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Empirical Mechanism Design for Optimizing Clearing Interval in Frequent Call Markets

Authors:

Michael P. WellmanAuthors Info & Claims

EC '17: Proceedings of the 2017 ACM Conference on Economics and Computation

Pages 205 - 221

https://doi.org/10.1145/3033274.3085153

Published: 20 June 2017 Publication History

Abstract

Several recent authors have advocated for financial markets to move from continuous clearing to discrete or batched clearing, as a way to defeat the latency arms race: the never-ending quest for small advantages in time to access markets. How frequently should such a modern batch auction clear? We conduct a systematic simulation-based investigation on the relationship between clearing frequency and metrics of market quality, such as allocative efficiency, comparing the performance of discrete and continuous auction mechanisms under empirical equilibrium behavior of all participating traders. In effect we perform empirical mechanism design on frequent batch auctions. We find that in a wide array of environments, equilibrium efficiency is improved for small positive intervals but falls off dramatically when there are too few opportunities to trade. The result is a large range of batch frequencies that are near optimally efficient; this range is wider in thick markets.

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Cited By

Dütting PFeng ZNarasimhan HParkes DRavindranath S(2024)Optimal Auctions through Deep Learning: Advances in Differentiable EconomicsJournal of the ACM10.1145/363074971:1(1-53)Online publication date: 11-Feb-2024
https://dl.acm.org/doi/10.1145/3630749
Alves TFlorescu IBozdog D(2023)Insights on the Statistics and Market Behavior of Frequent Batch AuctionsMathematics10.3390/math1105122311:5(1223)Online publication date: 2-Mar-2023
https://doi.org/10.3390/math11051223
Liu BPolukarov MVentre CLi LKanthan LWu FBasios MPelachaud CTaylor MFaliszewski PMascardi V(2022)The Spoofing Resistance of Frequent Call MarketsProceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems10.5555/3535850.3535943(825-832)Online publication date: 9-May-2022
https://dl.acm.org/doi/10.5555/3535850.3535943
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Index Terms

Empirical Mechanism Design for Optimizing Clearing Interval in Frequent Call Markets
1. Theory of computation
  1. Theory and algorithms for application domains
    1. Algorithmic game theory and mechanism design
      1. Algorithmic game theory

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Published In

cover image ACM Conferences

EC '17: Proceedings of the 2017 ACM Conference on Economics and Computation

June 2017

740 pages

ISBN:9781450345279

DOI:10.1145/3033274

General Chair:
Constantinos Daskalakis
Massachusetts Institute of Technology, USA
,
Program Chairs:
Moshe Babaioff
Microsoft Research, Israel
,
Hervé Moulin
University of Glasgow, UK

Copyright © 2017 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 the author(s) 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

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Published: 20 June 2017

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National Science Foundation
Future of Life Institute

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EC '17

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SIGecom

EC '17: ACM Conference on Economics and Computation

June 26 - 30, 2017

Massachusetts, Cambridge, USA

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EC '17 Paper Acceptance Rate 75 of 257 submissions, 29%;

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

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Cited By

Dütting PFeng ZNarasimhan HParkes DRavindranath S(2024)Optimal Auctions through Deep Learning: Advances in Differentiable EconomicsJournal of the ACM10.1145/363074971:1(1-53)Online publication date: 11-Feb-2024
https://dl.acm.org/doi/10.1145/3630749
Alves TFlorescu IBozdog D(2023)Insights on the Statistics and Market Behavior of Frequent Batch AuctionsMathematics10.3390/math1105122311:5(1223)Online publication date: 2-Mar-2023
https://doi.org/10.3390/math11051223
Liu BPolukarov MVentre CLi LKanthan LWu FBasios MPelachaud CTaylor MFaliszewski PMascardi V(2022)The Spoofing Resistance of Frequent Call MarketsProceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems10.5555/3535850.3535943(825-832)Online publication date: 9-May-2022
https://dl.acm.org/doi/10.5555/3535850.3535943
Qi JVentre C(2022)Accounting for Strategic Response in Limit Order Book DynamicsPRIMA 2022: Principles and Practice of Multi-Agent Systems10.1007/978-3-031-21203-1_41(630-639)Online publication date: 12-Nov-2022
https://doi.org/10.1007/978-3-031-21203-1_41
Liu BPolukarov MVentre CLi LKanthan LDignum FLomuscio AEndriss UNowé A(2021)Call Markets with Adaptive Clearing IntervalsProceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3463952.3464168(1587-1589)Online publication date: 3-May-2021
https://dl.acm.org/doi/10.5555/3463952.3464168
Liu BPolukarov MVentre CLi LKanthan LCalinescu ASzpruch L(2021)Agent-based marketsProceedings of the Second ACM International Conference on AI in Finance10.1145/3490354.3494389(1-8)Online publication date: 3-Nov-2021
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Wang XPennock DDevanur NRothschild DTao BWellman MBiró PChawla SEchenique F(2021)Designing a Combinatorial Financial Options MarketProceedings of the 22nd ACM Conference on Economics and Computation10.1145/3465456.3467634(864-883)Online publication date: 18-Jul-2021
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Wang XVorobeychik YWellman M(2018)A cloaking mechanism to mitigate market manipulationProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304415.3304492(541-547)Online publication date: 13-Jul-2018
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Melton H(2017)A Fairness-Oriented Performance Metric for Use on Electronic Trading Venues2017 International Conference on Computational Science and Computational Intelligence (CSCI)10.1109/CSCI.2017.177(1027-1030)Online publication date: Dec-2017
https://doi.org/10.1109/CSCI.2017.177

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