专家简介: 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JASA, Annals of Statistics,Econometrica, JPE, Journal of Econometrics，Journal of Finance，Review of Financial Studies上发表了研究成果。任Journal of Financial Econometrics的Co-Editor, Journal of Econometrics, Journal of Business & Economic Statistics, Journal of Empirical Finance, and Statistica Sinica的副主编。
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 images—stock-level price charts—from 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.
腾讯会议号：223 888 598
时间：2021年10月7日上午10:00
