Stock Trend Prediction Algorithm Based on Deep Recurrent Neural Network

Abstract

With the return of deep learning methods to the public eye, more and more scholars and industry researchers have tried to start exploring the possibility of neural networks to solve the problem, and some progress has been made. However, although neural networks have powerful function fitting ability, they are often criticized for their lack of explanatory power. Due to the large number of parameters and complex structure of neural network models, academics are unable to explain the predictive logic of most neural networks, test the significance of model parameters, and summarize the laws that humans can understand and use. Inspired by the technical analysis theory in the field of stock investment, this paper selects neural network models with different characteristics and extracts effective feature combinations from short-term stock price fluctuation data. In addition, on the basis of ensuring that the prediction effect of the model is not lower than that of the mainstream models, this paper uses the attention mechanism to further explore the predictive $K$ -line patterns, which summarizes usable judgment experience for human researchers on the one hand and explains the prediction logic of the hybrid neural network on the other. Experiments show that the classification effect is better using this model, and the investor sentiment is obtained more accurately, and the accuracy rate can reach 85%, which lays the foundation for the establishment of the whole stock trend prediction model. In terms of explaining the prediction logic of the model, it is experimentally demonstrated that the $K$ -line patterns mined using the attention mechanism have more significant predictive power than the general $K$ -line patterns, and this result explains the prediction basis of the hybrid neural network.