Minimal Shortfall Strategies for Liquidation of a Basket of Stocks using Reinforcement Learning

ArXiv ID: 2502.07868 “View on arXiv”

Authors: Unknown

Abstract

This paper studies the ubiquitous problem of liquidating large quantities of highly correlated stocks, a task frequently encountered by institutional investors and proprietary trading firms. Traditional methods in this setting suffer from the curse of dimensionality, making them impractical for high-dimensional problems. In this work, we propose a novel method based on stochastic optimal control to optimally tackle this complex multidimensional problem. The proposed method minimizes the overall execution shortfall of highly correlated stocks using a reinforcement learning approach. We rigorously establish the convergence of our optimal trading strategy and present an implementation of our algorithm using intra-day market data.

Keywords: stochastic optimal control, reinforcement learning, execution shortfall, highly correlated stocks, optimal trading strategy, equities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper applies advanced stochastic optimal control and reinforcement learning with multiple mathematical proofs and derivations, indicating high math complexity. It includes real intra-day market data implementation and a GitHub repository with code, demonstrating substantial empirical rigor.

  flowchart TD
    A["Research Goal:<br>Liquidation of Correlated Stocks"] --> B["Formulate as<br>Stochastic Optimal Control"]
    B --> C["Methodology:<br>Reinforcement Learning"]
    C --> D["Data:<br>Intra-day Market Data"]
    D --> E["Computational Process:<br>RL Algorithm Implementation"]
    E --> F["Key Findings:<br>Minimal Shortfall Strategy"]
    F --> G["Outcome:<br>Convergent & Optimal Execution"]