{"id":8357,"date":"2026-04-11T21:07:12","date_gmt":"2026-04-11T18:07:12","guid":{"rendered":"https:\/\/nti.ukrintei.ua\/?page_id=8357"},"modified":"2026-04-11T21:57:22","modified_gmt":"2026-04-11T18:57:22","slug":"quantitative-modeling-of-portfolio-strategies-using-machine-learning-based-alpha-signals-and-backtesting","status":"publish","type":"page","link":"https:\/\/nti.ukrintei.ua\/?page_id=8357&lang=en","title":{"rendered":"Quantitative Modeling of Portfolio Strategies Using Machine Learning-Based Alpha Signals and Backtesting"},"content":{"rendered":"\n
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T. V. SHABELNYK<\/strong>, D. Sc. in Economics, Professor
S. O. YEVSYEYEVA<\/strong>, Student<\/p>\n\n\n\n

DOI<\/strong>: https:\/\/doi.org\/10.35668\/2520-6524-2026-1-07<\/p>\n\n\n\n

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PDF (Ukrainian)<\/a><\/div>\n<\/div>\n\n\n\n

Keywords:<\/strong> quantitative modeling, machine learning, alpha signals, portfolio backtesting, equity market, mediumterm strategies, risk-adjusted performance metrics.<\/p>\n\n\n\n

ABSTRACT<\/strong><\/p>\n\n\n\n

This paper proposes a quantitative framework for constructing ML-based alpha signals and integrating them into a portfolio backtesting procedure over a medium-term investment horizon. The empirical analysis relies on daily financial time series of liquid equity market instruments, providing statistically consistent input data for quantitative modeling. A structured feature space is constructed, incorporating price-based, volatility-based, and intraday-aggregated characteristics. Model training is implemented in a time-aware setting using rolling or expanding window schemes and strict chronological separation of the training, validation, and test sets, ensuring robust quantitative out-of-sample evaluation and minimizing information leakage.
Alpha signals are generated at discrete rebalancing dates as numerical forecasts of expected returns over a predefined horizon and are directly embedded into portfolio construction. Performance is assessed through quantitative portfolio backtesting using standard risk-adjusted metrics, including returns, volatility, the Sharpe ratio, and maximum drawdown. The proposed algorithmic pipeline formalizes the interaction between machine learning and portfolio analysis and provides a methodological basis for the quantitative evaluation of the investment performance of ML-based strategies.<\/em><\/p>\n\n\n\n

Received by the Editorial Office on 21.02.2026
Accepted for publication on 09.03.2026<\/p>\n\n\n\n

REFERENCES<\/strong><\/p>\n\n\n\n

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    The license permits free non-commercial use, distribution, reproduction, and adaptation of the materials in any format, provided that proper attribution is given to the author(s) and the source of publication is cited. Commercial use of the materials is permitted only with the publisher\u2019s written consent.<\/cite><\/blockquote>\n","protected":false},"excerpt":{"rendered":"

    T. V. SHABELNYK, D. Sc. in Economics, ProfessorS. O. YEVSYEYEVA, Student DOI: https:\/\/doi.org\/10.35668\/2520-6524-2026-1-07 Keywords: quantitative modeling, machine learning, alpha signals, portfolio backtesting, equity market, mediumterm strategies, risk-adjusted performance metrics. ABSTRACT This paper proposes a quantitative framework for constructing ML-based alpha signals and integrating them into a portfolio backtesting procedure over a medium-term investment horizon. The empirical … <\/p>\n

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