Do management earnings forecasts incorporate information in accruals?

ARTICLE IN PRESS Journal of Accounting and Economics 49 (2010) 227–246

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Do management earnings forecasts incorporate information in accruals? Weihong Xu  Department of Accounting & Law, State University of New York at Buffalo, Buffalo, NY 14260, USA

a r t i c l e in fo

abstract

Article history: Received 19 June 2006 Received in revised form 16 September 2009 Accepted 20 November 2009 Available online 27 November 2009

I investigate whether management earnings forecasts fully reflect the implications of accruals for future earnings. I find that managers overestimate accrual persistence in range forecasts but not in point forecasts and that managers’ accrual-related forecast bias in range forecasts increases with forecast range and forecast horizon. My results suggest that managers overestimate accrual persistence when faced with greater difficulty forecasting earnings. Moreover, I find that managers’ accrual-related forecast bias in range forecasts is somewhat affected by managerial opportunism and fear of litigation. Finally, I find accrual mispricing for firms issuing range forecasts but not for firms issuing point forecasts. & 2009 Elsevier B.V. All rights reserved.

JEL classification: M41 G14 Keywords: Accruals Persistence Management forecasts Forecast errors

1. Introduction In an influential paper, Sloan (1996) reports that the accrual component of earnings is less persistent than the cash component of earnings. He also finds that investors generally fail to appreciate fully the lower persistence of accruals and, consequently, overprice accruals. Extending the evidence in Sloan, subsequent researchers examine whether sophisticated intermediaries understand better than naı¨ve investors the implications of the lower persistence of accruals for future earnings. For example, DeFond and Park (2001) find that analysts appear to anticipate the reversing implications of abnormal accruals. Specifically, they report that analysts’ forecast revisions following good (bad) news earnings surprises in conjunction with incoming-increasing abnormal accruals are less positive (more negative) than revisions following good (bad) news earnings surprises in conjunction with incoming-decreasing abnormal accruals. Moreover, Elgers et al. (2003) find that overweighting of accruals in analysts’ earnings forecasts is less than overweighting by investors that is implicit in stock prices. However, Bradshaw et al. (2001) document that analysts do not fully incorporate the predictable earnings declines associated with high accruals in their one-year-ahead earnings forecasts.1 They also find no evidence that auditors signal the future earnings problems associated with high accruals through either their auditor opinions or through auditor changes. Taken together, previous studies suggest that although sophisticated intermediaries have a better understanding of accruals than naı¨ve investors, they do not anticipate fully the effect of the lower persistence of accruals on future earnings.

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E-mail address: [email protected] Note that Bradshaw et al. (2001) do not exclude the possibility that analysts partially see through the subsequent accrual reversals.

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0165-4101/$ - see front matter & 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.jacceco.2009.11.005

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Prior studies, however, provide relatively little evidence on how well corporate managers understand the effect of the lower persistence of accruals on future earnings. Managers make accrual-related decisions in response to their firms’ business operations and likely have the most complete firm-specific information set regarding factors underlying the persistence of accruals. As such, managers are expected to at least partially anticipate the reversing implications of accruals. In a related study, Beneish and Vargus (2002) find that the persistence of income-increasing accruals is lower when accompanied by abnormal insider selling and greater when accompanied by abnormal insider buying. These results suggest that corporate insiders, including managers, exploit their superior knowledge of the lower persistence of their firms’ income-increasing accruals when making insider trading decisions.2 Beneish and Vargus, however, do not investigate whether managers are able to anticipate fully the effect of the lower persistence of their firms’ accruals on future earnings. Using a sample of 8244 firm-quarter observations during the period 1997–2005, I find that accruals are negatively associated with management forecast errors with respect to future earnings. Extending Sloan (1996) and Bradshaw et al. (2001), my results suggest that managers generally overestimate accrual persistence (i.e., underestimate accrual reversals) in their forecasts. To better understand the nature of this phenomenon, I perform several sets of analyses. First, I examine whether managers’ overestimation of accrual persistence is attributable to their difficulty forecasting earnings. Following Baginski et al.’s (1993) reasoning that forecast specificity reveals managers’ uncertainty in forecasting earnings (i.e., the greater difficulty the managers have forecasting earnings, the less specific the forecasts are), I use forecast specificity to capture forecast difficulty. Consistent with this forecast difficulty explanation, I find that managers who issue point forecasts appear to reflect fully the implications of accruals in their forecasts whereas managers who issue range forecasts underestimate accrual reversals in their forecasts. Furthermore, I find that managers’ accrual-related forecast bias reported in range forecasts increases with managers’ forecast difficulty as captured by forecast range and forecast horizon. Taken together, these results suggest that managers’ underestimation of accrual reversals is at least partly due to their difficulty forecasting earnings. In the second set of analyses, I investigate whether managers’ overestimation of accrual persistence found in range forecasts is affected by either managerial opportunism or litigation risk. I find only weak evidence that managers’ overestimation of accrual persistence is affected by self-interested motives to bias the forecasts before external financing and merger or acquisition. Meanwhile, I find no such evidence before net insider selling.3 Also, I find that managers facing higher litigation risk overestimate the persistence of income-increasing accruals to a lesser extent. This result suggests that managers’ tendency to overestimate the persistence of income-increasing accruals is mitigated by their downward forecast bias to reduce expected litigation costs. In the third set of analyses, I examine the robustness of the previous results. Because firms likely choose to issue an earnings forecast and then choose its forecast specificity based on firm characteristics and management incentives, I use a double-election model in which Heckman’s (1976) selection model is extended by including a second selection equation to correct for the potential selectivity bias associated with two choices. I find that my results are not driven by firms’ endogenous decisions to issue a forecast and to issue a point or range forecast. In addition, I find no evidence that managers’ accrual-related forecast bias arises from managers’ misestimation of the persistence of the cash component of earnings. In the last set of analyses, I examine whether accrual mispricing differs across firms issuing point forecasts and firms issuing range forecasts. I find accrual mispricing among firms issuing range forecasts but not among those issuing point forecasts. This evidence—coupled with the finding that managers who issue point forecasts incorporate fully into their forecasts the earnings reversals associated with accruals whereas managers who issue range forecasts do not—suggests that managers may mitigate accrual mispricing of their companies by issuing forecasts that better incorporate the persistence of prior accruals. My study makes several contributions to the existing literature. First, my results add to the research on market participants’ ability to interpret the lower persistence of accruals. Prior studies (e.g., Sloan, 1996; Bradshaw et al., 2001) suggest that investors as well as sophisticated financial intermediaries fail to comprehend fully the implications of the lower persistence of accruals for future earnings. I extend prior literature by showing that managers—even though they have superior firm-specific earnings information because of their position with the firm—do not always fully incorporate information contained in accruals into their forecasts. In particular, I find that the extent to which management earnings forecasts reflect past accrual information depends importantly on managers’ difficulty forecasting firms’ future earnings. Moreover, it is, to some degree, affected by their opportunistic incentives and fear of litigation. Second, my study contributes to research on accrual mispricing. Sloan (1996) suggests that investors fail to appreciate fully the lower persistence of accruals and, therefore, overprice accruals. My study extends the accrual mispricing literature by examining whether the extent to which management earnings forecasts reflect accrual information affects accrual mispricing. On one hand, I do not find accrual mispricing for firms issuing point forecasts, which—coupled with the finding that managers who issue point forecasts appear to incorporate fully into their forecasts the accrual

2 Beneish and Vargus (2002) define insiders as directors, officers (including CEOs, CFOs, and board chairs) and others such as nonmanagement shareholders holding more than 10% of the shares. 3 Note that my results for these three types of incentives are not consistent with each other. One possible explanation is that managers perceive circumstantial costs and benefits from biasing their forecasts. For example, potential legal costs imposed on improper insider trading may be sufficiently high to prevent managers from creating insider trading opportunities by issuing forecasts containing accrual-related bias.

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reversals—suggests that management point earnings forecasts may be sufficiently informative to reduce substantially or even eliminate accrual mispricing. On the other hand, I find accrual mispricing within firms issuing range forecasts, which—combined with the finding that managers who issue range forecasts overestimate accrual persistence in their forecasts—suggests that the accrual anomaly may, in part, arise from managers’ inability to incorporate fully the implications of accruals for future earnings into their forecasts. Third, my study contributes to research on management forecast bias. Prior research finds various factors that may induce management forecast bias, such as litigation risk and forecast horizon (e.g., Johnson et al., 2001; Ajinkya et al., 2005; Rogers and Stocken, 2005). I extend prior studies by providing evidence that management range earnings forecasts contain predictable bias with respect to accruals. My study, thus, complements existing studies on the determinants of management forecast errors. The remainder of the paper is organized as follows. Section 2 develops the research hypotheses. Section 3 describes the methodology and data. Section 4 reports the empirical findings, and Section 5 concludes the study. 2. Hypothesis development 2.1. The negative association between accruals and management forecast errors Given their internal role in their firms’ business-operating and financial-reporting processes, corporate managers possess valuable private information about future earnings. Consistent with this view, Hassell and Jennings (1986) and Waymire (1986) report that management forecasts are more accurate than contemporaneous analysts’ forecasts, implying that managers’ earnings information is superior to analysts’ earnings information. Similarly, managers likely have a superior ability to predict future earnings changes associated with accruals because, charged with making accrual-related decisions, managers probably have the most complete firm-specific information set on factors underlying the persistence of accruals.4 Although managers hold an advantage in understanding the actual process of accruals, the unresolved question is whether managers are able to incorporate fully accrual reversals into their earnings forecasts. That is, although managers’ information advantage provides them with a better foundation on which to base their estimation of accrual persistence, it may not completely free them from making erroneous estimations. Managers must, in fact, make difficult decisions that affect accrual persistence, such as accrual adjustments for impairments, write-downs, and restructuring charges. Thus, managers likely underestimate accrual reversals as do other market participants. Therefore, I conjecture that managers generally underestimate accrual reversals in their earnings forecasts. As a result, accruals will be negatively associated with management forecast errors—that is, the higher the accruals, the greater the overestimation of future earnings and the lower the management forecast errors ( =[actual earnings forecasted earnings]/share price).5 This reasoning leads to my first hypothesis (expressed in its alternative form): H1. A negative association exists between the level of accruals and management forecast errors with respect to future earnings. 2.2. Forecast difficulty and the negative association between accruals and management forecast errors Depending on the uncertainty of forthcoming earnings, managers face different degrees of difficulty forecasting future earnings accurately. When managers have greater forecast difficulty, they are less likely to predict future earnings changes induced by accrual reversals accurately. Hence, I explore forecast difficulty as a potential explanation for managers’ accrual-related bias in forecasting earnings. Baginski et al. (1993) argue that managers reveal their uncertainty about future earnings by issuing less specific forecasts, which implies that managers who issue range forecasts have greater difficulty forecasting earnings than managers who issue point forecasts. Therefore, I conjecture that managers who issue range forecasts have more difficulty correctly forecasting future earnings declines experienced by high accrual firms than managers who issue point forecasts. This reasoning leads to my second hypothesis (expressed in its alternative form): H2a. The negative association between accruals and management forecast errors is more (less) likely to occur within firms that issue range (point) forecasts. Baginski et al. (1993) also suggest that the width of the range in range forecasts captures the degree of forecast difficulty (i.e., the more difficulty the manager has forecasting earnings, the wider the range of the forecast). Thus, managers who issue forecasts with wider ranges implicitly have greater difficulty forecasting earnings and, consequently, likely have 4 For example, an increase in accounts receivable can be the result of solid sales growth or easier credit terms. Managers, who possess this information, can better anticipate that the increase in accounts receivable due to solid sales growth is more likely to lead to an increase in future earnings than that due to easier credit terms. 5 Note that I measure management forecast errors as the signed difference between actual earnings and forecasted earnings, similar to Karamanou and Vafeas (2005). Some studies measure management forecast errors as the signed difference between forecasted earnings and actual earnings (e.g., Ajinkya et al., 2005; Rogers and Stocken, 2005). These two approaches of measurement can result in opposite signs for forecast errors.

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greater difficulty anticipating the accrual-induced earnings changes. Consequently, I test the following hypothesis (expressed in its alternative form): H2b. For firms that issue range forecasts, the negative association between accruals and management forecast errors is stronger (weaker) for firms issuing wider (narrower) range forecasts. 2.3. Management opportunism and the negative association between accruals and management forecast errors Managers’ accrual-related bias in forecasting earnings is also likely to be affected by managerial opportunism. I consider three potential sources of incentives for managers to bias their estimation of accrual persistence. Specifically, managers who anticipate external financing, merger or acquisition, or insider trading likely have incentives to, respectively, influence stock prices by misleading investors about the firms’ prospects to increase the proceeds from external financing, reduce the cost of buying the target, or increase the profit from insider trading. Prior research finds that firms release good news, issue optimistic earnings forecasts, and manage earnings upward prior to external financing and merger or acquisition (e.g., Teoh et al., 1998; Erickson and Wang, 1999; Lang and Lundholm, 2000). Prior research also finds that managers issue optimistic forecasts in anticipating of selling stocks and pessimistic forecasts in anticipating of buying stocks (e.g., Rogers and Stocken, 2005). I posit that managers’ tendency to overestimate accrual persistence in formulating earnings forecasts is affected similarly by these opportunistic incentives. In particular, I expect that in anticipation of external financing, merger or acquisition, or net insider selling (i.e., after netting insider purchases), managers will overestimate the persistence of income-increasing (income-decreasing) accruals to a greater (lesser) degree to issue more optimistic earnings forecasts. This reasoning leads to the following hypotheses (expressed in their alternative forms): H3a. The negative association between accruals and management forecast errors is stronger (weaker) for firms with income-increasing (income-decreasing) accruals when managers anticipate seeking external financing. H3b. The negative association between accruals and management forecast errors is stronger (weaker) for firms with income-increasing (income-decreasing) accruals when managers anticipate seeking a merger or acquisition. H3c. The negative association between accruals and management forecast errors is stronger (weaker) for firms with income-increasing (income-decreasing) accruals when managers anticipate net selling their firms’ stocks.6 2.4. Litigation risk and the negative association between accruals and management forecast errors In addition, managers’ estimation of the effect of accruals on future earnings is likely to be affected by the threat of litigation. Litigation risk will likely increase if a firm issues a forecast that contains predictable bias with respect to accruals and that later proves to be inaccurate. Following Skinner (1994), I expect this litigation risk to be asymmetric. Skinner suggests that the U.S. legal system imposes an asymmetric loss function on firms because a firm is more likely to be sued when a large negative return occurs at its earnings announcement. This loss function provides managers with incentives to issue less optimistically or more pessimistically biased forecasts. Hence, managers’ tendency to overestimate accrual persistence in formulating earnings forecasts is likely to be affected by their incentives to produce more downward biased forecasts to mitigate litigation risk. Specifically, I expect that when facing higher litigation risk, managers will overestimate the persistence of income-increasing (income-decreasing) accruals to a lesser (greater) degree to issue less optimistic earnings forecasts. This reasoning leads to the following hypothesis (expressed in its alternative form): H4. The negative association between accruals and management forecast errors is weaker (stronger) for firms with income-increasing (income-decreasing) accruals when managers face higher litigation risk. 3. Methodology and data 3.1. Research design for H1 and H2 I use the following regression equation to examine the relation between accruals and management forecast errors with respect to future earnings as stated in H1: MFEt ¼ b0 þ b1 ACCRt1 þ b2 FINt þ b3 MAt þ b4 NetSellt þ b5 Litigationt þ b6 Horizont þ b7 Returnt X þ b8 Dispersiont þ b9 Abs_Surpriset þ b10 Bad_Newst þ b11 M=Bt1 þ b12 LNSizet1 þ bi Yeari þ et ;

ð1Þ

6 Because net selling is characterized by one (minus one) if a firm’s net insider transactions are sells (buys), and zero otherwise, H3c is consistent with both the notion that managers issue optimistic forecasts before selling stocks and the notion that managers issue pessimistic forecasts before buying stocks.

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MFEt is management forecast errors with respect to the current quarter’s earnings and is measured as actual earnings per share (EPS) of quarter t less management forecasted EPS for quarter t, deflated by stock price at the end of quarter t  1. For a range forecast, management forecasted EPS is the midpoint of the range. ACCRt  1 is total accruals of quarter t  1. I use both actual values and decile rank values to measure accruals. In particular, ACCRact t  1 is measured as the difference between earnings and cash flows from operations, deflated by the average total assets of quarter t  1. ACCRdec t  1 is measured as the decile rank obtained by ranking total accruals into ten deciles within the sample. If managers fully incorporate the information in last quarter’s accruals into their forecasts of the current quarter’s earnings, there should be no association between last quarter’s accruals and the current quarter’s forecast errors (i.e., b1 = 0). On the other hand, if managers underestimate accrual reversals as hypothesized in H1, then forecast errors will be more negative for firms with higher accruals (i.e., b1 r0). To examine the effect of forecast difficulty on the relation between accruals and management forecast errors as stated in H2a, I augment Eq. (1) by interacting ACCRt  1 with two indicator variables, Pointt and Ranget, for firms that issue point and range forecasts, respectively, for quarter t. I expect the coefficient on ACCRt  1  Ranget to be more negative than the coefficient on ACCRt  1  Pointt. To examine H2b, I focus on the range forecast subsample and augment Eq. (1) by interacting ACCRt  1 with three indicator variables for firms that issue a range forecast for quarter t with the lowest, medium and highest level of stock price deflated range width (Range1t, Range2t, and Range3t, respectively). I expect the coefficient on ACCRt  1  Range3t to be most negative and the coefficient on ACCRt  1  Range1t to be least negative. I include several control variables in the regressions to control for other factors that can potentially affect management forecast bias. First, I include Pointt, Range1t, and Range2t, when applicable, to control for possible main effects of issuing forecasts with different specificity on management forecast errors. Second, because managers of firms that engage in external financing, merger or acquisition, and insider selling activities may have incentives to bias their forecasts upward to exploit the stock mispricing these forecasts may induce, I include FINt, MAt, and NetSellt and expect their coefficients to be negative.7 Third, I use Litigationt to control for the effect of litigation risk on management forecast bias. Litigationt is the lagged probability of litigation risk estimated using a logit model where I regress the incidence of being sued onto a number of predictors (see Appendix A for estimation details).8 In line with Rogers and Stocken (2005), who find some evidence that managers of firms with higher litigation risk issue less optimistic earnings, I expect the coefficient on Litigationt to be positive.9 Fourth, I include forecast horizon (Horizont) because several studies (e.g., Johnson et al., 2001; Ajinkya et al., 2005) find that management forecasts are less optimistic when they are issued closer to the end of forecast period. Fifth, following McNichols (1989) and Rogers and Stocken (2005), who find that managers issue more pessimistic forecasts when prior stock returns are higher, I use Returnt to measure prior returns. Sixth, because Ajinkya et al. (2005) and Karamanou and Vafeas (2005) find that dispersion in analysts’ forecasts can affect managerial forecasting behavior, I include Dispersiont. Seventh, because Ajinkya et al. (2005) find that management forecasts are more optimistic when the absolute earnings surprise is higher, I include Abs_Surpriset. Eighth, I include Bad_Newst because Karamanou and Vafeas (2005) find that managers make more pessimistic forecasts when their forecasts convey bad news.10 Ninth, following Bamber and Cheon (1998), who find that growth opportunities affect a firm’s forecasting behavior, I use a firm’s market-to-book ratio, M/Bt  1, as a measure of a firm’s growth opportunities. Tenth, several studies find that forecast behavior is associated with firm size (e.g., Baginski and Hassell, 1997; Bamber and Cheon, 1998); therefore, I use the natural logarithm transformation of a firm’s market value of equity, denoted as LNSizet  1, to control for firm size. Finally, I include eight year dummy variables (Yeari) to account for possible variation in management forecast bias over time. Detailed definitions for all variables used in the empirical analyses are provided in Table 1. 3.2. Research design for H3 and H4 To examine the effects of management opportunism and litigation risk on the relation between accruals and management forecast errors as stated in H3 and H4, I estimate the following equation, which is an augmentation of Eq. (1) with eight interactive variables: MFEt ¼ g0 þ g1 ACCRt1 þ g2 ACCRt1  Post1  FINt þ g3 ACCRt1  Negt1  FINt þ g4 ACCRt1  Post1  MAt þ g5 ACCRt1  Negt1  MAt þ g6 ACCRt1  Post1  NetSellt þ g7 ACCRt1  Negt1  NetSellt þ g8 ACCRt1 Post1  Litigationt þ g9 ACCRt1  Negt1  Litigationt þ g10 FINt þ g11 MAt þ g12 NetSellt þ g13 Litigationt þ g14 Horizont þ g15 Returnt þ g16 Dispersiont þ g17 Abs_Surpriset þ g18 Bad_Newst þ g19 M=Bt1 þ g20 LNSizet1 X þ gi Yeari þ et ; ð2Þ

7 Rogers and Stocken (2005) find some evidence that management forecasts are more pessimistic when insider net buy is higher whereas Noe (1999) observes an insignificant relation between these two variables. 8 Table A1 shows that the pseudo R2 for my litigation model is 19.34%, which indicates a reasonably good model fit and is slightly higher than the pseudo R2s of 12.26% and 15.41%, respectively, in Rogers and Stocken (2005) and Brown et al.’s (2005) litigation risk models. Thus, I expect that Litigationt is a reasonable measure of litigation risk. 9 Rogers and Stocken (2005) also find that this result disappears when influential observations are deleted. 10 In contrast, Rogers and Stocken (2005) find no association between bad news forecasts and management forecast bias.

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Table 1 Variable definitions. MFEt = Pointt = Ranget = Range1(2;3)t = ACCRt  1 =

actual earnings per share (EPS) of quarter t less management forecasted EPS for quarter t, deflated by stock price (Compustat #14) at the end of quarter t  1; 1 (0) if the firm issues a point (range) forecast for quarter t; 1 (0) if the firm issues a range (point) forecast for quarter t; 1 if the firm issues a range forecast for quarter t with the lowest (medium; highest) level of stock price deflated range width, and 0 if the firm issues a point forecast for quarter t; total accruals of quarter t  1, measured using both actual values and decile rank values. In particular, ACCRact t1 is calculated using information from the cash flow statement following Collins and Hribar (2000) and measured as earnings from continuous operations (Compustat #76) of quarter t  1 less cash flow from continuous operations (Compustat #108  Compustat #78) of quarter t  1,

deflated by the average total assets (Compustat #44) of quarter t  1. ACCRdec t1 is the decile rank obtained by ranking total accruals into 10 deciles within the sample; 1 if total accruals of quarter t  1 are positive, and 0 otherwise; Post  1 = Negt  1 = 1 if total accruals of quarter t  1 are negative, and 0 otherwise; 1 if the firm’s long-term debt or outstanding shares, split-adjusted, increases at least 20% in quarter t, and 0 otherwise; FINt = 1 if the firm engages in merger or acquisition activities as reported in the SDC Platinum database within 60 days after the MAt = management forecast date (between the earnings announcement date of quarter t  1 and the fiscal ending date of quarter t) for forecasting (nonforecasting) firms, and 0 otherwise; 1 ( 1) if a firm’s net insider transactions by the CEO and CFO within 10 trading days after the management forecast date for NetSellt = forecasting firms (the fiscal ending date of quarter t for nonforecasting firms) are sells (buys), and 0 otherwise. The trade data are obtained from the Thomson Financial Insider Trading database; the lagged probability of litigation risk estimated using a logit model. See Appendix A for estimation details; Litigationt = the number of calendar days from the management forecast date to the fiscal ending date of quarter t; Horizont = the size-adjusted stock return for a 90-day window ending one day before the management forecast date (the fiscal ending date of Returnt = quarter t) for forecasting (nonforecasting) firms; the standard deviation of analyst earnings forecasts for quarter t, deflated by stock price at the end of quarter t  1. The standard Dispersiont = deviation is obtained from the latest summary statistics on First Call available within 90 days before the management forecast date; Abs_Surpriset = the absolute value of management forecasted EPS for quarter t minus the mean analyst forecasted EPS for quarter t, deflated by stock price at the end of quarter t  1. The mean analyst forecasted EPS is obtained from the latest summary statistics on First Call available within 90 days before the management forecast date; 1 if management forecasted EPS for quarter t is less than the mean analyst forecasted EPS for quarter t, and 0 otherwise; Bad_Newst = the market value of equity divided by the book value of equity (Compustat #59) at the end of quarter t  1; M/Bt  1 = the natural logarithm transformation of Sizet–1, the firm’s market value of equity (Compustat #14  Compustat #61; in $ billions) at LNSizet  1 = the end of quarter t  1; 1 if the firm issues as least one earnings forecast for quarter t between the earnings announcement date of quarter t  1 and the fiscal Forecastt = ending date of quarter t, and 0 otherwise; Abs_AFEt = the absolute value of actual EPS of quarter t minus the mean analyst forecasted EPS for quarter t before the management forecast date (the fiscal quarter ending date) for forecasting (nonforecasting) firms, deflated by stock price at the end of quarter t  1; Abs_EarnChgt = the absolute value of the seasonally differenced EPS in quarter t, deflated by stock price at the end of quarter t  1; 1 if CEO or CFO of the firm buys or sells the firm’s shares within 10 trading days after the management forecast date (the fiscal Ins_Tradet = ending date of quarter t) for forecasting (nonforecasting) firms, and 0 otherwise; the natural logarithm transformation of the number of analysts following the firm in quarter t before the management forecast date Analystt = (the fiscal quarter ending date) for forecasting (nonforecasting) firms; the percentage of the firm’s outstanding shares held by institutions within 90 days before the management forecast date (the fiscal INSTt = ending date of quarter t) for forecasting (nonforecasting) firms; EXPt = 1 if the firm issues an earnings forecast between the earnings announcement date of quarter t  2 and the fiscal ending date of quarter t  1, and 0 otherwise; 1 if the firm issues as least one earnings forecast for quarter t within 90 days before the earnings announcement date of quarter t  1, Updatet = and 0 otherwise; net operating assets at the end of quarter t  1, deflated by the average sales of quarter t  1; NOAt  1 = 1 if the firm reports a loss in quarter t, and 0 otherwise; Losst = Zmijewski’s (1984) financial condition score in quarter t  1. Distresst  1 =

where Post  1 equals one if total accruals of quarter t  1 are positive, and zero otherwise; and Negt  1 equals one if total accruals of quarter t 1 are negative, and zero otherwise. The coefficient on ACCRt  1  Post  1  FINt captures the incremental impact of external financing on managers’ estimation of the persistence of income-increasing accruals in forecasting earnings. A negative (positive) coefficient on ACCRt  1  Post  1  FINt is in the same (opposite) direction as the predicted coefficient on ACCRt  1 and thus implies a stronger (weaker) negative association between accruals and management forecast errors. Thus, based on H3a, which anticipates a stronger (weaker) negative association between income-increasing (income-decreasing) accruals and management forecast errors when managers anticipate external financing, I predict g2 will be negative and g3 will be positive. Similarly, based on H3b and H3c, I predict g4 and g6 will be negative and g5 and g7 will be positive. Based on H4, which hypothesizes a weaker (stronger) negative association between income-increasing (income-decreasing) accruals and management forecast errors when firms face higher litigation risk, I predict g8 will be positive and g9 will be negative.

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Table 2 Descriptive statistics. Variable

Mean

Median

MFEt

0.00041  0.01507

0.00046  0.01322

0.00448 0.03569

0.09207 0.11572 0.01262 0.01135 54.80204  0.01521 0.00153 0.00314 0.62858 3.46828 6.90937

0.00000 0.00000 0.00000 0.00533 64.00000  0.01350 0.00078 0.00107 1.00000 2.55588 1.33568

0.28914 0.31991 0.40719 0.02126 24.20160 0.22526 0.00229 0.00553 0.48321 3.05469 24.27942

ACCRact t1 FINt MAt NetSellt Litigationt Horizont Returnt Dispersiont Abs_Surpriset Bad_Newst M/Bt  1 Sizet  1

Standard deviation

The sample consists of 8244 firm-quarter observations. All variables are defined in Table 1. The extreme values of MFEt, ACCRact t1 , Litigationt, Dispersiont, Abs_Surpriset, and M/Bt  1 are winsorized at the 1 and 99 percentiles to avoid problems related to outliers.

3.3. Sample selection I obtain point (CIG Code: A or Z) and range (CIG Code: B) quarterly management earnings forecasts issued by companies during the period 1997–2005 from the First Call database, which leads to an initial sample of 28,297 observations (6364 point forecasts and 21,933 range forecasts). I consider only point and range forecasts because they can be clearly compared to actual earnings. I limit my sample period to 1997–2005 because the number of forecasts prior to 1997 is substantially lower (see Ajinkya et al., 2005). I next eliminate forecasts issued prior to the previous quarter’s earnings announcement dates and forecasts issued between the current quarter’s fiscal end and earnings announcement dates, which reduces the sample by 10,014 observations. I then remove observations with missing actual earnings numbers on First Call to measure management forecast errors, which results in a loss of an additional 917 observations. From the remaining 17,366 observations, I further drop 2053 forecasts from firms that make multiple forecasts for the same fiscal quarter to avoid problems of data interdependence. When a firm issues more than one forecast for a given quarter, I retain only the first forecast because it is likely to be more biased than updated forecasts. I also eliminate 953 observations issued by financial institutes (SIC codes: 6000–6999) and 690 observations with missing Compustat data on accruals and stock ending prices for the prior quarter.11 These eliminations reduce the sample to 13,670 observations. Finally, I exclude observations with missing data needed to measure control variables, including 2319 observations with missing analyst consensus data on First Call, 1651 observations with missing analyst dispersion data on First Call, and 1456 observations with missing data to measure other control variables. The final sample consists of 8244 management earnings forecasts with 1550 point forecasts and 6694 range forecasts. 3.4. Descriptive statistics Table 2 provides descriptive statistics for the sample.12 The mean and median values of management forecast errors (MFEt) are 0.00041 and 0.00046, respectively. The positive forecast errors suggest that quarterly management forecasts, on average, are pessimistically biased, which is consistent with Choi and Ziebart (2004), who report that short-term management forecasts are pessimistically biased. The relatively small magnitude of management forecast errors is not unexpected because the forecasts are issued for quarterly earnings about two months before the fiscal quarter end.13 This magnitude, which is comparable to that of contemporaneous analysts’ forecast errors, is economically important because even slightly beating or missing managers’ own forecasts at earnings announcements can trigger significant stock price effects (e.g., Chen, 2004).14 The mean and median total accruals (ACCRtact  1) are 0.01507 and 0.01322, respectively, which is consistent with prior findings that total accruals, on average, are negative. In addition, about 9.2% (11.6%) of the sample is engaged in external financing (merger or acquisition) activities. The mean value of net sells is positive, indicating that firms with insider net sells outnumber firms with insider net buys. The 11

Richardson et al. (2005) indicate that the separation between operating and financing activities is not clear in financial companies. The extreme values of MFEt, ACCRact t  1, Litigationt, Dispersiont, Abs_Surpriset, and M/Bt  1 are winsorized at the 1 and 99 percentiles to avoid problems related to outliers. 13 In contrast, Rogers and Stocken (2005) report that the mean and median values of annual management forecast errors are  0.017 and  0.005, respectively, and that the average forecast horizon of their forecasts is 237 days. 14 The mean and median values of analysts’ forecast errors for forecasts issued within 10 calendar days after the management forecasts are 0.00043 and 0.00040, respectively. 12

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Table 3 Pearson correlations. FINt ACCRact t1 MFEt ACCRact t1

MAt

NetSellt

Litigationt Horizont Returnt

 0.059  0.039  0.006 (0.000) (0.000) (0.603) 0.013 0.015

0.015  0.044 (0.188) (0.000) 0.006  0.035

(0.253)

(0.609) (0.001) 0.008  0.017 (0.487) (0.131)  0.000 0.006 (0.998) (0.591)  0.027 (0.015)

FINt MAt NetSellt Litigationt Horizont Returnt

(0.167) 0.064 (0.000)

Dispersiont Abs_Surpriset Bad_Newst M/Bt–1

LNSizet–1

 0.004 0.125 0.058 (0.685) (0.000) (0.000)  0.020  0.005  0.091

 0.009 (0.421)  0.027

 0.064 (0.000) 0.026

0.003  0.001 (0.786) (0.894)  0.007 0.009

(0.066) (0.627)  0.049  0.003 (0.000) (0.789) 0.023 0.017 (0.039) (0.122) 0.067 0.084 (0.000) (0.000) 0.004  0.265 (0.706) (0.000) 0.108 (0.000)

(0.015) 0.009 (0.423)  0.077 (0.000)  0.063 (0.000) 0.068 (0.000)  0.256 (0.000)  0.133 (0.000) 0.580 (0.000)

(0.020)  0.013 (0.234)  0.013 (0.235)  0.085 (0.000) 0.046 (0.000)  0.135 (0.000)  0.183 (0.000) 0.068 (0.000) 0.210 (0.000)

(0.546) 0.018 (0.099) 0.031 (0.006) 0.049 (0.000) 0.092 (0.000) 0.027 (0.014) 0.018 (0.103)  0.219 (0.000)  0.190 (0.000)  0.061 (0.000)

(0.000)  0.010 (0.382)  0.079 (0.000)  0.049 (0.000) 0.056 (0.000)  0.034 (0.002)  0.068 (0.000)

Dispersiont Abs_Surpriset Bad_Newst M/Bt–1

(0.406)  0.021 (0.058) 0.151 (0.000) 0.030 (0.007) 0.123 (0.000) 0.149 (0.000) 0.061 (0.000)  0.348 (0.000)  0.345 (0.000)  0.042 (0.000) 0.337 (0.000)

All variables are defined in Table 1. p-values are in parentheses. Bold figures indicate significance at the 10% or better level (two-tailed test).

estimated litigation risk for the sample firms is about 1.1%. The mean and median forecast horizons are 55 and 64 days before the fiscal quarter end, respectively. Bad_Newst has a mean of 0.62858, indicating that there are more bad news forecasts than good news forecasts. Also, the mean (median) market-to-book ratio (M/Bt  1) is 3.47 (2.56) and the mean (median) market value of equity (Sizet  1) is $6.91 billion ($1.34 billion), which suggests that the sample contains large firms with high market-to-book ratios. Table 3 provides Pearson correlation coefficients among the variables in Eq. (1).15 MFEt is significantly negatively related to ACCRtact  1, which is consistent with H1. Moreover, it is positively related to Returnt and Dispersiont and negatively related to FINt, Litigationt, and Bad_Newst. Correlation coefficients among the independent variables are lower than 0.40 except for the coefficient between Dispersiont and Abs_Surpriset, suggesting that severe multicollinearity is not present.

4. Empirical results 4.1. Association between accruals and management forecast errors—tests of H1 Table 4 presents my results of analyses of H1, which posits a negative relation between the level of accruals and management forecast errors with respect to future earnings for the full sample. Because some firms are represented multiple times in the sample, I use clustered (by firm) standard errors to adjust for possible cross-sectional dependence among observations. Column 1 presents the results using the actual values of accruals. The coefficient on ACCRact t1 ( 0.646%) is significantly negative at the 0.01 level, indicating that management forecast errors are more negative for firms with higher accruals. The magnitude of the coefficient on accruals provides an indication of the economic significance of the result. Given that the average share price of my sample firms is $27 and the average accruals is 0.01507, the coefficient on accruals,  0.646%, translates into a forecast error of 0.26 cents ( 0.646%   0.01507  $27  100). Because the mean management forecast error is about 1.11 cents (0.00041  27  100), approximately one-fourth of management forecast errors are attributable to managers’ overestimation of accrual persistence. Column 2 presents the results using the decile values of accruals. The coefficient on ACCRdec t1 (  0.008%) is significantly negative at the 0.01 level, which is similar to the result in column 1. Collectively, these findings are consistent with H1 and support the notion that managers as a whole underestimate the earnings reversals associated with accruals.16 15

Spearman correlation coefficients are similar. I also estimate Eq. (1) using 2230 forecasts issued for the fourth quarter and 6014 forecasts issued for the other quarters. The results (unreported) show that the coefficient on ACCRact t  1 is  1.367% (p = 0.002) and  0.395% (p = 0.029) for fourth quarter and nonfourth quarter forecasts, respectively, suggesting that the negative association between accruals and management forecast errors is not confined to forecasts for the fourth quarter. 16

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Table 4 Regression results of the association between accruals and management forecast errors. Variable

Pred. sign

Intercept

?

ACCRt  1

–

Control variables FINt

–

MAt

–

NetSellt

–

Litigationt

+

Horizont



Returnt

+

Dispersiont

?

Abs_Surpriset



Bad_Newst

?

M/Bt  1

?

LNSizet  1

?

Year dummies

?

Adj. R2 (%) N

Full sample Dep. = MFEt

Dep. = MFEt

Based on ACCRact t1 (1)

Based on ACCRdec t1 (2)

0.078 (0.393)  0.646nnn (0.000)

0.136 (0.133)  0.008nnn (0.000)

 0.049nnn (0.005)  0.004 (0.740) 0.006 (0.600)  0.091 (0.765)  0.001nnn (0.000) 0.225nnn (0.000) 18.950nnn (0.008)  2.236 (0.426)  0.031nn (0.011) 0.003 (0.171) 0.003 (0.416) Included

 0.050nnn (0.005)  0.004 (0.715) 0.006 (0.609)  0.091 (0.765)  0.001nnn (0.000) 0.224nnn (0.000) 19.114nnn (0.007)  2.269 (0.419)  0.031nn (0.011) 0.003 (0.182) 0.003 (0.425) Included

3.69 8244

3.72 8244

All variables are defined in Table 1. All coefficients are multiplied by 100 for expositional purposes. p-values (in parentheses) are based on standard errors clustered by firm. n, nn, and nnn indicate significance at the 10%, 5%, and 1% levels, respectively (two-tailed test).

The coefficients on the control variables FINt and Horizont are significantly negative whereas the coefficients on Returnt are significantly positive. These findings are consistent with my expectations. The coefficients on Dispersiont are significantly positive, indicating that managers issue less optimistic forecasts when analyst forecasts are more dispersed. On the other hand, the coefficients on Bad_Newst are significantly negative, indicating that managers issue more optimistic forecasts for bad news forecasts. The coefficients on the other control variables are not significant. 4.2. The effect of forecast difficulty on the association between accruals and management forecast error—tests of H2 Columns 1 and 2 of Table 5, Panel A, present my results of analyses of H2a, which posits that a negative association between accruals and management forecast errors is more (less) likely to occur within firms that issue range (point) forecasts. The coefficients on ACCRt  1  Pointt are 0.148% (p=0.750) and  0.005% (p=0.242) when ACCRt  1 are measured using the actual values and decile values, respectively. The insignificance of both coefficients suggests that accruals are not associated with management forecast errors for point forecasts. That is, I cannot reject the notion that managers fully adjust for future earnings reversals induced by accruals in their point forecasts. In contrast, the coefficients on ACCRt  1  Ranget are 0.768% (p=0.000) and  0.009% (p=0.000) when ACCRt  1 are measured using the actual values and decile values, respectively. The negative significance of these two coefficients suggests that managers who make range forecasts underestimate accrual reversals in their forecasts. Hence, the negative association between accruals and management forecast errors found in the full sample is primarily driven by the range forecast subsample. Taken together, the results in columns 1 and 2 are consistent with H2a and suggest that managers who issue range forecasts have greater difficulty correctly forecasting future earnings declines experienced by high accrual firms than managers who issue point forecasts.17 17 To alleviate the concern that the results are driven by large firms due to the sample selection criteria, I examine H1 and H2a using a less constrained sample—13,670 observations for which I do not require data availability for the control variables. The results (untabulated) are similar. Specifically, the act act coefficients on ACCRact t  1, ACCRt  1  Pointt and ACCRt  1  Ranget are  0.748% (p =0.000),  0.095% (p=0.851) and  0.899% (p=0.000), respectively.

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Table 5 Regression results of the effect of forecast difficulty on the association between accruals and management forecast errors. Panel A: Results for forecasts with different forecast specificity Variable

Intercept

Pred. sign

?

Forecast difficulty variables (H2a and H2b) ACCRt  1  Pointt ? ACCRt  1  Ranget



ACCRt  1  Range1t



ACCRt  1  Range2t



ACCRt  1  Range3t



Control variables Pointt

?

Range1t

?

Range2t

?

FINt



MAt



NetSellt



Litigationt

+

Horizont



Returnt

+

Dispersiont

?

Abs_Surpriset



Bad_Newst

?

M/Bt  1

?

LNSizet  1

?

Year dummies

?

Adj. R2 (%) N

Full sample

Range forecast subsample

Dep. =MFEt

Dep. =MFEt

Dep. =MFEt

Dep. =MFEt

Based on ACCRact t1 (1)

Based on ACCRdec t1 (2)

Based on ACCRact t1 (3)

Based on ACCRdec t1 (4)

0.075 (0.410)

0.140 (0.121)

0.047 (0.644)

0.167n (0.100)

 0.148 (0.750)  0.768nnn (0.000)

 0.005 (0.242)  0.009nnn (0.000) 0.025 (0.867) 0.605nn (0.027) 1.402nnn (0.001)

 0.002 (0.330)  0.008nnn (0.007)  0.018nnn (0.001)

 0.091nnn (0.009)  0.047 (0.205)  0.045nn (0.020)  0.011 (0.398) 0.013 (0.307)  0.279 (0.427)  0.001nnn (0.000) 0.231nnn (0.000) 18.710nn (0.022)  2.055 (0.503)  0.037nnn (0.005) 0.003 (0.175) 0.004 (0.351) Included 4.17 6694

0.017 (0.258)

 0.015 (0.655)

 0.049nnn (0.005)  0.004 (0.733) 0.006 (0.608)  0.090 (0.768)  0.001nnn (0.000) 0.225nnn (0.000) 18.933nnn (0.008)  2.239 (0.425)  0.030nn (0.014) 0.003 (0.175) 0.003 (0.421) Included

 0.050nnn (0.004)  0.004 (0.712) 0.006 (0.609)  0.093 (0.761)  0.001nnn (0.000) 0.224nnn (0.000) 19.124nnn (0.007)  2.282 (0.416)  0.030nn (0.014) 0.003 (0.190) 0.003 (0.433) Included

0.018 (0.396) 0.021 (0.289) 0.044nn (0.021) 0.010 (0.403) 0.014 (0.302) 0.296 (0.402) 0.001nnn (0.000) 0.232nnn (0.000) 18.386nn (0.023) 2.049 (0.504) 0.037nnn (0.005) 0.003 (0.153) 0.004 (0.352) Included

3.71 8244

3.71 8244

4.22 6694

Panel B: Results for forecasts with short vs. long forecast horizon Variable

Pred. sign

Point forecast subsample Short forecast horizon

ACCRact t1



Control variables Adj. R2 (%) N ACCRdec t1 Control variables Adj. R2 (%) N



Range forecast subsample Long forecast horizon

Short forecast horizon

Long forecast horizon

 0.004

 0.202

0.440n

 1.309nnn

(0.994) Included 2.42 862

(0.775) Included 3.29 688

(0.053) Included 4.43 3117

(0.000) Included 4.10 3577

 0.003

 0.007

0.007nn

 0.012nnn

(0.581) Included 2.46 862

(0.290) Included 3.45 688

(0.011) Included 4.52 3117

(0.000) Included 3.82 3577

All variables are defined in Table 1. All coefficients are multiplied by 100 for expositional purposes. For parsimony, coefficients on the control variables in Panel B are not reported. p-values (in parentheses) are based on standard errors clustered by firm. n, nn, and nnn indicate significance at the 10%, 5%, and 1% levels, respectively (two-tailed test).

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Columns 3 and 4 of Table 5, Panel A, present my results of analyses of H2b, which hypothesizes that the negative association between accruals and management forecast errors is stronger for firms issuing wider range forecasts. As expected, the coefficients on the interaction terms between accruals and range width dummies decease monotonically. For example, the coefficient on ACCRact t1  Range1t is 0.025% (p = 0.867) for firms with the narrowest forecast ranges, act and the coefficients on ACCRact t1  Range2t and ACCRt1  Range3t are 0.605% (p = 0.027) and  1.402% (p =0.001) for firms with medium and the widest forecast ranges, respectively. Thus, the results in columns 3 and 4 support H2b and suggest that managers who issue forecasts with wider ranges underestimate accrual reversals in their forecasts to a greater extent. The forecast difficulty explanation has one additional implication. Bradshaw et al. (2001) document that analysts appear to realize the implications of accruals for subsequent earnings gradually, as the year progresses and more earnings information becomes available. Applying this finding to managers, I expect managers to have greater difficulty anticipating accrual-induced earnings changes when they make forecasts earlier in the quarter (i.e., with long forecast horizon) as less earnings information is available. To test this prediction, I separate the point and range forecast subsamples into two groups, respectively, based on whether the forecast horizons of the observations are longer or shorter than the median forecast horizon of the sample (i.e., 64 days as reported in Table 2) and then estimate Eq. (1) for each group of firms. The results, presented in Panel B of Table 5, show that for the point forecast subsample, the coefficients on ACCRact t1 are 0.004% and 0.202% for forecasts with short and long forecast horizons, respectively. Both coefficients are insignificant. These results indicate that managers who issue point forecasts, regardless of forecast horizon, have fully adjusted for future earnings reversals induced by accruals in their forecasts. In contrast, for the range forecast subsample, the coefficient on ACCRact t1 is 0.440% (p = 0.053) for forecasts with short forecast horizon, which appears to be less negative than  1.309% (p= 0.000), the coefficient on ACCRact t1 for forecasts with long forecast horizon. Thus, managers who make range forecasts earlier in the quarter underestimate accrual reversals in their forecasts to a greater extent. Similar results are obtained when accruals are measured using decile values.18 Taken together, the evidence in Table 5 indicates that managers who issue point forecasts incorporate fully the earnings reversals associated with accruals into their forecasts, whereas managers who issue range forecasts underestimate accrual reversals in their forecasts. Furthermore, the degree of managers’ underestimation of accrual reversals in range forecasts increases with forecast range and forecast horizon. These findings support H2a and H2b and are consistent with the view that managers’ accrual-related forecast bias is at least partly attributable to their difficulty forecasting earnings as captured by forecast specificity and forecast horizon. 4.3. The effects of management opportunism and litigation risk on the association between accruals and management forecast errors—tests of H3 and H4 Because I find that only managers who issue range forecasts underestimate accrual reversals in their forecasts, I focus on range forecasts in my analyses of H3 and H4. Recall that I posit a stronger (weaker) negative association between accruals and management forecast errors for firms with income-increasing (income-decreasing) accruals when managers anticipate external financing, merger or acquisition, and insider selling (H3) and lower litigation risk (H4). Table 6 reports the estimation results for Eq. (2) based on actual values of accruals. The coefficient on ACCRact t1 is  1.100% (p= 0.000), which confirms my prior finding that managers who issue range forecasts underestimate accrual reversals in their forecasts. More important, looking at the effects of managerial opportunism, the coefficient on ACCRact t1  Post  1  FINt (  2.600%) is negative and marginally significant at the 0.10 level and the coefficient on ACCRact  Neg  FINt (1.217%) is t  1 t1 not significant. These results provide weak (no) evidence that managers overestimate the persistence of income-increasing (income-decreasing) accruals to a greater (lesser) extent before external financing. act The coefficient on ACCRact t1  Post  1  MAt (0.729%) is not significant and the coefficient on ACCRt1  Negt  1  MAt (1.335%) is positive and marginally significant at the 0.10 level. These results provide no (weak) evidence that managers overestimate the persistence of income-increasing (income-decreasing) accruals to a greater (lesser) extent before merger act or acquisition. In addition, the coefficients on ACCRact t1  Post  1  NetSellt and ACCRt1  Negt  1  NetSellt are both insignificant, failing to support H3c that managers overestimate the persistence of income-increasing (income-decreasing) accruals to a greater (lesser) extent before insider selling. Collectively, I find only limited evidence that managers’ overestimation of accrual persistence is affected by self-interested motives to bias the forecasts before external financing and merger or acquisition. Meanwhile, I find no such evidence before net insider selling. Turning to the effect of litigation risk, the coefficient on ACCRact t1  Post  1  Litigationt (41.111%) is significantly positive at the 0.01 level whereas the coefficient on ACCRact t1  Negt  1  Litigationt (7.742%) is insignificant, which suggests that managers faced with higher litigation risk overestimate the persistence of income-increasing accruals to a lesser degree but do not overestimate the persistence of income-decreasing accruals to a greater degree, thus partly supporting H4. That is, the effect of litigation risk on accrual-related bias is restricted to income-increasing accruals. The conservative forecast bias caused by litigation risk appears to partially offset managers’ tendency to overestimate the persistence of incomeincreasing accruals. 18

For brevity, I do not tabulate the estimation results on control variables in Panel B of Table 5.

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Table 6 Regression results of the effects of management opportunism and litigation risk on the association between accruals and management forecast errors. Variable

Pred. sign

Range forecast subsample Dep.= MFEt Based on ACCRact t1

Intercept

?

ACCRt  1



External financing variables (H3a) ACCRt  1  Post  1  FINt



ACCRt  1  Negt  1  FINt

+

Merger or acquisition variables (H3b) ACCRt  1  Post  1  MAt



ACCRt  1  Negt  1  MAt

+

Insider net selling variables (H3c) ACCRt  1  Post  1  NetSellt



ACCRt  1  Negt  1  NetSellt

+

Litigation risk variables (H4) ACCRt  1  Post  1  Litigationt

+

ACCRt  1  Negt  1  Litigationt



Control variables FINt



MAt



NetSellt



Litigationt

+

Horizont



Returnt

+

Dispersiont

?

Abs_Surpriset



Bad_Newst

?

M/Bt  1

?

LNSizet  1

?

Year dummies

?

Adj. R2 (%) N

0.046 (0.649)  1.100nnn (0.000)  2.600n (0.061) 1.217 (0.179) 0.729 (0.419) 1.335n (0.071)  1.071 (0.338) 0.284 (0.655) 41.111nnn (0.002) 7.742 (0.553)  0.000 (0.995) 0.010 (0.579) 0.024 (0.225)  0.451 (0.309)  0.001nnn (0.000) 0.231nnn (0.000) 18.838nn (0.016)  2.098 (0.487)  0.036nnn (0.007) 0.003 (0.212) 0.005 (0.317) Included 4.27 6694

All variables are defined in Table 1. All coefficients are multiplied by 100 for expositional purposes. p-values (in parentheses) are based on standard errors clustered by firm. n, nn, and nnn indicate significance at the 10%, 5%, and 1% levels, respectively (two-tailed test).

In sum, the results in Table 6 weakly support H3a and H3b and partly support H4 in that managers’ tendency to overestimate accrual persistence in range forecasts is to a small extent affected by management opportunism and, to a somewhat greater extent, affected by their fear of litigation.19

19 I also estimate Eq. (2) using 1550 point forecasts. The results (unreported) show that the coefficient on ACCRact t  1 (0.036%) is insignificant (p = 0.940). This result confirms my prior finding that managers who issue point forecasts appear to incorporate fully the earnings reversals associated with accruals

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4.4. Robustness checks 4.4.1. The control of firms’ self-selections to issue a management forecast and to issue a point or range forecast Previous research finds systematic differences between firms that voluntarily issue earnings forecasts and those that do not forecast and also between firms that issue point forecasts and those that issue range forecasts (e.g., Ajinkya and Gift, 1984; King et al., 1990; Kasznik and Lev, 1995; Baginski and Hassell, 1997). This prior literature suggests that managers make two endogenous decisions based on firm characteristics and managerial incentives. That is, managers first decide whether to issue an earnings forecast, and then, for those who decide to forecast, whether to issue a point or a range forecast. This double selectivity bias may result in inconsistent coefficient estimates when I use the standard ordinary least squares regressions to analyze the association between accruals and management forecast errors. Because two selectivity biases need to be corrected, the typical Heckman (1976) single-selection approach is not applicable. Instead, I use the double-selection model developed by Tunali (1986) that extends Heckman’s (1976) two-stage procedure by including a second selection equation. The double-selection approach simultaneously estimates managers’ forecast decision and forecast specificity decision in the first stage to derive appropriate selectivity correction variables. These selectivity correction variables are included in the second-stage regressions of management forecast errors on accruals to obtain consistent coefficient estimates. Specifically, in the first stage, I use full information maximum likelihood method to jointly estimate the following bivariate probit sample selection model and obtain the selectivity correction variables, IMR_Forecastt and IMR_Pointt, for managers’ decisions to issue a management forecast and to issue a point forecast, respectively: Forecastt ¼ d0 þ d1 Abs_AFEt þ d2 Abs_EarnChgt þ d3 Ins_Tradet þ d4 Litigationt þ d5 Analystt þ d6 INSTt þ d7 EXPt þ d8 Updatet þ d9 FINt þ d10 MAt þ d11 NOAt1 þ d12 Losst þ d13 Distresst1 þ d14 ACCRact t1 X þ d15 NetSellt þ d16 Returnt þ d17 M=Bt1 þ d18 LNSizet1 þ di Yeari þ et ;

ð3Þ

Pointt ¼ l0 þ l1 Dispersiont þ l2 Litigationt þ l3 Horizont þ l4 Bad_Newst þ l5 Losst þ l6 Analystt þ l7 INSTt þ l8 Distresst1 þ l9 FINt þ l10 MAt þ l11 Ins_Tradet þ l12 NOAt1 þ l13 ACCRact t1 þ l14 NetSellt þ l15 Returnt X þ l16 Abs_Surpriset þ l17 M=Bt1 þ l18 LNSizet1 þ li Yeari þ ut ;

ð4Þ

where Forecastt equals one if the firm issues as least one earnings forecast for quarter t between the previous quarter’s earnings announcement date and the current quarter’s fiscal end, and zero otherwise; Pointt equals one (zero) if the firm issues a point (range) forecast for quarter t. Eq. (3) models the probability of a firm issuing a management earnings forecast for quarter t. It includes explanatory variables based on prior research that likely capture managers’ incentives to provide an earnings forecast. These incentives include adjusting the market’s expectations toward managers’ expectations, reducing litigation risk, meeting information demands by analysts and institutional investors, updating prior forecasts, and so on. These incentives provide a rationale to understand why managers issue earnings forecasts despite their uncertainty about the effect of accrual reversals on the forecasts. Eq. (4) models the probability of a firm issuing a point forecast relative to a range forecast, conditional on its decision to issue a management earnings forecast. It controls for factors that likely affect management forecast specificity, such as dispersion among analysts, litigation risk, forecast horizon, good news versus bad news forecasts, the occurrence of earnings losses, and so on. Eqs. (3) and (4) are jointly estimated using 58,321 observations including 8244 forecasting observations and 50,077 nonforecasting observations. The nonforecasting observations are First Call firm-quarters for which managers do not issue any earnings forecasts between the previous quarter’s earnings announcement date and the current quarter’s fiscal end. Detailed description and estimation results of Eqs. (3) and (4) are provided in Appendix B. The correlation coefficient between the error terms of Eqs. (3) and (4) (i.e., RHO) equals 0.189 and is significant (p= 0.000), suggesting that managers’ decisions to make a forecast and to make a point or range forecast are jointly determined. In the second stage, I include the selectivity correction variables, IMR_Forecastt and IMR_Pointt, in the regression analyses to correct for possible selectivity bias. Columns 1 and 2 of Table 7 present the second-stage test results for H2 when ACCRact t1 is used. The coefficients on IMR_Forecastt are not significant and the coefficients on IMR_Pointt are significant at the 0.01 level. Column 1 shows that the coefficient on ACCRact t1  Pointt is not significant whereas the act coefficient on ACCRact t1  Ranget is significantly negative. Column 2 reports that the coefficients on ACCR t1  Ranget act decrease monotonically from 0.031% on ACCRact  Range1 to  1.355% on ACCR  Range3 . Similar results (unreported) t t t1 t1 are obtained when ACCRdec t1 is used. (footnote continued) into their forecasts. My tests of H3a, H3b, and H3c find little supportive evidence. Specifically, the coefficients on ACCRact t  1  Post  1  FINt, act act act ACCRact t  1  Post  1  MAt, ACCRt  1  Negt  1  MAt, and ACCRt  1  Post  1  NetSellt are all insignificant. The coefficient on ACCRt  1  Negt  1  FINt is significantly negative, which is inconsistent with H3a. The coefficient on ACCRact t  1  Negt  1  NetSellt is significantly positive, partly supporting H3c. In act addition, the coefficients on ACCRact t  1  Post  1  Litigationt and ACCRt  1  Negt  1  Litigationt are both insignificant, failing to support H4. Taken together, these results suggest that estimations of accrual reversals made by managers who issue point forecasts are not affected by managerial opportunism and litigation risk.

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Table 7 The second-stage regression results of the association between accruals and management forecast errors after controlling for firms’ self-selections into issuing a forecast and a point forecast. Variable

Pred. sign

Intercept

?

ACCRt  1



Forecast difficulty variables (H2a and H2b) ACCRt  1  Pointt ? ACCRt  1  Ranget



ACCRt  1  Range1t



ACCRt  1  Range2t



ACCRt  1  Range3t



External financing variables (H3a) ACCRt  1  Post  1  FINt



ACCRt  1  Negt  1  FINt

+

Merger or acquisition variables (H3b) ACCRt  1  Post  1  MAt



ACCRt11  Negt  1  MAt

+

Insider net selling variables (H3c) ACCRt  1  Post  1  NetSellt



ACCRt  1  Negt  1  NetSellt

+

Litigation risk variables (H4) ACCRt  1  Post  1  Litigationt

+

ACCRt  1  Negt  1  Litigationt



Control variables Pointt

?

Range1t

?

Range2t

?

FINt



MAt



NetSellt



Litigationt

+

Horizont



Returnt

+

Dispersiont

?

Abs_Surpriset



Bad_Newst

?

M/Bt  1

?

LNSizet  1

?

IMR_Forecastt

?

IMR_Pointt

?

Year dummies

?

R2 (%) N

Full sample

Range forecast subsample

Dep.=MFEt Based on ACCRact t1 (1)

Dep. =MFEt Based on ACCRact t1 (2)

Dep. =MFEt Based on ACCRact t1 (3)

0.095 (0.258)

0.047 (0.619)

0.046 (0.622) 1.064nnn (0.000)

 0.111 (0.716)  0.730nnn (0.000) 0.031 (0.914)  0.554nn (0.047)  1.355nnn (0.000) 2.524nn (0.023) 1.249n (0.073) 0.760 (0.542) 1.302n (0.089) 1.022 (0.262) 0.262 (0.613) 42.962nnn (0.001) 7.928 (0.286) 0.349nnn (0.000)

 0.047nnn (0.005)  0.010 (0.506) 0.006 (0.594)  0.350 (0.174)  0.001nnn (0.000) 0.217nnn (0.000) 20.455nnn (0.000)  2.951nn (0.013) 0.003 (0.849) 0.000 (0.980)  0.000 (0.937) 0.006 (0.565)  0.188nnn (0.000) Included

0.008 (0.624) 0.017 (0.268)  0.041nn (0.031)  0.018 (0.295) 0.014 (0.298)  0.594nn (0.047)  0.001nnn (0.000) 0.222nnn (0.000) 19.803nnn (0.000)  3.092nn (0.024) 0.001 (0.972) 0.001 (0.799) 0.001 (0.867) 0.007 (0.565)  0.264nnn (0.001) Included

0.003 (0.919) 0.002 (0.919) 0.023 (0.229) 0.746n (0.058) 0.001nnn (0.000) 0.221nnn (0.000) 20.521nnn (0.000) 3.005nn (0.026) 0.000 (0.978) 0.000 (0.974) 0.001 (0.840) 0.009 (0.441) 0.253nnn (0.002) Included

3.87 8244

4.37 6694

4.43 6694

IMR_Forecastt and IMR_Pointt are the selectivity correction variables from the first-stage bivariate probit estimation described in Appendix B and represent the self-selection corrections for firms choosing to issue an earnings forecast and to issue a point forecast conditional on their decision to forecast. All other variables are defined in Table 1. All coefficients are multiplied by 100 for expositional purposes. p-values (in parentheses) are based on standard errors adjusted using the procedure outlined by Tunali (1986, 272–279). n, nn, and nnn indicate significance at the 10%, 5%, and 1% levels, respectively (two-tailed test).

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Column 3 of Table 7 reports the second-stage test results for H3 and H4. Similarly, the coefficient on IMR_Forecastt is not significant and the coefficient on IMR_Pointt is significant at the 0.01 level. Consistent with the results reported in act Table 6, I find that the coefficients on ACCRact t1  Post  1  FINt (  2.524%), ACCRt1  Negt  1  MAt (1.302%), and  Pos  Litigation (42.962%) are significant at the 0.10 or better level with the expected signs. However, ACCRact t  1 t t1 unlike the result in Table 6, the coefficient on ACCRact t1  Negt  1  FINt (1.249%) becomes marginally significant with the expected positive sign. Collectively, my inferences drawn from tests of H2, H3 and H4 are not affected when I control for firms’ selectivity bias associated with their endogenous decisions to issue a management forecast and, if forecast, to issue a point forecast. 4.4.2. Tests of cash flows as an alternative explanation Prior research finds a strong negative correlation between accruals and cash flows (e.g., Bradshaw et al., 2001) and also that accrual anomaly may be driven by cash flow anomaly (e.g., Desai et al., 2004; Barone and Magilke, 2008). These results raise the concern that managers may systematically misinterpret the persistence of the cash flow component of earnings, causing a biased management earnings forecast and further a spurious negative association between accruals and management forecast errors. To alleviate this concern, I examine the association between management forecast errors and quarterly changes in accruals and cash flows. Specifically, I partition the sample firms into two sets of quintile portfolios based on the magnitude of accruals and cash flows in quarter t 1, with portfolio 1 (5) consisting of firmquarters in the lowest (highest) quintile of accruals or cash flows in quarter t  1. Then for each portfolio, I examine the mean quintile value of management forecast errors for quarter t and the mean quintile value of accruals or cash flows in quarter t. Panel A of Table 8 reports the results based on accruals. For firms in the first (fifth) quintile of accruals in quarter t 1, the average quintile value of management forecast errors for quarter t is greatest (smallest), indicating that management forecasts for these firms are most pessimistic (optimistic). Also, the average quintile value of accruals in quarter t for firms in the first (fifth) quintile of accruals in quarter t  1 is 2.911 (2.935), showing that the extreme accruals in quarter t 1 have very low persistence. Hence, the findings in Panel A indicate that managers of firms with lowest (highest) accruals overestimate the persistence of accruals and, consequently, issue overly pessimistic (optimistic) forecasts. Panel B of Table 8 reports the results based on cash flows. For firms in the first (fifth) quintile of cash flows in quarter t  1, the average quintile value of management forecast errors for quarter t is 3.050 (3.074), suggesting that cash flows in quarter t 1 are possibly not systematically associated with management forecast errors for quarter t. Moreover, firms with lowest (highest) cash flows in quarter t  1 continue to have lowest (highest) values of cash flows in quarter t. Thus, the findings in Panel B do not suggest that managers of firms with extreme cash flows systematically misestimate the persistence of cash flows and, as a result, issue biased forecasts. 4.4.3. Additional robustness checks I perform several additional tests to examine the sensitivity of my results to earnings management, special items, sales growth, and alternative measures for range forecasts. First, Kasznik (1999) suggests that managers, fearing legal actions by

Table 8 Tests of cash flows as an alternative explanation. Panel A: Association between quarterly change in accruals and management forecast errors Accrual quintile in quarter t  1 (1= lowest, 5= highest)

1 2 3 4 5

Mean quintile value in quarter t (1= lowest, 5 = highest) Management forecast errors

Accruals

3.094 3.045 2.992 2.951 2.933

2.911 2.925 3.066 3.177 2.935

Panel B: Association between quarterly change in cash flows and management forecast errors Cash flow quintile in quarter t  1 (1= lowest, 5= highest)

1 2 3 4 5

Mean quintile value in quarter t (1= lowest, 5 = highest) Management forecast errors

Cash flows

3.050 2.972 2.997 2.921 3.074

2.539 2.669 2.989 3.246 3.571

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investors as well as loss of reputation for accuracy, have incentives to manage reported earnings toward their forecasts.20 To control for the effect of earnings management to mitigate management forecast errors, I eliminate from my sample 2774 observations in which the difference between management forecasted earnings and actual earnings is 1 cent or less. I repeat the tests in Tables 4–7 and find the results are not affected. Second, First Call adjusts actual earnings by excluding any unusual items that a majority of the contributing analysts deem nonoperating or nonrecurring. Thus, if earnings numbers forecasted by managers include these unusual items, forecasted earnings and actual earnings are not measured on the same basis. To ensure the results are robust to this potential measurement problem, I delete 3742 management forecasts in which the firm-quarters record special items (Compustat #32) and rerun the regressions in Tables 4–7. Despite a smaller number of observations available, my main findings (unreported) are similar. Third, I examine whether my results are sensitive to the control of sales growth. In the extant literature, disagreement exists on the explanation for the lower persistence of the accrual component of earnings. Some (e.g., Xie, 2001; Richardson et al., 2005) argue that the lower persistence of accruals arises because of the greater subjectivity in estimating accruals, whereas others (e.g., Fairfield et al., 2003) argue that it results from the interaction of firm growth with the conservatism bias in accounting or the lower rate of economic profits associated with diminishing marginal returns to increased new investments.21 Although discriminating between these alternative explanations is beyond the scope of this paper, I examine whether my results are sensitive to the control of firm growth by including sales growth—measured as the change in sales revenue from quarter t  5 to quarter t  1—as an additional explanatory variable in regressions in Tables 4–7. The untabulated results based on a sample of 8220 observations reveal that when I control for the effect of sales growth, my findings remain unchanged. Finally, I repeat my analyses using alternative measures for range forecasts. I use the midpoint values of the ranges to capture management earnings expectations for range forecasts following prior research (e.g., Baginski et al., 1993; Bamber and Cheon, 1998; Rogers and Stocken, 2005). The midpoint value estimation, however, could contain measurement errors because the manager’s true expectation of future earnings likely lies anywhere within the forecast range. To examine whether my results are sensitive to alternative measures for range forecasts, I repeat the tests in Tables 4–7 using the lower bounds and the upper bounds of the range forecasts to estimate management earnings expectations. I obtain consistent results (unreported) except that the coefficients on ACCRact t1  Negt  1  Litigationt become unexpectedly positive when the upper bound measures are used. 4.5. Accrual mispricing and managers’ accrual-related forecast bias Thus far, I have focused on whether managers predict and incorporate the effect of the lower persistence of accruals on future earnings into their forecasts. Given that the lower persistence of accruals is a crucial element in the explanation for the accrual mispricing, I further investigate whether the degree to which management earnings forecasts reflect information in accruals affects investors’ assessment of the valuation implications of accruals. If point forecasts correctly reflect information about accrual persistence, as my findings suggest, and if share prices fully incorporate the accrual information contained in the forecasts, then I expect point forecasts to facilitate more accurate pricing of accruals than range forecasts. To test this conjecture empirically, I perform a portfolio analysis for the full sample, point forecast subsample, and range forecast subsample. Specifically, I rank each sample into five (i.e., quintile) portfolios each quarter based on accruals. Each quarter is required to have at least 30 observations. I then form hedge portfolios by taking a long (short) position for firms in the lowest (highest) accrual quintile. The hedge portfolio return is computed by subtracting the average size-adjusted return on the short portfolio from the average size-adjusted return on the long portfolio. The size-adjusted return is cumulated from 18 days after the earnings announcement for quarter t (i.e., the quarter for which management forecast earnings) to 17 days after the earnings announcement for quarter t+ 2. Unreported results show that the hedge return for the full sample is 0.061 (p= 0.006), suggesting that accrual mispricing is found in the full sample. The hedge return is 0.010 (p =0.787) for the point forecast subsample and 0.071 (p= 0.037) for the range forecast subsample, suggesting that the accrual mispricing for the whole sample is mainly caused by the accrual mispricing for the range forecast subsample. Taken together, these results indicate that managers who issue point forecasts better facilitate the market to translate into stock prices the future earnings information contained in the accrual component, which, in turn mitigates accrual mispricing. 5. Conclusion I investigate whether management earnings forecasts fully reflect the implications of accruals for future earnings. I find in the full sample that accruals are negatively associated with management forecast errors with respect to future earnings, 20 Specifically, Kasznik (1999) finds that managers use income-increasing discretionary accruals to manage reported earnings toward their forecast numbers when they have overestimated earnings but do not use income-decreasing discretionary accruals to manage reported earnings downward when they have underestimated earnings in their forecasts. 21 In a more recent paper, Richardson et al. (2006) suggest that although they cannot rule out the growth-related explanations, temporary accounting distortions provide the most compelling explanation for the lower persistence of accruals.

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suggesting that managers as a whole overestimate accrual persistence in their forecasts. Consistent with the notion that managers’ overestimation of accrual persistence depends importantly on their difficulty forecasting earnings, I find that the negative association exists in firms issuing range forecasts but not in firms issuing point forecasts and that the negative association within range forecasts increases with forecast difficulty as captured by forecast range and forecast horizon. In addition, I find that managers’ tendency to overestimate accrual persistence in range forecasts is to a small extent affected by management opportunism and, to a somewhat greater extent, affected by their fear of litigation. Finally, I find accrual mispricing for firms issuing range forecasts but not for firms issuing point forecasts. This evidence, coupled with the finding that managers who issue range forecasts but not those who issue point forecasts underestimate accrual reversals in their forecasts, suggests that managers may mitigate accrual mispricing of their companies by issuing forecasts that better incorporate the persistence of prior accruals. My paper leaves some questions unanswered, thus providing opportunities for future research. Although the accrual anomaly may partially arise from managers’ intentional or unintentional inefficiency in incorporating fully the future earnings implications of accruals into their forecasts, as reported in this study, additional research is needed to examine how much managers’ forecast inefficiency can explain the intriguing accrual anomaly. Another question that merits further examination is whether the predictable bias in management earnings forecasts also holds for other types of accounting information that relies heavily on managerial estimation. For example, special items are often viewed as transitory compared with remaining components of aggregate earnings, and hence managers’ estimation of the effect of special items on future earnings may contain similar bias.

Acknowledgement I thank Thomas Lys (the editor) and Darren Roulstone (the referee) for very constructive comments and suggestions. I also thank Joe Comprix and Feng Gu for their helpful comments. I gratefully acknowledge the summer research support from School of Management at the State University of New York at Buffalo. Appendix A. Litigation risk estimation The probability of litigation is estimated using the following model, which is similar to Rogers and Stocken (2005): PrðLawsuit ¼ 1Þ ¼ Gða0 þ a1 Size þ a2 Turnover þ a3 Beta þ a4 CumRet þ a5 StdRetþ a6 Skewness þ a7 MinRet þ a8 Bio-technology þ a9 ComputerHardwareþ a10 Electronicþ a11 Retailing þ a12 ComputerSoftware þ eÞ;

ðA:1Þ

Table A1 Estimation of litigation risk. Variable Intercept Size Turnover Beta CumRet StdRet Skewness MinRet Bio-technology ComputerHardware Electronic Retailing ComputerSoftware McFadden Pseudo R2 (%) N

Predicted sign ? + + +  +   + + + + +

Coefficient nnn

 13.350 0.345nnn 51.253nnn  0.115nnn  0.886nnn 10.798nnn  0.253nnn  4.074nnn 0.484nnn 0.059  0.204n  0.027 0.073

p-value (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.003) (0.711) (0.068) (0.844) (0.434)

19.34 184,380

Variables are measured over a calendar quarter from the fourth quarter of 1996 to the fourth quarter of 2005. Lawsuit equals 1 for a firm-quarter if a securities class action lawsuit was recorded by Stanford Law School’s Securities Class Action Clearinghouse during a quarter, and 0 otherwise. Size is the natural logarithm transformation of the average market value of equity. Turnover is the average daily trading volume deflated by the number of shares outstanding. Beta is the slope coefficient from regressing daily returns on the CRSP equal-weighted index. CumRet is the cumulative daily raw returns. StdRet is the standard deviation of the daily raw returns. Skewness is the skewness of the daily returns. MinRet is the minimum of the daily returns. The high-risk industry indicators represent bio-technology (SIC 2833–2836), computer hardware (SIC 3570–3577), electronics (SIC 3600–3674), retailing (SIC 5200–5961), and computer software (SIC 7370-7379). The extreme values of Turnover, Beta, and StdRet are winsorized at the 1 and 99 percentiles. n, nn, and nnn indicate significance at the 10%, 5%, and 1% levels, respectively (two-tailed test).

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Table B1 The first-stage bivariate probit estimation results of managers’ decisions to issue a forecast and to issue a point forecast. Variable

Eq. (3): Dep. =Forecastt Pred. sign

Intercept Abs_AFEt Abs_EarnChgt Ins_Tradet Dispersiont Litigationt Horizont Bad_Newst Analystt INSTt EXPt Updatet FINt MAt NOAt  1 Losst Distresst  1 ACCRact t1 NetSellt Returnt Abs_Surpriset M/Bt  1 LNSizet  1 Year dummies

Eq. (4): Dep. =Pointt

Coefficient nnn

p-value

? +  +

 2.670 20.126nnn  6.406nnn 0.561nnn

(0.000) (0.000) (0.000) (0.000)

+

0.525

(0.289)

+ + + + ? ? ? ? ? ? ? ?

0.359nnn 0.474nnn 1.406nnn 0.147nnn 0.005  0.035  0.043nnn  0.172nnn  0.013n 0.929nnn 0.056nn  0.512nnn

(0.000) (0.000) (0.000) (0.000) (0.865) (0.183) (0.000) (0.000) (0.073) (0.000) (0.028) (0.000)

? ? ?

 0.019nnn 0.025nnn Included

(0.000) (0.002)

N RHO Log-likelihood

58,321

Pred. sign

Coefficient

p-value

?

 2.591

nnn

(0.000)

     + +

0.098nn  10.345 1.690nn  0.001  0.374nnn 0.216nnn 0.293nnn

(0.024) (0.311) (0.040) (0.218) (0.000) (0.000) (0.003)

  +   ? ? ? ? ? ? ?

 0.032 0.061 0.020nnn  0.118nn  0.079nnn  0.196  0.018 0.073 15.202nnn 0.035nnn 0.022 Included

(0.584) (0.244) (0.004) (0.043) (0.000) (0.671) (0.658) (0.342) (0.000) (0.000) (0.156)

8244 0.189nnn (p= 0.000)  18,283.680

All variables are defined in Table 1. n,

nn

, and

nnn

indicate significance at the 10%, 5%, and 1% levels, respectively (two-tailed test).

where G(  ) is the logistic cumulative distribution function. Eq. (A.1) is estimated based on 184,380 firm-quarter observations with quarterly earnings information available on First Call and stock return information available on CRSP. The estimation results are reported in Table A1.

Appendix B. The first-stage bivariate probit analyses of managers’ decisions to issue a forecast and a point forecast I use the following bivariate probit sample selection model to jointly estimate the probability of a firm issuing a management forecast and the probability of the firm issuing a point forecast in a quarter: Forecastt ¼ d0 þ d1 Abs_AFEt þ d2 Abs_EarnChgt þ d3 Ins_Tradet þ d4 Litigationt þ d5 Analystt þ d6 INSTt þ d7 EXPt þ d8 Updatet þ d9 FINt þ d10 MAt þ d11 NOAt1 þ d12 Losst þ d13 Distresst1 þ d14 ACCRact t1 þ d15 NetSellt X þ d16 Returnt þ d17 M=Bt1 þ d18 LNSizet1 þ di Yeari þ et ; Pointt ¼ l0 þ l1 Dispersiont þ l2 Litigationt þ l3 Horizont þ l4 Bad_Newst þ l5 Losst þ l6 Analystt þ l7 INSTt þ l8 Distresst1 þ l9 FINt þ l10 MAt þ l11 Ins_Tradet þ l12 NOAt1 þ l13 ACCRact t1 þ l14 NetSellt þ l15 Returnt X þ l16 Abs_Surpriset þ l17 M=Bt1 þ l18 LNSizet1 þ li Yeari þut ;

ð3Þ

ð4Þ

Eq. (3) includes explanatory variables from prior research that can potentially affect managers’ decision to forecast. I include Abs_AFEt and Abs_EarnChgt because managers are more likely to forecast when the gap between the managers’ and the market’s expectations of earnings is large and when the volatility of earnings is low (e.g., Ajinkya and Gift, 1984; Waymire, 1985; Brown et al., 2005). I include Ins_Tradet, Litigationt, Analystt, and INSTt because managers are more likely to forecast when a firm has insiders who are expecting to trade and when a firm operates in a high litigation environment and has greater information demands from analysts and institutional owners (e.g., Skinner, 1994; Brown et al., 2005). I include EXPt and Updatet because managers are more likely to issue an earnings forecast if they have issued a forecast for the previous quarter and if they need to update an outstanding forecast for the current quarter. Following Wooldridge’s (2003) intuition, I include FINt, MAt, NOAt  1, Losst, Distresst  1, ACCRact t1 , NetSellt, and Returnt because these variables are

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included in Eq. (4) or Eq. (1).22 Lastly, I use M/Bt  1, LNSizet  1 and year dummies to control for the effects of market-tobook ratio, firm size and time, respectively. Eq. (4) includes explanatory variables from prior research that can potentially affect managers’ choice to issue a point forecast relative to a range forecast, conditional on their decision to issue a forecast. I include Dispersiont, Litigationt, Horizont, Bad_Newst, and Losst because managers of firms with greater dispersion among analysts, higher litigation risk, longer forecast horizon, bad news forecasts, and earnings losses, respectively, may be more likely to issue less specific forecasts, and I include Analystt and INSTt because managers of firms with greater analyst following and greater percentage of institutional ownership, respectively, may be more likely to issue more specific forecasts (e.g., Baginski and Hassell, 1997; Bamber and Cheon, 1998; Ajinkya et al., 2005; Karamanou and Vafeas, 2005). I include Distresst  1 because managers of firms in financial distress may find their ability to forecast more specific earnings circumscribed in a fashion similar to managers of firms reporting earnings losses. Moreover, I include FINt, MAt and Ins_Tradet to account for managers who, in anticipation of new debt or equity issuance, merger or acquisition, and insider trading, likely have incentives to use range forecasts to manipulate market expectations. Range forecasts encompass many possible outcomes of earnings and, thus, are less likely than point forecasts to prove inaccurate, ex post, and to expose firms to legal liabilities.23 On the other hand, managers’ incentives to use range forecasts to mislead may be constrained by their accounting flexibility, which is measured by net operating assets (NOAt  1) following Barton and Simko (2002). Again following Wooldridge’s (2003) intuition, I include ACCRact t1 , NetSellt, Returnt, and Abs_Surpriset because they are included in Eq. (1).24 Finally, M/Bt  1, LNSizet  1, and year dummies are included. Table B1 presents results from the maximum likelihood bivariate probit estimation of Eqs. (3) and (4). References Ajinkya, B., Bhojraj, S., Sengupta, P., 2005. The association between outside directors, institutional investors and the properties of management earnings forecasts. Journal of Accounting Research 43, 343–376. Ajinkya, B., Gift, M., 1984. Corporate managers’ earnings forecasts and symmetrical adjustments of market expectations. Journal of Accounting Research 22, 425–444. Baginski, S., Conrad, E., Hassell, J., 1993. The effects of management forecast precision on equity pricing and on the assessment of earnings uncertainty. The Accounting Review 68, 913–927. Baginski, S., Hassell, J., 1997. Determinants of management forecast precision. The Accounting Review 72, 303–312. Bamber, L., Cheon, Y., 1998. 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22 Wooldridge (2003, Chapter 17) suggests that any element that appears as an explanatory variable in the second-stage equation should also be an explanatory variable in the first-stage selection equation because excluding it incorrectly can lead to inconsistency. 23 For example, to boost stock prices, firms can issue range earnings forecasts with expected earnings falling into the lower bounds of the forecasts. 24 act The results in Table 7 are similar when FINt, MAt, NOAt  1, Losst, Distresst  1, ACCRact t  1, NetSellt, and Returnt are not included in Eq. (3) and ACCRt  1, NetSellt, Returnt, and Abs_Surpriset are not included in Eq. (4).

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