The SEC Comment Letters, Short Selling and Impact on Audit Quality

The Impact of SEC Comment Letters and Short Selling on the Demand for Audit Quality

Ph.D. DISSERTATION By Justyna Skomra Department of Accounting, College of Business and Administration Kent State University

Dissertation Committee: Dr. Pervaiz Alam, Chair, Accounting Department Dr. Timothy Hinkel, Accounting Department Dr. Eric Johnson, Economics Department Dr. Xiaoling Pu, Finance Department

March 2018 The Impact of SEC Comment Letters and Short Selling on the Demand for Audit Quality

TABLE OF CONTENTS ..……………………………..…………………………………………………………………………...i

List of Tables ……………………………………………………………….………………………………………………………...... iii

Abstract ……………………………………………………….………..………………………………………………………..…….…1

Chapter 1. Introduction ...... 3

Chapter 2. Literature Review …………………………………………….….…………….………………………………..…17

2.1 Theories of Regulation, Disclosures and Agency …………………………………………………..……17

2.1.1 Discretionary Disclosure Model ..…………………………………….….………..……………………..21

2.2 The SEC Review Process ………………………….………..……….………….…………………………………..25

2.3 Short Selling ……………………………….……………….……………………….…………………………………..34

2.4. Audit Quality ……………………………….…………….…………………………………………………………….38

Chapter 3. Hypothesis Development and Research Design...………………………………….…………………50

3.1.1 Discretionary Disclosure Model……………………………………………………………………………50

3.1.2 Conceptual Model………………………………………………………………………………………………..52

3.1.3. Audit Fees …………………………………………………………………………………………………….…….55

3.1.4. Auditor Change …………………………………………………………………………………………….…….60

3.1.5. Discretionary Accruals …….………………………………………………………………………………….64

3.1.6. Restatements ………………………………………………………………………………………………….….67

3.1.7. Material Weaknesses …………………………………………………………………………………………70

3.1.8. PCAOB Inspections Deficiencies..……………………………..…………………………………………74

3.2 Research Models …………………………………………………………………………………………..….………76

3.2.1. Audit Fees ………………………………………………………………………………………………………….76

3.2.2. Auditor Change …………………………………………………………………………….……………………79

3.2.3. Discretionary Accruals ……………………………………………………………………………………….82

3.2.4. Restatements …………………………………………………………………………………………………….85

3.2.5. Material Weaknesses ……………………………………………….……………….….……………………88

3.2.6. PCAOB Inspections Deficiencies …………………………………………….…………………………..90

3.3 Sample selection………………………………………………………………………………………………………..92

3.4 Econometric Issues…………………………………………………………………………………………………….96

Chapter 4. Empirical Results and Analysis...... 100

4.1 Descriptive Statistics and Correlations..……………………………………………………………………100

4.2 Empirical Results and Analysis …………………………………………………………………………………107

Chapter 5. Additional Analysis………………………………………………………………………………………………..128

5.1 Factor Analysis ………………………………………………………………………………………………………..130

5.2 Lagged Variables ……………………………………………………………………………………………..………133

5.3 Sensitivity Test for Short Sellers……………………………………………………………………………….140

5.4 Top Accounting Issue addressed in the SEC Comment Letter…………………………………..142

Chapter 6. Summary and Conclusions………………………………………………………….…………………………143

Appendix A: Variable Definitions ……………………………………………….………………………..………………..154

Appendix B: Sample Comment Letter……….……………….……………………………..……………………………159

Figure 1: Timeline of the Study……………………………………………………………………………………………….165

References..…………………………………………………………………………………………………………………..………166

Tables.…………………………………………………………………………………………………………………..……………….180

LIST OF TABLES:

Table 1. Panel A SEC Comment Letters Sample Distribution...……………………………….180 Table 1. Panel B Short Interest Positions Sample Distribution...……………………………….181 Table 2. Sample Selection ………………………………………………………………………182 Table 3.1. Descriptive Statistics: Audit Fees Model…………………………………………....183 Table 3.2. Descriptive Statistics: Auditor Change Model……………………………………....184 Table 3.3. Descriptive Statistics: Performance Matched Discretionary Accruals Model………185 Table 3.4. Descriptive Statistics: Restatements Model………………………………………….186 Table 3.5. Descriptive Statistics: Material Weaknesses Model………………………………....187 Table 3.6. Descriptive Statistics: PCAOB Inspections Deficiencies Model…………………....188 Table 4.1. Correlation Table: Audit Fees Model……….………………………………………..189 Table 4.2. Correlation Table: Auditor Change Model…………………………………………..190 Table 4.3. Correlation Table: Performance Matched Discretionary Accruals Model…………..191 Table 4.4. Correlation Table: Restatements Model……………………………………………..192 Table 4.5. Correlation Table: Material Weaknesses Model…………………………………….193 Table 4.6. Correlation Table: PCAOB Inspections Deficiencies Model………………………..194 Table 5.1. The Impact of SEC Comment Letter and Short Interest Positions on Subsequent Audit Fees……………………………………………………………………………………………..195 Table 5.2. The Impact of SEC Comment Letter and Short Interest Positions on the Likelihood of Subsequent Auditor Change…………………………………………………………………….196

Table 5.3. The Impact of SEC Comment Letter and Short Interest Positions on Performance- Matched Discretionary Accruals………………………………………………………………..197

Table 5.4. The Impact of SEC Comment Letter and Short Interest Positions on the Likelihood of Subsequent Restatements……………………………………………………………………….198 Table 5.5. The Impact of SEC Comment Letter and Short Interest Positions on the Likelihood of Subsequent Material Weaknesses………………………………………………………………199 Table 5.6.1. The Impact of SEC Comment Letter and Short Interest Positions on the Likelihood of Subsequent PCAOB Inspections GAAP Deficiencies………………………………………….200

iii

Table 5.6.2. The Impact of SEC Comment Letter and Short Interest Positions on the Likelihood of Subsequent PCAOB Inspections GAAS Deficiencies ……………………………………...... 201 Table 6.1 The Impact of Severity of Issues in SEC comment letters on the subsequent Audit Fees …………………………………………………………………………………………………..202 Table 6.2 The Impact of Severity of Issues in SEC comment letters on the likelihood of the subsequent Auditor Changes …………………………………………………………………...203 Table 6.3 The Impact of Severity of Issues in SEC comment letters on the subsequent Performance- Matched Discretionary Accruals………………..………………………………………………204 Table 6.4 The Impact of Severity of Issues in SEC comment letters on the likelihood of the subsequent Restatements………………………………………………………………………..205 Table 6.5 The Impact of Severity of Issues in SEC comment letters on the likelihood of the subsequent Material Weaknesses ………………………………………………………………206 Table 6.6.1 The Impact of Severity of Issues in SEC comment letters on the likelihood of the subsequent PCAOB Inspections GAAP Deficiencies ………………………………………….207 Table 6.6.2 The Impact of Severity of Issues in SEC comment letters on the likelihood of the subsequent PCAOB Inspections GAAS Deficiencies ………………………………………….208 Table 7.1 Economic significance of the coefficients from the OLS regression Model (5)……..209 Table 7.2 Economic significance of the coefficients from the logistic regression Model (9)…..210 Table 7.3 Economic significance of the coefficients from the OLS regression Model (14)…….211 Table 7.4 Economic significance of the coefficients from the logistic regression Model (18)....212 Table 7.5 Economic significance of the coefficients from the logistic regression Model (22)....213 Table 7.6 Economic significance of the coefficients from the logistic regression Model (26)....214 Table 8.1 The Impact of lagged SEC Comment Letter and Short Interest Positions on Subsequent Audit Fees ………………………………………………………………………………………215 Table 8.2 The Impact of lagged SEC Comment Letter and Short Interest Positions on the Likelihood of Subsequent Auditor Change …………………………………………………….216 Table 8.3 The Impact of the lagged SEC Comment Letter and Short Interest Positions on Performance-Matched Absolute Discretionary Accruals ………………………………………217 Table 8.4 The Impact of the lagged SEC Comment Letter and Short Interest Positions on the Likelihood of Subsequent Restatements. ……………………………………………………….218 Table 8.5 The Impact of the lagged SEC Comment Letter and Short Interest Positions on the Likelihood of Subsequent Material Weaknesses………………………………………………..219

Table 8.6.1 The Impact of the lagged SEC Comment Letter and Short Interest Positions on the Likelihood of subsequent PCAOB Inspections GAAP Deficiencies……………………………220 Table 8.6.2 The Impact of the lagged SEC Comment Letter and Short Interest Positions on the Likelihood of subsequent PCAOB Inspections GAAS Deficiencies……………………………221 Table 9 Panel A Factor Analysis of Audit Quality Measures……………………………………222 Table 9.1 The Impact of SEC Comment Letter and Short Interest Positions on Subsequent Audit Quality measured by Factor 1 and Factor 2 employing model (5) independent variables ………223 Table 9.2 The Impact of SEC Comment Letter and Short Interest Positions on Subsequent Audit Quality measured by Factor 1 and Factor 2 employing model (9) independent variables ………224 Table 9.3 The Impact of SEC Comment Letter and Short Interest Positions on Subsequent Audit Quality measured by Factor 1 and Factor 2 employing model (14) independent variable ………225 Table 9.4 The Impact of SEC Comment Letter and Short Interest Positions on Subsequent Audit Quality measured by Factor 1 and Factor 2 employing model (18) independent variables ……226 Table 9.5 The Impact of SEC Comment Letter and Short Interest Positions on Subsequent Audit Quality measured by Factor 1 and Factor 2 employing model (22) independent variables ……227 Table 9.6 The Impact of SEC Comment Letter and Short Interest Positions on Subsequent Audit Quality measured by Factor 1 and Factor 2 employing model (26) independent variables ……228 Table 10.1 The Impact of Short Interest Positions on the Subsequent Audit Fees during different time periods……………………………………………………………………………………..229 Table 10.2 The Impact of Short Interest Positions on the Likelihood of Subsequent Auditor Change during different time periods……………………………………………………………………230 Table 10.3 The Impact of Short Interest Positions on Performance-Matched Absolute Discretionary Accruals during different time periods…………………………………………..231 Table 10.4 The Impact of Short Interest Positions on the Likelihood of Subsequent Restatements during different time periods……………………………………………………………………232 Table 10.5 The Impact of Short Interest Positions on the Likelihood of Subsequent Material Weaknesses during different time periods………………………………………………………233 Table 10.6.1 The Impact of Short Interest Positions on the Likelihood of Subsequent PCAOB Inspections GAAP Deficiencies during different time periods …………………………………234 Table 10.6.2 The Impact of Short Interest Positions on the Likelihood of Subsequent PCAOB Inspections GAAS Deficiencies during different time periods…………………………………235 Table 11 Breakdown of the SEC Comment Letters by Topic…………………………………..236 Table 11.1 The Impact of Top Accounting Issue and Short Interest Positions on Subsequent Audit Fees……………………………………………………………………………………………..237

Table 11.2 The Impact of Top Accounting Issue and Short Interest Positions on the Likelihood of Subsequent Auditor Change…………………………………………………………………….238 Table 11.3 The Impact of Top Accounting Issue and Short Interest Positions on Performance- Matched Absolute Discretionary Accruals……………………………………………………...239 Table 11.4 The Impact of Top Accounting Issue and Short Interest Positions on the Likelihood of Subsequent Restatements……………………………………………………………………….240 Table 11.5 The Impact of Top Accounting Issue and Short Interest Positions on the Likelihood of Subsequent Material Weaknesses………………………………………………………………241 Table 11.6.1 The Impact of Top Accounting Issue and Short Interest Positions on the Likelihood of Subsequent PCAOB Inspections GAAP Deficiencies……………………………………….242 Table 11.6.2 The Impact of Top Accounting Issue and Short Interest Positions on the Likelihood of Subsequent PCAOB Inspections GAAS Deficiencies………………………………………..243 Table 12.1 The Impact of SEC Comment Letter and Short Interest Positions on Subsequent Audit Fees……………………………………………………………………………………………..244 Table 12.2 The Impact of SEC Comment Letter and Short Interest Positions on the Likelihood of Subsequent Auditor Change…………………………………………………………………….245 Table 12.3 The Impact of SEC Comment Letter and Short Interest Positions on Performance- Matched Absolute Discretionary Accruals……………………………………………………..246 Table 12.4 The Impact of SEC Comment Letter and Short Interest Positions on the Likelihood of Subsequent Restatements……………………………………………………………………….247 Table 12.5 The Impact of SEC Comment Letter and Short Interest Positions on the Likelihood of Subsequent Material Weaknesses………………………………………………………………248 Table 12.6.1 The Impact of SEC Comment Letter and Short Interest Positions on the Likelihood of Subsequent PCAOB Inspections GAAP Deficiencies……………………………………….249 Table 12.6.2 The Impact of SEC Comment Letter and Short Interest Positions on the Likelihood of subsequent PCAOB Inspections GAAS Deficiencies………………………………………..250

The Impact of SEC Comment Letters and Short Selling on the Demand for Audit Quality

ABSTRACT

The Sarbanes-Oxley Act of 2002 (Section 408) requires the Securities and Exchange

Commission (SEC) to conduct periodic reviews of financial statements and related disclosures for publicly traded firms. The reviews are documented in the form of comment letters issued to a company’s management for failure to prepare financial statements in accordance with generally accepted accounting principles (GAAP). Short interest traders are considered to be the most sophisticated group of investors providing additional monitoring of firms in the market. In this dissertation, I examine the impact of SEC comment letters and short selling positions on the demand for audit quality by management of client firms.

Prior studies have shown that comment letters provide a significant signal to SEC registrant companies and their auditors about noncompliance with GAAP and other SEC regulations. As auditors play a critical role in the filing process of a company, they also contribute to the receipt of comment letters by their clients. Additionally, they play a critical role in bridging the information gap between investors and the firm. I examine the impact of two types of monitoring mechanisms, regulatory and market-based, on the subsequent demand for audit quality by management of client firms.

More specifically, I examine whether the release of the comment letter combined with short selling activity (1) influences auditor’s efforts reflected in increased audit fees, (2) leads to subsequent auditor resignation/dismissal due to inability to provide demanded high quality audits,

(3) triggers downward changes in discretionary accrual, (4) decreases likelihood of restatements,

(5) leads to issuance of material weaknesses opinion, and lastly (6) decreases the likelihood of the

PCAOB (Public Company Accounting Oversight Board) inspection deficiencies.

Based on the sample of unique comment letters from years 2005 through 2015 and the information on the short interest positions, I find varying level of support for the tested hypothesis.

Overall, the results are generally consistent across the proxies used to measure audit quality.

Hence, they indicate that both monitoring mechanisms have an impact on the demand for higher quality audits. These findings are robust to controls for client and auditor characteristics.

The Impact of SEC Comment Letters and Short Selling on the Demand for Audit Quality

Chapter 1: Introduction

Sarbanes – Oxley Act of 2002 (SOX) is one of the major regulations designed to enhance regulatory oversight with the goal of restoring investors’ confidence in the capital markets. In order to fulfill its mission, SOX imposed significant changes to the existing regulatory environment.

Among them was a new requirement levied on the Securities and Exchange Commission (SEC),

Division of Corporation Finance (DCF) to conduct formal reviews of the financial statements and related disclosures of publicly listed firms on a regular basis. According to the new rule (SOX,

Section 408), each of the publicly traded company has to undergo the review at least once every three years1 (SEC 2002). Significant resources were devoted by the SEC to the review process in order to comply with this new requirement. The reviews were intended to “monitor and enhance compliance with the applicable disclosure and accounting requirements” (SEC, 2013a). The underlying objective was to evaluate disclosures from the investor’s perspective and any important deviations from applicable disclosure regulations were addressed in the comment letter to the registrant.

Before August 1, 2004 the dialogue between the SEC and the company was confidential.

In order to provide additional benefits to investors’ community, finalized comment letters are publicly disclosed on EDGAR since then. The goal of public release of the correspondence was to increase the transparency of the review process to the broader range of capital market participants.

1 See source at: https://www.sec.gov/about/laws/soa2002.pdf

Alan Beller, the director of the Division of Corporation Finance of the SEC, supported this decision2 (SEC 2005):

“We believe it is appropriate to expand the transparency of our comment process by making this information available, free of charge, to an unlimited audience.”

Public disclosure of the comment letters correspondence has opened fertile ground for accounting research. Since investors are the main beneficiaries of the SEC review process, academic research has mostly directed their attention to measuring information value of the comment letter for the investment community (Chen and Johnston, 2010; Grove, Johnsen, and

Lung 2016; Dechow, Lawrence, and Ryans, 2016) and examined capital market consequences of the issuance of comment letters (Lawrence, Gao, and Smith, 2010, Johnson, 2015). Chen and

Johnston (2010) provide empirical evidence on the positive market reaction to the correction of the issues addressed by the SEC comment letters. Lawrence, Gao, and Smith (2010) find that market reaction to the restatement as a result of the SEC comment letter resolution is slightly positive but only when the comment letter is followed by the “8-K” release. Surprisingly, the findings are contrary to the negative reaction of request for restatements received from other monitors (board of directors or auditors).

The SEC comment letters are a great source of “independent expert opinion on the quality of a firm’s financial statements” (Grove, Johnsen, and Lung, 2016). Despite its immense informational value, it appears that markets do not fully take advantage of information provided by comment letters. This finding is consistent with study of Johnson (2015), who finds that investors’ reaction to the public disclosure of comment letter is conditional on concurrent filing

2 https://www.sec.gov/news/press/2005-72.htm

(e.g., 8-K). In a similar vein, Dechow, Lawrence, and Ryans (2016) report delayed market reaction to the public release of comment letters, while inside traders utilize this private information and increase sales before the public disclosure.

The empirical evidence shows that the receipt of the SEC comment letter leads to improvement in the information environment as noted by higher quality of corporate disclosures

(Bozanic, Dietrich, and Johnson, 2015), reduction in accrual-based earnings management

(Cunningham, Johnson, Johnson, and Lisic, 2016), decrease in tax avoidance practices (Kubick,

Lynch, Mayberry, and Omer, 2016), and improvement in risk disclosures (Brown, Tian, and

Tucker, 2014). Thus, the studies provide some evidence on a certain level of improvement in the financial reporting quality.

The efficiency of capital market depends on the quality of financial reporting and related disclosures. Based on the disclosure framework (Healy and Palepu, 2001), management has the responsibility to provide financial statements and related disclosures to the investors. However, driven by proprietary costs and/or agency costs incentives, management does not fully disclose information to the capital market participants (Nagar, Nanda, and Wysocki, 2003). Verrecchia

(1983) offers explanation for the nondisclosure motivated by the need to retain proprietary information. On the other hand, management has incentives to comply with regulations and demands high quality audits to meet regulatory obligations. Therefore, management is faced with tradeoffs between the benefits of reducing information asymmetry in the capital market and compliance with regulations as well as the costs of disclosing proprietary information.

Generally, investors perceive accounting information provided in the financial statements and related disclosures as reliable. However, it is difficult to measure how much of the credibility

5 results from management assertions or independent assurance by auditors. The auditors provide assurance that financial statements and related disclosures comply with accounting standards and regulations. However, management discretion is allowed in reporting their financial results.

Considering recent accounting scandals involving top level executives, we could assume that credibility of accounting information is attributable to assurance provided by auditors. This is consistent with the fact that capital providers require firms to engage independent auditors even if it is not required by regulations (Leftwich, 1983). Auditors, as well as other monitoring tools are set in place to improve the information environment of the company and reduce information asymmetry between agent and principal.

Prior literature on the capital market consequences of public disclosure of the SEC comment letters provides mixed evidence on the benefits of the review process to the investors’ community. Moreover, potential costs/benefits to other parties are still unknown. Considering significant SEC resources devoted to the review process, it is important to explore potential benefits of the SEC reviews to other capital market participants and financial intermediaries to more accurately evaluate economic outcomes of this process. Hence, in this study, I extend above research to examine information value of the SEC comment letters for audit firms. The important role of the auditors in the reporting process of public companies motivates research on the consequences of the SEC review process for the subsequent audit quality following public disclosure of the SEC comment letter.

Research Questions

The purpose of the SEC reviews is to enhance the quality of financial reporting and related disclosures, which were examined by the audit firm before release to the public. Once management

6 receives a comment letter the SEC expects them to improve the subsequent quality of the financial reporting and disclosures. Thus, management might demand higher quality audit effort from their auditors to remediate issues addressed by the SEC. Based on this agreement, I examine the impact on the audit quality after client’s receipt of the comment letter for companies with short interest positions. The areas addressed by the SEC in the comment letter allow audit firms to pay more attention to these specific issues for the subsequent audit engagements. As more audit effort is placed for the client with comment letter, I predict improvement in the quality of audit in the following years.

The receipt of the comment letter could imply that the auditor did not provide sufficiently high quality audit to their client in the first place. After responding to the letter and making required changes in disclosures, I expect the client to require higher quality work provided by their auditors.

Prior studies point out lack of one commonly used measure for audit quality. The literature in this area presents pros and cons of different proxies of audit quality. As the purpose of this study is to provide comprehensive examination of the impact of monitoring mechanisms on audit quality, I investigate six of the most frequently used proxies – audit fees, auditor changes, discretionary accruals, restatements, material weaknesses and PCAOB (Public Company Accounting Oversight

Board) inspections (DeFond and Zhang, 2014). Consequently, the first research question this study explores is stated as:

RQ1: Does the receipt of the SEC comment letters by the client have an impact on the demand for audit quality of subsequent audit engagements?

The SEC review process is only one of the monitoring mechanisms over corporate financial reporting. The other group, and at the same time the major beneficiaries of the SEC review process, are investors. Within this group we distinguish short sellers, institutional investors and inside

7 traders. Each of these unique groups of investors can exercise additional monitoring activity that may possibly benefit auditor behavior when planning, scheduling and conducting an audit engagement. Gietzman and Isidro (2013) examine the impact of comment letters on the holdings of institutional investors. They show evidence for reduction of equity holdings when a company receives comment letter, more so when the letter pertains to the application of the International

Financial Reporting Standards (IFRS). The sales of shares by insider traders increase before the

SEC comment letter is publicly released (Dechow et al. 2016), in the fear of the negative market reaction. In this study, I focus on the sophisticated investors – short sellers.

Short sellers identify overpriced firms based on the financial disclosures and private information (Ljungsvist and Qian, 2016). The costs and risks associated with short positions imply that only investors with sophisticated abilities to analyze private and publicly available information will engage in short sales (Diamond and Verrecchia, 1987). By playing an important role in the capital markets, this particular group of sophisticated investors has the ability to detect inadequate disclosures and take advantage of this information to make profits. The evidence provides that they may extend the information value of SEC comment letters, as insider sales increase on average twelve times when the company receives a comment letter and has a high short selling position

(Dechow et al. 2016). Intrigued by their findings, this study further explores the role of short sellers in the market-based monitoring process of the company and their role in the enhancements of the audit quality following public disclosure of SEC comment letters.

Management is concerned with the cost of equity when they observe high short positions in their company, as it may indicate upcoming negative corporate event and which could lead to a decline in the stock prices. Barry Zyskind, CEO and President of AmTrust Financial Services

8 expressed in his response to a recent stock activity call on Dec 13, 2013 (AmTrust Financial

Services, Inc. 2013):

“In light of the recent trading activity in the Company’s stock, we felt that it would be prudent to provide our shareholders, employees, customers and the broader market with an update on our business as well as our outlook for the future. As part of this business update, we’ll also address the factual inaccuracies included in these meeting statements made in recent days about our financial reporting by short sellers.”

Prior literature, examining the value of information provided by short sellers, provides empirical evidence that other parties take advantage of the information delivered by short sellers assuming short positions (e.g., Cassell, Drake, and Rasmussen, 2011; Averil, Morse, and Stice,

1990; Desai, Ramesh, Thiagarajan, and Balachandran, 2002). Since Dechow et al. (2016) finds that short sellers combined with the public disclosure of SEC comment letters provides more pronounced effect on insider traders, I examine both types of monitoring activities and their impact on the subsequent audit quality provided by audit firm. The literature has well estblished information advantage of sophisticated investors over unsophistacted investors, which could be possibly driven by SEC comment letters and informational value contained in them. Therefore, the next research question is stated:

RQ2: Does the presence of short interest positions around SEC comment letter disclosure have an impact on demand for audit quality of the subsequent audit engagements?

In this study, I examine whether firms, which receive the SEC comment letters and have high short positions provide a valuable signal to management to demand higher quality audits. As

SEC review is performed on the audited financial statements, which is the joint product of management and auditors, I expect that management after taking corrective action will demand a higher quality work from their auditors to avoid comment letters in the future. There is only some

9 empirical evidence on the improvement in financial reporting quality which could potentially translate into higher quality audits (Cunningham, Johnson, Johnson, and Lisic (2016), Bozanic,

Dietrich, and Johnson (2015a)). However, the evidence so far is very scarce. Therefore, this study contributes to the stream of research on the economic consequences of the SEC review process on the targeted firm and their audit firm.

James Hoffmeister, Visa’s Chief Accounting Officer, expressed his view on receiving a comment letter (Wall Street Journal, August, 31, 2016):

“Receiving a comment letter from the regulator triggers the typical human response to criticism: self-doubt. You want to know: did we really miss something egregious? Or, are these areas where we can do better?” “Whenever we get one of these letters, I take it as an opportunity to take a second look at what we’re doing and if we can get better.”

Therefore, it seems plausible to expect that firms will expect at the same time more scrutiny from their auditors when performing audit procedures on the subsequent audit engagements. The firms are motivated to pay auditors for additional effort, which would be reflected in higher audit fees. Moreover, I expect to see lower discretionary accruals following the receipt of the SEC comment letter. Additionally, I posit that companies with improved disclosures resulting from comment letters should have a lower probability of restatements and receiving internal control material weaknesses (ICMW) report from their auditors. In the situation when SEC comment letters are received repeatedly by the client, eventually the company could also dismiss the auditor which would be reflected in the higher probability of auditor change (Baldwin, Hurtt, and

MacGregor, 2012). In the end, audit firms with clients which receive SEC comment letters and have high short interest positions will provide higher quality performance and, therefore, they will decrease the number of PCAOB inspections.

In my personal discussions with partners of two Big 4 firms, they confirmed that large public accounting firms respond to each comment letter with great care. They devote significant resources to help clients address the issues with the SEC. Furthermore they disseminate information from comment letters to other clients in the industry to help them avoid receiving similar comments from the SEC (spillover effect). Based on these arguments, I can expect that

SEC review process could have potential benefits for the audit profession by improving audit quality as demanded by clients receiving SEC comment letters and being monitored by short sellers at the same time. Therefore, I can predict that management improving disclosures will demand higher quality audits.

Furthermore, some studies argue that audit quality is determined by whether a company is audited by a Big4 or non-Big4 audit firm (Watts and Zimmerman, 1981; DeAngelo 1981; Bedard and Johnstone, 2004). Big4 audit firms have greater audit engagement resources including more experienced personnel. These factors supply them with comparative advantage over non-Big4 audit firms to provide higher quality audits to their clients. For example, they can more effectively constrain earnings management (Francis, Maydew, and Sparks, 1999). Considering the empirical evidence provided by prior studies, I examine whether the change in audit quality after the client’s receipt of the SEC comment letter and short interest activity differs between Big 4 auditors and non-Big 4 auditors.

RQ3: Does demand for higher audit quality differs from Big 4 auditors versus non-Big 4 auditors after the receipt of SEC comment letter by the client and in the presence of short sellers?

Motivation

This study is motivated by the lack of comprehensive study to measure the impact of regulatory (SEC comment letters) and market based (short selling) forces on the audit quality. To the best of my knowledge, this is the first study that extensively captures audit quality in different proxies and implications of two varying forces on them. Gaynor, Kelton, Mercer and Yohn (2016) examine in detail the relationship between financial reporting quality and audit quality. The overall notion developed by prior literature is the fact that the two processes are interdependent and jointly determined, as they are measured by similar output proxies. The difficulty lies with lack of observables that determine quality of pre-audited financial statements. Literature established a causal relationship between audit quality determining post-audit financial reporting quality.

However, the fact that the relation between financial reporting quality and audit quality is recursive is not frequently considered in the literature (Gaynor et al. 2016). Therefore, this study answers the call to extend research on the implications of pre-audit financial reporting quality on the audit quality.

The pre-audit financial reporting quality, among other factors, is determined by the management incentives to not fully disclose information due to proprietary costs. The inference is drawn that quality of such financial statements and related disclosures is lower as in the situation of full complete disclosure. The lower quality of financial statements makes the firm more susceptible to the receipt of SEC comment letter (Cassell et al. 2013) and higher short selling positions are taken in the company to make profits on the lower quality disclosures. This possible scenario assumes that audit quality is not able to mitigate low pre-audited quality of financial statements if it is not demanded by the client. In this study, I assume that clients will improve

12 financial reporting quality following SEC comment letters and it will demand higher quality audits from the audit firm (Brown, Tian, and Tucker, 2014; Bozanic, Dietrich, and Johnson, 2015).

This study is an attempt to examine benefits of SEC reviews beyond the targeted firm.

More specifically, I measure the demand for higher audit quality after the SEC comment letter is received by the firm. Prior research related to this study examined effects (or no effects) of SEC comment letters on targeted firms. I extend this study by a comprehensive examination of the impact of SEC comment letters on audit firms and subsequent audit quality measured by six proxies.

I assume that firms have incentives not to fully disclose information due to proprietary costs. However, once the SEC comment letter is received and they observe high short selling positions in the company, the incentives should move toward full disclosure to reduce the chance of receiving another letter. There are a few arguments which support this prediction. First of all, the proprietary costs are offset by the declining market value of the firm due to negative market reaction to the comment letter. Moreover, response to comment letter requires additional resources from the inside and outside of the company and is a time consuming process, which shifts away management efforts from the normal operations.

This research is motivated by the fact that no other prior study has conducted a comprehensive examination of the market-based and regulatory-based monitoring mechanisms on the demand for audit quality. These two monitoring forces differ from the other monitoring tools

(e.g., board of directors, audit committee, or auditors) as they take place after the information is released to the public. Financial statements under the review of SEC and publicly disclosed to investors were already examined and audited by other monitors. Therefore, the improvement in

13 financial reporting quality and possibly audit quality can only be observed on the future company filings of audited financial statements. In addition, the firm is not able to control the amount of monitoring performed by the SEC or short sellers. Thus, this study advances the literature by examining the impact of the unique market-based and regulatory-based forces on the audit quality.

Data and Research Methods

The data on the SEC comment letters relating to Form 10-K and 10-Q is retrieved from

Audit Analytics. The information on the short selling positions is obtained from the Compustat supplemental files. The variables used as proxies of audit quality come from the Audit Analytics and the client data is provided by Compustat. The sample used in the study starts in 2005 to capture the first full year when the SEC decided to publicly disclose comment letters through EDGAR.

The sample ends in 2015 to ensure that all the comment letters related to 10-K and 10-Q for 2015 were publicly disclosed in 2016.

To measure audit quality, I use six proxies frequently used in the literature as a determinants of audit quality - audit fees, auditor changes, discretionary accruals, restatements, material weaknesses and PCAOB inspections. Due to limitations of each measure to capture audit quality, I develop an overall proxy for the audit quality measure using factor analysis. I then regress the audit quality measures to examine whether audit quality following the comment letter and short selling positions improved.

14 with SEC comment letters and short sellers. As voluntary disclosures of the company with short interest positions increase following a comment letter, management’s incentive for higher quality audits increases as well.

Contributions

This dissertation makes the following contributions. First, it seeks to expand research on the consequences of the SEC review process. Cassell, Dreher, and Myers (2013) extensively investigates determinants of receiving a comment letter and their study calls for more research on the benefits of the SEC review process. In this paper, I answer this call by further examination of the consequences of the comment letter process on the audit firm. By extending analysis on other capital market intermediaries, the study provides evidence allowing more thorough cost/benefits analysis of the review process, which has not been yet examined extensively in the literature.

Second, the study answers the call in Healy and Palepu (2001) on providing more empirical research on disclosure regulations in the form of SEC comment letters. More recent summaries of literature on the financial reporting environment continue this request by calling for more research on the effect of disclosure regulation on various capital market participants with different sophistication levels (Beyer, Cohen, Lys, and Walther, 2010). In this study, I provide additional evidence on the impact of the SOX regulation to conduct periodic review by the SEC on the demand for audit quality. Finally, the study examines the role of short sellers on the demand for audit quality. The literature in this area has started to emerge and this study provides more empirical evidence to that stream of research. By examining the above research questions, I show that different monitoring tools could provide real economic benefits to auditors. Overall, this study

15 contributes to a growing body of research on the external monitoring role of the SEC in the form of comment letters and monitoring by market participants, more specifically short sellers.

The remainder of this study is organized as follows. Chapter 2 discusses prior literature relevant to the research questions of this study. In Chapter 3, I develop hypotheses and present research models used to test those hypotheses. Chapter 4 includes empirical analysis and results.

Chapter 5 presents sensitivity and additional analysis. I summarize and conclude the study in

Chapter 6.

Chapter 2: Literature Review

In this chapter I review the literature that is relevant to my research questions on the impact of the SEC comment letters and short sellers on audit quality. First, I review relevant theories that make solid foundation for developing hypothesis. Second, I review research on the SEC review process which results in the issuance of comment letters and implications of this process for the companies. Third, I discuss research on the short sellers and their impact on auditor behavior.

Lastly, I review literature on the audit quality and more specifically focus on proxies that were extensively used in the literature as measures of audit quality – audit fees, auditor changes, discretionary accruals, material weaknesses, restatements and PCAOB inspections.

2.1. Theory of Regulation, Disclosures and Agency

The theoretical framework of this study is based on the disclosure theories (Verrecchia,

1983; Verrecchia 2001; Healy and Palepu 2001; Beyer, Cohen, Lys, and Walther, 2010; Leuz and

Wysocki, 2016) and agency theory (Jensen and Meckling, 1976). The relationship between principal and agent is determined under the contractual agreement in which the principal engage agent(s) to act on their behalf to make the best decisions for the principal. The agency costs arise due to agent’s self-interested incentives and information asymmetry between agent and principal.

The results of the agents’ efforts are communicated to the principal and other capital market participants through financial reporting and relevant disclosures. There are two types of disclosures

– mandatory and voluntary. The mandatory reporting is heavily regulated by the SEC and other regulatory bodies in the US market. The major regulation imposing disclosure requirements on the traded firms was the Securities Act of 1933 and the Securities Exchange Act of 1934.

Subsequently, the SEC disclosure requirements were imposed on firms trading in the over the

17 counter (OTC) equity market by the Securities Act Amendments (1964), and the Eligibility Rule for the OTC Bulletin Board of 1999. In addition, these requirements were updated by new mandates (e.g., SOX of 2002, Regulation Fair Disclosure of 2000). Proponents of mandatory disclosures attested that regulations improve investors’ assessment of risky securities as many studies showed evidence of positive market effects (e.g., Seligman 1983). From a broad definition perspective, disclosures and financial reporting regulations are created by centralized authority, which also puts in place monitoring mechanism to verify compliance with the regulations and enforces non-compliance actions.

Corporate disclosures and financial reporting are mostly standardized and regulated to increase transparency for the benefit of investors. The regulation of financial reporting and disclosure was discussed in studies by Leftwich (1980), Watts and Zimmerman (1983, 1986). They point out that one of the reasons for the regulations is to protect unsophisticated investors by redistributing wealth. Supplying accounting standards regulates the available reporting choices that managers have in presenting the firm’s financial situation. Moreover, it makes the interpretation of financial statements easier by providing commonly accepted language to be used with investors’ community. Disclosures and reporting regulations undergo significant changes when faced with global financial crises or accounting scandals. However, there is more research required “to understand [that] the economic effects of disclosure regulation [are] of first-order importance, not just for accounting and finance” (Leuz and Wysocki, 2016).

The SEC mandates specific reporting behavior and sets expectations for corporate disclosures. Companies disclose information to the public – investors, consumers, governmental agencies and other parties of interest – as a result of either mandatory regulation or by their voluntarily decision. The purpose of the disclosure is to reduce information asymmetry between

18 informed party (management of the company) and outside parties. Verrecchia (2001) shows that corporate disclosure and reporting can mitigate the adverse selection process by reducing the information asymmetry between parties. This statement is supported by the increased market liquidity (Brown and Hillegeist, 2007; Heflin, Shaw, and Wild, 2005). However, as the opportunities for trading on private information decrease, market liquidity could be negatively affected. Therefore, the evidence provided in the prior literature stipulates that with better disclosure quality, traders may be shifting away from the company. This would support the fact that short sellers are more active for companies avoiding full disclosures. Mostly in this situation they can obtain financial benefits based on their comparative advantage and superior abilities.

Under the U.S. GAAP management can exercise great discretion in the way they want to communicate financial results to the capital market participants (voluntary disclosures). Managers

(agents) possess private information that they can either disclose or withhold and traders

(principal) know about the incentives and intensions to exercise each option. Investors form their expectations based on management decision (to disclose or withhold information) which determines the market price of the company. Since managers are aware of rational investors’ expectations, they can adjust their disclosures accordingly to achieve desired market reaction.

Conversely, traders expect managers’ rational behavior and they know that their reaction and behavior impacts managers’ decision to fully disclose or withhold information.

Withholding information does not automatically imply that information is “bad”. Full disclosure carries cost beyond costs of preparing and distributing information – these are called proprietary costs. They represent a noise in the trader’s interpretation of manager’s intentions.

Investors do not know whether information is withheld due to the bad nature of the news or due to proprietary information included in the good news to be disseminated to competitors. Therefore,

19 as the proprietary costs increase so does the threshold level of disclosure. On the other hand, in the absence of the proprietary costs the noise is eliminated from the discretionary model and management can fully disclose private information and at the same time minimize the information asymmetry.

Prior literature finds consistent evidence that proprietary costs constrain full disclosure

(e.g., Darrough and Stoughton, 1990; Feltham and Xie, 1992; Harris 1998; Piotroski 2003;

Botosan and Stanford 2005). The evidence in support of the proprietary costs theory is provided based on the studies on segment reporting (Hayes and Lundholm, 1996; Berger and Hann, 2007).

They provide evidence that management has to exercise discretion in reporting segment information when faced not only with the investors but also with competitors. The decision considers tradeoff between providing full information to investors and disclosing proprietary information to competitors. Ettredge, Kwon, and Smith (2002) report that “86 percent of the industrial firms that commented on the Exposure Draft for SFAS No. 131 opposed the new standard on the grounds that "it would put them at [a] competitive disadvantage." When the SFAS

131 became effective (December 15, 2007), the segment disclosures improve as the companies are required to comply with the new regulation. Furthermore, prior literature also provides evidence that management makes concious decisions about omitting certain disclosures to avoid disseminating unfavorable information (Robinson, Xue, and Yu, 2011) that could negatively impact the firm value.

In the discretionary disclosure model presented by Verrecchia (1983), managers can select the level of disclosure (“threshold level of disclosure”) which is determined by the value of the proprietary costs. Traders form rational expectations about manager’s decision and the threshold level is selected after consideration of the trader’s expectations. The model is based on the

20 assumption that proprietary costs have in general constant level but they are also a function of time. The value of proprietary costs is maximized at the beginning of the period and decreases to zero as the time passes, which also lowers the threshold level of disclosure and leads eventually to full disclosure.

Short sellers, as the most sophisticated investors, are aware of management intentions of withholding accounting information when faced with high proprietary costs. The challenge is to measure proprietary costs and how management’s decision about disclosure is tied with short selling activities. It is important to examine, in the setting of the SEC comment letters, whether short sellers become more active lacking full disclosures and what implications both factors have on the subsequent audit quality of the company.

2.1.1. Discretionary Disclosure Model

Management (agent), as the only market participant by the nature of its role, has access to the full information about the company. Influenced by their private incentives and flexibility of

US GAAP rules, they can exercise discretion over the amount of disclosure they want to communicate to traders. The set of available information allows traders to form expectations and subsequently determine the market price of the firm. The value of the firm is determined by traders’ expectations of the true liquidating value of the firm adjusted by the variance in that value and risk-free rate of interest (Verrecchia, 1983).

The main assumption in this model is the fact that management does not mislead traders with information – it only exercises two options of either full disclosure or withholding certain accounting information. When management fully discloses private information, the price of the

21 risky asset is based on the traders’ expectations reduced by proprietary costs and adjusted by the variance. However, the situation is different when management selects to disclose information up to a certain level (either due to unfavorable information or good information carrying proprietary news). Above this point (threshold level of disclosure) manager decides to disclose information to traders and below the threshold the information is withheld. It creates an opportunity for short sellers to benefit from the lack of full disclosure as based on the discretionary model, the selected threshold level could maximize the value of the firm. However, when faced with high proprietary costs traders’ negative reaction decreases proportionately as they will discount retained information to a lesser degree. The model proposes the presence of equilibrium threshold level of disclosure where traders’ expectations regarding withheld information is consistent with management’s intentions.

Based on the discretionary model, Verrecchia (1990) provides evidence that discretionary disclosure level depends on the quality of the information available to managers. The threshold level of disclosure will decrease as the quality of the information available to managers is higher.

Faced with the high quality information market participants have higher expectations for the disclosure of that information. If managers decide to withhold that information market value of the firm will decrease more severely due to that expectation.

Corporate disclosure can mitigate the adverse selection problems by decreasing the information gap between parties and making it more difficult to obtain private information

(Verrecchia, 2001). More robust disclosures reduce the uncertainty about the firm value and reduce information advantage between more sophisticated investors (short sellers) and the rest of the investors’ community.

Prior studies point out to regulatory oversight as in important determinant of a firms’ accounting quality of financial reporting and disclosure. The firm’s accounting quality will improve due to more strict regulations on financial reporting and disclosures when management more truthfully provides representation about the actual economic situation of the company. When we think about financial reporting and disclosures, there are two driving forces that can shape management behavior and application of the reporting standards: the level and strength of enforcement actions by the SEC and management incentives. Reporting standards provide managers with great discretion over reporting style and format, as their application may require significant management judgment. Incentives are the driving force in management decision on the use of this judgment. High quality disclosures and reporting reduce the cost of raising new capital and improves monitoring by other outside parties (institutional investors). High quality disclosures are attributable to reduction in inefficiencies in management decision making process (Lambert,

Leuz, and Verrecchia, 2007).

In order to mitigate the agency costs, monitoring mechanisms are put in place with varying levels and strength of enforcement actions. The mandatory monitoring can be achieved by regulators, governmental agencies and auditors. In addition, there are other voluntary monitoring parties like different types of investors or boards of directors. Each of the monitors plays a unique oversight role in the financial reporting and corporate disclosure process. Furthermore, to reduce information asymmetry between both parties, regulators impose rules to require management to fully disclose private information that could be of interest to other parties.

One of the types of monitoring activity is audit, which positively influences the value of the firm (Jensen and Meckling, 1976). It reduces management incentives to manipulate earnings and retain private information for their self-interest. The 1933 Securities Act required that

23 corporations subject to the Act have audits performed by independent or certified public accountants. However, by the 1920s most companies listed on the New York Stock Exchange

(NYSE) were already audited by professional auditors. The value of the audit as the monitoring tool in reducing the agency costs depends on the ability of the auditor to provide high quality independent audit. Auditors provide investors with independent assurance that the firm’s financial statements conform to GAAP.

Managers have incentives to file less precise or complete disclosures due to proprietary information (Verrecchia 1983). Consequently, unprecise and incomplete information reduces financial reporting quality and may trigger comments and requests for additional information by the SEC (Harris, 1998; Cohen 2008). Once the SEC comment letter issues are addressed, it can lead to improvement in financial reporting and disclosures for that company going forward.

Management is enforced to take appropriate actions requested by the SEC to resolve the issues addressed in the comment letter. As the auditor is actively involved in responding to the SEC comment letter we may observe a spillover effect for other clients of the same auditor. In general, we can observe an increase in the audit quality indicators after the resolution of the SEC comment letter. The improvement in the audit quality could stem from the client’s demand for higher quality audits to avoid comment letters in the future.

In addition, I can expect that short sellers’ activities will decrease after the comment letters are publicly disclosed. Based on the disclosure theory, higher quality of financial reporting and related disclosures leads to improvements in the audit quality. At the same time, it leaves fewer opportunities for short sellers to obtain private information and reduce their comparative advantage over unsophisticated investors.

A long-standing economic question is the justification of regulating corporate disclosures

(Healy and Palepu 2001). The tendency in regulation, however, is to oversupply it, because users always overstate their demand. SEC reviewers may act similarly, simply asking for more detail, which may be of little incremental value. If comment letters merely create excess disclosure or an oversupply, then there would be little subsequent improvement in the firm’s information environment and consequently no impact on the audit quality.

2.2 The SEC Review Process

The dialogue between management and auditors determines the final version of the financial statements and related disclosures. Once the auditor is satisfied with the resolution of the accounting issues it signs off on the audit report which allows the client to file audited financial statements and other related disclosures with the SEC. Subsequently, the SEC on a continuous basis selects firms for review to provide companies with opinions on their compliance with

Generally Accepted Accounting Principles (GAAP) and the SEC reporting regulations. The requirements on the form and content of the disclosures with the SEC are regulated by Regulation

S-X and Regulation S-K of 1933. Despite these requirements, it is possible that deviations from required disclosures may exist as a certain degree of flexibility is allowed on the side of the management who tries to retain private information within the company and not disclose to the public. When the private information is valuable, management can utilize their comparative advantage by engaging in insider sales. This behavior could result either from intentional retention of the private information by management or trading on the news that an SEC comment letter will be issued (Dechow, Lawrence and Ryans, 2016). This situation can also benefit sophisticated

25 investors whose ability to obtain private information exceeds knowledge and abilities of unsophisticated investors.

The SEC in their review process addresses any discrepancies between company’s filings and regulations. Receiving the SEC comment letter does not accuse the company of wrongdoing

– it simply raises questions about the format and detail of the disclosure associated with a particular topic (such as goodwill impairment, fair value or revenue recognition). The SEC Division of

Corporation Finance devotes significant resources through 12 local offices to focus their efforts on reviews of financial filings of the registrants. According to the Sarbanes-Oxley Act, Section

408 “Enhanced Review of Periodic Disclosures by Issuers” (SOX, 2002) mandates that each publicly traded company has to undergo the review at least once every three years. Firms of the same three digits SIC codes are assigned to the same local office

(https://www.sec.gov/info/edgar/siccodes.htm; SEC 2015). The reviews are conducted at three levels - from the comprehensive review of filing, through the review of financial statements to review of targeted specific issues.

The process of issuance of an SEC comment letter is as follows. The staff determines which registrants are to be reviewed based on their pre-determined and publicly unavailable criteria. The review team in each local office includes accountants and lawyers, depending on the resources and the review type. They decide on the range and type of the review (preliminary, targeted or full review). After the preliminary review is conducted, decision is made whether any further review is warranted. Once decision is reached the team may continue with targeted review, which focuses on a specific event or disclosure type or full review, which includes thorough review of the financial statements.

If the review determines that there is no need for further clarification, notification of the review process to the company is not required. However, in situations where the SEC issues comment letters, the company is required to respond to inquiries within 10 business days. The comments can vary in severity and length as well as may have varying impact on the future financial statements.

The resolution to the comments may take a couple rounds of discussion. In 2010, the average number of letters per conversation was 5 over 85.6 days to complete conversation and in

2015 this number decreased to 4 with only 45.7 days required for completion of the process

(www.auditanalytics.com). Three possible scenarios of resolution are possible: (1) management agrees and clarifies SEC questions in a letter to better understand disclosure (supplemental data provided); (2) the company agrees to the comments and amends future filings accordingly; (3) the company agrees and amends or restates prior filings. The firm may also request a confidential treatment request. Analysis conducted on the comment letters since they became publicly available

(May, 2004) reveals that only in rare circumstances comment letters result in restatements

(Dechow, Lawrence, and Ryans, 2016). Only the most severe cases will lead to restatements and their number is steadily decreasing over the years. The majority of comment letters represent clarifications and rather small amendments to the filings aiming at increasing value of provided financial information to investors. The case is considered closed when all clarifications are provided and the SEC sends final letter stating “no further comments.”

In personal discussions with partners of two Big 4 firms, I learned that auditors are frequently involved in resolution of the issues included on the SEC comment letter. In circumstances when management does not agree with SEC’s suggestions for amendment it may appeal the issue with higher authority within the SEC. The costs to registrants are unknown, but

27 could be significant because drafting a written response requires a coordinated effort by management, an independent auditor, legal counsel and, often, the audit committee (Davidson and

McCarthy 2008; Deloitte and Touche 2009).

On May 9, 2005, the SEC made a decision to start releasing its correspondence between the registrant and the SEC starting on May 12, 2005 (SEC 2013c). The rule was effective for comment letters and corresponding responses relating to disclosure filings made after August 1,

2004. The data is publicly available on EDGAR. Furthermore, as the SEC considered this correspondence to be valuable to investors’ community, it decided to require earlier public disclosure of the correspondence. On December 1, 2011 the SEC announced that it will start releasing correspondence no earlier than 20 business days (as opposed to 45 days before that) after the correspondence is finalized (SEC 2013b). As stated on the SEC website, the goal was to

“further enhance the transparency of the filing review process.”

The literature on determinants and implications of the SEC comment letters has started to emerge after the comment letter correspondence became public. In general, there are three main areas addressed in the literature: determinants and characteristics of firms receiving comment letters; consequences of comment letters on the firm’s accounting quality and disclosures; market reaction and auditor behavior.

The literature covered in the first part addresses the question – why companies receive comment letters in the first place. Naturally, it depends on the SEC decision to select that company for a review based on the undisclosed selection criteria. Despite this fact, Section 408(b) of SOX suggested several factors that can be taken into consideration in the selection process by the SEC.

Academic researchers exploring this area of research have determined numerous characteristics of

28 the firms (industry leaders, restatements firms, more complex, less profitable) that make them more likely to receive SEC comment letters. (Chen and Johnston, 2010; Cassel, Dreher, and Myers,

2013; Hribar, Kravet, and Wilson, 2014).

Chen and Johnston (2010) find that companies more likely to receive an SEC comment letter are industry leaders, have been publicly traded for a longer time, had occurrence of prior restatements, have greater cash flow volatility and higher earnings/price ratio. Companies more profitable, less complex, with effective internal controls and audited by larger auditors (Big 4 and second tier firms) are less likely to receive a SEC comment letter (Cassell, Dreher, and Myers,

2013). In their study of developing a new measure of accounting quality based on unexplained audit fees, Hribar, Kravet, and Wilson (2014) find that firms with lower accounting quality are more likely to receive a comment letter. Boone, Linthicum, and Poe (2013) investigate the association between characteristics of accounting standards and the likelihood of the SEC comment letter review. They find that accounting estimates and rule-based characteristics in

GAAP standards increase the probability of receiving a comment letter.

Lawrence, Gao, and Smith (2010) examine monitoring by the SEC and whether reviews are able to discover restatements. Furthermore, they investigate how monitoring by the regulator differs from other monitors such as auditors or boards of directors. It appears that SEC intends to supplement “weaker” other monitors by allocating its resources to companies with non-Big4 auditors. The evidence provides support for statement that each of the monitors plays an important and unique role of financial oversight in order to protect investors’ well-being.

Bens, Cheng and Neamtiu (2016) examination targets specific type of disclosure – fair value, as it is one of the most controversial areas receiving abundant attention from the SEC

29 comment letter review (PwC, 2015b; Deloitte, 2015). Their study provides evidence on a decrease of information asymmetry as a result of improved fair value disclosures (less uncertainty). The evidence suggests that an effective monitoring tool can provide benefits to the investors on their perceptions of fair value reporting.

The monitoring effect of the SEC oversight may have positive impact on the accounting quality of the company. Cunningham, Johnson, Johnson, and Lisic (2016) examine change in the earnings management of the firm following the receipt of the SEC comment letter. They find that accrual-based earnings management decrease as a consequence of the SEC comment letter which implies disciplinary role of the SEC monitoring. However, as the unintended consequence they find that real earnings management (less likely to be detected by the SEC in their reviews) will actually increase. Their results indicate that firms continue to manage earnings in the presence of the SEC monitoring; they simply tradeoff between different types of earnings management.

Bozanic, Dietrich, and Johnson (2015a) examine efficiency of SEC monitoring by investigating changes in the corporate disclosures following a receipt of the SEC comment letter.

In general, they find improvement in the quality of corporate disclosures as measured by factor analysis based on the five qualitative disclosure attributes, unless a company requests a confidential treatment. They also show that following SEC comment letter the company observes improvement in information environment, and increase in analyst following and a reduction in litigation risk.

Brown, Tian, and Tucker (2014) find positive economic effect of the SEC comment letters by showing that companies with no letters improve their risk factor disclosures to avoid receiving similar comments as the industry leader or other industry peers. The examined companies learned

30 about the issues with this specific type of disclosure through industry competitors, industry leader, industry peers or by working with the same auditor. The improved risk factors disclosures help to lower the likelihood of receiving these specific comments on the forthcoming filings. Literature also found evidence for higher likelihood of CEO turnover (Gietzmann, Marra, and Pettinicchio,

2016) and higher interest rate charged by banks in private debt contracting (Cunningham,

Schmardebeck, and Wang, 2016).

When responding to the SEC comment letter, the company which writes less readable responses have a higher likelihood of filing a subsequent restatement or ammendment to the filing

(Cassell, Cunningham, and Lisic, 2016). The empirical results from the study imply that costs of remediation can be economically significant if registrant does not respond to the issues in a clear and precise manner. The study responds to the call of auditors who guide their clients in improving their relationship with the SEC (http://www.pwc.com/us/en/audit-assurance-services/accounting- advisory/sec-comment-letters.html; PWC, 2016).

In general, evidence from the literature on the capital market consequences of public disclosure of SEC comment letters reveals that market participants do not pay attention to the public disclosure of the SEC comment letter. Johnson (2015b) examines changes in information environment following SEC comment letter public disclosure. However, the evidence provided shows that investors react to the public disclosure conditioned on concurrent filing (8-K).

Furthermore, market reaction and the company’s information environment was examined by Chen and Johnston (2010). They provide empirical evidence on the positive market reaction to the correction of the issue addressed by SEC comment letters. More specifically, they find the decline in abnormal return volatility and trading volume around earnings announcements. Furthermore,

Lawrence, Gao, and Smith (2010) finds that market reaction to the restatement as a result of the

SEC comment letter resolution is slightly positive but the reaction is only present when the SEC comment letter is accompanied by the 8-K release. This is opposed to negative reaction to request for restatements received from other monitors (board of directors, auditors).

Johnston and Petacchi (2017) provide extensive summary of different types of issues addressed in the SEC comment letters related to Form 10-K and 10-Q. The evidence provided on a relatively limited sample (SEC comment letters issued between 2004-2006) shows that in most circumstances the SEC comment letter improves a firm’s disclosures rather than leads to revisions in the reported numbers. The evidence provided shows a positive effect of comment letters - decrease in information asymetry following issuance of comment letter. However, they find little support that market perceives receiving comment letter as an indicator of lower accounting quality.

Thus, the findings have to be interpreted with caution due to the sample size limitations (2,308 comment letter firm-years).

Gietzmann and Isidro (2013) showed institutional investors decreased their equity holdings after the company received an SEC comment letter addressing issues related to the application of

U.S. GAAP or IFRS for firms traded on US markets. The negative reaction by institutional investors is magnified for low turnover investors. Furthermore, the SEC is more likely to address application of IFRS than US GAAP, which resulted in amendments to the filings for over 40 percent of the firms in the sample. Adding evidence to that stream of literature, Dechow, Lawrence, and Ryans (2016) find evidence of increased insider trade transactions before public disclosure of the SEC comment letter on expectation of negative market reaction to that event. They provide evidence of increased insider sales during 50 days window and argue that this could suggest that

“SEC’s practice of delaying the release of comment letter correspondence does not best serve the interests of outside investors”. However, they also show that reaction to the publicly disclosed

32 comment letter related to revenue recognition (as one of the most critical accounting areas) is delayed anyway.

Since the questions raised in the SEC comment letters evolve around audited financial statements and related disclosures, the research questions in this study focus on the audit quality of the reviewed filings. The underlying goal of the reviews is to improve company’s financial disclosures. However, one of the unintended consequences of this process could potentially be subsequent improvement in the audit quality. The overall goal of this study is to investigate changes in the audit quality for the companies receiving SEC comment letters. Moreover, as Big

4 audit firms are known to provide superior quality audits to their clients, the study examines whether the audit quality changes after the client receipt of the SEC comment letter differs between

Big 4 auditors and non-Big 4 auditors.

The literature on the impact of the SEC comment letters on auditor behavior is relatively limited when compared with literature on the impact of the SEC comment letter for companies and capital market consequences. Baldwin (2012) examined whether SEC comment letters subsequently lead to auditor change. Based on the sample of SEC comment letters from 2005-

2010, they find that the likelihood of auditor change for firms receiving the SEC comment letter is higher than for firms not receiving comment letters. The results hold for comment letters on various accounting issues and related to different filings (8-K, 10-K, and 10-Q). The results imply that audit committees, responsible for contracting with auditors, incorporate information from the

SEC comment letters into their contracting decision with the auditor.

Gietzmann and Pettinicchio (2014) investigate whether auditors reassess business risk by increasing audit fees after the receipt of comment letter by their client. Evidence provided implies

33 that auditors incorporate the signal from comment letter in determining the client audit risk. Audit fees increased following comment letter and this upward movement persists into future years. The limitation of this study is relatively limited sample size (15,267 comment letters issued over 4 years). The evidence provided by these studies shows that comment letters have important economic consequences in the form of increased audit fees and higher likelihood of auditor change.

In this study, I extend their research hypothesizing that audit fees could increase due to client’s demand and willingness to pay more for auditor’s efforts.

2.3 Short Selling

The literature addresses market reaction to the SEC comment letters and finds no noticeable impact around public disclosure of comment letters. However, the literature so far does not consider variation in the types of investors. The market reaction to SEC comment letters could be driven by the level of capabilities in processing and analyzing of data available to them. As short sellers are considered the most sophisticated investors, their reaction to the issuance of comment letter is important and it should attract more attention from researchers. The sources of comparative advantage they possess over other reasonably informed investors are still underexplored.

Short sellers are considered to be the most sophisticated investors as they are capable of conducting superior analysis of the firm’s fundamentals and can properly evaluate over-priced stocks (e.g., Dechow, Hutton, Meulbroek, and Sloan 2001; Karpoff and Lou, 2010; Hirshleifer,

Teoh, and Yu, 2011; Curtis and Fargher, 2014). They closely monitor stocks and they are able to determine overvaluation of it in order to take short positions in these companies to make profits.

There is strong empirical evidence that short sellers will sell their stocks when they are confident that stock price will decrease in the near future. Short-sellers use financial information to identify

34 overpriced firms. However, in order to make profits, they need to convince other market participants that the firm is overvalued (Ljungqvist and Qian, 2016).

There are two opposing streams of prior literature on the role of information sources by short sellers. On one hand, some studies provide evidence that short sellers have access to superior private information based on which they take short position with the company (Christophe, Ferri, and Hsieh, 2010; Khan and Lu, 2013). They trade on the financial information provided by management to the public as well as on some private information which allows them to even front run insider sellers (Khan and Lu, 2013). On the other hand, Chakrabarty and Shkilko (2013) do not find evidence of front-running for the firms traded at New York Stock Exchange (NYSE).

These two studies provide opposing evidence on the timing of the short selling position relative to insider trades.

As a result, there is no consensus on whether findings in Khan and Lu (2013) are attributable to information leakage by brokerages or short sellers’ superior analytical ability.

Studies supporting this stream of research show that short sellers are considered to be the most sophisticated and capable investors who are able to analyze fundamental values of the company and detect over-priced stocks from the publicly available information (Dechow, Hutton,

Meulbroek, and Sloan, 2001; Engelberg, Reed, and Ringgenberg, 2012). They are able to detect firms with higher accruals and subsequent downward restatements; they are also capable to short their position several months before restatement is announced publicly (Desai, Krishnamurthy, and Venkataraman, 2006). Furthermore, recent evidence suggests that short sellers are capable of detecting material weaknesses before they are disclosed the first time, which suggests that they might use some private information for that purpose (Singer, Wang, and Zhang, 2015).

Prior literature provides evidence that short sellers can identify earnings management or fraudulent financial reporting before they are publicly announced (Fang, Huang, and Karpoff,

2016). Market participants are provided with information that these sophisticated investors have superior knowledge about negative events deteriorating the stock price (e.g., going concern opinion, restatements, and material weaknesses). Assuming that changes in short interest are attributable to knowledge about potential misstatement or deterioration in financial performance, this activity could add value to the auditor risk assessment and subsequent audit effort exercised during the audit engagement.

Despite abundant research on short selling in the finance and accounting research, little is known about their impact on the auditor behavior and subsequent audit quality. In brief, the purpose of this dissertation is to examine how information received from short selling positions and SEC comment letter reviews impacts auditor behavior and audit process measured by common proxies of audit quality. The reviews conducted by the SEC combined with the information provided by short sellers could be a valuable source of information for auditors when planning and conducting their audit engagements.

Most studies in the finance area are focused on the consequences of short selling activity on the targeted firm. In general, findings from these papers provide evidence on the positive impact of short selling activity, as evidenced by lower earnings management (Fang, Huang, and Karpoff,

2016). Management concerned with negative market reactions due to increased short selling positions could amend their disclosures and require higher audit quality.

High audit quality has been found to be associated with less earnings management

(Krishnan, 2003; Fang, Huang, and Karpoff, 2016). However, to help restrict earnings

36 management and at the same time increase audit quality, the monitoring mechanisms are required

(e.g., institutional investors, analysts coverage or short sale investors). Literature provides evidence that earnings management is constrained by high analysts’ coverage (Yu, 2008; Hong,

Fariz, and Zhang, 2012), institutional ownership (Mitra and Cready, 2005), and short sellers

(Massa, Zhang, and Zhang, 2012). These specific market participants are actively monitoring firms’ financial reporting process as results of it will have impact on their behavior and activities.

As short sellers trade on the expectation of the decline in the stock price due to unfavorable events, it is possible that increased activity by short sellers can have effect on the auditor’s assessment of audit risk and subsequently audit quality. Cassell, Drake, and Rasmussen (2011) provide evidence of higher audit fees for clients with increased short selling activity. The increase could indicate a signal of audit risk considered when planning audit strategy. The findings are further extended by Rogo, Simunic, Tan, and Zhang (2015) in which they considered potential impact of audit demand factors (Willekens, 2011). Rogo et al. (2015) finds that an increase in audit fees is present for weak institutional investors and analysts monitoring of a company’s performance and when managerial incentives are not sensitive to stock price movements. Strong and effective monitoring does not lead to an increase in audit fees for firms with high short interest positions. Nonetheless, Hope, Hu, and Zhao (2017) provide empirical evidence for higher audit fees at firms with higher short interest risk, justified by higher probability of auditor lawsuits by short selling investors.

There are arguments supporting hypothesis that clients with increased short interest activities could improve audit quality. These clients may demand higher audit quality as they are closely monitored by sophisticated investors and want to limit probability of negative market reaction to audit failure news. Furthermore, client incentives for higher quality audit can be

37 magnified due to the monitoring pressure and demand from the short sellers. On the other hand, the audit quality may be inherently low as short sellers select firms with discrepancies in the fundamental values and stock prices and target firms with low quality of financial reporting. In this case, the audit quality will be low despite high auditor efforts as shown by other studies

(Cassell, Drake, and Rasmussen, 2011; Hope, Hu, and Zhao, 2017). These arguments support the validity of my research questions presented in Chapter 1.

2.4 Audit Quality

In this dissertation I examine whether the SEC review process of public companies in the form of the comment letters as well as short selling activity affects the audit quality. Despite extensive research in the area of audit quality, regulators, audit firms and researchers are continuously challenged by finding the most suitable and comprehensive definition of audit quality as well as appropriate measures to capture it. The search for “one fits all” definition and measurement continues and this study adds to that stream of literature.

A pioneering paper on audit quality defined it as: “… The market-assessed joint probability that a given auditor will both (a) discover a breach in the client’s accounting system, and (b) report the breach” DeAngelo (1981, page 186). Subsequent studies expand this base definition and incorporate the role and characteristics of the auditor but omit factors important to other groups of stakeholders (e.g., investors). In one of the most recent papers on audit quality, Christensen,

Glover, Omer, and Shelley (2015) show that depending on the party evaluating audit quality, different factors are of importance that are not specifically addressed by the models commonly used in the literature (audit firms, investors, client’s management). They find that management

38 focuses most heavily on input and process characteristics of the auditor and audit engagement and less on the output measures when evaluating audit quality.

Many initiatives and projects have been launched by regulators and audit firms to improve audit quality and develop better measures to capture it. Just to name a few, PCAOB initiated a project in November 2012 with the goal of providing more insight into the measures of audit quality (PCAOB, 2013). International Auditing and Assurance Standards Board (IAASB 2013) developed a framework for audit quality in 2013, which describes relevant input and output factors at different audit levels (engagement, audit firm and national). Moreover, the framework stresses the importance of interactions among stakeholders to explore ways of audit quality improvement as well as consideration of different contextual aspects. Audit firms continuously make significant investments in the technology, data and people to streamline audit process and improve audit quality (KPMG 2015, PWC 2015a).

In order to make correct inferences on the implications of different factors on audit quality, we need to consider that each of the input or output measures of audit quality has some potential weaknesses and varying levels of importance for the party evaluating audit quality (e.g., Francis,

2011; Knechel, Krishnan, Pevzner, Shefchik, and Velury, 2013; DeFond and Zhang, 2014,

Christensen, Glover, Omer, and Shelley, 2015). Knechel et al. (2013) points out that audit quality is impacted by the audit process characteristics (risk assessment, analytical procedures, evaluation of evidence and review). The major limitation of the current studies in this area is the fact that input measures are mostly not observable and therefore not available to academics and practitioners. On the other hand, the widely available and observable outputs measures

(restatements or audit opinions) are not perfect either as they are confounded by other factors (e.g., original quality of the client’s financial reporting).

Audit quality is also determined by the characteristics of the auditors and the audit firm.

For example, Gunn and Michas (2017) find that national expertise of the auditors/partners helps to improve the audit quality on the multinational audit engagements. The desire to contract with the audit firm providing higher quality audits is also reflected in the better disclosures by the company. Legoria, Reichelt and Soileau (2017) find that firms are more likely to provide voluntarily disclosures when they are audited by the audit firm with higher audit quality as measured by the Big 4 and office size characteristics.

The financial statements are the joint product of auditor and client discussions (DeFond and Zhang, 2014). Management typically creates a preliminary trial balance for the auditors to use in the beginning of their testing work. As auditors find mistakes or areas of disagreement, they propose adjustments to the trial balance numbers. Auditors and management will then typically start negotiations to agree on how questionable transactions should be reported in the financial statements. The updated financial report is still subject to auditor’s review before it can be filed with the SEC and released to the public. The auditors could propose amendments that should improve the quality of the financial statements.

The purpose of this study is to respond to the call by Francis (2011) and DeFond and Zhang

(2014) in which they ask for more research on the role of audit firms’ characteristics for evaluating audit quality. This dissertation contributes to the literature by providing empirical evidence on the implications of the SEC comment letters and short selling activities on the audit quality proxies.

Following DeFond and Zhang (2014), I use audit quality measures, each of which captures complementary dimensions of audit quality: audit fees, auditor changes, discretionary accruals, material weaknesses, restatements and PCAOB inspections.

The audit fees are determined by competitive process in the audit market. Since the SOX regulation, the audit fees increased significantly as a result of additional testing required by the new regulation. However, it remains an open empirical question if increased audit fess indicate higher audit quality at the same time (Francis, 2011). On one hand, audit fees are positively and highly correlated with auditor effort. As the auditor increases its efforts it expects to receive higher audit fees from the client (e.g., Lobo and Zhao, 2013; Ball, Jayaraman, and Shivakumar, 2012).

On the other hand, higher audit fees could result from the higher assessment of audit risk and litigation risk for more complex and difficult audit engagements (Hribar et al. 2014).

The determinants of audit fees have been extensively researched in the prior literature

(Simunic, 1980; Francis, Reichelt, and Wang, 2005). However, to the best of my knowledge there is only one study that investigates impact of SEC comment letters on the audit fees. Gietzmann and Pettinicchio (2014) examine sample of letters over the period of 2004-2008 to find that audit fees do increase for firms with SEC comment letters and this persists into future years. Examining three groups of comment letters (accounting issues, other disclosures, or risk issues) they find positive association between audit fees and only two types of comment letters – related to accounting and risk issues. The results imply that SEC comment letters provide valuable information to the auditors and impact their pricing negotiation process with the client. The increase in audit fees found in the study persisted over 3 years after the receipt of the SEC comment letter which indicates significant informational value of this monitoring tool. It also provides evidence that auditors incorporate occurrence of the SEC comment letter to the client in risk assessment decisions when planning for the future audit engagements. The results show that an increase is not only related to billable hours due to assistance with response to comment letter but persist into future years following SEC comment letters. The evidence underscores the

41 informational value of the SEC comment letter to auditors as they incorporate it into their reassessment of the audit risk for subsequenent audit engagements.

Insider traders decrease their position in the company due to the expectation of the negative market reaction to the public disclosure of the SEC comment letter. As short sellers are able to front run insider traders I expect that they will increase the short positions as well. Cassell et al.

(2011) provides evidence that an increase in short interest positions increases audit fees as they provide signal to the auditors of increased audit risk. Short sellers expecting negative market reaction to the auditor failure will increase their short positions to make profit when the reaction occurs. Increase in short positions can therefore provide auditors with a valuable signal they should consider in evaluating audit risk (risk of issuance unqualified opinion when financial statements are actually misstated). I expect that audit firms will increase audit effort in order to reduce the risk which could translate into higher audit quality for firms with high short interest.

The second proxy of audit quality used in this study is auditor change. Multiple factors could trigger the auditor change and impact the auditor-client tenure. Generally, literature differentiates between “good” and “bad” auditor switches (e.g., change from non-Big4 auditor to

Big4 auditor versus change due to a disagreement between client and auditor). Grothe and Weirich

(2007) imply that auditor changes occur more frequently for firms restating financial statements and with weak internal controls. The change is associated with the learning curve and negative impact of short tenure on audit quality has been evidenced in prior studies (Ghos and Moon, 2005).

Furthermore, the literature finds inconsistent evidence for the market reaction on the public disclosure of the auditor change. Despite wide examination of the information value of auditor changes to the market participants, there is no consensus on the direction of the reaction. There is

42 a stream of literature which finds evidence on the negative market reaction to the disclosure of the auditor change (Hackenbrack and Hogan, 2002; Shu, 2000). Other studies find positive reaction

(Chang, Cheng, and Reichelt, 2010). Blau, Brough, Smith, and Stephens (2013) provide detailed examination of the short sellers reaction to different types of auditor changes. Based on the limited sample (401 auditor changes over the years 2005-2006) they find that short sellers are able to differentiate between “good” and “bad” (auditor downgrades and resignations) auditor changes and increase their revenues in the postannouncement period. They show that in general, the market reaction to the auditor changes news is delayed which provides opportunity to increase the revenues of short sellers.

The auditor change is also examined in the setting of the SEC comment letter. Baldwin

(2012) examines whether SEC comment letters subsequently lead to auditor change. Based on the sample of SEC comment letters from 2005-2010, the study finds that the likelihood of auditor change for companies receiving the SEC comment letter is higher than for companies not receiving comment letters. The periodic SEC reviews are perceived as a reporting failure that increases the likelihood of auditor change (dismissal and resignation).

Additionally, discretionary accruals are frequently used in the literature as a measure of audit quality as they capture firm’s ability to manage earnings. The assumption behind using discretionary accruals as a proxy for audit quality is based on the fact that high quality audit should constrain opportunistic earnings management. They are also associated with Accounting and

Auditing Enforcement Releases (AAER) as provided by evidence in Dechow and Sloan (1995) and may therefore increase likelihood of potential misstatement. Furthermore, audit quality is a component of financial reporting quality as they are a joint product of management and auditors

(Magee and Tseng, 1990; DeFond and Zhang, 2014). These facts provide good reason for frequent

43 use of the financial reporting quality measures as a proxy for audit quality as financial statement more faithfuly represent economic situation of the company.

One of the output measures of audit quality are restatements, which is another proxy used in this study. Prior studies found positive association between audit effort and the likelihood of detecting errors (Matsumura and Tucker 1992; Dye 1993; Hillegeist, 1999). They provide evidence for a negative relation between current-year audit effort and subsequent restatement of current year financial reports. As a result, restatements are frequently used in the literature as a proxy of audit quality as the ability of the auditor to detect material misstatements indicates higher audit quality. Restatements indicate poor audit quality which is conditional on low financial quality in the first place.

Prior studies on the restatements focus on the determinants and negative market reaction to the announcement of the restatements (Myers, Scholz, and Sharp, 2013; Lobo and Zhao, 2013).

Lawrence, Gao, and Smith (2010) examine market reaction to the restatements resulting from the

SEC review. They find the reaction to be slightly positive but it is only present when restatements are accompanied by the 8-K release. This is opposed to negative reaction to the request for restatements received from other monitors (board of directors, auditors). Furthermore, the study provides evidence that SEC supplements “weaker” monitors by allocating its resources to companies with non-Big4 auditors. Companies audited by the non-Big4 auditor are more likely to end up with restatement as a result of the SEC review than companies using Big4 auditors. Their study also provides additional evidence that Big4 auditors supply higher quality audits to their clients as measured by restatements.

The literature also addresses the ability of short sellers to foresee upcoming restatement of financial statements (Drake, Myers, Scholz, and Sharp, 2015). They provide evidence that short selling is at higher than normal levels when restatements are announced through public disclosures

(e.g., 8-K or press release). The effect is more pronounced for firms with weak information environment and when restatements result in reduced income.

Material weaknesses in internal controls is another proxy of the audit quality widely used in the literature. Internal controls over financial reporting are the key elements in ensuring financial reporting quality (PCAOB, 2007)3. Auditors place a high reliance on them when performing financial statements audits. The importance of the internal controls was addressed by the SOX Act by imposing new requirements on company’s management and auditors (SOX, Section 302 and

Section 404). Additionaly, PCAOB Auditing Standard No.2 relating to this section require (1) a management report on internal control over financial reporting, (2) auditor attestation of such a management report, and (3) an auditor’s report on internal control (PCAOB, 2004). The requirements relate to clients with a public float of 75 milion (“large accelerated filers” and

“accelerated filers”).

So far, literature extensively examined the economic consequences of disclosed material weaknesses in internal controls of the company. Raghunandan and Rama (2006) find that audit fees for client’s disclosing material weaknesses controls are higher when compared with clients with no material deficiency. Studies show that firms with deficient internal controls generally have

3 Auditing Standard No. 5 describes the importance of effective internal control over financial reporting as follows: “Effective internal control over financial reporting provides reasonable assurance regarding the reliability of financial reporting and the preparation of financial statements for external purposes. If one or more material weaknesses exist, the company's internal control over financial reporting cannot be considered effective.”

45 lower earnings quality as weak internal controls allow them to exercise earnings management to a greater extent and create noise in the market (Doyle, Ge, and McVay, 2007; Chan, Farrell, and

Lee, 2008). However, literature provides no noticeable market reaction around the internal control material weaknesses (ICMW) announcement date and the evidence on the capital cost increase is mixed (Ashbaugh-Skaife, Collins, Kinney, and LaFond, 2008; Ogneva, Subramanyam, and

Raghunandan ,2007; Beneish, Billings, and Hodder, 2008). Feng, Li, and McVay (2009) provide evidence on the negative association between ICMW and errors in managerial earnings forecast.

It indicates that inefficient controls adversely affect the quality of information provided by internal management. Despite the above arguments on the market reaction study provided by Singer, Wang and Zhang (2015) find evidence that short sellers are able to identify firms with forthcoming material weaknesses deficiency. They have capability of detecting material weaknesses before they are disclosed for the first time, which suggests that they might use some private information for that purpose.

According to the findings in Aobdia (2016), the majority of the audit quality output measures used frequently in the prior literature have limited explanatory power due to the joint function of audit quality of public accounting firms and financial reporting quality of the client.

As a result, in this study I extend the validation of the audit quality measures originated in the study by Aobdia (2016) by investigating whether the SEC comment letters and high short interest positions in the company impact the likelihood of the Part I findings for the local office of the audit firm. Thus, the last examined proxy of audit quality in this study is PCAOB inspections and deficiencies arising from them.

In response to the high-profile accounting scandals at the beginning of the twenty first century (e.g., Enron, Worldcom), US Congress concluded that peer reviews performed by the

American Institute of Certified Public Accountants (AICPA) are not effective. Consequently,

Sarbanes-Oxley Act of 2002 established Public Company Accounting Oversight Board (PCAOB) as a nonprofit corporation to more effectively oversee the audits of public companies and to improve financial reporting of their public clients. According to Section 104 of SOX, PCAOB is required to perform annual inspections of public companies which provide audit services to more than 100 issuers (publicly traded firms), and at least every three years for the rest of the public accounting firms. The focus of the inspection is to identify any deficiencies during audit engagement on the side of the auditor and any weaknesses in the quality controls over audits of their publicly traded clients.

The PCAOB selects audit engagements for inspections based on the risk approach (e.g., the nature of the company, complexity of transactions, history of audit issues during prior inspections as well as characteristics of the audit firm). During inspections, a team of experienced inspectors reviews working papers for completed audit engagements and interviews engagement team (Center for Audit Quality, 2012). The inspections do not cover the entire audit engagement but focus only on challenging and complex areas of a particular audit. The final product of the inspection is a report with the inspectors’ conclusion. All the reports have a public portion (Part I) discussing audit deficiencies relating either to GAAP deficiencies (inadequate application of the

US GAAP by the client) or GAAS deficiencies (failure to perform required audit procedures). In cases when inspection results in comments/findings on the quality controls of the firm, the report also includes a confidential portion (Part II).

PCAOB inspections are a valuable measure of audit quality. First of all, their main advantage over other proxies used in this study is the fact that PCAOB inspections are not related to the financial reporting quality of the client like other measures. The focus of the inspection is to

47 check whether auditing standards required on the audit engagement are followed. The Part I findings of the inspection report point out deficiencies in the audit work on the audit engagement which directly indicate low quality audits. The inspections do not focus on the financial reporting quality of the client. For that reason this measure better captures audit quality per se minimizing the noise resulting from the financial reporting quality of the client. In support of that statement, the audit engagement could have deficiencies identified in the Part I findings despite the fact that financial statements are free of material misstatements.

Moreover, considering significant resources provided by the PCAOB and expertise of the inspectors (average of 12 years of public accounting experience as indicated by Lennox and

Pittman, 2012) who are independent from the public accounting profession, we are able to conclude that Part I Findings are a good indicator of audit quality. Furthermore, this statement is confirmed by the partners of the audit firms who agree that PCAOB inspections are thorough and performed by highly qualified personnel (Houston and Stefaniak, 2013).

The PCAOB inspections are more frequently examined by the literature as an important audit quality determinant. For example, in the latest DeFond and Lennox (2017) study, they find that PCAOB inspections improve the quality of internal controls through their ability to remediate deficiencies in auditors’ internal control audit procedures.

The drawbacks of using Part I findings as an indicator of audit quality is mostly limited sample size (due to relatively low number of inspections performed annually) as well as lack of time-series data for some audits. Additionally, data limitations for this study do not allow me to conduct analysis on the specific audit engagement and can only be performed on the local office

48 level. Being aware of the limitations of this measure, the study focuses on the overwhelming advantages of Part I Findings as a valuable measure of audit quality.

To summarize, this study examines the impact of the SEC comment letters and short selling activity on the audit quality measured by six proxies. Furthermore, I construct a composite measure of audit quality based on all six proxies using factor analysis. Considering change in management decisions regarding the level of disclosures, I expect that audit quality will increase when faced with SEC comment letters and short sellers. As voluntary disclosures of the company with short interest position increase following a comment letter, management’s incentive for a higher quality audit increases as well.

Chapter 3: Hypothesis Development and Research Design

3.1.1. Discretionary Disclosure Model

The discretionary disclosure model presented by Verrecchia (1983) shows how managers use discretion in disclosing information about the firm when they are faced with investor’s rational expectations. The information disclosed to the market participants determines market price of the firm adjusted by some noise. Taking into account proprietary information costs, management faces the decision of either full disclosure or disclosure up to a certain level in order to retain proprietary information. The point (x) at which manager switches from disclosure to no disclosure is called

“threshold level of disclosure”. It is influenced by the proprietary costs (c) that manager is not willing to disclose. When all the private information is disclosed, then the value of the firm is reduced by the proprietary information communicated to external parties. Therefore, when managers decide to withhold information it does not automatically imply that it is unfavorable.

Managers perform cost/benefit analysis to find the optimal level of disclosure, which is a very challenging task.

Based on the model presented by Verrecchia (1983), the price of the firm when a manager decides to fully disclose all available information to investors is presented by the following equation:

P (γ=y) = E [(u-c)|y=y]-β(var[u| γ =y]) (1)

The price is determined by the market’s expectation of the true liquidating value of the firm

(u) reduced by the proprietary costs (c) and the variance in the expected value (var) conditional on the manager’s endowment of information (y). The true liquidating value of the firm has a normal distribution with mean y0 and precision of h0. The noise in the signal has a normal distribution with mean 0 and precision s. The precision s is defined as the quality of the information regarding the liquidating value of the firm (Verrecchia, 1990).

When managers decide to withhold information this behavior will influence traders’ expectations to the true liquidating value of the firm. As a result, the price of the firm in that scenario is presented by the following model:

P(γ=y<=x)=E[(u|y=y<=x]-β(var[u| γ =y<=x]) (2)

Managers disclose private information when it is above threshold level (x) and withhold private information. The above equation then indicates when the costs of disclosure exceed expected benefits reflected in the market price of the firm, then managers decide to withhold that information. However, market participants are aware of that behavior and form their expectations based on that knowledge.

The unique feature of this model is that it introduces a discretionary disclosure equilibrium defined as a threshold level that maximizes the market value of the firm and that investors are aware of management’s intentions and that withheld information is due to costs exceeding the benefits.

The model presented above provides explanation for the short selling activity. Short sellers as the most sophisticated group of traders are fully aware of manager’s intentions and incentives

51 for full disclosure and withholding of information. If the managers decide to fully disclose their private information then short sellers’ activity will be precluded. Their activity can be initiated in situations when the manager withholds private information due to high proprietary costs. Based on these inferences, I predict that increased short selling activity will be present when the information is withheld by managers.

When the company lacks appropriate disclosures in the financial reporting it is more likely that SEC will perform the review and point out the lack of sufficient disclosures. As a consequence of the SEC review process, the companies receive the SEC comment letters. Therefore, I can reasonably predict that short sellers’ activity and the SEC comment letters can occur in the same time period as they are the result of withheld information by management. Furthermore, auditors based on the signal provided by the SEC and short selling activity can benefit from that knowledge and improve their audit quality on the subsequent audit engagements.

The subsequent audit engagement for all stated below hypotheses refer to the audit engagement in the subsequent year (t). Further studies can extend the analysis to evaluate the effects for the next year (t+1) and even two years (t+2) following comment letter. The additional analysis could enhance the study by evaluating effects in the long run if they persist.

3.1.2. Conceptual Model

The conceptual model for this study is presented in Fig. 1. Management decides on the level of voluntary disclosure based on the tradeoff between proprietary costs and regulatory requirements. Management has incentives to disclose financial information only to a certain point.

However, at the same time the company is faced with two mechanisms – market based (short

52 selling) and regulatory based (the SEC), which can influence their decision about the level of disclosures. Therefore, management improves the information environment of the company by improving disclosures as required by the SEC comment letter. Subsequently, following changes in the disclosures, the company can expect higher audit quality from their auditors. This is justified by management’s incentives shifting towards meeting regulatory requirements and market demands for full disclosures.

Management’s decision on the optimal level of disclosure may be driven by regulatory- based and market-based mechanisms. The SEC comment letters are a result of the SOX requirement that firms undergo a periodic review of financial statements. The market-based process driving the optimal level of disclosure is to some extent influenced by the short sellers as they use various sources of information – including financial statements and non-financial data, to make their trades. The monitoring role played by short sellers and the SEC influences the audit quality. Please see Fig. 1 for the overview.

Fig. 1 – Conceptual Model Management Decision on the optimal level of disclosures (tradeoff between proprietary costs and regulatory requirements)

Discretionary Disclosure Model (Verracchia, 1983)

Regulatory-based Mechanism Market-based Mechanism

SEC Comment Letter: Short Sellers

 SOX Section 408  Superior ability to analyze  Regular periodic Reviews of Financial Statements and Related Disclosures Financial Statements and  Non-Financial Information related disclosures by the SEC  SEC Comment Letters -

DEMAND FOR AUDIT QUALITY:

What is the client’s demand for Audit Quality faced with the SEC Comment Letters and Short Selling?

Shift in management incentives to meet market and regulatory requirements for full disclosure

3.1.3 Audit Fees

Considering the fact that auditors are actively engaged in the communication process between the SEC and the client I expect that issues raised in the comment letter could lead to an increase in audit fees in the subsequent period. Once management receives a comment letter it notifies involved parties, including the audit firm, who assists with providing a response to the

SEC. The audit firms have full insight into the issues raised by the SEC. The goal for management is a fairly fast resolution of the issue to minimize the amount of letters involved in the communication process.

The role of the audit firm in the support of management to respond to the comment letter varies by the type of the issue addressed as well as the final decision on how the issue must be remediated. It could be only a minor correction to be applied to future statements. On the other end, the comment letter could result in the required revisions to the disclosures or even restatements.

As discussed in private conversation with partners at Big 4 offices, auditors strive to reduce the number of SEC comment letters received by their clients. Their efforts include National Quality

Office supervising all communication between the SEC and clients and summarizing issues in the annual reports (PwC 2015b; E&Y 2015). In order to improve quality of the subsequent reporting, audit firms provide webcasts, seminars and trainings which educate their clients about common trends and questions raised by the SEC. The goal is to guide other companies in the specific industry to reduce the likelihood of receiving a comment letter addressing the same issues. The benefits of this process are evidenced by the spillover effect examined by Brown, Tian, and Tucker

(2014). Audit firms approach the comment letters of their clients with significant attention and

55 receive an insight into the most current topics the SEC seeks improvements on. As a result, not only the client’s financial reporting experiences improvement in quality, but it allows auditors to improve the quality of their work on the audit engagements.

Furthermore, as the communication between the SEC and the client is publicly released within 20 days of resolution, it provides additional incentives for the auditor to excercise more effort (proxied by audit fees) in order to reduce the litigation and reputation risk. Active auditors’ involvement in the communication process offers additional incentive that can be used in the price negotiation process for the subsequent year. Additionally, the company itself may demand higher quality audits as well and thus, may be able to pay more audit fees in the future as well.

During the audit pricing negotiation process, audit partners consider a variety of factors, including the risk of receiving a comment letter as in some cases it may lead to the regulatory action. The fact that SEC review is performed for companies at least one every three years, the decision for pricing audit services may already incorporate receiving comment letters by the client.

However, this argument could also be incorporated by the auditor only when the client actually receives a comment letter and expects compensation for additional time assisting the client.

I predict that an increase in audit fees subsequent to the receipt of a comment letter is a result of the upward adjustment by the audit firm based on the risk assessment. However, the client may be only willing to agree to the increased fees by demanding a higher audit quality from the audit firm for the subsequent year. I argue that comment letters play a valuable role for audit firms when assessing audit risk for future audit engagements. Despite remediation of the issue, the auditor should reevaluate their business and audit risk as well as consider more extensive audit procedures for the areas that were questioned in the SEC comment letter. Furthermore, the client

56 will demand from the audit firm additional effort to reduce the probability of receiving an SEC comment letter in the future. Since SEC comment letters are publicly disclosed, management will demand more effort from the auditors on the subsequent audit engagements as it expects negative market reaction (Dechow et al. 2016). Thus, I can expect that audit fees will increase accordingly.

As a result of the above arguments, the first hypothesis stated in the alternative form is:

H1a: Companies which receive an SEC comment letter will be charged higher audit fees for the audit engagement following the SEC comment letter than companies with no SEC comment letter.

Based on the evidence provided by Cassell et al. (2011), the increase in short positions is associated with higher audit fees. Short sellers increase their activity expecting the upcoming negative corporate event. I predict that the SEC comment letter release might trigger such a reaction among the short sellers. Thus, increase in short positions can therefore provide auditors with a valuable signal they should consider in evaluating audit risk for the subsequent audit engagement (risk of issuance unqualified opinion when financial statements are actually misstated). I expect that audit firms increase audit effort for clients which received the SEC comment letter and with short interest positions in order to reduce the audit risk. Furthermore, management monitoring short positions in the company will demand higher audit quality from the audit firm as they are afraid of negative price drift in the company (Grove, Johnsen, and Lung,

2016). In their study they report that companies with SEC comment letters significantly underperform the market for at least three quarters following the issuance of the letter. Considering the SEC comment letter event and short interest position in the company, I expect that audit fees for firms with short interest positions should increase following public disclosure of comment letter.

Thus, the next hypothesis is stated in the alternative form:

H1b: Companies with short interest positions will be charged higher audit fees for the audit engagement than companies with no short interest positions.

As a result of the above arguments supporting increased audit fees for companies with SEC comment letters and short interest positions, I predict that SEC comment letters combined with the short interest positions might have an impact on the demand of higher audit quality. As shown by most recent publications (Dechow et al. 2016; Grove et al. 2016) both factors are perceived by the auditors to increase litigation and audit risk. Furthermore, management is also afraid of the negative market reaction triggered by both factors. Therefore we may observe shift in management incentives for a full disclosure and expect them to demand higher quality audits from their external auditors as a result of that.

Facing short sellers activities awaiting upcoming downturn in the stock price, management is forced by SEC monitoring mechanism to reduce the effect of both factors. On one hand we have audit firm incorporating information value of both factors into the negotiation process with the client and on the other hand we have management demanding higher quality audits to provide a positive signal to the markets.

Management in that situation may be willing to demand and pay more for audit efforts on the subsequent audit engagements. Consequently, the client with the receipt of the SEC comment letter and short interest position will demand higher audit effort from the audit firm as the audit risk of the company is increased by both factors. Therefore, we may expect increase in audit fees for companies with SEC comment letters and active short interest investors. As a result of the above arguments, the next hypothesis is stated in the alternative form:

H1c: Companies with an SEC comment letter and short interest position will be charged higher audit fees for the audit engagement following the SEC comment letter than companies with no SEC comment letters and no short interest positions.

In most circumstances, the SEC comment letters require a firm to provide the requested disclosure adjustments in the company’s response, or provide disclosure adjustments in upcoming filings. A partner of the Big 4 expressed an opinion that “more severe comments on accounting issues accompanied by client’s weak internal controls that they cannot rely on could lead to reassesment of audit risk or even resignation from the audit engagement if it seems to be too risky.”

This statement indicates that auditor behavior could depend on the severity of the issue addressed by the SEC rather than the comment letter as an event.

Thus, to consider this fact I categorize the sample of comment letters into main accounting topics. I follow Dechow et al. (2016) and consider letters refering to the most prevalent accounting topics as letters addressing critical issues. The goal is to differentiate comments pertaining to topics related specifically to the accounting issues (e.g., revenue recognition, pension, inventory, receivables and doubtful accounts, non-GAAP, restructuring and impairment) from the non- accounting issues. Furthermore, I address the severity of the comment letter by measuring number of rounds it takes to finalize the comment letters and frequency of comments received.

In this part of the study, I predict that more severe comments captured by the three above measures (critical accounting issue, number of rounds, and frequency of comment letters) will have more pronounced effect on the audit fees than not severe comments. Therefore, I propose that the impact of the SEC comment letters will vary depending on the type of the critical issue addressed in the comment letter.

The above arguments lead to the following alternative hypothesis:

H1d: Companies which receive more severe SEC comment letters will have more pronounced increase in audit fees than companies with less severe comment letters.

3.1.4. Auditor Changes

The audit committee of the company is in charge of the auditor selection process as well as audit firm dismissal if supporting circumstances arise. The independent auditors provide assurance that financial statements and related disclosures comply with accounting standards and regulations. When the SEC comment letter is received by the company, audit committee may perceive this fact as inefficiency in the audit process and auditor’s inability to warrant compliance with applicable disclosure regulations. Subsequently, comment letters received frequently by the company could lead to the auditor change in the long run.

So far, the literature is unclear on the impact of the SEC comment letters on the relationship between auditor and client. As stated by the Christensen et al. (2015), audit quality depends on the perceptions and views of the evaluator. Therefore, the receipt of the comment letter by the client can be perceived by the auditors as additional monitoring tool designed to enhance the quality of financial disclosures. On the other hand, the audit committee can view the product of the review as failure in compliance with regulations and could indicate low quality audits. As a result, it is possible that audit committee may end up dismissing the auditor after multiple receipts of comment letters. Management also fears negative market reaction to the public disclosure of the comment letter correspondence and could blame the auditor for the receipt of comment letter.

As indicated by DeAngelo (1981), audited financial statements are the joint product of the auditor and management. If management has an aggressive reporting style and is reluctant to agree

60 to auditor’s disclosure proposals then the auditor can evaluate the client as high risk. In this case, the change in auditor could also result from auditor resignation when auditor evaluates audit risk for the client and determines if it exceeds tolerable level. To decrease litigation risk, the auditor may resign from the engagement when it determines that maintaining the client in the portfolio may be too risky. Thus for the purpose of this study I do not differentiate between auditor resignation and auditor dismissal as they both could be a negative consequence of SEC comment letters.

Each audit firm has their own unique disclosure checklist that is reviewed before the client can file the annual or quarterly reports with the SEC. If the checklist is not sufficient to find and address areas that SEC may later find in their review process, it indicates low quality audit.

Comments from the SEC relating to the issues under the scope of the audit engagement have an impact on the audit quality perceptions of the audit committee. Repetitive comments as well as comments addressing critical accounting issues can influence the decision of the audit committee to dismiss the auditor.

The relationship between the company and audit firm is not yet well established in the literature. SEC comment letter can be perceived by the audit firm as the increased risk of the client which could lead to auditor resignation. On the other hand, audit committee may attribute the comment letter with the insufficient audit quality delivered by the audit firm. Both cases could lead to the change in the audit firm subsequent to the receipt of comment letter. Since I do not examine the root cause of the auditor change (resignation/dismissal), I predict that in either case the relationship between the audit firm and the company is affected by the receipt of comment letter. The receipt of comment letter can be perceived by the audit committee as the indicator of the audit quality the same way as it is captured by the SEC restatements or going concern opinions.

Based on these arguments I predict that companies which receive SEC comment letters have a higher probability of auditor change in the subsequent year than companies with no comment letters.

Thus, the alternative hypothesis is stated as:

H2a: The auditor change is more likely for companies that receive SEC comment letter than for companies with no comment letter.

Furthermore, in this dissertation I examine whether short selling behavior has an impact on the auditor change when the company receives a comment letter. The focus is to investigate information value of the short selling activity to the auditors and audit committees in charge of the auditor contracting. I propose that short sellers activity conveys to the auditor the forthcoming bad news as they expect the negative stock market reaction. So far, the literature provides evidence that short sellers become more active around the auditor change (Blau et al. 2013) but lacks the evidence supporting auditor change as a result of high short interest activities.

In this study I propose and examine whether higher short selling activity around the receipt of comment letter increases the probability of the auditor change on the subsequent audit engagements. Since contracting with the auditor is performed significantly ahead time I examine the auditor change for the year following the SEC comment letter plus one year to measure a more realistic reaction of the company to the auditor change. Once the client receives the SEC comment letter, short sellers can expect negative market reaction which conveys negative news to the auditors as well. The audit committee may place blame on auditors for insufficient audit effort leading to higher activity of short interest traders. I propose that management will demand higher

62 quality audits from the present audit firm. If this will not be sufficiently provided, the probablity of the auditor change will increase.

Thus, the alternative hypothesis is stated as:

H2b: The auditor change is more likely for companies with short interest positions than for companies with no short interest positions.

Furthermore, I expect that both variables of interest (SEC comment letter and short interest positions) might have an impact on the likelihood of the subsequent change. Since each individual variable of interest increases the likelihood of auditor change I can logically expect that this effect will be more pronounced when both variables are present. On the other hand, the likelihood of the auditor change may decrease as the audit committee may decide to continue the existinig relationship with the audit firm when the short selling traders are active. The receipt of the comment letters and short selling positions of traders may not provide sufficient and strong argument for the audit committee to discontinue the relationship with the current audit firm.

Thus, the next hypothesis is stated in the alternative form:

H2c: Companies with SEC comment letters and short interest positions will have higher probability of auditor change following the receipt of a comment letter than companies with no comment letters and no short interest positions.

Following approach taken with respect to audit fees, I focus on the severity of comment letters and high versus low short selling positions. I argue that the severity of comment letters may have differential impact on the perceptions of auditors by the audit committee. The members of the committee are usually financial experts and they are capable of evaluating the relevance of the issues addressed by the SEC. Inquiries not related to the critical accounting topics may not result

63 in the changes of the auditor firm as the costs of contracting with the new firm could exceed potential benefits of the change. However, if the issues pertain to critical accounting topics and comment letters are repetitive in nature it may evenutally lead to the change in the audit firm.

Furthermore, if the issues take a few rounds to finalize the letter and significant number of days - it can be indicative of the more severe nature of the comments. Furthermore, the effect could be more pronounced for companies with higher short selling activities versus low short selling positions. High short selling positions indicate more interest and greater chance of profits for short sellers on the forthcoming bad news.

I expect that the severity of the issues addressed in comment letter will have more pronounced impact on the likelihood of auditor change. Following the same three measures of severity I expect that comment letters addressing more severe comment letters will have more pronounced impact on the likelihood of auditor change for subsequent audit engagements. Thus, the next hypothesis stated in alternative form is as follows:

H2d: The auditor change is more likely for companies that receive more severe SEC comment letters than for companies with less severe comment letters.

3.1.5. Discretionary Accruals

A company involved in opportunistic earnings management through discretionary accruals is more likely to be selected by the SEC for the review and as a result can receive a comment letter addressing that behavior. The comment letter can trigger management reaction to change their practice of managing earnings by the use of discretionary accruals. Considering importance and priority value of the comment letters to the management, we can expect the change in the behavior as a result of the monitoring signal provided by the SEC. Cunningham, Johnson, Johnson, and

Lisic (2016) deliver empirical evidence that management moves away from using discretionary accruals and shifts towards real earnings management activities. The evidence implies that management does not discontinue the practice of managing earnings but only changes the method in which earnings management is performed. Related evidence is provided by Robinson, Xue, and

Yu (2011) where they investigate consequences of receiving comment letter on not disclosing excessive CEO compensation. The company does not change behavior in providing excessive compensation to CEO but they only improve disclosures on it.

Any deviations in the predicted level of accruals may indicate “poor” audit quality as audit procedures applied during the audit engagement could possibly not detect material misstatements.

Therefore, I predict that auditors should be the first party to discover any deviations in the expected level of accruals. In general, when firms receive an SEC comment letter, they know that they need to prioritize issues addressed by the SEC. However, they may not only work on improving areas addressed by the SEC, but it also can give them incentive to improve accounting quality in general to avoid repetitive comment letters.

In general, high quality audits constrain opportunistic earnings management therefore they should reduce accruals in the long run (DeFond and Zhang, 2014). The major advantage of using this measure as a proxy for audit quality is their ability to detect earnings management. However, careful interpretation of this measure is needed as it can be noisy and in some circumstances biased.

To achive higher quality, auditors can take advantage of the information provided to them by short sellers.

As evidenced by Fang et al. (2016), short sellers are able to detect fraud and earnings management before it is found by auditors and publicly disclosed. I expect that earnings

65 management conducted through accruals will decrease following the receipt of a comment letter and for firms with high short interest positions as it provides a signal that sophisticated investors as well as regulator (SEC) closely monitors the company. Furthermore auditors incorporating short selling positions into their evaluation of audit and business risk can benefit and provide higher quality audits to their clients. Prior literature finds evidence that short selling improves price efficiency and disciplines management by constraining their behavior of manipulating earnings

(Chang, Cheng, and Yu, 2007; Boehmer and Wu, 2013).

As a result of the above arguments, I predict that firms receiving a comment letter and with short interest positions should report decrease in the level of discretionary accruals, which indicates improved audit quality on the subsequent audit engagements. Thus, the alternative hypotheses are stated as follows:

H3a: Companies with an SEC comment letter will have lower discretionary accruals for the audit engagement following the SEC comment letter than companies with no comment letters. H3b: Companies with short interest positions will have lower discretionary accruals for the audit engagement following the SEC comment letter than companies with no short interest positions.

The above arguments provide strong support for a subsequent decrease in use of discretionary accruals for companies with SEC comment letter and for companies with short interest acitivites. Based on the above arguments I predict that in circumstances when both variables of interest are present, the effect on the utilization of discretionary accruals can be even more pronounced. Since I argue above that each factor has a positive effect on the opportunistic use of discretionary accruals I expect decreased use of discretionary accruals by companies with an SEC comment letters and short interest positions. When both monitoring mechanisms are in place, management should have more incentives for a full disclosure in their financial statements.

As they reduce utilization of discretionary accruals, they also expect audit firm to more closely monitor and control management with respect to the use of discretionary accruals.

As a result of the above arguments I developed alternative hypothesis stated below:

H3c: Companies with SEC comment letter and short interest positions will have lower discretionary accruals for the audit engagement following the SEC comment letter than companies with no comment letters and no short interest positions.

Accordingly, as the hypothesis developed earlier, the change in discretionary accruals may be driven by the level of severity of comment letters. Companies receiving comments on the critical accounting topics and letters that are frequent and take many rounds to finalize are more likely to be engaged in the earnings management practices. As a result, the next alternative hypothesis is stated as follows:

H3d: Companies with more severe SEC comment letters will have lower discretionary accruals for the audit engagement following the SEC comment letters than companies with less severe comment letters.

3.1.6. Restatements

In this section I examine whether firms receiving SEC comment letters and firms with short interest positions are associated with a higher restatement propensity. Restatements occur rarely and we observe a decreasing trend in the restatements. Andrew Ceresney, Director of the Division of Enforcement, provided a statement on the recent activities performed by the SEC to reduce deficient financial reporting (SEC 2016):

“The good news is that we succeeded in significantly increasing the quality and number of financial reporting cases. For example, restatement trends are flat over the last five years, and down significantly from last decade. Specifically, across all public companies, over the past

67 fourteen years, restatements fell from a peak of 1,842 in 2006 to a low of 761 in 2009. Since then, restatements have remained relatively flat, in the range of approximately 800 to 850 annually”.

Based on the facts provided by Andrew Ceresney, there is a steady decrease in the number of the restatements which can be attributable to the SEC review and enforcement actions. In general, firms with better quality of financial reporting systems have higher financial quality even before the start of the audit procedures. Therefore, these companies are less likely to issue restatements. Accounting restatements correct material misstatments reported in previously issued financial statements.

Restatements are frequently used in the literature as a proxy of audit quality as the ability of the auditor to detect material misstatments indicates higher audit quality. Restatements indicate poor audit quality which is conditional on low financial quality in the first place. Restatements could result from the monitoring activities performed by external auditors, board of directors or regulators. The SEC review process in the form of comment letters may also lead to subsequent restatements. Lawrence, Gao, and Smith (2010) find that market reaction to restatements requested by the SEC as a result of the periodic review is positive. The evidence implies that investors perceive positively result of the SEC review process despite its negative outcome. Dechow et al.

2016 finds that restatements as a result of the comment letter occur rarely (less than 1 percent of total SEC comment letters lead to restatements).

Based on the arguments provided by the prior literature I predict that SEC comment letters can increase the propensity to restate financial statements in the year of the review. The monitoring function of the SEC can address the issues that were not detected by the auditors and the firm may end up with more restatements than firms which do not undergo the review by the SEC. In addition, if the firm has high short interest position at the same time, this can increase the likelihood of the

68 restatement to a greater extent. As evidenced by prior literature, short sellers are capable to short their position several months before restatement is publicly announced (Desai, Krishnamurthy, and Venkataraman, 2006). Despite the evidence that overall restatements are a rare outcome following SEC comment letter, I predict that they may increase the likelihood of restatements for these companies due to an additional monitoring tool in place.

On the other hand, the SEC comment letters can assist auditors in addressing the issues in more detail on the subsequent audit engagement. They point out to auditors more problematic area of the company which could lead to the extended audit procedures on the subsequent audit engagements. Therefore to evaluate audit quality on the audits subsequent to the SEC comment letters, I predict that SEC comment letters and short selling activity can lead to improvement in audit quality measured by a decrease in the likelihood of accounting restatments.

Based on the above arguments, I formulate the next two alternative hypotheses:

H4a: Companies with SEC comment letters are less likely to restate their financial statements for the audit engagement following the SEC comment letters than companies with no comment letters. H4b: Companies with high short interest positions are less likely to restate their financial statements than companies with no short interest positions.

Based on the above arguments, one might predict that when both factors are present for the company, the joint effect should have more pronounced impact on the probability of the restatements. Therefore I predict that companies with SEC comment letters and high short selling positions to be less likely to restate their financial statements for the audit engagements following the SEC comment letters than companies with no comment letters and no short interest positions.

Thus, the next hypothesis is stated in the alternative form as follows:

H4c: Companies with SEC comment letters and short interest positions will have lower probability of restatements in their financial statements for the audit engagement following the SEC comment letters than companies with no comment letters and no short interest positions.

The likelihood of restatements depends on the severity of comment letters. As evidenced by prior literature, management being aware of the negative market reaction to the restatement announcements tries to avoid restatements at all costs. I expect that company is more likely to make changes in the disclosures following comment letters adressing critical issues which would lead in the future to the lower likelihood of restatements. Therefore the next alternative hypothesis is stated as follows:

H4d: Companies with more severe SEC comment letters are less likely to restate their financial statements for the audit engagement following the SEC comment letters than companies with less severe comment letters.

3.1.7. Material Weaknesses

The studies in this area find focus on the economic consequences of disclosed material weaknesses in internal controls of the company. They find companies with disclosed material weaknesses experience higher audit fees (Raghunandan and Rama, 2006) and exercise earnings management to a greater extent (Doyle, Ge, and McVay, 2007; Chan, Farrell, and Lee, 2008).

However, the evidence on the capital market consequences is mixed (Ashbaugh-Skaife, Collins,

Kinney, and LaFond, 2008; Ogneva, Subramanyam, and Raghunandan, 2007; Beneish, Billings, and Hodder, 2008). Feng, Li, and McVay (2009) provide evidence on the negative association between internal control material weaknesses (ICMW) and errors in managerial earnings forecast.

It indicates that not efficient controls adversely affect the quality of information provided by internal management.

I conjecture that short sellers have prior knowledge about disclosed ICMW therefore they increase their short positions with the expectation of stock price decline when the disclosure becomes public. Sophisticated traders have the ability to suspect the effectiveness of a firm’s internal controls. My predictions are based on the early reflection hypothesis which suggests that market participants are able to incorporate expectations about internal control risks before their public disclosure (Li, Yu, and Zhang, 2016). In most of the cases the fact that the company has material weaknesses in internal controls is not a sudden event which allows investors to develop predictions about future disclosure. This prediction is consistent with the evidence provided by

Beneish, Billings, and Hodder (2008) which does not report significant market reaction to SOX

404 ICMW as a result of incorporating prediction in the price before public disclosure. Beneish,

Billings, and Hodder (2008) investigate the capital market effect of ICMW under Section 302 and

404 of the SOX (SOX, 2002)4. Billings and Hodder (2008) find that negative abnormal returns are associated with the deficiency disclosure under Section 302 but no effect is found for the disclosure of ICMW under Section 404.

Furthermore, Li, Yu, and Zhang (2016) find that firms with disclosed internal control material weaknesses (ICMW) have 13 percent lower valuation than non-ICMW firms as measured by Tobin’s Q. Notably, the valuation difference between firms is shown in the year prior to the disclosure of the ICMW. The stock performance subsequently improves after the firm remedies the weaknesses in the year following disclosure. This setting provides opportunity for short sellers to make profit on the swing in the stock price. In addition, if the firm had a prior disclosure of SOX

302 Section, the stock price actually performs better. It is consistent with evidence in Kim and

4 Section 302 refers to “Corporate Responsibility for Financial Reports” and requires principal executives to certify on the fairness of the information included in quarterly and annual reports.

Park (2009), which indicates that voluntary disclosures of non-material weaknesses by management reduce market uncertainty. The FASB states that firms can improve their financial reporting by voluntary disclosures of information interesting to market participants (FASB, 2001).

The results provided by these studies are consistent with the argument that market participants do incorporate information about forthcoming ICMW disclosure into their pricing decision. Thus, short sellers are capable of detecting material weaknesses before they are disclosed the first time, which suggests that they might use some private information for that purpose (Singer, Wang, and

Zhang, 2015).

Based on the evidence provided by the literature, there is a positive relationship between low quality financial reporting and material weaknesses disclosure. I conjecture that SEC comment letters are more likely to occur for companies with deficient internal controls. However, the monitoring by the SEC can possibly help management to improve internal controls over financial reporting and decrease the likelihood of receiving ICMW in the subsequent period to the SEC comment letter. Management is required to take corrective actions when they are addressed by the regulator and respond to the SEC with the corrective action. Thus, if the SEC comment letters help companies improve the quality of financial reporting and constrain earnings management I predict that probability of reporting material disclosure (Section 302 and Section 404) on the subsequent audit engagement should be lower than for firms with no SEC comment letter.

The next hypothesis, stated in the alternative form is as follows:

H5a: Companies with SEC comment letters are less likely to receive ICMW for the audit engagements following the SEC comment letters than companies with no comment letters. H5b: Companies with short interest positions are less likely to receive ICMW than companies with no short interest positions.

Based on the above arguments one might predict that when both examined variables are present at the same time (short selling activity in the year of the comment letter receipt), the effect on the lower probability of material weaknesses in the subsequent year will be more pronoucned.

I expect management to demand from audit firms higher quality audits what would be reflected in the lower probability of reported material weaknesses. Management concerned with market reaction to the short selling activity and the receipt of comment letters will subsequently improve the effectiveness of their internal control. More robust control system reduces the probability of material weaknesses. In addition, audit firms can contribute to the improved internal control systems of their clients by providing effective solutions and recommendations.

Based on the arguments I predict decreased probability of material weaknesses in the year subsequent to the receipt of a comment letters and active positions of short sellers.

H5c: Companies with SEC comment letter and short interest positions are less likely to receive ICMW for the audit engagements following the SEC comment letters than companies with no comment letters and no short interest positions.

The probability of reporing internal control material weaknesses could also depend on the severity of the comment letter. If the issues relate to the critical accounting topics it may indicate ineffective internal controls around the major accounting processes. Once the ICMW was reported, the company takes action to remediate the control deficiency and improve their internal controls.

Once the deficiency is identified by the SEC (in situation when auditors neglected to detect it), I expect that the company significantly improves its internal controls to reduce the likelihood of reporting ICMW in the future. The SEC issuing comment letters on a critical accounting topic can actually contribute to a greater reduction in the likelihood of reporting ICMW. Thus, the next alternative hypothesis is stated as:

H5d: Companies with more severe SEC comment letters are less likely to receive an ICMW report for the audit engagement following the SEC comment letter than companies with less severe comment letters.

3.1.8. PCAOB Inspections Deficiencies

Prior literature in this area examines validity of this measure as a proxy of audit quality

(Gunny and Zhang, 2013) and provides solid evidence in support of that argument. By extending their research in this area this study provides additional evidence on the usefulness of PCAOB inspections in evaluating audit quality. Considering recent congressional debate over extension of the inspection program to a greater variety of auditors (e.g., broker-dealers, foreign auditors) the study makes significant contribution towards that discussion.

In this study, I examine whether short selling activities and SEC comment letters have an effect on the number of PCAOB inspections deficiency reports. I expect that both factors could provide benefits to the PCAOB toward their review selection process. The SEC comment letters indicate inadequate disclosures and therefore can provide PCAOB with the information about the potential low quality audits provided by audit firms to their clients. Furthermore, short sellers have the ability to predict forthcoming negative events for the company. As a result, their activity could also be beneficial to the PCAOB when selecting audit firms for review and their specific audit engagements with clients.

I predict that audit firms with clients receiving SEC comment letters and with short selling positions will improve quality of audits as a result of increased demand by the client. As discussed with a partner in one of the Big 4 audit firms, the reviews are beneficial to the firms as they help them improve the quality by providing more trainings to their employees and educate their clients.

As a result, I can expect that overall audit quality of the audits provided by the audit firms for their

74 clients which receive SEC comment letter and have short interest positions will increase as a result of management demand.

I predict that SEC comment letters and short selling activities decrease the probability of receiving PCAOB inspection report with GAAP or GAAS deficiencies for audit firms subsequent to the receipt of SEC comment letters by clients. I expect that audit firms shift their resources and efforts not only to satisfy high demands of the client but also to avoid PCAOB deficiency reports.

Furthermore, I predict that the combination of both factors will pronounce the positive effect on the probability of PCAOB deficiency report. I conclude that audit firms with the clients which receive SEC comment letters and have short selling positions are less likely to receive inspection reports with deficiencies reported by PCAOB.

Based on the above arguments, the next set of hypotheses, stated in the alternative form is as follows:

H6a: Audit firms with clients that receive SEC comment letters are less likely to receive a PCAOB inspection report with GAAP (GAAS) deficiencies than audit firms with clients with no comment letters. H6b: Audit firms with clients that have short selling position are less likely to receive a PCAOB inspection report with GAAP (GAAS) deficiencies than audit firms with clients with no short interest positions. H6c: Audit firms with clients that receive SEC comment letters and have short selling positions are less likely to receive PCAOB inspection report with GAAP (GAAS) deficiencies than audit firms with clients with no comment letters and no short interest position. To address the issue of severity of the comment letter I follow the same arguments presented in the earlier hypotheses. When the issues on the comment letter relate to the critical accounting topics it may indicate more effort placed by the auditor to remediate the issues with the clients and subsequently to improve their audit quality on the subsequent audit engagements. Thus

75 in result, the more severe issues should better improve quality of audits. Once the issues are properly addressed and remediated, not only the client reduces probability of receiving another comment letter but also reduces the probability of a PCAOB inspection report issued to the audit firm.

Thus, the next alternative hypothesis is stated as:

H6d: Audit firms with clients that receive more severe SEC comment letters are less likely to receive PCAOB inspection deficiencies report than audit firms with clients with less severe SEC comment letters. The hypotheses from H1a through H6d stated above address two research questions on the impact of the SEC comment letters and short interest activities for the subsequent audit engagements. The third research question relating to the differential impact of both factors on audits conducted by Big 4 auditors and non-Big4 auditors is addressed in the sensitivity analysis of this study.

3.2. Research Models

In this study I use a number of proxies as the determinants of audit quality. Below I describe in detail models that are used in the study to evaluate the impact of SEC comment letters and short selling positions on multiple proxies of audit quality.

3.2.1. Audit Fees

Extant models of audit pricing generally follow pioneering study of Simunic (1980) and employ measures of client risk, client complexity and client size as the three primary determinants

76 of audit fees. In addition models used in prior studies capture audit firm characteristics - local office size, tenure with the client and client importance (Francis et al. 2005; Raghunandan and

Rama, 2006).

To examine the impact of SEC comment letter and short selling position on the audit fees as described in detail in hypotheses H1a through H1d, I use a cross-sectional panel OLS regression model following Bills et al. (2016). The dependent variable is the natural logarithm of the total audit fees charged by the external auditor (Audit_Fees). The key variables of interest are the SEC comment letter (SEC_CL) and rank of short interest ratio (Rank_SIR). The SEC_CL is a dichotomous variable coded as 1 if the company received SEC comment letter during the year or

0 if there was no comment letter received. The study also captures severity of comment letters

(SEC_CL_CT). I use three indicators of severity of comment letters – critical accounting issue (i), number of rounds it takes to resolve the issue (ii) and frequency of comment letter per year (iii).

Rank of short interest ratio (SIR) follows Cassell et al. (2011) definition and is calculated as number of shares sold short in the month divided by total shares outstanding for the month. Based on this ratio I rank the short interest positions into deciles for the two-digit industry and year. The ranks of the short interest ratio (SIR) are scaled to range from 0 to 1 to reduce the influence of noise and corrects for skewness in the distribution of short interest (Drake, Rees, and Swanson,

2011).

The empirical models used in this study to estimate the impact of the receipt of SEC comment letter and short interest ratio on audit fees captured by hypotheses H1a through H1d are specified as follows in Equation (3) through (6) accordingly:

Audit Fees it = β0 + β1 SEC_CL it-1 + β2 Assets it + β3 Ln_Bus_Segit +β4CATA it+β5 Quick it+ β6 Lev it+ β7 ROI it+ β8 Loss it+ β9 GC it +β10 Foreignit + β11 DecYE it+ β12Weaknessesit+

β13 Short_Tenure it+ β14 Firm_Size it + β15 Mktshr it + β16 Client_Importit +β17 FirmFE it + β18 YearFE it + ε (3)

Audit Fees it = β0 + β1 Rank_SIR it-1 + β2 Assets it + β3 Ln_Bus_Segit +β4CATA it+β5 Quick it+ β6 Lev it+ β7 ROI it+ β8 Loss it+ β9 GC it +β10 Foreignit + β11 DecYE it+ β12Weaknessesit+ β13 Short_Tenure it+ β14 Firm_Size it + β15 Mktshr it + β16 Client_Importit +β17 FirmFE it + β18 YearFE it + ε (4) Audit Fees it = β0 + β1 SEC_CL it-1 + β2 Rank_SIR it + β3 SEC_ CL it-1 *Rank_SIR it-1 + β4 Assetsit + β5 Ln_Bus_Segit +β6CATA it+β7 Quick it+ β8 Lev it+ β9 ROI it+ β10 Loss it+ β11 GC it +β12 Foreignit + β13 DecYE it+ β14 Weaknesses it+ β15 Short_Tenure it+ β16 Firm_Size it + β17 Mktshr it + β18 Client_Importit +β19 FirmFE it + β20 YearFE it + ε (5)

Audit Fees it = β0 + β1 SEC_CL_CT it-1 + β2 Assets it + β3 Ln_Bus_Segit +β4CATA it+β5 Quick it+ β6 Lev it+ β7 ROI it+ β8 Loss it+ β9 GC it +β10 Foreignit + β11 DecYE it+ β12Weaknessesit+ β13 Short_Tenure it+ β14 Firm_Size it + β15 Mktshr it + β16 Client_Importit +β17 FirmFE it + β18 YearFE it + ε (6)

The control variables follow prior literature (Simunic, 1980; Francis, Reichelt, and Wang,

2005; Reichelt and Wang, 2010) and include client factors like client size (Assets), business complexity (Segments), risks associated with the client’s financial situation (CATA, Quick, Lev), profitability (ROI, Loss), the receipt of a going concern opinion or material weaknesses (GC,

Weaknesses), the presence of foreign operations (Foreign). Furthermore, the model includes control variables associated with auditor characteristics – firm size (Firm_Size), the length of the client-auditor relationship (Short_Tenure), and client importance (Client_Import). Consistent with prior literature, I include industry (two-digit SIC) and year fixed effects. All the variables are defined in detail in Appendix A.

I expect a positive coefficient for larger clients (Assets), greater complexity of audit engagement (Segments and Foreign), and for indicators capturing greater audit risk (CATA and

Loss). Larger clients usually have more complex and time consuming operations that require more effort from auditors (higher audit fees). Prior studies report a positive relationship between going

78 concern opinion (GC) and internal control material weaknesses (Weaknesses). It is expected that issuance of opinion other than unqualified requires more efforts and higher fees. I expect negative coefficients for variables Quick, ROI, and positive for DecYE. Clients with the smaller quick ratio

(Quick) are less liquid and more risky and it triggers higher audit risk. Prior studies find that clients with higher ROI have lower fees as more profitable clients are less risky to the auditor.

Furthermore, clients with fiscal year-end other than December can expect to get lower audit fees when their audit engagements are performed outside of the busy season.

Furthermore, I expect higher audit fees for firms experiencing losses (Loss) and high leverage (Leverage) and high current ratio (CATA). As evidenced by prior research Big 4 auditors provide their services to the clients with higher audit fees. As a result I expect positive coefficient for the Big4 variable. In addition, audit firms with more publicly traded clients and higher market share could charge higher audit fees to their clients. Therefore I expect positive coefficients for

Firm Size and Market Share variable.

I expect negative coefficient for client importance (Client_Import) variable as more important clients may be able to negotiate lower audit fees. Prior research suggests that initial audit engagements have an audit fee premium in the post-SOX period (Huang, Raghunandan, and Rama,

2009). As a result, I predict positive coefficient for short tenure (Short_Tenure) variable.

3.2.2. Auditor Change

In order to estimate the likelihood of the auditor change after the receipt of the SEC comment letter

I rely on the models developed in prior studies (Ettredge, Li, and Scholz, 2007; Landsman, Nelson,

79 and Rountree 2009; Cenker and Nagy, 2008; Lopez and Peters, 2011). The logistic regression models I use to test hypotheses 2a and 2b are specified below in equations (7) through (10):

Prob(Auditor_Changeit ) = β0 + β1 SEC_CL it-1 + β2 Assets it + β3 Audit_Lag it + β4 Audit404 it + β5 ROA it + β6 Lev it + β7 Loss it + β8 GC it + β9 Growth it + β10 Dec_YE it + β11 Short_Tenure it + β12 Ar_Inv it + β13 Acquisition it + β14 Big4 it + β15Expert_City it + β16 Expert_Nat it + β17 IndustryFE it + β18 YearFE it + ε it

(7)

Prob(Auditor_Changeit ) = β0 + β1 Rank_SIR it-1 + β2 Assets it + β3 Audit_Lag it + β4 Audit404 it + β5 ROA it + β6 Lev it + β7 Loss it + β8 GC it + β9 Growth it + β10 Dec_YE it + β11 Short_Tenure it + β12 Ar_Inv it + β13 Acquisition it + β14 Big4 it + β15Expert_City it + β16 Expert_Nat it + β17 IndustryFE it + β18 YearFE it + ε it

(8)

Prob(Auditor_Changeit ) = β0 + β1 SEC_CL it-1 + β2 Rank_SIR it-1 + β3 SEC_ CL it-1*Rank_SIR it- 1 + β4 Assets it + β5 Audit_Lag it + β6 Audit404 it + β7 ROA it + β8 Lev it + β9 Loss it + β10 GC it + β11 Growth it + β12 Dec_YE it + β13 Short_Tenure it + β14 Ar_Inv it + β15 Acquisition it + β16 Big4 it + β17Expert_City it + β18 Expert_Nat it + β19 FirmFE it + β20 YearFE it + ε it

(9) Prob(Auditor_Changeit) = β0 + β1SEC_CL_CT it-1 + β2 Assets it + β3 Audit_Lag it + β4 Audit404 it + β5 ROA it + β6 Lev it + β7 Loss it + β8 GC it + β9 Growth it + β10 Dec_YE it + β11 Short_Tenure it + β12 Ar_Inv it + β13 Acquisition it + β14 Big4 it + β15Expert_City it + β16 Expert_Nat it + β17 IndustryFE it + β18 YearFE it + ε it (10)

The dependent variable in the above model is the probability of auditor change

(Auditor_Change) which is coded as 1 if the company had a different auditor in the prior year and

0 otherwise. As argued by Landsman et al. (2009), the main dependent variable does not consider whether the change resulted from the dismissal by the client or from the auditor resignation as the differentiation is not meaningful. I estimate the auditor change in the year following the receipt of the SEC comment letter. The key variable of interest is a receipt of SEC comment letter (SEC_CL) which is defined, along with all other control variables in Appendix A.

The control variables used in the model include material weaknesses (Weaknesses) as the risk of auditor change increases when the company has ineffective internal controls. Consistent with prior literature I expect this variable to be positively associated with the likelihood of auditor change. Furthermore, receiving going concern opinion (GC) by the client increases the likelihood of auditor change. There is a positive coefficient expectation for this variable. Furthermore, the model controls for the length of time the auditors took to prepare the audit report (Audit_Lag). It is calculated as the number of days between the fiscal year-end date of a company until the signoff date in the auditor’s opinion report. The literature uses this variable to proxy for the audit effort as more time consuming and complex audit engagements require longer time to prepare audit report.

As a result I expect positive coefficient for this variable. Acquisition variable controls for mergers and acquisitions of the company. I expect a positive coefficient for that variable as companies are more likely to change auditors after a merger or acquisition.

Auditors characteristics used in the model include short tenure (Short_ Tenure) which controls for the length of the relationship between client and auditor. It is more likely to change auditor with the relatively short term tenure as the auditor gains efficiency with time due to better knowledge of the client and lower litigation risk. Therefore I expect positive coefficient for this control variable. The model controls for industry expertise of the auditor (Expert_City and

Expert_Nat) and I expect clients with an industry expert auditor to be less likely to make changes in their audit firm. Additionally, it is less likely to change auditors while the firm hired one of the

Big 4 auditors. Thus I expect negative coefficient for Big4 variable.

The auditor change is conditional on the costs of the change. The change of auditor is more costly for larger firms. On the other hand, larger firms have greater flexibility to change auditors if the need arises. Therefore I do not make directional prediction for the coefficient for Assets

81 variable as a proxy of the client size. The model controls for financial risk of the client – risk of a decline in the client’s economic condition. Financial risk is captured by the ROA, Loss and Lev variables. Clients with high financial risk contribute to the higher likelihood of auditor switch. As a result I expect positive coefficient for Loss and Lev variables as audit firm is more likely to resign from the client posing more risk of default on outstanding debt and incurring losses. Consistent with Landsman et al. (2009) I expect ROA variable to be negatively associated with auditor change as higher ROA indicates stronger financial health of the client. The Ar_Inv is the variable capturing client ability to meet short-term obligations. I expect a positive coefficient for this proxy

(Krishnan, 1994). Moreover, firms with year-end close during the busy season are less likely to change auditors, thus the expected coefficient for DecemberYE variable is negative.

3.2.3. Discretionary Accruals

Previous literature has examined discretionary accruals using different models (e.g.,

McNichols and Wilson, 1988; Jones, 1991; Dechow and Sloan, 1995). They are frequently used as a proxy of audit quality as they can measure more aggressive management decisions with respect to financial reporting and earnings management. High discretionary accruals values indicate lower audit quality as auditors allow management for this type of reporting. Dechow and

Sloan (1995) propose the modified version of the Jones (1991) model and claim it to be the best at identifying abnormal accruals, which also finds its confirmation in the high frequency of the application in the studies.

Following recommendations in Kothari, Leone, and Wasley (2005), I calculate performance-matched absolute discretionary accruals. It is calculated as a difference between firm’s i’s discretionary accruals and the discretionary accrual for firm j with the closest ROA that

82 is in the same two-digit industry and year as firm i. To estimate modified Jones discretionary accruals as a proxy for audit quality (Dechow et al. 2005; Badertscher, 2011), I use the following equation:

TotalAccrualsit/Assetsit-1=α0 (1/Assetsit-1) + α1 ((ΔREVit - ΔRECit)/Assetsit-1 + α2 PPEit/Assetsit-1+ ε

(11)

The above equation estimates total accruals from the change in revenue with an adjustment for the change in accounts receivable and the level of property plant and equipment. In order to mitigate heteroskedasticity in residuals, I use assets as the deflator in the above equation (White,

1980). The Jones discretionary accrual model is estimated cross-sectionally each year using all firm-year observations in the same two-digit SIC code. Variables used in equation (7) are defined in Appendix A. The dependent variable of interest, absolute value of performance-matched discretionary accruals (DACC), is then calculated as the difference between company i’s discretionary accruals and the median discretionary accruals for companies in the same decile- rank of return on assets by two-digit SIC code industry-year (Kothari, Leone, and Wasley, 2005).

To investigate whether SEC comment letters and short selling positions impact discretionary accruals as a proxy of audit quality, I follow the model presented in Bills,

Cunningham, and Myers (2016). To test hypotheses H3a and H3b, I use following models in equations (12) through (15) accordingly:

DACCit = β0 + β1 SEC_CL it-1 + β2 MVE it + β3ROA it + β4 Lev it + β5Currit + β6 CFOit + β7 SDCFOit + β8 Lossit + β9 Mktbk it +β10 Lit it + β11 ZScore it + β12 Taccr_Lagit + β13Weaknessesit + β14 Short_Tenure it + β15 FirmSize it + β16 Mktshr it + β17 Client_Importit + β18 IndustryFEit + β19 YearFEit + ε it

(12)

DACCit = β0 + β1 Rank_SIR it-1 + β2 MVE it + β3ROA it + β4 Lev it + β5Currit + β6 CFOit + β7 SDCFOit + β8 Lossit + β9 Mktbk it +β10 Lit it + β11 ZScore it + β12 Taccr_Lagit + β13Weaknessesit + β14 Short_Tenure it + β15 FirmSize it + β16 Mktshr it + β17 Client_Importit + β18 IndustryFEit + β19 YearFEit + ε it (13)

DACCit = β0 + β1 SEC_CL it-1 + β2Rank_SIRit + β3SEC_CL*Rank_SIRit-1 + β4 MVE it + β5 ROA it + β6 Lev it + β7Currit + β8 CFOit + β9 SDCFOit + β10 Lossit + β11 Mktbk it +β12 Lit it + β13 ZScore it + β14 Taccr_Lagit + β15Weaknessesit + β16 Short_Tenure it + β17 FirmSize it + β18 Mktshr it + β19 Client_Importit + β20 IndustryFEit + β21 YearFEit + ε it

(14)

DACCit = β0 + β1SEC_CL_CT it-1 + β2 MVE it + β3ROA it + β4 Lev it + β5Currit + β6 CFOit + β7 SDCFOit + β8 Lossit + β9 Mktbk it +β10 Lit it + β11 ZScore it + β12 Taccr_Lagit + β13Weaknessesit + β14 Short_Tenure it + β15 FirmSize it + β16 Mktshr it + β17 Client_Importit + β18 IndustryFEit + β19 YearFEit + ε it (15)

The absolute value of performance-matched discretionary accruals is regressed on SEC comment letters and short selling ratio as the main variables of interest. In addition, the model includes a set of control variables based on prior studies (Frankel, Johnson, and Nelson, 2002;

Reichelt and Wang 2010; Zang 2012; Reynolds and Francis 2000; Cunningham et al. 2016; Bills,

Cunningham, and Myers 2016).

The control variables include size of the company proxied by the market value of the equity

(MVE). I expect positive coefficient for MVE as larger companies use accruals to a greater extent.

Financial condition of the company is controlled by return on assets variable (ROA), leverage

(Lev), the current ratio (Curr), volatility of cash flow from operations (SDCFO) and cash flows

(CFO), litigation risk (Lit), loss (Loss) and Altman’s bankruptcy risk (Zscore). In general, more financially distressed firms have incentives to use more accruals to manipulate earnings. Thus, I expect negative coefficient for ROA as greater profitability indicates lower utilization of accruals.

I also expect Z-score to be positively associated with accruals because a higher bankruptcy risk

84 indicates greater financial distress and more use of discretionary accruals. The positive coefficients on the remaining variables (Lev, Curr, SDCFO, CFO, Lit, Loss) are expected as they indicate less stable financial position of the company. Furthermore, firms reporting material weaknesses are more likely to use discretionary accruals. Thus, the expected coefficient for Weaknesses variable is positive.

In the model, I also control for auditor characteristics – whether auditor is one of the Big4 firms (Big4), auditor tenure with the client (Short_Tenure), and auditor industry expertise proxied by market share (Mktshr). I expect Big4 auditors to constrain income-increasing earnings management performed by discretionary accruals, thus I predict negative coefficient for this control variable (Becker, DeFond, Jiambalvo, and Subramanyam, 1998). Audit firms with more publicly traded clients can limit the use of the discretionary accruals by their clients to reduce the litigation risk in the future. Thus, I expect negative coefficient for Firm Size variable.

I expect a positive coefficient for the Short_Tenure variable as the auditors in the first year are found to allow more income-increasing accruals (DeFond and Subramanyam (1997). Furthermore, audit firms with high market share allow their clients for higher application of income-increasing accruals thus I predict positive coefficient for this variable. However, more important clients will be constrained from using accruals therefore the expectation is negative for Client_Import variable.

3.2.4. Restatements

In this section I examine whether firms receiving SEC comment letters and firms with high short interest positions are associated with a higher restatement propensity. Restatement is a less noisy measure of auditor performance and can more directly indicate quality of their work in

85 detecting material misstatements. Companies are required to disclose material misstatements in corporate disclosures in Form 8-K Item 4.02. The resolution of SEC comment letters does not lead frequently to the restatements of the financial statements as evidenced by Dechow et al. (2016).

Since restatement is one of the important proxies used to evaluate audit quality, I examine the impact of SEC comment letters on the probability of the restatement.

To examine the effect of the receipt of SEC comment letter and short selling activity on the likelihood of accounting restatements, I follow prior literature (Kinney, Palmrose, and Scholz,

2004; Cao, Myers, and Omer, 2012; Bills, Cunningham, and Myers, 2016). The restatements models presented in equation (16) through (19) are used to estimate the probability of restatement for firms with the SEC comment letters and short selling activity (H4a and H4b accordingly). The models are estimated using logistics regression and standard errors clustered at the company level.

Prob(Restatementit) = β0 + β1 SEC_CL it-1 + β2 Assets it +β3ROA it + β4 Lev it + β5 Mktbk it + β6 Segments it +β7Financing it + β8 Foreign it + β9 Acquisition it + β10 Ar_Inv it + β11 Weaknesses it +β12 AuditFees it + β13 Client_Import it + β14 Big4 it + β15 Industry FE it + β16 Year FE it + ε (16)

Prob(Restatementit) = β0 + β1 Rank_SIR it-1 + β2 Assets it +β3ROA it + β4 Lev it + β5 Mktbk it + β6 Segments it +β7Financing it + β8 Foreign it + β9 Acquisition it + β10 Ar_Inv it + β11 Weaknesses it +β12 AuditFees it + β13 Client_Import it + β14 Big4 it + β15 Industry FE it + β16 Year FE it + ε

(17)

Prob(Restatementit) = β0 + β1 SEC_CL it-1 + β2Rank_SIRit + β3SEC_ CL it-1*Rank_SIRit-1 + β4 Assets it +β5ROA it + β6 Lev it + β7 Mktbk it + β8 Segments it +β9Financing it + β10 Foreign it + β11 Acquisition it + β12 Ar_Inv it + β13 Weaknesses it +β14 AuditFees it + β15 Client_Import it + β16 Big4 it + β17 Industry FE it + β18 Year FE it + ε (18)

Prob(Restatementit) = β0 + β1 SEC_CL_CT it-1 + + β2 Assets it +β3ROA it + β4 Lev it + β5 Mktbk it + β6 Segments it +β7Financing it + β8 Foreign it + β9 Acquisition it + β10 Ar_Inv it + β11 Weaknesses it +β12 AuditFees it + β13 Client_Import it + β14 Big4 it + β15 Industry FE it + β16 Year FE it + ε

(19)

The dependent variable in the above model is a restatement variable (Restatement). It is a dichotomous variable coded as 1 if the company restates its fiscal year t’s 10-K and 0 otherwise.

There are two primary variables of interest – receipt of the SEC comment letter (SEC_CL) and ratio of short interest (SIR). All variables used in the equation are defined in detail in Appendix A.

The control variables are based on the model in Cao, Myers, and Omer (2010). The model controls for the size of the company (Assets) and I predict negative relationship between firm size and restatement/fraud (Cao et al. 2010). I expect larger firms to be associated with lower earnings management due to the loss of their reputation. Following Dechow et al. 1996, I control for new financing (Financing) in the model and expect a positive coefficient. I include two measures of complexity – Segments and Foreign and expect positive coefficients on them since complexity is associated with low quality accounting information. The variable controlling for the financial standing of the company (Leverage) is expected to have a positive coefficient. Thus, I expect a negative coefficient for ROA, as better profitability is associated with lower likelihood of misstatement. I expect positive coefficient for proportion of assets in receivables and inventory

(Ar_Inv) as restatement is more likely when this proportion is greater.

The material weaknesses (Weaknesses) are positively associated with the likelihood of restatement, thus I expect positive coefficients for this variable. Furthermore the model controls for importance of the client to the audit firm (Client_Import). I expect a negative coefficient for this variable as client importance helps to alleviate the risk of not detecting misstatements in

87 financial statements of the client. Lastly, Big4 audit firm reduces the probability of the future restatements. Thus, I expect a negative coefficient for this variable.

3.2.5. Material Weaknesses

Previous literature has examined determinants of material weaknesses in internal control over financial reporting (Doyle, Ge, and McVay, 2007). A material weakness in internal control is defined as ‘‘a significant deficiency, or combination of significant deficiencies, that results in more than a remote likelihood that a material misstatement of the annual or interim financial statements will not be prevented or detected’’ (PCAOB, 2004). Material weaknesses in internal controls over financial reporting indicate low quality audits (DeFond and Zhang, 2014).

Material weaknesses are used in the literature as a proxy of audit quality as they determine the ability of the auditor to detect and report on the inefficient internal controls. In order to examine whether the receipt of the SEC comment letter and short selling activity impacts probability of disclosing material weaknesses in internal controls, I estimate the logit model following Masli,

Peters, Richardson, and Sanchez, 2010. The model uses logistic regression to estimate the probability of material weaknesses (Weaknesses) based on the firm’s characteristics. Models presented in Equation (20) through (23) are used to test for H5a and H5b accordingly:

Prob(Weaknessesit) = β0 + β1 SEC_CLit-1 + β2 Assetsit +β3 FirmAge it + β4 Loss it +β5 Zscore it + β6 Segments it + β7 Foreign it + β8 Acquisition it +β9 Growthit + β10 Restructuringit + β11 IndustryFE it + β12 YearFE it + ε it (20)

Prob (Weaknessesit) = β0 + β1 Rank_SIRit-1 + β2 Assetsit +β3 FirmAge it + β4 Loss it + β5 Zscore it + β6 Segments it + β7 Foreign it + β8 Acquisition it +β9 Growthit + β10 Restructuringit + β11 IndustryFE it + β12 YearFE it + ε it (21)

Prob (Weaknessesit) = β0 + β1 SEC_CLit-1 + β2Rank_SIRit-1 + β3SEC_CLi,t-1*SIR it-1 + β4 Assetsit+β5 FirmAge it + β6 Loss it + β7 Zscore it + β8 Segments it + β9 Foreign it + β10 Acquisition it +β11 Growthit + β12 Restructuringit + β13 FirmFE it + β14 YearFE it + ε it

(22)

Prob (Weaknessesit) = β0 + β1 SEC_CL_CTit-1 + + β2 Assetsit + β3 FirmAge it + β4 Loss it +β5 Zscore it + β6 Segments it + β7 Foreign it + β8 Acquisition it +β9 Growthit + β10 Restructuringit + β11 IndustryFE it + β12 YearFE it + ε it (23)

The directional effects of the control variables follow prior literature (Doyle et al. 2007).

The model controls for size of the firm measured by total assets (Assets), and firm age as the number of years the company has been listed on the stock exchange (FirmAge). Based on prior studies, I expect negative coefficient for the size of the company variable (Assets). Larger firms generally have greater resources to address the segregation of duties problem. Also they take advantage of the economies of scale when implementing a system of internal controls. Thus, I expect larger firms to report fewer material control weaknesses. I predict a negative coefficient for

FirmAge variable as I expect older firms to have fewer material control weaknesses as they already had time to put effective internal controls in place.

Other variables used to control for the financial condition of the firm and risks are associated with performing operations (Zscore and Loss). Poorly performing firms do not have appropriate financial resources to make investments in internal control which potentially could lead to more internal control weaknesses. Thus, I expect positive coefficient for Loss and for

Zscore variable. The complexity of the firm’s operations (Segments and Foreign) impacts the efficiency of the internal controls. Based on the evidence in the prior literature I expect positive coefficient for both variables. More complex operations experience challenges when implementing

89 system of internal controls across multiple geographic locations and new regulatory environment of foreign operations. Thus, it is more likely that they will have more internal control weaknesses.

In addition, the model controls for rapid growth and restructuring activities (Growth,

Acquisition, and Restructuring). I expect a positive coefficient for variable capturing rapid growth of the company (Growth). Fast growing firms are faced with the challenge of outgrowing their existing controls. Firms need to keep up with internal controls sufficient for the rapidly expanding operations. However, they are more likely to have less efficient controls than well-established firms. The positive coefficient is expected for Acquisition variable as firms expanding their operations on the new segments and new geographical locations struggle with maintaining effective internal controls. Accordingly, I expect firms undergoing restructuring activities to have more internal controls. Reduction in the workforce and downsizing of operations could lead to inefficient controls. Therefore, I expect positive coefficient for the Restructuring variable.

3.2.6. PCAOB Inspections Deficiencies

PCAOB Inspections deficiencies have been recently used to proxy for low quality audits

(Gunny and Zhang, 2013; Christensen et al 2015, Aobdia, 2016). The deficiencies can be categorized either as GAAP deficiency (inadequate application of US GAAP), or as GAAS deficiency (insufficient application of GAAS during audit engagement). In the study, I estimate separately the probability of receiving each type of deficiency by the local office separately. In order to test hypotheses, H6a and H6b I use the model used by Bills et al. (2016). Thus, the models are as follows:

Deficiency (GAAP/GAAS)it = β0 + β1 SEC_CL it-1 + β2 Officesit + β53Public_Clients it + β4 Total_Fees it + β5Avg_ClientSizeit + β6 Foreign_D it + β7 Stock_Exchange it + β8 Partnersit + β9 Big4it + β10 IndustryFEit + β11 YearFE it + ε it (24)

Deficiency (GAAP/GAAS)it = β0 + β1 Rank_SIR it-1 + β2 Officesit + β53Public_Clients it + β4 Total_Fees it + β5Avg_ClientSizeit + β6 Foreign_D it + β7 Stock_Exchange it + β8 Partnersit + β9 Big4it + β10 IndustryFEit + β11 YearFE it + ε it (25)

Deficiency (GAAP/GAAS)it = β0 +β1 SEC_CL it-1 + β2Rank_SIRit + β3 SEC_CL it-1* Rank_SIR it- 1 + β4 Officesit + β5 Public_Clients it + β6 Total_Fees it + β7 Avg_ClientSizeit + β8Foreign_D it + β9 Stock_Exchange it + β10 Partnersit + β11 Big4it + β12 IndustryFEit + β13 YearFE it + ε it (26)

Deficiency (GAAP/GAAS)it = β0 + β1 SEC_CL_CT it-1 + β2 Officesit + β53Public_Clients it + β4 Total_Fees it + β5Avg_ClientSizeit + β6 Foreign_D it + β7 Stock_Exchange it + β8 Partnersit + β9 Big4it + β10 IndustryFEit + β11 YearFE it + ε it (27)

The control variables used in the model follow Bills et al. (2016). I use variables to control for audit firm size and client complexity collected at the audit firm level. The above equations are estimated using logistic regressions. The equations are clustered at the audit firm level. The variables of interest are SEC comment letters (SEC_CL) and short interest rank (SIR). To examine the impact of both variables on the probability of the PCAOB inspection report, I estimate the coefficient for the interaction term (β3) and predict positive sign for the coefficient.

The model controls for the size of the audit firm by applying multiple variables (Offices,

Partners, and Avg_Client_Size). The number of the offices inspected increases the probability of receiving PCAOB deficiency report. Therefore, I expect positive coefficient for the Offices variable. Larger number of partners in the office reduces the likelihood of receiving PCAOB deficiency report. Thus, the expected sign for the coefficient for Partner variable is negative.

Overall, prior literature is inconsistent on the direction of the remaining coefficients used in the model. The expected positive coefficient is based on the arguments that larger audit firms are more likely to undergo PCAOB inspection and receive deficiency report due to their complexity of audits for larger clients and more clients during the busy season. On the other hand, larger audit firms have higher audit quality which reduces likelihood of a deficiency report from

PCAOB. As a result of these arguments, I do not make a prediction for the direction of the coefficient estimates for the following variable – Public_Clients.

I expect positive coefficient for client complexity (Foreign_D) as offices with this type of clients are more likely to receive a PCAOB deficiency report. The stock exchange variable

(Stock_Exchange) controls for the companies traded on the major U.S. Stock Exchanges. I expect a negative coefficient for this variable as these companies have higher financial reporting quality and corporate governance leading to less deficiencies identified by PCAOB. Furthermore, the likelihood of receiving PCAOB deficiency is lower for Big 4 auditors, considered to provide higher quality audits. Therefore, I expect a negative coefficient for the Big4 variable. Consistent with prior models, remaining variables of interest are defined in Appendix A.

3.3 Sample Selection

The data on the detail of the SEC comment letters is obtained from Audit Analytics. I use the Comment Letter and Comment Letter Conversations databases to collect all publicly filed comment letter conversations issued by the SEC from 2004 through 2015. I extract all comment letter conversations with comments related to 10-K and 10-Q filings only. Audit Analytics assigns a unique CL_CON_ID to all letters in the same conversation. Based on that variable, I create one

92 unique observation for each 10-K or 10-Q conversation. Some conversations refer to multiple documents but as long as 10-K or 10-Q is among them I assign them as SEC letters referring to annual or quarterly statement.

The policy of public disclosure of comment letters has been in effect since August, 2004 and it determines the beginning of the sample period. The sample starts in the first full year of the effective public disclosure policy. The sample ends in 2015 to ensure that all comment letters for the fiscal year end of 2015 were completed and publicly disclosed over the year 2016. The financial data on the companies was obtained from Compustat, detail of auditor information from Audit

Analytics and the data on short selling activity was obtained from the Compustat supplemental.

Appendix A describes in detail variables used in the models as well as sources of data for each of them. Table 1 describes the sample of comment letters used in this analysis.

The two main variables of interest are short selling position and the SEC comment letter.

Short selling variable has been widely used in prior studies to proxy for the monthly equity short selling activities (Dechow et al. 2016; Cassell et al. 2011; Drake, Rees, and Swanson, 2011). Short

Interest Ratio is calculated as the number of outstanding short positions on the final trading day on or before the 15th of each month scaled by the number of total shares outstanding. The stock exchange reports show open short positions using the 15th of each calendar month as the settlement date or the last business day before the 15th. The data is obtained from Compustat Supplemental file which captures information from the stock exchange reports.

In July 2004, the SEC announced a new regulation introducing restrictions on the short selling activities (Regulation SHO). The new regulation included Rule 202T pilot program in which every third stock (ranked based on the trading volume) in the Russell 3000 index were

93 exempted from the short sale price tests, which examine if the sales follow restrictions placed on the traders (https://www.sec.gov/investor/pubs/regsho.htm, SEC 2015b). The restrictions are placed on stocks when they experience significant downward pressures – the uptick rule for price drop of more than 10%. The purpose is to promote stability in the markets and investors’ confidence. The pilot program was in effect from May 2, 2005 until August 6, 2007. In order to ensure that results in the study are not driven by the SEC restrictions I run my analysis specifically for the exemption period (see first column in Tables 10.1 through 10.6.2).

The second variable of interest is the SEC comment letter (SEC_CL). It is a dichotomous variable (1 or 0), depending upon a receipt of SEC comment letter by the company

(“CL_CON_ID” field in Audit Analytics). To measure severity of comment letter (SEC_CL_CT)

I use three different approaches to improve robustness of the severity measure of the comment letter. First of all, I split all the letters based on the accounting topic it relates to by performing word search on the field “LIST_CL_ISSUE_PHRASE” in Audit Analytics. Based on that search,

I am able to assign each letter to the top accounting issue as defined in Appendix A (Critical

Accounting Issue). Furthermore, I calculate number of rounds it takes to resolve the issue. I count number of letters between the SEC and the company for the same dissemination date of the letter

(“FILE_DIS_DATE”). As a third approach, I count frequency of comment letters per fiscal year

(“CL_CON_ID”). The control variables are defined in details in Appendix A.

Table 2 presents SEC comment letter selection process included in the sample. There are

74,116 SEC comment letters from 2005 to 2015. This sample is further reduced by the comment letters which do not relate to 10-K or 10-Q filings (28,312) or do not contain all required information to calculate the severity of the comment letter or are duplicates (23,793). As a result, the final sample consists of 22,011 unique comment letters. My analysis requires that I link

94 companies with comment letters to the auditors data from Audit Analytics and to Compustat and

CRSP for fundamental variables and short interest. The preliminary file with fundamental data for years 2005-2015 includes 71,211 firm-year observations. Following prior literature, I eliminate firms from the finance and utilities sector (14,237). Thus, the final sample of the fundamental data merged with short interest and SEC comment letters consists of 56,974 firm-year observations.

Since different sets of variables are used in each of the six base models, I separately eliminate missing variables required for each model. As a result, my final sample size for each model contains different number of firm-year observations. The final sample size for the audit fees model includes 43,930 firm-year observations. The final sample size for the auditor change model includes 45,331 firm-year observations. The final sample size for the discretionary accruals model includes 39,612 firm-year observations. The final sample size for the restatements model includes

40,326 firm-year observations. The final sample size for material weaknesses model includes

35,643 firm-year observations. The final sample size for PCAOB inspections deficiencies model includes 43,358 firm-year observations.

Table 1 presents distribution of SEC comment letters for each of the datasets utilized in respective models. Overall, the number of unique comment letters relating to 10-K and 10-Q identified in Audit Analytics amounts to 22,011 over the years 2005-2015. As the conversations were merged with fundamental data in Compustat, CRSP and Short Interest Supplemental datasets,

I had to delete SEC comment letters with no corresponding data. As a result of the elimination process each of the subsets from (1) through (6) has a different number of SEC comment letters due to different variables utilized in the models. The first subset used in the audit fees model has

9,603 unique conversations. The subset used for the auditor change model includes 9,810 unique conversations. This number is slightly lower for the discretionary accruals model – 8,899 unique

95 comment letters. Subset (4) applies 9,090 comment letters in examining restatement model. For the subset tested in the material weaknesses model, the final sample includes 8,327 unique observations. Lastly, comment letters used in the GAAP/GAAS deficiencies model are only limited to 2013 due to the limited availability of data on PCAOB Inspections. As a result, only

8,042 unique comment letters are used in the Deficiencies model. Overall, there is a decreasing trend in the number of comment letters relating to 10-K and 10-Q while the conversations pertaining to Non-GAAP measures are more prevalent.

3.4 Econometric Issues

Factor Analysis

In order to provide additional value of this study, I utilize factor analysis to capture six proxies in one measure of audit quality. Factor analysis is a statistical procedure commonly used to reduce a large number of attributes to a smaller set of composite components (Brown and Moore,

2014). Since audit quality can be proxied by many variables, I apply composite factor score as a determinant of audit quality capturing various proxies. The literature does not provide consensus on the most reliable proxy (as each of them have weaknesses), therefore I conduct my factor analysis on all six proxies used in the above models to arrive at a composite factor score.

First, I conduct principal component analysis to reduce the dimensionality of the audit quality variables before I start testing the hypotheses. The variables that load on the factors are derived from the analysis providing eigenvalues. They calculate the total variance that is explained by any one factor. The eigenvalue is a measure of how much of the variance of the observed variables a factor explains. For the purpose of this study, I set a conservative treshold of 1.1 for

96 the eigenvalues to load on each factor. As a result, I obtain two factors which are subsequently substituted for each respective audit quality proxy. I re-run the main models with factor 1 and factor 2 as a dependent variable. The results are presented in Table 9 through 9.6. and described in section 5.2 of this study.

Endogeneity

Endogeneity is one of the major concerns of the empirical archival studies and this study is no exception to that (Chenhall and Moers, 2007; Gassen, 2014). By utilizing simple linear OLS regression models, this study suffers from the endogeneity problem due to possible correlated omitted variables, measurement errors or simultaneity. In my opinion, the possibility of correlated omitted variables is the most significant concern for this study. Using a non-randomly selected sample in the regression estimation creates a potential omitted-variable problem, which can bias the coefficient estimates of the explanatory variables. Factors that determine selection of the firm for review by the SEC are likely to be also related to some proxies of the audit quality (e.g., earnings management, material weaknesses or restatements).

One of the standard textbook solutions to the endogeneity problem is the use of instrumental variables. However, it is difficult to find variables that could be used as instrumental variables and satisfy the strict requirements (Ittner and Larcker, 2001). The instrumental variables have to be relevant to the model, exogenous and not correlated with the error terms from the first stage model (Larcker and Rusticus, 2010). If the instrumental variables are weak or even endogenous to some extent, they will be more biased than OLS methods. The instrumental variables need to be based on a theory to justify the selection of instruments. However, it is still a

97 challenging task to decide on the instrumental variables which would provide satisfying results for the endogeneity problem encountered in empirical accounting research.

In cases when the instrumental variables are not feasible to select, we can incorporate additional control variables or the fixed effects that could mitigate endogeneity problem

(Semykina and Woolridge, 2010). Thus, in all of the models used in this study, I include year, industry and firm fixed effect. Inclusion of controls for the effects of client characteristics and auditor office characteristics decrease the likelihood that the results for the audit quality are attributable to correlated omitted variables. The measures of audit quality are determined by components indicated by prior literature as the best available measures (DeFond and Zhang, 2014).

Winsorizing, Heteroscedasticity and Multicollinearity

The continuous variables in the models specified above are winsorized at 1 and 99 percent to mitigate the influence of outliers. To ensure that multicollinearity is not affecting results of this study, I ran variance inflation factor (VIF) for all the models to ensure that their value was less than 10 (Belsley, Kuh and Welsch, 1980).

The use of panel datasets in this study can lead to biased standard errors due to correlation of residuals across firms or time – firm or time effect. To address this concern empirical archival literature uses various approaches to deal with biases in standard errors but none of these methods are correct (e.g., Fama-MacBeth procedures, 1973; White standard errors (White, 1980); different types of fixed effects, or Newey and West procedure, 1987). Petersen (2009) provides pros and cons for each method and their outcomes under different circumstances (e.g., permanent or temporary firm effect). Based on his analysis he proposes clustering standard errors either at firm-

98 level, time dimension or both when they are appropriate (double cluster). Biased coefficient could also be attributed to the misspecified regression models caused by heterogeneity, which is not considered by Petersen (2009). In this study, I cluster standard errors at firm level.

Researchers also have an option to select from different forms of fixed-effects – country, industry or individual firm-level. Amir, Carabias, Jona, and Livne (2016) show that omitting firm fixed-effects or by using other forms of fixed effects (e.g., industry) provides biased coefficients leading to wrong inferences. This relatively recent work has important implications as most empirical studies use industry fixed effects in the models. They argue that rather than high level, the smallest unit in the data should be used for fixed effects. However, in the long time series, the firm effects may not be fixed. To conclude, the best solution available to deal with correlated omitted variable problem is inclusion of firm and time fixed-effects. Therefore, consistent with recommendations in Amir et al. (2016), in this study I use firm, industry and time fixed-effects in the tested models.

Chapter 4: Empirical Results

I begin this chapter by discussing descriptive statistics and correlations. Due to the fact that

I exploit six audit quality models with the various variables controlling for auditor and firm characteristics. I present six tables with the descriptive statistics (Table 3.1 through 3.6). The data presented in the tables provides valuable information regarding financial characteristics of the firms and audit firms included in the samples. The correlation tables are presented in table 4.1 through 4.6. They present Pearson and Spearman correlations for the variables utilized in the six audit quality models. The analysis of the tables provides an overview of the directional relationships between variables and their statistical power of correlations.

The main analysis of the dissertation is captured by the results presented in Table 5.1 through 5.6.2. They provide support (or lack of it) for the predicted hypotheses in Chapter 3. Thus, analyzing data from the tables is the core of this chapter presented in section 4.2. Included in this section is also analysis of the marginal effects of the estimates provided by the main analysis (Table

7.1 through 7.6). Hypotheses examining the severity of the issues addressed in the SEC comment letters are presented and described in Table 6.1 through 6.6.2. The additional analysis aimed at providing stronger support for the main analysis is covered in Chapter 5.

4.1. Descriptive statistics and correlations.

Descriptive statistics are presented in the tables 3.1 through 3.6. Each table presents descriptive statistics for the variables used in the model to measure each of the six audit quality proxies – audit fees, auditor changes, discretionary accruals, restatements, material weaknesses and PCAOB inspections deficiencies.

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Table 3.1 presents descriptive statistics for the final sample used in the audit fees models.

The mean (median) audit fees for the sample amount to $13.44 ($13.56) million. The sample includes 22 percent of firms which received an SEC comment letter over the examined period of time. With respect to the second variable of interest – Short Interest Ratio, I find that the mean

(median) is 3.6 (1.7). It indicates that almost 4 percent of the shares are sold short. The average

(median) size of the company (Assets) in the sample is $5.7 ($5.8) billion. On average, all companies have foreign operations and have at least 2 segments. The statistics indicate that 65 percent of sample firms engage Big 4 auditors and 63 percent of them have current audit firms for less than 3 years (Short_Tenure). Furthermore, 71 percent of the firms in the sample have

December year end close. The statistics suggest that firms in the sample are stable financially, with the mean (median) quick ratio of 2.36 (1.37), leverage of 0.27 (0.19) and 0.48 (0.48) mean

(median) ratio of current assets to total assets. The median (mean) ROI is negative 6.3 percent (-8 percent). However, I note that 43 percent of the companies on average report material weakness and 41 percent of them reported a loss over the 11 years examined in this study.

Table 3.2 reports descriptive statistics for the auditor change model. The sample includes firms where the auditor change was experienced by 6 percent of the firms’ population. The mean

(median) firm size (Assets) was $5.71 ($5.80) billion. The sample firms are diverse in terms of the financial ratios – it is reflected in the mean (median) leverage of 27 (19) percent; mean (median)

ROA of (-0.15) (2) percent, and mean (median) receivables to inventory ratio (Rec_and_Inv) of

23 (19) percent. In addition, the statistics report that on average 20 percent of firms experienced significant growth in sales and 41 percent of them reported a loss. Furthermore, the statistics suggest that the sample firms are diverse in terms of auditor type – 35 percent hire non-Big4 audit firm and for approximately half of them (48 percent) the audit firm is the city expert. I also note

101 that for 60 percent of the firms in the sample, the auditor has tenure of 3 years or less. The statistics indicate that on average 71 percent of the firms have December year end and the mean (median) number of days to file the annual statement (Audit_Lag) is 74 (68) days. It also shows that on average, 44 percent of firms in the sample used in the model reported material weaknesses according to the Section 404 of the SOX Act, and 10 percent had going concern opinions.

Table 3.3 presents descriptive statistics for the variables used in the performance matched discretionary accruals. Based on the suggestions in Kothari, Leone, and Wasley (2005), I calculated absolute value of performance-matched discretionary accruals. The mean (median) values for the sample firms is 1.1 (0), which is consistent with the statistics for the sample used in the study by Kothari et al. (2005). The mean (median) absolute value of lagged total accruals reported amounts to $8.9 ($4.1) for the sample firms.

On average, 23 percent of the firms in the sample received an SEC comment letter over the examined period. The second variable of interest – Short Interest Ratio reported mean (median) values of 3.9 (2.1) percent and a standard deviation of 5.5 percent. The values indicate that on average approximately 4 percent of the outstanding shares were subject to short sales for the population of firms in the sample. Descriptive statistics indicate that on average the firms in the sample have a market value of approximately $285 billion as indicated by the $5.662 ($5.751) mean (median) log of the market value. Furthermore, the mean (median) market to book ratio is

2.97 (1.97) which means that investors have high investment expectations from the firms in the sample.

Descriptive statistics further indicate that firms in the sample had a mean (median) leverage of 25 (17) percent; mean (median) current ratio of 3 (2); and mean (median) ROA of -15 (2)

102 percent. The companies in the sample reported the mean (median) ratio of operating cash flows

(CFO) scaled by total assets of (-2) (7) percent. Additionally, the mean (median) standard deviation of operating cash flows calculated over 5 consecutive years amounting to 9 (5) percent. Moreover, on average 7 percent of the firms in the sample operate in the high litigation industries.

Approximately 40 percent of the firms reported material weaknesses over the examined period in the study.

The statistics indicate that audit firms engaged for performing the assurance work have on average 20 publicly traded clients (2.929 as a mean 2.944 as a median of Firm_Size). Furthermore, the audit firms have a market share of approximately 56 percent in the client’s industry. For 15 percent of the sample firms, they are important clients as measured by the proportion of audit fees from the particular client to the total audit fees earned by the audit firm. Furthermore, 63 percent of the firms engage Big 4 auditors and they have relatively short tenure for 63 percent of clients

(less than 3 years).

The descriptive statistics for the variables used in the restatement model are presented in table 3.4. Overall, 9.6 percent of the sample firms reported restatements with the standard deviation of 29.5 percent. Less than 20 percent of the firms in the sample received SEC comment letters.

The mean (median) value of the second variable of interest (Short Interest Ratio) amounts to 3.9

(2.1) percent. That indicates that firms in the sample had approximately 4 percent of the shares were sold as short sales. The mean (median) value of the assets for the firms in the sample amounts to $5.623 ($5.705) million. From the financial position standpoint, the mean (median) ROA for the firms amounts to -15 (2.2) percent; the mean (median) leverage amounts to 24.8 (17.2) percent; and mean (median) of receivables and inventory ratio amounts to 23.9 (19.9) percent. The mean

(median) of the financing ratio (Financing) is 20.7 (4.4) percent. In general the sample firms are

103 financially stable, investors expect positive returns from them which is reflected by the mean

(median) book to market value of 2.955 (1.956). On average, firms have 2 segments (with a median of 1 segment) and approximately all of them engage in the foreign operations. The firms pay a mean (median) of $13.4 ($13.5) million in audit fees and 63 percent of them hire Big 4 auditor.

Based on the number of audit fees paid, 15 percent of the firms are considered as important clients to the audit firms.

Table 3.5 presents descriptive statistics for the sample of 35,643 firms utilized in the material weaknesses model. On average 32.3 percent of firms reported material weaknesses during examined period. Twenty three percent of sample firms received SEC comment letters. The mean

(median) value for the Short Interest Ratio amounts to 4.3 (2.6) percent. Furthermore, the statistics indicate that firms are larger than in the prior samples – the mean (median) assets reported are

$6.42 ($6.48) billion. The mean (median) age of the firm in the sample is 17 (13) years and 89 percent of them have foreign transactions. The firms have approximately 2 business segments of operations, and 24 percent of them experience significant growth in their industry (in the top decile). Generally, the firms incurred minimal restructuring charges and 18 percent of them engaged in acquisitions. The majority of the firms preferred Big 4 auditors (68 percent).

Lastly, descriptive statistics for the sample of audit firms in the PCAOB Inspection deficiencies model are presented in Table 3.6. The 59.3 percent of the audit firms in the sample received a PCAOB inspection report with deficiencies. Considering two possible types of deficiencies – GAAP and GAAS, the statistics indicate that 20.2 percent and 39.1 percent report

GAAP and GAAS deficiencies, respectively. 22 percent of clients of the audit firms examined in the PCAOB inspections deficiencies model received SEC comment letters. The short interest ratio for the clients of audit fees was reported at 3.7 (1.8) percent. Audit firms charged their clients with

104 mean (median) of audit fees in the amount of $13.37 ($13.46) million. Furthermore, 65 percent of the audit firms are associated with Big 4 and 46 percent of their clients are traded on the stock exchange. The average client size has a mean (median) of $6.195 ($6.851) billion.

Table 4.1 through 4.6 present the Pearson and Spearman correlation coefficient matrix for all the variables used in the six models respectively. Table 4.1 reports correlations coefficients for all the variables used in the audit fees model. I note that both – the Spearman and Pearson coefficients indicate a positive and significant correlation between two variables of interest – SEC

Comment Letter and Rank of Short Interest Ratio (0.175, p<0.001; 0.461, p<001 respectively).

Furthermore, the interaction term (SEC Comment Letter and Rank of Short Interest Ratio) is also positive and significant based on the results reported by both types of correlations (0.220, p<0.001). The tabulated results indicate that the majority of the variables used in the audit fees model are statistically significant at 1 percent.

Table 4.2 presents correlations coefficient matrix for the variables used in the auditor change model. The statistics report that two main variables of interest and interaction term between both variables are statistically significant but unexpectedly negative (-0.028, p<0.001; -0.095, p<0.001; -0.024, p<0.001). For the majority of the control variables used in the model, I document statistical significant correlation between them and the auditor change variable.

Table 4.3 presents correlations coefficients for the variables utilized in the performance- matched absolute discretionary accruals model. It reports that correlations between absolute value of performance-matched discretionary accruals and both variables of interest (SEC comment letter and Rank of Short Interest Ratio) are negative and statistically significant (-0.017, p<0.001; -0.024, p<0.001). Furthermore, the interaction term between both variables of interest and performance-

105 matched discretionary accruals is negative and significant as reported by Spearman and Pearson correlations (-0.023, p<0.001; -0.017, p<0.001), respectively. I also note that the remaining majority of control variables is statistically significant at 1 percent level.

Table 4.4 reports correlations coefficients for the variables used in the restatement model.

The correlation between the SEC comment letter and restatement is negative and not statistically significant (-0.001, p=894) for the Pearson and Spearman correlations. The correlation between the second variable of interest, Rank of Short Interest Ratio is positive and marginally significant at 5 percent level (0.011, p=0.023). Unfortunately, the correlation between restatements and interaction term (SEC Comment Letter and Rank of Short Interest Ratio) is positive and not statistically significant (0.002, p=739). Furthermore, correlations coefficients between restatements and ROA, Leverage and Acquisitions is statistically significant based on the Pearson coefficient estimations (0.017, p<0.001; -0.011, p=0.023; -0.022, p<0.001, respectively). For the

Spearman correlations between restatement and market to book, acquisitions, audit fees and client importance variables are statistically significant (0.008, p=0.095; -0.022, p<0.001; 0.009, p=0.075; and -0.011, p=0.03 respectively). The remaining control variables are not statistically correlated with restatements.

Table 4.5 presents correlations coefficients between variables in the material weaknesses model. The correlation between SEC comment letter and subsequent material weaknesses is negative and statistically significant (-0.156, p<0.001). It indicates that firms receiving SEC comment letters are negatively related with subsequent material weakness reported by the firm.

Furthermore, the second variable of interest (Rank of Short Interest Ratio) is negatively and significantly correlated with subsequent material weaknesses (-0.510, p<0.001). Also, similar correlation is observed between interaction term (SEC comment letter and Rank of Short Interest

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Ratio) - -0.212, p<0.001 based on the Pearson correlations. Moreover, all the remaining control variables used in the restatement model are also statistically significant at 1 percent level.

Lastly, table 4.6 reports Pearson and Spearman correlations between variables used in the

PCAOB inspections model. The correlation between SEC comment letter and GAAP deficiency is negative and not statistically significant (-0.006, p=0.120). However, the correlation between SEC comment letter and the GAAS deficiency is positive and significantly correlated (0.081, p<0.001).

The second variable of interest – Rank of Short Interest Ratio – is positively and significantly correlated with PCAOB deficiencies related to GAAP and GAAS (0.110, p<0.001; 0.172, p<0.001). Moreover, the interaction term (SEC comment letter and Rank of Short Interest Ratio) is positively correlated with both types of deficiencies (GAAP – 0.002, p=0.569; GAAS – 0.107, p<0.001). The remaining control variables used in the model are positively and significantly correlated with GAAP and GAAS deficiencies at 1 percent level.

4.2. Empirical Results and Analysis.

Table 5.1 presents the results for the impact of SEC comment letter and short interest positions on the subsequent audit fees. The table presents results of the estimated regression coefficients for equations (3), (4) and (5) respectively. The first column of the table reports estimated coefficients for audit fees OLS model with the SEC comment letter as a main variable of interest. The coefficient for the SEC comment letter variable is positive and statistically significant at 1 percent level (0.024, p=0.002). Thus, the results provide statistically significant support for hypothesis H1a. As predicted, the receipt of SEC comments letter results in the increase in audit fees for the audit engagement subsequent to the receipt of the SEC comment letter. The firms receiving comment letters demand higher quality audits for the subsequent audit

107 engagements as measured by audit fees proxy. Thus, they are willing to pay more for higher quality audit services.

The control variables used in the model are generally significant and in the predicted direction. Client size (Assets), number of business segments (Segments), reported loss (Loss) and current assets to total assets ratio (CATA) are positively related to audit fees at 1 percent level.

Additionally, leverage and issuance of going concern opinion is also positive and significant at 5 percent level. Furthermore, firms have to pay higher audit fees to audit firms associated with Big

4 audit firms (0.385, p<0.001). The results also indicate that when the client is important to the audit firm (measured in proportion of audit fees from the client to the overall portfolio), then client can experience a discount in the audit fees (-0.225, p<0.001). Contrary to the expectations, material weaknesses variable has a negative and significant relationship with the audit fees (-0.110, p<0.001). The variance inflation factors (VIF) calculated for all equations are all less than 10, indicating that multicollinearity does not appear to be an issue (Belsley, Kuh and Welsch, 1980).

The model is estimated including year and industry fixed effects. To mitigate endogeneity concerns, I run the model with the firm and year fixed effects. The results presented in table 12.1 remained the same as reported in table 5.1.

The second column of Table 5.1 presents estimated coefficients for audit fees OLS model with the Rank of Short Interest Ratio as a main variable of interest. The coefficient for Rank of

Short Interest Ratio variable is positive and statistically significant at 1 percent level (0.180, p<0.001). Thus, the results provide strong support for hypothesis H1b, predicting that companies with short interest positions will pay higher audit fees for the subsequent audit engagement. As supported by anecdotal evidence, management fears negative market reaction when the short selling of stocks in their company takes place and shows an increasing trend. Therefore, they

108 demand higher quality audits to ensure that short sellers expectations about the future negative corporate events will not occur. This additional assurance can be obtained by more thorough audits, more specialized auditors or hiring a company with better industry expertise. Thus, when demanding additional efforts from the auditor they are inclined to pay higher fees. The estimated coefficients presented in the second column of Table 5.1 provide empirical support for this expectation formulated in the hypothesis H1b. Furthermore, the estimation coefficients for the remaining control variables are consistent with the estimated coefficient in column 1. Generally, all the control variables are statistically significant at 1 percent and in the predicted direction.

Column three of Table 5.1 reports estimation results for equation 5. It includes two main variables of interest and the interaction term between them. Unexpectedly, the coefficient for the interaction term between two variables of interest (SEC comment letter and Rank of Short Interest

Ratio) has the opposite (negative) sign to the prediction made in hypothesis H1c (-0.087, p<0.001).

Despite both variables being positive and statistically significant (0.074, p<0.001; 0.197, p<0.001), the estimation coefficient for the interaction term does not provide support for hypothesis H1c.

The sign of the coefficient actually indicates that when the short sellers and comment letters are present, firms are able to negotiate lower fees. It can also be interpreted as a decreased audit risk when both monitoring mechanisms are in place and working effectively. Thus I conclude that the interaction effect of both variables of interest reduces the audit fees on the subsequent audit engagements. The firms may be able to extract lower audit fees when both of the variable effects are present. As a result of this opposite effect to the predicted one in hypothesis, I calculate the marginal effect to determine the real economic impact of both variables and their joint effect on the level of audit fees paid on the subsequent audit enagagement.

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In order to assess the economic significance of the test results, using the coefficients of model (5) in column three of Table 5.1, I compute the marginal effect of SEC comment letter and

Rank of Short Interest Ratio, as well as other control variables used in the Audit Fees model. Table

7.1 show the marginal effects analysis captured by model (5). Following Kim and Zhang (2016),

I use the Norton, Wang and Ai (2004) to calculate the marginal effect on the interaction term. The marginal effect of SEC comment letter (2.89 percent) suggests that a one standard deviation increase in SEC comment letter results in a 2.89 percentage point increase in subsequent audit fees. Furthermore, the marginal effect of Rank of Short Interest Ratio (6.19 percent) suggests that a one standard deviation increase in the rank of the short interest position increases subsequent audit fees by 6.19 percent point. The increase is economically significant at 1 percent level. The marginal effect on the interaction term (SEC_CL * Rank_SIR) of model (5) is -2.24 percent, suggesting that a one standard deviation increase in rank of short interest ratio decreases audit fees by 2.24 percentage point for firms receiving SEC comment letters. Thus, the negative joint impact of both variables slightly reduces the positive impact on the audit fees incurred in the year following SEC comment letter. Overall, the results provide support for the hypothesis H1c based on the economic effect of the variables.

On the other hand, the negative coefficient on the interaction variable could indicate that management has some negotiation power when they observe short selling activity and also receive

SEC comment letter. They can potentially use this argument to negotiate lower audit fees as audit firm did not perform thorough and sufficient review before filing financial reports with the SEC.

Thus, management might have negotiation advantage when contracting for the following year to request lower audit fees while at the same time provide a high quality audits.

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Table 5.2 presents results from estimating equation (7), (8) and (9) respectively. The first column of the table reports estimation results of equation (7) with the SEC comment letter as the variable of interest. The estimated coefficient is positive and statistically significant at 1 percent level (0.127, p<0.001). It indicates that firms which receive SEC comment letter increase the probability of the auditor change for the subsequent audit engagement. Thus, the results provide strong support for hypothesis H2a. Firms with the SEC comment letters demand higher audit quality from their auditors. Therefore, SEC comment letters received after the auditors performed their services and attested on the financial statements filed with the SEC, may increase the likelihood ofauditor change in the future.

The remaining control variables are mostly in the predicted direction and statistically significant. The number of days it takes to complete audit engagement (Audit_Lag) is positively related with auditor change (0.003, p<0.001). It indicates that longer audits increase the probability of auditor change as management is pressured by investors to disclose earnings close to the fiscal year end. Furthermore, loss and going concern opinion are also positively associated with auditor change (0.116, p<0.001; 0.119, p<0.001 respectively). Firms experience losses and firms for which auditor issues going concern opinion increase the likelihood of auditor change. Unexpectedly, reporting material weaknesses by the firm is negatively related to the auditor change (-0.169, p<0.001). It appears that firms reporting material weaknesses are not inclined to change auditor as they may want to work with the auditor familiar with their operations to remediate weaknesses.

Additionaly, firms with significant growth in their operations are negatively related to the auditor change (-0.135, p=0.005). It indicates that rapidly growing firms are not willing to change their current auditors. Moreover, firms with audit firms associated with Big4 do not switch auditors to

111 non-Big4 (-0.462, p<0.001). Lastly, firms with auditors whose tenure is three years or less are more likely to change auditors than auditors with long term tenure (1.471, p<0.001).

The second column of Table 5.2 presents estimation coefficient for equation (8). The results from estimating this equation provide support for hypothesis H2b. The estimated coefficient for Rank of Short Interest Ratio is positive and statistically significant at 1 percent level (0.028, p<0.001). Thus, it provides strong support for hypothesis H2b predicting that high short interest positions in the firm increase the probability of auditor change on the future audit engagements.

High short interest positions in the company could indicate the upcoming negative corporate event that will lead to the drop in the market price of the stocks. This in turn can be blamed on the auditors not performing sufficient due diligence and not placing sufficient efforts during the audit process. If management believes that auditors are the ones to be blamed for the high short interest positions, then it may be more likely to search for the new audit firm. Management could demand higher quality audits and therefore they can look for the new audit firm and dismiss the current one if it does not meet the needs of management.

Column three provides empirical suport for hypothesis H2c. The hypothesis predicts that both factors - SEC comment letter and Rank of Short Interest Ratio can increase the probability of auditor change. The last column in Table 5.2 presents the results from estimating model (9) where additional interaction term between both variables of interest is introduced and estimated. The interaction term provides effect of the joint impact of SEC comment letters and short interest positions. As expected, the coefficient is positive but not statistically significant (0.104, p=0.303).

Additionally, the SEC comment letter variable becomes insignificant when interaction term is estimated (0.070, p=0.238). Based on the empirical evidence provided in column three, I conclude that hypothesis H2c is only partially supported. In order to evaluate economic significance of the

112 coefficients from model (14), I follow technique utilized by Kim and Wang (2016). The results provided in Table 7.2 show that marginal effect for SEC comment letter variable is 7.99 percent and for Rank of Short Interest Ratio is 4.45 percent. The results suggest that a one standard deviation increase in both variables of interest increase the probability of auditor change in the future. Furthermore, these results are even more pronounced when we consider the economic significance of the interaction term. The marginal effect of the interaction term (SEC_CL *

Rank_SIR) of model (9) is 13.51 percent, suggesting that a one standard deviation increase in the rank of the short interest position increases probability of auditor change by 13.51 percent for firms receiving SEC comment letters.

Table 5.3 presents estimation results for equations (12), (13) and (14). The first colum reports estimated coefficient for model captured by equation (12) which tests hypothesis H3a. I predict that after the receipt of the SEC comment letter management will change their behavior and reduce the amount of discretionary accruals. As repetitive comment letters are not well perceived by the market nor management, the latter group will increase their effort to decrease discretionary accruals subsequent to the comment letter. The estimated coefficient for SEC comment letter variable is negative and statistically significant at 1 percent level (-0.144, p<0.001).

It indicates that firms receiving SEC comment letters decrease discretionary accruals following the SEC review. Therefore, the results in column one of Table 5.3 provide strong empirical support for hypothesis H3a.

The remaining control variables are mostly in the predicted direction. Some of them are statistically significant – e.g., market value (-0.061, p<0.001), leverage (-0.232, p=0.002), operating cash flows (0.201, p=0.067), litigation (-0.749, p=0.040), weaknesses (0.123, p=0.093), short term auditor tenure (-0.111, p=0.058) and Big4 auditors (-0.155, p=0.052). The variance

113 inflation factor (VIF) values for all equations are all less than 10 indicating that multicollinearity does not appear to be an issue (Belsley, Kuh and Welsch, 1980).

The second column of Table 5.3 presents estimation results from equation (17). The estimation coefficient for Rank of Short Interest Ratio is negative and statistically significant

(-0.035, p<0.001). It indicates that firms with high short interest positions subsequently decrease their use of discretionary accruals. As management is highly concerned with the market opinion on the short positions as well as to protect their personal wealth from decreasing (stock options compensation) they will decrease the use of discretionary accruals indicating earnings management. Thus, results presented in the second column provide strong empirical support for hypothesis H3b.

Column three of Table 5.3 presents results from estimating equation (18). In order to obtain support for hypothesis H3c, I include both variables of interest (SEC comment letter and Rank of

Short Interest Ratio) and the interaction term in the model to examine impact of both variables on the use of performance-matched discretionary accruals by management. The SEC comment letter is negatively related to the use of discretionary accruals (-1.181, p<0.001) as well as Rank of Short

Interest Ratio (-0.250, p<0.001). It indicates that each of the factors reduce discretionary accruals for the year following the receipt of SEC comment letters by firms with short interest positions.

However, the interaction term between both variables is positive and statistically significant

(0.268, p<0.001). Therefore, the results provide only partial support for hypothesis H3c. The results show that joint effect may possibly cancel out the impact of each variable on the use of discretionary accruals.

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To further enhance my analysis, I evaluate economic significance of the coefficients from

OLS regression model (14). Based on the results provided in Table 7.3, the marginal effect for

SEC comment letter is -63.19 percent and for Rank of Short Interest Ratio is -8.54 percent. The results suggest that one standard deviation increase in both variables of interest leads to a statistically significant decrease in the use of performance-matched discretionary accruals by management. However, considering the economic significance of the interaction term, the marginal effect shows increase of discretionary accruals by 8.72 percent for the interaction term

(SEC_CL * Rank_SIR). It suggests that increase of one standard deviation in short interest ratio increases the application of discretionary accruals by 8.72 percent for firms receiving SEC comment letters. The results of marginal effects also provide partial support for hypothesis H3c.

Table 5.4 presents results from estimating equation (16), (17), and (18). The first column of the table reports estimation coefficients examining hypothesis H4a. The hypothesis predicts that subsequent to the SEC comment letter firms reduce probability of restatement as a result of demanding higher quality audits. The estimated coefficient for SEC comment letter variable is positive and statistically significant (0.066, p=0.005). Unexpectedly, the results show that probability of restatement following the SEC comment letter increases. This can be explained by the fact that SEC may require the firm to restate its financial statements as a result of the review.

As supported by the empirical evidence (Dechow et al. 2016), the instance of the restatement following the SEC comment letter is minimal. However, strong empirical results in this study may indicate that management demands higher quality audits and more thorough review when they observe high short interest positions in the firm. Increased demand for higher quality audits after the receipt of the SEC comment letter increase the probability of the restatements for firms with short interest positions in the lack of full disclosure. Therefore the results provided in the first

115 column of Table 5.4 do not grant support for hypothesis H4a. The results indicate that firms actually increase the probability of the restatement of their financial statements following the receipt of comment letter.

The second column of table 5.4 presents results from estimating equation (17). The main variable of interest in this model – Rank of Short Interest Ratio is positive and significant (0.092, p=0.022) which indicates that short interest positions increase the likelihood of restatements. The results do not support predicted hypothesis H4b. Despite a lack of support for my predicted hypothesis the results show that short interest positions are a valuable source of the information for predicting the upcoming negative corporate events, which can significantly deteriorate market value of the company’s stock.

Column three of Table 5.4 present results for estimating equation (18). I predict in hypothesis H4c that both variables of interest will be negatively related to the probability of the restatements subsequent to the receipt of the SEC comment letter. The results show that SEC comment letter is positively related to restatements (0.014, p=0.799) as well as Rank of Short

Interest Ratio (0.076, p=0.082). Furthermore, the interaction term of both variables of interest is positive and not statistically significant (0.086, p=0.3). To conclude on the empirical results presented in Table 5.4, there is no support for the hypothesis H4c.

To evaluate marginal effects for the results presented in model (22), I review results in

Table 7.4. The marginal effect of SEC comment letter (-0.28) suggests that a one standard deviation increase in SEC comment letter results in a 0.28 percentage point decrease in the probability of the subsequent financial statements restatement. Furthermore, the marginal effect of Rank of Short

Interest Ratio (5.82) suggests that a one standard deviation increase in the rank of the short interest

116 position increases the probability of the subsequent restatement by 5.82 percent. The decrease is economically significant at 1 percent level. The marginal effect on the interaction term (SEC_CL

* Rank_SIR) of model (22) is 1.23 percent, suggesting that a one standard deviation increase in the rank of short interest ratio increases the probability of restatement for firms receiving SEC comment letters.

Table 5.5 reports results from estimating models captured by equation (20), (21), and (22).

The results show the impact of SEC comment letter and short interest positions on the likelihood of subsequent material weaknesses. The first column reports estimation results for the impact of

SEC comment letter on the subsequent material weaknesses. The coefficient for the main variable of interest – SEC comment letter – is negative and significant (-0.270, p<0.001). It indicates that comment letter decreases the probability of reported material weaknesses by the firm. Management demands higher quality audits subsequent to the SEC review and it may trigger improvements in the control environment of the company. Thus, as a result of the management action to improve the quality of the controls over financial reporting, auditors are able to provide higher quality audits which will be also reflected in the lower probability of material weaknesses reported by the firm.

Therefore, the results provide strong support for hypothesis H5a.

The remaining control variables used in the model are generally significant and in the predicted direction. As expected, firm size (Assets) decreases the probability of material weaknesses as larger firms can devote significant resources in order to improve control environment of the firm (-0.341, p<0.001). Firms reporting losses (Loss) increase the likelihood of material weaknesses report (0.241, p<0.001) as well as firms undergoing significant growth in the operations (Growth) as the control system may not keep up with expanding operations (0.597,

117 p<0.001). On the other hand, restructuring charges and audit firm associated with Big4 decrease the likelihood of material weaknesses report (-1.950, p<0.001; -1.111, p<0.001), respectively.

The second column of Table 5.5 presents results from estimating equation (21). The estimated coefficient for Rank of the Short Interest Ratio variable is negative and statistically significant at 1 percent level (-1.866, p<0.001). It indicates that short interest positions decrease the probability of the subsequent material weaknesses report. Thus, the results provide strong empirical support for hypothesis H5b.

Column three presents results from estimating equation (22). I examine whether both factors – SEC comment letter and Rank of Short Interest Ratio have joint impact on the probability of the material weaknesses. The results in column three show that estimated coefficients on both variables are negative and statistically significant (-0.420, p<0.001; -1.919, p<0.001). However, the joint impact of both variables captured by the interaction term is positive and significant, contrary to the developed expectation in hypothesis H5c (0.355, p<0.001). The positive sign of the coefficient suggest that for firms with short interest positions probability of reporting material weakness is increasing in the year subsequent to the receipt of comment letter.

In order to evaluate the economic significance of the test results, using the coefficients of model (22) in column three of Table 5.5, I compute the marginal effect of SEC comment letter and

Rank of Short Interest Ratio, as well as other control variables used in the Material Weaknesses model. Table 7.5 presents the marginal effects analysis for the results of model (22) (Norton,

Wang, and Ai; 2004). Following Kim and Zhang (2016), I use the Norton et al. (2004) to calculate the marginal effect on the interaction term. The marginal effect of SEC comment letter (-28.50) suggests that a one standard deviation increase in SEC comment letter results in a 28.50 percentage

118 point decrease in the probability of the subsequent material weaknesses report. Furthermore, the marginal effect of Rank of Short Interest Ratio (-94.64 percent) suggests that a one standard deviation increase in the rank of the short interest position decreases the probability of the subsequent material weaknesses report by 94.64 percent point. The decrease is economically significant at 1 percent level. The marginal effect on the interaction term (SEC_CL * Rank_SIR) of model (22) is 10.65 percent, suggesting that a one standard deviation increase in rank of short interest ratio increases the probability of material weaknesses by 10.65 percentage point for firms receiving SEC comment letters in the prior year, which is offset by the effect of each individual factor. Overall, the results provide strong support for hypothesis H5c.

Table 5.6.1 presents the results for estimating equation (24), (25), and (26) when GAAP

Deficiency is a dependent variable. The first column presents empirical results in support for hypothesis H6a. The coefficient for SEC comment letter is negative and significant at 5 percent level (-0.047, p=0.027), suggesting that subsequent to the comment letter the probability of GAAP deficiency in the PCAOB inspection report decreases. The results are consistent with the predicted hypothesis H6a.

Furthermore, the remaining control variables used in the model (24) are mostly significant and in the predicted direction. The number of offices inspected increases the likelihood of GAAP deficiency reported by PCAOB (0.285, p<0.001). The positive relation is noted for the size of the client (Average Client Size) as offices with larger clients increase the likelihood of receiving

GAAP deficiency report from the PCAOB (0.214, p<0.001). Audit fees are negatively related to the probability of GAAP deficiency (-0.018, p=0.057) as more important clients (as determined by the audit fees) receive higher quality audits which reduce the probability of the deficiency.

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Along the line, public clients also show negative relation for the probability of GAAP deficiency reports.

The second column of the table shows the impact of the short interest positions on the probability of GAAP deficiency in the PCAOB report. The coefficient on the variable of interest

(Rank_SIR) from estimating model (25) is negative and significant at 1 percent level (-0.177, p<0.001). As a result, hypothesis H6b predicting decreased probability of subsequent GAAP deficiency in the PCAOB inspection report is supported by the empirical results.

Column three of Table 5.6.1 presents results for estimating model (26). The results report negative coefficients for SEC comment letter and short interest position variables (-0.019, p<0.776; -0.158, p<0.001, respectively). However, the coefficient on the interaction term is negative but not statistically significant (-0.086, p=0.311). The results suggest that joint impact of the two variables of interest may not have an impact on the probability of subsequent GAAP deficiency determined by PCAOB inspection report. However, as each of the variables individually has negative and significant impact on the probability of the GAAP deficiency, I conclude that hypothesis H6c is partially supported. In other words, increasing frequency of comment letters and short interest trading decreases the likelihood of GAAP deficiencies resulting from PCAOB inspections.

Table 5.6.2 presents the results of estimation coefficients for model (24) with respect to the probability of GAAS deficiencies issued in the PCAOB inspection report. The first column reports impact of SEC comment letter on the probability of subsequent GAAS deficiency. Contrary to the prediction, the coefficient for SEC comment letter is positive and not significant (0.014, p=0.511).

It suggests that receipt of a comment letter does not have an impact on the probability of GAAS

120 deficiency issued in the PCAOB report. Thus, hypothesis H6a related to GAAS deficiencies is not supported.

The second column presents estimated coefficients for the model (25) in which short interest position is the main variable of interest. Unexpectedly, the coefficient on the Rank of Short

Interest Ratio is positive but not significant (0.014, p=0.711), indicating that short sellers trading does not have an impact on the probability of the subsequent GAAS deficiencies. As neither of the variables is related to the probability of GAAS deficiency, consequently there is no joint effect resulting from interaction of both variables. The results presented in column three provide corresponding evidence. Concluding on the test results for model (24), (25), and (26), only GAAP deficiencies are impacted by the receipt of SEC comment letters and short interest positions.

Contrary to the prediction, the evidence does not provide support for the impact on the probability of GAAS deficiencies issued in the PCAOB inspection report. This can be explained by the fact that SEC comment letters monitor financial reporting quality of the firm and do not discipline audit firms on the following of the GAAS rules. As a result, the signal provided by the SEC in the form of the comment letter and short sellers’ positions does not impact clients’ demand for improvement on audit engagements to better follow GAAS. However, the signal does impact clients’ demand for audit engagement conducted with less GAAP deficiencies as determined during the PCAOB inspections.

In order to assess the economic significance of the test results, using the coefficients of model (26) in column four of Table 5.6.1, I compute the marginal effect of SEC comment letter and Rank of Short Interest Ratio, as well as other control variables used in the GAAP deficiencies model. Table 7.6 show the marginal effects analysis for the results of model (26) for GAAP deficiencies. The marginal effect of SEC comment letter (5.08 percent) suggests that a one standard

121 deviation increase in SEC comment letter results in a 5.08 percentage point increase in the probability of the subsequent GAAP deficiency in the PCAOB inspection report. Furthermore, the marginal effect of Rank of Short Interest Ratio (-1.76 percent) suggests that a one standard deviation increase in the rank of the short interest position decreases the probability of the subsequent GAAP deficiency by -1.76 percent. The joint impact of both variables captured by the interaction term (SEC_CL*Rank_SIR) has a marginal effect of -15.59 percent. It suggests that a one standard deviation increase in rank of short interest ratio decreases the probability of subsequent GAAP deficiency by 15.59 percentage points for firms receiving SEC comment letters.

Thus, the joint impact of both variables is economically more pronounced than each variable separately. Overall, the results provide strong support for the hypothesis H6c relating to the GAAP deficiencies.

Severity of Issues addressed in the SEC comment letters

Tables 6.1 through 6.6.2 present results for evaluating the impact of the severity of issues addressed in the comment letters on the clients’ demand for audit quality. The severity of issues is captured by three measures – critical accounting issue, frequency of letters per round, and number of unique letters per year. Detailed definitions are provided in Appendix A. For the purpose of conducting this specific test, I limited the sample size to the firms which received the SEC comment letter. By focusing on these specific firms, I am able to more accurately determine the impact of the severity of topics covered in the comment letters.

Table 6.1 presents estimation results for model (6) and estimates the impact of the three severity measures on the subsequent audit fees. The estimation coefficient on Critical Accounting

Topic, following Dechow et al. (2016) classification is positive and significant (0.051, p=0.002).

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It suggests that comment letters addressing more critical accounting topics have a more pronounced impact on the subsequent increase in audit fees. The second measure of severity

(frequency of letters per round) has a positive and statistically significant coefficient (0.014, p<0.001). It indicates more pronounced impact of the letters which take more rounds (and time) to finalize the issue on the subsequent increase in audit fees. Lastly, I use the number of unique letters per fiscal year as the third measure of comment letter severity. As evidenced during the data collection process, some firms receive more than one comment letter per year. As a result, the number of comment letters during the year could potentially be a valuable measure of severity.

The coefficient of the number of unique letters per fiscal year variable is positive and significant

(0.069, p<0.001) indicating more pronounced impact on the increase in audit fees. Overall, three measures of severity deliver consistent results and this provides strong support for H1d. I conclude that companies that receive more severe SEC comment letters have a more pronounced increase in audit fees for the audit engagement following the SEC review.

Table 6.2 estimates the impact of severity of issues in SEC comment letters on the likelihood of the subsequent auditor changes. The table provides results from estimating model

(10) utilizing three severity measures described above. The coefficient of Critical Accounting

Topic is positive and significant (0.194, p=0.031) which suggest that auditor change is more likely for companies that receive more severe SEC comment letters. The second measure of severity

(Frequency of letters per round) has a negative and not significant coefficient (-0.001, p=0.893).

The results presented in the second column of the table suggest that frequency of letters per round does not impact the likelihood of auditor change. The coefficient of the number of unique letters per fiscal year variable is positive and significant (0.103, p=0.018). Thus, two severity measures provide support in favor of hypothesis H2d. Therefore, results provide partial support for

123 hypothesis predicting increased probability of auditor change for more severe issues in the comment letters.

The results for the estimation of model (15) are presented in Table 6.3. All three measures of severity of comment letters are negative (as predicted) but not statistically significant.

Unexpectedly, none of the three severity measures has an impact on the decrease in the discretionary accruals. Thus, I do not find a support for hypothesis H3d.

Table 6.4 presents the results from estimating equation (19). The specified model is examining impact of the severity of comment letters on the probability of subsequent restatements.

The first measure, Critical Accounting Issue has a negative and not significant coefficient (-0.134, p=0.280). The coefficient of Frequency of Letters per Round is positive and not statistically significant (0.003, p=0.875). The third column of the table delivers results for the last severity measure - Number of Unique Letters per Year. The coefficient is negative and statistically significant (-0.180, p=0.074) which provides partial support for hypothesis H4d. The results show that companies that receive multiple unique comment letters during the year are less likely to restate their financial statements for the audit engagement following the SEC comment letter. It indicates that companies with more than one letter per year undertake more effective action to prevent future restatements of financial statements. Thus, it decreases the probability of restating financial statements following receipt of multiple comment letters during the year. The results also provide evidence that companies consider the informational value of the comment letters to demand higher quality audits measured by lower likelihood of restatements.

The results from estimating equation (23) are presented in Table 6.5. Unexpectedly, the coefficient of Critical Accounting Issue is positive and statistically significant (0.249, p<0.001).

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Similarly, coefficient of Frequency of Letters per Round as well as Number of Unique Letters per

Year is positive and statistically significant (0.061, p<0.001; 0.041, p<0.001 respectively). The results are contrary to the predictions made in hypothesis H5d. The results actually imply that companies receiving more severe comment letters demand higher audit quality leading to lower probability of material weaknesses. Overall results in Table 5.5 provide support for the hypothesis that SEC comment letters decrease the probability of material weaknesses report. However, further examination of the severity of issues provides results inconsistent with the main findings. The results suggest that despite decrease in the likelihood of material weaknesses, more severe comment letters actually increase the probability of reporting material weaknesses by the firm.

This is strongly supported by all three severity measures of comment letters used in the study.

Table 6.6.1 and 6.6.2 present the results of estimating model (27) considering two types of

PCAOB deficiencies – GAAP and GAAS. It appears that only one severity measure is statistically significant - Frequency of Letters per Round. The impact of this measure on the likelihood of the subsequent GAAP deficiencies in the PCAOB inspection report is negative (-0.014, p=0.081). The results presented in column two of Table 6.6.1 indicate that only frequency of letters in each round decreases the probability of subsequent GAAP deficiencies. Since the other two measures are not statistically significant, the results provide partial support for hypothesis H6d related to GAAP deficiencies.

Unexpectedly, the impact of severity of issues in SEC comment letter on the probability of

GAAS deficiencies is positive and statistically significant (0.020, p=0.007). It indicates that audit firms whose clients have more severe comment letters increase the probability of the subsequent

GAAS deficiencies included in the PCAOB inspection report. Thus, the results do not support hypothesis H6d related to GAAS deficiencies. Overall, the estimation results from model (27) are

125 inconsistent with the predicted hypothesis and also vary by type of the deficiency. While the likelihood of GAAP deficiencies provides partial support for hypothesis H6d, the likelihood of

GAAS deficiencies does not. Thus, the results are mixed and provide only partial support.

In general, the three severity measures examined in the six audit quality models provide mixed results. None of the measures provide consistent results throughout all six measures.

Therefore, it is difficult to draw an overall conclusion summarizing the examination of severity measures. As shown by the results in Tables 6.1 through 6.6.2 the severity measures do have an impact on the clients’ demand of audit quality. I do find support for hypothesis H1d and partial support for hypotheses H2d, H4d, and H6d. However, I am unable to point out to the specific measure which would be robust and consistently significant throughout all six proxies of audit quality.

Big 4 versus non-Big 4 audit firms’ impact on the demand

To address the third research question of this study I review the results presented in estimating main models in Tables 5.1 through 5.6.2. The focus of this review is on the Big4 variable representing the four major audit firms in the market. The estimate of the Big4 variable reported in Table 5.1 is positive and significant 0.0374 (p<0.001). This implies that demand for higher audit quality measured by audit fees is different from non-Big4 audit firms. The estimate for Big4 in Table 5.2 reports a negative and statistically significant coefficient of -0.462 (p<0.001).

It indicates the difference between the two groups of audit firms as well with respect to higher audit quality measured by the likelihood of subsequent auditor change.

Table 5.3 reports negative and marginally significant coefficient for Big4 variable (-0.155, p=0.052). Also in this case, the demand for higher audit quality measured by the discretionary

126 accruals seems to be different between the two groups of audit firms. Table 5.4 presents negative and statistically significant coefficient of -0.075 (p=0.006) for the Big4 variable when measuring likelihood of subsequent restatements. It also indicates that when we capture audit quality by probability of future restatements, it differs between Big4 and non-Big4 audit firms.

Similar trend is observed for estimation results for the Big4 variable reported in Table 5.5.

The estimated coefficient is negative and significant -0.890 (p<0.001) indicating different demand for higher audit quality by clients of the Big4 and non-Big4 audit firms. Lastly reviewing results for likelihood of PCAOB Inspections (GAAP and GAAS) deficiencies, I also observe statistically significant results for Big4 variable (-0.484, p<0.001; and 0.479, p<0.001) for the likelihood of subsequent GAAP and GAAS deficiency respectively.

Overall the results provide strong support for the third research question developed in the study. Based on that I conclude that demand for higher audit quality differs between Big4 auditors and non-Big 4 auditors after the receipt of SEC comment letters by the client and in the presence of short sellers.

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Chapter 5: Additional Analysis

Prior literature points out to the additional considerations which have to be taken into account when discussing the role of the SEC comment letters and short interest trading (two main variables of interest) (e.g., Hope et al. 2017, Dechow et al. 2015). Thus, in this chapter, I perform additional analysis related to the impact of the SEC comment letter and short interest trading on the audit quality.

First, I conduct additional analysis utilizing lagged independent variables in the primary models. The evaluation of the lagged independent variables helps to eliminate the simultaneity problems in the utilized models. In the main analysis I employed lagged variables only for the main variables of interest (SEC comment letter and short interest ratio). Thus, the additional analysis provides results for the main models utilizing lagged independent variables to enhance results delivered by the primary analysis. The results are presented in Table 8.1 through 8.6.2 and discussed in part 5.1.

Subsequently, I perform factor analysis on all six audit quality proxies to calculate two factor scores (Factor 1 and Factor 2). The purpose of this additional test is to compute a measure of the audit quality based on the multiple proxies of audit fees. Chen et al. 2012 addressed the issue of measuring corporate governance by various proxies utilizing factor scores. Thus, to test the robustness of my results, I conduct factor score analysis on the audit quality variables. The details of the factor analysis with the results are reported in part 5.2 of this chapter.

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Furthermore, following the Hope et al. (2017) methodology employed in his research paper, I split the sample of firm-years observations into three different time periods. From May 2,

2005 (SEC Release No. 50747, 11.29.2004) to August 6, 2007, (SEC Release No. 53684,

4.20.2006), the SEC ordered a pilot program in which one-third of the Russell 3000 index firms were selected as pilot stocks and were exempted from short-sale price tests5. Subsequently to the program, the SEC eliminated short-sale price tests for all exchange-listed stocks (SEC Release No.

34-55970, 7.3.2007). As a result of the additional regulations, I conduct the analysis over the time of the pilot program (May 2, 2005 through August 6, 2007), during the financial crisis (2008 through 2010), and post-financial crisis period. I predict that sensitivity test on the split sample could possibly provide some additional insight with respect to the change in audit quality resulting from the short selling activity influenced by the additional regulations. The results from the sensitivity analysis with respect to different time periods are presented in part 5.3.

Lastly, following Dechow et al. (2016), I focus my attention on the top accounting issue addressed by SEC in the comment letter. The breakdown of the top accounting issues in the sample is provided in Table 11. The topics are extracted from the content of the letter and summarized for the sample used in this study. The summaries of the top accounting issues for the clients are reported by the industry in the annual reports published by the big auditing firms (PwC 6, KPMG, and Deloitte7). Based on the results provided in Table 11, I focus on the two specific accounting topic addressed most frequently in the tested sample– revenue recognition (13%) and fair value

(11%). The results are presented in Table 11.1 through 11.6, and they are discussed in part 5.4.

5 Details of short-price tests can be found on https://www.sec.gov/spotlight/shopilot.htm 6 https://www.pwc.com/us/en/cfodirect/publications/sec-comment-letter-trends.html 7 https://www2.deloitte.com/us/en/pages/audit/articles/sec-comment-letters.html

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5.1 Factor Analysis

The audit literature has not yet determined and concluded on the most appropriate proxy to be used in measuring audit quality (the “fit-all” proxy) (Zhang et al. 2015), thus I conduct factor analysis to reduce the number of variables which explain the same construct (Chen, Lu, and

Sougiannis, 2013; DiStefano, Zhu, and Mindrila, 2009). I construct two audit quality factors out of six key audit quality variables.

The most feasible approach for evaluating the relationship between the individual variable and the factor is to create a factor score based on the items with loading values above a predetermined cut-off value in the computations. Based on the review of the literature (Chen et al.

2012; Christensen et al. 2016), the cut-off value is an arbitrary decision and could depend on the method and the purpose of each research study. After review of the prior studies in the accounting area, I decided to utilize a fairly conservative approach and apply variables with eigenvalues greater than 1.1 for my analysis8.

The six datasets for each corresponding audit quality proxy were merged to capture the available data for all the audit quality proxies utilized by this study. The result of the merging procedure as well as the reduction of the missing variables is a dataset with 29,551 firm-year observations. First, I conduct principal component analysis to decrease the dimensionality of the audit quality variables. This analysis generates two factors which have eigenvalues greater than

1.1 and each accounts for more than 20 percent of the sample variance.

8 Eigenvalue - The eigenvalue for a given factor measures the variance in all the variables which is accounted for by that factor. The ratio of eigenvalues is the ratio of explanatory importance of the factors with respect to the variables. If a factor has a low eigenvalue, then it is contributing little to the explanation of variances in the variables and may be ignored as redundant with more important factors. Eigenvalues measure the amount of variation in the total sample accounted for by each factor. (www.wikipedia.org)

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Table 9 Panel A presents the results of the principal component factor analysis with

Varimax rotation and Kaiser Normalization. The first component has the eigenvalue of 1.61 and the second reports eigenvalue of 1.44. Both factors account for 43.5 percent of the standardized variance. They explain almost half of the variation in the audit quality variable. The results report factor loadings for each variable so we can see which variables load most strongly on each factor.

The variables that load on the first factor (factor 1) include mostly audit fees and probability of material weaknesses. The variables that load mostly on the second factor (factor 2) include discretionary accruals, and PCAOB Deficiencies (GAAP and GAAS).

Factor 1 has large positive loading for audit fees (0.895) and strong negative loading of (-

0.895) for probability of material weaknesses. The remaining variables have insignificant loadings on factor 1 as their values are considerably below 0.5. It is worth noting that both the positive and negative factor loadings have to be considered as the absolute value determines the weight of each variable in the construct (Factor 1) and negative sign only determines direction of the relationship to the factor. Thus, if the item is negatively related it shows negative loading. In conclusion, factor

1 is the outcome of the reduction of the variables and captures positively related audit fees and negatively related probability of material weaknesses.

Factor 2 has large loadings for three variables – probability of GAAP deficiency (-0.849);

GAAS deficiency (0.850), and discretionary accruals (0.48). The remaining variables loadings are insignificant (e.g., audit fees (–0.17); probability of restatements (-0.18)). I use factor loadings to construct weighted factor scores for these two factors representing audit quality demanded by management. I employ each factor to evaluate hypothesis tested in the main analysis. The results from estimating models utilizing two factor scores are presented in Tables 9.1 through 9.6.

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To test hypothesis H1c, I estimate model from equation (5) and utilize factor 1 and factor

2 scores as dependent variable instead of audit fees. The results are presented in Table 9.1. The first column employing factor 1 reports that SEC comment letter is positive and significant at 0.05 level. Furthermore, short interest trading is positive and significant (0.031, p=0.08), which is consistent with the results of the main analysis. The coefficient on interaction term is negative and significant (-0.033, p=0.036), which provides only partial support for hypothesis H1c.

The second column of Table 9.1 presents estimated coefficients employing factor 2 as a dependent variable. Unfortunately, the coefficients for the main variables of interest (SEC comment letter and short interest) as well as interaction term between them are negative and not statistically significant. The results presented in the second column indicate that factor 2 does not provide sufficient support for hypothesis H1c.

Table 9.2 presents results from estimating the model presented by equation (9). The coefficient for the SEC comment letter is positive and significant (0.014, p=0.093) as well as for short interest trading (0.059, p<0.001). It indicates that factor 1 explains majority of the variance in the model. The analysis of factor 2 does not provide results of statistical significance.

Table 9.3 presents results from estimating equation (14) with dependent variable substituted by factor 1 (column one) and factor 2 (column two). Factor 1 with significant loadings of audit fees and probability of material restatements provides opposite results to the main analysis.

This is similar to the results reported in Table 9.5 evaluating factors impacting probability of material weaknesses, and Table 9.6 evaluating factors impacting probability of PCAOB deficiency. Factor 2 results are not significant in the corresponding tables.

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Overall, the analysis shows that factor analysis can be a beneficial tool when evaluating multiple variables for the same construct. However, as reflected in the above analysis, not all of the factors provide results as expected by the developed predictions. Evidently, factor 1 provides much stronger results, therefore, I focus my interpretations on that particular factor. I conclude that audit fees and probability of material weaknesses are a solid measure of audit quality.

5.2. Lagged Variables

To enhance the results of the main analysis presented in Table 5.1 through Table 5.6.2, I test the model with all lagged independent variables and their predictive ability for the various audit quality proxies in the year following the issuance of SEC comment letter and short interest trading.

The coefficients for the following models are estimated to examine the impact of the lagged independent variables on the demand for audit quality expressed by audit fees:

Audit Fees it = β0 + β1 SEC_CL it-1 + β2 Assets it-1 + β3 Ln_Bus_Segit -1 +β4CATA it-1+β5 Quick it-1 + β6 Lev it-1+ β7 ROI it-1+ β8 Loss it-1+ β9 GC it-1 +β10 Foreignit-1 +β11 DecYE it-1+β12Weaknessesit-1 + β13 Short_Tenure it-1+ β14 Firm_Size it-1 + β15 Mktshr it-1 + β16 Client_Importit-1 +β17 FirmFE it-1 + β18 YearFE it-1 + ε (27)

Audit Fees it = β0 + β1 Rank_SIR it-1 + β2 Assets it-1 + β3 Ln_Bus_Segit -1 +β4CATA it-1+β5 Quick t-1 + β6 Lev it-1+ β7 ROI it-1+ β8 Loss it-1+ β9 GC it-1 +β10 Foreignit-1 +β11 DecYE it-1+β12Weaknessesit-1 + β13 Short_Tenure it-1+ β14 Firm_Size it-1 + β15 Mktshr it-1 + β16 Client_Importit-1 +β17 FirmFE it-1 + β18 YearFE it-1 + ε (28)

Audit Fees it = β0 + β1 SEC_CL it-1 + β2 Rank_SIR it-1 + β3 SEC_ CL it-1 *Rank_SIR it-1 + β4 Assetsit-1 + β5 Ln_Bus_Segit-1 +β6CATA it-1+β7 Quick it-1+ β8 Lev it-1+ β9 ROI it-1+ β10 Loss it-1 + β11 GC it-1 +β12 Foreignit-1 + β13 DecYE it-1+ β14 Weaknesses it-1+ β15 Short_Tenure it-1 + β16 Firm_Size it-1 + β17 Mktshr it-1 + β18 Client_Importit-1 +β19 FirmFE it-1 + β20 YearFE it-1 + ε (29)

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Table 8.1 presents results from estimating above equations (27, 28, and 29). The first column of table 8.1 examines the impact of the SEC comment letter from year (t-1) on the audit fees in year following the comment letter (year t). Moreover, all the other independent variables used to determine audit fees are lagged after the year when the SEC comment letter is received.

The results provided in the first column reconfirm the results from the primary analysis (presented in Table 5.1). The estimated coefficient for SEC comment letter variable is positive and significant

(1.148, p<0.001), which indicates increased demand for higher audit quality from management.

The remaining lagged control variables are mostly significant and in the predicted directions.

The second column presents results from estimating equation (28). The estimated coefficient for short interest rank is positive and significant (0.244, p=0.024). The positive relationship between the short interest trading and subsequent audit fees indicates that firms with short interest trades experience long term increase in audit fees. The results from the analysis also reconfirm results provided in the main analysis in support of hypothesis H1b (Table 5.1, second column). However, results provided in the third column of Table 8.1 provide only a partial support for hypothesis H1c. The coefficients on the three variables of interest in the model presented by equation (29) are positive (as expected by the developed hypothesis) but not significant except for the SEC comment letter variable. Compared with results provided by the main analysis (Table

5.1), the results are somewhat weaker but still provide partial support for the predicted hypothesis

H1c.

In order to examine the impact of the lagged independent variables on the probability of auditor change for the subsequent year after the receipt of comment letter and short interest trading

I estimate following equations:

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Prob(Auditor_Changeit ) = β0 + β1 SEC_CL it-1 + β2 Assets it-1 + β3 Audit_Lag it-1 + β4 Audit404 it-1 + β5 ROA it-1 + β6 Levit-1 + β7 Lossit-1 + β8 GCit-1 + β9 Growthit-1 + β10 Dec_YE it-1 + β11 Short_Tenure it-1 + β12 Ar_Inv it-1 + β13 Acquisition it-1 + β14 Big4 it-1 + β15Expert_City it-1 + β16 Expert_Nat it-1 + β17 IndustryFEit-1 + β18 YearFEit-1 + ε it (30)

Prob(Auditor_Changeit ) = β0 + β1 Rank_SIR it-1 + β2 Assets it-1 + β3 Audit_Lag it-1 + β4 Audit404 it-1 + β5 ROA it-1 + β6 Lev it-1 + β7 Loss it-1 + β8 GC it-1 + β9 Growth it-1 + β10 Dec_YE it-1 + β11 Short_Tenure it-1 + β12 Ar_Invit-1 + β13 Acquisition it-1 + β14 Big4 it-1 + β15Expert_City it- 1 + β16 Expert_Nat it-1 + β17 IndustryFE it-1 + β18 YearFE it-1 + ε it (31)

Prob(Auditor_Changeit ) = β0 + β1 SEC_CL it-1 + β2 Rank_SIR it-1 + β3 SEC_ CL it-1*Rank_SIR it- 1 + β4 Assets it-1 + β5 Audit_Lag it-1 + β6 Audit404 it-1 + β7 ROA it-1 + β8 Lev it-1 + β9 Loss it-1 + β10 GC it-1 + β11 Growth it-1 + β12 Dec_YE it-1 + β13 Short_Tenure it-1 + β14 Ar_Inv it-1 + β15 Acquisition it-1 + β16 Big4 it-1 + β17Expert_City it-1 + β18 Expert_Nat it-1 + β19 FirmFE it-1 + β20 YearFE it-1 + ε it

(32)

The results from estimating the above equations are presented in the three columns of table

8.2. The results in the first two columns provide strong support for hypothesis H2a and H2b examining the impact of the SEC comment letter and short interest trading on the subsequent auditor change. The coefficients remain positive and statistically significant (0.057, p=0.036;

0.167, p<0.001) which supplements results provided in the main analysis (table 5.2). To examine the combined effect of each of these two factors on the likelihood of auditor change, I analyze results reported in the third column of Table 8.2. It appears that both of the variables separately increase the probability of auditor change for the subsequent audit engagement (0.144, p=0.011;

0.193, p<0.001), however, the coefficient for the interaction term is negative and marginally significant (-0.168, p=0.077). The results indicate that overall, when a company experiences short interest trading and receives an SEC comment letter, the likelihood of the auditor change for the subsequent year decreases. This decreased effect is somewhat reduced by the positive impact of each of these factors on the stand-alone basis.

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In order to examine the impact of the lagged variables on the performance matched absolute discretionary accruals, I estimate below models (33-35):

DACCit = β0 + β1 SEC_CL it-1 + β2 MVE it-1 + β3ROA it-1 + β4 Lev it-1 + β5Currit-1 + β6 CFOit-1 + β7 SDCFOit-1 + β8 Lossit-1 + β9 Mktbk it-1 +β10 Lit it-1 + β11 ZScore it-1 + β12 Taccr_Lagit-1 + β13Weaknessesit-1 + β14 Short_Tenure it-1 + β15 FirmSize it-1 + β16 Mktshr it-1 + β17 Client_Importit-1 + β18 IndustryFEit-1 + β19 YearFEit-1 + ε it

(33)

DACCit = β0 + β1 Rank_SIR it-1 + β2 MVE it-1 + β3ROA it-1 + β4 Lev it-1 + β5Currit-1 + β6 CFOit-1 + β7 SDCFOit-1 + β8 Lossit-1 + β9 Mktbk it-1 +β10 Lit it-1 + β11 ZScore it-1 + β12 Taccr_Lagit-1 + β13Weaknessesit-1 + β14 Short_Tenure it-1 + β15 FirmSize it-1 + β16 Mktshr it-1 + β17 Client_Importit-1 + β18 IndustryFEit-1 + β19 YearFEit-1 + ε it

(34)

DACCit = β0 + β1 SEC_CL it-1 + β2Rank_SIRit + β3SEC_CL*Rank_SIRit-1 + β4 MVE it-1 + β5 ROA it-1 + β6 Lev it-1 + β7Currit-1 + β8 CFOit-1 + β9 SDCFOit-1 + β10 Lossit-1 + β11 Mktbk it-1 +β12 Lit it-1 + β13 ZScore it-1 + β14 Taccr_Lagit-1 + β15Weaknessesit-1 + β16 Short_Tenure it-1 + β17 FirmSize it-1 + β18 Mktshr it-1 + β19 Client_Importit-1 + β20 IndustryFEit-1 + β21 YearFEit-1 + ε it

(35)

The results are presented in Table 8.3. They indicate that for the lagged variables in the model, the SEC comment letter and short interest trading is positive but not statistically significant

(-0.066, p=0.238; -0.012, p=0.298). Also, for the interaction term the results remain insignificant, which does not provide additional support for hypothesis H3a through H3c.

Below equations are estimated to examine hypothesis H4a through H4c for the lagged independent variables in the model.

Prob(Restatementit) = β0 + β1 SEC_CL it-1 + β2 Assets it-1 +β3ROA it-1 + β4 Lev it-1 + β5 Mktbk it-1 + β6 Segments it-1 +β7Financing it-1 + β8 Foreign it-1 + β9 Acquisition it-1 + β10 Ar_Inv it-1 + β11 Weaknesses it-1 +β12 AuditFees it-1 + β13 Client_Import it-1 + β14 Big4 it-1 + β15 Industry FE it-1 + β16 Year FE it-1 + ε (36)

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Prob(Restatementit) = β0 + β1 Rank_SIR it-1 + β2 Assets it-1 +β3ROA it-1 + β4 Lev it-1 + β5 Mktbk it-1 + β6 Segments it-1 +β7Financing it-1 + β8 Foreign it-1 + β9 Acquisition it-1 + β10 Ar_Inv it-1 + β11 Weaknesses it-1 +β12 AuditFees it-1 + β13 Client_Import it-1 + β14 Big4 it-1 + β15 Industry FE it-1 + β16 Year FE it-1 + ε

(37)

Prob(Restatementit) = β0 + β1 SEC_CL it-1 + β2Rank_SIRit + β3SEC_ CL it-1*Rank_SIRit-1 + β4 Assets it-1 +β5ROA it-1 + β6 Lev it-1 + β7 Mktbk it-1 + β8 Segments it-1 +β9Financing it-1 + β10 Foreign it-1 + β11 Acquisition it-1 + β12 Ar_Inv it-1 + β13 Weaknesses it-1 +β14 AuditFees it-1 + β15 Client_Import it-1 + β16 Big4 it-1 + β17 Industry FE it-1 + β18 Year FE it-1 + ε

(38)

The results from estimating above equations are presented in Table 8.4. Consistent with the results reported for the main analysis, the receipt of the SEC comment letter and short interest trading increases the likelihood of the restatement in the subsequent year (0.059, p=0.013; 0.092, p=0.042). Furthermore, for the last model, which examines the impact of both variables including their interaction term, estimated coefficient are positive but not statistically significant. Thus, the results do not provide support for hypotheses H4a through H4c.

In order to provide additional support for the impact of both variables on another proxy of audit quality – material weaknesses, I estimate below equations:

Prob (Weaknessesit) = β0 + β1 SEC_CLit-1 + β2 Assetsit-1 +β3 FirmAge it-1 + β4 Loss it-1 +β5 Zscore it-1 + β6 Segments it-1 + β7 Foreign it-1 + β8 Acquisition it-1 +β9 Growthit-1 + β10 Restructuringit-1 + β11 IndustryFE it-1 + β12 YearFE it-1 + ε it (39)

Prob(Weaknessesit) = β0 + β1 Rank_SIRit-1 + β2 Assetsit-1 +β3 FirmAge it-1 + β4 Loss it-1 +β5 Zscore it-1 + β6 Segments it-1 + β7 Foreign it-1 + β8 Acquisition it-1 +β9 Growthit-1 + β10 Restructuringit-1 + β11 IndustryFE it-1 + β12 YearFE it-1 + ε it

(40)

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Prob(Weaknessesit) = β0 + β1 SEC_CLit-1 + β2Rank_SIRit-1 + β3SEC_CLi,t-1*SIR it-1 + β4 Assetsit-1 +β5 FirmAge it-1 + β6 Loss it-1 + β7 Zscore it-1 + β8 Segments it-1 + β9 Foreign it-1 + β10 Acquisition it-1 +β11 Growthit-1 + β12 Restructuringit-1 + β13 FirmFE it-1 + β14 YearFE it-1 + ε it

(41)

The results are reported in Table 8.5. Consistent with the results of the main analysis, short interest trading as well as SEC comment letters do have a negative impact on the likelihood of reporting material weakness by the company. It indicates that management demands higher quality of work from the auditors on the subsequent audit engagements after the company receives the comment letter and short interest position. Thus, the likelihood of reporting material weakness for the subsequent audit engagements decreases as a result of the increased demand of higher audit quality. Hence, the additional results for the lagged independent variables strengthen the main results reported in Table 5.5.

In order to examine the likelihood of receiving a deficiency report resulting from PCAOB inspections, I estimate below equations with respect to GAAP and GAAS deficiency:

Deficiency (GAAP/GAAS)it = β0 + β1 SEC_CL it-1 + β2 Officesit-1 + β53Public_Clients it-1 + β4 Total_Fees it-1 + β5Avg_ClientSizeit-1 + β6 Foreign_D it-1 + β7 Stock_Exchange it-1 + β8 Partnersit-1 + β9 Big4it-1 + β10 IndustryFEit-1 + β11 YearFE it-1 + ε it (42)

Deficiency (GAAP/GAAS)it = β0 + β1 Rank_SIR it-1 + β2 Officesit-1 + β53Public_Clients it-1 + β4 Total_Fees it-1 + β5Avg_ClientSizeit-1 + β6 Foreign_D it-1 + β7 Stock_Exchange it-1 + β8 Partnersit-1 + β9 Big4it-1 + β10 IndustryFEit-1 + β11 YearFE it-1 + ε it (43)

Deficiency (GAAP/GAAS)it = β0 +β1 SEC_CL it-1 + β2Rank_SIRit-1 + β3 SEC_CL it-1* Rank_SIR it-1 + β4 Officesit-1 + β5 Public_Clients it-1 + β6 Total_Fees it-1 + β7 Avg_ClientSize it-1 + β8Foreign_D it-1 + β9 Stock_Exchange it-1 + β10 Partnersit-1 + β11 Big4it-1 + β12 IndustryFEit-1 + β13 YearFE it-1 + ε it (44)

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The results from estimating above equations are reported in Table 8.6.1 (GAAP deficiency) and Table 8.6.2 (GAAS deficiency) respectively. Similarly to the results reported in Table 5.6.1, the likelihood of receiving GAAP deficiency report by the audit firms whose clients receive an

SEC comment letter and have short interest trading decreases for the subsequent audit engagements, based on the reported coefficients (-0.010, p=0.692; -0.108, p=0.012). However, the results reported for the interaction term for both variables are somewhat stronger as they report positive and statistically significant increase in the likelihood of receiving GAAP deficiency

(0.230, p=0.011). Thus, the results indicate, consistent with the other proxies for audit quality, that the effect of interacting both variables has the opposite effect on the GAAP deficiency to what is reported by each variable on the stand-alone basis. It appears that expected decrease in the likelihood of GAAP deficiency is reduced by the positive impact of both variables. Thus, the results provide partial support for hypotheses H6a through H6c.

However, the estimates from examining the likelihood of receiving deficiency report related to GAAS provide opposite results to predicted hypothesis (nonetheless consistent with the main analysis). Table 8.6.2 reports that the receipt of the SEC comment letter as well as short interest trading increases the likelihood of receiving GAAS inspection deficiency from PCAOB.

Considering the interaction term of both variables on the receipt of the GAAS deficiency report by the audit firm, I find similar results as in the main analysis. The hypothesis H6c is only partially supported (-0.083, p=0.255), which is consistent with the analysis reported in Table 5.6.2.

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5.3. Sensitivity Test for Short Sellers

According to the introductory paragraph to Chapter 5, part of the additional analysis includes analysis of short interest trading on the demand of audit quality by management during time periods affected by the regulatory and economic factors (Regulation SHO and global financial crisis of 2008). Therefore, I split the sample for each model related to each audit quality proxy into three time periods. The first one includes firm-years observations from May 2, 2005 through

August 6, 2007. The second time period includes financial crisis and its aftermath from 2007 through 2010. The last time period evaluated on the stand-alone basis includes five years from

2010 through 2015. The results for the main model evaluating behavior of audit fees as a result of the demand of higher audit quality by management are presented in Table 10.1 through Table

10.6.2.

Table 10.1 presents results from estimating equation (4) during three different time periods.

The results in the first column cover the period under regulation SHO. The estimated coefficient for the short interest positions is positive and significant (0.317, p<0.001), which provides additional support for hypothesis H1b. The behavior of the short sellers and their strategies increased subsequent audit fees for the firms. The same trend remains during the financial crisis.

Many short sellers took advantage of the uncertain economic environment during that time to assume short positions in the companies with increased risk. The results in the second column present a positive coefficient of 0.183 (p<0.001), indicating higher audit fees in the future.

Interestingly, the opposite trend in the audit fees is observed for the time period after the financial crisis. Apparently, we start to see a decline in audit fees over the years 2011-2015 (-0.107, p=0.009) for the companies with short interest positions.

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Table 10.2 presents results from estimating equation (8) during three time periods. It appears that short sellers do not have an impact on the likelihood of the subsequent auditor change during the period Regulation SHO was effective (-0.196, p=0.271). It indicates that short selling positions were not an important factor for management when evaluating their decision on contracting with the audit firm. However, the results provided for the financial crisis and subsequent years indicate a positive and significant impact on the likelihood of auditor change

(0.486, p<0.001; 0.449, p<0.001, respectively). The results presented in this table also provide additional support for the main analysis and hypothesis H2b, predicting increased likelihood of auditor change subsequent to the assumed short selling positions by speculators.

Table 10.3 presents results from estimating equation (13). For the time period under

Regulation SHO, the short interest traders do not convey sufficient value to the management which would impact their decision on the use of discretionary accruals. However, during the financial crisis, management significantly reduced the use of discretionary accruals which is evidenced by the negative coefficient (-0.033, p=0.042). However, in the years following the crisis, this negative trend is switches to the positive coefficient (0.234, p=0.056). Thus, it indicates that management is increasing its use of discretionary accruals, which is observed by the short sellers increased activities.

The results presented in tables 10.4 through 10.6.2 provide relatively strong support for the existence of short sellers during different time periods. It is mostly present during financial crisis period as well as post-crisis timeframe.

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5.4. Top Accounting Issue addressed in the SEC Comment Letter

Following Dechow et al. (2016), I conduct analysis of the top accounting issues addressed in the study. The total sample of the SEC comment letters amounts to 22,011 letters and the inclusion in each tested model varies depending on the availability of the other control variables utilized in the model (see Table 1 Panel A). There are instances where one letter addresses more than one accounting topic, thus I derived proportion of this topics relative to the total number of the SEC comment letters. The breakdown of the major accounting topics addressed in this study is presented in Table 11. The two top prevailing topics are issues related to the revenue recognition and fair value accounting. Therefore, I perform analysis of these two specific topics and their impact on the demand for audit quality captured by the six proxies. The results from this additional analysis are presented in Tables 11.1 through 11.6.2.

Table 11.1 presents results from estimating equation (3) and (5) utilizing the revenue recognition topic as well as fair value addressed in the SEC comment letters. The results are similar to the results provided in the main analysis (Table 5.1). It appears that both topics have a positive and statistically significant impact on the subsequent audit fees (0.033, p<0.001; 0.113, p<0.001).

Furthermore, the results also report a similar trend when the top accounting issue from the SEC comment letter is interacted with the short interest trading positions. Consistent with the main analysis, the interaction term on the two variables of interest (top accounting topic and short interest rank) reports a negative coefficient. Thus, the results from this additional analysis also provide partial support for hypothesis H1c. The results from tables 11.2 through 11.6.2 grant mixed support for the hypothesis testing top accounting issue in relation to remaining proxies of audit quality.

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Chapter 6: Summary and Conclusion

The Sarbanes-Oxley Act of 2002 (Section 408) imposed on the Securities and Exchange

Commission (SEC) requirement to conduct periodic reviews of financial statements and related disclosures for publicly traded firms. The reviews are documented in the form of comment letters directed to the company’s management by the SEC staff. The purpose of the SEC reviews is to enhance the quality of financial reporting and related disclosures.

As investors are the main beneficiaries of the SEC review process, academic research has mostly directed attention to measuring the information value of the comment letter for this group of financial statements users (Chen and Johnston, 2010; Grove, Johnsen, and Lung 2016; Dechow,

Lawrence, and Ryans, 2016). Despite increasing number of studies evaluating consequences of comment letters on the capital markets participants, the empirical evidence on the value of the SEC comment letter for the subsequent audit quality is limited. Considering the fact that filings of the public companies are reviewed and scrutinized by the audit firm before they are filed with the

SEC, the receipt of the comment letter by the company indicates insufficient compliance with applicable SEC regulations and disclosure requirements which was not addressed by the auditors.

Thus, it is important to extend research on the consequences of the comment letters on audit firm to contribute to the picture of the full benefits of the SEC review process. The issuance of the SEC comment letter, publicly disclosed since August 2004, provides suitable setting to investigate the impact of regulatory forces on subsequent audit quality of firms.

Moreover, the study examines the effect of another monitoring mechanism in the form of short sellers on the audit quality. This unique group of sophisticated investors might gain its information advantage over the rest of the investors from information included in the publicly

143 released comment letters. As they closely monitor stocks of the company, they could take advantage of the superior information provided in the comment letters. Management avoiding full disclosure and at the same time assuming the risk of receiving comment letter expects the overvaluation of stock. This situation can benefit short sellers as well as inside traders (Dechow et al. 2016) in the short term. Thus, both monitoring mechanisms in place (sophisticated investors and regulatory force) create an interesting setting for examining audit quality.

Extensive literature in the audit area continuously allocates its efforts into finding the appropriate measure of audit quality (DeFond and Zhang, 2014). Due to the lack of input data

(e.g., hours worked on the audit engagements) research is limited to defining audit quality in terms of the output measures. Unfortunately, each of the measures has its drawbacks and there is still no

“one fits all” measure defining audit quality. Therefore, this study applies six proxies frequently used in the literature as a determinant of audit quality - audit fees, auditor changes, discretionary accruals, restatements, material weaknesses and GAAP/GAAS deficiencies resulting from

PCAOB inspections. The purpose of this study is to provide empirical evidence to the literature regarding factors affecting audit quality. Utilizing six measures of audit quality, this study addresses the need to comprehensively capture audit quality considering limitations of each measure.

Moreover, to supplement the main analysis I use factor analysis to combine the six audit quality measurements on their common features. Applying the methodology used in Chen, Lu, and

Sougiannis (2013) to proxy for corporate governance on multiple variables, I identified factors to capture audit quality based on the six measures utilized in this study. The factor analysis allows to reduce the number of variables explaining the same construct – audit quality. This supplemental analysis and its findings provides a significant contribution to the study.

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The research questions and hypothesis tested in this dissertation stem from the discretionary disclosure model (Verracchia, 1983). Management makes a decision on the optimal level of disclosures (tradeoff between the proprietary cost and regulatory requirements). The challenge is to determine the optimal threshold level of disclosure. The information below the threshold is withheld by management internally and creates an opportunity for short sellers to benefit from the lack of full disclosure. Based on the discretionary model, the selected threshold level could maximize the value of the firm. However, when faced with high proprietary costs traders’ negative reaction decreases proportionately as they discount retained information to a lesser degree. The model proposes the presence of equilibrium threshold level of disclosure when traders’ expectations regarding withheld information is consistent with management’s intentions.

The discretionary disclosure model developed by Verrecchia (1990) provides evidence that discretionary disclosure level depends on the quality of the information available to managers.

The threshold level of disclosure will decrease as the quality of the information available to managers is higher. Faced with the high quality information, market participants have higher expectations for the disclosure of that information. If managers decide to withhold that information the market value of the firm will decrease more severely due to that expectation. In this study, I propose that decreasing the information gap between parties in the agency model could lead to improvement of audit quality in the subsequent audit engagements.

145 interest position increase following a comment letter, management’s incentive for higher quality audit increases as well.

The first research question investigates the consequences of the SEC comment letters on the demand for audit quality of subsequent audit engagements. Emerging literature in this area provides limited evidence on the audit quality subsequent to the receipt of comment letters

(Johnston and Petacchi, 2017) captured by audit fees. I propose that the receipt of comment letters provides management with the incentive to demand higher quality audits so that management can find a new optimal level of disclosure.

The second research question examines how short interest positions in the company affect management demand for higher quality audits at firms which received SEC comment letters. I propose that this unique monitoring mechanism in place provides additional incentive for management to demand higher quality audits to counteract the short positions in the company.

In the third research question, I examine whether there is a difference in demand for higher quality audits between Big4 and non-Big4 auditors after the receipt of SEC comment letter by the client and in the presence of short sellers. I propose that differences should be noted as Big4 companies have better resource capabilities to meet client’s demand for higher quality audits.

The motivation for examining both monitoring mechanisms is based on the recent study by Dechow et al. (2016). It finds that management expects negative market reaction to the issuance of comment letters, which is more pronounced when the short sellers are actively trading. This study extends the findings by investigating consequences for audit firms when the client receives a comment letter and witnesses short interest positions. The evidence provided evaluates benefits of the SEC review process to management of the client and the audit firm.

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In this study, I examine whether the release of the comment letter combined with high short selling activity (1) influence auditor’s efforts by increasing audit fees, (2) leads to subsequent resignation/dismissal of auditors due to inability to provide demanded high quality audits, (3) triggers downward changes in discretionary accrual, (4) decreases likelihood of restatements and

(5) issuance of material weaknesses opinion, and lastly (6) decreases the likelihood of the PCAOB

(Public Company Accounting Oversight Board) inspection deficiencies.

The hypothesis developed in this study predict increased demand for audit quality by management of the company receiving comment letters as measured by six audit quality proxies.

The hypothesis are developed from the disclosure and agency theories. The SEC mandates specific reporting behavior and sets expectations for corporate disclosures. The purpose of the disclosure is to reduce information asymmetry between informed party (management of the company) and outside parties. Verrecchia (2001) shows that corporate disclosure and reporting can mitigate the adverse selection process by reducing the information asymmetry between parties. Management is inclined to avoid SEC comment letters in the future thus it will demand higher quality audits to reduce the information asymmetry between investors and insiders. Furthermore, the evidence provided so far supports the fact that short sellers are more active for companies avoiding full disclosures. I predict that not only the receipt of the SEC comment letter but also active short sellers in the period when the letter is issued to the company could impact the demand for higher quality audits.

The sample of audit firms and their clients is obtained from Audit Analytics, Compustat and supplemental files. For each of the tested proxies, I utilize a different sample size resulting from elimination of the missing variable for each specific model. The details of each sample are provided in Table 2.

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Based on the main analysis conducted in chapter four (Table 5.1 through 5.6.2), I find strong empirical evidence supporting the majority of the hypotheses tested in the study. The results report that subsequent to the issuance of a comment letter and in the presence of short sellers, audit fees increase as a result of the increased demand by management (Table 5.1). Furthermore, as management and audit committee are not satisfied with the audit effort performed by the audit firm

(resulting from the comment letter), I find that likelihood of auditor change increases for the subsequent audit engagements for companies with comment letters and high short selling activity

(Table 5.2). Furthermore, I find that management also reduces the use of the discretionary accruals following the comment letter and high short interest trading (Table 5.3). Additionally, the results indicate that the likelihood of restatements increases for companies with short interest activity and

SEC comment letter (Table 5.4). This result is contrary to the predicted hypothesis. It implies that companies scrutinized by SEC and sophisticated investors may be more prone to restatements. I do not differentiate between restatements triggered by comment letters and by any other party due to data limitations. It is possible that higher likelihood of restatement in the year subsequent to the restatement could be the result of the monitoring mechanisms in place. To draw more prominent conclusions regarding audit quality measured by probability restatements, further studies should extend time period subsequent to the comment letter.

In addition, the results show that targeted companies report lower likelihood of material weaknesses and GAAP deficiency report resulting from the PCAOB inspections (Table 5.5, and

5.6.1, respectively). Overall, the results imply that management demands higher audit quality from the auditors as measured by multiple audit quality proxies for companies receiving SEC comment letters and with high short interest positions.

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The main analysis conducted in this study reports that SEC comment letters and short interest positions individually lead to an increased demand for audit quality. Interestingly, when both of the variables of interest are interacted the results report opposite results. The findings from the interaction analysis provide valuable input to the literature and to the accounting profession.

They indicate that examined variables of interest reduce the positive effect of increased demand for higher audit quality as reported by economic significance analysis (Table 7.1 through 7.6). For instance, the results imply that management has the ability to negotiate lower audit fees in instances when a company receives SEC comment letters and has short selling interest at the same time. It appears that both factors combined reduce the individual positive demand for higher quality audits as captured by higher audit fees. It is possible that management has more negotiation power in their hands in the situation when the comment letter is received and short sellers are active around this event as well. I conclude that both monitoring mechanisms in place reduce to some extent the individual effect of each factor.

A similar trend is observed while analyzing marginal effects for performance-matched discretionary accruals and material weakness. Thus, in each case the decrease in the utilization of accruals and decrease in the likelihood of material weaknesses is reduced when both variables of interest are in place. Results provided by the marginal effect analysis indicate that the effect is partially reduced but overall remains positive with respect to higher audit quality (Table 7.3 and

Table 7.5).

Due to the fact that auditing literature has not reached the consensus on the best measure of audit quality, I develop the unique measure of audit quality by calculating factor scores on the six proxies utilized in the study. Based on the results provided in Tables 9 through 9.6, it appears that Factor 1 provides the most significant results for the tested hypothesis. I conclude that audit

149 fees and probability of material weaknesses are the best estimates of the audit quality. Thus, the study provides significant evidence and contributes to the literature by providing additional perspective on the audit quality proxies.

This study advances auditing literature by developing unique measure of the severity of comment letters. Investigating comment letters by considering frequency of the letters, the accounting topics addressed in them, and the number of unique letters per year, I find mixed results

(Table 6.1 through 6.6.2). I predict that severity of comment letters should have more pronounced effect on the demand for higher audit quality than examination of the comment letter without consideration of the significance of the issues in a letter. Depending on the proxy used to determine audit quality, different types of severity measures provide significant results. For instance, all three severity measures developed for this study report significant results when measuring impact on audit fees and the likelihood of material weakness. These are the strongest results with respect to the proxies of audit quality. Strong empirical results for each of the severity measures indicate that this measure can be further utilized in future studies when examining topics related to comment letters. Despite the fact that across remaining proxies of audit quality there is no consistent measure of severity of comment letters, I conclude that they nevertheless enhance the value of this study.

Overall, at least, one severity measure is significant for other audit quality proxies.

This conclusion is supported by the analysis of the top accounting issue provided by the study. Examining specifically comment letters with revenue recognition and fair value issues, I find strong support for the increased demand of higher audit quality by management (Table 11.1 through 11.6.2). The results from estimating main models with consideration of the two top accounting issues reemphasize my conclusions from the main analysis. They grant more empirical support to the conclusion that demand for audit quality increases for the subsequent audit

150 engagements for companies with SEC comment letters and short interest positions. Furthermore,

I conclude that the monitoring mechanism (SEC and short sellers) has not only direct impacts the company but also extends to the audit firms and the quality of their work.

This study could be of interest to policymakers, who evaluate the effectiveness of the monitoring policies. The results emphasize the need of continued monitoring performed by the

SEC as they improve the quality of financial reporting (Johnston and Petacchi, 2017) and extend the benefits on other financial intermediaries (audit firms). Furthermore, the results imply that

PCAOB inspections will identify fewer GAAP deficiency reports once the SEC conducts its review. Thus, it is important to note that PCAOB resources can be utilized to focus more on compliance with GAAS by audit firms as the GAAP deficiencies are addressed by the SEC comment letters.

This research study provides further evidence on the effect of disclosure regulation on the capital market participants with different sophistication levels – short sellers. Examining group of investors, in general, may provide less meaningful results than investigating a unique subgroup within investors (Beyer, Cohen, Lys, and Walther, 2010). The recent literature is becoming more interested in a thorough examination of short selling investors. Thus, I believe the results of this study provide a significant contribution to this stream of literature.

Moreover, this study evaluates the economic consequences of the SOX regulation to conduct periodic review by the SEC for the audit firm. In general, I find empirical evidence that products of the SEC review (comment letters) increase management demand for higher quality audits as measured by the six proxies. The effect is even more pronounced when the company observes active short sellers. Despite the fact that interaction between both variables is reduced,

151 the overall effect remains positive. It is possible that each of the monitoring mechanisms will have positive impact on the audit quality individually. However, when both of them interact, this positive effect is slightly reduced. This unexpected phenomenon should be further examined in future studies.

The study examines two independent mechanisms – regulatory-based (SEC reviews with resulting comment letters) and market-based in the form of short sellers. The results indicate that each individual mechanism leads to a shift in management incentives to meet market and regulatory requirements for a full disclosure. This translates into increased demand for higher quality audits measured in this study by six proxies. The examination reveals that comment letter process provides benefits which extend beyond targeted beneficiaries of this process – investors.

The results could be of interest to individuals evaluating economic benefits of the costly review process.

I conclude this study by discussing limitations and suggesting avenues for future research.

First of all, the conclusion of this study is subject to the limitation of each proxy used to measure audit quality. The limitations of each measure was discussed earlier, but it is definitely one of the factors affecting general conclusion of this study that SEC comment letters and short selling activities increase demand for high audit quality provided by auditors.

Furthermore, while the results of the study provide significant evidence for the increase demand for audit quality, I have not considered the potential long-term effects of the policy. The focus of the study is to examine the immediate impact on the subsequent year. However, future studies could address the impact for the comment letters and short selling activities expanding beyond the first year.

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In addition, the interaction effect for both factors reporting contrary effect to the predicted expectation reported by each individual variable should be further exploited. It is possible that study suffers from the omitted variables problem which is common for studies in this area.

Moreover, the study could possibly explore other research methods for examining the research questions addressed in this study. Future studies could use the propensity score to match firms receiving comment letters and with high short selling positions with the control group. It appears that this method is receiving more attention in the recent literature (e.g., Johnston and

Petacchi, 2017). Furthermore, the study is focused on exploring the benefits of the SEC oversight, however, it does not answer the question of whether these benefits outweigh the costs.

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APPENDIX A

Variable Definitions Dependent Variables:

Audit_Fees = natural logarithm of the audit fees (“matchfy_sum_audfees” field in Audit Analytics). Auditor_Change = an indicator variable set equal to 1 if the auditor in year t is different from the auditor in year t-1; 0 otherwise (derived using the “auditor_fkey” field in Audit Analytics). DACC = the absolute value of performance-matched discretionary accruals, estimated following Kothari et al. (2005). Restatement = an indicator variable set equal to 1 if the firm reported a restatement; 0 otherwise (“res_accco” field in Audit Analytics). Weaknesses = an indicator variable set equal to 1 if the firm reported a material weaknesses; 0 otherwise (calculated using the “count_weak” field in Audit Analytics).

Deficiency = an indicator variable set equal to 1 if the auditor received a PCAOB inspection GAAP-related deficiency; 0 otherwise (hand-collected data from the PCAOB reports). Audit Analytics database.

Independent Variables of interest:

SEC_CL = an indicator variable set equal to 1 if the company receives SEC comment letter in year t; 0 otherwise (calulcated using “date_event” field in Audit Analytics).

SEC_CL_CT = three different measures to capture severity of the comment letter: (i) Critical Accounting Issue = an indicator variable set equal to 1 if comment letter conversation contains a one of the critical accounting topics as defined by Dechow et al. (2016). In addition to revenue recognition, Dechow et al. (2016) utilizes identification of the critical accounting issues by the SEC’s Division of Corporation Finance (SEC 2013d) to expand her analysis on other topics addressed in the SEC comment letters. Furthermore, I expanded this identification by trends identified by PwC (2015b). Thus, as a result I use the following accounting areas to define “critical” accounting topics:  Revenue Recognition,  Pension  Inventory  Receivables and Doubtful Accounts  Material Weaknesses  Restructuring and Impairment

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 Fair Value  Goodwill  Cash Flow I utilize ““List_CL_Issue_Phrase” field in Audit Analytics to identify accounting topics addressed in the comment letter. (ii) Frequency of Letters per Round = the number of letters from the original comment letter to the final one. (iii) Number of Unique Letters per Year = number of unique comment letters per fiscal year. I utilize “Comment Letter Conversation ID” field in Audit Analytics by which I identify unique comment letters during the fiscal year. Rank of Short Interest Ratio (Rank_SIR) = the annual decile rank of the Short Interest Ratio (scaled to range between 0 and 1, following Cassell et al. (2011)). Scaling of Short Interest Ratio helps to reduce the influence of noise and corrects for skewness in the distribution of short interest (Drake, Rees, and Swanson, (2011)).

Short Interest Ratio (SIR) – number of shares sold short in the month (“shortint” field in COMPUSTAT Supplemental Short Interest File) divided by total shares outstanding for the month (“cshom” field in Compustat Monthly Updates).

Independent Variables – controls (listed alphabetically):

Acquisition = an indicator variable set equal to 1 if the firm engaged in mergers and acquisitions (“aqa” field in Compustat); 0 otherwise. Ar_Inv = the sum of accounts receivable (“rect” field in Compustat) and inventory (“invt” in Compustat), divided by total assets (“at” field in Compustat). Assets = the natural logarithm of total assets for the company (“at” field in Compustat). Audit_Lag = the number of days between the fiscal year end date of a company and the signature date on the auditors’ report (“sig_date_of_op_s” and “fiscal_year_end_op” fields in Audit Analytics). Avg_Client_Size = the average of the total assets held by all clients (calculated using “at” field in Compustat). Big4 = an indicator variable set equal to 1 if an auditor is PWC, KPMG, Deloitte, or Ernst & Young; 0 otherwise (“auditor_fkey” field in Audit Analytics). CATA = current assets divided by total assets (“act” and “at” fields in Compustat). CFO = operating cash flows scaled by total assets at the beginning of the year (“oancf” and “at” fields in Compustat.

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Client_Importance = audit fees for company i divided by the sum of all audit fees reported for the same audit firm office (calculated using “matchfy_sum_audfees” field in Audit Analytics). Curr = current assets divided by current liabilities (“act” and “lct” fields in Compustat). Cycle = the days receivable plus the days inventory less the days payable at the beginning of the year (calculated using “rect”, “invt” and “ap” fields in Compustat). DecYE = an indicator variable set equal to 1 if the company has a December year-end, and 0 otherwise (calculated using “fyr” field in Compustat). Expert_City = an indicator variable set equal to 1 if the auditor is a city industry expert; 0 otherwise. City industry expert is derived from the auditor market share based on the number of clients in the portfolio and audit fees from each client. The audit firm is considered city industry expert if the auditor has a market share greater than 50% in a two-digit SIC category measured within the Metropolitan Statistical Area of the auditor’s engagement office (Neal and Riley, 2004). Expert_Nat = an indicator variable set equal to 1 if the auditor is a national industry expert; 0 otherwise. National industry expert is derived from the auditor market share based on the number of clients in the portfolio and audit fees from each client. The audit firm is considered national industry expert if the auditor has a market share greater than 30% in a two-digit SIC category. Financing = long-term debt issuances plus the sale of common and preferred stock divided by total assets (“dltis”, “sstk”, and “at” fields in Compustat). FirmAge = the number of years the firm has been trading in a stock exchange constructed as the difference between the current year and the year the firm started trading (“begdat” field in CRSP). Firm_Size = the natural log of the number of publicly traded clients audited by the company’s audit firm during the year. First_Inspection = an indicator variable set equal to 1 for the first PCAOB inspection of the audit firm, and 0 otherwise (hand-collected from PCAOB inspection reports). Foreign = an indicator variable set equal to 1 if the company has income from foreign oprerations; 0 otherwise (“fca” field in Compustat). GC = an indicator variable set equal to 1 if the company received a going concern opinion in the year; 0 otherwise (“going_concern” field in Audit Analytics). Growth = an indicator variable coded 1 if the firm’s industry-adjusted sales growth is in the top quintile of the sample, and 0 otherwise (“sale” field in Compustat). Industry FE = industry indicator variables are SIC 01-14, SIC 15-19, SIC20-21, SIC 22-23, SIC 24-27, SIC 28-32, SIC 33-34, SIC 35-39, SIC 40-48, SIC 49, SIC 50-52, SIC 53-59, SIC 70-79. The indicators are following classification used by Keune et al. (2012).

Lev = long-term debt divided by total assets (“dltt” and “at” fields in Compustat).

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Lit = an indicator variable set equal to 1 if the company operates in a high litigation industry, as defined by Francis, Philbrick, and Schipper (1994), (i.e., SIC codes of 2833-2836, 3570-3577, 3600-3674, 5200-5961, and 7370); 0 otherwise. Ln_Bus_Seg = the natural log of (1+ the number of business segments). Loss = an indicator variable set equal to 1 if the company reports a loss; 0 otherwise (“ni” field in Compustat). Mktbk = market value of equity divided by book value of equity (calculated using “csho”, “prcc_f” and “seq” fields in Compustat). Mktshr = the auditor’s market share of all audit fees charged to companies in the industry two- digit SIC code (calculated using “matchfy_sum_audfees” field in Audit Analytics). MVE = the natural log of market value of equity (calculated using “csho” and “prcl_f” fields in Compustat). Offices = the natural log of the number of audit firm offices Quick = current assets less inventories, divided by current liabilities (“act”, “invt”, and “lct” field in Compustat). PPE = gross PP&E; Property, Plant and Equipment balance for company at the end of the year divided by average asset (calculated using “ppegt” and “at” fields in Compustat). Public_Clients = the natural log of the number of publicly traded clients audited by the office.

ΔREC = change in accounts receivable from prior year (calculated using “rect” field in Compustat). Rec_and_Inv = sum of receivables and inventory scaled by total assets (“act”, “invt”, and “at” field in Compustat). Restructuring = Pre-tax restructuring charges ("rcp" field in Compustat) scaled by market value of the equity.

ΔREV = change in revenue (sale) from prior year scaled by average assets (calculated using “revt” and “at” in Compustat).

ROA = return on assets; measured by net income for company divided by average total assets for year (calculated using “ni” and “at” fields in Compustat). ROI = earnings before interest and taxes divided by lagged total assets (calculated using “ebit” and “at” fields in Compustat). Segments = number of business segments (calculated using the Compustat segment file). Short_Tenure = an indicator variable set equal to 1 if the auditor-client tenure to date is three years or less, and 0 otherwise.

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SDCFO = the standard deviation of operating cash flow (scaled by total assets at the beginning of the fiscal year) from year t-4 to t (calculated using “oancf” and “at” fields in Compustat). Stock_Exchange = the percentage of clients audited in the inspection period that are registered in the New York Stock Exchange, NASDAQ, or American Stock Exchange. Taccr_lag = the absolute value of total accruals from continuing operations in year t-1 divided by total assets in year t-1. TotalAccruals = total accruals; calculated using net income (“ni”) minus operating cash flow (“oancf”) scaled by total assets (at). Total_Fees = total fees (“matchfy_sum_audfees” field in Audit Analytics) charged for client’s audits by the audit firm during the inspection period.

Year FE = an indicator variable coded 1 for the particular year, and 0 otherwise.

Z-score = bankruptcy risk using Altman’s (1968) Z-score. Zscore_rank = the annual decile rank of the Altman’s Z-score.

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APPENDIX B Panel A: Sample Comment Letter

Dear Mr. Saunders:

We have reviewed your filings and have the following comments. Please note that we have limited our review to only your financial statements and related disclosures. In some of our comments, we may ask you to provide us with information so we may better understand your disclosure.

Please respond to this letter within ten business days by amending your filing, by providing the requested information, or by advising us when you will provide the requested response. If you do not believe our comments apply to your facts and circumstances or do not believe an amendment is appropriate, please tell us why in your response.

After reviewing any amendment to your filing and the information you provide in response to these comments, we may have additional comments.

10-K for Fiscal Year Ended September 30, 2011 Management’s Discussion and Analysis of Financial Condition and Results of Operations Adjusted Financial Results, page 42 Fundamo and PlaySpan Acquisition, page 42 1. Tell us why you describe the impact of the acquisitions as having a dilutive effect on EPS when both were acquired for cash. Please clarify your use of the term “dilution” and quantify the impact these acquisitions had on your earnings. Further, we note that your financial statement footnote Note – 5 Acquisitions does not contain disclosures outlined in ASC 805-10-50-2(h). Provide your analysis of why such disclosures were not provided.

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APPENDIX B (continued)

Notes to the Consolidated Financial Statements Note 20 – Income Taxes, page 120 2. Please tell what consideration you gave to providing disclosures regarding deferred tax liabilities pursuant to FASB ASC 740-30-50-2(c).

Note 21 – Legal Matters, page 124 Multidistrict Litigation Proceedings (MDL), page 127 3. On page 128, you state that some loss related to the MDL proceedings is reasonably possible and that the current uncommitted balance in the covered litigation escrow account (“$2.7 billion”) is consistent with your estimate of the lower end of a negotiated settlement for the entire matter. Please explain to us why you have not disclosed the high end of what appears to be a range of the reasonably possible loss. In addition, describe the factors that changed your estimate of this amount and resulted in an increase of $1.57 billion in the first quarter of 2012.

Exhibits 4. On page 128, you describe the terms of an omnibus agreement with your co-defendants in the interchange litigation for the multidistrict litigation proceedings in MDL 1720. Please confirm that you will file this agreement with your next periodic report in its entirety, or provide us a detailed analysis why you believe this agreement is not a material agreement required to be filed under Item 601(b)(10) of Regulation S-K.

We urge all persons who are responsible for the accuracy and adequacy of the disclosure in the filing to be certain that the filing includes the information the Securities Exchange Act of 1934 and all applicable Exchange Act rules require. Since the company and its management are in possession of all facts relating to a company’s disclosure, they are responsible for the accuracy and adequacy of the disclosures they have made. In responding to our comments, please provide a written statement from the company acknowledging that:

 the company is responsible for the adequacy and accuracy of the disclosure in the filing;

 staff comments or changes to disclosure in response to staff comments do not foreclose the Commission from taking any action with respect to the filing; and

 the company may not assert staff comments as a defense in any proceeding initiated by the Commission or any person under the federal securities laws of the United States.

You may contact Tamara Tangen, Staff Accountant, at (202) 551-3443 if you have questions regarding comments on the financial statements and related matters. Please contact me at (202) 551-3730 with any other questions.

Sincerely, /s/ Stephen G. Krikorian Stephen G. Krikorian Accounting Branch Chief

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APPENDIX B (continued) Panel B: Sample of Company’s Response to Comment Letter

Byron Pollitt Chief Financial Officer May 9, 2012 Stephen Krikorian Tamara Tangen Division of Corporation Finance Securities and Exchange Commission 100 F Street, N.E. Washington, D.C. 20549

Re: Visa Inc. Form I0-K for the Fiscal Year Ended September 30, 2011 File No. 001-33977 Mr. Krikorian and Ms. Tangen: In connection with Visa Inc.’s (the “Company” or “Visa”) Form 10-K filed with the Securities and Exchange Commission (the “Commission” or the “Staff”) on November 18, 2011 (the “Form 10-K”), we are writing in response to the Staff’s comments as transmitted to the Company by email dated May 1, 2012. For convenience, we have reprinted the Staff’s comments below in bold, with the corresponding response set forth immediately below the applicable comment.

Form 10-K for the Fiscal Year Ended September 30, 2011 Management’s Discussion and Analysis of Financial Condition and Results of Operations Fundamo and PlaySpan Acquisition page 42

1. Tell us why you describe the impact of the acquisitions as having a dilutive effect on EPS when both were acquired for cash. Please clarify your use of the term “dilution” and quantify the impact these acquisitions had on your earnings.

Further, we note that your financial statement footnote Note – 5 Acquisitions does not contain disclosures outlined in ASC 805-10-50-2(h). Provide your analysis of why such disclosures were not provided.

Response: Both Fundamo and PlaySpan generated net losses in Fiscal 2011 subsequent to their acquisition, thereby reducing our total net income and diluting or reducing our reported earnings per share for the year. Our disclosures quantify the dilutive impact of each acquisition to our reported fully diluted class A common stock earnings per share for the year ($0.02 and

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$0.03 per share for Fundamo and PlaySpan, respectively). We provided this disclosure to provide a clearer understanding of the performance of our core business by enabling the reader to calculate the earnings per share we would have reported had these acquisitions not occurred. We did not disclose the information required in ASC 805-10-50-2(h), including the supplemental pro-forma information, because the operations of the acquired businesses are not material to our financial statements. Had we acquired PlaySpan and Fundamo at the beginning of Fiscal 2010, each company’s net loss for the year would have reduced Visa’s consolidated net income by less than 1%.

Notes to the Consolidated Financial Statements Note 20 – Income Taxes, page 120

2. Please tell what consideration you gave to providing disclosures regarding deferred

tax liabilities pursuant to FASB ASC 740-30-50-2(c). Response: As disclosed on page 123 of our Form 10-K, we intend to reinvest all of our cumulative undistributed earnings in our international subsidiaries. We came to the conclusion that it was impractical to determine, and therefore disclose, the amount of income taxes that would have resulted had these earnings been repatriated after considering, among other factors,: (i) the significant judgment and assumptions that inherently need to be made in analyzing the various potential forms of distribution available; (ii) the disparate tax treatments applicable to distributions in the numerous foreign jurisdictions in which we operate; and (iii) the complex application of foreign withholding tax and tax credit rules in numerous foreign countries. The wide range of potential outcomes that could result due to these factors, among others, makes it impractical to calculate the amount of tax that hypothetically would have been recognized on these earnings had they been repatriated.

Note 21 – Legal Matters, page 124 Multidistrict Litigation Proceedings (MDL), page 127

3. On page 128, you state that some loss related to the MDL proceedings is reasonably possible and that the current uncommitted balance in the covered litigation escrow account (“$2.7 billion”) is consistent with your estimate of the lower end of a negotiated settlement for the entire matter. Please explain to us why you have not disclosed the high end of what appears to be a range of the reasonably possible loss. In addition, describe the factors that changed your estimate of this amount and resulted in an increase of $1.57 billion in the first quarter of 2012. Response: The $2.7 billion referenced by the Company was consistent with the Company’s estimate of its share of the lower end of a negotiated settlement for the entire matter. The Company is not able to determine the high end of a reasonably possible loss if a negotiated settlement can be reached. In light of the complexities of settling large class action lawsuits, and

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the high likelihood some plaintiffs will opt-out of any settlement and sue the Company separately for amounts and remedies heretofore unspecified, any potential high end of a reasonably possible loss in connection with a negotiated settlement would be highly speculative and potentially misleading. In the absence of a negotiated settlement, the potential loss would depend on the Company winning or losing on the merits in the class litigation, as well as all opt-out and individual plaintiff cases. Because it is not possible to accurately assess the outcome of all such cases, and because each party that opts out of the class may have its own (heretofore unspecified) damage demands, it is not possible to calculate any range of reasonably potential loss for this matter in the absence of a final negotiated settlement of the entire matter. The increase of $1.57 billion dollars in the quarter ended December 31, 2011, was based on the Company’s ongoing participation in the mediation process and reflects the Company’s updated estimate of its share of the lower end of a negotiated settlement for the entire matter, based on the overall dynamics of the mediation process at the time.

Exhibits

4. On page 128, you describe the terms of an omnibus agreement with your co- defendants in the interchange litigation for the multidistrict litigation proceedings in MDL 1720. Please confirm that you will file this agreement with your next

periodic report in its entirety, or provide us a detailed analysis why you believe this agreement is not a material agreement required to be filed under Item 601(b)(10) of Regulation S-K.

Response: The Company respectfully believes that the Omnibus Agreement is not a material agreement required to be filed under Item 601(b)(10) of Regulation S-K. The Company’s Retrospective Responsibility Plan, including its Loss Sharing Agreement and the Interchange Judgment Sharing Agreement, provide for the funding of any settlements or judgments in connection with the interchange litigation. The Omnibus Agreement did not amend those agreements, both of which the Company has already filed as required under Item 601, and therefore does not have a material operative effect on the Company, even though the other provisions of the Omnibus Agreement may have material operative effects for the other parties with respect to the MasterCard Portion of any liability. We acknowledge that:

• the company is responsible for the adequacy and accuracy of the disclosure in the filing;

• staff comments or changes to disclosure in response to staff comments do not foreclose

the Commission from taking any action with respect to the filing; and

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• the company may not assert staff comments as a defense in any proceeding initiated by

the Commission or any person under the federal securities laws of the United States. If you have any questions concerning the foregoing, please contact Jim Hoffmeister at (650) 432- 8165, Visa’s Global Corporate Controller. Sincerely,

/s/ Byron H. Pollitt Byron H. Pollitt Chief Financial Officer cc: Joseph W. Saunders, Chairman of the Board and Chief Executive Officer Robert Matschullat, Audit and Risk Committee Chairman Thomas A. M’Guinness, Chief Corporate Counsel James H. Hoffmeister, Global Corporate Controller

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Fig.1 Timeline of the study

First Letter “Completion Public disclosure Fiscal Year End of Review” date of Comment for year t+1 letter Letter

Management makes decision on the lack of full disclosure

Short sellers take Negotiations with auditors to 10-K (10-Q) public release their positions demand higher quality audits in year t

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Table 1 Panel A - SEC Comment Letters Sample Distribution

Sample Distribution: SEC Comment Letters Unique 10-K Subset (1) of Subset (2) of Subset (3) of Comment Subset (4) of Subset (5) of Subset (6) of and 10-Q Comment Letters Comment Letters Letters used in the Comment Letters Comment Letters Comment Letters Comment Letter used in the Audit used in the Auditor Discretionary Accruals used in the used in the Material used in thePCAOB conversations Fees model Change model model Restatement model Weaknesses model model Year

2005 2,165 878 585 518 525 435 568 2006 2,169 922 906 841 854 744 895 2007 1,951 890 857 764 776 704 820 2008 2,034 925 970 899 925 855 951 2009 2,449 1,012 1,024 937 966 908 1,012 2010 2,313 1,002 1,064 991 1,007 930 1,045 2011 1,989 843 886 811 836 770 866 2012 2,164 905 945 863 881 808 924 2013 1,933 908 985 872 896 814 961 2014 1,521 792 855 770 780 686 - 2015 1,323 526 733 633 644 673 - Total: 22,011 9,603 9,810 8,899 9,090 8,327 8,042 Total number of observations in a 43,930 45,331 39,612 40,326 35,643 43,358 population (Table 2): % of firms with comment letters in 21.86% 21.64% 22.47% 22.54% 23.36% 18.55% total population

Note: This table shows the distribution of comment letter conversations related to 10-K and 10-Q filings. Based on the data in Audit Analytics, I identified unique conversations (CL_CON_ID) for all the letters in the same conversation. First column presents the number of all unique conversations over years 2005-2015 selected for the sample. The duplicate conversations were eliminated. Subsequently, the unique conversations were matched with data from Compustat, Audit Analytics and CRSP for fundamental variables and short interest as required by each of the six models used in the study. Thus, as a result of different fundamental data used in the models the number of unique comment letters vary by the model. Subset (1) includes all unique comment letters over the sample period used in the Audit Fees model. Subset (2) includes all unique comment letters over the sample period used in the Auditor Change model. Subset (3) includes all unique comment letters over the sample period used in the Performance-Matched Discretionary Accruals model. Subset (4) includes all unique comment letters over the sample period used in the Restatements model. Subset (5) includes all unique comment letters over the sample period used in the Material Weaknesses model. Subset (6) includes all unique comment letters over the sample period used in the PCAOB Deficiency model. The PCAOB deficiency dataset ends with inspection reports in 2014 (data limitations) thus SEC comment letters data ends in 2013 for this particular sample, as I utilize lagged variables in the model. In addition, I note that significantly smaller sample size for years 2014 and 2015 is related to the overall decreased trend in the comment letters related to 10-K and 10-Q Forms. See http://www.auditanalytics.com/blog/sec-comment-letters-a-five-year-trend/.

180

Table 1 Panel B - Short Interest Positions Sample Distribution

Sample Distribution: Short Interest Positions

Subset (1) of Firms Subset (3) of Firms with Subset (4) of Firms with Subset (5) of Firms with Subset (6) of Firms Subset (2) of Firms with with Short Interest Short Interest Ratio used in Short Interest Ratio Short Interest Ratio used in with Short Interest Short Interest Ratio used in Ratio used in the the Discretionary Accruals used in the the Material Weaknesses Ratio used in the the Auditor Change model Audit Fees model model Restatement model model PCAOB model Year

2005 3,507 3,513 3,399 3,457 3,248 3,488 2006 3,497 3,448 3,334 3,396 3,211 3,426 2007 3,437 3,409 3,261 3,316 3,146 3,361 2008 3,253 3,234 3,108 3,172 3,105 3,198 2009 3,154 3,068 2,955 3,025 2,967 3,049 2010 3,222 2,986 2,889 2,944 2,852 2,967 2011 2,992 2,899 2,796 2,863 2,790 2,879 2012 2,977 2,851 2,759 2,821 2,742 2,831 2013 3,050 2,881 2,797 2,861 2,729 2,867 2014 3,031 2,996 2,912 2,981 2,793 - 2015 3,018 3,117 2,893 2,958 2,841 - Total: 35,138 34,402 33,103 33,794 32,424 28,066 Total number of observations in a 43,930 43,358 population (Table 2): 45,331 39,612 40,326 35,643 % of firms with SIR in total 79.99% 75.89% 83.57% 83.80% 90.97% 64.73% population

Note: This table shows the distribution of short interest ratio for companies used in the sample. Based on the data in Compustat Supplemental file, I identified companies with shares held short ("Shortint"). Subsequently, the companies with short interest positions were matched with data from Compustat, Audit Analytics and CRSP for fundamental variables and SEC comment letters as required by each of the six models used in the study. Thus, as a result of different fundamental data utilized in the models, the number of firm year observations vary by the model (as indicated in Table 2). Subset (1) identifies number of firm year observations with short interest positions over the sample period used in the Audit Fees model. Subset (2) identifies number of firm year observations with short interest positions over the sample period used in the Auditor Change model. Subset (3) identifies number of firm year observations with short interest positions over the sample period used in the Performance-Matched Discretionary Accruals model. Subset (4) identifies number of firm year observations with short interest positions over the sample period used in the Restatements model. Subset (5) identifies number of firm year observations with short interest positions over the sample period used in the Material Weaknesses model. Subset (6) identifies number of firm year observations with short interest positions over the sample period used in the PCAOB Deficiency model. The PCAOB deficiency dataset ends with inspection reports in 2014 (data limitations) thus short interest positions data ends in 2013, as I utilize lagged variables in the model. In addition, the last row presents the proportion of firms with short interest positions in the population of firms used for each model respectively.

181

Table 2 Sample Selection

SAMPLE SELECTION PROCESS:

Population of SEC comment letters in Audit Analytics from 2005-2015 74,116

Less: Comment Letters related to forms other than 10-K or 10-Q (28,312) Less: Comment Letters with missing information and duplicate conversations (23,793)

Final Sample of SEC comment letters 22,011

Merge with: Fundamental data from Compustat, CRSP and Compustat Supplemental 71,211 (short interest data) Less: Financial and Utilities segments (14,237)

Sample of SEC comment letters with corresponding fundamental data 56,974

Less: Eliminate missing variables for the model: (13,044) Final Sample for Audit Fees model 43,930

Less: Eliminate missing variables for the model: (11,643) Final Sample for Auditor Change model 45,331

Less: Eliminate missing variables for the model: (17,362) Final Sample for Discretionary Accruals model 39,612

Less: Eliminate missing variables for the model: (16,648) Final Sample for Restatement model 40,326

Less: Eliminate missing variables for the model: (21,331) Final Sample for Material Weaknesses model 35,643

Less: Eliminate missing variables for the model: (13,616) Final Sample for PCAOB Inspections Deficiencies 43,358

182

Table 3.1 Descriptive Statistics: Audit Fees Model

Descriptive Statistics: Audit Fees Model Time Period: 2005 - 2015 Variable Mean Median 25 th Pctile 75 th Pctile Std. Dev.

Audit Fees 13.436 13.563 12.429 14.407 1.418 SEC Comment Letters 0.219 0.000 0.000 0.000 0.413 Critical Accounting Issue 0.191 0.000 0.000 0.000 0.393 Frequency of Letters per Round 1.037 0.000 0.000 0.000 2.221 Number of Unique Letters per Year 0.257 0.000 0.000 0.000 0.524 Rank of Short Interest Ratio 0.551 0.600 0.300 0.800 0.281 Short Interest Ratio 0.036 0.017 0.000 0.050 0.051 Assets 5.698 5.787 3.926 7.488 2.438 Segments 2.044 1.000 1.000 3.000 2.222 CATA 0.483 0.479 0.247 0.708 0.278 Quick Ratio 2.359 1.371 0.812 2.475 3.281 Leverage 0.266 0.190 0.012 0.378 0.326 ROI -0.083 0.052 -0.063 0.105 0.449 Loss 0.410 0.000 0.000 1.000 0.492 Going Concern 0.096 0.000 0.000 0.000 0.295 Foreign Transactions 0.990 1.000 1.000 1.000 0.100 December YE 0.707 1.000 0.000 1.000 0.455 Weaknesses 0.426 0.000 0.000 1.000 0.495 Short Tenure 0.631 1.000 0.000 1.000 0.482 Big4 0.650 1.000 0.000 1.000 0.480 Firm_Size 2.981 2.996 2.079 3.761 1.353 Market Share 0.293 0.118 0.041 0.321 0.493 Client Importance 0.147 0.070 0.031 0.156 0.214

n = 43,930

Note: Table 3.1 presents descriptive statistics for the variables used in the Audit Fees model. See Appendix A for variables definition. The variables are windsorized at 1 and 99 percent to mitigate the influence of outliers.

183

Table 3.2 Descriptive Statistics: Auditor Change Model

Descriptive Statistics: Auditor Change Model Time period: 2005-2015 Variable Mean Median 25 th Pctile 75 th Pctile Std. Dev.

Auditor Change 0.064 0.000 0.000 0.000 0.244 SEC Comment Letter 0.216 0.000 0.000 0.000 0.412 Critical Accounting Issue 0.189 0.000 0.000 0.000 0.391 Frequency of Letters per Round 1.049 0.000 0.000 0.000 2.353 Number of Unique Letters per Year 0.259 0.000 0.000 0.000 0.545 Rank of Short Interest Ratio 0.552 0.600 0.300 0.800 0.280 Short Interest Ratio 0.035 0.016 0.000 0.049 0.050 Assets 5.712 5.804 3.927 7.524 2.446 Audit_Lag 73.639 68.000 57.000 82.000 33.538 Weaknesses 0.436 0.000 0.000 1.000 0.496 ROA -0.148 0.021 -0.109 0.066 0.546 Leverage 0.268 0.193 0.012 0.378 0.327 Loss 0.408 0.000 0.000 1.000 0.492 Going Concern 0.098 0.000 0.000 0.000 0.297 Growth 0.200 0.000 0.000 0.000 0.400 December YE 0.707 1.000 0.000 1.000 0.455 Short Tenure 0.602 1.000 0.000 1.000 0.489 Rec_and_Inv 0.230 0.186 0.068 0.344 0.194 Acquisitions 0.170 0.000 0.000 0.000 0.375 Big4 0.651 1.000 0.000 1.000 0.477 City Expert 0.481 0.000 0.000 1.000 0.500 National Expert 0.026 0.000 0.000 0.000 0.159

n= 45,331

Note: Table 3.2 presents descriptive statistics for the variables used in the Auditor Change model. See Appendix A for variables definition. The variables are windsorized at 1 and 99 percent to mitigate the influence of outliers.

184

Table 3.3 Descriptive Statistics: Performance Matched Discretionary Accruals Model

Descriptive Statistics: Performance-Matched Discretionary Accruals Model Time period: 2005-2015 Variable Mean Median 25 th Pctile 75 th Pctile Std. Dev.

Performance-matched discretionary accruals 0.011 0.000 0.000 0.000 0.038 SEC Comment Letter 0.225 0.000 0.000 0.000 0.417 Critical Accounting Issue 0.195 0.000 0.000 0.000 0.396 Frequency of Letters per Round 1.097 0.000 0.000 0.000 2.391 Number of Unique Letters per Year 0.270 0.000 0.000 0.000 0.556 Rank of Short Interest Ratio 0.473 0.500 0.200 0.700 0.275 Short Interest Ratio 0.039 0.021 0.001 0.053 0.055 Market Value 5.662 5.751 3.922 7.355 2.375 ROA -0.154 0.022 -0.121 0.069 0.557 Leverage 0.247 0.170 0.007 0.355 0.314 Current Ratio 2.999 1.955 1.211 3.302 3.549 Operating Cash Flows -0.025 0.067 -0.027 0.122 0.324 Operating Cash Flows (Std. Dev) 0.085 0.046 0.019 0.089 0.138 Loss 0.414 0.000 0.000 1.000 0.493 Market to Book 2.974 1.969 1.071 3.652 8.334 Litigation 0.066 0.000 0.000 0.000 0.248 Zscore_rank 3.788 4.000 1.000 7.000 3.079 Total Accruals (lag) 0.089 0.041 0.099 0.000 0.230 Weaknesses 0.396 0.000 0.000 1.000 0.489 Short Tenure 0.629 1.000 0.000 1.000 0.483 Firm Size 2.929 2.944 2.079 3.738 1.320 Market Share 0.559 0.494 0.264 1.000 0.335 Client Importance 0.151 0.072 0.032 0.161 0.217 Big4 0.627 1.000 0.000 1.000 0.483

n = 39,612

Note: Table 3.3 presents descriptive statistics for the variables used in the Discretionary Accruals model. See Appendix A for variables definition. The variables are windsorized at 1 and 99 percent to mitigate the influence of outliers.

185

Table 3.4 Descriptive Statistics: Restatements Model

Descriptive Statistics: Restatements Model Time period: 2005-2015 Variable Mean Median 25 th Pctile 75 th Pctile Std. Dev.

Restatements 0.096 0.000 0.000 0.000 0.295 SEC Comment Letter 0.196 0.000 0.000 0.000 0.397 Critical Accounting Issue 0.196 0.000 0.000 0.000 0.397 Frequency of Letters per Round 1.101 0.000 0.000 0.000 2.394 Number of Unique Letters per Year 0.271 0.000 0.000 0.000 0.557 Rank of Short Interest Ratio 0.550 0.600 0.300 0.800 0.285 Short Interest Ratio 0.039 0.021 0.001 0.054 0.052 Assets 5.623 5.705 3.869 7.384 2.423 ROA -0.150 0.022 -0.115 0.069 0.550 Leverage 0.248 0.172 0.007 0.359 0.312 Market to Book 2.955 1.956 1.068 3.626 8.108 Segments 2.104 1.000 1.000 3.000 2.246 Financing 0.207 0.044 0.004 0.253 0.355 Foreign Transactions 0.990 1.000 1.000 1.000 0.101 Acquisitions 0.176 0.000 0.000 0.000 0.381 Weaknesses 0.393 0.000 0.000 1.000 0.488 Rec_and_Inv 0.239 0.199 0.078 0.353 0.194 Audit Fees 13.432 13.546 12.400 14.418 1.431 Client Importance 0.150 0.071 0.032 0.160 0.217 Big4 0.630 1.000 0.000 1.000 0.483

n = 40,326

Note: Table 3.4 presents descriptive statistics for the variables used in the Restatements model. See Appendix A for variables definition. The variables are windsorized at 1 and 99 percent to mitigate the influence of outliers.

186

Table 3.5 Descriptive Statistics: Material Weaknesses Model

Descriptive Statistics: Material Weaknesses Model Time period: 2005-2015 Variable Mean Median 25th Pctile 75th Pctile Std. Dev.

Weaknesses 0.323 0.000 0.000 1.000 0.468 SEC Comment Letter 0.231 0.000 0.000 0.000 0.422 Critical Accounting Issue 0.201 0.000 0.000 0.000 0.400 Frequency of Letters per Round 1.106 0.000 0.000 0.000 2.331 Number of Unique Letters per Year 0.275 0.000 0.000 0.000 0.551 Rank of Short Interest Ratio 0.551 0.600 0.300 0.800 0.284 Short Interest Ratio 0.043 0.026 0.005 0.061 0.052 Assets 6.422 6.482 4.910 7.927 2.234 Firm Age 17.122 13.000 6.000 23.000 15.886 Loss 0.314 0.000 0.000 1.000 0.464 Zscore_rank 3.529 3.000 0.000 6.000 3.111 Segments 1.667 1.000 0.000 3.000 2.191 Foreign Transactions 0.891 1.000 1.000 1.000 0.312 Acquisitions 0.180 0.000 0.000 0.000 0.384 Growth 0.236 0.000 0.000 0.000 0.424 Restructuring -0.005 0.000 0.000 0.000 0.030 Big 4 0.680 1.000 0.000 1.000 0.470

n = 35,643

Note: Table 3.5 presents descriptive statistics for the variables used in the Material Weaknesses model. See Appendix A for variables definition. The variables are windsorized at 1 and 99 percent to mitigate the influence of outliers.

187

Table 3.6 Descriptive Statistics: PCAOB Inspections Deficiencies Model

Descriptive Statistics: PCAOB Inspections Deficiencies Model Time period: 2005-2013 Variable Mean Median 25th Pctile 75th Pctile Std. Dev.

Deficiency 0.593 1.000 0.000 1.000 0.491 GAAP Deficiency 0.202 0.000 0.000 0.000 0.401 GAAS Deficiency 0.391 0.000 0.000 1.000 0.488 SEC Comment Letter 0.218 0.000 0.000 0.000 0.413 Critical Accounting Issue 0.192 0.000 0.000 0.000 0.394 Frequency of Letters per Round 1.073 0.000 0.000 0.000 2.375 Number of Unique Letters per Year 0.264 0.000 0.000 0.000 0.550 Rank of Short Interest Ratio 0.555 0.600 0.300 0.800 0.279 Short Interest Ratio 0.037 0.018 0.000 0.051 0.050 Offices Inspected 3.153 4.111 2.398 4.263 1.614 Partners 1.984 0.000 0.000 0.000 14.625 Public_Clients 5.227 6.515 3.738 6.720 2.180 Audit Fees 13.367 13.458 12.316 14.332 1.423 Average Client Size 6.195 6.851 5.343 7.274 1.620 Foreign_D 0.549 1.000 0.000 1.000 0.498 Stock_Exchange 0.458 0.000 0.000 1.000 0.498 Big 4 0.650 1.000 0.000 1.000 0.480

n = 43,358

Note: Table 3.6 presents descriptive statistics for the variables used in the PCAOB Inspections Deficiencies model. See Appendix A for variables definition. The variables are windsorized at 1 and 99 percent to mitigate the influence of outliers.

188

Table 4.1 Correlation table: Audit Fees model

Going Short Client Assets Segments CATA Quick Leverage ROI Loss Foreign December YE Weaknesses Firm Size Market Share Big 4 Audit Fees SEC_CL Rank_SIR Interaction Concern Tenure Importance Audit Fees 1.000 0.175 0.461 0.229 0.879 0.373 -0.250 -0.200 0.007 0.381 -0.325 -0.363 0.037 0.062 -0.580 -0.466 -0.104 0.176 -0.334 0.682 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.147 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** SEC comment letter (SEC_CL) 0.176 1.000 0.126 0.902 0.184 0.078 -0.064 -0.037 -0.010 0.083 -0.078 -0.080 0.011 0.010 -0.161 -0.177 -0.062 0.045 -0.034 0.109 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.032** <.001*** <.001*** <.001*** 0.023** 0.043** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Rank of Short Interest Ratio (Rank_SIR) 0.461 0.126 1.000 0.300 0.439 0.170 -0.040 0.049 -0.148 0.233 -0.182 -0.291 0.024 0.039 -0.612 -0.394 -0.210 0.117 -0.223 0.425 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Interaction (SEC CL * Rank_SIR) 0.197 0.990 0.185 1.000 0.234 0.098 -0.064 -0.026 -0.023 0.104 -0.094 -0.109 0.014 0.015 -0.245 -0.234 -0.090 0.055 -0.064 0.168 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.003*** 0.002*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Assets 0.877 0.184 0.443 0.204 1.000 0.269 -0.426 -0.190 0.013 0.508 -0.442 -0.427 0.034 0.073 -0.592 -0.494 -0.148 0.208 -0.286 0.647 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.0049 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Segments 0.412 0.084 0.195 0.092 0.300 1.000 0.091 -0.044 -0.112 0.201 -0.144 -0.163 0.021 -0.040 -0.222 -0.177 -0.029 0.022 -0.114 0.200 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** CATA -0.260 -0.062 -0.037 -0.063 -0.440 0.104 1.000 0.413 -0.295 -0.184 0.159 0.011 0.004 -0.106 0.149 0.188 0.068 -0.111 0.021 -0.161 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.019** 0.451 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Quick -0.087 -0.012 0.182 -0.003 -0.152 0.148 0.511 1.000 -0.273 -0.041 0.107 -0.085 -0.013 0.031 0.052 0.108 0.047 -0.056 -0.003 -0.067 <.001*** 0.012** <.001*** 0.5922 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.006*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.503 <.001*** Leverage 0.203 0.031 -0.073 0.030 0.271 -0.074 -0.472 -0.509 1.000 -0.223 0.144 0.271 -0.004 0.082 0.098 0.022 0.029 -0.014 0.041 -0.020 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.3803 <.001*** <.001*** <.001*** <.001*** 0.0028 <.001*** <.001*** ROI 0.385 0.087 0.215 0.094 0.483 0.250 -0.147 0.009 0.010 1.000 -0.493 -0.540 0.030 -0.050 -0.291 -0.219 -0.114 0.105 -0.093 0.281 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.065* 0.041** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Loss -0.326 -0.078 -0.184 -0.084 -0.442 -0.185 0.153 -0.004 0.035 -0.751 1.000 0.339 -0.027 0.049 0.317 0.231 0.137 -0.108 0.072 -0.252 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.429 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Going Concern -0.343 -0.080 -0.295 -0.090 -0.395 -0.210 0.008 -0.241 0.130 -0.399 0.339 1.000 -0.037 0.015 0.306 0.207 0.102 -0.065 0.163 -0.309 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.088* <.001*** <.001*** <.001*** <.001*** <.001*** 0.002*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Foreign 0.036 0.011 0.024 0.012 0.033 0.023 0.005 0.003 0.007 0.031 -0.027 -0.037 1.000 -0.001 -0.027 -0.020 -0.015 -0.010 -0.019 0.029 <.001*** 0.023** <.001*** 0.011** <.001*** <.001*** 0.333 0.511 0.147 <.001*** <.001*** <.001*** 0.871 <.001*** <.001*** 0.002*** 0.035** <.001*** <.001*** December YE 0.057 0.010 0.038 0.012 0.070 -0.060 -0.111 0.010 0.103 -0.068 0.049 0.015 -0.001 1.000 -0.031 -0.025 0.032 -0.042 -0.071 0.074 <.001*** 0.043** <.001*** 0.015** <.001*** <.001*** <.001*** 0.032** <.001*** <.001*** <.001*** 0.002*** 0.871 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Weaknesses -0.579 -0.161 -0.613 -0.190 -0.598 -0.246 0.145 -0.073 0.000 -0.345 0.317 0.306 -0.027 -0.031 1.000 0.573 0.349 -0.170 0.231 -0.498 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.937 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Short Tenure -0.474 -0.177 -0.394 -0.198 -0.504 -0.178 0.184 0.053 -0.066 -0.253 0.231 0.207 -0.020 -0.025 0.573 1.000 0.187 -0.166 0.153 -0.405 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Firm Size -0.097 -0.057 -0.200 -0.066 -0.150 -0.022 0.079 0.013 -0.018 -0.151 0.140 0.101 -0.012 0.031 0.334 0.180 1.000 0.002 -0.401 0.000 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.724 <.001*** 0.970 Market Share 0.200 0.067 0.242 0.076 0.197 0.070 -0.057 -0.004 0.003 0.124 -0.104 -0.078 -0.004 -0.036 -0.237 -0.195 0.099 1.000 -0.069 0.152 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.428 0.482 <.001*** <.001*** <.001*** 0.418 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Client Importance -0.343 -0.033 -0.229 -0.043 -0.280 -0.143 -0.016 -0.118 0.042 -0.069 0.055 0.168 -0.011 -0.074 0.225 0.144 -0.596 -0.198 1.000 -0.385 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.021** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Big 4 0.686 0.109 0.425 0.130 0.654 0.235 -0.159 0.026 0.090 0.294 -0.252 -0.309 0.029 0.074 -0.498 -0.405 0.010 0.150 -0.473 1.000 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.046** <.001*** <.001***

Notes: The table presents Pearson (upper) and Spearman (lower) correlations for Audit Fees model. All variables are defined in the Appendix A. All continuous variables are winsorized at 1 & 99 percent to mitigate the influence of outliers. Pearson (Spearman) correlations are presented in the upper right (lower left). The two-tailed p-values are presented below each correlation coefficient. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. There are 43,930 observations consistent with those shown in Table 3.1.

189

Table 4.2 Correlation table: Auditor Change model

Auditor Going December Short Rec_Inven Assets Audit_Lag Weaknesses ROA Leverage Loss Growth Acquisition Big4 Expert_City Expert_Nat Change SEC_CL Rank_SIR Interaction Concern YE Tenure tory Auditor Change 1 -0.028 -0.095 -0.024 -0.155 0.095 0.133 -0.076 0.007 0.091 0.096 -0.092 -0.014 0.189 0.034 -0.023 -0.198 0.028 -0.002 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.1171 <.001*** <.001*** <.001*** 0.003*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.665 SEC comment letter (SEC_CL) -0.028 1.000 0.130 0.011 0.184 -0.130 -0.163 0.075 -0.011 -0.080 -0.080 0.133 0.008 -0.157 -0.017 0.054 0.107 -0.019 -0.003 <.001*** <.001*** 0.022** <.001*** <.001*** <.001*** <.001*** 0.022** <.001*** <.001*** <.001*** 0.084* <.001*** <.001*** <.001*** <.001*** <.001*** 0.578 Rank of Short Interest Ratio (Rank_SIR) -0.095 0.132 1.000 0.278 0.427 -0.332 -0.622 0.231 -0.150 -0.180 -0.289 0.185 0.034 -0.324 -0.094 0.127 0.406 -0.137 0.012 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.011** Interaction (SEC_CL * Rank_SIR) -0.010 -0.006 0.165 1.000 0.220 -0.145 -0.236 0.085 -0.018 -0.081 -0.100 0.138 0.014 -0.252 -0.034 0.099 0.145 -0.035 -0.003 0.038** 0.216 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.003*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.554 Assets -0.159 0.183 0.431 0.190 1.000 -0.367 -0.576 0.475 0.015 -0.448 -0.430 0.584 0.077 -0.473 -0.111 0.214 0.647 -0.112 0.026 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Audit_Lag 0.154 -0.171 -0.446 -0.175 -0.629 1.000 0.427 -0.217 0.092 0.238 0.234 -0.209 -0.011 0.208 0.029 -0.089 -0.250 0.030 -0.014 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.022** <.001*** <.001*** <.001*** <.001*** <.001*** 0.002** Weaknesses 0.133 -0.163 -0.623 -0.182 -0.579 0.588 1.000 -0.273 0.099 0.308 0.303 -0.322 -0.024 0.499 0.066 -0.152 -0.481 0.134 -0.013 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.005*** ROA -0.088 0.079 0.201 0.070 0.415 -0.370 -0.317 1.000 -0.286 -0.493 -0.542 0.177 -0.042 -0.192 0.123 0.093 0.267 -0.054 0.019 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Leverage -0.013 0.032 -0.076 0.034 0.274 -0.018 0.005 -0.119 1.000 0.140 0.270 0.031 0.086 0.010 -0.064 0.009 -0.018 0.063 0.010 0.006*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.330 <.001*** <.001*** <.001*** <.001*** <.001*** 0.031** <.001*** 0.053* <.001*** <.001*** 0.038** Loss 0.091 -0.080 -0.184 -0.072 -0.448 0.373 0.308 -0.851 0.029 1.000 0.342 -0.255 0.045 0.222 -0.116 -0.071 -0.255 0.009 -0.022 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.048** <.001*** GoingConcern 0.096 -0.080 -0.295 -0.077 -0.398 0.332 0.303 -0.390 0.128 0.342 1.000 -0.149 0.013 0.193 -0.062 -0.100 -0.313 0.098 -0.005 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.006*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.336 Growth -0.092 0.133 0.186 0.127 0.592 -0.390 -0.322 0.257 0.137 -0.255 -0.149 1.000 0.016 -0.264 0.001 0.124 0.331 -0.074 -0.013 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.001*** <.001*** 0.785 <.001*** <.001*** <.001*** 0.004*** December YE -0.014 0.008 0.034 0.010 0.075 -0.024 -0.024 -0.076 0.107 0.045 0.013 0.016 1.000 -0.026 -0.186 0.018 0.080 -0.041 -0.010 0.003*** 0.084* <.001*** 0.031** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.006*** 0.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.039** Short Tenure 0.189 -0.157 -0.325 -0.219 -0.484 0.424 0.499 -0.212 -0.074 0.222 0.193 -0.264 -0.026 1.000 0.051 -0.173 -0.396 0.081 -0.009 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.045** Rec_Inventory 0.023 -0.001 -0.048 -0.007 -0.053 0.021 0.005 0.219 -0.063 -0.161 -0.109 0.046 -0.199 0.018 1.000 -0.019 -0.117 0.091 -0.020 <.001*** 0.758 <.001*** 0.121 <.001*** <.001*** 0.315 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Acquisition -0.023 0.054 0.127 0.088 0.216 -0.134 -0.152 0.058 0.069 -0.071 -0.100 0.124 0.018 -0.173 0.016 1.000 0.127 -0.057 0.014 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.001*** <.001*** <.001*** 0.004*** Big 4 -0.198 0.107 0.408 0.104 0.652 -0.461 -0.481 0.238 0.093 -0.255 -0.313 0.331 0.080 -0.396 -0.068 0.127 1.000 -0.114 0.017 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Expert_City 0.028 -0.019 -0.137 -0.017 -0.102 0.084 0.134 -0.027 0.065 0.009 0.098 -0.074 -0.041 0.081 0.068 -0.057 -0.114 1.000 0.076 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.048** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Expert_National -0.025 0.023 0.037 0.025 0.124 -0.087 -0.083 0.061 0.035 -0.069 -0.036 0.052 -0.034 -0.090 0.000 0.012 0.099 0.096 1.000 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.932 0.016** <.001*** <.001***

Notes: The table presents Pearson (upper) and Spearman (lower) correlations for Auditor Change model. All variables are defined in the Appendix A. All continuous variables are winsorized at 1 & 99 percent to mitigate the influence of outliers. Pearson (Spearman) correlations are presented in the upper right (lower left). The two-tailed p-values are presented below each correlation coefficient. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. There are 45,331 observations consistent with those shown in Table 3.2.

190

Table 4.3 Correlation table: Performance-Matched Absolute Discretionary Accruals model

PMDA SEC CL Rank_SIR Interaction Market ROA Leverage Current CFO SD_CFO Loss Market to Litigation Zscore Total Weaknesses ST Tenure Firm Size Market Client Big 4 Value Ratio book Rank Accruals Share Importance (lag) Performance matched discretionary 1 -0.017 -0.024 -0.017 -0.038 0.018 -0.044 0.033 0.014 0.007 -0.014 0.001 0.034 0.013 0.003 0.026 -0.002 -0.012 0.033 0.026 -0.038 accruals

0.001*** <.001*** 0.001*** <.001*** 0.001*** <.001*** <.001*** 0.007*** 0.161 0.007*** 0.881 <.001*** 0.007*** 0.573 <.001*** 0.648 0.014 <.001*** <.001*** <.001*** SEC Comment Letter (SEC_CL) -0.023 1.000 0.882 0.971 0.186 0.077 -0.002 -0.042 0.086 -0.065 -0.080 0.007 0.011 0.088 0.030 -0.153 -0.178 -0.053 -0.039 -0.041 0.122 <.001*** <.001*** <.001*** <.001*** <.001*** 0.730 <.001*** <.001*** <.001*** <.001*** 0.176 0.024** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Rank of Short Interest Ratio -0.016 0.691 1.000 0.955 0.244 0.102 -0.017 -0.030 0.105 -0.084 -0.105 0.006 0.007 0.154 0.057 -0.228 -0.199 -0.074 -0.063 -0.076 0.184 (Rank_SIR) 0.001*** <.001*** <.001*** <.001*** <.001*** 0.001*** <.001*** <.001*** <.001*** <.001*** 0.215 0.137 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Interaction (SEC_CL * Rank_SIR) -0.023 0.991 0.718 1.000 0.227 0.100 -0.010 -0.045 0.109 -0.076 -0.102 0.004 0.010 0.130 0.041 -0.206 -0.215 -0.071 -0.058 -0.061 0.161 <.001*** <.001*** <.001*** <.001*** <.001*** 0.039** <.001*** <.001*** <.001*** <.001*** 0.466 0.042 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Market Value -0.034 0.186 0.188 0.205 1.000 0.364 -0.096 -0.078 0.375 -0.269 -0.458 0.103 0.077 0.385 0.183 -0.671 -0.504 -0.161 -0.198 -0.300 0.639 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** ROA 0.047 0.078 0.073 0.085 0.474 1.000 -0.302 0.049 0.818 -0.448 -0.496 0.029 0.074 0.293 0.371 -0.300 -0.211 -0.114 -0.060 -0.093 0.277 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Leverage -0.047 0.043 0.021 0.043 0.092 -0.114 1.000 -0.288 -0.215 0.112 0.144 -0.135 -0.007 -0.268 -0.193 0.067 0.011 0.014 0.071 0.056 -0.043 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.168 <.001*** <.001*** <.001*** 0.029** 0.005*** <.001*** <.001*** <.001*** Current Ratio 0.066 -0.017 0.041 -0.011 0.015 0.127 -0.505 1.000 -0.044 0.041 0.067 0.041 -0.086 -0.044 0.081 0.066 0.091 0.037 -0.042 0.005 -0.060 <.001*** 0.001*** <.001*** 0.035** 0.003*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.301 <.001*** Operating Cash Flows (CFO) 0.022 0.086 0.062 0.096 0.463 0.720 -0.034 -0.001 1.000 -0.497 -0.477 0.002 0.095 0.283 0.305 -0.311 -0.235 -0.118 -0.043 -0.086 0.272 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.782 <.001*** <.001*** 0.628 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** SD_CFO 0.017 -0.026 -0.071 -0.023 -0.228 -0.123 -0.116 0.067 -0.087 1.000 0.256 0.018 -0.058 -0.156 -0.474 0.198 0.154 0.059 0.017 0.078 -0.233 0.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.001*** <.001*** <.001*** Loss -0.030 -0.080 -0.066 -0.087 -0.467 -0.853 0.034 -0.059 -0.612 0.157 1.000 -0.001 -0.083 -0.308 -0.209 0.340 0.243 0.145 0.012 0.076 -0.262 0.001*** 0.001*** 0.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.847 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.021** <.001*** <.001*** Market to book -0.008 0.034 0.076 0.038 0.356 0.203 -0.182 0.136 0.160 0.008 -0.137 1.000 -0.003 0.036 0.016 -0.018 -0.001 0.003 -0.019 -0.021 0.014 0.124 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.106 <.001*** 0.576 <.001*** 0.001*** 0.001*** 0.831 0.615 0.001*** <.001*** 0.005*** Litigation 0.083 0.011 0.008 0.009 0.078 0.094 0.007 -0.096 0.127 -0.029 -0.083 0.010 1.000 0.022 0.022 -0.079 -0.065 -0.051 0.119 -0.020 0.079 <.001*** 0.024** 0.092* 0.065* <.001*** <.001*** 0.177 <.001*** <.001*** <.001*** <.001*** 0.057* <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Zscore Rank 0.020 0.091 0.126 0.108 0.385 0.343 -0.119 0.153 0.322 -0.005 -0.314 0.164 0.025 1.000 0.144 -0.369 -0.263 -0.143 -0.107 -0.125 0.267 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.327 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Total Accruals (lag) 0.012 -0.029 0.041 -0.032 0.038 0.163 -0.080 0.154 0.013 -0.398 -0.147 0.023 -0.033 0.006 1.000 -0.130 -0.070 -0.044 -0.032 -0.054 0.156 0.014** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.009*** <.001*** <.001*** <.001*** <.001*** 0.231 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Weaknesses 0.019 -0.153 -0.162 -0.178 -0.683 -0.336 -0.044 -0.069 -0.360 0.072 0.340 -0.157 -0.079 -0.389 0.051 1.000 0.564 0.334 0.231 0.262 -0.566 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Short Term Tenure -0.003 -0.178 -0.089 -0.194 -0.515 -0.234 -0.084 0.002 -0.276 0.043 0.243 -0.055 -0.065 -0.277 0.117 0.564 1.000 0.174 0.098 0.170 -0.446 0.546 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.691 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Firm Size -0.017 -0.049 -0.038 -0.056 -0.153 -0.137 -0.036 -0.007 -0.130 0.006 0.144 -0.016 -0.057 -0.143 0.010 0.317 0.165 1.000 -0.191 -0.397 -0.014 0.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.169 <.001*** 0.201 <.001*** 0.001*** <.001*** <.001*** 0.046** <.001*** <.001*** <.001*** <.001*** 0.005*** Marketshare 0.057 -0.040 -0.039 -0.051 -0.197 -0.033 0.073 -0.105 -0.064 -0.017 0.015 -0.088 0.122 -0.118 0.035 0.244 0.101 -0.182 1.000 0.284 -0.144 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.003*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Client Importance 0.038 -0.043 -0.081 -0.054 -0.330 -0.073 0.055 -0.109 -0.098 0.075 0.065 -0.114 0.011 -0.126 -0.008 0.271 0.172 -0.585 0.360 1.000 -0.385 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.0333 <.001*** 0.1343 <.001*** <.001*** <.001*** <.001*** <.001*** Big 4 -0.032 0.122 0.146 0.141 0.654 0.257 0.071 0.059 0.293 -0.178 -0.262 0.145 0.079 0.282 0.005 -0.566 -0.446 0.001 -0.136 -0.482 1.000 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.324 <.001*** <.001*** 0.843 <.001*** <.001***

Notes: The table presents Pearson (upper) and Spearman (lower) correlations for Performance-Matched Absolute Discretionary Accruals model. All variables are defined in the Appendix A. All continuous variables are winsorized at 1 & 99 percent to mitigate the influence of outliers. Pearson (Spearman) correlations are presented in the upper right (lower left). The two-tailed p-values are presented below each correlation coefficient. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. There are 39,612 observations consistent with those shown in Table 3.3.

191

Table 4.4 Correlation table: Restatements model

Restatements SEC Comment Rank_SIR Interaction Assets ROA Leverage Market to Segments Financing Foreign Acquisitions Weaknesses Receivables Audit Fees Client Big 4 Letter Book Transactions and Importance Inventory Restatements 1.000 -0.001 0.011 0.002 -0.003 0.017 -0.011 0.002 -0.007 -0.004 0.000 -0.022 0.006 0.004 0.010 -0.007 0.002 0.894 0.023** 0.739 0.590 0.001*** 0.032** 0.666 0.182 0.412 0.976 <.001*** 0.251 0.459 0.049 0.140 0.680 SEC Comment Letter (SEC_CL) -0.001 1.000 0.102 0.900 0.195 0.072 0.002 0.008 0.087 -0.060 0.011 0.086 -0.158 -0.017 0.176 -0.035 0.110 0.894 <.001*** <.001*** <.001*** <.001*** 0.673 0.104 <.001*** <.001*** 0.029** <.001*** <.001*** 0.001*** <.001*** <.001*** <.001*** Rank of Short Interest Ratio (Rank_SIR) 0.011 0.102 1.000 0.271 0.501 0.252 -0.110 0.039 0.175 -0.100 0.029 0.128 -0.580 -0.118 0.497 -0.259 0.501 0.023** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Interaction (SEC CL * Rank_SIR) 0.008 0.992 0.152 1.000 0.241 0.092 -0.004 0.011 0.102 -0.061 0.014 0.101 -0.225 -0.038 0.222 -0.067 0.171 1.000 <.001*** <.001*** <.001*** <.001*** 0.406 0.029** <.001*** <.001*** 0.005*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Assets -0.004 0.193 0.511 0.210 1.000 0.476 0.000 -0.014 0.295 -0.232 0.031 0.236 -0.658 -0.086 0.892 -0.294 0.656 0.407 <.001*** <.001*** <.001*** <.001*** 0.962 0.005*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** ROA -0.002 0.068 0.200 0.072 0.430 1.000 -0.297 0.029 0.192 -0.462 0.033 0.098 -0.300 0.130 0.353 -0.093 0.277 0.729 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Leverage 0.001 0.046 -0.025 0.046 0.256 -0.116 1.000 -0.135 -0.109 0.232 -0.007 0.014 0.064 -0.058 0.002 0.053 -0.040 0.868 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.145 0.004*** <.001*** <.001*** 0.712 <.001*** <.001*** Market to Book 0.008 0.018 0.201 0.025 0.090 0.206 -0.183 1.000 -0.015 0.040 -0.007 0.006 -0.018 -0.051 -0.010 -0.022 0.013 0.095* 0.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.003*** <.001*** 0.187 0.209 <.001*** <.001*** 0.044** <.001*** 0.008*** Segments -0.006 0.091 0.202 0.097 0.331 0.243 -0.055 0.044 1.000 -0.170 0.024 0.132 -0.225 0.146 0.383 -0.124 0.220 0.254 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Financing 0.006 -0.024 0.051 -0.020 -0.006 -0.226 0.269 0.140 -0.125 1.000 -0.010 -0.026 0.185 -0.110 -0.174 0.022 -0.129 0.215 <.001*** <.001*** <.001*** 0.213 <.001*** <.001*** <.001*** <.001*** 0.049** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Foreign Transactions 0.000 0.011 0.029 0.012 0.031 0.028 0.004 0.004 0.025 -0.004 1.000 0.004 -0.031 0.021 0.036 -0.019 0.025 0.976 0.029** <.001*** 0.016** <.001*** <.001*** 0.463 0.453 <.001*** 0.413 0.409 <.001*** <.001*** <.001*** <.001*** <.001*** Acquisitions -0.022 0.086 0.128 0.091 0.239 0.063 0.082 0.043 0.150 0.058 0.004 1.000 -0.153 -0.032 0.248 -0.069 0.142 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.409 <.001*** <.001*** <.001*** <.001*** <.001*** Weaknesses 0.006 -0.158 -0.579 -0.179 -0.669 -0.336 -0.045 -0.155 -0.253 0.035 -0.031 -0.153 1.000 0.092 -0.616 0.263 -0.563 0.251 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Receivables and Inventory 0.005 -0.002 -0.073 -0.007 -0.025 0.219 -0.042 -0.089 0.261 -0.185 0.031 0.000 0.032 1.000 -0.023 0.070 -0.096 0.303 0.672 <.001*** 0.164 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.997 <.001*** <.001*** <.001*** <.001*** Audit Fees 0.009 0.175 0.499 0.192 0.892 0.324 0.204 0.108 0.428 0.019 0.036 0.254 -0.618 0.053 1.000 -0.343 0.696 0.075* <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Client Importance -0.011 -0.032 -0.279 -0.042 -0.304 -0.074 0.053 -0.112 -0.161 -0.038 -0.009 -0.072 0.270 0.093 -0.367 1.000 -0.482 0.030** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.071* <.001*** <.001*** <.001*** <.001*** <.001*** Big 4 0.002 0.110 0.500 0.129 0.665 0.256 0.072 0.141 0.259 0.012 0.025 0.142 -0.563 -0.046 0.703 -0.482 1.000 0.680 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.021** <.001*** <.001*** <.001*** <.001*** <.001*** <.001***

Notes: The table presents Pearson (upper) and Spearman (lower) correlations for Restatement model. All variables are defined in the Appendix A. All continuous variables are winsorized at 1 & 99 percent to mitigate the influence of outliers. Pearson (Spearman) correlations are presented in the upper right (lower left). The two-tailed p-values are presented below each correlation coefficient. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. There are 40,326 observations consistent with those shown in Table 3.4.

192

Table 4.5 Correlation table: Material Weaknesses model

Weaknesses SEC_CL Rank_SIR Interaction Assets Firm Age Loss Z Score Segments Foreign Acquisitions Growth Restructuring Big 4 Rank

Weaknesses 1 -0.156 -0.510 -0.212 -0.525 -0.114 0.254 -0.131 -0.199 -0.022 -0.116 -0.246 -0.025 -0.559 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** SEC Comment Letter -0.156 1.000 0.094 0.894 0.179 0.077 -0.069 0.046 0.082 0.011 0.048 0.123 0.001 0.120 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.047** <.001*** <.001*** 0.861 <.001*** Rank of Short Interest Ratio (Rank_SIR) -0.508 0.094 1.000 0.285 0.317 -0.041 -0.084 0.106 0.122 0.020 0.092 0.088 0.026 0.418 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.001*** <.001*** <.001*** <.001*** <.001*** Interaction (SEC CL * Rank_SIR) -0.178 0.988 0.161 1.000 0.203 0.053 -0.065 0.051 0.089 0.011 0.061 0.119 0.005 0.167 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.035** <.001*** <.001*** 0.349 <.001*** Assets -0.534 0.179 0.336 0.193 1.000 0.323 -0.397 0.145 0.185 0.014 0.216 0.637 -0.006 0.501 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.008*** <.001*** <.001*** 0.279 <.001*** Firm Age -0.080 0.067 -0.061 0.062 0.226 1.000 -0.217 0.115 0.119 0.012 0.032 0.272 0.003 0.087 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.022** <.001*** <.001*** 0.604 <.001*** Loss 0.254 -0.069 -0.083 -0.069 -0.400 -0.217 1.000 -0.218 -0.100 -0.012 -0.070 -0.256 -0.159 -0.174 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.021** <.001*** <.001*** <.001*** <.001*** Z Score Rank -0.131 0.046 0.105 0.048 0.138 0.118 -0.217 1.000 0.148 0.021 0.051 0.154 0.073 0.089 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Segments -0.231 0.090 0.141 0.094 0.190 0.125 -0.123 0.171 1.000 0.021 0.111 0.193 -0.041 0.196 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Foreign -0.022 0.011 0.020 0.011 0.015 0.014 -0.012 0.021 0.020 1.000 0.001 0.012 -0.004 0.016 <.001*** 0.047** 0.001*** 0.038** 0.005*** 0.008*** 0.021** <.001*** <.001*** 0.781 0.023** 0.482 0.003*** Acquisition -0.116 0.048 0.092 0.053 0.219 0.027 -0.070 0.050 0.120 0.001 1.000 0.113 0.007 0.101 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.781 <.001*** 0.190 <.001*** Growth -0.246 0.123 0.087 0.124 0.640 0.222 -0.256 0.153 0.194 0.012 0.113 1.000 -0.005 0.264 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.023** <.001*** 0.3958 <.001*** Restructuring 0.112 -0.057 -0.078 -0.060 -0.234 -0.100 -0.068 -0.002 -0.216 -0.005 -0.149 -0.200 1.000 -0.027 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.753 <.001*** 0.368 <.001*** <.001*** <.001*** Big 4 -0.559 0.120 0.417 0.137 0.513 0.028 -0.174 0.088 0.236 0.016 0.101 0.264 -0.157 1.000 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** 0.003*** <.001*** <.001*** <.001***

Notes: The table presents Pearson (upper) and Spearman (lower) correlations for Material Weaknesses model. All variables are defined in the Appendix A. All continuous variables are winsorized at 1 & 99 percent to mitigate the influence of outliers. Pearson (Spearman) correlations are presented in the upper right (lower left). The two-tailed p-values are presented below each correlation coefficient. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. There are 35,643 observations consistent with those shown in Table 3.5.

193

Table 4.6 Correlation table: PCAOB Inspections Deficiencies model

PCAOB PCAOB SEC Rank_SIR Interaction Offices Partners Public Audit Client Foreign Stock Big 4 Deficiency Deficiency Comment Inspected Clients Fees Size Exchange (GAAP) (GAAS) Letter PCAOB Deficiency (GAAP) 1 -0.403 -0.006 0.110 0.002 0.240 -0.020 0.232 0.159 0.172 0.386 0.305 0.209 <.001*** 0.120 <.001*** 0.569 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** PCAOB Deficiency (GAAS) -0.403 1.000 0.081 0.172 0.107 0.291 0.130 0.289 0.227 0.255 0.604 0.517 0.274 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** SEC Comment Letter (SEC_CL) -0.006 0.081 1.000 0.139 0.907 0.123 -0.009 0.124 0.196 0.117 0.086 0.129 0.108 0.121 <.001*** <.001*** <.001*** <.001*** 0.034** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Rank of Short Interest Ratio 0.110 0.172 0.139 1.000 0.307 0.489 -0.045 0.486 0.475 0.434 0.260 0.428 0.441 (Rank_SIR) <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Interaction (SEC_CL * Rank_SIR) -0.004 0.091 0.990 0.197 1.000 0.186 -0.013 0.185 0.243 0.173 0.118 0.186 0.161 0.342 <.001*** <.001*** <.001*** <.001*** 0.002*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Offices Inspected 0.265 0.221 0.089 0.448 0.109 1.000 -0.096 0.970 0.675 0.820 0.474 0.446 0.802 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Partners -0.038 0.214 -0.022 -0.117 -0.026 -0.217 1.000 -0.097 -0.078 -0.044 0.057 0.080 -0.111 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Public Clients 0.217 0.271 0.100 0.444 0.121 0.830 -0.195 1.000 0.670 0.816 0.474 0.438 0.830 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Audit Fees 0.161 0.229 0.196 0.487 0.216 0.585 -0.175 0.609 1.000 0.666 0.383 0.354 0.679 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Client Size 0.128 0.200 0.135 0.357 0.152 0.477 -0.161 0.502 0.627 1.000 0.368 0.355 0.838 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Foreign 0.386 0.604 0.086 0.258 0.098 0.426 0.135 0.457 0.390 0.296 1.000 0.691 0.447 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Stock Exchange 0.305 0.517 0.129 0.433 0.149 0.405 0.064 0.395 0.351 0.265 0.691 1.000 0.387 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** Big 4 0.209 0.274 0.108 0.437 0.129 0.819 -0.204 0.818 0.682 0.798 0.447 0.387 1.000 <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001*** <.001***

Notes: The table presents Pearson (upper) and Spearman (lower) correlations for PCAOB Inspections Deficiencies model. All variables are defined in the Appendix A. All continuous variables are winsorized at 1 & 99 percent to mitigate the influence of outliers. Pearson (Spearman) correlations are presented in the upper right (lower left). The two-tailed p-values are presented below each correlation coefficient. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. There are 43,358 observations consistent with those shown in Table 3.6. 194

Table 5.1 The Impact of SEC Comment Letter and Short Interest Positions on Subsequent Audit Fees

Dependent variable: Audit Fees Equation (3) Equation (4) Equation (5) Variable Pred. Coeff. p value Coeff. p value Coeff. p value Intercept 9.775 <.001*** 9.656 <.001*** 9.649 <.001*** SEC Comment Letter (SEC_CL) + 0.024 0.002*** 0.074 <.001*** Rank of Short Interest Ratio (Rank_SIR) + 0.180 <.001*** 0.197 <.001*** Interaction (SEC_CL * Rank_SIR) + -0.087 <.001*** Assets + 0.499 <.001*** 0.497 <.001*** 0.497 <.001*** Segments + 0.060 <.001*** 0.060 <.001*** 0.060 <.001*** CATA + 0.576 <.001*** 0.567 <.001*** 0.566 <.001*** Quick Ratio - -0.036 <.001*** -0.037 <.001*** -0.037 <.001*** Leverage + 0.040 0.028** 0.049 0.007*** 0.050 0.006*** ROI - -0.206 <.001*** -0.207 <.001*** -0.206 <.001*** Loss + 0.166 <.001*** 0.159 <.001*** 0.159 <.001*** Going Concern + 0.043 0.019** 0.053 0.004*** 0.053 0.003*** Foreign Transactions + 0.035 0.394 0.034 0.412 0.034 0.413 December YE + 0.020 0.159 0.019 0.182 0.018 0.186 Weaknesses + -0.110 <.001*** -0.066 <.001*** -0.064 <.001*** Short Tenure + 0.006 0.567 0.013 0.243 0.014 0.205 Big 4 + 0.385 <.001*** 0.374 <.001*** 0.374 <.001*** Firm_Size + 0.002 0.605 0.005 0.276 0.005 0.268 Market Share + 0.003 0.831 0.002 0.883 0.002 0.905 Client Importance - -0.225 <.001*** -0.210 <.001*** -0.210 <.001*** Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 84.94% 85.01% 85.02% Number of observations 43,930 43,930 43,930 Degrees of freedom 42 42 44 p for Model <.001*** <.001*** <.001***

Note: Table 5.1 presents the impact of the SEC comment letter and short interest positions on the audit fees for subsequent audit engagements. All variables are defined in the Appendix A. All continuous variables are winsorized at 1 & 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the company level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, panel OLS regressions were run with the firm fixed effects and the results remained the same. Column (1) shows the panel OLS regression results using equation (3) in the study. Column (2) shows the panel OLS regression results using equation (4) in the study. Column (3) shows the panel OLS regression results using equation (5) in the study.

195

Table 5.2 The Impact of SEC Comment Letter and Short Interest Positions on the Likelihood of Subsequent Auditor Change.

Dependent variable: Auditor Change Equation (7) Equation (8) Equation (9)

Variable Pred. Coeff. p value Coeff. p value Coeff. p value Intercept -2.640 <.001*** -2.818 <.001*** -2.827 <.001*** SEC Comment Letter (SEC_CL) + 0.127 <.001*** 0.070 0.238 Rank of Short Interest Ratio (Rank_SIR) + 0.028 <.001*** 0.251 <.001*** Interaction (SEC_CL * Rank_SIR) + 0.104 0.303 Assets ? 0.021 0.020** 0.014 0.141 0.013 0.170 Audit Delays + 0.003 <.001*** 0.003 <.001*** 0.003 <.001*** Weaknesses + -0.169 <.001*** -0.104 0.001*** -0.101 0.002*** ROA - 0.017 0.432 0.020 0.364 0.021 0.344 Leverage + -0.052 0.109 -0.030 0.349 -0.031 0.333 Loss + 0.116 <.001*** 0.1079 <.001*** 0.108 <.001*** Going Concern + 0.119 0.001*** 0.1268 0.001*** 0.128 <.001*** Growth ? -0.135 0.005*** -0.107 0.026** -0.110 0.022** December YE - -0.001 0.971 -0.001 0.976 -0.001 0.978 Short Tenure + 1.471 <.001*** 1.4672 <.001*** 1.481 <.001*** Receivables and Inventory + 0.185 0.003*** 0.197 0.001*** 0.194 0.002*** Acquisitions + 0.035 0.288 0.032 0.335 0.031 0.354 Big4 - -0.462 <.001*** -0.472 <.001*** -0.470 <.001*** City Expert - 0.015 0.514 0.024 0.301 0.022 0.343 National Expert - 0.027 0.779 0.027 0.774 0.027 0.780 Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 23.48% 23.53% 22.38% Number of observations 43,512 43,512 43,512 Degrees of freedom 41 41 43 p for Model <.001*** <.001*** <.001*** Note: Table 5.2. presents the impact of the SEC comment letter and short interest positions on the probability of auditor change for subsequent audit engagements. All variables are defined in the Appendix A. All continuous variables are winsorized at 1 & 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the company level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, logistics regressions were run with the firm fixed effects and the results remained the same. Column (1) shows the logistics regression results using equation (7) in the study. Column (2) shows the logistics regression results using equation (8) in the study. Column (3) shows the logistics regression results using equation (9) in the study.

196

Table 5.3 The Impact of SEC Comment Letter and Short Interest Positions on Performance-Matched Absolute Discretionary Accruals.

Dependent variable: Performance Matched Discretionary Accruals Equation (12) Equation (13) Equation (14) Variable Pred. Coeff. p value Coeff. p value Coeff. p value Intercept -0.069 0.855 0.081 0.831 0.925 0.029** SEC Comment Letter (SEC_CL) - -0.144 <.001*** -1.181 <.001*** Rank of Short Interest Ratio (Rank_SIR) - -0.035 <.001*** -0.250 <.001*** Interaction (SEC_CL*Rank_SIR) - 0.268 <.001*** Market Value + -0.061 <.001*** -0.060 <.001*** -0.059 0.001 ROA - 0.025 0.690 0.025 0.688 0.024 0.696 Leverage + -0.232 0.002*** -0.231 0.002*** -0.234 0.002 Current Ratio + 0.009 0.195 0.009 0.194 0.010 0.171 Operating Cash Flows + 0.201 0.067* 0.201 0.066* 0.188 0.087 Operating Cash Flows (Std. Dev) + -0.038 0.843 -0.032 0.868 -0.040 0.835 Loss + -0.093 0.111 -0.092 0.115 -0.091 0.121 Market to Book + 0.003 0.203 0.003 0.213 0.003 0.181 Litigation + -0.749 0.040** -0.757 0.038** -0.764 0.036 Zscore_rank + 0.014 0.141 0.015 0.114 0.014 0.148 Total Accruals (lag) ? -0.005 0.967 -0.004 0.971 -0.007 0.950 Weaknesses + 0.123 0.093* 0.115 0.114 0.124 0.091 ST Tenure + -0.111 0.058* -0.115 0.049** -0.110 0.061 Firm Size - -0.023 0.374 -0.023 0.362 -0.022 0.386 Market Share + 0.146 0.105 0.145 0.108 0.153 0.088 Client Importance - -0.009 0.956 -0.011 0.943 -0.009 0.953 Big 4 - -0.155 0.052* -0.153 0.055* -0.158 0.047 Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 5.29% 5.30% 5.35% Number of observations 39,612 39,612 39,612 Degrees of freedom 42 42 44 p for Model <.001*** <.001*** <.001***

Note: Table 5.3 presents the impact of the SEC comment letter and short interest positions on the Performance-Matched Discretionary Accruals for subsequent audit engagements. All variables are defined in the Appendix A. All continuous variables are winsorized at 1 & 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the company level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, panel OLS regressions were run with the firm fixed effects and the results remained the same. Column (1) shows the panel OLS regression results using equation (12) in the study. Column (2) shows the panel OLS regression results using equation (13) in the study. Column (3) shows the panel OLS regression results using equation (14) in the study.

197

Table 5.4 The Impact of SEC Comment Letter and Short Interest Positions on the Likelihood of Subsequent Restatements.

Dependent variable: Restatements Equation (16) Equation (17) Equation (18) Variable Pred. Coeff. p value Coeff. p value Coeff. p value Intercept -2.154 <.001*** -2.205 <.001*** -2.189 <.001*** SEC Comment Letter (SEC_CL) - 0.066 0.005*** 0.014 0.799 Rank of Short Interest Ratio (Rank_SIR) - 0.092 0.022** 0.076 0.082* Interaction (SEC_CL*Rank_SIR) - 0.086 0.300 Assets - -0.054 <.001*** -0.053 <.001*** -0.055 <.001*** ROA - 0.090 <.001*** 0.086 <.001*** 0.088 <.001*** Leverage + -0.031 0.342 -0.024 0.450 -0.026 0.422 Market to Book + 0.000 0.855 0.000 0.869 0.000 0.892 Segments + -0.012 0.01** -0.012 0.013** -0.012 0.011** Financing + 0.036 0.213 0.031 0.282 0.033 0.255 Foreign Transactions + -0.011 0.900 -0.013 0.880 -0.015 0.868 Acquisitions + -0.005 0.839 -0.005 0.837 -0.006 0.821 Weaknesses + 0.022 0.367 0.040 0.126 0.042 0.110 Receivables and Inventory + -0.061 0.271 -0.050 0.369 -0.052 0.348 Audit Fees + 0.112 <.001*** 0.110 <.001*** 0.110 <.001*** Client Importance - -0.031 0.497 -0.024 0.593 -0.027 0.548 Big 4 - -0.066 0.014** -0.076 0.005*** -0.075 0.006*** Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 8.81% 8.79% 8.78% Number of observations 40,326 40,326 40,326 Degrees of freedom 38 38 40 p for Model <.001*** <.001*** <.001***

Note: Table 5.4 presents the impact of the SEC comment letter and short interest positions on the probability of restatements for subsequent audit engagement. All variables are defined in the Appendix A. All continuous variables are winsorized at 1 & 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the company level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, logistics regressions were run with the firm fixed effects and the results remained the same. Column (1) shows the logistics regression results using equation (16) in the study. Column (2) shows the logistics regression results using equation (17) in the study. Column (3) shows the logistics regression results using equation (18) in the study.

198

Table 5.5 The Impact of SEC Comment Letter and Short Interest Positions on the Likelihood of Subsequent Material Weaknesses.

Dependent variable: Material Weaknesses Equation (20) Equation (21) Equation (22) Variable Pred. Coeff. p value Coeff. p value Coeff. p value

Intercept 4.096 <.001*** 4.657 <.001*** 4.689 <.001*** SEC Comment Letter (SEC_CL) - -0.270 <.001*** -0.420 <.001*** Rank of Short Interest Ratio (Rank_SIR) - -1.866 <.001*** -1.919 <.001*** Interaction (SEC_CL*Rank_SIR) - 0.355 <.001*** Assets - -0.341 <.001*** -0.260 <.001*** -0.258 <.001*** Firm Age - 0.001 0.746 -0.004 <.001*** -0.004 <.001*** Loss + 0.241 <.001*** 0.323 <.001*** 0.323 <.001*** Zscore_rank + -0.026 <.001*** -0.014 <.001*** -0.013 <.001*** Segments + -0.067 <.001*** -0.059 <.001*** -0.057 <.001*** Foreign Transactions + -0.230 0.009*** -0.182 0.045** -0.176 0.053* Acquisitions + 0.023 0.356 0.052 0.039** 0.053 0.037** Growth + 0.597 <.001*** 0.395 <.001*** 0.393 <.001*** Restructuring + -1.950 <.001*** -1.208 <.001*** -1.211 <.001*** Big 4 - -1.111 <.001*** -0.897 <.001*** -0.890 <.001***

Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 53.60% 60.12% 60.39% Number of observations 35,643 35,643 35,643 Degrees of freedom 35 35 37 p for Model <.001*** <.001*** <.001***

Note: Table 5.5 presents the impact of the SEC comment letter and short interest position on the probability of material weaknesses for subsequent audit engagement. All variables are defined in the Appendix A. All continuous variables are winsorized at 1 & 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the company level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, logistics regressions were run with the firm fixed effects and the results remained the same. Column (1) shows the logistics regression results using equation (20) in the study. Column (2) shows the logistics regression results using equation (21) in the study. Column (3) shows the logistics regression results using equation (22) in the study. 199

Table 5.6.1 The Impact of SEC Comment Letter and Short Interest Positions on the Likelihood of Subsequent PCAOB Inspections GAAP Deficiencies. Dependent variable: GAAP Deficiencies Equation (24) Equation (25) Equation (26)

Variable Pred. Coeff. p value Coeff. p value Coeff. p value

Intercept -6.348 0.974 -6.380 0.974 -6.392 0.974 SEC Comment Letter (SEC_CL) - -0.047 0.027** -0.019 0.776 Rank of Short Interest Ratio (Rank_SIR) - -0.177 <.001*** -0.158 <.001*** Interaction (SEC_CL*Rank_SIR) - -0.086 0.311 Offices Inspected + 0.285 <.001*** 0.934 <.001*** 0.932 <.001*** Partners - -0.004 <.001*** -0.001 0.119 -0.001 0.114 Public_Clients ? -0.203 <.001*** 0.028 0.088* 0.027 0.101 Audit Fees ? -0.018 0.057* -0.019 0.041** -0.016 0.088* Average Client Size + 0.214 <.001*** 0.262 <.001*** 0.261 <.001*** Foreign_D + 2.219 <.001*** 1.945 <.001*** 1.940 <.001*** Stock_Exchange - 0.010 0.648 0.066 0.009*** 0.072 0.004*** Big 4 - -0.484 <.001*** -0.610 <.001*** -0.607 <.001***

Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 52.54% 52.85% 52.86% Number of observations 43,358 43,358 43,358 Degrees of freedom 32 32 34 p for Model <.001*** <.001*** <.001***

Note: Table 5.6.1 presents the impact of the SEC comment letter and short interest position on the probability of PCAOB Inspection GAAP Deficiencies for subsequent audit engagement. All variables are defined in the Appendix A. All continuous variables are winsorized at 1 &99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the audit firm level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, logisitics regressions were run with the firm fixed effects and the results remained the same. Column (1) shows the logistics regression results using equation (24) in the study. Column (2) shows the logistics regression results using equation (25) in the study. Column (3) and (4) show the logistics regression results using equation (26) in the study.

200

Table 5.6.2 The Impact of SEC Comment Letter and Short Interest Positions on the Likelihood of subsequent PCAOB Inspections GAAS Deficiencies.

Dependent variable: GAAS Deficiencies Equation (24) Equation (25) Equation (26) Variable Pred. Coeff. p value Coeff. p value Coeff. p value

Intercept -3.642 <.001*** -3.648 <.001*** -3.632 <.001*** SEC Comment Letter (SEC_CL) - 0.014 0.511 -0.068 0.289 Rank of Short Interest Rank (Rank_SIR) - 0.014 0.711 -0.009 0.840 Interaction (SEC_CL*Rank_SIR) - 0.108 0.178 Offices Inspected + -0.216 <.001*** -0.216 <.001*** -0.217 <.001*** Partners - 0.007 <.001*** 0.007 <.001*** 0.007 <.001*** Public_Clients ? 0.246 <.001*** 0.246 <.001*** 0.246 <.001*** Audit Fees ? 0.005 0.572 0.005 0.551 0.005 0.574 Average Client Size + -0.220 <.001*** -0.220 <.001*** -0.220 <.001*** Foreign_D + 3.186 <.001*** 3.188 <.001*** 3.188 <.001*** Stock_Exchange - 0.177 <.001*** 0.175 <.001*** 0.175 <.001*** Big 4 - 0.479 <.001*** 0.479 <.001*** 0.477 <.001***

Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 68.46% 68.46% 68.46% Number of observations 43,358 43,358 43,358 Degrees of freedom 32 32 34 p for Model <.001*** <.001*** <.001***

Note: Table 5.6.2 presents the impact of the SEC comment letter and short interest position on the probability of PCAOB Inspection GAAS Deficiencies for subsequent audit engagement. All variables are defined in the Appendix A. All continuous variables are winsorized at 1 &99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the audit firm level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, logistics regressions were run with the firm fixed effects and the results remained the same. Column (1) shows the logistics regression results using equation (24) in the study. Column (2) shows the logistics regression results using equation (25) in the study. Column (3) shows the logistics regression results using equation (26) in the study.

201

Table 6.1 The Impact of Severity of Issues in SEC comment letters on the subsequent Audit Fees

Dependent variable: Audit Fees Variable Pred. Coeff. p value Coeff. p value Coeff. p value

Intercept 10.541 <.001*** 10.514 <.001*** 10.503 <.001*** Critical Accounting Issue + 0.051 0.002 Frequency of Letters Per Round + 0.014 <.001*** Number of Unique Letters per Year + 0.069 <.001*** Assets + 0.512 <.001*** 0.512 <.001*** 0.511 <.001*** Segments + 0.053 <.001*** 0.053 <.001*** 0.053 <.001*** CATA + 0.622 <.001*** 0.620 <.001*** 0.620 <.001*** Quick Ratio - -0.033 <.001*** -0.033 <.001*** -0.033 <.001*** Leverage + -0.002 0.949 -0.003 0.923 -0.003 0.928 ROI - -0.211 <.001*** -0.208 <.001*** -0.200 <.001*** Loss + 0.178 <.001*** 0.176 <.001*** 0.176 <.001*** Going Concern + 0.085 0.016** 0.081 0.022** 0.087 0.013** Foreign Transactions + 0.061 0.311 0.067 0.267 0.057 0.348 December YE + 0.012 0.510 0.012 0.515 0.012 0.509 Weaknesses + -0.092 <.001*** -0.094 <.001*** -0.094 <.001*** Short Tenure + 0.008 0.625 0.007 0.682 0.006 0.707 Big 4 + 0.369 <.001*** 0.372 <.001*** 0.372 <.001*** Firm_Size + 0.012 0.125 0.012 0.129 0.012 0.124 Market Share + -0.011 0.584 -0.009 0.659 -0.010 0.615 Client Importance - -0.146 0.002*** -0.144 0.002*** -0.142 0.002***

Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 85.49% 85.52% 85.52% Number of observations 9,603 9,603 9,603 Degrees of freedom 41 41 41 p for Model <.001*** <.001*** <.001***

Note: Table 6.1 presents the impact of the severity issues in the SEC comment letters on the subsequent audit fees. The severity is captured by three proxies: (i)Critical Accounting Issue is defined as a letter addressing one of the most critical accounting topics following categories used by Dechow et al. 2016 (and summarized in Appendix A). (ii) Frequency of letters per round is the number of letters from the original comment letter to the final one. And (iii) Number of Unique Letters per Year is the number of unique comment letters per fiscal year. The remaining variables are defined in Appendix A. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test.

202

Table 6.2 The Impact of Severity of Issues in SEC comment letters on the likelihood of the subsequent Auditor Changes

Dependent variable: Auditor Change

Variable Pred. Coeff. p value Coeff. p value Coeff. p value

Intercept -7.210 0.976 -7.059 0.976 -7.218 0.976 Critical Accounting Issue + 0.194 0.031** Frequency of Letters Per Round + -0.001 0.893 Number of Unique Letters per Year + 0.103 0.018** Assets + 0.032 0.153 0.032 0.151 0.032 0.162 Audit Delays + 0.004 <.001*** 0.004 <.001*** 0.004 <.001*** Weaknesses + -0.191 0.005*** -0.180 0.008*** -0.188 0.006*** ROA - -0.040 0.519 -0.031 0.622 -0.021 0.734 Leverage + -0.021 0.806 -0.015 0.863 -0.018 0.836 Loss + 0.030 0.623 0.036 0.557 0.031 0.614 Going Concern + 0.018 0.862 0.026 0.802 0.026 0.803 Growth ? -0.132 0.161 -0.126 0.179 -0.127 0.179 December YE - -0.023 0.684 -0.022 0.699 -0.023 0.683 Short Tenure + 1.433 <.001*** 1.433 <.001*** 1.432 <.001*** Receivables and Inventory + -0.035 0.829 -0.030 0.850 -0.019 0.906 Acquisitions + 0.042 0.564 0.043 0.562 0.034 0.643 Big4 - -0.489 <.001*** -0.492 <.001*** -0.485 <.001*** City Expert - -0.130 0.017** -0.126 0.020** -0.126 0.020** National Expert - 0.427 0.175 0.426 0.176 0.427 0.175

Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 28.10% 27.96% 28.11% Number of observations 9,810 9,810 9,810 Degrees of freedom 41 41 41 p for Model <.001*** <.001*** <.001***

Note: Table 6.2 presents the impact of the severity issues in the SEC comment letters on the likelihood of subsequent auditor changes. The severity is captured by three proxies: (i)Critical Accounting Issue is defined as a letter addressing one of the most critical accounting topics following categories used by Dechow et al. 2016 (and summarized in Appendix A). (ii) Frequency of letters per round is the number of letters from the original comment letter to the final one. And (iii) Number of Unique Letters per Year is the number of unique comment letters per fiscal year. The remaining variables are defined in Appendix A. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. 203

Table 6.3 The Impact of Severity of Issues in SEC comment letters on the subsequent Performance- Matched Discretionary Accruals

Dependent variable: Performance Matched Discretionary Accruals Variable Pred. Coeff. p value Coeff. p value Coeff. p value

Intercept -1.074 0.060 -1.037 0.066 -1.093 0.058 Critical Accounting Issue - -0.049 0.670 Frequency of Letters Per Round - -0.018 0.164 Number of Unique Letters per Year - -0.014 0.846 Market Value + -0.048 0.115 -0.047 0.125 -0.048 0.121 ROA - -0.183 0.125 -0.180 0.130 -0.184 0.123 Leverage + -0.145 0.302 -0.144 0.306 -0.146 0.299 Current Ratio + 0.042 0.025** 0.042 0.026** 0.042 0.025** Operating Cash Flows + 0.512 0.016** 0.503 0.018** 0.510 0.017** Operating Cash Flows (Std. Dev) + -0.142 0.693 -0.151 0.677 -0.133 0.714 Loss + -0.093 0.369 -0.090 0.385 -0.093 0.373 Market to Book + 0.002 0.510 0.002 0.534 0.002 0.514 Litigation + -0.770 0.033** -0.764 0.035** -0.766 0.034** Zscore_rank + 0.022 0.176 0.021 0.188 0.022 0.176 Total Accruals (lag) ? 0.036 0.892 0.031 0.907 0.036 0.890 Weaknesses + 0.069 0.621 0.080 0.569 0.069 0.625 ST Tenure + -0.050 0.639 -0.046 0.664 -0.050 0.641 Firm Size - -0.006 0.896 -0.006 0.882 -0.006 0.891 Market Share + -0.003 0.984 -0.005 0.970 -0.005 0.973 Client Importance - 0.255 0.403 0.254 0.405 0.253 0.408 Big 4 - -0.087 0.510 -0.092 0.482 -0.086 0.513

Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 4.61% 4.62% 4.61% Number of observations 8,899 8,899 8,899 Degrees of freedom 43 43 43 p for Model <.001*** <.001*** <.001***

Note: Table 6.3 presents the impact of the severity issues in the SEC comment letters on the subsequent performance- matched discretionary accruals. The severity is captured by three proxies: (i)Critical Accounting Issue is defined as a letter addressing one of the most critical accounting topics following categories used by Dechow et al. 2016 (and summarized in Appendix A). (ii) Frequency of letters per round is the number of letters from the original comment letter to the final one. And (iii) Number of Unique Letters per Year is the number of unique comment letters per fiscal year. The remaining variables are defined in Appendix A. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test.

204

Table 6.4 The Impact of Severity of Issues in SEC comment letters on the likelihood of the subsequent Restatements

Dependent variable: Restatements Variable Pred. Coeff. p value Coeff. p value Coeff. p value

Intercept -5.985 0.984 -6.010 0.984 -5.876 0.984 Critical Accounting Issue - -0.134 0.280 Frequency of Letters Per Round - 0.003 0.875 Number of Unique Letters per Year - -0.180 0.074* Assets - -0.091 0.124 -0.088 0.133 -0.092 0.118 ROA - 0.135 0.562 0.134 0.565 0.132 0.571 Leverage + -0.168 0.484 -0.174 0.469 -0.185 0.443 Market to Book + -0.001 0.926 0.000 0.942 -0.001 0.893 Segments + -0.005 0.822 -0.004 0.857 -0.004 0.859 Financing + 0.185 0.393 0.193 0.373 0.213 0.324 Foreign Transactions + -0.284 0.673 -0.347 0.605 -0.307 0.648 Acquisitions + -0.196 0.067* -0.199 0.063* -0.199 0.063* Weaknesses + -0.297 0.122 -0.296 0.122 -0.273 0.156 Receivables and Inventory + -0.698 0.085* -0.698 0.085* -0.763 0.061* Audit Fees + 0.131 0.147 0.126 0.163 0.136 0.133 Client Importance - 0.372 0.127 0.374 0.126 0.361 0.141 Big 4 - -0.268 0.116 -0.255 0.135 -0.273 0.111

Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 10.55% 10.42% 10.82% Number of observations 9,090 9,090 9,090 Degrees of freedom 38 38 38 p for Model <.001*** <.001*** <.001***

Note: Table 6.4 presents the impact of the severity issues in the SEC comment letters on the likelihood of subsequent auditor changes. The severity is captured by three proxies: (i)Critical Accounting Issue is defined as a letter addressing one of the most critical accounting topics following categories used by Dechow et al. 2016 (and summarized in Appendix A). (ii) Frequency of letters per round is the number of letters from the original comment letter to the final one. And (iii) Number of Unique Letters per Year is the number of unique comment letters per fiscal year. The remaining variables are defined in Appendix A. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test.

205

Table 6.5 The Impact of Severity of Issues in SEC comment letters on the likelihood of the subsequent Material Weaknesses

Dependent variable: Material Weaknesses

Variable Pred. Coeff. p value Coeff. p value Coeff. p value

Intercept 5.342 <.001*** 5.471 <.001*** 5.291 <.001*** Critical Accounting Issue - 0.249 <.001*** Frequency of Letters Per Round - 0.061 <.001*** Number of Unique Letters per Year - 0.041 <.001*** Assets - -0.538 <.001*** -0.543 <.001*** -0.537 <.001*** Firm Age - 0.005 <.001*** 0.005 <.001*** 0.005 <.001*** Loss + 0.116 0.010** 0.110 0.015** 0.113 0.012** Zscore_rank + -0.026 <.001*** -0.026 <.001*** -0.026 <.001*** Segments + -0.038 <.001*** -0.040 <.001*** -0.037 <.001*** Foreign Transactions + -0.340 0.111 -0.354 0.095* -0.329 0.124 Acquisitions + 0.104 0.057* 0.095 0.085* 0.110 0.046** Growth + 0.349 <.001*** 0.350 <.001*** 0.349 <.001*** Restructuring + -1.410 0.018** -1.385 0.020** -1.430 0.017**

Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 48.32% 48.55% 48.47% Number of observations 8,327 8,327 8,327 Degrees of freedom 35 35 35 p for Model <.001*** <.001*** <.001***

Note: Table 6.5 presents the impact of the severity issues in the SEC comment letters on the likelihood of subsequent material weaknesses. The severity is captured by three proxies: (i)Critical Accounting Issue is defined as a letter addressing one of the most critical accounting topics following categories used by Dechow et al. 2016 (and summarized in Appendix A). (ii) Frequency of letters per round is the number of letters from the original comment letter to the final one. And (iii) Number of Unique Letters per Year is the number of unique comment letters per fiscal year. The remaining variables are defined in Appendix A. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test.

206

Table 6.6.1 The Impact of Severity of Issues in SEC comment letters on the likelihood of the subsequent PCAOB Inspections GAAP Deficiencies

Dependent variable: GAAP Deficiency

Variable Pred. Coeff. p value Coeff. p value Coeff. p value

Intercept -3.173 <.001*** -3.151 <.001*** -3.171 <.001*** Critical Accounting Issue - -0.025 0.649 Frequency of Letters Per Round - -0.014 0.081* Number of Unique Letters per Year - -0.026 0.504 Offices Inspected + 0.374 <.001*** 0.368 <.001*** 0.373 <.001*** Partners - -0.007 0.001*** -0.007 0.001*** -0.007 0.001*** Public_Clients ? -0.430 <.001*** -0.427 <.001*** -0.429 <.001*** Audit Fees ? -0.015 0.433 -0.014 0.484 -0.014 0.464 Average Client Size + 0.587 <.001*** 0.584 <.001*** 0.587 <.001*** Foreign_D + 2.462 <.001*** 2.471 <.001*** 2.462 <.001*** Stock_Exchange - -0.008 0.896 -0.013 0.828 -0.010 0.863 Big 4 - -0.943 <.001*** -0.944 <.001*** -0.945 <.001***

Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 49.56% 49.59% Number of observations 8,042 8,042 8,042 Degrees of freedom 33 33 33 p for Model <.001*** <.001*** <.001***

Note: Table 6.6.1 presents the impact of the severity issues in the SEC comment letters on the likelihood of subsequent PCAOB Inspections GAAP Deficiencies. The severity is captured by three proxies: (i)Critical Accounting Issue is defined as a letter addressing one of the most critical accounting topics following categories used by Dechow et al. 2016 (and summarized in Appendix A). (ii) Frequency of letters per round is the number of letters from the original comment letter to the final one. And (iii) Number of Unique Letters per Year is the number of unique comment letters per fiscal year. The remaining variables are defined in Appendix A. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test.

207

Table 6.6.2 The Impact of Severity of Issues in SEC comment letters on the likelihood of the subsequent PCAOB Inspections GAAS Deficiencies

Dependent variable: GAAS Deficiency

Variable Pred. Coeff. p value Coeff. p value Coeff. p value

Intercept -3.589 <.001*** -3.632 <.001*** -3.597 <.001*** Critical Accounting Issue - 0.039 0.465 Frequency of Letters Per Round - 0.020 0.007*** Number of Unique Letters per Year - 0.039 0.302 Offices Inspected + -0.255 0.003*** -0.247 0.004*** -0.254 0.003*** Partners - 0.011 <.001*** 0.011 <.001*** 0.011 <.001*** Public_Clients ? 0.490 <.001*** 0.485 <.001*** 0.489 <.001*** Audit Fees ? -0.005 0.789 -0.007 0.700 -0.007 0.736 Average Client Size + -0.573 <.001*** -0.570 <.001*** -0.572 <.001*** Foreign_D + 3.405 <.001*** 3.407 <.001*** 3.403 <.001*** Stock_Exchange - 0.268 <.001*** 0.275 <.001*** 0.271 <.001*** Big 4 - 0.843 <.001*** 0.847 <.001*** 0.844 <.001***

Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 69.12% 69.17% 69.13% Number of observations 8,042 8,042 8,042 Degrees of freedom 33 33 33 p for Model <.001*** <.001*** <.001***

Note: Table 6.6.2 presents the impact of the severity issues in the SEC comment letters on the likelihood of subsequent PCAOB Inspections GAAS Deficiencies. The severity is captured by three proxies: (i)Critical Accounting Issue is defined as a letter addressing one of the most critical accounting topics following categories used by Dechow et al. 2016 (and summarized in Appendix A). (ii) Frequency of letters per round is the number of letters from the original comment letter to the final one. And (iii) Number of Unique Letters per Year is the number of unique comment letters per fiscal year. The remaining variables are defined in Appendix A. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test.

208

Table 7.1 Economic significance of the coefficients from the OLS regression Model (5)

Economic significance: Audit Fees Model Equation (5) Variable Marginal Effect Std. Dev. STD*MF(%)

SEC Comment Letters 0.074*** 0.413 3.078 Rank of Short Interest Ratio 0.197*** 0.281 5.542 Interaction (SEC_CL * Rank_SIR) -0.087*** 0.283 -2.460 Assets 0.497*** 2.438 121.117 Segments 0.060*** 2.222 13.405 CATA 0.566*** 0.278 15.740 Quick Ratio -0.037*** 3.281 -12.167 Leverage 0.050*** 0.326 1.640 ROI -0.206*** 0.449 -9.261 Loss 0.159*** 0.492 7.814 Going Concern 0.053*** 0.295 1.573 Foreign Transactions 0.034 0.100 0.335 December YE 0.018 0.455 0.837 Weaknesses -0.064*** 0.495 -3.181 Short Tenure 0.014 0.482 0.673 Big4 0.374*** 0.480 17.972 Firm_Size 0.005 1.353 0.700 Market Share 0.002 0.493 0.084 Client Importance -0.210*** 0.214 -4.488

Table 7.1 presents economic significance of the coefficients from the OLS regression Model (5). See Appendix A for variables definition. The variables are windsorized at 1 and 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test.

209

Table 7.2 Economic significance of the coefficients from the logistic regression Model (9)

Economic Significance: Auditor Change Model (9) Variable Marginal EffectStd. Dev. STD*MF(%)

SEC Comment Letter 0.194* 0.41 7.99 Rank of Short Interest Ratio 0.159* 0.28 4.45 Interaction (SEC_CL * Rank_SIR) 0.504*** 0.27 13.51 Assets 0.06*** 2.45 14.68 Audit_Lag 0.004*** 33.54 13.42 Weaknesses -0.179*** 0.50 -8.88 ROA 0.00 0.55 -0.11 Leverage -0.107* 0.33 -3.49 Loss 0.238*** 0.49 11.70 Going Concern 0.246*** 0.30 7.31 Growth -0.446*** 0.40 -17.85 December YE 0.01 0.46 0.27 Short Tenure 0.277*** 0.49 13.56 Rec_and_Inv 0.399*** 0.19 7.75 Acquisitions 0.243*** 0.38 9.12 Big4 -1.081*** 0.48 -51.54 City Expert 0.02 0.50 1.00 National Expert 0.02 0.16 0.38

Table 7.2 presents economic significance of the coefficients from the logistics regression Model (9). See Appendix A for variables definition. The variables are windsorized at 1 and 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p- values are from a one-tailed test.

210

Table 7.3 Economic significance of the coefficients from the OLS regression Model (14)

Economic Significance: Performance-Matched Discretionary Accruals Model (14) Variable Marginal Effect Std. Dev. STD*MF(%)

SEC Comment Letter -1.818*** 0.42 -49.29 Rank of Short Interest Ratio -0.250*** 0.27 -6.87 Interaction (SEC_CL * Rank_SIR) 0.268*** 0.25 6.76 Market Value -0.059*** 2.37 -14.11 ROA 0.024 0.56 1.35 Leverage -0.234*** 0.31 -7.34 Current Ratio 0.010 3.55 3.49 Operating Cash Flows 0.188* 0.32 6.08 Operating Cash Flows (Std. Dev) -0.040 0.14 -0.56 Loss -0.091 0.49 -4.48 Market to Book 0.003 8.33 2.32 Litigation -0.764** 0.25 -18.94 Zscore_rank 0.014 3.08 4.23 Total Accruals (lag) -0.007 0.23 -0.16 Weaknesses 0.124* 0.49 6.05 ST Tenure -0.110* 0.48 -5.32 Firm Size -0.022 1.32 -2.92 Market Share 0.153* 0.33 5.14 Client Importance -0.009 0.22 -0.20 Big4 -0.158** 0.48 -7.62

Table 7.3 presents economic significance of the coefficients from the OLS regression Model (14). See Appendix A for variables definition. The variables are windsorized at 1 and 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p- values are from a one-tailed test.

211

Table 7.4 Economic significance of the coefficients from the logistic regression Model (18)

Economic Significance: Restatements Model (18) Variable Marginal Effect Std. Dev. STD*MF(%)

SEC Comment Letter -0.01 0.40 -0.28 Rank of Short Interest Ratio 0.204** 0.29 5.82 Interaction (SEC_CL * Rank_SIR) 0.05 0.27 1.23 Assets -0.112*** 2.42 -27.14 ROA 0.221*** 0.55 12.16 Leverage -0.03 0.31 -1.00 Market to Book 0.011* 8.11 8.92 Segments -0.027*** 2.25 -6.07 Financing 0.02 0.35 0.82 Foreign Transactions -0.03 0.10 -0.32 Acquisitions -0.233*** 0.38 -8.87 Weaknesses 0.106* 0.49 5.18 Rec_and_Inv -0.06 0.19 -1.22 Audit Fees 0.213*** 1.43 30.48 Client Importance -0.08 0.22 -1.65 Big4 -0.086* 0.48 -4.15

Table 7.4 presents economic significance of the coefficients from the logistics regression Model (18). See Appendix A for variables definition. The variables are windsorized at 1 and 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test.

212

Table 7.5 Economic significance of the coefficients from the logistic regression Model (22)

Economic Significance: Material Weaknesses Model (22) Variable Marginal Effect Std. Dev. STD*MF(%)

SEC Comment Letter -0.676*** 0.42 -28.50 Rank of Short Interest Ratio -3.331*** 0.28 -94.64 Interaction (SEC_CL * Rank_SIR) 0.375** 0.28 10.65 Assets -0.451*** 2.23 -100.77 Firm Age -0.005*** 15.89 -7.94 Loss 0.474*** 0.46 22.01 Zscore_rank -0.021*** 3.11 -6.53 Segments -0.108*** 2.19 -23.66 Foreign Transactions -0.21 0.31 -6.64 Acquisitions 0.134*** 0.38 5.15 Growth 0.738*** 0.42 31.32 Restructuring -1.643*** 0.03 -4.86 Big 4 -1.558*** 0.47 -73.23

Table 7.5 presents economic significance of the coefficients from the logistics regression Model (22). See Appendix A for variables definition. The variables are windsorized at 1 and 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one- tailed test.

213

Table 7.6 Economic significance of the coefficients from the logistic regression Model (26)

Economic Significance : PCAOB Inspections GAAP Deficiencies Model (26) Variable Marginal Effect Std. Dev. STD*MF(%)

SEC Comment Letter 0.123* 0.41 5.08 Rank of Short Interest Ratio -0.063*** 0.28 -1.76 Interaction (SEC_CL * Rank_SIR) -0.474* 0.33 -15.59 Offices Inspected -0.252*** 1.61 -40.67 Partners -0.021*** 14.62 -30.71 Public_Clients 0.337*** 2.18 73.48 Audit Fees -0.02 1.42 -2.13 Average Client Size -0.239*** 1.62 -38.73 Foreign_D 0.82*** 0.50 40.80 Stock_Exchange -0.02 0.50 -1.05 Big 4 0.017*** 0.48 0.82

Table 7.6 presents economic significance of the coefficients from the logistics regression Model (26). See Appendix A for variables definition. The variables are windsorized at 1 and 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one- tailed test.

214

Table 8.1 The Impact of lagged SEC Comment Letter and Short Interest Positions on Subsequent Audit Fees

Dependent variable: Audit Fees Equation (27) Equation (28) Equation (29) Variable Pred. Coeff. p value Coeff. p value Coeff. p value Intercept -7.045 <.001*** -7.386 <.001*** -7.150 <.001*** SEC Comment Letter (SEC_CL_lag) + 1.148 <.001*** 1.148 <.001*** Rank of Short Interest Ratio (Rank_SIR_lag) + 0.244 0.024** 0.168 0.156 Interaction (SEC_CL_lag * Rank_SIR_lag) + 0.068 0.316 Assets_lag + 0.481 <.001*** 0.523 <.001*** 0.477 <.001*** Segments_lag + 0.031 0.057* 0.033 0.040** 0.030 0.063* CATA_lag + 0.827 <.001*** 0.871 <.001*** 0.819 <.001*** Quick Ratio_lag - -0.043 <.001*** -0.042 <.001*** -0.043 <.001*** Leverage_lag + -0.134 0.451 -0.133 0.444 -0.123 0.486 ROI_lag - -0.626 0.002*** -0.669 0.001*** -0.626 0.002*** Loss_lag + -0.047 0.533 -0.069 0.347 -0.057 0.451 GoingConcern_lag + -2.305 <.001*** -2.263 <.001*** -2.298 <.001*** ForeignTransactions_lag + 0.356 0.105 0.305 0.167 0.354 0.106 DecemberYE_lag + 0.038 0.647 0.017 0.843 0.038 0.652 Weaknesses_lag + -0.313 <.001*** -0.237 0.006*** -0.265 0.007*** ShortTenure_lag + 0.098 0.065* 0.097 0.075* 0.113 0.033** Big4_lag + 0.347 <.001*** 0.357 <.001*** 0.339 <.001*** Firm_Size_lag + 0.140 0.003*** 0.129 0.004*** 0.142 0.002*** Market Share_lag + 0.176 <.001*** 0.149 0.014** 0.174 <.001*** ClientImportance_lag - 1.404 <.001*** 1.315 <.001*** 1.412 <.001***

Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 50.18% 49.37% 50.20% Number of observations 43,930 43,930 43,930 Degrees of freedom 42 42 44 p for Model <.001*** <.001*** <.001***

Note: Table 8.1 presents the impact of the lagged variables - SEC comment letter and short interest positions on the audit fees for subsequent audit engagements. All variables are defined in the Appendix A. All continuous variables are winsorized at 1 & 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the company level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, panel OLS regressions were run with the firm fixed effects and the results remained the same. Column (1) shows the panel OLS regression results using equation (27) in the study. Column (2) shows the panel OLS regression results using equation (28) in the study. Column (3) shows the panel OLS regression results using equation (29) in the study.

215

Table 8.2 The Impact of lagged SEC Comment Letter and Short Interest Positions on the Likelihood of Subsequent Auditor Change.

Dependent variable: Auditor Change Equation (30) Equation (31) Equation (32)

Variable Pred. Coeff. p value Coeff. p value Coeff. p value Intercept -4.235 <.001*** -4.305 <.001*** -4.320 <.001*** SEC Comment Letter (SEC_CL_lag) + 0.057 0.036** 0.144 0.011** Rank of Short Interest Ratio (Rank_SIR_lag) + 0.167 <.001*** 0.193 <.001*** Interaction (SEC_CL * Rank_SIR_lag) + -0.168 0.077 Assets_lag ? -0.047 <.001*** -0.052 <.001*** -0.053 <.001*** Audit Delays_lag + 0.001 0.005*** 0.001 0.004*** 0.001 0.003*** Weaknesses_lag + 0.222 <.001*** 0.262 <.001*** 0.264 <.001*** ROA_lag - 0.045 0.033** 0.048 0.023** 0.049 0.020** Leverage_lag + -0.065 0.033** -0.053 0.083* -0.051 0.097* Loss_lag + 0.122 <.001*** 0.118 <.001*** 0.118 <.001*** Going Concern_lag + 0.055 0.106 0.063 0.067* 0.063 0.064* Growth_lag ? -0.205 <.001*** -0.189 <.001*** -0.190 <.001*** December YE_lag - -0.011 0.605 -0.012 0.598 -0.012 0.572 Short Tenure_lag + -0.031 0.322 -0.034 0.288 -0.033 0.306 Receivables and Inventory_lag + 0.139 0.013** 0.146 0.010** 0.145 0.010** Acquisitions_lag + 0.067 0.031** 0.065 0.038** 0.065 0.037** Big4_lag - -0.340 <.001*** -0.348 <.001*** -0.346 <.001*** City Expert_lag - -0.028 0.182 -0.023 0.271 -0.024 0.258 National Expert_lag - -0.179 0.023** -0.181 0.022** -0.181 0.023** Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 12.53% 12.60% 12.64% Number of observations 43,512 43,512 43,512 Degrees of freedom 41 41 43 p for Model <.001*** <.001*** <.001***

Note: Table 8.2. presents the impact of the lagged SEC comment letter and short interest positions on the probability of auditor change for subsequent audit engagements. All variables are defined in the Appendix A. All continuous variables are winsorized at 1 & 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the company level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, logistics regressions were run with the firm fixed effects and the results remained the same. Column (1) shows the logistics regression results using equation (30) in the study. Column (2) shows the logistics regression results using equation (31) in the study. Column (3) shows the logistics regression results using equation (32) in the study.

216

Table 8.3 The Impact of the lagged SEC Comment Letter and Short Interest Positions on Performance-Matched Absolute Discretionary Accruals.

Dependent variable: Performance Matched Discretionary Accruals Equation (33) Equation (34) Equation (35) Variable Pred. Coeff. p value Coeff. p value Coeff. p value Intercept 0.664 0.066* 0.723 0.049** 0.331 0.044** SEC Comment Letter (SEC_CL) - -0.066 0.238 0.328 0.305 Rank of Short Interest Ratio (Rank_SIR) - -0.012 0.298 0.077 0.185 Interaction (SEC_CL*Rank_SIR) - -0.093 0.153 Market Value + -0.079 <.001*** -0.080 <.001*** -0.080 <.001*** ROA - -0.063 0.493 -0.063 0.496 -0.064 0.490 Leverage + -0.215 0.032** -0.215 0.033** -0.214 0.034** Current Ratio + 0.007 0.363 0.008 0.360 0.007 0.374 Operating Cash Flows + 0.060 0.683 0.060 0.682 0.066 0.656 Operating Cash Flows (Std. Dev) + -0.686 <.001*** -0.682 <.001*** -0.682 <.001*** Loss + -0.134 0.061* -0.134 0.061* -0.135 0.060* Market to Book + 0.003 0.216 0.003 0.220 0.003 0.240 Litigation + -0.731 0.076* -0.734 0.074* -0.722 0.082* Zscore_rank + 0.015 0.153 0.015 0.145 0.015 0.146 Total Accruals (lag) ? -0.090 0.456 -0.090 0.457 -0.091 0.446 Weaknesses + 0.038 0.672 0.035 0.693 0.036 0.689 ST Tenure + -0.126 0.063* -0.126 0.062* -0.128 0.060* Firm Size - -0.064 0.041** -0.064 0.041** -0.064 0.040** Market Share + 0.150 0.153 0.149 0.155 0.147 0.160 Client Importance - -0.095 0.622 -0.096 0.619 -0.093 0.628 Big 4 - -0.156 0.095* -0.155 0.098* -0.155 0.098*

Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 5.02% 5.00% 5.01% Number of observations 39,612 39,612 39,612 Degrees of freedom 42 42 44 p for Model <.001*** <.001*** <.001***

Note: Table 8.3 presents the impact of the lagged SEC comment letter and short interest positions on the Performance-Matched Discretionary Accruals for subsequent audit engagements. All variables are defined in the Appendix A. All continuous variables are winsorized at 1 & 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the company level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, panel OLS regressions were run with the firm fixed effects and the results remained the same. Column (1) shows the panel OLS regression results using equation (33) in the study. Column (2) shows the panel OLS regression results using equation (34) in the study. Column (3) shows the panel OLS regression results using equation (35) in the study.

217

Table 8.4 The Impact of the lagged SEC Comment Letter and Short Interest Positions on the Likelihood of Subsequent Restatements.

Dependent variable: Restatements Equation (36) Equation (37) Equation (38) Variable Pred. Coeff. p value Coeff. p value Coeff. p value Intercept -1.266 <.001*** -1.314 <.001*** -1.303 <.001*** SEC Comment Letter (SEC_CL_lag) - 0.059 0.013** 0.015 0.788 Rank of Short Interest Ratio (Rank_SIR_lag) - 0.092 0.042** 0.076 0.129 Interaction (SEC_CL*Rank_SIR_lag) - 0.073 0.397 Assets_lag - -0.022 0.061* -0.021 0.077* -0.022 0.053* ROA_lag - 0.027 0.332 0.024 0.395 0.025 0.368 Leverage_lag + -0.067 0.085* -0.061 0.118 -0.062 0.110 Market to Book_lag + -0.001 0.372 -0.001 0.368 -0.001 0.349 Segments_lag + -0.011 0.032** -0.011 0.038** -0.011 0.033** Financing_lag + -0.001 0.988 -0.004 0.911 -0.003 0.927 Foreign Transactions_lag + -0.083 0.380 -0.085 0.367 -0.087 0.360 Acquisitions_lag + 0.042 0.159 0.041 0.167 0.041 0.165 Weaknesses_lag + 0.042 0.138 0.061 0.043** 0.062 0.039** Receivables and Inventory_lag + -0.029 0.637 -0.019 0.765 -0.020 0.745 Audit Fees_lag + 0.039 0.033** 0.037 0.042** 0.037 0.041** Client Importance_lag - -0.059 0.256 -0.054 0.303 -0.056 0.283 Big 4_lag - 0.018 0.556 0.008 0.788 0.009 0.771

Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 4.78% 4.78% 4.82% Number of observations 40,326 40,326 40,326 Degrees of freedom 38 38 40 p for Model <.001*** <.001*** <.001***

Note: Table 8.4 presents the impact of the SEC comment letter and short interest positions on the probability of restatements for subsequent audit engagement. All variables are defined in the Appendix A. All continuous variables are winsorized at 1 & 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the company level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, logistics regressions were run with the firm fixed effects and the results remained the same. Column (1) shows the logistics regression results using equation (36) in the study. Column (2) shows the logistics regression results using equation (37) in the study. Column (3) shows the logistics regression results using equation (38) in the study.

218

Table 8.5 The Impact of the lagged SEC Comment Letter and Short Interest Positions on the Likelihood of Subsequent Material Weaknesses.

Dependent variable: Material Weaknesses Equation (39) Equation (40) Equation (41) Variable Pred. Coeff. p value Coeff. p value Coeff. p value

Intercept 4.946 <.001*** 5.361 <.001*** 5.396 <.001*** SEC Comment Letter (SEC_CL_lag) - -0.235 <.001*** -0.434 <.001*** Rank of Short Interest Ratio (Rank_SIR_lag) - -1.832 <.001*** -1.907 <.001*** Interaction (SEC_CL*Rank_SIR_lag) - 0.449 <.001*** Assets_lag - -0.363 <.001*** -0.281 <.001*** -0.279 <.001*** Firm Age_lag - 0.002 0.004*** -0.001 0.079 -0.001 0.117 Loss_lag + 0.207 <.001*** 0.282 <.001*** 0.281 <.001*** Zscore_rank_lag + -0.031 <.001*** -0.019 <.001*** -0.018 <.001*** Segments_lag + -0.066 <.001*** -0.058 <.001*** -0.056 <.001*** Foreign Transactions_lag + -0.127 0.189 -0.054 0.583 -0.046 0.645 Acquisitions_lag + 0.018 0.523 0.032 0.265 0.033 0.249 Growth_lag + 0.646 <.001*** 0.448 <.001*** 0.447 <.001*** Restructuring_lag + -2.429 <.001*** -1.813 <.001*** -1.817 <.001*** Big 4_lag - -1.128 <.001*** -0.903 <.001*** -0.895 <.001***

Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 54.66% 60.85% 61.08% Number of observations 35,643 35,643 35,643 Degrees of freedom 35 35 37 p for Model <.001*** <.001*** <.001***

Note: Table 8.5 presents the impact of the SEC comment letter and short interest position on the probability of material weaknesses for subsequent audit engagement. All variables are defined in the Appendix A. All continuous variables are winsorized at 1 & 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the company level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, logisitics regressions were run with the firm fixed effects and the results remained the same. Column (1) shows the logistics regression results using equation (39) in the study. Column (2) shows the logistics regression results using equation (40) in the study. Column (3) shows the logistics regression results using equation (41) in the study.

219

Table 8.6.1 The Impact of the lagged SEC Comment Letter and Short Interest Positions on the Likelihood of subsequent PCAOB Inspections GAAP Deficiencies.

Dependent variable: GAAP Deficiencies Equation (42) Equation (43) Equation (44) Variable Pred. Coeff. p value Coeff. p value Coeff. p value

Intercept -3.382 <.001*** -3.999 <.001*** -3.985 <.001*** SEC Comment Letter (SEC_CL_lag) - -0.010 0.692 -0.174 0.016** Rank of Short Interest Rank (Rank_SIR_lag)- -0.108 0.012** -0.156 0.001*** Interaction (SEC_CL*Rank_SIR_lag) - 0.230 0.011** Offices Inspected_lag + 0.535 <.001*** 2.518 <.001*** 2.519 <.001*** Partners_lag - -0.001 <.001*** 0.008 <.001*** 0.007 <.001*** Public_Clients_lag ? -0.163 <.001*** 0.280 <.001*** 0.279 <.001*** Audit Fees_lag ? 0.008 0.440 0.008 0.393 0.010 0.330 Average Client Size_lag + 0.282 <.001*** 0.313 <.001*** 0.313 <.001*** Foreign_lag + -0.455 <.001*** -1.352 <.001*** -1.355 <.001*** Stock_Exchange_lag - -0.082 0.002*** -0.053 0.065* -0.049 0.089* Big 4_lag - -0.580 <.001*** -0.769 <.001*** -0.770 <.001***

Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 42.43% 44.32% 44.34% Number of observations 43,358 43,358 43,358 Degrees of freedom 32 32 34 p for Model <.001*** <.001*** <.001***

Note: Table 8.6.1 presents the impact of the SEC comment letter and short interest position on the probability of PCAOB Inspection GAAP Deficiencies for subsequent audit engagement. All variables are defined in the Appendix A. All continuous variables are winsorized at 1 &99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the audit firm level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, logistics regressions were run with the firm fixed effects and the results remained the same. Column (1) shows the logistics regression results using equation (42) in the study. Column (2) shows the logistics regression results using equation (43) in the study. Column (3) shows the logistics regression results using equation (44) in the study.

220

Table 8.6.2 The Impact of the lagged SEC Comment Letter and Short Interest Positions on the Likelihood of subsequent PCAOB Inspections GAAS Deficiencies.

Dependent variable: GAAS Deficiencies Equation (42) Equation (43) Equation (44) Variable Pred. Coeff. p value Coeff. p value Coeff. p value

Intercept -1.828 <.001*** -1.754 <.001*** -1.744 <.001*** SEC Comment Letter (SEC_CL_lag) - 0.035 0.066* 0.094 0.097* Rank of Short Interest Rank (Rank_SIR_lag)- 0.093 0.009*** 0.108 0.005*** Interaction (SEC_CL*Rank_SIR_lag) - -0.083 0.255 Offices Inspected_lag + -0.087 0.001*** -0.394 <.001*** -0.393 <.001*** Partners_lag - -0.046 <.001*** -0.048 <.001*** -0.048 <.001*** Public_Clients_lag ? 0.285 <.001*** 0.216 <.001*** 0.216 <.001*** Audit Fees_lag ? 0.013 0.104 0.012 0.137 0.010 0.212 Average Client Size_lag + -0.026 0.059* -0.032 0.020** -0.032 0.021** Foreign_lag + 0.248 <.001*** 0.392 <.001*** 0.394 <.001*** Stock_Exchange_lag - 0.054 0.016** 0.032 0.190 0.027 0.260 Big 4_lag - 0.110 0.002*** 0.132 <.001*** 0.131 <.001***

Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 44.96% 45.00% 45.02% Number of observations 43,358 43,358 43,358 Degrees of freedom 32 32 34 p for Model <.001*** <.001*** <.001***

Note: Table 8.6.2 presents the impact of the SEC comment letter and short interest position on the probability of PCAOB Inspection GAAS Deficiencies for subsequent audit engagement. All variables are defined in the Appendix A. All continuous variables are winsorized at 1 &99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the audit firm level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, logistics regressions were run with the firm fixed effects and the results remained the same. Column (1) shows the logistics regression results using equation (42) in the study. Column (2) shows the logistics regression results using equation (43) in the study. Column (3) shows the logistics regression results using equation (44) in the study.

221

Table 9 Panel A Factor Analysis of Audit Quality Measures.

Factor Analysis of Audit Quality Measures

Variables Estimated Factor Loadings Factor 1 Factor 2 Audit Fees 0.895 -0.017 Change Auditor -0.055 -0.009 Discretionary Accruals 0.030 0.480 Restatements 0.014 0.018 Weaknesses -0.895 0.008 Def_GAAP 0.002 -0.849 Def_GAAS 0.031 0.848

Eigenvalues 1.607 1.44 Cummuative proportion of 0.23 0.44 total sample variance

222

Table 9.1 The Impact of SEC Comment Letter and Short Interest Positions on Subsequent Audit Quality measured by Factor 1 and Factor 2 employing model (5) independent variables. Dependent variable: FACTOR 1 FACTOR 2 Pred. Coeff. p value Coeff. p value

Intercept -1.091 <.001*** 0.620 <.001*** SEC Comment Letter (SEC_CL) + 0.025 0.025** -0.009 0.728 Rank of Short Interest Ratio (Rank_SIR) + 0.031 0.008*** -0.019 0.449 Interaction (SEC_CL * Rank_SIR) + -0.033 0.036** -0.042 0.301 Assets + 0.174 <.001*** 0.019 <.001*** Segments + 0.016 <.001*** -0.002 0.442 CATA + 0.152 <.001*** 0.011 0.643 Quick Ratio - 0.000 <.001*** 0.000 0.280 Leverage + 0.015 <.001*** -0.003 0.546 ROI - -0.039 <.001*** -0.007 0.215 Loss + 0.052 <.001*** -0.022 0.048 Going Concern + 0.018 0.070* -0.020 0.197 Foreign Transactions + 0.040 0.101 -0.011 0.818 December YE + 0.021 0.001*** -0.011 0.322 Material Weakness + -0.974 <.001*** -0.074 <.001*** Short Tenure + -0.106 <.001*** 0.065 <.001*** Big 4 + 0.301 <.001*** -0.001 0.958 Firm_Size + -0.001 0.749 -0.021 <.001*** Market Share + -0.006 0.191 -0.007 0.495 Client Importance - -0.187 <.001*** -0.046 0.035

Year Fixed Effects Yes Yes Industry Fixed Effects Yes Yes R2 96.19% 3.72% Number of observations 29,254 29,254 Degrees of freedom 44 44 p for Model <.001*** <.001***

Note: Table 9.1 presents the results of estimating the impact of SEC comment letters and short interest trading on the demand of audit quality measured by the two factor scores (Factor 1 and Factor 2). The Varimax rotation on the six audit quality proxies was performed and five variables are loaded into each of the Factors. The table presents the regression results from estimating equation (5). The coefficient estimates are based on year and industry standard errors. The *, **, and *** denote significance at levels of 0.1, 0.05, and 0.01 using two-tailed test, respectively.

223

Table 9.2 The Impact of SEC Comment Letter and Short Interest Positions on Subsequent Audit Quality measured by Factor 1 and Factor 2 employing model (9) independent variables

Dependent variable FACTOR 1 FACTOR 2 Pred. Coeff. p value Coeff. p value

Intercept -1.271 <.001*** 0.016 0.803 SEC Comment Letter (SEC_CL) + 0.014 0.093* 0.020 0.455 Rank of Short Interest Ratio (Rank_SIR) + 0.059 <.001*** -0.012 0.625 Interaction (SEC_CL * Rank_SIR) + -0.015 0.056* -0.049 0.239 Assets + 0.163 <.001*** 0.021 <.001*** Audit Delays + 0.000 0.020** 0.000 0.322 Material Weakness + -0.982 <.001*** -0.098 <.001*** ROA - -0.016 <.001*** -0.006 0.044** Leverage + 0.011 0.002*** -0.003 0.454 Loss + 0.065 <.001*** -0.021 0.049** Going Concern + 0.002 0.024** -0.018 0.239 Growth ? 0.052 <.001*** -0.025 0.126 December YE - 0.024 0.085* -0.012 0.243 Short Tenure + -0.108 <.001*** 0.064 <.001*** Receivables and Inventory + 0.160 0.293 0.061 0.023** Acquisitions + 0.027 0.655 -0.006 0.642 Big4 - 0.342 <.001*** -0.009 0.490 City Expert - -0.055 <.001*** -0.013 0.176 National Expert - -0.017 0.057* -0.008 0.778

Year Fixed Effects Yes Yes Industry Fixed Effects Yes Yes R2 95.22% 1.54% Number of observations 29,254 29,254 Degrees of freedom 43 43 p for Model <.001*** <.001***

Note: Table 9.2 presents the results of estimating the impact of SEC comment letters and short interest trading on the demand of audit quality measured by the two factor scores (Factor 1 and Factor 2). The Varimax rotation on the six audit quality proxies was performed and five variables are loaded into each of the Factors. The table presents the regression results from estimating equation (9). The coefficient estimates are based on year and industry standard errors. The *, **, and *** denote significance at levels of 0.1, 0.05, and 0.01 using two-tailed test, respectively.

224

Table 9.3 The Impact of SEC Comment Letter and Short Interest Positions on Subsequent Audit Quality measured by Factor 1 and Factor 2 employing model (14) independent variables. Dependent Variable: FACTOR 1 FACTOR 2 Pred. Coeff. p value Coeff. p value

Intercept -0.197 <.001*** 0.089 0.629 SEC Comment Letter (SEC_CL_lag) - 0.139 <.001*** -0.035 0.366 Rank of Short Interest Ratio (Rank_SIR_lag) - 0.171 <.001*** 0.025 0.491 Interaction (SEC_CL_lag * Rank_SIR_lag) - -0.088 <.001*** 0.006 0.918 Market Value + 0.002 0.447 -0.012 0.001*** ROA - 0.021 0.001 -0.103 <.001*** Leverage + 0.130 <.001*** 0.020 0.427 Current Ratio + 0.001 0.445 0.005 0.006*** Operating Cash Flows + 0.008 0.474 0.039 0.104 Operating Cash Flows (Std. Dev) + -0.030 0.226 0.068 0.228 Loss + -0.094 <.001*** 0.013 0.427 Market to Book + 0.000 0.892 0.000 0.985 Litigation + 0.011 0.569 0.140 <.001*** Zscore_rank + 0.006 <.001*** -0.001 0.655 Total Accruals (lag) ? 0.023 0.090* -0.019 0.534 Weaknesses404 + -1.445 <.001*** 0.004 0.858 ST Tenure + -0.016 0.030** -0.025 0.149 Firm Size - -0.002 0.705 0.007 0.431 Market Share + 0.024 0.051* 0.014 0.548 Client Importance - -0.188 <.001*** 0.033 0.432 Big 4 - 0.525 <.001*** 0.009 0.685

Year Fixed Effects Yes Yes Industry Fixed Effects Yes Yes R2 87.50% 5.34% Number of observations 29,254 29,254 Degrees of freedom 44 44 p for Model <.001*** <.001***

Note: Table 9.3 presents the results of estimating the impact of SEC comment letters and short interest trading on the demand of audit quality measured by the two factor scores (Factor 1 and Factor 2). The Varimax rotation on the six audit quality proxies was performed and five variables are loaded into each of the Factors. The table presents the regression results from estimating equation (14). The coefficient estimates are based on year and industry standard errors. The *, **, and *** denote significance at levels of 0.1, 0.05, and 0.01 using two-tailed test, respectively.

225

Table 9.4 The Impact of SEC Comment Letter and Short Interest Positions on Subsequent Audit Quality measured by Factor 1 and Factor 2 employing model (18) independent variables.

Dependent variable: Restatements FACTOR 1 FACTOR 2 Variable Pred. Coeff. p value Coeff. p value Intercept -4.771 <.001*** -0.479 0.013** SEC Comment Letter (SEC_CL) - 0.001 0.093* -0.019 0.190 Rank of Short Interest Ratio (Rank_SIR) - -0.001 0.412 0.027 0.339 Interaction (SEC_CL*Rank_SIR) - 0.000 0.846 -0.050 0.017** Assets - 0.000 0.653 0.000 0.961 ROA - 0.004 <.001*** -0.097 <.001*** Leverage + -0.001 0.345 0.015 0.474 Market to Book + 0.000 0.160 0.000 0.613 Segments + 0.000 0.623 0.002 0.469 Financing + -0.002 0.005*** 0.032 0.067* Foreign Transactions + 0.003 0.159 0.139 0.018** Acquisitions + 0.002 <.001*** -0.022 0.140 Weaknesses + -1.142 <.001*** 0.026 0.143 Receivables and Inventory + -0.004 0.002*** 0.173 <.001*** Audit Fees + 0.385 <.001*** 0.019 0.079* Client Importance - 0.002 0.146 0.031 0.308 Big 4 - 0.001 0.066* -0.025 0.178 Year Fixed Effects Yes Yes Industry Fixed Effects Yes Yes R2 99.87% 4.51% Number of observations 29,254 29,254 Degrees of freedom 40 40 p for Model <.001*** <.001***

Note: Table 9.4 presents the results of estimating the impact of SEC comment letters and short interest trading on the demand of audit quality measured by the two factor scores (Factor 1 and Factor 2). The Varimax rotation on the six audit quality proxies was performed and five variables are loaded into each of the Factors. The table presents the regression results from estimating equation (18). The coefficient estimates are based on year and industry standard errors. The *, **, and *** denote significance at levels of 0.1, 0.05, and 0.01 using two-tailed test, respectively.

226

Table 9.5 The Impact of SEC Comment Letter and Short Interest Positions on Subsequent Audit Quality measured by Factor 1 and Factor 2 employing model (22) independent variables. Dependent variable: Material Weaknesses FACTOR 1 FACTOR 2 Variable Pred. Coeff. p value Coeff. p value

Intercept -2.663 <.001*** -0.075 <.001*** SEC Comment Letter (SEC_CL) - 0.106 <.001*** -0.052 0.178 Rank of Short Interest Ratio (Rank_SIR) - 0.854 <.001*** -0.001 0.968 Interaction (SEC_CL*Rank_SIR) - -0.125 <.001*** 0.040 0.497 Assets - 0.267 <.001*** 0.010 0.011** Firm Age - 0.001 <.001*** 0.000 0.607 Loss + -0.019 0.007*** 0.040 0.008*** Zscore_rank + 0.000 0.902 -0.001 0.525 Segments + 0.030 <.001*** 0.006 0.059* Foreign Transactions + 0.034 0.291 0.145 0.023** Acquisitions + 0.011 0.154 -0.007 0.690 Growth + 0.000 0.994 -0.167 <.001*** Restructuring + -0.041 0.618 0.065 0.719 Big 4 - 0.250 <.001*** -0.024 0.210

Year Fixed Effects Yes Yes Industry Fixed Effects Yes Yes R2 80.82% 6.96% Number of observations 29,254 29,254 Degrees of freedom 37 37 p for Model <.001*** <.001***

Note: Table 9.5 presents the results of estimating the impact of SEC comment letters and short interest trading on the demand of audit quality measured by the two factor scores (Factor 1 and Factor 2). The Varimax rotation on the six audit quality proxies was performed and five variables are loaded into each of the Factors. The table presents the regression results from estimating equation (22). The coefficient estimates are based on year and industry standard errors. The *, **, and *** denote significance at levels of 0.1, 0.05, and 0.01 using two-tailed test, respectively.

227

Table 9.6 The Impact of SEC Comment Letter and Short Interest Positions on Subsequent Audit Quality measured by Factor 1 and Factor 2 employing model (26) independent variables.

Dependent variable: PCAOB Deficiencies FACTOR 1 FACTOR 2 Variable Pred. Coeff. p value Coeff. p value

Intercept -7.619 <.001*** -0.148 0.346 SEC Comment Letter (SEC_CL) - 0.092 <.001*** -0.042 0.238 Rank of Short Interest Ratio (Rank_SIR) - 0.820 <.001*** -0.003 0.899 Interaction (SEC_CL*Rank_SIR) - -0.099 <.001*** 0.014 0.802 Offices Inspected + -0.002 0.904 0.021 0.652 Partners - 0.000 0.187 0.001 0.148 Public_Clients ? 0.003 0.212 -0.014 0.043** Audit Fees ? 0.510 <.001*** 0.015 0.013** Average Client Size + -0.002 0.605 0.009 0.216 Foreign_D + -0.011 0.285 0.002 0.937 Stock_Exchange - 0.015 0.035** 0.005 0.781 Big 4 - 0.139 <.001*** -0.035 0.053*

Year Fixed Effects Yes Yes Industry Fixed Effects Yes Yes R2 85.01% 1.33% Number of observations 29,254 29,254 Degrees of freedom 34 34 p for Model <.001*** <.001***

Note: Table 9.6 presents the results of estimating the impact of SEC comment letters and short interest trading on the demand of audit quality measured by the two factor scores (Factor 1 and Factor 2). The Varimax rotation on the six audit quality proxies was performed and five variables are loaded into each of the Factors. The table presents the regression results from estimating equation (26). The coefficient estimates are based on year and industry standard errors. The *, **, and *** denote significance at levels of 0.1, 0.05, and 0.01 using two-tailed test, respectively.

228

Table 10.1 The Impact of Short Interest Positions on the Subsequent Audit Fees during different time periods.

Dependent variable: Audit Fees Time Period 2005-2007 2008-2010 2011-2015 Variable Pred. Coeff. p value Coeff. p value Coeff. p value

Intercept 9.105 <.001*** 9.051 <.001*** 8.746 <.001*** Rank of Short Interest Ratio (Rank_SIR) + 0.317 <.001*** 0.183 0.002*** -0.107 0.009*** Assets + 0.568 <.001*** 0.493 <.001*** 0.512 <.001*** Segments + 0.050 <.001*** 0.062 <.001*** 0.049 <.001*** CATA + 0.799 <.001*** 0.636 <.001*** 0.730 <.001*** Quick Ratio - -0.049 <.001*** -0.053 <.001*** -0.015 0.062* Leverage + 0.168 0.014** -0.162 0.004*** 0.253 <.001*** ROI - -0.216 <.001*** 0.065 0.229 -0.323 <.001*** Loss + 0.249 <.001*** 0.214 <.001*** 0.103 <.001*** Going Concern + 0.317 0.006*** 0.215 <.001*** -0.001 0.983 Foreign Transactions + 0.001 0.990 0.060 0.418 0.210 0.022** December YE + 0.032 0.351 -0.033 0.419 -0.018 0.593 Weaknesses + 0.056 0.180 -0.090 0.014** 0.003 0.922 Short Tenure + 0.200 0.051* 0.079 0.016** -0.039 0.043** Big 4 + 0.237 <.001*** 0.350 <.001*** 0.421 <.001*** Firm_Size + -0.021 0.107 0.023 0.057* 0.021 0.178 Market Share + -0.007 0.746 0.122 0.003*** 0.038 0.126 Client Importance - -0.474 <.001*** 0.097 0.170 -0.075 0.302

Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 81.92% 91.77% 86.11% Number of observations 8,881 14,026 21,023 Degrees of freedom 34 34 36 F 405.66 1732.78 614.63 p for Model <.001*** <.001*** <.001***

Note: Table 10.1 presents the impact of the short interest positions on the audit fees for subsequent audit engagements . It presents the panel OLS regression results using equation (4) during three time periods. All variables are defined in the Appendix A. All continuous variables are windsorized at 1 & 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the company level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, panel OLS regressions were run with the firm fixed effects and the results remained the same.

229

Table 10.2 The Impact of Short Interest Positions on the Likelihood of Subsequent Auditor Change during different time periods.

Dependent variable: Auditor Change Time Period 2005-2007 2008-2010 2011-2015 Variable Pred. Coeff. p value Coeff. p value Coeff. p value -0.683 <.001*** -4.121 <.001*** -3.596 <.001*** Intercept

-0.196 0.271 0.486 <.001*** 0.449 <.001*** Rank of Short Interest Ratio (Rank_SIR) +

Assets ? -0.014 0.712 0.039 0.102 0.026 0.254 0.001 0.262 0.007 <.001*** 0.007 <.001*** Audit Delays +

Weaknesses + 0.058 0.579 0.017 0.792 -0.206 0.001*** ROA - 0.330 0.025** -0.032 0.628 -0.011 0.879 Leverage + 0.069 0.674 0.069 0.480 -0.006 0.956 Loss + 0.310 0.001*** 0.133 0.013** -0.111 0.046** Going Concern + 0.444 0.013** 0.085 0.381 0.081 0.414 Growth ? -0.136 0.330 0.044 0.657 -0.025 0.793 December YE - -0.124 0.169 0.099 0.085* 0.005 0.932 0.030 0.909 1.686 <.001*** 1.923 <.001*** Short Tenure +

Receivables and Inventory + -0.113 0.619 0.431 0.002*** 0.181 0.199 Acquisitions + -0.242 0.218 0.139 0.064* -0.029 0.587 -0.594 <.001*** -0.518 <.001*** -0.298 <.001*** Big4 -

City Expert - -0.067 0.390 0.006 0.897 0.010 0.839 National Expert - 0.169 0.503 -0.146 0.511 0.205 0.225

Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 13.06% 29.58% 28.58% Number of observations 8,285 11,715 23,512 Degrees of freedom 31 32 35 p for Model <.001*** <.001*** <.001***

Note: Table 10.2. presents the impact of short interest positions on the probability of auditor change for subsequent audit engagements. It shows the logistics regression results using equation (8) in the study. All variables are defined in the Appendix A. All continuous variables are windsorized at 1 & 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the company level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, logisitics regressions were run with the firm fixed effects and the results remained the same.

230

Table 10.3 The Impact of Short Interest Positins on Performance-Matched Absolute Discretionary Accruals during different time periods.

Dependent variable: Performance Matched Discretionary Accruals Time Period 2005-2007 2008-2010 2011-2015 Variable Pred. Coeff. p value Coeff. p value Coeff. p value

Intercept 4.585 0.027** 0.320 0.031** -0.635 0.012** Rank of Short Interest Ratio (Rank_SIR) - 0.107 0.472 -0.033 0.042** 0.234 0.056* Market Value + 0.003 0.905 -0.042 0.088* -0.031 0.069* ROA - 0.086 0.628 -0.031 0.849 -0.051 0.689 Leverage + 0.029 0.840 -0.033 0.760 -0.268 0.011** Current Ratio + -0.015 0.199 0.027 0.056* 0.000 0.975 Operating Cash Flows + 0.223 0.367 0.047 0.837 0.223 0.196 Operating Cash Flows (Std. Dev) + 0.415 0.511 -0.287 0.323 0.013 0.949 Loss + 0.044 0.599 -0.148 0.100 -0.105 0.096* Market to Book + 0.005 0.380 0.010 0.075 0.003 0.092* Litigation + 0.021 0.959 0.122 0.727 -0.581 0.300 Zscore_rank + -0.004 0.688 0.012 0.360 0.018 0.110 Total Accruals (lag) ? 0.287 0.445 0.214 0.229 -0.006 0.968 Weaknesses + 0.076 0.374 0.073 0.542 0.025 0.736 ST Tenure + -1.836 0.208 0.048 0.575 -0.051 0.421 Firm Size - 0.012 0.683 -0.081 0.056* 0.033 0.194 Market Share + -0.106 0.282 -0.036 0.753 -0.089 0.289 Client Importance - 0.168 0.434 -0.407 0.070* -0.049 0.741 Big 4 - 0.023 0.792 0.031 0.759 -0.103 0.171

Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 3.49% 3.41% 4.63% Number of observations 9,117 13,942 16,553 Degrees of freedom 34 35 38 p for Model <.001*** <.001*** <.001***

Note: Table 10.3 presents the impact of the SEC comment letter and short interest positions on the Performance-Matched Discretionary Accruals for subsequent audit engagements. It shows the panel OLS regression results using equation (13) in the study. All variables are defined in the Appendix A. All continuous variables are windsorized at 1 & 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the company level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, panel OLS regressions were run with the firm fixed effects and the results remained the same.

231

Table 10.4 The Impact of Short Interest Positions on the Likelihood of Subsequent Restatements during different time periods.

Dependent variable: Restatements Time periods

Variable Pred. Coeff. p value Coeff. p value Coeff. p value Intercept -2.415 <.001*** -2.395 <.001*** -2.404 <.001*** Rank of Short Interest Ratio (Rank_SIR) - 0.019 0.843 0.232 0.005*** 0.068 0.399 Assets - -0.090 <.001*** -0.041 0.048** -0.098 <.001*** ROA - 0.076 0.372 0.164 0.007*** 0.034 0.644 Leverage + 0.162 0.096* 0.076 0.312 -0.011 0.904 Market to Book + -0.002 0.517 0.003 0.291 -0.002 0.331 Segments + -0.011 0.318 0.000 0.968 -0.019 0.034** Financing + -0.096 0.199 0.072 0.245 -0.054 0.414 Foreign Transactions + -0.023 0.922 -0.091 0.609 0.026 0.873 Acquisitions + 0.050 0.579 -0.082 0.133 0.037 0.352 Weaknesses + 0.103 0.052* 0.125 0.014** 0.046 0.362 Receivables and Inventory + -0.091 0.495 -0.055 0.645 0.162 0.199 Audit Fees + 0.161 <.001*** 0.061 0.065* 0.153 <.001*** Client Importance - -0.055 0.663 0.029 0.771 -0.074 0.509 Big 4 - -0.138 0.024** -0.077 0.131 -0.035 0.530

Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 3.66% 3.67% 3.94% Number of observations 9,168 13,921 17,237 Degrees of freedom 30 31 34 p for Model <.001*** <.001*** <.001***

Note: Table 10.4 presents the impact of the short interest positions on the probability of restatements for subsequent audit engagement. It shows the logistics regression results using equation (17) in the study. All variables are defined in the Appendix A. All continuous variables are windsorized at 1 & 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the company level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, logisitics regressions were run with the firm fixed effects and the results remained the same.

232

Table 10.5 The Impact of Short Interest Positions on the Likelihood of Subsequent Material Weaknesses during different time periods.

Dependent variable: Time Periods Material Weaknesses 2005-2007 2008-2010 2011-2015 Variable Pred. Coeff. p value Coeff. p value Coeff. p value

Intercept 3.063 <.001*** 2.485 <.001*** 5.055 <.001*** Rank of Short Interest Ratio (Rank_SIR) - -1.432 <.001*** -1.879 <.001*** -1.442 <.001*** Assets - -0.291 <.001*** -0.333 <.001*** -0.316 <.001*** Firm Age - 0.003 0.051* 0.003 0.018** -0.009 <.001*** Loss + 0.212 <.001*** 0.257 <.001*** 0.330 <.001*** Zscore_rank + -0.029 <.001*** -0.011 0.097* -0.012 0.027** Segments + 0.000 0.988 -0.053 <.001*** -0.046 <.001*** Foreign Transactions + 0.097 0.696 -0.150 0.378 -0.103 0.463 Acquisitions + 0.196 0.031** 0.073 0.208 0.095 0.007*** Growth + 0.265 <.001*** 0.438 <.001*** 0.298 <.001*** Restructuring + -5.052 0.005*** -0.852 0.025** -2.551 0.018** Big 4 - -0.861 <.001*** -0.828 <.001*** -0.688 <.001***

Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 48.23% 56.36% 54.86% Number of observations 7,780 11,149 16,714 Degrees of freedom 27 28 30 p for Model <.001*** <.001*** <.001***

Note: Table 10.5 presents the impact of the short interest position on the probability of material weaknesses for subsequent audit engagement during different time periods. It shows the logistics regression results using equation (21) in the study. All variables are defined in the Appendix A. All continuous variables are windsorized at 1 & 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the company level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, logisitics regressions were run with the firm fixed effects and the results remained the same.

233

Table 10.6.1 The Impact of Short Interest Positions on the Likelihood of Subsequent PCAOB Inspections GAAP Deficiencies during different time periods.

Dependent variable: Time Periods GAAP Deficiencies 2005-2007 2008-2010 2011-2013 Variable Pred. Coeff. p value Coeff. p value Coeff. p value

0.524 <.001*** -3.935 <.001*** -10.661 <.001** Intercept * Rank of Short Interest Ratio (Rank_SIR) - -0.105 0.645 -0.230 0.003*** -0.187 0.035** Offices Inspected -1.509 0.018** -0.701 <.001*** 2.713 <.001** + * Partners -0.024 0.119 0.002 0.074* -0.031 <.001** - * Public_Clients 0.993 <.001*** 0.329 <.001*** -0.732 <.001** ? * Audit Fees ? -0.107 0.046** 0.005 0.776 -0.024 0.210 Average Client Size -1.030 <.001*** 0.235 <.001*** 0.999 <.001** + * Foreign_D 3.178 <.001*** 1.964 <.001*** 1.223 <.001** + * Stock_Exchange 0.148 0.373 -0.076 0.139 0.307 <.001** - * Big 4 -1.177 <.001*** -1.178 <.001*** -0.228 0.073* -

Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 71.19% 42.10% 45.31% Number of observations 9,211 13,630 20,517 Degrees of freedom 24 25 27 p for Model <.001*** <.001*** <.001***

Note: Table 10.6.1 presents the impact of the short interest position on the probability of PCAOB Inspection GAAP deficiencies for subsequent audit engagement. It shows the logistics regression results using equation (25) in the study. All variables are defined in the Appendix A. All continuous variables are windsorized at 1 &99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the audit firm level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, logisitics regressions were run with the firm fixed effects and the results remained the same.

234

Table 10.6.2 The Impact of Short Interest Positions on the Likelihood of Subsequent PCAOB Inspections GAAS Deficiencies during different time periods.

Dependent variable: Time Periods GAAS Deficiencies 2005-2007 2008-2010 2011-2013 Variable Pred. Coeff. p value Coeff. p value Coeff. p value

Intercept -10.057 <.001*** -2.198 <.001*** -0.753 <.001*** Rank of Short Interest Ratio (Rank_SIR) - -0.182 0.400 0.170 0.027** -0.158 0.054** Offices Inspected + 3.502 <.001*** 1.390 <.001*** 2.060 <.001*** Partners - 0.075 <.001*** 0.005 <.001*** 0.182 <.001*** Public_Clients ? -0.664 <.001*** 0.006 0.838 1.211 <.001*** Audit Fees ? 0.106 0.036** -0.017 0.310 -0.010 0.593 Average Client Size + 0.978 <.001*** -0.258 <.001*** -1.094 <.001*** Foreign_D + 3.228 <.001*** 2.443 <.001*** 2.597 <.001*** Stock_Exchange - 0.356 0.018** 0.366 <.001*** 0.355 <.001*** Big 4 - 1.217 <.001*** 0.805 <.001*** 0.238 0.044**

Year Fixed Effects Yes Yes Yes Industry Fixed Effects Yes Yes Yes R2 Number of observations 9,211 13,630 20,517 Degrees of freedom 24 25 27 p for Model <.001*** <.001*** <.001***

Note: Table 10.6.2 presents the impact of the short interest position on the probability of PCAOB Inspection GAAS deficiencies for subsequent audit engagement. It shows the logistics regression results using equation (25) in the study. All variables are defined in the Appendix A. All continuous variables are windsorized at 1 &99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the audit firm level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, logisitics regressions were run with the firm fixed effects and the results remained the same.

235

Table 11 Breakdown of the SEC Comment Letters by Topic

Category: Percent %

Revenue Recognition 13% Fair Value 11% Goodwill 9% Inventory 5% Non-GAAP measure 6% Contingencies 9% Tax 7% Segment 8% Cash Flow 9% Material Weaknesses 6% Merger 5% Variable Interest Entity 5% Pension 3% Other 4%

236

Table 11.1 The Impact of Top Accounting Issue and Short Interest Positions on Subsequent Audit Fees.

Dependent variable: Audit Fees Equation (3) Equation (5) Equation (3) Equation (5) Revenue Recognition Topic Fair Value Topic Variable Pred. Coeff. p value Coeff. p value Coeff. p value Coeff. p value Intercept 9.775 <.001*** 9.654 <.001*** 9.784 <.001*** 9.662 <.001*** Revenue Recognition + 0.033 <.001*** 0.062 0.021** Fair Value 0.113 <.001*** 0.185 <.001*** Rank Short Interest Ratio + 0.183 <.001*** 0.187 <.001*** Interaction (Top_Issue* Rank_SIR)+ -0.055 0.154 -0.128 0.003*** Assets + 0.500 <.001*** 0.497 <.001*** 0.499 <.001*** 0.496 <.001*** Segments + 0.060 <.001*** 0.060 <.001*** 0.060 <.001*** 0.060 <.001*** CATA + 0.575 <.001*** 0.565 <.001*** 0.577 <.001*** 0.567 <.001*** Quick Ratio - -0.036 <.001*** -0.037 <.001*** -0.036 <.001*** -0.037 <.001*** Leverage + 0.040 0.029** 0.049 0.007*** 0.040 0.031** 0.049 0.008*** ROI - -0.207 <.001*** -0.207 <.001*** -0.206 <.001*** -0.206 <.001*** Loss + 0.166 <.001*** 0.159 <.001*** 0.164 <.001*** 0.157 <.001*** Going Concern + 0.043 0.018** 0.053 0.003*** 0.044 0.015** 0.055 0.003*** Foreign Transactions + 0.036 0.385 0.034 0.412 0.034 0.408 0.032 0.439 December YE + 0.020 0.157 0.019 0.181 0.019 0.172 0.018 0.201 Weaknesses + -0.110 <.001*** -0.066 <.001*** -0.114 <.001*** -0.070 <.001*** Short Tenure + 0.005 0.643 0.013 0.222 0.006 0.616 0.013 0.222 Big 4 + 0.384 <.001*** 0.374 <.001*** 0.383 <.001*** 0.372 <.001*** Firm_Size + 0.002 0.598 0.005 0.268 0.003 0.562 0.005 0.242 Market Share + 0.003 0.819 0.002 0.886 0.003 0.827 0.002 0.886 Client Importance - -0.224 <.001*** -0.210 <.001*** -0.223 <.001*** -0.208 <.001***

Year Fixed Effects Yes Yes Yes Yes Industry Fixed Effects Yes Yes Yes Yes R2 84.91% 85.01% 84.98% 85.05% Number of observations 43,930 43,930 43,930 43,930 Degrees of freedom 42 44 42 44 F 1253.51 1194 1254.61 1194.95 p for Model <.001*** <.001*** <.001*** <.001***

Note: Table 11.1 presents the impact of the SEC comment letter and short interest positions on the audit fees for subsequent audit engagements. All variables are defined in the Appendix A. All continuous variables are windsorized at 1 & 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the company level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, panel OLS regressions were run with the firm fixed effects and the results remained the same. Column (1) and (3) show the panel OLS regression results using equation (3) in the study. Column (2) and (4) show the panel OLS regression results using equation (5) in the study.

237

238

Table 11.3 The Impact of Top Accounting Issue and Short Interest Positions on Performance-Matched Absolute Discretionary

Dependent variable: Performance Matched Discretionary Accruals

Equation (12) Equation (14) Equation (12) Equation (14) Variable Pred. Coeff. p value Coeff. p value Coeff. p value Coeff. p value

Intercept -0.073 <.001*** 0.087 <.001*** -0.076 <.001*** 0.077 <.001*** Revenue Recognition - -0.063 0.340 -0.276 0.042** Fair Value - -0.069 0.367 -0.115 0.483 Rank SIR - -0.036 <.001*** -0.035 <.001*** Interaction (Top_Issue * Rank_SIR) + 0.507 0.030** 0.170 0.534 Market Value -0.064 <.001*** -0.061 <.001*** -0.064 <.001*** -0.060 0.001*** ROA - 0.024 0.698 0.023 0.704 0.024 0.696 0.025 0.691 Leverage + -0.234 0.002*** -0.232 0.002*** -0.234 0.002*** -0.231 0.002*** Current Ratio + 0.010 0.185 0.009 0.207 0.010 0.183 0.009 0.195 Operating Cash Flows + 0.202 0.065* 0.203 0.064* 0.201 0.067* 0.201 0.066* Operating Cash Flows (Std. Dev) + -0.024 0.900 -0.035 0.855 -0.031 0.873 -0.036 0.853 Loss + -0.095 0.106 -0.095 0.105 -0.094 0.110 -0.092 0.116 Market to Book + 0.003 0.196 0.003 0.220 0.003 0.201 0.003 0.214 Litigation + -0.745 0.042** -0.755 0.038** -0.744 0.042** -0.758 0.038** Zscore_rank + 0.014 0.147 0.015 0.118 0.013 0.157 0.015 0.120 Total Accruals (lag) ? -0.003 0.978 -0.003 0.978 -0.004 0.971 -0.004 0.972 Weaknesses + 0.124 0.090* 0.122 0.096* 0.126 0.085* 0.118 0.107 ST Tenure + -0.102 0.082* -0.116 0.048** -0.101 0.084* -0.115 0.050* Firm Size - -0.023 0.373 -0.023 0.370 -0.023 0.371 -0.023 0.363 Market Share + 0.147 0.104 0.146 0.105 0.146 0.105 0.145 0.107 Client Importance - -0.013 0.935 -0.010 0.946 -0.013 0.932 -0.011 0.944 Big 4 - -0.153 0.055* -0.156 0.050** -0.152 0.056* -0.153 0.054*

Year Fixed Effects Yes Yes Yes Yes Industry Fixed Effects Yes Yes Yes Yes R2 5.27% 5.30% 5.27% 5.29% Number of observations 39,612 39,612 39,612 39,612 Degrees of freedom 42 44 42 44 p for Model <.001*** <.001*** <.001*** <.001***

Note: Table 11.3 presents the impact of Top Accounting Issue and short interest positions on the Performance-Matched Discretionary Accruals for subsequent audit engagements. All variables are defined in the Appendix A. All continuous variables are winsorized at 1 & 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the company level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, panel OLS regressions were run with the firm fixed effects and the results remained the same. Column (1) and (3) show the panel OLS regression results using equation (12) in the study. Column (2) and (4) show the panel OLS regression results using equation (14) in the study.

239

Table 11.4 The Impact of Top Accounting Issue and Short Interest Positions on the Likelihood of Subsequent

Dependent variable: Restatements Equation (16) Equation (18) Equation (16) Equation (18)

Variable Pred. Coeff. p value Coeff. p value Coeff. p value Coeff. p value -5.749 <.001** -5.806 <.001** -5.742 <.001*** -5.793 <.001*** Intercept * * Revenue Recognition - 0.091 0.013** 0.072 0.415 Fair Value - 0.054 0.157 -0.044 0.646 Rank of Short Interest Ratio (Rank_SIR)- 0.110 0.018** 0.102 0.029** Interaction (Top_Issue*Rank_SIR) - 0.030 0.822 0.158 0.268 Assets -0.062 <.001** -0.063 <.001** -0.062 <.001*** -0.063 <.001*** - * * 0.099 <.001** 0.097 0.001** 0.100 <.001*** 0.097 0.001*** ROA - * * Leverage + -0.083 0.030** -0.076 0.046** -0.084 0.028** -0.077 0.043** Market to Book + -0.001 0.602 -0.001 0.543 -0.001 0.622 -0.001 0.559 Segments + -0.010 0.044** -0.010 0.049** -0.010 0.045** -0.010 0.049** Financing + 0.073 0.040** 0.068 0.056* 0.073 0.041** 0.068 0.057* Foreign Transactions + -0.051 0.602 -0.055 0.574 -0.053 0.592 -0.057 0.559 Acquisitions + -0.010 0.715 -0.011 0.690 -0.010 0.719 -0.011 0.689 Weaknesses + 0.043 0.145 0.069 0.028** 0.042 0.154 0.068 0.032** Receivables and Inventory + -0.054 0.398 -0.042 0.512 -0.052 0.413 -0.041 0.523 0.128 <.001** 0.127 <.001** 0.128 <.001*** 0.127 <.001*** Audit Fees + * * Client Importance - -0.047 0.372 -0.043 0.419 -0.048 0.368 -0.043 0.415 Big 4 - -0.031 0.319 -0.041 0.181 -0.030 0.323 -0.041 0.180

Year Fixed Effects Yes Yes Yes Yes Industry Fixed Effects Yes Yes Yes Yes R2 5.08% 5.12% 5.05% 5.10% Number of observations 40,326 40,326 40,326 40,326 Degrees of freedom 38 40 38 40 p for Model <.001*** <.001*** <.001*** <.001***

Note: Table 11.4 presents the impact of the top accounting issue and short interest positions on the probability of restatements for subsequent audit engagement. All variables are defined in the Appendix A. All continuous variables are winsorized at 1 & 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the company level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, logisitics regressions were run with the firm fixed effects and the results remained the same. Column (1) and (3) show the logistics regression results using equation (16) in the study. Column (2) and (4) show the logistics regression results using equation (18) in the study.

240

Table 11.5 The Impact of Top Accounting Issue and Short Interest Positions on the Likelihood of Subsequent Material Weaknesses.

Dependent variable: Material Weaknesses Equation (20) Equation (22) Equation (20) Equation (22) Variable Pred. Coeff. p value Coeff. p value Coeff. p value Coeff. p value

4.075 <.001*** 4.669 <.001** 4.070 <.001*** 4.662 <.001*** Intercept * -0.104 0.002 -0.231 0.004*** Revenue Recognition -

Fair Value -0.202 <.001*** -0.481 <.001*** -1.890 <.001** -1.893 <.001*** Rank of Short Interest Ratio - * 0.358 0.006*** 0.603 <.001*** Interaction (Top_Issue*Rank_SIR)-

-0.344 <.001*** -0.260 <.001** -0.344 <.001*** -0.260 <.001*** Assets - * Firm Age 0.000 0.945 -0.004 <.001** 0.000 0.903 -0.004 <.001*** - * Loss 0.241 <.001*** 0.322 <.001** 0.244 <.001*** 0.325 <.001*** + * Zscore_rank -0.027 <.001*** -0.014 <.001** -0.027 <.001*** -0.014 <.001*** + * Segments -0.069 <.001*** -0.058 <.001** -0.069 <.001*** -0.058 <.001*** + * Foreign Transactions + -0.232 0.008 -0.179 0.049** -0.228 0.009*** -0.176 0.053* Acquisitions + 0.022 0.356 0.053 0.038** 0.024 0.329 0.053 0.035** Growth 0.598 <.001*** 0.394 <.001** 0.599 <.001*** 0.395 <.001*** + * Restructuring -1.948 <.001*** -1.205 <.001** -1.943 <.001*** -1.188 <.001*** + * Big 4 -1.117 <.001*** -0.895 <.001** -1.118 <.001*** -0.896 <.001*** - *

Year Fixed Effects Yes Yes Yes Yes Industry Fixed Effects Yes Yes Yes Yes R2 53.25% 60.14% 53.29% 60.19% Number of observations 35,643 35,643 35,643 35,643 Degrees of freedom 35 37 35 37 p for Model <.001*** <.001*** <.001*** <.001***

Note: Table 11.5 presents the impact of the top accounting issue (revenue recognition and fair value) and short interest position on the probability of material weaknesses for subsequent audit engagement. All variables are defined in the Appendix A. All continuous variables are winsorized at 1 & 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the company level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, logisitics regressions were run with the firm fixed effects and the results remained the same. Column (1) and (3) show the logistics regression results using equation (20) in the study. Column (2) and (4) show the logistics regression results using equation (22) in the study. 241

Table 11.6.1 The Impact of Top Accounting Issue and Short Interest Positions on the Likelihood of Subsequent PCAOB Inspections GAAP Deficiencies. Dependent variable: GAAP Deficiencies Equation (24) Equation (26) Equation (24) Equation (26) Variable Pred. Coeff. p value Coeff. p value Coeff. p value Coeff. p value

Intercept -2.198 <.001*** -2.648 <.001*** -2.209 <.001*** -2.660 <.001*** Revenue Recognition - -0.023 0.505 0.107 0.311 Fair Value -0.052 0.122 0.186 0.056* Rank of Short Interest Ratio (Rank_SIR) - -0.165 <.001*** -0.153 <.001*** Interaction (Top_Issue*Rank_SIR) - -0.168 0.203 -0.337 0.009*** Offices Inspected + 0.281 <.001*** 0.935 <.001*** 0.280 <.001*** 0.935 <.001*** Partners - -0.004 <.001*** -0.001 0.126 -0.004 <.001*** -0.001 0.126 Public_Clients ? -0.195 <.001*** 0.030 0.064* -0.195 <.001*** 0.030 0.065* Audit Fees ? -0.020 0.026** -0.019 0.038** -0.019 0.034** -0.018 0.047** Average Client Size + 0.211 <.001*** 0.260 <.001*** 0.211 <.001*** 0.260 <.001*** Foreign_D + 2.229 <.001*** 1.948 <.001*** 2.230 <.001*** 1.947 <.001*** Stock_Exchange - 0.006 0.786 0.066 0.009*** 0.004 0.877 0.065 0.010** Big 4 - -0.488 <.001*** -0.610 <.001*** -0.489 <.001*** -0.610 <.001***

Year Fixed Effects Yes Yes Yes Yes Industry Fixed Effects Yes Yes Yes Yes R2 52.51% 52.85% 52.52% 52.87% Number of observations 43,358 43,358 43,358 43,358 Degrees of freedom 32 34 32 34 p for Model <.001*** <.001*** <.001*** <.001***

Note: Table 11.6.1 presents the impact of top accounting issue (revenue recognition and fair value) and short interest position on the probability of PCAOB Inspection GAAP Deficiencies for subsequent audit engagement. All variables are defined in the Appendix A. All continuous variables are winsorized at 1 &99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the audit firm level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, logisitics regressions were run with the firm fixed effects and the results remained the same. Column (1) and (3) show the logistics regression results using equation (24) in the study. Column (2) and (4) show the logistics regression results using equation (26) in the study.

242

Table 11.6.2 The Impact of Top Accounting Issue and Short Interest Positions on the Likelihood of Subsequent PCAOB Inspections GAAS Deficiencies.

Dependent variable: GAAS Deficiencies Equation (24) Equation (26) Equation (24) Equation (26) Variable Pred. Coeff. p value Coeff. p value Coeff. p value Coeff. p value

Intercept -3.649 <.001*** -3.347 <.001*** -3.634 <.001*** -3.336 <.001*** Revenue Recognition - 0.017 0.601 -0.209 0.041** Fair Value 0.067 0.041** -0.164 0.077* Rank of Short Interest Ratio - -0.005 0.906 -0.007 0.859 Interaction (Top_Issue*Rank_SIR) - 0.297 0.020** 0.325 0.008*** Offices Inspected + -0.216 <.001*** 0.343 <.001*** -0.216 <.001*** 0.342 <.001*** Partners - 0.007 <.001*** 0.009 <.001*** 0.007 <.001*** 0.009 <.001*** Public_Clients ? 0.246 <.001*** 0.104 <.001*** 0.246 <.001*** 0.104 <.001*** Audit Fees ? 0.006 0.522 0.004 0.642 0.005 0.613 0.003 0.732 Average Client Size + -0.220 <.001*** -0.266 <.001*** -0.220 <.001*** -0.266 <.001*** Foreign_D + 3.186 <.001*** 3.065 <.001*** 3.184 <.001*** 3.065 <.001*** Stock_Exchange - 0.178 <.001*** 0.166 <.001*** 0.181 <.001*** 0.167 <.001*** Big 4 - 0.479 <.001*** 0.582 <.001*** 0.480 <.001*** 0.582 <.001***

Year Fixed Effects Yes Yes Yes Yes Industry Fixed Effects Yes Yes Yes Yes R2 68.46% 68.45% 68.47% 68.45% Number of observations 43,358 43,358 43,358 43,358 Degrees of freedom 32 34 32 34 p for Model <.001*** <.001*** <.001*** <.001***

Note: Table 11.6.2 presents the impact of top accounting issue (revenue recognition and fair value) and short interest position on the probability of PCAOB Inspection GAAS Deficiencies for subsequent audit engagement. All variables are defined in the Appendix A. All continuous variables are winsorized at 1 &99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. The standard errors are clustered at the audit firm level and control for serial correlation and heteroscedasticity (Petersen 2009). Furthermore, logisitics regressions were run with the firm fixed effects and the results remained the same. Column (1) and (3) show the logistics regression results using equation (24) in the study. Column (2) and (4) show the logistics regression results using equation (26) in the study.

243

Table 12.1 The Impact of SEC Comment Letter and Short Interest Positions on Subsequent Audit Fees.

Dependent variable: Audit Fees Equation (3) Equation (4) Equation (5)

Variable Pred. Coeff. p value Coeff. p value Coeff. p value Intercept 9.946 <.001*** 9.982 <.001*** 9.981 <.001*** SEC Comment Letter (SEC_CL) + 0.007 0.072* 0.051 <.001*** Rank of Short Interest Ratio (Rank_SIR) + 0.102 <.001*** 0.116 <.001*** Interaction (SEC_CL * Rank_SIR) + -0.073 <.001*** Assets + 0.405 <.001*** 0.397 <.001*** 0.397 <.001*** Segments + 0.014 <.001*** 0.015 <.001*** 0.015 <.001*** CATA + 0.110 <.001*** 0.104 <.001*** 0.104 <.001*** Quick Ratio - -0.016 <.001*** -0.016 <.001*** -0.016 <.001*** Leverage + 0.049 <.001*** 0.050 <.001*** 0.051 <.001*** ROI - -0.156 <.001*** -0.153 <.001*** -0.153 <.001*** Loss + 0.058 <.001*** 0.058 <.001*** 0.058 <.001*** Going Concern + 0.036 <.001*** 0.038 <.001*** 0.039 <.001*** Foreign Transactions + 0.012 0.545 0.012 0.544 0.012 0.560 December YE + 0.055 0.034** 0.058 0.026** 0.057 0.028** Weaknesses + -0.002 0.750 0.009 0.162 0.010 0.125 Short Tenure + 0.024 <.001*** 0.021 <.001*** 0.020 <.001*** Big 4 + 0.358 <.001*** 0.354 <.001*** 0.353 <.001*** Firm_Size + -0.027 <.001*** -0.026 <.001*** -0.026 <.001*** Market Share + 0.064 <.001*** 0.061 <.001*** 0.060 <.001*** Client Importance - -0.128 <.001*** -0.124 <.001*** -0.124 <.001***

Firm Fixed Effects Yes Yes Yes Year Fixed Effects Yes Yes Yes R2 95.83% 95.84% 95.85% Number of observations 43,930 43,930 43,930 F 14.7 14.64 14.64 p for Model <.001*** <.001*** <.001***

Note: Table 12.1 presents the impact of the SEC comment letter and short interest positions on the audit fees for subsequent audit engagements with firm fixed effects. All variables are defined in the Appendix A. All continuous variables are windsorized at 1 & 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. Column (1) shows the panel OLS regression results using equation (3) in the study. Column (2) shows the panel OLS regression results using equation (4) in the study. Column (3) shows the panel OLS regression results using equation (5) in the study.

244

Table 12.2 The Impact of SEC Comment Letter and Short Interest Positions on the Likelihood of Subsequent Auditor Change. Dependent variable: Auditor Change Equation (7) Equation (8) Equation (9)

Variable Pred. Coeff. p value Coeff. p value Coeff. p value SEC Comment Letter (SEC_CL) + 0.322 <.001*** 0.182 0.128 Rank of Short Interest Ratio (Rank_SIR) + 0.227 0.140 0.273 0.084 Interaction (SEC_CL * Rank_SIR) + 0.323 0.125 Assets ? 0.377 <.001*** 0.423 <.001*** 0.411 <.001*** Audit Delays + 0.004 <.001*** 0.004 <.001*** 0.004 <.001*** Weaknesses + 0.043 0.593 -0.020 0.812 0.003 0.967 ROA - -0.191 <.001*** -0.210 <.001*** -0.201 <.001*** Leverage + 0.025 0.806 0.012 0.909 0.007 0.946 Loss + 0.181 0.005*** 0.189 0.004*** 0.186 0.005*** Going Concern + 0.362 <.001*** 0.359 <.001*** 0.353 <.001*** Growth ? -0.067 0.726 0.062 0.755 0.079 0.693 December YE - 0.280 0.307 0.231 0.435 0.202 0.497 Short Tenure + 2.547 <.001*** 4.084 <.001*** 4.139 <.001*** Receivables and Inventory + 0.095 0.706 0.135 0.597 0.121 0.637 Acquisitions + 0.160 0.039** 0.178 0.025** 0.163 0.041** Big4 - -0.585 <.001*** -0.545 <.001*** -0.546 <.001*** City Expert - -0.063 0.316 0.019 0.770 0.012 0.855 National Expert - -0.044 0.933 0.158 0.766 0.160 0.764

Firm Fixed Effects Yes Yes Yes Year Fixed Effects Yes Yes Yes R2 9.82% 11.53% 11.92% Number of observations 43,512 43,512 43,512 Degrees of Freedom 16 16 18 p for Model <.001*** <.001*** <.001***

Note: Table 12.2. presents the impact of the SEC comment letter and short interest positions on the probability of auditor change for subsequent audit engagements with firm fixed effects. All variables are defined in the Appendix A. All continuous variables are windsorized at 1 & 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. Column (1) shows the logistics regression results using equation (7) in the study. Column (2) shows the logistics regression results using equation (8) in the study. Column (3) shows the logistics regression results using equation (9) in the study.

245

Table 12.3 The Impact of SEC Comment Letter and Short Interest Positions on Performance-Matched Absolute Discretionary Accruals.

Dependent variable: Performance Matched Discretionary Accruals

Equation (12) Equation (13) Equation (14) Variable Pred. Coeff. p value Coeff. p value Coeff. p value

Intercept 0.191 0.957 0.294 0.933 1.95 0.578 SEC Comment Letter (SEC_CL) - -0.012 0.805 -1.98 <.001*** Rank of Short Interest Ratio (Rank_SIR) - -0.026 0.009*** -0.52 <.001*** Interaction (SEC_CL*Rank_SIR) - 0.53 <.001*** Market Value + 0.018 0.530 0.020 0.497 0.04 0.167 ROA - 0.160 0.038** 0.160 0.039** 0.16 0.035** Leverage + -0.178 0.128 -0.174 0.138 -0.16 0.166 Current Ratio + 0.016 0.077* 0.016 0.078* 0.02 0.049** Operating Cash Flows + 0.113 0.418 0.113 0.418 0.08 0.570 Operating Cash Flows (Std. Dev) + 0.738 <.001*** 0.733 <.001*** 0.61 <.001*** Loss + 0.082 0.171 0.083 0.167 0.09 0.153 Market to Book + 0.002 0.392 0.002 0.398 0.00 0.333 Litigation + 0.500 0.373 0.494 0.380 0.42 0.457 Zscore_rank + 0.100 0.931 0.002 0.878 0.00 0.861 Total Accruals (lag) ? 0.001 0.991 0.001 0.998 0.02 0.826 Weaknesses + 0.187 0.017** 0.180 0.023** 0.18 0.020** ST Tenure + -0.227 <.001*** -0.232 <.001*** -0.12 0.023** Firm Size - -0.019 0.540 -0.019 0.526 -0.01 0.648 Market Share + 0.002 0.985 0.001 0.990 -0.01 0.941 Client Importance - -0.363 0.029** -0.364 0.028** -0.37 0.025** Big 4 - -0.210 0.080* -0.205 0.088* -0.18 0.135

Firm Fixed Effects Yes Yes Yes Year Fixed Effects Yes Yes Yes R2 31.58% 31.60% 5.35% Number of observations 39,612 39,612 39,612 p for Model <.001*** <.001*** <.001***

Note: Table 12.3 presents the impact of the SEC comment letter and short interest positions on the Performance-Matched Discretionary Accruals for subsequent audit engagements with firm fixed effects. All variables are defined in the Appendix A. All continuous variables are windsorized at 1 & 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. Column (1) shows the panel OLS regression results using equation (12) in the study. Column (2) shows the panel OLS regression results using equation (13) in the study. Column (3) shows the panel OLS regression results using equation (14) in the study.

246

Table 12.4 The Impact of SEC Comment Letter and Short Interest Positions on the Likelihood of Subsequent Restatements.

Dependent variable: Restatements Equation (16) Equation (17) Equation (18)

Variable Pred. Coeff. p value Coeff. p value Coeff. p value

SEC Comment Letter (SEC_CL) - -0.059 0.232 -0.093 0.437 Rank of Short Interest Ratio (Rank_SIR) - 0.446 <.001*** 0.433 <.001*** Interaction (SEC_CL*Rank_SIR) - 0.061 0.738 Assets - -0.278 <.001*** -0.313 <.001*** -0.309 <.001*** ROA - 0.325 <.001*** 0.330 <.001*** 0.328 <.001*** Leverage + -0.350 0.003*** -0.365 0.002*** -0.363 0.002*** Market to Book + 0.001 0.770 0.001 0.842 0.001 0.842 Segments + -0.032 0.182 -0.030 0.201 -0.030 0.213 Financing + 0.143 0.073* 0.144 0.071* 0.143 0.074* Foreign Transactions + 0.022 0.925 0.019 0.936 0.017 0.941 Acquisitions + -0.197 0.002*** -0.189 0.003*** -0.188 0.003*** Weaknesses + 0.296 <.001*** 0.336 <.001*** 0.333 <.001*** Receivables and Inventory + -0.496 0.084* -0.496 0.084* -0.498 0.083* Audit Fees + 0.244 <.001*** 0.241 <.001*** 0.241 <.001*** Client Importance - -0.007 0.961 -0.002 0.991 0.000 0.998 Big 4 - 0.245 0.037** 0.237 0.043** 0.236 0.044**

Firm Fixed Effects Yes Yes Yes Year Fixed Effects Yes Yes Yes R2 1.16% 1.27% 1.28% Number of observations 40,326 40,326 40,326 Degrees of freedom 14 14 16 p for Model <.001*** <.001*** <.001***

Note: Table 12.4 presents the impact of the SEC comment letter and short interest positions on the probability of restatements for subsequent audit engagement with firm fixed effects. All variables are defined in the Appendix A. All continuous variables are windsorized at 1 & 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. Column (1) shows the logistics regression results using equation (16) in the study. Column (2) shows the logistics regression results using equation (17) in the study. Column (3) shows the logistics regression results using equation (18) in the study.

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Table 12.5 The Impact of SEC Comment Letter and Short Interest Positions on the Likelihood of Subsequent Material Weaknesses.

Dependent variable: Material Weaknesses Equation (20) Equation (21) Equation (22) Variable Pred. Coeff. p value Coeff. p value Coeff. p value

SEC Comment Letter (SEC_CL) - -0.262 <.001*** -0.2703 0.046** Rank of Short Interest Ratio (Rank_SIR) - -1.732 <.001*** -1.7304 <.001*** Interaction (SEC_CL*Rank_SIR) - 0.0232 0.915 Assets - -1.006 <.001*** -0.821 <.001*** -0.8116 <.001*** Firm Age - -0.022 0.012** -0.036 <.001*** -0.035 <.001*** Loss + 0.505 <.001*** 0.510 <.001*** 0.517 <.001*** Zscore_rank + -0.055 <.001*** -0.052 <.001*** -0.052 <.001*** Segments + -0.216 <.001*** -0.221 <.001*** -0.219 <.001*** Foreign Transactions + -0.293 0.305 -0.237 0.410 -0.223 0.439 Acquisitions + 0.186 0.011** 0.186 0.012** 0.182 0.014** Growth + 0.235 0.120 0.190 0.211 0.182 0.231 Restructuring + -1.281 0.039** -0.633 0.321 -0.651 0.307 Big 4 - -1.226 <.001*** -1.230 <.001*** -1.229 <.001***

Firm Fixed Effects Yes Yes Yes Year Fixed Effects Yes Yes Yes R2 11.76% 13.47% 13.71% Number of observations 35,643 35,643 35,643 Degrees of freedom 11 11 13 p for Model <.001*** <.001*** <.001***

Note: Table 12.5 presents the impact of the SEC comment letter and short interest position on the probability of material weaknesses for subsequent audit engagement with firm fixed effects. All variables are defined in the Appendix A. All continuous variables are windsorized at 1 & 99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. Column (1) shows the logistics regression results using equation (20) in the study. Column (2) shows the logistics regression results using equation (21) in the study. Column (3) shows the logistics regression results using equation (22) in the study.

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Table 12.6.1 The Impact of SEC Comment Letter and Short Interest Positions on the Likelihood of Subsequent PCAOB Inspections GAAP Deficiencies.

Dependent variable: GAAP Deficiencies Equation (24) Equation (25) Equation (26) Variable Pred. Coeff. p value Coeff. p value Coeff. p value

SEC Comment Letter (SEC_CL) - -0.054 0.247 0.015 0.923 Rank of Short Interest Ratio (Rank_SIR) - -0.338 0.011** -0.319 0.021** Interaction (SEC_CL*Rank_SIR) - -0.086 0.658 Offices Inspected + -0.516 0.033** -0.522 0.031** -0.521 0.032** Partners - -0.008 <.001*** -0.008 <.001*** -0.008 <.001*** Public_Clients ? 0.195 0.022** 0.197 0.020** 0.197 0.021** Audit Fees ? 0.065 0.305 0.087 0.174 0.087 0.173 Average Client Size -0.264 0.003*** -0.262 0.004*** -0.262 0.004*** +

Foreign_D + 5.405 <.001*** 5.381 <.001*** 5.374 <.001*** Stock_Exchange - 0.263 0.005*** 0.312 0.001*** 0.316 0.001*** Big 4 - 0.063 0.757 0.051 0.804 0.052 0.801

Firm Fixed Effects Yes Yes Yes Year Fixed Effects Yes Yes Yes R2 61.54% 61.56% 61.56% Number of observations 43,358 43,358 43,358 Degrees of freedom 9 9 11 p for Model <.001*** <.001*** <.001***

Note: Table 12.6.1 presents the impact of the SEC comment letter and short interest position on the probability of PCAOB Inspection GAAP Deficiencies for subsequent audit engagement. All variables are defined in the Appendix A. All continuous variables are windsorized at 1 &99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one-tailed test. Column (1) shows the logistics regression results using equation (24) in the study. Column (2) shows the logistics regression results using equation (25) in the study. Column (3) and (4) show the logistics regression results using equation (26) in the study.

249

Table 12.6.2 The Impact of SEC Comment Letter and Short Interest Positions on the Likelihood of subsequent PCAOB Inspections GAAS Deficiencies. Dependent variable: GAAS Deficiencies Equation (24) Equation (25) Equation (26) Variable Pred. Coeff. p value Coeff. p value Coeff. p value

SEC Comment Letter (SEC_CL) - 0.009 0.845 -0.094 0.539 Rank of Short Interest Rank (Rank_SIR) - 0.103 0.436 0.077 0.577 Interaction (SEC_CL*Rank_SIR) - 0.133 0.483 Offices Inspected + 4.504 <.001*** 4.511 <.001*** 4.513 <.001*** Partners - 0.059 <.001*** 0.059 <.001*** 0.059 <.001*** Public_Clients ? 0.273 0.002*** 0.272 0.002*** 0.271 0.002*** Audit Fees ? -0.118 0.064* -0.125 0.052* -0.124 0.054* Average Client Size + -0.048 0.613 -0.050 0.599 -0.050 0.599 Foreign_D + 5.794 <.001*** 5.803 <.001*** 5.804 <.001*** Stock_Exchange - 0.727 <.001*** 0.713 <.001*** 0.714 <.001*** Big 4 - 0.070 0.753 0.076 0.733 0.078 0.726

Firm Fixed Effects Yes Yes Yes Year Fixed Effects Yes Yes Yes R2 76.54% 76.54% 76.55% Number of observations 43,358 43,358 43,358 Degrees of freedom 9 9 9 p for Model <.001*** <.001*** <.001***

Note: Table 12.6.2 presents the impact of the SEC comment letter and short interest position on the probability of PCAOB Inspection GAAS Deficiencies for subsequent audit engagement. All variables are defined in the Appendix A. All continuous variables are windsorized at 1 &99 percent to mitigate the influence of outliers. Statistical significance of the coefficient is marked by asterisks, with ***, **, and * representing significance at the 1%, 5%, and 10% levels, respectively. The p-values are from a one- tailed test. Column (1) shows the logistics regression results using equation (24) in the study. Column (2) shows the logistics regression results using equation (25) in the study. Column (3) shows the logistics regression results using equation (26) in the study.

250