Deep Reinforcement Learning for Optimum Order Execution: Mitigating Risk and Maximizing Returns

2026-01-08

AI Summary

This study addresses the order execution problem under given time and volume constraints by simultaneously optimizing investment returns and minimizing transaction risk. To this end, the authors propose a dynamic execution strategy based on deep reinforcement learning (DRL), which, to the best of their knowledge, is the first application of DRL to the joint optimization of return maximization and risk minimization, thereby overcoming the limitations of traditional approaches that focus on a single objective. Empirical experiments on U.S. market data demonstrate that the proposed method significantly outperforms benchmark strategies such as VWAP and TWAP in both return generation and risk control, exhibiting particularly robust performance during periods of market stress.

Technology Category

• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Reinforcement Learning

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Optimal Order Execution is a well-established problem in finance that pertains to the flawless execution of a trade (buy or sell) for a given volume within a specified time frame. This problem revolves around optimizing returns while minimizing risk, yet recent research predominantly focuses on addressing one aspect of this challenge. In this paper, we introduce an innovative approach to Optimal Order Execution within the US market, leveraging Deep Reinforcement Learning (DRL) to effectively address this optimization problem holistically. Our study assesses the performance of our model in comparison to two widely employed execution strategies: Volume Weighted Average Price (VWAP) and Time Weighted Average Price (TWAP). Our experimental findings clearly demonstrate that our DRL-based approach outperforms both VWAP and TWAP in terms of return on investment and risk management. The model's ability to adapt dynamically to market conditions, even during periods of market stress, underscores its promise as a robust solution.

Problem

• Optimal Order Execution
• Risk Minimization
• Return Maximization
• Deep Reinforcement Learning
• Market Impact

Innovation

• Deep Reinforcement Learning
• Optimal Order Execution
• Risk Management
• Return Optimization
• Market Adaptation