Research on Stock Trading Model Based on Deep Convolutional Neural Network
To explore the effectiveness of the Convolutional Neural Network(CNN)model,a stock selection strategy for the Shanghai and Shenzhen 300 based on the CNN model is constructed.Firstly,the effectiveness of the upward factor predicted by the CNN model is tested with the usage of the layering method and IC testing method;secondly,based on risk and return indicators such as annualized returns and maximum drawdown,the specific performance of the rising factor stock selection strategy is judged to further verify the effectiveness of the CNN stock selection model.Subsequently,a timing strategy based on broad-based index was constructed,and the results showed that the CNN model performed the best in predicting the Shanghai Stock Exchange 50 Index.The research conclusion of the quantitative stock selection and timing model of Convolutional Neural Network confirms that convolutional neural network can not only select stocks with better performance in the Shanghai and Shenzhen 300,but also be equally effective in quantitative timing.
deep learningConvolutional Neural Networkquantitative stock selectionquantitative timing
万方数据
维普
