Pessimistic Earnings Guidance before Annual Incentive Plan Approval

Abstract:
Corporate boards determine the performance metrics for CEOs’ annual incentive plans (AIPs) incompensation committee meetings at the beginning of each fiscal year. We provide evidence thatmanagement tends to issue pessimistic earnings guidance ahead of these meetings (“event-window
guidance”), and that this pessimistic guidance leads analysts to lower their earnings forecasts,
which commonly serve as an anchor for setting AIP performance goals. This pessimism in eventwindow earnings guidance is present when performance goals are linked to measures such as
Earnings-Per-Share (EPS), but not when they are linked to revenue, providing further evidence
that pessimistic event-window guidance is motivated by a desire to manipulate executive
compensation. In the cross section, pessimistic event-window guidance is more pronounced whenanalyst forecasts are optimistic, when the EPS performance target was missed in the previous year,
when the target payout amount is large, or when shareholders actively monitor the firm. Lastly,
pessimism in event-window guidance is associated with higher bonus payouts. Overall, our study
sheds light on the strategic role of management disclosure policy in the executive compensation
process.
Keywords: Annual Incentive Plan (bonus contracts), Performance Targets, Management Earnings
Guidance
JEL Codes: G34, M41, M52

I. Introduction
Annual incentive plans (AIPs, also referred to as annual bonus plans) have been widelyused at large public companies in the United States to incentivize corporate executives to improve
short-term performance (Murphy 1999). Bonus payouts have accounted for approximately 20% of
total CEO pay in recent years (see Figure 1). Failure to meet performance goals laid out in AIPs
adversely affects not only bonus payouts but also corporate boards’ CEO retention decisions(Bennett, Bettis, Gopalan, and Milbourn 2017). 1 However, little is known about the process
corporate boards follow to set AIP performance goals. Our study attempts to investigate thestrategic role of management disclosure policy in this process.
On December 15, 2006, the Securities and Exchange Commission (SEC), aiming toprovide investors with a clearer and more complete picture of the compensation earned by acompany’s named executive officers (NEOs), enhanced executive compensation disclosure
requirements for public companies. In particular, the new rules require companies to disclose thedetails of awards granted to each NEO (including the CEO) under an incentive plan or otherwise
contingent on the achievement of performance goals, including estimated future (minimum, target,
and maximum) payouts for (threshold, target and stretch) performance for both equity incentive
plans and AIPs.2
We argue that the enhanced disclosure rules allow shareholders to scrutinize executive
compensation structures more rigorously, which may force corporate boards to defend their choiceof performance goals for AIPs. In response, boards might rely on arguably more objective external
1

Bennett et al. (2017) provide evidence that the probability of a forced CEO turnover more than doubles if a firm has
missed a performance target (of an accounting-based absolute performance measure) in the previous year, after
controlling for other known determinants of forced CEO turnovers.
2
More details on the enhanced disclosure requirements are available at https://www.sec.gov/rules/final/2006/338732a.pdf.

1benchmarks to determine performance goals. In this study, we focus on firms that use earningsper-share (EPS) or net income as a performance measure in AIPs. We make this choice for two
reasons. First and foremost, it is common for CEO AIPs to use EPS to measure performance(Bennett et al. 2017).3, 4 Second, the natural external benchmarks for corporate boards are theprevailing consensus analyst earnings forecasts. For example, CFOs consider the consensus
analyst estimate to be one of the two most important earnings benchmarks (Graham, Harvey and
Rajgopal 2005).5 Similarly, corporate boards refer to the consensus analyst estimate as one of themost common benchmarks used to set performance goals in executive compensation (Burchman
and Emanuel 2015). By focusing on firms with EPS-based AIPs, we can draw a direct link between
management disclosure policy and managerial compensation.
Our empirical prediction is that if corporate boards rely on the prevailing consensus analyst
forecasts to determine CEO performance targets, we expect managers to issue more pessimistic
earnings guidance in the period immediately before board compensation meetings (“event-window
guidance”), compared to earnings guidance issued in other periods (“non-event-window
guidance”). We define the event window as the 90 days before the approval of an AIP by acompensation committee. We define event-window earnings guidance as the most recent
management earnings guidance issued during this event window. We presume that managers
release pessimistic guidance to walk down analyst earnings forecasts. Based on this lower
performance benchmark, corporate boards are likely to set lower EPS performance targets, which
are easier for managers to meet or beat.

3

In Bennett et al. (2017), 40% of AIPs have EPS performance goals, and 30% of AIPs have sales performance goals.
In untabulated statistics, we find similar results.
4
Some firms use net income figures rather than per-share performance measures as earnings-related performancetargets. We use both EPS and net income figures in our analyses.
5
The other is quarterly earnings for the same quarter in the prior year.
2Our empirical tests encompass three sets of analyses. Using 1,173 firm-year observations
between 2006 and 2015, we begin by testing the relationship between the prevailing consensus
analyst forecasts and the EPS performance targets in AIPs. We find that EPS performance targets
are significantly positively associated with prevailing consensus analyst forecasts ahead of thecompensation committee meetings in which AIPs are approved, incremental to past EPS
performance targets.6 This result validates our assumption that corporate boards use the prevailingconsensus analyst forecasts as an input in setting EPS performance targets.
We next investigate whether managers issue pessimistic earnings guidance to affect theprevailing consensus analyst forecasts. That is, we test revisions of consensus analyst forecasts in
response to event-window earnings guidance. We measure management forecast bias as thedifference between the management earnings forecast and the prevailing consensus analyst
forecasts (Ajinkya and Gift 1984; Baginski et al. 1993; Kothari, Shu, and Wysocki 2009; Rogers
and Van Buskirk 2013). We find that analysts do make more substantive negative revisions to theirforecasts in the period after the issuance of event-window guidance and before the approval dateof AIPs. Interestingly, we find that the extent to which analysts respond to the event-window
guidance is similar to their response to non-event-window guidance, suggesting that analysts may
be unable to see through managers’ opportunistic disclosure strategy.
After documenting the link between event-window management guidance and consensus
analyst forecasts, as well as the link between consensus analyst forecasts and the EPS performancetargets in AIPs, we examine directly whether managers issue more pessimistic event-window

6

It is plausible that compensation committees use management earnings guidance as a direct input in setting
performance targets. Our findings show that consensus analyst forecasts are incrementally, positively associated with
the EPS performance target over and above management earnings guidance issued before the approval of AIPs. In
contrast, management earnings guidance loses its significance once consensus analyst forecasts are included in the
regression. Therefore, we conclude that compensation committees are more likely to rely on consensus analyst
forecasts to determine performance targets, although we can’t completely rule out the alternative.
3

earning guidance to affect the target-setting process. We use 11,866 annual management earnings
forecasts issued between 2006 and 2015. As expected, we find that event-window earnings
guidance is on average more pessimistic than non-event-window earnings guidance, particularly
when the performance metric in AIPs contains an EPS measure rather than a revenue measure. In
contrast, when the performance measure in AIPs contains revenue rather than EPS measures, wefind no bias in event-window earnings guidance relative to non-event-window earnings guidance.
This reinforces our inference that EPS-linked managerial compensation motivates managers to
issue pessimistic event-window guidance. From an economic perspective, the average magnitudeof the pessimism in the event-window earnings guidance represents approximately 19% of thesample standard deviation of management EPS forecast bias.7
We conduct a falsification test by investigating whether our results hold for the period
before 2006. This exercise tests whether the post-2006 compensation disclosure regime haspressed corporate boards to rely on analyst consensus forecasts in setting performance goals inmanagerial AIPs, which in turn has motivated managers to issue pessimistic event-window
guidance. More specifically, we assign bonus plan approval dates to the period before 2006 using
typical bonus grant dates and the performance metrics disclosed in the each firm’s AIPs after 2006.
We then identify event-window earnings guidance, as we have done in our main analysis. We
don’t find evidence of pessimism in the event-window guidance, supporting our argument that ourfindings are mainly driven by the enhanced disclosure regime.8

7
We find that the management forecast bias in the event window is 0.16% lower relative to non-event-window
management forecasts, which represents approximately 19.3% of the sample standard deviation of the management
forecast bias variable.
8
We acknowledge that the approval dates assigned in this test might not be the actual approval dates, despite our best
efforts to identify them. Consequently, the inaccuracies in the pseudo approval dates might reduce the test power,
leading to insignificant results.

4

In the second set of analyses, we examine whether the pessimism in event-window
management guidance varies in the cross section. We find that the results are stronger when theprevailing consensus analyst forecasts during the event window are more optimistic or when thetarget payout amount of the AIP is larger. We also find that event-window management guidance
is more pessimistically biased when the firm has missed its EPS target for the prior year — i.e.,
when top executives are more concerned about their reputation and job security (Bennett et al.
2017). Moreover, we find that the results are stronger when external shareholders actively monitor
the firm, suggesting that corporate boards are more inclined to rely on external benchmarks to set
AIP performance targets in the presence of shareholder pressure.
In the final set of analyses, we investigate the consequences of pessimistic event-window
management guidance. We find that pessimism in event-window earnings guidance is associatedwith higher actual bonus payouts. A one-standard-deviation decrease in the management forecast
bias is associated with an increase of 10.3% of the standard deviation of the actual bonus payout
relative to the target payout. Furthermore, we find that the negative association between eventwindow guidance and CEO bonus payouts is fully mediated by post-guidance revisions of analyst
consensus forecasts and EPS performance targets. This suggests that management guidance affects
managerial bonus payouts through its effect on consensus analyst forecasts and the subsequent
EPS performance targets in AIPs. Taken together, these findings demonstrate that managers issue
more pessimistic earnings guidance during the event window to lowball performance targets andthat this artificially low guidance allows managers to collect bigger bonuses and possibly enhance
job security (Bennett et al. 2017).
Our paper makes four key contributions to the literature. First, the study provides evidence
on the effect of firm disclosure policy on the managerial compensation process. We find evidence5that strategic managerial pessimism in earnings guidance ahead of board compensation meetingssignificantly lowers AIP performance targets, allowing managers to obtain higher bonus payouts
and enhance job security. Prior studies find that managers manipulate earnings to meet
performance targets (Bennett et al. 2017) while taking the performance targets as given. Our paper
is the first, to the best of our knowledge, to study managers’ role in setting performance targets.
Our findings thus have important implications for corporate governance.
Second, our findings suggest that corporate boards rely on external benchmarks in response
to shareholder pressure, and this, in turn, incentivizes managers to distort corporate disclosures. In
this sense, shareholder activism might inadvertently promote managerial opportunism and allow
managers to manipulate their compensation or cement job security, which is contrary to the statedobjective of the SEC’s 2006 changes to compensation disclosure requirements.
Third, our paper is related to Cheng and Lo (2005), Brockman, Khurana, and Martin (2008),
Lennox and Ge (2011), and Dimitrov and Jain (2011). These studies show that management
earnings guidance is used opportunistically to facilitate insider trading, corporate repurchases, andM&A transactions, as well as to influence shareholders’ opinions ahead of shareholder meetings.
In the same vein, our paper is also related to Matsunaga and Park (2001). They show a significant
adverse effect on CEO annual cash bonuses when a firm’s quarterly earnings fall short of theconsensus forecast for at least two-quarters of a year. However, Matsunaga and Park do not haveaccess to CEOs’ AIP performance targets. Instead, they argue that the board may exercise theirdiscretion to reallocate the bonus pool based on performance outcomes relative to earnings
benchmarks later in the year; that is, their research focuses on the effect of (ex-post) performanceoutcomes on bonus payouts. In contrast, our study takes advantage of the availability of bonus

6formulas to examine the effect of pessimistic management earnings guidance on setting the (exante) AIP performance targets in the first quarter of the fiscal year.
Lastly, our paper contributes to the heated debate over the causes of the rapid increases in
CEO compensation in recent years. Murphy (2002), Murphy and Zabojnik (2004), Oyer (2004),
Gabaix and Landier (2008), Edmans, Gabaix, and Landier (2009), Core and Guay (2010), Kaplan
and Rauh (2010), Baranchuk, MacDonald, and Yang (2011), and Subramanian (2013) argue thatthe scarcity of managerial talent and the increasing importance of managerial skills largely explain
observed changes in the level and dispersion of CEO pay. In contrast, Bertrand and Mullainathan(2001), Bebchuk and Fried (2004), and Morse, Nanda, and Seru (2011) argue that CEO
entrenchment and ineffective board monitoring lead to “rigged” CEO pay. We add to this literatureby discovering a mechanism through which managers might influence the design of and inputs
into their compensation plans. This mechanism works because, under the increasing pressure ofshareholder activism, boards must rely on external performance benchmarks to justify their
managerial compensation plan designs.
The remainder of the paper is organized as follows. Section II discusses related literatureand develops testable predictions. Section III discusses empirical designs. Sections IV and V
present our empirical results. Section VI concludes.

II. Background and Hypothesis Development
Background of CEO annual incentive plans (AIPs)
CEO AIPs are designed to improve firms’ short-term performance. “Virtually every forprofit company offers a bonus plan covering its top executives and paid annually based on a single
year’s performance” (Murphy 1999). Figure 1 shows that, in our sample, annual cash bonuses

7account for approximately 20 percent of total annual CEO pay between 2006 and 2015.9 A typical
AIP contains performance measures, corresponding performance goals, and the structure of thepay-for-performance relationship. The relationship between pay and performance is often
nonlinear because no bonus is paid until a threshold performance goal is achieved, at which a“minimum bonus” is paid. The target bonus is paid for achieving the performance target.10 A cap
is usually placed on the bonus payout, commonly expressed as a percentage of the target bonus.
Companies select a variety of financial and non-financial performance measures based on
their strategic and operational goals. The choice of performance measures also depends on thesignal to noise ratios of accounting and market measures (Lambert and Larcker 1987). In practice,almost all companies rely on some measures of accounting profits in their executive AIPs.
Commonly used accounting measures include EPS, revenues, net income, pre-tax income,
operating profits (EBIT), and economic value added (EVA). Performance metrics (e.g., therelationship between pay and performance) are designed to strike a balance between improving
shareholder value and incentivizing and retaining managerial talent. Performance targets foraccounting-profit performance measures are mainly based on a single criterion. These criteria
include historical performance, peer-group comparisons, analyst expectations, budgets, board
discretion, and a combination of these criteria. Murphy (1999) argues that internal performance
metrics are problematic if executives can participate in setting performance standards. Similarly,
Anderson, Dekker, and Sedatole (2008) suggest that the benefits of pay-for-performance will beattenuated if managers are given the opportunity to influence performance goals. Interestingly,
Kim and Yang (2012) show that firms tend to set EPS targets for CEO AIPs below the prevailing

9

The corresponding figure based on the ExecuComp Universe is 23 percent, which is similar to the calculations fromour sample.
10
We use performance target and performance goal interchangeably in this paper.
8

consensus earnings forecasts to allow CEOs to achieve these performance goals more easily. This
paper provides insights into the mechanism through which downward-biased earnings guidanceissued by management helps lower consensus analyst forecasts before AIP approval, which in turnlowers the AIP performance targets.
The nonlinearity of the pay-for-performance relationship in bonus plans motivates
management to distort their firms’ financial reporting. Prior literature shows that firms tend tomanipulate earnings upward when performance falls just short of the threshold performance goal,and downward when performance substantially exceeds the stretch performance goal at which no
additional payout is awarded (Healy 1985; Gaver, Gaver, and Austin 1995; Holthausen, Larcker,
and Sloan 1995). Using a large dataset of performance goals employed in executive incentive
contracts, Bennett et al. (2017) find a disproportionately large number of firms exceeding theirperformance targets by a small margin, compared to the number that falls short of the goal by asimilar margin. They find that this asymmetry is stronger when AIPs use earnings and profits as
the performance goal and when the bonus payout is contingent on a single goal.
Literature on management forecasts
Management earnings forecasts have a significant influence on the market’s future cash
flow expectations, analysts’ forecast revisions and stock returns (Ajinkya and Gift 1984; Jennings
1987; Anilowski, Feng and Skinner 2007). Prior research shows that both CEOs and CFOs havesignificant influence in shaping management earnings forecasts though CEOs are relatively more
influential (Bamber, Jiang and Wang 2010). This study focuses on management earnings guidance
driven by CEO incentives. In providing guidance, managers must consider various factors. On the
one hand, providing earnings forecasts is associated with capital market benefits such as lower
information asymmetry between firms and investors (Ajinkya and Gift 1984; Ajinkya, Bhojraj,

9and Sengupta 2005; Karamanou and Vafeas 2005; Lennox and Park 2006; Hui, Matsunaga, andMorse 2009), and personal benefits such as stock-based compensation (Noe 1999; Aboody andKasznik 2000; Cheng and Lo 2006; Brockman et al. 2008). On the other hand, when managers
provide guidance, they bear an implicit responsibility to make reasonably accurate forecasts.
Accurate guidance is rewarded, for example, via career-advancement opportunities for the CEO(Zamora 2009), whereas inaccurate guidance is associated with a higher probability of CEO
turnover (Lee, Matsunaga and Park 2012).
Hypothesis development
As previously stated, performance standards in executive AIPs are generally set bycorporate boards at the beginning of a fiscal year at a compensation committee meeting. To
determine the performance targets, compensation committees may rely on external benchmarksthat are readily available, relevant, and unbiased, such as the earnings forecasts issued by securities
analysts. Anecdotally, Semler Brossy, one prestigious executive compensation consulting firm,
considers analyst expectations a common benchmark for setting performance goals in theexecutive compensation process (WorldatWork Journal, Third Quarter 2015, page 10).
Compensation committees may rely on consensus analyst forecasts for two reasons. One
is to obtain some reference points. The other is to make it easier for compensation committees todefend their performance target choices for executive compensation plans to shareholders.
Compensation-related shareholder activism has undergone several changes since the 1990s. For
example, union pension funds have become the major advocate for shareholder activism since
2002, supplanting individual shareholders. Such funds have introduced new types of proposals,
advocating enhanced shareholder voting rights on CEO pay, more transparent reporting, and atighter link between pay and performance. As a result, the frequency of and the voting support for10

compensation-related shareholder proposals have increased significantly (Gillan and Starks 2007;
Ertimur, Ferri, and Muslu 2010). Increased intervention by hedge fund activists has also forced
boards to moderate CEO compensation and tighten the relationship between pay and performance(Brav, Jiang, Partnoy, and Randall 2008; Fos 2017). Mandatory Say-On-Pay votes starting in 2011
have imposed additional pressure on corporate boards to justify their compensation policies.
Moreover, shareholders’ Just-Vote-No campaigns, which aim to change executive pay practices,
have become more frequent. In some high-profile cases (e.g., Home Depot, Pfizer, Bank ofAmerica), the campaigns have even contributed to the ouster of the CEO and board members.11
Regardless of the specific reasons, if managers believe that compensation committees rely on
analyst forecasts to set performance targets and that management earnings guidance has a direct
influence on analyst forecasts, they have an incentive to issue pessimistic earnings forecasts before
compensation committee meetings in order to lower performance targets linked to earnings. Basedon this line of reasoning, our main hypothesis is stated as follows:
Hypothesis: When executive AIPs link performance goals to earnings, management
earnings guidance is more pessimistic during the period immediately before AIP performancetarget approval than during other periods.
It is important to note that performance goals for executive AIPs are commonly determinedin the first fiscal quarter. The pessimistic first-quarter management guidance predicted in this study
is inconsistent with the typical guidance pattern documented in prior research; managers typicallyissue optimistic earnings guidance in the first fiscal quarter and gradually reduce this optimism in

11

For example, a "Just-Vote-No” campaign engineered at Bank of America in 2009 by labor-backed Change to Win
Investment Group intended to remove then-Chief Executive Ken Lewis and two other directors from the board. Even
though the campaign failed to garner a majority vote against any of the three, the effort helped hasten Lewis' departureat the end of the year.
11the later quarters of the same period to walk down analyst forecasts to a beatable level (Matsumoto
2002, Richardson, Teoh, and Wysocki 2004; Bergman and Roychowdhury 2008). Therefore, ourprediction differs from the characteristics of management guidance identified in prior studies.

III. Research Design
To examine our hypothesis, we follow prior research and use the following regression
model (Brockman et al. 2008):
MF Biast =β1 Event EPS Targett + β2 Sizet-1 + β3 Market-to-Bookt-1 + β4 ABRETt-1(1)+ β5 ROAt-1 + β6 EarnVolt-1 + β7 Losst-1 + β8 Litigation Riskt + β9 Horizont + εtwhere MF Biast is the dependent variable, defined as the management EPS forecast less theconsensus analyst forecast three days before the date of the management forecast, divided by thestock price three days before the date of management forecast. We multiply this variable by 100
to ease the interpretation of economic magnitude. We use all quantitative management annual
earnings forecasts. If the management forecast is a range, we choose the midpoint of the range(Rogers and Van Buskirk 2013; Bergman and Roychowdhury 2008). If the management forecast
is a one-sided range (e.g., it specifies only a maximum or a minimum value), we calculate
management forecast bias only if the mean consensus is above (below) the maximum (minimum)
value; otherwise, the management forecast is assumed to contain no bias (Bergman andRoychowdhury 2008). We calculate the daily EPS consensus analyst forecasts using the IBES
unadjusted detail file. We specifically calculate the daily consensus based on individual analyst
forecasts, which are required to be reported within the 90-day window immediately preceding thatspecific date, to ensure that our daily consensus is not based on stale analyst forecasts. We exclude
individual analyst forecasts if IBES excludes the forecasts from calculating IBES-reported EPS

12consensus. In addition, following Rogers and Van Buskirk (2013), we adjust daily consensus when
we calculate management forecast bias if a management earnings forecast is issued concurrently
with an earnings announcement (i.e., bundled management earnings forecasts).12
Our main independent variable, Event EPS Targett, is an indicator variable equal to one formanagement earnings forecasts issued in the event window for firms with an EPS (or earnings)
performance target and without a revenue performance target in AIPs in period t and zerootherwise. If multiple identical management forecasts are issued during the event period, we selectthe first one because subsequent management forecasts less likely contain new information.
Consequently they have little impact on analysts’ decision to revise forecasts. Figure 2 presents
the timeline of events.13
We also include a comprehensive set of control variables. We include firm size to control
for the overall information environment of a firm. We use the market-to-book ratio to control for
growth options and proprietary costs (Bamber and Cheon 1998). The abnormal return variable,
ABRETt-1, controls for the effect of momentum and performance on earnings forecasts (Brockman
et al. 2008). We also include return on assets to further control for the effect of performance onmanagement forecast news. The incidence of a loss (Losst-1) and earnings volatility (EarnVolt-1)
are further included to control for the difficulty in predicting earnings. Both analysts’ and managers’

12
Rogers and Van Buskirk (2013) argue that when bundled management forecasts are compared to the prevailing
analyst expectations, the prevailing analyst expectations need to be adjusted assuming that analysts update earnings
forecasts for subsequent periods using realized earnings from the earnings announcement. By using the analyst
forecast revision model in Rogers and Van Buskirk (2013), we specifically calculate management earnings bias forbundled forecasts by comparing prevailing analyst expectations with management earnings forecasts conditional on
the current period’s earnings surprise.
13
We identify the event-window management forecasts by using the grant date of bonus plans. If the grant date ofbonus plans is missing, we calculate the typical grant date of AIPs for each firm during our sample period and thenidentify the event-window management earnings forecast. We find that the mean difference between the current period
end date and the grant date of a bonus plan is 314 days (standard deviation of 18.55 days) and the mean difference
between the current period end date and the event-window management forecast is 340 days (standard deviation of
25.20 days), suggesting that the compensation committee meetings are typically held in the first fiscal quarter of eachyear and the last management earnings forecast in the event period is issued on average 26 days before the bonus plan
grant date.

13

earnings forecasts are likely to be less precise for firms with negative or volatile earnings, which
in turn might affect the tendency of managers to lowball earnings forecasts (Skinner 1994; Soffer,
Thiagarajan and Walther 2000; Matsumoto 2002; Richardson et al. 2004; Ke and Yu 2006). We
include a proxy for litigation risk, Litigation Riskt, because prior research suggests that litigation
risk is an important motive for managers to voluntarily disclosure forward-looking information(Francis, Philbrick, and Schipper 1994). Lastly, we include forecast horizon as a control variablebecause prior research documents that forecast biases depend on forecast horizon (Rogers andStocken 2005; Bergman and Roychowdhury 2008). See Appendix A for detailed variable
definitions. To align with the time frame of our main dependent variable (i.e., bias in management
annual earnings guidance), all flow variables are measured on an annual basis (e.g., return on assets)
and other variables are measured at the closest date before the release of management forecasts(i.e., firm size or market-to-book ratio).
We use an ordinary least square (OLS) regression model with standard errors clustered at
the firm level. We include firm and year fixed effects to control for firm-invariant and timeinvariant factors that might affect management forecast bias. We also include fiscal quarterindicators to account for variance in management incentives to issue biased forecasts across the
fiscal year (Matsumoto 2002).

IV. Data and Descriptive Statistics
We obtain data on performance goals and grant dates from the Incentive Lab database. The
SEC standardized disclosure requirements for grants of plan-based awards in 2006, requiring firms
to disclose plan details in the proxy filing starting in December 2006. Incentive Lab collects
detailed plan data on grants from firms’ proxy filings. For each disclosed grant, we identify

14whether the firm uses EPS (or EPS growth, earnings, or earnings growth) or revenue (or revenue
growth) as AIP performance measures.14 We further identify information on the corresponding
performance targets and the grant dates of the incentive plans for the CEO.15
According to Section 162(m) of the tax code, the formulas for performance-based pay inannual and long-term incentive plans must be approved in the first 90 days of a fiscal year to be
tax deductible. Committee grant dates are disclosed in the Grants of Plan-Based Awards table ineither a firm’s proxy filings or 8-K filings. Many companies have similar grant dates over time.
This helps us extrapolate the grant dates of some of the AIPs before 2006.16 In Appendix B, weprovide an example of a proxy filing disclosing the grant date of AIPs. Information below is
extracted from the AIP of Colgate-Palmolive in 2007: 17“Bonus payouts for a particular year are determined … [omitted by authors] by a formulabased on the level of growth achieved the prior year in Base Business Earnings-Per-Share or theapplicable division’s net sales and net profit after tax. The P&O Committee has discretion to adjust
the calculated awards downward, but not upward. For 2007, in order for Named Officers with

14

Specifically, we first identify bonus plans containing absolute performance goals and short-term cash awards. Next,
in each bonus plan, we identify the absolute earnings or revenue performance goals in which the measurement period
is annual and the performance target is available. We drop performance goals if the goal is specific to a certain business
unit or geographical area.
15
In case of multiple CEOs identified in the same fiscal year (e.g., due to CEO turnover), we use AIPs for the CFO.
We compare CFOs’ performance targets and CEOs’ performance targets when both are available and find that firms
typically use the same performance metrics for both CEOs and CFOs. Our inferences are not affected if we drop the
CFOs’ AIPs from our empirical analyses.
16
The typical term used in firms’ Grants of Plan-Based Awards Table is “grant date” rather than “approval date.”
Based on private communications with ISS Corporate Services and James F. Reda at Arthur J. Gallagher & Co., we
use the grant date as a proxy for the approval date for the AIP when only the former is available. For some companies,
we fill in the missing grant dates of the AIPs using the grant date of the long-term incentive plan if the grant occurredin the first quarter and these incentive plans tend to be granted on the same day historically.
17
Some companies state that they do not disclose performance targets because the information is confidential in acompetitive environment. For example, Affiliated Computer Services states the following on page 26 of its fiscal 2007
proxy statement. “We have not disclosed target levels with respect to specific quantitative or qualitative performancerelated factors considered by the Compensation Committee because disclosure of the specific performance goals
would give our competitors information that could be leveraged for competitive advantage which would result incompetitive harm to the Company.” Other companies simply do not disclose any information on performance targets.
15

corporate-wide responsibilities to earn bonuses at the top end of their range, Base Business
Earnings-Per-Share had to grow by 11.0% above the 2006 Base Business Earnings-Per-Share.”We merge the Incentive Lab data with COMPUSTAT for financial data, the IBES
Guidance database for management annual earnings forecasts, the IBES database for analyst
earnings forecasts and IBES-reported actual EPS data, and CRSP for stock return data. Our final
sample consists of 3,786 firm-year observations for 830 unique firms with available EPS (orearnings) or revenue performance targets in AIPs, and the sample period ranges from 2006 to 2015.
Panel A of Table 1 presents the sample selection procedure. Among 3,786 firm-year observations,
we identify 11,866 management annual earnings forecasts with available financial data (Firm-MF
sample), which correspond to 2,184 firm-year observations for 477 unique firms. Out of 2,184
firm-year observations, 1,173 firm-year observations are used to examine the relationship between
EPS performance targets in AIPs and the prevailing consensus analyst forecasts during the event
window as well as pessimism in event-window management earnings guidance. Following priorstudies (Rogers and Stocken 2005), we drop pre-earnings announcements (i.e., earnings forecasts
issued after the end of a fiscal period but before the earnings announcement date) because preearnings announcements are commonly issued to avoid negative earnings surprises and deter
litigation, and thus they are a part of earnings announcement strategies (Kasznik and Lev 1995;
Soffer et al. 2000). The number of observations might vary depending on data availability for eachtest. All continuous variables are winsorized at the 1st and 99th percentiles to mitigate the influenceof extreme observations.

16Panel B of Table 1 reports descriptive statistics for the Firm-MF sample.18 Event EPS REV
Targett is an indicator variable equal to one for the latest management earnings forecast issued
during the event period for firms that disclosed both EPS and revenue performance targets in AIPs
in period t and zero otherwise. Event REV Targett is an indicator variable equal to one for the latest
management earnings forecast issued during the event period for firms that disclosed revenue but
not EPS performance targets in AIPs in period t and zero otherwise. We find that approximately
5.4% (3.4%) of management earnings forecasts are Event EPS Targett forecasts (Event REV
Targett forecasts). Approximately 4.6% of management earnings forecasts are the latest
management earnings forecasts issued in the event period for firms that disclose both EPS andrevenue performance targets in AIPs.
In Panel C of Table 1, we present descriptive statistics for the Firm-Year sample requiring
EPS performance targets in AIPs, and the event-window management forecasts and financial data
available. AF Before MFt (AF After MFt) is the consensus analyst forecast three days before (fivedays after) the date of the management forecast in the event period scaled by stock price, andmultiplied by 100. MFt is the event-window management earnings forecast divided by stock priceand multiplied by 100. Figure 2 presents the detailed timeline of variable measurement. We findthat the mean value of the AF Before MFt variable (7.091) is greater than that of both the MFt
variable (6.79) and the AF After MFt variable (6.942), consistent with our argument thatmanagement earnings guidance in the event period contains pessimistic bias, which walks down
the analyst consensus forecasts. We also find that the mean value of EPSTARt is slightly lower

The Firm-MF sample also contains management forecast observations issued in fiscal period with no management
earnings forecast issued during the event-window. If we drop those observations, the sample is reduced to 9,241
observations. All results remain both qualitatively and quantitatively similar, however.
18

17

than that of AF After MFt (6.717 vs. 6.942). This is consistent with Kim and Yang (2012) who
document that at the time of bonus plan approval, performance targets are set lower than theprevailing consensus analyst forecasts, making it easier for firms to achieve the performance goals
ex-post.
Table 2 reports Pearson correlations for the Firm-MF sample in Panel A, and the FirmYear sample in Panel B. In Panel A, we first note that MF Biast is significantly negatively
correlated with Event EPS Targett (-0.05) but not significantly correlated with Event EPS REV
Targett or Event REV Targett, providing preliminary evidence that management EPS forecasts
during the event window are generally pessimistic for firms that use EPS to set performance targets
in their CEO AIPs but not firms that use revenue as their AIP metric. We also note that Event EPS
Targett is significantly negatively correlated with AF Revisiont (-0.05), which measures analyst
forecast revisions issued after management EPS forecasts. This correlation provides initialevidence that managers issue pessimistic earnings guidance during the event window to walk down
the consensus analyst forecast. AF Revisiont is positively correlated with MF Biast (0.23), implyingthat analysts revise forecasts in the direction consistent with management forecast bias.
In Panel B, we note that EPSTARt is significantly positively correlated with AF Before MFt
and AF After MFt, and the magnitude of the correlation between EPSTARt and AF After MFt (0.78)
is greater than that between EPSTARt and AF Before MFt (0.69). This evidence suggests that theprevailing consensus analyst forecast may serve as an input in setting the EPS performance target
in AIPs, which we will validate in the multivariate regression below. The correlation between AF
Before MFt and MF Biast is significantly negative (-0.16), suggesting that the event-window
management earnings guidance might be used to walk down the prevailing consensus analyst
forecasts.

18

V. Empirical results
Validity Check
We begin our empirical analyses by validating our assumption that corporate boards referto the prevailing consensus analyst forecasts to determine the EPS-based CEO performance target
in AIPs. It is also plausible that boards directly reference management earnings forecasts to set
performance targets. However, earnings forecasts issued by management are arguably lessobjective compared with those provided by security analysts, which in turn could undermine
boards’ ability to defend AIP performance metrics to shareholders. To determine which benchmark
boards are likely to rely on, we examine how the EPS performance target figures in AIPs (EPSTARt)
are related to earnings forecast by management (MFt), earnings forecast by security analysts threedays before management earnings guidance (AF Before MFt), and consensus analyst forecasts
issued five days after management earnings guidance (AF After MFt). We use multivariate OLS
regressions, clustering standard errors at the firm level.
Table 3 presents regression results using Firm-Year sample, with EPSTARt as thedependent variable. In columns 1, 2, and 3, AF Before MFt, MFt, and AF After MFt, respectively,
are the main explanatory variables. We find that each variable is significantly positively associatedwith the EPSTARt variable. Focusing on estimated coefficients and adjusted R2, we find the mostsignificant input appears to be the consensus analyst forecasts after management earnings guidance.
To compare the relative importance of these variables, we run a regression that includes both AF
Before MFt and AF After MFt. The results reported in column 4 show that the coefficient estimate
on the AF Before MFt variable becomes significantly negative at the 10% level (coefficient = 0.086 and t-stat = -1.688) while the AF After MFt variable remains significantly positive at the 1%

19

level (coefficient = 0.879 and t-stat = 10.111).19 In column 5, we include MFt and AF After MFt
simultaneously in the regression model, and find that the MFt variable becomes statisticallyinsignificant (coefficient = 0.012 and t-stat = 0.220). This evidence suggests that the consensus
analyst forecasts appear to be more relevant than management earnings guidance in setting the
EPS target, and consensus analyst forecasts subsume management guidance in affecting the EPS
target.
In column 6, we control for the EPS performance target in the previous year (EPSTARt-1).
We continue to find a positive coefficient on AF After MFt. We also find a positive coefficient on
EPSTARt-1, suggesting that the EPS performance target in the previous year is an also input insetting the current period’s EPS performance target. An F-stat testing the difference in the effectsof AF After MFt and EPSTARt-1 indicates that AF After MFt has more influence on the EPS target
for the current year than EPSTARt-1. Overall, the results in Table 3 lend support to our assumptionthat corporate boards use the prevailing consensus analyst forecast as an input in setting the EPS
performance target in AIPs.
Next, we use Firm-MF sample to examine whether managers issue pessimistic earnings
forecasts in the event window to guide analyst forecasts downward. Table 4 reports the results,
using AF Revisiont as the dependent variable. In column 1, we find a significantly positivecoefficient on the MF Biast variable, suggesting that analysts revise their outstanding earnings
forecasts in the direction consistent with the management earnings forecast bias. In column 2, we
separately include the Event EPS Targett variable in the regression and find a significantly negative
coefficient at the 5% level, implying that, on average, analysts revise forecasts downward in theevent window. In column 3, we interact MF Biast with Event EPS Targett to test whether analysts

19

The significantly negative coefficient on the AF Before MFt variable is possibly due to multicollinearity, given thehigh correlation between the AF Before MFt and AF After MFt variables (Pearson correlation 0.9; See Table 2).
20

react differently to management forecasts issued in the event window from those issued outside
the event window. We show that the interaction term is not statistically significant, implying thatanalysts react similarly to event-window and non-event-window management forecasts. Taken intandem, the findings in Table 3 and Table 4 verify our assumption that management forecasts
issued shortly before AIP approval dates guide analyst forecasts, which are then used by boards asa benchmark in determining earnings performance goals for the AIPs. Such a mechanism provides
incentives for managers to issue pessimistic earnings guidance immediately prior to AIP approval.

Main analysis
We examine whether managers issue more pessimistic earnings forecasts in the event
window than outside the event window by directly investigating the biases imbedded inmanagement earnings forecasts. Panel A of Table 5 reports results of testing our hypothesis using
equation (1). In column 1, our main variable of interest, Event EPS Targett, is loaded negatively
and statistically significant at the 1% level. This evidence is consistent with our expectation thatwhen managers’ bonuses are directly linked to a firm’s EPS, management earnings guidanceissued in the event period is more pessimistic relative to those issued in other periods. From an
economic perspective, management earnings forecasts issued in the event window are 0.16% lower
than those issued in other periods, which represents approximately 19.3% of the standard deviationof the MF Biast variable (0.193 = 0.16 / 0.828). Regarding control variables, we find that Sizet-1,
Market-to-Bookt-1, and Losst-1 are significantly positively associated with management forecast
bias, suggesting that large firms, firms with high growth, and firms incurring losses tend to issue
more optimistic forecasts (Bergman and Roychowdhury 2008; Hribar and Yang 2015). Litigation
Riskt is negatively associated with management forecast optimism, implying that management is21

more likely to be pessimistic in forecasting earnings when litigation risk is high (Francis et al.
1994; Skinner 1994). We find that Horizont is significantly positively associated with management
forecast bias, consistent with the prior literature that long-term forecasts tend to be more optimistic(Matsumoto 2002, Richardson, Teoh, and Wysocki 2004).
Next, we examine whether event-window management earnings forecasts demonstrate
similar pessimism when management bonuses are linked to both EPS and revenue or revenue only.
If it is management’s intention to lower performance targets that leads to forecast pessimism, thenwe would expect to find weaker results for firms whose managers’ bonuses are linked to both EPS
and revenue because the additional linkage to revenue might dilute managers’ incentive to lower
earnings forecasts. We expect to observe even weaker results for firms whose managers’ bonuses
are linked to revenue than EPS because management earnings forecasts are less likely to have a
direct influence on revenue targets.
In column 2, consistent with our expectation, the Event EPS REV Targett variable is loaded
negatively but statistically insignificant. In column 3, we find the coefficient on the Event REV
Targett variable is positive but insignificant. In column 4, we include all three indicator variablesin the same regression model to compare the economic magnitude of estimated coefficients. We
find that the results from columns 1 through 3 also hold in column 4. Specifically, Event EPS REV
Targett remains statistically insignificant. The absolute value of the coefficient of Event EPS
Targett is statistically larger than that of Event EPS REV Targett at the 10% level (F-stat = 3.72;
P-value = 0.054). The coefficient of Event REV Targett continues to be insignificant in column 4,
and its absolute magnitude is smaller than that of Event EPS Targett (F-stat = 7.034; p-value =0.008). There is no statistical difference between the coefficients of Event EPS REV Targett andEvent REV Targett.

22

Alternative explanations and robustness tests
Prior research finds that managers are more likely to release bad news before stock option
awards to dampen stock price (Aboody and Kasznik 2000). We next investigate whether our
findings can be explained by pessimism in management earnings forecasts driven by AIPs that
grant equity incentives (Table 5 Panel B). In column 1, we identify firms with stand-alone AIPs
that do not contain long-term incentive plans and examine the event-window earnings forecasts
issued by those firms for evidence of pessimism.20 We find a significantly negative coefficient on
the Event EPS Targett variable for firms with AIPs without long-term equity incentives in column
1. In column 2 of Panel B, using the remaining sample, we continue to find a significantly negative
coefficient on the Event EPS Targett variable. It is important to note that we do not find significant
differences between the coefficient estimates on Event EPS Targett in column 1 and column 2.
This evidence alleviates the potential concern that our results are merely driven by equity awards
rather than short-term EPS performance targets in AIPs.
Our main hypothesis is based on the argument that, due to enhanced compensation-related
disclosures, shareholder pressures lead corporate boards to rely on consensus analyst forecasts to
set performance targets in executive AIPs. Alternatively, corporate boards might have naïvely
relied on external benchmarks in setting the performance target regardless of shareholder pressures.
To rule out these possibilities, we conduct an analysis based on the period before 2006. If thealternative explanation is true, we would expect the results to hold even before 2006, indicatingthat the boards’ reliance on external benchmarks in setting performance targets does not depend
on whether these targets are publicly disclosed or not.

20

The sample for column 1 includes observations for which we cannot identify a long-term incentive plan granted at
the same date as the short-term AIP. We check the robustness of the results by further excluding observations in which
we could find a long-term incentive plan granted in the [-5, 5] window centered on the AIP approval dates. We findqualitatively similar results.
23

We face two challenges in attempting to test whether our results hold for the period before
2006. First, we do not observe performance targets in AIPs before 2006 because they were notdisclosed. Therefore, we have to make the assumption that firms’ compensation policies are stable
over time, which allows us to assign the performance measures observed in the post-2006 period
to the pre-2006 period.21 Second, we do not observe the actual date of compensation committee
meetings before 2006. Therefore, we assume that these meeting dates do not vary significantly
across years. More specifically, we assign compensation committee meeting dates for the period
before 2006 using the typical observed compensation committee meeting dates for the same firm
during the period after 2006.22 We then identify the latest management earnings guidance issued
before the assigned compensation committee meeting dates, Assigned Event EPS Targett, in thesame way as we do in our main analysis.
Table 5 Panel C reports the results for the period 2001-2005. In column 1, we find an
insignificant coefficient on the Assigned Event EPS Targett variable. Furthermore, the absolutevalue of the coefficient estimate is much smaller than that reported in Panel A. We also find an
insignificant coefficient on Assigned Event EPS REV Targett in column 2 and Assigned Event REV
Targett column 3. Overall, the combined evidence from Panel C suggests that corporate boards
accede to shareholder pressures rather than act naïvely to rely on external benchmarks in setting
performance targets.

21

Our final sample consists of 830 unique firms during the post-2006 sample period. We find that 83.25% (= 691
firms / 830 firms) of our sample firms have not changed their performance target metrics in the post-2006 period,
indicating that the choice is sticky. As a robustness check, we select a subsample of firms that have not changed theircompensation policies post-2006, and find qualitatively similar results to those reported in Panel C of Table 4.
22
We find that the average of each firm’s standard deviation of meeting dates during the post-2006 sample period is
8.97 days, suggesting that a firm’s grant dates do not vary significantly. We further check the sensitivity of our results
by assigning each firm’s first or last compensation committee meeting date during the post-2006 sample period as thedate for the years 2001-2005. We find similar results.
24

We find evidence that event-window management earnings forecasts are more pessimistic
than those non-event-window forecasts and we attribute these findings to strategic disclosures bymanagers to lower performance targets in AIPs. One concern with our results might be that theymight be driven by artifacts of the testing procedures we use. In other words, by construction, ourevent-window mainly concentrates in the first fiscal quarter. If management earnings forecasts
issued in the first fiscal quarter are somehow more pessimistic than those issued in other quarters
for reasons other than we hypothesize, then our results might be spurious. To alleviate this concern,
we conduct a falsification test in the similar spirit of Jacob and Jorgensen (2007). As they point
out, annual earnings can be measured in either fiscal year or by aggregating quarterly earnings.
We test management earnings forecast bias for alternative annual earnings forecasts. If strategic
disclosures are responsible for our findings, we will not expect to observe similar findings whenmanagement earnings forecasts for these alternative annual earnings are used.
To conduct this test, we compute management alternative-annual earnings forecasts thatare the sum of quarterly earnings forecasts for the current and following quarters. If the portion of
the alternative-annual earnings are realized at the time of management issuing quarterly forecasts,
then we use the realized quarterly earnings. 23 Similarly, we construct consensus analyst
alternative-annual earnings forecasts by using the consensus analyst quarterly earnings forecasts
measured three days before the corresponding quarterly management earnings forecasts. The
alternative management earnings forecast bias as the difference between management alternativeannual earnings forecasts and analyst consensus alternative-annual-earnings forecasts, scaled bystock price, and multiplied by 100. We then identify the latest management earnings guidance

23

For example, an alternative annual management earnings forecast for 2010/Q3 is calculated as the sum of therealized quarterly earnings for 2010/Q1 and 2010/Q2, management quarterly earnings forecast for 2010/Q3, andmanagement quarterly earnings forecasts for 2010/Q4, which is outstanding in 2010/Q3. If quarterly earnings forecasts
are not available, we use the annual earnings forecast to back out quarterly forecasts.
25

issued before compensation committee meeting dates in the same way as we do in our main
analysis.
Table 5 Panel D reports the results. We do not find statistically significant coefficient on
Event EPS Target in columns (1) and (4) when alternative management earnings forecasts bias
serves as the dependent variable. The coefficients on other control variables omitted from reportingare largely consistent with those from Panel A. Therefore, we conclude that our findings areunlikely to be driven by artifacts of our testing procedures.

Cross-sectional analyses in management incentives
In this section, we conduct cross-sectional analyses aiming to shed light on theheterogeneity in managers’ incentives to engage in compensation-induced opportunistic disclosure
strategy.
Analyst forecast optimism
First, we investigate whether management incentives for strategic disclosure vary withanalyst optimism. Intuitively, if compensation committees refer to analyst forecasts in setting
performance targets, then we expect event-window management forecasts to be more pessimistic
when the prevailing consensus analyst forecasts are more optimistic.
To examine this prediction, we use an indicator variable, High AF Biast, which measures
the optimism of the prevailing consensus analyst forecast in the event window, and interact thisvariable with the Event EPS Targett variable. More specifically, we first calculate analyst forecast
bias as the prevailing consensus analyst forecast three days before the date of the event-window
guidance less the actual EPS in period t scaled by stock price. We then give the High AF Biast
variable a value of one if analyst forecast bias is greater than the sample median and zero otherwise.

26

We report the results in column 1 of Table 6. We find a significantly negative coefficienton High AF Biast, suggesting that managers tend to issue pessimistic forecasts when analysts are
more optimistic in the non-event window. The coefficient on Event EPS Targett is positive but
insignificant, implying that event-window management forecasts do not differ from non-eventwindow forecasts when security analysts are not optimistic. More importantly, we find theinteraction term, Event EPS Targett × High AF Biast, is negative and statistically significant at the
1% level. The evidence suggests that management forecasts issued in the event window are more
pessimistic than non-event-window forecasts when the prevailing consensus analyst forecast is
optimistic. In an untabulated test, we replace the actual EPS in period t with the projected EPS,
which is measured as the actual EPS multiplied by the actual EPS growth rate in period t-1, todefine the High AF Biast variable to avoid potential endogeneity concerns. We find similar results.
Target bonus payout
Second, we investigate whether management incentives for strategic disclosure vary withtarget bonus amount. We expect that managers are more likely to issue pessimistic event-window
earnings forecasts if the expected bonus amount is greater. To test this prediction, we construct
High Target Bonust-1, which is an indicator equal to one if the target bonus (non-equity incentive)
payout in period t-1 scaled by total cash compensation in period t-1 is greater than the sample
median and zero otherwise. 24 We interact this conditioning variable with Event EPS Targett.
Column 2 of Table 6 shows that the coefficient on Event EPS Targett is negative but statisticallyinsignificant, suggesting that event-window management forecasts are similar to non-window
forecasts when the expected target bonus is low. However, when the expected target bonus is high,event-window management forecasts are more pessimistic than non-event-window forecasts, as

24

We find similar results if we use the target bonus payout in period t, assuming that managers know the target bonus
payout in the current period before the AIPs are approved by the compensation committee.
27

evidenced by the significant negative coefficient on the interaction term, Event EPS Targett × High
Target Bonust-1. Thus, we find evidence that management incentives to engage in compensationrelated strategic disclosure are stronger when the expected target bonus is high.
Missing the performance target in the previous year
Third, we investigate whether concerns about reputational losses and job security due tomissing performance targets affect management incentives for strategic disclosure (Bennett et al.
2017). We use the Miss EPS Targett-1 variable, which is an indicator equal to one if managers
missed the EPS performance target in period t-1 and zero otherwise, and interact this variable with
the Event EPS Targett variable. In column 3 of Table 6, we find a negative and significant
coefficient on Event EPS Targett, implying that event-window management forecasts are more
pessimistic than non-event-window forecasts even when the EPS performance target for the prioryear had been met. When managers had missed the EPS performance target in the prior year,
pessimism in event-window forecasts becomes even stronger, as evidenced by the significantly
negative coefficient on the interaction term, Event EPS Targett × Miss EPS Targett-1. The evidence
suggests that managers have stronger incentives to engage in strategic disclosure to avoid losing
their jobs.

Cross-sectional analyses of shareholder pressures
In developing our main hypothesis, we argued that increased shareholder activism fueledby enhanced disclosure requirements leads to opportunistic managerial disclosure behavior. We
explicitly test this argument in this section. If the argument is true, we would expect to observe
more pessimism in event-window management forecasts when shareholders are particularly active.

28

We capture the intensity of shareholder activism using four measures. First, we use theconcentration of top-five institutional ownership (Smith 1996; Gillan and Starks 2002; Hartzell
and Starks 2003). Prior studies suggest that institutions have greater monitoring influences when
they are large shareholders (Shleifer and Vishny 1986). Second, we use the concentration ofownership by institutions with long investment horizons (Bushee 1998; Bushee, Carter, andGerakos 2013). Bushee et al. (2013) find evidence that institutions with longer investment horizons
tilt their portfolio towards firms providing better shareholder rights and engage in shareholder
activism to improve corporate governance. Third, we use the concentration of ownership bypensions, endowments, and bank trusts. These institutions face a higher standard of prudence andmore stringent duties, and thereby they are more likely to invest in firms with better shareholder
rights as a defense against potential investor lawsuits (Del Guercio 1996; Bushee et al. 2013).25
Fourth, we use the actual occurrence of compensation-related shareholder activism to capture theintensity of shareholder activism.
Table 7 presents results. Following prior studies, we measure institutional ownershipvariables as the average over the four previous quarters (Bushee et al. 2013). We then compute theconcentration of institutional ownership, i.e., Herfindahl-Hirschman index (HHI), to capture theintensity of shareholder activism. 26 In column 1, we compute an indicator variable, High INST
TOP5t-1, which is equal to one if the concentration of top-five institutional ownership for firm i inperiod t-1 exceeds the sample median and zero otherwise, and interact this variable with EventEPS Targett. We find a significantly negative coefficient on Event EPS Targett × High INST
TOP5t-1 at the 5% level, suggesting that intense monitoring from large external shareholders

25

We obtain data on the classifications of institutional ownership (i.e., investment horizons and legal types) from
Brian Bushee’s website (http://acct.wharton.upenn.edu/faculty/bushee/IIclass.html). The data is available until 2013.
26
In untabulated tests, instead of the HHI of institutional ownership, we use the level of each institutional ownershipvariable and find that our results are qualitatively similar.
29

facilitates pessimism in event-window earnings guidance. In column 2, we use High INST DEDt-1
as a conditioning variable that is equal to one if the concentration of dedicated institutional
ownership for firm i in period t-1 exceeds its sample median and zero otherwise, and interact thisvariable with Event EPS Targett. We find a significantly negative coefficient on Event EPS Targett
× High INST DEDt-1 at the 10% level. This evidence is consistent with our expectation andsuggests that the event-window earnings guidance is more pessimistic when institutional investors’
investment horizons are longer.27 In column 3, we create the indicator variable High INST PNEBt1, which is equal to one if the concentration of ownership by pensions, endowments, and banktrusts for firm i in period t-1 exceeds its sample median and zero otherwise, and interact thisvariable with Event EPS Targett. Again, we find a significantly negative coefficient on Event EPS
Targett × High INST PNEBt-1 at the 5% level, suggesting that institutions with a higher standardof prudence are associated with more external pressure on corporate boards, leading to more
pessimism in the event-window earnings guidance.28
In column 1 through column 3, we examine the effects of the threat of shareholder activism
on pessimism in event-window earnings guidance. In column 4, we examine whether the actual
occurrence of compensation-related shareholder activism gives rise to more pessimistic event-27

In untabulated tests, we examine the relationship between the levels of ownership by quasi-indexers or transientinstitutions (i.e., institutions with relatively short investment horizons) and pessimism in event-window earnings
guidance. We find no evidence that concentrated ownership by institutions with short investment horizons is
associated with pessimistic event-window guidance, corroborating our finding in column 2. These results are also
consistent with prior research suggesting that these two kinds of institutions (i.e., transient versus dedicatedinstitutions) have different incentives to monitor firm policies (Ramalingegowda and Yu 2012; Dikolli, Kulp, andSedatole 2009).
28
In untabulated results, rather than using the ownership concentration of pensions, endowments, and bank trusts, we
use the ownership concentration of investment advisors and insurance companies, which are expected to have lower
standards of prudence (Bushee et al. 2013) to create conditioning variables and examine their relationship withpessimism in the event-window earnings guidance. Consistent with our earlier evidence, we don’t find more
pessimistic event-window earnings guidance when the ownership concentration of investment advisors and insurancecompanies is high, supporting our prediction that higher standards of prudence are associated with intense shareholder
activism threats that prompt managers to engage in opportunistic disclosure behavior.
30

window earnings guidance. 29 To test this prediction, we construct an indicator variable,
Shareholder Proposalt-1, which is equal to one if a firm’s shareholder votes for shareholdersponsored proposals and against management-sponsored proposals relating to compensation
exceed 30% in the current or previous period and zero otherwise, and interact this variable with
Event EPS Targett.30 In column 4, we find a significantly negative coefficient on Event EPS Targett
× Shareholder Proposalt-1 at the 10% level, suggesting that compensation-related shareholder
activism induces managers to guide consensus analyst forecasts downward. 31
Overall, results in Table 7 provide evidence consistent with our expectations that greater
external shareholder pressures and monitoring lead to corporate boards externally benchmarking
to set AIP performance targets, which facilitates the issuance of pessimistic event-window
management earnings guidance to affect the target setting process.

Do managers benefit from issuing pessimistic earnings forecasts?
In this section, we test whether managers receive higher bonus payouts as a result ofcompensation-induced strategic disclosures. Table 8 reports results. We measure the dependent
variable, Actual less Target Payoutt, as the difference between the actual non-equity incentive

29

We use the voting tallies for proxy items recorded in the Voting Analytics provided by ISS Corporate Services. For
each proxy item proposed by the management or shareholders, the database includes the number of total votes cast,
the number of votes “For” the proposal, the number of votes “Against” the proposal, and the number of “Abstained”
votes. For a shareholder proposal on compensation-related issues, S5***, voting support is calculated as the fractionof “For” votes out of the total votes cast. For a management proposal on compensation-related issues, M5***, voting
support is calculated as the fraction of “Against” and “Abstained” votes out of the total votes cast.
30
We focus on first-time shareholder proposals made during our sample period and assume that managers are awareof the possibility of significant compensation-related shareholder proposals in the current period.
31
We note that the marginally significant result could be attributed to the weak statistical power to detect the effect.
Descriptive Statistics in Panel B of Table 1 show that only 6.3% of our earnings guidance observations are subject tothe treatment, i.e., the occurrence of significant shareholder proposals, and the interaction between Shareholder
Proposalt-1 and Event EPS Targett further reduces observations that are subject to the treatment effect. To increase theempirical power to detect the effect, we use the past three-year or five-year period window to construct Shareholder
Proposalt-1. In untabulated tests, we find negative but marginally insignificant coefficients on the interaction terms inthese cases, possibly due to the effects becoming weaker as the measurement window becomes longer.
31

payout and the target non-equity incentive payout amount in period t scaled by total cash
compensation in period t-1. In column 1, we find a significantly negative coefficient on both AF
Before MFt and MF Biast, suggesting that the pessimism in both pre-MF consensus analyst
forecasts and event-window management forecasts contribute to a higher bonus payout tomanagers. A one-standard-deviation decrease in the event-window management forecast bias is
associated with an 0.04 (= -0.043 × 0.932) increase in Actual less Target Payoutt, which represents
approximately 10.3% of its standard deviation (= 0.04 / 0.389) and $0.117 million in actual bonus
payout in excess of target payout amount.32 In column 2, we replace AF Before MFt with AF After
MFt and find that the MF Biast variable is no longer statistically significant, implying that postMF consensus analyst forecasts subsume pessimism in event-window management forecasts inaffecting the actual bonus payout. In column 3, we replace the AF After MFt variable with theactual EPS performance target in period t (EPSTARt) and, again, find an insignificant coefficienton MF Biast, suggesting that the pessimism in event-window management forecasts isincorporated into EPS performance targets in affecting the actual bonus payments.33 Collectively,
our evidence suggests that strategic management disclosure in the event window lowers AIP
performance targets and increases the bonus payouts to managers.

VI. Conclusion
This study examines strategic management disclosures in the form of earnings forecasts
issued ahead of compensation meetings approving executive AIPs. We argue that managers have32
The average cash compensation in period t-1 is $2,939,770. Hence, an increase of 0.04 in the dependent variablecorresponds to an increase of $117,591 in actual bonus payout in excess of the target payout amount (117,591 = 0.04
× 2,939,770).
33
In untabulated tests, we find similar results using the EPS performance targets as a dependent variable. We findevidence that pessimistic event-window earnings guidance contributes to lower EPS performance targets in AIPs,
primarily through its effect on the prevailing consensus analyst forecasts.

32

incentives to make such strategic disclosures because corporate boards rely on analyst forecasts to
set AIP performance targets. We find evidence supporting the argument that the prevailingconsensus analyst forecasts are positively associated with the EPS performance targets in AIPs.
Next, we find that management earnings forecasts issued before compensation meetings
are more pessimistic than those issued in other periods. The result is unlikely to be driven byequity-related incentives for strategic disclosure (Aboody and Kasznik 2000). Further, we do notfind similar results before 2006, suggesting that the enhanced compensation disclosure
requirements post-2006 contributed to the increased pessimism in management earnings forecasts.
In response to pessimistic management forecasts issued during the event window, analysts revise
their forecasts downward. Analyst revisions made during the event window are similar inmagnitude to those made in other periods.
Cross-sectional tests suggest that management has a stronger incentive to make strategic
disclosures during the event window when existing consensus analyst forecasts are more optimistic,
the target payout amount of the AIP is larger, or the firm has missed its EPS performance target in
the previous year. Finally, we find evidence suggesting that pessimistic event-window earnings
forecasts increase the actual bonus payout to management.
More interestingly, we find that our results on strategic management disclosure are stronger
when shareholder activism is more intense, suggesting an unintended consequence of shareholder
activism.

33

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38

Appendix A Variable Definitions
Variables

Descriptions

MF Biast

MF Biast is measured as management’s quantitative earnings forecast less theprevailing mean consensus analyst forecast three days before the date of the
management forecast, scaled by stock price, and multiplied by 100.
AF Revisiont is defined as the mean consensus analyst forecast five days after thedate of the management forecast less the mean consensus analyst forecast threedays before the date of the management forecast, scaled by stock price, andmultiplied by 100.
Event EPS Targett is an indicator variable equal to one for the latest management
earnings forecast issued during the 90-day period before the AIP grant date forfirms with an EPS performance target but without a revenue performance target
and zero otherwise.
Event EPS REV Targett is an indicator variable equal to one for the latest
management earnings forecast issued during the 90 day period prior to the AIP
grant date for firms with both EPS and revenue performance targets and zerootherwise.
Event REV Targett is an indicator variable equal to one for the latest management
earning forecast issued during the 90 day period before the grant date of AIPs forfirms with revenue performance target but without EPS performance target andzero otherwise.
Sizet-1 is measured as the natural logarithm of firm i’s market value of equity atthe end of fiscal quarter preceding the date of the management forecast.
Market-to-Bookt-1 is measured as the market-to-book ratio at the end of the fiscal
quarter preceding the date of the management forecast.
ABRETt-1 is defined as cumulative abnormal returns, measured as the excess firm
returns over the CRSP value-weighted returns during the three months ending 2days before the issuance of the management forecast.
ROAt-1 is calculated as income before extraordinary items in period t-1 divided bylagged total assets.
EarnVolt-1 is measured as standard deviation of quarterly earnings scaled bylagged total assets over 12 quarters preceding the date of the management forecast.
Litigation Riskt is an indicator variable equal to one for firms in the biotechnology(SIC 2833–2836 and 8731–8734), computers (3570–3577 and 7370–7374),
electronics (3600–3674), and retail (5200–5961) industries and zero otherwise.
Losst-1 is an indicator variable equal to one if the firm reported losses in period t1 and zero otherwise
Horizont is measured as the difference between the date of fiscal year-end and thedate of the management forecast, divided by 365.
AF Biast is measured as the difference between the consensus analyst forecast
three days before the date of the event-window management forecast and theactual EPS for firm i in period t divided by stock price. When this variable is used

AF Revisiont

Event EPS Targett

Event EPS REV Targett

Event REV Targett

Sizet-1
Market-to-Bookt-1
ABRETt-1

ROAt-1
EarnVolt-1
Litigation Riskt

Losst-1
Horizont
AF Biast

39

Target Bonust-1

Miss EPS Targett-1
INST TOP5t-1

INST DEDt-1

INST PNEBt-1

Shareholder Proposalt-1

EPSTARt
Actual less Target
Payoutt
AF Before MFt

MFt
AF After MFt

Actual EPSt

as a conditioning variable, we create an indicator variable, High AF Biast, equal
to one if the AF Biast variable exceeds its sample median and zero otherwise.
Target Bonust-1 is defined as the non-equity incentive target in period t-1 divided
by total cash compensation (i.e., the sum of salary and bonus payout) in period t1. When this variable is used as a conditioning variable, we create an indicatorvariable, High Target Bonust-1, equal to one if the Target Bonust-1 variable exceeds
its sample median and zero otherwise.
Miss EPS Targett-1 is an indicator variable equal to one if firm i missed the EPS
performance target in period t-1 and zero otherwise.
INST TOP5t-1 is measured as the concentration of top-five institutional ownershipfor firm i in period t-1. When this variable is used as a conditioning variable, wecreate an indicator variable, High INST TOP5t-1, equal to one if INST TOP5t-1
exceeds its sample median, zero otherwise.
INST DEDt-1 is measured as the concentration of dedicated institutional ownershipfor firm i in period t-1. When this variable is used as a conditioning variable, wecreate an indicator variable, High INST DEDt-1, equal to one if INST DEDt-1
exceeds its sample median and zero otherwise.
INST PNEBt-1 is measured as the concentration of pensions’, endowments’, orbank trusts’ institutional ownership for firm i in period t-1. When this variable isused as a conditioning variable, we create an indicator variable, High INST PNEBt1, equal to one if INST PNEBt-1 exceeds its sample median and zero otherwise.
Shareholder Proposalt-1 is an indicator variable equal to one if a firm has received
votes for shareholder-sponsored proposals and against management-sponsoredproposals relating compensation exceeding 30% of total votes in the current
period or period t-1 and zero otherwise.
EPSTARt is the actual EPS performance target in the AIP, divided by stock priceand multiplied by 100.
Actual less Target Payoutt is measured as the difference between the actual nonequity incentive bonus payout and the target non-equity incentive bonus amountin period t, scaled by total cash compensation in period t-1.
AF Before MFt is the mean consensus analyst forecast three days before the dateof event-window management earnings guidance, scaled by stock price andmultiplied by 100.
MFt is the event-window management earnings guidance, divided by stock priceand multiplied by 100.
AF After MFt is the mean consensus analyst forecast five days after the date ofevent-window management earnings guidance, scaled by stock price andmultiplied by 100.
Actual EPSt is the IBES-reported actual EPS in period t divided by stock price andmultiplied by 100.

40

Appendix B An Example
Mar 25, 2010 Hess Corporation proxy statement

41

Figure 1 CEO Bonus
2500

40.0%
35.0%

2000

30.0%
25.0%

1500

20.0%
1000

15.0%
10.0%

500

5.0%
0.0%

0
2006

2007

2008

2009

2010

2011

Bonus

2012

2013

2014

2015

%Bonus

The figure represents the average dollar value of a bonus (in thousands) and the average percentage of the bonus
relative to total annual compensation earned by CEOs in our sample during the fiscal year between 2006 and 2015.
Bonus indicates the bonus amount earned by the CEO during the fiscal year (i.e., the sum of bonus and noneq_incent
in Execucomp). Bonus is inflation-adjusted (as of the beginning of 2006), and the inflation data is obtained from
CRSP-Indexes-US Treasury and Inflation. %Bonus is the bonus divided by total annual compensation of the CEO(i.e., TDC1 in Execucomp).

42

Figure 2 Timeline

The figure represents the timeline of events. Compensation committee meetings determining AIPs are typically held
during the first quarter of a fiscal year. First, we identify the date of compensation committee meetings (AIP date) inwhich performance targets in AIP (e.g., EPS target, EPSTAR) are determined. We then identify the latest management
earning guidance issued during the 90-day window, i.e. the event period, before the meeting for firms with either an
EPS (or earnings) performance target or a revenue performance target in bonus plans. Prevailing analyst expectations
to gauge the management forecast bias are measured three days before the date of latest management earnings
guidance issued in the event period (AF Before MF). Prevailing analyst expectations to gauge the impact ofmanagement forecast bias are measured five days after the date of latest management earnings guidance issued in theevent period but before the compensation committee meeting (AF After MF).

43

Table 1 Sample Selection and Descriptive Statistics
Panel A reports the sample selection procedure. Panel B and Panel C present descriptive statistics for the Firm-MF
sample and Firm-Year sample, respectively. The sample period ranges from 2006 to 2015. All variables are definedin the Appendix A.

Panel A Sample Selection# Firm-Year(# Firm-MF)

EPS w/oREV

EPS and
REV

REV w/oEPS# Firm-Year observations with available
EPS and REV targets in AIP

3,786

1,720

881

1,185

# Firm-Year observations with available EPS
and REV targets in AIP, management
forecasts, and financial data

2,184

1,011

673

500

# Firm-MF sample (Panel B, Table 1)(11,866)(5,581)(3,649)(2,636)

Firm-year observations with available EPS
targets in AIP, the Event MF, and financial
data

1,173

632

541

-

# Firm-Year sample (Panel C, Table 1)

Panel B Descriptive Statistics for the Firm-MF Sample
Variables
Dependent variables
MF Biast
AF Revisiont
Independent variables
Event EPS Targett
Event EPS REV Targett
Event REV Targett
Control variables
Sizet-1
Market-to-Bookt-1
ABRETt-1
ROAt-1
EarnVolt-1
Losst-1
Litigation Riskt
Horizont
Variables for cross-sectional tests
AF Biast
Target Bonust-1
Miss EPS Targett-1
INST TOP5t-1
INST DEDt-1
INST PNEBt-1
Shareholder Proposalt-1

N

Mean

Std

Q1

MedianQ3

11,866
11,624-0.208
0.044

0.828
0.666

-0.184-0.097-0.035
0.000

0.042
0.044

11,866
11,866
11,866

0.054
0.046
0.034

0.225
0.209
0.182

-

-

-

11,866
11,866
11,866
11,866
11,866
11,866
11,866
11,866

8.991
2.030
0.011
0.074
0.016
0.052
0.265
0.613

1.112
1.080
0.118
0.062
0.023
0.223
0.441
0.416

8.176
1.283
-0.057
0.034
0.005
0.322

8.939
1.678
0.007
0.064
0.009
0.642

9.697
2.389
0.076
0.105
0.016
0.869

9,206
11,866
6,972
11,866
9,231
9,231
11,866

0.058
0.453
0.310
0.014
0.004
0.004
0.063

1.792
0.304
0.463
0.021
0.012
0.006
0.244

-0.475
0.316
0.000
0.000
0.000
0.000
-

-0.028
0.451
0.000
0.012
0.001
0.003
-

0.430
0.592
1.000
0.020
0.005
0.005
-

44

Panel C Descriptive statistics for the Firm-Year Sample
Variables
Dependent variables
EPSTARt
Actual less Target Payoutt
Independent variables
AF before MFt
MFt
MF Biast
AF After MFt
EPSTARt-1
Actual EPSt
Control variables
Sizet-1
Market-to-Bookt-1
ABRETt-1
ROAt-1
EarnVolt-1
Losst-1
Litigation Riskt

N

Mean

Std

Q1

Median

Q3

1,173
996

6.717
0.138

2.634
0.389

5.272
-0.053

6.376
0.119

7.780
0.333

1,173
1,173
1,173
1,173
848
1,173

7.091
6.790
-0.333
6.942
6.801
6.885

2.871
3.061
0.920
2.441
2.365
2.582

5.447
5.098
-0.368
5.428
5.373
5.349

6.658
6.393
-0.110
6.582
6.534
6.548

8.079
7.814
0.023
7.893
7.784
7.855

1,173
1,173
1,173
1,173
1,173
1,173
1,173

9.015
2.046
0.008
0.077
0.016
0.049
0.242

1.136
1.081
0.107
0.063
0.022
0.215
0.429

8.181
1.307
-0.048
0.037
0.005
-

8.923
1.706
0.005
0.068
0.009
-

9.733
2.430
0.067
0.107
0.016
-

45

Table 2 Correlations
This table reports Pearson correlations. Sample period ranges from 2006 to 2015. Panel A presents Pearson correlations for Firm-MF sample, and Panel B presents
Pearson correlations for Firm-Year sample. Significance level at 5% is bolded. All variables are defined in the Appendix A.
Panel A Firm-MF Sample
Variables(1) MF Biast(2) AF Revisiont(3) Event EPS Targett(4) Event EPS REV Targett(5) Event REV Targett(6) Sizet-1(7) Market-to-Bookt-1(8) ABRETt-1(9) ROAt-1(10) EarnVolt-1(11) Losst-1(12) Litigation Riskt(13) Horizont(14) AF Biast(15) Target Bonust-1(16) Miss EPS Targett-1(17) INST TOP5t-1(18) INST DEDt-1(19) INST PNEBt-1(20) Shareholder Proposalt-1

(1)
0.23
-0.05
-0.02
0.00
0.09
-0.05
-0.01
-0.08
-0.01
0.01
0.01
0.01
0.00
-0.02
-0.02
-0.02
0.00
-0.06
0.03

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

-0.05
-0.03
-0.02
-0.03
0.01
0.04
-0.01
0.03
-0.01
0.03
-0.03
-0.44
0.02
-0.07
0.06
0.01
0.04
-0.01

-0.05
-0.04
-0.04
-0.04
-0.01
-0.02
-0.04
-0.01
-0.07
0.20
0.04
0.01
0.02
-0.01
0.00
0.00
0.00

-0.04
0.05
0.05
0.00
0.04
0.04
0.00
0.05
0.18
-0.01
-0.01
0.01
-0.01
-0.01
-0.03
-0.01

0.00
0.02
0.00
0.00
0.00
0.01
0.08
0.15
0.01
0.03
0.02
0.00
-0.01
0.01
0.03

0.22
0.01
0.16
-0.08
-0.15
0.14
0.06
-0.03
-0.06
-0.12
-0.14
-0.09
0.01
-0.02

0.07
0.67
0.16
-0.07
0.21
-0.03
-0.09
-0.10
-0.08
0.01
0.09
-0.06
-0.06

-0.01
0.01
0.03
0.01
0.02
-0.15
-0.01
-0.02
0.00
0.00
0.00
0.00

0.01
-0.43
0.08
-0.03
-0.04
-0.09
-0.12
-0.01
0.03
0.00
-0.08

0.26
0.20
0.07
0.04
0.00
-0.04
0.06
0.12
-0.08
0.05

46

Panel A Firm-MF Sample, cont'd
Variables(1)
MF Biast(2)
AF Revisiont(3)
Event EPS Targett(4)
Event EPS REV Targett(5)
Event REV Targett(6)
Sizet-1(7)
Market-to-Bookt-1(8)
ABRETt-1(9)
ROAt-1(10) EarnVolt-1(11) Losst-1(12) Litigation Riskt(13) Horizont(14) AF Biast(15) Target Bonust-1(16) Miss EPS Targett-1(17) INST TOP5t-1(18) INST DEDt-1(19) INST PNEBt-1(20) Shareholder Proposalt-1

(11)

(12)

(13)

(14)

(15)

(16)

(17)

(18)

(19)

(20)

0.05
-0.01
-0.02
0.08
0.11
0.02
0.03
-0.03
0.03

0.00
-0.09
0.01
-0.01
-0.03
0.01
-0.12
0.01

0.04
-0.03
-0.06
-0.01
0.00
0.00
0.04

0.00
0.12
0.01
0.03
0.01
0.05

0.30
0.01
0.00
0.05
-0.01

0.01
-0.03
0.00
0.01

0.63
0.46
-0.02

0.35
0.00

0.00

-

47

Panel B Firm-Year Sample
Variables(1) EPSTARt(2) AF Before MFt(3) MFt(4) MF Biast(5) AF After MFt(6) Actual less Target Payoutt(7) EPSTARt-1(8) Actual EPSt
(9) Sizet-1(10) Market-to-Bookt-1(11) ABRETt-1(12) ROAt-1(13) EarnVolt-1(14) Losst-1(15) Litigation Riskt

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

(15)

0.69
0.65
0.02
0.78
-0.13
0.60
0.64
-0.11
-0.35
-0.18
-0.14
0.02
0.03
-0.08

0.90
-0.16
0.90
-0.13
0.45
0.71
-0.08
-0.36
-0.19
-0.15
0.01
0.02
-0.14

0.20
0.83
-0.12
0.46
0.66
-0.05
-0.35
-0.16
-0.17
-0.01
0.01
-0.12

-0.05
0.01
0.04
0.03
0.09
0.01
0.05
-0.01
-0.06
-0.02
0.07

-0.10
0.49
0.83
-0.09
-0.38
-0.18
-0.14
0.02
0.02
-0.10

-0.01
0.16
0.07
-0.02
0.14
-0.06
0.01
0.03
-0.02

0.46
-0.08
-0.33
-0.02
-0.15
0.02
0.05
-0.05

-0.10
-0.31
-0.07
-0.10
0.05
0.05
-0.07

0.20
-0.01
0.14
-0.02
-0.13
0.22

0.01
0.72
0.16
-0.10
0.18

0.01
-0.01
0.01
-0.03

0.04
-0.41
0.08

0.25
0.18

0.05

-

48

Table 3 Determinants of the level of EPS performance targets in AIPs
This table presents the estimation results from the following regression model using Firm-Year sample.
EPSTARt=

β1 AF Before MFt + β2 MFt + β3 AF After MFt + β4 EPSTARt-1 + β5 Sizet-1+ β6 Market-to-Bookt-1 + β7 ABRETt-1 + β8 ROAt-1 + β9 EarnVolt-1 + β10 Losst-1+ β11 Litigation Riskt + ԑt

EPSTARt is measured as the EPS performance target in AIPs divided by stock price, and multiplied by 100. AF
Before MFt is defined as the mean consensus analyst forecast three days before the date of management forecast
divided by stock price, and multiplied by 100. MFt is defined as the event-window management earnings forecast
divided by stock price, and multiplied by 100. AF After MFt is defined as the mean consensus analyst forecast fivedays after the date of management forecast but before the compensation meetings divided by stock price, andmultiplied by 100. EPSTARt-1 is the lagged EPSTARt variable. Test statistics for the coefficient difference in column
6 are summarized toward the bottom of the table. All other variables are defined in the Appendix A. Standard errors
are clustered by firm. Robust t-statistics are in parentheses. *, **, and *** represent significance level at the 10%,5%, and 1%, respectively.
EPSTARt
Variables
AF Before MFt
MFt
AF After MFt
EPSTARt-1
Sizet-1
Market-to-Bookt-1
ABRETt-1
ROAt-1
EarnVolt-1
Losst-1
Litigation Riskt(1)
0.537***(6.886)-0.054(-0.628)-0.505***(-4.015)-1.859***(-2.872)
4.750**(2.186)2.089(0.518)0.356(0.978)0.200(0.834)

Fixed Effects (Industry, Year)
Yes
Number of observations
1,173
Adjusted R squared
0.496
Coefficient difference tests in column (6)AF After MFt = EPSTARt(2)
0.459***(6.472)-0.071(-0.813)-0.630***(-5.028)-2.539***(-3.920)
6.867***(2.844)4.659(1.069)0.522(1.380)0.143(0.557)(3)0.790***(8.204)-0.051(-0.676)-0.241**(-1.979)-1.240*(-1.917)1.249(0.728)0.315(0.088)0.137(0.472)0.019(0.093)(4)-0.086*(-1.688)0.879***(10.111)-0.047(-0.615)-0.245**(-2.011)-1.306**(-1.981)1.141(0.659)0.399(0.113)0.130(0.448)-0.005(-0.026)(5)
0.012(0.220)0.778***(8.496)-0.053(-0.688)-0.241**(-1.980)-1.237*(-1.905)
1.298(0.731)
0.348(0.096)0.141(0.483)0.021(0.103)(6)0.680***(5.422)0.305***(4.473)-0.040(-0.606)-0.065(-0.624)-1.199*(-1.782)-0.794(-0.602)-3.269(-1.161)-0.133(-0.468)-0.070(-0.437)

Yes
1,173
0.473

Yes
1,173
0.613

Yes
1,173
0.614
Coeff Diff
0.375

Yes
1,173
0.613
F-Stat
4.28

Yes
848
0.723
P-Value
0.040

49

Table 4 The effects of management forecast bias on analyst forecast revisions
This table presents the estimation results from the following regression model using Firm-MF sample.
AF Revisiont= β1 MF Biast + β2 Event EPS Targett + β3 MF Biast × Event EPS Targett+ β4 Sizet-1 + β5 Market-to-Bookt-1 + β6 ABRETt-1 + β7 ROAt-1 + β8 EarnVolt-1 + β9 Losst-1+ β10 Litigation Riskt + β11 Horizont + ԑt

AF Revisiont is defined as the mean consensus analyst forecast five days after the date of management forecast lessthe mean consensus analyst forecast three days before the date of management forecast scaled by stock price, andmultiplied by 100. All variables are defined in the Appendix A. Standard errors are clustered by firm. Robust tstatistics are in parentheses. *, **, and *** represent significance level at the 10%, 5%, and 1%, respectively.

Variables
MF Biast
Event EPS Targett
MF Biast × Event EPS Targett
Sizet-1
Market-to-Bookt-1
ABRETt-1
ROAt-1
EarnVolt-1
Losst-1
Litigation Riskt
Horizont
Fixed Effects (Firm, Year, FQ)
Number of observations
Adjusted R squared(1)
0.461***(11.307)-0.218***(-3.682)-0.047(-1.141)0.021(0.503)-0.408(-1.381)
1.137(0.849)-0.074*(-1.819)0.158(1.374)-0.021(-1.115)

AF Revisiont(2)-0.071**(-2.276)-0.133***(-2.911)-0.005(-0.159)
0.040(0.783)-0.200(-0.821)0.866(0.798)-0.005(-0.119)0.067(0.689)-0.019(-0.842)(3)0.458***(10.776)
0.002(0.081)0.030(0.778)-0.218***(-3.685)-0.047(-1.135)
0.019(0.463)-0.411(-1.387)1.134(0.848)-0.074*(-1.819)0.158(1.376)-0.018(-1.029)Yes
11,624
0.616

Yes
11,624
0.412

Yes
11,6240.616

50

Table 5 Management earnings forecast bias before the approval of AIPs
This table presents the estimation results from the following regression model using Firm-MF sample.
MF Biast=

β1 INDVARt + β2 Sizet-1 + β3 Market-to-Bookt-1 + β4 ABRETt-1 + β5 ROAt-1+ β6 EarnVolt-1 + β7 Losst-1 + β8 Litigation Riskt + β9 Horizont + ԑt

MF Biast is measured as the management earnings forecast less the prevailing mean consensus analyst forecast
three days before the date of management forecast scaled by stock price, and multiplied by 100. In Panel A,
INDVARt represents Event EPS Targett, Event EPS REV Targett, or Event REV Targett in column 1, column 2, andcolumn 3, respectively. In column 4, all three variables are included simultaneously. In Panel B, column 1 (column
2) uses the AIP observations approved without (with) long-term equity grants. In Panel C, INDVARt represents
Assigned Event EPS Targett, Assigned Event EPS REV Targett, or Assigned Event REV Targett in column 1, column
2, and column 3, respectively. In column 4, all three variables are included for estimation. In Panel C, the sample
period is between 2001 and 2005 and we assume that the performance targets after 2006 are used before 2006. We
assign hypothetical grant dates of bonus plans based on the typical grant date for each firm after 2006. In Panel D,
Alternative MF Biast is measured as the difference between management alternative-annual earnings forecasts andanalyst consensus alternative-annual earnings forecasts, scaled by stock price and multiplied by 100. Management
alternative-annual earnings forecasts are sum of quarterly earnings forecasts for four quarters ending with threealternative fiscal year end other than the actual fiscal year end. If the portion of the alternative-annual earnings are
realized at the time of forecasts issued, then we use the realized earnings instead. Similarly, consensus analyst
alternative-annual earnings forecasts use the consensus analyst quarterly earnings forecasts measured three days
before the corresponding quarterly management earnings forecasts. Control variables consisting of Size, Marketto-Book, ABRET, ROA, EarnVol, Loss, Litigation Risk, and Horizon are omitted from reporting in Panels B−D for
brevity. All other variables are defined in the Appendix A. Standard errors are clustered by firm. Robust t-statistics
are in parentheses. *, **, and *** represent significance level at the 10%, 5%, and 1%, respectively.
Panel A The effects of AIPs on management forecast bias
Independent Variables
Event EPS Targett
Event EPS REV Targett
Event REV Targett
Sizet-1
Market-to-Bookt-1
ABRETt-1
ROAt-1
EarnVolt-1
Losst-1
Litigation Riskt
Horizont(1)-0.160***(-4.011)
0.197**(2.574)0.074*(1.767)0.048(0.781)0.436(0.869)-0.244(-0.303)0.134**(2.085)-0.201*(-1.860)
0.130**(2.490)

MF Biast(2)-0.041(-0.961)
0.195**(2.564)0.074*(1.779)0.051(0.832)0.436(0.870)-0.217(-0.270)
0.133**(2.060)-0.194*(-1.818)0.120**(2.243)(3)
0.039(0.703)0.195**(2.565)0.074*(1.778)0.052(0.845)0.437(0.873)-0.208(-0.258)
0.133**(2.063)-0.195*(-1.825)
0.117**(2.205)(4)-0.169***(-4.072)-0.068(-1.498)
0.001(0.021)0.196**(2.576)0.074*(1.771)0.047(0.768)0.437(0.870)-0.258(-0.322)0.134**(2.084)-0.202*(-1.875)0.134**(2.523)

Yes
11,866
0.375

Yes
11,866
0.374

Yes
11,866
0.374

Yes
11,866
0.376

Fixed Effects (Firm, Year, FQ)
Number of observations
Adjusted R squared51

Coeff. Diff. tests in col. (4)β1 (Event EPS Targett) = β2 (Event EPS REV Targett)
β1 (Event EPS REV Targett) = β3 (Event REV Targett)β2 (Event EPS Targett) = β3 (Event REV Targett)

Coeff. Diff.
-0.101-0.067-0.170F-Stat.
3.720
1.328
7.034

p-Value
0.054
0.250
0.008

Panel B AIPs and long-term incentive plans (LTIP)
MF Biast
Independent Variables
Event EPS Targett
Event EPS REV Targett
Event REV Targett

Controls
Fixed Effects (Firm, Year, FQ)
Number of observations
Adjusted R squared
Coeff. Diff. between col. (1) and col. (2)Event EPS Targett
Event EPS REV Targett
Event REV Targett(1)
AIP only-0.203**(-2.037)-0.157(-0.936)
0.039-0.283(2)
AIP w/ LTIP
-0.167***(-3.646)-0.049(-1.164)-0.001(-0.016)

Included
Yes
2,175
0.378
Coeff. Diff.
-0.036-0.1090.040

Included
Yes
9,691
0.388
p-Value
0.736
0.513
0.788

Panel C The effects of AIPs on management forecast bias before 2006
MF Biast
Variables
Assigned Event EPS Targett
Assigned Event EPS REV Targett
Assigned Event REV Targett

Controls
Fixed Effects (Firm, Year, FQ)
Number of observations
Adjusted R squared

(1)
0.041
(0.386)
-

(2)
0.377
(0.955)
-

(3)
0.135
(1.032)(4)
0.107(0.968)0.428(1.070)0.195(1.491)

Included
Yes
5,570
0.815

Included
Yes
5,570
0.815

Included
Yes
5,570
0.815

Included
Yes
5,570
0.815

Panel D The effects of AIPs on alternative management forecast bias
Alternative MF Biast
Variables
(1)
(2)
(3)
Event EPS Targett-0.050(-0.721)
Event EPS REV Targett
0.006(0.087)
Event REV Targett
0.101*
52(4)-0.022(-0.292)
0.023(0.304)0.103

Controls
Fixed Effects (Firm, Year, FQ)
Number of observations
Adjusted R squared--(1.901)(1.583)

Included
Yes
3,801
0.498

Included
Yes
3,801
0.498

Included
Yes
3,801
0.498

Included
Yes
3,801
0.498

53

Table 6 Cross-sectional variation: Management heterogeneous incentives
This table presents the estimation results from the following regression model using Firm-MF sample.
MF Biast=

β1 Event EPS Targett + β2 Sizet-1 + β3 Market-to-Bookt-1 + β4 ABRETt-1 + β5 ROAt-1+ β6 EarnVolt-1 + β7 Losst-1 + β8 Litigation Riskt + β9 Horizont + ԑt

In column 1, 2, and 3, we interact Even EPS targett with High AF Biast, High Target Bonust-1, and Miss EPS Targett1, respectively. High AF Biast is an indicator variable equal to one if the mean consensus analyst forecast three days

before the date of event management forecast less the actual EPS figure in period t divided by stock price is above
the sample median, zero otherwise. High Target Bonust-1 is an indicator variable equal to one if non-equity incentive
target amount in period t-1 divided by cash compensation in period t-1 is above the sample median, zero otherwise.
Miss EPS Targett-1 is an indicator variable equal to one if the firm missed the EPS performance target in period t-1,
zero otherwise. All variables are defined in the Appendix A. Standard errors are clustered by firm. Robust t-statistics
are in parentheses. *, **, and *** represent significance level at the 10%, 5%, and 1%, respectively.

Variables
Event EPS Targett
High AF Biast
Event EPS Targett × High AF Biast
High Target Bonust-1
Event EPS Targett × High Target Bonust-1
Miss EPS Targett-1
Event EPS Targett × Miss EPS Targett-1

Controls
Fixed Effects (Firm, Year, FQ)
Number of observations
Adjusted R squared(1)

MF Biast(2)

0.047(1.251)-0.061**(-2.432)-0.432***(-6.636)--0.045(-1.064)-0.011(-0.463)-0.258***(-3.902)-(3)-0.107**(-2.580)
0.008(0.188)-0.220**(-2.390)

Included
Yes
9,206
0.399

IncludedYes
11,866
0.377

Included
Yes
6,972
0.402

54

Table 7 Cross-sectional variation: Shareholder activism
This table presents the estimation results from the following regression model using Firm-MF sample.
MF Biast=

β1 Event EPS Targett + β2 Sizet-1 + β3 Market-to-Bookt-1 + β4 ABRETt-1 + β5 ROAt-1+ β6 EarnVolt-1 + β7 Losst-1 + β8 Litigation Riskt + β9 Horizont + ԑt

In column 1, 2, 3, and 4, we interact Event EPS Targett with High INST TOP5t-1, High INST DEDt-1, High INST
PNEBt-1, or Shareholder Proposalt-1, respectively. High INST TOP5t-1 is an indicator variable equal to one if theconcentration of top-five institutional ownership for firm i in period t-1 exceeds its sample median, zero otherwise.
High INST DEDt-1 is an indicator variable equal to one if the concentration of dedicated institutional ownership forfirm i in period t-1 exceeds its sample median, zero otherwise. High INST PNEBt-1 is an indicator variable equal to
one if the concentration of pensions’, endowments’, or bank trusts’ institutional ownership for firm i in period t-1
exceeds its sample median, zero otherwise. Shareholder Proposalt-1 is an indicator variable equal to one if a firm
has received votes for (against) shareholder-proposed (management-sponsored) compensation-related issues thatexceed 30% of total votes in the current or period t-1, zero otherwise. All variables are defined in the Appendix A.
Standard errors are clustered by firm. Robust t-statistics are in parentheses. *, **, and *** represent significancelevel at the 10%, 5%, and 1%, respectively.
MF Biast
Variables
Event EPS Targett
High INST TOP5t-1
Event EPS Targett × High INST TOP5t-1
High INST DEDt-1
Event EPS Targett × High INST DEDt-1
High INST PNEBt-1
Event EPS Targett × High INST PNEBt-1
Shareholder Proposalt-1
Event EPS Targett × Shareholder Proposalt-1
Controls
Fixed Effects (Firm, Year, FQ)
Number of observations
Adjusted R squared(1)-0.072(-1.639)-0.000(-0.009)-0.181**(-2.382)-(2)-0.093*(-1.734)-0.014(-0.353)-0.144*(-1.687)-(3)-0.077*(-1.679)
0.014(0.456)-0.163**(-2.196)-(4)-0.146***(-3.533)
0.033(0.690)-0.229*(-1.814)

IncludedYes
11,866
0.376

Included
Yes
9,225
0.428

Included
Yes
9,225
0.428

IncludedYes
11,8660.376

55

Table 8 The effect of management forecasts and performance targets on ex-post payout
This table presents the estimation results from the following regression model using Firm-Year sample.
DEPVARt=

β1 INDVARt + β2 Actual EPSt + β3 Sizet-1 + β4 Market-to-Bookt-1 + β5 ABRETt-1 + β6 ROAt-1+ β7 EarnVolt-1 + β8 Losst-1 + β9 Litigation Riskt + ԑt

DEPVARt is Actual less Target Payoutt, which is measured as the non-equity incentive payout less non-equity
incentive target in period t divided by cash compensation in period t-1. In column 1, INDVARt represents AF Before
MFt and MF Biast. In column 2, INDVARt represents MF Biast and AF After MFt. In column 3, INDVARt represents
MF Biast and EPSTARt. Actual EPSt is the actual IBES-reported EPS for firm i in period t divided by stock price,and multiplied by 100. All variables are defined in the Appendix A. Standard errors are clustered by firm. Robust
t-statistics are in parentheses. *, **, and *** represent significance level at the 10%, 5%, and 1%, respectively.

Variables
AF Before MFt
MF Biast
AF After MFt
EPSTARt
Actual EPSt
Sizet-1
Market-to-Bookt-1
ABRETt-1
ROAt-1
EarnVolt-1
Losst-1
Litigation Riskt
Fixed Effects (Industry, Year)
Number of observations
Adjusted R squared(1)-0.063***(-6.029)-0.043**(-2.478)
0.076***(8.097)0.046***(2.916)0.030(1.465)0.293**(2.279)-1.263***(-3.449)-0.591(-0.873)-0.084(-0.871)-0.019(-0.344)

Actual less Target Payoutt(2)-0.025(-1.520)-0.117***(-7.746)
0.116***(9.207)0.046***(2.981)0.011(0.544)0.175(1.397)-1.006***(-2.859)-0.383(-0.565)-0.089(-0.967)-0.009(-0.166)(3)-0.004(-0.244)-0.047***(-5.446)
0.058***(5.966)
0.038**(2.335)0.033(1.558)0.351***(2.759)-1.223***(-3.148)-0.530(-0.673)-0.072(-0.750)-0.005(-0.095)

Yes
996
0.186

Yes
996
0.233

Yes
996
0.154

56