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A neural network framework for portfolio optimization under second-order stochastic dominance

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  • Babapour-Azar, Ali
  • Khanjani-Shiraz, Rashed

Abstract

Traditional portfolio optimization strategies often rely on statistical methods and linear programming tools to achieve a balance between return and risk. Despite their usefulness for portfolio optimization, they cannot efficiently capture the specific differences and complexity of real-world financial markets. Several modern approaches attempt to overcome these limitations by using nonlinear models, machine learning, and advanced risk measures. In this study, we propose a novel strategy for optimizing portfolios that incorporates second-order stochastic dominance constraints and solves them with neural networks. we show that portfolios subject to second-order stochastic dominance constraints outperform their traditional counterparts, especially in tail-risk situations.

Suggested Citation

  • Babapour-Azar, Ali & Khanjani-Shiraz, Rashed, 2024. "A neural network framework for portfolio optimization under second-order stochastic dominance," Finance Research Letters, Elsevier, vol. 66(C).
    • Handle: RePEc:eee:finlet:v:66:y:2024:i:c:s1544612324006561
    DOI: 10.1016/j.frl.2024.105626
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    References listed on IDEAS

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