This project demonstrates how to apply machine learning algorithms to distinguish "good" stocks from the "bad" stocks.
This project 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-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 Stacking Ensemble model, are trained to solve the classification task. Genetic Algorithm is also used to implement features selection. The effectiveness of the stock selection strategy is validated in Chinese stock market from both statistical and practical aspects, showing that: