IJCOPE Journal

UGC Logo DOI / ISO Logo

International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
Journal Information
ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 8

Published on: August 2026

PORTFOLIO OPTIMIZATION OF SELECTED NIFTY COMPANIES USING MODERN PORTFOLIO THEORY

SUBHASH NAIDU B DR R N KULKARNI

Ballari Institute of Technology and Management, Ballari

Article Status

Plagiarism Passed Peer Reviewed Open Access

Available Documents

Abstract

The stock market provides several investment opportunities, but selecting suitable securities and deciding their portfolio allocation requires careful consideration of expected return and risk. This study develops a portfolio optimization framework that combines machine learning-based stock price prediction with Modern Portfolio Theory (MPT). Historical daily data for 21 companies listed on the National Stock Exchange (NSE) were collected from Yahoo Finance for January 2020 to December 2025. The data were cleaned and prepared for exploratory analysis, return calculation and prediction. Linear Regression, Decision Tree, Random Forest and XGBoost were considered for stock-price prediction and evaluated using MAE, MSE, RMSE and R² Score. The project results identify Random Forest as the overall best-performing model. Predicted prices were then used with MPT to determine portfolio weights. The final recommendation selected M&M, SUNPHARMA, BHARTIARTL, NTPC and LT, with allocations of 22.37%, 21.21%, 21.01%, 18.29% and 17.12%, respectively. Portfolio performance was evaluated using annual return, annual volatility, Sharpe ratio, CAGR and maximum drawdown. The framework provides a systematic decision-support approach for combining predictive analytics with portfolio optimization.

Keywords- Machine Learning, Modern Portfolio Theory, NSE, Portfolio Optimization, Stock Price Prediction.

How to Cite this Paper

B, S. N. & KULKARNI, D. R. N. (2026). Portfolio Optimization of Selected Nifty Companies Using Modern Portfolio Theory. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(8), 1-9. https://doi.org/10.55041/ijcope.v2i8.209

B, SUBHASH, and DR KULKARNI. "Portfolio Optimization of Selected Nifty Companies Using Modern Portfolio Theory." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 8, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijcope.v2i8.209.

B, SUBHASH, and DR KULKARNI. "Portfolio Optimization of Selected Nifty Companies Using Modern Portfolio Theory." International Journal of Creative and Open Research in Engineering and Management 02, no. 8 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijcope.v2i8.209.

Search & Index

References

[1] A. Chaweewanchon and R. Chaysiri, “Markowitz mean-variance portfolio optimization with predictive stock selection using machine learning,” International Journal of Financial Studies, vol. 10, no. 3, p. 64, 2022.

[2] A. Naccarato, A. Pierini, and G. Ferraro, “Markowitz portfolio optimization through pairs trading cointegrated strategy in long-term investment,” Annals of Operations Research, vol. 299, pp. 81–99, 2021.

[3] P. Zhao, S. Gao, and N. Yang, “Solving multi-objective portfolio optimization problem based on MOEA/D,” in Proc. 12th Int. Conf. Advanced Computational Intelligence, 2020, pp. 30–37.

[4] Y. Ma, R. Han, and W. Wang, “Prediction-based portfolio optimization models using deep neural networks,” IEEE Access, vol. 8, pp. 115393–115406, 2020.

[5] J. Gao, H. Hu, Z. Zou, and H. Ahmadzade, “Mean–variance–skewness portfolio optimization with uncertain returns via co-skewness,” Soft Computing, vol. 29, pp. 4233–4246, 2025.

[6] B. Joemon, N. Z. Jhanjhi, M. A. Ghazanfar, A. A. Khan, and M. A. Azam, “Novel heuristics for stock portfolio optimization using machine learning and Modern Portfolio Theory,” in Proc. ICBATS, 2023.

[7] K. Raja, U. Shaikh, D. Sidhwani, and A. Tewari, “Hybrid stock market forecasting and portfolio optimization using LSTM, ARIMA, and Modern Portfolio Theory,” in Proc. WCONF, 2025, pp. 1–6.

[8] S. Vikash, N. Murali, and S. Rajendran, “AI-enhanced portfolio optimization framework leveraging Modern Portfolio Theory,” in Proc. ICDSAAI, 2025.

[9] Y. Zhao, J. Wang, Y. Wang, and M. Lv, “How to optimize Modern Portfolio Theory? A systematic review and research agenda,” Expert Systems with Applications, vol. 263, p. 125780, 2025.

[10] R. N. R. Sa’diyah, R. Nooraeni, W. A. Sofa, and M. I. Falahuddin, “Portfolio optimization using the mean-variance method with a prototype-based segmentation approach,” Procedia Computer Science, vol. 245, pp. 601–616, 2024.

Ethical Compliance & Review Process

  • All submissions are screened under plagiarism detection.
  • Review follows editorial policy.
  • Authors retain copyright.
  • Peer Review Type: Double-Blind Peer Review
  • Published on: Aug 25 2026
CCBYNC

This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.

View License
Scroll to Top