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DRL Optimizes Bi-Objective Portfolios with Reliability and Downside Risk

Sounaq Das, Tanmay Sen, Raghu Nandan Sengupta, Aditya Gupta· July 9, 2026 View original

Summary

This research introduces MORP-DRL, a deep reinforcement learning framework for bi-objective portfolio optimization that jointly optimizes expected return and downside risk. It incorporates advanced risk measures and market modeling to handle uncertainty and sequential decision-making, outperforming traditional methods.

This paper presents a novel deep reinforcement learning (DRL) framework, called MORP-DRL, designed for bi-objective portfolio optimization. The framework addresses the inherent complexities of investment decisions under uncertainty, aiming to simultaneously optimize expected returns and mitigate downside risks. Unlike traditional static optimization methods, MORP-DRL is capable of handling sequential decision-making, tail risks, and market frictions such as transaction costs. The proposed system integrates three complementary risk measures: variance, Conditional Value-at-Risk (CVaR), and Entropic Value-at-Risk (EVaR). To accurately model market dynamics and heavy-tailed behavior, asset returns are represented using GARCH(1,1), Extreme Value Theory, and a t-copula dependence structure, with realistic scenarios generated via quasi-Monte Carlo simulation. A Proximal Policy Optimization (PPO) strategy is employed, incorporating practical constraints like transaction costs and portfolio bounds. Experimental results across various market regimes on global equity indices demonstrate that MORP-DRL achieves superior risk-return performance and enhanced downside risk reduction compared to the NSGA-II benchmark, while also showing scalability for high-dimensional portfolios.

Why it matters

This framework offers a more robust and adaptive approach to portfolio management, potentially leading to better investment outcomes and improved risk mitigation, especially in volatile markets.

How to implement this in your domain

  1. 1Explore integrating DRL-based portfolio optimization into existing quantitative trading strategies.
  2. 2Pilot MORP-DRL or similar frameworks for managing specific investment portfolios, focusing on downside risk reduction.
  3. 3Evaluate the performance of DRL models against traditional optimization techniques in various market conditions.
  4. 4Develop internal expertise in deep reinforcement learning and advanced financial modeling for investment applications.

Who benefits

BFSIFinTechAsset ManagementInvestment Banking

Key takeaways

  • Deep reinforcement learning can effectively optimize portfolios for both return and downside risk.
  • MORP-DRL integrates multiple risk measures and sophisticated market modeling for robust performance.
  • The framework handles sequential decisions, tail risks, and transaction costs, surpassing static methods.
  • It demonstrates competitive risk-return performance and scalability across diverse market regimes.

Original post by Sounaq Das, Tanmay Sen, Raghu Nandan Sengupta, Aditya Gupta

"arXiv:2607.06610v1 Announce Type: cross 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 portfol…"

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Originally posted by Sounaq Das, Tanmay Sen, Raghu Nandan Sengupta, Aditya Gupta on X · view source

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