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Unravelling the Probabilistic Forest: Arbitrage in Prediction Markets - arXiv

paper

Authors

Andrew Stershic·Kritee Gujral

Credibility Rating

3/5
Good(3)

Good quality. Reputable source with community review or editorial standards, but less rigorous than peer-reviewed venues.

Rating inherited from publication venue: arXiv

Empirical analysis of arbitrage opportunities in Polymarket prediction markets, relevant to AI safety for understanding how prediction market mechanisms function and potential vulnerabilities in decentralized platforms that could be used for forecasting AI-related events.

Paper Details

Citations
2
1 influential
Year
2020
Methodology
peer-reviewed
Categories
The Journal of Prediction Markets

Metadata

arxiv preprintanalysis

Summary

This paper empirically analyzes arbitrage opportunities in Polymarket, a decentralized prediction market platform. The authors identify two types of arbitrage—Market Rebalancing Arbitrage (intra-market) and Combinatorial Arbitrage (inter-market)—that arise when dependent assets are mispriced, allowing traders to guarantee profits by exploiting price inconsistencies. Using on-chain order book data and a heuristic-driven reduction strategy to overcome computational scalability challenges, the study estimates that approximately $40 million in arbitrage profits were extracted during their measurement period, demonstrating that such opportunities not only exist theoretically but are actively exploited in practice.

Cited by 1 page

PageTypeQuality
PolymarketOrganization33.0

Cached Content Preview

HTTP 200Fetched Mar 15, 202692 KB
[2508.03474] Unravelling the Probabilistic Forest: Arbitrage in Prediction Markets 
 
 
 
 
 
 
 
 
 
 
 

 
 

 
 
 
 
 
 
 
 \hideLIPIcs 
 IMDEA Networks, Madrid, Spain oriol.saguillo@imdea.orghttps://orcid.org/0009-0000-6636-8527
Oxford Internet Institute, Oxford, UK vahid.ghafouri@oii.ox.ac.ukhttps://orcid.org/0000-0001-9068-8854
IMDEA Networks, Madrid, Spainlucianna.kiffer@networks.imdea.orghttps://orcid.org/0000-0003-2022-7993
IMDEA Networks, Madrid, Spain guillermo.suarez-tangil@networks.imdea.orghttps://orcid.org/0000-0002-0455-2553
 \Copyright Jane Open Access and Joan R. Public \ccsdesc [100]Security and privacy → \to Economics of security and privacy 

 
 
 Acknowledgements.

This work was supported by a Flashbots Research Proposal FRP-51. Additionally, we thank the anonymous AFT reviewers for the helpful feedback. \EventEditors John Q. Open and Joan R. Access
 \EventNoEds 2
 \EventLongTitle 42nd Conference on Very Important Topics (CVIT 2016)
 \EventShortTitle CVIT 2016
 \EventAcronym CVIT
 \EventYear 2016
 \EventDate December 24–27, 2016
 \EventLocation Little Whinging, United Kingdom
 \EventLogo 
 \SeriesVolume 42
 \ArticleNo 23

 
 Unravelling the Probabilistic Forest: 
 Arbitrage in Prediction Markets

 
 
 Oriol Saguillo
 
    
 Vahid Ghafouri
 
    
 Lucianna Kiffer
 
    
 Guillermo Suarez-Tangil
 
 

 
 Abstract

 Polymarket is a prediction market platform where users can speculate on future events by trading shares tied to specific outcomes, known as conditions .
Each market on Polymarket is associated with a set of one or more such conditions.
To ensure proper market resolution, the condition set must be exhaustive —collectively accounting for all possible outcomes—and mutually exclusive —only one condition may resolve as true. Thus, the collective prices (probabilities) of all related outcomes (whether in a condition or market) should be $1, representing a combined probability of 1 of any outcome.
Despite this design, Polymarket exhibits cases where dependent assets are mispriced, allowing for purchasing (or selling) a certain outcome for less than (or more than) $1, guaranteeing profit.
This phenomenon, known as arbitrage, could enable sophisticated participants to exploit such inconsistencies.

 In this paper, we conduct an empirical arbitrage analysis on Polymarket data to answer three key questions:
(Q1) What conditions give rise to arbitrage?
(Q2) Does arbitrage actually occur on Polymarket?, and
(Q3) Has anyone exploited these opportunities?
A major challenge in analyzing arbitrage between related markets lies in the scalability of comparisons across a large number of markets and conditions, with a naive analysis requiring O ​ ( 2 n + m ) O(2^{n+m}) comparisons.
To overcome this, we employ a heuristic-driven reduction strategy based on timeliness, topical similarity, and combinatorial relationships, further validated by expert input.

 Our study reveals two distinct forms of arbitrage on Polymarket: Market Rebalancing

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Resource ID: 33b0fb9003952a61 | Stable ID: NjkwMmRkOT