the role of accounting conservatism in management forecast bias

Journal of Contemporary Accounting & Economics 8 (2012) 64–77

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Journal of ContemporaryAccounting & Economics

journal homepage: www.elsevier .com/locate / jcae

The role of accounting conservatism in management forecast bias q

Yan Sun a,1, Weihong Xu b,⇑a Department of Accounting, John Cook School of Business, Saint Louis University, St. Louis, MO 63108, United Statesb Department of Accounting & Law, State University of New York at Buffalo, Buffalo, NY 14260, United States

a r t i c l e i n f o a b s t r a c t

Article history:Received 2 March 2012Revised 21 May 2012Accepted 21 May 2012Available online 30 May 2012

Keywords:ConservatismManagement earnings forecastsManagement forecast errors

1815-5669/$ - see front matter Published by Elsevihttp://dx.doi.org/10.1016/j.jcae.2012.05.002

q We appreciate the helpful comments of Bin SriniSun acknowledges the summer research grant fromresearch support from School of Management at th⇑ Corresponding author. Tel.: +1 716 645 5434.

E-mail addresses: [email protected] (Y. Sun), wxu41 Tel.: +1 314 977 3818.

We investigate whether management earnings forecasts fully incorporate information inhistorical accounting conservatism. We find that management earnings forecasts are moreoptimistic for firms with greater accounting conservatism in the previous year. We furtherexamine whether this conservatism-related optimistic bias in management earnings fore-casts varies with managers’ difficulty predicting earnings accurately, managers’ opportu-nistic incentives, and the firms’ litigation risk. We find that the negative associationbetween management forecast errors and conservatism increases, to various extent, withthe firms’ operating cycles, earnings volatility, and the width of forecast range but doesnot change with proxies for opportunistic incentives or litigation risk. These results suggestthat forecast difficulty is the primary reason for managers’ failure to incorporate conserva-tism fully in their earnings forecasts.

Published by Elsevier B.V.

1. Introduction

Management earnings forecasts are one of the key voluntary disclosure mechanisms by which managers adjust marketearnings expectations, reduce litigation risk, and influence their reputation for transparent financial reporting. Managementearnings forecasts are influential as they have been shown to affect stock prices, analysts’ forecasts, and information asym-metry (e.g., Baginski and Hassell, 1997; Coller and Yohn, 1997; Pownall et al., 1993). A question of particular interest iswhether characteristics of a firm’s accounting system, such as the extent of accounting conservatism, affect managementforecast bias. Evidence on this issue can further our understanding of the information incorporated by managers whenmaking earnings forecasts and the relevance of accounting conservatism to financial performance prediction.

Conservatism refers to the tendency to require a higher degree of verification to recognize good news as gains than torecognize bad news as losses in earnings (Basu, 1997). Conservatism implies that bad news is recognized in earnings moretimely. Given that managers are the decision makers of a firm and are in charge of the financial reporting process, they likelypossess superior information regarding the firm’s operations and reporting policies. As such, we expect that managers have abetter understanding than other market participants of the implications of the firm’s accounting conservatism for futureearnings and, therefore, at least partially adjust their forecasts for the effect of conservatism. However, managers’ informa-tion on the firm’s earnings prospects may be noisy as well because of uncertainty in the economy and in the firm’s operatingenvironment. Besides that, managers may neglect the implications of accounting choices when making operating decisions

er B.V.

dhi (editor), an anonymous reviewer, and workshop participants at the 2011 AAA Annual Meeting. YanJohn Cook School of Business at Saint Louis University. Weihong Xu also acknowledges the summer

e State University of New York at Buffalo.

@buffalo.edu (W. Xu).

http://dx.doi.org/10.1016/j.jcae.2012.05.002

mailto:[email protected]

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Y. Sun, W. Xu / Journal of Contemporary Accounting & Economics 8 (2012) 64–77 65

or earnings forecasts.2 Moreover, managers’ forecasts can be influenced by self-serving incentives other than by the incentive topredict earnings accurately. Thus, managers’ earnings forecasts are not necessarily free from errors associated with historicalaccounting conservatism. We posit that if managers underestimate the implications of historical conservatism for current earn-ings, their forecasts for the current year’s earnings will be greater than the actual earnings, leading to optimistically biased earn-ings forecasts. Our conjecture is in line with prior studies showing that management forecast errors are associated withhistorical information, such as past stock returns and accruals (e.g., Gong et al., 2009; McNichols, 1989; Xu, 2010).

The purpose of our study is two-fold. First, we investigate whether management earnings forecasts fully incorporateinformation in historical conservatism by examining the association between management forecast errors for the currentyear’s earnings and accounting conservatism of the previous year. Second, we explore possible reasons for managers’ failureof complete adjustment for the effect of historical conservatism on current-year earnings, if any, by examining whether theassociation between management forecast errors and conservatism varies with managers’ difficulty predicting earningsaccurately, managers’ opportunistic incentives, and firms’ litigation risk.

Our empirical analyses are based on 7236 firm-year observations during the period 1997–2008. We find that accountingconservatism measured in the previous year is negatively associated with management forecast errors for the current year’searnings (= [actual earnings � forecasted earnings]/share price). This result suggests that managers generally underestimatethe implications of historical conservatism for current-year earnings in their forecasts, resulting in optimistic earnings fore-casts. Furthermore, we find evidence that this negative association is stronger for firms with longer operating cycles, greaterearnings volatility, and less specific forecasts. In contrast, we find no evidence that managers’ conservatism-related optimis-tic forecast bias is greater when firms anticipate external financing, merger or acquisition, or net insider selling activities. Wealso find that litigation risk does not mitigate the negative relation between conservatism and management forecast errors.Taken together, these results suggest that earnings forecast difficulty, rather than managers’ opportunistic incentives, is theprimary reason that managers fail to fully incorporate information in historical conservatism in their earnings forecasts.

Our study contributes to the research on accounting conservatism. Conservatism is considered an important property ofaccounting earnings. Existing studies on conservatism focus on consequences of conservatism for the firms and the capitalmarkets (e.g., Ahmed et al., 2002; LaFond and Watts, 2008; Watts, 2003; Zhang, 2008) and factors that are associated with orinfluence conservatism (e.g., Jenkins and Velury, 2008; LaFond and Watts, 2008; Li, 2010). Yet little effort has been made toexamine whether managers consider the impact of accounting conservatism on reported earnings when they predict futurefirm performance. We extend the literature by showing that although managers have superior information on the firm’soperations and financial reporting process, they do not fully incorporate the earnings implications of the adopted conserva-tive accounting practices into their forecasts. We further find that the extent to which management earnings forecasts reflectthe implications of conservatism is associated with managers’ difficulty forecasting future earnings but not with managers’opportunistic incentives or the fear of litigation.

Furthermore, our study contributes to research on management earnings forecasts. Prior research finds various factorsassociated with management forecast bias, such as firm performance, litigation risk, and forecast horizon (e.g., Ajinkyaet al., 2005; Johnson et al., 2001; Rogers and Stocken, 2005). We extend these studies by providing evidence that manage-ment earnings forecasts contain predictable bias with respect to historical conservatism. Our results thus provide practicaladvice to information users to consider a firm’s accounting conservatism when assessing management earnings forecasts,especially for firms with longer operating cycles, greater earnings volatility, and less specific forecasts.

The remainder of the paper is organized as follows. Section 2 reviews the related literature and develops the researchhypotheses. Section 3 describes the methodology and data. Section 4 reports the empirical findings, and Section 5 concludesthe study.

2. Prior literature and hypothesis development

2.1. The relation between conservatism and management forecast errors

Prior research provides evidence that the extent of accounting conservatism can affect managers’ forecasting behavior.Hui et al. (2009) find a negative association between historical conservatism and the frequency, specificity, and timelinessof management forecasts. They suggest that accounting conservatism acts as a substitute for management forecasts bydecreasing information asymmetry and reducing potential litigation through timely reporting of bad news. In contrast, Li(2008) finds a positive association between conservatism and a firm’s likelihood to issue management earnings forecastsand its forecast frequency. She argues that managers have incentives to issue earnings guidance to help analysts adjust theirforecasts for the effect of conservatism. However, Hui et al. (2009) and Li (2008) do not examine the association betweenaccounting conservatism and management forecast bias.

Several studies examine whether financial analysts fully adjust accounting conservatism when they issue future earningsforecasts. Louis et al. (2009) find that analysts do not include in their initial forecasts information about conservatism eventhough that information is available at the time of the forecasts. They argue that analyst inefficiency with respect to

2 McNichols and Stubben (2008) find that firms with earnings management over-invest substantially in the misreporting period. Gong et al. (2009) and Xu(2010) find that management earnings forecasts contain optimistic bias related to past accruals.

66 Y. Sun, W. Xu / Journal of Contemporary Accounting & Economics 8 (2012) 64–77

conservatism contributes to the optimistic analyst forecast bias. Similarly, Pae and Thornton (2010) show that analysts’ earn-ings forecasts do not fully incorporate the implications of earnings conservatism. Yet it is still an open question whethermanagers fully adjust the effect of accounting conservatism on future earnings when they issue earnings forecasts.

We argue that management earnings forecast errors are associated with historical conservatism because the demand forconservatism increases with a firm’s business uncertainty, and meanwhile business uncertainty increases the variance ofpossible outcomes and the difficulty for managers to forecast earnings accurately. Firms with greater business uncertaintyare more likely to have unfavorable outcomes. Lenders of these firms thus demand a greater level of conservatism to reduceagency problems such as shareholder distributions and sub-optimal investment. Since conservatism leads to a downwardbias in realized accounting earnings due to more timely recognition of bad news versus good news, ceteris paribus, realizedearnings for conservative firms are lower than realized earnings for non-conservative firms. If managers fail to adjust theirforecasts fully for the effect of conservatism on realized earnings, managers’ forecasted earnings for conservative firms willbe greater than their realized earnings, which translates into optimistically biased management forecasts. As a result, con-servatism will be negatively associated with management forecast errors—that is, the higher the degree of accounting con-servatism, the greater the overestimation of future earnings and the lower the management forecast errors (= [actualearnings � forecasted earnings]/share price).3 This reasoning leads to our first hypothesis (stated in the alternative form):

H1. A negative association exists between the level of accounting conservatism and management forecast errors withrespect to future earnings.

2.2. Forecast difficulty and the relation between conservatism and management forecast errors

Khan and Watts (2009) show that accounting conservatism is positively associated with firm-specific uncertainty. Spe-cifically, they find that firms with greater conservatism have smaller size, longer investment cycles, and higher stock returnvolatility. Business uncertainty likely increases managers’ forecast difficulty and leads to unintentional errors in managers’earnings forecasts. Therefore, we examine whether the negative association between accounting conservatism and manag-ers’ forecast errors is associated with managers’ forecast difficulty. If firm-specific uncertainty drives the conservatism-re-lated optimistic bias in management earnings forecasts, we expect the bias to increase with managers’ forecast difficultyas a result of the firm’s business uncertainty.

We use three alternative proxies for forecast difficulty: the length of operating cycle, earnings volatility and the width ofmanagement earnings forecast. Operating cycle refers to the time that a firm takes to convert purchases into cash throughsales. A longer operating cycle indicates that longer time transpires for a firm to complete productions, to sell its products,and to realize profits. Longer operating cycle is associated with forecast difficulty because it potentially increases managers’uncertainty in measuring and forecasting earnings. Earnings volatility can arise from economic shocks or accounting choicesand estimations. Volatile earnings can be less persistent because they are likely extreme earnings that revert toward themean more rapidly (e.g., Dichev and Tang, 2009; Freeman et al., 1982).4 Earnings volatility is associated with forecast difficultybecause firms with volatile earnings are likely to have fluctuating earnings over time. As a result, managers of such firms willhave greater difficulty assessing whether earnings and earnings properties, including conservatism, in the previous period willpersist into a new period. Management forecast width is associated with forecast difficulty because it reveals managers’ uncer-tainty about future earnings. Prior research shows that managers issue less specific forecasts if they are less certain about thefirm’s prospects (e.g., Baginski et al., 1993). We thus expect that managers who issue forecasts with a wider range have greaterdifficulty correctly forecasting earnings or assessing the persistence of earnings properties, such as accounting conservatism.

Collectively, the length of operating cycle, earnings volatility and the width of management earnings forecast parsimoni-ously capture managers’ difficulty forecasting earnings and assessing the implications of historical conservatism for futureearnings. If the negative relation between management forecast errors and accounting conservatism is due to managers’forecast difficulty, we predict:

H2. The negative association between conservatism and management forecast errors is stronger for firms with longeroperating cycles, greater earnings volatility, and wider management forecast range.

2.3. Management opportunism and the relation between conservatism and management forecast errors

Besides forecast difficulty, managerial opportunism can also affect managers’ conservatism-related bias in their earningsforecasts. We consider three potential sources of opportunistic incentives that may motivate managers to bias their forecastsintentionally by not fully adjusting for the effect of historical conservatism in their earnings forecasts for the current period.The first incentive source is managers’ anticipation of external financing in the current year. The literature suggests that

3 Our measurement of management forecast errors is consistent with many prior studies such as Karamanou and Vafeas (2005). Some studies measuremanagement forecast errors as the signed difference between forecasted earnings and actual earnings (e.g., Ajinkya et al., 2005; Rogers and Stocken, 2005),which will lead to opposite signs for management forecast errors.

4 In a related study, Graham et al. (2005) survey 401 financial executives and find that managers believe that volatile earnings are less persistent.


managers issue optimistic earnings forecasts (Frankel et al., 1995; Lang and Lundholm, 2000) or recognize income-increasingaccruals (Teoh et al., 1998) before they raise additional external capital to mislead investors about the firms’ prospects toreduce the cost of capital and to increase the proceeds from external financing. Because accounting conservatism leads tounderstated earnings, managers’ incentive to correctly incorporate conservatism in their earnings forecasts may decreasewhen managers anticipate the need for external capital. Therefore, if the negative association between management forecasterrors and accounting conservatism is due to managerial opportunism related to external financing, we predict:

H3a. The negative association between conservatism and management forecast errors is stronger when managers anticipateseeking external financing.

The second source of managers’ opportunistic incentives that we consider is managers’ expectation for mergers or acqui-sitions. Prior studies report that firms have incentives to manage earnings upward before mergers or acquisitions to reducethe cost of business growth (e.g., Erickson and Wang, 1999). To reduce the cost to acquire the target, managers may under-estimate the earnings implications of conservatism to a greater degree to issue more optimistic earnings forecasts. Therefore,if the negative association between management forecast errors and accounting conservatism is due to managerial oppor-tunism related to mergers or acquisitions, we predict:

H3b. The negative association between conservatism and management forecast errors is stronger when managers anticipatea merger or acquisition.

The last source of managers’ opportunistic incentives that we consider is managers’ plan to trade their shares of the firm.Prior research finds that managers issue optimistic forecasts in anticipation of selling stocks and pessimistic forecasts inanticipation of buying stocks (e.g., Rogers and Stocken, 2005). If managers plan to net sell their shares, we expect that man-agers will issue optimistically biased earnings forecasts by not fully adjusting the effect of conservatism. Therefore, if thenegative association between management forecast errors and accounting conservatism is due to managerial opportunismrelated to their plan to net sell firm stocks, we predict:

H3c. The negative association between conservatism and management forecast errors is stronger when managers anticipatenet selling their firms’ stocks.

2.4. Litigation risk and the relation between conservatism and management forecast errors

Managers’ underestimation of the effect of historical conservatism on future earnings is likely to be affected by the threatof litigation. A firm’s litigation risk is expected to increase if the managers issue biased forecasts that later prove to be inac-curate. Since the US legal system imposes an asymmetric loss function on firms in that a firm is more likely to be sued whena large negative return occurs at its earnings announcement (Skinner, 1994), we expect that managers of firms with greaterlitigation risk are motivated to issue less optimistic earnings forecasts and even pessimistic forecasts to preempt bad news.Therefore, we anticipate that managers’ tendency to issue optimistically biased forecasts by not fully incorporating the effectof conservatism will be mitigated by their incentive to provide pessimistic forecasts to alleviate litigation risk.

H4. The negative association between conservatism and management forecast errors is weaker when the firm faces higherlitigation risk.

3. Methodology and data

3.1. Measure of accounting conservatism

To examine our research questions, we need a firm-year-specific accounting conservatism measure that reflects earnings’tendency to recognize bad news as losses more rapidly than to recognize good news as gains. We use Khan and Watts (2009)C_Score to measure conservatism because it has several advantages over the other conservatism measures used in the lit-erature. First, it is firm-year specific. Second, its estimation does not require a series of historical data and thus imposes littledata restrictions. Finally, it captures the asymmetric-timeliness property of earnings that we examine. C_Score has been usedin recent studies on conservatism, such as Chen et al. (2010), Chi et al. (2009), and DeFond et al. (2010).

Khan and Watts (2009) C_Score is based on Basu’s (1997) measure of the asymmetric timeliness of earnings, as in thefollowing regression equation:

Et ¼ b1 þ b�2DRt þ b�3RETt þ b�4RET�t DRt þ et ; ð1Þ

where Et is earnings before extraordinary items for year t, deflated by market value of equity at the beginning of year t; DRt isan indicator variable that equals 1 if RETt is negative (i.e., when there is bad news), and zero otherwise; RETt is the buy-and-hold stock returns over the 12-month period starting with the fourth month of year t. The coefficient on RETt, b3, captures


earnings’ timeliness to recognize good news, and the coefficient on RETt � DRt, b4, captures earnings’ asymmetric timelinesswith respect to the recognition of bad news.

Khan and Watts (2009) incorporate into Basu’s (1997) model the theoretical relation between conservatism and threefirm characteristics: leverage (LEV), market-to-book ratio (M/B), and size (SIZE). They then express b3 and b4, respectively,as the following linear functions of LEV, M/B, and SIZE:

5 Rog

G Score ¼ b3 ¼ l0 þ l1 � LEVt þ l2 �M=Bt þ l3 � SIZEt ð2Þ

C Score ¼ b4 ¼ k0 þ k1 � LEVt þ k2 �M=Bt þ k3 � SIZEt ð3Þ

Khan and Watts (2009) show that by allowing all four coefficients in Basu’s regression (Eq. (1)) to vary with LEV, M/B, andSIZE, the derived C_Score is better able to predict earnings’ asymmetric timeliness for up to 3 years ahead. Thus, we also al-low coefficients b1 and b2 in regression Eq. (1) to vary with LEV, M/B, and SIZE as follows:

b1 ¼ q0 þ q1 � LEVt þ q2 �M=Bt þ q3 � SIZEt ð4Þ

b2 ¼ r0 þ r1 � LEVt þ r2 �M=Bt þ r3 � SIZEt ð5Þ

Replacing coefficients in regression Eq. (1) with coefficients in Eqs. (2)–(5), we obtain the following expanded regressionequation:

Et ¼ q0 þ q1 � LEVt þ q2 �M=Bt þ q3 � SIZEt þ r0 � DRt þ r1 � LEVt � DRt þ r2 �M=Bt � DRt þ r3 � SIZEt � DRt

þ l0 � RETt þ l1 � LEVt � RETt þ l2 �M=Bt � RETt þ l3 � SIZEt � RETt þ k0 � RETt � DRt þ k1 � LEVt � RETt

� DRt þ k2 �M=Bt � RETt � DRt þ k3 � SIZEt � RETt � DRt þ et ð6Þ

Following Khan and Watts (2009), we estimate regression Eq. (6) cross-sectionally in each year of the sample period using allobservations with necessary data from COMPUSTAT and CRSP. For each firm-year observation, C_Scoret is computed usingEq. (3). To assess the impact of outliers and estimation errors for C_Scoret on our empirical results, we use both the actualC_Scoret and its percentile rank in our empirical analyses.

3.2. Research design

To examine H1 on the relation between historical conservatism and management forecast errors with respect to earningsfor the current period, we estimate the following regression:

MFEt ¼ b0 þ b1C Scoret�1 þ b2FINt þ b3MAt þ b4NetSellt þ b5Litigationt þ b6Horizont þ b7Returnt

þ b8Abs Surpriset þ b9Bad Newst þ b10ROAt�1 þ b11ACCRt�1 þ b12LnSalest�1 þX

biYeari þ et ð7Þ

The dependent variable, MFEt, is management earnings forecast errors for year t. It is measured as actual earnings per share(EPS) of year t less management forecasted EPS for year t, deflated by stock price at the end of year t � 1. For a range forecast,management forecasted EPS is the midpoint of the range. Our variable of interest, C_Scoret�1, is the firm’s accounting con-servatism for year t � 1, measured following Khan and Watts (2009) as previously discussed. If managers underestimate theimplications of historical conservatism for earnings of the current period, as hypothesized in H1, management forecast errorswill be more negative for firms with greater conservatism (i.e., b1 6 0).

We include the remaining independent variables to control for other factors that can potentially affect management fore-cast bias. We include FINt, MAt, and NetSellt because, as previously discussed, managers of firms that anticipate externalfinancing, a merger or acquisition, and insider selling activities, respectively, may have incentives to bias their forecasts up-ward to mislead investors and to exploit possible stock mispricing. FINt is an indicator variable that equals 1 if the firm’sexternal financing in year t is greater than 10% of total assets at the beginning of the year, and zero otherwise. MAt equals1 if the firm engages in a merger or acquisition in year t, and zero otherwise. NetSellt equals 1 if the firm’s CEO and CFO en-gage in net insider selling within 10 trading days after the management forecast date, and zero otherwise. We expect neg-ative coefficients for all three incentive variables.

We include Litigationt to control for the effect of litigation risk on management forecast bias. Litigationt is measured asthe probability of litigation risk in the quarter before the management forecast date. The estimates of litigation risk are ob-tained from a logit model in which we regress the incidence of being sued on a number of predictors (see Appendix A forestimation details). Rogers and Stocken (2005) find evidence that managers of firms with higher litigation risk issue less opti-mistic earnings.5 We thus expect the coefficient on Litigationt to be positive.

We control for forecast horizon (Horizont) and prior stock returns (Returnt) because prior studies find that managers’forecasts are less optimistic when they are issued closer to the end of the forecast period and when prior stock returnsare higher (e.g., Johnson et al., 2001; McNichols, 1989). Horizont is defined as the number of calendar days from the

ers and Stocken (2005) also find that this result disappears when influential observations are deleted.


management forecast date to the fiscal ending date of year t. Returnt is measured as the size-adjusted stock return for a 90-day window ending 1 day before the management forecast date.

We include Abs_Surpriset and Bad_Newst because previous research shows that management forecasts are more optimis-tic when the absolute surprise contained in the forecasts is greater and are more pessimistic when the forecasts convey badnews (e.g., Ajinkya et al., 2005; Karamanou and Vafeas, 2005).6 Abs_Surpriset is defined as the absolute value of managementEPS forecast for year t minus the mean analyst EPS forecast issued within 90 days before the management forecast date, deflatedby stock price at the end of year t � 1. Bad_Newst equals 1 if management EPS forecast for year t is less than the mean analystEPS forecast issued within 90 days before the management forecast date, and zero otherwise.7

We use return on assets for year t � 1 (ROAt�1), accruals in year t � 1 (ACCRt�1), and a firm’s sales revenue in year t � 1(LnSalet�1), respectively, to control for the impact of firm performance, past accruals, and firm size on management forecast-ing behavior. Firms with poor performance may issue more pessimistic earnings forecasts to preempt bad news. Alterna-tively, poor performance may induce managers to issue less pessimistic or more optimistic forecasts to boost earningsexpectations in the capital markets (e.g., Rogers and Stocken, 2005). Taking into account the mixed views, we do not predictthe sign of the coefficient on ROAt�1. Gong et al. (2009) and Xu (2010) find that management earnings forecasts contain pre-dictable bias with respect to past accruals; therefore, we expect a negative coefficient on ACCRt�1. We use the natural log-arithm transformation of a firm’s sales revenue, instead of market value of equity, to capture firm size because market valueof equity is one of the three firm characteristics used to compute Khan and Watts (2009) C_Score and is, therefore, highlycorrelated with C_Score, our variable of interest. Finally, we include 11 year indicator variables (Yeari) to account for possiblevariation in management forecast bias over time.

To examine H2, the effect of forecast difficulty on the relation between historical conservatism and management forecasterrors, we augment Eq. (7) by interacting C_Scoret�1 individually with Ope_Cyclet�1, Earn_Volt�1, and MFE_Widtht.Ope_Cyclet�1 is the decile rank of the length of operating cycle in year t � 1 where operating cycle is measured as averageaccounts receivable divided by sales plus average inventory divided by cost of goods sold then multiplied by 365. Earn_Volt�1

is the decile rank of earnings volatility from year t�5 to year t � 1 where earnings volatility is measured as the standard devi-ation of return on assets over the 5-year period, scaled by the magnitude of average return on assets over the same period.We use decile ranks of operating cycle and earnings volatility because the distributions of the actual values of these two vari-ables are highly skewed to the right. MFE_Widtht is the width of management earning forecast, measured as 3 (2; 1) if thefirm issues a range forecast for year t with the highest (medium; lowest) level of stock price deflated range width, and zero ifthe firm issues a point forecast for year t. We predict in H2 that the negative association between conservatism and man-agement forecast errors is stronger for firms with longer operating cycles, more volatile earnings, and less specific forecasts.We thus expect the coefficients on C_Scoret�1 � Ope_Cyclet�1, C_Scoret�1 � Earn_Volt�1, and C_Scoret�1 �MFE_Widtht to benegative. In addition, we include Ope_Cyclet�1, Earn_Volt�1, and MFE_Widtht as additional independent variables to controlfor their possible main effects on management forecast errors.

To examine H3 and H4, the effect of managerial opportunism and litigation risk on the relation between conservatism andmanagement forecast errors, we augment Eq. (7) by interacting C_Scoret�1 individually with FINt, MAt, NetSellt, andLitigationt. Recall that we hypothesize that the negative association between conservatism and management forecast errorsis stronger when managers anticipate external financing, a merger or acquisition, and net selling activities and is weakerwhen firms face higher litigation risk. Accordingly, we predict the coefficients on C_Scoret�1 � FINt, C_Scoret�1 �MAt, andC_Scoret�1 � NetSellt to be negative and the coefficient on C_Scoret�1 � Litigationt to be positive. Table 1 provides detaileddefinitions for all variables used in the empirical analyses.

3.3. Sample selection

We begin with point and range annual management earnings forecasts issued by U.S. firms between January 1, 1996 andOctober 10, 2009 from First Call’s Company Issued Guidance (CIG) database.8 To compute management forecast errors, we donot include forecasts with missing corresponding actual annual earnings on First Call. We also eliminate forecasts issued prior tothe previous year’s earnings announcement date and forecasts issued between the current year’s fiscal end and earnings announce-ment date to ensure that managers have information on their firm’s conservatism for the previous year when forecasting and toexclude earnings pre-announcements. This procedure yields 28,156 forecasts. To avoid problems of data interdependence, we re-tain only the first forecast when a firm issues more than one forecast for a given year. This step results in a loss of 18,757 forecasts.

From the remaining 9399 observations, we next remove 761 observations without an analyst earnings forecast on IBES forthe current fiscal year issued within 90 days before the management forecast date in order to measure the surprise or newscontained in the management earnings forecast. Furthermore, we eliminate 1292 observations with missing required Com-pustat or CRSP data. Because the number of management forecasts prior to 1997 is substantially lower, we use fiscal years1997–2008 as the sample period and thereby eliminate another 96 observations. To avoid a small deflator problem, we

6 In contrast, Rogers and Stocken (2005) find no association between bad news forecasts and management forecast bias.7 Ajinkya et al. (2005) suggest that dispersion in analysts’ forecasts can affect managerial forecasting behavior. When we re-estimate our regressions by

including analyst dispersion as an additional independent variable, our results (unreported) are qualitatively similar. We do not control for analyst dispersion inthe paper because its measurement requires at least three analyst forecasts available and the resultant sample loss is about 20%.

8 October 10, 2009 is the last forecast date for management forecasts on First Call CIG tape updated on November 9, 2009 by Wharton Research Data Services.

Table 1Detailed variable definitions.

Variable Definition

MFEt Actual earnings per share (EPS) of year t less management forecasted EPS for year t, deflated by stock price (#14) at the end of yeart � 1

C_Scoret�1 The firm’s conservatism score for year t � 1, defined using the approach of Khan and Watts (2009)Ope_Cyclet�1 Decile rank of the length of operating cycle in year t � 1 where operating cycle is measured as average accounts receivable (#2)

divided by sales (#12) plus average inventory (#3) divided by cost of goods sold (#41) then multiplied by 365Earn_Volt�1 Decile rank of earnings volatility over a 5-year period from year t�5 to year t � 1 where earnings volatility is measured as the

standard deviation of return on assets over the 5-year period, scaled by the magnitude of average return on assets over the sameperiod

MFE_Widtht 3 (2; 1) if the firm issues a range forecast for year t with the highest (medium; lowest) level of stock price deflated range width, andzero if the firm issues a point forecast for year t

FINt 1 if the firm’s external financing in year t is greater than 10% of total assets (#6) at the beginning of year t, and zero otherwise.External financing is measured as net equity financing plus net debt financing, where net equity financing equals cash proceeds fromthe sale of common and preferred stock (#108) minus cash payments for the purchase of common and preferred stock (#115) minuscash payments for dividends (#127), and net debt issuance equals cash proceeds from the issuance of long-term debt (#111) minuscash payments for long-term debt reductions (#114) minus the net changes in current debt (#301)

MAt 1 if the firm engages in a merger or acquisition (Footnote code #1) in year t, and zero otherwiseNetSellt 1 if the firm’s net insider transactions by the CEO and CFO within 10 trading days after the management forecast date are sells, and

zero otherwise. The trade data are obtained from the Thomson Financial Insider Trading databaseLitigationt The lagged probability of litigation risk estimated using a logit model. See Appendix A for estimation detailsHorizont The number of calendar days from the management forecast date to the fiscal ending date of year tReturnt The size-adjusted stock return for a 90-day window ending 1 day before the management forecast dateAbs_Surpriset The absolute value of management forecasted EPS for year t minus the mean analyst forecasted EPS for year t, deflated by stock price

at the end of year t � 1. The mean analyst forecasted EPS is computed using IBES analyst forecasts issued within 90 days before themanagement forecast date

Bad_Newst 1 if management forecasted EPS for year t is less than the mean analyst forecasted EPS for year t, and zero otherwiseROAt�1 Earnings before extraordinary items (#123) of year t � 1, divided by total assets (#6) at the beginning of year t � 1ACCRt�1 Total accruals of year t � 1, measured as income before extraordinary items (#123) less cash flows from operating activities (#308),

divided by total assets (#6) at the beginning of year t � 1LnSalet�1 The natural logarithm transformation of the firm’s sales revenue (#12) of year t � 1


delete 14 observations with a stock price at the end of the previous year smaller than $1. Our final sample consists of 7236management earnings forecasts for 2257 distinct firms.

3.4. Descriptive statistics

Table 2 reports descriptive statistics for the sample. Continuous variables are winsorized at the 1st and the 99th percen-tiles of their respective distributions. The mean and median values of management forecast errors (MFEt) are �0.0081 and�0.0003, respectively, indicating that initial annual management forecasts, on average, are optimistically biased. The meanand median conservatism (C_Scoret�1) are 0.1133 and 0.1017, respectively, suggesting that, on average, earnings are asym-metrically timely in the recognition of bad news.

Table 2 also shows that about 10.07%, 25.58%, and 12.98% of the sample is engaged in external financing (FINt), merger oracquisition (MAt), and net insider selling (NetSellt) activities, respectively. The average estimated litigation risk (Litigationt)for the sample firms is about 0.66%. The mean and median forecast horizon (Horizont) are 273 and 315 days before the fiscalyear end, respectively. The size-adjusted stock returns prior to management earnings forecasts (Returnt) are negative, with amean (median) of �0.0009 (�0.0056). Bad_Newst has a mean of 0.5299, indicating that bad news forecasts outnumber goodnews forecasts. ROAt�1 is positive at the median and the 25th percentile, suggesting that the majority of the sample firms areprofitable in the previous year. The mean and median accruals (ACCRt�1) are �0.0539 and �0.0488, respectively, suggestingthat accruals are negative on average.

Table 3 reports Pearson correlation coefficients among the variables in regression (7).9 Bold figures indicate significance atthe 10% or better level. Consistent with H1, MFEt is significantly negatively related to C_Scoret�1. In addition, MFEt is positivelyrelated to MAt, Returnt, ROAt�1, and LnSalet�1 and negatively related to Litigationt, Horizont, Abs_Surpriset, and ACCRt�1.

4. Empirical results

4.1. Relation between conservatism and management forecast errors

Table 4 reports our regression analyses of the relation between historical conservatism and management forecast errorsfor the current year’s earnings (H1). For ease of exposition, we multiply all coefficients by 1000. Our inferences are based onstandard errors clustered by firm and year to account for both time-series and cross-sectional dependence (Gow et al., 2010;

9 Spearman correlation coefficients are similar.

Table 2Descriptive statistics.

Variable Mean Std. dev. 25% Median 75%

MFEt �0.0081 0.0316 �0.0114 �0.0003 0.0037C_Scoret�1 0.1133 0.1562 0.0187 0.1017 0.1896FINt 0.1007 0.3010 0.0000 0.0000 0.0000MAt 0.2558 0.4363 0.0000 0.0000 1.0000NetSellt 0.1298 0.3361 0.0000 0.0000 0.0000Litigationt 0.0066 0.0081 0.0025 0.0041 0.0073Horizont 273.2477 85.7798 247.0000 315.0000 331.0000Returnt �0.0009 0.1759 �0.0949 �0.0056 0.0863Abs_Surpriset 0.0063 0.0114 0.0007 0.0020 0.0061Bad_Newst 0.5299 0.4991 0.0000 1.0000 1.0000ROAt�1 0.0623 0.1093 0.0261 0.0596 0.1052ACCRt�1 �0.0539 0.0868 �0.0893 �0.0488 �0.0155LnSalet�1 6.8678 1.6842 5.6976 6.8476 8.0067

The sample consists of 7236 firm-year observations. Variables are defined in Table 1. Continuous variables are winsorized at the 1st and 99th percentiles oftheir respective distributions.

Table 3Pearson correlations.

C_Scoret�1 FINt MAt NetSellt Litigationt Horizont Returnt Abs_Surpriset Bad_Newst ROAt�1 ACCRt�1 LnSalet�1

MFEt �0.106 �0.005 0.034 0.013 �0.026 �0.059 0.071 �0.210 0.012 0.051 �0.071 0.053(0.000) (0.7002) (0.0041) (0.2845) (0.027) (<.0001) (<.0001) (<.0001) (0.2952) (<.0001) (<.0001) (<.0001)

C_Scoret�1 0.038 �0.021 �0.046 �0.178 �0.012 0.027 0.146 �0.047 �0.155 0.001 �0.335(0.0013) (0.0692) (<.0001) (<.0001) (0.2949) (0.0215) (<.0001) (<.0001) (<.0001) (0.9158) (<.0001)

FINt 0.212 �0.002 �0.025 �0.043 0.048 0.053 �0.017 �0.034 0.038 �0.141(<.0001) (0.8524) (0.0302) (0.0002) (<.0001) (<.0001) (0.153) (0.0038) (0.0013) (<.0001)

MAt 0.012 0.018 �0.017 0.010 �0.032 �0.004 0.035 0.019 �0.022(0.3048) (0.1343) (0.1547) (0.3928) (0.0064) (0.7155) (0.0031) (0.1000) (0.0588)

NetSellt 0.026 0.106 �0.025 0.004 0.025 0.010 0.021 0.061(0.0273) (<.0001) (0.0315) (0.7224) (0.0327) (0.4131) (0.0759) (<.0001)

Litigationt 0.036 �0.190 �0.006 0.030 0.055 �0.034 0.161(0.0021) (<.0001) (0.5865) (0.0113) (<.0001) (0.0037) (<.0001)

Horizont 0.023 �0.045 �0.022 0.028 �0.004 0.182(0.0524) (0.0001) (0.0647) (0.0162) (0.731) (<.0001)

Returnt �0.007 �0.157 0.021 0.014 0.034(0.5423) (<.0001) (0.0801) (0.2279) (0.0039)

Abs_Surpriset �0.021 �0.151 �0.020 �0.127(0.0712) (<.0001) (0.0844) (<.0001)

Bad_Newst �0.011 0.038 0.028(0.3653) (0.0012) (0.016)

ROAt�1 0.339 0.109(<.0001) (<.0001)

ACCRt�1 0.068(<.0001)

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


Petersen, 2009). Column (1) presents the results using actual values of C_Scoret�1; column (2) reports the results using thepercentile ranks of C_Scoret�1. Both the coefficient on actual C_Scoret�1 (�41.626/1000, t = 2.30) and the coefficient onranked C_Scoret�1 (�28.446/1000, t = 4.11) are significantly negative at the 5% or better level after controlling for variousfactors that may influence managerial forecast biases. To interpret our results in a meaningful manner, we compute the eco-nomic significance of the coefficient on C_Scoret�1 in column (1). Given that the 25th (75th) percentile of C_Scoret�1 is0.0187 (0.1896) and that the coefficient on C_Scoret�1 is �41.626/1000, an interquartile change in C_Scoret�1 (=0.1896 � 0.0187 = 0.1709) is associated with a change in MFEt of �0.0071 (=�41.626/1000 � 0.1709). Because the mean va-lue of MFEt for the sample firms is �0.0081, a change of �0.0071 represents an increase in negative forecast errors (i.e., anincrease in optimistic bias) of 87.7% of the average forecast error.

To summarize, the results in Table 4 are consistent with H1 that management forecast errors are more negative for firmswith greater conservatism in the previous year. That is, managers do not appear to incorporate the information in historicalconservatism fully into their earnings forecasts, resulting in optimistically biased forecasts.

4.2. Forecast difficulty and the relation between conservatism and management forecast error

Table 5 presents our test results of the effect of managers’ forecast difficulty on the relation between historical conser-vatism and management forecast errors (H2). Panels A and B report the regression results when conservatism is measured

Table 4Regression results of the relation between conservatism and management forecast errors.

(1) (2)Based on actual C_Scoret�1 Based on ranked C_Scoret�1

Variable Predicted sign Coef.(t-stat.) Coef.(t-stat.)

Intercept ? �2.272 12.428(�0.29) (1.45)

C_Scoret�1 � �41.626** �28.446***

(�2.30) (�4.11)

Control variablesFINt � 0.594 0.474

(0.41) (0.33)MAt � 1.491** 1.394*

(1.99) (1.92)NetSellt � 2.247*** 2.201**

(2.64) (2.49)Litigationt + �123.889** �158.661***

(�2.27) (�3.25)Horizont � �0.038*** �0.039***

(�4.86) (�5.03)Returnt + 12.575*** 12.479***

(3.32) (3.33)Abs_Surpriset � �489.936*** �472.169***

(�4.40) (�4.19)Bad_Newst + 1.363 1.335

(1.12) (1.10)ROAt�1 ? 4.479 2.513

(0.89) (0.54)ACCRt�1 � �26.615*** �23.957***

(�3.94) (�3.73)LnSalet�1 ? �0.302 �1.225

(�0.33) (�1.55)Year indicators Included IncludedAdj. R2 (%) 9.9 10.7N 7236 7236

Variables are defined in Table 1. The dependent variable in the regressions is management forecast error (MFEt). Conservatism is measured by actualC_Scoret�1 in column (1) and ranked C_Scoret�1 in column (2). All coefficients are multiplied by 1000 for expositional purposes. t-Statistics (in parentheses)are based on standard errors using two-way clustering by firm and year to control for both time-series and cross-sectional dependence.

* Significance at the 10% levels.** Significance at the 5% levels.

*** Significance at the 1% level.


using actual C_Scoret�1 and ranked C_Scoret�1, respectively. For both panels, columns (1–3) show the results with each of thethree forecast difficulty proxies included. As reported in Panel A of Table 5, the coefficient on C_Scoret�1 is significant in allthree columns, which confirms our prior finding that managers underestimate the effect of conservatism in their earningsforecasts. More importantly, we find that the coefficient on C_Scoret�1 � Earn_Volt�1 is negative and significant at the 1%level in column (2). Thus, the results in Panel A of Table 5 provide strong support to our expectation that managers of firmswith higher earnings volatility underestimate the effect of historical conservatism on current-year earnings to a greater ex-tent in their earnings forecasts.

Turning to the results in Panel B of Table 5 where ranked C_Scoret�1 is used to measured conservatism, we find that thecoefficient on C_Scoret�1 is significant at the 1% level in columns (1–3), which again confirms our earlier results for H1. Thecoefficient on C_Scoret�1 � Ope_Cyclet�1 is negative and significant at the 10% level in column (1), suggesting that managers’conservatism-related optimistic bias in their earnings forecasts is positively related to (i.e., increases with) the firm’s oper-ating cycle. The coefficient on C_Scoret�1 � Earn_Volt�1 is negative and significant at the 1% level in column (2), which againstrongly supports our prediction that earnings volatility is associated with managers’ conservatism-related optimistic bias.In addition, the coefficient on C_Scoret�1 �MFE_Widtht is negative and significant at the 10% level in column (3), indicatingthat managers of firms with wider range forecasts underestimate the effect of historical conservatism on current-year earn-ings to a greater extent in their earnings forecasts.

Taken together, the results in Table 5 suggest that managers’ conservatism-related optimistic bias in their earnings fore-casts is strongly related to the firm’s earnings volatility and is to some extent related to its operating cycle and the width offorecast range.10 These results collectively are consistent with H2 that when managers have difficulty forecasting earnings

10 We also estimate regressions in Tables 4 and 5 by including lagged MFEt as an additional control variable. The requirement of lagged MFEt substantiallyreduces the sample size to 4144. The coefficients on C_Scoret�1 are significantly negative in all regressions, consistent with H1 that management earningsforecasts contain conservatism-related optimistic bias. However, the interaction terms between C_Scoret�1 and Ope_Cyclet�1, Earn_Volt�1, and MFE_Widtht arenot significant except for the coefficient on the interaction term of ranked C_Scoret�1 and Ope_Cyclet�1. Hence, readers should exercise caution in interpretingthe results related to H2 in Table 5.

Table 5Regressions results of the effect of forecast difficulty on the relation between conservatism and management forecast errors.

(1) (2) (3)Forecast difficulty proxy:operating cycle

Forecast difficulty proxy:earnings volatility

Forecast difficulty proxy:width of forecast range

Variable Predicted sign Coef. (t-stat.) Coef. (t-stat.) Coef. (t-stat.)

Panel A: Results when conservatism is measured using actual C_Scoret�1

Intercept ? �0.221 �1.669 �3.342(�0.03) (�0.22) (�0.51)

C_Scoret�1 � �34.867** �27.299* �26.096***

(�2.16) (�1.87) (�2.81)C_Scoret�1 � Ope_Cyclet�1 � �1.559

(�1.40)C_Scoret�1 � Earn_Volt�1 � �3.246***

(�3.14)C_Scoret�1 �MFE_Widtht � �7.388

(�1.04)Control variablesOpe_Cyclet�1 ? �0.235***

(�8.41)Earn_Volt�1 ? 0.041

(0.22)MFE_Widtht ? �0.771

(�0.76)FINt � 0.594 0.460 0.478

(0.41) (0.32) (0.32)MAt � 1.377* 1.558** 1.413*

(1.84) (2.12) (1.91)NetSellt � 2.402*** 2.164*** 2.126**

(3.04) (2.70) (2.54)Litigationt + �120.308** �115.772* �129.176**

(�2.21) (�1.96) (�2.54)Horizont � �0.038*** �0.038*** �0.035***

(�4.87) (�4.90) (�4.55)Returnt + 12.757*** 13.208*** 12.768***

(3.37) (3.47) (3.27)Abs_Surpriset � �490.708*** �479.054*** �471.099***

(�4.41) (�4.30) (�4.11)Bad_Newst + 1.382 1.356 1.543

(1.14) (1.09) (1.35)ROAt�1 ? 2.911 3.506 2.351

(0.60) (0.71) (0.47)ACCRt�1 � �22.627*** �26.962*** �25.674***

(�3.76) (�4.09) (�3.81)LnSalet�1 ? �0.367 �0.405 �0.201

(�0.40) (�0.47) (�0.24)Year indicators Included Included IncludedAdj. R2 (%) 10.1 10.2 10.3N 7236 7236 7236

Panel B: Results when conservatism is measured using ranked C_Scoret�1

Intercept ? 13.225 10.643 8.831(1.49) (1.27) (1.23)

C_Scoret�1 � �23.991*** �20.641*** �19.950***

(�3.43) (�3.28) (�3.75)C_Scoret�1 � Ope_Cyclet�1 � �1.045*

(�1.69)C_Scoret�1 � Earn_Volt�1 � �1.754***

(�5.65)C_Scoret�1 �MFE_Widtht � �4.299*

(�1.76)Control variablesOpe_Cyclet�1 ? 0.097

(0.47)Earn_Volt�1 ? 0.593***

(3.32)MFE_Widtht ? 0.910

(0.65)FINt � 0.455 0.312 0.362

(0.32) (0.22) (0.25)MAt � 1.252* 1.465** 1.326*

(1.71) (2.06) (1.86)

(continued on next page)


Table 5 (continued)

(1) (2) (3)Forecast difficulty proxy:operating cycle

Forecast difficulty proxy:earnings volatility

Forecast difficulty proxy:width of forecast range

Variable Predicted sign Coef. (t-stat.) Coef. (t-stat.) Coef. (t-stat.)

NetSellt � 2.368*** 2.129** 2.076**

(2.87) (2.55) (2.34)Litigationt + �156.018*** �152.367*** �163.362***

(�3.18) (�2.85) (�3.45)Horizont � �0.039*** �0.039*** �0.037***

(�5.08) (�5.10) (�4.84)Returnt + 12.650*** 13.109*** 12.586***

(3.38) (3.52) (3.34)Abs_Surpriset � �474.082*** �463.200*** �456.262***

(�4.19) (�4.12) (�3.96)Bad_Newst + 1.339 1.331 1.458

(1.12) (1.07) (1.26)ROAt�1 ? 0.795 1.749 0.836

(0.18) (0.38) (0.18)ACCRt�1 � �19.568*** �24.369*** �23.368***

(�3.47) (�3.89) (�3.64)LnSalet�1 ? �1.306 �1.329* �1.124

(�1.61) (�1.78) (�1.51)Year indicators Included Included IncludedAdj. R2 (%) 11.0 11.0 11.1N 7236 7236 7236

Variables are defined in Table 1. The dependent variable in the regressions is management forecast error (MFEt). Conservatism is measured by actualC_Scoret�1 in Panel A and ranked C_Scoret�1 in Panel B. All coefficients are multiplied by 1000 for expositional purposes. t-Statistics (in parentheses) arebased on standard errors using two-way clustering by firm and year to control for both time-series and cross-sectional dependence.




correctly because of business uncertainty, they are less likely to accurately incorporate the implications of historical conserva-tism for current earnings and issue optimistic forecasts. Accordingly, managers’ forecast difficulty plays an important role intheir failure to adjust their earnings forecasts fully for the effect of conservatism.

4.3. Management opportunism, litigation risk, and the relation between conservatism and management forecast errors

Table 6 reports the results of the effect of managers’ opportunistic incentives and their firms’ litigation risk on the relationbetween historical conservatism and management forecast errors (H3 and H4). Results in column (1) and column (2) arebased on actual and ranked C_Scoret�1, respectively. Again, we find that, consistent with H1, the coefficients on C_Scoret�1

are significantly negative. However, none of the coefficients on the interaction terms between C_Scoret�1 and our three prox-ies for management opportunistic incentives (FINt, MAt, and NetSellt) are significant. Similarly, we find that the coefficientson the interaction terms between C_Scoret�1 and litigation risk (Litigationt) are not significant.11

Overall, these results do not support H3 and H4. Managers’ tendency to underestimate the effect of conservatism on cur-rent-year earnings in their earnings forecasts does not seem to be heightened by management opportunistic incentives toissue more upward biased forecasts or be mitigated by their litigation concerns to issue less upward biased forecasts.

5. Conclusion

We investigate whether management earnings forecasts fully incorporate information in historical conservatism byexamining the association between Khan and Watts (2009) conservatism measure for the previous year, C_Scoret�1, andmanagers’ earnings forecast errors for the current year. We find that management earnings forecasts are more optimisticfor firms with greater accounting conservatism in the previous year. To understand the possible reasons for this conserva-tism-related optimistic bias, we examine whether the negative association between management forecast errors and con-servatism is positively related to managers’ difficulty predicting earnings and managers’ opportunistic incentives andnegatively related to firms’ litigation risk. We find that managers’ conservatism-related optimistic bias increases, to variousextent, with the firms’ operating cycles, earnings volatility, and the width of forecast range but does not change with proxies

11 All results reported in Table 6 remain similar when Ope_Cyclet�1, Earn_Volt�1, and MFE_Widtht are added as additional explanatory variables in theregressions to control for their possible main effects on management forecast errors.

Table 6Regression results of the effect of management opportunism and litigation risk on the relation between conservatism and management forecast errors.

(1) (2)Based on actual C_Scoret�1 Based on ranked C_Scoret�1

Variable Predicted sign Coef. (t-stat.) Coef. (t-stat.)

Intercept ? �2.244 12.944(�0.29) (1.53)

C_Scoret�1 � �42.066** �29.352***

(�2.26) (�4.32)C_Scoret�1 � FINt � �7.913 �3.904

(�0.83) (�0.68)C_Scoret�1 �MAt � 5.347 4.331

(1.18) (1.34)C_Scoret�1 � NetSellt � �5.556 �0.836

(�0.91) (�0.23)C_Scoret�1 � Litigationt + 87.034 41.421

(0.29) (0.22)Control variablesFINt � 1.631 2.567

(1.23) (1.01)MAt � 0.886 �0.763

(1.24) (�0.56)NetSellt � 2.804*** 2.611*

(3.45) (1.77)Litigationt + �130.069* �176.360**

(�1.95) (�2.03)Horizont � �0.038*** �0.039***

(�4.91) (�5.12)Returnt + 12.591*** 12.505***

(3.32) (3.33)Abs_Surpriset � �488.599*** �470.080***

(�4.40) (�4.18)Bad_Newst + 1.385 1.353

(1.13) (1.11)ROAt�1 ? 4.461 2.445

(0.90) (0.53)ACCRt�1 � �26.627*** �23.944***

(�3.96) (�3.73)LnSalet�1 ? �0.296 �1.225

(�0.33) (�1.54)Year indicators Included IncludedAdj. R2 (%) 9.9 10.8N 7236 7236

Variables are defined in Table 1. The dependent variable in the regressions is management forecast error (MFEt). Conservatism is measured by actualC_Scoret�1 in column (1) and ranked C_Scoret�1 in column (2). All coefficients are multiplied by 1000 for expositional purposes. t-Statistics (in parentheses)are based on standard errors using two-way clustering by firm and year to control for both time-series and cross-sectional dependence.




for opportunistic incentives or litigation risk. These results suggest that forecast difficulty, rather than managerial opportun-ism, is the primary reason for managers’ failure to incorporate conservatism fully in their earnings forecasts.

This study improves our understanding of the bias in management earnings forecasts and possible reasons for an opti-mistic bias. This study also provides evidence on the relevance of accounting conservatism to earnings prediction. Our find-ing that managers do not fully incorporate the implications of conservatism for earnings into their earnings forecastssuggests that investors and other information users should evaluate management earnings forecasts carefully by consideringthe firm’s accounting conservatism.

Appendix A

A.1. Estimation of litigation risk

Litigation risk (Litigationt) in our analyses is the predicted probability that a firm is sued in the quarter before the man-agement forecast date. Our litigation model as shown in Eq. (A1) is similar to Rogers and Stocken’s (2005). The dependentvariable, Lawsuit, equals 1 for a firm-quarter if the firm is a defendant in a securities class action lawsuit filed in that quarter,and zero otherwise. The class action filing data from 1996 through 2008 are obtained from the Stanford Securities Class Ac-tion Clearinghouse website.


PrðLawsuit ¼ 1Þ¼ Gða0 þ a1Sizeþ a2Turnoverþ a3Betaþ a4CumRetþ a5StdRetþ a6Skewnessþ a7MinRet

þ a8Bio—technologyþ a9ComputerHardwareþ a10Electronicþ a11Retailing

þ a12ComputerSoftwareþ eÞ; ðA1Þ

We measure the explanatory variables over each firm-quarter. Size is the natural logarithm transformation of the averagemarket value of equity. Turnover is the average daily trading volume deflated by the number of shares outstanding. Beta isthe slope coefficient from regressing daily returns on CRSP’s equal-weighted index. CumRet is the cumulative daily raw re-turns. StdRet is the standard deviation of the daily raw returns. Skewness is the skewness of the daily returns. MinRet is theminimum of the daily returns. Four indicator variables for high-risk industries are included because prior research finds thatfirms in these industries are more likely to be sued. 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) industries, respectively.

Our litigation model is estimated using 30,255 firm-quarter observations with quarterly earnings available on First Calland stock returns available on CRSP during the 1996–2008 period. The estimation results are reported in Table A1. As shown,the coefficients on Size, Turnover, CumRet, StdRet, Skewness, and MinRet are significant with the predicted signs. The pseudoR2 for our litigation model is 18.22%, indicating a reasonably good model fit.

Table A1Estimation of litigation risk.

Variable Predicted sign Coefficient p-Value

Intercept ? �13.321*** 0.000Size + 0.340*** 0.000Turnover + 49.545*** 0.000Beta + �0.041 0.138CumRet � �0.823*** 0.000StdRet + 5.252** 0.027Skewness � �0.184*** 0.000MinRet � �5.015*** 0.000Bio-technology + 0.045 0.772ComputerHardware + 0.062 0.682Electronic + �0.077 0.436Retailing + �0.058 0.652ComputerSoftware + 0.095 0.283McFadden pseudo R2 (%) 18.22N 230,255

The extreme values of Turnover, Beta, and StdRet are winsorized at the 1st and 99th percentiles.** Significance at the 5% levels.


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