On Parametric Optimal Execution and Machine Learning Surrogates

TL;DR

This paper develops a neural network-based framework for optimal order execution, enabling scalable, accurate, and parametric approximation of strategies under complex price impact models, with practical implementation insights.

Contribution

It introduces a neural network surrogate approach for parametric optimal execution, extending existing models to nonlinear impact and demonstrating scalable, accurate solutions.

Findings

Neural network surrogates accurately approximate optimal strategies across parameters.

The approach scales well with multiple input dimensions.

Provides a reproducible implementation demonstrating practical utility.

Abstract

We investigate optimal order execution problems in discrete time with instantaneous price impact and stochastic resilience. First, in the setting of linear transient price impact we derive a closed-form recursion for the optimal strategy, extending the deterministic results from Obizhaeva and Wang (J Financial Markets, 2013). Second, we develop a numerical algorithm based on dynamic programming and deep learning for the case of nonlinear transient price impact as proposed by Bouchaud et al. (Quant. Finance, 2004). Specifically, we utilize an actor-critic framework that constructs two neural-network (NN) surrogates for the value function and the feedback control. The flexible scalability of NN functional approximators enables parametric learning, i.e., incorporating several model or market parameters as part of the input space. Precise calibration of price impact, resilience, etc., is known to be extremely challenging and hence it is critical to understand sensitivity of the execution policy to these parameters. Our NN learner organically scales across multiple input dimensions and is shown to accurately approximate optimal strategies across a wide range of parameter configurations. We provide a fully reproducible Jupyter Notebook with our NN implementation, which is of independent pedagogical interest, demonstrating the ease of use of NN surrogates in (parametric) stochastic control problems.