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

2025/02/11 by Pemy, Moustapha, Zhang, Na
#Computational Finance (q-fin.CP) #FOS: Economics and business #FOS: Mathematics #Optimization and Control (math.OC) #Trading and Market Microstructure (q-fin.TR)

paper · doi:10.48550/arxiv.2502.07868

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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