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Bandit Market Makers

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dc.contributor.authorDella Penna, Nicolas
dc.contributor.authorReid, Mark
dc.date.accessioned2019-11-25T23:35:10Z
dc.date.available2019-11-25T23:35:10Z
dc.date.issued2011
dc.date.updated2019-05-19T08:23:15Z
dc.description.abstractWe propose a flexible framework for profit-seeking market making by combining cost function based automated market makers with bandit learning algorithms. The key idea is to consider each parametrisation of the cost function as a bandit arm, and the minimum expected profits from trades executed during a period as the rewards. This allows for the creation of market makers that can adjust liquidity and bid-asks spreads dynamically to maximise profits.en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.urihttp://hdl.handle.net/1885/186624
dc.language.isoen_AUen_AU
dc.provenanceAttribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)en_AU
dc.rights© 2011 The Author(s)en_AU
dc.rights.licenseAttribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)en_AU
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/3.0/en_AU
dc.sourceComputing Research Repository (CoRR)en_AU
dc.source.urihttps://arxiv.org/abs/1112.0076en_AU
dc.titleBandit Market Makersen_AU
dc.typeConference paperen_AU
dcterms.accessRightsOpen Accessen_AU
local.bibliographicCitation.lastpage7en_AU
local.bibliographicCitation.startpage1en_AU
local.contributor.affiliationDella Penna, Nicolas, College of Engineering and Computer Science, ANUen_AU
local.contributor.affiliationReid, Mark, College of Engineering and Computer Science, ANUen_AU
local.contributor.authoruidDella Penna, Nicolas, u4941783en_AU
local.contributor.authoruidReid, Mark, u4466898en_AU
local.description.notesImported from ARIESen_AU
local.identifier.absfor080109 - Pattern Recognition and Data Miningen_AU
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciencesen_AU
local.identifier.ariespublicationu4963866xPUB167en_AU
local.type.statusPublished Versionen_AU

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