Managerial Overconfidence and Accounting Conservatism*
Anwer S. Ahmed
Ernst & Young Professor of Accounting
Texas A & M University
Scott Duellman
Assistant Professor of Accounting
St. Louis University
March 2012
Abstract
Overconfident managers overestimate IXWXUHUHWXUQVIURPWKHLUILUPV¶LQYHVWPHQWV7KXVZH
predict that overconfident managers will tend to accelerate good news recognition, delay loss
recognition, and generally use less conservative accounting. Furthermore, we test whether
external monitoring helps to mitigate this effect. Using measures of both conditional and
unconditional conservatism, we find robust evidence of a negative relation between CEO
overconfidence and accounting conservatism. We further find that external monitoring does not
appear to mitigate this effect. Our findings add to the growing literature on overconfidence and
complements findings in Schrand and Zechman [2011] that overconfidence affects financial
reporting behavior.
*
We gratefully acknowledge comments received from Philip Berger, Carol Ann Frost, Christo Karuna, Mary Lea
McAnally, Mike Neel, Kaye Newberry, Ananth Seetharaman, Yan Sun, Senyo Tse, Ross Watts, an anonymous
reviewer, and workshop participants at the University of Houston and the University of North Texas. M anagerial O verconfidence and A ccounting Conservatism
1. Introduction
2YHUFRQILGHQWRURSWLPLVWLFPDQDJHUVRYHUHVWLPDWHIXWXUHUHWXUQVIURPWKHLUILUP¶V
investment projects (Heaton [2002], Malmendier and Tate [2005]).1 Previous research in finance
documents that overconfidence affects corporate investment, financing, and dividend policies
(e.g., Malmendier and Tate [2008], Cordeiro [2009], Deshmukh, Goel, and Howe [2010],
Hirshleifer, Low, and Teoh [2010], Malmendier, Tate, and Yan [2011]). Recent work in
accounting examines the impact of overconfidence on the likelihood of an AAER (Schrand and
Zechman [2011]) and the likelihood of issuing a management forecast (Hribar and Yang [2011],
Libby and Rennekamp [2012]). We extend this line of research by investigating the effects of
managerial overconfidence on accounting conservatism. We find consistent and robust evidence
of a significant negative effect of CEO overconfidence on both conditional and unconditional
accounting conservatism.
Investigating the effects of overconfidence on corporate policies, including accounting
policies, is important because overconfidence can induce decisions that destroy firm value. For
example, Roll [1986] argues that managerial hubris (or overconfidence) explains why firms
engage in value-destroying mergers or acquisitions. Similarly, distortions in other investment,
financing, or accounting policies can be costly (Malmendier and Tate [2005, 2008], Ben-David,
Graham, and Harvey [2010]). More specifically, conservatism is viewed as playing an important
role in debt contracting as well as in governance (e.g., Ahmed et al. [2002], Watts [2003], Ball
and Shivakumar [2005], Ahmed and Duellman [2007], LaFond and Roychowdhury [2008],
1
0DOPHQGLHUDQG7DWH>@XVHWKHWHUPµRYHUFRQILGHQFH¶WRUHIHUWRPDQDJHUVZKRRYHUHVWLPDWHIXWXUHUHWXUQV
IURPWKHLUILUPV¶SURMHFWV+HDWRQ>@XVHVWKHWHUPµRSWLPLVP¶WRUHIHUWRPDQDJHUVZKRsystematically
overestimate the probability of good firm performance and underestimate the probability of bad firm performance.
Following most of the literature in finance and accounting, we use the term overconfidence and consider it
equivalent to optimism.
1 Garcia Lara, Garcia Osama, and Penalva [2009]). To the extent that overconfidence reduces
conservatism, it can adversely affect the efficacy of conservatism in debt contracting as well as
in governance.
We hypothesize that if overconfident managers overestimate future returns from their
firmV¶SURMHFWVWKH\DUHOLNHO\WRdelay recognition of losses and use less conditionally
conservative accounting. For example, poorly performing negative NPV projects may be
erroneously perceived as positive NPV projects by overconfident managers leading to delayed
loss recognition. Furthermore, overestimation of future returns from projects may also cause
overconfident managers to use optimistic estimates in determining asset values (such as
inventory, receivables, or long-lived assets) leading to lower levels of unconditional
conservatism. Thus, our first set of hypotheses predict a negative relation between
overconfidence and both conditional and unconditional conservatism respectively.2 In addition,
we examine whether these effects vary with the strength of external monitoring based on the
argument in Kahneman and Lovallo [1993] that overconfidence or optimism is best alleviated by
introducing an outside view. Thus, external monitoring could mitigate the predicted negative
relation between overconfidence and conservatism.
Our tests are based on a sample of 14,641 firm-years over 1993-2009 from S&P 1500
firms that have the available data needed to carry out our tests. Our primary measure of
overconfidence is based on the timing of CEO option exercise following Malmendier and Tate
[2005, 2008], Hirshleifer, Low, and Teoh [2010], and Campbell et al. [2011]. Intuitively, CEOs
are under-diversified and should exercise their options and sell shares obtained from exercising
options to minimize their exposure to idiosyncratic risk. However, an overconfident CEO
2
While we predict a negative relation between overconfidence and conservatism, we also note in section 2 that there
are reasons why this relation could be positive.
2 believes that firm value will continue to increase and thus chooses to delay exercise and hold
options that are deep in-the-money. We classify a CEO as being overconfident if the average
intrinsic value of his/her exercisable unexercised options exceeds 67% of the average exercise
SULFHDWOHDVWWZLFHRYHURXUVDPSOHSHULRG&(2VWKDWGRQ¶WPHHWWKLVFULWHULRQDUHFODVVLILHGDV
not being overconfident. Our second measure of overconfidence is based on net purchases of the
ILUP¶VVKDUHVE\WKH&(2 Intuitively, an overconfident CEO will tend to buy more of their
ILUPV¶VWRFNV relative to other CEOs.
In addition to these measures, we use two other measures related to overinvestment
which is a potential consequence of overconfidence: (i) capital expenditures above the industry
median, and (ii) excess asset growth. Intuitively, firms with overconfident managers will tend to
overinvest in assets resulting in above-average capital expenditures and/or above-average growth
in assets (relative to sales growth).
We measure conditional conservatism using L%DVX¶V[1997] asymmetric timeliness
measure, and (ii) firm-specific C-Scores following Khan and Watts [2009]. We measure
unconditional conservatism using an accrual based measure (Ahmed et al. [2002]), and the
difference between cash flow skewness and earnings skewness (Givoly and Hayn [2000]). We
use a simple measure of external monitoring based on the percentage of outside directors on the
board, outside director ownership, CEO/Chairman separation, and institutional ownership.
Our findings can be summarized as follows. First, all four of our conservatism measures
are negatively related to each of the four overconfidence measures respectively. Furthermore,
except for the relation between net purchases and the Basu [1997] measure, the relations between
conservatism measures and overconfidence are statistically significant at conventional levels.
3 The results survive a battery of robustness checks including the use of first differences, controls
for firm-fixed effects, and use of alternative measures of overconfidence (over longer horizons).
An alternative explanation for the negative relation between managerial overconfidence
and conservatism is that overconfident managers self-select into firms with less conservative
accounting. To rule out this alternative explanation, we examine the relation between changes in
firm-specific measures of conservatism and changes in overconfidence following a change in
CEO. If our findings are driven by self-selection, CEOs would self-select into firms with their
preferred level of conservatism and there should be no change in conservatism after the new
CEO takes over. However, we find evidence of a negative relation between changes in
conservatism and changes in overconfidence resulting from CEO turnover albeit the significance
levels drop because of a drastic reduction in sample size. Overall, we conclude that the evidence
strongly supports the prediction that overconfidence and conservatism are negatively related.
With respect to the potential mitigating effect of external monitoring, we do not find
evidence that the relation between conservatism and overconfidence weakens for firms with
higher external monitoring. A potential explanation for this result may be that overconfidence
helps CEOs in their appointment to their positions and external monitors are less sensitive to the
adverse effects of overconfidence on corporate policies (Goel and Thakor [2008]).
We contribute to the literature by demonstrating that overconfidence significantly affects
both conditional and unconditional conservatism. To our knowledge, prior work has not
demonstrated the presence of these effects. In related work, Schrand and Zechman [2011] show
that overconfidence affects the likelihood of an AAER. However, given the rarity of an AAER,
we cannot infer whether overconfidence affects accounting policies more broadly based on their
study. Our paper extends and complements their work.
4 The remainder of the paper is organized as follows. Section 2 presents the discussion of
the previous literature and develops the hypothesis. Section 3 presents the research design and
data definitions. Section 4 presents the empirical results and Section 5 concludes the paper.
2. Literature Review and Hypothesis Development
2.1. MANAGERIAL OVERCONFIDENCE
In the finance literature, an overconfident manager is viewed as a manager who
systematically overestimates IXWXUHUHWXUQVIURPWKHILUP¶VSURMHFWVRUHTXLYDOHQWO\
systematically overestimates the likelihood and impact of favorable events on his/her ILUP¶VFDVK
flows; and/or underestimates the likelihood and impact of negative (adverse) events on hLVILUP¶V
cash flows (Heaton [2002], Malmendier and Tate [2005]). The notion of managerial
overconfidence (or optimism) in this literature is based on a stylized fact in social psychology
NQRZQDVWKHµEHWWHU-than-DYHUDJH¶HIIHFW:HLQVWHLQ>@6YHQVRQ [1981], Weinstein and
Klein [1996]).3 Camerer and Lovallo [1999] experimentally document that this effect extends to
economic decision making.4
One of the earliest uses of this concept in finance was by Roll [1986], who argued that
managerial hubris (i.e. overconfidence) is one explanation for value-destroying mergers and for
overpayment for target firms. Heaton [2002] analytically investigates the implications of
RSWLPLVPRYHUFRQILGHQFHDQGVKRZVWKDWRSWLPLVWLFPDQDJHUVRYHUYDOXHWKHLUILUP¶VSUojects
and equity as well as invest in negative NPV projects mistakenly perceiving them to be positive
3
Such biases have also been shown to affect executives or managers. For example, Kidd and Morgan [1969]
document that production managers consistently overestimate their performance (relative to subsequent
UHDOL]DWLRQV/DUZRRGDQG:KLWWDNHU>@GRFXPHQWWKDWH[HFXWLYHVRYHUHVWLPDWHWKHLUILUPV¶IXWXUHSHUIRUPDQFH
and growth rates relative to their competitors.
4
See recent surveys by Baker, Ruback, and Wurgler [2007] and Skata [2008] for a thorough review of managerial
overconfidence in psychology and behavioral corporate finance.
5 NPV investments. Malmendier and Tate [2005] introduce measures of overconfidence based on
PDQDJHUV¶LQFUHDVLQJWKHLUH[SRVXUHWRWKHLUILUP¶VVWRFNSULFH through their stock option
holdings and provide empirical evidence consistent with the notion that overconfidence leads to
overinvestment. Malmendier and Tate [2008] find that overconfident managers engage in more
acquisitions and value-destroying mergers FRQVLVWHQWZLWK5ROO¶V>@FRQMHFWXUH&RUGHLUR
[2009] and Deshmukh, Goel, and Howe [2010] document that overconfident managers tend to
pay less dividends than other managers. Malmendier, Tate, and Yan [2011] document evidence
consistent with overconfidence leading to distortions in corporate financial policies. In summary,
a growing literature documents that overconfidence results in distortions in corporate investment,
financing, and dividend policies.5
Recent work in accounting examines the implications of overconfidence for managerial
forecasts of earnings (Hilary and Hsu [2011], Hribar and Yang [2011], Libby and Rennekamp
[2012]) and misreporting or fraud (Schrand and Zechman [2011]). Hilary and Hsu [2011] find
that managers that have accurately predicted earnings in the previous quarters are less likely to
accurately forecast future earnings. Thus, the managers that successfully forecasted previous
earnings tend to attribute too much of their past success to superior ability and too little to
exogenous events and begin to disregard public information such as analyst forecasts.
Additionally, the market recognizes this overconfidence and reacts less strongly when earnings
fall short of the forecasts issued by overconfident managers. Hribar and Yang [2011], using a
sample of firms listed in the Fortune 500 from 2000-2007, find that overconfident managers are
more likely to issue a management forecast. Furthermore, Hribar and Yang [2011] find these
forecasts tend to be more optimistic and are more likely to issue point forecasts and/or forecasts
5
Researchers have also looked empirically at other implications of overconfidence such as CEO decisions to sell
equity (Jin and Kothari [2008]), investment in innovation (Hirshleifer, Low, and Teoh [2010]) and CEO turnover
(Campbell et al. [2011]).
6 with a narrower range. Additionally, Libby and Rennekamp [2012] explore two components of
overconfidence, miscalibration and dispositional optimism, in an experiment and find both are
related to confidence in improved performance and the likelihood of issuing a forecast. Schrand
and Zechman [2011] find that managerial overconfidence is positively related to the likelihood
of financial statement fraud and that higher internal/external monitoring through governance
mechanisms does not mitigate this effect. We add to the literature by examining the effects of
overconfidence on accounting choices more broadly.
2.2. THE EFFECT OF OVERCONFIDENCE ON ACCOUNTING CONSERVATISM
Conservatism is viewed as requiring higher verification standards for recognizing good
news than for recognizing bad news (Basu [1997], Watts [2003]). Managerial estimates play a
critical role in applying conservative accounting. For example, managers have to estimate the net
realizable value oILQYHQWRU\LQDSSO\LQJWKHµORZHURIFRVWRUPDUNHW¶UXOHIRULQYHQWRU\
valuation. Overconfident managers overestimate IXWXUHUHWXUQVIURPWKHLUILUPV¶SURMHFWV7KXV
they are likely to overestimate the probability and magnitude of positive shocks to future cash
flows from current projects and underestimate the probability and magnitude of negative or
adverse shocks to cash flows.
Overestimation of future returns or cash flows from current projects or assets has at least
two implications for managers¶ accounting decisions. First, it implies that they are likely to be
reluctant to recognize losses and likely to underestimate losses even when they choose to
recognize them. Thus, overconfidence would lead to less conditionally conservative financial
reporting. This suggests the following hypothesis:
7 H1a: There is a negative relation between overconfidence and conditional
conservatism.
A second implication for accounting choices is that overconfident managers will tend to
over-value assets and undervalue liabilities. For example, an overconfident manager will tend to
overestimate the probability of collection of accounts receivables and therefore understate the
allowance for bad debts thereby overstating net receivables. Similarly, an overconfident manager
will tend to overestimate salvage values or useful lives of long-lived assets, thus overstating asset
values. Such overestimations will lead to more aggressive reporting of assets and lower
unconditional conservatism.6 This suggests the following hypothesis:
H1b: There is a negative relation between overconfidence and unconditional
conservatism.
While the above hypotheses are intuitive, it is possible for overconfidence to be
positively related to conservatism. Gervais, Heaton, and Odean [2011] study the design of
optimal contracts when the principal can distinguish between rational and overconfident agents.
They show that in some cases both firms and managers benefit from overconfidence.
Furthermore, they predict that the most overconfident managers will self-select into risky growth
firms. If this argument holds, then a positive relation between conservatism and overconfidence
could result because growth firms tend to have more conservative reporting. Another reason why
we may not observe a negative relation between conservatism and overconfidence is if boards
are able to identify overconfident managers and require them to use conservative reporting to
6
While we develop separate predictions for conditional and unconditional conservatism, we note that Watts [2003,
S@DUJXHVWKDW³$QLPSRUWDQWFRQVHTXHQFHRIFRQVHUYDWLVP¶VDV\PPHWULFWUHDWPHQWRIJDLQVDQGORVVHVLVWKH
SHUVLVWHQWXQGHUVWDWHPHQWRIQHWDVVHWYDOXHV´,QRWKHUZRUGVFRQGLWLRQDOFRQVHUYDWLVPshould lead to lower book
values relative to market values and lower (more negative) accruals (i.e. lower unconditional conservatism). Further
discussion for additional reasons why conditional and unconditional conservatism would be related can be found in
Ryan [2006] and Roychowdhury and Watts [2007].
8 offset the adverse effects of overconfidence on financial reporting (Ahmed and Duellman [2007],
Garcia Lara, Garcia Osama, and Penalva [2009]). In light of these counter arguments, whether or
not accounting conservatism is negatively related to overconfidence is an empirical question.
2.3. EXTERNAL MONITORING, CONSERVATISM, AND OVERCONFIDENCE
Kahneman and Lovallo [1993] argue that managerial optimism (or overconfidence) is
best alleviated by introducing an outside view. Similarly, Heaton [2002] argues that the most
effective prescription for managerial optimism combines strong incentives with strong outside
monitoring. Previous research in accounting has documented a relation between corporate
governance and accounting conservatism (Ahmed and Duellman [2007], Garcia Lara, Garcia
Osama, and Penalva [2009]). These studies suggest that strong boards can require more
conservative reporting. Thus, firms with a strong board of directors may also effectively
constrain the effects of managerial overconfidence. These arguments suggest that strong external
monitoring can potentially mitigate the negative relation between overconfidence and
conservatism predicted above and lead to the following hypothesis:
H2: The relation between overconfidence and conservatism is weaker for firms
with stronger external monitoring.
Conversely, Goel and Thakor [2008] model the selection process of a CEO and find that
overconfident managers are more likely to be promoted to the CEO position. Thus, the benefits
of strong monitoring mechanisms may impact the level of accounting conservatism but not
directly mitigate the effect of managerial overconfidence. In addition, Baker, Ruback, and
Wurgler [2007] argue that monitoring mechanisms may be limited in constraining overconfident
managers. Consistent with strong monitoring not mitigating the negative effects of managerial
9 overconfidence, Schrand and Zechman [2011] find that corporate governance structures of firms
that misreport earnings are similar to corporate governance structures of control firms. Thus,
whether strong external monitoring will mitigate the effects of managerial overconfidence is an
open empirical question.
3. Research Design
3.1 MEASURES OF OVERCONFIDENCE
3.1.1. C E O Option and Purchase Based Measures of Overconfidence
We use four measures of overconfidence in our main tests. The first two measures focus
on &(2¶V option holding behavior and stock purchases whereas the other two measures focus on
their investment decisions. The first measure of overconfidence is based on Malmendier and Tate
[2005, 2008] who use the timing of CEO option exercises to identify overconfidence. CEOs are
typically under-diversified and therefore exposed to the idiosyncratic risk of their company¶V
stock. To minimize their exposure to this risk the CEO should minimize the holdings of their
stock, and following vesting, exercise options fairly quickly. However, overconfident CEOs are
more likely to believe that their company will continue to outperform a hedged portfolio and thus
postpone option exercise. Consistent with Hall and Murphy [2002] and Malmendier and Tate
[2005, 2008] we classify a CEO as being overconfident if the average option value (per share) of
exercisable options held by the CEO is more than or equal to 67% of the average exercise price
at least twice during the sample period.7
However, as we do not have the detailed private dataset of Malmendier and Tate [2005,
2008] we estimate managerial overconfidence from Execucomp by following Hirshleifer, Low,
7
This corresponds to a risk aversion of three in a constant relative risk aversion utility specification with a
percentage of wealth invested in the company of 66%. 10 and Teoh [2010] and Campbell et al. [2011].8 First, we divide the value of exercisable
unexercised options by the number of exercisable unexercised options and subtract this value
from the stock price at the fiscal year end to obtain the average exercise price per option. Second,
we divide the value of exercisable unexercised options per option by the average exercise price
per option to calculate the ratio of the options in-the-money. Third, we set Holder67, our
measure of overconfidence, equal to one when the ratio of the options in-the-money exceeds 0.67
at least twice during the sample period, zero otherwise. Consistent with Malmendier and Tate
[2005] and Campbell et al. [2011] a CEO is classified as overconfident in the first fiscal year
he/she exhibits the overconfident behavior and continues to be classified as overconfident for the
remainder of the sample.9
Our second measure of overconfidence is based on Malmendier and Tate [2005] who
utilize the net purchases by the CEO to identify overconfident executives. As top executives
often have restrictions on the sale of stock, and lack the ability to hedge against the risk by short
selliQJWKHSXUFKDVHRIDGGLWLRQDOVKDUHVDQH[HFXWLYHPXVWEHFRQILGHQWDERXWKLVKHUILUP¶V
future profitability and prospects to purchase additional shares. Thus, consistent with Campbell
et al. [2011] we classify a CEO as overconfident using a dichotomous variable where Purchase
is set equal to one LIWKH&(2¶VQHWSXUFKDVHVSXUFKDVHV-sales) are in the top quintile of the
GLVWULEXWLRQRIQHWSXUFKDVHVE\DOO&(2¶VDQGWKRVHSXUFKDVHVLQFUHDVHWKHLURZQHUVKLSLQWKH
firm by 10% during the fiscal year, otherwise zero.10
8
Additionally, Malmendier, Tate, and Yan [2011] utilize managerial overconfidence measures based on Execucomp
and Thomson Financial option portfolios to validate their measures of overconfidence outside of the 1980-1994 time
period of their previous work (Malmendier and Tate [2005, 2008]). 9
Results are similar to those reported if we classify CEOs as overconfident starting with the second time they
exhibit the overconfident behavior. Also, results are similar to those reported if we only require the CEO to exhibit
the overconfident behavior once to become classified as overconfident as in Malmendier and Tate [2008] and
Hirshleifer, Low, and Teoh [2010].
10
In our Purchase measure we exclude purchases due to option exercises; although our results remain qualitatively
similar to those reported if we include purchases due to option transactions.
11 3.1.2. Investment Measures of Overconfidence
Malmendier and Tate [2005, 2008] and Ben-David, Graham, and Harvey [2010]
GHPRQVWUDWHWKDWILUPV¶LQYHVWPHQWGHFLVLRQVDUHUHODWHGWRPDQDJHULDORYHUFRQILGHQFH This
suggests that these decisions may contain information regarding the level of overconfidence
(Campbell et al. [2011]). Thus, we utilize two measures of overconfidence based on the
investment decisions of the current CEO. These measures assume that the CEO is consistently
overconfident in their decisions across multiple contexts (option exercising, investment policy,
etc.) and we can therefore obtain measures of overconfidence based on these decisions.
Our first investing based proxy for overconfidence ( CAPEX) is a dichotomous variable
set equal to one if the capital expenditures deflated by lagged total assets in a given year is
greater than the median level of capital expenditures to lagged total assets for the firm¶s FamaFrench industry in that year, otherwise zero. This proxy is based on the findings in Ben-David,
Graham, and Harvey [2010] that firms with overconfident CEOs have larger capital
expenditures; and the findings of Malmendier and Tate [2005] that overconfident managers tend
to overinvest in capital projects. A similar measure of overconfidence is utilized by Campbell et
al. [2011] who consider a CEO overconfident (low optimism) if the firm is in the top (bottom)
quintile of industry adjusted investment rates for two consecutive years.11
Our second investing based proxy for overconfidence, following Schrand and Zechman
[2011], is the amount of excess investment in assets from the residual of a regression of total
asset growth on sales growth run by industry-year ( Over-Invest). We set Over-Invest equal to one
if the residual from the excess investment regression is greater than zero, and equal to zero
11
Results are qualitatively similar if we define CAPEX as firms with industry adjusted capital expenditures in the
top quintile, quartile, or tercile.
12 otherwise. Intuitively, if assets are growing at a faster rate than sales, this suggests that managers
are over-investing in their company relative to their peers.
3.2 MEASURES OF ACCOUNTING CONSERVATISM
3.2.1. Measures of Conditional Conservatism
We use two measures of conditional conservatism in our tests. Our first measure of
conditional FRQVHUYDWLVPLV%DVX¶V>@DV\PPHWULFWLPHOLQHVVPHDVXUH12 We use the control
variables in LaFond and Roychowdhury [2008] who find that firms with higher managerial
ownership have lower asymmetric timeliness. To test our hypotheses we estimate the following
regression:
X t = ȕ0 + ȕ1 Dt + ȕ2 Owntí1 + ȕ3 MTBtí1 + ȕ4 Leveragetí1 + ȕ5 Sizetí1+ ȕ6 Litigationtí1 + ȕ7
OverCont-1 + ȕ8 Dt*Owntí1 + ȕ9 Dt*MTBtí1 + ȕ10 Dt*Leveragetí1 + ȕ11 Dt*Sizetí1+ ȕ12 Dt*
Litigationtí1 + ȕ13 Dt*OverCont-1 + ȕ14 Rett + ȕ15 Rett *Owntí1 + ȕ16 Rett *MTBtí1 + ȕ17
Rett *Leveragetí1 + ȕ18 Rett *Sizetí1+ ȕ19 Rett * Litigationtí1 + ȕ20 Rett *OverCont-1+ ȕ21
Dt * Rett + ȕ22 Dt * Rett *Owntí1 + ȕ23 Dt * Rett *MTBtí1 + ȕ24 Dt * Rett *Leveragetí1 +
ȕ25 Dt * Rett *Sizetí1+ ȕ26 Dt * Rett * Litigationtí1 + ȕ27 Dt * Rett *OverCont-1 + İ
Where X is net income before extraordinary items deflated by the market value of equity at the
beginning of the fiscal year; D is an indicator variable set equal to one if Ret is negative, zero
otherwise, Ret is the annual buy and hold return beginning four months after the prior fiscal year
end; Own is the decile rank of CEO ownership for the industry year; MTB is the decile rank of
the market value of equity divided by the book value of equity for the industry year; Leverage is
the decile rank of total debt divided by total assets for the industry-year; Size is the decile rank of
12
Recent research has questioned the measurement of accounting conservatism via asymmetric timeliness (e.g.
Givoly, Hayn, and Nataranjan [2007], Dietrich, Muller, and Riedl [2007], and Patatoukas and Thomas [2011a]).
Thus, we also utilize unconditional conservatism proxies to demonstrate the robustness of our results. 13 the market value of equity; Litigation is the probability of litigation for the firm-year estimated
using the coefficients from the litigation risk model of Kim and Skinner [2011] in Table 7 model
(2); and OverCon is one of the four managerial overconfidence measures defined in the previous
section.
We control for current CEO ownership (Own) because LaFond and Roychowdhury
[2008] find that asymmetric timeliness of earnings decreases with managerial ownership. We
control for beginning Market-to-book ( MTB) as Roychowdhury and Watts [2007] find that the
asymmetric timeliness is related to the level of conservatism since the inception of the firm. We
control for leverage (Leverage) as Ahmed et al. [2002] find that firms with greater bondholdershareholder conflicts have higher levels of conservatism. We control for firm size (Size) because
Givoly, Hayn and Natarajan [2007] find that large firms have lower asymmetric timeliness than
small firms. Finally, we control for litigation risk ( Litigation) as firms that face higher litigation
risk may use more conservative accounting to mitigate these risks. Conversely, the lower levels
of asymmetric timeliness of these firms may lead to increased litigation risk.
Our second measure of conditional conservatism is the firm-specific asymmetric
timeliness score developed by Khan and Watts [2009]. Khan and Watts [2009] utilize a firm
specific estimation of the timeliness of good news (G-Score) and bad news (C-Score) and
document evidence consistent with conservatism increasing in the C-score. The G-Score and CScore are estimated as follows:
X ȕ1 ȕ2 D ȕ3 RET ȕ4 D RET + İ
(2)
G Score ȕ3 = µ 1 + µ 2 MV + µ 3 MTB + µ 4 /(9İ
(3)
C Score ȕ4 Ȝ1 Ȝ2 MV Ȝ3 MTB Ȝ4 /(9İ
(4)
14 Where, MV is market value of equity; MTB is market-value of equity divided by the book value
of equity; and LEV is WRWDOGHEWGLYLGHGE\WRWDODVVHWV5HSODFLQJȕ3 DQGȕ4 from equations (3)
and (4) into regression equation (2) yields:
X ȕ1 ȕ2 D + RET * (µ 1 + µ 2 MV + µ 3 MTB + µ 4 LEV) + D RET (Ȝ1 Ȝ2 MV Ȝ3 MTB +
Ȝ4 LEV) į1 6L]Hį2 07%į3 /(9į4 D*Size į5 '/(9į6 D*Lev) + İ
(5)
We estimate equation (5) using annual cross-sectional regressions. All variables are as
previously defined. The estimates from equation 5 are applied to equation 4 to obtain the firmspecific level of conservatism.
3.2.2. Measures of Unconditional Conservatism
We use two measures of unconditional conservatism in our tests. Our first measure, Con-
AC C , is based on the persistence use of negative accruals following Givoly and Hayn [2000] and
Ahmed et al. [2002]. We define Con-AC C as income before extra-ordinary items less cash flows
from operations plus depreciation expense deflated by average total assets, and averaged over the
previous three years, multiplied by negative one. Positive values of Con-AC C indicate greater
unconditional conservatism.
Our second unconditional conservatism measure, Skewness, is the difference between
cash flow skewness and earnings skewness developed by Givoly and Hayn [2000]. The skewness
of earnings (cash flows) is equal to (x-µ)3 ı3 ZKHUH—DQGıDUHWKHPHDQDQGVWDQGDUGGHYLDWLRQ
of the earnings (cash flows) over the last five years. All variables are deflated by total assets.
This measure has been used by Beatty, Weber, and Yu [2008] in documenting the role of
conservatism in contract modifications and by Zhang [2008] in demonstrating the ex post and ex
ante benefits of conservatism to lenders and borrowers. The difference between cash flow
15 skewness and earnings skewness captures conservatism as a conservative reporting system will
fully recognize unfavorable events in earnings while favorable events will be recognized in a
gradual manner resulting in the negative skewness of earnings.
3.2.3. Specification for Tests with F irm-Specific Conservatism Measures
To test H1a and H1b, we use the firm-specific measures of conservatism in the following
regression:
Con t = ȕ0 + ȕ1 OverCont-1 + ȕ2 Firm Sizet + ȕ3 Sales Growtht + ȕ4 R&D_ADt + ȕ5 CFOt +
ȕ6 Leveraget + ȕ7 Litigationt + ȕInd Industry + ȕYear Year + İ
(6)
Where, Con is one of the three firm-specific measures of accounting conservatism outlined
above; Overcon is one of the four firm-specific overconfidence measures outlined in Section 3.1;
F irm Size is the natural log of average total assets; Sales Growth is the percentage of annual
growth in total sales; R&D_AD is total research and development expense plus advertising
expense deflated by total sales; C F O is cash flows from operations deflated by average total
assets; Leverage is total liabilities divided by total assets; Litigation is the probability of
litigation for the firm-year estimated using the coefficients from the litigation risk model of Kim
and Skinner [2011] in Table 7 model (2); Additionally, we include indicator variables for the
firms Fama-French industry as well as the firm year. These control variables are consistent with
those utilized in Ahmed and Duellman [2007] and are also defined in Table 1.
We control for large firms as Watts and Zimmerman [1986] argue that firms face higher
political costs that may induce them to use more conservative accounting. However, as
information asymmetry is smaller for large firms due to the amount of public information there
could be a lower demand for conservative accounting (LaFond and Watts [2008]). We control
16 for sales growth as the demand for accounting conservatism may affect measures of
conservatism such as Con-AC C and Skewness due to the increase in accruals in accounts such as
inventory and accounts receivable (Ahmed and Duellman [2007]). We control for the level of
research and development (R&D _AD ) as this is GAAP mandated conservatism and could affect
measures of conservatism utilizing accruals. We control for cash flows from operations to
control for firm profitability. Firms with greater profitability may use more conservative
DFFRXQWLQJWRFUHDWH³FRRNLHMDU´UHVHUYHVDQGPDQDJHUVRISURILWDEOHILUPVDUHOHVVOLNHO\WREH
terminated for missing an earnings target. To control for the bondholder-shareholder conflict we
include firm leverage (Leverage). We also control for the litigation risk ( Litigation Risk) as the
expected cost of litigation is higher for firms that overstate their asset base and conservative
accounting could reduce these agency costs (Watts [2003]).
4. Sample Selection and Results
We utilize a sample of S&P 1,500 firms with available information in Compustat and
Execucomp from 1993-2009 (25,500 firm-years). As our main tests require that we have option
holding data available for the CEO we drop firms that do not have information on the number of
options held by the CEO (1,228 firm-years). We also remove financial services and insurance
firms (SIC 6000-7000) from the sample as these firms have relatively unique financial structures
and are subject to regulatory constraints that may affect their reporting (3,469 firm-years). We
lose an additional 3,796 firm-years due to missing data in Compustat, an additional 1,448 firmyears are removed due to CEO turnover during the year, and 918 firm-years due to missing data
in CRSP leaving a final sample of 14,641 firm-years. Furthermore, in our tests utilizing
Purchase we require the firm to have information on the trading activities of the CEO available
17 from Thomson Reuters. The inclusion of purchase and sales information of the CEO causes us to
lose an additional 2,525 firm-years leaving a final sample of 12,116 in our sample when
Purchase is the measure of managerial overconfidence.
We present the descriptive statistics of our sample in Table 2, Panel A. Using the
measure of overconfidence based on option holding, Holder67, we find that 35.1% of our firmyears have an overconfident CEO. This finding is consistent with Campbell et al. [2011], who
use a similar measure of overconfidence constructed using Execucomp data from 1992-2005,
find that 34.1% of firm-years can be classified as having an overconfident CEO. For the stock
purchase based measure of overconfidence, Purchase, 26.1% of the firm-years have an
overconfident CEO. This finding is slightly below the 34.6% reported in Campbell et al. [2011].
Using our investing measures of overconfidence we find that 43.1% of our sample firms overinvest in assets relative to sales growth ( Over-Invest) and 56.5% of firms have capital
expenditures greater than the median firm in the industry.13
The firm-specific measure of conditional conservatism, C-Score, is 0.060 (0.063) and
consistent with previous research. The accrual based measure of unconditional conservatism,
Con-AC C , has a mean (median) value of 0.008 (0.006) and is consistent with the values reported
in Ahmed et al. [2002] and Ahmed and Duellman [2007]. The mean (median) value of the
skewness based measure of conservatism is 0.224 (0.004).14 The mean and median values of our
control variables are generally consistent with previous research (Ahmed and Duellman [2007],
LaFond and Roychowdhury [2008].
13
We measure overconfidence using all firms with the relevant available data in Compustat, Execucomp, and
Thomson Financial to effectively capture if the manager is overconfident.
14
The values of Skewness in our study are slightly lower than those reported in Beatty, Weber, and Yu [2008] as we
use annual data rather than quarterly data.
18 In Table 2 Panel B we present the means and medians of our sample after partitioning on
each dichotomous overconfidence proxy ( Holder67, Purchase, CAPEX, and Over-Invest).
Consistent with H1a and H1b, the mean value for conservatism is significantly lower for the high
overconfidence group, using all four measures of managerial overconfidence, in comparison to
the low overconfidence group. Furthermore, the median level of overconfidence is significantly
lower for the high overconfidence group using Holder67 as the measure of managerial
overconfidence. The difference in means and medians also demonstrate how Holder67 and
Purchase tend to capture firms with high equity returns as well as firms with large sales growth.
Table 3 presents the correlations between our overconfidence measures, firm-specific
conservatism measures, and control variables. The stock option based measure of overconfidence
( Holder67) is positively correlated with Purchase (0.08) as well the investing measures of
overconfidence of CAPEX (0.12) and Over-Invest (0.13). Additionally, CAPEX has a
Pearson/Spearman correlation with Over-Invest of 0.16. The correlation between Purchase and
the two investing based measures of overconfidence are positive and significant at the 5% level
but small in magnitude. Con-AC C is positively correlated with Skewness but is uncorrelated with
C-Score. The lack of correlation between Con-AC C and C-Score may be due to C-Score
capturing conditional conservatism while Con-AC C is a measure of unconditional conservatism.
Consistent with H1, all three firm-specific measures of conservatism are negatively correlated
with all four measures of managerial overconfidence at the 1% level of significance.15 However,
these univariate results should be interpreted with caution as they do not reflect how the
variables jointly impact accounting conservatism.
15
Our results are not sensitive to using either continuous measures of overconfidence or their decile ranks as
independent variables instead of the dichotomous measures throughout our main tests. 19 4.1. ASYMMETRIC TIMELINESS OF EARNINGS
Table 4 presents the estimation of equation (1). All p-values are based on two-tailed
significance tests using firm and year clustered standard errors. In column (i) we present the
results from Table 2 of LaFond and Roychowdhury [2008] for comparative purposes.16 In
columns (ii) thru (v) we report the effects of managerial overconfidence on asymmetric
timeliness of earnings. The coefficient on Ret is positive and significant (p-value <0.001) and the
coefficient on D*Ret is positive and significant (p-value <0.001) across all columns indicating
that bad news is reflected in earnings on a timelier basis. We expect overconfident managers to
accelerate good news recognition and delay loss recognition. The coefficient on the interaction
term Ret*Overcon captures the effect of overconfidence on timeliness of good news recognition.
Except for the Purchase measure of overconfidence, the coefficient is positive and significant at
conventional levels consistent with our expectations. Similarly, except for the Purchase measure
of overconfidence, the incremental coefficient on bad news timeliness in columns (ii) to (v) is
negative and significant consistent with our expectations. However, this coefficient by itself does
not indicate whether loss recognition is less timely for firms with overconfident managers
relative to other firms. Thus, in untabulated tests we perform a joint test of the sum of the
coefficients of D*Ret*Overcon and Ret*Overcon and find that this sum is significantly negative
(p-value <0.001) for CAPEX and Over-Invest but not significantly less than zero for Holder67
and Purchase. In summation, consistent with H1a, we find evidence consistent with
RYHUFRQILGHQW&(2¶VLEHLQJPRUHOLNHO\WRDFFHOHUDWHJRRGQHZVLQWRHDUQLQJVXVLQJWKUHHRI
our four overconfidence proxies, and (ii) being more likely to delay loss recognition using two of
our four overconfidence proxies.
16
The p-values reported in Table 4 in column (i) are estimated based on the reported t-statistics of Model 1, Table 2
of LaFond and Roychowdhury [2008, p.117].
20 Consistent with LaFond and Roychowdhury [2008] all control variables, except for
litigation risk, are measured in decile ranks. The coefficient on D*Ret*Own is negative and
significant (p-value <0.001) across columns (ii-v) consistent with the findings of LaFond and
Roychowdhury [2008] that firms with greater executive ownership have less conservative
accounting. Controlling for the ownership of the CEO demonstrates that our results for the
Holder67 PHDVXUHRIRYHUFRQILGHQFHDUHQRWDWWULEXWDEOHWR&(2V¶VWRFNKROGLQJV, which may
explain why the CEO has a significant amount of vested unexercised in-the-money options.
However, we do not find a significant relation between the market-to-book decile (MTB) and
asymmetric timeliness similar to Roychowdhury and Watts [2007]. This finding is likely due to
(i) the drastic changes in the market that take place during our sample period as the
Roychowdhury and Watts [2007] sample ends in fiscal year 2000 and avoids the market turmoil
of 2002, and (ii) the use of additional control variables. Furthermore, in untabulated results when
we use the three-year backwards cumulation technique of Roychowdhury and Watts (2007) we
do find a positive and significant coefficient on D*RET*MTB. The coefficients on
D*Ret*Overcon in these alternative specifications remain qualitatively similar to those reported
in Table 4.
We find a positive and significant (p-value <0.001) coefficient on D*Ret*Leverage in
columns (ii) through (v) indicating that firms with greater outstanding debt tend to use more
conservative accounting. The coefficient on D*Ret*Size is negative and significant (p-value
<0.001) consistent with larger firms having less conservative accounting. Increased litigation risk
is positively related to the asymmetric timeliness of earnings as the coefficient on
D*Ret*Litigation is positive and significant. Overall, the signs and significance of the control
variables are consistent with those reported in LaFond and Roychowdhury [2008].
21 We also estimate equation (1) including firm, industry, and year fixed effects as
suggested by Ball, Kothari, and Nikolaev [2011] to control for information that is incorporated in
lagged earnings.17 In the fixed effects models, we find results qualitatively similar to those
reported in Table 4. Additionally, we continue to find support for our hypothesis when we utilize
Fama-MacBeth regressions as an alternative estimation technique.
4.2. FIRM-SPECIFIC MEASURES OF CONSERVATISM
Table 5 presents the estimation of equation (6), using the Khan and Watts [2009] CScores as the dependent variable, which tests for the relation between managerial overconfidence
and the firm-specific measures of conditional conservatism. All p-values are based on two-tailed
significance tests using firm and year clustered standard errors. Consistent with H1a, we find a
negative and significant (p-value <0.001) coefficient on all four measures of managerial
overconfidence.
The coefficients on the control variables are fairly consistent across columns (i) thru (iv).
The coefficient on F irm Size is negative and significant (p-value <0.001) consistent with larger
firms using less conditionally conservative accounting as found in LaFond and Watts [2008].
Consistent with the findings of Ahmed and Duellman [2007], where growing firms use less
conservative accounting, Sales Growth is negatively related (p-value <0.001) to the C-Score. The
coefficient on R&D AD is negative and significant (p-value <0.001). We find a negative and
significant (p-value <0.001) relation between cash flows from operation ( C F O ) and conditional
conservatism measured by the C-Score. Consistent with the findings in Ahmed et al. [2002], we
17
Patatoukas and Thomas [2011b] argue that the revised Basu [1997] measures suggested by Ball, Kothari, and
Robin [2011] are not reliable and should be interpreted with caution. However, we provide evidence consistent with
a negative relation between managerial overconfidence and accounting conservatism using both measures of
asymmetric timeliness and unconditional conservatism. Thus, our findings are unlikely an artifact of the properties
of the Basu [1997] regression.
22 find a positive and significant coefficient on Leverage as firms with greater bondholdershareholder conflict demand greater accounting conservatism. Finally, we find no relation
between litigation risk (Litigation) and conditional conservatism measured by the C-Score. We
continue to find results qualitatively similar to those reported using Fama-MacBeth regressions.
Table 6 presents the estimation of equation (6) using the accrual-based measures of
unconditional conservatism as the dependent variable. Consistent with H1b, we find a negative
and significant (p-value <0.001) coefficient on both the option and purchases based measures of
overconfidence ( Holder67 and Purchase) as well as the investment measures of overconfidence
( CAPEX and Over-Invest ) respectively.
Despite utilizing an unconditional measure of accounting conservatism the control
variable coefficients are fairly consistent with those reported in Table 5. However, in Table 6 the
coefficient on R&D AD is positive and significant (p-value <0.001) consistent with firms with
greater uncertainty about future cash flows via their investment in future technologies (mandated
by GAAP) using more unconditionally conservative accounting; and we find a positive and
significant (p-value <0.001) relation between cash flows from operation ( C F O ) and accrualbased conservatism. Overall, the findings on the control variables are similar to the findings
reported by Ahmed and Duellman [2007] in their tests utilizing Con-AC C .
Table 7 presents the regression of the difference between cash flow and earnings
skewness (Skewness) on managerial overconfidence and control variables. We continue to find
support for H1b using all four measures of overconfidence as the coefficient on overconfidence
is negative and significant (p-value <0.001) in columns (i)-(iv). The control variables are
consistent with those reported in Table 6 where F irm Size and Sales Growth are negatively
related to accounting conservatism; and R&D AD , C F O , and Leverage are positively related to
23 accounting conservatism. Furthermore, the sign on Litigation is positive and significant
consistent with firms facing higher litigation risk utilizing more conservative accounting. The
consistency between the control variables in Table 6 and Table 7 indicate our measures of
unconditional conservatism are capturing a common component. Results remain qualitatively
similar when we use Fama-MacBeth regressions.
Overall, the negative relation between managerial overconfidence and accounting
conservatism is consistent across multiple measures of overconfidence as well as alternative
estimation methods such as Fama-MacBeth regressions.
4.3. MODERATING EFFECTS OF STRONG EXTERNAL MONITORING
To investigate the effects of external monitoring on the relation between conservatism
and overconfidence we utilize data from The &RUSRUDWH/LEUDU\¶VGLUHFWRULQIRUPDWLRQGDWDVHW
from 2001-2009 and obtain institutional shareholding data from Thomson Reuters. The year
based data limitations cause a loss of (5,078 firm-years), the lack of available corporate
governance data causes a loss of an additional 1,386 firm-years, and the lack of available
institutional shareholdings an additional 949 firm-years, leaving a final sample of 7,228 firmyears. We utilize four common monitoring attributes (percentage of inside directors, ownership
of outside directors, institutional ownership, and CEO/Chair duality) in our tests of the
moderating effects of external monitoring on managerial overconfidence.18 The proxies used for
external monitoring are blanket proxies that do not effectively capture the complex nature of the
overall governance and oversight structure of the firm. However, these crude proxies may still
18
These proxies were selected given their prevalence in the accounting and finance literature. Furthermore, two of
the four proxies (percentage of inside directors and the ownership of outside directors) were directly related to
accounting conservatism in Ahmed and Duellman [2007]. 24 provide evidence on the ability of strong external monitoring to mitigate the effects of
managerial overconfidence.
As different mechanisms may act as substitutes we focus on firms that have higher levels
of external monitoring across multiple dimensions. Thus, we classify firms as ³VWURQJ´external
monitoring firms if the firm meets three of the following four criteria: (i) a lower percentage of
inside directors than the median firm in the sample, (ii) a higher percentage of outside director
ownership than the median firm in the sample, (iii) a higher percentage of institutional ownership
than the median firm in the sample, and (iv) the CEO is not also serving as Chairman of the
Board.19 Of our 7,228 firm-years (2,041) firm-years are classified as strong monitoring firms.
We then modify equation (6) as follows:
Con t = ȕ0 + ȕ1 OverCont-1 + ȕ2 Strong Monitoringt + ȕ3 Strong Monitoringt * Overcont-1 +
ȕ4 Firm Sizet + ȕ5 Sales Growtht + ȕ6 R&D_ADt + ȕ7 CFOt + ȕ8 Leveraget +
ȕ9 Litigationt + ȕInd Industry + ȕYear Year + İ
(7)
Where, Strong Monitoring is a dummy variable set equal to one if the firm has strong external
monitoring (as defined above), and zero otherwise. All other variables remain as previously
defined. If strong external monitoring forces overconfident managers to utilize more
conservatLYHDFFRXQWLQJZHZRXOGH[SHFWWKHFRHIILFLHQWRQȕ3 to be significantly positive.
In untabulated univariate tests we find that the average percentage of inside directors on
the board of directors is 19.3%, the average total ownership of outside directors is 0.7%, the
average amount of shares held by institutional investors is 67.6%, and 58.8% of our sample have
the same person operating as both CEO and Chairman of the Board. Furthermore, when we test
for differences between firms with high managerial overconfidence (2,488 firm-years) to firms
19
Results are qualitatively similar to those reported if we require Strong Monitoring firms to meet all four of the
criteria.
25 with low managerial overconfidence (4,740 firm-years), as defined by Holder67, we find that
&(2¶V with higher overconfidence have boards with a larger percentage of inside directors
(20.2% to 19.1%), similar levels of outside director ownership (0.07% to 0.07%), lower
institutional ownership (58.9% to 71.6%), and greater CEO duality (59.8% to 58.4%).20
We present the results of our estimation of equation (7) in Table 8. Consistent with our
findings in Tables 5 through 7, we continue to find a strong negative relation between all four
managerial overconfidence measures and accounting conservatism across the three firm-specific
conservatism measures. Additionally, consistent with Ahmed and Duellman [2007] and Garcia
Lara, Garcia Osama, and Penalva [2009] we find a positive relation between strong external
monitoring and our measures of accounting conservatism. Furthermore, we do not find any
consistent evidence of a moderating effect of strong external monitoring as Strong Monitoring *
Overcon is insignificant at conventional levels in ten of the twelve columns of Table 8 Panel A
and B. Similar to the findings reported in Table 8, in untabulated testing we do not find that
strong external monitoring mitigates the relation between asymmetric timeliness and managerial
overconfidence.
Because our measures of external monitoring are somewhat ad-hoc, we perform
additional tests using different specifications for strong external monitoring such as separating
the sample into weak and strong monitoring based on each individual monitoring measure
(percentage of inside directors, outside director ownership, institutional ownership, and CEO
duality). The results of these tests (not tabulated) are similar to those reported in Table 8;
however, the coefficient on Strong Monitoring is insignificant when we define strong monitoring
as firms with the different people acting as CEO and Chairman of the Board or as firms with
20
The differences between the percentage of inside directors, institutional ownership are significant at the 1% level
of significance; while the difference in CEO duality and outside director ownership are insignificant.
26 high institutional ownership. Overall, it does not appear that external monitoring mitigates the
negative relation between managerial overconfidence and accounting conservatism. These
findings are consistent with Schrand and Zechman [2011] who find that the corporate
governance structures of firms that misreport earnings are very similar to governance structures
of their control firms.
4.4. ENDOGENEITY AND SELF SELECTION
While our results discussed above show evidence of a significant negative effect of
managerial overconfidence on both conditional and unconditional conservatism, it is possible
that these results are driven by self-selection or endogeneity. Thus, we perform a number of
additional tests to investigate this possibility.
First, the negative relation between overconfidence and conservatism could result from
overconfident managers self-selecting into firms with less conservative accounting. To rule out
this alternative explanation, we examine the relation between changes in firm-specific measures
of conservatism and changes in overconfidence following a change in CEO. If our findings are
driven by self-selection, CEOs would self-select into firms with their preferred level of
conservatism and there would be no change in conservatism after the new CEO takes over.
In our sample period we have 1,448 CEO changes. However, we require that the
incoming CEO remains in office for a minimum of 3 years (loss of 632 CEO changes) and the
outgoing CEO to have been in office for a minimum of 4 years (loss of 476 CEO changes). We
require these minimum tenure requirements so that the incoming/outgoing CEOs have sufficient
time to impact their UHVSHFWLYHILUPV¶accounting and investment policies. These requirements
leave us with a sample of 340 CEO changes. We then take the values of accounting
27 conservatism, managerial overconfidence, and the control variables measured three years after
the CEO change and subtract the values of the corresponding variable two-years prior to the
CEO change. This specification provides direct evidence on whether a change in managerial
overconfidence leads to changes in accounting conservatism.
For these 340 CEO changes, we do not find evidence consistent with CEOs self-selecting
into firms with their desired level of conservatism. For example, using the Holder67 measure of
managerial overconfidence, a firm where the previous CEO was classified as overconfident
(non-overconfident) brought in a non-overconfident (overconfident) manager 66.4% (30.0%) of
the time; which is consistent with the rate of non-overconfident (non-overconfident) CEOs for
the entire sample as shown in Table 2.21 Furthermore, following a CEO change, the mean and
median differences in the firm specific measures of conservatism ( C-Score, Con-AC C , and
Skewness) are not statistically different from zero.
Table 9 provides the results of our changes specification of equation (6) using the CEO
turnover sub-sample. Despite the small sample size of 340 observations, we find consistent
evidence that changes in managerial overconfidence, following a CEO change, are negatively
related to changes in accounting conservatism. In Table 9, Panel A we find a negative relation
between changes in the option and purchase measures of overconfidence and changes in
accounting conservatism at the 5% level of significance in 5 out of the 6 specifications.
Similarly, when using our investment based measures of overconfidence we find a negative
relation between changes in overconfidence and accounting conservatism at the 5% level of
significance in 3 out of the 6 specifications; and at the 10% level of significance in 4 out of the
21
Using the Purchases, CAPEX , and Over-Invest proxies for managerial overconfidence a firm where the previous
CEO was overconfident (non-overconfident) brought in a non-overconfident (overconfident) CEO only 82.4%
(17.3%), 38.9% (44.3), and 57.6% (47.0%), respectively.
28 six specifications. The coefficients on the control variables in Table 9 are generally consistent
with expectations and the previous results reported in Tables 5-8.
Second, we estimate equation (6) including firm fixed effects that capture static innate
characteristics of the firm which may affect both conservatism and overconfidence. The results
of this test are qualitatively similar to those reported in Tables 5-7 although the relation between
Purchase and Skewness is only significant at the 5% level. Thus, none of our inferences change
after controlling for firm fixed effects.
Third, we also run equation (6) using a first differences approach. For this test, we utilize
a firm-year based measure of Holder67. Overall, we find that changes in managerial
overconfidence are negatively related to changes in accounting conservatism. Thus, the
inferences from this test are also qualitatively similar to the inferences based on the reported
Tables 5-7. However, we note that because accounting conservatism is measured over a three
(five) year period using Con-ACC (Skewness), these first differences are not independent over
time. Overall, these additional tests suggest that our results are unlikely to be driven by selfselection or endogeneity.
4.5. ADDITIONAL ROBUSTNESS TESTS
In addition to the above tests we perform several additional robustness and sensitivity
checks. First, three of our measures of overconfidence are measured on an annual basis.
However, overconfidence is a behavioral trait that should remain relatively static over time.22
Thus, we repeat our tests utilizing overconfidence proxies measured over a three-year period
which may better reflect relatively stable overconfidence. We calculate these overconfidence
22
The annual proxies of overconfidence are fairly stable over time. For exaPSOHZHILQGWKDW&(2¶VFKDQJH
overconfidence partitions between firm years for Purchase, CAPEX , and Over-Invest approximately 21.3%, 16.9%,
and 17.9% respectively.
29 measures by calculating each of the dichotomous annual overconfidence variables for years t-3
through t-1 and dividing the sum of these overconfidence proxies by 3.23 We require that the firm
has the same CEO for years t-3 through t for our three-year overconfidence measure which
reduces our sample size to 9,281 (8,211) firm-years for our three-year proxies for CAPEX and
Over-Invest (Purchase). Using the three-year measure of overconfidence we find over 65% of all
firms are classified as either overconfident or not overconfident for three consecutive years for
each of the managerial overconfidence measures indicating the stability of these behavioral
proxies. When we use these three-year overconfidence measures to test our hypotheses using
equations (5) and (6) we find results qualitatively similar to those reported in the tables.
Second, we utilize a measure of overconfidence that incorporates investment in
intangibles as well as capital expenditures, CAPEX-Intangible, as an overconfident CEO may not
only over-invest in tangible assets but intangible assets as well. We set CAPEX-Intangible equal
to one if the capital expenditures plus research and development expense plus advertising
expense all deflated by lagged total assets is greater than the median level of capital expenditures
plus research and development plus advertising expense (all deflated by lagged total assets) for
WKHILUP¶V)DPD-French industry, zero otherwise. Consistent with H1a and H1b CAPEX-
Intangible is negatively related to both conditional and both unconditional conservatism
measures at the 1% level of significance.
Third, Campbell et al. [2011] document a non-monotonic relation between managerial
overconfidence and the likelihood of forced turnover. They find that firms with both high and
low levels of overconfidence are more likely to force out their CEO. Thus, we also examine
whether there is a non-monotonic relation between managerial overconfidence and accounting
23
Each overconfidence proxy varies from 0 to 1 where a CEO that was overconfident (not overconfident) in each of
the previous three years would have a score of 1 (0) and a firm that was overconfident (not overconfident) in one
(two) of the previous three years would have a score of 0.333 (0.667).
30 conservatism. To test for non-monotonic effects (untabulated) we include a dummy variable
equal to one if the continuous variable of overconfidence being tested is in the lowest quintile.
Overall, we find the low overconfidence dummy variable has a positive and significant
coefficient and the overconfidence dummy maintains a negative and significant coefficient.24 As
we find similar results using indicators for low overconfidence, continuous measures of
overconfidence, and different cutoff points for the overconfidence indicator variables
(quintiles/quartiles/terciles), it does not appear that the relation between managerial
overconfidence and accounting conservatism is non-monotonic.
Fourth, in untabulated tests we utilize an additional measure of managerial
overconfidence based on the cash paid for acquisitions from the cash flow statement ( AC Q ).
Previous research by Malmendier and Tate [2008] find that overconfident CEOs are more likely
to engage in acquisitions; and conditional on performing an acquisition tend to pay more for the
acquired firm. Additionally, Schrand and Zechman [2011] use acquisitions as a component in
their composite overconfidence score. We set AC Q equal to one when cash paid for acquisitions
deflated by beginning total assets is higher than the industry median. Overall, we find partial
support for our hypotheses where AC Q is positively related to accounting conservatism when
C ON-AC C and Skewness are the dependent variable. However, we do not find a significant
relation between AC Q and the asymmetric timeliness of earnings or the Khan and Watts (2009)
C-Score.25
24
Results are similar when we consider high overconfidence in the top quintile rather than as defined in Table 1.
Furthermore, the result is not sensitive to defining low/high overconfidence by deciles or quartiles.
25
The results utilizing the AC Q proxy for overconfidence may be weaker as overconfidence represents a behavioral
trait by management persistent across time while cash acquisitions are infrequent events conditional on having both
the opportunity and resources to purchase another firm. Thus, the overconfidence proxy based on acquisition is
likely to contain considerable error as to what constitutes an overconfident manager.
31 Fifth, Malmendier and Tate [2005, 2008] find that overconfident CEOs respond to
financing constraints. Thus, we test whether the relation between accounting conservatism varies
for firms with greater cash holdings (untabulated). We classify our firms as cash constrained
(unconstrained) if they have a ratio of beginning cash to beginning total assets below (above) the
industry-year median. Using a test similar to our strong monitoring tests in Table 8, we create a
cash constrained dummy variable equal to one if the firm is cash constrained, zero otherwise, and
interact the variable with our measures of managerial overconfidence. We do not find the
relation between accounting conservatism and overconfidence varies with financing
constraints.26
5. Conclusion
Recent studies in accounting and finance investigate the relation between managerial
overconfidence and corporate investment, financing, and dividend policies, as well as managerial
forecasts and financial misreporting. We contribute to this literature by providing evidence on
the effects of overconfidence on both conditional and unconditional accounting conservatism.
Because overconfident managers overestimate future UHWXUQVIURPWKHLUILUPV¶SURMHFWV, we
predict that overconfidence and conservatism will be negatively related. Using 14,641 firm-years
from 1993-2009, we find evidence of a significant negative relation between overconfidence and
both conditional and unconditional conservatism. Furthermore, we find that changes in
managerial overconfidence are negatively related to changes in accounting conservatism
following a CEO change. Our results are robust to a battery of robustness and specification tests.
Overall, our results are consistent with overconfidence having a significant negative effect on
accounting conservatism.
26
Results are qualitatively similar when we define cash constraints as free cash flows.
32 R E F E R E N C ES
AHMED, A.; B. BILLINGS; R. MORTON; DQG067$1)25'³7KH5ROHRI$FFRXQWLQJ
Conservatism in Mitigating Bondholder ±Shareholder Conflicts Over Dividend Policy in
5HGXFLQJ'HEW&RVWV´ The Accounting Review 77 (2002): 867-890.
$+0('$DQG6'8(//0$1³$FFRXQWLQJ&onservatism and Board of Director
&KDUDFWHULVWLFV$Q(PSLULFDO$QDO\VLV´Journal of Accounting and Economics 43
(2007): 411-437.
%$.(50558%$&.DQG-:85*/(5³%HKDYLRUDO&RUSRUDWH)LQDQFH$6XUYH\´LQ
The Handbook of Corporate F inance: E mpirical Corporate F inance, edited by B. Espen
Eckbo. New York: Elsevier/North-Holland, 2007.
BALL, R.; S. P. KOTHARI; DQG91,.2$(9³2Q(VWLPDWLQJ&RQGLWLRQDO&RQVHUYDWLVP´
Working paper, University of Chicago, 2011. Available at
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1758702.
%$//5DQG/6+,9$.80$5³(DUQLQJV4XDOLW\LQ8.3ULYDWH)LUPV´Journal of
Accounting and Economics 23 (2005): 83-128.
%$686³7KH&RQVHUYDWLVP3ULQFLSOHDQGWKH$V\PPHWULF7LPHOLQHVVRI(DUQLQJV´Journal of
Accounting and Economics 24 (1997): 3-37.
%($77<$-:(%(5DQG-<8³&RQVHUYDWLVPDQG'HEW´Journal of Accounting and
Economics 45 (2008): 154-175.
BEN-'$9,',-5*5$+$0DQG&5+$59(<³0DQDJHULDO0LVFDOLEUDWLRQ´:RUNLQJ
paper, Duke University, 2010. Available at
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1640552
33 &$0(5(5&DQG'/29$//2³2YHUFRQILGHQFHDQG([FHVV(QWU\$Q(PSLULFDO
$SSURDFK´American Economic Review 89 (1999): 306-318.
CAMPBELL, T. C.; M. GALLEYER; S. A. JOHNSON; J. RUTHERFORD; and B. W.
67$1/(<³&(22SWLPLVPDQG)RUFHG7XUQRYHU´Journal of F inancial Economics
101 (2011): 695-712.
&25'(,52/³0DQDJHULDO2YHUFRQILGHQFHDQG'LYLGHQG3ROLF\´:RUNLQJSDSHU/RQGRQ
Business School, 2009. Available at
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1343805.
'(6+08.+6$*2(/DQG.+2:(³&(22YHUFRQILGHQFHDQG'LYLGHQG3ROLF\´
Working paper, DePaul University, 2010. Available at
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1496404.
',(75,&+'.08//(5DQG(5,('/³$V\PPHWULF7LPHOLQHVV7HVWVRI$FFRXQWLQJ
&RQVHUYDWLVP´5HYLHZRI$FFRXQWLQJ6WXGLHV-124
*$5&,$/$5$-%*$5&,$26$0$DQG)3(1$/9$³$ccounting Conservatism and
&RUSRUDWH*RYHUQDQFH´Review of Accounting Studies 14 (2009): 161-201.
*(59$,66-%+($721DQG72'($1³2YHUFRQILGHQFH&RPSHQVDWLRQ&RQWUDFWVDQG
&DSLWDO%XGJHWLQJ´Journal of F inance 66 (2011): 1735-1777.
GIVOLY, D., DQG&+$<1³7KH&KDQJLQJ7LPH6HULHV3URSHUWLHVRI(DUQLQJV&DVK)ORZV
DQG$FFUXDOV+DV)LQDQFLDO5HSRUWLQJ%HFRPH0RUH&RQVHUYDWLYH"´Journal of
Accounting and Economics 29 (2000): 287-320.
GIVOLY, D.; C. HAYN; DQG$1$7$5$-$1³0HDVXULQJ5HSRUWLQJ &RQVHUYDWLVP´ The
Accounting Review 82 (2007): 65-106.
34 *2(/$0DQG$-7+$.25³2YHUFRQILGHQFH&(26HOHFWLRQDQG&RUSRUDWH
*RYHUQDQFH´Journal of F inance 63 (2008): 2737-2784.
+$//%-DQG.-0853+<³6WRFN2SWLRQVIRU8QGLYHUVLILHG([HFXWLYHV´Journal of
Accounting and Economics 33 (2002): 3-42.
+($721-³0DQDJHULDO2SWLPLVPDQG&RUSRUDWH)LQDQFH´ F inancial Management 31 (2002):
33-45.
+,/$5<*DQG&+68³(QGRJHQRXV2YHUFRQILGHQFHLQ0DQDJHULDO)RUHFDVWV´Journal of
Accounting and Economics 51 (2011): 300-313.
+,56+/(,)(5'$/2:DQG67(2+³$UH2YHUFRQILGHQW&(2V%HWWHU,QQRYDWRUV"´
Working paper, University of California at Irvine, 2010. Available at
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1598021.
+5,%$53DQG+<$1*³&(22YHUFRQILGHQFHDQG0DQDJHPHQW)RUHFDVWLQJ´Working
paper, University of Iowa, 2011. Available at
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=929731.
-,1/DQG63.27+$5,³(IIHFWRI3HUVRQDO7D[HVRQ0DQDJHUV'HFLVLRQVWR6HOO7KHLU
6WRFN´Journal of Accounting and Economics 46 (2008): 23-46.
.$+1(0$1'DQG'/29$//2³7LPLG&KRLFHVDQG%ROG)RUHFDVWV$&RJQLWLYH
3HUVSHFWLYHRQ5LVN7DNLQJ´ Management Science 39 (1993): 17-31.
.+$10DQG5/:$776³(VWLPDWLRQDQG(PSLULFDO3URSHUWLHVRID)LUP-Year Measure of
$FFRXQWLQJ&RQVHUYDWLVP´Journal of Accounting and Economics 48 (2009): 132-150.
.,''-DQG-025*$1³$3UHGLFWLYH,QIRUPDWLRQ6\VWHPIRU0DQDJHPHQW´ Operational
Research Quarterly 20 (1969): 149-170.
35 .,0,DQG'-6.,11(5³0HDVXULQJ6HFXULWLHV5LVN´Journal of Accounting and
Economics 53 (2012): 290-310.
/$)21'5DQG652<&+2:'+85<³0DQDJHULDO2ZQHUVKLSDQG$FFRXQWLQJ
&RQVHUYDWLVP´Journal of Accounting Research 46 (2008): 101-135.
/$)21'5DQG5/:$776³7KH,QIRUPDWLRQ5ROHRI&RQVHUYDWLVP´The Accounting
Review 83 (2008): 447-478.
/$5:22'/DQG::+,77$.(5³0DQDJHULDO0\RSLD6HOI-Serving Biases in
2UJDQL]DWLRQDO3ODQQLQJ´Journal of Applied Psychology 62 (1977): 194-198.
/,%%<5DQG.5(11(.$03³6HOI-Serving Attribution Bias, Overconfidence, and the
,VVXDQFHRI0DQDJHPHQW)RUHFDVWV´Journal of Accounting Research 50 (2012): 197231.
0$/0(1',(58DQG*7$7(³&(22YHUFRQILGHQFHDQG&RUSRUDWH,QYHVWPHQW´Journal
of F inance 60 (2005): 2661-2700.
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0DUNHW¶V5HDFWLRQ´Journal of F inancial Economics 89 (2008): 20-43.
0$/0(1',(58*7$7(DQG-<$1³2YHUFRQILGHQFHDQG(DUO\/LIH([SHULHQFHV7KH
Effect of Managerial Traits on CRUSRUDWH)LQDQFLDO3ROLFLHV´Journal of F inance 66
(2011): 1687-1733.
3$7$728.$63DQG-7+20$6³0RUH(YLGHQFHRI%LDVLQWKH'LIIHUHQWLDO7LPHOLQHVV
0HDVXUHRI&RQGLWLRQDO&RQVHUYDWLVP´ The Accounting Review 86 (2011a): 1765-1793.
PATATOUKAS, P., aQG-7+20$6³&RPPHQWRQµ(VWLPDWLQJ&RQGLWLRQDO&RQVHUYDWLVP¶E\
%DOO.RWKDULDQG1LNRODHY´Working paper, University of California at Berkeley,
2011b. Available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1922833.
36 52//5³7KH+XEULV+\SRWKHVLVRI&RUSRUDWH7DNHRYHUV´ The Journal of Business 59 (1986):
197-216.
52<&+2:'+85<6DQG5/:$776³$V\PPHWULF7LPHOLQHVVRI(DUQLQJV0DUNHW-toBook and Conservatism in )LQDQFLDO5HSRUWLQJ´Journal of Accounting and Economics
44 (2007): 2-31.
5<$16³,GHQWLI\LQJ&RQGLWLRQDO&RQVHUYDWLVP´ European Accounting Review 17 (2006):
207-221.
6&+5$1'&0DQG6/=(&+0$1³([HFXWLYH2YHUFRQILGHQFHDQGWKH6OLSSHU\6ORSHWo
)LQDQFLDO0LVUHSRUWLQJ´Journal of Accounting and Economics 53 (2011): 311-329.
6.$7$'³2YHUFRQILGHQFHLQ3V\FKRORJ\DQG)LQDQFH± An Interdisciplinary Literature
5HYLHZ´Bank and Credit 39 (2008): 33-50.
69(1621$³$UH:H$OO/HVV5LVN\7KDQ2XU)HOORZ'ULYHUV"´ Acta Psychologica 47
(1981): 143-148.
WATTS, R. L. ³&RQVHUYDWLVPLQ$FFRXQWLQJ3DUW,([SODQDWLRQVDQG,PSOLFDWLRQV´ Accounting
Horizons 17 (2003): 207-221.
WATTS, R. L., and J. ZIMMERMAN. Positive Accounting Theory. Prentice Hall, Upper Saddle
River, NJ, 1986.
:(,167(,11'³8QUHDOLVWLF2SWLPLVPDERXW)XWXUH/LIH(YHQWV´Journal of Personality
and Social Psychology 39 (1980): 806-820.
:(,167(,11'DQG:0./(,1³8QUHDOLVWLF2SWLPLVP3UHVHQWDQG)XWXUH´Journal of
Social and Clinical Psychology 15 (1996): 1-8.
=+$1*-³7KH&RQWUDFWLQJ%HQHILWVRI$FFRXQWLQJ&RQVHUYDWLVPWR/HQGHUVDQG%RUURZHUV´
Journal of Accounting and Economics 45 (2008): 27-54.
37 T able 1
V ariable Definitions
M anagerial O verconfidence M easures
Holder67
First, we divide the value of exercisable unexercised options by the
number of exercisable unexercised options and subtract this value from
the stock price at the fiscal year end to obtain the average exercise price
per option. Second, we divide the value of exercisable unexercised
options per option by the average exercise price per option to calculate
the ratio of the options in-the-money. Third, we set Holder67
(overconfidence) equal to one when the ratio of the options in-the-money
exceeds 0.67 at least twice during the sample period, zero otherwise.
Consistent with Malmendier and Tate [2005, 2008], a CEO is classified
as overconfident in the first fiscal year he/she exhibits the overconfident
behavior and continues to be classified as overconfident for the
remainder of the sample.
Purchase
$GLFKRWRPRXVYDULDEOHVHWHTXDOWRRQHLIWKH&(2¶VQHWSXUFKDVHV
(purchases-sales) are in the top quintile of the distribution of net
pXUFKDVHVE\DOO&(2¶VDQGWKRVHSXUFKDVHVLQFUHDVHWKHLURZQHUVKLSLQ
the firm by 10%, otherwise equal to zero.
CAPEX
A dichotomous variable set equal to one if the capital expenditures
deflated by lagged total assets is greater than the median level of capital
expenditures to lagged total assets for WKHILUP¶V)DPD-French industry,
otherwise equal to zero.
Over-Invest
A dichotomous variable set equal to one if the residual of a regression of
total asset growth on sales growth run by industry-year ( Over-Invest) is
greater than zero, otherwise equal to zero.
F irm-Specific Conservatism Measures
C-Score
Firm-specific asymmetric timeliness score developed by Khan and Watts
(2009) detailed in Section 3.2.1.
CON-ACC
Income before extra-ordinary items less cash flows from operations plus
depreciation expense deflated by average total assets, and averaged over
the previous three years, multiplied by negative one. Positive values of
CON-ACC indicate greater conservatism.
Skewness
Difference between the cash flow skewness and earnings skewness.
Skewness of earnings (cash flows) is equal to (x-µ)3 ı3 ZKHUH—DQGı
are the mean and standard deviation of the earnings (cash flows) over the
last five years. All variables are deflated by average total assets.
38 Control V ariables
Own
PHUFHQWDJHRIWKHILUP¶VRXWVWDQGLQJVKDUHVKHOGE\WKH&(2DWWKHHQGRI
the fiscal year.
MTB
Market-to-book value at the beginning of the fiscal year.
Firm Size
Natural log of average total assets at the end of the fiscal year.
Litigation
Probability of litigation for the firm-year estimated using the coefficients
from the litigation risk model of Kim and Skinner [2011] in Table 7
model (2). This measure estimates the probability of litigation based on
industry, lagged assets, lagged sales growth, market-adjusted return,
return skewness, return standard deviation, and turnover.
Sales Growth
Percentage of annual growth in total sales for the fiscal year.
R&D AD
Total research and development expense plus advertising expense
deflated by total sales (for a given fiscal year).
CFO
Cash flows from operations divided by average total assets.
Leverage
Total liabilities divided by total assets at the end of the fiscal year.
V ariables UHTXLUHGWRHVWLPDWH%DVX¶V[1997] Asymmetric T imeliness M easure
Ret
Annual buy and hold return beginning four months after the prior fiscal
year end.
D
A dichotomous variable equal to one if Ret is less than zero, zero
otherwise.
X
Net income before extraordinary items deflated by the market value of
equity at the beginning of the fiscal year.
39 T able 2
Descriptive Statistics
Mean
Std. Dev
Q1
Median
0.351
0.261
0.565
0.431
0.477
0.445
0.496
0.495
0.000
0.000
0.000
0.000
0.000
0.000
1.000
0.000
1.000
1.000
1.000
1.000
Conservatism Measurse
C-Score
Con-ACC
Skewness
0.060
0.008
0.224
0.085
0.045
2.097
0.014
-0.014
-0.771
0.063
0.006
0.004
0.112
0.027
1.147
Control Variables
Own
MTB
Firm Size
Litigation
Sales Growth
R&D AD
CFO
Leverage
0.020
2.950
7.247
0.047
0.125
0.055
0.106
0.511
0.052
2.677
1.457
0.079
0.272
0.101
0.082
0.194
0.001
1.525
6.183
0.003
0.008
0.000
0.061
0.376
0.003
2.249
7.176
0.014
0.087
0.016
0.102
0.529
0.012
3.496
8.231
0.051
0.191
0.062
0.152
0.653
Asymmetric Timeliness Variables
Return
0.120
D
0.417
X
0.037
0.451
0.493
0.083
-0.165
0.000
0.025
0.069
0.000
0.052
0.320
1.000
0.074
All variables are defined in Table 1. The sample contains 14,641 firm years from 1993-2009.
40 Q3
Overconfidence Measures
Holder 67
Purchase
CAPEX
Over-Invest
T able 2, Panel B
Mean and Median Differences in F irm-Specific Conservatism M easures, Control V ariables, and other
variables for H igh and Low O verconfidence F irms
Holder 67
C-Score
Con-ACC
Skewness
Own
MTB
Firm Size
Litigation
Sales Growth
R&D AD
CFO
Leverage
Return
D
X
N
Purchase
Low
High
Low
High
Overconfidence Overconfidence Overconfidence Overconfidence
Mean Median Mean Median Mean Median Mean Median
0.069 0.073 0.044 0.048
0.069 0.069 0.035 0.040
0.011 0.007 0.004 0.005
0.010 0.009 0.007 0.007
0.411 0.061 -0.121 -0.073
0.264 0.007 0.102 -0.014
0.018 0.003 0.023 0.005
0.020 0.000
0.015 0.003
2.409 1.879 3.947 3.162
2.755 2.200 3.497 2.714
7.341 7.251 7.084 7.010
7.056 7.194 7.789 7.713
0.051 0.016 0.037 0.009
0.041 0.015 0.066 0.027
0.078 0.057 0.209 0.150
0.115 0.084 0.157 0.104
0.053 0.014 0.059 0.021
0.052 0.015 0.060 0.018
0.094 0.092 0.128 0.124
0.106 0.102 0.118 0.112
0.533 0.556 0.472 0.485
0.507 0.533
0.528 0.549
0.032 0.003 0.279 0.208
0.099 0.048 0.176 0.123
0.491 0.000 0.281 0.000
0.435 0.000 0.368 0.000
0.026 0.048 0.056 0.059
0.036 0.051 0.045 0.054
9,502
5,139
8,952
3,161
CAPEX
C-Score
Con-ACC
Skewness
Own
MTB
Firm Size
Litigation
Sales Growth
R&D AD
CFO
Leverage
Return
D
X
N
Over-Invest
Low
High
Low
High
Overconfidence Overconfidence Overconfidence Overconfidence
Mean Median Mean Median Mean Median Mean Median
0.066 0.069 0.055 0.060
0.061 0.064 0.058 0.062
0.010 0.007 0.007 0.006
0.011 0.007 0.005 0.005
0.315 0.027 0.155 -0.004
0.306 0.017 0.116 -0.006
0.018 0.003 0.021 0.003
0.020 0.003
0.020 0.004
2.569 1.989 3.243 2.485
2.756 2.112 3.207 2.445
7.328 7.257 7.186 7.084
7.286 7.204 7.197 7.088
0.047 0.014
0.046 0.014
0.047 0.014
0.045 0.014
0.085 0.064 0.156 0.106
0.116 0.078 0.138 0.101
0.056 0.013
0.054 0.019
0.054 0.015 0.056 0.018
0.085 0.084 0.123 0.119
0.100 0.098 0.115 0.110
0.531 0.555 0.495 0.187
0.523 0.541 0.495 0.513
0.111 0.063
0.127 0.073
0.114 0.068
0.129 0.070
0.421 0.000
0.414 0.000
0.415 0.000
0.421 0.000
0.029 0.051 0.044 0.053
0.029 0.051 0.049 0.054
6,368
8,273
8,339
6,302
All variables are defined in Table 1. Significant differences at the 1% level between the High and Low
Overconfidence partitions for each measures of managerial overconfidence are denoted by italic typeface in
the High Overconfidence partition.
41 T able 3
Cor relations between O verconfidence measures, Conservatism measures, and other variables
Spearman (Pearson) cor relation is above (below) the diagonal
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Holder67
Purchase
CAPEX
Over-Invest
C-Score
Con-ACC
Skewness
Own
MTB
Firm Size
Litigation
Sales Growth
R&D AD
CFO
Leverage
Return
D
X
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
1
0.08
0.12
0.13
-0.15
-0.07
-0.12
0.06
0.29
-0.09
-0.09
0.23
0.01
0.21
-0.17
0.26
-0.21
0.18
0.08
1
0.03
0.02
-0.12
-0.03
-0.03
-0.03
0.09
0.14
0.08
0.05
0.03
0.06
0.03
0.06
-0.05
0.05
0.12
0.03
1
0.16
-0.06
-0.04
-0.03
0.04
0.12
-0.06
-0.01
0.13
-0.01
0.24
-0.11
0.01
-0.01
0.09
0.13
0.02
0.16
1
-0.03
-0.06
-0.04
0.00
0.08
-0.04
-0.02
0.03
0.01
0.09
-0.08
0.01
0.02
0.12
-0.18
-0.13
-0.06
-0.02
1
0.01
0.08
0.06
-0.30
-0.50
-0.33
-0.10
-0.02
-0.21
-0.02
-0.04
0.07
-0.13
-0.05
-0.03
-0.02
-0.05
0.00
1
0.14
-0.05
0.05
-0.03
0.00
-0.09
0.23
0.23
0.00
-0.01
0.02
-0.33
-0.12
-0.02
-0.03
-0.04
0.11
0.11
1
0.01
-0.06
-0.03
0.02
-0.08
0.02
0.17
0.00
-0.09
0.09
-0.28
0.14
0.01
0.04
0.05
0.23
-0.05
0.03
1
0.01
-0.16
-0.09
0.02
-0.02
0.03
-0.10
0.04
-0.01
0.01
0.41
0.11
0.17
0.11
-0.42
0.03
-0.11
-0.07
1
-0.01
-0.04
0.16
0.21
0.29
0.00
0.22
-0.15
0.04
-0.09
0.14
-0.06
-0.04
-0.51
-0.01
-0.03
-0.39
0.02
1
0.61
-0.04
-0.22
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0.49
-0.06
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0.13
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0.11
-0.01
-0.02
-0.37
0.00
0.02
-0.31
-0.06
0.77
1
0.13
-0.02
-0.07
0.24
-0.28
0.23
-0.04
0.32
0.05
0.17
0.06
-0.17
-0.09
-0.14
0.07
0.26
-0.04
0.06
1
0.07
0.07
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0.13
-0.09
0.15
0.03
0.03
0.04
0.04
-0.08
0.11
0.02
-0.06
0.25
-0.18
-0.07
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1
-0.18
-0.30
-0.01
0.06
-0.32
0.22
0.06
0.25
0.09
-0.24
0.28
0.18
0.01
0.40
-0.05
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0.15
0.05
1
-0.22
0.12
-0.12
0.34
-0.17
0.03
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-0.01
0.00
-0.15
-0.07
0.49
0.33
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0.00
0.00
0.26
0.07
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0.18
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0.27
-0.14
0.06
-0.12
0.00
-0.86
1
-0.19
0.17
0.05
0.04
0.08
-0.08
-0.20
-0.28
0.02
0.03
0.12
-0.04
0.20
-0.28
0.26
0.11
0.27
-0.23
1
Italic typeface indicates significance at the 1% level and bold typeface indicates significance at the 5% level. All variables are defined
in Table 1.
42 T able 4
T he E ffect of M anagerial O verconfidence on the Asymmetric T imeliness of E arnings
(i)
Overcon Measure
Intercept
D
Own
MTB
Leverage
Size
Litigation
OverCon
D*Own
D*MTB
D*Leverage
D*Size
D*Litigation
D*OverCon
Ret
Ret*Own
Ret*MTB
Ret*Leverage
Ret*Size
Ret*Litigation
Ret*OverCon
D*Ret
D*Ret*Own
D*Ret*MTB
D*Ret*Leverage
D*Ret*Size
D*Ret*Litigation
D*Ret*OverCon
R2
N
Coef
0.066
-0.011
-0.005
-0.017
0.000
0.005
-0.012
0.009
0.017
0.023
-0.019
0.003
0.014
0.030
-0.025
-0.003
0.024
-0.018
0.319
-0.051
-0.120
0.117
-0.238
0.016
-
p-value
<0.001
0.128
0.009
<0.001
0.968
0.472
0.003
0.038
0.322
<0.001
0.194
0.569
0.042
0.003
0.018
0.367
0.002
0.166
<0.001
0.003
0.047
<0.001
<0.001
0.482
0.340
(ii)
Holder67
Coef p-value
0.020 0.030
-0.012 0.021
0.001 0.087
0.001 0.971
-0.001 0.274
0.007 <0.001
-0.055 <0.001
0.011 <0.001
0.005 0.924
0.001 0.555
0.001 0.617
0.002 0.008
-0.019 0.617
0.005 0.588
0.012 <0.001
0.008 <0.001
0.008 <0.001
-0.001 0.056
0.009 <0.001
-0.048 0.617
0.025 <0.001
0.287 <0.001
-0.009 <0.001
-0.005 0.364
0.013 <0.001
-0.031 <0.001
0.112 0.338
-0.032 <0.001
(iii)
Purchase
Coef p-value
0.024 0.178
-0.009 0.273
0.001 0.060
0.001 0.943
-0.001 0.139
0.007 <0.001
-0.046 <0.001
0.001 0.824
0.001 0.739
0.000 0.773
0.001 0.655
0.002 0.021
-0.019 0.444
-0.003 0.555
0.015 <0.001
0.002 <0.001
0.005 <0.001
-0.001 0.051
0.008 <0.001
-0.051 0.303
0.006 0.460
0.297 <0.001
-0.005 <0.001
-0.006 0.314
0.016 <0.001
-0.027 <0.001
0.790 0.551
-0.010 0.611
(iv)
CAPEX
Coef p-value
0.022 0.265
-0.013 0.216
0.001 0.041
-0.001 0.785
-0.001 0.197
0.007 <0.001
-0.051 <0.001
0.002 0.255
0.000 0.711
0.001 0.536
0.001 0.501
0.002 0.026
-0.019 0.459
0.003 0.534
0.013 <0.001
0.007 <0.001
0.006 <0.001
-0.001 0.088
0.009 <0.001
-0.058 0.565
0.012 <0.001
0.298 <0.001
-0.008 <0.001
-0.005 0.409
0.013 <0.001
-0.032 <0.001
0.139 0.238
-0.049 <0.001
(v)
Over-Invest
Coef p-value
0.019 0.325
-0.014 0.339
0.001 0.020
0.001 0.848
-0.001 0.155
0.007 <0.001
-0.052 <0.001
0.007 <0.001
0.001 0.877
0.001 0.499
0.000 0.566
0.002 0.008
-0.014 0.553
0.001 0.739
0.014 <0.001
0.003 <0.001
0.007 <0.001
-0.002 0.010
0.009 <0.001
-0.043 0.665
0.015 <0.001
0.305 <0.001
-0.008 <0.001
-0.005 0.299
0.013 <0.001
-0.031 <0.001
0.096 0.434
-0.089 <0.001
0.285
14,641
0.271
12,113
0.279
14,641
0.290
14,641
All variables are defined in Table 1. All p-values are based on two-tailed tests using firm and year clustered standard errors. Column
(i) provides the estimated coefficients from Lafond and Roychowdhury (2008) for comparative purposes. Consistent with LaFond and
Rocyhowdhury (2008) all control variables, except Litigation, were measured as decile ranks in the regression.
43 T able 5
Regression of K han and W atts C-Scores on M anagerial O verconfidence and Control V ariables
Intercept
Holder67
Purchase
CAPEX
Over-Invest
Firm Size
Sales Growth
R&D AD
CFO
Leverage
Litigation
Year Dummies
Industry Dummies
R2
N
(i)
(ii)
Coef. p-value Coef. p-value
0.455 <0.001 0.483 <0.001
-0.018 <0.001
-0.005 <0.001
-0.036 <0.001 -0.039 <0.001
-0.008 <0.001 -0.015 <0.001
-0.065 <0.001 -0.076 <0.001
-0.160 <0.001 -0.210 <0.001
0.084 <0.001 0.086 <0.001
0.010 0.351 0.009 0.451
(iii)
(iv)
Coef. p-value Coef. p-value
0.454 <0.001 0.453 <0.001
-0.005
-0.036
-0.012
-0.064
-0.164
0.086
0.011
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.314
-0.003
-0.037
-0.013
-0.064
-0.173
0.085
0.009
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.408
Included
Included
Included
Included
Included
Included
Included
Included
0.599
14,641
0.609
12,113
0.593
14,641
0.592
14,641
All variables are defined in Table 1. All p-values are based on two-tailed tests using firm and year clustered standard errors.
44 T able 6
Regression of A ccrual-Based Conservatism (Con-A C C) on M anagerial O verconfidence and Control V ariables
Intercept
Holder67
Purchase
CAPEX
Over-Invest
Firm Size
Sales Growth
R&D AD
CFO
Leverage
Litigation
Year Dummies
Industry Dummies
R2
N
(i)
(ii)
Coef. p-value Coef. p-value
0.008 0.269 0.005 0.478
-0.008 <0.001
-0.005 <0.001
-0.003 <0.001 -0.003 <0.001
-0.018 <0.001 -0.018 <0.001
0.155 <0.001 0.156 <0.001
0.186 <0.001 0.178 <0.001
0.054 <0.001 0.053 <0.001
0.009 0.383 0.015 0.065
(iii)
(iv)
Coef. p-value Coef. p-value
0.010 0.161 0.011 0.126
-0.007
-0.003
-0.019
0.154
0.188
0.055
0.013
<0.001
-0.008 <0.001
<0.001 -0.003 <0.001
<0.001 -0.019 <0.001
<0.001 0.155 <0.001
<0.001 0.183 <0.001
<0.001 0.055 <0.001
0.141 0.012 0.208
Included
Included
Included
Included
Included
Included
Included
Included
0.256
14,641
0.252
12,113
0.256
14,641
0.258
14,641
All variables are defined in Table 1. All p-values are based on two-tailed tests using firm and year clustered standard errors.
45 T able 7
Regression of the Skewness Based Conservatism Measure on M anagerial O verconfidence and Control V ariables
Intercept
Holder67
Purchase
CAPEX
Over-Invest
Firm Size
Sales Growth
R&D AD
CFO
Leverage
Litigation
Year Dummies
Industry Dummies
R2
N
(i)
(ii)
Coef. p-value Coef. p-value
0.807 <0.001 0.637 0.009
-0.587 <0.001
-0.288 <0.001
-0.169 <0.001 -0.175 <0.001
-0.559 <0.001 -0.691 <0.001
1.713 <0.001 1.934 <0.001
6.349 <0.001 5.918 <0.001
0.957 <0.001 0.919 <0.001
2.215 <0.001 1.863 <0.001
(iii)
(iv)
Coef. p-value Coef. p-value
0.912 <0.001 0.905 <0.001
-0.341
-0.180
-0.697
1.702
6.233
1.102
2.533
<0.001
-0.306 <0.001
<0.001 -0.175 <0.001
<0.001 -0.737 <0.001
<0.001 1.742 <0.001
<0.001 5.911 <0.001
<0.001 1.141 <0.001
<0.001 2.455 <0.001
Included
Included
Included
Included
Included
Included
Included
Included
0.104
14,641
0.092
12,113
0.094
14,641
0.094
14,641
All variables are defined in Table 1. All p-values are based on two-tailed tests using firm and year clustered standard errors.
46 T able 8, Panel A
T he Moderating E ffects of Strong Monitoring on the Relation between Conservatism and M anagerial O verconfidence
Conservatism Measure
Intercept
Holder67
Purchase
Strong Monitoring
Strong Monitoring * Holder67
Strong Monitoring * Purchase
Firm Size
Sales Growth
R&D AD
CFO
Leverage
Litigation
Year Dummies
Industry Dummies
R2
N
(i)
C-Score
Coef. p-value
0.518 <0.001
-0.016 <0.001
0.004 0.021
0.002 0.545
-0.045 <0.001
-0.003 0.605
-0.062 <0.001
-0.192 <0.001
0.095 <0.001
0.009 0.141
(ii)
Con-ACC
Coef. p-value
0.005 0.655
-0.005 <0.001
0.003 0.026
0.000 0.898
-0.004 <0.001
-0.019 <0.001
0.126 <0.001
0.151 <0.001
0.044 <0.001
0.020 0.090
(iii)
Skewness
Coef. p-value
1.272 0.013
-0.621 <0.001
0.174 <0.001
0.410 <0.001
-0.210 <0.001
-0.936 <0.001
1.704 <0.001
6.820 <0.001
0.710 <0.001
2.889 <0.001
(iv)
C-Score
Coef. p-value
0.296 <0.001
-0.005 <0.001
0.004 0.049
0.001 0.902
-0.039 <0.001
-0.004 0.481
-0.088 <0.001
-0.253 <0.001
0.115 <0.001
0.003 0.484
(v)
Con-ACC
Coef. p-value
0.000 0.978
-0.005 <0.001
0.003 0.012
0.000 0.955
-0.004 <0.001
-0.019 <0.001
0.131 <0.001
0.152 <0.001
0.043 <0.001
0.022 0.049
(vi)
Skewness
Coef. p-value
1.295 0.031
-0.294 <0.001
0.177 <0.001
-0.037 0.785
-0.200 <0.001
-1.015 <0.001
2.008 <0.001
6.299 <0.001
0.790 <0.001
3.125 <0.001
Included
Included
Included
Included
Included
Included
Included
Included
Included
Included
Included
Included
0.624
7,228
0.221
7,228
0.146
7,228
0.612
6,063
0.227
6,063
0.158
6,063
Where, Strong Monitoring is a dichotomous variable set equal to one (zero otherwise) if the firm meets three of the following criteria:
(i) a lower percentage of inside directors than the median firm in the sample, (ii) a higher percentage of outside director ownership
than the median firm in the sample, (iii) a higher percentage of ownership held by institutional investors than the median firm in the
sample, and (iv) the CEO does not also serve as Chairman of the Board. All p-values are based on two-tailed tests using firm and year
clustered standard errors.
47 T able 8, Panel B
T he Moderating E ffects of Strong Monitoring on the Relation between Conservatism and M anagerial O verconfidence
(i)
Conservatism Measure
C-Score
Coef. p-value
Intercept
0.518 <0.001
CAPEX
-0.007 <0.001
Over-Invest
Strong Monitoring
0.005 0.066
Strong Monitoring * CAPEX
0.004 0.307
Strong Monitoring * Over-Invest
Firm Size
-0.045 <0.001
Sales Growth
-0.007 0.251
R&D AD
-0.062 <0.001
CFO
-0.202 <0.001
Leverage
0.097 <0.001
Litigation
0.028 0.047
Year Dummies
Industry Dummies
R2
N
(ii)
Con-ACC
Coef. p-value
0.008 0.496
-0.007 <0.001
0.002 0.081
-0.001 0.646
-0.004 <0.001
-0.019 <0.001
0.124 <0.001
0.156 <0.001
0.044 <0.001
0.024 0.046
(iii)
Skewness
Coef. p-value
1.374 0.011
-0.355 <0.001
0.185 <0.001
0.132 0.355
-0.220 <0.001
-1.034 <0.001
1.877 <0.001
6.757 <0.001
0.778 <0.001
3.178 <0.001
(iv)
C-Score
Coef. p-value
0.295 <0.001
-0.004 <0.001
0.004 0.056
-0.004 0.115
-0.040 <0.001
-0.004 0.422
-0.080 <0.001
-0.241 <0.001
0.114 <0.001
0.005 0.195
(v)
Con-ACC
Coef. p-value
0.008 0.503
-0.006 <0.001
0.002 0.078
-0.005 0.042
-0.004 <0.001
-0.020 <0.001
0.127 <0.001
0.150 <0.001
0.044 <0.001
0.024 0.045
(vi)
Skewness
Coef. p-value
1.367 0.011
-0.277 <0.001
0.176 <0.001
0.080 0.530
-0.216 <0.001
-1.085 <0.001
1.774 <0.001
6.460 <0.001
0.757 <0.001
3.069 <0.001
Included
Included
Included
Included
Included
Included
Included
Included
Included
Included
Included
Included
0.619
7,228
0.225
7,228
0.138
7,228
0.619
7,228
0.225
7,228
0.136
7,228
Where, Strong Monitoring is a dichotomous variable set equal to one (zero otherwise) if the firm meets three of the following criteria:
(i) a lower percentage of inside directors than the median firm in the sample, (ii) a higher percentage of outside director ownership
than the median firm in the sample, (iii) a higher percentage of ownership held by institutional investors than the median firm in the
sample, and (iv) the CEO does not also serve as Chairman of the Board. All p-values are based on two-tailed tests using firm and year
clustered standard errors.
48 T able 9, Panel A
Relation between C hanges in A ccounting Conservatism and C hanges in M anagerial O verconfidence following a C E O C hange
Conservatism Measure
Intercept
ǻ+ROGHU
ǻ3XUFKDVH
ǻ)LUP6L]H
ǻ6DOHV*URZWK
ǻ5'$'
ǻ&)2
ǻ/HYHUDJH
Litigation
Year Dummies
Industry Dummies
2
R
N
(i)
ǻ&6FRUH
Coef. p-value
-0.049 0.009
-0.019 0.015
-0.038 <0.001
-0.013 0.270
-0.176 0.007
-0.166 0.002
0.101 0.003
0.063 0.122
(ii)
ǻ&RQ$&&
Coef. p-value
-0.049 0.015
-0.006 0.006
0.002 0.505
-0.030 0.013
0.096 <0.001
0.205 <0.001
0.052 0.002
0.008 0.792
(iii)
ǻ6NHZQHVV
Coef. p-value
1.817 <0.001
-0.734 0.017
-0.146 0.269
-0.477 0.427
0.820 0.497
7.967 <0.001
0.474 0.599
-0.370 0.831
(iv)
ǻ&6FRUH
Coef. p-value
-0.057 <0.001
-0.018 0.045
-0.038 <0.001
-0.018 0.135
-0.182 0.004
-0.196 <0.001
0.104 <0.001
0.049 0.237
(v)
ǻ&RQ$&&
Coef. p-value
-0.048 0.025
-0.003 0.258
0.002 0.525
-0.032 0.009
0.096 <0.001
0.199 <0.001
0.054 <0.001
0.004 0.885
(vi)
ǻ6NHZQHVV
Coef. p-value
1.705 0.007
-0.384 0.016
-0.154 0.210
-0.726 0.175
0.636 0.605
6.913 0.002
0.664 0.411
0.207 0.904
Included
Included
Included
Included
Included
Included
Included
Included
Included
Included
Included
Included
0.599
340
0.341
340
0.270
340
0.595
340
0.338
340
0.252
340
Of the firms in our primary sample, 340 firms made a change in CEO where the previous CEO occupied the position for at least four
years and the incoming CEO remained in the position for at least three years. We then take the values of accounting conservatism,
managerial overconfidence, and the control variables measured three years after the CEO change and subtract their values two-years
prior to the CEO change. Using this changes analysis, we are able to determine if changes in managerial overconfidence are related to
changes in accounting conservatism after the turnover of the CEO position. All p-values are based on two-tailed tests using firm and
year clustered standard errors.
49 T able 9, Panel B
Relation between C hanges in A ccounting Conservatism and C hanges in M anagerial O verconfidence following a C E O C hange
Conservatism Measure
Intercept
ǻ&$3(;
ǻ2YHU,QYHVW
ǻ)LUP6L]H
ǻ6DOHV*URZWK
ǻ5'$'
ǻ&)2
ǻ/HYHUDJH
Litigation
Year Dummies
Industry Dummies
2
R
N
(i)
ǻ&6FRUH
Coef. p-value
-0.046 0.019
-0.007 0.078
-0.039 <0.001
-0.019 0.113
-0.176 0.009
-0.181 <0.001
0.109 <0.001
0.042 0.331
(ii)
ǻ&RQ$&&
Coef. p-value
-0.500 0.011
-0.010 0.023
0.001 0.651
-0.030 0.012
0.099 <0.001
0.212 <0.001
0.055 <0.001
0.001 0.963
(iii)
ǻ6NHZQHVV
Coef. p-value
1.965 0.015
-0.206 0.305
-0.190 0.121
-0.735 0.176
0.793 0.522
7.333 <0.001
0.790 0.316
0.400 0.809
(iv)
ǻ&6FRUH
Coef. p-value
-0.041 0.040
-0.015 0.009
-0.039 <0.001
-0.020 0.133
-0.177 0.006
-0.185 <0.001
0.112 <0.001
0.040 0.350
(v)
ǻ&RQ$&&
Coef. p-value
-0.047 0.025
-0.004 0.436
0.002 0.579
-0.032 0.009
0.096 <0.001
0.200 <0.001
0.055 <0.001
0.003 0.929
(vi)
ǻ6NHZQHVV
Coef. p-value
1.993 0.002
-0.315 0.022
-0.177 0.165
-0.769 0.160
0.719 0.562
7.049 <0.001
0.772 0.331
0.287 0.865
Included
Included
Included
Included
Included
Included
Included
Included
Included
Included
Included
Included
0.591
340
0.353
340
0.249
340
0.597
340
0.341
340
0.248
340
Of the firms in our primary sample, 340 firms made a change in CEO where the previous CEO occupied the position for at least four
years and the incoming CEO remained in the position for at least three years. We then take the values of accounting conservatism,
managerial overconfidence, and the control variables measured three years after the CEO change and subtract their values two-years
prior to the CEO change. Using this changes analysis, we are able to determine if changes in managerial overconfidence are related to
changes in accounting conservatism after the turnover of the CEO position. All p-values are based on two-tailed tests using firm and
year clustered standard errors.
50