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How Wise is the Crowd in Prediction Markets

June 5, 2026

If you’ve ever scrolled through Polymarket or Kalshi wondering whether the “wisdom of crowds” is actually wisdom—or just organized noise—you’re not alone. A new paper, “How Wise is the Crowd? Bias and Edge in Prediction Markets,” tears into the microstructure of modern prediction markets to ask a practical question: Who’s actually making money, and who’s just paying for the privilege of being loud? By engineering a high-frequency data pipeline that ingests tick-level order flow, on-chain wallet histories, and social commentary across decentralized finance and regulated venues, the authors expose structural inefficiencies that most traders overlook. The verdict? Market efficiency in Web3 betting isn’t dead—but it’s wearing a very clever disguise.

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Who Profits from Prediction Markets?

May 18, 2026

In the high-stakes arena of prediction markets, a counterintuitive pattern emerges: retail traders who correctly pick winners more than half the time still lose money, while automated traders with coin-flip accuracy pocket nine-figure profits. Using 222 million prediction market tradeswith directly observable terminal payoffs, the paper “Who Profits from Prediction? Execution, Not Information” presents a clean answer to why it is so. The authors decompose trader returns into a directional component and an execution component, revealing that the execution component, not the directional component, determines which trader types earn positive returns. 

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Exploiting Mean-Reversion in Decentralized Prediction Markets: Evidence from Polymarket Binary Contracts

April 17, 2026

This study examines the profitability of mean-reversion trading strategies applied to binary outcome contracts on Polymarket, the world’s largest decentralized prediction market platform. We analyze three distinct contracts representing varying risk profiles: a quasi-risk-free instrument (No to “Will Jesus Christ return in 2025?”) and two high-yield speculative contracts (No to “Will China invade Taiwan in 2025?” and “Will the US confirm that aliens exist in 2025?”). Using high-frequency price data sampled at 10-minute intervals over approximately one year, we implement a parameterized mean-reversion framework across twelve strategy variants, testing robustness under varying liquidity constraints and transaction cost assumptions. Our findings reveal that while mean-reversion signals generate substantial alpha under passive limit-order execution (zero-spread scenario), strategy performance degrades significantly when more aggressive market orders are accounted for.

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Systematic Edges in Prediction Markets

November 27, 2025

Prediction markets are financial platforms where participants trade contracts linked to future events, with prices reflecting collective probabilities. While these markets efficiently aggregate information, systematic inefficiencies create trading opportunities. Notable strategies include inter- and intra-market arbitrage, exploiting price differences across platforms or mispricing within a single market. Behavioral biases, such as the longshot bias, lead traders to overvalue underdogs and undervalue favorites, while bookmakers may manipulate odds to mislead naive participants. Experienced traders can exploit these patterns to secure profits. This article reviews common systematic edges in prediction markets, illustrates their practical application, and highlights the potential for profitable trading.

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