SearcharxivSearch

arXiv subjects

Andrei Kornilov

Publications and source records attributed to Andrei Kornilov.

2 recordsLinked to original sources

Liquidity provision in CLMMs: evidence from transactions data

The emergence of Concentrated Liquidity Market Makers (CLMMs) has made liquidity provision on decentralized exchanges an active and risk-sensitive task. However, the standalone profitability of liquidity provision remains unclear for liquidity providers (LPs) who neither hedge their inventory risk nor receive off-pool profits. This paper studies the actual outcomes of LP activity using historical transaction-level data from WETH/USD liquidity pools on the Base chain across the Uniswap, Aerodrome, PancakeSwap and SushiSwap protocols. We propose a methodology for reconstructing LP PnL dynamics from on-chain events and introduce an original metric that captures both the terminal state of LP capital and its path over time. Based on this framework, we estimate the share of successful LPs, classify their behavior and develop a taxonomy of 15 position types as structural components of PnL. We further identify a distinct class of multi-LPs operating across several pools and show that the dominant profitable position configurations are concentrated around the current pool price. The results show that only about one out of six LPs avoids losses in the selected market segment, raising an open question about the true economic motives of LP participation. Evidence also suggests that successful LPs often close positions before the full range is traversed, making observed behavior closer to profit-target-based strategies.

q-fin.TR

Dynamic Liquidity Provision in Decentralized Markets: Strategy Optimization and Performance Evaluation in Concentrated Liquidity AMMs

Concentrated Liquidity Market Makers (CLMMs) represent a fundamental innovation in market microstructure, transforming liquidity provision from passive portfolio allocation to active risk management. This evolution creates significant challenges for performance evaluation and strategy optimization, particularly due to the absence of comprehensive historical liquidity data. We address these challenges through a novel methodological framework that reconstructs historical liquidity states from swap transaction data, enabling rigorous backtesting of dynamic liquidity provision strategies. Our parametric reconstruction method achieves high accuracy (approximation errors averaging around 2\%) without relying on historical liquidity snapshots, addressing a critical data gap in decentralized finance research. We apply this framework to evaluate tau-reset strategies--dynamic liquidity reallocation approaches that respond to market movements--across multiple Uniswap v3 pools. Using machine learning to optimize strategy parameters based on market conditions, we identify consistent outperformance (13--23\% higher fees) compared to uniform allocation benchmarks. Our analysis reveals important insights into the risk-return tradeoffs in automated market making, including the critical role of impermanent loss as a dominant risk factor and the effectiveness of asymmetric strategy modifications for capital preservation. These findings contribute to the broader understanding of market microstructure in decentralized exchanges, providing both methodological innovations for performance evaluation and practical insights for liquidity providers navigating this evolving financial landscape.

q-fin.MF