The Confirmatory Role of Mandatory Accounting:

Accounting as a Disciplinary Mechanism for

Managers’ Estimates and Analysts’ Forecasts

Yoel Beniluz

Rutgers University

April, 2005

Comments Welcome yoel@business.rutgers.edu

(732) 445-5107

I am grateful for the insightful comments of Ray Ball, Sasson Bar-Yosef, Bo Becker, Zahi Ben-David, Daniel Bens, Phil Berger, Valentin Dimitrov, Ellen Engel, Douglas Hanna, Rachel Hayes, Feng Li, Steve Monahan, Joseph Piotroski, Darren Roulstone, Haresh Sapra, Jagadeesh Sivadasan, Douglas Skinner, Abbie Smith, Kendrew Witt, and Franco Wong.

The Confirmatory Role of Mandatory Accounting: Accounting as a Disciplinary

Mechanism for Managers’ Estimates and Analysts’ Forecasts

Yoel Beniluz

Rutgers University

ABSTRACT

This paper explores the interaction between information systems. More specifically, the paper

empirically investigates whether higher quality of accounting information enhances the quality of information from other sources, in particular, managers and analysts. In general, managers face incentives to optimistically bias the forward-looking statements they release about their company. Future accounting information will confirm or undermine current voluntary disclosures provided by managers, thereby disciplining managers’ current forward-looking statements. Higher quality of accounting information better reveals systematic optimistic bias in managers’ disclosures and therefore imposes higher costs on such behavior. Similarly, under the view that analysts also face incentives to optimistically bias their information, this confirmatory role of accounting applies to analysts as well. Two measures of accounting quality are used to capture two dimensions of accounting quality that are most relevant for the confirmatory role of accounting: reliability and the extent of earnings management. I predict and find a negative relation between the magnitude of the optimistic bias in managers and analysts forecasts and the measures of accounting quality. The paper also addresses the question of whether the importance of accounting as a disciplinary mechanism increases with the intensity of incentives to optimistically bias the information. The empirical findings are consistent with the hypothesis that the negative relation between the magnitude of the optimistic bias and the accounting quality measures is more pronounced when intense incentives to bias the information are present. Additional empirical tests provide evidence that the 3 days abnormal return around managers’ forecast is significantly lower for firms with lower accounting quality, suggesting that market participants correct, at least partially, for the higher optimistic bias in managers’ forecasts of firms with lower accounting quality. Finally, I find that managers’ decision to issue an earnings guidance is negatively related to the accounting quality measures.

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1. Introduction

The accounting system and its financial reports represent a major source of information to investors,

creditors, employees and other parties contracting with the firm. Yet, there are other important sources of

information such as managers’ voluntary disclosures, security analysts’ reports and forecasts, and the

financial media. In this paper, I explore the interactions between information systems; more specifically, I

investigate the role of the accounting system in enhancing the quality of information from other sources,

in particular, managers and analysts.

An important function of mandatory accounting information is its confirmatory role. Ball (2001)

discusses how high quality mandatory accounting information is complementary to managers’ voluntary

disclosures. High quality accounting information - information that is verifiable and auditable by

independent auditors - disciplines managers’ voluntary forecasts. Gigler and Hemmer (1998) investigate

the confirmatory role of mandatory accounting and use the frequency of mandatory accounting signals to

highlight analytically the difference between the confirmatory role of accounting and accounting as the

“primary” source of information.

The following illustrates the confirmatory role of accounting. In general, managers have incentives to

optimistically bias the information they provide about their company.1 These incentives stem from

managers interest in higher stock prices, compensation contracts, performance evaluations, prestige

concerns, promotion prospects, and future job opportunities. Due to the asymmetry of information

inherent in the manager-shareholders relation, managers possess private information and specific

knowledge about the future prospects and the economic strength of their firm. Based on their private

information and specific knowledge, managers provide information about their company. Some of these

disclosures are about future plans and strategies, and estimates regarding future outcomes, which will

only be observable at some future time. Verifiable and independently audited mandatory accounting

information released in future periods confirms, refutes, corroborates or undermines the past information

provided by managers. Therefore, the confirmation of future accounting reports serves as a disciplinary

mechanism for managers’ forward-looking statements.

The purpose of this study is to empirically investigate the confirmatory role of accounting and to

probe the following two research questions. The first research question is whether higher quality of

1 Consistent with this assertion, Jennings (1987) and Hutton, Miller and Skinner (2003) provide evidence suggesting that managers’ disclosures of bad news are more credible than managerial disclosures of good news. Also consistent with the assertion are the findings in Penman (1980) and Waymire (1984) that the majority of management forecasts are favorable relative to market expectation; the findings in Patell (1976) and Lev and Penman (1990) that on average forecast news is good news; and the finding in Miller (2002) that firms increase discretionary disclosures during periods of increased earnings. Sansing (1992) provides an analytical model that yields a separating equilibrium that predicts these findings.

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accounting information enhances the quality of information provided by managers. The costs involved

with biasing the information limit the magnitude and the frequency of such behavior. Systematic

intentional optimistic bias is harmful for managers’ reputation; it reduces the credibility of future

voluntary disclosures (Stocken [2000]; Williams [1996]), and increases litigation risks.2 An accounting

signal that challenges the honesty of managers’ prior forward-looking statements imposes higher costs on

managers when the signal comes from a higher quality accounting system. In other words, since higher

quality accounting information better reveals the bias in managers’ disclosures, it increases the costs of

such behavior. Therefore, I predict that higher quality accounting information better disciplines managers

and reduces the optimistic bias in their disclosures and estimates regarding the future.

The second research question I investigate is whether the importance of accounting as a disciplinary

mechanism increases with the incentives to bias the information. In the absence of incentives to

optimistically bias the information there is no need for a disciplinary mechanism. On the other hand, in

the presence of intense incentives to optimistically bias the information, it is crucial to have a disciplinary

mechanism. Therefore, I predict that the negative relation between accounting quality and the magnitude

of the optimistic bias is more pronounced when the incentives to bias the information are stronger.

The discussion above raises the issue of why market participants would believe managers’ disclosures

if they know that managers have incentives to optimistically bias their information.3 One possible

explanation is that the costs involved with biasing the information in terms of litigation risks and

reputation give credibility to the information.4

Another issue is why investors cannot simply undo the bias and thereby remove any incentive to bias

the information in the first place. The theoretical literature sheds light on this matter. One branch of this

literature results in a fully revealing equilibrium in which the bias is perfectly removed by the receiver of

the signal. Nevertheless, even in this case, an equilibrium where managers bias their information can

exist. Furthermore, as Stein (1989) illustrates one may observe optimistic bias even when it actually

reduces managers’ utility relative to a situation where they are not biasing. This prisoner’s dilemma

situation arises because investors expect a bias and hence discount managers’ information accordingly;

consequently, managers are forced to bias in order to convey their information. The more efficient 2 For research on managers’ disclosures and litigation see, among others, Francis, Philbrick, and Schipper (1994), Skinner (1994), Kasznik and Lev (1995), Skinner (1997) and Baginski, Hassell and Kimbrough (2002). 3 There is a longstanding literature documenting that managers’ earnings estimates are informative and that the informativeness of managers’ estimates varies with management credibility, the sign of the earnings news, the horizon, the form and venue of the estimates, and supplementary information. See for example, Patell (1976), Penman (1980), Ajinkya and Gift (1984), Waymire (1984; 1985; 1986), Jennings (1987), Pownall and Waymire (1989a), Lev and Penman (1990), Baginski, Conrad, and Hassell (1993), Pownall, Wasley and Waymire (1993), Williams (1996), Bamber and Cheon (1998), and Hutton, Miller and Skinner (2003). 4 See for example, Ajinkya and Gift (1984), and Pownall and Waymire (1989b). Stocken (2000) shows analytically that in a repeated game setting a manager’s concern for the credibility of his future disclosures often results in truthful disclosure.

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situation of no bias is not achieved because when investors do not expect a bias, managers will benefit

from introducing optimistic bias. In another branch of this literature, the resulting equilibrium is not fully

revealing and investors can only undo the bias on average, not perfectly. In these circumstances, Fischer

and Verrecchia (2000) show that managers may be better with the option to bias. Similarly, Guttman,

Kadan, and Kandel (2003) who study analytically the discontinuity of reported earnings, demonstrate that

in their model managers are better off in the pooling equilibrium interval relative to the (fully revealing)

separating equilibrium. They show that a partial pooling equilibrium exists where investors discount

managers’ reports by a constant even though the extent of the earnings management varies within the

pooling interval. Therefore, even if investors can undo the bias on average across firms, they may be

unable to perfectly undo it at the individual firm level, hence, each individual manager may still face

incentives to introduce an optimistic bias.

In this paper, I suggest the existence of the following equilibrium. Managers who face higher costs

when biasing their information introduce smaller optimistic bias. Market participants recognize that there

is a cross-sectional variation in the cost of biasing, and thus they expect a cross-sectional variation in the

bias as well, and discount managers’ information accordingly. One important factor of the cross-sectional

variation in the cost of biasing is the confirmatory role of accounting. To explore this avenue, I examine

how the market reaction to managers’ forecasts varies with the quality of accounting information. I find

that the three days abnormal return around managers’ earnings forecasts is significantly lower for firms

with lower accounting quality. This result is consistent with market participants correcting, at least

partially, for higher optimistic bias in managers’ forecasts of firms with lower accounting quality.

Security analysts represent another important source of information. Research in the financial and

accounting literature suggests that analysts also face incentives to produce optimistic forecasts because it

helps maintain good relationships with firms’ management and generates brokerage fees and investment

banking businesses.5,6 Under this view, the confirmatory role of accounting applies to analysts as well.

Therefore, if the assertions that analysts have incentives to optimistically bias their information are right,

then I also expect higher quality of accounting information to discipline analysts and reduce the

magnitude of the optimistic bias in their forecasts and reports.

To empirically explore the two research questions of this study, I investigate the relation between two

measures of accounting quality and the optimistic bias in managers’ estimates and analysts’ forecasts of

annual earnings per share. Obviously, accounting information serves a variety of users and purposes. An 5 See for example, Chen and Jiang (2003), Das, Levine, and Sivaramakrishnan (1998), Dugar and Nathan (1995), Francis and Philbrick (1993), Hong and Kubik (2003), Irvine (forthcoming), Lim (2001), Lin and McNichols (1998), Lin, McNichols and O’Brien (2003), and Michaely and Womack (1999). 6 There are increasing concerns among market participants and regulatory authorities regarding the conflict of interests of brokerage houses analysts. Recently ten of the biggest brokerage firms settled with state and federal regulators and agreed to pay $1.4 billion over analysts’ conflict of interest surrounding stock recommendations.

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accounting feature that increases the usefulness of the accounting numbers for one purpose or group of

users may reduce their usefulness for another purpose or group of users. The dimensions of accounting

quality that are most important for the confirmatory role of accounting are the reliability feature and the

extent of earnings management. When the accounting numbers are less verifiable and hence less auditable

by an independent auditor, managers can more easily manage the numbers to fit their forecasts, thereby

lowering the cost of biasing the forecasts.7 In addition, when the realized earnings numbers are generated

from a system with greater degree of earnings management they are less credible, and hence impose

lower costs on analysts and managers for a given forecast error or systematic optimistic bias. Therefore,

for the purpose of this study, I measure the inverse of accounting quality as the extent to which a firm

engages in earnings management, or has the flexibility to engage in earnings management. The first

measure for accounting quality is an accruals based measure. I proxy for the extent to which a firm

engages in earnings management by taking the average over the last five years of the absolute value of

discretionary total accruals. Discretionary total accruals are estimated as the residual from the yearly

cross-sectional regressions of the modified Jones model. I find a significant positive association between

the measure of the inverse of accounting quality and managers’ and analysts’ optimistic bias. The

relationship remains significantly positive and economically important when controlling for other

possible explanations for the bias and under several robustness checks. However, this finding should be

interpreted cautiously. The accruals based earnings management proxy may also capture fundamentals of

the firm that are not related to earnings management. The finer tests of the second research question,

discussed next, mitigate this concern.

To reinforce the interpretation of the results with the accruals based measure, I use a second measure

of accounting quality which is based on restatement of financial statements. The second measure is an

indicator variable, receiving the value of one if the firm had restated its financial statements and zero

otherwise. Only restatements that were a result of accounting irregularities or errors, and not simply

because of a legitimate change in accounting method, were considered. The results with the restatement

indicator variable are consistent with those of the accruals based measure.

In order to address the second research question, that is, does the importance of accounting as a

disciplinary mechanism increase with the incentives to bias, I explore two cases of variation in the

incentives to bias the information: forecast horizon and activity of external financing. I argue that there

are stronger incentives to optimistically bias forecasts of earnings that will be realized further in the future

(referred to as a longer horizon). First, a longer horizon allows a longer period to reap the benefits from

7 Kasznik (1999) provides evidence consistent with managers managing earnings upward in order to meet their prior high earnings forecasts. In this paper, I argue that when managers know they can more easily manage earnings, they will issue more optimistic forecasts because the costs of such behavior are lower in these cases. In the empirical tests, I control for the relation suggested by Kasznik (1999).

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the bias before it is revealed. Second, a longer horizon reduces the costs of biasing because it would be

easier to blame changes in the economic environment and unexpected events for the optimistic error.

Third, people would tend to pay closer attention to more recent information provided by managers and

analysts rather than to information provided long ago. Therefore, I predict that the relationship between

the accounting quality measures and the optimistic bias is more pronounced for longer horizons. The

empirical findings are consistent with this prediction.

External financing activity provides another opportunity to explore the variation in incentives to

introduce an optimistic bias. Firms that issue equity or bonds have stronger incentives to be optimistic in

order to receive higher considerations for their securities. Conversely, firms that repurchase their stock

face a counter incentive to avoid optimistic bias in order to buy their stock at a lower price. Therefore, I

predict that the relation between the accounting quality measure and the optimistic bias is more (less)

pronounced for firms that increase (decrease) their external financing. The comparison of the relation

across groups of firms with different level of external financing activities yields results that are in general

consistent with this prediction.

The confirmatory role of accounting also raises the following question: How managers’ decision to

provide voluntary disclosures is affected by the ability of the accounting system to serve as a disciplinary

mechanism? Therefore, I investigate how managers’ decision to issue long-horizon annual earnings

guidance is related to their ability to optimistically bias the forecast. I define long-horizon to mean more

than 180 days prior to the earnings announcements. On one hand, lower accounting quality increases

managers’ abilities to introduce an optimistic bias, and hence manager may be more likely to issue an

earnings guidance. On the other hand, when the accounting quality is lower, the credibility of managers

forecasts is lower, which reduces the benefits and the likelihood of issuing an earnings guidance. The

empirical findings are consistent with the idea that managers would be more likely to issue an earnings

guidance when the accounting quality is lower.

The important ongoing debate on relevance versus reliability motivates this study. Mandatory

accounting information and managers’ voluntary disclosures are complementary and possess different

attributes. While the accounting numbers should be based on observable outcomes, managers’ disclosures

are based also on their expectations and unobservable private information and knowledge. This paper

attempts to contribute to the relevance versus reliability debate by suggesting that the scope of the

consideration of the tradeoff between relevance and reliability should not be restricted to the accounting

information set, but to encompass the total set of information available. In particular, the tradeoff

consideration should include the information provided by managers and analysts. For example, an

increase in the reliability of an accounting item may reduce its relevance, but at the same time may

increase the credibility of relevant and timely information provided by managers.

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The central role of managers’ disclosures and analysts’ reports and forecasts also motivates this

paper. Managers and analysts provide information that is a crucial element of the process of capital

formation and the efficient functioning of the financial markets. Therefore, it is important to study how

the intensity of incentives faced by managers and analysts affects the quality of information they provide,

and how high quality accounting information could discipline other providers of information, particularly

managers and analysts.

This study contributes to the accounting literature by providing empirical evidence on the

confirmatory role of accounting and on accounting as a disciplinary mechanism. This is of special

importance in light of the large accounting literature that explores the value relevance and information

content of accounting information on the one hand, and the sparse accounting literature that investigates

the confirmatory role of accounting on the other hand. An accounting signal could have little or even no

correlation with stock returns because prior managers’ disclosures or analysts’ reports have already

provided the information to market participants. Yet, the accounting signal could be of great importance,

since in its absence the prior managers’ disclosures or analysts’ reports would not be credible. Providing

evidence on the confirmatory role of accounting highlights that researchers should consider and evaluate

the role of accounting not only as the primary source of information, but also its confirmatory role and the

discipline that it imposes on other sources of information. Put another way, one also needs to consider the

effect of the accounting system on the total level of information from all sources as opposed to accounting

as the primary source of information.

The study also contributes to the literature on managers’ disclosures and analysts’ forecast bias by

providing empirical evidence on the cross-sectional variation in managers’ and analysts’ forecasts bias

with respect to their incentives and the accounting quality measure. Finally, the paper highlights the

importance of the issue of externalities between information systems. For example, when standard setters

evaluate the costs-benefits of an accounting procedure, the effect of the procedure on the quality of

information from other providers should be part of the considerations.

The paper proceeds as follows. The next section reviews the literature and develops the hypotheses.

Section three discusses the research design. Data and univariate analyses are presented in section four.

The fifth section contains the regressions results while the sixth section describes the robustness tests.

Section seven concludes.

2. Literature Review and Hypotheses Development

2.1 Literature Review

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The literature on the confirmatory role of accounting and on accounting as a disciplinary mechanism

is scarce. Ball (2001) discusses the criteria for efficient accounting and corporate disclosure. One of the

criteria he illustrates is: “Accounting and non-accounting disclosures interact: they are complements.

Routine reporting of accounting information based on independently observable outcomes provides an

important reality check, particularly for managers making non-financial disclosures (which generally are

a function of their unobservable expectations).” Gigler and Hemmer (1998) explore analytically the

interaction between mandatory accounting and manager’s voluntary disclosures. They show that

increasing the frequency of mandated disclosures may eliminate manager’s voluntary disclosures and

reduce the informational efficiency of prices. This result highlights the importance of considering the

externalities between information systems. Kanodia and Lee (1998) provide a theoretical study involving

the disciplinary role of periodic performance reports on investment decisions. In their model they show

that the anticipation of performance reports such as accounting earnings disciplines managers’ investment

incentives and allows the firm’s observable investment to credibly signal management’s prior

information.

Similarly, Lundholm (1999, 2001) suggests reporting ex post accuracy of a firm’s prior estimates and

argues that such a check can be sufficient to ensure that the voluntary disclosures are credible most of the

time. Hirst, Jackson, and Koonce (2003) use an experimental setting to provide evidence on this proposal

and conclude that information about previous estimate accuracy is useful to investors.

Finally, Rogers and Stocken (2003) provide empirical evidence on the relation between managers’

forecasts bias and managers’ incentives interacted with the difficulty to detect managers’

misrepresentations. Their measure of the difficulty to detect misrepresentation is a combination of

analysts disagreement, the standard deviation of analyst forecast error over the prior five years, return

volatility, bid-ask spread and the width of the range of managers’ estimate. Note that this difficulty

measure captures uncertainty and volatility which indeed reduce the cost of biasing. In this paper, the

focus is on the role of accounting in disciplining managers; therefore, I control for uncertainty and

volatility in the regression analyses.

2.2 Hypotheses Development

In contrast to the papers discussed above, in this study, the confirmatory role of accounting is

explored empirically. In general, when managers issue forward-looking statements based on their

unobservable private information and specific knowledge, they face incentives to optimistically bias the

information. These incentives stem from managers’ interest in higher stock price, compensation contracts,

future jobs opportunities, prestige concerns, and promotion prospects. Certainly, overly optimistic

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forecasts of information that will be eventually revealed by the release of the accounting reports affect the

stock price and the views of market participants and the board of directors only temporarily. Nevertheless,

at each point in time managers face incentives to be overly optimistic about future periods (e.g. Stein

[1989]). Compensation contracts and evaluations, takeover defenses, and the use of shares for mergers

and acquisitions drive managers to inflate their stock price even if it is temporary. Obviously, at times,

other factors are present that induce counter incentives to pessimistically bias the information, e.g., prior

to stock repurchases, management buyouts (Perry and Williams [1994]), or granting of stock options so

the strike price is set at a lower level (Aboody and Kasznik [2000]; Yermack [1997]). Nevertheless, in

most cases the incentives are to introduce an optimistic bias. Supporting this view, Jennings (1987) and

Hutton, Miller and Skinner (2003) provide evidence consistent with the idea that managerial forecasts of

good news are inherently less credible than managerial forecasts of bad news.

However, biasing the information is costly. It is harmful to managers’ reputations and increases

litigation risks. In fact, without such costs the signal or information would not be credible. Furthermore,

as Spence (1973) illustrates, it is the variation in the cost of the signal that makes the signal useful.

Managers anticipate that in future periods the released accounting reports will corroborate or contradict

the forward-looking statements they issue today. Therefore, future accounting reports discipline

managers’ current disclosures because they impose costs on biasing the information. Moreover, variation

in the quality of accounting information leads to variation in the cost it imposes on biasing the

information. An accounting signal that challenges the honesty of managers’ prior disclosures imposes

higher costs when it comes from a higher quality system because it better reveals the bias.8 Obviously,

some firms operate in more uncertain environments, which would affect both the ability of mandatory

accounting and managers to provide good information about the firm; however, without the incentives to

bias this should affect the accuracy of managers’ information not the bias.

The accounting system is not the only source of costs for biasing the information. In general, in more

volatile and uncertain environments it is easier to hide intentional bias, lowering the costs on such

behavior. While this is an interesting issue on its own, the focus of this paper is on the role of accounting.

Therefore, I control for general volatility and uncertainty. The first hypothesis of the paper, stated in the

alternative form, follows:

8 I am silent about the cross-sectional variation in the benefits from optimistically biasing the information because the relation between the variation in the benefits and the confirmatory role of accounting is not clear. On one hand, lower quality of the accounting system and its ability to serve its confirmatory role reduce the credibility of managers’ disclosures and the benefit from biasing. On the other hand, when the accounting quality is low the information set available is smaller and the market depends more on managers information, which increase the benefit from biasing.

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H1: Controlling for other factors, managers’ optimistic bias is negatively related to the quality of

the firm’s accounting information.

Under the null hypothesis, if accounting information has no role in disciplining managers’

disclosures then there should be no relation between accounting quality and managers’ optimistic bias,

after controlling for other factors that affect the cost of biasing the information.

The next two hypotheses address the second research question: does the role of accounting as a

disciplinary mechanism become more important with the intensity of the incentives to optimistically bias

the information? Absent incentives to bias the information there is no need for a disciplinary mechanism,

while it is crucial to have one when intense incentives are present. Variation in forecast horizons and

external financing activities provide interesting settings to investigate this question. For several reasons, I

argue that there are stronger incentives to optimistically bias information about outcomes that will be

realized further in the future. For instance, there is not a strong incentive to bias estimates of earnings that

will be announced in one month. First, such bias would be quickly revealed, reducing the benefits from

the bias. Second, it would be hard to blame unexpected changes in the economic environment for the bias.

Third, the cost of such bias, in terms of reputation and litigation risk, is much higher. Fourth, people

would tend to focus more on recent managers and analysts information rather than things they said a long

time ago. Finally, managers’ ability to bias short horizon forecast of annual earnings per share is smaller.

There is simply less room to optimistically bias annual earnings per share when the results of the first,

second and third quarters are already known. In fact, there is growing empirical evidence that in recent

years firms’ management manage analysts’ expectations in this short-term interval in order to achieve a

positive surprise on the earnings announcement date, creating a pessimistic bias rather than an optimistic

bias (e.g. Bartov, Givoly, and Hayn [2002], Soffer, Thiagarajan and Walther [2000], Chan, Karceski and

Lakonishok [2003]). Using the last analyst’s forecast of quarterly earnings, Brown (2001) documents a

temporal shift from slightly optimistic forecasts errors in the period 1984-1990, to no bias in the 1991-

1993 period, to slightly pessimistic errors in the 1994-1999 period. In addition, Kang, O’Brien, and

Sivaramakrishnan (1994) and Lim (2001) report that analysts’ forecast bias decreases as the forecasts are

made closer to the announcement dates. The second hypothesis stated in the alternative form follows:

H2: The negative relation between managers’ optimistic bias and the quality of accounting

information is more pronounced for estimates of earnings that will be realized further in the

future.

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Under the null hypothesis, if accounting has no role in disciplining managers’ disclosures then the

relation between managers’ optimistic bias and the quality of accounting information should not vary with

the horizon of the estimate.

External financing activity provides another setting to explore variation in incentives to optimistically

bias the information. Trying to maximize the proceeds for their issues, firms that issue stock or bonds

have stronger incentives to optimistically bias information about their economic strength and

performance. On the other hand, firms that repurchase their stock have incentives to avoid optimistic

disclosures in order to accomplish the repurchase at a lower price. The third hypothesis is then:

H3: The negative relation between managers’ optimistic bias and the quality of accounting

information is more (less) pronounced for firms that increase (decrease) their external

financing.

Under the null hypothesis, if accounting has no role in disciplining managers’ disclosures then the

relation between managers’ optimistic bias and the quality of accounting information should not vary with

external financing activity.

The next hypothesis addresses the issue of whether market participants appreciate that managers’

long-horizon (more than 180 days prior to earnings announcement) annual earnings forecasts of firms

with lower accounting quality are more optimistically biased. Given that the accruals based measure is

computed using public information, and assuming that market participants were aware of the lower

accounting quality of firms that had to restate their financial statements, I predict that market participants

will correct for the higher optimistic bias of firms with lower accounting quality. This leads to the fourth

hypothesis:

H4: The abnormal return in the three-day window around managers’ long-horizon forecasts of

annual earnings is negatively related to the quality of the firm’s accounting information.

The next hypothesis deals with the relationship between managers’ decision to issue long-horizon

annual earnings guidance and the accounting quality of their firm. I do not have a prediction for this

relationship a priori. On one hand, lower quality of accounting information enables managers to be more

aggressive in their optimistic bias. This would increase managers’ likelihood to issue an earnings

guidance if they believe that market participants can “back out” the bias only on average, not perfectly.

As Fischer and Verrecchia (2000) illustrate under this condition managers may benefit from the option to

bias. Another explanation for the negative association between the accounting quality and managers’

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decision to issue earnings guidance is not based on managerial opportunism. That is, managers may

simply respond to higher demand for managers’ private information when the quality of accounting

information is lower, and the set of information available to market participants is lower. On the other

hand, lower quality of accounting information reduces the credibility of managers’ earnings guidance, and

hence reduces the benefits and the likelihood of issuing an earnings forecast. Due to these competing

explanations the fifth hypothesis is stated in its null form:

H5: there is no relationship between managers’ decision to issue long-horizon annual earnings

guidance and the quality of the firm’s accounting information.

It is true that managers may decide to provide voluntary disclosure for the “right” reasons, for

instance, to mitigate the information asymmetry problem, thereby reducing the firm’s cost of capital; yet,

once they endorsed a policy of voluntary disclosure they face incentives to behave opportunistically and

to optimistically bias their disclosure. Nevertheless, it is interesting to explore how the ability of

accounting information to perform its role as a disciplinary mechanism is related to managers’ decision to

issue an earnings guidance?

2.3 The Confirmatory Role of Accounting: the Link to Analysts

Research on analysts’ forecast properties suggests that analysts also face incentives to optimistically

bias their information. Analysts’ optimistic bias helps generate investment banking businesses and

brokerage fees, and maintain a good relationship with management. For example, Das, Levine, and

Sivaramakrishnan (1998) and Lim (2001) document positive analysts’ forecast bias and hypothesize that

analysts optimistically bias their forecasts in order to maintain a good relationship with management,

thereby getting superior access to managers’ private information. Francis and Philbrick (1993) suggest

that analysts optimistically bias their forecasts to repair their relationships with management following

sell recommendations. Dugar and Nathan (1995), Lin and McNichols (1998), and Michaely and Womack

(1999) provide evidence that analysts issue more positive predictions for firms with which their brokerage

house has investment banking businesses. Lin, McNichols, and O’Brien (2003) document that affiliated

analysts downgrade recommendations more slowly than unaffiliated analysts. And Hong and Kubik

(2003) document that after controlling for accuracy analysts who issue relatively optimistic forecasts are

more likely to experience favorable job separation, and that this effect is stronger for underwriting

brokerages and during the boom market of the late 1990s.

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Under the view that analysts have incentives to optimistically bias their information, the confirmatory

role of accounting applies to analysts as well. Note that according to the investment banking and access to

management information arguments, analysts’ incentives stem from analysts’ need to please managers.

Hence, managers own incentives have an effect on analysts’ incentives.

However, whether analysts’ bias is a result of opportunistic behavior or cognitive bias is still debated

in the literature (e.g. Chen and Jiang [2003]). In addition, Gu and Wu (2003) argue that if analysts’

objective is to minimize the mean absolute forecast errors, then the optimal forecast is the median. They

also demonstrate that the median error of forecasts made up to 90 days prior to earnings announcement is

approximately zero. Similarly, Basu and Markov (2003) find no evidence of inefficiency with least

absolute deviation regressions for annual forecasts made up to 60 days prior to earnings announcement.

Both papers, however, look at short-term forecasts for which the incentives to bias are much weaker.

In this paper, I rely on the evidence in the literature that analysts have incentives to optimistically bias

their information, and use it as a maintained assumption to empirically investigate the confirmatory role

of accounting with respect to analysts.

Testing the confirmatory role of accounting with both managers’ estimates and analysts’ forecasts is

not redundant. Each sample has its own advantages and shortcomings. Obviously, the manager sample is

more closely related to the conventional notion of the confirmatory role of accounting. It is also free from

the joint-hypotheses issue in the analyst sample, in which I use a maintained assumption that analysts

have incentives to optimistically bias their information. On the other hand, the manager sample is much

smaller and concentrated in the 1997-2002 period. In addition, it might suffer from an endogenity issue

because managers affect both the magnitude of the bias and the measures of accounting quality. Self

selection concern is also more serious in the manager sample.9 Regarding the analyst sample, it covers the

period 1983-2002 and its vast data availability mitigates the measurement error problem (discussed

below) of the accounting quality measure. Furthermore, the endogenity concern is mitigated since

analysts cannot directly affect the accounting numbers. Finally, and most important, both managers and

analysts are important providers of information, and provide interesting settings to study the relation

between information systems.

3. Research Design

3.1 The Measures for Accounting Quality

9 For example, Lev and Penman (1990) provide evidence that firms with good news issue estimates to separate themselves from firms with “worse news.”

13

Accounting information serves a variety of users and different purposes. An accounting feature that

increases the usefulness of the accounting numbers for one purpose or group of users may decrease the

usefulness of the accounting numbers for another purpose or group of users. Consequently, researchers

have motivated and used several accounting quality measures, which capture different dimensions of

accounting quality. The accounting quality dimensions that are important for the confirmatory role of

accounting are reliability and the extent of earnings management. The reliability of the accounting

numbers is lower when they are less verifiable and thus less auditable by an independent auditor. Lower

reliability reduces the costs imposed on managers when biasing their forward-looking statements because

the future accounting numbers can be more easily manipulated to fit managers prior statements. In

addition, accounting numbers with a higher degree of earnings management are less credible and hence

impose lower costs on managers and analysts for a given error or systematic optimistic bias. Therefore,

for the purpose of this study, I use the extent of earnings management, or the flexibility to engage in

earnings management, as a measure of the inverse of accounting quality; the higher the proxy of the

extent of earnings management, the lower the quality of the accounting information.

Notice that the earnings management proxy aims to capture both the extent of earnings management

and the reliability feature. Holding other things equal, earnings management and reliability should be

negatively correlated because the independent auditor task can be done more thoroughly when it is based

on more verifiable outcomes.

Independence is an essential element of the confirmatory role of accounting because without it the

realization of the actual accounting signal can be manipulated which impairs its ability to reveal the bias.

This raises the concern that when the accounting quality measure is low there is more measurement error

in detecting the bias. However, this would work against finding supportive results.

An important feature of the earnings management proxy as a measure for accounting quality is that it

only relies on accounting information, which in turn depends on observable past outcomes when possible,

with the exception of conservatism. This feature is important for the crux of the confirmatory role of

accounting idea. Other measures of accounting quality will not do as well with regard to the confirmatory

role of accounting notion. For example, the association between stock returns or prices and earnings, or

the timeliness index as constructed in Bushman, Chen, Engel, and Smith (forthcoming) as a measure for

the accounting confirmation ability, will not fit well. The reason is that these measures are affected by the

extent to which accounting incomes capture changes in future cash-flow and not by their ability to

confirm past forward-looking statements. For the same reason, earnings conservatism (e.g. Ball and

Shivakumar [2002]) does not seem to capture the accounting quality aspects that are important for the

confirmatory role of accounting.

14

The use of the extent of earnings management as an accounting quality measure is also supported by

the growing frequency and magnitude of earnings management in most of the accounting scandals in the

last several years, as well as in restatements of accounting statements from prior periods. Earnings

management is a single but nevertheless important factor of accounting quality.

The following definitions describe the meaning of the term earnings management in this paper. The

definition of Schipper (1989): “... A purposeful intervention in the external financial reporting process,

with the intent of obtaining some private gain (as opposed to, say, merely facilitating the neutral

operation of the process)...” (emphasis added). And that of Healy and Wahlen (1999): “Earnings

management occurs when managers use judgment in financial reporting and in structuring transactions to

alter financial reports to either mislead some stakeholders about the underlying economic performance of

the company, or to influence contractual outcomes that depend on reported accounting numbers”

(emphasis added).10

The above definitions should be distinguished from the notion that managers can use judgment and

intervene in the financial reporting process to better convey their private information (Watts and

Zimmerman [1986]).

In this study, two measures are used to capture accounting quality. The first measure is based on

discretionary accruals. I employ a modified version of the Jones Model (Jones [1991]) to estimate the

non-discretionary level of total accruals. The fitted values from the modified Jones model represent the

non-discretionary level of total accruals, while the residuals capture the level of discretionary total

accruals. The model is estimated using cross-sectional regressions in each year and industry with more

than six observations (industries are defined using two-digit SIC codes). Dechow, Sloan and Sweeney

(1995) compare five earnings management models, and show that the modified Jones model is the best

among the five models in detecting earnings management. Similarly, Guay, Kothari, and Watts (1996)

compare the same five models and conclude that only the Jones model and the modified Jones model

appear to have the potential to provide reliable estimates of discretionary accruals.

Over the life of the firm, accounting earnings is equal to economic profits, hence, positive abnormal

accruals in some periods should reverse to negative abnormal accruals in other periods. Therefore, the

proxy for the extent to which a firm engages in earnings management, ABS_DTA, is the average of the

absolute values of discretionary total accruals over the last five years. When less than five years are

available, I use all available years.11 This discretionary accruals based measure of earnings management is

the first proxy for a firm’s accounting quality.

10 Dechow and Skinner (2000) discuss different views and several definitions of earnings management. 11 In the manager sample, for about 80% of the firm-announcements observations, ABS_DTA, the measure for earnings management, is computed using 5 years of data. And at the firm level, the average number of years available is 4.2 with a median of 5 years. In the analyst sample, for 70% of the firm-year observations, ABS_DTA is

15

I use averages over the last five years rather than the concurrent discretionary total accruals for two

reasons. First, averages over five years reduce the noise in the measure for earnings management.

Reducing the noise in the discretionary accruals measure is important because the model estimates

discretionary accruals with considerable imprecision (Guay, Kothari and Watts [1996]). Second, the

purpose of the measure is to capture the firm overall level of earnings management or its flexibility to

engage in earnings management, and not to detect whether the firm engages in earnings management in a

specific direction at some point in time as a result of incentives.

To reinforce the results with the accruals based measure, a second measure of accounting quality is

used. The second measure, RESTATEMENT, is an indicator variable which receives the value of one if

the firm had restated its financial statements in the period starting form 1997 and up to the year when

managers’ forecast was made. Only restatements that are a result of accounting irregularities or an error

are considered. Restatements due to a legitimate change in accounting method are ignored. After the

restatement year, it is likely the firm is “under fire” from investors and regulators which limit managers’

ability to bias their information and may lead to improvement in the firm’s accounting quality. Therefore,

after the restatement year the RESTATEMENT indicator variable is equal to zero. The advantage of the

restatement indicator variable is that it provides a clearer indication of lower accounting quality. When a

firm reaches the point where it needs, or is forced by the auditor, to restate its financial statements due to

an “error” or “accounting irregularities,” one can be more confident that there is a problem with the way

the accounting numbers are generated. The disadvantage is its limited variation with only 8% of the firm-

announcement observations receiving a value of one.

A caveat is in order. Guay, Kothari and Watts (1996) suggest that caution should be exercised in

interpreting research on managements’ use of accruals motivated by opportunism. As mentioned above,

the modified Jones model detects earnings managements with considerable imprecision. The model may

capture fundamentals of the firm that are not related to earnings management (e.g. Hribar and Collins

[2002]). To the extent that these fundamentals are not random noise and are related to the forecast errors,

it could induce the association between the bias and the accruals based accounting quality measure.

Moreover, managers may use discretion in accruals to better convey (truthfully) their private information

(Watts and Zimmerman [1986], Sankar and Subramanyam [2001]). To address this concern, I add the

signed value of discretionary accruals in the year when the forecast was made as a control variable. If the

forecast error results from unexpected fundamentals then the signed value of discretionary total accruals

of the forecasted year should capture this effect. In addition, the empirical tests of the incentive

hypotheses further alleviate this concern. If the measure simply captures fundamentals and is not related

computed using 5 years of data, and only 16% of the observations are based on one or two years. At the firm level, the median is 4 years and the average is 3.6 years.

16

to earnings management, then the relation between the measure and the optimistic bias should not vary

with the intensity of the incentives in the predicted directions. Furthermore, this supportive pattern is

provided by two different kinds of variation in incentives, horizon and external financing. Finally, the

consistent set of results with the restatement indicator variable supports the interpretation of the results

with the accruals based measure.

3.2 The Measure for the Optimistic Bias

The next main variable used in this study is the optimistic bias of managers’ estimates and analysts’

forecasts. For managers’ estimates, I use all available annual earnings per share estimates on the First

Call, Company Issued Guidelines (CIG) database, made up to three years ahead, and partition them into

three groups by forecast horizon. The first group, HORIZON1, includes all estimates made up to 180 days

prior to the earnings announcement dates, the second group, HORIZON2, contains estimates made more

than 180 days prior to the earnings announcement date and up to 360 days. Finally, the third group,

HORIZON3, comprises the estimates made more than 360 days ahead of the earnings announcement date

but less than three years ahead. However, 88% of the observations in the third group are in the window of

361-540 days prior to the earnings announcement date.

As for analysts’ forecasts, I use forecasts of annual earnings per share from the I/B/E/S database and

partition them into four groups. The first group, HORIZON1, includes forecasts made up to 90 days prior

to the earnings announcement date. Note that although these are annual forecasts, the results of the prior

quarters are already known by the release time of the forecasts, so these forecasts represent, in general,

forecasts of the current quarter results. The second group, HORIZON2, consists of forecasts of annual

earnings per share of the current fiscal year made after the announcement of last year earnings, and before

the announcement of the first quarter earnings of the current fiscal year. This window assures that when

the forecast is made the analyst knows the earnings of last year but does not know the results of the first

quarter of the forecasted year. The third group, HORIZON3, comprises forecasts made in the same

window of time as the second group but for the next fiscal year, as opposed to the current fiscal year in

the second group. Finally, the fourth group, HORIZON4, contains forecast of three years ahead made in

the same window as the forecasts of the second and third groups. For all groups, if an analyst issued more

than one forecast in the window frame, the last one is considered.

This partition is done in order to address the second hypothesis (H2) that the incentives to introduce

an optimistic bias increase with the horizon because as the horizon gets shorter, the benefits from the bias

are lower and, at the same time, the costs are higher. The measure for the level of optimistic bias for each

firm is constructed as follows. For the manager sample, the earnings estimate error for firm i in year t is:

17

1,

,,,

−

−=

ti

tititi P

AEBIAS

where BIASi,t is the scaled error for firm i in year t, Ei,t is the manager’s estimate for firm i in year t,

Ai,t is the actual value for firm i in year t, and Pi,t-1 is the stock price of firm i at the end of the prior fiscal

year. Then, for each firm and horizon the average of the scaled error is taken.

Regarding the analyst sample, the forecast error for firm i in year t is defined as:

1,

,,,_

−

−=

ti

tititi P

AFErrFor

where For_Erri,t stands for forecast error for firm i in year t, Fi,t is the analyst forecast for firm i in

year t, Ai,t is the actual value for firm i in year t, and Pi,t-1 is the stock price of firm i at the end of the prior

fiscal year.12 The mean and the median of the forecast errors are calculated for each firm-year and

horizon. Finally, the bias measures for each firm and horizon, Ave_Bias and Med_Bias, are defined as:

∑=

=T

ttii ErrForMean

TBiasAve

1, )_(1_

and

∑=

=T

ttii ErrForMedian

TBiasMed

1, )_(1_

where Mean(For_Erri,t) and Median(For_Erri,t) are the mean and median of analysts forecast errors for

firm i in year t, respectively, and T is the number of years the firm appears in the sample.13 Note that the

same periods are used for averaging the bias measures, the accounting quality measure, and the control

variables. For example, if a firm has forecast errors data for the period 1992-1996 then the measure of

earnings management and the control variable are also averaged over the period 1992-1996. This process

yields a sample in which each firm appears only once in the sample and it is done for two reasons. First,

the issue of accounting quality is at the firm level, and I do not expect it to vary considerably over a time

span of few years. Second, having firms appear more than once in the sample might raise a concern of

non-independent error terms in the regression tests.

3.3 One-time Charges

12 Although it is common in the literature on analysts’ forecast errors to define the forecast errors as actual minus forecast divided by a scaler, the definition used in this paper is more suitable for this study. 13 In the analysts sample for HORIZON2, for 8,203 out of 36,866 firm-year observations (22.3%) there is only one forecast. In these cases, the mean and the median of the forecast errors are simply the forecast error of that forecast.

18

To better understand the relation between the accounting quality measures and the optimistic bias, a

closer look at one-time charges is in order. This is because one-time charges affect both the accruals

based earnings management proxy, ABS_DTA, and the optimistic bias.14 One-time charges tend to be

negative and more transitory, and thus are less predictable. Due to earnings conservatism, accounting

earnings is a mixture of processes: transitory losses, due to timely loss recognition, and persistent profits,

as a result of recognition of gains as they accrue (e.g., Ball and Shivakumar [2002]). Therefore, it is

possible that in earlier stages, when information about one-time charges is unavailable, managers and

analysts predict the persistent component of earnings. Since one-time charges tend to be negative this

creates an optimistic error. At the same time, the one-time charges are more likely to be part of the

accruals component of earnings rather than the cash-flow component (e.g. Ball and Shivakumar [2002];

Abarbanell and Lehavy [2003]). Thus, to the extent that the one-time charges are firm specific and not

captured by the cross-sectional modified Jones model, they affect the accruals based accounting quality

measure as well.

It is important to mention here, however, that the existence of one-time charges could be a result of

either good or bad accounting practices. A negative special item that is the product of timely loss

recognition represents higher quality accounting, while a negative special item that results from the

reversal of inflated accruals in past years, or the creation of “cookie jar” corresponds to lower quality

accounting, as it is probable that managers would tend to categorize such negative effect on earnings as

something transitory and nonrecurring (McVay [2005]). To address this issue, the main tests of the paper

are replicated using only observations with no special items. In addition, in the regressions tests below the

inclusion of the signed value of discretionary total accruals as a control variable also addresses this

concern.

3.4 Control Variables

DTA. DTA is the signed value of discretionary total accruals in the year where the estimate or the

forecast was made. It is added to the multivariate regressions as a control variable for several reasons.

First, Kasznik (1999) suggests that managers manage earnings toward their forecasts.15 If managers

manage current period earnings in order to meet their prior high level estimates, or high level analysts’

forecasts, then to the extent that DTA and the proxies for the extent of earnings management (ABS_DTA

14 See for example Hanna (2000) who explores the relation between analysts’ forecasts and nonrecurring charges; and Gu and Chen (2003) who investigate analysts’ decisions to include or exclude nonrecurring items from their short-term (next quarter) forecasts. 15 See Dutta and Gigler (2002) for a theoretical analysis on the relation between managers’ earnings estimates and earnings management.

19

and RESTATEMENT) are correlated there might be a problem of reverse causality. Second, since the

accruals based measure for earnings management (ABS_DTA) is over the last five years, including the

year in which the estimate or the forecast was made, one could argue that there is a concern of a

mechanical relation between ABS_DTA and the dependent variable (the forecast bias). This is due to the

fact that DTA and total accruals (TA) are highly positively correlated, and that high values of total

accruals increase earnings, which is part of the dependent variable. This concern is largely solved by the

fact that to arrive at ABS_DTA the absolute value of DTA is taken, and the fact that DTA is quite

symmetric around zero.16 Nevertheless, adding DTA as a control variable further alleviates this concern.17

Finally, as mentioned above, DTA also addresses the concern that the relation between the bias and the

accounting quality measure is driven by one-time charges.

Size. The larger the size of the firm, the richer the information set about the firm, making it harder for

managers and analysts to credibly bias their predictions, and imposing a higher cost on such behavior. In

addition, larger firms have “deeper pockets,” increasing the cost of biasing in case of the litigation. The

negative correlation between size and ABS_DTA, and the negative correlation between the size and the

bias measures suggest that omitting size as a control variable would bias upward the relation between

ABS_DTA and the bias measure. The natural log of the average over-time of the firm market-value

proxies for the firm size.

FOLLOWING. Financial analysts collect and disseminate information about the firm, and the number

of analysts following can be viewed as a proxy for the total resources spent on private information

acquisition about the firm (Bhushan [1989]). Therefore, the higher the number of analysts following the

firm, the richer the information set about the firm, which limits the ability of managers to bias their

information, and increases the cost of biasing. On the contrary, the higher level of analysts following

means that there is more interest in the firm, which increases the benefits of biasing.

Regarding analysts, on one hand, the higher the number of analysts issuing forecasts about the firm,

the more difficult it is for an analyst to bias her forecast and the greater the costs. On the other hand, a

larger number of analysts following the firm increases the probability of having very optimistic forecasts

but not very pessimistic forecasts, because in the pessimistic cases analysts tend to stop their coverage,

shifting the mean and the median upward. The analysts following variable, FOLLOWING, is the average

over-time of the number of analysts following the firm during the year.

16 In the manager sample at the firm-announcement level DTA is distributed as follow (not reported): P1= -0.286, Q1=-0.040, median=0.001, Q3=0.042, P99=0.330; and in the analysts sample at the firm-year level: P1= -0.313, Q1= -0.046, median= -0.003, Q3=0.039, P99=0.308. 17 For the manager sample, I have replicated the analysis using the five years prior to the year when the estimate was issued, instead of the last five years, and the results, in general, are qualitatively the same.

20

Non-Discretionary Accruals. To strengthen the inferences from the earnings management measure for

accounting quality, the absolute value of non-discretionary level of accruals is added to the regressions. If

the model for earnings management does not capture the extent to which a firm engages in earnings

management, then the discretionary and the non-discretionary components of total accruals should have

similar association with the optimistic bias. On the other hand, if the model residuals capture earnings

management then only the discretionary components should be positively associated with the optimistic

bias.18 The measure for the level of non-discretionary accruals, ABS_NDTA, is the average over the last

five years of the absolute values of the fitted values from the cross-sectional modified Jones model.

Analysts Disagreement. When there is more uncertainty, it is easier to hide the bias, lowering the

costs of such behavior. In addition, when there is more agreement among analysts about the next period

earnings, it is more difficult for a manager or an analyst to optimistically bias her forecast, because her

high forecast will stand out relative to other analysts’ agreement. Analysts’ disagreement, ANA_DIS, is

measured by the average over-time of the standard-deviation of analysts’ forecast errors, scaled by the

prior fiscal year end stock price.

Abnormal Return Volatility. Analysts disagreement only captures the uncertainty about the forecasted

earnings; therefore, a return measure for uncertainty is also used. The measure for the general uncertainty

environment of the firm, RET_VOL, is the standard deviation of the residuals from the market model,

estimated over the last three years with monthly returns. The average over-time of these standard-

deviations is added to the regressions.

Earnings Volatility. Lim (2001) asserts that companies with more uncertain information environments

and analysts for whom building management access is more important are predicted to be associated with

more optimistic forecasts. To control for this explanation, a measure of earnings volatility is added to the

regressions. In addition, earnings volatility is a more direct measure of the difficulty of forecasting

earnings, and the easiness of hiding the bias in volatile environments, which lowers the cost of biasing.

The measure for earnings volatility, STD_EPS, is the standard deviation or earnings per share (before

extraordinary items) in the prior five years, with the requirement of having at least four years.

Earnings Predictability. Das, Levine, and Sivaramakrishnan (1998) argue that analysts will issue

more optimistic forecasts for firms with low predictability of earnings in order to facilitate access to

management’s non-public information. They also point out the difference between earnings predictability

and earning variability. For example, a firm with seasonal income could have volatile yet predictable

earnings. Under a random walk model, the change in earnings captures the prediction error. The measure

for earnings predictability, STD_D_EPS, is the standard deviation of the changes in earnings per share

over the prior five years, for observations with data availability of at least four years. 18 Teoh and Wong (2002) similarly interpret the inclusion of non-discretionary accruals in their study.

21

Institutional Ownership. Corporate governance mechanisms monitor management performances and

actions. Enhanced corporate governance mechanisms better discipline managers and mitigate managers’

incentives to optimistically bias their information (Ajinkya, Sanjeev and Sengupta [2004]). I control for

the corporate governance role in disciplining managers’ disclosures by percentage of institutional

ownership.

Litigation Risk. Higher litigation risk increases the cost of biasing. The biotechnology industry (sic

codes 2833-2836 and 8731-8734), computers (3570-3577 and 7370-7379), electronics (3600-3674) and

retailing (5200-5961) have high incidence of litigation (Francis et al. [1994]). I use an indicator variable,

LITIGATE, which receives the value of one if the firm belongs to one of these industries and zero

otherwise.

Growth Opportunities. Managers of firms with higher growth opportunities may be more optimistic

about the future prospects of their company. TOBIN_Q, measured as the market value of total assets

divided by the book value of total asset, controls for growth opportunities.

Industry. I include industry fixed-effect variables to investigate whether the results are driven by the

accounting quality measure capturing variation across industries with respect to the bias. Including

industry fixed effects also alleviates concern regarding cross-sectional correlation in the error term due to

common economic shocks. I use the same industry grouping as in Teoh and Wong (2002) and form 17

industry groups.

4. Data and Univariate Analysis

4.1 Data

I obtain from the First Call’s Company Issued Guidelines database data on managers’ estimates of

annual earnings per share, the corresponding actual values, estimate announcement dates, earnings

announcement dates and the factors to adjust for stock splits and stock dividends. The data cover the

period 1992-2002, with very few observations prior to 1995, and about 80% of the observations from the

period 1999-2002. The Institutional Brokerage Estimate Systems (I/B/E/S) database is the source for data

on analysts’ annual earnings per share forecasts, the matching actual values, earnings announcement dates

and the adjustment factors. These data span the years 1982-2002, with very limited coverage in 1982.

Information about the individual analysts and the brokerage firms is also obtained from I/B/E/S and is

used to compare affiliated analysts forecasts errors with those of non-affiliated analysts. I use the Security

Data Company (SDC) database to obtain data regarding stock issues, main underwriters and co-

underwriters involved with the issues. Finally, stock returns and prices are taken from CRSP, and firms’

22

accounting data, as well as prices, adjustment factors for stock splits and stock dividends, and earnings

announcement dates are obtained from Compustat.19

To mitigate the effect of small scalers, observations with a lag price smaller than $1.75 are deleted

(approximately 1% of the observations). Baber and Kang (2002) and Payne and Thomas (2003) point out

that research conclusions may be affected by the use of I/B/E/S and First Call adjusted numbers, which

are rounded to the nearest penny. To address this issue, observations with an extreme split factor, i.e.,

smaller than 0.1 or bigger than 16, are deleted. Baber and Kang (2002) also call attention to the fact that

firms with large split factors do not comprise a random sample, and eliminating them may cause a sample

selection problem. This issue should not be a problem in this study as it only eliminates, approximately,

the 99th and 1st percentiles. Financial institutions are also excluded from the analysis because the

accounting rules and numbers are significantly different for these firms. Except for the rank regressions,

to reduce the effect of data errors and extreme outliers the main variables are winsorized at the firm-year

or firm-announcement level at values near their 99th percentiles and/or first percentiles.

4.2 Univariate Statistics

Table 1, panel a provides the descriptive statistics of the manager sample for the second and the third

horizons combined (more than 180 days prior to earnings announement). The statistics are at the firm

level, meaning that the mean of the available observations for each firm is calculated first, so each firm

becomes one observation, and only then the descriptive statistics are computed. To the extent the smaller

firms issue estimates less frequently, this procedure over weights small firms. Robustness checks show

that weighting firm-level observations by the square root of the number of times a firm appears in the

sample does not affect the inferences from the results. The median of the managers’ estimate errors of

1.2% of stock price and the mean of 3.1% support the existence of an optimistic bias in managers’

estimates in these horizons. The large difference between the mean and the median, as well as the first

quartile of 0% together with the median and the third quartile of 3.8% indicate the right skewness of the

estimate errors distribution. The variable N_Announcement shows that the average number of times a

firm appears in the sample in these horizons is 3.3 with a median of 2.

Panel b of table 1 documents, at the firm level, the optimistic bias of one year ahead analysts’

earnings forecasts, i.e., the second group (HORIZON2). Recall that these forecasts are made after the 19 Earnings announcement dates and adjustment factors from First Call or I/B/E/S are compared with those from Compustat. In general, there is agreement between the sources, for example, I/B/E/S and Compustat earnings announcement dates are identical for approximately 80% of the observations, and differ by more than 5 days in about 2% of the observations. I this paper, I use the earnings announcement dates and adjustment factors from First Call and I/B/E/S, and I complete missing values with data from Compustat if available. In addition, when big discrepancies between the sources exist, I use the values that seem more appropriate.

23

announcement of last year earnings and before the announcement of the first quarter earnings of the

forecasted year. During this window, the means of Ave_Bias and Med_Bias are equal to 3.4% of stock

price, and the medians are equal to 1.2% of stock price. Notice that, in line with the manager sample,

these numbers are at the firm level, i.e., first the averages over the years the firm appears in sample are

taken, and then the descriptive statistics are calculated across firms. 21.7% of the firms appear only one

year in the sample, 18.0% appear twice, the median is 3, the average is 5.1 and the third quartile is 7

years. This suggests that taking averages over-time is meaningful. Over long periods, taking averages

over-time may be problematic because firm characteristics could vary considerably. Panel c of table 1

presents the descriptive statistics, at the firm level, for two years ahead forecasts, i.e., the third group

(HORIZON3). Note that, consistent with the horizon hypothesis, the mean of Ave_Bias and Med_Bias of

5.3% and the median of 2.9% are much larger than those of one year ahead forecasts.

Table 2 documents the correlation matrices at the firm level. Panel a exhibits the correlation matrix

for the manager sample in the second and third horizons combined. As expected, the correlations between

the inverse of accounting quality, as measured by average over the last five years of the absolute value of

discretionary total accruals (ABS_DTA), and the bias measures (BIAS) is significantly positive. The

Pearson correlation (below the diagonal) and the Spearman correlation (above the diagonal) are both 0.10.

Similar pictures arise from the analyst sample for both one year ahead annual forecasts (panel b) and two

years ahead annual forecasts (panel c).

Table 3 reports selected statistics by horizon. To aid comparability with other studies on manager

estimates and analyst forecast errors, the statistics are given at both the firm level and the firm-year or

firm-announcement level. Panel a shows the statistics of the manager sample; the optimistic bias indeed

increases with the horizon. For example, while the median for the first horizon is zero, it increases as one

moves to the second and the third horizons. More importantly, the spearman correlations between

ABS_DTA and BIAS increase with the horizon, consistent with the hypothesis that the importance of

accounting as a disciplinary mechanism increases with the incentives to bias the information. The Pearson

correlations, however, although increase from the first to the second horizon, decrease when moving from

the second to the third horizon. The differences between the patterns of the Pearson and Spearman

correlations suggest that the relation may not be linear. Therefore, in the regression tests below, I also

analyze rank regressions. To assure that the results are not driven by one-time charges, panel b of table 3

replicates panel a but only with observations for which the special item value on Compustat is zero. In

this panel, consistent with the incentive hypothesis, both the Pearson and the Spearman correlations

increase with the horizon. Panel c and panel d of table 3 provide the results for the analyst sample. At the

firm-year level, a consistent pattern of higher correlations with horizons is observed. At the firm level, the

24

pattern is less consistent but still similar, especially when only observations with special item equal to

zero are considered.

It is interesting to note that in the manager sample the importance of special items, which tend to be

negative, increases with the horizon (table 3, panel a), suggesting that when managers expect to have

large negative charges they are more likely to issue earlier the estimates of its implication on earnings per

share. This is along the lines of Kasznik and Lev (1995), Skinner (1994) and Skinner (1997) which

provide evidence that managers use voluntary earnings disclosures to preempt large negative earnings

surprises more frequently than other type of news.

4.3 Plot of the bias by horizon and ABS_DTA

Figure 1 exhibits the medians of the estimate errors by horizon and quartiles of the accruals based

accounting quality measure. In the first horizon, when there is no incentive to bias the information, or at

least the incentives to bias are weak, there is no variation in the median across quartiles of accounting

quality, and it is zero for all of them. In contrast, the difference in the median of the errors across the

quartiles is very pronounced for the third horizon. As expected, for the second horizon the variation is in

between. Figure 2 provides similar evidence with respect to the analyst sample, with the only difference

that I exploit the larger sample and form quintiles instead of quartiles. Using only observations with no

special items yields similar figures.

The results discussed so far were based on a univariate analysis and examine the simple relation

between the accounting quality measure and the optimistic bias. The more important analysis, though, is

the multivariate one, which is discussed in the next section.

5. Multivariate Analysis

5.1 Managers’ Estimates

5.1.1 Basic Regressions

Table 4 presents the results of the basic regressions using managers’ estimates of annual earnings per

share for the second and the third horizons combined. Model 1 is the simple regression, while model 2

and model 3, the multiple regressions, control for other variables that may affect the optimistic bias.

Compared with model 2, model 3 uses the entire set of the control variables, including 17 industry fixed

effect dummy variables. I emphasize the tests based on model 2 for two reasons. First, it includes only

25

one of STD_EPS and STD_D_EPS, which are highly correlated (0.93) and hence avoids multicolinearity

problems. The reason why both variables are added to model 3 in spite of the multicolinearity is to try to

capture the different aspects of variability versus predictability as discussed in the control variables

section above. Second, although it is interesting to add the industry fixed effect variables to assure that the

accounting quality measure does not simply capture variation across industries with respect to the bias, it

risks controlling for the relation under investigation. The first three columns of table 4 show that,

consistent with the confirmatory role of accounting hypothesis, the association between the accounting

quality measure (ABS_DTA) and the optimistic bias (BIAS) is significantly positive in all of the three

models. The relation is not only statistically significant but also economically important. For instance, in

model 2, an increase of one standard deviation in ABS_DTA increases the bias by 2.2% of stock price,

which is 70% of the mean bias of 3.1% of stock price, and 1.8 times the median bias of 1.2% of stock

price.

To assure that the results are not driven by special items, which tend to be negative and less

predictable, the next three columns of table 4 provide the regressions results using only observations with

no special items, i.e., the value of special items is zero. The coefficient on ABS_DTA remains

significantly positive in all three models.

The last three columns of table 4 report the results of the rank regressions, in which the values of the

variables are replaced with their rank index, ranging from 0 to 100.20 The motivation for analyzing the

rank regressions stems from the large differences between the Pearson and Spearman correlations

between ABS_DTA and BIAS, which suggest that the relation may not be linear. As can be seen from

table 3, this is of special importance when analyzing the relation across horizons. Consistent with the

regular regressions, the coefficient on ABS_DTA is significantly positive in all three models. However,

the results of table 4 should be interpreted cautiously. As mentioned in the research design section, the

accruals based earnings management proxy, ABS_DTA, may capture fundamentals of the firm that are

not related to earnings management. To mitigate this concern, I add as a control variable the signed value

of discretionary total accruals in the year when the forecast was made. The incentive tests below and the

replication of the results with the restatement indicator variable (described below) further mitigate this

concern.

Turning to the control variables, Size, as expected, is negatively correlated with the bias.

FOLLOWING is consistently significantly positive, consistent with the interpretation that when there is

more interest in the company the incentives to introduce a bias are stronger. For example, around a SEO

20 The index values are calculated by ordering the values of each variable from the smallest to the largest, with the smallest value assigned a ranked value of 1, the second smallest assigned a ranked value of 2 and so on until the largest value which is assigned the ranked value of the number of observations with non-missing values for the particular variable. The ranked values are then divided by the number of observations and multiplied by 100.

26

or an IPO one would expect more analysts to follow a firm. Considering that size may already capture the

level of information availability about the firm, the marginal effect of FOLLOWING seems to fit with

this interpretation. ABS_NDTA is significantly negative, and the fact that it is not positive corroborates

the use of ABS_DTA to capture earnings management, however, it is not clear why it is significantly

negative. The analysts disagreement coefficient is positive and significant at the 10% level in the rank

regressions, consistent with the argument that when there is more uncertainty it is easier to hide the bias,

which lowers the cost of biasing. In the regular regressions, the ANA_DIS coefficients are positive as

expected but are not significant. When only observations with no special items are considered, the

coefficient on ANA_DIS flips its sign, but it is significantly negative only in model 2.

As for the general uncertainty control variable, RET_VOL, in spite of the positive simple correlation

between RET_VOL and BIAS, the multivariate relation is significantly negative in the regular

regressions, and is insignificantly negative in the rank regressions. Given that ANA_DIS, STD_D_EPS

and/or STD_EPS already control for uncertainty and volatility, it is possible that the marginal effect of

RET_VOL captures another effect. A possible explanation could be that if higher volatility is correlated

with negative abnormal returns, then perhaps it is more difficult for managers of companies that had past

negative abnormal returns to introduce an optimistic bias without “hard evidence.”

STD_EPS and STD_D_EPS are significantly positive, as expected, but only when just one of them is

added to the regressions. Due to the high correlation between these two variables (0.94), when both are

added to the regressions none is significant. In the rank regression both are not significant even if just one

is added to the regressions.

The control variable for litigation risk, LITIGATE, is significantly positive in models 2 in the regular

and the rank regressions. One would expect that higher litigation risk would reduce the bias in managers

earnings guidance, but recall that LITIGATE is an indicator variable of industries with high incidence of

litigation, and positive association could arise because in industries where managers’ forecasts are more

optimistic more litigation follows. The proxy for corporate governance and monitoring, INS_OWN,

although has negative coefficient as expected, is not significant. Finally, adding industry fixed effects to

the regressions does not have a real effect on any of the explanatory variables (except for LITIGATE

which is based on industry classification).

A concern worth noting here is whether the relation just documented between ABS_DTA and BIAS

is simply a result of a “mechanical” relation. Suppose there is an inherent positive bias in BIAS and that it

increases with the variance of BIAS, then if in environments with higher uncertainty and volatility the

variance of BIAS and the value of ABS_DTA increase then the relation follows. However, the following

addresses this concern. First, the control variables are constructed to capture the effect of uncertainty and

volatility. Second, the tests of the second and third hypotheses, which explore how the relation varies with

27

the incentives, would not yield supportive results if the relation were “mechanical.” Third, absent

incentives to optimistically bias the information why would the bias increase with the variance? Finally,

the replication of the results described above with the restatement indicator variable discussed next further

alleviates this concern.

Table 5 reports the results with the restatement indicator variable. Since the indicator variable

receives a value of one up to the year when the firm had restated its financial statements, the analysis is

done at the firm-announcement level. Consistent with the results when using the accruals based measure,

the coefficient on RESTATEMENT is positive in all the regressions at significance level of 10% or

lower.

5.1.2 Measurement Errors in the Proxy for Accounting Quality

As mentioned above, the accruals based earnings management measure has considerable

measurement error, which biases the coefficient on ABS_DTA toward zero when the measurement error

is uncorrelated with the other explanatory variables. In addition, the low R2 of 1% in the univariate

regressions questions the importance of the role that ABS_DTA plays. Grouping is one approach to

mitigate the measurement error problem. Grouping by ABS_DTA, however, would not solve the

problem. As Beaver, Lambert and Morse (1980) point out, the measurement error problem is reduced if

the grouping procedure is uncorrelated with the error and is highly correlated with the ‘underlying’

variable. Therefore, 100 groups are formed based on the estimate bias. The R2 from the group univariate

regression rises from 1% to 13%, and the coefficient on ABS_DTA rises from 0.096 to 1.45 (not

reported).

5.1.3 Tests of the Second Hypothesis: Horizon

The second research question of this paper is whether importance of accounting as a disciplinary

mechanism increases with the incentives to optimistically bias the information. The rationale here is that

absent incentives to optimistically bias the information there is no need for a disciplinary mechanism. On

the contrary, when such incentives are intense it is crucial to have a disciplinary mechanism. For the

manager sample, I explore two cases of variation in intensity of incentives: different horizons and activity

of external financing. In the analyst sample, in addition to these two cases, I also investigate the

differences between affiliated and unaffiliated analysts.

For the reasons discussed above, I argue that the incentives to optimistically bias estimates of

earnings of periods further in the future are stronger than those for the near future. To test this hypothesis,

28

I rerun the basic regressions from table 4 using observations from all three horizons combined, and add

dummy variables for the first horizon, D1, and the third horizons, D3, as well as the interaction of these

dummies with ABS_DTA. Table 6 reports the results of these regressions. The rank regressions provide

the most supportive results. Consistent with the prediction of the horizon hypothesis, the coefficient on

the interaction term for the third horizon, D3_ABS_DTA, is significantly positive in all three models, and

the coefficient on the interaction term for the first horizon, D1_ABS_DTA, is significantly negative in all

three models. Note that ABS_DTA remains significantly positive in all three models. As for the regular

regressions, the first three columns show that consistent with the predictions the coefficient on

D1_ABS_DTA is significantly negative in all three models, however, the coefficient on D3_ABS_DTA is

not statistically different from zero and has the opposite sign. In the sub-sample where only observations

with no special items are considered, consistent with the predictions the coefficient on D3_ABS_DTA is

significantly positive, but the coefficient on D1_ABS_DTA is not statistically different form zero in

model 2 and model 3.

In sum, in all regressions when the coefficients are significant they are consistent with the predictions,

that is, there is no case of a significant coefficient with opposite sign. In addition, in all cases at least one

of D1_ABS_DTA and D3_ABS_DTA has a significant coefficient in the predicted direction. Therefore,

overall, these results are consistent with the horizon hypothesis.

A caveat is in order. Table 3 shows that the standard deviation of BIAS increases with the horizon,

therefore, one could argue that the results of the horizon test may simply be an outcome of the different

variation in BIAS across horizons. However, the rank regressions are free from this concern and yet

provide the most supportive results.21 In addition, in the regular regressions case, the fact that the

variation of ABS_DTA also increases with the horizon (table 3) mitigates this concern.

5.1.4 Tests of the Third Hypothesis: External Financing Activity

Activity of external financing provides another interesting setting to test how the importance of

accounting as a disciplinary mechanism varies with the intensity of the incentives to bias the information.

Firms that issue external financing have stronger incentives to optimistically bias the information

regarding their economic strengths and performances in order to issue the stock or bonds at a higher price,

while firms that repurchase their stock have incentives to avoid optimistic information in order to buy the

stock at a lower price. Therefore, I hypothesize that the relationship between the accounting quality

21 In the ranked sample, the standard deviations of BIAS across horizons at the firm-announcement level are 28.97, 28.84, and 28.86, for the first, second and third horizon respectively, and at the firm level the corresponding values are 25.90, 26.97 and 27.12.

29

measures (ABS_DTA and RESTATEMENT) and the bias is stronger for firms with positive external

financing activities (issuers) relative to firms with negative external financing activities (repurchasers).

I use the same method as in Bradshaw, Richardson and Sloan (2003) to compute change in external

financing (EXT_FIN), which is equal to the sum of change in equity (∆EQUITY) and the change in debt

(∆DEBT), scaled by average book value of total assets. ∆EQUITY is measured as the annual change in

common equity (Compustat item #60) plus the change in preferred stock (Compustat item #130) minus

net income (Compustat item #172). ∆DEBT is measured as the annual change in total long-term debt

(Compustat item #9) plus the change in short-term debt included in current liabilities (Compustat item

#34).

In table 7, which reports the results of the external financing test with the ABS_DTA measure,

ABS_DTA_XFIN is the interaction between ABS_DTA and EXT_FIN. Indeed, in model 1 and model 2

the coefficient on EXT_FIN is significantly positive, suggesting that firms that increase their external

financing issue more optimistic forecasts. In addition, consistent with the incentive hypothesis, the

coefficient on ABS_DTA_XFIN is significantly positive at 10% and 5% significance level in model 3 and

model 4, respectively. Table 8 replicates the external financing test with the restatement indicator variable

instead of ABS_DTA. Here, RES_XFIN is the interaction of RESTATEMENT and EXT_FIN. Consistent

with the results of table 7, the coefficient on RES_XFIN is significantly positive.

5.1.5 Market Reaction to Managers’ Forecasts and the Quality of Accounting Information

In efficient markets, if market participants can estimate or at least have some judgment regarding the

variation in the ability of the accounting system to serve its confirmatory role, then one would expect the

reaction to managers’ information to vary accordingly. More specifically, if firms with lower accounting

quality issue forecasts with higher optimistic bias, then market participants should discount their

information to a greater extent. The results of table 9 support this argument. In table 9, the dependent

variable, CAR, is the cumulative size adjusted abnormal return during the three days window around

managers’ forecasts. MF_NEWS is managers’ forecast news, measured as the managers’ forecast of

annual earnings minus the median of analysts’ forecast in the prior 60 days, scaled by the stock price 2

days prior to the managers’ forecast. Since 59% of managers’ forecasts in my sample are released

together with quarterly earnings, it is crucial to control for the earnings announcement news.

EAR_NEWS is quarterly earnings announcement news released together with managers’ forecast, and is

measured as actual earnings per share minus median analysts forecast in the prior 30 days (or 90 days if

there is no analyst’s forecast in the last 30 days), scaled by the stock price 2 days prior to the earnings

announcement. In models 1 and 2, the coefficient on ABS_DTA is indeed significantly negative,

30

consistent with market participants correcting for higher optimistic bias in managers’ forecasts of firms

with lower accounting quality. To assure that this result is not influenced by the release of quarterly

earnings, the regressions are reran using only observations of managers’ forecasts released without

announcement of quarterly earnings. Models 3 and 4 in table 9 show that the coefficient on ABS_DTA

remains significantly negative. This finding is replicated in table 10 using the restatement indicator

variable instead of ABS_DTA.

5.1.6 Managers’ Decision to Issue an Earnings Guidance and the Quality of Accounting Information

The confirmatory role of accounting and the empirical findings documented above raise the following

question: How the ability of accounting information to perform its role as a disciplinary mechanism

affects managers’ decision to issue an earnings guidance? Table 11 and table 12 document the empirical

evidence regarding this question. In these tables the dependent variable is FORECAST, an indicator

variable receiving the value of one if the firm issued an annual earnings guidance during the fiscal year

and zero otherwise. Since incentives to bias exist mainly in the long-horizon, for FORECAST=1 only

observations in the second and third horizons are included (more than 180 days prior to earnings

announcements). Similarly, forecast of quarterly earnings are ignored because it is hard to relate them to

long horizon due to their relatively short duration. Anilowski, Feng and Skinner (2004) document that

earnings guidance is increasingly pervasive, and that there is a consistent increase in the proportion of

firms issuing guidance. Therefore, a trend variable, TREND, which is equals one for 1997, 2 for 1998

etc., is added to the control variables. In the multivariate regressions of table 11, the coefficient on

ABS_DTA is significantly positive, consistent with the argument that when managers have higher ability

to optimistically bias their forecast they are more likely to issue an earnings guidance. However, this

result is also consistent with managers responding to higher demand for managers’ private information

when quality of accounting information is lower, and information set available regarding the firm is

smaller. Table 12 replicates the finding of table 11 using the restatement indicator variable,

RESTATEMENT, instead of the accruals based measure, ABS_DTA.

5.2 Analysts’ Forecasts

5.2.1 Regressions Results

The regressions results from the analyst sample are in general consistent with those from the manager

sample. Panel a of table 13 reports the results of the basic regressions using one year ahead forecasts

31

(HORIZON2), while panel b reports the results of two years ahead forecasts (HORIZON3). As predicted,

the coefficient on ABS_DTA is significantly positive in all cases. Table 14 documents the results of the

horizon test. The coefficients on ABS_DTA, D1_ABS_DTA, and D3_ABS_DTA have the predicted sign

and are significant in all cases except for the coefficient D3_ABS_DTA in the univariate rank regression

where its t-stat is 1.57 (in the predicted direction).

Table 15 reports the results of the external financing tests. Three sub-samples are formed base on

external financing activities. The benchmark group comprises observations with no changes or small

changes in external financing, -2%<∆XFIN<2%, expressed in term of percentage of total assets; the high

external financing group (issuers) includes observations with ∆XFIN>10%, and the large negative

external financing group (repurchasers) contains observations with ∆XFIN<-10%. D1 (D3) is a dummy

variable for the repurchasers (issuers) group, and D1_ABS_DTA (D3_ABS_DTA) is the interaction of

D1 (D3) and ABS_DTA. Panel a reports the results of one year ahead forecasts, as predicted, the

coefficient on D1_ABS_DTA is significantly negative in all of the multiple regressions, but not in the

univariate regressions. The coefficient on D3_ABS_DTA is significantly negative, consistent with the

argument that increased scrutiny from regulators and the due-diligence process involved with the issuance

of stock provide another channel of disciplining in this horizon and that optimistic bias is more likely to

occur in forecasts of earnings of periods further in the future. This result is similar to the findings in

Bradshaw, Richardson and Sloan (2003). Panel b reports the results of two years ahead forecasts. When

using all observations, in the regular multivariate regressions the coefficient on D1_ABS_DTA is

significant in the predicted direction, but the coefficient on D3_ABS_DTA is not statistically different

from zero. A possible explanation for this pattern is that incentives other than external financing that are

present in the benchmark group already induce analysts to bias their information to the limit, hence,

adding another incentive does not increase the bias. In the rank regressions, the negative coefficients on

D3_ABS_DTA are inconsistent with the prediction, while the negative coefficients on D1_ABS_DTA are

consistent with the incentive hypothesis.

5.2.2 Affiliated versus Non-Affiliated Analysts

The analyst sample provides an additional setting to test the incentive question by comparing

affiliated and non-affiliated analysts’ forecasts and their relation to the accounting quality measure.

However, the evidence in the literature suggests that the difference between affiliated and non-affiliated

analysts are mainly in recommendations, long-term growth forecasts and price targets, which are affected

by outcomes in the long-run, while there is mixed evidence regarding forecasts of earnings, which are

more sensitive to short-term effects. Nevertheless, it is worth exploring. To classify forecasts into

32

affiliated and non-affiliated groups, I used information regarding lead underwriters and co-underwriters of

IPO and SEO from the SDC database together with I\B\E\S’s analyst/broker translation code data. The

analysis did not detect significant differences between the two groups (not reported).

6. Robustness Tests

6.1 Cross-Sectional Correlation and Heteroskedasticity

The results presented above may suffer from cross-sectional correlation and heteroskedasticity issues.

The reason for the cross-sectional correlation is that some firms have common shocks and therefore the

error-terms are not independent. The heteroskedasticity problem results from the fact that each

observation is an average over-time, but the number of years or announcements used in the computation

varies across firms. In general, the higher the number of years or announcements used in the computation

the lower the variance of the error-term of that observation. The Fama and MacBeth (1973) method to

address cross-sectional correlation is not ideal here due to serial correlation of the explanatory variables

and the limited number of cross sections in the manager sample. Since it is probable that common shocks

that have a common effect on the error terms would be more pronounced at the industry level, I use a

cluster analysis to address the cross-sectional correlation issue, where the clustering is done by industry. I

have replicated the basic tests for the manager and the analyst samples with robust standard errors that

account for heteroskedasticity and with clustering by industry group and the coefficient of ABS_DTA

remain significantly positive in both the manager and analyst tests.

6.2 Measuring Accruals: Balance Sheet Approach vs. Cash-Flow Approach

Hribar and Collins (2002) document that using the balance sheet approach instead of the cash-flow

approach could result in measurement error in accruals estimates. They show that this measurement error

is correlated with firms’ characteristics such as the occurrence of mergers and acquisitions and

discontinued operations. To address this concern, I have rerun basic regressions using the cash-flow

approach instead of the balance-sheet approach to estimate the level of accruals. The results (not reported)

are similar to those using the balance sheet approach.22

6.3 Independence of the Error Terms in Regressions of the Horizon Test 22 The reasons for using the balance sheet approach are data availability and to maintain comparability between the manager and analyst samples, and the existing literature. The cash-flow data for calculating accruals are only available starting from 1987 (with limited coverage in 1987).

33

In the horizon tests presented in table 6 and table 14, each firm may have up to three observations in

the computation of the regressions. This raises the concern that the error term of a firm in one horizon

may be correlated with its error term in another horizon. To address this issue I have rerun the regressions

of tables 6 and 14 with clustering the observations by firm. The results are, in general, similar to those

reported.

6.4 Overweighting Small Firms in the analyst sample

The results presented in the analyst sample may overweight small firms.23 The reason for this concern

stems from the use of averages over time and the fact that the number of years that small firms appear in

the analyst sample is smaller than that of large firms, as indicated by the large positive correlation

between N_Year and size (not reported). In addition, about 40% of the firms appear in the analyst sample

only in one or two years, which limits the benefits of taking averages overtime. Therefore, I have rerun

the empirical results of table 13 panel a for two sub-samples: firms that appear only in one or two years

(about 40% of firms) and firms that appear in four years or more (about 48% of the firms). The results

(not reported) for each sub-sample are similar to those of the entire sample.

7. Conclusion

This paper explores the confirmatory role of accounting and how the importance of accounting as a

disciplinary mechanism varies with the incentives to optimistically bias the information. The empirical

evidence is consistent with the confirmatory role of accounting. Controlling for other explanatory

variables, I find a negative association between the measures of the quality of accounting information and

managers’ and analysts’ optimistic bias. Furthermore, the results are, in general, consistent with the

incentive hypotheses, namely, the importance of the role of accounting as a disciplinary mechanism

increases with the incentives to optimistically bias the information.

However, caution should be used with the interpretation of the results. The support of the results

depends on the ability of the cross-sectional modified Jones model to capture earnings management. I

recognize the limitations of the modified Jones model in capturing earnings management; nevertheless,

for the reasons discussed in the research design section, it seems that an earnings management proxy is

23 This concern is not relevant to the manager sample for two reasons. First, there is much less variation in the number of years a firm appears in the manager sample. The standard deviation of N_Year is 1.1, the mean is 1.8, with a median of 1 and an upper quartile of 2. Second, the relation between N_Year and size is not as clear as in the analyst sample.

34

the most suitable to capture the accounting quality dimensions that are important for the confirmatory role

of accounting. Moreover, having the signed value of discretionary total accruals as a control variable and

the finer tests of the incentives hypotheses mitigate this concern. In addition, the replication of the results

with a second measure of accounting quality, the restatement indicator variable, supports the

interpretation of the results.

Further analysis reveals that market participants recognize that managers’ earnings forecasts of firms

with lower accounting quality are more optimistically biased. Finally, the empirical evidence suggests

that managers are more likely to issue long-horizon earnings guidance when the ability of accounting to

serve its confirmatory role is lower.

35

References

Abarbanell, Jeffery, and Reuven Lehavy, 2003, Biased forecasts or biased earnings? The role of reported

earnings in explaining apparent bias and over/underreaction in analysts’ earnings forecasts, Journal of

Accounting and Economics, forthcoming.

Aboody, David and Ron Kasznik, 2000, CEO stock options awards and the timing of corporate voluntary

disclosures, Journal of Accounting and Economics 29, 73-100.

Ajinkya, Bipin B., Sanjeev Bhojraj, and Partha Sengupta, 2004, The association between outside

directors, institutional investors and the properties of management earnings forecasts, working paper.

Ajinkya, Bipin B., and Michael J. Gift, 1984, Corporate managers’ earnings forecasts and symmetrical

adjustments of market expectations, Journal of Accounting Research 22, 425-444.

Anilowski, Carol, Mei Feng, and Douglas Skinner, 2004, Is guidance a macro factor? The nature and

information content of aggregate earnings guidance, University of Michigan working paper.

Baber, William R., and Sok-Hyon Kang, 2002, The impact of split adjusting and rounding on analysts’

forecast error calculations, Accounting Horizon 16, 277-289.

Baginski, Stephen P., Edward J. Conrad, and John M. Hassell, 1993, The effects of management forecast

precision on equity pricing and on the assessment of earnings uncertainty, Accounting Review 68,

913-927.

Baginski, Stephen P., John M. Hassell, and Michael D. Kimbrough, 2002, The effect of legal environment

on voluntary disclosure: evidence from management earnings forecasts issued in U.S. and Canadian

markets, Accounting Review 77, 25-50.

Ball, Ray, 2001, Infrastructure requirements for an economically efficient system of public financial

reporting and disclosure, Brooking-Wharton Papers on Financial Services, 127-182.

Ball, Ray, and Lakshmanan Shivakumar, 2002, Earnings quality in U.K. private firms, University of

Chicago and LBS working paper.

Bamber, Linda S., Youngsoon S. Cheon, 1998, Discretionary management earnings forecast disclosures:

antecedents and outcomes associated with forecast venue and forecast specificity choices, Journal of

Accounting Research 36, 167-190.

Bartov, Eli, Dan Givoly, and Carla Hayn, 2002, The rewards to meeting or beating earnings expectations,

Journal of Accounting and Economics 33, 173-204.

Basu, Sudipta, and Stanimir Markov, 2003, Loss function assumptions in rational tests on financial

analysts’ earnings forecasts, Emory University working paper.

Beaver, William, Richard Lambert, and Dale Morse, 1980, The information content of security prices,

Journal of Accounting and Economics 2, 3-28.

36

Bhushan, Ravi, 1989, Firm characteristics and analyst following, Journal of Accounting and Economics

11, 255-274.

Bradshaw, Mark T., Scott A. Richardson, and Richard G. Sloan, 2003, Pump and dump: an empirical

analysis of the relation between corporate financing activities and sell-side analyst research,

University of Michigan, HBS and Wharton working paper.

Brown, Lawrence D., 2001, A temporal analysis of earnings surprises: profits versus losses, Journal of

Accounting Research 39, 221-241.

Bushman, Robert M., Qi Chen, Ellen Engel, and Abbie J. Smith, forthcoming, Financial accounting

information, organizational complexity and corporate governance systems, Journal of Accounting and

Economics.

Chan, Louis K.C., Jason Karceski, and Josef Lakonishok, 2003, Analysts’ conflict of interest and biases

in earnings forecasts, NBER working paper #9544.

Chen, Qi, and Wei Jiang, 2003, Analysts’ weighting of private and public information, Duke University

and Columbia University working paper.

Das, Somnath, Carolyn B. Levine, K. Sivaramakrishnan, 1998, Earnings predictability and bias in

analysts’ earnings forecasts, Accounting Review 73, 277-294.

Dechow, Patricia M., and Douglas J. Skinner, 2000, Earnings management: reconciling the views of

accounting academics, practitioners, and regulators, Accounting Horizon 14, 235-250.

Dechow, Patricia M., Richard G. Sloan, Amy P. Sweeney, 1995, Detecting earnings management,

Accounting Review 70, 193-225.

Dugar, Amitabh, and Siva Nathan, 1995, The effects of investment banking relationships on financial

analysts’ earnings forecasts and investment recommendations, Contemporary Accounting Research,

131-160.

Dutta, Sunil, and Frank Gigler, 2002, The effect of earnings forecasts on earnings management, Journal of

Accounting Research 40, 631-655.

Fama, F. Eugene, James D. MacBeth, 1973, Risk, return, and equilibrium: empirical tests, Journal of

Political Economy 81, 607-636.

Fischer, Paul E., and Robert E. Verrecchia, 2000, Reporting bias, Accounting Review 75, 229-245.

Francis, Jennifer, and Donna R. Philbrick, 1993, Analysts’ decisions as products of multi-task

environment, Journal of Accounting Research 31, 216-230.

Francis, Jennifer, Donna Philbrick, and Katherine Schipper, 1994, Shareholder litigation and corporate

disclosures, Journal of Accounting Research 32, 137-164.

Gigler, Frank, and Thomas Hemmer, 1998, On the frequency, quality, and informational role of

mandatory financial reports, Journal of Accounting Research 36, 117-147.

37

Gu, Zhaoyang and Ting Chen, 2003, In or out: do analysts know what they are doing with nonrecurring

items?, Carnegie Mellon University working paper.

Gu, Zhaoyang, and Joanna Shuang Wu, 2003, Earnings skewness and analysts forecast bias, Journal of

Accounting and Economics 35, 5-29.

Guay, Wayne R., S. P. Kothari, and Ross L. Watts, 1996, A market-based evaluation of discretionary

accrual models, Journal of Accounting Research 34, 83-105.

Guttman, Ilan, Ohad Kadan, and Eugene Kandel, 2003, Adding the noise: a theory of compensation-

driven earnings management, Washington University and Hebrew University working paper.

Hanna, Douglas J., 2000, Analysts’ earnings forecasts and the recognition of nonrecurring charges,

University of Chicago working paper.

Healy, Paul M., and James M. Wahlen, 1999, A review of the earnings management literature and its

implications for standard setting, Accounting Horizon 13, 365-383.

Hirst, Eric D., Kevin E. Jackson, Lisa Koonce, 2003, Improving financial reports by revealing the

accuracy of prior estimates, Contemporary Accounting Research 20, 165-193.

Hong, Harrison, and Jeffrey D. Kubik, 2003, Analyzing the analysts: career concerns and biased earnings

forecasts, Journal of Finance, 313-351.

Hribar, Paul, and Daniel W. Collins, 2002, Errors in estimating accruals: implications for empirical

research, Journal of Accounting Research 40, 105-134.

Hutton, Amy P., Gregory S. Miller, and Douglas J. Skinner, 2003, The role of supplementary statements

with management earnings forecasts, Journal of Accounting Research 41, 867-890.

Irvine, Paul J., 2003, Analysts’ forecasts and brokerage-firm trading, Accounting Review, forthcoming.

Jennings, Robert, 1987, Unsystematic security price movements, management earnings forecasts, and

revisions in consensus analyst earnings forecasts, Journal of Accounting Research 25, 90-110.

Jones, Jennifer J., 1991, Earnings management during import relief investigations, Journal of Accounting

Research 29, 193-228.

Kang, Sok-Hyon, John O’Brien, and K. Sivaramakrishnan, 1994, Analysts’ interim earnings forecasts:

evidence on the forecasting process, Journal of Accounting Research 32, 103-112.

Kanodia, Chandra, and Deokheon Lee, 1998, Investment and disclosure: the disciplinary role of periodic

performance reports, Journal of Accounting Research 36, 33-55.

Kasznik, Ron, 1999, On the association between voluntary disclosure and earnings management, Journal

of Accounting Research 37, 57-81.

Kasznik, Ron, and Baruch Lev, 1995, To warn or not to warn: management disclosures in the face of an

earnings surprise, Accounting Review 70, 113-134.

38

Lev, Baruch, and Stephen H. Penman, 1990, Voluntary forecast disclosure, nondisclosure, and stock

prices, Journal of Accounting Research 28, 49-76.

Lim, Terence, 2001, Rationality and analysts’ forecast bias, Journal of Finance, 369-385

Lin, Hsiou-wei, and Maureen F. McNichols, 1998, Underwriting relationships, analysts’ earnings

forecasts and investment recommendations, Journal of Accounting and Economics 25, 101-127.

Lin, Hsiou-Wei, Maureen F. McNichols, and Patricia C. O’Brien, 2003, Analyst Impartiality and

investment banking relationships, Stanford University working paper.

Lundholm, Russell J., 1999, Reporting on the past: a new approach to improving accounting today,

Accounting Horizons 13, 315-322.

Lundholm, Russell J., 2003, Historical accounting and the endogenous credibility of current disclosures,

Journal of Accounting, Auditing & Finance 18, 207-229.

McVay, Sara E., Earnings management using classification shifting: an examination of core earnings and

special items, New York University working paper.

Michaely, Roni, and Kent Womack, 1999, Conflict of interest and the credibility of underwriter analyst

recommendations, Review of Financial Studies 12, 653-686.

Miller, Gregory, 2002, Earnings performance and discretionary disclosure, Journal of Accounting

Research 40, 173-204.

Patell, James M., 1976, Corporate forecasts of earnings per share and stock price behavior: empirical

tests, Journal of Accounting Research, 246-276.

Payne, Jeff L., and Wayne B. Thomas, 2003, The implications of using stock-split adjusted I/B/E/S data

in empirical research, Accounting Review 78.

Penman, Stephen H., 1980, An empirical investigation of the voluntary disclosure of corporate earnings

forecasts, Journal of Accounting Research 18, 132-160.

Perry, Susan E., and Thomas H. Williams, 1994, Earnings management preceding management buyout

offers, Journal of Accounting and Economics 18, 157-179.

Pownall, Grace, Charles Wasley, and Gregory Waymire, 1993, The stock price effects of alternative types

of management earnings forecasts, Accounting Review 68, 896-912.

Pownall, Grace, and Gregory Waymire, 1989a, Voluntary disclosure credibility and securities prices:

evidence from management earnings forecasts, 1969-73, Journal of Accounting Research 27, 227-

245.

Pownall, Grace, and Gregory Waymire, 1989b, Voluntary disclosure choice and earnings information

transfer, Journal of Accounting Research 27, 85-105.

Rogers, Jonathan L., and Phillip C. Stocken, 2003, Credibility of management forecasts, working paper.

39

Sankar, Mandira Roy, and K. R. Subramanyam, 2001, Reporting discretion and private information

communication through earnings, Journal of Accounting Research 39, 365-386.

Sansing, Richard C., 1992, Accounting and the credibility of management forecasts, Contemporary

Accounting Research 9, 33-45.

Schipper, Katherine, 1989, Commentary on earnings management, Accounting Horizon 3, 91-102.

Skinner, Douglas J., 1994, Why firms voluntarily disclose bad news, Journal of Accounting Research 32,

38-60.

Skinner, Douglas J., 1997, Earnings disclosures and stockholder lawsuits, Journal of Accounting and

Economics 23, 249-282.

Soffer, Leonard, Ramu S. Thiagarajan, and Beverly R. Walther, 2000, Earnings preannouncement

strategies, Review of Accounting Studies 5, 5-26.

Spence, Michael, 1973, Job market signaling, Quarterly Journal of Economics, 355-374.

Stein, Jeremy C., 1989, Efficient capital markets, inefficient firms: a model of myopic corporate behavior,

Quarterly Journal of Economics, 655-669.

Stocken, Phillip C., 2000, Credibility of voluntary disclosure, RAND Journal of Economics 31, 359-374.

Teoh, Siew Hong, and T. J. Wong, 2002, Why new issues and high-accrual firms underperform: the role

of analysts’ credulity, Review of Financial Studies 15, 869-900.

Watts, Ross L., and Jerold L. Zimmerman, 1986, Positive Accounting Theory, Prentice Hall, New Jersey.

Waymire, Gregory, 1984, Additional evidence on the information content of management earnings

forecasts, Journal of Accounting Research 22, 703-718.

Waymire, Gregory, 1985, Earnings volatility and voluntary management forecast disclosure, Journal of

Accounting Research 23, 268-295.

Waymire, Gregory, 1986, Additional evidence on the accuracy of analyst forecasts before and after

voluntary management earnings forecasts, Accounting Review, 129-142.

Williams, Patricia A., 1996, The relation between a prior earnings forecast by management and analyst

response to a current management forecast, Accounting Review 71, 103-115.

Yermack, David, 1997, Good timing: CEO stock option awards and company news announcements,

Journal of Finance 52, 449-476.

40

Figure 1

Median Bias by Quartile and Horizon, Managers' Estimates

0.0000.0020.0040.0060.0080.0100.0120.0140.0160.0180.020

HORIZON1 HORIZON2 HORIZON3

Horizon

Bia

s

Q1Q2Q3Q4

HORIZON1 refers to the period of up to 180 days prior to the earnings announcement date, HORIZON2 refers to period from 181 to 360 days prior to the earnings announcement date, and HORIZON3 refers to period from 361 days and up to 3 years (with 88% of the observations in this group in the 361-540 days window) prior to the earnings announcement date. Quartiles are formed by ABS_DTA, defined in table 1. Figure 2

Median Bias by Quantile and Horizon, Analysts' Forecasts

-0.0050.0000.0050.0100.0150.0200.0250.0300.0350.0400.045

HORIZON1 HORIZON2 HORIZON3 HORIZON4

Horizon

Bia

s

Q1Q2Q3Q4Q5

HORIZON1 refers to the period of up to 90 days prior to the earnings announcement date, HORIZON2, HORIZON3 and HORIZON4 refer to the time period after the earnings announcement of last year and before the announcement of the first quarter results of the year when the forecast is issued, the “window.” In HORIZON2 the forecast is for the current year, and in HORIZON3 (HORIZON4) the forecasts is for two (three) years ahead. Quintiles are formed by ABS_DTA, defined in table 1.

41

Table 1, Descriptive Statistics at the Firm Level Panel a, Managers’ Estimates

Variable Mean STD P5 Q1 Median Q3 P95 n BIAS 0.031 0.076 -0.019 0.000 0.012 0.040 0.141 1978 ABS_DTA 0.084 0.079 0.017 0.037 0.061 0.101 0.235 1856 TA -0.042 0.103 -0.190 -0.083 -0.044 -0.005 0.105 1837 DTA -0.004 0.104 -0.161 -0.040 -0.001 0.034 0.143 1837 ABS_NDTA 0.062 0.050 0.014 0.031 0.047 0.076 0.157 1856 SIZE 4409.2 18843.6 45.4 187.0 621.6 1950.8 15871.2 1976 FOLLOWING 8.39 6.48 1 4 7 11 22 1930 STD_EPS 0.055 0.070 0.009 0.018 0.031 0.061 0.191 1416 STD_D_EPS 0.076 0.091 0.010 0.024 0.043 0.085 0.258 1301 ANA_DIS 0.006 0.008 0.000 0.002 0.003 0.007 0.023 1718 RET_VOL 0.139 0.068 0.060 0.090 0.123 0.171 0.273 1587 INS_OWN 0.506 0.237 0.100 0.324 0.517 0.699 0.873 1868 TOBIN_Q 2.45 2.15 0.93 1.22 1.71 2.67 7.05 1976 Special Items -0.023 0.052 -0.136 -0.023 -0.003 0.000 0.006 1925 N_Announcement 3.3 3.1 1 1 2 4 9 1978 Fiscal Year 1999.9 1.8 1996 1999 2001 2001 2002 1978 No. of Yeats 1.8 1.2 1 1 1 2 4 1978

Panel b, Analysts’ Forecasts, One Year Ahead

Variable Mean STD P1 Q1 Median Q3 P99 n AVE_BIAS 0.034 0.072 -0.071 0.001 0.012 0.043 0.331 7230 MED_BIAS 0.034 0.072 -0.072 0.001 0.012 0.043 0.332 7230 ABS_DTA 0.088 0.077 0.0077 0.040 0.065 0.108 0.426 7230 TA -0.038 0.092 -0.331 -0.075 -0.038 -0.001 0.243 7230 DTA -0.006 0.085 -0.289 -0.035 -0.003 0.026 0.238 7230 ABS_NDTA 0.061 0.054 0.004 0.029 0.047 0.073 0.293 7230 Size 1619.7 8597.9 12.2 78.8 212.8 668.0 26365.2 7224 N 4.3 4.3 1 2 3 5 22 7230 FOLLOWING 8.0 7.5 1 3 6 10 37 7230 STD_EPS 0.076 0.082 0.008 0.027 0.048 0.090 0.447 4538 STD_D_EPS 0.100 0.101 0.010 0.035 0.064 0.121 0.500 4081 ANA_DIS 0.010 0.014 0.000 0.002 0.006 0.012 0.076 6111 RET_VOL 0.140 0.070 0.046 0.091 0.125 0.171 0.388 5343 Special Items -0.027 0.097 -0.401 -0.024 -0.004 0.000 0.059 7178 Fis_Year 1995.0 5.2 1983 1992 1997 1999 2002 7230 N_Year 5.1 4.8 1 2 3 7 20 7230

42

Table 1, Panel c, Analysts’ Forecasts, Two Years Ahead

Variable Mean STD P1 Q1 Median Q3 P99 n AVE_BIAS 0.053 0.086 -0.081 0.006 0.029 0.075 0.416 5865 MED_BIAS 0.053 0.086 -0.083 0.006 0.029 0.075 0.416 5865 ABS_DTA 0.085 0.076 0.0071 0.039 0.063 0.105 0.423 5865 TA -0.033 0.097 -0.320 -0.073 -0.036 0.005 0.278 5865 DTA -0.005 0.087 -0.292 -0.036 -0.002 0.029 0.257 5865 ABS_NDTA 0.059 0.054 0.004 0.028 0.045 0.072 0.296 5865 Size 1810.3 9213.1 14.3 94.1 250.1 760.0 28885.9 5862 N 3.6 3.5 1 1 2 4 18 5865 FOLLOWING 8.1 7.3 1 3 6 10 36 5865 STD_EPS 0.069 0.078 0.007 0.024 0.042 0.080 0.451 3604 STD_D_EPS 0.092 0.099 0.009 0.032 0.057 0.109 0.500 3212 Ana_Dis 0.012 0.015 0.000 0.003 0.006 0.014 0.088 4630 RET_VOL 0.130 0.063 0.043 0.086 0.118 0.160 0.350 4236 Special Items -0.025 0.094 -0.325 -0.025 -0.005 0.000 0.075 5779 Fis_Year 1994.8 4.8 1983 1992 1996 1999 2001 5865 N_Year 4.7 4.5 1 1 3 6 19 5865

Managers’ estimates of earnings per share and their corresponding actual values are taken from First Call. Analysts’ earnings per share forecasts and their corresponding actual values are taken from IBES. Returns, prices, and firms’ data are obtained from CRSP and COMPUSTAT. BIAS is the average over time of managers’ estimate error of annual earnings per share, scaled by the price of the stock at the end of the prior fiscal year. Estimates were made more then 180 days but less then 3 years prior to the earnings announcement date, with 96% of the observations in the 181-540 days window. Errors are defined as forecasts minus actual values. AVE_BIAS (MED_BIAS) is the average over-time of the mean (median) analysts’ forecast error of annual earnings per share, scaled by the price of the stock at the end of the prior fiscal year, made after the announcement of last year annual earnings and before the announcement of the first quarter earnings of the year when the forecast was issued. I refer to this time frame as the “window.” TA is the average over-time of total accruals scaled by total assets at the end of the prior fiscal year. DTA is the average over-time of discretionary total accruals (also referred as the abnormal level of total accruals), scaled by total assets at the end of the prior fiscal year, and is measured as the residuals from cross-sectional regressions of a modified version of the Jones model in each year and industry. ABS_DTA is the average over the last five years, relative to the time when the forecast was issued, of the absolute value of discretionary total accruals scaled by total assets at the end of the prior fiscal year. ABS_NDTA is the average over the last five years of the absolute value of non-discretionary total accruals (also referred as the “normal” level of accruals), scaled by total assets at the end of the prior fiscal year, and is measured as the fitted values from the accruals model. Size is the natural log of the average over-time of the market value at the end of the prior fiscal year. N is the average over-time of the number of analysts issuing forecasts during the window between the announcement of last year annual earnings and the announcement of the first quarter earnings of the year when the forecast was issued. FOLLOWING in the manager sample is the average over-time of the number of analysts following the firm in the 365 days period prior to the manager’s forecast. FOLLOWING in the analyst sample is the average over-time of the number of analysts FOLLOWING the firm. (An analyst is included in this variable but not in N if his first forecast is after the announcement of the first quarter earnings of the forecasted year.) STD_EPS (STD_D_EPS) is the standard deviation of (the change in) earnings per share before extraordinary item (Compustat item # 58) over the prior five years, scaled by price of the stock at the end of the prior fiscal year. At least four observations are required to be calculated.

43

ANA_DIS in the manager sample is the average over-time of analysts’ disagreement and is measured as the standard deviation of analysts’ forecasts made during the 180 days period prior to the manager forecast, scaled by the stock price prior to that period. ANA_DIS in the analyst sample is the average over-time of analysts’ disagreement and is measured as the standard deviation of analysts’ forecast errors made during the window, scaled by the price of the stock at the end of the prior fiscal year. RET_VOL is the average over-time of a measure of the abnormal return volatility and is measured as the standard deviation of the residuals from the market model, estimated with monthly returns over the 3 years period ending at the end of the prior fiscal year. At least 30 monthly observations are required to be included in the estimation. INS_OWN is institutional ownership and is measured as the percentage of outstanding common shares held by institutional investors. TOBIN_Q is measured as market value of total assets (market value of common equity plus the book value of total debt) divided by the book value of total assets. LITIGATE is an indicator variable equal 1 if the company belong to an industry with high risk of litigation (this variable is almost identical to being in a high-tech industry). Special items, Compustat item # 17, represents unusual or nonrecurring items presented above taxes by the company, divided by the number of shares and scaled by price of the stock at the end of the prior fiscal year. N_Announcement is the number of earnings estimate announcements made by the company within the corresponding horizon (in the case of panel a, the second and the third horizon), that were used to compute the average over time for each firm. N_Year is the number of years a firm appears in the sample. In panel b and c it also represents the number of observations used to compute the average over time for each firm. Fis_Year is the average over time of fiscal year.

44

Table 2, Correlations Matrixes at the Firm Level Panel a, Managers’ Estimates, Second and Third Horizons Combined

BIAS ABS_ DTA

ABS_ DTA2 TA DTA ABS_

NDTA Size FOLLO-WING

STD_ EPS

STD_D_ EPS

ANA_ DIS

RET_ VOL

INS_ OWN

TOBIN_Q

Special Items

BIAS 1 0.10 0.06 -0.03 -0.10 -0.06 -0.13 -0.05 0.06 0.08 0.06 0.06 -0.03 -0.06 -0.24

ABS_DTA 0.10 1 0.71 -0.02 -0.04 0.26 -0.24 -0.12 0.29 0.27 0.06 0.48 -0.12 0.20 -0.13

ABS_DTA2 0.05 0.70 1 -0.06 -0.05 0.25 -0.19 -0.08 0.31 0.31 0.07 0.48 -0.10 0.28 -0.14

TA -0.07 0.01 -0.08 1 0.66 -0.15 -0.05 -0.08 -0.04 -0.03 -0.11 -0.05 0.01 0.01 0.14

DTA -0.09 -0.03 -0.04 0.77 1 0.15 0.02 -0.01 -0.07 -0.07 -0.08 -0.07 0.06 0.01 0.15

ABS_NDTA -0.01 0.41 0.37 -0.01 0.12 1 0.00 0.06 0.05 0.03 0.07 0.11 -0.04 0.07 0.02

Size -0.19 -0.16 -0.14 -0.07 -0.02 -0.04 1 0.78 -0.39 -0.40 -0.26 -0.38 0.41 0.33 -0.02

FOLLOWING -0.09 -0.02 -0.01 -0.11 -0.05 0.06 0.75 1 -0.26 -0.25 -0.12 -0.16 0.37 0.27 -0.08

STD_EPS 0.16 0.25 0.26 -0.07 -0.07 0.08 -0.28 -0.19 1 0.93 0.35 0.44 -0.08 -0.22 -0.14

STD_D_EPS 0.15 0.27 0.27 -0.05 -0.06 0.06 -0.30 -0.19 0.94 1 0.34 0.44 -0.07 -0.22 -0.15

Ana_Dis 0.06 0.08 0.10 -0.11 -0.08 0.09 -0.27 -0.14 0.28 0.25 1 0.19 -0.12 -0.35 -0.11

Ret_Vol 0.05 0.43 0.43 -0.05 -0.05 0.18 -0.32 -0.11 0.35 0.38 0.18 1 -0.08 0.16 -0.12

INS_OWN -0.08 -0.15 -0.13 -0.01 0.04 -0.09 0.38 0.29 -0.09 -0.10 -0.17 -0.11 1 0.13 -0.06

TOBIN_Q -0.09 0.25 0.30 -0.01 -0.03 0.12 0.26 0.25 -0.08 -0.09 -0.17 0.23 0.04 1 0.04

Special Items -0.29 -0.12 -0.18 0.14 0.15 0.00 0.12 0.01 -0.15 -0.17 -0.20 -0.08 0.05 0.11 1

Pearson correlations are below the diagonal and Spearman correlations are above the diagonal. All variables are defined in Table 1.

45

Table 2, Panel b, Analysts’ Forecasts, One Year Ahead

AVE_BIAS MED_BIAS ABS_DTA ABS_DTA2 TA DTA ABS_NDTA Size FOLLOWING STD_EPS STD_D_EPS Ana_Dis Ret_Vol Special Items

AVE_BIAS 1 0.997 0.171 0.137 -0.078 -0.057 0.056 -0.398 -0.297 0.357 0.346 0.337 0.143 -0.213

MED_BIAS 0.999 1 0.172 0.139 -0.078 -0.056 0.056 -0.398 -0.298 0.359 0.346 0.336 0.144 -0.215

ABS_DTA 0.138 0.139 1 0.653 0.047 -0.052 0.225 -0.200 -0.125 0.255 0.264 0.038 0.473 -0.136

ABS_DTA2 0.106 0.107 0.670 1 -0.037 -0.064 0.178 -0.135 -0.091 0.326 0.327 0.074 0.540 -0.208

TA -0.149 -0.149 -0.027 -0.121 1 0.668 -0.200 -0.114 -0.084 -0.104 -0.078 -0.190 -0.017 0.113

DTA -0.138 -0.138 -0.120 -0.129 0.778 1 0.080 -0.048 -0.036 -0.102 -0.092 -0.112 -0.114 0.139

ABS_NDTA 0.032 0.032 0.385 0.314 -0.092 0.031 1 0.022 0.052 0.142 0.137 0.088 0.124 -0.032

Size -0.316 -0.315 -0.120 -0.067 -0.109 -0.042 0.026 1 0.755 -0.344 -0.352 -0.204 -0.257 -0.043

FOLLOWING -0.196 -0.194 -0.074 -0.043 -0.096 -0.048 0.044 0.721 1 -0.251 -0.258 -0.061 -0.225 -0.087

STD_EPS 0.278 0.277 0.179 0.287 -0.101 -0.065 0.121 -0.240 -0.171 1 0.953 0.501 0.424 -0.218

STD_D_EPS 0.279 0.276 0.207 0.304 -0.088 -0.070 0.112 -0.268 -0.195 0.946 1 0.471 0.448 -0.218

Ana_Dis 0.315 0.318 0.041 0.086 -0.164 -0.100 0.052 -0.185 -0.060 0.445 0.410 1 0.119 -0.111

Ret_Vol 0.084 0.084 0.397 0.485 -0.073 -0.108 0.172 -0.199 -0.210 0.363 0.388 0.125 1 -0.189

Special Items -0.207 -0.208 -0.103 -0.235 0.120 0.133 -0.025 0.026 0.003 -0.237 -0.188 -0.157 -0.107 1

Pearson correlations are below the diagonal and Spearman correlations are above the diagonal. All variables are defined in Table 1.

46

Table 2, Panel c, Analysts’ Forecasts, Two Years Ahead

AVE_BIAS MED_BIAS ABS_DTA ABS_DTA2 TA DTA ABS_NDTA Size FOLLOWING STD_EPS STD_D_EPS Ana_Dis Ret_Vol Special Items

AVE_BIAS 1 0.999 0.176 0.149 0.012 0.018 0.037 -0.406 -0.314 0.364 0.363 0.323 0.236 -0.217

MED_BIAS 0.999 1 0.177 0.149 0.012 0.017 0.036 -0.407 -0.315 0.363 0.362 0.320 0.235 -0.218

ABS_DTA 0.133 0.134 1 0.647 0.075 -0.030 0.202 -0.196 -0.116 0.248 0.278 0.009 0.472 -0.125

ABS_DTA2 0.106 0.107 0.657 1 0.015 -0.031 0.159 -0.141 -0.086 0.309 0.326 0.030 0.521 -0.155

TA -0.026 -0.026 0.016 -0.083 1 0.680 -0.176 -0.136 -0.102 -0.087 -0.066 -0.179 0.037 0.015

DTA -0.018 -0.019 -0.099 -0.128 0.789 1 0.069 -0.060 -0.044 -0.088 -0.077 -0.102 -0.090 0.053

ABS_NDTA 0.021 0.020 0.360 0.295 -0.055 0.005 1 0.033 0.075 0.122 0.124 0.059 0.086 -0.035

Size -0.339 -0.339 -0.111 -0.066 -0.145 -0.072 0.033 1 0.742 -0.328 -0.346 -0.162 -0.326 -0.048

FOLLOWING -0.222 -0.222 -0.059 -0.037 -0.125 -0.076 0.058 0.727 1 -0.233 -0.247 -0.020 -0.263 -0.106

STD_EPS 0.287 0.286 0.185 0.279 -0.091 -0.067 0.103 -0.228 -0.163 1 0.949 0.450 0.405 -0.161

STD_D_EPS 0.295 0.295 0.221 0.307 -0.080 -0.072 0.111 -0.256 -0.191 0.944 1 0.423 0.456 -0.177

Ana_Dis 0.295 0.292 0.001 0.059 -0.151 -0.076 0.026 -0.161 -0.036 0.409 0.401 1 0.152 -0.045

Ret_Vol 0.148 0.148 0.395 0.451 -0.041 -0.115 0.145 -0.259 -0.244 0.344 0.401 0.180 1 -0.153

Special Items -0.247 -0.248 -0.094 -0.123 0.045 0.077 -0.033 0.034 -0.017 -0.088 -0.083 -0.056 -0.069 1

Pearson correlations are below the diagonal and Spearman correlations are above the diagonal. All variables are defined in Table 1.

47

Table 3, Selected Statistics by Horizon Panel a, Managers’ Estimates Firm Level Firm-Announcement Level HORIZON1 HORIZON2 HORIZON3 HORIZON1 HORIZON2 HORIZON3 BIAS: Mean 0.011 0.024 0.038 0.012 0.020 0.032 Median 0.000 0.007 0.016 0.000 0.003 0.011 STD 0.056 0.071 0.082 0.063 0.067 0.078 ABS_DTA: Mean 0.076 0.080 0.083 0.067 0.070 0.076 Median 0.058 0.060 0.059 0.052 0.053 0.054 STD 0.068 0.073 0.081 0.060 0.064 0.074 Special Items: Mean -0.015 -0.020 -0.026 -0.014 -0.017 -0.024 Median -0.001 -0.003 -0.004 0.000 -0.001 -0.002 STD 0.041 0.049 0.054 0.043 0.048 0.054 Pearson Correlation: ABS_DTA and BIAS 0.015 0.114 0.068 0.004 0.078 0.066 P-Value 0.548 0.000 0.022 0.804 0.000 0.002 Spearman Correlation: ABS_DTA and BIAS -0.015 0.051 0.141 0.001 0.048 0.129 P-Value 0.549 0.695 0.000 0.969 0.940 0.000 N 1612 1651 1228 3455 4084 2362

Panel b, Managers’ Estimates, Special Items Equal Zero Firm Level Firm-Announcement Level HORIZON1 HORIZON2 HORIZON3 HORIZON1 HORIZON2 HORIZON3 BIAS: Mean 0.016 0.020 0.030 0.018 0.020 0.027 Median 0.000 0.004 0.009 0.000 0.002 0.006 STD 0.066 0.061 0.084 0.075 0.070 0.080 ABS_DTA: Mean 0.074 0.074 0.075 0.067 0.067 0.068 Median 0.056 0.055 0.053 0.052 0.051 0.048 STD 0.068 0.069 0.071 0.061 0.060 0.065 Pearson Correlation: ABS_DTA and BIAS -0.014 0.094 0.189 -0.016 0.107 0.171 P-Value 0.691 0.013 0.000 0.575 0.000 0.000 Spearman Correlation: ABS_DTA and BIAS -0.050 0.028 0.121 -0.040 0.028 0.135 P-Value 0.162 0.449 0.009 0.153 0.714 0.000 N 861 768 500 1378 1407 790

In panel a and panel b, HORIZON1 refers to the period of up to 180 days prior to the earnings announcement date, HORIZON2 refers to period from 181 to 360 days prior to the earnings announcement date, and HORIZON3 refers to period from 361 days and up to 3 years (with 88% of the observations in this group in the 361-540 days window) prior to the earnings announcement date.

48

Table 3, Panel c, Analysts’ Forecasts Firm Level Firm-Year Level HORIZON1 HORIZON2 HORIZON3 HORIZON4 HORIZON1 HORIZON2 HORIZON3 HORIZON4

MED_BIAS: Mean 0.008 0.034 0.053 0.055 0.005 0.023 0.036 0.043 Median 0.000 0.012 0.029 0.031 0.000 0.005 0.017 0.022 STD 0.049 0.072 0.086 0.096 0.047 0.075 0.090 0.097 ABS_DTA: Mean 0.090 0.088 0.085 0.069 0.074 0.073 0.071 0.058 Median 0.066 0.065 0.063 0.052 0.055 0.055 0.053 0.042 STD 0.082 0.077 0.076 0.065 0.069 0.066 0.064 0.055 Special Items: Mean -0.023 -0.027 -0.025 -0.021 -0.018 -0.020 -0.019 -0.016 Median -0.003 -0.004 -0.005 0.000 0.000 0.000 0.000 0.000 STD 0.096 0.097 0.094 0.168 0.122 0.138 0.115 0.126 Pearson Correlation: ABS_DTA & MED_BIAS 0.052 0.139 0.134 0.105 0.030 0.083 0.093 0.118

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Spearman Correlation: ABS_DTA & MED_BIAS 0.054 0.172 0.177 0.161 0.022 0.092 0.123 0.149

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 N 7440 7230 5865 1761 36823 36866 27851 4521

Table 3, Panel d, Analysts’ Forecasts, Special Items Equal Zero Firm Level Firm-Year Level HORIZON1 HORIZON2 HORIZON3 HORIZON4 HORIZON1 HORIZON2 HORIZON3 HORIZON4

MED_BIAS: Mean 0.004 0.019 0.033 0.041 0.003 0.013 0.022 0.035 Median 0.000 0.006 0.016 0.022 0.000 0.003 0.010 0.017 STD 0.037 0.058 0.075 0.094 0.035 0.058 0.076 0.093 ABS_DTA: Mean 0.086 0.083 0.080 0.065 0.074 0.072 0.069 0.059 Median 0.063 0.062 0.061 0.050 0.055 0.054 0.052 0.044 STD 0.079 0.073 0.069 0.057 0.068 0.064 0.061 0.054 Pearson Correlation: ABS_DTA & MED_BIAS 0.044 0.084 0.091 0.112 0.026 0.057 0.073 0.124 P-Value 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Spearman Correlation: ABS_DTA & MED_BIAS 0.049 0.090 0.109 0.103 0.014 0.054 0.087 0.119 P-Value 0.000 0.000 0.000 0.001 0.047 0.000 0.000 0.000 N 5713 5512 4187 1044 19287 18845 13320 1879

Variables are defined in table 1. Firm Level means that the average over time for each firm is calculated first, and then the statistics are computed. In panel c and panel d, HORIZON1 refers to the period of up to 90 days prior to the earnings announcement date, HORIZON2, HORIZON3 and HORIZON4 refer to the time period after the earnings announcement of last year and before the announcement of the first quarter results of the year when the forecast is issued, the “window.” In HORIZON2 the forecast is for the current year, and in HORIZON3 (HORIZON4) the forecast is for two (three) years ahead.

49

Table 4, Managers’ Estimates: Firm-Level

The dependent variable is BIAS. The basic regressions for the second and the third horizons combined.

All Observations Special Items = 0 Rank Regressions Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 ABS_DTA 0.096 0.284 0.292 0.120 0.202 0.190 0.101 0.117 0.110

(4.31)*** (6.11)*** (6.05)*** (3.73)*** (2.38)** (2.12)** (4.76)*** (3.42)*** (3.08)***

DTA -0.041 -0.035 -0.022 -0.024 -0.021 -0.010

(1.59) (1.36) (0.55) (0.57) (0.65) (0.30)

SIZE -0.007 -0.007 -0.007 -0.008 -0.166 -0.170

(3.34)*** (3.12)*** (2.32)** (2.55)** (3.11)*** (3.06)***

FOLLOWING 0.001 0.001 0.002 0.002 0.120 0.156

(1.94)* (2.46)** (2.27)** (2.74)*** (2.45)** (3.07)***

ABS_NDTA -0.121 -0.113 -0.217 -0.135 -0.093 -0.092

(2.19)** (1.87)* (2.57)** (1.39) (3.23)*** (2.90)***

ANA_DIS 0.416 0.530 -0.873 -0.617 0.065 0.072

(1.31) (1.61) (2.09)** (1.37) (1.83)* (1.96)*

RET_VOL -0.179 -0.161 -0.153 -0.157 -0.036 -0.028

(4.05)*** (3.55)*** (2.42)** (2.40)** (0.96) (0.73)

STD_D_EPS 0.105 0.097 -0.050 -0.105 -0.001 0.027

(4.06)*** (1.45) (1.23) (0.90) (0.02) (0.40)

LITIGATE 0.015 0.006 0.016 0.009 3.649 -1.884

(3.09)*** (0.76) (2.35)** (0.85) (2.09)** (0.72)

INS_OWN -0.011 -0.014 -0.017 -0.018 -0.010 -0.028

(1.13) (1.37) (1.25) (1.30) (0.35) (0.94)

TOBIN_Q -0.001 -0.000 0.000 0.000 0.032 0.060

(0.52) (0.20) (0.12) (0.04) (0.88) (1.52)

STD_EPS 0.004 0.072 -0.015

(0.05) (0.53) (0.22)

Constant 0.023 0.073 0.033 0.016 0.087 0.053 47.385 49.981 30.941

(8.94)*** (4.89)*** (1.60) (4.69)*** (4.07)*** (1.83)* (36.17)*** (11.97)*** (4.33)***

Industry Fixed Effect

Included Included Included

Observations

1856 1034 1034 882 475 475 1856 1034 1034

R-squared 0.01 0.10 0.12 0.02 0.07 0.10 0.01 0.05 0.09

Note: Each firm is assigned to one of 17 industry groups. Industry fixed effect variables are included in models 3. Horizons are defined in table 3. All other variables are defined in Table 1. t-statistics are under the coefficients. * Significant at 10%; ** significant at 5%; *** significant at 1%

50

Table 5, Managers’ Estimates: Restatements, Firm-Announcement Level The dependent variable is BIAS. The basic regressions for the second and the third horizons combined. (1) (2) (3) (4) (5) (6) (7) All Observations Special Items = 0 Rank Regressions Rank-Cluster RESTATEMENT 0.008 0.006 0.014 0.012 6.527 5.002 5.002

(0.020)** (0.090)* (0.019)** (0.067)* (0.000)*** (0.002)*** (0.068)*

DTA -0.046 -0.039 -0.007 -0.007

(0.000)*** (0.090)* (0.641) (0.753)

SIZE -0.010 -0.011 -4.337 -4.337

(0.000)*** (0.000)*** (0.000)*** (0.000)***

FOLLOWING 0.001 0.002 0.119 0.119

(0.000)*** (0.000)*** (0.000)*** (0.007)***

ANA_DIS 0.226 -0.170 0.030 0.030

(0.125) (0.544) (0.089)* (0.255)

RET_VOL -0.169 -0.157 -0.134 -0.134

(0.000)*** (0.000)*** (0.000)*** (0.000)***

STD_EPS 0.096 0.008 0.030 0.030

(0.000)*** (0.796) (0.092)* (0.366)

LITIGATE 0.014 0.015 5.280 5.280

(0.000)*** (0.001)*** (0.000)*** (0.004)***

INS_OWN -0.010 -0.009 -0.017 -0.017

(0.032)** (0.288) (0.313) (0.540)

TOBIN_Q 0.001 0.001 0.094 0.094

(0.041)** (0.459) (0.000)*** (0.011)**

Constant 0.024 0.098 0.021 0.096 49.554 72.958 72.958

(0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)***

Observations

6446 3980 2197 1286 6446 3980 3980

R-squared 0.001 0.060 0.002 0.053 0.004 0.041 0.041 RESTATEMENT is an indicator variable receiving the value of one for the years starting at 1997 and up to the restatement year if the firm had restated its financial statements. Horizons are defined in table 3. All other variables are defined in Table 1. p-values are under the coefficients. * Significant at 10%; ** significant at 5%; *** significant at 1%

51

Table 6, Horizon Tests, Managers Estimates: Firm Level The dependent variable is BIAS. Observations in all three horizons are included. All Observations Special Items = 0 Rank Regressions Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 ABS_DTA 0.107 0.278 0.288 0.083 0.060 0.050 0.062 0.091 0.083

(4.52)*** (6.62)*** (6.73)*** (2.20)** (0.68) (0.56) (2.66)*** (2.80)*** (2.48)**

D1_ABS_DTA -0.095 -0.213 -0.212 -0.096 0.024 0.008 -0.078 -0.091 -0.087

(2.71)*** (3.76)*** (3.76)*** (1.87)* (0.24) (0.08) (2.35)** (2.12)** (2.02)**

D3_ABS_DTA -0.039 -0.083 -0.079 0.139 0.255 0.245 0.074 0.091 0.089

(1.14) (1.43) (1.36) (2.36)** (2.09)** (2.02)** (2.08)** (1.97)** (1.93)*

D1 -0.006 0.001 0.002 0.002 -0.006 -0.006 2.987 3.456 3.296

(1.66)* (0.30) (0.36) (0.41) (0.88) (0.78) (1.47) (1.45) (1.39)

D3 0.016 0.017 0.016 -0.001 -0.011 -0.011 -3.929 -5.435 -5.356

(4.19)*** (3.55)*** (3.46)*** (0.19) (1.26) (1.23) (1.82)* (2.17)** (2.15)**

DTA -0.052 -0.047 -0.093 -0.087 0.010 0.013

(3.42)*** (3.02)*** (3.32)*** (3.11)*** (0.50) (0.63)

SIZE -0.006 -0.005 -0.006 -0.007 -0.177 -0.163

(4.79)*** (4.23)*** (3.13)*** (3.12)*** (5.12)*** (4.54)***

FOLLOWING 0.001 0.001 0.002 0.002 0.131 0.142

(3.19)*** (3.14)*** (3.52)*** (3.74)*** (4.18)*** (4.38)***

ABS_NDTA -0.096 -0.099 -0.063 0.042 -0.093 -0.089

(2.94)*** (2.75)*** (1.05) (0.64) (4.84)*** (4.26)***

ANA_DIS 0.070 0.143 -0.194 -0.041 0.050 0.053

(0.36) (0.72) (0.62) (0.13) (2.30)** (2.38)**

RET_VOL -0.127 -0.115 -0.126 -0.125 -0.101 -0.089

(4.77)*** (4.21)*** (2.82)*** (2.72)*** (4.12)*** (3.53)***

STD_D_EPS 0.084 0.153 -0.041 -0.007 0.026 0.096

(5.58)*** (4.06)*** (1.53) (0.09) (1.26) (2.19)**

LITIGATE 0.008 0.003 0.015 0.009 2.156 -2.185

(2.72)*** (0.78) (3.16)*** (1.30) (1.87)* (1.25)

INS_OWN -0.002 -0.002 -0.007 -0.005 0.008 -0.005

(0.41) (0.36) (0.74) (0.47) (0.43) (0.26)

TOBIN_Q 0.000 0.000 0.001 0.000 0.083 0.092

(0.12) (0.51) (0.42) (0.19) (3.57)*** (3.64)***

STD_EPS -0.108 -0.041 -0.072

(2.22)** (0.41) (1.59)

Constant 0.015 0.051 0.033 0.014 0.067 0.023 48.834 49.538 41.637 (6.00)*** (5.65)*** (2.62)*** (3.73)*** (4.33)*** (1.10) (34.20)*** (16.24)*** (8.54)*** Industry Fixed Effect

Included Included Included

Observations 4217 2420 2420 1959 1064 1064 4217 2420 2420 R-squared 0.03 0.09 0.10 0.02 0.05 0.09 0.01 0.04 0.06 Note: Each firm is assigned to one of 17 industry groups. Industry fixed effect variables are included in models 3. D1 (D3) is a dummy variable for the first (third) horizon. Horizons for the manager sample are defined in table 3. D1_ABS_DTA (D3_ABS_DTA) is the interaction of D1 (D3) and ABS_DTA. All other variables are defined in Table 1. t-statistics are under the coefficients. * Significant at 10%; ** significant at 5%; *** significant at 1%

52

Table 7, Managers’ Estimates: External Financing Tests, Firm-Announcement Level The dependent variable is BIAS. Observations in the second and third horizons are included.

Model 1 Model 2 Model 3 Model 4 ABS_DTA 0.073 0.120 0.105 0.094

(0.000)*** (0.000)*** (0.000)*** (0.001)***

EXT_FIN 0.016 0.029 0.012 0.013

(0.004)*** (0.000)*** (0.274) (0.249)

DTA -0.059 -0.062 -0.061

(0.000)*** (0.000)*** (0.000)***

SIZE -0.009 -0.009 -0.009

(0.000)*** (0.000)*** (0.000)***

FOLLOWING 0.001 0.001 0.001

(0.000)*** (0.000)*** (0.000)***

ABS_NDTA -0.051 -0.044 -0.012

(0.057)* (0.106) (0.682)

ANA_DIS 0.321 0.334 0.349

(0.039)** (0.031)** (0.028)**

RET_VOL -0.182 -0.182 -0.176

(0.000)*** (0.000)*** (0.000)***

STD_D_EPS 0.058 0.057 0.073

(0.000)*** (0.000)*** (0.032)**

LITIGATE 0.013 0.013 0.009

(0.000)*** (0.000)*** (0.011)**

INS_OWN -0.010 -0.010 -0.013

(0.053)* (0.052)* (0.014)**

TOBIN_Q 0.000 0.000 0.000

(0.678) (0.639) (0.796)

ABS_DTA_XFIN 0.235 0.266

(0.073)* (0.043)**

STD_EPS -0.027

(0.545)

Constant 0.019 0.090 0.090 0.069

(0.000)*** (0.000)*** (0.000)*** (0.000)***

Industry Fixed Effect Included Observations 5929 3680 3680 3680 R-squared 0.008 0.070 0.071 0.080

Note: Each firm is assigned to one of 17 industry groups. Industry fixed effect variables are included in model 4. EXT_FIN is the change in external financing, which is equal to the sum of change in equity (∆EQUITY) and the change in debt (∆DEBT), scaled by average book value of total assets. ∆EQUITY is measured as the annual change in common equity (Compustat item #60) plus the change in preferred stock (Compustat item #130) minus net income (Compustat item #172). ∆DEBT is measured as the annual change in total long-term debt (Compustat item #9) plus the change in short-term debt included in current liabilities (Compustat item #34). ABS_DTA_XFIN is the interaction of ABS_DTA and EXT_FIN. All other variables are defined in Table 1. p-values are under the coefficients. * Significant at 10%; ** significant at 5%; *** significant at 1%

53

Table 8, Managers’ Estimates: External Financing Tests, Firm-Announcement Level, Restatements The dependent variable is BIAS. Observations in the second and third horizons are included.

Model 1 Model 2 Model 3 Model 4 RESTATEMENT 0.008 0.006 0.004 0.005

(0.021)** (0.111) (0.260) (0.206)

EXT_FIN 0.017 0.031 0.025 0.027

(0.001)*** (0.000)*** (0.000)*** (0.000)***

DTA -0.063 -0.063 -0.060

(0.000)*** (0.000)*** (0.000)***

SIZE -0.009 -0.009 -0.010

(0.000)*** (0.000)*** (0.000)***

FOLLOWING 0.001 0.001 0.001

(0.000)*** (0.000)*** (0.000)***

ANA_DIS 0.318 0.331 0.334

(0.040)** (0.032)** (0.035)**

RET_VOL -0.161 -0.160 -0.156

(0.000)*** (0.000)*** (0.000)***

STD_D_EPS 0.063 0.063 0.072

(0.000)*** (0.000)*** (0.035)**

LITIGATE 0.014 0.014 0.011

(0.000)*** (0.000)*** (0.003)***

INS_OWN -0.010 -0.010 -0.013

(0.046)** (0.041)** (0.009)***

TOBIN_Q 0.001 0.001 0.001

(0.144) (0.125) (0.373)

RES_XFIN 0.085 0.084

(0.001)*** (0.002)***

STD_EPS -0.017

(0.700)

Constant 0.023 0.094 0.094 0.074

(0.000)*** (0.000)*** (0.000)*** (0.000)***

Observations 6314 3687 3687 3687 R-squared 0.003 0.066 0.068 0.078

Note: Each firm is assigned to one of 17 industry groups. Industry fixed effect variables are included in model 4. RESTATEMENT is an indicator variable receiving the value of one for the years starting at 1997 and up to the restatement year if the firm had restated its financial statements. EXT_FIN is the change in external financing, which is equal to the sum of change in equity (∆EQUITY) and the change in debt (∆DEBT), scaled by average book value of total assets. ∆EQUITY is measured as the annual change in common equity (Compustat item #60) plus the change in preferred stock (Compustat item #130) minus net income (Compustat item #172). ∆DEBT is measured as the annual change in total long-term debt (Compustat item #9) plus the change in short-term debt included in current liabilities (Compustat item #34). RES_XFIN is the interaction of RESTATEMENT and EXT_FIN. All other variables are defined in Table 1. p-values are under the coefficients. * Significant at 10%; ** significant at 5%; *** significant at 1%

54

Table 9, Market Reaction to Managers’ Forecast News Controlling for Accounting Quality, Firm-Announcement Level The dependent variable is CAR, the cumulative size adjusted abnormal return during the three days window around managers’ forecasts. Observations in the second and third horizons are included.

All Observations Forecasts without Earnings Releases

Model 1 Model 2 Model 3 Model 4 MF_NEWS 0.856 0.854 1.006 1.061

(17.58)*** (15.02)*** (14.23)*** (12.38)***

ABS_DTA -0.063 -0.134 -0.116 -0.219

(3.23)*** (3.63)*** (3.61)*** (3.30)***

EAR_NEWS 6.813 6.794

(14.18)*** (11.03)***

SIZE 0.003 0.004

(2.47)** (1.67)*

FOLLOWING -0.000 -0.000

(1.40) (0.73)

ANA_DIS 0.649 1.474

(2.93)*** (3.94)***

STD_EPS -0.007 0.024

(0.30) (0.61)

RET_VOL 0.107 0.027

(3.26)*** (0.46)

LITIGATE -0.001 0.000

(0.32) (0.06)

TOBIN_Q -0.003 -0.004

(2.95)*** (2.53)**

INS_OWN -0.002 -0.007

(0.32) (0.55)

ABS_NDTA 0.061 0.081

(1.61) (1.21)

DTA -0.007 -0.009

(0.41) (0.31)

Constant 0.989 0.961 0.978 0.952

(509.15)*** (85.61)*** (300.04)*** (50.06)***

Observations 6599 4626 2727 1838 R-squared 0.08 0.08 0.08 0.10

MF_NEWS is managers’ forecast news measured as the managers’ forecast of annual earnings minus the median of analysts’ forecast in the prior 60 days scaled by the stock price 2 days prior to the managers’ forecast. EAR_NEWS is quarterly earnings announcement news released together with managers’ forecast, and is measured as actual earnings per share minus median analysts forecast in the prior 30 (or 90 if there is no analyst’s forecast in the last 30 days) days scaled by the stock price 2 days prior to the earnings announcement. All other variables are defined in Table 1. t-statistics appear under the coefficients. * Significant at 10%; ** significant at 5%; *** significant at 1%

55

Table 10, Market Reaction to Managers’ Forecast News Controlling for Accounting Quality - Restatement, Firm-Announcement Level The dependent variable is CAR, the cumulative size adjusted abnormal return during the three days window around managers’ forecasts. Observations in the second and third horizons are included.

All Observations Forecasts without

Earnings Releases Model 1 Model 2 Model 3 Model 4 MF_NEWS 0.861 0.857 0.994 1.065

(18.56)*** (15.78)*** (14.79)*** (13.11)***

RESTATEMENT -0.011 -0.015 -0.017 -0.024

(2.26)** (2.80)*** (2.16)** (2.93)***

EAR_NEWS 6.850 7.014

(14.75)*** (11.84)***

SIZE 0.004 0.006

(3.06)*** (2.56)**

FOLLOWING -0.001 -0.001

(2.06)** (1.18)

ANA_DIS 0.788 1.661

(3.79)*** (4.77)***

STD_EPS -0.015 0.014

(0.62) (0.36)

RET_VOL 0.069 -0.004

(2.24)** (0.08)

LITIGATE -0.000 0.000

(0.09) (0.03)

TOBIN_Q -0.003 -0.005

(3.61)*** (3.55)***

INS_OWN -0.004 -0.005

(0.60) (0.42)

Constant 0.986 0.962 0.972 0.943

(730.68)*** (91.95)*** (430.49)*** (55.07)***

Observations 7056 4960 2912 1966 R-squared 0.08 0.09 0.07 0.10

MF_NEWS is managers’ forecast news measured as the managers’ forecast of annual earnings minus the median of analysts’ forecast in the prior 60 days scaled by the stock price 2 days prior to the managers’ forecast. EAR_NEWS is quarterly earnings announcement news released together with managers’ forecast, and is measured as actual earnings per share minus median analysts forecast in the prior 30 (or 90 if there is no analyst’s forecast in the last 30 days) days scaled by the stock price 2 days prior to the earnings announcement. RESTATEMENT is an indicator variable receiving the value of one for the years starting at 1997 and up to the restatement year if the firm had restated its financial statements. All other variables are defined in Table 1. t-statistics appear under the coefficients. * Significant at 10%; ** significant at 5%; *** significant at 1%

56

Table 11, Managers’ Decision to Issue Earnings Guidance: Firm Level The dependent variable is FORECAST, an indicator variable receiving the value of 1 if the firm issued an annual earnings guidance during the fiscal year and zero otherwise. For FORECAST=1 only observations in the second and third horizons are included.

All Observations Rank Regressions Model 1 Model 2 Model 3 Model 4 ABS_DTA -1.639 2.754 -0.004 0.009

(0.000)*** (0.003)*** (0.000)*** (0.000)***

DTA -0.533 0.000

(0.478) (0.932)

SIZE 0.207 0.014

(0.000)*** (0.000)***

FOLLOWING -0.015 0.002

(0.183) (0.488)

ABS_NDTA -1.287 -0.003

(0.304) (0.169)

ANA_DIS -15.492 -0.006

(0.002)*** (0.007)***

RET_VOL -3.635 -0.009

(0.000)*** (0.002)***

STD_EPS -1.044 -0.003

(0.149) (0.225)

LITIGATE -0.064 -0.102

(0.554) (0.343)

INS_OWN 1.439 0.013

(0.000)*** (0.000)***

TOBIN_Q -0.017 -0.002

(0.586) (0.422)

TREND 1.405 1.413

(0.000)*** (0.000)***

Constant -1.134 -7.120 -1.063 -6.929

(0.000)*** (0.000)*** (0.000)*** (0.000)***

Observations

9286 3766 9286 3766

Pseudo R^2 0.003 0.344 0.002 0.348 Trend equals one for 1997, 2 for 1998 etc. All other variables are defined in Table 1. p-values are under the coefficients. * Significant at 10%; ** significant at 5%; *** significant at 1%

57

Table 12, Managers’ Decision to Issue Earnings Guidance: Firm-Year Level, Restatements. The dependent variable is FORECAST, an indicator variable receiving the value of 1 if the firm issued an annual earnings guidance during the fiscal year and zero otherwise. For FORECAST=1 only observations in the second and third horizons are included.

All Observations Cluster by Firm

Rank Regression Cluster by Firm

Model 1 Model 2 Model 3 RESTATEMENT 0.411 0.516 0.546

(0.000)*** (0.000)*** (0.000)***

DTA 0.537 0.002

(0.124) (0.053)*

SIZE 0.348 0.018

(0.000)*** (0.000)***

FOLLOWING -0.011 0.004

(0.190) (0.080)*

ANA_DIS -26.545 -0.011

(0.000)*** (0.000)***

RET_VOL -2.819 -0.007

(0.000)*** (0.001)***

STD_EPS 0.342 -0.001

(0.563) (0.406)

LITIGATE 0.043 0.007

(0.650) (0.941)

INS_OWN 0.597 0.006

(0.000)*** (0.001)***

TOBIN_Q -0.079 -0.005

(0.000)*** (0.010)***

TREND 0.631 0.645

(0.000)*** (0.000)***

Constant -1.008 -4.613 -3.575

(0.000)*** (0.000)*** (0.000)***

Observations

33532 10627 10627

Pseudo R^2 0.002 0.229 0.222 Trend equals one for 1997, 2 for 1998 etc. RESTATEMENT is an indicator variable receiving the value of one for the years starting at 1997 and up to the restatement year if the firm had restated its financial statements. All other variables are defined in Table 1. p-values are under the coefficients. * Significant at 10%; ** significant at 5%; *** significant at 1%

58

Table 13, Analysts’ Forecasts Panel a, Basic regressions for second horizon (one year ahead forecasts) The dependent variable is MED_BIAS. All Observations Special Items = 0 Rank Regressions Independent Variable

Model 1

Model 2

Model 3 Model

1 Model

2 Model

3 Model 1

Model 2

Model 3

Intercept 0.023 0.059 0.050 0.014 0.046 0.042 47.12 44.63 39.06 18.15 13.22 8.43 11.52 9.60 6.77 82.43 27.04 18.29 ABS_DTA 0.129 0.105 0.103 0.066 0.047 0.045 0.128 0.047 0.046 11.90 6.25 5.90 6.23 2.66 2.48 13.75 3.80 3.59 DTA -0.039 -0.043 -0.015 -0.017 0.003 -0.002 -2.63 -2.91 -1.13 -1.28 0.19 -0.13 Size -0.009 -0.009 -0.006 -0.007 -0.172 -0.160 -11.87 -11.91 -8.13 -8.55 -10.99 -9.92 FOLLOWING 0.0004 0.0004 0.0002 0.0003 -0.025 -0.028 2.84 3.04 1.90 2.15 -1.57 -1.76 ABS_NDTA -0.016 -0.026 -0.031 -0.029 0.025 0.018 -0.72 -1.10 -1.43 -1.24 2.35 1.58 Ana_Dis 0.890 0.827 0.645 0.576 0.224 0.221 12.78 11.22 9.01 7.80 17.85 16.71 Ret_Vol -0.087 -0.089 -0.087 -0.085 -0.041 -0.042 -5.47 -5.28 -4.94 -4.56 -3.26 -3.23 STD_EPS 0.126 0.195 0.184 3.72 5.74 5.65 STD_D_EPS 0.088 -0.002 0.039 -0.106 0.046 -0.114 9.26 -0.07 3.96 -3.93 3.71 -3.70 Industry Fixed Effect Included Included Included

n 7230 3539 3539 5512 2622 2622 7230 3539 3539 Adjusted R2 0.019 0.200 0.210 0.007 0.099 0.114 0.025 0.252 0.267

Note: Each firm is assigned to one of 17 industry groups. Industry fixed effect variables are included in models 3. Horizons are defined in table 3. All other variables are defined in Table 1. t-statistics are under the coefficients.

59

Table 13, Panel b, Basic regressions for third horizon (two years ahead forecasts) The dependent variable is MED_BIAS. All Observations -0.01<Special Items<0.01 Rank Regressions Independent Variable

Model 1

Model 2

Model 3 Model

1 Model

2 Model

3 Model 1

Model 2

Model 3

Intercept 0.040 0.079 0.059 0.029 0.060 0.048 47.60 35.52 29.76 24.29 11.81 7.07 18.67 8.88 5.71 70.98 17.59 11.78 ABS_DTA 0.151 0.163 0.141 0.105 0.078 0.070 0.149 0.060 0.046 10.34 6.58 5.52 7.41 2.93 2.51 13.74 3.86 2.92 DTA -0.017 -0.017 0.003 0.005 0.054 0.051 -0.81 -0.85 0.18 0.24 2.71 2.55 Size -0.011 -0.011 -0.008 -0.008 -0.167 -0.167 -10.21 -10.45 -7.40 -7.73 -8.52 -8.33 FOLLOWING 0.0004 0.0006 0.0003 0.0004 -0.031 -0.025 2.13 2.89 1.53 2.14 -1.59 -1.27 ABS_NDTA -0.061 -0.056 -0.078 -0.080 0.007 0.006 -2.10 -1.77 -2.60 -2.41 0.54 0.46 Ana_Dis 1.119 1.137 0.972 0.980 0.251 0.250 12.84 12.53 11.53 11.07 17.19 16.34 Ret_Vol -0.081 -0.076 -0.057 -0.041 0.009 0.006 -3.13 -2.75 -2.18 -1.45 0.55 0.37 STD_EPS 0.095 0.066 0.219 1.97 1.45 5.63 STD_D_EPS 0.086 0.016 0.041 -0.010 0.092 -0.102 6.21 0.43 2.92 -0.29 6.26 -2.75 Industry Fixed Effect Included Included Included

n 5865 2638 2638 5041 2260 2260 5865 2638 2638 Adjusted R2 0.018 0.209 0.223 0.011 0.135 0.143 0.031 0.303 0.320

Note: Each firm is assigned to one of 17 industry groups. Industry fixed effect variables are included in models 3. Horizons are defined in table 3. All other variables are defined in Table 1. t-statistics are under the coefficients.

60

Table 14, Analysts’ Forecasts: Horizon Tests The dependent variable is MED_BIAS. Observations in the first, second and third horizons are included. All Observations -0.01<Special Items<0.01 Rank Regressions Independent Variable

Model 1

Model 2

Model 3 Model

1 Model

2 Model

3 Model 1

Model 2

Model 3

Intercept 0.020 0.046 0.034 0.012 0.032 0.028 48.51 54.89 50.97 16.34 16.54 9.75 10.89 12.31 8.44 83.09 37.58 30.66

D1 -0.017 -0.006 -0.006 -0.010 -0.004 -0.004 -8.82 -4.97 -4.99 -9.49 -3.46 -3.44 -6.73 -2.60 -2.62 -10.73 -5.98 -6.04

D3 0.019 0.007 0.007 0.014 0.006 0.006 8.62 4.40 4.50 10.84 3.75 3.88 9.38 3.49 3.67 10.64 5.14 5.30

ABS_DTA 0.101 0.100 0.089 0.049 0.035 0.032 0.109 0.054 0.052 8.92 6.87 6.03 4.68 2.38 2.16 11.28 4.93 4.62

D1_ABS_DTA -0.083 -0.127 -0.129 -0.038 -0.059 -0.060 -0.077 -0.075 -0.075 -5.25 -6.44 -6.57 -2.59 -3.00 -3.05 -5.68 -5.13 -5.16

D3_ABS_DTA 0.041 0.080 0.079 0.049 0.047 0.046 0.021 0.039 0.038 2.66 3.83 3.84 3.39 2.22 2.17 1.57 2.58 2.48

DTA -0.028 -0.033 -0.006 -0.009 0.037 0.032 -3.19 -3.66 -0.78 -1.05 3.55 3.04

Size -0.006 -0.007 -0.004 -0.005 -4.74 -4.58 -15.64 -15.52 -11.01 -11.42 -16.26 -15.42

FOLLOWING 0.0003 0.0003 0.0002 0.0002 -0.021 -0.021 3.52 3.98 2.39 2.91 -2.42 -2.43

ABS_NDTA -0.019 -0.016 -0.027 -0.028 0.002 -0.002 -1.63 -1.29 -2.36 -2.26 0.29 -0.27

Ana_Dis 0.951 0.923 0.765 0.728 0.202 0.200 23.56 21.90 19.44 17.76 26.15 24.73

Ret_Vol -0.065 -0.066 -0.042 -0.038 -0.014 -0.021 -6.79 -6.58 -4.54 -3.85 -1.86 -2.65

STD_EPS 0.108 0.089 0.145 5.36 4.93 7.40

STD_D_EPS 0.060 -0.016 0.024 -0.042 0.019 -0.108 10.88 -1.04 4.40 -2.94 2.57 -5.77

Industry Fixed Effect Included Included Included

n 16842 9026 9026 14070 7719 7719 16842 9026 9026

Adjusted R2 0.101 0.243 0.253 0.075 0.146 0.152 0.203 0.342 0.350

Note: Each firm is assigned to one of 17 industry groups. Industry fixed effect variables are included in models 3. D1 (D3) is a dummy variable for the first (third) horizon. Horizons for the analyst sample are defined in table 3. D1_ABS_DTA (D3_ABS_DTA) is the interaction of D1 (D3) and ABS_DTA. All other variables are defined in Table 1. t-statistics are under the coefficients.

61

Table 15, Analysts’ Forecasts: External Financing Tests Panel a Regressions for second horizon (one year ahead forecasts). The benchmark group comprises observations with no or small changes in external finance, -2%<∆XFIN<2%, expressed in term of percentage of total assets, the high external finance group includes observations with ∆XFIN>10%, and the negative external finance group contains observations with ∆XFIN<-10%. The dependent variable is MED_BIAS. All Observations Special Items = 0 Rank Regressions Independent Variable

Model 1

Model 2

Model 3 Model

1 Model

2 Model

3 Model 1

Model 2

Model 3

Intercept 0.018 0.035 0.031 0.009 0.019 0.030 45.35 45.88 40.66 7.17 5.61 3.83 4.14 2.67 3.44 49.21 22.55 15.38

D1 -0.007 0.004 0.004 -0.013 -0.002 -0.002 -0.723 2.83 3.14 -1.48 0.78 0.88 -3.10 -0.34 -0.33 -0.45 1.56 1.72

D3 0.009 0.010 0.010 0.008 0.012 0.013 3.25 4.39 4.51 2.70 2.55 2.62 2.52 3.00 3.05 2.68 2.87 2.95

ABS_DTA 0.175 0.238 0.230 0.116 0.152 0.164 0.125 0.102 0.095 6.24 6.23 5.93 4.51 3.61 3.82 8.11 4.88 4.48

D1_ABS_DTA 0.024 -0.194 -0.201 0.033 -0.147 -0.143 0.019 -0.062 -0.065 0.52 -3.39 -3.52 0.72 -2.18 -2.12 0.70 -1.89 -1.96

D3_ABS_DTA -0.124 -0.179 -0.175 -0.096 -0.159 -0.164 -0.054 -0.110 -0.112 -3.81 -3.92 -3.83 -3.21 -3.16 -3.26 -2.62 -3.82 -3.87

DTA -0.037 -0.039 -0.012 -0.009 -0.020 -0.021 -2.83 -2.98 -0.81 -0.66 -1.38 -1.41

Size -0.005 -0.005 -0.002 -0.002 -0.086 -0.080 -5.23 -5.04 -2.07 -2.37 -4.44 -4.01

FOLLOWING -0.000 -0.000 -0.000 -0.000 -0.080 -0.078 -0.80 -0.87 -1.18 -1.25 -4.03 -3.84

ABS_NDTA -0.013 -0.027 -0.013 -0.039 0.004 0.006 -0.45 -0.89 -0.44 -1.16 0.31 0.39

Ana_Dis 1.411 1.327 1.107 1.008 0.218 0.219 12.96 11.78 9.07 8.04 15.56 15.08

Ret_Vol -0.084 -0.091 -0.071 -0.067 -0.041 -0.045 -4.07 -4.19 -2.95 -2.61 -2.85 -2.91

STD_EPS 0.174 0.148 0.071 4.53 3.44 2.13

STD_D_EPS 0.051 -0.075 0.005 -0.107 0.009 -0.052 4.20 -2.49 0.33 -3.17 0.60 -1.61

Industry Fixed Effect Included Included Included

n 10005 4609 4609 6161 2672 2672 10005 4609 4609

Adjusted R2 0.008 0.095 0.102 0.009 0.052 0.055 0.013 0.110 0.114

Note: Each firm is assigned to one of 17 industry groups. Industry fixed effect variables are included in models 3. D1 (D3) is a dummy variable for the large negative (positive) change in external finance group. D1_ABS_DTA (D3_ABS_DTA) is the interaction of D1 (D3) and ABS_DTA. All other variables are defined in Table 1. t-statistics are under the coefficients.

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Table 15, Panel b Regressions for third horizon (two year ahead forecasts). The benchmark group comprises observations with no or small changes in external finance, -2%<∆XFIN<2%, expressed in term of percentage of total assets, the high external finance group includes observations with ∆XFIN>10%, and the negative external finance group contains observations with ∆XFIN<-10%. The dependent variable is MED_BIAS. All Observations Special Items = 0 Rank Regressions Independent Variable

Model 1

Model 2

Model 3 Model

1 Model

2 Model

3 Model 1

Model 2

Model 3

Intercept 0.034 0.055 0.036 0.018 0.028 0.021 40.53 31.69 27.33 10.90 6.03 3.28 5.67 2.75 1.75 37.61 12.33 8.37

D1 -0.018 -0.007 -0.006 -0.026 -0.013 -0.012 3.97 5.28 6.42 -3.31 -1.16 -0.91 -4.48 -1.96 -1.83 1.95 2.20 2.67

D3 0.008 0.001 0.002 0.012 0.009 0.009 3.829 2.169 2.453 1.91 0.23 0.34 2.77 1.55 1.51 2.60 1.13 1.27

ABS_DTA 0.212 0.183 0.162 0.108 0.086 0.077 0.190 0.117 0.106 6.06 3.10 2.70 2.96 1.43 1.25 10.17 4.38 3.89

D1_ABS_DTA 0.006 -0.197 -0.205 0.138 -0.061 -0.064 -0.074 -0.068 -0.081 0.10 -2.33 -2.42 2.22 -0.68 -0.71 -2.20 -1.57 -1.87

D3_ABS_DTA -0.101 -0.037 -0.031 -0.053 -0.048 -0.035 -0.076 -0.066 -0.069 -2.50 -0.55 -0.46 -1.24 -0.66 -0.47 -2.99 -1.78 -1.85

DTA -0.002 -0.001 0.027 0.023 0.054 0.052 -0.10 -0.08 1.33 1.15 3.19 3.05

Size -0.006 -0.007 -0.003 -0.004 -0.077 -0.073 -4.88 -4.96 -2.13 -2.38 -3.06 -2.83

FOLLOWING 0.000 0.000 0.000 0.000 -0.070 -0.067 0.19 0.78 0.31 0.53 -2.81 -2.68

ABS_NDTA -0.065 -0.068 -0.028 -0.019 -0.022 -0.026 -1.74 -1.67 -0.69 -0.41 -1.33 -1.43

Ana_Dis 1.212 1.232 0.209 0.171 0.225 0.220 9.41 9.20 1.50 1.16 13.38 12.50

Ret_Vol -0.038 -0.027 0.020 0.021 0.006 0.001 -1.19 -0.80 0.54 0.52 0.31 0.04

STD_EPS 0.069 0.105 0.147 1.39 1.79 3.77

STD_D_EPS 0.062 0.009 -0.017 -0.094 0.084 -0.043 3.68 0.23 -0.88 -2.04 4.74 -1.12

Industry Fixed Effect Included Included Included

n 7725 3350 3350 4547 1873 1873 7725 3350 3350

Adjusted R2 0.016 0.087 0.098 0.018 0.028 0.027 0.021 0.138 0.148

Note: Each firm is assigned to one of 17 industry groups. Industry fixed effect variables are included in models 3. D1 (D3) is a dummy variable for the large negative (positive) change in external finance group. D1_ABS_DTA (D3_ABS_DTA) is the interaction of D1 (D3) and ABS_DTA. All other variables are defined in Table 1. t-statistics are under the coefficients.