AI 中文总结
本研究以以太坊区块链上的USDT和USDC交易为对象，分析约3.7亿笔交易数据，发现稳定币交易价值存在两种幂律缩放机制，为区块链金融系统的相关研究提供了基础。
AI 中文摘要
稳定币已迅速成为一类重要的数字资产，也是数字金融生态系统的组成部分。尽管其重要性日益提升，稳定币交易活动的统计特性在很大程度上仍未得到探索。据我们所知，本研究是首个针对稳定币交易数据中缩放行为开展的研究，聚焦于USDT与USDC。我们分析了2024年6月至2026年2月期间六个时段内记录在以太坊区块链上的约3.7亿笔USDT和USDC交易。基于外部拥有账户（EOA）与智能合约（SC）之间的交互，我们将交易分为四类：EOA-EOA、EOA-SC、SC-EOA和SC-SC。通过对幂律指数进行最大似然估计，我们发现两类稳定币在所有时段及交互类别下的交易价值分布均呈现重尾缩放特征。我们识别出两种不同的缩放机制：涉及EOA的类别集中在1.45-1.60之间，而SC-SC交易的指数更高，约为1.72-1.73。敏感性分析证实，这种分离在不同时段、稳定币及拟合样本量下均具有稳健性。反事实分析显示，仅类别权重的变化无法解释观测到的总指数变异；在不同样本量下，反事实路径仅能解释实际数据中观测到的总时间范围的约10%-35%。总体而言，我们的结果表明，稳定币交易价值尾部存在两种明显分化的缩放机制。幂律尾部行为在稳定币交易活动中普遍存在，但指数取决于交易是否由EOA或SC驱动。这些发现为进一步研究基于区块链的金融系统中的缩放行为与交易异质性提供了基础。
英文摘要
Stablecoins have rapidly emerged as an important class of digital assets and a component of the digital financial ecosystem. Despite their growing importance, the statistical properties of stablecoin transaction activity remain largely unexplored. To the best of our knowledge, this is the first study to investigate scaling behavior in stablecoin transaction data, focusing on USDT and USDC. We analyze approximately 370 million USDT and USDC transactions recorded on the Ethereum blockchain across six periods spanning June 2024 to February 2026. Based on interactions between Externally Owned Accounts (EOAs) and Smart Contracts (SCs), we classify transactions into four categories: EOA-EOA, EOA-SC, SC-EOA, and SC-SC. Using maximum-likelihood estimation of power-law exponents, we find that transaction value distributions exhibit heavy-tailed scaling for both stablecoins across all periods and interaction categories. We identify two distinct scaling regimes: EOA-involved categories cluster around 1.45-1.60, whereas SC-SC transactions exhibit higher exponents of approximately 1.72-1.73. Sensitivity analysis confirms that this separation is robust across periods, stablecoins, and fitting sample sizes. Counterfactual analysis shows that changes in category weights alone cannot explain the observed variation in the overall exponent. Across different sample sizes, the counterfactual path accounts for only about 10%-35% of the total temporal range observed in the actual data. Overall, our results indicate two broadly differentiated scaling regimes in the tail of stablecoin transaction values. Power-law tail behavior is observed throughout stablecoin transaction activity, but the exponent depends on whether transactions are driven by EOAs or SCs. These findings provide a basis for further research on scaling behavior and transaction heterogeneity in blockchain-based financial systems.
