Cross-Category Information Transmission and Price Comovement in Academy Awards Prediction Markets

Abstract

Prediction market research has largely focused on elections and other political events, leaving a gap for the exploration of other domains such as entertainment markets. This thesis investigates cross-category price transmission in the Academy Awards prediction markets on Polymarket, focusing on whether price changes in the Best Picture category spill over to contracts in other categories that are co-nominated under the same film. The analysis is conducted on daily prices across six major categories from 2025 and 2026. Using Principal Component Analysis, Granger causality, and pooled OLS regression, the study characterizes cross-category comovement, measures the lagged spillover effects, and decomposes the contract premium. The findings indicate that Best Picture price movements significantly predict changes in co-nominee prices within one day, with the effect amplified in more competitive seasons as co-nominee prices deviate from levels justified by their prior award records. This informs how cross-contract dependencies may affect pricing efficiency and contributes to the broader understanding of informational spillover dynamics in multi-category event markets.