1. ZHAW digitalcollection
  2. School of Management and Law
  3. Publikationen
  4. Publikationen School of Management and Law
Full metadata record
DC FieldValueLanguage
dc.contributor.authorPosth, Jan-Alexander-
dc.contributor.authorHadji Misheva, Branka-
dc.contributor.authorKotlarz, Piotr Kamil-
dc.contributor.authorOsterrieder, Jörg-
dc.contributor.authorSchwendner, Peter-
dc.date.accessioned2021-03-04T07:33:40Z-
dc.date.available2021-03-04T07:33:40Z-
dc.date.issued2020-11-
dc.identifier.urihttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3737714de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/21895-
dc.description.abstractThe 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.de_CH
dc.format.extent15de_CH
dc.language.isoende_CH
dc.publisherSSRNde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectArtificial intelligencede_CH
dc.subjectMachine learningde_CH
dc.subjectFinancial marketde_CH
dc.subjectSelf-playde_CH
dc.subjectTradingde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc332: Finanzwirtschaftde_CH
dc.titleThe applicability of self-play algorithms to trading and forecasting financial markets : a feasibility studyde_CH
dc.typeWorking Paper – Gutachten – Studiede_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.departementSchool of Management and Lawde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
zhaw.organisationalunitInstitut für Wealth & Asset Management (IWA)de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen School of Management and Law

Files in This Item:
There are no files associated with this item.
Show simple item record
Posth, J.-A., Hadji Misheva, B., Kotlarz, P. K., Osterrieder, J., & Schwendner, P. (2020). The applicability of self-play algorithms to trading and forecasting financial markets : a feasibility study. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3737714
Posth, J.-A. et al. (2020) The applicability of self-play algorithms to trading and forecasting financial markets : a feasibility study. SSRN. Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3737714.
J.-A. Posth, B. Hadji Misheva, P. K. Kotlarz, J. Osterrieder, and P. Schwendner, “The applicability of self-play algorithms to trading and forecasting financial markets : a feasibility study,” SSRN, Nov. 2020. [Online]. Available: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3737714
POSTH, Jan-Alexander, Branka HADJI MISHEVA, Piotr Kamil KOTLARZ, Jörg OSTERRIEDER und Peter SCHWENDNER, 2020. The applicability of self-play algorithms to trading and forecasting financial markets : a feasibility study [online]. SSRN. Verfügbar unter: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3737714
Posth, Jan-Alexander, Branka Hadji Misheva, Piotr Kamil Kotlarz, Jörg Osterrieder, and Peter Schwendner. 2020. “The Applicability of Self-Play Algorithms to Trading and Forecasting Financial Markets : A Feasibility Study.” SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3737714.
Posth, Jan-Alexander, et al. The Applicability of Self-Play Algorithms to Trading and Forecasting Financial Markets : A Feasibility Study. SSRN, Nov. 2020, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3737714.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.