Toward an Enhanced Stock Market Forecasting with Machine Learning and Deep Learning Models

Abstract

The use of both machine learning and deep learning in forecasting the stock market has attracted the interest of finance, as it holds the promise of better and more efficient prediction of stock market trends. With the markets becoming more intricate and fluid with every passing moment, relying on sophisticated predictive models becomes indispensable. This work is intended to investigate how good machine learning versus deep learning can be for stock market forecasts — based on their ability to predict stock market trends — and analyze techniques' effectiveness inside each methodological field. We compared the exploration of advances in long short-term memory with the traditional methods that demonstrate improved accuracies in predicting stock markets. Therefore, this study attempts to place itself within the framework of the discussion about using artificial intelligence for financial decisions by contributing new knowledge. Indeed, the complexity and unpredictability of stock market dynamics necessitate a diverse range of data sources for accurate prediction, and this research aims to shed light on the potential of integrating advanced technologies to enhance forecasting capabilities within the finance sector.