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In this paper we reviewed the empirical literature on the determinants and consequences of stock price crash risk. We categorize the determinants into (i) financial reporting and corporate disclosures, (ii) managerial incentives and managerial characteristics, (iii) capital market transactions, and (iv) formal and informal corporate governance mechanisms.

Managerial incentives for hoarding bad news have been the primary focus of the burgeoning literature on crash risk. However, incentives alone would not be sufficient to withhold bad news. Managers would have to devise mechanisms for concealing negative information. Earnings manipulation (both accruals and real), tax avoidance, and voluntary disclosures (CSR disclosures and management earnings guidance) have been identified as some of the mechanisms used by managers. Finally, reporting conservatism, external auditing, and corporate governance mechanisms (e.g. institutional monitoring) can curb managerial opportunistic use of mechanisms for concealing negative information.

Finally, in terms of country coverage on the determinants of crash risk research, we note with no surprise that USA dominates the empirical research followed by China. Although there are a couple of international studies, we were surprised to find no published study on either crash risk determinants or consequences of crash from other parts of the world. Of course, inferences

34 may be similar for countries with institutional environments similar to the US, e.g. the UK, Australia, and Canada; and this may restrain researchers from pursuing research on crash risk in other similar institutional settings. Yet it is well known that, despite the institutional similarities, similar research questions or some variants thereof have been pursued across these countries.

Therefore, we call for more research in an international context to better understand the effect of country-specific idiosyncratic features on the determinants and consequences of price crash.

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40 TABLE1: Determinants and consequences of stock price crash risk

Authors CRASH NCSKEW DUVOL Main IV CRASH NCSKEW DUVOL

Mean STD Mean STD Mean STD

Hutton et al.

(2009)

Opacity 0.17 - OPAQUE 0.855*** - -

Cohen et al.

(2014)

Earnings management in banks

Not reported

Not reported

- - - - EARN_MGT

EARN_MGT*CRISIS

8.22*

22.70**

- -

Kim et al.

(2011a)

CEO/CFO equity incentives

0.172 0.377 0.034 0.693 0.004 0.332 CEO_OPTION -0.029 -0.009 -0.005

CFO_OPTION 0.757** 0.278*** 0.131**

Kim et al.

(2011b)

Tax avoidance 0.161 0.368 -0.079 0.739 - - SHELTER 0.270*** 0.253*** -

BTD 0.233*** 0.07*** -

Kim & Zhang (2014)

Implied volatility Descriptive statistics not reported OPAQUE 0.005*** IV_SKEW as the dependent variable

Kim & Zhang (Forthcoming)

CEO

overconfidence

0.172 0.378 0.068 0.74 0.027 0.345 OC_CJRS 0.103* 0.049*** 0.022***

OC_SZ 0.162*** 0.045** 0.023***

Kim et. al.

(2015)

Accounting conservatism

0.12 0.327 -0.20 0.71 - - CSCORE -1.27** -0.57*** -

Kim et al.

(2016)

Financial statement comparability

IV_SKEW: 0.042 (0.031) FCOM -0.004*** [Table 2 column 4]

DeFond et al.

(2015)

Adoption of IFRS

- - -0.285 0.615 - - Mandatory

Adopters*POST

- -0.11*** -

Francis et al.

(2014)

Real earnings management

0.195 0.396 -0.072 0.792 -0.141 0.51 REM [DISX] 0.089** - -

REM [PROD] 0.172*** - -

Ertugrul et al.

(Forthcoming)

Annual report readability

0.21 0.41 0.16 0.87 0.008 0.38 log(File size) 0.032** 0.026*** 0.009**

Kim et al. Corporate social - - 0.035 0.81 -0.002 0.37 CSR_SCORE - -0.064** -0.027**

41 (2014) responsibility

Robin & Zhang (2014)

Auditor industry specialization

0.19 0.39 -0.10 0.76 -0.05 0.35 SPECIALIST* -0.07*** -0.03*** -0.012***

Habib & Hasan (2016)

Auditor-provided tax services

- - -0.07 1.17 -0.37 0.91 FEERATIO - -0.017** -0.014***

Callen and Fang (2013)

Institutional investors

-0.46 1.67 -0.07 1.24 -0.15 0.56 StdI *100 0.30*** 0.23*** 0.14***

DED -0.50*** -0.37*** -0.17***

TRA 1.09*** 1.14*** 0.47***

Callen & Fang (2015)

Short interest -0.43 1.68 -0.14 1.59 -0.17 0.66 SIR 0.64** 0.70*** 0.31***

An & Zhang (2013)

Institutional ownership

-0.09 0.70 -0.17 0.94 -0.10 0.41 IO

IO_DED IO_TRA

0.054**

-0.19***

0.39***

0.13***

-0.26***

0.66***

0.06***

-0.10***

0.30***

Lee & Wang (2016)

Political connections

-0.173 0.676 -0.06 0.242 PCD*IDENTITY - 0.407*** 0.165***

Andreou et al.

(2016)

Corporate governance

- - 0.101 0.811 -0.00 0.364 Nine PCF scores from

21 individual CG variables

- Too many variables to be tabled

An & Zhang (2013)

Institutional investor

-0.09 0.70 -0.17 0.94 -0.10 0.41 IO 0.05** 0.13*** 0.06***

DED -0.19*** -0.26*** -0.01***

Xu et al. (2014) Excess perks - - −0.365 0.630 −0.273 0.457 ExcessPerk - 0.247*** 0.136***

Aman (2013) Media Coverage - 0.706 - - - - MEDIA 0.26*** - -

Li and Chan (2016)

Communist Party Control

- - - - - - PARTY_DIR - -0.061** -

Xu et al. (2013) Analyst coverage, forecast optimism and conflict of interest

0.09 0.29 -0.14 0.66 -0.1 0.48 Analyst

Analyst_OPT Analyst_IB Analyst_Affiliated

0.019***

0.024***

0.02***

0.023***

0.008**

0.011**

0.009***

0.009***

0.005**

0.007**

0.007***

0.006***

Yuan et al.

(2016)

D&O liability insurance

- - −0.215 0.793 −0.251 0.733 D&O - −0.137*** −0.113***

He (2015) CEO inside debt - - - - - - InsiDebt -0.1458** - -

Chang et al.

(Forthcoming)

Stock liquidity 0.19 0.39 0.008 0.76 - - LIQ 0.216*** 0.057*** -

Piotroski et al. Political - - -0.650 0.674 - - Political - -0.253*** -

42

(2015) incentives Post-Political 0.049*

Kubick &

Lockhart (2016)

Proximity to SEC

0.19 0.39 0.008 0.67 -0.011 0.32 SEC Distance 0.05** 0.03*** 0.018***

Callen & Fang (2016)

Religion -0.66 1.70 -0.21 0.59 -0.23 1.15 REL -0.13** -0.077** -0.13**

Li & Cai (Forthcoming)

Religion - - -0.19 0.61 -0.18 0.45 RELIGION - -0.81*** -0.69***

Zhang et al.

(2016)

Donations - - -0.24 0.82 -0.17 0.69 Donations - -0.0024** -0.0026***

Donations*SOE - 0.0051** 0.0058***

Note: In the following we define those variables which have been used infrequently in the surveyed studies

Tax fee ratio (FEERATIO): tax fees received by audit firms as a proportion of audit fees.

Incentive ratio (ONEPCT): The dollar change in the value of a manager’s option holdings that would come from a one-percentage-point increase in the company stock price.

Institutional ownership stability (INSTOWN_Stable): Average standard deviation of institutional shareholding proportions across all investors in a firm over a 5-year period including sample year and the 4 years preceding it (i.e., 20 quarters).

Dedicated institutional investor (DED) is the percentage of shares outstanding held by dedicated institutions at the end of the year.

Transient institutional investor (TRA) is the percentage of shares outstanding held by transient institutions at the end of the year.

Short interest (SIR): The number of shares sold short divided by total shares outstanding from the last month of fiscal year t, with a range from 0 to 1. Compustat Supplemental Short Interest File provides the available data to calculate short interest.

CEO overconfidence (OC_CJRS): Dummy variable coded 1 if the CEO holds options at least twice during the sample period that are more than 100 percent in the money, and zero otherwise.

CEO overconfidence (OC_SZ): It takes the value of one if the firm meets the requirements of at least three of the following five criteria and zero otherwise: (i) Excess investment is in the top quartile within industry years; (ii) net acquisitions from the statement of cash flows are in the top quartile within industry-years; (iii) the debt-to equity ratio is in the top quartile within industry-years; (iv) either convertible debt or preferred stock is greater than zero; and (v) the dividend yield is zero.

Religion (RELIGION) (Li & Cai, Forthcoming): The distance between the registered address of the listed company and the site of religious activity.

Religion (REL) (Callen & Fang 2015): Religiosity data from the Association of Religion Data Archives (ARDA). Once every decade, the Glenmary Research Center collects data from surveys on religious affiliation in the United States (1971, 1980, 1990, and 2000). Based on the survey results, the center reports county-level data on the number of churches and the number of total adherents by religious affiliation.

Political connection (PCON): Number of politically connected directors on the board divided by the total number of directors. A board member as a politically connected director if he or she formerly (currently) served (serves) in one of the following posts: (1) government

43 official, (2) member of the Chinese People’s Congress (CPC), (3) member of the Chinese People’s Political Consultative Conference (CPPCC), or (4) military official.

Media coverage (MEDIA): Iinformation from Nikkei Telecom 21 on the number of citations over one year in four commercial newspapers, The Nikkei, the Nikkei Business Daily, the Nikkei Finance Journal, and the Nikkei Marketing Journal.

Annual report readability (log(File size): The natural logarithm of the file size in megabytes of the SEC EDGAR “complete submission text file” for the 10-K filing.

Excess perks (ExcessPerk): Actual perk consumption minus expected perk consumption whereby the latter is derived by regressing perks consumption (scaled by revenue) on natural log of total compensation for all firm employees, firm size, and the natural log of total income per capita of the region in which the firm is located.

Communist party control (CPC): A dummy variable coded 1 if any members of the CPC committee are also directors, supervisors, or senior executives; otherwise it is zero.

Inside debt (InsiDebt): A dummy variable, which takes value of 1 if CEO relative leverage exceeds 1 and 0 otherwise (See Appendix 2 of the paper for the detailed calculation of CEO leverage).

Proximity to the SEC (SEC distance): The distance between the firm’s headquarters and the closest SEC regional or national office. SEC national office in Washington DC and regional offices in New York City, Miami, Chicago, Denver, and Los Angeles.

Stock liquidity (LIQ): -100 times relative effective spread, which is the ratio of the difference between the trade price and the midpoint of the bid-ask quote over the trade price.

Political incentives (Political and Post-Political): An indicator variable equal to one if the firm-year relates to a specific political event (i.e., National Congress or Provincial-Level Political Promotion), zero otherwise. An indicator variable equal to one in the year immediately following the political event, zero otherwise.

44 TABLE2: Determinants of stock price crash risk

Authors Research question Sample Sample period justification

Findings Economic significance of

the coefficient of interest Financial reporting quality, bad news hoarding and crash risk

Hutton et al.

(2009)

To examine the effect of transparency of financial statement on crash risk.

1991-2005 sample period with a final sample of 40,882 firm-years.

Direct method CF data because first available in 1987.

Sample periods begin in 1991 as three annual lags of cash flow data is needed for estimating accruals.

Firms with opaque financial statements are more prone to stock price crashes.

However, this relation more is pronounced before the passage of the Sarbanes-Oxley Act.

Financial reporting opacity accounts for 15.8% of the variation in crash risk.

Francis et al.

(2014)

To examine the impact of real earnings management on future stock price crash.

1989-2009 sample period with 44,731 firm-year

observations

No justification for sample period.

Firms that deviate in real operations from industry norms are positively associated with future crash risk.

One standard deviation change around the mean of 3 years’ absolute value of deviation in real operations increases crash likelihood by 0.94 %

Kim and Zhang (2015)

To investigate whether conditional conservatism in financial reporting affects price crashes.

1962-2007 sample period with 114,548 firm-year

observations

No justification for the start of sample period in 1962.

Conditional conservatism reduces the likelihood of a firm experiencing future price crashes. Furthermore, changes in the degree of conditional conservatism are also negatively associated with changes in future crash risk.

A one standard deviation increase in the Basu (1997) coefficient reduces crash probability by 46.4%.

Kim et al.

(2011b)

To examine the association between tax avoidance and crash risk

1995-2008 sample period with 87,162 firm-year

observations

To ensure the consistent

measurement of tax avoidance variables, following the enactment of FAS 109, Accounting for Income Taxes, the authors start sample

Various forms of tax avoidance increase crash risk.

Logistic regression revels that the marginal effect of the tax avoidance measure on crash risk is 3.6%for SHELTER, 4.1% for LRETR, and 3.1% for BTDFACTOR. Economic significance for tax avoidance variable for two

45

period in 1995. other crash risk measures is

not reported.

Zhu (2016) Investigates the relation between accruals and price crashes.

A total of 108,184 firm-year

observations between 1965 and 2013.

None provided A strong positive association between total accruals and price crashes is found which is consistent with the hidden bad news explanation. With respect to the components of accruals, Zhu (2016) finds that less (more) reliable accrual components are significantly positively (not significantly) associated with price crashes,

The probability of observing price over the next year increases from 12.88 % for the lowest decile of the current year’s accruals to

17.27 % for the highest decile.

Kim et al.

(2014)

To examine the effect of CSR disclosures on crash risk

1995-2009 sample period and 12,978 firm-year

observations

Availability of CSR data from 1994 from KLD database and use of one year lagged value allow the authors to begin the sample period from 1995.

Firms with better CSR scores are less prone to crash risk. The role of CSR in reducing stock price crash risk is particularly important when internal monitoring by the boards or external monitoring by institutional investors is weak.

On average, an increase of one standard deviation in CSR_SCORE is associated with a decrease of 0.052 in NCSKEW in year t. DUVOL, too, is economically significant.

Ertrugrul et al.

(Forthcoming)

Examines the effects of annual report readability and tone ambiguity on crash risk and borrowing costs

A total of 32,207 firm-year

observations and 1995-2013 sample period

None provided Firms with larger reports and a higher proportion of uncertain and weak expressions experience greater crash risk supporting the argument that readability of a firm’s financial disclosures are related to managerial information hoarding.

A one SD increase in readability score would lead to an 18.2% increase in the average value of NCskew

Kubick &

Lockhart (2016)

Examines whether proximity to SEC (‘geography of crash risk’) impacts crash risk

A total of 18,081 firm-year

observations from 1996 to 2012

YES. “…sample begins in 1996 [to]

obtain historical zip codes from firm 10-K filings…”

Firms further away from SEC office are more likely to experience crash. This effect is more pronounced for firms with larger 10-K file sizes. This is consistent with managerial influence over annual report disclosures with an intent to obfuscate bad news when there is greater distance between managers and the SEC.

A change from 1st to the 3rd quartile of the distance from SEC variable increases crash risk by 0.75%.

46 External monitoring and crash risk

Robin and Zhang (2014)

To examine whether the industry-specialist auditors influence crash risk

1990-2009 sample period with 58,365 firm-year

observations.

No justification for the sample period.

Information intermediary and corporate governance roles of auditor industry specialization reduce crash risk.

Moreover, industry-specialist auditors moderate the effects of opacity, accounting conservatism, and tax avoidance on crash risk.

No

Habib & Hasan (2016)

To investigate the association between NATS and crash risk.

2002-2012 sample period with 21,950 firm-year

observations

Sample period begins from 2002 as

Congress ratified SOX in this year.

NATS attenuate crash risk by constraining both tax expenses management and tax avoidance.

Further, NATS reduce crash risk for firms following innovative business strategies. Empirical findings, therefore, support knowledge spillover benefits.

No

Callen and Fang (2013)

To examine the association between institutional

investor stability and stock price crash risk.

1981-2008 sample period with 61,705 firm-year

observations.

No justification for the sample period.

Institutional investor stability is negatively associated with one-year-ahead stock price crash risk. Moreover, this relationship varies depending on whether institutional owners are public pension funds or bank trusts, investment companies and independent investment advisors.

One year ahead crash risk increase by 5.3% with a shift from the 25th

to the 75th percentile of the distribution of standard deviation of institutional shareholding.

An and Zhang (2013)

To examine the impact of institutional investors on stock price synchronicity and crash risk

From 1987 through 2010 sample period with 79,932 firm-year

observations

Sample period begins from 1987 as the historical SIC data is missing in Compustat before 1987.

Monitoring by dedicated institutional investors mitigates managerial bad-news hoarding, which reduces crash risk and stock price synchronicity

A one-standard deviation increase of dedicated institutional ownership will lead to an 17% decrease in crash risk at the mean, while a similar increase of transient institutional investors will increase crash risk by 46% at the mean.

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