• David Iyanuoluwa Ajiga Independent Researcher, Chicago, Illinois, USA
  • Rhoda Adura Adeleye Information Technology & Management, University of Texas, Dallas, USA
  • Tula Sunday Tubokirifuruar Department of Accounting, Ignition Ajuru University of Education, Rivers State, Nigeria
  • Binaebi Gloria Bello Kings International School, Port-Harcourt, Rivers State, Nigeria
  • Ndubuisi Leonard Ndubuisi Spacepointe Limited Rivers State, Nigeria
  • Onyeka Franca Asuzu Dangote Sugar Refinery Plc, Lagos, Nigeria.
  • Oluwaseyi Rita Owolabi Independent Researcher, Indianapolis Indiana, USA



As financial markets become increasingly complex and dynamic, the application of machine learning (ML) techniques for stock market forecasting has garnered significant attention. This paper presents a comprehensive review of various ML models employed in the realm of stock market forecasting, focusing on their methodologies and the accuracy achieved in predicting market trends. The review begins by examining traditional time-series models such as autoregressive integrated moving average (ARIMA) and moving average convergence divergence (MACD) and their limitations in capturing the intricate patterns present in financial data. Subsequently, the discussion transitions to more advanced ML models, including support vector machines (SVM), artificial neural networks (ANN), and ensemble methods like random forests and gradient boosting. Each model's strengths and weaknesses are scrutinized in the context of stock market forecasting. The paper explores the pivotal role of feature selection and engineering in enhancing the predictive power of ML models. Feature sets encompassing financial indicators, macroeconomic variables, sentiment analysis from news articles, and social media data are analyzed for their impact on forecasting accuracy. Additionally, the incorporation of technical indicators and alternative data sources is explored as potential avenues to improve model robustness. A critical aspect of this review is the assessment of accuracy in predicting stock market movements. The evaluation is conducted through a comparative analysis of model performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and accuracy rates. The study also addresses the challenge of model overfitting and proposes strategies to mitigate this issue for more reliable predictions. This review provides a nuanced understanding of the landscape of ML models for stock market forecasting, highlighting the diverse approaches, challenges, and opportunities in the quest for improved accuracy. It contributes valuable insights for researchers, practitioners, and investors seeking to leverage the potential of ML in navigating the complexities of financial markets.

Keywords:  Machine Learning, Stock Market, Forecasting, Models, Review.


2024-02-14 — Updated on 2024-03-03