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Forecasting without Consequence? Evidence on the Properties of Retiring CEOs’ Forecasts of Future Earnings

Cory A. Cassell University of Arkansas

Shawn X. Huang*

Arizona State University

Juan Manuel Sanchez Texas Tech University

January 2013

Editor’s note: Accepted by Beverly R. Walther. Submitted April 2011 Accepted June 2013

Keywords: forecast bias; horizon problem; management earnings forecasts; managerial myopia; CEO retirement. Data Availability: All data used in the study are publicly available from the sources cited in the text. *Corresponding author. We thank John Harry Evans III (Senior Editor), Beverly Walther (Editor), two anonymous referees, Richard Cazier, Mike Drake, Steve Kachelmeier, Edward Li, James Myers, Linda Myers, Ray Pfeiffer, and Stephanie Rasmussen for comments that have helped us improve the quality of the paper. We also thank workshop participants at Texas Christian University and the University of Arkansas, and participants at the 2011 American Accounting Association Annual Meeting and the 2013 FARS mid-year meeting for insightful comments. We thank Benjamin Anderson, Cari Burke, Ben Coulter, Lauren Dreher, Taylor Joo, Stacey Kaden, Roy Schmardebeck, Timothy Seidel, and Michael Stuart for excellent research assistance. We gratefully acknowledge the financial assistance of the Sam M. Walton College of Business at the University of Arkansas.

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Forecasting without Consequence? Evidence on the Properties of Retiring CEOs’ Forecasts of Future Earnings

ABSTRACT: We investigate whether retiring CEOs engage in opportunistic terminal-year forecasting behavior and the circumstances in which such behavior is likely to be more or less pronounced. Using a within-CEO empirical design, we find that retiring CEOs are more likely to issue forecasts of future earnings, and that they issue such forecasts more frequently in their terminal year relative to other years during their tenure with the firm. Further, retiring CEOs’ terminal-year forecasts of future earnings are more likely to convey good news and are more optimistically biased relative to pre-terminal years. Opportunistic terminal-year forecasting behavior is: i) more pronounced in the presence of higher CEO equity incentives and when discretionary expenditures are cut in the terminal year, and ii) less pronounced in the presence of stronger monitoring mechanisms (e.g., higher institutional ownership). Collectively, our results provide evidence on a potential implication of the CEO horizon problem that has not been investigated previously. Keywords: forecast bias; horizon problem; management earnings forecasts; managerial myopia; CEO retirement. Data Availability: All data used in the study are publicly available from the sources cited in the text.

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I. INTRODUCTION

Theory suggests that Chief Executive Officers (CEOs) with short horizons with their firm

have weaker incentives to act in the best interest of shareholders (Smith and Watts 1982). To

date, research examining the “horizon problem” focuses on whether CEOs adopt myopic

investment and accounting policies in their final years in office (e.g., Dechow and Sloan 1991;

Davidson et al. 2007; Kalyta 2009; Antia et al. 2010). We extend this line of research by

investigating whether retiring CEOs are more likely to engage in opportunistic forecasting

behavior in their terminal year relative to other years during their tenure with the firm.

Specifically, we contrast the properties (issuance, frequency, news, and bias) of earnings

forecasts issued by retiring CEOs during pre-terminal years (when the CEO will be in office

when the associated earnings are realized) with forecasts issued by retiring CEOs during their

terminal year (when the CEO will no longer be in office when the associated earnings are

realized). We also examine circumstances in which opportunistic terminal-year forecasting

behavior is likely to be more or less pronounced.

Our predictions are based on several incentives that arise or increase during retiring

CEOs’ terminal year with their firms. Specifically, relative to CEOs who will continue with their

firms, retiring CEOs face strong incentives to engage in opportunistic terminal-year forecasting

behavior in an attempt to inflate stock prices during the period leading up to their retirement.

Although it is only necessary for our hypotheses that managers believe such opportunistic

behavior will influence stock prices, prior work shows that managers’ opportunistic voluntary

disclosures influence stock prices (Noe 1999; Aboody and Kasznik 2000; Cheng and Lo 2006;

Hamm et al. 2012). Moreover, this work suggests that managers use opportunistic earnings

forecasts to manipulate analysts’ (Cotter et al. 2006) and investors’ perceptions (Cheng and Lo

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2006; Hamm et al. 2012) in an effort to maximize the value of their stock-based compensation

(Aboody and Kasznik 2000). Because SEC trading rules related to CEOs’ post-retirement

security transactions are less stringent than those in effect during their tenure with the firm, post-

retirement transactions can be made before the earnings associated with the opportunistic

forecast are realized and with reduced regulatory scrutiny.1

To test our predictions, we identify all CEO turnover events in Execucomp from 1997

through 2009 and perform detailed searches of SEC filings, executive biographies, press

releases, and related disclosures to determine whether the CEO turnover was due to retirement.2

We require that each of these retiring CEOs have at least two years of data prior to their terminal

year so that we can compare within-CEO forecasting patterns. These procedures yield a sample

of 862 CEO retirements for the forecast issuance/frequency tests and 272 CEO retirements for

the forecast bias tests.

Our results indicate that retiring CEOs are more likely to issue forecasts of future

earnings and that they issue such forecasts more frequently in their terminal year relative to other

years during their tenure with the firm. Moreover, we find that retiring CEOs’ terminal-year

forecasts of future earnings are more likely to convey good news and are more optimistically

biased relative to pre-terminal years. Our findings that retiring CEOs engage in opportunistic

terminal-year forecasting behavior represent a previously undocumented implication of the

“horizon problem.”

1 Anecdotal evidence suggests that a number of retiring CEOs sell a significant proportion of their holdings soon after the retirement date. For example, Nicholas Taubman, former CEO and chairman of Advance Auto Parts, sold about 17% of his holdings for a total of approximately $19 million soon after retiring from Advance Auto Parts in early 2003 (Kincaid 2003). Similarly, Michael Ahearn, former CEO and chairman of First Solar, sold approximately $160 million in First Solar shares shortly after his retirement as CEO (Ahanotu 2010). 2 We classify CEO retirements as turnover events where the CEO does not secure a position with another public company in the years subsequent to their departure. For all turnover events, our search for subsequent employment extends to the end of our data collection in late 2011.

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Next, we examine circumstances in which the terminal-year forecasting behavior we

document for the average retiring CEO is likely to be more or less pronounced. Specifically, we

investigate the effects of eight factors we hypothesize should influence the extent of terminal-

year opportunistic forecasting behavior. We find that opportunistic terminal-year forecasting

behavior is more pronounced in the presence of higher CEO equity incentives and discretionary

expenditure cuts in the terminal year, and less pronounced in the presence of stronger monitoring

mechanisms, such as greater institutional ownership. Other factors have a significant effect on

some, but not all, of the forecast characteristics in the predicted direction.

Although we attribute our results to changes in retiring CEOs’ incentives during their

terminal year with the firm, our results could potentially be confounded by over-time trends in

management forecasts (e.g., the effects of CEO tenure, secular trends, or regulatory changes) or

by a preference for conservative accounting by the incoming CEO. Based on the results from a

battery of sensitivity analyses, we conclude that our inferences are most likely not an artifact of

longer CEO tenure, over-time trends in management forecasts, or incoming CEOs making more

conservative reporting choices.

Our results should be of interest to market participants, such as investors and analysts,

who use information from management earnings forecasts. However, market participants’ ability

to use our evidence is contingent on their knowledge of, or ability to anticipate, a given CEO’s

impending retirement. Our study should also be of interest to stakeholders, such as boards of

directors and regulators, who seek to implement incentive mechanisms that mitigate agency

conflicts. Interestingly, our results suggest that equity incentives, a tool commonly used to align

incentives and minimize agency costs, can have the unintended consequence of creating or

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exacerbating opportunistic forecasting in a CEO’s terminal year.3 Thus, CEO and firm

characteristics, such as equity incentives, may have competing effects on various horizon-

problem induced behaviors.

Our study contributes to two broad research streams in the academic accounting and

finance literatures. First, our study contributes to an emerging line of research that investigates

how individual managers or their particular characteristics influence corporate policy decisions

(Bertrand and Schoar 2003; Dyreng et al. 2010; Ge et al. 2011), including voluntary disclosure

choices (Bamber et al. 2010; Brochet et al. 2011). Our results that CEOs’ forecasting behavior

becomes more opportunistic when their horizon becomes very short are consistent with

practitioners’ survey responses indicating that management philosophy is an important

determinant of forecasting policies (NIRI 2007). Second, we extend the stream of research that

investigates the effects of the CEO horizon problem by identifying a new implication of the CEO

horizon problem, opportunistic terminal-year forecasting behavior.

Section II next describes prior literature and develops our hypotheses. In Section III, we

discuss the data and the empirical model. Section IV presents the empirical results and Section V

concludes the paper.

II. HYPOTHESIS DEVELOPMENT

Terminal-Year Forecasting Behavior for Retiring CEOs

Our primary argument, that retiring CEOs face incentives to issue deliberately misleading

forecasts in an attempt to influence stock prices during their terminal year, requires that CEOs

believe that opportunistic forecasting yields at least temporary market mispricing whereby stock

3 For example, Cheng (2004) provides evidence that firms use option compensation to mitigate horizon-problem induced reductions in R&D spending.

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prices are influenced by biased forecasts. Considerable empirical evidence indicates that

managers employ misleading accounting and disclosure policy choices, suggesting that these

activities yield non-trivial benefits (Erickson and Wang 1999; Noe 1999; Aboody and Kasznik

2000; Lang and Lundholm 2000; Louis 2004; Cheng and Lo 2006; Brockman et al. 2008; Daniel

et al. 2008; Gong et al. 2008).4

We expect that retiring CEOs will be more likely to succumb to such pressures during the

terminal year because the regulations surrounding their stock transactions are less stringent

during the post-retirement period. Specifically, SEC insider trading rules generally do not require

retired CEOs to disclose their trades after retirement.5 As a result, post-retirement trades can be

made before the associated earnings are realized and with reduced regulatory and market

scrutiny. Thus, relative to CEOs who will continue with their firm, retiring CEOs face stronger

incentives to inflate stock prices during the period leading up to their retirement.

The preceding discussion forms the basis for our primary hypotheses, that retiring CEOs

will be more likely to engage in opportunistic forecasting behavior during their terminal year

with the firm than in previous years. Specifically, we posit that, relative to pre-terminal years,

retiring CEOs will be more likely to issue forecasts of future earnings, and to issue them more

frequently, during their terminal year and that such forecasts will be more likely to convey news

that is viewed positively by market participants (e.g., investors and analysts) and more

optimistically biased. We examine forecast issuance because a manager cannot issue good news

4 Theoretical work by Bloomfield (2002), termed The Incomplete Revelation Hypothesis, predicts a negative association between the cost of information extraction and the extent to which market prices reflect the information. Moreover, Hirshleifer and Teoh (2003) argue that investors’ limited attention contributes to incomplete market responses to public information, including information conveyed by deliberately misleading disclosures. 5 Transactions executed after an officer or director relinquishes their insider status are subject to Rule 16 and the related SEC filing requirements only for trades that are executed within a period of less than six months of an opposite transaction; i.e., an acquisition following a disposition, or vice versa, where the opposite transaction was made when the officer or director held insider status. See SEC Rule 16a-2(b) for more details.

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or biased forecasts unless a forecast is issued. Thus, retiring CEOs who have not previously

issued forecasts, but would like to issue good news or biased forecasts in their terminal year,

must change their behavior.6 The preceding argument also supports our interest in terminal-year

forecasting frequency because forecast frequency is influenced by forecast issuance. Our primary

hypotheses, stated in the alternative form, are as follows:

Hypothesis 1: Retiring CEOs are more likely to issue forecasts of future earnings, and to issue such forecasts more frequently, in their terminal year relative to other years during their tenure with the firm.

Hypothesis 2: Retiring CEOs’ terminal-year forecasts of future earnings are more likely to

convey good news relative to prevailing expectations than are forecasts issued in other years during their tenure with the firm.

Hypothesis 3: Retiring CEOs’ terminal-year forecasts of future earnings are more

optimistically biased relative to forecasts issued in other years during their tenure with the firm.

Factors that Affect Terminal-Year Forecasting Behavior

We also investigate a number of factors that we expect to influence the extent of

terminal-year opportunistic forecasting behavior. Specifically, we examine the effect of CEO

incentives to engage in such behavior in terms of the magnitude of CEO equity incentives, the

incidence of cuts in discretionary expenditures, and whether the CEO retains a position in the

firm after retirement.7 We also analyze the effect of the strength of internal monitoring in the

form of whether the firm hires the incoming CEO internally, whether the firm announces the

CEO’s upcoming retirement in advance, the proportion of outside directors on the board, and

whether the CEO also chairs the board, as well as external monitoring in terms of the percentage

of shares held by institutional investors on such behavior. Below, we develop hypotheses for

6 Consistent with these arguments, we find that approximately 18% of sample CEOs (152 out of 862) issue at least one forecast in their terminal year but do not issue any forecasts during pre-terminal years. 7 We acknowledge that cuts in discretionary expenditures are not CEO incentives per se; rather they are ex post observed actions from which we are inferring CEO incentives.

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each of these factors under two broad categories, CEO Incentives and Internal and External

Monitors.

CEO Incentives

Because the benefits to the CEO of influencing stock prices through opportunistic

forecasting behavior are increasing in the extent of CEO equity-based incentives resulting from

option and share holdings, we posit that such behavior will be more pronounced among CEOs

with large equity-based incentives. Equity incentives can also encourage CEOs to manipulate

earnings (Cheng and Warfield 2005; Bergstresser and Philippon 2006; Efendi et al. 2007). Thus,

retiring CEOs with large equity holdings face stronger incentives to issue forecasts of future

earnings and this behavior could benefit the retiring CEO in part because SEC trading rules

related to CEOs’ post-retirement security transactions are less stringent than those in effect

during their tenure with the firm.

Similar arguments apply to retiring CEOs who engage in real earnings management

during their terminal year. Extant research suggests that CEOs adopt myopic investment and

accounting policy choices in the years leading up to their retirement. Consistent with theoretical

expectations (Smith and Watts 1982), several empirical studies find that CEOs with short

horizons reduce both R&D spending (Dechow and Sloan 1991; Barker and Mueller 2002;

Naveen 2006) and capital expenditures (Capex) (Xu 2010).8 Because these policy choices

artificially boost current period income, we expect retiring CEOs to issue forecasts of future

earnings that imply that the current period results represent performance that will continue into

8 Evidence on the association between the horizon problem and myopic investment and accounting policy choices is mixed. A number of studies have either: 1) failed to find an association between the horizon problem and myopic behavior, or 2) documented an association between the horizon problem and myopic behavior only in specific circumstances (Butler and Newman 1989; Gibbons and Murphy 1992; Murphy and Zimmerman 1993; Pourciau 1993; Wells 2002; Cheng 2004; Cazier 2011).

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the future. Thus, we expect that opportunistic terminal-year forecasting behavior will be greater

among CEOs who cut R&D and Capex.

Finally, we expect that opportunistic terminal-year forecasting behavior will be less

pronounced among CEOs who retain a position on the firm’s board of directors after retirement.

These CEOs are more likely to be concerned about the labor market costs associated with

opportunistic forecasting, such as reputation loss and/or the loss of their board position (Fama

1980; Gibbons and Murphy 1992). In addition, firms where former CEOs remain on the board

have better accounting performance and higher turnover-performance sensitivity of the successor

CEO (Fahlenbrach et al. 2011), suggesting that CEOs who remain as board members continue to

have an interest in the firm. The preceding arguments suggest that CEOs who retain a position

within the firm will be less likely to engage in opportunistic forecasting behavior in their

terminal year. Our next set of hypotheses, stated in the alternative form, reflect the influence of

CEO incentives on terminal-year opportunistic forecasting:

Hypothesis 4: Terminal-year opportunistic forecasting behavior will be more pronounced among CEOs with large equity incentives.

Hypothesis 5: Terminal-year opportunistic forecasting behavior will be more pronounced

among CEOs who cut R&D and Capex. Hypothesis 6: Terminal-year opportunistic forecasting behavior will be less pronounced

among CEOs who retain a position on the firm’s board of directors after retirement.

Internal and External Monitoring

We also posit that retiring CEOs will be less likely to engage in terminal-year

opportunistic forecasting behavior in the presence of strong monitoring mechanisms. First, we

expect that such behavior will be less likely among CEOs where the firm hires the successor

CEO internally. An internal hire is more likely to be aware of the retiring CEO’s impending

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departure, which should allow the incoming CEO to make efforts to prevent the retiring CEO

from engaging in opportunistic forecasting behavior.9

Opportunistic terminal-year forecasting may also be less likely when the firm announces

the CEO’s retirement in advance because internal and external monitors in the form of investors

and other stakeholders are likely better able to detect and prevent such behavior. On the other

hand, however, early retirement announcements also help distinguish retiring CEOs who know

that they are about to leave the firm from those whose retirements are unplanned. In this case,

early announcements would be associated with more opportunistic terminal-year forecasting

behavior because CEOs whose retirements are unplanned have little opportunity to engage in

opportunistic terminal-year forecasting behavior. Thus, we cannot predict the direction in which

early retirement announcements affect opportunistic forecasting.

Finally, we expect stronger monitoring from the board of directors and external monitors

to attenuate opportunistic terminal-year forecasting behavior. Our prediction is based on prior

work suggesting that financial reporting quality is higher in the presence of stronger monitoring

mechanisms (Beasley 1996; Dechow et al. 1996; Klein 2002; Imhoff 2003; Farber 2005;

Dechow et al. 2010). Moreover, prior work finds a positive association between management

forecast quality and the strength of internal and external monitoring. Specifically, CEOs of firms

with higher institutional ownership and more independent board members are more likely to

issue management earnings forecasts and their forecasts are more precise (Ajinkya et al. 2005;

Karamanou and Vafeas 2005). We consider two measures of the quality of internal monitoring,

the proportion of outside directors and the presence of CEO/Chairman of the Board duality, and

9 This argument assumes that, relative to an external successor, an internal successor will: i) have better knowledge about the departure date of the retiring CEO, ii) be more involved in decision making during the retiring CEO’s terminal year, and iii) have sufficient power or influence to mitigate opportunistic behavior.

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one measure of the quality of external monitoring, the percentage of shares held by institutional

investors. Our final set of hypotheses, stated in the alternative form, is as follows:

Hypothesis 7: Terminal-year opportunistic forecasting behavior will be less pronounced

when the firm hires the successor CEO internally. Hypothesis 8: Terminal-year opportunistic forecasting behavior differs based on whether

the firm announces the CEO’s retirement in advance. Hypothesis 9: Terminal-year opportunistic forecasting behavior will be less pronounced

when the board of directors includes a higher percentage of outside directors.

Hypothesis 10: Terminal-year opportunistic forecasting behavior will be more pronounced when the retiring CEO also chairs the board of directors.

Hypothesis 11: Terminal-year opportunistic forecasting behavior will be less pronounced

when institutional investors hold a greater percentage of the firm’s shares.

III. DATA AND EMPIRICAL DESIGN

Sample Selection

We identify all 3,548 CEO turnover events in the ExecuComp database during the period

1997-2009.10 For each CEO turnover event identified, we perform detailed searches of SEC

filings (e.g., 10-K, DEF 14A), executive biographies appearing on various social media outlets

such as LinkedIn and Forbes People Finder, press releases, and related disclosures to determine

whether the CEO turnover was due to retirement. We classify CEO retirements as turnover

events where the CEO does not secure a position with another public company in the years

subsequent to their departure.11 Because we are interested in contrasting retiring CEOs’ terminal-

year forecasts of future earnings with those in other years during their tenure with the firm, the

10 We start the sample in 1997 because First Call did not have comprehensive coverage of management earnings forecasts until 1995 and we require that retiring CEOs have at least two years of data prior to their terminal year. 11 We extend our search for subsequent employment to late 2011. For sample CEOs, the average (median) age and tenure at retirement are 61 (61) and 10.4 (8) years, respectively. Approximately 65 (76) percent of sample CEOs retired when they were at least 60 (58) years old.

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sample only includes observations for retiring CEOs. Further, we exclude observations for

retiring CEOs whose tenure with the firm is shorter than three years. These procedures yield a

sample of 1,175 CEO retirements. Subsequent data restrictions involving coverage in the First

Call database, complete data for the control variables and moderating variables, and the

exclusion of firms from regulated industries limit the sample to 862 CEO retirements for the

forecast issuance/frequency tests.12 For the forecast bias tests, we exclude 590 observations

where the firm did not issue a quantitative forecast in either pre-terminal or terminal years,

leading to a final sample of 272 CEO retirements.13 Because the first measure of forecast news,

based on the difference between management forecasts and analyst expectations, requires

quantitative forecasts, the sample for tests using this measure is comprised of 272 CEO

retirements. The sample for tests using the second measure of forecast news, based on the market

return to the management forecast, is comprised of 862 CEO retirements because this measure

does not require quantitative forecasts. Table 1 outlines the sample selection. To mitigate the

effects of outliers on our inferences, we winsorize all continuous variables with values above the

99th percentile or below the 1st percentile.

[insert Table 1 here]

In general, the sample firms are from a broad spectrum of industries. We compared the

distribution of sample observations by industry to the industry distribution of the Compustat

12 To ensure proper identification of firms that do not provide forecasts of future earnings, we require that each sample firm be covered by the First Call database. A firm is covered by First Call if it is included in the First Call company issued guidelines, summary, detailed analysts’ forecasts, or actual earnings files. 13 Management forecasts could involve quantitative information, qualitative information, or both. Quantitative forecasts include discrete (point) forecasts, range forecasts, or open-ended forecasts. We cannot construct measures of forecast bias based on qualitative forecasts. To contrast forecast bias of retiring CEOs’ terminal-year forecasts with that of forecasts made by the same CEO in other years during their tenure with the firm, we require the retiring CEO to provide at least one point, range, or open-ended forecast during both his/her terminal year and pre-terminal years. We exclude 384 observations where no forecasts were provided or where actual earnings are missing from the First Call database. In addition, we exclude 206 observations where point/range/open-ended forecasts are provided during the terminal year but not during pre-terminal years (or vice versa).

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population over the sample period after excluding observations in regulated industries (two-digit

SIC codes 60 through 69). The sample industry distribution is comparable to the Compustat

population with the following exceptions: our sample includes fewer observations in two-digit

SIC code 48 (Communications) than would be expected based on the Compustat population (0.8

percent versus 4.7 percent), our sample includes fewer observations in two-digit SIC code 73

(Business Services) than would be expected based on the Compustat population (7.1 percent

versus 15.2 percent), and our sample includes more observations in two-digit SIC code 49

(Electric, Gas, and Sanitary Services) than would be expected based on the Compustat

population (8.9 percent versus 5.0 percent). For all other two-digit SIC codes, the difference

between the percentage of observations in the sample and the percentage of observations in the

Compustat population is less than two percent.

Measurement of Management Earnings Forecasts

We extract management earnings forecasts data from the First Call Company Issued

Guidance file. Because we are interested in contrasting the properties of forecasts of future

earnings made by retiring CEOs during their terminal and pre-terminal years, we begin by

partitioning the forecast data into the retiring CEO’s terminal year and pre-terminal years. Our

hand collection efforts enable us to identify a specific retirement date, so we classify forecasts as

terminal-year forecasts if: i) the forecast is issued during the CEO’s last year in office, and ii) the

forecast relates to earnings that will not be realized until after the CEO leaves office. Pre-

terminal year forecasts are forecasts that are issued by the retiring CEO where: i) the forecast is

issued during the period before the CEO’s terminal year, and ii) the forecast relates to earnings

that will be realized before the CEO leaves office. We limit the pre-terminal year period to four

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years to ensure the benchmark against which we compare terminal-year forecasting behavior is

relevant.14

To test our predictions, we construct within-CEO measures of changes in forecast

issuance, frequency, news, and bias from the pre-terminal period to the terminal year. For

forecast issuance, we first construct a measure of forecast issuance for each year during the

CEO’s tenure with the firm. Specifically, forecast of future earnings (FFE) occurrence,

FFE_Occurrence, takes a value of one if the firm issued at least one forecast of future earnings

during the year, and zero otherwise. Chg_FFE_Occurence is equal to FFE_Occurrence in the

terminal year less the average of FFE_Occurrence in pre-terminal years. For forecast frequency,

we count the number of forecasts issued during each year of the CEO’s tenure with the firm

(FFE_Frequency).15 Chg_FFE_Frequency is equal to FFE_Frequency in the terminal year less

the average of FFE_Frequency in pre-terminal years.

We construct two measures of forecast news. The first measure, FFE_News1, is the

difference in the management forecast and prevailing analyst expectations in the form of the

median analyst forecast measured during the month closest to the management forecast

announcement date, scaled by the firm’s stock price at the beginning of the year. We use the

midpoint of range forecasts and the value provided in open-ended forecasts to construct

FFE_News1. To measure changes in forecast news, Chg_FFE_News1, we take the average of

FFE_News1 during the terminal year less the average of FFE_News1 during pre-terminal years.

For the second measure of forecast news, FFE_News2, we differentiate between good and bad

news forecasts. We classify each forecast as either good or bad news based on the cumulative

14 Inferences are unchanged if we include all available pre-terminal year observations. 15 Firms can provide multiple forecasts on the same day. Given that these forecasts tend to be issued in the same press release, we treat these forecasts as one forecast for the firm in calculating the frequency of management forecasts.

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abnormal return (CAR) during the three-day window (-1, 1) around the forecast announcement

date (Chen et al. 2008). If the CAR is positive, the forecast is deemed to convey good news.

FFE_News2 is the proportion of good news forecasts in a given year. To measure changes in

forecast news, Chg_FFE_News2, we take FFE_News2 during the terminal year less the average

of FFE_News2 during pre-terminal years. Larger values of Chg_FFE_News1 and

Chg_FFE_News2 indicate that terminal year forecasts of future earnings are more likely to

convey good news, relative to pre-terminal years.

Finally, for firms that issue quantitative forecasts of future earnings during both pre-

terminal years and the terminal year, we construct measures to capture changes in the bias

associated with the forecasts. We measure forecast bias (FFE_Bias) as the difference between

forecasted and actual earnings scaled by the firm’s stock price at the beginning of the fiscal year

(Ajinkya et al. 2005; Karamanou and Vafeas 2005). We use the midpoint of range forecasts and

the value provided in open-ended forecasts to construct FFE_Bias. To measure changes in

forecast bias, Chg_FFE_Bias, we take the average of FFE_Bias during the terminal year less the

average of FFE_Bias during pre-terminal years. Larger values of Chg_FFE_Bias indicate that

terminal year forecasts of future earnings are more optimistically biased, relative to pre-terminal

years.16

Empirical Models

Baseline Tests

The within-CEO empirical design allows us to test hypotheses H1 – H3 using univariate

tests of Chg_FFE_Occurence, Chg_FFE_Frequency, Chg_FFE_News1, Chg_FFE_News2, and

Chg_FFE_Bias. Significant increases in FFE_Occurrence, FFE_Frequency, FFE_News1,

16 For both Chg_FFE_News1 and Chg_FFE_Bias, inferences are unchanged if we exclude open-ended forecasts.

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FFE_News2, and FFE_Bias would be consistent with the notion that retiring CEOs are more

likely to engage in opportunistic forecasting behavior during their terminal year with the firm.

However, to ensure that our results are not driven by changes in other firm characteristics that

influence management earnings forecasts, we also estimate the following empirical model:

Chg_FFE_Properties i,t = β0 + β1Chg_Tenurei,t + β2Chg_Returni,t + β3Chg_StdReti,t (1) + β4Chg_BMi,t + β5Chg_Sizei,t + β6Chg_EarnVoli,t + β7Chg_HHIi,t + β8RegFDi,t + β9 Chg_Horizon i,t + ei,t

Where: Chg_FFE_Properties = one of five variables: Chg_FFE_Occurrence, Chg_FFE_Frequency,

Chg_FFE_News1, Chg_FFE_News2, or Chg_FFE_Bias (all are defined above);

Chg_Tenure = CEO tenure, the number of consecutive years that the CEO has been in

office in the CEO’s terminal year less the average tenure during pre-terminal years;

Chg_Return = the firm’s stock return during the CEO’s terminal year less the average

stock return during pre-terminal years;

Chg_StdRet = the standard deviation of the firm’s daily stock returns during the CEO’s terminal year less the average of the standard deviations of daily stock returns during pre-terminal years;

Chg_BM = the book-to-market ratio in the CEO’s terminal year less the average

book-to-market ratio during pre-terminal years;

Chg_Size = the natural logarithm of the firm’s market capitalization in the CEO’s terminal year less the average market capitalization during pre-terminal years;

Chg_EarnVol = the standard deviation of the firm’s net income over the previous five

years (calculated in the CEO’s terminal year) less the average of the standard deviations calculated during pre-terminal years;

Chg_HHI = the Hirschman-Herfindahl (HH) index in the CEO’s terminal year less

the average HH index during pre-terminal years;17

17 The HHI index is equal to ∑[si/S]2 where si is firm i’s net sales and S is the total sales in firm i’s industry (two digit SIC code level).

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RegFD = an indicator variable set equal to one if the CEO's retirement occurs in 2001, the first year under Regulation Fair Disclosure (Reg FD), and zero otherwise; and

Chg_Horizon = the average forecast horizon (earnings announcement date minus the

forecast date) in the terminal year, less the average horizon in pre-terminal years.18

The coefficient of interest in equation (1) is the intercept (β0), which captures the average

change in retiring CEOs’ forecasting behavior after controlling for changes attributable to

changes in our control variables. To the extent that retiring CEOs engage in opportunistic

forecasting behavior during their terminal year, we expect the coefficient β0 will be positive and

significant for each dependent variable because retiring CEOs will be more likely to issue

forecasts of future earnings, will issue such forecasts more frequently, and their forecasts will be

more likely to convey good news and more biased in their terminal year relative to pre-terminal

years.

In estimating equation (1), we control for a number of firm and manager characteristics

that are associated with firm disclosure practices. We include Chg_Tenure to ensure that our

results on terminal year forecasting behavior are not simply a manifestation of changes in

forecasting behavior that occur throughout the CEO’s tenure with the firm (Ajinkya et al. 2005).

We include Chg_Return and Chg_StdRet because prior work finds that firm disclosure is

associated with firm performance and the volatility of firm performance (Lang and Lundholm

1993; Nagar et al. 2003; Bergman and Roychowdhury 2008; Chen et al. 2008). Chg_BM is

included to control for firm growth opportunities. On one hand, firms with high growth

opportunities tend to have high proprietary costs and are less likely to issue high-quality

disclosures (Cohen 2008). On the other hand, because firms with high growth opportunities often

18 We only include this variable in regressions where the dependent variable is Chg_FFE_News1 or Chg_FFE_Bias.

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require external financing, high-growth firms may undertake greater disclosure to obtain access

to lower cost funds (Frankel et al. 1995). We control for firm size (Chg_Size) because prior

research finds that large firms are more willing to provide disclosures (Kasznik and Lev 1995).

We control for Chg_EarnVol because Ajinkya et al. (2005) find a negative association between

earnings volatility and forecast frequency and a positive association between earnings volatility

and forecast error. Finally, we include the change in the Hirschman-Herfindahl index (Chg_HHI)

to control for the effect of changes in proprietary costs on disclosure behavior (Li 2010), and

RegFD to control for changes in forecasting behavior due to the implementation of Reg FD

(Wang 2007). We estimate equation (1) using Ordinary Least Squares (OLS). We use “robust”

standard errors that are adjusted for heteroskedasticity (White 1980) and clustered by retirement

year to derive the p-values in all regressions.

We provide descriptive statistics for the control variables in Table 2, Panel A. The results

indicate that, relative to their pre-terminal years, retiring CEOs’ firms are larger (Chg_Size), have

poorer performance (Chg_Return), and have more volatile returns and earnings (Chg_StdRet and

Chg_EarnVol) during the terminal year. Approximately six percent of sample CEOs retire in the

first year of Reg FD.

[insert Table 2 here]

Tests of Factors that Affect Terminal-Year Forecasting Behavior

In H4 – H11, we hypothesize that certain factors influence the extent of terminal-year

opportunistic forecasting behavior. H4 – H6 relate to the incentives that CEOs face to behave

opportunistically during their terminal year, while H7 – H11 relate to the internal and external

monitoring factors we expect to help mitigate opportunistic behavior. We test H4 – H11 by

modifying equation (1) as follows:

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Chg_FFE_Properties i,t = δ0 + δ1Incent_Ratioi,t + δ2RD_Capex_Cuti,t +δ3Retain_Positioni,t (2) + δ4Internal_Hirei,t + δ5Early_Announcei,t + δ6Board_Indi,t + δ7CEO_Dualityi,t + δ8Inst_Holdingsi,t + δkChg_Controlsi,t + µi,t

Where: Incent_Ratio = the natural logarithm of the incentive ratio plus one, where the incentive

ratio is the dollar change in the value of the CEO's stock and option holdings coming from a one percent increase in the firm’s stock price (DELTA) divided by DELTA plus salary and bonus (all calculated during the retiring CEO’s terminal year) (Bergstresser and Philippon 2006);

RD_Capex_Cut = an indicator variable set equal to one if the sum of terminal-year R&D

and Capex is less than average expenditures in pre-terminal years, and zero otherwise (see, e.g., Bushee 1998; Bens et al. 2002);

Retain_Position = an indicator variable set equal to one if the retiring CEO retains a

position on the board of directors after retirement, and zero otherwise;

Internal_Hire = an indicator variable set equal to one if the incoming CEO is hired from within the firm, and zero otherwise;

Early_Announce = an indicator variable set equal to one if the number of days between the

announcement date of the CEO’s retirement and the actual departure date is greater than the median value for our sample firms, and zero otherwise;

Board_Ind = the percentage of independent members of the board of directors

(measured during the retiring CEO’s terminal year);

CEO_Duality = an indicator variable set equal to one if the CEO also serves as the chairman of the board, and zero otherwise (measured during the retiring CEO’s terminal year);

Inst_Holdings = the proportion of shares held by institutional owners at fiscal year t

(measured during the retiring CEO’s terminal year); Chg_Controls = a vector of all control variables included in equation (1); and All other variables are as defined previously. We provide descriptive statistics for these

moderating variables in Table 2, Panel B. We find that the mean value of Incent_Ratio is 0.166,

suggesting that approximately 17% of total compensation comes from a one percentage point

increase in the value of the CEO’s equity holdings in the firm for the average sample CEO

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(Bergstresser and Philippon 2006). Approximately 35 percent of sample CEOs cut discretionary

expenditures in their terminal year (RD_Capex_Cut) and 70 percent retain a position with the

firm after retirement. With respect to our monitoring variables, approximately 71 percent of

successor CEOs are hired internally, 48 percent of retiring CEOs announce their retirement in

advance, and 84 percent of retiring CEOs also chair the board of directors. Finally, for the

average retiring CEO, the board of directors is comprised of mostly independent directors

(approximately 71 percent) and the firm’s shares are largely held by institutional investors

(approximately 73 percent).

We estimate equation (2) using OLS for each of the five dependent variables

(Chg_FFE_Occurrence, Chg_FFE_Frequency, Chg_FFE_News1, Chg_FFE_News2, and

Chg_FFE_Bias). In H4 - H11, we predict the following: δ1 (Incent_Ratio), δ2 (RD_Capex_Cut)

and δ7 (CEO_Duality) should be positive, and δ3 (Retain_Position), δ4 (Internal_Hire), δ6

(Board_Ind), and δ8 (Inst_Holdings) should be negative. We do not make a directional prediction

for δ5 (Early_Announce).

IV. RESULTS

The results of tests performed to investigate H1 – H3 are presented in Table 3 (univariate

tests) and Table 4 (estimation results for equation (1)). Results of tests performed to investigate

H4 – H11 are presented in Table 5 (univariate tests) and Table 6 (estimation results for equation

(2)).

Baseline Tests

Table 3 provides the results of univariate tests for H1 – H3. The results indicate that the

differences in the properties of forecasts of future earnings between terminal and pre-terminal

years are significant in the predicted direction. Specifically, relative to pre-terminal years,

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retiring CEOs are significantly more likely to issue forecasts of future earnings

(Chg_FFE_Occurrence) and issue such forecasts more frequently (Chg_FFE_Frequency) during

terminal years. Moreover, retiring CEOs’ terminal-year forecasts of future earnings are more

likely to convey good news (Chg_FFE_News1 and Chg_FFE_News2) relative to their pre-

terminal year forecasts. Finally, retiring CEOs’ terminal-year forecasts of future earnings are

more optimistically biased (Chg_FFE_Bias) relative to their pre-terminal year forecasts. The

changes in the forecast characteristics are also economically significant as all terminal year

values exceed values in pre-terminal years by at least 70 percent.

[insert Table 3 here]

Figures 1 – 3 illustrate mean FFE_Occurrence, FFE_Frequency, FFE_News1,

FFE_News2, and FFE_Bias during retiring CEOs’ pre-terminal years (years t – 4 through t – 1),

retiring CEOs’ terminal year (year t), and the first four years of newly appointed CEOs’ tenure

(years t + 1 through t + 4). The figures show an increase in the aggressiveness of retiring CEOs’

forecasting behavior in the years leading up to their terminal year. Importantly, we observe a

substantial spike in all measures at year t and a substantial reduction in all measures in the first

year of the newly appointed CEO’s tenure. Collectively, the results in Table 3 and Figures 1 – 3

provide univariate evidence consistent with our prediction that retiring CEOs engage in

opportunistic forecasting behavior during their terminal year. However, because the properties of

managers’ earnings forecasts are likely to be influenced by a number of firm characteristics, it is

important to examine our predictions in a multivariate setting.

[insert Figures 1 – 3 here]

The estimation results for equation (1), which includes controls for changes in firm and

manager characteristics, are presented in Table 4. The intercept captures the average change in

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retiring CEOs’ forecasting behavior after controlling for changes attributable to changes in our

control variables. For each dependent variable (Chg_FFE_Occurrence, Chg_FFE_Frequency,

Chg_FFE_News1, Chg_FFE_News2, and Chg_FFE_Bias), we find that the coefficient on the

intercept is positive and significant. These results provide further support for H1 – H3 and

suggest that retiring CEOs are more likely to engage in opportunistic forecasting behavior during

their terminal year with the firm.19

[insert Table 4 here]

Turning to the control variables, we find a significantly negative association between

changes in several properties of managers’ earnings forecasts (Chg_FFE_Occurrence,

Chg_FFE_Frequency, Chg_FFE_News1, and Chg_FFE_Bias) and Chg_Return, suggesting that

poor stock performance increases managers’ willingness to issue forecasts to guide market

expectations. Moreover, consistent with prior research (e.g., Ajinkya et al. 2005), we find that

Chg_Size is positively related with Chg_FFE_Occurrence and Chg_FFE_Frequency, which

suggests that larger firms are more willing to provide forecasts. Finally, the coefficient on

Chg_BM is positive and marginally significant in the model where the dependent variable is

Chg_FFE_Bias, consistent with the notion that firms with low growth opportunities need less

external financing, which reduces firms’ need to supply high quality disclosure (Cohen 2008).

The Effect of CEO Incentives and Internal and External Monitoring on Terminal-Year

Forecasting Behavior

In H4 – H6, we predict that opportunistic forecasting behavior will be more pronounced

when the retiring CEO’s equity incentives are larger and when discretionary expenditures are cut

19 In untabulated tests, we find that retiring CEOs’ terminal-year forecasts of future earnings are less accurate relative to their pre-terminal year forecasts. In the moderator tests (H4 – H11), we find support for H10 (CEO/Chairman Duality) and H11 (institutional ownership), but the other moderator variables are insignificant. We measure forecast accuracy as the absolute value of the difference between forecasted earnings and actual (realized) earnings.

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and less pronounced when the CEO retains a position with the firm after retirement. In the

univariate tests presented in Table 5, we find consistent support for the first two predictions and

partial support for the third prediction. Specifically, Chg_FFE_Occurrence,

Chg_FFE_Frequency, Chg_FFE_News1, Chg_FFE_News2, and Chg_FFE_Bias are higher

when Incent_Ratio is above the median and when RD_Capex_Cut is equal to one. In addition,

Chg_FFE_Frequency, Chg_FFE_News1, and Chg_FFE_Bias are lower when Retain_Position is

equal to one. There is no significant difference, however, in Chg_FFE_Occurrence or

Chg_FFE_News2.

In H7 – H11, we predict that opportunistic forecasting behavior will be less pronounced

in the presence of stronger monitoring mechanisms. The univariate tests presented in Table 5

partially support these predictions. Specifically, Chg_FFE_Occurrence and Chg_FFE_News2

are lower when the successor CEO is hired internally, but there is no difference in

Chg_FFE_Frequency, Chg_FFE_News1, or Chg_FFE_Bias. For retiring CEOs who announce

their retirement in advance, Chg_FFE_Occurrence, Chg_FFE_Frequency, and

Chg_FFE_News2 are higher, but there is no difference in Chg_FFE_News1 or Chg_FFE_Bias.

Chg_FFE_Frequency, Chg_FFE_News1, and Chg_FFE_Bias are also lower when board

independence is high but CEO duality has no effect on changes in our forecast characteristics.

Finally, we find that high institutional ownership is associated with lower

Chg_FFE_Occurrence, Chg_FFE_Frequency, Chg_FFE_News1, Chg_FFE_News2, and

Chg_FFE_Bias.

[insert Table 5 here]

Multivariate tests performed to investigate H4 – H11 are presented in Table 6. Consistent

with the univariate results described above, we find that opportunistic terminal-year forecasting

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behavior is more pronounced when equity incentives are large and when discretionary

expenditures are cut. These results hold across all dependent variables. Moreover, the results

indicate that Chg_FFE_Frequency and Chg_FFE_News1 are lower when the CEO retains a

position with the firm after retirement. Collectively, the results in Tables 5 and 6 provide strong

support for H4 (equity incentives) and H5 (cuts in discretionary expenditures) and partial support

for H6 (retaining a position). We conclude that CEO incentives have a significant influence on

the extent of opportunistic terminal-year forecasting behavior.

Consistent with the univariate results described above, we find that hiring an internal

successor CEO has a negative effect on Chg_FFE_Occurrence and Chg_FFE_News2, but not

other forecast characteristics. We also find consistent results for the effect of early

announcements. Chg_FFE_Frequency, Chg_FFE_News1, and Chg_FFE_Bias are lower when

board independence and institutional ownership are higher (Chg_FFE_Occurrence and

Chg_FFE_News2 are also lower when institutional ownership is higher). Finally, we find no

effect of CEO duality on any of our dependent variables. Collectively, the results in Tables 5 and

6 provide strong support for H11 (institutional ownership), partial support for H6 (internal hire)

and H8 (board independence), and no support for H9 (CEO/Chairman duality). The results also

provide some evidence suggesting that early retirement announcements are associated with more

opportunistic terminal-year forecasting behavior (H7).20

[insert Table 6 here]

Robustness Tests

Our main results are consistent with the notion that CEOs engage in opportunistic

forecasting behavior during their terminal year with the firm. However, a possible alternative 20 Inferences are unchanged if we introduce each conditioning variable separately with one exception. Specifically, the coefficient on Retain_Position is negative and significant when the dependent variable is Chg_FFE_Bias, consistent with H6 (retaining a position).

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explanation for these results is that forecasts of future earnings are more likely to occur, occur

more frequently, are more likely to convey good news, and are more biased in later years during

our sample period. These concerns arise because terminal-year forecasts of future earnings

always take place later in calendar time than forecasts made by the same CEO during their earlier

tenure with the firm. If forecasts of future earnings are more likely to occur, more likely to

convey good news, and/or more optimistically biased in more recent years (or later in the CEO’s

tenure), this time trend would bias in favor of finding support for our predictions. Although we

control for CEO tenure and a fixed effect for whether retirements occurred in the first year of

Reg FD in all multivariate analyses, we perform a number of additional tests to further mitigate

these concerns.

First, we contrast: i) the properties of future earnings forecasts made by retiring CEOs in

their terminal year with forecasts made by newly appointed CEOs, and ii) the properties of future

earnings forecasts made by retiring CEOs in pre-terminal years with forecasts made by newly

appointed CEOs.21 The results in Panel A of Table 7 indicate that, relative to retiring CEOs’

terminal-year, newly appointed CEOs are less likely to issue forecasts of future earnings (and

issue such forecasts less frequently), and that their forecasts are less likely to convey good news

and are less biased. In Panel B of Table 7, we contrast the properties of future earnings forecasts

made by retiring CEOs in pre-terminal years with forecasts made by newly appointed CEOs and

find no significant difference in the properties of their forecasts.

[insert Table 7 here]

To further mitigate concerns that our results are a manifestation of CEO tenure-related

effects, we construct a sample of retiring CEOs with tenures of at least five years and contrast: i)

21 Similar to the data requirement we impose on retiring CEOs, we require newly appointed CEOs to have at least two years of data.

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the properties of future earnings forecasts made by retiring CEOs in a “pseudo” terminal year

(year t-2) with forecasts made by retiring CEOs in years t-5 through year t-3, and ii) the

properties of future earnings forecasts made by retiring CEOs in the actual terminal year (year t)

with forecasts made by retiring CEOs in years t-5 through year t-3. The second test is similar to

our primary test except that we exclude years t-1 and t-2 to ensure that both tests are performed

with similar power. We find similar results if, instead of using pre-terminal years t-5 through t-3,

we include all pre-terminal years through year t-3. The results in Panel C of Table 7 provide

some evidence of an increase in both forecast issuance and forecast frequency during CEOs’

pseudo terminal year (year t-2) relative to years t-5 through t-3. However, we find no significant

difference in forecast news or bias during the pseudo terminal year (year t-2) relative to years t-5

through t-3. Importantly, the increase in opportunistic forecasting behavior observed during the

actual terminal year is significantly larger than that observed during the pseudo terminal year.

Next, we perform analyses after de-trending our management forecast measures.

Specifically, we calculate the mean value of each of the management forecast characteristics for

each year of CEO tenure based on the FirstCall population, and then subtract the mean values

from our firm-level management forecast measures.22 The results, reported in Panel D of Table

7, are consistent with those from our primary tests.

Finally, prior studies find that Reg FD has a significant impact on firm disclosure

behavior (Wang 2007). If a retiring CEO’s pre-terminal period occurs before Reg FD, while the

terminal year occurs in the first year under Reg FD, the change in regulation could contribute to

the observed change in forecasting behavior. To rule out this alternative explanation, we perform

our main analyses separately in periods surrounding the implementation of Reg FD. Specifically, 22 For example, we take the population of CEOs with tenure of ten years and construct the mean value of their forecast characteristics during their tenth year in office. The mean value is used to de-trend the forecast characteristics of sample CEOs during their tenth year in office.

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we form three groups of sample CEOs: 1) where the CEO’s retirement year is before 2001, 2)

where the CEO’s retirement year is 2001, and 3) where the CEO’s retirement year and pre-

terminal period are both after 2001. In Table 7, Panel E, we find evidence of opportunistic

terminal-year forecasting behavior during all three periods. Thus, our findings do not appear to

be attributable to the implementation of Reg FD.

Collectively, the results in Table 7, Panels A through E suggest that the terminal-year

opportunistic forecasting behavior that we document is not simply a secular trend. Indeed,

contrary to an increasing trend in our forecast characteristics over time, we find that newly

appointed CEOs engage in less opportunistic forecasting behavior than retiring CEOs during

their terminal year. While this result contradicts the secular time-trend explanation for our

primary results, it raises another possible alternative explanation. If newly appointed CEOs

prefer conservative accounting policies in their early years, this would result in lower actual

earnings, which would make retiring CEOs’ terminal-year forecasts appear optimistically biased.

Although we cannot completely eliminate this alternative explanation, we offer the

following evidence that incoming CEO conservatism is not likely to significantly affect our

results. First, as discussed above, we find no difference in the properties of management earnings

forecasts when we contrast retiring CEOs’ pre-terminal year forecasts with those issued by

newly appointed CEOs. Thus, newly appointed CEOs’ forecasts are not significantly more

conservative than retiring CEOs’ forecasts during pre-terminal years. Second, our results suggest

that retiring CEOs engage in more terminal-year opportunistic forecasting behavior when the

retiring CEO has substantial equity incentives and when discretionary expenditures are cut.

While these factors plausibly affect the retiring CEO’s terminal-year opportunistic forecasting

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behavior, outgoing CEO’s equity incentives and discretionary cost cutting are not likely to affect

the incoming CEO’s preference for conservative accounting.

Limitations of the Within-CEO Research Design

Our primary tests are based on a within-CEO research design where we compare the

CEO’s terminal-year forecasting behavior to that in pre-terminal years. Although this approach

has the advantage of eliminating the need to control for stationary CEO-level differences, it

presents two significant disadvantages: 1) the comparison involves different samples sizes

because the pre-terminal period includes multiple years versus a single terminal year, which

could result in systematic differences in sample variances,23 and 2) the loss of information when

we take the average in the pre-terminal period to construct our measures of changes in forecast

characteristics. To mitigate concerns relating to the first issue, we restrict the pre-terminal period

to four years in our main tests. For the univariate tests reported in Table 3, we perform tests to

determine whether the mean value of the change in the forecast characteristic is different from

zero rather than comparing sample means in the two periods where the sample means may have

different variances.24

To further mitigate concerns relating to the comparison of observations from samples of

different sizes and to address concerns relating to the potential loss of information in the pre-

terminal period, we conduct our analysis using a “levels” design.25 Here, we modify equations

23 Specifically, the variances of the sample means differ in the pre-terminal period relative to the terminal year because the number of forecasts per CEO per year and the number of years per CEO in the pre-terminal period differ from the terminal period. 24 For the tests reported in Table 3, we also directly compare sample means across the two periods (pre-terminal years and terminal years) by performing t-tests that assume unequal variances between the two periods. The results from these tests are consistent with the tabulated results. 25 The insignificant coefficients Chg_Horizon in Table 6 highlight the loss of information incurred by employing a within-CEO research design. Prior work provides strong evidence of a positive association between forecast horizon and forecast bias (Ajinkya et al. 2005). However, when the change in horizon is constructed as the difference in the average horizon in the terminal year less the average in pre-terminal years, where pre-terminal year horizon is averaged across multiple forecasts in a given year and across multiple years in the pre-terminal period, the resulting

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(1) and (2) by replacing the changes variables with the actual values in a given firm-year and by

introducing Terminal, a new categorical variable that equals one in the CEO’s terminal year, and

zero otherwise. In untabulated tests, we find consistent support for H1 – H3. Specifically, the

coefficient on Terminal is positive and significant in regressions for each dependent variable

(FFE_Occurence, FFE_Frequency, FFE_News1, FFE_News2, and FFE_Bias).26

To investigate H4 – H11 in this setting, we include interactions between Terminal and

each of the moderator variables. The results are generally consistent with the tabulated results.

Specifically, we continue to find strong support for both H4 (equity incentives) and H11

(institutional ownership) and partial support for H6 (retaining a position), H7 (internal hire), and

H8 (early announcement). Consistent with our main results, we find no support for H10

(CEO/Chairman duality). Moreover, while we find only partial support for H8 (board

independence) in the tabulated results, the levels results provide strong support in that the

coefficient on the interaction between Terminal and Board_Ind is negative and significant in

regressions for each dependent variable. One noteworthy exception in the level tests is that we do

not find support for H5 involving cuts in discretionary expenditures.

V. CONCLUSION

We examine whether retiring CEOs are more likely to issue forecasts of future earnings

in their terminal year and whether such forecasts are more likely to convey good news and to be

more optimistically biased relative to forecasts in other years during their tenure with the firm.

measure (Chg_Horizon) exhibits little variation. Because the levels design does not pose this problem, we find results that are consistent with prior work in that the coefficient on Horizon is positive and significant in untabulated tests where FFE_Bias is the dependent variable. 26 The levels results for tests of H1 – H3 are important due to another limitation of the within-CEO research design. Specifically, for tests of H1 – H3, the estimated intercept is the focal test when we use the within-CEO research design. This situation is not ideal because potential model misspecification in terms of non-stationary variables that are excluded from the model or measurement error in the included variables could result in a biased estimate of the intercept.

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Specifically, we contrast the properties of earnings forecasts issued by retiring CEOs during pre-

terminal years, when the CEO will be in office when the associated earnings are realized, with

forecasts issued by retiring CEOs during their terminal year, when the CEO will no longer be in

office when the associated earnings are realized.

We find that retiring CEOs are more likely to issue forecasts of future earnings and that

they issue such forecasts more frequently in their terminal year relative to other years during

their tenure with the firm. Further, retiring CEOs’ terminal-year forecasts of future earnings are

more likely to convey news that is viewed favorably by market participants and more

optimistically biased relative to pre-terminal years. We find supporting results when we perform

tests designed to mitigate concerns that our results are capturing an over-time trend as opposed to

a manifestation of the CEO horizon problem. We also examine circumstances in which

opportunistic terminal-year forecasting behavior is likely to be more or less pronounced. We find

that opportunistic terminal-year forecasting behavior is more pronounced when CEO equity

incentives are stronger and when discretionary expenditures are cut in the terminal year, and less

pronounced in the presence of stronger monitoring mechanisms, such as higher institutional

ownership. Other factors, including the percentage of independent board members, whether the

CEO retains a position on the board of directors after retirement, whether the incoming CEO is

hired internally, and whether the retiring CEO’s retirement is announced early, have a significant

effect on some, but not all, of the forecast characteristics in the predicted direction.

Our results should be of interest to market participants who use information from

management earnings forecasts. Specifically, we provide evidence which suggests that retiring

CEOs engage in opportunistic forecasting behavior during their terminal year and that the extent

of such behavior varies with CEO incentives and the strength of monitoring mechanisms. Thus,

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our evidence should facilitate market participants’ efforts to identify earnings forecasts that may

be misleading. Our study should also be of interest to stakeholders who seek to implement

incentive mechanisms that mitigate agency conflicts. For example, our results suggest that equity

incentives, a tool commonly used to align incentives and minimize agency costs, can have the

unintended consequence of creating or exacerbating terminal-year opportunistic earnings

forecasting behavior. Finally, our results should be of interest to academics because our

empirical approach highlights the importance of: i) investigating changes in policy choices that

are implemented during retiring CEO’s terminal year rather than over the years leading up to the

CEO’s retirement, and ii) investigating circumstances in which horizon problem induced myopic

behavior is likely to be more prevalent. The mixed findings from prior research investigating

various implications of the horizon problem may be attributable to a failure to adopt one or both

of these empirical design choices.

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Figure 1 FFE_Occurrence and FFE_Frequency by Year

Figure 2 FFE_Bias and FFE_News1 by Year

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Figure 3 FFE_News2 by Year

See text for variable definitions. Figures 1-3 illustrate mean FFE_Occurrence, FFE_Frequency, FFE_News1, FFE_News2, and FFE_Bias during retiring CEOs’ pre-terminal years (years t – 4 through t – 1), retiring CEOs’ terminal year (year t), and the first four years of newly appointed CEOs’ tenure (years t + 1 through t + 4).

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TABLE 1 Sample Selection and Distribution

# of CEO turnover events from Execucomp during the period 1997-2009 3,548 Less: non-retirement CEO turnover (2,373) Less: retirements with missing data (185) Less: retirements occurred in financial and utility firms (SIC codes 4900-4999 and 6000-6999) (128) Final # of CEO retirements used in tests of Chg_FFE_Occurrence, Chg_FFE_Frequency, and Chg_FFE_News2 8621 Less: retirements without quantitative forecasts of future earnings in both (590) pre-terminal and terminal years and retirements with missing actual EPS in First Call Final # of CEO retirements used in tests of Chg_FFE_News1 and Chg_FFE_Bias 2722

See text for variable definitions. 1 For tests of Chg_FFE_Occurrence, Chg_FFE_Frequency, and Chg_FFE_News2, there are 3,015 firm-year observations in the pre-terminal period and 862 firm-year observations in the terminal period. With respect to the number of forecasts, there are 912 forecasts in the pre-terminal period and 702 forecasts in the terminal period. 2 For tests of Chg_FFE_News1 and Chg_FFE_Bias, there are 393 firm-year observations in the pre-terminal period and 272 firm-year observations in the terminal period. With respect to the number of forecasts, there are 416 forecasts in the pre-terminal period and 279 forecasts in the terminal period.

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TABLE 2

Descriptive Statistics for the Control Variables and the Factors that Influence Terminal-Year Forecasting Behavior

Panel A - Control Variables

N Mean Std. Dev. Q1 Median Q3 Chg_Tenure 862 2.051 0.624 2.000 3.000 3.000 Chg_Return 862 -0.093 0.487 -0.366 -0.097 0.154 Chg_StdRet 862 0.002 0.011 -0.004 0.001 0.006 Chg_BM 862 0.060 0.313 -0.068 0.016 0.120 Chg_Size 862 0.023 0.574 -0.221 0.054 0.320 Chg_EarnVol 862 0.208 0.667 -0.165 0.136 0.546 Chg_HHI 862 0.001 0.016 -0.002 0.000 0.002 RegFD 862 0.061 0.240 0.000 0.000 0.000 Chg_Horizon 272 0.208 0.288 0.042 0.209 0.359

Panel B - Factors that Influence Terminal-Year Forecasting Behavior

N Mean Std. Dev. Q1 Median Q3 CEO Incentives: Incent_Ratio 862 0.166 0.149 0.047 0.121 0.255 RD_Capex_Cut 856 0.351 0.478 0.000 0.000 1.000 Retain_Position 862 0.700 0.459 0.000 1.000 1.000 Internal and External Monitoring Internal_Hire 862 0.706 0.456 0.000 1.000 1.000 Early_Announce 862 0.476 0.500 0.000 0.000 1.000 Board_Ind 780 0.705 0.158 0.625 0.727 0.818 CEO_Duality 780 0.838 0.368 1.000 1.000 1.000 Inst_Holdings 862 0.728 0.538 0.552 0.713 0.845

See text for variable definitions.

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TABLE 3 Changes in the Properties of Retiring CEOs’ Forecasts of Future Earnings,

Univariate Tests of H1 – H3 FFE_Occurrence FFE_Frequency FFE_News1 FFE_News2 FFE_Bias Pre-terminal Years 0.199 0.297 0.002 0.054 0.002 Terminal Year 0.346 0.814 0.004 0.184 0.005 Difference 0.147*** 0.518*** 0.002*** 0.129*** 0.004*** P-value (0.00) (0.00) (0.00) (0.00) (0.00)

N 862 862 272 862 272

See text for variable definitions. *, **, and *** denote significance at the 10%, 5%, and 1% levels (two-tailed), respectively.

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TABLE 4 Changes in the Properties of Retiring CEOs’ Forecasts of Future Earnings, Multivariate Tests of H1 – H3

pred (1)

Chg_FFE_Occurrence(2)

Chg_FFE_Frequency (3)

Chg_FFE_News1(4)

Chg_FFE_News2(5)

Chg_FFE_BiasIntercept + 0.123*** 0.360** 0.003** 0.095** 0.005** (0.01) (0.02) (0.04) (0.01) (0.04) Chg_Tenure ? 0.053** 0.143** -0.001 0.014 -0.001 (0.04) (0.04) (0.51) (0.47) (0.44) Chg_Return ? -0.078* -0.260** -0.008*** -0.003 -0.008** (0.06) (0.02) (0.01) (0.91) (0.02) Chg_StdRet ? -3.680** -5.654 0.136 -2.558** -0.139 (0.02) (0.18) (0.33) (0.02) (0.49) Chg_BM ? 0.018 -0.060 -0.002 0.046 0.016* (0.74) (0.66) (0.83) (0.25) (0.09) Chg_Size ? 0.071** 0.176** -0.003 0.041* -0.005 (0.02) (0.03) (0.47) (0.07) (0.22) Chg_EarnVol ? -0.005 -0.062 0.000 -0.002 -0.002 (0.85) (0.39) (0.85) (0.14) (0.47) Chg_HHI ? -0.991 -2.016 0.244 -0.913 -0.010 (0.20) (0.28) (0.24) (0.12) (0.83) RegFD ? 0.114* 0.121 -0.007 0.056 0.013** (0.10) (0.51) (0.20) (0.26) (0.04) Chg_Horizon ? 0.009* 0.005 (0.07) (0.29) N 862 862 272 862 272 Adj. R2 0.029 0.052 0.151 0.028 0.160

See text for variable definitions. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. P-values are two-tailed unless a prediction is made.

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TABLE 5 Changes in the Properties of Retiring CEOs’ Forecasts of Future Earnings Conditioned on the

Extent of CEO Incentives and Internal/External Monitoring, Univariate Tests of H4 – H11

Average Values of Chg_FFE_Occurence Yes No Diff P-value

High Incent_Ratio1 0.301 0.177 0.125** (0.02) RD_Capex_Cut 0.281 0.176 0.105*** (0.00) Retain_Position 0.244 0.228 0.015 (0.65) Internal_Hire 0.228 0.265 -0.037* (0.10) Early_Announce 0.276 0.227 0.049* (0.08) High Board_Ind 0.226 0.273 -0.047 (0.15) CEO_Duality 0.250 0.239 0.011 (0.80) High Inst_Holdings 0.227 0.251 -0.023* (0.08)

Average Values of Chg_FFE_Frequency Yes No Diff P-value High Incent_Ratio 0.903 0.417 0.485*** (0.00) RD_Capex_Cut 0.791 0.461 0.330*** (0.00) Retain_Position 0.466 0.691 -0.225* (0.07) Internal_Hire 0.638 0.711 -0.073 (0.48) Early_Announce 0.794 0.615 0.179* (0.09) High Board_Ind 0.504 0.884 -0.380*** (0.00) CEO_Duality 0.698 0.638 0.060 (0.65) High Inst_Holdings 0.571 0.749 -0.178* (0.06)

Average Values of Chg_FFE_News1 Yes No Diff P-value High Incent_Ratio 0.003 0.002 0.001*** (0.01) RD_Capex_Cut 0.006 0.002 0.005** (0.04) Retain_Position 0.001 0.005 -0.004* (0.09) Internal_Hire 0.002 0.003 -0.002 (0.56) Early_Announce 0.002 0.002 0.000 (0.48) High Board_Ind 0.002 0.003 -0.001* (0.08) CEO_Duality 0.002 0.001 0.001 (0.79) High Inst_Holdings 0.001 0.003 -0.002** (0.03)

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Average Values of Chg_FFE_News2 Yes No Diff P-value High Incent_Ratio 0.139 0.111 0.028** (0.02) RD_Capex_Cut 0.135 0.106 0.029* (0.08) Retain_Position 0.128 0.133 -0.004 (0.12) Internal_Hire 0.125 0.140 -0.015* (0.10) Early_Announce 0.163 0.118 0.045* (0.09) High Board_Ind 0.125 0.140 -0.015 (0.12) CEO_Duality 0.159 0.128 0.031 (0.35) High Inst_Holdings 0.108 0.151 -0.042* (0.06)

Average Values of Chg_FFE_Bias Yes No Diff P-value High Incent_Ratio 0.005 0.003 0.002*** (0.01) RD_Capex_Cut 0.014 0.003 0.011*** (0.00) Retain_Position 0.003 0.008 -0.005* (0.07) Internal_Hire 0.004 0.006 -0.002 (0.44) Early_Announce 0.002 0.005 -0.003 (0.38) High Board_Ind 0.002 0.007 -0.005* (0.06) CEO_Duality 0.005 0.005 0.000 (0.88) High Inst_Holdings 0.002 0.006 -0.004*** (0.01)

1 For non-dichotomous variables, the cut-off for the yes/no classification is the sample median. See text for variable definitions. *, **, and *** denote significance at the 10%, 5%, and 1% levels (two-tailed), respectively.

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TABLE 6 Changes in the Properties of Retiring CEOs’ Forecasts of Future Earnings Conditioned on the Extent of CEO Incentives and

Internal/External Monitoring, Multivariate Tests of H4 – H11

pred (1)

Chg_FFE_Occurrence (2)

Chg_FFE_Frequency (3)

Chg_FFE_News1 (4)

Chg_FFE_News2 (5)

Chg_FFE_Bias Intercept + 0.066 -0.159 0.004 0.084* -0.001 (0.12) (0.55) (0.30) (0.07) (0.40) Incent_Ratio + 0.152* 1.252*** 0.008** 0.039* 0.012* (0.09) (0.00) (0.03) (0.06) (0.07) RD_Capex_Cut + 0.096*** 0.290*** 0.007** 0.046** 0.002* (0.01) (0.00) (0.02) (0.04) (0.07) Retain_Position - -0.013 -0.126* -0.004* -0.030 -0.001 (0.72) (0.07) (0.08) (0.13) (0.43) Internal_Hire - -0.047* -0.073 -0.001 -0.015* -0.001 (0.08) (0.25) (0.42) (0.09) (0.30) Early_Announce ? 0.036* 0.103* -0.001 0.043* -0.003 (0.08) (0.07) (0.41) (0.07) (0.15) Board_Ind - 0.077 -0.718** -0.008* 0.025 -0.011* (0.46) (0.01) (0.10) (0.35) 0..06 CEO_Duality + -0.001 0.041 0.001 -0.040 0.003 (0.98) (0.36) (0.42) (0.12) (0.21) Inst_Holdings - -0.053** -0.180* -0.001** -0.052** -0.003*** (0.03) (0.06) (0.04) (0.01) (0.00) Chg_Tenure ? 0.064** 0.097 -0.002 0.027 -0.002 (0.02) (0.20) (0.21) (0.24) (0.31) Chg_Return ? -0.056 -0.172 -0.005 0.005 -0.007* (0.21) (0.14) (0.21) (0.87) (0.07) Chg_StdRet ? -5.574*** -11.752** 0.146 -3.562*** -0.146 (0.00) (0.02) (0.31) (0.01) (0.50) Chg_BM ? 0.001 -0.077 -0.004 0.028 0.017* (0.99) (0.67) (0.65) (0.56) (0.07) Chg_Size ? 0.021 -0.022 -0.007* 0.018 -0.006 (0.60) (0.85) (0.09) (0.57) (0.13) Chg_EarnVol ? 0.002 -0.026 0.002 -0.001 -0.001 (0.95) (0.73) (0.49) (0.11) (0.61) Chg_HHI ? -1.334 -2.927 0.284 -1.191 0.081 (0.18) (0.20) (0.15) (0.12) (0.54) RegFD ? 0.129* 0.189 -0.008 0.074 0.011* (0.08) (0.33) (0.15) (0.16) (0.06) Chg_Horizon ? 0.008 0.002 (0.13) (0.71) N 778 778 256 778 256 Adj. R2 0.056 0.087 0.225 0.038 0.199

See text for variable definitions. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. P-values are two-tailed unless a prediction is made.

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TABLE 7 Robustness Tests

Panel A: New CEO Early Years Minus Retiring CEO Terminal Years Chg_FFE_Occurrence Chg_FFE_Frequency Chg_FFE_News1 Chg_FFE_News2 Chg_FFE_Bias Mean -0.248*** -0.598*** -0.002** -0.138*** -0.004** P-Value (0.00) (0.00) (0.04) (0.00) (0.05) N 855 855 216 855 216 Panel B: New CEO Early Years Minus Retiring CEO Pre-Terminal Years Chg_FFE_Occurrence Chg_FFE_Frequency Chg_FFE_News1 Chg_FFE_News2 Chg_FFE_Bias Mean -0.007 0.007 -0.001 -0.007 -0.001 P-Value (0.42) (0.37) (0.28) (0.21) (0.77) N 855 855 216 855 216 Panel C: Pseudo Terminal Year Chg_FFE_Occurrence Chg_FFE_Frequency Chg_FFE_News1 Chg_FFE_News2 Chg_FFE_Bias Mean in Pseudo Terminal Year 0.033** 0.056** 0.000 0.007 0.000 (0.02) (0.03) (0.83) (0.47) (0.93) Mean in Actual Terminal Year 0.278*** 0.747*** 0.002** 0.144*** 0.003** (0.00) (0.00) (0.05) (0.00) (0.04) Difference 0.245** 0.692** 0.002** 0.136*** 0.003** P-Value (0.00) (0.00) (0.04) (0.00) (0.02) N 548 548 101 548 101 Panel D: Detrended Forecast Chacteristics Chg_FFE_Occurrence Chg_FFE_Frequency Chg_FFE_News1 Chg_FFE_News2 Chg_FFE_Bias Mean 0.240*** 0.660*** 0.003** 0.129*** 0.005*** P-Value (0.00) (0.00) (0.04) (0.00) (0.00) N 862 862 272 862 272

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Panel E: Reg FD Tests Chg_FFE_Occurrence Chg_FFE_Frequency Chg_FFE_News1 Chg_FFE_News2 Chg_FFE_Bias Both Pre-Terminal and 0.101*** 0.301*** 0.004* 0.092*** 0.010*** Terminal Years are Pre-Reg FD (0.00) (0.00) (0.09) (0.00) (0.00) N 285 285 35 285 35 Pre-Terminal Years are Pre-Reg 0.132*** 0.659*** -0.003 0.147*** 0.016 FD and Terninal Year is 2001 (0.00) (0.00) (0.61) (0.00) (0.11) N 53 53 12 53 12 Pre-Terminal and Terminal 0.228*** 0.757*** 0.002* 0.174** 0.002* Years are Post-Reg FD (0.00) (0.00) (0.06) (0.00) (0.09) N 342 342 170 342 170

See text for variable definitions. *, **, and *** denote significance at the 10%, 5%, and 1% levels (two-tailed), respectively