IRO Journals
Journal of Soft Computing Paradigm

Stock Market Prediction Using A 3-ML Fusion Approach

Open Access
Published: 13 October, 2026
Pages: 341-358
  • Janani M. , Janani M.
    Assistant Professor, Department of Computer Science and Engineering, N P R College of Engineering and Technology, Tamil Nadu, India
    Assistant Professor, Department of Computer Science and Engineering, N P R College of Engineering and Technology, Tamil Nadu, India
  • Surekha M. , Surekha M.
    Students, Department of Computer Science and Engineering, N P R College of Engineering and Technology, Tamil Nadu, India
    Students, Department of Computer Science and Engineering, N P R College of Engineering and Technology, Tamil Nadu, India
  • Sabura Shifana J. , Sabura Shifana J.
    Students, Department of Computer Science and Engineering, N P R College of Engineering and Technology, Tamil Nadu, India
    Students, Department of Computer Science and Engineering, N P R College of Engineering and Technology, Tamil Nadu, India
  • Vishnu Priya D. Vishnu Priya D.
    Students, Department of Computer Science and Engineering, N P R College of Engineering and Technology, Tamil Nadu, India
    Students, Department of Computer Science and Engineering, N P R College of Engineering and Technology, Tamil Nadu, India
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How to Cite

M., Janani, Surekha M., Sabura Shifana J., and Vishnu Priya D. 2026. “Stock Market Prediction Using A 3-ML Fusion Approach”. Journal of Soft Computing Paradigm 8 (4): 341-58. https://doi.org/10.36548/jscp.2026.4.002.
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Keywords

Stock Market Prediction
3-ML Fusion Framework
NIFTY-50 Prediction
Ensemble Learning
Stock Price Forecasting
Market Trend Classification
XGBoost Regression

Abstract

Stock market prediction is still a significant challenge due to the non-linearity, dynamism, and volatility of the financial time series. Accurate stroke price prediction plays a major role in investment decisions, risk management, and financial planning. This research presents a novel 3-ML framework, which uses a combination of the Random Forest, Decision Tree and XGBoost Regression models to predict the next day's closing price of NIFTY-50 index. Moreover, a Random Forest (RF) classifier is used to classify market trends such as UP, DOWN or NEUTRAL, improving the decision-making process. Data from the market is preprocessed and enhanced by feature engineering through the use of technical indicators like moving averages and daily returns. The proposed framework implements a form of a Flask-based web application along with a MySQL database. It has been experimentally evaluated and shown that the framework developed has an accuracy of 97.7% and better stability of prediction and lower forecasting errors.

References

  1. Usmani, Mehak, Syed Hasan Adil, Kamran Raza, and Syed Saad Azhar Ali. "Stock Market Prediction using Machine Learning Techniques." International Conference on Computer and Information Sciences, IEEE, 2016: 322-327.
  2. Yu, Pengfei, and Xuesong Yan. "Stock Price Prediction Based on Deep Neural Networks." Neural Computing and Applications, 2020, vol 32, no. 6: 1609-1628.
  3. Billah, Mustain, Sajjad Waheed, and Abu Hanifa. "Stock Market Prediction using an Improved Training Algorithm of Neural Network." International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE), IEEE, 2016: 1-4.
  4. Samunderu, Eyden, and Yvonne T. Murahwa. "Return Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach." Journal of Risk and Financial Management 2021, vol 14, no. 11: 540.
  5. Sonkavde, Gaurang, Deepak Sudhakar Dharrao, Anupkumar M. Bongale, Sarika T. Deokate, Deepak Doreswamy, and Subraya Krishna Bhat. "Forecasting Stock Market Prices using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications." International Journal of Financial Studies, 2023, vol 11, no. 3: 94.
  6. Chen, Tianqi, and Carlos Guestrin. "Xgboost: A Scalable Tree Boosting System." International Conference on Knowledge Discovery and Data Mining, 2016, 785-794.
  7. Bhattacharjee, Indronil, and Pryonti Bhattacharja. "Stock Price Prediction: A Comparative Study Between Traditional Statistical Approach and Machine Learning Approach." International Conference on Electrical Information and Communication Technology, IEEE, 2019: 1-6.
  8. Hiransha, M. E. A. G., E. Ab Gopalakrishnan, and K. P. Soman. "NSE Stock Market Prediction using Deep-Learning Models." Procedia Computer Science, 2018, vol 132: 1351-1362.
  9. Grinberg, Miguel. Flask web development. " O'reilly Media, Inc.", 2018: Second Edition.
  10. Pedregosa, Fabian, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel et al. "Scikit-Learn: Machine Learning in Python." The Journal of Machine Learning Research, 2011, vol 12: 2825-2830.
  11. Yahoo Finance. "Nifty 50 (ˆNsei) Historical Data." Available: https://ca.finance.yahoo.com/quote/%5ENSEI/history