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.

10.30699/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
Agrawal, M., Shukla, P. K., Nair, R., Nayyar, A., & Masud, M. (2021). Stock Prediction Based on Technical Indicators Using Deep Learning Model. Computers, Materials & Continua/Computers, Materials & Continua (Print), 70(1), 287. https://doi.org/10.32604/cmc.2022.014637
Almahdi, S. (2015). Smart Beta Portfolio Optimization. Journal of Mathematical Finance, 5(2), 202. https://doi.org/10.4236/jmf.2015.52019
Altman, E. I. (1968). FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY. The Journal of Finance, 23(4), 589. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
Arnott, R. D., Hsu, J., & Moore, P. (2005). Fundamental Indexation. Financial Analysts Journal, 61(2), 83. https://doi.org/10.2469/faj.v61.n2.2718
Basak, S., Kar, S., Saha, S., Khaidem, L., & Dey, S. R. (2018). Predicting the direction of stock market prices using tree-based classifiers. The North American Journal of Economics and Finance, 47, 552. https://doi.org/10.1016/j.najef.2018.06.013
Beasley, J. E., Meade, N., & Chang, T.-J. (2003). An evolutionary heuristic for the index tracking problem. European Journal of Operational Research, 148(3), 621. https://doi.org/10.1016/s0377-2217(02)00425-3
Beaver, W. H. (1966). Financial Ratios As Predictors of Failure. Journal of Accounting Research, 4, 71. https://doi.org/10.2307/2490171
Benartzi, S., & Thaler, R. H. (2001). Naive Diversification Strategies in Defined Contribution Saving Plans. American Economic Review, 91(1), 79. https://doi.org/10.1257/aer.91.1.79
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5. https://doi.org/10.1023/a:1010933404324
Chen, L., Pelger, M., & Zhu, J. (2023). Deep Learning in Asset Pricing. Management Science, 70(2), 714. https://doi.org/10.1287/mnsc.2023.4695
Chen, W., Zhang, H., & Jia, L. (2022). A novel two-stage method for well-diversified portfolio construction based on stock return prediction using machine learning. The North American Journal of Economics and Finance, 63, 101818. https://doi.org/10.1016/j.najef.2022.101818
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273. https://doi.org/10.1007/bf00994018
Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A., & Vapnik, V. (1996). Support Vector Regression Machines. Neural Information Processing Systems, 9, 155. https://papers.nips.cc/paper/1238-support-vector-regression-machines.pdf
Huang, C. (2011). A hybrid stock selection model using genetic algorithms and support vector regression. Applied Soft Computing, 12(2), 807. https://doi.org/10.1016/j.asoc.2011.10.009
Huang, M., Dang, S., & Bhuiyan, M. A. (2025). Multi-objective portfolio optimization for stock return prediction using machine learning. Expert Systems with Applications, 298, 129672. https://doi.org/10.1016/j.eswa.2025.129672
Kamdem, J. S., & Selambi, D. (2022). Cyber-Risk Forecasting using Machine Learning Models and Generalized Extreme Value Distributions. HAL (Le Centre Pour La Communication Scientifique Directe). https://hal.archives-ouvertes.fr/hal-03814979
Ma, Y., Han, R., & Wang, W. (2020). Portfolio optimization with return prediction using deep learning and machine learning. Expert Systems with Applications, 165, 113973. https://doi.org/10.1016/j.eswa.2020.113973
Malkiel, B. G. (2014). Is Smart Beta Really Smart? The Journal of Portfolio Management, 40(5), 127. https://doi.org/10.3905/jpm.2014.40.5.127
Ohlson, J. A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1), 109. https://doi.org/10.2307/2490395
Richard, J.-C., & Roncalli, T. (2015). Smart Beta: Managing Diversification of Minimum Variance Portfolios. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2595051
Shetty, S., Musa, M., & Brédart, X. (2022). Bankruptcy Prediction Using Machine Learning Techniques. Journal of Risk and Financial Management, 15(1), 35. https://doi.org/10.3390/jrfm15010035
Sun, H., & Yu, B. (2019). Forecasting Financial Returns Volatility: A GARCH-SVR Model. Computational Economics, 55(2), 451. https://doi.org/10.1007/s10614-019-09896-w
Ta, V.-D., Liu, C.-M., & Addis, D. (2018). Prediction and Portfolio Optimization in Quantitative Trading Using Machine Learning Techniques. 98. https://doi.org/10.1145/3287921.3287963
Trivedi, S. K. (2020). A study on credit scoring modeling with different feature selection and machine learning approaches. Technology in Society, 63, 101413. https://doi.org/10.1016/j.techsoc.2020.101413
Zhao, C., Yang, S., Qin, C., Zhou, J., & Chen, L. (2023). A Novel Smart Beta Optimization Based on Probabilistic Forecast. Computers, Materials & Continua/Computers, Materials & Continua (Print), 75(1), 477. https://doi.org/10.32604/cmc.2023.034933
Zhu, S., Zhou, C., Liu, H., & Ren, Y. (2023). Commodity factor investing via machine learning. Pacific-Basin Finance Journal, 83, 102231. https://doi.org/10.1016/j.pacfin.2023.102231
Iranian Journal of Finance
Volume 9, Issue 4
2025
Pages 91-116

Home

Submit Manuscript

Reviewers

Contact Us

Journal Metrics

Article Processing Charges

Call for Papers