Title: A Machine Learning Framework for Stock Selection,
Abstract: This paper demonstrates how to apply machine learning algorithms to
distinguish good stocks from the bad stocks. To this end, we construct 244
technical and fundamental features to characterize each stock, and label stocks
according to their ranking with respect to the return-to-volatility ratio.
Algorithms ranging from traditional statistical learning methods to recently
popular deep learning method, e.g. Logistic Regression (LR), Random Forest
(RF), Deep Neural Network (DNN), and the Stacking, are trained to solve the
classification task. Genetic Algorithm (GA) is also used to implement feature
selection. The effectiveness of the stock selection strategy is validated in
Chinese stock market in both statistical and practical aspects, showing that:
1) Stacking outperforms other models reaching an AUC score of 0.972; 2) Genetic
Algorithm picks a subset of 114 features and the prediction performances of all
models remain almost unchanged after the selection procedure, which suggests
some features are indeed redundant; 3) LR and DNN are radical models; RF is
risk-neutral model; Stacking is somewhere between DNN and RF. 4) The portfolios
constructed by our models outperform market average in back tests.