Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization

Researchers propose a deep reinforcement learning framework (MORP-DRL) for multi-objective portfolio optimization that jointly maximizes expected return and minimizes downside risk using CVaR and EVaR. The PPO-based strategy outperforms NSGA-II on ten global equity indices, reducing tail risk during market stress while handling transaction costs and portfolio constraints.

arXiv:2607.06610v1 Announce Type: new 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.