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Adapting Pairs Trading to Gambling Markets A Case Study of the U.S. Presidential Election

Haoyu Liu, Len Thomas, Benjamin Baer, Carl Donovan · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2609.22639 · cited by 0 works (OpenAlex) · open-access PDF

Spanish

Los pares de negociación explotan la reversión media de datos en la relación entre activos relacionados. Adaptamos esta idea a los mercados de apuestas políticas modelando la probabilidad implícita combinada de los dos nominados de los partidos principales con un proceso latente Ornstein-Uhlenbeck cuyo nivel de reversión media varía con el tiempo y cuyas observaciones contienen ruido adicional. Los parámetros del modelo se estiman a partir de los datos de probabilidades de muestreo regulares utilizando una probabilidad de estado-espacio implícito, con valores repetidos consecutivos representados por una observación única retenida y el número de intervalos de muestreo perdido preservados en la transición de tiempo continuo. El modelo de selección de candidatos es un trabajo de trayectoria en el año 2020. Los resultados de la predicción de los parámet

Machine translation (Meta M2M100); the English original follows.

Abstract

Pairs trading exploits mean reversion in the relationship between related assets. We adapt this idea to political betting markets by modelling the combined implied probability of the two major-party nominees with a latent Ornstein-Uhlenbeck process whose mean-reversion level varies over time and whose observations contain additive noise. Model parameters are estimated from regularly sampled odds data using a state-space likelihood, with consecutive repeated values represented by a single retained observation and the elapsed number of sampling intervals preserved in the continuous-time transition. Parametric-bootstrap upper prediction bounds identify signal times at which the combined implied probability is likely to decline, and a no-intercept Bradley-Terry-type model selects the candidate-specific odds quote. The candidate-selection model is trained on 2020 U.S. presidential-election data and evaluated out of sample on 2024 data. The 2024 analysis produced 130 signals, empirical one-step coverage of 95.1%, a mean synthetic odds-price return of 1.86%, and an unannualized per-trade Sharpe-type ratio of 1.12. These returns are frictionless descriptive quantities rather than executable betting-exchange profits. The results support the integrated framework as a proof of concept for two-candidate electoral markets.

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Topics: Odds, Econometrics, Sample (material), Economics, Presidential election, Executable

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