Key Points
- The SuperStacks program achieved 53.7M retained 30 days after the program concluded. This translates to 21.5/OP after costs, respectively.
- Findings supported the theory that strong demand-side activity can lift post-incentive equilibrium levels, though a more rigorous analysis is still needed.
- Incentivized lending pools generally demonstrated higher liquidity retention.
- However, TVL retention in DEXs appeared inflated by Uniswap's concurrent Gauntlet campaign. Specifically, when the two co-incentivized DEX pools were excluded, retained TVL inflows dropped to $48.2M, revealing a significant divergence between the lending and DEX verticals.
- Metrics for measuring demand-side traction included trading volume per TVL for DEX pools and utilization rate for lending pools. For DEX pools, a moderate correlation was observed between peak net TVL inflows and trading volume per TVL, hinting at possible synergistic effects.
- A key limitation was that isolating the program's causal effect alone was extremely difficult due to numerous confounding variables, such as external co-incentives and broader market volatility.
Background
SuperStacks was the Optimism Foundation's first proactive DeFi incentive pilot program. It was designed with the goal of increasing liquidity for interoperable assets (e.g., USD₮0) across the Superchain. At its core, the program aimed to help these assets overcome the cold start problem and test new mechanisms for establishing sustainable DeFi growth loops.
The program's design was based on a theoretical framework suggesting that a two-pronged approach, focusing on both supply-side and demand-side activity, could initiate a sustainable flywheel effect for interoperable assets on the Superchain, ultimately catalyzing lasting liquidity growth.
This theory is broken down into three phases:
- Supply-Side Growth: Available incentives attract deposits into DEX pools, which creates deeper liquidity and improves the execution quality for trades. On the lending side, an increase in lending supply reduces the borrow rate.
- Demand-Side Growth: Improved trading conditions draw in more trades that are routed through the incentivized DEX pools, which increases trading volumes and generates more fees for LPs. Similarly, more competitive borrow rates attract more borrowers, which raises utilization rates and generates a higher yield for lenders.
- Sustainable Traction: Once incentives are switched off, a portion of the TVL (Total Value Locked) and net liquidity leaves the incentivized pools, but due to higher fee and yield generation for LPs and lenders, liquidity settles at higher baseline levels compared to the pre-incentives period.
Analysis Method
Dataset
The datasets and methods used for the SuperStacks analysis were:
- Period: 76-day incentive period (April 16 – June 30) and a 30-day retention evaluation period after program end (through July 30).
- Data sources: 25 pools and vaults targeted by SuperStacks incentives.
- Metrics:
- TVL (Total Value Locked): Net TVL inflows during the program and retained inflows after program end.
- Trading volume: Indicator of demand-side activity in DEX pools.
- Utilization: Indicator of demand-side activity in lending pools.
- Cost efficiency: Net TVL inflows per OP token ($/OP).
Intervation / Explanatory Variable
SuperStacks Program:
SuperStacks was the Optimism Foundation’s first attempt at a “proactive DeFi incentive,” designed as a pilot program to increase liquidity of interoperable assets across the Superchain. The program was built on a two-pronged approach targeting both supply-side and demand-side activities. Specifically, incentives were provided to encourage supply-side actions (deposits into DEX pools, increased lending supply), which in turn aimed to stimulate demand-side activities (higher trading volume, increased utilization).
Dependent Variable
Trading volume and utilization: Determining whether TVL growth led to higher demand-side trading volume and lending utilization, and whether these metrics remained at elevated equilibrium levels after incentives stopped.
Identification Strategy
- A pro-rata model was applied to disentangle the complexity of overlapping incentive programs, attributing impact based on each program’s share of total USD incentives.
- To evaluate pool-level performance, incentivized pools (treatment group) were paired with comparable non-incentivized pools (control group) on the same chain and protocol. One-sided t-tests were conducted on changes in TVL and trading volume.
- The analysis focused on DEX pools (9 pairs) that passed statistical filtering.
Results
- DEX Trading Volume:
- In DEX pools, trading volume per TVL was used as a measure of liquidity “productivity.”
- CL100-USD₮0/kBTC on OP Mainnet: Alongside rapid TVL growth, trading volume increased by 126.7% during the program, and remained 42.7% above baseline after incentives ended (p < 0.01).
- CL1-USD₮0/USDC on OP Mainnet: TVL rose, while trading volume saw a modest 2.5% increase during the program. However, after incentives ended, volume accelerated and stabilized 25.1% above baseline (p < 0.01).
- BV-WETH/weETH on OP Mainnet: Beyond TVL growth, trading volume rose even more sharply, increasing 123.8% during the program and remaining 49.4% above baseline afterward (p < 0.01).
- Lending Market Utilization:
- In lending pools, utilization rate was used as the demand-side traction metric.
- The lending pool with the highest performance at peak net TVL inflow showed a healthy 60.2% utilization rate, but the relationship between supply and demand momentum was less clear than in DEXs. Theoretically, competitive borrowing rates were expected to attract more borrowers, boost utilization, and deliver higher yields to lenders.