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

TL;DR

This study uses an explainable AI framework with a zero-inflated GLM to analyze Ethereum wallet behaviors before and after the FTX collapse, revealing shifts in transaction patterns and stablecoin usage.

Contribution

It introduces a wallet-level, bottom-up analysis method employing XAI and a zero-inflated GLM to quantify behavioral shifts following a major crypto event.

Findings

Increased transaction activity post-FTX collapse

Shift towards stablecoin usage as a flight to safety

Demonstrates the effectiveness of wallet-level analysis for blockchain shocks

Abstract

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.