A study of cross-industry return predictability in the Chinese stock market

We investigate cross-industry return predictability for the Shanghai and Shenzhen stock exchanges, by constructing 6- and 26- industry portfolios. The dominance of retail investors in these markets, in conjunction with the gradual diffusion of information hypothesis provide the theoretical background that allows us to employ machine learning methods to test for cross-industry predictability. We find that Oil, Telecommunications and Finance industry portfolio returns are significant predictors of other industries. Our out-of-sample forecasting exercise shows that the OLS post-LASSO estimation outperforms a variety of benchmarks and a long-short trading strategy generates an average annual excess return of 13%.

Automated summary

This study investigates the predictability of cross-industry returns for the Shanghai and Shenzhen stock exchanges by constructing portfolios from different industries. The research findings show that the returns of the Oil, Telecommunications, and Finance industries are significant predictors for other industries. The machine learning methods used in the study outperform various benchmarks in the out-of-sample forecasting exercise, with an average annual excess return of 13%.