Forecasting Ability, Firm Welfare and Managerial Skill

Abstract
We examine whether the ability to deliver high quality forecasts reflects greater
managerial skills. We expect that managers are more likely to provide consistent forecasts
when they can better anticipate future events in the business environment and their impact onearnings.

Increased information processing ability as evidenced by greater forecast

consistency should be associated with better corporate decisions and financial disclosures.
Consistent with this view, we find that firms with CEOs who deliver more consistent
forecasts experience higher profitability and higher valuation. We also find that thisforecasting ability is, to a large extent, a CEO rather than a firm characteristic. Finally, we
find that CEOs with greater forecasting skill experience better careers.

Key words: Managerial skills, Management Forecast, Firm performance, Variance analysis

Forecasting Ability, Firm Welfare and Managerial Skill
1. Introduction
Management earnings forecasts play an important role in capital markets. They areuseful to set or alter market earnings expectations, to preempt litigation concerns, and to be asource of transparent and accurate reporting (Hirst, Koonce, and Venkataraman 2008).
Trueman (1986) suggests that these voluntarily disclosed earnings forecasts are also useful tomeasure broader managerial skills. The basic intuition is that management forecasts reveal
managers’ knowledge of the firm’s economic environment and their ability to forecast futurebusiness prospects. Consistent with the idea that managers draw on similar skills whengenerating external earnings forecasts and internal cash flow predictions for their investment
decisions, Goodman et al. (2014) provide empirical evidence suggesting that the quality ofmanagement forecasts can be used as a valuable measure of managerial forecasting ability
related to investment. We extend these studies by investigating the implications ofmanagement forecast skills for firm overall welfare and managerial career outcomes.
We first examine if greater forecasting ability leads to superior firm welfare. CEOs
that demonstrate greater forecast quality should also have greater information processing
ability, which in turn should reflect better quality corporate decisions.

In particular,

operational, investment, and financing decisions should be better for executives who canbetter calibrate their expectations. Consistent with this conjecture, Malmendier and Tate[2005, 2008] find that managerial overconfidence can account for corporate investment andmergers and acquisitions distortions. Ben-David, Graham, and Harvey [2010] also find thatmanagerial miscalibration affects real investment. We expect that managers who understandthe firm’s future payoffs well enough to deliver high quality forecasts can run the firm more
efficiently and thus deliver better firm performance.

1

Next, we examine whether forecasting ability is a distinctive organizational trait or amanager specific skill. We argue that management earnings forecast is important to anorganization and its investors but likely secondary to the organization’s core competencies.
A core competency is a deep proficiency that enables a company to deliver unique value tocustomers and hence is essential or central to the overall performance and success of the firm
(Prahalad and Hamel, 1990). It provides the foundation that allows a firm to be more
profitable than its competitors (e.g., Honda’s engine expertise). Such competencies are, by
nature and by design, organizational traits. Firms may be tempted to protect competencies
that provide consistent superior profit, but there is a limit to the number of core competencies
an organization can develop and maintain (Prahalad and Hamel, 1990) and organizational
overreach is costly (Bruch and Menges, 2010). To address this dilemma, one solution may be
to rely on (distinctive) managerial skills instead of distinctive (organizational) competencies,that is, on the capacity of individuals to provide rare and valuable services instead of relying
on institutional know-how. We argue that, in delivering high quality management forecasts,
firms likely rely more on individual managerial skill rather than on organizational
competency. Thus, we expect executive quality to matter more than organizational trait inthis context.
Finally, to the extent that forecast quality is determined by managerial skill, we
examine whether managers can extract the economic surplus associated with the supply ofthis skill. That is, are executives who possess this skill compensated for providing it, or is theorganization that employs them the sole beneficiary? We consider three aspects of CEO
welfare: the probability of finding another CEO appointment in case of departure, theprobability of being replaced, and overall CEO compensation. In a broad cross-section offirms, Chang, Dasgupta, and Hilary (2010) find CEO performance to be positively correlatedwith both labor progress and compensation. If forecast ability is a positive and manager-2

specific attribute, we expect boards of directors to exert a greater effort to retain and attract
more skilled CEOs.

In turn, we expect CEOs who are skilled in making high-quality

forecasts to receive higher compensation.
One challenge involved in our investigations is to find a good proxy for forecastingskill in a large sample of firms. To address this, we examine forecasts of their own earnings
issued by firms and we rely on Bayesian theory which describes the quality of a forecast bymeasuring the informativeness of the signal. Building on recent work (Hilary and Hsu, 2013;
Hilary, Hsu, and Wang, 2014) that provides an empirical application of this intuition, we use
forecast consistency to measure forecast quality to determine whether forecasting is apredictor of firm performance in a large cross-section of firms.
Our empirical results are consistent with our hypotheses. We find that the ability tomake superior forecasts is associated with greater firm performance, and that this ability is
supported by managerial skill, and that firms compensate managers who provide it. First,
firms that forecast their earnings more consistently experience higher profitability (measured
by their ROA), higher productivity (measured using a data envelopment analysis) and higher
valuation (measured by the firm Tobin's Q).
Second, we find that forecast ability is to a large extent a managerial characteristic.
Specifically, past forecasting consistency of a CEO is a strong predictor of future consistency
for the same CEO, even after controlling for firm characteristics. In addition, we find thatCEO fixed effects significantly affect forecast consistency in a sample of executives whochange employers. The CEO-specific factor is the largest contributor to the variance inforecasting quality (contributing close to 50 percent of the explained variance). Contrary tofirm-fixed effects and other explanatory variables, this factor is reliably outside the range ofvalues that could be obtained using random data (Fitza, 2014).

3

Finally, we find that CEOs who provide better forecasts are more likely to findanother CEO position when they depart. This result supports the idea that the labor market
attributes the capacity to issue consistent forecasts to managerial skill rather than to the firm’s
information environment. Consistent with the view that boards of directors value forecastingskill, we find that CEOs with greater forecast quality have a lower probability of dismissal
and earn higher compensation. These results hold when we control for firm performance andfinancial distress.
We contribute to the existing literature by establishing a link between forecast quality
and broad managerial skill. In particular, we show that firms managed by CEOs who canform better expectations of future earnings experience higher valuation and profitability.
Goodman et al. (2014) find that managerial forecasting quality is positively associated with
the quality of investment decisions. Our study extends their finding by establishing a link
between firm overall welfare and managerial forecasting skill. Moreover, we demonstratethat managerial forecasting ability is, to a large extent, a CEO rather than a firm characteristic.
Our study also extends the literature that examines the relation between management
forecast ability and managers’ career development.

Previous studies mainly use statedaccuracy as a signal of managers’ ability to adapt to future changes in the businessenvironment (Zamora 2009; Lee, Matsunaga, and Park 2012).

Our study shows thatconsistency is more important than accuracy in determining managerial turnover andsubsequent progress in the labor market. Our results suggest that consistent managers are
information suppliers and decision makers with greater information processing ability.

2. Measure of Forecast Quality and Sample Selection
2.1. Measure of Forecast Quality4

We want to measure the quality of the private expectations that firms and their
managers form (i.e., their private forecasts) about their own future earnings. However, thisquality is not directly observable as earnings forecasts that are released are affected bydifferent personal and firm incentives. Our approach to circumvent this issue is to follow
Hilary and Hsu (2013) and Hilary et al. (2014) and to invert the quality of the private
forecasts from the (biased) public ones.

As these authors note, accuracy (the absoluteforecast error), bias (the signed error), and consistency (the standard deviation of the signed
error) are three different properties of forecasts. Hirst, Koonce, and Venkataram (2008)
indicate that managers systematically and intentionally issue biased forecasts. These biases
mechanically affect the stated accuracy of the forecast. In other words, accuracy is not only
limited by the firm's or the CEO's ability to make accurate forecasts but also by theircommunication needs. 1

However, if users of these forecasts (e.g., financial market

participants) can ‘undo’ predictable forecast biases, then these biases should not affect users’
predictions. A forecast that is a predictable transformation of the realization (e.g., realization
minus three units) without a random error should be more informative to users than an
unbiased forecast with a small unpredictable error, even if the biased forecast is less accurate.
Potentially biased but consistent estimates should therefore have a greater impact on users’
expectations than estimates with inconsistent random errors. In essence, Hilary and Hsu(2013) and Hilary et al. (2014) argue that if users are Bayesian, forecast usefulness should bebased on the extent to which a forecaster delivers consistent forecast errors, as captured by
the volatility of unexpected errors. We follow their approach to invert the quality of private
forecasts from the public ones.
1

Specifically, managers may want to adjust investor expectations to lower cost of capital, to avoid legal
liability, to signal their managerial ability, or to maximize their payoffs from stock-based compensation.
Managers' ability to achieve any of these goals depends on being able to make their disclosures as persuasive
and credible as possible but, as noted by Hilary et al. (2014), that does not necessarily mean as accurate as
possible. A similar point can be made for other characteristics of public forecasts such as the choice of venue,
time horizon, and the amount of supporting information provided.

5

According to previous studies, corporate transparency in general is important for thewelfare of the firm (e.g., Jovanovic, 1982). Greater transparency and lower information
asymmetry is viewed as economically desirable for firms because it is associated with higher
liquidity (Diamond and Verrecchia, 1991) and a lower cost of capital (Leuz and Verrecchia,
2000). More specifically, it has been showed that managerial forecasts play an importanteconomic role in financial markets. Hirst et al. (2008, p.2) note that, ‘‘they represent one of
the key voluntary disclosure mechanisms by which managers establish or alter market
earnings expectations, preempt litigation concerns, and influence their reputation fortransparent and accurate reporting.’’ Firms therefore have incentives to provide optimal
forecasts, as indicated by Hilary et al. (2014), who show that managers who issue consistent
forecasts have a greater ability to influence investor and analyst, and Hilary and Hsu (2013),
who show a similar result with analyst forecasts. Overall, their evidence suggests that theability to deliver consistent forecasts improves the information environment surrounding thefirm. Both studies show that, on average, consistency provides a more powerful measure of
forecast quality than accuracy.
Following Hilary et al. (2014), we compute FQfirm, our measure of forecast quality, as
follows. First, we calculate a raw measure of forecast quality (RawFQfirm) as the standard
deviation of the management forecast errors over the last two years (with at least six nonmissing observations) for firm i before the current actual earnings announcement, where
forecast errors are calculated as the difference between realized earnings and thecorresponding management forecast. The smaller RawFQfirm implies that the firm is moreconsistent. Second, we rank all firms by industry in quarter t based on the standard deviationof forecast errors scaled by the stock price at the beginning of the quarter. Finally, we
compute a consistency ranking score using the formula: FQfirm = 1 – (rank – 1) ∕ (number offirms within the industry – 1). A higher FQ score thus implies that the firm is more6

consistent. Using a rank measure instead of a raw measure allows us to mitigate the effectsof the common shocks affecting all of the managers within the same industry at a given point
in time. However, using this coarser partition based on the ranking score (instead of thecontinuous variable) leads to a loss of information that may reduce the power of our tests.

2.2. Sample Selection
Our sample comes from the management forecasts of quarterly earnings per share(EPS) in the FirstCall database over the 1993 to 2010 period. 2 We obtain executive
information from the ExecuComp database. To calculate our control variables, we intersect
our sample with accounting data from the Compustat quarterly files and stock return datafrom the CRSP daily files. Following recent research literature (e.g., Hilary and Hsu, 2011;
Hilary et al., 2014), we focus on the last forecast made by a given manager before the end of
the fiscal period, and require that each firm in our sample issue forecasts for at least six
quarters over the previous two years. This sampling procedure results in 7,321 firm-quarter
observations from 525 firms spanning 34 industries.

3. Empirical Analyses
We conduct three sets of analyses. First, we consider if the ability to deliver highquality forecasts benefits firm performance. Next, we investigate whether forecast ability is amanagerial or organizational characteristic.

Finally, we supplement our analysis byexamining whether managers who issue high quality earnings forecasts are rewarded for suchskill, or if the benefits mainly accrue to the organization employing them.
2

The database starts coverage in 1993 and stops in early 2011. Chuk, Matsumoto, and Miller (2013) document
the presence of several problems with the First Call CIG database, but these are mitigated by cross-sectional
characteristic of our sample. We require at least six management forecasts for each CEO – it is unlikely that
CIG omits a given CEO who issues a series of forecasts (Christensen, Merkley, Tucker and Venkataraman 2011
makes a similar point).

7

3.1. Forecast quality and firm performance
We hypothesize that firms that demonstrate greater forecast quality in a broad range
of industries should benefit from this ability. First, as discussed earlier, informative forecasts
are directly valued by the financial markets. Specifically, firms that issue management
forecasts experience higher stock prices (Coller and Yohn, 1997) and access capital markets
more often (Frankel, McNichols, and Wilson, 1995). In turn, cheaper and more flexible
capital acquisition should lead to more profitable investments. Second, firms that have more
informative forecasts should have a greater information processing ability and a greater
capacity to understand the impact of their decisions on their welfare, which should make forbetter quality corporate decisions. For example, capital budgeting and deployment should be
more efficient (Goodman et al., 2014). Better projects should be selected, while negative netpresent value (NPV) investments should be avoided.

Production schedules should be

optimized, leading to less inefficiency in the utilization of capital. As a consequence, thevalue of the firm, given the assets in place, should be higher. We expect that firms which
understand their future payoffs well enough to deliver consistent forecasts will be more
efficiently run and thus deliver better performance. This leads to our first hypothesis:

H1. Firms that release higher quality forecasts experience higher performance thanfirms releasing lower quality forecasts.

To test this hypothesis, we measure firm performance from three different
perspectives. First, we examine if firms with more consistent forecasts have higher operating
performance, measured by return on assets (ROA). In addition to measuring profitability, we
also examine the effect on productivity using data envelopment analysis (DEA). The DEA8

based firm efficiency score provides an ordinal ranking of efficiency relative to the Paretoefficient frontier—the best performance that a firm can practically achieve. Finally, we
explore the association between management forecast quality and valuation. We expect firmsthat are able to release higher quality forecasts to successfully identify and capitalize on
investment opportunities, and thus our forecast ability measure to be positively associatedwith profitability, productivity, and valuation.
We estimate the following three regressions to test this hypothesis:
AROAi,t=a0 + a1 FQfirmi,t + ∑ akCONTROLSi,t + εi,t(1)

FIRMEFFi,t=b0 + b1 FQfirmi,t + ∑ bkCONTROLSi,t + εi,t(2)

ATOBINQi,t=c0 + c1 FQfirmi,t + ∑ ckCONTROLSi,t + εi,t,(3)where AROA is return on assets (adjusted for industry median), FIRMEFF is within-industry
ranking of firm efficiency score based on DEA (Demerjian et al., 2012), and ATOBINQ is themarket-to-book ratio of total assets (adjusted for industry median). 3 Our results are notaffected if we use an industry ranking approach for the three dependent variables similar to
one used for FQ (untabulated results). If the ability to deliver high quality forecasts is related
to firm performance, we expect a1, b1, and c1 to be positive in Models (1) – (3).
The vector CONTROLS contains those variables identified in previous studies that are
related to information environment, management forecast characteristics, and firm
performance. Following prior literature (e.g., Bourgeois, 1985; Chaganti and Damanpour,
1991; Gentry and Shen, 2013; Nielsen and Nielsen, 2013), we control for firm size (SIZE),
leverage (LEV), analyst following (COVER), earnings volatility (EARNVOL), stock return

3

Following Demerjian et al. (2012), we conduct a DEA and calculate firm efficiency score by excluding
financial services firms (banks, insurance, real estate, and finance companies) because of the uniqueness of theirasset structure and earnings generating process as well as utility companies because of regulation of the output
price. As a result, the sample size is reduced to 6,380 when we use FIRMEFF as the dependent variable inModel (2).

9

(RET), forecast horizon (HOR), and institutional ownership (INSTO). We also include acontrol for growth opportunities (BTM) when AROA or FIRMEFF is the dependent variable.
In addition, we include ROA (return on assets) in Models (2) and (3) (our results are notaffected if this variable is excluded).

We calculate AROA, FIRMEFF, ATOBINQ, andCONTROLS as moving averaged values over the last two years but our conclusions are notaffected if we consider the value at the end of the last quarter. These variables are defined ingreater detail in the Appendix. Except for the ranking variables, all of our variables are
winsorized at one percent in either tail of the distribution to remove the effects of outliers andextremely misrecorded data. We adjust the standard errors for heteroskedasticity and theclustering observations by firm and quarter (Cameron, Gelbach, and Miller, 2011).

[Insert Table 1 here]

We present the descriptive statistics in Table 1. Most means and medians are close toeach other, suggesting that skewness is not an issue in our setting. The one exception is
ATOBINQ. Our results are not affected when we replicate our tests using the log of TOBINQ.

[Insert Table 2 here]

We present the univariate correlations in Table 2. Consistent with hypothesis H1,
FQfirm is positively and significantly (at less than the one percent level) associated with ourthree measures of firm performance (AROA, FIRMEFF, and ATOBINQ). The correlation
between the different control variables is reasonably low, suggesting that multicollinearity is
not an issue in our setting. The exception is the correlation between COVER and SIZE that ispredictably high (0.6).

10[Insert Table 3 here]
We present the results from the estimation of Models (1) – (3) in Table 3. They
indicate that firms releasing higher quality forecasts experience higher performance,
supporting H1. Specifically, FQfirm is positively associated with AROA, FIRMEFF andATOBINQ, with z-statistics of 2.42, 2.20, and 6.83, respectively. The economic effect of
FQfirm on AROA is approximately 25 percent of the effect of LEV, one of the most significant
drivers of AROA. Similarly, the economic effect of FQ firm on FIRMEFF is approximately 80
percent of the effect of LEV while the effect on ATOBINQ is five percent greater than theeffect of LEV. 4 Untabulated results indicate that FQfirm is positively associated with the profit
margin (PM) of the firm (z-statistic of 2.40). We also re-estimate Models (1) – (3) using
RawFQfirm instead of FQfirm.

Untabulated results indicate that our conclusions are notaffected (z-statistics of -2.10, -2.37, and -2.05, respectively).
To ensure that our results are not driven by earnings management, we regress ameasure of accrual management (ACCRMGT, Brown and Pinello, 2007) and a measure of
real earnings management (REALMGT, Roychowdhury, 2006) on FQfirm and our control
variables.

See the Appendix for a detailed description of the variables.

Untabulated

coefficients on FQfirm are insignificantly different from zero in both regressions, which do notsupport a manipulation explanation.

4

Since the mean and median values of ROA are close to zero, we cannot meaningfully estimate the economic
effect by comparing the effect of FQ to these statistics. We can, however, compare the effect of FQ to the effectof other significant control variables. The effect of FQ on AROA is 25 percent of the effect of LEV (we multiply
the coefficient on FQ by the standard deviations of the respective variables divided by the product of thecoefficient on LEV and its standard deviation). Specifically, the economic effect of FQ on AROA is based on thefollowing ratio: (0.003×0.345) / (0.028×0.149) = 0.248. The comparable ratios for FIRMEFF and ATOBINQ
are (0.039×0.345)/(0.113×0.149) = 0.799; and (0.497×0.345)/(1.097×0.149) = 1.049, respectively.

113.2. Is forecast quality a managerial characteristic?
We next investigate whether forecast ability is a managerial characteristic.

As

discussed in the introduction, we expect there to be a limit to what a firm can handle as anorganization. Prahalad and Hamel (1990) note (p. 83) that “few companies are likely to build
world leadership in more than five or six fundamental competencies.” Bruch and Menges(2010) argue that organizational overstretch is costly. They argue that many organizations
increase the number and speed of their activities, raise performance goals, shorten innovation
cycles, and introduce new management technologies or organizational systems; while thisfrenzy of activity may initially be successful, it quickly generates organizational overload,
dilutes the company’s focus, and severely weakens the organization. As a consequence, we
expect companies to rely on individual managers to acquire important but non-corecompetencies such as the issuance of high quality management forecasts. Results in Table 3
indicate that superior forecasting ability generates superior firm performance, but they do notdisentangle the individual effects of organizational competency and managerial forecastingskill. Our theory posits that firms will rely on managerial skill to obtain this ability.

H2. Forecasting ability is a managerial characteristic.

To test this hypothesis, we focus on the role played by CEOs. Anecdotal evidence
suggests that forecasting ability is a managerial skill, and more specifically a CEO skill. For
example, after General Electric (GE) missed an earnings forecast, Jack Welch (former
chairman and CEO) stated in an April 16, 2008 interview on CNBC that ‘‘Jeff [Immelt, GE’s
current CEO; emphasis added] has a credibility issue.’’ Similarly, Daniel Vasella, CEO of
Novartis, indicated "The practice by which CEOs [emphasis added] offer guidance about
their expected quarterly earnings performance […] is an old one.” James Hesket, writing his

12

Harvard Business School blog, asked “Should CEOs [emphasis added] of Public Companies
Offer Earnings Guidance?” Brochet, Faurel, and McVay (2011) indicate that the decision tofollow a policy of issuing earnings guidance or not is largely a CEO decision and that CFOs
have little effect on this decision. 5
To investigate H2, we first consider a sample of CEOs who leave a firm to take up an
executive position at another firm.

We define forecast quality for the CEO, FQceo(RawFQceo), as the analog of FQfirm (RawFQfirm) based on the executive tenure. The key
difference between FQceo (RawFQceo) and FQfirm (RawFQfirm) is that we require that the CEO
does not change during the time these forecasts (at least six over the previous two years) weremade when we calculate FQceo (RawFQceo). We then regress our measure of forecast quality
on a vector of time-varying explanatory variables (SIZE, BTM, LEV, COVER, EARNVOL,
ROA, RET, HOR, and INSTO), industry and year fixed effects, as well as on CEO and firm
fixed effects. We use the method proposed by Abowd, Kramarz, and Margolis (1999). 6 Oursample includes 43 different "connected" CEOs and 877 observations. However, we notethat the discrete and bounded nature of FQ may create additional econometric issues in thiscase. Thus, we focus on RawFQ for this test. The untabulated F-test indicates that the CEO

5

Bamber, Jiang, and Wang (2010) establish the presence of CEO fixed effects on forecast accuracy and biases.
Our approach differs from theirs in several aspects. First, and most important, our goal is to show thatmanagerial effects are larger than firm effects, while theirs is merely to establish the presence of managerial
fixed effects. Second, we focus on forecast consistency rather than on forecast accuracy or bias. Hilary and Hsu(2013) and Hilary et al. (2014) suggest that consistency is a better measure than accuracy and biases to capture
forecast quality and informativeness. This is not an issue for the Bamber et al. (2010) study as it attempts toestablish the presence of managerial styles rather than the managerial effect on forecast quality. However, theirtests yield a different interpretation from ours. Lastly, Fitza (2014) suggests that the CEO effect can be
exaggerated by sheer luck. The benefit of that study was not available to Bamber et al. (2010), but we address
this issue in Table 4.
6

Abowd et al. (1999) (AKM) lever the potentially small number of mover observations (i.e., managers who
move across companies) to deduce information about non-movers who work in firms that have employed atleast one mover (i.e., the “connected” managers). Using the AKM method allows us to separate firm andmanager fixed effects not only for movers but also for some non-movers, which increases the sample size andpower (e.g., Bertrand and Schoar, 2003). This method has been used in previous studies (e.g., Graham, Li, andQiu, 2012).

13

fixed effects are jointly significant with an F-value of 9.36. For completeness, we repeat theprocedure using FQceo and find the F-value to be 14.62 (we do not include industry and year
fixed effects when we use FQceo as this variable is estimated at the industry-year level).
Next, we re-estimate models (1) – (3) using FQceo instead of FQfirm.

Results(untabulated) indicate that we reach similar conclusions. However, both the economic andstatistical significance increase when we use FQceo instead of FQfirm (the point estimates of
the coefficient increase by approximately 30 percent on average and the z-statistics by 6
percent on average). These results are consistent with the notion that forecasting ability is amanagerial skill rather than a firm characteristic.

[Insert Table 4 here]

Finally, we perform a variance decomposition analysis (e.g., Abowd et al., 1999) in
Table 4. We again note the potential econometric issues created by the discrete and boundednature of FQceo and focus on RawFQceo for this decomposition. However, as discussed below,
we obtain similar estimates if we use FQceo instead of RawFQceo in this test. Following Fitza(2014), we also replace RawFQceo as dependent variable with a randomly created variable
based on 100 draws from a normal distribution with the same mean and standard deviation as
RawFQceo. This procedure allows us to estimate the range of explanatory power we could
obtain by sheer luck. Results in Table 4 indicate that that CEO fixed effects is the main
explanatory variable to explain variations in forecast quality. The managerial fixed effects
contribute 49 percent of the explained variance (47 percent of the overall variance). In
contrast, the contribution of firm-fixed effects is 50 percent smaller (33 percent of the
explained variance) while other explanatory variables (including the year and industry fixed
effects) contribute only 18 percent to the explained variance. When we compare the estimate

14

to the simulated data, we observe that only the effect from the managerial fixed effects ismaterially outside the 90 percent confidence interval (47 percent of total variance versus anupper bound of 29 percent for the confidence interval). In contrast, the value obtained for thefirm fixed effects is within the 90 percent confidence interval (31 percent of total varianceversus an upper bound of 46 percent).

Thus we cannot reject the hypothesis that thecontribution of the firm fixed effects is obtained by sheer luck. For completeness, we also
consider a similar analysis using FQceo and obtain similar estimates. 7 These results support
the notion that CEOs matter more for forecast quality than firm-specific capital.
Next, H2 suggests that, since forecasting is a managerial skill, its quality should beconsistent (i.e., not a by-product of luck) and independent of firm characteristics. To further
investigate these claims, we regress the current forecast quality (FQfirm or FQceo) on the past
forecast quality (LagFQfirm or LagFQceo) for the same firm or CEO-firm. We consider only
non-overlapping observations of eight quarters and use the value lagged by one period (i.e.,eight quarters) to define past consistency. We also control for the current value of our usual
variables (SIZE, BTM, LEV, COVER, EARNVOL, ROA, RET, HOR, and INSTO) as well as
industry and year fixed effects. 8 Specifically, we next estimate the following two models:
FQfirmi,t=

a0 + a1 LagFQfirmi,t + ∑ ak CONTROLSi,t + εi,t(4a)FQceoi,t=

a0 + a1 LagFQceoi,t + ∑ ak CONTROLSi,t + εi,t(4b)[Insert Table 5 here]

7

Only the effect from the managerial fixed effects is materially outside the 90 percent confidence interval (44percent versus an upper bound of 32 percent for the confidence interval). The value for the firm fixed effects isonly slightly higher than the 90 percent confidence interval (38 percent versus an upper bound of 37 percent)and within the 95 percent confidence interval.
8

Controlling for the additional variables used by Gong, Li, and Wang (2011), including ZSCORE, HHI, XFINQ,NETSALES, LITIG, LOSS, and ACCRUAL (all of which are defined in the Appendix), does not affect ourconclusions.

15

Results indicate that past consistency is a strong predictor of current consistency, evenafter controlling for firm characteristics. This is true in both columns of Table 5, but theeffect is stronger when we focus on the definition based on CEO tenure in column (2) rather
than on the definition based on the firm-level. In column (1), when FQfirm is the dependent
variable, the coefficient on past values of FQfirm is 0.288 with a z-statistic of 10.40. In
Column (2), when FQceo is the dependent variable, the coefficient on past values of FQceo
increases to 0.333 with a z-statistic of 10.79. 9 These results indicate that forecast quality is
persistent. The fact that the results hold after controlling for firm characteristics is consistent
with the notion that forecast quality is an individual executive skill. For completeness, wedefine RawFQceo as the analog of RawFQfirm and regress RawFQceo on LagRawFQceo (and ourstandard control variables).

Results (untabulated) are not affected. Taken together, ourresults from various tests discussed above support hypothesis H2.
Naturally, CEOs do not complete the process of preparing forecasts alone.
Management forecasts are the output of a lot of work within firms (e.g., data from different
departments are aggregated and projected).

Forecasting skill comprises the ability toorganize the firm in such way that the different functions provide high quality informationand the capacity to synthesize the information collected by the different functions. Our
results suggest that it is managerial skill rather than firm functional quality that has a firstorder effect on forecast quality, suggesting that the first element is more significant than thesecond. Consistent with this view, Simon (1973, 270) argues that “…the scarce resource is
not information; it is processing capacity to attend to information.”

9This specification is the only one in which the VIFs are above the conventional level. In untabulated
specifications, we drop the industry and year fixed effects. Doing this reduces the average VIF to 1.51 and thehighest one to 2.10). Our conclusions are not affected. The point estimates of the coefficient slightly increase
from 0.288 to 0.305 for FQfirm and from 0.333 to 0.363 for FQceo. The z-statistics change from 10.40 to 10.16 for
FQfirm, and from 10.79 to 11.99 for FQceo.

16

3.3. Forecasting ability and CEO welfare
Finally, we ask whether CEOs who issue high-quality earnings forecasts are rewarded
for such skill. If forecasting skills are rare and valuable and are provided by managers, we
expect this to be the case (Geletkanycz, Boyd, and Finkelstein, 2001). In contrast, if theability to provide high-quality forecasts is part of the organizational capital, managers
running the firm may not be able to extract the economic surplus for themselves. We
consider three aspects of CEO welfare; the probability of finding another CEO appointment
in case of departure, the probability of being replaced, and overall CEO compensation. In a
broad cross-section of firms, Chang et al. (2010) find CEO performance to be positively
correlated with both labor progress and compensation. If forecast ability is a positive andmanager-specific attribute, we expect boards of directors to exert a greater effort to retain andattract more skilled CEOs. In turn, we expect CEOs who are skilled in making high-quality
forecasts to receive higher compensation. This leads to the following set of hypotheses:

H3a. The probability that a CEO who leaves one firm to work as a CEO for another ispositively related to management forecast consistency.
H3b. The probability of CEO turnover is negatively related to management forecast
consistency.
H3c. CEO compensation is positively related to management forecast consistency.
To test H3a, we compare the CEOs who left one firm to work as a CEO for another(covered by the ExecuComp database) with the same number of CEOs who also left a firm
but did not find similar employment. We then estimate the following model for manager i inquarter t:
NEWJOBceoi,t =a0 + a1 FQceoi,t + ∑ ak CONTROLSi,t + εi,t,

(5)

17

where NEWJOBceo is a binary variable equal to one if the CEO found a new appointment as
CEO of a listed firm after leaving the firm, and zero if she did not. 10 We focus on FQ insteadof RawFQ because prior research (e.g., Antle and Smith, 1986) suggests that relative performance
evaluation (RPE) is a key element of CEO selection and evaluation. We control for size (SIZE),

growth opportunities (BTM), leverage (LEV), analyst following (COVER), earnings volatility(EARNVOL), forecast horizon (HOR), institutional ownership (INSTO), profitability (ROA),
and stock performance (RET). We calculate CONTROLS as moving averages over the last
two years but our conclusions are not affected if we consider the value at the end of theprevious quarter.

Our results are not affected if we use a propensity score matchingprocedure to obtain our control sample. The score is based on the control variables defined
above (CONTROLS) (naturally excluding FQceo). H3a predicts the coefficient on FQceo, a1,
to be positive.
To test H3b, we estimate the following model for manager i in quarter t:Prob(TURNOVERceoi,t+1=1) = b0 + b1FQceoi,t + ∑ bk CONTROLSi,t + εi,t,(6)where TURNOVERceoi,t+1 is an indicator variable that equals one if CEO i is replaced inquarter t+1, otherwise zero. 11 Our results are not affected if we extend the measurement
window of the turnover to four quarters in the future (i.e. from quarter t+1 to t+4). In Model(6), we control for our standard set of variables. H3b predicts the coefficient on FQceo, b1 to
be negative.
To test Hypothesis 3c, we estimate the following model for manager i in quarter t:

10

We calculate the value of FQceo in the final years before the CEO leaves her position, i.e., there is only one
observation per CEO-firm. Our results do not change when we set NEWJOB to one if the new position is either
as CEO or a Chairperson of a listed firm.
11

Our results are not affected when we identify involuntary turnover events (instead of turnover for any reason).

18

ACOMPceoi,t = c0 + c1 FQceoi,t + ∑ ck CONTROLSi,t + εi,t,(7)where ACOMPceo is the natural logarithm of the overall compensation of the CEO (adjusted
for the industry median). 12 We calculate ACOMPceo as moving averaged values over the last
two years but our conclusions are not affected if we consider the value at the end of the lastquarter.

Our conclusions do not change if we use an industry ranking approach for

ACOMPceo similar to the one used for FQceo. The variables FQceo are as previously defined.
CONTROLS are our standard set of variables. H3c predicts the coefficient on FQceo, c1, to bepositive.
Before considering the regression results, we split the sample based on whether
NEWJOBceo is equal to 1 or to 0. Untabulated results indicate that FQceo is 25 percent larger
in the former than in the latter (t-statistic of 2.14). We then repeat the procedure with CFOs
but fail to find support for the hypothesis that CFOs who are able to deliver high quality
forecasts have better career development than CFOs who are not. These preliminary results
are consistent with the idea that the labor market attributes high forecast consistency to CEO
skill rather than to the managerial environment or to CFO skill. By separating the CEO-firmfrom the CFO-firm observations we gain further confidence that the effect is driven by theCEOs rather than by the characteristics of the firms they manage.

[Insert Table 6 here]

We report the results of estimating Model (5) in Column 1 of Table 6. Consistent
with H3a, the coefficient associated with FQceo is positive (3.949) and significant at the one
percent level. The marginal effect is approximately four percent.

Column 2 of Table 6

12

Executive compensation (ACOMPceo) is an annual item. Therefore, we only include the fourth quarter of eachfiscal year to estimate Model (6). This reduces our sample to 1,558 CEO-years. Our results are not affected if
we include all quarters.

19

reports the results of testing Model (6). The coefficient on FQceo is negative (-0.398) andsignificant at less than the five percent level (z-statistic of -2.31). The untabulated marginal
effect of FQceo on quarterly turnover is approximately two percent. These results support
H3b. Column 3 of Table 5 reports the results of testing Model (7). Consistent with H3c, thecoefficient on FQceo is 0.222 and is significant at less than five percent level (z-statistic of2.39). The economic effect of FQceo is such that it represents approximately 14 percent, 50
percent, and 45 percent of the effects of SIZE, RET, and INSTO respectively, probably themost important determinants of compensation.

Results (untabulated) indicate that ourconclusions are not affected if we define FQceo using five or seven quarters, or if we omit all
the control variables. Untabulated results show that our conclusions in Table 6 are notaffected if we control for financial stress by including LOSS (an indicator variable that equals
one if the firm is reporting a loss, zero otherwise) or if we use an extended list of controls(DUAL, HHI, AGE, EXPERIENCE and MBAF). 13
Overall, our results suggest that employers reward managers for their forecasting skill.
We note that this effect is incremental to standard drivers of compensation. To obtain further
confidence that this result is not driven by the superior performance of firms associated withhigh-quality forecasts, we further control for profitability, productivity and valuation by
including PM (profit margin, ROA is already included), FIRMEFF, and Tobin’s Q (we drop
BTM in this case). Results (untabulated) indicate that FQceo remains significant (z-statistics
of 2.60, -2.08, and 1.94, respectively), consistent with the notion that forecasting skills are
directly rewarded. More generally, they are consistent with the notion that firms acquire
forecast competences by hiring and compensating managers who can directly provide them.

13

The additional variables are defined in the Appendix. Our conclusions are not affected if we control for
current total compensation (COMPceo) and include a fourth quarter indicator (Q4) to control for year-end effects
in Model (6).

20

4. Conclusions
We examine whether the ability to deliver high quality forecasts reflects greater
managerial skill. We expect that managers are more likely to provide consistent forecasts
when they can better anticipate future events in the business environment and their impact onearnings.

Increased information processing ability as evidenced by greater forecast

consistency should be associated with better corporate decisions and financial disclosures.
Consistent with this view, we find that firms with CEOs who deliver more consistent
forecasts experience higher profitability and higher valuation.
Next, our results suggest that forecast consistency is, to a large extent, a CEO rather
than a firm characteristic. We find that past forecast consistency is a strong predictor of
future forecast consistency, even after controlling for firm characteristics. In addition, we
find that CEO fixed effects significantly affect forecast consistency in a sample of executives
who change employers. The CEO-specific factor is the largest contributor to the variance inforecasting quality (contributing close to 50 percent of the explained variance). Contrary tofirm-fixed effects and other explanatory variables, this factor is reliably outside the range ofvalues that could be obtained using random data (Fitza, 2014). We do not find a comparable
effect for CFOs, however, suggesting that the consistency of the forecasts is attributed toCEOs rather than to CFOs.
Lastly, we investigate whether managers are rewarded for providing consistent
forecasts. We expect that information processing ability should be an important attribute of amanager’s quality that is valued by a firm’s board of directors. Our empirical results are
consistent with this prediction and indicate that CEOs who provide better forecasts are more
likely to find another CEO position when they depart. This result supports the idea that thelabor market attributes the capacity to issue consistent forecasts to managerial skill rather
than to the firm’s information environment. Consistent with the view that boards of directors

21

value forecasting skill, we find that CEOs with greater forecast quality have a lower
probability of dismissal and earn higher compensation. These results hold when we control
for firm performance and financial distress.

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25

Appendix: Variable Definitions
Variables

Definitions

FQfirmMeasure of consistency for a firm based on the industry ranking. We
first calculate the standard deviation of the management forecast errors
over the last two years for firm i in quarter t. We then rank all of thefirms by industry (4-digit SIC codes) in quarter t based on the standard
deviation of forecast errors scaled by stock price two days before themanagement forecast. Finally, we obtain a consistency ranking scoreusing the formula (Hilary, Hsu, and Wang, 2014): 1 – (rank – 1) ∕(number of CEOs within the industry – 1). Management forecast error
is the difference between the management forecast and the realized
earnings.

FQCEO

Measure of consistency for a CEO based on the industry ranking. We
first calculate the standard deviation of the management forecast errors
over the last two years for CEO i in quarter t. We then rank all of theCEOs by industry (4-digit SIC codes) in quarter t based on the standard
deviation of forecast errors scaled by stock price two days before themanagement forecast. Finally, we obtain a consistency ranking scoreusing the formula ((Hilary et al., 2014): 1 – (rank – 1) ∕ (number ofCEOs within the industry – 1).

ACCRMGT

Abnormal accruals, measured as the residual from a specification that
regresses total accruals on assets, change in sales minus change inaccounts receivables, and plant, property, and equipment (Brown andPinello, 2007).

ACCRUAL

Total accruals, measured as the difference between income before
extraordinary items and operating cash flows, deflated by average total
assets.

MBAF

The frequency of meeting or beating consensus analyst forecasts overthe previous two years before the current management forecast date,divided by the total number of forecasts issued by CEO i over the twoyear period.

AGE

The current age of a CEO.

ACOMPceo

The natural logarithm of total annual compensation for a CEO, adjusted
for the industry-median (based on Fama French 48 industries).

26

AROA

Return on a firm’s assets adjusted for the industry-median (based on
Fama French 48 industries) over the last three months.

ATOBINQ

The market-to-book ratio of total assets adjusted for the industry-median(based on Fama French 48 industries) over the last three months.

BTM

Book-to-market ratio, measured as book value of equity divided bymarket value of equity.

COMPceo

The natural logarithm of the total annual compensation for a CEO.

COVER

The natural logarithm of one plus the number of analysts covering thefirm in a quarter. If the number of analysts covering the firm in a quarteris missing, we set it as zero.

DUAL

Indicator variable that equals one if a CEO is also holding the positionof Chairman within a firm, and zero otherwise.

EARNVOL

The standard deviation of the quarterly return on assets over thepreceding eight quarters.

EXPERIENCE

Forecast experience of a CEO, measured as the natural logarithm of one
plus the number of quarters in which a CEO has issued forecasts before
the current management forecast.

HHI

Industry concentration, measured by the Herfindahl-Hirschman index
and calculated as the sum of the squares of the market shares of thefirms’ sales within each 4-digit SIC industry.

HOR

The natural logarithm of one plus the number of days between theissuance of the forecast and the fiscal period end.

LEV

Leverage ratio, measured as total liability over book value of equity.

LITIG

Indicator variable that equals one for litigious industries, including
Biotechnology (SIC 2833 to 2836), Computer Hardware (SIC 3570 to3577), Electronics (SIC 3600 to 3674), Retailing (SIC 5200 to 5961)
and Computer Software (SIC 7371 to 7379), and zero otherwise.

LOSS

Indicator variable that equals one if the firm is reporting a loss, zerootherwise.

MFE

Management forecast errors, measured as the management forecast ofquarter t earnings per share minus quarter t realized earnings per sharescaled by the stock price at the beginning of quarter t.

NETSALES

Net sales (in millions of shares) from open market transactions by CEOs
during the 30-day period following management forecasts.

27

NEWJOBceo

Indicator variable equal to one if the CEO found a new CEO
appointment after leaving the firm, zero if she did not.

PM

Profit margin, measured as income before extraordinary items divided
by sales.

Q4

Indicator variable that equals one for the 4th quarter, and zero otherwise.

ROA

Return on a firm’s assets over the last three months, measured as income
before extraordinary items divided by total assets.

REALMGT

A combination of the abnormal levels of cash flow from operations(ABOCF), discretionary expenses (ABDE), and production costs (ABPC)(-1×(ABOCF+ABDE)+ABPC) (Roychowdhury, 2006).

RET

The raw stock return of a firm over the last three months.

SIZE

Natural logarithm of market value of equity.

RawFQceo

The standard deviation of the management forecast errors over the last
two years for a CEO, scaled by the stock price.

RawFQfirm

The standard deviation of the management forecast errors over the last
two years, scaled by the stock price.

TURNOVERceo

Indicator variable that equals one if CEO i is replaced in quarter t+1,
and zero otherwise.

XFINQ

External financing, measured as the sum of net equity financing and net
debt financing scaled by total assets.

ZSCORE

Altman’s Z-score.

28

Table 1
Summary statistics

variable
AROA
FIRMEFFATOBINQ
FQfirm
SIZE
BTM
LEV
COVER
EARNVOL
RET
HOR
INSTO

N
7,321
6,380
7,321
7,321
7,321
7,321
7,321
7,321
7,321
7,321
7,321
7,321

MEAN
0.003
0.765
0.473
0.551
7.744
0.427
0.178
1.814
0.015
0.034
3.902
0.761

STD
0.022
0.213
1.126
0.345
1.480
0.242
0.149
0.602
0.019
0.067
0.374
0.171

MEDIAN
0.002
0.800
0.142
0.571
7.544
0.375
0.172
1.816
0.008
0.031
4.020
0.793

All of the variables are defined in the Appendix.

29

Table 2
Correlations

FIRMEFFATOBINQ
FQfirm
SIZE
BTM
LEV
COVER
EARNVOL
RET
HOR
INSTO

AROA
0.260
0.476
0.233
0.167
-0.408-0.1800.076-0.0940.274-0.019-0.015FIRMEFFATOBINQ

FQfirm

SIZE

BTM

LEV

COVER

EARNVOL

RET

HOR

0.149
0.235
0.521
-0.2160.084
0.271-0.0920.080-0.016
0.036

0.305
0.217
-0.651-0.303
0.154
0.044
0.115
0.038
-0.0930.318-0.336-0.0680.137-0.1680.089-0.0160.040-0.3920.103
0.595-0.139-0.096-0.079-0.0440.029-0.1920.127-0.136-0.0260.096-0.040-0.077-0.016-0.100-0.0500.062-0.125-0.2470.135-0.0400.021-0.032-0.005-0.1350.053

All of the variables are defined in the Appendix. The Pearson correlations in bold are significant at the 5% level or less.

30

Table 3
Managerial forecast ability and firm performance

FQfirm
SIZE
BTM
LEV
COVER
EARNVOL
RET
HOR
INSTO

(1)
AROA
0.003**(2.42)
0.000(0.41)-0.034***(-13.08)-0.028***(-7.07)-0.000(-0.42)-0.252***(-6.69)0.025***(3.96)-0.003***(-2.80)0.005(1.39)ROA
N
adj. R2

7,321
0.446

(2)
FIRMEFF
0.039**(2.20)
0.077***(11.38)0.097**(2.32)
0.113**(2.33)-0.009(-0.61)0.577(1.41)
0.343***(4.26)
0.024(1.26)
0.074(1.58)
1.884***(4.01)
6,380
0.311

(3)
ATOBINQ
0.497***(6.83)
0.028(0.78)-1.097***(-4.54)0.152*(1.91)
16.197***(8.49)
0.204(0.54)
0.284***(4.12)-0.541**(-2.08)38.768***(12.05)
7,321
0.537

This table reports of the relationship between managerial forecast ability and firm performance. The estimation
results are based on the following models:

AROAi,t=a0 + a1FQfirmi,t + ∑ akCONTROLSi,t + εi,tFIRMEFFi,t=b0 + b1FQfirmi,t + ∑ bkCONTROLSi,t + εi,t
ATOBINQi,t=c0 + c1FQfirmi,t +∑ ckCONTROLSi,t + εi,t

(1)
(2)
(3)

All of the variables are defined in the Appendix. The constant terms are included, but not tabulated. z-statistics,
which are reported in parentheses, are corrected for heteroskedasticity and are adjusted for clustering of observations
by firm and quarter. Coefficients that are significant at the 10, 5, and 1% levels are marked with *, ** and ***,
respectively.

31

Table 4
Variance analysis
Components
Managerial fixed effects(1)
RawFQceo
0.47(0.01-0.29)

Firm fixed effects

0.31(0.01-0.46)

Explanatory variables

0.17(0.09-0.65)

Unexplained variance0.05(0.21-0.65)

This table reports results of the variances analysis on RawFQceo using the connectedness sample (i.e., the sample that
includes all the CEOs who have worked in the firms that have hired at least one mover) (Abowd, Kramarz, andMargolis, 1999). Column 1 presents the covariance between RawFQceo and each of the components, normalized by
the variance of RawFQceo, respectively. The normalized covariance (excluding “unexplained variance”) can beinterpreted as decomposition of model R2. Explanatory variables include year and industry indicator variables. The
results in parentheses indicate the 90% interval of the variances based on randomly created numbers (Fitza, 2014).

32

Table 5
The Persistence of Management Forecast Quality(1)
FQfirmLagFQfirm
0.288***

(2)
FQceo

(10.40)0.333***(10.79)

ceo

LagFQ

SIZE
BTM
LEV
COVER
EARNVOL
ROA
RET
HOR
INSTO
N
adj. R2

0.056***(6.45)-0.243***(-4.68)-0.136**(-1.96)-0.047***(-2.60)-1.567***(-3.48)0.302(0.44)
0.848***(6.61)-0.011(-0.45)0.173***(2.70)

0.040***(4.14)-0.232***(-4.33)-0.012(-0.18)-0.025(-1.21)-1.997***(-3.63)0.310(0.42)
0.901***(6.87)-0.012(-0.46)0.187***(2.77)

3,987

2,986

0.309

0.322

This table reports persistence of management forecast quality. The estimation results are based on the following
regression models:
FQfirmi,t=a0 + a1LagFQfirmi,t +∑ akCONTROLSi,t + εi,tFQceoi,t=a0 + a1LagFQceoi,t +∑ akCONTROLSi,t + εi,t

(4a)
(4b)

All of the variables are defined in the Appendix. The constant terms, industry and year fixed effects are included, but
not tabulated. All of the continuous variables are winsorized at the 1st and 99th percentiles. Column (1) reports theestimation results of Model (4a). Column (2) reports the estimation results of Model (4b). z-statistics, which arereported in parentheses, are corrected for heteroskedasticity and are adjusted for clustering of observations by CEO
and quarter. Coefficients that are significant at the 10, 5, and 1% levels are marked with *, ** and ***, respectively.

33

Table 6
Managerial forecast ability and welfare

FQceo
SIZE
BTM
LEV
COVER
EARNVOL
ROA
RET
HOR
INSTO
N
adj. R2 (pseudo R2)(1)
NEWJOBceo
3.949***(2.68)
0.250(0.31)
1.689(1.63)-16.114**(-2.08)-2.228(-1.11)-2.562(-0.24)-1.687(-0.13)6.185**(2.07)-1.457***(-5.71)
0.236(0.06)
628
0.436

(2)
TURNOVERceo-0.398**(-2.31)0.205*(1.93)-0.199(-0.46)0.424(0.52)-0.290(-1.28)4.229(0.95)-20.505**(-2.08)-1.480(-1.22)0.102(0.32)
0.263(0.33)
5,415
0.050

(3)
ACOMPceo
0.222**(2.39)
0.377***(6.29)
0.055(0.24)
0.593(1.62)-0.094(-0.95)0.285(0.16)-0.515(-0.15)2.223***(2.69)-0.259*(-1.69)1.029***(3.54)
1,558
0.204This table reports the effect of managerial forecast ability on CEO welfare. The estimation results are based on thefollowing regression models:Prob(NEWJOBceoi,t=1)=c0 + c1FQceo,t +∑ckCONTROLSi,t + εi,tProb(TURNOVERceoi,t+1=1)=b0 + b1FQceo,t +∑ akCONTROLSi,t + εi,t
ACOMPceoi,t=a0 + a1FQceoi,t +∑ akCONTROLSi,t + εi,t

(5)
(6)
(7)

All of the variables are defined in the Appendix. In Model (4), we only keep one observation per CEO-firm. The
constant terms, year and industry fixed effects are included in Columns (1) and (2), but not tabulated. The constantterm is included in Column (3), but not tabulated. z-statistics, which are reported in parentheses, are corrected forheteroskedasticity and are adjusted for clustering of observations by industry and quarter (Column (1)), by CEO andquarter (Columns (2)), or by CEO and year (Column (3)). Coefficients that are significant at the 10, 5, and 1% levels
are marked with *, ** and ***, respectively.

34