Stock price forecasting based on improved time convolution network. (22nd March 2022) - Guo, Wenchao
Abstract: Stock forecasting is difficult because of its complexity and uncertainty. To better predict stock prices and then provide stockholders with reasonable suggestions, this paper proposes an improved time convolution network (TCN) model for predicting stock prices. The model used can make up for some of the shortcomings of the traditional neural network, use the trading data in the stock market, and put the preprocessed data of financial news into the model for training to improve the accuracy of prediction. Using the Shanghai Securities Exchange (SSE) 50 Index (Shanghai Securities Exchange 50 Index selects the most representative 50 stocks with large scale and good liquidity in Shanghai stock market as sample stocks) and news text crawled from financial web pages as samples, predict the direction of the SSE 50 Index's rise and fall. After using different network structure hyperparameters to adjust the model structure, the prediction effect is compared with other models, and it is found that the proposed improved TCN model can effectively improve the effect of predicting the rise and fall of the SSE 50 index, and can complete the model training and predict the stock price faster.