Foreign Institutional Ownership, Risk-Taking, and Crash ... · Crash Risk around the World Abstract...
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Foreign Institutional Ownership, Risk-Taking, and
Crash Risk around the World
Abstract
Using a large sample of 48,548 firms in 72 countries from 2000 to 2008, we examine how foreign
institutional ownership affects corporate risk-taking and stock price crash risk. We find foreign
institutional ownership is positively associated with corporate risk-taking, while domestic institutional
ownership is negatively associated with it. Foreign institutional ownership substitutes for country-level
corporate governance in determining corporate risk-taking. Crash risk is a potential negative side effect
associated with risk-taking. We find when a firm takes more risks, foreign direct investors’ ownership
significantly reduces the firm’s crash risk. In contrast, domestic institutional investors’ ownership
significantly increases it.
Key words: Institutional ownership, risk taking, crash risk
JEL classification: G32, G34
Garland Huang
Australian Business
School
University of New South
Wales
Kensington NSW 2033 Sydney, Australia
Email:garland.huang@st
udent.unsw.edu.au
Donghui Li
Australian Business
School
University of New South
Wales
Kensington NSW 2033 Sydney, Australia
Email:[email protected].
au
Sheng Xiao
Gore Business School
Westminster College
1840S 1300 S Salt Lake City, UT 84105
USA
Email:
Zhe An
Australian Business
School
University of New South
Wales
Kensington NSW 2033 Sydney, Australia
Email:[email protected]
.au
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1. Introduction
Corporate innovations are a major determinant of firm performance and economic growth.1 To
innovate, firms must take risks. On one hand, such risk-taking activities may lead to breakthroughs in
business models and technology. On the other hand, such risk-taking activities may also magnify the
crash risk of a firm’s stock price, especially when the firm manager hoards bad news. The ownership
structure of a firm, especially the firm’s institutional ownership, has significant effects on both corporate
risk-taking activities and stock price crash risk (henceforth “crash risk”).2However, not all institutional
investors play the same role in affecting firms’ risk-taking activities and crash risk. We examine how a
particular type of institutional owner, the foreign institutional owner, affects a firm’s risk-taking
behaviors and crash risk. For the purpose of comparison, we also examine how domestic institutional
owners affect a firm’s risk-taking behaviors and crash risk. Our research is inspired by cross-country
research by Ferreira and Matos (2008), who find that firms with higher foreign institutional ownership
have higher valuations and better operating performance. Our research provides an important channel
through which foreign institutional ownership leads to better firm performance: promoting corporate risk-
taking.
We focus on foreign institutional ownership for several reasons: first, foreign institutional owners
are likely to be stronger monitors, as they tend to have fewer conflicts of interest with the firm. In
contrast, domestic owners are unlikely to have the same authority over a firm due to their existing
business relationships with managers, which may prevent them from being efficient monitors (Ferreira
and Matos 2008). Second, diversification is more prominent among foreign owners due to their
1See, e.g., Hirshleifer, Hsu, and Li (2013); Mankiw, Romer, and Weil (1992).
2Papers on how institutional investors affect corporate risk-taking and innovative activities include:
Wright, Perris, Sarin, and Awasthi (1996); Faccio, Marchica, and Mura (2011); Aghion, Reenen, and
Zingales (2013), among others. Papers on how institutional investors affect crash risks include: An and
Zhang (2013); Callen and Fang (2013), among others.
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internationally diversified portfolios. Such international diversification motivates foreign owners to push
managers to take more risks (Faccio, Marchica, and Mura 2011).Third, foreign institutional investors
bring additional funds that reduce the reliance of the invested firm on banks, which usually influence a
firm to pursue conservative investment policies (Morck and Nakamura 1999). These three factors imply
that foreign institutional investors, compared with domestic institutional investors, can better motivate
managers to take sufficient risks. Therefore, foreign institutional ownership is expected to be positively
associated with corporate risk-taking. On the other hand, foreign institutional owners may face more
severe information asymmetry (Chan, Menkveld, and Yang2008; Portes and Rey 2005). As a result, they
may be less effective monitors. This implies that foreign institutional ownership is expected to be
negatively associated with corporate risk-taking. Therefore, whether foreign institutional ownership
increases or decreases corporate risk-taking activities is an empirical question.
John, Litov, and Yeung (2008) show that country-level governance institutions such as investor-
protection mechanisms also promote corporate risk-taking. A natural question to ask is how foreign
institutional ownership and country-level governance institutions jointly affect corporate risk-taking. That
is, are they substitutes or complements? We empirically test their interactions in the determination of
corporate risk-taking.
Existing literature appears to focus on the positive effects of corporate risk-taking on corporate
growth and economic growth (John, Litov, and Yeung 2008). However, it is also important to consider its
potential negative side effects. One of the most fundamental negative side effects associated with risk-
taking is the increase in the firm’s crash risk. This is because managers who are motivated to actively
seek risky projects will inherently face increased exposure to losses as a result of these risky projects.
Managers who incur these losses tend to regularly manage earnings by withholding bad news due to
managerial incentives such as career concerns and compensation contracts (Kothari, Shu, and Wysocki
2009). As a result, bad news associated with poorly performing projects tends to stockpile within a firm.
When bad news accumulates to a certain threshold, managers are no longer able to hide the bad news
effectively, and all the negative information will be released to the market at once. This leads to an
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extreme decline in stock price, which is the “crash risk” (Hutton, Marcus, and Tehranian 2009; Kim, Li,
and Zhang 2011a, b).
How do foreign institutional owners affect firms’ crash risk? Cornett, Marcus, and Tehranian
(2008) find that institutional investors are able to constrain firm earnings management. Therefore, we
hypothesize that if foreign institutional owners are effective monitors, they will reduce their firms’ crash
risk. This hypothesis is also based on the fact that foreign owners face more severe information
asymmetry than domestic owners. Therefore, they generally demand more corporate transparency, which
allows shareholders to discriminate between good and bad projects at an early stage, thus reducing the
crash risk associated with bad projects. For example, Bleck and Liu (2007) argue that greater financial
reporting opacity increases the crash risk of the firm’s stock price.
Considering the heterogeneity of foreign institutional owners, we further examine the effect of
foreign institutional ownership in two different forms of investments: (1) Foreign direct investment (FDI),
defined as a long-term-relationship investment that reflects a lasting interest and control of a resident
entity in an economy other than that of the foreign direct investor (UNCTAD(United Nations Conference
on Trade and Development)2002) and (2) Foreign portfolio investment (FPI), representing passive
holdings of foreign securities such as ownership through stocks and bonds. FPI investors generally have
shorter investment time horizons and do not exert control over firms. We hypothesize that FDI ownership
significantly reduces crash risk while FPI ownership has insignificant effects on crash risk. This is
because FDI investors are long-term institutional investors who tend to be stronger monitors than short-
term institutional investors (Chen, Harford, and Li 2007).
Examining 48,548 firms in 72 countries from the years 2000 to 2008, we find that foreign
institutional ownership is positively associated with corporate risk-taking. We also find that foreign
institutional ownership substitutes for country-level governance institutions (information transparency,
legal environment, and investor protection) in determining corporate risk-taking. Further, we examine
how foreign institutional ownership affects crash risk, a potential negative side effect associated with
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additional risk-taking. We find that when a firm takes more risks, foreign direct ownership significantly
reduces the firm’s crash risk. In contrast, risk-taking by domestic owners significantly increases it.
Our study contributes to the literature in several ways. First, to the best of our knowledge, we are
the first to show that foreign institutional ownership promotes corporate risk-taking in a large sample of
48,548 firms in 72 developing and developed countries. This shows that foreign institutional owners are
indeed stronger monitors, despite their information disadvantages compared with domestic institutional
owners. We are also the first to show that foreign institutional ownership and country-level governance
institutions are substitutes. The research that most resembles ours is Boubakri, Cosset, and Saffar (2013),
who show that foreign institutional owners promote corporate risk-taking in a smaller sample of 381
newly privatized firms in 57 countries. But Boubakri, Cosset, and Saffar (2013)find foreign institutional
ownership and country-level governance institutions are complements in the determination of corporate
risk-taking while we find they are substitutes. Our research not only significantly expands their dataset
beyond 381 newly privatized firms (a special group of firms that have experienced the change of control
from the government to private owners), but also finds some results that are exactly opposite to those in
Boubakri, Cosset, and Saffar (2013).
Second, to the best of our knowledge, we are the first to show that despite the fact that foreign
institutional investors lead to more corporate risk-taking, they significantly lower the crash risk. To
further investigate which foreign institutional investors are responsible for the decrease in crash risk, we
differentiate between foreign direct investors and foreign portfolio investors. We find that foreign direct
investors significantly reduce the crash risk of a firm while foreign portfolio investors have an
insignificant effect on the crash risk. In contrast, domestic institutional owners exacerbate crash risk. Our
research contributes to the nascent literature on the determinants of crash risk.
Our research is important in the context of increasing globalization, a result of the recent wave of
capital market liberalizations around the world. According to the World Investment Report2010 and 2013,
global FDI rose from $154 billion in 1991 to $1.35 trillion in 2013, and global foreign portfolio equity
investments increased from $106 billion in 1991 to $744 billion in 2010. As foreign capital becomes an
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increasingly important source of financing around the world (Bekaert, Harvey, and Lumsdaine2002), it is
essential to understand the effects of foreign institutional ownership on corporate decisions and
performance.
2. Literature Review and Hypothesis Development
Due to career and reputation concerns, managers tend to avoid taking risky projects even when
the investment enhances firm value (Amihud and Lev 1981; Hirshleifer and Thakor1992; Holmstrom and
Ricart I Costa 1986; Myers 1977). Further, unlike shareholders who are able to diversify their portfolios,
managers are unable to diversify their labor income. Consequently, managers tend to be risk averse to
new projects. However, corporate risk-taking by managers is of fundamental importance because it is
directly linked to corporate and economic growth (John, Litov, and Yeung 2008). As a result, motivating
managerial risk-taking has become a key concern to academia and industry practitioners. Existing
research focuses on how to align the interests of managers with those of shareholders by using various
macroeconomic mechanisms (e.g., investor protection) and microeconomic mechanisms (e.g., equity-
based compensation) so that managers are incentivized to take sufficient risks.3
In this paper, we focus on how foreign institutional owners affect corporate risk-taking activities
around the world. As discussed in the Introduction of this paper, foreign institutional ownership is
expected to have two opposite effects on corporate risk-taking. On one hand, the following features of
foreign institutional ownership are expected to promote corporate risk-taking: (1) the relative
independence of foreign institutional owners makes them better monitors; (2) they tend to be more
3For example, researchers have analyzed how corporate risk-taking is influenced by country-level
investor protection (John, Litov, and Yeung 2008), equity-based compensation (Jensen and Meckling
1976; Haugen and Senbet 1988; Smith and Stulz1985; Coles, Daniel, and Naveen, 2006; Low 2009;
Rajgopal and Shevlin 2002; Chen, Steiner, and Whyte 2006; Hagendorff and Valascas 2011), large-
shareholder diversification (Faccio, Marchica, and Mura 2011) and ownership structure (Boubakri,
Cosset, and Saffar2008; Anderson, Mansi, and Reeb 2003).
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diversified internationally; (3) they reduce firms’ reliance on bank financing, while banks tend to push
firms to adopt more conservative investment policies. On the other hand, compared with domestic
owners, foreign institutional investors may suffer from more severe information asymmetry with
managers, which will weaken the monitoring role of foreign institutional owners and lead to less
corporate risk-taking. Our above analysis leads to the following hypotheses regarding the net effect of
foreign institutional ownership on corporate risk-taking:
Hypothesis 1a(H1a): Foreign institutional ownership is significantly and positively associated
with corporate risk-taking.
Hypothesis 1b(H1b): Foreign institutional ownership is significantly and negatively associated
with corporate risk-taking.
In contrast, domestic institutional owners have information advantages over foreign institutional
owners which imply that they may be better monitors and would more effectively motivate managers to
take risks. On the other hand, domestic institutional owners may be less independent than foreign
institutional owners, which imply that they may be worse monitors and would less effectively motivate
managers to take risks. Therefore, we have the following hypotheses:
Hypothesis 2a(H2a): Domestic institutional ownership is significantly and positively associated
with corporate risk-taking.
Hypothesis 2b(H2b): Domestic institutional ownership is significantly and negatively associated
with corporate risk-taking.
Existing research indicates that good country-level corporate governance institutional
environments such as stronger investor protection and greater information transparency promote
corporate risk-taking (John, Litov, and Yeung 2008).It would be interesting to examine how foreign
institutional ownership and country-level corporate governance interact in influencing corporate risk-
taking. Due to the information disadvantage of foreign institutional owners (Brennan and Cao 1997; Kang
and Stulz 1997; Choe, Kho, and Stulz 2005; Leuz2006; Chan, Menkveld, and Yang 2008),foreign
institutional owners’ influence on managers is expected to be stronger in more transparent institutional
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environments where information is regularly available and investors are well protected. This implies that
foreign institutional ownership and country-level institutional environments are complementary.
On the other hand, Aggarwal, Erel, Ferreira, and Matos(2011) find that good corporate
governance practices “travel around the world” through institutional investors. Therefore, foreign
investors may substitute for poor institutional environments via the corporate governance spill-over effect
(i.e., foreign owners from countries with better governance institutions bring about more substantial
improvements in the firm-level governance of local firms in countries with worse governance institutions)
(Albuquerque, Durnev, and Koskinen 2013). For example, Rossi and Volphin (2004) find that firms
based in weak legal environments are more frequently acquired by firms from stronger legal
environments. On the other hand, in a country with good governance institutions, domestic investors are
able to advance their interests successfully and more easily influence managers to adopt riskier projects.
In such environments, the presence of foreign owners no longer exerts significant effects on corporate
risk-taking. The above analysis implies that foreign institutional ownership and country-level institutional
environments are substitutes. Our above analysis leads to the following hypotheses:
Hypothesis 3a(H3a): Foreign institutional ownership and country-level corporate governance
institutional environments are complements in motivating managers to take risks.
Hypothesis 3b(H3b): Foreign institutional ownership and country-level corporate governance
institutional environments are substitutes in motivating managers to take risks.
Although corporate risk-taking has been shown to boost corporate growth and economic growth
(John, Litov, and Yeung 2008), it may lead to an undesirable consequence: the crash risk of the firm’s
stock price may rise. This is because when risk-taking activities result in losses, managers tend to try to
hoard bad news until the negative shocks pile up to a level where managers can no longer hide them.
When negative shocks accumulate above a threshold, the firm finally releases huge amount of negative
information to the market at once, causing its stock price to drop precipitously. Managers hoard bad news
due to their earnings-based compensation contracts, as well as career and reputation concerns
(Kirschenheiter and Melumad 2002; Ball 2009). Kim, Li, and Zhang (2011b) find that the sensitivity of a
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chief financial officer’s (CFO’s) option portfolio value to stock price is significantly and positively
associated with the firms’ future crash risk. Hutton, Marcus, and Tehranian (2009) and Kim, Li, and
Zhang (2011a) find that firms that publish more opaque financial reports and actively manage earnings or
avoid taxes tend to be more prone to stock-price crashes, confirming Jin and Myers’s (2006) conjecture.
How do foreign owners affect a firm’s crash risk? Foreign owners are generally more
independent from firm managers than domestic owners, but they also face more severe information
asymmetry. Therefore, they demand more transparency than domestic owners to alleviate information
asymmetry. Foreign institutional owners’ commitment to increasing firm transparency reduces the
opaqueness of corporate financial reporting and lowers crash risk. Further, Mieno (2009) and Gurunlu and
Gursoy (2010) provide evidence that increased foreign institutional ownership reduces firm leverage. This
is because greater foreign ownership increases the capital available to the firm (Stiglitz 2000), and firms
prefer less costly internal funding over more costly debt (Myers and Majluf 1984). Meanwhile, the
presence of foreign owners increases the firm’s credit worthiness and expands its financing channels
(Csermely and Vincze2000). Hence, foreign institutional ownership reduces a firm’s likelihood of
financial distress and bankruptcy, which in turn reduces the firm’s crash risk. Based on the above
analysis, we propose the following hypothesis:
Hypothesis (H4): Foreign institutional ownership significantly lowers a firm’s crash risk.
We further classify foreign institutional investors into foreign direct investors and foreign
portfolio investors. Foreign direct investors generally have longer investment horizons and greater
commitments than foreign portfolio investors. These longer horizons and greater commitments make
foreign direct investors more active monitors than foreign portfolio investors. We therefore propose the
following hypothesis:
Hypothesis 5(H5): Foreign direct investor ownership significantly lowers a firm’s crash risk,
while foreign portfolio investor ownership has insignificant effects on a firm’s crash risk.
3. Data
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We obtain data from several sources. Firm ownership data are from two sources: Datastream and
FactSet Ownership (LionShares). Firm accounting data are from Worldscope. Country-level control
variables are from the World Development Indicators (WDI) compiled by the World Bank. Country-level
governance institutional environment data are from existing papers. Finally, to construct our crash-risk
measure, we collect weekly return data at both firm level and market level from Datastream.
3. 1. Sample
We construct our sample using all firms available in Datastream. Following previous studies, we
exclude firms in heavily-regulated financial and utility industries. To create our risk-taking variables, we
require that each firm have at least five consecutive years of earnings data available from Worldscope. To
preserve the consistency of our crash-risk measure, we require that there be no fewer than 26 weekly
stock returns available for a firm-year. If any variable of interest is missing for a given year, we remove
the firm-year observation. The resulting sample consists of 48,548 public firms from 72 countries
between 2000 and 2008. Table 1 reports the distribution of the sample by year, industry, and region.
[Insert Table 1 about here]
3. 2. Corporate Risk-Taking Measures
The primary measure of corporate risk-taking (RISK1) used in this study is the volatility of a
firm’s earnings (ROA) over overlapping five-year periods over the entire sample period (e.g., year 0 to
year 4; year 1 to year 5; year 2 to year 6). As alternative measures of corporate risk-taking, we also use
RISK2: company earnings range (defined as the maximum return on assets (ROA) over the overlapping
five-year window less the minimum ROA over the same period), RISK3: country-adjusted company-
earnings volatility, and RISK4: country-and-industry adjusted earnings volatility.4The firm’s earnings,
measured by the firm’s ROA, are computed by dividing earnings before interest and taxes by total assets.
4More details on the data definitions are available in the Appendix.
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The firm’s earnings volatility is measured by the sample standard deviation of ROA.5 This measure is the
same as the measure used in previous studies (Boubakri, Cosset, and Saffar 2013; John, Litov, and Yeung
2008; Acharya, Amihud, and Litov 2011; Faccio, Marchica, and Mura 2011; Hilary and Hui 2009). We
also include a market measure of risk-taking: stock return volatility (SRVOL) over a two-year period
beginning from the current fiscal year. As an alternative measure of corporate risk-taking, we also use
research and development (R&D) expenses over total assets over overlapping five-year periods. In this
paper, we focus on the first five measures: RISK1, RISK2, RISK3, RISK4 and SRVOL because a large
proportion of sample firms report zero R&D expenses over the sample period, resulting in insufficient
variations in the R&D variable.
3. 3. Crash-Risk Measures
Following previous studies (Chen, Hong, and Stein 2001; Jin and Myers 2006; Hutton, Marcus,
and Tehranian 2009; Kim, Li, and Zhang 2011a,b; An and Zhang 2013), we construct three crash-risk
measures: NCSKEW, DUVOL, and COUNT. First, following Jin and Myers (2006), we calculate the
demeaned firm-specific continuously compounded weekly returns for firm i in week t as the
demeaned natural logarithm of one plus the residual from the expanded market model regression:
where: is the stock return for firm i in week t, is the local market return for country j in week t,
is the United States (U.S.) market return in week t, is the change in country j’s exchange rate
vs. the U.S. dollar in week t.
5We filter out extremely large outliers from the sample. After filtering out these values, 98 percent of our
sample observations remain. We then winsorize ROA at the 1 percent level on both tails of the sample
distribution when constructing our risk-taking measures.
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The expanded market model includes two lead and lag terms to control for the non-synchronous
trading that affects both the local market returns and U.S. market returns (Dimson1979). Individual stock
returns that are not explained by the local and U.S. markets are considered firm specific and are captured
by the residual term . We use such firm-specific returns to calculate the firm-specific continuously
compounded weekly returns , which will be used to compute the three crash-risk measures below.
The first crash-risk measure, NCSKEW, is a measure of stock return asymmetry, which is the
negative skewness of the firm-specific weekly return for a given year. This measure is computed by
taking the negative of the third central moment of firm-specific weekly return scaled by the sample
variance of firm-specific weekly return raised to the power of 3/2. We follow the literature by putting a
minus sign in front of the skewness so that an increase in NCSKEW corresponds to higher crash risk(i.e.,
a more negatively skewed stock return distribution)(Chen, Hong, and Stein 2001). Specifically:
The second crash-risk measure, DUVOL, is also a measure of stock return asymmetry, computed
by taking the natural logarithm of the ratio of the standard deviation on down weeks to the standard
deviation on up weeks (Chen, Hong, and Stein2001). Specifically:
A firm-week is considered an up (down)-week if the firm-specific weekly return is above (below) the
annual mean weekly return. The convention is that a higher value of DUVOL indicates a more left-
skewed distribution, thus higher crash risk.
The third crash-risk measure, COUNT, is computed in the following manner: we first detect crash
(jump), which occurs when the firm-specific weekly return is 3.09 standard deviations below (above) its
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mean over the fiscal year,6we then compute COUNT as the number of crashes minus the number of jumps
over the fiscal year (Hutton, Marcus, and Tehranian 2009; Kim, Li, and Zhang 2011a,b; An and Zhang
2013).
3.4. Foreign Institutional Ownership and Domestic Institutional Ownership Measures
From Datastream and FactSet (LionShares), we construct four measures of foreign institutional
ownership: (1) foreign strategic institutional ownership (FSIO); (2) foreign institutional ownership (FIO);
(3) foreign direct investment (FDI) ownership; (4) foreign portfolio investment (FPI) ownership, and one
measure of domestic institutional ownership (DIO).
Datastream provides data on strategic holdings, defined as any disclosed holdings above the 5%
threshold of the total number of shares outstanding. We extract data on the year-end foreign strategic
institutional ownership, which is the aggregate percentage of the total shares outstanding by foreign
investors with disclosed holdings greater than 5%. We also utilize these data as a proxy for the level of
FDI (i.e., the percentage of the total shares outstanding owned by foreign institutional investors who exert
control over the domestic firm). Datastream has two limitations. The first is that it does not include
institutional ownership of less than 5%. Therefore, foreign portfolio investors are not included. The
second limitation is that it does not include domestic institutional ownership data. Therefore, we cannot
use it to test H2about how domestic institutional ownership affects corporate risk-taking.
To overcome the above two limitations of Datastream, we construct foreign institutional
ownership and a proxy for FPI from FactSet Ownership database which provides fund holdings
information for various funds such as mutual funds and pension funds. In contrast with Datastream,
FactSet includes ownership above, below or equal to 5% of the total shares.7Foreign institutional
6We follow Hutton, Marcus, and Tehranian (2009) and choose 3.09 to generate top and bottom 0.1
percent in the normal distribution.
7The fund positions are gathered globally from mutual fund reports, regulatory authorities, mutual fund
associations and fund management companies. Due to international differences in the regulatory
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ownership is derived by summing up the percentage ownership by each unique fund domiciled in a
foreign country. Further, we single out foreign institutional investors with percentage ownership of less
than 5 percent, and aggregate their percentage ownership to compute FPI ownership. FPI thus represents
foreign institutional investors who gain ownership of domestic firms without gaining control.
Domestic institutional ownership is included in our models so that we are able to contrast the
effects of foreign institutional ownership and domestic institutional ownership on corporate risk-taking
and crash risk. We construct domestic institutional ownership from FactSet Ownership database.
Specifically, we aggregate the equity holdings of domestic institutions as a percentage of the total shares
outstanding at the end of the previous year. The domestic ownership used for each of the overlapping
five-year windows is again the year-end value of the first year within the window.
3.5. Country-level Corporate Governance Institution Measures
We use two groups of country-level corporate governance institution measures: (1) Information
transparency measures from La Porta, Lopez-de-Silanes, and Shleifer(2006), Djankov, McLiesh, and
Shleifer (2007), and Bushman, Piotroski, and Smith (2004); (2) Judicial system efficiency and investor
protection measures from La Porta, Lopez-de-Silanes, Shleifer, and Vishny(1998) and Djankov, La Porta,
Lopez-de-Silanes, and Shleifer(2008). We expect that managers are monitored more intensely in countries
where information is more transparent, the judicial system is more efficient, and investors are better
protected. As a result, we expect firms to take more risks in countries with greater information
transparency, a more efficient judicial system and better investor protection.
requirements and disclosure conventions, the reporting frequency of fund positions varies from a monthly
basis to an annual basis, therefore we scale the last reported holdings in a year by the total shares
outstanding at the end of the previous year to construct a percentage ownership. Using an alternative
definition, such as the year end shares outstanding, we find that our results are insensitive to the choice of
scale.
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3.6. Control Variables
We include both firm-level and country-level controls in our risk-taking regressions. Standard
controls that have been shown previously to significantly influence corporate risk-taking are included.
Specifically, we include the following firm-level controls: (1) the annual growth rate of total sales
(SALESGROWTH)(to control for the effect of firm growth opportunities on risk-taking) (John, Litov,
and Yeung 2008); (2) firm size (SIZE)(i.e., the natural logarithm of total sales in millions of U.S. dollars);
(3) firm profitability (ROA)(i.e., earnings before interests and taxes scaled by total assets); (4) leverage
(LEVERAGE)(i.e., net debt over assets); (5) capital expenditure (CAPEX) (i.e., capital expenditure over
total assets). We include the following country-level controls: (1) growth in real gross domestic product
(GDPGROWTH); (2) market interest rates (MARKETRATES); (3) economic freedom
(ECONFREEDOM) (greater economic freedom provides investors and managers with more incentives to
implement riskier innovative projects) (Gwartney, Lawson, and Norton 2008); (4) economic development
(GDP, measured by the natural logarithm of GDP per capita). In addition to these variables, we include
year, industry and country dummy variables. To reduce the effects of outliers, we winsorize all firm-level
variables at 1% at both tails of the distribution.
In crash-risk regressions, we include firm-level control variables that have been shown to
significantly affect crash risks in Chen, Hong, and Stein (2001), Hutton, Marcus, and Tehranian (2009),
and Kim, Li, and Zhang (2011a,b). We also include country-level control variables that potentially affect
crash risks. Specifically, we include the following firm-level control variables: (1) de-trended average
monthly stock turnover (DTURN)(i.e., the average monthly turnover minus the average monthly turnover
from the previous year)—we expect stocks with higher turnover to be more negatively skewed; (2) the
standard deviation of firm-specific return (SIGMA), derived from the expanded market model (equation
1)(higher volatility of firm-specific return is expected to increase the crash risk of the firm); (3) average
firm-specific weekly return (RET)(lower average firm-specific return indicates that a firm is more likely
to experience more down weeks than up weeks, which should lead to more crash risk); (4) lagged three-
year moving sum of the absolute value of discretionary accruals (OPACITY)(this is a measure of accrual
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manipulation and proxies for the ability of a manager to hide adverse information from the financial
markets. This should increase a firm’s crash risk, because when the threshold of bad news that the firm
can sustain is higher, more extreme crashes are more likely); (5) leverage (LEVERAGE)(i.e., net debt
over total assets. Higher leverage increases the probability of bankruptcy and crash risk); (6) corporate
risk-taking (RISK1)(the volatility of a firm’s earnings over overlapping five-year periods during the entire
sample period. If a firm takes more risks, then the firm could incur more losses. Hiding such losses from
the market could contribute to a firm’s crash risk); (7)profitability (ROA)(i.e., earnings before interest and
tax over total assets—high profitability is expected to be associated with more stability and lower crash
risk);(8) market-to-book ratio (MTB) (a higher market-to-book ratio has previously been shown to be
associated with higher distress risks (Griffin and Lemmon 2002), which are expected to lead to higher
crash risk); (9) the natural logarithm of the market value of equity in US dollar (SIZE); (10) lagged crash
risk (lagged NCSKEW) (Kim, Li, and Zhang 2011a,b).
In crash-risk regressions, we also include the following country-level controls:(1) growth in real
Gross Domestic Product (GDPGROWTH); (2) market interest rates (MARKETRATES);(3) economic
development (GDP)(i.e., the natural logarithm of GDP per capita). In addition to these variables, we
include year and industry dummy variables. We winsorize all the variables at 1% level at both tails of the
distribution, to reduce the effects of outliers. Table 2 reports the summary statistics of the variables used
in this paper.
[Insert Table 2 about here]
4. Empirical Models
To test H1, we estimate the following regression equation:
where “risk-taking” is measured by five measures of corporate risk-taking, FSIO is the percentage of
shares held by foreign strategic institutional owners. CONTROLS denotes the set of control variables,
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and denotes the error term. Subscript “i” indexes firm and subscript “t” indexes year. Robust standard
errors clustered at both firm and country levels are estimated. If is positive and significant, then H1a is
supported (i.e., foreign institutional owners promote corporate risk-taking). If is negative and
significant, then H1b is supported (i.e., foreign institutional owners lower corporate risk-taking).
To further test H1, we estimate the following regression equation:
where FIO is foreign institutional ownership, as an alternative measure to FSIO, and DIO is domestic
institutional ownership. If is positive and significant, then H1a is supported (i.e., domestic institutional
owners promote corporate risk-taking). If is negative and significant, then H1b is supported (i.e.,
domestic institutional owners lower corporate risk-taking).
To test H3 regarding the interactions between foreign institutional ownership and
country-level corporate governance institutions, we estimate the following regression equation:
Where GI is country-level governance institutions, including information transparency (IT) and institutional
environment (IE). If and are positive, then H3a is supported (i.e., foreign institutional ownership and
country-level governance institutions are complements). If and are negative, then H3b is supported
(i.e., foreign institutional ownership and country-level governance institutions are substitutes).
The crash-risk regression model is as follows:
where CRASH is our crash-risk measures (NCSKEW, DUVOL, and COUNT). FSIO is foreign strategic
institutional ownership, RISK1 is our primary measure of corporate risk-taking, CONTROLS is a vector
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of control variables (firm characteristics and country characteristics).Subscript “i” indexes firm and
subscript “t” indexes year. Robust standard errors clustered at both firm and country levels are estimated.
If is negative and significant, then H4 is supported (i.e., foreign institutional owners reduce crash risk).
To test H5, we estimate the following regression model:
where CRASH is our crash-risk measures, FDI, FPI, and DIO are foreign direct investors’ ownership,
foreign portfolio investors’ ownership, and domestic institutional ownership in a firm, respectively.
RISK1 is our primary measure of corporate risk-taking, CONTROLS is a vector of control variables (firm
characteristics and country characteristics). If is negative and significant, and is insignificant, then
H5 is supported (i.e., foreign direct investors’ ownership significantly reduces crash risk while foreign
portfolio investors’ ownership has an insignificant effect on crash risk).
5. The Effects of Foreign Institutional Ownership on Corporate Risk-Taking
5.1. The Effects of Foreign Institutional Ownership and Domestic Institutional Ownership on
Corporate Risk-Taking
Table 3 reports the estimation results when we use foreign strategic institutional ownership as our
measure of foreign institutional ownership in equation (2). We find that foreign strategic institutional
ownership is positively and significantly associated with all four corporate risk-taking measures.
Regressions of all five risk-taking measures on foreign strategic institutional ownership show consistent
results that foreign institutional owners promote corporate risk-taking.
[Insert Table 3 about here]
Table 4 presents our results using the FactSet Ownership database. When we include both foreign
and domestic institutional ownership in our model, we find that foreign and domestic institutional
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ownership enter the regression positively and negatively, respectively, both at the 1% significance level.
These results strongly support hypotheses 1a and 2b.
[Insert Table 4 about here]
We further classify foreign institutional ownership into foreign direct investment (FDI)
ownership and foreign portfolio investment (FPI) ownership and examine their respective effects on
corporate risk-taking. Table 5 reports that both FDI ownership and FPI ownership significantly promote
corporate risk-taking. In contrast, domestic institutional ownership significantly lowers corporate risk-
taking. These results support hypotheses 1a and 2b.
[Insert Table 5 about here]
5.2. The Joint Effects of Foreign Institutional Ownership and Country-level Corporate Governance
Institutions on Corporate Risk-Taking
To examine whether foreign institutional owners and country-level corporate governance
institutions complement or substitute for each other, we use various country-level governance institution
measures used in previous studies. We first examine the following six information-transparency
measures: (1) AUDIT, measuring the credibility of financial accounting disclosure (an index ranging from
1 to 4 depending on the percentage of the firms in the country audited by the Big 5 Accounting Firms);
(2) ANALYST, the number of analysts following the largest 30 companies in the country in 1996; (3)
ITENF, measuring insider trading regulation, a dummy variable that equals one if a country has enforced
insider trading laws before 1995 (measures (1), (2) and (3) are from Bushman, Piotroski, and Smith
2004); (4) INFSHA03 (“information sharing dummy” from Djankov, McLiesh, and Shleifer 2007), a
dummy variable that indicates whether a public or a private credit bureau operates in the country in 2003;
(5) DISREQ, the disclosure requirement index; (6) LIASTA, the liability standard index (measures (5)
and (6) are from La Porta, Lopez-de-Silanes, and Shleifer 2006) Each of the measures we have used is
designed in such a way that higher scores reflect greater country-level information transparency.
For brevity, in Table 6, we report results only when we measure corporate risk-taking by RISK1,
but the results are qualitatively similar when we use other corporate risk-taking measures. For almost all
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regressions in Table 6, we find that both FDI and FPI are positively and significantly related to corporate
risk-taking. The interactions between foreign institutional ownership and country-level information-
transparency measures are negative and significant. Evidence from these regressions supports H3b that
foreign institutional ownership and country-level governance institutions are substitutes.
[Insert Table 6 about here]
In addition to country-level information-transparency measures, we also use the following three
country-level legal-environment and investor-protection measures: (1) EFFJUD, a measure of the
efficiency of judicial system (La Porta, Lopez-de-Silanes, Shleifer, and Vishny 1998); (2) LEGCOM, a
dummy variable for English common law origin (La Porta, Lopez-de-Silanes, Shleifer, and Vishny (1998)
find that countries with English common law origin protect investors more effectively than countries with
civil law origin); (3) ANTISELF, the anti-self-dealing index (Djankov, La Porta, Lopez-de-Silanes, and
Shleifer 2008), which ranges from zero to one and captures the ex-ante and ex-post private control of self-
dealing. The higher the values of the three variables, the more efficient is the judicial system, and the
more effectively investors are protected. In almost all regressions in Table 7, we find that the interactions
between foreign institutional ownership and country-level legal enforcement and investor protection
measures are negative and significant. Evidence from these regressions again supports H3b that foreign
institutional ownership and country-level governance institutions are substitutes.
[Insert Table 7 about here]
Following our above findings we separate foreign institutional holdings into four different
groups. The groups are categorized according to the development (developed/developing) of the country
of the foreign institutional investor and firm in which they invest in8. This is beneficial because it
provides us with a more aggregate position on governance. After categorizing the institutional holdings
we run a separate regression for each group to determine the effect of foreign institutional investor
ownership under each classification.
8 We classify the development based on the classification provided by the International Monetary Fund
(IMF).
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The results are reported in Table 8. We find that the risk-taking effects from foreign institutional
investors are almost entirely driven by the groups: (1) Developed to Developed, and (2) Developed to
Developing, since foreign institutional ownership does not enter significantly otherwise. This provides
some evidence for our hypothesis regarding the substitution effect between foreign institutional investors
and corporate governance. This is because we expect developed countries to have a more highly
developed economy and subsequently higher standards of country-level governance.
[Insert Table 8 about here]
In summary, we find that there is evidence for a substitution effect between foreign institutional
ownership and corporate governance. This suggests that foreign institutional investors are able to
motivate risk-taking by motivating domestic firms to undertake improvements in corporate governance.
5.3. The Effect of Foreign Institutional Ownership from Different Institution Types on Corporate
Risk-Taking
Prior literature suggests that investor behavior depends on both nationality and institution type
due to differences in preferences and potential business ties (Brickley, Lease, and Smith 1988; Almazan,
Hartzell, and Starks 2005; Chen, Li, and Harford 2006; Ferreira and Matos 2008). While different
institution types have different preferences, the presence of business ties between institutional investors
and firm can also affect both their ability to be active in monitoring managers’ decisions and their ability
to act as independent shareholders (Ferreira and Matos 2008). Different institution types are also exposed
to distinct investment mandates and regulations. Therefore, we cannot expect all institutions to be equally
equipped or motivated to be active monitors.
Following Ferreira and Matos (2008) we classify institutions into seven different categories: (1)
Banks; (2) Insurance Companies; (3) Investment Companies (typically Mutual Fund Management
Companies); (4) Investment Advisors; (5) Pension Funds and Endowments; (6) Hedge Funds and Venture
Capital; (7) Government9. We then further classify these categories into two groups, namely, independent
and grey institutions based on both the institutions preferences and potential for business ties to a firm.
9 Note that Government and Endowment holdings does not enter into our dataset.
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We characterize independent institutions as institutions that are subject to fewer regulatory restrictions
and potential business relationship, on the other hand grey institutions tend to have higher monitoring
costs and are more loyal to corporate management. In our study, we classify Investment Companies and
Investment Advisors as independent, meanwhile Banks and Insurance Companies as the typical grey
institutions (Brickley, Lease, and Smith 1988; Almazan, Hartzell, and Starks 2005; Chen, Li, and Harford
2006; Ferreira and Matos 2008). For example, Brickley, Lease, and Smith (1988) finds that investment
advisers and mutual funds tend to be very active monitors, while banks and insurance companies are more
supportive of management actions.
Although many also classify Pension and Hedge funds as grey institutions there are
disagreements amongst researchers on whether they can actually be considered grey. In particular, since
the 1980s we have seen an increase in the involvement of public pension funds in regards to submitting
shareholder proposals, pressuring management for corporate reforms, and using the press to target the
management and boards of poorly governed or performing companies (Gillian and Starks 2007).
Similarly, hedge funds have also seen a rise in activism since the beginning of the 21st century and have
become particularly important in their ability as monitors of corporate performance and agents of change
(Gillian and Starks 2007). Yet few empirical studies have been able to show that this has impacted the
value of the target firms especially in the long-term operating or stock-market performance (Barber 2006;
DelGuercio and Hawkins 1999). Several studies do attempt to show that the firms do end up
implementing governance reforms or even significant changes in business activities after activism by
Pension and Hedge funds (Wahal 1996; Carleton, Nelson, and Weisbach 1998; Gillian and Starks 2007;
Ertimur, Ferri, and Stubben 2009), however it is difficult to establish a causal relationship between these
changes and activism. Consequently, there is no solid empirical evidence to classify Pension or Hedge
funds as either independent or grey.
In order to determine institution type effect we divide both foreign and domestic institutional
ownership into the seven categories. We then report separate regressions for each of the categories.
Following our discussion we anticipate that more independent institutions are less likely to have business
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relations with the firm, they will subsequently be less compelled to be loyal to management and will take
a more active stance when it comes to monitoring and firm management.
The results are reported in Table 9. We find that foreign institutional ownership is positively and
significantly associated with risk-taking for the categories (3) Investment Companies, (4) Investment
Advisors, and (5) Pension Funds & Endowments, each at the 1% significance level. On the other hand,
(1) Banks, (2) Insurance Companies, and (6) Hedge Funds & Venture Capital are insignificant at all
conventional significance levels. Based on our discussion we can see that only independent institutions
types (Investment Companies and Investment Advisors) have a significant effect on risk-taking for both
foreign and domestic ownership. On the other hand for all typical grey institution types (Banks and
Insurance Companies) both foreign and domestic ownership has no significant effect on risk-taking. This
implies that only institutions with the ability to actively monitor have the capacity to influence firms’
risk-taking characteristics. Even though this result may seem straightforward it contributes to our study in
number of ways. First, this reinforces the reliability of our results since it confirms the results from past
studies. Second, this result indicates that our main findings are only driven by institution types with the
ability or incentive to actively monitor, since they are able to effectively impose their risk-taking
preferences onto the firm they invest in. Lastly, this shows foreign institutional owners have less business
ties with the firms they invest in therefore they are able to motivate risk-taking through active and
effective monitoring of managerial behavior.
[Insert Table 9 about here]
5.4. Endogeneity
Our evidence indicates that foreign institutional ownership is positively and significantly related
to corporate risk-taking. However, it is possible that foreign institutional investors are attracted to firms
that take more risks (reverse causality). It is also possible that a third factor affects both foreign
institutional ownership and corporate risk-taking. For example, if a firm has highly effective corporate-
governance mechanisms, it may attract more foreign institutional owners (Leuz, Lins, and Warnock
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2009). Meanwhile, such effective corporate-governance mechanisms may motivate managers to take
sufficient risks.
To tackle endogeneity, we adopt four different approaches. The first three approaches are
routinely used to account for endogeneity and simply make use of our previously described ownership
dataset. The results are reported for our primary risk-taking measure RISK1 in section 5.3.1.
Alternatively, in section 5.3.2 we employ an event study approach that allows us to both
effectively account for the endogeneity problem while emphasizing the causal relationship between
foreign ownership and risk-taking. This approach incorporates data sources other than our primary
ownership dataset. The results are qualitatively similar when we use alternative risk-taking measures.
5.4.1. Regression Based Approaches
In this section we carefully address endogeneity concerns by using three different approaches.
The first approach is to lag every independent variable to mitigate the concerns about reverse causality.
More specifically, it is difficult to argue that the foreign owners are able to accurately predict the future
risk-taking activities of a firm and as a result invest in it. The results of Model 1 in Table 10 indicate that
even after lagging all the independent variables, our key findings remain the same: FDI and FPI both
display a positive and significant effect on risk-taking, while domestic institutional ownership has a
negative and significant effect.
The second approach is to take the first difference of each variable in the regressions and then run
regressions with these differenced variables. Additionally, we lag every differenced independent variable
to mitigate reverse causality. By taking first difference, we remove any time-invariant firm characteristics
that could be driving the relationship between foreign institutional ownership and corporate risk-taking
(e.g., firm-specific corporate governance mechanisms that are generally persistent over time). Model 2 of
Table 10 reports the results of regressions with first-differenced variables. Our key results still hold that
foreign institutional ownership promotes corporate risk-taking.
The third approach is 2SLS regressions. We use political rights index (POLRIGHTS) from
Freedom House as an instrument for FDI ownership. Boubakri, Cosset, and Saffar (2008) posit that
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foreign owners are more likely to invest in countries with stronger political institutions. Meanwhile,
political rights do not have direct unambiguous effects on corporate risk-taking. Therefore, political rights
satisfy both the relevance and exogeneity conditions for a valid instrumental variable. We report the
second-stage regression results in Model 3 of Table 10. We find our key results still hold that foreign
institutional ownership is positively and significantly related to corporate risk-taking.
[Insert Table 10 about here]
5.4.2. Event Study Approach
For our final approach we must first establish that one of the main channels allowing foreign
investors to access domestic firms is through M&A transactions. By identifying this link it now becomes
apparent to employ an event study framework to illustrate the true causal relationship between foreign
ownership and risk-taking. Consequently, we are required to supplement our ownership dataset with
additional data sources for the announcements of these M&A transactions, namely, the Securities Data
Company (SDC) Platinum Mergers and Acquisitions Database.
We construct our initial sample by specifying that these firms have to be readily available in both
Datastream and SDC. These firms also have to have been the target of large block purchases (>5% of
issued shares reported by SDC) by foreign investor during our sample period since it is closest in nature
to our definition of FDI. Finally, to assure that the event windows are independent of each other we limit
our sample to firms that have only been the target of a foreign large block purchase no more than once.
Using the above criterion we find only 255 event firms in our sample in which foreign ownership
increases due to large block purchases. The announcement dates allows us to confirm whether these
sample firms reporting large foreign shareholders only exhibit risk-taking changes after these
transactions.
We calculate our risk-taking measure (ROA volatility) for both pre- and post-event periods of up
to 5 years. More specifically, for the pre-event period we compute the ROA volatility using ROA values
ranging from year -5, -4, and -3 up until year -1 before the announcement date. Similarly, for the post-
event period we compute the ROA volatility using ROA values from year +1 to year +3, +4, and +5 after
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the announcement date. These risk-taking measures are computed for each event firm and corresponding
matched control firm. The control firm set contains firms that do not report any foreign block purchases.
For this study, we form a set of 1-to-1 matched firms by matching each event firm to a control firm that
belongs in the same market, industry and closest market capitalization, however if not possible, a firm in
the same market with the closest market capitalization.
The result of this study is reported in Table 11. The results using our aggregate sample presented
in Panel A clearly indicates that there is indeed an increase in risk-taking after large block purchases by
foreign investors. In fact, prior to the transaction the event firm has a similar risk-taking characteristic to
that of the control firm. However, after the announcement of these transactions our risk-taking measure is
significantly higher than that of the control set. This implies that the increase in risk-taking of the event
firm is driven by large foreign block purchases rather than as a result of foreign investors being attracted
to riskier firms. Furthermore, given the event study design, the only difference between the event and
control set is in the presence of a large foreign block purchase. This eliminates the possibility of a third
factor skewing our results. In Panel B, we divide our aggregate sample into different groups based on the
development indicator (developed/developing) of the acquirer and target nation involved in the
transaction. Again, even in these groups we find that the risk-taking measure between the event and
control firms prior to the announcement of the transaction is never significantly different, with significant
differences only occurring after the announcement of these transactions. This provides further evidence
against reverse causality. Furthermore, we find that the results from Panel A are mainly driven by
transactions between the same development groups. However, the fact that there is a significantly positive
difference in the group with transactions from developed to developing countries and none when reversed
again lends support to our previous findings that portray foreign ownership as a substitute for corporate
governance.
[Insert Table 11 about here]
In summary, we find that foreign institutional ownership continues to have a positive and
significant effect on corporate risk-taking even after addressing endogeneity. It is therefore unlikely that
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our main findings are driven by endogeneity. For this reason, foreign ownership does appear to bring
important risk-taking characteristics to domestic firms.
6. The Effects of Foreign Institutional Ownership on Crash Risk
Our results in the previous section show that foreign institutional ownership significantly
increases corporate risk-taking. Does it also lead to greater crash risk for the firm? To answer this
question, we estimate OLS (Ordinary Least Square) regression equation (4). Robust standard errors
clustered at both country and firm levels are estimated, and corresponding p-values are reported in the
tables.
6.1. The Effects of Foreign Strategic Institutional Ownership on Crash Risk
We first examine how foreign strategic institutional ownership affects a firm’s crash risk, where
foreign strategic institutional ownership is defined as the total ownership by foreign owners with more
than 5% ownership in the firm. In Models 1 and 2 of Table 12, we use NCSKEW as the dependent
variable and include lagged foreign strategic institutional ownership as the key independent variable. We
also include lagged control variables that are commonly used in existing crash-risk literature (Chen,
Hong, and Stein 2001; Hutton, Marcus, and Tehranian 2009; Kim, Li, and Zhang 2011a,b; An and Zhang
2013). Meanwhile, we control for industry and year fixed effects. We find that the proportion of foreign
strategic institutional ownership is negatively and significantly related to a firm’s crash risk. In Models 3
and 4, we use DUVOL as an alternative measure of a firm’s crash risk, and the results are qualitatively
similar. In Models 5 and 6, we use COUNT as the third measure of a firm’s crash risk, and we obtain
qualitatively consistent results in Model 6 as in Models 2 and 4. These results strongly support H4.
[Insert Table 12 about here]
6.2. The Effects of Foreign Direct Investor (FDI) Ownership, Foreign Portfolio Investor (FPI)
Ownership and Domestic Institutional Ownership on Crash Risk
In this section, we examine the effects of FDI, FPI and domestic institutional ownership on crash
risk, respectively. The proxy we use for FDI is the foreign strategic institutional ownership from
Datastream. As a proxy for FPI ownership, we aggregate all foreign mutual fund holdings below 5% to
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represent small foreign portfolio owners who are interested only in the firm for the purpose of
international diversification.
In Model 1 of Table 13, we find that while FDI ownership is negatively and significantly related
to a firm’s crash risk, FPI ownership is insignificantly related to it. In Model 2, we include the
interactions between risk-taking and various types of institutional ownership (FDI, FPI and domestic) to
investigate how a firm’s ownership structure affects the effect of corporate risk-taking on crash risk. We
find that while domestic institutional ownership significantly increases a firm’s crash risk when the firm
takes more risks, neither FDI nor FPI ownership significantly affects the relationship between corporate
risk-taking and crash risk. In Models 3 and 4, we use DUVOL as an alternative measure of crash risk, and
get qualitatively similar results as in Models 1 and 2. In Models 5 and 6, we use COUNT as the third
measure of crash risk, and we get qualitatively similar results in Model 6 as in Models 2 and 4. These
results strongly support H5.
[Insert Table 13 about here]
In summary, the results of this section show that a firm’s ownership structure plays a significant
role as a determinant of the firm’s crash risk. Specifically, our results show that FDI ownership
significantly decreases crash risk, while domestic institutional ownership significantly increases crash
risk. Moreover, domestic institutional ownership significantly increases a firm’s crash risk when it takes
more risks, but foreign institutional ownership does not have a significant impact on the relationship
between a firm’s risk-taking and its crash risk.
6.3. Endogeneity
The negative relationship between foreign institutional ownership and corporate crash risk may
have an alternative interpretation: foreign institutional investors choose to invest in firms with greater
information transparency, which tend to have lower crash risk. To rule out the possibility of reverse
causality, we regress the first-differenced crash risk on the first-differenced foreign institutional
ownership. We also use the 2SLS method, with foreign sales over total assets as the instrumental variable
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for FDI ownership. We do not lag all the independent variables as we do in Section 5 because our
baseline specification already lags each independent variable. Table 14 reports the results.
[Insert Table 14 about here]
We first run a regression of first-differenced crash risk on first-differenced foreign institutional
ownership. By examining the change instead of the level, we remove any time-invariant firm
characteristics that could be driving the relationship between ownership structure and crash risk. Model 1
of Table 14 shows that the change in FDI ownership is negatively and significantly associated with the
change in a firm’s crash risk. In Model 2, we use foreign sales scaled by total assets (FSALES) as an
instrumental variable because while it is expected to directly influence FDI ownership, it is difficult to
argue that FSALES has direct effects on a firm’s crash risk.10
This instrument is highly correlated with
foreign institutional ownership because foreign institutional investors tend to prefer investing in firms
with more international exposure, which will consequently lead to more visibility to foreign investors
(Covrig, Lau, and Ng 2006; Ferreira and Matos 2008).
In Model 2, we report the second-stage regression results, which indicate that the coefficient of
FDI ownership is still significantly negative, while the coefficient of domestic institutional ownership
remains significantly positive. Interestingly, after we address the endogeneity issue, FPI ownership begins
to show positive and significant effects on crash risk, again highlighting the different effects of FDI and
FPI ownership on crash risk. For brevity, we have reported the results only when we use NCSKEW as the
dependent variable. When we use DUVOL and COUNT as the dependent variables, we obtain
qualitatively similar results, which are available upon request.
7. Conclusion
10We use FSALES as opposed to POLRIGHTS since FSALES appears to be much more relevant as an
instrument. However, we disregard FSALES as an instrument for our previous 2SLS analysis since it is
very likely that FSALES is endogenous there, given that the measure of corporate risk-taking is the
volatility of earnings.
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We study a large sample of 48,548 firms in 72 countries from 2000 to 2008 to investigate the
effect of ownership structure on corporate risk-taking and crash risk, with a focus on how foreign
institutional ownership affects corporate risk-taking and crash risk. Motivating corporate risk-taking is
pivotal because corporate risk-taking is essential to corporate growth and economic growth (John, Litov,
and Yeung 2008; Baumol, Litan, and Schramm 2009). However, corporate risk-taking can also be
detrimental to a firm, because more risk-taking is associated with potentially more severe losses, and
severe losses create incentives for managers to manage earnings and hide bad news from the market.
When bad news eventually accumulates beyond a threshold, all the negative information will be released
at once, leading to an excessive fall in the firm’s stock price. Therefore, more corporate risk-taking may
lead to greater crash risk. We evaluate the effects of two different types of foreign institutional ownership:
foreign direct investor (FDI) ownership and foreign portfolio investor (FPI) ownership on corporate risk-
taking and crash risk.
We have made several discoveries. First, we find that both foreign direct investor ownership and
foreign portfolio investor ownership in a firm significantly increases the firm’s risk-taking. We believe
that foreign institutional investors monitor the managers more intensely due to their lack of extensive
existing business ties with the managers. As a result, foreign institutional investors are able to motivate
managers more effectively to take risks. On the other hand, compared with domestic institutional
investors, foreign institutional investors generally face more information asymmetry with the managers.
As a result, they may be less effective monitors, and may lead to significantly lower corporate risk-taking.
Our results show that the positive effects of foreign institutional ownership on corporate risk-taking
dominate the negative effects.
Second, we examine whether foreign institutional ownership and country-level corporate
governance institutions are substitutes or complements. We find that the relationship between foreign
institutional ownership and corporate risk-taking is stronger in countries with poorer governance
institutions. This supports the view that foreign institutional owners play stronger roles in motivating
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managers to take risks in countries with weaker governance institutions as domestic institutional investors
there are unable to incentivize managers effectively to take risks.
Third, we examine the effects of FDI, FPI and domestic institutional ownership on a firms’ crash
risk. We find that among these three types of owners, only FDI ownership significantly decreases a firms’
crash risk. Similar to FPI owners, FDI owners are more independent of management compared with
domestic institutional owners. As a result, FDI owners have stronger incentives to improve corporate
governance. Different from FPI owners, FDI owners have control over corporate operations. Since FDI
owners have both the incentives to improve firm-level corporate governance and the controlling power to
influence corporate operations, they can effectively reduce the earnings management activities of
managers, which consequently decrease the crash risk of a firm.
Finally, we find that when firms take more risks, domestic institutional ownership significantly
increases the crash risk, while foreign institutional ownership does not.
Our findings are robust to the inclusion of industry and year fixed effects, the use of alternative
measures of corporate risk-taking and crash risk, lagging all the independent variables, taking the first
differences, and 2SLS estimation techniques.
Our findings highlight the benefits associated with foreign institutional investors. These findings
have broad implications for academia, practitioners and policy makers. When considering the crash risk
of a firm, researchers should consider the ownership identity of its institutional investors since it has a
strong effect on the firm’s crash risk. When policy makers reduce the barriers to foreign investments in
the hope of developing the local markets, they should consider the costs and benefits associated with
foreign investment. For example, based on our findings, foreign investors are particularly effective at
motivating corporate risk-taking in countries with poor governance environments. This provides a new
channel through which foreign investment can promote economic growth in developing countries.
However, attracting FPI investors as opposed to FDI investors could significantly increase the crash risk
of local firms’ stock prices.
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Table 1. Sample Distribution
This table reports the distribution of our sample of 48,548 firms by year, industry and region.
Panel A: Distribution by year
Year Number Percentage
2000 5463 2.34
2001 8936 3.83
2002 28665 12.28
2003 29166 12.49
2004 31073 13.31
2005 31012 13.28
2006 31941 13.68
2007 33542 14.36
2008 33701 14.43
Total 233499 100.00
Panel B: Distribution by industry
Industry Number Percentage
Basic Materials 5703 11.75
Consumer Goods 7305 15.05
Consumer Services 6969 14.35
Health Care 4573 9.42
Industrials 12413 25.57
Oil & Gas 3252 6.7
Technology 7279 14.99
Telecommunications 945 1.95
Other 109 0.22
Total 48548 100
Panel C: Distribution by region
Region (Countries) Number Percentage
East Asia and the Pacific (14) 17795 36.65
Europe and Central Asia (28) 10547 21.72
Latin America and the Caribbean (8) 717 1.48
Middle East and North Africa (8) 475 0.98
North America (2) 16934 34.88
South Asia (4) 1469 3.03
Sub-Saharan Africa (8) 611 1.26
Total (72) 48548 100
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Table 2. Summary Statistics
This table reports the summary statistics for the variables used in this paper. The full sample contains 48,548 firms from 72 countries. Panel A reports the summary statistics for variables in our corporate risk-taking regressions. Panel B reports the summary statistics for variables in our crash-risk regressions. The definitions and data sources for the variables are available in the Appendix. Panel A: Corporate risk-taking regression variables
Mean Median S.D. Minimum Maximum
RISK1 0.076 0.041 0.091 0.004 0.462
RISK2 0.185 0.100 0.221 0.009 1.114
RISK3 0.076 0.041 0.089 0.006 0.459
RISK4 0.075 0.043 0.086 0.004 0.449
R&D 0.059 0.020 0.103 0.000 0.632
FSIO 0.061 0.000 0.160 0.000 1.000
FIO 0.05 0.019 0.077 0.000 0.416
FDI 0.061 0.000 0.160 0.000 1.000
FPI 0.047 0.020 0.067 0.000 0.350
DINST 0.115 0.024 0.176 0.000 0.777
ROA 0.006 0.046 0.197 -1.032 0.355
LEVERAGE 0.217 0.185 0.202 0.000 1.004
SIZE 13.926 13.881 3.372 5.352 21.793
SALESGROWTH 0.252 0.091 0.830 -0.752 6.349
CAPEX 0.055 0.035 0.063 0.000 0.362
GDPGROWTH 3.189 2.660 2.627 -1.04 12.700
ECONFREEDOM 7.879 8.020 0.686 6.060 9.110
GDP 10.08 10.485 1.003 6.681 10.922
MARKETRATES (%) 3.389 3.000 2.325 -3.280 11.710
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Panel B: Stock price crash-risk regression variables
Mean Median S. D. Minimum Maximum
NCSKEW -0.061 -0.073 0.685 -1.989 2.194
DUVOL -0.04 -0.045 0.339 -0.875 0.884
FSIO 0.046 0.000 0.139 0.000 0.990
FIO 0.047 0.019 0.071 0.000 0.414
FDI 0.046 0.000 0.139 0.000 0.990
FPI 0.044 0.020 0.062 0.000 0.348
DINST 0.134 0.038 0.184 0.000 0.775
ROA -0.002 0.037 0.192 -1.028 0.355
LEVERAGE 0.219 0.184 0.205 0.000 1.000
MV 5.054 4.938 1.973 0.820 10.232
MTB 2.177 1.460 3.217 -8.160 20.510
RISK1 0.071 0.036 0.088 0.003 0.442
OPACITY 0.762 0.245 1.384 0.026 8.510
SIGMA 0.060 0.053 0.030 0.016 0.155
RET -0.218 -0.138 0.224 -1.163 -0.012
DTURN 0.000 0.000 0.007 -0.038 0.024
GDPGROWTH 3.134 2.550 2.783 -4.240 12.700
GDP 10.101 10.491 0.976 6.681 10.922
MARKETRATES (%) 3.217 3.000 2.163 -3.280 11.710
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Table 3. Foreign Strategic Institutional Ownership and Corporate Risk-Taking
This table reports the OLS estimation results of the following regression model:
where “Risk-taking” refers to five risk-taking measures, FSIO is the percentage of strategic ownership
held by foreign institutional investors and CONTROLS is a vector of control variables (firm and country
characteristics). All regressions includes year, industry and country fixed effects whose coefficients are
suppressed. Beneath each coefficient estimate is p-statistics in parentheses based on robust standard errors
clustered at both the firm- and country-levels. ***, **, and * denote statistical significance at the 1%, 5%,
and 10% levels, respectively. Variable definitions and data sources are available in the Appendix.
Dependent Variable RISK1 RISK2 RISK3 RISK4 SRVOL
Model (1) (2) (3) (4) (5)
FSIO 0.010** 0.025** 0.009** 0.009** 0.029***
(0.020) (0.018) (0.012) (0.034) (0.000)
ROA -0.192*** -0.461*** -0.184*** -0.171*** -0.114***
(0.000) (0.000) (0.000) (0.000) (0.000)
LEVERAGE 0.018*** 0.045*** 0.018*** 0.018*** 0.079***
(0.000) (0.000) (0.000) (0.000) (0.000)
SIZE -0.010*** -0.024*** -0.010*** -0.010*** -0.014***
(0.000) (0.000) (0.000) (0.000) (0.000)
SALESGROWTH 0.005*** 0.012*** 0.005*** 0.005*** 0.004***
(0.000) (0.000) (0.000) (0.000) (0.000)
CAPEX -0.012* -0.030* -0.014** -0.018** -0.032***
(0.092) (0.081) (0.021) (0.010) (0.007)
GDPGROWTH -0.007*** -0.019*** -0.009*** -0.009*** 0.001
(0.009) (0.008) (0.004) (0.003) (0.617)
ECONFREEDOM 0.001** 0.003** 0.001* 0.001 0.013*
(0.022) (0.027) (0.070) (0.136) (0.088)
GDP -0.015** -0.035* -0.020*** -0.017** 0.102**
(0.039) (0.056) (0.005) (0.014) (0.028)
MARKETRATES 0.001*** 0.002*** 0.001** 0.001** -0.001
(0.009) (0.008) (0.013) (0.015) (0.312)
Intercept YES YES YES YES YES
YEAR DUMMIES YES YES YES YES YES
COUNTRY DUMMIES YES YES YES YES YES
INDUSTRY DUMMIES YES YES YES YES YES
Adjusted R-Squared 0.432 0.429 0.435 0.444 0.383
No. of Observations 199693 199693 199693 199693 174714
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Table 4. Foreign Institutional Ownership and Corporate Risk-Taking
This table reports the OLS estimation results of the following regression model: , where “Risk-taking” refers to five risk-taking measures, FIO is the percentage of
foreign institutional ownership in a firm, DIO is the percentage of total domestic institutional ownership
in a firm, and CONTROLS is a vector of control variables (firm and country characteristics). All
regressions include year, industry and country fixed effects whose coefficients are suppressed. Beneath
each coefficient estimate is p-statistics in parentheses based on robust standard errors clustered at both the
firm- and country-levels. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels,
respectively. Variable definitions and data sources are available in the Appendix.
Dependent Variable RISK1 RISK2 RISK3 RISK4 SRVOL R&D
Model (1) (2) (3) (4) (5) (6)
FIO 0.055*** 0.134*** 0.047*** 0.047*** 0.066*** 0.117***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
DIO -0.018*** -0.046*** -0.018*** -0.022*** -0.046*** -0.013**
(0.000) (0.000) (0.000) (0.000) (0.000) (0.039)
ROA -0.196*** -0.476*** -0.186*** -0.172*** -0.137*** -0.171***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
LEVERAGE 0.004 0.012 0.005 0.006 0.062*** -0.028***
(0.329) (0.279) (0.280) (0.138) (0.000) (0.000)
SIZE -0.009*** -0.022*** -0.009*** -0.009*** -0.012*** -0.006***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
SALESGROWTH 0.008*** 0.020*** 0.008*** 0.007*** 0.006*** 0.000
(0.000) (0.000) (0.000) (0.000) (0.000) (0.892)
CAPEX -0.001 -0.003 -0.003 -0.008 -0.002 -0.023
(0.944) (0.890) (0.752) (0.359) (0.848) (0.163)
GDPGROWTH 0.001* 0.002* 0.001 0.001* -0.000 -0.000
(0.054) (0.061) (0.136) (0.062) (0.847) (0.665)
ECONFREEDOM -0.000 -0.001 -0.001 -0.000 0.040*** -0.012***
(0.888) (0.793) (0.800) (0.899) (0.000) (0.001)
GDP
-0.008 -0.018 -0.015** -0.014** 0.022 0.017***
(0.232) (0.284) (0.010) (0.023) (0.478) (0.008)
MARKETRATES 0.000 0.001 0.000 0.000 0.001 0.000
(0.111) (0.110) (0.116) (0.172) (0.184) (0.815)
Intercept YES YES YES YES YES YES
YEAR DUMMIES YES YES YES YES YES YES
COUNTRY DUMMIES YES YES YES YES YES YES
INDUSTRY DUMMIES YES YES YES YES YES YES
Adjusted R-Squared 0.417 0.414 0.421 0.431 0.436 0.529
No. of Observations 91211 91211 91211 91211 110187 45263
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Table 5. FDI, FPI Ownership and Corporate Risk-Taking
This table reports the OLS estimation results of the following regression model: where “Risk-taking” refers to five risk-taking
measures, FDI is the percentage of foreign direct investors’ ownership in a firm, FPI is the percentage of
foreign portfolio investors’ ownership in a firm, DIO is the percentage of total domestic institutional
ownership in a firm, and CONTROLS is a vector of control variables (firm and country characteristics).
All regressions include year, industry and country fixed effects whose coefficients are suppressed.
Beneath each coefficient estimate is p-statistics in parentheses based on robust standard errors clustered at
both the firm- and country-levels. ***, **, and * denote statistical significance at the 1%, 5%, and 10%
levels, respectively. Variable definitions and data sources are available in the Appendix.
Dependent Variable RISK1 RISK2 RISK3 RISK4 SRVOL R&D
Model (1) (2) (3) (4) (5) (6)
FDI 0.007** 0.019** 0.008*** 0.006** 0.011*** 0.003
(0.011) (0.010) (0.005) (0.045) (0.003) (0.480)
FPI 0.057*** 0.138*** 0.049*** 0.044*** 0.049** 0.135***
(0.000) (0.000) (0.000) (0.000) (0.016) (0.000)
DIO -0.017*** -0.045*** -0.017*** -0.021*** -0.051*** -0.011*
(0.000) (0.000) (0.000) (0.000) (0.000) (0.073)
ROA -0.193*** -0.469*** -0.183*** -0.172*** -0.121*** -0.185***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
LEVERAGE 0.010** 0.025** 0.010*** 0.010*** 0.068*** -0.021***
(0.012) (0.011) (0.009) (0.005) (0.000) (0.001)
SIZE -0.009*** -0.022*** -0.009*** -0.009*** -0.011*** -0.007***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
SALESGROWTH 0.006*** 0.016*** 0.006*** 0.006*** 0.005*** 0.006***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
CAPEX 0.000 0.000 -0.003 -0.009 -0.006 -0.022
(0.987) (0.989) (0.684) (0.275) (0.516) (0.264)
GDPGROWTH -0.008** -0.019** -0.008** -0.008** -0.002* -0.015***
(0.016) (0.014) (0.025) (0.027) (0.086) (0.000)
ECONFREEDOM 0.001** 0.003** 0.001* 0.001 0.021*** 0.000
(0.018) (0.021) (0.063) (0.113) (0.003) (0.618)
GDP 0.003 0.011 -0.007 -0.004 0.052 0.032***
(0.756) (0.651) (0.495) (0.676) (0.214) (0.001)
MARKETRATES 0.000** 0.001** 0.000* 0.000* -0.001 0.001*
(0.049) (0.036) (0.072) (0.084) (0.389) (0.065)
Intercept YES YES YES YES YES YES
YEAR DUMMIES YES YES YES YES YES YES
COUNTRY DUMMIES YES YES YES YES YES YES
INDUSTRY DUMMIES YES YES YES YES YES YES
Adjusted R-Squared 0.409 0.407 0.414 0.427 0.410 0.530
No. of Observations 63430 63430 63430 63430 79806 32571
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Table 6.FDI and FPI Ownership, Country-level Information Transparency and Corporate Risk-
Taking
This table reports the OLS estimation results of the following regression model:
where “Risk-
taking” refers to five risk-taking measures, FDI is the percentage of foreign direct investors’ ownership
in a firm, FPI is the percentage of foreign portfolio investors’ ownership in a firm, DIO is the percentage
of domestic institutional investors’ ownership in a firm, IT is country-level information transparency, and
CONTROLS is a vector of control variables (firm and country characteristics). All regressions include
year, industry and country fixed effects whose coefficients are suppressed. Beneath each coefficient
estimate is p-statistics in parentheses based on robust standard errors clustered at both the firm- and
country-levels. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
Variable definitions and data sources are available in the Appendix. For brevity, this table only reports the
regression when we measure risk-taking by RISK1. The results when we use other risk-taking measures
are qualitatively similar.
Dependent Variable RISK1 Information
Transparency AUDIT ANALYST ITENF INFSHA03 DISREQ LIASTA
FDI 0.053*** 0.032*** 0.025*** 0.032*** 0.037*** 0.024***
(0.001) (0.000) (0.000) (0.000) (0.009) (0.006)
FPI 0.141*** 0.118*** 0.040 0.090*** 0.083* 0.054**
(0.000) (0.000) (0.113) (0.000) (0.061) (0.028)
DIO -0.061* -0.028 -0.014 -0.046** -0.044 0.002
(0.079) (0.217) (0.593) (0.017) (0.326) (0.929)
FDI*IT -0.014*** -0.001*** -0.026*** -0.032*** -0.038** -0.023***
(0.006) (0.001) (0.001) (0.000) (0.031) (0.002)
FPI*IT -0.032*** -0.005*** -0.017 -0.072*** -0.073 -0.031
(0.003) (0.000) (0.542) (0.000) (0.191) (0.361)
DIO*IT 0.014 0.001 0.008 0.040* 0.038 -0.020
(0.127) (0.336) (0.777) (0.055) (0.428) (0.472)
IT 0.007*** 0.000 0.007 0.002 0.012 0.026***
(0.010) (0.569) (0.331) (0.831) (0.469) (0.002)
ROA -0.205*** -0.205*** -0.204*** -0.204*** -0.203*** -0.204***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
LEVERAGE 0.006 0.005 0.004 0.005 0.008* 0.005
(0.219) (0.325) (0.378) (0.310) (0.297) (0.281)
SIZE -0.007*** -0.007*** -0.007*** -0.007*** -0.007*** -0.007***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
SALESGROWTH 0.007*** 0.007*** 0.007*** 0.007*** 0.007*** 0.007***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
CAPEX 0.009 0.005 0.007 0.008 0.006 0.006
(0.472) (0.622) (0.573) (0.500) (0.606) (0.627)
GDPGROWTH 0.002 0.001 0.001 0.000 0.001 0.001
(0.257) (0.453) (0.431) (0.838) (0.583) (0.495)
ECONFREEDOM 0.006 0.008 0.007 0.011** 0.007 0.002
(0.258) (0.146) (0.173) (0.023) (0.324) (0.644)
GDP -0.005 -0.003 -0.004 -0.007 -0.004 -0.003
(0.167) (0.353) (0.238) (0.145) (0.381) (0.403)
MARKETRATES 0.001* 0.001** 0.001** 0.001** 0.001* 0.001**
(0.070) (0.031) (0.041) (0.027) (0.076) (0.033)
Intercept YES YES YES YES YES YES YEAR DUMMIES YES YES YES YES YES YES COUNTRY DUMMIES YES YES YES YES YES YES INDUSTRY DUMMIES YES YES YES YES YES YES
Adjusted R-Squared 0.398 0.397 0.396 0.388 0.394 0.394
No. of Observations 60558 60770 60770 63373 61153 61398
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Table 7.FDI and FPI Ownership, Country-level Institutional Environment (Legal Environment and
Investor Protection) and Corporate Risk-Taking
This table reports the OLS estimation results of the following regression model where “Risk-taking”
refers to four risk-taking measures: , FDI is the percentage of foreign direct investors’
ownership, FPI is the percentage of foreign portfolio investors’ ownership, DIO is the percentage of
domestic institutional investors’ ownership, IE is country-level institutional environment (legal
environment and investor protection here), and CONTROLS is a vector of control variables (firm and
country characteristics). All regressions include year, industry and country fixed effects whose
coefficients are suppressed. Beneath each coefficient estimate is p-statistics in parentheses based on
robust standard errors clustered at both the firm- and country-levels. ***, **, and * denote statistical
significance at the 1%, 5%, and 10% levels, respectively. Variable definitions and data sources are in the
Appendix. For brevity, this table only reports the regression when we measure risk-taking by RISK1. The
results when we use other risk-taking measures are qualitatively similar.
Dependent Variable RISK1
Institutional environment (IE) EFFJUD LEGCOM ANTISELF
FDI 0.041*** 0.018*** 0.026**
(0.002) (0.000) (0.024)
FPI 0.069 0.043*** 0.037
(0.147) (0.004) (0.217)
DIO -0.155 -0.020 -0.030
(0.131) (0.414) (0.367)
FDI*IE -0.004** -0.018*** -0.033*
(0.018) (0.003) (0.084)
FPI*IE -0.005 -0.043** -0.026
(0.377) (0.022) (0.508)
DINST*IE 0.015 0.010 0.036
(0.140) (0.672) (0482)
IE -0.002 0.007* -0.005
(0.220) (0.094) (0.669)
ROA -0.205*** -0.206*** -0.204***
(0.000) (0.000) (0.000)
LEVERAGE 0.005 0.005 0.005
(0.281) (0.345) (0.289)
SIZE -0.007*** -0.006*** -0.007***
(0.000) (0.000) (0.000)
SALESGROWTH 0.007*** 0.007*** 0.007***
(0.000) (0.000) (0.000)
CAPEX 0.005 0.006 0.007
(0.647) (0.625) (0.585)
GDPGROWTH 0.000 0.001 0.000
(0.694) (0.643) (0.927)
ECONFREEDOM 0.011** 0.006 0.014***
(0.017) (0.266) (0.003)
GDP
-0.004 -0.003 -0.008**
(0.299) (0.411) (0.042)
MARKETRATES 0.001** 0.001* 0.001**
(0.031) (0.062) (0.012)
Intercept YES YES YES
YEAR DUMMIES YES YES YES COUNTRY DUMMIES YES YES YES INDUSTRY DUMMIES YES YES YES
Adjusted R-Squared 0.393 0.392 0.389
No. of Observations 61398 61398 62867
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Table 8. Development of Foreign institutional Investor and Corporate Risk-Taking
This table reports the OLS estimation results of the following regression model:
after restricting the where “Risk-taking” refers to five risk-taking measures, FIO is the percentage of
foreign institutional ownership in a firm, DIO is the percentage of total domestic institutional ownership
in a firm, and CONTROLS is a vector of control variables (firm and country characteristics). All
regressions include year, industry and country fixed effects whose coefficients are suppressed. Beneath
each coefficient estimate is p-statistics in parentheses based on robust standard errors clustered at both the
firm- and country-levels. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels,
respectively. Variable definitions and data sources are available in the Appendix.
Dependent Variable RISK1
Holding Type
Developed to
Developed
Developed to
Developing
Developing to
Developing
Developing to
Developed
Model (1) (2) (3) (4)
FIO 0.056*** 0.011* 2.029 -0.068
(0.000) (0.099) (0.346) (0.786)
DIO -0.011** -0.015 -0.029 0.008
(0.014) (0.322) (0.683) (0.215)
ROA -0.201*** -0.044** -0.012 -0.191***
(0.000) (0.036) (0.614) (0.000)
LEVERAGE 0.004 0.025*** -0.010 -0.011
(0.505) (0.001) (0.523) (0.100)
SIZE -0.009*** -0.007*** -0.003** -0.008***
(0.000) (0.000) (0.046) (0.000)
SALESGROWTH 0.008*** 0.004*** 0.012*** 0.011***
(0.000) (0.007) (0.000) (0.004)
CAPEX 0.014 -0.024*** -0.022 0.032**
(0.210) (0.007) (0.280) (0.012)
GDPGROWTH 0.001*** -0.001 -0.001 0.001
(0.000) (0.253) (0.238) (0.148)
ECONFREEDOM -0.003 0.011** -0.006 0.007
(0.114) (0.012) (0.328) (0.117)
GDP 0.003 -0.010 -0.058* -0.000
(0.857) (0.544) (0.064) (0.991)
MARKETRATES 0.001* 0.000 0.000 0.000
(0.081) (0.395) (0.774) (0.974)
Intercept YES YES YES YES
YEAR DUMMIES YES YES YES YES
COUNTRY DUMMIES YES YES YES YES
INDUSTRY DUMMIES YES YES YES YES
Adjusted R-Squared 65938 11007 738 7151
No. of Observations 0.419 0.145 0.261 0.305
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Table 9. Institution Type and Corporate Risk-Taking
This table reports the OLS estimation results of the following regression model:
after restricting the institutional holdings by both foreign and domestic to the distinct institution
type.“Risk-taking” refers to five risk-taking measures, FIO is the percentage of foreign institutional
ownership in a firm, DIO is the percentage of total domestic institutional ownership in a firm, and
CONTROLS is a vector of control variables (firm and country characteristics). All regressions include
year, industry and country fixed effects whose coefficients are suppressed. Beneath each coefficient
estimate is p-statistics in parentheses based on robust standard errors clustered at both the firm- and
country-levels. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
Variable definitions and data sources are available in the Appendix.
Dependent Variable RISK1
Institution Type Banks Insurance
Companies
Investment
Companies
Investment
Advisors
Pension
Funds &
Endowments
Hedge
Funds &
Venture
Capital
Model (1) (2) (3) (4) (5) (6)
FIO 0.429 1.351 0.064*** 0.070*** 0.301*** -0.184
(0.435) (0.258) (0.001) (0.000) (0.003) (0.112)
DIO -0.005 -0.109 -0.017*** -0.030*** -0.096*** -0.068
(0.986) (0.697) (0.003) (0.000) (0.006) (0.554)
ROA -0.191*** -0.191*** -0.189*** -0.197*** -0.179*** -0.187***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
LEVERAGE -0.005 -0.007*** -0.002 0.003 -0.003 -0.009***
(0.248) (0.005) (0.517) (0.508) (0.365) (0.001)
SIZE -0.007*** -0.008*** -0.009*** -0.009*** -0.007*** -0.008***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
SALESGROWTH 0.010*** 0.012*** 0.009*** 0.008*** 0.010*** 0.006***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
CAPEX 0.022 -0.008 0.002 0.004 0.006 0.002
(0.113) (0.271) (0.835) (0.655) (0.580) (0.809)
GDPGROWTH 0.000 0.001** 0.001 0.001** 0.001* 0.001
(0.478) (0.023) (0.137) (0.039) (0.074) (0.104)
ECONFREEDOM 0.005 -0.002 0.000 -0.001 -0.002 -0.000
(0.128) (0.590) (0.996) (0.589) (0.377) (0.933)
GDP -0.004 0.009 -0.013* -0.004 -0.010 0.001
(0.796) (0.812) (0.067) (0.605) (0.344) (0.892)
MARKETRATES -0.000 0.001*** 0.001** 0.000* 0.001** 0.000
(0.650) (0.001) (0.042) (0.055) (0.042) (0.388)
Intercept YES YES YES YES YES YES
YEAR DUMMIES YES YES YES YES YES YES
COUNTRY DUMMIES YES YES YES YES YES YES
INDUSTRY DUMMIES YES YES YES YES YES YES
Adjusted R-Squared 0.368 0.381 0.405 0.415 0.351 0.391
No. of Observations 13790 22648 67155 86720 25219 25717
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Table 10. FDI, FPI Ownership and Corporate Risk-Taking: Correcting for Endogeneity
This table reports the regression results for three regressions estimated to tackle the endogeneity issue in
Table 5. Model 1 is an OLS model with lagged independent variables. Model 2 is an OLS model with the
first difference of the dependent variable and the independent variables. Model 3 is a 2SLS model using
political rights as the instrumental variable for FDI. All regressions include year, industry and country
fixed effects whose coefficients are suppressed. Beneath each coefficient estimate is p-statistics in
parentheses based on robust standard errors clustered at both the firm- and country-levels. ***, **, and *
denote statistical significance at the 1%, 5%, and 10% levels, respectively. Variable definitions and data
sources are available in the Appendix.
Dependent Variable RISK1
Model Specification
Lagged independent
variables First Difference 2SLS
Model (1) (2) (3)
FDI 0.008** 0.002* 0.090**
(0.038) (0.067) (0.045)
FPI 0.058*** 0.010*** 0.047***
(0.000) (0.004) (0.001)
DIO -0.015*** 0.000 -0.011***
(0.000) (0.881) (0.006)
ROA -0.144*** 0.014*** -0.195***
(0.000) (0.000) (0.000)
LEVERAGE 0.013*** 0.004* 0.011***
(0.000) (0.091) (0.002)
SIZE -0.010*** 0.004*** -0.009***
(0.000) (0.009) (0.000)
SALESGROWTH 0.007*** -0.001*** 0.006***
(0.000) (0.008) (0.000)
CAPEX -0.014 -0.003 -0.005
(0.209) (0.286) (0.548)
GDPGROWTH -0.010** -0.004* -0.006
(0.038) (0.098) (0.185)
ECONFREEDOM 0.001 0.000 0.001**
(0.224) (0.425) (0.019)
GDP -0.006 0.012 -0.002
(0.516) (0.602) (0.846)
MARKETRATES 0.000 0.000 0.001**
(0.810) (0.171) (0.035)
Intercept YES YES YES
YEAR DUMMIES YES YES YES
COUNTRY DUMMIES YES YES YES
INDUSTRY DUMMIES YES YES YES
Adjusted R-Squared 0.349 0.0150 0.394
No. of Observations 48255 35060 60813
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Table 11. Event Study of ROA volatility around Large Block Purchases by Foreign Investors
This table presents our results for an event study around the announcement of acquisitions of large block
of shares (>5%) by foreign investors available in the SDC Platinum M&A Database. Panel A compares
the volatility of ROA between event and control firms that are matched 1-to-1 by finding firms in the
same market and industry with the closest market capitalization, or if not possible, a firm in the same
market with the closest market capitalization. ROA volatility is reported for pre-event periods from year
-5, year -4, and year -3 up to year -1 before the announcement date and post-event periods from year +1
to year +3, year +4, and year +5 after the announcement date. Column (1) reports the average ROA
volatility for our event sample. Column (2) reports the average ROA volatility for our matched control
sample. Panel B provides further analysis by differentiating between acquisitions from developed or
developing acquirers to developed or developing targets. See Appendix I for variable definitions. The
difference and the t-statistic associated with the difference are reported in the last two columns. ***, **, *
denotes statistical significance at the 1%, 5%, and 10% levels, respectively.
Average ROA
Volatility -
Event Sample
Average ROA
Volatility -
Control Sample
Difference t-test of
Difference
(1) (2) (1)-(2) (1)-(2)
Panel A. ROA Volatility Before and After Large Block Purchases by Foreign Investors (N=255)
Year -5 0.084 0.078 0.006 0.703
Year -4 0.073 0.070 0.003 0.383
Year -3 0.066 0.062 0.005 0.630
Year +3 0.084 0.058 0.026 2.817***
Year +4 0.086 0.060 0.026 2.970***
Year +5 0.097 0.063 0.034 3.775***
Panel B. ROA Volatility Before and After Large Block Purchases by Foreign Investors from:
Developing to Developing Countries (N=15)
Year -5 0.046 0.051 -0.006 -0.418
Year -4 0.040 0.038 0.002 0.227
Year -3 0.034 0.037 -0.003 -0.303
Year +3 0.064 0.029 0.035 2.113**
Year +4 0.062 0.032 0.031 2.120**
Year +5 0.071 0.032 0.039 2.214**
Developing to Developed Countries (N=36)
Year -5 0.051 0.060 -0.008 -0.451
Year -4 0.045 0.057 -0.012 -0.716
Year -3 0.044 0.043 0.001 0.046
Year +3 0.037 0.041 -0.004 -0.247
Year +4 0.045 0.041 0.004 0.245
Year +5 0.048 0.040 0.009 0.641
Developed to Developing Countries (N=12)
Year -5 0.111 0.078 0.033 0.811
Year -4 0.088 0.079 0.009 0.249
Year -3 0.093 0.069 0.024 0.736
Year +3 0.129 0.071 0.058 1.117
Year +4 0.145 0.065 0.081 1.608
Year +5 0.165 0.060 0.105 2.044*
Developed to Developed Countries (N=192)
Year -5 0.091 0.084 0.007 0.606
Year -4 0.080 0.074 0.005 0.542
Year -3 0.072 0.067 0.005 0.475
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Year +3 0.092 0.062 0.030 2.607***
Year +4 0.093 0.066 0.027 2.478**
Year +5 0.104 0.070 0.034 3.075***
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Table 12. Foreign Strategic Institutional Ownership and Crash Risk
This table reports the OLS estimation results of the following regression model:
where FSIO is the percentage of foreign strategic institutional ownership in a
firm, RISK1 is a corporate risk-taking measure, and CONTROLS is a vector of control variables (firm
and country characteristics). All regressions include year and industry fixed effects whose coefficients
are suppressed. Beneath each coefficient estimate is p-statistics in parentheses based on robust standard
errors clustered at both the firm- and country-levels. ***, **, and * denote statistical significance at the
1%, 5%, and 10% levels, respectively. Variable definitions and data sources are available in the
Appendix.
Dependent Variable NCSKEW DUVOL COUNT
Model (1) (2)
(3) (4)
(5) (6)
FSIO -0.103* -0.175***
-0.059** -0.097***
-0.054 -0.088***
(0.061) (0.000)
(0.036) (0.000)
(0.105) (0.000)
FSIO*RISK1
0.490
0.275
0.277
(0.152)
(0.144)
(0.226)
RISK1
0.016
-0.025
0.091
(0.861)
(0.581)
(0.162)
Lagged NCSKEW 0.061*** 0.057***
0.030*** 0.029***
0.034*** 0.031***
(0.000) (0.000)
(0.000) (0.000)
(0.000) (0.000)
SIGMA -1.501 -1.279
-1.075 -0.902
-0.632 -0.827
(0.379) (0.490)
(0.198) (0.318)
(0.584) (0.489)
RET -0.408** -0.390**
-0.236*** -0.222**
-0.254** -0.280**
(0.011) (0.030)
(0.002) (0.010)
(0.021) (0.015)
DTURN 1.614*** 1.730***
0.866*** 0.976***
0.919*** 1.153***
(0.001) (0.000)
(0.001) (0.001)
(0.010) (0.006)
OPACITY 0.016*** 0.015***
0.007*** 0.007***
0.012*** 0.012***
(0.000) (0.001)
(0.000) (0.001)
(0.000) (0.001)
LEVERAGE 0.072*** 0.066**
0.041*** 0.037***
0.027 0.024
(0.005) (0.014)
(0.002) (0.009)
(0.187) (0.269)
ROA -0.003 0.029
-0.002 0.010
-0.010 0.020
(0.952) (0.678)
(0.936) (0.790)
(0.765) (0.680)
MCAP 0.023*** 0.026***
0.012*** 0.013***
0.015*** 0.018***
(0.000) (0.000)
(0.001) (0.001)
(0.000) (0.000)
MTB 0.004** 0.004***
0.002** 0.002**
0.002*** 0.002***
(0.013) (0.004)
(0.040) (0.023)
(0.001) (0.004)
GDPGROWTH 0.007 0.006
0.004 0.003
0.004 0.003
(0.278) (0.383)
(0.298) (0.387)
(0.252) (0.365)
GDP 0.018 0.018
0.008 0.009
0.014 0.013
(0.376) (0.380)
(0.436) (0.427)
(0.266) (0.317)
MARKETRATES -0.007 -0.008
-0.004 -0.005
-0.003 -0.003
(0.225) (0.223)
(0.166) (0.164)
(0.368) (0.329)
Intercept YES YES
YES YES
YES YES
YEAR DUMMIES YES YES
YES YES
YES YES
INDUSTRY DUMMIES YES YES
YES YES
YES YES
Adjusted R-Squared 0.034 0.035
0.042 0.043
0.017 0.017
No. of Observations 82705 74423 82705 74423 82787 74489
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Table 13.FDI and FPI Ownership and Crash Risk
This table reports the OLS estimation results of the following regression model:
where FDI is foreign direct investors’ ownership, FPI is foreign portfolio
investors’ ownership, DIO is the percentage of total domestic institutional ownership in a firm, RISK1 is
a corporate risk-taking measure, and CONTROLS is a vector of control variables (firm and country
characteristics). All regressions include year and industry fixed effects whose coefficients are suppressed.
Beneath each coefficient estimate is p-statistics in parentheses based on robust standard errors clustered at
both the firm- and country-levels. ***, **, and * denote statistical significance at the 1%, 5%, and 10%
levels, respectively. Variable definitions and data sources are available in the Appendix.
Dependent Variable NCSKEW DUVOL COUNT
Model (1) (2)
(3) (4)
(5) (6)
FDI -0.085** -0.107***
-0.045** -0.063***
-0.048 -0.047*
(0.037) (0.000)
(0.025) (0.000)
(0.112) (0.053)
FPI 0.114 0.097
0.038 0.039
0.134* 0.117
(0.270) (0.311)
(0.477) (0.422)
(0.079) (0.123)
DIO 0.247*** 0.215***
0.126*** 0.108***
0.153*** 0.133***
(0.000) (0.000)
(0.000) (0.000)
(0.000) (0.000)
FDI*RISK1 -0.008
0.090
-0.108
(0.981)
(0.552)
(0.679)
FPI*RISK1
-0.019
-0.097
-0.122
(0.968)
(0.724)
(0.854)
DIO*RISK1 0.305**
0.191**
0.233**
(0.045)
(0.021)
(0.042)
RISK1
0.072
0.004
0.117
(0.469)
(0.927)
(0.156)
Lagged NCSKEW 0.031*** 0.030***
0.016*** 0.016***
0.016*** 0.015***
(0.000) (0.000)
(0.000) (0.000)
(0.007) (0.009)
SIGMA -0.653 -0.920
-0.465 -0.486
-0.295 -0.832
(0.735) (0.639)
(0.602) (0.596)
(0.813) (0.479)
RET -0.292 -0.338
-0.144 -0.152
-0.211 -0.285**
(0.157) (0.118)
(0.100) (0.102)
(0.107) (0.025)
DTURN 1.213* 1.417*
0.338** 0.423**
0.468 0.724
(0.097) (0.095)
(0.036) (0.036)
(0.425) (0.283)
OPACITY 0.008* 0.006
0.003 0.002
0.007** 0.006*
(0.066) (0.143)
(0.134) (0.220)
(0.010) (0.051)
LEVERAGE 0.015 0.007
0.009 0.005
-0.005 -0.008
(0.628) (0.826)
(0.587) (0.789)
(0.822) (0.768)
ROA 0.054 0.110***
0.029 0.053***
0.025 0.066**
(0.168) (0.008)
(0.147) (0.009)
(0.309) (0.039)
MCAP 0.023*** 0.025***
0.012*** 0.013***
0.015*** 0.016***
(0.000) (0.000)
(0.000) (0.000)
(0.000) (0.000
MTB 0.003*** 0.003**
0.001* 0.001
0.002*** 0.002**
(0.008) (0.015)
(0.056) (0.112)
(0.002) (0.022)
GDPGROWTH -0.009* -0.011**
-0.006* -0.006**
-0.005 -0.006*
(0.085) (0.043)
(0.061) (0.038)
(0.184) (0.083)
GDP 0.019 0.019
0.008 0.009
0.012 0.010
(0.156) (0.162)
(0.215) (0.204)
(0.161) (0.227)
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MARKETRATES -0.005 -0.006
-0.002 -0.003
-0.004 -0.005
(0.297) (0.249)
(0.313) (0.271)
(0.225) (0.150)
Intercept YES YEs
YES YES
YES YES
YEAR DUMMIES YES YES
YES YES
YES YES
INDUSTRYDUMMIES YES YES
YES YES
YES YES
Adjusted R-Squared 0.040 0.041
0.046 0.047
0.018 0.019 No. of Observations 56626 52199 56626 52199 56651 52218
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Table 14. FDI and FPI Ownership and Crash Risk: Correcting for Endogeneity
This table reports the regression results for two regressions estimated to tackle the endogeneity issue in
Table 13. Table 1 is an OLS model with the first difference of the dependent variable and the independent
variables. Model 2 is a 2SLS model where FDI is instrumented on foreign sales scaled by total assets. All
regressions include year and industry fixed effects whose coefficients are suppressed. Beneath each
coefficient estimate is p-statistics in parentheses based on robust standard errors clustered at both the
firm- and country-levels. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels,
respectively. Variable definitions and data sources are available in the Appendix.
Dependent Variable NCSKEW
Model specification First-differenced Variables 2SLS
Model (1) (2) FDI -0.117*** -0.752**
(0.009) (0.030)
FPI 0.367** 0.228**
(0.027) (0.000)
DIO 0.221*** 0.199***
(0.000) (0.000)
Lagged NCSKEW -0.489*** 0.026***
(0.000) (0.000)
SIGMA -1.136 0.004
(0.318) (0.998)
RET -0.164 -0.209
(0.156) (0.224)
DTURN 0.706 0.798
(0.101) (0.131)
OPACITY -0.017*** 0.004
(0.000) (0.410)
LEVERAGE 0.141** 0.006
(0.011) (0.854)
ROA 0.102*** 0.083*
(0.004) (0.081)
MCAP 0.215*** 0.023***
(0.000) (0.000)
MTB
0.002* 0.002*
(0.086) (0.069)
GDPGROWTH 0.009 0.011
(0.463) (0.293)
GDP -0.411 0.047**
(0.507) (0.020)
MARKETRATES -0.012 0.005
(0.110) (0.379)
Intercept YES YES YEAR DUMMIES YES YES INDUSTRY DUMMIES YES YES
Adjusted R-Squared 0.264 0.020 No. of Observations 41108 39628
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Appendix: Variable Definitions and Data Sources
Variable Acronym Definition Data Source
Panel A: Corporate risk-taking variables
Company Earnings Volatility RISK1
is the earnings before interest and taxes of firm i in year t; is the total
assets of firm i in year t; with T over the windows (0 to +4, +1 to +5, +2 to +6, +3
to +7, +4 to +8, +5 to +9, +6 to +10, etc.).
Worldscope
Company Earnings Range RISK2
is the earnings before interest and taxes of firm i in year t; is the total
assets of firm i in year t; with T over the windows (0 to +4, +1 to +5, +2 to +6, +3
to +7, +4 to +8, +5 to +9, +6 to +10 etc.).
Worldscope
Company Earnings Volatility
(Adjusted for Country)
RISK3
indexes the number of firms within the country c and year t, is the
earnings before interest and taxes of firm i in year t; is the total assets of
firm i in year t; with T over the windows (0 to +4, +1 to +5, +2 to +6, +3 to +7, +4
to +8, +5 to +9, +6 to +10, etc.).
Worldscope
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Company Earnings Volatility
(Adjusted for Country and Industry)
RISK4
indexes the number of firms within the country c, industry d and year
t, is the earnings before interest and taxes of firm i in year t; is the
total assets of firm i in year t; with T over the windows (0 to +4, +1 to +5, +2 to
+6, +3 to +7, +4 to +8, +5 to +9, +6 to +10, etc.).
Worldscope
Stock Return Volatility SRVOL Standard deviation of monthly stock returns over the next two years including the
current fiscal year.
Datastream
Research and Development Ratio R&D
is the research and development expenses of firm i in year t; is the total
assets of firm i in year t; with T over the windows (0 to +4, +1 to +5, +2 to +6, +3
to +7, +4 to +8, +5 to +9, +6 to +10, etc.).
Worldscope
Panel B: Crash-Risk Measures
Negative Skewness NCSKEW
is de-meaned firm-specific weekly return of firm i in year t. This is the
negative sample skewness of the firm-specific weekly returns over the year.
Datastream
Return Asymmetries DUVOL
and are de-meaned firm-specific weekly return for up and down weeks
respectively; and are the number of up and down weeks respectively.
Datastream
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Number of crashes minus number of
jumps
COUNT We first detect crash (jump), which occurs when the firm-specific weekly return is
3.09 standard deviations below (above) its mean over the fiscal year, and then we
compute COUNT as the number of crashes minus the number of jumps over the
fiscal year.
Datastream
Panel C: Firm Ownership Measures
Foreign Strategic Institutional
Ownership
FSIO Aggregate strategic holdings by investors domiciled in a foreign country denoted
as a percentage of the total number of shares outstanding at the end of the previous
year, with a disclosed holding above 5 percent of the total number of shares
outstanding.
Datastream
Foreign Institutional Ownership FIO Aggregate equity portfolio holdings by institutions domiciled in a foreign country
denoted as a percentage of the total number of shares outstanding at the end of the
previous year.
FactSet Ownership (LionShares)
Database
Foreign Direct Investment
Ownership
FDI Aggregate strategic holdings by investors domiciled in a foreign country denoted
as a percentage of the total number of shares outstanding at the end of the previous
year, with a disclosed holding above 5 percent of the total number of shares
outstanding.
Datastream
Foreign Portfolio Investment
Ownership
FPI Aggregate equity portfolio holdings by institutions domiciled in a foreign country
denoted as a percentage of the total number of shares outstanding at the end of the
previous year, with individual firm holdings below 5 percent.
FactSet Ownership (LionShares)
Database
Domestic Institutional Ownership DIO Aggregate equity holdings by domestic institutional investors as a percentage of
total number of shares outstanding at the end of the previous year.
FactSet Ownership (LionShares)
Database
Panel D: Firm-level control variables
Return on Assets ROA
is the earnings before interest and taxes of firm i in year t; is the total
assets of firm i in year t;
Worldscope
Leverage LEVERAGE
is net debt of firm i in year t; is the total assets of firm i in year t;
Worldscope
Firm Size SIZE The natural logarithm of total sales denominated in U.S. dollar Worldscope
Annual Sales Growth SALESGROWTH The annual change in total sales. Worldscope
Market Capitalization MCAP The natural logarithm of market capitalization Worldscope
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Market to Book Ratio MTB Market value of equity divided by the book value of equity. Worldscope
Sigma SIGMA The standard deviation of firm-specific weekly returns Worldscope
Return RET The mean of the firm-specific weekly returns for firm i in given year t. Worldscope
Detrended Turnover DTURN The average monthly share turnover in the current year minus the average monthly
share turnover in the previous year, where monthly share turnover is given by the
monthly trading volume divided by the number of shares outstanding.
Worldscope
Earning Opacity OPACITY The sum of the absolute value of discretionary accruals over the prior three years,
where discretionary accruals are estimated from the modified Jones model.
Worldscope
Panel E: Country-level control variables
Economic Freedom ECONFREEDOM The index published in Economic Freedom of the World measures the degree to
which the policies and institutions of countries are supportive of economic
freedom. The cornerstones of economic freedom are personal choice, voluntary
exchange, freedom to compete, and security of privately owned property. Forty-
two variables are used to construct a summary index and to measure the degree of
economic freedom in five broad areas: (1) Size of Government; (2) Legal System
and Property Rights; (3) Sound Money; (4) Freedom to Trade Internationally; (5)
Regulation.
Economic Freedom of the World
(Gwartney et al., 2008)
Market Interest Rates MARKETRATES The real market interest rate. World Development Indicators.
Annual GDP Growth GDPGROWTH Annual percentage growth rate of GDP, at 2005 U.S. dollar. Where GDP is the
sum of gross value added by all resident producers in the economy plus any
product taxes and minus any subsidies not included in the value of the products. It
is calculated without making deductions for depreciation of fabricated assets or for
depletion and degradation of natural resources.
World Development Indicators
Logarithm of GDP per capita GDP The natural logarithm of GDP per capita, which is the gross domestic product
divided by midyear population of a given country, at constant 2005 U.S. dollar.
World Development Indicators
Panel F: Country-level Information Transparency Variables
Credibility of financial accounting
disclosure
AUDIT Variable indicating the percentage of firms in the country audited by the Big 5
accounting firms. AUDIT equals 1, 2, 3 or 4 if the percentage ranges between
[0,25%], (25%,50%], (50%, 75%] and (75%, 100%], respectively.
Bushman et al. (2004)
Number of financial analysts ANALYST Number of analysts following the largest 30 companies in each country in 1996. Bushman et al. (2004)
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Extent of insider trading activities ITENF Indicator variable equal to one if the country enforced insider trading laws before
1995, zero otherwise.
Bushman et al. (2004)
Disclosure requirements index DISREQ The index of disclosure equals the arithmetic mean of: (1) Prospect; (2)
Compensation; (3) Shareholders; (4) Inside ownership; (5) Contracts Irregular; (6)
and Transactions.
La Porta et al. (2006)
Liability standard index LIASTA The index of liability standards equals the arithmetic mean of: (1) Liability
standard for the issuer and its directors; (2) Liability standard for the distributor;
and (3) Liability standard for the accountant.
La Porta et al. (2006)
Information sharing INFSHA03 Dummy equals to one if information sharing operates in 2003, and zero otherwise. Djankov et al. (2007)
Panel G: Legal Environment and Shareholder Protection Variables
Efficiency of judicial system EFFJUD
Assessment of the ‘‘efficiency and integrity of the legal environment as it affects
business, particularly foreign firms’’ produced by the country risk rating agency
Business International Corp. It ‘‘may be taken to represent investors’ assessments
of conditions in the country in question.’’ Average between 1980 and 1983. Scale
from zero to 10; with lower scores, lower efficiency levels.
La Porta et al. (1998)
Anti-self-dealing index ANTISELF Average of ex-ante and ex-post private control of self-dealing. Djankov et al. (2008)
English common law LEGCOM A dummy equals 1 if a country adopts the common law system, zero otherwise. La Porta et al. (1998)
Panel H: Instrumental Variables
Political rights index POLRIGHTS
A higher political rights rating indicates a political system that includes free and
fair elections, those who are elected rule, competitive political parties or other
political groupings, the opposition plays an important role and has actual power,
and minority groups have reasonable self-government or can participate in the
government through informal consensus. The political rights index ranges from
one to seven, where a higher rating corresponds to stronger political rights. (Qi,
Roth, and Wald, 2010)
Freedom House (2013)
Foreign sales scaled by total assets FSALES A firm’s sales in foreign countries divided by its total assets. Worldscope
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