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 regressio...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.展开更多
基金We are grateful to seminar participants at Beijing University,Central University of Finance and Economics,Georgia State University,Hunan University,Indiana University,Renmin University,Shanghai University of Finance and Economics,Washington University in St.Louis,and conference partidpants at the 20(71872195,71602198)Beijing Natural Science Foundation(9174045)+1 种基金Hunan Natural Science Foundation(2019JJ50058)the Fundamental Research Funds for the Central Universities.
文摘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.
