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An optimization-based framework for automated market-making

Authors:

Jacob Abernethy,

Jennifer Wortman VaughanAuthors Info & Claims

EC '11: Proceedings of the 12th ACM conference on Electronic commerce

Pages 297 - 306

https://doi.org/10.1145/1993574.1993621

Published: 05 June 2011 Publication History

Abstract

We propose a general framework for the design of securities markets over combinatorial or infinite state or outcome spaces. The framework enables the design of computationally efficient markets tailored to an arbitrary, yet relatively small, space of securities with bounded payoff. We prove that any market satisfying a set of intuitive conditions must price securities via a convex cost function, which is constructed via conjugate duality. Rather than deal with an exponentially large or infinite outcome space directly, our framework only requires optimization over a convex hull. By reducing the problem of automated market making to convex optimization, where many efficient algorithms exist, we arrive at a range of new polynomial-time pricing mechanisms for various problems. We demonstrate the advantages of this framework with the design of some particular markets. We also show that by relaxing the convex hull we can gain computational tractability without compromising the market institution's bounded budget.

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Cited By

Tran TTran DNguyen T(2024)Order Book Inspired Automated Market MakingIEEE Access10.1109/ACCESS.2024.337240212(36743-36763)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3372402
Gampe HGriffin C(2023)Dynamics of a binary option market with exogenous information and price sensitivityCommunications in Nonlinear Science and Numerical Simulation10.1016/j.cnsns.2022.106994118(106994)Online publication date: Apr-2023
https://doi.org/10.1016/j.cnsns.2022.106994
Haider AWang HScotney BHawe G(2022)Predictive Market Making via Machine LearningOperations Research Forum10.1007/s43069-022-00124-03:1Online publication date: 26-Jan-2022
https://doi.org/10.1007/s43069-022-00124-0
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Index Terms

An optimization-based framework for automated market-making
1. Applied computing
  1. Law, social and behavioral sciences
    1. Economics

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Information

Published In

cover image ACM Conferences

EC '11: Proceedings of the 12th ACM conference on Electronic commerce

June 2011

384 pages

ISBN:9781450302616

DOI:10.1145/1993574

General Chair:
Yoav Shoham
Stanford University, USA
,
Program Chairs:
Yan Chen
University of Michigan, USA
,
Tim Roughgarden
Stanford University, USA

Copyright © 2011 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

Publication History

Published: 05 June 2011

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EC '11

Sponsor:

SIGecom

EC '11: ACM Conference on Electronic Commerce

June 5 - 9, 2011

California, San Jose, USA

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

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EC '25

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The 25th ACM Conference on Economics and Computation

July 7 - 11, 2025

Stanford , CA , USA

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Cited By

Tran TTran DNguyen T(2024)Order Book Inspired Automated Market MakingIEEE Access10.1109/ACCESS.2024.337240212(36743-36763)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3372402
Gampe HGriffin C(2023)Dynamics of a binary option market with exogenous information and price sensitivityCommunications in Nonlinear Science and Numerical Simulation10.1016/j.cnsns.2022.106994118(106994)Online publication date: Apr-2023
https://doi.org/10.1016/j.cnsns.2022.106994
Haider AWang HScotney BHawe G(2022)Predictive Market Making via Machine LearningOperations Research Forum10.1007/s43069-022-00124-03:1Online publication date: 26-Jan-2022
https://doi.org/10.1007/s43069-022-00124-0
Haider AHawe GWang HScotney B(2022)Multi-Asset Market Making via Multi-Task Deep Reinforcement LearningMachine Learning, Optimization, and Data Science10.1007/978-3-030-95470-3_27(353-364)Online publication date: 2-Feb-2022
https://doi.org/10.1007/978-3-030-95470-3_27
Dudík MWang XPennock DRothschild DDignum FLomuscio AEndriss UNowé A(2021)Log-time Prediction Markets for Interval SecuritiesProceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3463952.3464011(465-473)Online publication date: 3-May-2021
https://dl.acm.org/doi/10.5555/3463952.3464011
Engel DHerlihy MBaldimtsi FRoughgarden T(2021)Composing networks of automated market makersProceedings of the 3rd ACM Conference on Advances in Financial Technologies10.1145/3479722.3480987(15-28)Online publication date: 26-Sep-2021
https://dl.acm.org/doi/10.1145/3479722.3480987
Nakshatri NMenon AGiles CRajtmajer SGriffin C(2021)Design and analysis of a synthetic prediction market using dynamic convex setsResults in Control and Optimization10.1016/j.rico.2021.100052(100052)Online publication date: Sep-2021
https://doi.org/10.1016/j.rico.2021.100052
Olefir OLastovsky O(2021)Application of a Modified Logarithmic Rule for ForecastingAdvances in Computer Science for Engineering and Education IV10.1007/978-3-030-80472-5_23(270-280)Online publication date: 21-Jul-2021
https://doi.org/10.1007/978-3-030-80472-5_23
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
Carvalho A(2016)An Overview of Applications of Proper Scoring RulesDecision Analysis10.1287/deca.2016.033713:4(223-242)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1287/deca.2016.0337
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