【学术通知】(Re-)Imag(in)ing Price Trends(价格再现)---- 修大成 (芝加哥大学)

发布者:统计与数据科学学院发布时间:2021-10-04浏览次数:28

报告题目(Re-)Imag(in)ing Price Trends(价格再现); 

 

报告人:修大成  ;


报告时间:2021/10/07 10:00 ;


线上腾讯会议号:223 888 598  


报告摘要

    We reconsider the idea of trend-based predictability using methods that flflexibly learn price patterns that are most predictive of future returns, rather than testing hypothesized or pre-specifified patterns (e.g., momentum and reversal). Our raw predictor data are imagesstock-level price chartsfrom which we elicit the price patterns that best predict returns using machine learning image analysis methods. The predictive patterns we identify are largely distinct from trend signals commonly analyzed in the literature, give more accurate return predictions, translate into more profifitable investment strategies, and are robust to a battery of specifification variations. They also appear context-independent: Predictive patterns estimated at short time scales (e.g., daily data) give similarly strong predictions when applied at longer time scales (e.g., monthly), and patterns learned from US stocks predict equally well in international markets.


报告人简介:

   修大成,现任芝加哥大学布斯商学院计量经济学和统计学教授,兼任清华大学五道口金融学院特聘教授、上海交通大学上海高级金融学院特聘教授。研究兴趣包括:设计统计方法并将其应用于金融数据,来研究数据中所反映的经济学涵义。他早期的研究涉及风险测量和投资组合管理,包括高频数据和衍生产品的计量经济学模型。他的研究主要集中在设计机器学习方法来解决资产定价领域的大数据问题。在JASAAnnals of Statistics,Econometrica, JPE,  Journal of EconometricsJournal of FinanceReview of Financial Studies上发表了研究成果。Journal of Financial EconometricsCo-EditorJournal of EconometricsJournal of Business & Economic StatisticsJournal of Empirical Finance, and Statistica Sinica的副主编