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

Ali Babapour-Azar and Rashed Khanjani-Shiraz

Finance Research Letters, 2024, vol. 66, issue C

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.

Keywords: Portfolio optimization; Stochastic dominance; Neural networks; Multilayer perceptron; Deep learning (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:66:y:2024:i:c:s1544612324006561

DOI: 10.1016/j.frl.2024.105626

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