Prediction Market Accuracy:
Crowd Wisdom or Informed Minority?
A Seminar by Theis Ingerslev Jensen
Tuesday September 8th, 2026
6:00 PM Seminar Begins
7:30 PM Reception
Hybrid Event
Fordham University
McNally Amphitheater
140 West 62nd Street
New York, NY 10023
Free Registration!
For Virtual Attendees: Please select virtual instead of member type upon registration.
Abstract:
Prediction markets produce remarkably accurate forecasts, but the source of this accuracy is poorly understood. Two explanations dominate: crowd wisdom and insider trading. Using the universe of Polymarket transactions, we show it is neither. Instead, accuracy comes from a minority of persistently skilled traders, around 3% of accounts. Unlike insiders, whose private edge is localized, these traders exhibit depth and breadth. They react to public news when it arrives, eliminate law-of-one-price violations, and trade against the crowd’s behavioral mistakes. The crowd, in turn, generates most of the volume but little of the information, and its losses fund the minority’s profits.
Bio:
Theis Ingerslev Jensen is an Assistant Professor of Finance at the Yale School of Management. Prior to joining Yale, he obtained his Ph.D. in financial economics from Copenhagen Business School. He conducts research in empirical asset pricing. His projects are often data-intensive, and he is especially interested in studying expectations. Much of Theis Ingerslev Jensen's recent work focuses on prediction markets.
We derive the optimal long-term growth rate for an agent investing in a market composed of a numéraire asset, a risky asset subject to transaction costs, and a liquidity pool within an Automated Market Maker (AMM). We first establish the necessary conditions to ensure a no-arbitrage environment within this market structure. Under these conditions, we determine the asymptotically optimal trading strategy for liquidity providers. Finally, we provide economic intuition for the strategy’s sensitivity to various market parameters, supported by numerical illustrations of our theoretical results.
Maxim Bichuch holds a M.S. from NYU and a Ph.D. from Carnegie Mellon University both in Financial Mathematics. He was a Postdoctoral Research Associate & Lecturer in the ORFE department in Princeton, and an Assistant Professor at Worcester Polytechnic Institute and Johns Hopkins University, before joining the department of Mathematics at The University at Buffalo. Prior to obtaining his Ph.D. He has also gained corporate experience working for Citigroup and Bear Stearns. His research interests include optimal investment, optimal control, stochastic volatility, credit, funding and counterparty risks, and most recently electricity markets, machine learning and AI, decentralized finance and fintech.
