Ethereum Portfolio Management
Liquid staking tokens (LSTs) have become a core component of any Ethereum portfolio. 26% of all ETH is staked, and capital allocators of all sizes have taken positions in one or more competing liquid staking protocols.
A popular method for optimizing ETH portfolios involves composing LSTs, AMM liquidity pools, and lending into productized financial strategies. From Yearn to Balancer, the technical implementations of these strategies and their resulting financial payoffs differ significantly.
With DFMM, Primitive has the all building blocks required to bring automated, objective-oriented portfolios onchain. Our efforts have resulted in important questions: What do allocators want? What does Ethereum need? How do we balance confilicting objectives?
Conflicting Objectives
Due to inconsistent LST performance, varied cost of leverage, and opaque technical risk, it can be challenging for any allocator to distill a single, optimal ETH portfolio.
At Primitive, we deem it critical to communicate the conflicting objectives of an "optimal" ETH portfolio as we iteratively discover a healthy balance.
Supporting Network Health
After The Merge, Ethereum network health is adverse to the dominance of any particular liquid staking protocol. This conflict is worsened by memetic effects of user behavior. Lido is widely known as the safest, highest-performing LST. Regardless of its objective merit, recommending Lido en-masse as a primary source of ETH yield is detrimental to the security of our ecosystem.
Portfolio managers and DeFi developers rarely assume responsibility for these network effects. Let's dig into a couple simple strategies that work to alleviate this risk.
Even Weighting
Liquid staking protocol dominance can be corrected by taking a simple diversification approach. A liquidity pool or index may be constructed for a given basket of LSTs to ensure that each LST is held in balanced amounts (i.e., four tokens, 25% per token), given their relative price. The resulting strategy is known as a fixed, even-weight ETH LST portfolio.
While this is a rudimentary solution to liquid staking dominance, it requires minimal external dependencies and is highly intuitive.
Inverse Market Cap
Popular indexes such as the S&P 500 typically weight their portfolios based on market capitalization. To actively fight against LST dominance, ETH portfolio managers and strategy developers should consider taking the opposite approach. Re-weight any given basket of LSTs inverse to their network dominance, inferred by market capitalization.
As you may already know, the optimal portfolio for supporting Ethereum network health is not directly aligned with the quest for optimal yield. Re-weighting based on inverse market capitalization may lead to high demand for low-quality LSTs with lackluster staking yield. For risk-averse or yield-hungry depositors, these downsides are significant. A balanced approach is necessary to productize ETH network health successfully.
Optimizing Yield
An ETH portfolio's most obvious objective is maximizing ETH yield. Sources of ETH yield and their correlated risks differ broadly across various products. For this example, we will focus on optimizing two familiar non-leveraged sources of ETH yield: liquid staking yield and LST liquidity provision.
Liquid Staking Yield
LSTs represent claims on Ethereum validator income. Given the wide variety of liquid staking protocols, different LSTs' yield rates and net present value (NPV) vary at any given time. A basket of LSTs may be constructed and dynamically rebalanced based on current yield rates to optimize a given ETH portfolio for staking yield.
Referencing LST prices against ETH on primary markets such as Uniswap is an accurate method of determining NPV, and changes in this price over time define the yield rate. A portfolio built under this strategy will increase the weights of a particular LST based on its current staking yield rate. Buying high performers and selling losers grants an allocator the flexibility to dynamically inject capital into a wide range of non-dominant liquid staking protocols, increasing network security and optimizing yield.
For more details, read @casperchwa's post on staking economics.
LST Liquidity Provision
LST portfolios constructed from Automated Market Maker (AMM) liquidity pools extract additional yield through swap fees. Tokens such as wstETH and rETH, when traded against each other in an AMM, experience sporadic price swings that generate swap fees due to trading activity and arbitrage. When prices subsequently mean-revert, liquidity providers make a profit.
LST AMM pools optimized for maximized exposure to this volatility capture increased swap fee income, but there is no free lunch. These portfolios risk drastically re-allocating into poor-performing LSTs and experience losses over time as prices diverge.
To learn more about LST liquidity provision, check out our previous blog post.
Lending Loops & Leverage
Yield optimization strategies will frequently "bake-in" leverage via lending loops, leaving allocators with little control over their risk profile. At Primitive, we evangelize a modular approach to portfolio management. While these high-leverage products may be a convenient solution for the max-risk degen, allocators should be able to clearly understand their "no-leverage" returns and apply leverage when desired. Portfolio composibility via tokenization is critical to enabling this flexibility.
A Healthy Balance
When constructing a portfolio, financial objectives must be directly weighted against existential risks. It is our goal is to create LST portfolios optimized under this philosophy.
With the advent of LST re-staking via EigenLayer and various projects injecting billions of USD into Lido dominance, aligning incentives between ETH allocators and network participants is a critical, time-sensitive mission.
Let's get to work.