An Empirical Study of DeFi Liquidations: Incentives, Risks, and Instabilities
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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.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Jun 2021.
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Citing papers per year: 2023: 1; 2024: 3; 2025: 1; 2026: 3.
8 papers.
- 2024Detecting Financial Bots on the Ethereum Blockchaincited by 6
- 2023Leveraging Machine Learning for Multichain DeFi Fraud Detectioncited by 3
- 2025DeXposure: A Dataset and Benchmarks for Inter-protocol Credit Exposure in Decentralized Financial Networkscited by 3
- 2024Decentralized Intelligence in GameFi: Embodied AI Agents and the Convergence of DeFi and Virtual Ecosystemscited by 2
- 2024Leveraging Ensemble-Based Semi-Supervised Learning for Illicit Account Detection in Ethereum DeFi Transactionscited by 1
- 2026Paper Agents, Paper Gains: An Empirical Analysis of DeFi Investment Agentscited by 1
- 2026zLend: A Dual-Scope Cash-Flow Reconstruction Framework for On-Chain Credit Underwritingcited by 1
- 2026From Risk to Rescue: An Agentic Survival Analysis Framework for Liquidation Preventioncited by 0
- 2026zLend: A Dual-Scope Cash-Flow Reconstruction Framework for On-Chain Credit Underwriting17 Aug 2026
- 2026Paper Agents, Paper Gains: An Empirical Analysis of DeFi Investment Agents27 May 2026
- 2026From Risk to Rescue: An Agentic Survival Analysis Framework for Liquidation Prevention16 Apr 2026
- 2025DeXposure: A Dataset and Benchmarks for Inter-protocol Credit Exposure in Decentralized Financial Networks27 Nov 2025
- 2024Decentralized Intelligence in GameFi: Embodied AI Agents and the Convergence of DeFi and Virtual Ecosystems24 Dec 2024
- 2024Leveraging Ensemble-Based Semi-Supervised Learning for Illicit Account Detection in Ethereum DeFi Transactions3 Dec 2024
- 2024Detecting Financial Bots on the Ethereum Blockchain28 Mar 2024
- 2023Leveraging Machine Learning for Multichain DeFi Fraud Detection17 May 2023