Price Formation in Field Prediction Markets: the Wisdom in the Crowd

TL;DR

This paper applies a Kyle model-based method to field prediction market data, revealing how traders' trades influence prices and demonstrating the model's effectiveness in real-world settings.

Contribution

It introduces a novel Kyle model-derived approach to identify informed traders and analyze price impact in prediction markets, validated across multiple datasets.

Findings

The method successfully identifies traders with significant price impact.

Traders not identified as informed exhibit noise-like trading behavior.

Results support the Kyle model's applicability in real-world prediction markets.

Abstract

Prediction markets are a popular, prominent, and successful structure for a collective intelligence platform. However the exact mechanism by which information known to the participating traders is incorporated into the market price is unknown. Kyle (1985) detailed a model for price formation in continuous auctions with information distributed heterogeneously amongst market participants. This paper demonstrates a novel method derived from the Kyle model applied to data from a field experiment prediction market. The method is able to identify traders whose trades have price impact that adds a significant amount of information to the market price. Traders who are not identified as informed in aggregate have price impact consistent with noise trading. Results are reproduced on other prediction market datasets. Ultimately the results provide strong evidence in favor of the Kyle model in a field market setting, and highlight an under-discussed advantage of prediction markets over alternative group forecasting mechanisms: that the operator of the market does not need to have information on the distribution of information amongst participating traders.