Disclosure, Analyst Forecast Bias, and the Cost of Equity Capital

DISCLOSURE, ANALYST FORECAST BIAS, AND THE COST OF EQUITY CAPITAL

Stephannie Larocque

A thesis submitted in conformity with the requirements

for the degree of Doctor of Philosophy

Joseph L. Rotman School of Management

University of Toronto

DISCLOSURE, ANALYST FORECAST BIAS, AND THE COST OF EQUITY CAPITAL by Stephannie Larocque (2009)

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy, Joseph L. Rotman School of Management, University of Toronto.

ABSTRACT

This dissertation investigates the relation between firm disclosure, analyst forecast bias, and the cost of equity capital (COEC). Since analyst forecast bias is associated with both implied COEC estimates and disclosure, it is important to control for or remove it from COEC estimates when estimating the relation between disclosure and ex ante expected returns. I begin my analysis by predicting and removing systematic ex ante bias from analyst forecasts to produce de-biased analyst forecasts that better proxy for the market‟s ex ante earnings expectations. I use these de-biased analyst forecasts to produce estimates of ex ante expected returns, both at the portfolio- and the firm-level. In addition, I develop a novel estimate of ex ante expected returns by applying Vuolteenaho‟s (2002) return decomposition framework to ex post realized returns and accounting data. Finally, using several techniques to control for analyst forecast bias and self-selection bias, I find theoretically consistent evidence of a negative association between regular disclosure and ex ante expected returns. I predict and show that inferences can change when analyst forecast bias is controlled for.

ACKNOWLEDGEMENTS

Thank you to my supervisors, Jeffrey Callen and Gordon Richardson, for patiently guiding me through my doctoral studies; to Franco Wong and Hai Lu for offering helpful comments on and constructive criticism of my thesis; and to Larry Brown, the external examiner, for his detailed assessment of my dissertation. I am also indebted to Gus De Franco and Ole-

Kristian Hope for their counsel and support over the last few years.

I would also like to thank the numerous PhD students whom I had the pleasure to study with and learn from while at the University of Toronto. These include Gauri Bhat, Bill Bobey,

Xiaohua Fang, Yuyan Guan, Justin Jin, Ambrus Kecskes, Mozaffar Khan, Alastair Lawrence,

Fatma Sonmez Saryal, Florin Vasvari, Dushyant Vyas, and Yibin Zhou.

Like many academics, I developed a love for learning early in life. For this gift, I thank my wonderful teachers, including Mr. McEvoy, Ms. Brophy, Miss Leslie, Mr. Smith, Father

Nugent, and Ann Holtz.

Finally, I deeply appreciate the love, support, and encouragement of my family including, in particular, my husband, Scott. I dedicate this dissertation to our two wonderful children, Zoe and Jackson.

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TABLE OF CONTENTS

ABSTRACT………………………………………………………………………………………ii

ACKNOWLEDGEMENTS………………………………………………………………………iii

TABLE OF CONTENTS…………………………………………………………………………iv

LIST OF TABLES. ……………………………………………………….……………………..vii

LIST OF FIGURES……………………………………………………….……………………viii

LIST OF APPENDICES….……………………………………………….……………………viii

CHAPTER 1: INTRODUCTION………………………………………...………………………1

CHAPTER 2: EXTRACTING EX ANTE EARNINGS EXPECTATIONS FROM ANALYST

FORECASTS……………………………………………………………….…………………….3

2.1 INTRODUCTION………………………………………………………………………….3

2.2 PREDICTION OF ANALYST FORECAST BIAS …………………...…………………...4

2.3 EMPIRICAL METHODOLOGY………….……………………………..…………….…..8

2.3.1 Sample selection…………………………………………………….…………….....8

2.3.2 Univariate analyses………………………………………………..…………….…...9

2.3.3 Estimation of the predictable bias component in analyst forecasts……………….....9

2.3.4 Analysis and validation of de-biased analyst earnings forecasts…………………...10

2.4 SENSITIVITY ANALYSES………………………………………………………………12

2.5 SUMMARY AND CONCLUSIONS……………………………………………………...13

CHAPTER 3: EXTRACTING EX ANTE ANALYST BIAS FROM COST OF EQUITY

CAPITAL ESTIMATES……………………………………………………………..……….….14

3.1 INTRODUCTION……………………………………………………………..……….….14

3.2 REVIEW OF THE IMPLIED COST OF EQUITY CAPITAL LITERATURE…………..15

3.3 EMPIRICAL METHODOLOGY…………………………………………………………19

3.3.1 Sample selection…………………………………………………………………….21

3.3.2 Portfolio-level cost of equity capital estimates……………………………………..21

3.3.3 Firm-level cost of equity capital estimates……………………………………….....21

3.3.4 Analysis and validation of de-biased cost of equity capital estimates…...…………23

3.4 SENSITIVITY ANALYSES……………………………………………………………....27

3.5 SUMMARY AND CONCLUSIONS……………………………………………………...29

CHAPTER 4: ESTIMATING EX ANTE EXPECTED RETURNS USING RETURN

DECOMPOSITION……………………………………………………………..……….……....30

4.1 INTRODUCTION……………………………………………………………..……….….30

4.2 RETURN DECOMPOSITION …………………………………………………...………30

4.3 EMPIRICAL METHODOLOGY…………………………………………………...…….31

4.3.1 Sample selection……………………………………………………………..….…..31

4.3.2 Empirical estimation of return news and earnings news………………………...….32

4.3.3 Analysis and validation of ex ante expected return estimates…………….……..….33

4.4 SENSITIVITY ANALYSES……………………………………………………….….…..34

4.5 SUMMARY AND CONCLUSIONS………………………………………………..….…34

CHAPTER 5: DISCLOSURE, ANALYST FORECAST BIAS, AND THE COST OF EQUITY

CAPITAL……………………………………………………………..……….…………………36

5.1 INTRODUCTION……………………………………………………………..……….….36

5.2 THE EFFECT OF ANALYST FORECAST BIAS ON DISCLOSURE – COEC

STUDIES……………………………………………………………..……….…………………38

5.3 EMPIRICAL METHODOLOGY….…………………………………………..……….…40

5.3.1 Sample selection……………………………………………………………..……...40

5.3.2 Portfolio-level tests…………………………………………………...…………..…42

5.3.3 Firm-level tests ……………………………………………………………..………43

5.3.4 Return decomposition tests…………………………………………………………45

5.3.5 Asset-pricing tests ………………………………………………….…………..…..46

5.4 SENSITIVITY ANALYSES………………………………………………………………47

5.5 SUMMARY AND CONCLUSIONS……………………………………………………...49

CHAPTER 6: CONCLUSION………………………………………………………………….50

APPENDIX 1: VARIABLE DEFINITIONS……………………………………………………52

REFERENCES…………………………………………………………………………………..55

LIST OF TABLES

Table 1: Sample selection

Table 2: Descriptive statistics for analyst forecast bias sample

Table 3: Tests of the predictable bias in analyst forecasts

Table 4: Comparison of adjusted and unadjusted analyst forecasts

Table 5: Earnings response coefficient tests

Table 6: Cost of equity capital estimates following Easton and Sommers (2007)

Table 7: Cost of equity capital estimates following Gebhardt, Lee, and Swaminathan (2001)

Table 8: Cost of equity capital estimates following Claus and Thomas (2001)

Table 9: Cost of equity capital estimates following Easton (2004)

Table 10: Summary and correlation of cost of equity capital estimates

Table 11: Multivariate analysis of cost of equity capital estimates and realized returns

Table 12: Comparison of cost of equity capital estimates and risk factor proxies

Table 13: Ex post estimates of ex ante expected returns, using return decomposition and direct estimation of return news

Table 14: Ex post estimates of ex ante expected returns, using return decomposition and residual estimation of return news

Table 15: Descriptive statistics for regular guidance vs. non-guidance firms

Table 16: Portfolio-level tests of association between disclosure and cost of equity capital

Table 17: Firm-level tests of association between disclosure and cost of equity capital

Table 18: Asset-pricing tests

Table 19: Analyst forecast bias and disclosure

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LIST OF FIGURES

Figure 1a: Comparison of bias in unadjusted and adjusted analyst forecasts for year t

Figure 1b: Comparison of bias in unadjusted and adjusted analyst forecasts for year t+1

Figure 2a: Comparison of bias in unadjusted and adjusted COEC estimates using the methodology of Gebhardt, Lee, and Swaminathan (2001)

Figure 2b: Comparison of bias in unadjusted and adjusted COEC estimates using the methodology of Claus and Thomas (2001)

Figure 2c: Comparison of bias in unadjusted and adjusted COEC estimates using the methodology of Easton (2004)

Figure 3: Timeline

LIST OF APPENDICES

Appendix 1: Variable definitions

viii Chapter 1: Introduction The relation between disclosure and expected returns continues to be debated. Theoretical research generally predicts a lower cost of capital for firms that commit to higher quality disclosures (Verrecchia 1990; Diamond and Verrecchia 1991; Easley and O‟Hara 2004; Lambert, Leuz, and Verrecchia 2007). At the same time, Hughes, Liu, and Liu (2007) claim that information risk can be diversified away in large economies. The empirical evidence regarding the disclosure – cost of equity capital (COEC) association is also mixed. Using a self-constructed index of disclosure quality, Botosan (1997) finds a negative COEC – disclosure association for industrial firms with low analyst coverage. Botosan and Plumlee (2002) associate firm COEC with AIMR disclosure rankings; for certain types of disclosure they find a negative COEC – disclosure association but for other disclosures they find evidence of a positive association. Francis, Nanda, and Olsson (2008) show that a negative relation between their disclosure index and COEC disappears after controlling for earnings quality. Analyst forecast bias is associated with both implied COEC estimates and firm disclosure. Easton and Sommers (2007) show that analyst forecast optimism inflates implied COEC estimates, while Easton (2006) notes that implied expected rates of return may not equal ex ante expected returns if analyst earnings forecasts are not the market‟s earnings expectations. Botosan and Plumlee (2005) discuss the effects of unsystematic and systematic bias that is not impounded in price on implied COEC estimates: “If forecast bias is unsystematic, only the magnitude of the estimates is affected. … If analysts‟ forecasts are systematically biased, both the magnitude and cross-sectional distribution of the estimates are affected.” Given the empirical association between management disclosure and analyst forecast bias documented in the literature (Baik and Jiang 2006; Cotter, Tuna, and Wysocki 2006), it is potentially important to control for or remove analyst forecast bias from COEC estimates when associating these with disclosure. Recent COEC research controls for ex post analyst forecast bias in cross-sectional tests of the impact of cross-country legal institutions and securities regulation (Hail and Leuz 2006), Regulation FD (Chen, Dhaliwal, and Xie 2006), and auditor fees (Hope, Kang, Thomas, and Yoo 2008). Yet, Sharpe (1978) emphasizes the need to estimate the market‟s expectations on an ex ante basis when estimating ex ante expected returns. Of course, ex ante analyst forecast optimism cannot be observed. This leaves the estimation of ex ante analyst forecast bias and its impact on firm-level COEC estimates as an unanswered question in the literature. Removing ex ante analyst forecast bias is important to the extent it affects firms systematically, and to the

extent that this could impact inferences about cross-sectional variation in the COEC. Easton and Sommers (2007) discuss the potential impact of analyst forecast bias on COEC estimation: “since all observations are not equally affected by the bias (due to varying degrees of optimism), variation in the estimates of the implied expected rates of return may be partially caused by bias and not by the factors that are the focus of the research question.” Furthermore, in an efficient market, investors should see through analyst optimism in setting price, which determines expected returns. In effect, the efficient market‟s (unobservable) expectations of future earnings should be based on de-biased analyst forecasts. This dissertation examines the relation between disclosure, analyst forecast bias, and the cost of equity capital. In my empirical tests, I measure disclosure using management guidance and distinguish firms that regularly issue such disclosures from those that do so irregularly. Management guidance issuance is voluntary and thus subject to self-selection bias. I control for the decision to regularly issue guidance using a two-step design. Given the ongoing debate over the robustness of COEC estimation (Easton and Monahan 2005), I use several techniques to estimate ex ante expected returns without the influence of analyst forecast bias and associate them with disclosure. I begin in Chapter 2 by predicting and removing ex ante analyst forecast bias from analyst earnings forecasts, producing de-biased analyst forecasts1 that better proxy for the market‟s ex ante earnings expectations. Chapters 3 and 4 offer three novel estimates of ex ante expected returns. In Chapter 3, I estimate ex ante expected returns at the portfolio-level and the firm-level, respectively, using de-biased analyst forecasts. In Chapter 4, I develop a third measure of ex ante expected returns by applying the Vuolteenaho (2002) return decomposition framework to ex post realized returns and accounting data. Using the ex ante expected returns measures developed in Chapters 3 and 4, I study the association between firms‟ commitment to disclosure and COEC in Chapter 5. I further investigate whether controlling for analyst forecast bias in COEC estimates affects inferences, after controlling for self-selection bias. Chapter 6 concludes the dissertation and discusses my contribution to the literature.

1 I refer to de-biased forecasts as consensus analyst forecasts from which I have removed predicted ex ante bias. 2

Chapter 2: Extracting Ex Ante Earnings Expectations from Analyst Forecasts 2.1 Introduction In models where there is uncertainty that is resolved during the course of events, the ex ante values are those that are calculated in advance of the resolution of uncertainty – About.com.

Market participants and academic researchers generally rely on analyst forecasts as a surrogate for the market‟s earnings expectations (Schipper 1991; Kothari 2001) and as a superior surrogate than time-series models (Brown and Rozeff 1978; Fried and Givoly 1982; Brown, Griffin, Hagerman, and Zmijewski 1987). That longer-term analyst forecasts exhibit, on average, ex post optimism (Fried and Givoly 1982; Brown, Foster, and Noreen 1985; O‟Brien 1988) suggests that they form an imperfect proxy for the market‟s ex ante expectations. Bagnoli, Beneish, and Watts (1999) find analyst „whisper‟ estimates to be a better proxy for the market‟s short-term ex ante earnings expectations relative to First Call analyst forecasts. Gu and Xue (2008) compare the forecasts of independent security analysts with those of non-independent analysts to determine which group‟s earnings forecasts better reflect the market‟s ex ante earnings expectations. They find that the forecasts of independent analysts are less accurate ex post, but yield forecast errors that are more strongly associated with abnormal stock returns. Of course, ex ante earnings expectations cannot be observed. In this study, I estimate the market‟s ex ante earnings expectations for a broad cross-section of firms by removing predictable ex ante bias from analyst earnings forecasts. Removing ex ante analyst forecast bias is important to the extent it affects firms systematically. Were analyst forecasts unsystematically biased, the use of analyst forecasts to proxy for the market‟s earnings expectations in tests that correlate variables of interest with market earnings expectations might be less of a concern. However, the use of analyst forecasts to proxy for the market‟s earnings expectations might impact inferences if analyst forecast optimism is systematic, as has been shown in the extant literature.2 In an efficient market, investors should see through analyst optimism in setting price and thus base future earnings expectations on de-biased analyst forecasts. This Chapter contributes to the analyst forecast literature by offering a means to predict and remove ex ante bias from consensus analyst forecasts. My focus on ex ante bias departs from the extant literature, which generally associates ex post analyst forecast bias with variables of

2 Ex post analyst forecast bias has been shown to vary with firm size (Easton and Sommers 2007), recent returns (Ali, Klein, and Rosenfeld 1992), and analyst incentives including trading (Irvine 2004) and investment banking activities (Dugar and Nathan 1995), among other characteristics. 3

interest. Ali, Klein, and Rosenfeld (1992) (hereafter AKR) show that analyst forecast bias can be predicted based upon lagged analyst forecast bias and lagged returns. I extend AKR‟s model to include size and a control for ex post measurement error. Using out-of-sample predictions, my ex ante model of analyst forecast bias effectively removes the mean 1.4% (2.5%) upward bias in year t (t+1) analyst earnings forecasts.3 Diagnostic tests show that de-biased earnings forecasts provide an improved proxy for the market‟s ex ante expectations relative to unadjusted consensus analyst forecasts. The Chapter is organized as follows. In Section 2.2, I discuss the prediction of analyst forecast bias. In Sections 2.3 and 2.4, I present empirical results and sensitivity analyses. Section 2.5 concludes with a discussion of possible applications of ex ante earnings expectations in accounting and finance research.

2.2 Prediction of analyst forecast bias A large literature investigates whether analyst forecasts are rational, as well as the cross- sectional variation in analyst forecast bias. According to Muth (1961), expectations are rational if the economy does not waste information in setting them. Keane and Runkle (1998) extend Muth‟s rationality hypothesis to analyst forecasts with the observation that rational analyst EPS forecasts “are equal to the actual expectation of EPS conditional on the information available to analyst n at time t.” Researchers have associated analyst forecast bias with factors including previous forecast bias (DeBondt and Thaler 1990; Mendenhall 1991), prior stock returns (Abarbanell 1991; AKR), and firm characteristics such as book-to-market ratio (Doukas, Kim, and Pantzalis 2002). These authors generally claim that analysts fail to incorporate publicly- available information when they issue forecasts, which bears directly on how analyst forecasts should be employed as a proxy for market expectations (see, for example, Abarbanell 1991). On the other hand, analyst forecast optimism may be intentional (Brown 1993). Researchers have associated analyst forecast bias with the incentives facing analysts, such as brokerage trading revenues (Hayes 1998; Jackson 2005), analyst affiliation (Dugar and Nathan 1995), and access to management (Frances and Philbrick 1993). Other explanations put forward for analysts‟ optimism include analysts‟ self-selection in their coverage decisions (McNichols and O‟Brien 1997) and asymmetries in the distribution of reported earnings that exaggerate or

3 Analyst forecast bias is scaled by price. For example, for a firm with $1.00 of earnings and a $10 share price, 2% upward bias would amount to $0.20 or 20% of earnings. 4

obscure evidence of analyst bias (Abarbanell and Lehavy 2003). This research is necessarily indirect; academic researchers know little about analysts‟ motivations (Schipper 1991).

My model assumes that the consensus analyst forecast for firm j in period t, AFjt, equals

ex _ ante analysts‟ earnings expectations for period t, Et(epsjt), plus some ex ante bias, Bias : jt

AF  E (eps )  Bias ex _ ante (2.1) jt t jt jt To infer analysts‟ true earnings expectations for period t, one must remove the ex ante bias

ex _ ante ( Bias ) from the observed consensus analyst forecast (AFjt): jt

E (eps )  AF  Bias ex _ ante (2.2) t jt jt jt Equation 2.2 assumes an efficient market where investors see through analyst forecast bias. “Just as consumers know to be somewhat skeptical of the commercials they see on television, so do professional investors know how to de-bias information they receive from analysts” (Womack in Knowledge@Wharton 2002). For example, if analysts issue more optimistic forecasts for smaller firms, the market should both anticipate and correct for this optimism. I thus predict that de- biased analyst forecasts better proxy for the market‟s ex ante earnings expectations:

H1: De-biased analyst forecasts better proxy for the market’s ex ante earnings expectations than (unadjusted) analyst forecasts.

That the market is efficient when analysts are biased is consistent with the theoretical models of Stein (1989), Fischer and Verrecchia (2000), and Beyer (2005), where managers issue optimistic forecasts but the market anticipates and corrects for this optimism. To de-bias analyst forecasts I extend Ali, Klein, and Rosenfeld (1992). These authors build on Muth (1961) and Keane and Runkle (1990) to remove the portion of analyst forecast bias that can be predicted based on the prior year‟s forecast bias and on recent returns. Elgers and Lo (1994) use a similar framework to reduce analyst forecast errors using information in prior earnings changes and returns. Frankel and Lee (1998) predict analyst forecast bias using sales growth, book-to-market ratio, analyst long-term growth forecasts, and a valuation-based measure of analyst optimism. Guay, Kothari, and Shu (2005) follow AKR to remove the sluggishness (related to recent returns) but not the bias, from analyst forecasts. Hughes, Liu, and Su (2008) predict analyst forecast errors using a comprehensive set of variables including recent returns and analyst forecast revisions, growth, and accruals. Each of these authors investigates whether

analyst forecast bias can be predicted based on the information set (Χ) available at the time the forecast is issued, as in Equation 2.3: Bias =    (2.3) AKR note that rejecting the null that B = 0 indicates that analysts don‟t fully use the information available when forming their earnings forecasts.4 Some other researchers conclude that rejecting the null that B = 0 indicates that analysts strategically add bias to their forecasts. In particular, AKR relate the bias in analyst forecasts to predictive variables as follows: Bias ex _ post    Bias ex _ post  RET   (2.4) jt 0 1 jt 1 2 jt t In Equation 2.4, the dependent variable equals the ex post bias in the consensus analyst forecast for year t. Bias ex _ post represents lagged ex post bias, measured a year prior to measurement of the t 1 dependent variable. RETt is the stock return for the 12 months prior to measurement of the forecast. AKR find that ex post analyst bias is positively associated with lagged ex post analyst forecast bias and negatively associated with recent returns. Guay, Kothari, and Shu (2005) posit that this association stems from analysts‟ sluggishness in updating their forecasts. My ex ante bias prediction model adds two variables to AKR‟s prediction model: firm size, and a control for ex post measurement error:   Bias ex _ post  RET  ln(MV )  RET _ EZ   (2.5) 0 1 jt 1 2 jt 3 jt 4 jt t In Equation 2.5, the dependent variable is measured as the consensus median analyst forecast from the April I/B/E/S unadjusted summary reports less reported EPS for year t, for December fiscal year-end firms. Bias is scaled by lagged price, as in Ali, Klein, and Rosenfeld (1992): AF  eps (2.6) jt jt price jt1

ex _ post 5 Thus Bias jt is increasing in analysts‟ optimism. As in much of the analyst forecast literature, the dependent variable represents ex post analyst forecast bias. Ideally I would use ex ante bias, but this is unobservable. Unlike ex ante bias, ex post bias is affected by events that occur between the forecast date and the earnings announcement date. To control for some of the noise in the dependent variable, I include

RET_EZt, the abnormal return between the date the consensus I/B/E/S analyst forecast was

4 AKR, like many researchers, assume that analysts face a quadratic loss function. Basu and Markov (2004) argue that, if analysts face a linear loss function, there is limited evidence of analyst forecast inefficiency. Recently, Hughes, Liu, and Su (2008) find evidence of analyst forecast inefficiency when a linear loss function is assumed. 5 AKR measure bias as reported EPS less the consensus analyst forecast, scaled by price; thus, AKR‟s bias measure is decreasing in analysts‟ optimism. 6

issued and the year t earnings announcement date following Easton and Zmijewski (1989). Ceteris paribus, I expect that analyst forecasts appear less (more) biased ex post when news between the forecast issuance date and the earnings announcement date, as reflected in returns, is positive (negative). Thus, the coefficient on RET_EZt is expected to be negative. In Equation 2.6, ln(MVt) is the natural log of total market capitalization at the end of March of year t. I include size in the bias prediction model following Easton and Sommers (2007), who show that analyst optimism is decreasing in firm size. Thus, the coefficient on ln(MVt) is expected to be negative. I follow AKR by using rolling, out-of-sample estimates to adjust the consensus analyst forecast. Simple means of the coefficients from Equation 2.5 for three consecutive years6 are computed (denoted as ˆ ) and used to predict ex ante bias for firm j in year t ( Bias ex _ ante ) and in i jt year t+1 ( Bias ex _ ante ), using independent variables that are observable at the time the forecast is jt 1 issued. Importantly, I thus exclude RET_EZt, which is not observable at the time the forecast is issued:

^ ^ ^ ^ ex _ ante ex _ post (2.7) Bias   0  1* Bias  2 * RET jt  3*ln(MV jt ) jt jt 1

The predicted ex ante analyst forecast bias for firm j in year t ( Bias ex _ ante ) is deducted from the jt consensus analyst forecast to obtain the adjusted analyst forecast (AdjAFjt): ex _ ante (2.8) AdjAF jt  AF jt  Bias jt To test my first hypothesis, I investigate whether de-biased analyst forecasts better proxy for the market‟s ex ante earnings expectations using both ex post and ex ante tests. In tests of analyst forecasts as a proxy for market earnings expectations, predictive ability and association are two sides of the same coin (see Schipper 1991; Brown 1993; Ramnath, Rock, and Shane 2008). At the same time, O‟Brien (1988) finds that ex ante association tests and ex post accuracy tests may not produce consistent results. While ex post tests examine how well forecasts predict earnings, ex ante association tests may be a better test of the market efficiency assumption. In other words, association tests can help us observe whether the market actually de-biases analyst forecasts in setting ex ante earnings expectations. Following Bagnoli, Beneish, and Watts (1999), I first evaluate the bias and accuracy of the unadjusted and adjusted analyst forecasts. Adjusted bias is defined as follows:

6 For example, 2006 forecasts are adjusted using coefficients from 2003-2005 data. 7

AdjAF  eps (2.9) AdjBias  jt jt jt price jt1 Second, I conduct long-horizon earnings-response coefficient (ERC) tests. Such market association tests are used in the analyst forecast literature by researchers including Fried and Givoly (1982), O‟Brien (1988), and Gu and Xue (2008). I measure the closeness of forecasts to ex ante market expectations by the strength of the association between abnormal stock returns and unexpected earnings (i.e., actual earnings less forecasted earnings), as in Equation 2.10:

(eps jt  E(eps jt )) (2.10) URjt  0   *  jT price jt1

In the above specification, URjt is the abnormal return of the stock (i.e., the total raw return adjusted for the value-weighted return on a market portfolio from CRSP) from the day preceding the release of the I/B/E/S Summary report to one day following the announcement of year t earnings. I use each of unadjusted analyst forecasts (AFjt) and de-biased analyst forecasts (both

AdjAFjt) to proxy for expected earnings (E(epsjt)). If analyst earnings forecasts offer a noisy proxy for the market‟s ex ante earnings expectations, this measurement noise will bias the ERC towards zero and result in lower explanatory power.

2.3 Empirical methodology 2.3.1 Sample selection From the April I/B/E/S unadjusted summary reports (Payne and Thomas 2003), I extract U.S. firms with December fiscal year-ends and with median consensus analyst forecasts (AF) and reported EPS (eps) for the current fiscal year, as well as for the preceding year. Prices and returns are from CRSP. To reduce the effect of extreme observations, observations with Bias ex _ post and/or Bias ex _ post above (below) the top (bottom) 1% level are deleted, as are firm- jt jt 1 years with lagged price below $1 or above $500. These requirements yield a sample of 26,774 firm-year observations for 1991-2006, of which 22,141 firm-years have the data necessary to compute year t+1 bias. Table 1 details the sample selection procedure used in this dissertation. The research design demands a comprehensive set of variables from I/B/E/S and CRSP. The inclusion of all these variables in the ex ante prediction model means that firm observations will be excluded from out-of-sample predictions if merely one variable is unavailable for a given firm. As in Ou and Penman (1989), “the elimination of observations because of missing

descriptors is the price of demanding a comprehensive” analysis. In particular, the requirement of non-missing lagged analyst forecast bias reduces the sample size by nearly 20%.

2.3.2 Univariate analyses Table 2 Panel A provides the distribution of the sample by year. Analyst forecast bias decreases over the sample period, in particular following the October 2000 implementation of Regulation Fair Disclosure (hereafter, Reg FD) which may have lowered analyst incentives to curry favor with management in order to receive access to proprietary information. Table 2 Panel B presents descriptive statistics. For the full sample of firms, mean ex post analyst forecast bias is 0.016 for year t and 0.026 for year t+1, consistent with analyst forecast optimism increasing in the forecasting horizon (Kang, O‟Brien, and Sivaramakrishnan 1994). Because of the requirement that sample firms be covered by I/B/E/S, the sample firms are larger than the typical Compustat firm, with mean market value of $3.5 billion. Table 2 Panel C presents positive correlations between Bias ex _ post and Bias ex _ post , and negative correlations between Bias ex _ post t t1 t and each of RET , ln(MV ), and RET_EZ . Similar correlations exist for Bias ex _ post . These results t t t t1 validate the inclusion of these variables in the ex ante bias prediction model.

2.3.3 Estimation of the predictable bias component in analyst forecasts I first regress analyst forecast bias on the full set of explanatory variables in Equation 2.5 on a yearly basis from 1991 to 2005. Table 3 reports results of these regressions for year t bias (in Panel A) and year t+1 bias (Panel B)7 including annual coefficients and t-statistics, following Fama and MacBeth (1973). The models‟ explanatory power (adjusted R2) ranges from 6.6% to 22.9% with a mean of 13.3% for year t bias (Panel A), and from 3.6% to 18.8% with a mean of 12.2% for year t+1 bias (Panel B). For both the year t and t+1 bias regressions, the signs of each coefficient are in line with my predictions. I find a positive estimated coefficient on Bias ex _ post , jt 1 consistent with AKR‟s finding of positive serial correlation in analyst forecast bias. I estimate a negative coefficient on RETt, consistent with the findings of Guay, Kothari, and Shu (2005). The estimated coefficient on ln(MVt) is negative, consistent with Easton and Sommers‟ (2007) finding that analyst forecast bias is decreasing in firm size. The estimated coefficient on

RET_EZt is negative, as expected, and shows that ex post analyst bias appears smaller (larger) for

7 In Chapter 4, firm-level COEC estimation requires analyst forecasts for both year t and t+1. Thus, in this Chapter, I de-bias analyst forecasts for both year t and t+1. 9

firms that enjoy positive (negative) news between the forecast release date and the earnings announcement date. Additional analysis shows the robustness of these results. For example, in the year t bias regressions, the estimated coefficients on Bias ex _ post are positive and significant in jt 1 all 15 years, while the coefficients on each of RETt, ln(MVt), and RET_EZt are negative and significant in all 15 years. In addition, for the year t+1 bias regressions, the estimated coefficients on ln(MVt) and RET_EZt+1 are negative in all 15 years, while the coefficients on Bias ex _ post are positive in 14 out of 15 years.8 jt 1 Second, I estimate analyst forecast bias using rolling out-of-sample estimates from each of the two bias prediction models. Following AKR, I compute simple means of the coefficient estimates from Equation 2.5 for three consecutive years. These mean coefficients are used to predict analyst forecast bias in the following year. Finally, using my predicted ex ante bias estimates, I obtain an adjusted analyst forecast (AdjAF) for years t and t+1 in 1994 through 2006 after deducting the predicted bias from the consensus analyst earnings forecast. Figure 1a (1b) presents a histogram of the bias in year t (t+1) unadjusted and adjusted analyst forecasts, for the same set of firm-years. A visual inspection shows that the bias in adjusted analyst forecasts is more symmetric around zero, suggesting that de-biased analyst forecasts on average more correctly estimate earnings.

2.3.4 Analysis and validation of de-biased analyst earnings forecasts Table 4 presents the ex post bias and accuracy of unadjusted and adjusted analyst forecasts. A significant decrease in bias is found: in Table 4 Panel A, bias for unadjusted year t analyst forecasts averages 0.014 (p < 0.001) while bias for my de-biased year t analyst forecasts averages -0.002 (p = 0.380) and is not significantly different from zero. In Panel B, bias for unadjusted year t+1 forecasts averages 0.025 (p < 0.001), while bias for my de-biased year t+1 analyst forecasts averages -0.004 (0.291) and is not significantly different from zero. I further find that my de-biased analyst forecasts are significantly more accurate than unadjusted analyst forecasts, based on mean squared error.9 To test the usefulness of de-biased forecasts as a proxy for the market‟s ex ante earnings expectations, I conduct long-horizon earnings-response coefficient tests (commonly referred to

8In untabulated results, using Equation 2.4, I replicate AKR‟s tests by regressing ex post analyst forecast bias for year t on lagged ex post analyst forecast bias and recent returns on a yearly basis from 1991 to 2005. The model‟s mean explanatory power (adjusted R2) is 7.5%. The mean estimated coefficient (following Fama and MacBeth 1973) on lagged bias is 0.211, while the mean estimated coefficient on RETt is -0.009. 9 I obtain similar results using AKR‟s methodology to de-bias analyst forecasts. 10

as ERC tests). Results for year t in Table 5 Panel A show a larger and more statistically significant estimated coefficient on unexpected earnings in the specification using adjusted analyst forecasts (Column 2) relative to the specification using analyst forecasts (Column 1) to proxy for expected earnings. The estimated coefficient on year t unexpected earnings of 2.16 (p

< 0.001) using unadjusted analyst forecasts (AFt) compares with the estimated coefficient of 2.37

(p < 0.001) using de-biased analyst forecasts (AdjAFt), although the difference between the two earnings-response coefficients is not statistically significant. In addition, the explanatory power in the specification using adjusted forecasts (adjusted R2 of 0.051) exceeds that in the specification using unadjusted forecasts (adjusted R2 of 0.048). Moreover, Vuong‟s Z-statistic (Z = -5.61; p < 0.0001) rejects unadjusted analyst forecasts in favor of adjusted analyst forecasts in the year t ERC tests. Results for year t+1 are stronger. Table 5 Panel B presents shows an estimated coefficient on unexpected earnings of 1.85 (p < 0.001) using analyst forecasts (AFt+1) and of 1.97

(p < 0.001) using de-biased analyst forecasts (AdjAFt+1), with a statistically significant difference between the two earnings-response coefficients. Thus the estimated coefficient on unexpected earnings for year t+1 is 6% larger when my de-biased analyst forecasts are used than when unadjusted analyst forecasts are used to proxy for expected earnings.10 In addition, the explanatory power in the specification using adjusted forecasts (adjusted R2 of 0.071) exceeds that in the specification using unadjusted forecasts (adjusted R2 of 0.064). In the year t+1 ERC tests, Vuong‟s Z-statistic (Z = -5.22; P < 0.0001) rejects unadjusted analyst forecasts in favor of adjusted analyst forecasts. Further, in the spirit of Gu and Xue‟s (2008) analysis, I estimate the following model to obtain an ERC-based measure of the incremental contribution of (unadjusted) analyst forecasts, which contain ex ante bias, relative to de-biased analyst forecasts:

(eps jt  AdjAF jt ) (AdjAF jt  AF jt ) (2.11) URjt  0  1 *  2 *   price jt1 price jt1

If (epsjt – AdjAFjt) fully captures the surprise to the market, we would expect that λ2 = 0. In untabulated results for both year t and year t+1, I find that λ1 is positive and statistically different from zero while λ2 is not statistically different from zero. I conclude that analyst forecasts do not incrementally contribute to de-biased analyst forecasts, in estimating the ex ante earnings surprise measure.

10 In untabulated results I find that the estimated ERC using my de-biased analyst forecasts is statistically larger (at the 5% level) than the ERC obtained using AKR‟s methodology to de-bias analyst forecasts. 11

Based on the above analyses, I conclude that my de-biased analyst forecasts better proxy for the market‟s ex ante earnings expectations relative to unadjusted analyst. On an ex post basis, de-biased analyst forecasts are less biased and more accurate than unadjusted analyst forecasts. On an ex ante basis, de-biased analyst forecasts better represent the market‟s earnings expectations in association tests. Both the ex post and ex ante test results provide support for H1.

2.4 Sensitivity analyses Inferences throughout this Chapter are unchanged when the mean consensus forecast from I/B/E/S is used (in place of the median consensus forecast) to measure analyst forecast bias, when outliers (i.e., the top and bottom 1% of Bias ex _ post and Bias ex _ post ) are retained in the jt jt 1 sample, and when simple means of the coefficient estimates from Equation 2.5 are computed for five, rather than three, consecutive years and used to predict ex ante bias. Other candidates for inclusion as bias prediction variables include firms‟ book-to-market ratio, institutional ownership, trading volumes, analyst following, recommendations and long- term growth forecasts, and the standard deviation of analyst earnings forecasts, as well as industry controls. In untabulated results, I add each of these variables to the bias prediction model without appreciably improving results. I exclude these variables to avoid shrinking the sample or over-fitting the bias prediction model. Results vary somewhat when analyst forecast bias is scaled by the absolute value of the median analyst forecast, rather than by lagged price, as in Equation 2.12:

ex _ post Bias  AF jt  eps jt (2.12) jt AF jt

For year t bias, when bias is scaled by the absolute value of the median analyst forecasts, the bias prediction equations perform similarly, although the model‟s explanatory power is somewhat 2 lower (with a 3.2% adjusted R ). I further find that year t de-biased analyst forecasts (AdjAFt) are both less biased and more accurate than unadjusted analyst forecasts (AFt). However, in ERC tests, year t unadjusted analyst forecasts better proxy for the market‟s ex ante earnings expectations relative to de-biased forecasts. Results for year t+1 bias, when bias is scaled by the absolute value of the median analyst forecast, are similar to results when bias is scaled by price. The models‟ explanatory power is 2 somewhat lower (with a 4.4% adjusted R ), and the mean estimated coefficient on RETt is no longer significantly different from zero. I further find that de-biased analyst forecasts (AdjAFt+1)

outperform unadjusted analyst forecasts (AFt+1) as far as both bias and accuracy, as well as in ERC tests, where year t+1 adjusted (de-biased) analyst forecasts better proxy for the market‟s ex ante earnings expectations.

2.5 Summary and conclusions In this Chapter, I extract ex ante earnings expectations from consensus analyst earnings forecasts by predicting and removing systematic ex ante analyst bias. The method proposed effectively removes the upward bias in year t and t+1 analyst earnings forecasts for 1994-2006, using out-of-sample predictions. Validity tests show that these de-biased analyst forecasts better proxy for the market‟s ex ante earnings expectations, in support of my hypothesis. Analyst forecasts are often used to proxy for the market‟s ex ante earnings expectations in the accounting and finance literature. The de-biased analyst forecasts put forward in this Chapter, could be applied in studies that use analyst forecasts to investigate earnings response coefficients, to estimate the cost of equity capital, and to value firms, among others. In Chapter 3 of this dissertation, I estimate implied COEC estimates at both the portfolio-level and firm-level using the de-biased ex ante earnings expectations discussed in this Chapter.

Chapter 3: Extracting Ex Ante Analyst Bias from Cost of Equity Capital Estimates 3.1 Introduction Financial economists have exerted extensive efforts to quantify expected returns. Recently researchers (for example, Botosan 1997; Gebhardt, Lee, and Swaminathan 2001; and Easton 2004) have developed reverse-engineering approaches to estimate the cost of equity capital using current price and forecasted earnings. Moreover, by relying on earnings forecasts, implied COEC techniques allow for better estimation of expected returns relative to the historically-based CAPM and Fama-French (1992, 1993) three-factor models, in situations where the COEC has changed (perhaps as a result of an event) and the researcher wants to know by how much (Easton 2008). Evidence on whether implied COEC estimates successfully estimate ex ante expected returns is mixed. In a comparison of five implied COEC proxies, Botosan and Plumlee (2005) conclude that only Easton‟s (2004) PEG (hereafter, rPEG) estimate and the COEC estimates derived in Botosan and Plumlee (2002) are consistently and predictably related to risk factor proxies including beta, size, book-to-market, and leverage. Errors in estimating the COEC could stem from either the model implemented or the inputs to the model, or both. In this Chapter, I focus on inputs to COEC models and in particular, on the systematic ex ante bias in analysts‟ forecasts. That longer-horizon analyst forecasts are generally optimistic leads to upward bias in implied COEC estimates (Claus and Thomas 2001; Easton and Sommers 2007). Easton (2006) notes that implied expected rates of return may not equal the COEC if analyst earnings forecasts are not the market‟s earnings expectations. Easton and Monahan‟s (2005) results suggest that removing bias from analyst forecasts, could generate a more reliable COEC proxy. This Chapter extends Easton and Sommers (2007) to show the effects of ex ante analyst forecast bias on both portfolio-level and firm-level implied COEC estimation. Easton and Sommers estimate the impact of analyst forecast bias on portfolio-level COEC estimates, as the 2.84% difference between COEC estimates based on analyst forecasts and based on current (realized) earnings for their sample of firms. Using Easton and Sommers‟ (2007) portfolio-level estimation, I estimate 2.4% upward bias in COEC estimates obtained using unadjusted analyst forecasts relative to COEC estimates obtained using de-biased analyst forecasts. Using the firm- level estimation techniques of Gebhardt, Lee, and Swaminathan (2001), Claus and Thomas (2001), and Easton (2004), I similarly estimate respective upward biases of 1.7%, 1.1%, and

5.5%. I validate the de-biased firm-level COEC estimates using realized returns, following Guay, Kothari, and Shu (2005), as well as risk factor proxies, following Botosan and Plumlee (2005). The Chapter is organized as follows. Section 3.2 reviews the implied cost of equity capital literature. Section 3.3 provides de-biased COEC estimates at the portfolio- and the firm- level, respectively, and validates these de-biased COEC estimates. Section 3.4 provides sensitivity analyses while Section 3.5 concludes the Chapter.

3.2 Review of the implied cost of equity capital literature Easton (2006) refers to three models from which to estimate the implied cost of equity capital: the dividend capitalization model, the residual income valuation model, and the abnormal growth in earnings model. I discuss each of these models below.

Dividend capitalization model In their study of capital investment, Gordon and Shapiro (1956) estimate the rate of profit required for a firm‟s capital outlay. Their research builds upon Lutz and Lutz (1951), Dean (1951), and Soule (1953) who show that firms maximize value when they set their capital budgets to equate the marginal return on investment with the rate of return at which the firm‟s stock sells in the market. Gordon and Shapiro seek to improve upon such measures as dividend yield and earnings yield. Gordon and Shapiro begin with the valuation equation:  E (D ) (3.1) 0 t P0   t t1 (1 r) where P0 is share price at t = 0, Et(•) is the expectations operator at time t, Dt is the dividend payment in year t, and r is the rate which equates the left-hand side of Equation 3.1 with the right-hand side. Assuming constant dividend growth: D (3.2) P  0 0 r  g where g is a continuous expected growth rate, with the condition that r > g. From Equation 3.2:

D0 (3.3) r   g P0 such that r is the current dividend yield, plus the rate at which the dividend is expected to grow.

Residual income valuation model

Researchers have advanced Gordon and Shapiro to incorporate expected earnings. This is appealing, as expected earnings can easily be proxied for with analyst forecasts. One stream of COEC research builds off the residual income valuation (RIV) model:

 t (3.4) P0  BV0  (1 r) E0[X t  rBVt1] t1 where X = earnings and BV = book value. Equation 3.4 is equivalent to: T  (3.5) t t BV0  (1 r) E0[X t  rBVt1 ] (1 r) E0[X t  rBVt1 ] t1 tT 1 where the third term on the right-hand side of Equation 3.5 can be described as terminal value.

Botosan (1997) implements the RIV model using ValueLine forecasts and target prices, PT: T (3.6)  T BV0  (1 r) E0[X t  rBVt 1] (1 r) E0[PT  BVT ]  1 From the above equation, Botosan (1997) iterates to solve for r. Gebhardt, Lee, and Swaminathan (2001) assume that firm return on equity (ROE) fades to the industry median ROE by year T, and iterate to find r: E (ROE )  r E (ROE )  r (3.7) BV  0 t1 BV  0 t2 BV 0 (1 r) 0 (1 r)2 1

T 1 E0 (ROEti )  r E0 (ROEtT )  r   i BVti1  T 1 BVtT 1 i3 (1 r) r(1 r)

Claus and Thomas (2001) assume that expected abnormal earnings (E(aet) = E(epsjt) – rBVt-1) grow at the expected inflation rate beyond year five, and also iterate to find r: E (ae ) E (ae ) E (ae ) E (ae ) (3.8) BV  0 1  0 2  0 3  0 4 0 (1 r) (1 r)2 (1 r)3 (1 r)4

E (ae ) E (ae ) *(1 g)  0 5  0 5 (1 r)5 (r  g)(1 r)5 Easton, Taylor, Shroff, and Sougiannis (2002) (hereafter, ETSS) use Equation 3.5 to isolate the finite period for which they have earnings forecasts:

4  t t (3.9) BV0  (1 r) E0[X t  rBVt1] (1 r) E0[X t  rBVt1 ] t1 t5

Using clean surplus accounting (such that BVt = BVt-1 + Xt – Dt), ETSS obtain: R  E (X )  (R 1)BV (3.10) BV  0 cT 0 0 R  G

where R = (1 + r)4 is one plus the four-year expected return on equity, G = (1 + g)4 is one plus the expected rate of growth in four-year residual income, and XcT is aggregate four year cum- dividend earnings. Rearranging, ETSS obtain:

E0 (X jcT ) Pj0 (3.11)   0   1 BV j0 BV j0 where γ0 = G – 1 and γ1 = R – G. With two coefficients and two unknown variables (R and G), ETSS can estimate both r and g at the portfolio level. This allows the researcher to empirically estimate, rather than assume, a long-term growth rate.

Abnormal growth in earnings model Ohlson and Juettner-Nauroth (2005) build on Equation 3.1. They create a series of valuation attributes, y1, y2, … yT, that satisfy the following relation: 1 2 (3.12) 0  y0  R (y1  Ry 0 )  R (y2  Ry1)  ... -∞ which holds so long as R (y∞ – Ry∞-1)→0. (Note that R = 1 + r.) Adding together Equation 3.1 and Equation 3.12:  E (y  D  Ry ) (3.13) 0 t t t1 P0  y0   t t1 (1 r) eps Ohlson and Juettner-Nauroth now define y  t1 . Equation 3.13 can be rewritten as: t r 1 (3.14)   E0 (epst1  r  Dt  R  epst ) E0 (eps1) r   t r t1 (1 r) E (eps )  E (z ) (3.15) 0 1 0 t   t r t1 (1 r) where zt represents abnormal earnings for period t+1. Thus, Ohlson and Juettner-Nauroth (2005) characterize price as capitalized expected earnings for the next period, plus the present value of capitalized future abnormal earnings. Easton (2004) builds on Ohlson and Juettner-Nauroth (2005). Easton uses the no arbitrage assumption: E (P  D ) (3.16) 0 1 1 (1 r) where P1 = share price at t = 1 and r > 0 is a fixed constant. Easton adds and subtracts expected eps capitalized accounting earnings ( E ( 1 ) ) to Equation 3.16 to yield: 0 r

eps eps (P  D ) (3.17) P  E ( 1 )  E [ 1  1 1 ] 0 0 r 0 r (1 r) Similarly, eps eps (P  D ) (3.18) P  E ( 2 )  E [ 2  2 2 ] 1 0 r 0 r (1 r) Substituting Equation 3.18 into Equation 3.17, Easton obtains: eps agr (rD  (1 r)eps ) P (3.19) E [ 1  ( 1  2 2  2 ] 0 r r(1 r) r(1 r)2 (1 r)2 where E(agr1) = [eps2 + rD1 – (1+r)eps1], i.e., expected abnormal growth in earnings.

Recursively substituting for P2, P3, P4, etc. in Equation 3.19 yields: E (eps ) 1  E (agr ) (3.20) 0 1   0 t r r t1 (1 r) Equation 3.20 is equivalent to Equation 3.15 above. Easton (2004) defines a perpetual rate of change in abnormal growth in earnings (∆agr) beyond the forecast horizon, and allows Equation 3.19 to encompass earnings forecasts for two periods: E (eps ) E (agr ) (3.21) 0 1  0 1 r r(r  agr)

Now considering the special case where ∆agr = 0 and D1 = 0, an estimate of the COEC, rPEG, can be derived, using only earnings estimates and current price: (3.22) (E0 (eps2 )  E0 (eps1)) rPEG  P0

Further, when there is no abnormal growth in earnings (i.e., agrt = 0), Equation 3.23 obtains: E (eps ) (3.23) 0 1 r such that a second COEC estimate, rPE, can be derived: E (eps ) (3.24) 0 1 rPE  P0

3.3 Empirical methodology In this Chapter, I estimate the implied COEC at both the portfolio- and the firm-level.11 In my portfolio-level tests, I estimate r and g following Easton and Sommers (2007). Easton and Sommers build on Easton, Taylor, Shroff, and Sougiannis (2002) to simultaneously estimate r and g using expected earnings, lagged book value, and price (Pt):

E(eps jt ) Pjt (3.25)   0  1   jt BV j,t1 BV j,t1

In Equation 3.25, γ0 estimates g and γ1 estimates r – g, such that the COEC, r, for the entire portfolio of firms can be estimated as γ0 + γ1. Easton and Sommers then estimate r and g at the portfolio level using current earnings realizations, following O‟Hanlon and Steele (2000). Easton and Sommers conclude that the bias in their portfolio-level estimate of r, computed as the difference between the COEC based on analyst forecasts (using the ETSS procedure) and that based on current earnings (using the O‟Hanlon and Steele procedure), is 2.84%. I estimate portfolio-level implied COEC using each of three different proxies for expected earnings: (unadjusted) analyst forecasts, adjusted (de-biased) analyst forecasts, and perfect foresight forecasts (i.e., realized earnings). In Equation 3.26, I use analyst forecasts to proxy for expected earnings: AF (3.26) jt  BV j,t1 In Equation 3.27, I use de-biased analyst earnings forecasts to proxy for expected earnings: AdjAF (3.27) jt  BV j,t1 In Equation 3.28, I use perfect foresight forecasts (i.e., realized earnings) to proxy for expected earnings: eps (3.28) jt  BV j,t1 A comparison of the COEC based on analyst forecasts, and that based on adjusted analyst forecasts, will yield an estimate of the ex ante bias in my portfolio-level COEC estimates. To consider the impact of ex ante forecast bias on firm-level implied COEC estimates, I use estimates commonly used in the extant literature, specifically, those of Gebhardt, Lee, and

11 Easton et al. (2002), Easton and Sommers (2007) and Easton (2008) emphasize portfolio-level COEC estimation, which allows for simultaneous estimation of r and g. Yet, numerous researchers consider firm-level COEC estimates. 19

Swaminathan (2001) („GLS‟), Claus and Thomas (2001) („CT‟), and Easton (2004) („PEG‟). I outline each of these below. Gebhardt et al. (2001) estimate the COEC using Equation 3.7, as discussed in section 3.2. To implement Equation 3.7, I first use analyst forecasts to proxy for expected earnings, as in Equation 3.29:

AFt  r AFt1  r T 1 AFt1i  r AFtT  r (3.29) P  BVt1 BVt BVti BVtT 1 0 BVt1  BVt1  2 BVt   i2 BVti  T 1 BVtT 1 (1 r) (1 r) i1 (1 r) r(1 r) I next use de-biased analyst forecasts to proxy for expected earnings, as in Equation 3.30:

AdjAF AdjAF AdjAF t  r t1  r T 1 t1i  r (3.30) BV  BVt1 BV  BVt BV  BVti BV t1 (1 r) t1 (1 r)2 t  (1 r)i2 ti i1 AdjAFtT  r  BVtT 1 BV r(1 r)T 1 tT 1 Claus and Thomas (2001) estimate the COEC using Equation 3.8, as discussed in section 3.2. To implement Equation 3.8, I first use analyst forecasts to proxy for expected earnings: AF  rBV AF  rBV AF  rBV AF  rBV (3.31) BV  t t1  t1 t  t2 t1  t3 t2 t1 (1 r) (1 r)2 (1 r)3 (1 r)4

AF  rBV (AF  rBV )*(1 g)  t4 t3  t4 t3 (1 r)5 (r  g)(1 r)5 I next use de-biased forecasts to proxy for expected earnings, as in Equation 3.32: AdjAF  rBV AdjAF  rBV AdjAF  rBV (3.32) BV  t t1  t1 t  t2 t1 t1 (1 r) (1 r)2 (1 r)3

AdjAF  rBV AdjAF  rBV (AdjAF  rBV )*(1 g)  t3 t2  t4 t3  t4 t3 (1 r)4 (1 r)5 (r  g)(1 r)5

Easton‟s (2004) rPEG equation is set out in Equation 3.22, as discussed in section 3.2. To implement Equation 3.22, I first use analyst forecasts to proxy for expected earnings, as in Equation 3.33:

(AF  AF ) (3.33) est t1 t rPEG  P0 I next use de-biased analyst forecasts to proxy for expected earnings, as in Equation 3.34:

(AdjAF  AdjAF ) (3.34) adj t1 t rPEG  P0 For each of the above firm-level COEC estimates, a comparison of the COEC based on analyst forecasts and that based on de-biased analyst forecasts, will yield an estimate of the ex ante bias in those estimates.

3.3.1 Sample selection I use a matched set of unadjusted and adjusted (de-biased) analyst forecasts for the 20,422 firm-years from 1994-2006 for which I can estimate de-biased analyst EPS forecasts for year t (AdjAFt), and for which lagged, non-negative book value (BVt-1) is available from Compustat and price is available from CRSP.12 I limit the sample to firms with December fiscal year-ends, following Easton and Sommers (2007).

3.3.2 Portfolio-level cost of equity capital estimates From the sample of firms described in Section 3.3.1, I delete those firm-years with dependent or independent variables in the top and bottom 2% of observations following Easton and Sommers (2007), to obtain a sample of 17,313 firm-years from 1994-2006. (In sensitivity analysis in Section 3.4, I discuss how results are unchanged when I delete only the top and bottom 1%.) Using analyst forecasts to proxy for expected earnings, I obtain a mean portfolio- level COEC estimate of 11.1% (Table 6 Panel A). Using de-biased forecasts, I obtain a mean portfolio-level COEC estimate of 8.7% (Table 6 Panel B).13 Using perfect foresight forecasts, I obtain a mean portfolio-level COEC estimate of 9.4% (Table 6 Panel C). Therefore, both the de- biased COEC estimate (8.7%) and the perfect foresight COEC estimate (9.4%) are below the unadjusted COEC estimate (11.1%). From this analysis, I conclude that there is ex ante bias of 2.4% in the COEC estimate for my sample of firms (i.e., 11.1% - 8.7%). This compares with Easton and Sommers‟ (2007) estimate of 2.8% ex ante bias for an earlier 1992-2003 sample, which provides some validation of my estimate of the ex ante bias in COEC estimates.

3.3.3 Firm-level cost of equity capital estimates To estimate GLS COEC estimates, I require the following additional variables relative to the sample described in Section 3.3.1: analyst forecasts and de-biased analyst forecasts for year t+1, the dividend payout ratio from year t-1 (k), and industry median ROE for the previous five years. This results in a sub-sample of 18,877 firm-years. AFt+2 is estimated by applying the year t+1 growth rate to year t+1 forecasted earnings (i.e., AFt+2 = AFt+1 * AFt+1/AFt). Future book value (BVt+τ) is estimated using the clean surplus relation (BVt+τ = BVt+τ-1 + AFt+τ - k * AFt+τ).

12 I use the earliest price available from CRSP during the five trading days following the release of the April I/B/E/S Summary report. 13 Relative to a 5% average risk-free rate (based on 10-year Treasury bonds) during the sample period, an 8.7% COEC estimate suggests a 3.7% risk premium, which is in the range of estimates put forward by Pastor and Stambaugh (2001), Claus and Thomas (2001), Gebhardt et al. (2001), and Fama and French (2002). 21

Beyond year three, ROE is linearly interpolated to the industry median following GLS.14 Table 7

est shows a mean (median) COEC estimate ( rGLS ) of 9.17% (8.78%) using analyst forecasts to proxy

adj for expected earnings, as in Equation 3.29, and a mean (median) COEC estimate ( rGLS ) of 7.50% (7.07%) using de-biased analyst forecasts to proxy for expected earnings, as in Equation 3.30. The estimates are similar to those obtained by Guay, Kothari, and Shu (2005). The difference between and is positive (reflecting analyst optimism) and statistically significant at the 1% level in all 13 years, with a mean of 0.0167 for 1994-2006. I conclude that ex ante analyst forecast bias has an average 1.7% impact on GLS COEC estimates. To estimate CT COEC estimates, I require the following additional variables: non- negative analyst forecasts and de-biased analyst forecasts for year t+1, and the dividend payout ratio from year t-1. This results in a sub-sample of 16,872 firm-years. AFt+2 is again estimated as

AFt+1 * AFt+1/AFt. Future book value of equity (BVt+τ) is estimated using the clean surplus relation. Beyond year three, abnormal earnings are forecast to grow at a rate equal to the risk-free rate in year t minus 3%, following Claus and Thomas.15 Table 8 shows a mean (median) COEC

est estimate ( rCT ) of 9.24% (8.50%) using analyst forecasts to proxy for expected earnings, as in

adj Equation 3.31, and a mean (median) COEC estimate ( rCT ) of 8.13% (6.05%) using de-biased forecasts to proxy for expected earnings, as in Equation 3.32. The estimates are similar to

est adj those obtained by Guay, Kothari, and Shu (2005). The difference between rCT and rCT is positive (reflecting analyst optimism) and statistically significant at the 1% level in 10 of 13 years, and significantly negative in one year (1994), with a mean of 0.0111 for 1994-2006. I conclude that ex ante analyst forecast bias has an average 1.1% impact on CT COEC estimates. To estimate PEG COEC estimates following Easton (2004), I require non-negative and increasing analyst forecasts and de-biased analyst forecasts for year t+1. These restrictions, in particular the requirement of increasing de-biased analyst forecasts for year t+1, generate a smaller sub-sample of 7,383 firm-years and likely bias the sample toward higher growth firms.16

est Table 9 shows a mean (median) COEC estimate ( rPEG ) of 15.22% (12.71%) for 1994-2005 using

14 A 9% estimate is used in the first iteration. The algorithm converges when the price obtained from the algorithm deviates from the actual stock price by no more than $0.005. The GLS estimates usually converged in 4 – 6 iterations. I thank Maria Ogneva for programming assistance. 15 A 9% estimate is used in the first iteration. The algorithm converges when the stock price obtained deviates from the actual stock price by no more than $0.005. The CT estimates usually converged in 5 – 10 iterations. 16 The requirement of increasing de-biased analyst forecasts for year t+1 may limit the applicability of de-biased PEG COEC estimation. In sensitivity analysis in section 3.4, I discuss how estimating rPEG using analyst long-term growth forecasts can lessen the problem of non-increasing year t+1 de-biased analyst forecasts. 22

analyst forecasts to proxy for expected earnings, as in Equation 3.33. I further obtain a mean

adj (median) COEC estimate ( rPEG ) of 9.69% (7.48%) using de-biased analyst forecasts to proxy for

est expected earnings, as in Equation 3.34. The rPEG estimates are higher than those obtained by

adj Guay et al. (2005). The difference between and rPEG is positive (reflecting analyst optimism) and statistically significant at the 1% level in all 13 years, with a mean of 0.0553 for 1994-2006. I conclude that ex ante analyst bias has a 5.5% upward impact on PEG COEC estimates. Figures 2a, 2b, and 2c illustrate the distribution of de-biased and unadjusted implied COEC estimates. Table 10 Panel A summarizes the COEC estimates obtained in Tables 6 through 9 and Table 10 Panel B reports correlations between the firm-level COEC estimates. As in Guay et al. (2005) and Hope et al. (2008), the COEC measures are quite highly correlated, with the cross-correlations ranging from 0.30 to 0.54 (0.06 to 0.21) among the unadjusted (adjusted) COEC estimates. This is not surprising: the models rely on many of the same inputs, albeit with different growth and terminal value assumptions.

3.3.4 Analysis and validation of implied cost of equity capital estimates To investigate whether de-biased implied COEC estimates form a good proxy for ex ante expected returns, I use realized returns as a benchmark following Guay, Kothari, and Shu (2005) as well as risk factor proxies following Botosan and Plumlee (2005). First, in untabulated results, I use univariate analysis to compare COEC estimates with returns realized in the year following estimation of the cost of equity capital. I find for each of the firm-level COEC estimates (formed following GLS, CT, and PEG), that the de-biased COEC estimate (formed using adjusted analyst forecasts) is less biased, but less accurate, than its unadjusted COEC estimate counterpart (formed using unadjusted analyst forecasts).17 Second, in multivariate analysis, I test for an association between expected returns and realized returns, following Guay, Kothari, and Shu (2005), as well as with return news and earnings news, following Vuolteenaho (2002) and Easton and Monahan (2005). Realized returns often proxy for expected returns in the empirical asset-pricing literature. Using realized returns as a proxy for expected returns assumes rational expectations, i.e. that ex ante unknown information will cancel out in aggregate. Vuolteenaho (2002) shows that realized returns equal expected returns plus expected earnings news (NROE) less return news (NRET):

17 The (relative) lower accuracy of de-biased COEC estimates stems in part from the positive skewness in realized returns, which results from several large outliers. 23

RET t1  Et (RETt1 )  N ROE,t1  N RET ,t1 (3.35) Vuolteenaho (2002) defines earnings news, which encompasses both earnings surprises and revisions to future expected earnings over the firm‟s lifetime, as:

 j (3.36) NROE  Et   roet j j0 where ρ is a discount factor, roe represents the natural logarithm of one plus return on equity (i.e. net income before extraordinary items over last year‟s common equity), and ∆Et(·) = Et(·) – Et-

1(·) denotes the revision or shock. Similarly, Vuolteenaho (2002) defines return (or discount rate) news, which encompasses shocks to expected future discount rates over the firm‟s lifetime, as:  (3.37) j NRET  Et   rett j j1 where ret represents the natural logarithm of one plus returns. (Earnings news and return news are empirically estimated in Section 4.2.) Equation 3.35 underlines the need to control for expected earnings news (i.e., revisions or shocks to expected earnings over the firm‟s lifetime) as well as return news (i.e., revisions or shocks to expected returns) when associating realized returns with expected returns. Table 11 presents firm-level regressions of year t+1 realized returns on cost of equity capital estimates formed using either unadjusted or de-biased analyst forecasts, as well as return news and earnings news, for the sub-sample of firm-years for which the two news variables can be estimated. GLS, CT, and PEG COEC estimates are considered in Panels A, B, and C respectively. My analysis focuses on the improvement in the association between de-biased COEC estimates and realized returns, relative to that between unadjusted COEC estimates and realized returns. Columns 1, 2, and 3 present results of variations of the following test, which excludes the news variables:

RETt1   0  1Et (RETt1 )   t1 (3.38) If expected returns predict realized returns, we would expect to see a coefficient of 1 on

Et(RETt+1). Columns 4, 5, and 6 present results of variations of the following test:

0  1Et (RETt1)  2NRET  3NROE t1 (3.39)

Following Vuolteenaho (2002), I expect respective coefficients of -1 and 1 on NRET and NROE. Table 11 Panel A presents firm-level regressions of year t+1 realized returns on GLS cost of equity capital estimates as well as return news (NRET) and earnings news (NROE), for the sub- sample of 11,492 firm-years for which NRET and NROE can be estimated. I report mean

coefficients across the 13 sample years, the t-statistic testing whether the mean coefficient is different from zero, and the mean adjusted R2 from the annual regressions, following Fama and MacBeth (1973). In Table 11 Panel A Columns 1 – 3, results of the regression of realized returns on expected returns (as in Equation 3.38) are somewhat difficult to interpret given low levels of statistical significance, as in Guay, Kothari, and Shu (2005). Column 1 presents an estimated

adj coefficient of 0.239 (p=0.229) on rGLS while Column 2 presents an estimated coefficient of 0.266

est adj est adj on rGLS (p=0.221). In Column 3, where expected returns are broken into rGLS and (rGLS  rGLS ) ,

adj the estimated coefficient on rGLS is a larger 0.344 but not statistically significant (p=0.205). Columns 4 – 6 present results when the news variables are included in the regression, as in Equation 3.39. In Column 4, the estimated coefficient on is 0.141 and is significantly different from zero (p = 0.007). In Column 5, when is used to proxy for expected returns, the estimated coefficient on is 0.148 and is statistically different from zero (p=0.002).

Finally, in Column 6, when expected returns are separated into two components ( and

), the estimated coefficient on is stronger at 0.210 (p=0.002).18 In conclusion, there appears to be a stronger relation between realized returns and expected returns estimated following Gebhardt, Lee, and Swaminathan (2001) after controlling for ex ante bias in the expected returns estimate. Table 11 Panel B presents firm-level regressions of year t+1 realized returns on CT cost of equity capital estimates as well as return news and earnings news, for the sub-sample of

10,419 firm-years for which NRET and NROE can be estimated. In Table 11 Panel B Columns 1 – 3, results of the regression of realized returns on expected returns are again difficult to interpret given low levels of statistical significance. Column 1 presents an estimated coefficient of 0.022

adj est (p=0.550) on rCT while Column 2 presents an estimated coefficient of 0.252 on rCT (p=0.305).

adj est In Column 3, where expected returns are broken into rCT and ( rCT - ), the estimated coefficient on is larger at 0.269 but not statistically different from zero (p=0.275). Results are strengthened when the news variables are included in the regression, as in Equation 3.39. In Column 4, the estimated coefficient on is 0.024 (p = 0.178). In Column 5, the estimated

est coefficient on rCT is 0.289 and is statistically different from zero (p=0.002). Finally, in Column

18 The estimated coefficients on NRET and on NROE are not significantly different from their respective predicted values of -1 and 1 throughout Table 11. 25

adj est 6, when expected returns are separated into two components ( rCT and ( rCT - )), the estimated coefficient on is stronger at 0.303 and statistically different from zero (p=0.007). To conclude, there appears to be a stronger relation between realized returns and expected returns estimated following Claus and Thomas (2001) after controlling for ex ante bias in the expected returns estimate, as well as for return and earnings news. Table 11 Panel C presents firm-level regressions of year t+1 realized returns on PEG cost of equity capital estimates as well as return news and earnings news, for the sub-sample of 4,409 firm-years for which NRET and NROE can be estimated. In Table 11 Panel C, results of the regression of realized returns on expected returns (as in Equation 3.38) are again difficult to interpret given low levels of statistical significance. Column 1 presents an estimated coefficient

adj est of 0.115 (p=0.642) on rPEG while Column 2 presents an estimated coefficient of 0.094 on rPEG

est adj (p=0.717). In Column 3, where expected returns are broken into and (rPEG  rPEG ) , the estimated coefficient on is -0.045 and not statistically different from zero (p=0.883). Results are strengthened when the news variables are included in the regression, as in Equation 3.39. In Column 4, the estimated coefficient on is 0.101 and is significantly different from zero (p =

0.015). In Column 5, when is used to proxy for expected returns, the estimated coefficient on is 0.242 and is statistically different from zero (p=0.002). Finally, in Column 6, when expected returns are separated into two components ( and ( - )), the estimated coefficient on is stronger at 0.292 (p=0.003). Thus, there appears to be a stronger relation between realized returns and PEG expected returns after controlling for ex ante bias, as well as for shocks to expected returns and shocks to expected earnings.

Additional validation In addition to the above tests, I test the association between COEC estimates and risk factor proxies. Researchers including Gebhardt, Lee, and Swaminathan (2001), Gode and Mohanram (2003), and Botosan and Plumlee (2005) evaluate expected returns proxies based on their association with variables that may be correlated with risk factors, such as size, book-to- market, firm beta, and leverage. Thus, this analysis requires Betat-1, which I estimate on a 5-year rolling basis for firms with at least 24 months of returns data, as well as Leveraget-1 which I

measure as the ratio of total long-term debt to total assets as of year t-1. MVjt-1 and BMjt-1 are as defined previously. Descriptive statistics for the risk factor proxies are provided in Table 12 Panel A for the

13,015 firm-years with non-missing Betat-1 and Leveraget-1. Mean (median) MVjt-1 for this sample is $4,427 (744) million while mean (median) BMjt-1 is 1.764 (0.474). Mean (median)

Leveraget-1 is 1.693 (0.181) while mean (median) Betat-1 is 0.984 (0.843). To test the association between COEC estimates and risk factor proxies, I use two approaches. The first analysis in Table 12 Panels B – E compares the COEC estimates obtained for portfolios of firms based on their ranking of a given risk factor proxy. For example, Table 12 Panel B Column 6 shows that firms in the first size quintile (i.e., the smallest firms) have a mean

adj rPEG estimate of 10.88%, while firms in the fifth quintile (i.e. the largest firms) have a mean estimate of 7.08%, with statistically significant differences between the fifth and first quintiles. Similar comparisons are made in Table 12 Panels B – E for COEC estimates generated following GLS (Columns 1 and 2) and CT (Columns 3 and 4), and for portfolios formed according to book- to-market (Panel C), leverage (Panel D), and beta (Panel E). In Table 12 Panel B, COEC estimates, both unadjusted and de-biased, generally decrease across size portfolios, as expected. In Table 12 Panel C, all COEC estimates increase across book-to-market portfolios, as expected.

est adj est In Table 12 Panel D, the rGLS , rGLS , and rCT estimates increase across leverage portfolios. In Table 12 Panel E, only the estimates increase across beta portfolios. The second provided analysis in Table 12 Panel F estimates the following regression for each firm-level estimate, similar to Francis, Lafond, Olsson, and Schipper (2004):

rjt   0 1 *ln(MV jt1 )  2 * BM jt1  3 * Leveragejt1  4 * Beta jt   jt (3.40)

Following Fama and MacBeth (1973), coefficients are calculated as the mean of the estimated coefficients from annual cross-sectional regressions estimated over the years 1994 to 2006. From

est this analysis, I conclude that each of and rPEG is strongly associated with each of the risk factor proxies. Not surprisingly, the explanatory power of the above model is lower for de-biased COEC estimates, given that such COEC estimates exclude ex ante analyst bias, which is associated with firm size (Easton and Sommers 2007). Although suggestive, it is, however, difficult to draw conclusions from the above analysis given that the risk factor proxies are measured with error and, more importantly, there is no theoretically correct level of association between expected returns and risk factor proxies.

3.4 Sensitivity analyses In portfolio-level COEC estimation, inferences are unchanged when two years of forecasts are used as in Easton et al. (2002). In doing so, I obtain COEC estimates of 11.1% using unadjusted analyst forecasts, 8.9% using adjusted forecasts, and 8.1% using perfect foresight forecasts, for a smaller sample of 13,947 firm-years. Inferences are also similar when I delete firm-years with dependent or independent variables in the top and bottom 1% (rather than 2%) of observations. In doing so, I obtain COEC estimates of 10.9% using unadjusted analyst forecasts, 8.4% using adjusted forecasts, and 8.9% using perfect foresight forecasts, for a slightly larger sample of 17,228 firm-years. Moreover, including firms with negative analyst forecasts in portfolio-level estimation, yields lower COEC estimates of 10.6% using unadjusted analyst forecasts, 7.9% using adjusted forecasts, and 8.6% using perfect foresight forecasts. It is possible that the use of RET_EZ (the return between the date the analyst forecast is issued, and the earnings release date) in the analyst forecast bias prediction model, might spuriously lead to a stronger association between de-biased COEC estimates and future realized returns. To address this concern, I have re-run the de-biasing tests to exclude RET_EZ. The results are similar, and inferences regarding the association between de-biased COEC estimates and future realized returns (in Table 11) are unchanged. In the estimation of firm-level COEC, it is possible to use analyst long-term growth forecasts (LTG) to compute GLS, CT, and PEG COEC estimates. For example, under the GLS methodology, AFt+2 can be estimated as AFt+1 * (1+LTG) where long-term growth forecasts are available, following Guay, Kothari, and Shu (2005). Doing so, I obtain a mean COEC estimate

est ( rGLS ) of 8.88% using analyst forecasts to proxy for expected earnings and of 7.51% using de-

adj biased analyst forecasts ( rGLS ) to proxy for expected earnings. In this estimation, the difference between and is positive and statistically significant at the 1% level in all 13 years, with a mean of 0.0137 for 1994-2006. Similarly, under the CT methodology, AFt+2 can be estimated as AFt+1 * (1+LTG) where long-term growth forecasts are available. Doing so, I obtain a mean

est COEC estimate ( rCT ) of 8.80% using analyst forecasts to proxy for expected earnings and a

adj mean estimate ( rCT ) of 8.19% using de-biased analyst forecasts to proxy for expected earnings. In this estimation, the difference between and is positive and statistically significant at the 1% level in 9 of 13 years, with a mean of 0.0061 for 1994-2006.

Gode and Mohanram (2008) offer an alternative way to estimate rPEG using analyst long- term growth forecasts. Gode and Mohanram estimate AFt+1 as AFt+1 * (1+STG) where STG 28

(short-term growth) is the average of the growth in year t+1 relative to year t analyst forecasts, and of analysts‟ long-term growth (LTG) forecasts. (This is appealing, as it lessens the aforementioned problem of non-increasing year t+1 de-biased earnings forecasts.) Using Gode

est and Mohanram‟s methodology, I obtain a mean COEC estimate ( rPEG ) of 11.08% using analyst

adj forecasts to proxy for expected earnings and a mean estimate ( rPEG ) of 6.91% using de-biased analyst forecasts to proxy for expected earnings, for a larger sample of 11,881 firm-years. In this estimation, the difference between and is positive (i.e., reflecting analyst optimism) and statistically significant at the 1% level in all 13 years, with a mean of 0.0417 for 1994-2006. Moreover, inferences throughout this Chapter are unchanged when I use LTG to compute GLS, CT, and PEG COEC estimates.

3.5 Summary and conclusions This Chapter estimates the COEC using both unadjusted and de-biased analyst EPS forecasts. As an extension of Easton and Sommers (2007), I show 2.4% upward ex ante bias in portfolio-level COEC estimates based on adjusted (de-biased) analyst EPS forecasts relative to those based on (unadjusted) analyst forecasts. I find that the effect of ex ante analyst forecast bias on firm-level COEC estimation amounts to respective upward bias of 1.7%, 1.1%, and 5.5% when using the estimation methods of Gebhardt et al. (2001), Claus and Thomas (2001), and Easton (2004). I validate the firm-level COEC estimates based on their association with ex post realized returns and with risk factor proxies. From these analyses, I conclude that de-biased

adj analyst forecasts better predict realized returns, and that rPEG is most closely associated with risk factor proxies. In Chapter 5, I implement these de-biased COEC estimates in tests of the association between disclosure and the cost of equity capital.

Chapter 4: Estimating Ex Ante Expected Returns Using Return Decomposition 4.1 Introduction Ex post returns are often used in the empirical asset-pricing literature to proxy for ex ante expected returns.19 However, realized returns are noisy (Elton 1999). In particular, realized returns might deviate from expected returns when earnings forecasts are revised (Liu and Thomas 2000) or when discount rate expectations change. Vuolteenaho (2002) shows theoretically that realized returns differ from expected returns when expectations about future expected earnings and/or about expected returns (i.e., future discount rates) change. In this Chapter, I apply Vuolteenaho‟s (2002) return decomposition framework to develop a novel firm-level estimate of ex ante expected returns. Simply put, this COEC estimate corrects realized returns for estimated shocks to expected discount rates (“return news”) and estimated shocks to expected earnings (“earnings news”) over the firm‟s lifetime. Using Vuolteenaho‟s framework, I separate realized returns into expected earnings, earnings news, and return news on an ex post basis, in order to back out ex ante expected returns. Thus, I contribute to the literature that estimates expected returns. I show that this ex ante expected returns estimate correlates well with risk factor proxies including size and beta. An advantage of using return decomposition to estimate ex ante expected returns, is that it is does not rely on analyst forecasts and thus is not impacted by analyst forecast bias. This should prove meaningful in associating expected returns with factors such as disclosure that are associated with analyst forecast bias. Section 4.2 of this Chapter discusses return decomposition. Sections 4.3 and 4.4 outline empirical methodology and sensitivity analysis, respectively. Section 4.5 concludes.

4.2 Return decomposition Invoking the clean surplus assumption and using a Taylor series approximation, Vuolteenaho expands the log book-to-market ratio to extend the return decomposition framework of Campbell and Shiller (1988a, 1998b), Campbell (1991), and Campbell and Ammer (1993). Vuolteenaho (2002) shows that the change in expectations about period t returns equals earnings news and expected-return news during year t:

RETt  Et1(RETt )  N ROE,t  N RET ,t  . (4.1)

In Equation 4.1, Et-1(·) is the expectations operator, NROE represents earnings news, NRET represents news about expected returns, and κ is the approximation error.

19 See, for example, Fama and French (1992). 30

As discussed in section 3.3.4, Vuolteenaho (2002) defines earnings news, which encompasses both earnings surprises and revisions to future expected earnings over the firm‟s lifetime, as:

 j (4.2) NROE  Et   roet j j0 where ρ is a discount factor, roe represents the natural logarithm of one plus return on equity (i.e. net income before extraordinary items over last year‟s common equity), and the revision or shock is denoted by ∆Et(·) = Et(·) – Et-1(·). Similarly, Vuolteenaho (2002) defines return (or discount rate) news, which encompasses shocks to expected future discount rates over the firm‟s lifetime, as:  (4.3) E  jret NRET  t  t j j1 where ret represents the natural logarithm of one plus returns. Rolling forward Equation 4.1 by one year, we can estimate expected returns (i.e.,

Et(RETt+1)) from realized returns (RETt+1) and the two news variables. I refer to this estimate of ex ante expected returns, as rNEWS:

rNEWS = Et (RETt1 )  RETt1  N ROE,t1  N RET ,t1 (4.4) In other words, by adjusting realized returns for both earnings news and return news, one can estimate the market‟s ex ante expected returns using ex post data.

4.3 Empirical methodology 4.3.1 Sample selection

To implement the above VAR specification and generate estimates of NRET and NROE, I require the following variables: market value and returns from CRSP, and book value (DATA60) and net income before extraordinary items (DATA18) from Compustat. Following Callen and Segal (2008), I delete firms in the financial industry with SIC codes 6000-6999, and following both Vuolteenaho (2002) and Callen and Segal (2008), I delete the top and bottom 1% of dependent and independent variables for the VAR specification. Similarly, I assume that ρ = 0.96. This leaves a sample of 24,624 firm-years for 1990-2006. From this sample, I estimate the news variables for 18,734 firm-years for 1994-2006, after trimming the top and bottom 1% of news variables for the VAR specification.

4.3.2 Empirical estimation of return news and earnings news

This section contains details on the estimation of each of NRET and NROE. I follow Vuolteenaho (2002), Callen and Segal (2004), Callen, Hope, and Segal (2005), Callen, Livnat, and Segal (2006), and Callen and Segal (2008) in using a log-linear vector autoregression

(VAR). If we define zi,t to be a vector of firm-specific state variables, the state vector can be assumed to follow the following multivariate log-linear dynamic:

zi,t  zi,t1 i,t . (4.5) The VAR matrix (Γ) of coefficients is assumed to be constant over time and over firms, and the error terms (ηi,t,) are assumed to be independent of variables known at time t-1.

ret      i,1t   i,t   1 2 3    If we let zi,t  bmi,t  ,   1 2  3  , and i,t  i,2t  in Equation 4.5, with ret equal to the             roei,t   1 2 3  i,3t  natural logarithm of returns, bm equal to the natural logarithm of the book-to-market ratio, and roe equal to the natural logarithm of return on equity, this gives rise to the (short) VAR specification:

rett = α1rett-1 + α2bmt-1 + α3roet-1 + η1t (4.6)

bmt = β1rett-1 + β2bmt-1 + β3roet-1 + η2

roet = γ1rett-1 + γ2bmt-1 + γ3roet-1 + η3t Combining the residuals from the VAR estimation with Equation 4.3 allows expected-return news to be expressed (as in Campbell and Shiller 1988b) as:

 1 (4.7) N RET  e1 (1 ) i,t

1    where e1  0 .   0 Similarly, earnings news can be expressed directly as:

N   1 (4.8) ROE e3 (1 ) i,t

0   where e3  0 .   1  Alternatively, earnings news can be expressed residually as:

N   1 (4.9) ROE e1 (1 ) i,t

Similarly, return news can be expressed residually as:

 1  (4.10) N RET  [e3 (1 )  e1 ]i,t . Callen (2009) notes that in return decomposition, typically all but one variable is estimated directly with the contribution of the remaining variable estimated residually so as to guarantee equality. Alternatively, one can estimate all variables directly, bearing in mind that equality in equation 4.1 may not be satisfied. In my empirical tests, I use both direct and residual formulation following research by Chen and Zhao (2008) that points to possible differences in inferences when news variables are estimated residually vs. directly.

4.3.3 Analysis and validation of ex ante expected return estimates

Table 13 Panel A presents descriptive statistics for my sample when both NRET and NROE are estimated directly. Mean (median) NROE,t+1 for my sample is 0.020 (0.084), indicating that earnings news is positive, on average. Mean (median) NRET,t+1 for my sample is 0.002 (0.007), similar to the findings of Vuolteenaho (2002), while mean (median) RETt+1 is 0.156 (0.084). Consistent with Callen and Segal (2004, 2008), I find that shocks to expected earnings significantly exceed shocks to expected returns, indicating that earnings news is the main driver of firm-level unexpected returns.

I estimate rNEWS following Equation 4.4. rNEWS has a mean (median) of 0.139 (0.056) across 18,734 firm-years from 1994-2006. In Table 13 Panel B, I use rNEWS (with both NRET and

NROE estimated directly) in multivariate tests to test for an association between expected returns and risk factor proxies, using Equation 4.11:

rNEWS = 0 1 ln(MV jt )  2 ln(BM jt 1) 3Beta jt   jt (4.11) I estimate each model as a panel and cluster the standard errors at the firm level. In Column 1, when rNEWS is regressed on risk factor proxies, I find a significant negative estimated coefficient on ln(MVt), and a significant positive estimated coefficient on Betat. This provides some validation of rNEWS as a measure of expected returns. Inferences from estimation of Equation 4.11 are similar when I exclude firm-years with negative values of rNEWS in Column 2 with the exception of the coefficient on ln(BMt-1), which is surprisingly negative and significantly different from zero.

In Table 14 Panel B, I similarly use rNEWS (with NRET estimated residually and NROE estimated directly) in multivariate tests to test for an association between expected returns and risk factor proxies. In Column 1, when rNEWS is regressed on risk factor proxies, I find a

significant negative estimated coefficient on ln(MVt), and significant positive estimated coefficients on ln(BMt-1) and Betat. This provides validation of rNEWS as a measure of expected returns. In Column 2, when I exclude firm-years with negative values of rNEWS, I continue to find a significant negative estimated coefficient on ln(MVt), and a significant positive estimated coefficient on Betat.

4.4 Sensitivity analyses Inferences in Table 14 Panel B are similar when I estimate earnings news, rather than return news, residually.

That rNEWS provides a new estimate of ex ante expected returns based upon both realized returns and accounting data is appealing. At the same time, rNEWS estimates are often negative – particularly for large firms with low stock returns, or very high – particularly for small firms with high stock returns. This former problem is similar to the challenge in estimating rPEG, which cannot be estimated for firms with non-increasing expected earnings (see Section 3.3 for more detail).

In untabulated analysis, I test for the correlation between rNEWS and firm-level implied COEC estimates. (See Chapter 3 for detail on estimation of implied COEC.) I find Pearson

est est est correlations between NEWS (with both news variables estimated directly) and rGLS , rCT , and rPEG of 0.072 (p < 0.001), 0.104 (p < 0.001), and 0.261 (p<0.001), respectively. When return news is estimated residually, I find respective Pearson correlations between rNEWS and , , and of 0.150 (p < 0.001), 0.150 (p < 0.001), and 0.060 (p<0.001).

4.5 Summary and conclusions This Chapter contributes to the accounting and finance literature with a novel estimate of ex ante expected returns, rNEWS, that I estimate using Vuolteenaho‟s return decomposition framework applied to ex post realized returns and accounting data. I show that rNEWS correlates well with risk factor proxies including firm size and beta. An advantage of using return decomposition to estimate ex ante expected returns, is that it is does not rely on analyst forecasts and thus is not impacted by analyst forecast bias. This should prove meaningful in associating expected returns with factors such as disclosure that are associated with analyst forecast bias. In Chapter 5, I use rNEWS, in addition to implied COEC

estimates from which I have removed predictable analyst forecast bias, to test for an association between disclosure and ex ante expected returns. My empirical methodology follows Vuolteenaho (2002) and Callen and Segal (2008) to estimate return news and earnings news, which are in turn used to estimate ex ante expected returns. In future research, it might be possible to estimate the news variables using historical data (either cross-sectionally or in time-series) or to estimate earnings news using revisions to analyst forecasts, as in Easton and Monahan (2005).

Chapter 5: Disclosure, Analyst Forecast Bias, and the Cost of Equity Capital 5.1 Introduction The association between the level of information about a firm and its cost of capital is an important question for managers, standard-setters, and researchers. Early theoretical studies posit that managers have incentives to disclose positive news but withhold negative news (Verrecchia 1983; Dye 1985; Jung and Kwon 1988). More recently theorists have associated firms‟ disclosure policy choices with expected returns. Verrecchia (1990) discusses a manager‟s decision to commit to a level of disclosure in order to maximize firm value, while Diamond and Verrecchia (1991) model how the revelation of public information can reduce information asymmetry and, in turn, reduce the cost of capital by attracting increased demand from large investors through increased liquidity. Easley and O‟Hara (2004) posit that firms can influence their cost of capital by choosing the quantity and precision of information available to investors. In their model, private information induces a form of systematic risk, which affects the cost of capital. Hughes, Liu, and Liu (2007) demonstrate that information asymmetry can lead to higher factor risk premiums and, thus, higher costs of capital. Hughes et al. (2007) also claim that information risk can be diversified away in large economies. Lambert, Leuz, and Verrecchia (2007) demonstrate that the quality of accounting information influences the COEC both directly by affecting perceptions about the distribution of future cash flows, and indirectly by affecting real decisions that in turn affect the distribution of future cash flows. Gao (2008) models how disclosure‟s impact on the cost of capital depends on the firm‟s investment decisions. Empirical evidence of the association between disclosure and the cost of capital is mixed. On one hand, Botosan (1997) estimates that, in a sample of manufacturing firms with low analyst following, firms making the most forthcoming disclosures enjoy a 9.7% reduction in COEC relative to the least forthcoming firm. She scores annual report disclosures using a self- constructed index. Sengupta (1998) finds that high disclosure ratings are associated with lower costs of issuing debt. Leuz and Verrecchia (2000) relate firm disclosure commitments to the information asymmetry component of the COEC. In particular, Leuz and Verrecchia find that German firms that switch to international accounting standards or US GAAP enjoy narrower bid- ask spreads and increased trading volume, relative to firms employing German GAAP. On the other hand, research by Botosan and Plumlee (2002) and Francis, Nanda, and Olsson (2008) calls into question the COEC – disclosure association. Botosan and Plumlee (2002) find that the COEC is decreasing in the level of AIMR annual report disclosure ranking but increasing in the level of AIMR timely disclosure ranking. Francis et al. (2008) show that, after conditioning on

earnings quality, voluntary disclosure (measured using a self-constructed index) is not associated with the cost of equity capital. Kim and Shi (2007) find a decrease (increase) in COEC following management guidance issuance containing positive (negative) news. Using content analysis, Kothari, Li, and Short (2008) find no evidence of a relation between firm disclosures and COEC. More recent evidence by Leuz and Schrand (2008) suggests that, in the face of an exogenous positive shock to their cost of capital, firms increase voluntary disclosures. Tests of the relation between disclosure and COEC face several challenges. Healy and Palepu (2001) and Core (2001) discuss the difficulty in measuring the extent of voluntary disclosure. Moreover, disclosure studies are subject to self-selection bias. Disclosure is voluntary and hence it is possible that the decision to disclose is related to the characteristics influencing expected returns. For instance, Lang and Lundholm (1993) show that firm disclosure rankings are increasing in firm size and decreasing in the variability of stock returns. Further, as Leuz and Verrecchia (2000) highlight, the COEC cannot be directly observed. COEC measurement continues to be refined by empirical researchers. Claus and Thomas (2001) and Easton and Sommers (2007) point to the optimistic bias in longer-term analyst forecasts as a potential source of error. Easton and Monahan (2005) suggest that removing bias from analyst forecasts could generate a more reliable COEC proxy. This Chapter examines the relation between disclosure, analyst forecast bias, and the cost of equity capital. In my empirical tests, I measure disclosure using management guidance and distinguish firms that regularly issue such disclosures from those that do so irregularly. Management guidance issuance is voluntary and thus subject to self-selection bias. I control for the decision to regularly issue guidance using a two-step design. Given the ongoing debate over the robustness of COEC estimation (Easton and Monahan 2005), I use several techniques to estimate ex ante expected returns without the influence of analyst forecast bias and associate them with disclosure. I contribute to the literature in several ways. As predicted by theory I find that ex ante expected returns are lower for firms that regularly issue management guidance. The extant literature fails to allow for the potential impact of ex ante analyst forecast bias on the COEC– disclosure association, as well as self-selection bias.20 I predict and show that inferences can change when analyst forecast bias and self-selection bias are controlled for.

20 An exception is Leuz and Verrecchia (2000), who control for self-selection bias in their study of information asymmetry and disclosure commitments. Nikolaev and van Lent (2005) discuss endogeneity problems in tests associating disclosure with the cost of debt capital. 37

The Chapter proceeds as follows. Section 5.2 discusses the effect of analyst forecast bias on studies of the association between disclosure and the cost of equity capital, and develops my hypotheses. Section 5.3 outlines my empirical methodology and tests. Section 5.4 concludes.

5.2 The effect of analyst forecast bias on disclosure – COEC studies It is possible that the association between analyst forecast bias and disclosure, and between analyst forecast bias and implied COEC estimates, could influence empirical tests of the association between the COEC and disclosure. For example, if analysts set more optimistic longer-term forecasts for firms issuing guidance, these firms will appear to have a higher implied COEC when in fact these firms should have a lower true COEC because of their extra level of voluntary disclosure.21 The extant literature has associated disclosure with both the accuracy and bias of analyst forecasts. Analyst forecast accuracy is shown to improve with firm-level disclosures by Hassel, Jennings, and Lasser (1988), Bowen, Davis, and Matsumoto (2002), Lang and Lundholm (1996), and Hope (2003). The link between analyst forecast bias and disclosure is generally tied to expectations management wherein firms are more likely to disclose news when analysts‟ estimates are optimistic. Bartov, Givoly, and Hayn (2002) document a valuation premium for firms that meet or beat analysts‟ earnings expectations relative to firms that fail to meet expectations while Kasznik and Lev (1995) show that firms that warn investors following earnings disappointments suffer significant share price declines. Thus firms have incentives to manage down analyst expectations through their disclosures. Baik and Jiang (2006) and Cotter, Tuna, and Wysocki (2006) find that management guidance is more likely to be issued when analysts‟ estimates are optimistic. Hutton (2005) finds that analysts‟ quarterly forecasts are less optimistic after firms provide management guidance. Bagnoli, Li, and Watts (2008) discuss how analysts respond more optimistically to guidance from managers that have historically provided downward-biased forecasts. While the extant literature focuses on the impact of firm disclosure on current-period analyst forecasts, I show that analysts are over-optimistic in setting future-period forecasts following management guidance issuance. (See section 5.4.) This is consistent with analysts issuing overly optimistic forecasts and managers attempting to lower analysts‟ expectations over

21 To my knowledge, there is no theoretical reason why analyst optimism would be associated with guidance issuance. In my sample, I find that analysts set more optimistic longer-term forecasts for firms issuing guidance, which in turn could impact tests of the association between the COEC and regular guidance issuance. 38

time (Bagnoli, Li, and Watts 2008; Tan, Libby, and Hunton 2007). Hirst, Koonce, and Venkataraman (2008) highlight the “multi-period” nature of management guidance issuance. In an efficient market, investors should see through expected analyst optimism in setting prices, which determine expected returns. Consistent with the overall assumption of market efficiency in any model using implied cost of equity capital, it is also reasonable to assume that professional investors know how to de-bias information they receive from analysts. For example, if analysts issue more optimistic forecasts for smaller firms, the market should both anticipate and correct for this optimism. Sharpe (1978) emphasizes the need to estimate the market‟s expectations on an ex ante basis when estimating ex ante expected returns. Of course, ex ante analyst forecast optimism cannot be observed. This leaves estimation of ex ante analyst forecast bias and its impact on COEC estimates an unanswered question. To consider how inferences about the relation between the COEC and disclosure might be affected by ex ante analyst forecast bias, I use a framework similar to that of McNichols and Wilson (1988). I assume that the true COEC is estimated with error: rest  rtrue  (5.1) According to McNichols and Wilson, the variance of η and its correlation with other variables affect both power and bias of statistical inferences relating to rtrue. If rtrue were observable, COEC tests would be expressed as follows: rtrue     *PART  (5.2) where PART is an indicator variable that partitions the data into two, α is the average value of rtrue in the first group, and α + β is the average value of rtrue for the second group. Because researchers use a proxy (rest), COEC tests are characterized as follows: rest     * PART  (5.3) where

 (5.4)     PART , *  PART

In Equation 5.4, ρPART,η is the correlation between PART and η, and ση (σPART) is the variance of η (PART). This means that γ is a biased estimate of β when the partitioning variable and η are true correlated. This correlation can be particularly problematic when r is poorly estimated (i.e., ση is large). In the current study, β (the association between disclosure and firm COEC) should be negative while ρ (the correlation between disclosure and the error in estimating rtrue, due to analyst forecast bias) should be positive. In other words, in the current study, γ (the estimate of β)

will be biased towards zero. Thus, researchers investigating the COEC – disclosure relation should consider analyst forecast bias in their research design. Hypothesis 2 investigates the association between disclosure and the COEC. H2 is stated in the alternative, and is consistent with the theoretical literature that predicts a lower cost of equity capital for firms that commit to disclose information:

H2: Disclosure is negatively associated with ex ante expected returns.

Hypothesis 3, also stated in the alternative, considers the effect of ex ante analyst forecast bias on the association between the firm‟s COEC and its commitment to disclose information:

H3: Disclosure is more negatively associated with ex ante expected returns after controlling for ex ante analyst forecast bias.

5.3 Empirical methodology To the extent that analyst forecast bias is associated with disclosure, researchers need to control for bias when measuring ex ante expected returns and estimating the COEC–disclosure relation. I do so in a number of ways. In section 5.3.2, I conduct portfolio-level tests following Easton and Sommers (2007) and Easton (2008). In section 5.3.3, I use firm-level COEC estimates following Easton (2004). These portfolio-level and firm-level implied COEC estimates are generated using de-biased analyst earnings forecasts, which I develop in Chapter 2. In section 5.3.4, I conduct firm-level tests using a novel measure of ex ante expected returns that I develop in Chapter 4 from Vuolteenaho‟s (2002) return decomposition framework. This measure does not rely upon analyst forecasts and thus is not impacted by analyst forecast bias. Each of these ex ante expected returns estimates is tested for an association with disclosure. Last, in section 5.3.5, I implement asset-pricing tests which, because they rely upon realized returns, are not subject to analyst forecast bias.

5.3.1 Sample selection From the sample of firms from 1997 to 2006 for which I can estimate de-biased analyst forecasts, I keep firms with non-missing book value and beta. Prices and returns are extracted from CRSP. These requirements yield a sample of 13,656 firm-year observations for 1997-2006. I distinguish between firms that regularly issue voluntary disclosures and those that do not. To measure disclosure, I use management guidance. Other studies, including King, Pownall, and Waymire (1990), Coller and Yohn (1997), Rogers and Van Buskirk (2008), Rogers, Skinner, 40

and Van Buskirk (2008), and De Franco, Hope, and Larocque (2009) use management guidance as a direct measure of a firm‟s disclosure policy (see also Hirst, Koonce, and Venkataraman 2008). King, Pownall, and Waymire (1990) and Ajinkya and Gift (1984) show that managers issue guidance to align the market‟s expectations with their own. Coller and Yohn (1997) and Marquardt and Wiedman (1998) document that management guidance reduces information asymmetry measured as bid-ask spreads. Overall, management guidance is considered to be a credible voluntary disclosure about firms‟ future earnings potential by these researchers. I estimate both analyst forecast bias and the cost of equity capital using April I/B/E/S summary reports; thus I measure guidance issuance for the year ending March, as depicted in Figure 3. From First Call‟s Company-Issued Guidelines (CIG) database, I extract all 67,948 management guidance events from April 1996 through March 2006. These comprise 19,582 unique firm-years and 5,954 unique firms for which guidance is issued via press releases, interviews, and conference calls. After the guidance variables are merged in, the final sample consists of 6,146 firm-years (45% of the full sample) from 1997 to 2006 with management guidance during the year ending March of year t. (See Figure 1.) Of these firms, 3,819 firm-years (28% of the full sample) issue guidance regularly, as outlined in Table 1 Panel B, with an increase in the number of firms issuing regular guidance over time. To proxy for a firm‟s implicit commitment to disclose, I refer to firms that issue guidance in each of years t-1, t, and t+1 as regular guidance firms, following Chen, Matsumoto, and Rajgopal (2006). Inferences throughout this study are unchanged when I classify firms that issue guidance in each of years t-1 and t as regular guidance firms. I focus in this study on regular guidance firms relative to firms that do not issue guidance, thus excluding from the analysis the 2,327 firm-years that issue guidance only sporadically. Including sporadic guidance firms in the analysis (for example, in a comparison of all guidance firm-years with non-guidance firm-years) introduces news effects that confound results. For example, if firm ABC issues a one-time earnings warning that results in a lower financial outlook, firm ABC‟s COEC could increase following this guidance event (see Verrecchia and Weber 2008 and Kothari, Li, and Short 2008). Table 15 presents descriptive statistics for the regular guidance firms and non-guidance firms. Regular guidance firms are larger, have lower book-to-market ratios, have higher analyst coverage, and are more likely to be profitable than non-guidance firms.

5.3.2 Portfolio-level tests Easton, Taylor, Shroff, and Sougiannis (2002), Easton (2006), Easton and Sommers (2007), and Easton (2008) highlight the need to estimate the cost of equity capital at the portfolio-level. As discussed in section 3.2 of this dissertation, portfolio-level estimation allows the researcher to empirically estimate, rather than assume, long-term growth rates (g) as is common in firm-level COEC estimation. I test for an association between disclosure and COEC estimates at the portfolio level by following Easton (2008), who builds on Easton and Sommers (2007) to estimate the difference in COEC (r) among portfolios of stocks, which differ in the attribute of interest (Q). Easton (2008) sets forth the following “dummy-variable regression”:

E(eps jt ) Pjt Pjt (5.5)   0   1   2Q   3Q *   jt BV j,t1 BV j,t1 BV j,t1

In Equation 5.5, γ2 captures the difference in g across the two portfolios of firms, and γ3 captures the difference in r – g across the two portfolios of firms, such that the difference in r across the two portfolios of firms can be estimated as γ2 + γ3. Turning to the difference in r across regular guidance and non-guidance firms, it is important to take into account the impact of analyst bias within this analysis given the evidence in the prior literature that analyst forecast bias is associated with disclosure and with implied

COEC estimates. I use each of three different proxies for expected earnings (i.e., E(eps)jt): (unadjusted) analyst forecasts, de-biased analyst forecasts, and perfect foresight forecasts. In this and other tests of the impact of disclosure on COEC, I focus on the impact of regular guidance issuance, following theoretical research that links firms‟ commitments to disclosure to lower COEC (see, for example, Leuz and Verrecchia 2000). I use the following specification: E(eps ) P P (5.6) jt     jt   REGULAR   REGULAR * jt   ln(MV ) BV 0 1 BV 2 jt 3 jt BV 4 t j,t1 j,t1 j,t1 Pjt Pjt   5 ln(MVt )*   6 Betat1   7 Betat1 *   jt BV j,t1 BV j,t1

In Equation 5.6, REGULARt is an indicator variable that equals one if a firm issues guidance during each of the years ending March of year t-1, t, and t+1, and zero otherwise. Betat-1 is CAPM beta for the five years ending December of year t-1. Firm size and beta are included to control for risk factors that might be correlated with REGULARt, following Easton (2008). As in Equation 5.5, the difference in r across the two portfolios of firms (i.e., non-disclosers and

regular disclosers) can be estimated as γ2 + γ3. A negative value for γ2 + γ3 would provide evidence of a lower COEC for regular disclosers, in support of H2.22 In Table 16 Column 1, using (unadjusted) analyst forecasts to proxy for expected earnings, the COEC is 0.6% lower for the portfolio of regular guidance firms relative to the portfolio of non-guidance firms. However, the F-test of the linear restriction (γ2 + γ3 = 0), which represents the increment to r for the regular guidance firms relative to the non-guidance firms, is not significant at the 5% level in one-tailed tests (p-value = 0.067). In Column 2, using de-biased analyst forecasts to proxy for expected earnings, I find that the COEC is 0.8% lower for regular guidance firms. In addition, the F-test of the increment to r for guidance firms relative to non- guidance firms is significant at the 5% level in one-tailed tests (p-value = 0.033). Results using perfect foresight forecasts provide a benchmark for my findings using de-biased analyst forecasts. Using realized earnings to proxy for expected earnings in Column 3, I find that the COEC is 1.4% lower for regular guidance firms. Here the F-test on the increment to r for guidance firms relative to non-guidance firms is significant at the 1% level in one-tailed tests (p- value = 0.004).23 In summary, the portfolio-level COEC tests provide support for both H2 and H3. I find a significantly lower COEC for the portfolio of regular guidance firms relative to that of non- guidance firms. In my tests, inferences are changed using either de-biased analyst forecasts or perfect foresight forecasts relative to analyst forecasts. This evidence as well as the association between analyst bias and the increment to COEC for regular guidance firms underlines the need to control for or exclude analyst bias in such tests.

5.3.3 Firm-level tests I now test for an association between firm-level cost of equity capital estimates and disclosure. Since issuing guidance is voluntary, my tests are subject to selection bias. I follow Heckman (1979) and Francis and Lennox (2008) and estimate the decision to issue regular guidance in a first-stage model that includes variables that are observable prior to the period in

22 Similarly, a negative value for γ4 + γ5 would provide evidence of a lower COEC for larger firms and a positive value for γ6 + γ7 would provide evidence of a higher COEC for higher-beta firms, as is typically assumed in the asset-pricing literature. 23 To further investigate the impact of bias on portfolio-level results, I regress analyst forecast bias on the independent variables in Equation 5.6. (The portfolio-level tests use different dependent variables, so that one cannot simply compare the coefficients across tests.) Column 4 (5) of Table 16 tests the association between ex ante (ex post) bias and the COEC increment for regular guidance firms. I find a significant positive association between the COEC increment for regular guidance firms (i.e., γ2 + γ3) and each of ex ante bias (p = 0.024, one-tailed) and ex post bias (p = 0.005, one-tailed). 43

which management guidance is issued (or not). In the second stage, I estimate the association between the COEC and regular guidance issuance, controlling for the decision to issue regular guidance. The first-stage probit model follows Cotter, Tuna, and Wysocki (2006), and Ajinkya, Bhoraj, and Sengupta (2005): Pr(REGULAR 1)     ln(MV )   ln(BM )   Bias ex _ post   ln(ANF ) (5.7) jt 0 1 jt1 2 jt1 3 t1 4 jt1

  5 Beta jt1   6 Loss jt1   7 Litigate jt  8 FD jt   t

In the above specification, ln(ANFt-1) is the natural logarithm of the number of analysts issuing earnings forecasts for the firm as of April of year t-1. Losst-1 is an indicator variable that equals one if earnings per share in year t-1 is less than zero, and zero otherwise. Litigatet is an indicator variable that equals one for all firms in the biotechnology, computers, electronics, and retail industries, and zero otherwise, as in Ajinkya et al. (2005). FDt is an indicator variable that equals one for years 2000 and later, and zero otherwise, and takes into account the impact of Regulation Fair Disclosure which prohibits firms from selectively disclosing material information.

Following Ajinkya et al. (2005), I expect positive coefficients on each of ln(MVt-1), ln(ANFt-1),

Litigatet, and FDt and negative estimated coefficients on each of BMt-1 and Betat-1. Following Cotter, Tuna, and Wysocki (2006), I predict a positive coefficient on Bias ex _ post. t 1 Table 17 Panel A presents the results of estimating Equation 5.7. Each of the estimated coefficients on the explanatory variables is of the predicted sign and is statistically significant, with the exception of firm size.24 In particular, regular management guidance is more likely to be issued for firms that have greater analyst following, lower book-to-market ratios, and positive earnings, and following the implementation of Regulation FD. To test H2 and H3, each firm-level COEC estimate is regressed on the disclosure proxy, as well as firm size, book-to-market, and beta which proxy for risk factors. I use variations of the following specification, which follows Francis, LaFond, Schipper, and Olsson (2004): E (RET )    ln(MV )  ln(BM )  Beta  REGULAR (5.8) t jt1 0 1 jt 2 jt1 3 jt 4 jt  5 jt   jt I estimate Equation 5.8 using both ordinary least squares (OLS) and as a second-stage model that includes the inverse Mills ratio (λt) from the first-stage model. I estimate each model as a panel and cluster the standard errors at the firm level. Firm fixed effects are included since a Breusch- Pagan test rejects the null hypothesis of homoscedasticity.

24 The coefficient on firm size is positive and significant, as predicted, when this model is estimated for the full sample of firms. 44

Table 17 Panel B presents the results of the OLS analysis, in Columns 1 – 3. In Column

est 1, using rPEG to proxy for Et(RETt+1), the estimated coefficient on REGULARt is negative and significantly different from zero. I obtain similar results in Column 2 when ex post analyst forecast bias is included, following Hail and Leuz (2006) and Hope, Kang, Thomas, and Yoo

adj (2008), and in Column 3, using rPEG to proxy for Et(RETt+1). Results for the second-stage model, however, highlight the need to exclude analyst forecast bias from the analysis. Table 17 Panel C presents the second-stage model results in

Columns 1 – 3. In Column 1, using to proxy for Et(RETt+1), the estimated coefficient on

REGULARt is negative but not significantly different from zero. In Column 2 when ex post analyst forecast bias is included as a control variable, the estimated coefficient on REGULARt is again negative but not significantly different from zero. Only in Column 3, using to proxy for Et(RETt+1), is the estimated coefficient on REGULARt negative (-0.0045; p = 0.027, one- sided) and significantly different from zero. As in section 3.2, the explanatory power of the above model is lower for de-biased COEC estimates, given that such COEC estimates exclude ex ante analyst bias, which is associated with firm size (Easton and Sommers 2007). I conclude from this analysis that directly de-biasing COEC estimates is a more effective control for analyst forecast bias, relative to including ex post bias as a covariate.25 At the same time, as in Section 3.3.3, the requirement of increasing de-biased analyst forecasts for year t+1 may limit the applicability of de-biased PEG COEC estimation. To summarize the above firm-level analysis, in support of H2, I find a significant negative association between the COEC and management guidance that is regularly issued, using PEG COEC estimates. In support of H3, I again show that inferences can change when analyst forecast bias is controlled for.

5.3.4 Return decomposition tests

This section uses a similar methodology to that employed in section 5.3.3, but uses rNEWS, which I develop using Vuolteenaho‟s return decomposition framework in Chapter 4, to proxy for ex ante expected returns. I again test for an association between expected returns and disclosure,

25 To further investigate the impact of bias on firm-level results, I regress the estimated ex ante bias in rPEG on the independent variables in Equation 5.8. (The above tests use different dependent variables, so that one cannot simply compare the coefficients across tests.) Column 4 of Table 20 Panel B shows no association between ex ante bias and regular guidance using OLS. Column 4 of Table 6 Panel C shows a significant association between the ex ante bias in rPEG and regular guidance issuance (p < 0.001) in the second-stage model. 45

using variations of Equation 5.8. Table 17 Panel D presents results of these tests. As in section 5.3.3, I estimate each model as a panel and cluster the standard errors at the firm level, and I include firm fixed effects since a Breusch-Pagan test rejects the null hypothesis of homoscedasticity.

In Column 1 of Table 17 Panel D, when rNEWS (with both NRET and NROE estimated directly) is regressed on risk factor proxies and the regular guidance variable, I find a significant negative estimated coefficient on ln(MVt), significant positive estimated coefficients on BMt-1 and Betat, and a significant negative estimated coefficient of -0.0244 on REGULARt, in support of H2. In Column 2, I include the Inverse Mills ratio from the first-stage model described in Equation 5.7 for a sub-sample of firms with the required data to estimate the first-stage model. The coefficient on the Inverse Mills ratio is statistically significant, consistent with self-selection affecting the COEC. The estimated coefficient on REGULARt in Column 3 is again negative and significantly different from zero (-0.0433; p < 0.001).

Results are somewhat similar in Columns 3 and 4 of Table 17 Panel D when NRET is estimated directly and NROE is estimated residually to estimate rNEWS. In Column 3, I obtain a negative estimated coefficient on REGULARt that is insignificantly different from zero. In Column 4, when I include the Inverse Mills ratio from the first-stage model, the estimated coefficient on REGULARt is negative and significantly different from zero (-0.0172; p < 0.001), in support of H2. To conclude, the above results which do not rely upon analyst forecasts (and thus are not impacted by analyst forecast bias), provide support for H2.

5.3.5 Asset-pricing tests To supplement the tests associating disclosure with accounting-based measures of the cost of equity capital, I test whether disclosure predicts stock returns following Pastor and Stambaugh (2003). To do so, I collect monthly stock returns for all firms on CRSP from 1997-

2006, as well as Rm, Rf, SMB, HML, and MOM from Professor Kenneth French‟s website via Wharton Research Data Services (WRDS). At the beginning of each calendar year, I create portfolios of regular guidance and non-guidance firms. Equally-weighted returns are calculated for each portfolio of firms. This yields portfolio returns for 120 months (from January 1997 – December 2006) for each of the two portfolios.

I next conduct asset-pricing tests following Pastor and Stambaugh (2003). Monthly portfolio returns for regular and non-guidance firms are regressed on the risk factors, as in Equation 5.9:

RETPt  0  1 *(Rm  Rf )t  2 *SMBt  3 *HMLt  4 *MOMt  jt (5.9) where Pt = the portfolio of regular or non-guidance firms. In Table 18, I estimate variations of Equation 5.9 using each of CAPM (Panel A), the Fama-French three-factor model (Panel B), and the four-factor model including momentum (Panel C). Following Pastor and Stambaugh (2003), I compare the alphas of the portfolios. For each specification, I find a negative difference between the intercepts of regular guidance and non-guidance portfolios. The difference between the intercepts is statistically significant at the 1% level in all three specifications. This suggests that regular guidance firms have lower returns than non-guidance firms, which provides support for H2.

5.4 Sensitivity analyses In this section, I assess the association between analyst forecast bias and disclosure as well as other sensitivity analyses. Given that analyst forecast bias has been shown to vary cross- sectionally, I use multivariate analysis to evaluate the association between future-period analyst forecast bias and disclosure. In addition, given that the decision to issue regular guidance is discretionary, I follow Heckman and use a two-stage model. Thus, I test for an association between future-period analyst forecast bias and regular management disclosure while controlling for variables shown in the extant literature to be associated with analyst forecast bias, as well as the Inverse Mills ratio from the first-stage model outlined in Equation 5.7: Bias ex _ post     Bias ex _ post   RET   ln(MV )   REGULAR (5.10) jt 0 1 jt 1 2 jt 3 jt 4 jt   5 jt   t Table 19 presents the results of estimating Equation 5.10 for year t (t+1) analyst forecast bias in Column 1 (2). After controlling for variables shown by the extant literature to be associated with analyst optimism, I find that analyst forecast bias is positively associated with management guidance issuance. In both Columns 1 and 2, the estimated coefficient on REGULARt is positive and significantly different from zero. Overall, it appears that year t (t+1) analyst forecast bias increases by approximately 0.2% (0.7%) of lagged share price in the presence of regular management guidance. Thus, it would appear that analysts are over-optimistic in setting future-

period forecasts following management guidance issuance. This is consistent with analysts issuing overly optimistic forecasts and managers attempting to lower expectations over time. It is possible that the relation between analyst forecast bias and disclosure changed following the October 2000 implementation of Reg FD which apparently lessened analyst incentives to curry favor with management in order to gain access to non-public information. I thus test for a disclosure – COEC association both before and after the implementation of Reg FD as well as whether the impact of ex ante analyst bias continues to affect inferences following 2000. Inferences are unchanged for portfolio-level tests for the period of 2001 – 2006 relative to the results reported for the full sample period of 1997 – 2006. Inferences are unchanged for firm- level tests for the period of 2001 – 2006 relative to the OLS results reported for the full sample period of 1997 – 2006 in Table 17 Panel B. Inferences are strengthened for firm-level tests for the period of 2001 – 2006 relative to the two-stage results reported for the full sample period of 1997 – 2006 in Table 17 Panel C. I focus in this study on regular guidance firms relative to firms that do not issue guidance, thus excluding from the analysis the firms that issue guidance only sporadically. Inferences throughout this study are similar, however, when I compare regular guidance firms with both non-guidance firms and firms that issue guidance sporadically. To address selection bias in the portfolio-level COEC tests, I conduct similar tests using a matched sample design. Where possible, I match each regular guidance firm-year with a non- guidance firm-year from the same industry and year that is closest in size. For this matched sample of 3,392 firm-years, I continue to find a significant negative association between regular disclosure and COEC. Francis, Nanda, and Olsson (2008) show that a negative relation between their disclosure index and COEC disappears when they control for earnings quality. When I add similar measures of earnings quality to the two-step analysis in section 5.3.3 (in other words, when I add either earnings variability or earnings quality or the absolute value of abnormal accruals to the first-stage model that predicts the firm‟s decision to issue regular guidance), inferences are unchanged. Inferences in section 5.3.3 are unchanged when firm fixed effects are excluded, and are similar when I replace the control for ex post forecast bias, with the bias prediction variables from Equation 2.5 in Chapter 2, and when I control for growth, following Hughes, Liu, and Liu (2008). Inferences in section 5.3.4 are unchanged, or slightly strengthened, when firm fixed effects are excluded. In addition, inferences in section 5.3.4 are unchanged or slightly

strengthened when I exclude firm-years with negative values of rNEWS. In the asset-pricing tests, I obtain similar results using value-weighted returns rather than equal-weighted returns.

5.5 Summary and conclusions

I investigate the association between disclosure, analyst forecast bias, and the cost of equity capital. After excluding ex ante analyst forecast bias from COEC estimates in a number of novel ways, I find that disclosure and the COEC are negatively associated for firms that regularly issue guidance. I predict and show that inferences can change when analyst forecast bias is controlled for or removed from COEC estimation. My findings add to the ongoing debate over the relation between disclosure and expected returns and are supported by theoretical research that generally predicts a lower cost of capital for firms that commit to higher disclosure levels (Verrecchia 1990, Diamond and Verrecchia 1991, Easley and O‟Hara 2004, and Lambert, Leuz, and Verrecchia 2007). Using regular guidance issuance to proxy for voluntary disclosure, I find evidence that supports the early findings of Botosan (1997). In addition, my findings demonstrate the need to control for analyst forecast bias in future cross-sectional COEC studies, particularly when analyst forecast bias is associated with the variable of interest. In portfolio-level tests, I show that controlling for ex ante analyst forecast bias is as effective as controlling for ex post analyst forecast bias. In firm-level tests, I show that removing ex ante analyst bias from COEC estimates outperforms a control for ex post analyst bias tests, as is common in the literature (see for example, Hail and Leuz 2006).

Chapter 6: Conclusion This dissertation examines the relation between disclosure, analyst forecast bias, and the cost of equity capital. In Chapter 2, I predict and remove ex ante analyst forecast bias from consensus analyst earnings forecasts, to produce de-biased analyst earnings forecasts that better proxy for the market‟s ex ante earnings expectations. In Chapters 3 and 4, I develop three novel estimates of ex ante expected returns. In Chapter 3, I estimate ex ante expected returns at the portfolio-level and the firm-level, respectively, using de-biased analyst forecasts. In Chapter 4, I develop a third measure of ex ante expected returns by applying the Vuolteenaho (2002) return decomposition framework to realized returns and accounting data. Chapter 5 studies the association between firms‟ commitment to disclose and COEC, by considering whether controlling for or excluding analyst forecast bias from COEC estimates affects inferences, after controlling for self-selection bias. I contribute to several branches of the accounting and finance literature. First, I contribute to the analyst forecast literature by predicting and removing systematic ex ante bias from analyst forecasts. I depart from the extant literature which generally analyzes the ex post bias in analyst forecasts. I extend Ali, Klein, and Rosenfeld (1992) with an ex ante model of analyst bias that incorporates the firm characteristics related to analyst bias, to effectively remove the mean 1.4% (2.5%) upward bias in year t (t+1) analyst earnings forecasts, using out-of-sample predictions. Using earnings-response coefficient (ERC) tests, I show that de-biased analyst forecasts better proxy for the market‟s ex ante earnings expectations relative to unadjusted analyst forecasts. Second, I offer three novel estimates of ex ante expected returns. The first two measures of ex ante expected returns are estimated using de-biased analyst forecasts at the portfolio-level and the firm-level, respectively. I further estimate a 2.4% mean ex ante bias in portfolio-level COEC estimates formed using analyst forecasts relative to estimates formed with de-biased analyst forecasts. My 2.4% estimate of ex ante bias in portfolio-level COEC estimates compares with Easton and Sommers‟ (2007) 2.8% estimate of ex ante bias in their estimates.26 I also consider the effect of ex ante analyst bias on firm-level COEC estimation.27 In my analysis, I find respective mean upward biases of 1.7%, 1.1%, and 5.5% when using the COEC estimation methods of Gebhardt et al. (2001), Claus and Thomas (2001), and Easton (2004). Using realized

26 Easton and Sommers (2007) measure bias as the difference between COEC estimates based on analyst forecasts and those based on current (realized) earnings for their sample of firms. 27 Along these lines, a recent working paper by Gode and Mohanram (2008) creates firm-level implied COEC estimates using analyst forecasts adjusted for predictable bias related to variables including firm growth, accruals, and investment. 50

returns I show that de-biased COEC estimates better proxy for expected returns, relative to unadjusted COEC estimates. The third measure of ex ante expected returns is an ex post estimate that I develop using the Vuolteenaho (2002) return decomposition framework. It does not rely upon analyst forecasts, and thus, is not impacted by analyst forecast bias. I show that this measure correlates well with risk factor proxies including size and beta. Finally, I extend the branch of literature that considers the association between information risk and the COEC by considering whether use of a de-biased, firm-level COEC estimate affects inferences, after controlling for risk. McInnis (2007) considers the relation between earnings smoothness and COEC using both (un-biased) realized returns and COEC estimates reverse-engineered from (biased) analyst forecasts to proxy for expected returns; he concludes that an inverse relation between earnings smoothness and the COEC results primarily from the optimistic bias in analysts‟ long-term earnings projections. Ogneva, Raghunandan, and Subramanyam (2007) find that, after controlling for predictable analyst forecast bias, there is no relation between internal control weakness and the COEC. As predicted by theory, I find that ex ante expected returns are lower for firms that regularly issue management guidance. The extant literature fails to allow for the potential impact of ex ante analyst forecast bias on the COEC– disclosure association, and largely overlooks self-selection bias. In my analysis, I predict and show that inferences can change when analyst forecast bias is controlled for or removed from COEC estimation. In portfolio-level tests, I find a significant negative relation between COEC and regular disclosure using either de-biased analyst forecasts or perfect foresight forecasts to proxy for expected earnings, but an insignificantly negative relation using unadjusted analyst forecasts. In firm-level tests, after controlling for self-selection bias, I find a significant negative relation between COEC and regular disclosure using de-biased analyst forecasts to proxy for expected earnings, but no relation using unadjusted analyst forecasts. Tests using realized returns (with and without adjustment for news), which do not rely on analyst forecasts and thus are not impacted by analyst bias, support my findings.

APPENDIX 1: Variable Definitions

Variable Definition

ex _ ante AdjAFT = ex _ ante, where T = t or t+1, where Bias is predicted using AF jT  Bias jT jT

Equation 2.5

AdjBias = AdjAF  eps T T T , where T = t or t+1 pricet1 aet = Abnormal earnings, i.e. epst  r  BVt1

AFT = Median EPS forecast for year T (where T = t or t+1), according to the I/B/E/S unadjusted summary report released during April of year t

ANFt = Number of I/B/E/S analysts that issue earnings forecasts for the firm for year t, according to the I/B/E/S summary report released during April of year t

Betat-1 = CAPM beta for the five years ending in December of year t-1 ex _ post Bias = AFt1  epst1 jt 1 pricet2 ex _ post Bias = AFT  epsT jT , where T = t or t+1 pricet1 Bias ex _ ante = Predicted ex ante bias in the consensus analyst forecast issued in April of jT year t, for year T earnings (and scaled by pricet-1), where T = t or t+1

BMt-1 = Ratio of common equity to market value of equity for the firm as of the end of year t-1

BVt-1 = Book value per share as of the end of year t-1

Dt = Dividend payment in year t

Et(●) = Expectations operator

Et(epsT) = Expected earnings per share for year T, as of year t epst = Actual earnings per share in year t, according to I/B/E/S unadjusted reports or, if I/B/E/S actual unavailable, according to Compustat

FDt = Indicator variable that equals 1 in the years 2000 and higher, and 0 otherwise HML = The performance of value stocks relative to growth stocks („High Minus Low‟) , according to Professor Kenneth French‟s website k = Dividend payout ratio

APPENDIX 1: Variable Definitions (continued)

Variable Definition

Leveraget-1 = Total long-term debt divided by total assets, as of the end of year t-1

Losst-1 = Indicator variable that equals 1 when EPS for year t-1 is negative, and 0 otherwise

Litigatet = Indicator variable that equals 1 for high-litigation industries, and 0 otherwise (as in Ajinkya, Bhoraj, and Sengupta 2005) MOM = The performance of high return relative to low return stocks, according to Professor Kenneth French‟s website

MVt = Market value of the firm as of March of year t

NRET = Return news, according to Vuolteenaho‟s (2002) return decomposition framework

NROE = Earnings news, according to Vuolteenaho‟s (2002) return decomposition framework

Pt = Earliest price available from CRSP in the five trading days following release of the April I/B/E/S Summary report pricet-1 = Price at the end of year t-1, from CRSP adj = Portfolio-level COEC estimated following Easton and Sommers (2007), rES 07 and using adjusted analyst forecasts est = Portfolio-level COEC estimated following Easton and Sommers (2007), rES 07 and using unadjusted analyst forecasts adj = Portfolio-level COEC estimated following Claus and Thomas (2001), and rCT using adjusted analyst forecasts est = Portfolio-level COEC estimated following Claus and Thomas (2001), and rCT using unadjusted analyst forecasts adj = Portfolio-level COEC estimated following Gebhardt, Lee, and rGLS Swaminathan (2001), and using adjusted analyst forecasts est = Portfolio-level COEC estimated following Gebhardt, Lee, and rGLS Swaminathan (2001), and using unadjusted analyst forecasts adj = Portfolio-level COEC estimated following Easton and Sommers (2007), rPEG and using adjusted analyst forecasts est = Portfolio-level COEC estimated following Easton and Sommers (2007), rPEG and using unadjusted analyst forecasts

REGULARt = Indicator variable that equals 1 when firm j issued management guidance during each of the years ending in March of year t-1, t and t+1, and 0 otherwise

APPENDIX 1: Variable Definitions (continued)

Variable Definition rett = Natural logarithm of (one plus the return for the year t)

RETt = Stock return for the 12 months ending in March of year t

RETt+1 = Stock return for the 12 months ending in May of year t+1

RET_EZt = Total raw return to shareholders adjusted for the value-weighted return on a market portfolio from CRSP, from the day following the release of the April summary report during year t through to the day preceding the release of earnings for year t

Rf = Risk-free rate roet = Natural logarithm of (one plus return on equity for the year t) ROE = Return on equity rNEWS = RET – NROE + NRET

SPORADICt = Indicator variable that equals 1 when firm j issued management guidance during the year ending of March of year t but not in both years t-1 and t+1, and 0 otherwise SMB = The performance of small stocks relative to big stocks (“Small Minus Big”), according to Professor Kenneth French‟s website

URjt = Abnormal return of the stock (i.e., the total raw return to shareholders adjusted for the value-weighted return on a market portfolio from CRSP) from the day preceding the release of the I/B/E/S Summary report to one day following the announcement of year t earnings

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Figure 1a – Comparison of bias in unadjusted and adjusted analyst forecasts for year t

Figure 1b – Comparison of bias in unadjusted and adjusted analyst forecasts for year t+1

Figure 2a – Comparison of unadjusted and adjusted COEC estimates using the methodology of Gebhardt, Lee, and Swaminathan (2001)

Figure 2b – Comparison of unadjusted and adjusted COEC estimates using the methodology of Claus and Thomas (2001)

Figure 2c – Comparison of unadjusted and adjusted COEC estimates using the methodology of Easton (2004)

Figure 3 - Timeline

management guidance issuance

April of year t-1 April of year t Bias ex _ post measured Bias ex _ post and t 1 t ex _ post Bias t1 measured

TABLE 1 Sample selection

This table summarizes the procedure used to select the samples used in this dissertation. Variable definitions are in Appendix 1.

Panel A: Sample selection procedure

Criteria Firm-years

U.S. firms with December fiscal year-ends and I/B/E/S EPS forecasts for year t-1 and year t from April I/B/E/S unadjusted summary reports, from 1991 to 2006 40,480 Less: Observations with missing I/B/E/S or Compustat actual EPS figure for year t-1 and/or year t (5,520) Observations with missing CRSP returns or prices, or with price below $1 or above $500 (7,656) Observations with Bias ex _ post or Bias ex _ post in top or bottom 1% t t 1 (530)

Sample used to estimate adjusted (de-biased) analyst forecasts (Chapter 2) 26,774 Sample for which de-biased analyst forecasts are estimated for year t, from 1994 to 2006 23,131

Less: Observations with missing or negative Compustat year t-1 book-value data (2,709)

Sample used to estimate implied cost of equity capital estimates (Chapter 3) 20,422 Sub-sample used to estimate portfolio-level cost of equity capital estimates 17,313 Sub-sample used to estimate GLS cost of equity capital estimates 18,877 Sub-sample used to estimate CT cost of equity capital estimates 16,872 Sample used to estimate PEG cost of equity capital estimates 7,383

Less: Observations from 1994 to 2006 (2,071) Observations for which beta cannot be estimated (1,815) Observations with missing or negative Compustat year t-2 book-value data (2,880) Sporadic guidance firm-years (2,327)

Sample of regular guidance and non-guidance firms (Chapter 5) 11,329

Separate samples

Observations with rNEWS estimated using direct estimation of NRET , from 1994 to 2006 (Chapter 4) 18,734

Observations with rNEWS estimated using residual estimation of NRET, from 1994 to 2006 (Chapter 4) 18,730

Observations from 1997 to 2006 used in asset-pricing tests (Chapter 5) 69,560

TABLE 2 Descriptive statistics for analyst forecast bias sample

This table provides the breakdown of the analyst forecast bias sample by year (Panel A), descriptive statistics for this sample (Panel B), and the average of annual cross-sectional Pearson correlations of Bias ex _ post and Bias ex _ post jt jt 1 with variables used to predict analyst forecast bias (Panel C). Variable definitions are in Appendix 1. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

Panel A: Distribution of analyst forecast bias sample by year

Number of Number of Year firm-years with Mean firm-years with Mean ex _ post ex _ post Biast data Biast1 data

1991 1,165 0.040 959 0.053 1992 1,159 0.024 976 0.032 1993 1,319 0.017 1,156 0.025 1994 1,488 0.013 1,230 0.023 1995 1,577 0.018 1,305 0.035 1996 1,715 0.017 1,342 0.029 1997 1,822 0.017 1,415 0.037 1998 1,967 0.020 1,522 0.033 ` 1999 1,921 0.016 1,444 0.030 2000 1,779 0.017 1,374 0.042 2001 1,654 0.024 1,360 0.031 2002 1,695 0.011 1,498 0.022 2003 1,647 0.009 1,437 0.011 2004 1,745 0.004 1,565 0.011 2005 2,019 0.008 1,769 0.010 2006 2,102 0.007 1,830 0.018

Total 26,774 0.016 22,141 0.026

TABLE 2 (continued) Descriptive statistics for analyst forecast bias sample

Panel B: Descriptive statistics for analyst forecast bias sample

N Mean Median Std. Dev. pricet-1 26,774 28.35 23.39 21.64

AFt 26,774 1.707 1.480 1.797 epst 26,774 1.352 1.220 1.826 ex _ post 26,774 0.016 0.006 0.041 Biast

AFt+1 22,141 2.110 1.800 1.886 epst+1 22,141 1.443 1.300 2.033 ex _ post 22,141 0.026 0.017 0.052 Biast1 ex _ post 26,774 0.013 0.005 0.044 Biast1

MVt 26,774 3,523 582 14,821

RETt 26,774 0.202 0.122 0.630

RET_EZt 26,774 0.020 -0.024 0.470

Panel C: Pearson correlation of analyst forecast bias with prediction variables

Variable Bias ex _ post Bias ex _ post jt jt 1

Bias ex _ post 0.238*** 0.143*** jt 1 Number of years correlation is significant (16/16) (14/15)

RETt -0.137*** -0.073*** Number of years correlation is significant (15/16) (10/15) ln(MVt) -0.161*** -0.163*** Number of years correlation is significant (16/16) (15/15)

RET_EZt -0.200*** -0.238*** Number of years correlation is significant (16/16) (15/15)

TABLE 3 Tests of the predictable bias in analyst forecasts

This table reports the results of regressions of Bias ex _ post (Panel A) and Bias ex _ post (Panel B) on the prediction jt jt 1 variables used in Ali, Klein, and Rosenfeld (1992) plus firm size and a control for ex post measurement error, as in the following equation: Bias ex _ post    Bias ex _ post  RET  ln(MV )  RET _ EZ   jt 0 1 jt 1 2 jt 3 jt 4 jt t

Figures in parentheses are t-statistics. Mean coefficients and t-statistics are calculated following Fama and MacBeth (1973). Significance levels are based on one-tailed tests where there is a prediction for the sign of the coefficient and based on two-tailed tests otherwise. ***, ** and * denote significance at the 1%, 5% and 10% levels, respectively. Variable definitions are in Appendix 1.

Panel A: Year t bias Bias ex _ post Intercept jt 1 RETt ln(MVt) RET_EZt Adjusted (?) (+) (-) (-) (-) N R2

1991 0.074 0.308 -0.021 -0.006 -0.060 1,165 0.152 (8.61) (7.52) (-3.81) (-4.71) (-8.60) 1992 0.044 0.299 -0.009 -0.004 -0.031 1,159 0.229 (8.46) (14.42) (-2.77) (-5.11) (-7.00) 1993 0.036 0.216 -0.012 -0.003 -0.018 1,319 0.164 (10.16) (10.80) (-5.60) (-5.82) (-6.73) 1994 0.034 0.049 -0.005 -0.004 -0.023 1,488 0.097 (11.08) (3.71) (-2.25) (-7.61) (-8.09) 1995 0.036 0.210 -0.008 -0.003 -0.022 1,577 0.131 (11.13) (8.45) (-3.30) (-6.43) (-7.97) 1996 0.022 0.170 -0.008 -0.001 -0.036 1,715 0.158 (7.00) (9.05) (-5.08) (-2.27) (-12.33) 1997 0.026 0.251 -0.005 -0.002 -0.014 1,822 0.130 (9.78) (12.59) (-2.48) (-4.85) (-7.55) 1998 0.031 0.189 -0.007 -0.002 -0.019 1,967 0.145 (9.85) (9.84) (-5.88) (-4.70) (-9.79) 1999 0.039 0.330 -0.006 -0.005 -0.004 1,921 0.102 (9.27) (10.50) (-3.16) (-7.11) (-3.87) 2000 0.048 0.280 -0.004 -0.004 -0.026 1,779 0.195 (12.47) (12.70) (-5.77) (-7.49) (-12.65) 2001 0.063 0.212 -0.007 -0.006 -0.029 1,654 0.127 (14.31) (6.38) (-3.41) (-8.60) (-11.14) 2002 0.026 0.143 -0.002 -0.003 -0.018 1,695 0.108 (7.50) (8.61) (-1.42) (-5.37) (-8.32) 2003 0.029 0.288 -0.003 -0.003 -0.013 1,647 0.107 (8.60) (10.76) (-1.45) (-6.42) (-6.78) 2004 0.011 0.073 -0.001 -0.001 -0.019 1,745 0.066 (4.28) (5.94) (-0.98) (-2.50) (-9.55) 2005 0.023 0.078 -0.003 -0.002 -0.022 2,019 0.083 (7.74) (4.84) (-1.36) (-4.61) (-11.15)

Sign (+/-) (15/0) (15/0) (0/15) (0/15) (0/15)

Mean 0.036*** 0.206*** -0.007*** -0.003*** -0.024*** 24,672 0.133 (8.60) (8.90) (-5.24) (-8.06) (-7.14)

TABLE 3 (continued) Tests of the predictable bias in analyst forecasts

Panel B: Year t+1 bias Bias ex _ post Intercept jt 1 RETt ln(MVt) RET_EZt+1 Adjusted (?) (+) (-) (-) (-) N R2

1991 0.105 0.278 -0.014 -0.008 -0.050 959 0.153 (10.87) (5.74) (-2.56) (-5.66) (-10.12) 1992 0.049 0.238 -0.004 -0.003 -0.020 976 0.142 (7.95) (9.08) (-1.30) (-3.71) (-7.12) 1993 0.056 0.148 0.002 -0.005 -0.016 1,115 0.094 (11.60) (5.14) (0.80) (-7.15) (-6.77) 1994 0.058 0.015 0.002 -0.006 -0.015 1,230 0.085 (12.30) (0.67) (0.63) (-8.11) (-6.93) 1995 0.054 0.129 -0.0004 -0.004 -0.030 1,305 0.088 (9.39) (2.72) (-0.10) (-3.97) (-9.87) 1996 0.041 0.162 -0.006 -0.002 -0.023 1,342 0.096 (9.03) (5.21) (-3.08) (-3.11) (-9.04) 1997 0.057 0.256 -0.009 -0.004 -0.015 1,415 0.091 (11.50) (6.57) (-2.62) (-5.24) (-7.03) 1998 0.067 0.078 -0.006 -0.005 -0.002 1,522 0.036 (12.38) (1.91) (-2.85) (-6.10) (-2.06) 1999 0.089 0.352 -0.002 -0.010 -0.014 1,444 0.113 (11.28) (5.86) (-0.64) (-8.26) (-8.64) 2000 0.113 0.167 -0.006 -0.009 -0.029 1,374 0.161 (17.21) (4.14) (-4.98) (-9.42) (-13.45) 2001 0.088 -0.023 -0.006 -0.008 -0.026 1,360 0.168 (16.49) (-0.54) (-2.27) (-10.07) (-12.67) 2002 0.054 0.199 -0.001 -0.005 -0.022 1,498 0.148 (11.43) (8.59) (-0.34) (-7.49) (-12.38) 2003 0.043 0.298 0.004 -0.004 -0.026 1,437 0.108 (8.45) (6.43) (1.11) (-6.11) (-10.78) 2004 0.025 0.218 0.001 -0.002 -0.035 1.565 0.188 (5.50) (9.11) (0.80) (-3.17) (-17.03) 2005 0.032 0.152 0.002 -0.003 -0.035 1,769 0.168 (8.23) (5.47) (0.78) (-5.43) (-16.89)

Sign (+/-) (15/0) (14/1) (5/10) (0/15) (0/15)

Mean 0.062*** 0.178*** -0.003*** -0.005*** -0.024*** 20,311 0.122 (9.38) (6.71) (-2.24) (-8.31) (-8.15)

TABLE 4 Comparison of adjusted and unadjusted analyst earnings forecasts

This table compares unadjusted and adjusted analyst forecasts and their bias and accuracy, from 1994-2006. Panels A and B present unadjusted and adjusted analyst forecasts for years t and t+1, respectively. ***, ** and * denote significance at the 1%, 5% and 10% levels, respectively. Variable definitions are in Appendix 1.

Panel A: Annual comparison of unadjusted and adjusted bias in year t forecasts AFt AdjAFt epst Mean t-statistic Mean t-statistic Mean t-statistic N

1.661 21.59 1.319 17.14 1.337 17.38 23,131

ex _ post Bias AdjBiast t Mean t-statistic Mean t-statistic Difference (1) (2) (1) – (2)

0.014 8.76 -0.002 -0.91 0.016***

MSEt AdjMSEt Mean t-statistic Mean t-statistic Difference (3) (4) (3) – (4)

0.002 7.56 0.001 8.54 0.0003***

Panel B: Annual comparison of unadjusted and adjusted bias in year t+1 forecasts AFt+1 AdjAFt+1 epst+1 Mean t-statistic Mean t-statistic Mean t-statistic N

2.050 26.65 1.323 17.20 1.405 15.91 19,122

ex _ post Bias AdjBiast+1 t 1 Mean t-statistic Mean t-statistic Difference (1) (2) (1) – (2)

0.025 8.77 -0.004 -1.11 0.029***

MSEt+1 AdjMSEt+1 Mean t-statistic Mean t-statistic Difference (4) (5) (4) – (5)

0.003 7.82 0.003 8.47 0.001***

TABLE 5 Earnings response coefficient tests

This table presents the results of earnings response coefficient tests using each of unadjusted (AF) and adjusted (AdjAF) analyst forecasts to proxy for expected earnings (E(eps)) in computation of the earnings surprise variable, as in the following equation: (eps  E(eps )) jt jt URjt  0  1 *  jT price jt1

Panel A (B) presents results for year t (t+1). Figures in parentheses are mean t-statistics and are calculated following Fama and MacBeth (1973). Significance levels are based on one-tailed tests where there is a prediction for the sign of the coefficient and based on two-tailed tests otherwise. ***, ** and * denote significance at the 1%, 5% and 10% levels, respectively. Variable definitions are in Appendix 1.

Panel A: Year t tests E(EPSjt)= AFjt AdjAFjt Difference (1) (2) (2) – (1)

Intercept 0.052* 0.019 (t-stat.) (1.82) (0.65)

(epst-E(epst))/pricet-1 2.162*** 2.367*** 0.204 (t-stat.) (1.41) (17.03) (1.63)

Adj. R2 0.048 0.051 0.004 N 23,131 23,131

Panel B: Year t+1 tests E(EPSjt)= AFjt+1 AdjAFjt+1 Difference (1) (2) (2) – (1) and are Intercept 0.109* 0.054 (t-stat.) (2.09) (1.10)

(epst-E(epst+1))/pricet-1 1.847*** 1.969*** 0.122** (t-stat.) (9.57) (9.30) (2.57)

Adj. R2 0.064 0.071 0.007** N 19,122 19,122

73 TABLE 6 Cost of equity capital estimates following Easton and Sommers (2007)

This table provides portfolio-level COEC estimates for a sample of 17,313 firm-years with positive I/B/E/S analyst forecasts and non-missing adjusted forecasts and realized earnings for year t and non-negative book value for year t-1. Panel A estimates the COEC using analyst forecasts; Panel B uses adjusted forecasts; and Panel C uses perfect foresight forecasts (i.e., realized earnings) to proxy for expected earnings. Panel D summarizes and compares the COEC estimates from Panels A – C. Mean estimated coefficients and t-statistics are calculated following Fama and MacBeth (1973). Chapter 2 provides details on how analyst forecasts are adjusted. ***, ** and * denote significance at the 1%, 5% and 10% levels, respectively. Variable definitions are in Appendix 1.

AF P Panel A: Using unadjusted analyst forecasts. Equation is jt est est jt .   0   1   jt BV j,t1 BV j,t1  est est est est est 2 0 t-statistic  1 t-statistic rES   0   1 N Adj. R

1994 0.0746 25.11 0.0350 30.74 0.1096 1,021 0.481 1995 0.0860 27.32 0.0350 30.25 0.1210 1,114 0.451 1996 0.0912 32.81 0.0271 29.37 0.1183 1,209 0.416 1997 0.0800 30.67 0.0325 36.29 0.1124 1,297 0.504 1998 0.0911 33.56 0.0217 33.28 0.1128 1,396 0.442 1999 0.1024 38.86 0.0203 28.48 0.1228 1,356 0.374 2000 0.1204 41.91 0.0161 21.43 0.1365 1,294 0.262 2001 0.0983 32.98 0.0220 24.54 0.1202 1,229 0.329 2002 0.0656 21.31 0.0270 29.44 0.0926 1,223 0.415 2003 0.0634 21.53 0.0381 33.90 0.1015 1,310 0.467 2004 0.0666 23.74 0.0300 35.18 0.0965 1,440 0.462 2005 0.0565 20.60 0.0378 41.12 0.0943 1,667 0.502 2006 0.0729 24.72 0.0289 35.71 0.1018 1,677 0.422 Mean 0.0819 16.01 0.0286 14.91 0.1105 17,313 0.422

TABLE 6 (continued) Cost of equity capital estimates following Easton and Sommers (2007)

Panel B: Using adjusted analyst forecasts. Equation is AdjAF jt adj adj Pjt .   0   1   jt BV j,t1 BV j,t1 adj adj adj adj adj 2  0 t-statistic  1 t-statistic rES   0   1 N Adj. R

1994 0.0572 16.65 0.0181 13.77 0.0753 1,021 0.156 1995 0.0726 21.42 0.0267 21.43 0.0993 1,114 0.292 1996 0.0722 24.73 0.0235 24.18 0.0957 1,209 0.326 1997 0.0655 23.57 0.0240 25.20 0.0896 1,297 0.328 1998 0.0632 23.70 0.0198 30.95 0.0831 1,396 0.407 1999 0.0787 31.20 0.0118 17.36 0.0905 1,356 0.181 2000 0.0892 31.06 0.0192 25.62 0.1084 1,294 0.336 2001 0.0865 24.41 0.0127 11.93 0.0992 1,229 0.103 2002 0.0393 10.99 0.0208 19.50 0.0601 1,223 0.237 2003 0.0450 14.38 0.0289 24.14 0.0739 1,310 0.308 2004 0.0513 16.21 0.0254 26.46 0.0767 1,440 0.327 2005 0.0493 16.36 0.0340 33.72 0.0832 1,667 0.404 2006 0.0637 21.04 0.0263 31.63 0.0901 1,677 0.364 Mean 0.0641 14.89 0.0224 12.96 0.0865 17,313 0.290

TABLE 6 (continued) Cost of equity capital estimates following Easton and Sommers (2007)

eps P Panel C: Using perfect foresight forecasts. Equation is jt PF PF jt .   0   1   jt BV j,t1 BV j,t1 PF PF PF PF PF 2  0 t-statistic  1 t-statistic rES   0   1 N Adj. R

1994 0.0680 18.28 0.0283 19.88 0.0963 1,021 0.279 1995 0.0757 19.55 0.0251 17.62 0.1008 1,114 0.218 1996 0.0769 22.07 0.0191 16.48 0.0960 1,209 0.183 1997 0.0665 20.87 0.0254 23.20 0.0919 1,297 0.293 1998 0.0668 19.46 0.0165 20.01 0.0833 1,396 0.223 1999 0.0889 26.28 0.0151 16.54 0.1040 1,356 0.168 2000 0.1043 29.97 0.0139 15.32 0.1182 1,294 0.153 2001 0.0677 19.16 0.0179 16.89 0.0856 1,229 0.188 2002 0.0554 15.59 0.0246 23.32 0.0800 1,223 0.308 2003 0.0543 15.44 0.0363 27.00 0.0906 1,310 0.357 2004 0.0663 19.78 0.0261 25.65 0.0923 1,440 0.313 2005 0.0501 15.12 0.0349 31.48 0.0849 1,667 0.371 2006 0.0682 20.16 0.0263 28.26 0.0944 1,677 0.314 Mean 0.0699 17.39 0.0238 12.16 0.0937 17,313 33.47

Panel D: Summary of mean portfolio-level cost of equity capital estimates est adj PF est adj est PF adj PF rES rES rES rES  rES rES  rES rES  rES

Mean 0.1105*** 0.0865*** 0.0937*** 0.0240*** 0.0169*** -0.0094*

76 TABLE 7 Cost of equity capital estimates following Gebhardt, Lee, and Swaminathan (2001)

This table provides cost of equity capital estimates for a sample of 18,877 firm-years from 1994-2006 with non-missing I/B/E/S analyst earnings forecasts and adjusted analyst forecasts for years t and t+1, as well as book value from year t-1, dividend payout ratio from year t-1, and industry median ROE for the previous five years. Columns (1) through (4) estimate the COEC using I/B/E/S analyst forecasts to proxy for expected earnings and the following equation: AF AF AF AF t  r t 1  r T 1 t 1i  r t T  r P  BV  BVt 1 BV  BVt BV  BVt i BV  BVt T 1 BV 0 t1 t1 2 t  i2 ti T 1 tT 1 (1 r) (1 r) i1 (1 r) r(1 r) Columns (5) through (8) use adjusted analyst forecasts to proxy for expected earnings and the following equation: AdjAF AdjAF AdjAF AdjAF t  r t 1  r T 1 t 1i  r t T  r P  BV  BVt 1 BV  BVt BV  BVt i BV  BVt T 1 BV 0 t1 t1 2 t  i2 ti T 1 tT 1 (1 r) (1 r) i1 (1 r) r(1 r) est adj Columns (9) and (10) compare rGLS and rGLS from Columns (1) and (5). Mean t-statistics are calculated following Fama and MacBeth (1973). ***, ** and * denote significance at the 1%, 5% and 10% levels, respectively. Chapter 2 provides details on how analyst forecasts are adjusted. Variable definitions are in Appendix 1.

est adj rGLS  rGLS Mean Median STD t-statistic Mean Median STD t-statistic Mean t-statistic N (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

1994 0.0982 0.0962 0.0675 47.71 0.0762 0.0739 0.0604 41.36 0.0220*** 9.04 1,077 1995 0.0997 0.0985 0.0533 63.26 0.0845 0.0852 0.0390 73.26 0.0152*** 9.23 1,144 1996 0.0935 0.0942 0.0505 65.85 0.0776 0.0774 0.0407 67.80 0.0159*** 10.68 1,266 1997 0.0966 0.0942 0.0625 57.43 0.0768 0.0750 0.0611 46.64 0.0198*** 9.35 1,379 1998 0.0824 0.0800 0.0566 56.97 0.0667 0.0599 0.0892 29.29 0.0157*** 6.07 1,532 1999 0.0951 0.0918 0.0730 49.39 0.0760 0.0681 0.0964 29.87 0.0191*** 6.37 1,437 2000 0.0985 0.0955 0.0796 45.75 0.0785 0.0759 0.0606 47.90 0.0199*** 8.15 1,367 2001 0.0914 0.0887 0.0476 69.77 0.0744 0.0667 0.0900 30.06 0.0170*** 6.89 1,321 2002 0.0840 0.0817 0.0708 45.44 0.0657 0.0586 0.1018 24.72 0.0183*** 5.75 1,466 2003 0.0970 0.0922 0.0767 48.26 0.0761 0.0700 0.0652 44.58 0.0209*** 9.07 1,456 2004 0.0847 0.0822 0.0633 53.35 0.0699 0.0675 0.0622 44.86 0.0148*** 6.95 1,593 2005 0.0894 0.0863 0.0736 52.41 0.0779 0.0746 0.0757 44.42 0.0115*** 5.12 1,861 2006 0.0819 0.0798 0.0765 47.66 0.0744 0.0715 0.0809 40.91 0.0076*** 3.51 1,978

Mean 0.0917 0.0878 0.0674 50.47 0.0750 0.0707 0.0748 53.47 0.0167*** 15.09 18,877

TABLE 8 Cost of equity capital estimates following Claus and Thomas (2001)

This table provides cost of equity capital estimates for a sample of 16,872 firm-years from 1994-2006 with non-missing I/B/E/S analyst earnings forecasts and adjusted analyst forecasts for years t and t+1, as well as non-negative analyst forecasts and adjusted analyst forecasts for year t+1, book value for year t-1, and the dividend payout ratio from year t-1. Columns (1) through (4) estimate the COEC using I/B/E/S analyst earnings forecasts to proxy for expected earnings and the following equation: AF  rBV AF  rBV AF  rBV AF  rBV AF  rBV (AF  rBV )*(1 g) P  BV  t t1  t1 t  t2 t1  t3 t2  t4 t3  t4 t3 0 t1 (1 r) (1 r) 2 (1 r)3 (1 r) 4 (1 r)5 (r  g)(1 r)5 Columns (5) through (8) use adjusted analyst forecasts to proxy for expected earnings and the following equation: AdjAF  rBV AdjAF  rBV AdjAF  rBV AdjAF  rBV AdjAF  rBV (AdjAF  rBV )*(1 g) P  BV  t t1  t1 t  t2 t1  t3 t2  t4 t3  t4 t3 0 t1 (1 r) (1 r) 2 (1 r)3 (1 r) 4 (1 r)5 (r  g)(1 r)5 est adj Columns (9) and (10) compare rCT and rCT from Columns (1) and (5). Mean t-statistics are calculated following Fama and MacBeth (1973). ***, ** and * denote significance at the 1%, 5% and 10% levels, respectively. Chapter 2 provides details on how analyst forecasts are adjusted. Variable definitions are in Appendix 1.

est adj rCT  rCT Mean Median STD t-statistic Mean Median STD t-statistic Mean t-statistic N (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

1994 0.1126 0.1033 0.0811 43.34 0.1749 0.0741 0.3748 14.56 -0.0622*** -5.03 974 1995 0.1028 0.0969 0.0337 101.22 0.0884 0.0748 0.1273 23.06 0.0144*** 3.70 1,103 1996 0.0963 0.0939 0.0281 118.60 0.0942 0.0713 0.1739 18.72 0.0021 0.41 1,193 1997 0.0977 0.0927 0.1485 100.28 0.0839 0.0666 0.1485 20.20 0.0138*** 3.26 1,274 1998 0.0784 0.0727 0.0301 96.77 0.0600 0.0480 0.1140 19.55 0.0184*** 5.88 1,373 1999 0.1027 0.0929 0.0710 51.56 0.0946 0.0610 0.2019 16.68 0.0081 1.34 1,257 2000 0.1052 0.0985 0.0779 46.88 0.0802 0.0667 0.1158 24.02 0.0251*** 6.39 1,205 2001 0.0957 0.0903 0.0347 88.94 0.0789 0.0578 0.1606 15.83 0.0168*** 3.33 1,039 2002 0.0824 0.0768 0.0413 66.10 0.0523 0.0435 0.0938 18.49 0.0301*** 9.77 1,099 2003 0.0909 0.0854 0.0452 71.65 0.0636 0.0506 0.1184 19.14 0.0272*** 7.73 1,270 2004 0.0774 0.0728 0.0450 66.27 0.0602 0.0489 0.0858 22.60 0.0174*** 6.03 1,484 2005 0.0831 0.0781 0.0495 70.03 0.0673 0.0611 0.0844 33.29 0.0158*** 6.80 1,744 2006 0.0758 0.0734 0.0294 111.00 0.0664 0.0614 0.0792 36.15 0.0094*** 5.09 1,857

Mean 0.0924 0.0850 0.0498 27.73 0.0813 0.0605 0.1559 9.85 0.0111* 1.84 16,872

TABLE 9 Cost of equity capital estimates following Easton (2004)

This table provides cost of equity capital estimates for a sample of 7,383 firm-years from 1994-2006 with non-negative and non-decreasing analyst earnings forecasts and adjusted analyst forecasts for year t+1. Columns (1) through (4) estimate the COEC using I/B/E/S analyst forecasts to proxy for expected earnings as in the following equation:

est (AF2  AF 1) rPEG  P0 Columns (5) through (8) use adjusted analyst forecasts to proxy for expected earnings as in the following equation:

adj (AdjAF2  AdjAF 1) rPEG  P0 est adj Columns (9) and (10) compare rPEG and rPEG from Columns (1) and (5). Mean t-statistics are calculated following Fama and MacBeth (1973). ***, ** and * denote significance at the 1%, 5% and 10% levels, respectively. Chapter 2 provides details on how analyst forecasts are adjusted. Variable definitions are in Appendix 1.

est adj rPEG  rPEG Mean Median STD t-statistic Mean Median STD t-statistic Mean t-statistic N (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

1994 0.1426 0.1310 0.0447 77.38 0.0860 0.0722 0.0599 34.85 0.0566*** 60.38 588 1995 0.1348 0.1225 0.0470 88.86 0.0920 0.0791 0.0601 47.39 0.0429*** 63.32 959 1996 0.1460 0.1348 0.0478 72.08 0.0944 0.0812 0.0636 35.10 0.0516*** 50.56 558 1997 0.1600 0.1437 0.0599 62.36 0.0980 0.0793 0.0771 29.67 0.0620*** 56.12 545 1998 0.1663 0.1514 0.0578 53.80 0.1047 0.0879 0.0732 26.73 0.0616*** 51.10 349 1999 0.1794 0.1657 0.0639 55.47 0.1138 0.0960 0.0782 28.74 0.0657*** 41.74 390 2000 0.1666 0.1542 0.0725 44.31 0.1000 0.0776 0.0778 24.79 0.0667*** 39.63 372 2001 0.1746 0.1715 0.0643 38.49 0.1083 0.1004 0.0724 21.22 0.0663*** 31.02 201 2002 0.1630 0.1492 0.0687 42.60 0.1123 0.0944 0.0804 25.06 0.0507*** 29.82 322 2003 0.1846 0.1692 0.0729 40.93 0.1180 0.0940 0.0873 21.85 0.0666*** 35.72 261 2004 0.1283 0.1166 0.0511 60.50 0.0792 0.0658 0.0604 31.82 0.0492*** 53.50 589 2005 0.1273 0.1123 0.0542 68.78 0.0793 0.0635 0.0619 37.51 0.0480*** 67.09 857 2006 0.1049 0.0964 0.0335 116.65 0.0741 0.0665 0.0397 71.97 0.0308*** 71.97 1,392

Mean 0.1522 0.1271 0.0580 23.15 0.0969 0.0748 0.0654 24.39 0.0553*** 18.11 7,383

TABLE 10 Summary and correlation of cost of equity capital estimates

This table provides a summary of cost of equity capital estimates calculated using the methodologies of Gebhardt, Lee, and Swaminathan (2001), Claus and Thomas (2001), Easton (2004), and Easton and Sommers (2007). In Panel A, Columns (1), (3), (5), and (7) estimate the COEC using consensus I/B/E/S analyst EPS forecasts to proxy for expected earnings while Columns (2), (4), (6), and (8) use adjusted analyst forecasts to proxy for expected earnings. Panel B presents the average of annual cross-sectional correlations between the six firm-level COEC estimates, with Pearson (Spearman) correlations below (above) the diagonal. ***, ** and * denote significance at the 1%, 5% and 10% levels, respectively. Chapter 2 provides details on how analyst forecasts are adjusted. Variable definitions are in Appendix 1.

Panel A: Summary of cost of equity capital estimates est adj est adj est adj est adj Risk-free rGLS rGLS rCT rCT rPEG rPEG rES 07 rES 07 rate (1) (2) (3) (4) (5) (6) (7) (8) (9)

1994 0.0982 0.0762 0.1126 0.1749 0.1426 0.0860 0.1096 0.0753 0.0781 1995 0.0997 0.0845 0.1028 0.0884 0.1348 0.0920 0.1210 0.0993 0.0571 1996 0.0935 0.0776 0.0963 0.0942 0.1460 0.0944 0.1183 0.0957 0.0630 1997 0.0966 0.0768 0.0977 0.0839 0.1600 0.0980 0.1124 0.0896 0.0581 1998 0.0824 0.0667 0.0784 0.0600 0.1663 0.1047 0.1128 0.0831 0.0465 1999 0.0951 0.0760 0.1027 0.0946 0.1794 0.1138 0.1228 0.0905 0.0628 2000 0.0985 0.0785 0.1052 0.0802 0.1666 0.1000 0.1365 0.1084 0.0524 2001 0.0914 0.0744 0.0957 0.0789 0.1746 0.1083 0.1202 0.0992 0.0509 2002 0.0840 0.0657 0.0824 0.0523 0.1630 0.1123 0.0926 0.0601 0.0403 2003 0.0970 0.0761 0.0909 0.0636 0.1846 0.1180 0.1015 0.0739 0.0427 2004 0.0847 0.0699 0.0774 0.0602 0.1283 0.0792 0.0965 0.0767 0.0423 2005 0.0894 0.0779 0.0831 0.0673 0.1273 0.0793 0.0943 0.0832 0.0447 2006 0.0819 0.0744 0.0758 0.0664 0.1049 0.0741 0.1018 0.0901 0.0456

Mean 0.0917 0.0750 0.0924 0.0813 0.1522 0.0969 0.1105 0.0865 0.0527 N 18,877 18,877 16,872 16,872 7,383 7,383 17,313 17,313

Panel B: Correlation of firm-level cost of equity capital estimates adj rPEG (1) (2) (3) (4) (5) (6)

est r 0.1930 0.5444 0.0344 0.2960 0.2647 GLS *** *** *** *** *** adj r 0.1930 0.3007 0.0601 0.2231 0.2122 GLS *** *** ** *** *** est r 0.5444 0.3007 0.0395 0.4208 0.3883 CT *** *** *** *** adj r 0.0344 0.0601 0.0395 0.0861 0.0823 CT *** *** *** *** 0.2960 0.2231 0.4208 0.0861 0.9371 *** *** *** *** *** 0.2647 0.2122 0.3883 0.0823 0.9371 *** *** *** *** ***

TABLE 11 Multivariate analysis of cost of equity capital estimates and realized returns

This table presents firm-level regressions of year t+1 realized returns on cost of equity capital estimates, return news (Nret), and earnings news (Nroe) for a sub-sample of firms for which Nret and Nroe can be estimated. COEC estimates are formed using either unadjusted analyst forecasts ( r est ) or adjusted analyst forecasts ( r adj ). Panels A, B, and C employ cost of equity capital estimates formed following Gebhardt, Lee, and Swaminathan (2001) („GLS‟), Claus and Thomas (2001) („CT‟), and Easton (2004) („PEG‟), respectively. Columns (1) to (3) of each Panel use variations of the following test:

RET t1   0  1Et (RETt1 )   t1

Columns (4) to (6) use variations of the following test:

 0  1Et (RETt1 )   2 N RET ,t1   3 N ROE,t1   t1

Mean t-statistics are in parentheses, and are calculated following Fama and MacBeth (1973). Significance levels are based on one-tailed tests where there is a prediction for the sign of the coefficient and based on two-tailed tests otherwise. ***, ** and * denote significance at the 1%, 5% and 10% levels, respectively. Chapter 2 provides details on how analyst forecasts are adjusted. Chapter 4 discusses the estimation of return news and earnings news. Variable definitions are in Appendix 1.

Panel A: Regression of year t+1 returns on GLS COEC estimates

Predicted sign (1) (2) (3) (4) (5) (6)

Intercept ? 0.126** 0.119* 0.114* 0.123* 0.120* 0.116* (2.32) (2.15) (2.08) (2.04) (1.97) (1.93) r adj + 0.239 0.344 0.141*** 0.210*** GLS (1.27) (1.34) (3.27) (3.91) est r + 0.266 0.148*** GLS (1.29) (3.83) r est  r adj ? 0.191 0.103*** GLS GLS (0.23) (3.46) Nroe + 1.103*** 1.103*** 1.102*** (13.96) (13.97) (13.96) Nret - -1.181*** -1.182*** -1.182*** (-13.54) (-13.48) (-13.50)

N 11,492 11,492 11,492 11,492 11,492 11,492 Adj. R2 0.004 0.006 0.007 0.906 0.906 0.906

TABLE 11 (continued) Multivariate analysis of cost of equity capital estimates and realized returns

Panel B: Regression of year t+1 returns on CT COEC estimates

Predicted sign (1) (2) (3) (4) (5) (6)

Intercept ? 0.148** 0.125** 0.124** 0.132* 0.106* 0.105* (3.10) (2.67) (2.68) (2.16) (1.81) (1.79) r adj + 0.022 0.269 0.024* 0.303*** CT (0.61) (1.15) (1.43) (4.55) est r + 0.252 0.289*** CT (1.07) (3.86) r est  r adj ? 0.256 0.283*** CT CT (1.08) (3.76) Nroe + 1.113*** 1.112*** 1.112*** (14.89) (14.90) (14.91) Nret - -1.193*** -1.192*** -1.193*** (-15.32) (-15.10) (-15.12)

N 10,419 10,419 10,419 10,419 10,419 10,419 Adj. R2 0.00001 0.008 0.008 0.907 0.908 0.908

Panel C: Regression of year t+1 returns on PEG COEC estimates

Predicted sign (1) (2) (3) (4) (5) (6)

Intercept ? 0.138** 0.131** 0.171** 0.133** 0.105* 0.067 (2.68) (2.76) (2.24) (2.19) (1.79) (1.17) r adj + 0.115 -0.045 0.101** 0.292** PEG (0.48) (-0.15) (2.85) (3.65) est r + 0.094 0.242*** PEG (0.37) (4.10) r est  r adj ? -0.427 0.813** PEG PEG (-0.43) (3.26) Nroe + 1.123*** 1.123*** 1.123*** (13.37) (13.40) (13.25) Nret - -1.259*** -1.256*** -1.256*** (-13.79) (-13.89) (-13.74)

N 4,409 4,409 4,409 4,409 4,409 4,409 Adj. R2 0.003 0.005 0.019 0.911 0.912 0.913

TABLE 12 Comparison of cost of equity capital estimates and risk factor proxies

This table analyzes firm-level cost of equity capital estimates and risk factor proxies. Panel A contains descriptive statistics for the risk factor proxies. Panels B through D present cost of equity capital estimates for portfolios of firms formed yearly according to the level of firm size (Panel B), book-to-market (C), leverage (C), and firm beta (D), with Portfolio 1 (5) containing the smallest (largest) level of a given variable. Panel E presents results of regression tests of firm-level COEC estimates on risk factor proxies as in the following equation: r  jt  0 1 *ln(MV jt1 )  2 * BM jt1  3 * Leveragejt1  4 * Beta jt   jt

Mean t-statistics are calculated following Fama and MacBeth (1973). Significance levels are based on one-tailed tests where there is a prediction for the sign of the coefficient and based on two-tailed tests otherwise. ***, ** and * denote significance at the 1%, 5% and 10% levels, respectively. Variable definitions are in Appendix 1.

Panel A: Descriptive statistics Mean Median STD N

MVt-1 4,427 744 17,524 17,427

BMt-1 1.764 0.474 13.557 17,427

Leveraget-1 1.693 0.181 22.330 17,427

Betat-1 0.984 0.843 0.759 17,427

Panel B: Comparison across size portfolios est adj est adj est adj Predicted rGLS rGLS rCT rCT rPEG rPEG sign (1) (2) (3) (4) (5) (6)

Q1 0.1011 0.0833 0.0983 0.0805 0.1682 0.1088 Q2 0.0955 0.0774 0.0904 0.0670 0.1482 0.0926 Q3 0.0916 0.0757 0.0867 0.0636 0.1385 0.0849 Q4 0.0877 0.0712 0.0828 0.0677 0.1240 0.0768 Q5 0.0801 0.0692 0.0770 0.0681 0.1059 0.0708 Q5 – Q1 - -0.0210*** -0.0141*** -0.0213*** -0.0124* -0.0623*** -0.0380*** t-statistic -6.45 -4.52 -4.00 -1.83 -5.51 -5.47

Panel C: Comparison across book-to-market portfolios

Predicted sign (1) (2) (3) (4) (5) (6)

Q1 0.0779 0.0561 0.0720 0.0606 0.1144 0.0718 Q2 0.0810 0.0668 0.0866 0.0636 0.1287 0.0781 Q3 0.0887 0.0737 0.0885 0.0784 0.1210 0.0849 Q4 0.0976 0.0847 0.0928 0.0695 0.1379 0.0873 Q5 0.1107 0.0955 0.0998 0.0848 0.1592 0.1069 Q5 – Q1 + 0.0328*** 0.0394*** 0.0278*** 0.00242* 0.0448*** 0.0351*** t-statistic 9.84 7.02 4.93 2.63 3.61 4.70

TABLE 12 (continued) Comparison of cost of equity capital estimates and risk factor proxies

Panel D: Comparison across leverage portfolios est adj est adj est adj Predicted rGLS rGLS rCT rCT rPEG rPEG sign (1) (2) (3) (4) (5) (6)

Q1 0.0867 0.0672 0.0793 0.0661 0.1378 0.0850 Q2 0.0840 0.0681 0.0781 0.0627 0.1159 0.0716 Q3 0.0904 0.0768 0.0856 0.0683 0.1250 0.0767 Q4 0.0936 0.0773 0.0894 0.0722 0.1344 0.0854 Q5 0.1004 0.0866 0.0987 0.0758 0.1545 0.1046 Q5 – Q1 + 0.0137*** 0.0194*** 0.0194* 0.0097 0.0167 0.0196 t-statistic 4.05 6.26 3.14 1.42 0.70 1.63

Panel E: Comparison across beta portfolios

Predicted sign (1) (2) (3) (4) (5) (6)

Q1 0.0885 0.0720 0.0843 0.0676 0.1157 0.0683 Q2 0.0884 0.0764 0.0845 0.0729 0.1235 0.0752 Q3 0.0916 0.0763 0.0864 0.0637 0.1310 0.0824 Q4 0.0939 0.0778 0.0895 0.0678 0.1398 0.0911 Q5 0.0927 0.0735 0.0884 0.0749 0.1486 0.0986 Q5 – Q1 + 0.0042 0.0015 0.0041 0.0073 0.0329 0.0303** t-statistic 1.09 0.22 0.86 1.24 1.65 2.79

Panel F: Regression of adjusted cost of equity capital estimates on risk factors

Predicted sign (1) (2) (3) (4) (5) (6)

Intercept ? 0.0920*** 0.0771*** 0.0912*** 0.0821*** 0.1417*** 0.0827*** (35.07) (42.78) (25.87) (8.95) (20.22) (16.95) ln(MVt-1) - -0.0003** -0.0002*** -0.0002*** -0.0004 -0.0008*** -0.0005*** (-6.31) (-3.82) (-6.10) (-1.27) (-6.22) (-3.83) BMt-1 + 0.0003** 0.0002*** 0.00002 0.0003 0.0018* 0.0017* (2.65) (3.43) (0.17) (0.49) (1.50) (1.65) Leveraget-1 + -0.0002 -0.00004 0.0002* 0.0004 0.0006* 0.0007* (-1.36) (-0.60) (2.18) (0.48) (1.63) (1.43) Betat-1 + 0.0006 -0.0013 0.0013 0.0030* 0.0085*** 0.0100*** (0.48) (-1.10) (1.14) (1.97) (5.61) (5.91)

Adj. R2 0.0067 0.0018 0.0129 0.0054 0.0952 0.0338 N 16,891 16,891 15,592 15,592 5,850 5,850

TABLE 13 Ex post estimates of ex ante expected returns, using return decomposition and direct estimation of return news

This table presents ex post estimates of ex ante expected returns (rNEWS) which I develop using Vuolteenaho‟s (2002) return decomposition methodology. rNEWS is estimated as:

rNEWS  Et (RETt1)  RETt1  NROE,t1  NRET ,t1 with NROE estimated directly and and NRET estimated directly. Panel A presents descriptive statistics for rNEWS and its components, while Panel B reports results of the following test, with rNEWS used to proxy for Et(RETt+1):

Et (RET jt1)  0 1 ln(MV jt )  2BM jt1 3Beta jt   jt

In Panel B, I estimate each model as a panel and cluster the standard errors at the firm level. Figures in parentheses are t-statistics. Significance levels are based on one-tailed tests where there is a prediction for the sign of the coefficient and based on two-tailed tests otherwise. ***, ** and * denotes significance at the 1%, 5% and 10% levels, respectively. Variable definitions are in Appendix 1.

Panel A: Descriptive statistics Mean STD Median Min Max

RETt+1 0.156 0.545 0.084 -0.893 6.360 NROE,t+1 0.020 0.504 0.084 -4.242 2.469 NRET,t+1 0.002 0.128 0.007 -0.765 0.765 rNEWS 0.139 0.536 0.056 -2.197 5.145

Panel B: Multivariate tests of association between rNEWS and risk factor proxies (dependent variable is rNEWS, with both news variables estimated directly) Predicted Sign All observations Excluding negative rNEWS (1) (2)

Intercept ? 0.3138*** 0.5707*** (3.28) (8.83) ln(MVt) - -0.0401*** -0.0501*** (-4.47) (-6.19) ln(BM t-1) + -0.0052 -0.0573*** (-0.37) (-4.32)

Betat + 0.0537** 0.0806*** (2.30) (3.38)

N 18,734 10,696

Adj. R2 0.042 0.065

TABLE 14 Ex post estimates of ex ante expected returns, using return decomposition and residual estimation of return news

This table presents ex post estimates of ex ante expected returns (rNEWS) which I develop using Vuolteenaho‟s (2002) return decomposition methodology. rNEWS is estimated as:

rNEWS  Et (RETt1)  RETt1  NROE,t1  NRET ,t1 with NROE estimated directly and and NRET estimated residually. Panel A presents descriptive statistics for rNEWS and its components, while Panel B reports results of the following test, with rNEWS used to proxy for Et(RETt+1):

Et (RET jt1)  0 1 ln(MV jt )  2BM jt1 3Beta jt   jt

Panel A: Descriptive statistics Mean STD Median Min Max

RETt+1 0.147 0.512 0.083 -0.855 4.363 NROE,t+1 0.019 0.510 0.083 -4.242 2.440 NRET,t+1 -0.007 0.801 0.056 -21.743 9.200 rNEWS 0.145 0.287 0.099 -0.415 2.730

Panel B: Multivariate tests of association between rNEWS and risk factor proxies (dependent variable is rNEWS, with NROE estimated directly and and NRET estimated residually) Predicted Sign All observations Excluding negative rNEWS (1) (2)

Intercept ? 0.2032** 0.2717*** (2.82) (4.88)

ln(MVt) - -0.0139*** -0.013*** (-3.93) (-3.73)

ln(BM t-1) + 0.0131*** 0.0031 (2.87) (0.52)

Betat + 0.0265** 0.0264** (2.58) (2.52)

N 18,730 11,737

Adj. R2 0.062 0.053

TABLE 15 Descriptive statistics for regular guidance vs. non-guidance firms

This table provides the distribution of observations by year (Panel A) and descriptive statistics (Panel B) for the firm-years used to test the disclosure – COEC association. ***, ** and * denotes significance at the 1%, 5% and 10% levels, respectively. Variable definitions are in Appendix 1.

Panel A: Distribution of observations by year

Number of % of firm-years where % of firm-years where Year firm-years SPORADICt = 1 REGULARt = 1

1997 1,185 21.4 1.8 1998 1,279 18.4 9.4 ` 1999 1,304 27.2 12.2 2000 1,311 18.9 19.6 2001 1,252 26.1 23.6 2002 1,320 20.5 36.8 2003 1,311 13.4 42.3 2004 1,421 12.0 43.8 2005 1,553 9.3 41.0 2006 1,720 8.6 38.8

Total 13,656 17.0 28.0

Panel B: Descriptive statistics for regular guidance vs. non-guidance firms Regular guidance Non-guidance firms firms (REGULARt = 1) (REGULARt = 0) Difference (1) (2) (1) – (2)

pricet-1 33.044 30.524 2.520***

MV t-1 7,601 2,867 4,734***

Betat-1 0.911 0.906 0.005

BMt-1 0.479 3.301 -2.822***

ANFt-1 10.178 6.764 3.414***

Losst-1 0.023 0.078 -0.055***

Litigate t-1 0.243 0.167 0.076***

FDt-1 0.854 0.543 0.311*** ex _ post 0.008 0.010 -0.002*** Biast1 ex _ post 0.009 0.012 -0.003*** Biast ex _ post 0.018 0.021 -0.003*** Biast1

N 3,819 7,510

TABLE 16 Portfolio-level tests of association between disclosure and cost of equity capital

This table provides portfolio-level COEC estimates, using the following equation:

E(eps jt ) Pjt Pjt   0  1   2REGULARt   3REGULARt * BV BV BV j,t1 j,t 1 j,t 1

Pjt Pjt   4 ln(MVt )   5 ln(MVt ) *   6Betat   7 Betat *   jt BV j,t 1 BV j,t 1

In the above specification, γ2 + γ3 represents the COEC increment for regular guidance firms relative to non- guidance firms. To proxy for expected earnings [E(epsjt)], Column 1 uses analyst forecasts, Column 2 uses adjusted (de-biased) analyst forecasts, and Column 3 uses perfect-foresight forecasts. Column 4 (5) uses ex ante (ex post) analyst forecast bias (scaled by lagged book value) as the dependent variable. Mean estimated coefficients and t- statistics are calculated following Fama and MacBeth (1973). Figures in brackets are t-statistics. Significance levels are based on one-tailed tests where there is a prediction for the sign of the coefficient and based on two-tailed tests otherwise. ***, ** and * denote significance at the 1%, 5% and 10% levels, respectively. Chapter 2 provides details on how analyst forecasts are adjusted. Variable definitions are in Appendix 1.

Dependent variable AF AdjAF eps AF  AdjAF AF  eps AdjAF  eps jt jt jt jt jt jt jt jt jt BV j,t1 BV j,t1 BV j,t1 BV j,t1 BV j,t1 BV j,t1 (1) (2) (3) (4) (5) (6)

γ0 0.0252** 0.0160 0.0026 0.0089** 0.0236*** 0.0131** (2.29) (1.61) (0.25) (2.68) (5.97) (3.12) γ1 0.0537*** 0.0268*** 0.0487*** 0.0271*** 0.0045* -0.0222*** (16.03) (10.60) (13.01) (8.51) (1.85) (7.98) γ2 -0.0083 -0.0108* -0.0179* 0.0021 0.0082*** 0.0073* (-1.67) (-1.76) (-2.77) (1.45) (3.57) (1.95) γ3 0.0025 0.0024 0.0040 0.00002 -0.0019* -0.0015 (0.84) (0.76) (1.38) (0.03) (-2.06) (1.00) γ4 0.0052*** 0.0060*** 0.0083*** -0.0009*** -0.0033*** -0.0023*** (4.98) (6.04) (6.85) (-5.06) (-6.78) (3.91) γ5 -0.0017*** 0.0005 -0.0018** -0.0022*** 0.0002 0.0024*** (-5.14) (1.34) (-4.13) (-7.51) (0.69) (10.40) γ6 0.0114** 0.0074* 0.0026 0.0044* 0.0086* 0.0048 (2.90) (2.12) (1.35) (1.91) (2.20) (1.17) γ7 -0.0080*** -0.0058*** -0.0069*** -0.0023* -0.0010 0.0010 (-7.03) (-5.78) (-5.20) (-2.16) (-1.15) (0.67)

N 10,835 10,835 10,835 10,835 10,835 10,835

Adj. R2 0.5624 0.4714 0.4100 0.4530 0.0470 0.0887

Regular guidance -0.0058* -0.0083** -0.0139*** 0.0021** 0.0063*** 0.0058* COEC – non- guidance firms COEC (γ2 + γ3) p-value of F-test of 0.067 0.033 0.004 0.024 0.005 0.053 γ2+ γ3= 0, one-tailed

TABLE 17 Firm-level tests of association between disclosure and cost of equity capital

This table reports results of tests of the association between firm-level cost of equity capital estimates and disclosure. Panel A presents the results of estimating the following first-stage model that predicts whether firms issue regular management guidance: Pr(REGULAR  1)     ln(MV )   ln(BM )   Bias ex _ post   ln(ANF ) jt 0 1 jt1 2 t1 3 t1 4 jt1   5 Beta jt1   6 Loss jt1   7 Litigate jt  8 FD jt   t est Panel B reports results of variations of the following test, with the dependent variable equal to either rPEG (in adj est adj Columns 1 and 2), rPEG (in Column 3), or ( rPEG  rPEG ) (in Column 4): ex _ post Et (RET jt1)  0 1 ln(MV jt )  2BM jt1  3Beta jt1  4REGULARjt  5Bias jt   jt Panel C repeats the analysis in Panel B, and includes the Inverse Mills ratio (λ) from the first-stage model in Panel A. Panel D reports results of the above test, with the dependent variable equal to rNEWS. In Panel A, coefficient standard errors are in parentheses. In Panels B, C, and D, I estimate each model as a panel and cluster the standard errors at the firm level. Significance levels are based on one-tailed tests where there is a prediction for the sign of the coefficient and based on two-tailed tests otherwise. ***, ** and * denotes significance at the 1%, 5% and 10% levels, respectively. Figures in brackets are z-statistics. Variable definitions are in Appendix 1.

Panel A: First-stage model predicting firm guidance decision Predicted sign Pr(REGULARt=1)

Intercept ? -1.448*** (0.123)

ln(MV t-1) + -0.010 (0.021)

ln(BM t-1) - -0.267*** (0.028) ex _ post + 2.475*** Biast1 (0.816)

ln(ANFt-1) + 0.293*** (0.041)

Beta t-1 - -0.121*** (0.033)

Losst-1 ? -0.400*** (0.103)

Litigatet + 0.327*** (0.062)

FDt + 0.964*** (0.061)

N 3,294

Pseudo R2 0.234 % Concordant 73.6

TABLE 17 (continued)

Panel B: Firm-level tests of association between disclosure and cost of equity capital Dependent est adj est adj variable is: rPEG rPEG rPEG  rPEG Predicted Sign (1) (2) (3) (4)

Intercept ? 0.2164*** 0.2024*** 0.1175*** 0.0989*** (40.71) (41.40) (18.91) (47.25) ln(MVt) - -0.0118*** -0.0105*** -0.0050*** -0.0068*** (-17.43) (-16.93) (6.37) (-25.01) ln(BMt-1) + 0.0066*** 0.0063*** 0.0074*** -0.0081* (5.07) (5.22) (5.14) (-1.57) Betat + 0.0079*** 0.0079*** 0.0100*** -0.0021*** (6.25) (6.59) (7.01) (-3.88) REGULARt - -0.0056*** -0.0051*** -0.0068*** 0.0012 (-2.81) (-2.68) (-2.97) (1.34) Bias ex _ post + 0.3863*** jt (9.45)

Firm fixed effects Yes Yes Yes Yes N 3,294 3,294 3,294 3,294

Adj. R2 0.242 0.291 0.088 0.192

Panel C: Firm-level tests of association between disclosure and cost of equity capital Dependent est adj variable is: rPEG  rPEG Predicted Sign (5) (6) (7) (8)

Intercept ? 0.1750*** 0.1606*** 0.0982*** 0.0768*** (23.53) (22.61) (11.08) (23.77) ln(MVt) - -0.0097*** -0.0084*** -0.0041*** -0.0056*** (-13.60) (-12.74) (-4.84) (-18.72) ln(BMt-1) + 0.0030*** 0.0027** 0.0057*** -0.0028*** (2.13) (2.02) (3.58) (-4.78) Betat + 0.0065*** 0.0066*** 0.0093*** -0.0028*** (5.42) (5.71) (6.65) (-5.22) REGULARt - -0.0006 -0.0001 -0.0045** 0.0039*** (-0.27) (-0.04) (-1.85) (4.09) + 0.3871*** (9.92) λt ? 0.0247*** 0.0248*** 0.0115** 0.0132*** (7.90) (8.13) (2.94) (8.47)

Firm fixed effects Yes Yes Yes Yes N 3,294 3,294 3,294 3,294

Adj. R2 0.260 0.309 0.092 0.213

TABLE 17 (continued)

Panel D: Firm-level tests of association between disclosure and cost of equity capital

(dependent variable is RNEWS) Return news estimated directly Return news estimated residually Predicted Sign (1) (2) (3) (4)

Intercept ? 0.3497*** 0.4868*** 0.4339*** 0.4176*** (18.48) (12.34) (25.59) (28.31) ln(MVt) - -0.0412*** -0.0462*** -0.0405*** -0.0182*** (-14.50) (-11.31) (-15.61) (-12.54) ln(BMt-1) + 0.0148*** 0.0351*** 0.0097* 0.0602*** (2.34) (4.35) (1.59) (18.96) Betat + 0.0626*** 0.0409*** 0.0524*** 0.0096** (9.72) (4.99) (9.10) (3.05) REGULARt - -0.0244*** -0.0433*** -0.0038 -0.0172*** (-2.69) (-3.91) (-0.41) (-3.87) λt ? 0.0742*** -0.1478*** (-4.42) (-26.28)

Firm fixed effects Yes Yes Yes Yes N 12,895 7,288 13,288 6,843

Adj. R2 0.037 0.036 0.035 0.094

TABLE 18 Asset-pricing tests

These tables test the relation between realized returns and disclosure. At the beginning of each calendar year, I create portfolios of regular guidance and non-guidance firms. Using regular guidance as an example, at the beginning of each year, eligible firms are included in the regular guidance portfolio if the firm issued guidance in each of the prior two years, as well as in the current year. I then calculate equally-weighted returns for each portfolio of firms. This yields portfolio returns for 120 months (from January 1997 – December 2006) for each portfolio. I conduct asset-pricing tests following Pastor and Stambaugh (2003). Monthly portfolio returns for the disclosing and non-disclosing firms are regressed on risk factors, as in the following equation:

rPt   1 *(Rm  R f )t  2 *SMBt  3 *HMLt t  4 *MOMt  jt

The tests are run using each of CAPM (Panel A), the Fama-French three-factor model (B), and the four factor model including momentum (C). For each of the three specifications, I compare the estimated coefficients from the regular guidance and non-guidance portfolios, in Column 3. T-statistics (provided in parentheses) are based on robust standard errors. Significance levels are based on one-tailed tests. ***, ** and * denotes significance at the 1%, 5% and 10% levels, respectively. Variable definitions are in Appendix 1.

TABLE 18 (continued)

Panel A: Asset-pricing tests using CAPM REGULARt = 1 REGULARt = 0 (1) (2) (1) – (2)

Intercept 0.0076*** 0.0141*** -0.0073*** (2.93) (6.10)

Rm – Rf 0.9558*** 0.8779*** 0.0850* (17.73) (17.47)

N 120 120

Adj. R2 0.7477 0.7212

Panel B: Asset-pricing tests using Fama-French three factors REGULARt = 1 REGULARt = 0 (1) (2) (1) – (2)

Intercept 0.0027 0.0115*** -0.0094*** (1.45) (10.42)

Rm – Rf 1.1135*** 0.8417*** 0.2688*** (24.71) (30.88) SMB 0.4400*** 0.5829*** -0.1454*** (9.45) (20.82) HML 0.5313*** 0.1956*** 0.3298*** (8.88) (5.42)

N 120 120

Adj. R2 0.8764 0.9420

Panel C: Asset-pricing tests using Fama-French three factors, plus momentum REGULARt = 1 REGULARt = 0 (1) (2) (1) – (2)

Intercept 0.0049*** 0.0120*** -0.0078*** (3.17) (10.97)

Rm – Rf 1.0274*** 0.8200*** 0.2063*** (26.58) (29.44) SMB 0.4851*** 0.5958*** -0.1127*** (12.59) (21.46) HML 0.4850*** 0.1849*** 0.2962*** (9.84) (5.22) MOM -0.1904*** -0.0497 -0.1380*** (-7.28) (-2.62)

N 120 120

Adj. R2 0.9176 0.9448

TABLE 19 Analyst forecast bias and disclosure

This table investigates the association between disclosure and analyst forecast bias, as in the following equation: ex _ post ex _ post Bias t  0  1Bias t1   2 RETt  3 ln(MVt1 )   4 REGULARt  5t   t In the above specification, λ represents the Inverse Mills ratio from a first-stage model similar to that in Table 17 Panel A. Column 1 (2) presents this analysis for year t (t+1) analyst forecast bias. Figures in parentheses are t- statistics. Mean estimated coefficients and t-statistics are calculated following Fama and MacBeth (1973). Significance levels are based on one-tailed tests where there is a prediction for the sign of the coefficient and based on two-tailed tests otherwise. ***, ** and * denotes significance at the 1%, 5%, and 10% levels, respectively. Variable definitions are in Appendix 1.

Dependent variable ex _ post ex _ post Biast Biast1 Predicted sign (1) (2)

Intercept ? 0.031*** 0.073*** (3.92) (4.55) Bias ex _ post + 0.189*** 0.296** jt 1 (5.91) (2.75)

RETt-1 - -0.004** -0.006* (-2.41) (-1.73)

ln(MVt) - -0.003*** -0.006*** (-4.26) (-5.11)

REGULAR t + 0.002* 0.007** (1.79) (2.57)

λt ? -0.002 -0.009 (-0.72) (-1.53)

N 11,329 9,862

Adj. R2 0.080 0.050