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Stablecoin depegging risk prediction

Author

Listed:
  • Lee, Yi-Hsi
  • Chiu, Yu-Fen
  • Hsieh, Ming-Hua

Abstract

This study aims to identify and analyze key factors contributing to depegging risks in stablecoins, consolidating insights from the literature into four critical categories: trading price and volume, market information, sentiment, and volatility. Utilizing these insights, we develop predictive models using three machine learning algorithms—logistic regression, random forest, and XGBoost—to accurately and timely predict stablecoin depegging events. Our primary subjects are the top four stablecoins by daily trading volume: USDT, USDC, BUSD, and DAI. Diverging from previous studies that employed static depegging thresholds, we adopt a dynamic threshold adjusted for trading volume. Additionally, this study is the first to incorporate sentiment indicators from news sources alongside traditional on-chain price and volume data. Covering the empirical period from January 1, 2022, to December 31, 2023.

Suggested Citation

  • Lee, Yi-Hsi & Chiu, Yu-Fen & Hsieh, Ming-Hua, 2025. "Stablecoin depegging risk prediction," Pacific-Basin Finance Journal, Elsevier, vol. 90(C).
    • Handle: RePEc:eee:pacfin:v:90:y:2025:i:c:s0927538x24003925
    DOI: 10.1016/j.pacfin.2024.102640
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    More about this item

    Keywords

    Stablecoins; Depegging; Machine learning;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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