Paper: arXiv 2601.04896

Authors: Khabbab Zakaria, Jayapaulraj Jerinsh, Andreas Maier, Patrick Krauss, Stefano Pasquali, Dhagash Mehta

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.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 5.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced mathematical modeling and reinforcement learning algorithms, requiring significant quantitative expertise to understand and implement, while also demonstrating a rigorous empirical setup with specific backtesting periods and performance comparisons.

Research Flowchart

  flowchart TD
  A["Research Goal: Improve<br>Order Execution"] --> B["Methodology: Deep Reinforcement Learning"]
  B --> C["Inputs: US Market Data"]
  C --> D["Computational Process:<br>Dynamic Policy Training"]
  D --> E["Outcome: DRL Model"]
  E --> F["Comparison: DRL vs<br>VWAP & TWAP"]
  F --> G["Findings: DRL outperforms<br>in ROI & Risk Management"]