报告题目：Trading Volume Alpha

报 告 人：Yinan Su（Johns Hopkins University）

报告时间：2024年6月19日（周三）9:00-10:30

报告地点：ZOOM平台在线交流（会议ID： 950 1081 9321）

ZOOM App下载链接：//zoom.us/download, 亦可点击以下链接直接参加会议：//ucincinnati.zoom.us/j/95010819321

主办单位：bet365中国备用网址

【报告人简介】

Yinan Su is an Assistant Professor of Finance at Johns Hopkins University Carey Business School. His primary research fields are asset pricing, financial machine learning, and financial econometrics. His recent research focuses on risk and return modeling, portfolio optimization, and price impacts, leveraging advanced techniques such as neural networks, transfer learning, spectral analysis, and news textual analysis. Yinan Su's research has been published in top academic journals in finance and econometrics, including the Journal of Financial Economics, the Review of Financial Studies, and the Journal of Econometrics. His contributions have earned notable recognition, including the Fama/DFA Prize for Best Paper in Capital Markets and Asset Pricing, awarded by the Journal of Financial Economics in 2019. Yinan Su holds a PhD in Financial Economics from the University of Chicago (Booth and Econ Joint Program), and a BA in Economics and Finance from Tsinghua University.  

【内容摘要】

Portfolio optimization chiefly focuses on risk and return prediction, yet implementation costs also play a critical role. Predicting trading costs is challenging, however, since costs depend endogenously on trade size and trader identity, thus impeding a generic solution. We focus on a key, yet general, component of trading costs that abstracts from these challenges – trading volume. Individual stock trading volume is highly predictable, especially with machine learning. We model the economic benefits of predicting stock volume through a portfolio framework that trades off portfolio tracking error versus net-of-cost performance – translating volume prediction into net-of-cost portfolio alpha. We find the benefits of predicting individual stock volume to be substantial, and potentially as large as those from stock return prediction.

撰稿：赵鹏辉  审核：史永东  单位：bet365中国备用网址