Proceedings of the 3rd International Conference on Artificial Intelligence in Economics, Finance and Management (ICAIEFM 2025)

<Previous Article In Volume
Next Article In Volume>

Portfolio Optimization Using Machine Learning Techniques

Authors
G. Sai Kartheek1, *, G. Gowtham Kumar1, B. Sai Mani Ram1, G. Mohan Krishna1, V. Veeravel2
1Department of Computer Science and Engineering, School of Engineering and Sciences, SRM University – AP, Amaravati, 522 240, Andhra Pradesh, India
2Department of Management, Paari School of Business, SRM University – AP, Amaravati, 522 240, Andhra Pradesh, India
*Corresponding author. Email: saikartheek.gathram@srmap.edu.in
Corresponding Author
G. Sai Kartheek
Available Online 6 November 2025.
DOI
10.2991/978-94-6463-896-7_4How to use a DOI?
Keywords
Portfolio selection; Portfolio optimization; XG Boost; Sharpe Ratio
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.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 3rd International Conference on Artificial Intelligence in Economics, Finance and Management (ICAIEFM 2025)
Series
Advances in Economics, Business and Management Research
Publication Date
6 November 2025
ISBN
978-94-6463-896-7
ISSN
2352-5428
DOI
10.2991/978-94-6463-896-7_4How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - G. Sai Kartheek
AU  - G. Gowtham Kumar
AU  - B. Sai Mani Ram
AU  - G. Mohan Krishna
AU  - V. Veeravel
PY  - 2025
DA  - 2025/11/06
TI  - Portfolio Optimization Using Machine Learning Techniques
BT  - Proceedings of the 3rd International Conference on Artificial Intelligence in Economics, Finance and Management (ICAIEFM 2025)
PB  - Atlantis Press
SP  - 42
EP  - 67
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-896-7_4
DO  - 10.2991/978-94-6463-896-7_4
ID  - Kartheek2025
ER  -