Aloke Ghosh Speaks at Fox Business School, Temple University, Philadelphia

Accounting Losses versus Profits
and CEO Turnover
Aloke (Al) Ghosh
Stan Ross Department of Accountancy
Baruch College, The City University of New York,
Box B12-225, One Bernard Baruch Way
New York, NY 10010
E-mail address: Aloke.Ghosh@baruch.cuny.edu
Phone: 646.312.3184
___________________
We benefited from our discussions with Ray Ball, Sudipta Basu, Jeremy Bertomeu, Scott
Bronson, Ting Chen, Sonali Hazarika, John Elliott, Chris Hogan, Yinghua Li, Antonio Marra,
Pietro Mazzola, Christina Mashruwala, Serena Morricone, Daniel Oyon, Annalisa Prencipe, Bill
Ruland, Sasson Bar-Yosef and the participants at 2011 AAA Annual Meeting in Denver
(Colorado), University of Lauzanne (UNIL), Bocconi University, and Yonsei University. Our
special thanks to Masako Darrough, Val Dimitrov, Carol Marquardt, and Terry Shevlin for their
many comments and suggestions on some of our prior versions.

Accounting Losses versus Profits
and CEO Turnover
ABSTRACT
Relying on a linear specification, several studies examine the importance of accounting
and stock performance measures for CEO turnover. We suggest that accounting losses reflect
managerial effort and quality that are not fully captured in the prior performance measures
including profits. Using a non-linear specification around losses, we find a statistically and
economically significant relationship between accounting losses and subsequent CEO turnover.
Further, the magnitude of the loss also increases the likelihood of CEO turnover. A crucial
finding is that once we include losses, accounting performance is no longer incrementally
important in explaining CEO turnover. We additionally hypothesize and find that: (1) the impact
of losses on CEO turnover depends on whether other firms in the industry also report losses, (2)
CEO turnover following losses leads to more outside CEO appointments, and (3) the sensitivity
of CEO turnover to losses is affected by the strength of the board and the level of growth
opportunities. Collectively, our results suggest that CEOs are penalized for losses and that boards
consider other factors along with losses to arrive at CEO retention decisions.
Keywords: accounting losses; accounting performance; CEO turnover; managerial quality.
Data Availability: All data used in the paper are available from publicly available sources
noted in the text.

2
Accounting Losses versus Profits and CEO Turnover
I. INTRODUCTION
Relying on accounting (e.g., earnings levels and changes, industry-adjusted earnings) and
market measures (e.g., stock returns) as metrics for poor performance, several studies document
a positive relationship between CEO turnover and poor performance (see Hermalin and
Weisbach 2003). Almost all prior studies, with the exception of Jenter and Lewellen (2010),
assume a linear relationship between turnover and performance.1
A key criticism of prior studies
has been that the economic significance of performance is “arguably quite small” (Brickley 2003,
p. 228). One possible explanation for the weak association is that it becomes challenging for
researchers to develop a uniform metric for poor performance. In this study, we investigate
whether accounting loss is one such indicator of poor performance and whether losses dominate
other accounting measures of performance including accounting profits in determining CEO
turnover. Our fundamental contention is that a non-linear specification around losses is a more
parsimonious representation of turnover-performance relationship.
The classification of accounting performance into profits and losses presents a simple yet
powerful device for assessing managerial performance. Reporting of losses might act as a
heuristic for boards to calibrate managerial competence, which ultimately affects CEO retention
decisions (Pinnuck and Lillis 2007; Watts 2003). The idea that CEOs are penalized for losses
has been referenced by researchers and practitioners. Watts (2003) concludes that “managers
have incentives to hide losses to avoid being fired before their tenure is over.” Based on a survey
of key executives, Graham et al. (2005) find that three-fourths of the survey respondents believe
1
For instance, Engel et al. (2003) find that a decline in accounting performance by 10% increases
the likelihood of CEO departure by 0.31%. Farrell and Whidbee (2003) find that the implied probability
of CEO turnover increases by 1.14% when ROA declines by one standard deviation. DeFond and Park
(1999) find that a one standard deviation decrease in earnings increases the probability of CEO turnover
by 1.3%. Kaplan and Minton (2006) conclude that the relationship is stronger in recent years.

3
that losses might lead to job losses. One recent anecdotal example is the removal of Howard
Stringer as the CEO of Sony Corp following losses (Wall Street Journal, February 2, 2012).
There are many reasons for losses serving as a barometer for failed performance and,
therefore, leading to more frequent CEO turnover. First, because boards are unable to observe
CEOs’ innate ability, they must rely on public signals, e.g., accounting earnings, and private
signals to assess managerial performance (Taylor 2010; Hermalin and Weisbach 1998; Huson et
al. 2004; Murphy and Zimmerman 1993). Losses are expected to be more informative in judging
managerial performance than profits because, under accounting conservatism, reported income
includes current and future losses while reported income is precluded from anticipating future
profits (Basu 1997; Watts 2003; Givoly and Hayn 2000). Because losses include future negative
payoffs, losses are more likely to indicate whether a CEO invested in negative net present value
(NPV) projects than profits (Ball and Shivakumar 2005; Watts 2003). Hence, the association
between CEO turnover and accounting performance is expected to be stronger for loss firms than
for profit firms.
Second, a loss indicates that a CEO failed in his/her stewardship role to manage the assets
of the firm thereby triggering questions about competence and ability. Consequently, some board
members might feel compelled to learn more about the CEO’s true ability following a loss
(Bowen 2008). Contrary to a routine annual evaluation, a loss might precipitate more critical
evaluation of the incumbent CEO. A critical review, or an escalation in the intensity with which
boards monitor CEOs, increases the likelihood of boards collecting adverse information (i.e.,
private information) which might lead to more frequent CEO turnover (Hermalin and Weisbach
1998).2
Finally, board members might also feel the pressure to hold CEOs accountable for losses
2
In the extreme scenario, a board member might consider losses as sufficient justification for
removing a CEO from office. Degeorge et al. (1999) discuss three psychological effects for the zero-

4
because dissident shareholders often wage proxy fights to replace directors when boards fail to
initiate management changes following losses (DeAngelo 1988).
Based on a comprehensive sample of CEO turnovers from S&P 1500 firms between 1997
and 2007, we find, as in prior studies, a strong negative relationship between CEO turnover and
firm performance using both accounting and stock performance measures. Consistent with
concerns that the economic magnitude of accounting performance on CEO turnover is small, our
estimates suggest that a 10% increase in industry-adjusted return on assets decreases the
probability of CEO turnover by 2%. More importantly, when we additionally include an
indicator variable Loss for firms with negative net income while controlling for the other
determinants of CEO turnover, we find that the coefficient on Loss is positive and highly
significant. Additionally, when we examine whether the loss-size might be an added factor
determining CEO turnover (Klein and Marquardt 2006) by including Magnitude (decile ranks of
the absolute value of net income to book value equity) and interaction between Loss and
Magnitude, we find that the coefficients on Loss and the interaction term are positive and
significant. While losses increase the overall probability of a CEO turnover, the likelihood of
CEO turnover further increases with the magnitude of the loss.
In sharp contrast to the results from prior studies, we document that the economic
magnitude of losses on CEO turnover is large. Holding the other variables constant, some
estimates suggest that the odds of a CEO losing a job within two years are 54% higher for a firm
with losses compared to one with profits which suggests that losses substantially increase the
likelihood of the termination of CEO contracts. A crucial finding is that, once we include Loss,
earnings threshold acting as a heuristic or decision rule: (1) there is something fundamental between
positive and non-positive numbers in the human thought process, (2) prospect theory predicts that
individuals evaluate outcomes as changes from a reference point and losses can serve as one reference
point, and (3) heuristics or a ‘rule of thumb’ can reduce transactions costs.

5
accounting performance is no longer incrementally important in explaining CEO turnover
regardless of how we measure accounting performance (e.g., earnings-to-total assets, industry-
adjusted earnings-to-total assets or change in earnings). In contrast, stock performance continues
to be incrementally important in explaining CEO turnover decisions. Thus, losses appear to
dominate as a metric for judging managerial competence while small or declining profits do not
appear to be useful for CEO retention decisions.3
We also develop several related hypotheses on the cross-sectional variations in the loss-
turnover relationship. The added tests are aimed to increase the confidence in our conclusion that
boards consider losses along with other factors to arrive at CEO retention decisions. First, while
losses from continuing operations are expected to lead to higher turnover, we also conjecture that
losses from non-recurring operations as a result of plant closings, rearrangement and shedding of
lines of businesses lead to more frequent CEO turnover because it suggests that a CEO made
poor investment decisions in the past that are currently being reversed (Elliott and Hanna 1996).
Decomposing net losses into recurring and non-recurring components, where non-recurring
losses include losses from discontinued operations and extraordinary items (e.g., Gaver and
Gaver 1998; Dechow et al. 1994), we find that both recurring and non-recurring losses are
incrementally important in explaining CEO turnover.
Second, boards are expected to hold CEOs accountable for poor performance when losses
result from mismanagement or incompetence (i.e., losses are idiosyncratic to the firm), but they
are expected to shelter CEOs from losses that are the outcome of industry-wide shocks affecting
many firms (Bushman et al. 2010; Gibbons and Murphy 1990; Sloan 1993). Therefore, we posit
3
In a regression of CEO turnover on stock returns, Jenter and Lewellen (2010) allow the slope
coefficient of stock returns to vary by including quintile ranks instead of using a continuous variable.
When we use a similar specification and include quintile ranks of accounting return, our results and
conclusions remain unchanged.

6
that the relative frequency of industry-wide losses affects the sensitivity of CEO turnover to
losses. We find that the likelihood of a CEO turnover increases subsequent to losses when losses
are idiosyncratic or unique to the firm. However, as losses become more symptomatic of the
industry, the sensitivity of CEO turnover to losses is muted.
Third, we conjecture that losses increase the likelihood that an outsider would replace an
incumbent CEO. Parrino (1997) claims that outside candidates are more suitable for the top
position when boards want changes in the direction of the firm than when they want to maintain
status quo because “outside candidates…by virtue of their employment at other firms often have
a broader exposure to, and experience with, alternative ways of running a firm” (p. 167).
Because losses might suggest that a firm is in trouble (DeAngelo 1988), and if board members
feel that the current problems are an outcome of failed business policies, boards are more likely
to consider outsiders than insiders when replacing the incumbent CEO for poor performance.
Concentrating on the sub-sample with CEO turnovers, we find that turnovers following losses
lead to more frequent outside CEO appointments than those following profits.
Fourth, because stronger boards are more effective in disciplining top management for
poor performance (e.g., Huson et al. 2001; Goyal and Park 2002; Core et al. 1999; Hermalin and
Weisbach 1998; Byrd and Hickman 1992), we posit that the board strength affects the sensitivity
of turnover to losses. Our results indicate that the strength of the relationship between CEO
turnover and losses is stronger for firms with more effective boards, i.e., stronger boards are
more likely to hold CEOs accountable for reporting losses.
Fifth, while reported earnings might serve as reliable signals of current and past
managerial performance, accounting measures are less likely to capture future investment
opportunities. Therefore, boards might consider future growth opportunities along with current

7
performance to determine CEO retention decisions. Accordingly, we conjecture that the
sensitivity of CEO turnover to accounting losses is less pronounced for firms with large
investment opportunities. We find that the CEO turnover-loss relationship is weaker for firms
with large investment opportunities, implying that boards are willing to shield CEOs from losses
for growing firms to encourage long-term investments.4
Overall, our results suggest that boards rely on accounting losses for CEO retention
decisions. We also hypothesize and find that other firm- and industry-specific attributes affect
the loss-turnover relationship. 5
Prior studies provide persuasive evidence that accounting
recognition of losses is valuable to lenders and that it has a positive impact on debt contract
efficiency. Extending this line of literature, we provide evidence that accounting recognition of
losses is also valued by boards to monitor CEOs.
Our study also improves our understanding of three distinct areas of research. Relying on
a non-linear specification around losses, we document that once we control for losses,
accounting performance is no longer statistically significant in explaining CEO turnover. Also,
prior studies generally conclude that CEOs manage earnings to avoid losses.6
Our results suggest
that protecting jobs might be a key consideration for CEOs managing earnings to avoid losses.
Finally, prior studies conclude that the stock market reacts to profits, but not losses, because
4
Additionally, similar to the studies examining the rewards from sustained growth for positive
earnings (Elliott et al. 2010; Ghosh et al. 2005; Barth et al. 1999), we analyze and find that CEO turnover
likelihood increases with the frequency of annual losses.
5
Our analyses assume that accounting loss is a pre-determined variable. Because prior research
suggests that accounting loss might be endogenously determined (Klein and Marquardt 2006; Joos and
Plesko 2005), we also use a two-stage least squares estimation procedure to address endogeneity concerns
and continue to find unusually high frequency of CEO turnover for firms reporting losses.
6
The discontinuity in the frequency of firm-years around zero earnings (e.g., Hayn 1995;
Burgstahler and Dichev 1997) is widely cited as evidence of earnings management to avoid reporting
losses. Similarly, Roychowdhury (2006) provides evidence consistent with the premise that managers
manipulate operating (‘real’) activities to avoid reporting losses.

8
CEOs are expected to exercise the ‘liquidation or abandonment option’ when losses are
persistent (Collins et al. 1999; Burgstahler and Dichev 1997; Hayn 1995; Berger and Ofek 1996).
Our results suggest that the enhanced threat of a job loss provides incumbent CEOs with strong
incentives to abandon operations when a firm reports a loss.
The rest of the paper is organized as follows. Section II develops the hypotheses, Section
III outlines our research design to test our hypotheses, and Section IV describes the sample
selection procedure and the data. Section V reports the empirical results, Section VI discusses
sensitivity analyses, and finally Section VII concludes the paper.
II. THE RELATIONSHIP BETWEEN ACCOUNTING LOSSES AND CEO TURNOVER
In a survey and interview of 400 key executives directly involved in the financial
reporting process, Graham et al. (2005) find that 78% of the executives admit to sacrificing long-
term growth to report immediate profits rather than a loss. Why are CEOs so concerned about
reporting accounting losses and why would they go to such lengths to manage earnings so as not
to report losses? We hypothesize that a key reason for CEOs avoiding losses is related to career
concerns.
Losses versus Profits and CEO Turnover
The board of directors is primarily charged with the responsibility of monitoring,
evaluating, and rewarding management and ultimately firing a CEO for poor performance (e.g.,
Hermalin and Weisbach 1998). Because board members cannot directly observe the ability of a
CEO, they must rely on various performance measures (public signals) and inside information
(private signals) to evaluate the performance of a CEO (Taylor 2010). Prior studies document
that CEO turnover is higher for firms performing poorly which suggests that boards rely on

9
public signals, along with their private information, to make CEO turnover decisions.7
We
propose a new bright-line test that may be used in CEO retention decisions—whether a firm
reports a loss or a profit.
Researchers tend to agree that reported earnings in the U.S., and those around the world,
follow the ‘accounting conservatism principle’ (e.g., Ball et al. 2000). One interpretation of this
principle is that the income statement anticipates all losses (current and future) but not future
profits (Basu 1997). Lower of the cost or market for inventories, immediate recognition of future
losses on long-term contracts, recognition of future losses for operations designated as
discontinued, and asset impairments are some examples of reporting conservatism. A key
implication of accounting conservatism principle is that losses are more timely and reliable
signals of deteriorating managerial performance than small or declining profits. Therefore, the
relationship between CEO turnover and accounting performance is stronger for losses than for
profits.
Additionally, losses might act as a heuristic for ultimate failure (Pinnuck and Lillis
2007).8
Accounting losses are a signal that the underlying business model has failed under the
present leadership. Consistent with the premise of failed leadership, Graham et al. (2005) find
that three-fourths of the survey respondents believe that their inability to avoid losses is seen as a
“managerial failure” by the executive labor market and by corporate boards. According to one of
the surveyed executives, “if I miss the target, I’m out of a job.” One such target includes
7
Most analytical CEO turnover models assume that over time the board learns more about the
CEO’s ability based on firm performance and other private information. The board decides to replace the
incumbent manager whenever a CEO’s ability falls below a threshold which is less than the ability of a
replacement manager (Jenter and Kanaan 2011).
8
Some board members might favor CEO turnover for loss firms because of a loss aversion. If a
board member is more sensitive to losses than profits because a loss serves as a heuristic for failure
(Barberis and Huang 2001; Kahneman and Tversky 1979), he/she might conclude that a loss is sufficient
justification of a change.

10
avoiding losses; failure to report a profit may be seen as a sign of failed policies. Losses might
also indicate that a firm is experiencing serious difficulties and these problems are unlikely to be
resolved under the current leadership. These arguments suggest that boards might hold CEOs
accountable for losses.
In a related study, Watts (2003, 213) posits that “managers have incentives to hide losses
to avoid being fired before their tenure is over.” Admitting to losses might indicate that the CEO
invested in negative NPV projects and that the possibility of future profits under the incumbent
management might be remote. Therefore, losses provide board of directors with a signal to
investigate the reasons for those losses, to better understand why previously set goals and
objectives were not met, and to reevaluate the CEO’s future goals (Bowen 2008). A heightened
scrutiny of the CEO following a loss is more likely to lead to CEO turnover for at least two
reasons. First, holding other factors constant, an in-depth evaluation increases the chances of the
board uncovering more detrimental information about the CEO’s ability than through a more
routine annual evaluation. Second, if boards do not have the assurance that a change in business
strategy is likely even when confronted with losses, they are expected to replace the incumbent
CEO.9
Finally, shareholders might expect the board to dismiss the CEO when a firm reports a
loss because of erosion in equity value and the board might be acting to placate shareholders
(Watts 2003). For instance, DeAngelo (1988, 15) reports that dissident shareholders waging a
proxy contest “tend to emphasize losses as necessitating a management change.” Therefore,
boards might be willing to remove a CEO from office to placate dissident shareholders.
9
For instance, instead of abandoning loss-making projects, CEOs may continue to operate their
pet projects by subsidizing the losses with the profits from other segments. Similarly, entrenched and
powerful CEOs may be unwilling to discontinue projects with losses either because they are reluctant to
acknowledge their mistakes or because of some personal benefits from managing a larger firm.

11
Hypothesis 1: Accounting losses increase the likelihood of CEO turnover.
In the subsequent sub-sections, we theorize how the loss-turnover relationship is expected
to vary with several firm- and industry-specific attributes.
Recurring versus Non-Recurring Losses
Accounting performance includes recurring and non-recurring operations. A reasonable
question is whether boards discriminate between losses from recurring and non-recurring
operations. If CEOs are held accountable for losses, it seems plausible to assume that CEOs are
more likely to be held accountable for losses from continuing operations because it is indicative
of failed managerial performance. We conjecture that losses from discontinued operations also
provide useful additional information about the CEO’s competence. For instance, losses arising
from discontinued operations suggest that the CEO is likely to have made poor investment
decisions in the past that are currently being reversed. Therefore, we posit that
Hypothesis 2: Accounting losses from recurring and non-recurring operations both
increase the likelihood of CEO turnover.
Idiosyncratic versus Systematic Losses
Boards are expected to hold CEOs accountable for poor performance when losses result
from mismanagement of the firm, lack of leadership or incompetence, or CEO’s inability to take
optimal decisions. In contrast, boards are expected to shelter CEOs from losses when poor
performance is systematic in nature because of industry- or economy-wide shocks which affect
many firms (Bushman et al. 2010; Gibbons and Murphy 1990; Jenter and Kanaan 2011).
Hypothesis 3: The sensitivity of CEO turnover to accounting losses declines as more
firms from the same industry report losses.
Losses and Outside Replacement
The decision to fire a poorly performing CEO benefits shareholders only when the board
appoints a more capable successor. CEOs who are appointed from outside the firm are more

12
likely to change pre-existing firm policies that have resulted in losses. Consistent with this
perspective, Borokhovich et al. (1996) find that the stock market views the appointment of an
outside CEO more favorably than the appointment of an insider, especially when the incumbent
CEO is forced to resign. Therefore, accounting losses increase the likelihood that the board
might prefer an outside replacement to send a strong signal to investors that the CEO is
committed to turning around the firm.
Hypothesis 4: CEO turnover following losses leads to more outside CEO appointments.
Board Strength
Several studies find that CEO turnover is more frequent following poor performance
when boards are more effective and independent (e.g., Faleye et al. 2011; Huson et al. 2001;
Goyal and Park 2002; Core et al. 1999; Hermalin and Weisbach 1998; Byrd and Hickman 1992).
This suggests that more effective and independent boards are more likely to hold CEOs
responsible for poor performance as measured by losses. Therefore,
Hypothesis 5: The sensitivity of CEO turnover to accounting losses increases with
stronger boards.
III. RESEARCH DESIGN
Based on the extant literature on CEO turnover (e.g., Bushman et al. 2010; Fich and
Shivdasani 2006; Engel et al. 2003; Desai et al. 2006; Farrell and Whidbee 2003; Huson et al.
2001), we test the relationship between CEO turnover and accounting losses using the following
logistic regression.
Turnover = 0 + 1Loss + 2Accounting-return + 3ΔAccounting-return + 4Stock-return +
5Stock-volatility + 6Earnings-volatility + 7Forecast-error + 8Concentration +
9Size + 10Growth + 11Restructure + 12Restatement + 13Age + 14Tenure +
Industry/Year Fixed effects +  (1)

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Where Turnover is an indicator variable that equals 1 when there is a change in the CEO in the
two years subsequent to the current fiscal year and 0 otherwise.10
Our main independent variable
is Loss, which is also an indicator variable with a value of 1 when net income is negative for the
current year and 0 otherwise. The predicted sign of the coefficient on Loss is positive; CEO
turnover for firms with losses is expected to be higher than firms with profits.
We also include the following performance and control variables. Accounting-return is
industry-adjusted return on assets measured as the difference between the firm-specific and the
industry-median income before extraordinary items deflated by total assets at the beginning of
the year. Accounting-return is the difference between current period income before
extraordinary items and the corresponding number in the prior year deflated by total assets at the
beginning of the year. Stock-return is the difference between the raw returns and the value-
weighted CRSP market returns over a twelve-month fiscal period. Stock-volatility is the standard
deviation of Stock-return based on prior twenty-four monthly returns. Earnings-volatility is the
standard deviation of Accounting-return over the previous five years. Forecast-error is the
difference between reported annual EPS and the mean forecast EPS deflated by stock price at the
beginning of the year. Concentration is the industry level Herfindahl index. Size is the
logarithmic transformation of the fiscal year-end market value of equity. Growth is the sum of
the market value of equity and the book value of debt scaled by the book value of total assets.
Restructure is an indicator variable that equals 1 if special items as a percentage of total assets
are less than or equal to -5 percent and 0 otherwise. Restatement is an indicator variable equal to
1 when a firm restates its financial statement in the current or prior year and 0 otherwise. Age is
10
Some studies measure performance and CEO turnover concurrently. Because we are interested
in a causal relationship, it becomes critical that turnover is measured subsequent to losses. Also, if
incumbent CEOs tend to take a “big bath” in the first year of their tenure, CEO turnover and losses should
not be measured concurrently to avoid a mechanical relationship. Our results are similar when we limit
changes in the CEO in the year subsequent to the current fiscal year.

14
an indicator variable equal to 1 when the CEO is over the age of 60 years and 0 otherwise.
Tenure is the number of years that the CEO has been in office as of the fiscal year-end.
We include two accounting and one market measures of performance (Accounting-return,
Accounting-return, and Stock-return) because prior studies find that CEO turnover is related to
stock and accounting performance (e.g., Bushman et al. 2010; Fich and Shivdasani 2006; Farrell
and Whidbee 2003; DeFond and Park 1999; Murphy and Zimmerman 1993; Weisbach 1988).
We include two measures of volatility, one market (Stock-volatility) and another accounting
(Earnings-volatility), because firms with higher volatility are more prone to severe shocks that
lead to more frequent CEO turnovers (Bushman et al. 2010; Engel et al. 2003; DeFond and Park
1999). We control for analysts’ forecast errors because Farrell and Whidbee (2003) find that firm
performance expectations affect CEO turnover. We control for industry concentration because
CEO turnover is greater in highly concentrated industries than in less concentrated industries
(DeFond and Park 1999). We control for firm size (Size) and investment opportunity (Growth)
because larger firms and growing firms have a greater demand for high quality CEOs (Smith and
Watts 1992). We include indicator variables for restructuring activities (Restructure) and
financial restatements (Restatement) because firms with structural or reporting problems are
more likely to be associated with CEO turnovers (Desai et al. 2006; Pourciau 1993). Because not
all CEO turnovers are performance related, as in DeFond and Park (1999) and Desai et al.
(2006), we include an indicator variable for CEOs who are 60 years or older (Age) and a tenure
variable to measure the number of years in office (Tenure). Finally, we include fixed effects for
years and industry to control for variations in CEO turnover over time and across industries.
We also estimate an augmented equation that includes governance variables in addition to
the control variables included in Equation (1) because prior studies find that CEO turnover is

15
associated with board characteristics (e.g., Faleye et al. 2011; Dahya et al. 2002; Weisbach 1988;
Goyal and Park 2002). We include the number of directors on the board (Board-size), the
percentage of independent directors on the board (Board-independence), indicator variables
when a firm has a separate CEO and board chair (Separate-chair) and when a firm has separate
audit, nominating, and compensation committees (Separate-committees), and the percentage of
common stock held by the CEO (Ownership).
IV. DATA AND DESCRIPTIVE STATISTICS
Data and Sample Selection
Our sample consists of Standard and Poor’s (S&P) 1500 firms from Compustat’s
ExecuComp files during the period 1997 to 2007. Included in the ExecuComp files are the names
of the top executives in the firm, a CEOANN variable indicating which of the executives has the
title of a CEO, and the starting date of the CEO. Our CEO turnover indicator variable is
constructed from the information contained in ExecuComp files. If the name of the executive
listed as a firm’s CEO for the current year is different from the one listed as the CEO for the
prior year, we conclude that there is a change in the CEO, or a new CEO is hired, for the current
year. Because we define Turnover as one when there is a change in a CEO for the subsequent
two years, and our sample period ends with 2007, we consider accounting loss from 1997 to
2005.
Ideally, our sample would only consist of involuntary or forced turnovers. However, it is
often difficult to categorize CEO turnovers into voluntary and involuntary turnovers by reading
press articles (Engel et al. 2003). For instance, prior studies discuss the unreliable nature of the
press articles and how press releases often present involuntary turnovers as retirements (DeFond
and Park 1999; Warner et al. 1988). Therefore, as in prior studies we control for voluntary

16
turnovers by including a separate indicator variable for retirement age in our regression analyses
(e.g., Engel et al. 2003).
We also obtain CEO ownership, age and tenure data from the ExecuComp files. The data
on earnings and other firm characteristics are obtained from Compustat annual files. Stock return
data are obtained from CRSP files. We obtain analyst earnings forecast data from the IBES
summary files and board characteristics (size, composition, and structure) from the RiskMetrics
database (also previously known as IRRC). We construct one combined sample by merging the
CEO, accounting, stock return, forecast, and governance data. To remove the effect of outliers,
we winsorize the top or bottom 1 percent of the observations for Accounting-return,
Accounting-return, Stock-return, Earnings-volatility, Concentration, and Growth. 11
This
sample selection procedure results in 11,031 firm-year observations over fiscal years 1997
through 2005 with information about CEO turnover included up to 2007. However, when we
include analysts’ forecasts, the sample size reduces to 9,459.
Descriptive Statistics
Table 1 reports the descriptive statistics for the variables included in Equation (1). CEO
turnover levels are higher than those typically reported by prior studies; the frequency of CEO
turnover is 24.6% over the entire sample period. Losses are fairly common; of all the firm years,
17.6% report negative net income. The mean (median) industry-adjusted return on assets
(Accounting-return) and changes in income before extraordinary items deflated by total assets
(Accounting-return) are 5.1% (2.9%) and 0.9% (0.6%), respectively. The mean (median)
cumulative market-adjusted stock returns (Stock-return) are 8% (1.5%). The mean (median)
11
Our results are not sensitive to other outlier identification methods and they remain qualitatively
unchanged when we remove the top and/or bottom 0.5 or 1 percent of observations or even retain all the
outliers.

17
return volatility (Stock-volatility) is 0.115 (0.104), whereas the mean (median) earnings volatility
(Earnings-volatility) is 0.057 (0.029). The mean (median) analysts’ earnings forecast errors
(Forecast-error) are -0.007 (-0.000). The Herfindahl index (Concentration) has a median of
0.041. The mean fiscal-year end market value of equity (Market-equity) is $8.03 billion, while
the median number is much smaller ($1.52 billion). The mean (median) market-to-book ratio
(Growth) is 1.68 (1.20). 8.6% of firm years report special items less than or equal to -5 percent of
total assets and 8.2% of firm years are involved with restatements in the current or prior year.
The mean and median values of CEO age are very close at 56 years, whereas the mean (median)
CEO tenure (Tenure) is 8 (6) years.
Table 2 presents the relative frequency of CEO turnover for firms reporting losses and
those reporting profits. Consistent with our expectations that accounting losses are more likely to
lead to a CEO turnover, Turnover in Panel A is higher among loss firms than among profit firms.
More specifically, the frequency of a CEO turnover in the subsequent two years is 34% when
firms report negative net income in the current year while the corresponding number is 23%
when firms report a non-negative number as net income. The difference in frequency of turnover
between the two groups of firms is statistically significant at the 1 percent level. The preliminary
results indicate that firms with losses have higher CEO turnover than firms with profits.
Panel B of Table 2 reports the frequency of CEO turnover for loss and profit firms from
1997 to 2005. The frequency of CEO turnover for firms reporting profits appears to be constant
around 20% over the sample years. On the other hand, the frequency of CEO turnover for firms
reporting losses fluctuates over time but is always higher than that for profit firms. The
difference in the frequency of CEO turnover between loss and profit firms is statistically
significant for each of the sample years indicating that our hypothesis that losses lead to higher

18
CEO turnover is statistically reliable across each of the years.
V. EMPIRICAL RESULTS
CEO Turnover and Accounting Losses
Table 3 presents the logistic regression results for Equation (1). The dependent variable
Turnover is an indicator variable that equals 1 when there is a change in the CEO in the two
years subsequent to the current year and 0 otherwise. Our interest is in the sign and magnitude of
the coefficient on Loss. In regression (1), we first replicate prior studies by including accounting
and stock performance measures but without including Loss. Consistent with prior research, we
find that accounting and stock performance measures are significantly negatively associated with
turnover. The coefficients on Accounting-return and Stock-return are -1.083 (2
=15.57) and -
0.398 (2
=53.48), respectively. As in prior studies, we find that the economic significance of
accounting performance on CEO turnover is small. Holding the values of the other explanatory
variables at their mean values, a 10% increase in industry-adjusted return on assets decreases the
probability of CEO turnover by 1.9%. Also, as in Huson et al. (2001) and Weisbach (1988), the
coefficient on Accounting-return is negative but insignificant (-0.114, 2
=0.15).
More importantly, when we additionally include Loss in regression (2), we find a
statistically significant relationship between CEO turnover and accounting losses. The
coefficient on Loss is 0.435 (2
=31.22). The economic magnitude of the coefficient is large.
Holding the other variables constant, the odds of a CEO losing his/her job within two years of
reporting a loss are about 54 percent higher than the odds of CEO turnover of reporting a profit.
Another key empirical result is that the coefficient on Accounting-return becomes insignificant (-
0.497, 2
=2.86) when we include Loss as an additional explanatory variable. In sharp contrast,

19
the coefficient on Stock-return remains significant (-0.375, 2
=47.46). The coefficient on
Accounting-return continues to be insignificant (-0.003, 2
=0.01).
When we include all the control variables in regression (3), the results are very similar to
those in regression (2). Although the magnitude of the coefficient on Loss becomes smaller
(0.349, 2
=14.25), we find it reassuring that the results continue to be significant even with the
reduced sample size from additionally including analysts’ forecasts errors (Forecast-error). Our
results in regressions (2) and (3) indicate that while accounting and stock performance is
important in CEO performance evaluations, accounting losses as a metric for judging CEO
competence dominate other accounting performance measures.
The results of the control variables are generally consistent with our expectations and
similar to those reported in prior studies (e.g., Bushman et al. 2010; Desai et al. 2006; Farrell and
Whidbee 2003; Engel et al. 2003; Huson et al. 2001; DeFond and Park 1999). In regression (3),
the coefficient estimate on Forecast-error is negative and highly significant, which indicates that
poor earnings performance relative to analyst forecasts significantly increases the likelihood of
CEO turnover. The coefficient estimates on Stock-volatility, Size, Restructure, Restatement, Age,
and Tenure are all positive and significant. The results suggest that the likelihood of CEO
turnover is higher for firms with higher stock return volatility, bigger firms, firms with
restructuring activities, restating firms, older firms, and firms with longer CEO-tenure. The
coefficients on Earnings-volatility, Concentration, and Growth are insignificant.
One concern with Table 3 is that our regression specification excludes governance
measures for various agency problems which might impact CEO turnover (e.g., Faleye et al.
2011; Fich and Shivdasani 2006; Dahya et al. 2002; Weisbach 1988; Goyal and Park 2002). For
instance, a CEO with a higher equity ownership has more power which might affect CEO

20
turnover decisions. Accordingly, we additionally include the size, composition, and structure of
the board, and CEO ownership in Equation (1). Specifically, we include the number of members
on the board (Board-size), the percentage of independent directors on the board (Board-
independence), the separation of CEO and board chair positions (Separate-chair), the presence
of separate standing sub-committees (Separate-committees), and the percentage of common
stock held by the CEO (Ownership). The additional data requirement reduces our sample to
8,077 and 7,233 firm-year observations, respectively, without and with controlling for Forecast-
error.
The results in Table 4 show that the inclusion of the additional board and ownership
variables does not alter the relation between CEO turnover and accounting losses. Consistent
with the results in Table 3, the positive relation between CEO turnover and losses continues to
hold. The coefficient on Loss is 0.383 (2
=15.80) and 0.289 (2
=6.81), respectively, without and
with the inclusion of Forecast-error in regressions (1) and (2). The parameter estimate in
regression (2) suggests that the odds of a CEO turnover are about 34 percent higher for loss firms
than for profit firms. We also find that, consistent with the findings in prior studies (e.g., Goyal
and Park 2002), the coefficient on Separate-chair is positive and significant, suggesting that
firms with separate CEO-Chair positions have higher turnover than firms with combined
positions. The coefficient on Separate-committees is also positive and significant, which
suggests that CEO turnover is more frequent when the board structure includes separate sub-
committees. The coefficient on Ownership is negative and significant, indicating that high equity
ownership by the CEO makes it more difficult for a board to remove a CEO. The coefficients on
Board-size and Board-independence are insignificant.

21
We use an indicator variable for losses to examine the relation between accounting
losses and CEO turnover. The underlying assumption that the impact of losses on CEO turnover
does not depend on the magnitude of accounting losses may be too restrictive. Therefore, we
relax this assumption by examining whether the strength of the relation between losses and
turnover varies with the size of losses by adding the magnitude of the loss (Magnitude).
Magnitude represents decile ranks of the absolute value of net income deflated by book value of
equity at the beginning of the year.12
Table 5 reports the results after including LossMagnitude to estimate how Magnitude
affects the sensitivity of top executive turnover to losses. We find that the coefficient on Loss
remains positive and significant in regression (1). More importantly, the sensitivity of CEO
turnover to accounting losses becomes larger as the magnitude of accounting losses increases.
The coefficient on LossMagnitude is 0.052 (2
=4.31) in regression (1). When we include
governance variables in regression (2), the results are very similar to those in regression (1). The
coefficient on LossMagnitude is 0.045 (2
=4.02). Our results suggest that boards take into
account both incidence and size of accounting losses in holding CEOs accountable for poor
performance.
Overall, the results from Tables 3 to 5 suggest that CEOs reporting losses are more likely
to lose their jobs within the two-year period compared to CEOs reporting profits.
Decomposition of Losses
Our analysis in the previous section partitions firms into profit and loss groups based on
net income, which is the bottom-line measure of accounting performance. We further examine
12
We deflate loss by book value of equity because it indicates the degree of erosion in shareholder
equity, which aids the economic interpretation. We use deciles instead of including the continuous
variable to minimize the effect of outliers (firms can report extremely large losses). The results are very
similar when we interact Loss with Accounting-return.

22
whether the loss findings vary depending on whether income is recurring or non-recurring. By
partitioning net income, we are able to test whether losses from recurring or non-recurring
earnings components are treated differently when evaluating CEO performance. Similar to Gaver
and Gaver (1998), and Dechow et al. (1994), we decompose net income into: (1) recurring items
defined as income before extraordinary items and discontinued operations, and (2) non-recurring
items defined as the difference between net income and income before extraordinary items and
discontinued operations. Accordingly, we decompose Loss into two indicator variables measured
based on each earnings element. LossIB equals 1 when income before extraordinary items and
discontinued operations is negative. LossNR equals 1 when the difference between net income and
income before extraordinary items and discontinued operations is negative.
Table 6 reports the regression results of CEO turnover on the loss components. We find
that both LossIB and LossNR are significantly related to CEO turnover. In regression (1) when we
include all the variables except Forecast-error, the coefficients on LossIB and LossNR are 0.294
(2
=8.01) and 0.211 (2
=8.57), respectively. In regression (2) when we additionally include
Forecast-error, the coefficients on LossIB and LossNR continue to be significant (0.203, 2
=5.86;
0.196, 2
=6.30). The coefficients on recurring losses (11) and non-recurring losses (12) are
statistically indistinguishable; 11−12 is 0.083 (2
=0.41) and 0.007 (2
=0.01), respectively, in
regressions (1) and (2). Our results indicate that both recurring and non-recurring losses are
incrementally important in explaining CEO turnover.
Part of the earnings decomposition result might be mechanical. We find that 90% of the
firm-years with losses for net income also have negative recurring income (the correlation
between Loss and LossIB is also 0.923). Therefore, it is not surprising that losses based on
recurring earnings also lead to higher turnover as losses for net income. However, the correlation

23
between Loss and LossNR and that between LossIB and LossNR are low (0.148 and 0.042,
respectively). Low correlations imply that recurring income and non-recurring income can
provide distinct insights into the CEO’s ability. For instance, discontinued operations with losses
might indicate to boards that the CEO invested in negative NPV projects (Elliott and Hanna 1996)
which might be useful in assessing the CEO’s competence.
Idiosyncratic versus Systematic Losses
A related but important question is whether CEOs are held responsible for reporting
losses when other firms in the industry also report losses, i.e., losses are the outcome of
systematic negative shocks to the industry. Following Heflin and Hsu (2008), we construct a
variable, Industry-loss, which measures the proportion of firms reporting losses in each industry
for a given year where industry is defined using two-digit Standard Industry Classification (SIC)
codes. Our measure is constructed to capture industry-wide shocks. For instance, a negative
shock to the industry affecting many firms in that industry is likely to result in a significant
number of firms with losses. Therefore, as Industry-loss increases, a firm-specific loss is more
likely to be the outcome of industry-wide effects (systematic loss). In contrast, a smaller value
for Industry-loss indicates that a firm-specific loss is more likely to be the result of firm specific
factors rather than industry-wide effects (idiosyncratic loss).
Accordingly, in Table 7, we also examine whether Industry-loss affects the sensitivity of
turnover to losses by estimating an augmented logistic regressions after additionally including
LossIndustry-loss and Industry-loss along with the other variables. In regression (1) when we
include all control variables except Forecast-error, we find that the coefficient on Loss is
positive and significant (0.798, 2
=40.08) while that on LossIndustry-loss is negative and
significant (-1.402, 2
=11.97). Our results suggest that industry wide losses reduce the impact of

24
firm specific losses on CEO turnover. We get similar results from regression (2) when we
additionally include governance variables and Forecast-error.
The results from Table 7 provide direct evidence that boards are discriminating in
holding CEOs responsible for reporting losses. When losses are the outcome of poor managerial
performance, i.e., idiosyncratic in nature, the likelihood of CEO turnover is high following losses.
However, when losses are more systematic to the industry, boards are less likely to hold CEOs
accountable for losses.13
Outside Replacement and Accounting Losses
We also examine whether accounting losses increase the likelihood of an outside
replacement. We hand collect data to establish whether the successor CEO is an outsider by
reading press releases, 10-K reports, and associated proxy statements. The sample to examine the
impact of losses on outside appointments consists of 1,379 CEO turnover observations. Table 8
presents the regression results on the relationship between accounting losses and the likelihood
of outside succession, conditional on CEO turnover. We use a dichotomous dependent variable
that equals 1 when the incumbent CEO is replaced with a successor CEO from outside the firm
and 0 if the replacement CEO is appointed from within the firm. The independent variables are
based on two prior studies examining outside CEO turnover, Farrell and Whidbee (2003) and
Borokhovich et al. (1996).
We find that the coefficient on Loss is positive and significant in regressions (1) and (2).
Our results suggest that accounting losses lead to more frequent appointments of CEOs from
outside the firm. We also find that the coefficient on Separate-committees is positive and
13
When we also use an alternative specification of Industry-loss by taking the natural logarithm of
the value plus 1, we find that our results remain unchanged. For example, the coefficients on Loss,
LossIndustry-loss, and Industry-loss are 0.848 (2
=38.80), -1.866 (2
=12.85), and 0.251 (2
=0.72),
respectively, in regression (1).

25
significant, which indicates that the likelihood of outside succession is higher for firms with
specialized committees on audit, appointment, and remuneration issues. Among the other
variables in regression (2), we find that the coefficients on Stock-return, Size, and Tenure are
negative and significant, implying that the boards of larger firms with higher stock performance
and longer tenured CEOs tend to hire an insider to replace the incumbent CEO.
Boards, CEO Turnover, and Reporting of Losses
We also examine whether board strength affects the sensitivity of turnover to losses by
augmenting the logistic regressions reported in Table 3 after additionally including an interaction
term between accounting losses and a composite measure of board strength. Drawing on prior
studies (e.g., Bertrand and Mullainathan 2001), we construct a measure for board strength based
on six individual measures of board characteristics that are transformed into standardized values.
Specifically, we demean each of the six variables and divide it by its standard deviation. Board-
strength is then computed by summing the following six transformed measures: (1) board size,
(2) board independence, (3) separate CEO-Chair, (4) separate committees, (5) CEO ownership,
and (6) CEO tenure. For board size, CEO ownership, and CEO tenure, we use negative of board
size, CEO ownership, and CEO tenure before transforming them into standardized values so that
increasing values of individual characteristics indicate stronger boards.
The results from including LossBoard-strength and Board-strength in the turnover
regressions are presented in Table 9. In regression (1), we find that the coefficient on Loss
continues to be positive and significant (0.330, 2
=11.59), but the coefficient on LossBoard-
strength is also positive and significant (0.077, 2
=6.41). The results indicate that the strength of
the relationship between CEO turnover and losses is stronger for firms with stronger and more

26
effective boards. To the extent that Board-strength measures effectiveness of boards, our results
suggest that stronger boards are more likely to hold CEOs accountable for reporting losses.
In regression (2) when we additionally include Forecast-error, we get similar results to
those in regression (1). The coefficients on Loss and LossBoard-strength are 0.239 (2
=4.55)
and 0.074 (2
=4.77), respectively. Thus, our results from Table 9 corroborate the results from
prior tables. We additionally find direct evidence that stronger boards are more proactive in
holding CEOs accountable for eroding shareholder’s equity.
Key Implications
Our first hypothesis has broader implications. Several valuation studies find that the
relation between returns and earnings is weaker for loss firms than for profit firms (Collins et al.
1999; Burgstahler and Dichev 1997; Hayn 1995). The “liquidation/abandonment option” to
redeploy existing assets is often used as an explanation for the differential results between firm
values and earnings for profit and loss firms. Assuming that CEOs are willing to liquidate a firm
or to discontinue a segment when losses are expected to perpetuate, investors perceive losses as
being temporary. Therefore, the stock market reaction to losses is muted.
However, in the presence of agency problems, it is unclear whether incumbent CEOs
would exercise the liquidation/abandonment option when losses are expected to continue. For
example, Ofek (1993) finds that entrenched managers continue operations even when a firm is
distressed. Our study helps us better understand why losses are temporary. Because boards play a
proactive role in replacing entrenched CEOs that are unwilling or unable to change their failed
business strategies, losses are more likely to be temporary because new CEOs are more likely to
reverse the failed strategies of the predecessor CEO.
VI. SENSITIVITY ANALYSIS

27
CEO Turnover and the Frequency of Accounting Losses
Prior studies show that debt and equity markets reward firms with sustained earnings
growth because sustained earnings increases are indicative of the firms’ competitive advantages
and a higher probability of future earnings and cash flow growth (Elliott et al. 2010; Ghosh et al.
2005; Barth et al. 1999). Similar to the studies on the information content of sustained earnings
growth, we analyze whether the likelihood of a CEO turnover is affected by the frequency of
losses. Accordingly, we decompose Loss into 3 indicator variables depending on the frequency
of annual losses for the past five years including the current year. Loss1 equals 1 for firms with a
loss in the current year but not in the prior four years. Similarly, Loss2 (Loss3) equals 1 for firms
with two (three) years of losses in the past five years including the current year.14
Our results (not reported) show that the frequency of losses is incrementally important in
explaining CEO turnover. In the first regression when we include all the control variables except
Forecast-error, the coefficients on Loss1, Loss2, and Loss3 are all positive and statistically
significant (0.422, 2
=16.71; 0.528, 2
=25.06; 0.312, 2
=6.29). In the second regression when
we additionally include Forecast-error, the coefficients on Loss1 and Loss2 continue to be
significant but that on Loss3 becomes insignificant (0.239, 2
=2.60) probably because of lack of
power (i.e., there are few observations with CEO turnover and three annual losses).
Finally, when we include all the control and governance variables in the regression, the
results are very similar to those in the second regression. These results suggest that boards of
directors play a proactive role in replacing CEOs for reporting losses and, as the frequency of
14
We do not include more than three years of annual losses in our analysis because very few firms
report losses more than three years over a five-year period. We get very similar results when we construct
indicator loss variables to measure consecutive years of losses over a five year period including the
current year.

28
losses increases, the likelihood of a CEO being replaced also increases. Our results provide a key
explanation why investors treat losses as being temporary.
CEO Turnover and Accounting Losses for Firms with High Growth Opportunities
For firms in emerging, high-tech, and high-growth industries, current earnings may not
serve as an adequate proxy for future earnings potential because of the mismatching of revenues
and expenses. Growing firms need to make large investments which are expected to yield future
revenues. However, R&D costs are expensed as incurred and growth in tangible/intangible assets
leads to depreciation/amortization expenses which could result in more frequent losses for
growing firms than non-growing firms (Hayn 1995). If CEOs are penalized for all losses
regardless of future growth considerations, they might reduce long-term investments to avoid
losses which would be detrimental for the long-run profitability of the firm. Therefore, boards of
firms with future growth opportunities are expected to shield CEO from losses.
As in prior studies, we use the market-to-book ratio (Growth) to measure future growth
opportunities. Similar to Magnitude, we include decile ranks of Growth to minimize the effect of
outliers. Our untabulated results indicate that the sensitivity of CEO turnover to losses becomes
smaller for firms with higher growth opportunities. In the first regression when we include all the
control variables except Forecast-error, the coefficient on Loss remains positive and significant
(0.570, 2
=30.12). In contrast, the coefficient on the interaction between Loss and Growth is
negative and significant (-0.047, 2
=4.53). When we additionally include Forecast-error in the
second regression, the results are very similar to those in the first regression. The coefficients on
Loss and interaction term are 0.500 (2
=15.41) and -0.046 (2
=4.09), respectively. Our results
suggest that boards of growing firms shield their CEOs from losses to encourage long-term
investments.

29
Non-linear Effects of Accounting Returns on CEO Turnover
Jenter and Lewellen (2010) find that the sensitivity of CEO turnover and stock price
performance increases substantially when they allow for non-linear effects of performance on
turnover. Specifically, instead of using a continuous variable, they include stock return quintiles
as separate explanatory variables in a CEO turnover regression. Similar to their approach, we
also allow for the Accounting-return slope to vary by including Accounting-return quintiles.
When we use Accounting-return quintile (each as an indicator variable) in our regression
specification, we find that our main conclusions remain unchanged. For instance, similar to
Table 3, regression (2) results, the coefficient on Loss remains positive and significant; it is 0.445
(2
=28.40). On the other hand, treating top quintile Accounting-return as the benchmark, none of
the coefficients on Accounting-return quintiles are significant.
Endogeneity
A potential concern with our prior results is that accounting losses are likely to be
endogenously determined (Klein and Marquardt 2006; Joos and Plesko 2005). There are two
sources of endogeneity in our study. First, there might be a simultaneity problem; losses might
lead to CEO turnover, but CEO turnover might lead newly appointed CEOs to take a “big bath”
and report a loss. We avoid the simultaneity problem by examining CEO turnover subsequent to
the years following losses, i.e., we examine CEO turnover in years +1 and +2 where year 0
denotes a loss-year. Second, random shocks might affect the occurrence of losses which might
also affect CEO turnover decisions.
We address the second type of an endogeneity problem using a two-stage least squares
(2SLS) estimation procedure to obtain consistent and efficient estimates for losses. Specifically,
drawing on prior studies, we model losses in the first stage and then, in the second stage, we

30
regress CEO turnover on the probability of a firm reporting a loss obtained from the first stage
regression. We estimate the occurrence of losses by including four categories of variables: (1)
profitability measures including Cash-flow and Accrual (Joos and Plesko 2005), (2) Size because
of the strong link between losses and firm size (Hayn 1995), and Sales-growth because current
earnings may not capture future prospects of growing firms, (3) lag(Loss) to measure the
incidence of past losses; firms with prior losses are less likely to return to profitability in the
subsequent year, and (4) Dividend (Dividend-stop) because firms continuing to pay dividends
(firms stopping paying dividends) have a lower (higher) probability of incurring future losses
(Joos and Plesko 2005). We include governance and control variables in the first stage (Larcker
and Rusticus 2010). Because of the additional data requirements, the sample decreases to 6,906
observations in the first stage regression.
The results for the first stage estimation are presented in the first column of Table 10. The
coefficients on Cash-flow and Accrual are negative and significant, which suggests that firms
with higher cash flow from operations and larger accruals are less likely to report accounting
losses. In contrast, the coefficient on lag(Loss) is positive and significant, indicating that firms
with losses in the prior year are more likely to incur losses in the current year. The coefficients
on Sales-growth, Dividend, and Dividend-stop are not significant. The Nagelkerke R2
from the
first-stage regression which includes the instruments, governance and control variables is around
65% which is high indicating that our model explains substantial variations across firms
reporting losses. More importantly, we find that the partial Nagelkerke R2
after excluding the
governance and control variables is 30.43% and the partial likelihood ratio 2
–statistic is
statistically significant at 2,452.66, which means that the instruments do a reasonably good job
of explaining cross sectional variations in losses.

31
In the second stage, we use the estimated values of losses (Pred-loss) from the first stage
as an instrumental variable and re-estimate Equation (1). After controlling for the endogeneity of
accounting losses, our results confirm the earlier findings on the positive relationship between
CEO turnover and losses. The coefficient on Pred-loss is 0.304 (2
=4.23) when we include the
governance variables in the second column. Thus, our results once again confirm that the
likelihood of CEO turnover is higher for firms reporting accounting losses even after controlling
for issues related to endogeneity.
VII. CONCLUSIONS
Several studies examine the importance of earnings and stock returns as measures of firm
performance on CEO turnover considerations (e.g., Weisbach 1988; Murphy and Zimmerman
1993; Goyal and Park 2002; Bushman et al. 2010; Jenter and Kanaan 2011). We suggest that
accounting losses reflect managerial effort and quality that are not fully captured in the
traditional measures of firm performance. In this paper, we investigate whether accounting losses
provide incremental information that can be used to assess CEO retention/dismissal decision.
Specifically, we examine how accounting losses affect subsequent top executive turnover in
relation to the other measures of performance.
Based on a comprehensive sample of CEO turnover between 1997 and 2007, we find
that compared to profit firms, the likelihood of CEO turnover is significantly higher for loss
firms. Controlling for the other determinants of CEO turnover including accounting and stock
performance measures, the relative odds of a CEO losing a job within two years are more than 50
percent higher for firms with losses than with profits. More importantly, when we include losses,
accounting performance measures (earnings levels, earnings changes, or industry-adjusted
earnings) are no longer incrementally significant in explaining CEO turnover. Our results

32
suggest that only losses result in a higher likelihood of a subsequent CEO turnover and variations
in profit levels do not impact CEO turnover decisions.
Additionally, we find the following key results: (1) when we decompose losses (defined
using net income) into losses before extraordinary items and discontinued operations and losses
from extraordinary items and discontinued operations, we find that both measures affect CEO
turnover, (2) the magnitude of the loss also affects CEO turnover, (3) the impact of firm-specific
losses on CEO turnover is significant when losses tend to be confined to the firm but is muted
when losses are more systematic to the industry, (4) CEO turnover following losses leads to
more outside CEO appointments, and (5) the sensitivity of CEO turnover to losses is affected by
the strength of the board and the level of growth opportunities. Collectively, the results suggest
that CEOs are penalized for losses and that boards consider other factors along with losses to
arrive at CEO retention decisions.
Our results suggest that boards view losses as an additional indicator of management
failure and are more likely to penalize CEOs that report losses. Additionally, prior studies often
presume that effective managers exercise the liquidation/abandonment option when losses are
expected to persist. Our results suggest that board actions that threaten higher turnover following
losses increase CEO focus on the liquidation/abandonment option. Finally, our results also
provide one explanation why firms manage earnings to avoid reporting losses.

33
REFERENCES
Ball, R., S. P. Kothari, and A. Robin. 2000. The effect of international institutional factors on properties
of accounting earnings. Journal of Accounting and Economics 29: 1-51.
Ball, R., and L. Shivakumar. 2005. Earnings quality in U.K. private firms: Comparative loss recognition
timeliness. Journal of Accounting and Economics 39: 83-128.
Barberis, N., and M. Huang. 2001. Mental accounting, loss aversion, and individual stock returns. Journal
of Finance 56: 1247-1292.
Barth, M. E., J. A. Elliott, and M. A. Finn. 1999. Market rewards associated with patterns of increasing
earnings. Journal of Accounting Research 37: 387-413.
Basu, S. 1997. The conservatism principle and the asymmetric timeliness of earnings. Journal of
Accounting and Economics 24: 3-37.
Berger, P. G., and E. Ofek. 1996 . Bustup takeovers of value-destroying diversified firms. Journal of
Finance 51: 1175-200.
Bertrand, M., and S. Mullainathan. 2001. Are CEOs rewarded for luck? The ones without principals are.
The Quarterly Journal of Economics 116: 901-932.
Borokhovich, K. A., R. Parrino, and T. Trapani. 1996. Outside directors and CEO selection. Journal of
Financial and Quantitative Analysis 31: 337-355.
Bowen, W. G. 2008. The Board Book: An Insider’s Guide for Directors and Trustees. New York, NY: W.
W. Norton and Company.
Brickley, J. A. 2003. Empirical research on CEO turnover and firm-performance: A discussion. Journal
of Accounting and Economics 36: 227-233.
Burgstahler, D., and I. Dichev. 1997. Earnings management to avoid earnings decreases and losses.
Journal of Accounting and Economics 24: 99-126.
Bushman, R., Z. Dai, and X. Wang. 2010. Risk and CEO turnover. Journal of Financial Economics 96:
381-398.
Byrd, J. W., and K. A. Hickman. 1992. Do outside directors monitor managers? Evidence from tender
offer bids. Journal of Financial Economics 32: 195-221.
Collins, D. W., M. Pincus, and H. Xie. 1999. Equity valuation and negative earnings: The role of book
value of equity. The Accounting Review 74: 29-62.
Core, J. E., R. W. Holthausen, and D. F. Larcker. 1999. Corporate governance, chief executive officer
compensation, and firm performance. Journal of Financial Economics 51: 371-406.
Dahya, J., J. J. McConnell, and N. G. Travlos. 2002. The Cadbury committee, corporate performance, and
top management turnover. Journal of Finance 57: 461-483.
DeAngelo, L. 1988. Managerial competition, information costs, and corporate governance. Journal of
Dechow, P., M. Huson, and R. Sloan. 1994. The effect of restructuring charges on executives' cash
compensation. The Accounting Review 69: 138-156.
DeFond, M. L., and C. W. Park. 1999. The effect of competition on CEO turnover. Journal of Accounting
and Economics 27: 35-56.
Degeorge, F., J. Patel, and R. Zeckhauser. 1999. Earnings management to exceed thresholds. Journal of
Business 72: 1-33.
Desai, H., C. E. Hogan, and M. S. Wilkins. 2006. The reputational penalty for aggressive accounting:
Earnings restatements and management turnover. The Accounting Review 81: 83-112.
Elliott, J. A., A. Ghosh, and D. Moon. 2010. Asymmetric valuation of sustained growth by bond- and
equity-holders. Review of Accounting Studies 15: 833-878.
Elliott, J. A., and J. D. Hanna. 1996. Repeated accounting write offs and the information content of
earnings. Journal of Accounting Research 34: 135-155.
Engel, E., R. M. Hayes, and X. Wang. 2003. CEO turnover and properties of accounting information.

34
Faleye, O., R. Hoitash, and U. Hoitash. 2011. The costs of intense board monitoring. Journal of Financial
Economics 101: 160-181.
Farrell, K. A., and D. A. Whidbee. 2003. Impact of firm performance expectations on CEO turnover and
replacement decisions. Journal of Accounting and Economics 36: 165-196.
Fich, E. M., and A. Shivdasani. 2006. Are busy boards effective monitors? Journal of Finance 61: 689-
724.
Gaver, J. J., and K. M. Gaver. 1998. The relation between nonrecurring accounting transactions and CEO
cash compensation. The Accounting Review 73: 235-253.
Ghosh, A., Z. Gu, and P. Jain. 2005. Sustained earnings and revenue growth, earnings quality, and
earnings response coefficients. Review of Accounting Studies 10: 33-57.
Gibbons, R., and K. J. Murphy. 1990. Relative performance evaluation for chief executive officers.
Industrial and Labor Relations Review 43: 30S-51S.
Givoly, D., and C. Hayn. 2000. The changing time-series properties of earnings, cash flows and accruals:
Has financial reporting become more conservative? Journal of Accounting and Economics 29: 287-
320.
Goyal, V., and C. W. Park. 2002. Board leadership structure and CEO turnover. Journal of Corporate
Finance 8: 49-66.
Graham, J. R., C. R. Harvey, and S. Rajgopal. 2005. The economic implications of corporate financial
reporting. Journal of Accounting and Economics 40: 3-73.
Hayn, C. 1995. The information content of losses. Journal of Accounting and Economics 20: 125-154.
Heflin, F., and C. Hsu. 2008. The impact of the SEC’s regulation of non-GAAP disclosures. Journal of
Hermalin, B. E., and M. S. Weisbach. 1998. Endogenously chosen boards of directors and their
monitoring of the CEO. American Economic Review 88: 96-117.
Hermalin, B. E., and M. S. Weisbach. 2003. Boards of directors as an endogenously determined
institution: A survey of the economic literature. Economic Policy Review 9: 7-26.
Huson, M. R., P. H. Malatesta, and R. Parrino. 2004. Managerial succession and firm performance.
Journal of Financial Economics 74: 237-275.
Huson, M. R., R. Parrino, and L. T. Starks. 2001. Internal monitoring mechanisms and CEO turnover: A
long-term perspective. Journal of Finance 55: 2265-2297.
Jenter, D., and F. Kanaan. 2011. CEO Turnover and relative performance evaluation. Journal of Finance:
Forthcoming.
Jenter, D., and K. Lewellen. 2010. Performance-induced CEO Turnover. Working paper, Stanford
University.
Joos, P., and G. Plesko. 2005. Valuing loss firms. The Accounting Review 80: 847-870.
Kahneman, D., and A. Tversky. 1979. Prospect theory: An analysis of decision under risk. Econometrica
47: 263-291.
Kaplan, S., and B. A. Minton. 2006. How has CEO turnover changed? Increasingly performance sensitive
boards and increasingly uneasy CEOs, Working paper, University of Chicago.
Klein, A., and C. Marquardt. 2006. Fundamentals of accounting losses. The Accounting Review 81: 179-
206.
Larcker, D. F., and T. O. Rusticus. 2010. On the use of instrumental variables in accounting research.
Murphy, K. J., and J. L. Zimmerman. 1993. Financial performance surrounding CEO turnover. Journal of
Ofek, E. 1993. Capital structure and firm response to poor performance. Journal of Financial Economics
34: 3-30.
Parrino, R. 1997. CEO turnover and outside succession: A cross-sectional analysis. Journal of Financial
Economics 46: 165-197.
Pinnuck, M., and A. Lillis. 2007. Profits versus losses: Does reporting an accounting loss act as a
heuristic trigger to exercise the abandonment option and divest employees?’ The Accounting Review

35
82: 1031-1053.
Pourciau, S. 1993. Earnings management and nonroutine executive change. Journal of Accounting and
Roychowdhury, S. 2006. Earnings management through real activities manipulation. Journal of
Sloan, R. 1993. Accounting earnings and top executive compensation. Journal of Accounting and
Smith, C. W., and R. L. Watts. 1992. The investment opportunity set and corporate financing, dividend,
and financing policies. Journal of Financial Economics 32: 262-292.
Taylor, L. A. 2010. Why are CEOs rarely fired? Evidence from structural estimation. Journal of Finance
65: 2051-2087.
Warner, J. B., R. L. Watts, and K. H. Wruck. 1988. Stock prices and top management changes. Journal of
Financial Economics 20: 461-492.
Watts, R. L. 2003. Conservatism in accounting, Part I: Explanations and implications. Accounting
Horizons 17: 207-221.
Weisbach, M. 1988. Outside directors and CEO turnover. Journal of Financial Economics 20: 431- 460.

36
TABLE 1
Descriptive Statistics
Mean First quartile Median Third quartile
Standard
deviation
Turnover 0.246 0.000 0.000 0.000 0.431
Loss 0.176 0.000 0.000 0.000 0.381
Accounting-return 0.051 -0.002 0.029 0.098 0.117
Accounting-return 0.009 -0.010 0.006 0.028 0.088
Stock-return 0.080 -0.231 0.015 0.287 0.504
Stock-volatility 0.115 0.074 0.104 0.146 0.055
Earnings-volatility 0.057 0.013 0.029 0.066 0.077
Forecast-error -0.007 -0.012 -0.000 0.004 0.037
Concentration 0.057 0.027 0.041 0.067 0.049
Market-equity 8.033 0.566 1.520 5.121 25.938
Growth 1.683 0.828 1.195 1.958 1.481
Restructure 0.086 0.000 0.000 0.000 0.280
Restatement 0.082 0.000 0.000 0.000 0.274
Age 56.151 51.000 56.000 61.000 7.509
Tenure 7.994 3.000 6.000 11.000 7.566
Turnover is an indicator variable that equals 1 when there is a change in the CEO in the two years subsequent to the
current fiscal year and 0 otherwise. Loss is an indicator variable with a value of 1 when net income is negative for
the current year and 0 otherwise. Accounting-return is the industry-adjusted return on assets measured as the
difference between the firm-specific and the industry-median income before extraordinary items deflated by total
assets at the beginning of the year. Accounting-return is the difference between current period income before
extraordinary items and the corresponding number in the prior year deflated by total assets at the beginning of the
year. Stock-return is the difference between the raw returns and the value-weighted CRSP market returns over a
twelve-month fiscal period. Stock-volatility (Earnings-volatility) is the standard deviation of Stock-return
(Accounting-return) based on prior twenty-four monthly (five year) data. Forecast-error is the difference between
reported annual EPS and the mean forecast EPS deflated by stock price at the beginning of the year. Concentration
is the industry level Herfindahl index. Market-equity is the fiscal year-end market value of equity. Growth is the sum
of the market value of equity and the book value of debt scaled by the book value of total assets. Restructure is an
indicator variable that equals 1 if special items as a percentage of total assets are less than or equal to -5 percent and
0 otherwise. Restatement is an indicator variable equal to 1 when a firm restates its financial statement in the current
or prior year and 0 otherwise. Age is the age of the CEO in years (for the outgoing CEO in turnover firms) as of the
fiscal-year end. Tenure is the number of years that the CEO has been in office as of the fiscal year-end. Descriptive
statistics are based on a sample with 11,031 firm-year observations between 1997 and 2005 for all the variables
other than Forecast-error which has 9,459 observations.

37
TABLE 2
CEO Turnover for Loss and Profit Firms
Firms with
Profits Losses Differences
Panel A: Full Sample
0.226 0.339 -0.113**
Panel B: By Fiscal Year
1997 0.216 0.383 -0.167**
1998 0.243 0.363 -0.120**
1999 0.256 0.457 -0.201**
2000 0.227 0.315 -0.088**
2001 0.194 0.280 -0.086**
2002 0.194 0.293 -0.099**
2003 0.214 0.314 -0.100**
2004 0.220 0.360 -0.140**
2005 0.293 0.473 -0.180**
Firms with losses have negative net income for the current year and the rest of the firms are classified as profit firms.
Turnover is an indicator variable that equals 1 when there is a change in the CEO in the two years subsequent to the
current fiscal year and 0 otherwise. The significance test of differences in means between profit and loss firms is
based on the t-tests. ** denotes statistical significance at the 1 percent level based on a two-tailed test.

38
TABLE 3
CEO Turnover and Losses
Dependent variable: Turnover
(1) (2) (3)
Intercept -1.875 (105.09)**
-1.940 (111.98)**
-2.046 (98.46)**
Performance measures
Loss 0.435 (31.22)**
0.349 (14.25)**
Accounting-return -1.083 (15.57)**
-0.497 (2.86) -0.507 (2.24)
Accounting-return -0.114 (0.15) -0.003 (0.01) 0.348 (0.92)
Stock-return -0.398 (53.48)**
-0.375 (47.46)**
-0.357 (32.23)**
Control variables
Stock-volatility 2.745 (18.14)**
2.073 (9.96)**
1.614 (4.68)*
Earnings-volatility -0.092 (0.05) -0.187 (0.21) -0.539 (1.30)
Forecast-error -2.731 (13.13)**
Concentration 0.220 (0.15) 0.264 (0.22) -0.482 (0.57)
Size 0.070 (18.47)**
0.077 (22.18)**
0.086 (20.61)**
Growth -0.025 (1.31) -0.027 (1.58) -0.003 (0.02)
Restructure 0.321 (13.52)**
0.200 (4.95)*
0.217 (4.55)*
Restatement 0.263 (10.60)**
0.249 (9.46)**
0.292 (11.53)**
Age 0.642 (156.63)**
0.653 (161.07)**
0.712 (162.59)**
Tenure 0.007 (4.93)*
0.007 (5.43)*
0.007 (5.31)*
Fixed effects
Industry Yes Yes Yes
Year Yes Yes Yes
Observations 11,031 11,031 9,459
Nagelkerke R2
5.88% 6.27% 6.64%
The dependent variable Turnover is an indicator variable that equals 1 when there is a change in the CEO in the two
years subsequent to the current fiscal year and 0 otherwise. The independent variables are as follows. Loss is an
indicator variable with a value of 1 when net income is negative for the current year and 0 otherwise. Accounting-
return is the industry-adjusted return on assets measured as the difference between the firm-specific and the
industry-median income before extraordinary items deflated by total assets at the beginning of the year.
Accounting-return is the difference between current period income before extraordinary items and the
corresponding number in the prior year deflated by total assets at the beginning of the year. Stock-return is the
difference between the raw returns and the value-weighted CRSP market returns over a twelve-month fiscal period.
Stock-volatility (Earnings-volatility) is the standard deviation of Stock-return (Accounting-return) based on prior
twenty-four monthly (five year) data. Forecast-error is the difference between reported annual EPS and the mean
forecast EPS deflated by stock price at the beginning of the year. Concentration is the industry level Herfindahl
index. Size is the logarithmic transformation of the fiscal year-end market value of equity. Growth is the sum of the
market value of equity and the book value of debt scaled by the book value of total assets. Restructure is an
indicator variable that equals 1 if special items as a percentage of total assets are less than or equal to -5 percent and
0 otherwise. Restatement is an indicator variable equal to 1 when a firm restates its financial statement in the current
or prior year and 0 otherwise. Age is an indicator variable equal to 1 when the CEO is over the age of 60 years and 0
otherwise. Tenure is the number of years that the CEO has been in office as of the fiscal year-end. We report the
estimated coefficients from a logistic regression and the corresponding 2
–statistics in parenthesis. ** and * denote
statistical significance at the 1 percent and 5 percent levels, respectively, based on a two-tailed test.

39
TABLE 4
CEO Turnover and Losses: Inclusion of Additional Governance Variables
(1) (2)
Intercept -3.171 (141.55)**
-3.522 (145.52)**
Loss 0.383 (15.80)**
0.289 (6.81)**
Accounting-return -0.438 (1.24) -0.321 (0.53)
Accounting-return 0.085 (0.04) 0.079 (0.03)
Stock-return -0.380 (28.76)**
-0.356 (20.52)**
Governance variables
Board-size 0.008 (0.33) 0.017 (1.40)
Board-independence 0.004 (3.47) 0.004 (3.49)
Separate-chair 0.708 (136.69)**
0.759 (136.76)**
Separate-committees 0.222 (7.05)**
0.242 (7.01)**
Ownership -0.021 (11.10)**
-0.016 (5.80)*
Control variables
Stock-volatility 1.961 (5.58)*
1.685 (3.45)
Earnings-volatility -0.678 (1.49) -0.697 (1.26)
Forecast-error -2.231 (4.70)*
Concentration 0.286 (0.18) -0.270 (0.13)
Size 0.087 (13.58)**
0.097 (14.10)**
Growth -0.001 (0.01) 0.010 (0.11)
Restructure 0.257 (5.13)*
0.277 (4.93)*
0.314 (10.38)**
Age 0.688 (126.62)**
0.749 (132.61)**
Tenure 0.030 (53.89)**
0.028 (42.96)**
Fixed effects
Industry Yes Yes
Year Yes Yes
Observations 8,077 7,233
Nagelkerke R2
8.80% 9.60%
The dependent variable Turnover is an indicator variable that equals 1 when there is a change in the CEO in the two years
subsequent to the current fiscal year and 0 otherwise. The independent variables are as follows. Loss is an indicator variable with
a value of 1 when net income is negative for the current year and 0 otherwise. Accounting-return is the industry-adjusted return
on assets measured as the difference between the firm-specific and the industry-median income before extraordinary items
deflated by total assets at the beginning of the year. Accounting-return is the difference between current period income before
extraordinary items and the corresponding number in the prior year deflated by total assets at the beginning of the year. Stock-
return is the difference between the raw returns and the value-weighted CRSP market returns over a twelve-month fiscal period.
Board-size is the number of directors on the board. Board-independence is the percentage of independent directors on the board.
Separate-chair and Separate-committees are indicator variables set to 1 when a firm has a separate CEO and board chair and
when a firm has separate audit, nominating, and compensation committees, respectively. Ownership is the percentage of common
stock held by the CEO. Stock-volatility (Earnings-volatility) is the standard deviation of Stock-return (Accounting-return) based
on prior twenty-four monthly (five year) data. Forecast-error is the difference between reported annual EPS and the mean
forecast EPS deflated by stock price at the beginning of the year. Concentration is the industry level Herfindahl index. Size is the
logarithmic transformation of the fiscal year-end market value of equity. Growth is the sum of the market value of equity and the
book value of debt scaled by the book value of total assets. Restructure is an indicator variable that equals 1 if special items as a
percentage of total assets are less than or equal to -5 percent and 0 otherwise. Restatement is an indicator variable equal to 1 when
a firm restates its financial statement in the current or prior year and 0 otherwise. Age is an indicator variable equal to 1 when the
CEO is over the age of 60 years and 0 otherwise. Tenure is the number of years that the CEO has been in office as of the fiscal
year-end. We report the estimated coefficients from a logistic regression and the corresponding 2
–statistics in parenthesis. **
and * denote statistical significance at the 1 percent and 5 percent levels, respectively, based on a two-tailed test.

40
TABLE 5
CEO Turnover and the Magnitude of Losses
(1) (2)
Intercept -2.237 (128.33)**
-3.512 (142.87)**
Loss 0.228 (4.33)*
0.211 (4.26)*
LossMagnitude 0.052 (4.31)*
0.045 (4.02)*
Magnitude -0.007 (0.31) 0.004 (0.08)
Stock-return -0.339 (29.47)**
-0.356 (20.46)**
Board-size 0.017 (1.39)
Board-independence 0.005 (3.68)
Ownership -0.016 (5.98)*
Control variables
Stock-volatility 1.146 (2.36) 1.604 (3.09)
Forecast-error -2.420 (10.00)**
-2.075 (3.93)*
Concentration -0.412 (0.41) -0.281 (0.14)
Size 0.091 (22.95)**
0.094 (13.01)**
Growth -0.015 (0.40) 0.002 (0.01)
Restructure 0.158 (2.32) 0.242 (3.63)
0.314 (10.35)**
Age 0.716 (164.30)**
0.749 (132.68)**
Tenure 0.007 (4.79)*
0.028 (43.19)**
Fixed effects
Industry Yes Yes
Year Yes Yes
Nagelkerke R2
6.72% 9.65%
a value of 1 when net income is negative for the current year and 0 otherwise. Magnitude represents decile ranks of the absolute
value of net income deflated by book value of equity at the beginning of the year. Accounting-return is the industry-adjusted
return on assets measured as the difference between the firm-specific and the industry-median income before extraordinary items
deflated by total assets at the beginning of the year. Accounting-return is the difference between current period income before
extraordinary items and the corresponding number in the prior year deflated by total assets at the beginning of the year. Stock-
return is the difference between the raw returns and the value-weighted CRSP market returns over a twelve-month fiscal period.
Board-size is the number of directors on the board. Board-independence is the percentage of independent directors on the board.
Separate-chair and Separate-committees are indicator variables set to 1 when a firm has a separate CEO and board chair and
when a firm has separate audit, nominating, and compensation committees, respectively. Ownership is the percentage of common
stock held by the CEO. Stock-volatility (Earnings-volatility) is the standard deviation of Stock-return (Accounting-return) based
on prior twenty-four monthly (five year) data. Forecast-error is the difference between reported annual EPS and the mean
forecast EPS deflated by stock price at the beginning of the year. Concentration is the industry level Herfindahl index. Size is the
logarithmic transformation of the fiscal year-end market value of equity. Growth is the sum of the market value of equity and the
book value of debt scaled by the book value of total assets. Restructure is an indicator variable that equals 1 if special items as a
percentage of total assets are less than or equal to -5 percent and 0 otherwise. Restatement is an indicator variable equal to 1 when
a firm restates its financial statement in the current or prior year and 0 otherwise. Age is an indicator variable equal to 1 when the
CEO is over the age of 60 years and 0 otherwise. Tenure is the number of years that the CEO has been in office as of the fiscal
year-end. We report the estimated coefficients from a logistic regression and the corresponding 2

41
TABLE 6
CEO Turnover and Loss: Decomposition of Losses
(1) (2)
Intercept -3.132 (138.11)**
-3.497 (143.35)**
LossIB (11) 0.294 (8.01)**
0.203 (5.86)*
LossNR (12) 0.211 (8.57)**
0.196 (6.30)*
Test: 11  12  0 0.083 (0.41) 0.007 (0.01)
Stock-return -0.383 (29.19)**
-0.358 (20.67)**
Board-size 0.007 (0.28) 0.017 (1.35)
Board-independence 0.003 (3.26) 0.004 (3.79)
0.764 (138.17)**
0.243 (7.09)**
Ownership -0.021 (11.18)**
-0.016 (5.78)*
Control variables
Stock-volatility 1.957 (5.54)*
1.667 (3.36)
Concentration 0.253 (0.14) -0.314 (0.18)
Size 0.080 (11.64)**
0.092 (12.64)**
Growth 0.004 (0.02) 0.015 (0.25)
0.296 (5.53)*
0.302 (9.57)**
Age 0.685 (125.52)**
0.746 (131.68)**
Tenure 0.030 (54.84)**
0.029 (43.88)**
Fixed effects
Industry Yes Yes
Year Yes Yes
Nagelkerke R2
8.84% 9.65%
The dependent variable Turnover is an indicator variable that equals 1 when there is a change in the CEO in the two years subsequent to
the current fiscal year and 0 otherwise. The independent variables are as follows. LossIB is an indicator variable with a value of 1 when
income before extraordinary items and discontinued operations is negative for the current year and 0 otherwise. LossNR is an indicator
variable with a value of 1 when the difference between net income and income before extraordinary items and discontinued operations is
negative for the current year and 0 otherwise. Accounting-return is the industry-adjusted return on assets measured as the difference
between the firm-specific and the industry-median income before extraordinary items deflated by total assets at the beginning of the year.
Accounting-return is the difference between current period income before extraordinary items and the corresponding number in the
prior year deflated by total assets at the beginning of the year. Stock-return is the difference between the raw returns and the value-
weighted CRSP market returns over a twelve-month fiscal period. Board-size is the number of directors on the board. Board-
independence is the percentage of independent directors on the board. Separate-chair and Separate-committees are indicator variables set
to 1 when a firm has a separate CEO and board chair and when a firm has separate audit, nominating, and compensation committees,
respectively. Ownership is the percentage of common stock held by the CEO. Stock-volatility (Earnings-volatility) is the standard
deviation of Stock-return (Accounting-return) based on prior twenty-four monthly (five year) data. Forecast-error is the difference
between reported annual EPS and the mean forecast EPS deflated by stock price at the beginning of the year. Concentration is the
industry level Herfindahl index. Size is the logarithmic transformation of the fiscal year-end market value of equity. Growth is the sum of
the market value of equity and the book value of debt scaled by the book value of total assets. Restructure is an indicator variable that
equals 1 if special items as a percentage of total assets are less than or equal to -5 percent and 0 otherwise. Restatement is an indicator
variable equal to 1 when a firm restates its financial statement in the current or prior year and 0 otherwise. Age is an indicator variable
equal to 1 when the CEO is over the age of 60 years and 0 otherwise. Tenure is the number of years that the CEO has been in office as of
the fiscal year-end. We report the estimated coefficients from a logistic regression and the corresponding 2

42
TABLE 7
CEO Turnover and the Impact of Industry Losses
(1) (2)
Intercept -2.001 (117.11)**
-3.512 (142.95)**
Loss 0.798 (40.08)**
0.492 (5.34)*
LossIndustry-loss -1.402 (11.97)**
-0.561 (4.26)*
Industry-loss 0.144 (0.34) -0.236 (0.57)
Accounting-return -0.006 (0.01) 0.037 (0.01)
Stock-return -0.379 (48.18)**
-0.360 (20.88)**
Board-size 0.017 (1.41)
Ownership -0.016 (5.89)*
Control variables
Stock-volatility 2.110 (10.26)**
1.768 (3.77)
Concentration 0.303 (0.29) -0.310 (0.17)
Size 0.080 (23.64)**
0.097 (14.19)**
Growth -0.030 (2.01) 0.007 (0.06)
0.282 (5.11)*
0.311 (10.15)**
Age 0.654 (161.51)**
0.749 (132.83)**
Tenure 0.007 (5.97)*
0.028 (42.23)**
Fixed effects
Industry Yes Yes
Year Yes Yes
Nagelkerke R2
6.45% 9.62%
a value of 1 when net income is negative for the current year and 0 otherwise. Industry-loss is the proportion of firms reporting
losses in each industry for a given year when industry is defined using SIC 2-digit codes. Accounting-return is the industry-
adjusted return on assets measured as the difference between the firm-specific and the industry-median income before
extraordinary items deflated by total assets at the beginning of the year. Accounting-return is the difference between current
period income before extraordinary items and the corresponding number in the prior year deflated by total assets at the beginning
of the year. Stock-return is the difference between the raw returns and the value-weighted CRSP market returns over a twelve-
month fiscal period. Board-size is the number of directors on the board. Board-independence is the percentage of independent
directors on the board. Separate-chair and Separate-committees are indicator variables set to 1 when a firm has a separate CEO
and board chair and when a firm has separate audit, nominating, and compensation committees, respectively. Ownership is the
percentage of common stock held by the CEO. Stock-volatility (Earnings-volatility) is the standard deviation of Stock-return
(Accounting-return) based on prior twenty-four monthly (five year) data. Forecast-error is the difference between reported
annual EPS and the mean forecast EPS deflated by stock price at the beginning of the year. Concentration is the industry level
Herfindahl index. Size is the logarithmic transformation of the fiscal year-end market value of equity. Growth is the sum of the
market value of equity and the book value of debt scaled by the book value of total assets. Restructure is an indicator variable
that equals 1 if special items as a percentage of total assets are less than or equal to -5 percent and 0 otherwise. Restatement is an
indicator variable equal to 1 when a firm restates its financial statement in the current or prior year and 0 otherwise. Age is an
indicator variable equal to 1 when the CEO is over the age of 60 years and 0 otherwise. Tenure is the number of years that the
CEO has been in office as of the fiscal year-end. We report the estimated coefficients from a logistic regression and the
corresponding 2
–statistics in parenthesis. ** and * denote statistical significance at the 1 percent and 5 percent levels,
respectively, based on a two-tailed test.

43
TABLE 8
Outside CEO Appointment Following Losses
Dependent variable: Outsider replacement
(1) (2)
Intercept 0.099 (0.03) -0.164 (0.03)
Loss 0.490 (4.38)*
0.612 (3.98)*
Accounting-return 0.199 (0.08) 0.743 (0.57)
Accounting-return -0.411 (0.36) 0.370 (0.10)
Stock-return -0.292 (3.56) -0.541 (4.51)*
Board-size -0.018 (0.17)
Separate-chair -0.104 (0.34)
Separate-committees 0.623 (4.41)*
Ownership -0.015 (0.57)
Control variables
lag(Stock-return) -0.264 (3.62) -0.293 (2.12)
Stock-volatility -0.593 (0.10) 2.207 (0.73)
Earnings-volatility 1.231 (1.42) 0.337 (0.04)
Forecast-error 0.376 (0.03)
Concentration -0.971 (0.30) -0.333 (0.02)
Size -0.132 (7.87)**
-0.147 (3.87)*
Growth 0.070 (1.31) -0.001 (0.01)
Restructure 0.067 (0.10) 0.146 (0.24)
Restatement -0.020 (0.01) -0.264 (1.30)
Tenure -0.026 (7.52)**
-0.026 (3.89)*
Fixed effects
Industry Yes Yes
Year Yes Yes
Observations 1,379 886
Nagelkerke R2
10.23% 13.10%
The dichotomous dependent variable Outsider replacement equals 1 when the incumbent CEO is replaced with a successor CEO
from outside the firm and 0 if the replacement CEO is appointed from within the firm. The independent variables are as follows.
Loss is an indicator variable with a value of 1 when net income is negative for the current year and 0 otherwise. Accounting-
return is the industry-adjusted return on assets measured as the difference between the firm-specific and the industry-median
income before extraordinary items deflated by total assets at the beginning of the year. Accounting-return is the difference
between current period income before extraordinary items and the corresponding number in the prior year deflated by total assets
at the beginning of the year. Stock-return is the difference between the raw returns and the value-weighted CRSP market returns
over a twelve-month fiscal period. Board-size is the number of directors on the board. Board-independence is the percentage of
independent directors on the board. Separate-chair and Separate-committees are indicator variables set to 1 when a firm has a
separate CEO and board chair and when a firm has separate audit, nominating, and compensation committees, respectively.
Ownership is the percentage of common stock held by the CEO. lag(Stock-return) is the one-year lagged value of Stock-return.
Stock-volatility (Earnings-volatility) is the standard deviation of Stock-return (Accounting-return) based on prior twenty-four
monthly (five year) data. Forecast-error is the difference between reported annual EPS and the mean forecast EPS deflated by
stock price at the beginning of the year. Concentration is the industry level Herfindahl index. Size is the logarithmic
transformation of the fiscal year-end market value of equity. Growth is the sum of the market value of equity and the book value
of debt scaled by the book value of total assets. Restructure is an indicator variable that equals 1 if special items as a percentage
of total assets are less than or equal to -5 percent and 0 otherwise. Restatement is an indicator variable equal to 1 when a firm
restates its financial statement in the current or prior year and 0 otherwise. Tenure is the number of years that the CEO has been
in office as of the fiscal year-end. We report the estimated coefficients from a logistic regression and the corresponding 2
–
statistics in parenthesis. ** and * denote statistical significance at the 1 percent and 5 percent levels, respectively, based on a two-
tailed test.

Aloke Ghosh Speaks at Fox Business School, Temple University, Philadelphia

Aloke Ghosh Speaks at Fox Business School, Temple University, Philadelphia

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