Stock Market Prediction Using Machine Learning: Techniques, Challenges, and Future Directions

Year : 2026 | Volume : 13 | Issue : 01 | Page : 10 16
    By

    Nirav Shukla,

  • Vishal Dahiya,

  1. Research Scholar and Assistant Professor, Chimanbhai Patel Institute of Computer Applications, Sardar Vallabhbhai Global University, Ahmedabad, Gujarat, India
  2. Professor, Department of MCA, Chimanbhai Patel Institute of Computer Applications, Sardar Vallabhbhai Global University, Ahmedabad, Gujarat, India

Abstract

The continuous advancement of machine learning (ML) technologies has significantly transformed the field of financial forecasting, particularly in the area of stock market prediction. The ability to accurately forecast stock price movements and market trends plays a crucial role in supporting informed investment strategies and effective risk management. This paper provides a comprehensive review of recent developments in the application of ML techniques for predicting stock market behavior. It classifies the various ML approaches used in this domain and explores the growing importance of alternative data sources, such as financial news, social media sentiment, and market-related textual information, in enhancing prediction accuracy. In addition, the study presents a comparative assessment of different ML models based on their performance in real-world forecasting scenarios. It highlights several major challenges faced in stock market prediction, including the nonstationary nature of financial data, the risk of model overfitting, limited interpretability of complex algorithms, issues related to data reliability, and practical economic constraints. These factors often limit the effectiveness and applicability of ML models in financial decision-making. The paper concludes by outlining potential future research directions aimed at developing more reliable, interpretable, and economically practical ML models that can better support decision-making processes in the financial sector.

Keywords: Financial time series, machine learning, model interpretability and robustness, sentiment, and alternative data, stock market forecasting

[This article belongs to E-Commerce for Future & Trends ]

How to cite this article:
Nirav Shukla, Vishal Dahiya. Stock Market Prediction Using Machine Learning: Techniques, Challenges, and Future Directions. E-Commerce for Future & Trends. 2026; 13(01):10-16.
How to cite this URL:
Nirav Shukla, Vishal Dahiya. Stock Market Prediction Using Machine Learning: Techniques, Challenges, and Future Directions. E-Commerce for Future & Trends. 2026; 13(01):10-16. Available from: https://journals.stmjournals.com/ecft/article=2026/view=242149


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Regular Issue Subscription Review Article
Volume 13
Issue 01
Received 27/12/2024
Accepted 15/01/2026
Published 31/03/2026
Publication Time 459 Days
3

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