Incentive Spend: Efficiency and Effectiveness
Part One: The historical context behind incentive spend
Over the last 500 years of financial history, trading venues have seen tremendous evolution in speed, access, and complexity. However, their purpose has remained the same since the earliest organized exchanges in medieval Europe: to match buyers and sellers of financial assets quickly and at a mutually agreed upon price.
As electronic trading made venues compete in a global marketplace, some operators started designing features to attract more market share to their trading systems. Since venues compete on speed of execution and quality of price matching, these features have mainly focused on either incentivizing faster trading or improving execution prices achievable by traders. As trading continues to evolve with the emergence of Decentralized Finance (DeFi), new types of venues such as decentralized exchanges have come to grapple with some similar optimization questions.
To unpack how trading venues use incentives to make their systems more attractive to customers, let’s first look at how order matching works in theory and what customers look for in choosing a venue. When an order to buy or sell an asset at a certain price is submitted, one of two things can happen: it can be matched instantly with an available counterparty willing to take the other side of the trade, or, if no such counterparty exists, it can be made available until someone willing to trade at that price comes along.
In the first case, the order is called a “taker” order, as it instantly accepts an available price and removes liquidity from the venue. The latter case is a “maker” order, which does the opposite: supplying liquidity to the venue for taker orders to trade against. At venues with a central order book, taker orders represent buying at the offer price or selling at the bid price, while maker orders represent submitting a new bid or offer to the market that is not instantly executed.
For a trader submitting a taker order, it is advantageous to have a large number of maker orders available, as this ensures ample competition to provide a fair price and allows the trader to execute large trades immediately. On the other hand, a trader submitting a maker order would prefer to have as many taker orders as possible at the venue, as this increases the chances of their order being executed quickly and reduces the need to compete with other maker orders on price.
Finally, it is important to also consider the incentives of the venue itself. While venues can make money in a variety of ways, most charge a small fee on every trade as a primary source of revenue. By fine-tuning the fees charged to each side of the trade, venues can optimize trading conditions on their system to attract customers and maximize earned fees. For the rest of this article, let’s look at how US equity venues have tried to optimize their fee structures over the last few decades, and how DeFi exchanges (on which tokens, not equities, are traded) have dealt with similar questions more recently.
Incentive Optimization at US Equity Venues
The first US equity venue to notably calibrate its fees in an attempt to attract customer volume was the Island Electronic Communications Network (Island ECN) launched in 1996. Driven by what they saw as unfavorable trading practices of market makers on the Nasdaq exchange, affiliates of Datek Securities launched Island ECN as a competing trading venue for most Nasdaq-listed equities. Since Datek was primarily an online retail brokerage looking to start its own trading venue to route client orders, they needed a way to encourage institutional traders to participate in the new system.
Their solution was to pay members of Island ECN a small rebate if their orders were matched on the maker side of a trade. To subsidize this rebate and generate sufficient revenue for the venue, Island ECN charged a modest fee to orders matched on the taker side. By breaking with the then standard practice of charging equal fees on either side of the trade, Island ECN quickly attracted maker volume and ensured that plentiful liquidity was available for its retail clients. By 1999, the venture was hailed as a huge success and a series of rival trading venues with similar fee models sprung up, bringing the practice into the mainstream. As of early 2021, industry research estimates that venues using this type of fee model — called the maker/taker model — make up about 50% of US equity exchange trading.
As specialized fee models became more widespread, some venues experimented with reversing the roles in the original maker/taker model, charging a fee to provide passive maker orders to the venue and paying a rebate to aggressive taker orders. Known as the inverted fee model, this scheme was first tried by the Nasdaq Boston Exchange in 2009 and now makes up about 5% of US equity exchange volume. To visualize how the maker/taker and inverted fee structures differ, we can imagine a hypothetical market in which the available bid and offer prices are the same across all venues, excluding the impact of fees.
Assuming trading happens only at these prices, we can then see the net result for makers or takers after fees in either type of venue, as shown in the diagram. Note that the inverted fee venue effectively creates a narrower spread between the bid and offer price, as some of the advantage makers have from trading on the less aggressive side is eroded by the fee structure. Conversely, the maker/taker venue has an effectively wider spread as taker orders must pay a fee in addition to trading on the more aggressive side of the bid or offer.
In reality, however, available quotes and market behavior are not the same across different types of trading venues. The higher incentive to provide maker quotes in maker/taker venues produces more competition on the maker side, which offsets some of the disadvantages for the taker side by leading to narrower spreads. In inverted venues, the disadvantage to the maker side created by fees is offset by incentivizing more aggressive trading, which leads to wider spreads and faster execution for orders on the maker side.
Since trading fees at US equity venues have been competitive for over 20 years, the nuances of trading at major exchanges are well-known and small inefficiencies are mostly arbitraged by established players. The costs passed through to end users in this mature market are fairly uniform once market impact and the opportunity cost of execution speed are taken into account, as suggested by a recent study on a full year of trading data from a large US hedge fund. Due to the highly competitive nature of the equity exchange business and the widespread presence of sophisticated arbitrage traders, microstructure tradeoffs in US equity markets occur outside the view of most participants, who can nonetheless benefit from the quick execution and favorable pricing they facilitate.
New Venue Structures in DeFi
Up until this point, we have looked primarily at traditional equity venues with a central order book, but these same principles apply to all order book venues, such as commodity futures exchanges and centralized crypto exchanges. Most of these venues use some version of the maker/taker model or charge flat fees to each leg of the trade, though recently some (like eToroX) have experimented with an inverted fee model as well.
But what about new types of venues where there is no central order book? In the last few years, an increasing volume of crypto trading has occurred through automated market makers (AMMs) where prices are set by a formula rather than a traditional order book structure. To understand how fee optimization occurs at these new venues, let’s first look at the theoretical concept of a simple AMM and how it matches orders.
At the most basic level, an AMM is a smart contract that creates a market between Asset X and Asset Y by holding both assets and trading them according to agreed rules. To supply the needed inventory for trading, assets are deposited into the AMM by market participants called Liquidity Providers (LPs), who retain fractional ownership of the pooled assets. Though some AMMs have given LPs the option to provide price input, let’s first consider the simpler case where their role is merely passive. In this case, the AMM is programmed to maintain quantities of those assets based on a predetermined bonding curve. If a trader swaps a certain quantity of Asset X with the AMM, they will receive a quantity of Asset Y as given by the slope of the bonding curve around the current level of both assets in the AMM.
For any convex bonding curve, this creates a situation where the price of Asset Y denominated in Asset X increases as more Asset Y is removed, leading to an equilibrium level at which prices match those available at other venues. As the price fluctuates around the equilibrium point, arbitrage traders are incentivized to remove small amounts of the temporarily underpriced asset from the AMM and sell it at other venues, pocketing a small profit and correcting the AMM price to keep it in line with the broader market.
This trading dynamic splits AMM markets into three categories of participants: LPs, Arbitrageurs, and Traders. LPs passively provide assets for others to trade with at any price and maintain fractional ownership of the combined asset pool. Arbitrageurs trade small quantities with the AMM frequently to maintain price alignment with outside venues. And finally, traders who transact with the AMM for the same reasons they would transact with any other venue. While traders who transact with the AMM strictly as a trading venue can be seen as an analogy to takers in an order book market, price making in an AMM market is done by both LPs and arbitrageurs.
Since arbitrage needs a profit incentive, the profit of the arbitrageurs comes at a cost to LPs, called an “impermanent” or “divergence loss”. LPs also need a profit incentive to provide their assets to an AMM for trading, so they must be compensated for their losses and opportunity cost from fees charged to arbitrageurs and other traders. The way most AMMs go about this is charging a fee on every transaction made by traders and arbitrageurs, and passing on some of the revenue to LPs as a yield or incentive. Though early generations of AMMs used a flat fee set mostly by subjective judgment, recent developments have been made in calibrating fees for market conditions and the business needs of the venue, creating some familiar trade-offs.
Incentive Optimization in DeFi
The end goal of AMM optimization is the same as for any other type of venue: to attract user volume and generate higher earnings from the trading the venue facilitates. One possible approach is to pay higher yields to LPs for depositing their assets at the AMM, with the goal of accumulating a large pool of assets that can support heavy trading volume. Making these yields sustainable, however, requires charging a high fee on transacting with the AMM and limits the activity of arbitrageurs, as they become unable to profit off dislocations in prices that are smaller than the fee rate.
This friction leads to noisier prices and lower arbitrage volume, both of which are undesirable for the overall success of the venue. Fees that are too low, on the other hand, cannot support a high yield for LPs to make up for loss due to price divergence, and may struggle to attract assets from competitors. As AMM markets have started to mature and enter the mainstream, progress has been made on quantifying these trade-offs in an actionable way and tailoring AMM markets to the needs of their users.
As a more advanced generation of AMMs enters the market and brings a number of changes to the trading environment, fee structures will need to adjust for the new features being offered to users. Dynamic fees that rise and fall based on market inputs will allow more fine tuning of incentives to provide consistently balanced trading conditions. Tiered fee pools, like those used for Uniswap v3, allow traders and LPs to choose between AMMs at different fee levels depending on their individual risk tolerance and trading needs. Finally, concentrated liquidity features, which are also being used for Uniswap v3, give LPs the ability to limit the range of price over which their assets can be used for trading. This last innovation is especially interesting in the context of optimization as allowing LPs to have some price input could conceivably reduce the compensation they require for providing their assets for trading, opening the door for more experimentation with fee and incentive structures. As trading venues continue to evolve, there is a lot of work to be done in creating venues that most efficiently permit users to use all these features. By being aware of the historical trade-offs in market design, and the relation of DeFi markets to this context, we can approach these design decisions aware of their impact on both trading environments and the overall competitiveness of new market venues.
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