Ramzi Benkraiem
email: rbenkraiem@audencia.com

Stéphane Goutte
email: stephane.goutte@uvsq.fr

Samir Saadi
email: samir.saadi@telfer.uottawa.ca

Hui Zhu
email: hui.zhu@uoit.ca

Steven Zhu Bauer

Investor Heterogeneity and Negative Skewness in Stock Returns: Evidence from Institutional Investors

Keywords: JEL classification: G12, G14 Investors' horizons, Crash risk, Skewness, Institutional Investors ' !, #, $ ( #

We examine the relation between the probability of future stock price crash and investors' investment horizons. Using negative skewness as a proxy for firm-specific crash risk, we document a positive association between institutional ownership and stock price crash risk. The relation is, however, driven by short-term institutional investors, while the presence of long-term institutional investors has a negative effect on stock price crash risk. In addition, we find that the presence of short-term institutional investors induces corporate risk-taking behavior. Our results are robust to alternative model specifications, endogeneity concerns, and different measures of crash risk and proxies of investors' horizons.

Introduction

Stock crashes have become commonplace in the last few decades. [START_REF] Hutton | Opaque Financial Reports, R2, and Crash Risk[END_REF], for instance, document that 17.1% of the firm-years in their sample experience at least one crash over the period 1991-2005. The scale and rising frequency of crash in the equity market have attracted much attention with particular emphasis on identifying its causes. In fact, several studies in the finance and accounting literature show that crash risk increases with the lack of transparency and decreases with the level corporate governance. 1 The premise is that managers tend to use the lack of transparency and weak monitoring environment to delay or conceal bad news and accelerate the disclosure of good news to the market. However, such selective disclosure strategy is not sustainable as the accumulated bad news will end up coming out all at once after crossing a tipping point leading to a sharp decline in the stock price.

In this paper, we examine whether and how institutional investors' investment horizon influences the probability of firm-specific stock price crash. 2 We focus on institutional investors because of their key role in influencing corporate governance and managerial decisions. In fact, given their large shareholdings, in comparison to retail investors, institutional investors tend to monitor managers and discipline those who deviate from the long-term objective of maximizing 1 See, among others, [START_REF] Jiang | Firms' digitalization and stock price crash risk[END_REF], [START_REF] Cao | Idiosyncratic volatility and stock price crash risk: Evidence from china[END_REF], [START_REF] Hsu | Non-GAAP earnings and stock price crash risk[END_REF], [START_REF] Lobo | Material Weakness in Internal Controls and Stock Price Crash Risk[END_REF], [START_REF] Balachandran | Does takeover activity affect stock price crash risk? Evidence from international M&A laws[END_REF], [START_REF] Kim | Accounting Conservatism and Stock Price Crash Risk: Firm-Level Evidence[END_REF], [START_REF] Kim | Financial Reporting Opacity and Expected Crash Risk: Evidence from Option Implied Volatility Smirks[END_REF], [START_REF] Defond | Does Mandatory IFRS Adoption Affect Crash Risk?[END_REF], and Hu, Kim and Zhang (2013). Kim, Li andZhang (2011a, 2011b), and [START_REF] Hutton | Opaque Financial Reports, R2, and Crash Risk[END_REF].

2 Institutional investors are legal entities that are widely considered as being more sophisticated than retails investors. Institutional investors can include large funds (e.g., mutual fund, hedge fund, VC funds, pension funds) as well as insurance companies, credit unions and banks.

firm's value (e.g., [START_REF] Shleifer | A Survey of Corporate Governance[END_REF][START_REF] Monks | Corporate governance[END_REF]. Our emphasis on institutional investors is also motivated by their dominant market share in U.S. equity ownership and trading volume (e.g., [START_REF] Zalewska | Mutual funds' exits, financial crisis and Darwin[END_REF][START_REF] Cella | Investors' Horizons and the Amplification of Market Shocks[END_REF]Boehmer and Kelly, 2009). Boehmer and Kelly (2009), for instance, document that the volume of trading by institutional investors account for up to 96% of the total trading in the New York Stock Exchange.

While the importance of institutional investors in the corporate world and financial markets is undeniable, it is inaccurate to assume that they represent a homogeneous group of sophisticated investors. In fact, several studies argue and empirically show that institutional investors are not homogeneous, and they do not have the same incentives to monitor managers. One of the main dimensions that institutional investors differ across is their investment horizon (e.g., [START_REF] Bushee | The influence of Institutional Investors on Myopic R&D Investment Behavior[END_REF][START_REF] Bushee | Do Institutional Investors Prefer Near-term Earnings over Long-run Value?[END_REF][START_REF] Bushee | Corporate Disclosure Practices, Institutional Investors, and Stock Return Volatility[END_REF][START_REF] Gaspar | Shareholder Investment Horizons and the Market for Corporate Control[END_REF][START_REF] Derrien | Investor Horizons and Corporate Policies[END_REF]. For example, pension funds have a long-term investment horizon as they have long-term liabilities; whereas mutual funds are likely to have a short-term investment horizon, as they may face a large liquidation in the short-term [START_REF] Derrien | Investor Horizons and Corporate Policies[END_REF]. Differences in investment horizons, in turn, are likely to affect institutional investors' governance role and trading strategies. [START_REF] Attig | Institutional Investment Horizon, Corporate Governance, and the Cost of Equity Capital[END_REF] and [START_REF] Gaspar | Shareholder Investment Horizons and the Market for Corporate Control[END_REF], among others, show that institutional investors with a short-term horizon have fewer incentives to engage in efficient monitoring because they tend to trade more frequently. On the contrary, long-term institutional investors assume a more active corporate governance role. [START_REF] Burns | Institutional Ownership and Monitoring: Evidence from Financial Misreporting[END_REF] find that, unlike the case of long-term institutional investors, the presence of short-term institutional investors is associated with lower quality of financial information and weak corporate governance.

As stated above, stock price crash risk tends to increase with financial reporting opacity as the lack of transparency allows managers to withhold bad news from the market. Because short-term (long-term) institutional investors are associated with weaker (stronger) monitoring of corporate information disclosure, we posit that the probability of stock price crash risk would increase (decrease) with the level of ownership by short-term (long-term) institutional investors. Using a large sample of U.S. public firms over the period 1981-2018, we find strong support for our prediction. First, we report that stock price crash risk is positively related to institutional investors ownership. However, we find that this relation is actually driven by short-term institutional investors. Moreover, in contrast to short-term institutional investors, long-term institutional investors' holdings and trading are negatively associated with stock price crash risk. This paper contributes to the literature in several ways. First, it adds to the growing literature investigating the factors influencing stock crash risk (e.g., [START_REF] Zuo | Employee relations and stock price crash risk: Evidence from employee lawsuits[END_REF][START_REF] Cao | Idiosyncratic volatility and stock price crash risk: Evidence from china[END_REF][START_REF] Jiang | Firms' digitalization and stock price crash risk[END_REF][START_REF] Hsu | Non-GAAP earnings and stock price crash risk[END_REF][START_REF] Hutton | Opaque Financial Reports, R2, and Crash Risk[END_REF]. For example, crash risk increases with aggressive tax strategy (Kim, Li and Zhang, 2011a), and divergence in ownership and control [START_REF] Hong | Divergence of Cash Flow and Voting Rights, Opacity, and Stock Price Crash Risk: International Evidence[END_REF], and decreases with financial reporting transparency [START_REF] Defond | Does Mandatory IFRS Adoption Affect Crash Risk?[END_REF][START_REF] Kim | Financial Reporting Opacity and Expected Crash Risk: Evidence from Option Implied Volatility Smirks[END_REF] and accounting conservatism [START_REF] Kim | Accounting Conservatism and Stock Price Crash Risk: Firm-Level Evidence[END_REF]. We extend the extant literature by investigating the relation between investment horizon of institutional investors and firm-specific stock price crash risk.

Because we use negative skewness as a proxy for firm-specific crash risk, our paper is also related to the literature on the impact of skewness on asset pricing (e.g., [START_REF] Anghel | Asset Pricing with Systematic Skewness: Two Decades Later[END_REF][START_REF] Dong | Co-skewness and expected return: Evidence from international stock markets[END_REF][START_REF] Harvey | Conditional Skewness in Asset Pricing Tests[END_REF]. Other studies incorporate the impact of return skewness on investor decision making and show that investors have greater preference for skewness (e.g., [START_REF] Martellini | Improved Estimates of Higher-order Comovements and Implications for Portfolio Selection[END_REF][START_REF] Boyer | Expected Idiosyncratic Skewness[END_REF]. As a result, skewness is a priced component of security returns and is relevant to optimal or efficient portfolios. A growing set of studies also examines market skewness as a priced risk factor of security returns (e.g., Chang, Christoffersen and Jacob, 2013).

The current paper also contributes to the debate about which type of institutional ownership is more desirable to a firm. Studies such as by [START_REF] Porter | Capital Choices: Changing the Way America Invests in Industry[END_REF], [START_REF] Brancato | Institutional Investors and Corporate Governance: Best Practices for Increasing Corporate Value[END_REF][START_REF] Bushee | Identifying and Attracting the 'Right' Investors: Evidence on the Behavior of Institutional Investors[END_REF] suggest that firms benefit from attracting long-term institutional investors rather than short-term ones. Short-term institutional investors are weak monitors [START_REF] Gaspar | Shareholder Investment Horizons and the Market for Corporate Control[END_REF] and pressure managers into short-run objectives, thereby negatively affecting firm value in the long run [START_REF] Bushee | The influence of Institutional Investors on Myopic R&D Investment Behavior[END_REF][START_REF] Bushee | Do Institutional Investors Prefer Near-term Earnings over Long-run Value?[END_REF]. Nevertheless, Yan and [START_REF] Zhang | Institutional Investors and Equity Returns: Are Short-term Institutions Better Informed?[END_REF] show that short-term institutional investors are better informed and can predict future stock returns. Yan and Zhang call into question the benefits of attracting only long-term investors since more informative prices facilitate better financing and investment decisions and may reduce cost of capital. More recently, however, [START_REF] Attig | Institutional Investment Horizon, Corporate Governance, and the Cost of Equity Capital[END_REF] find that holding by long-term institutional investors leads to a lower firm's cost of equity capital than short-term institutional ownership. Moreover, [START_REF] Bushee | Corporate Disclosure Practices, Institutional Investors, and Stock Return Volatility[END_REF] show that firms with short-term (transient) institutional holdings have higher stock return volatility. More recently, however, [START_REF] Giannetti | Adapting to Radical Change: The Benefits of Short Horizon Investors[END_REF] show firms with more short-term investors adapt better to industry shocks that change its competitive environment. Our study shows that the presence of short-term institutional investors is positively associated with crash risk. Thus, our findings are more consistent with the view that firms benefit from attracting longterm institutional investors.

Our paper is also related to an emergent stream of literature examining institutional trading during the 2008 finance crisis (e.g., [START_REF] Zalewska | Mutual funds' exits, financial crisis and Darwin[END_REF][START_REF] Cella | Investors' Horizons and the Amplification of Market Shocks[END_REF].

For instance, [START_REF] Cella | Investors' Horizons and the Amplification of Market Shocks[END_REF] examine institutional trading during the Lehman Brothers' bankruptcy in September 2008 and find that investors' short horizons amplify the effects of market-wide negative shocks.

Finally, we add to the literature linking investment horizon to corporate behavior, such as capital structure [START_REF] Boubaker | Does Institutional Investor Horizon Influence US Corporate Financing Decisions?[END_REF], corporate innovation and R&D investment [START_REF] Barrot | Investor Horizon and the Life Cycle of Innovative Firms: Evidence from Venture Capital[END_REF][START_REF] Bushee | The influence of Institutional Investors on Myopic R&D Investment Behavior[END_REF], corporate social responsibility [START_REF] Boubaker | It's About Time! The Influence of Institutional Investment Horizon on Corporate Social Responsibility[END_REF], corporate payout policy [START_REF] Amin | Institutional Shareholding and Information Content of Dividend Surprises: Re-examining the Dynamics in Dividend-reappearance Era[END_REF][START_REF] Gaspar | Payout Policy Choices and Shareholder Investment Horizons?[END_REF], mergers and acquisitions [START_REF] Gaspar | Shareholder Investment Horizons and the Market for Corporate Control[END_REF], firm's performance (Elyasiani and Jai, 2010), and investment to cash flow sensitivity [START_REF] Attig | Institutional Investment Horizon and Investment-cash Flow Sensitivity[END_REF].

As noted by Hartzel and Starks (2003), though long-term investors could affect corporate policies by active monitoring, short-term investors could affect corporate policies through their trading strategies and preferences. We augment the existing literature by investigating whether the institutional investment horizon influences corporate risk-taking. Related studies attempt to identify the determinants of corporate risk-taking behavior. For instance, [START_REF] Hilary | Does Religion Matter in Corporate Decision Making in America?[END_REF] find that U.S. firms located in more religious counties are less likely to engage in risk-taking behavior. [START_REF] John | Corporate Governance and Risk-Taking[END_REF] find that corporate risk-taking and growth are positively associated with better investor protection.

The remainder of the paper is organized as follows. Section 2 describes the data and variables we use in our empirical analyses. In Section 3, we investigate the impact of the level and the change in short-and long-term institutional investors on return skewness. In Section 4, we conduct robustness checks and endogeneity tests of institutional ownership. In Section 5, we provide additional empirical analyses by examining institutions' holdings and trading behavior around earnings announcements, and by testing the relation between corporate risk-taking behavior and investors' horizons. Section 6 concludes the paper.

Data, Variables and Descriptive Statistics

Data and sample selection

We obtain stock returns, number of shares outstanding, and turnover data from CRSP for all common stocks listed on NYSE, AMEX and Nasdaq. We gather quarterly institutional holdings from Thomson Financial 13 File. We exclude firms that do not have a full quarter of uninterrupted daily price data. To alleviate the concern that our sample is biased towards firms without institutional ownership, we conduct the propensity score matching approach to obtain our baseline sample. Specifically, we match firms with institutional ownership with firms without institutional ownership based on firm size, leverage, and book-to-market ratio in a given year.

We pursue a non-repetitive matching scheme that firms without institutional ownership can be used only once for the matching. Our post-matched sample has 457,188 observations including the first quarter of 1981 and ends in the fourth quarter of 2018.

Variables

We follow, among others, [START_REF] Chen | Forecasting Crashes: Trading Volume, Past Returns, and Conditional Skewness in Stock Prices[END_REF] and use negative skewness of stock returns as our main measure of crash risk. Skewness, SKEWi,t, is defined as the negative third moment of daily returns divided by its standard deviation of daily returns raised to the third power for any stock i over quarter t.

𝑆𝐾𝐸𝑊 !,# = -(𝑛(𝑛 -1) $/& ∑ 𝑅 !,# $ )/((𝑛 -1)(𝑛 -2)(∑ 𝑅 !,# & ) $/& ) (1)
where Ri,t is the sequence of de-meaned daily returns to stock i during quarter t and n is the number of observations on daily returns during quarter t.

The daily returns we use to compute skewness are log changes in stock price. We compute the skewness of an individual stock return based on raw stock returns (i.e., the log changes in price); market-adjusted returns (i.e., the log change in stock i less the log change in the value-weighted CRSP index for that day), and excess returns (i.e., the log change in stock i less the T-bill return).

We follow this procedure to compute each stock's past returns as well.

In addition to SKEWi,t, we also consider an alternative measure of return asymmetries, which we denote by SKEW_FF3i,t, for idiosyncratic skewness. Specifically, we use the [START_REF] Fama | Multifactor Explanations of Asset Pricing Anomalies[END_REF] model to correct for systematic risk and to better match stock return co-movements.

𝑅𝐸𝑇 !,# = 𝛽 ',!,( + 𝛽 ),!,( 𝑀𝐾𝑇 # + 𝛽 &,!,( 𝑆𝑀𝐵 # + 𝛽 $,!,( 𝐻𝑀𝐿 # + 𝜖 !,# ** (2)
where RETi,t is daily return of stock i on day t, MKTt is the excess return on the market portfolio, SMBt is the size factor, HMLt is the value factor, and 𝜖 !,# ** is the residual of the regression.

In the robustness check, we use two other proxies for crash risk. The first alternative is defined as volatility ratio (VOLRATIO). Following [START_REF] Bae | Corporate governance and conditional skewness in the world's stock markets[END_REF], we define VOLRATIO as the logarithm of the ratio of the variance from the daily returns below the sample mean to the variance from the daily returns above the sample mean. Our second alternative defines extreme return ratio EXTRATIO as the logarithm of the ratio of the number of negative extreme return days to the number of positive extreme return days, where we define a daily return as the negative (positive) extreme return if the daily stock return is less (larger) than twice of its standard deviation. Based on the above definition, greater negative values in VOLRATIO and EXTRATIO are associated with a more left-skewed return distribution (e.g., higher likelihood of crash risk).

We obtain data on Fama-French factors from Kenneth French's website and estimate the model every quarter with daily data. The idiosyncratic skewness for stock i is the skewness of the residual of the Fama-French regression, 𝑠𝑘𝑒𝑤(𝜖 !,# ** ).

The other key variable we use in our analysis is institutional ownership. Following several empirical studies such as by [START_REF] Giannetti | Adapting to Radical Change: The Benefits of Short Horizon Investors[END_REF], [START_REF] Ke | Do Institutional Investors Exploit the Post-earnings Announcement Drift?[END_REF], we measure institutional investor horizon using Bushee's classification of 13F investors [START_REF] Bushee | The influence of Institutional Investors on Myopic R&D Investment Behavior[END_REF][START_REF] Bushee | Do Institutional Investors Prefer Near-term Earnings over Long-run Value?[END_REF][START_REF] Bushee | Corporate Disclosure Practices, Institutional Investors, and Stock Return Volatility[END_REF]. In particular, using factor analysis and cluster analysis to classify institutional ownership into transient, dedicated, and quasi-indexer groups based on their past investment behavior. We first use the portfolio diversification, portfolio turnover, and momentum trading factors to obtain standardized factor scores. We then conduct k-means cluster analysis on the factor scores to assign institutions into three groups. Finally, we calculate the proportion of ownership held by each group of institutions for each firm, identifying the groups as transient (TRA), dedicated (DED), and quasi-indexers (QIX).

Based on the mean factor scores for each cluster, we find that transient institutional investors exhibit the highest portfolio turnover and highest trading sensitivity to current earnings, along with relatively high portfolio diversification. Thus, based on the frequency of portfolio turnover we expect TRAs to have characteristics similar to short-term institutional investors. We identify dedicated institutional investors as having low portfolio turnover with almost no use of momentum trading strategies and high concentration. Thus, we expect DEDs to exhibit characteristics similar to those of long-term institutional investors. Quasi-indexing institutional investors3 are also identified as having low turnover and low use of momentum trading strategies, but high diversification. Our classification of institutional investment horizons is consistent with, among others, [START_REF] Bushee | The influence of Institutional Investors on Myopic R&D Investment Behavior[END_REF][START_REF] Bushee | Do Institutional Investors Prefer Near-term Earnings over Long-run Value?[END_REF] and [START_REF] Giannetti | Adapting to Radical Change: The Benefits of Short Horizon Investors[END_REF].

In the robustness check, we use alternative proxies for institutional investors' horizon. Following [START_REF] Gompers | Institutional Investors and Equity Prices[END_REF] and Yan and [START_REF] Zhang | Institutional Investors and Equity Returns: Are Short-term Institutions Better Informed?[END_REF], we define aggregate institutional ownership as the ratio of the number of shares held by institutional investors relative to the total number of shares outstanding. We then categorize institutional investors into short-and longterm investors on the basis of how frequently they rotate their positions on all the stocks of their portfolio over the previous four quarters. Specifically, if we denote the set of companies held by investor j by Q, then we define the aggregate buy and sell for each investor as follows:

𝐶𝑅_𝑏𝑢𝑦 +,# = ∑ |𝑁 +,!,# 𝑃 !,# -𝑁 +,!,#,) 𝑃 !,#,) -𝑁 +,!,#,) ∆𝑃 !,# | !∈.,/ !,#,$ 0/ !,#,$%& (3) 𝐶𝑅_𝑠𝑒𝑙𝑙 +,# = ∑ |𝑁 +,!,# 𝑃 !,# -𝑁 +,!,#,) 𝑃 !,#,) -𝑁 +,!,#,) ∆𝑃 !,# | !∈.,/ !,#,$ 1/ !,#,$%& (4)
where CR_buyj,t is investor j's aggregate purchase for quarter t, CR_sellj,t is investor j's aggregate sale for quarter t, Nj,i,t is the number of shares of stock i held by investor j at the end of quarter t,

and Pi,t is the share prices for stock i at the end of quarter t. We also adjust for stock splits and stock dividends by using the CRSP price adjustment factor.

Next we compute investor j's churn rate for quarter t as in Yan and [START_REF] Zhang | Institutional Investors and Equity Returns: Are Short-term Institutions Better Informed?[END_REF]:

𝐶𝑅 +,# = 234(67_9:; !,$ ,67_<=>> !,$ ) ∑ (5)
To minimize the impact of investor's cash flows on portfolio turnover, we use the minimum of aggregate purchase and sale. Further, we calculate each institution's average churn rate over the past four quarters as:

𝐴𝑉𝐺_𝐶𝑅 +,# = ) A ∑ 𝐶𝑅 +,#,BC) A BD) (6)
Based on the average churn rate, we rank all institutional investors in three tertile portfolios for each quarter. We define short-term institutional investors (SIO) as those with the highest AVG_CRj,t in the top tertile and those in the bottom tertile as long-term institutional investors (LIO). For each stock, we define short-term (long-term) institutional ownership as the ratio of the number of shares held by short-term (long-term) institutional investors to the total number of shares outstanding.

Following [START_REF] Chen | Forecasting Crashes: Trading Volume, Past Returns, and Conditional Skewness in Stock Prices[END_REF], our control variables include RETi,t-1, defined as the cumulative return on stock i over the one-quarter period t-1; SIZEi,t-1, measured as the log of firm i's stock market capitalization at the end period of t-1; BKMKTi,t-1, defined to firm i's book-tomarket ratio at the end of period t-1; LEVERAGEi,t-1, defined as firm i's total liabilities divided by its total asset over period t-1; DTURNOVERi,t-1, measured as the detrending turnover by subtracting from the turnover variable a moving average of its value over the prior 18 months; and time dummies, defined as an indicator variable that equals 1 if firm i is in quarter t.

Descriptive statistics

We compute quarterly measures of skewness, institutional ownership, and stock characteristics for the period beginning with the first quarter of 1981 and continuing to the fourth quarter of 2008. Insert Table I about here

The Impact of Institutional Ownership on Stock Return Skewness

As stated above, we measure the probability of crashes using skewness of returns over quarter t.

We use the lagged institutional holdings based on the information available at the end of quarter t-1 as our proxy for contemporary demand shocks, and use changes in the institutional holdings over quarter t-1 as a proxy for institutional trading. 4 Because institutional investors trade 4 To see whether the effect of institutional ownership is due to contemporary demand shock or to the informational advantage of institutional ownership, [START_REF] Gompers | Institutional Investors and Equity Prices[END_REF] use the lagged frequently and aggressively in an effort to safeguard their investment, then if institutional investors experience larger demand shock, we would expect IOt-1 to lead to large negative return skewness. If institutional investors trade frequently on the basis of information quality, then ΔIOt-1 should be positively related to negative skewness (i.e., stock price crash).

Effect of the level of institutional holdings on skewness

Using skewness as a proxy for crash risk, we examine the extent to which short-and long-term institutional investors influence the probability of a stock's crash risk. Prior studies show that some stock characteristics, such as the market value of equity, detrended average monthly turnover, firm's leverage, and book-to-market ratio, have a strong influence on crash risk [START_REF] Chen | Forecasting Crashes: Trading Volume, Past Returns, and Conditional Skewness in Stock Prices[END_REF][START_REF] Hutton | Opaque Financial Reports, R2, and Crash Risk[END_REF]. To ensure that the impact of investors' horizons is not driven by their relationship with those stock characteristics, we use the same set of stock characteristics as control variables. Specifically, we regress SKEWt against institutional ownership: IOt-1, TRAt-1, DEDt-1; various stock characteristics: SKEWt-1, VOLATILITYt-1, SIZEt-1, LEVERAGEt-1, DTURNOVERt-1, BKMKTt-1; and one lag of past returns:

RETt-1. We also include time dummy variables for each quarter t.

Table II reports the cross-sectional regression specifications. We measure the dependent variable, SKEWt, on the basis of raw returns. We use Bushee's (1998) classifications of investor horizons:

Transient (TRAt-1) and dedicated (DEDt-1). In column (1), we include only aggregate institutional institutional holdings as a proxy for contemporary demand shocks and use changes in the institutional holdings as a proxy for informational advantage.

ownership and the control variables. The coefficient on IOt-1 is 0.084 which is statistically significant at the 1% level, indicating that skewness increases with the degree of aggregate institutional ownership. In columns ( 2) and (3) we refine this finding by considering transient (short-term) and dedicated (long-term) institutional ownership, respectively. The coefficient of TRAt-1 is 0.566 and statistically significant at the 1% level, but the coefficient of DEDt-1 is negative (i.e., -0.043) and significant at the 10% level. Results hold when we include both TRAt-1 and DEDt-1 in the regression (column 4): the coefficient on TRAt-1 is positive while the coefficient of DEDt-1 is negative, and both are significant at the 1% level. The positive sign for TRAt-1 suggests that an increase in short-term institutional holdings is associated with the increasing probability of crashes. In contrast, long-term institutional ownership is inversely related to the likelihood of crashes.

Insert Table II about here

The effect of change in institutional holdings on skewness

To differentiate between institutional holdings and trading on the basis of investment horizon, we include both lagged holdings and changes in holdings of aggregate, short-and long-term investors. We designate these factors as IOt-2, ΔIOt-1, TRAt-2, ΔTRAt-1, DEDt-2, and ΔDEDt-1, in the cross-sectional regression specifications.

Table III presents the impact on the probability of crashes of lagged holdings and changes in holdings for aggregate, short-, and long-term investors. In column (1) we focus on the effect of the level and the change in aggregate institutional ownership. In column (2) we examine the effect of the level and the change in short-term investors; in column (3) we address the effect of the level and the change in long-term investors; and in column (4) we consider the combined effect of both short-and long-term investors at the level and the change in ownership. When we include both lagged and changes in aggregate institutional holdings, the coefficients on both IOt-2 and ΔIOt-1 are significantly positive. This result suggests that increasing the level and magnitude of change in aggregate institutional holdings increases a firm's crash risk.

More importantly, when we include both lagged and changes in short-term institutional holdings (column 2), we find that the coefficients on both TRAt-2 and ΔTRAt-1 remain significantly positive at the 1% level. However, when we include both lagged and changes in long-term institutional holdings (column 3), we find that the coefficient on ΔTRAt-1 is negative and statistically significant. Further, when we consider the level and the change in both short-and long-term institutional investors (column 4), the coefficients on TRAt-2 and ΔTRAt-1 are positive (i.e., 0.684 and 0.445, respectively) and statistically significant at the 1% level whereas the coefficients on DEDt-2 and ΔDEDt-1 are negative (i.e., 0.234 and 0.186, respectively) and statistically significant at the 1% level. These results suggest that short-term institutional investors are the major driver of firms' crash risk.

Insert Table III about here

Robustness checks

Alternative measures of crash risk

Table IV provides the regression results for various proxies of negative return skewness. In columns ( 1) and ( 2), we use excess returns as the basis for computing the SKEW_EXt measure. In columns ( 3) and ( 4), we use market-adjusted returns for SKEW_MKt. In columns ( 5) and ( 6) we use the residual of the regression from the Fama-French three-factor model as the basis for computing idiosyncratic skewness, SKEW_FF3t. By definition, SKEW_FF3t should have more ability to explain skewness in the purely idiosyncratic component of stock returns.

The results are qualitatively similar for all three measures. The coefficients on short-term institutional stock ownership are positive and strongly statistically significant in each of the three columns. This implies that, all else equal, short-term institutional holdings are more likely to have large negative skewness (i.e., to become more crash-prone). The coefficients on aggregate institutional ownership remain positive and have a smaller effect on the negative skewness than do short-term institutional investors. In contrast, the coefficients on long-term institutional ownership are negative. All the coefficients are statistically significant. The results from Table IV corroborate our previous findings that the positive relation between institutional ownership and crash risk is mainly driven by short-term institutional investors, and that long-term institutional ownership is negatively associated with crash risk.

Insert Table IV about here

Table V presents the regression results by using alternative measures of crash risk. In columns ( 1) and ( 2), we use volatility ratio (VOLRATIO) to measure crash risk whereas in columns ( 3) and ( 4)

we use extreme return ratio (EXTRATIO) 5 to measure crash risk. The coefficients on aggregate institutional ownership (IOt-1) in columns ( 1) and ( 3) are positive at the 1% level. The coefficients on short-term institutional ownership (TRAt-1) in Columns ( 2) and ( 4) are positive at the 1% level whereas those on long-term institutional ownership (DEDt-1) are negative and significant.

Overall, our findings remain qualitatively similar as our main tests by using the alternative measures of crash risk.

Insert Table V about here

Alternative proxies of institutional investor horizon

In Table VI, we report additional regression specifications to assess the robustness of our results

to the use of alternative proxies of institutional investors' investment horizon, which are shortterm and long-term institutional holdings [START_REF] Gompers | Institutional Investors and Equity Prices[END_REF][START_REF] Zhang | Institutional Investors and Equity Returns: Are Short-term Institutions Better Informed?[END_REF]. In columns (1)-(3), we respectively examine the effect of the short-term institutional ownership (SIOt-1) and long-term institutional ownership (LIOt-1), as well as the combined effect of the two group of institutions on the probability of crashes. Our results show that, consistent with the effect of transient institutional investors, lagged short-term institutional holdings have a positive 5 The reason why observations in models (3) and ( 4) drop dramatically is that the number of negative (positive) extreme return days can be zero. According to the EXTRATIO definition of log(# negative extreme return days/# positive extreme return days), if any of the number of negative (positive) extreme return days is zero, then the observation will not be incorporated into the regression estimation.

effect on crash risk. We also find that holdings by long-term institutional investors have a negative effect on crash risk, which is also consistent with our results using dedicated institutional investors.

Insert Table VI about here

Endogeneity of institutional ownership

To eliminate the potential endogeneity concerns of institutional ownership, we further use an instrumental variables approach. The instrumental variables for IOt-1, TRAt-1, and DEDt-1 are the level of institutional holdings at quarter t-2 (IOt-2), share price at quarter t-1 (PRCt-1), Amihud liquidity measure at t-1 (Amihudt-1), skewness of residuals of Fama-French-Carhart four-factor model (Skew_FF4), and a set of past five-year returns augmented with RETt-2 as the marketadjusted cumulative return in the three-month period of t-2, RETt-3 as the market-adjusted cumulative return in the three-month period of t-3, similarly RETt-4, RETt-5, and RETt-6. A firststage estimation is a regression of IOt-1, TRAt-1, and DEDt-1 on instrumental variables and on a set of control variables. A second-stage regression is an estimation of future crash risk on fitted values of estimated IOt-1, TRAt-1, and DEDt-1 from the first-stage regressions and the control variables. Table VII shows that the coefficient on the predicted IOt-1 is positive and significant at the 1% level. The coefficients on the predicted TRAt-1, and DEDt-1 are positive and negative at the 1% level, respectively. The results are consistent with our main tests, suggesting that short-term institutional investors have a positive effect on crash risk.

Insert Table VII about here

Additional Analyses

In this section we perform three additional analyses. First, we study institutional investment horizon and trading behavior around earnings announcements. Second, we investigate whether investors' horizons influence corporate risk-taking behavior. Finally, we test whether our main results are sensitive to the current financial crisis.

Institutions' investment horizon and trading behavior around earnings announcements

Meeting short-term earnings goals induces institutions to buy or sell when there is unexpected earnings news. To better understand how unexpected earnings news affects institutions' trading strategies, we examine the relation between different institutional ownership and earnings surprises.

We obtain data on earnings surprises from the IBES and the CRSP daily database. The sample period spans fiscal years 1996 through 2009 (56 fiscal quarters). Following previous studies on post-earnings-announcement drift (e.g., [START_REF] Bernard | Evidence that Stock Prices Do not Fully Reflect the Implications of Current Earnings for Future Earnings[END_REF][START_REF] Bartov | Investor Sophistication and Patterns in Stock Returns after Earnings Announcements[END_REF][START_REF] Ke | Do Institutional Investors Exploit the Post-earnings Announcement Drift?[END_REF][START_REF] Zhang | Analyst Responsiveness and the Post-earnings-announcement Drift[END_REF], we define standardized unexpected earnings (SUE) as decile-adjusted unexpected earnings. To obtain SUE, we first calculate the firms' raw earnings surprises as the actual earnings per share minus the average of individual analyst forecasts, scaled by the standard deviation of individual analyst forecasts. Next, based on the sample distribution of earnings surprises by quarter, we sort the earnings surprises into ten deciles indexed from zero to nine, and then scale them by nine to obtain the decile-adjusted unexpected earnings. [START_REF] Ke | Do Institutional Investors Exploit the Post-earnings Announcement Drift?[END_REF] show that earnings surprises have a positive effect on transient institutional investors' ownership changes. To the extent that transient institutions have a shortterm focus, we expect positive and negative earnings surprises to have different impacts on institutions' holding and trading behavior.

Table VIII reports the regression results of the level and the change in different institutional ownership for positive and negative earnings surprises. In columns (1) to (3), we focus on the effect of positive and negative earnings surprises on institutions' holdings. In columns ( 4) to (6),

we examine the effect of positive and negative earnings surprises on institutions' trading behavior. Panel A reports aggregate institutions' holdings and trading behavior around earnings announcements. The coefficients on both SUEt-1 and positive SUEt-1 are significantly positive at the 5% and 10% level, respectively. But the coefficients on negative SUEt-1 are insignificant for both the level and change in institutions' holdings. This result suggests that, as a whole, institutions tend to hold their positions around earnings announcements, especially when there is good news.

Insert Table VIII about here

Panel B reports the results for short-term institutional investors' holding and trading behavior around earnings announcements. The coefficients on SUEt-1, positive SUEt-1, and negative SUEt-1 are positive and significant (at 1% level) for the level of short-term institutional holdings.

However, the coefficients on positive SUEt-1 and negative SUEt-1 are positive and significant at the 10 and 5% level, respectively, for short-term institutional investors' trading behavior. These results suggest that short-term institutional investors are more likely to hold their positions around earnings announcements and are also more likely to trade frequently around earnings announcements if there is either good or bad earnings news. Short-term institutional investors appear to overreact to the information they possess. Thus, meeting short-term earnings goals seems to be the main focus of short-term institutional investors.

Panel C considers long-term institutional investors' holding and trading behavior around earnings announcements. With respect to the level of and changes in long-term institutional holdings, the coefficients on SUEt-1, positive SUEt-1, and negative SUEt-1 are not statistically different from zero. This suggests that long-term institutional investors are less likely to trade around the earnings announcements.

Overall, the above findings suggest that earnings news could induce short-term investors to trade frequently, and that long-term investors can be liquidity providers for short-term investors.

Moreover, short-term institutional investors could induce managers to maximize short-run earnings at the expense of long-run shareholders' value.

Corporate risk-taking and investor horizon

We examine whether the presence of short-term institutional investors induces corporate risktaking behavior by managers. Following studies, such as, by [START_REF] John | Corporate Governance and Risk-Taking[END_REF] and [START_REF] Hilary | Does Religion Matter in Corporate Decision Making in America?[END_REF], we construct three different measures of corporate risk-taking behavior:

(1) StdROA1t, which we define as the standard deviation of quarterly ROA1 from quarter t to t+5, where ROA1 is the log of the ratio of net income to total assets lagged by one quarter; (2) StdROA2t, measured as the standard deviation of quarterly ROA2 from quarter t to t+5, where ROA2 is the log of the ratio of operating income before depreciation to total assets lagged by one quarter; (3) GROWTHt, which is the log of the ratio of market capitalization to book value of equity. Thus, StdROA1t and StdROA2t capture the total risk taken by the corporation and GROWTHt captures the expected growth rate of the corporation. We control for firm size, bookmarket-ratio, monthly detrended turnover, leverage and return on equity (ROEt-1) measured as the log of the ratio of net income to the value of equity lagged by one quarter.

The results, reported in Table IX, show that consistent with our prediction, the presence of shortterm institutional investors induces corporate-risk taking. The coefficients of IOs are positive for all the three proxies of corporate risk-taking but statistically insignificant for StdROA2t and GROWTHt. When we consider investment horizon, all coefficients on TRAs are positive and statistically significant at the 1% level in all regressions. Similarly, all coefficients for DEDs are negative and statistically significant at the 1% level except for StdROA2t, which is significant at the 10% level.

Insert Tables IX about here

To eliminate the concerns of of institutional ownership, we further conduct an instrumental variable two-stage estimation. The instrumental variables for IOt-1, TRAt-1, and DEDt-1 are the level of institutional holdings at quarter t-2 (IOt-2), share price at quarter t-1 (PRCt-1), Amihud liquidity measure at t-1 (Amihudt-1), skewness of residuals of Fama-French-Carhart four-factor model at quarter t-1 (Skew_FF4t-1), and a set of past five-year returns augmented with RETt-2 as the market-adjusted cumulative return in the three-month period of t-2, RETt-3 as the market-adjusted cumulative return in the three-month period of t-3, similarly RETt-4, RETt-5, and RETt-6. A first-stage estimation is a regression of IOt-1, TRAt-1, and DEDt-1 on instrumental variables and on a set of control variables. A second-stage regression is an estimation of future corporate risk-taking on fitted values of estimated IOt-1, TRAt-1, and DEDt-1 from the first-stage regressions and the control variables.

The results in Table X are qualitatively similar as in Table IX. In particular, the coefficients on the predicted TRAt-1 are statistically significant at the 1% level and positive with magnitude of 0.178, 0.121, and 0.357, respectively. The results support our prediction that the presence of short-term institutional investors induces corporate risk-taking behavior by managers.

Insert Table X for non-and financial crisis periods, is rejected at the 1% significant level, suggesting that there is significantly different impacts of non-financial crisis and financial crisis periods on crash risk.

Insert Table XI about here

Conclusions

Using negative skewness as a proxy for the probability of crashes, we document a positive relation between institutional investors holding and a stock's crash risk. More importantly, we find strong and robust evidence that the positive relation between institutional ownership and stock price crash is driven by transient (short-term) institutional investors, while the presence of dedicated (long-term) institutional investors is negatively related to stock price crash. We also report that short-term institutional investors' trading is positively related to negative skewness, indicating that short-term institutional investors overact to the information they possess. We also find that short-term institutional investors are positively related to corporate risk-taking behavior while long-term institutional investors are negatively associated with corporate risk-taking.

Our results are consistent the view that a firm is better off targeting and attracting long-term institutional investors [START_REF] Porter | Capital Choices: Changing the Way America Invests in Industry[END_REF][START_REF] Brancato | Institutional Investors and Corporate Governance: Best Practices for Increasing Corporate Value[END_REF][START_REF] Bushee | Identifying and Attracting the 'Right' Investors: Evidence on the Behavior of Institutional Investors[END_REF]. Even though short-term institutional investors are better informed and can better predict future stock returns (Yan and [START_REF] Zhang | Institutional Investors and Equity Returns: Are Short-term Institutions Better Informed?[END_REF], they tend to exacerbate a firm's stock return volatility [START_REF] Bushee | Corporate Disclosure Practices, Institutional Investors, and Stock Return Volatility[END_REF] and,

as we have shown, increase the likelihood of stock price crash risk. Note: This table presents an instrumental variable estimation result. The instrumental variables for IOt-1, TRAt-1, and DEDt-1 are the level of institutional holdings at quarter t-2 (IOt-2), share price at quarter t-1 (PRCt-1), Amihud liquidity measure at t-1 (Amihudt-1), skewness of residuals of Fama-French-Carhart four-factor model (Skew_FF4t-1), and a set of past five-year returns augmented with RETt-2 as the market-adjusted cumulative return in the three-month period of t-2, RETt-3 as the market-adjusted cumulative return in the three-month period of t-3, similarly RETt-4, RETt-5, and RETt-6. A first-stage estimation is a regression of IOt- Individual analyst forecasts quarterly earnings per share are from I/B/E/S while institutional ownership is obtained from Thomson Financial 13 File. The dependent variables IOt, TRAt, and DEDt, are as defined in Table I. ΔIOt, ΔTRAt, and ΔDEDt are changes in holdings of aggregate, short-and long-term investors, respectively. Independent variable SUEt is standardized unexpected earnings, defined as decileadjusted unexpected earnings. Positive SUEt is standardized unexpected earnings when the raw earnings surprises are positive. Negative SUEt is standardized unexpected earnings when the raw earnings surprises are negative. CAR [-1, 1] is the cumulative abnormal return around the earnings announcement, where zero represents the announcement date. All the other independent variables are as defined in Table I , and GROWTHt on investor horizon. The sample period is from the first quarter of 1981 to the fourth quarter of 2018 Corporate risk-taking variables are from COMPUSTAT while institutional ownership is obtained from Thomson Financial 13 File. The dependent variables are as follows. StdROA1t (StdROA2t) is the standard deviation of quarterly ROA1 (ROA2) from quarter t to t+5, where ROA1 is the log of the ratio of net income (COMPUSTAT Fundamentals Quarterly: Item IBQ) to total assets (Item ATQ) lagged by one quarter and ROA2 is the log of the ratio of operating income before depreciation (Item OIBDPQ) to total assets (Item ATQ) lagged by one quarter. GROWTHt is the log of the ratio of market capitalization to book value of equity (Item SEQQ). The independent ROEt-1 is quarterly return on equity defined as the log of the ratio of net income (Item IBQ) to the value of equity (Item SEQQ) lagged by one quarter. All the other independent variables are as defined in Table I. Continous variables are winsorized at their 1 st and 99 th percentiles. Robust t-statistics adjusted for clustering by firm are reported inside the parentheses and ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. 4) are for non-financial crisis periods whereas model ( 5) to (8) are for financial crisis periods of 1987, 1997, 2007, and 2008. Chow statistics for non-financial crisis and financial crisis groups corresponding to model (1) vs (5), model ( 2) vs (6), model (3) vs (7), and model ( 4) vs (8) are F(9, 457170)=14.95, F(9, 457170)=4.95, F(9, 457170)=7.40, and F(10, 457168)=5.92, respectively. The null hypothesis, where there is no structural break for non-and financial crisis periods, is rejected at the 1% significant level, suggesting that there is significantly different impacts of nonfinancial crisis and financial crisis periods on crash risk. Robust t-statistics adjusted for clustering by firm are reported inside the parentheses and ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

  TableIprovides summary statistics of the main variables used in our study. Panel A reports the mean, median, 25 th percentile, 75 th percentile, and standard deviations of all variables. For individual stocks, there is positive skewness at the 75 th percentile and negative skewness at the mean, median, and 25 th percentiles for all different measures of skewness. The average institutional ownership is 31.3%, which is similar to Yan and[START_REF] Zhang | Institutional Investors and Equity Returns: Are Short-term Institutions Better Informed?[END_REF] who report a 25.1%

	average institutional ownership for the period 1980-2003. On average, transient (short-term)
	institutional investors hold 6.18% of total shares outstanding and dedicated (long-term)
	institutional investors hold 5.0% of total shares outstanding. The monthly detrended turnover for
	the average firm is 0.35% comparable to the 0.1% reported by Chen, Hong and Stein (2001) for
	the period 1962-1998. All continuous control variables are winsorized at the 1 st and 99 th
	percentiles.

Panel B of Table

I

provides correlations between skewness, institutional ownership, and various firm characteristics. The aggregate institutional ownership (IO) is positively correlated with past skewness, firm size, leverage, detrended turnover, and past returns, but it is negatively related to past volatility and book-to-market ratio.

  about here5.3 Financial CrisesCrash risk could be more serious in the crisis period in comparison to the non-crisis period. Since our sample covers the 1987, 1997, and 2007-08 financial periods, it allows us to conduct additional tests of examining whether our main results are sensitive to the financial vs. non-financial crises periods. Models (1) to (4) in Table XI are for non-financial crisis periods whereas model (5) to(8)

	are for financial crisis periods of 1987, 1997, and 2007-08. Chow statistics for non-financial crisis
	and financial crisis groups corresponding to model (1) vs. (5), model (2) vs. (6), model (3) vs. (7),
	and model (4) vs. (8) are F(9, 457170)=14.95, F(9, 457170)=4.95, F(9, 457170)=7.40, and F(10,
	457168)=5.92, respectively. The null hypothesis of Chow tests, where there is no structural break

TABLE Descriptive Statistics

 Descriptive Note: This table summarizes the descriptive statistics and correlation coefficients between negative skewness, institutional ownership, and various firm characteristics. The sample period is from the first quarter of 1981 to the fourth quarter of 2018. Individual stock characteristics are from the CRSP while institutional ownership is obtained from Thomson Financial 13 File. SKEWt is the skewness, measured using daily raw returns (log changes in price), excess returns (SKEW_EXt), and market-adjusted returns (SKEW_MKt) in quarter t, respectively. SKEW_FF3t is idiosyncratic skewness of the residual of the regression, estimated using Fama-French threes factors model. IOt-1 is total institutional ownership.[START_REF] Bushee | The influence of Institutional Investors on Myopic R&D Investment Behavior[END_REF] classifications of investment horizons: Transient (TRAt-1), dedicated (DEDt-1), and quasi-indexers (QIXt-1). Institutional investors are categorized as

	Panel B: Correlation
	VARIABLES	SKEWt	SKEW_EXt	SKEW_MKt	SKEW_FF3t	IOt-1	TRAt-1		DEDt-1		QIXt-1
	Panel A: Variables statistics SKEW_EXt 1.0000
	SKEW_MKt	0.9633	0.9633
	VARIABLES SKEW_FF3t	0.9644	Mean 0.9644	Median 0.9665	25 th Percentile	75 th Percentile	Standard Dev.
	SKEWt IOt-1	0.0609	-0.0375 0.0609	-0.1326 0.0568	0.0554	-0.5636		0.2673			1.3927
	SKEW_EXt TRAt-1	0.0606	-0.0375 0.0607	-0.1325 0.0622	0.0594	-0.5636 0.6261		0.2673			1.3927
	SKEW_MKt DEDt-1	0.0065	-0.0372 0.0065	-0.1345 0.0089	0.0064	-0.5609 0.4109	0.2197	0.2618			1.3883
	SKEW_FF3t QIXt-1	0.0435	-0.0544 0.0435	-0.1437 0.0383	0.0391	-0.5681 0.7719	0.4999	0.2502	0.2094		1.3538
	IOt-1 SIOt-1 TRAt-1 LIOt-1 DEDt-1 SKEWt-1 QIXt-1 VOLATILITYt-1 SIZEt-1	0.0675 0.0279 0.1715 -0.0388 0.1106	0.3126 0.0675 0.0618 0.0279 0.0498 0.1715 0.1467 -0.0388 0.1106	0.2424 0.0642 0.0123 0.0237 0.0011 0.1575 0.0629 -0.0383 0.0999	0.0624 0.0235 0.1631 -0.0319 0.0994	0.0181 0.9157 0.0000 0.6436 0.0000 0.0579 0.0000 -0.2231 0.5108	0.5340 0.0898 0.0705 0.2321 -0.0261 0.6927 0.256 0.0429 0.2461	0.3179 0.3822 0.0095 -0.0803 0.0758		0.7076 0.2989 0.4271 0.0979 0.0464 0.0881 -0.1931 0.1936 0.402
	SIOt-1 LEVERAGEt-1	-0.0115	0.1656 -0.0115	0.1015 -0.0144	-0.0131	0.0005 0.0418	0.0171	0.2761	0.0129		0.1863 0.0125
	LIOt-1 DTRUNOVERt-1	0.0267	0.0597 0.0267	0.0383 0.0252	0.0237	0.0002 0.0541	0.0639	0.0912	-0.005		0.0775 0.0393
	SKEWt-1 BKMKTt-1	-0.0733	-0.0295 -0.0733	-0.1328 -0.0645	-0.063	-0.5610 -0.134	-0.118	0.2660	-0.0167		1.3727 -0.1195
	VOLATILITYt-1 RETt-1	0.0681	0.0343 0.0681	0.0268 0.0664	0.0574	0.0176 0.0155	0.013	0.0425	0.0122		0.0259 0.0025
	SIZEt-1	SIOt-1	19.1322 LIOt-1	19.0153 SKEWt-1	VOLATILITYt-1	17.6037 SIZEt-1	20.5402 LEVERAGEt-1 DTRUNOVERt-1	2.0567 BKMKTt-1
	LEVERAGEt-1 LIOt-1	0.4037	0.1778	0.1320		0.0168		0.2860			0.1802
	DTRUNOVERt-1 SKEWt-1 BKMKTt-1 VOLATILITYt-1 RETt-1 SIZEt-1 LEVERAGEt-1	0.061 -0.1664 0.4632 0.0458	0.0035 0.0286 0.7388 -0.1875 -0.0110 0.3665 0.0282	-0.0016 0.5923 0.1178 0.0063 0.0888 -0.0086	-0.4321 -0.062	-0.0162 0.3420 -0.1263 0.0944		0.0139 0.9299 0.1263			0.1037 0.6572 0.2903
	DTRUNOVERt-1	0.0643	0.0166	0.0102	0.0993	0.0561	0.0074
	BKMKTt-1	-0.1574	-0.0669	-0.0211	0.0709	-0.2924	0.0036		-0.054
	RETt-1	0.0192	0.0053	-0.3594	-0.1412	0.0884	-0.0129	0.1435		-0.1452

VII Influence of Investor Horizon on Skewness: An Instrumental Variables Approach

		(1)	(2)	(3)	(4)
	VARIABLES	SKEWt	SKEWt	SKEWt	SKEWt
	Predicted IOt-1	0.084***
		(8.01)
	Predicted TRAt-1		0.741***		0.810***
			(23.22)		(24.63)
	Predicted DEDt-1			-0.408***	-0.286***
				(-5.30)	(-7.55)
	SKEWt-1	0.218***	0.217***	0.219***	0.217***
		(39.56)	(39.29)	(39.76)	(39.32)
	VOLATILITYt-1	-0.661***	-0.768***	-0.796***	-0.839***
		(-4.75)	(-5.49)	(-5.69)	(-5.99)
	SIZEt-1	0.043***	0.041***	0.049***	0.041***
		(26.73)	(27.04)	(32.38)	(27.63)
	LEVERAGEt-1	-0.126***	-0.126***	-0.125***	-0.126***
		(-9.86)	(-9.82)	(-9.76)	(-9.83)
	DTRUNOVERt-1	0.016	-0.013	0.023	-0.016
		(0.61)	(-0.55)	(0.87)	(-0.66)
	BKMKTt-1	-0.054***	-0.046***	-0.052***	-0.044***
		(-11.66)	(-9.98)	(-11.52)	(-9.77)
	RETt-1	0.685***	0.683***	0.684***	0.682***
		(47.14)	(47.06)	(47.01)	(47.06)
	INTERCEPT	-0.827***	-0.808***	-0.887***	-0.814***
		(-21.13)	(-20.67)	(-22.92)	(-21.00)
	Quarter Effects	Yes	Yes	Yes	Yes
	N	440,970	440,970	440,970	440,970
	R 2	0.068	0.069	0.067	0.069

TABLE VIII Institutions' and Trading Behavior around Earnings Announcement

 VIII This table presents the results of regressions of the level and the change in investor horizons on the earnings surprises. The sample period is from the first quarter of 1996 to the fourth quarter of 2018.

	SIZEt-1		-0.038***	-0.031***	-0.002***	-0.002***	-0.001*
		(-20.79)	(-20.19)	(-16.32)	(-4.20)	(-3.17)	(-1.85)
	BKMKTt-1	-0.070***	-0.081***	-0.054***	0.009***	0.011***	0.007*
	(1) (-6.92) IOt 0.083*** (4.61) Panel A: Institutional ownership VARIABLES CAR[-1,1]t SUEt-1 0.025** CAR[-1,1]t-1 0.088*** (2.49) (4.45) Positive SUEt-1 RETt-1 0.016*** (3.10) Negative SUEt-1 INTERCEPT 0.850*** (19.51) SUEt-2 0.025*** Quarter Effects Yes (2.71) N 13,921 SUEt-5 0.022** R 2 0.234 (2.04) Panel C: Institutional ownership by long-term institutional investors (2) (3) (-6.21) (-5.96) IOt IOt 0.080*** 0.057** (3.41) (2.09) 0.101*** 0.065** (4.03) (2.16) 0.022* 0.035*** -0.009 (1.89) (4.83) (-1.08) -0.013 0.921*** 0.766*** (-0.84) (19.28) (15.53) 0.033*** 0.012 Yes Yes (2.75) (0.80) 8,606 5,315 0.024* 0.016 0.241 0.234 (1.89) (1.10) SIZEt-1 -0.028*** -0.032*** -0.023*** VARIABLES DEDt DEDt DEDt (-4.59) (-4.87) (-3.35) SUEt-1 -0.002 BKMKTt-1 -0.095*** -0.103*** -0.083*** (-0.81) (-3.36) (-3.40) (-2.67) Positive SUEt-1 -0.000 CAR[-1,1]t 0.103*** 0.124*** 0.059 (-0.01) (2.77) (2.61) (1.01) Negative SUEt-1 -0.001 CAR[-1,1]t-1 0.087** 0.065 0.094 (-0.30) (2.17) (1.24) (1.53) SUEt-2 0.001 0.002 0.000 RETt-1 0.023* 0.024 0.012 (0.31) (0.48) (0.01) (1.89) (1.56) (0.61) SUEt-5 0.002 0.001 0.003 INTERCEPT 1.277*** 1.158*** 1.197*** (0.77) (0.35) (0.86) (8.41) (6.74) (7.34) SIZEt-1 -0.007*** -0.008*** -0.006*** Quarter Effects Yes Yes Yes (-7.61) (-7.64) (-5.43) N 14,350 8,811 5,539 BKMKTt-1 -0.010* -0.012 -0.009 R 2 0.191 0.186 0.217 (-1.73) (-1.64) (-1.29) CAR[-1,1]t 0.018* 0.014 0.024 Panel B: Institutional ownership by short-term institutional investors VARIABLES TRAt TRAt TRAt (1.82) (1.14) (1.48) SUEt-1 0.017*** CAR[-1,1]t-1 -0.000 -0.007 0.007 (4.88) (-0.04) (-0.52) (0.43) Positive SUEt-1 0.016*** RETt-1 0.011*** 0.013*** 0.009 (3.20) (3.61) (3.63) (1.58) Negative SUEt-1 0.016*** INTERCEPT 0.181*** 0.197*** 0.160*** (2.83) (7.19) (7.10) (5.40) SUEt-2 0.025*** 0.025*** 0.024*** Quarter Effects Yes Yes Yes (7.87) (5.54) (4.58) N 13,921 8,606 5,315 SUEt-5 0.022*** 0.024*** 0.019*** R 2 0.150 0.169 0.129 Note:	(4) (4.29) ΔIOt 0.041*** (3.98) 0.034*** 0.003 (2.81) (0.78) 0.005 (1.59) 0.039*** (3.75) -0.004 Yes (-1.44) 13,921 -0.001 0.058 (-0.23) -0.001 ΔDEDt (-1.46) -0.002 -0.002 (-0.94) (-0.59) 0.028** (2.24) 0.046*** (3.63) -0.000 0.009*** (-0.03) (2.89) 0.004*** 0.050*** (2.78) (3.27) 0.001*** Yes (3.99) 14,350 0.002 0.074 (1.58) 0.001 ΔTRAt (0.14) -0.011 0.000 (-1.55) (0.11) 0.007*** (3.39) -0.027*** (-2.92) -0.004** Yes (-2.28) 13,921 -0.002 0.145	(5) (3.63) ΔIOt 0.035*** (2.61) 0.035** (2.42) 0.010** -0.000 (2.40) (-0.10) 0.005 (0.17) -0.004 Yes (-1.12) 8,606 -0.002 0.059 (-0.51) -0.001 ΔDEDt (-1.24) 0.001 (0.27) -0.003 0.035** (-1.16) (1.98) 0.031** (2.07) 0.000 0.008** (0.05) (1.99) 0.004** -0.012 (2.22) (-0.32) 0.001** Yes (2.02) 8,811 0.004 0.065 (1.58) 0.002 ΔTRAt (0.28) -0.014 (-1.50) 0.008*** 0.005* (3.04) (1.92) -0.036*** (-2.84) -0.005* Yes (-1.86) 8,606 -0.004* 0.154	(1.93) (6) 0.040** ΔIOt (2.48) 0.031* (1.69) -0.001 (-0.24) 0.011 0.007 (0.45) (1.41) Yes -0.003 5,315 (-0.75) 0.064 0.002 (0.44) ΔDEDt -0.001 (-0.62) -0.004 (-0.80) 0.010 0.003 (0.51) (0.97) 0.065*** 0.001 (2.94) (0.31) 0.011** 0.005* (2.15) (1.76) 0.007 0.001* (0.33) (1.91) Yes -0.000 5,539 (-0.12) 0.100 0.000 (0.03) ΔTRAt -0.002 (-0.15) 0.008** (2.45) -0.029** 0.007** (-2.14) (2.23) Yes -0.005* 5,315 (-1.70) 0.141 0.001
		(5.91)	(4.79)	(3.66)	(-1.05)	(-1.78)	(0.47)

  . Robust t-statistics adjusted for clustering by firm are inside the parentheses and ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. This table presents the results of regressions of corporate risk-taking measured as StdROA1t, StdROA2t

				IX
		Corporate Risk-taking and Investor Horizon
		(1)	(2)	(3)	(4)	(5)	(6)
	VARIABLES	StdROA1t	StdROA2t	GROWTH
	IOt-1	0.018*		0.002		0.006
		(1.71)		(0.27)		(0.56)
	TRAt-1		0.142***		0.093***		0.334***
			(6.22)		(5.67)		(13.00)
	DEDt-1		-0.043*		-0.047***		-0.130***
			(-1.80)		(-2.78)		(-5.75)
	SIZEt-1	-0.044***	-0.044***	-0.037***	-0.038***	0.020***	0.018***
		(-27.82)	(-29.94)	(-33.00)	(-35.89)	(9.47)	(9.05)
	LEVERAGEt-1	0.356***	0.357***	-0.021*	-0.021*	-0.083***	-0.084***
		(22.09)	(22.11)	(-1.91)	(-1.89)	(-4.80)	(-4.86)
	DTRUNOVERt-1	0.112***	0.099***	0.091***	0.082***	0.241***	0.210***
		(6.54)	(5.81)	(7.38)	(6.73)	(10.05)	(9.63)
	BKMKTt-1	-0.035***	-0.032***	-0.018***	-0.016***	-0.997***	-0.991***
		(-7.55)	(-6.92)	(-5.03)	(-4.48)	(-82.06)	(-81.85)
	ROEt-1	-0.124***	-0.124***	-0.022***	-0.022***	0.122***	0.122***
		(-64.06)	(-64.12)	(-14.47)	(-14.48)	(40.75)	(40.74)
	INTERCEPT	0.866***	0.867***	0.983***	0.992***	1.198***	1.235***
		(25.23)	(26.30)	(39.60)	(41.46)	(27.23)	(29.79)
	Quarter Effects	Yes	Yes	Yes	Yes	Yes	Yes
	N	257,907	257,907	239,307	239,307	270,610	270,610
	R 2	0.124	0.124	0.065	0.066	0.695	0.697
	Note:

In this study, we use transient institutional investors to proxy for the short-term institutional investors and dedicated institutional investors to proxy for the long-term institutional investors. Our inference, however, still remains if we classify both dedicated and quasi-indexing institutional investors as the longterm institutional investors as in[START_REF] Giannetti | Adapting to Radical Change: The Benefits of Short Horizon Investors[END_REF].

is defined as the logarithm of the ratio of the variance from the daily returns below the sample mean to the variance from the daily returns above the sample mean. Extreme return ratio (EXTRATIO) is defined as the logarithm of the ratio of the number of negative extreme return days to the number of positive extreme return days, where a daily return is defined as the negative (positive) extreme return if the daily stock return is less (larger) than twice of its standard deviation. All of the other variables are as defined in Table I. Robust t-statistics adjusted for clustering by firm are reported inside the parentheses and ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.


H Larroque

E Dugas

F Woloszyn

F Plisson-Petit

J Gayraud

J.-C Duchêne

J Frayssignes

A Gille

S Brenot

D Allain

L Drouilhet
email: laurence.drouilhet@inrae.fr

Genetic determinism of quality of lamb leather in Lacaune dairy sheep breed

Lacaune lamb skins are well known for their high quality and their use in the leather industry and luxury ready-to-wear clothing. In France, for several decades, the quality of this leather has deteriorated, mainly due to two skin defects: pinhole and straw-like bushiness. To explore the genetic determinism of these defects, around 1400 Lacaune lambs, from 72 rams, were phenotyped for the 2 skin defects and 4 wool traits. The pinhole defect showed a high heritability (0.59 ± 0.07) and was highly genetically correlated to the wool predicted core bulk (rg = 0.54 ± 0.08). The straw-like bushiness defect had a lower heritability (0.25 ± 0.06), and was correlated to the mean fiber diameter (rg = 0.41 ± 0.15). A genome wide association study revealed for the pinhole defect one major signal on chromosome 3 close to a cluster of keratin genes, known to play an important role in skin development.

Introduction

Lamb skins are generally considered to be a by-product of sheep production and genetic studies of traits directly related to sheep skin are infrequent in the literature. The Lacaune sheep milk breed is bred in south of France, in the Roquefort cheese production area. Lacaune lamb skins are well known for their high quality and their use in the leather industry and luxury ready-to-wear clothing. Therefore, historically, a large majority of French tanneries was located in the same geographical region. However, for several decades, a degradation of this leather quality was observed, mainly due to the presence of two skin defects: pinhole and straw-like bushiness. Pinhole consists of small holes in the grain surface of the dewooled pelt, which decreases the leather quality. The straw-like bushiness defect is defined as perforations due to the presence of forage debris planted in the thickness of the dermis. In the present study, we explored the genetic determinism for these two main skin defects and their possible relationship with wool traits.

Materials & Methods

Animals and phenotypes.

Around 1400 Lacaune lambs (from 72 rams) were born around February 2020 and 2021 (in 16 different farms) and identified at birth with electronic ear tags. At around 35 days-old lambs were brought together in a unique fattening farm. Around 60 days-old, a blood and a wool samples of each animal were collected (they were carried out in the fattening herd within the framework of the usual practices of breeding by authorized persons from OVI-test AI organization). After an average fattening period of 79 days, animals were slaughtered and their skins were sent to the ALRIC tannery to proceed to the tanning. Each skin was identified in order to be assigned to one lamb. The two defects were scored at the end of the tanning procedure: skin pinhole score (from 0=absent to 4=abundant corresponding to several heavily marked regions), and straw-like bushiness score (from 0=absent to 2=abundant). Wool samples were analysed by Optical Fibre Diameter Analyser (OFDA) for determining mean fibre diameter (MFD, µ), coefficient of variation of fibre diameter (CVD, %) and mean fibre curvature (MFC, °/mm). Predicted core bulk (PCB), expressed in cm3/g, represents the occupied volume by 1 gram of wool under a pressure of 10 -3 N [START_REF] Sumner | [END_REF]. At the end, 1379 lambs were phenotyped for wool traits, 1166 for leather defects, and 1158 for both (Table 1).

SNP genotypes.

DNA of a subset of 1248 animals was extracted from blood sample and genotyped using the Illumina OvineSNP50 BeadChip. Genomic analyses were performed using 1248 phenotyped and genotyped lambs, plus their 72 rams' genotypes (available from a previous project). SNP quality was checked (i.e. SNP call rates >95%, minor allele frequency >5%, inconsistent for Hardy-Weinberg disequilibrium). Finally, 38,439 autosomal SNPs were retained and 14 lambs failed QC and were removed.

Statistical analysis.

Variance-covariance components were estimated with the airemlf90 software [START_REF] Misztal | Proc. of the 7 th WCGALP[END_REF] using pedigree information (8575 animals over 6 generations) combined with SNPs information. Heritabilities were estimated with linear single trait animal models, and genetic correlations with linear bivariate animal models. For leather defects, the models included environmental fixed effects of birth herd (16 levels), year*housing condition during the fattening period (3 levels), litter size (2 levels: 1, 2 or more), duration of fattening period (as co-variable, 79d ± 15.71) and a random additive genetic animal effect. For wool traits, the models were the same excepting for duration of fattening period replaced by the age at sampling (61d ± 12.07). Genome wide association analysis (GWAS) was performed using GEMMA software (Zhou and Stephens, 2012) using phenotypes adjusted for environmental effects defined above with the procedure GLM of SAS software, the threshold of significance was at the genome level (probability of 0.05 divided by the number of SNP used).

Results

Phenotypes characteristics of leather and wool traits are presented in Table 1. The mean of the pinhole score was 1.47, and 25% of skins did not express the defect whereas the percentages of skins classified of 1 to 4 were of 28%, 27%, 17% and 4% respectively. For Straw-like bushiness, the mean was 0.86, and 45% of skins showing no defect, whereas 27% presented this defect with a low density (score 1) and 28% with a high density (score 2). Straw-like bushiness presented a moderate estimated heritability of 0.25, whereas the other traits had a high estimated heritability ranging from 0.48 for MFD to 0.61 for MFC (Table1). Phenotypic and genetic correlations estimated between each leather defect and wool traits are shown in Figure 2. The two leather defects showed low to moderate phenotypic correlations with wool traits and reach 0.33 and 0.17 with PCB (for pinhole and straw-like bushiness defects respectively). Although the two defects were genetically highly correlated (0.66 ± 0.11), their correlations with wool traits were somewhat different. Pinhole defect presented a strong positive genetic correlation with MFC (0.38 ± 0.09) and PCB (0.54 ± 0.08). Lambs with high MFC or high PCB wool had genetically a skin with more pinhole defects. Strawlike bushiness defect showed a moderate positive genetic correlation with PCB (0.31 ± 0.14) and a higher genetic correlation with MFD (0.41 ± 0.15). The two defects were related to PCB.

Figure 1. Phenotypic and genetic correlations between leather defects and wool traits

(rp: phenotypic correlation, rg: genetic correlation, vertical line: approximated standard error).

Genome wide association studies were performed on 1147 animals for the pinhole defect and the straw-like bushiness defect. A significant signal was detected at the genome-wide threshold for the pinhole defect, localized on OAR3 (Figure 2). We identified the most significant SNP at a position of 146.2 Mb on the ovine reference genome (Oar_rambouillet_v1.0). Considering the significant region at the chromosome-wide threshold, it encompasses 4Mb and the cluster of Keratin genes localised between 143-144 Mb. In contrast, no significant signal was found for the straw-like bushiness defect.

Discussion

A defect similar to the pinhole defect was studied in New-Zealand and Australia. We found an estimation of pinhole heritability of 0.59, which was close to the estimation of 0.6 found by [START_REF] Campbell | Proc. of AAABG[END_REF] on fewer animals. We have found a high genetic correlation (0.54) between pinhole defect and PCB, a higher level of wool PCB being related to a higher presence of pinhole defect. This can be related to previous studies carried out in New-Zealand, where a phenotypic positive relationship was found between the presence of the pinhole defect and PCB [START_REF] Scobie | Proc. of NZSAP[END_REF][START_REF] Cooper | Proc. of NZSAP[END_REF]. In these previous studies, high PCB was related to a more pronounced curvature of fibers. These authors proposed that when removing the fleece, fibers that have a high curvature remove part of the collagen matrix at the same time, affecting the skin [START_REF] Scobie | Proc. of NZSAP[END_REF].

No data on genetic parameters for the straw-like bushiness defect are available in literature. Its heritability estimation was 0.25, suggesting a large influence of the environment on this skin defect. Length of the fattening period (highly correlated with age at slaughter) seemed to influence the presence of this defect. Indeed, lambs with a longer fattening period had more straw-like bushiness defects, which could be linked to a longer exposition of their skin to the plant debris.

Our genetic study led to the identification of a region nearby the cluster of Keratin genes localized on OAR3. Keratin genes is a family of structural fibrous proteins also known as scleroproteins. It is the key structural material making up scales, hair, nails, feathers, horns, claws, hooves, and the outer layer of skin among vertebrates. Keratin also protects epithelial cells from damage or stress. Sequencing of finely chosen animals is currently on-going to identify one or several candidate mutations in our region of interest.

For pinhole defect, its high heritability, its strong genetic correlation with a wool trait measurable on live animals and the detection of a QTL affecting it, indicate that a genetic selection could be considered in order to decrease its frequency in Lacaune breed. However, additional studies must be conducted in order to better understand its genetic determinism and to evaluate its relationship with traits currently selected in this dairy sheep breed.

Figure 2 .

 2 Figure 2. Manhattan plot for GWAS of pinhole defect. This plot is based on -log 10 (P value) from GWAS against chromosome position, each colour represents different chromosome. Red line indicates genome-wide significant threshold.

Table 1 . Summary of phenotypes used for estimation of genetic parameters and heritability estimations.

 1

		Records	Mean	Std	Heritability	SE 1
	Pinhole	1166	1.47	1.14	0.59	0.07
	Straw-like bushiness	1166	0.86	0.84	0.25	0.06
	MFD	1379	19.31	2.02	0.48	0.07
	CVD	1379	25.75	5.85	0.51	0.06
	MFC	1379	93.45	10.38	0.61	0.06
	PCB	1379	28.02	1.43	0.57	0.06

1

: approximated standard error of heritability

Acknowledgements

This project has received funding from Occitanie region's Readynov program "L'A-PROPOS".