| Publikationstyp: | Working Paper – Gutachten – Studie |
| Titel: | The applicability of self-play algorithms to trading and forecasting financial markets : a feasibility study |
| Autor/-in: | Posth, Jan-Alexander Hadji Misheva, Branka Kotlarz, Piotr Kamil Osterrieder, Jörg Schwendner, Peter |
| et. al: | No |
| Umfang: | 15 |
| Erscheinungsdatum: | Nov-2020 |
| Verlag / Hrsg. Institution: | SSRN |
| Sprache: | Englisch |
| Schlagwörter: | Artificial intelligence; Machine learning; Financial market; Self-play; Trading |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren 332: Finanzwirtschaft |
| Zusammenfassung: | 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. |
| URI: | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3737714 https://digitalcollection.zhaw.ch/handle/11475/21895 |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Engineering School of Management and Law |
| Organisationseinheit: | Institut für Datenanalyse und Prozessdesign (IDP) Institut für Wealth & Asset Management (IWA) |
| Enthalten in den Sammlungen: | Publikationen School of Management and Law |
Dateien zu dieser Ressource:
Es gibt keine Dateien zu dieser Ressource.
Zur Langanzeige
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.
Alle Ressourcen in diesem Repository sind urheberrechtlich geschützt, soweit nicht anderweitig angezeigt.