Analysis of ARIMA, LightGBM, XGBoost, and LSTM models for stock prediction

Abstract

As a matter of fact, stock market prediction has always been a popular problem. Many investors and scholars think that its impossible because they believe its random and doesnt have any patterns. However, many studies have found that long term prediction is possible, and that the existence of patterns in stocks makes it able to be predicted. Because of the possibility of predicting the stock market, many studies and investors have thought of new methodologies, ranging from statistical, economical, and others, employing techniques from a variety of practices. One methodology that has recently gained momentum is machine learning, which shows great promise and improvement. This study looks at four prevalent stock market prediction models, which include ARIMA, LightGBM, XGBoost, and LSTMs, explains some research done with them, the problems they have, and future improvements. It finally briefly discusses other methods researchers have used to predict the stock market that werent explained in the paper.