标题： 基于FTX崩溃后的钱包级行为变化建模：一种基于XAI的GLM对以太坊交易的研究 显示英文标题

标题： Modeling Wallet-Level Behavioral Shifts Post-FTX Collapse: An XAI-Driven GLM Study on Ethereum Transactions

摘要： 以太坊区块链在更广泛的加密货币生态系统中发挥着核心作用，通过智能合约的使用促进了各种金融活动。 本文研究了个人以太坊钱包如何应对FTX的崩溃，这是最大的中心化加密货币交易所之一。 超越基于价格的事件研究，我们采用自下而上的方法，使用细粒度的钱包级别数据。 我们构建了一个代表性的以太坊地址样本，并利用可解释的人工智能（XAI）框架分析其在崩溃前后交易行为。 我们提出的框架通过采用校准的零膨胀广义线性固定效应模型，解决了高分辨率钱包级别每日交易的数据稀缺问题。 我们的分析量化了交易强度和稳定币使用方面的显著变化，突显了生态系统内的避险行为。 这些发现强调了自下而上方法在量化基于区块链的冲击对用户层面影响的价值，通过钱包级别数据提供了超越传统价格层面分析的见解。 显示英文摘要

摘要： The Ethereum blockchain plays a central role in the broader cryptocurrency ecosystem, enabling a wide range of financial activity through the use of smart contracts. This paper investigates how individual Ethereum wallets responded to the collapse of FTX, one of the largest centralized cryptocurrency exchanges. Moving beyond price-based event studies, we adopt a bottom-up approach using granular wallet-level data. We construct a representative sample of Ethereum addresses and analyze their transaction behavior before and after the collapse using an explainable artificial intelligence (XAI) framework. Our proposed framework addresses data scarcity in high-resolution wallet-level daily transactions by employing a calibrated zero-inflated generalized linear fixed effects model. Our analysis quantifies distinct shifts in transaction intensity and stablecoin usage, highlighting a flight to safety within the ecosystem. These findings underscore the value of a bottom-up methodology for quantifying the user-level impact of blockchain-based shocks, offering insights beyond traditional price-level analysis through wallet-level data.