A statistical learning approach for stock selection in the Chinese stock market - Wenbo Wu; Jiaqi Chen; Liang Xu; Qingyun He; Michael L. Tindall (Financial Innovation, 2019)
Forecasting stock returns is extremely challenging in general, and this task becomes even more difficult given the turbulent nature of the Chinese stock market. We address the stock selection process as a statistical learning problem and build cross-sectional forecast models to select individual stocks in the Shanghai Composite Index. Decile portfolios are formed according to rankings of the forecasted future cumulative returns. The equity market's neutral portfolio-formed by buying the top decile portfolio and selling short the bottom decile portfolio-exhibits superior performance to, and a low correlation with, the Shanghai Composite Index. To make our strategy more useful to practitioners, we evaluate the proposed stock selection strategy's performance by allowing only long positions, and by investing only in A-share stocks to incorporate the restrictions in the Chinese stock market. The long-only strategies still generate robust and superior performance compared to the Shanghai Composite Index. A close examination of the coefficients of the features provides more insights into the changes in market dynamics from period to period.
Keywords: 650; Stock selection; Stock return prediction; Statistical learning; Lasso; Elastic net