Early-Warning Signals of Political Risk in Stablecoin Markets: Human and Algorithmic Behavior Around the 2024 U.S. Election

ArXiv ID: 2512.00893 “View on arXiv”

Authors: Kundan Mukhia, Buddha Nath Sharma, Salam Rabindrajit Luwang, Md. Nurujjaman, Chittaranjan Hens, Suman Saha, Tanujit Chakraborty

Abstract

We study how the 2024 U.S. presidential election, viewed as a major political risk event, affected cryptocurrency markets by distinguishing human-driven peer-to-peer stablecoin transactions from automated algorithmic activity. Using structural break analysis, we find that human-driven Ethereum Request for Comment 20 (ERC-20) transactions shifted on November 3, two days before the election, while exchange trading volumes reacted only on Election Day. Automated smart-contract activity adjusted much later, with structural breaks appearing in January 2025. We validate these shifts using surrogate-based robustness tests. Complementary energy-spectrum analysis of Bitcoin and Ethereum identifies pronounced post-election turbulence, and a structural vector autoregression confirms a regime shift in stablecoin dynamics. Overall, human-driven stablecoin flows act as early-warning indicators of political stress, preceding both exchange behavior and algorithmic responses.

Keywords: cryptocurrency, stablecoins, structural break analysis, political risk, market microstructure

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced econometric techniques like structural break tests, Hilbert-Huang Transform (energy-spectrum analysis), and structural vector autoregression, indicating significant mathematical density. It is heavily data-driven, using real-world blockchain transaction data, implementing robustness checks (surrogate tests), and providing clear methodological details for backtesting.

  flowchart TD
    A["Research Goal<br>Human vs. Algorithmic Response<br>to Political Risk in Crypto Markets"] --> B["Methodology<br>Structural Break Analysis & Energy-Spectrum"]
    B --> C["Data Input<br>ERC-20 P2P Transactions & Exchange Volumes"]
    C --> D["Computational Process<br>Detected Structural Breaks"]
    D --> E["Nov 3: Human-Driven Shift<br>(2 Days Pre-Election)"]
    D --> F["Nov 5: Exchange Volume Shift<br>(Election Day)"]
    D --> G["Jan 2025: Algorithmic Shift<br>(Post-Election Regime)"]
    E & F & G --> H["Key Findings<br>Human Flows are Early-Warning Signals<br>Predicting Algorithmic & Exchange Responses"]