Download Do executives have fixed effects on firm

Do executives have fixed effects on firm-level stock price crash risks
Preliminary Version (Please Do Not Quote)
Jiaxin Liu
City University of New York, Baruch College
09/2015
Abstract
Past literature suggests that managers’ idiosyncratic characteristics affect a number of firm-level
policies, such as financing, investing, organization, financial reporting and tax strategies (e.g.
Mason and Hambrick (1984), Bertrand and Schoar (2003), Bamber et al. (2010), etc.). However,
there has been limited evidence on how idiosyncratic managerial characteristics manifest into
capital market consequences. This paper investigates whether individual CEOs and CFOs have
“styles” (i.e. manager’s fixed effects) in corporate bad news withholding which is captured using
firm-level future stock price crash risks. Tracking managers that move across firms and
employing a manager fixed-effect model, I find that individual CEOs exert an incremental effect
on their firm’s future stock price crash risk using multiple crash risk measures. While individual
CFOs have a marginal incremental effect on their firm’s future stock crash incidence measure,
they are significantly associated with the negative skewness of future returns and the down-to-up
return volatility ratio of the firm. In addition, I find that the magnitude of a CFO’s effect on the
negative skewness and down-to-up volatility ratio of returns is positively related to a CFO’s
managerial ability. Similary, the magnitude of a CEO’s effect on future crash incidence is
positively related to a CEO’s managerial ability. Lastly, I find that CEOs who are born before
World War II are less prone to crash risks than CEOs born after the war, consistent with prior
findings that the former are more conservative in corporate voluntary disclosures (Bamber et al.
2010).
1. Introduction:
Studies on firm-specific stock price declines suggest that firm-level bad news withholding
behaviors manifest into firm-specific accruals, tax avoidance and biased earnings guidance and
are also associated with the likelihood of a future stock price crash (Kim et al.( 2011a, b), Kim
and Zhang (2014), Hamm et al. (2014)). These studies, however, do not distinguish between the
effects of manager- and firm-specific bad news withholding behaviors on stock price crash risks.
This paper explores this issue by examining whether management fixed-effects exist in stock
price crash risk.
After the collapse of Enron and other high-profile companies in the early 2000s, as well as the
recent financial crisis, a stream of research has investigated the cause of extreme price declines
(e.g. Jin and Meyers (2006); Hutton et al. (2009); Kim et al. (2011)). Studies highlight the
agency problem behind stock price crashes in which managers tend to hide the privately
observed bad news from investors due to personal reputation and career concerns. They stockpile
this bad news until it reaches a tipping point where they are no longer able to hoard more bad
news, and they release it all at once, resulting in a stock price crash.
Managers with heterogeneous personal values of honesty, conservatism, risk-taking, and
cognitive basis (i.e. knowledge of assumptions, alternatives and consequences of future events)
exhibit different degrees of tolerance towards negative news and preferences for the timely
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release of the bad news. Therefore, it is expected that there exists a management-specific
idiosyncratic component in stock price crash risk. Prior studies seem to suggest such a link
given the findings of manager fixed-effects in firms’ financial reporting opacity (Ge et al.
(2011)), voluntary disclosure quality (Bamber et al. 2010) and tax avoidance (Dyreng et al.
(2010)), as well as the evidence that stock price crashes are associated with firm-level financial
reporting (Hutton et al. 2009; Hamm et al. 2014) and tax avoidance activities (Kim et al. (2011)).
However, detecting a manager fixed-effect in stock crash risk may be difficult for a few reasons.
First, the neoclassical theory suggests that managers do not bear idiosyncratic decision-making
power that affects organizational strategies because managers’ decisions are motivated and
constrained by economic incentives in compensation contracts and by corporate governance
mechanisms (Weintraub (2002); Bertrand and Schoar (2003)). Thus, it is possible that managers
are passively selected into the firm by the board of directors based on a profile of firm
characteristics, e.g. organizational culture and goals. Therefore, the variation in future stock
performance may be completely predicted by firm-level characteristics with minimum influence
from the manager. To address this concern, I include firm fixed-effects in panel regressions to
control for unobservable firm-specific characteristics.
Second, there may not be an association between individual manager and firm crash risks if the
risk of concealing bad news attached to idiosyncratic managerial characteristics are anticipated
and priced into firm valuation by investors before the bad news surprises arrive in the market. In
a semi-efficient market, it is not unreasonable to expect that some observable demographic and
personal information of managers (e.g. age, gender, educational and working background,
religious beliefs, etc.) are factored into the discount rate in stock valuation and into the market’s
crash risk assessments.
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Third, there may not be any incremental, manager-specific effects on firm crash risk if new
managers release accumulated bad news in the initial year of their appointment. Pouraciau
(1993) suggests that incoming executives tend to record large write-offs and special items that
decrease earnings in the year they enter management and to increase earnings in the following
year. Meanwhile, the departing executives tend to decrease earnings during their last year of
tenure. Yu (2012 working paper) develops a model suggesting that the new CEO is likely to
create a “big bath” at his or her initial appointment when the benefits of a lower risk-premium
demanded by investors, that is associated with low earnings, outweighs the cost of a reduction in
the CEO’s compensation value. Therefore, if the big-bath associated with the departing and the
newly appointed managers has exhausted all bad news within the firm, we may not be able to
detect an empirical association between individual managers and crash risk in the remaining
tenure of the new manager as there is not sufficient bad news that can manifest into stock crashes
in subsequent years.
Taken together, it remains an empirical question whether individual manager’s idiosyncratic
characteristics are associated with future firm-level crash risk. To examine whether managers
carry their individual style of withholding bad news and the associated impact on firm-level
crash risk from one firm to another, I track managers who worked for multiple firms in the
ExecuComp database for the sample period of 1992-2013. I identify 553 CEOs and 513 CFOs
that have consecutive employments with at least two firms and investigate for each firm whether
the crash risks of the executives’ appointment period is significantly different from the crash
risks in the period in which they are absent. The research design aims to delineate managerspecific effects on crash risk from the unobserved, firm-specific confounding factors and other
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time-varying firm characteristics that are correlated with crash risk. In addition, I include firmspecific fixed-effects in the panel data regression to further address this concern.
Following prior studies, I use four crash risk measures (Hutton et al. 2009). The first, the discrete
crash risk measure, equals 1 if a firm-specific weekly return falls below 3.09 standard deviations
from its mean annual weekly return, and 0 otherwise. This measure captures the likelihood the
firm experiences extreme stock return declines in a year. The second is a variation of the first
crash risk measure and is calculated as the ratio of the number of crash incidences in a firm-year
to the total number of crash incidences of firms in the same industry during the year. This
measure aims to capture the relative crash likelihood of a specific firm to its industry peers in a
year. The third measure is the negative conditional skewness of firm-specific weekly returns,
capturing the downside risks of firms having extreme negative stock returns (Chen, Hong and
Stein (2001)). The last measure is the down-to-up return volatility ratio constructed by Chen,
Hong and Stein (2001) that is an alternative measure of the negative skewness of stock returns,
but is less subject to extreme outlier days.
Applying these measures, I find some evidence that individual managers do exert significant
influence on firm-level crash risk. For a sample of 648 CEOs 1, I find that individual CEOs are
associated with the cross-sectional variation in all crash risk measures. Additionally, for a sample
of 513 CFOs, after controlling for the effect of concurrent CEOs, I find that CFOs are associated
with the negative skewness of future stock returns and with the down-to-up return volatility ratio.
Having established the association between individual manager’s fixed-effects and firm-specific
crash risk, I then examine whether some observable managerial characteristics, such as age,
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Stata drops a number of CFOs and CEOs that switched between firms due to multicollinearity issues in estimating
the manager fixed-effects in the OLS regression. Therefore, the sample size of CFOs (and CEOs) who have
estimable manager fixed-effects is reduced to 461 (and 449). To be consistent with Stata’s output, I report sample
sizes before the omission in the tables in Section 4.
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gender and managerial ability, can contribute to firm crash risk. The “upper echelon theory”
suggests that demographic managerial characteristics are associated with managers’ key
formative experiences and managerial knowledge, and are a reflection of managers’ values and
cognitive basis that shape their decisions and choices.
I estimate the magnitude of fixed-effects of each individual manager on firm crash risks.
Following Bamber et al. (2010), I separate managers into pre- and post-World War II age groups
and into male/female gender groups to account for the differences in disclosure conservatism
associated with each group. In addition, I estimate the portion of managerial ability attributed to
each switching manager (e.g. fixed managerial ability) from the ability measure developed by
Demerjian (2012).
I find that the CFO’s fixed effect on two crash risk measures, negative skewness of future
weekly returns and the down-to-up return volatility ratio, are positively associated with the
CFO’s fixed managerial ability, but not with the CFO’s age cohort or gender. In addition, I find
that the CEO’s fixed-effect on the discrete crash measure is positively associated with the CEO’s
fixed managerial ability. Meanwhile, the CEO’s fixed-effect on all crash risk measures is
negatively (positively) associated with the CEO’s age-cohort variable, suggesting that CEOs
born before WWII are less aggressive in bad news withholding and less prone to firm-specific
crash risk. This finding is consistent with the prediction of rent extraction theory that more able
(reputable) CEOs are more likely to be subject to the “winner’s curse”, in which they are
distracted by social activity and more likely to shirk and to engage in earnings manipulation to
meet the market’s expectations (Francis et al. (2008) ; Malmendier and Tate (2009)).
In sum, this paper finds managers’ idiosyncratic characteristics (“style”) have an incremental
influence on future firm-level crash risk. Combined with the evidence on the association between
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observable managerial characteristics and crash risk, I capture the systematic differences in
managers’ unique bad news withholding and disclosure styles in an indirect way.
The study contributes to the literature in several ways. First, to the best of my knowledge, this
paper is the first in the crash risk literature to examine the determinants of crash risks from the
dimension of people (i.e. corporate managers). Prior research focuses on the impact of firm-level
characteristics (e.g. reporting opacity, accounting conservatism, executive’s equity incentives,
tax avoidance strategy and management earnings forecasts) on corporate bad news withholding
and disclosure and thus on the crash risks in the future (e.g. Kim and Zhang (2015), Kim et al.
(2010) a,b, Hutton et al. (2009), Hamm et al. (2014), etc.). This paper investigates the primary
underlying causes of firms’ bad news withholding behaviors—the idiosyncratic characteristics of
managers that shape their decision choices on the disclosure of bad news—and its consequences
on firm-level crash risk. In addition, none of the previous studies have included firm fixedeffects in the crash risk model. My results are robust to the inclusion of firm fixed-effects,
solidifying the direct association between managers and crash risk.
Second, there have been limited studies examining whether market perceives individual
managers differently. Hayes and Schaefer (1997) suggest that the market has a negative reaction
towards the departure of managers who are hired by another firm, implying investors do value
manager’s ability. Pan, Wang and Weisback (2014 working paper) find that the market views the
uncertainty associated with a CEO’s ability as risky, which is reflected in greater stock price
volatility. Kim et al. (2014 working paper) find that overconfident CEOs are associated with
greater stock price crash risk. In a semi-efficient market, there should not be any systematic
differences in stock price crash events across managers if the market is able to distinguish the
tendency of individual managers to “not tell the ugly truth” and to price that probability in the
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stock valuation ex-ante. My study is the first to provide indirect evidence on this issue, and my
findings suggest that the market cannot see through the differences in individual managers’ bad
news withholding behaviors.
Third, the findings of this paper should be of interest to the board of directors during the
executive selection process. The selection committee may want to consider manager’s
idiosyncratic, personal characteristics which is associated with future stock price crash if they are
concerned about the negative impact of stock price crash on firm valuation and public image.
The remainder of the paper is divided as follows: Section 2 reviews related literature and
develops the hypotheses; Section 3 describes the research methodologies; Section 4 details the
data and sample selection; Section 5 presents the results of the empirical tests; and Section 6
concludes the paper.
2. Literature review and hypotheses development:
This study draws from two streams of literature: (1) research on managers’ fixed-effects and
firm-level policies, and (2) research on managers’ bad news withholding and firm-level stock
price crash risks. After reviewing each area in the subsequent sections, I join the two streams of
literature to develop and present the hypotheses.
2.1.Managers’ idiosyncratic characteristics (fixed effects):
The “upper echelons theory” suggests that organizational outcomes, such as strategic choices and
performance levels, are partially predicted by manager characteristics (Hambrick and Mason
(1984). The authors propose that organizational strategic choices, in contrast to operational
decisions, may reflect decision maker’s idiosyncratic characteristics, arising from “knowledge or
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assumptions about future events, knowledge of alternatives and consequences attached to each
alternative” (pg.195).
Following the “upper echelons theory”, several studies report empirical findings of manager’s
fixed-effects on firm-level policies (Bertrand and Schoar 2003, Ge et al.2011, Bamber et al.
2010, Dyreng et al.2010).
Bertrand and Schoar (2003) find that individual managers have fixed effects on firm-level
financial policies (i.e. borrowing, cash holding, dividend payout, etc.), investment policies (i.e.
capital expenditure, acquisition, etc.), organizational policies (i.e. R&D, cost-cutting strategy,
etc.), and firm performance (i.e. return on assets, etc.). For example, the paper finds that CEOs
with MBA degrees take on more aggressive strategies than non-MBA CEOs, such as higher
levels of capital expenditures, holding more debt and paying less dividends.
Ge et al. (2011) find that individual CFO’s reporting style explains a significant portion of
variation in firm’s earnings-related reporting characteristics, such as discretionary accruals, the
probability of accounting manipulations and financial reporting conservatism. For example, the
study finds that older CFOs are more conservative in earnings reporting than younger CFOs,
which is evidenced by lower non-operating accruals and more timely disclosure of bad news.
In addition, Bamber et al. (2011) document similar results that the CEO, CFO and general
counsel of the firm have idiosyncratic influence on the firm’s voluntary disclosure
characteristics, such as frequency, accuracy, precision and errors of management guidance. For
example, executives with legal and military backgrounds appear to be more conservative and
provide less good news forecasts than their counterparts, while executives with MBA degrees
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display more aggressive forecasting strategies by providing a greater number of both earnings
forecasts and good news forecasts than non-MBA executives.
This literature extends to tax reporting as well, where Dyreng et al. (2010) document that top
executives have idiosyncratic influence on both the GAAP effective tax rate and cash effective
tax rate of firms, suggesting that individual managers do exert incremental, fixed-effects on firm
tax avoidance policy.
2.2 Bad news withholding and firm-specific crash risk:
Kothari, Shu and Wysocki (2009) show that managers withhold bad news and delay bad news
disclosure out of reputational and career concerns. When managers conceal and accumulate bad
news until it reaches a tipping point, there is no additional room for absorption, forcing the bad
news to be released suddenly to the market which causes an abrupt stock price decline, i.e. stock
price crash (Jin and Meyers (2007); Hutton et al. (2009); Kim et al. (2011)). In addition,
concealment of bad news also prevents investors and board of directors from discerning negative
NPV projects from positive NPV projects on a timely basis. A stock price crash will occur when
bad performance of the negative NPV projects accumulates and eventually materializes (Bleck
and Liu (2007)).
Managers can withhold bad news by manipulating financial statement information. Hutton et al.
(2008) find that firm-level financial reporting transparency is negatively associated with firmlevel crash risk. Kim and Zhang (2014) document that higher accounting conservatism deters bad
news withholding and is negatively associated with firm crash risk.
In addition, managers can camouflage bad news in forecasted earnings, thereby issuing biased
management forecast to guide market expectations. Hamm et al. (2014) find that higher
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management earnings forecast frequency is associated with higher crash risk, suggesting that
managers use more frequent management guidance to conceal bad news and manipulate the
disclosure of bad news.
Moreover, Kim, Li and Zhang (2011) find that tax avoidance activities enable managers to bury
bad news in complicated tax-related transactions. The paper suggests that higher tax avoidance is
associated with higher future firm crash risk.
2.3. Managers’ idiosyncratic characteristics and firm-level crash risks:
Connecting and extending the literature of (1) manager bad news hoarding and firm stock price
crash risks and (2) manager fixed-effects on firm-level policies, I investigate whether individual
CEOs/CFOs have idiosyncratic effects on corporate bad news withholding behaviors, measured
using the firm-level crash risk.
It is widely suggested that managers can conceal and embed a great extent of bad news in
financial statement information and management guidance through a variety of accounting and
tax transactions that obfuscate firm’s financial reporting transparency. Thus, firm-level
characteristics, such as discretionary accruals, earnings guidance frequency, effective tax rate
and accounting conservatism, are used as indicators of the degree of bad news hoarding, and are
associated with future firm-level crash risk (e.g. Kim et la. (2011)a,b; Kim and Zhang (2014);
Hutton et al. (2009); Hamm et la. (2014), etc.).
However, managerial bad news hoarding behaviors associated with negative operational and
strategic events may not be fully captured by these firm characteristics documented in existing
literature. For example, managers may choose to withhold and delay the announcement of a
manufacturing glitch discovered in the current production period. The production glitch may
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increase the probability of future product recalls, resulting in lower future earnings. Another
example is a cost overrun. Managers may conceal the additional cost, as well as the delayed
deadline, from investors in the hope that future costs will drop and progress may recover in the
future. Managers may choose to disclose the cost overrun only when the room of absorbing bad
news becomes less than minimal. Other examples include the potential departure of a key
management member and the failure in negotiating a long-term contract with an incumbent
outsourcing partner, distributor or supplier, all of which can adversely affect future firm
performance. Managers may not choose financial statement or management guidance as vehicles
to hide this bad news, and instead they may simply withhold, accumulate and carry the bad news
into the future, leading to possible abrupt stock declines when a cluster of bad news is released in
later periods.
The “upper echelon theory” suggests that individual managers possess different sets of values
and cognitive basis that determine their perceptions and decision-making processes and
outcomes. Given the same operational and strategic scenario, each manager can exercise
discretion on the time, content and extent of bad news disclosure, resulting in different levels of
bad news withholding and disclosure by each individual. It is reasonable to expect that managers
associated with different personal values (e.g. honesty, conservatism and risk-taking preference),
in conjunction with different cognitive basis (i.e. the knowledge of assumptions, alternatives and
consequences of future events, Mason and Hamribrick (1984)), will exemplify varied degrees of
bad news tolerance and varied bad news disclosure timeliness, which will manifest into the
cross-sectional variation of future firm-level crash risk. For example, it is possible that a
manager who values honesty above personal gains and who is more conservative-oriented may
choose to disclose bad news on a more timely basis, and thus be less prone to future crash risk
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than a manager who places a stronger emphasis on personal gain and has a more aggressive
disclosure style. Natovich et al. (2011) show that IT project managers with higher psychological
capacity 2 are able to disclose bad news on a more prompt basis without excessively considering
the wellbeing of themselves than managers with lower psychological capacity.
However, it is also possible that there is no association between idiosyncratic managerial
characteristics and corporate bad news withholding behaviors (and associated stock price crash
risks).
Firstly, the neoclassical theory suggests that managers are “identical substitutes” and are
homogenous, self-less inputs into the production process (Weintraub 2002). Agency theory
focuses on governance monitoring mechanisms, such as independent boards and incentive
contracting, on constraining manager’s choice of firm policies, being consistent with Hambrick
(2007) argument that managerial influence limited to the decision power one possesses. Betrand
and Schoar (2003) also point out the possibility that homogenous managers are passively chosen
by boards based on a firm’s strategic needs which manifest into a persistent pattern of firm
policies. Therefore, it is possible that the variation in firm-specific stock price crash is purely a
manifestation of firm-level determinants, such as a specific strategy pursued by a firm in a
particular life-cycle stage or the firm-level governance mechanisms that constrain managerial
discretion in rent extraction. In other words, the firm-level characteristics may co-move with
management appointment and fully explain the timing and extent of firm’s bad news disclosure.
Secondly, stock price crash is a market behavior which cannot be easily predicted in the semistrong form of market efficiency. Since much of manager-specific information (e.g. age, gender,
2
Luthans et al. (2007a) develop four metrics to measure a person’s psychological capacity: self-efficacy, optimism,
hope and resilience.
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educational and working background, religion, etc.) is readily available to the public via the
internet, 10-K reports and media interviews, it is highly probable that market has observed such
information, factored it into the crash likelihood and discounted the stock price beforehand. If
that is the case, there should not be any statistically or economically significant manager fixedeffects on firm-level crash risks.
Thirdly, there may not be any incremental, manager-specific effect on firm crash risk if new
managers always tend to release accumulated bad news in the initial year of their appointment.
Pouraciau (1993) suggests that incoming executives tend to record large write-offs and incomedecreasing special items in the year they enter management and increase earnings in the
following year. Meanwhile, the departing executives tend to decrease earnings during their last
year of tenure. Yu (2012 working paper) develops a model suggesting that the new CEO is likely
to create a “big bath” at his or her initial appointment when benefits of a lower risk-premium
demanded by investors outweigh the cost of the CEO’s compensation value reduction. If the
big-bath associated with the departing and the newly appointed manager has consumed the
majority of bad news in the firm, we may not detect an association between the moving manager
and crash risks in firm-years subsequent to her/his appointment because there is insufficient
remaining bad news to generate crash incidences following the initial big-bath.
Therefore, it remains an empirical question whether individual managers have “style” in bad
news withholding, illustrated in their idiosyncratic influence on firm-level crash risks. I examine
the association between the individual chief executive officer (CEO) (and chief financial officer
(CFO)) and firm-level crash risks with the following hypotheses:
H10: There is no association between individual CFOs and firm-level crash risk in the future.
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H1a: There is an association between individual CFOs and firm-level crash risk in the future.
H20: There is no association between individual CEOs and firm-level crash risk in the future.
H2a: There is an association between individual CEOs and firm-level crash risk in the future.
3. Research Methodology and Sample:
3.1. Research model and sample for managers’ fixed-effects on crash risks:
Following prior research (Bertrand and Schoar (2003); Bamber et al. (2011); Ge et al. (2010)), I
use a manager fixed-effect model to analyze CEO/CFO’s idiosyncratic effect on firm future
crash risk. I tracked down CEOs/CFOs who worked for at least two firms, denoting them as
“Switching CFO” (“Switching CEO”). To disentangle firm-level characteristics and other timevarying factors that may be correlated with the presence of individual switching managers and
with firm crash risks, I match the switching managers to the matching managers, i.e. managers
who worked in the same firm before and after the switching managers (Diagram 1). For example,
in Diagram 1, a switching manager worked for firm A in period 2 and move to firm B in period
3. The switching manager is the manager of interest. I track her/him from firm A to firm B.
Matching Manager A1(B1) and A2(B2) are used as the control group of managers since these
managers are subject to the same unobserved, firm-specific characteristics as the switching
managers in each firm.
Firm A
Firm B
Period1
Matching
Manager A1
Diagram 1
Period2
Period3
Switcher
Matching Manager B1
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Period 4
Matching ManagerA2
Switcher
Matching
Manager B2
I start with the CEOs and CFOs listed in the ExecuComp database in the period from 1992to
2013 3. The initial sample consists of 6,754 CFOs and 6,898 CEOs. I tracked CFOs/CEOs who
worked for least two firms (i.e. switching CFOs/CEOs), and find there are 576 switching CFOs
and 759 switching CEOs. I then merge the data with the CRSP database and as a result only 561
switching CFOs and 687 switching CEOs remain due to missing returns observations in
calculating crash risk measures. I further merge the data with Compustat which results in 513
switching CFOs and 648 switching CEOs remaining due
[INSERT TABLE 1 HERE]
to missing financial data in calculating the control variables. The estimation sample is reduced to
12,439 firm-year observations (7,103 in the CFO sample and 5,536 in the CEO sample)
including matching firm-years of non-switching managers. However, Stata drops a number of
switching CFOs/CEOs in the process of estimating managers’ fixed-effects due to
multicollinearity. Therefore, the tests on managers’ fixed-effects are estimated for 461 switching
CFOs and 449 switching CEOs 4.
To estimate managers’ fixed-effects on firm-level crash risks, I employ Model (3-1) and Model
(3-2) as follows. I include an indicator variable for each switching manager, firm and year in the
model. Prior studies document that CFOs may succumb to the CEO’s pressure to manipulate
earnings (Feng et al.(2010)). Thus, it is possible that the CFO’s style of bad news withholding is
a manifestation of the CEO’s decision style. Therefore, for the CFO’s fixed effect sample, I
3
I identify CEOs and CFOs using the annual title and executive title variables in ExecuComp. I include any
CFO/CEO that has a “CFO” or “CEO” annual title in the field of “ceoann” or “cfoann”, or that has the words “Chief
Financial Officer”, “Chief Executive Officer”, “Comptroller”, “Treasurer”, or “Vice President of Finance” in the
field of “anntitle”.
4
To be consistent with Stata, I report the sample before the omissions (i.e. 513 CFOs and 648 CEOs with 12,439
firm-year observations) in the resulting tables.
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include an indicator variable for the concurrent CEO to delineate the CFO’s effect from the
CEO’s effect.
CFO Sample:
Crash Risksj,t+ 1= α + β1 Controls jt + β2 Firm fixed-effectjt + β3 Year fixed-effectjt + ɛ jt ;
Model (2-1),
Crash Risksj,t+1 = α + β1 CFO Switching CFO Dummiesjt+ β2 Controlsjt +
β3 Firm fixed- effectjt+ β4 Year fixed-effectjt + β5 ConcurrentCEOjt + ɛ jt;
Model (3-1),
CEO Sample:
Crash Risksj,t+1 = α + β1 Controls jt + β2 Firm fixed-effect jt + β3 Year fixed-effect jt + ɛ jt;
Model (2-2),
Crash Risksj,t+1 = α + β1 CEO Switching CEO Dummiesjt + β2 Controlsjt +
β3 Firm fixed-effectjt + β4 Year fixed-effectjt + ɛjt;
Model (3-2),
The null hypothesis is that there is no fixed-effect of individual switching CEOs/CFOs on firm
future crash risks. Thus, the F-test for whether the coefficients of individual switching
CEOs/CFOs are jointly zero (i.e. Not all β1 CFO s = 0; and not all β1 CEO s = 0) is performed. If
the F-statistic values are significant at <0.1 level, i.e. the coefficient of at least one switching
manager is not zero, I can reject the null hypothesis, suggesting that individual switching
CEOs/CFOs in my sample do exert idiosyncratic effects on the one-year ahead firm-level crash
risk.
Model (2-1) and Model (3-1) are the restricted models from past crash risk studies, controlling
for firm and year fixed-effects. The adjusted R2 of these models will be compared against the full
models to identify the incremental effects of switching managers on existing crash risk
determinants.
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Crash Risksj,t+ 1 are the four crash risk measures which capture the probability the firm
experiences extreme stock price declines in the future, as adopted from prior studies (Hutton et
al. (2009), Kim et al. (2011), Hamm et al. (2014)). Crash1j,t+1 is coded 1 if in a given fiscal firmyear, the firm experiences one or more firm-specific weekly returns 5 that decrease 3.09 or more
standard deviations below the annual mean firm-specific weekly return (Wjt) measured over the
entire fiscal-year, and 0 otherwise. In addition, I create a variation of Crash1j,t+1 , Crash%j,t+1,
which is the ratio of the number of firm-specific crash incidences (defined by Crash1j,t+1) per
year to the total number of crash incidences of all firms in the same year and industry. This
measure aims to capture the crash likelihood of a firm relative to its industry peers 6. Crash2j,t+1,
is the negative conditional skewness of firm-specific weekly returns developed by Chen, Hong
and Stein (2001), and it is calculated for a firm-year by taking the negative of the third moment
of firm-specific weekly returns of the sample firm-year and dividing it by the standard deviation
of firm-specific weekly returns over the year raised to the third power, as shown in the following
equation:
Crash2j,t+1=-[n(n-1)3/2ƩW3 j,t+1]/[(n-1)(n-2)( ƩW2jt+1)3/2]
5
.All crash risk measures are constructed using the firm-specific weekly returns calculated from the expanded
market model – Model (1).
rjt= α +β1jrm t-1+β2j rmt+ β3j rmt+1 + β4j rit-1+ β5j rit + β6j rit +1+ ɛjt Model (1)
rj,t is the CRSP return on firm j in week t , and rm,t is the industry-weighted market index in week t. I include the lead
and lag terms for market returns to allow for nonsynchronous trading (Dimson 1979). I am interested in the residual
ɛjt from the market model, which is the firm-specific portion of weekly stock returns. The firm-specific weekly
return, denoted by Wjt, is calculated as the natural log of one plus the residual return from the above model, i.e.
Wjt=ln(1+ ɛ).
6
I construct this measure based on Crash1j,t+1 to address the non-convergence issue of the logistic regression in
model (3-1) and model (3-2) with respect to Crash1j,t+1. Due to the inability of the Stata program to converge in the
logistic regression, I use an OLS regression to approximate the test results of manager’s fixed-effect on Crash1j,t+1.
However, I recognize that the discrete dependent variable is not normally distributed and thus, coefficient estimation
is biased. The alternative measure, Crash%j,t+1, is a continuous measure of the value between 0 and 1, which is a
better specified dependent variable in the OLS regression, theoretically producing less biased but qualitatively
comparable results to Crash1j,t+1.
17
Crash3j,t+1 is defined as log of the ratio of the standard deviation of firm-specific weekly returns
that are above the annual mean returns to the standard deviation of returns that are below the
annual mean returns, capturing the relative volatility of firm-specific weekly returns of the
“down weeks” to the “up weeks”.
Crash3j,t+1= Log( σdown j,t+1 / σup j,t+1)
All crash risk measures are one-year ahead measures (i.e. year t+1) since it takes time for the
bad news to accumulate in the current period and to be released in the next period when the
tipping point is reached. I aim to capture the bad news withholding behaviors of managers rather
than the “whistle blowing” behaviors.
I also include a set of control variables following Chen et al. (2001), Hutton et al. (2009) and
Kim et al. (2011). DTURN is the detrended stock trading volume, which is a proxy for investor
heterogeneity, or the difference of opinions among investors. Firms with high stock turnovers are
more likely to have stock price crashes in the future. The NCSKEW is the negative skewness of firmspecific stock returns in the prior year, capturing the potential persistence of the third moment of
stock returns. sdW is the standard deviation of past firm-specific stock returns, controlling for the
fact that more volatile stock is more prone to crash in the future. w is the average firm-specific
weekly return over the past year, with higher past returns associated with a greater probability of
crashing in the future (Chen et al. (2011)). I include the standard control variables for firm size
(SIZE), market-to-book ratio (MB), financial leverage (LEV), and return on assets (ROA) (Hutton et
al. 2009). In addition, DISACC is the modified Jones’ discretionary accruals, controlling for the
financial reporting opacity of the firm. Hutton et al. (2009) find that firms can conceal bad news in
manipulated earnings and financial reporting opacity is positively associated with stock crash risks in
the future.
3.2. Observable manager characteristics and managers’ impact on crash risks:
18
Following prior studies, I examine whether observable manager characteristics, such as
managerial ability (Demerjian (2012)), age cohort 7 and gender (Bamber et al. (2011), Ge et al.
(2011)) are associated with the magnitude of manager’s fixed-effects on firm’s future crash risk.
The OLS model is as follows, with all variables winsorized at 1% and 99%:
λmt = α+γ1 MA_Coeffmt + γ2Age_Cohortmt + γ3 Gendermt + ɛmt.
Model (4)
λmt are the coefficients from models (3-1) and (3-2), representing individual CFO’s/CEO’s
fixed/incremental effects on firm crash risk. MA_Coeffmt is manager’s fixed-effect on
Demerjian’s managerial ability score (i.e. fixed managerial ability), estimated using the OLS
regression in Model (5).
MA_scorejt (Demerjian et al. (2012) ability measure)
= α+β1SwitcherDummiesjt+ β2Firms jt+ β3Yearsj,t+ β4 ConcurrentCEO/CFOjt + ɛjt
Model (5)
In Model (5), I regress the overall managerial ability (MA_scorejt) from Demerjian et al. (2012) 8
on the switching manager dummies (SwitcherDummiesjt) to obtain MA_Coeffmt (i.e. a manager’s
fixed-effect on overall managerial ability). Footnote 5 illustrates the estimation of the overall,
general managerial ability measure, MA_scorejt developed by Demerjian et al.(2012) and shows
that it may contain unobserved firm-level noise. MA_Coeffmt is the β1 estimated for each
switching manager in Model (5), purging off firm noise and better capturing the manager-
7
Age corhort is an indicator variable that equals 1 if a manager is born before 1945 (i.e. pre-World War II), and 0 if
a manager is born after 1945 (i.e. post-World War II), following Zemke et al. (2000) and Bamber et al. (2010).
8
Demerjian et al. (2012) develop a measure for managerial ability by regressing firm efficiency on firm size, market
share, free cash flow, firm age and business segment concentration using aTobit regression.
Firm Efficiencyit = α + β1ln(Total Assets) it + β2Market Share it + β3 Free Cash Flow Indicatorit + β4ln(Age) it + β5
Business Segment Concentration it + Year Dummies it + Industry Dummies it + ɛit.
The residual is deemed to capture firm operating efficiency that cannot be explained by firm-level variables, and is
considered to measure managerial ability. This measure can be obtained from Demerjian’s website directly:
https://community.bus.emory.edu/personal/PDEMERJ/Pages/Download-Data.aspx
19
specific contribution to general managerial ability. Age_Cohortmt is defined 1 if a manager is
born before 1945 (World War II), and 0 otherwise. Gendermt is 1 if the manager is male and 0 if
the manager is female.
4. Results:
4.1 Main results:
Table 2 summarizes the descriptive statistics of the crash risk measures and control variables.
The mean Crash1t+1 is 29.4% 9, which is relatively higher than the 10-20% range reported by
previous studies, suggesting that on average 29.4% of firm-years in the sample period of 19922013 experience one or more firm-specific weekly returns that fall 3.09 standard deviations
below (above) their annual mean stock return. The mean weekly stock return of sample firms is
0.31%.
[INSERT TABLE 2 HERE]
Table 3 shows the pairwise Pearson Correlation matrix of the dependent and independent
variables. It shows that that the four crash risk measures (Crash1jt+1, Crash1%jt+1, Crash2jt+2 and
Crash3jt+3) are highly correlated with each other at 0.01 level.
[INSERT TABLE 3 HERE]
Table 4 tabulates the result from Models (2) and Models (3) – the models of CFO’s and CEO’s
fixed-effects on future crash risk. For the CFO sample, after controlling for the concurrent CEO
9
The mean Crash1 t+1 reported here is relatively higher than the 10-20% range reported by previous studies (Kim et al. 2011,
Hutton et al. 2009, Kim and Zhang 2013, Hamm et al 2014). My sample average Crash1 t+1 is closest to Hamm et al. (2014)’s
sample average of 19.75%. Ham et al. (2014) cover a sample period of 1996-2009, which partially includes the most recent
financial crisis. One explanation of my higher Crash1 measure is that my sample is from 1992-2013, a more comprehensive
coverage of the entire financial crisis period, including both the head and tail. Thus, it would not be surprising to see a greater
percentage of crash incidence in my sample than Hamm et al (2014)’s.
20
and firm and year fixed-effects, the F-statistic has a p-value of 0.06 (<0.1) for the Crash1jt+1
measure, suggesting that individual CFOs do exhibit idiosyncratic style in bad news withholding
that affects firm-level crash risks. However, for the alternative measure, Crash%jt+1, the Fstatistic is not significant at <0.1 level. It appears that the association between individual CFOs
and future crash incidences of the firm is weak. Nonetheless, the F-statistic is 1.22 for Crash2jt+1
and is significant at the level of 0.0017. In addition, for the Crash3jt+1 measure, the F-statistic is
1.26 and is significant at the level of <0.001. The results suggest that individual CFOs have
incremental influence on the negative sknewness of future returns and on the down-to-up
volatility ratio of future returns.
[INSERT TABLE 4a HERE]
I also examine the relative change in the adjusted R2s 10 between the restricted and the full
models. It is of note that controlling for firm fixed-effects in the crash risk model reduces the
adjusted R2 to negative. Past literature on crash risks does not include controls for firm fixedeffects (e.g. Hutton et al. 2008, Hamm et al .2014; Kim and Zhang 2014, Kim et al. 2011, etc.).
The adjusted R2 s are between 3%-8% in those models. Untabulated results show that, without
controlling for the firm fixed-effect, I achieve the adjusted R2 in a similar range (3%-5%) in my
paper, which is comparable to prior studies. To control for the time-invariant, firm-specific
characteristics that may be correlated with firm crash risk, I include firm and time fixed-effects
in the model.
For the CEO sample, I find that individual CEOs are associated with all of the crash risk
measures. In Table 4b, the F-statistic for Crash1jt+1 is 1.22 and is significant at the 0.0086 level.
10
Adjusted R2 is calculated from the within R-squared of the firm fixed-effect regression (Stata: xtreg, fe command).
21
The F-statistic for the alternative measure, Crash%jt+1, is 1.26 and significant at the 0.0000 level.
The adjusted-R2 of the full model has increased by 17.43% ((-8.39%-(-10.16%))/10.16% =
17.43%) relative to the restricted model of Crash1jt+1, suggesting that individual CEO’s
idiosyncratic characteristics exert influence on the probability of a firm’s future stock price
crash. The adjusted-R2 of Crash%jt+1 in the full model increases from -13.59% to -8.64%, a
relative increase of 36.42% from the restricted model. For Crash2jt+1, the negative stock return
skewness of future’s firm returns, the F-statistic is 1.17 and is significant at the 0.0102 level,
with an adjusted R2 that increases from -5.52% to -3.87%, a relative increase of 29.84% from the
restricted model. For Crash3jt+1, the down-to-up volatility ratio of future stock returns, the Fstatistic is 1.22 and is significant at the <0.01 level. The adjusted R2 in the restricted model is 9.06% and has increased to -6.90% in the full model, a relative increase of 23.85%. In sum, I
find evidence that individual CEOs have an idiosyncratic style in bad news hoarding that
influences future firm-level crash risk.
[INSERT TABLE 4b HERE]
4.2 Additional results on manager demographic characteristics and manager’s fixed-effect
on crash risk:
Table 5a and 5b present the results of model (4), which examines the association between
manager’s effect on firm future crash risk and manager’s personal and demographic
characteristics.
[INSERT TABLE 5a HERE]
Table 5a presents the results for the CFO sample. Panel A shows the distribution of CFOs’ fixed
effect on the crash risk measures and the value of the general managerial ability of the firm in the
22
CFO sample (e.g. MA_score from Demerjian et al.2012). Approximately 3-12% of the sample of
CFOs has a fixed effect, at the 0.1 level, on the crash risk measures. The mean fixed effect of an
individual CFO on Crash1jt+1 (Crash%jt+1, Crash2 jt+1, Crash3 jt+1) is -0.005 (-0.001, 0.123 and
0.047, respectively). The mean fixed effect of an individual manager on the general managerial
ability is -0.008, while the median fixed effect is -0.005. About 20% of CFO sample has a fixed
managerial ability significant at the <0.1 level. Panel B provides evidences that on average, an
individual CFO does have a fixed managerial ability that she brings with her from firm to firm
given that the Fischer’s F-statistic is significant at the <0.000 level.
Panel C shows the descriptive statistics of CFOs’ personal and demographic characteristics. The
mean MA_score is 0.012 for the sample of CFOs, and 51.7% (48.3%) of CFOs have positive
(negative) managerial ability, suggesting they contribute to firm operations in a positive
(negative) way. About 6.99% of the sample of CFOs are born before World War II, and are
considered to be more conservative in their disclosure styles (Bamber et al. 2010) while 91.13%
of CFOs are male and are considered to be more aggressive than female CFOs.
Panel D presents the results of Model 5 for the CFO sample. After controlling for age and
gender, Crash2jt+1 and Crash3jt+1 are associated with CFO-fixed managerial ability. The
coefficients on Crash2jt+1 and Crash3jt+1 are negative, consistent with the explanation that more
able managers are associated with greater reputational risk 11 and are more likely to engage in bad
news withholding to meet the market’s expectations. However, I do not find any association
between managerial ability and the dichotomous crash measures Crash1jt+1 and Crash%jt+1.
11
Rent extraction theory predicts that reputable CEOs are more likely to manipulate earnings to “pretense and
maintain” the good performance of firms since in the past they were awarded for actual good performance based on
their high ability (Francis et al. 2008). In addition, Malmendier and Tate (2008) find that media covered “superstar”
CEOs underperform after such recognition by correspondingly increasing the level of earnings management. They
argue these CEOs spend more time on public figure events and as a result have to manipulate earnings to meet the
market’s expectation, which is based on prior “star” performance.
23
Table 5b presents the results for the CEO sample. Panel A shows the distribution of CEOs’
fixed-effects on the crash risk measures and the value of the general managerial ability of the
firm in the CEO sample. Approximately 3-13% of the sample of CEOs has a fixed effect, at the
0.1 level, on the crash risk measures. The mean fixed effect of individual CEOs on Crash1jt+1
(Crash%jt+1, Crash2 jt+1, Crash3 jt+1) is -0.010 (-0.001, 0.035, 0.025, respectively). Panel B
demonstrates that an individual CEO does have fixed managerial ability that she brings with her
from firm to firm (the F-statistic is significant at the <0.001 level). The mean fixed-effect of an
individual CEO on the general managerial ability is -0.072, while the median fixed effect is
0.000. About 18.28% of sample CFOs have fixed managerial ability significant at the <0.1 level.
Panel B shows that CEOs do have individual-specific managerial ability, suggested by the Fstatistic that is significant at the <0.000 level.
Panel C shows the descriptive statistics of CEOs’ personal and demographic characteristics. The
mean CEO MA_score is -0.0003 with 46.25% (53.75%) of CEOs having positive (negative)
managerial ability, suggesting they contribute to firm operations in a positive (negative) way.
About 30.28% of sample CEOs are born before World War II and 97.78% are male.
Panel D presents the results of Model 5 for the CEO sample. After controlling for age and
gender, Crash1jt+1 and Crash% jt+1 are associated with the CEO’s fixed managerial ability.
However, I do not find any evidence on the association between the CEO’s managerial ability
and the other two crash risk measures. Interestingly, Age_Cohort is negatively associated with
all of thecrash risk measures, consistent with the notion that CEOs born before WWII are more
conservative and tend to disclose bad news on time and accumulate and delay good news release
(Bamber et al. 2010).
24
[INSERT TABLE 5b HERE]
In sum, I find some evidence that top executives’ managerial ability is associated with future
firm-specific crash risks. Specifically, the CFO’s ability is associated with the negative skewness
of stock returns and the down-to-up return volatility ratio of the firm. However, the CEO’s
ability, but not the CFO’s ability, is associated with the discrete crash risk measures and its
variation form (Crash1jt+1 and Crash% jt+1 ). In addition, the Age_Cohort is negatively associated
with the CEO’s fixed effect on crash measures, corroborating the finding of Bamber et al. (2010)
that managers born before WWII have more conservative voluntary disclosure styles.
5. Sensitivity test – replication of Bamber et al. (2010):
I replicate Table 2 “Testing Individual Top Managers’ Fixed Effects on the Number of
Management Earnings Forecasts” of Bamber et al. (2010) using my switching CEO/CFO sample.
The replication has two purposes: (1) ensuring the identification of switching managers and the
relevant sample selection procedures are correct; and (2) ensuring that my Stata coding and
regression techniques are consistent with Bamber et al.’s model.
Following Bamber et al. (2010), I use the regression model (6-2) shown below, substituting the
dependent variable by using the number of earnings forecasts a firm issues per year and using
standard control variables from the management guidance literature. All variable definitions are
in Appendix A.
NumFj, t+1= α + β1 Controls jt + β2 Firm fixed-effect + β3 Year fixed-effect + ɛ jt
Model (6-1)
NumFj, t+1= α + β1 SwitchingCFO/CEO_Dummiesjt+ β2 Controls jt + β3 Firm fixed-effect
+ β4 Year fixed-effect + ɛjt
Model (6-2)
25
Bamber et al. used a sample period of 1995-2005 while my sample period is 1992-2013,
resulting in a larger sample size compared to their study. Management earnings forecasts are
obtained from the Company Issued Guidance database (CIG). Control variables are constructed
from observations in IBES and Compustat. Results are presented in Appendix B. Table B1
presents the sample selection process. The sample consists of 413 switching CFOs (3,891 firmyear observations) and 411 switching CEOs (3,384 firm-year observations).
[INSERT TABLE B1 HERE]
Table B2 is the descriptive statistics of the main and control variables. On average, the sample
firms issue 4.971 management forecasts in a sample-year, which is relatively larger than Bamber
et al.’s sample average of 1.77. I further looked at annual forecasts and quarterly forecasts
separately, and the average number of annual forecasts is 2.815 and quarterly forecasts 2.156,
which is closer to Bamber et al.’s sample average. The mean, median and standard deviation of
other variables are comparable to Bamber et al.’s sample.
[INSERT TABLE B2 HERE]
Table B3 presents the results of managers’ fixed-effects on management earnings forecast
frequency. In general, I find that there is an association between individual CFOs (CEOs) and
firm-specific management earnings forecast frequency. Table 3a shows that CEOs and CFOs
have an incremental, fixed-effect on the frequency of annual earnings forecast with an F-statistic
of 1.62 (1.96) that is significant at the <0.000 level for the sample CFOs (CEOs). Table B3b and
Table B3c show similar results for the frequency of annual and quarterly management forecasts.
[INSERT TABLE B3 HERE]
26
Table B4 is the original table in Bamber et al. (2010), the results of which are comparable to my
results 12.
[INSERT TABLE B4 HERE]
6. Conclusion:
Past theory and evidence suggests that individual managers bring idiosyncratic managerial styles
to the firms in which they work and such styles affect firm-level policies on multiple levels (e.g.
Hambrick and Mason 1984; Bertrand and Schoar 2003, Bamber et al. 2010 and Ge et al. 2011). I
investigate whether managers’ idiosyncratic characteristics exert influence on firm-level bad
news withholding decisions, reflected in the future stock price crash risks of the firm.
Following prior literature and using the ExecuComp database, I track managers who switched
firms in a sample period ranging from 1992 to 2013 and examine whether these managers’
idiosyncratic styles affect firm stock price crash risks. Following Hutton et al. (2009) and related
studies, I use the discrete stock price crash risk measure, the negative skewness of future returns
and the down-to-up returns volatility ratio to capture firm’s stock price crash likelihood.
For the sample of 461 switching CFOs and 449 switching CEOs whose fixed effects are
estimable, I find some evidence that individual CFOs and CEOs are associated with the future
stock price crash risks of their firms. I find that CFOs exert an incremental effect on the three
crash risk measures after controlling for the concurrent CEO and year and firm fixed-effects,
12
Note that in Table B4, the F-statistic in Bamber et al.’s paper is larger in value than the F-statistic in my paper,
and this is because my sample includes a greater number of manager dummies and thus, greater degrees of freedom.
An F-statistic of Fischer’s F-distribution with larger degrees of freedom (both the denominator and numerator d.o.f.)
that falls in the critical region is usually smaller in value than an F-statistic of distributions with smaller degrees of
freedom. In sum, the replication findings I undertake are qualitatively consistent with Bamber et al. (2010)’s Table
2.
27
while the results for the discrete crash risk measure are marginally significant. Meanwhile, I find
that CEOs are significantly associated with all three crash risks measures after controlling for
firm and year fixed-effects.
Building on prior studies on manager personal and demographic characteristics and their impact
on firm-level policies (Baik et al. 2010, Demerjian et al. 2012, Get et al. 2011, Bamber et al.
2010), I examine whether managerial ability, age and gender are associated with the magnitude
of manager’s fixed effects on crash risks. Specifically, I estimate manager’s fixed effects on
managerial ability (Demerjian et al. 2012) to separate any firm-level confounding factors from
manager-specific efficiency, and examine whether the magnitude of manager fixed-effects on
crash risk is associated with their managerial ability, age and gender. The findings show that
CFOs’ fixed managerial ability is positively associated with their idiosyncratic impact on the
negative skewness and down-to-up volatility ratio of firms’ future stock returns. Similarly,
CEOs’ fixed managerial ability is associated with firms’ future crash incidence (on a marginal
basis). This is consistent with the notion that more able managers tend to be overconfident about
future outcomes and withhold more bad news (Baik et al. 2010, Hamm et al. 2014, Kim and
Zhang WP 2013). One explanation is that the market underestimates the likelihood of “superstar
CEOs” shirking, manipulating earnings (Tate and Malmendier 2009) and concealing bad news.
Lastly, I find that CEOs born before WWII are more conservative (Bember et al 2010), and thus,
are less prone to crash risk than CEOs born after WWII.
This paper, to the best of my knowledge, is the first to examine individual managers and the
effect of their idiosyncratic managerial style on firm’s future extreme stock price declines.
28
While prior studies show that firm-level accounting and tax characteristics are predictors of
future stock crash incidence (Hutton et al. 2009, Kim et al. 2011ab, Hamm et al.2014), I direct
the investigation towards managers, the ultimate decision-makers of firm-level policies, and
examine if there is a direct association between individual managers (CFOs/CEOs) and bad news
withholding behaviors, reflected in firm stock price crash risk.
My study also controls for firm fixed-effects in the crash risk model. After controlling for firm,
year and the other concurrent executive (CFO or CEO), my findings are robust to the inclusion
of the unobserved, firm-level characteristics, suggesting that investors and board of directors
may need to take caution in assessing managers’ bad news withholding intention and severity
given the board’s concern for extreme stock price declines in the future.
29
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34
Table 1 Sample Selection
Sample Selection Steps
ExComp
Switching Managers
Data available for crash risk measures calculation on CRSP
CFO
6,754
576
CEO
6,898
759
Total Observations (including
matching managers)
44,118
20,728
561
687
17,023
Data available for control variables calculation on Compustat
513
461
648
449
12,639
11,177
Managers whose fixed effects are estimable
This table presents the sample selection procedures in constructing the sample for manager's fixed effect model:
1. CFO are identified by (1) extracting keywords from the "anntitle" field, including excerpts of "Chief Financial Officer, chief
finance officer, VP in Finance, vice president in finance, vp-finance, treasurer, comptroller", (2) using "cfoann" field indicator;
CEO are identified by (1) extracting keywords from the "anntitle" field, including excerpts of CEO, Chief Executive Officer, (2)
2. Identified switching managers/CFO/CEO are managers that worked for at least two firms in the sample period.
3. There are 513 switching CFOs and 648 switching CEOs identified using my method. However, Stata drops a number of
observations/manager-years in the regression process due to multicollinearity issues. So only 461 CFOs and 449 CEO of whose
fixed effects are estimable. Firm-years attached to the 461 CFOs is 6,308 and to the 449 CEOs is 4,869. Stata reports the estimation
sample sizes as before the omission of the multicollinear managers. To be consistent with Stata, I report in tables the sample size
before the omission: CFO test is 7,103 and CEO test is 5,536
35
Table2
Variable
Crash1 t+1
Crash%t+1
Crash2 t+1
Crash3 t+1
DTURNt
NSKEWt
sdWt
wt
Size t
MB t
LEVt
DISACCt
ROAt+1
Descriptive Statistics (CEO and CFO sample combined)
Obs
Mean Std. Dev. 5%
50%
95%
12,639
0.294
0.456
0.000
0.000
1.000
12,639
0.011
0.047
0.000
0.000
0.053
12,638
0.036
1.495
-1.276
0.194
3.288
12,635
0.133
0.512
-0.512
0.079
1.040
12,639
0.056
0.880
-1.326
0.038
1.487
12,639
0.469
1.531
-1.262
0.207
3.578
12,639
0.063
0.036
0.024
0.054
0.134
12,639
-0.048
0.056
-0.149
-0.031
-0.001
12,639
7.551
1.535
5.076
7.489
10.178
12,639
3.027
4.161
0.578
2.190
9.241
12,639
0.193
0.168
0.000
0.172
0.507
12,639
-0.007
0.121
-0.163
-0.004
0.144
12,639
0.043
0.104
-0.135
0.047
0.194
This table presents the descriptive statistics of crash risk measures and control
variables in manager's fixed effect model. This sample is the combination of CEO and
CFO manager firm-years in the sample period of 1992-2013.
1. All continuous variables are winsorized at 1% level both sides.
2. This table presents descriptive statistics for dependent and independnet variables
for the combined CEO and CFO sample (12,639), with 7,103 from CFO sample and 5,336
from CEO sample
36
a
b
c
d
e
f
g
h
i
j
k
l
m
a
Crash1 t+1
Crash1 t+1
1
Crash%t+1 0.3366*
Crash2 t+1 0.6375*
Crash3 t+1 0.6282*
DTURN t 0.0251*
NSKEW t 0.0059
sdW t
0.0032
wt
0.0024
Size t
-0.0149
MB t
0.0272*
LEV t -0.0349*
DISACC t 0.0212*
ROA t+1 0.0332*
Table 3 Pearson Pairwise Correlation Matrix
b
c
d
e
f
g
h
Crash%t+1 Crash2 t+1 Crash3 t+1 DTURN t NSKEW t sdW t
wt
1
0.2326*
0.2295*
0.0006
-0.0039
-0.0384*
0.0284
0.0413*
0.0389*
0.0151
0.0275
0.0269
1
0.9437*
0.0204
-0.0246*
-0.0476*
0.0340*
0.0234*
0.0138
-0.0621*
0.0273*
0.0584*
1
0.0118
-0.0349*
-0.0532*
0.0378*
0.0611*
0.0119
-0.0484*
0.0346*
0.0662*
1
0.0230*
0.1682*
-0.0913*
0.0157
-0.0087
0.0229*
0.0148
0.0046
1
0.2172*
-0.1979*
0.0293*
-0.0088
-0.0365*
-0.0295*
0.0068
1
-0.5061*
-0.1817*
0.0098
-0.0168
-0.0951*
-0.1000*
1
0.0851*
-0.0081
0.0128
0.0821*
0.0850*
i
Size t
j
MB t
k
LEV t
l
m
DISACC t ROA t+1
1
-0.0044
1
0.1445* 0.0182
1
0.0597* -0.0071 0.0206*
1
0.0516* -0.0011 -0.0293* 0.3473*
1
This table presents the pariwise Pearson Correlatin matrix for variables included in our main test for the sample period of 1992-2013. All variables are defined in Table2 and Appendix
A. Bold face and asterisks indicates significance level <=0.01.
37
Table 4a Testing CFO fixed effect on future crash risk, controlling firm CEO fixed effect
Economic
Economic
Economic determinants
Economic determinants
and CFO
and CFO
determinants
determinants
fixed effect
fixed effect
only
only
(model 3-1)
(model 3-1)
(model 2-1)
(model 2-1)
CFO=513
Crash1 jt+1
N=7,103
N=7,103
Testing Economic Determinatns = 0
F-statistics
1.76 *
2.53***
p value
0.0790
0.0095
Constraints
F( 8, 6277) F( 8, 4941)
Crash% jt+1
N=7,103
Testing Economic Determinatns = 0
F-statistics
1.89
2.3500
p value
0.0570
0.0161
Constraints
F( 8, 6277) F( 8, 4941)
Testing Manager Fixed Effect = 0
F-statistics
1.11*
p value
0.0607
Constraints
F(461, 4941)
Testing Manager Fixed Effect = 0
F-statistics
0.71
1.0000
p value
Constraints
F(461, 4941)
Year fixed effect
Firm fixed Effect
Year fixed effect
Firm fixed Effect
Adjusted R2
CFO=513
N=7,103
yes
yes
yes
yes
-7.79%
-4.35%
Adjusted R2
3.44%
Improvement in
R2 relative to
restricted model
In Raw Percentage
Improvement in
R2 relative to
restricted model
In Raw Percentage
2
As a % of restricted model R
yes
yes
yes
yes
-12.25%
-17.02%
-4.77%
2
44.16%
As a % of restricted model R
-38.93%
1.This table presents the results from model (2-1) and (3-1). I report the F-statisitc of testing the whether coefficeints of
the economic determinants are jointly zero, and whether the coefficients of switching CFO dummies are jointly zero for
hypothesis 1. A p-value of the f-test smaller than 0.1 suggests that the CFO dummies are not jointly zero, i.e. individual
CFO having idiosyncratic effect on firm's crash risk in the future.
2. Definitions of variables in the model can be found in Appendix A.
3. I use the "xtreg, fe" command in Stata for all tests. Stata is not able to converge for the sample under the "xtlogit, fe"
regression, so I use this alternative to approximate the coefficient. However, I am aware that this could potential bias my
coefficeint estimates and this is subject to future refinement of the data.
4. I have calculated the adjusted R2 for both models and take the difference between the expanded model (3-1) and the
restricted model (2-1) to see how much more explanatory power the switching CFO and corresponding control CEO adds
to the model. "As a % of restricted model R2 is calculated as (expanded model R2 - restricted model R2)/restricted model
R2 .
5. Significance level of 0.1, 0.05 and <0.01 is denoted as *, ** and ***.
38
Table 4a Testing CFO fixed effect on future crash risk, controlling firm CEO fixed effect (cont'd)
Economic
Economic
Economic determinants
Economic determinants
determinants
determinants
and CFO
and CFO
only
only
fixed effect
fixed effect
(model 2-1)
(model 2-1)
(model 3-1)
(model 3-1)
CFO=513;
CFO=513;
Crash2 jt+1
N=7,103
N=7103
Testing Economic Determinatns = 0
F-statistics
6.03***
5.29***
p value
0.0000
0.0000
Constraints
F( 8, 6276)
F( 8, 4940)
Crash3 jt+1
N=7,103
N=7103
Testing Economic Determinatns = 0
F-statistics
4.78***
2.94***
p value
0.0000
0.0028
Constraints
F( 8, 6275) F( 8, 4939)
Testing Manager Fixed Effect = 0
F-statistics
1.22***
p value
0.0017
Constraints
F(460, 4940)
Year fixed effect
yes
yes
Firm fixed Effect
yes
yes
Testing Manager Fixed Effect = 0
F-statistics
1.26***
p value
0.0003
Constraints
F(457, 4939)
Year fixed effect
yes
yes
Firm fixed Effect
yes
yes
Adjusted R2
Adjusted R2
-3.82%
-1.22%
Improvement in
R2 relative to
restricted model
-7.62%
-3.92%
Improvement in
R2 relative to
restricted model
In Raw Percentage
2.60%
2
As a % of restricted model R
In Raw Percentage
3.70%
2
67.97%
As a % of restricted model R
48.56%
1.This table presents the results from model (2-1) and (3-1). I report the F-statisitc of testing the whether coefficeints of
the economic determinants are jointly zero, and whether the coefficients of switching CFO dummies are jointly zero for
hypothesis 1. A p-value of the f-test smaller than 0.1 suggests that the CFO dummies are not jointly zero, i.e. individual
CFO having idiosyncratic effect on firm's crash risk in the future.
2. Definitions of variables in the model can be found in Appendix A.
3. I use the "xtreg, fe" command in Stata for all tests to control for firm fixed-effect.
4. I have calculated the adjusted R2 for both models and take the difference between the expanded model (3-1) and the
restricted model (2-1) to see how much more explanatory power the switching CFO and corresponding control CEO adds
to the model. "As a % of restricted model R2 is calculated as (expanded model R2 - restricted model R2 )/restricted model
R2 .
5. Significance level of 0.1, 0.05 and <0.01 is denoted as *, ** and ***.
39
Table 4b Testing CEO fixed effect on future crash risk
Economic
Economic
Economic
Economic
determinants
and
determinants
and
determinants
determinants
CEO fixed effect
CEO fixed effect
only
only
(model 3-2)
(model 3-2)
(model 2-2)
(model 2-2)
CEO=648
CEO=648
Crash1 jt+1
N= 5,536
N= 5,536
Crash% jt+1
N= 5,536
N= 5,536
Testing Economic Determinatns = 0
Testing Economic Determinatns = 0
F-statistics
3.06***
2.78***
F-statistics
1.87
1.8*
p value
0.0020
0.0045
p value
0.0596
0.0726
Constraints
F( 8, 4836)
F( 8, 4387)
Constraints
F( 8, 4836)
F( 8, 4387)
Testing Manager Fixed Effect = 0
F-statistics
1.22***
p value
0.0086
Constraints
F(449, 4385)
Year fixed effect
yes
yes
Firm fixed Effect
yes
yes
Testing Manager Fixed Effect = 0
F-statistics
p value
Constraints
Year fixed effect
yes
Firm fixed Effect
yes
Adjusted R2
Improvement in
R2 relative to
restricted model
In Raw Percentage
Adjusted R2
Improvement in
R2 relative to
restricted model
In Raw Percentage
-10.16%
-8.39%
1.77%
2
As a % of restricted model R
17.43%
-13.59%
1.49***
0.0000
F(449, 4387)
yes
yes
-8.64%
4.95%
2
As a % of restricted model R
36.42%
1.This table presents the results from model (2-1) and (3-1). I report the F-statisitc of testing the whether coefficeints of the
economic determinants are jointly zero, and whether the coefficients of switching CEO dummies are jointly zero for hypothesis 1. A pvalue that is smaller than 0.1 suggests the coefficients of CEO dummies are not jointly zero, i.e. individual CEO having idiosyncratic
effect on the crash risk measures of the firm.
2. Definitions of variables in the model can be found in Appendix A.
3. I use the "xtreg, fe" command in Stata for all tests. Stata is not able to converge for the sample under the "xtlogit, fe" regression,
so I use this alternative to approximate the coefficient. However, I am aware that this could potential bias my coefficeint estimates
4. I have calculated the adjusted R2 for both models and take the difference between the expanded model (3-1) and the restricted
model (2-1) to see how much more explanatory power the switching CEO adds to the model. "As a % of restricted model R2 is
calculated as (expanded model R2 - restricted model R2 )/restricted model R2 .
5. Significance level of 0.1, 0.05 and <0.01 is denoted as *, ** and ***.
40
Table 4b Testing CEO fixed effect on future crash risk (cont'd)
Economic
Economic
Economic
Economic
determinants
and
determinants
and
determinants
determinants
CEO fixed effect
CEO fixed effect
only
only
(model 3-2)
(model 3-2)
(model 2-2)
(model 2-2)
CEO=648
CEO=648
Crash2 jt+1
N= 5,536
N= 5,536
Crash3 jt+1
N= 5,536
N= 5,536
Testing Economic Determinatns = 0
Testing Economic Determinatns = 0
F-statistics
6.57***
5.34***
F-statistics
4.76***
3.32***
p value
0.0000
0.0000
p value
0.0000
0.0009
Constraints
F( 8, 4836)
F( 8, 4387)
Constraints
F( 8, 4834)
F( 8, 4385)
Testing Manager Fixed Effect = 0
F-statistics
1.17**
p value
0.0102
Constraints
F(449, 4387)
Year fixed effect
yes
yes
Firm fixed Effect
yes
yes
Testing Manager Fixed Effect = 0
F-statistics
p value
Constraints
Year fixed effect
yes
Firm fixed Effect
yes
Adjusted R2
Improvement in
R2 relative to
restricted model
In Raw Percentage
Adjusted R2
Improvement in
R2 relative to
restricted model
In Raw Percentage
-5.52%
-3.87%
1.65%
2
As a % of restricted model R
29.84%
-9.06%
1.22***
0.0018
F(449, 4385)
yes
yes
-6.90%
2.16%
2
As a % of restricted model R
23.85%
1.This table presents the results from model (2-1) and (3-1). I report the F-statisitc of testing the whether coefficeints of the
economic determinants are jointly zero, and whether the coefficients of switching CEO dummies are jointly zero for hypothesis 1. A pvalue that is smaller than 0.1 suggests the coefficients of CEO dummies are not jointly zero, i.e. individual CEO having idiosyncratic
effect on the crash risk measures of the firm.
2. Definitions of variables in the model can be found in Appendix A.
3. I use the "xtreg, fe" command in Stata for all tests. Stata is not able to converge for the sample under the "xtlogit, fe" regression,
so I use this alternative to approximate the coefficient. However, I am aware that this could potential bias my coefficeint estimates
and this is subject to future refinement of the data.
4. I have calculated the adjusted R2 for both models and take the difference between the expanded model (3-1) and the restricted
model (2-1) to see how much more explanatory power the switching CEO adds to the model. "As a % of restricted model R2 is
calculated as (expanded model R2 - restricted model R2 )/restricted model R2 .
5. Significance level of 0.1, 0.05 and <0.01 is denoted as *, ** and ***.
41
Table 5a CFO fixed effect on crash risk and CFO's personal and demographic
characteristics
PanelA: CFO fixed effect distribution for each crash(jump) risk measure and for
managerial ability
Crash1 j t+1 Crash% j t+1 Crash2 j t+1 Crash3 j t+1 MA_score jt
Number of CFO
coefficients significant at
53
90
52
14
53
10%
% of total CFO sample
significance at 10%
Mean Effect
(Coefficient)
25th percentile
Median
75th percentile
11.28%
3.04%
11.52%
11.60%
20.59%
-0.005
-0.001
0.123
0.047
-0.008
-0.274
0.001
0.252
-0.007
0.000
0.008
-0.700
0.076
0.847
-0.234
0.023
0.317
-0.056
-0.006
0.036
Panel B : CFO's fixed effect on MA_score (control for CEO)
2.09
F-stat
p-value
0.000
Constraint
F(437, 4847)
Panel C: Descriptive of CFO personal, demographic characteristics
Obs
Mean
5%
50%
95%
Managerial Ability
2,250
0.012
MA_score<0.000
1,164
0.517
MA_score>0.000
1,086
0.483
372
26
346
372
339
33
Age_Cohort
Pre-War
Post-War
Gender
Male
Female
-0.193
-0.005
0.267
0.0669
0.000
0.000
1.000
0.9110
0.000
1.000
1.000
42
Table 5a CFO fixed effect on crash risk and CFO's personal and demographic
characteristics (continued)
Panel D: CFO fixed effect on crash risk and managerial ability, age and gender:
Simplied
Model (5)
Model (5)
Crash1 jt+1
n=403
n=372
MA_Coeff jt
0.2347
1.01
0.0038
0.26
0.0003
0.07
-0.0026
-0.52
-0.0009
-0.22
Gender jt
Age_Corhort jt
Constant
-0.0074
-0.35
Crash% jt+1
MA_Coeff jt
n=398
0.0087
0.01
Gender jt
Age_Corhort jt
Constant
Crash2 jt+1
MA_Coeff jt
0.0087
-0.84
n=402
2.2776 **
2.96
Gender jt
Age_Corhort jt
Constant
0.0661
0.97
43
n=372
0.0038
0.26
0.0003
0.07
-0.0026
-0.52
-0.0009
-0.22
n=372
1.7092 **
2.2
0.0552
0.23
0.2087
0.79
0.0032
0.01
Table 5a CFO fixed effect on crash risk and CEO's personal and demographic
characteristics (continued)
Panel D: CFO fixed effect on crash risk, and managerial ability, age and gender(continued ):
Simplied
Model (5)
Model (5)
Crash3 jt+1
n=401
n=370
MA_Coeff jt
0.7338 **
0.4924 *
2.63
1.77
0.0766
Gender jt
0.89
Age_Corhort jt
0.0399
0.42
Constant
0.0296
-0.0443
0.228
-0.54
1. Panel A of Table 5a prestents the distribution of manager's fixed effect on crash risk measures and on
managerial ability measure. The fixed effect on crash (jump) measure is the coefficients of switching CFO
dummies estimated in model (3-1). And the fixed effect on managerial ability is the coefficeints of
switching CFO dummies estimated in model (5).
2. Panel B presents the statistics of estimating switching CFO's fixed effect on managerial ability. The
results show that individual CFO does have fixed ability after controlling for concurrent CEO, i.e. F-stat is
3. Panel C presents the descriptive statistics of fixed managerial ability, age and gender for the switching
CFO sample in the test in PanelD.
4. Panel D shows result of regression model (4) that examines if CFO's fixed effect on ability, their age and
gender are associated with the magnitude of their fixed effect on crash (jump) risk measures. I performed
two-tailed t-test on coefficients to examine whether fixed effect on crash (jump) risk is associated with
5. Significance level of 0.1, 0.05 and <0.01 is denoted as *, ** and ***.
44
Table5b CEO fixed effect on crash risk and CEO's personal and demographic
characteristics:
PanelA: CEO fixed effect distribution for each crash risk measure
Crash1 j t+1 Crash% j t+1 Crash2 j t+1 Crash3 j t+1 MA_score jt
Number of CEO significant
at 10%
% of total CEO sample
significance at 10%
Mean Effect (Coefficient)
25th percentile
Median
75th percentile
51
14
58
47
66
12.91%
3.11%
14.68%
11.66%
18.28%
-0.010
-0.704
-0.043
0.731
-0.001
-0.008
-0.001
0.006
0.035
-0.544
0.012
0.535
0.025
-0.072
0.000
0.136
-0.072
-0.072
0.000
0.136
Panel B : CEO's fixed effect on MA_score (controlling for CFO)
F-stat
1.96
p-value
0.000
Constraint
F(379, 3440)
Panel C: Distribution of personal and demographic characteristics of switching CEOs:
Obs
Mean
5%
50%
95%
Managerial Ability
1,734
-0.0003
-0.1962 -0.0105
0.2333
MA_score<0.000
802
0.1066
0.0048
0.0786
0.2993
MA_score>0.000
932
-0.0922
-0.2383 -0.0751
-0.0082
Age_Cohort
Pre-War
Post-War
Gender
Male
Female
361
109
252
360
353
7
0.3028
0.0000
0.0000
1.0000
0.9778
0.0000
0.0000
1.0000
45
Table5b CEO fixed effect on crash risk and CEO's personal and
demographic characteristics(continued):
Panel D: CEO fixed effect on crash risk, and managerial ability, age
and gender:
Simplied
Model(5)
Model (5)
N=309
N=309
Crash1 jt+1
0.5788 **
0.5511 **
MA_Coeff jt
2.30
2.19
0.2185
Gender jt
1.26
-0.1011 *
Age_Corhort jt
-1.68
Constant
0.0154
-0.1673
0.56
-0.97
Crash% jt+1
MA_Coeff jt
N=309
0.5788 **
2.30
Gender jt
Age_Corhort jt
Constant
0.0154
0.56
Crash2 jt+1
MA_Coeff jt
N=309
1.2432
1.32
Gender jt
Age_Corhort jt
Constant
0.0776
0.76
46
N=309
0.5511 **
2.19
0.2185
1.26
-0.1011 *
-1.68
-0.1673
-0.97
N=309
1.1449
1.22
0.5650
0.88
-0.4459 **
-2.00
-0.3410
-0.54
Table5b CEO fixed effect on crash risk and CEO's personal and
demographic characteristics(continued):
Panel D: CEO fixed effect on crash risk, and managerial ability, age
and gender (continued):
Crash3 jt+1
MA_Coeff jt
N=309
0.0490
0.16
Gender jt
Age_Corhort jt
Constant
0.0546
1.66
N=309
0.0003
0.00
0.1885
0.91
-0.1794 **
-2.50
-0.0753
-0.37
1. Panel A of this table prestents the distribution of manager's fixed effect on crash
(jump) risk measures and on managerial ability measure. The fixed effect on crash
(jump) measure is the coefficients of switching CEO dummies estimated in model (3-2).
And the fixed effect on managerial ability is the coefficeints of switching CEO dummies
estimated in model (5).
2. Panel B presents the statistics of estimating switching CEO's fixed effect on
managerial ability. The results show that individual CEO does have fixed ability after
controlling for concurrent CFO, i.e. F-stat is <0.001 significant in testing that
coefficients of switching CEO dummies are joint zero.
3. Panel C shows result of regression model (4) that examines if CEO's fixed effect on
ability, their age and gender are associated with the magnitude of their fixed effect on
crash (jump) risk measures. For the 553 CEO to begin with in the main test, after stata
drops some managers due to multicollinearity and after merging with age and gender
variables from ExComp, I have slightly smaller sample than the main test.
4. Significance level of 0.1, 0.05 and <0.01 is denoted as *, ** and ***.
47
Appendix A Variable Definitions
Crash Risk Measures:
Firm-Specific-Weekly Return (W t+1 ) is equal to ln(1+ɛ), where ɛ is the residual from the following expanded market model regression:
r
Crash1 t+1
j,t = α
+β 1j r
m, t-1 +β 2j
r m,t + β 3j r m,t+1 + β 4j r i,t-1 + β 5j r i,t + β 6j r i,t +1 + ɛ ji ,
where rj,t is the CRSP return on firm j in week t , and rm,t is the fama-french industry-weighted
market index in week t.
is the future crash incidence, defined as an indicator variable that equals to 1 for a firm-year that
experiences one or more firm-specific weekly returns falling 3.09 standard deviation below the
mean firm-specific weekly returns over the fiscal year, following prior literature (Hutton et al.
2009, Kim et al.2010ab, Kim and Zhang 2014, etc).
Crash% t+1 is a variation of Crash1 t+1 It is the percentage of total crash incidences of a given industry-year
the firm has experienced in that firm-year. Crash%t+1 is ratio of number of firm crash incidences
in that year divided by the total firm crash incidences in the industry the firm was operating in
during the same year.
Crash2 t+1 is the negative skewness of future firm-specific-weekly return over the fiscal year period,
calculated in the following equation
Crash2 j,t+1 =-[n(n-1) 3/2 ƩW 3
Crash3 t+1
j,t+1 ]/[(n-1)(n-2)( ƩW
2
jt+1 )
3/2
]
where n is the number of firm-specific weekly returns in a year, and W is the firm-spcific
weekly return.
is the log of the ratio of the standard deviations of down-week to up-week firm-specific returns.
Control Variables of Crash Risk Models
DTURN t is the average monthly share turnover over the current fiscal year period, minus average
monthly share turnover over the previous fiscal year period, where monthly share turnover is
calculated as the monthly trading volume divided by total number of shares outstanding during
h
NSKEW t ish the current
year negative skewness of firm-specific weekly returns, defined in Crash2
t+1.
SdW t is the standard deviation of the firm-specific-weekly return over the fiscal year period.
Ret t is the mean of the firm-specific-weekly return over the fiscal year period.
Size t is the log of total asset.
48
Appendix A Variable Definitions (Continued)
MB t is the market value of equity divided by book value of equity.
LEV t is the total long-term debts divided by total assets.
ROA t+1 is the future return on asset, return on asset is defined as income before extraordinary items
divided by lagged asset.
DISACC t+1 is the absolute value of discretionary accruals, where discretionary accruals are estimated
from the modified Jones model (Dechow et al., 1995).
Variables for earnings forecast model:
NumF t is the number of earnings forecast management issued per fiscal-year (including both annual and
quarterly forecasts).
NumForcann t is the number of annual earnings forecast management issued per fiscal-year.
NumForcq t is the number of quarterly earnings forecast management issued per fiscal-year.
Size t is the log of total asset.
Leverage t is the total long-term debts divided by total assets.
Abs_EPSChg t is the absolute value of the change in firm's eranings per share from year t-1 to t, deflated by
stock price at the end of year t-1.
ROA t is defined as income before extraordinary items divided by lagged asset.
Growth t is the sales growth from year t-1 to year t.
OpCF t is the cashflow from opeartion deflated by total assets.
EPS_UP t equals 1 if firm's EPS in this is greater or equal to its EPS last year, 0 otherwise.
MB t is the market value of equity divided by book value of equity.
RDxp t is the expenditures on research and development scaled by total asset.
Num_Analyst t is the number of analyst following the firm.
DispForecas t is the standard deviation of analysts' forecasts of firm's earnings, devided by the absolute value
of the median forecast.
F-horizon t is the forecast horizon, defined as the number of days between the forecast date and the end of
the fiscal period of forecasted earnings. I scaled forecast horizon by 360 days for annual and 90
for quarterly forecast.
49
Appendix B Replication of Bamber et al. (2010)
Table B1 Sample Selection on replicating Bamber et al. (2010)
Total Observations
(including matching
managers)
Sample Selection Steps
CFO
CEO
ExComp
6,754
6,898
44,118
Switching Managers
576
759
20,728
Data available for management earnings forecast in
438
460
8,674
CIG
Data available for control variables calculation on
Compustat and IBES
413
441
7,275
Managers whose fixed effects are estimable
325
283
6,119
This table presents the sample selection procedures in constrcuting the sample for manager's fixed effect model:
1. CFO are identified by (1) extracting keywords from the "anntitle" field, including excerpts of "Chief Financial
Officer, chief finance officer, VP in Finance,vice president in finance, vp-finance, treasurer, comptroller", (2) using
"cfoann" field indicator; CEO are identified by (1) extracting keywords from the "anntitle" field, including
excerpts of CEO, Chief Executive Officer, (2) using "cfoann" field indicator.
2. Identified switching managers/CFO/CEO are managers that worked for at least two firms in the sample period.
3. Identified matching maangers/CFO/CEO are managers who worked for the same firm of switching managers,
but that have not shown to have switched working firms in the sample period. Firm-years pertain to the matching
managers served as firm-level control for switching manager fixed effect model.
50
Table B2. Descriptive Statistics of variables used in Bamber et al (2010)
replication model (CEO and CFO sample combined)
Variables
NumF
NumForcann
NumForcq
Size
Leverage
Abs_EPSChg
ROA
Growth
OpCF
EPS_UP
LOSS
MB
RDxp
Num_Analyst
DispForecast
F-horizon
Observations Mean
7,275
4.971
7,275
2.815
7,275
2.156
7,275
7.666
7,275
0.087
7,275
0.007
7,275
0.155
7,275
0.104
7,275
0.101
7,275
0.566
7,275
0.158
7,275
3.016
7,275
0.036
7,275
16.707
7,275
0.267
7,275
0.555
Stdev.
3.910
2.845
2.456
1.499
0.168
0.139
0.115
0.271
0.084
0.496
0.365
23.595
0.059
10.598
1.166
0.319
5%
1.000
0.000
0.000
5.312
0.000
-0.136
0.012
-0.202
-0.010
0.000
0.000
0.709
0.000
4.000
0.013
0.000
50%
4.000
2.000
1.000
7.596
0.034
0.006
0.142
0.072
0.097
1.000
0.000
2.212
0.005
14.000
0.071
0.564
95%
13.000
8.000
7.000
10.207
0.419
0.132
0.348
0.481
0.235
1.000
1.000
8.586
0.152
37.000
0.824
1.081
This table presents the descriptive statistics of crash risk measures and control variables in
manager's fixed effect model. This sample is the combination of CEO and CFO manager firm-years in
1. All continuous variables are winsorized at 1% level both sides.
2. This table presents descriptive statistics for dependent and independnet variables for the
combined CEO and CFO sample (7,275), with 3,891 from CFO sample and 3,384 from CEO sample.
51
Table B3a. Replication of Table 3 of Bamber et al.(2010)
Testing individual CFO/CEO fixed effect on forecast frequency
Economic
Economic
determinants
determinants
Economic
and CFO fixed
and CEO fixed
determinants
effect
effect
CEO/CFO=608
CFO=325
CEO=283
D.V. : Number of
Forecast per year
N= 7,275
Testing Economic Determinatns = 0
F-statistics
27.68***
p value
0.000
Constraints
F( 13, 6342)
N=3,891
N=3,384
12.57***
0.000
F( 13, 2897)
10.16***
0.000
F( 13, 2546)
Testing Manager Fixed Effect = 0
F-statistics
p value
Constraints
1.62***
0.000
F(325, 2897)
1.96***
0.000
F(283, 2546)
Year fixed effect
Firm fixed Effect
yes
yes
Adjusted R2
17.99%
2
Improvement in R
relative to
restricted model
In Raw Percentage
As a % of restricted model R2
yes
yes
yes
yes
18.51%
22.53%
0.53%
4.55%
2.92%
21.73%
1.This table presents the results from model (2-1) and (3-1). I report the F-statisitc of testing the
whether coefficeints of the economic determinants are jointly zero, and whether the coefficients of
switching CFO dummies are jointly zero for hypothesis 1. F-statsitics is the value from this coefficient
testing, and a p-value smaller than 0.1 indicates there is CFO fixed effect on firm crash risk.
2. Definitions of variables in the model can be found in Appendix A.
3. I use the "xtreg, fe" command in Stata for all tests to control for firm fixed-effect.
4. I have calculated the adjusted R2 for both models and take the difference between the expanded
model (3-1) and the restricted model (2-1) to see how much more explanatory power the switching CFO
and corresponding control CEO adds to the model. "As a % of restricted model R2 is calculated as
(expanded model R2 - restricted model R2 )/restricted model R2 .
5. Significance level of 0.1, 0.05 and <0.01 is denoted as *, ** and ***.
52
Table B3b. Testing individual CFO/CEO fixed effect on forecast frequency of
ANNUAL earnings forecasts
Economic
determinants
Economic
determinants and
CFO fixed effect
Economic
determinants and
CEO fixed effect
CEO/CFO=608
CFO=325
CEO=283
D.V. : Number of
N= 7,275
Annual Forecast per
year
Testing Economic Determinatns = 0
F-statistics
15.88***
p value
0.000
Constraints
F( 13, 6342)
N=3,891
N=3,384
8.13***
0.000
F( 13, 2546)
5.35***
0.000
F( 13, 2546)
Testing Manager Fixed Effect = 0
F-statistics
p value
Constraints
Year fixed effect
yes
Firm fixed Effect
yes
1.65***
0.000
F(325, 2897)
yes
yes
2.14***
0.000
F(283, 2546)
yes
yes
19.55%
22.94%
1.70%
5.08%
9.50%
28.46%
Adjusted R2
17.86%
2
Improvement in R
relative to restricted
model
In Raw Percentage
As a % of restricted model R2
1.This table presents the results from model (2-1) and (3-1) on switching CEO or CFO's fixed effect
on annual earnings forecast frequency. I report the F-statisitc of testing the whether coefficeints
of the economic determinants are jointly zero, and whether the coefficients of switching CFO of
CEO dummies are jointly zero for hypothesis 1. F-statsitics is the value from this coefficient
testing, and a p-value smaller than 0.1 indicates there is CFO fixed effect on firm crash risk.
2. Definitions of variables in the model can be found in Appendix A.
3. I use the "xtreg, fe" command in Stata for all tests to control for firm fixed-effect.
4. I have calculated the adjusted R2 for both models and take the difference between the expanded
model (3-1) and the restricted model (2-1) to see how much more explanatory power the switching
CFO and corresponding control CEO adds to the model. "As a % of restricted model R2 is
calculated as (expanded model R2 - restricted model R2 )/restricted model R2 .
5. Significance level of 0.1, 0.05 and <0.01 is denoted as *, ** and ***.
53
Table B3c. Testing individual CFO/CEO fixed effect on forecast frequency of
QUARTERLY earnings forecasts
Economic
Economic
Economic
determinants and determinants and
determinants
CFO fixed effect
CEO fixed effect
CEO/CFO=608
CFO=325
CEO=283
D.V. : Number of
N= 7,275
N=3,891
N=3,384
Quarterly Forecast
per year
Testing Economic Determinatns = 0
F-statistics
18.79
8.38***
8.06
p value
0.000
0.000
0.000
Constraints
F( 13, 6342)
F( 13, 2897)
F( 13, 2546)
Testing Manager Fixed Effect = 0
F-statistics
p value
Constraints
1.88***
0.000
F(325, 2897)
1.57***
0.000
F(283, 2546)
3.87%
2.86%
3.01%
2.01%
351.33%
234.18%
Year fixed effect
Firm fixed Effect
Adjusted R2
0.86%
2
Improvement in R
relative to restricted
model
In Raw Percentage
As a % of restricted model R2
1.This table presents the results from model (2-1) and (3-1) of switching CEO or CFO fixed effect on
the frequency of quarterly earnings forecast. I report the F-statisitc of testing the whether
coefficeints of the economic determinants are jointly zero, and whether the coefficients of
switching CFO or CEO dummies are jointly zero for hypothesis 1. F-statsitics is the value from this
coefficient testing, and a p-value smaller than 0.1 indicates there is CFO fixed effect on firm crash
risk.
2. Definitions of variables in the model can be found in Appendix A.
3. I use the "xtreg, fe" command in Stata for all tests to control for firm fixed-effect.
4. I have calculated the adjusted R2 for both models and take the difference between the expanded
model (3-1) and the restricted model (2-1) to see how much more explanatory power the switching
CFO and corresponding control CEO adds to the model. "As a % of restricted model R2 is
calculated as (expanded model R2 - restricted model R2 )/restricted model R2 .
5. Significance level of 0.1, 0.05 and <0.01 is denoted as *, ** and ***.
54
Table B4. -- The Table2 of Bamber et al. (2010) on top managers' fixed effects on the Number of
Management Earnings Forecasts
55