Management equity incentives and stock price crash risk: “Golden handcuffs” or “gold watch”

Abstract

This paper expands the previous research on management equity incentives (MEIs) and stock price crash risk by distinguishing between the "gold watch" region and the "golden handcuff" regions in MEIs. By using an estimation of the gold watch region and the golden handcuff regions based on 6,675 annual observations of China’s A-share listed companies, the stock price crash risk is found to be negatively correlated with MEIs in the golden handcuff regions (0–10%, 30%-100%) and is positively correlated with MEIs in the gold watch region (10%-30%). A further investigation of the mediating effects of peer effects on MEIs and the stock price crash risk reveals that peer effects have a partial mediation effect at the level of peer managers’ shareholding and mediate the relationship between MEIs and the stock price crash risk.

Equity incentive contract characteristics and company operational performance—An empirical study of Chinese listed companies

“…We further specified formula (3) to analyze the effect of the independent variables on the ROA of specific individual years from t to t + 2.…”

“…Past literature has determined the validity or invalidity of the equity incentive mechanism from various perspectives, combined with empirical analysis. For example, research finds that equity incentives are negatively correlated to the stock price crash risk in the golden hand cuff regions, while equity incentives are positively correlated to the stock price crash risk in the golden watch region [ 3 ]. In addition, research reveals that high equity incentives could strengthen CEOs’ degree of earnings management activities [ 4 ].…”

Expression of Concern: Management equity incentives and stock price crash risk: “Golden handcuffs” or “gold watch”

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The PLOS ONE Editors After this article [1] was published, concerns were raised that aspects of [1] are similar to preexisting but unpublished work by another research group, and were published without the appropriate attributions and permissions.In addition, concerns were raised about some aspects of the methodology in [1]. Specifically, that the data in S1 Data indicate that the managerial ownership consists of positive numbers, but that the code multiplied by -1 for each region of managerial ownership when the regions were divided into three distinct groups.In response to queries about the methodology, the corresponding author stated that the results and hypothesis development/theoretical framework in [1] are correct because the two explained variables have a negative skewness problem. The corresponding author also stated that the method [2] used to divide mos into mos_l, mos_m and mos_h in [1] addresses cases where the explained variable has positive skewness, and to ensure the skewness of the explanatory variable agrees with that of the explained variable, mos_l, mos_m and mos_h were multiplied by-1 when calculating intervals, but not for regression.

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“…Specifically, that the data in S1 Data indicate that the managerial ownership consists of positive numbers, but that the code multiplied by -1 for each region of managerial ownership when the regions were divided into three distinct groups.In response to queries about the methodology, the corresponding author stated that the results and hypothesis development/theoretical framework in [1] are correct because the two explained variables have a negative skewness problem. The corresponding author also stated that the method [2] used to divide mos into mos_l, mos_m and mos_h in [1] addresses cases where the explained variable has positive skewness, and to ensure the skewness of the explanatory variable agrees with that of the explained variable, mos_l, mos_m and mos_h were multiplied by-1 when calculating intervals, but not for regression. The corresponding author further noted that mos is a static variable, so the variable mei was used in [1]; mei is close to equal to mos in numerical terms, but provides a preferable theoretical explanation of why mei_l, mei_m and mei_h are inverted.…”

“…The corresponding author also stated that the method [2] used to divide mos into mos_l, mos_m and mos_h in [1] addresses cases where the explained variable has positive skewness, and to ensure the skewness of the explanatory variable agrees with that of the explained variable, mos_l, mos_m and mos_h were multiplied by-1 when calculating intervals, but not for regression. The corresponding author further noted that mos is a static variable, so the variable mei was used in [1]; mei is close to equal to mos in numerical terms, but provides a preferable theoretical explanation of why mei_l, mei_m and mei_h are inverted. An expert Academic Editor reviewed the methodology and S1 Data file and stated that if multiplication by -1 was done when calculating intervals, the methodology in [1] is adequate.The complainants' institution investigated the concern that aspects of [1] overlap with preexisting work, and were published without the appropriate attributions and permissions.…”