research-article

A bayesian market maker

Authors:

Mithun Chakraborty,

Malik Magdon-IsmailAuthors Info & Claims

EC '12: Proceedings of the 13th ACM Conference on Electronic Commerce

Pages 215 - 232

https://doi.org/10.1145/2229012.2229031

Published: 04 June 2012 Publication History

Abstract

Ensuring sufficient liquidity is one of the key challenges for designers of prediction markets. Variants of the logarithmic market scoring rule (LMSR) have emerged as the standard. LMSR market makers are loss-making in general and need to be subsidized. Proposed variants, including liquidity sensitive market makers, suffer from an inability to react rapidly to jumps in population beliefs. In this paper we propose a Bayesian Market Maker for binary outcome (or continuous 0-1) markets that learns from the informational content of trades. By sacrificing the guarantee of bounded loss, the Bayesian Market Maker can simultaneously offer: (1) significantly lower expected loss at the same level of liquidity, and, (2) rapid convergence when there is a jump in the underlying true value of the security. We present extensive evaluations of the algorithm in experiments with intelligent trading agents and in human subject experiments. Our investigation also elucidates some general properties of market makers in prediction markets. In particular, there is an inherent tradeoff between adaptability to market shocks and convergence during market equilibrium.

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Cited By

Курков М(2023)МОДЕЛІ ШТУЧНОГО ІНТЕЛЕКТУ В ІНВЕСТІЦІЙНІЙ ДІЯЛЬНОСТІ НА ФІНАНСОВИХ РИНКАХКиївський економічний науковий журнал10.32782/2786-765X/2023-3-9(56-61)Online publication date: 30-Nov-2023
https://doi.org/10.32782/2786-765X/2023-3-9
Xu JParuch KCousaert SFeng Y(2023)SoK: Decentralized Exchanges (DEX) with Automated Market Maker (AMM) ProtocolsACM Computing Surveys10.1145/357063955:11(1-50)Online publication date: 9-Feb-2023
https://dl.acm.org/doi/10.1145/3570639
Carvalho ASilveira DEly RCajueiro D(2023)A logarithmic market scoring rule agent-based model to evaluate prediction marketsJournal of Evolutionary Economics10.1007/s00191-023-00822-w33:4(1303-1343)Online publication date: 13-Jun-2023
https://doi.org/10.1007/s00191-023-00822-w
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Index Terms

A bayesian market maker
1. Applied computing
  1. Law, social and behavioral sciences
    1. Economics

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Published In

cover image ACM Conferences

EC '12: Proceedings of the 13th ACM Conference on Electronic Commerce

June 2012

1016 pages

ISBN:9781450314152

DOI:10.1145/2229012

General Chair:
Boi Faltings
École Polytechnique Fédérale de Lausanne (EPFL)
,
Program Chairs:
Kevin Leyton-Brown
University of British Columbia
,
Panos Ipeirotis
New York University

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGecom: Special Interest Group on Economics and Computation

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Association for Computing Machinery

New York, NY, United States

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Published: 04 June 2012

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EC '12: ACM Conference on Electronic Commerce

June 4 - 8, 2012

Valencia, Spain

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Overall Acceptance Rate 664 of 2,389 submissions, 28%

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Cited By

Курков М(2023)МОДЕЛІ ШТУЧНОГО ІНТЕЛЕКТУ В ІНВЕСТІЦІЙНІЙ ДІЯЛЬНОСТІ НА ФІНАНСОВИХ РИНКАХКиївський економічний науковий журнал10.32782/2786-765X/2023-3-9(56-61)Online publication date: 30-Nov-2023
https://doi.org/10.32782/2786-765X/2023-3-9
Xu JParuch KCousaert SFeng Y(2023)SoK: Decentralized Exchanges (DEX) with Automated Market Maker (AMM) ProtocolsACM Computing Surveys10.1145/357063955:11(1-50)Online publication date: 9-Feb-2023
https://dl.acm.org/doi/10.1145/3570639
Carvalho ASilveira DEly RCajueiro D(2023)A logarithmic market scoring rule agent-based model to evaluate prediction marketsJournal of Evolutionary Economics10.1007/s00191-023-00822-w33:4(1303-1343)Online publication date: 13-Jun-2023
https://doi.org/10.1007/s00191-023-00822-w
Kumar PEl Fallah Seghrouchni ASukthankar GAn BYorke-Smith N(2020)Deep Reinforcement Learning for Market MakingProceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3398761.3399018(1892-1894)Online publication date: 5-May-2020
https://dl.acm.org/doi/10.5555/3398761.3399018
Qu HRyzhov IFu MBergerson EKurka MKopacek L(2020)Learning Demand Curves in B2B Pricing: A New Framework and Case StudyProduction and Operations Management10.1111/poms.1316129:5(1287-1306)Online publication date: 1-May-2020
https://doi.org/10.1111/poms.13161
Spooner TFearnley JSavani RKoukorinis AAndre EKoenig SDastani MSukthankar G(2018)Market Making via Reinforcement LearningProceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3237383.3237450(434-442)Online publication date: 9-Jul-2018
https://dl.acm.org/doi/10.5555/3237383.3237450
Lekwijit SSutivong D(2018)Optimizing the liquidity parameter of logarithmic market scoring rules prediction marketsJournal of Modelling in Management10.1108/JM2-06-2017-006613:3(736-754)Online publication date: 13-Aug-2018
https://doi.org/10.1108/JM2-06-2017-0066
Kinathil SSanner SDas SPenna N(2016)A symbolic closed-form solution to sequential market making with inventoryProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3061053.3061124(3609-3615)Online publication date: 9-Jul-2016
https://dl.acm.org/doi/10.5555/3061053.3061124
Wah ELahaie SPennock D(2016)An empirical game-theoretic analysis of price discovery in prediction marketsProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3060621.3060693(510-516)Online publication date: 9-Jul-2016
https://dl.acm.org/doi/10.5555/3060621.3060693
Chakraborty MDas S(2016)Trading on a rigged gameProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3060621.3060644(158-164)Online publication date: 9-Jul-2016
https://dl.acm.org/doi/10.5555/3060621.3060644
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