Machine Learning Techniques for Stock Market Prediction: A State-of-the-Art Review

Abstract

Machine learning techniques for stock market prediction have been the subject of research and application for several decades. These techniques are currently undergoing rapid evolution, with researchers constantly refining existing models and developing new approaches to enhance predictive capabilities. Therefore, staying well-informed with the latest developments in this field is an important but also challenging task.

To navigate the extent of current research on machine learning methods for stock market prediction, this thesis conducts a state-of-the-art review on seventeen relevant and highly cited papers, published between 2019 to 2024. The review offers two main contributions: (1) a comprehensive examination of 112 unique feature variables employed and (2) a review of machine learning methods implemented. Additionally, the review provides a yearly distribution of the utilized machine learning methods, a summary of the best machine learning models, and a discussion of the latest developments in the field.

The findings of the review indicate rapid and significant advancements in the field of stock market forecasting over the last six years. The primary trend in the reviewed papers is the dominance of deep learning techniques, demonstrating superior forecasting power over traditional machine learning methods. Deep learning classifiers were frequently utilized for sentiment analysis of textual data. Furthermore, ensemble learning has been integrated into to deep learning models to combine the predictive capabilities of multiple deep learning predictors. In terms of the utilized data, the majority of the employed variables were technical indicators, whereas no fundamental variable was used. Papers published after 2021 generally utilized fewer variables, and there has also been a growing interest in integrating textual data extracted from different media sources.