Hitotsubashi University Repository

Title ISS’s Proxy Voting Guidelines and ROE Management

Author(s) Ishida, Souhei; Kochiyama, Takuma

Citation

Issue Date 2020-06

Type Technical Report

Text Version publisher

URL https://hdl.handle.net/10086/31155

Right

ISS’s Proxy Voting Guidelines and ROE Management

Souhei Ishida Associate Professor, Saitama University

Takuma Kochiyama

Associate Professor, Hitotsubashi University

June 2020

No.235

1

ISS’s Proxy Voting Guidelines and ROE Management *

Souhei Ishida a

Graduate School of Humanities and Social Sciences

Saitama University

Takuma Kochiyama

Graduate School of Business Administration,

Hitotsubashi University

This version: June 2020

a Corresponding author: Graduate School of Humanities and Social Science, Saitama University.

255, Shimo Okubo, Sakura, Saitama city, Sitama, 338-8570, Japan.

E-mail: souhei@mail.saitame-u.ac.jp

Acknowledgements: We appreciate helpful comments and suggestions form Tetsuyuki Kagaya,

Kim Hyonok, Takashi Ebihara, Shirou Ichinomiya, Yasushi Yoshida, and participants in the annual

meeting of Japanese Association for Research in Disclosure 2019, the annual conference of Japanese

Accounting Association 2019, and research workshops in Hitotsubashi University. All errors

remaining are the responsibility of the authors.

2

ISS’s Proxy Voting Guidelines and ROE Management

ABSTRACT

We examine the impact of the proxy voting guidelines of the Institutional Shareholder Services (ISS)

on managers’ opportunistic reporting behaviours. In January 2015, the ISS introduced a new

advisory guideline based on firms’ return on equity (ROE), and started to recommend that

shareholders should vote against the proposed director election for firms whose ROE is lower than

5%. Focusing on this benchmark, we find that managers are more likely to achieve this threshold

since the publication of the guideline and, in particular, they do so by accrual management and

increased dividends. Moreover, firms with higher institutional ownership are more likely to beat the

threshold. This paper thus contributes to the literature by providing new evidence that earnings

management can be motivated by a unique soft law and detects managers’ discretions in the context

of ROE.

Keywords: Earnings Management; Return on Equity; Institutional Shareholder Services

JEL Classification: M41

3

1. Introduction

In this study, we use a unique accounting-based proxy voting guideline for the Japanese market to

examine whether it leads managers to resort to opportunistic reporting behaviours. In January 2015,

Institutional Shareholder Services (ISS), the world leading provider of corporate governance and

responsible investment solutions (i.e. proxy advisor), introduced a new guideline for Japanese listed

firms and started to issue a negative recommendation on proposed director elections for firms whose

reported return on equity (ROE) were below 5%.1 The main aim of the guideline was to provide

market discipline to underperforming firms and improve their capital efficiency, as well as corporate

governance (Ishida 2015). However, in the accounting literature, it is argued that, if a contract or

regulation is based on reported accounting numbers, managers have the incentive to manipulate

these numbers to serve their own or the firm’s interests (Chen and Yuan 2004; Fan et al. 2015).

Therefore, the proposed guideline can provide the motivation to manipulate the reported ROE to

avoid negative recommendations and for managers to secure their positions.

Prior research on opportunistic reporting behaviours has focused on earnings benchmarks

stemmed from market-based heuristics. It finds that managers manipulate earnings to report profit,

increase earnings, and meet analysts and management’s earnings forecasts (Healy and Wahlen 1999;

Graham et al. 2005; Dechow et al. 2010). Moreover, given the growing interests of companies,

market participants, and regulators, scholars have devoted increasing attention to understanding the

influence of proxy advisors on firms’ corporate governance, such as director elections, voting

outcomes, and executive compensation (Ertimur et al. 2018; Hitz and Lehmann 2018; Hayne and

1 We investigate ISS’s voting guidelines for each country and find that ISS includes clear guideline based on ROE for board member appointments only for Japan. For other countries, such as in New Zealand, the guideline includes recommendation based on ‘performance’ but leave the definition and threshold undefined.

4

Vance 2019). However, we have even less evidence on whether a guideline issued by a proxy

advisory firm affects firms’ reporting behaviours. As a result, our study differs from prior studies in

the following two points. Fist, we focus on a soft law issued by a private company. The ISS guideline

is unique, in that it provides certain pressure for managers in all Japanese listed firms, but its

consequences remain uncertain. According to the guideline, reporting poor ROE enhances the

likelihood of forced managerial turnover. However, it is not yet understood whether such a soft law

leads to managers’ opportunistic reporting behaviours, as ISS is an advisory firm that provides only

recommendations, largely leaving the consequences to the shareholders. Second, we focus on ROE

rather than a single earnings item. ROE is a financial ratio affected by a firm’s various financial

policies, such as earnings management, dividend payouts, asset sales, and debt issuance. Although

ROE has long been considered an important indicator in equity valuation (Ohlson 1995), there is a

surprising paucity of evidence on whether and how managers discretionally manage ROE and its

drivers. We thus comprehensively examine managers’ discretions over ROE and provide unique

evidence on ROE management. In other words, we extend the literature in this area by considering

the new benchmark set by the ISS guideline and revealing the impact on managers’ opportunistic

behaviours regarding ROE.

To detect managers’ opportunistic reporting, we use the histogram approach of Burgstahler

and Dichev (1997) and test the discontinuities of the reported ROE distribution. If managers

opportunistically achieve the ROE threshold in the ISS guideline, the ROE distribution should be

skewed around 5%, with more observations that report ROE slightly above the threshold. Moreover,

in accordance with prior studies, we focus on firms that just beat the threshold as suspects and assess

how they do so by considering their determinants (Beatty et al. 2002; Burgstahler et al. 2006; Fan

et al. 2015; Shuto and Iwasaki 2015). Specifically, we conduct regression analysis for examining

5

managers’ discretionary activities that can improve the reported ROE: accrual management, real

activity manipulation, sales growth, asset sales, debt financing, and corporate payout policies.

Using a sample of Japanese listed companies, we first provide evidence consistent with firms

managing ROE to achieve the 5% threshold. Our histogram analysis shows that the ROE distribution

is significantly skewed at 5% after the publication of the ISS guideline, that is, the frequency of

firms just beating the ROE threshold is disproportionately high. Moreover, the regression analysis

shows that suspect firms (i.e. those that just beat the benchmark) are more likely to use discretionary

accruals and increase dividends to improve the reported ROE compared to those that just miss the

benchmark. These results are robust to several sensitivity tests, including the definition of suspect

firms (i.e. bin width for constructing histogram intervals), model specifications, alternative

measurements for earnings management and dividend payouts, and a subsample of firms with poor

ROE history.

Additionally, we investigate whether the monitoring mechanisms relate to the likelihood of

achieving the ISS threshold. This additional analysis reveals that firms with higher institutional

ownership have a higher probability of achieving the threshold. Consistent with the economic role

of ISS, the evidence highlights that the presence of institutional investors increases managers’ career

concerns stemming from the ISS recommendation and, therefore, their sensitivities to beat the ISS

threshold.

We contribute to the literature in several ways. First, we provide new evidence on firms’

opportunistic reporting. Past research on earnings management largely focuses on market-based

heuristics and reports that managers manipulate their earnings to avoid losses, increase earnings,

and meet analyst/management forecasts (e.g. Burgstahler and Dichev 1997; Degeorge et al. 1999;

Enomoto and Yamaguchi 2017). By contrast, this study highlights the importance of the soft law

issued by a proxy advisory firm and extends the literature by revealing the effects of the ISS

6

regulation on accounting and corporate behaviours. Second, we provide new insights on managers’

discretionary activities over ROE. While ROE has been considered a key financial indicator in

equity valuation, the literature on earnings management tends to focus on a single earnings item.

Our findings indicate that, for improving ROE, managers use increased dividends, as well as accrual

management. Moreover, the evidence in this study should be of interest to both regulators and proxy

advisory firms. When a regulation or guideline is based on accounting numbers or specific financial

indicators, the managers can have incentives to beat the benchmark using discretionary behaviours,

which leads to a lower quality of financial reporting. Our findings support this view and suggest that

rule-makers and advisory firms should pay attention to the use of accounting numbers and its

consequences.

2. Literature review and hypotheses development

2.1. Institutional background

Over the past decade, the Japanese economy has experienced drastic changes in its corporate

governance and financial regulations. In the wake of Prime Minister Shinzo Abe’s economic reforms

(Abenomics), the Japanese government published the ‘Japan Revitalization Strategy’ in June 2013

and advocated enhancing corporate governance to improve investor confidence and facilitate a more

aggressive business management, as well as increase the earnings capacity of firms. This strategy

was followed by rapid and significant responses from financial regulators (Yanagi 2018; Kochiyama

and Ishida 2020). These include the Stewardship Code of June 2014 (Financial Service Agency),

the Ito Review of August 2014 (Ministry of Economy, Trade and Industry), and Japan’s Corporate

Governance Code of February 2015 (Tokyo Stock Exchange). The common underlying goal of these

publications was the improvement of corporate governance and capital efficiency towards a

7

sustainable growth of corporate value, represented by ROE. For example, the Ito Review shows that

the capital efficiency of Japanese firms has been significantly lower than those of US and European

firms and suggests Japanese listed firms should achieve a minimum ROE of 8% to create corporate

value. As another example, the Japan Exchange Group encourages the improvement of capital

efficiency by launching a new and prestigious stock index in 2014, the JPX-Nikkei 400, where ROE

is used as an index-inclusion criterion (Chattopadhyay et al. 2020). Consequently, ROE has been

the centre of the Japanese economy and has attracted significant attention from managers, regulators,

and investors.2

Among these changes, what is particularly unique to the Japanese market is that ISS issued

a new proxy voting guideline based on firms’ reported ROE. ISS has is the most influential proxy

voting advisor and plays a significant role in Japanese capital markets, where foreign institutional

ownership is predominant.3 Specifically, in January 2015, ISS published a revised proxy voting

guideline for Japanese listed companies and introduced a new capital efficiency criterion for director

elections. The revised guideline states that ISS ‘vote(s) for the election of directors, except top

executive at a company that has underperformed in terms of capital efficiency (i.e. when the

company has posted average ROE of less than 5% over the last 5 years), unless an improvement (i.e.

defined as ROE of 5% or greater for the most recent fiscal year) is observed’. The representative of

the ISS Japanese office, Takeyuki Ishida, explains that the new guideline follows the preceding Ito

2 For example, according to a survey by The Life Insurance Association of Japan (2016), more than half of

the responding companies publish their ROE as management goal in their medium-term management plans and 80% of the responding investors consider ROE should be emphasised as a management goal.

3 According to a survey of the Tokyo Stock Exchange, institutional ownership accounts for more than 80% of the overall ownership in Japanese listed firms and, among them, foreign investors have been the largest shareholder group, exhibiting more than 30% of the ownership in Japanese markets (Tokyo Stock Exchange 2019). From an economic perspective, proxy advisors represent information intermediaries that economise information and monitoring costs, particularly for institutional shareholders covering firms from different countries (Hitz and Lehmann 2018).

8

Review and JPX-400 index in considering ROE as a useful measurement for encouraging

improvements in firms’ capital efficiencies (Ishida 2015).

The key implication of the new guideline is the presence of direct punishments for existing

top managers in all Japanese listed firms. Unlike other recent capital market reforms, such as the Ito

Review and JPX-400 index that utilise ROE as a desirable target and positive screening of excellent

companies, the revised ISS guideline is more likely to use ROE in the context of negative screening:

scrutinising underperformed listed companies and dismissing incompetent managers. As in many

other countries, the Japanese Companies Act states that shareholders have statutory rights to appoint

company directors (Article 329) and the selection of directors must be resolved by more than half

of the affirmative votes from the shareholders attending the annual meeting (Article 341). Moreover,

the tenure of company directors must be no more than 2 years (Article 332), indicating that existent

directors can remain in the same position when they are repeatedly approved at shareholder meetings.

Therefore, the revised guideline can have a direct influence on managers’ sensitivities on ROE and

their reporting behaviours, to the extent that reporting poor ROE increases the likelihoods of

managers’ turnover. This study thus focuses on this unique ROE-based advisory rule and extends

our knowledge on the influence of proxy voting advisors.

2.2. Prior studies on earnings management around benchmarks

In discussing the drivers of earnings management, prior studies have shown that managers often

manipulate reported earnings to meet or beat certain earnings benchmarks (e.g. Burgstahler and

Dichev 1997; Degeorge et al. 1999; Beatty et al. 2002; Dichev and Skinner 2002; Thomas et al.

2004; Shuto and Iwasaki 2015; Enomoto and Yamaguchi 2017).4 These benchmarks stem from

4 See, for example, Healy and Wahlen (1999) and Dechow et al. (2010) for a literature review on earnings

management.

9

market-based heuristics and include reporting a profit (i.e. avoiding loss), increasing earnings (i.e.

beating previous earnings), and beating analyst and management earnings forecasts. In other words,

managers will manipulate earnings to achieve these benchmarks, so that they can receive rewards

or avoid penalties from the capital markets. Moreover, Schipper (1989) argues that managers tend

to conduct earnings management to obtain favourable treatment from regulators, particularly when

regulation is based on accounting numbers. Chen and Yuan (2004) and Fan et al. (2015) use unique

Chinese regulations and find that managers manipulate earnings to achieve regulatory benchmarks.

We extend this literature stream by examining whether ISS’s ROE-based guideline leads to

managers’ opportunistic reporting behaviours. Our study is remarkably different from previous

studies in the following two points. First, we focus on a soft law issued from a proxy advisor firm,

the ISS guideline, as a benchmark, while prior studies have highlighted the importance of earnings

benchmarks for investors’ and creditors’ expectations, such as loss avoidance, analyst forecasts, and

debt covenants. However, it is unknown whether a guideline published by a private advisory firm

gives rise to managers’ opportunistic reporting behaviours. Specifically, while ISS is only an

advisory firm to shareholders and investors, its influence has attracted significant attention and

concern from both financial regulators and academics (Ertimur et al. 2018; Hitz and Lehmann 2018).

Hence, the impact of the ISS guideline on managers’ discretionary reporting is an important

empirical question. Second, we focus on ROE, a financial indicator, rather than a single earnings

item. As argued in the previous section, the new ISS guideline uses ROE as a key benchmark for

the election of company directors. Compared to single earnings items, such as net income, managers

have greater options to increases ROE. As suggested by the DuPont decomposition equation, ROE

can be illustrated as a function of return on sales (ROS, profitability), asset turnover (productivity

relative to revenues), and financial leverage (financial policies including debt issuance and payouts

to shareholders). However, we have little evidence whether and how managers use these options to

10

discretionally enhance the reported ROE. We thus extend the literature on earnings management by

decomposing the drivers of ROE and examining managers’ ROE management.

2.3. Hypotheses development

In the Japanese markets, the new ISS guideline explicitly uses firms’ reported ROE in the context

of recommendations for director elections. Regarding the effectiveness of proxy advisor opinions,

prior studies document that a negative recommendation significantly decreases the number of

supportive votes for the receiving directors (Cai et al. 2009; Ertimur et al. 2018; Hitz and Lehmann

2018).5 Therefore, to the extent that it alters shareholders’ voting behaviours, reporting a poor ROE

after publication will increase the likelihood of managers’ dismissal. Particularly, targeted managers

will face career concerns, as they may lose expected executive compensation, social status, and

external reputation based on the voting outcome of the shareholder meeting. These potential

negative consequences can generate a managerial incentive to achieve a ROE of 5%, which is the

threshold suggested in the guideline, and thus reduce the likelihood of dismissal. Consistent with

this argument, the survey investigation of Graham et al. (2005) shows that more than 75% of

executive respondents agree that the concerns about career and external reputation give rise to a

motivation to achieve earnings benchmarks. Using a sample of Japanese listed firms, Shuto (2007)

documents that mangers are more likely to beat earnings benchmarks, particularly firms where CEO

turnover is historically associated with earnings, indicating that they conduct earnings management

to decrease the likelihood of CEO turnover.

5 Although it is yet unclear whether Western findings can translate to Japanese markets, it is plausible that

ISS has a similar influence on listed companies as its activity and coverage are consistent worldwide. ISS (2019) reports that the ratio of negative recommendations for Japanese firm director elections was 9% in 2019 (out of all individual director candidates), which is similar to the 7% in the U.S., as reported by Ertimur et al. (2018).

11

Moreover, even if ISS fails to garner the support of shareholders, it can create pressure for

targeted managers by attracting media’s and investors’ attention. In the context of shareholder

proposals, David et al. (2007) and Hadani et al. (2011) argue that activists are often successful in

attracting significant public attention and, therefore, intensify the public scrutiny for managers’

legitimacy. Consistently, Ertimur et al. (2018) report that, while negative recommendations from

proxy advisors rarely result in director turnover, they are likely to oblige targeted firms to respond

by addressing the concerns in a public manner. Given that this triggered public scrutiny can

challenge managers’ legitimacy and potentially affect their careers, managers will behave as to avoid

such situations ex ante by beating the threshold of ISS.

Therefore, we predict that, after the publication of the new ISS guideline, managers have the

incentives to conduct discretionary activities to achieve the ROE threshold. The hypothesis is

expressed as follows:

Hypothesis: After the publication of the revised ISS guideline, managers are more likely to

engage in discretionary activities to achieve the 5% ROE.

While the revised guideline states that ISS will issue negative recommendations for firms

whose average ROE over the past 5 years is below 5%, we first focus on the current reported ROE

and examine whether managers opportunistically achieve the threshold after publication. This is

because reporting a ROE below 5% will more or less increase the probability of forced turnover

after publication; this career concern is applicable to all managers, not being limited to firms with

poor ROE history. Moreover, when we only focus on underperforming firms, it can be difficult to

detect opportunistic reporting behaviours by the histogram approach (see the next section) as the

distribution of ROE for these firms can be irregularly skewed, regardless of the threshold. We will

come back to this issue in the subsequent section.

12

3. Research design and data

3.1. Definition of ROE in the Japanese context

In the Japanese markets, ROE is statutory defined by the Financial Service Agency as mandatorily

reported in the first page of the statutory annual report (Yuka Shoken Houkokusho, which is

equivalent to 10-K filing in the US). Although the ISS guideline does not provide an explicit

definition of ROE, we conjecture that it applies to the reported ROE in the statutory report, as this

is an official public document submitted to the Prime Minister. Specifically, the ROE is defined as

follows:

ROE = Net income attributable to shareholders of the parent company

Average owned equity capital

ROE =Net income attributable to shareholders of the parent company

Ave. net assets - Ave. stock options - Ave. non-controlling interests, (1)

where ‘average’ denotes the arithmetical mean of current and previous fiscal years. According to

the Japanese accounting standards, the net assets in the balance sheet consists of four items:

shareholder equity, accumulated other comprehensive income (OCI), stock options, and non-

controlling interests. Among these items, it is traditional to call the sum of first two items Jiko Shihon

(self-owned equity capital) and use it as the ROE denominator. Accordingly, the denominator in

Equation (1) equals the sum of shareholder equity and accumulated OCI, which is largely equivalent

to ‘shareholders’ equity’ in the US balance sheet. We hereafter use this definition of ROE and treat

firm observations whose average owned equity capital is less than or equal to 0 as missing data

observations.

13

3.2. Empirical approach for testing hypothesis

To test the research hypothesis, we apply the histogram approach and investigate the firms that

report values just above the ROE threshold. As previously mentioned, the methodology was

introduced by Burgstahler and Dichev (1997) and has been used for detecting earnings management

that meet/beat certain earnings targets (e.g. Beatty et al. 2002; Dichev and Skinner 2002; Fan et al.

2015; Shuto and Iwasaki 2015; Enomoto and Yamaguchi 2017). The general assumption of this

methodology is that, when firms discretionarily beat certain earnings targets, the distribution of

earnings should be skewed at the threshold. In our context, if firms attempt to achieve the ISS

guideline, the distribution of the reported ROE will irregularly skew around 5%. This approach is

particularly useful for identifying suspect firms with a specific target, as well as incentives for

earnings management by providing more direct and clear evidence (Dichev and Skinner 2002).

Moreover, we use multiple regression analysis to examine how managers discretionarily

achieve the ROE benchmark after the publication of the guideline. Following Fan et al. (2015) and

other relevant prior studies, we estimate the following probit regression model:

ROE Managementi,t = β0 + β1SUSPECTi,t + β2ln(TAi,t−1) + β3MBi,t−1

+ β4OCFi,t−1 + β5LEVi,t−1 + YEAR + εi,t, (2)

ROE Managementi,t = {DAi,t, ACFi,t, AEXPi,t, APRODi,t, CSi,t, SALEi,t, DEBTi,t, DIVi,t, REPi,t}

where ROE Management consists of a set of indicator variables that take values of 1 if the firm

engages in discretionary activities that increase ROE, and 0 otherwise.6 Specifically, we follow the

6 Fan et al. (2015) and other relevant studies have used continuous variables to examine whether suspect

firms discretionarily achieve certain earnings benchmarks. However, the use of continuous variables may not be appropriate, as it tests whether suspect firms report more discretionary accruals, thus focusing on the degree rather than the existence of opportunistic behaviours. For instance, firms with small deficits compared to the benchmark should report small amounts of discretionary accruals to compensate them. Tests using continuous variables can underestimate this type of discretionary behaviour. Therefore, we use binominal indicator variables that identify whether the suspect firms conduct discretionary activities. We

14

DuPont decomposition equation, using the following seven factors as dependent variables: DA, ACF,

AEXP, APROD, CS, SALE, DEBT, DIV, and REP. First, to increase the ROS level, firms may

conduct accrual management and real activities manipulation (Roychowdhury 2006; Fan et al. 2015;

Shuto and Iwasaki 2015). We consider these earnings management activities by including

discretionary accruals based on Dechow et al. (1995) and the abnormal cash flows, abnormal

discretionary expenses, and abnormal production costs introduced by Roychowdhury (2006). Each

measurement is converted to an indicator variable based of the sign of estimated residuals (see

Appendices A and B for the estimation process).

Second, managers can enhance their ROE by improving asset turnover. Particularly, they

can do so by increasing sales and/or selling idle assets. To capture these activities, we include an

indicator variable that takes the value of 1 if a firm increases net sales from previous year, and 0

otherwise (CS), and an indicator that takes the value of 1 if a firm’s amount of net asset sale is

positive, and 0 otherwise (SALE).

Third, we consider firms’ financial policies for increasing ROE. As implied by the DuPont

equation, ROE can be driven by capital structure and be subject to the effect of debt issuance and

payout policies. Therefore, we use three indicator variables to identify whether firms conduct debt

issuance (DEBT), dividend increase (DIV), and stock repurchases (REP).

Our variable of interests is SUSPECT, an indicator variable that takes the value of 1 if the

firm reports a ROE in the interval between 0.050 and 0.062, and 0 for the interval between 0.038

and 0.050. In accordance with prior studies, we focus on firms within two intervals, one between

0.050 and 0.062 (just-above firms) and the other between 0.038 and 0.050 (just-below firms)

(Degeorge et al. 1999; Beatty et al. 2002; Shuto and Iwasaki 2015). In constructing the histograms

also conduct tests using continuous variables and find the results remains unchanged. We report the findings in Section 4.4.2.

15

and intervals, we calculate a bin width twice the interquartile range of ROE, multiplied by the

negative cube root of the sample size (i.e. −2(IQR)n−1/3). This formula provides a bin width of 0.006.

Finally, following Beatty et al. (2002) and Shuto and Iwasaki (2015), we apply an interval size twice

the bin width (namely 0.012) used in constructing the histograms and variable SUSPECT. If

managers use either of these discretionary activities to achieve the ISS guideline, the coefficient on

SUSPECT should be significant and positive.

We consider the following control variables, which Fan et al. (2015) and prior studies have

linked to managers’ earnings management incentives. In Equation (2), we control for the effect of

firm size (ln(TA)), market-to-book ratio (MB), cash flows from operations (OCF), and leverage

(LEV). Specifically, ln(TA) is the natural log of total assets, MB is the ratio of market capitalisation

to net assets, OCF is the cash flows from operations scaled by total assets, and LEV is the ratio of

total liabilities to total assets. We also control for year-fixed effects by including a year dummy

(YEAR). Table 1 shows the definitions of all testing variables.

[Insert Table 1 about here]

3.3. Sample selection

We summarise our sample selection procedure in Table 2. We collect the initial sample from a

database called Nikkei NEEDS-FinancialQUEST, the most comprehensive commercial database for

Japanese listed companies. The database provides financial and stock price data for all listed

companies in Japan, including delisted companies.

Our initial sample consists of 23,032 firm-year observations for 2012–2017. As the revised

ISS guideline was officially published in January 2015 and has been in effect since February 2015,

we focus on the 3 years before and after its publication to examine its impact. We exclude financial

16

sector firms, that is, those in the banking, securities, and insurance sectors, because of their

uniqueness in terms of the balance sheet structure and business model.7 Moreover, we exclude the

firms that prepare financial statements in accordance with the U.S. GAAP or IFRS to consider the

differences in the underlying accounting procedures from the Japanese GAAP. We also exclude

firms whose ROE is not available. Specifically, when firms report negative net assets, ROE likely

loses its continuous meaning. As a result, the testing sample used for histogram analysis consists of

20,554 firm-years with available ROEs. For the multiple regression analysis, we use firm-years

whose ROEs fall in the intervals adjacent to the threshold after the publication of revised ISS

guideline. Therefore, the sample used for Equation (2) decreases to 1,438 firm-years (see the next

section).

[Insert Table 2 about here]

4. Empirical results

4.1. Histogram analysis

First, we construct ROE histograms and examine whether the distributions show significant

discontinuities around the 5% ROE threshold. If firms manage their ROEs to achieve this threshold,

the distribution will be irregularly skewed around 5%. Figure 1 compares the ROE distributions for

the pre- (Panel A) and post-ISS periods (Panel B). Each panel uses the bin width of 0.006, as argued

above, and illustrates ROE histograms between −0.076 and 0.170. For the pre-ISS period, Panel A

reveals that the distribution tends to be smooth, with a single-peak in the interval left of the threshold

(i.e. between 0.038 and 0.044), suggesting that ROE management towards 5% is less likely to occur

before the guideline. Panel B shows irregular distributions around the threshold after the publication

7 We use the Tokyo Stock Exchange (TSE) industry classification (33 industries).

17

of the ISS guideline. Specifically, the distribution is peaked in the interval immediately right to the

threshold (i.e. between 0.050 and 0.056) and fluctuates around 5%.

To test the significance of the irregularities near the threshold, we apply the standardised

differences, following Burgstalhler and Dichev (1997). This method tests for deviations from

smoothness, where under the null hypothesis of no abnormal behaviours, the expected number of

observations in any given bin is equal to the average of the number of observations in the two

immediately adjacent bins (Dichev and Skinner 2002; Shuto and Iwasaki 2015).8 As shown in

Figure 1, while the standardised differences in Panel A exhibit insignificance for both intervals

adjacent to the threshold, those in Panel B present statistically significant irregularities for both

intervals left and right of the threshold. The results support our hypothesis and suggest that the ROE-

based guideline induces opportunistic ROE reporting to beat the threshold.

[Insert Figure 1 about here]

4.2. Univariate analysis

Next, we examine how firms achieve the ROE benchmark after the publication of the revised ISS

guideline. To do this, we focus on firm-years whose ROEs fall in the two intervals adjacent to the

threshold in the post-ISS period and test whether just-above suspect firms are associated with certain

discretionary activities to enhance ROE.

Table 3 shows the results of the univariate comparison tests between firms slightly above

and below the 5% threshold. We test the difference in each variable in Equation (2) by conducting

8 Specifically, we first calculate the difference between the actual and expected number of observations in an interval, defined as the arithmetical mean of the numbers in the two adjacent intervals. Second, we divide the difference by the estimated standard deviation, derived from the variance of the difference, approximately expressed as Npi(1 − pi) + (1/4)N(pi−1 + pi+1)(1 − pi−1 − pi+1), where N is the number of observations in testing sample and pi the probability that an observation will fall into interval i.

18

Welch’s t-Test and the Wilcoxon rank sum test for means and medians, respectively. Suspect firm

group exhibits statistically higher values for the three dependent variables (DA, DIV, and REP),

suggesting that, among others, managers are more likely to achieve a 5% ROE by conducting accrual

management, dividend increase, and stock repurchases. Moreover, firm size (ln(TA)) and market-

to-book ratio (MB), the control variables in Equation (2), are significantly higher for suspect firms.

This finding implies that large and high-growth firms have higher incentives to achieve the threshold

because they attract more attentions from investors and face a relatively large reputation loss.

[Insert Table 3 about here]

4.3. Regression analysis

Table 4 reports the results of the multiple regression analysis based on Equation (2). Panels A and

B report the estimated coefficients and the marginal effects at the means of the covariates,

respectively. We again use a sample of firm-years in the two intervals adjacent to the threshold after

the publication of the revised ISS guideline. We estimate Equation (2) by probit regression and

report the p-values based on standard errors clustered at both the firm and year levels (Petersen

2009).

First, we investigate whether suspect firms conduct earnings management. Column (1)

reports the results using DA as a dependent variable. The coefficient on SUSPECT, 0.185, is

significantly positive (p-value < 0.001), indicating that managers tend to conduct accrual

management to achieve the ROE threshold. Regarding economic significance, the marginal effect

of 0.073 suggests that the probability of suspect firms reporting positive discretionary accruals is

7.3 percentage points higher than that of non-suspect firms. Given that the mean value of DA is

53.0% (see Table 3), the result appears to be economically meaningful. Columns (2), (3), and (4)

19

focus on real activity manipulation and report the results using ACF, AEXP, and APROD,

respectively. The coefficients on SUSPECT are not significant for all dependent variables. Therefore,

in contrast to accrual management, we find no evidence that managers rely on real activity

manipulation to achieve the ROE threshold.

Second, we examine whether suspect firms achieve the threshold by improving asset

turnover. Column (5) in Table 4 reports the results using CS, and the coefficient on SUSPECT is not

significant. Similarly, column (6) shows the results using SALE, and the coefficient on SUSPECT is

not significant. These findings indicate that suspect firms are not likely to depend on increasing sales

or selling idle assets in terms of ROE management.

Finally, we examine whether suspect firms relate to the change in capital structure. Columns

(7), (8), and (9) in Table 4 report the results using DEBT, DIV and, REP, respectively. The

coefficients on SUSPECT in columns (7) and (9) are not statistically significant, providing no

evidence that managers use new debt issuance and stock repurchases to manage ROE around the

threshold. However, the coefficient on SUSPECT in column (8), 0.261, is positive and significant

(p-value < 0.001), indicating that managers tend to increase dividends to achieve the ROE threshold.

In terms of economic significance, the marginal effect of 0.103 suggests that the probability of

suspect firms of increasing dividends is 10.3 percentage points higher than for non-suspect firms.

Given that the mean value of DIV is 45.0% (Table 3), the results appear economically meaningful.

Overall, the findings are consistent with univariate analysis and suggest managers are more

likely to achieve the 5% ROE after the publication of the new ISS guideline by conducting accrual

management and increasing dividends.

[Insert Table 4 about here]

20

4.4. Robustness tests

4.4.1. Alternative bin width

We conduct additional tests to assess the robustness of our empirical results. First, we use an

alternative bin width to define SUSPECT. Following Beatty et al. (2002) and Shuto and Iwasaki

(2015), we used twice the bin width based on the interquartile formula in the main analyses.

However, there is no theory that dictates the correct bin width (Dichev and Skinner 2002), and some

studies have used a bin width strictly following formula of −2(IQR)n−1/3 (e.g. Beaver et al. 2003).

As such, our results may be affected by the bin width definition. To assess this possibility, we apply

an alternative bin width of 0.006 based on the formula and reconstruct variable SUSPECT.

Specifically, we focus on firms that fall between 0.044 and 0.056 and redefine SUSPECT to take the

value of 1 if the firm’s reported ROE is between 0.050 (inclusive) and 0.056 (exclusive), and 0 if

the firm’s reported ROE is between 0.044 (inclusive) and 0.050 (exclusive). This procedure

decreases the testing sample used for Equation (2) to 755 firm-years.

Table 5 shows the results. The estimated coefficients on SUSPECT in columns (1) and (8)

are positive and significant. These results are consistent with the main analysis and suggest that

managers are more likely to achieve the 5% ROE by conducting accrual management and increasing

dividends. However, unlike in Table 4, we find a significantly negative coefficient on SUSPECT in

column (4) using the APROD. However, the estimated coefficients on SUSPECT in columns (2) and

(3) using other real activity manipulations are not significant, which is consistent with the main

results. Accordingly, our results generally remain unchanged and suggest that suspect firms are less

likely to be associated with real activities management.

[Insert Table 5 about here]

21

4.4.2. Continuous dependent variables

In the main analyses, we use indicator variables that take the value of 1 if the firm engages in

discretionary activities to increase ROE, and 0 otherwise, as dependent variables. However, Fan et

al. (2015) and other relevant prior studies use continuous dependent variables to test whether suspect

firms engage in discretionary activities to meet or beat benchmarks. To check sensitivity, we

alternatively use continuous dependent variables.

Table 6 reports the results. The estimated coefficients on SUSPECT in columns (1) and (8)

are positive and significant, which is consistent with main results. However, we observe that the

coefficient on SUSPECT in column (6) using SALE is significantly negative. While this is

inconsistent with our main results, Table 6 indicates that, to achieve the threshold, managers are

more likely to engage in accrual management and dividend policies rather than improve assets

turnover.

[Insert Table 6 about here]

4.4.3. Alternative measures for discretionary accruals and dividend policy

To further assess the robustness of the results, we apply alternative measurements for discretionary

accruals. In the main analysis, we use Dechow et al.’s (1995) model for discretionary accruals. To

examine whether the choice of the estimation model affects our results, we additionally estimate

discretionary accruals following Kasznik (1999) and Kothari et al. (2005) (see Appendix A).

Columns (1) and (2) in Table 7 use alternative discretionary accruals to define DA and report the

expected positive and significant coefficients on SUSPECT. This suggests that the choice of

estimation model for discretionary accruals does not change the results.

22

Moreover, we also apply alternative measurements for the dividend policy. In the previous

analysis, we used total dividends to construct DIV. However, Takasu and Nakano (2012) report that

maintaining the level of dividends per share (DPS) is a baseline for Japanese dividend practices.

Additionally, the change in dividends may not capture managers’ opportunistic behaviours, as it can

reflect the underlying economic performance (Lintner 1956; Brav et al. 2005). To consider these

potential concerns, we use two alternative measurements based on DPS and the discretionary portion

of dividend changes. Columns (3) and (4) in Table 7 report the results. In column (4), we regress

Lintner’s (1956) model and use regression residuals as a proxy for discretionary dividend changes

(see Appendix C). The coefficients on SUSPECT are positive and significant for both DPS-based

measurements and discretionary dividend changes, being similar to the main analysis.

[Insert Table 7 about here]

4.4.4. Average ROE for the past 5 years below 5%

Finally, we conduct an additional analysis using firm-years whose average ROE over the past 5

years was less than the 5% threshold. In the previous section, we consider all firm-years to examine

whether the ISS guideline provides additional motivation to manage the reported ROE. However,

the revised ISS states that it will issue negative recommendations, particularly for firms that have

reported average ROEs below 5% over the past 5 years. Accordingly, the incentives for ROE

management will be more pronounced for these underperforming firms.

Table 8 reports regression results using firm-years whose average ROEs were below 5%

over the past 5 years. We calculate the average ROE as cumulative net income attributable to

shareholders of the parent company divide by cumulative average owned equity capital for years

23

t−4 to t.9 The sample size decreases to 792 firm-years due to the additional testing sample condition.

As shown, the coefficients on SUSPECT are positive and significant for columns (1), (5), (8), and

(9), using DA, CS, DIV, and REP, respectively. These results are consistent with the main analysis

but different for CS and REP, implying that managers tend to achieve the ROE benchmark by

increasing sales and conducting stock repurchases, as well as accrual management and dividend

increases. Given that the managers in underperforming firms are more exposed to pressure, they

may utilise various activities to achieve the ROE and avoid negative recommendations.

Overall, a series of robustness tests shows that our main results are by and large robust to the

alternative definition of suspect firms and ROE management, measurement of earnings management

and dividend policy, and the subsample of firm-years whose average ROE over the past 5 years was

below 5%. The evidence consistently supports our hypothesis and suggests that managers are more

likely to achieve a ROE of 5% by conducting accrual management and increasing dividends.

[Insert Table 8 about here]

5. Additional tests: The effect of monitoring mechanisms

In the previous section, we provided evidence that managers use accrual management and dividend

payouts to achieve the ROE threshold introduced by the ISS guideline. Such activities result in the

number of firm-years that just beat the threshold being disproportionately high. Here, we follow Fan

et al. (2015) and examine whether monitoring mechanisms relate to the probability that firms just

beat the threshold. We consider shareholder and board structures as monitoring mechanisms,

because they are most likely to affect managers’ sensitivity to beat the ISS threshold. Specifically,

9 Although the ISS guideline explicitly states the use of ROE in their voting policy, it does not provide any

definitions for ROE or average ROE. Accordingly, we follow the definition used in JPX-400.

24

we estimate the following probit regression model:

SUSPECTi,t = β0 + β1INSTi,t + β2CROSSi,t + β3OWNi,t + β4OUTi,t

+ β5ln(TAi,t−1) + β6MBi,t−1 + β7OCFi,t−1 + β8LEVi,t−1 + YEAR + εi,t, (3)

where INST is the percentage of ownership held by institutional investors. Since ISS represents

information intermediaries and provides voting recommendations to institutional investors, the

managers with higher institutional ownership are exposed to more severe pressure to achieve the

ROE threshold. Consistent with this argument, Miyajima et al. (2018) find that institutional investors

strengthen the top executive turnover sensitivity to ROE. Therefore, we predict that the probability

of just beating the threshold of the new ISS guideline increases with the existence of institutional

investors.

CROSS is the percentage of ownership held by cross-shareholders. Cross-shareholding is a

unique feature of the ownership structure in the Japanese stock market and represents stable and

silent shareholders. Prior research argues that cross-shareholders do not encourage managers to

inflate their earnings to achieve short-term earnings goals (Abegglen and Stalk 1985; Porter 1992;

Jacobson and Aaker 1993; Osano 1996). Therefore, we expect cross-shareholdings to reduce the

incentives for mangers to just beat the threshold of the new ISS guideline.

OWN is the percentage of ownership held by managers. It indicates the power of top

executives through voting control. Denis et al. (1997) document that the likelihood of top executive

turnover is less sensitive to performance for firms with higher managerial ownership. As such,

higher managerial ownership can protect managers from ISS pressure, diminishing the incentive to

achieve the threshold. Therefore, we predict that the probability of just beating the threshold

decreases with managerial ownership.

25

OUT is the percentage of outside directors on the board of directors. The board directors are

responsible for evaluating top managers and replacing them if they fail to perform. However, this

task is likely to fall on outside directors. Weisbach (1988) reports that the association between firm

performance and managers’ resignation is more pronounced for firms with outsider-dominated

boards. Given this monitoring role, the presence of outside directors can enhance managers’

sensitivity to the ISS recommendation. Accordingly, we posit that firms with more outsider directors

are more likely to just beat the ISS threshold.

Following Fan et al. (2015), we include the control variables previously defined in Equation

(2), namely firm size (ln(TA)), market-to-book ratio (MB), cash flows from operations (OCF), and

leverage (LEV). We also control for year-fixed effects by YEAR. We collect our data on shareholder

and board structures using Nikkei NEEDS-Cges, a commercial database for corporate governance in

Japanese listed companies.

Table 9 reports the results of Equation (3). Columns (1) and (2) show the results using

SUSPECT based on the bin widths of 0.012 and 0.006, respectively. The estimated coefficients on

INST are significantly positive (p-value < 0.001) in both columns (1) and (2). However, the

coefficients on CROSS and OWN are not significant. Similarly, although OUT exhibits a slightly

significant coefficient in column (1), but not in column (2). As a result, we find consistent evidence

on institutional investors suggesting that institutional ownership is likely to increase managers’

sensitivity to ISS pressure and lead to a higher probability of just beating the threshold. This result

corroborates our assertion that the ISS and its guideline matter for managers and become a

motivation for opportunistic reporting.

[Insert Table 9 about here]

26

6. Conclusions

In this study, we examine the impact of the proxy voting guideline of Institutional Shareholder

Services (ISS) on ROE management. In January 2015, ISS published a unique guideline for Japanese

listed firms and started to use firms’ reported ROE for proxy voting recommendation on director

elections. Particularly, ISS set a ROE of 5% as a benchmark for negative recommendations. Given

managers’ concerns on career and social reputation, we hypothesise that managers are more likely

to engage in discretionary activities to achieve the threshold of the new ISS guideline.

We test this hypothesis using the histogram approach and find that, after the publication of

the new guideline, the distribution of ROEs is irregularly skewed around 5%, with an increased

number of firm observations that report values just above the threshold. Moreover, regression

analyses show that managers tend to achieve the threshold by conducting accrual management and

increasing dividends. These results are robust to an alternative definition of suspect firms and ROE

management, measurement of earnings management and dividend policy, and a subsample of firm-

years whose average ROEs over the past 5 years are lower than the 5% threshold. Additionally, we

investigate how monitoring mechanisms relate to the probability that firms just beat the threshold

and find that firms with more institutional ownership are more likely to achieve the threshold.

Overall, the evidence highlights the importance of proxy voting advisors and their accounting-based

guidelines in financial reporting and managers discretionary behaviours.

We contribute to the literature in several ways. First, the evidence suggests that a soft law

issued by a private advisory firm can lead to opportunistic reporting, if based on accounting numbers.

Second, we provide new evidence on ROE management. Unlike a single earnings item, ROE can be

affected by managers’ financial policies, as well as earnings management. This study sheds new

light on how managers discretionally manipulate the ROE level rather than earnings. As such, our

27

findings should be of interest to both regulators and proxy advisors. Specifically, an accounting-

based guideline can induce managers’ discretionary activities, which deteriorate the quality of

financial reporting. Given the growing interest and concern on proxy voting advisors, the evidence

in this study is useful to better understand their economic consequences.

28

Appendix A: Estimation Models for Accrual Management Measurements

To measure discretionary accruals, we apply the following three models: namely, the modified Jones

model in Dechow et al. (1995) (Equation (A.1)); the CFO modified Jones model in Kasznik (1999)

(Equation (A.2)); and the performance-matched modified Jones model in Kothari et al. (2005)

(Equation (A.3)). We require at least 15 observations for each industry-year group and estimate the

following each equation cross-sectionally:

TAi,t = β0 + β1(1/TAi,t−1) + β2(ΔSi,t − ΔARi,t) + β3PPEi,t + εi,t, (A.1)

TAi,t = β0 + β1(1/TAi,t−1) + β2(ΔSi,t − ΔARi,t) + β3PPEi,t + β4ΔCFOi,t−1 + εi,t, (A.2)

TAi,t = β0 + β1(1/TAi,t−1) + β2(ΔSi,t − ΔARi,t) + β3PPEi,t + β4ROAi,t−1 + εi,t, (A.3)

where TA is the total accruals scaled by lagged total assets; TA is the total assets; ΔS is the change

in sales scaled by lagged total assets; ΔAR is the change in account receivables scaled by lagged

total assets; PPE is the net property, plant and equipment scaled by lagged total assets; ΔCFO is the

change in cash flow from operations scaled by lagged total assets; and ROA is the net income

attributable to shareholders of the parent company scaled by total assets. We measure discretionary

accruals as the value of estimated residuals.

29

Appendix B: Estimation Models for Real Activities Manipulation Measurements

We measure real activities manipulation by following the methodology of Roychowdhury (2006).

Specifically, we expect that suspect firms that just beat the threshold are more likely to perform

manipulations and discretionary activities, such as control of their sales, reducing discretionary

costs/expenses, and overproduction. To measure these manipulations, we use the following three

models: abnormal cash flow model (Equation (B.1)); abnormal discretionary expenses model

(Equation (B.2)); and abnormal production cost model (Equation (B.3)). We require at least 15

observations for each industry-year group and estimate the following each equation cross-

sectionally:

CFOi,t = β0 + β1(1/TAi,t−1) + β2Si,t + β3ΔSi,t + εi,t, (B.1)

EXPi,t = β0 + β1(1/TAi,t−1) + β2Si,t−1 + εi,t, (B.2)

PRODi,t = β0 + β1(1/TAi,t−1) + β2Si,t + β3ΔSi,t + β4ΔSi,t−1 + εi,t, (B.3)

where CFO is the cash flow from operations scaled by lagged total assets; TA is the total asssets; S

is the sales scaled by lagged total assets; ΔS is the change in sales scaled by lagged total assets; EXP

is the selling, general, and administrative expenses scaled by lagged total assets; and PROD is the

sum of cost of goods and change in inventory scaled by lagged total assets. We define real activities

manipulation as the value of estimated residuals.

30

Appendix C: Estimation Model for Discretionary Dividend Changes

To measure discretionary dividend changes, we use Lintner (1956) partial adjustment model. Lintner

(1956) shows that managers have a target payout ratio and gradually adjust their dividend levels

towards the targets by considering earnings stream. More specifically, he formulates that dividend

changes are function of earnings and past dividends.

When managers discretionarily change their dividends, this should be irrelevant with their

earnings and past dividend levels. Therefore, we posit that such discretionary portion of dividend

changes can be captured by estimated residuals of Linter model (Equation (C.1)). In a sample of

Japanese listed firms, we estimate the following equation by year:

ΔDi,t = β0 + β1Di,t−1 + β2E i,t + εi,t, (C.1)

where ΔD is the change in dividends scaled by lagged total assets; D is dividends scaled by total

assets; and E is net income attributable to shareholders of the parent company scaled by lagged total

assets. We define discretionary dividend policy as the value of estimated residuals.

31

References

Abegglen, J. C., and G. Stalk. 1985. Kaisha: The Japanese corporation. Basic Books.

Beatty, A. L., B. Ke, and K. R. Petroni. 2002. Earnings management to avoid earnings declines

across publicly and privately held banks. The Accounting Review 77 (3): 547–570.

Beaver, W. H., M. F. McNichols, and K. K. Nelson. 2003. Management of the loss reserve accrual

and the distribution of earnings in the property-casualty insurance industry. Journal of

Accounting and Economics 35 (3): 347–376.

Brav, A., J. R. Graham, C. R. Harvey, and R. Michaely. 2005. Payout policy in the 21st century.

Journal of Financial Economics 77 (3): 483–527.

Burgstahler, D., and I. Dichev. 1997. Earnings management to avoid earnings decreases and losses.

Journal of Accounting and Economics 24 (1): 99–126.

Burgstahler, D. C., L. Hail, and C. Leuz. 2006. The importance of reporting incentives: Earnings

management in European private and public firms. The Accounting Review 81 (5): 983–1016.

Cai, J., J. L. Garner, and R. A. Walking. 2009. Electing directors. The Journal of Finance 64 (5):

2389–2421

Chattopadhyay, A., M. D. Shaffer, and C. C. Y. Wang. 2020. Governance through shame and

aspiration: Index creation and corporate behavior. Journal of Financial Economics 135 (3):

704–724.

Chen, K. C. W., and H. Yuan. 2004. Earnings management and capital resource allocation: Evidence

from China’s accounting-based regulation of rights issues. The Accounting Review 79 (3):

645–665.

David, P., M. Bloom, and A. J. Hillman. 2007. Investor activism, managerial responsiveness, and

corporate social performance. Strategic Management Journal 28 (1): 91–100.

Dechow, P. M., W. Ge, and C. Schrand. 2010. Understanding earnings quality: A review of the

proxies, their determinants and their consequences. Journal of Accounting and Economics

50 (2–3): 344–401.

Dechow, P. M., R. G. Sloan, and A. P. Sweeney. 1995. Detecting earnings management. The

Accounting Review 70 (2): 193–225.

Degeorge, F., J. Patel, and R. Zeckhauser. 1999. Earnings management to exceed thresholds. The

Journal of Business 40 (4): 1091–1123.

32

Denis, D. J., D. K. Denis, and A. Sarin. 1997. Ownership structure and top executive turnover.

Journal of Financial Economics 45 (2): 193–221.

Dichev, I., and D. J. Skinner. 2002. Large-sample evidence on the debt covenant hypothesis. Journal

of Accounting Research 40 (4): 1091–1123.

Enomoto, M., and T. Yamaguchi. 2017. Discontinuities in earnings and earnings change

distributions after J-SOX implementation: Empirical evidence from Japan. Journal of

Accounting and Public Policy 36 (1): 82–98.

Ertimur, Y., F. Ferri, and D. Oesch. 2018. Understanding uncontested director elections.

Management Science 64 (7): 3400–3420.

Fan, Y., W. B. Thomas, and C. Wang. 2015. The effect of regulatory benchmarks on firm reporting

behavior. Journal of International Accounting Research 14 (1): 85–107.

Graham, J. R., C. R. Harvey, and S. Rajgopal. 2005. The economic implications of corporate

financial reporting. Journal of Accounting and Economics 40 (1-3): 3–73.

Hadani, M., M. Goranova, and R. Khan. 2011. Institutional investors, shareholder activism, and

earnings management. Journal of Business Research 64 (12): 1352–1360.

Hayne, C., and M. Vance. 2019. Information intermediary or de facto standard setter? Field evidence

on the indirect and direct influence of proxy advisors. Journal of Accounting Research 57

(4): 969–1011.

Healy, P. M., and J. M. Wahlen. 1999. A review of the earnings management literature and its

implications for standard setting. Accounting Horizons 13 (4): 365–383.

Hitz, J., and N. Lehmann. 2018. Empirical evidence on the role of proxy advisors in European capital

markets. European Accounting Review 27 (4): 713–745.

Institutional Shareholder Services (ISS). 2019. Japan: Proxy voting guidelines, benchmark policy

recommendations (in Japanese). Retrieved from

https://www.issgovernance.com/file/policy/active/asiapacific/Japan-Voting-Guidelines-

Japanese.pdf.

Ishida, T. 2015. 2015 ISS proxy voting advisory policy (in Japanese). Shoji Homu 2061: 49– 62.

Jacobson, R., and D. Aaker. 1993. Myopic management behavior with efficient, but imperfect,

financial markets: A comparison of information asymmetries in the US and Japan. Journal

of Accounting and Economics 16 (4): 383–405.

Kasznik, R. 1999. On the association between voluntary disclosure and earnings management.

Journal of Accounting Research 37 (1): 57–81.

33

Kothari, S. P., A. J. Leone, and C. E. Wasley. 2005. Performance matched discretionary accrual

measures. Journal of Accounting and Economics 39 (1): 163–197.

Kochiyama, T., and S. Ishida. 2020. Unnatural selection of outside directors: Consequences of

Japanese corporate governance reforms. SSRN.

Lintner, J. 1956. Distribution of incomes of corporations among dividends, retained earnings, and

taxes. The American Economic Review 46 (2): 97–113.

Miyajima, H., R. Ogawa, and T. Saito. 2018. Changes in corporate governance and top executive

turnover: The evidence from Japan. Journal of the Japanese and International Economies

47: 17–31.

Ohlson, J. 1995. Earnings, book values, and dividends in security valuation. Contemporary

Accounting Research 11 (2): 661–687.

Osano, H. 1996. Intercorporate shareholdings and corporate control in the Japanese firm. Journal of

Banking and Finance 20 (6): 1047–1068.

Petersen, M. A. 2009. Estimating standard errors in finance panel data sets: Comparing approaches.

Review of Financial Studies 22 (1): 435–480.

Porter, M. E. 1992. Capital choices: Changing the way America invests in industry. Journal of

Applied Corporate Finance 5 (2): 4–16.

Roychowdhury, S. 2006. Earnings management through real activities manipulation. Journal of

Accounting and Economics 42 (3): 335–370.

Schipper, K. 1989. Commentary on earnings management. Accounting Horizons 3: 91–102.

Shuto, A. 2007. The analysis of earnings management incentives to meet earnings thresholds (in

Japanese). Accounting Progress (8): 76–92.

Shuto, A., and T. Iwasaki. 2015. The effect of institutional factors on discontinuities in earnings

distribution: Public versus private firms in Japan. Journal of Accounting, Auditing, and

Finance 30 (3): 283–317.

Takasu, Y., and M. Nakano. 2012. What do smoothed earnings tell us about the future? The Japanese

Accounting Review (2): 1–32.

The Life Insurance Association of Japan. 2016. Approaches toward enhancing equity values

(Overview). Retrieved from https://www.seiho.or.jp/info/news/2015/pdf/20160323_2.pdf.

Thomas, W. B., D. R. Herrmann, and T. Inoue. 2004. Earnings management through affiliated

transactions. Journal of International Accounting Research 3 (2): 1–25.

34

Tokyo Stock Exchange. 2019. 2018 Nendo kabushiki bunpu jyoukyou chousa kekka no gaiyou (in

Japanese). Retrieved from https://www.jpx.co.jp/markets/statistics-

equities/examination/nlsgeu0000043n00-att/report2018.pdf.

Weisbach, M. S. 1988. Outside directors and CEO Turnover. Journal of Financial Economics 20:

431–460.

Yanagi, R. 2018. Corporate governance and value creation in Japan: Prescriptions for boosting

ROE. Springer.

35

FIGURE 1

The distributions of ROE

Panel A: Periods before the ROE-based guideline of ISS (2012–2014)

Panel B: Periods after the ROE-based guideline of ISS (2015–2017)

Notes: This figure shows the distribution of ROE using a bin width of 0.006. ROE is the net income attributable to

shareholders of the parent company scaled by average owned equity capital. The bin width is based on the formula

of −2(IQR)n−1/3. The interval in gray is one just below the threshold of ROE of 5%; and the interval in black indicates

one just above the threshold.

0

100

200

300

400

500

600

[-0.076,-0.07] [-0.016,-0.01] [0.044,0.05] [0.104,0.11] [0.164,0.17]

Num

ber

of

firm

-yea

rs

ROE interval

std diff for the left of 0.05 = 0.697

std diff for the right of 0.05 = −0.538

0

100

200

300

400

500

600

[-0.076,-0.07] [-0.016,-0.01] [0.044,0.05] [0.104,0.11] [0.164,0.17]

Num

ber

of

firm

-yea

rs

ROE interval

std diff for the left of 0.05 = −1.496*

std diff for the right of 0.05 = 2.967***

36

TABLE 1

Definitions of testing variables

Variables Definition

ROEi,t The net income attributable to shareholders of the parent company scaled by average “owned

equity capital,” defined as the sum of shareholder equity and accumulated other

comprehensive income.

DAi,t An indicator variable that takes a value of 1 if the firm report positive discretionary accruals

and 0, otherwise. Discretionary accruals are based on Dechow et al. (1995) model (see

Appendix A).

ACFi,t An indicator variable that takes a value of 1 if the firm report negative abnormal cash flow

and 0, otherwise. Abnormal cash flow is based on Roychowdhury (2006) model (see

Appendix B).

AEXPi,t An indicator variable that takes a value of 1 if a firm’s amount of net asset sale is positive,

and 0 otherwise. Abnormal discretionary expenses are based on Roychowdhury (2006)

model (see Appendix B).

APRODi,t An indicator variable that takes a value of 1 if the firm report positive abnormal production

costs and 0, otherwise. Abnormal production costs are based on Roychowdhury (2006)

model (see Appendix B).

CSi,t An indicator variable that takes a value of 1 if the firm’s sales increases from the previous

year and 0 otherwise.

SALEi,t An indicator variable that takes a value of 1 if the firm’s net asset sale is positive and 0

otherwise. Net asset sale is defined as the difference of cash inflow from selling fixed assets

and cash outflow from acquiring fixed assets.

DEBTi,t An indicator variable that takes a value of 1 if the firm’s net debt finance is positive and 0

otherwise. Net debt finance is defined as the difference of cash inflow from debts and cash

outflow from debt repayments.

DIVi,t An indicator variable that takes a value of 1 if the firm’s dividends increase from the previous

year and 0 otherwise.

REPi,t An indicator variable that takes a value of 1 if the firm conducts stock repurchases and 0

otherwise.

SUSPECTi,t An indicator variable that takes a value of 1 if the firm’s reported ROE is in the interval

between 0.050 (inclusive) and 0.062 (exclusive) and 0 if the firm’s reported ROE is the

interval between 0.038 (inclusive) and 0.050 (exclusive).

ln(TAi,t−1) The natural log of total assets.

MBi,t−1 The ratio of market capitalization to net assets.

OCFi,t−1 Cash flow from operations scaled by total assets.

LEVi,t−1 The ratio of total liabilities to total assets.

INSTi,t The percentage of ownership held by institutional investors.

CROSSi,t The percentage of ownership held by cross-shareholders.

OWNi,t The percentage of ownership held by managers.

OUTi,t The percentage of outside directors on the board of directors.

37

TABLE 2

Sample selection reconciliation

Criteria # firm-year observations

Firm-years that listed on Japanese stock markets for 2012–2017 23,032

Less:

Fiscal year period does not have just 12 months (771) 22,261

The industry classification cannot be identified nor is as financial (405) 21,856

Firm-years with financial statements prepared in U.S. GAAP or IFRS (618) 21,238

Firm-years whose ROE is not available (684) 20,554

Full sample used for the histogram analysis 20,554

Less:

Firm-years that listed on Japanese stock markets for 2015–2017 (10,240) 10,314

Firm-years whose ROE is NOT in the interval between 0.038 and 0.062 (8,755) 1,559

Firm-years with missing data for Equation (2) (121) 1,438

The final sample used for regression analysis 1,438

38

TABLE 3

Descriptive statistics and univariate tests

Full sample

(N = 1,438)

SUSPCETi,t = 1

(N =748)

SUSPECTi,t = 0

(N = 690) Two group comparison

Mean Median Mean Median Mean Median Welch’s

t-Test

Wilcoxon

rank sum test

DAi,t 0.530 1.000 0.557 1.000 0.500 0.500 (0.029) (0.029)

ACFi,t 0.538 1.000 0.528 1.000 0.549 1.000 (0.421) (0.421)

AEXPi,t 0.657 1.000 0.668 1.000 0.645 1.000 (0.349) (0.348)

APRODi,t 0.618 1.000 0.614 1.000 0.623 1.000 (0.710) (0.710)

CSi,t 0.581 1.000 0.587 1.000 0.574 1.000 (0.619) (0.618)

SALEi,t 0.053 0.000 0.051 0.000 0.055 0.000 (0.718) (0.718)

DEBTi,t 0.305 0.000 0.310 0.000 0.299 0.000 (0.633) (0.633)

DIVi,t 0.450 0.000 0.497 0.000 0.399 0.000 (<0.001) (<0.001)

REPi,t 0.389 0.000 0.421 0.000 0.355 0.000 (0.010) (0.010)

ln(TAi,t−1) 10.538 10.442 10.635 10.504 10.433 10.339 (0.008) (0.014)

MBi,t−1 0.949 0.750 1.018 0.816 0.874 0.712 (<0.001) (<0.001)

OCFi,t−1 0.050 0.051 0.052 0.052 0.048 0.047 (0.076) (0.013)

LEVi,t−1 0.436 0.434 0.438 0.436 0.433 0.430 (0.606) (0.664)

Notes: This table shows the descriptive statistics of testing variables used in Equation (2) and results of univariate comparison tests. We construct two subsamples

of firms with slightly above and below the threshold of ROE of 5%. We test the differences of variables using Welch’s t-Test and Wilcoxon rank sum test for

means and medians, respectively. DA is an indicator variable that takes a value of 1 if the firm report positive discretionary accruals (based on Dechow et al.

(1995)) and 0, otherwise; ACF is an indicator variable that takes a value of 1 if the firm report negative abnormal cash flow (based on Roychowdhury (2006))

and 0, otherwise; AEXP is an indicator variable that takes a value of 1 if the firm report negative abnormal discretionary expenses (based on Roychowdhury

(2006)) and 0, otherwise; APRPD is an indicator variable that takes a value of 1 if the firm report positive abnormal production costs (based on Roychowdhury

(2006)) and 0, otherwise; CS is an indicator variable that takes a value of 1 if the firm’s sales increases from the previous year and 0 otherwise; SALE is an

indicator variable that takes a value of 1 if the firm’s net asset sale is positive and 0 otherwise; DEBT is an indicator variable that takes a value of 1 if the firm’s

net debt finance is positive and 0 otherwise; DIV is an indicator variable that takes a value of 1 if the firm’s dividends increase from the previous year and 0

otherwise; REP is an indicator variable that takes a value of 1 if the firm conducts stock repurchases and 0 otherwise; SUSPECT is an indicator variable that

takes a value of 1 if the firm’s reported ROE is in the interval between 0.050 (inclusive) and 0.062 (exclusive) and 0 if the firm’s reported ROE is the interval

between 0.038 (inclusive) and 0.050 (exclusive); ln(TA) is the natural log of total assets; MB is the ratio of market capitalization to net assets; OCF is cash flow

from operations scaled by total assets; and LEV is the ratio of total liabilities to total assets. Continuous variables are winsorized by year at the 1 percent and 99

percent levels.

39

TABLE 4

Regression results

Panel A: Main results DAi,t ACFi,t AEXPi,t APRODi,t CSi,t SALEi,t DEBTi,t DIVi,t REPi,t (1) (2) (3) (4) (5) (6) (7) (8) (9)

Constant 0.714 −0.153 0.485 0.280 −0.025 0.339 −1.942 −1.177 −4.429

(0.001) (0.536) (0.011) (0.133) (0.952) (0.245) (<0.001) (<0.001) (<0.001)

SUSPECTi,t 0.185 −0.039 0.120 0.020 0.018 −0.009 −0.010 0.261 0.122

(<0.001) (0.587) (0.110) (0.630) (0.740) (0.893) (0.855) (<0.001) (0.134)

ln(TAi,t−1) −0.046 −0.011 −0.010 −0.020 0.039 −0.191 0.090 0.143 0.415

(0.040) (0.597) (0.631) (0.227) (0.267) (<0.001) (<0.001) (<0.001) (<0.001)

MBi,t−1 −0.105 −0.079 −0.319 −0.283 0.173 −0.031 0.105 −0.138 −0.044

(<0.001) (0.097) (<0.001) (<0.001) (<0.001) (0.569) (0.051) (<0.001) (0.384)

OCFi,t−1 −1.894 −2.303 −2.172 −2.118 −1.580 −4.389 0.311 −1.161 0.788

(<0.001) (<0.001) (<0.001) (0.004) (0.048) (<0.001) (0.179) (0.485) (0.273)

LEVi,t−1 −0.082 1.198 0.861 1.450 −0.433 0.529 0.898 −0.953 −0.740

(0.058) (<0.001) (0.001) (<0.001) (0.007) (0.210) (<0.001) (<0.001) (<0.001)

YEAR included included included included included included included included included

Pseudo R2 0.013 0.030 0.038 0.050 0.016 0.069 0.026 0.045 0.148

N 1,438 1,438 1,438 1,438 1,438 1,438 1,438 1,438 1,438

Panel B: Marginal effects

SUSPECTi,t 0.073 −0.016 0.044 0.008 0.007 −0.001 −0.003 0.103 0.046

Notes: This table shows the results of probit regression for Equation (2). Panels A and B report the estimated coefficients and the marginal effects at means of

covariates respectively. DA is an indicator variable that takes a value of 1 if the firm report positive discretionary accruals (based on Dechow et al. (1995)) and

0, otherwise; ACF is an indicator variable that takes a value of 1 if the firm report negative abnormal cash flow (based on Roychowdhury (2006)) and 0,

otherwise; AEXP is an indicator variable that takes a value of 1 if the firm report negative abnormal discretionary expenses (based on Roychowdhury (2006))

and 0, otherwise; APRPD is an indicator variable that takes a value of 1 if the firm report positive abnormal production costs (based on Roychowdhury (2006))

and 0, otherwise; CS is an indicator variable that takes a value of 1 if the firm’s sales increases from the previous year and 0 otherwise; SALE is an indicator

variable that takes a value of 1 if the firm’s net asset sale is positive and 0 otherwise; DEBT is an indicator variable that takes a value of 1 if the firm’s net debt

finance is positive and 0 otherwise; DIV is an indicator variable that takes a value of 1 if the firm’s dividends increase from the previous year and 0 otherwise;

REP is an indicator variable that takes a value of 1 if the firm conducts stock repurchases and 0 otherwise; SUSPECT is an indicator variable that takes a value

of 1 if the firm’s reported ROE is in the interval between 0.050 (inclusive) and 0.062 (exclusive) and 0 if the firm’s reported ROE is the interval between 0.038

(inclusive) and 0.050 (exclusive); ln(TA) is the natural log of total assets; MB is the ratio of market capitalization to net assets; OCF is cash flow from operations

scaled by total assets; and LEV is the ratio of total liabilities to total assets. Continuous variables are winsorized by year at the 1 percent and 99 percent levels.

p-values reported in parentheses are based on standard errors clustered at both firm and year levels (Petersen 2009).

40

TABLE 5

Robustness test: Alternative bin width

DAi,t ACFi,t AEXPi,t APRODi,t CSi,t SALEi,t DEBTi,t DIVi,t REPi,t

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Constant 0.182 −0.575 0.141 0.087 −0.278 0.116 −2.221 −0.851 −4.536

(0.703) (0.048) (0.559) (0.722) (0.647) (0.873) (<0.001) (<0.001) (<0.001)

SUSPECTi,t 0.274 0.101 0.037 −0.062 0.025 −0.124 0.076 0.186 0.074

(<0.001) (0.273) (0.699) (<0.001) (0.782) (0.504) (0.082) (0.001) (0.543)

ln(TAi,t−1) −0.014 0.008 0.023 0.001 0.061 −0.164 0.110 0.135 0.415

(0.715) (0.777) (0.238) (0.942) (0.231) (<0.001) (<0.001) (<0.001) (<0.001)

MBi,t−1 −0.054 −0.007 −0.272 −0.257 0.247 −0.244 0.143 −0.139 −0.098

(0.259) (0.951) (<0.001) (<0.001) (<0.001) (0.186) (0.051) (0.093) (0.243)

OCFi,t−1 −1.573 −1.829 −1.272 −1.371 −1.930 −4.890 0.115 −2.650 1.562

(0.004) (0.001) (0.033) (<0.001) (<0.001) (0.019) (0.902) (0.077) (0.019)

LEVi,t−1 −0.033 1.368 0.759 1.257 −0.474 0.847 0.843 −1.082 −0.701

(0.884) (<0.001) (<0.001) (<0.001) (0.256) (0.173) (<0.001) (<0.001) (<0.001)

YEAR included included included included included included included included included

Pseudo R2 0.013 0.034 0.031 0.040 0.034 0.079 0.032 0.054 0.155

N 755 755 755 755 755 755 755 755 755

Notes: This table shows the probit regression results for Equation (2) using alternative bin width. SUSPECT is an indicator variable that takes a value of 1 if the

firm’s reported ROE is in the interval between 0.050 (inclusive) and 0.056 (exclusive) and 0 if the firm’s reported ROE is the interval between 0.044 (inclusive)

and 0.050 (exclusive); and all other variables are defined in Table 1. Continuous variables are winsorized by year at the 1 percent and 99 percent levels. p-values

reported in parentheses are based on standard errors clustered at both firm and year levels (Petersen 2009).

41

TABLE 6

Robustness test: Continuous dependent variables

DAi,t ACFi,t AEXPi,t APRODi,t CSi,t SALEi,t DEBTi,t DIVi,t REPi,t

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Constant 0.018 0.006 −0.072 0.037 0.000 0.002 0.001 0.001 0.002

(0.004) (0.590) (0.001) (0.183) (0.995) (0.498) (0.862) (0.033) (0.291)

SUSPECTi,t 0.004 0.001 −0.009 0.005 -0.001 −0.002 −0.000 0.000 0.000

(<0.001) (0.333) (0.205) (0.436) (0.803) (0.086) (0.785) (<0.001) (0.533)

ln(TAi,t−1) −0.001 0.000 0.005 −0.004 0.002 −0.001 −0.000 −0.000 0.000

(0.128) (0.983) (0.002) (0.028) (0.396) (<0.001) (0.939) (0.563) (0.012)

MBi,t−1 −0.002 −0.002 0.035 −0.022 0.029 −0.007 0.008 −0.000 0.000

(<0.001) (0.060) (<0.001) (0.005) (<0.001) (0.047) (0.022) (0.261) (0.041)

OCFi,t−1 −0.110 0.142 0.167 −0.324 -0.261 −0.222 −0.013 −0.002 −0.006

(<0.001) (<0.001) (0.135) (0.006) (<0.001) (<0.001) (<0.001) (0.397) (0.036)

LEVi,t−1 −0.005 −0.039 −0.104 0.143 -0.047 −0.002 −0.013 −0.001 −0.008

(0.398) (<0.001) (<0.001) (<0.001) (<0.001) (0.786) (0.006) (<0.001) (<0.001)

YEAR included included included included included included included included included

Adjusted R2 0.012 0.050 0.058 0.067 0.049 0.113 0.022 0.012 0.032

N 1,438 1,438 1,438 1,438 1,438 1,438 1,438 1,438 1,438

Notes: This table shows the regression results for Equation (2) using continuous dependent variables. DA is discretionary accruals (based on Dechow et al. (1995));

ACF is abnormal cash flow (based on Roychowdhury (2006)); AEXP is abnormal discretionary expenses (based on Roychowdhury (2006)); APROD is abnormal

production costs (based on Roychowdhury (2006)); CS is the changes in sales scaled by beginning total assets; SALE is net asset sales scaled by beginning total

assets; DEBT is net debt finance scaled by beginning total assets; DIV is the changes in dividends scaled by beginning total assets; REP is stock repurchases

scaled by beginning total assets; and all other variables are defined in Table 1 Continuous variables are winsorized by year at the 1 percent and 99 percent levels.

p-values reported in parentheses are based on standard errors clustered at both firm and year levels (Petersen 2009).

42

TABLE 7

Robustness test: Alternative measures for discretionary accruals and dividend policy

DAi,t DIVi,t

Kasznik (1999) Kothari et al. (2005) DPS Discretionary

dividend policy

(1) (2) (3) (4)

Constant 0.696 1.190 −1.815 −0.026

(<0.001) (<0.001) (<0.001) (0.931)

SUSPECTi,t 0.156 0.169 0.250 0.182

(<0.001) (0.008) (<0.001) (<0.001)

ln(TAi,t−1) −0.039 −0.023 0.168 0.004

(0.120) (0.043) (<0.001) (0.890)

MBi,t−1 −0.115 −0.130 −0.153 0.316

(0.019) (<0.001) (0.001) (0.001)

OCFi,t−1 −2.268 −12.848 −1.067 1.086

(<0.001) (<0.001) (0.397) (0.550)

LEVi,t−1 −0.003 −0.306 −0.707 −3.155

(0.970) (0.062) (0.006) (<0.001)

YEAR included included included included

Pseudo R2 0.015 0.122 0.047 0.153

N 1,438 1,438 1,436 1,438

Notes: This table shows the probit regression results for Equation (2) using alternative discretionary accrual measures

and dividend policy measures. DA in column (1) is an indicator variable that takes a value of 1 if the firm report

positive discretionary accruals (based on Kasznik (1999)) and 0, otherwise; DA in column (2) is an indicator

variable that takes a value of 1 if the firm report positive discretionary accruals (based on Kothari et al. (2005)) and

0, otherwise; DIV in column (3) is an indicator variable that takes a value of 1 if the firm’s dividends per share

increases from the previous year and 0 otherwise; DIV in column (4) is an indicator variable that takes a value of 1

if the firm report positive discretionary dividend changes (based on Lintner (1956)) and 0 otherwise; and all other

variables are defined in Table 1. Continuous variables are winsorized by year at the 1 percent and 99 percent levels.

p-values reported in parentheses are based on standard errors clustered at both firm and year levels (Petersen 2009).

43

TABLE 8

Robustness test: Average ROE for the past 5 years below 5%

DAi,t ACFi,t AEXPi,t APRODi,t CSi,t SALEi,t DEBTi,t DIVi,t REPi,t

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Constant 0.969 0.220 0.644 0.313 0.191 0.709 −1.749 −0.775 −4.913

(<0.001) (0.491) (0.182) (0.292) (0.381) (0.077) (<0.001) (<0.001) (<0.001)

SUSPECTi,t 0.269 0.112 0.054 0.025 0.211 0.026 −0.023 0.374 0.211

(<0.001) (0.219) (0.644) (0.782) (<0.001) (0.873) (0.888) (<0.001) (0.001)

ln(TAi,t−1) −0.070 −0.034 −0.038 −0.032 0.034 −0.212 0.084 0.132 0.456

(<0.001) (0.127) (0.468) (0.262) (0.278) (0.001) (<0.001) (<0.001) (<0.001)

MBi,t−1 −0.184 −0.296 −0.266 −0.214 0.120 −0.038 0.052 −0.215 −0.001

(0.006) (0.001) (0.002) (0.111) (0.246) (0.643) (0.016) (0.001) (0.993)

OCFi,t−1 −1.975 −2.798 −1.719 −2.362 −1.128 −3.840 −0.746 −0.700 0.489

(0.001) (0.007) (0.080) (0.004) (0.052) (<0.001) (0.189) (0.476) (0.752)

LEVi,t−1 0.056 1.199 1.082 1.535 −0.695 0.405 0.609 −1.192 −0.690

(0.800) (<0.001) (<0.001) (<0.001) (0.016) (0.505) (<0.001) (<0.001) (0.001)

YEAR included included included included included included included included included

Pseudo R2 0.022 0.040 0.030 0.044 0.020 0.075 0.016 0.061 0.163

N 792 792 792 792 792 792 792 792 792

Notes: This table shows the probit regression results for Equation (2) using a subsample of firm-years that average ROE for the past 5 years are lower than 5%.

All variables are defined in Table 1. Continuous variables are winsorized by year at the 1 percent and 99 percent levels. p-values reported in parentheses are

based on standard errors clustered at both firm and year levels (Petersen 2009).

44

TABLE 9

Additional Test

SUSPECTi,t

[Bin width = 0.012]

SUSPECTi,t

[Bin width = 0.006]

(1) (2)

Constant −0.318 −0.352

(0.125) (0.153)

INSTi,t 0.969 0.692

(0.001) (0.001)

CROSSi,t −0.563 −0.488

(0.285) (0.373)

OWNi,t −0.818 0.253

(0.260) (0.750)

OUTi,t −0.435 −0.254

(0.083) (0.313)

ln(TAi,t−1) 0.020 0.048

(0.216) (0.088)

MBi,t−1 0.257 0.168

(<0.001) (0.023)

OCFi,t−1 0.748 −0.480

(0.241) (0.544)

LEVi,t−1 −0.224 −0.381

(0.068) (0.178)

YEAR included included

Pseudo R2 0.024 0.020

N 1,102 593

Notes: This table shows the probit regression results for Equation (3). SUSPECT in column (1) is an indicator variable

that takes a value of 1 if the firm’s reported ROE is in the interval between 0.050 (inclusive) and 0.062 (exclusive)

and 0 if the firm’s reported ROE is the interval between 0.038 (inclusive) and 0.050 (exclusive); SUSPECT in

column (2) is an indicator variable that takes a value of 1 if the firm’s reported ROE is in the interval between 0.050

(inclusive) and 0.056 (exclusive) and 0 if the firm’s reported ROE is the interval between 0.044 (inclusive) and

0.050 (exclusive); INST is the percentage of ownership held by institutional investors; CROSS is the percentage of

ownership held by cross-shareholders; OWN is the percentage of ownership held by managers; OUT is the

percentage of outside directors on the board of directors; and all other variables are defined in Table 1. Continuous

variables are winsorized by year at the 1 percent and 99 percent levels. p-values reported in parentheses are based

on standard errors clustered at both firm and year levels (Petersen 2009).