Unravelling the Probabilistic Forest: Arbitrage in Prediction Markets

Paper: arXiv 2508.03474

Authors: 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 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 of all related outcomes 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 $O(2^{“n+m”})$ 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 Arbitrage, which occurs within a single market or condition, and Combinatorial Arbitrage, which spans across multiple markets. We use on-chain historical order book data to analyze when these types of arbitrage opportunities have existed, and when they have been executed by users. We find a realized estimate of 40 million USD of profit extracted.

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 6.0/10 (capped from 8.5: no out-of-sample test; see penalty flags)
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper relies heavily on empirical data from on-chain order books and quantitative profit estimates (40M USD), but the mathematics is primarily combinatorial heuristics and probability concepts rather than advanced derivations, resulting in high empirical rigor with moderate math complexity.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Identify & Analyze Arbitrage<br>in Polymarket"] --> B["Data & Preprocessing<br>On-Chain Order Book Data"]
  B --> C["Methodology: Heuristic Reduction<br>Timeliness, Similarity, Combinatorics"]
  C --> D["Computational Analysis<br>Scalable Arbitrage Detection"]
  D --> E{"Arbitrage Types Found?"}
  E --> F["Market Rebalancing Arbitrage"]
  E --> G["Combinatorial Arbitrage"]
  F --> H["Outcome: $40M+ Profit Extracted"]
  G --> H

Cited References (49)

1. DeepSeek‑R1‑Distill‑Qwen‑32B (2025)
2. Tweet on unusual trading activity (2024)
3. Tweet on market dynamics and liquidity (2024)
4. Linq‑Embed‑Mistral
5. MTEB Leaderboard – Embedding Benchmark
6. Journalists Wake Up to the Power of Prediction Markets (2022)
7. Presidential Election Winner– 2024 Market (2024)
8. Flash boys 2.0: Frontrunning in decentralized exchanges, miner extractable value, and consensus instability (2020)

2 of 49 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.