skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Characterizing Common Quarterly Behaviors in DeFi Lending Protocols
The emerging decentralized financial ecosystem (DeFi) is comprised of numerous protocols, one type being lending protocols. People make transactions in lending protocols, each of which is attributed to a specific blockchain address which could represent an externally-owned account (EOA) or a smart contract. Using Aave, one of the largest lending protocols, we summarize the transactions made by each address in each quarter from January 1, 2021, through December 31, 2022. We cluster these quarterly summaries to identify and name common patterns of quarterly behavior in Aave. We then use these clusters to glean insights into the dominant behaviors in Aave. We show that there are three kinds of keepers, i.e., a specific type of users tasked with the protocol’s governance, but only one kind of keeper finds consistent success in making profits from liquidations. We identify the largest-scale accounts in Aave and the highest-risk kinds of behavior on the platform. Additionally, we use the temporal aspect of the clusters to track how common behaviors change through time and how usage has shifted in the wake of major events that impacted the crypto market, and we show that there seem to be problems with user retention in Aave as many of the addresses that perform transactions do not remain in the market for long.  more » « less
Award ID(s):
2113906
PAR ID:
10514673
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
The International Conference on Mathematical Research for Blockchain Economy
Date Published:
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Pardalos, Panos; Kotsireas, Ilias; Guo, Yike; Knottenbelt, William (Ed.)
    We propose a decentralized finance (DeFi) survival analysis approach for discovering and characterizing user behavior and risks in lending protocols. We demonstrate how to gather and prepare DeFi transaction data for survival analysis. We demonstrate our approach using transactions in AAVE, one of the largest lending protocols. We develop a DeFi survival analysis pipeline which first prepares transaction data for survival analysis through the selection of different index events (or transactions) and associated outcome events. Then we apply survival analysis statistical and visualization methods such as median survival times, Kaplan–Meier survival curves, and Cox hazard regression to gain insights into usage patterns and risks within the protocol. We show how by varying the index and outcome events, we can utilize DeFi survival analysis to answer three different questions. What do users do after a deposit? How long until borrows are first repaid or liquidated? How does coin type influence liquidation risk? The proposed DeFi survival analysis can easily be generalized to other DeFi lending protocols. By defining appropriate index and outcome events, DeFi survival analysis can be applied to any cryptocurrency protocol with transactions. 
    more » « less
  2. We propose a survival analysis approach for discovering and characterizing user behavior and risks for lending protocols in decentralized finance (DeFi). We demonstrate how to gather and prepare DeFi transaction data for survival analysis. We illustrate our approach using transactions in Aave, one of the largest lending protocols. We develop a DeFi survival analysis pipeline that first prepares transaction data for survival analysis through the selection of different index events (or transactions) and associated outcome events. Then we apply survival analysis statistical and visualization methods modified for competing risks when appropriate, such as Kaplan–Meier survival curves, cumulative incidence functions, Cox hazard regression, and Fine-Gray models for sub-distribution hazards to gain insights into usage patterns and risks within the protocol. We show how, by varying the index and outcome events as well as covariates, we can use DeFi survival analysis to answer questions like “How does loan size affect the repayment schedule of the loan?”; “How does loan size affect the likelihood that an account gets liquidated?”; “How does user behavior vary between Aave markets?”; “How has user behavior in Aave varied from quarter to quarter?” The proposed DeFi survival analysis can easily be generalized to other DeFi lending protocols. By defining appropriate index and outcome events, DeFi survival analysis can be applied to any cryptocurrency protocol with transactions. 
    more » « less
  3. Permissioned Blockchain systems rely mainly on Byzantine fault-tolerant protocols to establish consensus on the order of transactions. While Byzantine fault-tolerant protocols mostly guarantee consistency (safety) in an asynchronous network using 3f+1 machines to overcome the simultaneous malicious failure of any f nodes, in many systems, e.g., blockchain systems, the number of available nodes (resources) is much more than 3f + 1. To utilize such extra resources, in this paper we introduce a model that leverages transaction parallelism by partitioning the nodes into clusters (partitions) and processing independent transactions on different partitions simultaneously. The model also shards the blockchain ledger, assigns different shards of the blockchain ledger to different clusters, and includes both intra-shard and cross-shard transactions. Since more than one cluster is involved in each cross-shard transaction, the ledger is formed as a directed acyclic graph. 
    more » « less
  4. Böhme, Rainer; Kiffer, Lucianna (Ed.)
    Decentralized finance (DeFi) borrowing and lending platforms are crucial to the decentralized economy, involving two main participants: lenders who provide assets for interest and borrowers who offer collateral exceeding their debt and pay interest. Collateral volatility necessitates over-collateralization to protect lenders and ensure competitive returns. Traditional DeFi platforms use a fixed interest rate curve based on the utilization rate (the fraction of available assets borrowed) and determine over-collateralization offline through simulations to manage risk. This method doesn't adapt well to dynamic market changes, such as price fluctuations and evolving user needs, often resulting in losses for lenders or borrowers. In this paper, we introduce an adaptive, data-driven protocol for DeFi borrowing and lending. Our approach includes a high-frequency controller that dynamically adjusts interest rates to maintain market stability and competitiveness with external markets. Unlike traditional protocols, which rely on user reactions and often adjust slowly, our controller uses a learning-based algorithm to quickly find optimal interest rates, reducing the opportunity cost for users during periods of misalignment with external rates. Additionally, we use a low-frequency planner that analyzes user behavior to set an optimal over-collateralization ratio, balancing risk reduction with profit maximization over the long term. This dual approach is essential for adaptive markets: the short-term component maintains market stability, preventing exploitation, while the long-term planner optimizes market parameters to enhance profitability and reduce risks. We provide theoretical guarantees on the convergence rates and adversarial robustness of the short-term component and the long-term effectiveness of our protocol. Empirical validation confirms our protocol’s theoretical benefits. 
    more » « less
  5. We introduce ABIDES, an open source Agent-Based Interactive Discrete Event Simulation environment. ABIDES is designed from the ground up to support agent-based research in market applications. While proprietary simulations are available within trading firms, there are no broadly available high-fidelity market simulation environments. ABIDES enables the simulation of tens of thousands of trading agents interacting with an exchange agent to facilitate transactions. It supports configurable pairwise noisy network latency between each individual agent as well as the exchange. Our simulator's message-based design is modeled after NASDAQ's published equity trading protocols ITCH and OUCH. We introduce the design of the simulator and illustrate its use and configuration with sample code, validating the environment with example trading scenarios. The utility of ABIDES for financial research is illustrated through experiments to develop a market impact model. The core of ABIDES is a general-purpose discrete event simulation, and we demonstrate its breadth of application with a non-finance work-in-progress simulating secure multiparty federated learning. We close with discussion of additional experimental problems it can be, or is being, used to explore, such as the development of machine learning trading algorithms. We hope that the availability of such a platform will facilitate research in this important area. 
    more » « less