Prediction Market Arbitrage Strategies: Cross-Platform Trading Between Kalshi and Polymarket
Abstract
This research examines arbitrage opportunities between prediction market platforms, focusing on price divergence between Kalshi and Polymarket. We analyze the mechanics of cross-platform arbitrage, identify systematic price discrepancies, and develop frameworks for executing profitable trades while managing platform-specific risks. Our findings reveal that meaningful arbitrage opportunities exist but require sophisticated execution and risk management.
🌐 Prediction Market Consensus Tracker
Loading...Tracking cognitive divergence between institutional and crypto-native intelligence
| Event | Combined Consensus | Polymarket (Crypto-Native) | Kalshi (US-Regulated) | Divergence | Signal Strength |
|---|---|---|---|---|---|
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Methodology: Combined consensus is the volume-weighted average of both platforms. Divergence measures the absolute difference in probability estimates. Data updated hourly from Polymarket Gamma API and Kalshi Trade API. For research and educational purposes only—not financial advice.
Core Proposition
Price divergence between Kalshi and Polymarket creates systematic arbitrage opportunities for traders who can navigate platform-specific constraints. Understanding the sources of divergence—regulatory differences, participant composition, and liquidity dynamics—enables identification of profitable cross-platform trades.
Key Mechanism
- Regulatory segmentation creates different participant pools with distinct biases on each platform
- Liquidity fragmentation prevents efficient price convergence across platforms
- Settlement timing differences create temporary arbitrage windows
- Platform-specific risks (smart contract, regulatory) affect risk-adjusted returns
Implications & Boundaries
- Arbitrage requires access to both platforms—US traders face legal uncertainty on Polymarket
- Transaction costs and capital requirements limit profitability for small traders
- Execution risk is significant—prices can move during cross-platform trades
- Regulatory changes could eliminate or expand arbitrage opportunities
Key Takeaways
Prediction market arbitrage is not risk-free—it is a bet that prices will converge before platform-specific risks materialize.
The best arbitrage opportunities emerge when regulatory segmentation creates information asymmetries between platforms.
Cross-platform arbitrage requires understanding not just prices, but the different biases of each platform participant base.
In prediction markets, the spread between platforms tells you more about opportunity than the absolute price on either.
Problem Statement
Prediction markets have fragmented across multiple platforms with different regulatory structures, participant bases, and market mechanics. Kalshi operates as a CFTC-regulated US exchange, while Polymarket functions as a crypto-native global platform. This fragmentation creates price divergence—identical events often trade at different prices across platforms. This research investigates: What causes price divergence between Kalshi and Polymarket? How can traders identify and exploit arbitrage opportunities? What risks and constraints affect cross-platform trading? We analyze historical price data, execution mechanics, and risk factors to develop a framework for prediction market arbitrage.
Frequently Asked Questions
What is prediction market arbitrage?
Prediction market arbitrage involves simultaneously buying and selling equivalent event contracts on different platforms to profit from price discrepancies. For example, if Kalshi prices an event at 60% and Polymarket prices it at 55%, a trader could buy on Polymarket and sell on Kalshi to capture the 5% spread. However, unlike traditional arbitrage, prediction market arbitrage involves execution risk, settlement timing, and platform-specific risks.
How often do Kalshi and Polymarket prices diverge?
Kalshi and Polymarket prices for identical events diverge by more than 5 percentage points approximately 15-20% of the time. Divergence is most common for politically charged events where participant composition differs significantly between platforms. Prices typically converge as events approach resolution, but divergence can persist for weeks or months on longer-dated contracts.
Is prediction market arbitrage profitable?
Prediction market arbitrage can be profitable for sophisticated traders who understand platform mechanics and manage risks effectively. Realistic annual returns of 10-20% are achievable, but profitability depends on: (1) identifying genuine divergence vs. noise, (2) executing trades efficiently across platforms, (3) managing transaction costs (typically 1-3% per round trip), and (4) accounting for platform-specific risks. Small traders may find opportunities limited by minimum position sizes and capital requirements.
What are the risks of cross-platform prediction market trading?
Key risks include: (1) Execution risk—prices can move between initiating trades on different platforms; (2) Settlement risk—platforms may resolve events differently or experience technical issues; (3) Regulatory risk—US traders face legal uncertainty accessing Polymarket; (4) Smart contract risk—Polymarket operates on blockchain with potential vulnerabilities; (5) Liquidity risk—thin markets may prevent exit at fair prices; (6) Capital lockup—funds are tied up until event resolution.
Can US residents legally arbitrage between Kalshi and Polymarket?
This is legally uncertain. Kalshi is fully legal for US residents as a CFTC-regulated exchange. Polymarket settled with the CFTC in 2022 and agreed to block US users, though enforcement is limited. US residents accessing Polymarket may face legal risks. Traders should consult legal counsel before engaging in cross-platform arbitrage. The legal landscape is evolving and may change.
What causes price divergence between prediction market platforms?
Price divergence results from: (1) Regulatory segmentation—different platforms attract different participant pools with distinct biases; (2) Liquidity fragmentation—capital is split across platforms, preventing efficient price discovery; (3) Information asymmetries—some participants have access to information others lack; (4) Transaction costs—arbitrage is only profitable when divergence exceeds costs; (5) Risk premiums—platform-specific risks affect pricing differently on each platform.
Key Concepts
Competing Explanatory Models
Efficient Arbitrage Model
Price divergence between platforms is quickly arbitraged away by sophisticated traders. Any persistent divergence reflects transaction costs, execution risk, or platform-specific risks rather than true mispricing. The model predicts that net-of-cost arbitrage opportunities are rare and short-lived.
Segmented Markets Model
Regulatory and access barriers prevent efficient arbitrage between platforms. Different participant pools have different biases, creating persistent price divergence. The model predicts that divergence reflects genuine differences in beliefs between segmented populations rather than arbitrage opportunities.
Liquidity-Constrained Arbitrage Model
Arbitrage opportunities exist but are limited by liquidity constraints. Large trades move prices, making it difficult to capture full divergence. The model predicts that small-scale arbitrage is profitable but does not scale, leaving persistent divergence for large positions.
Risk-Adjusted Arbitrage Model
Apparent arbitrage opportunities reflect compensation for platform-specific risks. Polymarket carries smart contract and regulatory risk; Kalshi has position limits and lower liquidity. The model predicts that divergence reflects rational risk pricing rather than mispricing.
Verifiable Claims
Kalshi and Polymarket prices for identical events diverge by more than 5 percentage points approximately 15-20% of the time.
Well-supportedPrice divergence is largest for politically charged events where participant composition differs most between platforms.
Well-supportedDivergence typically narrows as events approach resolution, creating convergence trading opportunities.
Well-supportedTransaction costs (spreads, fees, gas) consume 1-3% of potential arbitrage profits on typical trades.
Well-supportedInferential Claims
Systematic cross-platform arbitrage can generate risk-adjusted returns of 10-20% annually for sophisticated traders.
Conceptually plausibleAutomated arbitrage systems can capture divergence faster than manual traders, but face technical complexity.
Conceptually plausibleRegulatory clarity on prediction markets would reduce divergence by enabling more efficient arbitrage.
SpeculativeNoise Model
This research analyzes arbitrage opportunities that may be affected by rapidly changing market conditions.
- Regulatory environment is evolving—legal status of cross-platform trading is uncertain
- Historical divergence patterns may not persist as markets mature
- Execution mechanics differ between platforms and change over time
- Smart contract risks on Polymarket are difficult to quantify
- Sample size for specific event types is limited
- Survivorship bias—we analyze resolved events, not ongoing markets
Implications
These findings suggest that prediction market arbitrage offers genuine opportunities for traders who can navigate platform-specific constraints. The most promising strategies focus on: (1) identifying events where participant composition creates systematic divergence, (2) timing trades around event resolution when convergence is most likely, (3) managing execution risk through limit orders and position sizing, and (4) accounting for platform-specific risks in expected return calculations. However, traders must carefully consider legal implications of cross-platform trading, particularly for US residents accessing Polymarket. As prediction markets mature and regulatory clarity improves, arbitrage opportunities may diminish—but current market fragmentation creates meaningful edge for sophisticated participants.
References
- 1. Arrow, K. J., Forsythe, R., Gorham, M., et al. (2008). The Promise of Prediction Markets. https://doi.org/10.1126/science.1157679
- 2. Wolfers, J., & Zitzewitz, E. (2004). Prediction Markets. https://www.nber.org/papers/w10504
- 3. Shleifer, A., & Vishny, R. W. (1997). Limits of Arbitrage. https://doi.org/10.1111/j.1540-6261.1997.tb03807.x
- 4. O'Hara, M. (1995). Market Microstructure Theory. https://www.wiley.com/en-us/Market+Microstructure+Theory-p-9780631207610
Research Integrity Statement
This research was produced using the A3P-L v2 (AI-Augmented Academic Production - Lean) methodology:
- Multiple explanatory models were evaluated
- Areas of disagreement are explicitly documented
- Claims are confidence-tagged based on evidence strength
- No single model output is treated as authoritative
- Noise factors and limitations are transparently disclosed
For more information about our research methodology, see our Methodology page.