Earnings Warnings: Market Reaction and Management Motivation

to the Faculty of the Graduate School of The University of Texas at Austin in Partial Fulfillment of the Requirements for the Degree of Doctoral of Philosophy The University of Texas at Austin May, 2003 UMI Number: 3116199 ________________________________________________________ UMI Microform 3116199 Copyright 2004 by ProQuest Information and Learning Company. All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code. ____________________________________________________________ ProQuest Information and Learning Company 300 North Zeeb Road PO Box 1346 Ann Arbor, MI 48106-1346 Dedication To my parents Acknowledgements I would like to express my most sincere gratitude to my committee members: Rowland Atiase (Chair), Robert Freeman, Tom Shively, Laura Starks, and Senyo Tse. Rowland has been an invaluable mentor to me, and has been generous with his time and advice throughout my doctoral studies. In addition, I wish to acknowledge with sincere thanks to many helpful suggestions of Peggy Weber. Also, I would like to thank First Call Corporation, and Steve Sommers in particular, for the corporate earnings guidance data. I appreciate the financial support of Ministry of University Affairs (Thailand) and Faculty of Commerce and Accountancy, Thammasat University (Thailand). I would like to thank my colleagues and friends at Thammasat University (Thailand) for their support and encouragement. Finally, my deepest thanks go to my parents, my sister and brothers, for their unconditional love and support. They have always been my source of strength. Without them this would have not been possible. v Earnings Warnings: Market Reaction and Management Motivation Publication No._____________ Somchai Supattarakul, Ph.D. The University of Texas at Austin, 2003 Supervisor: Rowland K. Atiase This dissertation provides empirical evidence on the market reaction to earnings warnings as well as management’s motivation to issue earnings warnings. Specifically, this study first investigates whether self-selection bias exists in a firm’s warning choice and if so, whether the warning effect (i.e., a differential market reaction associated with earnings news between warning and no-warning scenarios) is positive (negative) for good (bad) news warnings after controlling for potential self-selection bias. I find that self-selection does exist in a firm’s warning choice and it creates a downward bias in the warning effect. I also find that the warning effect after controlling for self-section bias, on average, is positive (negative) for good (bad) news warnings suggesting that empirical evidence in Kasznik and Lev [1995] and Atiase, Supattarakul, Tse [2003] is robust after controlling for self-selection bias. More importantly, this study vi investigates whether and how the warning effect affects a firm’s warning choice (i.e., to warn or not to warn). I find that a firm’s tendency to warn is positively associated with the warning effect after controlling for other management motives to issue earnings warnings, i.e., litigation concerns, reputation concerns, and information asymmetry consequence concerns, suggesting that the warning effect itself provides management with an economic motivation to issue earnings warnings. vii Table of Contents List of Tables........................................................................................................... x List of Figure .........................................................................................................xii Chapter 1: Introduction .......................................................................................... 1 Chapter 2: Prior Studies and Hypotheses............................................................. 10 2.1 Differential market reaction (The warning effect) ................................ 10 2.2 Self-selection in a firm’s warning choice.............................................. 12 2.3 Management motives to issue earnings warnings ................................. 15 Chapter 3: Model Specification and Estimation Procedures................................ 20 3.1 Model specification ............................................................................... 20 3.2 Limited dependent variable or self-selectivity problem........................ 22 3.3 Lee [1978]’s approach........................................................................... 23 3.4 Methodological problems in Shu [2001]............................................... 27 Chapter 4: Sample Design and Variable Definitions ........................................... 30 4.1 Sample selection criteria ....................................................................... 30 4.2 Sample Description ............................................................................... 31 4.3 Variable Definitions .............................................................................. 32 4.3.1 Market reaction associated with earnings news (MRW and MRN) ......................................................................................... 32 4.3.2 Determinants of market reaction associated with earnings news (Z*) .................................................................................... 33 4.3.3 Warning choice (WARN).......................................................... 37 4.3.4 Management motives to issue earnings warnings (S*).............. 37 4.4. Sample descriptive statistics................................................................. 41 Chapter 5: Empirical Tests and Results ............................................................... 43 5.1 Self-selection and its impacts on the warning effect............................. 43 viii 5.2 A firm’s tendency to warn and the warning effect................................ 57 Chapter 6: Summary and Conclusions ................................................................. 62 6.1 Summary ............................................................................................... 62 6.2 Contributions and future research ......................................................... 63 Figure and Tables .................................................................................................. 66 References ............................................................................................................. 97 Vita….................................................................................................................. 101 ix List of Tables Table 1 – Reconciliation of Sample Data.............................................................. 68 Table 2 – Distribution of Earnings Warnings by Year and Quarter...................... 69 Table 3 – Sample Descriptive Statistics ................................................................ 70 Table 4 – Correlations of Market Reaction Associated with Earnings News (MR), Unexpected Earnings (UE) and Analyst Forecast Revisions (AFR)............................................................................... 72 Table 5 – Results of Probit Maximum Likelihood Estimation of Warning Choice Model to Obtain Parameters to Calculate “Inverse Mills Ratio” ............................................................................................... 73 Table 6 – Results of OLS Estimation of Market Reaction Model under Warning Scenario – Controlling for Self-selection Bias.................. 76 Table 7 – Results of OLS Estimation of Market Reaction Model under No-Warning Scenario – Controlling for Self-selection Bias ........... 79 Table 8 – Distribution of the Warning Effect after Controlling for Self-selection Bias ( RMˆ∆ ).............................................................. 82 Table 9 – Results of OLS Estimation of Market Reaction Model under Warning Scenario – without Controlling for Self-selection Bias .... 84 Table 10 – Results of OLS Estimation of Market Reaction Model under No-Warning Scenario – without Controlling for Self-selection Bias................................................................................................... 87 Table 11 – Distribution of the Warning Effect without Controlling for Self-selection Bias ( RMˆ ′∆ )............................................................. 90 x Table 12 – Distribution of Self-selection Bias (S ) in the Warning Effect ...... 92 BSˆ Table 13 – Results of Probit Maximum Likelihood Estimation of Warning Choice Model ................................................................................... 94 xi List of Figure Figure 1 – Timeline of Events............................................................................... 67 xii Chapter 1: Introduction This dissertation provides empirical evidence on the market reaction to earnings warnings and management’s motivation to issue earnings warnings. Earnings warnings are any earnings-related management voluntary disclosures made prior to the earnings announcement date.1 Firms use earnings warnings to provide timely information to their shareholders and investors as well as financial analysts regarding their expected current period performance prior to the earnings announcement date (Ip [1997], McLean [2001] and Stone [2002]). Kasznik and Lev [1995; hereafter KL] and Atiase, Supattarakul, and Tse [2003; hereafter AST] find a differential market reaction to earnings news between warning and no- warning scenarios (i.e., “the warning effect”).2 Specifically, AST document that the warning effect is positive for good news warnings while KL and AST find that the warning effect is negative for bad news warnings.3 Both KL and AST document that the majority of good news firms do not warn despite a positive warning effect, but that a significant number of bad news firms do warn despite a negative warning effect.4 These counter-intuitive findings raise a number of 1 Consistent with prior research, I use the terms an earnings warning and earnings guidance interchangeably for both good and bad news. Earnings warnings are also referred to as earnings preannouncements (Soffer, Thiagarajan, and Walther [2000]). 2 Market reaction to earnings news under a warning scenario is the reaction to the news in both the earnings warnings and earnings announcements combined while market reaction to earnings news under a no-warning scenario is measured as the returns over a comparable window to that of a warning scenario. 3 Good (bad) news firms are those with positive (negative) total earnings news revealed through a warning (if any) and an earnings announcement. 4 KL document that 90% of firms with relatively large good news do not warn while 21% of firms with relatively large bad news do warn. Similarly, AST document that 97% of all good news firms do not warn while 13% of all bad news firms do warn. Moreover, the business press reports 1 questions: First, is the warning effect indeed positive (negative) for good (bad) news warnings? Second, does the warning effect motivate managers to issue earnings warnings, and if so, how? This study empirically investigates these questions. Healy and Palepu [2001] and Core [2001] point out that a firm’s warning choice is likely to be endogenous. Thus, empirical evidence on the warning effect estimated by OLS regression (as in KL and AST) may be sensitive to self- selection bias due to biased estimates (Maddala [1991]).5 To address this concern, I investigate (1) whether self-selection bias exists in a firm’s warning choice, (2) whether, after controlling for self-selection bias (if any), the warning effect is indeed positive for good news warnings, as documented in AST, and (3) whether, after controlling for self-selection bias (if any), the warning effect remains negative for bad news warnings, as documented in KL and AST. KL and Shu [2001] implicitly assume that the warning effect is the ultimate motive for management to issue earnings warnings. Prior research suggests has documented several differential firm-characteristics between warning and no-warning firms. I classify them into three management motives (other than the warning effect) to warn: (1) a litigation-concern motive (i.e., management issue warnings to reduce or even avoid shareholder litigation risk), (2) a reputation-concern motive (i.e., management warn to establish or maintain good relationship or reputation with financial analysts and investors), and (3) an that bad news warnings are more common than good news warnings (Ip [1997], Hovanesian [2000], Stone [2002], and Wahlegren [2002]). 5 A problem of self-selection bias arises whenever there is non-random sampling caused by individual choices (Maddala [1991]). 2 information asymmetry consequence-concern motive (i.e., management warn to mitigate consequences of information asymmetry, such as a higher cost of capital). Despite the implicit presumption in KL and Shu [2001] that the warning effect motivates managers to issue earnings warnings, this motive has not been explicitly examined in the warnings literature. This study is the first to explicitly consider the warning effect as a motive for management to issue earnings warnings. I examine how the warning effect affects a firm’s warning choice after controlling for three other management motives affect a firm’s warning choice. Specifically, this study investigates whether and how the warning effect is associated with a firm’s tendency to warn after controlling for the effects of the litigation-concern, reputation-concern, and information asymmetry consequence- concern motives on a firm’s tendency to warn. Because the warning effect is both a motive and a result of earnings warnings, I use a simultaneous equations model with qualitative and limited dependent variables as introduced by Lee [1978]. This approach allows me to examine (1) the presence of self-selection bias in a firm’s warning choice, (2) the warning effect after controlling for self-selection bias, and more importantly (3) the relations between a firm’s tendency to warn and the warning effect after controlling for three other management motives to warn. Lee’s [1978] approach is briefly described as follows. First, three models are established: (1) a warning choice model, (2) a market reaction model under a warning scenario, and (3) a market reaction model under a no-warning scenario. A firm’s warning choice is likely to be determined by the warning effect as well 3 as three other management motives to issue earnings warnings (i.e., litigation- concern, reputation-concern, and information-asymmetry-concern motives). Therefore, all four motives need to be specified as independent variables in the warning choice model. The dependent variable for this model is the choice made by management to warn or not to warn. The warning effect is a difference between the market reaction when a firm warns and the market reaction when it does not warn, ceteris paribus. However, the no-warning market reaction cannot be observed for a firm that chooses to warn. Similarly, the warning market reaction cannot be observed for a firm that does not warn. Lee [1978] therefore suggests that both market reaction models be substituted directly into the warning choice model as the warning effect. The resulting warning choice model is estimated using Probit Maximum Likelihood Estimation and the resulting estimated coefficients are used to calculate self-selectivity variables (Inverse Mills Ratio), which are in turn included as a dependent variable in both market reaction models to control for potential self-selection bias. The resulting market reaction models are estimated with Ordinary Least Square (OLS) regression. OLS regression should yield unbiased estimators once self-selection bias is controlled for, allowing empirical assessment of the warning effect.6 Finally, the original warning choice model is estimated with the unbiased warning effect using Probit Maximum Likelihood Estimation and thus associations between a firm’s tendency 6 Conceptually, this approach allows me to estimate the “what would have been” market reaction. For example, for a warning firm, I am able to estimate what its market reaction would have been if it had not warned. Similarly, for a no-warning firm, I am able to estimate what its market reaction would have been if it had warned. 4 to warn and the warning effect after controlling for three other management motives are investigated. My sample consists of all firms included in the Institutional Broker Estimate System (I/B/E/S) database from 1998 to 2000. I obtain reported quarterly earnings and financial analysts’ quarterly earnings estimates from I/B/E/S. Earnings warnings are obtained from the First Call Historical database. Quarterly earnings announcement dates and selected quarterly accounting information are obtained from the Compustat database while daily security returns are obtained from the Center for Research in Security Prices (CRSP) daily stock database. Security Data Company (SDC) is the source for debt and equity issuance data. My final sample consists of 23,018 firm-quarters (4,482 firms) of which, 13,818 firm-quarters are good news and 9,200 are bad news.7 Consistent with prior research, I find that firms are more apt to warn about bad news than good news. Specifically, 1,258 (9.10%) firm-quarters with good news have earnings warnings while 2,045 (22.23%) firm-quarters with bad news have earnings warnings. As expected, I find evidence of self-selection bias in a firm’s warning choice. Moreover, after controlling for the bias, I find that the warning effect, on average, is positive for good news warnings and negative for bad news warnings. This suggests that even though self-selection bias exists in a firm’s warning choice, the empirical findings in KL and AST do not appear to be materially 7 Types of news (i.e., good news and bad news) are classified by total earnings news (UE or a price-deflated analyst forecast error) revealed through the warning (if any) and the earnings announcement. See a definition of UE in chapter 4. 5 altered by it. I also find that self-selection in a firm’s warning choice appears to create a downward bias in the warning effect. More importantly, I find that a firm’s tendency to warn is significantly positively associated with the warning effect after controlling for three other management motives, suggesting that the warning effect itself provides management with an economic incentive to issue earnings warnings. For other management motives, I document that, consistent with prior research, the litigation-concern motive and the reputation-concern motives determine a firm’s warning choice. That is, my empirical evidence suggests that firms that are more vulnerable to shareholder litigation are more likely to warn than other firms and that firms that are more concerned with their reputation with financial analysts and investors are more likely to warn than other firms. The association between a firm’s tendency to warn and the information asymmetry consequence-concern motives, however, is insignificant. In summary, my findings suggest that although the warning effect provides management with an economic motivation to issue earnings warnings, it is only one of several management motives. Therefore, the notion that a firm will warn only if the warning effect is positive and will not warn otherwise, as implied by KL and Shu [2001], is not completely accurate. A more appropriate depiction is that that a firm prefers a more positive warning effect, all else being equal. My findings contribute to the literature on management earnings warnings in two ways. First, I provide empirical evidence suggesting that self-selection does exist in a firm’s warning choice and it creates a downward bias in the 6 warning effect and that after controlling for the bias, the warning effect, on average, remains positive (negative) for good (bad) news warnings. Thus, contrary to Shu [2001] findings, I find that the results in KL and AST are not altered by the self-selection bias. Shu [2001] re-examines KL’s results on bad news warnings of firms with bad news of at least 1% of a stock price and finds that the warning effect is positive. Her results, however, could be spurious due to logical inconsistency resulting in model misspecification, and methodological problems avoided in my design.8 Furthermore, Shu [2001] only examines the warning effect for bad news warnings of firms with relatively large bad news while I examine the warning effect for both good news and bad news warnings of firms with all magnitudes of news. Second, the main contribution of this study is that it is the first to investigate an association between a firm’s tendency to warn and the warning effect. Prior research has not specified a warning choice model with the warning effect as a determinant of the warning choice. Overall, my findings suggest that the stock market responds to earnings warnings and that managers consider a capital-market incentive (i.e., the warning effect) when they are making a warning decision, among other things. 8 Shu [2001] implicitly assumes that the warning effect is the sole determinant of a firm’s warning choice. She models her warning choice model without the warning effect as an independent variable, instead includes certain firm-specific characteristics, which prior research has found to be other determinants of a firm’s warning choice. This logical inconsistency clearly results in the model misspecification. Furthermore, Maddala [1983; 1991], among others, explicitly suggests that (1) self-selectivity variables should be used to estimate unbiased OLS estimates, and never be used to estimate dependent variables; and (2) the warning effect should be estimated based on estimated values of both market reaction associated with earnings news under warning and no- warning scenarios. Shu [2001] fails to follow these guidelines. 7 Potential avenues for future research may include an investigation of management motives to issue different types of earnings warnings, namely, point, range, open-ended, and qualitative warnings as well as a use of self-selection analysis in other settings. Specifically, an investigation of whether and how the warning effect determines a firm’s choice of warning type would give insights regarding management voluntary disclosure choice. In addition, management may self-selectively choose one accounting choice over others and management’s decision may affect how the stock market reacts to his accounting choice decision. The remainder of this dissertation is organized as follows. Chapter 2 discusses prior studies on the warning effect, self-selection bias in a firm’s warning choice, and management motives to issue earnings warnings. Chapter 2 also develops hypotheses regarding the existence of self-selection bias in a firm’s warning choice and the association between a firm’s tendency to warn and the warning effect. Chapter 3 specifies the market reaction and warning choice models, and describes estimation procedures that address self-selection bias. Chapter 4 addresses the sample selection criteria, defines all empirical proxies employed in the study and discusses the basic characteristics of the sample firms. Chapter 5 presents empirical tests and discusses the results of these tests related to the presence of self-selection bias in a firm’s warning choice and its impacts on the warning effect as well as an association between a firm’s tendency to warn and the warning effect after controlling for three other management motives to 8 issue earnings warnings. Chapter 6 reviews the contribution of the study, proposes possible avenues for future research and concludes the dissertation. 9 Chapter 2: Prior Studies and Hypotheses This chapter discusses prior studies on differential market reactions induced by earnings warnings (i.e., the warning effect), management motives for issuing earnings warnings and issues related to self-selection bias. It also develops hypotheses regarding the existence of the self-selection bias in a firm’s warning choice and the association between a firm’s tendency to warn and the warning effect. 2.1 DIFFERENTIAL MARKET REACTION (THE WARNING EFFECT) Kasznik and Lev [1995; hereafter KL] examine whether there is a differential market reaction associated with earnings news between firms that warn and firms that do not warn (i.e., a differential market reaction induced by earnings warnings or “the warning effect”). Market reaction to earnings news for warning firms is the reaction to the news in both the earnings warnings and the earnings announcements (i.e., a combination of cumulative abnormal returns around the warning date and the earnings announcement date). Market reaction to earnings news for no-warning firms is measured as the cumulative abnormal returns over a comparable window to that of warning firms. Their sample is limited to firms with large earnings news (i.e., the absolute value of earnings news of at least 1% of the beginning-of-quarter stock price) from 1988 to 1990. They find that, all else being equal, market reaction associated with earnings news of bad news firms that warn is more negative than that of bad news firms that do not 10 warn (i.e., a negative warning effect for bad news warnings). KL specifically state that “We consider this finding [a negative warning effect] counter-intuitive because a warning generally provides partial information about the subsequent earnings surprise, and is therefore expected to be rewarded by investors” (pp. 128, KL). However, they find insignificant results for good news firms. Atiase, Supattarakul, and Tse [2003, hereafter AST] extend KL by examining the warning effect for firms with earnings news covering a broad range of magnitude over the period 1995 to 1999. They find the market reaction associated with earnings news of good news firms that warn is more positive than that of good news firms that do not warn (i.e., a positive warning effect for good news warnings).9 In addition, consistent with empirical results in KL, they find the market reaction associated with earnings news of bad news firms that warn is more negative than that of bad news firms that do not warn (i.e., a negative warning effect for bad news warnings). Libby and Tan [1999] provide a possible explanation for a negative warning effect for bad news warnings. They conjecture that a negative warning effect for bad news warnings arises from financial analysts’ (or investors’) sequential information processing. Their experimental evidence suggests that a bad news warning itself does not give rise to a negative warning effect, but rather financial analysts’ sequential information processing induces the negative warning effect. Specifically, they find that financial analysts’ forecasts of future 9 This seems to be consistent with King, Pownell, and Waymire’s [1990] conjecture that management voluntary disclosures reduce investors’ need to privately acquire information, thus reducing capital-market transaction costs (i.e., the transaction cost saving argument). This transaction cost savings may induce investors’ positive response to earnings warnings. 11 earnings are lower when they receive a bad news warning first and then later receive an earning announcement than when they receive no warnings whatsoever. In addition, they find that financial analysts’ forecasts of future earnings are higher when they receive a bad news warning concurrently with an earnings announcement than when they receive no warnings whatsoever. This suggests that the bad news warning itself has a positive warning effect, but the fact that financial analysts have to revise their forecasts twice (i.e., once after receiving a bad news warning and again after receiving an earnings announcement) induces a negative warning effect. Experimental results in Libby and Tan [1999] corroborate empirical results in KL and AST that the warning effect is negative for bad news warnings. 2.2 SELF-SELECTION IN A FIRM’S WARNING CHOICE Healy and Palepu [2001] and Core [2001] point out in their review papers that a firm’s warning choice is likely to be endogenous.10 Specifically, a firm chooses to warn or not to warn based on certain exogenous factors, which are likely to represent management motives to issue earnings warnings, and thus characteristics of warning firms and no-warning firms are likely to be systematically different (e.g., Cox [1985], Waymire [1985], KL, Shu [2001], Chen [2002], and AST). As a consequence, the warning effect estimated by OLS regression (as in KL and AST) may be sensitive to potential self-selection bias due to biased estimators (Maddala [1983; 1991]). Market reaction associated with 10 A firm does not randomly choose to warn or not to warn or a firm self-selectively chooses to warn or not to warn. 12 earnings news is observable only in the state chosen by the firm (warning or no- warning) while market reaction associated with earnings news in the alternative condition (no-warning or warning) is unobservable. For example, if firm A decides to warn, the market reaction associated with its earnings ne._.ws in a warning scenario is observable, but the market reaction associated with its earnings news in a no-warning scenario is unobservable. This creates a limited dependent variable or self-selectivity problem. In the presence of a limited dependent variable or self-selectivity problem, OLS regression cannot be used to obtain unbiased estimated coefficients (Maddala [1983; 1991]). This study examines how earnings warnings affect the market reaction associated with earnings news, after controlling for potential self-selection bias. I investigate (1) whether self-selection bias exists in a firm’s warning choice, (2) whether, after controlling for potential self-selection bias, the warning effect remains positive for good news warnings, as documented in AST, and (3) whether, after controlling for potential self-selection bias, the warning effect remains negative for bad news warnings, as found in KL and AST. Shu [2001] re-examines KL’s results by using Heckman two-stage regression to control for potential self-selection bias in an attempt to explain KL’s counter-intuitive findings that the warning effect is negative for bad news warnings of firms with relatively large bad news. She finds that after controlling for the self-selection bias, the warning effect is positive for warning firms, but the warning effect would have been negative for no-warning firms had they decided to warn. She concludes that the firms in her sample, on average, make rational 13 warning choices. Her findings, however, could be spurious due to a problem of logical inconsistency resulting in model misspecification, and some methodological problems as briefly discussed below. Shu’s conclusion rests on the unstated premise that a firm will warn only if the warning effect is positive and will not warn otherwise, i.e., the warning effect is the sole management motive to issue earnings warnings. However, the warning choice model she employs fails to include the warning effect as an independent variable; instead she includes certain firm-specific characteristics that proxy for management motives to issue earnings warnings. As a result, her model is misspecified. Furthermore, according to Maddala [1983; 1991], among others, self- selectivity variables should only be used to obtain unbiased OLS estimates (in the market reaction models), and never be used to estimate dependent variables (i.e., market reactions associated with earnings news); and differential market reactions in this case, i.e., the warning effect, should be calculated based on estimated market reactions associated with earnings news under warning and no-warning scenarios. Shu [2001] fails to follow these guidelines. I use a simultaneous equations model with qualitative and limited dependent variables as introduced by Lee [1978] to more properly address the self-selection issue. Lee’s [1978] approach avoids the logical inconsistency and methodological problems found in Shu [2001]. Chapter 3 describes this approach in detail. 14 As discussed earlier, prior research has documented that firm characteristics of warning and no-warning firms are systematically different and thus it is likely that the firm characteristics are related to the firm’s decision to warn or not to warn. As a result, I hypothesize that self-selection bias exists in a firm’s warning choice. However, it is not clear whether or not extant empirical findings that the warning effect is positive for good news warnings (AST) and negative for bad news warnings (KL and AST) are influenced by the potential self-selection bias. Therefore, I do not make any predictions regarding the sensitivity of the empirical results in KL and AST to the possible self-selection bias. 2.3 MANAGEMENT MOTIVES TO ISSUE EARNINGS WARNINGS Prior research has documented seven differential firm-characteristics between warnings and no-warning firms: (1) membership in high-litigation risk industries, (2) the magnitude of earnings news, (3) market capitalization, (4) past warning pattern, (5) analyst following, (6) membership in regulated industries, and (7) future external finance offering. I classify these firm-characteristics into three management motives to issue earnings warnings: (1) a litigation-concern motive (items 1-3), (2) a reputation-concern motive (items 4-6), and (3) an information asymmetry consequence-concern motive (item 7). Therefore, these three motives need to be controlled for in the warning choice model. 15 Skinner [1994] suggests that management may issue earnings warnings to reduce or even avoid shareholder litigation risk.11 Skinner [1997] provides empirical evidence suggesting that earnings warnings can reduce settlement amounts in shareholder lawsuits consistent with the litigation-concern motive. Baginski, Hassell, and Kimbrough [2002] also find that a firm’s shareholder litigation environment has a significant impact on a firm’s warning decision to warn or not to warn. Similarly, KL and Shu [2001] provide empirical evidence suggesting that firms in industries that tend to be vulnerable to shareholder litigation (mostly high-tech industries) are more likely to warn than firms in other industries. In addition, they find that firms with relatively large earnings news, especially bad news, and those with large market capitalization, who are likely to be targets of shareholder lawsuits, are more likely to warn than other firms. All these studies support the litigation-concern motive. Skinner [1994] also conjectures that management may issue earnings warnings to establish or maintain a good reputation with financial analysts and investors. Miller and Piotroski [2000] and Chen [2002] provide empirical evidence, consistent with this reputation-concern motive. Specifically, Miller and Piotroski [2000] find that firms that have issued earnings warnings in prior periods are likely to do so in the current period, suggesting that these firms intend to establish or maintain a reputation as warning firms. 11 Vicker [1999] states in her article in Business Week that if companies fail to meet analysts’ estimates (i.e., have bad news on the earnings announcement date), they risk shareholder lawsuits. In fact, she reports that shareholders recently filed suit against Compaq Company Corp. charging that the company did not timely inform them about its disappointing performance. 16 Chen [2002] finds that analyst following is positively associated with a firm’s tendency to warn. Firms with a large analyst following need to maintain a good reputation with their analysts. Issuing earnings warnings is one way to maintain a good relationship with financial analysts.12 In addition, KL find that firms in regulated industries (i.e., utility, communication, and financial firms) are less likely to warn than other firms. KL speculate that this may be because firms in the regulated industries are required to disclose financial information in more detail and thus the incremental benefit of earnings warnings to financial analysts and investors is likely to be minimal, compared to the benefit for firms in unregulated industries. Lang and Lundholm [1993] conjecture that high quality disclosures may reduce information asymmetry and increase firm value at the time of debt or equity issuance. Botosan [1997] documents that disclosure quality is adversely associated with cost of equity capital and similarly, Sengupta [1998] finds an adverse relationship between disclosure quality and cost of debt. Ruland, Tung, and George [1990], Frankel, McNichols, and Wilson [1995] and Miller and Piotroski [2000] provide empirical results that firms planning to issue public offerings (either debt or equity) are more likely to provide voluntary disclosures to the stock market. Shu [2001] also finds a positive association between a firm’s tendency to warn and a firm’s tendency to issue debt or equity in the market. Taken as a whole, these studies indicate that earnings warnings may be a mechanism used to reduce potential consequences of information asymmetry 12 Skinner [1994] argues that financial analysts may impose costs on firms whose managers are less than candid about a potential earnings surprise. For example, analysts may choose not to follow firms that continue not to warn analysts about their earnings surprise. 17 (e.g., a high cost of capital). This conforms to the information asymmetry consequence-concern motive to issue earnings warnings. KL and Shu [2001] tacitly imply that the warning effect is management’s sole motive to issue earnings warnings, i.e., a firm will warn only if the warning effect is positive and will not otherwise. For example, KL specifically state that their finding that the warning effect is negative for bad news warnings is “counter-intuitive.” Shu [2001] likewise concludes that bad news firms in her sample, on average, make rational warning choices based on her problematic findings that the warning effect is positive for warning firms but the warning effect would have been negative for no-warning firms if they had decided to warn. It is unlikely that the warning effect is the sole management motive to issue earnings warnings since prior research shows litigation concerns, reputation concerns, and information asymmetry consequence concerns all appear to motivate management to issue earnings warnings. Despite the implicit presumption in KL and Shu [2001] that the warning effect motivates managers to issue earnings warnings, this motive has not been explicitly examined in the warnings literature. This study is the first to explicitly consider the warning effect itself as a management motive for issuing earnings warnings and examine how the warning effect affect a firm’s warning choice. How the warning effect affects a firm’s warning decision (to warn or not to warn) is a fundamental question that has not been addressed in the earnings warnings literature. This study therefore investigates whether and how the warning effect affects a firm’s tendency to warn after controlling for litigation concerns, reputation concerns, and information 18 asymmetry consequence concerns. Since intuitively management prefers a more positive market reaction, all else being equal, I hypothesize that a firm’s propensity to warn is positively associated with the warning effect. Because the warning effect is both a motive and a result of earnings warnings, I use a simultaneous equations model with qualitative and limited dependent variables introduced by Lee [1978]. This approach allows me to measure the warning effect after controlling for self-selection bias and to explicitly examine the associations between a firm’s tendency to warn and the warning effect after controlling for three other management motives. 19 Chapter 3: Model Specification and Estimation Procedures This chapter describes the models and estimation procedures used in the study. I specify market reaction models for warning and no-warning scenarios and a warning choice model. I also employ estimation procedures to deal with possible self-selection bias. This study implements a simultaneous equations model with qualitative and limited dependent variables as introduced by Lee [1978]. Lee’s [1978] approach allows me to estimate the unbiased warning effect as well as to explicitly examine the association between a firm’s tendency to warn and the warning effect, controlling for other management motives to issue earnings warnings. 3.1 MODEL SPECIFICATION The two separate market reaction models under warning and no-warning scenarios and the warning choice model appear as follows: Market reaction model under a warning scenario: )σN(0,~ε where;εZββMRW 2W W i W i * i *W 1 *W 0i ++= (1) Market reaction model under a no-warning scenario: )σN(0,~ε where;εZββMRN 2N N i N i * i *N 1 *N 0i ++= (2) Warning choice model: i * i * 2i * 1 * 0i εSδMRδδ WARN ++∆+= ; (3)   ≤ >= 0 WARNif 0 0 WARNif 1 WARNwhere * i * i i In Eqs. (1) and (2), MRWi and MRNi denote firm i’s market reactions associated with earnings news under warning and no-warning scenarios, 20 respectively, and denotes a vector of firm i’s firm-specific characteristics that determine firm i’s market reaction to earnings news. denote firm i’s random residuals under warning and no-warning scenarios and are assumed to be and , respectively. * iZ σN(0, N i W i ε andε )σN(0, 2W ) 2 N In Eq. (3), iii MRNMRW∆MR −= , is firm i’s warning effect (i.e., the differential market reaction to earnings news between warning and no-warning scenarios) and S represents a vector of firm i’s firm-specific characteristics that proxy for other management motives to issue earnings warnings: the litigation- concern motive, the reputation-concern motive, and the information asymmetry consequence-concern motive. * i * iWARN WARN is a latent variable that represents firm i’s unobserved tendency to warn; denotes firm i’s observable warning choice, where i 1WARNi = if firm i chooses to warn and 0WARNi = if firm i chooses not to warn. The models assume that 1iWARN = if and if . This study is the first to specify a warning choice model using the warning effect as one of determinants of warning choice. 0WARN*i > 0WARNi = 0*i ≤WARN Since vectors in Eqs. (1) and (2) and S in Eq. (3) contain common variables, let and S , where X * iZ Zi * i ]X[Z i * i = ]SX[ ii*i = i denotes a vector of variables common to the warning choice and market reaction models for firm i. Thus, Eqs. (1), (2), and (3), respectively, are re-written as follows: W ii W 2i W 1 W 0i εXβZββMRW +++= (4) N ii N 2i N 1 N 0i εXβZββMRN +++= (5) ii3i2ii10i εSδXδ)MRN(MRWδδ WARN +++−+= (6) 21 3.2 LIMITED DEPENDENT VARIABLE OR SELF-SELECTIVITY PROBLEM If, for any particular firm i, both MRWi and MRNi were observable, firm i’s warning effect ( ) could be easily measured as , and the warning choice model (Eq. (6)) could be estimated using Probit Maximum Likelihood Estimation. For any particular firm i, either MRW i∆MR N 1 N β and ii MRNMRW − iW iNRˆM i or MRNi is observable depending upon firm i’s warning choice (WARNi), but not both. For example, if firm i decides to warn (WARNi = 1), MRWi is observable, but MRNi is not. Similarly, if firm i decides not to warn (WARNi = 0), MRNi is observable, but MRWi is not. Even if only one state is observable, if the waning choice is random, OLS regression gives unbiased estimates coefficients ( ) for Eqs. (4) and (5); and are then easily obtained. As a result, is measurable and the warning choice model (Eq. (6)) can again be estimated using Probit Maximum Likelihood Estimation. 1 N 0 W 2 W 1 W 0 β ,β,β,β,β RˆM iRMˆ∆ It follows from the warning choice model (Eq. (6)) that firm i self- selectively chooses to warn or not to warn. That is, firm i chooses to warn or not to warn based on the warning effect ( ) and other management motives to issue earnings warnings ( ). This creates a limited dependent variable or self-selectivity problem. In this situation, the error terms, , in Eqs. (4) and (5) are conditioned on firm i’s warning choice (WARN i∆MR ii S and X N i W i ε and ε i). In this setting, Eqs. (4) and (5) are re-written as follows: ;1)WARN(εXβZββMRW i W ii W 2i W 1 W 0i =+++= (7) 0 1)WARNE(ε where i W i ≠= 22 ;0)WARN(εXβZββMRN i N ii N 2i N 1 N 0i =+++= (8) 0 0)WARNE(ε where i N i ≠= Since 01)WARNE(ε i W i ≠= and 00)WARNE(ε iNi ≠= )σN(0,~ε 2W W i , the OLS regression assumption that and is clearly violated. As a result, using OLS regression to estimate Eqs. (4) and (5) gives biased estimated coefficients and the warning effect estimated based on these biased coefficients is biased as well. )σN(0,~ε 2W W i If 1)WARNE(ε i W i = and 0)WARNε iNi =E( can be estimated and incorporated into Eqs. (7) and (8), the conditional error terms will have a mean of zero, yielding unbiased OLS estimates. 3.3 LEE [1978]’S APPROACH Generally, to correct for self-selectivity (i.e., to obtain 1)WARNE(ε i W i = and 0)WARNE(ε i N i = in Eqs. (7) and (8)), Heckman two-stage regression (as described in Maddala [1983; 1991]) is used.13 If the warning choice is modeled without the warning effect (∆MR), the warning choice model can be estimated using Probit Maximum Likelihood Estimation. Next, the self-selectivity variables for warning and no-warning firms are calculated based on estimated coefficients from the warning choice model. The self-selectivity variables are then incorporated into the corresponding market reaction models to control for potential self-selection bias and OLS regression is used to estimate the resulting market reaction models, yielding unbiased estimated coefficients. Finally, 13 Shu [2001] uses Heckman two-stage regression. 23 unbiased and are obtained and an unbiased estimate of is measured as . iWRˆM RˆM iNRˆM iNRˆM− M iRMˆ∆ iW R MRN i 0δ= (+ W 1β -MRW ∆MR iWARN W 0 ,β W 01(βδ+ W 21(βδ − N 0 W 2 β ,β , The warning choice model (Eq. (6)) in this study is specified with the warning effect (∆ ) and other management motives (S ). Since , Eqs. (7) and (8) need to be estimated before estimating Eq. (6). Equations (7) and (8), however, cannot be estimated properly without Eq. (6) due to the self-selectivity problem. Therefore, Heckman two-stage regression is not appropriate. Lee [1978] introduces a simultaneous equations model with qualitative and limited dependent variables that can be used in this situation. * i ∆MR = Lee’s [1978] procedures require using Eqs. (4) and (5) to substitute MRWi and MRNi as in Eq. (6), resulting in the following equation. i N 1 W 11 N i W i1 N 0 )Zβ(βδ)ε(εδ)β −+−+− ii3i2 N 2 εSδ)Xδ)β +++ (9) Since are not identified in Eq. (9), this model cannot be used to obtain estimates of these coefficients. Letting , , , and , Eq. (9) may be re-written as N 2 N 1 β and ,β , )ε(εδ)ββ NW1 N 0 W 0 −+− γ1(δδγ 100 += 33 δγ = )β(βδ N1 W 11 −= 3N2W212 δ)β(βδγ +−= ii3i2i10i εSγXγZγγWARN ++++= (10) 3210 γand , γ, γ,γ are identified in Eq. (10) and can be estimated using Probit Maximum Likelihood Estimation. Assume that in Eqs. (4), (5), and (10), respectively, are trivariate normally distributed, with mean vector zero and covariance matrix i N i W i ε and ,ε ,ε Σ , where 24         == 1 σσ σσσ Σε),ε,cov(ε Nε 2 N WεWN 2 W NW (11) Note that var is assumed since the ( ) 1ε i = γ ’s in Eq. (10) are estimable only up to a scale factor (Maddala [1983])14. Note also that the conditional distribution of , given , is normal, with mean and variance and the conditional distribution of ε , is normal, with mean and variance (Maddala [1983]). Consistent with the Heckman two-stage approach, W iε 2 Nσ iε ε iWεεσ 2 Wε 2 W σσ − iNεεσi N i εgiven , 2 Nσ − 1)WARNi =E(εWi and 0)WARNE(εNi i = can be measured as follows: 1)WARNE(ε i W i = )SγXγZγγ ε E(ε i3i2i10iWi +++≤= )iSγXγZγγε εE(σ 3i2i10iiWε +++≤= )SiγXγZγγε E(ε σ 3i2i10iiWε +++≤=    +++ +++= )SγXγZγΦ(γ )SγXγZγφ(γ- σ i3i2i10 i3i2i10 Wε (12) 0)WARNE(ε i N i = )SγXγZγγ ε E(ε i3i2i10iNi +++≥= )iSγXγZγγ ε εE(σ 3i2i10iiNε +++≥= )SiγXγZγγ ε E(ε σ 3i2i10iiNε +++≥=    +++ +++= )SγXγZγΦ(γ-1 )SγXγZγφ(γσ i3i2i10 i3i2i10 Nε (13) where and are the standard normal density function and cumulative distribution function, respectively. φ(.) Φ(.) 14 The probit method gives a scale estimate, σγ , where σ 2 ( )iεvar= . 25 As noted earlier, can be estimated from Eq. (10). Letting , , and . Thus, the market reaction model under a warning scenario (Eq. (7)) is re-written as 3210 γˆ and ,γˆ ,γˆ ,γˆ i3Sγˆ Wε W σπ =i2i10i XγˆZγˆγˆψ +++= NεN σ π = e )Φ(ψ )φ(ψπXβZββMRW Wi i iW i W 2i W 1 W 0i +  −+++= (14) 0 1)WARNE(e where i W i == . The market reaction model under a no-warning scenario is similarly re-written as e )Φ(ψ-1 )φ(ψπXβZββMRN Ni i iN i N 2i N 1 N 0i +  +++= (15) 0 0)WARNE(e where i N i == N 2 N 1 N 0 βˆ and ,βˆ ,βˆ . Thus, Eqs. (14) and (15) are now estimated using OLS regression to obtain the unbiased OLS estimated coefficients, β . ,βˆ ,βˆ ,ˆ W2 W 1 W 0 From Eqs. (7), (8), (14) and (15), it follows that   −== )Φ(ψ )φ(ψ π)1WARNE(ε i iW i W i and    −== )Φ(ψ1 )φ(ψπ)0WARNε i iN i N i Wπ Nπ Wπ Nπ E( . The self-selection bias exists in a firm’s warning choice if and are non- zero since the expected values of are then non-zero.15 As I hypothesize that the self-selection bias exists in a firm’s warning choice, I expect and to be significantly different from zero. N i W i ε andε After obtaining unbiased β from Eqs. (14) and (15), the warning effect (∆ ) is estimated as follows: N 2 N 1 N 0 W 2 W 1 W 0 βˆ and ,βˆ ,βˆ ,βˆ ,βˆ ,ˆ MR 15 Specifically, non-zero and imply that OLS estimated coefficients in Eqs. (7) and (8) are biased due to omitted variables – self-selectivity variables (i.e., additional terms in squared blankets in Eqs. (14) and (15)). These self-selectivity variables are generally referred to as the “Inverse Mills Ratio.” Wπ Nπ 26 iRMˆ∆ ii NRˆM WRˆM −= βˆZβˆβˆ i W 1 W 0 ++= W 1 N 0 W 0 βˆ()βˆβˆ( +−= )XβˆZβˆβˆ(X i N 2i N 1 N 0i W 2 ++− i N 2 W 2i N 1 )Xβˆβˆ()Zβˆ −+− (16) It is now possible to proceed to determine whether the warning effect ( RMˆ∆ ) is positive (negative) for good (bad) news warnings after controlling for the self-selection bias.16 In order to investigate whether and how the warning effect affects a firm’s tendency to warn after controlling for other possible management motives, I estimate Eq. (6), the warning choice model, using Probit Maximum Likelihood Estimation as follows: ii3i2i10i εSδXδRMˆδδWARN +++∆+= (17) Since I expect a firm’s tendency to warn to be positively associated with the warning effect, I predict to be significantly positive.17 1δˆ 3.4 METHODOLOGICAL PROBLEMS IN SHU [2001] Both in Eq. (16) are calculated without self-selectivity variables. According to Maddala [1983; 1991], self-selectivity variables should only be used to estimate unbiased OLS estimated coefficients of independent variables (i.e., and β ) and should not be used to estimate dependent variables ii NRˆM and WRˆM Wβˆ Nˆ 17 δ1 estimated from eq. (17) may be biased (or underestimated) since ∆MR is the estimated value, not the observed one. 16 For a warning firm, M is the “what would have been” market reaction if it had not warned. Similarly, for a no-warning firm, is the “what would have been” market reaction if it had warned. NRˆ WRˆM 27 (i.e., ). Moreover, ∆ is calculated based on both estimated and (Lee [1978] and Maddala [1983; 1991]). Observed MRW ii NRˆM and WRˆM iW iNRˆM W N ii NRˆMMRWR −= ii WRˆMRMˆ∆ −= iRMˆ RˆM RˆM RˆM Mˆ∆ i and MRNi should not be used to calculate ∆ . iRMˆ RˆM Shu [2001], in attempting to control for the self-selection bias, violates both of these guidelines. Shu [2001] estimates M for warning firms and for no-warning firms by including the self-selectivity variables along with other independent variables but fails to estimates for warning firms and for no-warning firms. As a result, she calculates the warning effect for warning firms with observed MRW and estimated M (i.e., if firm i warns), and calculates the warning effect for no-warning firms with estimated and observed MRN (i.e., if firm i does not warn). Therefore, her results could be spurious due to these methodological mistakes. NRˆ WRˆM W NRˆ i iMRN More importantly, her model is misspecified, reflecting a logical inconsistency. Specifically, she assumes that a firm will warn only if the warning effect is positive which is equivalent to assuming that the warning effect is the sole motive underlying the warning decision. However, she specifies her warning choice model without including the warning effect as an independent variable, instead including certain firm-specific characteristics (i.e., tentative proxies for management motives other than the warning effect). In summary, Shu’s [2001] empirical findings that, on average, the warning effect is positive for warning firms and it would have been negative for no- warning firms had they warned, could be spurious due to the methodological 28 problems and the logical inconsistency that compromises her model specification. Her conclusion that bad news firms in her sample make rational warning decisions may be unfounded. 29 Chapter 4: Sample Design and Variable Definitions 4.1 SAMPLE SELECTION CRITERIA Firms must meet the following selection criteria to be included in the sample. The sample firm must have (1) quarterly earnings announcements during the period 1998-2000 available on the Institutional Broker Estimate System (I/B/E/S) database; (2) the corresponding quarterly earnings announcement date (quarters t), and the prior quarter’s earnings announcement date (quarters t-1) as well as price per share, book value per share, and number of shares outstanding at the beginning of quarter t available on the Quarterly Compustat database; (3) at least one individual analyst forecast of quarters t’s and t+1’s EPS made between the announcement of quarter t-1’s earnings and (i) the 30th day following the announcement of quarter t-1’s earnings or (ii) quarter t’s fiscal quarter end, whichever comes first, plus at least one individual analyst forecast of quarter t+1’s EPS made within 30 days following the announcement of quarter t’s earnings available on the I/B/E/S database (See a timeline in figure 1); and (4) daily security returns for the period extending from 98 days preceding quarter t- 1’s earnings announcement date to one day after quarter t’s earnings announcement date available on the Center for Research in Security Prices (CRSP) Daily Stock database. Criteria (1) - (3) are required to compute unexpected earnings, analyst forecast revisions, market values and book-to-market ratios. Criterion (4) is required to calculate cumulative abnormal returns, betas, and return variations. In 30 addition, data on debt and equity issuances are obtained from the Security Data Company (SDC) database. Earnings warnings include any earnings-related voluntary disclosures made by management in either quantitative or qualitative form available in the First Call Historical database. The warnings included in the sample must have been released during the warning period, which is defined as the period between (i) the 31st day after quarter t-1’s earnings announcement date, or (ii) quarter t’s fiscal quarter end, whichever comes first, and quarter t’s earnings announcement date (See a timeline in figure 1). 4.2 SAMPLE DESCRIPTION Table 1 provides a reconciliation of sample data and reports the sequential filters applied to obtain the final sample: 91,561 firm-quarters (10,840 firms) meet criterion (1). Criterion (2) eliminates 32,520 firm-quarters. I remove 34,612 firm-quarters that do not meet criterion (3). 412 firm-quarters fail to meet criterion (4) resulting in a sample of 24,017 firm-quarters (4,564 firms) that meet all four criteria. Of these, 999 firm-quarters with extreme values (i.e., the highest and lowest 1%) of cumulative abnormal returns, unexpected earnings, and/or analyst forecast revisions are eliminated leaving a final sample of 23,018 firm- quarters (4,482 firms), consisting of 13,818 (60.03%) firm-quarters with good news and 9,200 (39.97%) firm-quarters with bad news.18 Of the 13,818 good 18 Good (bad) news firms are those with positive (negative) total earnings news revealed through a warning (if any) and an earnings announcement. Total earnings news is UE defined in section 4.3.2. 31 news firm-quarters, there are 1,258 (9.10%) earnings warnings and of the 9,200 bad news firm-quarters, there are 2,045 (22.23%) earnings warnings. This distribution is consistent with empirical findings in Skinner (1994), Baginski, Hassell, and Waymire (1994), KL, and AST that bad news firms are more likely to warn than good news firms. Table 2 reports the distribution of earnings warnings in the final sample by year and quarter. There are 1,078 (14.76%) firm-quarters of earnings warnings in 1998, 1,019 (13.09%) in 1999 and 1,206 (15.21%) in 2000. In each of the three years, bad news firms issue more earnings warnings than good news firms: In 1998, there are 363 (9.11%) good news warnings and 715 (21.54%) bad news warnings, in 1999 there are 427 (8.82%) good news warnings and 592 (20.09%) bad news warnings and in 2000 there are 468 (9.37%) good news warnings and 738 (25.15%) bad news warnings. 4.3 VARIABLE DEFINITIONS 4.3.1 Market reaction associated with earnings news (MRW and MRN) The market reaction associated with earnings news under a warning scenario of firm i in quarter t, denoted MRWit, and the market reaction associated with earnings news under a no-warning scenario of firm i in quarter t, denoted MRNit, are defined as the cumulative abnormal (market-adjusted) returns from two days following quarter t-1’s earnings announcement date to one day following quarter t’s earnings announcement date. Warning firms._. Panel A: Good News Warning Firms RMˆ∆ Pooled Sample 1998-2000 Subsample 1998 Subsample 1999 Subsample 2000 Mean 0.092 0.079 0.050 0.096 Std. Dev. 0.037 0.047 0.052 0.061 Max 0.169 0.156 0.161 0.217 90% 0.142 0.131 0.098 0.186 75% 0.105 0.091 0.049 0.116 Median 0.080 0.075 0.034 0.078 25% 0.070 0.067 0.029 0.061 10% 0.066 0.080 0.022 0.055 Min -0.103 -0.179 -0.001 -0.086 No. of Obs. 1,258 (100.00%) 363 (100.00%) 427 (100.00%) 468 (100.00%) Positive RMˆ∆ 1,250 ( 99.36%) 353 ( 97.25%) 422 ( 98.83%) 458 ( 97.86%) Negative RMˆ∆ 10 ( 0.64%) 10 ( 2.75%) 5 ( 1.17%) 10 ( 2.14%) Panel B: Good News No-warning Firms RMˆ∆ Pooled Sample 1998-2000 Subsample 1998 Subsample 1999 Subsample 2000 Mean 0.082 0.070 0.047 0.078 Std. Dev. 0.037 0.043 0.090 0.063 Max 0.148 0.128 0.182 0.185 90% 0.122 0.108 0.089 0.140 75% 0.092 0.080 0.036 0.093 Median 0.075 0.067 0.023 0.069 25% 0.068 0.062 0.018 0.059 10% 0.064 0.050 0.013 0.045 Min -0.057 -0.101 -0.020 -0.169 No. of Obs. 12,560 (100.00%) 3,620 (100.00%) 4,412 (100.00%) 4,528 (100.00%) Positive RMˆ∆ 12,344 ( 98.28%) 3,502 ( 96.74%) 4,174 ( 98.83%) 4,352 ( 96.11%) Negative RMˆ∆ 216 ( 1.72%) 118 ( 3.26%) 138 ( 3.13%) 176 ( 3.89%) 82 Table 8 (Continued) Distribution of the Warning Effect after Controlling for Self-selection Bias ( RMˆ∆ ) ititit NRˆMWRˆMRMˆ∆ −= , where and M are estimated market reactions associated with earnings news under warning and no-warning scenarios of firm i in quarter t. itWRˆM itNRˆ Panel C: Bad News Warning Firms RMˆ∆ Pooled Sample 1998-2000 Subsample 1998 Subsample 1999 Subsample 2000 Mean -0.011 -0.029 -0.025 -0.006 Std. Dev. 0.043 0.050 0.076 0.036 Max 0.066 0.029 0.069 0.067 90% 0.033 0.013 0.042 0.036 75% 0.016 0.001 0.023 0.021 Median -0.005 -0.016 -0.007 -0.003 25% -0.031 -0.047 -0.050 -0.033 10% -0.060 -0.077 -0.108 -0.054 Min -0.085 -0.117 -0.154 -0.072 No. of Obs. 2,045 (100.00%) 715 (100.00%) 592 (100.00%) 738 (100.00%) Positive RMˆ∆ 901 ( 44.06%) 189 ( 26.43%) 263 ( 44.43%) 334 ( 45.26%) Negative RMˆ∆ 1,114 ( 55.94%) 526 ( 73.57%) 329 ( 55.57%) 404 ( 54.74%) Panel D: Bad News No-warning Firms RMˆ∆ Pooled Sample 1998-2000 Subsample 1998 Subsample 1999 Subsample 2000 Mean -0.014 -0.020 -0.020 -0.011 Std. Dev. 0.035 0.043 0.051 0.031 Max 0.035 0.022 0.020 0.050 90% 0.014 0.009 0.016 0.018 75% 0.009 0.004 0.012 0.010 Median -0.005 -0.007 -0.002 -0.005 25% -0.029 -0.031 -0.034 -0.030 10% -0.058 -0.061 -0.075 -0.055 Min -0.077 -0.084 -0.115 -0.070 No. of Obs. 7,155 (100.00%) 2,604 (100.00%) 2,355 (100.00%) 2,196 (100.00%) Positive RMˆ∆ 3,043 (42.53%) 968 ( 37.17%) 1,083 ( 45.99%) 961 ( 43.76%) Negative RMˆ∆ 4,112 (57.47%) 1,636 ( 62.83%) 1,272 ( 54.01%) 1,235 ( 56.24%) 83 Table 9 Results of OLS Estimation of Market Reaction Model under Warning Scenario – without Controlling for Self-selection Bias )FGx (UEβ)FSx (UEβ)FRx (UEβUEββMRW itit W 4itit W 3itit W 2it W 1 W 0it ′+′+′+′+′= it W 7itit W 6itit W 5 AFRβ)LOSSx (UEβ)LUEx (UEβ ′+′+′+ W ititit W 8 e)LAFRx (AFRβ ′+′+ Panel A: Good News Warning Firms Independent Variables Est. Coeff. Pooled Sample 1998-2000 Subsample 1998 Subsample 1999 Subsample 2000 Intercept W 0βˆ′ 0.025(0.011) ** 0.006 (0.015) 0.072 (0.018) *** 0.008 (0.019) UE W 1βˆ′ 11.115(3.506) *** 12.461 (3.984) *** 10.427 (3.989) *** 11.874 (3.125) *** UE x FR W 2βˆ′ 8.219(3.933) ** 9.144 (4.137) ** 9.302 (3.870) ** 5.637 (3.167) * UE x FS W 3βˆ′ -1.501(3.691) -2.358 (3.373) -1.279 (3.999) -1.837 (3.908) UE x FG W 4βˆ′ 1.775(3.907) 1.988 (3.077) 0.721 (3.797) 2.731 (2.730) UE x LUE W 5βˆ′ -5.424(2.855) * -6.105 (3.022) ** -5.622 (2.854) ** -6.409 (3.270) ** UE x LOSS W 6βˆ′ -5.076(2.851) * -6.957 (3.532) ** -4.900 (2.311) ** -6.143 (2.939) ** AFR W 7βˆ′ 8.597(3.035) *** 8.719 (2.813) *** 10.692 (3.962) *** 7.238 (3.531) ** AFR x LAFR W 8βˆ′ -5.880(3.111) * -5.325 (2.926) * -5.021 (2.656) * -3.018 (1.658) * Adj. R2 0.087 0.117 0.110 0.125 No. of Obs. 1,258 363 427 468 84 Table 9 (Continued) Results of OLS Estimation of Market Reaction Model under Warning Scenario – without Controlling for Self-selection Bias )FGx (UEβ)FSx (UEβ)FRx (UEβUEββMRW itit W 4itit W 3itit W 2it W 1 W 0it ′+′+′+′+′= it W 7itit W 6itit W 5 AFRβ)LOSSx (UEβ)LUEx (UEβ ′+′+′+ W ititit W 8 e)LAFRx (AFRβ ′+′+ Panel B: Bad News Warning Firms Independent Variables Est. Coeff. Pooled Sample 1998-2000 Subsample 1998 Subsample 1999 Subsample 2000 Intercept W 0βˆ′ -0.134(0.012) *** -0.120 (0.018) *** -0.132 (0.024) *** -0.131 (0.022) *** UE W 1βˆ′ 6.660(2.321) *** 5.544 (1.293) *** 6.736 (2.723) ** 6.792 (2.302) *** UE x FR W 2βˆ′ 3.111(1.095) *** 2.805 (1.550) * 3.970 (2.603) 4.992 (1.739) *** UE x FS W 3βˆ′ -0.640(1.422) -0.514 (1.142) -0.463 (2.012) -0.831 (1.278) UE x FG W 4βˆ′ 3.685(1.423) *** 3.158 (1.611) ** 3.355 (2.470) 5.231 (1.887) *** UE x LUE W 5βˆ′ -2.282(1.201) * -2.228 (1.252) * -2.418 (1.321) * -2.052 (1.122) * UE x LOSS W 6βˆ′ -2.995(1.655) * -3.966 (1.825) ** -2.078 (1.148) * -2.838 (1.594) * AFR W 7βˆ′ 11.353(2.366) *** 13.622 (2.624) *** 8.906 (2.356) *** 13.144 (3.151) *** AFR x LAFR W 8βˆ′ -6.979(2.109) *** -7.301 (2.333) *** -5.099 (2.601) ** -8.813 (3.005) *** Adj. R2 0.49 0.097 0.047 0.047 No. of Obs. 2,045 715 592 738 85 Table 9 (Continued) Results of OLS Estimation of Market Reaction Model under Warning Scenario – without Controlling for Self-selection Bias * Statistically significant at two-tailed 0.10 level. ** Statistically significant at two-tailed 0.05 level. *** Statistically significant at two-tailed 0.01 level. Variable definitions: MRWit = market reaction associated with earnings news of warning firm i in quarter t, UEit = price-deflated unexpected earnings of warning firm i in quarter t, FRit = a dichotomous variable taking a value of 1 if warning firm i’s return variation in quarter t exceeds the median return variation of all sample firm-quarters (i.e., high market risk firms) and 0 otherwise, FSit = a dichotomous variable taking a value of 1 if warning firm i’s market capitalization at the beginning of quarter t exceeds the median market capitalization of all sample firm-quarters (i.e., large firms) and 0 otherwise, FGit = a dichotomous variable taking a value of 1 if warning firm i’s book-to-market ratio at the beginning of quarter t is at least the median book-to-market ratio of all sample firm-quarters (i.e., high growth firms) and 0 otherwise, LUEit = a dichotomous variable taking a value of 1 if the absolute value of price-deflated unexpected earnings of warning firm i in quarter t is at least 0.01 (i.e., large earning news firms) and 0 otherwise, LOSSit = a dichotomous variable taking a value of 1 if warning firm i’s reported earnings in quarter t is negative (i.e., loss firms) and 0 otherwise, AFRit = price-deflated analyst forecast revisions of warning firm i in quarter t, and LAFRit = a dichotomous variable taking a value of 1 if the absolute value of price-deflated analyst forecast revisions of warning firm i in quarter t is at least 0.01 (i.e., large forecast revision firms) and 0 otherwise. 86 Table 10 Results of OLS Estimation of Market Reaction Model under No-warning Scenario – without Controlling for Self-selection Bias )FGx (UEβ)FSx (UEβ)FRx (UEβUEββMRN itit N 4itit N 3itit N 2it N 1 N 0it ′+′+′+′+′= it N 7itit N 6itit N 5 AFRβ)LOSSx (UEβ)LUEx (UEβ ′+′+′+ N ititit N 8 e)LAFRx (AFRβ ′+′+ Panel A: Good News No-warning Firms Independent Variables Est. Coeff. Pooled Sample 1998-2000 Subsample 1998 Subsample 1999 Subsample 2000 Intercept N 0βˆ′ 0.018(0.003) *** 0.026 (0.004) *** 0.003 (0.006) 0.032 (0.006) *** UE N 1βˆ′ 10.884(1.410) *** 10.168 (2.759) *** 9.591 (2.524) *** 11.483 (2.665) *** UE x FR N 2βˆ′ 5.476(1.530) *** 6.831 (2.623) *** 3.790 (2.256) * 4.761 (2.561) * UE x FS N 3βˆ′ -2.877(2.387) -2.333 (2.722) -0.515 (2.344) -3.048 (2.234) UE x FG N 4βˆ′ 3.082(2.352) 0.932 (2.432) 3.173 (2.219) 2.257 (2.315) UE x LUE N 5βˆ′ -4.825(1.327) *** -5.512 (2.366) ** -4.576 (2.158) ** -4.567 (2.330) ** UE x LOSS N 6βˆ′ -5.961(1.251) *** -5.551 (2.636) ** -6.385 (2.396) *** -5.360 (2.615) ** AFR N 7βˆ′ 11.563(1.027) *** 14.335 (1.624) *** 10.045 (1.722) *** 11.645 (1.864) *** AFR x LAFR N 8βˆ′ -7.971(1.221) *** -8.114 (2.154) *** -5.261 (2.073) ** -9.429 (2.116) *** Adj. R2 0.036 0.048 0.072 0.042 No. of Obs. 12,560 3,620 4,412 4,528 87 Table 10 (Continued) Results of OLS Estimation of Market Reaction Model under No-warning Scenario – without Controlling for Self-selection Bias )FGx (UEβ)FSx (UEβ)FRx (UEβUEββMRN itit N 4itit N 3itit N 2it N 1 N 0it ′+′+′+′+′= it N 7itit N 6itit N 5 AFRβ)LOSSx (UEβ)LUEx (UEβ ′+′+′+ N ititit N 8 e)LAFRx (AFRβ ′+′+ Panel B: Bad News No-warning Firms Independent Variables Est. Coeff. Pooled Sample 1998-2000 Subsample 1998 Subsample 1999 Subsample 2000 Intercept N 0βˆ′ -0.034(0.005) *** -0.028 (0.007) *** -0.047 (0.009) *** -0.026 (0.010) ** UE N 1βˆ′ 4.550(1.827) ** 4.143 (2.021) ** 4.400 (1.496) *** 5.125 (2.429) ** UE x FR N 2βˆ′ 0.322(0.850) 0.469 (1.264) 0.773 (1.679) 0.141 (0.828) UE x FS N 3βˆ′ -1.936(1.057) * -0.813 (1.332) * -2.324 (1.139) * -0.422 (2.244) UE x FG N 4βˆ′ 1.279(0.822) 0.892 (1.079) 0.270 (1.565) 1.537 (1.537) UE x LUE N 5βˆ′ -2.234(1.346) * -3.410 (1.916) * -2.578 (1.386) * -3.895 (2.188) * UE x LOSS N 6βˆ′ -2.394(0.892) *** -3.117 (1.383) ** -2.526 (1.164) ** -2.834 (1.461) * AFR N 7βˆ′ 9.905(1.197) *** 10.278 (1.639) *** 9.692 (2.121) *** 10.414 (2.455) *** AFR x LAFR N 8βˆ′ -4.848(1.174) *** -3.125 (1.594) ** -5.990 (2.100) *** -5.497 (2.409) ** Adj. R2 0.027 0.047 0.035 0.038 No. of Obs. 7,155 2,604 2,355 2,196 88 Table 10 (Continued) Results of OLS Estimation of Market Reaction Model under No-warning Scenario – without Controlling for Self-selection Bias * Statistically significant at two-tailed 0.10 level. ** Statistically significant at two-tailed 0.05 level. *** Statistically significant at two-tailed 0.01 level. Variable definitions: MRNit = market reaction associated with earnings news of no-warning firm i in quarter t, UEit = price-deflated unexpected earnings of no-warning firm i in quarter t, FRit = a dichotomous variable taking a value of 1 if no-warning firm i’s return variation in quarter t exceeds the median return variation of all sample firm-quarters (i.e., high market risk firms) and 0 otherwise, FSit = a dichotomous variable taking a value of 1 if no-warning firm i’s market capitalization at the beginning of quarter t exceeds the median market capitalization of all sample firm-quarters (i.e., large firms) and 0 otherwise, FGit = a dichotomous variable taking a value of 1 if no-warning firm i’s book-to-market ratio at the beginning of quarter t is at least the median book-to-market ratio of all sample firm-quarters (i.e., high growth firms) and 0 otherwise, LUEit = a dichotomous variable taking a value of 1 if the absolute value of price-deflated unexpected earnings of no-warning firm i in quarter t is at least 0.01 (i.e., large earning news firms) and 0 otherwise, LOSSit = a dichotomous variable taking a value of 1 if no-warning firm i’s reported earnings in quarter t is negative (i.e., loss firms) and 0 otherwise, AFRit = price-deflated analyst forecast revisions of no-warning firm i in quarter t, and LAFRit = a dichotomous variable taking a value of 1 if the absolute value of price-deflated analyst forecast revisions of no-warning firm i in quarter t is at least 0.01 (i.e., large forecast revision firms) and 0 otherwise. 89 Table 11 Distribution of the Warning Effect without Controlling for Self-selection Bias ( RMˆ∆ ′ ) ititit NRˆMWRˆMRMˆ∆ ′−′=′ , where and M are estimated market reactions associated with earnings news under warning and no-warning scenarios without IMR, of firm i in quarter t. itWRˆM ′ itNRˆ ′ Panel A: Good News Warning Firms RMˆ∆ ′ Pooled Sample 1998-2000 Subsample 1998 Subsample 1999 Subsample 2000 Mean 0.085 0.065 0.036 0.093 Std. Dev. 0.040 0.050 0.054 0.060 Max 0.168 0.147 0.141 0.212 90% 0.141 0.120 0.091 0.180 75% 0.099 0.078 0.035 0.113 Median 0.071 0.058 0.018 0.076 25% 0.061 0.049 0.012 0.061 10% 0.057 0.044 0.006 0.055 Min -0.110 -0.186 -0.012 -0.101 No. of Obs. 1,258 (100.00%) 363 (100.00%) 427 (100.00%) 468 (100.00%) Positive RMˆ∆ ′ 1,248 ( 99.21%) 347 ( 95.59%) 412 ( 96.49%) 455 ( 97.22%) Negative RMˆ∆ ′ 10 ( 0.79%) 16 ( 4.41%) 15 ( 3.51%) 13 ( 2.78%) Panel B: Good News No-warning Firms RMˆ∆ ′ Pooled Sample 1998-2000 Subsample 1998 Subsample 1999 Subsample 2000 Mean 0.074 0.057 0.040 0.074 Std. Dev. 0.042 0.048 0.087 0.064 Max 0.146 0.117 0.172 0.177 90% 0.117 0.098 0.086 0.135 75% 0.084 0.069 0.032 0.090 Median 0.066 0.054 0.016 0.067 25% 0.059 0.048 0.011 0.057 10% 0.055 0.039 0.007 0.038 Min -0.094 -0.129 -0.025 -0.190 No. of Obs. 12,560 (100.00%) 3620 (100.00%) 4,412 (100.00%) 4,528 (100.00%) Positive RMˆ∆ ′ 12,305 ( 97.97%) 3468 ( 95.80%) 4,228 ( 95.83%) 4,328 ( 95.58%) Negative RMˆ∆ ′ 255 ( 2.03%) 152 ( 4.20%) 184 ( 4.17%) 200 ( 4.42%) 90 Table 11 (Continued) Distribution of the Warning Effect without Controlling for Self-selection Bias ( RMˆ∆ ′ ) ititit NRˆMWRˆMRMˆ∆ ′−′=′ , where and M are estimated market reactions associated with earnings news under warning and no-warning scenarios without IMR, of firm i in quarter t. itWRˆM ′ itNRˆ ′ Panel C: Bad News Warning Firms RMˆ∆ ′ Pooled Sample 1998-2000 Subsample 1998 Subsample 1999 Subsample 2000 Mean -0.030 -0.037 -0.043 -0.036 Std. Dev. 0.034 0.047 0.058 0.032 Max 0.055 0.022 0.031 0.079 90% -0.007 -0.001 0.001 0.001 75% -0.014 -0.010 -0.009 -0.027 Median -0.024 -0.024 -0.028 -0.042 25% -0.042 -0.050 -0.059 -0.055 10% -0.064 -0.080 -0.102 -0.067 Min -0.084 -0.124 -0.143 -0.073 No. of Obs. 2,045 (100.00%) 715 (100.00%) 592 (100.00%) 738 (100.00%) Positive RMˆ∆ ′ 151 ( 7.38%) 70 ( 9.79%) 67 ( 11.32%) 81 ( 10.98%) Negative RMˆ∆ ′ 1,894 ( 92.62%) 645 ( 90.21%) 525 ( 88.68%) 657 ( 89.02%) Panel D: Bad News No-warning Firms RMˆ∆ ′ Pooled Sample 1998-2000 Subsample 1998 Subsample 1999 Subsample 2000 Mean -0.032 -0.027 -0.029 -0.029 Std. Dev. 0.025 0.040 0.037 0.019 Max 0.030 0.016 0.005 0.040 90% -0.019 -0.003 -0.006 -0.012 75% -0.021 -0.007 -0.008 -0.025 Median -0.026 -0.016 -0.017 -0.030 25% -0.039 -0.034 -0.036 -0.038 10% -0.057 -0.063 -0.067 -0.048 Min -0.074 -0.084 -0.095 -0.054 No. of Obs. 7,155 (100.00%) 2,604 (100.00%) 2,355 (100.00%) 2,196 (100.00%) Positive RMˆ∆ ′ 184 ( 2.57%) 162 ( 6.22%) 18 ( 0.76%) 132 ( 6.01%) Negative RMˆ∆ ′ 6,971 (97.43%) 2,442 ( 93.78%) 2,337 ( 99.24%) 2,064 ( 93.99%) 91 Table 12 Distribution of Self-selection Bias ( ) in the Warning Effect BSˆS ititit RMˆRMˆBSˆS ∆−′∆= , where is the warning effect without controlling for self-selection bias and is the warning effect after controlling for self-selection bias of firm i in quarter t. itRMˆ ′∆ itRMˆ∆ Panel A: Good News Warning Firms BSˆS Pooled Sample 1998-2000 Subsample 1998 Subsample 1999 Subsample 2000 Mean -0.007 -0.014 -0.013 -0.003 Std. Dev. 0.004 0.008 0.006 0.004 Max 0.005 0.010 0.014 0.002 90% -0.002 -0.003 -0.007 -0.000 75% -0.005 -0.011 -0.012 -0.001 Median -0.007 -0.016 -0.015 -0.002 25% -0.008 -0.019 -0.016 -0.004 10% -0.009 -0.020 -0.017 -0.008 Min -0.022 -0.028 -0.031 -0.020 No. of Obs. 1,258 (100.00%) 363 (100.00%) 427 (100.00%) 468 (100.00%) Positive BSˆS 26 ( 7.16%) 15 ( 3.51%) 20 ( 4.27%) Negative S BSˆ 1,194 ( 94.91%) 337 ( 92.84%) 412 ( 96.49%) 448 ( 95.73%) 64 ( 5.09%) Panel B: Good News No-warning Firms BSˆS Pooled Sample 1998-2000 Subsample 1998 Subsample 1999 Subsample 2000 Mean -0.008 -0.012 -0.006 -0.003 Std. Dev. 0.005 0.008 0.007 0.004 Max 0.002 0.008 0.015 0.001 90% -0.004 -0.004 -0.001 -0.001 75% -0.006 -0.010 -0.004 -0.001 Median -0.008 -0.013 -0.007 -0.002 25% -0.009 -0.014 -0.008 -0.004 10% -0.010 -0.016 -0.009 -0.008 Min -0.035 -0.035 -0.033 -0.023 No. of Obs. 12,560 (100.00%) 3,620 (100.00%) 4,412 (100.00%) 4,528 (100.00%) Positive S BSˆ 298 ( 2.37%) 192 ( 5.30%) 332 ( 7.52%) 106 ( 2.34%) Negative S BSˆ 12,262 ( 97.63%) 3,428 ( 94.70%) 4,080 ( 92.48%) 4,422 ( 97.66%) 92 Table 12 (Continued) Distribution of Self-selection Bias ( ) in the Warning Effect BSˆS ititit RMˆRMˆBSˆS ∆−′∆= , where is the warning effect without controlling for self-selection bias and is the warning effect after controlling for self-selection bias of firm i in quarter t. itRMˆ ′∆ itRMˆ∆ Panel C: Bad News Warning Firms BSˆS Pooled Sample 1998-2000 Subsample 1998 Subsample 1999 Subsample 2000 Mean -0.018 -0.007 -0.018 -0.029 Std. Dev. 0.019 0.006 0.021 0.031 Max 0.041 0.007 0.073 0.073 90% 0.012 0.001 0.008 0.009 75% -0.006 -0.003 -0.007 -0.010 Median -0.019 -0.008 -0.021 -0.031 25% -0.032 -0.012 -0.033 -0.052 10% -0.042 -0.015 -0.041 -0.069 Min -0.048 -0.018 0.047 -0.076 No. of Obs. 2,045 (100.00%) 715 (100.00%) 592 (100.00%) 738 (100.00%) Positive S BSˆ 316 ( 15.45%) 100 ( 13.99%) 99 ( 16.72%) 112 ( 15.18%) Negative S BSˆ 1,729 ( 84.55%) 615 ( 86.01%) 493 ( 83.28%) 626 ( 84.82%) Panel D: Bad News No-warning Firms BSˆS Pooled Sample 1998-2000 Subsample 1998 Subsample 1999 Subsample 2000 Mean -0.018 -0.007 -0.009 -0.018 Std. Dev. 0.016 0.006 0.016 0.024 Max 0.030 0.008 0.043 0.053 90% 0.005 0.001 0.012 0.015 75% -0.008 -0.003 -0.001 -0.003 Median -0.022 -0.009 -0.014 -0.022 25% -0.031 -0.012 -0.021 -0.036 10% -0.034 -0.014 -0.024 -0.042 Min -0.041 -0.016 -0.028 -0.058 No. of Obs. 7,155 (100.00%) 2,604 (100.00%) 2,355 (100.00%) 2,196 (100.00%) Positive S BSˆ 1,039 ( 14.52%) 366 ( 14.06%) 525 ( 22.29%) 459 ( 20.90%) Negative S BSˆ 6,116 ( 85.48%) 2,238 ( 85.94%) 1,830 ( 77.71%) 1,737 ( 79.10%) 93 Table 13 Results of Probit Maximum Likelihood Estimation of Warning Choice Model it5it4it3it2it10it PWPδLMVδUEδHLRδRMˆ∆δδWARN +++++= itit8it7it6 εFXFδREGδNAFδ ++++ Panel A: Good News Firms Independent Variables Est. Coeff. Pooled Sample 1998-2000 Subsample 1998 Subsample 1999 Subsample 2000 Constant 0δˆ -2.401 (0.085) *** -2.700 (0.179) *** -2.233 (0.137) *** -2.505 (0.142) *** RMˆ∆ 1δˆ 2.764 (0.476) *** 3.390 (0.695) *** 1.703 (0.373) *** 2.927 (0.542) *** HLR 2δˆ 0.033 (0.034) 0.028 (0.063) 0.002 (0.058) 0.038 (0.058) UE 3δˆ 13.070(5.131) ** 13.447 (5.384) ** 12.025 (6.135) ** 13.167 (6.670) ** LMV 4δˆ 0.087(0.012) *** 0.121 (0.023) *** 0.065 (0.020) ** 0.088 (0.019) *** PWP 5δˆ 0.624(0.032) *** 0.582 (0.060) *** 0.672 (0.055) *** 0.646 (0.053) *** NAF 6δˆ 0.001(0.004) 0.012 (0.010) 0.017 (0.008) ** 0.007 (0.007) REG 7δˆ -0.318(0.057) *** -0.381 (0.117) ** -0.315 (0.101) ** -0.307 (0.090) *** FXF 8δˆ 0.071(0.043) 0.026 (0.085) 0.018 (0.077) 0.195 (0.069) *** No. of Obs. 13,818 3,983 4,839 4,996 % Correct 93.62% 93.30% 93.80% 92.25% 94 Table 13 (Continued) Results of Probit Maximum Likelihood Estimation of Warning Choice Model it5it4it3it2it10it PWPδLMVδUEδHLRδRMˆ∆δδWARN +++++= itit8it7it6 εFXFδREGδNAFδ ++++ Panel B: Bad News Firms Independent Variables Est. Coeff. Pooled Sample 1998-2000 Subsample 1998 Subsample 1999 Subsample 2000 Constant 0δˆ -1.794 (0.077) *** -1.515 (0.127) *** -1.924 (0.140) *** -1.855 (0.143) *** ∆MR 1δˆ 1.585 (0.443) *** 1.847 (0.695) *** 1.194 (0.553) ** 1.722 (0.803) ** HLR 2δˆ 0.166 (0.034) *** 0.156 (0.057) *** 0.126 (0.061) ** 0.152 (0.058) *** UE 3δˆ -13.180(3.952) *** -13.234 (3.766) *** -13.916 (4.125) *** -11.738 (3.826) *** LMV 4δˆ 0.087(0.012) *** 0.060 (0.020) *** 0.096 (0.021) *** 0.099 (0.021) *** PWP 5δˆ 0.546(0.031) *** 0.549 (0.054) *** 0.567 (0.056) *** 0.545 (0.054) *** NAF 6δˆ 0.006(0.005) 0.001 (0.008) 0.003 (0.008) 0.014 (0.008) * REG 7δˆ -0.433(0.054) *** -0.463 (0.097) *** -0.375 (0.095) *** -0.470 (0.092) *** FXF 8δˆ 0.056(0.048) 0.009 (0.084) 0.070 (0.092) 0.089 (0.079) No. of Obs. 9,200 3,319 2,947 2,934 % Correct: 83.63% 83.94% 83.71% 81.60% 95 Table 13 (Continued) Results of Probit Maximum Likelihood Estimation of Warning Choice Model * Statistically significant at two-tailed 0.10 level. ** Statistically significant at two-tailed 0.05 level. *** Statistically significant at two-tailed 0.01 level. Variable definitions: WARNit = a dichotomous variable taking a value of 1 if firm i issues earnings warnings for quarter t and 0 otherwise, ∆MRit = the warning effect, calculated as the estimated market reaction associated with earnings news under a warning scenario minus that under a no-warning scenario of firm i in quarter t. 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Ho-hum, Another Earnings Surprise. Business Week, May 24. Waymire, G. 1985. Earnings Volatility and Voluntary Management Forecast Disclosure. Journal of Accounting Research, Spring: 268-95. 100 101 Vita Somchai Supattarakul was born in Saraburi, Thailand on November 18, 1967, the son of Orapin and Prasert Supattarakul. He earned the degrees of Bachelor of Business Administration in Accounting in October 1988 and Master of Business Administration in May 1994 from Thammasat University, Thailand. He joined SGVN Arthur Andersen – Thailand as an auditor in October 1988 and Thammasat University as an accounting lecturer in January 1993. In May 1996, he received the degree of Master of Professional Accounting from The University of Texas at Austin with the financial support of Faculty of Commerce and Accountancy, Thammasat University where he returned to work for after graduation. In September 1998 he entered the Ph.D. Program in Accounting at The University of Texas at Austin. Permanent address: 11 Saeree-Thai Rd., Klongjun, Bangkapi, Bangkok 10240 Thailand This dissertation was typed by the author. ._.