Financial News Quantization and Stock Market Forecast Research Based on CNN and LSTM

Shubin Cai, Xiaogang Feng, Ziwei Deng, Zhong Ming, Zhiguang Shan

Abstract: The changes of stock market and the predictions of the price have become hot topics. When machine learning emerged, it has been used in the stock market forecast research. In recent years, the vertical development of machine learning has led to the emergence of deep learning. Therefore, this paper proposes and realizes the CNN and LSTM forecasting model with financial news and historical data of stock market, which uses deep learning methods to quantify text and mine the laws of stock market changes and analyze whether they can predict changes. According to the results from this paper, this method has certain accuracy in predicting the future changes of the stock market, which provides help to study the inherent laws of stock market changes.