Who Wins and Who Loses In Prediction Markets? Evidence from Polymarket
Pat Akey (ESSEC Business School), et al.
April 2026
We study pricing efficiency in decentralized prediction markets by comparing marketimplied probabilities from Polymarket with benchmarks derived from option-implied riskneutral distributions extracted from the derivatives market. We study Bitcoin and Ethereum prediction bets and find that, although Polymarket prices broadly track option-implied benchmarks, they show systematic price differences driven by behavioral factors and market frictions. Price differences are most pronounced in tail events, during periods of high volatility, and in response to major macroeconomic shocks, and they reflect the influence of sentiment, attention, and blockchain-specific risks. These results reveal both efficiency and behavioral distortions in prediction markets.
How Wise is the Crowd? Bias and Edge in Prediction Markets
Avaneesh Deleep (University of California, Berkeley), et al.
March 2026
Prediction markets are increasingly relied upon as real-time probability oracles, yet their predictive signals remain polluted by structural inefficiencies. While prior literature documents anomalies like the favorite-longshot bias at an aggregate level, the microstructural origins of these distortions—specifically, who generates and exploits them—remain unstudied in modern ecosystems. To investigate this, we engineer a scalable, multi-threaded data architecture capable of synchronously ingesting and persisting tick-level order flow, decentralized wallet histories, and user commentary across Polymarket and Kalshi… Our findings challenge the idea that favorite-longshot bias is present in every prediction market. In the markets we find it to be present, such as Mention Markets, the classic favorite-longshot bias may in fact be a statistical artifact masking a pervasive “Yes Bias”, driven by extreme temporal volatility and not controlling for the time to market completion in previous methodologies. Furthermore, we find that “Whales”, or the most capitalized players, are not the most sophisticated. By dynamically reconstructing participant positions, we demonstrate that Whales, on average, systematically bleed expected value to small-order traders. Rather than acting as sharp informed players, these large actors likely trade on ideological conviction, structurally overpaying for specific narratives and suffering from adverse selection against smaller participants.
Kalshi and the Rise of Macro Markets
Anthony M. Diercks (Board of Governors of the Federal Reserve System), et al.
February 2026
Prediction markets offer a new market-based approach to measuring macroeconomic expectations in real-time. We evaluate the accuracy of prediction market-implied forecasts from Kalshi, the largest federally regulated prediction market overseen by the CFTC. We compare Kalshi with more traditional survey and market-implied forecasts, examine how expectations respond to macroeconomic and financial news, and how policy signals are interpreted by market participants. Our results suggest that Kalshi markets provide a high-frequency, continuously updated, distributionally rich benchmark that is valuable to both researchers and policymakers.
Market Efficiency in Prediction Markets – A Comparison with Derivatives
Michele Fabi (CREST-ENSAE), et al.
April 2026
We study pricing efficiency in decentralized prediction markets by comparing marketimplied probabilities from Polymarket with benchmarks derived from option-implied riskneutral distributions extracted from the derivatives market. We study Bitcoin and Ethereum prediction bets and find that, although Polymarket prices broadly track option-implied benchmarks, they show systematic price differences driven by behavioral factors and market frictions. Price differences are most pronounced in tail events, during periods of high volatility, and in response to major macroeconomic shocks, and they reflect the influence of sentiment, attention, and blockchain-specific risks. These results reveal both efficiency and behavioral distortions in prediction markets.
Minority Report: Contrarian Traders, Prediction Markets, and the Return of Post-Earnings Drift
Chloe Feng (Stanford U., Graduate School of Business, Students)
March 2026
Prediction markets on the Polymarket platform allow traders to bet on whether a company will beat or miss an earnings-per-share consensus target. Using 338 resolved markets matched to IBES analyst consensus forecasts, I document four findings… Taken together, the results suggest that a small contrarian minority drives prediction market accuracy, and that their signal is most valuable as a short-side veto: when the crowd assigns a low beat probability, shorting into earnings produces significant risk-adjusted returns over a 10-day horizon.
Skilled Liquidity Provision in Prediction Markets: Evidence from 150 Million Trades
Hsiang-Chieh (Alex) Yang (Augusta University)
March 2026
Do skilled traders provide liquidity, and when? I study this question using 150 million trades across more than 200,000 markets on Polymarket, a zero-fee prediction market with observable outcomes and wallet-level identification. The zero-fee setting isolates the information channel from fee confounds present in prior work on Kalshi. The central finding is dual-role profitability: skilled traders (top 5% by rolling historical accuracy) earn $121 as makers and $63 as takers per market entered, extracting $228 million over three years, while ordinary traders lose on both sides. Aggregate spread transfer is economically negligible, but this null masks the skilled-ordinary asymmetry. Skilled traders strategically choose their role, providing liquidity more often in highervolume and shorter-duration markets. Within-trader variation confirms this reflects strategy, not selection. Placebo tests, wash-trading exclusions, out-of-sample persistence, and domain-specific skill classifications that measure accuracy within rather than across market categories validate the skill classification and confirm that the findings are not artifacts of cross-domain luck. Trader skill, not the maker-taker distinction, determines who profits in prediction markets.
From Iran to Taylor Swift: Informed Trading in Prediction Markets
Joshua Mitts (Columbia Law School) and Moran Ofi (U. of Haifa)
March 2026
This Article presents a systematic empirical and legal study of informed trading on prediction markets. We document a series of case studies in which traders appear to have exploited material nonpublic information on Polymarket and Kalshi, spanning events from the joint U.S.-Israel February 2026 strike on Iran to pre-announcement trading in Taylor Swift’s engagement. Building on these cases, we develop a statistical screening of all Polymarket markets from February 2024 through February 2026, analyzing over 210,000 suspicious wallet-market pairs using a composite score that combines bet size anomalies, profitability, pre-event timing, and directional concentration. Flagged traders achieve a 69.9% win rate well in excess of chance, and we estimate approximately $143 million in aggregate anomalous profit. We then analyze the legal framework governing this conduct, finding that neither the classical nor misappropriation theories of securities fraud map cleanly onto geopolitical or macroeconomic event contracts, and that the CFTC’s principal anti-fraud vehicle, Rule 180.1, is narrower in critical respects than SEC Rule 10b-5 and has rarely been applied to prediction markets. We argue that a comprehensive regulatory response requires mandatory registration and surveillance obligations for any platform serving U.S. persons, contract-level rules targeting high-risk information channels, and an extended misappropriation theory directed at informed traders on decentralized platforms that resist operator-level regulation.
Learn To Use R For Portfolio Analysis
Quantitative Investment Portfolio Analytics In R:
An Introduction To R For Modeling Portfolio Risk and Return
By James Picerno
