Publications & Working Papers
Analysing systematic mispricing of high- and low-probability events across 2,400+ resolved Polymarket markets to identify category-specific trading opportunities.
A framework for integrating polling aggregates into prediction market positioning, backtested against 2022 midterm and 2024 presidential data.
Deriving practical bet-sizing rules that account for estimation error in prediction markets, with simulation and historical backtests.
How We Work
All research begins with data. We pull historical Polymarket data via API, clean and structure it, and let empirical patterns guide our hypotheses.
Research that cannot inform a trading decision is deprioritised. Every paper we produce should have a clear section on trading implications.
We engage with the academic literature on prediction markets and forecasting (Tetlock, Hanson, Wolfers, Arrow et al.) and hold our own work to the standard of peer-reviewed publication where possible.
Collaborate
We welcome collaboration with UChicago faculty, researchers at other institutions, and industry practitioners. Reach out on X or through our sponsor contacts.