Political Alpha: High-Frequency Analysis of U.S.A Electoral-Market Dynamics

Abstract

This study examines the bi-directional relationship between electoral expectations and market behavior, challenging conventional unidirectional frameworks in election forecasting. Traditional electoral prediction models, heavily reliant on economic indicators and polling data, have demonstrated significant limitations in recent U.S. presidential elections. This research introduces two methodological innovations: the integration of comprehensive betting market data with traditional variables, and the development of "Party Portfolios" that track market sectors demonstrating systematic sensitivity to electoral expectations. Using high-frequency data analysis and Granger causality testing, the study reveals complex patterns of information flow across multiple channels. Betting markets demonstrate stronger predictive power for economic indicators than vice versa, while sector-specific market behavior serves as a leading indicator for electoral developments. The mid-cycle period (60-20 days before elections) emerges as a critical window when Party Portfolios exhibit their strongest predictive power. During this phase, sophisticated investors express electoral expectations through sector allocation decisions, creating potential market opportunities. These findings have significant implications for investors, campaign strategists, and policymakers, suggesting that integrated approaches incorporating both political and financial metrics yield more accurate forecasts than isolated methodologies. The research advances our understanding of how democratic processes interact with economic systems, demonstrating that markets not only respond to political developments but also discover and express political information in ways traditional metrics may miss.