DBJ Discussion Paper Series, No. 1901

Foreign Ownership, Bank Information Environments, andthe International Mobility of Corporate GovernanceYiwei Fang
（Stuart School of Business）
Iftekhar Hasan（Fordham University）
Woon Sau Leung（University of Edinburgh）
Qingwei Wang
（Centre for European Economic Research）

April 25, 2019

Discussion Papers are a series of preliminary materials in their draft form. No quotations,reproductions or circulations should be made without the written consent of the authors in order toprotect the tentative characters of these papers. Any opinions, findings, conclusions or
recommendations expressed in these papers are those of the authors and do not reflect the views of
the Institute.

Foreign Ownership, Bank Information Environments, andthe International Mobility of Corporate GovernanceYiwei Fang,1 Iftekhar Hasan,2† Woon Sau Leung,3 and Qingwei Wangc,dApril 25, 20191

Illinois Institute of Technology, Stuart School of Business, Chicago, IL, USA.
Gabelli School of Business, Fordham University, New York, New York 10023, USA; Bank of Finland, FI-00101
Helsinki, Finland; University of Sydney Business School, NSW, Australia.
†
Corresponding author. E-mail: ihasan@fordham.edu.
3
University of Edinburgh, Edinburgh, Scotland, UK.
dCentre for European Economic Research, L7 1, 68161 Mannheim, Germany.
2

Part of this work was completed by Hasan while he was a Shimomura Fellow at the Development Bank of Japan. We
are grateful for helpful comments from Davide Avino, Jie Chen, Hans Degryse, Julian Franks, Simone Giansante(discussant), Marc Goergen, Liuling Liu, Jens Hagendorff, Khelifa Mazouz, Wei Song, Yuliang Wu, Jason Xiao, Tina
Xu, Shuxing Yin, and Hong Zou, as well as participants at the 2016 Empirical Finance Group Conference (Cardiff).
All remaining errors are our own.

Foreign Ownership, Bank Information Environments, andthe International Mobility of Corporate Governance
ABSTRACTThis paper investigates how foreign ownership shapes bank information environments. Using a sample oflisted banks from 60 countries over 1997-2012 we show that foreign ownership is significantly associatedwith greater (lower) informativeness (synchronicity) in bank stock prices. We also find that stock returnsof foreign-owned banks reflect more information about future earnings. In addition, the positive association
between price informativeness and foreign ownership is stronger for foreign-owned banks in countries withstronger governance, stronger banking supervision, and lower monitoring costs. Overall, our evidence
suggests foreign ownership reduces bank opacity by exporting governance, yielding important implications
for regulators and governments.

Keywords: Foreign bank ownership; Bank information environments; Stock price synchronicity; Earnings
coefficients; International mobility of corporate governance.

JEL Classification: G14; G15; G21; G38.

2

1

INTRODUCTION

Despite an extensive body of international business (IB) research on globalization (see a review by Verbeke
et al., 2018), researchers pay relatively little attention to banking globalization. However, banks are
instrumental in promoting IB because of their roles in financing global trade and foreign investments ofmultinational companies (MNCs) (Niepmann and Schmidt-Eisenlohr, 2017), as well as in maintaining thestability of international financial systems (Laeven, 2013). The 2008-09 global credit crisis is a vivid
example of how instability in the banking sectors could impair global trade and investment flows.
Many argue that weak bank transparency is a major cause of the crisis because poor-quality
information makes asset risk opaque and the lack of disclosure aggravates conflicts between different bankstakeholders (Healy and Palepu, 2001; Hyytinen and Takalo, 2002; Bushman and Williams, 2012; Bushman,2014). Over the decades, various international institutions, including the International Monetary Fund, theBasel Committee on Banking Supervision (BCBS), and the Financial Stability Board, have proposed
prudential regulations for banks. They have also campaigned for greater emphasis on effective corporate
governance and improved disclosure for banks. For instance, Pillar Three of the Basel II Accord focuses
specifically on enhancing the transparency of the global banking sectors by raising bank disclosure
requirements and strengthening market discipline (BCBS, 2015). A number of scholars, such as Berlin etal. (1991), Bhattacharya et al. (1998), Laeven (2013), and Bushman (2014), also advocate increasedtransparency in banking systems. Given these increasing calls from policymakers and scholars, andconsidering the importance of bank stability to global trade, it is essential for IB researchers to have a better
understanding of what enhances bank transparency within a globalization context to ensure efficacy andsustainability in international trade.
In this paper, we examine whether foreign ownership increases bank transparency throughimproved corporate governance. In an agency-theory framework, effective governance, such as monitoringby owners, boards of directors, and other market participants, could improve corporate transparency by
reducing managers’ incentives to exploit or hoard private information for their own advantage (see, e.g.,
Bertrand and Mullainathan, 2003; Ferreira and Laux, 2007; Gul et al., 2011; Armstrong et al., 2012;
Armstrong et al., 2014). Also, a recent, growing body of IB and international finance literature theorizes
and documents that foreign owners who actively monitor corporate insiders play a significant role inexporting corporate governance to subsidiaries or invested entities in host countries (Gillian and Starks,
2003; Ferreira and Matos, 2008; Aggarwal et al., 2011; He et al., 2013; Han, 2015; Bena et al., 2017;
Cumming et al., 2017).
In line with this governance-mobility view, several cross-border M&A studies show that target
firms benefit from importing superior governance and contracting devices from acquirers headquartered ingood-governance countries (Ferreira and Matos, 2008; Martynova and Renneborg, 2008; Aggarwal et al.,3

2011; Ellis et al., 2017; Renneboog et al., 2017). The banking literature also suggests that foreign owners
bring better governance and risk-management systems to their local counterparts, in turn improving
efficiency (Berger et al., 2009). Based on these arguments, we hypothesize that foreign ownership is
positively associated with bank transparency because governance spills over from home countries withstronger governance or banking regulations.
To test this hypothesis, we measure bank transparency by the amount of firm-specific variation inbank stock returns, which reflects the incorporation of private information (see, e.g., Morck et al., 2000;
Jiang et al., 2009). When information environments improve, stock prices incorporate more variation in
firm-specific factors and thus synchronize less with market factors. Following this literature, our dependent
variable is price synchronicity, estimated as the logistic-transformed R2 from an expanded market model.
Our explanatory variable of interest is an indicator for majority foreign ownership (Foreign) that equals 1
for banks with 50% or more foreign ownership, and 0 otherwise. Using a sample of 710 banks from 60
countries over 1997-2012, our baseline results show that foreign bank ownership is associated withsignificantly lower (higher) price synchronicity (informativeness). This relationship is robust to alternative
fixed effects, estimation approaches, sample periods, standard errors, and use of discretionary loan loss
provisions as an alternative proxy for bank transparency.
A potential concern is endogeneity. Unobserved country or bank characteristics that codetermine
foreign ownership and price synchronicity could bias our estimates. Additionally, foreign investors may
prefer to invest in countries with more transparent banking systems (Leuz et al., 2009). Because thetreatment status is not randomly assigned to the sample banks, our results may be subject to potential
selection issues or reverse causality. To address these concerns, we examine the results using bank fixed
effects, instrumental variable estimation, and dynamic panel generalized method of moments (GMM)
estimation, all showing that endogeneity does not drive our results.
Another concern recent studies raise (see, e.g., Dasgupta et al., 2010; Xing and Anderson, 2011) isthat stock price synchronicity may be noisy and unreliable in capturing information flows. To address this,we study the extent to which stock prices incorporate future earnings information, and we examine its
relationship to foreign bank ownership. Our results show that stock prices of foreign-owned banks containsignificantly more future earnings information than those of local banks, consistent with our hypothesis.
To test the theory of governance mobility, we evaluate whether the quality of corporate governanceand banking regulations in the home countries governs the relationship between foreign bank ownershipand stock price informativeness. If foreign owners export better governance practices and improve bankinformation environments, then increases in stock price informativeness should be more pronounced forforeign-owned banks from home countries with relatively strong corporate governance or banking
regulations. Moreover, because monitoring foreign subsidiaries and invested companies incurs significant
4transportation and communication costs (Degryse and Ongena, 2005; Mian, 2006; Kang and Kim, 2008),
physical distance between the home and host countries could reduce the spillover of corporate governanceacross borders. Our results support these predictions.
Our paper makes several important contributions to the literature. First, we add to an emerging
body of IB literature theorizing and documenting that governance does indeed travel abroad through, forexample, foreign ownership, cross-border M&As, etc. (see, e.g., Aguilera, et al., 2017; Cumming, et al.,2017; Ellis et al., 2017; Miletkov et al., 2017). We complement this literature by analyzing banks, anindustry of central importance to IB regarding the stability of global trade, revealing that foreign bankownership is another effective channel through which governance moves across borders.
Second, we add to the growing literature on the economic consequences of banking globalization.1
However, relatively few studies analyze its role in shaping banks’ information environments. This question
has far-reaching implications for IB because bank opacity, often considered a major cause of banking crises,
could seriously disrupt global trade. Our findings uncover a bright side of banking globalization in
mitigating information asymmetry.
Third, using more general samples of firms, our paper relates to studies on the link between foreign
investors and the information content of stock prices (see, e.g., Gul et al., 2010). For instance, He et al.
(2013) and Han (2015) show that foreign shareholders enhance price informativeness due to a greater ability
in processing value-relevant information. However, our paper differs from theirs in several ways. First, oursample contains global banks; theirs consist of mainly nonfinancial firms. Banks are distinct from
nonfinancial firms in that they are opaque (Morgan, 2002), highly levered, and interconnected; their failures
could have adverse systemic consequences that disrupt global markets and trade. As such, we extend thesignificant contribution these studies make by offering practical implications regarding market and tradestability for regulators as well as scholars in IB. Second, our theoretical framework, motivated by recent,growing IB literature, asserts that increased corporate governance exported by foreign bank owners
enhances information environments. Although more intense monitoring by foreign shareholders is apotential channel that He et al. (2013) consider, their study only analyzes the host countries’ governancecharacteristics. However, because we know the home countries of foreign-owned banks, we are able toexamine in a governance context whether relative distance between home and host countries explains priceinformativeness, directly testing the theory.
Our findings yield several policy implications. First, despite the growing literature, researchers still
do not fully understand the costs and benefits of foreign bank ownership, as well as their tradeoffs. Somestudies document a few dark sides of foreign banks, such as cherry-picking elite clients and not venturing
out of urban territories in host markets. They conclude that foreign banks fail to enhance domestic credit
and financial inclusion (Detragiache et al., 2008; Allen et al., 2011; Claessens and van Horen, 2014; Beck5

and Brown, 2015; Allen et al., 2017). Recent financial crises also raise concerns that foreign bank ownershipis a potential channel for transmitting liquidity shocks across borders (Dages et al., 2000; Cetorelli andGoldberg, 2012). As such, foreign ownership of banks remains controversial, especially in emerging
markets where foreign bank ownership is limited or banned.
Our study adds to this policy debate by uncovering a new bright side of foreign bank ownership:enhanced banking information environments through governance spillovers. Because transparency isimportant for creating a domestic banking environment that fosters credit creation and efficiency in capital
allocation (Greenspan, 1996; Morgan, 2002), emerging economies with low incomes, high opacity, weak
governance, and ineffective banking regulations likely benefit the most from governance spillovers inducedby foreign bank ownership. Regulators should consider such benefits when evaluating policies regarding
foreign bank entry.
Second, reduced information asymmetry due to foreign bank ownership strengthens market-based
oversight, regulatory efficiency, and market discipline. 2 Third, by reducing price synchronicity amongbanks, foreign ownership reduces the likelihood of concurrent price declines during difficult economic
conditions, contributing to market and trade stability. Finally, as shown in events such as the Great
Depression and “Brexit” in the UK, and amid nationalism’s surging popularity in some countries, we add
to the partisan debate about globalism versus nationalism. Our evidence suggests that governments should
foster legislation supporting the convergence of governance.
We structure the rest of the paper as follows: section 2 reviews the literature and develops our
hypotheses. Section 3 explains our sample, variable construction, and empirical methodology, and it
presents our descriptive statistics. Section 4 presents our test results, and section 5 concludes.

2
2.1

THEORETICAL FRAMEWORK AND HYPOTHESIS DEVELOPMENT

The Implications of Banking Globalization

There has been a dramatic globalization trend in banking and a constantly increasing demand fortransparency around the world over the past several decades.3 Although a core body of IB research seeks
to understand the antecedents, processes, and consequences of globalization in modern corporations (seeVerbeke et al., 2018), researchers pay relatively little attention to banking globalization and its relevancefor market stability.
In fact, increased banking globalization has several important implications for IB. First, foreignowned banks, often major credit suppliers for MNCs, play a key role in providing liquidity locally as wellas in facilitating foreign direct investments (Detragiache et al., 2008; Gormley, 2010; Claessens et al., 2017).
Second, increases in banking competition and thus the potential shifts in the quality of financial
intermediation due to the entry of foreign banks could benefit or harm the export businesses in the host6

countries (Beck et al., 2006; Berger et al., 2009; Giannetti and Ongena, 2009, 2012; Bruno and Hauswald,2014). Third, foreign-owned banks could help facilitate information flows among global trading partners(Portes and Rey, 2005), thereby narrowing the information gap and fostering more trustworthy traderelationships. Fourth, because the efficacy and extent of international trade depend on the health of the
global financial and banking sectors, foreign-owned banks have implications for IB through their role inmarket stability (Iacovone and Zavacka, 2009; Gormley, 2010; Amiti and Weinstein, 2011; Cetorell andGoldberg, 2012). In particular, the extent to which banking systems should integrate into the global
financial economy is an important question for economic growth, financial stability, and global businesses.

2.2

The Importance of Bank Information Environments

A bank shapes its information environment through the quality of its financial disclosure, including thetimeliness, reliability, and discretionary choices of its financial reporting. Improving bank transparency has
been one of the most important prudential policy initiatives globally over the past decades (Rochet, 1992;
Bushman and Williams, 2015). There are at least four reasons.
First, informational transparency strengthens market discipline in bank risk-taking. From an ex ante
perspective, anticipating that informed investors will respond swiftly to increased risk by demanding higher
returns, bank managers would be less prone to taking excessive risks, thereby enhancing stability. In an ex
post sense, bank transparency also disciplines managers to the extent that distressed securities could trigger
regulatory interventions to restore stability (Flannery, 1998, 2001), thereby threatening careers and casting
doubt on managerial abilities.
Second, informational transparency improves bank stability by supporting governance mechanisms(Bushman and Smith, 2001; Bushman, 2014, 2016). Banks are prone to moral-hazard problems, and their
managers have incentives to invest in excessively risky projects because of high leverage (Laeven, 2013),
limited discipline from insured depositors (Merton, 1977; Hett and Schmidt, 2017), asset opacity (Morgan,
2002; Acharya et al., 2016), and implicit government guarantees (Gropp et al., 2011). In this regard, bankinformation that is reliable and publicly available not only helps appraise the performance of bank managers,
but also supports monitoring bank directors, owners, and regulators, which in turn mitigates agency costs
and systemic risk.
Third, bank transparency reduces uncertainty about solvency, as well as panic and rollover risk
among depositors and short-term creditors, and thus helps prevent bank failures and runs (Gorton and
Huang, 2006; Ratnovski, 2013). Finally, managerial incentives such as stock grants, option grants, andother forms of equity-related pay often tie to stock prices. Because stock prices convey useful information
about managers’ efforts and performance, the information content of bank stock prices is useful in7

structuring executive incentives to better align the interests of owners and agents, thereby increasing theeffectiveness of such incentives (Holmstrom and Tirole, 1993; Kang and Liu, 2008; Alok et al., 2016).

2.3

Corporate Governance, Banking Regulations, and Information Environments

Among the factors that shape a bank’s information environment is corporate governance, which researchers
widely discuss (see, e.g., Enriques and Volpin, 2007; Mehran et al., 2011; Laeven, 2013). Better governanceimproves corporate transparency by encouraging management to increase public disclosures and provide
more relevant and timely information to external parties (e.g., Bertrand and Mullainathan, 2003; Ferreiraand Laux, 2007; Gul et al., 2011; Armstrong et al., 2012; Armstrong et al., 2014). Consistent with this view,
empirical studies document that companies with better governance have higher-quality disclosures, lower
incidences of financial statement fraud (Beasley, 1996), less earnings management (Dechow et al., 1996),
and more precise earnings forecasts (Ajinkya et al., 2005).4
Because banks are heavily regulated, one governance-related factor that shapes a bank’s
information environment is its external supervisory and regulatory environment. The Basel Committee on
Banking Supervision (BCBS) has published a number of microprudential and macroprudential regulatory
guidance documents that emphasize the importance of capital adequacy, effective corporate governance,
and improved accounting and disclosure practices for banks. In particular, the Basel II Accord, proposed in
2004, contains three pillars: minimum capital requirements, official supervisory power, and market
discipline mechanisms. The first pillar concerns the ongoing maintenance of regulatory capital that isrequired to protect banks against different kinds of risks, such as credit risk, operational risk, and market
risk. The second pillar gives supervisory authorities more power to take actions in preventing and correcting
problems. The third pillar promotes market discipline and bank transparency by imposing various
regulatory disclosure requirements that enable market participants to access key information about a bank’s
capital adequacy and risk exposures (BCBS, 2015). More recently, in response to the global financial crisis,
the Basel III Accord raised the regulatory capital requirements, set up new requirements for funding
illiquidity, and introduced new prudential regulations specifically targeting systemically important financial
institutions (SIFIs).
Several studies confirm the significant role of banking supervision and regulations in determiningbank efficiency (Barth et al., 2013b) and risk-taking behaviors (Laeven and Levine, 2009). Importantly,
Francis et al. (2015) document that more stringent capital regulations and increased incentives for private
monitoring are associated with less synchronized bank stock prices, consistent with improved bankinformation environments.

8

2.4

The International Mobility of Corporate GovernanceAlthough an extensive amount of IB and international finance literature uncovers various governancefactors that may determine MNCs’ global strategies, most research views corporate governance as“location-specific” (Filatotchev and Wright, 2011). Recently, however, some researchers challenge thisview. Cumming et al. (2017), for example, discuss at least two theoretical perspectives that could explain
why governance travels across countries. The first is the agency perspective, focusing on the achievementof efficiency outcomes. Under this perspective, MNCs import or export corporate governance in order togain access to superior resources, managerial expertise, and monitoring competencies. The second is theinstitutional perspective, positing that MNCs adjust their corporate governance structures and processes toincrease their legitimacy among local stakeholders who may have different expectations of governancequality. Although the two perspectives differ in emphases, they both suggest there is a demand-side
explanation for governance mobility.
Two of the most important ways corporate governance travels across countries are international
mergers and acquisitions (M&As) and foreign ownership. In the context of cross-border M&As, Ellis et al.
(2017) propose that governance moves under an agency theoretical framework (Jensen and Meckling, 1976).
Part of the synergy value created in cross-border M&As in which acquirers are from countries withrelatively strong shareholder protections is due to improved post-merger governance over the target’s assets(Martynova and Renneborg, 2008).
Governance can also travel internationally through foreign ownership. Specifically, foreign
investors who are active monitors change governance mechanisms and standards across borders (Gillian
and Starks, 2003; Cumming et al., 2017). For example, Ferreira and Matos (2008) and Aggarwal et al.
(2011) document that foreign investors from countries with strong shareholder protections significantly
improve the corporate governance structures in target firms. Among Japanese firms, Aguilera et al. (2017)
show that foreign investors’ institutional backgrounds affect managerial discretion and optimism inearnings forecasts and subsequent revisions. Moreover, a related strand of banking literature documentsthat foreign owners improve bank efficiency in China, consistent with banks importing better governancesystems from abroad (see, e.g., Berger et al., 2009).
However, not all firms can effectively learn and copy governance practices from their foreigncounterparts, because such learning often requires acquiring new and refining existing managerial andmonitoring skills, investing in new technologies, and interacting with people with the right expertise(Cumming et al., 2017). In the process of importing governance, the degree of governance mobility likely
depends on differences in regulatory and legal environments between the host and home countries.
Moreover, because monitoring is more costly for foreign owners that are farther away from the host9

countries, geographical proximity may determine the extent of governance mobility (Degryse and Ongena,
2005; Mian, 2006; Kang and Kim, 2008).

2.5

Hypotheses Development

Our previous discussions suggest that if governance travels across countries via foreign ownership, thenbanking globalization could improve bank informational transparency. Our first hypothesis is as follows:
Hypothesis 1: Foreign bank ownership improves the quality of bank information
environments.
The governance-mobility explanation yields several additional testable predictions. First, if foreign
owners export and promote improved corporate governance abroad, we expect a larger improvement in
information environments for foreign-owned banks originating from economies with better governance orregulatory environments. Second, among foreign-owned banks, we expect higher-quality information
environments when governance quality or the regulatory environment is relatively strong in the homecountries; that is, the relative differences between the home and host countries are larger. Third, becausemonitoring incurs significant transportation and communication costs (Degryse and Ongena, 2005; Mian,
2006; Kang and Kim, 2008), learning governance practices from abroad may be less effective the farther
the home and host countries are from each other physically. Based on these arguments, our second set of
hypotheses are as follows:
Hypothesis 2a: The positive association between foreign bank ownership and the qualityof the bank information environment is more pronounced when foreign owners come fromcountries with stronger corporate governance and banking regulations.
Hypothesis 2b: Among foreign-owned banks, the quality of the bank information
environment increases with the relative difference in corporate governance or in banking
regulatory environment between the home and host countries.
Hypothesis 2c: Among foreign-owned banks, the positive association between the relative
distance in corporate governance or banking regulatory environment (between the homeand host countries), and the quality of bank information environment decreases with thephysical distance between the home and host countries.

3
3.1

SAMPLE, VARIABLE MEASUREMENTS, AND DESCRIPTIVE STATISTICS

SampleOur initial sample begins with all banks that have an International Securities Identification Number (ISIN)
from the Bankscope database from 1996 to 2012. We collect bank information such as headquarters location,10

total assets, capital, earnings, bad loans, etc., from Bankscope; we collect information about foreign bankownership from Claessens and van Horen (2014).5 We download bank security information, including stock
prices, return indexes, shares outstanding, and exchange listings from Worldscope/DataStream (DS). We
download the daily return series of the DS-calculated local market indexes, bank indexes, and a world
market index to proxy for the local market, the local industry, and the world market risk factors, respectively.
All variables are denominated in U.S. dollars.6 We exclude banks whose primary stock exchanges aredifferent from their host countries. Our final sample consists of 710 banks (7,550 bank-year observations)from 60 countries between 1997 and 2012.7 In all, 137 (19.3%) of these banks are foreign-owned. We
collect several macroeconomic indicators from the World Bank, and we collect country-level governanceindexes from Djankov et al. (2008) and Kaufmann et al. (2006). The location information is fromwww.mapsofworld.com and the distances are from the CEPII database.8

3.2

Variable Measurements
3.2.1

Bank-specific return variation. We construct a proxy for the bank information environment― bank-specific return variation. French and Roll (1986) and Roll (1988) propose that a significant portionof stock return variation is unexplained by market movements and that a firm-specific component of return
variation captures the amount of private information embedded in stock prices. Many accounting andfinance studies verify it as a measure of information flow in prices (see, e.g., Morck et al., 2000; Jin andMyers, 2006; Hutton et al., 2009; Gul et al., 2011). They broadly agree that when information environments
improve, stock prices incorporate more (fewer) variation in firm-specific (market) factors. Following thisstrand of literature, we estimate bank-specific return variation, or price synchronicity, as the estimated R2from an expanded market model:
rit = β0i + β1i ritW + β2i rit-1L + β3i ritL + β4i rit+1L + β5i rit-1I + β6i ritI + β7i rit+1I + εit ,(1)

where i is a bank and t is a week. The variable rit represents weekly Wednesday-to-Wednesday returns ofbank stock i in week t.9 The ritW variable is the weekly return of a DS-calculated world market equity index,
ritL is the weekly return of a DS-calculated local market equity index, and ritI is the weekly return of a DScalculated local bank equity index. We allow for nonsynchronous trading by including lead and lag terms
for the local market and bank indexes (Dimson, 1979). We estimate equation (1) for each bank year andrequire a minimum of 25 weeks of available observations for the estimation.
The estimated R2 from equation (1) measures the proportion of return variation explained by theworld market, local market, and local bank equity indexes; it is thus a proxy for price synchronicity. Because
R2 is bounded between 0 and 1, such boundedness may complicate the empirical estimation. Following the11extant literature, we apply a logistic transformation to the estimated R2 to obtain our price synchronicity
measure, SYNCH, as shown in equation (2):SYNCHit = log [ R2it / ( 1 – R2it )]
3.2.2

(2)

Foreign bank ownership. Our foreign bank ownership data is from Claessens and van

Horen (2014), who manually collect the data from various sources, including bank websites, annual reports,
web resources by banking regulators and central banks, corporate governance reports, local stock exchanges,
SEC filings, newspaper articles, and country experts.10 Because tracing all changes in ownership throughtime is impossible due to the large sample of banks, the authors follow standard practice in the literatureand identify a bank as foreign-owned if foreign owners hold 50% or more of the shares in a given year, andas domestic otherwise. Following their definition, we construct a foreign bank indicator variable that equals
1 when foreign owners hold 50% or more of a bank’s shares, and 0 otherwise.

3.2.3

Control variables. We consider various bank characteristics that may affect pricesynchronicity, including bank size, profitability, income from nontraditional banking activities, deposit
funding, capital ratios, and credit quality of banks’ loan portfolios. The detailed definitions are in appendix
A.1. Finally, cross-listing could reduce price informativeness in emerging markets because the associatedincrease in analyst coverage encourages the production of marketwide information (Fernandes and Ferreira,2008). We construct a dummy that equals 1 for banks with cross-listing in a given year, and zero otherwise.
We also control for several country characteristics in our analyses to account for differences in
economic conditions and the degree of financial development across countries. Less developed or
malfunctioning stock markets likely have thinly traded stocks and less informative stock prices. Following
Bekaert and Harvey (1997), we measure stock market development as the ratio of market capitalization to
GDP (Stock market cap/GDP). To account for differences in trading intensity across stock markets, we
include annualized turnover ratios of local stocks (Stock market turnover ratio). To account for differencesin economic growth, we include annual rates of change in real GDP (GDP growth) and consumer priceindex (CPI) in our tests. Moreover, following Stulz and Williamson (2003), we use the ratio of total private
credit to GDP as a proxy for financial development (Financial development). Finally, countries’ market
size is captured by log total population (Ln(Population)). All country controls are from the World Bank
Development Indicators database.

12

3.2.4

Descriptive statistics. Table 1 presents descriptive statistics. Panel A reports sample

distribution by year. Covering 1997-2012, our sample contains 472 banks per year on average. The statistics
show that SYNCH increases during crisis periods (in 1998 and 2008-2009), consistent with possible
contagion. The proportion of foreign-owned banks increases over time from 6.1% in 1997 to 19.3% in 2012.
[Insert Table 1 about here]
Panel B reports sample distribution by country. The top five countries in terms of coverage are
Japan (12.4%), Denmark (7.5%), India (4.1%), Italy (3.9%), and France (3.6%). Among the 10 countries
with the most (least) informative bank stock prices, three (six) are member states of the European Union;
the United States is ranked 39th in ascending order. The five countries with the largest foreign ownership
are Luxembourg (100%), Slovakia (97.1%), Czech Republic (88.1%), Poland (74.4%), and Hong Kong
(61.3%). These statistics closely resemble those in Claessens and van Horen (2014). The detailed sample
distribution by country and year is in Table A-2.1 of the appendix A-2.
Panel A of table 2 reports summary statistics. Bank size is heavily skewed, and mean (median)
total assets is $45.6 ($8.1) billion. The mean (median) R2 and SYNCH are 48.2% (46.5%) and -0.06 (-0.14),
respectively. Foreign-owned banks constitute 14.7% of our observations. The other statistics resemble thosereported by Houston et al. (2010).
Panel B presents univariate comparisons between foreign-owned and non-foreign-owned banks.
The analysis shows that R2 and SYNCH are significantly lower among foreign-owned banks than nonforeign-owned banks in both mean and median tests, showing preliminary support for hypothesis 1. Besides,
foreign-owned banks are more profitable, more reliant on deposit funding, smaller, have higher noninterest
income, and have larger capital buffers. The host countries of foreign-owned banks have higher inflation,
more GDP growth, less stock market turnover, smaller populations, and lower financial development thanthose of non-foreign-owned banks.
[Insert Table 2 about here]

4

MULTIVARIATE ANALYSISThis section reports results on the association between foreign ownership and bank stock priceinformativeness based on a panel multivariate regression framework, written as follows:
SYNCHijt = β0 + β1 Foreignijt-1 + δ Xijt-1 + Country FE + Year FE + εijt ,(3)

where i is a bank, j is a country, and t is a year. SYNCHijt is the estimated stock price synchronicity variable
as described in section 3.2.1; Foreignijt-1 is an indicator variable that equals 1 when foreign ownership is
50% or more, and 0 otherwise; Xijt-1 is a vector of lagged bank control variables as defined in section 3.2.3.
We include country and year fixed effects in the baseline specification to control for time-invariant,13

unobserved country characteristics and marketwide shocks. In alternative specifications, we include thecountry control variables defined in section 3.2.3 in the regressions. All explanatory variables lag by one
year and thus are determined prior to year t to alleviate potential endogeneity concerns. Standard errors are
clustered at the bank level to account for serial correlations. To reduce the effects of outliers, we winsorize
the variables at the 1st and 99th percentiles.

4.1

The Relationship between Foreign Bank Ownership and Bank Stock Price InformativenessPanel A of table 3 reports the results of regressing price synchronicity on foreign bank ownership. In
addition to Foreign, model (1) includes country and year fixed effects; model (2) includes bank- andcountry-level controls and year fixed effects. Models (3) to (5) apply alternative fixed effects.
The results from model (1) suggest a negative and significant (at the 1% level) relationship between
Foreign and SYNCH, supporting hypothesis 1. Model (2) controls for bank and country characteristics andshows that the significantly negative association between Foreign and SYNCH remains robust. Model (3)replaces the country characteristics with country fixed effects and shows that the negative coefficient for
Foreign remains significant. Our results are economically significant. Using the estimates from model (3)and holding all other variables at mean values, a move of Foreign from 0 to 1 decreases implied R2 by
17.3% (from 0.497 to 0.412).11
To alleviate the concern that time-variant, unobservable country factors codetermine foreign
ownership and price synchronicity, model (4) uses country-year interacted fixed effects to absorb timevarying country characteristics, confirming that our results are robust. Model (5) introduces bank fixed
effects to absorb time-invariant bank characteristics. Note that bank fixed-effects regression only analyzes
within-bank changes in foreign ownership and price synchronicity. As model (5) shows, the negative
coefficient for Foreign remains significant at the 10% level, revealing a within-bank increase in priceinformativeness after a change in major ownership.
As for the control variables, larger banks have more synchronized prices, consistent with Gul et al.
(2011). Cross-listings are positively associated with price synchronicity, consistent with Fernandes andFerreira (2008). Bank profitability, deposit funding, and capital ratios also positively correlate with pricesynchronicity. Overall, our results support hypothesis 1.
[Insert Table 3 about here]

4.2

Robustness Tests

Panel B of table 3 presents several robustness tests. All models include the baseline controls and country
and year fixed effects, if not stated otherwise. We only report the estimates for Foreign for brevity.

14

In row (1), we replace SYNCH, which is log-transformed, with the estimated R-squared. Our results
are similar in magnitude (a foreign bank has 13.7% higher R-squared) and significance. In rows (2) and (3),
we apply alternative estimation techniques, including the between-effects estimators and Fama-MacBeth(1973) cross-sectional regressions with Newey and West (1987) correction (with no year fixed effects).Our
results hold. Row (4) tests whether the over-representation of certain countries drives our results. We
estimate a weighted-least-squares (WLS) regression and calculate weights as the inverse of the number ofobservations in each country; each country receives equal weight in the estimation. As shown, our results
are similar and significant.
In row (5), we estimate a median regression that is less influenced by potential outliers than an OLS
regression, confirming that our results are intact. Row (6) examines whether the global financial crisis
drives our results by restricting our sample to end in 2006. Our results are not much affected. Rows (7) and(8) apply alternative standard errors double-clustered by bank and year, and by country and year. Our results
remain robust.
Prior studies document that the influence of foreign banks on local credit growth and othereconomic outcomes may vary with the market and economic development of the host countries. For
instance, Allen et al. (2017) find that in Kenya foreign banks play a less positive role in access to financing
than local and government banks do, indicative of foreign banks’ strategies in targeting clients with higher
incomes. Detragiache et al. (2008) show that foreign bank concentration in emerging markets is associatedwith lower credit and welfare due to their lower ability to monitor soft information. In light of the potential
dark sides of foreign bank ownership, rows (9) to (13) examine whether the level of economic and financial
development of the host countries may affect the coefficient of interest.
We consider five indicators of economic and financial development for the host countries. The firstis an indicator that equals 1 for advanced economies and 0 for emerging countries following the IMF’sclassification. The second, Financial development, is the ratio of total private credit to GDP. The third is
Stock market cap/GDP, which is a proxy of the degree of stock market development. The fourth is GDP
growth, which captures the rate of economic growth. The fifth is a legal origin indicator that equals 1 forcivil law countries and 0 for common law countries.12 We interact each economic indicator with Foreignand find that the interaction terms between Foreign and the host country variables are all insignificant.
Importantly, Foreign remains negatively and significantly associated with price synchronicity. The
unabridged version of these results is in Table A-2.3 of the appendix A-2.

4.3

Endogeneity Tests

If unobserved bank or country characteristics determine foreign bank ownership and price informativeness,
our estimations would be biased (omitted-variable bias). Reverse causality or selection issues might also15

drive our results if foreign investors prefer to invest in firms in countries with better governance ordisclosure quality (Leuz et al., 2009). We employ an instrumental variable approach and dynamic panel
system GMM estimation to address these concerns.13,14
First, we employ the instrumental variables approach and estimate a two-stage least squares (2SLS)
regression to isolate the effect of foreign bank ownership on price synchronicity. 15 Our first instrument is
log absolute geographical distance from the equator (Ln(Latitude)). The endowment theory (Acemoglu etal., 2001) posits that during the colonial period, Europeans adopted different colonization strategies
depending on the feasibility of settlement. In colonies where mortality rates were low, Europeans endowed
more in developing institutions to support private property. These institutions endured after independence
and shaped subsequent developments, such as property rights and financial development. 16 We thus
measure initial endowments by their absolute distance from the equator, as climates and diseaseenvironments are less hospitable nearer to the equator (Easterly and Levine, 2003). If foreign investors
prefer countries with better institutions (Leuz et al., 2009), we thus expect greater foreign ownership incountries farther from the equator.17 Latitudes are from www.mapsofworld.com.
Our second instrument is the log of 1 plus the distance between the bank’s home city and the hostcountry’s capital city (Ln(1+Distance from bank city to capital city)). The economic geography literature
posits that firms have incentives to place businesses near large markets because of increasing returns toscale, lower transportation costs, and greater consumer preferences for variety (Krugman, 1991; Redding,2013; Detragiache et al., 2008). Capital cities, which are often heavily populated, usually represent one of
the largest markets within a country. Driven by such market-size-related benefits, we expect greater foreignbank presence near capital cities and thus a negative coefficient for Ln(1+Distance from bank city to capitalcity) in the first-stage regression. The location information is from the CEPII database.
[Insert Table 4 about here]
Model (1) of table 4 reports the first-stage results of regressing Foreign on the two instruments,bank and country controls, and year fixed effects. The coefficients for both instruments are significant andhave the expected signs. The F-statistics of excluded instruments in the first stage show that the instruments
are jointly different from zero at the 1% level. The Cragg-Donald F-statistic is significant at the 1% level,
rejecting the null hypothesis of weak instruments (Cragg and Donald, 1993). Further, the Hansen’s J
overidentification test has a p-value of 0.258, increasing the likelihood that the two instruments are valid
or uncorrelated with the error term (Hansen, 1982). Model (2) reports the second-stage results using theexogenous component of Foreign to explain SYNCH. The coefficient for the fitted Foreign remains
negative and significant at the 5% level and is larger in magnitude than our baseline tests.
Another potential concern is that the relation between price informativeness and foreign bankownership may be dynamically endogenous. If past realizations of the dependent variable affect current
16levels of some or all independent variables, both OLS and fixed effects regressions that do not take into
account such dynamic relations would be biased (Wintoki et al., 2012). We estimate a two-step dynamic
panel system GMM estimator (Arellano and Bover, 1995; Blundell and Bond, 1998) that controls for lagged
SYNCH in the regressions and exploits past bank information as instruments. Following Wintoki et al.
(2012), we include the first- and second-period lagged dependent variables in the regressions.18 Model (3)of table 4 presents the estimation results, showing that the coefficient for Foreign remains significantly
negative (at the 5% level). To check instrument validity, we test for the absence of second-order serial
correlation in the residuals, finding that the test is passed. The Hansen test of overidentifying restrictions
fails to reject the null hypothesis of instrument validity.

4.4

Foreign Bank Ownership and Earning Innovations

Although the R-squared-based stock return synchronicity measure is a common proxy for information flow,
recent studies question its validity and reliability.19 To address this concern, we examine the extent to whichinvestors incorporate future earnings information into stock prices, and we test its association with foreignowned banks. If stock prices contain future earnings information (Collins et al., 1994; Durnev et al., 2004)
and stock prices of foreign-owned banks have greater earnings informativeness, the positive association
between future earnings innovations and current stock returns would be stronger among foreign-ownedbanks.
The accounting literature shows that stock returns follow this return-earning relation:
Rt = β0 + β1 UEt + ∑3𝑘=1 𝛽𝑘+1 ∆𝔼t(Et+k ) + et ,(4)

where Rt is annual stock returns in year t. UEt is Et - 𝔼t-1(Et ), the unexpected earnings in year t. ∆𝔼t(Et+k) is
the revision of market expectations about future earnings in year t+k from the beginning to the end of yeart (𝔼t-1(Et+k) - 𝔼t(Et+k)). Following Collins et al. (1994) and Lundholm and Myers (2002), we choose k=3
because investors’ revisions of earnings growth rates tend to be over short horizons and returns have limited
ability to anticipate earnings more than three years in advance (Kothari and Sloan, 1992).
Following Collins et al. (1994), we proxy for UEt using the annual growth rate in bank net income
in year t (∆Et). The term ∆𝔼t(Et+k) is proxied by realized future growth rates in bank net income. Because
market expectations are unobservable, we must control for several sources of measurement errors. We
control for two — 𝔼t-1(Et) and 𝔼t-1(Et+k) — using the earnings-to-price ratio (E/MVt-1) in year t-1. Anothersource of measurement error is the future earnings growth in year t+k that is unanticipated in year t (UEt+k,defined as Et+k – 𝔼t(Et+k)), which we control for via the realized buy-and-hold future returns for year t+1 to
t+3 following Lundholm and Myers (2002). As such, we formulate the following regression model andinteract these terms with our foreign ownership indicator variable (bank subscript i and country subscript j
are both suppressed for brevity):17

Rt = β0 + β1 E/MVt-1 + β2 ∆Et + ∑3𝑘=1 𝛽2+𝑘 ∆𝐸𝑡+𝑘 + β6 R(t+1 to t+3) + β7 Foreign+ (β8 E/MVt-1 + β9 ∆Et + ∑3𝑘=1 𝛽9+𝑘 ∆𝐸𝑡+𝑘 + β13 R(t+1 to t+3)) × Foreign+ δ1 Xt-1+ (δ2 E/MVt-1 + δ3 ∆Et + ∑3𝑘=1 𝛿3+𝑘 ∆𝐸𝑡+𝑘+ δ7 R(t+1 to t+3)) × Xt-1 + Country FE + εit ,(5)where Rt is the buy-and-hold annual bank stock returns in year t. E/MVt-1 is net income in year t-1, scaledby market capitalization at the end of year t-1; we expect it to have a positive coefficient. ∆Et is the annualchange in net income from year t-1 to t, scaled by market capitalization at the end of year t-1. R(t+1 to t+3) isthe buy-and-hold stock return from the beginning of year t+1 to the end of year t+3, and we expect it to be
negatively related to stock returns. Xt-1 is a vector of baseline bank controls; εit is the residual. Country fixed
effects are included to account for time-invariant country differences in stock returns. We control for theinteraction between our earnings and future returns variables with the bank controls in the regressions, but
we only report the estimates for the variables of our interest for brevity.
[Insert Table 5 about here]
Table 5 reports the estimation for equation (5). As model (1) shows, the coefficient for Foreign ×
ΔEt+2 is positive and significant at the 5% level, consistent with hypothesis 1, suggesting that stock prices
of foreign-owned banks incorporate more information about earnings growth two years in advance. The
estimate for E/MVt-1 is significantly positive as expected, but it is significantly smaller for foreign-ownedbanks (at the 10% level). The estimate for future returns (R(t+1 to t+3)) is significantly positive, which issurprising, suggesting that the sources of measurement errors may be different among global banks.
Model (2) presents an alternative model specification that aggregates the three future earnings
growth variables: ∆Ei(t+1 to t+3) is the sum of annual changes in net income over years t+1 to t+3, scaled bymarket capitalization at the end of year t-1, similarly interacted with Foreign and other bank controls in the
regression. Our results show that the estimate for Foreign × ΔE(t+1 to t+3) is significantly positive (at the 5%
level), again consistent with stock prices of foreign-owned banks having greater informativeness.
For robustness, we estimate an alternative cross-sectional earnings model following Bai et al.
(2016). We again find that stock prices of foreign-owned banks reflect more earnings information two years
in advance compared to non-foreign-owned banks. These unreported results are in Table A-2.9 of theappendix A-2.

18

4.5

Foreign Bank Ownership, Corporate Governance Mobility, and Price Informativeness
4.5.1

Do the home country’s governance and banking regulatory environment matter? In thissection, we test whether the negative association between foreign bank ownership and price synchronicity
varies with home countries’ governance and bank regulatory environment (hypothesis 2a).
We use three country governance indexes. The first is the revised anti-director-rights index; thesecond is the anti-self-dealing index, both from Djankov et al. (2008). The anti-director-rights index
measures how well a country protects minority shareholders based on six legal rights granted to them. The
anti-self-dealing index measures how well a country protects shareholders against corporate insiders’ selfdealing. The third is the World Governance Index (WGI), measured as the average of six governanceindicators capturing government effectiveness, political stability, regulatory quality, rule of law, voice andaccountability, and control of corruption, respectively, from Kaufmann et al. (2006).
We focus on three aspects of bank regulations relating to the three pillars of the Basel II Accord,
namely, minimum capital requirement (Pillar One), supervisory review process (Pillar Two), and market
discipline (Pillar Three). In particular, we collect three country-level variables from the four worldwide
surveys on bank regulations of Barth et al. (2004, 2006, 2008, and 2013a), namely, Capital regulatory index,
Official supervisory power, and Private monitoring index to proxy for Pillar One, Pillar Two, and Pillar
Three of the Basel II Accord, respectively. 20 Capital regulatory index is an index of overall capitalstringency and is the sum of the answers to several survey questions relating to the extent of regulatory
requirements in bank capital and to the sources of funds that count as regulatory capital. Official supervisory
power is a measure of the power of supervisory agencies; we measure it using answers to 14 questions on
the extent to which supervisory authorities have the authority to take action in preventing and correcting
problems. Private monitoring index is a measure of the extent of bank disclosure requirements andincentives to increase private monitoring. We construct it using answers to several survey questions, suchas “Is an external audit compulsory for banks?” and “Are off-balance sheet items disclosed to the public?”
For each of the three governance indexes and three banking regulatory variables, we divide Foreign
into two indicator variables based on its respective sample median. For instance, Foreign (High antidirector-rights index) equals 1 when a foreign bank’s home country has an above-median anti-directorrights index; it equals 0 otherwise. We estimate the baseline models using the split Foreign variables andreport the estimation in table 6.
[Insert Table 6 about here]
As shown in models (1) to (3), the negative association of foreign bank ownership with pricesynchronicity is only significant when home countries have above-median values in the anti-director-rights
index, anti-self-dealing index, and WGIs, supporting hypothesis 2a. The Wald tests show that thecoefficients for Foreign are significantly different between the high- and low-governance groups for the19

anti-director-rights index and the anti-self-dealing index. These results suggest that the increased priceinformativeness due to foreign ownership likely stems from home countries’ governance, consistent withforeign-owned banks exporting governance overseas.
As for the banking regulatory variables, the results from models (4) to (6) exhibit a similar pattern,
despite being less significant: the negative association between Foreign and price synchronicity is more
pronounced when home countries have above-median values in the three banking regulation variables (suchnegative association is only significant when home countries have better supervisory power). Nonetheless,the results from the Wald tests show that the coefficients for Foreign between the high and low regulatory
variables are not significantly different. These findings suggest that foreign-owned banks from homecountries with stronger supervision likely have better practices and thus better information environments.

4.5.2

Does relative distance in governance and regulation between home and host countries

matter? In this section, we test hypotheses 2b and 2c to determine whether relative differences ingovernance and banking regulatory environments between the home and host countries determine priceinformativeness, and whether such relation varies with geographical distance.
For each foreign bank in our sample, we subtract each of the three governance indexes and the threebanking regulation variables of the host country from that of the home country, and we examine therelationship between the relative differences with price synchronicity. To illustrate, a positive value of Antidirector-rights index (Home-Host) indicates that the home country has better shareholder protections thanthe host country. Geographical distance is the distance (in miles) between a foreign bank’s city (in the hostcountry) and its home country’s capital city.21, 22[Insert Table 7 about here]
Table 7 reports these results. Models (1) to (3) show that the differences in anti-self-dealing index
and WGI enter negatively and significantly (at the 10% level or better). Models (4) to (6) show that, of thethree bank regulation variables, only the differences in Official supervisory power are negatively andsignificantly (at the 10% level) associated with price synchronicity. These findings support hypothesis 2b.
In models (7) to (9), we interact the governance distance with log geographical distance to explain
price synchronicity; in model (10), we interact the three banking regulatory variables with log geographicaldistance. In line with our predictions, differences in the anti-self-dealing index and WGI are negatively andsignificantly associated with SYNCH (at the 5% level or better) (see models 8 and 9). For both the anti-selfdealing index and the WGI, their interaction terms with geographical distance are positive and significant
at the 10% level or better. In model (10), we find that the relative distance in Official supervisory power isnegatively associated with price synchronicity, and such negative relation strengthens when geographicaldistance decreases. These results are consistent with hypothesis 2c.
20

Specifically, for geographical distances (between the home and host countries) in the 25th (345.5
miles) and 75th (4464.7 miles) percentiles, an increase in the home-minus-host difference in the anti-selfdealing index [WGI] {Official supervisory power} from the 25th to the 75th percentiles reduces stock pricesynchronicity by -15.9% [-33.3%] {-13.3%} and -4.0% [-2.1%] {+12.2%}, respectively, holding othervariables at mean values. Overall, these results support both hypotheses 2b and 2c.

4.6

Other Considerations

Our findings thus far suggest that foreign bank ownership plays an important role in improving banks’
information environments. To shed more light on the economic mechanism behind these findings, we
examine the disclosure quality of foreign-owned banks, and specifically, we focus on their loan loss
provisions ― a major device banks use to manage earnings and regulatory capital. We follow Beatty andLiao (2014) and Jiang et al. (2016) and estimate a bank’s “abnormal” loan loss provisions, or discretionary
loan loss provisions, that cannot be explained by bank fundamentals. Consistent with improved information
environments, our tests (untabulated) show that foreign-owned banks have significantly lower discretionary
loan loss provisions. Our results are robust to excluding positive discretionary loan loss provisions (which
reduce earnings) that may represent accounting discretion to enhance transparency; they are also robust to
using an alternative extended sample consisting of 2,639 banks from 134 countries. Moreover, suchnegative association is more pronounced when home countries have lower governance quality. For
conciseness, these results are unreported but are in Table A-2.10 of the appendix A-2.
An issue we have not explored thus far in our study is the possibility that governance mechanisms
substitute or complement one another. Prior studies analyze the interaction between internal and external
governance mechanisms, such as between directors and the takeover market (Hirshleifer and Thakor, 1994),
between executive compensation and the takeover market (Agrawal and Knoeber, 1998; John et al., 2000),
between the legal environment and board monitoring (e.g., Marcel and Cowen, 2014), between ownership
structure and board composition (Hermalin and Weisbach, 1991), and between external audits and board
monitoring (Desender et al., 2016) (see John and Senbet, 1998; and Aguilera et al., 2015).
We first shed light on the potential interaction between foreign bank ownership and an external
governance mechanism: the banking regulatory environment. Specifically, we collect from the worldwide
surveys of Barth et al. (2004, 2006, 2008, and 2013a) the three variables for Basel II’s three pillars and an
external governance index, which aggregates survey answers relating to the strength of external audit, banks’financial disclosure requirements, accounting practices, and external ratings and creditor monitoring. To
the extent that the banking regulatory environment and monitoring by foreign owners are partial
complements, our results may be more pronounced for host countries that have stronger regulations in bank21

supervision and disclosure. However, our results (untabulated) show little evidence of interaction between
Foreign and these external governance variables in explaining SYNCH.
Furthermore, we test whether foreign ownership and board governance are complements or
substitutes. Because firm-level board data is unavailable for our sample of global banks, we collect thetiming of country-specific board-related major reforms for 40 of our sample countries from Fauver et al.
(2017). We examine whether the effect of such reforms in relation to (1) board independence and (2) audit
committee and audit independence on price synchronicity depends on foreign ownership. We find that thenegative association between foreign bank ownership and price synchronicity significantly (at the 10%
level) strengthens after major reforms in board independence, but not with audit committee reforms,
consistent with a complementary relation. To keep our paper concise, these results are in Table A-2.11 of
the appendix A-2. Nonetheless, because the board reform variables are at the country level, our results are
subject to measurement errors and are merely suggestive.

5

CONCLUSION

Under both the agency and institutional theoretical perspectives, recent IB research posits that corporate
governance travels across countries through, for example, foreign ownership, international M&As, etc. (see,e.g., Cumming et al., 2017; Ellis et al., 2017). This study tests this emerging theory of governance mobility
in the context of banking and, more specifically, investigates whether foreign ownership increases banktransparency via governance spillover from home countries. Because bank opacity is likely a major causeof banking crises (Bushman, 2014, 2016), a better understanding of what determines bank opacity in thecontext of globalization helps regulators and IB scholars stabilize financial markets and international trade.
Based on a large sample of global banks from 60 countries over 1997-2012, we find that foreign
ownership is associated significantly with higher (lower) price informativeness (synchronicity) among bankstock prices. Further tests show that stock prices of foreign-owned banks reflect more information about
future earnings growth than those of non-foreign-owned banks. The positive association between foreign
ownership and price informativeness is more pronounced for foreign-owned banks from home countries
with relatively strong governance and banking supervision, as well as when monitoring costs are low. Ourevidence is consistent with foreign ownership increasing price informativeness through improved corporate
governance.
Our findings contribute to the IB literature by uncovering a positive effect of globalization on bankinformation environments. Consistent with the emerging theory of international governance mobility, ourstudy shows that foreign bank ownership is an effective channel through which governance travels acrossborders. Our results have direct implications for regulators, as more informative bank stock prices due toforeign ownership could help increase market discipline and thus the efficiency of supervision. Enhanced
22

bank transparency also helps international investors better discriminate between banks, improving theefficiency of capital allocation. For policymakers, fostering legislation that supports the importation orexportation of good governance may help stabilize markets and global trade.
We acknowledge a few limitations of our study that may offer possible directions for future
research. First, firm-level data on corporate governance, such as governance provisions and board
information, are unavailable for our sample of global banks. Although our results are in line with the theoryof international governance mobility, we are unable to offer direct evidence of precisely what components
of bank governance improve when foreign ownership increases and whether foreign ownership substitutes
or complements other governance mechanisms. Future research efforts in compiling and analyzing moregranular bank data are warranted.
Second, although our results are robust to alternative empirical approaches and tests, our main
measure of stock price informativeness ― firm-specific return variation ― is nonetheless noisy in capturing
information flows. This concern about measurement errors could be more severe for developing economies
whose stock markets are undeveloped, inefficient, or malfunctioning, and where the role of stock prices insignaling and directing market activities is ineffective. As such, our findings have less to say about whether
the effects of foreign ownership on bank transparency diverge across countries. Finally, because we exclude
countries that ban foreign ownership in banks (e.g., Ethiopia), our findings may not generalize to suchcountries.

23

References
Acemoglu, D., Johnson, S. & Robinson, J.A., 2001. The colonial origins of comparative development: An
empirical investigation. American Economic Review, 91(5), 1369–1401.
Acharya, V.V., Mehran, H. & Thakor, A.V., 2016. Caught between Scylla and Charybdis? Regulating bankleverage when there is rent seeking and risk shifting. The Review of Corporate Finance Studies, 5(1), 36–
75.
Aggarwal, R., Erel, I., Ferreira, M. & Matos, P., 2011. Does governance travel around the world? Evidence
from institutional investors. Journal of Financial Economics, 100(1), 154–181.
Agrawal, A. & Knoeber, C. R., 1998. Managerial compensation and the threat of takeover. Journal of
Financial Economics, 47(2), 219–239.
Aguilera, R.V., Desender, K.A., Lamy, M.L.P. & Lee, J.H., 2017. The governance impact of a changing
investor landscape. Journal of International Business Studies, 48(2), 195–221.
Ajinkya, B., Bhojraj, S. & Sengupta, P., 2005. The association between outside directors, institutional
investors and the properties of management earnings forecasts. Journal of Accounting Research, 43(3),343–376.
Allen, F., Carletti, E., Cull, R., Qian, J., Senbet, L. & Valenzuela, P., 2017. Improving access to banking:
Evidence from Kenya. World Bank Group, policy research working paper.
Allen, F., Otchere, I., Senbet, L. W., 2011. African financial systems: A review. Review of Development
Finance, 1(2), 79–113.
Alok, S., Cortes, F. & Gopalan, R., 2016. Stock price informativeness and executive compensation,
available at SSRN: https://ssrn.com/abstract=2659167
Amiti, M. & Weinstein, D.E., 2011. Exports and financial shocks. The Quarterly Journal of Economics,
126(4), 1841–1877.
Arellano, M. & Bover, O., 1995. Another look at the instrumental variable estimation of error-components
models. Journal of Econometrics, 68(1), 29–51.
Armstrong, C.S., Balakrishnan, K. & Cohen, D., 2012. Corporate governance and the information
environment: Evidence from state antitakeover laws. Journal of Accounting and Economics, 53(1–2), 185–
204.
Armstrong, C.S., Core, J.E. & Guay, W.R., 2014. Do independent directors cause improvements in firmtransparency? Journal of Financial Economics, 113(3), 383–403.
Bai, J., Philippon, T. & Savov, A., 2016. Have financial markets become more informative? Journal of
Financial Economics, 122(3), 625–654.
Barth, J.R., Caprio, G. & Levine, R., 2004. Bank regulation and supervision: what works best? Journal of
Financial Intermediation, 13(2), 205–248.
Barth, J.R., Caprio, G. & Levine, R., 2006. Rethinking Bank Regulation: Till Angels Govern. Cambridge
University Press, Cambridge, UK.
Barth, J.R., Caprio, G. & Levine, R., 2008. Bank regulations are changing: But for better or worse? World
Bank. Policy Research Working Paper, 4646.

24

Barth, J.R., Caprio, G. & Levine, R., 2013a. Bank regulation and supervision in 180 countries from 1999
to 2011. National Bureau of Economic Research Working Paper 18733.
Barth, J.R., Lin, C., Ma, Y., Seade, J. & Song, F.M., 2013b. Do bank regulation, supervision and monitoringenhance or impede bank efficiency? Journal of Banking & Finance, 37(8), 2879–2892.
Basel Committee on Banking Supervision, 2015 https://www.bis.org/bcbs/publ/d309.pdf
Beasley, M., 1996. An empirical analysis of the relation between the board of directors composition andfinancial statement fraud. The Accounting Review, 71(4), 443–465.
Beatty, A. & Liao, S., 2014. Financial accounting in the banking industry: A review of the empiricalliterature. Journal of Accounting and Economics, 58(2–3), 339–383.
Beck, T. & Brown, M., 2015. Foreign bank ownership and household credit. Journal of Financial
Intermediation, 24(4), 466–486.
Beck, T., Demirgüҫ-Kunt, A. & Levine, R., 2003. Law, endowments, and finance. Journal of Financial
Economics, 70(2), 137–181.
Beck, T., Demirgüç-Kunt, A., Laeven, L. & Maksimovic, V. 2006. The determinants of financing obstacles.
Journal of International Money and Finance, 25(6), 932–952.
Bekaert, G. & Harvey, C.R., 1997. Emerging equity market volatility. Journal of Financial Economics,
43(1), 29–77.
Bena, J., Ferreira, M. A., Matos, P. & Pires, P. 2017. Are foreign investors locusts? The long-term effects
of foreign institutional ownership. Journal of Financial Economics, 126(1), 122–146.
Berger, A.N., Hasan, I. & Zhou, M., 2009. Bank ownership and efficiency in China: What will happen in
the world’s largest nation? Journal of Banking and Finance, 33(1), 113–130.
Berlin, M., Saunders, A. & Udell, G. F. (1991) Deposit insurance reform: What are the issues and whatneeds to be fixed? Journal of Banking and Finance, 15(4–5), 735–752.
Bertrand, M. & Mullainathan, S., 2003. Enjoying the quiet life? Corporate governance and managerial
preferences. Journal of Political Economy, 111(5), 1043–1075.
Bhattacharya, S., Boot, A.W. & Thakor, A.V., 1998. The economics of bank regulation. Journal of Money,
Credit and Banking, 30(4), 745–770.
Blundell, R. & Bond, S., 1998. Initial conditions and moment restrictions in dynamic panel data models.
Journal of Econometrics, 87(1), 115–143.
Bose, N., Murshid, A.P. & Rath, C., 2014. Finance and property rights: Exploring other directions. Journalof Money, Credit and Banking, 46(2-3), 503–517.
Bronzini, R. & D'Ignazio, A., 2017. Bank internationalization and firm exports: Evidence from matchedfirm-bank data. Review of International Economics, 25(3), 476–499.
Bruno, V. & Hauswald, R., 2014. The real effect of foreign banks. Review of Finance, 18(5), 1683–1716.
Bushman, R.M., 2014. Thoughts on financial accounting and the banking industry. Journal of Accounting
and Economics, 58(2-3), 384–395.
Bushman, R.M., 2016. Transparency, accounting discretion, and bank stability. Economic Policy Review,(Aug), 129–149.

25

Bushman, R.M. & Smith, A.J., 2001. Financial accounting information and corporate governance. Journalof Accounting and Economics, 32(1-3), 237–333.
Bushman, R.M. & Williams, C.D., 2012. Accounting discretion, loan loss provisioning, and discipline ofbanks’ risk-taking. Journal of Accounting and Economics, 54(1), 1–18.
Bushman, R.M. & Williams, C.D., 2015. Delayed expected loss recognition and the risk profile of banks.
Journal of Accounting Research, 53(3), 511–553.
Cetorell, N. & Goldberg, L., 2012. Banking globalization and monetary transmission. Journal of Finance,
67(5), 1811–1843.
Claessens, S., Hassib, O. & van Horen, N., 2017. The role of foreign banks in trade. CEPR discussion paper
no. 11821.
Claessens, S. & van Horen, N., 2014. Foreign banks: Trends and impact. Journal of Money, Credit andBanking, 46(s1), 295–326.
Claessens, S. & van Horen, N., 2015. The impact of the global financial crisis on banking globalization.
IMF Economic Review, 63(4), 868–918.
Clarke, G., Cull, R., Peria, M.S.M. & Sánchez, S.M., 2005. Bank lending to small businesses in Latin
America: Does bank origin matter? Journal of Money, Credit and Banking, 37(1), 83–118.
Collins, D.W., Kothari, S.P., Shanken, J. & Sloan, R.G., 1994. Lack of timeliness and noise as explanations
for the low contemporaneous return-earnings association. Journal of Accounting and Economics, 18(3),289–324.
Cragg, J.G. & Donald, S.G. 1993. Testing identifiability and specification in instrumental variable models.
Econometric Theory, 9(2), 222–240.
Cumming, D., Filatotchev, I., Knill, A., Reeb, D.M. & Senbet, L., 2017. Law, finance, and the international
mobility of corporate governance. Journal of International Business Studies, 48(2), 123–147.
Dages, B.G., Goldberg, L. & Kinney, D., 2000. Foreign and domestic bank participation in emerging
markets: Lessons from Mexico and Argentina. Economic Policy Review, 6, 17–36.
Dasgupta, S., Gan, J. & Gao, N., 2010. Transparency, price informativeness, and stock return synchronicity:
theory and evidence. Journal of Financial and Quantitative Analysis, 45(5), 1189–1220.
Dechow, P.M., Sloan, R.G. & Sweeney, A.P., 1996. Causes and consequences of earnings manipulation:
An analysis of firms subject to enforcement actions by the SEC. Contemporary Accounting Research, 13(1),
1–36.
Degryse, H. & Ongena, S., 2005. Distance, lending relationships, and competition. Journal of Finance,
60(1), 231–266.
Desender, K. A., Aguilera, R. V., Lópezpuertas‐Lamy, M. & Crespi, R. (2016). A clash of governancelogics: Foreign ownership and board monitoring. Strategic Management Journal, 37(2), 349–369.
Detragiache, E., Tressel, T. & Gupta, P., 2008. Foreign banks in poor countries: theory and evidence.
Journal of Finance, 63(5), 2123–2160.
Dimson, E., 1979. Risk measurement when shares are subject to infrequent trading. Journal of Financial
Economics, 7(2), 197–227.

26

Distinguin, I., Rous, P. & Tarazi, A., 2006. Market discipline and the use of stock market data to predictbank financial distress. Journal of Financial Services Research, 30(2), 151–176.
Djankov, S., La Porta, R., López-de-Silanes, F. & Shleifer, A., 2008. The law and economics of self-dealing.
Journal of Financial Economics, 88(3), 430–465.
Durnev, A., Morck, R. & Yeung, B., 2004. Value-enhancing capital budgeting and firm-specific stock
return variation. Journal of Finance, 59(1), 65–105.
Easterly, W. & Levine, R., 2003. Tropics, germs, and crops: How endowments influence economic
development. Journal of Monetary Economics, 50(1), 3–39.
Ellis, J.A., Moeller, S.B., Schlingemann, F.P. & Stulz, R.M., 2017. Portable country governance and crossborder acquisitions. Journal of International Business Studies, 48(2), 148–173.
Enriques, L. & Volpin, P., 2007. Corporate governance reforms in continental Europe. Journal of Economic
Perspectives, 21(1), 117–140.
Fama, E. & MacBeth, J., 1973. Risk, return and equilibrium: Empirical tests. Journal of Political Economy,
81(3), 607–636.
Fauver, L., Hung, M., Li, X. & Taboada, A. G. (2017). Board reforms and firm value: Worldwide
evidence. Journal of Financial Economics, 125(1), 120–142.
Fernandes, N. & Ferreira, M.A., 2008. Does international cross-listing improve the information
environment? Journal of Financial Economics, 88(2), 216–244.
Ferreira, M.A. & Laux, P.A., 2007. Corporate governance, idiosyncratic risk, and information flow. Journalof Finance, 62(2), 951–989.
Ferreira, M.A. & Matos, P., 2008. The colors of investors’ money: The role of institutional investors aroundthe world. Journal of Financial Economics, 88(3), 499–533.
Filatotchev, I. & Wright, M., 2011. Agency perspectives on corporate governance of multinational
enterprises. Journal of Management Studies, 48(2), 471–486.
Flannery, M.J., 1998. Using market information in prudential bank supervision: A review of the U.S.
empirical evidence. Journal of Money, Credit and Banking, 30(3), 273–305.
Flannery, M.J., 2001. The faces of market discipline. Journal of Financial Services Research, 20(2-3), 107–
119.
Francis, B.B., Hasan, I., Song, L. & Yeung, B., 2015. What determines bank-specific variations in bankstock returns? Global evidence. Journal of Financial Intermediation, 24(3), 312–324.
French, K.R. & Roll, R., 1986. Stock return variances: The arrival of information and the reaction of
traders. Journal of Financial Economics, 17(1), 5–26.
Giannetti, M. & Ongena, S., 2009. Financial integration and firm performance: Evidence from foreign bankentry in emerging markets. Review of Finance, 13(2), 181–223.
Giannetti, M. & Ongena, S., 2012. “Lending by example”: Direct and indirect effects of foreign banks inemerging markets. Journal of International Economics, 86(1), 167–180.
Gillan, S., & Starks, L. T. 2003. Corporate governance, corporate ownership, and the role of institutional
investors: A global perspective. Journal of Applied Finance, 13(2), 4–22.

27

Gormley, T., 2010. The impact of foreign bank entry in emerging markets: Evidence from India. Journalof Financial Intermediation, 19(1), 26–51.
Gorton, G. & Huang, L., 2006. Bank panics and the endogeneity of central banking. Journal of Monetary
Economics, 53(7), 1613–1629.
Greenspan, A. 1996. “Remarks.” Financial Markets Conference of the Federal Reserve Bank of Atlanta,
Coral Gables, Florida, February 23.
Greenspan, A., 2001. The financial safety net. Remarks to the 37th annual conference on bank structure
and competition of the Federal Reserve Bank of Chicago, Chicago, IL (May 10).
Gropp, R., Hakenes, H. & Schnabel, I., 2011. Competition, risk-shifting, and public bailout policies. The
Review of Financial Studies, 24(6), 2084–2120.
Gul, F.A., Kim, J.B. & Qiu, A.A., 2010. Ownership concentration, foreign shareholding, audit quality, andstock price synchronicity: Evidence from China. Journal of Financial Economics, 95(3), 425–442.
Gul, F.A., Srinidhi, B. & Ng, A.C., 2011. Does board gender diversity improve the informativeness of stock
prices? Journal of Accounting and Economics, 51(3), 314–338.
Hall, R.E. & Jones, C.I., 1999. Why do some countries produce so much more output per worker thanothers? The Quarterly Journal of Economics, 114(1), 83–116.
Han, S.J., 2015. Foreign investors, stock price informativeness, and multinational corporations (Doctoral
dissertation, The University of North Carolina at Chapel Hill).
Hansen, L.P., 1982. Large sample properties of generalized method of moments estimators. Econometrica,
50(4), 1029–1054.
He, W., Li, D., Shen, J. & Zhang, B., 2013. Large foreign ownership and stock price informativeness aroundthe world. Journal of International Money and Finance, 36, 211–230.
Healy, P.M. & Palepu, K.G., 2001. Information asymmetry, corporate disclosure, and the capital markets:
A review of the empirical disclosure literature. Journal of Accounting and Economics, 31(1-3), 405–440.
Heckman, J.J., 1979. Sample selection bias as a specification error. Econometrica, 47(1), 153–161.
Hermalin, B.E. & Weisbach, M.S., 1991. The effects of board composition and direct incentives on firm
performance. Financial management, 20(4), 101–112.
Hett, F. & Schmidt, A., 2017. Bank rescues and bailout expectations: The erosion of market discipline
during the financial crisis. Journal of Financial Economics, 126(3), 635–651.
Hirshleifer, D. & Thakor, A., 1994. Managerial performance, boards of directors and takeover bidding.
Journal of Corporate Finance, 1(1), 63–90.
Holmström, B. & Tirole, J. (1993). Market liquidity and performance monitoring. Journal of Political
Economy, 101(4), 678–709.
Houston, J.F., Lin, C., Lin, P. & Ma, Y., 2010. Creditor rights, information sharing, and bank risk taking.
Journal of Financial Economics, 96(3), 485–512.
Houston, J.F., Lin, C. & Ma, Y., 2012. Regulatory arbitrage and international bank flows. Journal of
Finance, 67(5), 1845–1895.
Hutton, A.P., Marcus, A.J. & Tehranian, H., 2009. Opaque financial reports, R2, and crash risk. Journal of
Financial Economics, 94(1), 67–86.
28

Hyytinen, A. & Takalo, T., 2002. Enhancing bank transparency: A re-assessment. Review of Finance, 6(3),429–445.
Iacovone, L. & Zavacka, V., 2009. Banking crises and exports: Lessons from the past for the recent trade
collapse. Baldwin, R.E. ed., The Great Trade Collapse: Causes, Consequences and Prospects, CEPR.
Jensen, M.C. & Meckling, W.H., 1976. Theory of the firm: managerial behavior, agency costs, andownership structure. Journal of Financial Economics, 3(4), 305–360.
Jiang, G.J., Xu, D. & Yao, T., 2009. The information content of idiosyncratic volatility. Journal of
Financial and Quantitative Analysis, 44(1), 1–28.
Jiang, L., Levine, R. & Lin. C., 2016. Competition and bank opacity. The Review of Financial Studies,
29(7), 1911–1942.
Jin, L. & Myers, S., 2006. R-squared around the world: New theory and new tests. Journal of Financial
Economics, 79(2), 257–292.
John, K., Saunders, A. & Senbet, L. W. (2000). A theory of bank regulation and managementcompensation. The Review of Financial Studies, 13(1), 95–125.
John, K. & Senbet, L.W., 1998. Corporate governance and board effectiveness. Journal of Banking &Finance, 22(4), 371–403.
Kang, J.K. & Kim, J.M., 2008. The geography of block acquisitions. Journal of Finance, 63(6), 2817–2858.
Kang, Q. & Liu, Q., 2008. Stock trading, information production, and executive incentives. Journal of
Corporate Finance, 14(4), 484–498.
Kaufmann, D., Kraay, A. & Mastruzzi, M., 2006. Governance matters V: Worldwide governance indicators,
1996–2005. World Bank.
Knaup, M. & Wagner, W., 2012. A market-based measure of credit portfolio quality and banks'
performance during the subprime crisis. Management Science, 58(8), 1423–1437.
Kothari, S.P. & Sloan, R.G., 1992. Information in prices about future earnings: Implications for earnings
response coefficients. Journal of Accounting and Economics, 15(2-3), 143–171.
Krugman, P., 1991. Increasing returns and economic geography. Journal of Political Economy, 99(3), 483–
499.
Laeven, L., 2013. Corporate governance: What’s special about banks? Annual Review of Financial
Economics, 5, 63–92.
Laeven, L. & Levine, R. 2009. Bank governance, regulation and risk taking. Journal of financial economics,
93(2), 259–275.
La Porta, R., Lopez-de-Silanes, F., Shleifer, A. & Vishny, R.W., 1998. Law and finance. Journal of
Political Economy, 106(6), 1113–1155.
La Porta, R., Lopez-de-Silanes, F. & Shleifer, A., 2008. The economic consequences of legal
origins. Journal of Economic Literature, 46(2), 285–332.
Lee, D.W. & Liu, M.H., 2011. Does more information in stock price lead to greater or smaller idiosyncratic
return volatility? Journal of Banking and Finance, 35(6), 1563–1580.
Leuz, C., Lins, K. & Warnock, F., 2009. Do foreigners invest less in poorly governed firms? The Review
of Financial Studies, 23(3), 3245–3285.
29

Lundholm, R. & Myers, L.A., 2002. Bringing the future forward: the effect of disclosure on the returnsearnings relation. Journal of Accounting Research, 40(3), 809–839.
Maddala, G. S. 1983. Limited Dependent und Qualitative Variables in Econometrics. New York:
Cambridge University Press.
Marcel, J. J. & Cowen, A. P. (2014). Cleaning house or jumping ship? Understanding board upheaval
following financial fraud. Strategic Management Journal, 35(6), 926–937.
Martynova, M. & Renneboog, L., 2008. Spillover of corporate governance standards in cross-bordermergers and acquisitions. Journal of Corporate Finance, 14(3), 200–223.
Mehran, H. & Morrison, A. D. & Shapiro, J. D., Corporate governance and banks: What have we learned
from the financial crisis? (June 1, 2011). FRB of New York Staff Report No. 502.
Merton, R.C., 1977. An analytic derivation of the cost of deposit insurance and loan guarantees: An
application of modern option pricing theory. Journal of Banking and Finance, 1(1), 3–11.
Mian, A., 2006. Distance constraints: The limits of foreign lending in poor economies. Journal of Finance,
61(3), 1465–1505.
Miletkov, M., Poulsen, A. & Wintoki, M.B., 2017. Foreign independent directors and the quality of legal
institutions. Journal of International Business Studies, 48(2), 267–292.
Morck, R., Yeung, B. & Yu, W., 2000. The information content of stock markets: Why do emerging markets
have synchronous stock price movements? Journal of Financial Economics, 58(1–2), 215–260.
Morgan, D., 2002. Rating banks: risk and uncertainty in an opaque industry. American Economic Review,
92(4), 874–888.
Newey, W.K. & West, K.D., 1987. A simple, positive semi-definite, heteroskedasticity and autocorrelationconsistent covariance matrix. Econometrica, 55(3), 703–708.
Niepmann, F. & Schmidt-Eisenlohr, T., 2017. International trade, risk and the role of banks. Journal ofInternational Economics, 107, 111–126
Nier, E. & Baumann, U., 2006. Market discipline, disclosure and moral hazard in banking. Journal of
Financial Intermediation, 15(3), 332–361.
Portes, R. & Rey, H., 2005. The determinants of cross-border equity flows. Journal of International
Economics, 65(2), 269–296.
Ratnovski, L., 2013. Liquidity and transparency in bank risk management. Journal of Financial
Intermediation, 22(3), 422–439.
Redding, S.J., 2013. Economic geography: A review of the theoretical and empirical literature. In Palgrave
Handbook of International Trade (pp. 497–531). Palgrave Macmillan, London.
Renneboog, L., Szilagyi, P.G. & Vansteenkiste, C., 2017. Creditor rights, claims enforcement, and bondperformance in mergers and acquisitions. Journal of International Business Studies, 48(2), 174–194.
Rochet, J.C., 1992. Capital requirements and the behavior of commercial banks. European Economic
Review, 36(5), 1137–1178.
Roll, R., 1988. R2. Journal of Finance, 43(3), 541–566.
Stulz, R.M. & Williamson, R., 2003. Culture, openness, and finance. Journal of Financial Economics, 70(3),313–349.
30

Verbeke, A., Coeurderoy, R. & Matt, T., 2018. The future of international business research on corporate
globalization that never was.... Journal of International Business Studies, 49(9), 1101–1112.
Wintoki, M.B., Linck, J.S. & Netter, J.M., 2012. Endogeneity and the dynamics of internal corporate
governance. Journal of Financial Economics, 105(3), 581–606.
Xing, X. & Anderson, R., 2011. Stock price synchronicity and public firm-specific information. Journal of
Financial Markets, 14(2), 259–276.

31

TABLE 1
Descriptive Statistics
This table provides the descriptive and summary statistics for our international bank sample. The sample period isfrom 1997 to 2012. All bank fundamental information is from Bankscope. All stock information is from Datastream
and Worldscope. Foreign bank ownership data is from the dataset compiled by Claessens and van Horen (2014).
The final sample consists of 7,550 bank-year observations for 710 unique banks from 60 countries. SYNCH is bankstock price synchronicity; Foreign equals 1 for banks with 50% or more foreign ownership, and zero otherwise.
Panel A reports the number of observations plus average SYNCH and Foreign by year. Panel B reports the numberof observations, proportion, and the average SYNCH and Foreign by country.

Panel A: By Year
Year
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Total

Obs.
328
341
378
420
429
446
449
465
478
528
547
567
558
559
543
514
7550

SYNCH
-0.252-0.107-0.401-0.326-0.329-0.257-0.327-0.088-0.2020.057-0.016
0.267
0.128
0.146
0.273
0.019

Foreign
0.061
0.076
0.093
0.110
0.117
0.123
0.131
0.127
0.132
0.157
0.177
0.183
0.192
0.181
0.190
0.193

Panel B: By Country
Country
Argentina
Australia
Austria
BahrainBelgiumBrazilBulgaria
CanadaChile
China
Colombia
Croatia
CyprusCzech Republic
Denmark
Egypt
Finland

Obs.
90
129
126
51
11
246
30
144
104
54
6
96
30
42
565
176
16

%
1.2%
1.7%
1.7%
0.7%
0.1%
3.3%
0.4%
1.9%
1.4%
0.7%
0.1%
1.3%
0.4%
0.6%
7.5%
2.3%
0.2%
32

SYNCH
0.349
1.042
0.120
-1.048-0.278-0.053
0.674
0.740
0.101
-0.698-1.314-0.5000.279
0.989-0.913-0.8701.915

Foreign
0.322
0.093
0.079
0.275
0.000
0.089
0.200
0.056
0.288
0.000
0.000
0.375
0.033
0.881
0.012
0.369
0.000

France
Germany
GreeceHong Kong
Hungary
India
Indonesia
IrelandIsraelItaly
Japan
JordanKorea, Republic of
KuwaitLuxembourg
MexicoMoroccoNetherlands
Nigeria
NorwayOman
Pakistan
Peru
Philippines
PolandPortugal
Qatar
Romania
Russian Federation
Singapore
Slovakia
Slovenia
South Africa
Spain
Sri Lanka
SwedenSwitzerland
Taiwan, Province of China
ThailandTurkeyUnited Kingdom
United States
Venezuela, Bolivarian Republic of
Total

275
147
48
124
28
309
66
35
140
293
934
69
124
61
57
13
62
54
31
228
33
234
80
171
215
80
40
37
96
59
35
42
74
127
110
48
136
210
178
155
93
148
135
7550

3.6%
1.9%
0.6%
1.6%
0.4%
4.1%
0.9%
0.5%
1.9%
3.9%
12.4%
0.9%
1.6%
0.8%
0.8%
0.2%
0.8%
0.7%
0.4%
3.0%
0.4%
3.1%
1.1%
2.3%
2.8%
1.1%
0.5%
0.5%
1.3%
0.8%
0.5%
0.6%
1.0%
1.7%
1.5%
0.6%
1.8%
2.8%
2.4%
2.1%
1.2%
2.0%
1.8%
100%

33

-0.247-0.0500.211-0.1061.632
0.032-1.4640.636
0.518-0.033-0.243-0.709-0.287-0.4210.168-0.477-0.0250.459-0.316-0.7210.084-0.055-0.581-0.364
0.263
0.538
0.282
1.488
0.003
0.818
0.167
0.254
-0.100
0.386
0.297
1.580
-0.158
0.123
0.423
1.011
0.190
0.261
-0.494

0.044
0.156
0.208
0.613
0.107
0.045
0.409
0.000
0.050
0.003
0.003
0.362
0.129
0.115
1.000
0.000
0.484
0.037
0.097
0.000
0.000
0.282
0.538
0.082
0.744
0.075
0.000
0.324
0.042
0.000
0.971
0.500
0.095
0.055
0.000
0.000
0.103
0.043
0.202
0.103
0.000
0.115
0.356

TABLE 2
Summary Statistics and Univariate Analysis
This table presents the summary statistics (panel A) and the univariate analysis (panel B). Panel A reports thenumber of observations, means, medians, standard derivations, and percentile statistics of the main variables in thisstudy. Panel B compares the means and medians of these variables between the foreign-owned banks (1,107
observations) and the non-foreign-owned banks (6,443 observations). It reports their differences with statisticalsignificance based on the two-sample t-tests (mean tests) and the Wilcoxon signed rank tests (median tests). The *,**, and *** symbols denote statistical significance at the 10%, 5%, and 1% levels, respectively.
Panel A: Summary Statistics
Variable
SYNCH
R2
Foreign
ROA
Noninterest income/TA
Deposits/TA
Total assets
Ln(TA)
Capital
LLP/TA
Cross-listing indicator
CPI
GDP growth
Stock market cap/GDP
Stock market turnover ratio
Ln(Population)
Financial development

Obs.
7550
7550
7550
7550
7550
7550
7550
7550
7550
7550
7550
7029
7340
5743
5624
7340
7002

Mean
-0.061
0.482
0.147
0.008
0.016
0.714
45611
8.928
0.090
0.006
0.329
0.038
0.029
0.642
0.780
17.412
1.698

Std. dev.
1.219
0.235
0.354
0.014
0.019
0.195
122129
2.014
0.061
0.009
0.470
0.051
0.035
0.824
0.539
1.594
27.62325th-0.881
0.293
0.000
0.003
0.006
0.630
1838
7.517
0.054
0.001
0.000
0.014
0.012
0.180
0.409
15.884
0.406

Median-0.140
0.465
0.000
0.008
0.012
0.777
8194
9.011
0.074
0.004
0.000
0.025
0.028
0.458
0.686
17.711
0.878

75th
0.675
0.663
0.000
0.013
0.019
0.857
29007
10.275
0.111
0.007
1.000
0.046
0.048
0.777
1.068
18.663
1.490

Panel B: Univariate Analysis

SYNCH
R2
Foreign
ROA
Noninterest income/TA
Deposits/TA
Total assets
Ln(TA)
CapitalLoan loss provisions/TA
Cross-listing indicator
CPI
GDP growth
Stock market cap/GDP
Stock market turnover ratio
Ln(Population)
Financial development

Foreign=1(1107 obs.)
Mean
Median-0.160-0.158
0.466
0.460
1.000
1.000
0.011
0.010
0.018
0.015
0.742
0.786
19780
5328
8.599
8.581
0.103
0.091
0.007
0.004
0.291
0.000
0.046
0.033
0.036
0.037
0.650
0.140
0.490
0.415
16.96817.3100.636
0.473

Foreign=0(6443 obs.)
Mean Median-0.045-0.139
0.485
0.465
0.000
0.000
0.008
0.007
0.016
0.012
0.710
0.774
50050
9153
8.984
9.122
0.088
0.071
0.006
0.004
0.336
0.000
0.036
0.024
0.028
0.026
0.640
0.486
0.831
0.740
17.49017.877
1.887
0.948

34

Difference (1-0)
Mean-0.116 ***-0.019 **1.000 ***
0.003 ***
0.002 ***
0.032 ***
-30269 ***-0.385 ***0.015 ***
0.001 *
-0.045 ***0.010 ***
0.007 ***
0.010
-0.340 ***-0.522 ***-1.251

Median-0.020 **-0.005 **1.000 ***
0.003 ***
0.003 ***
0.012
-3825 ***
-0.541 ***0.020 ***
0.000 *
0.000 ***
0.009 ***
0.011 ***
-0.346 ***-0.325 ***-0.567 ***-0.476 ***TABLE 3
Foreign Ownership and Price Synchronicity of Bank Stocks
Panel A of this table reports the results of our baseline tests. The dependent variable is the price synchronicity of bankstocks (SYNCH). The main variable of interest is Foreign, which equals 1 when a bank has 50% or more foreign
ownership, and zero otherwise. The bank controls include return on assets (ROA), noninterest income to total assets(Noninterest income/TA), total deposits to total assets (Deposits/TA), log total assets (Ln(TA)), equity capital to total
assets (Capital), loan loss provisions to total assets (Loan loss provisions/TA), and an indicator variable for crosslisting (Cross-listing indicator). Model 2 controls for six country characteristics, including the ratio of stock market
capitalization to GDP (Stock market cap/GDP), the growth in real GDP (GDP growth), the rate of change in thenational consumer price index (CPI), annualized turnover ratios of domestic stocks (Stock market turnover ratio), thelog of total population (Ln(Population)), and the ratio of total private credit to GDP (Financial development). The
coefficient estimates for these country controls are suppressed for brevity. The standard errors are clustered at thebank level, unless stated otherwise. Panel B reports the robustness results. For brevity, we report only the coefficientestimates on Foreign. Model specifications in panel B follow model (3) of panel A. In row 1, the nontransformed Rsquared is the dependent variable. Rows 2 and 3 report the between-effect (BE) estimator and the Fama and MacBeth(FM) cross-sectional regression, respectively. Row 4 reports a weighted least squares (WLS) regression in which theweight is the inverse of the number of observations in each country. Row 5 reports a median regression that is lesssubject to the influences of outliers. Row 6 uses a subsample covering the period from 1997 to 2006. Rows 7 and 8
use alternative standard errors, clustered by bank and year, and by country and year, respectively. The *, **, and ***symbols denote statistical significance at the 10%, 5%, and 1% levels, respectively.
Panel A: Baseline Tests

Foreign(1)-0.322***(0.094)

ROA
Noninterest income/TA
Deposits/TA
Ln(TA)
CapitalLoan loss provisions/TA
Cross-listing indicatorIntercept
Country controls
Bank FECountry FE
Year FECountry×Year FE
Obs.
Adj. R2

0.279(0.260)(2)-0.294***(0.101)
5.487**(2.241)
2.697*(1.395)0.160(0.151)0.373***(0.023)
1.104**(0.525)7.770***(2.857)0.339***(0.071)-2.185***(0.338)
Yes

SYNCH
(3)
-0.347***(0.068)4.620***(1.567)1.090(1.107)0.452***(0.151)0.459***(0.024)3.070***(0.475)-1.830(1.923)0.250***(0.056)-4.194***(0.346)(4)-0.315***(0.071)
5.398***(1.753)1.051(1.107)0.442***(0.155)0.464***(0.026)2.887***(0.499)0.155(2.134)0.290***(0.060)-3.442***(0.641)(5)-0.183*(0.097)2.363*(1.364)1.559(1.215)-0.098(0.169)0.236***(0.054)1.354***(0.439)0.743(1.892)0.171**(0.070)-2.179***(0.521)
Yes

Yes
Yes

Yes

Yes
Yes

7550
0.234

5191
0.381

7550
0.482

Panel B: Robustness Tests

35

Yes
7550
0.567

Yes
7550
0.352

R2

Coef.

S.E.

-0.407***(0.088)Fama-MacBeth regression-0.268***(0.039)Weighted-least-squares (WLS)-0.396***(0.095)(5)Median regression-0.311***(0.048)(6)

Subsample: 1996-2006-0.310***(0.086)(7)

Cluster standard errors at the bank-year level-0.347***(0.067)(8)

Cluster standard errors at the country-year level-0.347***(0.069)(9)Controlling for the interaction between
Foreign and Advanced
Controlling for the interaction between
Foreign and Financial development
Controlling for the interaction between
Foreign and Stock market cap/GDP
Controlling for the interaction between
Foreign and GDP Growth
Controlling for the interaction between
Foreign and Civil law-0.329***(0.085)-0.279**(0.118)-0.380***(0.091)-0.345***(0.072)-0.328***(0.097)(1)(2)

Use R2 as dependent variable
Between-estimator

(3)
(4)

(10)
(11)
(12)
(13)

Coef.
-0.066***

SYNCH

36

S.E.
(0.013)

TABLE 4
Endogeneity Tests
This table presents the instrumental variable estimation using the two-stage least squares (2SLS) approach and the dynamic
panel system generalized method of moments (DPS GMM) approach. For the 2SLS regression, the dependent variables are
the foreign bank ownership indicator (Foreign) for the first-stage regression (in model 1) and bank stock price synchronicity(SYNCH) for the second-stage regression (model 2). Ln(latitude) is the natural logarithm of transformed latitude (in decimalform), which measures the geographical distance between the bank’s city (in the host country) and the equator;Ln(1+Distance from bank city to capital city) is the natural logarithm of 1 plus the geographical distance (in miles) fromthe bank’s city of location to the host country’s capital city. All bank controls are identical to those in the baseline modeland are defined in the appendix. We control for the five country characteristics and year fixed effects and cluster the standard
errors at the bank level. Model 3 presents the results estimated using the two-step dynamic panel system GMM estimator
developed by Arellano and Bover (1995) and Blundell and Bond (1998). Following Wintoki, Linck, and Netter (2012), we
control for the first-period (SYNCH (-1)) and second-period lagged dependent variables (SYNCH (-2)) in the regression. We
treat all explanatory variables except the year dummies as endogenous and instrument them with the t-3 to t-5 lagged
dependent and independent variables for the difference equation and their respective lagged differences for the level
regression. We report the tests of first-order and second-order serial correlations in the residuals and the Hansen test ofoveridentification under the null of instrument validity. The *, **, and *** symbols denote statistical significance at the
10%, 5%, and 1% levels, respectively.
Estimation approaches
Dependent variables
Ln(Latitude)
Ln(1+Distance from bank city to capital city)
Foreign (Fitted Foreign for model 2)
ROA
Noninterest income/TA
Deposits/TA
Ln(TA)
CapitalLoan loss provisions/TA
Cross-listing indicator

2SLS IV
1st stage
2nd stage
Foreign
SYNCH
(1)
(2)
0.074***(0.022)-0.015**(0.007)-1.301**(0.662)-1.0974.127(1.043)(2.544)0.010
3.091**(0.590)(1.551)0.166***
0.304*(0.058)(0.172)0.010
0.384***(0.010)(0.023)0.482*
1.437**(0.275)(0.640)0.715
7.898**(1.291)(3.069)-0.0380.304***(0.029)(0.075)SYNCH (-1)
SYNCH (-2)
Country controls
Year FE
Obs.
Adj. R2
F-statistics of the excluded IVs
Cragg-Donald F-statistics of the excluded IVs
AR(1) test p-value
AR(2) test p-value
Hansen test of overidentification p-value

Yes
Yes
5132
0.121
7.750***
54.320***

Yes
Yes
5132
0.278

0.258

37

DPS GMM
SYNCH(3)-0.692**(0.294)-17.981(12.012)-5.612(6.935)0.016(0.694)0.130(0.222)3.215*(1.742)
14.268(13.301)0.300*(0.163)0.424*(0.251)-0.221(0.216)
Yes
Yes
4272

0.004
0.194
0.603

TABLE 5
Foreign Bank Ownership and Earnings Innovations
This table reports the results of regressions examining the extent to which current stock prices reflect future
earnings information. The dependent variable is buy-and-hold bank stock annual returns (Rt). E/MVt-1 is the net
income in year t-1, scaled by market capitalization at the end of year t-1. ∆Et is the annual growth rate in net
income from year t-1 to t, scaled by market capitalization at the end of year t-1 (i.e., at the beginning of returncalculation). R(t+1 to t+3) is the buy-and-hold stock returns from the beginning of year t+1 to the end of year t+3; we
expect it to be negatively related to stock returns. ∆Ei(t+1 to t+3) is the sum of the annual changes in net income overyear t+1 to t+3, scaled by market capitalization at the end of year t-1. The *, **, and *** symbols denote statisticalsignificance at the 10%, 5%, and 1% levels, respectively.

Rt(1)
0.043***(0.016)-0.044(0.047)0.017(0.053)0.029(0.045)-0.056(0.037)

E/MVt-1
∆Et
∆Et+1
∆Et+2
∆Et+3
∆E(t+1 to t+3)
R(t+1 to t+3)0.111***(0.020)-0.309*(0.161)-0.032(0.035)-0.007(0.126)
0.316**(0.132)-0.004(0.088)Foreign × E/MVt-1
Foreign × ∆Et
Foreign × ∆Et+1
Foreign × ∆Et+2
Foreign × ∆Et+3
Foreign × ∆E (t+1 to t+3)
Foreign × R(t+1 to t+3)
ForeignInteraction between earnings innovations and all other controls
Country FE
Obs.
Adj. R2

38

0.015(0.031)0.034(0.031)
Yes
Yes
6,140
0.462

(2)
0.038***(0.014)-0.017(0.036)-0.005(0.017)0.107***(0.020)-0.213**(0.084)-0.039(0.030)0.122**(0.053)0.006(0.030)0.018(0.030)
Yes
Yes
6,140
0.448

Table 6
Does Home Country’s Governance and Banking Regulatory Environment Matter?
This table presents the results of the regressions examining whether foreign ownership’s effect on bank stock price synchronicity
depends on the home country’s governance quality and banking regulatory environment. The dependent variable is bank stock
price synchronicity (SYNCH). We measure country governance using three proxies: the revised anti-director-rights index (Antidirector-rights index), the anti-self-dealing index (Anti-self-dealing index), and the World Governance Index (WGI). We measurebanking regulatory environment in three aspects, corresponding to Basel II Pillar One, Pillar Two, and Pillar Three, respectively,
using worldwide survey data from Barth et al. (2004, 2006, 2008, and 2013a). Capital regulatory index is an index of overall
capital stringency and is the sum of the answers to several survey questions relating to the extent of regulatory requirements inbank capital and to the sources of funds that count as regulatory capital. Official supervisory power is a measure of the power ofsupervisory agencies, measured using answers to 14 questions on the extent to which supervisory authorities have the authority
to take actions in preventing and correcting problems. Private monitoring index is a measure of the extent of bank disclosure
requirements and incentives to increase private monitoring. We divide the foreign ownership (Foreign) variable into two variables
based on the sample median of the governance indexes and banking regulatory variables (in the foreign bank sample). To illustrate,
Foreign (High [Low] governance index) equals 1 when a bank has 50% or more foreign ownership and its home country has anabove-median (below-median) governance index; it equals zero otherwise. The bank controls are identical to those used in thebaseline model, defined in the appendix, and their coefficients are suppressed for brevity. Year fixed effects are included. Standard
errors are clustered at the bank level. The p-values of the Wald tests of coefficient equality between the divided foreign ownershipvariables are reported. The *, **, and *** symbols denote statistical significance at the 10%, 5%, and 1% levels, respectively.
Dependent variables
Foreign (High Anti-director index)
Foreign (Low Anti-director index)(1)-0.359**(0.163)-0.010(0.112)Foreign (High Anti-self-dealing index)

(2)

SYNCH
(3)
(4)

(5)

-0.365***(0.125)0.035(0.121)

Foreign (Low Anti-self-dealing index)
Foreign (High World Governance Index)-0.192**(0.091)-0.129(0.096)

Foreign (Low World Governance Index)
Foreign (High Capital regulatory index)-0.353(0.219)-0.092(0.100)Foreign (Low Capital regulatory index)
Foreign (High Official supervisory power)-0.245*(0.130)-0.041(0.114)Foreign (Low Official supervisory power)
Foreign (High Private monitoring index)
Foreign (Low Private monitoring index)
Bank controls
Year FE
Obs.
Adj. R2
Wald test of coefficient equality: H0: High-Low=0

(6)

Yes
Yes
7550
0.235
0.069

39

Yes
Yes
7550
0.236
0.014

Yes
Yes
7550
0.233
0.788

Yes
Yes
7,550
0.233
0.262

Yes
Yes
7,550
0.234
0.183

-0.165(0.143)-0.090(0.103)
Yes
Yes
7,550
0.233
0.600

Table 7
Does the Relative Difference in Governance and Banking Regulatory Environment Matter?
This table presents the regressions that examine whether differences in governance and banking regulatory environment between home and host countries explain bank stock price synchronicity on the foreignbank sample. The dependent variable is bank stock price synchronicity (SYNCH). We measure country governance using three proxies: the revised anti-director-rights index (Anti-director-rights index), the antiself-dealing index (Anti-self-dealing index), and the World Governance Index (WGI). Banking regulatory environment is measured in three aspects, corresponding to the Basel II Pillar One, Pillar Two, and Pillar
Three, respectively, using worldwide survey data from Barth et al. (2004, 2006, 2008, and 2013a). Capital regulatory index is an index of overall capital stringency and defined as the sum of the answers to several
survey questions relating to the extent of regulatory requirements in bank capital and to the sources of funds that count as regulatory capital. Official supervisory power is a measure of the power of supervisoryagencies; we measure it using 14 questions on the extent to which supervisory authorities have the authority to take actions to prevent and correct problems. Private monitoring index is a measure of the extent ofbank disclosure requirements and incentives to increase private monitoring. We calculate the relative difference in the governance indexes and banking regulatory variables between the home and host countries
by subtracting each of these indexes and variables for the host country from those of the home country. We calculate geographical distance (in miles) between the bank’s city of location (in the host country) and
its home country’s capital city (both measured at the center) by applying spherical geometry and trigonometric math functions to the latitudes and longitudes of the locations. The bank controls are identical tothose used in the baseline model, and their coefficients are suppressed for brevity. Year fixed effects are included in all specifications. Country fixed effects are included only when WGI is used. Standard errors
are clustered at the bank level. The *, **, and *** symbols denote statistical significance at the 10%, 5%, and 1% levels, respectively.
Dependent variables
SYNCH
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
Anti-director-rights index (Home-Host)-0.001-0.240(0.055)(0.328)Anti-self-dealing index (Home-Host)-0.681**-3.087**(0.338)(1.502)
WGI (Home-Host)-0.206*-1.622***(0.119)(0.489)
Capital regulatory index (Home-Host)
0.031-0.190(0.029)(0.211)
Official supervisory power index (Home-Host)-0.039*-0.500*(0.023)(0.260)Private monitoring index (Home-Host)-0.055-0.031(0.044)(0.403)Anti-director-rights index (Home-Host)×Ln(Geographical distance)
0.032(0.041)Anti-self-dealing index (Home-Host)×Ln(Geographical distance)
0.348*(0.199)WGI (Home-Host)×Ln(Geographical distance)
0.190***(0.064)
Capital regulatory index (Home-Host)×Ln(Geographical distance)
0.035(0.032)
Official supervisory power index (Home-Host)×Ln(Geographical distance)
0.069**(0.034)
Private monitoring index (Home-Host)×Ln(Geographical distance)-0.016(0.052)
Ln(Geographical distance)-0.038-0.0540.011(0.050)(0.054)(0.065)
Bank controls
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Country FE
Yes
YesYear FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obs.
865
865
1,047
519
707
868
865
865
1,047
370
Adj. R2
0.218
0.234
0.418
0.196
0.168
0.178
0.219
0.231
0.431
0.181

40

APPENDIX A.1
Variable Definitions
Variables
SYNCH

Description
A bank's stock price synchronicity measure, calculated based on the R2 estimated from an expanded market modelusing weekly bank stock returns. We apply a logistic transformation to the estimated R 2 as follows:

SourceDataStream

SYNCHit = log [ R2it / ( 1 – R2it )].
Foreign

An indicator variable that equals 1 when a bank has 50% or more foreign ownership, and zero otherwise, in a given
year.

Claessens and van
Horen (2014)

ROA

Net income to total assets.

Bankscope

Noninterest income/TA

Noninterest income to total assets.

Bankscope

Deposits/TA

Total deposits to total assets.

Bankscope

Ln(TA)Natural logarithm of total assets in millions of U.S. dollars.

Bankscope

Capital

Total equity capital to total assets.

Bankscope

Loan loss provisions/TA

Loan loss provisions to total assets.

Bankscope

Cross-listing indicator

An indicator variable equal to 1 when a bank stock has at least one cross-listing, and zero otherwise, in a given year.

Worldscope

CPI

Rate of change in the national consumer price index.

World Bank

GDP growth

Rate of change in real gross domestic product (GDP).

World Bank

Stock market cap/GDP

The ratio of market capitalization to GDP.

World Bank

Stock market turnover ratio

The annualized turnover ratios of domestic stocks. Turnover ratio is the value of domestic stocks traded divided by theirmarket capitalizations.

World Bank

Ln(Population)

The natural logarithm of the total population in a country in a given year.

World Bank

Financial development

The ratio of total private credit to gross domestic product (GDP).

World Bank

Anti-director-rights index

The revised anti-director-rights index, which ranges from 0 to 6. This index measures how well a country protects its
minority shareholders based on six legal rights granted to them. It is formed by adding 1 to the index for each legal
right, including (1) shareholders can mail proxy votes; (2) shareholders are not required to deposit their shares prior to
the general shareholders meeting; (3) cumulative voting or proportional representation of minorities on the board of
directors is allowed; (4) there is an oppressed minorities mechanism; (5) shareholders have preemptive rights that can
only be waived by a shareholders meeting; and (6) the minimum percentage of share capital that entitles a shareholder
to call for an extraordinary shareholders meeting is less than or equal to 10%.

Djankov, La Porta,
López-de-Silanes, andShleifer (2008)

Anti-self-dealing index

The anti-self-dealing index, which measures a country’s legal protections for minority shareholders against corporate
insiders’ self-dealing. It is the average of ex ante and ex post private control of self-dealing indexes. The index of ex
ante control of self-dealing transactions is the average of approval by disinterested shareholders and ex ante disclosure.
The index of ex post control of self-dealing transactions is the average of disclosures in periodic filings and ease ofproving wrongdoing.

Djankov, La Porta,
López-de-Silanes, andShleifer (2008)

41

WGI

The World Governance Index, which is the average of six country-level governance indicators, measures (1) government
effectiveness; (2) political stability; (3) regulatory quality; (4) rule of law; (5) voice and accountability; and (6) control
of corruption. For more details about the construction of these six governance indicators, please see Kaufmann, Kraay,
and Mastruzzi (2006).

Kaufmann, Kraay, andMastruzzi (2006)

Capital regulatory index

Capital regulatory index is an index of overall capital stringency and is the sum of the answers to several survey
questions relating to the extent of regulatory requirements in bank capital and to the sources of funds that count asregulatory capital. See Barth et al. (2004, 2013a) for more details.

Barth et al. (2004,
2006, 2008, and2013a)

Official supervisory power

Official supervisory power is a measure of the power of supervisory agencies, measured using answers to 14 questions
on the extent to which supervisory authorities have the authority to take actions to prevent and correct problems. See
Barth et al. (2004, 2013a) for more details.

Barth et al. (2004,
2006, 2008, and2013a)

Private monitoring index

Private monitoring index is a measure of the extent of bank disclosure requirements and incentives to increase private
monitoring. See Barth et al. (2004, 2013a) for more details.

Barth et al. (2004,
2006, 2008, and2013a)

Ln(Geographical distance)

The geographical distance (in miles) between the foreign-owned bank's city of location in the host country and its homecountry's capital city. We make use of spherical geometry and trigonometric math functions to calculate the accurate
distance between the two locations. Specifically, we first convert each pair of latitude and longitude from decimaldegrees into radians by dividing their values by 180/π or 57.296. Lat1 (Lat2) and Long1 (Long2) represent the latitudes
and longitudes in radians of the foreign bank’s city of location (home country’s capital city). We then use the great
circle distance formula to calculate the distance in miles between two pairs of latitudes and longitudes as follows:

www.mapsofworld.com; CEPII

Distance = 3963 × Arccos[Sin(Lat1)Sin(Lat2) + Cos(Lat1)Cos(Lat2)Cos(Long2-Long1)],
where 3,963 is the radius of the Earth in miles.
Ln(Latitude)

Natural logarithm of the absolute value of latitude in decimal degrees. It measures the distance of the bank's city oflocation (in the host country) from the equator.

www.mapsofworld.com; CEPII

Ln(1+Distance from bank city
to capital city)

Natural logarithm of 1 plus the distance (in miles) from the bank's city of location (in the host country) from thecapital city of the host country.

www.mapsofworld.com; CEPII

E/MVt-1

Net income in year t-1, scaled by market capitalization at the end of year t-1.

Bankscope;
DataStream∆Et

The annual change in net income from year t-1 to t, scaled by market capitalization at the end of year t-1 (i.e., at the
beginning of return calculation).

Bankscope;
DataStream

R(t+1 to t+3)

The buy-and-hold stock returns from the beginning of year t+1 to the end of year t+3.

DataStream∆Ei(t+1 to t+3)

The sum of the annual changes in net income over year t+1 to t+3, scaled by market capitalization at the end of year t1.

Bankscope;
DataStream

MV/TAt-1

The ratio of market capitalization to total assets at the end of year t-1.

Bankscope;
DataStream

42

Appendix A-2

ARGENTINA
AUSTRALIA
AUSTRIA
BAHRAINBELGIUM
BRAZIL
BULGARIA
CANADACHILECHINACOLOMBIACROATIACYPRUSCZECH REPUBLIC
DENMARKEGYPTFINLANDFRANCEGERMANYGREECEHONG KONG
HUNGARY
INDIA
INDONESIAIRELANDISRAELITALY
JAPANJORDANKOREA, REPUBLIC OFKUWAITLUXEMBOURG
MEXICO
MOROCCONETHERLANDS

1997
5
8
5
0
0
12
0
8
6
0
1
0
1
3
35
6
1
9
5
1
7
2
2
12
3
7
17
54
0
5
0
3
1
3
2

TABLE A-2.1
Descriptive Statistics by Year and Country
This table reports the descriptive statistics by year and country for our international bank sample.
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
6
6
6
6
6
5
5
5
5
5
6
6
6
8
8
8
8
8
8
8
8
8
8
9
8
7
6
6
7
7
7
8
8
9
9
9
9
9
9
0
0
0
0
0
0
5
5
6
6
6
6
6
0
0
0
0
1
1
1
1
1
1
1
1
1
12
13
13
13
16
16
16
15
13
17
20
17
18
0
0
0
1
1
1
2
1
2
4
3
3
4
8
9
9
9
9
9
9
10
10
10
10
10
10
7
7
7
7
6
7
6
6
6
6
6
6
7
0
0
0
0
0
0
0
3
4
5
8
8
8
1
1
0
0
0
0
1
2
0
0
0
0
0
0
0
0
0
0
0
0
0
12
13
13
14
15
2
2
2
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
2
2
2
2
34
35
36
36
36
36
36
36
35
37
40
38
34
6
12
13
12
12
10
10
9
8
10
13
13
14
1
1
1
1
1
1
1
1
1
1
1
1
1
11
14
14
15
17
19
19
19
19
19
20
20
20
5
6
8
9
10
9
11
11
11
11
11
10
10
1
2
3
3
3
3
2
3
5
5
5
5
5
7
7
7
7
7
8
8
8
8
8
8
8
8
2
2
2
3
3
2
2
2
2
1
1
1
1
7
13
15
19
20
25
24
23
23
23
24
23
23
0
0
0
0
0
0
0
3
5
7
6
9
7
3
3
3
3
2
2
2
2
2
2
2
2
2
9
9
11
10
9
8
8
8
8
8
9
9
9
17
19
20
21
21
19
20
21
21
18
16
16
16
53
54
55
55
57
57
59
60
61
63
62
63
63
0
0
0
0
0
0
0
0
10
10
10
9
10
5
7
8
7
9
7
8
9
8
9
11
9
3
0
0
0
0
0
0
6
6
7
7
7
7
7
3
3
4
4
4
4
3
3
3
3
4
4
4
1
1
1
0
1
1
1
1
1
1
1
1
1
4
4
4
4
4
4
4
4
4
3
4
4
4
3
3
3
3
3
3
3
3
4
4
4
4
4

43

2011
6
9
9
6
1
18
4
4
7
9
0
15
2
2
33
14
1
20
10
1
9
1
22
7
2
9
16
60
10
10
7
4
0
4
4

2012
6
8
9
5
1
17
4
10
7
9
0
14
1
2
28
14
1
20
10
1
9
1
23
10
0
9
15
58
10
9
7
4
0
4
4

Total
90
129
126
51
11
246
30
144
104
54
6
96
30
42
565
176
16
275
147
48
124
28
309
66
35
140
293
934
69
124
61
57
13
62
54

%
1.2%
1.7%
1.7%
0.7%
0.1%
3.3%
0.4%
1.9%
1.4%
0.7%
0.1%
1.3%
0.4%
0.6%
7.5%
2.3%
0.2%
3.6%
1.9%
0.6%
1.6%
0.4%
4.1%
0.9%
0.5%
1.9%
3.9%
12.4%
0.9%
1.6%
0.8%
0.8%
0.2%
0.8%
0.7%

NIGERIANORWAYOMANPAKISTANPERUPHILIPPINESPOLANDPORTUGALQATARROMANIARUSSIAN FEDERATIONSINGAPORESLOVAKIASLOVENIA
SOUTH AFRICASPAIN
SRI LANKASWEDENSWITZERLAND
TAIWANTHAILAND
TURKEYUNITED KINGDOMUNITED STATESVENEZUELA
Total

0
8
0
12
0
9
11
4
0
0
0
3
0
0
3
7
5
2
6
10
11
0
4
7
2

0
9
0
11
0
11
13
4
0
1
0
3
0
0
3
7
5
2
7
12
12
0
5
7
4

0
11
0
13
0
13
14
4
0
1
0
3
0
0
4
7
5
2
8
12
12
0
5
8
6

0
13
0
12
7
11
13
4
0
2
1
4
0
2
8
8
6
2
9
13
12
9
5
9
7

0
13
0
12
6
12
12
5
0
2
1
5
0
2
6
9
6
3
9
13
12
9
6
9
9

0
14
0
13
6
13
14
6
0
2
1
5
0
2
6
9
6
3
9
13
11
11
7
8
9

0
14
0
13
6
13
13
6
0
2
1
5
0
2
5
9
7
4
8
14
12
11
7
9
10

0
14
0
16
6
13
11
6
4
3
1
4
0
2
4
8
7
3
9
14
11
11
6
10
9

44

0
15
0
16
5
12
14
6
4
3
4
3
0
2
3
8
7
3
8
15
10
10
6
10
12

0
16
4
17
6
11
14
5
4
3
9
4
5
3
5
8
9
4
9
15
11
11
6
10
12

0
17
4
17
7
9
15
6
4
3
10
4
5
3
6
8
8
3
10
15
11
15
7
10
12

0
17
5
18
7
8
15
5
4
3
15
4
5
4
5
8
8
4
9
16
11
14
7
11
10

0
17
5
18
7
9
15
5
5
3
14
3
5
5
4
8
8
3
10
16
11
14
6
10
9

14
17
5
16
7
9
14
5
5
3
11
3
5
5
4
8
8
3
10
16
11
14
6
11
8

7
17
5
15
7
9
14
5
5
3
17
3
5
5
4
8
6
3
7
16
10
13
5
10
8

10
16
5
15
3
9
13
4
5
3
11
3
5
5
4
7
9
4
8
0
10
13
5
9
8

31
228
33
234
80
171
215
80
40
37
96
59
35
42
74
127
110
48
136
210
178
155
93
148
135
7,550

0.4%
3.0%
0.4%
3.1%
1.1%
2.3%
2.8%
1.1%
0.5%
0.5%
1.3%
0.8%
0.5%
0.6%
1.0%
1.7%
1.5%
0.6%
1.8%
2.8%
2.4%
2.1%
1.2%
2.0%
1.8%
100%

TABLE A-2.2
Robustness Tests
This table reports results for our robustness tests. Model 1 uses the nontransformed R-squared as dependent variable. Models 2 and 3 report the betweeneffect (BE) estimator and the Fama and MacBeth (FM) cross-sectional regression. Model 4 is a weighted least squares (WLS) regression in which theweight is the inverse of the number of observations in each country. Model 5 reports a median regression that is less subject to the influences of outliers.
Model 6 uses a subsample covering 1997 to 2006. Models 7 and 8 use alternative standard errors, which are clustered by bank and year, and by country
and year, respectively. The standard errors are clustered at the bank level, unless stated otherwise. The *, **, and *** symbols denote statisticalsignificance at the 10%, 5%, and 1% levels, respectively.
Dependent variable

Foreign
ROA
Noninterest income/TA
Deposits/TA
Ln(TA)
CapitalLoan loss provisions/TA
Cross-listing indicatorIntercept
Country FE
Year FE
Obs.
Adj. R2/Avg. R2

R2
OLS
(1)
-0.066***(0.013)0.910***(0.303)0.242(0.218)0.083***(0.028)0.087***(0.004)0.566***(0.089)-0.444(0.372)0.054***(0.011)-0.291***(0.065)
Yes
Yes
7,550
0.483

SYNCH
BE
(2)
-0.407***(0.088)7.588**(3.222)-1.223(1.881)0.739***(0.203)0.433***(0.022)
3.288***(0.684)-2.544(4.829)0.377***(0.086)-4.209***(0.373)YesFM(3)-0.268***(0.039)7.099***(1.498)0.473(0.800)0.488***(0.090)0.466***(0.009)2.674***(0.238)0.266(2.152)0.303***(0.032)-4.339***(0.108)
Yes

7,550
0.600

7,550
0.615

WLS
(4)
-0.396***(0.095)2.357(2.939)1.556(1.935)0.253(0.234)0.506***(0.034)3.231***(0.665)-4.367(3.220)0.159(0.129)-4.420***(0.465)
Yes
Yes
7,550
0.492

45

QREG
(5)
-0.311***(0.048)8.080***(1.436)0.650(0.890)0.436***(0.096)0.454***(0.012)2.588***(0.311)-1.919(1.745)0.238***(0.036)-3.999***(0.195)
Yes
Yes
7,550
0.477

1996-2006(6)-0.310***(0.086)4.326**(1.958)1.404(1.250)0.464***(0.170)0.459***(0.025)3.091***(0.582)-0.402(2.491)0.249***(0.068)-4.080***(0.365)
Yes
Yes
4,262
0.476

Clustered two-way by
Bank & YearCountry & Year(7)
(8)-0.347***-0.347***(0.067)(0.069)4.620**
4.620**(1.964)(1.923)1.090
1.090(1.228)(1.368)0.452***
0.452***(0.136)(0.162)0.459***
0.459***(0.023)(0.029)3.070***
3.070***(0.423)(0.436)-1.830-1.830(2.126)(2.287)0.250***
0.250***(0.059)(0.063)-4.194***-4.194***(0.322)(0.303)
Yes
Yes
Yes
Yes
7,550
7,550
0.487
0.487

TABLE A-2.3
Does Host Countries’ Country Characteristics Matter?
This table reports regression results examining the role of certain host countries’ country characteristics
on the association between foreign bank ownership and stock returns synchronicity. The dependent
variable is the price synchronicity of bank stocks (SYNCH). The main variable of interest is Foreign,
which equals 1 when a bank has 50% or more foreign ownership, and zero otherwise. The bank controls
are identical to those in the baseline model and are unreported for brevity. Intercepts are also suppressed.
Advanced is an indicator of advanced economies for the host countries following the classification by
IMF, and zero otherwise. Civil law is an indicator for civil law host countries, and zero for the common
laws host countries or others. Standard errors are clustered at the bank level. The *, **, and *** symbols
denote statistical significance at the 10%, 5%, and 1% levels, respectively.

Foreign
Advanced
Foreign × Advanced(1)-0.329***(0.085)-1.200***(0.234)-0.064(0.131)

Financial development(2)-0.279**(0.118)SYNCH(3)-0.380***(0.091)(4)-0.345***(0.072)0.000(0.000)Foreign × Financial
development-0.092(0.119)Stock market cap/GDP-0.052(0.035)

Foreign × Stock market
cap/GDP0.024(0.042)

GDP Growth-0.889(0.563)-0.188(0.966)

Foreign × GDP Growth
Civil law
Foreign × Civil law
Country FE
Year FE
Obs.
Adj. R2(5)-0.328***(0.097)

Yes
Yes
7,550
0.482

Yes
Yes
7,002
0.487

46

Yes
Yes
5,743
0.509

Yes
Yes
7,340
0.488

0.316
(0.233)-0.027(0.129)
Yes
Yes
7,550
0.482

TABLE A-2.4
Propensity Score Matching Estimator
This table reports the propensity score matching estimation results. Panel A reports the coefficientestimates of the logit regression used to estimate the propensity scores and the logit regression estimatesusing the post-match sample. The dependent variable is Foreign, which equals one when a bank has 50%
or more foreign ownership, and zero otherwise. The bank controls are identical to those in the baselinemodel and are defined in the appendix. Country and year fixed effects are included, and the standard
errors are clustered at the bank level. Panel B compares the means of the bank controls across the foreignowned banks (treatment group) and the matched banks (control group) and reports the differences andthe corresponding t-statistics. Panel C reports the average treatment effects for the treated. SYNCH isbank stock price synchronicity. The *, **, and *** symbols denote statistical significance at the 10%,5%, and 1% levels, respectively.
Panel A: Logit results
Dependent variables

ROA
Noninterest income/TA
Deposits/TA
Ln(TA)
CapitalLoan loss provisions/TA
Cross-listing indicatorIntercept
Country FE
Year FE
Obs.
Pseudo R2
Wald χ2

ForeignPre-match(1)-4.731(6.337)-1.548(6.225)0.257(0.695)0.145(0.089)3.379*(1.891)-0.764(7.814)-0.361(0.375)-5.078***(1.029)
Yes
Yes
7,550
0.306
290.860***

Post-match
(2)
3.664
(6.312)-0.996(6.097)0.021(0.795)0.056(0.099)-0.225(2.069)6.522(7.727)0.044(0.331)-0.751(1.219)
Yes
Yes
1,722
0.015
12.740Panel B: Differences in observables
ROA
Noninterest income/TA
Deposits/TA
Ln(TA)
CapitalLoan loss provisions
Cross-listing indicator

Treatment
0.011
0.018
0.721
8.582
0.105
0.007
0.305

Control
0.011
0.019
0.723
8.512
0.105
0.007
0.282
47

Difference
0.000-0.001-0.001
0.070
0.000
0.000
0.023

t-stat0.030-0.630-0.136
0.780
0.042
0.844
1.058

Panel C: Average treatment effects for the treatedTreatedControls
SYNCH-0.271
0.011

Difference-0.282

48

S.E.
0.061

t-stat
-4.600Table A-2.5
Endogenous Treatment-Regression Model for the Earnings Innovation Tests (Table 5 in the main paper)This table reports the estimation results of the endogenous treatment-regression models for the two earnings innovation tests in Table 5 of the main paper.
The earnings innovation tests examine the extent to which future earnings information is reflected in current stock prices. In the treatment equation (probit
regression) (columns 1 and 4), we model the likelihood of foreign-bank status (Foreign) as a function of two instrumental variables, bank and country
controls, and year fixed effects. The two instrumental variables are Ln(latitude), which is the natural logarithm of transformed latitude (in decimal form)
measuring the geographical distance between the bank’s city of location (in the host country) and the equator, and Ln(1+Distance from bank city to capitalcity), which is the natural logarithm of 1 plus the geographical distance (in miles) from the bank’s city of location to the host country’s capital city. The
dependent variable is the price synchronicity of bank stocks (SYNCH). The bank and country controls are identical to those used in the baseline models.
The *, **, and *** symbols denote statistical significance at the 10%, 5%, and 1% levels, respectively. In columns (2) to (3), the dependent variable isbuy-and-hold bank stock annual returns (Rt). E/MVt-1 is the net income in year t-1, scaled by market capitalization at the end of year t-1. ∆Et is the annual
growth rate in net income from year t-1 to t, scaled by market capitalization at the end of year t-1 (i.e., at the beginning of return calculation). R(t+1 to t+3) isthe buy-and-hold stock returns from the beginning of year t+1 to the end of year t+3; we expect it to be negatively related to stock returns. ∆Ei(t+1 to t+3) isthe sum of the annual changes in net income over year t+1 to t+3, scaled by market capitalization at the end of year t-1. In columns (5) to (7), the dependent
variable is the ratio of net income to total assets, measured at year t, t+1, and t+2. MV/TAt-1 is the ratio of market capitalization to total assets. The bankcontrol variables are identical to those used in the baseline models. For brevity, we only report the estimates for the variables of interest and all intercepts
are suppressed. The *, **, and *** symbols denote statistical significance at the 10%, 5%, and 1% levels, respectively.
Treatment equation
Stock returnst
Treatment equation
ROAt
ROAt+1
ROAt+2
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Ln(Latitude)
0.340***
0.320***(0.127)(0.117)Ln(1+Distance of bank city to capital city)-0.038-0.036(0.040)(0.037)
E/MVt-1
0.039*
0.034(0.023)(0.023)∆Et-0.156**-0.099*(0.068)(0.054)∆Et+1
0.064(0.073)∆Et+2
0.064(0.081)∆Et+3-0.094(0.073)∆E(t+1 to t+3)-0.045(0.028)R(t+1 to t+3)0.120*** 0.134***49

(0.026)-0.011(0.042)-0.281*(0.160)0.285(0.332)0.504(0.382)-0.214(0.271)

Foreign × E/MVt-1
Foreign × ∆Et
Foreign × ∆Et+1
Foreign × ∆Et+2
Foreign × ∆Et+3
Foreign × ∆E (t+1 to t+3)
Foreign × R(t+1 to t+3)
Foreign(0.027)0.005(0.024)-0.334***(0.091)-0.058*(0.034)-0.016(0.027)0.120**(0.058)-0.005(0.032)-0.010(0.029)-0.181***(0.059)-0.091(0.059)

Yes

Yes

0.001
-0.002(0.002)(0.002)-0.0040.022***(0.006)(0.007)-0.022***0.004(0.008)(0.009)-0.005** -0.023***(0.002)(0.003)Foreign × MV/TAt-1
MV/TAt-1
Inverse Mills ratio
Interaction between earnings innovations(or MV/TA) and all bank controls
Bank controls
Country controls
Country FE
Year FE
Observations

Yes
Yes

-0.000(0.002)0.019***(0.007)0.010(0.010)-0.013***(0.003)

Yes

Yes

Yes

Yes

Yes

Yes

Yes
Yes
Yes

Yes

Yes

Yes
4,232

5,048

50

Table A-2.6
Endogenous Treatment-Regression Model for the Host Country Characteristics Tests (in Panel B of Table 3 in the main paper)This table reports the estimation of the endogenous treatment-regression models for our robustness tests in Panel B of Table 3 in the main paper.
In the treatment equation (probit regression) (column 1), we model the likelihood of foreign-bank status (Foreign) as a function of two instrumental
variables, bank and country controls, and year fixed effects. The dependent variable is the price synchronicity of bank stocks (SYNCH). The main
variable of interest is Foreign, which equals 1 when a bank has 50% or more foreign ownership, and zero otherwise. The bank controls are
identical to those in the baseline model and are reported in appendix A-2. Intercepts are also suppressed. Advanced is an indicator of advanced
economies for the host countries following the classification by IMF, and zero otherwise. Civil law is an indicator for civil law host countries, andzero for the common laws host countries or others. Standard errors are clustered at the bank level. The *, **, and *** symbols denote statisticalsignificance at the 10%, 5%, and 1% levels, respectively.
Treatment equation
SYNCH
(1)
(2)
(3)
(4)
(5)
(6)
Ln(Latitude)
0.327***(0.048)Ln(1+Distance of bank city to capital
city)-0.040***(0.012)Foreign-1.083***-1.061***-1.119***-0.993***-1.000***(0.158)(0.157)(0.179)(0.172)(0.179)Advanced-0.039(0.130)Foreign × Advanced-0.050(0.088)Financial development
0.000(0.000)Foreign × Financial development-0.090(0.084)
Stock market cap/GDP-0.020(0.036)
Foreign × Stock market cap/GDP
0.048(0.031)
GDP Growth-1.653**(0.705)
Foreign × GDP Growth
0.223(1.104)
Civil law
0.399***
51

Foreign × Civil law
ROA
Non-interest income/TA
Deposits/TA
Ln(TA)
CapitalLoan loss provisions/TA
Cross-listing indicator-3.037(2.370)-1.556(1.691)0.919***(0.167)0.068***(0.018)2.589***(0.535)2.419(3.128)-0.082(0.059)4.082***(1.368)0.578(0.827)0.365***(0.087)0.450***(0.011)2.859***(0.316)1.842(1.763)0.261***(0.034)0.373***(0.087)
Yes
Yes
Yes

Inverse Mills ratios
Country controls
Country FE
Year FE
Observations

Yes
Yes

52

4.057***(1.372)0.588(0.829)0.371***(0.087)0.450***(0.011)2.872***(0.317)1.806(1.767)0.259***(0.034)
0.391***(0.088)
Yes
Yes
Yes
5,132

4.015***(1.368)0.549(0.826)
0.362***(0.087)0.449***(0.011)2.864***(0.318)1.829(1.763)0.264***(0.034)0.362***(0.094)
Yes
Yes
Yes

4.299***(1.359)0.641(0.819)0.358***(0.086)0.447***(0.011)2.803***(0.314)1.587(1.750)0.263***(0.034)0.309***(0.085)
Yes
Yes
Yes

(0.110)-0.080(0.089)4.113***(1.364)0.543(0.824)0.359***(0.086)0.449***(0.011)2.853***(0.314)1.874(1.757)0.264***(0.034)0.346***(0.084)
Yes
Yes
Yes

Table A-2.7
Endogenous Treatment-Regression Model for the baseline modelThis table reports the estimation results of an endogenous treatment-regression model. In the treatment
equation (probit regression), we model the likelihood of foreign-bank status (Foreign) as a function of
two instrumental variables, bank and country controls, and year fixed effects. The two instrumental
variables are Ln(latitude), which is the natural logarithm of transformed latitude (in decimal form)
measuring the geographical distance between the bank’s city of location (in the host country) and theequator, and Ln(1+Distance from bank city to capital city), which is the natural logarithm of 1 plus thegeographical distance (in miles) from the bank’s city of location to the host country’s capital city. The
dependent variable is the price synchronicity of bank stocks (SYNCH). The bank and country controls
are identical to those used in the baseline models. The *, **, and *** symbols denote statisticalsignificance at the 10%, 5%, and 1% levels, respectively.
Treatment equation
SYNCH
(1)
(2)
Ln(Latitude)
0.327***(0.048)Ln(1+Distance of bank city to capital city)-0.040***(0.012)Foreign-0.306**(0.136)
ROA-3.0375.349***(2.370)(1.398)Non-interest income/TA-1.5562.789***(1.691)(0.846)
Deposits/TA
0.919***
0.145*(0.167)(0.081)
Ln(TA)
0.068***
0.368***(0.018)(0.010)Capital2.589***
1.006***(0.535)(0.310)Loan loss provisions/TA
2.419
7.203***(3.128)(1.780)
Cross-listing indicator-0.0820.352***(0.059)(0.032)Inverse Mills ratios-0.016(0.077)
Intercept-2.273***-2.183***(0.465)(0.190)Country controls
Yes
Yes
Year FE
Yes
Yes
Observations
5,132

53

Table A-2.8
Endogenous Treatment-Regression Model for the Disclosure Quality Tests (in Table 8 of the main paper)This table reports the estimation results of endogenous treatment-regression models examining the link between
foreign bank ownership and disclosure quality. In the treatment equation (probit regression), we model the likelihoodof foreign-bank status (Foreign) as a function of two instrumental variables, bank and country controls, and year fixed
effects. The two instrumental variables are Ln(latitude), which is the natural logarithm of transformed latitude (indecimal form) measuring the geographical distance between the bank’s city of location (in the host country) and theequator, and Ln(1+Distance from bank city to capital city), which is the natural logarithm of 1 plus the geographicaldistance (in miles) from the bank’s city of location to the host country’s capital city. the dependent variables are ALLP
1 and ALLP 2, which are the estimated discretionary loan loss provisions. ALLP 2 is the absolute of the negative
residuals from the first-step regression of equation (4). The bank controls used are identical to those of the baselinemodels. The estimates for the bank controls are reported in appendix A-2. We cluster standard errors at the bank level.
The *, **, and *** symbols denote statistical significance at the 10%, 5%, and 1% levels, respectively.
Treatment equation(1)
Ln(Latitude)
0.349***(0.064)Ln(1+Distance of bank city to capital city)-0.006(0.014)Foreign
ROA
Non-interest income/TA
Deposits/TA
Ln(TA)
CapitalLoan loss provisions/TA
Cross-listing indicator-9.559**(3.824)4.837*(2.675)0.482**(0.217)0.047**(0.024)
3.112***(0.854)0.801(4.703)-0.147**(0.073)Inverse Mills ratios
Intercept
Country controls
Bank FE
Year FE
Observations-1.819***(0.616)
Yes
Yes
Yes
3,499

54

ALLP 1(2)-1.193***(0.359)-8.644**(3.510)16.348***(3.360)-0.535*(0.281)-0.081(0.060)-0.924(1.060)7.538*(4.103)-0.075(0.077)
0.526***(0.170)-3.670***(0.691)Yes
Yes

Treatment equation(3)0.309***(0.082)0.001(0.018)-10.274**(4.894)9.779***(3.357)
0.521**(0.264)0.084***(0.030)2.524**(1.097)3.108(7.223)-0.271***(0.090)-1.560**(0.765)
Yes
Yes
Yes
2,389

ALLP 2(4)-0.857**(0.370)-11.897***(3.866)12.355***(4.293)-0.715**(0.310)0.097(0.062)
1.573(1.107)-12.775**(4.976)-0.088(0.074)0.276*(0.167)-5.475***(0.709)Yes
Yes

Table A-2.9
Alternative Earnings Innovation Tests Following Bai et al. (2016)
This table reports the results for an alternative test of earnings innovations following the specification of
Bai et al. (2016). Specifically, we estimate the following cross-sectional regression model (estimatedusing the between-effects estimator):
ROAit+h = β0 + β1 MV/TAit-1 + β2 Foreignit-1 + β3 MV/TAit-1 × Foreignit-1+ δ1 Xit-1 + δ2 (Xit-1 × MV/TAit-1) + Country FE + εit ,where ROAit+h is net income to total assets of bank i at year t+h where h ranges from 0 to 2. MV/TAit-1 ismarket capitalization to total assets for bank i at year t-1. We control for the interaction between MV/TAit1 and the set of baseline bank controls.
In the table, the dependent variable is the ratio of net income to total assets (ROA), measured at year t,
t+1, and t+2. MV/TAt-1 is the ratio of market capitalization to total assets. The bank control variables are
identical to those used in the baseline models. We only report the estimates for the variables of interests
for brevity. Standard errors are clustered at the bank level. The *, **, and *** symbols denote statisticalsignificance at the 10%, 5%, and 1% levels, respectively.
ROAt
(1)
0.001
(0.005)0.002(0.001)-0.021***(0.006)
Yes
Yes
7,361
0.653

Foreign × MV/TAt-1
Foreign
MV/TAt-1
Interaction between MV/TAt-1 and other controls
Country FE
Obs.
Adj. R2

55

ROAt+1(2)
0.021***(0.006)0.002(0.002)0.002(0.008)
Yes
Yes
7,212
0.600

ROAt+2(3)0.013**(0.007)0.004**(0.002)0.005(0.009)
Yes
Yes
7,070
0.511

Table A-2.10
Foreign Bank Ownership and Disclosure Quality
This table examines the relationship between foreign bank ownership and disclosure quality. The main variable of interest is Foreign, whichequals one when a bank has 50% or more foreign ownership, and zero otherwise. Panel A reports the estimation on our current sample consistingof 60 countries of all listed banks. Panel B reports the estimation using an extended sample consisting of 134 countries with both listed and unlistedbanks.
Following Beatty and Liao (2014) and Jiang et al. (2016), we use a two-step procedure to estimate a bank’s “abnormal” loan loss provisions. In
the first-step, we estimate a model to separate “normal” loan loss provisions (explained by bank fundamentals) from “abnormal” or discretionary
loan loss provisions (unaccounted for by bank fundamentals) written as follows:
LLP/TLijt = β0 + β1 ∆NPL/TLijt+1 + β2 ∆NPL/TLijt + β3 ∆NPL/TLijt-1+ β4 Ln(TA)ijt-1 + β5 ∆TL/TLijt-1 + Country FE + Year FE + εijt ,where LLP/TL is the ratio of loan loss provisions to total loans, ∆NPL/TL is the change in nonperforming loans scaled by total loans, and ∆TL/TL
is the change in total loans scaled by total loans. Following Bushman and Williams (2012) and Jiang et al. (2016), we include the lead,contemporaneous, and lag terms of ∆NPL/L because banks may make loan loss provisions based on forward-looking, current, or historicalinformation about nonperforming loans. We include country and year fixed effects in the model to account for the effects of time-invariant country
characteristics and market-wide shocks on loan loss provisions. On the current sample (extended sample), we estimate this pooled OLS modelusing all publicly listed (all publicly listed and private) banks from the 60 (134) countries over the sample period.
In the second step, we take the natural logarithm of the absolute residuals from the model and use this measure (ALLP 1) to capture discretionary
loan loss provisions that are unaccounted for by the regressors in the above equation. A potential concern is that the positive residuals (which
reduce earnings) may represent accounting discretion that enhances transparency and, hence, the absolute-residuals measure may be subject tomeasurement errors. To this end, we exclude the positive residuals and take the natural logarithm of the absolute negative residuals to construct
an alternative disclosure-quality measure (ALLP 2).
For models (1) and (2), the dependent variables are ALLP 1 and ALLP 2, which are the estimated discretionary loan loss provisions. ALLP 2 isthe absolute of the negative residuals from the first-step regression of equation (4). In models (3) to (5) of panel A (panel B), we construct strong
and weak (strong, medium, and low) governance indicators based on sample medians (using the 30th and 70th breakpoints) for the anti-director
rights index, anti-self-dealing index, and the WGI. We interact Foreign with these governance indicators to explain ALLP 2. The bank controls
used are identical to those of the baseline models. The estimates for the bank controls are reported in appendix A-2. We cluster standard errors at
the bank level. The *, **, and *** symbols denote statistical significance at the 10%, 5%, and 1% levels, respectively.
Panel A: Our Current Sample of 60 Countries – All Listed Banks
56

Dependent variables

ALLP 1

ALLP 2

ALLP 2Governance indexes
Foreign(1)-0.239**(0.121)(2)-0.382***(0.136)

Foreign×Strong governanceForeign×Weak governanceBank controls
Bank FE
Year FE
Obs.
Adj. R2

Yes
Yes
Yes
4914
0.323

Yes
Yes
Yes
3305
0.412

Anti-director rights
index
(3)

Anti-self-dealing
index
(4)

-0.439*(0.249)-0.403(0.272)
Yes
Yes
Yes
3305
0.412

-0.546*(0.279)-0.314(0.219)
Yes
Yes
Yes
3305
0.412

WGI
(5)

-0.773***(0.181)-0.204*(0.112)
Yes
Yes
Yes
3305
0.414

Panel B: An Extended Sample Consisting of 134 Countries, Including Both Listed and Unlisted Banks
Dependent variables

ALLP 1

ALLP 2Governance indexes

Foreign(1)-0.297***57(2)-0.288***ALLP 2
Antidirector
rightsindex(3)Anti-selfdealing
index

WGI

(4)

(5)

(0.090)(0.109)Foreign×Strong Governance (>70%)
Foreign×Medium Governance (>30% and <=70%)
Foreign×Weak Governance (<=30%)
Bank controls
Bank FE
Year FE
Obs.
Adj. R2

Yes
Yes
Yes
14378
0.373

58

Yes
Yes
Yes
9536
0.432

-0.498***(0.193)-0.293**(0.139)-0.376***(0.146)
Yes
Yes
Yes
9536
0.432

-0.482***(0.186)-0.372***(0.137)-0.279*(0.158)
Yes
Yes
Yes
9536
0.432

-0.451***(0.145)-0.312***(0.119)-0.163(0.124)
Yes
Yes
Yes
9536
0.432

Table A-2.11
Foreign Bank Ownership vs. Alternative Governance Mechanisms
This table examines whether foreign bank ownership and alternative governance mechanisms are complements or substitutes in explaining pricesynchronicity. The dependent variable is the price synchronicity of bank stocks (SYNCH). The main variable of interest is Foreign, which equals
1 when a bank has 50% or more foreign ownership, and zero otherwise. Capital regulatory index is an index of overall capital stringency anddefined as the sum of the answers to several survey questions relating to the extent of regulatory requirements in bank capital and to the sourcesof funds that count as regulatory capital. Official supervisory power is a measure of the power of supervisory agencies, measured using 14
questions on the extent to which supervisory authorities have the authority to take actions in preventing and correcting problems. Private
monitoring index is a measure of the extent of bank disclosure requirements and incentives to increase private monitoring. External governanceindex is the sum of six indicators of external governance, in the four worldwide surveys, relating to the strength of external audit, banks’ financial
disclosure requirements, accounting practices, and external ratings and creditor monitoring. Board major reform (Independence) is a dummy that
equals one after a country has adopted a major board reform relating to board independence, and zero prior to the reform. Board major reform(Audit committee) is a dummy that equals one after a country has adopted a major board reform relating to audit committee and auditor
independence, and zero prior to the reform. The timing of the board reforms is collected from Fauver et al. (2017). The bank controls are identicalto those used in the baseline model. Country and year fixed effects are included in all models. Standard errors are clustered at the bank level. The*, **, and *** symbols denote statistical significance at the 10%, 5%, and 1% levels, respectively.
SYNCH

Foreign
Capital regulatory index
Foreign × Capital regulatory index
Official supervisory power
Foreign × Official supervisory power(1)-0.183(0.304)-0.020(0.026)-0.022(0.040)External governance(2)(3)-0.157-0.438(0.355)(0.399)0.027*(0.015)-0.017(0.030)Private monitoring index

0.007(0.019)0.007(0.047)

Foreign × Private monitoring index

59(4)-0.306(0.478)Internal governance Board major reforms(5)(6)-0.256***-0.324***(0.096)(0.117)External governance index

0.033(0.022)0.000(0.032)Foreign × External governance index
Board major reform (Independence)

0.049(0.055)-0.195*(0.118)Foreign × Board major reform (Independence)
Board major reform (Audit committee)
Foreign × Board major reform (Audit committee)
ROA
Non-interest income/TA
Deposits/TA
Ln(TA)
CapitalLoan loss provisions/TA
Cross-listing indicatorIntercept
Country FE
Year FE
Obs.
Adj. R2

4.592*(2.393)1.765(1.855)0.574***(0.197)
0.483***(0.028)3.642***(0.617)-2.000(3.245)0.219***(0.076)-4.258***(0.431)
Yes
Yes
3,526
0.499

4.698***(1.710)0.911(1.223)0.390**(0.165)0.462***(0.025)3.080***(0.514)-1.569(2.137)0.262***(0.061)-4.453***(0.405)
Yes
Yes
5,898
0.489

60

3.945**(1.601)
1.192(1.153)0.429***(0.155)0.465***(0.024)
3.166***(0.491)-2.299(2.018)0.255***(0.058)-4.329***(0.390)
Yes
Yes
6,668
0.495

5.874**(2.313)-0.547(1.681)0.470**(0.193)0.448***(0.030)3.366***(0.695)-1.498(3.051)0.278***(0.070)-4.651***(0.488)
Yes
Yes
3,765
0.478

4.180**(1.659)-0.012(1.200)0.514***(0.159)0.456***(0.024)3.305***(0.515)-1.309(2.220)0.277***(0.058)-4.227***(0.341)
Yes
Yes
6,216
0.531

0.016
(0.052)-0.060(0.135)4.095**(1.660)0.055(1.198)0.511***(0.159)0.457***(0.024)3.321***(0.514)-1.304(2.241)0.280***(0.058)-4.214***(0.347)
Yes
Yes
6,216
0.530

Additional References
Bai, J., Philippon, T. & Savov, A., 2016. Have financial markets become more informative? Journal of
Financial Economics, 122(3), 625–654.
Beatty, A. & Liao, S., 2014. Financial accounting in the banking industry: A review of the empiricalliterature. Journal of Accounting and Economics, 58(2-3), 339–383.
Bushman, R.M. & Williams, C.D., 2012. Accounting discretion, loan loss provisioning, and discipline ofbanks’ risk-taking. Journal of Accounting and Economics, 54(1), 1–18.
Fama, E. & MacBeth, J., 1973. Risk, return and equilibrium: Empirical tests. Journal of Political Economy,
81(3), 607–636.
Fauver, L., Hung, M., Li, X. & Taboada, A. G. (2017). Board reforms and firm value: Worldwide
evidence. Journal of Financial Economics, 125(1), 120-142.
Jiang, L., Levine, R. & Lin. C., 2016. Competition and bank opacity. The Review of Financial Studies,
29(7), 1911–1942.

61

NOTES
1

Many posit that foreign banks benefit local markets and trade by boosting credit supply, local banking
competition, financial development, and exports (see, e.g., Clarke et al., 2005; Giannetti and Ongena, 2009, 2012;
Bruno and Hauswald, 2014; Bronzini and D'Ignazio, 2017; Claessens et al., 2017). Others find that foreign banks
transmit adverse shocks from home to host countries and threaten stability (see, e.g., Detragiache et al., 2008; Gormley,
2010; Cetorell and Goldberg, 2012; Houston et al., 2012).
2
Market-based oversight includes the use of stock market information by regulators or supervisors to assess bankfinancial conditions, gauge managerial performance, and inform disciplinary actions and intervention policies (see,e.g., Flannery, 1998, 2001; Greenspan, 2001; Distinguin et al., 2006; Nier and Baumann, 2006; Kang and Liu, 2008;
Knaup and Wagner, 2012).
3
The increasing trends in banking globalization are best described in Claessens and van Horen (2014), whomanually compile a database of bank ownership covering over 5,000 banks from 137 countries over 1995-2009. Theirstatistics show that the number of local banks decreases by about 18%, whereas the number of foreign-owned banks
increases by 69% over the period. Such increases in foreign-owned banks are more pronounced in developing markets(111% increase) than in OECD countries (38% increase). Most foreign-owned banks are from OECD countries, andthis composition remains similar over the years. Moreover, the local share of foreign bank assets increased from 20%
in 1995 to 34% in 2009 (Claessens and van Horen, 2015).
4
Foreign bank ownership could be viewed as an internal governance mechanism in which foreign owners directly
monitor management and control their operational and accounting choices. In principle, different governancemechanisms could complement or substitute each other (see John and Senbet, 1998; Aguilera et al., 2015). For instance,
greater monitoring by blockholders such as foreign bank owners may reduce the need for monitoring by bank directors.
On the other hand, the foreign owners’ monitoring may become more effective as a result of advisory board directors
who possess strong industry-specific skills and experience. Moreover, foreign bank owners may be more efficient inmonitoring management in host countries with strong legal systems, shareholder protections, and banking disclosure
regulations. In section 4.6, we use untabulated analysis to explore potential interactions between foreign bankownership and several country-level proxies of external and internal governance mechanisms.
5
We thank the authors for making the data publicly available.
6
Our results are qualitatively similar if we instead denominate the variables in the local currencies (unreported).
7
Because the independent variables are lagged one year, the independent variables of 1996 explain the dependent
variable of 1997. Therefore, our final sample covers 1997-2012.
8
The distance database is available via http:// www.cepii.fr/anglaisgraph/bdd/distances.htm.
9
The use of Wednesday-to-Wednesday returns helps mitigate problems of thin trading as well as otherconfounding seasonal effects, such as the Monday effect. Following Francis et al. (2015), we trim bank stock weekly
returns that are below -25% or above +25% to avoid potential coding errors in DataStream.
10
The authors exclude countries with few banks, remove smaller banks and holding companies, and adjust forbank mergers, as well as the timing of entry and exit. For more details, please refer to Claessens and van Horen (2014).
11
After we obtain the predicted SYNCH from the fitted model (holding all other variables at their mean values)
for both foreign and local banks, we invert equation (2) to solve for the implied R2:
R2= exp(SYNCH)/(exp(SYNCH)+1).
Hutton et al. (2009) use a similar approach in quantifying economic magnitude.
12
Common law countries have greater shareholder protections than civil law countries, which could in turninfluence financial market development (La Porta et al., 1998; La Porta et al., 2008).
13
We also use a propensity score matching approach to address potential endogeneity problems. Our results showthat foreign-owned banks have on average 16.2% lower implied R2 than domestic banks (significant at the 1% level),matched by baseline bank characteristics and country and year fixed effects using propensity score matching
techniques. The details and are reported results are in Table A-2.4 of the appendix A-2.
14
Furthermore, we estimate Heckman-type endogenous treatment-regression models following the two-stepestimator of Maddala (1983). In the first stage, we obtain probit estimates from a treatment equation that models theforeign bank status as a function of the two instrumental variables (in section 4.3), bank and country controls, andyear fixed effects. We augment the regression model via the inverse Mills ratio. We estimate the endogenous
treatment-regression models for the baseline model specification (in table 3), the robustness tests controlling for hostcountry characteristics (panel B of table 3), the earnings innovations tests (in table 5), and the robustness tests basedon discretionary loan loss provisions (in Table A-2.10 of the appendix A-2). Although estimates for the inverse Mills

62

ratio are significant in most cases (except for the model under the baseline specification), the coefficients of Foreign
remain similar in significance. These results are in tables IA.5, IA.6, IA.7, and IA.8 of the appendix A-2, respectively.
15
We need instruments that are correlated with Foreign (the inclusion restriction) but are uncorrelated withSYNCH except through the variable (the exclusion restriction). Finding such instruments is extremely difficult; hence,
we acknowledge that our instruments are far from perfect but instead represent a best effort.
16
See, for example, Acemoglu et al. (2001), Beck et al. (2003), Bose et al. (2014), etc.
17
To be a valid instrument, initial endowments should not directly determine price synchronicity. There is some
support from the literature. For instance, Hall and Jones (1999) find evidence, later confirmed by Acemoglu et al.
(2001), that distance from the equator does not determine economic performance (Hall and Jones, 1999). Though theassumption of instrument exogeneity is untestable, we shed some light by analyzing the pairwise correlation between
Ln(Latitude) and SYNCH in our sample. The correlation coefficient is 0.01 and is statistically insignificant.
18
We treat all explanatory variables except year dummies as endogenous and instrumented them with the t-3 tot-5 lagged dependent and independent variables for the difference equation and their respective lagged differences forthe level regression.
19
Dasgupta et al. (2010) show that stock price synchronicity is positively associated with market transparency
because more informative stock prices today imply that less private information is incorporated into stock prices in
the future. Lee and Liu (2011) find a U-shaped relationship between idiosyncratic return variation and several
alternative measures of price informativeness. Xing and Anderson (2011) document an inversely U-shaped
relationship between proxies for public firm-specific information and stock return synchronicity.
20
Because the surveys were completed in 1999, 2003, 2007, and 2012, following Barth et al. (2013b), we use thefirst survey of 1999 for the regulation variables for 1997-2001. The values for the regulatory variables in 2002-2004
are from the second survey of 2003. We use the third survey of 2007 for the regulatory variables for 2005-2007. The
last survey of 2012 determines the values of the regulatory variables for 2008-2012.
21
We collect the pair of latitude and longitude coordinates (in decimals) for each foreign bank’s city of location(in the host city) and its home country’s capital city from www.mapsofworld.com. We use spherical geometry andtrigonometric math functions to calculate accurate distances between locations. Specifically, we first convert each pairof latitudes and longitudes from decimal degrees to radians by dividing their values by 180/π or 57.296. Lat1 (Lat2)and Long1 (Long2) represent the latitude and longitude in radians of a foreign bank’s city of location (home country’s
capital city). We then use the great circle distance formula to calculate the distance in miles between two pairs oflatitudes and longitudes, as follows:
Distance = 3963 × Arccos[Sin(Lat1)Sin(Lat2) + Cos(Lat1)Cos(Lat2)Cos(Long2-Long1)],
where 3,963 is the radius of the Earth in miles.
22
Because we do not observe relative distance in governance and banking regulation variables for the nonforeign-owned banks, our sample is incidentally truncated and thus subject to a potential sample-selection bias. If theunobserved variables that determine whether a bank receives foreign ownership correlate with those that determine
its price synchronicity, our regression estimates would be biased. In an unreported analysis, to control for this potential
sample-selection bias, we use the Heckman (1979) two-stage procedure. Using the bank and country controls, year
fixed effects, and the two instruments as defined in section 4.3, we estimate a first-stage probit regression to identify
the likelihood of the bank being foreign-owned. We include the estimated inverse Mills ratio in the second-stage pricesynchronicity regressions to account for a potential correlation between the error of the first-stage probit and thesecond-stage OLS regressions. Because the inverse Mills ratios in all models are insignificant, the sample-selection
bias is likely not severe, and, hence, we drop them in the main analysis. These unreported results are available uponrequest.

63