Does CEO Overconfidence Affect Management Forecasting and Subsequent Earnings Management?

Paul Hribar Henry B. Tippie College of Business

University of Iowa paul-hribar@uiowa.edu

Holly Yang

The Wharton School University of Pennsylvania hollyy@wharton.upenn.edu

March 2010

Comments Welcome

We would like to thank the helpful comments and suggestions made by Sanjeev Bhojraj, Rob Bloomfield, Julia D’Souza, Werner DeBondt, Ming Huang, Bob Libby, Mark Nelson, Vefa Tarhan, Merle Erickson (the editor), an anonymous referee and workshop participants at Cornell University, The University of Iowa, and the 2007 People and Money conference on Behavioral Finance. We also thank Theo Chen, Lin Qiao, Justin Kim, Cindy Na, and Jason Vigushin for their excellent research assistance.

1

Does CEO overconfidence affect management forecasting and subsequent earnings management?

Abstract

This paper examines whether overconfidence increases the likelihood of issuing overly

optimistic management earnings forecasts. Using a press-based measure of overconfidence introduced by Malmendier and Tate (2005), we provide evidence that higher scores are associated with a greater likelihood of a CEO missing his/her voluntary forecast of earnings. Controlling for known determinants of forecast precision, we also find evidence that overconfidence decreases the width of forecasts when issued as a range. Finally, controlling for other determinants of earnings management, we show that overconfidence is positively associated with the use of income-increasing abnormal accruals subsequent to the issuance of a range forecast. Taken together, our results suggest that overconfidence increases the optimistic bias in voluntary forecasts, leading to both an increased likelihood of missing management forecasts and greater earnings management.

2

I. Introduction

Finance and accounting researchers have studied extensively how individual decision

biases affect asset prices in equilibrium (e.g. Kyle and Wang 1997, Daniel, Hirshleifer, and

Subrahmanyam 1998, Fischer and Verrecchia 1999, Odean 1998, Gervais and Odean 2001,

Libby, Bloomfield, and Nelson 2002). These papers use evidence in social psychology on

individual decision-making biases as potential explanations for observed empirical regularities in

asset prices, such as returns momentum or post-earnings announcement drift. In contrast,

research studying the effects of these biases on corporate policies and decision-making has been

sparse until recently. Heaton (2002) discusses this discrepancy between the literatures. He notes

that the lack of behavioral economics in corporate decision-making research is somewhat

puzzling, because the common objections to behavioral economics have less vitality in this

setting than in asset pricing. Evidence of individual decision-making biases should be easier to

detect in the context of corporate decisions, where there exists little or no arbitrage mechanism,

and where significant decisions, such as corporate acquisitions, are relatively infrequent with

delayed and noisy feedback.1 With the introduction of archival measures of overconfidence,

more recent studies are examining links between overconfidence and corporate decisions such as

acquisitions, cash flow sensitivity, earnings management, and personal equity sales (Malmendier

and Tate, 2005, 2008; Ben-David, Graham and Harvey, 2007: Jin and Kothari, 2008; Schrand

and Zechman, 2010).

In this paper, we examine the role of CEO overconfidence in two important accounting

decisions: earnings forecasting and earnings management. We argue that overconfidence

manifests itself either as excessive optimism about future firm performance, or as an

1 Roll (1986) expresses similar sentiments, arguing that managerial hubris is likely to contribute to corporate takeovers, because of the substantial influence of the executives in these decisions.

3

underestimate of the variance underlying future performance. Overconfident managers are more

likely to issue optimistically biased forecasts because they overestimate their ability to affect

their financial results and/or underestimate the probability of random events. We test for three

potential consequences of this behavior. First, we examine whether overconfidence is associated

with a greater probability of missing their voluntary forecasts, controlling for other determinants

of forecast accuracy. Second, we examine whether overconfidence affects the form of the

forecast they issue, with overconfident CEOs being more likely to issue forecasts of greater

precision (points and narrower ranges) than other CEOs. Third, we examine whether

overconfidence affects the use of abnormal accruals, conditional on issuing a forecast.

Our primary measure of overconfidence is based on popular press characterizations of the

CEO (Malmendier and Tate, 2008; Jin and Kothari, 2008). Malmendier and Tate (2008) classify

a CEO as overconfident if he/she is more frequently described as confident and optimistic

relative to descriptors such as frugal, conservative, cautious, practical, reliable, or steady. From

an econometric perspective, this measure serves as a good instrument because it correlates

positively with equity-based measures of overconfidence, but does not suffer from the same

endogeneity. However, because of the inherent noise in the press-based measure, we examine an

alternative measure of overconfidence based on CEO equity purchases similar to Malmendier

and Tate (2005). For consistency with prior research, we use the term ‘overconfident’ to

describe the construct of interest, despite the fact that the measure we use is actually a relative

measure of confidence. Although individuals at one end of the continuum are more likely to be

overly confident, we have no way of calibrating our proxies to an appropriate level of

4

confidence.2 The tenor of our results are unchanged, however, if one views it simply as the

effect of confidence on management forecasting and earnings management.

We test our predictions empirically on a sample of 640 firms listed on the Fortune 500

from 2000 to 2007. We use a two-stage procedure to control for the fact the sample of firms that

voluntarily forecast earnings is a self-selected sample. For example, overconfidence might affect

the decision to forecast, in addition to the content of the forecast. Controlling for self-selection,

we find that overconfident managers are more likely to issue forecasts that they subsequently

miss. This finding holds after controlling for other predictors of managerial forecast errors such

as accounting flexibility, litigation risk, forecast horizon, and growth prospects. We also find

evidence that confidence affects forecast precision, whereby overconfident CEOs issue range

forecasts with a narrower width. Finally, we find that among the sample of CEOS that

voluntarily forecast earnings, greater overconfidence is positively associated with the use of

income-increasing accruals, although this behavior is most pronounced when the forecast is

issued as a range, not a point.

Our paper contributes to several streams of literature. First, we add to the growing

literature on how behavioral biases can affect executives’ corporate decisions. Prior studies

contend that overconfident CEOs are more acquisitive and over-invest in projects they perceive

as less risky leading to reductions in firm value in the long run. Firms with overconfident CFOs

also have a lower propensity to pay out dividends and a higher propensity to engage in market

timing and are more likely to issue voluntary disclosures (Ben-David et al., 2007). Our results

2 The basis for this claim follows from the psychological evidence that individuals are, on average, overconfident (see, for example, Weinstein 1980). In fact, managers tend to be more prone to display overconfidence, both in terms of the better than average effect as well as having too narrow confidence intervals for probabilistic events (e.g. Larwood and Whittaker 1977; Moore 1977).

5

complement this study by showing that overconfidence affects two key financial reporting

decisions: the content of voluntary earnings forecasts and the use of discretionary accruals.

We also contribute to the literature on the accuracy of management forecasts. Chen

(2004) investigates why managers fail to meet their own forecasts. Her results suggest that firms

that miss their own forecasts have less accounting flexibility, less forecasting experience, and

have incurred exogenous negative shocks. Bhojraj, Libby, and Yang (2010) examine the

association between forecast frequency and forecast properties. Their results suggest that firms

committed to providing forecasts issue forecasts that are less optimistic, more accurate, and more

precise. We provide evidence that forecasting decisions and forecast errors are also affected by

overconfidence.

Last, we add to the earnings management literature by providing evidence that

overconfidence is associated with more aggressive accounting after issuing a voluntary forecast.

Much of the prior archival research on earnings management examines the association between

personal and corporate economic incentives to manage earnings, such as contracting costs,

compensation contracts, and capital market incentives.3 We add to this literature by providing

evidence that individual psychology also contributes to the decision to manage earnings.

II. Literature Review and Hypotheses Development

Our research draws from three streams of literature. We begin by reviewing the

literature on overconfidence as it has evolved in the corporate finance literature.4 We then

briefly discuss the accounting literature on voluntary earnings forecasts and earnings

management.

3 See Dechow, Ge, and Schrand (2009) for a recent review of the literature on earnings management. 4 The notion of investor overconfidence has also been used extensively in the asset pricing literature in finance and accounting. We do not review these studies here.

6

2.1 Managerial Overconfidence

Research in psychology shows that individuals are overconfident. When assessing their

acumen in a distribution of peers on almost any desirable trait (e.g. driving ability, longevity,

income prospects), a vast majority claim that they are above average (Weinstein, 1980; Svenson,

1981). This belief appears to be more pronounced when there is the illusion of control, limited

tangible feedback or abstract reference points, and for outcomes to which the individual is highly

committed (e.g. Weinstein 1980).

The notion of overconfidence in finance builds from this research and examines whether

this optimistic bias affects economic decisions. One of the earliest examples of research linking

overconfidence to corporate decision making is Roll (1986), who claims that ‘managerial hubris’

as an explanation for corporate takeovers is at least as descriptive as the alternative hypotheses,

which include taxes, synergy and inefficient target management. Camerer and Lovallo (1999)

use an experimental setting to show that overconfidence affects the decision to enter into a

business market where success is dependent on the individual’s skill.5 Heaton (2002) develops a

simple theoretical model that assumes managerial over-optimism and derives testable predictions

from it. His model predicts that overoptimistic managers will have biased cash flow forecasts,

exhibit a preference for internal financing of projects because of the perceived undervaluation of

the firm, and have a stronger resistance to external takeovers.

Until recently, however, little empirical research regarding managerial confidence has

been done using naturally occurring data (i.e. archival studies). This is most likely due to the

difficulty associated with measuring the degree of confidence in corporate decision makers.

5 Interestingly, the degree of over-entry into a market is even stronger when individuals are allowed to self-select into the experimental group where the payoffs are skill-dependent.

7

Recently, different empirical measures have been developed in an attempt to measure

overconfidence in corporate executives, typically the CEO.

The first measure, used by Malmendier and Tate (2005), classifies managers as

overconfident if they overexpose themselves to the idiosyncratic risk of their firms. They

classify CEOs as overconfident if they exercise options later than the optimal date, hold their

options until expiration, or increase their holdings of company stock. Using these measures of

overconfidence, they show that overconfident CEOs of 477 large U.S. companies between 1980

and 1994 have a heightened sensitivity of corporate investment to cash flow. They attribute this

finding to the fact that overconfident CEOs are more likely to overestimate the returns of

investment projects, hence investing more when internal funds are sufficient. However, because

overconfident CEOs also believe their firm shares are undervalued by the market, they will not

issue new equity to increase investment in projects if internal funds are not sufficient. As a result,

additional cash flow provides an opportunity for overconfident CEOs to invest closer to their

desired levels. Employing the same methodology, Malmendier and Tate (2008) also find that

overconfident CEOs are more acquisitive and engage in more value-destroying mergers because

they overestimate their ability to generate returns.

Malmendier and Tate (2008) develop a second measure to corroborate the findings of the

option-based measure, and to help rule out omitted variable explanations for their findings. One

concern with the option-based measure is the potential endogeneity in a model that links the

CEO’s equity holdings to his/her corporate decisions. Their second measure is therefore based

on outsiders’ perceptions of the CEO, as published in the popular press. CEOs are ranked based

on the number of times they are described as confident or optimistic relative to the number of

times they are described as prudent, cautious, conservative, practical or frugal. The advantage of

8

using this as an instrumental variable for overconfidence is that it does not suffer from the same

endogeneity and omitted variable explanations as the equity-based measure of overconfidence.

For example, it is more difficult to argue that the manner in which a CEO is described in the

press subsequently alters his/her behavior in a manner consistent with the description (e.g.

describing a CEO as aggressive causes him/her to make more aggressive decisions).

Furthermore, although it is really a relative measure of confidence with no benchmark to

measure an appropriate level of confidence, it correlates positively with the equity-based

measure. Thus, individuals who score highly on the press-based measure are more likely to

overexpose themselves to the idiosyncratic risk of their firms. The disadvantage is that it is

likely to be a noisy instrument, measuring the true degree of CEO confidence with a significant

amount of error.

Jin and Kothari (2008) also use the press-based measure in their study of the effects of

taxes on CEOs' personal equity selling decisions. They argue that the option-based measure

relies on assumptions about the gains from diversification relative to the costs of early exercise,

and may in fact be nearly rational if, for example, the firm has low idiosyncratic risk. Their

results suggest that although managerial overconfidence plays a role in determining a manager's

investment in firm equity, taxes have a first order effect.

A third measure of overconfidence is employed by Ben-David, Graham, and Harvey

(2007). They measure the confidence bounds that executives provide when asked to estimate the

future performance of a stock index. Overconfidence can then be defined as having too narrow

of confidence intervals relative to the historical distribution (i.e. variance) of the stock index.

They find that firms managed by CFOs with narrower confidence intervals (higher subjective

confidence) invest more, pay out fewer dividends, are more leveraged, engage in market timing,

9

and have a higher pay for performance ratio. This measure has the advantage of directly

measuring the construct of interest, but the downside is that the measure is proprietary, and only

exists for a self-selected set of managers.

It is important to recognize the difference in the notion of overconfidence examined in

Malmedier and Tate (2005, 2008) and Jin and Kothari (2008) versus Ben-David et al. (2007).

Malmedier and Tate (2005, 2008) and Jin and Kothari (2008) are motivated from the “better than

average” effect, where individuals over-estimate their acumen relative to others. This upward

bias in the assessment of future events is also referred to as over-optimism, although we retain

the term overconfidence for consistency with the finance literature. In contrast, the Ben-David et

al. paper (2007) defines overconfidence as too narrow confidence intervals when predicting

probabilistic events, regardless of whether the expectation is biased or not. Examining

management forecasts allows us to gain insight into both dimensions, because we can examine

the likelihood of exceeding the forecast (i.e. upward bias) as well as the implicit confidence

bounds that the manager places around his/her forecast by observing forecast forms (i.e. the

width of the range forecast).

2.2 Management Earnings Forecasts

Prior research shows that managers face a number of penalties for voluntarily disclosing

inaccurate earnings information and that these penalties are often sufficient to deter managers

from issuing biased forecasts (McNichols, 1989). Despite the penalties identified including loss

of reputation, probability of legal actions, and negative capital market consequences, firms still

fail to meet their own earnings forecasts (Trueman, 1986; Kasznik, 1999; Chen, 2004).

Research investigating the cause of upwardly biased forecasts has focused primarily on

10

economic rationales, where the perceived benefits of providing optimistic forecasts outweigh the

potential costs of issuing inaccurate disclosures.

Lang and Lundholm (2000) examine corporate disclosure activity around equity offerings.

They find that companies not only increase the frequency of disclosures regarding firm

performance but also make more optimistic statements and detailed interpretations relative to

previous periods. Koch (2002) uses a sample of 517 quantitative management forecasts issued

from 1993 to 1997 and finds that forecasts by financially distressed firms are biased relative to

forecasts by non-distressed firms. His results suggest that management credibility decreases as

financial distress intensifies but that analysts are able to discount the optimism contained in these

biased management forecasts. Chen (2004) studies why managers fail to meet their own

forecasted quarterly targets. Using various proxies for accounting flexibility, she finds that firms

with greater accounting constraints are more likely to miss their own earnings forecasts.

Overall, the literature on management forecast accuracy has demonstrated a number of

different economic incentives to issue biased forecasts. Our goal is to complement this research

by examining whether a personal characteristic of the CEO also explains differences in the ex-

post accuracy of management forecasts.6 We conjecture that confidence will be positively

associated with the likelihood of issuing upwardly biased (i.e. more optimistic) earnings

forecasts, leading overconfident CEOs to miss their voluntary earnings forecasts more frequently.

Our interpretation of confidence in this setting is consistent with its use by Malmendier and Tate

(2005, 2008) and Jin and Kothari (2008), where it affects one’s assessment of their acumen

relative to the average. Overconfident managers overestimate the precision of their own

judgments and underestimate the variance of random processes. Therefore, they are more likely

to issue an overly optimistic forecast because they overestimate their ability to influence the 6 See Bamber, Jiang, and Wang (2009) on the effects of management styles on firms’ voluntary disclosures.

11

earnings of the firm, and underestimate the probability of unexpected events, such as fluctuations

in the business cycle. This leads to our first hypothesis, in alternative form:

H1: Overconfidence is positively associated with the likelihood of a firm missing its management earnings forecast. A second implication of CEO overconfidence is derived from the Ben-David et al.

(2007) study that defines overconfidence as having too narrow confidence intervals when

forecasting future returns for the S&P500. Our measure might also capture this aspect of

overconfidence because it is based on outsiders’ perceptions of the CEO. A priori, it is

not clear whether a characterization of the CEO as overconfident refers to a general sense

of over-optimism or a tendency to underestimate random events. To the extent that our

measure also captures the latter, we expect overconfident managers will issue

management forecasts with a narrower range. Specifically, we expect that overconfident

managers will issue more ‘point’ forecasts, and have narrower range forecasts. This

leads to our next hypotheses:

H2a: Conditional on issuing a forecast, overconfidence is positively associated with the issuing of point forecasts relative to range or open-ended forecasts. H2b: Conditional on issuing a range forecast, overconfidence is negatively associated with the width of the range.

2.3 Management Forecasts and Earnings Management

Our third research question examines the link between overconfidence and the use of

aggressive accounting practices. Given that missing forecasts is costly, managers have economic

incentives to issue beatable forecasts or make attempts to meet their forecasts when feasible.

Kasznik (1999) investigates whether firms that issue management forecasts are more likely to

manage earnings upwards because they fear litigation or reputation costs from missing their

12

forecast. He finds that firms that miss their own forecasts have significant income-increasing

accruals, and that the level of earnings management is increasing in expected litigation costs. He

interprets this result as firms using income-increasing accruals to reduce their forecast errors.

Chen (2004) finds that managers of larger firms with a larger analyst following and more

forecasting experience are more likely to update their forecasts. Both of these studies suggest

that managers take actions to reduce their own forecast errors.

We predict that overconfidence affects CEOs’ decisions to manage earnings in two ways.

First, if more confident managers issue forecasts that are, on average, too optimistic, then the

economic incentives for meeting or beating a forecast implies that managers will need to manage

earnings upwards to try to meet or beat their forecast. Using firm-level proxies for managerial

overconfidence, Schrand and Zechman (2010) find a positive association between executive

overconfidence and fraud. Ceteris paribus, an overconfident manager will need to use more

income-increasing accruals to meet or beat their forecast when it is overly optimistic.

Second, Miller and Ross (1975) argue that confidence affects the attribution of causality.

Individuals who expect their behavior to produce success are more likely to attribute bad

outcomes to luck, and good outcomes to skill. Therefore, faced with missing his/her forecast, an

overconfident CEO would be more likely to attribute the shortfall in earnings to luck, and be

more willing to make it up through earnings management, believing that he/she will be able to

make up the difference in the next period. These two explanations lead to our third hypothesis:

H3: Conditional on issuing a management forecast, overconfidence is positively associated with the use of income-increasing discretionary accruals.

13

III. Research Design

3.1 Sample Selection and Variable Definitions

Our initial sample consists of 640 firms listed on the Fortune 500 from 2000 to 2007. We

collect press coverage information on CEOs of these firms for our sample period. Following

Malmendier and Tate (2008), we employ a financial press-based measure of CEO

overconfidence. We search for articles referring to the CEOs in the New York Times, Business

Week, Financial Times, the Economist, Forbes, Fortune, Time and the Wall Street Journal using

Factiva.7 We record four statistics for each CEO in our sample: the number of articles describing

the CEO using the terms “confident” or “confidence” (confident); the number of articles

describing the CEO using the terms “optimistic” or “optimism” (optimistic); the number of

articles describing the CEO using the terms “reliable”, “steady”, “practical”, “conservative”,

“frugal”, or “cautious” (cautious); and the number of articles describing the CEO using the terms

“not confident” or “not optimistic” (not optimistic). TOTAL is the sum of confident, optimistic,

cautious and not optimistic. We read each article to verify that the word is used in an appropriate

context and relevant to the CEO of interest.

We develop two variables using these article counts. The first variable (CONF1) is an

indicator variable that classifies the manager as overconfident if the number of articles

describing the CEO as optimistic or confident exceeds the number of articles describing the CEO

as reliable, steady, practical, conservative, frugal, cautious, not optimistic, or not confident. This

is the same definition used in Malmendier and Tate (2008) and Jin and Kothari (2008). Note that

using this definition, firms with zero press coverage end up in the ‘not-overconfident’ group.

In addition to the binary variable introduced by Malmendier and Tate (2005), we develop

a second variable (CONF2), which is continuous. This variable captures the frequency with 7 As in Jin and Kothari (2008), we expand the media source by including Time, Forbes, and Fortune.

14

which a CEO is described as confident or optimistic relative to conservative or not optimistic,

and is computed as follows:

CONF2 = [(confident + optimistic) – (conservative + not optimistic)] / TOTAL (2)

The measure ranges from -1 to 1. Observations for which there are no press mentions describing

the CEO as confident or conservative in a given year are assigned a value of 0, and tend to fall in

the middle of the distribution. We use both measures in the empirical tests to ensure that our

results are not sensitive to the categorization of CEOs into discreet groups of overconfident and

not overconfident. However, we focus primarily on the CONF2 measure, primarily because we

believe there is information in the relative frequency with which a CEO is referred to as

overconfident.8 Additionally, this measure does a better job of neutralizing the CEOs with no

press coverage because they fall in the middle of the distribution instead of the non-

overconfident group.

To further address the concern that we include CEOs with no press coverage in our

sample, we also use a second sample that eliminates all CEOs with zero press coverage.

Although this reduces the number of observations, it alleviates the concern that the press is likely

to feature a CEO when there is something to discuss, leading to an overrepresentation of CEOs

with no press coverage in the non-overconfident group. Therefore, all of our main analysis (i.e.

Tables 5 through 8) includes a column showing the results when all CEOs with zero press

coverage are eliminated from the sample (i.e. omitting observations where TOTAL equals zero).

We employ two checks to avoid the potential for reverse causality in the data (i.e. that

over-optimistic forecasts cause reporters to describe the CEO as overconfident). First, during the

data collection process, we noticed that CEOs are more likely to be described as confident in the

8 For example, if CEO X had 10 overconfident mentions and zero conservative mentions, and CEO Y had 4 overconfident mentions and 3 conservative mentions, CONF1 would assign both CEOs a value of 1, whereas CONF2 would assign CEO X a value of 1, and CEO Y a value of 0.14.

15

context of articles discussing certain events, such as mergers and acquisitions. Therefore, for

each CEO description in our sample we verify that the terms are not used to describe beliefs

about management’s past forecasts or forecasting ability. Second, we control for a firm’s

acquisition activity in our multivariate analyses by including an indicator variable coded equal to

one if the firm’s annual acquisition or merger-related costs exceeded 5% of net income (loss) in

year t. Last, we classify the press articles by type and source and do not find any systematic

differences between the subsample of articles using the “confident” or “cautious” terms.

For consistency with prior research, the set of CEOs that are more often described as

confident or optimistic are referred to as “overconfident”, despite the fact that this measure does

not allow us to calibrate to an appropriate level of confidence.9 However, Malmendier and Tate

(2008) show that this measure is positively correlated with the extent to which executives hold

disproportionately large positions in the equity of their firms. More importantly, unlike equity

holdings, the popular-press characterizations are not a choice of the CEO. Thus, there are less

concerns with endogeneity or omitted variables. Nonetheless, following Malmendier and Tate

(2005), we construct a third measure of overconfidence (NETBUYER), based on the CEO’s

acquisition of company stock. Given their high exposure to company risk, CEOs should be less

inclined to purchase more shares unless if it reflects their optimistic beliefs of the company’s

future performance. Therefore, we identify CEOs as overconfident if they were net buyers of

company stock during the year. We recognize this measure is affected by the fact that CEOs

have private information about the firm, which will be reflected in their purchase decisions.

However, to the extent that net buying reflects positive private information, it should work

9 Ben-David et al. (2007) use historical returns on the S&P500 and Malmendier and Tate (2005) use portfolio theory to provide a benchmark to against which they measure overconfidence.

16

against finding an association between this measure of overconfidence and the probability of

missing their forecast.

Panel A of Table 1 shows the summary statistics for the descriptors used to characterize

the CEOs. 15% of the CEOs in our sample are classified as overconfident, because they are

described as confident and optimistic more often than frugal, steady, practical, reliable, cautious,

or conservative. This ratio is not significantly different from the 13% reported in Malmendier

and Tate (2005) and Jin and Kothari (2008). Looking at the individual search terms, about

29.8% of the sample receive “confident” mentions, 17.9% receive “optimistic” mentions, and

18.7% receive “reliable, conservative, practical, frugal, steady, or cautious” mentions. The

median number of mentions for a CEO in our sample is twice per year, although the distribution

is skewed, with an average of almost eleven mentions per year.

Following Malmendier and Tate (2008), we identify whether the overall focus of the

article is about the CEO, the firm, or about the market or industry. We also identify whether the

terms used to describe the CEO were the CEO’s own quotes, the journalist’s assessment, or

another source. Panel B of Table 1 provides descriptive statistics on the press articles. First, it is

worth noting that the distribution of article types are similar between the “confident” and

“cautious” subsamples. This mitigates the concern that our overconfidence measure is capturing

certain events that could lead to the firm missing its own forecast. Second, 29% of the “confident”

articles and 22% of the “cautious” articles are about the CEO compared to 9% and 18% in

Malmendier and Tate (2008). This is likely due to CEOs gaining more public attention in recent

years compared to the earlier sample period (1980-1994) examined in their study. Last,

consistent with confident CEOs being more assertive and outspoken, we find that 35% of the

“confident” articles are CEO quotes compared to 24% for the “cautious” articles.

17

[Table 1]

To provide evidence on management forecasting behavior, we combine the CEO data

with a sample of quantitative annual management EPS forecasts issued from 2000 to 2007 as

reported in the Company Issued Guidelines File (CIG) maintained by First Call. This procedure

reduces our sample to 401 firms and 1879 observations. Table 2 reports the descriptive statistics

for our sample of management forecasts. Except for fiscal 2000 and 2001, forecasts are evenly

distributed over the sample period. The frequency of point, range, and open-ended forecasts are

17.94%, 80.31%, and 1.76%, respectively. Since management earnings forecasts in our sample

come in varying forms (e.g. point, range, and open-ended), we categorize firms into two groups.

MISS (MEETBEAT) is an indicator variable that is set to 1 if a manager misses (beats or exactly

meets) his own earnings forecast for the fiscal year. For open-ended and point estimates, the

forecast is coded as MISS=1 (MEETBEAT=1) if the actual EPS is less than (greater than or equal

to) the estimated EPS. For range estimates, the forecast is coded as MISS=1 (MEETBEAT=1) if

the actual EPS is less than (greater than or equal to) the mid-point of the range forecast.10 We

obtain actual earnings from the First Call Actuals File to ensure consistency between

management forecasts and EPS realizations. When a firm issues multiple forecasts during the

year, we restrict our sample to the first forecast issued in the fiscal period.11 We also exclude

qualitative forecasts from our sample because we have no objective criterion for determining

whether such forecasts were missed.12

[Table 2]

10 We use the mid-point for range estimates because prior research suggests that investors use the mid-point when forming their expectations of earnings (Baginski et al., 1993) 11 Our inferences are unchanged if we use the last forecast issued for the same fiscal period. 12 We remove the 33 forecasts issued in open-ended form from our multivariate analyses because unlike with point or range forecasts, it is difficult to interpret the manager’s confidence interval around a lower/upper bound estimate.

18

Panels A and B of Table 3 report the summary statistics for our full initial sample and the

sample of firms issuing forecasts. Out of the 640 firms in our initial sample, 401 firms issued at

least one forecast during our sample period. Using the continuous measure of confidence

(CONF2), we find some evidence that overconfident CEOs are more likely to issue voluntary

forecasts. The average CONF2 is 2.8% for the forecasting firms compared to 2.1% for the

overall sample. Additionally, 7.4% of forecasting CEOs are classified as a NETBUYER

compared with only 6.8% for the overall sample. The average number of articles mentioning the

CEO is 7.62 in the forecasting sample versus 10.98 among non-forecasters, and the medians are

the same. The accounting flexibility variable, proxied by beginning-of-year net operating assets

scaled by sales, is smaller for forecasting firms. This is consistent with firms considering their

accounting flexibility when making a forecast decision. Analyst following is larger for

forecasting firms, consistent with the notion that more informative disclosure policies lead to

larger analyst following (Lang and Lundholm, 1996). Forecasting firms are also more likely to

be from high litigation risk industries and have lower ROAs.

[Table 3]

Table 3, Panel C reports Pearson product-moment correlations for our main variables of

interest. The categorical measure of overconfidence (CONF1) and the continuous measure

(CONF2) are both included, and highly correlated with each other. Examining the categorical

measure CONF1, we see that it is positively correlated with the extent of press coverage a CEO

receives, the market value of equity, analyst following, and M&A activity. The continuous

measure is correlated with all of the same variables. Consistent with prior research, size is

positively correlated with analyst following, total press mentions of the CEO, and accounting

flexibility (Lang and Lundholm, 1996; Chen, 2004; Francis et al., 2008).

19

Table 4, Panel A provides descriptive statistics for the two forecast outcome groups:

MISS and MEETBEAT. 58% of the firm-years in our sample fall into the MISS group, higher

than the 44% reported in Kasznik (1999).13 As initial evidence of the association between

overconfidence and management forecast errors, we find that CEOs in the MISS group have

higher confidence scores and are more likely to be net buyers. The continuous measure shows

that CEOs who miss their forecast score significantly higher on this measure (CONF2=0.035)

versus those that meet or beat (CONF2=0.019) their forecast. CEOs in the MISS group are also

more likely to be net purchasers of company stock than those in the MEETBEAT group

(NETBUYER=0.083 vs. NETBUYER=0.061). The total number of article mentions is marginally

different between MISS firms and MEETBEAT firms. Consistent with expectations based on

prior research, MISS firms are smaller (p<0.01), have lower analyst following (p<0.05), and

greater forecast errors (p<0.01).

[Table 4]

4. Empirical Results

4.1 Controlling for Self-Selection

Our first hypothesis is whether overconfident CEOs are more likely to issue forecasts that

are optimistically biased. However, the set of firms that provide voluntary forecasts of earnings

are a self-selected sample, having made the choice to issue a forecast. This gives rise to a

censored dependent variable, in that we observe forecast errors for CEOs who make the decision

to forecast. To the extent that the determinants of the forecast decision are potential

determinants of the forecast error, ordinary least squares estimates of the parameters in the

forecast error equation will be biased (Maddala 1983, p.222). In our setting, we believe that

13 For range forecasts, Kasznik (1999) defines MISS=1 (BEAT=1) if the actual EPS is below (above) the lower (upper) bound and MEET=1if the actual EPS is between the upper and lower bound. This would result in fewer range forecasts being classified as MISS than otherwise would be using our criterion.

20

confidence could affect both the decision to forecast, as well as the forecast error conditional on

issuing a forecast, and use a two-stage Heckman procedure to try to remove the bias.

We start by modeling the forecast decision itself as a function of several factors,

including the degree of confidence of the CEO and other variables that have been associated with

the voluntary disclosure decision. The first stage logit model is as follows:

0 1 2 3 4 5

6 7 8 9

Pr( ) 2

(1)

FORECAST CONF TOTAL FLEX SIZE MB

DISTRESS LITRISK ANALYSTS MA

Our continuous measure of overconfidence (CONF2) is included based on the discussion

in Ben-David et al. (2007) that overconfident CEOs are more likely to voluntarily forecast and

our descriptive statistics in Table 3. We include the total number of mentions (TOTAL) because

it has been used as a proxy for CEO reputation (Francis et al., 2008), and is positively correlated

with our measures of confidence. We control for accounting flexibility because firms are likely

to consider the flexibility of their balance sheet when deciding whether to forecast (Chen, 2004).

We use FLEX as a proxy for accounting flexibility, measured as beginning-of-year net operating

assets relative to sales (NOAt-1/Salest-1). Barton and Simko (2002) argue that this measure

captures the cumulative effect of a firm’s overstated net operating asset values. Firms with

higher NOAs are more constrained in their ability to manage earnings upwards because of prior

optimistic accounting choices. Therefore, a higher value of FLEX indicates less accounting

flexibility and a greater likelihood of missed forecasts.14 We include the total number of analysts

following the firm (ANALYSTS) and the market value of equity (SIZE), because prior research

finds a positive association between disclosure, analyst following, and size (Lang and Lundholm,

1996; Koch, 2002; Bhojraj et al., 2010). Following Koch (2002), we also control for financial

14 As noted in Chen (2004), since NOA/Sales is the inverse of asset turnover, this result can also be interpreted as missed forecasts attributed to deteriorating operating efficiency.

21

distress where DISTRESS=1 if the Altman’s Z-score is less than 2.65 (Altman, 1968). Finally,

we include an indicator variable for high litigation risk industries (LITRISK) because Skinner

(1994) suggests that firms with bad news will disclose to minimize the expected cost of

litigation.15 Following Francis, Philbrick, and Schipper (1994), high risk industries are defined

as biotech (SIC 2833-2836), computer (SIC 3570-3577, 7370-7374), electronics (SIC 3600-

3674), and retailing (SIC 5200-5961). We also control for merger-related activities (MA) to the

extent that firms may supply more information in an attempt to reduce information asymmetry

when undergoing significant events. MA is an indicator variable coded equal to one if the firm’s

annual acquisition or merger-related costs exceeded 5% of net income (loss) in year t. Together,

these variables constitute the forecast prediction model.

The results of estimating the forecast prediction model are presented in Table 5 Column

(1). Although this regression is used only to correct for the self-selection bias, it is worth noting

that consistent with Ben-David et al. (2007), our measure of overconfidence is related to the

likelihood of issuing a forecast. We also see that ANALYSTS and LITRISK are positively related

to the probability of issuing a forecast, while FLEX and MA are negatively associated with the

issuance of a forecast. Results using the equity-based confidence measure (NETBUYER) and the

sub-sample of observations where TOTAL is greater than zero are presented in Columns (2) and

(3). NETBUYER and CONF2 continue to be positively associated with forecast likelihood. The

signs and significance of the control variables are also similar.16

15 Kasznik (1999) uses number of analysts as a proxy for litigation risk in his tests. This variable is already included in our model based on the findings of Lang and Lundholm (1996). 16 Prior research suggests that firms with greater institutional ownership are more likely to provide management forecasts and that their forecasts are also more accurate and precise (Ajinkya, Bhojraj, and Sengupta, 2005). However, we do not require data on institutional holdings because doing so would reduce our sample size by 272 observations. Untabulated results indicate that the sign and significance of the confidence variables are unchanged when we control for the percentage of institutional holdings in the forecast prediction model and the tests of hypothesis one. Consistent with Ajinkya et al. (2005), we also find that institutional ownership is positively associated with forecast issuance and negatively associated with the likelihood of issuing an optimistic forecast.

22

Using the forecast prediction model, we construct the inverse Mills ratio to control for the

self-selection problem (Heckman, 1979). The inverse Mills ratio is the ratio of the standard

normal probability density function to the standard normal cumulative density function. We

include the inverse Mills ratio (denoted MILLS) when the sample includes only firms who

choose to forecast to control for the role of overconfidence in the decision to forecast.

[TABLE 5]

4.2 Is Overconfidence Associated with Missing Voluntary Forecasts?

To examine our first research question of whether overconfident CEOs issue overly

optimistic forecasts, we estimate the likelihood of missing a forecast as a function of

overconfidence and other variables that are expected to affect forecast accuracy. Specifically,

our logit model is as follows:

0 1 2 3 4 5

6 7 8 9 10

11

Pr( 1) 2

iMISS CONF TOTAL FLEX SIZE MB

LITRISK ANALYSTS DACC POINT HORIZON

MILLS

(2)

MISS is an indicator variable equal to 1 if a firm fails to meet its earnings forecast, and

zero if it meets or beats its forecast. Notice that this tests whether managers fall short of their

forecast, which is more consistent with the notion of overconfidence employed in Malmendier

and Tate (2005). CONF2 is our primary variable of interest, which is our proxy for

overconfidence. In untabulated results, we run the regression using the indicator variable,

CONF1, and the results are qualitatively unchanged. The inverse Mills ratio (MILLS) is also

included as discussed in the previous section.

We include several control variables, some of which are the same variables used in the

first stage model to capture the likelihood of issuing a forecast. Prior research suggests that

firms from high litigation risk industries and firms with less accounting flexibility, smaller size,

23

and fewer analysts following are less likely to meet their estimated earnings target. Therefore,

we continue to include LITRISK, FLEX, SIZE, and ANALYSTS in our forecast outcome

prediction model. We also include the firm’s market to book ratio (M/B) as prior research on

meeting or beating analyst forecasts shows that high M/B firms are more prone to large stock

price declines when they miss analyst forecasts (Skinner and Sloan, 2002). Finally, we include

an indicator variable for the form of the forecast (POINT), because a more precise forecast is

easier to miss, and our second hypothesis suggests that overconfidence is associated with

forecast form.17 We also control for forecast horizon (HORIZON) because we expect managers

to have less information about realized earnings the earlier the forecast is issued. Last, we

include a firm’s discretionary accruals (DACC) estimated from the modified Jones model since

Kasznik (1999) finds that firms are likely to manage earnings to avoid missing their own forecast.

Table 6 reports the results of estimating equation (2) and provides a test of the first

hypothesis. Consistent with overconfidence affecting the likelihood of missing a voluntary

forecast, our measure of confidence (CONF2), has a statistically significant positive coefficient

(α=0.571, p<0.01). Thus, the probability of missing a forecast is increasing in our measure of

confidence. The coefficients on SIZE (α=-0.268, p<0.01) and MB (α=-0.011, p<0.05) are

consistent with large firms and firms with higher market-to-book ratios being less likely to miss

their forecast. We also find that firms from high litigation risk industries have greater difficulty

forecasting earnings and are more likely to miss their own forecast (α=0.898, p<0.05).18 The

negative and significant coefficient on DACC (α=-4.539, p<0.01) also suggests that firms that

17 Note that we include only point or range forecasts in this analysis so only one indicator variable (POINT) is necessary. 18 While one might expect MA firms to issue overly optimistic forecasts, we do not find an association between MA and MISS in unreported results. Combined with the negative association between MA and FORECAST from the forecast prediction model, this suggests that MA firms will only forecast when there’s a greater possibility of meeting or beating their own forecast.

24

have high abnormal accruals are less likely to miss their forecast. Results using NETBUYER

reported in column (2) are similar albeit weaker (α=0.300, p<0.10). We also find that CONF2

(α=0.517, p<0.01) remains positive and significant when we test our hypothesis using the

subsample of observations with positive article mentions. Overall, the results in Table 6 are

consistent with overconfidence increasing the likelihood of a CEO issuing an overly optimistic

forecast.19

[Table 6]

4.3 Overconfidence and Forecast Form

Our second hypothesis relates overconfidence to the form of the management earnings

forecasts. To examine our second hypothesis, we use the unique feature of using management

forecast data that managers choose various forms for the issuance of forecasts – points, ranges,

and open-ended forecasts. This test is consistent with the notion of overconfidence used in Ben-

David et al. (2007), as an overestimation of judgmental precision or underestimation of the

variance of random processes. For management forecasts, this suggests that overconfident

managers are more likely to issue forecasts either as a point or as a narrow range, relative to non-

overconfident managers. Therefore, we first use an ordered logit model to examine whether

overconfidence leads to more specific forecasts, controlling for other determinants of forecast

specificity.20

0 1 2 3 4 5 6

7 8 9 10 11 12

13

2

SPECIFICITY CONF TOTAL FLEX SIZE MB LITRISK

DISTRESS ANALYSTS ROA HORIZON CONC BLOCKSHR

MILLS

(3)

19 Significance levels for tests of hypotheses 1 to 3 are based on one-tailed tests for the overconfidence proxies (CONF2 and NETBUYER) and two-tailed tests for the control variables. 20 We define precision as the range-width of forecasts and specificity as an ordinal variable that gives the highest value to the most specific forecast form. Point, range, and open-ended forecasts are coded as 3, 2, and 1, respectively.

25

We include two additional control variables in equation 3. Bamber and Cheon (1998)

find that firms with greater product-market concentration, their proxy for proprietary costs, issue

less specific forecasts. Therefore, CONC is the firm’s product-market concentration ratio,

defined as sales of the top-five firms in the two-digit SIC industry, divided by total sales in the

same industry in year t. Prior research also finds that higher institutional concentration leads to

less specific forecasts (Bamber and Cheon, 1998; Ajinkya et al., 2005). BLOCKSHR is the

percentage of the firm’s shares held in blocks (greater than 5% of the total shares outstanding) by

institutional investors at the end of the fiscal year. In untabulated estimation of equation (3), we

find no association between overconfidence and forecast specificity. Part of the explanation for

the null result might be the lack of variation in the dependent variable. As reported in Panel B of

Table 2, more than 80% of the forecasts in our sample are range forecasts while only about 18%

are point forecasts.

We next estimate the following model to investigate whether, conditional on issuing a

range forecast, overconfident managers use narrower ranges:

0 1 2 3 4 5 6

7 8 9 10 11 12

13

2

RANGE CONF TOTAL FLEX SIZE MB LITRISK

DISTRESS ANALYSTS ROA HORIZON CONC BLOCKSHR

MILLS

(4)

The dependent variable RANGE is the unscaled width of range forecasts measured in

cents per share. Consistent with overconfident CEOs having tighter confidence intervals, the

coefficients on CONF2 (α=-0.021, p<0.10) and NETBUYER (α=-0.023, p<0.01) are significantly

negative. We also find that firms with less accounting flexibility (α=0.011, p<0.10) and higher

distress risk (α=0.049, p<0.01) issue forecasts with wider width. The signs on SIZE, ANALYSTS,

HORIZON, CONC, and BLOCKSHR are consistent with prior studies that examine the

determinants of forecast form (Baginski and Hassell, 1997; Bamber and Cheon, 1998; Ajinkya et

26

al., 2005). Overall, the data in Table 7 suggests that conditional on issuing a range forecast,

overconfidence decrease the width of the range.

[Table 7]

4.4 Are Overconfident CEOs More Likely to Manage Earnings After Forecasting?

Our third test examines the effect of overconfidence on the extent of earnings

management subsequent to issuing voluntary forecasts. To test whether overconfident managers

use accruals more aggressively after issuing a forecast, we use total accruals and discretionary

accruals as alternative dependent variables. Discretionary accruals are estimated using the

following time series model, using data from a minimum of five years and a maximum of 15

years for the estimation21:

tititittiiiti CFOPPEREVTACC ,,3,,2,,1,0. (5)

TACC is total accruals, ∆REV is the change in revenues, ∆REC is the change in

receivables, PPE is gross property, plant, and equipment, and ∆CFO is the change in cash flow

from operations. We estimate eqn. (5) for each of the 640 sample firms in our sample. We

follow Kasznik (1999) and include the change in cash flow from operations in the accruals

estimation model because Dechow (1994) finds that it is negatively correlated with total accruals,

and it controls for the measurement error in discretionary accrual models that is correlated with

performance (e.g. Kothari, Leone, Wasley, 2005).22 We also include the return on assets (ROA)

into the regressions that use accruals as the dependent variable to control for the association

between accruals and earnings. Using the estimated coefficients from the previous regression,

we are able to estimate the nondiscretionary component of total accruals. The discretionary

21 Because we measure accruals and cash flows from the statement of cash flows, our earliest year for estimating the parameters of the Jones model is 1988. 22 Our results are not sensitive to excluding the change in CFO explanatory variable from the accruals estimation model.

27

component is obtained by using the predicted values from the above equation and applying them

with the modification for the change in revenue as in Dechow, Sloan, and Sweeney (1995).

titittitiiititi CFOPPERECREVTACCDACC ,3,,2,,,1,0,. )( (6)

Hypothesis 3 predicts that conditional on issuing a management forecast, overconfident

CEOs are more likely to use income-increasing accruals to manage their reported earnings

upwards. Table 7 suggests that overconfidence affects the width of the range forecast, but does

not affect the likelihood of issuing a point forecast. In addition, Xu (2009) examines whether

managers take into account the lower persistence of accruals in their forecasts and finds that

managers underestimate accrual reversals when they issue range forecasts but not point forecasts,

consistent with the range forecasts being issued under greater uncertainty. Therefore, we

examine both the full sample and the sample of range forecasts only. Because results are

stronger in the sample of range forecasts only, we report and discuss these results.23 We use the

following models to test our prediction that the magnitude of discretionary accruals is higher for

overconfident managers using a sample of range forecasts.

0 1 2 3 4

5 6 7 8 9 10

11 12 13 14 15

2 2*

*

(7)

DACC CONF MEETBEAT CONF MEETBEAT TOTAL

TOTAL MEETBEAT FLEX SIZE MB LITRISK DISTRESS

ANALYSTS ROA RANGE HORIZON MILLS

If overconfident managers issue optimistically biased earnings forecasts, then we expect

that they will use more income-increasing accruals to try to meet the forecast. In addition,

psychology research suggests that overconfidence is associated with attribution bias, suggesting

that overconfident CEOs will be more likely to attribute lower than expected earnings to bad

luck, and therefore be willing to temporarily inflate earnings which can be recovered in future 23 The results are qualitatively similar, but exhibit less statistical significance using both point and range forecasts. In both cases the main effect of overconfidence is significant but the interaction with MEETBEAT is not.

28

periods. The coefficient on CONF2 is expected to be significantly positive if overconfidence is

associated with more aggressive use of accruals. MEETBEAT is an indicator variable that equals

one if the firm’s earnings equal or exceed their earlier forecast, and CONF2*MEETBEAT is an

interaction between our confidence classification and MEETBEAT. The coefficient on the

interaction is expected to be significantly positive if there is greater evidence of income-

increasing earnings management among firms that meet or beat their management forecasts.

We also include controls for other variables expected to affect earnings management

subsequent to issuing a management forecast. Kasznik (1999) posits that litigation costs and

reputation both provide incentives for firms to increase earnings after issuing an earnings

forecast. Following Francis et al. (2004) we include TOTAL to proxy for reputation costs. We

include both ANALYSTS and LITRISK to capture potential litigation risk (Kasznik, 1999). We

include ROA, measured as net income divided by lagged total assets, to control for the fact that

discretionary accrual estimates are correlated with performance (Kothari et al., 2005). We

include FLEX to measure potential constraints on earnings management, and MB because

glamour firms have greater incentives to meet or beat their forecasts (Skinner and Sloan, 2002).

We also control for RANGE and HORIZON because we expect discretionary accruals to increase

with forecast difficulty.

Table 8 reports the estimation results for equation (7). Because the untabulated results

using total accruals are qualitatively similar, we focus our discussion on the discretionary

accruals specification, reported in Table 8. The main effect on MEETBEAT is positive in all

three specifications, suggesting that firms that meet or beat their forecast have larger

discretionary accruals. The sign on the main effect CONF2 is positive and statistically

significant in both columns (1) and (3) (α=0.007, p<0.001). However, the interaction term is not

29

significant. The coefficient on NETBUYER is also insignificant which suggests that managers

who have net purchases in firm stocks do not use more accruals earnings management. These

results suggest that firms with more confident CEOs use greater income-increasing accruals, but

this tendency is not more pronounced among the set of firms that meet or beat their forecasts.

Taken together, our results suggest that conditional on issuing a management forecast,

overconfidence is associated with using more aggressive accounting (i.e. greater income-

increasing accruals) to try to meet these forecasts.24

[Table 8]

V. Conclusion

Contrary to prior studies that focus on personal or economic incentives, we relax the

assumption of management rationality to examine the effect of executive overconfidence on two

extensively researched themes in the accounting literature: management earnings forecasts and

earnings management. We provide evidence consistent with the notion that managerial

overconfidence manifests itself as excessive optimism about future earnings, leading

overconfident CEOs to issue upwardly biased forecasts. This has two implications. First, we

find that overconfident CEOs are more likely to miss their own forecasts, controlling for other

predictors of ex-post forecast accuracy such as accounting flexibility, litigation costs, forecast

horizon, and growth prospects. Second, we show that overconfidence is associated with the use

of aggressive accounting subsequent to the forecast.

24 The coefficient on CONF2 is significant at the 10% level when we estimate equation 7 on a sample of point and range forecasts and insignificant when we use only point forecasts. This suggests that when the information environment allows the manager to issue a more specific (point) forecast, the manager can better incorporate the persistence of accruals in the forecast, such that they do not need to use accruals to manage earnings ex post. However, conditional on issuing a range forecast that tends to underestimate accrual reversals (Xu, 2009), we document an association between overconfidence and the level of income-increasing discretionary accruals.

30

Our study therefore contributes to our understanding of why managers miss their own

forecasts when the costs of failing to meet their own earnings expectations are so high. Our

findings also add to the vast earnings management literature as we find evidence that in addition

to economic incentives, individual psychology also plays a role in determining earnings

management. Given that Malmendier and Tate (2008) find that the market discounts mergers

undertaken by overconfident CEOs, future research in this topic could also investigate whether

investors or analysts take managerial overconfidence into consideration when determining a

firm’s stock price based on its forecasts.

31

Appendix - Sample Articles for Press-Based CEO Confidence Measure Examples of Confidence and Optimism articles “International Paper outlines second-quarter loss”, 17 July 2001, Financial Times

However, John Dillon, chairman and chief executive officer, remained confident that internal improvements will help offset external market conditions. He said: "We have continued to reduce our capacity to match demand from our customers, improved our operations and began to realign staff resources to better our financial results."

“Aetna: a long way to the recovery room its CEO is promising a turnaround by 2004. Doubters abound.” 16 July 2001, Business Week

Aetna plans to reexamine every medical contract, employer by employer, and study every benefit it offers and every price it charges. Rowe, who helped turn around an ailing WellPoint 10 years ago, is confident: ``There is nothing here that I have not seen before. There is nothing here that I believe cannot be fixed.''

“Will You Get a Bonus This Year? --- Surprisingly, Some Companies Are Paying More Than Last Year, But Wall Street and Tech Suffer” 22 October 2002 The Wall Street Journal

Even companies that have gone through the wringer recently express optimism. John W. Rowe, chairman of health insurer Aetna, last month sent an e-mail to the company's roughly 30,000 employees announcing that if Aetna continues to meet financial objectives for the year, there probably would be greater funding for the bonus pool.

“Mistress of the turnround answers Avon's calling - Andrea Jung has led a revival” 6 November 2003, Financial Times

Trying to shake off persistent concern from investors and analysts that the cosmetics and skin-care-products company's strategies aren't working, Chief Executive Andrea Jung said, "We are as confident as ever about the fundamentals of this business." Since taking the helm in November 1999, Ms. Jung has been trying to freshen up the Avon brand.

“Bank of America Aims to Boost Corporate, Investment Banking --- Operation to Get Infusion of as Much as $600 Million In an Effort to Fuel Growth,” 17 March 2004, The New York Times

The consumer business has been robust for Bank of America and other banks the last three years, but Mr. Lewis acknowledges that growth will slow once interest rates rise and as consumers shift money away from banks. He expects the bank's deposits to grow just 3 percent to 4 percent this year, compared with 10 percent in 2003. Still, Mr. Lewis is confident he will prove his doubters wrong. ''Whatever our growth winds up being, we will outperform the market,'' he said in an interview. As evidence, he points to his earnings record. ''We've exceeded expectations for the last 12 quarters,'' he said, ''and we've gotten to like that.''

“Arkansas' Telco Kingpins; Alltel grew by picking up others' scraps. With telecom a mess, now it can really clean up.” 27 May 2002, Fortune

Nothing guarantees that Alltel itself won't end up being acquired, either. Compared with the boom years, says Scott Ford, "it is much more difficult to predict today how things will play

32

out for us." But he's confident that Alltel's assets will end up in gainful employment--not on the scrapheap.

Examples of Conservative or Steady Articles “Coke and Procter & Gamble In Joint Marketing Venture” 22 February 2001, The New York Times

The venture is a departure for Procter, a company that prides itself on traditional business practices. Mr. Lafley became chief executive last July with a mandate to restore conservative leadership and cut costs.

“Is Rejected SunTrust the Way for Wachovia?” 23 May 2001, The Wall Street Journal

SunTrust, though, may have some explaining of its own to do. While First Union has completed about 90 takeovers, conservative SunTrust doesn't have a lot of acquisitions under its belt. But those it has made haven't always gone well. It was singed by the 1987 acquisition of Third National, a Nashville, Tenn., acquisition "It was one hell of a mess," acknowledged L. Phillip Humann, SunTrust's chairman and chief executive, in an interview last week. Mr. Humann blames an outside vendor for the problems, which he says were solved quickly.

“ CEO Parsons to Head AOL's Board --- New Chairman Expected To Take Conservative Tack, Putting His Stamp on Firm, 17 January 2003, The Wall Street Journal

Mr. Parsons is giving signs of taking a very conservative approach to management. At an investment conference last month, he said his priorities were to run the businesses better than they had been run, to "avoid any more transforming transactions that only take us back in the wrong direction," and to continue to focus on the balance sheet.

“Northrop Elevates Its President To Complete Succession Plan,” February 2003,The New York Times

''Ron Sugar is a talented and seasoned executive who possesses a thorough understanding of our business, our strategy and our potential,'' Mr. Kresa said. ''Under his leadership, Northrop Grumman will be in very capable hands and will continue on a steady and consistent course.''

“Earnings Fall At Times Co. But Increase At Gannett,” 13 April 2004 The New York Times

''We're cautious people these days, and we've had reason to be,'' said Russell T. Lewis, who has announced he will retire as the company's chief executive later this year. ''We're not going to declare victory prematurely.''

33

References

Ajinkya, B., S. Bhojraj, and P. Sengupta. 2005. The association between outside directors, institutional investors and the properties of management earnings forecasts. Journal of Accounting Research 43: 343-374. Altman, E. 1968. Financial ratios, discriminate analysis and the prediction of corporate bankruptcy. Journal of Finance 23: 589-609. Baginski, S., E. Conrad, and J. Hassell. 1993. The effects of management forecast precision on equity pricing and on the assessment of earnings uncertainty. The Accounting Review 68: 913-927. Baginski, S. and J. Hassell. 1997. Determinants of management forecast precision. The Accounting Review 72: 303-312. Bamber, L. and Y. 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. Bamber, L., J. Jiang, and I. Wang. 2009. What’s my style? The influence of top managers on voluntary corporate disclosure. Forthcoming, The Accounting Review. Barton, J. and P. Simko. 2002. The balance sheet as an earnings management constraint. The Accounting Review 77: 1-27. Ben-David, I., J. Graham, and C. Harvey. 2007. Managerial overconfidence and corporate policies. Working Paper, Ohio State University and Duke University. Bhojraj, S., R. Libby, and H. Yang. 2010. Analyzing guidance at the firm level: The association between guidance frequency, guidance properties, and market reactions. Working Paper, Cornell University and University of Pennsylvania. Camerer, C. and D. Lovallo. 1999. Overconfidence and excess entry: an experimental approach. American Economic Review 89: 306-318. Chen, S. 2004. Why do firms fail to meet their own forecasts? Working Paper, University of Texas. Daniel, K., D. Hirshleifer, and A. Subrahmanyam. 1998. Investor psychology and security market under- and overreactions. Journal of Finance 53: 1839-1885. Dechow, P. 1994. Accounting earnings and cash flows as measures of firm performance: the role of accounting accruals. Journal of Accounting and Economics 18: 3-42.

34

Dechow, P., W. Ge, and C. Schrand. 2009. Understanding earnings quality: A review of the proxies, their determinants and their consequences. Working Paper, University of California, Berkeley, University of Washington, and University of Pennsylvania. Dechow, P., R. Sloan, and A. Sweeney. 1995. Detecting earnings management. The Accounting Review 70: 193-225. Fischer, P. and R. Verrecchia. 1999. Public information and heuristic trade. Journal of Accounting and Economics 27: 89-124. Francis, J., A. Huang, S. Rajgopal, and A. Zang. 2008. CEO reputation and earnings quality. Contemporary Accounting Research 25: 109-147. Francis, J., D. Philbrick, and K. Schipper. 1994. Shareholder litigation and corporate disclosure. Journal of Accounting Research 32 (2): 137-164. Gervais, S. and T. Odean. 2001. Learning to be overconfident. Review of Financial Studies 14: 1-27. Heaton, J. 2002. Managerial optimism and corporate finance. Financial Management 31: 33-45. Jin, L. and S. P. Kothari. 2008. Effects of personal taxes on managers’ decision to sell their stock. Journal of Accounting and Economics 46: 23-46. Kasznik, R. 1999. On the association between voluntary disclosure and earnings management. Journal of Accounting Research 37: 57-81. Koch, A. 2002. Financial distress and the credibility of management earnings forecasts. Working Paper, University of Virginia. Kothari, S. P., A. Leone, and C. Wasley. 2005. Performance matched discretionary accrual measures. Journal of Accounting and Economics 39: 163-197. Kyle, A. and F. Wang. 1997. Speculation duopoly with agreement to disagree. Can overconfidence survive the market test? Journal of Finance 52: 2073-2090. Lang, M. and R. Lundholm. 1996. Corporate disclosure policy and analyst behavior. The Accounting Review 71: 467-492. Lang, M. and R. Lundholm. 2000. Voluntary disclosure and equity offerings: reducing information asymmetry or hyping the stock? Contemporary Accounting Research 17: 623-662. Larwood, L. and W. Whittaker. 1977. Managerial myopia: self-serving biases in organizational planning. Journal of Applied Psychology 62: 194-198.

35

Libby, R., R. Bloomfield, and M. Nelson. 2002. Experimental research in financial accounting. Accounting Organizations Society 27: 777-812. Maddala, G. 1983. Limited Dependent and Qualitative Variables in Econometrics. Econometric Society Monographs. Malmendier, U. and G. Tate. 2005. CEO overconfidence and corporate investment. Journal of Finance 60: 2660-2700. Malmendier, U. and G. Tate. 2008. Who makes acquisitions? CEO overconfidence and the market’s reaction. Journal of Financial Economics 89: 20-43. McNichols, M. 1989. Evidence of informational asymmetries from management earning forecasts and stock returns. The Accounting Review 64: 1-27. Miller, D. and M. Ross. 1975. Self-serving biases in the attribution of causality: fact or fiction? Psychological Bulletin 82: 213-225. Moore, P. 1977. The manager’s struggle with uncertainty. Journal of The Royal Statistical Society 149: 129-165.

Odean, T. 1998. Volume, volatility, price, and profit when all traders are above average. Journal of Finance 53: 1887-1934. Roll, R. 1986. The hubris hypothesis of corporate takeovers. Journal of Business 59: 197-217. Schrand, C. and S. Zechman. 2010. Executive overconfidence and the slippery slope to fraud. Working Paper, University of Chicago and University of Pennsylvania. Skinner, D. 1994. Why do firms voluntarily disclose bad news? Journal of Accounting Research 32(1): 38-60. Skinner, D. and R. Sloan. 2002. Earnings surprises, growth expectations, and stock returns. Review of Accounting Studies 7: 289-312. Svenson, O. 1981. Are we all less risky and more skillful than our fellow drivers? Acta Psychologica 47: 143-148. Trueman, B. 1986. Why do managers voluntarily release earnings forecasts? Journal of Accounting and Economics 8: 53-72. Weinstein, N. 1980. Unrealistic optimism about future life events. Journal of Personality and Social Psychology 39: 806-820.

Xu, W. 2009. Do management earnings forecasts incorporate information in accruals? Forthcoming, Journal of Accounting and Economics.

36

Table 1.  Summary Statistics of Press Data for Sample of Fortune 500 CEOs The  sample  consists of 3,980 CEO‐year observations  from 2000‐2007. CONF1  is  an  indicator  variable equal to 1 (0) if the number of articles describing the CEO as optimistic or confident is (not) greater than the number of articles describing  the CEO as  reliable,  steady, practical, conservative,  frugal, cautious, not optimistic, or not confident. Total Mentions  is  the number of articles  in  leading business  journals which mention  the CEO’s name  in  calendar year  t. Confident  (Optimistic) Mentions  is  the number of articles  in  leading  business  journals which  portray  the  CEO  as  confident  (optimistic).  Reliable  et  al. Mentions is the number or articles in leading business journals, which portray the CEO as reliable, steady, practical, conservative, frugal, cautious, not optimistic, or not confident.    Panel A 

  

Mean  Median  Minimum  Maximum  Std Dev 

Overconfident (CONF1 =1)  0.153  0.000  0.000  1.000  0.360 

Total Mentions  10.977  2.000  0.000  755.000  33.122 

“Confident or Confidence” Mentions  0.298  0.000  0.000  20.000  1.051 

“Optimistic or Optimism” Mentions  0.179  0.000  0.000  22.000  0.798 

“Reliable, Conservative, Practical, Frugal, Steady, or Cautious” Mentions  

0.187  0.000  0.000  10.000  0.719 

37

Table 1 (cont’d) Descriptive  statistics  of  all  articles  in  The  New  York  Times,  Business  Week,  Financial  Times,  The Economist, Forbes, Fortune, Time, and The Wall Street  Journal during the 2000 to 2007 sample period which describe the sample CEOs using the terms confident (confidence), optimistic (optimism), reliable, steady, practical, conservative, frugal, cautious, not optimistic, or not confident. Some articles have both Confident and Optimistic Mentions and more than one Article Type may apply so that percentages need not add to 100.    Panel B 

            Full Sample CEO Confident  CEO Cautious

Number of Articles  2668 1900 768

Article Type 

About the CEO  27% 29% 22%

About the Firm  59% 56% 68%

   About Company Earnings  21% 21%  21%

   About a M&A  5% 6%  3%

   About a Company Product  7% 7%  9%

   About the Company's Culture 6% 4%  11%

   Other  20% 18%  24%

About the Market or Industry  10% 11% 10%

Classification Source 

CEO Quote  32% 35% 24%

Journalist's assessment  50% 48% 58%

Other           14% 13% 17%

38

Table 2.  Descriptive Statistics for Sample of Management Forecasts The sample consists of 1,879 management annual EPS forecasts from 2000 to 2007.  Calendar year is the calendar year  in which management  forecasted earnings.   Forecast type  is the type of  forecast  issued including point, range, and open‐ended forecasts.   

  

Number of  Forecasts 

Percentage Cumulative Percentage 

Panel A: Fiscal Year       

2000  175  9.31  9.31 

2001  199  10.59  19.90 

2002  254  13.52  33.42 

2003  260  13.84  47.26 

2004  270  14.37  61.63 

2005  248  13.20  74.83 

2006  246  13.09  87.92 

2007  227  12.08  100.00 

  1879     

Panel B: Forecast Type       

Point  337  17.94  17.94 

Range  1509  80.31  98.25 

Open‐ended  33  1.76  100.00 

  1879     

39

Table 3.  Summary Statistics of Firm Data This  table presents  summary data on  the  full  sample of  firms and  the  sample of  firms  that  issue  voluntary management  forecasts. The  full sample  in Panel A consists of 3,980 CEO‐year observations  from 2000  to 2007. The  forecasting sample  in Panel B consists of 1,879 CEO‐year observations  from 2000  to 2007 with management earnings  forecasts. CONF1  is an  indicator variable equal  to 1  (0)  if  the number of articles describing the CEO as optimistic or confident is (not) greater than the number of articles describing the CEO as frugal, reliable, steady, practical, conservative, cautious, not optimistic, or not confident. CONF2 is the difference between the confident/optimistic mentions and the frugal et al. mentions divided by the sum of the two article types. NETBUYER  is an  indicator variable equal to one  if the CEO was a net buyer of company stock in year t. TOTAL is the number of articles in leading business journals, which mention the CEO’s name in calendar year t. FLEX is beginning net operating assets divided by lagged sales in year t. SIZE is the firm’s logged market value at fiscal year end. MB is market‐to‐book at year t. DISTRESS is an indicator variable equal to one if the firm’s Z‐score is less than 2.65. LITRISK is an indicator variable equal to one if the firm is in the biotech, retailing, electronics, or computer industry. ANALYSTS is the number of analysts following in year t. ROA is the return on assets in year t. MA is in indicator variable equal to one if the firm’s annual acquisition or merger‐related costs exceeded 5% of net income(loss) in year t.  Panel A Full Sample 

  Mean  Median  Min  Max  Std Dev. 

CONF1  0.153  0.000  0.000  1.000  0.360 

CONF2  0.021  0.000  ‐0.250  0.500  0.174 

NETBUYER  0.068  0.000  0.000  1.000  0.252 

TOTAL  10.977  2.000  0.000  755.000  33.122 

FLEX  0.831  0.541  ‐5.525  17.082  1.180 

SIZE  9.051  9.062  2.530  13.131  1.420 

MB  3.071  2.249  ‐367.904  273.220  11.700 

DISTRESS  0.731  1.000  0.000  1.000  0.443 

LITRISK  0.117  0.000  0.000  1.000  0.322 

ANALYSTS  8.306  6.500  0.000  36.667  8.940 

ROA  0.084  0.041  ‐5.346  102.115  1.677 

MA  0.005  0.000  0.000  1.000  0.069 

    

40

Panel B Forecast Sample 

  Mean  Median  Min  Max  Std Dev. 

CONF1  0.145  0.000  0.000  1.000  0.352 

CONF2  0.028  0.000  ‐0.250  0.750  0.214 

NETBUYER  0.074  0.000  0.000  1.000  0.262 

TOTAL  7.622  2.000  0.000  260.000  18.982 

FLEX  0.728  0.520  ‐5.525  14.277  0.955 

SIZE  9.055  9.061  4.403  13.073  1.323 

MB  3.101  2.475  ‐376.904  110.952  12.298 

DISTRESS  0.674  1.000  0.000  1.000  0.469 

LITRISK  0.166  0.000  0.000  1.000  0.372 

ANALYSTS  11.309  11.500  0.000  36.667  8.396 

ROA  0.065  0.052  ‐0.581  1.610  0.098 

MA  0.002  0.000  0.000  1.000  0.046 

   

41

Table 3 (cont’d) Panel C Pearson Correlation Coefficients for Full Sample 

  CONF1  CONF2  TOTAL  FLEX  SIZE  MB  DISTRESS  LITRISK  ANALYSTS ROA  MA 

CONF1  1  0.438166  0.349415  0.01697  0.310336  0.069731  ‐0.01731  ‐0.02261  0.089769  ‐0.0053  0.021127 

CONF2    1  0.009465  0.031447  0.085308  0.031807  ‐0.00355  ‐0.00087  0.063173  ‐0.00103  0.010522 

TOTAL      1  ‐0.0028  0.313092  0.012144  0.028509  ‐0.05158  0.022462  0.007497  ‐0.00292 

FLEX        1  0.117048  ‐0.02466  0.226352  ‐0.13217  ‐0.04611  ‐0.01388  0.006188 

SIZE          1  0.076608  0.000863  ‐0.12729  0.326112  0.061271  0.011111 

MB            1  ‐0.04196  ‐0.01207  0.07289  0.001717  ‐0.00972 

DISTRESS              1  ‐0.3767  ‐0.17403  ‐0.04248  0.041997 

LITRISK                1  0.139077  ‐0.00093  ‐0.02522 

ANALYSTS                  1  ‐0.00465  ‐0.00444 

ROA                    1  ‐0.00656 

MA                      1 

  

Table 4.  Comparison of MISS and MEETBEAT Firms The  sample  consists  of  1,879  CEO‐year  observations  from  2000  to  2007 with management  earnings forecasts. Firms are assigned a value of MISS  (MEETBEAT) =1  if  it misses  (meets or beats)  its earnings forecast for the fiscal year. CONF2 is the difference between the number of articles describing the CEO as  confident  or  optimistic  and  the  number  of  articles  describing  the  CEO  as  frugal,  reliable,  steady, practical, conservative, cautious, not optimistic, or not confident, divided by the sum of the two article types. NETBUYER  is an  indicator variable equal to one  if the CEO was a net buyer of company stock  in year t. TOTAL  is the number of articles  in  leading business  journals, which mention the CEO’s name  in calendar year t. FLEX is beginning net operating assets divided by lagged sales in year t. SIZE is the firm’s logged market  value  at  fiscal  year‐end. MB  is market‐to‐book  at  year  t. ANALYSTS  is  the  number  of analyst following in year t. ROA is the return on assets in year t. MA is in indicator variable equal to one if the firm’s annual acquisition or merger‐related costs exceeded 5% of net income (loss) in year t. POINT is an indicator variable equal to one for point forecasts, and zero otherwise. RANGE is the width of range forecasts.  FORECASTERROR  is  reported  earnings  minus  the  management  forecast.  HORIZON  is  the number of days between forecast issuance and the fiscal year end.  

  MISS (n=1108)  MEETBEAT (n=771)   

  Mean  Median  Mean  Median  P‐Valuea 

CONF2  0.035  0.000  0.019  0.000  <0.05 

NETBUYER  0.083  0.000  0.061  0.000  <0.05 

TOTAL  7.108  2.000  8.359  2.000  <0.10 

FLEX  0.685  0.494  0.791  0.567  <0.05 

SIZE  8.922  8.911  9.247  9.253  <0.01 

MB  2.730  2.360  3.633  2.597  <0.05 

ANALYSTS  11.005  11.167  11.747  12.000  <0.05 

ROA  0.063  0.049  0.067  0.054  0.21 

MA  0.003  0.000  0.001  0.000  0.25 

POINT  0.185  0.000  0.171  0.000  0.22 

RANGE  0.170  0.100  0.129  0.100  <0.01 

FORECASTERROR  ‐1.204  ‐0.625  0.422  0.120  <0.01 

HORIZON  356.613  339.000  345.396  338.000  <0.05 aOne‐tail p‐value of a two‐sample t‐test comparing the means for the MISS and MEETBEAT groups. 

43

Table 5.  First‐Stage Estimation of the Probability of Management Forecast Issuance The sample consists of 3,980 CEO‐year observations from 2000 to 2007. The dependent variable  is an indicator variable FORECAST=1. CONF2 is the difference between the confident/optimistic mentions and the frugal et al. mentions divided by the sum of the two article types. NETBUYER is an indicator variable equal to one  if the CEO was a net buyer of company stock in year t. TOTAL is the number of articles in leading  business  journals, which mention  the  CEO’s  name  in  calendar  year  t.  FLEX  is  beginning  net operating assets divided by lagged sales in year t. SIZE is the firm’s logged market value at fiscal year‐end. MB is market‐to‐book at year t. DISTRESS is an indicator variable equal to one if the firm’s Z‐score is less than 2.65. LITRISK is an indicator variable equal to one if the firm is in the biotech, retailing, electronics, or computer industry. ANALYSTS is the number of analyst following in year t. MA is in indicator variable equal to one if the firm’s annual acquisition or merger‐related costs exceeded 5% of net income in year t. Column (3) reports results using observations with TOTAL article mentions greater than zero. Standard errors clustered by industry and year are reported in parentheses.

0 1 2 3 4 5 6

7 8 9

Pr( 1) 2

FORECAST CONF TOTAL FLEX SIZE MB DISTRESS

LITRISK ANALYSTS MA

    

COLUMN (1) COLUMN (2) COLUMN (3)CONF2  0.336* 0.358**

(0.217)  (0.216) 

NETBUYER  0.267** 

(0.154) 

TOTAL  ‐0.010***  ‐0.011***  ‐0.010*** 

(0.002)  (0.002)  (0.002) 

FLEX  ‐0.118**  ‐0.118**  ‐0.113* 

(0.052)  (0.051)  (0.064) 

SIZE  ‐0.051  ‐0.043  ‐0.054 

(0.035)  (0.035)  (0.040) 

MB  ‐0.003  ‐0.004  0.003 

(0.003)  (0.003)  (0.003) 

DISTRESS  ‐0.044  ‐0.051  ‐0.087 

(0.105)  (0.105)  (0.121) 

LITRISK  0.580***  0.592***  0.544*** 

(0.134)  (0.134)  (0.162) 

ANALYSTS  0.078***  0.078***  0.075*** 

(0.006)  (0.006)  (0.006) 

MA  ‐1.595*  ‐1.576*  ‐1.544 

(0.816)  (0.811)  (1.155) 

INTERCEPT  ‐0.039  ‐0.123  0.006 

(0.293)  (0.295)  (0.353) 

PSUEDO RSQ  0.0983  0.0986  0.1011 

OBSERVATIONS  3595  3595  2643 

44

Table 6.  Are Overconfident CEOs More Likely to Miss Forecasts? The  sample  consists  of  1,755  CEO‐year  observations  from  2000  to  2007  with  point  or  range management forecasts. The dependent variable is an indicator variable MISS=1. See Table 3 for CONF2, NETBUYER, TOTAL, FLEX, SIZE, MB, LITRISK, ANALYSTS, POINT, and HORIZON variable definitions. DACC is discretionary  accruals  estimated  from  the  modified  Jones  model.  MILLS  is  the  Inverse  Mill’s  Ratio estimated  from  stage one of  the Heckman model. Column  (3)  reports  results using observations with TOTAL article mentions greater than zero. Standard errors clustered by industry and year are reported in parentheses.

0 1 2 3 4 5 6

7 8 9 10 11

Pr( 1) 2

MISS CONF TOTAL FLEX SIZE MB LITRISK

ANALYSTS DACC POINT HORIZON MILLS

 

   COLUMN (1) COLUMN (2) COLUMN (3)CONF2  0.571*** 0.517***

(0.233)  (0.226) 

NETBUYER  0.300* 

(0.187) 

TOTAL  ‐0.012  ‐0.011  ‐0.011 

(0.008)  (0.008)  (0.009) 

FLEX  ‐0.263**  ‐0.255**  ‐0.232* 

(0.113)  (0.116)  (0.129) 

SIZE  ‐0.268***  ‐0.250***  ‐0.233*** 

(0.067)  (0.068)  (0.077) 

MB  ‐0.011**  ‐0.011**  ‐0.009* 

(0.005)  (0.005)  (0.005) 

LITRISK  0.898**  0.875**  0.879** 

(0.377)  (0.385)  (0.419) 

ANALYSTS  0.097*  0.092*  0.077 

(0.053)  (0.055)  (0.060) 

DACC  ‐4.539***  ‐4.411***  ‐5.207*** 

(1.351)  (1.338)  (1.498) 

POINT  0.175  0.179  0.223 

(0.144)  (0.144)  (0.162) 

HORIZON  0.001  0.001  0.001 

(0.000)  (0.000)  (0.000) 

MILLS  3.555*  3.346*  2.942 

(1.898)  (1.944)  (2.127) 

INTERCEPT  ‐0.989  ‐0.961  ‐0.554 

(1.561)  (1.599)  (1.765) 

PSUEDO RSQ  0.0203  0.0389  0.0226 

OBSERVATIONS  1755  1755  1280 

    

45

Table 7.  Evidence on Precision of Management Forecasts and CEO Confidence The  sample  consists  of  1,405  CEO‐year  observations  from  2000  to  2007  with  range  forecasts.  The dependent variable RANGE  is the width of range forecasts. See Table 3 for CONF2, NETBUYER, TOTAL, FLEX, SIZE, MB, LITRISK, DISTRESS, ANALYSTS, ROA, and HORIZON variable definitions. CONC is the firm’s product‐market  concentration  ratio.  BLOCKSHR  is  the  percentage  of  shares  held  in  blocks  by institutional investors. MILLS is the Inverse Mill’s Ratio estimated from stage one of the Heckman model. Column (3) reports results using observations with TOTAL article mentions greater than zero. Standard errors clustered by industry and year are reported in parentheses.

   

   COLUMN (1) COLUMN (2) COLUMN (3)CONF2  ‐0.021* ‐0.019*

(0.013)  (0.013) 

NETBUYER  ‐0.023*** 

(0.011) 

TOTAL  0.001**  0.001**  0.001 

(0.000)  (0.000)  (0.000) 

FLEX  0.011*  0.010  0.008 

(0.007)  (0.007)  (0.008) 

SIZE  0.006  0.005  0.003 

(0.005)  (0.005)  (0.005) 

MB  ‐0.000  ‐0.000  ‐0.000 

(0.000)  (0.000)  (0.000) 

LITRISK  ‐0.131***  ‐0.128***  ‐0.105*** 

(0.023)  (0.023)  (0.023) 

DISTRESS  0.049***  0.050***  0.052*** 

(0.008)  (0.008)  (0.009) 

ANALYSTS  ‐0.010***  ‐0.009***  ‐0.007** 

(0.003)  (0.003)  (0.003) 

ROA  ‐0.090***  ‐0.091***  ‐0.113*** 

(0.029)  (0.029)  (0.039) 

HORIZON  0.000***  0.000***  0.000*** 

(0.000)  (0.000)  (0.000) 

CONC  0.093***  0.092***  0.078*** 

(0.022)  (0.023)  (0.023) 

BLOCKSHR  0.035  0.034  0.012 

(0.026)  (0.026)  (0.032) 

MILLS  ‐0.336***  ‐0.315***  ‐0.231** 

(0.110)  (0.112)  (0.114) 

INTERCEPT  0.300***  0.291***  0.238*** 

(0.084)  (0.086)  (0.090) 

RSQ  0.142  0.142  0.145 

OBSERVATIONS  1405  1405  1006 

46

Table 8.  Earnings Management Subsequent to Forecast Issuance  The  sample  consists  of  1,447  CEO‐year  observations  from  2000  to  2007  with  range  forecasts.  The dependent variable DACC is discretionary accruals estimated from the modified Jones model. CONF2 is the difference between  the number of articles describing  the CEO as  confident or optimistic and  the number of articles describing  the CEO as  frugal,  reliable, steady, practical, conservative, cautious, not optimistic,  or  not  confident,  divided  by  the  sum  of  the  two  article  types. NETBUYER  is  an  indicator variable equal to one  if the CEO was a net buyer of company stock  in year t. TOTAL  is  the number of articles  in  leading  business  journals,  which mention  the  CEO’s  name  in  calendar  year  t.  Firms  are assigned a value of MEETBEAT=1  if  it meets or beats  its earnings  forecast  for  the  fiscal year and zero otherwise.  See  Table  3  for  definitions  of  the  control  variables.  Column  (3)  reports  results  using observations with TOTAL article mentions greater than zero. Standard errors clustered by  industry and year are reported in parentheses.

0 1 2 3 4

5 6 7 8 9

10 11 12 13 14

15

2 2 *

*

DACC CONF MEETBEAT CONF MEETBEAT TOTAL

TOTAL MEETBEAT FLEX SIZE MB LITRISK

DISTRESS ANALYSTS ROA RANGE HORIZON

MILLS

   COLUMN (1) COLUMN (2) COLUMN (3)

CONF2  0.007***  0.007*** 

(0.002)  (0.002) 

NETBUYER  0.000 

(0.003) 

MEETBEAT  0.007***  0.007***  0.008*** 

(0.002)  (0.002)  (0.003) 

CONF2*MEETBEAT  ‐0.012  ‐0.013 

(0.018)  (0.018) 

NETBUYER*MEETBEAT    ‐0.006 

(0.007) 

TOTAL  0.000  0.000  ‐0.000 

(0.000)  (0.000)  (0.000) 

TOTAL*MEETBEAT  0.000  0.001*  0.000 

(0.000)  (0.000)  (0.000) 

CONTROLS INCLUDED 

RSQ  0.059  0.058  0.072 

OBSERVATIONS  1447  1447  1036