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Common Institutional Ownership and Crash Risk
C.S. Agnes Cheng1, Jing Xie2, and Yuxiang Zhong3
This Version: January 25, 2018
Abstract
Crash risk of a firm is positively correlated with concurrent crash risk of other firms that share
common institutional blockholders (CIBs) with the focal firm. Using exogenous formations of
CIBs around annual Russell index reconstitutions, we find that crash risk of two firms, which do
not share CIBs in the pre-reconstitution period, comoves more with each other in the post-
reconstitution period when they share CIBs than the pre-reconstitution period. The effect is mainly
driven by peers that have similar level of stock liquidity to the focal firm and implies that CIBs
induce commonality of crash risk through trading. The effect is robust to a battery of alternative
empirical designs and is unlikely to be explained by alternative stories based on similarity in firm
fundamentals, industry affiliation, and headquarter location. Overall, our paper provides a novel
source of variations in crash risk and highlight the role of institutional investors in asset prices.
Keywords: Crash risk; institutional investors; peer effects
JEL codes: G12; G23; D82; M40
1 Chair Professor of Accounting, Hong Kong Polytechnic University, School of Accounting and Finance, M730a, Li
Ka Shing Tower, Hung Hom, Kowloon, Hong Kong. Phone: +852-2766-7771.Email: [email protected]. 2 Assistant Professor of Finance, Hong Kong Polytechnic University, School of Accounting and Finance, M853, Li
Ka Shing Tower, Hung Hom, Kowloon, Hong Kong. Phone: +852-2766-4071. Email: [email protected]. 3 Ph.D. Student, Xi’an Jiaotong University, School of Management, Email: [email protected].
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1. Introduction
Stock price crashes as widely viewed as a result of accumulated negative firm-specific information
suddenly becomes publicly available (see, e.g., Jin and Myers, 2006; Hutton, Marcus, and Tehranian, 2009).
Prior empirical studies about determinants of crash risk have explored various proxies for opacity or agency
conflicts that affect firm disclosure). However, existing studies ignore the fact that firms are not operating
in isolation in financial markets which are features by various linkages across firms. In this paper, we
analyze the peer effects of crash risk stemming from the interconnections between firms through the linkage
of common institutional ownership.
Our investigation is motivated by the prominent role of institutional investors in setting stock prices
(see, e.g., Coval and Stafford, 2007; Anton and Polk, 2014). In particular, we focus on institutional investors
that hold blocks of stakes in multiple firms, and explore the similarity of crash risk between firms sharing
common institutional blockholders (hereafter, CIBs). We hypothesize that a negative shock to funding
liquidity of CIBs (e.g., outflows by investors) tends to result in fire sale of stocks held by these CIBs,
leading to common downward pressure on prices of these stocks and commonality in crash risk between
these firms.
Using institutional block holdings data of U.S. firms over the period from year 1980 to 2014, we
provide the first piece of evidence in the literature that crash risk of a firm is positively correlated with
concurrent crash risk of other firms sharing CIBs with the focal firm. We refer to firms that share CIBs as
peers to each other, and refer to the commonality of crash risk between these peer firms as peer effect. The
above peer effect is significant both economically and statistically. One standard deviation increases in the
average of peers’ likelihood of experiencing crash risk is associated with a 0.515 percentage point increase
in the focal firm’s likelihood of experiencing crash risk in the same year.4 The results are similar when we
use continuous measures of crash risk following Chen, Hong, and Stein (2001) and Kim, Li, and Zhang
4 The economic magnitude of peers’ average crash risk, on the focal firm’ crash risk, is higher than the effect of the
focal firm’s return on asset (0.438) and leverage (0.205) on its crash risk.
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(2011b). Our results suggest that the presence of CIBs between two firms facilitate the transmission of crash
risk from one firm to another.
We are aware of the possibility that institutional investors choose to hold large blocks of stakes in
firms with similar characteristics or styles. In other words, both the presence of CIBs and commonality of
crash risk between two firms are possibly driven by an omitted variable. To overcome this issue, we explore
the shocks to peer relation around the annual Russell index reconstitutions. This analysis is motivated by
the intuition that firms will attract different clientele as their index membership change. The ensuing
changes in investor base of firms are primarily driven by index investing by a large group of index investors
(Barberis and Shleifer, 2003). Consistent with our hypothesis, we find that crash risk of two firms, which
do not share CIBs in the pre-reconstitution period, comoves more with each other in the post-reconstitution
period when they share CIBs than the pre-reconstitution period. Similarly, we find that crash risk of two
firms, which (do not) share CIBs in the pre (post)-reconstitution, comoves less with each other in the post-
reconstitution period than the pre-reconstitution period. The evidence of time-series difference in the
commonality of crash risk between these two firms around index reconstitutions help to establish a causal
relation between the presence of CIBs and commonality of crash risk between two firms.
Although our empirical results fit well with the hypothesis that CIBs induce common pressure on
stock prices through their fire sales and lead to commonality of crash risk among these firms, there is an
alternative explanation for our results. CIBs can exert common influence on disclosure practices (e.g.,
discourage withholding bad news through their communications with firm managers), which are the most
important determinants of crash risk, of firms that they hold. Therefore, the second explanation is that CIBs
induce commonality crash risk through their influence on the disclosure patterns.
To differentiate these two non-exclusive channels through which CIBs induce peer effect of crash
risk, we group all peers of a focal firm based on proxies for the likelihood of each channel to operate. Lou
(2012) shows that institutions experiencing outflows consider stock liquidity when they choose which firm
to sell. Given the importance of stock liquidity in the fire sale decision, the fire-sale based explanation for
our results predict that the commonality of crash risk is stronger between two firms with smaller difference
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in stock liquidity. Consistent with this hypothesis, we find that a firm’s crash risk comove significantly
more with crash risk of its peers that have smaller absolute value of difference in stock liquidity with the
focal firm. The second explanation would predict that commonality of crash risk is stronger between two
firms with smaller difference in disclosure quality. We use the disclosure quality (DQ) measure following
Chen, Miao and Shevlin (2015) because it captures the “fineness” of data and is based on a comprehensive
set of accounting line items in annual reports. We find that a firm’s crash risk comove slightly more with
crash risk of its peers that have smaller absolute value of difference in DQ with the focal firm. However,
the difference between the effects from two groups of peers is not statistically significant. Overall, we find
strong evidence for the fire-sale based channel through which CIBs influence peer effect of crash risk, while
the evidence for the disclosure-based channel is limited.
Lastly, we implement a battery of robustness tests. We find that our main results are robust to
alternative constructions of main explanatory variable (i.e., use concurrent equal-weighted peer average of
crash risk or lagged the value-weighted peer average as the main explanatory variable), to control for
average of peers’ fundamentals that are related to their own crash risk, to control for other contagion effects
stemming from commonality in industry affiliation or headquarter location in our main regression.
Our paper contributes to two strands of literature. First, we enrich the understanding of determinants
of crash risk. We focus on a factor that is external to the firm, i.e., peers’ crash risk, and explore its relation
with the focal firm’s crash risk. This peer effect goes above and beyond the extent implied by the similarity
of fundamentals between firms and primarily operates through the channel of trading by CIBs. Our paper
highlights the importance of externality of crash risk on other firms’ performance.
Second, our paper also contributes to the literature of the institutional investors and asset prices.
Different from prior studies that also explore the effect of flow-induced institutional trading on subsequent
short-run stock returns (Coval and Stafford, 2007; Cella, Ellul and Giannetti; 2013), we focus on the
realization of extremely negative returns in the context of crash risk. Our paper is related to a growing
literature about the role of common institutional ownership in asset pricing (Anton and Polk, 2014; Koch,
Ruenzi and Starks, 2016). Unlike the above two papers, we focus on institutional blockholders only and
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offer a clean identification strategy with an analysis based on exogenous formations of CIBs around annual
Russell index reconstitutions. Moreover, we differentiate two channels through which CIBs affect crash
risk, the direct channel through fire-sale trading or the indirect channel through its effect on convergence
of disclosure patterns among peers.
The paper is organized as follows. Section 2 presents the literature review of related studies. Section
3 presents the variable constructions and empirical methodology to test the commonality of crash risk.
Section 4 provides empirical results. Section 5 presents analyses of two possible channels through which
the CIBs induce peer effects. Section 6 provides a battery of robustness tests. Section 7 concludes.
2. Literature review
2.1 Determinants of crash risk
Jin and Myers (2006) argue that crash risk results from the information asymmetry between managers
and outsider investors, which allows managers to withhold bad news from public disclosure for an extended
period. Once the accumulated “bad news” reaches a certain threshold and is suddenly released at once, the
stock prices experience a crash, leading to a negative skewness in the distribution of individual stock returns.
Jin and Myers (2006)’s theoretical framework spurs a large body of follow-on empirical research that
investigate determinate crash risk along the line of opacity of financial reporting and corporate governance.
The first strand of empirical studies analyze determinants that related to financial reporting. Hutton,
Marcus, and Hassan (2009) measure financial reporting opacity by summing previous three years’ abnormal
accruals estimated by the modified Jones model and find that firms with higher financial reporting opacity
are associated with higher future crash risk. Kim and Zhang (2014) use earnings management, the presence
of financial statement restatements, and the presence of auditor-attested material internal control weakness
(ICW) over financial reporting as proxy for financial reporting opacity and find similar results. Zhu (2016)
show total accruals increase information noise and lead to crash risk. Kim and Zhang (2016) find higher
accounting conservatism reduces the probability of future crash risk. Kim, Li, Lu, and Yu (2016) also find
financial statement comparability reduces firm’s expected crash risk because higher financial statement
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comparability facilitates invertors to understand firm’s financial reporting information though peers’
information easily. Francis, Hasan, and Li (2016) show that firms engage more real earnings manger are
more likely to experience crash risk. Kim, Li, and Zhang (2011a) focus on tax avoidance behavior and find
that higher tax avoidance is associated with higher prone to price crash. DeFond, Huang, Li, and Li (2015)
extend the literature by investigating how accounting principles affect crash risk. Specifically, they find
IFRS adoption decreases crash risk. Kim, Li, and Li (2014) provide evidence that firms’ CSR performance
is negatively correlated with crash risk, which suggests non-financial information opacity also affect crash
risk.
The second strand of literature focuses on determinants related to corporate governance. An and
Zhang (2013) find that the firm’s ownership by dedicated institutional investors is negatively correlated
with crash risk, consistent with the notion that dedicated investors have strong incentives to monitor
managers. Kim, Li, and Zhang (2011b) find the sensitivity of a CFO’s option portfolio to stock price is
significantly positively related to future crash risk, but the evidence on CEO is weak. Because managers of
China’s state-owned firms work in a closed pyramidal managerial labor market, they have higher career
and wealth concern. Based on the argument, Chen, Kim, Li, and Liang (2017) provide evidence that the
political ranks of managers in China’s state-owned firms are negatively correlated with crash risk. In
another paper, Hong, Kim, and Welker (2017) investigate how divergence of cash flow and voting rights
influence crash risk. They report positive effect of the divergence of cash flow and voting rights on crash
risk, especially in firms with higher opacity. In addition, more effective external monitoring mitigates the
positive relationship.
Our paper differs from above papers by focusing on a factor that is external to the firm, i.e., peers’
crash risk, and explore its relation with the focal firm’s crash risk. This peer effect goes above and beyond
the extent implied by the similarity of fundamentals between firms and primarily operates through the
channel of trading by CIBs. Our paper highlights the importance of externality of crash risk on other firms’
performance.
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2.2 Institutional investors and asset pricing
Coval and Stafford (2007) study how mutual funds flows generate pressure on the stock price when
they experience large outflows or large inflows. Funds that experience large outflows tend to decrease
existing positions, which in turn introduce downward price pressure on the stocks hold by distressed funds.
On the contrary, when mutual funds experience large inflows, they are willing to holding more shares and
introduce positive price pressure on the stocks hold by them. Cella, Ellul, and Giannetti (2013) extend Coval
and Stafford (2007) by investigating the effect of investors’ horizons on the price pressure. They find that
13F institutional investors with short horizons sell more stockholdings than these with longer trading
horizons during the financial distress. Their evidence implies that 13F institutional investors with short
horizons induce more downward price pressure on the stocks during market turmoil.
A growing literature extend the flow-based effect on asset price to a more general setting by analyzing
the effect of common institutional investors on asset prices and other outcomes. For example, Anton and
Polk (2014) focus on how the ownership hold by common active mutual funds affect the return correlation
between connected stocks, i.e., stocks hold by the active mutual funds at the same time. They find that the
degree of common ownership predicts return correlations when the common mutual funds suffer from large
outflows or inflows. Koch, Ruenzi, and Starks (2016) investigate the role mutual funds played in
commonality in liquidity. They find the liquidity comovement between an individual stock and a portfolio
of stocks with high mutual fund ownership are significant higher when the mutual fund ownership of the
individual stock is higher. Furthermore, they study through which channel mutual funds can induce
commonality in liquidity. The results show that one important channel is common mutual funds among
stocks. If stocks are holding by same mutual funds at the same time, then they exhibit higher commonality
in liquidity.
Unlike the above two papers, we focus on institutional blockholders only and the realization of
extremely negative returns in the context of crash risk. We offer a clean identification strategy with an
analysis based on exogenous formations of CIBs around annual Russell index reconstitutions. Moreover,
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we differentiate two channels through which CIBs affect crash risk, the direct channel through fire-sale
trading or the indirect channel through its effect on convergence of disclosure patterns among peers.
3. Variable Definition and Empirical Design
3.1 Measuring firm-specific crash risk
Following Chen, Hong, and Stein (2001), Hutton, Marcus, and Tehranian (2009), and Kim, Li, and
Zhang (2011b), we use three measures of crash risk. To calculate these crash risk measures, we first generate
frim-specific weekly return (𝑊𝑟𝑒𝑡𝑖,𝜏)from the following market model regression:
ri,τ = 𝛽0 + 𝛽1𝑖𝑟𝑚,𝜏−2 + 𝛽2𝑖𝑟𝑚,𝜏−1 + 𝛽3𝑖𝑟𝑚,𝜏 + 𝛽4𝑖𝑟𝑚,𝜏+1 + 𝛽5𝑖𝑟𝑚,𝜏+2 + 𝜀𝑖,𝜏, (1)
where riτ is the return on stock i in week τ, and 𝑟𝑚,𝜏 is the return on the CRSP value-weighted market index
in week τ. To allow for non-synchronous trading, the expanded market model includes the lead and lag
terms for the market index return (Dimson 1979; Scholes and Williams 1977). In estimating the model,
each firm-year is required to have at least 26 weekly stock return observations. The firm i’s specific weekly
return in week τ is measured as the natural log of one plus the residual return from equation (1), i.e.,
𝑊𝑟𝑒𝑡𝑖,𝜏 = ln (1 + 𝜀𝑖,𝜏).
We define three crash risk measures calculated based on 𝑊𝑟𝑒𝑡𝑖,𝜏. The first measure, CRASH, is an
indicator variable equal to 1 if a firm experiences one or more firm-specific weekly returns falling 3.2 or
more standard deviations below the mean firm-specific weekly return within the fiscal year, and zero
otherwise.
The second measure, NCSKEW, is the negative conditional return skewness of Chen, Hong, and Stein
(2001) and Kim, Li, and Zhang (2011b). For a given firm in a fiscal year, NCSKEW is calculated by taking
the negative value of the third moment of firm-specific weekly returns during the fiscal year, scaled by the
standard deviation of firm-specific weekly returns raised to the third power as follows,
𝑁𝐶𝑆𝐾𝐸𝑊𝑖𝑡 = −[𝑛(𝑛 − 1)3
2 ∑ 𝑊𝑖𝜏3]/[(𝑛 − 1)(𝑛 − 2) ∑ 𝑊𝑖𝜏
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2]. (2)
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The third measure, DUVOL, is computed without relying on the third moment and thus it is less likely
to be influenced by a few extreme return observations (Chen, Hong, and Stein 2001). For each firm i over
fiscal year t, all the weeks with firm-specific weekly returns below the annual mean (“down” weeks) are
distinguished from those with firm-specific weekly returns above the annual mean (“up” weeks). Then the
standard deviation of firm-specific weekly returns for the up weeks and down weeks is calculated separately.
DUVOL is the natural logarithm of the ratio of standard deviation of down-week to up-week firm-specific
weekly returns calculated as,
𝐷𝑈𝑉𝑂𝐿𝑖𝑡 = 𝑙 𝑛 {(𝑛𝑢 − 1) ∑ 𝑊𝑖𝜏
2𝐷𝑜𝑤𝑛
(𝑛𝑑 − 1) ∑ 𝑊𝑖𝜏2
𝑢𝑝} . (3)
3.2 Identify peers sharing CIBs and construct main explanatory variable
Since institutional holdings information is available at quarterly level, we use institutional block
holdings information over the four quarters ending at the fiscal year end of a firm to identify peers sharing
CIBs with this firm. Firm j is regarded as a peer of firm i in year t if these two firms are both held by at
least one institutional investor that possess a block of shares (i.e., >= 5% of shares outstanding) in at least
one quarter during the four quarters ending at firm i's fiscal year end in fiscal year t. For example, consider
a firm i with fiscal year end at 2005Dec, a firm j is regarded as the focal firm i's peer if j shares a CIB with
i in any quarter over 2005Q1-2005Q4.
Next, we use the following way to calculate peers’ crash risk measures in year t. Assume there are n
peers (j) share common blockholders with focal firm i in year t-1 and t. Then the peers’ crash risk in fiscal
year t is computed as,
𝑉𝑊_𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾𝑖𝑡 =∑ 𝑤𝑖,𝑗,𝑡𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾𝑗𝑡
𝑛𝑗=1
∑ 𝑤𝑖,𝑗,𝑡𝑛𝑗=1
, (4)
𝑤𝑖,𝑗,𝑡 =∑ (𝑆𝑖,𝑡
𝑓𝑃𝑖,𝑡 + 𝑆𝑗,𝑡
𝑓𝑃𝑗,𝑡)𝐹
𝑓=1
𝑆𝑖,𝑡𝑃𝑖,𝑡 + 𝑆𝑗,𝑡𝑃𝑗,𝑡, (5)
where 𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾 indicates any of three crash risk measures, CRASH, NCSKEW, DUVOL. 𝑤𝑖,𝑗,𝑡 is the
weight used to generate the value-weighted crash risk measures for these peers. In 𝑤𝑖,𝑗,𝑡, F is the set of
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common blockholders shared by focal firm i and peer j in year t. 𝑆𝑖,𝑡𝑓
is number of shares hold by fund f in
firm i in year t, 𝑆𝑖,𝑡 is firm i’s number of shares outstanding in year t and 𝑃𝑖,𝑡 is firm i’s stock price in year
t. 𝑆𝑗,𝑡𝑓
is number of shares hold by fund f in firm j in year t, 𝑆𝑗,𝑡 is firm j’s number of shares outstanding in
year t and 𝑃𝑗,𝑡 is firm j’s stock price in year t. If firm i and firm j share CIBs in multiple quarters in year t,
then we use the information of shares held and stock prices in the latest quarter in which such CIBs exist.
𝑉𝑊_𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾𝑖𝑡 is value-weighed crash risk in year t over all peers and serves as our key explanatory
variable. Since we have three versions of crash risk of each firm, we also have three corresponding versions
of 𝑉𝑊_𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾𝑖𝑡.
3.3 Model specification
Following Chen, Hong, and Stein (2001), Hutton, Marcus, and Tehranian (2009) and Kim, Li, and
Zhang (2011b), we estimate the following regression:
𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾𝑖𝑡 = 𝛽0 + 𝛽1𝑉𝑊_𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾𝑖𝑡 + +𝛽2𝑁𝐶𝑆𝐾𝐸𝑊𝑖𝑡−1 + 𝛽3𝐷𝑇𝑈𝑅𝑁𝑖𝑡−1 + 𝛽4𝑆𝐼𝐺𝑀𝐴𝑖𝑡−1
+ 𝛽5𝑅𝐸𝑇𝑖𝑡−1 + 𝛽6𝑆𝐼𝑍𝐸𝑖𝑡−1 + 𝛽7𝐿𝐸𝑉𝑖𝑡−1 + 𝛽8𝑀𝑇𝐵𝑖𝑡−1 + 𝛽9𝑅𝑂𝐴𝑖𝑡−1 + 𝛽10𝐴𝐶𝐶𝑀𝑖𝑡−1
+ 𝜀𝑖𝑡 , (6)
where for firm i in fiscal year t, 𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾 indicate three crash risk measures, CRASH, NCSKEW,
DUVOL. CRASH is dummy variables for crash risk and NCSKEW, DUVOL are continuous variables for
crash risk. 𝑉𝑊_𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾𝑖𝑡 refers to peers’ value-weighted crash risk in year t, i.e., VW_PCRASH,
VW_PNCSKEW and VW_PDUVOL. The three peers’ crash risk measures correspond to focal firm i’s three
crash risk measures in equation (6). Following Chen, Hong, and Stein (2001) and Hutton, Marcus, and
Tehranian (2009), we include the following control variables that are deemed to be potential predictors of
crash risk. DTURNt-1 is the detrended share turnover over year t-1. SIGMAt-1 is the standard deviation of
firm-specific weekly return in year t-1. RETt-1 is the mean of the firm-specific weekly return times 100 in
year t-1. SIZEt-1 is the natural logarithm of firm's total market capitalization in year t-1. LEVt-1 is the ratio
of total long-term debt divided by total assets at the end of the fiscal year t-1. MTBt-1 is the market-to-book
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ratio of a firm at the end of year t-1. ROAt-1 is income before extraordinary scaled by beginning total assets
in year t and ACCMt-1 is the three-year moving sum of absolute abnormal accruals ending in year t-1. We
use logistic regression when the dependent variable is CRASH, and OLS regression otherwise. We control
for year and industry fixed effects. Reported t-values (or z-value) are based on robust standard errors
adjusted for firm-level clustering (Petersen 2009) and heteroskedasticity (White 1980). Our research
question suggests that the coefficient on 𝑉𝑊_𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾𝑖𝑡 is expected to be positive (𝛽1 > 0).
4. Empirical Results
4.1 Sample selection
Initially, our sample starts from common blockholders data which includes all pairs connected by
common blockholders. If focal firm i and peer j are connected by more than one common blockholders in
one year, we only keep one observation in the data. The data covers 35 years from 1980 to 2014. We extract
stock files from Center for Research in Security Prices (CRSP) and financial data from Compustat. The
following selection criteria are imposed to generate the final dataset: only keep focal firm and peers
connected by common blockholders in both year t-1 and year t; exclude firm fiscal year with fewer than 26
weekly stock return data; exclude firm fiscal year with insufficient data to calculate control variables. We
are left with 59,289 firm-years and the sample period cover 34 years from 1981 to 2014.
4.2 Descriptive statistics and univariate analysis
Table 1 presents descriptive statistics and correlations. In panel A, the mean of CRASH is 0.197,
which suggests that 19.7% firm-years in our sample experience at least one firm-specific weekly return
crash per year on average. As a comparison, the mean of CRASH measure in Kim, Li, and Zhang (2011b)
is 0.172. The mean of NCSKEW and DUVOL are -0.089 and -0.061, respectively5. Regarding to peers’
5 The mean of NCSKE and DUVOL measure in our paper are larger than An and Zheng (2013) (-0.165
and -0.10, respectively) but smaller than Kim, Li and Zhang (2011b) (0.034 and 0.004, respectively). The
difference is mainly due to different sample period and sample size. For example, the mean of NCSKE and
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crash risk, the average of VW_PCRASH is 0.198 and close to the average of CRASH. In addition, the average
of VW_PNCSKEW and VW_PDUVOL is -0.074 and -0.055. The distribution of other control variables is
similar with prior literature (Kim and Zhang, 2016; Hutton, Marcus, and Tehranian, 2009).
Panel B reports Pearson’s correlation coefficients (Lower triangle) and Spearman’s Rank correlations
(Above the diagonal). Panel B shows that crash risk measures are highly correlated. For example, the
Pearson (Spearman) coefficient between CRASH and NCSKEW is 0.625 (0.593). The Pearson (Spearman)
coefficient between NCSKEW and DUVOL is 0.961(0.98). Furthermore, peers’ crash risk measures are also
significantly positively correlated with focal firms crash risk measures, which is consistent with our
prediction. VW_PCRASH is positively correlated with CRASH, the Pearson (Spearman) coefficients is 0.07
(0.099). The Pearson (Spearman) coefficient between NCSKEW and VW_PNCSKEW is 0.142(0.159). The
Pearson (Spearman) coefficient between DUVOL and VW_PDUVOL is 0.152(0.164).
Table 2 reports univariate analysis. We sort all firms into quintile groups based on peers’ value-
weighted crash risk measures separately for each year. The sorting variables in the three columns are
VW_PCRASHt, VW_PNCSKEWt, and VW_PDUVOLt, respectively. We report the mean of focal firm’s
corresponding concurrent crash risk (CRASH, NCSKEW, and DUVOL) in each quintile in the three columns.
We report the mean difference between Quintile 5 (with the highest peer average of crash risk) and Quintile
1 (with the lowest peer average of crash risk) and the T-value of the difference. Table 2 shows that focal
firm’s crash risks increase monotonously from Quintile 1 to Quintile 5. For example, CRASH increases
from 0.185 in Quintile 1 to 0.21 in Quintile 5. The difference (Q5-Q1) is 0.025 and is significantly different
from 0. In addition, the difference of NCSKEW and DUVOL(Q5-Q1) are 0.173 and 0.081, both are
significantly positive. The results provide initial evidence that peers’ crash risk is positively associated with
focal firm’s crash risk.
DUVOL measure of Kim, Wang, and Zhang (2016) is 0.068 and 0.027, even higher than Kim, Li and Zhang
(2011b).
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4.3 Baseline results
We hypothesize that a negative shock to funding liquidity of CIBs (e.g., outflows by investors) tends
to result in fire sale of stocks held by these CIBs, leading to common downward pressure on prices of these
stocks and commonality in crash risk between these firms.
To investigate how peers’ crash risk affects the concurrent crash risk of the focal firm, we estimate
Equation (6) with different crash risk measures. The baseline results are reported in table 3. We use logistic
regression when the dependent variable is CRASH, and OLS regression otherwise. We control for year and
industry fixed effects. Reported t-values (or z-value) are based on robust standard errors adjusted for firm-
level clustering (Petersen 2009) and heteroskedasticity (White 1980). The coefficient on VW_PCRASH
RISKt is positive and significant (0.291, z=2.78) when we use CRASH as the dependent variables. When
NCSKEW is used as the dependent variable, the coefficient on VW_PCRASH RISKt is 0.175 with t-value
equal to 10.13, which is significantly positive. In column (3), we replace NCSKEW with DUVOL and the
coefficient on VW_PCRASH RISKt remains significantly positive (0.173, t=10.52). We also calculate the
economic significance of the coefficients on VW_PCRASH RISKt. In column (1), one standard deviation
increases in VW_PCRASH RISKt results in a 0.515 percentage point increase in the probability of one crash6,
which is higher than those of ROAt-1 (0.438) and LEVt-1 (0.205). When we use NCSKEW as dependent
variable, one standard deviation increases in VW_PCRASH RISKt results in 0.039 (0.175*0.224) increase
in NCSKEW. As a comparison, one standard deviation increases in ACCMt-1 only results in 0.021 increase
in NCSKEW. The economic significance of VW_PCRASH RISKt is about twice relative to those of ACCMt-
1 and NCSKEWt-1 when we use DUVOL as dependent variable. These results support that peers’ crash risk
have strong positive effects on the concurrent crash risk of the focal firm.
The coefficients of last year’s NCSKEW is significantly positive, consistent with Chen, Hong, and
Stein (2001). The coefficients on DTURNt-1 are significantly positive across three models, implying that
6 In column (1), we use logistic model to predict the coefficient. Therefore, when we estimate economic significance,
we need to estimate margin effect for each coefficient first, then times the standard deviation to get the economic
significance, which is different from the OLS regression.
14
investor heterogeneity increases crash risk (Chen, Hong, and Stein, 2001; Kim and Zhang, 2016). Higher
past return volatility (SIGMAt-1) and stock return (RETt-1) predicts higher future crash risk. The coefficients
on SIZEt-1 are all significantly positive. The coefficients of LEVt-1 are significantly negative in column (2)
and (3). The coefficients on MTBt-1 are all significantly positive, consistent with the notion that higher
growth firm are more likely to have crash risk. The coefficients on ROAt-1 are all significantly positive.
Finally, consistent with Hutton, Marcus, and Tehranian (2009), higher accrual opacity (ACCMt-1 l) leads to
higher future crash risk.
4.4 Shocks to CIBs - An analysis based on Russell Index reconstitutions
This part studies the peer effects for firms that experiences exogenous change to investor bases
around annual Russell index reconstitution. Annual Russell index reconstitutions have been widely used in
finance and accounting literature as a shock to investor base (see, e.g., Appel, Gormley, and Keim, 2015).
We focus on firm-years for firms that experience any of the following three events around annual Russell
index reconstitution: index switching, index addition and index deletion. The first event refers to firms
switching from Russell 1000 (2000) to Russell 2000 (1000) (Switching); the second event refers to firms
joining in Russell 1000 or Russell 2000 (Addition); the last event refers to firm is deleted from Russell
Index (Deletion). This analysis is motivated by the intuition that firms will attract different clientele as their
index membership change. The ensuing changes in investor base of firms are primarily driven by index
investing by a large group of index investors (Barberis and Shleifer, 2003).
If firm i in year t experience a Russell Index event, for example, switches from Russell 1000 to
Russell 2000, firm i will stay in Russell 2000 from 1 July, year t to 30 June, year t+1. We identify firm i’s
(common blockholders) peers in both fiscal year t-2 and t-1 as SETPRE, and peers in both fiscal year t+1
and t+2 as SETPOST. SETA consists of firms that are in SETPRE but not in SETPOST, and they are referred
to as departing peers of the focal firm i. SETB consists of firms that are in SETPOST but not in SETPRE,
and they are referred to as newly-connected peers. We calculate average crash risk of peers in SETA in year
t-2, t-1, t+1, t+2 respectively and defined as CRASH RISK(SETA). Similarly, we calculate average crash
15
risk of peers in SETB in year t-2, t-1, t+1, t+2 respectively and defined as CRASH RISK(SETB). For each
firm, we retain four observations in the four years t-2, t-1, t+1, and t+2 (skipping the event year t). POST is
a dummy variable which equals one for observations in year t+2 or year t+1, and zero otherwise. We
conduct the analysis by estimating coefficients from the following regression model,
𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾𝑖𝑡 = 𝛽0 + 𝛽1𝑃𝑂𝑆𝑇𝑖𝑡 + 𝛽2𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾(𝑆𝐸𝑇𝐴)𝑖𝑡 + 𝛽3𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾(𝑆𝐸𝑇𝐵)𝑖𝑡
+ 𝛽4𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾(𝑆𝐸𝑇𝐴)𝑖𝑡 ∗ 𝑃𝑂𝑆𝑇𝑖𝑡 + 𝛽5𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾(𝑆𝐸𝑇𝐵)𝑖𝑡 ∗ 𝑃𝑂𝑆𝑇𝑖𝑡
+ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝑖𝑡 + 𝜀𝑖𝑡 . (7)
If only the peers’ crash risk affects focal firm’s crash risk, then we expect β4 to be significantly
negative because peers in SETA depart from focal firm i in year t. and we expect β5 to be significantly
positive because peers in SETB share common blockholders with firm i since year t+1. There are 14476
observations in the test dataset, which generated from 3619 firm-years experiencing Switch (1328), Add
(1092) or Delete (1199) around annual Russell index reconstitution. The results are reported in table 4.
When we use CRASH to proxy for crash risk, the coefficient on POST*CRASH RISKt(SETA) is -
0.488, with expected sign but insignificant. We conjecture that the insignificant coefficient is due to the lag
effect of peers in SETA. Although peers in SETA depart in year t, but their effect on focal firm’s crash risk
may not disappear immediately. Consistent with our prediction, the coefficient on POST*CRASH
RISKt(SETB) is 1.064 with z-value equal to 2.18, which is significantly positive7 . The coefficient of
POST*CRASH RISKt(SETA) is significantly negative (-0.284, t= -4.38), and the coefficient on
POST*CRASH RISKt(SETB) is positive and significant (0.144, t=1.97) when we use NCSKEW as dependent
variable. DUVOL is used to proxy for crash risk in column (3), the results are similar with these in column
(2), coefficient on POST*CRASH RISKt(SETA) is significantly negative (-0.292, t=-4.57) and coefficient
on POST*CRASH RISKt(SETB) is significantly positive (0.119, t=1.75). Using annual Russell index
reconstitution as an exogenous shock, the results imply that the effect of departing peers’ crash risk decrease
7 The observation of column (1) is less than 14476 due to perfect collinearity between some industry dummies and
dependent variable.
16
significantly after the shock while the effect of newly-connected peers’ crash risk increase significantly
after the shock, consistent with the notion that only the peers’ crash risk affects focal firm’s crash risk.
5. Analyses of two channels
Although our earlier empirical results fit well with the hypothesis that CIBs induce common pressure
on stock prices through their fire sales and lead to commonality of crash risk among these firms, there is an
alternative explanation for our results. CIBs can exert common influence on disclosure practices (e.g.,
discourage withholding bad news through their communications with firm managers), which are the most
important determinants of crash risk, of firms that they hold. Therefore, the second explanation is that CIBs
induce commonality crash risk through their influence on the disclosure patterns.
To differentiate these two non-exclusive channels through which CIBs induce peer effect of crash
risk, we group all peers of a focal firm based on proxies for the likelihood of each channel to operate. Lou
(2012) shows that institutions experiencing outflows consider stock liquidity when they choose which firm
to sell. Given the importance of stock liquidity in the fire sale decision, the fire-sale based explanation for
our results predict that the commonality of crash risk is stronger between two firms with smaller difference
in stock liquidity. The second explanation would predict that commonality of crash risk is stronger between
two firms with smaller difference in disclosure quality.
We conduct analyses about the channel through which peers’ crash risk affects focal firm’s crash risk
in this section. We propose two possible channels. The first is similarity of common blockholder trading-
induced pressure on stock returns between focal firm and its peers. The peers’ crash risk is more likely to
affect focal firm’s crash risk when they have similar common blockholder trading-induced pressure on
stock returns with focal firm. We use the distance of liquidity (ILIQ) between a firm and its peers to proxy
for the similarity of blockholder trading-induced pressure on their stock returns. Following prior literature,
we calculate liquidity (ILIQ) based on Amihud (2002). Specifically, for each focal firm i in year t, we sort
its peers into two groups by distance of ILIQ between focal firm i and peer j. The ILIQ distance is calculated
as ABS(ILIQit-ILIQjt). Peers are sort into two groups by the median of ILIQ distance. We define the peer as
17
CLOSE group when its ILIQ distance is lower than the median, FAR group otherwise. Then we calculated
value weighted crash risk measure for COLSE and FAR group and generate VW_CRASH RISKt(Close) and
VW_CRASH RISKt(Far), respectively. Finally, we add VW_CRASH RISKt(Close) and VW_CRASH
RISKt(Far) to model (6) and drop VW_CRASH RISKt to conduct channel tests for ILIQ.
The second is common disclosure practices between focal firm and its peers. Jin and Myers (2006)
argue that information opacity is positively associated with stock price risk. Thus, if focal firm and its peers
have similar disclosure practices, they are likely to have similar crash risk pattern. We use the distance of
disclosure quality (DQ) between a firm and its peers to proxy for the commonality of crash risk stemming
from common disclosure practices. We generate disclosure quality (DQ) measure following Chen, Miao
and Shevlin (2015). We use the disclosure quality (DQ) measure following Chen, Miao and Shevlin (2015)
because it captures the “fineness” of data and is based on a comprehensive set of accounting line items in
annual reports. The DQ measure uses the degree of disaggregation in GAAP line items in firm’s annual
financial reporting (Balance sheet and income statement) to proxy for the disclosure quality. Specifically,
Chen, Miao and Shevlin (2015) extract firm’s financial reporting data from Compustat and count the
number of nonmissing Compustat items as DQ. Analogous to ILIQ, we generate VW_CRASH RISKt(Close)
and VW_CRASH RISKt(Far) and conduct channel test for DQ. The results are reported in table 5. To
compare the coefficients on VW_CRASH RISKt (Close) and VW_CRASH RISKt (Far), we provide estimate
of coefficients based standardized variables in table 5.
Panel A provides the results of ILIQ. The coefficients of VW_CRASH RISKt(Close) is positive and
significant (0.019, z=3.36), but the coefficient on VW_CRASH RISKt(Far) (0.002, z=0.55) is insignificant
when we use CRASH as dependent variable. Furthermore, the coefficient on VW_CRASH RISKt(Close) is
nearly 10 (0.019/0.002=9.5) times of that on VW_CRASH RISKt(Far). We also test the difference between
these two coefficients, the P-value is 0.085, implying that the coefficient on VW_CRASH RISKt(Close) is
significantly higher than that on VW_CRASH RISKt(Far). When we use NCSKEW or DUVOL as dependent
variable, the coefficients on VW_CRASH RISKt(Close) are significantly positive and significantly higher
than these on VW_CRASH RISKt(Far). These results support that the effect of peers’ crash risk on focal
18
firm’s crash risk is stronger when peers have more similar liquidity with focal firm. When peers have quite
different liquidity relative to focal firm, their crash risk have lower effect on focal firm’s crash risk.
Panel B provides the results of DQ. In column (1), the coefficient on VW_CRASH RISKt(Close) is
positive and significant (0.015, z=2.33) and the coefficient on VW_CRASH RISKt(Far) (0.008, z=1.19) is
insignificant. However, the difference between these two coefficients are insignificant (P=0.4572). The
coefficients on VW_CRASH RISKt(Close) and VW_CRASH RISKt(Far) are all significantly positive when
we use NCSKEW or DUVOL to proxy for crash risk. Different from Panel A, the coefficients on
VW_CRASH RISKt(Close) are quite close to these on VW_CRASH RISKt(Far) in column (2) and (3) of
Panel B. The coefficient difference between VW_CRASH RISKt(Close) and VW_CRASH RISKt(Far) are
insignificant in column (2) and (3) (P=0.5729 and 0.4346, respectively). The results in Panel B suggest that
the common disclosure practices between focal firm and its peers doesn’t necessarily lead to the similar
crash risk pattern. In summary, the results in table 5 provides evidence that peers’ crash risk is more likely
to affects focal firm’s crash risk through ILIQ channel rather than DQ channel.
Overall, we find strong evidence for the fire-sale based channel through which CIBs influence peer
effect of crash risk, while the evidence for the disclosure-based channel is limited.
6. Robustness tests
In this section, we implement a battery of robustness tests including: use alternative constructions of
main explanatory variable (i.e., concurrent equal-weighted peer average of crash risk or lagged the value-
weighted peer average), control for average of peers’ fundamentals that are related to their own crash risk,
and control for other contagion effects stemming from commonality in industry affiliation or headquarter
location in our main regression.
6.1 Use equal-weighted peer average of crash risk as main explanatory variable
In our main test, we use value-weighted method to calculate peers’ crash risk and generate
𝑉𝑊_𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾𝑖𝑡 . Here we use equal-weighted method to calculate peers’ crash risk, replace
𝑉𝑊_𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾𝑖𝑡 by 𝐸𝑊_𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾𝑖𝑡 in model (6). The re-estimate results are reported in Panel A
19
of table 6 (all controls are included in the regressions but untabulated to conserve space). The coefficient
on 𝐸𝑊_𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾𝑖𝑡 is positive and significant (0.268, z=2.49) when we use CRASH as dependent
variable. The coefficients on 𝐸𝑊_𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾𝑖𝑡 remain positive and significant in column (2) and (3)
when NCSKEW and DUVOL are used to proxy for crash risk, respectively. Therefore, our main results are
not sensitive to the way to generate peers’ crash risk.
6.2 Use lagged value-weighted peer average of crash risk as main explanatory variable
Panel B uses VW_CRASH RISK in year t-1 instead of year t as the main independent variable, and
use the crash risk in year t as dependent variable to allow for a lead-lag regression. In addition, peers in
panel B are identified using holding information as of year t-1: the focal firm and peers only share common
blockholders in year t-18. The coefficient on 𝑉𝑊_𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾𝑖𝑡−1 is positive and significant (0.547,
z=4.49) when we use CRASH as dependent variable. The coefficient on 𝑉𝑊_𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾𝑖𝑡−1 is
significantly positive (0.204, t=10.36) when we use NCSKEW as dependent variable. The dependent
variable of column (3) is DUVOL and the coefficient on 𝑉𝑊_𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾𝑖𝑡−1 remains significantly
positive (0.196, t=10.38). The results in Panel B are consistent with our main results in table 3.
6.3 Add average of peers' fundamentals as additional control variables
We study how peers’ crash risk affects the concurrent crash risk of the focal firm. To mitigate the
concern that the focal firm’ crash risk may be affected by peers’ fundamental characteristics, we add value-
weighted average of peers’ fundamental variables as additional control variables. Specifically, we add
value-weighted average of peers' DTURN, SIGMA, RET, SIZE, LEV, MTB, ROA and ACCM in year t to
model (6). The estimated results are presented in Panel C. The coefficients on VW_CRASH RISKt are smaller
than these in table 3 but remain significantly positive no matter which crash risk measure is used as
dependent variable. The main results still hold after controlling for peers’ fundamental characteristics.
8 The results still hold if we require focal firm and peers share common blockholders in both year t-1 and t.
20
6.4 Control for contagion effects stemming from commonality in industry affiliation or headquarter
location
Finally, we add industry (two-digit SIC code) peers’ and headquarter state peers’ crash risk to model
(6), which is calculated as equal-weighted crash risk in year t measured over all other firms operating in the
same industry and over all other firms located in the same state as the focal firm. The re-estimated results
are reported in Panel D. The sample size reduces to 59,089 after adding these two additional variables. In
column (1), the coefficient on VW_CRASH RISKt is positive and significant (0.275, z=2.63) when we use
CRASH as dependent variable. In column (2) and (3), the coefficient on VW_CRASH RISKt are all positive
and significant when we use NCSKEW and DUVOL as dependent variable, respectively. Our main results
are similar after we add industry peers’ and headquarter state peers’ crash risk to model (6).
7. Conclusion
In this paper, we find that crash risk of a firm is positively correlated with concurrent crash risk of
other firms that share common institutional blockholders (CIBs) with the focal firm. Using exogenous
formations of CIBs around annual Russell index reconstitutions, we find that crash risk of two firms, which
do not share CIBs in the pre-reconstitution period, comoves more with each other in the post-reconstitution
period when they share CIBs than the pre-reconstitution period. The effect is mainly driven by peers that
have similar level of stock liquidity to the focal firm and suggests CIBs induce commonality of crash risk
through trading. The effect is robust to a battery of alternative empirical designs and is unlikely to be
explained by alternative stories based on similarity in firm fundamentals, industry affiliation, and
headquarter location. Overall, our paper provides a novel source of variations in crash risk and highlight
the role of institutional investors in asset prices.
21
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Table 1 Descriptive Statistics
Panel A presents descriptive statistics on focal firm’s crash risk, value weighted average of peers’ crash
risk and control variables. For Firm i, CRASHt is dummy variables for crash risk and NCSKEWt, DUVOLt
are continuous variables for crash risk in fiscal year t. VW_PCRASHt, VW_PNCSKEWt and VW_PDUVOLt
are value-weighted crash risk measures of peers. NCSKEWt-1 is negative firm-specific weekly return
skewness in year t-1. DTURNt-1 is the detrended share turnover over year t-1. SIGMAt-1 is the standard
deviation of firm-specific weekly return in year t-1. RETt-1 is the mean of the firm-specific weekly return
times 100 in year t-1. SIZEt-1 is the natural logarithm of firm's total market capitalization in year t-1. LEVt-1
is the ratio of total long-term debt divided by total assets at the end of the fiscal year t-1. MTBt-1 is the
market-to-book ratio of a firm at the end of year t-1. ROAt-1 is income before extraordinary scaled by lagged
total assets in year t-1 and ACCMt-1 is the three-year moving sum of absolute abnormal accruals ending in
year t-1. There are 59,289 observations in our final sample and the sample period ranges from 1981 to 2014.
All continuous variables are winsorized at 1% and 99%.
Panel A Descriptive Statistics
Variable N Mean Std Lower Quartile Median Upper Quartile
CRASHt 59,289 0.197 0.398 0 0 0
NCSKEWt 59,289 -0.089 0.782 -0.527 -0.117 0.302
DUVOLt 59,289 -0.061 0.359 -0.301 -0.073 0.165
VW_PCRASHt 59,289 0.198 0.115 0.144 0.198 0.256
VW_PNCSKEWt 59,289 -0.074 0.224 -0.203 -0.049 0.058
VW_PDUVOLt 59,289 -0.055 0.108 -0.117 -0.043 0.009
NCSKEWt-1 59,289 -0.088 0.761 -0.519 -0.120 0.293
DTURNt-1 59,289 0.002 0.075 -0.021 0.000 0.022
SIGMAt-1 59,289 0.057 0.029 0.036 0.050 0.070
RETt-1 59,289 -0.200 0.231 -0.241 -0.121 -0.062
SIZEt-1 59,289 5.864 1.818 4.537 5.746 7.097
LEVt-1 59,289 0.182 0.183 0.009 0.143 0.290
MTBt-1 59,289 2.547 3.129 1.117 1.798 3.014
ROAt-1 59,289 0.001 0.157 0.000 0.037 0.069
ACCMt-1 59,289 0.190 0.156 0.089 0.145 0.237
24
Panel B Correlations
A B C D E F G H I J K L M N
CRASHt A 1.000 0.593 0.548 0.099 0.102 0.101 0.023 -0.007 0.007 0.048 -0.031 0.079 0.025 0.011 <.001 <.001 <.001 <.001 <.001 <.001 0.107 0.083 <.001 <.001 <.001 <.001 0.007
NCSKEWt B 0.625 1.000 0.980 0.119 0.159 0.160 0.054 -0.064 0.065 0.151 -0.014 0.145 0.083 -0.026 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 0.001 <.001 <.001 <.001
DUVOLt C 0.573 0.961 1.000 0.123 0.163 0.164 0.053 -0.068 0.068 0.151 -0.015 0.145 0.084 -0.027 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001
VW_PCRASHt D 0.070 0.083 0.088 1.000 0.734 0.709 0.009 -0.097 0.098 0.268 -0.040 0.193 0.029 -0.089 <.001 <.001 <.001 <.001 <.001 0.035 <.001 <.001 <.001 <.001 <.001 <.001 <.001
VW_PNCSKEWt E 0.088 0.142 0.152 0.642 1.000 0.980 0.037 -0.120 0.121 0.347 -0.015 0.268 0.087 -0.093 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001
VW_PDUVOLt F 0.088 0.142 0.152 0.611 0.977 1.000 0.040 -0.122 0.123 0.346 -0.013 0.256 0.089 -0.097 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001
DTURNt-1 G 0.025 0.047 0.048 0.013 0.033 0.033 1.000 0.092 -0.091 0.060 0.036 0.120 0.017 -0.032 <.001 <.001 <.001 0.002 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001
SIGMAt-1 H -0.015 -0.063 -0.073 -0.043 -0.084 -0.085 0.140 1.000 -1.000 -0.453 -0.125 -0.094 -0.351 0.360 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001
RETt-1 I 0.020 0.067 0.076 0.038 0.075 0.074 -0.143 -0.960 1.000 0.455 0.125 0.094 0.352 -0.360 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001
SIZEt-1 J 0.044 0.134 0.144 0.159 0.302 0.302 0.030 -0.418 0.352 1.000 0.373 0.117 0.142 -0.342 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001
LEVt-1 K -0.024 -0.021 -0.022 -0.019 -0.002 0.000 0.021 -0.042 0.030 0.310 1.000 -0.136 -0.162 -0.162 <.001 <.001 <.001 <.001 0.618 0.985 <.001 <.001 <.001 <.001 <.001 <.001 <.001
MTBt-1 L 0.048 0.087 0.088 0.089 0.139 0.133 0.082 0.022 -0.030 0.021 -0.084 1.000 0.204 0.025 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001
ROAt-1 M 0.012 0.066 0.074 -0.009 0.046 0.051 0.028 -0.443 0.452 0.254 -0.030 -0.061 1.000 -0.069 0.004 <.001 <.001 0.030 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001
ACCMt-1 N 0.013 -0.007 -0.015 -0.036 -0.057 -0.061 -0.015 0.318 -0.274 -0.293 -0.110 0.072 -0.152 1.000
0.002 0.079 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001
Pearson's correlation coefficients are shown in the lower triangle while Spearman's rank correlations appear above the diagonal.
25
Table 2 Univariate Analysis
We sort all firms into quintile groups based on peers’ value-weighted crash risk measures separately for
each year. The sorting variables in the three columns are VW_PCRASH, VW_PNCSKEW, and
VW_PDUVOL, respectively. We report the mean of focal firm’s corresponding concurrent crash risk
(CRASH, NCSKEW, and DUVOL) in each quintile in the three columns. We report the mean difference
between Quintile 5 (with the highest peer average of crash risk) and Quintile 1 (with the lowest peer average
of crash risk) and the T-value of the difference. ***, ** and * represent significance levels at 1%, 5% and
10%, respectively.
Quintile rank of peers' crash risk CRASH NCSKEW DUVOL
1 (Low) 0.185 -0.191 -0.11
2 0.191 -0.136 -0.085
3 0.195 -0.075 -0.053
4 0.206 -0.023 -0.029
5 (High) 0.21 -0.019 -0.029
Difference (Q5 - Q1) 0.025*** 0.173*** 0.081***
T-value of the diff. 4.82 16.95 17.5
26
Table 3 The Effect of Peers’ Crash Risk on Focal Firm’s Crash Risk – Baseline Result
This table presents how peers’ crash risk affects the concurrent crash risk of the focal firm. Firm j is regarded
as the focal firm i’s peer in fiscal year t if it shares common blockholders with the focal firm i in both fiscal
year t-1 and t, and we measure peers crash risk in year t as follows,
𝑉𝑊_𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾𝑖𝑡 =∑ 𝑤𝑖,𝑗,𝑡𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾𝑗𝑡
𝑛𝑗=1
∑ 𝑤𝑖,𝑗,𝑡𝑛𝑗=1
, (1)
𝑤𝑖,𝑗,𝑡 =∑ (𝑆𝑖,𝑡
𝑓𝑃𝑖,𝑡 + 𝑆𝑗,𝑡
𝑓𝑃𝑗,𝑡)𝐹
𝑓=1
𝑆𝑖,𝑡𝑃𝑖,𝑡 + 𝑆𝑗,𝑡𝑃𝑗,𝑡. (2)
In equation (1), we use CRASH RISK to proxy for CRASH, NCSKEW, and DUVOL and use the
corresponding VW_CRASH RISKt in columns 1 to 3, respectively. Assume there are n peers (j) share common
blockholders with focal firm i in year t-1 and t. 𝑤𝑖,𝑗,𝑡 is the weight used to generate the value-weighted
crash risk measures for these n peers. In 𝑤𝑖,𝑗,𝑡, F is the set of common blockholders shared by focal firm i
and peer j in year t. 𝑆𝑖,𝑡𝑓
is number of shares hold by fund f in firm i in year t, 𝑆𝑖,𝑡 is firm’s number of shares
outstanding and 𝑃𝑖,𝑡 is firm i’s stock price in year t. 𝑆𝑗,𝑡𝑓
is number of shares hold by fund f in firm j in year
t, 𝑆𝑗,𝑡 is firm’s number of shares outstanding and 𝑃𝑗,𝑡 is firm j’s stock price at in year t. If firm i and firm j
share CIBs in multiple quarters in year t, then we use the information of shares held and stock prices in the
latest quarter in which such CIBs exist. Our regression model is as follows,
𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾𝑖𝑡 = 𝛽0 + 𝛽1𝑉𝑊_𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾𝑖𝑡 + +𝛽2𝑁𝐶𝑆𝐾𝐸𝑊𝑖𝑡−1 + 𝛽3𝐷𝑇𝑈𝑅𝑁𝑖𝑡−1 + 𝛽4𝑆𝐼𝐺𝑀𝐴𝑖𝑡−1 + 𝛽5𝑅𝐸𝑇𝑖𝑡−1
+ 𝛽6𝑆𝐼𝑍𝐸𝑖𝑡−1 + 𝛽7𝐿𝐸𝑉𝑖𝑡−1 + 𝛽8𝑀𝑇𝐵𝑖𝑡−1 + 𝛽9𝑅𝑂𝐴𝑖𝑡−1 + 𝛽10𝐴𝐶𝐶𝑀𝑖𝑡−1 + 𝜀𝑖𝑡 . (3)
All other variables are defined in appendix B. We use logistic regression when the dependent variable is
CRASH, and OLS regression otherwise. We control for year and industry fixed effects. Reported t-values
(or z-value) are based on robust standard errors adjusted for firm-level clustering (Petersen 2009) and
heteroskedasticity (White 1980). ***, ** and * represent significance levels at 1%, 5% and 10%,
respectively.
(1) (2) (3)
Variables CRASHt NCSKEWt DUVOLt
VW_CRASH RISKt 0.291*** 0.175*** 0.173*** (2.78) (10.13) (10.52)
NCSKEWt-1 0.053*** 0.023*** 0.011*** (3.64) (4.81) (5.08)
DTURNt-1 0.872*** 0.431*** 0.202*** (6.21) (9.41) (9.89)
SIGMAt-1 6.683*** 3.084*** 1.094*** (4.23) (6.66) (5.24)
RETt-1 1.080*** 0.488*** 0.197*** (5.73) (9.09) (8.18)
SIZEt-1 0.047*** 0.050*** 0.023*** (5.20) (19.01) (19.10)
LEVt-1 -0.073 -0.146*** -0.077*** (-1.04) (-6.56) (-7.63)
MTBt-1 0.018*** 0.015*** 0.007***
(5.22) (12.73) (13.25)
ROAt-1 0.180** 0.159*** 0.076***
27
(2.18) (6.00) (6.60)
ACCMt-1 0.379*** 0.136*** 0.055***
(5.12) (5.76) (5.21)
Intercept -3.104*** -0.831*** -0.389***
(-9.33) (-9.50) (-8.26)
Year, industry fixed effect YES YES YES
N 59,289 59,289 59,289
Pseudo R-sq or Adj R-sq 0.026 0.051 0.057
28
Table 4 The Effect of Peers’ Crash Risk on Focal Firm’s Crash Risk: An Analysis Based on Russell
Index Reconstitutions
This table presents the peer effects for firms that experiences exogenous change to investor bases around
annual Russell index reconstitution. We focus on firm-years for firms that experience any of the following
three events around annual Russell index reconstitution: index switching, index addition and index deletion.
The first event refers to firms switching from Russell 1000 (2000) to Russell 2000 (1000) (Switch); the
second event refers to firms joining in Russell 1000 or Russell 2000 (Add); the last event refers to firm is
deleted from Russell Index (Delete). We expect blockholders will change their position when the stocks
they hold suffer from Russell Index events (i.e., Switch, Add or Delete). If firm i in year t experience Russell
Index event, for example, switches from Russell 1000 to Russell 2000, firm i will stay in Russell 2000 from
1 July, year t to 30 June, year t+1. We identify firm i’s (common blockholders) peers in both fiscal year t-
2 and t-1 as SETPRE, and peers in both fiscal year t+1 and t+2 as SETPOST. SETA consists of firms that
are in SETPRE but not in SETPOST, and they are referred to as departing peers of the focal firm i. SETB
consists of firms that are in SETPOST but not in SETPRE, and they are referred to as newly-connected
peers. We calculate average crash risk of peers in SETA in year t-2, t-1, t+1, t+2 respectively and defined
as CRASH RISK(SETA). Similarly, we calculate average crash risk of peers in SETB in year t-2, t-1, t+1,
t+2 respectively and defined as CRASH RISK(SETB). For each event happens in year t, we retain four
observations in t-2, t-1, t+1, and t+2 (skipping the event year t). POST is a dummy variable which equals
one for observations in year t+2 or year t+1, and zero otherwise. We estimate coefficients from the
following regression model,
𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾𝑖 = 𝛽0 + 𝛽1𝑃𝑂𝑆𝑇 + 𝛽2𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾(𝑆𝐸𝑇𝐴) + 𝛽3𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾(𝑆𝐸𝑇𝐵) + 𝛽4𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾(𝑆𝐸𝑇𝐴)
∗ 𝑃𝑂𝑆𝑇 + 𝛽5𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾(𝑆𝐸𝑇𝐵) ∗ 𝑃𝑂𝑆𝑇 + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝑖 + 𝜀𝑖 . (1)
If only the peers’ crash risk affects focal firm’s crash risk, then we expect β4 to be significantly negative
because peers in SETA depart from focal firm i in year t. and we expect β5 to be significantly positive
because peers in SETB share common blockholders with firm i since year t+1. All other variables are
defined in appendix B. We use logistic regression when the dependent variable is CRASH, and OLS
regression otherwise. We control for year and industry fixed effects. Reported t-values (or z-value) are
based on robust standard errors adjusted for firm-level clustering (Petersen 2009) and heteroskedasticity
(White 1980). ***, ** and * represent significance levels at 1%, 5% and 10%, respectively.
(1) (2) (3)
Variables CRASHt NCSKEWt DUVOLt
POST -0.134 -0.039*** -0.021***
(-1.02) (-2.78) (-2.92)
CRASH RISKt (SETA) 0.722** 0.345*** 0.363***
(2.43) (7.18) (7.71)
CRASH RISKt (SETB) 0.209 0.163*** 0.213***
(0.62) (3.01) (4.22)
POST*CRASH RISKt (SETA) -0.488 -0.284*** -0.292***
(-1.19) (-4.38) (-4.57)
POST*CRASH RISKt (SETB) 1.064** 0.144** 0.119*
(2.18) (1.97) (1.75)
NCSKEWt-1 0.111*** 0.021* 0.011**
(3.15) (1.85) (2.26)
29
DTURNt-1 1.166*** 0.421*** 0.207***
(3.95) (4.66) (5.09)
SIGMAt-1 4.565 1.139 0.339
(1.29) (1.10) (0.72)
RETt-1 0.853** 0.242** 0.093*
(2.00) (2.01) (1.69)
SIZEt-1 0.106*** 0.055*** 0.027***
(4.43) (7.20) (7.76)
LEVt-1 0.044 -0.143*** -0.082***
(0.27) (-2.98) (-3.83)
MTBt-1 0.017** 0.016*** 0.008***
(2.15) (5.84) (6.05)
ROAt-1 0.103 0.130** 0.072***
(0.54) (2.18) (2.73)
ACCMt-1 0.428** 0.122** 0.052**
(2.36) (2.27) (2.11)
Intercept -2.716*** -0.705*** -0.334***
(-3.59) (-3.87) (-3.28)
Year, industry fixed effect YES YES YES
N 14,396 14,476 14,476
Pseudo R-sq or Adj R-sq 0.032 0.051 0.059
30
Table 5 The Effect of Peers’ Crash Risk on Focal Firm’s Crash Risk: Two Possible Channels
This table presents analyses about the channel through which peers’ crash risk affects focal firm’s crash
risk. We use the distance of liquidity (ILIQ) between a firm and its peers to proxy for the similarity of
blockholder trading-induced pressure on their stock returns. We use the distance of disclosure quality (DQ)
between a firm and its peers to proxy for the commonality of crash risk stemming from common disclosure
practices. In panel A, for each focal firm i in year t, we sort its peers into two groups by distance of ILIQ
between focal firm i and peer j. The ILIQ distance is calculated as ABS(ILIQit-ILIQjt). Peers are sort into
two groups by the median of ILIQ distance. We define the peer as CLOSE group when its ILIQ distance is
lower than the median, FAR group otherwise. Then we calculated value weighted crash risk measure for
COLSE and FAR group and generate VW_CRASH RISKt(Close) and VW_CRASH RISKt(Far), respectively.
Finally, we add VW_CRASH RISKt(Close) and VW_CRASH RISKt(Far) to model (3) (See legend of table
3) and drop VW_CRASH RISKt to conduct channel tests for ILIQ. Similarly, in panel B, we generate
VW_CRASH RISKt(Close) and VW_CRASH RISKt(Far) and conduct channel test for DQ. All other
variables are defined in appendix B. We use logistic regression when the dependent variable is CRASH,
and OLS regression otherwise. We provide estimate of coefficients based standardized variables. We
control for year and industry fixed effects. Reported t-values (or z-value) are based on robust standard errors
adjusted for firm-level clustering (Petersen 2009) and heteroskedasticity (White 1980). ***, ** and *
represent significance levels at 1%, 5% and 10%, respectively.
Channel Panel A Iliquidity Panel B Disclosure Quality
(1) (2) (3) (1) (2) (3)
Variables CRASHt NCSKEWt DUVOLt CRASHt NCSKEWt DUVOLt
VW_CRASH RISKt (Close) 0.019*** 0.064*** 0.063*** 0.015** 0.037*** 0.038*** (3.36) (12.94) (12.64) (2.33) (7.47) (7.79)
VW_CRASH RISKt (Far) 0.002 0.007 0.009* 0.008 0.032*** 0.031*** (0.55) (1.50) (1.95) (1.19) (6.52) (6.29)
Difference test between coefficients
of Close and Far VW_CRASH RISKt P=0.084 P<0.001 P<0.001 P=0.4572 P=0.5729 P=0.4346
NCSKEWt-1 0.022*** 0.022*** 0.022*** 0.023*** 0.021*** 0.022*** (3.62) (4.51) (4.78) (3.68) (4.41) (4.63)
DTURNt-1 0.034*** 0.039*** 0.040*** 0.033*** 0.039*** 0.040*** (5.91) (8.77) (9.24) (5.68) (8.44) (8.84)
SIGMAt-1 0.105*** 0.110*** 0.085*** 0.102*** 0.132*** 0.109*** (4.17) (6.27) (4.93) (3.90) (7.16) (6.00)
RETt-1 0.133*** 0.138*** 0.122*** 0.131*** 0.153*** 0.140*** (5.66) (8.65) (7.82) (5.32) (9.04) (8.39)
SIZEt-1 0.045*** 0.107*** 0.107*** 0.045*** 0.119*** 0.119*** (5.16) (17.12) (17.23) (4.95) (18.52) (18.79)
LEVt-1 -0.007 -0.033*** -0.038*** -0.016** -0.045*** -0.050*** (-1.07) (-6.24) (-7.28) (-2.23) (-8.60) (-9.72)
MTBt-1 0.029*** 0.059*** 0.061*** 0.036*** 0.073*** 0.075*** (5.12) (12.09) (12.57) (6.08) (14.09) (14.84)
ROAt-1 0.016** 0.033*** 0.034*** 0.019*** 0.035*** 0.037*** (2.20) (6.11) (6.68) (2.61) (6.46) (7.10)
ACCMt-1 0.031*** 0.027*** 0.024*** 0.03*** 0.026*** 0.023*** (4.87) (5.64) (5.14) (4.69) (5.29) (4.81)
Year, industry fixed effect YES YES YES YES YES YES
N 57,668 57,668 57,668 54,786 54,786 54,786
Pseudo R-sq or Adj R-sq 0.027 0.051 0.057 0.027 0.051 0.058
31
Table 6 Robustness tests
This table presents four robustness tests. Panel A uses equal-weighted method to calculate peers’ crash risk,
replace 𝑉𝑊_𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾𝑖𝑡.by 𝐸𝑊_𝐶𝑅𝐴𝑆𝐻 𝑅𝐼𝑆𝐾𝑖𝑡 in model (3) (See legend of table 3). Panel B uses
VW_CRASH RISK in year t-1 instead of year t as the main independent variable, and use the crash risk in
year t as dependent variable to allow for a lead-lag regression. In addition, peers in panel B are identified
using holding information as of year t-1: the focal firm and peers only share common blockholders in year
t-1. Panel C adds average of peers’ fundamental variables as additional control variables. Crash risk control
variables from Peers include: value weighted average of peers' DTURN, SIGMA, RET, SIZE, LEV, MTB,
ROA and ACCM in year t. Panel D presents results with average of crash risk measured over all other firms
sharing the same industry affiliation (two-digit SIC code) or the same headquarter state as the focal firm
added as additional control variables. We add two variables, equal-weighted crash risk in year t measured
over all other firms operating in the same industry (or located in the same state) as the focal firm. All other
variables are defined in the appendix B. We use logistic regression when the dependent variable is CRASH,
and OLS regression otherwise. We control for year and industry fixed effects. Reported t-values (or z-value)
are based on robust standard errors adjusted for firm-level clustering (Petersen 2009) and heteroskedasticity
(White 1980). ***, ** and * represent significance levels at 1%, 5% and 10%, respectively.
Panel A Equal weighted
(1) (2) (3)
Variables CRASHt NCSKEWt DUVOLt
EW_CRASH RISKt 0.268** 0.169*** 0.167*** (2.49) (9.50) (9.85)
N 59,289 59,289 59,289
Pseudo R-sq or Adj R-sq 0.026 0.050 0.056
Panel B VW_CRASH RISK in year t-1 (1) (2) (3)
Variables CRASHt NCSKEWt DUVOLt
VW_CRASH RISKt-1 0.547*** 0.204*** 0.196*** (4.49) (10.36) (10.38)
N 66,997 66,997 66,997
Pseudo R-sq or Adj R-sq 0.027 0.052 0.058
Panel C Add peers' control variables (1) (2) (3)
Variables CRASHt NCSKEWt DUVOLt
VW_CRASH RISKt 0.238** 0.133*** 0.133*** (2.22) (7.35) (7.78)
N 59,289 59,289 59,289
Pseudo R-sq or Adj R-sq 0.027 0.051 0.058
Panel D Control for industry and state peers' crash risk (1) (2) (3)
Variables CRASHt NCSKEWt DUVOLt
VW_CRASH RISKt 0.275*** 0.173*** 0.170*** (2.63) (9.99) (10.34)
N 59,089 59,097 59,097
Pseudo R-sq or Adj R-sq 0.027 0.051 0.058
32
Appendix A Illustration of focal firm, CIBs and peers
We illustrate in figure 1 the relation among focal firm, CIBs and peers in fiscal year t. In fiscal year t, focal
firm i is connected to its peers by CIBs. There could be more than 1 CIBs between focal firm and peers. In
our tests, the average crash risk of peers are value (equal)-weighted average in the main (robustness)
regressions.
Figure 1 The relation between focal firm, CIBs and peers
CIB1, CIB2,
CIB3… Focal firm i
Peer 1
Peer 2
Peer…
33
Appendix B Variable Definitions
All crash risk measures are generated from firm-specific weekly returns. The frim-specific return is estimated from
the market model regression:
riτ = 𝛽0 + 𝛽1𝑖𝑟𝑚,𝜏−2 + 𝛽2𝑖𝑟𝑚,𝜏−1 + 𝛽3𝑖𝑟𝑚,𝜏 + 𝛽4𝑖𝑟𝑚,𝜏+1 + 𝛽5𝑖𝑟𝑚,𝜏+2 + 𝜀𝑖𝜏, (1)
where riτ is the return on stock i in week τ, and 𝑟𝑚,𝜏 is the return on the value-weighted market index in week τ. To
allow for non-synchronous trading, the expanded market model includes the lead and lag terms for the market index
return (Dimson 1979; Scholes and Williams 1977). In estimating the model, each firm-year is required to have at
least 26 weekly stock return observations. The residual is extracted after the regressions and firm-specific return is
equal to ln(1+residual).
Dependent variables: crash risk of focus firm
CRASH
CRASH is an indicator variable equal to 1 if a firm experiences one or more firm-specific
weekly returns falling 3.2 or more standard deviations below the mean firm-specific weekly
return within the fiscal year and 0 otherwise.
NCSKEW NCSKEW is the negative coefficient of skewness of firm-specific weekly returns over the
fiscal year.
DUVOL The log of the ratio of the standard deviations of down-week to up-week Firm-Specific-
Weekly Return
Independent variables: crash risk of peer firms
VW_PCRASH Value weighted (by market cap adjusted holdings) CRASH for all peer firms which have
common blockholders with focal firm in both year t-1 and t.
VW_PNCSKEW Value weighted (by market cap adjusted holdings) NCSKEW for all peer firms which have
common blockholders with focal firm in both year t-1 and t.
VW_PDUVOL Value weighted (by market cap adjusted holdings) DUVOL for all peer firms which have
common blockholders with focal firm in both year t-1 and t.
Control variables
DTURN
The average monthly share turnover over the current year, minus the average monthly share
turnover over the previous year, where monthly share turnover is calculated as the monthly
trading volume divided by total number of shares outstanding during the month.
SIGMA Standard deviation of firm-specific weekly return over the year.
RET Average firm-specific weekly returns times 100 during the year.
SIZE The natural logarithm of firm's total market capitalization.
LEV Total long-term debt divided by beginning total assets at the end of the fiscal year.
MTB Market value of equity divided by the book value of equity at the end of the fiscal year.
ROA Income before extraordinary scaled by beginning total assets.
ACCM Pervious three-year moving sum of absolute abnormal accruals, the abnormal accruals are
estimated by modified Jones model.
