# An Empirical Study of DeFi Liquidations: Incentives, Risks, and Instabilities

arXiv: https://arxiv.org/abs/2106.06389 · HTML: https://mlsanity.com/paper/2106.06389/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2021.

## Built on by (most cited first, 8 of 8)

- [Detecting Financial Bots on the Ethereum Blockchain](https://mlsanity.com/paper/2403.19530/) (2024)
- [Leveraging Machine Learning for Multichain DeFi Fraud Detection](https://mlsanity.com/paper/2306.07972/) (2023)
- [DeXposure: A Dataset and Benchmarks for Inter-protocol Credit Exposure in Decentralized Financial Networks](https://mlsanity.com/paper/2511.22314/) (2025)
- [Decentralized Intelligence in GameFi: Embodied AI Agents and the Convergence of DeFi and Virtual Ecosystems](https://mlsanity.com/paper/2412.18601/) (2024)
- [Leveraging Ensemble-Based Semi-Supervised Learning for Illicit Account Detection in Ethereum DeFi Transactions](https://mlsanity.com/paper/2412.02408/) (2024)
- [Paper Agents, Paper Gains: An Empirical Analysis of DeFi Investment Agents](https://mlsanity.com/paper/2605.29174/) (2026)
- [zLend: A Dual-Scope Cash-Flow Reconstruction Framework for On-Chain Credit Underwriting](https://mlsanity.com/paper/2608.16856/) (2026)
- [From Risk to Rescue: An Agentic Survival Analysis Framework for Liquidation Prevention](https://mlsanity.com/paper/2604.14583/) (2026)

## Abstract (arXiv)

Financial speculators often seek to increase their potential gains with leverage. Debt is a popular form of leverage, and with over 39.88B USD of total value locked (TVL), the Decentralized Finance (DeFi) lending markets are thriving. Debts, however, entail the risks of liquidation, the process of selling the debt collateral at a discount to liquidators. Nevertheless, few quantitative insights are known about the existing liquidation mechanisms. In this paper, to the best of our knowledge, we are the first to study the breadth of the borrowing and lending markets of the Ethereum DeFi ecosystem. We focus on Aave, Compound, MakerDAO, and dYdX, which collectively represent over 85% of the lending market on Ethereum. Given extensive liquidation data measurements and insights, we systematize the prevalent liquidation mechanisms and are the first to provide a methodology to compare them objectively. We find that the existing liquidation designs well incentivize liquidators but sell excessive amounts of discounted collateral at the borrowers' expenses. We measure various risks that liquidation participants are exposed to and quantify the instabilities of existing lending protocols. Moreover, we propose an optimal strategy that allows liquidators to increase their liquidation profit, which may aggravate the loss of borrowers.