报告题目1：Employee Sentiment and Stock Returns
报告题目2：Stock Option Predictability for the Cross-Section
报 告 人：周国富（Guofu Zhou）
报告地点：ZOOM平台在线交流（会议ID：919 4888 4614 ；会议密码：288120）
Guofu Zhou is Frederick Bierman and James E. Spears Professor of Finance at Olin Business School of Washington University in St. Louis. He has a BS degree from Chengdu College of Geology, China, and a PhD in economics from Duke University. Prior to his PhD studies, he was interested in mathematics with publications in number theory, function theory, and numerical solutions to partial differential equations. After his PhD, he has been working at Washington University since 1990, conducting research in finance in a number of asset pricing areas with numerous publications in Journal of Financial Economics, Review of Financial Studies, Journal of Financial and Quantitative Analysis, and Journal of Finance, as well as in industry journals such as Journal of Portfolio Management and Financial Analyst Journal. He has won awards for teaching MBA and MSF students and for his research.
His current research interests are primarily in big data and machine learning with innovations applicable to finance. His recent works (with co-authors) include exploring limitations and extensions of factor models, constructing macro factors, trend factors, lottery factors, and information factors to explain cross-section of stock returns and corporate bond returns, and proposing combination Lasso to best select firm characteristics for forecasting expected asset returns.
报告1：We propose an employee sentiment index, which complements investor sentiment and manager sentiment indices, and find that high employee sentiment predicts a subsequent low market return, significant both in- and out-of-sample. The predictability can also deliver sizable economic gains for mean-variance investors. The employee sentiment’s impact is stronger among employees who work in the headquarters state and among less experienced employees. The economic driving force of the predictability is distinct: high employee sentiment leads to high contemporaneous wage growth due to immobility, which in turn results in subsequently lower firm cash flow and lower stock return.
报告2：We provide the first comprehensive analysis of the information content from options markets for predicting the cross-section of stock returns. We jointly examine an extensive set of firm characteristics and an exhaustive set of option predictors, filling the void between two largely
disjoint literatures. Using both portfolio sorts and machine learning methods, we find that options have strong predictive power for the cross-section of returns after controlling for firm characteristics. A structural analysis shows that the strongest predictors are associated with tail risk premia and leverage. Our findings imply that these risks are estimated more accurately from options data,providing annualized Sharpe ratios in excess of 1.5.
程航，东北财经大学应用金融与行为科学学院讲师，2019年获得东北财经大学金融工程学博士学位。研究领域为资产定价、风险管理、金融科技。已经在Pacific-Basin Finance Journal、系统工程理论与实践发表多篇论文。