A Comprehensive Review of Machine Learning for Financial Market Prediction Methods

Abstract

Financial market prediction is an important task for placing an investor's hard-earned money in the financial market to earn profit. Many parameters affect the financial market's valuation, making it volatile, which is challenging for investors. This review study gives a full overview of 53 research articles that were chosen based on the trend of machine learning algorithms, calculation methods, and other performance parameters. Primarily, it is seen that artificial neural network (ANN) and support vector machine (SVM) techniques are used for forecasting the financial market. For prediction purposes, stock selection is also an important task. A genetic algorithm (GA) is used to choose stocks, and it is a very important part of managing a portfolio. The K-means algorithm is used to create a group of stocks that have a similar pattern and behavior. Hybrid approaches also provide better results. This review paper makes it easier for researchers to understand the terms and key ideas of predicting the financial market using machine learning so they can make the right choices for their needs.