Machine learning and deep learning predictive models for the stock market

School of Mathematical Physics, Xi’an Jiaotong-Liverpool University, Suzhou, China, 215123

^* Corresponding author: Sunye.Wang23@student.xjtlu.edu.cn

Abstract

Accurately predicting the movement of stock prices can help people make more informed investment decisions and thus obtain higher returns. They can also assess market trends, develop investment strategies and provide investment advice. In this paper, we used 5 models including Random Forest, XGBoost, ANN, RNN, LSTM to predict and verify the fit of 3 companies (AMZN, BABA and MSFT). It is found that LSTM and random forest model can predict well in most cases. The development of the financial industry does have some shortcomings, and the future financial field will be a field full of challenges and opportunities, so some machine learning and deep learning methods can be used to solve the prediction and modeling problems of financial aspects such as the stock market.