IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2609.22639.html

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

Author

Listed:
  • Haoyu Liu
  • Len Thomas
  • Benjamin Baer
  • Carl Donovan

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.

Suggested Citation

  • Haoyu Liu & Len Thomas & Benjamin Baer & Carl Donovan, 2026. "Adapting Pairs Trading to Gambling Markets A Case Study of the U.S. Presidential Election," Papers 2609.22639, arXiv.org.
    • Handle: RePEc:arx:papers:2609.22639
    as

    Download full text from publisher

    File URL: https://arxiv.org/pdf/2609.22639
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bryan Kelly & Hao Jiang, 2014. "Editor's Choice Tail Risk and Asset Prices," The Review of Financial Studies, Society for Financial Studies, vol. 27(10), pages 2841-2871.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shi, Yun & Cui, Xiangyu & Zhou, Xunyu, 2020. "Beta and Coskewness Pricing: Perspective from Probability Weighting," SocArXiv 5rqhv, Center for Open Science.
    2. Jung‐Soon Shin & Minki Kim & Dongjun Oh & Tong Suk Kim, 2019. "Do hedge funds time market tail risk? Evidence from option‐implied tail risk," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(2), pages 205-237, February.
    3. Xu, Dezhong & Li, Bin & Singh, Tarlok & Chen, Xiaoyue & Li, Jinze, 2025. "Cross-market overnight time-series momentum," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 105(C).
    4. Abdullah, Mohammad & Chowdhury, Mohammad Ashraful Ferdous & Wali Ullah, G.M., 2025. "Asymmetric tail risk dynamics, efficiency and risk spillover among FinTech stocks, cryptocurrencies and traditional assets," Global Finance Journal, Elsevier, vol. 64(C).
    5. Chen, Zhimin & Ibragimov, Rustam, 2019. "One country, two systems? The heavy-tailedness of Chinese A- and H- share markets," Emerging Markets Review, Elsevier, vol. 38(C), pages 115-141.
    6. Hou, Yanxi & Leng, Xuan & Peng, Liang & Zhou, Yinggang, 2024. "Panel quantile regression for extreme risk," Journal of Econometrics, Elsevier, vol. 240(1).
    7. Long, Huaigang & Zhu, Yanjian & Chen, Lifang & Jiang, Yuexiang, 2019. "Tail risk and expected stock returns around the world," Pacific-Basin Finance Journal, Elsevier, vol. 56(C), pages 162-178.
    8. Dai, Yingtong & Harris, Richard D.F., 2023. "Average tail risk and aggregate stock returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
    9. Laurent Gauthier & Julien Chevallier & Antoine Parent & Vincent Touzé, 2025. "Energy Real Options as a Predictor of War: 1920s–2020s," International Finance, Wiley Blackwell, vol. 28(3), pages 142-157, December.
    10. Saketh Aleti & Tim Bollerslev & Mathias Siggaard, 2025. "Intraday Market Return Predictability Culled from the Factor Zoo," Management Science, INFORMS, vol. 71(9), pages 7731-7751, September.
    11. Conlon, Thomas & Cotter, John & Ropotos, Ioannis, 2026. "Drivers of firm-level tail dependence: A machine learning approach," Journal of Economic Dynamics and Control, Elsevier, vol. 182(C).
    12. Yao, Haixiang & Huang, Jinbo & Li, Yong & Humphrey, Jacquelyn E., 2021. "A general approach to smooth and convex portfolio optimization using lower partial moments," Journal of Banking & Finance, Elsevier, vol. 129(C).
    13. Lu, Xinjie & Zeng, Qing & Zhong, Juandan & Zhu, Bo, 2024. "International stock market volatility: A global tail risk sight," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    14. José Afonso Faias & Juan Arismendi Zambrano, 2022. "Equity Risk Premium Predictability from Cross-Sectoral Downturns [International asset allocation with regime shifts]," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 12(3), pages 808-842.
    15. Lee, Kuan-Hui & Yang, Cheol-Won, 2022. "The world price of tail risk," Pacific-Basin Finance Journal, Elsevier, vol. 71(C).
    16. Yun Feng & Weijie Hou & Yuping Song, 2024. "Tail risk forecasting and its application to margin requirements in the commodity futures market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1513-1529, August.
    17. Xue, Wenjun & He, Zhongzhi & Hu, Yu, 2023. "The destabilizing effect of mutual fund herding: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 88(C).
    18. Abdelaziz, Fouad Ben & Chibane, Messaoud & Kuhanathan, Ano, 2024. "Can corporate social performance mitigate the risk of extreme stock returns?," The Quarterly Review of Economics and Finance, Elsevier, vol. 98(C).
    19. Li, C. Wei & Tiwari, Ashish & Tong, Lin, 2022. "Mutual fund tournaments and fund Active Share," Journal of Financial Stability, Elsevier, vol. 63(C).
    20. Zhao, Zifeng & Zhang, Zhengjun & Chen, Rong, 2018. "Modeling maxima with autoregressive conditional Fréchet model," Journal of Econometrics, Elsevier, vol. 207(2), pages 325-351.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2609.22639. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: https://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.