The Association of the Relative Informativeness of Market Risk Disclosures with
Liquidity and Investment Efficiency: Evidence from Textual Analyses
by
Xin Luo
A Dissertation Submitted to the Faculty of
The College of Business
In Partial Fulfilment of the Requirements for the Degree of
Doctor of Philosophy
Florida Atlantic University
Boca Raton, FL
May 2018
Copyright 2018 by Xin Luo
ii The Association of the Relative Informativeness of Market Risk Disclosures with
Liquidity and Investment Efficiency
by
XinLuo
This dissertation was prepared under the direction of the candidate's dissertation advisor, Dr. Mark Kohlbeck, School of Accounting, and has been approved by the members of her supervisory committee. It was submitted to the faculty of the College of Business and was accepted in partial fulfillment of the requirements for the de ee of Doctor of Philosophy.
~, Jahyun Goo, Ph.D.
Date
iii Acknowledgements I wish to extend my deepest gratitude to my advisor and dissertation committee chair, Dr. Mark Kohlbeck, for his support, inspiration, and guidance throughout my Ph.D. program. It is a great pleasure to work with him and learn from him. I am grateful for the suggestions and comments that I received from my committee members, Dr. Jian Cao,
Dr. Maya Thevenot, and Dr. Jahyun Goo. I appreciate the encouragement and advices from Dr. Julia Higgs and the company of my fellow doctoral students. I wish to thank
Xiaofeng Zhu for her friendship, insights, and technical support as I pursue textual analyses.
I am blessed to have many great professors as I pursue higher education. I wish I can express my gratitude for all the professors I had at Yantai University, Florida Gulf
Coast University, Marquette University, and Florida Atlantic University. Their patience, dedication, inspiration, and encouragement are the reasons that I chose this career and motivate me to be the best researcher and professor that I can.
Finally, I thank my family for teaching me the importance of independence, dedication, integrity, and humbleness. I am indebted to my family for sharing my happiness, supporting me through ups and downs, and always surrounding me with love.
iv Abstract
Author: Xin Luo
Title: The Association of the Relative Informativeness of Market Risk Disclosures with Liquidity and Investment Efficiency
Institution: Florida Atlantic University
Dissertation Advisor: Dr. Mark Kohlbeck
Degree: Doctor of Philosophy
Year: 2018
In a 2016 comment letter, the SEC summarizes the ongoing debate regarding the usefulness of market risk disclosures and calls for additional discussion (SEC Concept
Release 2016). In response to the SEC’s call, I investigate whether investors and firms benefit from market risk disclosures. Prior literature suggests that informative corporate disclosure is associated with improved liquidity and investment efficiency. I find that informative textual contents of market risk disclosures improve investors’ information environment, and as a result, are associated with higher liquidity level, lower liquidity uncertainty, and improved investment efficiency. My study is relevant to the ongoing debate regarding the usefulness of market risk disclosures, calls for more detailed regulatory guidance for market risk disclosures, and contributes to the literature on liquidity, investment efficiency, and risk factor disclosures.
v
Dedication
I dedicate this dissertation to my beloved parents. I thank them for their unconditional love and support throughout my life.
The Association of the Relative Informativeness of Market Risk Disclosures with
Liquidity and Investment Efficiency
List of Tables ...... ix
List of Equations ...... x
Chapter 1 Introduction ...... 1
Chapter 2 Literature Review ...... 7
2.1 Capital Market Risk Disclosure Requirement and Research ...... 7
2.2 Corporate Disclosure and Stock Liquidity Research ...... 10
2.3 Corporate Disclosure and Investment Efficiency Research ...... 13
Chapter 3 Hypotheses Development ...... 17
3.1 Market Risk Disclosures and Information Asymmetry...... 17
3.2 Market Risk Disclosures and Stock Liquidity Hypotheses ...... 19
3.3 Market Risk Disclosure and Investment Efficiency Hypotheses ...... 21
Chapter 4 Research Design ...... 24
4.1 Liquidity ...... 24
4.1.1 Liquidity Level ...... 24
4.1.2 Liquidity Uncertainty ...... 25
4.2 Investment Efficiency ...... 25
4.3 Textual Characteristics of Market Risk Disclosures ...... 27
4.3 Regression Model ...... 28
Chapter 5 Sample Selection and Descriptive Statistics ...... 31
vii Chapter 6 Empirical Results ...... 34
6.1 Results for Hypothesis 1 ...... 34
6.2 Results for Hypothesis 2 ...... 35
6.3 Results for Hypothesis 3 ...... 36
Chapter 7 Additional Analyses and Robustness Check ...... 40
7.1 Alternative Measures for Readabilities and Similarity ...... 40
7.2 Evidence from Other Textual Characteristics ...... 42
7.3 Impact of Market Risk Disclosures on Sensitivity of Stock Return to Market
Liquidity ...... 45
7.4 Information Asymmetry Level ...... 46
7.5 Other Robustness Tests ...... 48
Chapter 8 Conclusion ...... 50
Appendices ...... 52
References ...... 86
viii
List of Tables
Table 1: Sample Selection ...... 62
Table 2: Descriptive Statistics ...... 63
Table 3: Pearson Correlation Coefficients ...... 65
Table 4: The Effect of Market Risk Disclosures on Stock Liquidity Level ...... 67
Table 5: The Effect of Market Risk Disclosures on Stock Liquidity Uncertainty ...... 68
Table 6: The Effect of Market Risk Disclosures on Investment Efficiency ...... 69
Table 7: The Effect of Market Risk Disclosure on Over- and Under- Investment ...... 70
Table 8: Alternative Measures for Readability and Similarity ...... 72
Table 9: The Effect of Topics of Market Risk Disclosures on Stock Liquidity and
Investment Efficiency ...... 75
Table 10: The Effect of Market Risk Disclosures on Co-movement between
Stock Return and Market Liquidity ...... 79
Table 11: The Effect of Market Risk Disclosure Conditional on Ex-ante Information
Asymmetry ...... 80
ix
List of Equations
(1) %ΔDPIi,d = αi + βi,1%ΔDPIm,d-1 + βi,2%ΔDPIm,d + βi,3%ΔDPIm,d+1 +εi,d ...... 25
(2) CAPXi,t /Ai,t-1 = β0 + β1 CFi,t /Ai,t-1 + β2 MTBi,t + εi,t ...... 26
(3) CAPXi,r/Ai,t-1 = β0 + β1 MTBi,t-1 + β2 CAPXi,t-1 /Ai,t-2 + β3 CASH_RATIOi,t-1 +
β4 LEVi,t-1 + β5 ROAi,t-1 + β6 LNASSETi,t-1 + εi,t ...... 26
(4) LIQLEVEL or LIQRISKi,t+1 = α0 + β1INFORMi,t + β2TURNOVERi,t +
β3PRIORRETi,t + β4Z_SCORE + β5STDRETi,t+ β6SIZEi,t + β7MTBi,t +
β8CAPINTENSi,t + β9CASH_RATIOi,t + β10LOSSi,t+ Industry FE+
Year FE + εi,t ...... 28
(5) INVEFFi,t+1 = α0 + β1INFORMi,t + β2LOGASSETi,t + β3STDCFOi,t+
β4STDSALESi,t + β5MTBi,t + β6STDINVi,t + β7Z_SCOREi,t + β8AGEi,t +
β9DIVi,t + β10CAPINTENSi,t + β11LOSSi,t + Industry FE + Year FE + εi,t ...... 30
x
The Association of the Relative Informativeness of Market Risk Disclosures with
Liquidity and Investment Efficiency
Chapter 1 Introduction
I study the association of the textual characteristics of market risk disclosures with stock liquidity and investment decisions. In 1997, the U.S. Securities and Exchange
Commission (SEC) issued Financial Reporting Release No.48 (FRR No.48) mandating registrants to disclose quantitative and qualitative information about their capital market
risk exposures and how they manage these risk exposures (SEC 1997). 1 In practice, most companies disclose their market risk exposure and management in Item 7A, Quantitative and Qualitative Disclosures about Market Risk, of annual reports. Market risks mainly include risks caused by changes in interest rate, foreign currency exchange rates, commodity prices, and equity prices. Market risks also include risks arising from other market fluctuations that affect market risk sensitive instruments. Early studies suggest
1 See FRR No.48, Disclosure of Accounting Policies for Derivative Financial Instruments, and Derivative
Commodity Instruments and Disclosure of Quantitative and Qualitative Information about Market Risk
Inherent in Derivative Financial Instruments, Other Financial Instruments, and Derivative Commodity
Instruments (SEC 1997). Smaller reporting companies are not required to disclose market risk exposures. A smaller reporting company is currently defined as a company that has a public float of less than $75 million in common equity as of the last business day of its most recently completed second fiscal quarter, or if a public float of zero, has less than $50 million in annual revenues as of its most recently completed fiscal year end.
1 that market risk disclosures are informative to the capital market (e.g., Rajgopal and
Venkatachalam 2000; Guo 2002; Thornton and Welker 2004).
Although the SEC requires firms to disclose their exposure to capital market risks and how they manage these risks in Item 7A, the extent to which firms disclose their anticipated market risks varies across firms. In a 2016 comment letter, the SEC summarizes the ongoing debate regarding the usefulness of market risk disclosures and calls for additional discussion. Specifically, the SEC is interested in whether current disclosure practices provide information that investors consider important and can effectively assess. Furthermore, the SEC seeks public opinion on how to revise instructions to market risk disclosures to make it more readable and beneficial for both registrants and investors, and on how to enhance comparability of disclosures among registrants, etc. (SEC Concept Release 2016). In response to the SEC’s call, I investigate whether investors and registrants benefit from the market risk disclosures by testing if the characteristics of the qualitative content of these disclosures is associated with stock liquidity and investment efficiency.
My first research question addresses whether investors benefit from more informative market risk disclosures. The liquidity level and liquidity uncertainty capture the idiosyncratic and systematic components of liquidity, respectively. In a semi-efficient market, information asymmetry among market participants translates into higher costs for trading shares and lower stock liquidity level (e.g., Diamond and Verrecchia 1991). I argue that when managers release their superior information on market risks through forward-looking disclosures, information asymmetry between firms and capital providers
2 is reduced resulting in improved liquidity level. My first hypothesis predicts that the informativeness of the market risk disclosures is positively associated with liquidity level.
Stocks with more uncertainty about intrinsic value have less predictable liquidity.
They are also more likely to face market maker and investors’ outflows when market aggregate liquidity declines (Lang and Maffett 2011; Ng 2011). I argue that informative market risk disclosures reduce investors’ uncertainty about stocks’ intrinsic value. My second hypothesis predicts that the informativeness of the market risk disclosures is negatively associated with liquidity uncertainty.
My second research question focuses on whether firms benefit from informative market risk disclosures. I address this research question by testing the impact of informative market risk disclosures on investment efficiency. Tobin’s Q theory suggests that, in the neo-classical setting, corporate investments are driven by the investment opportunities and have no associations with internal generated cash flow (Tobin 1969).
However, corporate investments may deviate from the optimal level due to capital rationing and agency problems. These two frictions are derived from managers’ exploitation of their private information. When facing capital constraints, firms tend to under-invest and rely primarily on internal financing (e.g., Myers 1984; Myers and
Majluf 1984). When managers have empire-building incentives (agency problems), firms tend to use excess cash to over-invest. My third hypothesis predicts that informative market risk disclosures reduce the frictions derived from information asymmetry and further enhance investment efficiency. Answering this question helps us understand whether the benefits firms receive from providing informative market risk disclosures outweigh the costs.
3 My hypotheses center on the informativeness of market risk disclosures. I capture the informativeness through qualitative characteristics of market risk disclosures. I use the readability and the similarity between current and prior disclosures to capture the qualitative characteristics of the market risk disclosures. In my additional analyses, I also investigate whether certain topics contribute more to the information environment of market participants and firms.
I use the illiquidity measure developed by Amihud (2002) and the fraction of trading days with zero returns (Goyenko , Holden, Trzcinka 2009) to capture liquidity level. I use liquidity volatility and co-movement between firm level liquidity and market aggregate liquidity to capture liquidity uncertainty (e.g., Pastor and Stamboug 2003; Lang and Maffett 2011). I then regress stock liquidity proxies on the qualitative characteristics of market risk disclosures and control variables based on prior literature. I document a positive relationship between the readable and updated market risk disclosure and the liquidity level and a negative relationship between the readable and updated market risk disclosures and liquidity uncertainty.
I use investment-cash flow sensitivity and deviation from optimal investment level to proxy for investment efficiency (e.g., Biddle and Hilary 2006; Biddle, Hilary, and
Verdi 2009; Blaylock 2016). I regress these two measures on qualitative characteristics of market risk disclosures and control variables. Consistent with my third hypothesis, I document that more readable market risk disclosures are associated with higher investment efficiency, as captured by lower investment-to-operating cash flow sensitivity and the deviation from optimal investment level. I also find that investment decisions of
4 firms with more updated market risk disclosures are less sensitive to operating cash flow but have greater deviation from the optimal investment level.
I contribute to the emerging debate on the usefulness of capital market risk disclosures (SEC Concept Release 2016) by documenting the positive impact of the informative market risk disclosures on stock liquidity and corporate investment decisions.
This is the first study that relates the qualitative characteristics of capital market risk disclosures to investors’ risk assessments. My results suggest that quality market risk disclosure is informative for investors and firms. I extract topics covered in market risk disclosures and find some topics, especially those with explained risk exposure and risk management strategy in details, have more significant impact on stock liquidity and investment efficiency than others. My results thus provide preliminary suggestions on how the SEC can improve the disclosure requirement for market risk disclosures.
Previous research documents rich evidence of the impact of disclosure policy on liquidity level and liquidity risk (e.g., Diamond and Verrecchia 1991; Welker 1995; Kim and Verrecchia 1994; Lang and Maffett 2011; Ng 2011). However, the link between managers’ forward-looking risk disclosures and liquidity is still missing. I highlight the importance of informing investors of potential capital market risks in stock liquidity. I contribute to the stream of studies on the relation between corporate disclosure and liquidity and the broader stream of literature that studies the association between capital market risks and stock performance.
I also extend the growing literature on the role of the information environment in corporate capital allocation. Financing conditions have direct impact on investment decisions. The qualitative disclosures of market risk help investors understand firms’
5 financing uncertainties and draw better inferences about firms’ investment efficiency.
Results of this research contribute to our understanding of the value relevance and efficiency of market risk disclosures and may call for more detailed regulatory guidance for market risk disclosure.
The rest of my dissertation is organized as follows. Chapter 2 reviews prior literature, Chapter 3 develops hypotheses, Chapter 4 covers the research design, Chapter
5 presents the sample selection and descriptive statistics, Chapter 6 discusses the main empirical results, and Chapter 7 presents the result of additional analyses and robustness check, and Chapter 8 concludes this research.
6
Chapter 2 Literature Review
In this section, I review literature related to my study. I first review the requirement of FRR No.48 and prior research that is relevant to market risk disclosure. I then review research on the relation between corporate disclosure and stock liquidity. I next discuss prior literature on the relation between corporate disclosure and investment efficiency.
2.1 Capital Market Risk Disclosure Requirement and Research
In 1997, the SEC issued FRR No. 48, mandating registrants to disclose quantitative and qualitative information about their market risk exposures and how their risk exposures are managed (SEC 1997). The rule addresses risks caused by changes in interest rates, foreign currency exchange rates, commodity prices, and equity prices. It also addresses risks arising from other market fluctuations that affect market risk sensitive instruments. Appendix A provides an example of the 2017 market risk disclosure for American Airline.
To disclose quantitative information about market risk, registrants can choose one disclosure format among three alternatives, including tabular presentation, sensitivity analysis, and value at risk disclosure, for each risk exposure category. Under the tabular format, registrants need to present information of fair value, expected cash flow, and contract terms of market risk sensitive instruments. Sensitivity analysis format requires registrants to disclose estimated potential loss in cash flow, earnings, and fair value of market risk sensitive instruments caused by reasonable and hypothetical changes in
7 market rates and prices. American Airline presented their interest risks, commodity risks, and foreign currency risks using sensitivity analysis (Appendix A). Value at risk disclosure presents potential loss (average, high, and low amount) in cash flow, earnings, and fair value of market risk sensitive instruments over a certain period and with a selected probability of occurrence from changes in market rates and prices. Registrants should provide a description of the model, assumptions, and parameters necessary to understand the disclosure under three format alternatives (FRR No.48).
To disclose the qualitative information about market risk, registrants need to identify their primary market risk exposures in the near future and describe how those risks are managed. For example, registrants can discuss the objectives, strategies, and instruments used in risk management and accounting policies for derivatives. Registrants also need to describe changes in market risk exposures and risk management if different than what was in effect during the most recent fiscal year.
Several studies assess the usefulness of disclosures for specific market risks (e.g., interest rate risk and commodity price risk) but do not directly use market risk disclosures under FRR No. 48. Using banks’ maturity gap disclosure to proxy for FRR No. 48 disclosure, Ahmed, Beatty, and Bettinghaus (2000) find interest risk disclosure helps predict changes in interest income. Rajgopal (1999) investigates whether commodity price risk disclosures under Statement of Financial Accounting Standards (SFAS) No.69,
Disclosures about Oil and Gas Producing Activities, and SFAS No. 119, Disclosures about Fair Values of Derivative Financial Instruments and Fair Values of Financial
Instruments, are associated with oil and gas firm’s stock-price sensitivity to the risk associated with changes in oil and gas prices. He finds that stock prices of these oil and
8 gas firms are sensitive to the commodity risk disclosures, indicating the market risk disclosure do reveal market-related risks. Rajgopal and Venkatachalam (2000) document a positive relation between market risk disclosures, specifically commodity price risk disclosures, and market risk perception based on a sample of 25 US petroleum refiners.
Thornton and Welker (2004) examine actual effect of FRR No.48 and document that when FRR No. 48 takes effect, firms disclosing oil and gas price sensitivity or value at risk tend to experience shifts in stock return sensitivity to commodity prices.2
Jorion (2002) and Lin and Lin (2017) study the usefulness of general market risk disclosures. Jorion (2002) documents that value at risk disclosures of commercial banks are informative as they help predict variability of trading revenues. Lin and Lin (2017) find that value at risk and tabular format are more informative, as captured by higher analyst forecast accuracy, than sensitivity analysis format.
A few studies specifically investigate the impact of the adoption of market risk disclosures on cost of capital. Linsmeier, Thornton, Venkatachalam, and Welker (2002) argue that after FRR No. 48 information is available, investors obtain more precise assessments of firms’ exposures to underlying market rate/price changes. They find that trading volume sensitivity to magnitude of underlying market rate/price changes is lower after FRR No.48 disclosure. Guo (2002) examines the impact of the adoption of market risk disclosures on cost of debt. She finds that market risk disclosures increase cost of debt for the speculative firms and reduces cost of debt for hedging firms. These effects
2 Data from the market risk disclosure rules (FRR No.48) were not available when Rajgopal (1999) conducted this research. Oil and gas firms were required to provide commodity price risk disclosures under
SFAS No.69 and SFAS No. 119, which were similar to some disclosure requirements under FRR No.48.
9 are more pronounced for firms taking the value at risk format than firms taking sensitivity analysis format.
Overall, existing literature confirms the usefulness of market risk disclosures in financial report users’ decision making. However, there are several limitations in market risk disclosure research. First, prior literature predominantly focuses on the impact of first-time adoption of market risk disclosure and evidence from following period is limited. There is a large variation in the format and estimation bases used in firms’ market risk disclosure. The lack of details regarding quantitative measure of market risks and the lack of discussion regarding risk managements’ strategies may not help fulfill the objectives of FRR No.48 (Roulstone 1999). Flexibility accorded firms in FRR No.48 can adversely affect information users’ risk judgments (Hodder, Koonce, and McAnally
2001; Sribunnak and Wong 2004; Lin and Lin 2017).
To summarize, there is limited empirical evidence on firm-level market risk disclosure characteristics. Second, the financial crisis largely changed investors’ risk evaluation behavior but evidence from a more recent period is still missing. The sample periods of existing empirical studies concentrate at late 1990s. Third, prior studies emphasize the quantitative information disclosed in the market risk disclosures and overlook the importance of the textual content.
2.2 Corporate Disclosure and Stock Liquidity Research
Market participants expect the existence of investors with superior information about the stocks’ true value. Uninformed investors price protect against adverse selection and likely reduce stock liquidity (Bagehot 1971; Kyle 1985; Grossman and Miller 1988).
Consistent with this notion, Diamond and Verrechia (1991) theorize that, when there is
10 trading in an illiquid market with limited risk bearing capacity of risk-averse market makers, revealing public information that reduces information asymmetry increases stock liquidity. Kim and Verrecchia (1994) further suggest that the relation between public information disclosure and liquidity is conditional on the precision of disclosure. Less precise public information triggers information asymmetry among market participants and reduces the liquidity.
As investors’ potential liquidity needs increase, the optimal level of disclosure rises. Firms that increase their disclosure commitment to meet investors’ needs experience higher liquidity and lower cost of capital (Baiman and Verrechia 1996). High information asymmetry firms are likely to switch to accounting techniques that reduce information asymmetry and improve stock liquidity (Bartov and Bodnar 1996).
Leuz and Verrecchia (2000) document that increased disclosure commitment of
German firms after switching to an international reporting regime leads to higher stock liquidity. Bischof and Daske (2013) find that, after the mandatory one-time sovereign risk and stress-test disclosures, European banks which do not commit to voluntarily disclose sovereign risk exposures experience decreases in stock liquidity. Lang, Lins, and Maffett
(2012) examine the impact of corporate transparency —as measured by earnings management, auditor quality, adoption of international accounting standard, analyst coverage, and analyst forecasts accuracy—on liquidity in an international setting. They find that greater transparency is associated with lower transaction costs and higher liquidity level, especially when overall investor uncertainty is greater. Blankespoor,
Miller, and White (2013) document that dissemination of firm-initiated news via Twitter is associated with reduced information asymmetry among investors and improved
11 liquidity. Balakrishnan, Billings, Kelly, and Ljungqvist (2014) document that firms respond to loss of public information by providing more timely and informative earnings guidance, which improved firms’ information environment and further increases stock liquidity. Schoenfeld (2017) document that voluntary disclosure increases as firms join the S&P 500 index, voluntary disclosure increases and further improve stock liquidity.
Stock liquidity level has two key dimensions: bid-ask spread and the number of shares available for trading at the quoted bid and ask price (heareafter, quoted depth) (e.g.
Lee, Mucklow, and Ready 1993; Callahan, Lee, and Yohn 1997; Bacidore, Battalio, and
Jennings 2002). Early empirical studies predominantly focus on bid-ask spread. Welker
(1995) and Healy, Hutton, and Palepu (1999) document that firms’ expanded voluntary disclosures, as measured by AIMR rankings, lead to lower bid-ask spread. Coller and
Yohn (1997) find firms that issue management forecasts have greater bid-ask spread than firms that do not preceding the forecast. However, the difference of bid-ask spread between them is eliminated after the issuance of management forecasts.
Bloomfield and Wilks (2000) incorporate the quoted depth into the disclosure and liquidity research. They examine the impact of disclosure quality in a laboratory financial market. They find that disclosure has larger effects on liquidity at greater market depth, which suggests that a significant portion of disclosure effects on liquidity may not be revealed by the quoted bid-ask spread that is driven by small transaction. Heflin, Shaw, and Wild (2005) further investigate the relation between disclosure policy rating and quoted depth. Consistent with the notion that advanced disclosure reduces market making reward and, therefore, causes some market makers to quit (Diamond and Verrechia
1991), they document a negative relation between disclosure level and quoted depth.
12 They further find that, after adjusting for the quoted depth, bid-ask spread is still lower when disclosure level is high.
Corporate disclosures impact not only the liquidity level but also liquidity risk.
Liquidity risk refers to the unexpected stock return to investors as market liquidity changes (Pastor and Stambaugh 2003) and the uncertainty of firm-level stock liquidity
(Brunnermeier and Perdersen 2009). Lang and Maffett (2011) document that, in an international setting, firms with greater transparency have less volatility in liquidity and fewer extreme illiquidity events. This negative relation between transparency and liquidity uncertainty is more pronounced during crisis periods, suggesting that liquidity providers avoid assets with high level uncertainty of fundamental values. Ng (2011) conducts a within-country study and find a negative association between information quality and liquidity risk. This association is more pronounced in times of large shocks to market liquidity.
In summary, theoretical arguments and empirical evidence support the association between corporate disclosure and both firm level liquidity and liquidity risk. Evidence from the textual content of corporate disclosure is still missing. Although prior research suggests that liquidity level is largely conditional on the capital market risks faced by firms, existing literature does not examine whether improved market risk disclosure helps investors access firms’ market risk exposures and further impacts stock liquidity.
2.3 Corporate Disclosure and Investment Efficiency Research
Biddle and Hilary (2006) are the first to directly link corporate disclosure and investment efficiency. They argue that higher accounting quality reduces both adverse selection and moral hazard issues derived from information asymmetry between firms
13 and capital providers and further improves investment efficiency. They find that higher accounting quality is associated with lower investment-cash flow sensitivity in the US but not in Japan. They interpret the difference as the result of the fact that more capital in the
US is provided by investors who do not have access to private information.
Biddle, Hilary, and Verdi (2009) extend Biddle and Hilary (2006) by testing whether higher accounting quality is associated with either lower over- or under- investment in capital assets. They document that higher accounting quality is associated with lower (higher) investment among firms that are cash rich (cash constrained) and unlevered (highly levered) and firms with higher accounting quality invest less (more) when aggregate investment is high (low). Using an alternative proxy for investment efficiency, they find that higher accounting quality is associated with less deviation from optimal investment level. Thus, higher accounting quality reduces both over- and under- investment.
A few studies investigate the impact of capital providers’ access to private information and monitoring on the relation between accounting quality and investment decisions. Beatty, Liao, and Weber (2010 a) extend Biddle and Hilary (2006) and investigate how different sources of financing affect the importance of accounting quality on firms’ investment-cash flow sensitivity. They document that for firms with high financial constraints, debt providers’ private information mitigates the role of accounting quality in reducing the investment-cash flow sensitivity. Beatty, Liao, and Weber (2010 b) find that firms with low accounting quality are more likely to lease than purchase assets and the association between accounting quality and leasing decreases when banks have higher monitoring incentives and when loans contain capital expenditure provisions.
14 Chen, Hope, Li, and Wang (2011) study a sample of private firms from emerging markets and find that financial reporting quality mitigates both under-and over-investment. This impact is stronger if a firm’s investment is mainly funded by banks, which have access to more private information.
Investment decisions are largely driven by expectations of the benefits of investment. McNichols and Stubben (2008) argue that misstated financial performance can distort expectations by those unaware of the misstatement. They find that firms manipulating earnings over-invest in fixed assets in the misreporting period and discontinue the over-investment after the misreporting period.
Two studies examine the effect of specific accounting choices on investment decisions. Liang and Wen (2007) developed a theoretical model to investigate how output-based and input-based accounting measurement impact firms’ investment decisions through capital market pricing. They document that output-based measurement, which measures a firm’s activity by recording the estimated financial benefits of production, reduces the negative impact of mispricing on investment efficiency but may be noisy and subject to more managerial manipulation. On the other hand, input-based measurement, which measures a firm’s activity by recording the estimated cost factors of production, induces more efficient investment decisions when certain noise is unavoidable. Jackson, Liu, and Cecchini (2009) investigate the impact of firms’ depreciation method choice on capital investment decisions. They find that firms that use accelerated depreciation method make larger capital investments than firms that use straight-line depreciation method. They also find that firms that switch from accelerated to straight-line method experience reduced capital investment in the post-change period.
15 Two other studies focus on the influence of broader information environment, specifically, information spillover within industry, on investment decisions. Compared with private firms, public firms disclose more information and have information intermediaries help disseminate information. The presence of public firms in an industry enriches the information environment of the industry and, thus, facilitates the investment decisions of private firms (Badertscher, Shroff, and White 2013). Beatty, Liao, and Yu
(2013) argue that firms may rely on peer firms’ fraudulent financial report to mitigate product market uncertainty. They find that firms make more investment during peer firms’ scandal period and the association between investment and future performance is weaker during the scandal period.
In summary, prior literature suggests that financial reporting quality, accounting method, and information environment have significant impacts on firm-level investment decisions. To the best of my knowledge, the impact of textual contents of corporate disclosures on investment efficiency is still an open question.
16
Chapter 3 Hypotheses Development
Theory suggests that information asymmetry is one of the economic forces that drive liquidity and investment efficiency (e.g., Diamond and Verrecchia 1991; Welker
1995; Kim and Verrecchia 1994; Myers 1984; Jensen 1986; Blanchard, Lopez-de-
Silanes, and Shleifer 1994). High quality corporate disclosures improve information environment and reduces information asymmetry among market participants (e.g., Healy and Palepu 2001). Thus, it is important to understand the impact of market risk disclosures on information asymmetry before drawing an inference on whether market risk disclosures have an effect on liquidity and managers’ investment decisions.
In this section, I first discuss the role of market risk disclosures in reducing information asymmetry and improving the information environment. I next consider the association of informative market risk disclosures may have with the liquidity level and liquidity risk and develop my first two hypotheses. I then discuss how informative market risk disclosures facilitate firms’ investment efficiency and develop my third hypothesis.
3.1 Market Risk Disclosures and Information Asymmetry
Informative market risk disclosures can reduce information asymmetry and uncertainty over firm value faced by market participants and further facilitate capital providers’ decisions making for at least three reasons. First, Item 7A provides detailed forward-looking information about firms’ exposures to capital market risks, such as interest, foreign currency exchange, and equity price risks, which are important inputs of firms’ decision making. Here are a few examples of how market risk exposures impact
17 corporate policy. Interest rate uncertainty leads firms to substitute long-term for short- term financing (Titman 1992). Firms issue more debt, more debt relative to investment spending, and more debt compared to equity when interest rates are low relative to historical rates (Barry, Mann, Mihov, and Rodríguez 2008). Firms may hold foreign currency for operating, financing, or investment purpose and their assets are exposed to currency exchange risks (Adler and Dumas 1980). Exchange rate risk exposure is important in explaining temporal variation in expected return on bonds and stocks
(Chow, Lee, and Solt 1997). Informative market risk disclosures help investors develop clearer prospects regarding firms’ financing strategy and future performance.
Second, forward-looking disclosures in Item 7A allow market participants to assess firms’ risk management strategy and risk preference. Intertemporal CAPM (e.g.,
Merton 1973; Campbell 1993, 1996) predicts that firms have incentives to hedge against unexpected changes in market volatility that affect the evolution of investment opportunities. Financial derivatives are widely used to hedge risks (Bodnar, Hayt, and
Marston 1998; Guay 1999). According to Statement No. 133 issued by FASB in 1998, fair value is the most relevant measure for financial instruments and the only relevant measure for derivatives (FASB 1998). Firms have information advantage and discretion in the estimation of fair value. Firms can inform market participants the objectives and instruments used to manage specific risks in Item 7a. Firms can also communicate the use and valuation of derivatives and the effectiveness of risk hedging using derivatives with market participants. Risk hedging decisions and outcomes help investors observe managerial ability and further improve the informativeness of earnings (DeMarzo and
Duffie 1995).
18 Third, market risk disclosures, to some degree, serve as a safe harbor and protect firms from certain lawsuits. The safe harbor provided in Section 27A of the Securities
Act of 1933 (15 U.S.C. 77z-2) and Section 21E of the Securities Exchange Act of 1934
(15 U.S.C. 78u-5) shields firms from liability for forward-looking statement. Managers can avoid certain lawsuits and protect firm value if they warn the market participants by voluntarily disclosing negative information and potential risks in a timely manner (e.g.,
Skinner 1994; Skinner 1997; Campbell, Chen, Dhaliwal, Lu, and Steele 2014; Nelson and
Pritchard 2016). Investors can also expect firms with informative market risk disclosures to experience fewer lawsuit settlements and reputation loss (Muradoglu and Huskey
2008).
Overall, registrants’ Item 7A disclosures are not only about potential market risk exposures but also managers’ interpretation of market risks and risk management decisions. Moreover, market risk disclosures serve as a safe harbor and result in lower litigation risks and costs. Informative market risk disclosures reduce the information asymmetry among market participants and investors can use information contained in
Item 7A to revise or confirm their expectations about a firms’ market and litigation risk exposure and firm value. My hypotheses test the usefulness of information disclosed in
Item 7A by investigating the association of the characteristics of the textual disclosures with stock liquidity and investment efficiency.
3.2 Market Risk Disclosures and Stock Liquidity Hypotheses
Liquidity refers to how easy it is to trade shares without significantly affecting stock price. Liquidity is a function of the adverse selection in the equity market and stock prices are not efficient due to the information asymmetry (Kyle 1985; Glosten and
19 Milgrom 1985). Cost of trading shares is relatively high when information asymmetry exists among market participants for at least two reasons. First, informed traders have incentives to exploit their information advantage and place large orders. Market makers bear more risks when informed traders place large orders. Market makers adjust share prices to their information content, which includes both public and private information, but still charge a risk premium for taking a large position. This risk premium is paid by both informed and uninformed investors (Diamond and Verracchia 1991). Second, uninformed investors also price protect against potential losses from trading with informed traders (Bhattacharya and Spiegel 1991). The high cost of trading shares disables investors to trade a large number of shares without significantly affecting the stock price.
Theory suggests that corporate disclosures which reduce the information asymmetry among market participants decrease the cost for trading shares and lead to higher stock liquidity level (e.g., Diamond and Verracchia 1991; Welker 1995; Kim and
Verrecchia 1994). Consistent with this theory, I anticipate a positive relationship between the informativeness of market risk disclosure and liquidity level.
H1: Ceteris paribus, the informativeness of market risk disclosure is positively
associated with stock liquidity level.
Liquidity risk, the systematic component of liquidity, captures stock liquidity uncertainty (Brunnermeier and Perdersen 2009) and unexpected stock return to investors as market liquidity changes (Pastor and Stambaugh 2003). Stocks with more uncertainty about intrinsic value have less predictable liquidity. Therefore, the volatility of stock liquidity is positively associated with information asymmetry (Lang and Maffett 2011).
20 When market aggregate liquidity declines, the investor and market maker outflow is likely to be more severe for stocks associated with greater uncertainty and adverse selection issues (Pastor and Stambaugh 2003; Vayanos 2004; Brunnermeier and Pedersen
2009).
Item 7A contains information characteristics of a firm that affect the degree of uncertainty about firm value and adverse selection. Therefore, I expect a negative association between the informativeness of market risk disclosure and liquidity uncertainty.
H2: Ceteris paribus, the informativeness of market risk disclosure is negatively
associated with liquidity uncertainty.
3.3 Market Risk Disclosure and Investment Efficiency Hypotheses
Modigliani and Miller (1958) suggest that the financing and investment decisions are independent in the absence of market friction. In neoclassical setting, firms’ investment decisions are solely driven by potential return from investments (e.g., Tobin
1969; Hayashi 1982). However, two market frictions affect the alignment of firms’ financing policy with investment decisions and consequently investments may deviate from optimal levels.
The first friction is financing constraints associated with adverse selection. Myers
(1984) utilizes pecking order theory to explain firms’ use of capital in investment.
Pecking order theory suggests that firms prefer to use internal financing before seeking debt and equity financing due to the cost of capital. Myers and Majluf (1984) further extend the pecking order theory. When firms totally rely on the internal generated cash flow to finance investment project, the information asymmetry issue is resolved. Myers
21 and Majluf (1984) document that managers, who have information advantage over investors, act in favor of existing shareholders and may refuse to raise external capital at a discounted rate (e.g., underpriced security) even if it means bypassing profitable investments. In this case, financing constraints may cause under-investment. On the other hand, firms may issue overpriced securities and, therefore, use excess free cash flow to overinvest (e.g., Baker, Stein, and Wurgler 2003; Shleifer and Vishny 2003).
The second friction derives from moral hazard issues. Agency theory suggests that managers have incentives to maximize their personal utility even if it is at the cost of shareholders’ interest (Jensen and Meckling 1976). Jensen (1986) argues that entrenched managers have incentives to make firms grow beyond the optimal level so that managers can gain more power. To avoid the monitoring from external capital providers, managers may overinvest using internally generated cash flow. However, once capital providers anticipate managers’ empire building incentives and the information asymmetry, they may ration capital ex-ante, which causes under-investment (e.g., Stiglitz and Weiss 1981;
Lambert, Leuz, and Verrecchia 2007).
As discussed in Section 3.1, informative market risk disclosures help investors develop a better understanding of firms’ financing constraints, uncertainty in firms’ capital holding, and investment return after adjusting market risk. Investors can better assess firms’ financing constraints by evaluating disclosed interest risks and foreign currency risks. Investors can identify managers’ risk preference through the disclosed risk management strategy and outcome and further ration capital ex-ante. Efficient risk management further helps ensure that firms have sufficient internally generated funding to finance investment opportunities (Froot, Scharfstein, and Stein 1993). Reduced
22 information asymmetry can alleviate both adverse selection and moral hazard issues. I anticipate that informative market risk disclosures mitigate both over-and under- investment and are therefore associated with more efficient investment.
H3: Ceteris paribus, the informativeness of market risk disclosure is positively
associated with investment efficiency.
23
Chapter 4 Research Design
In this section, I first introduce the proxies for the liquidity level and liquidity risks. I next discuss two measures for investment efficiency. I then introduce the constructs for informativeness of market risk disclosure. Lastly, I present the empirical model used to test my hypotheses.
4.1 Liquidity
4.1.1 Liquidity Level
I employ two measures to proxy the level of liquidity: illiquidity and fraction of zero return days. The first measure reflects the price impact associated with trading volume. Following Amihud (2002), I calculate illiquidity (ILLIQ) as a daily ratio of absolute stock return to its dollar volume averaged over a one-year period starting from the release date of the annual report (this measure is then multiplied by 106 to facilitate its presentation). Higher ILLIQ is associated with lower liquidity.
The second measure is fraction of zero return days (ZERORET). Following
Lesmond, Ogden, and Trizcinka (1999) and Goyenko et al. (2009) I calculate this measure as the proportion of trading days with both zero daily return and positive trading volume out of all trading days over one year period starting from the release date of the annual report. Higher ZERORET is associated with lower liquidity. 3
3 As a robustness check, I exclude trading days with zero return from the calculation of ILLIQ, following research such as Daske, Hail, Leuz, and Verdi (2008).
24 4.1.2 Liquidity Uncertainty
I adopt two proxies for liquidity uncertainty: volatility of stock liquidity and co- movement between firm-level liquidity and market liquidity. Following Lang and Maffett
(2011), I calculate the volatility of stock liquidity (LIQVOL) as the standard deviation of the daily ratio of absolute stock return to its dollar volume over a one-year period starting from the release date of annual report (this ratio is multiplied by 106 to facilitate its presentation). Higher LIQVOL suggests higher liquidity uncertainty.
Co-movement between firm-level liquidity and market liquidity is a systematic risk factor (Acharya and Pedersen 2005). Following Lang and Maffett (2011), I regress firm-level daily percentage in price impact of trade on past, current, and future market- level percentage change in price impact of trade for each firm. Specifically, I run the following time-series regression by firm using daily data over the year following the release date of the annual report to capture the co-movement between firm-level liquidity and market liquidity (COM(FL, ML)). The co-movement is equal to the R-squared of the estimation. Higher R-square suggests higher liquidity uncertainty because in this case firms’ liquidity level are more likely to change due to fluctuation of market level liquidity rather than firm specific factors.
%ΔDPIi,d = αi + βi,1%ΔDPIm,d-1 + βi,2%ΔDPIm,d + βi,3%ΔDPIm,d+1 +εi,d (1) where d denotes day, i denotes individual firm, and m denotes market aggregate.
Variables are defined in Appendix B.
4.2 Investment Efficiency
I adopt two measures to capture firm level investment efficiency. The first one is investment-cash flow sensitivity. As discussed in Section 3.3, firms under financial
25 constraints heavily rely on cash flow generated internally and underinvest while firms with entrenched managers overinvest using internal cash flow to avoid monitoring from external capital providers (Myers and Majluf 1984; Jensen 1986). Therefore, investment- cash flow sensitivity has been widely used to measure investment efficiency. Following prior research (e.g., Fazzari, Hubbars, and Peterson 2000; Hoshi, Kashyap, and
Scharfstein 1991; Biddle and Hilary 2006; McLean, Zhang, and Zhao 2012), I regress the capital investment scaled by lagged assets on operating cash flow scaled by lagged assets and Tobin’s Q proxied by market to book ratio for each firm over the ten-year rolling window. I use the coefficient of cash flow scaled by lagged asset to proxy for the investment-cash flow sensitivity (ICFS). Higher ICFS suggests lower investment efficiency. Equation (3) presents this regression model.
CAPXi,t /Ai,t-1 = β0 + β1 CFi,t /Ai,t-1 + β2 MTBi,t + εi,t (2)
Variables are defined in Appendix B.
The second measure is the deviation from optimal investment level. Following
Blaylock (2016), I regress investment on lagged growth opportunities and other firm characteristics for each industry-year. A more negative residual suggests more underinvestment while a more positive residual suggests more overinvestment. I take the absolute value of residual as deviation from the optimal level (SUBOPT). Equation (4) presents this regression model.
CAPXi,r/Ai,t-1 = β0 + β1 MTBi,t-1 + β2 CAPXi,t-1 /Ai,t-2 + β3 CASH_RATIOi,t-1 + β4
LEVi,t-1 + β5 ROAi,t-1 + β6 LNASSETi,t-1 + εi,t (3)
Variables are defined in Appendix B.
26 4.3 Textual Characteristics of Market Risk Disclosures
I use textual characteristics of market risk disclosures to assess their informativeness. The first dimension of the informativeness captures Item 7A readers’ information processing cost, using the readability of Item 7A. Consistent with the notion that managers can obfuscate information by making it harder for investors to interpret, prior studies document that more readable information is associated with lower information processing cost and better information environment (e.g., Li 2008; Biddle et al. 2009; Lehavy, Li, and Merkley 2011; Bozanic and Thevenot 2015). I capture readability using the Fog Index (FOG_INDEX). The Fog Index captures two aspects of readability: average sentence length and the fraction of complex words. Thus, more readable market risk disclosures (lower FOG_INDEX) are more informative.
The second dimension is the update in the textual content of Item 7A, which is measured by the similarity between market risk disclosures in the current year and the previous year (SIM). I use Java programming to calculate the similarity score. First, I calculate the frequency a word occurs in a document and normalize the frequency based on the document size because the frequency of a word is likely to be larger in large document. In this first step, all words are considered equally important. Second, I increase (decrease) the weight for the effects of more (less) frequently occurring terms and calculate the inverse document frequency. Third, I take the product of normalized word frequency and the inverse document frequency calculated in the first and second steps. Lastly, I derive a vector for each document using the numbers from the third step.
The cosine of the vectors of a pair of documents is the similarity score for a pair of documents. SIM is between 0 and 1 with 1 for identical disclosure. Similar disclosures
27 provide less up-to-date information. Nelson and Pritchard (2016) document that less similar risk factor disclosures are less likely to be boilerplate and are more meaningful for investors. 4
4.3 Regression Model
I estimate Equation (4) to investigate the association between the informativeness of market risk disclosure and liquidity level and liquidity risk and test H1 and H2. I regress either the liquidity level (LIQLEVEL) or liquidity uncertainty (LIQRISK) measures on one or more of my Item 7A disclosure metrics (INFORM) and control variables following recent research (e.g., Francis, Lafond, Olsson, and Schipper 2004;
Lang and Maffett 2011). LIQLEVEL takes the value of ILLIQ and ZERORET, while
LIQRISK takes the value of LIQVOL and COM (FL, ML) . The informativeness of the market risk disclosure takes the value of FOG_INDEX and SIM.
Equation (4) is as follows:
LIQLEVEL or LIQRISKi,t+1 = α0 + β1INFORMi,t + β2TURNOVERi,t +
β3PRIORRETi,t + β4Z_SCORE + β5STDRETi,t+ β6SIZEi,t + β7MTBi,t +
β8CAPINTENSi,t + β9CASH_RATIOi,t + β10LOSSi,t+ Industry FE+
Year FE + εi,t (4)
Variables are defined in Appendix B.
4 Alternatively, some research finds that higher similarity between disclosure of current period and prior period reduces financial report users’ uncertainty. Bozanic and Thevenot (2015) document that higher similarity in 10-K is associated with decreases in consensus and increases in private information precision.
More detailed information about the calculation of similarity score can be found at “http://www.ir- facility.org/scoring-and-ranking-techniques-tf-idf-term-weighting-and-cosine-similarity”.
28 I use prior stock return (PRIORRET) and stock volatility (STDRET) to control for short term return dynamics and firm value volatility. Investors respond to past performance stocks by chasing trends and stocks that have good recent performance are less likely to fluctuate as market liquidity changes. Thus, I expect PRIORRET to be positively (negatively) associated with liquidity level and uncertainty. Stock return volatility increases the inventory risk of market makers and shareholders’ uncertainty, and reduce liquidity trading (e.g., Amihud and Mendelson 1980, Benston and Hagerman
1974). Thus, I expect STDRET to be negatively (positively) associated with liquidity level (uncertainty).
I control for firm characteristics that may impact stock performance, including market capitalization (SIZE), growth opportunities (MTB), capital intensity
(CAPINTENS), financial liquidity (CASH_RATIO), and whether firms have accounting losses (LOSS). I expect SIZE to be positively (negatively) associated with liquidity level
(uncertainty) because smaller firms have lower investor awareness and less information available (Stoll and Whaley 1983). CAPINTENS, CASH_RATIO, and LOSS capture firms’ financial stress. Investors have more uncertainty regarding stock value of stressed firms. I therefore anticipate that both CAPINTENS and LOSS are negatively (positively) associated with liquidity level (uncertainty) while CASH_RATIO is positively
(negatively) associated with liquidity level (uncertainty). I do not make prediction for
MTB.
Consistent with my first and second hypotheses, I expect higher readability (lower
FOG_INDEX) and more updates (lower SIM) to be associated with (1) higher liquidity
29 level, as captured by lower ILLIQ and ZERORET, and (2) lower liquidity uncertainty, as captured by lower LIQVOL and COM (FL, ML).
I use Equation (5) to assess the association between the informativeness of market risk disclosures and investment efficiency and test my H3. I regress INVEFF (ICFS or
SUBOPT) on INFORM and control variables based on prior research.
Equation (5) is as follows:
INVEFFi,t+1 = α0 + β1INFORMi,t + β2LOGASSETi,t + β3STDCFOi,t+
β4STDSALESi,t + β5MTBi,t + β6STDINVi,t + β7Z_SCOREi,t + β8AGEi,t +
β9DIVi,t + β10CAPINTENSi,t + β11LOSSi,t + Industry FE + Year FE + εi,t (5)
Variables are defined in Appendix B.
I follow Biddle et al. (2009) and control for factors that may impact corporate disclosure behavior and investment decisions. I control for firm size (LOGASSET), the volatility of cash flow, sales, and investment (STDCFO, STDSALES, STDINV), growth opportunity (MTB), bankruptcy likelihood (Z_SCORE), firm age (AGE), whether firms pay dividend (DIV), tangibility (CAPINTENS), and whether firms have losses (LOSS).
Firms that are likely to bankrupt or have losses face more financial constraint, and therefore are more likely to rely on internal generated cash flow and deviate from optimal investment level. Therefore, I expect ZSCORE and LOSS are both negatively associated with investment efficiency. I do not make prediction for the rest of the control variables.
Consistent with my third hypothesis, I expect higher readability (lower
FOG_INDEX) and more updates (lower SIM) to be associated with higher investment efficiency, as captured by lower ICFS and SUBOPT.
30
Chapter 5 Sample Selection and Descriptive Statistics
Table 1 presents my sample selection procedure. My initial sample consists of
50,743 annual observations from the cross-section of the Computstat, CRSP, and SEC
Edgar database for the period of 2003 to 2015. I limit the sample period to after 2002 to so that the effect of Sarbanes-Oxley Act is not varied and to avoid the influence from the dotcom bubble. I eliminate 6,431 observation that are small reporting firms because they are not mandated to disclose market risks and may have different incentives when they voluntarily disclose market risks. Regulated industries, including utilities (SIC 4813-
4999) and financial institutions (SIC 6000-6999), are excluded from my sample. Firms from mines, coal, and oil industry (SIC 1000-1499; 2900-2912; 2990-2999) are most vulnerable to commodity risks and self-select to have longer Item 7As. I therefore exclude firms from these industries to reduce the self-selection bias in my sample. I then remove 2,807 observations whose Item 7A cannot be successfully parsed and 7,196 observations whose Item 7As have less than 100 words because their market risks are either not material or disclosed under other sections of 10-K, such as Item 7 and footnotes. After removing Item 7As that cannot be parsed or that are too short, the sample of firm-year observations with usable Item 7A data totals 19,283.
To construct my sample for stock liquidity, I further remove 9,751 observations that do not have sufficient information to calculate stock liquidity level, stock liquidity uncertainty, and control variables in Equation (4). My final sample for stock liquidity analyses consists of 9,532 observations. To construct my sample for investment
31 efficiency, I eliminate 8,560 observations which have missing information to calculate investment efficiency measures and control variables from the 19,283 observations that have usable Item 7A data. My final sample for investment efficiency analyses consists of
10,723 observations. I winsorize all continuous variables at the top and bottom 1% to reduce the effect of outliers.
Panel A of Table 2 present the descriptive statistics for the stock liquidity sample.
The average FOG_INDEX and SIM are 9.6697 and 0.8172, respectively. The mean value of ILLIQ and LIQVOL are 0.1893 and 0.4756, respectively, and both fall into their top quartile. Similar to Lang et al. (2011), the distributions of ILLIQ and LIQVOL are skewed. On average, observations in my sample have 2.2% of their trading days ending with zero return. The mean value of COM (FL, ML) is 16.53%, suggesting on average fluctuation of market aggregate liquidity explains 16.53% of firms’ stock liquidity.
Panel B of Table 2 reports the descriptive statistics for investment efficiency sample. Similar to the stock liquidity sample, the average FOG_INDEX and SIM are about 9.6511 and 0.8101, respectively. The mean values of ICFS is 0.19, suggesting that after controlling for investment opportunity, firms’ investment on average increase by
$0.19 for every $1 increase in cash flow. The mean value for SUBOPT is 0.31, indicating the average deviation from optimal investment is 31% of lagged assets. My descriptive statistics for control variables are consistent with prior research (e.g., Francis et al. 2004;
Biddle et al. 2009).
32 Panel A and B of Table 3 report the Pearson correlation coefficients for both the liquidity and investment efficiency sample, respectively. FOG_INDEX and SIM are positively associated with each other, suggesting that firms with more consistent Item 7A over time also tend to provide more readable Item 7A. Panel A shows that two liquidity level variables and two liquidity uncertainty variables are positively related to each other, suggesting firms with lower stock liquidity level also experiences more uncertainty in liquidity. FOG_INDEX is not significantly associated with any liquidity variables and
SIM is negatively associated with ZERORET and LIQVOL. Panel B suggests that two investment efficiency variables are positively associated with each other. FOG_INDEX and SIM are positively and negatively associated with SUBOPT, respectively. It is difficult to identify the impact of textual characteristics on liquidity and investment efficiency from the Pearson correlation. The correlation between control variables and stock liquidity level, liquidity uncertainty, and investment efficiency are consistent with my predictions in Section 4.3.5
5 I calculated the VIF and multicollinearity is not an issue in this study.
33
Chapter 6 Empirical Results
6.1 Results for Hypothesis 1
I estimate Equation (4) to test the affect the readability and year-to-year similarity of Item 7A on two measures of stock liquidity level and report the results in Table 4. The explanatory powers for estimated regressions are similar to prior research (e.g., Fang,
Noe, and Tice 2009; Lang et al. 2012).
The estimated coefficients for most control variables across the four models are consistent with my predictions. Higher PRIORRET and SIZE are associated with higher liquidity level, captured by lower ILLIQ and ZERORET. Higher STDRET,
CAPINTENS, and LOSS are associated with lower liquidity level, captured by higher
ILLIQ and ZERORET.
The results for my test variables show strong positive relationships between
FOG_INDEX and both liquidity level proxies, ILLIQ (0.0274; p-value < 0.01) and
ZERORET (0.0008; p-value < 0.01). As FOG_INDEX increases by 1, ILLIQ increases by 0.0274 and ZERORET increases by 0.0008, which represents 14.4% and 4% of the sample means of ILLIQ and ZERORET, respectively. My results suggest that firms providing less readable market risk disclosures are likely to have higher illiquidity and higher fraction of zero return days.
Estimation results also show significant associations between SIM and both
ILLIQ (0.1083; p-value < 0.05) and ZERORET (0.002; p-value < 0.10), suggesting firms that update market risk disclosures to a greater degree have higher illiquidity level and
34 higher fraction of zero return days. As SIM increase by 0.1, ILLIQ increases by 0.0108 and ZERORET increases by 0.0002, which represent 5.7% and 1% of their sample means. Results indicate that firms with less updates in market risk disclosures have lower liquidity level, captured by higher illiquidity and higher zero return days.
Results in Table 4 support the argument that higher information processing cost and less updated information are associated with higher information asymmetry among market participants and further lead to lower stock liquidity. My first hypothesis is supported.
6.2 Results for Hypothesis 2
Next, I test the association of readability and year-to-year similarity of Item 7A with stock liquidity uncertainty. Table 5 reports the estimates of Equation (4) using liquidity uncertainty, namely LIQVOL and COM (FL, ML), as dependent variables. The
R2s for the models explaining LIQVOL and COM (FL, ML) are approximately 18.8% and 3.5%, respectively.
The estimated coefficients for the control variables for each model reported in
Table 5 are generally consistent with my predictions. Higher PRIORRET and SIZE are associated with lower liquidity uncertainty. Higher STDRET, CAPINTENS, and LOSS are associated with higher liquidity uncertainty.
FOG_INDEX is positively associated with LIQVOL (0.0649; p-value < 0.05) and
COM (FL, ML) (0.0005; p-value < 0.05). The magnitudes of FOG_INDEX coefficients represent 13.6% and 0.3% of sample mean of LIQVOL and COM (FL, ML), respectively. SIM is positively associated with LIQVOL (0.2693; p-value < 0.05). The
35 magnitude of SIM coefficient suggests when the SIM increases by 10% point, LIQVOL increases by 0.0269, accounting for 5.7 % of sample mean of LIQVOL. SIM is not significantly associated with COM (FL, ML).
Results in Table 5 suggest that firms with more readable Item 7A exhibit less volatile stock liquidity and their stock liquidity varies less with the movement of market aggregate liquidity. Firms that actively update their market risk disclosures have less volatile stock liquidity, but their updates do not seem to impact the co-movement between firm-level and market-level liquidity. Overall, when market participants are provided more readable Item 7A and observe updates in Item 7A, their information environment is improved. Improved information environment reduces market participants’ uncertainty about stocks’ intrinsic value and leaves less opportunity for trading on private information. Firms, therefore, experience less liquidity uncertainty, supporting my second hypothesis.
6.3 Results for Hypothesis 3
I estimate Equation (5) to test the effect of the readability and year-to-year similarity of the market risk disclosure on investment efficiency. I regress proxies for investment efficiency (ICFS and SUBOPT) on textual characteristics of Item 7A
(FOG_INDEX and SIM) and control variables. Regression results are reported in Table
6. The explanatory power for the models explaining the association of FOG_INDEX with
ICFS and SUBOPT are 13.9% and 23.3%, respectively. The explanatory power for the models explaining the association of SIM with ICFS and SUBOPT are 14% and 22.7%, respectively.
36 Control variables capture firm characteristic that help explain variations in firms’ investment decisions. I use the same control variables for both ICFS and SUBOPT models, but the estimated coefficients of some control variables have different signs.
Higher ICFS captures both financing constraint and excess of cash flow, which lead to under- and over-investment, respectively. Higher SUBOPT also captures both under- and over-investment. The conflicting signs of coefficients of control variables in the two models may be caused by the dominating effect of over- or under- investment observations.
FOG_INDEX is positively associated with ICFS (0.0059; p-value < 0.1) and
SUBOPT (0.0121; p-value < 0.01). SIM is positively associated with ICFS (0.0121; p- value < 0.01) and not significantly associated with SUBOPT. The magnitudes of
FOG_INDEX coefficients account for 3% and 3.8% of the sample means of ICFS and
SUBOPT, respectively. As SIM increases by 10% point, SUBOPT increases by 0.0121, accounting for 3.8% of the sample means of SUBOPT.
Results from Table 6 indicate that the investment decisions of firms with more readable and updated market risk disclosures are less sensitive to their cash holdings.
Firms with more readable market risk disclosures are also less likely to deviate their investment from the optimal level. My third hypothesis is supported.
To further investigate whether the textual characteristics of market risk disclosure help resolve under-investment caused by financial constraints or over-investment by managers’ over-confidence or empire building incentives, I partition my sample into over- and under-investment groups. I classify an observation as over-investment if the
37 residual from the estimation of Equation (3) is positive and classify an observation as under-investment if the residual is negative. I then re-estimate Equation (5) for each group and report the results in Table 7.
Panel A shows the regression results using ICFS as the dependent variable.
FOG_INDEX and SIM are only significantly and positively associated with ICFS when firms over-invest. Results suggest that when firms have more readable and updated market risk disclosures, managers are less likely to exploit the excess cash flow and over- invest. I do not find any evidence that readable and updated market risk disclosures reduce under-investment caused by financing constraints.
Panel B shows the regression results using SUBOPT as the dependent variable.
FOG_INDEX is positively associated with SUBOPT. This positive association is significant at the 10% and 1% level (one-tail test) for the overinvestment subsample and the underinvestment subsample, respectively. These positive association suggest that when firms’ market risk disclosures are more difficult to interpret, firms are more likely to deviate from optimal investment level. SIM has significant and negative effect on
SUBOPT for both over- and under-investment subsamples. Item 7A discloses firms’ uncertainty in external financing. When firms confirm their market risk exposure disclosed in prior years, they have access to less volatile external financing. Thus, they are less likely to face financial constraints and under-invest and managers are less likely to exploit existing capital to over-invest. This is consistent with Bozanic and Thevenot’s
(2015) findings that higher similarity may also be informative. After partitioning the over- and under-investment observations, the explanatory power is largely improved,
38 suggesting the impact of textual characteristics on SUBOPT have different magnitude for over- and under-investment firms.
39
Chapter 7 Additional Analyses and Robustness Check
7.1 Alternative Measures for Readabilities and Similarity
As a robustness check, I use alternative measures to access the readability and similarity of Item 7A and re-run my main regressions. It is possible that firms with high liquidity and investment efficiency are less likely to hoard information and therefore disclose market risks in a more readable way. To address this reverse causality issue, I use FOG_INDEX of Item 7A from the prior fiscal year as an instrument variable. Past
FOG_INDEX captures firms’ style of disclosing market risks in the annual report and cannot predict current capital market momentum. Besides the style of disclosing market risks, the content of Item 7A also largely depends on the extent and the type of market risks faced by individual firms. I anticipate firms’ business complexity, existence of foreign segments, and derivative holdings may also impact the content of market risk disclosures and investors’ information processing cost of market risk disclosures. I estimate Equation (6) and use the predicted value of FOG_INDEX (FOG_PRED) as an alternative measure for readability. 6
FOG_INDEXi,t = α0 + β1FOG_INDEXi,t-1 + β2%FOREIGNSALEi,t +
β3DERIVATIVEi,t + β4FORSEGi,t + β5GEOSEGi,t+ β6BUSSEGi,t + β7EQFINi,t +
6 R-square of this regression is 41.3%, suggesting this model makes reasonable predictions for
FOG_INDEX. In untabulated analyses, I also replace FOG_INDEX with lagged FOG_INDEX in main analyses, results continue to be consistent.
40 β8DEBTFINi,t + β9LITRISKi,t + β10SIZEi,t+ β11MTBi,t + β12AGEi,t + β13STDRETi,t
+Industry FE + Year FE + εi,t (6)
Variables are defined in Appendix B.
Estimation results (untabulated) of Equation (6) suggest that lagged FOG_INDEX is positively associated with FOG_INDEX, suggesting firms’ disclosure style is consistent. Firms with more geographic segments (GEOSEG) and less business segments
(BUSSEG) have more readable ITEM 7A. SIZE and MTB are positively associated with
FOG_INDEX. The mean value of FOG_PRED is 9.61, similar to the mean value of
FOG_INDEX.
Table 8 reports the regression results using alternative Fog Index constructs in the models explaining liquidity, stock liquidity uncertainty, and investment efficiency.
Similar to results reported earlier, FOG_PRED is positively associated with ILLIQ,
ZERORET, LIQVOL, COM (FL, ML), ICFS, and SUBOPT, suggesting higher predicted readability is associated with higher stock liquidity level, liquidity uncertainty, and investment efficiency.
I reduce the weight of frequently occurring words in a document on similarity score when I calculate SIM, which partially reduce the effect of document length on the similarity score. As a robustness check, I further reduce weight of the impact of the document length on the similarity score. I follow Brown and Tucker (2011) and incorporate a Taylor Expansion in the estimation of document similarity. Specifically, I regress SIM on the first five polynomial of the number of words (L) in Item 7A (Equation
(7)).
2 3 4 5 SIMi,t = α0 + β1Li,t + β2L i,t + β3L i,t + β4 L i,t + β5 L i,t + εi,t (7)
41 Variables are defined in Appendix B. I use the residual from this estimation
(SIM_TAYLOR), as an alternative measure for year-to-year similarity. SIM_TAYLOR represents the fraction of SIM that is not explained by the length of Item 7A.
The untabulated estimation results of Equation (7) shows only L has a positive association with SIM. The R-square is less than 1%, suggesting that variation in SIM is not largely explained by the length of Item 7A. The mean value of SIM_TAYLOR is about zero. Table 8 reports the regression results using SIM_TAYLOR as an alternative measure for similarity. Results suggest that SIM is positively associated with ILLIQ,
ZERORET, LIQVOL, and ICFS.
Overall, results using alternative methods reported in Table 8 are generally consistent with the empirical results reported earlier.
7.2 Evidence from Other Textual Characteristics
In untabulated analyses, I use the length of market risk disclosure as an alternative textual characteristic to test whether higher information processing cost outweigh the benefit from more careful disclosure. The length of market risk disclosure is calculated as the logarithm of the number of words used in the disclosure.
On one hand, longer disclosures contain more information. For example,
Campbell, Chen, Dhaliwal, Lu, and Steele (2011) document that the length of risks disclosed under Item 1A of 10-K are associated with lower information asymmetry.
Disclosures tend to be longer if firms provide more careful description for market risk exposures and more detailed explanation regarding their choice of certain risk management strategies in terms of underlying assumption, objectives, and achievement.
42 On the other hand, some accounting research documents that readers may find longer disclosures hard to understand and interpret. For example, Li (2008) argues that the information procession cost of longer disclosure is higher, everything else equal. He finds that longer annual report is associated with lower earnings and less persistent positive earnings. You and Zhang (2009) also find that investors underreact to longer 10-
K. However, since the market risk disclosure section only accounts for a small portion of full 10-K, it is uncertain whether the length of Item 7A significantly increases the information procession costs by itself.
I regress the liquidity level, liquidity uncertainty, and investment efficiency on the length of Item 7A and control variables and re-estimate Equations (4) and (5).
Untabulated results suggest that the length of Item 7A is not significantly associated with liquidity level, liquidity uncertainty, or investment efficiency. It appears that although longer market risk disclosures may provide valuable information for market participants and firms, users of market risk disclosures find them difficult to interpret.
I further investigate the influence of content in Item 7A on stock liquidity level, stock liquidity uncertainty, and investment efficiency by testing which topics are more informative for users of market risk disclosures. I extract 20 topics from Item 7A using
Latent Dirichlet Allocation (LDA) model. LDA is a three-level hierarchical Bayesian model which considers text documents as mixtures of topics composed of individual words with certain probabilities (Blei, Ng, and Jordan 2003). This methodology has recently been used in several accounting studies, such as Brown, Crowley, and Elliott
(2016) and Dyer, Lang, and Stice-Lawrence (2017). First, I use Python to read all Item
7A disclosures in my sample and analyze word frequency from the pool of all Item 7A
43 disclosures. Second, I use LDA to analyze the words from the pooled text to identify 20 topics that the words fit in. Third, the LDA model analyzes each Item 7A disclosure and predict the probabilities that each topic appears in a specific disclosure based on the words mixture of this disclosure. Finally, I calculate the probability that each topic appears in each of the market risk disclosures (PROBTOPIC1-PROBTOPIC20). Key words for each topic are disclosed in Appendix C.
I regress liquidity level, liquidity uncertainty, and investment efficiency on the probability that each topic appears in a document (PROBTOPIC1-PROBTOPIC19) and control variables in Equation (4) and (5), respectively.7 This arrangement allows me to identify topics that have the most influences on liquidity and investment efficiency. Table
9 presents the regression results. Results show that most of the extracted topics help explain the fraction of zero-return days. Both market participants and firms benefit from the disclosure of Topic (2), (7), (11), (13), and (16) because they help explain stock liquidity level and uncertainty and reduce the investment-to- cash flow sensitivity and deviation from optimal investment level. Disclosure of Topic (4), (6), and (12) is more helpful for improving investment efficiency but is not very informative to market participants.
Overall, results suggest some topics of Item 7A are associated with improved stock liquidity level, lower liquidity uncertainty, and improved investment efficiency more than other topics. Thus, although some contents of market risk disclosures tend to be less informative, market participants and firms do benefit from the disclosure of
7 I exclude PROBTOPIC 20 from regression due to multi-collinearity issues. 44 certain topics. These findings, to certain degree, also explain why longer market risk disclosures are not necessarily informative.
7.3 Impact of Market Risk Disclosures on Sensitivity of Stock Return to Market Liquidity
Liquidity risk has multiple aspects (Sadka 2011). My research centers on the liquidity uncertainty. As an additional analysis, I investigate whether the sensitivity of stock return to market aggregate liquidity, another aspect of liquidity risk, is affected by the textual content of market risk disclosure. A stock with higher systematic risk will perform relatively better (worse) when the market aggregate liquidity is high (low) (Ng
2011). Thus, stocks with greater uncertainty and adverse selection tend to have higher unexpected returns and higher fluctuations in liquidity upon changes in market aggregate liquidity.
Liquidity beta captures the sensitivity of stock returns to unexpected changes in market liquidity (Pastor and Stambaugh 2003). I follow Pastor and Stambaugh (2003) and include a market liquidity factor which captures unexpected changes in market liquidity as an additional factor in the Fama-French (1993) three-factor asset pricing model. I regress monthly excess return on monthly Fama-French (1993) factors and market aggregate liquidity for each firm over the twelve months following the third month after the firm’s fiscal year end. Liquidity beta (βL) is defined as the coefficient of unexpected changes in market liquidity in Equation (8).
M S H L RETi,t = αi,t + βi,t MKTt + βi,tSMBt + βi,tHMLt + βi,t MLIQt + εi,t (8)
Variables are defined in Appendix B.
45 I regress βL on the readability and year-to-year similarity of Item 7A and report results in Table 10. FOG_INDEX is negatively associated with βL (-0.0055; p-value <
0.01) and SIM is not significantly associated with βL. Results suggest that when firms provide more readable Item 7A, their stock return is higher (lower) as market liquidity increases (decreases), suggesting higher systematic risks. It appears that investors incorporate disclosed capital market risks as they make decisions.
7.4 Information Asymmetry Level
I argue that informative market risk disclosures reduce the information asymmetry among market participants and market friction to improved liquidity and investment efficiency. However, firms’ ex-ante information asymmetry may impact investors’ demand and reaction for public available information. On one hand, when firms have higher ex-ante information asymmetry, informative market risk disclosures fill a greater void in the information environment and may lead to a more pronounced improvement in liquidity and investment efficiency. On the other hand, investors and capital providers prefer firms with lower ex-ante information asymmetry and have confirmation bias.
When firms with lower information asymmetry provide informative market risk disclosures, information users’ existing judgment regarding firms’ information quality are supported.
I test whether my inferences from Chapter 6 are sensitive to firms’ ex-ante information asymmetry. I follow prior literature (e.g., Hu, Liu, and Zhu 2015) and use
R&D cost, a non-market-based measure, to access firms’ ex-ante information asymmetry.
I partition my sample based on the industry-median R&D cost (scaled by total asset) for
46 each fiscal year. If a firm’s R&D cost (scaled by total asset) is above the industry median,
I classify this firm as a high (low) information asymmetry firm. I re-run main regressions from Chapter 6 and report results in Table 10.
The associations between FOG_INDEX and ILLIQ and ZERORET and the association between SIM and ILLIQ and LIQVOL are significant and positive for both high and low information asymmetry observations. It appears that readable and updated
Item 7A improve stock liquidity level for both high and low information asymmetry firms. Updated Item 7A reduce the liquidity volatility for both high and low information asymmetry firms.
These positive associations between FOG_INDEX and LIQVOL and between
SIM and ZERORET are only significant for firms with higher information asymmetry.
Readable and updated Item 7A contribute to high information asymmetry firms’ information environment and reduce liquidity volatility and fraction of zero-return days, respectively. The association between FOG_INDEX and COM (FL, ML) is significant and positive for low information asymmetry observations. Low information asymmetry firms with readable Item 7A experience smaller stock liquidity decline as the market liquidity drops than low information asymmetry firms with less readable Item 7A.
Overall, investors benefit from more readable and updated market risk disclosure despite their ex-ante information asymmetry level.
These positive associations between FOG_INDEX and ICFS and SUBOPT are significant only for the high information asymmetry group. Readable market disclosures reduce high information asymmetry firms’ dependence on cash availability when making investment decisions, reduce their deviation from optimal investment level and thus
47 improve their investment efficiency. The association between SIM and ICFS is significant and positive for low information asymmetry observations. Updated market risk disclosures further improve the information environment of firms that already have less information asymmetry and reduce their investment-to-cash flow sensitivity. The negative association between SIM and SUBOPT is significant for both high and low information asymmetry firms. Firms with more consistent market risk disclosures have less deviation from the optimal investment level despite of their ex-ante information asymmetry.
Overall, these results suggest that the impacts of textual content of Item 7A on liquidity level, liquidity uncertainty, and investment efficiency may be conditional on firms’ ex-ante information asymmetry. These impacts can be driven by filled void in information environment, investors’ confirmation bias, or both.
7.5 Other Robustness Tests
It is possible that the associations between the textual characteristics of Item 7A and liquidity and investment efficiency are driven by the correlation between textual characteristics and financial reporting quality. In untabulated analysis, I calculate firms’ accrual quality following Dechow and Dichev (2002) method and include accrual quality in all regressions to control for the effect of financial reporting quality and the results continue to support my hypotheses. Therefore, textual characteristics of Item 7A have an effect on stock liquidity and investment efficiency incremental to accrual quality.
Nelson and Pritchard (2016) document that the readability and similarity of firms’ general risk disclosures (Item 1A) are associated on firms’ litigation risk. To rule out the
48 possibility that my results are driven by firms with higher litigation risks, I control for ex- ante firm specific security litigation risk using Kim and Skinner (2012) model (their
Table 7, Model 3). Results continue to support my hypotheses.
Firms with equity size just around the cut-off for smaller reporting firms may have different reporting incentives. I exclude firms with less than $100 million market floats from my sample and re-run main analyses. Regression results (untabulated) are similar to results reported in Chapter 6. Thus, my findings are not sensitive to the $75 million cut-off for smaller reporting firms.
In my main analyses, I use one year as the horizon for liquidity level and uncertainty. However, stock liquidity over this relatively long horizon could be impacted by unexpected events during the year. My results are generally robust. To address this issue, I limit the horizon to one month after the release of 10-K when calculate liquidity variables. As stated in Chapter 5, the distribution of ILLIQ and LIQVOL are skewed. I take the square root of ILLIQ and LIQVOL to reduce the skewness and use them as dependent variables to re-estimate Equation (5). Results continue to support my findings.
49
Chapter 8 Conclusion
In response to the SEC’s call for the discussion about the usefulness of mandatory market risk disclosures, I study the association between relative informativeness of market risk disclosures and stock liquidity and investment efficiency. My study highlights the benefit of providing informative market risk disclosures and may interest regulators and firms.
Theory suggests that reduced information asymmetry among market participants limits the exploitation of private information, drives down the trading costs, and resolves investors’ uncertainty about stock’ intrinsic value. I find that more readable market risk disclosures are associated with higher stock liquidity level and lower liquidity uncertainty, supporting the argument that readable market risk disclosures improve market participants’ information environment by lowering market participants’ information processing cost. I also document that updated market risk disclosures are also associated with higher stock liquidity level and lower liquidity uncertainty, indicating that market participants do incorporated the updates in market risk disclosures to facilitate their decision making.
Financial constraints and moral hazard are two market frictions that deviate firms from optimal investment. Improved information environment reduces the inefficient investment decisions caused by market frictions. I find that firms with more readable market risk disclosures rely less on the availability of cash when making investment decisions and are less likely to exploit available cash to overinvest. I also find that firms’
50 investments are less sensitive to cash flow when they update market risk disclosures and firms’ investments are closer to the optimal investment level when they confirm market risk disclosed in prior year. In the additional analyses, I find that some topics in market risk disclosures are more informative than others and market participants and firms may benefit from the disclosure of different topics. I also find that firms’ ex-ante information asymmetry may impact the benefit that market participants and firms receive from informative market risk disclosures. Firms can strategically plan their market risk disclosure based on investors’ interests and their ex-ante information environment.
My study has several limitations. First, as suggested by Amihud (2002) and Sadka
(2011), liquidity level and risks are elusive concepts and have multiple aspects. My research focuses on two aspects of liquidity level, illiquidity and zero-return days, and the liquidity uncertainty. Future studies can explore the impact of market risk disclosures on other dimensions of liquidity level and risks. Second, my measures for liquidity are calculated using low frequency data due to the data availability. Results may be more convincing if high frequency trading information is factored in. Third, although I controlled for accrual quality in the robustness test, I cannot fully control the impact of other sections of annual report on investor and firms’ behavior. Annual report users interpret the market risk disclosure along with other information. Fourth, market risk disclosures have both qualitative and quantitative information. My research design does not capture the quantitative information disclosed in Item 7A, which may also be informative to users.
51
Appendices
52
Appendix A: Market Risk Disclosure of American Airline for Fiscal Year 2017 ITEM 7A. QUANTITATIVE AND QUALITATIVE DISCLOSURES ABOUT MARKET RISK. The risk inherent in our market risk sensitive instruments and positions is the potential loss arising from adverse changes in the price of fuel, foreign currency exchange rates and interest rates as discussed below. The sensitivity analyses presented do not consider the effects that such adverse changes may have on overall economic activity, nor do they consider additional actions we may take to mitigate our exposure to such changes.
Therefore, actual results may differ. See Note 7 to AAG’s Consolidated Financial
Statements in Part II, Item 8A and Note 5 to American’s Consolidated Financial
Statements in Part II, Item 8B for additional discussion regarding risk management matters.
Aircraft Fuel
Our operating results are materially impacted by changes in the availability, price volatility and cost of aircraft fuel, which represents one of the largest single cost items in our business. Because of the amount of fuel needed to operate our airlines, even a relatively small increase or decrease in the price of fuel can have a material effect on our costs and liquidity. Jet fuel market prices have fluctuated substantially over the past several years with market spot prices ranging from a low of approximately $0.80 per gallon to a high of approximately $2.00 per gallon during the period from January 1,
2015 to December 31, 2017.
As of December 31, 2017, we did not have any fuel hedging contracts outstanding to hedge our fuel consumption. As such, and assuming we do not enter into any future transactions to hedge our fuel consumption, we will continue to be fully exposed to
53 fluctuations in fuel prices. Our current policy is not to enter into transactions to hedge our fuel consumption, although we review that policy from time to time based on market conditions and other factors. Based on our 2018 forecasted mainline and regional fuel consumption, we estimate that a one cent per gallon increase in aviation fuel price would increase our 2018 annual fuel expense by $45 million.
Foreign Currency
We are exposed to the effect of foreign exchange rate fluctuations on the U.S. dollar value of foreign currency-denominated operating revenues and expenses. Our largest exposure comes from the British pound, Euro, Canadian dollar and various Latin
American currencies, primarily the Brazilian real. We do not currently have a foreign currency hedge program. A uniform 10% strengthening in the value of the U.S. dollar from 2017 levels relative to each of the currencies in which we have foreign currency exposure would have resulted in a decrease in operating income of approximately
$203 million for the year ended December 31, 2017.
Generally, fluctuations in foreign currencies, including devaluations, cannot be predicted by us and can significantly affect the value of our assets located outside the United States.
These conditions, as well as any further delays, devaluations or imposition of more stringent repatriation restrictions, may materially adversely affect our business, results of operations and financial condition. See Part I, Item 1A. Risk Factors –“We operate a global business with international operations that are subject to economic and political instability and have been, and in the future may continue to be, adversely affected by numerous events, circumstances or government actions beyond our control” for additional discussion of this and other currency risks.
54 Interest
Our earnings and cash flow are also affected by changes in interest rates due to the impact those changes have on our interest expense from variable rate debt instruments and our interest income from short-term investments.
Our largest exposure with respect to variable rate debt comes from changes in LIBOR.
We had variable rate debt instruments representing approximately 40% of our total long- term debt at December 31, 2017. We currently do not have an interest rate hedge program. If annual interest rates increase 100 basis points, based on our December 31,
2017 variable-rate debt and short-term investments balances, annual interest expense on variable rate debt would increase by approximately $95 million and annual interest income on short-term investments would increase by approximately $51 million.
Additionally, the fair value of fixed-rate debt would have decreased by approximately
$691 million for AAG and $664 million for American.
55 Appendix B: Variable Definitions
%∆DPIi = firm-level daily percentage change in DPI
%∆DPIm = market level daily percentage change in DPI, which is daily
percentage in equal-weighted average DPI of the individual
stocks
%FOREIGNSALE = percentage of sales from foreign segment to total sales
A = total assets
AGE = difference between first year the firm appear on COMPUSTAT
and fiscal year
BUSSEG = number of business segments
CAPINTENS = ratio of property, plant and equipment to total assets
CAPX = capital investment
CASH_RATIO = ratio of cash and cash equivalent to total current liability
CF = net income plus depreciation expense
COM (FL, ML) = R-square from the following firm-specific regression:
%∆DPIi,d = αi + βi,1%∆DPIm,d−1 + βi,2%∆DPIm,d
+ βi,3%∆DPIm,d+1 + εi,d
DEBTFIN =change in total debts scaled by lagged total assets
DERIVATIVE =1 if any derivative-related item in COMPUSTAT has a non-
zero and non-missing value; 0 otherwise
DIV = 1 if the firm pays dividend; 0 otherwise
DPI = daily ratio of absolute stock return to its dollar volume
56 EQFIN = change in the book value of equity plus change in deferred
taxes minus the change in retained earnings, scaled by lagged
assets
FOG_INDEX = Fog Index of Item 7A
FORSEG =1 if the firm has foreign segments; 0 otherwise
GEOSEG = number of geographic segments
HML = average return on the two value portfolios minus the average
return on the two growth portfolios. This variable is publicly
available on WRDS.
ICFS = coefficient of CF/At-1 from the estimation of the following
regression by firm:
CAPXi,t⁄Ai,t−1 = α0 + β1 CFi,t⁄Ai,t−1 + β2MTBi,t + εi,t
ILLIQ = daily ratio of absolute stock return to its dollar volume
averaged over a year multiplied by 106
INFORM = FOG_INDEX or SIM
INVEFF = ICFS or SUBOPT
LEN = natural logarithm of the number of words used in Item 7A
LIQLEVEL = one of the following variables: ILLIQ, SPREAD, and
ZERORET
LIQRISK = one of the following variables: LIQVOL, COM (FL, ML), and
βL
LIQVOL = annual standard deviation of the daily ratio of absolute stock
return to its dollar volume
57 LITRISK = Kim and Skinner’s (2012) litigation score (their Table 7,
Model 3)
LOGASSET = natural logarithm of total assets
LOSS = 1 if the firm has accounting loss; 0 otherwise
MKT = return on the value-weighted return on all NYSE, AMEX, and
NASDAQ stocks minus the one-month Treasury bill rate.
MLIQ = innovations in market aggregate liquidity. This measure is
developed by Pastor and Stambaugh (2003) and publicly
available on WRDS.
MTB = market to book ratio
PRIORRET = stock return of previous year
PROBTOPICn = probability of nth topic appears in Item 7A
RET = monthly return in excess of risk-free rate
SALES_GROWTH = percentage change in sales from previous year
SIM = textual similarity between Item 7A of current year and Item 7A
of previous year
SIZE = natural logarithm of market value
SMB = average return on the three small portfolios minus the average
return on the three big portfolios. This variable is publicly
available on WRDS.
STDCFO = standard deviation of operating cash flow to average assets
ratio over years t-5 to t-1
58 STDINV = standard deviation of capital investment to average assets ratio
over years t-5 to t-1
STDRET = standard deviation of daily return
STDSALES = standard deviation of sales to average assets ratio over years t-5
to t-1
SUBOPT = residual from the following regression:
CAPXi,t = β0 + β1SALES_GROWTHi,t + εi,t
TURNOVER = trading volume scaled by outstanding shares
ZERORET = proportion of trading days with both zero daily return and
positive trading volume out of all trading days in a year
Z_SCORE = Altman z-score
βL = coefficient of MLIQ from the estimation of the following
regression:
M S H L RETi,t = αi,t + βi,tMKTt + βi,tSMBt + βi,tHMLt + βi,tMLIQt
+ εi,t
59 Appendix C: Keywords of Topics Covered in Item 7A
Topic Keywords
instruments, financial, derivative, trading, speculative, enter, interest, manage, 1 rate, risk, market, foreign, exposure, hedging foreign, currency, exchange, enter, forward, contracts, hedge, exposure, 2 denominated, risk, rate, transactions, fluctuations interest, rate, risk, exposure, debt, manage, changes, fixed, market, variable, 3 swap, use, risks, swaps interest, market, risk, foreign, currency, exchange, rate, exposure, risks, 4 primarily, fluctuations, financial value, losses, gains, fair, recorded, income, comprehensive, net, loss, 5 recognized, unrealized, contracts, accumulated financial, statements, consolidated, information, internal, control, accounting, 6 reporting, included, risk, discussion, management, market, material foreign, currency, exchange, dollar, rates, denominated, currencies, functional, 7 rate, net, assets, subsidiaries, liabilities foreign, denominated, sales, currencies, currency, dollar, operations, expenses, 8 local, products, international, operating, revenue, primarily interest, increase, approximately, rate, million, expense, decrease, basis, 9 change, point, debt, hypothetical, annual, result interest, market, rate, value, changes, fair, risk, investment, cash, securities, 10 income, debt, investments, fixed price, commodity, raw, market, fuel, contracts, purchase, natural, risk, gas, 11 cost, materials, changes, material interest, rate, fair, value, cash, average, debt, maturity, following, principal, 12 instruments, related forward, contracts, foreign, currency, exchange, fair, value, notional, rate, 13 interest, amount, cash, hedge, outstanding fair, value, stock, price, common, shares, market, convertible, notes, debt, 14 interest, rate, senior, amount
60 results, financial, foreign, exchange, rate, impact, cash, material, currency, 15 operations, changes, effect, fluctuations, interest securities, investments, cash, market, equivalents, portfolio, money, 16 marketable, debt, interest, rate, auction, corporate interest, rate, credit, million, variable, fixed, debt, outstanding, swap, 17 borrowings, facility market, economic, credit, ability, business, products, future, financial, 18 conditions, changes, risk, including, impact, capital, costs credit, investment, risk, financial, exposure, principal, preserve, quality, 19 primary, cash, policy, objective, market, amount fair, value, market, interest, hypothetical, rates, change, sensitivity, potential, 20 analysis, exchange, foreign, risk, currency
61 Appendix D: Tables Table 1: Sample Selection
Cross-section of Compustat, CRSP, and Edgar: 50,743 Less firm-year observations: Small Reporting Company (6,431) In the mines, coal, oil, utility, and financial service industry (SIC 1000-1499 2900-2912 2990-2999 4813-4999 6000-6999) (15,026) Not successfully parsed (2,807) With less than 100 words in Item 7A (7,196) Firm-year observations with usable Item 7A 19,283
Less firm-year observations missing information for liquidity and control variables in Equation (4) (9,751) Firm-year observations for liquidity sample 9,532
Firm-year observations with usable Item 7A 19,283 Less firm-year observations missing information for investment efficiency and control variables in Equation (5) (8,560) Firm-year observations for investment efficiency sample 10,723
62 Table 2:Descriptive Statistics
Panel A: Stock Liquidity Sample VARIABLES (N= 9,532) Mean SD Q1 Median Q3
ILLIQ 0.1893 0.8336 0.0006 0.0033 0.0267 ZERORET 0.0222 0.0257 0.0077 0.0121 0.0279 LIVOL 0.4756 2.3153 0.0005 0.0031 0.0286 COM (FL, ML) 0.1653 0.0366 0.1390 0.1625 0.1883 FOG_INDEX 9.6697 1.5604 8.4000 9.6000 10.4000 SIM 0.8172 0.2019 0.7699 0.8895 0.9493 TURNOVER 9.4236 7.7629 4.2733 7.5149 12.0710 PRIORRET 0.2003 0.6876 -0.1639 0.0838 0.3768 Z_SCORE 4.4334 4.8216 2.2801 3.5587 5.5469 STDRET 0.0291 0.0140 0.0193 0.0258 0.0351 SIZE 6.6500 1.7459 5.4223 6.6044 7.8316 MTB 2.9066 3.9325 1.3792 2.1691 3.5560 CAPINTENS 0.2241 0.1854 0.0840 0.1703 0.3122 CASH_RATIO 0.8041 1.1129 0.1676 0.4493 0.9596 LOSS 0.2484 0.4321 0.0000 0.0000 0.0000
63 Table2: Descriptive Statistics (continued)
Panel B: Investment Efficiency Sample VARIABLES (N=10,723) Mean SD Q1 Median Q3
ICFS 0.1909 0.4930 -0.0158 0.1008 0.3022 SUBOPT 0.3177 0.2375 0.1277 0.2687 0.4584 FOG_INDEX 9.6511 1.5613 8.4000 9.6000 10.4000 SIM 0.8101 0.2083 0.7598 0.8850 0.9480 LNASSET 6.3776 1.7464 5.1540 6.3509 7.5690 STDCFO 0.0730 0.0754 0.0293 0.0497 0.0863 STDRET 0.0304 0.0145 0.0202 0.0271 0.0368 MTB 2.9048 4.1450 1.3335 2.1389 3.5840 STDINV 0.0225 0.0255 0.0068 0.0135 0.0278 Z_SCORE 4.2965 5.2627 2.1050 3.4626 5.5144 AGE 22.1483 14.5519 11.0000 17.0000 29.0000 DIV 0.3910 0.4880 0.0000 0.0000 1.0000 CAPINTENS 0.2139 0.1913 0.0704 0.1530 0.2977 LOSS 0.2828 0.4504 0.0000 0.0000 1.0000
64
Table 3: Pearson Correlation Coefficients
Panel A: Stock Liquidity Sample 1 2 3 4 5 6 7 8 9 10 11 12 13 14
1 ILLIQ 1 2 ZERORET 0.4719* 1 3 LIQVOL 0.9755* 0.4276* 1 4 COM(FL, ML) 0.1008* 0.1672* 0.0966* 1 5 FOG_INDEX 0.0193 0.0009 0.0139 0.0113 1 6 SIM -0.0195 -0.0493* -0.0210* -0.0028 0.0516* 1 7 TURNOVER -0.1995* -0.2267* -0.1803* -0.0616* -0.0117 -0.0112 1 8 PRIORRET -0.0134 -0.0305* -0.0125 0.0043 -0.0085 -0.0643* 0.0163 1 9 Z_SCORE -0.0753* -0.2213* -0.0641* -0.0336* 0.0054 0.0154 0.0441* 0.0680* 1
65 10 STDRET 0.2013* 0.3503* 0.1862* 0.0460* -0.0385* -0.0886* 0.2587* 0.1606* -0.1826* 1
11 SIZE -0.4063* -0.5944* -0.3686* -0.1037* 0.0843* 0.0657* 0.2601* -0.0118 0.1518* -0.5159* 1 12 MTB -0.0752* -0.0961* -0.0677* -0.0145 0.0418* 0.0024 0.0585* 0.1290* 0.1950* -0.0726* 0.1989* 1 13 CAPINTENS 0.0147 -0.0715* 0.0145 -0.0321* 0.0108 -0.0159 0.0029 -0.0391* -0.1286* -0.0501* 0.0366* -0.0822* 1 14 CASH_RATIO -0.0276* 0.0724* -0.0286* 0.0257* 0.0059 0.0064 0.1246* 0.0612* 0.3081* 0.1340* -0.0895* 0.0544* -0.2378* 1 15 LOSS 0.1616* 0.3742* 0.1434* 0.0662* -0.0054 -0.0404* 0.0496* -0.0229* -0.2684* 0.4293* -0.3505* -0.0332* -0.0597* 0.1280*
Table 3: Pearson Correlation Coefficients (continued)
Panel B: Investment Efficiency Sample 1 2 3 4 5 6 7 8 9 10 11 12 13
1 ICFS 1 2 SUBOPT 0.0169* 1 3 FOG_INDEX 0.0126 0.1015* 1 4 SIM 0.0104 -0.0133* 0.0301* 1 5 LNASSET 0.1470* 0.3611* 0.0649* 0.0634* 1 6 STDCFO -0.1779* -0.0355* 0.0213* -0.0667* -0.4333* 1 7 STDRET -0.1093* -0.1059* -0.0240* -0.0862* -0.4339* 0.2969* 1 8 MTB -0.0464* 0.0362* 0.0245* 0.003 -0.0148* 0.1194* -0.0552* 1 9 STDINV 0.1695* -0.0507* -0.0301* -0.0446* -0.1470* 0.1719* 0.1198* 0.0153* 1 10 Z_SCORE -0.0179* -0.0282* 0.0072 0.0186* -0.0416* -0.0111 -0.1830* 0.2171* -0.005 1
66 11 AGE 0.0565* 0.0302* 0.0933* 0.0583* 0.4039* -0.2506* -0.2555* -0.0407* -0.1962* -0.0408* 1
12 DIV 0.0869* 0.1055* 0.0926* 0.0362* 0.3229* -0.2172* -0.2879* -0.0017 -0.1132* 0.0116 0.3985* 1 13 CAPINTENS 0.3308* 0.0566* -0.0001 -0.0067 0.1877* -0.2452* -0.0727* -0.0805* 0.4201* -0.1174* 0.0988* 0.1681* 1 14 LOSS -0.1174* -0.0635* 0.0101 -0.0443* -0.3128* 0.2880* 0.4432* -0.0398* 0.0656* -0.2697* -0.1895* -0.2383* -0.0871*
* denotes significance at 5% level. Variables are defined in Appendix B.
Table 4: The Effect of Market Risk Disclosures on Stock Liquidity Level
Eq. (4): LIQLEVELi,t+1 = α0 + β1INFORMi,t + β2TURNOVERi,t + β3PRIORRETi,t + β4Z_SCORE + β5STDRETi,t+ β6SIZEi,t + β7MTBi,t + β8CAPINTENSi,t + β9CASH_RATIOi,t + β10LOSSi,t+ Industry FE + Year FE + εi,t
Prediction ILLIQ ZERORET
Constant ? 0.4117 0.5649** 0.0458*** 0.0509*** (1.585) (2.344) (4.561) (5.181) FOG_INDEX H1: + 0.0274*** 0.0008*** (2.810) (3.701) SIM H1: + 0.1083** 0.0020* (2.071) (1.744) TURNOVER - -0.0128*** -0.0130*** -0.0005*** -0.0005*** (-6.128) (-6.215) (-10.053) (-10.180) PRIORRET - -0.0559*** -0.0564*** -0.0031*** -0.0031*** (-3.688) (-3.728) (-8.501) (-8.537) Z_SCORE - 0.0043 0.0042 -0.0006*** -0.0006*** (1.318) (1.272) (-8.143) (-8.188) STDRET + 6.1662*** 6.2751*** 0.2426*** 0.2458*** (2.881) (2.931) (6.196) (6.265) SIZE - -0.1510*** -0.1483*** -0.0062*** -0.0061*** (-10.933) (-10.850) (-19.922) (-19.631) MTB ? 0.0033 0.0037 0.0002** 0.0002** (1.435) (1.612) (2.308) (2.493) CAPINTENS + 0.1864* 0.1802* -0.0064*** -0.0066*** (1.852) (1.802) (-2.591) (-2.661) CASH_RATIO - -0.0359** -0.0350** 0.0006* 0.0007* (-2.290) (-2.227) (1.829) (1.917) LOSS + 0.0731** 0.0762** 0.0073*** 0.0073*** (2.450) (2.541) (9.042) (9.100)
Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes
Observations 9,532 9,532 9,532 9,532 R-squared 0.211 0.209 0.465 0.463 The t-statistics are reported in parentheses ***, **, * denote significance at 1%, 5%, and 10% levels (two-tail tests). Standard errors are clustered at firm level. Variables are defined in Appendix B.
67
Table 5: The Effect of Market Risk Disclosures on Stock Liquidity Uncertainty
Equation (4): LIQRISKi,t+1 = α0 + β1INFORMi,t + β2TURNOVERi,t + β3PRIORRETi,t + β4Z_SCORE + β5STDRETi,t+ β6SIZEi,t + β7MTBi,t + β8CAPINTENSi,t + β9CASH_RATIOi,t + β10LOSSi,t+ Industry FE + Year FE + εi,t
Prediction LIQVOL COM(FL, ML)
Constant ? 0.8354 1.1877* 0.1164*** 0.1200*** (1.186) (1.827) (13.342) (14.182) FOG_INDEX H2: + 0.0649** 0.0005** (2.417) (2.108) SIM H2: + 0.2693* 0.0015 (1.813) (0.747) TURNOVER - -0.0346*** -0.0351*** -0.0002*** -0.0002*** (-5.941) (-6.013) (-3.387) (-3.477) PRIORRET - -0.1638*** -0.1648*** -0.0003 -0.0003 (-3.866) (-3.900) (-0.507) (-0.525) Z_SCORE - 0.0146 0.0142 -0.0002* -0.0002** (1.554) (1.516) (-1.944) (-1.975) STDRET + 20.1112*** 20.3666*** 0.0676 0.0698 (3.335) (3.379) (1.494) (1.543) SIZE - -0.3766*** -0.3703*** -0.0018*** -0.0017*** (-10.639) (-10.578) (-5.303) (-5.177) MTB ? 0.0085 0.0095 -0.0000 0.0000 (1.358) (1.515) (-0.062) (0.012) CAPINTENS + 0.4942* 0.4796* -0.0029 -0.0030 (1.763) (1.723) (-0.982) (-1.028) CASH_RATIO - -0.1026** -0.1006** 0.0006 0.0006 (-2.392) (-2.333) (1.359) (1.403) LOSS + 0.1589* 0.1662* 0.0021* 0.0021* (1.876) (1.956) (1.902) (1.958)
Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes
Observations 9,532 9,532 9,532 9,532 R-squared 0.188 0.187 0.036 0.035 The t-statistics are reported in parentheses ***, **, * denote significance at 1%, 5%, and 10% levels (two-tail tests). Standard errors are clustered at firm level. Variables are defined in Appendix B.
68
Table 6: The Effect of Market Risk Disclosures on Investment Efficiency
Equation (5): INVEFFi,t+1 = α0 + β1INFORMi,t + β2LOGASSETi,t + β3STDCFOi,t+ β4STDSALESi,t + β5MTBi,t + β6STDINVi,t + β7Z_SCOREi,t + β8AGEi,t + β9DIVi,t + β10CAPINTENSi,t + β11LOSSi,t + Industry FE + Year FE + εi,t
Prediction ICFS SUBOPT
Constant ? 1.4055*** 1.4040*** -0.3634*** -0.2291** (27.562) (29.557) (-3.411) (-2.166) FOG_INDEX H3: + 0.0059* 0.0121*** (1.948) (4.883) SIM H3: + 0.0614*** -0.0134 (2.895) (-1.118) LNASSET ? 0.0176*** 0.0182*** 0.0716*** 0.0722*** (5.050) (5.226) (15.312) (15.308) STDCFO ? -0.4155*** -0.3993*** 0.2478*** 0.2644*** (-6.850) (-6.570) (3.804) (4.046) STDRET ? -2.2889*** -2.2483*** 2.0019*** 2.0223*** (-5.536) (-5.431) (6.171) (6.215) MTB ? -0.0018** -0.0017* 0.0019** 0.0020** (-1.984) (-1.899) (2.417) (2.568) STDINV ? 1.6518*** 1.6401*** -0.5511*** -0.5659*** (3.837) (3.812) (-3.303) (-3.393) Z_SCORE - -0.0012 -0.0011 0.0008 0.0009 (-1.540) (-1.520) (0.828) (0.957) AGE ? 0.0003 0.0003 -0.0024*** -0.0023*** (0.830) (0.997) (-5.668) (-5.462) DIV ? -0.0168 -0.0156 0.0305*** 0.0332*** (-1.542) (-1.420) (3.311) (3.583) CAPINTENS ? 0.5872*** 0.5892*** 0.0512 0.0508 (12.957) (13.036) (1.397) (1.384) LOSS + -0.0505*** -0.0498*** 0.0029 0.0050 (-4.606) (-4.549) (0.361) (0.610)
Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes
Observations 10,723 10,723 10,723 10,723 R-squared 0.139 0.140 0.233 0.227 The t-statistics are reported in parentheses ***, **, * denote significance at 1%, 5%, and 10% levels (two-tail tests). Standard errors are clustered at firm level. Variables are defined in Appendix B.
69
Table 7: The Effect of Market Risk Disclosure on Over- and Under- Investment
Panel A: ICFS
ICFS
Overinvest Underinvest Overinvest Underinvest
Constant 0.4925*** 1.5200*** 0.5458*** 1.4549*** (5.031) (19.939) (5.688) (22.184) FOG_INDEX 0.0129*** -0.0042 (3.137) (-1.134) SIM 0.0893*** 0.0128 (2.979) (0.474) LNASSET -0.0206*** 0.0366*** -0.0185*** 0.0372*** (-3.223) (5.947) (-2.947) (6.098) STDCFO -0.9534*** -0.0960 -0.9216*** -0.0988 (-8.122) (-1.566) (-7.831) (-1.602) STDRET -3.9527*** -0.3284 -3.8727*** -0.3246 (-5.915) (-0.761) (-5.768) (-0.753) MTB -0.0026** -0.0006 -0.0025* -0.0007 (-1.982) (-0.536) (-1.886) (-0.551) STDINV 3.2511*** -0.4238 3.2388*** -0.4125 (5.155) (-0.984) (5.134) (-0.957) Z_SCORE -0.0049*** 0.0016** -0.0047*** 0.0016** (-3.436) (2.093) (-3.333) (2.048) AGE 0.0014*** 0.0014*** 0.0015*** 0.0014*** (3.177) (2.767) (3.355) (2.735) DIV -0.0179 -0.0011 -0.0157 -0.0026 (-1.207) (-0.081) (-1.051) (-0.189) CAPINTENS 0.5823*** 0.3789*** 0.5887*** 0.3798*** (10.611) (5.192) (10.760) (5.200) LOSS -0.0810*** -0.0004 -0.0792*** -0.0011 (-4.815) (-0.029) (-4.715) (-0.086)
Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes
Observations 6,590 3,505 6,590 3,505 R-squared 0.160 0.081 0.159 0.081
70
Table 7: The Effect of Market Risk Disclosure on Over- and Under- Investment (continued)
Panel B: SUBOPT
SUBOPT Overinvest Underinvest Overinvest Underinvest
Constant -0.9257*** 0.8709*** -0.9007*** 0.9652*** (-48.014) (26.838) (-48.591) (36.130) FOG_INDEX 0.0012 † 0.0045*** (1.431) (2.948) SIM -0.0275*** -0.0369*** (-3.348) (-3.027) LNASSET 0.1916*** -0.1626*** 0.1918*** -0.1634*** (116.333) (-45.059) (116.876) (-45.750) STDCFO 0.3377*** 0.0888* 0.3368*** 0.0883** (6.126) (1.958) (6.156) (1.970) STDRET -0.1087 0.6738*** -0.1262 0.6618*** (-0.716) (3.103) (-0.830) (3.050) MTB 0.0015*** 0.0000 0.0016*** 0.0000 (3.205) (0.020) (3.231) (0.016) STDINV 0.0008 0.3585*** 0.0039 0.3471*** (0.010) (2.943) (0.049) (2.860) Z_SCORE -0.0065*** 0.0059*** -0.0065*** 0.0060*** (-11.858) (11.008) (-11.855) (11.091) AGE -0.0087*** 0.0078*** -0.0087*** 0.0078*** (-89.271) (38.759) (-89.463) (38.763) DIV 0.0125*** 0.0016 0.0125*** 0.0034 (3.730) (0.307) (3.741) (0.671) CAPINTENS 0.1365*** -0.2570*** 0.1372*** -0.2585*** (16.075) (-14.650) (16.281) (-14.678) LOSS 0.0334*** -0.0289*** 0.0337*** -0.0280*** (7.925) (-5.203) (8.024) (-5.017)
Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes Observations 6,946 3,777 6,946 3,777 R-squared 0.807 0.631 0.808 0.631 The t-statistics are reported in parentheses ***, **, * denote significance at 1%, 5%, and 10% levels (two-tail tests). † denotes significance at 10% level (one-tail tests). Standard errors are clustered at firm level. Variables are defined in Appendix B. 71
Table 8: Alternative Measures for Readability and Similarity
Panel A: Stock Liquidity Level
ILLIQ ZERORET
Constant 0.5420* 0.6621*** 0.0544*** 0.0524*** (1.868) (2.853) (4.748) (5.340) FOG_PRED 0.0337** 0.0007** (2.174) (2.034) SIM_TAYLOR 0.1041** 0.0020* (2.281) (1.807) TURNOVER -0.0123*** -0.0126*** -0.0004*** -0.0005*** (-5.030) (-6.712) (-8.486) (-10.208) PRIORRET -0.0526*** -0.0537*** -0.0031*** -0.0031*** (-2.949) (-3.618) (-7.239) (-8.462) Z_SCORE 0.0026 0.0039 -0.0006*** -0.0006*** (0.623) (1.245) (-6.556) (-8.179) STDRET 4.9607* 5.9457*** 0.1856*** 0.2424*** (1.931) (2.912) (4.046) (6.195) SIZE -0.1605*** -0.1486*** -0.0063*** -0.0061*** (-10.060) (-10.804) (-17.598) (-19.602) MTB 0.0076** 0.0040* 0.0002** 0.0002** (2.062) (1.731) (2.427) (2.551) CAPINTENS 0.1401 0.1789* -0.0071*** -0.0065*** (1.335) (1.786) (-2.589) (-2.631) CASH_RATIO -0.0217 -0.0393*** 0.0004 0.0006* (-1.044) (-2.981) (1.060) (1.817) LOSS 0.0745** 0.0759** 0.0070*** 0.0073*** (2.131) (2.539) (7.556) (9.071)
Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes
Observations 7,525 9,532 7,525 9,532 R-squared 0.209 0.208 0.455 0.462
72
Table 8: Alternative Measures for Readability and Similarity (continued)
Panel B: Stock Liquidity Uncertainty
LIQVOL COM(FL, ML)
Constant 1.1677 1.4303** 0.1321*** 0.1213*** (1.490) (2.294) (17.403) (14.621) FOG_PRED 0.0801* 0.0008** (1.872) (2.154) SIM_TAYLOR 0.2594** 0.0014 (1.989) (0.762) TURNOVER -0.0335*** -0.0339*** -0.0002*** -0.0002*** (-4.861) (-6.518) (-3.679) (-3.403) PRIORRET -0.1533*** -0.1568*** -0.0003 -0.0003 (-3.096) (-3.787) (-0.447) (-0.497) Z_SCORE 0.0097 0.0135 -0.0002* -0.0002** (0.821) (1.517) (-1.872) (-2.021) STDRET 17.8381** 19.4479*** 0.0572 0.0679 (2.460) (3.388) (1.109) (1.495) SIZE -0.3955*** -0.3709*** -0.0016*** -0.0017*** (-9.706) (-10.521) (-4.142) (-5.141) MTB 0.0188** 0.0103* -0.0001 -0.0000 (1.969) (1.647) (-0.516) (-0.044) CAPINTENS 0.3769 0.4743* -0.0045 -0.0030 (1.285) (1.701) (-1.342) (-1.033) CASH_RATIO -0.0552 -0.1127*** 0.0008 0.0006 (-0.958) (-3.191) (1.454) (1.420) LOSS 0.1579 0.1651* 0.0016 0.0021* (1.575) (1.947) (1.269) (1.939)
Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes
Observations 7,525 9,532 7525 9,532 R-squared 0.186 0.186 0.037 0.035
73
Table 8: Alternative Measures for Readability and Similarity (continued)
Panel C: Investment Efficiency
ICFS SUBOPT
Constant 1.4287*** 1.4519*** -0.4452*** -0.2409** (31.216) (34.475) (-4.476) (-2.305) FOG_PRED 0.0081** 0.0149*** (2.073) (3.295) SIM_TAYLOR 0.0549*** -0.0119 (2.667) (-1.005) LNASSET 0.0224*** 0.0188*** 0.0722*** 0.0724*** (5.846) (5.390) (13.475) (15.332) STDCFO -0.4767*** -0.3979*** 0.2801*** 0.2636*** (-6.085) (-6.543) (3.441) (4.055) STDRET -2.5543*** -2.2775*** 1.6656*** 2.0003*** (-5.267) (-5.492) (4.267) (6.160) MTB -0.0033*** -0.0018** 0.0013 0.0020*** (-3.052) (-1.966) (1.376) (2.647) STDINV 1.9307*** 1.5775*** -0.5358*** -0.5472*** (3.993) (3.643) (-2.936) (-3.299) Z_SCORE -0.0013 -0.0012 -0.0005 0.0007 (-1.319) (-1.603) (-0.469) (0.783) AGE 0.0001 0.0003 -0.0027*** -0.0023*** (0.385) (1.097) (-6.040) (-5.512) DIV -0.0055 -0.0170 0.0342*** 0.0334*** (-0.454) (-1.542) (3.311) (3.623) CAPINTENS 0.5452*** 0.5868*** 0.0816** 0.0506 (10.677) (12.940) (2.255) (1.379) LOSS -0.0583*** -0.0499*** 0.0009 0.0040 (-4.772) (-4.551) (0.105) (0.508)
Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes
Observations 8,201 10,723 8,201 10,723 R-squared 0.139 0.139 0.242 0.229 The t-statistics are reported in parentheses ***, **, * denote significance at 1%, 5%, and 10% levels (two-tail tests). Standard errors are clustered at firm level.
74
Table 9: The Effect of Topics of Market Risk Disclosures on Stock Liquidity and Investment Efficiency
Panel A: Stock Liquidity
ILLIQ ZERORET LIQVOL COM (FL, ML)
PROBTOPIC1 0.0811 -0.0060 0.2082 -0.0010 (0.217) (-0.803) (0.201) (-0.120) PROBTOPIC2 -0.3717* -0.0112* -1.0029* 0.0034 (-1.902) (-1.805) (-1.849) (0.490) PROBTOPIC3 -0.1891 -0.0163** -0.6146 -0.0135* (-0.638) (-2.555) (-0.766) (-1.796) PROBTOPIC4 -0.4890 -0.0149** -1.2291 -0.0077 (-1.604) (-2.411) (-1.415) (-1.149) PROBTOPIC5 -0.3356 -0.0107* -0.7514 0.0055 (-1.177) (-1.680) (-0.921) (0.726) PROBTOPIC6 -0.3011 -0.0172*** -0.7506 0.0002 (-1.233) (-2.858) (-1.104) (0.025) PROBTOPIC7 -0.4947* -0.0189*** -1.3078* -0.0116* (-1.834) (-3.419) (-1.722) (-1.903) PROBTOPIC8 -0.3885 -0.0034 -1.1322 -0.0040 (-1.541) (-0.518) (-1.621) (-0.586) PROBTOPIC9 -0.3344 -0.0212*** -0.8247 0.0011 (-1.183) (-3.512) (-1.038) (0.165) PROBTOPIC10 -0.7235** -0.0131** -1.9577** -0.0051 (-2.400) (-2.162) (-2.295) (-0.771) PROBTOPIC11 -0.6075** -0.0199*** -1.5706** -0.0079 (-2.222) (-3.772) (-2.056) (-1.255) PROBTOPIC12 -0.1019 -0.0090 -0.1856 -0.0061 (-0.334) (-1.481) (-0.218) (-0.843) PROBTOPIC13 -0.3767 -0.0163*** -1.0372 -0.0201*** (-1.573) (-2.871) (-1.543) (-2.781) PROBTOPIC14 -0.3406 -0.0178*** -1.0830 0.0022 (-1.356) (-3.095) (-1.561) (0.290) PROBTOPIC15 -0.8341*** -0.0149** -2.2840** 0.0039 (-2.639) (-2.494) (-2.561) (0.578) PROBTOPIC16 -0.6553** -0.0178*** -1.7451** -0.0079 (-2.205) (-3.081) (-2.064) (-1.227) PROBTOPIC17 -0.2806 -0.0171*** -0.7184 -0.0008 (-1.168) (-3.348) (-1.061) (-0.138) PROBTOPIC18 -0.1807 -0.0087 -0.6626 -0.0105 (-0.803) (-1.465) (-1.121) (-1.620)
75
PROBTOPIC19 -0.4708 -0.0017 -1.2542 0.0009 (-1.372) (-0.222) (-1.327) (0.118) TURNOVER -0.0118*** -0.0005*** -0.0319*** -0.0002*** (-6.140) (-9.995) (-5.978) (-3.454) PRIORRET -0.0595*** -0.0031*** -0.1726*** -0.0003 (-3.958) (-8.552) (-4.110) (-0.537) Z_SCORE 0.0063* -0.0007*** 0.0194* -0.0002* (1.738) (-8.284) (1.876) (-1.782) STDRET 6.1049*** 0.2368*** 19.9346*** 0.0720 (2.931) (6.153) (3.392) (1.598) SIZE -0.1603*** -0.0064*** -0.4015*** -0.0017*** (-10.340) (-19.240) (-9.947) (-4.750) MTB 0.0047** 0.0002** 0.0121* 0.0000 (2.026) (2.453) (1.938) (0.086) CAPINTENS 0.1327 -0.0053** 0.3413 -0.0023 (1.281) (-2.055) (1.182) (-0.786) CASH_RATIO -0.0226 0.0005 -0.0655 0.0006 (-1.361) (1.594) (-1.422) (1.457) LOSS 0.0888*** 0.0069*** 0.1999** 0.0021* (2.833) (8.682) (2.249) (1.887) Constant 1.0539*** 0.0676*** 2.4980*** 0.1247*** (3.529) (6.045) (3.050) (12.835)
Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes
Observations 9,532 9,532 9,532 9,532 R-squared 0.218 0.470 0.196 0.039
76
Table 9: The Effect of Topics of Market Risk Disclosures on Stock Liquidity and Investment Efficiency (continued)
Panel B: Investment Efficiency
ICFS SUBOPT
PROBTOPIC1 0.0836 -0.0287 (0.824) (-0.596) PROBTOPIC2 -0.1647** -0.1590*** (-2.094) (-4.236) PROBTOPIC3 -0.0373 -0.1247*** (-0.417) (-3.252) PROBTOPIC4 -0.1482* -0.1443*** (-1.787) (-3.678) PROBTOPIC5 -0.0187 -0.1627*** (-0.203) (-3.897) PROBTOPIC6 -0.1615** -0.0893** (-2.167) (-2.154) PROBTOPIC7 -0.1415* -0.2113*** (-1.926) (-6.056) PROBTOPIC8 -0.1106 -0.1488*** (-1.446) (-3.874) PROBTOPIC9 -0.0534 -0.2096*** (-0.622) (-5.776) PROBTOPIC10 -0.0907 -0.2193*** (-1.164) (-6.068) PROBTOPIC11 -0.1573** -0.2755*** (-1.988) (-8.383) PROBTOPIC12 -0.2746*** -0.1801*** (-3.172) (-4.725) PROBTOPIC13 -0.2026** -0.1353*** (-2.245) (-3.483) PROBTOPIC14 -0.2246*** -0.1107*** (-2.622) (-2.875) PROBTOPIC15 -0.0779 -0.2783*** (-0.988) (-7.788) PROBTOPIC16 -0.2145*** -0.1840*** (-2.863) (-5.067) PROBTOPIC17 -0.0661 -0.1668*** (-0.925) (-5.359) PROBTOPIC18 -0.1449* -0.0613 (-1.818) (-1.627)
77
PROBTOPIC19 -0.1199 -0.1376*** (-1.412) (-3.061) LNASSET 0.0186*** 0.0697*** (5.081) (30.567) STDCFO -0.3874*** 0.2821*** (-6.197) (6.419) STDRET -2.2052*** 1.8429*** (-5.324) (7.862) MTB -0.0017* 0.0021*** (-1.866) (3.543) STDINV 1.6542*** -0.5166*** (3.830) (-4.904) Z_SCORE -0.0008 0.0010* (-1.098) (1.813) AGE 0.0002 -0.0025*** (0.778) (-13.401) DIV -0.0160 0.0319*** (-1.463) (6.357) CAPINTENS 0.5806*** 0.1101*** (12.275) (7.636) LOSS -0.0467*** 0.0065 (-4.168) (1.081) Constant 1.5039*** -0.2082*** (21.500) (-6.450)
Industry FE Yes Yes Year FE Yes Yes
Observations 10,723 10,723 R-squared 0.143 0.224 The t-statistics are reported in parentheses ***, **, * denote significance at 1%, 5%, and 10% levels (two-tail tests). Standard errors are clustered at firm level.
78
Table 10: The Effect of Market Risk Disclosures on Co-movement between Stock Return and Market Liquidity
Beta Constant 0.1844 0.1453 (0.522) (0.415) FOG_INDEX -0.0055*** (-2.587) SIM -0.0117 (-0.677) TURNOVER 0.0014** 0.0014*** (2.494) (2.596) PRIORRET 0.0066 0.0068 (0.895) (0.913) Z_SCORE 0.0016* 0.0017* (1.712) (1.738) STDRET -2.3828*** -2.4068*** (-5.072) (-5.121) SIZE -0.0062** -0.0067** (-2.322) (-2.536) MTB 0.0024** 0.0023** (2.168) (2.096) CAPINTENS -0.0637*** -0.0623*** (-2.652) (-2.596) CASH_RATIO -0.0092** -0.0094** (-2.100) (-2.147) LOSS 0.0079 0.0073 (0.780) (0.720)
Industry FE Yes Yes Year FE Yes Yes
Observations 9,532 9,532 R-squared 0.030 0.029 The t-statistics are reported in parentheses ***, **, * denote significance at 1%, 5%, and 10% levels (two-tail tests). Standard errors are clustered at firm level. Variables are defined in Appendix B.
79
Table 11: The Effect of Market Risk Disclosure Conditional on Ex-ante Information Asymmetry
Panel A: Stock Liquidity Level
ILLIQ ZERORET Information Asymmetry Low High Low High Low High Low High
Constant 0.7650** 0.1866 0.8422*** 0.3550** 0.0565*** 0.0378*** 0.0613*** 0.0412*** (2.440) (1.221) (2.951) (2.522) (5.400) (5.912) (6.045) (6.407) FOG_INDEX 0.0226* 0.0242** 0.0006** 0.0007*** (1.731) (2.511) (2.242) (2.749) SIM 0.1492** 0.0788* 0.0006 0.0048*** (2.043) (1.736) (0.388) (2.974) 80 TURNOVER -0.0158*** -0.0057*** -0.0160*** -0.0059*** -0.0005*** -0.0004*** -0.0005*** -0.0004***
(-5.133) (-3.607) (-5.179) (-3.658) (-7.029) (-7.441) (-7.118) (-7.445) PRIORRET -0.0738*** -0.0025 -0.0739*** -0.0030 -0.0030*** -0.0030*** -0.0030*** -0.0030*** (-3.672) (-0.153) (-3.692) (-0.187) (-6.331) (-5.104) (-6.372) (-5.106) Z_SCORE 0.0077 -0.0015 0.0076 -0.0016 -0.0007*** -0.0005*** -0.0007*** -0.0005*** (1.547) (-0.814) (1.531) (-0.874) (-6.659) (-5.266) (-6.671) (-5.306) STDRET 6.6695** 3.4054* 6.6999** 3.5147* 0.1959*** 0.2934*** 0.1978*** 0.2965*** (2.412) (1.778) (2.431) (1.807) (4.071) (4.761) (4.106) (4.808) SIZE -0.2158*** -0.0567*** -0.2145*** -0.0524*** -0.0078*** -0.0041*** -0.0077*** -0.0040*** (-10.487) (-4.394) (-10.466) (-4.329) (-17.189) (-9.636) (-17.032) (-9.498) MTB 0.0050 0.0013 0.0054 0.0016 0.0003*** 0.0000 0.0003*** 0.0000 (1.527) (0.430) (1.644) (0.508) (2.630) (0.262) (2.745) (0.385) CAPINTENS 0.3006** 0.0566 0.2939** 0.0627 -0.0048 -0.0051 -0.0050* -0.0049 (2.280) (0.685) (2.255) (0.761) (-1.637) (-1.119) (-1.712) (-1.080)
CASH_RATIO -0.0207 -0.0135** -0.0206 -0.0127** 0.0014*** 0.0002 0.0014*** 0.0002 (-0.849) (-2.391) (-0.843) (-2.302) (2.944) (0.449) (3.008) (0.533) LOSS 0.1461*** 0.0015 0.1494*** 0.0045 0.0077*** 0.0074*** 0.0078*** 0.0074*** (3.375) (0.056) (3.431) (0.173) (7.450) (6.444) (7.485) (6.511)
Industry FE Yes Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Yes
Observations 6,048 3,484 6,048 3,484 6,048 3,484 6,048 3,484 R-squared 0.256 0.123 0.256 0.118 0.489 0.415 0.488 0.414
81
Table 11: The Effect of Market Risk Disclosure Conditional on Ex-ante Information Asymmetry (continued)
Panel B: Stock Liquidity Uncertainty
LIQVOL COM(FL, ML) Information Asymmetry Low High Low High Low High Low High
Constant 1.6495* 0.4441 1.7987** 0.8490** 0.1203*** 0.1679*** 0.1248*** 0.1699*** (1.938) (1.083) (2.338) (2.321) (12.670) (13.924) (13.706) (14.567) FOG_INDEX 0.0509 0.0603** 0.0006* 0.0004 (1.408) (2.226) (1.734) (1.082) SIM 0.3648* 0.2195* 0.0008 0.0030 (1.731) (1.837) (0.338) (0.933) TURNOVER -0.0438*** -0.0139*** -0.0441*** -0.0142*** -0.0002** -0.0002* -0.0002** -0.0002*
82 (-5.095) (-3.588) (-5.125) (-3.631) (-2.423) (-1.799) (-2.521) (-1.820)
PRIORRET -0.2178*** -0.0117 -0.2179*** -0.0130 0.0002 -0.0014 0.0001 -0.0014 (-3.908) (-0.263) (-3.924) (-0.294) (0.205) (-1.189) (0.178) (-1.195) Z_SCORE 0.0266* -0.0048 0.0264* -0.0051 -0.0002** -0.0001 -0.0002** -0.0001 (1.871) (-0.974) (1.860) (-1.033) (-2.082) (-0.921) (-2.092) (-0.947) STDRET 23.4368*** 8.6787* 23.4960*** 8.9492* 0.0294 0.1308 0.0311 0.1326* (3.004) (1.791) (3.022) (1.813) (0.523) (1.647) (0.552) (1.670) SIZE -0.5401*** -0.1426*** -0.5372*** -0.1319*** -0.0023*** -0.0007 -0.0022*** -0.0006 (-10.114) (-4.086) (-10.103) (-4.051) (-4.915) (-1.191) (-4.829) (-1.073) MTB 0.0105 0.0062 0.0113 0.0068 0.0000 -0.0000 0.0000 -0.0000 (1.168) (0.749) (1.264) (0.824) (0.091) (-0.118) (0.166) (-0.087) CAPINTENS 0.8106** 0.1263 0.7961** 0.1418 -0.0029 -0.0014 -0.0032 -0.0013 (2.210) (0.552) (2.195) (0.621) (-0.868) (-0.230) (-0.933) (-0.210) CASH_RATIO -0.0661 -0.0366** -0.0661 -0.0345** 0.0008 0.0005 0.0008 0.0005 (-0.993) (-2.303) (-0.990) (-2.221) (1.377) (0.672) (1.414) (0.703)
LOSS 0.3429*** -0.0053 0.3504*** 0.0021 0.0028** 0.0008 0.0029** 0.0009 (2.785) (-0.073) (2.835) (0.029) (2.031) (0.457) (2.076) (0.481)
Industry FE Yes Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Yes
Observations 6,048 3,484 6,048 3,484 6,047 3,484 6,047 3,484 R-squared 0.232 0.106 0.232 0.101 0.042 0.031 0.042 0.030
83
Table 11: The Effect of Market Risk Disclosure Conditional on Ex-ante Information Asymmetry (continued) Panel C: Investment Efficiency
ICFS SUBOPT Information Asymmetry Low High Low High Low High Low High
Constant 1.3855*** 0.1488 1.3347*** 0.2445*** -0.9257*** 0.8709*** -0.9007*** 0.9652*** (18.988) (1.645) (19.497) (2.946) (-48.014) (26.838) (-48.591) (36.130) FOG_INDEX 0.0026 0.0137*** 0.0012 0.0045*** (0.646) (3.198) (1.431) (2.948) SIM 0.0767*** 0.0363 -0.0275*** -0.0369*** (2.595) (1.323) (-3.348) (-3.027) 84
LNASSET 0.0259*** 0.0105** 0.0261*** 0.0129*** 0.1916*** -0.1626*** 0.1918*** -0.1634*** (4.673) (2.248) (4.720) (2.795) (116.333) (-45.059) (116.876) (-45.750) STDCFO -0.4783*** -0.2985*** -0.4636*** -0.2749*** 0.3377*** 0.0888* 0.3368*** 0.0883** (-6.095) (-3.159) (-5.916) (-2.889) (6.126) (1.958) (6.156) (1.970) STDRET -2.3375*** -2.3647*** -2.3106*** -2.3041*** -0.1087 0.6738*** -0.1262 0.6618*** (-4.483) (-3.657) (-4.426) (-3.562) (-0.716) (3.103) (-0.830) (3.050) MTB -0.0006 -0.0031** -0.0005 -0.0030** 0.0015*** 0.0000 0.0016*** 0.0000 (-0.444) (-2.559) (-0.389) (-2.450) (3.205) (0.020) (3.231) (0.016) STDINV 1.7075*** 1.6423*** 1.6926*** 1.6783*** 0.0008 0.3585*** 0.0039 0.3471*** (3.051) (2.652) (3.030) (2.704) (0.010) (2.943) (0.049) (2.860) Z_SCORE -0.0012 -0.0010 -0.0012 -0.0009 -0.0065*** 0.0059*** -0.0065*** 0.0060*** (-1.264) (-0.834) (-1.266) (-0.803) (-11.858) (11.008) (-11.855) (11.091) AGE -0.0005 0.0012*** -0.0005 0.0012*** -0.0087*** 0.0078*** -0.0087*** 0.0078*** (-1.154) (2.930) (-1.041) (2.904) (-89.271) (38.759) (-89.463) (38.763)
DIV -0.0316** 0.0120 -0.0312** 0.0145 0.0125*** 0.0016 0.0125*** 0.0034 (-2.159) (0.814) (-2.129) (0.980) (3.730) (0.307) (3.741) (0.671) CAPINTENS 0.5827*** 0.5193*** 0.5849*** 0.5220*** 0.1365*** -0.2570*** 0.1372*** -0.2585*** (10.685) (6.662) (10.774) (6.673) (16.075) (-14.650) (16.281) (-14.678) LOSS -0.0373** -0.0604*** -0.0368** -0.0586*** 0.0334*** -0.0289*** 0.0337*** -0.0280*** (-2.520) (-3.775) (-2.492) (-3.675) (7.925) (-5.203) (8.024) (-5.017)
Industry FE Yes Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Yes
Observations 6,946 3,777 6,946 3,777 6,946 3,777 6,946 3,777 R-squared 0.142 0.125 0.143 0.122 0.802 0.604 0.802 0.605 The t-statistics are reported in parentheses. 85 ***, **, * denote significance at 1%, 5%, and 10% levels (two-tail tests). Standard errors are clustered at firm level. Variables are defined in Appendix B.
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