Reinforcement-learning-based optimal trading in a simulated futures market with heterogeneous agents

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dc.authoridNadi Serhan Aydın / 0000-0002-1453-0016en_US
dc.authorscopusidNadi Serhan Aydın / 55904216900
dc.authorwosidNadi Serhan Aydın / X-8938-2018
dc.contributor.authorAydın, Nadi Serhan
dc.date.accessioned2021-12-17T08:59:11Z
dc.date.available2021-12-17T08:59:11Z
dc.date.issued2021en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractThis paper simulates a futures market with multiple agents and sequential auctions, where agents receive long-lived heterogeneous signals on the true value of an asset and with a known deadline. The evolution of the amount of differential information and its impact on the distribution of overall gains and the pace of truth discovery is examined for various depth levels of the limit order book (LOB). The paper also formulates a dynamic programming model for the problem and presents an associated reinforcement learning (RL) algorithm for finding optimal strategy in exploiting informational disparity. This is done from the perspective of an agent whose information is superior to the collective information of the rest of the market. Finally, a numerical analysis is presented based on a futures market example to validate the proposed methodology for finding the optimal strategy. We find evidence in favor of a waiting strategy where agent does not reveal her signal until the last auction before the deadline. This result may help bring more insight into the micro-structural dynamics that work against market efficiency.en_US
dc.identifier.citationAydin, N. S. (2021). Reinforcement-learning-based optimal trading in a simulated futures market with heterogeneous agents. SIMULATION, 00375497211061114.en_US
dc.identifier.doi10.1177/00375497211061114en_US
dc.identifier.issn0037-5497en_US
dc.identifier.issn1741-3133en_US
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85120971934en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1177/00375497211061114
dc.identifier.urihttps://hdl.handle.net/20.500.12713/2326
dc.identifier.wosWOS:000727883700001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAydın, Nadi Serhan
dc.language.isoenen_US
dc.publisherSAGE PUBLICATIONS LTDen_US
dc.relation.ispartofSIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONALen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMulti-Agent Simulationen_US
dc.subjectPrice Discoveryen_US
dc.subjectHeterogeneous Signalsen_US
dc.subjectMutual Learningen_US
dc.subjectOptimal Tradingen_US
dc.subjectDynamic Programmingen_US
dc.subjectReinforcement Learningen_US
dc.titleReinforcement-learning-based optimal trading in a simulated futures market with heterogeneous agentsen_US
dc.typeArticleen_US

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