The Applicability of Self-Play Algorithms to Trading and Forecasting Financial Markets: A Feasibility Study

15 Pages Posted: 11 Mar 2021

See all articles by Piotr Kotlarz

Piotr Kotlarz

University of Liechtenstein; Zurich University of Applied Sciences

Branka Hadji-Misheva

University of Pavia - Department of Economics and Management

Jan-Alexander Posth

ZHAW School of Management and Law

Joerg Osterrieder

Zurich University of Applied Sciences

Peter Schwendner

Zurich University of Applied Sciences

Date Written: November 25, 2020

Abstract

The central research question to answer in this feasibility study is whether the Artificial Intelligence (AI) methodology of Self-Play can be applied to financial markets. In typical use-cases of Self-Play, two AI agents play against each other in a particular game, e.g. chess or Go. By repeatedly playing the game, they learn its rules as well as possible winning strategies. When considering financial markets, however, we usually have one player – the trader – that does not face one individual adversary but competes against a vast universe of other market participants. Furthermore, the optimal behaviour in financial markets is not described via a winning strategy, but via the objective of maximising profits while managing risks appropriately. Lastly, data issues cause additional challenges, since, in finance, they are quite often incomplete, noisy and difficult to obtain.

We will show that academic research using Self-Play has mostly not focused on finance, and if it has, it was usually restricted to stock markets, not considering the large FX, commodities and bond markets. Despite those challenges, we see enormous potential of applying self-play concepts and algorithms to financial markets.

Keywords: artificial intelligence, self-play, machine learning, financial markets, trading

JEL Classification: G00

Suggested Citation

Kotlarz, Piotr and Hadji-Misheva, Branka and Posth, Jan-Alexander and Osterrieder, Joerg and Schwendner, Peter, The Applicability of Self-Play Algorithms to Trading and Forecasting Financial Markets: A Feasibility Study (November 25, 2020). Available at SSRN: https://ssrn.com/abstract=3737714 or http://dx.doi.org/10.2139/ssrn.3737714

Piotr Kotlarz

University of Liechtenstein ( email )

Chair in Finance
Fürst-Franz-Josef-Strasse
Vaduz, 9490
Liechtenstein

HOME PAGE: http://www.uni.li

Zurich University of Applied Sciences

ZHAW School of Engineering
Rosenstrasse 3
Winterthur, CH 8400
Switzerland

Branka Hadji-Misheva

University of Pavia - Department of Economics and Management ( email )

Strada Nuova, 65
Pavia, 27100
Italy

Jan-Alexander Posth (Contact Author)

ZHAW School of Management and Law ( email )

Technoparkstrasse 2, P.O. Box
Winterthur, 8401
Switzerland

Joerg Osterrieder

Zurich University of Applied Sciences ( email )

Economics and Finance
Technikumstrasse 9
Winterthur, 8401
Switzerland

HOME PAGE: http://www.zhaw.ch

Peter Schwendner

Zurich University of Applied Sciences ( email )

School of Management and Law
Technoparkstrasse 2
Winterthur, CH 8401
Switzerland
8400 (Fax)

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
98
Abstract Views
410
rank
318,968
PlumX Metrics