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dc.contributor.advisorLeonid Kogan.en_US
dc.contributor.authorXu, ZihaoS.M.Massachusetts Institute of Technology.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2020-09-15T21:57:58Z
dc.date.available2020-09-15T21:57:58Z
dc.date.copyright2020en_US
dc.date.issued2020en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/127437
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020en_US
dc.descriptionCataloged from the official PDF of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 53-54).en_US
dc.description.abstractThe study of the market with adverse selection risks is an appealing topic in the field of market microstructure. Multiple theoretical models have been proposed to address this issue over the past few years, such as the Kyle model (1985), the Glosten-Milgrom model (1985) and so on. The main goal of these theoretical models is to provide an optimal pricing strategy based on the market condition they used. However, the market is a competitive but not always efficient environment. The optimal pricing strategy cannot provide enough insights in the markets with multiple interacting agents. Also, the theoretical models cannot be easily extended to other complex market. In our work, we apply the deep reinforcement learning techniques to train neural agents in our designed multiagent environment. The result shows that the neural agents could learn the best strategy conditioned on the pricing behaviors of other competitors. It suggests a new approach to study the price formation process in the complex market..en_US
dc.description.statementofresponsibilityby Zihao Xu.en_US
dc.format.extent54 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleReinforcement learning in the market with adverse selectionen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.oclc1192966235en_US
dc.description.collectionS.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienceen_US
dspace.imported2020-09-15T21:57:58Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentEECSen_US


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