Incentive Spend: Elasticity and Optimization
Recap of AMM Economics
As new types of trading venues have become popular in Decentralized Finance, much attention has been focused on the mechanics of trading and how it differs from market structures of the past. However, DeFi innovations such as Automated Market Makers (AMMs) also represent an evolution in venue economics. In this article, we will look at some of the unique business decisions faced by DeFi venues and metrics that can drive their success in a rapidly developing market.
To start, let’s pick up the model of an AMM from a previous article and evaluate the three major groups of participants in terms of their business relationship with the venue. Starting from the left, end users pay a fee for the service of being able to trade, acting as the main source of revenue for an AMM. In order to meet customer trading needs, the venue needs assets, which are supplied by Liquidity Providers (LPs) in exchange for a fee. Incentive payments to LPs are the first major cost center of an AMM operation, as they must continuously be made to maintain a sufficient base of assets and attract customers.
The other major cost center from an AMM’s perspective is arbitrage. Since LPs usually have limited or no price input, the venue must rely on profit-motivated arbitrage traders to keep prices in line with other markets. Though arbitrage traders also pay fees, their profits in excess of fees come at the expense of the AMM’s assets, thus representing another cost center. For an AMM to be sustainably profitable, its fees earned from end users must exceed the costs of incentive payments and losses to arbitrage in the long-term.
While there are also interesting ways to think about managing arbitrage costs, this article will focus mainly on the tools AMMs can use to better understand their spending on LP incentives. Since payments to LPs are a cost that DAOs have complete control over, it can be a competitive advantage to adjust payments accurately and spend only how much is needed to secure a sufficient asset base. In practice, however, it is difficult to know how much this is and how it changes with market conditions. To dig deeper into this problem, let’s look at a theoretical model of a market reacting to LP payments and outline a possible approach.
Optimizing Incentive Payments
One of the biggest challenges with optimizing AMM incentive payments is that market response can be highly nonlinear, with regimes of high or low sensitivity to marginal spending. To see how nonlinear effects may arise, we can consider the relationship between incentive spending and the total value assets contributed by LPs for trading, a stylized example of which is shown in the chart.
While real venues may have more complex behavior, we can see why in theory an AMMs reaction to incentive payments could look something like this. When incentive payments are relatively low, changes have little effect on total assets, as prospective LPs have options that offer more competitive returns and stay away regardless of small variations. This type of market condition is called inelastic, since it does not respond to marginal changes in inputs. At higher levels of incentive spend, the AMM becomes competitive with other sources of yield and the market condition becomes elastic. In the elastic region, small changes in incentive payments do affect decision making for prospective LPs, and assets available for trading can change significantly from a relatively minor adjustment. Finally, as incentive payments become much larger than competitive yields available elsewhere, the market again enters an inelastic region. In this third scenario, the universe of potential LPs is saturated and further increases in incentive do not attract much additional growth in assets.
In practice, the elastic and inelastic behavior of LP assets is only one piece of the puzzle. To operate its full business model, an AMM must use LP assets to attract customer trading and earn fees on this volume, which can introduce additional nonlinear factors. Taken together, these effects result in high marginal returns on incentive spending within a certain range, with the effects of spending diminishing the further an AMM is from competitive pricing. Though the curve may be difficult to measure and change constantly with market conditions, we can imagine overall marginal return on incentive spend — the spend efficiency — looking something like the stylized chart version below.
Supposing a DAO can accurately track spend efficiency in real-time, it can bring significant benefits to their ability to maximize their venue performance. While distributing incentives based on subjective judgment or simple rules of thumb is enough to maintain basic market functioning, it is hard to coordinate a longer term strategy with no view on where spending stands relative to the competitive range. By providing a direct measure of whether the venue is over or underspending, a view like the example chart could help AMMs target spending more productively.
Driven by this emerging customer need, Gauntlet has built its incentive optimization platform with the goal of giving users quantitative data on both end-to-end spend efficiency and the elasticity of their market across a range of metrics. With this information, decision makers can take a more strategic approach to deploying their funds in a way that maximizes value to LPs, investors, and end users.
With the general growth of decentralized exchanges, the notional amount at stake in optimization choices has risen dramatically as well. In recent months, over $100 billion of volume has regularly traded on DeFi venues in aggregate. Since this trading was enabled by LP incentives and served as the primary source of income for the venues involved, the value of even a small improvement in efficiency can be very material when applied at scale. In the coming years, AMMs will develop new ways of competing for customer flow and spend efficiency will ultimately determine which venues are able to sustainably generate value from the opportunity. As DeFi market structures continue to mature, market participants will need tools to evaluate the new economic decisions they face in this stage of trading venue evolution.
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If you’re interested in learning more about Gauntlet’s incentive optimization platform, contact sales@gauntlet.network.
