Prediction markets are a successful information aggregation structure, however the exact mechanism by which private information is incorporated into the price remains poorly understood. We introduce a novel method based on the “Kyle model” to identify traders who contribute valuable information to the market price. Applied to a large field prediction market dataset, we identify traders whose trades have positive informational price impact. In contrast to others, these traders realize profit (on average) in excess of a theoretical expected informed lower bound. Results are replicated on other field prediction market datasets, providing strong evidence in favor of the Kyle model.