A reinforcement learning approach to optimal execution

Ciamac C. Moallemi and Muye Wang

Quantitative Finance, 2022, vol. 22, issue 6, 1051-1069

Abstract: We consider the problem of execution timing in optimal execution. Specifically, we formulate the optimal execution problem of an infinitesimal order as an optimal stopping problem. By using a novel neural network architecture, we develop two versions of data-driven approaches for this problem, one based on supervised learning, and the other based on reinforcement learning. Temporal difference learning can be applied and extends these two methods to many variants. Through numerical experiments on historical market data, we demonstrate significant cost reduction of these methods. Insights from numerical experiments reveals various tradeoffs in the use of temporal difference learning, including convergence rates, data efficiency, and a tradeoff between bias and variance.

Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1080/14697688.2022.2039403 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:quantf:v:22:y:2022:i:6:p:1051-1069

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RQUF20

DOI: 10.1080/14697688.2022.2039403

Access Statistics for this article

Quantitative Finance is currently edited by Michael Dempster and Jim Gatheral

More articles in Quantitative Finance from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().