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In the contemporary digital asset landscape, Decentralized Finance (DeFi) has evolved as a transformative ecosystem, enabling novel means of financial engagement via autonomous smart contracts. Within the expanding universe of DeFi protocols, yield farming has emerged as a popular and potentially profitable practice for crypto-asset investors. Yield farming refers to the strategic deployment of crypto-assets in various liquidity pools to maximize return on investment, an endeavor with a set of complexities and challenges that requires effective and data-driven management strategies.
This research aims to provide insight into the performance and sustainability of various DeFi yield farming portfolios, with a particular emphasis on differing rebalancing periods. By analyzing portfolio performance over a 12-month period, we examine the impact of weekly, biweekly, and monthly rebalancing strategies compared to a fixed portfolio with no rebalancing.
Our investigation considers both the potential for high yield and the inherent risks associated with these strategies. Furthermore, we incorporate the analysis of Arbitrum gas fees, which are integral to transactions and interactions with DeFi protocols and can significantly influence overall portfolio performance. However, our focus will not extend to the phenomenon of impermanent loss, a unique risk associated with liquidity provision in DeFi, which while crucial to the broader conversation, falls beyond the scope of this specific research.
Through a systematic backtesting approach, we will establish a comprehensive understanding of how different rebalancing periods can impact the overall performance of a DeFi yield farming portfolio. The findings of this research are intended to guide crypto investors, financial advisors, and DeFi enthusiasts in their portfolio management strategies, ultimately promoting greater efficiency and profitability in the fast-paced and ever-evolving DeFi space.
This research is premised on the expectation that yield farming portfolios with a higher frequency of rebalancing could potentially outperform those that are fixed or infrequently rebalanced. The logic behind this hypothesis is that, by adjusting the portfolio's allocations more often, an investor can better navigate the rapidly shifting DeFi landscape, capitalizing on the high-yielding opportunities that emerge.
However, it is also proposed that the benefit of frequent rebalancing may diminish or even reverse for portfolios with lower investment amounts. This assumption is based on the economic principle that pits fixed costs against variable gains. In the context of DeFi yield farming, Arbitrum gas fees associated with each rebalancing act as the fixed cost, and the yield from various liquidity pools represents the variable gain. For smaller portfolios, these fixed costs could, over time, outweigh the benefits derived from capturing fleeting high-yield opportunities, thereby eroding overall performance.
To empirically test these hypotheses, we will employ a backtesting methodology using historical data from the top 50 TVL pools on Arbitrum, excluding Uniswap v3 pools due to their significant impermanent loss component. This data will be utilized to calculate the optimal portfolio for each week over a 12-month period, under different rebalancing frequencies: weekly, biweekly, monthly, and fixed. The subsequent comparison of these results will provide empirical evidence to support or refute our initial expectations, contributing to a more robust understanding of the dynamics at play within DeFi yield farming portfolio management.
In conducting this research, several assumptions will be applied to provide structure to the analysis, as well as to narrow the scope to a manageable domain. The assumptions guiding our study are as follows:
Investment size: Initially we will assume an investment size of $10,000, we will later explore the effect of lower investment size and examine the effect of investment size on profitability.
Portfolio Optimization: We will use the Modern Portfolio Theory (MPT) as our guiding principle for portfolio optimization. Thus, we will use mean-variance optimization to construct our portfolios.
Transaction Costs: The cost of Arbitrum gas fees for each rebalancing operation will be considered from self testing and then pro-rating to an average using data obtained from Dune Analytics, in a precautionary overestimation. In this study, it is assumed that every asset in the portfolio will require four separate transactions - two approvals (one for the underlying token conversion and one for liquidity provision) and two actual transactions (one for asset conversion and one for liquidity provision). The cost of each approval 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 asset in the portfolio to four. This constraint is based on the notion that rebalancing strategies are typically short-term and having too many assets can add unnecessary complexity and transaction costs, potentially diminishing the effectiveness of frequent rebalancing. Furthermore each pool has a max weight of 30%.
Impermanent Loss: In this research, we will not consider impermanent loss. Although impermanent loss can be a significant factor affecting the profitability of liquidity provision in certain DeFi protocols, it will be excluded from this particular study to isolate the impact of rebalancing frequency and gas fees on portfolio performance.
By applying these assumptions, we aim to create a structured framework within which we can conduct our analysis and generate meaningful insights into the implications of different rebalancing strategies on DeFi yield farming portfolios.
The following approach was utilized in our research to rigorously examine and compare the performance of DeFi yield farming portfolios under various rebalancing strategies:
Dataset: We used a dataset comprising the top 50 TVL pools on Arbitrum, excluding Uniswap v3 due to their significant impermanent loss component. Our data is specifically looking at the time period 25th April 2022 to 25th April 2023. Each included pool is from a protocol with a risk score of 6/10 or higher, as assessed by our proprietary risk evaluation method. It is crucial to note that not all pools have return data extending back to the start of the 12-month period; thus, the set of available assets for portfolio optimization expands as the backtest progresses. Rather than a limitation, this situation realistically mirrors the dynamic nature of the DeFi space, where new investment opportunities constantly emerge. Full list of included pools can be found in the bibliography.
Portfolio Construction: To construct the portfolios, we segmented the 12-month period into individual weeks. For each week, we employed mean-variance optimization to construct the maximum return portfolio, setting the number of assets in the portfolio to four. This constraint was applied for reasons of consistency, simplicity, and comparability across the varying rebalancing periods.
Return Computation: Returns were calculated at the end of each week based on the optimal portfolio from the previous week, as future returns are, of course, unknowable. For instance, for the weekly rebalance portfolio, the week 10 portfolio tracks the return of those assets in week 11. Essentially, at time T, we tracked the returns of the portfolio from time T-1. When rebalancing occurs, gains are compounded; conversely, when no rebalancing takes place, gains are simply added without compounding.
Gas Fees and Compounding: The gas fees were deducted at the start of any rebalancing period and incorporated within the compounding calculations. This approach allows for a realistic evaluation of the net returns that can be expected after considering the transaction costs associated with portfolio rebalancing.
This methodology provides a robust and realistic framework to evaluate the impact of varying rebalancing frequencies and transaction costs on DeFi yield farming portfolios' performance over a 12-month period.
Monthly Rebalanced vs Static portfolio.
Fixed Portfolio ending balance after 52 weeks: $12522.65
Monthly Changing Portfolio ending balance after 52 weeks: $13527.94
Fixed Portfolio gain after 52 weeks: $2522.65 (25.23%)
Monthly Rebalanced Portfolio gain after 52 weeks: $3527.94 (35.28%)
Monthly / Fixed = +28.5%
Monthly Rebalanced vs. Biweekly Rebalanced.
Monthly Changing Portfolio ending balance after 52 weeks: $13527.94
Biweekly Changing Portfolio ending balance after 52 weeks: $13657.88
Monthly Changing Portfolio gain after 52 weeks: $3527.94 (35.28%)
Biweekly Changing Portfolio gain after 52 weeks: $3657.88 (36.58%)
Biweekly / Monthly = +3.55%
Weekly Rebalanced vs. Biweekly Rebalanced
Weekly Changing Portfolio ending balance after 52 weeks: $13435.20
Biweekly Changing Portfolio ending balance after 52 weeks: $13657.88
Weekly Changing Portfolio gain after 52 weeks: $3435.20 (34.35%)
Biweekly Changing Portfolio gain after 52 weeks: $3657.88 (36.58%)
Weekly/ Biweekly = -6.09%
The simulation results indicate that the frequency of portfolio rebalancing has a significant impact on the overall return, with different frequencies being optimal for different initial investment amounts.
For an initial investment of $10,000, the bi-weekly rebalancing strategy yielded the highest returns, outperforming the monthly strategy by 3.55% and the weekly strategy by 6.09%. This suggests that for this investment size, rebalancing every two weeks was the most effective way to take advantage of market fluctuations and maintain an optimal asset allocation.
Meanwhile, the monthly rebalancing strategy offered a substantial advantage over the fixed strategy, providing a 28.5% higher return over a 52-week period. This underlines the benefit of regular portfolio adjustments to adapt to changing market conditions.
However, as the size of the investment increases, the weekly rebalancing strategy starts to outperform. This is due to the ability of the weekly strategy to more promptly capture market opportunities and mitigate risks, which can have a greater impact on larger portfolios, but at the cost of a higher maintenance, which outweighed the benefits in a $10,000 simulation.
It is also important to note that the fixed portfolio performed better than we might expect, this can be due to survivorship bias from selecting pools based on top 50 TVL at the end of the period, the worse performing pools may have lost their TVL and not have been included, nevertheless the rebalancing strategies all outperformed substantially for the $10,000 investment size.
While these results provide valuable insights, it's essential to remember that the optimal rebalancing frequency may vary depending on various factors such as the investment amount, market volatility, transaction costs, and individual risk tolerance. Frequent rebalancing can help optimize returns and manage risk, but it also involves higher transaction costs. On the other hand, less frequent rebalancing may miss out on short-term market opportunities but are more cost-efficient.
Our previous experiments hinted at an interesting relationship between the size of an initial investment and the optimal frequency of portfolio rebalancing. While these initial findings are intriguing, we've only scratched the surface.
Now, we'll broaden our range and run simulations with initial investments from $2,000 all the way up to $50,000, using increments of $250. This expanded scope will give us a comprehensive view of how investment size interacts with rebalancing frequency.
We'll apply each of our four strategies - the fixed portfolio, and weekly, bi-weekly, and monthly rebalancing - to this wider range of investment sizes. Our aim is to pinpoint the specific investment sizes where one rebalancing strategy starts to outshine the others.
With this approach, we're hoping to gain a clearer understanding of how different scales of investment affect the profitability of various rebalancing strategies. We will be specifically looking into profitability and profit margin as this is the most objective measure of performance.
Based on our data visualization, we can derive several intriguing insights regarding the impact of investment size on the profitability of different rebalancing strategies.
Here were the optimal strategies for each range:
Due to the effect of compounding, we have an inverse exponential (logarithmic) effect on the profit margin when increasing our investment amount with these rebalancing strategies. This is because we have fixed costs against a variable gain, with a compounding effect.
These findings underscore the nuanced relationship between investment size and optimal rebalancing frequency. At smaller investment sizes, less frequent rebalancing appears more beneficial, due to lower transaction costs. However, as the investment size increases, higher frequency rebalancing strategies, particularly weekly rebalancing, start to deliver superior returns.
This dynamic interplay between investment size and rebalancing frequency adds another layer of complexity to portfolio management. It emphasizes the importance of tailoring the rebalancing strategy to the investment size to maximize profitability.
For individual users who do not have large amounts of capital to invest, these costs may make yield farming on their own less profitable or even unprofitable. However, there are potential solutions to this problem:
Our research provides a comprehensive examination of the interaction between rebalancing frequency, transaction costs, and investment size in Decentralized Finance (DeFi) yield farming portfolios. Our findings indicate a complex relationship between these variables, demonstrating the importance of strategic portfolio management in the dynamic DeFi landscape.
We found that less frequent rebalancing strategies, such as monthly, outperformed a fixed strategy for smaller investment sizes due to lower transaction costs. However, as the investment size increased, these benefits diminished and higher frequency rebalancing strategies, particularly weekly, became more advantageous. This dynamic highlights the necessity of tailoring rebalancing strategies according to the investment size and prevailing market conditions, underscoring the intricacies of portfolio management within DeFi.
However, it's important to note that our findings are derived from a dataset that, while extensive, may not perfectly represent the entire DeFi landscape. This limitation arises from the rapid evolution and inherent volatility of the DeFi ecosystem, where market conditions and asset values can shift significantly over short periods. As such, while our results provide a useful guide, they should not be taken as precise predictions. Instead, they offer general insights into the impact of rebalancing frequency and transaction costs on DeFi yield farming portfolios, which can aid investors in formulating more informed and effective portfolio management strategies.
In terms of practical implications, our research suggests that investors with smaller portfolios could potentially benefit from pooling their resources. By doing so, they could achieve a larger investment size, potentially making higher frequency rebalancing strategies more beneficial. Alternatively, opting for singular asset portfolios could help reduce transaction costs associated with rebalancing, thereby improving portfolio performance.
Overall, our study offers a robust understanding of the nuances of DeFi yield farming portfolio management, highlighting the significant opportunities for profit and growth within this innovative financial ecosystem. However, as the DeFi landscape continues to evolve, ongoing research and continual adjustment of investment strategies will be paramount. With a clear understanding of these dynamics, investors, financial advisors, and DeFi enthusiasts can navigate this vibrant financial frontier with greater confidence and foresight.
Included pools:
We regularly prepare insightful reports and case studies about crypto trading and the blockchain industry.
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