Is it profitable to rebalance DeFi positions and how often?
In today’s digital asset landscape, Decentralized Finance (DeFi) has emerged as a transformative ecosystem, enabling novel ways of financial engagement via autonomous smart contracts. In the growing universe of DeFi protocols, yield farming has become a popular and potentially profitable practice for crypto asset investors. Yield farming refers to the strategic deployment of crypto assets across different liquidity pools to maximize return on investments, an endeavor with a number of complexities and challenges that requires effective and data-driven management strategies.
The aim of this research is to provide insights into the performance and sustainability of different DeFi yield farming portfolios, with a particular focus on different rebalancing timeframes. By analyzing portfolio performance over a 12-month period, we examine the impact of weekly, bi-weekly and monthly rebalancing strategies compared to a fixed portfolio without rebalancing.
Our research considers both the potential for high returns and the risks associated with these strategies. In addition, we integrate the analysis of Arbitrum gas fees, which are an integral part of transactions and interactions with DeFi protocols and can significantly affect the overall performance of the portfolio. However, our focus will not extend to the phenomenon of temporary loss, a unique risk associated with liquidity provision in DeFi that, while crucial to the broader discussion, is beyond the scope of this specific investigation.
Through a systematic backtesting approach, we will develop a comprehensive understanding of how different rebalancing periods can impact the overall performance of a DeFi yield farming portfolio. The results of this research are expected to assist crypto investors, financial advisors, and DeFi enthusiasts in their portfolio management strategies, ultimately contributing to increased efficiency and profitability in the fast-paced and ever-evolving DeFi space.
This research is based on the expectation that yield farming portfolios with a higher frequency of rebalancing could potentially perform better than those that are fixed or rarely rebalanced. The logic behind this hypothesis is that by adjusting portfolio allocations more frequently, an investor can better navigate the rapidly changing DeFi landscape and take advantage of the high-yield opportunities that arise.
However, it is also suggested that the benefits of frequent rebalancing may diminish or even reverse for portfolios with smaller invested amounts. This assumption is based on the economic principle that fixed costs are compared to variable profits. In the context of DeFi yield farming, the arbitrum gas fees associated with each rebalancing act as a fixed cost, and the yield from different liquidity pools represents the variable profit. For smaller portfolios, over time, these fixed costs could outweigh the benefits derived from usage near-term high yield opportunities, thereby impacting overall performance.
To test these hypotheses empirically, we will apply a backtesting methodology using historical data of the 50 largest TVL pools on Arbitrum, excluding Uniswap v3 pools due to their significant impermanent loss component. This data is used to calculate the optimal portfolio for each week over a 12-month period with different rebalancing frequencies: weekly, bi-weekly, monthly and fixed. The subsequent comparison of these results will provide empirical evidence that supports or refutes our initial expectations and will contribute to a more informed understanding of the dynamics at play in DeFi yield farming portfolio management.
In conducting this research, several assumptions are applied to provide structure to the analysis and to narrow the scope to a manageable range. The assumptions guiding our study are as follows:
Investment size: First we assume an investment size of $10,000, later we examine the impact of a smaller investment size and examine the impact of investment size on profitability.
Portfolio Optimization: We use Modern Portfolio Theory (MPT) as the guiding principle for portfolio optimization. Therefore, we will use mean-variance optimization to construct our portfolios.
Transaction costs: The cost of Arbitrum Gas Fees for each balancing event is self-tested and then pro-rated and averaged as a precautionary overestimate using data received from Dune Analytics. This study assumes that four separate transactions are required for each asset in the portfolio – two approvals (one for converting the underlying token and one for providing liquidity) and two actual transactions (one for converting assets and one for providing liquidity). The cost of each permit is assumed to be $0.225 while the cost of each transaction is assumed to be $0.4. This results in a total cost of $1.25 per asset per rebalancing period.
Portfolio constraint: We will limit the number of assets in the portfolio to four. This limitation is based on the notion that rebalancing strategies tend to be short-term in nature and too many assets can introduce unnecessary complexity and transaction costs, potentially reducing the effectiveness of frequent rebalancing. In addition, each pool has a maximum weight of 30%.
Ephemeral Loss: In this investigation, we will not consider the temporary loss. Although transient losses can be a significant factor affecting the profitability of liquidity provision in certain DeFi protocols, they are excluded in this particular study to isolate the impact of rebalancing frequency and gas fees on portfolio performance.
By applying these assumptions, we aim to provide a structured framework within which to conduct our analysis and generate meaningful insights into the impact of different rebalancing strategies on DeFi yield farming portfolios.
The following approach was used in our research to thoroughly examine and compare the performance of DeFi yield farming portfolios under different rebalancing strategies:
Record: We used a dataset that includes the top 50 TVL pools on Arbitrum, excluding Uniswap v3 due to its significant transient loss component. Our data specifically covers the period from April 25, 2022 to April 25, 2023. Each included pool is from a protocol with a risk score of 6/10 or higher, assessed by our proprietary risk scoring methodology. It is important to note that not all pools have return data going back to the beginning of the 12 month period. Thus, the set of assets available for portfolio optimization expands as the backtest progresses. This situation does not represent a limitation, but realistically reflects the dynamics of the DeFi space, where new investment opportunities are constantly emerging. See the bibliography for a complete list of included pools.
Portfolio structure: To compile the portfolios, we have segmented the 12-month period into individual weeks. For each week, we used mean-variance optimization to create the maximum return portfolio, setting the number of assets in the portfolio to four. This limitation has been applied across the various rebalancing periods for reasons of consistency, simplicity and comparability.
Yield calculation: Returns were calculated at the end of each week based on the previous week’s optimal portfolio, as future returns are of course unpredictable. For example, in the weekly rebalancing portfolio, the week 10 portfolio tracks the returns of those assets at week 11. Essentially, at time T, we tracked the portfolio’s returns from time T-1. When there is a rebalancing, the gains are multiplied; On the other hand, if there is no rebalancing, the profits are simply added together without compounding.
Gas Fees and Compounding: Gas charges were deducted at the beginning of each rebalancing period and included in compounding calculations. This approach allows for a realistic assessment of the expected net returns, taking into account the transaction costs associated with the portfolio shift.
This methodology provides a robust and realistic framework to assess the impact of different rebalancing frequencies and transaction costs on the performance of DeFi yield farming portfolios over a 12-month period.
Monthly rebalanced vs. static portfolio.
Fixed portfolio ending balance after 52 weeks: $12522.65
Monthly rolling portfolio ending balance after 52 weeks: $13527.94
Fixed portfolio gain at 52 weeks: $2522.65 (25.23%)
Rebalanced portfolio monthly gain after 52 weeks: $3527.94 (35.28%)
Monthly / Fixed = +28.5%
Monthly rebalancing vs bi-weekly rebalancing.
Monthly rolling portfolio ending balance after 52 weeks: $13527.94
Biweekly rotating portfolio ending balance after 52 weeks: $13657.88
Monthly portfolio gain at 52 weeks: $3527.94 (35.28%)
Bi-Weekly Rotating Portfolio Gain After 52 Weeks: $3657.88 (36.58%)
Biweekly / Monthly = +3.55%
Weekly Rebalancing vs. Biweekly Rebalancing
Weekly changing portfolio ending balance after 52 weeks: $13435.20
Biweekly rotating portfolio ending balance after 52 weeks: $13657.88
Weekly rotating portfolio gain after 52 weeks: $3435.20 (34.35%)
Bi-Weekly Rotating Portfolio Gain After 52 Weeks: $3657.88 (36.58%)
Weekly/biweekly = -6.09%
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