Stock Market Prediction with RNN-LSTM and GA-LSTM

School of Mathematics and Science, Xi’an Jiaotong-Liverpool University, Suzhou, China, 215123

^* Corresponding author: Xinyue.Liang23@xjtlu.edu.cn

Abstract

The stock price reflects various factors such as the rate of economic growth, inflation, overall economy, trade balance, and monetary system, all of which impact the stock market as a whole. Investors often find the principle of stock price trends unclear because of the many important variables involved. When creating an investment strategy or determining the timing for buying or selling stocks, forecasting stock market trends plays a critical role. It is difficult to estimate the value of the stock market due to the non-linear and dynamic nature of the stock index. Numerous studies using deep learning techniques have been successful in making such predictions. The Long Short Term Memory (LSTM) has become popular for predicting stock market prices. This paper thoroughly examines methods for predicting stock market performance using RNN-LSTM and GA-LSTM, provides explanations of these methods, and performs a comparative analysis. We will discuss future directions and outline the significance of using RNN-LSTM and GA-LSTM for forecasting stock market trends, based on the papers we have reviewed.