Portfolio Optimization Using Machine Learning Techniques

G. Sai Kartheek (), G. Gowtham Kumar, B. Sai Mani Ram, G. Mohan Krishna and V. Veeravel
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G. Sai Kartheek: SRM University – AP, Department of Computer Science and Engineering, School of Engineering and Sciences
G. Gowtham Kumar: SRM University – AP, Department of Computer Science and Engineering, School of Engineering and Sciences
B. Sai Mani Ram: SRM University – AP, Department of Computer Science and Engineering, School of Engineering and Sciences
G. Mohan Krishna: SRM University – AP, Department of Computer Science and Engineering, School of Engineering and Sciences
V. Veeravel: Paari School of Business, SRM University – AP, Department of Management

A chapter in Proceedings of the 3rd International Conference on Artificial Intelligence in Economics, Finance and Management (ICAIEFM 2025), 2025, pp 42-67 from Springer

Abstract: Abstract Portfolio selection and optimization are crucial processes that determine how much capital to allocate to each chosen individual stock or sector. However, the portfolio risk is generally less than or equal to the total risk of all constituent stocks in the portfolio. Moreover, the Integration of Machine Learning Techniques into Investing offers substantial opportunities to maximise returns and minimise risks. Further, it is impossible for investors to invest in all publicly traded companies. The present study considers the sample of NSE 500 companies from the NSE 500 index based on several fundamental criteria, such as potential financial ratios. Next, the top 50 companies are considered to build a portfolio. We use weekly data from January 2010 to March 2025. Next, the study employs XGBoost and K-Means Algorithms. However, the selected stocks are segregated into three different sorts of portfolios: market-cap-weighted, equally-weighted, and maximum Sharpe ratio. The results document that when compared to the NSE 500 companies, the top 50 companies’ portfolios perform better than the market-cap-weighted and Maximum Sharpe Ratio portfolios.

Keywords: Portfolio selection; Portfolio optimization; XG Boost; Sharpe Ratio (search for similar items in EconPapers)
Date: 2025
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DOI: 10.2991/978-94-6463-896-7_4

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