Discussion Papers In Economics
And Business

Cross-Shareholdings and Information Environment
Katsuhiko Muramiya and Tomomi Takada

Discussion Paper 15-20

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANCross-Shareholdings and Information Environment
Katsuhiko Muramiya and Tomomi Takada

Discussion Paper 15-20

July 2015

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANCross-Shareholdings and Information Environment∗
Katsuhiko Muramiya† and Tomomi Takada‡
July 2015

Abstract: We examine the relationship between cross-shareholdings and the information
environment. This issue is important because the separation of ownership and control allows managers to act exclusively in their own interests. Despite numerous studies on theinﬂuences of other types of ownership structures, including family, institutional investor,and block ownership structures, little is known about how cross-shareholdings inﬂuence
management incentives. We highlight the Japanese market, where cross-shareholding ishistorically one of the prominent ownership structures. Using a unique database detailing the level of cross-shareholdings, we ﬁnd that higher cross-shareholdings relate to (1)greater information asymmetry in the market, (2) higher earnings quality, and (3) lower
ﬁrm value. The results are consistent with the quiet life hypothesis, which predicts management avoids diﬃcult decisions and costly actions when isolated from market pressures.
Keywords: Corporate governance, cross-shareholding, information asymmetry, earnings
quality, ﬁrm value.
JEL classification: G32, M41∗

We are grateful to Woo-Jong Lee, Lee-Seok Hwang, and Byungcherl Charlie Sohn for their many
intellectual contributions to this paper. We acknowledge the valuable comments and suggestions of
Takashi Kamihigashi, Yoichi Matsumoto, Yoshitaka Fukui, Takashi Obinata, and seminar participantsat Kobe University and Musashi University. This research was supported by the Zengin Foundation for
Studies on Economics and Finance and JSPS KAKENHI Grant Number 25780283 (Muramiya). Takada
acknowledges the ﬁnancial support from Kobe University.
†
Corresponding author. Associate Professor of Accounting at the Graduate School of Economics,
Osaka University.
Address 1-7, Machikaneyama, Toyonaka, Osaka 560-0043, JAPAN.
Tel. +81 6 6850 5238; E-mail address muramiya@econ.osaka-u.ac.jp.
‡
Visiting Scholar at the Carroll School of Management, Boston College and Associate Professor of
Accounting at the Graduate School of Business Administration, Kobe University.
Address 140 Commonwealth Avenue, Chestnut Hill, MA 02467.
E-mail address takadat@bc.edu.

1Introduction

Ever since the issues surrounding the separation of security ownership and control wereﬁrst addressed, it has been argued that ownership structures inﬂuence the information environment (Fama 1980; Fama and Jensen 1983). Prior literature illustrates how diﬀerent
classes of stakeholders, such as corporate insiders, family owners, institutional investors,
and block shareholders, aﬀect the information environment diﬀerently (e.g., El-Gazzar
1998; Heﬂin and Shaw 2000; Fan and Wong 2002; Ali, Chen, and Radhakrishnan 2007;
Gul, Kim, and Qiu 2010).1 Ownership structures vary between countries, but similarities exist. Cross-shareholding, a common format in several European and East Asian
countries, is but one example (La Porta, Lopez-De-Silanes, and Shleifer 1999; Claessens,
Djankov, and Lang 2000; Khanna and Yafeh 2007; Masulis, Pham, and Zein 2011). Despite such common usage, cross-shareholding has not been widely studied, particularly
with regard to how its use aﬀects the information environment in the market. We aim to
ﬁll this gap by providing empirical evidence of the relationship between cross-shareholding
and the information environment, focusing on information asymmetry between investors.
Japan provides an ideal research opportunity because it exhibits great variation in ownership structure (Jackson and Miyajima 2007; Nitta 2008) while cross-shareholding is still
one of the important ownership structures characterizing the market.
Previous studies do not pay much attention on the impact of management incentives
on the relationship between concentrated ownership and the information environment.
Such a stream of research could, however, be reasonable, because concentrated ownership is likely to allow controlling owners to eﬀectively control corporate policy decisions.
However, corporate owners, by nature, delegate decisions to managers and only take action when necessary, since the cost to monitor management is a salient factor. In turn,there should be conﬂict between concentrated owners and managers. We therefore aim
not only to investigate the relationship between cross-shareholding and the information
1

Armstrong, Guay, and Weber (2010) provide an excellent review about recent studies that investigate
the relationship between ownership structure and corporate disclosure.

1

environment, but also to clarify the management incentives behind the relationship.
Cross-held corporate shareholders could play the role of supportive allies who protect
managers from external monitoring forces, thereby reducing the market’s disciplinaryeﬀectiveness in the corporate control and monitoring of managerial opportunism. Crossshareholding thus increases information asymmetry between managers and outside investors because isolating managers from market discipline allows them to become entrenched and less responsive to outside investors (entrenchment hypothesis).2 As a result,less private information is available to outsiders because of the decreased level of disclosure.3 It is also possible for sophisticated investors to take advantage of this situation,
since this insider information is available for their exclusive use, thereby creating information asymmetry among outside (informed and uninformed) investors. For example,
institutional investors have superior abilities and more resources to seek and obtain private information than individual investors do. When managers are isolated from the dayto-day operations of capital markets, they become less apt to divulge private or insider
information to the public, thus providing more opportunities for institutional investors
to leverage their informational advantages over more naive investors. Therefore, in crossshareholding structures, the beneﬁciaries of informational advantages are not necessarily
limited to company insiders.
Conversely, it is argued that cross-shareholding structures improve a ﬁrm’s information
environment by reducing information asymmetry between investors. The literature suggests that cross-shareholding structures are developed to protect against outside takeovers(Aoki and Patrick 1994; Morck and Nakamura 1999). Managers tend to be operationally2

Withholding private information could be a good corporate strategy, because cross-shareholding canprevent proprietary information from leaking to the public (proprietary cost hypothesis). The proprietary cost hypothesis also predicts a negative association between the level of cross-shareholding andinformation asymmetry. Further details of the proprietary cost hypothesis are discussed later.
3
However, cross-shareholding could improve a ﬁrm’s information environment because there is a direct
way to share information between corporate insiders and shareholders through their mutual ownership
relationship. For instance, Berglöf and Perotti (1994) show that the cross-shareholdings of Japanesecompanies mitigate information asymmetry between management and investors. Nevertheless, suchinformation ﬂows could enhance information asymmetry between shareholders. Thus, even if crossshareholdings mitigate information asymmetry between managers and investors to some extent, it could
have negative eﬀects on information asymmetry between investors.

2

shortsighted when faced with takeover threats, while the existence of such protective measures promotes more long-term perspectives (Kang and Stulz 1996). In this scenario, theinterests of management teams beneﬁting from these protective structures could become
more aligned with those of shareholders, which could result in higher-quality ﬁnancial
reporting (alignment hypothesis). Moreover, because a ﬁrm’s higher-quality ﬁnancial disclosures improve its information environment, information asymmetry between outside
and inside investors could be reduced (Armstrong, Balakrishnan, and Cohen 2012). In
keeping with this spirit, Ferreira and Laux (2007) report that ﬁrms with fewer antitakeover provisions exhibit a higher probability of informed trading (PIN).
Although the above discussion assumes that managers’ interests are likely to be either
entrenched or aligned with shareholder interests when they are insulated from the market’s disciplinary eﬀect, there is another behavioral scenario in which some managers may
ﬁnd themselves. Speciﬁcally, it is plausible that managers enjoy a “quiet life” when they
are insulated from takeover threats (quiet life hypothesis). Such managers are likely to
avoid diﬃcult decisions (Bertrand and Mullainathan 2003; Giroud and Mueller 2010). In
keeping with this notion, Zhao and Chen (2008, 2009) show that managers enjoying thequiet life are less likely to engage in earnings management when their ﬁrms have takeoverprotection. Although better earnings quality could improve the ﬁrm’s information environment, managers enjoying the quiet life are less likely to take proactive measures toimprove the information environment: Proactive disclosure activities involve additionalcosts and attract public attention that could jeopardize their quiet life. Accordingly, underthe quiet life hypothesis, cross-shareholding structures are likely to increase information
asymmetry between investors.
Using cross-shareholding data for Japanese listed companies, we ﬁnd that cross-shareholdings
are positively related to the PIN. This implies that information asymmetry between investors is high for ﬁrms with higher proportions of cross-shareholdings. The evidence
is robust for other information asymmetry proxies, such as an alternative PIN measure(adjusted PIN) and the bid-ask spread. The results indicate that either the entrenchment

3hypothesis or the quiet life hypothesis is more plausible than the alignment hypothesis.
To distinguish between the entrenchment hypothesis and the quiet life hypothesis, we
conduct further analysis of the eﬀect of cross-shareholding on earnings quality. Entrenchedmanagers are more likely to engage in opportunistic earnings management because theyneed to conceal expropriation activities and the weakened monitoring of management behavior could overlook such opportunistic behavior. In contrast, managers enjoying thequiet life are unlikely to engage in earnings management. Such managers have less incentive to expropriate shareholder wealth, since this would require concealment or otherwise
entail diﬃcult decisions and costly eﬀorts. The results show that higher cross-shareholding
is correlated with higher earnings quality, which is consistent with the quiet life hypothesis. The evidence is consistent with four alternative earnings quality measures using
accruals. This suggests that managers working within higher-cross-shareholding structures have less incentive to distort earnings and tend to enjoy quiet lives with stable
positions.
To assure the robustness of our ﬁndings, which support the quiet life hypothesis, we
conduct an additional analysis. Speciﬁcally, we examine the association between crossshareholdings and ﬁrm value and discover that more cross-shareholdings lower ﬁrm value,
as measured by Tobin’s Q and the return on net operating assets. This relationship again
supports the quiet life hypothesis. Taken together with this study’s other results, the quiet
life hypothesis is the most likely theory to explain management behavior when working
within highly cross-held structures that are protected from market pressures.
Our research contributes to the literature in the following ways. First, to the best of
our knowledge, this study is the ﬁrst to comprehensively investigate the relationship between cross-shareholdings and the information environment in the market. Furthermore,
we ﬁnd that management teams that enjoy the quiet life cause a positive relationshipbetween cross-shareholdings and information asymmetry. Our results enhance the discussion concerning the eﬀects of cross-shareholdings, which are common structures in several
countries.

4Second, we contribute to the literature concerning ownership structures and theirconsequences by using a unique database detailing the level of cross-shareholdings, whichto date has not been adequately measured. Recent debates have explored how familyﬁrms and/or companies’ control-enhancing mechanisms (e.g., cross-shareholdings, stock
pyramids, and issues of dual-class shares) work in the marketplace, particularly outsideof the United States (Bebchuk, Kraakman, and Triantis 2000; Khanna and Palepu 2000;
Almeida, Park, Subrahmanyam, and Wolfenzon 2011; Masulis, Pham, and Zein 2011).
Our results provide new evidence on how one particular mechanism, cross-shareholding,aﬀects the information environment.
Third, our results support the quiet life view in situations where management is isolated from market discipline. The results imply that management teams working within
high-cross-shareholding environments are less likely to distort accounting information;
they also do not invest in projects that maximize ﬁrm value and do not try to resolve
information asymmetry between investors. This adds to the discussion concerning theconsequences of introducing anti-takeover protection devices, which will be useful not
only for academics but also for policy makers.
The remainder of the paper is organized as follows: In the next section, we present
the institutional background and hypothesis development, followed by an explanation of
the research methodology. We then describe the sample and empirical model and present
the results of the empirical analyses. The ﬁnal section concludes the paper.

2

Institutional Background and Hypothesis Development

2.1 Institutional background in Japan
Corporate ownership in Japan has been characterized by stable shareholders, with reciprocal shareholdings among many corporations and banks (Nakatani 1984; Hoshi, Kashyap,
and Scharfstein 1990; Aoki and Patrick 1994). While the largest single shareholder typically had less than a 5% stake, the web of small reciprocal cross-shareholdings often

5accounted for 20% of shares, which resulted in approximately 40% of all Japanese stock
being held by stable shareholders, at least until the early 1990s (Jackson and Miyajima2007). When the Japanese economy was hit by the banking crisis of the 1990s, theseclose relationships between companies began to fade. Nitta (2008) illustrates the gradualdecline of cross-shareholding relationships from the middle of the 1990s to the early 2000s.
However, the level of cross-shareholding remained relatively stable in the 2000s, although much lower than in the 1990s. Importantly, it is argued that cross-shareholdings
resurged in the middle of the 2000s (Nitta 2009). Cross-shareholding was highlighted as ananti-takeover provision in the Japanese market, since hostile takeover bids by international
investment funds increased in the 2000s.4 In the meanwhile, this trend began to reverse
in 2008 as the unwinding of cross-shareholdings became prevalent.5 While the importance of cross-shareholdings within the Japanese economy has decreased moderately overthe last two decades, it is still a distinguishing practice. Interestingly, cross-shareholding
is argued as having been an eﬀective corporate group strategy for information sharing
between stakeholders in the past, whereas it has been recently highlighted as a takeoverprotection device.

2.2 Hypothesis development
According to the literature, when companies are isolated from market discipline throughownership structures such as block ownership, family ownership, or corporate insider
ownership, shareholders and management are likely to be either entrenched or to bealigned with (Shleifer and Vishny 1986; La Porta et al. 1999; Attig, Fong, Gadhoum,and Lang 2006). There is also another plausible theory: that management is simply

4

They were mostly failed, but it was enough to cause Japanese ﬁrms to take measures to lower takeover
threat (e.g., “Increased steady shareholdings,” Nikkei Kinyu Shimbun, July 30, 2007; Yamazaki 2007).
5
This reversal was again partly due to globalization, as Japanese ﬁrms were forced to comply with global
accounting standards (International Financial Reporting Standards) with regard to ﬁnancial statement
preparation and expanded corporate disclosures. As a result, it became diﬃcult for these ﬁrms tojustify these cross-holding relationships. According to a survey conducted by Nomura Securities, theratio of cross-shareholding of listed Japanese companies reached record low levels of 10.9% as of theﬁscal year-end in 2012 (Nikkei Quick News, June 27, 2013).

6

reluctant to make diﬃcult decisions and prefers to just enjoy the quiet life when theﬁrm is less subject to takeover threat (Bertrand and Mullainathan 2003). Although
our ultimate purpose is to investigate the relationship between cross-shareholdings andinformation asymmetry in the market, we also aim to clarify management incentives
behind the structure. We therefore suggest that the management of cross-held companies
behave in certain ways, as explained by either the entrenchment, alignment, or quiet life
hypothesis. To determine the relevant hypothesis, we ﬁrst investigate the information
environment in the market and subsequently highlight earnings quality and ﬁrm value.
Entrenchment hypothesis assumes that management does not behave in the best interests of other shareholders because cross-held shareholders could play the role of supportive
allies and are thereby isolated from market disciplinary eﬀects. Because such managers
behave opportunistically at the expense of shareholder interests, they would adversely
aﬀect ﬁrm value, engage in opportunistic earnings manipulations, and not be forthcomingin corporate disclosures in order to protect their personal interests. Therefore, if the entrenchment hypothesis is supported, we will obtain the following results: (1) Information
asymmetry in the market is higher, (2) earnings quality is lower, and (3) ﬁrm value islower for ﬁrms with higher cross-shareholdings.
One could think that the incentive to prevent proprietary information from leaking
out to the public can also explain the same relationships as the entrenchment hypothesis
does. Since the disclosure of proprietary information should be unfavorable to a ﬁrm,
managers prefer to withhold information (e.g., Verrecchia 1983). Fan and Wong (2002)
show that more highly concentrated ownership allows managers to engage in less public
disclosure. A proprietary cost explanation would predict a negative relationship between
cross-shareholdings and information asymmetry or earnings quality while withholding
proprietary information does not necessarily jeopardize corporate value. We do not formally develop the hypothesis concerning proprietary cost but discuss it when relevant tointerpreting the results.
The alignment hypothesis argues that management’s interests are likely to align with7

shareholder’s interests. Managers are inclined to be myopic in situations where the threatof hostile takeover is high and, in turn, tend to invest in projects that provide only
short-term proﬁts, even if those projects would deteriorate ﬁrm value in the long run.
Thus, when increasing cross-shareholding reduces takeover threats, managers enjoy moresecurity, have longer-term perspectives, and are more motivated to act in the best interestsof shareholders. Accordingly, the alignment hypothesis expects favorable consequences tocross-shareholdings and consequently (1) information asymmetry in the market is lower,(2) earnings quality is higher, and (3) ﬁrm value is higher for ﬁrms with higher proportionsof cross-shareholdings.
Finally, under the quiet life hypothesis, management is expected to avoid diﬃcult
decisions and costly eﬀorts when they are protected from takeovers. Such managers
will not create earnings distortions nor will they invest in projects that maximize ﬁrm
value because such acts tend to cost them time and money. With respect to information
asymmetry in the market, they are unlikely to actively disclose corporate information
to external parties, especially voluntarily, again because of costs. Such management
behaviors will exacerbate information asymmetry in the market. Taken together, thequiet life hypothesis expects (1) information asymmetry in the market to be greater,(2) earnings quality to be higher, and (3) ﬁrm value to be lower for ﬁrms with morecross-shareholdings.

3

Research Methodology

3.1 Information asymmetry proxy
We use the PIN as a proxy for information asymmetry in the market. Easley, Hvidkjaer,
and O’Hara (2002, hereafter EHO) propose a structural model based on the work of
Glosten and Milgrom (1985) and Easley and O’Hara (1987) to estimate the PIN. As

8

EHO, we deﬁne the PIN as follows:

PIN =

αµ
,
αµ + εb + εs(1)where α is the probability of private information event, µ is the rate of informed tradearrival, and εb (εs ) is the arrival rate of uninformed buy (sell) orders.6
Although the PIN developed by EHO is one of the most common measures of information asymmetry in prior studies, it has room for improvement. Duarte and Young(2009, hereafter DY) extend the EHO model and decompose the PIN into two elements:
an information asymmetry component (adjusted PIN, or adj .PIN ) and a liquidity-related
component by introducing symmetric buy and sell order ﬂow shocks. In the extended DY
model,

adj .PIN =α [(1 − δ) µb + δµs ],α [(1 − δ) µb + δµs ] + (∆b + ∆s ) [αθ0 + (1 − α) θ] + εb + εs(2)where δ is the probability of a low signal, µb (µs ) is the arrival rate of informed buy (sell)orders, θ is the probability of buy and sell symmetric order ﬂow shocks on days withprivate information, and θ0 is the same probability on days with no private information.
In the event of symmetric order-ﬂow shock, the additional arrival rate of buy (sell) ordersis ∆b (∆s ). Following DY, we employ adj .PIN as an alternative measure of information

6

In EHO model, the likelihood of observing B buys and S sells on a certain day is:(µ + εb )B −εs (εs )S
e
B!
S!
B
S
(ε
)
(εb )B −εs (εs )S
(µ
+
ε
)
b
s
+ αδe−εbe−(µ+εs )+ (1 − α)e−εb
e
,
B!
S!
B!
S!

L(θ | B, S) = α(1 − δ)e−(µ+εb )where (B, S) is the total number of buys and sells for the day and θ = (µ, εb , εs , α, δ) is the parameter
vector. This function consists of the sum of three Poisson probabilities weighted by the probability ofeach event type. The data set over multiple days allows us to estimate the parameters. Because days
are independent, the likelihood function for T days is a product of the above likelihood over days,
L(θ | M ) =T∏

L(θ | Bi , Si ),iwhere (Bi , Si ) is trading data for day i = 1 . . . , T and M = ((B1 , S1 ), . . . , (BT , ST )) is the data set.

9

asymmetry in the market. We use trading days during April of year t − 1 to March ofyear t and estimate the parameter vectors of the EHO and DY models by maximizing the
likelihood function. We also employ a widely accepted measure, the bid-ask spread, as
an additional measure of information asymmetry in the market. Our measure based on
the bid-ask spread is deﬁned as the natural log of the daily average relative spread from
April of year t − 1 to March of year t.
3.2 Earnings quality measures
To test the hypotheses, we need to estimate our measures of earnings quality. Althoughabnormal accruals, which are known to measure opportunistic earnings management behavior, are common measures of earnings quality, measurement error issues in their estimation are noted in prior studies (e.g., Kothari, Leone, and Wasley 2005). We therefore
employ four models to estimate earnings quality to ensure the robustness of the results:
the Jones (1991) model, the modiﬁed Jones model (Dechow, Sloan, and Sweeney 1995),
the Dechow-Dichev (2002, hereafter DD) model, and the modiﬁed DD model (Francis,
LaFond, Olsson, and Schipper 2005). The basic concept behind these models is to detect abnormal portions or errors in accruals. Researchers use these calculated abnormal
portions of accruals as a measure of earnings quality (e.g., Wang 2006; Zhao and Chen
2009).
Our sample period starts in 1999, but Japanese ﬁrms were required to disclose cash
ﬂow statements only on and after the ﬁscal year beginning April 1, 1999. We therefore
cannot calculate accruals in a direct and less biased way by deducting operating cash ﬂow
from earnings (Hribar and Collins 2002). Instead, we calculate accruals (ACC ) using thebalance sheet approach according to Shuto’s (2007) accrual estimations for listed Japanesecompanies.7

7

Speciﬁcally, total accruals are deﬁned as (∆current assets − ∆cash and cash equivalents) −(∆current liabilities − ∆ﬁnancing item) − ∆other allowance − depreciation. Current accruals (CACC )for DD and modiﬁed DD models are deﬁned as (∆current assets − ∆cash and cash equivalents) −(∆current liabilities − ∆ﬁnancing item).

10

3.3 Firm value measures
Measures of ﬁrm value are required to test the hypotheses. We therefore utilize Tobin’s
Q and RNOA, which are generally used in the literature as measures of ﬁrm value or
performance. Tobin’s Q (TobinQ) is deﬁned as total assets minus the book value of equity
plus the market value of equity, all deﬂated by total assets and the RNOA (RNOAt ) isdeﬁned as operating income deﬂated by average net operating assets.

4

Sample Selection and Empirical Model

4.1 Sample and data
To construct our sample, we begin with all observations having March ﬁscal year-ends
listed on the Tokyo Stock Exchange from 1999 to 2007.8
The ﬁnancial statement, price, and cross-shareholding data are collected from the
Nikkei Economic Electronic Databank System (NEEDS), the Nikkei Portfolio Master
Database, and the Data Package of Cross-Shareholding and Stable-Shareholding of the
NLI Research Institute. To estimate the PIN, we need data on the numbers of buyerand seller-initiated trades each day. These data are gathered from the Nikkei NEEDS
TICK Database. Our sample is composed of all ﬁrm-years that have all of the required
data, excluding ﬁnancial ﬁrms. We investigate the three hypotheses using three measures, namely, information asymmetry, earnings quality, and ﬁrm value. Because thesample requirements vary with the variables utilized, the sample sizes diﬀer across regressions. Speciﬁcally, the sample size is 12,445 for the information asymmetry regression(PIN ), 14,175 for the earnings quality regression (abs(Jones)), and 16,530 for the ﬁrm
value regression (TobinQ).9 We winsorize TobinQ, signed abnormal accruals, RNOA, and8

Because our database of crossholding includes data as of March end every year, we limit our sample toﬁrms with March ﬁscal-year end to reconcile ﬁnancial data with crossholding data. About 75% of listed
Japanese ﬁrms have March ﬁscal-year end.
9
While we have alternative measures to conﬁrm the validity of the results for each test, we do not provide
the number of observations for all the measures due to brevity. Because the DD and modiﬁed DD models
require cash ﬂow data at t + 1, earnings quality based on these models is estimated up to 2006.

11

GROWTH at the top and bottom percentiles for each regression models to avoid outlier
problems.10

4.2 Cross-shareholding data
We obtain cross-shareholding data from the Data Package of Cross-Shareholding andStable-Shareholding. The database includes the ratios of cross-shareholding and stableshareholdings as of every March year-end. According to the NLI Research Institute’s
deﬁnition, cross-shareholding occurs when two companies hold each other’s shares.11 TheNLI Research Institute’s database includes the ratio of cross-shareholdings, which is calculated by dividing the sum of total cross-shareholding with other companies by the numberof shares outstanding.

4.3 Empirical model
The main regression equation to test the hypotheses is as follows:

DEP = α + β1 log(MVE ) + β2 log(TURNOVER) + β3 log(AGE ) + β4 RNOAt−1+ β5 GROWTH + β6 LEV + β7 log(sd(RNOA)) + β8 MO + β9 CROSS + ε, (3)where DEP measures information asymmetry (PIN , adj .PIN , or log(SPREAD)), earnings quality (abs(JONES ), abs(MJONES ), abs(DD), or abs(MDD)), and ﬁrm value(TobinQ or RNOAt ). All dependent variables are measured at the end of the year t.
The model includes various control variables to capture the incremental eﬀect of crossshareholdings after taking other factors’ eﬀects into account. Speciﬁcally, we control

10

Unsigned abnormal accruals are used in the regression model, but outlier treatments of earnings quality
measures are based on their signed values.
11
They also deﬁnes stable shareholdings as the sum of the following shares: (1) cross-shares, (2) shares
owned by ﬁnancial institutions, (3) shares owned by trust banks using their own accounts, and (4)shares owned by other companies as shares of aﬃliate companies. However, because our focus is on
cross-shareholding, we use cross-shareholding data for the analysis. Moreover, institutional owners,
including ﬁnancial institutions, may play an active governance role in deterring management’s opportunistic behavior. Using data of stable shareholders could complicate the interpretation of the results.

12

for market variables, ﬁrm age, proﬁtability, growth, ﬁnancial status, and managerial
ownership. The term log(MVE ) is the natural logarithm of the market value of equity, log(TURNOVER) is the natural logarithm of average daily turnover for the year,
log(AGE ) is the natural logarithm of ﬁrm age, RNOAt−1 is the return on net operation
assets for year t − 1, GROWTH is annual sales growth, LEV is leverage, log(sd(RNOA))
is the natural logarithm of the standard deviation of RNOA, and MO is managerial ownership. The test variable in our study is CROSS , which is the cross-shareholding ratio.
The independent variables are measured at the beginning of the year or at t − 1 period tosee causality to the dependent variables. Table 1 provides the detailed variable deﬁnitions.
(Insert Table 1 around here.)5

Empirical Results

5.1 Cross-shareholding and information asymmetry
Table 2 reports descriptive statistics of the variables used to estimate the model with PIN .
For brevity, we show descriptive statistics for only the samples using PIN . The statisticsof the estimated PIN are similar to those reported by Kubota and Takehara (2009), who
also estimate the PIN for Japanese ﬁrms. The mean and median diﬀerences of the variables in the PIN model according to the degree of cross-shareholdings are exhibited inPanels A and B of Table 3, respectively. These panels show that cross-shareholding is positively associated with PIN . Untabulated analysis also reveals that univariate correlation
between cross-shareholding and adj .PIN and log(SPREAD) is positive.
(Insert Table 2 and 3 around here.)
To understand the link between the level of cross-shareholdings and PIN more clearly,
we categorize the ﬁrms into six groups according to the level of cross-shareholdings.
The mean and median diﬀerences in these variables between observations with no crossshareholdings (C0) and those with the most cross-shareholdings (C6) are statistically13signiﬁcant. We also test the diﬀerences in variables between ﬁrms with the least crossshareholdings (C1) and with the most cross-shareholdings (C6) to determine the association between the level of cross-shareholdings and the PIN. Untabulated results indicatethat the diﬀerence in the PIN is more pronounced for the tests comparing C6 and C1
relative to those comparing C6 and C0.
Table 4 presents the results of the regression analysis. It reports that cross-shareholding
is positively related to PIN , adj .PIN , and log(SPREAD). This result is inconsistent withthe alignment hypothesis. The positive relation between cross-shareholdings and PIN supports the entrenchment and quiet life hypotheses. We next examine earnings quality toclarify which hypothesis is more plausible.
(Insert Table 4 around here.)

5.2 Cross-shareholding and earnings quality
With respect to the relation between cross-shareholding and earnings quality, we test
the diﬀerences in variables between ﬁrms with no cross-shareholdings or the least crossshareholdings and with the most cross-shareholdings, as does the investigation on information asymmetry. Higher values imply lower earnings quality and untabulated results
show that the measures decrease roughly monotonically with increasing levels of crossshareholdings. The diﬀerences in the means and medians between the lowest-cross-shareobservations and the highest are both signiﬁcant for all of the measures. We expect
entrenched managers to be more likely to expropriate shareholder wealth and distortearnings, while managers enjoying the quiet life would avoid such costly actions. The
results support the latter notion, which is the quiet life hypothesis.
(Insert Table 5 around here.)
The results of the regression model are reported in Table 5. The results show thatcross-shareholdings and all the earnings quality measures have a negative and signiﬁcant association, even after controlling for other factors. This indicates that more cross14

shareholding lessens the incentive management may have to distort earnings, supporting
the quiet life hypothesis.

5.3 Cross-shareholding and firm value
The entrenchment and quiet life hypotheses predict a negative relationship between crossshareholding and ﬁrm value, whereas the alignment hypothesis posits a positive relationship. In combination with the results obtained in the previous sections, however, our mainconcern at this stage is whether the results are consistent with the quiet life hypothesis.
We therefore now focus on the consistency of the results with the quiet life hypothesis.
(Insert Table 6 around here.)
We again examine the mean and median diﬀerences, respectively, in TobinQ and
RNOAt , respectively, according to the degree of cross-shareholdings. Untabulated results show that cross-shareholding has a negative association with ﬁrm value and themean and median diﬀerences in TobinQ and RNOAt between the lowest and highest
cross-shareholdings are statistically signiﬁcant. The result indicates that more crossshareholding decreases ﬁrm value. According to the results in Table 6, the regression
results also reveal negative and signiﬁcant relationships between cross-shareholdings andTobinQ as well as RNOAt . These results imply that more cross-shareholdings lower ﬁrmvalue, which is consistent with the quiet life hypothesis.
Collectively, the results of the three measures: information asymmetry, earnings quality, and ﬁrm value as a whole support the quiet life hypothesis. We bear in mind thatthe management incentive to withhold proprietary information could be another possible scenario that explains the relationship between cross-shareholdings and information
asymmetry, although we do not formally develop the hypothesis. The overall results ofthe current study, however, are not consistent with such an incentive. In sum, managers in greater cross-shareholding are neither entrenched nor aligned with those of othershareholders. The results indicate that they would rather enjoy the quiet life.
15

6Conclusion

Since Regulation Fair Disclosure was promulgated in 2000, the importance of fair disclosures has attracted much attention. Although the regulation is mainly applied to U.S.
listed companies, it should be of concern internationally, by regulatory bodies and academics alike. Uncovering the factors that inﬂuence the information environment in themarket is thus important.
Our results show that higher levels of cross-shareholding are related to greater information asymmetry. We also ﬁnd that ﬁrms with more cross-shareholdings exhibit higher
earnings quality and lower ﬁrm value. These results are robust, since we conﬁrm that using
alternative measures of information asymmetry, earnings quality, and ﬁrm value do notalter the ﬁndings. The evidence suggests that cross-shareholdings exacerbate information
asymmetry between investors in Japan. In conjunction with the results on earnings quality and ﬁrm value, our evidence supports the quiet life hypothesis. Management avoids
diﬃcult decisions and costly actions as the level of cross-shareholding increases, because
they are isolated from takeover threats or external market forces, thereby enjoying thequiet life.
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20

TABLE 1: Variable Deﬁnitions
Variable

Deﬁnition
probability of informed trading by Equation (1) estimated over year t(April of year t − 1 to March of year t)
adj.PINadjusted PIN by Equation (2) estimated over year t (April of year t − 1
to March of year t)log(SPREAD)
natural logarithm of the average daily percentage spread for ﬁscal yeart (April of year t − 1 to March of year t)abs(Jones)
the absolute value of abnormal accruals based on Jones (1991) for ﬁscal
year tabs(MJones)
the absolute value of abnormal accruals based on Dechow et al. (1995)
for ﬁscal year tabs(DD)the absolute value of abnormal accruals based on Dechow and Dichev(2002) for ﬁscal year tabs(MDD)the absolute value of abnormal accruals based on Francis et al. (2005)
for ﬁscal year t
TobinQ(total assets(t) − book value of equity(t) + market value ofequity(t))/total assets(t)RNOAtreturn on net operating assets for ﬁscal year t (= operating
income(t)/average net operating assets(t − 1 to t))log(MVE )natural logarithm of the market value of equity (= shares ×price) atthe end of year t − 1log(TURNOVER)
natural logarithm of the average daily turnover over year t − 1log(AGE )natural logarithm of the number of years since the ﬁrm was established
RNOAt−1
return on net operating asset for ﬁscal year t−1 (= operating Income(t−1)/average net operating assets(t − 2 to t − 1))
GROWTHsale growth (= (sale(t − 1) − sale(t − 2))/sale(t − 2))LEV
ﬁnancial obligations divided by total assets at the end of ﬁscal year t − 1log(sd(RNOA))
natural logarithm of the standard deviation of the RNOA over the past
ﬁve years (t − 5 to t − 1), requiring a minimum of three years of dataMO
percentage of shares held by all directors at the end of ﬁscal year t − 1
CROSScross-shareholding ratio at the end of ﬁscal year t − 1————————————————PIN

This table provide detailed deﬁnitions for all variables used in this paper. To mitigate the inﬂuence of outliers on the
results, the continuous variables, TobinQ, RNOA, and GROWTH , were winsorized at the ﬁrst and 99th percentiles of
the annual distribution for the respective variables across the year. Regarding earnings quality measures (i.e., abs(Jones),abs(MJones), abs(DD), and abs(MDD)), signed values were winsorized at the ﬁrst and 99th percentiles, and these absolutevalues are used in the empirical model estimations.

21

22

RNOAt−1

12,445
0.220
0.104
0.153
0.200
0.266

GROWTH

MVE(billions JPY)
12,445
191
875
11
28
99

12,445
0.265
0.207
0.084
0.241
0.405

LEV

12,44524.302
1.647
23.14224.06425.317log(MVE )12,445
0.129
1.475
0.016
0.030
0.062

sd(RNOA)

12,445
0.003
0.005
0.001
0.002
0.003

TURNOVER

This table reports the descriptive statistics. See Table 1 for the variable deﬁnitions.

Obs.
12,445
12,445
Mean
0.035
0.023
SD
0.109
0.134
Q1
0.008
−0.045Median
0.032
0.014
Q3
0.068
0.076
————————————————

Obs.
Mean
SD
Q1
MedianQ3

PIN

TABLE 2: Descriptive Statisticslog(sd(RNOA))
12,445−3.435
1.154−4.143−3.490−2.782

log(TURNOVER)
12,445−6.5591.099−7.304−6.570−5.873

12,445
0.039
0.084
0.002
0.005
0.029

MO

12,445
57.1
19.5
47
56
68

AGE

12,445
0.110
0.091
0.036
0.095
0.165

CROSS

12,445
3.973
0.416
3.850
4.025
4.220

log(AGE )

23

0.041
0.028
0.016
0.009
0.005
0.003
−0.0030.014−0.044∗∗∗(−9.53)

GROWTH

0.067
0.038
0.022
0.013
0.011
0.004
0.002
0.023
−0.065∗∗∗(−9.28)GROWTH

0.165
0.182
0.244
0.266
0.263
0.267
0.276
0.241
0.111∗∗∗(8.62)

LEV

0.224
0.240
0.272
0.280
0.276
0.292
0.282
0.265
0.058∗∗∗(6.33)

LEV

log(sd(RNOA))−2.853−3.345−3.496−3.569−3.644−3.711−3.648−3.490−0.795∗∗∗(−16.91)log(sd(RNOA))−2.550−3.311−3.529−3.610−3.596−3.684−3.616−3.435−1.066∗∗∗(−18.37)

0.005
0.004
0.005
0.005
0.005
0.005
0.005
0.005
−0.001∗∗∗(−3.22)

MO

0.094
0.059
0.037
0.026
0.020
0.019
0.014
0.039
−0.080∗∗∗(−17.37)

MO————————————————
This table reports the mean (Panel A) and median (Panel B) diﬀerences in variables according to the level of cross-shareholdings. Here C0 (C6) represents ﬁrms with no(most) cross-shareholdings. We categorize observations into six groups according to the level of cross-shareholding, as follows: C0, CROSS = 0%; C1, 0% < CROSS < 5%;
C2, 5% ≤ CROSS < 10%; C3, 10% ≤ CROSS < 15%; C4, 15% ≤ CROSS < 20%; C5, 20% ≤ CROSS < 25%; C6, 25% ≤ CROSS . The superscripts *** and * indicate
statistical signiﬁcance at the 1% and 10% levels, respectively. The panels exhibit the diﬀerences and their statistical signiﬁcance in the variables between the ﬁrms with the mostcross-shareholdings (C6) and those with none (C0). See Table 1 for the variable deﬁnitions.

Panel B: Median Diﬀerences for the Variables in the PIN Regression According to Cross-Shareholdings
CROSS
Obs.
PIN
log(MVE )log
log(AGE )RNOAt−1
Category(TURNOVER)
C0
1, 122
0.198
23.871−6.109
3.689
0.052
C1
2, 7940.193
24.183−6.488
3.932
0.042
C2
2, 5290.198
24.051−6.626
4.007
0.032
C3
2, 3380.196
24.200−6.531
4.078
0.029
C4
1, 6140.203
24.089−6.625
4.094
0.026
C5
1, 0400.209
24.020−6.693
4.127
0.022
C6
1, 0080.226
23.746−6.937
4.127
0.025
Total
12, 4450.200
24.064−6.570
4.025
0.032
C6−C0
0.028∗∗∗−0.125∗−0.828∗∗∗0.438∗∗∗−0.028∗∗∗C6−C0 (z-stat.)(7.19)(−1.75)(−16.72)(25.40)(−10.12)

Panel A: Mean Diﬀerences for the Variables in the PIN Regression According to Cross-Shareholdings
CROSS
Obs.
PIN
log(MVE )log
log(AGE )RNOAt−1
Category(TURNOVER)
C0
1, 1220.215
24.069−6.065
3.516
0.055
C1
2, 7940.214
24.387−6.514
3.833
0.051
C2
2, 5290.218
24.361−6.590
3.995
0.035
C3
2, 3380.220
24.450−6.535
4.077
0.028
C4
1, 6140.220
24.343−6.628
4.127
0.023
C5
1, 0400.228
24.183−6.735
4.142
0.019
C6
1, 0080.244
23.892−6.919
4.156
0.020
Total
12, 4450.220
24.302−6.559
3.973
0.035
C6−C0
0.029∗∗∗−0.178∗∗∗−0.854∗∗∗0.640∗∗∗−0.035∗∗∗
C6−C0 (t-stat.)(6.40)(−2.62)(−17.82)(30.05)(−5.28)TABLE 3: Mean and Median Diﬀerences in the Test Variables

TABLE 4: Information Asymmetry Regression Results
PIN
Constantlog(MVE )log(TURNOVER)
log(AGE )RNOAt−1
GROWTHLEV
log(sd(RNOA))
MO
CROSS

adj .PIN

0.596∗∗∗(12.2)−0.023∗∗∗(−15.8)−0.031∗∗∗(−9.39)−0.009∗∗(−2.25)0.024(1.36)
0.020∗∗∗(2.61)−0.004(−0.33)−0.001(−0.51)0.038(1.49)
0.036∗(1.65)

Observations
12, 4452
Adjusted R
0.3233————————————————log(SPREAD)0.422∗∗∗
1.940∗∗∗(11.2)(5.02)−0.015∗∗∗−0.344∗∗∗(−11.5)(−27.4)−0.020∗∗∗−0.248∗∗∗(−9.89)(−10.5)−0.006∗∗∗−0.044(−2.61)(−1.14)−0.010−0.250∗∗∗(−1.20)(−3.04)−0.008−0.223(−1.04)(−1.09)−0.0020.561∗∗∗(−0.27)(8.14)−0.002
0.066∗∗∗(−1.11)(4.12)
0.007−0.286∗(0.58)(−1.77)0.045∗∗∗
0.274∗(2.63)(1.83)

9, 9170.3091

12, 3400.6807This table reports the results of the measures of information asymmetry regressed on cross-shareholdings and control
variables. The information asymmetry measures employed are PIN , adj .PIN , and log(SPREAD). The t-statistics, reported
in parentheses, are based on two-way cluster-robust standard errors, following Petersen (2009). The superscripts ***, **,and * indicate statistical signiﬁcance at the 1%, 5%, and 10% levels, respectively. See Table 1 for the variable deﬁnitions.

24

TABLE 5: Earnings Quality Regression Results

Constantlog(MVE )log(TURNOVER)
log(AGE )RNOAt−1
GROWTHLEV
log(sd(RNOA))
MO
CROSS

abs
abs
(JONES )(MJONES )0.161∗∗∗
0.168∗∗∗(9.77)(10.9)−0.000−0.001(−1.10)(−1.36)0.001∗∗∗
0.001∗∗∗(3.07)(3.13)−0.019∗∗∗−0.020∗∗∗(−4.66)(−4.62)−0.016∗∗∗−0.018∗∗∗(−3.30)(−3.32)0.021∗∗
0.022∗(2.06)(1.93)
0.006
0.007∗(1.29)(1.65)
0.006∗∗∗
0.006∗∗∗(6.77)(6.34)
0.008
0.007(0.93)(0.78)−0.022∗∗∗−0.024∗∗∗(−3.23)(−3.41)

Observations
14, 175
2
Adjusted R
0.0779————————————————

14, 126
0.0776abs(DD)
0.099∗∗∗(10.0)−0.000(−1.27)0.002∗∗∗(5.24)−0.010∗∗∗(−5.54)−0.018∗∗∗(−4.51)−0.001(−0.31)0.005∗∗∗(2.83)
0.004∗∗∗(8.14)
0.000(0.04)−0.011∗∗∗(−3.44)
11, 8310.0804abs(MDD)
0.078∗∗∗(9.15)−0.000(−0.80)0.001∗∗∗(3.37)−0.007∗∗∗(−5.01)−0.015∗∗∗(−3.04)−0.001(−0.41)0.000(0.28)
0.004∗∗∗(9.96)
0.004(0.86)−0.012∗∗∗(−5.09)10, 9230.0837This table reports the results of the measures of earnings quality regressed on cross-shareholdings and control variables. The
earnings quality measures employed are abs(JONES ), abs(MJONES ), abs(DD), and abs(MDD). The t-statistics, reported
in parentheses, are based on two-way cluster-robust standard errors, following Petersen (2009). The superscripts ***, **,and * indicate statistical signiﬁcance at the 1%, 5%, and 10% levels, respectively. See Table 1 for the variable deﬁnitions.

25

TABLE 6: Firm Value Regression Results

Constantlog(MVE )log(TURNOVER)
log(AGE )RNOAt−1
GROWTHLEV
log(sd(RNOA))
MO
CROSS

TobinQ
0.883(1.51)
0.089∗∗∗(5.94)
0.053∗∗∗(3.54)−0.292∗∗∗(−4.36)0.484∗∗∗(4.06)
0.371∗∗∗(2.81)
0.195∗∗∗(3.06)
0.110∗∗∗(8.82)
0.121(0.84)−0.447∗∗∗(−5.68)Observations
16, 530
2
Adjusted R
0.3098————————————————

RNOAt−0.235∗∗∗(−5.79)0.009∗∗∗(9.44)−0.003∗(−1.88)0.012∗∗(2.56)
0.431∗∗∗(16.8)
0.044∗∗(2.16)−0.041∗∗∗(−5.17)0.006∗∗∗(2.99)
0.088∗∗∗(3.65)−0.021∗∗∗(−3.34)16, 5880.2569This table reports the results of the measures of ﬁrm value regressed on cross-shareholdings and control variables. The
ﬁrm value measures employed are TobinQ and RNOAt . The t-statistics, reported in parentheses, are based on two-way
cluster-robust standard errors, following Petersen (2009). The superscripts ***, **, and * indicate statistical signiﬁcanceat the 1%, 5%, and 10% levels, respectively. See Table 1 for the variable deﬁnitions.

26