学术讲座：Dynamic Effectiveness of Stock Pricing Factors via Random Forest Models

金融学院SBF论坛2019年第30讲

讲座题目：Dynamic Effectiveness of Stock Pricing Factors via Random Forest Models

时间：2019年10月28日（周一）12:20-13:30

地点：博学925

主讲人：向巨

主讲人简介：向巨，南方科技大学金融系助理教授。目前的主要研究方向为金融人工智能、智能投资及投顾、量化投资和社会科学中的人工智能方法，其论文发表于国际知名期刊如Journal of Empirical Finance、 Journal of Financial Econometrics、Computational Economics、Mathematical Social Sciences等，并获得多项研究基金。向博士曾在哥伦比亚大学作过研究，并曾全职工作于美国、欧洲、中国的多家金融机构，享有深圳市地方级领军人才和孔雀人才称号，并持有CFA及FRM证书。他研发的智能系统在2016年3月Alphago与李世石挑战赛前，成功预测出AlphaGo获胜并给出五盘比赛的胜率为3.1比 1.9。Email: judexiang@yahoo.com, xiangj@sustech.edu.cn

讲座内容简介：In his paper, we select 17 common stock pricing factors from the aspects of company value, growth potential, quality and technical indicators, and analyze the relationship between factors and future stock returns via random forest (RF) and linear regression (LR) models. We then dynamically construct portfolios according to factors’ changing effectiveness. Portfolio returns from Q1 of 2010 to Q2 of 2018 show that both RF and LR multi-factor models have achieved excess returns relative to the benchmark CSI-500 Index. We also find that nonlinear RF models were significantly better than LR ones in terms of risk-adjusted returns, and short-term RF models are better than the long-term ones. Through the trend analysis of factor effectiveness, we believe that the reason lies in short-term inertia in the A-share market, which is related to the speculative nature of A-share market.