Two Essays on the Post-Earnings-Announcement Drift

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2010 Two Essays on the Post-Earnings- Announcement Drift Anomaly: Information Content and Uncertainty Steven Mckay Price

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COLLEGE OF BUSINESS

TWO ESSAYS ON THE POST-EARNINGS-ANNOUNCEMENT DRIFT

ANOMALY: INFORMATION CONTENT AND UNCERTAINTY

STEVEN MCKAY PRICE

A Dissertation submitted to the Department of Finance in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Degree Awarded: Summer Semester, 2010

The members of the committee approve the dissertation of Steven McKay Price defended on April 23, 2010.

______David R. Peterson Professor Directing Dissertation

______Dean H. Gatzlaff University Representative

______Clemon F. Sirmans Committee Member

______James S. Doran Committee Member

Approved:

______William A. Christiansen, Chair, Department of Finance

______Caryn L. Beck-Dudley, Dean, College of Business

The Graduate School has verified and approved the above-named committee members.

To my wife, children, and parents…thank you.

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ACKNOWLEDGEMENTS

I would like to acknowledge the thoughtful guidance of my committee members; David Peterson (chair), C.F. Sirmans, Dean Gatzlaff, and James Doran. Their wisdom and insight have helped me keep blunders to a minimum. I also wish to thank Brandon Porter and Alex Wawer for General Inquirer technical support, Barbara Bliss for data collection assistance, and the Graduate School at The Florida State University for financial assistance in the form of the Presidential University Fellowship. Any errors in this manuscript are my own.

TABLE OF CONTENTS

List of Tables ...... vi List of Figures ...... vii Abstract ...... viii

1. INTRODUCTION ...... 1

2. EARNINGS CONFERENCE CALL CONTENT, UNCERTAINTY, AND THE POST-EARNINGS-ANNOUNCEMENT DRIFT ...... 2

Literature Review ...... 5 Data ...... 11 Content Analysis Techniques and Empirical Methods ...... 12 Results ...... 21 Conclusion ...... 26

3. INFORMATION UNCERTAINTY AND THE POST-EARNINGS- ANNOUNCEMENT DRIFT ANOMALY: INSIGHT FROM REITS 41

Literature Review and Hypothesis Development ...... 44 Data ...... 49 Analysis and Results ...... 51 Conclusion ...... 59

APPENDICES ...... 69

A CHAPTER 2 ADDITIONAL SUPPORT ...... 69 B CHAPTER 2 MARKET ADJUSTED RETURN RESULTS ...... 90 C CHAPTER 3 ADDITIONAL SUPPORT ...... 119

REFERENCES ...... 132

BIOGRAPHICAL SKETCH ...... 139

LIST OF TABLES

Table 1: Earnings Announcement and Conference Call Timing ...... 28

Table 2: Descriptive Statistics and Correlations ...... 29

Table 3: Test of Differences of Means and Medians, by TONE1 Quintiles . 30

Table 4: Test of Differences of Means and Medians, by TONE2 Quintiles . 31

Table 5: Test of Differences of Means and Medians, by H-TONE1 Quintiles ...... 32

Table 6: Test of Differences of Means and Medians, by H-TONE2 Quintiles ...... 33

Table 7: Regression Results of Cumulative Abnormal Returns on Earnings Surprise and H-Tone1 ...... 34

Table 8: CARs for Sorts by Earnings Surprise and H-TONE1 ...... 36

Table 9: Sample Characteristics and Returns Volatilities ...... 65

Table 10: Regression Results of Cumulative Abnormal Returns on Earnings Surprise, REIT Indicator, and Controls, 1982-2008 .... 66

Table 11: Test of Differences, Means and Medians, by SURP Quintiles, 1982-2008 ...... 67

Table 12: Test of Differences, Means and Medians, by SURP Quintiles and Decade...... 68

LIST OF FIGURES

Figure 1: Cumulative Abnormal Returns across All Conference Calls and by High and Low H-TONE1 Quintiles ...... 38

Figure 2: Cumulative Abnormal Returns across All Conference Calls and by High and Low SURP Quintiles ...... 39

Figure 3: Cumulative Abnormal Returns by Earnings Surprise and H-TONE1 Terciles ...... 40

Figure 4: Cumulative Abnormal Returns for all REITs by SURP Deciles for the 1982-2008 sample period ...... 61

Figure 5: Cumulative Abnormal Returns for all NonREITs by SURP Deciles for the 1982-2008 sample period ...... 62

Figure 6: Drift spread for REITs, NonREITs, and Small NonREITs over the 121 day period around earnings announcements, by year ...... 63

Figure 7: Post-earnings-announcement drift spread for REITs, NonREITs, and Small NonREITs over the 60 day post earnings announcement period, by year ...... 64

vii

ABSTRACT

This dissertation examines the post-earnings-announcement drift in two essays. In the first essay, computer aided content analysis is used to examine the incremental informativeness of quarterly earnings conference calls for earnings announcement window abnormal returns as well as the post-earnings-announcement drift. We find that conference call linguistic tone is a significant predictor of cumulative abnormal returns (CARs) in both the initial reaction and drift windows. Furthermore, conference call tone dominates earnings surprises over the longer period. Holding unexpected earnings constant, portfolios formed based on differences in call tone have CARs that are significantly different from one another. Returns for calls with a highly positive tone and a poor earnings surprise are essentially unaffected by the negative numerical signal, suggesting that new information is coming to light in the conference call discussion. Call tone matters more for dividend paying firms, illustrating differences in investor behavior based on the level of cash flow uncertainty. Additionally, we find that a context specific linguistic dictionary is more powerful than a more widely used general dictionary (Harvard IV-4 Psychosocial). The second essay is the first study to examine the post-earnings-announcement drift anomaly in a Real Estate Investment Trust (REIT) context. The efficient markets hypothesis suggests that unexpected earnings should be fully incorporated into asset prices soon after being publicly announced. We hypothesize that publicly announced earnings signals may be more certain for REITs due to the presence of a parallel (private) asset market, suggesting less drift for REIT stocks. However, we find a large REIT drift component that is both statistically and economically significant. Furthermore, while the initial earnings surprise response is more muted for REITs, we find that the magnitude of the drift is significantly larger for REITs than for ordinary common stocks (NonREITs). Thus, information does not appear to move between the private and public asset markets in such a way as to render REIT earnings signals more certain than NonREIT earnings signals.

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CHAPTER 1

INTRODUCTION

This dissertation is comprised to two related essays. Although each was written in a manner which is largely self contained, the underlying inspiration stems from a common source. In the summer of 2008 I started work on a research project, which was overseen by Professors David Peterson and James Doran, as part of the requirements for the investments seminar portion of the finance doctoral curriculum. Affectionately known as “the Peterson project”, the final product was titled “Earnings Conference Call Content and Stock Price: The Case of REITs” (Doran, Peterson, and Price 2009). As noted in the paper‟s abstract, Doran et al. (2009) use computer based content analysis to quantify the linguistic tone of quarterly earnings conference calls for publicly traded Real Estate Investment Trusts (REITs). After controlling for the earnings announcement, they examine the relation between conference call tone and the contemporaneous stock price reaction. They find that the tone of the conference call dialogue has significant explanatory power for the abnormal returns surrounding earnings announcements. While Doran et al. (2009) provides a meaningful contribution regarding the relation between call tone and the initial stock price reaction, further questions are raised. First, is conference call tone related to the well documented post-earnings- announcement drift1 anomaly (Ball and Brown 1968; Foster, Olsen and Shevlin 1984; Bernard and Thomas 1989, 1990), and if so, how? Second, why is no evidence of the drift manifest in the REIT sample using benchmark models to estimate abnormal returns? The first question provides the springboard for the first essay (Chapter 2) where the linguistic content of earnings conference calls are examined relative to the drift across a broad sample of firms. The latter question is addressed in the second essay (Chapter 3) where the presence of the drift is both documented and examined for a sample of all publicly traded REITs over three decades.

1 Or, the drift.

CHAPTER 2

EARNINGS CONFERENCE CALL CONTENT, UNCERTAINTY, AND THE POST- EARNINGS-ANNOUNCEMENT DRIFT

Managers regularly send signals to market participants through quarterly earnings announcements. The most widely studied signal is the numerical value of quarterly earnings relative to market expectations. However, earnings numbers are rarely communicated to the market in isolation. Performance results are generally embedded within the text of a carefully crafted earnings press release and followed shortly thereafter by an accompanying conference call to further discuss the results. Several recent studies have explored the relation between the text of the press releases and the abnormal returns that surround earnings announcements (Davis, Piger, and Sedor 2007; Henry 2008; Sadique, In, and Veeraraghavan 2008; and Demers and Vega 2008). However, such efforts are limited to the analysis of managerial expression. Quarterly earnings conference calls provide a forum in which to more fully examine corporate disclosure as executives interact with call participants. Recent literature argues that earnings conference calls have become an increasingly important medium through which firms convey value relevant information to the market (Frankel, Johnson, and Skinner 1999; Kimbrough 2005; Frankel, Mayew, and Sun 2010). Conference calls typically begin with prepared statements by management, which usually reiterate the press release, and are then opened up to questions from analysts. The dialogue between management and analysts relative to firm performance is a potentially rich information source that has yet to be studied within a tone oriented content analysis framework. We employ computer based content analysis to quantify the linguistic tone of quarterly earnings conference calls in order to examine the relation between conference call disclosure and both earnings announcement abnormal returns and the post-earnings-announcement drift. The post-earnings-announcement drift2 has puzzled financial market researchers for over four decades. This phenomenon of abnormally high (low) returns following

2 Also referred to as simply “the drift”.

2 positive (negative) earnings announcements has not been successfully attributed to risk adjustments, market frictions, or the psychological biases of market participants (Bernard and Thomas 1989, 1990; Bernard 1992; Chan, Jegadeesh, and Lakonishok 1996). However, recent studies contend that the drift may be due to uncertainty (Lewellen and Shanken 2002; Brav and Heaton 2002; Liang 2003; Zhang 2006; and Francis, LaFond, Olsson, and Schipper 2007). They argue that in cases of greater uncertainty, where investors must learn about cash flows and future firm prospects, information may be more slowly incorporated into asset prices. While these studies use various proxies to measure uncertainty, no universally accepted uncertainty measure has been established in the literature (Zhang 2006). We perform our analysis using an uncertainty indicator that is closely related to investor cash flows by separately considering dividend paying firms and those that do not pay dividends. Investor cash flow uncertainty is not a question of whether or not investors receive cash in the form of dividends, since investors can easily replicate dividend payments by selling shares. Rather it is the potential for agency conflicts in large, well established, cash rich firms (dividend payers) to prevent investors from realizing the full value of their investment (Jensen 1986; Allen and Michaely 2002; DeAngelo, DeAngelo, and Skinner 2004). For the most part, research has maintained an exclusive focus on the numerical value of earnings when trying to explain the post-earnings-announcement drift anomaly. Two notable exceptions are Engelberg (2008) and Demers and Vega (2008). Both studies utilize text based content analysis to glean information from the words related to the earnings announcement event. Engelberg attempts to link the qualitative information in the financial media to the drift, while Demers and Vega examine the relation between the drift and the wording of the earnings press releases. However, neither study examines the additional disclosures embedded in the language of the earnings conference calls.3 We focus our analysis on the information found in the text of quarterly conference calls to explore the relation between the linguistic tone of the conference call dialogue

3 Mayew and Venkatachalam (2009) do control for conference call linguistic content in their examination of managerial voice inflection and abnormal returns.

3 and both the initial market reaction and the post-earnings-announcement drift. We do so using a hand collected sample of over 2,800 conference call transcripts spanning sixteen consecutive quarters in the post Regulation Fair Disclosure era. We differentiate between the two major components of an earnings conference call by separating management‟s prepared remarks and the more spontaneous question and answer portion of the call. This allows us to control for the information conveyed in the written press release to determine the incremental disclosure found in the conference call dialogue between management and analysts. Content analytical techniques are accomplished using the General Inquirer package following Tetlock (2007).4 However, unlike Tetlock, who relies on the program‟s default dictionary (Harvard IV-4 Psychosocial) we also incorporate a custom dictionary with earnings specific language. Using the two separate dictionaries allows us to conduct an out of sample examination of the argument by Loughran and McDonald (2009) that custom, context specific dictionaries are most appropriate for use in financial research. We find support for the assertion of Loughran and McDonald (2009) in that the custom, earnings specific dictionary is much more powerful in detecting relevant conference call tone. We also find that conference call discussion tone has highly significant explanatory power for both initial reaction window abnormal returns as well as the post-earnings-announcement drift. This holds after controlling for both the magnitude of the earnings surprise and the tone of the prepared managerial statements. Furthermore, conference call tone is better able to predict sixty-day cumulative abnormal returns than the numerical value of unexpected earnings. We find dramatic differences in the predictive ability of call tone between dividend payers and non-payers in firm-level and portfolio-level analyses. Consistent with the uncertainty explanation for the drift, conference call tone matters more when firms pay dividends. In other words, nonfinancial information is relied on more heavily by investors when firms pay dividends. This suggests that in addition to providing a forum

4 While many text based content analysis programs exist, thus far the General Inquirer has been the most widely used application in the burgeoning linguistic analysis finance literature since many studies follow Tetlock (2007) and Tetlock, Saar-Tsechansky, and Macskassy (2008).

4 for disclosure, conference calls provide a monitoring function in cases of greater investor cash flow uncertainty. This is the first study of which we are aware to utilize tone oriented content analysis for earnings conference calls to examine the relation between the management/analyst discussion and the post-earnings-announcement drift anomaly. While many studies examine media-expressed negativity or management-expressed tone, we look at the tone of the interaction between all conference call participants to examine the incremental information content outside that found in the prepared statements of management. Our evidence suggests that conference calls provide information above and beyond both the earnings press releases and the earnings surprise. When earnings are less than expected, conference call tone plays a significant role in explaining both the initial reaction abnormal returns and the post-earnings- announcement drift. More specifically, when the earnings surprise is low, calls with a low tone see a pronounced drop in abnormal returns both initially and over the subsequent sixty days. However, calls with a high tone and low surprise do not see the same drop. This study is developed in the following sections. We review the literature in the next section. The third section describes our sample. In the fourth section we outline the content analytical techniques and empirical methods. The fifth section provides results. The final section concludes.

Literature Review Beaver (1968) shows that capital markets react to the information content in earnings reports. Ball and Brown (1968) find evidence that stock returns continue to drift in the same direction as unexpected earnings. That is, stock returns tend to drift upward (downward) after earnings announcements with positive (negative) earnings surprises. This post-earnings-announcement drift phenomenon stands in stark contrast to the concept of efficient markets, where the information content of the numerical value of earnings should be fully incorporated into stock prices within a short period of time after the information becomes publicly known. Even the most staunch of market

5 efficiency advocates acknowledge that this anomaly has survived across samples and variations in empirical methods (Fama 1998). Researchers have been unable to fully attribute the drift to risk adjustments, market frictions, or the psychological biases of market participants. Bernard and Thomas (1989, 1990) show that the drift is robust to risk adjustments, is not likely caused by transactions costs, and is not due to model misspecification or flaws in research design. They argue that the evidence is consistent with a delayed response explanation for the drift, where the market fails to immediately incorporate the information in the earnings surprise signal. Although inconclusive, other research suggests that psychological biases cause investors to overreact (DeBondt and Thaler 1985, 1987). Chopra, Lakonishok, and Ritter (1991) observe returns consistent with the overeaction hypothesis for short windows around quarterly earnings announcements. Daniel, Hirshleifer, and Subrahmanyam (1998) contend that investor overreaction, or overconfidence, lead investors to improperly weight earnings signals. The improper weighting is then slowly resolved over time, resulting in the drift.5 Abarbanell and Bernard (1992) note that the literature on both delayed response (underreaction) and overreaction includes some evidence to indicate that the drift may be rooted in the failure of market participants to fully appreciate the information content of current earnings. Such information is not limited to an exclusive focus on the numerical value of the earnings alone, but includes statements that typically accompany the earnings announcement. These statements provide context and add to our understanding of firm performance. Recently, a technique new to economic disciplines, called content analysis, has helped us better understand the relation between the wording in statements and stock prices. Content analysis is defined as the analysis of the manifest and latent content of a body of communicated material through classification, tabulation, and evaluation of its key symbols and themes in order to ascertain its meaning and probable effect.6 As applied to economic research, this involves the quantification of text based information through word classification and tabulation.

5 Bernard (1992) provides a good summary of the evidence for competing drift theories. 6 http://www.merriam-webster.com/dictionary/content%20analysis

Tetlock (2007) and Tetlock et al. (2008) are the first finance studies to rigorously incorporate text based content analysis. 7 Both studies capture the sentiment of the media by applying content analysis to the articles found in widely disseminated media sources. Tetlock (2007) analyzes the daily Wall Street Journal column, Abreast of The Market, and finds that high media pessimism predicts downward pressure on market prices and subsequent reversion. Tetlock et al. (2008) examine the relation between simple quantitative language measures and firm fundamentals. They find that the negative words in Wall Street Journal and Dow Jones News Service stories about individual S&P 500 firms can be used to predict individual firms‟ accounting earnings and stock returns. Engelberg (2008) counts the number of negative words as defined by the Harvard IV-4 Psychosocial Dictionary in the text of Dow Jones News Service stories about firms‟ earnings announcements following the method outlined in Tetlock (2007). In his attempt to link the qualitative earnings information embedded in the financial media with the post-earnings-announcement drift, Engelberg finds that text based “soft” information has additional predictability for equity prices beyond the “hard” information of the numerical earnings surprise. Specifically, the linguistic content of the news stories predicts larger price changes at longer horizons than unexpected earnings, suggesting that frictions in information processing may generate the drift. However, Engelberg does not consider more direct sources of information such as earnings announcements or the corresponding earnings conference calls. Several recent studies incorporate content analysis to examine the information content of the wording of earnings announcements (Davis et al. 2007; Henry 2008; Sadiqueet al. 2008; Demers and Vega 2008). Davis et al. (2007) examine whether managers use linguistic style in earnings press releases to provide information about expected future firm performance using a broad sample of firms. They find a positive association between optimistic tone in the press release and future return-on-assets. Using a sample in the telecommunications and computer industry, Henry (2008) measures the relative proportion of positive and negative words in earnings press

7 Text based content analysis techniques, developed decades ago, were originally applied in journalism, psychology, communications, and other social sciences. See Krippendorf (2004) for a complete history of content analysis, including its development and application across disciplines.

7 releases to capture the statements‟ tone. She shows that the tone affects the market‟s initial reaction to the earnings announcements. Sadique et al. (2009) extend this work by analyzing the content of both earnings announcements and related press coverage in relation to stock returns and volatility. They find that positive tone is related to increased returns and decreased volatility in a sample of S&P 100 firms. Demers and Vega (2008) examine whether the “soft” information in earnings press releases is incrementally informative over a company‟s reported “hard” earnings news. They find that managerial word choice is related to both abnormal returns and idiosyncratic volatility for up to sixty days following the announcement. Demers and Vega argue that it takes longer for the market to understand the implications of the wording of the earnings announcements relative to the numerical value of the earnings surprise and that the market response varies by firm characteristics. Overall, these studies (Henry 2008; Davis, Piger, and Sedor 2007; and Sadique, In, and Veeraraghavan 2008; Demers and Vega 2008) suggest that the information content of the words accompanying an earnings announcement provides additional insight beyond that which we learn by scrutinizing the numerical value of the earnings surprise in isolation. However, none of these studies analyze earnings conference calls, an information rich source with potentially greater information content than in the more narrow earnings press release. Frankel, Johnson, and Skinner (1999) note that conference calls are often used to supplement quarterly earnings releases and argue that managers use the calls to explain the implications of unusual or extraordinary items. This, in turn, aids analysts in determining the extent to which earnings surprises are permanent or temporary. Frankel et al. (1999) find that conference calls provide information to market participants over and above the information contained in the corresponding press release. Elevated return volatilities and trading volume during the conference calls suggests that large investors trade in real time based on the information released during the calls.

Sunder (2002) shows that firms disclose value relevant information to the public during conference calls following the passage of Regulation Fair Disclosure (FD). 8 Irani (2004) indicates that conference calls are a medium increasingly used by firms to disclose information to market participants and shows that post-FD conference calls are more informative than pre-FD conference calls. The National Investor Relations Institute indicates that next to news releases to wire services, conference calls are the most widely used means for disseminating corporate information to the investment community (NIRI, 2004). 9 They further state that conference calls are often used as a forum in which firms provide detailed information. Kimbrough (2005) explains that conference calls typically include opening remarks by management, which generally reiterate the press release, followed by a question and answer session where details not contained in the press releases are often disclosed. He notes that conference call discussions provide managers with an opportunity to comment on recent results and emphasize their implications for future financial performance. Kimbrough finds that the initiation of conference calls is associated with a significant reduction in the post-earnings-announcement drift, suggesting that conference calls improve the efficiency of the market reaction to earnings announcements. The linguistic information content of earnings conference calls has only recently been utilized in financial research. Frankel, Mayew, and Sun (2010) use conference call tone as a proxy for the prevailing state of relations between a firm and its investors in their examination of investor relations costs. They find a positive relation between conference calls returns and linguistic tone. Mayew and Venkatachalam (2009) analyze conference call audio files using vocal emotion analysis software. They examine the relation between managerial affective states (voice inflection), firm fundamentals, and performance. The number of negative words in each call is used as a control variable. Unlike these conference call studies, we focus our analysis on the information found in the text of quarterly conference calls to explore the relation between the linguistic tone of the conference call dialogue and both the contemporaneous market

8 The SEC rule commonly known as “Regulation FD” opened up earnings conference calls to the public, effective October 23, 2000. The final SEC rule is available at http//www.sec.gov/rules/final/33-7881.htm. 9 This is also noted in Frankel, Johnson, and Skinner (1999) using a NIRI (1996) report.

9 reaction and the post-earnings-announcement drift. We differentiate between the two major components of an earnings conference call, by separating management‟s prepared remarks and the unscripted question and answer portion of the call. This is the first study of which we are aware to utilize tone oriented content analysis to investigate the incremental information content of the Q&A component of quarterly earnings conference calls, above and beyond that found in the prepared statements of management, and relate it to the post-earnings-announcement drift. Furthermore, unlike other conference call studies, we examine differences across firms based on their level of certainty. The role of uncertainty in asset pricing has received increasing attention in the literature in recent years10. While numerous potential uncertainty proxies exist (Zhang, 2006), no universally accepted single measure of uncertainty has been established in the literature. We use dividends as an indicator of relative certainty. The dividend paying distinction enables us to check for differences between firms based on the certainty of the cash flows to investors. 11 DeAngelo et al. (2004) document that dividend payout has become concentrated among well established firms who generate substantial and stable earnings, while those firms with more growth opportunities tend to not pay dividends. As such, we would expect firms with fewer growth prospects (dividend payers) to have greater opportunities to squander the excess cash from operations, reducing the certainty of the cash flows to investors (Jensen 1986). Henry (2008) deliberately chooses a sample of firms drawn from the telecommunications and computer services industries for the period 1998-2002 because of the inherent uncertainty in both the industry and time period. She argues that, according to Amir and Lev (1996), non-financial information is relied on by investors more heavily during periods of uncertainty. Dividend payout differentiation allows us to examine this conjecture across a broad sample by comparing the non-

10 For example, Lewellen and Shanken (2002), Brav and Heaton (2002), Liang (2003), Zhang (2006), and Francis, LaFond, Olsson and Schipper (2007) all focus on uncertainty in asset pricing from either a theoretical or empirical perspective. 11 Although the relevance of dividend payout has been the subject of much study, and controversy, over the past fifty years (Miller and Modigliani 1961), recent theoretical, empirical, and survey evidence suggests that dividend payout is a nontrivial matter that has implications for firm value (Allen and Michaely 2002; Brav, Graham, Harvey, and Michaely 2005; DeAngelo and DeAngelo 2006).

10 numerical information content of earnings conference calls between firms with greater and lesser cash flow uncertainty.

Data This study covers sixteen consecutive quarters during the post Regulation FD four-year period 2004-2007. Prior to this time, conference call transcript availability is limited.12 After this time, markets experienced a precipitous decline initiated by the bursting of the housing bubble. Calls held during the sample period discuss firm performance starting with the fourth quarter of 2003 and run through the third quarter of 2007 due to the several week lag between the close of each quarter and the corresponding earnings announcements and conference calls. We use a pseudo-random sample comprised of 2,880 observations. REITs, ADRs, closed end funds, and units are excluded by limiting CRSP data to share codes 10, 11, and 12. Other financials (SIC 6000-6999) and utilities (SIC 4900-4949) are excluded as well. Prior to random selection, we drop all firms which fail to meet data sufficiency requirements. Firms need at least 60 days of daily returns on CRSP prior to the conference call and 60 days of daily returns following the call in order to estimate abnormal returns and show the drift (Bernard and Thomas 1989). In addition, seasonally lagged earnings-per-share (EPS) data obtained from Compustat are necessary to calculate the earnings surprise. For each quarter we sequentially sort the universe of merged CRSP and Compustat firms into terciles by size13 and book-to-market equity (BM)14 in order to

12 While Regulation FD took effect at the end of 2000, it took a few years before electronic conference call transcripts became widely available. Starting the sample earlier would have resulted in a more pronounced bias towards large, well established firms since they are more likely to receive greater attention and merit transcription coverage. We acknowledge that even during the 2004-2007 period there may be some bias inherent in each of our firm-quarter observations since we require each observation to have an electronically available transcript. We attempt to mitigate this to some extent by drawing a pseudo-random sample wherein we force the sample to have an equal number of small, medium, and large firms. 13 Size sort based on NYSE market capitalization breakpoints obtained from the website of Ken French. Since the breakpoints are only available at five percent increments, terciles are extrapolated as follows: ((p35-p30)/5*3=p33) & ((p70-p65)/5*2=p67). 14 Negative BM equity firms are dropped.

11 ensure that our sample contains a cross section of firms with varying characteristics.15 Size is the market capitalization of each firm calculated as the number of shares outstanding times the market price of the stock at the end of the preceding quarter. BM is calculated as the two-fiscal-quarter lagged Compustat book value of equity divided by the market capitalization of each firm at the end of the preceding quarter. Within the nine size-BM portfolios we further separate dividend and non-dividend paying firms, resulting in eighteen characteristic portfolios for each quarter. Ten firms are then randomly selected from each of the characteristic portfolios for each of the sixteen quarters, resulting in (18 x 16 x 10 =) 2,880 firm quarter observations. Conference call transcripts are obtained from two sources, Fair Disclosure Wire and The American Intelligence Wire. Call dates generally accompany the transcripts and are confirmed using Thomson Streetevents. Table 1 shows the relative timing of earnings announcements and conference calls for our sample of 2,880 firm-quarter observations.16 For the number of trading days after announcement, a zero indicates that a conference call is held on the same day as the earnings announcement. Likewise, a one indicates that a conference call is held one day after the earnings announcement date, a two indicates that a call takes place two trading days after the announcement date, and so forth. At 84%, the vast majority of calls take place on the same day as the earnings announcement. Over 15% of the calls are held on the following day.17 The remaining calls (0.6%) are all held within the next few days.

Content Analysis Techniques and Empirical Methods Nearly fifty years ago Harvard researcher Philip J. Stone created the first content analysis computer program, the General Inquirer (GI).18 Since that time, computer based content analysis has been widely used across many disciplines to quantify text based information in an effort to better understand the text‟s meaning. To accomplish

15 As such, we avoid the potential biases of having a sample skewed towards either large or small firms, or value or growth firms. 16 Earnings announcement dates are obtained from the Compustat Quarterly database, item “RDQ – Report Date of Quarterly Earnings”. 17 For the 439 calls that fall within the “one trading day after announcement” category, the earnings announcements were typically released after hours on the previous day. 18 Stone, Dunphy, Smith, and Ogilvie (1966) describe the program and its potential application across numerous disciplines.

12 this in a rigorous way, content analysis software applies predefined rules which aid in word recognition, categorization, and tabulation. With continued refinement over the years, the GI has maintained its place as a powerful tool for quantifying linguistic information. Under its current iteration, the GI processes text by first applying over 6,300 disambiguation rules as part of the word recognition process. Following word recognition, the program applies a dictionary that categorizes each word according to its meaning and then tabulates a raw count with the number of words that fall within each predefined category. By default, the GI applies the Harvard IV-4 Psychosocial Dictionary which contains approximately 12,000 words and 77 separate categories within which a word can be classified.19 It is also possible to create custom dictionaries with categories and word lists that are specifically tailored to the context of interest. Other computer based content analysis programs operate in a similar fashion, however none of them currently have the capacity to handle a dictionary as large and robust as the Harvard dictionary. In recent work, Tetlock (2007), Tetlock et al. (2008), Engelberg (2008), Mayew and Venkatachalam (2008), and Frankel et al. (2010) all utilize the GI and the Harvard dictionary to quantify the linguistic tone of written material. Davis et al. (2007), Henry (2008), and Sadique et al. (2008) use the Diction software package and a custom dictionary as found in Henry (2008). Demers and Vega (2008) use both the GI and Diction in their linguistic analysis and rely on the “off the shelf” dictionaries that each of the programs uses by default. In comparing the two packages, Demers and Vega find the default GI word list (Harvard dictionary) to be more powerful in predicting stock returns. While content analysis techniques are well established and have been widely used across many disciplines such as journalism, communications, psychology, sociology and other social sciences, its application to financial research is still in its infancy. The question of interdisciplinary dictionary transferability comes into play. In other words, is it appropriate to apply a dictionary that is well grounded in psychological research in a financial context? Henry (2008) and Loughran and McDonald (2009) both

19 For completeness, we note that the GI outputs a total of 182 separate categories since it also incorporates the Lasswell general purpose dictionary by default.

13 contend that each discipline has its own dialect, where words take on specific meanings in specific contexts which may not translate effectively across disciplines. Loughran and McDonald examine the Harvard dictionary in relation to a dictionary of their own creation using corporate 10-K filings and find that the Harvard dictionary often misclassifies terms in annual reports. That is, words which take on a negative meaning in every day speech do not necessarily have a negative meaning in the context of corporate annual reports. In order to provide an out of sample examination of dictionary power, we use both the Harvard dictionary and a custom, finance oriented, dictionary. Unlike previous studies that use the Harvard dictionary and only incorporate two word categories, positive and negative, we use additional categories that contain words that would generally be considered either positive or negative in nature. Specifically, we employ eight of the Harvard dictionary categories that are relevant to this study including positive, negative, active, passive, strong, weak, overstatement, and understatement and condense them into a single measure of conference call tone. For the custom dictionary we use the word lists defined in Henry (2008). The Henry dictionary is a simple list of “domain relevant” words that are categorized as either positive or negative. The custom dictionary was created specifically using quarterly earnings press releases which is appropriate for our examination of quarterly earnings conference calls. 20 We use the GI for word recognition and apply both the Harvard and Henry dictionaries for comparison. After processing the sample of conference call text files through the content analysis software, raw word counts are divided by the total words in their respective conference calls to produce a relative measure (REL_Mi,j) for each category i and conference call j:

_ = , (1) , � � where the REL_Mi,j categories� are Positivej, Negativej, Activej, Passivej, Strongj, Weakj,

Overstatedj, and Understatedj.

20 See Appendix A for further detail including a description of the categories and word lists. We use the Henry dictionary instead of the negative word list created by Loughran and McDonald (2009) because it is both more concise and specifically relevant to the context of our examination of earnings conference calls. Loughran and McDonald‟s list was created using 10-K filings and has not been tested in an earnings announcement/ conference call context. However, we feel the Loughran/McDonald word list would likely produce results similar to the Henry dictionary since they are both financially oriented.

The eight relevant Harvard dictionary categories comprise four opposing pairs. Expressed in ratio form we can tell, for example, how positive a call is relative to how negative it is. To arrive at an overall numerical representation of conference call tone for each conference call j (TONE1,j), we take the first principal component of the four relevant category ratios:

1, = , , , (2) This measure is used in place of simply isolating the positive- �to -negative ratio since investors will likely see words that fall within the active (passive), strong (weak), or overstated (understated) categories as “positive” (“negative”). Taking the first principal component allows collapsing of the variation across the four ratios into one simple measure of overall conference call tone. Thus, we treat the TONE variable as a metric of relative positivity. For robustness, we also construct the tone variable using an alternative form of the Harvard dictionary category ratios described above. First we take the difference in the opposing REL_Mi,j categories and divide by the sum of the two. Specifically, for each conference call j we calculate the following four ratios using the REL_Mi,j categories:

= (3) + − = (4) + − = (5) + − � � = � (6) + − In this form the ratios are bounded between -1 and 1, consistent with the tone measures of Uang, Citron, Sudarsanam, and Taffler (2006) and Henry (2008). For the second overall numerical representation of conference call tone for each conference call j

(TONE2,j), we take the first principal component of the four relevant category ratios:

2, = , , , (7)

�

For the Henry measure for each conference call j (H-TONEj), we simply express the REL_Mi,j categories Positivej and Negativej, as defined in the earnings specific Henry dictionary, in the two ratio forms described previously:

 = (8) 1, and

 = (9) 2, + − To isolate the stock price reaction to the earnings announcements and corresponding conference calls we calculate cumulative abnormal returns. Daily abnormal returns are defined as size adjusted returns calculated as the difference between the raw return for stock j on day t and the mean return of a portfolio of all firms in the same size decile (Foster, Olsen, and Shevlin 1984; Bernard and Thomas 1989, 1990) 21:

, = , , (10) where ARj,t is− the abnormal return for firm j on day t, Rj,t is the return for firm j on day t, and Rp,t is the mean return on day t for all firms in the same size decile as firm j. We cumulate abnormal returns over two different windows, days minus one through one and days two through sixty, where day zero is the date of the quarterly earnings conference call. We analyze the effect of conference call tone on the contemporaneous stock price reaction using CARs from the initial three-day period, while the CARs from the longer period allow us to examine the relation between tone and the post-earnings-announcement drift.22 While Bernard and Thomas (1990) show evidence of the drift through three quarters for a sample of earnings announcements from 1974 to 1986, others find that the magnitude, and perhaps duration, of the drift has declined over time. Campbell, Ramadorai, and Schwartz (2009) find that the drift

21 Size deciles are determined using NYSE size (market capitalization) breakpoints as found on Ken French‟s website. While the literature shows that short window cumulative abnormal returns are unaffected by risk adjustment, we find that our sample is also mostly unaffected by size adjustment. We obtain nearly identical results using simple Market Adjusted Returns (MAR) without size adjustment and the CRSP value-weighted index as the market proxy. See Appendix B for tabulated MAR results. 22 Davis et al. (2007), Henry (2008), Demers and Vega (2008), Engelberg (2008), and Mayew and Venkatachalam (2009) all use three day CARs to examine the initial reaction period. Foster et al. (2004), Bernard and Thomas (1989), and many subsequent studies use CARs of roughly sixty trading days to examine the post-earnings-announcement drift.

16 survives over the sixty days following the announcement in their 1995 to 2000 sample period, but that the effect is weaker than in Bernard and Thomas. Thus, we use two cumulative abnormal returns measures for each stock associated with conference call j,

(CAR(-1,1)j and CAR(2,60)j, where: 1 1,1 = = 1 , (11) 60 −2,60 = =2− , (12) After calculating the cumulative abnormal returns measures we sort the sample into tone quintiles and check for differences in CARs between the high and low tone portfolios. We compare the CAR means and medians of the high and low tone quintiles using t-tests and Wilcoxon rank-sum tests, respectively. The comparison is done for all four tone measures and both series of estimated CARs for the whole sample in addition to isolating dividend paying firms and firms that do not pay dividends. To further illustrate the differences across tone portfolios, we plot the cumulative abnormal returns from ten days prior to the conference call through sixty days after the conference call for firms in the high tone portfolio, firms in the low tone portfolio, and an average of all firms in the sample across the tone quintiles. Unexpected earnings, , are calculated using a seasonal random walk model where the difference between the earnings-per-share and the earnings-per-share in the same quarter of the prior year is scaled by the stock price at the close of the lagged quarter:

= , 4 (13)

− −, 4 The seasonal measure − of earnings surprise is widely used in the literature along with a measure calculated as the stock price scaled difference in actual and mean analyst forecast earnings.23 We choose the seasonal measure because the data are judged to be more reliable than analyst forecast data. Recent research shows that the analyst

23 For example, Akbas, Boehmer, Erturk, and Sorescu (2010) only use the seasonal measure. Sadique et al. (2008) use both measures in their analysis and find no substantive differences between them. However, they focus on, and only tabulate results for, the seasonal measure. Henry (2008) also uses the seasonal measure to control for unexpected earnings, although she incorporates an indicator variable set to one if earnings are greater than the consensus analyst forecast as well. Davis et al. (2007) and Engelberg (2008) both use an analyst forecast method exclusively. However, Engelberg notes that his sample is biased against small firms since analysts are more likely to cover large firms. Brown, Hagerman, Griffin, and Zmijewski (1987) show that both the seasonal and analyst forecast based earnings surprise proxies are prone to measurement error.

17 forecast based measure suffers from the finding that analysts only forecast GAAP earnings 65% of the time. In other words, 35% of the time analysts forecast an earnings number that does not correspond to the actual GAAP earnings number used in calculating GAAP earnings surprises (Doyle, Lundholm, and Soliman 2003; Collins, Li, and Xie 2009). Sadique et al. (2008) argue that analysts forecast pro forma earnings. Ljungqvist, Malloy, and Marston (2009) show I/B/E/S data to be unreliable. By comparing identical I/B/E/S downloads taken at different times Ljungqvist et al. find that up to 22% of matched observations are different from one download to the next. Differences include alterations of recommendations, the addition and deletion of records, and the removal of analyst names. The suspect nature of analyst forecast data sources is not new. Barron and Stuerke (1998) indicate that analyst forecast recording processes have undergone changes over time in an effort to correct practices that are fraught with issues and inaccuracies. Furthermore, survey evidence shows that managers place the greatest weight on four quarter lagged earnings per share as their earnings benchmark. Graham, Harvey, and Rajgopal (2005) find that 85.1% of CFO‟s consider earnings in the same quarter of the prior year to be the most important earnings benchmark, followed secondly by mean analyst forecast estimates. Thus, using the seasonal measure provides the most appropriate context in which to examine the relation between earnings and conference call tone since managerial tone may be more directly related to their performance relative to the benchmark they consider to be most important. To demonstrate performance variation in the cross section of earnings surprises, we sort the sample into SURP quintiles and visually check for differences in CARs between the high and low SURP portfolios. In the same manner as the tone based illustration described above, we plot the cumulative abnormal returns from ten days prior to the conference call through sixty days after the conference call for firms in the high SURP portfolio, firms in the low SURP portfolio, and an average of all firms in the sample across the SURP quintiles. We examine the separate effects of tone and earnings on returns in two ways. Our first analysis is at the firm level. To determine the effect of conference call tone on stock price reaction, we separately regress the CARs on the different tone measures,

18 controlling for the unexpected earnings from the announcement, by running cross- sectional regressions of the following form:

= 0, + 1, , + 2, , + (14)

where � represent� the cumulative� abnormal� return measures for conference call j defined above; , is interchangeable with  , , both as defined above and where i = 1 or 2. Standard errors are corrected for potential heteroscedasticity following White (1980). We also adjust the standard errors for time- and firm-effects by clustering by firm and by quarter using the methodology outlined in Petersen (2009).24 We expect the earnings surprise control variable to be the dominant predictor of abnormal returns during the initial reaction period (t = -1 to t = 1) and anticipate the coefficient will be positive and significant. Similarly, if additional information is disclosed during the conference calls, as proxied by call tone, we expect the tone measures to be both significant and positively related to the initial reaction window CARs. The efficient markets hypothesis suggests that the full impact of both the earnings surprise and the tone measures should be completely incorporated into the initial response. However, the coefficients on both the earnings surprise measure and the tone measures may be significant over a longer horizon (t = 2 to t = 60) given the pervasive post-earnings- announcement drift (Bernard and Thomas 1989, 1990; Chan et al. 1996). Engelberg (2008) and Demers and Vega (2008) suggest that soft information, such as our tone measures, has greater predictability for returns at longer horizons. In other words, the market may impound numerical information more quickly than qualitative information since the numerical information may be comparatively more certain. Finding significance for the conference call tone measures may indicate that we are simply picking up the impact of the earnings press releases, which is shown to be significantly related to abnormal returns by Davis et al. (2007), Henry (2008), Sadique et al. (2008), and Demers and Vega (2008). To differentiate between the earnings announcements and the corresponding conference calls, we control for the tone of the press releases by separating the conference calls into two sections as in Matsumoto,

24 Firm effects denote dependence within observations of a firm across time. Time effects refer to dependence within a quarter across firms.

Pronk, and Roelofsen (2007).25 Kimbrough (2005) and Matsumoto et al. (2007) contend that the prepared statements by management at the beginning of a call essentially reiterate the information in the carefully crafted press release. Thus, we use the tone of the introductory statements prepared by management as a proxy for the tone of the earnings announcements. Following the prepared remarks, the remainder of the time is opened up and management fields questions from call participants. Accordingly, we incorporate new tone measures which represent these separate conference call components for both of the dictionaries used in this study (INTRO TONE and Q&A TONE, as well as INTRO H-TONE and Q&A H-TONE). INTRO TONE is constructed exactly as the TONE variable above, only it is limited to the manager‟s introductory presentation text. Likewise, Q&A TONE represents the TONE measure for the question and answer portion of the call. INTRO H-TONE and Q&A H-TONE follow the same pattern using the Henry dictionary. In light of previous studies which demonstrate that conference calls convey information beyond that contained in the earnings announcements (Frankel, et al, 1999; Kimbrough, 2005), we expect that, after controlling for the tone of the press releases using the conference call introductory statements, the tone of the question and answer dialogue between management and analysts will contain explanatory power for the cumulative abnormal returns. Our second analysis is at the portfolio level. To further determine the extent of the relation between abnormal returns and conference call tone, we conduct two-way sequential sorts on tone and earnings surprise for both CAR representations and all four tone measures. For each dimension of the sort we form terciles that contain the mean CAR for their respective portfolios. The mean differences between the high and low portfolios are compared using t-tests. When holding the earnings surprise constant, we expect to find significant CAR differences in high and low tone portfolios.

25 Matsumoto et al. (2007) examine the relation between firm performance and quantity and type of disclosures made in conference calls. They split conference calls into their two primary components (the manager‟s introductory presentation and the question and answer session) and count the length of the calls, the number of financial versus non-financial words, and the number of backward-looking versus forward-looking verbs.

Results

Descriptive statistics for the CARs, earnings surprise, and various tone measures are shown in Table 2, Panel A. The CARs for the initial reaction period range from - .5135 to .4864, although most of the observations are less dramatic with a 25th percentile of -.0351 and a 75th percentile of .0400. The drift period CARs are more pronounced with a range of -1.2894 to .8992. The drift period interquartile range also implies that the majority of observations are less extreme with the 25th percentile at - .0865 and the 75th percentile at 0.0749. However, regardless of whether we look at the full range or interquartile range, CAR(2,60) is roughly double the levels we see with CAR(-1,1), suggesting that the information content of the earnings surprises and accompanying conference calls are not fully impounded into prices during the initial reaction period. The mean (median) SURP of .0007 (.0014) is near zero and ranges from -.8348 to .8947 which indicates that the sample is fairly evenly distributed between positive and negative earnings surprises, albeit with a slight negative skew. The tone measures reveal that the overall tone of the conference calls in the sample is positive. With a mean of 5.21, H-TONE1 shows that the conference call participants use over five times more positive words than negative words, on average. In the extreme positive case, call tone is 28 times more positive than negative, while in the extreme negative case a ratio of .82 indicates that call tone is only slightly more negative than positive. This is not surprising since we would expect management to put their best foot forward and present their case in the best possible light. However, the overall positivity of the sample is not nearly as important as the substantial dispersion we see in the tone 26 measures. The H-TONE2 measure, which is bounded between -1 and 1, shows a minimum value of -.10 and a maximum value of 1. The minimum value indicates a call tone that is slightly more negative than positive, but is near the point of tone neutrality. However, there is only one observation out of nearly three thousand in our sample that has an H-TONE2 measure less than zero. Likewise, there is only one call with an H-

26 We could also limit our focus to only the proportion of negative words as in Tetlock (2007), Tetlock et al (2008), and others. However, we contend that it is not so important how positive or negative a call is in and of itself. Rather, the relative proportions of the two tonal dimensions taken together provide a richer information set for our analysis.

TONE2 measure of 1.00, which represents a call that is completely void of negative words. Although not shown, the 1st and 99th percentiles are at .27 and .86, respectively.

Descriptive statistics for TONE1 and TONE2 show the same levels of dispersion even though the numerical representations are somewhat different because we use the first principal component of the four ratios. The correlations in Panel B of Table 2 show that the tone measures are all highly correlated with one another. However, the measures obtained using the Harvard and Henry dictionaries are not perfectly correlated, providing an indication that the two dictionaries materially differ from one another. The two dictionaries also differ with respect to the level of correlation between the tone measures and both the initial reaction window CARs and drift window CARs. With coefficients of .10 and .11, H-

TONE1 and H-TONE2 are slightly more correlated with CAR(-1,1) than the Harvard dictionary based TONE1 (.09) and TONE2 (.09) measures. The difference is more distinct when focusing on the drift period. The correlation coefficients for H-TONE1 and

H-TONE2 with CAR(2,60) are .04 and .06, respectively. While the coefficients for

TONE1 and TONE2 with CAR(2,60) are both .01.

Table 3 shows the CARs sorted into TONE1 quintile portfolios. Overall, differences are highly significant during the initial reaction window, but lose their significance during the drift period. Interestingly, there are significant differences between dividend paying firms and non-paying firms. 27 Initial reaction returns to a zero investment strategy long dividend paying stocks in the highest TONE1 quintile and short dividend paying stocks in the lowest TONE1 quintile are roughly 3% per quarter. In contrast, the same strategy for firms that do not pay dividends only produces returns of a statistically insignificant 0.7%. We see nearly identical results in terms of both magnitudes and significance in Table 4 where the quintile sorts are based on the

TONE2 measure. Quintile sorts based on the tone measures derived from the Henry dictionary are shown in Tables 5 and 6. With H-TONE1 and H-TONE2, the sorting breakpoints are identical for the highest and lowest quintile portfolios. The middle quintile portfolio

27 Although not shown, the p-value for the difference test between dividend payers (3%) and non-payers (0.7%) is 0.0131.

22 breakpoints are so close between the two H-TONE measures that only minor differences can be seen. This results in identical zero investment strategy returns in terms of both magnitude and significance across the two tables. Unlike the Harvard dictionary based tone measures in Tables 3 and 4, we find the Henry dictionary measures in Tables 5 and 6 to be much more powerful in their ability to detect differences in the context of earnings conference calls. This provides out of sample support for the argument of Loughran and McDonald (2009) that customized dictionaries with context specific terminology are more appropriate for research in financial settings.28 With sorts based on the tone measures derived from the finance- specific dictionary, we find highly significant CARs for the entire sample during both the initial reaction window and the drift period. Furthermore, we continue to see differences between dividend paying firms and those that do not pay dividends, particularly in the drift period where the CAR for the zero investment strategy with dividend paying firms is a highly significant 3.33% per quarter compared to an insignificant 1.16% for non- dividend payers. Figure 1 provides a graphical illustration of the CAR differences between the high and low H-TONE1 quintile portfolios plotted against the average of all conference calls in the sample. Starting ten days prior to the conference calls, we see some initial spreading due to earnings anticipation, followed by the initial reaction and continued spreading of the two portfolios through sixty days after the calls. The total spread of roughly 5.5% at the end of the 71 day period is dramatically different than the overall average which hovers near zero. In Figure 2 we demonstrate performance variation based on differences in earnings surprises. Similar to the graphical representation in Figure 1, we plot the cumulative abnormal returns from ten days prior to the conference call through sixty days after the conference call for firms in the high SURP quintile, firms in the low SURP quintile, and an average of all firms in the sample. With a total spread of approximately 4.5% at the end of the 71 day period, Figure 2 illustrates CAR differences in the cross

28 Accordingly, we focus on the results obtained using the earnings specific Henry dictionary from this point forward. Specifically, we show results using the H-TONE1 measure, which are either identical or nearly identical to the results obtained using the H-TONE2 measure. Results obtained using the Harvard dictionary based TONE1 and TONE2 measures are less powerful, but qualitatively the same. All results using H-TONE2, TONE1, and TONE2 are shown in Appendix A.

23 section of earnings surprises of a magnitude similar to, but slightly less than, we see in the H-TONE1 cross section. At the firm level, the separate effects of tone and earnings surprises for both the initial reaction period CARs and the drift period CARs are shown in the regression results of Table 7. The results for all firms in the sample are in Panel A. As expected, the coefficients for both SURP and H-TONE1 are positive and highly significant when the dependent variable is CAR(-1,1). When the dependent variable is CAR(2,60) tone remains an important predictor, however SURP loses its significance. Consistent with Engelberg (2008) and Demers and Vega (2008), qualitative information appears to have greater explanatory power for returns at longer horizons than the actual earnings number. In other words, numerical data may be easier for markets to comprehend. When conference calls are separated into their two main components, the primary source of additional information is contained in the question and answer portion of the calls. Q&A H-TONE1 is significant at the 1% level in both the initial reaction window and the drift period, while INTRO H-TONE1 is not significant in either case. After controlling for the linguistic information in the earnings announcement, the conference calls do indeed provide additional value relevant information to market participants. Thus, using content analysis techniques we find support for the arguments of Frankel et al. (1999) and Kimbrough (2005).29 Table 7 also provides regression results for dividend paying firms (Panel B) and firms that do not pay dividends (Panel C). Consistent with the quintile tone sorts above, we find differences between firms based on the certainty of the cash flows to investors. While tone appears to have explanatory power in both cases, there is more overall significance for dividend paying firms. With dividend payers, we find that the tone in the introductory portion of the conference calls (INTRO H-TONE1) has explanatory power during the drift window in addition to the significant explanatory power of Q&A H-

TONE1. Thus, investors in dividend paying firms rely on the information content in each portion of the conference call independently, suggesting that nonfinancial information is relied on more heavily in cases of greater cash flow uncertainty. Furthermore, the

29 Frankel et al. (1999) and Kimbrough (2005) do not use content analysis or examine conference call characteristics in any way. Rather they examine return volatility and trading volume at the time of the call (Frankel et al.) and the initiation of conference calls (Kimbrough).

24 significance of both portions of the call suggests that conference calls provide not only a forum for additional disclosure, but provide a monitoring function as well. At the portfolio level, we further differentiate the effects of tone and earnings surprise by conducting two-way sequential (tercile) sorts on tone and earnings surprise for both CAR(-1,1) and CAR(2,60) as shown in Table 8. In Panel A, which includes all 2,880 firms in the sample, we see that sorting on the earnings surprise results in significantly different initial reaction CARs regardless of the level of call tone. Looking at the other dimension of the sort we find that call tone matters for any earnings surprise outside of the highest tercile. Consistent with the firm level analysis, SURP loses its significance in the drift period. However, we still find modest significance for H-TONE1 when earnings surprises fall within the lowest SURP tercile. This result becomes much stronger when dividend paying firms are isolated (Panel B) and is nonexistent when firms that do not pay dividends are considered (Panel C). Thus, the two-way portfolio sorts also suggest that conference call tone matters more for dividend paying firms, particularly when quarterly earnings are below market expectations. In cases of greater investor cash flow uncertainty, investors rely more heavily on the information conveyed by conference call tone to the extent that a positive tone can effectively counteract the negative repercussions of an earnings surprise that is less than expected in both the initial reaction and drift windows.

Figure 3 illustrates the interaction between H-TONE1 and SURP by plotting CARs over a 71 day period from 10 days prior to the conference call to 60 days after the call. The CARs are separated into four separate portfolios which correspond to the four corners of the two-way sort matrices shown in Panel A of Table 8. When holding SURP constant we see that there is a remarkable difference between CARs based on the level of H-TONE1. That is, when the earnings surprises fall within the lowest third of all sample surprises, firms with low call tone see abnormal returns drop precipitously while those firms with high call tone are largely unaffected by the negative surprise. Essentially, a positive call tone offsets the damaging effect of a poor earnings announcement. When the earnings surprises fall within the highest third of all sample surprises, firms with high call tone experience strong cumulative abnormal returns. However, those firms with high surprises and low call tone see returns that increase

25 very little. By comparison, the CARs are indistinguishable from the High H-TONE1 Low SURP portfolio by day t=60. In contrast, the 71 day difference between the High H-

TONE1 High SURP portfolio and the Low H-TONE1 Low SURP is roughly 8% on a quarterly basis.

Conclusion

This study examines abnormal stock returns in the context of uncertainty using computer aided content analysis and the transcripts of quarterly earnings conference calls. We employ a general word categorization dictionary that is widely used across disciplines (Harvard IV-4 Psychosocial) and a custom, earnings specific, dictionary (Henry) in order to quantify the linguistic tone of earnings conference calls. We find that call tone is significantly related to both the initial earnings announcement window abnormal stock returns and the post-earnings-announcement drift after controlling for the numerical representation of the earnings surprise. However, the custom, “domain relevant” dictionary is shown to be much more powerful in detecting cumulative abnormal returns beyond the initial reaction window, providing out of sample support for the argument of Loughran and McDonald (2009) that context specific dictionaries are most appropriate for use in financial research. Unlike previous studies, which either limit their focus to media expressed sentiment or management expressed tone, we examine the marginal contribution of the tone of the dialogue between management and analysts in helping us better understand the behavior of stock returns following earnings announcements. We differentiate between the two major components of an earnings conference call by separating management‟s prepared introductory remarks and the more spontaneous question and answer portion of the call. Since the prepared statements essentially reiterate the content of the earnings press release (Frankel et al. 1999; Kimbrough 2005), including the two separate call components in our analysis allows us to determine the contribution of the informal, unscripted discussion between management and analysts above and beyond the information in the earnings number and accompanying announcement.

We find that the Q&A portion of the call has significant predictive ability for both the initial reaction CARs and the post-earnings-announcement drift. Our results show that Q&A call tone subsumes the predictive ability of the numerical earnings surprise in the longer window, suggesting that it takes longer for the market to fully comprehend the value relevant information in the conference call dialogue than in the reported earnings. While this may suggest that text based information may be inherently more uncertain than numerical information, we directly examine the uncertainty explanation for the drift by separating our by dividend paying status. Both firm-level and portfolio-level results suggest that nonfinancial information is relied upon more heavily by investors for dividend paying firms. In other words, conference call tone has more explanatory power for the post-earnings-announcement drift with firms where there is greater potential for agency conflicts to reduce future investor cash flows. This provides support for the uncertainty explanation and suggests that there is a significant monitoring component in the Q&A portion of conference calls. Additionally, when examining the interaction between the earnings surprise and conference call tone, we find that new information is revealed in the call discussion to the extent that a highly positive call tone can effectively offset the usual damaging effects of poor earnings performance. More specifically, when holding surprise constant in the lowest tercile, there are significant differences across portfolios of firms in the highest and lowest tone terciles. These differences are primarily clustered in the subsample of dividend paying firms, providing further support for the notion that additional disclosures are important for firms with greater investor cash flow uncertainty.

Table 1 Earnings Announcement and Conference Call Timing

Number of Trading Days Number of Percent of Cumulative After Announcement Calls Observations Percent 0 2,423 84.13 84.13 1 439 15.24 99.38 2 6 0.21 99.58 3 7 0.24 99.83 4 3 0.10 99.93 5 1 0.03 99.97 6 1 0.03 100 Total 2,880 100

This table shows the timing relation between earnings announcements and conference calls. Most of the calls take place on either the same day as the announcement or on the following day.

Table 2 Descriptive Statistics and Correlations

Panel A CAR (-1,1) CAR (2,60) SURP TONE1 TONE2 H-TONE1 H-TONE2 Mean 0.0005 -0.0062 0.0007 0.00 -0.00 5.21 0.64 Min -0.5135 -1.2894 -0.8348 -2.94 -4.95 0.82 -0.10 P25 -0.0351 -0.0865 -0.0033 -0.94 -0.89 3.56 0.56 P50 0.0012 -0.0089 0.0014 -0.18 0.04 4.73 0.65 P75 0.0400 0.0749 0.0058 0.74 0.94 6.28 0.73 Max 0.4864 0.8992 0.8947 15.16 5.55 28.00 1.00 Std.Dev. 0.0754 0.1498 0.0500 1.36 1.36 2.43 0.13 N 2880 2880 2880 2880 2880 2880 2880 Panel B

CAR (-1,1) 1

CAR (2,60) 0.0172 1

SURP 0.0979 -0.0158 1

TONE 0.0858 0.0093 0.0414 1 1 TONE 0.0865 0.0097 0.0484 0.9551 1 2 H-TONE1 0.1039 0.0389 0.0629 0.5851 0.5632 1

H-TONE2 0.1124 0.0567 0.0927 0.5512 0.5874 0.8718 1

This table provides descriptive statistics (Panel A) and correlations (Panel B) for the full sample of 2,880 conference calls. CAR(-1,1) is the three-day cumulative abnormal return, where day 0 is the conference call date, where the abnormal returns are estimated using size-adjusted returns calculated as ARj,t = Rj,t – Rp,t, where the abnormal return for firm j on day t is the difference between the return for firm j on day t and the mean return on day t for all firms in the same size decile as firm j. CAR(2,60) is calculated in the same manner as CAR(-1,1) only it is cumulated from days two through sixty. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock

Price (end of qtr-4)}. TONE1 is the first principal component of the four ratios Positive/Negative, Strong/Weak, Active/Passive, and Overstated/Understated and TONE2 is the first principal component of the four ratios (Positive – Negative)/(Positive + Negative), (Strong – Weak)/(Strong + Weak), (Active – Passive)/(Active + Passive), and (Overstated – Understated)/(Overstated + Understated), where each category reflects the proportion of words in a given call as defined by the Harvard IV-4 Psychosocial Dictionary. H-TONE1 is the ratio of Positive words to Negative words and H-TONE2 is the ratio (Positive – Negative)/(Positive + Negative) where each category reflects the proportion of words in a given call as defined by the custom earnings dictionary of Henry (2008).

Table 3 Test of Differences of Means and Medians, by TONE1 Quintiles TONE1 CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) Quintiles All Dividend NonDiv All Dividend NonDiv 1 (Low) Mean -0.0104 -0.0148 -0.0036 -0.0090 -0.0150 0.0002 Median -0.0100 -0.0119 -0.0028 -0.0144 -0.0151 -0.0116

2 Mean -0.0028 -0.0007 -0.0054 -0.0054 0.0098 -0.0245 Median 0.0008 0.0033 -0.0052 -0.0006 0.0128 -0.0134

3 Mean 0.0051 0.0072 0.0032 -0.0093 -0.0156 -0.0040 Median 0.0051 0.0063 0.0035 -0.0098 -0.0152 -0.0072

4 Mean 0.0027 0.0049 0.0008 -0.0118 -0.0208 -0.0039 Median 0.0020 0.0048 -0.0021 -0.0154 -0.0266 -0.0064

5 (High) Mean 0.0079 0.0145 0.0033 0.0045 -0.0047 0.0110 Median 0.0088 0.0068 0.0094 -0.0001 -0.0074 0.0030

Mean Q5 – Q1 0.0183*** 0.0293*** 0.0069 0.0135 0.0102 0.0108 t-statistic (4.16) (5.40) (0.96) (1.59) (1.00) (0.77) Wilcoxon rank-sum test

Median Q5 – Q1 0.0188*** 0.0187*** 0.0123* 0.0143* 0.0077 0.0146 z-statistic (5.40) (5.60) (1.95) (1.79) (1.28) (0.92) *** p<0.01, ** p<0.05, * p<0.1

This table shows the differences in CARs when sorted into TONE1 quintile portfolios for the full sample (All), dividend paying firms (Dividend), and firms that do not pay dividends (NonDiv). Test statistics (t and z) are in parentheses. CAR(-1,1) is the three-day cumulative abnormal return, where day 0 is the conference call date, where the abnormal returns are estimated using size-adjusted returns calculated as ARj,t = Rj,t – Rp,t, where the abnormal return for firm j on day t is the difference between the return for firm j on day t and the mean return on day t for all firms in the same size decile as firm j. CAR(2,60) is calculated in the same manner as CAR(-1,1) only it is cumulated from days two through sixty. TONE1 is the first principal component of the four ratios Positive/Negative, Strong/Weak, Active/Passive, and Overstated/Understated, where each category reflects the proportion of words in a given call as defined by the Harvard IV-4 Psychosocial Dictionary.

Table 4 Test of Differences of Means and Medians, by TONE2 Quintiles

TONE2 CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) Quintiles All Dividend NonDiv All Dividend NonDiv 1 (Low) Mean -0.0116 -0.0157 -0.0053 -0.0138 -0.0199 -0.0044 Median -0.0107 -0.0119 -0.0069 -0.0156 -0.0178 -0.0109

2 Mean 0.0000 0.0007 -0.0007 0.0045 0.0152 -0.0080 Median 0.0029 0.0067 -0.0014 0.0063 0.0178 -0.0051

3 Mean 0.0005 0.0023 -0.0013 -0.0144 -0.0144 -0.0144 Median 0.0008 0.0010 -0.0005 -0.0146 -0.0193 -0.0111

4 Mean 0.0065 0.0096 0.0038 -0.0078 -0.0152 -0.0016 Median 0.0036 0.0053 0.0009 -0.0124 -0.0154 -0.0073

5 (High) Mean 0.0071 0.0140 0.0022 0.0006 -0.0111 0.0089 Median 0.0089 0.0076 0.0104 -0.0059 -0.0184 0.0024

Mean Q5 – Q1 0.0187*** 0.0296*** 0.0075 0.0143 0.0088 0.0133 t-statistic (4.24) (5.49) (1.04) (1.62) (0.85) (0.89) Wilcoxon rank-sum test

Median Q5 – Q1 0.0197*** 0.0195*** 0.0173** 0.0097 -0.0005 0.0133 z-statistic (5.86) (5.91) (2.30) (1.48) (0.87) (0.74) *** p<0.01, ** p<0.05, * p<0.1

This table shows the differences in CARs when sorted into TONE2 quintile portfolios for the full sample (All), dividend paying firms (Dividend), and firms that do not pay dividends (NonDiv). Test statistics (t and z) are in parentheses. CAR(-1,1) is the three-day cumulative abnormal return, where day 0 is the conference call date, where the abnormal returns are estimated using size-adjusted returns calculated as ARj,t = Rj,t – Rp,t, where the abnormal return for firm j on day t is the difference between the return for firm j on day t and the mean return on day t for all firms in the same size decile as firm j. CAR(2,60) is calculated in the same manner as CAR(-1,1) only it is cumulated from days two through sixty. TONE2 is the first principal component of the four ratios (Positive – Negative)/(Positive + Negative), (Strong – Weak)/(Strong + Weak), (Active – Passive)/(Active + Passive), and (Overstated –Understated)/ (Overstated + Understated), where each category reflects the proportion of words in a given call as defined by the Harvard IV-4 Psychosocial Dictionary.

Table 5 Test of Differences of Means and Medians, by H-TONE1 Quintiles

H-TONE1 CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) Quintiles All Dividend NonDiv All Dividend NonDiv 1 (Low) Mean -0.0125 -0.0172 -0.0067 -0.0186 -0.0229 -0.0132 Median -0.0095 -0.0117 -0.0047 -0.0146 -0.0140 -0.0161

2 Mean -0.0014 -0.0023 -0.0004 -0.0113 -0.0178 -0.0039 Median -0.0045 -0.0060 -0.0032 -0.0161 -0.0258 -0.0051

3 Mean -0.0008 0.0017 -0.0033 -0.0004 0.0029 -0.0036 Median 0.0015 0.0052 -0.0018 -0.0072 0.0018 -0.0105

4 Mean 0.0054 0.0131 -0.0020 -0.0042 -0.0123 0.0035 Median 0.0041 0.0100 -0.0021 -0.0074 -0.0153 0.0040

5 (High) Mean 0.0119 0.0135 0.0106 0.0035 0.0104 -0.0016 Median 0.0137 0.0119 0.0180 0.0056 0.0148 -0.0063

Mean Q5 – Q1 0.0244*** 0.0307*** 0.0174** 0.0221** 0.0333*** 0.0116 t-statistic (5.43) (5.34) (2.49) (2.57) (3.05) (0.86) Wilcoxon rank-sum test

Median Q5 – Q1 0.0232*** 0.0235*** 0.0227*** 0.0201*** 0.0288*** 0.0098 z-statistic (6.53) (6.02) (3.17) (2.85) (2.99) (1.15) *** p<0.01, ** p<0.05, * p<0.1

This table shows the differences in CARs when sorted into H-TONE1 quintile portfolios for the full sample (All), dividend paying firms (Dividend), and firms that do not pay dividends (NonDiv). Test statistics (t and z) are in parentheses. CAR(-1,1) is the three-day cumulative abnormal return, where day 0 is the conference call date, where the abnormal returns are estimated using size-adjusted returns calculated as ARj,t = Rj,t – Rp,t, where the abnormal return for firm j on day t is the difference between the return for firm j on day t and the mean return on day t for all firms in the same size decile as firm j. CAR(2,60) is calculated in the same manner as CAR(-1,1) only it is cumulated from days two through sixty. H-TONE1 is the ratio of positive words to negative words as defined by the custom earnings dictionary of Henry (2008).

Table 6 Test of Differences of Means and Medians, by H-TONE2 Quintiles

H-TONE2 CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) Quintiles All Dividend NonDiv All Dividend NonDiv 1 (Low) Mean -0.0125 -0.0172 -0.0067 -0.0186 -0.0229 -0.0132 Median -0.0095 -0.0117 -0.0047 -0.0146 -0.0140 -0.0161

2 Mean -0.0017 -0.0028 -0.0004 -0.0111 -0.0173 -0.0039 Median -0.0047 -0.0061 -0.0032 -0.0161 -0.0257 -0.0051

3 Mean -0.0006 0.0023 -0.0033 -0.0006 0.0025 -0.0036 Median 0.0016 0.0052 -0.0018 -0.0074 0.0017 -0.0105

4 Mean 0.0054 0.0131 -0.0020 -0.0042 -0.0123 0.0035 Median 0.0041 0.0100 -0.0021 -0.0074 -0.0154 0.0040

5 (High) Mean 0.0119 0.0135 0.0106 0.0035 0.0104 -0.0016 Median 0.0137 0.0119 0.0180 0.0056 0.0148 -0.0063

Mean Q5 – Q1 0.0244*** 0.0307*** 0.0174** 0.0221** 0.0333*** 0.0116 t-statistic (5.43) (5.34) (2.49) (2.57) (3.05) (0.86) Wilcoxon rank-sum test

Median Q5 – Q1 0.0232*** 0.0235*** 0.0227*** 0.0201*** 0.0288*** 0.0098 z-statistic (6.53) (6.02) (3.17) (2.85) (2.99) (1.15) *** p<0.01, ** p<0.05, * p<0.1

This table shows the differences in CARs when sorted into H-TONE2 quintile portfolios for the full sample (All), dividend paying firms (Dividend), and firms that do not pay dividends (NonDiv). Test statistics (t and z) are in parentheses. CAR(-1,1) is the three-day cumulative abnormal return, where day 0 is the conference call date, where the abnormal returns are estimated using size-adjusted returns calculated as ARj,t = Rj,t – Rp,t, where the abnormal return for firm j on day t is the difference between the return for firm j on day t and the mean return on day t for all firms in the same size decile as firm j. CAR(2,60) is calculated in the same manner as CAR(-1,1) only it is cumulated from days two through sixty. H-TONE2 is the ratio of (Positive – Negative)/(Positive + Negative) where each category reflects the proportion of words in a given call as defined by the custom earnings dictionary of Henry (2008).

Table 7 Regression Results of Cumulative Abnormal Returns on Earnings Surprise and H-TONE1

TONE1 is the ratio of positive words to negative words as defined by the custom earnings dictionary of Henry (2008). The variable prefixes INTRO and Q&A indicate that the referenced tone measures are for the introductory managerial statement and the question and answer portions of the conference calls, respectively. Standard errors are adjusted for heteroscedasticity following White(1980) and for clustering by firm and quarter following Petersen (2009). t- statistics are in parentheses.

Panel A: All CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) SURP 0.1480*** 0.1380*** 0.1470*** 0.1510*** 0.1500*** -0.0474 -0.0549 -0.0514 -0.0382 -0.0410 (5.13) (4.52) (5.11) (4.86) (4.88) (-0.45) (-0.54) (-0.50) (-0.38) (-0.41)

H-TONE 0.0031*** 0.0025* 1 (6.16) (1.71)

INTRO H-TONE 0.0001 0.0001 0.0006 0.0004 1 (0.76) (0.31) (1.16) (0.67)

Q&A H-TONE 0.0009*** 0.0009*** 0.0025*** 0.0024*** 1 (3.55) (3.56) (3.96) (3.78)

Constant 0.0004 -0.0155*** -0.0005 -0.0045** -0.0047** -0.0062 -0.0190** -0.0103** -0.0200*** -0.0220*** (0.25) (-5.08) (-0.30) (-2.04) (-2.10) (-1.46) (-2.57) (-2.18) (-4.02) (-4.08)

Observations 2880 2880 2880 2880 2880 2880 2880 2880 2880 2880 R-Squared 0.010 0.019 0.010 0.013 0.013 0.000 0.002 0.001 0.006 0.007 *** p<0.01, ** p<0.05, * p<0.1

Table 7 (Continued)

Panel B: Dividend CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) SURP 0.1110** 0.0878** 0.1070** 0.1050** 0.1030** -0.0270 -0.0531 -0.0505 -0.0361 -0.0563 (2.27) (1.96) (2.19) (2.19) (2.13) (-0.15) (-0.30) (-0.28) (-0.20) (-0.31)

H-TONE 0.0038*** 0.0043*** 1 (4.71) (2.77)

INTRO H-TONE 0.0003 0.0002 0.0017*** 0.0016*** 1 (0.76) (0.56) (3.59) (3.28)

Q&A H-TONE 0.0012** 0.0011** 0.0019*** 0.0015** 1 (2.15) (2.09) (2.69) (2.34)

Constant 0.0010 -0.0180*** -0.0007 -0.0049 -0.0059 -0.0090** -0.0306*** -0.0202*** -0.0187*** -0.0273*** (0.45) (-3.65) (-0.25) (-1.13) (-1.29) (-2.39) (-2.93) (-3.39) (-2.81) (-3.45)

Observations 1440 1440 1440 1440 1440 1440 1440 1440 1440 1440 R-Squared 0.006 0.024 0.007 0.011 0.012 0.000 0.007 0.011 0.004 0.014 Panel C: NonDiv CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) SURP 0.1740*** 0.1720*** 0.1740*** 0.1820*** 0.1820*** -0.0651 -0.0655 -0.0642 -0.0356 -0.0310 (4.17) (4.11) (4.17) (4.11) (4.10) (-0.47) (-0.47) (-0.46) (-0.27) (-0.24)

H-TONE 0.0025*** 0.0005 1 (3.33) (0.31)

INTRO H-TONE 0.0000 -0.0001 -0.0007 -0.0011 1 (0.03) (-0.45) (-0.89) (-1.36)

Q&A H-TONE 0.0008* 0.0008** 0.0030*** 0.0033*** 1 (1.91) (2.01) (3.75) (4.05)

Constant -0.0003 -0.0138*** -0.0004 -0.0048 -0.0043 -0.0033 -0.0062 0.0013 -0.0205** -0.0148** (-0.12) (-3.08) (-0.11) (-1.26) (-1.04) (-0.40) (-0.71) (0.18) (-2.34) (-2.04)

Observations 1440 1440 1440 1440 1440 1440 1440 1440 1440 1440 R-Squared 0.013 0.018 0.013 0.015 0.015 0.000 0.000 0.001 0.008 0.010 *** p<0.01, ** p<0.05, * p<0.1

Table 8 CARs for Sorts by Earnings Surprise and H-TONE1

This table shows mean CARS sorted sequentially by SURP and H-TONE1 for the full sample (Panel A), dividend paying firms (Panel B), and firms that do not pay dividends (Panel C). H-L is the difference between the highest and lowest tercile. CAR(-1,1) is the three-day cumulative abnormal return, where day 0 is the conference call date, where the abnormal returns are estimated using size-adjusted returns calculated as ARj,t = Rj,t – Rp,t, where the abnormal return for firm j on day t is the difference between the return for firm j on day t and the mean return on day t for all firms in the same size decile as firm j. CAR(2,60) is calculated in the same manner as CAR(-1,1) only it is cumulated from days two through sixty. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-

4)]/Stock Price (end of qtr-4)}. H-TONE1 is the ratio of positive words to negative words as defined by the custom earnings dictionary of Henry (2008). t- statistics are in parentheses.

Panel A: All CAR (-1,1) CAR (2,60)

SURP SURP

H-TONE1 Low (L) Medium High (H) H - L t-statistic H-TONE1 Low (L) Medium High (H) H - L t-statistic Low (L) -0.0186 -0.0143 0.0137 0.0323 (5.16)*** Low (L) -0.0155 -0.0190 -0.0027 0.0128 (0.99) Medium -0.0141 -0.0019 0.0112 0.0253 (4.50)*** Medium -0.0089 -0.0066 -0.0012 0.0077 (0.61) High (H) -0.0026 0.0096 0.0216 0.0242 (3.84)*** High (H) 0.0054 -0.0182 0.0107 0.0053 (0.40) H – L 0.0160 0.0239 0.0079 H - L 0.0209 0.0009 0.0134 t-statistic (2.67)*** (4.59)*** (1.20) t-statistic (1.67)* (0.09) (0.99)

Panel B: Dividend CAR (-1,1) CAR (2,60)

SURP SURP

H-TONE1 Low (L) Medium High (H) H - L t-statistic H-TONE1 Low (L) Medium High (H) H - L t-statistic Low (L) -0.0234 -0.0172 0.0141 0.0376 (4.95)*** Low (L) -0.0285 -0.0205 -0.0056 0.0230 (1.42) Medium -0.0142 0.0057 0.0157 0.0298 (4.00)*** Medium -0.0121 -0.0128 0.0033 0.0155 (1.10) High (H) 0.0050 0.0119 0.0237 0.0187 (2.81)*** High (H) 0.0069 -0.0140 0.0172 0.0103 (0.64) H – L 0.0284 0.0291 0.0096 H – L 0.0354 0.0065 0.0227 t-statistic (4.04)*** (4.22)*** (1.23) t-statistic (2.19)** (0.55) (1.37)

Table 8 (Continued)

Panel C: NonDiv CAR (-1,1) CAR (2,60)

SURP SURP

H-TONE1 Low (L) Medium High (H) H - L t-statistic H-TONE1 Low (L) Medium High (H) H - L t-statistic Low (L) -0.0112 -0.0104 0.0133 0.0245 (2.34)** Low (L) 0.0046 -0.0171 0.0000 -0.0046 (-0.22) Medium -0.0141 -0.0109 0.0073 0.0214 (2.55)** Medium -0.0057 0.0007 -0.0051 0.0006 (0.03) High (H) -0.0079 0.0072 0.0202 0.0280 (2.91)*** High (H) 0.0044 -0.0226 0.0061 0.0018 (0.09) H – L 0.0033 0.0175 0.0068 H – L -0.0002 -0.0055 0.0061 t-statistic (0.33) (2.20)** (0.67) t-statistic (-0.01) (-0.35) (0.30)

*** p<0.01, ** p<0.05, * p<0.1

Cumulative Size Adjusted Returns

by High, Average, & Low H-TONE1 Quintiles 4 3 2 1 0 -1 Percent CAR Percent -2 -3 -4 -10 0 10 20 30 40 50 60

High H-Tone1 Conference Calls All Conference Calls Low H-Tone1 Conference Calls

Fig. 1 Cumulative Abnormal Returns (CARs) across All Conference Calls and by High and Low H-TONE1 Quintiles. Abnormal returns are estimated using returns that have been size-adjusted and are calculated as {ARjt = Rjt - Rpt} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rpt is the equally weighted mean return on day t for all firms in the same size decile as firm j. Abnormal returns are cumulated over a 71 day period starting ten days prior to the earnings conference call through sixty days after the call. Day zero is the date of the earnings conference call. H-TONE1 is the ratio of Positive words to Negative words where each category reflects the proportion of words in a given call as defined by the custom earnings dictionary of Henry (2008). High H-TONE1 Conference Calls shows the CARs for the highest H-TONE1 quintile conference calls. Low H-TONE1 Conference Calls shows the CARs for the lowest H-TONE1 quintile conference calls. All Conference Calls represents the CARs for all conference calls in the sample. CARs are shown in percent.

Cumulative Size Adjusted Returns by High, Average, & Low SURP Quintiles 4

-1 Percent CAR Percent -2

-3

-4 -10 0 10 20 30 40 50 60

High SURP Conference Calls All Conference Calls Low SURP Conference Calls

Fig. 2 Cumulative Abnormal Returns (CARs) across All Conference Calls and by High and Low SURP Quintiles. Abnormal returns are estimated using returns that have been size-adjusted and are calculated as {ARjt = Rjt - Rpt} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rpt is the equally weighted mean return on day t for all firms in the same size decile as firm j. Abnormal returns are cumulated over a 71 day period starting ten days prior to the earnings conference call through sixty days after the call. Day zero is the date of the earnings conference call. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (end of qtr-4)}. High SURP Conference Calls shows the CARs for the highest SURP quintile conference calls. Low SURP Conference Calls shows the CARs for the lowest SURP quintile conference calls. All Conference Calls represents the CARs for all conference calls in the sample. CARs are shown in percent.

Cumulative Size Adjusted Returns

by H-TONE1 and SURP Terciles 6 5 4 3 2 1 0

Percent CAR Percent -1 -2 -3 -4 -5 -10 0 10 20 30 40 50 60

Low H-Tone1 Low Surp High H-Tone1 Low Surp Low H-Tone1 High Surp High H-Tone1 High Surp

Fig. 3 Cumulative Abnormal Returns by Earnings Surprise and H-TONE1 Terciles. Abnormal returns are estimated using returns that have been size-adjusted and are calculated as {ARjt = Rjt - Rpt} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rpt is the equally weighted mean return on day t for all firms in the same size decile as firm j. Abnormal returns are cumulated over a 71 day period starting ten days prior to the earnings conference call through sixty days after the call. Day zero is the date of the earnings conference call. Cumulative abnormal returns (CAR) are shown in percent. H-TONE1 is the ratio of Positive words to Negative words where each category reflects the proportion of words in a given call as defined by the custom earnings dictionary of Henry (2008). SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (end of qtr-4)}. CARs are plotted for High H-TONE1 High SURP conference calls , High H-TONE1 Low SURP conference calls, Low H-TONE1 High SURP conference calls , and Low H-TONE1 Low SURP conference calls. High H-TONE1 High SURP contains the conference calls within the highest H- TONE1 tercile and lowest SURP tercile. High H-TONE1 Low SURP contains the conference calls within the highest H-TONE1 tercile and lowest SURP tercile. Low H-TONE1 High SURP contains the conference calls within the lowest H-TONE1 tercile and highest SURP tercile. Low H-TONE1 Low SURP contains the conference calls within the lowest H-TONE1 tercile and lowest SURP tercile.

CHAPTER 3

INFORMATION UNCERTAINTY AND THE POST-EARNINGS-ANNOUNCEMENT DRIFT ANOMALY: INSIGHT FROM REITS

The post-earnings-announcement drift (PEAD) 30 is the phenomenon where stock returns continue to drift in the same direction as the surprise portion of an earnings announcement. That is, positive earnings surprises tend to be followed by abnormal returns that continue to drift up and negative earnings surprises tend to be followed by abnormal returns that continue to drift down. While semi-strong form market efficiency suggests that information should be fully incorporated into stock prices soon after the information becomes publicly known, the information in earnings announcements seems to be incorporated slowly into asset prices over a period of several months after the announcements are made (Ball and Brown 1968; Foster, Olsen, and Shevlin 1984; Bernard and Thomas 1989, 1990). Few anomalies to market efficiency have stood the test of time. Fama (1998) argues that most of them are the statistical byproducts of flawed empirical methods. However, he also acknowledges that two anomalies legitimately remain “above suspicion”, PEAD and the short-term continuation of returns (momentum). 31 While momentum in the returns of Real Estate Investment Trusts (REITs) has been the subject of recent study (Chui, Titman, and Wei 2003; Hung and Glascock 2008, 2009) PEAD in a REIT setting has yet to receive any attention. This apparent gap in the literature is curious, given that returns momentum has been shown to be related to PEAD (Chan, Jegadeesh, and Lakonishock 1996; Chordia and Shivakumar 2006). This is the first study of which we are aware to examine REIT PEAD. We do so in the context of recent literature which contends that uncertainty may be the underlying cause of the drift (Lewellen and Shanken 2002; Brav and Heaton 2002; Liang 2003;

30 Also referred to in this paper as simply “the drift”. 31 First documented by Jegadeesh and Titman (1993), momentum is the phenomenon where stocks that have performed well (poorly) in recent months tend to continue to perform well (poorly).

Francis, LaFond, Olsson, and Schipper 2007)32. The efficiency with which markets fully incorporate information into asset prices is called into question when firms earn abnormally high (low) returns following positive (negative) earnings announcements. The uncertainty theory suggests that the initial signal sent by an earnings announcement is only partially incorporated into a stock‟s price due to investor uncertainty regarding the full meaning of the signal. Then, as time passes and the information slowly becomes more certain, the signal becomes more fully incorporated into the stock price, resulting in the drift. The REIT market provides a natural setting in which to examine the relation between information uncertainty and PEAD. Capozza and Lee (1994) suggest that REIT assets may be valued with much greater precision than the assets of typical corporations (NonREITs) because real estate assets also trade individually as properties. When securitized, commercial real estate assets are indirectly held and traded as REIT shares in the public markets. However, commercial real estate assets are also traded in the directly held private market as well.33 Private market participants competing with REITs for larger high-quality commercial property assets are primarily sophisticated institutional investors that collectively generate substantial commercial real estate data. As a result, the parallel asset market may provide a greater level of industry and asset performance information than can be found for typical firms. Essentially, investors might be able to effectively pierce through to the performance of the underlying asset base for REITs with much greater clarity than they can for NonREITs. This, in turn, should enable REIT investors to more fully understand, and quickly incorporate, the signal in the earnings announcement. Accordingly, we would expect to find less PEAD among REITs when compared to NonREITs due to the greater

32 The literature is replete with unsuccessful attempts to explain the drift. Researchers have not been able to attribute PEAD to various risk adjustments, poor research design, market frictions such as transactions costs and liquidity constraints, or the psychological biases of market participants. 33 Mahoney, McCarron, Miles, and Sirmans (1996) show that while there are some differences in terms of geographic concentration and proportionate allocation to various property types (retail, multi-family, office/industrial, etc) in the asset bases of publicly (NAREIT) and privately (NCREIF) held commercial real estate, there is nontrivial overlap as well. The overall location correlation is 0.79 and both NAREIT and NCREIF have roughly 85% of their holdings in the top 100 MSAs. In 1995, REIT allocations include 35% to retail, 24% to apartments, 12% to industrial/office, and 28% to „other‟. 1995 NCREIF allocations are 36% in retail, 14% in apartments, 44% in industrial/office, and 6% to „other‟.

42 certainty that stems from the rich commercial real estate performance information environment. We document a statistically and economically significant drift for REITs over the 1982-2008 sample period of approximately 5%. This translates into annualized returns to a zero investment portfolio long in high earnings surprise REITs and short in low earnings surprise REITs of just under 20%, on average. In contrast, NonREIT PEAD over the same period is markedly less at 3% (12% annualized). This difference either provides evidence against the uncertainty theory or suggests that the parallel asset market does not render REIT earnings signals more certain than NonREIT signals. At 4% (just under 17% annualized) small stock (Small NonREIT) PEAD is noted to be less pronounced than REIT PEAD, but the difference is not statistically significant. We also find that REITs have a more muted response to the information contained in the earnings surprise compared to both ordinary common stocks and small stocks. With abnormal announcement period returns of 1% for REITs, the magnitude of the initial earnings surprise response is less than half of the magnitude we observe for NonREITs and less than one third of the initial response for Small NonREITs. These differences are highly significant. We further examine the uncertainty theory on an intra-industry basis by comparing PEAD differences within the REIT industry over time. REIT evolution has been marked by points in time where rule changes have significantly altered the industry. Ling and Ryngaert (1997) contend that REIT valuation is more uncertain post- 1990 than pre-1990 on account of greater ownership structure complexity. They posit that active management and the introduction of the umbrella partnership REIT (UPREIT) structure injected greater subjectivity in REIT valuation when compared to the earlier, simpler pass-through arrangement. Overall, this paper makes the following contributions to the literature. First, we document PEAD in a REIT context. Second, we find that REIT PEAD is significantly greater in overall magnitude than that which we see in ordinary common stocks, and similar in magnitude to small stocks. Third, we show that the initial incorporation of earnings information into stock prices is more muted for REITs than for both typical equities and small stocks. Additionally, our results suggest that despite the rich

43 information environment created by the parallel asset market, information does not appear to flow from the private to the public market in such a way that it renders REIT earnings signals more certain than NonREIT earnings signals. Finally, comparisons within the REIT industry are consistent with both Chui et al. (2003) and the uncertainty explanation for PEAD. This study is developed in the following sections. In the next section we review the literature and develop our hypotheses. The third section describes our sample. In the fourth section we present our analysis and discuss the results. The fifth section concludes.

Literature Review and Hypothesis Development First documented by Ball and Brown (1968), PEAD continues to be an actively researched topic which has yet to be explained. The finance and accounting literature is replete with potential explanations for the incomplete market reaction to earnings announcements34. Many attribute the anomaly to human behavioral shortcomings such as investor irrationality or bias. Some contend that investor bias results in initial underreaction to earnings news (Bernard and Thomas 1989, 1990; Freeman and Tse 1989; Mendenhall 1991; and Wiggins 1991, among others) while others assert that the cause is investor over-reaction (DeBondt and Thaler 1985, 1987). Regardless of the direction of the reaction, investor biases are likely to be more pronounced in cases of greater information uncertainty (Daniel, Hirshleifer, and Subrahmanyam 1998, 2001; Hirshleifer 2001; Zhang 2006). A recent vein of literature has explored the combination of rational learning and information uncertainty as a potential cause of the drift. Lewellen and Shanken (2002) show that predictability arises from the learning process. They argue that in cases of information uncertainty, where investors must learn about expected cash flows, empirical tests can find patterns in the data which may depart from what we would expect in a world of perfect information. Parameter uncertainty “drives a wedge” between the empirical distributions estimated by econometricians and the distributions as perceived by rational investors.

34 See Bernard (1992) for a detailed review of possible explanations for the PEAD anomaly.

Brav and Heaton (2002) further develop this argument in their comparison of behavioral and rational structural uncertainty theories as explanations for financial anomalies. Behavioral explanations relax the assumption of rational information processing. In this framework, cognitive biases prevent investors from fully and accurately processing news, which can give rise to less-than-efficient financial markets. Alternatively, if the assumption of complete information is relaxed, it is possible to see the same anomalies manifest in the data based on rational investor learning. Under such models, the anomalies are the result of either mistakes or risk premiums attributable to incomplete information. Francis, LaFond, Olsson and Schipper (2007) describe how fully Baysian investors appear to under-react to earnings news by placing less weight on signals when there is greater information uncertainty. Investors then revise the weight placed on the original news as the uncertainty is resolved, resulting in the drift. Using an accounting based measure of earnings quality as a proxy for information uncertainty, Francis et al. (2007) test whether rational investor responses to information uncertainty explain PEAD. Lending support for a rational learning explanation, they find the market response to earnings surprises is less pronounced for firms with greater information uncertainty. Furthermore, they find that firms in the extreme earnings surprise portfolios tend to be characterized by greater information uncertainty and that these firms earn larger abnormal returns than low information uncertainty firms. However, Francis et al. (2007) do not investigate the source of information uncertainty, nor do they explore the nature of the uncertainty associated with the firms themselves. Liang (2003) also investigates PEAD and uncertainty, but in contrast to Francis et al. (2007), considers investors‟ non-Baysian behaviors. Liang argues that non- Baysian investors tend to underestimate the likelihood of highly reliable signals and overestimate the probability of highly unreliable signals. In other words, Liang posits that the drift is related to more reliable information since investors tend to be under- confident (over-confident) and under-react (over-react) more in cases of greater (lesser) informational reliability. The empirical results support this hypothesis in that the change in uncertainty around earnings announcements is found to have a negative relation with PEAD.

Initially, the results of Liang (2003) and Francis et al. (2007) appear diametrically opposed to one another. However, Shivakumar (2007) suggests that the coefficient sign difference may be attributed to the fact that Francis et al. (2007) measure information uncertainty prior to the earnings announcement, while Liang (2003) focuses on the change in information uncertainty from before to after the announcement. Liang (2003) and Francis et al. (2007) each rely on differently constructed proxies for uncertainty. As of yet there is no universally accepted authoritative measure of information uncertainty. Liang uses the change in expected squared error of average analysts‟ forecasts. Furthermore, to be included in his sample he requires firms to have at least two one-quarter-ahead quarterly forecasts in addition to two one-year-ahead annual forecast revisions. Francis et al. use the volatility of unexplained accruals, an accounting based measure derived from earnings that requires seven years of data to estimate. Shivakumar (2007) points out that such approaches bias their samples towards large firms, raising questions regarding the generalizability of their results. This criticism is particularly important since it is well established that PEAD is most pronounced in small stocks. The open question of uncertainty measurement may be best addressed by analyzing specific industries where the information diffusion process is more unambiguous than in the stock market as a whole. Hou (2007) finds that industries are the primary channel for news dissemination in the equity markets. Intra-industry information transfer between large and small firms (the lead-lag effect) is related to PEAD in small firms following the earnings releases of large firms. Excluding real estate investment trusts, Hou suggests that information diffuses slowly across firms. Kovacs (2007) analyzes intra-industry PEAD and finds that a firm‟s post-earnings returns depend on subsequently arriving industry earnings news. Specifically, PEAD is observed when there is uncertainty about subsequent industry earnings news. The REIT market provides a unique setting in which to examine the relation between information uncertainty and PEAD. REITs have at least three unique characteristics which are relevant to the examination of information uncertainty and asset pricing anomalies. First, REITs operate in tandem with a parallel asset market which may provide an additional layer of information beyond the already relatively

46 transparent nature of commercial real estate assets. Second, REITs are required to disgorge most of the cash generated by the operation of their underlying assets in order to maintain their pass-through tax status. Third, REITs have undergone documented changes in their level of uncertainty due to regulatory changes which have significantly altered their structure (Ling and Ryngaert 1997). When securitized, commercial real estate assets are indirectly held and traded as REIT shares in the public markets. However, many of these same assets are traded in the directly held commercial real estate asset market as well.35 The private market is much larger than the publicly traded REIT market and is dominated by sophisticated, institutional investors who generate volumes of information36. Starting in the late 1970s investment managers agreed to form a not-for-profit entity to foster research on commercial real estate assets.37 This cooperation led to the creation of the National Council of Real Estate Investment Fiduciaries (NCREIF). As noted on their website, NCREIF was established to serve the institutional real estate investment community as a non-partisan collector, processor, validator and disseminator of real estate performance information.38 Other organizations, such as the Investment Property Databank (IPD), also provide extensive commercial real estate performance data and benchmarks. Pagliari, Scherer, and Monopoli (2005) and Riddiough, Moriarty, and Yeatman (2005) both show that returns between the two parallel markets are not significantly different from one another after adjusting for characteristics such as leverage, liquidity, management structure, property type, and taxes. MacKinnon and Al Zaman (2009) provide additional evidence of linkages between the publicly and privately held commercial real estate markets. While both REITs and typical industrial firms are required to file quarterly and annual statements with the SEC, the existence of this parallel commercial real estate

35 See footnote #4 for more clarification on the overlap of underlying assets for public and private firms. 36 The private market can only be considered to be “much larger” than the public market if property not tracked by NCREIF is taken into consideration in addition to NCRIEF property. Otherwise, the two markets are close in size. NCREIF tracks just over six thousand properties that had an estimated combined market value of $238 billion as of the end of 2009. According to NAREIT, publicly traded REIT market capitalization at the end of 2009 was approximately $248 billion. 37 http://www.ncreif.com/data/ 38 http://www.ncreif.com/about/

47 asset market should enable investors to effectively see through to the underlying performance of REIT assets much more clearly than trying to pierce through to the underlying performance of typical industrial firms. Overall, the parallel asset market may potentially provide a greater level of industry and asset (e.g. retail, office, industrial, apartment) performance information for REITs than can be found for NonREITs. 39 Additionally, regulations stipulate that REITs must payout at least 90% of their taxable earnings in the form of dividends. 40 Kallberg, Liu, and Srinivasan (2003) note that this distinguishing feature removes much of the discretion REITs have over dividend payout. Unable to retain substantial levels of earnings, REITs must constantly turn to the capital markets in order to capitalize on growth opportunities. By subjecting REITs to the scrutiny and due diligence that accompany securities offerings, frequent trips to the capital markets provide an unusual incentive for REITs to remain transparent (Danielsen, Harrison, and Van Ness 2009). Thus, signals sent by REIT earnings announcements should be characterized as being much more certain than earnings announcements for typical equities due to the unique REIT information environment. This leads to the following two hypotheses: Hypothesis 1 – REIT PEAD should be less pronounced than NonREIT PEAD. Hypothesis 2 – The initial reaction to earnings signals should be more complete for REITs compared to NonREITs. The REIT market also provides an ideal intra-industry setting in which to examine PEAD due to industry changes which have been argued to cause REIT valuation to become comparatively more uncertain after 1990 (Ling and Ryngaert 1997). Ling and Ryngaert argue that the introduction of active management and the creation of the UPREIT structure around this time caused REIT valuation to become more uncertain. Specifically, increased organizational complexity infused greater subjectivity into the valuation process. Chui et al. (2003) examine momentum profits before and after 1990 and find that momentum profits increase in the latter, more uncertain time period. Similarly, we expect a greater manifestation of REIT PEAD in the more uncertain time

39 With respect to the price discovery processes, the direction of information flow is the subject of debate in the literature. Some contend that information flows from the private to the public real estate sector (Tuluca, Myer, and Webb 2000). Others argue that information flows from the public market to the private market (Barkham and Geltner 1995; Chiang 2009). 40 Internal Revenue Code Sect. 857(a)(1).

48 period when compared to the earlier, more certain era. This leads to the additional hypothesis: Hypothesis 3 – PEAD within the REIT industry should increase over time as regulatory changes cause the REIT structure to become more uncertain.

Data Our sample period runs from 1982 through 2008. Prior to this time quarterly Compustat data is less reliable and the number of publicly traded REITs is prohibitively small. Furthermore, starting the sample at 1982 provides for direct comparability with Chui, Titman, and Wei (2003). Following Chui et al. (2003) and Hung and Glascock (2008, 2009) we include all types of REITS (Equity, Mortgage, and Hybrid) in order to obtain a sufficient sample size in the pre-1990 period.41 Daily returns data for firms listed on NYSE, AMEX, and Nasdaq42 are obtained from CRSP for share codes 10, 11, and 12 for ordinary common stocks (NonREITs), and share code 18 for REITs. We exclude financials (sic 6000-6999)43 and utilities (sic 4600-4699). Earnings announcement dates and quarterly earnings come from Compustat. Firms must be listed on CRSP/Compustat for at least two years for inclusion in our sample, consistent with Fama and French (1992) and Chui et al. (2003). We end up with 212,455 firm- quarter observations over the 1982-2008 period. 203,971 of these are attributable to NonREITs and 8,484 are REIT observations.44 Table 9 provides descriptive statistics for all firms in our sample broken out into REIT, NonREIT, and Small NonREIT portfolios, where Small NonREITs are the subset

41 Equity REITs comprise the largest proportion of total publicly traded REITs and this proportion has increased over time. According to data available on the NAREIT website, Equity REITs made up 46%, 74%, and 81% of the total number of REITs in the 1982-1989, 1990-1999, and 2000-2008 time periods, respectively. Consequently, Mortgage (35%, 17%, 15%) and Hybrid (20%, 10%, 4%) REITs decreased as a percentage of total REITs during the same time periods. Although untabulated, the relative proportions based on market capitalization are even more pronounced in favor of Equity REITs. 42 Some of the PEAD literature, such as Bernard and Thomas (1989, 1990), restricts their samples to NYSE and AMEX firms. However, recent literature that investigates market anomalies in a REIT setting includes Nasdaq firms as well (Chui et al. 2003; Hung and Glascock 2008, 2009). In untabulated analyses we find that our results are not sensitive to the inclusion of Nasdaq firms. 43 Except REITs, most of which are classified as SIC 6798. 44 This essentially represents the population of REITs and NonREITs during the 1982-2008 sample period for which sufficient data are available. However, we also check our results using a size matched sample, a book-to-market equity matched sample, and a random sample and find consistent results. See Appendix C.

49 of NonREITs that fall within the lowest market capitalization quintile based on NYSE size breakpoints. We look at Small NonREITs separately because REIT returns behavior has historically been most highly correlated with small stocks (Goldstein and Nelling 1999) although the correlation has reduced over time (Ghosh, Miles, and Sirmans 1996). Panel A shows the distributional characteristics based on firm size for each portfolio. Firm size is measured by market capitalization, calculated as the stock price times the number of shares outstanding as of 45 days prior to the earnings announcement date. As expected, NonREITs are much larger than REITs, with average market capitalizations of $2.8 billion and $846 million, respectively. However, the mean NonREIT size figure is lowered by the inclusion of Nasdaq firms which predominantly fall within the smallest NYSE size quintile. The Small NonREIT subsample mean size is only $121 million. Panel B of Table 9 shows the distributional characteristics for each portfolio based on book-to-market (BM) ratios. Book-to-market equity is calculated as the two- quarter lagged book value of equity divided by firm market capitalization as defined above. With a median value of 0.71, REITs have higher book-to-market ratios than both NonREITs (0.49) and Small NonREITs (0.60). That is, REITs are primarily value oriented given the income oriented nature of their underlying assets and the requirement to pay out at least 90% of their annual book earnings in the form of dividends. Returns volatilities for the three portfolios are shown in Table 9, Panel C. Volatility is calculated as the standard deviation of daily returns for firm j in month m. All monthly observations are then averaged. REIT volatility of 2.15% over the entire sample period is significantly less than 3.63% for NonREITs and 4.39% for Small NonREITs, lending support to the idea that REITs are relatively more certain than their ordinary common stock counterparts.45 However, consistent with Ling and Ryngaert (1997) and Chui et al. (2003), we find that REITs become relatively more uncertain following industry structural changes as volatility increases from the 1982-1989 sub-

45 Both Chui et al. (2003) and Zhang (2006) use returns volatility as a measure of uncertainty. Greater volatility indicates greater uncertainty.

50 period to the 1990-1999 sub-period. 46 While volatility is somewhat less during the 2000-2008 sub-period, is does not revert back to the same levels we see in 1982-1989.

Analysis and Results We follow the standard methodology for PEAD calculation (Ball and Brown 1968; Foster et al. 1984; Bernard and Thomas 1989) and sort stocks into earnings surprise deciles based on the magnitude of the earnings surprise and cumulate market adjusted returns for each of the portfolios. Earnings surprises, , are calculated using the seasonal random walk model where the difference between earnings-per-share and earnings-per-share lagged four quarters is scaled by the stock price 45 days prior to the earnings announcement date47:

= , 4 (1)

− −, 45 We use the −seasonal random walk model for several reasons. First, it avoids the biases that stem from analyst forecasts. Johnson (2004) notes that analysts‟ forecasts can be biased by conflicts of interests, incentives, and career concerns. Further bias can be introduced since analysts are more likely to cover larger firms. Since one of our primary concerns is documenting the extent of PEAD for REITs relative to both NonREITs and Small NonREITs, size bias and forecast availability are issues of particular concern. Bradshaw, Drake and Myers (2009) find that simple random walk models are more accurate than analysts‟ forecasts for firms that are smaller, younger, or have limited analyst following. We also use the seasonal random walk model because the data are available for our entire sample period and are judged to be more reliable than analyst forecast data. First Call data only dates back to 1989 and the I/B/E/S data has repeatedly been shown to be unreliable, particularly prior to the 1990s. Barron and Stuerke (1998) indicate that I/B/E/S forecast recording processes improved in the 1990s, but were fraught with issues in the 1980s. Ljungqvist, Malloy, and Marston (2009) find that identical I/B/E/S downloads taken at different times reveal problems such as alterations of recommendations, the addition and deletion of records,

46 Although not shown, all volatility measurements are statistically different from one another at the 1% level. 47 We choose the stock price as of day t=-45 as the SURP scalar following Liang (2003). However, we find that our results are not sensitive to the choice of SURP scalar.

51 and the removal of analyst names. Hence, analyst forecast data sources remain suspect. Lastly, survey evidence shows that managers place greater weight on earnings in the same quarter of the prior year as their earnings benchmark. Graham, Harvey and Rajgopal (2005) find that 85.1% of CFO‟s consider four quarter lagged earnings to be the most important earnings benchmark, followed secondly by analyst consensus estimates. Abnormal returns are estimated using market adjusted returns calculated as the difference between the raw return for stock j on day t and the mean return of a portfolio of all firms in the same size decile:

, = , , (2) where ARj,t is− the abnormal return for firm j on day t, Rj,t is the total return for firm j on day t, and Rp,t is the mean return on day t for all firms in the same size decile as firm j. In this form market adjusted returns are size-adjusted, which is particularly useful in our case where we are concerned with a large sample that contains firms of all sizes. 48 To determine whether or not PEAD exists in our sample we cumulate abnormal returns over the 121 day window surrounding earnings announcement dates and plot the mean for each SURP decile. The cumulative abnormal returns (CARs) are the sum of the individual abnormal returns calculated as: 60 60,60 = = 60 , (3)

where − CAR(-60,60) j represents− the cumulative abnormal return for stock j from day t=- 60 to day t=60. The earnings announcement date is set to t=0.

Figure 4 shows the mean of all CAR(-60,60)j for all REITs sorted into SURP deciles on a quarterly basis for the entire sample period. We can see that the decile portfolios start to spread even prior to the earnings announcements. Campbell et al. (2009) describe the spreading seen on the left side of the figure as the tendency for returns to anticipate earnings surprises. This is followed by a clear jump immediately surrounding the earnings announcements. If the earnings announcement signals were immediately and fully incorporated into stock prices we would expect the right side of the figure to be flat. However, we observe a continued spreading of the decile portfolios

48 Bernard and Thomas (1989) find that PEAD is not a result of risk mismeasurement and, thus, is unaffected by risk adjustment.

52 over the following sixty days, with the decile portfolios lining up monotonically from positive (Decile 10) to negative (Decile 1). Thus, PEAD is manifest in the REIT subsample and firms with the most extreme positive earnings surprises (Decile 10) continue to earn abnormally high returns, while REITs with the lowest earnings surprises (Decile 1) continue to earn abnormally low returns.

Figure 5 shows the mean of all CAR(-60,60)j for all NonREITs sorted into SURP deciles on a quarterly basis for the entire sample period. The same patterns we see with REITs can be seen with NonREITs, with a few notable differences. The ability of NonREIT returns to anticipate the direction and relative magnitude of the earnings surprise is much more pronounced than with REITs. This, in turn, causes the drift (spread between Decile 10 and Decile 1) cumulated over the entire 121 day window to be of greater magnitude for NonREITs than that which we observe for REITs. Visual inspection shows the NonREIT spread to approximately double the REIT spread. However, if we focus on the PEAD window (days t=1 through t=60) it is difficult to determine if the divergence of the extreme SURP deciles for the REITs and NonREITs is substantially different. This is contrary to our hypothesis that less drift should be manifest in REITs when compared to their common stock counterparts. This suggests that either the uncertainty explanation for PEAD does not hold, or that REITs are actually more uncertain than NonREITs over our sample period. To check this on a more continuous basis we plot the drift quintile spread by year over the 1982-2008 period similar to Bernard and Thomas (1989).49 Figure 6 illustrates the spread, which is the 121 day CAR for the highest SURP quintile minus the 121 day CAR for the lowest SURP quintile for REITs, NonREITs, and Small NonREITs. We see that Small NonREITs have the largest overall spread, on average, followed by NonREITs. When looking at the 121 day CAR, which includes the anticipation, initial reaction, and PEAD windows, REITs have the lowest average spread50.

49 See Bernard and Thomas (1989), Figure 5. 50 There are large differences in the amount of total drift (pre- and post-earnings announcement) for REITs, NonREITs, and Small NonREITs as shown in Figures 1 – 3. Upon closer visual inspection of Figures 1 and 2, it appears that much of this is attributable to differences in decile spreading during the anticipation window. While the primary focus of this study is on stock returns behavior during the post- announcement window, with a secondary focus is on stock returns during the initial reaction window, we acknowledge the related nature of the anticipation window returns as well. As such, later analysis includes tabulated results for the total drift period [CAR(-60,60)], although we do not attempt to render a

To isolate PEAD we cumulate abnormal returns for the post announcement window, days t=1 through t=60, following Liang (2003): 60 1,60 = =1 , (4)

Figure 7 shows the drift spread of the high SURP quintile minus the low SURP quintile for the 60 day post earnings announcement CARs. The plot illustrates that REITs tend to have a greater manifestation of PEAD than both NonREITs, and the Small NonREIT subsample. Although there is some variation from year to year, this suggests that, in contrast to our initial hypothesis, earnings information is more slowly incorporated into REIT prices than the prices of both ordinary common stocks and small stocks.51 If this is the case, then we should find a more muted initial reaction to the earnings announcement for REITs. The initial reaction is captured in the manner of Francis et al. (2007) by summing abnormal returns for the day immediately prior to the earnings announcement date and the abnormal return on the earnings announcement date itself. Including the day prior to the announcement helps account for any substantial information leakage. For each stock associated with earnings announcement j: 0 1,0 = = 1 , (5)

−We check the− relation between REITs and CARs at the firm level by regressing CARs for the post-earnings-announcement drift period (1,60), for the initial reaction period (-1,0), and the full 121 day window (-60,60) on SURPj and an indicator variable set equal to 1 if firm j is a REIT, and 0 otherwise. To eliminate extreme outliers we th winsorize SURPj at the 0.25 percentile in each tail. We also check for interaction between REITj and SURPj in addition to adding controls for firm characteristics using size, SIZEj, and book-to-market equity, BMj.

, = 0 + 1 + 2 + (6)

, = �0 + �1 + �2 + �3 + (7) detailed interpretation.� � We conclude� that REIT� returns ∗ are less able� to anticipate future earnings surprises than NonREITs (Bernard and Thomas 1989; Campbell et al. 2009). However, there is little literature to build upon in deciphering the exact meaning of the anticipation window differences and we leave a more in depth look at this component to future study. 51 We also observe that PEAD, as plotted in Figure 7, seems to be counter-cyclical, particularly for REITs relative to NonREITs. Lending support to the idea that the drift is related to uncertainty, REIT PEAD increases dramatically in times of general economic uncertainty such as the recessions in the early 1990s, early 2000s, and late 2000s.

, = 0 + 1 + 2 + 3 + 4 + 5 + (8)

where CAR� w,j indicates� the� CARs for �a particular ∗ window � [CAR(1,60), � CAR(-1,60), � CAR(-60,60)] for each firm j. Standard errors are corrected for potential heteroscedasticity (White 1980), dependence within observations of a firm across time, and dependence within a quarter across firms, following the methodology outlined in Petersen (2009). Regardless of the nature of the relation between REITs, uncertainty, and the drift, we expect a positive significant coefficient on SURPj. If REITs are more certain than NonREITs and the uncertainty explanation for the drift is correct, then we expect a negative significant coefficient for 2 when the dependent variable is CAR(1,60) since REITs should experience less drift when compared to stocks with greater informational uncertainty. However, based on the graphical evidence from Figures 4 – 7, which suggest that either REITs are less certain than NonREITs or that the uncertainty theory is invalid, we expect a positive significant coefficient for 2 when the dependent variable is CAR(1,60). Our original expectations lead us to anticipate a more muted initial reaction for NonREITs compared to REITs. Thus, when the dependent variable is CAR(-1,0) we expect the coefficient on REITj to be positive. On the other hand, Figures 4 – 7 lead us to the opposite expectation, where the initial reaction to earnings surprise information is less complete for REITs compared to NonREITs.

Table 10 presents the firm-level regression results. As expected, SURPj is positive and significant at the 1% level in all specifications. The coefficients on REITj and the interaction between REITj and SURPj are highly significant as well. SIZEj is always significant, but the magnitude is small since we control for size differences on the left hand side of the equation by size-adjusting the market adjusted returns. Book- to-market equity is significant in the initial reaction period and full 121 day window, but is insignificant in the PEAD window. PEAD results are shown in Table2, Panel A, where the dependent variable is

CAR(1,60). The estimated coefficient on REITj indicates the average magnitude of the drift to REITs relative to NonREITs, while the REIT*SURPj interaction term allows the response to vary relative to the size of the surprise. In the PEAD window the magnitude

55 of the average difference in the REIT response is small, but statistically significant.

However, the coefficient on REIT*SURPj, which captures the marginal difference in the cumulative abnormal returns for REITs, suggests the response is almost twice that of NonREITs. This holds both with and without including SIZE and BM control variables. Thus, consistent with the graphical results presented above, the evidence suggests the drift is more pronounced in REIT stocks. The regressions results for the initial reaction window are shown in Table 10,

Panel B, where the dependent variable is CAR(-1,0). The coefficient on REITj provides the average magnitude of the initial earnings surprise response for REITs relative to NonREITs. While the magnitude is small, it is negative and significant, suggesting a more muted initial response to the unexpected earnings signal for REITs compared to

NonREITs. The coefficient on REIT*SURPj shows the level of the initial response conditional on the size of the earnings surprise. The magnitude of the negative and significant interaction coefficient suggests that the immediate REIT response is roughly half of what we observe for the initial surprise. All together, firm-level regression results show that REITs have a more muted initial response to earnings news and subsequently show a greater emergence of the drift.52 Since firm-level analysis includes all firms across the full range of earnings surprises, we examine the magnitude of the drift at the portfolio level in order to isolate the extreme surprise quintile portfolios. Portfolio-level comparisons approximate a potential trading strategy using zero-investment portfolios long in high earnings surprise firms and short low earnings surprise firms. We sort firms into SURP quintiles for REITs, NonREITs, and the Small NonREIT subsample. Differences in CARs between the High and Low SURP quintile portfolios are reported in Table 11. On the portfolio level, REITs, NonREITs, and Small NonREITs all exhibit statistically significant abnormal returns for the three cumulative abnormal returns windows considered. For the post announcement window, CAR(1,60), REITs have the highest PEAD at a mean of 4.88% compared to 2.97% for NonREITs and 4.22% for Small NonREITs. With a

52 We find the regression results to be robust to alternative regression methods which are much less powerful. We run Fama and MacBeth (1973) cross sectional regressions where the first stage consists of 109 quarterly cross sectional regressions and the second stage effectively evaluates the results of the cross sectional regressions using the time series standard errors. See Appendix C and Table C-2.

56 quarterly difference of nearly 2% (8% annualized), the drift magnitude for REITs is significantly higher than we see for NonREITs. However, the magnitude of the drift in the post announcement window for REITs and Small NonREITs are not significantly different from one another. For the initial reaction window, CAR(-1,0), the REIT response is clearly the lowest with a portfolio mean difference of 1.04% compared to 2.38% for NonREITs and 3.29% for Small NonREITs. All three reactions are significantly different from one another, at the 1% level. When the pre-earnings announcement anticipation window, the announcement window, and the post announcement window are considered together, CAR(-60,60), we again see evidence suggesting that REIT returns do not anticipate earnings surprises as well as ordinary common stocks with significant mean differences of 9.73% for REITs, 13.33% for NonREITs, and 17.92% for Small NonREITs. Taken together, the results of the graphical, firm-level, and portfolio-level analyses can be interpreted as either evidence against the uncertainty theory or evidence that suggests the parallel asset market does not render REIT earnings signals more certain than earnings signals for NonREITs. For clarification, we test the uncertainty theory on an intra-industry basis given the documented changes within the REIT industry over time (Ling and Ryngaert 1997). Following Chui et al. (2003) we compare the magnitude of the anomaly before and after 1990. Based on the uncertainty hypothesis, we expect the magnitude of the drift to be greater in the latter sub-period (1990-1999), where REITs have been argued to have become more uncertain, relative to the earlier period (1982-1989). For completeness, we include a third sub-period (2000-2008) as well since the data are now available. Table 12 shows the PEAD magnitude comparisons by sub-period. For REITs, zero-investment portfolios long in stocks in the high surprise quintile and short in stocks within the low surprise quintile earn higher cumulative abnormal returns after 1990 than before 1990. The magnitude increases from a statistically insignificant 2.20% in the 1982-1989 sub-period to a highly significant 4.37% and 5.70% in the 1990-1999 and 2000-2008 sub-periods, respectively. However, while the magnitude increases over the whole sample period, we are unable to pick up significant differences when comparing the 1982-1989 and 1990-1999 sub-periods. We do find a statistically significant

57 increase in the drift when comparing the 1982-1989 and 2000-2008 sub-periods.53 These results are consistent with Chui et al. (2003), who document increases in the momentum anomaly following REIT industry structural changes, and lend support for the uncertainty hypothesis in an intra-industry context.54 Strong statistical significance for PEAD raises the question of economic significance. To profit from the drift using zero-investment portfolios the rewards for doing so must exceed the round trip transactions costs. On the high end, Bernard and Thomas (1989) argue that round trip transactions costs for large (small) stocks are approximately 3% (6%) per quarter when defined to include both bid-ask spreads and commissions. To arrive at these cost estimates the authors rely on Stoll and Whaley (1983). Other studies estimate much lower transactions costs. Berkovitch, Logue, and Noser (1988) find round trip transactions costs for a large institutional investor to be roughly 0.5%. Chan and Lakonishok (1995) provide evidence that round trip transactions costs for a broad cross section of firms that is weighted somewhat more heavily by larger stocks to be just under 1%. Chan and Lakonishok (1997) show that institutional investors incur round trip transactions costs of slightly over 2% when trading small stocks. Ling, Naranjo, and Ryngaert (2000) argue that REIT transactions costs fall somewhere between those we see for large and small firms. Based on the aforementioned literature Ling et al. (2000) apply round trip transactions costs of 0.48% for large stocks, 1% for REITs, and 2% for small stocks. However, even if the costs applied by Ling et al. (2000) were doubled, a PEAD investment strategy would be profitable given the magnitudes we observe in Tables 3 and 4, particularly for REITs. Shivakumar (2007) notes that while this anomaly violates semi-strong form market efficiency, the strategy remains profitable. Campbell et al. (2009) find that institutional investors do, in fact, trade on PEAD. They show that institutional trading

53 We acknowledge that the increased proportion of Equity REITs may be driving this result. As a percentage of total REIT market capitalization, Equity REITs made up 46% in 1982-1989, 74% in 1990- 1999, and 81% in 2000-2008. However, most of the increase can be seen when comparing the first two sub-periods. Thus, if the increase in Equity REITs is driving the result, it would seem likely that we would also find significant differences between the 1982-1989 and 1990-1999 sub-periods as well. However, when the 1990s are included by arbitrarily cutting the sample in the middle of the decade, the differences become more difficult to detect. In this scenario, the earlier and latter sub-periods are not significantly different from one another. 54 Neither NonREITs nor Small NonREITs show this same upward trajectory across the three sub- periods. We find all NonREIT and Small NonREIT differences to be statistically insignificant.

58 anticipates both earnings surprises and PEAD. In other words, institutions buy (sell) stocks prior to positive (negative) earnings surprises and the stocks they buy (sell) tend to drift up (down).

Conclusion Given the transparent nature of commercial real estate assets and the presence of a parallel market in which these assets trade, we hypothesized that REITs would exhibit considerably less post-earnings-announcement drift than ordinary common stocks. The greater ability for investors to pierce through to the performance of the underlying REIT assets should render REIT earnings signals relatively more certain than the earnings signals of their NonREIT counterparts. According to the uncertainty argument, less uncertainty should translate into less drift since investors should be able to quickly and fully incorporate the new information into asset prices. As the first study to analyze the post-earnings-announcement drift in a REIT context, we document a strong manifestation of the anomaly during the 1982-2008 period. Graphical, firm-level regression, and portfolio-level comparison evidence indicate that REIT PEAD is both large and significant. The economically significant drift of nearly 20% on an annualized basis is significantly larger than that which we observe for NonREITs (12% on an annual basis). REIT drift also appears to be somewhat larger than the drift we find in the Small NonREIT sub-sample (roughly 17% annualized), however, the difference is not significant. We find strong differences between REITs, NonREITs, and Small NonREITs when comparing the initial reaction to the earnings surprises. Earnings announcement window REIT returns are significantly more muted than their NonREIT and Small NonREIT counterparts. REIT abnormal announcement window returns are less than half of what we find for NonREITs and less than one third of what we find for Small NonREITs. Taken together, the muted initial response and large subsequent drift either provides evidence against the uncertainty hypothesis or suggests that the parallel asset market does not render REIT earnings signals relatively more certain than their NonREIT counterparts. The latter explanation is consistent with Barkham and Geltner

(1995) and Chiang (2009) who contend that information and price discovery originate in the publicly traded real estate market and flow to the private market. For additional comparison, we also analyze REIT PEAD on an intra-industry basis. Within REIT industry comparisons provide weak support the both the uncertainty hypothesis and findings of earlier studies (Ling and Ryngaert 1997; Chui et al. 2003) that argue that the REIT industry has become relatively more uncertain over time. The corresponding returns to zero investment portfolios long in high earnings surprise REITs and short in low earnings surprise REITs increase from a statistically insignificant 2.2% in the 1982-1989 sub-period, to a highly significant 4.4% in the 1990-1999 sub-period, and a highly significant 5.7% in the 2000-2008 sub-period. However, using this same measure of uncertainty, the question is raised regarding why REITs show greater drift than ordinary common stocks and small stocks while their returns volatility is not nearly as high. While the results are robust to variations in methodology and sample composition, we note a few questions that are raised. First, why do REITs show greater drift than ordinary common stocks and small stocks when their returns volatility is not nearly as high? Assuming returns volatility can be used as a measure to proxy for uncertainty (Chui et al. 2003; Zhang 2006), we would expect much less drift for REITs compared to NonREITs and Small NonREITs. However, we do not find this to be the case. Second, why do investors appear to be able to anticipate earnings surprises to a greater extent for NonREITs and Small Nonreits compared to REITs? This result suggests that the overall information environment for REITs is not as rich as the information environments of NonREITs and Small NonREITs. Third, how long does REIT PEAD last and is there a reversion, or convergence with NonREIT returns behavior, at longer horizons? The finance literature provides mixed evidence regarding the duration of the PEAD anomaly among NonREITs due to the shortcomings of long term abnormal performance estimation. We leave a complete exploration of these findings to future study.

REIT Cumulative Market Adjusted Returns by Earnings Surprise Deciles, 1982-2008 10 8 Decile 10 6 Decile 9 Decile 10 4 9 Decile 8 8 Decile 7 2 7 6 5 Decile 6 0 4 3 Decile 5 -2

Percent CAR Percent 2 Decile 4 -4 Decile 1 Decile 3 -6 Decile 2 -8 Decile 1 -10 -60 -40 -20 0 20 40 60 Day

Fig. 4 Cumulative Abnormal Returns for all REITs by SURP deciles for the 1982-2008 sample period. Abnormal returns are estimated using size-adjusted market adjusted returns calculated as {ARjt = Rjt - Rpt} where ARjt is the abnormal return for firm j, day t; Rjt is the raw return for firm j, day t; and Rpt is the equally weighted mean return on day t for all firms in the same size decile as firm j. Size is the market capitalization of firm j as of day t = -45 and size deciles are based on NYSE size breakpoints. Abnormal returns are cumulated over a 121 day period starting sixty days prior to the earnings announcement through sixty days after the announcement. Day zero is the date of the earnings announcement. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (day t = -45)}. Firms are sorted into SURP deciles each quarter. REITs are defined as stocks with CRSP share code 18.

Nonreit Cumulative Market Adjusted Returns by Earnings Surprise Deciles, 1982-2008 10 8 Decile 10 6 Decile 9 9 Decile 8 4 8 Decile 7 2 7 Decile 6 0 6 Decile 5 -2 Percent CAR Percent 5 Decile 4 -4 4 3 Decile 3 -6 2 Decile 2 -8 Decile 1 -10 -60 -40 -20 0 20 40 60 Day

Fig. 5 Cumulative Abnormal Returns (CARs) for all NonREITs by SURP deciles for the 1982-2008 sample period. Abnormal returns are estimated using size-adjusted market adjusted returns calculated as {ARjt = Rjt - Rpt} where ARjt is the abnormal return for firm j, day t; Rjt is the raw return for firm j, day t; and Rpt is the equally weighted mean return on day t for all firms in the same size decile as firm j. Size is the market capitalization of firm j as of day t = -45 and size deciles are based on NYSE size breakpoints. Abnormal returns are cumulated over a 121 day period starting sixty days prior to the earnings announcement through sixty days after the announcement. Day zero is the date of the earnings announcement. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (day t = -45)}. Firms are sorted into SURP deciles each quarter. NonREITs include all stocks traded on NYSE, AMEX, and NASDAQ with CRSP share codes 10, 11, and 12 (excluding financials and utilities).

Drift Spread, Day -60 to 60 High Minus Low Surprise Quintiles, by Year 35%

30%

25%

20%

15% Small Nonreits 10% REITs Mean HML MeanSpread

-5%

Fig. 6 Drift spread for REITs, NonREITs, and Small NonREITs over the 121 day period around earnings announcements, by year. The drift spread is calculated as the mean 121 day Cumulative Abnormal Return (CAR) for firms in the high SURP quintile minus the mean 121 day CAR for firms in the low SURP quintile. Firms are sorted into SURP quintiles each quarter. Abnormal returns are estimated using size-adjusted market adjusted returns calculated as {ARjt = Rjt - Rpt} where ARjt is the abnormal return for firm j, day t; Rjt is the raw return for firm j, day t; and Rpt is the equally weighted mean return on day t for all firms in the same size decile as firm j. Size is the market capitalization of firm j as of day t = -45 and size deciles are based on NYSE size breakpoints. Abnormal returns are cumulated over a 121 day period starting sixty days prior to the earnings announcement through sixty days after the announcement. Day zero is the date of the earnings announcement. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (day t = -45)}. REITs are defined as stocks with CRSP share code 18. NonREITs include all stocks traded on NYSE, AMEX, and NASDAQ (excluding financials and utilities) with CRSP share codes 10, 11, and 12. Small NonREITs are the subset of NonREITs that fall within the lowest size quintile based on NYSE size breakpoints.

Drift Spread, Day 1 to 60 High Minus Low Surprise Quintiles, by Year 20%

15%

10%

REITs Small 5% Nonreits Mean HML MeanSpread

-5%

Fig. 7 Post-earnings-announcement drift spread for REITs, NonREITs, and Small NonREITs over the 60 day post earnings announcement period, by year. The drift spread is calculated as the mean 60 day Cumulative Abnormal Return (CAR) for firms in the high SURP quintile minus the mean 60 day CAR for firms in the low SURP quintile. Firms are sorted into SURP quintiles each quarter. Abnormal returns are estimated using size- adjusted market adjusted returns calculated as {ARjt = Rjt - Rpt} where ARjt is the abnormal return for firm j, day t; Rjt is the raw return for firm j, day t; and Rpt is the equally weighted mean return on day t for all firms in the same size decile as firm j. Size is the market capitalization of firm j as of day t = -45 and size deciles are based on NYSE size breakpoints. Abnormal returns are cumulated over a 60 day period starting one day after the earnings announcement through sixty days after the announcement. Day zero is the date of the earnings announcement. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (day t = -45)}. REITs are defined as stocks with CRSP share code 18. NonREITs include all stocks traded on NYSE, AMEX, and NASDAQ (excluding financials and utilities) with CRSP share codes 10, 11, and 12. Small NonREITs are the subset of NonREITs that fall within the lowest size quintile based on NYSE size breakpoints.

Table 9 Sample Characteristics and Returns Volatilities

Panel A: Size Mean Min P25 P50 P75 Max SD N REITs 846 1 64 284 887 26,381 1,703 8,484 NonREITs 2,830 1 108 369 1,371 540,649 13,655 203,971 Small NonREITs 121 1 42 78 149 787 125 76,597

Panel B: BM REITs 1.19 0.00 0.48 0.71 1.11 87.27 2.82 8,484 NonREITs 0.59 0.00 0.29 0.49 0.75 242.35 0.94 203,971 Small NonREITs 0.73 0.00 0.36 0.60 0.91 242.35 1.45 76,597

Panel C: Volatility 82-08 82-89 90-99 00-08 REITs 2.15% 1.88% 2.25% 2.10% NonREITs 3.63% 2.90% 4.09% 3.66% Small NonREITs 4.39% 3.43% 5.12% 4.29%

This table provides summary statistics for the REIT, NonREIT, and Small NonREIT subsamples. Size (Panel A) is the market capitalization, in millions, as of 45 days prior to the earnings announcement date, calculated as the stock price times the number of shares outstanding. P25, P50, and P75 represent the 25th, 50th, and 75th percentiles. SD is the standard deviation and N represents the number of firm-quarter observations. BM (Panel B) is the Book-to-Market ratio with book value lagged two calendar quarters. Volatility (Panel C) is in percent and is calculated for a given firm as the standard deviation of daily returns in a month. All monthly observations in the particular time period (e.g. 82-08) are averaged. All volatilities are significantly different from one another at the 1% level. To be included, stocks must have been listed on CRSP/Compustat for at least two years. REITs are defined as stocks with CRSP share code 18. NonREITs include all stocks traded on NYSE, AMEX, and NASDAQ with CRSP share codes 10, 11, and 12. Small NonREITs are the subset of NonREITs that fall within the lowest size quintile based on NYSE size breakpoints.

Table 10 Regression Results of Cumulative Abnormal Returns on Earnings Surprise, REIT Indicator, and Controls, 1982-2008

Panel A: PEAD CAR(1,60) t-statistic CAR(1,60) t-statistic CAR(1,60) t-statistic SURP 0.1400*** (8.23) 0.1250*** (7.12) 0.1230*** (8.09) REIT 0.0087*** (4.07) 0.0091*** (4.24) 0.0098*** (4.22) REIT*SURP 0.1300*** (2.69) 0.1290*** (2.63) SIZE 0.0002*** (4.03) BM -0.0008 (-0.72) Constant -0.0077*** (-3.99) -0.0077*** (-4.00) -0.0077*** (-4.18) Observations 212455 212455 212455 R-squared 0.001 0.001 0.001

Panel B: Announcement CAR(-1,0) CAR(-1,0) CAR(-1,0) SURP 0.1150*** (18.25) 0.1220*** (18.00) 0.1210*** (18.06) REIT -0.0014*** (-2.62) -0.0015*** (-3.04) -0.0011** (-1.98) REIT*SURP -0.0659*** (-3.20) -0.0672*** (-3.134) SIZE -0.0000*** (-3.25) BM -0.0008** (-2.04) Constant 0.0030*** (12.93) 0.0030*** (12.95) 0.0035*** (10.34) Observations 212455 212455 212455 R-squared 0.007 0.008 0.008

Panel C: Full Window CAR(-60,60) CAR(-60,60) CAR(-60,60) SURP 0.6770*** (18.43) 0.6980*** (18. 29) 0.6740*** (17. 14) REIT 0.0095** (2.41) 0.0089** (2.28) 0.0186*** (3.01) REIT*SURP -0.1830** (-2.098) -0.2080** (-2.01) SIZE 0.0002*** (2.78) BM -0.0158** (-1.96) Constant -0.0074*** (-2.61) -0.0074*** (-2.61) 0.0015 (0.27) Observations 212455 212455 212455 R-squared 0.010 0.010 0.015 *** p<0.01, ** p<0.05

This table provides results for cross-sectional regressions of CARs on SURP, REIT, REIT*SURP, SIZE and BM for the drift window (Panel A), the announcement window (Panel B), and the full 121 day window (Panel C). Standard errors are adjusted for heteroscedasticity following White(1980) and for clustering by firm and quarter following Petersen (2009). t-statistics are in parentheses. CAR(1,60) is the sixty-day cumulative abnormal return, starting the day after the earnings announcement, where the abnormal returns are estimated using size-adjusted market adjusted returns. CAR(-1,0) is the two-day cumulative abnormal return, starting the day prior to the earnings announcement. CAR(-60,60) is calculated the same way as CAR(1,60) and CAR(-1,0), only it is cumulated from 60 days prior to the earnings announcement through 60 days after the earnings announcement. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (day t = -45)}. To eliminate extreme outliers SURP has been winsorized at the 0.25th percentile in each tail. REIT is a 1/0 indicator variable set to 1 if the firm is a REIT. REIT*SURP is the interaction of REIT and SURP. SIZE is the market capitalization of each firm, in billions, as of 45 days prior to the earnings announcement date, calculated as the stock price times the number of shares outstanding. BM is the Book-to-Market ratio with book value lagged two calendar quarters.

Table 11 Test of Differences, Means and Medians, by SURP Quintiles, 1982-2008

SURP CAR(1,60) CAR(-1,0) CAR(-60,60) CAR(1,60) CAR(-1,0) CAR(-60,60) CAR(1,60) CAR(-1,0) CAR(-60,60) Quintiles REITs REITs REITs NonREITs NonREITs NonREITs Small Small Small

High Mean 0.0264 0.0072 0.0518 0.0084 0.0164 0.0672 0.0001 0.0220 0.0728 Median 0.0168 0.0035 0.0355 0.0040 0.0095 0.0500 -0.0064 0.0127 0.0491

Low Mean -0.0223 -0.0032 -0.0456 -0.0213 -0.0075 -0.0661 -0.0420 -0.0109 -0.1064 Median -0.0119 -0.0019 -0.0270 -0.0213 -0.0063 -0.0660 -0.0459 -0.0095 -0.1192

Mean H-L 0.0488*** 0.0104*** 0.0973*** 0.0297*** 0.0238*** 0.1333*** 0.0422*** 0.0329*** 0.1792*** t-statistic (7.51) (5.47) (10.31) (22.87) (57.28) (63.87) (20.28) (48.83) (51.56)

Median H-L 0.0287*** 0.0054*** 0.0625*** 0.0253*** 0.0159*** 0.1160*** 0.0395*** 0.0222*** 0.1683*** z-statistic (8.72) (7.47) (13.14) (23.39) (61.39) (66.45) (20.91) (52.55) (55.43) *** p<0.01, ** p<0.05

This table shows the differences in CARs when sorted into High and Low SURP quintile portfolios for the entire sample period, 1982-2008. Test statistics (t and z) are in parentheses. CAR(1,60) is the sixty-day cumulative abnormal return, starting the day after the earnings announcement, where the abnormal returns are estimated using size-adjusted market adjusted returns. CAR(-1,0) is the two-day cumulative abnormal return, starting the day prior to the earnings announcement. CAR(-60,60) is calculated the same way as CAR(1,60) and CAR(-1,0), only it is cumulated from 60 days prior to the earnings announcement through 60 days after the earnings announcement. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (day t = -45)}. REITs indicates that the test is limited to real estate investment trusts. NonREITs are all firms listed on NYSE, AMEX, and NASDAQ that are not real estate investment trusts, financials, or utilities. SMALL is a subsample of NONREITS that includes firms that fall within the bottom market capitalization size quintile based on NYSE size breakpoints. All same window comparisons between REITs, NonREITs, and Small NonREITs are significantly different from one another at the 1% level, except REITs and Small NonREITs during the CAR(1,60) window, which are not significantly different.

Table 12 Test of Differences, Means and Medians, by SURP Quintiles and Decade

CAR(1,60) CAR(1,60) CAR(1,60) CAR(1,60) CAR(1,60) CAR(1,60) CAR(1,60) CAR(1,60) CAR(1,60) SURP REITs REITs REITs NonREITs NonREITs NonREITs Small Small Small Quintiles 82-89 90-99 00-08 82-89 90-99 00-08 82-89 90-99 00-08

High Mean 0.0188 0.0309 0.0239 0.0111 0.0035 0.0123 0.0125 -0.0113 0.0056 Median 0.0030 0.0149 0.0187 0.0078 0.0008 0.0050 0.0072 -0.0188 -0.0048

Low Mean -0.0032 -0.0128 -0.0331 -0.0178 -0.0248 -0.0194 -0.0316 -0.0506 -0.0398 Median -0.0015 -0.0137 -0.0116 -0.0181 -0.0250 -0.0194 -0.0377 -0.0548 -0.0445

Mean H-L 0.0220 0.0437*** 0.0570*** 0.0289*** 0.0283*** 0.0318*** 0.0441*** 0.0393*** 0.0454*** t-statistic (1.60) (3.79) (6.79) (12.13) (12.91) (15.08) (11.30) (10.78) (14.22)

Median H-L 0.0045 0.0286*** 0.0302*** 0.0259*** 0.0259*** 0.0243*** 0.0448*** 0.0360*** 0.0396*** z-statistic (1.19) (5.24) (6.98) (12.50) (13.52) (14.67) (12.19) (11.20) (13.66) *** p<0.01, ** p<0.05

This table shows the differences in CARs when sorted into High and Low SURP quintile portfolios for each of the three decades in the sample period, 1982-1989, 1990-1999, and 2000-2008. Test statistics (t and z) are in parentheses. CAR(1,60) is the sixty-day cumulative abnormal return, starting the day after the earnings announcement, where the abnormal returns are estimated using size-adjusted market adjusted returns. CAR(-1,0) is the two- day cumulative abnormal return, starting the day prior to the earnings announcement. CAR(-60,60) is calculated the same way as CAR(1,60) and CAR(- 1,0), only it is cumulated from 60 days prior to the earnings announcement through 60 days after the earnings announcement. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (day t = -45)}. REITs indicates that the test is limited to real estate investment trusts. NonREITs are all firms listed on NYSE, AMEX, and NASDAQ that are not real estate investment trusts, financials, or utilities. SMALL is a subsample of NONREITS that includes firms that fall within the bottom market capitalization size quintile based on NYSE size breakpoints. All within sample comparisons between 1982-1989, 1990-1999, and 2000-2008 are not significantly different from one another, except for the REIT sample where the mean difference between 1982-1989 and 2000-2008 is significant at the 5% level.

APPENDIX A

CHAPTER 2 ADDITIONAL SUPPORT

The categories of the Harvard IV-4 Psychosocial Dictionary, which is the default dictionary for the General Inquirer package, are far more expansive than just the two that have been incorporated into recent finance studies (positive and negative words). The broad dictionary covers such diverse areas as political, religious, academic, gender specific, or even race related words. We identified eight relevant categories for the purpose of establishing an overall measure of positive and negative tone. Category descriptions, the number of root words, and a corresponding description of the type of words found in each category are in Table A-1. One of the limitations of applying the general Harvard dictionary is the potential misclassification of words due to context specific meanings (Loughran and McDonald, 2009). For example, the Harvard dictionary categorizes Volatility as a negative word, which is correct in many settings. However, option traders view Volatility positively. In light of such limitations, content analysis should be viewed as a fairly blunt instrument. One way to overcome some misclassification is through the use of custom dictionaries. Similar to Loughran and McDonald (2009), Henry (2008) argues that the Harvard IV-4 Psychosocial Dictionary is too broad to be properly applied within the realm of finance and related disciplines. As such she creates her own list of “domain relevant” words that connote either a positive or negative outlook using earnings announcement specific terminology. The list is shown in Table A-2. Of the collection of 199 words in the Henry list, all are found within the Harvard dictionary categorized under one of the eight categories listed in Table A-1, except four words – Deteriorate, Downturn, Hurdle, and Hurdles. We programmed the Henry word list into the General Inquirer as a custom dictionary which still allowed us to take advantage of the GI‟s powerful disambiguation (word recognition) capacity. The GI applies over 6,300 disambiguation rules in order to properly identify words within a body of text. For example, the program is able to identify Accomplished, Accomplishes, and Accomplishing as having the same common root, Accomplish, which

69 it then classifies according to the definitions or categorizations of the dictionary applied to the analysis. The GI will recognize Accomplishments, as a separate word with the root Accomplishment, which then receives its own categorization based on the dictionary. The remaining tables in Appendix A (Table A-3 through Table A-8) provide firm- level and portfolio-level results for the TONE1, TONE2, and H-TONE2 measures that correspond to the results shown in Tables 7 and 8, where the H-TONE1 measure is used. The remaining figures in Appendix A (Figure A-1 through Figure A-5) provide additional graphical detail. Figure 1 shows size-adjusted CARs sorted into SURP terciles and plotted over a 121 day window (day t=-60 to t=60) in the spirit of the decile plots found in Bernard and Thomas (1989) and elsewhere. The plot shows the market‟s ability to anticipate much of the earnings surprise during the days prior to the announcement, the initial reaction jump, and a small amount of subsequent post- earnings-announcement drift. Figure A-2 is similar to Figure 1 only Figure A-2 shows the 71-day CARs by high and low H-TONE2 quintiles for dividend paying firms.

Likewise, Figure A-3 shows the 71-day CARs by high and low H-TONE2 quintiles for firms that do not pay dividends. Figures A-4 and A-5 are essential the same as Figure 3, however Figure A-4 isolated dividend paying firms and Figure A-5 isolated firms that do not pay dividends. Furthermore, Figures A-4 and A-5 are sorted using H-TONE2, while Figure 3 is sorted using H-TONE1. However, since the sorting breakpoints are identical for H-TONE1 and H-TONE2, the plots do not change based on which H-TONE construction is used.

Table A-1 Harvard Dictionary Categories, Description, and Word Counts Select Harvard IV-4 Number of words and Description Psychosocial Dictionary Categories Positive: 1,915 words of positive outlook Negative: 2,291 words of negative outlook

Strong: 1902 words implying strength Weak: 755 words implying weakness

Active: 2045 words implying an active orientation Passive: 911 words indicating a passive orientation

Overstated: 696 words indicating emphasis in realms of speed, frequency, causality, inclusiveness, quantity or quasi-quantity, accuracy, validity, scope, size, clarity, exceptionality, intensity, likelihood, certainty and extremity Understated: 319 words indicating de-emphasis and caution in these realms

Table A-2 Henry Word List Positive Tone Negative Tone Above Expands Rewarded Below Fell Weakness Accomplish Expansion Rewarding Challenge Hurdle Weaknesses Accomplished Good Rewards Challenged Hurdles Worse Accomplishes Greater Rise Challenges Least Worsen Accomplishing Greatest Risen Challenging Less Worsening Accomplishment Grew Rises Decline Low Worsens Accomplishments Grow Rising Declined Lower Worst Achieve Growing Rose Declines Lowest Achieved Grown Solid Declining Negative Achievement Grows Strength Decrease Negatives Achievements Growth Strengthen Decreased Obstacle Achieves High Strengthened Decreases Obstacles Achieving Higher Strengthening Decreasing Penalties Beat Highest Strengthens Depressed Penalty Beating Improve Strengths Deteriorate Risk Beats Improved Strong Deteriorated Risks Best Improvement Stronger Deteriorates Risky Better Improvements Strongest Deteriorating Shrink Certain Improves Succeed Difficult Shrinking Certainty Improving Succeeded Difficulty Shrinks Definite Increase Succeeding Disappoint Shrunk Deliver Increased Succeeds Disappointed Slump Delivered Increases Success Disappointing Slumped Delivering Increasing Successes Disappointment Slumping Delivers Larger Successful Disappoints Slumps Encouraged Largest Up Down Smaller Encouraging Leader Downturn Smallest Enjoy Leading Drop Threat Enjoyed More Dropped Threats Enjoying Most Dropping Uncertain Enjoys Opportunities Drops Uncertainty Exceed Opportunity Fail Under Exceeded Pleased Failing Unfavorable Exceeding Positive Fails Unsettled Exceeds Positives Failure Weak Excellent Progress Fall Weaken Expand Progressing Fallen Weakened Expanded Record Falling Weakening Expanding Reward Falls Weakens

Table A-3 Regression Results of Cumulative Abnormal Returns on Earnings Surprise, and TONE1

TONE1 is the first principal component of the four ratios Positive/Negative, Strong/Weak, Active/Passive, and Overstated/Understated, where each category reflects the proportion of words in a given call as defined by the Harvard IV-4 Psychosocial Dictionary. The variable prefixes INTRO and Q&A indicate that the referenced tone measures are for the introductory managerial statement and the question and answer portions of the conference calls, respectively. Standard errors are adjusted for heteroscedasticity following White(1980) and for clustering by firm and quarter following Petersen (2009). t-statistics are in parentheses.

Panel A: All CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) SURP 0.1480*** 0.1430*** 0.1440*** 0.1410*** 0.1390*** -0.0474 -0.0486 -0.0457 -0.0446 -0.0426 (5.13) (4.89) (4.96) (4.70) (4.58) (-0.45) (-0.47) (-0.44) (-0.43) (-0.41)

TONE 0.0045*** 0.0011 1 (4.68) (0.39)

INTRO TONE 0.0032*** 0.0025** -0.0017 -0.0025 1 (2.97) (2.20) (-0.73) (-1.06)

Q&A TONE 0.0040*** 0.0033*** 0.0029 0.0036 1 (3.79) (2.95) (0.90) (1.09)

Constant 0.0004 0.0004 0.0004 0.0004 0.0003 -0.0062 -0.0062 -0.0062 -0.0061 -0.0061 (0.25) (0.25) (0.24) (0.23) (0.21) (-1.46) (-1.46) (-1.45) (-1.41) (-1.41)

Observations 2880 2880 2878 2876 2874 2880 2880 2878 2876 2874 R-Squared 0.010 0.016 0.013 0.013 0.015 0.000 0.000 0.000 0.001 0.001 *** p<0.01, ** p<0.05, * p<0.1

Table A-3 (Continued)

Panel B: Dividend CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) SURP 0.1110** 0.0990** 0.1050** 0.1050** 0.1010** -0.0270 -0.0265 -0.0250 -0.0275 -0.0256 (2.27) (2.14) (2.20) (2.21) (2.16) (-0.15) (-0.15) (-0.14) (-0.15) (-0.14)

TONE 0.0078*** -0.0003 1 (5.26) (-0.09)

INTRO TONE 0.0038** 0.0028* -0.0013 -0.0015 1 (2.36) (1.94) (-0.58) (-0.65)

Q&A TONE 0.0056*** 0.0049*** 0.0008 0.0012 1 (3.97) (4.05) (0.22) (0.31)

Constant 0.0010 0.0025 0.0015 0.0015 0.0018 -0.0090** -0.0091*** -0.0091** -0.0090** -0.0091** (0.45) (1.08) (0.61) (0.67) (0.75) (-2.39) (-2.61) (-2.52) (-2.44) (-2.54)

Observations 1440 1440 1438 1439 1437 1440 1440 1438 1439 1437 R-Squared 0.006 0.028 0.012 0.017 0.020 0.000 0.000 0.000 0.000 0.000 Panel C: NonDiv CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) SURP 0.1740*** 0.1730*** 0.1720*** 0.1670*** 0.1660*** -0.0651 -0.0662 -0.0637 -0.0590 -0.0572 (4.17) (4.13) (4.14) (4.11) (4.09) (-0.47) (-0.47) (-0.45) (-0.42) (-0.41)

TONE 0.0022* 0.0018 1 (1.77) (0.52)

INTRO TONE 0.0027** 0.0024 -0.0025 -0.0041 1 (2.06) (1.41) (-0.58) (-0.86)

Q&A TONE 0.0025 0.0017 0.0047 0.0060 1 (1.33) (0.79) (1.29) (1.46)

Constant -0.0003 -0.0007 -0.0007 -0.0007 -0.0009 -0.0033 -0.0036 -0.0030 -0.0034 -0.0030 (-0.12) (-0.28) (-0.25) (-0.26) (-0.35) (-0.40) (-0.46) (-0.37) (-0.42) (-0.38)

Observations 1440 1440 1440 1437 1437 1440 1440 1440 1437 1437 R-Squared 0.013 0.014 0.015 0.013 0.015 0.000 0.001 0.001 0.001 0.002 *** p<0.01, ** p<0.05, * p<0.1

Table A-4 Regression Results of Cumulative Abnormal Returns on Earnings Surprise, and TONE2

This table provides results for cross-sectional regressions of CARs on SURP and measures of TONE2 for the full sample (Panel A), dividend paying firms (Panel B), and firms that do not pay dividends (Panel C). CAR(-1,1) is the three-day cumulative abnormal return, where day 0 is the conference call date, where the abnormal returns are estimated using size-adjusted returns calculated as ARj,t = Rj,t – Rp,t, where the abnormal return for firm j on day t is the difference between the return for firm j on day t and the mean return on day t for all firms in the same size decile as firm j. CAR(2,60) is calculated in the same manner as CAR(-1,1) only it is cumulated from days two through sixty. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (end of qtr-4)}.

TONE2 is the first principal component of the four ratios (Positive – Negative)/(Positive + Negative), (Strong – Weak)/(Strong + Weak), (Active – Passive)/(Active + Passive), and (Overstated –Understated)/ (Overstated + Understated), where each category reflects the proportion of words in a given call as defined by the Harvard IV- 4 Psychosocial Dictionary. The variable prefixes INTRO and Q&A indicate that the referenced tone measures are for the introductory managerial statement and the question and answer portions of the conference calls, respectively. Standard errors are adjusted for heteroscedasticity following White(1980) and for clustering by firm and quarter following Petersen (2009). t-statistics are in parentheses.

Panel A: All CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) SURP 0.148*** 0.142*** 0.143*** 0.145*** 0.142*** -0.0474 -0.0489 -0.0463 -0.0482 -0.0465 (5.129) (4.857) (4.872) (4.861) (4.739) (-0.452) (-0.473) (-0.447) (-0.462) (-0.448)

TONE 0.00456*** 0.00116 2 (4.802) (0.401)

INTRO TONE 0.00345*** 0.00222** -0.000729 -0.00134 2 (3.812) (2.465) (-0.321) (-0.618)

Q&A TONE 0.00478*** 0.00392*** 0.00144 0.00196 2 (4.922) (4.158) (0.488) (0.670)

Constant 0.000404 0.000408 0.000407 0.000406 0.000407 -0.00617 -0.00617 -0.00617 -0.00617 -0.00617 (0.254) (0.254) (0.251) (0.257) (0.255) (-1.456) (-1.456) (-1.456) (-1.458) (-1.460)

Observations 2880 2880 2880 2880 2880 2880 2880 2880 2880 2880 R-Squared 0.010 0.016 0.014 0.016 0.018 0.000 0.000 0.000 0.000 0.001 *** p<0.01, ** p<0.05, * p<0.1

Table A-4 (Continued)

Panel B: Dividend CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) SURP 0.1110** 0.0974** 0.0995** 0.1030** 0.0968** -0.0270 -0.0268 -0.0258 -0.0268 -0.0258 (2.27) (2.10) (2.15) (2.15) (2.08) (-0.15) (-0.15) (-0.14) (-0.15) (-0.14)

TONE 0.0077*** -0.0001 2 (5.65) (-0.03)

INTRO TONE 0.0054*** 0.0039*** -0.0006 -0.0006 2 (4.58) (3.42) (-0.22) (-0.22)

Q&A TONE 0.0070*** 0.0054*** -0.0002 0.0000 2 (4.65) (3.87) (-0.07) (0.00)

Constant 0.0010 0.0025 0.0019 0.0018 0.0022 -0.0090** -0.0090*** -0.0091** -0.0090** -0.0091*** (0.45) (1.09) (0.82) (0.79) (0.97) (-2.39) (-2.61) (-2.55) (-2.52) (-2.60)

Observations 1440 1440 1440 1440 1440 1440 1440 1440 1440 1440 R-Squared 0.006 0.030 0.020 0.022 0.029 0.000 0.000 0.000 0.000 0.000 Panel C: NonDiv CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) SURP 0.1740*** 0.1730*** 0.1730*** 0.1740*** 0.1740*** -0.0651 -0.0665 -0.0638 -0.0651 -0.0628 (4.17) (4.11) (4.14) (4.11) (4.11) (-0.47) (-0.48) (-0.46) (-0.47) (-0.45)

TONE 0.0018 0.0018 2 (1.30) (0.52)

INTRO TONE 0.0014 0.0003 -0.0015 -0.0027 2 (1.12) (0.23) (-0.39) (-0.67)

Q&A TONE 0.0031** 0.0030* 0.0025 0.0035 2 (2.05) (1.72) (0.75) (1.00)

Constant -0.0003 -0.0007 -0.0006 -0.0007 -0.0007 -0.00328 -0.0036 -0.0030 -0.0036 -0.0032 (-0.12) (-0.25) (-0.20) (-0.25) (-0.27) (-0.40) (-0.47) (-0.38) (-0.45) (-0.41)

Observations 1440 1440 1440 1440 1440 1440 1440 1440 1440 1440 R-Squared 0.013 0.014 0.013 0.015 0.015 0.000 0.001 0.001 0.001 0.001 *** p<0.01, ** p<0.05, * p<0.1

Table A-5 Regression Results of Cumulative Abnormal Returns on Earnings Surprise, and H-TONE2

This table provides results for cross-sectional regressions of CARs on SURP and measures of H-TONE2 for the full sample (Panel A), dividend paying firms (Panel B), and firms that do not pay dividends (Panel C). CAR(-1,1) is the three-day cumulative abnormal return, where day 0 is the conference call date, where the abnormal returns are estimated using size-adjusted returns calculated as ARj,t = Rj,t – Rp,t, where the abnormal return for firm j on day t is the difference between the return for firm j on day t and the mean return on day t for all firms in the same size decile as firm j. CAR(2,60) is calculated in the same manner as CAR(-1,1) only it is cumulated from days two through sixty. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (end of qtr-4)}. H- TONE2 is the ratio of (Positive – Negative)/(Positive + Negative) where each category reflects the proportion of words in a given call as defined by the custom earnings dictionary of Henry (2008). The variable prefixes INTRO and Q&A indicate that the referenced tone measures are for the introductory managerial statement and the question and answer portions of the conference calls, respectively. Standard errors are adjusted for heteroscedasticity following White(1980) and for clustering by firm and quarter following Petersen (2009). t-statistics are in parentheses.

Panel A: All CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) SURP 0.1480*** 0.1330*** 0.1350*** 0.1450*** 0.1370*** -0.0474 -0.0637 -0.0652 -0.0513 -0.0621 (5.13) (4.10) (4.21) (4.73) (4.21) (-0.45) (-0.64) (-0.66) (-0.50) (-0.62)

H-TONE 0.0621*** 0.0694** 2 (5.37) (2.20)

INTRO H-TONE 0.0341*** 0.0234*** 0.0488*** 0.0322* 2 (4.41) (2. 90) (2.67) (1.77)

Q&A H-TONE 0.0436*** 0.0320*** 0.0658** 0.0499* 2 (3.89) (2.68) (2.54) (1.89)

Constant 0.0004 -0.0390*** -0.0216*** -0.0269*** -0.0347*** -0.0062 -0.0502*** -0.0376*** -0.0474*** -0.0581*** (0.25) (-5.32) (-4.311) (-3.82) (-4.70) (-1.46) (-2.63) (-3.24) (-3.16) (-3.49)

Observations 2880 2880 2880 2880 2880 2880 2880 2880 2880 2880 R-Squared 0.010 0.020 0.015 0.016 0.018 0.000 0.004 0.003 0.004 0.005 *** p<0.01, ** p<0.05, * p<0.1

Table A-5 (Continued)

Panel B: Dividend CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) SURP 0.1110** 0.0707* 0.0759* 0.0922** 0.0723* -0.0270 -0.0675 -0.0808 -0.0414 -0.0814 (2.27) (1.74) (1.74) (2.01) (1.68) (-0.15) (-0.38) (-0.46) (-0.23) (-0.47)

H-TONE 0.0861*** 0.0870*** 2 (5.82) (2.79)

INTRO H-TONE 0.0482*** 0.0360*** 0.0742*** 0.0721*** 2 (5.15) (3.96) (3.92) (3.72)

Q&A H-TONE 0.0579*** 0.0389*** 0.0449 0.0067 2 (4.34) (2.84) (1.57) (0.23)

Constant 0.0010 -0.0527*** -0.0296*** -0.0346*** -0.0458*** -0.0090** -0.0633*** -0.0562*** -0.0366* -0.0590*** (0.45) (-5.56) (-4.61) (-3.95) (-4.67) (-2.39) (-2.91) (-3.87) (-1.87) (-2.77)

Observations 1440 1440 1440 1440 1440 1440 1440 1440 1440 1440 R-Squared 0.006 0.033 0.022 0.020 0.028 0.000 0.008 0.011 0.002 0.011 Panel C: NonDiv CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) SURP 0.1740*** 0.1710*** 0.1720*** 0.1790*** 0.1770*** -0.0651 -0.0680 -0.0668 -0.0541 -0.0517 (4.17) (4.06) (4.10) (4.22) (4.23) (-0.47) (-0.49 (-0.48) (-0.39) (-0.37)

H-TONE 0.0411*** 0.0473 2 (2.61) (1.18)

INTRO H-TONE 0.0204* 0.0102 0.0165 -0.0143 2 (1.88) (0.75) (0.56) (-0.43)

Q&A H-TONE 0.0340** 0.0294 0.0826*** 0.0891*** 2 (2.12) (1.53) (2.76) (2.61)

Constant -0.0003 -0.0269*** -0.0137* -0.0220** -0.0257*** -0.0033 -0.0338 -0.0141 -0.0559*** -0.0506*** (-0.12) (-2.67) (-1.88) (-2.08) (-2.63) (-0.40) (-1.47) (-0.85) (-3.39) (-2.81)

Observations 1440 1440 1440 1440 1440 1440 1440 1440 1440 1440 R-Squared 0.013 0.016 0.014 0.016 0.016 0.000 0.002 0.001 0.005 0.005 *** p<0.01, ** p<0.05, * p<0.1

Table A-6 CARs for Sorts by Earnings Surprise and TONE1

This table shows mean CARS sorted sequentially by SURP and TONE1 for the full sample (Panel A), dividend paying firms (Panel B), and firms that do not pay dividends (Panel C). H-L is the difference between the highest and lowest tercile. CAR(-1,1) is the three-day cumulative abnormal return, where day 0 is the conference call date, where the abnormal returns are estimated using size-adjusted returns calculated as ARj,t = Rj,t – Rp,t, where the abnormal return for firm j on day t is the difference between the return for firm j on day t and the mean return on day t for all firms in the same size decile as firm j. CAR(2,60) is calculated in the same manner as CAR(-1,1) only it is cumulated from days two through sixty. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr- 4)]/Stock Price (end of qtr-4)}. TONE1 is the first principal component of the four ratios Positive/Negative, Strong/Weak, Active/Passive, and Overstated/Understated, where each category reflects the proportion of words in a given call as defined by the Harvard IV-4 Psychosocial Dictionary. t- statistics are in parentheses.

Panel A: All CAR (-1,1) CAR (2,60)

SURP SURP

TONE1 Low (L) Medium High (H) H - L t-statistic TONE1 Low (L) Medium High (H) H - L t-statistic Low (L) -0.0173 -0.0103 0.0083 0.0256 (4.51)*** Low (L) -0.0116 -0.0121 0.0050 0.0167 (1.32) Medium -0.0077 -0.0027 0.0196 0.0272 (4.64)*** Medium -0.0178 -0.0130 0.0045 0.0223 (1.79)* High (H) -0.0104 0.0063 0.0187 0.0290 (4.36)*** High (H) 0.0105 -0.0186 -0.0027 -0.0132 (-0.98) H – L 0.0070 0.0166 0.0104 H - L 0.0221 -0.0065 -0.0077 t-statistic (1.19) (3.08)*** (1.59) t-statistic (1.72)* (-0.68) (-0.58)

Panel B: Dividend CAR (-1,1) CAR (2,60)

SURP SURP

TONE1 Low (L) Medium High (H) H - L t-statistic TONE1 Low (L) Medium High (H) H - L t-statistic Low (L) -0.0192 -0.0106 0.0078 0.0270 (3.89)*** Low (L) -0.0153 -0.0046 0.0105 0.0258 (1.76)* Medium -0.0117 0.0008 0.0213 0.0330 (4.37)*** Medium -0.0127 -0.0155 -0.0045 0.0082 (0.50) High (H) -0.0043 0.0107 0.0274 0.0317 (4.12)*** High (H) -0.0119 -0.0305 0.0054 0.0173 (1.12) H – L 0.0149 0.0213 0.0196 H – L 0.0034 -0.0259 -0.0051 t-statistic (2.15)** (2.97)*** (2.46)** t-statistic (0.23) (-2.24)** (-0.32)

Table A-6 (Continued)

Panel C: NonDiv CAR (-1,1) CAR (2,60)

SURP SURP

TONE1 Low (L) Medium High (H) H - L t-statistic TONE1 Low (L) Medium High (H) H - L t-statistic Low (L) -0.0144 -0.0099 0.0089 0.0233 (2.42)** Low (L) -0.0059 -0.0235 -0.0018 0.0041 (0.18) Medium -0.0040 -0.0069 0.0183 0.0222 (2.55)** Medium -0.0224 -0.0101 0.0112 0.0336 (1.84)* High (H) -0.0147 0.0023 0.0132 0.0279 (2.84)*** High (H) 0.0264 -0.0076 -0.0077 -0.0341 (-1.72)* H – L -0.0002 0.0122 0.0043 H – L 0.0323 0.0159 -0.0060 t-statistic (-0.02) (1.46) (0.42) t-statistic (1.48) (1.00) (-0.29)

*** p<0.01, ** p<0.05, * p<0.1

Table A-7 CARs for Sorts by Earnings Surprise and TONE2

4)]/Stock Price (end of qtr-4)}. TONE2 is the first principal component of the four ratios (Positive – Negative)/(Positive + Negative), (Strong – Weak)/(Strong + Weak), (Active – Passive)/(Active + Passive), and (Overstated –Understated)/ (Overstated + Understated), where each category reflects the proportion of words in a given call as defined by the Harvard IV-4 Psychosocial Dictionary. t-statistics are in parentheses.

Panel A: All CAR (-1,1) CAR (2,60)

SURP SURP

TONE2 Low (L) Medium High (H) H - L t-statistic TONE2 Low (L) Medium High (H) H - L t-statistic Low (L) -0.0174 -0.0116 0.0107 0.0280 (4.91)*** Low (L) -0.0141 -0.0102 0.0109 0.0250 (1.99)** Medium -0.0079 -0.0013 0.0168 0.0247 (4.07)*** Medium -0.0118 -0.0138 -0.0040 0.0078 (0.63) High (H) -0.0102 0.0062 0.0190 0.0292 (4.51)*** High (H) 0.0070 -0.0197 -0.0001 -0.0070 (-0.52) H – L 0.0072 0.0178 0.0083 H - L 0.0211 -0.0095 -0.0110 t-statistic (1.23) (3.34)*** (1.32) t-statistic (1.69)* (-0.98) (-0.81)

Panel B: Dividend CAR (-1,1) CAR (2,60)

SURP SURP

TONE2 Low (L) Medium High (H) H - L t-statistic TONE2 Low (L) Medium High (H) H - L t-statistic Low (L) -0.0203 -0.0128 0.0097 0.0300 (4.35)*** Low (L) -0.0218 -0.0035 0.0076 0.0295 (2.05)** Medium -0.0102 0.0028 0.0175 0.0277 (3.57)*** Medium -0.0039 -0.0141 0.0039 0.0079 (0.46) High (H) -0.0040 0.0110 0.0283 0.0323 (4.29)*** High (H) -0.0119 -0.0329 0.0005 0.0124 (0.80) H – L 0.0163 0.0238 0.0186 H – L 0.0099 -0.0294 -0.0072 t-statistic (2.35)** (3.42)*** (2.39)** t-statistic (0.70) (-2.55)** (-0.44)

Table A-7 (Continued)

Panel C: NonDiv CAR (-1,1) CAR (2,60)

SURP SURP

TONE2 Low (L) Medium High (H) H - L t-statistic TONE2 Low (L) Medium High (H) H - L t-statistic Low (L) -0.0124 -0.0098 0.0118 0.0242 (2.47)** Low (L) -0.0013 -0.0205 0.0146 0.0159 (0.70) Medium -0.0058 -0.0059 0.0163 0.0221 (2.44)** Medium -0.0187 -0.0134 -0.0101 0.0085 (0.48) High (H) -0.0144 0.0015 0.0131 0.0275 (2.89)*** High (H) 0.0202 -0.0069 -0.0004 -0.0206 (-1.03) H – L -0.0020 0.0113 0.0013 H – L 0.0216 0.0136 -0.0150 t-statistic (-0.20) (1.35) (0.13) t-statistic (1.00) (0.84) (-0.70)

*** p<0.01, ** p<0.05, * p<0.1

Table A-8 CARs for Sorts by Earnings Surprise and H-TONE2

Panel A: All CAR (-1,1) CAR (2,60)

SURP SURP

H-TONE2 Low (L) Medium High (H) H - L t-statistic H-TONE2 Low (L) Medium High (H) H - L t-statistic Low (L) -0.0186 -0.0143 0.0137 0.0323 (5.16)*** Low (L) -0.0155 -0.0190 -0.0027 0.0128 (0.99) Medium -0.0141 -0.0019 0.0112 0.0253 (4.50)*** Medium -0.0089 -0.0066 -0.0012 0.0077 (0.61) High (H) -0.0026 0.0096 0.0216 0.0242 (3.84)*** High (H) 0.0054 -0.0182 0.0107 0.0053 (0.40) H – L 0.0160 0.0239 0.0079 H - L 0.0209 0.0009 0.0134 t-statistic (2.67)*** (4.59)*** (1.20) t-statistic (1.67)* (0.09) (0.99)

Panel B: Dividend CAR (-1,1) CAR (2,60)

SURP SURP

H-TONE2 Low (L) Medium High (H) H - L t-statistic H-TONE2 Low (L) Medium High (H) H - L t-statistic Low (L) -0.0234 -0.0172 0.0141 0.0376 (4.95)*** Low (L) -0.0285 -0.0205 -0.0056 0.0230 (1.42) Medium -0.0142 0.0057 0.0157 0.0298 (4.00)*** Medium -0.0121 -0.0128 0.0033 0.0155 (1.10) High (H) 0.0050 0.0119 0.0237 0.0187 (2.81)*** High (H) 0.0069 -0.0140 0.0172 0.0103 (0.64) H – L 0.0284 0.0291 0.0096 H – L 0.0354 0.0065 0.0227 t-statistic (4.04)*** (4.22)*** (1.23) t-statistic (2.19)** (0.55) (1.37)

Table A-8 (Continued)

Panel C: NonDiv CAR (-1,1) CAR (2,60)

SURP SURP

H-TONE2 Low (L) Medium High (H) H - L t-statistic H-TONE2 Low (L) Medium High (H) H - L t-statistic Low (L) -0.0112 -0.0104 0.0133 0.0245 (2.34)** Low (L) 0.0046 -0.0171 0.0000 -0.0046 (-0.22) Medium -0.0141 -0.0109 0.0073 0.0214 (2.55)** Medium -0.0057 0.0007 -0.0051 0.0006 (0.03) High (H) -0.0079 0.0072 0.0202 0.0280 (2.91)*** High (H) 0.0044 -0.0226 0.0061 0.0018 (0.09) H – L 0.0033 0.0175 0.0068 H – L -0.0002 -0.0055 0.0061 t-statistic (0.33) (2.20)** (0.67) t-statistic (-0.01) (-0.35) (0.30)

*** p<0.01, ** p<0.05, * p<0.1

Cumulative Size Adjusted Returns 5.00 4.00 3.00 2.00 1.00 0.00 -1.00 Percent CAR Percent -2.00 -3.00 -4.00 -5.00 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60

Top SURP Tercile Bottom SURP Tercile

Fig. A-1 Cumulative Abnormal Returns (CARs) to the Top and Bottom SURP terciles for the 2004-2007 sample period. Abnormal returns are estimated using returns that have been size-adjusted and are calculated as {ARjt = Rjt - Rpt} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rpt is the equally weighted mean return on day t for all firms in the same size decile as firm j. Abnormal returns are cumulated over a 121 day period starting sixty days prior to the earnings announcement through sixty days after the announcement. Day zero is the date of the earnings conference call. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (end of qtr-4)}.

Cumulative Size Adjusted Returns, Dividend Payers

by High, Average, & Low H-TONE2 Quintiles 4 3 2 1 0 -1

Percent CAR Percent -2 -3 -4 -5 -10 0 10 20 30 40 50 60

High H-Tone2 Quintile Average Firm Low H-Tone2 Quintile

Fig. A-2 Cumulative Abnormal Returns for dividend paying firms across All Conference Calls and by High and Low H-TONE2 Quintiles. Abnormal returns are estimated using returns that have been size-adjusted and are calculated as {ARjt = Rjt - Rpt} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rpt is the equally weighted mean return on day t for all firms in the same size decile as firm j. Abnormal returns are cumulated over a 71 day period starting ten days prior to the earnings announcement through sixty days after the announcement. Day zero is the date of the earnings conference call. Cumulative abnormal returns (CAR) are shown in percent. H-TONE2 is the ratio of (Positive – Negative)/(Positive + Negative) where each category reflects the proportion of words in a given call as defined by the custom earnings dictionary of Henry (2008). High H-TONE2 Conference Calls shows the cumulative abnormal returns for the highest H-TONE2 quintile conference calls. Low H-TONE2 Conference Calls shows the cumulative abnormal returns for the lowest H-TONE2 quintile conference calls. All Conference Calls represents the cumulative abnormal returns for all conference calls in the sample.

Cumulative Size Adjusted Returns, Non-Dividend

by High, Average, & Low H-TONE2 Quintiles 3

-1 Percent CAR Percent -2

-3

-4 -10 0 10 20 30 40 50 60

High H-Tone2 Quintile Average Firm Low H-Tone2 Quintile

Fig. A-3 Cumulative Abnormal Returns for firms that do not pay dividends across All Conference Calls and by High and Low H-TONE2 Quintiles. Abnormal returns are estimated using returns that have been size-adjusted and are calculated as {ARjt = Rjt - Rpt} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rpt is the equally weighted mean return on day t for all firms in the same size decile as firm j. Abnormal returns are cumulated over a 71 day period starting ten days prior to the earnings announcement through sixty days after the announcement. Day zero is the date of the earnings conference call. Cumulative abnormal returns (CAR) are shown in percent. H-TONE2 is the ratio of (Positive – Negative)/(Positive + Negative) where each category reflects the proportion of words in a given call as defined by the custom earnings dictionary of Henry (2008). High H-TONE2 Conference Calls shows the cumulative abnormal returns for the highest H-TONE2 quintile conference calls. Low H-TONE2 Conference Calls shows the cumulative abnormal returns for the lowest H-TONE2 quintile conference calls. All Conference Calls represents the cumulative abnormal returns for all conference calls in the sample.

Cumulative Size Adjusted Returns, Dividend Payers

by H-TONE2 and SURP Terciles 6

-2 Percent CAR Percent -4

-6

-8 -10 0 10 20 30 40 50 60

Low H-Tone2 Low Surp High H-Tone2 Low Surp Low H-Tone2 High Surp High H-Tone2 High Surp

Fig. A-4 Cumulative Abnormal Returns for dividend paying firms by Earnings Surprise and H-TONE2 Terciles. Abnormal returns are estimated using returns that have been size-adjusted and are calculated as {ARjt = Rjt - Rpt} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rpt is the equally weighted mean return on day t for all firms in the same size decile as firm j. Abnormal returns are cumulated over a 71 day period starting ten days prior to the earnings announcement through sixty days after the announcement. Day zero is the date of the earnings conference call. Cumulative abnormal returns (CAR) are shown in percent. H-TONE2 is the ratio of (Positive – Negative)/(Positive + Negative) where each category reflects the proportion of words in a given call as defined by the custom earnings dictionary of Henry (2008). SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (end of qtr-4)}. CARs are plotted for High H-TONE2 High SURP conference calls , High H-TONE2 Low SURP conference calls, Low H- TONE2 High SURP conference calls , and Low H-TONE2 Low SURP conference calls. High H-TONE2 High SURP contains the conference calls within the highest H-TONE2 tercile and lowest SURP tercile. High H-TONE2 Low SURP contains the conference calls within the highest H-TONE2 tercile and lowest SURP tercile. Low H-TONE2 High SURP contains the conference calls within the lowest H-TONE2 tercile and highest SURP tercile. Low H- TONE2 Low SURP contains the conference calls within the lowest H-TONE2 tercile and lowest SURP tercile.

Cumulative Size Adjusted Returns, Non-Dividend

by H-TONE2 and SURP Terciles 5 4 3 2 1 0

Percent CAR Percent -1 -2 -3 -4 -10 0 10 20 30 40 50 60

Low H-Tone2 Low Surp High H-Tone2 Low Surp Low H-Tone2 High Surp High H-Tone2 High Surp

Fig. A-5 Cumulative Abnormal Returns for firms that do not pay dividends by Earnings Surprise and H-TONE2 Terciles. Abnormal returns are estimated using returns that have been size-adjusted and are calculated as {ARjt = Rjt - Rpt} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rpt is the equally weighted mean return on day t for all firms in the same size decile as firm j. Abnormal returns are cumulated over a 71 day period starting ten days prior to the earnings announcement through sixty days after the announcement. Day zero is the date of the earnings conference call. Cumulative abnormal returns (CAR) are shown in percent. H-TONE2 is the ratio of (Positive – Negative)/(Positive + Negative) where each category reflects the proportion of words in a given call as defined by the custom earnings dictionary of Henry (2008). SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (end of qtr-4)}. CARs are plotted for High H-TONE2 High SURP conference calls , High H-TONE2 Low SURP conference calls, Low H-TONE2 High SURP conference calls , and Low H-TONE2 Low SURP conference calls. High H-TONE2 High SURP contains the conference calls within the highest H-TONE2 tercile and lowest SURP tercile. High H-TONE2 Low SURP contains the conference calls within the highest H-TONE2 tercile and lowest SURP tercile. Low H- TONE2 High SURP contains the conference calls within the lowest H-TONE2 tercile and highest SURP tercile. Low H-TONE2 Low SURP contains the conference calls within the lowest H-TONE2 tercile and lowest SURP tercile.

APPENDIX B

CHAPTER 2 MARKET ADJUSTED RETURN RESULTS

Tables and figures shown in Appendix B replicate the results shown in the body of the text and in Appendix A using Market Adjusted Returns (MAR) which have not been size-adjusted. In the body of the text daily abnormal returns are estimated using size corrected market adjusted returns calculated as the difference between the raw return for stock j on day t and the mean return of a portfolio of all firms in the same size decile:

, = , , (B-1) where ARj,t is− the abnormal return for firm j on day t, Rj,t is the total return for firm j on day t, and Rp,t is the mean return on day t for all firms in the same size decile as firm j. However, in Appendix B we simply substitute the CRSP Value Weighted Index return for Rp,t. and cumulate returns in the same manner as noted previously. Inferences remain unchanged when using non-size adjusted MARs.

Table B-1 Descriptive Statistics and Correlations (Using Market Adjusted Returns)

Panel A CAR (-1,1) CAR (2,60) SURP TONE1 TONE2 H-TONE1 H-TONE2 Mean 0.0007 0.0063 0.0007 0.00 -0.00 5.21 0.64 Min -0.5248 -1.3234 -0.8348 -2.94 -4.95 0.82 -0.10 P25 -0.0353 -0.0724 -0.0033 -0.94 -0.89 3.56 0.56 P50 0.0018 0.0075 0.0014 -0.18 0.04 4.73 0.65 P75 0.0408 0.0881 0.0058 0.74 0.94 6.28 0.73 Max 0.4876 0.9035 0.8947 15.16 5.55 28.00 1.00 Std.Dev. 0.0759 0.1537 0.0500 1.36 1.36 2.43 0.13 N 2880 2880 2880 2880 2880 2880 2880 Panel B

CAR (-1,1) 1

CAR (2,60) 0.0163 1

SURP 0.0923 -0.0096 1

TONE 0.0851 -0.0025 0.0414 1 1 TONE 0.0862 0.0001 0.0484 0.9551 1 2 H-TONE1 0.1109 0.0410 0.0629 0.5851 0.5632 1

H-TONE2 0.1168 0.0645 0.0927 0.5512 0.5874 0.8718 1

This table provides descriptive statistics (Panel A) and correlations (Panel B) for the full sample of 2,880 conference calls. CAR(-1,1) is the three-day cumulative abnormal return, where day 0 is the conference call date, where the abnormal returns are estimated using market adjusted returns calculated as {ARjt = Rjt – Rmkt,t} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rmkt,t is the CRSP value weighted return on day t. CAR(2,60) is calculated in the same manner as CAR(-1,1) only it is cumulated from days two through sixty.

SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (end of qtr-4)}. TONE1 is the first principal component of the four ratios Positive/Negative, Strong/Weak, Active/Passive, and

Overstated/Understated and TONE2 is the first principal component of the four ratios (Positive – Negative)/(Positive + Negative), (Strong – Weak)/(Strong + Weak), (Active – Passive)/(Active + Passive), and (Overstated – Understated)/(Overstated + Understated), where each category reflects the proportion of words in a given call as defined by the Harvard IV-4 Psychosocial Dictionary. H-TONE1 is the ratio of Positive words to Negative words and H-TONE2 is the ratio (Positive – Negative)/(Positive + Negative) where each category reflects the proportion of words in a given call as defined by the custom earnings dictionary of Henry (2008).

Table B-2 Test of Differences of Means and Medians, by TONE1 Quintiles (Using Market Adjusted Returns)

TONE1 CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) Quintiles All Dividend NonDiv All Dividend NonDiv 1 (Low) Mean -0.0103 -0.0148 -0.0033 0.0060 -0.0004 0.0157 Median -0.0092 -0.0124 -0.0014 0.0024 0.0022 0.0049

2 Mean -0.0025 0.0001 -0.0058 0.0071 0.0215 -0.0111 Median 0.0004 0.0045 -0.0054 0.0169 0.0252 0.0018

3 Mean 0.0054 0.0077 0.0033 0.0033 -0.0022 0.0080 Median 0.0065 0.0066 0.0064 0.0035 0.0037 0.0030

4 Mean 0.0031 0.0057 0.0009 0.0008 -0.0063 0.0070 Median 0.0010 0.0047 -0.0036 -0.0014 -0.0064 0.0137

5 (High) Mean 0.0078 0.0146 0.0030 0.0144 0.0081 0.0188 Median 0.0092 0.0091 0.0092 0.0131 0.0109 0.0180

Mean Q5 – Q1 0.0181*** 0.0294*** 0.0063 0.0084 0.0085 0.0031 t-statistic (4.06) (5.39) (0.87) (0.96) (0.80) (0.21) Wilcoxon rank-sum test

Median Q5 – Q1 0.0184*** 0.0215*** 0.0107* 0.0107 0.0087 0.0131 z-statistic (5.32) (5.71) (1.76) (1.29) (1.16) (0.46) *** p<0.01, ** p<0.05, * p<0.1

This table shows the differences in CARs when sorted into TONE1 quintile portfolios for the full sample (All), dividend paying firms (Dividend), and firms that do not pay dividends (NonDiv). Test statistics (t and z) are in parentheses. CAR(-1,1) is the three-day cumulative abnormal return, where day 0 is the conference call date, where the abnormal returns are estimated using market adjusted returns calculated as {ARjt = Rjt – Rmkt,t} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rmkt,t is the CRSP value weighted return on day t. CAR(2,60) is calculated in the same manner as CAR(-1,1) only it is cumulated from days two through sixty. TONE1 is the first principal component of the four ratios Positive/Negative, Strong/Weak, Active/Passive, and Overstated/Understated, where each category reflects the proportion of words in a given call as defined by the Harvard IV-4 Psychosocial Dictionary.

Table B-3 Test of Differences of Means and Medians, by TONE2 Quintiles (Using Market Adjusted Returns)

TONE2 CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) Quintiles All Dividend NonDiv All Dividend NonDiv 1 (Low) Mean -0.0114 -0.0157 -0.0049 0.0002 -0.0060 0.0097 Median -0.0099 -0.0119 -0.0025 0.0024 0.0020 0.0058

2 Mean 0.0002 0.0014 -0.0011 0.0166 0.0262 0.0053 Median 0.0036 0.0094 -0.0029 0.0211 0.0250 0.0055

3 Mean 0.0011 0.0033 -0.0009 -0.0010 0.0004 -0.0024 Median 0.0029 0.0033 0.0026 0.0020 0.0037 0.0009

4 Mean 0.0066 0.0101 0.0037 0.0048 -0.0010 0.0099 Median 0.0022 0.0049 -0.0012 0.0028 0.0004 0.0129

5 (High) Mean 0.0070 0.0140 0.0020 0.0110 0.0024 0.0171 Median 0.0094 0.0093 0.0096 0.0082 0.0022 0.0172

Mean Q5 – Q1 0.0184*** 0.0297*** 0.0069 0.0108 0.0084 0.0074 t-statistic (4.15) (5.49) (0.95) (1.18) (0.78) (0.48) Wilcoxon rank-sum test

Median Q5 – Q1 0.0193*** 0.0211*** 0.0121** 0.0057 0.0001 0.0113 z-statistic (5.77) (6.03) (2.11) (1.03) (0.84) (0.30) *** p<0.01, ** p<0.05, * p<0.1

This table shows the differences in CARs when sorted into TONE2 quintile portfolios for the full sample (All), dividend paying firms (Dividend), and firms that do not pay dividends (NonDiv). Test statistics (t and z) are in parentheses. CAR(-1,1) is the three-day cumulative abnormal return, where day 0 is the conference call date, where the abnormal returns are estimated using market adjusted returns calculated as {ARjt = Rjt – Rmkt,t} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rmkt,t is the CRSP value weighted return on day t. CAR(2,60) is calculated in the same manner as CAR(-1,1) only it is cumulated from days two through sixty. TONE2 is the first principal component of the four ratios (Positive – Negative)/(Positive + Negative), (Strong – Weak)/(Strong + Weak), (Active – Passive)/(Active + Passive), and (Overstated –Understated)/ (Overstated + Understated), where each category reflects the proportion of words in a given call as defined by the Harvard IV-4 Psychosocial Dictionary.

Table B-4 Test of Differences of Means and Medians, by H-TONE1 Quintiles (Using Market Adjusted Returns)

H-TONE1 CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) Quintiles All Dividend NonDiv All Dividend NonDiv 1 (Low) Mean -0.0129 -0.0171 -0.0076 -0.0105 -0.0160 -0.0036 Median -0.0097 -0.0130 -0.0047 -0.0064 -0.0095 -0.0007

2 Mean -0.0011 -0.0017 -0.0004 0.0034 -0.0035 0.0113 Median -0.0042 -0.0052 -0.0030 0.0054 -0.0052 0.0195

3 Mean -0.0006 0.0021 -0.0031 0.0120 0.0175 0.0066 Median 0.0007 0.0037 -0.0016 0.0080 0.0163 -0.0011

4 Mean 0.0056 0.0136 -0.0020 0.0109 0.0045 0.0170 Median 0.0048 0.0098 -0.0039 0.0083 0.0014 0.0207

5 (High) Mean 0.0125 0.0143 0.0112 0.0158 0.0261 0.0081 Median 0.0144 0.0108 0.0189 0.0198 0.0249 0.0053

Mean Q5 – Q1 0.0254*** 0.0315*** 0.0188*** 0.0263*** 0.0421*** 0.0117 t-statistic (5.62) (5.48) (2.66) (2.99) (3.78) (0.85) Wilcoxon rank-sum test

Median Q5 – Q1 0.0241*** 0.0238*** 0.0236*** 0.0262*** 0.0344*** 0.0060 z-statistic (6.83) (6.32) (3.33) (3.19) (3.65) (1.02) *** p<0.01, ** p<0.05, * p<0.1

This table shows the differences in CARs when sorted into H-TONE1 quintile portfolios for the full sample (All), dividend paying firms (Dividend), and firms that do not pay dividends (NonDiv). Test statistics (t and z) are in parentheses. CAR(-1,1) is the three-day cumulative abnormal return, where day 0 is the conference call date, where the abnormal returns are estimated using market adjusted returns calculated as {ARjt = Rjt – Rmkt,t} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rmkt,t is the CRSP value weighted return on day t. CAR(2,60) is calculated in the same manner as CAR(-1,1) only it is cumulated from days two through sixty. H-TONE1 is the ratio of positive words to negative words as defined by the custom earnings dictionary of Henry (2008).

Table B-5 Test of Differences of Means and Medians, by H-TONE2 Quintiles (Using Market Adjusted Returns)

H-TONE2 CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) Quintiles All Dividend NonDiv All Dividend NonDiv 1 (Low) Mean -0.0129 -0.0171 -0.0076 -0.0105 -0.0160 -0.0036 Median -0.0097 -0.0130 -0.0047 -0.0064 -0.0095 -0.0007

2 Mean -0.0014 -0.0022 -0.0004 0.0036 -0.0030 0.0113 Median -0.0042 -0.0052 -0.0030 0.0058 -0.0050 0.0195

3 Mean -0.0003 0.0027 -0.0031 0.0117 0.0170 0.0066 Median 0.0008 0.0044 -0.0016 0.0080 0.0157 -0.0011

4 Mean 0.0056 0.0136 -0.0020 0.0109 0.0045 0.0170 Median 0.0048 0.0098 -0.0039 0.0083 0.0014 0.0207

5 (High) Mean 0.0125 0.0143 0.0112 0.0158 0.0261 0.0081 Median 0.0144 0.0108 0.0189 0.0198 0.0249 0.0053

Mean Q5 – Q1 0.0254*** 0.0315*** 0.0188*** 0.0263*** 0.0421*** 0.0117 t-statistic (5.62) (5.48) (2.66) (2.99) (3.78) (0.85) Wilcoxon rank-sum test

Median Q5 – Q1 0.0241*** 0.0238*** 0.0236*** 0.0262*** 0.0344*** 0.0060 z-statistic (6.83) (6.32) (3.33) (3.19) (3.65) (1.02) *** p<0.01, ** p<0.05, * p<0.1

This table shows the differences in CARs when sorted into H-TONE2 quintile portfolios for the full sample (All), dividend paying firms (Dividend), and firms that do not pay dividends (NonDiv). Test statistics (t and z) are in parentheses. CAR(-1,1) is the three-day cumulative abnormal return, where day 0 is the conference call date, where the abnormal returns are estimated using market adjusted returns calculated as {ARjt = Rjt – Rmkt,t} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rmkt,t is the CRSP value weighted return on day t. CAR(2,60) is calculated in the same manner as CAR(-1,1) only it is cumulated from days two through sixty. H-TONE2 is the ratio of (Positive – Negative)/(Positive + Negative) where each category reflects the proportion of words in a given call as defined by the custom earnings dictionary of Henry (2008).

Table B-6 Regression Results of Cumulative Abnormal Returns on Earnings Surprise, and TONE1 (Using Market Adjusted Returns)

This table provides results for cross-sectional regressions of CARs on SURP and measures of TONE1 for the full sample (Panel A), dividend paying firms (Panel B), and firms that do not pay dividends (Panel C). CAR(-1,1) is the three-day cumulative abnormal return, where day 0 is the conference call date, where the abnormal returns are estimated using market adjusted returns calculated as {ARjt = Rjt – Rmkt,t} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rmkt,t is the CRSP value weighted return on day t. CAR(2,60) is calculated in the same manner as CAR(-1,1) only it is cumulated from days two through sixty. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (end of qtr-4)}. TONE1 is the first principal component of the four ratios Positive/Negative, Strong/Weak, Active/Passive, and Overstated/Understated, where each category reflects the proportion of words in a given call as defined by the Harvard IV-4 Psychosocial Dictionary. The variable prefixes INTRO and Q&A indicate that the referenced tone measures are for the introductory managerial statement and the question and answer portions of the conference calls, respectively. Standard errors are adjusted for heteroscedasticity following White(1980) and for clustering by firm and quarter following Petersen (2009). t-statistics are in parentheses.

Panel A: All CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) SURP 0.1400*** 0.1350*** 0.1370*** 0.1340*** 0.1320*** -0.0291 -0.0288 -0.0269 -0.0248 -0.0226 (4.71) (4.47) (4.54) (4.31) (4.19) (-0.27) (-0.27) (-0.25) (-0.23) (-0.21)

TONE 0.0045*** -0.0003 1 (4.72) (-0.10)

INTRO TONE 0.0031*** 0.0024** -0.0021 -0.0026 1 (2.94) (2.17) (-0.85) (-0.99)

Q&A TONE 0.0038*** 0.0031*** 0.0012 0.0019 1 (3.59) (2.77) (0.37) (0.58)

Constant 0.0006 0.0006 0.0006 0.0006 0.0005 0.0063 0.0063 0.0063 0.0065 0.0065 (0.35) (0.35) (0.33) (0.33) (0.31) (0.80) (0.80) (0.80) (0.81) (0.81)

Observations 2880 2880 2878 2876 2874 2880 2880 2878 2876 2874 R-Squared 0.009 0.015 0.012 0.012 0.014 0.000 0.000 0.000 0.000 0.001 *** p<0.01, ** p<0.05, * p<0.1

Table B-6 (Continued)

Panel B: Dividend CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) SURP 0.1060** 0.0947** 0.1010** 0.1000** 0.0971** 0.0006 0.0013 0.0026 -0.0001 0.0018 (2.27) (2.14) (2.20) (2.21) (2.16) (0.00) (0.01) (0.01) (-0.00) (0.01)

TONE 0.0077*** -0.0005 1 (5.22) (-0.13)

INTRO TONE 0.0036** 0.0026* -0.0013 -0.0015 1 (2.21) (1.81) (-0.50) (-0.58)

Q&A TONE 0.0054*** 0.0048*** 0.0009 0.0013 1 (3.69) (3.78) (0.26) (0.35)

Constant 0.0015 0.0029 0.0019 0.0020 0.0022 0.0045 0.0044 0.0043 0.0045 0.0044 (0.63) (1.24) (0.76) (0.84) (0.90) (0.71) (0.72) (0.70) (0.71) (0.70)

Observations 1440 1440 1438 1439 1437 1440 1440 1438 1439 1437 R-Squared 0.005 0.028 0.011 0.016 0.019 0.000 0.000 0.000 0.000 0.000 Panel C: NonDiv CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) SURP 0.1650*** 0.1630*** 0.1630*** 0.1570*** 0.1560*** -0.0521 -0.0518 -0.0502 -0.0442 -0.0423 (3.76) (3.72) (3.73) (3.68) (3.65) (-0.36) (-0.35) (-0.34) (-0.31) (-0.29)

TONE 0.0022* -0.0005 1 (1.71) (-0.15)

INTRO TONE 0.0027** 0.0025 -0.0034 -0.0041 1 (2.09) (1.45) (-0.73) (-0.81)

Q&A TONE 0.0023 0.0015 0.0013 0.0026 1 (1.20) (0.67) (0.34) (0.62)

Constant -0.0003 -0.0007 -0.0007 -0.0007 -0.0009 0.0083 0.0084 0.0087 0.0085 0.0089 (-0.12) (-0.26) (-0.24) (-0.24) (-0.32) (0.71) (0.75) (0.76) (0.74) (0.78)

Observations 1440 1440 1440 1437 1437 1440 1440 1440 1437 1437 R-Squared 0.011 0.013 0.013 0.012 0.013 0.000 0.000 0.001 0.000 0.001 *** p<0.01, ** p<0.05, * p<0.1

Table B-7 Regression Results of Cumulative Abnormal Returns on Earnings Surprise, and TONE2 (Using Market Adjusted Returns)

This table provides results for cross-sectional regressions of CARs on SURP and measures of TONE2 for the full sample (Panel A), dividend paying firms (Panel B), and firms that do not pay dividends (Panel C). CAR(-1,1) is the three-day cumulative abnormal return, where day 0 is the conference call date, where the abnormal returns are estimated using market adjusted returns calculated as {ARjt = Rjt – Rmkt,t} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rmkt,t is the CRSP value weighted return on day t. CAR(2,60) is calculated in the same manner as CAR(-1,1) only it is cumulated from days two through sixty.

SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (end of qtr-4)}. TONE2 is the first principal component of the four ratios (Positive – Negative)/(Positive + Negative), (Strong – Weak)/(Strong + Weak), (Active – Passive)/(Active + Passive), and (Overstated –Understated)/ (Overstated + Understated), where each category reflects the proportion of words in a given call as defined by the Harvard IV-4 Psychosocial Dictionary. The variable prefixes INTRO and Q&A indicate that the referenced tone measures are for the introductory managerial statement and the question and answer portions of the conference calls, respectively. Standard errors are adjusted for heteroscedasticity following White(1980) and for clustering by firm and quarter following Petersen (2009). t-statistics are in parentheses.

Panel A: All CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) SURP 0.1400*** 0.1340*** 0.1350*** 0.1380*** 0.1350*** -0.0291 -0.0292 -0.0279 -0.0292 -0.0280 (4.71) (4.44) (4.45) (4.47) (4.34) (-0.27) (-0.27) (-0.26) (-0.27) (-0.26)

TONE 0.0045*** 0.0000 2 (4.94) (0.01)

INTRO TONE 0.0034*** 0.0022** -0.0008 -0.0010 2 (3.95) (2.54) (-0.35) (-0.41)

Q&A TONE 0.0047*** 0.0038*** 0.0001 0.0005 2 (5.05) (4.17) (0.05) (0.17)

Constant 0.0006 0.0006 0.0006 0.0006 0.0006 0.0063 0.0063 0.0063 0.0063 0.0063 (0.35) (0.35) (0.34) (0.35) (0.35) (0.80) (0.80) (0.80) (0.80) (0.80)

Observations 2880 2880 2880 2880 2880 2880 2880 2880 2880 2880 R-Squared 0.009 0.015 0.013 0.015 0.016 0.000 0.000 0.000 0.000 0.000 *** p<0.01, ** p<0.05, * p<0.1

Table B-7 (Continued)

Panel B: Dividend CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) SURP 0.1060** 0.0930** 0.0952** 0.0989** 0.0924** 0.0006 0.0008 0.0007 0.0007 0.0007 (2.27) (2.09) (2.15) (2.17) (2.07) (0.00) (0.00) (0.00) (0.00) (0.00)

TONE 0.0078*** -0.0001 2 (5.69) (-0.03)

INTRO TONE 0.0054*** 0.0039*** -0.0000 -0.0000 2 (4.6) (3.41) (-0.01) (-0.01)

Q&A TONE 0.0069*** 0.0053*** -0.0001 -0.0000 2 (4.54) (3.72) (-0.02) (-0.01)

Constant 0.0015 0.0029 0.0023 0.0022 0.0027 0.0045 0.0044 0.0045 0.0045 0.0045 (0.63) (1.25) (0.98) (0.96) (1.13) (0.71) (0.73) (0.73) (0.72) (0.73)

Observations 1440 1440 1440 1440 1440 1440 1440 1440 1440 1440 R-Squared 0.005 0.030 0.020 0.021 0.028 0.000 0.000 0.000 0.000 0.000 Panel C: NonDiv CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) SURP 0.1650*** 0.1630*** 0.1640*** 0.1650*** 0.1640*** -0.0521 -0.0519 -0.0503 -0.0521 -0.0500 (3.76) (3.71) (3.73) (3.72) (3.71) (-0.36) (-0.36) (-0.34) (-0.36) (-0.34)

TONE 0.0017 -0.0003 2 (1.24) (-0.08)

INTRO TONE 0.0014 0.0004 -0.0022 -0.0025 2 (1.14) (0.28) (-0.55) (-0.61)

Q&A TONE 0.0030** 0.0028* 0.0000 0.0009 2 (2.01) (1.67) (0.0098) (0.26)

Constant -0.0003 -0.0007 -0.0006 -0.0007 -0.0007 0.0083 0.0083 0.0087 0.0083 0.0086 (-0.12) (-0.23) (-0.20) (-0.23) (-0.25) (0.71) (0.74) (0.76) (0.73) (0.77)

Observations 1440 1440 1440 1440 1440 1440 1440 1440 1440 1440 R-Squared 0.011 0.012 0.012 0.013 0.013 0.000 0.000 0.001 0.000 0.001 *** p<0.01, ** p<0.05, * p<0.1

Table B-8 Regression Results of Cumulative Abnormal Returns on Earnings Surprise, and H-TONE1 (Using Market Adjusted Returns)

This table provides results for cross-sectional regressions of CARs on SURP and measures of H-TONE1 for the full sample (Panel A), dividend paying firms (Panel B), and firms that do not pay dividends (Panel C). CAR(-1,1) is the three-day cumulative abnormal return, where day 0 is the conference call date, where the abnormal returns are estimated using market adjusted returns calculated as {ARjt = Rjt – Rmkt,t} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rmkt,t is the CRSP value weighted return on day t. CAR(2,60) is calculated in the same manner as CAR(-1,1) only it is cumulated from days two through sixty. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (end of qtr-4)}. H-TONE1 is the ratio of positive words to negative words as defined by the custom earnings dictionary of Henry (2008). The variable prefixes INTRO and Q&A indicate that the referenced tone measures are for the introductory managerial statement and the question and answer portions of the conference calls, respectively. Standard errors are adjusted for heteroscedasticity following White(1980) and for clustering by firm and quarter following Petersen (2009). t-statistics are in parentheses.

Panel A: All CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) SURP 0.1400*** 0.1310*** 0.1390*** 0.1430*** 0.1430*** -0.0291 -0.0368 -0.0337 -0.0222 -0.0261 (4.71) (4.09) (4.67) (4.48) (4.48) (-0.27) (-0.35) (-0.31) (-0.21) (-0.25)

H-TONE 0.0031*** 0.0025 1 (6.29) (1.56)

INTRO H-TONE 0.0001 0.0001 0.0007 0.0005 1 (0.77) (0.34) (1.24) (0.89)

Q&A H-TONE 0.0009*** 0.0009*** 0.0019** 0.0018** 1 (3.43) (3.44) (2.53) (2.31)

Constant 0.0006 -0.0157*** -0.0003 -0.0041* -0.0044* 0.0063 -0.0067 0.0017 -0.0041 -0.0069 (0.35) (-5.02) (-0.17) (-1.77) (-1.81) (0.80) (-0.66) (0.23) (-0.53) (-0.95)

Observations 2880 2880 2880 2880 2880 2880 2880 2880 2880 2880 R-Squared 0.009 0.019 0.009 0.011 0.011 0.000 0.002 0.001 0.003 0.004 *** p<0.01, ** p<0.05, * p<0.1

100

Table B-8 (Continued)

Panel B: Dividend CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) SURP 0.1060** 0.0831* 0.1030** 0.1010** 0.0987** 0.0006 -0.0287 -0.0259 -0.0077 -0.0308 (2.27) (1.93) (2.18) (2.19) (2.11) (0.00) (-0.15) (-0.13) (-0.04) (-0.16)

H-TONE 0.0038*** 0.0048*** 1 (4.85) (2.63)

INTRO H-TONE 0.0003 0.0002 0.0019*** 0.0018*** 1 (0.70) (0.50) (3.75) (3.54)

Q&A H-TONE 0.0012** 0.0011** 0.0017** 0.0013* 1 (2.25) (2.19) (2.02) (1.65)

Constant 0.0015 -0.0179*** -0.0002 -0.0046 -0.0055 0.0045 -0.0198 -0.0082 -0.0044 -0.0142 (0.63) (-3.65) (-0.06) (-1.07) (-1.20) (0.71) (-1.57) (-1.10) (-0.53) (-1.54)

Observations 1440 1440 1440 1440 1440 1440 1440 1440 1440 1440 R-Squared 0.005 0.024 0.006 0.011 0.011 0.000 0.008 0.014 0.003 0.015 Panel C: NonDiv CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) SURP 0.1650*** 0.1630*** 0.1650*** 0.1720*** 0.1720*** -0.0521 -0.0522 -0.0511 -0.0321 -0.0277 (3.76) (3.69) (3.76) (3.72) (3.71) (-0.36) (-0.36) (-0.35) (-0.23) (-0.19)

H-TONE 0.0026*** 0.0001 1 (3.34) (0.07)

INTRO H-TONE 0.0000 -0.0001 -0.0008 -0.0011 1 (0.13) (-0.32) (-0.98) (-1.27)

Q&A H-TONE 0.0007* 0.0007* 0.0020** 0.0023** 1 (1.73) (1.80) (2.13) (2.40)

Constant -0.0003 -0.0145*** -0.0005 -0.0045 -0.0042 0.0083 0.0076 0.0135 -0.0034 0.0021 (-0.12) (-3.09) (-0.15) (-1.13) (-0.97) (0.71) (0.60) (1.21) (-0.29) (0.19)

Observations 1440 1440 1440 1440 1440 1440 1440 1440 1440 1440 R-Squared 0.011 0.017 0.011 0.013 0.013 0.000 0.000 0.001 0.004 0.005 *** p<0.01, ** p<0.05, * p<0.1

101

Table B-9 Regression Results of Cumulative Abnormal Returns on Earnings Surprise, and H-TONE2 (Using Market Adjusted Returns)

This table provides results for cross-sectional regressions of CARs on SURP and measures of H-TONE2 for the full sample (Panel A), dividend paying firms (Panel B), and firms that do not pay dividends (Panel C). CAR(-1,1) is the three-day cumulative abnormal return, where day 0 is the conference call date, where the abnormal returns are estimated using market adjusted returns calculated as {ARjt = Rjt – Rmkt,t} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rmkt,t is the CRSP value weighted return on day t. CAR(2,60) is calculated in the same manner as CAR(-1,1) only it is cumulated from days two through sixty. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (end of qtr-4)}. H-TONE2 is the ratio of (Positive – Negative)/(Positive + Negative) where each category reflects the proportion of words in a given call as defined by the custom earnings dictionary of Henry (2008). The variable prefixes INTRO and Q&A indicate that the referenced tone measures are for the introductory managerial statement and the question and answer portions of the conference calls, respectively. Standard errors are adjusted for heteroscedasticity following White(1980) and for clustering by firm and quarter following Petersen (2009). t-statistics are in parentheses.

Panel A: All CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) SURP 0.1400*** 0.1250*** 0.1270*** 0.1380*** 0.1290*** -0.0291 -0.0477 -0.0502 -0.0330 -0.0475 (4.71) (3.68) (3.77) (4.33) (3.79) (-0.27) (-0.47) (-0.49) (-0.31) (-0.46)

H-TONE 0.0647*** 0.0793** 2 (5.71) (2.30)

INTRO H-TONE 0.0359*** 0.0249*** 0.0577*** 0.0431** 2 (4.73) (3.15) (2.72) (2.04)

Q&A H-TONE 0.0452*** 0.0329*** 0.0649** 0.0435 2 (4.21) (2.88) (2.24) (1.49)

Constant 0.0006 -0.0405*** -0.0225*** -0.0277*** -0.0360*** 0.0063 -0.0440** -0.0308* -0.0343** -0.0487** (0.35) (-5.59) (-4.48) (-4.05) (-4.97) (0.80) (-2.01) (-1.96) (-2.00) (-2.48)

Observations 2880 2880 2880 2880 2880 2880 2880 2880 2880 2880 R-Squared 0.009 0.020 0.015 0.015 0.018 0.000 0.004 0.004 0.004 0.005 *** p<0.01, ** p<0.05, * p<0.1

102

Table B-9 (Continued)

Panel B: Dividend CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) SURP 0.1060** 0.0654* 0.0706* 0.0875** 0.0669 0.0006 -0.0503 -0.0649 -0.0188 -0.0663 (2.27) (1.67) (1.67) (1.98) (1.61) (0.00) (-0.26) (-0.35) (-0.10) (-0.35)

H-TONE 0.0882*** 0.1090*** 2 (6.22) (3.33)

INTRO H-TONE 0.0495*** 0.0371*** 0.0904*** 0.0857*** 2 (5.48) (4.10) (4.08) (3.67)

Q&A H-TONE 0.0593*** 0.0397*** 0.0604** 0.0151 2 (4.58) (2.94) (1.98) (0.47)

Constant 0.0015 -0.0537*** -0.0300*** -0.0350*** -0.0465*** 0.0045 -0.0637*** -0.0530*** -0.0327 -0.0593** (0.63) (-5.90) (-4.85) (-4.17) (-4.98) (0.71) (-2.72) (-2.92) (-1.58) (-2.56)

Observations 1440 1440 1440 1440 1440 1440 1440 1440 1440 1440 R-Squared 0.005 0.034 0.023 0.020 0.028 0.000 0.012 0.015 0.004 0.016 Panel C: NonDiv CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (-1,1) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) CAR (2,60) SURP 0.1650*** 0.1620*** 0.1620*** 0.1700*** 0.1680*** -0.0521 -0.0549 -0.0539 -0.0432 -0.0419 (3.76) (3.64) (3.67) (3.83) (3.81) (-0.36) (-0.38) (-0.37) (-0.30) (-0.28)

H-TONE 0.0443*** 0.0450 2 (2.76) (1.02)

INTRO H-TONE 0.0227** 0.0122 0.0170 -0.0074 2 (2.04) (0.89) (0.54) (-0.22)

Q&A H-TONE 0.0358** 0.0303 0.0670* 0.0704* 2 (2.26) (1.61) (1.90) (1.84)

Constant -0.0003 -0.0290*** -0.0152** -0.0231** -0.0276*** 0.0083 -0.0208 -0.0028 -0.0344* -0.0317 (-0.12) (-2.80) (-1.99) (-2.18) (-2.74) (0.71) (-0.77) (-0.14) (-1.66) (-1.37)

Observations 1440 1440 1440 1440 1440 1440 1440 1440 1440 1440 R-Squared 0.011 0.015 0.013 0.015 0.015 0.000 0.001 0.001 0.003 0.003 *** p<0.01, ** p<0.05, * p<0.1

103

Table B-10 CARs for Sorts by Earnings Surprise and TONE1 (Using Market Adjusted Returns)

This table shows mean CARS sorted sequentially by SURP and TONE1 for the full sample (Panel A), dividend paying firms (Panel B), and firms that do not pay dividends (Panel C). H-L is the difference between the highest and lowest tercile. CAR(-1,1) is the three-day cumulative abnormal return, where day 0 is the conference call date, where the abnormal returns are estimated using market adjusted returns calculated as {ARjt = Rjt – Rmkt,t} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rmkt,t is the CRSP value weighted return on day t. CAR(2,60) is calculated in the same manner as CAR(-1,1) only it is cumulated from days two through sixty. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (end of qtr-4)}. TONE1 is the first principal component of the four ratios Positive/Negative, Strong/Weak, Active/Passive, and Overstated/Understated, where each category reflects the proportion of words in a given call as defined by the Harvard IV-4 Psychosocial Dictionary. t-statistics are in parentheses.

Panel A: All CAR (-1,1) CAR (2,60)

SURP SURP

TONE1 Low (L) Medium High (H) H - L t-statistic TONE1 Low (L) Medium High (H) H - L t-statistic Low (L) -0.0173 -0.0097 0.0083 0.0256 (4.47)*** Low (L) 0.0001 0.0032 0.0172 0.0171 (1.32) Medium -0.0076 -0.0014 0.0194 0.0270 (4.59)*** Medium -0.0074 0.0015 0.0176 0.0251 (1.99)** High (H) -0.0103 0.0068 0.0180 0.0283 (4.22)*** High (H) 0.0199 -0.0018 0.0065 -0.0133 (-0.95) H – L 0.0070 0.0165 0.0096 H - L 0.0198 -0.0050 -0.0107 t-statistic (1.18) (3.45)*** (1.47) t-statistic (1.48) (-0.51) (-0.78)

Panel B: Dividend CAR (-1,1) CAR (2,60)

SURP SURP

TONE1 Low (L) Medium High (H) H - L t-statistic TONE1 Low (L) Medium High (H) H - L t-statistic Low (L) -0.0190 -0.0101 0.0084 0.0274 (3.94)*** Low (L) -0.0041 0.0094 0.0228 0.0269 (1.79)* Medium -0.0116 0.0020 0.0213 0.0329 (4.39)*** Medium -0.0021 -0.0029 0.0117 0.0138 (0.83) High (H) -0.0034 0.0112 0.0272 0.0306 (3.95)*** High (H) 0.0013 -0.0133 0.0201 0.0187 (1.16) H – L 0.0156 0.0213 0.0188 H – L 0.0054 -0.0227 -0.0027 t-statistic (2.24)** (2.96)*** (2.34)** t-statistic (0.35) (-1.94)* (-0.17)

104

Table B-10 (Continued)

Panel C: NonDiv CAR (-1,1) CAR (2,60)

SURP SURP

TONE1 Low (L) Medium High (H) H - L t-statistic TONE1 Low (L) Medium High (H) H - L t-statistic Low (L) -0.0147 -0.0090 0.0082 0.0229 (2.34)** Low (L) 0.0066 -0.0062 0.0102 0.0036 (0.16) Medium -0.0039 -0.0055 0.0180 0.0219 (2.50)** Medium -0.0123 0.0069 0.0220 0.0343 (1.87)* High (H) -0.0153 0.0028 0.0122 0.0275 (2.78)*** High (H) 0.0330 0.0089 -0.0020 -0.0350 (-1.70)* H – L -0.0006 0.0118 0.0040 H – L 0.0264 0.0151 -0.0122 t-statistic (-0.06) (1.41) (0.38) t-statistic (1.17) (0.91) (-0.56)

*** p<0.01, ** p<0.05, * p<0.1

105

Table B-11 CARs for Sorts by Earnings Surprise and TONE2 (Using Market Adjusted Returns)

This table shows mean CARS sorted sequentially by SURP and TONE2 for the full sample (Panel A), dividend paying firms (Panel B), and firms that do not pay dividends (Panel C). H-L is the difference between the highest and lowest tercile. CAR(-1,1) is the three-day cumulative abnormal return, where day 0 is the conference call date, where the abnormal returns are estimated using market adjusted returns calculated as {ARjt = Rjt – Rmkt,t} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rmkt,t is the CRSP value weighted return on day t. CAR(2,60) is calculated in the same manner as CAR(-1,1) only it is cumulated from days two through sixty. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (end of qtr-4)}. TONE2 is the first principal component of the four ratios (Positive – Negative)/(Positive + Negative), (Strong – Weak)/(Strong + Weak), (Active – Passive)/(Active + Passive), and (Overstated – Understated)/ (Overstated + Understated), where each category reflects the proportion of words in a given call as defined by the Harvard IV-4 Psychosocial Dictionary. t- statistics are in parentheses.

Panel A: All CAR (-1,1) CAR (2,60)

SURP SURP

TONE2 Low (L) Medium High (H) H - L t-statistic TONE2 Low (L) Medium High (H) H - L t-statistic Low (L) -0.0171 -0.0110 0.0106 0.0277 (4.81)*** Low (L) -0.0023 0.0048 0.0230 0.0254 (1.97)** Medium -0.0081 0.0002 0.0169 0.0250 (4.11)*** Medium -0.0028 0.0010 0.0081 0.0109 (0.85) High (H) -0.0100 0.0066 0.0182 0.0283 (4.33)*** High (H) 0.0176 -0.0028 0.0102 -0.0073 (-0.53) H – L 0.0070 0.0176 0.0076 H - L 0.0199 -0.0076 -0.0128 t-statistic (1.19) (3.28)*** (1.19) t-statistic (1.54) (-0.77) (-0.92)

Panel B: Dividend CAR (-1,1) CAR (2,60)

SURP SURP

TONE2 Low (L) Medium High (H) H - L t-statistic TONE2 Low (L) Medium High (H) H - L t-statistic Low (L) -0.0200 -0.0124 0.0101 0.0301 (4.36)*** Low (L) -0.0100 0.0098 0.0211 0.0311 (2.13)** Medium -0.0102 0.0040 0.0180 0.0282 (3.65)*** Medium 0.0062 -0.0005 0.0180 0.0117 (0.68) High (H) -0.0032 0.0115 0.0279 0.0310 (4.08)*** High (H) 0.0010 -0.0161 0.0159 0.0148 (0.92) H – L 0.0168 0.0238 0.0177 H – L 0.0111 -0.0259 -0.0052 t-statistic (2.44)** (3.21)*** (2.45)** t-statistic (0.75) (-2.23)** (-0.32)

106

Table B-11 (Continued)

Panel C: NonDiv CAR (-1,1) CAR (2,60)

SURP SURP

TONE2 Low (L) Medium High (H) H - L t-statistic TONE2 Low (L) Medium High (H) H - L t-statistic Low (L) -0.0122 -0.0088 0.0112 0.0234 (2.35)** Low (L) 0.0105 -0.0028 0.0252 0.0147 (0.63) Medium -0.0063 -0.0043 0.0160 0.0223 (2.45)** Medium -0.0107 0.0027 0.0004 0.0111 (0.61) High (H) -0.0149 0.0018 0.0121 0.0270 (2.80)*** High (H) 0.0292 0.0101 0.0066 -0.0226 (-1.09) H – L -0.0027 0.0107 0.0009 H – L 0.0187 0.0129 -0.0186 t-statistic (-0.27) (1.27) (0.09) t-statistic (0.83) (0.76) (-0.85)

*** p<0.01, ** p<0.05, * p<0.1

107

Table B-12 CARs for Sorts by Earnings Surprise and H-TONE1 (Using Market Adjusted Returns)

This table shows mean CARS sorted sequentially by SURP and H-TONE1 for the full sample (Panel A), dividend paying firms (Panel B), and firms that do not pay dividends (Panel C). H-L is the difference between the highest and lowest tercile. CAR(-1,1) is the three-day cumulative abnormal return, where day 0 is the conference call date, where the abnormal returns are estimated using market adjusted returns calculated as {ARjt = Rjt – Rmkt,t} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rmkt,t is the CRSP value weighted return on day t. CAR(2,60) is calculated in the same manner as CAR(-1,1) only it is cumulated from days two through sixty. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (end of qtr-4)}. H-TONE1 is the ratio of positive words to negative words as defined by the custom earnings dictionary of Henry (2008). t-statistics are in parentheses.

Panel A: All CAR (-1,1) CAR (2,60)

SURP SURP

H-TONE1 Low (L) Medium High (H) H - L t-statistic H-TONE1 Low (L) Medium High (H) H - L t-statistic Low (L) -0.0189 -0.0136 0.0131 0.0320 (5.07)*** Low (L) -0.0067 -0.0048 0.0049 0.0116 (0.88) Medium -0.0138 -0.0012 0.0112 0.0250 (4.42)*** Medium 0.0023 0.0091 0.0121 0.0098 (0.76) High (H) -0.0024 0.0106 0.0215 0.0240 (3.77)*** High (H) 0.0168 -0.0014 0.0243 0.0075 (0.55) H – L 0.0165 0.0241 0.0084 H - L 0.0235 0.0034 0.0194 t-statistic (2.75)*** (4.59)*** (1.27) t-statistic (1.82)* (0.34) (1.40)

Panel B: Dividend CAR (-1,1) CAR (2,60)

SURP SURP

H-TONE1 Low (L) Medium High (H) H - L t-statistic H-TONE1 Low (L) Medium High (H) H - L t-statistic Low (L) -0.0234 -0.0167 0.0144 0.0378 (4.99)*** Low (L) -0.0212 -0.0098 0.0066 0.0277 (1.71)* Medium -0.0137 0.0066 0.0159 0.0296 (3.96)*** Medium 0.0006 0.0030 0.0164 0.0158 (1.07) High (H) 0.0058 0.0125 0.0238 0.0180 (2.69)*** High (H) 0.0234 0.0033 0.0351 0.0117 (0.70) H – L 0.0292 0.0291 0.0094 H – L 0.0446 0.0130 0.0285 t-statistic (4.18)*** (4.22)*** (1.20) t-statistic (2.70)** (1.09) (1.70)*

108

Table B-12 (Continued)

Panel C: NonDiv CAR (-1,1) CAR (2,60)

SURP SURP

H-TONE1 Low (L) Medium High (H) H - L t-statistic H-TONE1 Low (L) Medium High (H) H - L t-statistic Low (L) -0.0120 -0.0093 0.0118 0.0239 (2.25)** Low (L) 0.0157 0.0021 0.0034 -0.0123 (-0.57) Medium -0.0139 -0.0104 0.0070 0.0210 (2.49)** Medium 0.0039 0.0162 0.0083 0.0044 (0.21) High (H) -0.0081 0.0086 0.0199 0.0280 (2.88)*** High (H) 0.0123 -0.0063 0.0167 0.0045 (0.23) H – L 0.0039 0.0179 0.0080 H – L -0.0035 -0.0084 0.0133 t-statistic (0.38) (2.20)** (0.79) t-statistic (-0.17) (-0.52) (0.63)

*** p<0.01, ** p<0.05, * p<0.1

109

Table B-13 CARs for Sorts by Earnings Surprise and H-TONE2 (Using Market Adjusted Returns)

This table shows mean CARS sorted sequentially by SURP and H-TONE1 for the full sample (Panel A), dividend paying firms (Panel B), and firms that do not pay dividends (Panel C). H-L is the difference between the highest and lowest tercile. CAR(-1,1) is the three-day cumulative abnormal return, where day 0 is the conference call date, where the abnormal returns are estimated using market adjusted returns calculated as {ARjt = Rjt – Rmkt,t} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rmkt,t is the CRSP value weighted return on day t. CAR(2,60) is calculated in the same manner as CAR(-1,1) only it is cumulated from days two through sixty. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (end of qtr-4)}. H-TONE2 is the ratio of (Positive – Negative)/(Positive + Negative) where each category reflects the proportion of words in a given call as defined by the custom earnings dictionary of Henry (2008). t-statistics are in parentheses.

Panel A: All CAR (-1,1) CAR (2,60)

SURP SURP

H-TONE2 Low (L) Medium High (H) H - L t-statistic H-TONE2 Low (L) Medium High (H) H - L t-statistic Low (L) -0.0189 -0.0136 0.0131 0.0320 (5.07)*** Low (L) -0.0067 -0.0048 0.0049 0.0116 (0.88) Medium -0.0138 -0.0012 0.0112 0.0250 (4.42)*** Medium 0.0023 0.0091 0.0121 0.0098 (0.76) High (H) -0.0024 0.0106 0.0215 0.0240 (3.77)*** High (H) 0.0168 -0.0014 0.0243 0.0075 (0.55) H – L 0.0165 0.0241 0.0084 H - L 0.0235 0.0034 0.0194 t-statistic (2.75)*** (4.59)*** (1.27) t-statistic (1.82)* (0.34) (1.40)

Panel B: Dividend CAR (-1,1) CAR (2,60)

SURP SURP

H-TONE2 Low (L) Medium High (H) H - L t-statistic H-TONE2 Low (L) Medium High (H) H - L t-statistic Low (L) -0.0234 -0.0167 0.0144 0.0378 (4.99)*** Low (L) -0.0212 -0.0098 0.0066 0.0277 (1.71)* Medium -0.0137 0.0066 0.0159 0.0296 (3.96)*** Medium 0.0006 0.0030 0.0164 0.0158 (1.07) High (H) 0.0058 0.0125 0.0238 0.0180 (2.69)*** High (H) 0.0234 0.0033 0.0351 0.0117 (0.70) H – L 0.0292 0.0291 0.0094 H – L 0.0446 0.0130 0.0285 t-statistic (4.18)*** (4.22)*** (1.20) t-statistic (2.70)** (1.09) (1.70)*

110

Table B-13 (Continued)

Panel C: NonDiv CAR (-1,1) CAR (2,60)

SURP SURP

H-TONE2 Low (L) Medium High (H) H - L t-statistic H-TONE2 Low (L) Medium High (H) H - L t-statistic Low (L) -0.0120 -0.0093 0.0118 0.0239 (2.25)** Low (L) 0.0157 0.0021 0.0034 -0.0123 (-0.57) Medium -0.0139 -0.0104 0.0070 0.0210 (2.49)** Medium 0.0039 0.0162 0.0083 0.0044 (0.21) High (H) -0.0081 0.0086 0.0199 0.0280 (2.88)*** High (H) 0.0123 -0.0063 0.0167 0.0045 (0.23) H – L 0.0039 0.0179 0.0080 H – L -0.0035 -0.0084 0.0133 t-statistic (0.38) (2.20)** (0.79) t-statistic (-0.17) (-0.52) (0.63)

*** p<0.01, ** p<0.05, * p<0.1

111

Cumulative Market Adjusted Returns 8.00

6.00

4.00

2.00

Percent CAR Percent 0.00

-2.00

-4.00 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60

Top SURP Tercile Bottom SURP Tercile

Fig. B-1 Cumulative Abnormal Returns (CARs) to the Top and Bottom SURP terciles for the 2004-2007 sample period. Abnormal returns are estimated using market adjusted returns calculated as {ARjt = Rjt – Rmkt,t} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rmkt,t is the CRSP value weighted return on day t. Abnormal returns are cumulated over a 121 day period starting sixty days prior to the earnings announcement through sixty days after the announcement. Day zero is the date of the earnings conference call. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (end of qtr-4)}.

112

Cumulative Market Adjusted Returns, Whole Sample

by High, Average, & Low H-TONE2 Quintiles 4

-1 Percent CAR Percent -2

-3

-4 -10 0 10 20 30 40 50 60

High H-Tone2 Quintile Average Firm Low H-Tone2 Quintile

Fig. B-2 Cumulative Abnormal Returns across All Conference Calls and by High and Low H-TONE2 Quintiles. Abnormal returns are estimated using market adjusted returns calculated as {ARjt = Rjt – Rmkt,t} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rmkt,t is the CRSP value weighted return on day t. Abnormal returns are cumulated over a 71 day period starting ten days prior to the earnings announcement through sixty days after the announcement. Day zero is the date of the earnings conference call. Cumulative abnormal returns (CAR) are shown in percent. H-TONE2 is the ratio of (Positive – Negative)/(Positive + Negative) where each category reflects the proportion of words in a given call as defined by the custom earnings dictionary of Henry (2008). High H-TONE2 Conference Calls shows the cumulative abnormal returns for the highest H-TONE2 quintile conference calls. Low H-TONE2 Conference Calls shows the cumulative abnormal returns for the lowest H- TONE2 quintile conference calls. All Conference Calls represents the cumulative abnormal returns for all conference calls in the sample.

113

Cumulative Market Adjusted Returns, Dividend Payers

by High, Average, & Low H-TONE2 Quintiles 6 5 4 3 2 1 0

Percent CAR Percent -1 -2 -3 -4 -5 -10 0 10 20 30 40 50 60

High H-Tone2 Quintile Average Firm Low H-Tone2 Quintile

Fig. B-3 Cumulative Abnormal Returns for dividend paying firms across All Conference Calls and by High and Low H- TONE2 Quintiles. Abnormal returns are estimated using market adjusted returns calculated as {ARjt = Rjt – Rmkt,t} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rmkt,t is the CRSP value weighted return on day t. Abnormal returns are cumulated over a 71 day period starting ten days prior to the earnings announcement through sixty days after the announcement. Day zero is the date of the earnings conference call. Cumulative abnormal returns (CAR) are shown in percent. H-TONE2 is the ratio of (Positive – Negative)/(Positive + Negative) where each category reflects the proportion of words in a given call as defined by the custom earnings dictionary of Henry (2008). High H-TONE2 Conference Calls shows the cumulative abnormal returns for the highest H-TONE2 quintile conference calls. Low H-TONE2 Conference Calls shows the cumulative abnormal returns for the lowest H-TONE2 quintile conference calls. All Conference Calls represents the cumulative abnormal returns for all conference calls in the sample.

114

Cumulative Market Adjusted Returns, Non-Dividend

by High, Average, & Low H-TONE2 Quintiles 4

0 Percent CAR Percent -1

-2

-3 -10 0 10 20 30 40 50 60

High H-Tone2 Quintile Average Firm Low H-Tone2 Quintile

Fig. B-4 Cumulative Abnormal Returns for firms that do not pay dividends across All Conference Calls and by High and Low H-TONE2 Quintiles. Abnormal returns are estimated using market adjusted returns calculated as {ARjt = Rjt – Rmkt,t} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rmkt,t is the CRSP value weighted return on day t. Abnormal returns are cumulated over a 71 day period starting ten days prior to the earnings announcement through sixty days after the announcement. Day zero is the date of the earnings conference call. Cumulative abnormal returns (CAR) are shown in percent. H-TONE2 is the ratio of (Positive – Negative)/(Positive + Negative) where each category reflects the proportion of words in a given call as defined by the custom earnings dictionary of Henry (2008). High H-TONE2 Conference Calls shows the cumulative abnormal returns for the highest H-TONE2 quintile conference calls. Low H-TONE2 Conference Calls shows the cumulative abnormal returns for the lowest H-TONE2 quintile conference calls. All Conference Calls represents the cumulative abnormal returns for all conference calls in the sample.

115

Cumulative Market Adjusted Returns, Whole Sample

by H-TONE2 and SURP Terciles 8

Percent CAR Percent 0

-2

-4 -10 0 10 20 30 40 50 60

Low H-Tone2 Low Surp High H-Tone2 Low Surp Low H-Tone2 High Surp High H-Tone2 High Surp

Fig. B-5 Cumulative Abnormal Returns by Earnings Surprise and H-TONE2 Terciles. Abnormal returns are estimated using market adjusted returns calculated as {ARjt = Rjt – Rmkt,t} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rmkt,t is the CRSP value weighted return on day t. Abnormal returns are cumulated over a 71 day period starting ten days prior to the earnings announcement through sixty days after the announcement. Day zero is the date of the earnings conference call. Cumulative abnormal returns (CAR) are shown in percent. H-TONE2 is the ratio of (Positive – Negative)/(Positive + Negative) where each category reflects the proportion of words in a given call as defined by the custom earnings dictionary of Henry (2008). SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (end of qtr-4)}. CARs are plotted for High H- TONE2 High SURP conference calls , High H-TONE2 Low SURP conference calls, Low H-TONE2 High SURP conference calls , and Low H-TONE2 Low SURP conference calls. High H-TONE2 High SURP contains the conference calls within the highest H-TONE2 tercile and highest SURP tercile. High H-TONE2 Low SURP contains the conference calls within the highest H-TONE2 tercile and lowest SURP tercile. Low H-TONE2 High SURP contains the conference calls within the lowest H-TONE2 tercile and highest SURP tercile. Low H-TONE2 Low SURP contains the conference calls within the lowest H-TONE2 tercile and lowest SURP tercile.

116

Cumulative Market Adjusted Returns, Dividend Payers

by H-TONE2 and SURP Terciles 8

0 Percent CAR Percent -2

-4

-6 -10 0 10 20 30 40 50 60

Low H-Tone2 Low Surp High H-Tone2 Low Surp Low H-Tone2 High Surp High H-Tone2 High Surp

Fig. B-6 Cumulative Abnormal Returns for dividend paying firms by Earnings Surprise and H-TONE2 Terciles. Abnormal returns are estimated using market adjusted returns calculated as {ARjt = Rjt – Rmkt,t} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rmkt,t is the CRSP value weighted return on day t. Abnormal returns are cumulated over a 71 day period starting ten days prior to the earnings announcement through sixty days after the announcement. Day zero is the date of the earnings conference call. Cumulative abnormal returns (CAR) are shown in percent. H-TONE2 is the ratio of (Positive – Negative)/(Positive + Negative) where each category reflects the proportion of words in a given call as defined by the custom earnings dictionary of Henry (2008). SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (end of qtr-4)}. CARs are plotted for High H-TONE2 High SURP conference calls , High H-TONE2 Low SURP conference calls, Low H-TONE2 High SURP conference calls , and Low H-TONE2 Low SURP conference calls. High H-TONE2 High SURP contains the conference calls within the highest H-TONE2 tercile and highest SURP tercile. High H-TONE2 Low SURP contains the conference calls within the highest H-TONE2 tercile and lowest SURP tercile. Low H-TONE2 High SURP contains the conference calls within the lowest H-TONE2 tercile and highest SURP tercile. Low H-TONE2 Low SURP contains the conference calls within the lowest H-TONE2 tercile and lowest SURP tercile.

117

Cumulative Market Adjusted Returns, Non-Dividend

by H-TONE2 and SURP Terciles 6 5 4 3 2 1 0 Percent CAR Percent -1 -2 -3 -4 -10 0 10 20 30 40 50 60

Low H-Tone2 Low Surp High H-Tone2 Low Surp Low H-Tone2 High Surp High H-Tone2 High Surp

Fig. B-7 Cumulative Abnormal Returns for firms that do not pay dividends by Earnings Surprise and H-TONE2 Terciles. Abnormal returns are estimated using market adjusted returns calculated as {ARjt = Rjt – Rmkt,t} where ARjt is the abnormal return for firm j, day t; Rjt is the return for firm j, day t; and Rmkt,t is the CRSP value weighted return on day t. Abnormal returns are cumulated over a 71 day period starting ten days prior to the earnings announcement through sixty days after the announcement. Day zero is the date of the earnings conference call. Cumulative abnormal returns (CAR) are shown in percent. H-TONE2 is the ratio of (Positive – Negative)/(Positive + Negative) where each category reflects the proportion of words in a given call as defined by the custom earnings dictionary of Henry (2008). SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (end of qtr-4)}. CARs are plotted for High H-TONE2 High SURP conference calls , High H-TONE2 Low SURP conference calls, Low H-TONE2 High SURP conference calls , and Low H-TONE2 Low SURP conference calls. High H-TONE2 High SURP contains the conference calls within the highest H-TONE2 tercile and highest SURP tercile. High H-TONE2 Low SURP contains the conference calls within the highest H-TONE2 tercile and lowest SURP tercile. Low H-TONE2 High SURP contains the conference calls within the lowest H-TONE2 tercile and highest SURP tercile. Low H-TONE2 Low SURP contains the conference calls within the lowest H-TONE2 tercile and lowest SURP tercile.

118

APPENDIX C

CHAPTER 3 ADDITIONAL SUPPORT

The 1990 cutoff point from Ling and Ryngaert (1997) used by Chui et al. (2003) is somewhat arbitrary given the numerous industry changes that took place in the early 1990s. We check for time period sensitivity by establishing an arbitrary breakpoint of our own at 1995. Prior to this time the industry was adjusting to significant structural changes including the introduction of internal management, the creation of the UPREIT structure, and enacting the look-through provision which relaxed the five-or-fewer rule. 55 In combination, these changes dramatically altered the REIT structure and allowed a rush of capital to pour into the sector. The National Association of Real Estate Investment Trusts (NAREIT) indicates that 95 REITs went public in 1993 and 1994 combined, compared to just 8 IPOs in 1995.56 Thus, to a certain extent, 1995 marks a point in time where the industry settled down. As shown in Table C-1, Panel A, REIT returns reflect this settling down with volatility of 2.65% in the 1982-1994 sub-period and 1.97% in the 1995-2008 sub-period. Panel B shows that REIT PEAD appears to decline from 6.2% to 4.5% over the same time, although the difference is not statistically significant. In general, this suggests that REIT valuation uncertainty did not experience a permanent upward shift as of 1990 and demonstrates how sensitive REIT study results can be to the time period selected. Providing weak support for the uncertainty explanation for PEAD, REITs appear to be more certain in the latter sub- period and experience a corresponding reduction in PEAD. Table C-1 also shows that returns volatility and PEAD increase slightly for both NonREITs and Small NonREITs. On a within-sample basis, this provides evidence in support of the uncertainty hypothesis as well. However, it raises the question as to why REIT PEAD is of a greater magnitude than that which we observe for NonREITs and Small NonREITs when the level of uncertainty, as reflected by returns volatility, is always lower.

55 Internal management was allowed by the Tax Reform Act of 1986. The UPREIT structure was introduced in 1992. The look-through provision was passed in 1993. Prior to its implementation, the five-or-fewer rule prevented more than 50% of REIT shares from being held by five or fewer owners, which severely limited institutional involvement in REITs. The look-through provision relaxed five-or-fewer rule by recognizing the individual institutional beneficiaries as separate owners. 56 There were a total of 121 REIT IPOs during the entire 1995-2008 period, an average of 8.6 per year. IPO data is available on the NAREIT website, www.reit.com. 119

Figure C-1 provides a more continuous look at returns volatility over the entire sample period for REITs, NonREITs, and Small NonREITs. Volatilities are shown in percent and are calculated for firm j in month t as the standard deviation of its daily returns in month t. All monthly observations are then averaged by year. Clearly, all three groups move together and tend to peak during market downturns or crashes. Consistent with the sub-periods analyzed above, REIT returns volatility is always substantially lower than ordinary common stocks and small stocks. We can also see that REIT returns were fairly stable over the period that started around 1995 and continued through 2006. Immediately prior to and following this time REIT returns were much more volatile. Table C-2 shows the results of the firm-level regressions following the empirical methods outlined in Fama and MacBeth (1973). The Fama-MacBeth two step procedure is widely used in the finance literature as a standard method to control for cross-sectional dependence in finance panel data sets.57 In the first step, cross-sectional regressions are run for each time period in the sample. In the second step, final coefficient estimates are obtained as the average of the first step coefficient estimates using seemingly unrelated regression (Zellner 1962). Since the second step only uses the time series to produce final coefficients, the statistical power of Fama-MacBeth regressions are often criticized. Our inferences remain unchanged when comparing Table C-2 with the results in Table 10. Coefficient estimates and t-statistics for the variables of interest are nearly identical. SURP is positive and highly significant in all regressions. The REIT indicator and interaction variables are positive and highly significant in the PEAD window shown in Table C-2, Panel A. In Panel B, the REIT indicator and interaction variables are negative and generally significant, although the REIT indicator coefficient loses significance once the size and book- to-market equity characteristic control variables are added. However, the REIT interaction term remains highly significant. Table C-3 provides portfolio-level regression results following Liang (2003) and Bernard and Thomas (1990). In these regressions CARs are regressed on the earnings surprise and the REIT indicator interacted with the earnings surprise:

, = 0 + 1 + 2 + (C-1) where CAR� w,j indicates� the CARs� for a ∗particular window � [CAR(1,60), CAR(-1,60), CAR(-

60,60)] for each firm j. However, rather than use the actual surprise, SURPj, we use decile

57 Where the residuals in a given time period (year or quarter) may be correlated across different firms. 120 numbers, SURPDECj, where the decile rankings (1 to 10) are reduced by one and divided by nine in order to range from 0 to 1. With regressions constructed in this manner, the coefficient on SURPDEC can be interpreted as the abnormal return on a zero-investment portfolio long in high surprise NonREITs and short low surprise NonREITs. We interpret 2 as measuring the incremental change in the abnormal return for the REIT portfolio. The convenient economic interpretation provides for direct comparability with the results of portfolio sorts and t-tests. However, conducting the portfolio-level analysis in this manner has the added advantage of testing for significance with standard errors that are robust to both firm and time effects. Standard errors are corrected for potential heteroscedasticity (White 1980), dependence within observations of a firm across time, and dependence within a quarter across firms, following the methodology outlined in Petersen (2009). In Panel A of Table C-3 we see that the PEAD magnitude for NonREITs is 3.4%, while REITS experience and additional 1.6% drift for total REIT PEAD of 5.0% over the entire sample period. These results are slightly larger than we found in our portfolio sorts in Table 11 and they remain significant at the 1% level. For the initial announcement window, CAR(- 1,0), the interaction coefficient is negative and highly significant, providing additional support for the finding that REIT initial reactions to earnings surprise information are more muted than NonREITs. Table C-3, Panels B – F provide sub-period results for all of the sub-period considered in this study and the results are entirely consistent with the previous portfolio sorts. In the initial sub-period, 1982-1989 (Panel B), we find only marginally significant incremental REIT PEAD of just an additional 0.7%. While in the following period, 1990-1999 (Panel C), we see a considerable marginal increase in REIT PEAD. However, the changes in incremental REIT PEAD tend to move in the same direction as the volatilities reported in Tables 1 and 5. For example, when breaking the sample at 1995 (Panels E and F) we find a slight decrease in the incremental PEAD of 0.1%. Table C-4 provides regression results using equations (5), (6), and (7) and a sample that includes all REITs and an equal number of randomly selected of NonREITs. Table C-5 shows results for the same regressions using a sample of all REITs and sized matched NonREITs. Likewise, the same regressions are run using all REITs and book-to-market equity matched NonREITs with the results tabulated in Table C-6. The results across all

121 three tables are consistent with our earlier analysis using the larger NonREIT sample. The coefficient on the REIT indicator variable is positive and significant during the PEAD window [CAR(1,60)] and negative during the initial reaction window [CAR(-1,0)], with mixed levels of significance. We then compare the magnitudes of high minus low SURP quintile portfolios for the random and matched samples with the REIT sample in Table C-7. All portfolio differences are significant for all windows examined. Consistent with previous results, REIT PEAD is significantly larger than NonREIT PEAD and, for the most part, indistinguishably larger than Small NonREIT PEAD. Additionally, the magnitude in the initial response window is significantly less for REITs than for both NonREITs and Small NonREITs. Thus even when checking alternative sample constructions, we find that REITs show a greater manifestation of the drift and a more muted initial reaction to earnings surprises.

122

Table C-1 Returns Volatility and PEAD Magnitude, Split at 1995

Panel A REITs REITs NonREITs NonREITs Small Small 82-94 95-08 82-89 90-99 82-89 90-99 Returns Volatility 2.65% 1.97% 3.42% 3.80% 4.20% 4.57%

Panel B CAR(1,60) CAR(1,60) CAR(1,60) CAR(1,60) CAR(1,60) CAR(1,60) SURP REITs REITs NonREITs NonREITs Small Small Quintiles 82-94 95-08 82-89 90-99 82-89 90-99

High Mean 0.0480 0.0204 0.0124 0.0057 0.0079 -0.0054 Median 0.0157 0.0169 0.0089 0.0003 0.0026 -0.0133

Low Mean -0.0142 -0.0248 -0.0163 -0.0247 -0.0322 -0.0482 Median -0.0183 -0.0100 -0.0173 -0.0245 -0.0380 -0.0507

Mean H-L 0.0622*** 0.0452*** 0.0288*** 0.0304*** 0.0401*** 0.0428*** t-statistic (3.34) (7.05) (14.88) (17.42) (12.09) (16.01)

Median H-L 0.0341*** 0.0269*** 0.0262*** 0.0249*** 0.0406*** 0.0373*** z-statistic (4.25) (7.65) (15.84) (17.36) (13.26) (15.85) *** p<0.01, ** p<0.05

This table shows returns volatilities and PEAD magnitude differences for REITs, NonREITs, and Small NonREITs for the two sub-periods, 1982-1994 and 1995-2008. In Panel A, volatility is in percent and is calculated for a given firm as the standard deviation of daily returns in a month. All monthly observations in the particular time period (e.g. 82-94) are averaged. Panel B shows the differences in CARs when sorted into High and Low SURP quintile portfolios. Test statistics (t and z) are in parentheses. CAR(1,60) is the sixty-day cumulative abnormal return, starting the day after the earnings announcement, where the abnormal returns are estimated using size-adjusted market adjusted returns. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (day t = -45)}. REITs indicates that the test is limited to real estate investment trusts. NonREITs are all firms listed on NYSE, AMEX, and NASDAQ that are not real estate investment trusts, financials, or utilities. SMALL is a subsample of NONREITS that includes firms that fall within the bottom market capitalization size quintile based on NYSE size breakpoints. Although not shown, none of the means in the early period, 1982-1994, are statistically significantly different from the means in the latter period, 1995-2008.

123

Table C-2 Fama MacBeth Cross Sectional Regressions

Panel A: PEAD CAR(1,60) t-statistic CAR(1,60) t-statistic CAR(1,60) t-statistic SURP 0.1660*** (8.80) 0.1460*** (7.85) 0.1340*** (7.25) REIT 0.0069*** (2.86) 0.0068*** (3.07) 0.0101*** (3.66) REIT*SURP 0.2130*** (2.70) 0.2090** (2.54) SIZE -0.0058 (-0.85) BM -0.0059** (-2.31) Constant -0.0053** (-2.18) -0.0056** (-2.47) -0.0032 (-1.07) Number of groups 109 109 109 Average R-squared 0.006 0.007 0.024

Panel B: Announcement CAR(-1,0) CAR(-1,0) CAR(-1,0) SURP 0.1250*** (12.05) 0.1330*** (11.88) 0.1350*** (15.86) REIT -0.0017* (-1.90) -0.0022** (-2.29) -0.0014 (-1.44) REIT*SURP -0.0550*** (-2.76) -0.0608*** (-2.85) SIZE -0.0016 (-0.52) BM -0.0039*** (-5.12) Constant 0.0030*** (4.78) 0.0034*** (4.49) 0.0063*** (5.36) Number of groups 109 109 109 Average R-squared 0.021 0.022 0.036

Panel C: Full Window CAR(-60,60) CAR(-60,60) CAR(-60,60) SURP 0.7730*** (19.91) 0.7740*** (15.83) 0.6660*** (12.77) REIT 0.0079** (2.54) 0.0077** (2.39) 0.0305*** (6.26) REIT*SURP -0.0202 (-0.16) -0.0712 -(0.45) SIZE -0.0016 (-1.26) BM -0.0525*** (-8.96) Constant -0.0048 (-1.63) -0.0057* (-1.95) 0.0251*** (5.62) Number of groups 109 109 109 Average R-squared 0.020 0.023 0.051 *** p<0.01, ** p<0.05

This table provides results for the Fama-MacBeth cross-sectional regressions of CARs on SURP, REIT, REIT*SURP, SIZE and BM for the drift window (Panel A), the announcement window (Panel B), and the full 121 day window (Panel C). t- statistics are in parentheses. CAR(1,60) is the sixty-day cumulative abnormal return, starting the day after the earnings announcement, where the abnormal returns are estimated using size-adjusted market adjusted returns. CAR(-1,0) is the two-day cumulative abnormal return, starting the day prior to the earnings announcement. CAR(-60,60) is calculated the same way as CAR(1,60) and CAR(-1,0), only it is cumulated from 60 days prior to the earnings announcement through 60 days after the earnings announcement. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (day t = -45)}. To eliminate extreme outliers SURP has been winsorized at the 0.25th percentile in each tail. REIT is a 1/0 indicator variable set to 1 if the firm is a REIT. REIT*SURP is the interaction of REIT and SURP. SIZE is the market capitalization of each firm, in billions, as of 45 days prior to the earnings announcement date, calculated as the stock price times the number of shares outstanding. BM is the Book-to-Market ratio with book value lagged two calendar quarters. Number of groups indicates how many cross-sectional regressions are run in the first step. Average R-squared reports the mean R-squares of all 109 quarterly cross-sectional regressions.

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Table C-3 Portfolio-Level Regressions

Panel A: 1982-2008 CAR(-60,60) t-statistic CAR(-1,0) t-statistic CAR(1,60) t-statistic SURPDEC 0.1470*** (31.29) 0.0263*** (31.41) 0.0340*** (13.29) REIT*SURPDEC -0.0042 (-0.66) -0.0070*** (-8.44) 0.0161*** (4.64) Constant -0.0810*** (-22.79) -0.0102*** (-20.51) -0.0248*** (-11.22) Observations 212,171 212,171 212,171 R-squared 0.032 0.025 0.005

Panel B: 1982-1989 SURPDEC 0.1590*** (17.60) 0.0337*** (24.80) 0.0348*** (7.92) REIT*SURPDEC -0.0198** (-2.31) -0.0091*** (-6.65) 0.0071* (1.69) Constant -0.0833*** (-15.42) -0.0157*** (-17.12) -0.0216*** (-7.10) Observations 47,012 47,012 47,012 R-squared 0.050 0.057 0.006

Panel C: 1990-1999 SURPDEC 0.1580*** (21.13) 0.0274*** (23.28) 0.0311*** (7.25) REIT*SURPDEC -0.0089 (-0.83) -0.0070*** (-4.71) 0.0209*** (3.26) Constant -0.0896*** (-17.59) -0.0097*** (-14.10) -0.0285*** (-7.05) Observations 87,104 87,104 87,104 R-squared 0.031 0.026 0.003

Panel D: 2000-2008 SURPDEC 0.1280*** (18.63) 0.0207*** (19.90) 0.0369*** (8.54) REIT*SURPDEC 0.0054 (0.60) -0.0064*** (-5.59) 0.0134*** (3.01) Constant -0.0700*** (-10.83) -0.0073*** (-14.78) -0.0227*** (-6.62) Observations 78,055 78,055 78,055 R-squared 0.026 0.013 0.006 *** p<0.01, ** p<0.05, * p<0.1

This table shows portfolio-level results where CARs are regressed on earnings surprise deciles scaled from 0 to 1 (SURPDEC) and the interaction of the REIT indicator and SURPDEC for the whole sample period (Panel A) and each of the sub-periods analyzed in this study (Panels B – F). Standard errors are adjusted for heteroscedasticity following White(1980) and for clustering by firm and quarter following Petersen (2009). t-statistics are in parentheses. CAR(1,60) is the sixty-day cumulative abnormal return, starting the day after the earnings announcement, where the abnormal returns are estimated using size-adjusted market adjusted returns. CAR(-1,0) is the two-day cumulative abnormal return, starting the day prior to the earnings announcement. CAR(-60,60) is calculated the same way as CAR(1,60) and CAR(-1,0), only it is cumulated from 60 days prior to the earnings announcement through 60 days after the earnings announcement. SURPDEC represents the SURP deciles, scaled from 0 to 1, where SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (day t = -45)}. REIT is a 1/0 indicator variable set to 1 if the firm is a REIT. REIT*SURPDEC is the interaction of REIT and SURPDEC.

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Table C-3 Portfolio-Level Regressions (continued)

Panel E: 1982-1994 SURPDEC 0.1670*** (23.17) 0.0333*** (33.90) 0.0339*** (8.63) REIT*SURPDEC -0.0088 (-0.63) -0.0045** (-2.12) 0.0184** (2.18) Constant -0.0865*** (-20.44) -0.0146*** (-23.00) -0.0216*** (-8.55) Observations 85574 85574 85574 R-squared 0.044 0.048 0.005

Panel F: 1995-2008 SURPDEC 0.1330*** (23.94) 0.0216*** (26.55) 0.0340*** (10.03) REIT*SURPDEC 0.0021 (0.27) -0.0074*** (-8.63) 0.0173*** (3.93) Constant -0.0773*** (-15.18) -0.0072*** (-18.32) -0.0270*** (-8.35) Observations 126597 126597 126597 R-squared 0.025 0.015 0.004 *** p<0.01, ** p<0.05, * p<0.1

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Table C-4 Regression Results of Cumulative Abnormal Returns on Earnings Surprise, REIT Indicator, and Controls, using all REITs and an equal number of randomly sampled NonREITs, 1982-2008

Panel A: PEAD CAR(1,60) t-statistic CAR(1,60) t-statistic CAR(1,60) t-statistic SURP 0.1050*** (3.99) 0.0630** (2.32) 0.0634** (2.34) REIT 0.0090*** (3.22) 0.0087*** (3.19) 0.0060** (2.07) REIT*SURP 0.0574 (1.30) 0.0590 (1.36) SIZE 0.0001* (1.70) BM 0.0051*** (3.62) Constant -0.0083*** (-3.23) -0.0080*** (-3.16) -0.0114*** (-4.30) Observations 16968 16968 16968 R-squared 0.006 0.006 0.001

Panel B: Announcement CAR(-1,0) CAR(-1,0) CAR(-1,0) SURP 0.0335*** (3.95) 0.0505*** (4.95) 0.0507*** (4.96) REIT -0.0009 (-1.14) -0.0008 (-1.01) -0.0008 (-1.10) REIT*SURP -0.0233 (-1.39) -0.0233 (-1.40) SIZE 0.0001* (1.79) BM 0.0004 (1.07) Constant 0.0022*** (3.87) 0.0021*** (3.76) 0.0017*** (2.89) Observations 16968 16968 16968 R-squared 0.006 0.007 0.007

Panel C: Full Window CAR(-60,60) CAR(-60,60) CAR(-60,60) SURP 0.2720*** (8.15) 0.3290*** (6.40) 0.3290*** (6.41) REIT 0.0076 (1.56) 0.0080* (1.65) 0.0088* (1.73) REIT*SURP -0.0779 (-1.22) -0.0788 (-1.23) SIZE 0.0002* (1.84) BM -0.0006 (-0.15) Constant -0.0072* (-1.77) -0.0075* (-1.72) -0.0079* (-1.72) Observations 16968 16968 16968 R-squared 0.016 0.016 0.016 *** p<0.01, ** p<0.05

This table provides results for cross-sectional regressions of CARs on SURP, REIT, REIT*SURP, SIZE and BM for the drift window (Panel A), the announcement window (Panel B), and the full 121 day window (Panel C). The sample includes all REITs and an equal number of randomly selected NonREITs, 1982-2008. Standard errors are adjusted for heteroscedasticity following White(1980) and for clustering by firm and quarter following Petersen (2009). t-statistics are in parentheses. CAR(1,60) is the sixty-day cumulative abnormal return, starting the day after the earnings announcement, where the abnormal returns are estimated using size-adjusted market adjusted returns. CAR(-1,0) is the two-day cumulative abnormal return, starting the day prior to the earnings announcement. CAR(-60,60) is calculated the same way as CAR(1,60) and CAR(-1,0), only it is cumulated from 60 days prior to the earnings announcement through 60 days after the earnings announcement. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (day t = -45)}. To eliminate extreme outliers SURP has been winsorized at the 0.25th percentile in each tail. REIT is a 1/0 indicator variable set to 1 if the firm is a REIT. REIT*SURP is the interaction of REIT and SURP. SIZE is the market capitalization of each firm, in billions, as of 45 days prior to the earnings announcement date, calculated as the stock price times the number of shares outstanding. BM is the Book-to-Market ratio with book value lagged two calendar quarters.

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Table C-5 Regression Results of Cumulative Abnormal Returns on Earnings Surprise, REIT Indicator, and Controls, using all REITs and a size matched sample of NonREITs, 1982-2008

Panel A: PEAD CAR(1,60) t-statistic CAR(1,60) t-statistic CAR(1,60) t-statistic SURP 0.1050*** (3.79) 0.0541 (1.18) 0.0575 (1.25) REIT 0.0160*** (3.98) 0.0160*** (3.99) 0.0133*** (3.36) REIT*SURP 0.0610 (1.00) 0.0604 (1.02) SIZE 0.0028*** (2.93) BM 0.0037*** (2.61) Constant -0.0152*** (-3.90) -0.0151*** (-3.90) -0.0192*** (-4.54) Observations 16917 16917 16917 R-squared 0.006 0.006 0.011

Panel B: Announcement CAR(-1,0) CAR(-1,0) CAR(-1,0) SURP 0.0356*** (3.57) 0.0781*** (4.49) 0.0782*** (4.52) REIT -0.0049*** (-5.57) -0.0049*** (-5.54) -0.0048*** (-5.07) REIT*SURP -0.0511** (-2.29) -0.0511** (-2.30) SIZE -0.0005** (-2.38) BM 0.0002 (0.57) Constant 0.0063*** (8.40) 0.0063*** (8.33) 0.0064*** (8.41) Observations 16917 16917 16917 R-squared 0.006 0.008 0.008

Panel C: Full Window CAR(-60,60) CAR(-60,60) CAR(-60,60) SURP 0.2810*** (7.19) 0.4630*** (4.17) 0.4620*** (4.14) REIT 0.0096 (1.33) 0.0097 (1.33) 0.0097 (1.33) REIT*SURP -0.2190* (-1.85) -0.2190* (-1.83) SIZE 0.0026* (1.87) BM -0.0020 (-0.58) Constant -0.0089 (-1.31) -0.0090 (-1.33) -0.0088 (-1.20) Observations 16917 16917 16917 R-squared 0.010 0.011 0.011 *** p<0.01, ** p<0.05

This table provides results for cross-sectional regressions of CARs on SURP, REIT, REIT*SURP, SIZE and BM for the drift window (Panel A), the announcement window (Panel B), and the full 121 day window (Panel C). The sample includes all REITs and NonREITs matched on size by quarter, 1982-2008. Standard errors are adjusted for heteroscedasticity following White(1980) and for clustering by firm and quarter following Petersen (2009). t-statistics are in parentheses. CAR(1,60) is the sixty-day cumulative abnormal return, starting the day after the earnings announcement, where the abnormal returns are estimated using size-adjusted market adjusted returns. CAR(-1,0) is the two-day cumulative abnormal return, starting the day prior to the earnings announcement. CAR(-60,60) is calculated the same way as CAR(1,60) and CAR(-1,0), only it is cumulated from 60 days prior to the earnings announcement through 60 days after the earnings announcement. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (day t = -45)}. To eliminate extreme outliers SURP has been winsorized at the 0.25th percentile in each tail. REIT is a 1/0 indicator variable set to 1 if the firm is a REIT. REIT*SURP is the interaction of REIT and SURP. SIZE is the market capitalization of each firm, in billions, as of 45 days prior to the earnings announcement date, calculated as the stock price times the number of shares outstanding. BM is the Book-to-Market ratio with book value lagged two calendar quarters.

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Table C-6 Regression Results of Cumulative Abnormal Returns on Earnings Surprise, REIT Indicator, and Controls, using all REITs and a book-to-market equity matched sample of NonREITs, 1982-2008

Panel A: PEAD CAR(1,60) t-statistic CAR(1,60) t-statistic CAR(1,60) t-statistic SURP 0.0918*** (3.19) -0.0164 (-0.40) -0.0081 (-0.20) REIT 0.0171*** (3.64) 0.0176*** (3.72) 0.0159*** (3.31) REIT*SURP 0.1300** (2.39) 0.1280** (2.46) SIZE 0.004** (2.43) BM 0.0053*** (3.65) Constant -0.0163*** (-3.47) -0.0167*** (-3.53) -0.0216*** (-4.37) Observations 16481 16481 16481 R-squared 0.006 0.007 0.011

Panel B: Announcement CAR(-1,0) CAR(-1,0) CAR(-1,0) SURP 0.0353*** (3.31) 0.0754*** (3.77) 0.0762*** (3.81) REIT 0.0002 (0.21) 0.0000 (0.03) -0.0002 (-0.23) REIT*SURP -0.0481** (-1.99) -0.0482** (-2.00) SIZE -0.0000 (-0.79) BM 0.0005* (1.74) Constant 0.0012 (1.48) 0.0014* (1.70) 0.0011 (1.22) Observations 16481 16481 16481 R-squared 0.005 0.006 0.007

Panel C: Full Window CAR(-60,60) CAR(-60,60) CAR(-60,60) SURP 0.2450*** (5.89) 0.2380*** (3.11) 0.2360*** (3.09) REIT 0.0426*** (5.14) 0.0427*** (5.16) 0.0441*** (5.23) REIT*SURP 0.0077 (0.09) 0.0082 (0.10) SIZE 0.0006** (2.55) BM -0.0010 (-0.24) Constant -0.0418*** (-5.37) -0.0418*** (-5.38) -0.0425*** (-5.01) Observations 16481 16481 16481 R-squared 0.016 0.016 0.017 *** p<0.01, ** p<0.05

This table provides results for cross-sectional regressions of CARs on SURP, REIT, REIT*SURP, SIZE and BM for the drift window (Panel A), the announcement window (Panel B), and the full 121 day window (Panel C). The sample includes all REITs and NonREITs matched on book to market equity by quarter, 1982-2008. Standard errors are adjusted for heteroscedasticity following White(1980) and for clustering by firm and quarter following Petersen (2009). t-statistics are in parentheses. CAR(1,60) is the sixty-day cumulative abnormal return, starting the day after the earnings announcement, where the abnormal returns are estimated using size-adjusted market adjusted returns. CAR(-1,0) is the two-day cumulative abnormal return, starting the day prior to the earnings announcement. CAR(-60,60) is calculated the same way as CAR(1,60) and CAR(-1,0), only it is cumulated from 60 days prior to the earnings announcement through 60 days after the earnings announcement. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (day t = -45)}. To eliminate extreme outliers SURP has been winsorized at the 0.25th percentile in each tail. REIT is a 1/0 indicator variable set to 1 if the firm is a REIT. REIT*SURP is the interaction of REIT and SURP. SIZE is the market capitalization of each firm, in billions, as of 45 days prior to the earnings announcement date, calculated as the stock price times the number of shares outstanding. BM is the Book-to-Market ratio with book value lagged two calendar quarters.

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Table C-7 High Minus Low SURP Quintile Magnitudes, for REITs, Random, and Matched Samples, 1982-2008

Random Size Matched BM Matched Random Size Matched BM Matched Panel A: REITs NonREITs NonREITs NonREITs Small Small Small

CAR(1,60) 0.0488*** 0.0283*** 0.0296*** 0.0209*** 0.0396*** 0.0410*** 0.0185* t statistic (7.51) (4.37) (4.03) (3.24) (3.81) (3.83) (1.93)

CAR(-1,0) 0.0104*** 0.0266*** 0.0369*** 0.0260*** 0.0356*** 0.0520*** 0.0313*** t statistic (5.47) (12.83) (15.40) (12.40) (10.81) (14.54) (10.16)

CAR(-60,60) 0.0973*** 0.1296*** 0.1609*** 0.1114*** 0.1749*** 0.2086*** 0.1298*** t statistic (10.31) (12.39) (12.51) (10.24) (9.99) (10.43) (7.67)

Panel B: Differences CAR(1,60) ** ** *** *** CAR(-1,0) *** *** *** *** *** *** CAR(-60,60) ** *** *** *** * *** p<0.01, ** p<0.05, *p<0.10

This table shows the differences in CARs when sorted into High and Low SURP quintile portfolios for REITs and several different samples of NonREITs and Small NonREITs for the entire sample period, 1982-2008. RANDOM indicates that a random sample of NonREITs has been selected. SIZE MATCHED indicates that a size matched sample was drawn from the full NonREIT sample. BM MATCHED indicates that a book-to-market equity matched sample was drawn from the full NonREIT sample. CAR(1,60) is the sixty-day cumulative abnormal return, starting the day after the earnings announcement, where the abnormal returns are estimated using size-adjusted market adjusted returns. CAR(-1,0) is the two-day cumulative abnormal return, starting the day prior to the earnings announcement. CAR(-60,60) is calculated the same way as CAR(1,60) and CAR(-1,0), only it is cumulated from 60 days prior to the earnings announcement through 60 days after the earnings announcement. SURP is the earnings surprise calculated as {[EPS(qtr) – EPS(qtr-4)]/Stock Price (day t = -45)}. REITs indicates that the test is limited to real estate investment trusts. NonREITs are all firms listed on NYSE, AMEX, and NASDAQ that are not real estate investment trusts, financials, or utilities. SMALL is a subsample of NONREITS that includes firms that fall within the bottom market capitalization size quintile based on NYSE size breakpoints. Panel A shows the magnitudes of the High minus Low SURP quintiles for all the various sub-samples. t statistics are in parentheses and denote the level of magnitude significance. Panel B shows the level of significance for differences between the REIT sample and the random or matched samples.

130

Returns Volatility 7.00%

6.00%

5.00%

4.00% Small Nonreit 3.00% Nonreit REIT 2.00%

1.00%

0.00%

Fig. C-1 Volatilities are in percent and are calculated for a given firm in month t as the standard deviation of its daily returns in month t. All monthly observations are then averaged by year. REITs are defined as stocks with CRSP share code 18. NonREITs include all stocks traded on NYSE, AMEX, and NASDAQ with CRSP share codes 10, 11, and 12. Small NonREITs are the subset of NonREITs that fall within the lowest size quintile based on NYSE size breakpoints.

131

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BIOGRAPHICAL SKETCH

Steven McKay Price was born in Robbinsdale (Minneapolis), Minnesota and later moved with his family to Connecticut, Utah, Idaho, New Jersey, and later back to Utah where the family eventually “settled” in the Murray area. In adulthood, McKay has lived in South Korea, Utah, Massachusetts, Minnesota, and Florida. Next up, Pennsylvania. While at FSU, McKay has majored in Finance with support study in both Econometrics and Real Estate. In addition to doctoral study, his academic background includes a master‟s degree from MIT in Real Estate Development and an undergraduate degree in Finance from The University of Utah. McKay also has six years of professional experience which includes managing the commercial real estate (re)development process for Target Corporation in Boston, New York, Philadelphia and Washington DC, as well as commercial real estate brokerage in Salt Lake City with NAI Utah Commercial Real Estate, Inc. Most notably, McKay‟s greatest accomplishment has been in convincing Tricia Griffiths to marry him ten years ago. They have since welcomed three wonderful children into their home who are the light of their lives.

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