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A hybrid optimization and data-driven approach to understand the role of the risk-aversion profile parameter in portfolio optimization problems with shorting constraints

Author

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  • Carbonero-Ruz, Mariano
  • Fernández-Navarro, Francisco
  • Durán-Rosal, Antonio M.
  • Pérez-Rodríguez, Javier

Abstract

This study contributes to the optimization literature with an approach that would help investors understand how the risk-aversion profile hyperparameter affects excess returns, risk, and Sharpe ratio curves in portfolio optimization problems with short selling constraints. These curves were characterized by studying the original optimization problem and reducing it to a one-dimensional optimization problem. The problem variable was the excess return, and the minimum level of risk is expressed as a function of it. An approach to the functional form of the minimum risk level curve was also proposed, which allows us to determine an analytical expression for the aforementioned curves. The study provides significant results for the financial literature, such as (i) an upper and lower bound for the risk aversion profile hyperparameter; (ii) the optimal value for the risk aversion profile hyperparameter; (iii) a reduced version of the optimization problem that is easier to solve, and of course (iv) an analytical expression for the excess return, risk and Sharpe ratio curves as functions of the aforementioned hyperparameters. All of these results are reported using the Mean Squared Variance (MSV) portfolio optimization problem as the baseline model, representing the two objectives of the problem minimization function (excess return and risk) in the same unit.

Suggested Citation

  • Carbonero-Ruz, Mariano & Fernández-Navarro, Francisco & Durán-Rosal, Antonio M. & Pérez-Rodríguez, Javier, 2025. "A hybrid optimization and data-driven approach to understand the role of the risk-aversion profile parameter in portfolio optimization problems with shorting constraints," Operations Research Perspectives, Elsevier, vol. 15(C).
  • Handle: RePEc:eee:oprepe:v:15:y:2025:i:c:s2214716025000296
    DOI: 10.1016/j.orp.2025.100353
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    References listed on IDEAS

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    1. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    2. Qi, Yue & Liao, Kezhi & Liu, Tongyang & Zhang, Yu, 2022. "Originating multiple-objective portfolio selection by counter-COVID measures and analytically instigating robust optimization by mean-parameterized nondominated paths," Operations Research Perspectives, Elsevier, vol. 9(C).
    3. Taras Bodnar & Yarema Okhrin & Valdemar Vitlinskyy & Taras Zabolotskyy, 2018. "Determination and estimation of risk aversion coefficients," Computational Management Science, Springer, vol. 15(2), pages 297-317, June.
    4. Victor DeMiguel & Lorenzo Garlappi & Raman Uppal, 2009. "Optimal Versus Naive Diversification: How Inefficient is the 1-N Portfolio Strategy?," The Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 1915-1953, May.
    5. Francisco Fernández-Navarro & Luisa Martínez-Nieto & Mariano Carbonero-Ruz & Teresa Montero-Romero, 2021. "Mean Squared Variance Portfolio: A Mixed-Integer Linear Programming Formulation," Mathematics, MDPI, vol. 9(3), pages 1-13, January.
    6. Wan-Yi Chiu, 2024. "Portfolio Selection with Hierarchical Isomorphic Risk Aversion," Mathematics, MDPI, vol. 12(21), pages 1-22, October.
    7. Lorenzo Garlappi & Raman Uppal & Tan Wang, 2007. "Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach," The Review of Financial Studies, Society for Financial Studies, vol. 20(1), pages 41-81, January.
    8. Vincent Guigues, 2011. "Sensitivity analysis and calibration of the covariance matrix for stable portfolio selection," Computational Optimization and Applications, Springer, vol. 48(3), pages 553-579, April.
    9. Vijay K. Chopra & William T. Ziemba, 2013. "The Effect of Errors in Means, Variances, and Covariances on Optimal Portfolio Choice," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 21, pages 365-373, World Scientific Publishing Co. Pte. Ltd..
    10. Angela J. Black & David G. McMillan, 2004. "Non‐linear Predictability of Value and Growth Stocks and Economic Activity," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 31(3‐4), pages 439-474, April.
    11. Andrew Grant & Oh Kang Kwon & Steve Satchell, 2024. "Properties of risk aversion estimated from portfolio weights," Journal of Asset Management, Palgrave Macmillan, vol. 25(5), pages 427-444, September.
    12. Laurens Swinkels & Liam Tjong-A-Tjoe, 2007. "Can mutual funds time investment styles?," Journal of Asset Management, Palgrave Macmillan, vol. 8(2), pages 123-132, July.
    13. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    14. Leonid Churilov & Immanuel M. Bomze & Moshe Sniedovich & Daniel Ralph, 2004. "Hyper Sensitivity Analysis Of Portfolio Optimization Problems," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 21(03), pages 297-317.
    15. Garcia-Bernabeu, Ana & Hilario-Caballero, Adolfo & Tardella, Fabio & Pla-Santamaria, David, 2024. "ESG integration in portfolio selection: A robust preference-based multicriteria approach," Operations Research Perspectives, Elsevier, vol. 12(C).
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