Harnessing Machine Learning for Stock Movement Prediction: A Review of Current Approaches

Year : 2025 | Volume : 12 | Issue : 02 | Page : 29 40
    By

    Nisar Shaikh,

  • Pratik Hage,

  • Jay Chakole,

  • Krushnali Gulumkar,

  • Purvesh Chaudhari,

  1. Assistant Professor, Department of Artificial Intelligence and Data Science, Marathwada Mitra Mandal’s Institute of Technology, Pune, Maharashtra, India
  2. Student, Department of Artificial Intelligence and Data Science, Marathwada Mitra Mandal’s Institute of Technology, Pune, Maharashtra, India
  3. Student, Department of Artificial Intelligence and Data Science, Marathwada Mitra Mandal’s Institute of Technology, Pune, Maharashtra, India
  4. Student, Department of Artificial Intelligence and Data Science, Marathwada Mitra Mandal’s Institute of Technology, Pune, Maharashtra, India
  5. Student, Department of Artificial Intelligence and Data Science, Marathwada Mitra Mandal’s Institute of Technology, Pune, Maharashtra, India

Abstract

Stock price prediction is a crucial task in financial analysis, aiding investors and traders in making informed decisions. This study investigates the use of deep learning methods, particularly Long Short-Term Memory (LSTM) networks, for predicting stock prices based on historical market data. The dataset, sourced from Yahoo Finance, consists of time-series stock price data, which is preprocessed, feature-engineered, and visualized to improve prediction accuracy. The model’s performance is assessed using metrics like R² Score, Mean Absolute Error (MAE), and Mean Squared Error (MSE), showcasing its ability to capture market trends effectively. However, challenges such as market volatility, data sensitivity, and external influencing factors limit the model’s accuracy in highly dynamic environments. To address these limitations, future improvements include integrating sentiment analysis, using hybrid models, and incorporating additional financial indicators to refine predictions. Despite these challenges, the findings suggest that LSTM-based models can serve as valuable tools for financial forecasting, offering insights into market behavior and potential price movements. With further refinements and real-time data integration, such models can contribute to more robust decision-making strategies for investors and analysts.

Keywords: CNN-BiLSTM, Artificial Rabbits Optimization (ARO), Long Short-Term Memory (LSTM), Mean Absolute Error (MAE), stock price

[This article belongs to Journal of Advances in Shell Programming ]

How to cite this article:
Nisar Shaikh, Pratik Hage, Jay Chakole, Krushnali Gulumkar, Purvesh Chaudhari. Harnessing Machine Learning for Stock Movement Prediction: A Review of Current Approaches. Journal of Advances in Shell Programming. 2025; 12(02):29-40.
How to cite this URL:
Nisar Shaikh, Pratik Hage, Jay Chakole, Krushnali Gulumkar, Purvesh Chaudhari. Harnessing Machine Learning for Stock Movement Prediction: A Review of Current Approaches. Journal of Advances in Shell Programming. 2025; 12(02):29-40. Available from: https://journals.stmjournals.com/joasp/article=2025/view=225894


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Regular Issue Subscription Review Article
Volume 12
Issue 02
Received 11/04/2025
Accepted 15/05/2025
Published 08/09/2025
Publication Time 150 Days
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