Portfolio Optimization Based on Almost Second-Degree Stochastic Dominance

Chunling Luo (), Piao Chen () and Patrick Jaillet ()
Additional contact information
Chunling Luo: Alibaba Business School, Hangzhou Normal University, Hangzhou 310030, China
Piao Chen: Zhejiang University - University of Illinois Urbana-Champaign Institute, Zhejiang University, Haining 314400, China
Patrick Jaillet: Department of Electrical Engineering and Computer Science, Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139; and Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139

Management Science, 2025, vol. 71, issue 8, 7029-7055

Abstract: In portfolio optimization, the computational complexity of implementing almost stochastic dominance has limited its practical applications. In this study, we introduce an optimization framework aimed at identifying the optimal portfolio that outperforms a specified benchmark under almost second-degree stochastic dominance (ASSD). Our approach involves discretizing the return range and establishing both sufficient and necessary conditions for ASSD. We then propose a three-step iterative procedure: first, identifying a candidate portfolio; second, assessing its optimality; and third, refining the discretization scheme. Theoretical analysis guarantees that the portfolio identified through this iterative process improves with each iteration, ultimately converging to the optimal solution. Our empirical study, utilizing industry portfolios, demonstrates the efficacy of our approach by consistently identifying an optimal portfolio within a few iterations. Furthermore, comparative analysis against other decision criteria, such as mean-variance, second-degree stochastic dominance, and third-degree stochastic dominance, reveals that ASSD generally leads to portfolios with higher out-of-sample average excess returns but also entails increased variations and risks.

Keywords: portfolio optimization; almost stochastic dominance; stochastic dominance constraints; quadratically constrained programming; cutting-plane algorithm (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.2022.01092 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:71:y:2025:i:8:p:7029-7055

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().