Stablecoin depegging risk prediction

Yi-Hsi Lee, Yu-Fen Chiu and Ming-Hua Hsieh

Pacific-Basin Finance Journal, 2025, vol. 90, issue C

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.

Keywords: Stablecoins; Depegging; Machine learning (search for similar items in EconPapers)
JEL-codes: G12 G14 G15 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:pacfin:v:90:y:2025:i:c:s0927538x24003925

DOI: 10.1016/j.pacfin.2024.102640

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