Firm Characteristics and Chinese Stocks

• 来源期刊: 管理科学学报（英文版）
• 关键词: Partial least squares Machine learning Firm characteristics Chinese stock market Return predictability
• 年，卷（期）: 2018,（4）
• 页码范围: 259-283
• 页数: 25页
• 语种: 中文
• DOI: 10.3724/SP.J.1383.304014

摘要

This paper presents a comprehensive study on predicting the cross section of Chinese stock market returns with a large panel of 75 individual firm characteristics.We use not only the traditional Fama-MacBeth regression,but also the "big-data" econometric methods:principal component analysis (PCA),partial least squares (PLS),and forecast combination to extract information from all the 75 firm characteristics.These characteristics are important return predictors,with statistical and economic significance.Furthermore,firm characteristics that are related to trading frictions,momentum,and profitability are the most effective predictors of future stock returns in the Chinese stock market.