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

TL;DR

This paper presents a deep reinforcement learning approach for optimal order execution in finance, aiming to improve returns and reduce risk compared to traditional strategies, and demonstrating superior performance in US market conditions.

Contribution

It introduces a novel DRL-based method for holistic order execution optimization, outperforming standard strategies like VWAP and TWAP.

Findings

DRL approach outperforms VWAP and TWAP in ROI and risk management.

Model adapts dynamically to market conditions, including stress periods.

Experimental results validate the effectiveness of the proposed method.

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.