Deep Learning Models for Stock Market Forecasting: GARCH, ARIMA, CNN, LSTM, RNN

Abstract

Stock price prediction has long been a pivotal area of interest for investors, financial analysts, and researchers alike. The ability to forecast future stock prices accurately can provide substantial benefits in investment decision-making. With the advent of machine learning techniques and the ever-increasing availability of financial data, the field of stock price prediction has witnessed significant advancements. This paper presents a comprehensive review of stock price prediction methods using machine learning approaches. The primary objective is to provide an in-depth analysis of the various techniques, their strengths, limitations, and their overall performance in the context of stock market forecasting. Stock price prediction is categorizing into three main groups—(1) Statistical Models which include traditional time series models like ARIMA and GARCH. (2) Supervised Learning Models which explores the application of regression, decision trees, support vector machines, and various ensemble methods for stock price prediction. (3) Deep Learning Models like recurrent neural networks (RNNs), LSTM, and convolutional neural networks (CNNs). To evaluate the effectiveness of these methods, we review empirical studies and compare their performance on real-world stock market datasets. The paper concludes by summarizing the key findings, identifying challenges that still need to be addressed, and highlighting potential future directions in this dynamic field. This comprehensive review aims to serve as a valuable resource for researchers, practitioners, and investors interested in leveraging machine learning techniques for stock price prediction, shedding light on the current state of the art, and guiding future research endeavors.