Leveraged positions on decentralized lending platforms
Researchers have developed a new mathematical framework to optimize leveraged staking, or 'loopy' staking, in decentralized finance (DeFi) lending markets. Their model simplifies complex strategies into solvable equations for three major interest rate types, including those used by Aave and Morpho. Results show small investors can double returns to 6.2% annual yield, but larger positions face higher costs. The study uses real blockchain data and accounts for fees, though it assumes perfect liquidity and no competition.
What it examines
This paper develops a mathematical framework to optimize leveraged staking strategies in Decentralized Finance (DeFi). It provides closed-form solutions for allocating capital across multiple lending markets with different interest rate models, aiming to maximize returns while considering leverage limits, borrowing costs, and transaction fees.
What it concludes
The study shows that optimized leveraged staking can significantly boost returns, especially for smaller investments. However, results depend on market size, rebalancing frequency, and liquidity. Applications include automated DeFi portfolio management. Future research should address multi-user competition, risk from price changes, and more realistic market conditions.
Evidence objects
Researchers unveil a mathematical framework that simplifies 'loopy' leveraged staking in DeFi, enabling closed-form solutions for three major interest rate models: linear, kinked (Aave), and adaptive (Morpho).
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Leveraged staking can double returns for small investors, reaching up to 6.2% APY versus 3.1% for simple staking, but larger positions face higher borrowing costs, reducing profitabilitya surprising 'size effect.'.
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The study incorporates transaction fees and leverage limits, validating models with real blockchain data, but assumes perfect liquidity and a single agent, missing real-world slippage and competition in DeFi markets.
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This paper presents a novel convex optimization framework for leveraged staking in DeFi, offering closed-form solutions under diverse interest rate models and market constraints. Its originality lies in rigorous transaction cost modeling and multi-market exposure analysis, validated with real blockchain data, making it compelling for DeFi, quantitative finance, and risk management.
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Raw abstract and provenance
Abstract: We develop a mathematical framework to optimize leveraged staking ("loopy") strategies in Decentralized Finance (DeFi), in which a staked asset is supplied as collateral, the underlying is borrowed and re-staked, and the loop can be repeated across multiple lending markets. Exploiting the fact that DeFi borrow rates are deterministic functions of poo… ▽ More We develop a mathematical framework to optimize leveraged staking ("loopy") strategies in Decentralized Finance (DeFi), in which a staked asset is supplied as collateral, the underlying is borrowed and re-staked, and the loop can be repeated across multiple lending markets. Exploiting the fact that DeFi borrow rates are deterministic functions of pool utilization, we reduce the multi-market problem to a convex allocation over market exposures and obtain closed-form solutions under three interest-rate models: linear, kinked, and adaptive (Morpho's AdaptiveCurveIRM). The framework incorporates market-specific leverage limits, utilization-dependent borrowing costs, and transaction fees. Backtests on the Ethereum and Base blockchains using the largest Morpho wstETH/WETH markets (from January 1 to April 1, 2025) show that rebalanced leveraged positions can reach up to 6.2% APY versus 3.1% for unleveraged staking, with strong dependence on position size and rebalancing frequency. Our results provide a mathematical basis for transparent, automated DeFi portfolio optimization. △ Less
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