Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-22664
Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: The applicability of self-play algorithms to trading and forecasting financial markets
Authors: Posth, Jan-Alexander
Kotlarz, Piotr Kamil
Hadji Misheva, Branka
Osterrieder, Jörg
Schwendner, Peter
et. al: No
DOI: 10.3389/frai.2021.668465
10.21256/zhaw-22664
Published in: Frontiers in Artificial Intelligence
Volume(Issue): 4
Issue: 668465
Issue Date: 31-May-2021
Publisher / Ed. Institution: Frontiers Research Foundation
ISSN: 2624-8212
Language: English
Subjects: Artificial intelligence; Machine learning; Financial market; Trading
Subject (DDC): 006: Special computer methods
332: Financial economics
Abstract: The central research question to answer in this study is whether the 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 and economic forecasts.
URI: https://digitalcollection.zhaw.ch/handle/11475/22664
Fulltext version: Published version
License (according to publishing contract): CC BY 4.0: Attribution 4.0 International
Departement: School of Management and Law
School of Engineering
Organisational Unit: Institute of Wealth & Asset Management (IWA)
Institute of Data Analysis and Process Design (IDP)
Appears in collections:Publikationen School of Management and Law

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