Melco Management Accounting Research
Discussion Paper Series
No.MDP2024-001

Institutional Investors and Stock Price Crash Risk
January 2024
Yunfan He
Zhong Lun Law Firm
Zheng Li
Sichuan Industrial Revitalization Development Investment Fund Co. Ltd
Yingxuan Xie
School of Finance, Southwestern University of Finance and Economics
Yang Xu*
Professor, Faculty of Economics, Nagasaki University
4-2-1, Katafuchi, Nagasaki City, Nagasaki 850-8506 Japane-mail: jxuyang@nagasaki-u.ac.jp
Wan Zhang
School of Finance, Southwestern University of Finance and Economics*Corresponding author

Maki Makoto Foundation
Nagoya, Japan
Discussion Paper Series of the Melco Management Accounting Research, in order to promote
the study of management accounting, has published this unfinished paper onto the Web. Pleaseobtain the permission of the author when citing this paper.

Institutional Investors and Stock Price Crash Risk

Yunfan He
Zhong Lun Law Firm

Zheng Li
Sichuan Industrial Revitalization Development Investment Fund Co. Ltd

Yingxuan Xie
School of Finance, Southwestern University of Finance and Economics

Yang Xu*
Professor, Faculty of Economics, Nagasaki University, 4-2-1, Katafuchi, Nagasaki 850-8506,
Japan. Tel.: 0958206373.
e-mail: jxuyang@nagasaki-u.ac.jp

Wan Zhang
School of Finance, Southwestern University of Finance and Economics* Corresponding author

Institutional Investors and Stock Price Crash Risk

Yunfan He
Zhong Lun Law Firm
heyunfan@zhonglun.com

Zheng Li
Sichuan Industrial Revitalization Development Investment Fund Co. Ltd
939307317@qq.com

Yingxuan Xie
School of Finance, Southwestern University of Finance and Economics
939307317@qq.com

Yang Xu
Professor, Faculty of Economics, Nagasaki University, 4-2-1, Katafuchi, Nagasaki 850-8506,
Japan. Tel.: 0958206373. E-mail: jxuyang@nagasaki-u.ac.jp (corresponding author)

Wan Zhang
School of Finance, Southwestern University of Finance and Economics
zhangwan@smail.swufe.edu.cn

1

Abstract
This study conducted a profound analysis of the impact of external corporate governance,
focusing on the characteristics of institutional investors, on stock price crash risk. A total of
21,642 annual sample observations from the listed companies in China’s A-share market from2004 to 2021 were selected as the research data. The results showed that the increase inshareholding ratio of institutional investors among the shareholders of listed companies
significantly increased the risk of a stock price crash. Moreover, a higher stability among
institutional investors correlated with a higher inclination among companies to conceal
negative news, thus increasing the risk of a stock price crash. However, herd behavior among
institutional investors did not impose a significant impact on the stock price crash risk among
the overall samples. In listed companies with qualified foreign institutional investors holding
shares, institutional investors increased the risk of stock price collapse and exacerbated thecrash risk of stock prices. The results challenge the role of institutional investors as“supervisors” and provide new evidence for research on the influence of institutional investors
in the capital market.

Key words: External Corporate Governance; Stock Price Crash Risk; Institutional Investors2

1 Introduction
Stock price crash risk refers to a risk of a sharp decline in stock prices over a relatively
short period of time. This situation typically occurs under the influence of various factors, suchas drastic market fluctuation, severe economic environment, internal corporate problems, andchanges in market sentiment. Stock price crash risk may cause losses to investors, lower market
liquidity, and increase uncertainty in the entire market. Institutional investors play an importantrole in the capital market by improving information flow, enhancing market transparency,ugrading corporate governance, and promoting market supervision. These investors manage
large amounts of funds and represent many small and medium-sized investors (e.g., pension
funds, hedge funds, publicly offered funds, investment banks, insurance companies).
Meanwhile, all listed companies are subject to external corporate governance, in which external
authorities, such as regulatory bodies, the government, media, and public, supervise, regulate,and impact microcosmic entities (Weir et al., 2002; Kim et al., 2011; Bharath, 2019). In thisstudy, we explored the impact of external corporate governance on stock price crash risk.
Currently, the structure of investors in China’s A-share market has become less dominatedby retail investors, and an era of “institutionalization” is gradually emerging 1 . The China
Securities Regulatory Commission has also encouraged and advocated the “unconventionaland creative cultivation and development of institutional investors” and successively releaseda series of policies and measures to support and standardize this practice, benefiting from which
the shareholding ratio of institutional investors continued to grow. By the end of 2022,
according to the data released by the CSRC, the ratio of the institutional investors’ free float
market cap had increased to 56.7% in China’s A-share market.
Scholars hold different views regarding the impact of institutional investors on external
corporate governance. Institutional investors actively participate in corporate operation andmanagement. Their functions of supervision and governance have been deemed by scholars
from Western developed countries as crucial in mitigating agency problems and strengthening
corporate governance. As mature and rational investors in the capital market, institutional
investors own tremendous capital advantages and employ professional technical teams, which

1 For the detailed report, please refer to https://www.stcn.com/article/detail/500460.html

3enable them to function effectively as “supervisors” by means of active utterance (voting byhand), exit threat (voting by foot), and market survey. As such, they can control misconduct inmanagers. For example, institutional investors may reduce the management’s earnings
management behaviors initiated for self-interest motives (Cornett et al., 2008) or replace CEOs
with unfavorable performance (Kang et al., 2018).
Some scholars have raised their doubts on the governance effect of institutional investors,
given that the profit-driven nature of institutional investors makes them prioritize the inflow ofinvestment funds over corporate performance. In particular, trade-oriented institutional
investors who focus on short-term returns are more inclined to change investment targets
frequently and maintain shorter shareholding periods. In this case, they are less motivated toparticipate in corporate governance (Jiang & Kim, 2015; Firth et al., 2016). Moreover,institutional investors may become “cooperators,” conspiring with the management andexacerbating information asymmetry between investors and companies, resulting in increased
agency conflicts and decreased supervision and restriction of the operation management oflisted companies.
In consideration of the foregoing, we adopted the risk of a stock price crash as a specificresearch object to profoundly explore the effect of the external corporate governance ofinstitutional investors in China’s A-share market and analyze the role of institutional investors
in the stock market as “supervisors” or “cooperators.” We expected our findings to provide
novel insights for investors and for the market supervision of the government in reevaluating
institutional investors.

2

Literature Review

2.1 External Corporate Governance of Institutional Investors
The external corporate governance of institutional investors refers to the effect of large
institutional investors (e.g., pension, publicly offered, and hedge funds) on the behaviors ofcompanies in which they hold shares. With the continuous expansion of the scale and influenceof institutional investors, their role in corporate governance has sparked extensive discussion.
Currently, the effect of the external corporate governance of institutional investors is4

controversial in academic circles. One view is that institutional investors, as holders ofcorporate shares, would supervise internal corporate governance and relevant corporate
behaviors on behalf of minority shareholders, thereby benefiting the long-term sustainable
development of these companies. Another view holds that the primary purpose of institutional
investors is to gain interest by purchasing and selling assets (stock) in the capital market. As
such, they will not perform the function of supervision nor truly participate in corporate
governance. We discussed these two competing views, to explore the contribution margin.
With respect to the former view that supports the effects of external corporate governanceof institutional investors, many scholars have discussed the governance motive, methods, andperformance of institutional investors from multiple dimensions. First, the motive of
institutional investors for governance mainly originates from financial income. Lewellen et al.
(2022) found that the motive of institutional investors to participate in corporate governance is
to increase the capital inflows managed by these institutions by improving the corporate value,
thereby bringing better returns on investments to the institutional investors.
Second, institutional investors typically adopt two corporate governance methods, namely,
active utterance (voting by hand) and exit threat (voting by foot). Diversified strategies areavailable for active utterance, including private negotiation, public censure, voting by proxy,and shareholder litigation. Carleton et al. (1998) investigated letters of pension funds and foundthat these funds privately negotiate with the companies in which they held shares to participate
in governance. McCahery et al. (2016) gathered statistics on institutional investors’ survey
reports and proved that they participate in corporate governance; they further found that
institutional investors participate in corporate governance by adopting the strategy of active
utterance rather than exit threat. For example, institutional investors set meetings with the
management and publicly denounce the misconducts and unfavorable performance ofenterprises. Aggarwal et al. (2015) reported that institutional investors participate in corporate
governance through voting by proxy. Cheng et al. (2010) found that institutional investors, as
lead plaintiffs in litigation, better facilitate defendant companies to make improvements after
litigation, indicating that institutional investors may also improve corporate governance bymeans of litigation. Jiang et al. (2018) revealed that institutional investors significantly improvetheir corporate governance capacity through on-the-spot surveys, and this effect is more
5

prominent in companies with relatively low information environment quality and inferior
corporate governance. With respect to relevant research on the exit threat, Parrino et al. (2003)
asserted that institutional investors participate in corporate governance through voting by foot,and the shareholding ratio of institutional investors tends to decline drastically after a forced
CEO change, which is a way to express their dissatisfaction with the management.
Third, regarding governance performance, the impact of institutional investors on
corporate governance and corporate behaviors may be explored from multiple perspectives,
including shareholding characteristics and types of institutional investors. Hartzell et al. (2003)
found a correlation between the ownership centralization of institutional investors and thesensitivity of senior managers’ remuneration. Moreover, the former is negatively correlatedwith the remuneration level, indicating a role of “supervisor” played by institutional investors
in the mitigation of agency problems between shareholders and the management. Cornett et al.
(2008) reported that the increase in shareholding ratio of institutional investors reduces the
management’s earnings management behaviors initiated for self-interest motives, which
demonstrates the supervisory function of institutional investors. Kim et al. (2011) suggested
that the equity ratio of CEOs and the positive effect on risk taking may be weakened when theownership centralization of institutional investors is high, indicating the restricting effect of
institutional investors’ supervision on the management’s behaviors. Kang et al. (2018) pointed
out that institutional investors who serve as substantial shareholders tend to improve thegovernance effect. When these institutional investors hold a higher equity ratio in companies,
they are inclined to replace CEOs with unfavorable performance, and consequently, thecorporate performance improves after the CEO change. Dyck et al. (2019) analyzed data from
41 countries and demonstrated that the ownership of institutional investors is positively
correlated with the performance of corporate social responsibility, with their motive originatingfrom the dual effect of economy and society. He et al. (2019) suggested that cross-shareholding
motivates institutional shareholders to play a more active supervisory role, as specificallyreflected in the positive correlation of cross-shareholding with the voting of institutional
investors against governance proposals initiated by management shareholders, resulting in theloss of the latter’s decision-making rights. Crane et al. (2019) confirmed that the connection
between investors can be improved and corporate governance can be strengthened upon the6common shareholding of institutional investors; the increase in their common shareholding
ratio contributes to the increase in votes cast against low-quality management reports.
Regarding the different types of institutional investors, Chen et al. (2007) concluded intheir research on merger and acquisition (M&A) performance that institutional investors who
adhere to long-termism play their supervisory role better, as reflected in the better performanceof companies with this type of institutional investors after the M&A and the higher possibilityof the withdrawal of bad mergers and acquisitions. Ferreira et al. (2008) used data on listedcompanies in 27 countries and argued that institutional investors less involved in corporate
business (e.g., foreign-funded and independent institutional investors) have a stronger effecton corporate supervision, reflected in the higher valuation, better operating performance, andlower capital expenditure of companies in which these institutional investors hold a higher
shareholding ratio. Bena et al. (2017) also found that foreign institutional investors do not
simply focus on short-term income, and that the increase of their shareholding ratio can
improve the investments of enterprises in intangible assets, tangible assets, and human capital,
as well as promote the output of innovation achievements and make enterprises go global.
Huang et al. (2015) studied the reform of non-tradeable shares in China and confirmed that the
influence of qualified foreign institutional investors (QFIIs) on state-owned shareholders tends
to be greater compared with local mutual funds because of a lower vulnerability to politicalinfluences and higher likelihood of participating in the fair negotiation and supervision of stateholding companies. Brav et al. (2008) suggested that hedge funds participate in corporate
governance and the target companies’ performance and CEO remuneration change evensignificantly after fund intervention, together with the changes in the enterprises’ stock
dividend policies. Appel et al. (2016) indicated that passive mutual funds influence the choicesof corporate governance, including the hiring of more independent directors, reduction of
takeover defense, and construction of more equal voting rights to improve long-term corporate
performance. Borochin et al. (2017) indicated that long-term institutional investors contributefewer misguided valuations of companies and better governance effects compared with shortterm institutional investors.
With respect to questions on the effect of external corporate governance of institutional
investors, Bebchuk et al. (2017) proposed that a principal–agent problem exists between
7

investor managers from institutional investors and actual investors; investment managers mightmake the actual investors suffer investment losses to pursue their own interests. They also
found that the principal–agent problem in hedge funds might be insignificant compared with
passive index and active mutual funds. Schmidt et al. (2017) suggested that the positive
exogenous impact of the ownership of passive index funds may result in the increase of the
CEO’s power, reduction of the appointment of independent directors, and decline of M&A
quality. These results indicate that the increase in equity held by passive institutional investors
will lead to the rise of the principal–agent cost. Heath et al. (2022) indicated that the supervisory
effect of passive funds is worse compared with active funds. Passive funds are unlikely to voteagainst the management of the companies regarding disputed governance issues, which thenresults in the decline of the independence of the board of directors and sensitivity to the
remuneration performance.
2.2 Factors Triggering Stock Price Crash Risk
The triggers of stock price crash risk may be deliberated from the internal and external
factors of enterprises. Based on existing theories, the internal core factors of enterprises that
contribute to their stock price crash risk lie in the management’s hoarding and concealment ofnegative news. Therefore, research has investigated how the internal factors of enterprises,
including corporate behaviors, management behaviors and characteristics, remuneration andwelfare system, and stock characteristics, potentially trigger the risk of a stock price crash.
From the perspective of corporate behaviors, research indicates that factors like tax
behavior, decision-making on innovation, decision-making on investment, social responsibility
disclosure, and earnings management can increase the risk of a stock price crash. Thesebehaviors are adopted by management to cover negative news. Alternatively, the management
may overestimate the corporate value to pursue their own interests, thus leading to theaccumulation of negative news and finally causing stock prices to plummet.
Kim et al. (2019) used samples of American companies and proved that enterprises’ tax
avoidance increases the risk of a stock price crash, because tax avoidance, as an opportunistic
behavior, can lead to the management’s corruption and the accumulation of negative measures.
Once the concealed negative news accumulatively exceeds a critical point, the release of8negative news hoarded for a long period will result in a stock price crash.
Regarding decision-making on innovation, Zhou et al. (2017) found that the innovation
investment of GEM-listed companies is negatively correlated with stock price crash risk—
investors’ attention to enterprise innovation can increase the flow and transparency of
information. Jia et al. (2018) studied the relation between enterprise innovation strategies andfuture stock price crash risk, and found that enterprises with explorative innovation strategy
are more vulnerable to stock price crash compared with development-oriented innovation
strategy. The explorative innovation strategy indicates subversive exploration and innovationof previous technology, making the failure rate of innovation higher. Consequently, theenterprises’ motive to disclose negative news related to the innovation project is reduced,
resulting in a stock price crash.
From the perspective of decision-making on investment, Jiang et al. (2015) indicated thatenterprises’ overinvestment can aggravate the stock price crash risk, arising from a principal–
agent problem. In other words, the management overinvests to seek private interest, leading tothe overestimation of the actual value of the enterprise and eventually causing plummetingstock prices. Peng et al. (2018) constructed a triangular game model containing market,
company, and manager; they found that enterprises’ financial investment increases thepossibility of a stock price crash when the management will hold financial assets with favorable
liquidity to cover the negative news. In other words, the higher the financial investment levelof an enterprise, the higher the degree of information asymmetry with the outside world.
Meanwhile, Quan et al. (2015) reported that corporate social responsibility disclosure
positively impacts the risk of a stock price crash. Enterprises disclose reports mandatorily, andsome tend to cover up misconducts by disclosing social responsibility information, thus
resulting in the concealment and accumulation of negative news.
Research has also noted that earnings operation is an important behavior that affects
enterprises. Hutton et al. (2009) used earnings management as an indicator to measure thetransparency of corporate financial statements, proving a positive correlation between opacity
and stock price crash risk. However, the adoption of the Sarbanes–Oxley Act may, at least in
the US, help improve this negative impact, indicating that regulatory policies benefit the
reduction of the accumulation of negative news to realize the goal of preventing a stock price9

crash. Chen et al. (2017) found that the higher the degree of income smoothing in an enterprise,
the greater the stock price crash risk, indicating that earnings smoothing also provides
opportunities for the management to manipulate financial information. Notably, themanagement would conceal negative news in consideration of their position and remuneration.
From the perspective of the management’s behaviors and characteristics, the risk of astock price crash is positively affected by shareholding reduction of senior managers andsubstantial shareholders, management overconfidence, and management age. Zhang et al.
(2017) analyzed the desterilization of non-tradable shares and found that substantial
shareholders increase stock prices and reduce shareholding at a high level through capital
operation after desterilization, exacerbating the stock price crash risk. Sun et al. (2017) reported
that senior managers’ reduction of shareholding has a significantly positive impact on the stock
price crash risk, with shareholding reduction being a signal of the management’s suppressionof negative news. When the negative news accumulates to a certain extent, one potential
outcome is a stock price crash. Yi et al. (2019) pointed out that the peer effect of senior
managers’ shareholding reduction can significantly increase the risk of a stock price crash.
Although insiders may not conceal negative news during the period of shareholding reduction,
external investors can lower the stock prices to seek risk compensation, given that shareholding
reduction can intensify the separation of interests with external investors, thus increasing thestock price crash risk.
Kim et al. (2016) demonstrated that overconfidence makes CEOs overestimate the returnsof investment projects and neglect the feedback of privately acquired negative news, resulting
in the accumulation of unfavorable performance and increasing the possibility of a stock pricecrash. Andreou et al. (2017) found that CEO age has a significantly positive impact on the riskof a stock price crash: young CEOs are more likely to hoard some negative news for the purposeof self-interest and career success in the early stage. This negative effect is even moresignificant in companies with ineffective management supervision.
From the perspective of remuneration and welfare system, research has explored theimpact of senior managers’ option system and allowance and welfare system on stock pricecrash risk. Kim et al. (2011) found that the value of option portfolios of CFOs significantly
improves the stock price crash risk of companies. The effect is more obvious in companies
10with high financial leverage in non-competitive industries. Their research indicated that senior
managers will conceal negative news if the stock prices are closely related to their interests.
Benmelech et al. (2010), using a dynamic rational-expectation model featuring information
asymmetry, indicated that stock-based compensation incentives induce managers, including
CEOs, to conceal negative news regarding the influence on the future growth of options andchoose next-best investment decisions, leading to the overestimation of projects and slump ofstock prices.
Xu et al. (2014) found that the excess benefits provided by state-owned enterprises are
positively correlated with the risk of a stock price crash. Senior managers of state-owned
enterprises are motivated to conceal negative news for a longer period to enjoy extra benefits,leading to the increase of the stock price crash risk in the future. In companies where senior
managers have at least two years to reach the age of retirement, the impact of excess benefits
on the stock price crash risk is even more significant. Ben et al. (2018) pointed out that
employee welfare practice is positively correlated with future stock price crash risk, whichaccords with principal–agent theory. In other words, the management will attempt to use
generous employee welfare plans to reduce the possibility of employees’ reporting of the
management’s misconduct, thereby helping the management control negative news. Thisphenomenon is especially evident in labor-intensive enterprises with poor management,
ineffective investor protection, and low requirements for information disclosure.
Finally, from the perspective of stock characteristics, stock volatility, liquidity, andanchoring ratio have a prominent impact on the risk of a stock price crash. Chen et al. (2001)
found that stock volatility had a predictive effect on stock price crash risk. Chang et al. (2017)
adopted a decimal trading system as an exogenous impact of stock liquidity to explore theimpact of stock liquidity on the risk of a stock price crash, and found that companies withstronger stock liquidity are more likely to release negative income news, making short-term
investors sell off the stocks, and facilitating the management to conceal negative news.
Consequently, the accumulated negative news would break forth in the late stage to cause astock price crash. Yang et al. (2020) adopted anchoring ratio to describe the overestimation ofstock prices and revealed a positive correlation with stock price crash risk. Less analyst tracking,more shareholding by retail investors, and higher stock liquidity strengthen the positive impact
11

of the anchoring ratio on the stock price crash risk.
The catalytic effect of multiple dimensions, including humanistic environment, market
participants’ behaviors, industrial environment, and institutional environment, on the risk of astock price crash has been verified from the perspective of the external factors of enterprises.
An et al. (2018) concluded that companies in countries with a higher degree of individualism
face a higher stock price crash risk. Individualism spreads to overseas markets through foreign
investors, thus affecting the local enterprises’ stock price crash risk and intensifying the impact
of enterprises’ risk-taking and earnings management on the said risk. In addition, the positive
correlation between individualism and stock price crash risk is amplified during global
financial crises, whereas it is weakened by the improvement of financial informationtransparency at the national level and application of international financial reporting standards.
From the perspective of market participants’ behaviors, some factors that exacerbate thestock price crash risk include the behaviors adopted by analysts and institutional investors. Xu
et al. (2012) noted that analysts’ “optimistic bias” positively impacts the risk of a stock pricecrash. Xu et al. (2013) later pointed out that the herd behavior of institutional investors presents
a positive impact on companies’ stock price crash risk, as evident in the herd behavior of“sellers,” which the existence of QFII could not mitigate. Callen et al. (2015) found a positive
correlation between investors’ short selling and the risk of a stock price crash, indicating theability of short sellers to identify the negative news controlled by the management. They further
indicated that the predictive effect of short selling is more prominent in companies with weak
governance mechanisms, excessive risk-taking, and a high degree of information asymmetry
between the management and the shareholders.
Meanwhile, Li et al. (2019) observed that competition pressure from the product market
intensifies the management’s motive to conceal negative news, leading to an unexpected anddrastic decline of stock prices in the late stage and an increase risk of a stock price crash. Ye etal. (2018) indicated that the constituent stock of Shanghai and Shenzhen 300 index will
significantly increase the companies’ stock price crash risk owing to pressure possibly broughtby the name and liability of constituent stock to the management. Thus, the management would
try its best to conceal negative measures for the sake of their performance, subsequently
increasing the probability of a stock price crash.
12

Finally, from the perspective of institutional environment, scholars have noticed that somepolicies and regulations accelerate the stock price crash. For example, Chu et al. (2016) foundthat the margin trading system implemented since 2010 would exacerbate the risk of a stock
price crash. You et al. (2022) stated that the policies implemented by local governments affectcompanies’ stock price crash risk. Specifically, the more progressive the policies, the higher
the stock price crash risk of local enterprises. This kind of connection is closely related to thegovernment’s motives to intervene and enterprises’ motives to accede. The intrinsic
mechanism test indicates that the administration style of the local government will act on theenterprises’ stock price crash risk by impacting the legal construction, informationtransparency, and corporate information disclosure behavior in the region.

3

Research Hypotheses

In accordance with the analysis of Section 2, the primary reason contributing toplummeting stock prices lies in the management’s cover-up of negative news, which can neverlast. When the accumulated negative news exceeds a threshold, an extreme outcome is in theform of plummeting stock prices (Hutton et al., 2009; Jiang, 2015; Chen et al., 2017). The
management’s motive to cover up negative news can be explained by principal–agent theory.
The separation between managerial rights and company ownership enables the management toavoid sharing adverse information to the investors to pursue its own interests. Managers may
conceal negative news out of their concern for their careers and to build false confidence ininvestors. If the performance of the company is highly undesirable, the management will face
the reputation loss and even dismissal. Therefore, dressing up performance in financial
statements is a common occurrence. Another motive is to acquire higher remuneration. If the
management’s remuneration is linked with the company’s stock price, managers may choose
to hide negative news to increase or prevent the decline of the stock price; they may then reduce
their shareholding before the problem breaks out to cash out at a high stock price level.
Therefore, managers may adopt strategies such as limited disclosure, delayed disclosure, andeven non-disclosure to blind the investors of negative information, thus worsening the problem
of information asymmetry between the company and the market.
13

As an important market participant and an information transmitter, institutional investors
are controversial in the existing research with respect to their role in external corporate
governance. Therefore, the impact of institutional investors on the stock price crash risk may
also appear two-sided. On the one hand, institutional investors supplement and assist corporate
governance. They are able to impose a vital impact on corporate decision-making and operation
by means of active utterance and exit threat (Carleton et al., 1998; McCahery, 2016; Jiang etal., 2018). Scholars have also found that institutional investors can supervise and restrict the
management of companies to a certain extent and benefit the reduction of the principal–agent
problem between shareholders and the management. Therefore, institutional investors may
supervise the senior managers of a company and participate in corporate governance by playing
the role of “supervisor” with the objective to make the management reduce “hoarding behavior,”
improve information transparency, and lower the risk of a stock price crash. With a higher ratioof equity held, institutional investors may grow more motivated to implement supervision.
On the other hand, institutional investors cannot have an active effect on external
corporate governance. As onlookers, institutional investors may even bring a negative impact
on corporate governance. According to research on the different types of institutional investors,
index funds and mutual funds seldom actively participate in the supervision, inquiries, andother relevant activities of their holding companies compared with hedge funds. The reason isthat the income sources of these institutional investors come from managerial fees rather thanprice earnings from invested companies. Moreover, these institutional investors have a weakerdiscourse power compared with the management, and they intend to remain silent and play therole of onlooker regarding disagreements on important issues (Schmidt et al., 2017; Heath etal., 2022). Another negative impact arises in principal–agent problems that may exist between
institutional investor managers and the actual investors, which will further exacerbate theseverity of information asymmetry (Bebchuk, 2017). The reason is that the interests ofinvestment managers are not same as those of their clients, the actual investors. Typically,
investment managers still acquire large remunerations even when losses occur to the funds they
manage. Meanwhile, institutional investors may engage in short-term speculation in a company,
affecting the company’s long-term development (Borochin et al., 2017). Institutional investors
normally pursue high incomes and may focus on short-term financial performance rather than14

corporate social responsibility, environmental impact, and other relevant aspects. Notably,
institutional investors are essentially enterprises that pursue their own interests. Therefore, they
may have be motivated to conspire with the management or substantial shareholders to benefit
their interests regardless of the interests of other medium and small investors (Fu et al., 2008;
Pan et al., 2011). In summary, the increase in shareholding ratio of institutional investors maylead them to conspire with the management and exacerbate the concealment of negative news,
thus increasing the risk of a stock price crash. Based on the discussion above, we hypothesized
as follows:
Hypothesis 1: The higher the shareholding ratio of institutional investors, the higher therisk of a stock price crash.

The stability of shareholding of these investors is also an important factor to consider.
Notably, the impact of such stability on the risk of a stock price crash remains unclear. On the
one hand, based on available evidence regarding the “supervisor” role played by institutional
investors, the stability of shareholding of institutional investors may lower the stock price crashrisk. Stable institutional investors have a good understanding of the holding companies and areable to maximize insider information on these companies and impose restrictions on the
management, thereby participating in corporate governance in an effective manner (Chen et al.,2007). Koh (2007) found that institutional investors holding shares in companies for a long
duration can reduce the companies’ earnings management behaviors. Elyasiani et al. (2010)
reported that stable institutional investors are motivated and have effective supervision ability,
making them capable of playing an important role in the mitigation of principal–agent conflicts
and reduction of the risk of opacity of corporate information. On the contrary, institutional
investors who hold shares for a short period, or institutional investors with undesirable
performance, may not be strongly motivated to participate in corporate governance because
they tend to pursue short-term income, which will have an adverse influence on the company’s
long-term development. Bushee (1998) pointed out that enterprises are more likely to reduce
long-term R&D projects to realize the goal of short-term profitability upon the frequent change
of institutional investors.
On the other hand, owing to long-term shareholding, institutional investors may establish15

a “firm” relationship with managers and access insider information, making them motivated torake higher profits through insider transactions or conspire with the management (Cai et al.,2010). Indeed, institutional investors’ participation in insider transactions have been reported(Fu et al., 2008; Pan et al., 2011). Such research evidence indicates that the stability of
institutional investors may further urge their collusion with the management, resulting in theexacerbation of the risk of a stock price crash. Thus, we hypothesized as follows:
Hypothesis 2: The higher the stability of institutional investors, the higher the risk of astock price crash.

The characteristics of institutional investors also merit exploration. Research has notedthe characteristic of herd behavior among institutional investors, which can affect the risk of astock price crash. The herd behavior adopted by institutional investors may have a dual effect,
depending on the motive of the herd behavior. On the one hand, the motive of the herd behavior
of institutional investors may result from the management blindly following public information.
Managers may choose to ignore private information and intend to process and analyze
information known by the public (Deng et al., 2018). The herd behavior resulting from suchkind of motive is called “genuine herding,” the consequences of which include the failure tocompletely reflect the private information owned by the management in the stock price. The
subsequent information mismatch can further exacerbate the information opacity in the market
and increase the possibility of a stock price crash (Xu et al., 2013).
On the other hand, the herd behavior of institutional investors may be informed by acomprehensive and reasonable analysis of the public and private information they have. Thiskind of herd behavior is called “pseudo herding” (Bikhchandani & Sharma, 2001; Xu et al.,2013). Pseudo herding can comprehensively reflect information in the stock prices, and the
management does not conceal the private information. Thus, instead of being exacerbated, thestock price crash risk may even be reduced owing to the effective release of information. Thus,
we hypothesized as follows:
Hypothesis 3: The herd behavior of institutional investors will not exacerbate the risk ofa stock price crash.

16

Finally, the types of institutional investors have been distinguished, and whether thebenchmark results would be affected has been observed. On the former, the effect of QFII has
received much attention, given that most QFIIs include capital from Western developed
countries. Notably, the viewpoints on the role played by these institutional investors have notbeen completely unified in the market.
According to scholars, QFIIs have no motive to participate in corporate governancebecause they have a relatively low shareholding ratio (Li et al., 2013; Shi et al., 2009). Thus,
this type of institutional investors also lack any motive to actively negotiate with managers orplay a more powerful supervisor role. Therefore, QFIIs would not actively supervise themanagement, meaning that their presence does not affect the management’s motive to coverup negative news. As such, QFIIs, as profit-seeking entities, may choose to conspire with the
management for short-term gains from high stock prices, which then intensifies the positive
impact of the shareholding ratio of institutional investors on the risk of a stock price crash.
According to another viewpoint, the reason QFIIs have been introduced in the first place
pertains to their maturity and professionalism in investment decision-making (Aggarwal et al.,2011). Moreover, given that QFII’s capital comes from other countries, they appear more
independent and less vulnerable to intimidation from the management and substantial
shareholders that could make them conspire with the latter to cover up negative news.
Therefore, the introduction of QFIIs can strengthen the mitigation effect of institutional
investors’ shareholding ratio on the stock price crash risk. Thus, we hypothesized as follows:
Hypothesis 4: The introduction of QFIIs would intensify the positive impact of
institutional investors’ shareholding ratio on the risk of a stock price crash.

4 Sample Selection and Data Sources
Our initial sample included all A-share listed companies in the Shanghai Stock
Exchange and Shenzhen Stock Exchange of China. We set the time range of the data as startingfrom 2004, when the data disclosure of institutional investors began to become relatively
complete, to 2022, when the data of the annual financial statements for 2022 had not been
disclosed in the official database. To ensure data accuracy and reliability, we screened the17initial samples according to the following procedure: (1) We removed the data of listedcompanies in the financial industry that had been put under the state’s strict regulation. (2) We
removed the data of listed companies marked with ST and *ST; companies of this category
suffered great losses, which could result in sample deviation. (3) We removed the data of listedcompanies that had missing data on the main variable. After screening, the total sample datacontained 21,642 observations. To eliminate the impact of sample extremes on the empiricalresults, we winsorized all the variables per the quantiles of 1% and 99%. The data wereacquired from the China Stock Market & Accounting Research database. After reasonable
processing and screening, the data provided reliable support for our research.
4.1

Variable Definitions

1) Explained Variable
The explained variable was the risk of a stock price crash, measured by two indicators.
First, negative skewness (NCSKEW) in price earnings can be used to capture the asymmetry ofstock returns (Chen et al., 2021; Xu et al., 2013). If the skewness is negative, then the data lean
to the right as a whole. The specific calculation method is as follows:
3

𝑁𝐶𝑆𝐾𝐸𝑊𝑖,𝑡 = −3[𝑛(𝑛 − 1)2 ∑ 𝑊𝑖,𝑡
]
3

(1)

2 2
[(𝑛 − 1)(𝑛 − 2)(∑ 𝑊𝑖,𝑡)]where 𝑊𝑖,𝑡 represents the return of stock of company 𝑖 in week 𝑡 after market adjustment. It
specifically refers to the residual in the market model, calculated as follows:
𝑟𝑖,𝑡 = 𝛼𝑖 + 𝛽1 𝑟𝑚,𝑡−2 + 𝛽2 𝑟𝑚,𝑡−1 + 𝛽3 𝑟𝑚,𝑡 + 𝛽4 𝑟𝑚,𝑡+1 + 𝛽5 𝑟𝑚,𝑡+2 + 𝜀𝑖,𝑡(2)

𝑊𝑖,𝑡 = 𝐿 𝑛(1 + 𝜀𝑖,𝑡 )(3)where 𝑟𝑖,𝑡 represents the logarithm return of company 𝑖 at the end of week 𝑡 , and 𝑟𝑚,𝑡
represents the market return at the end of week 𝑡.
The second indicator used to measure stock price crash risk is down-to-up volatility(DUVOL). We divided the stock return data into two subsamples, namely, up weeks and down
weeks, according to whether the weekly return ( 𝑟𝑖,𝑡 ) of stock 𝑖 after market adjustment
18

exceeded the annual average return. We then calculated the standard deviations of the stock
return (𝑅𝑢 , 𝑅𝑑 ) in these two subsamples. The calculation method of DUVOL is as follows:
𝐷𝑈𝑉𝑂𝐿𝑖,𝑡 = 𝐿𝑛 {[(𝑛𝑢 − 1) ∑𝑑𝑜𝑤𝑛 𝑅𝑑2 ]}𝑛𝑑 − 1 ∑𝑑𝑜𝑤𝑛 𝑅𝑢2(4)

where 𝑛𝑢 and 𝑛𝑢 represent the number of weeks in which the weekly return of company 𝑖exceeds the annual average return.
2) Explanatory Variables
Our first explanatory variable was the shareholding ratio of institutional investors, which
could measure external corporate governance (INSH). The specific calculation method is theratio of shares held by institutional investors to the total share capital (Cheng, 2006; Yang etal., 2012). The second explanatory variable was the shareholding stability of institutional
investors (Instability), calculated as the average of standard deviations of the shareholding ratio
of institutional investors within two or three years (Liu et al., 2012). The third explanatory
variable was the herd behavior of institutional investors (HERD), calculated as the absolutevalue obtained from the deduction of the standard deviation from the mean value of theadjustment item of HERD (Xu et al., 2013).
3) Control Variables
We selected the following control variables, with reference to prior research (Xu et al.,2013; Kim et al., 2014; Hu et al., 2020): (1) Annual average weekly return of the company
stock (RET), or the annual average of the weekly return of the company stock; (2) Standard
deviation of the annual weakly return of the company stock (SIGMA); (3) Monthly average
excess turnover rate (MDIFF), or the annual average of the balance obtained from thededuction of the de-trended monthly turnover rate of the previous year from the de-trended
monthly turnover rate of the stock in the current year; (4) Asset–liability ratio of the company(LEV), or the ratio between total liabilities and total assets of the company; (5) Book-to-market
ratio of the company (BM), or the ratio between market and book value of the company; (6)Degree of the company’s earnings management (ABSACC), or the absolute value of accrued
earnings for the correction of the Jones model estimation; and (7) Information transparency ofthe company (OPAQUE), providing three-year moving averages of accrued earnings for the19correction of the Jones model estimation. We also added industry fixed effects (INDUSTRY
fixed effects) and year fixed effects (YEAR fixed effects) to the regression model, to control forother influencing factors that may be omitted. The definitions of specific variable data aredetailed in Table 1.
Table 1 Definitions and Explanations of Primary Variables
Category
Symbol of variable Name of variable
Explained
Indicator 1 of stock price
NCSKEW
variablecrash risk
Indicator 2 of stock price
DUVOL
crash risk
Explanatory
Shareholding ratio of
INSH
variables
institutional investors
Instability
Stability of institutional
investors

HERD

Herd behavior of
institutional investors

RET

Annual average weekly
return of the company
stockStandard deviation ofannual weekly return ofthe company stock
Monthly average excess
turnover rate

Control variables
SIGMA

MDIFF

SIZE

Company size

ROA

Return on assets of thecompany
Asset–liability ratio of
the company
Book–market ratio of thecompany
Degree of earnings
management of thecompany
Information transparencyof the company

LEV
BM
ABSACCOPAQUE

4.2

Definition of variable
Negative skewness
Down-to-up volatility of the company’s
stock price
Ratio of shares held by institutional
investors to the total share capital
Average of standard deviation ofshareholding ratio of each institutional
investor of company stock within a threeyear period, including the current year andthe past two years, for the measurement of
the stability of institutional investors
Absolute value obtained from the deductionof the standard deviation from the meanvalue as adjustment item
Annual average of weekly return of thecompany stock
Annual standard deviation of the weekly
return of the company stock
Annual average of the balance obtained
from the deduction of the de-trended
monthly turnover rate of the previous year
from the de-trended monthly turnover rateof the stock in the current year
Natural logarithm of the total assets of thecompany
Ratio between the net profits and total assetsof the company
Ratio between the total liabilities and total
assets of the company
Ratio between the market and book value ofthe company
Absolute value of accrued earnings for thecorrection of Jones model estimation
Three-year moving average of accrued
earnings for the correction of Jones model
estimation

Model Design
To verify the hypothesis, we constructed the following benchmark regression model, withreference to prior research (Cheng, 2006; Yang et al., 2012; Chen et al., 2021):
20

𝐶𝑟𝑎𝑠ℎ_𝑟𝑖𝑠𝑘𝑖,𝑡 = 𝛼𝑖 + 𝛽1 𝐼𝑁𝑆𝐻𝑖,𝑡−1 + 𝛽2 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑖,𝑡−1 + 𝐹𝑖𝑥𝑒𝑑 𝐸𝑓𝑓𝑒𝑐𝑡𝑠 + 𝜀𝑖𝑡(5)where explained variable 𝐶𝑟𝑎𝑠ℎ_𝑟𝑖𝑠𝑘𝑖,𝑡 represents the degree of the stock price crash risk ofcompany 𝑖 in year 𝑡. It is measured by the two indicators 𝑁𝐶𝑆𝐾𝐸𝑊𝑖,𝑡 and 𝐷𝑈𝑉𝑂𝐿𝑖,𝑡 . To reducethe disturbance of endogenous problems, we lagged the explanatory and control variables for
one period. The core explanatory variable is 𝐼𝑁𝑆𝐻𝑖,𝑡−1 . 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑖,𝑡−1 represents all control
variables (Table 1). Fixed Effects represents industry and year fixed effects.
Table 2 shows the descriptive statistical analysis indicators of our primary variables,
whereas Table 3 shows the correlation test coefficients between our primary variables.
Table 2 Descriptive Statistics
Name of variable

Obs

Mean

SD

Min

MedianMax

NCSKEW

21,642-0.266

1.294-3.714-0.248

2.779

DUVOL

21,642-0.1480.963-2.502-0.168

2.271

RET

21,642

0.003

0.011

-0.019

0.002

0.036

SIGMA

21,642

0.063

0.023

0.026

0.058

0.139

MDIFF

21,642-57.041385.977-1484.38-17.764896.547

HERD

21,64219.71010.076

5.227

17.79247.321

INSH

21,64248.95324.5050.405

52.46991.780Instability

6,067

0.256

0.502

0.000

0.084

3.313

SIZE

21,642

8.380

1.355

5.440

8.221

12.511

ROA

21,642

0.033

0.069

-0.352

0.033

0.204

LEV

21,642

0.477

0.209

0.070

0.478

1.085

BM

21,642

0.629

0.240

0.113

0.634

1.138

ABSACC

21,642

0.059

0.059

0.000

0.040

0.284

OPAQUE

14,760

0.003

0.056

-0.273

0.001

0.282

QFII

21,642

0.108

0.310

0.000

0.000

1.000

Note: Observation of variable (Obs), mean value (Mean), standard deviation (SD), minimum (Min), median(Median), and maximum (Max).

We found that NCSKEW and DUVOL were highly positively correlated. In other words,
both of them can be used to reasonably measure the risk of a stock price crash. This finding isof great significance for investors and analysts who are interested in the potential crash risk ofstock investments. As for the shareholding ratio of institutional investors (INSH), we observed
a positive correlation with the correlation coefficients of the aforesaid two stock price crashrisk indicators. The results indicated that the bigger the shareholding ratio of institutional
investors, the more significantly positive the impact will be on the stock price crash risk. We
21also found that the correlation coefficients between all control variables were not abnormallyhigh. Apart from indicating the absence of any multicollinearity problem in the samples, thesevalues suggested that the data could be used to conduct regression analysis, to acquire accurate
and reliable results.
However, the causal relations of the different variables could not be established, or theeffect provided could not be quantified. The correlation coefficients in Table 3 can only provide
information regarding intensity and direction of relations between different variables.
Therefore, we conducted further analysis to elucidate the relations between different variables,
as well as the possible impact of these variables on the dependent variables. In summary, Table
3 provides a useful starting point for the exploration of the relations between the variables andthe pursuit of possible explanatory factors.

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

5 Multiple Regression Analysis
We formulated a regression model to verify Hypothesis 1. The results in Table 4 present
the impact of the shareholding ratio of institutional investors on the risk of a stock price crash.
Specifically, the regression coefficients in Columns (1) and (2) of the explained variable(NSCKEW) and core explanatory variable (INSH) are significantly positive. The results in
Column (1) present the separate impact of the shareholding ratio of institutional investors on
the risk of a stock price crash, whereas the results in Column (2) are obtained after other22

characteristic variables of companies are controlled. The coefficients of the two columns fully
demonstrated that the bigger the shareholding ratio of institutional investors, the higher thestock price crash risk. The regression coefficient in Column (2) explains the economic
significance—the stock price crash risk rises by 0.8% for every 1% increase in shareholding
ratio of institutional investors.
To ensure the robustness of the results, we adopted another indicator to measure the stock
price crash risk (DUVOL). Columns (3) and (4) show the regression results when this indicator
is used as the explained variable. The regression coefficient of the core explanatory variable(INSH) remained positive and was significant at the 1% level regardless of the addition ofcontrol variables.
In summary, the results in Table 4 explained the positive impact of the shareholding ratio
of institutional investors onstock price crash risk—the higher the shareholding ratio of
institutional investors, the higher the stock price crash risk of the company. This positive
relation does not accord with previous findings. An et al. (2013) found that with a bigger
shareholding ratio of institutional investors in the US, both the synchronism of stock prices andthe stock price crash risk would be reduced. Moreover, they concluded that institutional
investors are strongly motivated to supervise because of their control over stock rights.
However, our analysis presented an opposite result, which may be explained when institutional
investors are viewed as essentially profit-seeking microcosmic individuals. They may gain
more discourse power at the level of shareholders if they have a higher equity ratio.
Subsequently, it will be convenient for them to negotiate and communicate with the
management. Rather than supervising the managers to achieve better corporate governance,
institutional investors may desire to conspire with the managers for higher gains, during which
the interests of small and medium-sized investors may be harmed. Thus, institutional investors
in the Chinese stock market do not play a supervisory role but rather facilitate themanagement’s concealment of negative news, in turn increasing the stock price crash risk.
Therefore, the results supported Hypothesis 1.
Table 4 Shareholding Ratio of Institutional Investors and Stock Price Crash Risk

𝐼𝑁𝑆𝐻𝑡−1(1)
NSCKEW
0.008***(2)
NSCKEW
0.008***
23

(3)
DUVOL
0.006***(4)
DUVOL
0.006***𝑁𝑆𝐶𝐾𝐸𝑊𝑡−1(4.404)(3.612)-0.103***-0.111***(-3.503)(-3.420)

𝐷𝑈𝑉𝑂𝐿𝑡−1
𝑅𝐸𝑇𝑡−1𝑆𝐼𝐺𝑀𝐴𝑡−1
𝑀𝐷𝐼𝐹𝐹𝑡−1
𝑅𝑂𝐴𝑡−1𝑆𝐼𝑍𝐸𝑡−1
𝐵𝑀𝑡−1
𝐿𝐸𝑉𝑡−1
𝐴𝐵𝑆𝐴𝐶𝐶𝑡−1
cons(4.415)(3.523)-0.106***-0.118***(-3.486)(-3.317)

3.112

0.483

(0.501)(0.091)-0.959-0.412(-0.310)(-0.164)-0.000**-0.000*(-2.226)(-2.024)

0.592

0.401

(1.559)(1.292)-0.028-0.000(-0.381)(-0.007)-0.584*-0.447(-1.843)(-1.648)0.063-0.002(0.421)(-0.019)0.403*

0.259(1.872)(1.492)-0.719***-0.169-0.504***-0.239(-5.742)(-0.368)(-5.309)(-0.636)

Firm FE

Yes

Yes

Yes

Yes

Year FE

Yes

Yes

Yes

Yes

N

21,53021,53021,527

21,527

2

Adj-R
0.009
0.024
-0.0010.016
Note: Value t calculated per corporate cluster. Numbers in brackets indicate robust standard error. *, **, and ***indicate significance at the 10%, 5%, and 1% levels, respectively.

Next, we added an indicator of stability of institutional investors (Instability) to thebenchmark model. We used Instability, as a control variable, to profoundly study whether thecharacteristics of institutional investors would affect the relation between institutional investors
and stock price crash risk. The specific model is as follows:
𝐶𝑟𝑎𝑠ℎ_𝑟𝑖𝑠𝑘𝑖,𝑡 = 𝛼𝑖 + 𝛽1 𝐼𝑁𝑆𝐻𝑖,𝑡−1 + 𝛽2 𝐼𝑛𝑠𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝑖,𝑡−1 + 𝛽2 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑖,𝑡−1 + 𝐹𝑖𝑥𝑒𝑑 𝐸𝑓𝑓𝑒𝑐𝑡𝑠 + 𝜀𝑖𝑡 (5)Table 5 presents the regression results. We adopted the standard deviation of theshareholding ratio of institutional investors as the stability indicator of institutional investors.
Therefore, the bigger this value, the more unstable the institutional investors would become.
24

The results in Columns (1) and (2) show that the shareholding ratio of institutional investors
has a significantly positive impact on the stock price crash risk (NSCKEW) even after
controlling for the stability of institutional investors. In addition, this positive relation remained
valid even after we measured the stock price crash risk using another method. The results in
Columns (3) and (4) give the regression coefficient between shareholding ratio of institutional
investors and stock price crash risk (DUVOL) after the stability of institutional investors has
been controlled. Their t statistics indicated a significant impact. Meanwhile, in Columns (2)and (4), the stability of institutional investors shows a negative impact on the stock price crashrisk, indicating that the risk is greater if the institutional investors are more unstable. However,this impact was very weak, and only significant at the 10% level. In summary, the empiricalresults in Table 5 indicate that the stability of institutional investors has a positive impact on
the stock price crash risk after the former has been introduced to the model of shareholding
ratio of institutional investors. Thus, the management is more inclined to conceal negative news
when the institutional investors of the company are more stable, thereby increasing the risk ofa stock price crash. These results supported the role of “conspirator” played by institutional
investors, and accorded with Hypothesis 2.
Meanwhile, our finding regarding the relation between institutional investor stability(Instability) and stock price crash risk contradicted the conclusion drawn by Callen et al. (2013):
The higher the stability of institutional investors, the lower the stock price crash risk in the next
year. Their conclusion supports the role of “supervisor” played by institutional investors who
are not defined as short-term profit-seekers. However, their finding may only apply to listedcompanies in the US. Our research, conducted based on Chinese listed companies, yielded acompletely opposite conclusion. The supervisory role of institutional investors has not beeneffectively adopted in the Chinese stock market. Instead, Chinese institutional investors
conspire with managers. Therefore, the government should not only strengthen the regulationof listed companies but also effectively supervise institutional investors, to lower their motive
to harm the interests of small and medium-sized investors.
Table 5 Shareholding Ratio of Institutional Investors, Stability of Institutional Investors, and Stock Price
Crash Risk
(1)
𝑁𝑆𝐶𝐾𝐸𝑊𝑡(2)
𝑁𝑆𝐶𝐾𝐸𝑊𝑡
25(3)
𝐷𝑈𝑉𝑂𝐿𝑡(4)
𝐷𝑈𝑉𝑂𝐿𝑡𝐼𝑁𝑆𝐻𝑡−1
𝐼𝑛𝑠𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝑡−1
𝑁𝑆𝐶𝐾𝐸𝑊𝑡−1

0.017***

0.018***

0.014***

0.014***(5.891)(6.140)(6.391)(6.481)-0.103-0.106*-0.073-0.074*(-1.632)(-1.712)(-1.642)(-1.690)-0.179***-0.190***(-12.185)(-11.340)-0.177***-0.199***(-12.426)(-11.983)

𝐷𝑈𝑉𝑂𝐿𝑡−1
𝑅𝐸𝑇𝑡−1𝑆𝐼𝐺𝑀𝐴𝑡−1
𝑀𝐷𝐼𝐹𝐹𝑡−1
𝑅𝑂𝐴𝑡−1𝑆𝐼𝑍𝐸𝑡−1
𝐵𝑀𝑡−1-3.996-6.249***(-1.288)(-2.795)

4.164***

4.117***(3.294)(4.548)-0.000***-0.000***(-6.506)(-8.008)1.314***

0.954***(3.222)(3.160)-0.114***-0.083***(-3.251)(-3.186)-0.385**-0.255**(-2.513)(-2.203)

0.250

0.111

(1.061)(0.626)0.737*

0.414(1.835)(1.371)

𝐿𝐸𝑉𝑡−1
𝐴𝐵𝑆𝐴𝐶𝐶𝑡−1_cons-1.216***-0.441-0.902***-0.373(-7.737)(-1.186)(-7.689)(-1.378)

Firm FE

Yes

Yes

Yes

Yes

Year FE

Yes

Yes

Yes

Yes

N

5,889

5,889

5,888

5,888

2

Adj-R
0.048
0.068
0.048
0.075
Note: Value t calculated per corporate cluster. Robust standard errors are shown in brackets. *, **, and ***indicate significance at the 10%, 5%, and 1% levels, respectively.

Nonetheless, the functions provided and role played by institutional investors may changewith the continual improvement of China’s capital market. The available research on thecharacteristics of institutional investors and stock price crash risk has used data prior to theyear 2013 (Xu et al., 2013; An et al., 2013; Callen et al., 2013). However, institutional investors
in China’s capital market may have been underdeveloped during this period. Therefore, thetime dimension should be considered in a staged re-survey of the relation between thecharacteristics of institutional investors and stock price crash risk.
26

The results in Table 6 present the relation of shareholding ratio and institutional investor
stability with stock price crash risk before and after 2014. Specifically, Columns (1) and (2)show the regression analysis results through the use of NSCKEW as an indicator of stock pricecrash risk. We noted that the impact of the shareholding ratio of institutional investors on thestock price crash risk was positive before and after 2014, and that institutional investors had
never played a supervisory role well in the capital market.
We also noted a difference in the regression coefficients of the stability of institutional
investors. Among the samples before 2014, as shown in Column (1), the stability of
institutional investors did not have a significant impact on the stock price crash risk; however,among the samples after 2014, as shown in Column (2), the same had a significant impact on
the stock price crash risk. This difference in impact before and after 2014 proved the change inthe role played by institutional investors in the development of the capital market. Thisdifference remained even when we used DUVOL to measure the stock price crash risk.
Columns (3) and (4) show the consistent positive impact of shareholding of institutional
investors on the stock price crash risk before and after 2014. The difference in the impact wasvery prominent.
On the whole, through the piecewise regression analysis of samples per time dimension(i.e., 2004–2014 and 2014–2021), we found that the shareholding ratio of institutional investors
always positively impacted the risk of a stock price crash, whereas the impact of the stabilityof institutional investors was more significant after 2014. Thus, the role of institutional
investors as “cooperators” has been highlighted with the development of China’s capital market.
Table 6: Shareholding Ratio of Institutional Investors, Stability of Institutional Investors, and Stock Price
Crash Risk: Piecewise Analysis before and after 2014
(1)
(2)
(3)
(4)
Before 2014
After 2014
Before 2014
After 2014
𝑁𝑆𝐶𝐾𝐸𝑊𝑡
𝑁𝑆𝐶𝐾𝐸𝑊𝑡
𝐷𝑈𝑉𝑂𝐿𝑡
𝐷𝑈𝑉𝑂𝐿𝑡0.022***
0.009**
0.016***
0.008**
𝐼𝑁𝑆𝐻𝑡−1(2.724)(2.008)(2.635)(2.401)

𝐼𝑛𝑠𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝑡−1-0.076-0.167**0.018-0.153***(-0.571)(-2.221)(0.186)(-2.858)

𝑁𝑆𝐶𝐾𝐸𝑊𝑡−1-0.363***-0.247***(-8.652)(-13.907)-0.365***-0.265***(-8.600)(-14.988)

0.554-3.914

𝐷𝑈𝑉𝑂𝐿𝑡−1
𝑅𝐸𝑇𝑡−1

4.862-0.90527

(0.561)(-0.261)(0.083)(-1.600)

𝑆𝐼𝐺𝑀𝐴𝑡−1

0.717

1.916

3.192

2.077**(0.181)(1.353)(1.145)(2.041)𝑀𝐷𝐼𝐹𝐹𝑡−10.001***-0.000***0.000***-0.000***(3.382)(-7.272)(2.608)(-8.557)

1.830**

0.554

1.518***

0.214(2.497)(1.115)(2.758)(0.607)-0.1980.067-0.164*0.085(-1.516)(0.721)(-1.719)(1.269)0.557-1.744***

0.637**-1.373***(1.527)(-8.924)(2.276)(-9.461)

0.355-0.3750.396-0.428(0.559)(-0.909)(0.816)(-1.498)

0.259

1.065**-0.2970.788**(0.334)(2.156)(-0.492)(2.166)-0.482-0.383-0.505-0.512(-0.399)(-0.496)(-0.564)(-0.923)

Yes

No

No

No

𝑅𝑂𝐴𝑡−1𝑆𝐼𝑍𝐸𝑡−1
𝐵𝑀𝑡−1
𝐿𝐸𝑉𝑡−1
𝐴𝐵𝑆𝐴𝐶𝐶𝑡−1_cons
Firm FE
Year FE

Yes

Yes

Yes

Yes

N

1,530

4,213

1,530

4,213

Adj-R2
0.156
0.110
0.182
0.114
Note: Value t calculated per corporate cluster. Robust standard errors are shown in brackets. *, **, and ***indicate significance at the 10%, 5%, and 1% levels, respectively.

Another characteristic of institutional investors we explored was the impact of their herd
behavior (HERD) on stock price crash risk. We added the HERD indicator to the benchmark
model as a new control variable. The specific model is as follows:
𝐶𝑟𝑎𝑠ℎ_𝑟𝑖𝑠𝑘𝑖,𝑡 = 𝛼𝑖 + 𝛽1 𝐼𝑁𝑆𝐻𝑖,𝑡−1 + 𝛽2 𝐻𝑒𝑟𝑑𝑖,𝑡−1 + 𝛽3 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑖,𝑡−1 + 𝐹𝑖𝑥𝑒𝑑 𝐸𝑓𝑓𝑒𝑐𝑡𝑠 + 𝜀𝑖𝑡(5)

We used the absolute value obtained from the deduction of the standard deviation fromthe mean value as the adjustment item of HERD. The regression results in Table 7 indicate thatthe shareholding ratio of institutional investors still has a significantly positive impact on thestock price crash risk (with either NSCKEW or DUVOL as measurement indicator) even after
the characteristic of herd behavior is added. The regression coefficients indicated that the herd
behavior of institutional investors would not cause a significant deviation in the impact on themain outcome. In addition, only the results of Column (3) indicate that the herd behavior of
institutional investors has a slightly positive impact on stock price crash risk.
The herd behavior of institutional investors did not have an intensifying or mitigating28

effect on the stock price crash risk during the period from 2004 to 2021. This finding countersthat of Xu et al. (2013), in which the data of listed companies from 2005 to 2010 showed thatthe herd behavior of institutional investors increases the possibility of a stock price crash. Xu
et al. (2013) posited that institutional investors choose to follow the public blindly and ignore
their own private information. Consequently, some information is not reflected in the stock
prices, further exacerbating the stock price crash risk. After considering data from 2013 to 2021,
we observed the herd behavior of institutional investors did not have a significant impact on
the stock price crash risk. Thus, institutional investors tended to make investment decisions
based on the information they obtain rather than by following the public blindly. These findings
lent support to Hypothesis 3.
Table 7: Shareholding Ratio of Institutional Investors, Herd Behavior of Institutional Investors, andStock Price Crash Risk

𝐼𝑁𝑆𝐻𝑡−1
𝐻𝐸𝑅𝐷𝑡−1
𝑁𝑆𝐶𝐾𝐸𝑊𝑡−1(1)
𝑁𝑆𝐶𝐾𝐸𝑊𝑡(2)
𝑁𝑆𝐶𝐾𝐸𝑊𝑡(3)
𝐷𝑈𝑉𝑂𝐿𝑡(4)
𝐷𝑈𝑉𝑂𝐿𝑡0.008***

0.005***

0.006***

0.004***(7.589)(3.443)(8.028)(3.550)

0.001

0.000

0.002*

0.001(0.609)(0.185)(1.659)(0.797)-0.103***-0.123***(-14.550)(-13.203)-0.106***-0.127***(-15.260)(-13.709)

𝐷𝑈𝑉𝑂𝐿𝑡−1
𝑅𝐸𝑇𝑡−1𝑆𝐼𝐺𝑀𝐴𝑡−1
𝑀𝐷𝐼𝐹𝐹𝑡−1
𝑅𝑂𝐴𝑡−1𝑆𝐼𝑍𝐸𝑡−1
𝐵𝑀𝑡−1
𝐿𝐸𝑉𝑡−1𝑂𝑃𝐴𝑄𝑈𝐸𝑡−1

7.418***

4.255***(5.647)(4.230)-0.546-0.088(-0.856)(-0.187)-0.000***-0.000***(-7.887)(-8.924)

0.269

0.112

(1.051)(0.594)0.051**

0.067***(2.311)(3.957)-1.087***-0.853***(-13.402)(-13.920)

0.208*

0.089(1.682)(0.965)0.748***

0.502**

29

(2.614)_cons(2.367)-0.737***-0.504**-0.539***-0.554***(-12.565)(-2.499)(-12.231)(-3.614)

Firm FE

Yes

Yes

Yes

Yes

Year FE

Yes

Yes

Yes

Yes

N

21,53014,387

21,527

14,387

2

Adj-R
0.009
0.038
-0.0010.031
Note: Value t calculated per corporate cluster. Robust standard errors are shown in brackets. *, **, and ***indicate significance at the 10%, 5%, and 1% levels, respectively.

The aforementioned findings similarly highlighted the importance of the time dimension
in analyses. As such, we again conducted piecewise analysis of the samples. We performed
regression analysis for the samples in two stages, namely, 2004–2014 and 2014–2021. The
results are presented in Table 8. As shown in the regression results in Columns (1) and (2), theimpact of the shareholding ratio of institutional investors on the stock price crash risk
(NSCKEW) was not significant before the year 2014 and after the herd behavior of institutional
investors was controlled. During this period, the impact of the herd behavior of institutional
investors on the stock price crash risk (NSCKEW) was positively significant. However, after
the year 2014, the impact of the herd behavior of institutional investors on the stock price crashrisk (NSCKEW) disappeared, replaced by the positive impact of the shareholding ratio of
institutional investors.
This difference remained even after we used DUVOL to measure the stock price crash risk.
According to the results in Columns (3) and (4), the impact of the shareholding ratio of
institutional investors on the stock price crash risk was not significant before 2014 and after
the herd behavior of institutional investors was controlled. The impact of the latter was
positively significant. However, after the year 2014, the impact of investors’ herd on the stock
price crash risk also disappeared, and the positive impact of the shareholding ratio of
institutional investors was highlighted.
The difference before and after 2014 proved that institutional investors were not maturein the initial development stage of China’s capital market and they tended to choose to follow
the public blindly and process known public information instead of paying attention to private
information. As a consequence, information in the capital market was even more opaque, which30

increased the possibility of a stock price crash. During this earlier period, the herd behavior of
institutional investors had a significantly positive impact on the risk of a stock price crash, inline with the findings of Xu et al. (2013). However, as time passed, the positive impact of theherd behavior of institutional investors on stock price crash risk disappeared, as reflected inour results for data from 2014 to 2021. Thus, institutional investors with growing power may
be more confident in the information they have as they navigate the developing and improving
capital market. These institutional investors were no longer affected by public information. At
this point, the impact of the shareholding ratio of institutional investors on the stock price crashrisk gradually became a dominant factor.
Table 8: Shareholding Ratio of Institutional Investors, Herd Behavior of Institutional Investors, andStock Price Crash Risk: Piecewise Analysis before and after 2014

𝐼𝑁𝑆𝐻𝑡−1
𝐻𝐸𝑅𝐷𝑡−1
𝑁𝑆𝐶𝐾𝐸𝑊𝑡−1(1)
Before 2014
𝑁𝑆𝐶𝐾𝐸𝑊𝑡(2)
After 2014
𝑁𝑆𝐶𝐾𝐸𝑊𝑡(3)
Before 2014
𝐷𝑈𝑉𝑂𝐿𝑡(4)
After 2014
𝐷𝑈𝑉𝑂𝐿𝑡-0.0040.005*-0.0030.004**(-1.585)(1.672)(-1.541)(2.048)0.005*

0.003

0.005*

0.004(1.656)(0.715)(1.902)(1.417)-0.146***-0.190***(-8.918)(-14.867)-0.162***-0.202***(-9.888)(-15.823)

𝐷𝑈𝑉𝑂𝐿𝑡−1
𝑅𝐸𝑇𝑡−1𝑆𝐼𝐺𝑀𝐴𝑡−1
𝑀𝐷𝐼𝐹𝐹𝑡−1
𝑅𝑂𝐴𝑡−1𝑆𝐼𝑍𝐸𝑡−1
𝐵𝑀𝑡−1
𝐿𝐸𝑉𝑡−1𝑂𝑃𝐴𝑄𝑈𝐸𝑡−1_cons

10.953***

10.638***8.108***

4.732**(3.330)(3.585)(3.166)(2.167)4.372**

1.788

3.731***

1.595*(2.539)(1.349)(2.893)(1.665)-0.000**-0.000**-0.000***-0.000**(-2.310)(-2.015)(-2.678)(-2.541)-0.156-0.136-0.281-0.067(-0.390)(-0.361)(-0.928)(-0.254)0.338***

0.297***

0.287***

0.237***(5.244)(4.251)(5.839)(4.740)-1.481***-1.529***-1.159***-1.178***(-8.574)(-10.082)(-8.516)(-10.520)-0.220-0.158-0.255-0.117(-0.947)(-0.650)(-1.457)(-0.676)

0.144

0.859*

0.214

0.528

(0.294)(1.845)(0.600)(1.570)-2.263***-2.520***-1.896***-2.008***31

(-5.021)(-4.197)(-5.485)(-4.671)

Firm FE

Yes

Yes

Yes

Yes

Year FE

Yes

Yes

Yes

Yes

N

6,076

7,946

6,076

7,946

2

Adj-R
0.105
0.136
0.100
0.152
Note: Value t calculated per corporate cluster. Robust standard errors are shown in brackets. *, **, and ***indicate significance at the 10%, 5%, and 1% levels, respectively.

We also introduced QFII to assess its impact on the risk of a stock price crash. In theempirical operation, we divided all samples into two groups, with one group containing the
QFIIs samples and the other group containing none. Columns (1) and (2) in Table 9 present the
regression results of the shareholding ratio of institutional investors against NSCKEW in thetwo groups of samples. The results indicated that the positive impact of the shareholding ratio
of institutional investors was more significant in sample companies share-held by QFIIs. Thisdifference remained even after the indicator DUVOL was used to measure the risk of a stock
price crash, as shown in Columns (3) and (4) of Table 9. The results enhanced the robustnessof the aforesaid findings.
In sum, the shareholding ratio of institutional investors had a greater impact in companies
share-held by QFIIs compared with those without shareholding by QFIIs. In listed companies
share-held by QFIIs, the bigger the shareholding ratio of institutional investors, the greater thestock price crash risk. Moreover, QFIIs could not consolidate the supervision of corporate
governance, and, instead of effectively stabilizing the market, they tended to boost the stock
price crash. Therefore, our results supported Hypothesis 4.
Table 9: Shareholding Ratio of Institutional Investors and Stock Price Crash Risk: Considering the
Impact of QFIIs
(1)
(2)
(3)
(4)
QFII
Non-QFII
QFII
Non-QFII
𝑁𝑆𝐶𝐾𝐸𝑊𝑡
𝑁𝑆𝐶𝐾𝐸𝑊𝑡
𝐷𝑈𝑉𝑂𝐿𝑡
𝐷𝑈𝑉𝑂𝐿𝑡0.011*
0.008***
0.009*
0.006***
𝐼𝑁𝑆𝐻𝑡−1(1.766)(3.345)(1.963)(3.425)-0.114***-0.118***𝑁𝑆𝐶𝐾𝐸𝑊𝑡−1(-2.996)(-3.530)-0.140***-0.124***
𝐷𝑈𝑉𝑂𝐿𝑡−1(-3.644)(-3.423)-4.5743.940-5.776
0.990
𝑅𝐸𝑇𝑡−1(-0.719)(0.632)(-1.172)(0.184)1.912-1.2451.612-0.564
𝑆𝐼𝐺𝑀𝐴𝑡−1(0.699)(-0.398)(0.747)(-0.220)-0.000-0.000**-0.000**-0.000*
𝑀𝐷𝐼𝐹𝐹𝑡−132

(-1.599)(-2.272)(-2.135)(-2.002)
0.884
0.638
0.612
0.438
(0.857)(1.623)(0.783)(1.379)-0.024-0.0340.018-0.006
𝑆𝐼𝑍𝐸𝑡−1(-0.296)(-0.468)(0.276)(-0.104)-0.467-0.584*-0.243-0.448𝐵𝑀𝑡−1(-1.450)(-1.798)(-0.896)(-1.625)-0.2440.070-0.3460.011
𝐿𝐸𝑉𝑡−1(-0.473)(0.471)(-0.860)(0.091)-0.2280.435*
0.317
0.264
𝐴𝐵𝑆𝐴𝐶𝐶𝑡−1(-0.309)(1.771)(0.594)(1.350)_cons-0.480-0.081-0.708-0.168(-0.563)(-0.186)(-1.094)(-0.468)
Firm FE
Yes
Yes
Yes
Yes
Year FE
Yes
Yes
Yes
Yes
N
1,819
19,164
1,818
19,162
Adj-R2
0.033
0.026
0.031
0.018
Note: Value t calculated per corporate cluster. Robust standard errors are shown in brackets. *, **, and ***indicate significance at the 10%, 5%, and 1% levels, respectively.
𝑅𝑂𝐴𝑡−1

6

Conclusion

We investigated the impact of the characteristics of institutional investors on the stock
risk of a price crash by examining companies listed in China’s A-share market from 2004 to
2021. The main findings are as follows. First, we found that the increase in the proportion of
institutional investors in the shareholders of listed companies significantly increased the riskof a stock price crash. The results remained robust even after the replacement of explanatory
variables. This finding challenges the role of institutional investors as “supervisors” and affirms
their role as “accomplices” in the existing studies. Moreover, this finding elucidates the role ofinstitutional investors in the stock market, offering insights for investors and government
regulators alike.
Second, we found that when institutional investors were more stable, companies weremore inclined to hide negative news, which then increased the risk of a stock price crash. Thisfinding lends support to the idea that institutional investors play the role of “co-conspirators.”
After we empirically analyzed the samples divided into time periods, we found that the
influence of institutional investors’ stability on stock price crash risk was more significant after
2014. This indicated that in the continuous development of China’s capital market, the situationof institutional investors participating in “collusion” was also increasing. This finding sounds
33the alarm for the increasingly “institutional” structure of investors in the current market.
Preventing collusion between institutional investors and enterprises represents a new problem
that needs to be considered in the design and implementation of future regulatory systems.
Third, our study also revealed that the herd behavior of institutional investors had no
significant effect on the stock price crash risk of the entire sample. However, the herding
behavior of early institutional investors played a leading role in the influence of institutional
investors on corporate behavior. With the gradual development of China’s capital market, theshareholding ratio of institutional investors plays an increasingly dominant role in the influenceof institutional investors on corporate behavior.
Finally, we confirmed that among the listed companies held by QFII, shares held byinstitutional investors exacerbated the crash risk of stock prices. As such, foreign institutional
investors may not be an effective external corporate governance mechanism. They do not playa role in stabilizing the market but rather boost the risk of a stock price crash.
We expected our study to contribute, first, to the exploration of the effect of external
corporate governance of institutional investors on listed companies. The existing evidence
regarding the effect of external corporate governance of institutional investors has supported
their supervisory role. In other words, institutional investors’ participation in the capital market
is reported as an effective external corporate governance mechanism (Kim et al., 2011; Kang
et al., 2018; Cornett et al., 2008; Dyck et al., 2019). However, the data adopted in such research
are mostly obtained from Western developed countries where the capital markets are mature,and the development history of institutional investors has far exceeded China’s progress.
Therefore, institutional investors may play a different role in China’s capital market. Our
research, focusing on the risk of a stock price crash, indicated that the increase in theshareholding ratio of institutional investors would result in a higher crash risk, and the stabilityof their shareholding would worsen this situation. This finding challenges the view of
institutional investors as “supervisors.” Our study provided new evidence on the impact of
institutional investors on the capital market.
Second, our work will enrich the research on the stock price crash risk of listed companies.
The existing research regarding the triggers of the risk of a stock price crash and thecorresponding preventive measures has focused on the catalytic effect of internal corporate
34

factors (Jiang et al., 2015; Quan et al., 2016; Chen et al., 2017; Zhang et al., 2017; Sun et al.,2017; Yi et al., 2019; Kim et al., 2016) and the preventive mechanism of internal corporate
governance (Wang et al., 2015; Liang et al., 2016; Ye et al., 2015; Kim et al., 2014; Song etal., 2017). However, research on how the external corporate governance mechanism prevents
stock price crashes is relatively limited. In particular, analysis and research regarding thecharacteristics of institutional investors lack depth. Our study supplied relevant evidence that
can help thoroughly elucidate the capital market and stock price crash risk.
In summary, our research investigated the effect of the external corporate governance ofinstitutional investors among important participants of China’s capital market. Our findings
shed light on the hidden dangers possibly brought by the phenomenon of gradual“institutionalization” of the style of investment subjects in the current stock market. The topic
of our study is innovative to a certain extent, and we have presented relevant researchconclusions that beneficially supplement the current understanding of the relation between
institutional investors and stock price crash risk. We have also provided new empiricalevidence for the role played by institutional investors in external corporate governance. At thesame time, our research findings provide novel insights for the prevention of stock price crashes
and strengthening of investor protection.

Acknowledgments: This study was supported by the Maki Makoto Foundation (research no.
2019014). We would also like to acknowledge Editage for providing us with English language
editing services.

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