Stock Price Prediction Using Deep-Learning Models: CNN, RNN, and LSTM

International Business School Suzhou, Xi’an Jiaotong-Liverpool University, Suzhou, China, 215123

Corresponding author: Ruixun.Cao23@student.xjtlu.edu.cn

Abstract

With the rapid development of the economy, stock markets or equity markets have an important role nowadays. More and more people participate in stock investment, the rise or the fall in prices is vital and closely related to investors’ earnings. The basic way uses linear or non-linear algorithms, but the stock market has many factors, so it is highly non-linear prediction, so it is helpless to use one simple model, so this paper proposes to figure out a good deep-learning model to capture and analyze the data of six companies from Yahoo Finance by comparing the fitness of three famous neural network: CNN, RNN, and LSTM. The Sliding-Window model was applied to make future predictions in time series. The results of the models were calculated by using MSE, MAE, and MAPE.