DP2023-06

Top Executive Turnover and Loan Loss
Provisions: Evidence from Japanese
Regional Banks
Masahiro ENOMOTO
Yusuke FUKAYA

March 8, 2023

Top executive turnover and loan loss provisions: Evidence from Japaneseregional banks
Masahiro Enomoto*Research Institute for Economics and Business Administration
Kobe University
2-1 Rokkodai, Nada, Kobe 657-8501 JAPAN
Email: menomoto@rieb.kobe-u.ac.jp

Yusuke Fukaya
College of Business Management
J. F. Oberlin University
3-23-1 Hyakunincho, Shinjuku, Tokyo 169-0073 JAPAN
Email: fukaya_y@obirin.ac.jp*Corresponding Author

March 2023

We thank the financial support of JSPS KAKENHI Grant Number 20K02009 and the Kobe University
Center for Social Systems Innovation (Masahiro Enomoto). We also thank Fumihiko Kimura andTomoyasu Yamaguchi for their helpful comments and suggestions.

Top executive turnover and loan loss provisions: Evidence from Japanese regional
banks
Abstract
This study examines loan loss provisions following top executive turnovers in Japanese banks. The
study differentiates between voluntary and forced turnover and inside and outside succession. The
results show that incoming top executives, following forced turnover or outside succession, tend to
reduce loan loss provisions in the second year of their tenure. This suggests that incoming top
executives attempt to create a positive impression of their abilities by increasing earnings in the second
year. This evidence differs from previous research showing “big bath” accounting in the first year.
Additionally, outgoing top executives recognize greater loan loss provisions in the final year before
outside succession. This study further shows that incoming top executives attempt to increase earnings
following succession through gains and losses from securities sales, commissions, and fees.
1. Introduction
Japan has a bank-based financial system centered on bank lending, while the U.S. has a market-based
financial system (Aoki and Patrick 1994; Levine 2002). The ratio of borrowings to total assets of listed
firms in Japan was 20 percent in 2020, and banks remain an important source of funding, even though
Japan has the third largest capital market worldwide.1 More than 99 percent of Japanese firms were(unlisted) small- and medium-sized enterprises in 2016 (The Small and Medium Enterprise Agency
2019, 2022) and are heavily dependent on bank loans. Therefore, the management policies of bankexecutives are crucial to most firms in Japan and ultimately have a significant impact on the entire
Japanese economy.
As the management turnover of banks often results in changing their management policies, it is ofinterest to many firms under the bank-based financial system. A bank’s lending activities are
determined by its management policies, under which the calculation of the allowance for loan losses
requires management estimates. These estimates strongly impact financial performance and are
influenced by management reporting incentives. The recognition of loan loss provisions (LLP) is
subject to the regulation and supervision of regulatory authorities, but since stakeholders cannot fully
monitor it, discretion through estimates that reflect management’s preferences still exists (Nichols,
Wahlen, and Wieland 2009, 95). Hence, a CEO facing poor performance may inflate earnings through
LLP to avoid dismissal. Outside CEOs can also resort to underestimating LLP to secure jobs in theearly years of their tenure.

1

We calculate the ratio by short- and long-term borrowing divided by total assets for the listed firms.

1

Over the past decades, many studies have focused on banks’ earnings management, such as incomesmoothing (e.g., Ahmed, Takeda, and Thomas 1999; Kanagaretnam, Lobo, and Mathieu 2003;
Umezawa 2016) and benchmark beating (e.g., Beatty, Ke, and Petroni 2002; Shen and Chih 2005;
Fukaya 2022). LLP play a pivotal role in the earnings management literature.2 However, few studies
examine the relationship between management turnover and earnings management in the banking
industry (Bornemann, Kick, Pfingsten, and Schertler 2015; Watanabe 2017; Sarkar, Subramanian, andTantri 2019; Shen and Wang 2019). These studies consistently showed that incoming CEOs following
turnover often increase discretionary expenses such as LLP. Reducing current earnings byoverestimating LLP lowers future performance benchmarks.
Incoming top executives in the early years of their tenure have the incentive to engage in earnings
management in both income-increasing and income-decreasing directions. 3 The goals of both
directions of earnings management are common: incoming top executives seek to boost current or
future earnings to enhance their reputation and secure their jobs. Our study builds on previous literatureby considering four types of management turnover in Japanese regional banks: forced-voluntary
turnover, and outside-inside succession.
Our study centers on the analysis of LLP, as they represent the primary accrual component for mostbanks (Kanagaretnam et al. 2003). McNichols (2000) suggested focusing on specific accruals rather
than aggregate accruals to examine further the relationship between specific accruals and earnings
management incentives.
This study employs the LLP model developed by Umezawa (2016), a comprehensive model that
captures LLP under the nonperforming loan categories in Japanese regulations. The model also
considers the size, income-smoothing incentive, and signaling hypotheses on the determinants of LLP
discussed in the literature (e.g., Ahmed et al. 1999; Kanagaretnam et al. 2003). We further decompose
LLP into general loan loss provisions (GLLP) and specific loan loss provisions (SLLP). In an
additional analysis, we examine discretionary gains and losses from security sales, commissions, andfees, using Ertan’s (2022) model.
Using a sample of 1,418 bank-year observations from Japanese regional banks from 2001 to 2020, we
find that incoming top executives following a forced turnover and outside succession tend to recognize
2

Beatty and Liao (2014), Lobo (2017), and Ozili and Outa (2017) reviewed recent empirical studies on LLPs.
In the majority of Japanese banks, the title of the top executive is “President” (Todori or Shatyo). Some financial
holding banks have a CEO as their highest-ranking executive, when they do not have a president. Consequently, thisstudy refers to both titles collectively as “top executives,” and uses the term CEO when referring to previous studies.
32

a lower level of LLP in the second year of their tenure, in contrast to the prior literature’s findings ofa “big bath” in the first year. This lower LLP can lead to an increase in earnings in the second tenure
year. It is possible that incoming top executives may be unable to recognize significantly higher LLP
in the first year due to regulatory capital requirements and/or that the income-decreasing andincreasing LLP may offset each other.
The findings from splitting LLP into GLLP and SLLP are consistent with the main results. The results
for gains and losses from security sales, commissions, and fees coincide with those for LLP. We alsofind that outgoing top executives before outside succession increase LLP in their final years. This
helps incoming top executives to improve their performance. To provide robustness to our findings,
we substitute the LLP model with alternative models, in line with Beatty and Liao (2014) and Bushman
and Williams (2012), for data from the U.S. and other countries. However, our results did not
significantly change.
This study makes three main contributions to the literature. First, it is the first study to focus on andpresent evidence for income-increasing behavior in the second year of a bank’s top executive tenure
following a forced turnover and outside succession. The bank’s income-increasing behavior through
LLP has been treated as a part of income smoothing in prior studies, but we find evidence after
controlling for it. The literature shows big bath accounting through LLP in the first year. We also foundthat outgoing top executives before outside succession are likely to increase LLP. The absence of priorresearch documenting similar findings suggests that this behavior is exclusive to Japanese banks.
Second, we shed light on the association of GLLP and SLLP with management turnover bydecomposing LLP into GLLP and SLLP. Although some studies have examined the relationshipbetween LLP and management turnover, research has yet to investigate the effect of management
turnover on GLLP and SLLP. Our results indicate that the effects of SLLP have an important influenceon those of LLP, and the findings for GLLP are consistent with those of LLP.
Third, this study adds evidence to the literature on real earnings management in the banking sector.
We find that incoming top executives tend to increase earnings through gains and losses from security
sales during the second year of tenure. Furthermore, we extend the existing literature on banks’ real
activities by demonstrating discretionary commissions and fees following management turnover.
Previous studies have paid little attention to commissions and fees. This is the first study to show the
relationship between management turnover and commissions and fees.
The remainder of this paper is organized as follows. Section 2 discusses the related literature and3hypothesis development. Section 3 describes the research design of the study. Section 4 presents ourempirical results. Section 5 provides additional analyses and robustness checks, and Section 6
concludes the paper.
2. Prior literature and hypotheses development
Incoming top executives can engage in income-decreasing and income-increasing earnings
management during the early years of their tenure. First, we consider income-decreasing earnings
management. For non-financial firms, previous studies document that incoming top executives take a
big bath to lower future target earnings in the early years of their tenure (e.g., Elliott and Shaw 1988;
Pourciau 1993; Francis, Hanna, and Vincent 1996).
Previous studies in the banking industry consistently find that incoming top executives engage inincome-decreasing earnings management in the early years of their tenure. Bornemann et al. (2015)
document an increase in discretionary expenses, such as LLP, to lower target earnings the following
year, using data from 691 German savings banks from 1993 to 2012. Their results are promoted whenincoming CEOs are from outside the bank, and the previous CEO has not retired.4
Sarkar et al. (2019) examined Indian government-owned banks (GOBs) from 2002 to 2013. They
focus on CEO turnover, subject to a mandatory retirement system that is exogenously determined andindependent of firm performance. They find that newly appointed CEOs of GOBs increase LLP by
8.5% and reduce lending to lower their personal risks. However, CEOs who retire at a mandatory
retirement age do not increase LLP.
Using data on Taiwanese banks between 2002 and 2010, Shen and Wang (2019) classified turnover
into three categories: forced and voluntary turnover in private banks, turnover in government-ownedbanks, and turnover through mergers and acquisitions (M&A), and investigate the relationshipbetween management turnover and “big bath” behavior. They find that CEOs following a forced
turnover in private banks tend to inflate LLP and provide loan charge-offs to take a big bath, whereas
incoming CEOs in government-owned banks engage only in increasing LLP. Incoming CEOs after
M&A do not induce either big baths.
Watanabe (2017) explored the use of Japanese banks from 2002 to 2013 and incoming top executives
dispose of nonperforming loans (consisting of SLLP and loan charge-offs of loans) following thedeparture of the top executive or the chairman who experienced the top executive. However, Watanabe

4

Bornemann et al. (2015, 192) defined retirement as a turnover in which the CEO has attained the age of 60 years andit is observed that it was their final appointment.

4

(2017) did not extensively use the total LLP or elaborate on voluntary and forced turnover.5
Based on the previous discussion, we posit that incoming top executives utilize earnings management,
which decreases earnings in their first year in office, to demonstrate their ability to improve
performance in subsequent years to stakeholders. At the same time, the incoming top executive canattribute this loss to the outgoing top executive without damaging their reputation and emphasize
dramatic recovery in future performance.
Taking a big bath is particularly likely when an outgoing top executive is not retained on the board
after a forced turnover. When an outgoing top executive remains involved in management following
a voluntary turnover, it is challenging to hold the former CEO accountable for the loss (Choi, Kwak,
and Choe 2014). Voluntary turnover results from the outgoing top executive's decision to retire oraccept another position rather than being prompted by poor performance. Incoming top executives
following a voluntary turnover are under less pressure to perform better than those following a forced
turnover. Thus, they are more inclined to take a “big bath” following forced turnover than voluntary
turnover (Shen and Wang 2019).
To avoid taking responsibility for the losses incurred by their predecessors, incoming top executives
following outside succession may also wish to recognize and address these losses as early as possible
in their first year for their reputation and job security. Then, incoming top executives following outside
succession tend to overestimate the expenses in the first year to improve their performance in the earlystages relative to those following inside succession. Bornemann et al. (2015) proposed that insiders
should not be able to effectively blame past poor performance and revise their predecessor decisions
through a “big bath,” whereas it is relatively easy for outsiders to do so. Thus, incoming top executives
following outside succession have a stronger incentive to demonstrate their capabilities in the years
after their appointment.
This income-decreasing behavior enables incoming top executives to alleviate the pressure fromoutsiders, such as shareholders and debtholders, and insiders, such as a board of directors, to facilitate
future earnings recovery.
The opposite hypothesis is that incoming top executives are incentivized to increase earnings
intentionally. Regardless of the timing of management turnover, meeting or beating earnings
benchmarks such as zero earnings, earnings in the previous year, or analyst forecasts are critical to the5

In Watanabe (2017), the dummy variable for incoming top executives after outside succession was insignificant.

5

evaluations of the CEO (Graham, Harvey, and Rajgopal 2005). Missing the earnings target would
negatively affect their reputation with the market and stakeholders; thus, they should avoid it fromtheir career concerns. The managerial ability of incoming CEOs may be uncertain for stakeholders,
and they may pay attention to their initial performance (Gibbons and Murphy 1992).
Ali and Zhang (2015) argued that CEOs are highly motivated to present favorable performance in theearly years of their tenure to maintain their reputation and avoid adverse effects on futurecompensation, autonomy, and termination. CEOs who report poor performance may be labeled ashaving the low ability, which could damage their entire career. Even high-ability CEOs would inflate
earnings early in their tenure to avoid reporting poor performance. Ali and Zhang (2015) found that
CEOs inflate earnings in their tenures’ early and final years. However, they do not distinguish between
CEOs who are promoted from inside or recruited from outside.
Incoming CEOs appointed from outside the firm have an incentive to enhance earnings compared toinsider CEOs (Choi et al. 2014; Kuang, Qin, and Wielhouwer 2014; Jongjaroenkamol and Laux 2017).
Outsider CEOs are highly expected by the board of directors and shareholders and must deliver strong
performance in their early years. Outsider CEOs have less firm-specific knowledge and skills andgreater risk than insider CEO (Harris and Helfat 1997; Parrino 1997). Management, unknown to
stakeholders, must excel in demonstrating its managerial skills. Kuang et al. (2014) posited that outside
CEOs are motivated by a stronger desire to succeed and a lower likelihood of long-term employment.
They find incoming CEOs are more likely to manage earnings upward in the short run after outside
succession. Moreover, outside CEOs cannot wait to benefit from the cookie jar reserve of deferred
earnings later in their tenures. They provide evidence of the positive impact of a CEO's outside origin
on earnings management.
In a forced turnover, since predecessors do not already exist or retain power after a forced turnover,incoming top executives are motivated by demonstrating their managerial ability through theirperformance, unlike incoming top executives after inside succession. Incoming top executives wholack the protection of their predecessors are in a relatively vulnerable position. As a result, they arelikely to prioritize actions that generate earnings in the early stages of their tenure.6
The two scenarios discussed above share the incentive to boost earnings in the early years of tenure
but differ in whether the first year should be increased. Therefore, the first-year hypothesis does not
6

Takasu and Nakano (2017), who studied the conservativeness and earnings management of the allowance for loan
losses in the Japanese banking industry, reported that the capital adequacy ratio regulation restricts banks from inflating
the LLP (big bath). This suggests that it is difficult for the incoming CEOs of Japanese banks to take a big bath under
the regulation.

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specify a direction for earnings management.
Hypothesis 1a: Incoming top executives engage in earnings management in the first year
following a turnover.
Hypothesis 1b: Incoming top executives engage in earnings management in the first year
following a forced turnover.
Hypothesis 1c: Incoming top executives engage in earnings management in the first year
following an outside succession.
In their second year, incoming top executives are incentivized to inflate their earnings. Our second
hypothesis, H2, is as follows:
Hypothesis 2a: Incoming top executives engage in income-increasing earnings management in
the second year following a turnover.
Hypothesis 2b: Incoming top executives engage in income-increasing earnings management in
the second year following a forced turnover.
Hypothesis 2c: Incoming top executives engage in income-increasing earnings management in
the second year following an outside succession.
3. Research design, sample selection, and data
3.1. Loan loss provision model
We develop a testing model based on Umezawa (2016), an LLP model according to Japanese standards
for allowances and write-offs. In Japan, the Financial Reconstruction Act (FRA) requires banks toclassify their loans into “Normal credits,” “Needs special attention credits,” “Doubtful credits,” and“Bankrupt or de facto bankrupt credits” based on their risk categories.7 Low-risk loans, “Normal
credits” and “Needs special attention credits,” are assigned to the general loan loss allowance (GLLA),
while high-risk loans, “Doubtful credits” and “Bankrupt or de facto bankrupt credits,” are allocated tothe specific loan loss allowance (SLLA).

7“Normal (Non-classified) credits” are credits with no problems in terms of the financial position or businessperformance of the borrower, i.e., all credits not classified as “Needs special attention,” “Doubtful,” or “Bankrupt or
De facto bankrupt” credits. “Needs special attention credits” are credits to “needs attention” borrowers that are “threemonths or more in arrears” or have been given relaxed lending conditions. “Doubtful credits” are credits with a highlikelihood that the principal will not be collected and interest not received according to the contract because the financial
position and business performance of the borrower have worsened, although the borrower is not yet bankrupt. In otherwords, these are credits to “in danger of bankruptcy” borrowers. “Bankrupt or De facto bankrupt credits” are credits toborrowers that have fallen into bankruptcy, corporate rehabilitation, composition or the like, or similar credits. These
are credits to “De facto bankrupt” borrowers and “bankrupt” borrowers (FSA 2014).

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Umezawa (2016) incorporates “Normal credits” and “Needs special attention credits” as independent
variables in his GLLA estimation model and “Doubtful credits” and “Bankrupt or de facto bankrupt
credits” as the independent variables in the SLLA estimation model. He built an estimation model for
the GLLP and SLLP based on the difference between each LLA required at the end of period t and the
LLA in period t-1 after adjusting for direct write-offs and loan sales. Details are provided in AppendixA.
The GLLP and SLLP models include the change in nonperforming loans (CHNPLit) in the current
period and the amount of nonperforming loan (NPL) in the previous period (NPLit-1) as independent
variables. The former variable captures the difference in allowance, while the latter captures reversals
of direct write-offs and loan sales. Finally, the GLLP and SLLP models are combined to form acomprehensive LLP model. Umezawa (2016) adds variables (AdjCAPit-1, EBTPit, CHEBTPit+1) toexamine the capital adequacy ratio adjustment hypothesis, income smoothing hypothesis, andsignaling hypothesis, as suggested by Ahmed et al. (1999) and Kanagaretnam et al. (2003). Thesevariables served as control variables in this study. Equation (1) is the baseline model without
management turnover variables and includes bank- and year-fixed effects.
LLPit = β0 + β1CHNPL0it + β2CHNPL1it + β3CHNPL2it + β4CHNPL3it + β5NPL0it-1 + β6NPL1it-1 +β7NPL2it-1 + β8NPL3it-1 + β9SIZEit-1 + β10AdjCAPit-1 + β11EBTPit + β12CHEBTPit+1 + β13
DNEGEBTPit + βBankFixedEffectit + βYearFixedEffectit + εit(1)

NPL0, NPL1, NPL2, and NPL3 represent “Normal (Non-classified),” “Needs special attention,”“Doubtful,” and “Bankrupt or De facto bankrupt” credits, respectively. CHNPL0, CHNPL1, CHNPL2,
and CHNPL3 indicate the changes in NPL0, NPL1, NPL2, and NPL3, respectively. SIZE is the natural
logarithm of total assets, and DNEGEBTP captures the effect of a negative EBTP. These were addedby Umezawa (2016). Appendix B provides the definitions of the variables.
First, this study incorporates two dummy variables of managerial turnover in equation (1) to test therelationship between turnover and LLP.
LLPit = β0 + β1YearOneit + β2YearTwoit + β3CHNPL0it + β4CHNPL1it + β5CHNPL2it + β6CHNPL3it +β7NPL0it-1 + β8NPL1it-1 + β9NPL2it-1 + β10NPL3it-1 + β11SIZEit-1 + β12AdjCAPit-1 + β13EBTPit+ β14CHEBTPit+1 + β15DNEGEBTPit + βBankFixedEffectit + βYearFixedEffectit + εit (2)YearOne and YearTwo are test variables for H1a and H2a, respectively. YearOne (YearTwo) is anindicator variable that equals one for the bank year, the first (second) year of top executives’ tenure.

8

β1 has a positive (negative) sign if incoming top executives in the first year of tenure use LLP to deflate(inflate) earnings relative to a bank-year without turnover. β2 is predicted to have a positive sign underH2, which assumes that incoming top executives reduce LLP to increase their earnings in the second
year. β2 also represents the difference in LLP from bank years with no turnover.
I divide YearOne and YearTwo into two variables representing the years following voluntary andforced turnover.
LLPit = β0 + β1VYearOneit + β2VYearTwoit + β3FYearOneit + β4FYearTwoit + β5CHNPL0it +β6CHNPL1it + β7CHNPL2it + β8CHNPL3it + β9NPL0it-1 + β10NPL1it-1 + β11NPL2it-1 +β12NPL3it-1 + β13SIZEit-1 + β14AdjCAPit-1 + β15EBTPit + β16CHEBTPit+1 + β17DNEGEBTPit +βBankFixedEffectit + βYearFixedEffectit + εit(3)

As in equation (2), VYearOne (VYearTwo) is an indicator variable that equals one for bank year, whichis the first (second) year of top executives’ tenure following a voluntary turnover. FYearOne(FYearTwo) was constructed similarly for forced turnover. The predicted signs for β1 and β2 (β3 andβ4) correspond to those in equation (2) and represent the difference in LLP from non-turnover bankyears. We also test β1 (β2) ＝ β3 (β4) to examine the difference between voluntary and forced
turnovers.
Next, we replace the management turnover variables for voluntary and forced turnover with those forinternal and external succession, respectively.
LLPit = β0 + β1IYearOneit + β2IYearTwoit + β3OYearOneit + β4OYearTwoit + β5CHNPL0it +β6CHNPL1it + β7CHNPL2it + β8CHNPL3it + β9NPL0it-1 + β10NPL1it-1 + β11NPL2it-1 +β12NPL3it-1 + β13SIZEit-1 + β14AdjCAPit-1 + β15EBTPit + β16CHEBTPit+1 + β17DNEGEBTPit +βBankFixedEffectit + βYearFixedEffectit + εit(4)

IYearOne (IYearTwo) is an indicator variable that equals one for the bank year, the first (second) yearof top executives’ tenure following insider succession. OYearOne (OYearTwo) was created in a similarvein for outsider succession. The predicted signs for β1 and β2 (β3 and β4) correspond to those inequation (2) and represent the difference in LLP from the non-turnover bank year. We also test β1 (β2)＝ β3 (β4), which indicates the difference between inside and outside succession.
3.2. Sample selection, data, and variable measurements for management turnover
3.2.1. Sample selection and data

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Our sample consists of listed regional banks that are members of the Regional Banks Association ofJapan and the Second Association of Regional Banks.8 All regional banks belong to one of theseorganizations. The sample period is March 2001 to March 2020. All bank fiscal years in our sample
begin in April and end in March of the following year.
We used data from the parent-only financial statements of Japanese banks. The data on each categoryof nonperforming loans for the estimation model are defined in the FRA (Act No.132 of 1998) anddisclosed only in a parent-only financial statement.9 Specific loan loss provisions are disclosed inparent-only data. We exclude bank years in which mergers and acquisitions (M&A) occur, as well as
all consecutive observations of management turnover and observations one year before or after amanagement turnover that overlaps one year before or after another management turnover.
Observations with missing data were also removed. Banks with fewer than two observations are
dropped to allow for bank fixed effects. As a result, our final LLP consists of 1,418 observations.
Financial data were obtained from Nikkei NEEDS-FinancialQUEST (Nikkei Media Marketing). Dataon specific loan loss provisions, reversal of allowance for loan losses before 2005, and nonperforming
loans for some banks in 1999 and 2000 were manually collected from the website of the Japanese
Bankers Association.
3.2.2. Variables’ measurement of management turnover
Our data on management turnover were collected using the “Yakuin (Directors)” in the Kigyo-Kihon
Database (Nikkei Media Marketing). This database lists the directors in the annual reports of all listed
firms and provides the name, position, date of birth, date of joining the firms, and related informationfor each director. We first define the top executive of a bank as an individual with the title Todori or
Shacho (President) in the database. In almost all Japanese regional banks, both titles denote the highest
level of executives.10 Management turnover is identified as a change in the name of the top executive
within the same bank code. Since this database is available from the fiscal year ending March 2003,
we rely on the “new presidents” list in Yakuin Shikiho (Executive Officers Handbook) before March
2003.11 To specify YearOne, YearTwo, and non-turnover bank-year data for the entire sample period,8

We cover regional banks in this study. City banks differ from regional banks in terms of business lines and operatingareas as well as their regulatory exposure (e.g., Spiegel and Yamori, 2004; 2007). In addition, this study covers publicly
traded banks that disclose parent-only financial statements, so subsidiary banks of holding ones are not included in thesample (these banks are not listed). In the early 2000s, all city banks established financial holding banks and became
their subsidiary banks of them. Since this leads to a significant restriction in the number of observations of listed city
banks, city banks are excluded from the sample. Umezawa (2016) and Fukaya (2022), who conduct research on
earnings management, also target only Japanese regional banks for the same reason.
9 Our sample period begins in 2001 to create nonperforming loan variables for the estimation of equation (1).
10 Some financial holding banks have a CEO as a position but are not included in our sample.
11 Yakuin Shikiho, published annually by Toyo Keizai Inc, provides information on directors of all listed firms and major
non-listed firms in Japan. It stopped providing the list of new presidents in 2010.

10

we collected data on management turnover from April 1999 onwards. As mentioned before, all thebank fiscal years in our sample end in March, when management turnover occurs between April of theprevious year and March of the current year. YearOne in the current fiscal year is set to one. Thismethod specifies the year the incoming top executive performs fiscal year-end closures.
We distinguish between forced-voluntary turnover and inside-outside succession to test our hypotheses.
First, following Kaplan (1994) and Miyajima, Ogawa, and Saito (2018), forced turnover is defined as
the case in which the outgoing top executive does not remain in the position of chairman or vicechairman.12 The chairman in Japan is generally regarded as less powerful than the president (Kaplan
1994). Miyajima et al. (2018, 21) state that “the standard practice in Japanese firms has been to appoint
the incumbent president to the post of a chairman or vice chairman after his tenure ends, as thepresidency is filled by a successor promoted from within the firm.” Indeed, they show that 60% of
outgoing top executives retain the positions of the chairman and vice chairman of the board.
Next, outside succession in this study is determined by the length of the period between joining thefirm and being promoted to a top executive position. A director from outside the firm who would be
nominated as president in the future would normally be expected to promote upon joining the board.
It is reasonable to posit that they would have a period to demonstrate performance before being
promoted. Shuto (2010) and Ishida and Hachiya (2021), who focus on management turnover inJapanese firms, used a four-year interval between these events to define outside succession. Following
these studies, we define inside succession as when the incoming top executive is promoted within four
years of joining the bank. Some studies on management turnover in Japanese firms (e.g., Yamaguchi2021) define a promotion within one year as inside succession, but Kang and Shivdasani (1995) pointout that short-term performance may not be sufficient to evaluate a promotion to the CEO.
4. Empirical results
Table 1 presents the descriptive statistics for the variables in equations (2)–(4). The value for YearOne
was approximately 14 percent. On average, one in seven Japanese regional banks experienced
management turnover per year. The mean value of LLP is 0.00170, indicating that LLP significantly
impacts ROA, with a mean value of 0.00114 (not tabulated). The mean value of GLLP isapproximately zero, and that of SLLP has a larger value than LLP, indicating the impact of GLLP on
LLP.

12

Miyajima et al. (2018) defined turnover as a voluntary turnover even if the former president remains on the board
except for the position of chairman or vice chairman. Since such a turnover is rare in the banking industry, we do notinclude the turnover as a voluntary turnover. Kaplan (1994) also classified the turnover into “non-standard turnover”.
The main results do not change substantially when we classify the turnover as voluntary turnover.

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[Insert Table 1 here]
Table 2 reports the mean and median of LLPs by type of management turnover. All available turnoverdata (YearOne, YearTwo, and others) are included to maintain a sufficient sample size.13 In the firstyear, 27.2 percent (54 / 199) of incoming top executives are appointed following a forced turnover. It
is worth noting that more than 70 percent of outgoing top executives in the Japanese banking industry
become chairman or vice-chairman with the appointment of incoming top executives. The ratio ofoutside top executives is less than 20 percent, and 80 percent of top executives are bank insiders. The
LLP in the second year (0.00147) is 25 percent lower than that of the first year (0.00195). Especially,
incomings top executives following a forced turnover and outside succession tend to report a greater
reduction in LLP than the other types of turnover. Table 3 is the Pearson correlation matrix. LLP, GLLP,
and SLLP have a positive (negative) correlation with the variables representing the first (second) year
following management turnover.
[Insert Table 2 here][Insert Table 3 here]
Table 4 presents the estimation results for equations (2)–(4). We winsorize all the dependent andindependent variables at the 1st and 99th percentiles, except for the dummy variables. The standard
error is clustered by firm and year. In Column [1], neither the coefficient of YearOne (β1) nor YearTwo(β2) is significant. Thus, H1a and H2a are not supported. In Column [2], we replace YearOne and
YearTwo in equation (2) with VYearOne, FYearOne, VYearTwo, and FYearTwo to test hypotheses
H1b and H2b. The coefficient of FYearTwo (β4) is significantly negative (but not significantly lower
than that of VYearTwo). These results are consistent with H2b and suggest that incoming top
executives following forced turnover have lower LLP in the second year. Neither VYearOne (β1) nor
FYearOne (β3) has a significant coefficient, and H1b is not supported.
[Insert Table 4 here]
In Column [3], we replace YearOne and YearTwo with IYearOne, OYearOne, IYearTwo, andOYearTwo to test H1c and H2c. The coefficient of OYearTwo (β4) was significantly negative andlower than that of IYearTwo (β2). These results are consistent with H2c and indicate that incoming top
executives following outside succession have lower LLP in the second year. Taken together with theresults shown in Column [2], incoming top executives in Japanese banks do not take a big bath in total.

13 When the observations with only management turnover data in the first or second year, the primary results arebroadly retained.

12

Insignificant coefficients of YearOne, VYearOne, and FYearOne variables imply that the incomedecreasing LLP (big bath) may offset each other’s income-increasing LLPs. Panel A of Table 2 in Ali
and Zhang (2015) also shows an insignificant coefficient for their YearOne, suggesting a similar
offsetting relationship. Alternatively, incoming top executives may not recognize significant incomedecreasing LLPs in the first year because of the capital adequacy ratio.14
Among the control variables, the results for loan-related variables are generally consistent with thosereported by Umezawa (2016). The size variable is significant in all equations, but the coefficients of
EBTP and DNEGEBTP are insignificant.
In summary, the results in Table 4 support hypotheses H2b and H2c, suggesting that incoming top
executives, following forced turnover and outside succession, tend to inflate earnings using LLP in thesecond year of their tenure.
Next, we decompose the LLP model into GLLP and SLLP models, considering that the LLP model
combines the GLLP and SLLP models (see Appendix A). To test the hypotheses, management turnovervariables are included in equations (A5) and (A6), as in equations (2)–(4). Following Umezawa (2016),
GLLP is calculated by the change in the general allowance for loan losses, and SLLP is the net transfer
to the specific allowance for loan losses. We regress equations (5)–(10) for GLLP and SLLP.
GLLPit = β0 + β1YearOneit + β2YearTwoit + β3CHNPL0it + β4CHNPL1it + β5NPL0it-1 + β6NPL1it-1 +β7SIZEit-1 +β8AdjCAPit-1 +β9EBTPit +β10CHEBTPit+1 +β11DNEGEBTPitβBankFixedEffectit + βYearFixedEffectit + εit+(5)

GLLPit = β0 + β1VYearOneit + β2VYearTwoit + β3FYearOneit + β4FYearTwoit + β5CHNPL0it +β6CHNPL1it + β7NPL0it-1 + β8NPL1it-1 + β9SIZEit-1 + β10AdjCAPit-1 + β11EBTPit +β12CHEBTPit+1 + β13 DNEGEBTPit + βBankFixedEffectit + βYearFixedEffectit + εit(6)

GLLPit = β0 + β1IYearOneit + β2IYearTwoit + β3OYearOneit + β4OYearTwoit + β5CHNPL0it +β6CHNPL1it + β7NPL0it-1 + β8NPL1it-1 + β9SIZEit-1 + β10AdjCAPit-1 + β11EBTPit+
β12CHEBTPit+1 + β13 DNEGEBTPit + βBankFixedEffectit + βYearFixedEffectit + εit(7)

SLLPit = β0 + β1YearOneit + β2YearTwoit + β3CHNPL2it + β4CHNPL3it + β5NPL2it-1 + β6NPL3it-1 +14

One possible interpretation of the results is that earnings management in the first year is relatively difficult forincoming top executives following outside succession as they lack familiarity with the bank’s specific information andhuman capital.

13

β7SIZEit-1 +β8AdjCAPit-1 +β9EBTPit +β10CHEBTPit+1 +β11DNEGEBTPitβBankFixedEffectit + βYearFixedEffectit + εit+(8)

SLLPit = β0 + β1VYearOneit + β2VYearTwoit + β3FYearOneit + β4FYearTwoit + β5CHNPL2it +β6CHNPL3it + β7NPL2it-1 + β8NPL3it-1 + β9SIZEit-1 + β10AdjCAPit-1 + β11EBTPit +β12CHEBTPit+1 + β13DNEGEBTPit + βBankFixedEffectit +βYearFixedEffectit + εit(9)

SLLPit = β0 + β1IYearOneit + β2IYearTwoit + β3OYearOneit + β4OYearTwoit + β5CHNPL2it +β6CHNPL3it + β7NPL2it-1 + β8NPL3it-1 + β9SIZEit-1 + β10AdjCAPit-1 + β11EBTPit + β12
CHEBTPit+1 + β13 DNEGEBTPit + βBankFixedEffectit + βYearFixedEffectit + εit(10)

Table 5 reports the results of the decomposition of LLP. Panel A of Table 5 for GLLP shows that thecoefficient of OYearTwo (β4) has a significantly negative sign and is lower than that of IYearTwo (β2).
The results are consistent with the primary results and support our hypothesis H2b, but FYearTwo (β4)
has no significant coefficient. The likely reason is that reversing the general allowance for loan losses
by use is very rare (Umezawa 2016). Panel B of Table 5 for SLLP illustrates that the results support
our hypothesis H2b more than GLLP. The coefficient of FYearTwo (β4) is significantly lower than thatof VYearTwo (β2) in Column [2] as well as IYearTwo in Column [3]. These results imply that incoming
top executives following forced turnover (outside succession) in the second year exhibit lower SLLP
than those following voluntary turnover (inside succession) and those for firms with no turnover. This
supports H2b and H2c and is consistent with the results in Table 4. It appears that the findings for
SLLP have a greater influence on the overall results for LLP and outweigh the effects of GLLP.
[Insert Table 5 here]
5. Additional analyses and sensitivity checks
5.1. Additional analysis
We conduct several additional analyses to gain a better understanding of incoming top executives’comprehensive earnings management. First, we investigate two types of real manipulation associatedwith management turnover in the banking industry. One is commissions and fees, and the other is gains
and losses from security sales. We employed Ertan's (2022) model to test for discretionary
commissions and fee income. Ertan (2022) finds that banks that slightly exceed earnings benchmarks
report significantly higher origination fees for syndicated loans.15 For Japanese banks, mutual fundsales commissions and fees are important sources of revenue and origination fees. However, the annual

15

Ozili (2017) and Ozili and Outa (2019) also examined commissions and fees as real earnings management.

14

reports of Japanese banks do not disclose each commission or fee separately. Thus, we use “Other Fees
and Commissions (income)” and “Other Fees and Commissions (expense)” in the income statement
as commission and fee-related income affected by real manipulation.16,17 Equation (12) is the baselinemodel before the inclusion of management turnover variables by Ertan (2022). Fee1, the independent
variable, represents “Other Fees and Commissions (income),” and Fee2 is Fee1 minus the expenses
for “Other Fees and Commissions (expense).” Both variables were divided by total assets. The control
variables are largely the same as those in Ertan (2022) and include a one-year lag, except for the bankand year-fixed effects. We also include the same turnover variables as those in equations (2)–(4).
Fee1it (Fee2it) = β0 + β1log(Size)it-1 + β2Leverageit-1 + β3Profitabilityit-1 + β4LoanIntensityit-1 +β5GrowthinNPLsit-1+

β6LoanLossReserveit-1+

β7AgriculturalLoansit-1+

β8CommercialandIndustrialLoansit-1 + β9RealEstateLoansit-1 + β10IndividualLoansit-1 +βBankFixed Effectit + βYearFixedEffectit + εit(12)

Table 6 shows the regression results. When Fee1 is the independent variable, none of the coefficientsof the test variables is statistically significant. In contrast, when using Fee2, the coefficient of
OYearTwo (β4) is significantly positive and larger than that of VYearTwo (β2). This evidence indicatesthat incoming top executives may engage in earnings management to boost earnings in the second year
following outside succession.
[Insert Table 6 here]
The second additional test is the relationship between gains and losses from securities sales andmanagement turnovers. Gains and losses from securities sales have been examined as managerial
discretion in banking studies (e.g., Beatty et al. 2002). We also utilize Ertan’s (2022) model for gains
and losses from securities sales, which proposes the same model for commissions and fees. Equation(13) is the baseline model before including the management turnover variables. The independent
variables are the same as those in equation (12).

16 “Other Fees and Commissions (income)” includes agency loans accepted as compensation for acceptances andguarantees, and guarantee charges received from the guarantee of obligation. It represents fees and commissions otherthan “Fees and Commissions on Domestic and Foreign Exchanges”, which is Commission received from domestic
exchange business (includes inter-bank fees). The commission received from foreign exchange business includes aguarantee charge received related to foreign exchange transactions. “Other Fees and Commissions (expense)” includes
guarantee charges paid for the guarantee of payment and guarantee fee paid to credit supplementation system for small
and medium-sized enterprises. Costs paid to receive other services. It represents Fees and commissions paid for a
portion other than “Fees and Commissions on Domestic and Foreign Exchanges”, which is Commission paid to otherconsignee banks for domestic exchange business (includes inter-bank fees). Commission paid for foreign exchange
business. Fees and commissions and related expenses are included in these items.
17 Fukaya (2022) reported the manipulation of commissions and fees for banks that meet and slightly exceed theearnings benchmarks. He uses “Fees and Commissions (income)” to capture a wide range of real earnings management.

15

SGLit (SGLit ≥0 or SGLit < 0) = β0 + β1log(Size)it-1 + β2Leverageit-1 + β3Profitabilityit-1 +β4LoanIntensityit-1 + β5GrowthinNPLsit-1 + β6LoanLossReserveit-1 + β7AgriculturalLoansit-1+ β8CommercialandIndustrialLoansit-1 + β9RealEstateLoansit-1 + β10IndividualLoansit-1 +βBankFixedEffectit + βYearFixedEffectit + εit(13)

SGL denotes the gains and losses from security sales divided by the total assets. In addition to SGL,
SGL is divided into non-negative SGL (SGL ≧ 0) and negative SGL (SGL ＜ 0); the former
represents income-increasing earnings management, and the latter represents income-decreasing
earnings management through gains and losses from security sales.
Table 7 reports the relationship between gains and losses from securities sales and management
turnover. When the independent variable is SGL, none of the turnover variables are statisticallysignificant. However, when SGL is non-negative, the coefficient of VYearTwo (β2) is significantly
positive and larger than the coefficient of FYearTwo (β4) in Column [5]. In Column [6], the coefficientof IYearTwo (β2) is insignificant but is significantly larger than OYearTwo (β4). These findings suggestthat when the bank has an unrealized gain from securities, incoming top executives following
voluntary turnover or inside succession tend to boost earnings in the second year of their career, incontrast to the results for LLP. The associations between voluntary turnover and inside succession and
LLP are interesting. It should be noted that the non-negative SGL (SGL ≧ 0) subsample are limited
to banks that report gains from security sales. Therefore, they do not mean that incoming top
executives following forced turnover or outside succession reduce earnings through losses fromsecurity sales in the second year. Thus, the results are not inconsistent with H2b and H2c.
[Insert Table 7 here]
When negative SGL is the independent variable in Columns [8] and [9], the coefficients of FYearOne(β2) and OyearTwo (β4) are significantly positive and greater than those of VyearOne and IyearTwo,
respectively. The results of OyearTwo suggest that incoming top executives following a forced
turnover and outside succession tend not to realize losses from securities to maintain earnings in thesecond year. This result is consistent with those of H2b and H2c. The result of FyearOne also indicatesthat incoming top executives following forced turnover do not attempt to reduce earnings in the firstyear, which is consistent with the primary results.
For the third additional test, we incorporate four types of management turnover in line with Choi et al.
(2014). We add eight turnover variables to equation (1). For the eight variables, “VI,” “VO,” “FI,” and16

“FO” attached before YearOne or YearTwo means voluntary turnover and inside succession, voluntary
turnover and outside succession, forced turnover and inside succession, and forced turnover andoutside succession, respectively.
LLPit = β0 + β1VIYearOne + β2VIYearTwo + β3VOYearOne + β4 VOYearTwo + β5FIYearOne +β6FIYearTwo + β7FOYearOne + β8FOYearTwo +β9CHNPL0it + β10CHNPL1it +β11CHNPL2it + β12CHNPL3it + β13NPL0it-1 + β14NPL1it-1 + β15NPL2it-1 + β16NPL3it-1 +β17SIZEit-1 + β18AdjCAPit-1 + β19EBTPit + β20CHEBTPit+1 + β21DNEGEBTPit +βBankFixedEffectit + βYearFixedEffectit + εit(14)

Only the coefficient of FOYearTwo (β8) was significantly negative. These findings imply that forced
turnover and external succession strongly affect LLP.
Finally, we examine the relationship between LLP immediately before management turnover and thetype of turnover. Dechow and Sloan (1991) demonstrate that CEOs in the year before turnover reduce
research, development, and advertising expenses. Pourciau (1993) hypothesizes that outgoing CEOs
engage in earnings management to increase earnings before forced turnover.
At the time of the outgoing top executives’ final year, they may not know whether turnover will occur,whether it will be forced or voluntary, or whether it will be an inside or outside succession. Therefore,
we did not develop a related hypothesis regarding the behavior of outgoing top executives because ofthis forward-looking problem. While the behavior of outgoing top executives has not been examinedin previous studies on management turnover in the banking industry, some studies on non-financial
firms hypothesize and test the relationship between the position of the outgoing CEO after turnover
and the background of the incoming CEO and earnings management (e.g., Choi et al. 2014).
The outgoing top executive in the final year is incentivized to boost earnings management for his/hercompensation or career after the turnover, regardless of whether the succession is voluntary or forced
and whether the successor is an insider or an outsider. From the perspective of economic rationality, it
is unlikely that the top executive engages in income-decreasing earnings management.
We add FinalYear, which represents the final year of tenure, to equation (2); VFinalYear andFFinalYear for voluntary and forced turnover to equation (3); and IFinalYear and OFinalYear for
inside and outside succession to equation (4), assuming that outgoing top executives know not only
their retirement but also their future position.

17

LLPit = β0 + β1FinalYearit + β2YearOneit + β3YearTwoit + β4CHNPL0it + β5CHNPL1it + β6CHNPL2it+ β7CHNPL3it + β8NPL0it-1 + β9NPL1it-1 + β10NPL2it-1 + β11NPL3it-1 + β12SIZEit-1 +β13AdjCAPit-1 + β14EBTPit + β15CHEBTPit+1 + β16DNEGEBTPit + βBankFixedEffectit+βYearFixedEffectit + ε(15)

LLPit = β0 + β1VFinalYearit + β2VYearOneit + β3VYearTwoit + β4FFinalYearit + β5FYearOne +β6FYearTwo + β7CHNPL0it + β8CHNPL1it + β9CHNPL2it + β10CHNPL3it + β11NPL0it-1 +β12NPL1it-1 + β13NPL2it-1 + β14NPL3it-1 + β15SIZEit-1 + β16AdjCAPit-1 + β17EBTPit+
β18CHEBTPit+1 + β19DNEGEBTPit + βBankFixedEffectit + βYearFixedEffectit + εit (16)
LLPit = β0 + β1IFinalYear + β2IYearOneit + β3IYearTwoit + β4OFinalYearit + β5OYearOneit +β6OYearTwoit + β7CHNPL0it + β8CHNPL1it + β9CHNPL2it + β10CHNPL3it + β11NPL0it-1 +β12NPL1it-1 + β13NPL2it-1 + β14NPL3it-1 + β15SIZEit-1 + β16AdjCAPit-1 + β17EBTPit+
β18CHEBTPit+1 + β19DNEGEBTPit + βBankFixedEffectit + βYearFixedEffectit + εit (17)Table 8 reports the regression results. In Column [3], the coefficient of OFinalYear (β4) is significantly
negative and lower than that of IFinalYear (β2), but FinalYear (β1) and other final year variables do
not have a significant coefficient in Columns [1] to [3]. The results suggest that the outgoing top
executives employ income-decreasing earnings management before the outside succession. However,this is the benefit of the incoming top executive, who has the incentive to improve earnings in the earlyyears of their tenure but does not appear to be economically rational behavior in terms of the outgoing
top executive’s career concerns. Forced turnover with low performance will likely limit the outgoing
top executives to their future job prospects. Moreover, the results are not consistent with the horizon
problem of the departing top executives. Given the absence of prior literature documenting similarfindings, this behavior may be specific to Japanese banks.
[Insert Table 8 here]
5.2. Robustness checks
Five robustness checks were conducted. In line with recent studies on management turnover in Japan,
we assume in our main analysis that when the top executive’s promotion occurs within four years of
joining the bank, the incoming top executive is an insider. Changing this period to three years, as inKang and Shivdasani (1995), did not substantially alter the primary results.

18

Second, we include voluntary turnover in cases where outgoing top executives remain on the board,
except for the position of the chairman or vice chairman (e.g., Kang and Shivdasani 1995; Miyajima
et al. 2018). This does not affect the results of the main findings.18
Third, the variables in equation (1) are standardized by lagged total assets following Umezawa (2016),
whereas the model developed by Beatty and Liao (2014) uses lagged total loans instead. Therefore,
we replace the standardized variable with lagged total loans. The results are robust to the use of totalloans for standardization.
Fourth, Beatty and Liao (2014) argued that it is less comfortable to include changes in NPLs and thelevel of (lagged) NPLs in the same regression. Hence, we exclude the level of lagged NPLs and add
the one-year-ahead change in NPLs, the lagged change in NPLs, and the two-year-lagged change in
NPLs, in line with their argument and Beatty and Liao (2014). The baseline regression equation is asfollows: we add a variable for management turnover.
LLPit = β0 + β1CHNPL0it+1 + β2CHNPL1it+1 + β3CHNPL2it+1 + β4CHNPL3it+1 + β5CHNPL0it +β6CHNPL1it + β7CHNPL2it + β8CHNPL3it + β9CHNPL0it-1 + β10CHNPL1it-1 + β11CHNPL2it-1 +β12CHNPL3it-1 + β13CHNPL0it-1 + β14CHNPL1it-2 + β15CHNPL2it-2 + β16CHNPL3it-2 + β17SIZEit1 + β18AdjCAPit-1 + β19EBTPit + β20CHEBTPit+1 + β21DNEGEBTPit + βBankFixedEffectit +βYearFixedEffectit + εit(18)

These results were similar to those of the primary ones. Our results are robust to dropping the NPLlevel variables and adding the future and past variables to the change in NPLs.
Finally, we replaced the original model with Bushman and Williams’s (2012) model. They estimate
discretionary LLP with earnings management (income-smoothing) variables using banks across 27
countries, including Japan. We exclude GDP from their model because our model already includes
year fixed effects. The baseline model for these robustness checks is as follows: 19 We included
management turnover variables.
LLPBLit = β0 + β1CHNPLBLit+1 + β2CHNPLBLit-1 + β3CHNPLBLit-1 + β4CHNPLBLit-2 + β5SIZEit-1 +β6AdjCAPit-1 + β7EBTPit + βBankFixedEffectit + βYearFixedEffectit + εit(19)

18 In addition to the first and second robustness checks, the main results remain largely consistent when using only thebank year with both YearOne and YearTwo for the management turnover sample.
19 AdjCAP is the book value of equity reported at the end of the period, scaled by total assets at end of the period in
Bushman and Williams (2012). We replace it with AdjCAP to adjust each bank’s capital adequacy ratio.

19

We replicated the primary results using the same approach as that of Bushman and Williams (2012).
6. Conclusion
This study examines discretionary LLP in the years following management turnover in the Japanesebanking industry. Categorizing management turnover into voluntary and forced turnover and insideand outside succession, we find that incoming top executives following a forced turnover and outside
succession inflate earnings in the second year of their tenure through LLP. These findings suggest that
incoming top executives attempt to demonstrate a positive effect on their capabilities by increasing
earnings. This study’s evidence differs from previous studies that showed a “big bath” in the first year.
Additionally, this study identifies that banks that have an outside succession subsequently occurringin the following year have a large amount of LLP in the year before the turnover. Further investigationis necessary to understand this behavior, which does not appear economically reasonable.
We also shed light on commissions and fees as well as gains and losses from security sales as a meansof earnings management. We find evidence suggesting that incoming top executives use gains fromsecurity sales, commissions, and fees to contribute to the increase in earnings in the second year of
their tenure after forced turnover and outside succession. The relationship between commissions, fees,and management turnover has not yet been documented in the literature.
Given that Japan is characterized as a country with a prevalent indirect financial system and significant
economic importance of banks, management policies change due to management turnoversignificantly impacting the economy through financial positions and lending policies. Unlike nonfinancial firms, banks have limited earnings management capabilities and are subject to scrutiny byregulatory authorities and central banks. In this context, providing evidence that incoming top
executives simultaneously engage in accounting discretion and real activities is of considerable
importance in the existing literature.
Appendix A. LLP estimation model in Umezawa (2016)
The derivation of Umezawa’s (2016) LLP estimation model consists of the following three steps: (i)Construct two models by dividing the allowance for loan losses (LLA) into general allowance for loan
losses (GLLA) and specific allowance for loan losses (SLLA). (ii) The relationship between the current
period’s GLLP (SLLP) and the current and prior periods’ GLLA (SLLA), given the reversal method.
(iii) The models for GLLP and SLLP obtained in (ii) are combined.
(i) Umezawa (2016) constructs models for the GLLA and SLLA using the loan categories defined in
the FRA. Japanese banks calculate the amount of GLLA and SLLA according to the borrower and20

loan categories in the Inspection Manual self-assessment. The Inspection Manual was repealed in
December 2019, and the framework with allowances and write-offs through self-assessment remained(FSA 2019). However, the number of loans in each self-assessment category was not disclosed to the
public. Japanese banks must disclose loan amounts under the FRA and the Banking Law (riskmanaged credit amounts). We employ the LLP model using loan categories in the FRA following
Umezawa (2016) because the loan categories correspond highly with the borrower classifications inthe self-assessment. Specifically, among the four categories of FRA loans, “Normal credits (NPL0)”and “Needs special attention credits (NPL1)” are subject to the general allowance, while “Doubtful
credits (NPL2)” and “Bankrupt or de facto bankrupt credits (NPL3)” are subject to the specificallowance. Equations (A1) and (A2) represent the expected model of GLLA and SLLA, respectively,
using this relationship, where i is the bank, t is the year, g is the general loan loss allowance, and s is
the specific loan loss allowance.
GLLAit = α0 + α1NPL0it + α2NPL1it + εgit

(A1)

SLLAit = β0 + β1NPL2it + β2NPL3it + εsit(A2)(ii) Assuming the reversal method, we formulate the relationship between the LLP for the current
period and the LLA for the current and prior periods. LLP is calculated as the difference between thecurrent period's LLA (LLAt) and the residual of the prior period’s LLA after accounting for direct writeoffs and loan sales [(1 – ωt) × LLAt-1], where ωt (0 ≤ ωt < 1) is the ratio of the number of reversals fromdirect write-offs and loan sales in period t to the LLA in the prior period. Based on the above, GLLP
and SLLP are described by equations (A3) and (A4), respectively.
GLLPit = GLLAit – (1 – ωgit) GLLAit-1(A3)

SLLPit = SLLAit – (1 – ωsit) SLLAit-1(A4)

Substituting equations (A1) and (A2) into equations (A3) and (A4) yields equations (A5) and (A6).
CHNPL0, CHNPL1, CHNPL2, and CHNPL3 indicate the changes in NPL0, NPL2, NPL2, and NPL3,
respectively.
GLLPit = α0 + α1NPL0it + α2NPL1it– (1 – ωgit) (α0 + α1NPL0it-1 + α2NPL1it-1) + εgit – (1-ωgit) εgit-1= δ0 + δ1CHNPL0it + δ2CHNPL1it + δ3NPL0it-1 + δ4NPL1it-1 + ugit
SLLPit = β0 + β1NPL2it + β2NPL3it– (1–ωsit) (β0 + β1NPL2it-1 + β2NPL3it-1) + εsit – (1-ωsit) εsit-1

21

(A5)

= γ0 + γ1CHNPL2it + γ2CHNPL3it + γ3NPL2it-1 + γ4NPL3it-1 + usit

(A6)(iii) Equation (A7), which is an estimation model of LLP, is derived by combining equation (A5) for
the GLLP and equation (A6) for the SLLP.
LLPit = (δ0 + γ0) + δ1CHNPL0it + δ2CHNPL1it + γ1CHNPL2it + γ2CHNPL3it + δ3NPL0it-1 + δ4NPL1it-1+ γ3NPL2it-1 + γ4NPL3it-1 + (ugit + usit)(A7)

A positive coefficient of the change in nonperforming loans during the period (CHNPLt) implies anincrease in credit risk. The effect of reversals due to direct write-offs and sales of loans during period
t is captured by the variable of the amount of normal and nonperforming loans at the end of theprevious period (NPLt-1).
Appendix B. Definition of variables
Definitions of variables
LLP= Loan loss provisions (LLP) divided by lagged total assets. When reversalof allowance for loan losses occurs, this value is negative.

GLLP= General loan loss provisions (GLLP) divided by lagged total assets. GLLP
is change in general allowance for loan losses. When reversal of allowance
for loan losses occurs, this value is negative.

SLLP= Specific loan loss provisions (SLLP) divided by lagged total assets. SLLP
is the net transfer to the specific allowance for loan losses. When reversalof allowance for loan losses occurs, this value is negative.

YearOne= 1 if the bank-year is the first year following management turnover, 0
otherwise.

YearTwo= 1 if the bank-year is the second year following management turnover, 0
otherwise.

VYearOne= 1 if the bank-year is the first year following voluntary turnover, 0 otherwise.

VYearTwo= 1 if the bank-year is the second year following voluntary turnover, 0
otherwise.

FYearOne= 1 if the bank-year is the first year following forced turnover, 0 otherwise.

FYearTwo= 1 if the bank-year is the second year following forced turnover, 0 otherwise

IYearOne= 1 if the bank-year is the first year following inside succession, 0 otherwise.

IYearTwo= 1 if the bank-year is the second year following inside succession, 0
otherwise.

OYearOne= 1 if the bank-year is the first year following outside succession, 0 otherwise

22

OYearTwo= 1 if the bank-year is the second year following outside succession, 0
otherwise.

CHNPL0= Change in normal (non-classified) credits divided by lagged total assets.

CHNPL1= Change in needs special attention credits divided by lagged total assets.

CHNPL2= Change in doubtful credits divided by lagged total assets.

CHNPL3= Change in bankrupt or de facto bankrupt credits divided by lagged total
assets.

NPL0= Normal (non-classified) credits divided by lagged total assets.

NPL1= Needs special attention credits divided by lagged total assets.

NPL2= Doubtful credits divided by lagged total assets.

NPL3= Bankrupt or de facto bankrupt credits divided by lagged total assets.

AdjCAP= Capital adequacy ratio minus benchmark ratio. The benchmark ratio is 8
percent for international and 4 percent for domestic banks.

EBTP= Net income before tax plus loan loss provisions (Earnings before tax + LLP)
divided by lagged total assets.

CHEBTP= Change in EBTP between the next year and the current year.

DNEGEBTP= 1 if EBTP is less than 0, 0 otherwise.

BankFixedEffect= Bank fixed effects.

YearFixedEffect= Year fixed effects.

Additional analysis
Fee1= Other Fees and Commissions (income). “Other Fees and Commissions(income)” includes agency loans accepted as compensation for acceptancesand guarantees, guarantee charge received from guarantee of obligation.
Includes agency loans accepted as compensation for acceptances andguarantees, guarantee charge received from guarantee of obligation.

Fee2= “Other Fees and Commissions (income)” minus “Other Fees andCommissions (expense)” divided by lagged total assets. “Other Fees andCommissions (expense)” includes guarantee charge paid for guarantee of
payment and guarantee fee paid to credit supplementation system for small
and medium-sized enterprise. Costs paid to receive other services.

SGL= Gains and losses from security sales divided by lagged total assets.
Specifically, gains and losses from security sales = (gain on sales of stocks
and other securities + gains on sales of bonds) – (losses on sales of stocks
and other securities + losses on devaluation of stocks and other securities +loss on sales of bonds + loss on devaluation of bonds).

log(Size)= Natural logarithm of total assets.

23

Leverage= The ratio of total debt divided by total assets.

Profitability= Net income divided by lagged total assets.

LoanIntensity= Total loan divided by total assets.

GrowthinNPL= Nonperforming loan divided by total loans.

LoanLossReserve= Loan loss reserves divided by total loans.

AgriculturalLoans = Agricultural loans divided by total loans (Fishery, Agriculture, Forestry)
CommercialandIn = Commercial and industrial loans divided by total loans (Manufacturing,
dustrialLoans

Mining, Construction, Wholesale and Retail trade, Finance and Insurance,
Transportation and Communication Services, Electric Power, Gas, Water
Utility, and Services).

RealEstateLoans= Real-estate loans divided by total loans (Real Estate).

IndividualLoans= Loans to individuals divided by total loans (Individual and Others).

FinalYear= 1 if the bank-year is the year prior to management turnover, 0 otherwise.

VFinalYear= 1 if the bank-year is the first year prior to voluntary turnover, 0 otherwise.

FFinalYear= 1 if the bank-year is the first year prior to forced turnover, 0 otherwise.

IFinalYear= 1 if the bank-year is the first year prior to inside succession, 0 otherwise.

OFinalYear= 1 if the bank-year is the first year prior to outside succession, 0 otherwise.

VIYearOne= 1 if the bank-year is the first year following voluntary turnover and inside
succession, 0 otherwise.

VIYearTwo= 1 if the bank-year is the second year following voluntary turnover andinside succession, 0 otherwise.

VOYearOne= 1 if the bank-year is the first year following voluntary turnover and outside
succession, 0 otherwise.

VOYearTwo= 1 if the bank-year is the second year following voluntary turnover andoutside succession, 0 otherwise.

FIYearOne= 1 if the bank-year is the first year following forced turnover and inside
succession, 0 otherwise.

FIYearTwo= 1 if the bank-year is the second year following forced turnover and inside
succession, 0 otherwise.

FOYearOne= 1 if the bank-year is the first year following forced turnover and outside
succession, 0 otherwise.

FOYearTwo= 1 if the bank-year is the second year following forced turnover and outside
succession, 0 otherwise.

Robustness checks
LLPBL= Loan loss provisions (LLP) divided by lagged total loans.

CHNPL= Nonperforming loans divided by lagged total loans.

24

EBTPBL= Net income before tax plus loan loss provisions divided by lagged totalloans.

Industries in parentheses are based on the classification in the Nikkei NEEDS-FinancialQUEST.
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Table 1. Descriptive statistics
Variables

Mean

p25

median

p75

S.D.

N

YearOne0.140340000.34746

1,418

YearTwo0.133990000.34076

1,418

VYearOne

0.102260000.30309

1,418

VYearTwo

0.095200000.29360

1,418

FYearOne

0.038080000.19146

1,418

FYearTwo

0.038790000.19316

1,418

IYearOne

0.114250000.318221,418

IYearTwo

0.106490000.30857

1,418

OYearOne

0.026090000.15947

1,418

OYearTwo

0.027500000.16360

1,418

LLP

0.001700.000140.00076

0.002160.00280

1,418

GLLP

-0.00008-0.00050-0.00011

0.000230.00100

1,418

SLLP

0.001790.000310.00098

0.002360.00237

1,418

CHNPLU00.012280.00103

0.01351

0.024710.02011

1,418

CHNPLU1-0.00023-0.00133-0.00015

0.000440.00326

1,418

CHNPLU2-0.00035-0.00172-0.00041

0.00105

0.00388

1,418

CHNPLU3-0.00045-0.00125-0.00034

0.00037

0.00257

1,418

NPLU0

0.649840.60086

0.653800.699190.06561

1,418

NPLU1

0.006930.002170.00467

0.00969

0.00656

1,418

NPLU2

0.01549

0.009620.013510.01934

0.00850

1,418

NPLU3

0.00799

0.003310.00604

0.010640.00641

1,418

SIZE

14.64998

14.14957

14.70731

15.20850

0.79637

1,418

AdjCAP

0.05827

0.04600

0.05695

0.069200.01829

1,418

EBTP

0.00401

0.002760.004020.00559

0.00280

1,418

CHEBTP

0.00001-0.00096

0.00006

0.00087

0.00310

1,418

DNEGEBTP

0.497180010.50017

1,418

Variable definitions are in Appendix B.

29

Table 2. LLP by four management turnover types

First year

Total

Voluntary

ForcedInsideOutsideMean

0.00195

0.00179

0.002380.00171

0.00300

Median0.000750.000780.000740.000690.00151

199

145

54

162

37

Mean

0.001470.001560.001260.001420.00169Median0.000860.000900.000620.000760.00113

190

135

55

151

39

N
Second YearN

The definition of LLP is in Appendix B. “First year” (“Second year”) means the first (second) year of
the CEO’s tenure. “Voluntary,” “Forced,” “Inside,” and “Outside” represent the bank-year after
voluntary turnover, forced turnover, inside succession, and inside succession, respectively.

30

Table 3. Pearson correlation matrix
[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

[1]

YearOne1[2]

YearTwo-0.1589

1[3]

VYearOne

0.8353-0.1328

1[4]

VYearTwo-0.13110.8247-0.1095

1[5]

FYearOne

0.4925-0.0783-0.0672-0.0645

1[6]FYearTwo-0.08120.5107-0.0678-0.0652-0.0400

1[7]

IYearOne

0.8889-0.14130.7715-0.1165

0.3919-0.07211[8]IYearTwo-0.1395

0.8777-0.11650.7682-0.0687

0.3806-0.1240

1[9]OYearOne

0.4051-0.06440.2806-0.05310.2910-0.0329-0.0588-0.0565

1[10]

OYearTwo-0.0679

0.4275-0.0568

0.2687-0.0335

0.3459-0.0604-0.0581-0.0275

1

[11]

LLP

0.0367-0.03100.0116-0.0157

0.0483-0.0309

0.0020-0.03410.0761-0.0003

1

[12]

GLLP

0.0335-0.05170.0236-0.0187

0.0235-0.06280.0308-0.0155

0.0115-0.0784

0.5710

1

[13]

SLLP

0.0296-0.01770.0059-0.01240.0443-0.0124-0.0113-0.03700.08700.03280.93900.2891

1[14]

CHNPLU0-0.0281-0.00140.0077-0.0286-0.06310.0410-0.0060-0.0160-0.04930.0273-0.4614-0.2244-0.4591[15]

CHNPLU1-0.0151-0.0017-0.02800.04010.0169-0.0639-0.0061

0.0251-0.0209-0.05080.21080.3976

0.0945[16]

CHNPLU2

0.0049-0.01280.01310.0114-0.0118-0.0399-0.0003-0.01900.01130.00910.33920.2536

0.3232[17]

CHNPLU3-0.02410.0031-0.00510.0207-0.0357-0.0261-0.01630.0303-0.0201-0.05070.24720.18890.2183

[18]

NPLU0

-0.0044-0.02590.0015-0.0139-0.0104-0.0244-0.0530-0.0686

0.09610.07560.18060.10350.1752

[19]

NPLU1

-0.0200-0.0176-0.0382-0.04380.02420.0355-0.0309-0.03760.01810.03420.3849-0.04400.4610

[20]

NPLU2

0.00320.0241-0.0632-0.0367

0.10590.0984-0.0303-0.0155

0.06760.07940.2420-0.08900.3122

[21]

NPLU3

0.01270.0167-0.0227-0.0174

0.05890.0558-0.0435-0.03640.11450.10340.45410.02530.5290[22]

SIZE

0.0087-0.00210.04400.0412-0.0537-0.0665

0.05540.0531-0.0916-0.1047-0.16450.0069-0.2016[23]

AdjCAP

0.01230.01130.04820.0526-0.0539-0.0601

0.06180.0651-0.0965-0.0993-0.2708-0.0183-0.3052

[24]

EBTP

-0.0125-0.0157-0.0124-0.0004-0.0030-0.0272-0.0172-0.03170.00710.02690.0324-0.1023

0.0611[25]

CHEBTP

0.00480.00690.01470.0114-0.0145-0.0052-0.0026

0.00550.01560.00400.0596

0.05280.0604[26]

DNEGBTP0.02060.02290.02750.0283-0.0062-0.0025

0.04640.0271-0.0477-0.0034

0.06550.07850.0546

31

[14]

[15]

[16]

[17]

[18]

[19]

[20]

[21]

[22]

[23]

[24]

[25]

[26]

[14]

CHNPLU0

1[15]

CHNPLU1-0.2875

1[16]

CHNPLU2-0.2265

0.1491

1[17]

CHNPLU3-0.1086

0.0571

0.0537

1

[18]

NPLU0

-0.0204

0.0731

0.0547

0.0613

1

[19]

NPLU1

-0.2072-0.2375-0.0671-0.1278

0.0915

1

[20]

NPLU2

-0.2408

0.0005-0.3427-0.1206

0.06380.4653

1

[21]

NPLU3

-0.35800.0858-0.0376-0.30220.18060.56230.5751

1

[22]

SIZE

0.15900.0125-0.0059

0.0261-0.2428-0.1645-0.3154-0.4357

1[23]

AdjCAP

0.1729-0.0274

0.02470.0733-0.2840-0.3951-0.3218-0.39350.2381

1

[24]

EBTP

0.1102-0.0461-0.0103-0.12510.13010.1432-0.03210.05870.1643-0.0183

1[25]

CHEBTP-0.0021-0.0776-0.0953

0.0569-0.01010.08740.06920.03970.0121-0.0374-0.5445

1[26]

DNEGBTP-0.07100.0331-0.0002

0.07330.0061-0.0509

0.0112-0.0022-0.04770.0248-0.3598

0.1379Variable definitions are in Appendix B. N = 1,418.

32

1

Table 4. Management turnover effects on LLP

YearOne
YearTwo[1]
LLP
0.00029(1.510)-0.00020(-1.493)

VYearOne[2]
LLP

0.00029(1.214)-0.00012(-0.733)
0.00030(0.975)-0.00038**(-2.175)

VYearTwo
FYearOneFYearTwo
IYearOne
IYearTwo
OYearOneOYearTwoCHNPLU0CHNPLU1CHNPLU2CHNPLU3
NPLU0
NPLU1
NPLU2
NPLU3
SIZE
AdjCAP
EBTP

[3]
LLP

-0.01564***(-3.881)0.12954**(2.791)0.24317***(8.373)0.35415***(7.913)0.00287(1.352)0.05411**(2.272)0.00664(0.249)0.15627***(7.678)0.00233***(3.815)0.00302(0.726)0.02852(0.658)33-0.01549***(-3.827)0.12910**(2.745)0.24314***(8.579)0.35386***(7.881)0.00281(1.302)0.05464**(2.278)0.00695(0.261)0.15614***(7.671)0.00233***(3.823)0.00293(0.707)0.02855(0.659)0.00031(1.566)-0.00007(-0.466)
0.00021(0.529)-0.00068***(-3.545)-0.01531***(-3.839)0.12699**(2.782)0.24438***(8.291)0.35287***(7.863)0.00282(1.333)0.05212**(2.131)0.00736(0.273)0.15670***(7.662)0.00239***(3.911)0.00272(0.655)0.03023(0.703)

CHEBTPDNEGBTP
Constant

0.04694(1.080)0.00000(0.005)-0.03585***(-3.623)
Yes
Yes

0.04651(1.085)-0.00000(-0.006)-0.03583***(-3.632)
Yes
Yes

0.04827(1.111)-0.00000(-0.005)-0.03667***(-3.722)
Yes
Yes

BankFixedEffect
YearFixedEffect
Test for the equality of regression
coefficients
VYearOne = FYearOne
0.979
VYearTwo = FYearTwo
0.311
IYearOne = OYearOne
0.815
IYearTwo = OYearTwo
0.018
2
Adj-R
0.632
0.631
0.632
N
1,418
1,418
1,418
***
**
and indicate significance at the 1 percent and 5 percent levels (two-tailed), respectively. The tstatistics in parentheses are based on the standard errors clustered by bank and year. The values of the
Test for the equality of regression coefficients are p-values from the tests of the null hypothesis thatthe two coefficients are equal. Appendix B provides the variable definitions.

34

Table 5. Management turnover effects on GLLP and SLLP
Panel A. Regression results for GLLPYearOne
YearTwo[1]
GLLP
0.00010(1.136)-0.00011(-1.425)

VYearOne[2]
GLLP

0.00009(0.879)-0.00011(-1.254)0.00013(0.883)-0.00011(-0.850)

VYearTwo
FYearOneFYearTwo
IYearOne
IYearTwo
OYearOneOYearTwoCHNPLU0CHNPLU1
NPLU0
NPLU1
SIZE
AdjCAPEBTP
CHEBTPDNEGBTP
Constant
BankFixedEffect

[3]
GLLP

-0.00612***(-3.615)0.12840***(3.861)0.00251**(2.679)0.01387**(2.492)0.00125**(2.745)0.00499*(1.994)-0.01413(-0.480)
0.01644(1.032)-0.00003(-0.419)-0.02029***(-3.160)
Yes

35-0.00610***(-3.492)
0.12825***(3.885)0.00253**(2.700)0.01379**(2.468)0.00126**(2.753)0.00499*(1.971)-0.01428(-0.488)0.01637(1.022)-0.00003(-0.420)-0.02032***(-3.171)
Yes

0.00011(1.650)-0.00005(-0.741)0.00007(0.243)-0.00032**(-2.488)-0.00598***(-3.569)
0.12745***(3.889)0.00249**(2.614)0.01320**(2.300)0.00127**(2.748)0.00485*(1.861)-0.01346(-0.464)0.01689(1.070)-0.00003(-0.427)-0.02056***(-3.158)
Yes

Yes
Yes
Yes
YearFixedEffect
Test for the equality of regression
coefficients
VYearOne = FYearOne
0.819
VYearTwo = FYearTwo
0.975
IYearOne = OYearOne
0.890
IYearTwo = OYearTwo
0.036
2
Adj-R
0.209
0.208
0.209
N
1,418
1,418
1,418
*** **, , and * indicate significance at the 1 percent, 5 percent, and 10 percent levels (two-tailed),
respectively. The t-statistics in parentheses are based on the standard errors clustered by bank and year.
The values of the Test for the equality of regression coefficients are p-values from the tests of the null
hypothesis that the two coefficients are equal. Appendix B provides the variable definitions.

36Panel B. Regression results for SLLP.

YearOne
YearTwo[1]
SLLP
0.00019(1.577)-0.00013(-0.990)

VYearOne[2]
SLLP

0.00020(1.396)-0.00004(-0.284)
0.00017(0.759)-0.00037**(-2.222)

VYearTwo
FYearOneFYearTwo
IYearOne
IYearTwo
OYearOneOYearTwoCHNPLU2CHNPLU3
NPLU2
NPLU3
SIZE
AdjCAPEBTP
CHEBTPDNEGBTP
Constant
BankFixedEffect
YearFixedEffect

[3]
SLLP

0.23548***(10.228)0.33757***(8.785)0.02559(1.290)0.17104***(10.888)0.00142***(4.226)-0.00044(-0.124)0.02554(0.700)0.03579(0.926)0.00005(0.578)-0.02061***(-4.067)
Yes
Yes

37

0.23534***(10.405)
0.33689***(8.747)0.02626(1.316)0.17077***(10.529)0.00141***(4.543)-0.00053(-0.150)
0.02575(0.707)0.03535(0.921)0.00005(0.538)-0.02055***(-4.350)
Yes
Yes

0.00021(1.609)-0.00003(-0.199)0.00012(0.482)-0.00054**(-2.488)0.23596***(10.233)
0.33587***(8.737)0.02594(1.300)0.17083***(10.966)0.00147***(4.271)-0.00068(-0.193)0.02715(0.722)0.03698(0.955)0.00005(0.548)-0.02133***(-4.109)
Yes
Yes

Test for the equality of regression
coefficients
VYearOne = FYearOne
0.900
VYearTwo = FYearTwo
0.047
IYearOne = OYearOne
0.725
IYearTwo = OYearTwo
0.042
Adj-R2
0.690
0.690
0.690
N
1,418
1,418
1,418
***
**
and indicate significance at the 5 percent, and 10 percent levels (two-tailed), respectively. The tstatistics in parentheses are based on the standard errors clustered by bank and year. The values of the
Test for the equality of regression coefficients are p-values from the tests of the null hypothesis thatthe two coefficients are equal. Appendix B provides the variable definitions.

38

Table 6. Management turnover effects on commissions and fees

YearOne
YearTwo[1]
Fee1-0.00135(-0.643)-0.00040(-0.277)

VYearOne

[2]
Fee1

[3]
Fee1

-0.00251(-1.245)-0.00122(-0.870)0.00115(0.347)0.00259(0.752)

VYearTwo
FYearOneFYearTwo
IYearOne

LoanIntensity
GrowthinNPLs
LoanLossReserve
AgriculturalLoans
CommercialandIndustrialLoans
RealEstateLoans
IndividualLoans
Constant-0.00121(-0.631)0.00040(0.245)-0.00054(-0.121)0.00428(0.930)-0.06141*** -0.06195***(-3.024)(-2.973)
0.047400.03801(0.524)(0.426)0.02231*0.01678(2.085)(1.693)0.15060*** 0.15699***(3.947)(4.099)-0.08061-0.09107(-1.083)(-1.205)-0.12802-0.11637(-0.655)(-0.679)
0.235170.15619(0.448)(0.293)-0.04822*(-1.823)-0.02897(-0.198)
0.01163(0.779)0.02164(0.406)-0.06085(-0.732)-0.13053(-0.516)-0.27440(-0.318)-0.04749*(-1.788)-0.04256(-0.294)
0.00278(0.223)0.03030(0.579)-0.07481(-0.781)-0.10352(-0.430)-0.31338(-0.361)0.085450.085410.08599

0.108630.111450.11093(1.653)0.10917(1.621)0.07694(1.337)0.89293***(2.853)(1.662)
0.09829(1.525)0.07168(1.196)0.90033**(2.795)(1.674)0.09871(1.535)0.07191(1.211)0.92050***(2.885)(1.338)0.06428(0.599)-0.08756(-1.355)
0.72162*(1.789)(1.387)0.04938(0.482)-0.08660(-1.315)0.70590*(1.742)(1.370)0.04720(0.462)-0.08768(-1.334)
0.73154*(1.830)OYearTwo

Profitability

[6]
Fee2

-0.00236(-1.044)-0.00096(-0.590)0.00552(0.960)0.01101*(1.919)-0.04912*(-1.870)-0.04178(-0.292)
0.00442(0.341)0.02973(0.575)-0.06963(-0.748)-0.12510(-0.516)-0.36694(-0.421)

OYearOne

Leverage[5]
Fee2-0.00186(-0.966)-0.00167(-1.273)0.00018(0.039)0.00569(1.421)-0.06324***(-3.060)
0.03726(0.420)0.01613(1.608)0.15503***(4.075)-0.08478(-1.141)-0.12620(-0.740)0.13213(0.250)

IYearTwo

Log (Size)

[4]
Fee2
-0.00158(-0.680)0.00061(0.339)

39

Yes
Yes
Yes
Yes
Yes
Yes
BankFixedEffect
YearFixedEffect
Yes
Yes
Yes
Yes
Yes
Yes
Test for the equality ofregression coefficients
VYearOne = VYearOne
0.201
0.866
FYearTwo = FYearTwo
0.358
0.442
IYearOne = OYearOne
0.611
0.187
IYearTwo = OYearTwo
0.109
0.063
2
AdjR
0.824
0.825
0.826
0.808
0.807
0.808
N
1,516
1,501
1,501
1,516
1,501
1,501
*** ***, , and indicate significance at the 1 percent, 5 percent, and 10 percent levels (two-tailed), respectively.
The t-statistics in parentheses are based on the standard errors clustered by bank and year. The values of
the Test for the equality of regression coefficients are p-values from the tests of the null hypothesis that thetwo coefficients are equal. Appendix B provides the variable definitions.

40

Table 7. Management turnover effects on gain and loss from securities sales

YearOne
YearTwo[1]
SGL
0.00005(0.477)-0.00009(-0.735)

VYearOne[2]
SGL

FYearOneFYearTwo
IYearOne
IYearTwo
OYearOneOYearTwo

Leverage
Profitability
LoanIntensity
GrowthinNPLs
LoanLossReserve

[4]
SGL≧0
-0.00001(-0.064)0.00004(0.655)0.00001(0.058)-0.00011(-0.540)0.00017(1.260)-0.00006(-0.321)

VYearTwo

Log (Size)

[3]
SGL

0.00072(0.976)-0.00514(-1.120)-0.00055(-0.755)
0.00011(0.066)-0.00146(-0.213)0.00277(0.246)0.00073(0.975)-0.00516(-1.113)-0.00052(-0.682)0.00017(0.099)-0.00164(-0.236)0.00267(0.234)

[5]
SGL≧0

[6]
SGL≧0

[7]
SGL＜0
0.00013(0.672)-0.00012(-0.658)

0.00001(0.107)0.00014**(2.144)-0.00005(-0.334)-0.00019(-1.662)0.00006(0.518)-0.00009(-0.532)
0.00000(0.010)-0.00012(-0.728)0.00072(1.013)-0.00514(-1.118)-0.00057(-0.733)
0.00011(0.063)-0.00147(-0.212)0.00280(0.247)0.00068(1.204)-0.00316(-0.906)
0.00093*(1.858)0.00029(0.253)-0.00216(-0.594)0.00102(0.110)410.00065(1.146)-0.00305(-0.889)0.00091*(1.880)0.00025(0.216)-0.00188(-0.485)0.00133(0.144)

[8]
SGL＜0

[9]
SGL＜0

0.00004(0.188)-0.00012(-0.598)
0.00042***(3.262)-0.00012(-0.302)0.00003(0.300)0.00011(1.678)-0.00018(-1.059)-0.00028(-1.421)0.00069(1.258)-0.00336(-0.980)0.00092*(1.858)0.00026(0.213)-0.00184(-0.514)0.00155(0.165)0.00116(1.218)-0.01142(-1.567)-0.00057(-0.346)
0.00296(0.899)-0.00573(-0.892)-0.00830(-0.680)0.00120(1.285)-0.01186(-1.629)-0.00054(-0.321)0.00306(0.961)-0.00609(-0.923)-0.00830(-0.670)0.00020(1.038)-0.00027(-1.190)-0.00026(-0.489)0.00035*(2.033)0.00102(1.110)-0.01145(-1.490)-0.00070(-0.418)0.00326(0.965)-0.00545(-0.872)-0.00862(-0.702)

AgriculturalLoans
CommercialandIndustri
alLoans
RealEstateLoans
IndividualLoans
Constant

0.00793(0.387)0.00779(0.366)0.00821(0.379)-0.00137(-0.079)0.00162(0.095)0.00072(0.042)0.00180(0.040)0.00487(0.111)0.00024(0.005)0.00230

0.00228

0.002300.00098

0.00101

0.00095-0.00149-0.00146-0.00191(0.829)-0.00113(-0.438)-0.00157(-0.638)-0.01105(-0.963)
Yes
Yes(0.821)-0.00116(-0.451)-0.00159(-0.641)-0.01126(-0.964)
Yes
Yes(0.820)-0.00110(-0.434)-0.00157(-0.638)-0.01114(-0.998)
Yes
Yes(0.549)-0.00121(-0.622)-0.00207(-1.207)-0.00902(-1.091)
Yes
Yes(0.571)-0.00114(-0.597)-0.00200(-1.178)-0.00862(-1.038)
Yes
Yes(0.532)-0.00117(-0.603)-0.00212(-1.261)-0.00923(-1.143)
Yes
Yes(-0.371)-0.00872(-1.530)-0.00729(-1.655)-0.01620(-1.105)
Yes
Yes(-0.367)-0.00885(-1.553)-0.00722(-1.658)-0.01692(-1.180)
Yes
Yes(-0.460)-0.00943(-1.545)-0.00806(-1.668)-0.01374(-0.983)
Yes
Yes

BankFixedEffect
YearFixedEffect
Test for the equality ofregression coefficients
VYearOne = VYearOne
0.441
0.737
0.064
FYearTwo = FYearTwo
0.894
0.017
0.988
IYearOne = OYearOne
0.839
0.298
0.375
IYearTwo = OYearTwo
0.887
0.073
0.035
Adj-R2
0.334
0.333
0.333
0.255
0.257
0.258
0.442
0.440
0.443
N
1,395
1,395
1,395
914
914
914
475
475
475
*** **, , and * indicate significance at the 1 percent, 5 percent, and 10 percent levels (two-tailed), respectively. The t-statistics in the parentheses are based on standard
errors clustered by bank and year. The t-statistics in parentheses are based on the standard errors clustered by bank and year. The values of the Test for the equality ofregression coefficients are p-values from the tests of the null hypothesis that the two coefficients are equal. Appendix B provides the variable definitions.

42

Table 8. Management turnover effects on LLP including the final year of the tenure

FinalYear
YearOne
YearTwo[1]
LLP
0.00014(1.069)0.00032(1.660)-0.00017(-1.223)

VFinalYear

[2]
LLP

0.00002(0.169)0.00031(1.295)-0.00010(-0.596)0.00048(1.335)0.00036(1.107)-0.00032*(-1.755)

VYearOne
VYearTwo
FFinalYear
FYearOneFYearTwo
IFinalYear
IYearOne
IYearTwo
OFinalYear
OYearOneOYearTwo
Constant
Control Variables
BankFixedEffect
YearFixedEffect
Test for the equality of regression
coefficients
VFinalYear = FFinalYear
VYearOne = FYearOne
VYearTwo = FYearTwo
IFinalYear = FOinalYear
IYearOne = OYearOne
IYearTwo = OYearTwo
Adj-R2

[3]
LLP

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes

-0.00008(-0.695)
0.00031(1.528)-0.00008(-0.475)0.00121**(2.224)0.00041(1.058)-0.00051**(-2.433)
Yes
Yes
Yes
Yes

0.251
0.897
0.399

0.632

43

0.632

0.034
0.795
0.095
0.636

1,418
1,418
1,418
N
*
and indicate significance at the, 5 percent, and 10 percent levels (two-tailed), respectively. The tstatistics in parentheses are based on the standard errors clustered by bank and year. The values of the
Test for the equality of regression coefficients are p-values from the tests of the null hypothesis thatthe two coefficients are equal. Appendix B provides the variable definitions.
**44