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Robust Portfolio Selection Under Model Ambiguity Using Deep Learning

Author

Listed:
  • Sadegh Miri

    (Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Tehran 15916-34311, Iran)

  • Erfan Salavati

    (Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Tehran 15916-34311, Iran)

  • Mostafa Shamsi

    (Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Tehran 15916-34311, Iran)

Abstract

In this study, we address the ambiguity in portfolio optimization, particularly focusing on the uncertainty related to the statistical parameters governing asset returns. We propose a novel method that combines robust optimization with artificial neural networks (ANNs). Our approach effectively handles both the randomness inherent in asset prices and the ambiguity in their governing parameters. Through our method, we consider both simulated data, using the Exponential Ornstein–Uhlenbeck process, and real-world stock price data. The results showcase that our ANN-based method outperforms traditional benchmark methods such as equally weighted portfolio and adaptive mean–variance portfolio selection.

Suggested Citation

  • Sadegh Miri & Erfan Salavati & Mostafa Shamsi, 2025. "Robust Portfolio Selection Under Model Ambiguity Using Deep Learning," IJFS, MDPI, vol. 13(1), pages 1-18, March.
    • Handle: RePEc:gam:jijfss:v:13:y:2025:i:1:p:38-:d:1604707
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    References listed on IDEAS

    as
    1. Lotfi, Somayyeh & Zenios, Stavros A., 2018. "Robust VaR and CVaR optimization under joint ambiguity in distributions, means, and covariances," European Journal of Operational Research, Elsevier, vol. 269(2), pages 556-576.
    2. 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.
    3. Guo, Sini & Gu, Jia-Wen & Ching, Wai-Ki, 2021. "Adaptive online portfolio selection with transaction costs," European Journal of Operational Research, Elsevier, vol. 295(3), pages 1074-1086.
    4. Michael J. Best & Robert R. Grauer, 1991. "Sensitivity Analysis for Mean-Variance Portfolio Problems," Management Science, INFORMS, vol. 37(8), pages 980-989, August.
    Full references (including those not matched with items on IDEAS)

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