mlsanityData to 14 Oct 2026

Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization

arXiv 2607.06610 ↗First posted 7 Jul 2026cs.LG cs.AI math.OCcited by 0

On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 35 references that are in the map. First posted on arXiv 7 Jul 2026.

Builds on

5 of 35 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Advancing Investment Frontiers: Industry-grade Deep Reinforcement Learning for Portfolio Optimization27 Feb

Other references · 4

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2019Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimizationcited by 22
  3. 2020Application of Deep Q-Network in Portfolio Managementcited by 11
  4. 2019Capturing Financial markets to apply Deep Reinforcement Learningcited by 3

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Deep Reinforcement Learning Framework for Diversified Portfolio Management Across Global Equity Markets
  2. 2025Deep Reinforcement Learning for Investor-Specific Portfolio Optimization: A Volatility-Guided Asset Selection Approach
  3. 2026Deep Reinforcement Learning for Optimal Portfolio Allocation: A Comparative Study with Mean-Variance Optimization
  4. 2024Deep Reinforcement Learning and Mean-Variance Strategies for Responsible Portfolio Optimization
  5. 2025DeepAries: Adaptive Rebalancing Interval Selection for Enhanced Portfolio Selection
  6. 2025Deep Reinforcement Learning for Optimal Asset Allocation Using DDPG with TiDE
  7. 2025Diffusion-Augmented Reinforcement Learning for Robust Portfolio Optimization under Stress Scenarios
  8. 2025Regret-Optimized Portfolio Enhancement through Deep Reinforcement Learning and Future Looking Rewards