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RESEARCH ARTICLE

A machine learning framework for multi-market portfolio optimization: Evidence from U.S. stocks and cryptocurrencies

Pejman Peykani1* ,  Daniyal Sabour1 ,  Cristina Tanasescu2
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1 Department of Industrial Engineering, Faculty of Engineering, Khatam University, Tehran, Iran
2 Faculty of Economic Sciences, Lucian Blaga University of Sibiu, Sibiu, Romania
IJOCTA, 026240111 https://doi.org/10.36922/IJOCTA026240111
Received: 9 June 2026 | Revised: 9 July 2026 | Accepted: 13 July 2026 | Published online: 3 August 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

This study presents an integrated framework for multi-market portfolio optimization that integrates machine-learning-based return forecasting with classical and downside-oriented risk models. Using daily data for Bitcoin, Ethereum, BNB, Microsoft, and Tesla, the XGBoost algorithm is employed to predict short-term returns, after which the forecasts are used to construct optimal portfolios under the Mean-Variance (MV), Mean-Semi-Variance (MSV), and Mean-Absolute Deviation (MAD) models. A naïve equal-weighted portfolio is included as a benchmark. The results show that the predictive models provide useful signals for portfolio construction, with all optimized strategies achieving markedly lower risk than the naïve portfolio for the same expected return level. Tangency portfolios, particularly the Maximum Sortino and Maximum Sharpe-AD configurations, delivered the highest efficiency gains and the strongest performance on out-of-sample data. Overall, the findings demonstrate that combining machine learning with risk-sensitive optimization tools can improve decision-making in markets characterized by volatility and mixed asset dynamics.

Keywords
Portfolio optimization
Machine learning
XGBoost Mean-variance
Semi-variance
Absolute deviation
Cryptocurrency
Funding
None.
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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An International Journal of Optimization and Control: Theories & Applications, Electronic ISSN: 2146-5703 Print ISSN: 2146-0957, Published by AccScience Publishing
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