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BIMSA Digital Economy Lab Seminar
BIMSA Digital Economy Lab Seminar
Robustifying Conditional Portfolio Decisions via Optimal Transport
Robustifying Conditional Portfolio Decisions via Optimal Transport
Organizers
Johansson Anders
,
Ruize Gao
,
Liyan Han
,
Zhen Li
,
Jin Liu
,
Fei Long
,
Dongbo Shi
,
Ke Tang
,
Xing Yan
,
Qi Zhang
Speaker
Time
Friday, July 3, 2026 3:00 PM - 4:00 PM
Venue
A3-2-303
Online
Zoom 435 529 7909
(BIMSA)
Abstract
This paper proposes a data-driven portfolio selection model that integrates side information, conditional estimation, and robustness using the framework of distributionally robust optimization. Conditioning on the observed side information, the portfolio manager solves an allocation problem that minimizes the worst-case conditional risk-return trade-off, subject to all possible perturbations of the covariate-return probability distribution in an optimal transport ambiguity set. Despite the nonlinearity of the objective function in the probability measure, it shows that the distributionally robust portfolio allocation with a side information problem can be reformulated as a finite-dimensional optimization problem. If portfolio decisions are made based on either the mean-variance or the mean-Conditional Value-at-Risk criterion, the reformulation can be further simplified to second-order or semi-definite cone programs. Empirical studies in the US equity market demonstrate the advantage of this integrative framework against other benchmarks.