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

TL;DR

This paper introduces a reinforcement learning-based method for optimal liquidation of highly correlated stocks, effectively minimizing execution shortfall in high-dimensional trading scenarios.

Contribution

It presents a novel stochastic optimal control approach that overcomes the curse of dimensionality in liquidating large baskets of stocks.

Findings

Proposed method effectively minimizes execution shortfall.

Proven convergence of the optimal trading strategy.

Validated with intra-day market data.

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.