一、主讲学生与论文题目：
1. 安苏伟（2018级博士生）：Relative Performance Evaluation and Stock Price Crash Risk
2. 毕嘉（2016级博士生）：Alternative lottery measure and cross-sectional returns
3. 逯晓玮（2016级博士生）：Litigation Risk, Political Connections and Corporate Performance
4. 陈媛先（2018级博士生）：Borrowing Decisions and Future Financing Opportunities
二、时间：2021年10月23日（周六）下午14:00-16:30
三、地点：腾讯会议
四、点评与讨论教师：
朱一峰 bwin必赢唯一官方网站 副教授
王盈 bwin必赢唯一官方网站 助理教授
丁娜 bwin必赢唯一官方网站 助理教授
五、主持人：朱一峰 bwin必赢唯一官方网站 副教授
六、论文摘要：
1. Relative Performance Evaluation and Stock Price Crash Risk
We study how RPE utilization affects stock price crash risk. Using a sample of 22,776 U.S. listed firms from 2006 to 2017, we find RPE can decrease the probability of stock price crash risk, and the effect can attribute to market-based performance metrics and relative benchmarks of self-selected peers. The RPE-crash risk relation mainly manifests in firms with higher managerial ability, lower industry concentration, and higher managerial myopia. We further illustrate the analyst coverage as the primary channel relative to the industry tournament incentive, emphasizing the role of analyst pressure. Finally, we demonstrate that RPE firms are associated with higher information disclosure quality and lower analyst optimism.
2. Alternative lottery measure and cross-sectional returns
We construct a new lottery measure (ALM) to evaluate the lottery preference feature of stocks. The new measure is different from the common used lottery proxies: maximum daily return (MAX) and skewness (SKEW). In the U.S. stock market, the relationship between the ALM and expected returns is negative and it can be explained by MAX. Additionally, the negative predictability of ALM is significant and cannot be explained by other controls when the investor sentiment is high. While no significant relationship has been detected between the ALM and the stock returns during periods of low investor sentiment.
3. Litigation Risk, Political Connections and Corporate Performance
This study first document models that measure litigation risk in China and to benchmark these models against the industry measure widely used in the literature. While the industry measure alone does a relatively poor job of predicting litigation, supplementing this variable with measures of firm capital market characteristics (such as stock return, and stock volatility) and political connection factor considerably improves predictive ability. The additional variables such as those that proxy for firms’ managerial opportunism and governance quality don’t add much to litigation risk predictive ability. Moreover, I examine the effect of litigation risk in shaping corporate performance, and document a negative relationship between litigation risk and corporate performance. I further examine the mediating effect of political connection factor on the relationship between litigation risk and corporate performance, finding that having political connections to government could mitigate the negative impacts caused by litigation risk on corporate performance.
4. Borrowing Decisions and Future Financing Opportunities
We study borrowers' decisions related to their future financing opportunities. We measure borrowers’ future financing opportunities by two proxy variables: their income and their observed overall financing difficulty. We find that low-income borrowers and those aware of the overall difficulty of financing in the credit market increases are more likely to borrow more (in terms of percentage of their income). They are also willing to accept higher interest rates. Our findings help interpret several patterns reported in the empirical literature, such as debt puzzle in the low-income group.
