講座主題：金融經濟學中的機器學習方法

主講嘉賓：修大成，芝加哥大學布斯商學院教授

講座時間：2020年6月3日（周三），6月10日（周三），上午8：00-下午13：00（北京時間）

講座形式：騰訊會議

嘉賓簡介：修大成，芝加哥大學布斯商學院計量與統計教授。中國科學技術大學數學,理工學學士，美國普林斯頓大學應用數學碩士、博士。主要研究領域為Financial Econometrics, Empirical Asset Pricing, Machine Learning in Finance, High-Dimensional Statistics, Quantitative Finance等。研究成果發表于Journal of Econometrics，Review of Financial Studies，Journal of Finance，Annals of Statistics，Journal of Business & Economic Statistics等國際知名期刊。

内容摘要：Because machine learning can handle a large number of predictive variables and has a variety of functional forms, the application of machine learning methods in the financial field is always a concerned issue in the cademia and industry.

This paper applies a variety of representative machine learning methods to solve the most studied and classic problem in the field of empirical asset pricing: measuring the risk premium of assets. This paper focuses on comparing the different methods. It is found that using machine learning to predict can bring huge economic benefits to investors, which is better than the long-term regression analysis strategy in the literature. Among them, classification tree and neural network are the two learning methods with the best performance. Compared with other methods, they take into account the nonlinear relationship and interaction between variables and effectively improve the prediction accuracy.