Application of Machine Learning Algorithm in Financial Market Risk Prediction

Abstract

Due to the complexity and openness of financial market system, there are complex relationships among its internal economic variables. Understanding the fluctuation law of the financial market is of great value for improving people's ability to prevent financial risks. This paper mainly studies the application of machine learning algorithm in financial market risk prediction. In this paper, LSTM algorithm is analyzed firstly, and the forgetting gate, input gate and output gate of repetitive structure are analyzed in detail. LSTM xgboo hybrid forecasting method is designed by combining LSTM and xgboost methods. The traditional LSTM algorithm is compared with the improved LSTM algorithm by using the collected data of Shanghai Stock Exchange Index and Shenzhen Stock Exchange Index. The results show that the improved LSTM algorithm is superior to the traditional machine learning algorithm.