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Beyond the Polls: Quantifying Early Signals in Decentralized Prediction Markets with Cross-Correlation and Dynamic Time Warping

Author

Listed:
  • Francisco Cordoba Otalora

    (Department of Digital Innovation of the School of Business, University of Nicosia, 46 Makedonitissas Avenue, Nicosia CY-241, Cyprus)

  • Marinos Themistocleous

    (Department of Digital Innovation of the School of Business, University of Nicosia, 46 Makedonitissas Avenue, Nicosia CY-241, Cyprus)

Abstract

In response to the persistent failures of traditional election polling, this study introduces the Decentralized Prediction Market Voter Framework (DPMVF), a novel tool to empirically test and quantify the predictive capabilities of Decentralized Prediction Markets (DPMs). We apply the DPMVF to Polymarket, analysing over 11 million on-chain transactions from 1 September to 5 November 2024 against aggregated polling in the 2024 U.S. Presidential Election across seven key swing states. By employing Cross-Correlation Function (CCF) for linear analysis and Dynamic Time Warping (DTW) for non-linear pattern similarity, the framework provides a robust, multi-faceted measure of the lead-lag relationship between market sentiment and public opinion. Results reveal a striking divergence in predictive clarity across different electoral contexts. In highly contested states like Arizona, Nevada, and Pennsylvania, the DPMVF identified statistically significant early signals. Using a non-parametric Permutation Test to validate the observed alignments, we found that Polymarket’s price trends preceded polling shifts by up to 14 days, a finding confirmed as non-spurious with a high confidence ( p < 0.01) and with an exceptionally high correlation (up to 0.988) and shape similarity. At the same time, in states with low polling volatility like North Carolina, the framework correctly diagnosed a weak signal, identifying a “low-signal environment” where the market had no significant polling trend to predict. This study’s primary contribution is a validated, descriptive tool for contextualizing DPM signals. The DPMVF moves beyond a simple “pass/fail” verdict on prediction markets, offering a systematic approach to differentiate between genuine early signals and market noise. It provides a foundational tool for researchers, journalists, and campaigns to understand not only if DPMs are predictive but when and why, thereby offering a more nuanced and reliable path forward in the future of election analysis.

Suggested Citation

  • Francisco Cordoba Otalora & Marinos Themistocleous, 2025. "Beyond the Polls: Quantifying Early Signals in Decentralized Prediction Markets with Cross-Correlation and Dynamic Time Warping," Future Internet, MDPI, vol. 17(11), pages 1-26, October.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:11:p:487-:d:1778978
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    References listed on IDEAS

    as
    1. Jiahua Xu & Krzysztof Paruch & Simon Cousaert & Yebo Feng, 2021. "SoK: Decentralized Exchanges (DEX) with Automated Market Maker (AMM) Protocols," Papers 2103.12732, arXiv.org, revised Mar 2023.
    2. Klitos Christodoulou & Elias Iosif & Antonios Inglezakis & Marinos Themistocleous, 2020. "Consensus Crash Testing: Exploring Ripple’s Decentralization Degree in Adversarial Environments," Future Internet, MDPI, vol. 12(3), pages 1-12, March.
    3. Paulo Rupino da Cunha & Piotr Soja & Marinos Themistocleous, 2021. "Blockchain for development: a guiding framework," Information Technology for Development, Taylor & Francis Journals, vol. 27(3), pages 417-438, July.
    4. Hanna Halaburda & Guillaume Haeringer & Joshua Gans & Neil Gandal, 2022. "The Microeconomics of Cryptocurrencies," Journal of Economic Literature, American Economic Association, vol. 60(3), pages 971-1013, September.
    5. Conlon, T. & Ruskin, H.J. & Crane, M., 2009. "Cross-correlation dynamics in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(5), pages 705-714.
    6. Lin William Cong & Zhiguo He, 2019. "Blockchain Disruption and Smart Contracts," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1754-1797.
    7. Evgenia Kapassa & Marinos Themistocleous, 2022. "Blockchain Technology Applied in IoV Demand Response Management: A Systematic Literature Review," Future Internet, MDPI, vol. 14(5), pages 1-19, April.
    8. Sang Hyuk Kim & Hee Soo Lee & Han Jun Ko & Seung Hwan Jeong & Hyun Woo Byun & Kyong Joo Oh, 2018. "Pattern Matching Trading System Based on the Dynamic Time Warping Algorithm," Sustainability, MDPI, vol. 10(12), pages 1-18, December.
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