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

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  • Moustapha Pemy
  • Na Zhang

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.

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  • Moustapha Pemy & Na Zhang, 2025. "Minimal Shortfall Strategies for Liquidation of a Basket of Stocks using Reinforcement Learning," Papers 2502.07868, arXiv.org.
    • Handle: RePEc:arx:papers:2502.07868
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    File URL: http://arxiv.org/pdf/2502.07868
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    References listed on IDEAS

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    1. Bahman Angoshtari & Tim Leung, 2020. "Optimal trading of a basket of futures contracts," Annals of Finance, Springer, vol. 16(2), pages 253-280, June.
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