Iranian Journal of Finance

Smart-Beta Portfolio Optimization Using Machine Learning Techniques

Document Type : Original Article

Authors

Fatemeh Salehirad 1
Farid Tondnevis 2

1 MSc. in Financial Engineering and Risk Management, Department of Financial Engineering, College of Management, University of Tehran, Tehran, Iran.

2 Assistant Prof., Department of Financial Engineering, College of Management, University of Tehran, Tehran, Iran.

https://doi.org/10.66224/ijf.2025.524928.1518
Abstract
This study examines the integration of machine learning techniques with smart beta investment strategies to enhance portfolio performance. Traditional market indices often fail to meet investors' expectations, especially during volatile market periods, leading to a growing interest in alternative strategies such as smart beta methodologies. These strategies combine the cost and risk efficiency of passive investing with the performance advantages of active strategies by employing alternative weighting schemes based on financial factors such as value, quality, and momentum. In this research, Return on Invested Capital (ROIC) is selected as a value-based factor due to its strong reflection of a company's operational efficiency and value creation driver. We employ three machine learning models—Support Vector Regression (SVR), Random Forest, and XGBoost—to forecast ROIC based on various financial ratios. Each model is fine-tuned using Bayesian optimization techniques to achieve the highest forecasting accuracy. The dataset includes financial data from 85 manufacturing companies listed on the Tehran Stock Exchange. Model performance is evaluated using R², Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), with the optimized Random Forest model achieving the best results based on higher R² and lower error values compared to the other models. The forecasted ROIC values are then used to construct a smart beta portfolio, which is compared to a traditional market-cap-weighted portfolio. The findings demonstrate that a machine learning-enhanced, ROIC-based smart beta strategy can significantly outperform traditional approaches, offering investors a more robust and data-driven method for portfolio construction and risk-adjusted return enhancement.

Keywords

ROIC
Machine learning
Bayesian optimization
Smart-beta strategy
Factor investing
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Iranian Journal of Finance
Volume 9, Issue 4
2025
Pages 91-116

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