Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization
This paper proposes a multi-objective reliability-based portfolio optimization framework using deep reinforcement learning (MORP-DRL) that jointly optimizes expected return and downside risk. It incorporates three risk measures (variance, CVaR, EVaR), models market uncertainty with GARCH and extreme value theory, and uses PPO under practical constraints, outperforming NSGA-II on ten global equity indices across different market regimes.
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[Submitted on 7 Jul 2026]
Title:Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization
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Abstract:Portfolio optimization under uncertainty is inherently a multi-objective decision problem involving complex interactions among return, risk, market dynamics, and practical investment constraints. Existing reliability based portfolio optimization approaches primarily rely on static optimization frameworks and often fail to capture sequential decision making, tail risk, and market frictions such as transaction costs. To address these limitations, we propose a deep reinforcement learning framework for multi-objective reliability based portfolio optimization (MORP-DRL). The proposed framework jointly optimizes expected return and downside risk using three complementary risk measures: variance, Conditional Value-at-Risk (CVaR), and Entropic Value-at-Risk (EVaR). To model uncertainty and heavy-tailed market behavior, asset returns are represented using GARCH(1,1), Extreme Value Theory, and a t-copula dependence structure, while realistic scenarios are generated through quasi-Monte Carlo simulation. A Proximal Policy Optimization (PPO) based strategy is developed under practical constraints including transaction costs and portfolio bounds, and is benchmarked against NSGA-II. Experiments on ten global equity indices across pre-COVID, COVID, and post-COVID market regimes demonstrate that MORP-DRL achieves competitive risk-return performance, reduced downside risk during periods of market stress, and scalability to high-dimensional portfolio settings.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
Cite as: arXiv:2607.06610 [cs.LG]
(or arXiv:2607.06610v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.06610
arXiv-issued DOI via DataCite
Submission history
From: Tanmay Sen [view email] [v1] Tue, 7 Jul 2026 06:24:32 UTC (12,547 KB)
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