Market Makers Weekly insights and analysis on the transformative forces shaping the global economy. By Phil Mackintosh, Nasdaq Chief Economist Aligning with the Fed, rising energy prices and why many trading businesses are platforms: Here’s what’s moving markets this week. Macro in a Minute In mid-January, markets were pricing in 170 basis points in rate cuts this year. After a series of stronger-than-expected data – capped off by somewhat higher-than-expected consumer and producer inflation data this week – market expectations are now in line with the Federal Reserve’s projected 75 basis points in cuts. This shift helps explain why 10-year Treasury yields are up 20 basis points in the last week to 4.3%. Equity markets seem less concerned, however, with the major equity indices roughly flat. That may be because the underlying details of the inflation reports were less concerning than the headline numbers suggest. Headline CPI inflation increased 0.4% after a surprise 0.3% increase in January, pushing the year-over-year rate up to 3.2% p.a. from 3.1%. Core CPI inflation, however, did slip to 3.8% p.a. from 3.9%. Still, this report suggests that January’s surprise strength may have been noise, with rents reverting to its recent trend and core services excluding housing growing at half January’s pace. Core producer price inflation rose 0.3%, with goods and services both increasing, more than offsetting the 0.3% drop in margins. And unexpected 1% and 4.4% increases in food and energy prices, respectively, pushed up headline PPI inflation 0.6% from January – double expectations. Market measures show energy prices have increased in March. Oil prices are up about 5% in the last week, with U.S. stockpiles falling, a drone strike hitting a Russian oil refinery, and the International Energy Agency now estimating global oil markets will face a deficit throughout 2024. Friday’s jobs report provided the one bit of below-expectations inflation news, with wages only increasing 0.1%. On the whole, the report was mixed. While the economy added 275,000 jobs in February – well above the 200,000 estimates – the unemployment rate rose to a two-year high of 3.9%. Still, with the unemployment rate low by historical standards, the labor market remains relatively tight. Many Trading Businesses Are Platforms The United States Securities and Exchange Commission’s (SEC) rules and guidance sometimes mention “platform” as a basis for understanding competition in the industry. That’s not entirely surprising — almost all businesses in the financial system compete as platforms, as we discuss today. What is a platform? In economics, the widely accepted definition of platforms is that they are at least two-sided markets, enabling interactions between users. That means they need to get the two sides ‘on board’ by appropriately charging each side. That is often done by bundling, or even rewarding, one side to each transaction. This may include not charging or even paying one side to participate. A common feature of platforms is the production of “joint products,” or what some might call “by-products.” That means the business creates more than one product, often that very different customers want, from the same process. One problem for accountants working in platforms is that the costs of producing joint products are also shared. In reality, that means it is difficult to allocate total costs to different products correctly. Finally, platform businesses also often benefit from network effects. As the user base grows, the platform becomes more efficient for the buyers and the sellers – and often the platform achieves economies of scale. Exchanges are platforms Based on the definitions above, exchanges are clearly platforms. The whole point of an exchange is to match buyers to sellers and issuers to investors. Exchanges also produce joint products with network effects — quotes, trades, co-location, and sometimes tickers (by competing for capital formation and listing services). Without a quote, it would be hard to attract trades. More trades increase the economies of scale for co-location, and more trading and quoting increase the quality of market data coming from that exchange. Done well, all those things combined increase market quality, and tighten spreads, making an exchange attractive for issuers to list on too. Done really well, an active exchange with competitive quotes also creates economic benefits (or “positive externalities”) for issuers, workers and investors, too. If you want to learn more, you can read our new paper. In a platform, free or fixed isn’t fair (or good economics) We have also shown in the past that free stuff creates free riding. We may soon see that fixed prices don’t allow for economies of scale or network benefits to grow. Both are bad economics. We’ve shown data that confirms this has happened in the stock market before, too. For example: Exchanges can compete with very different trading cost models, because the “all in costs” to customers matters more than line-by-line costs. Some platforms increase spread capture using quotes that fade, which free-rides off market data but are not good for issuers. Some exchange customers profit from data without trading at all. Even for the “same” product, we see customers actively choosing different levels of trading, data and co-location setups based on their own cost-benefit and the behaviors of their own customer base (Chart 2). Despite all this, many fees in the exchange space are approved on a line-by-line basis, and fee guidance continues to focus on “costs” rather than the value customers see in the product, as well as efficiency, economic benefits and competition with other platforms. What economics matter for exchange customers This brings us to one of the common myths about exchanges — that all customers are basically the same. In reality, exchange platform customers see the economics of the stock market in a variety of very different ways. Consider the simplified example below for a maker-taker market’s different customers. Imagine a customer who does: Statistical arbitrage (totally aggressive) and trades a lot, but only ever crosses the spread to take liquidity. They likely have reasonably high volumes (width of the bar) and need co-location, and fast data, to lock in mis-pricings before others – but they always pay take fees. In terms of market-wide benefits (externalities), they would provide liquidity and keep markets efficient (plus sign), but never set the NBBO (red dot). Market Makers (totally passive), in contrast, set two-sided quotes across a wide variety of stocks all the time. They probably need even more hardware to manage all their quotes and adjust prices and they likely have the highest message volumes. They also need co-location and fast data but earn rebates from providing liquidity, which helps them offset some of their fixed costs and adverse selection. These market makers set the NBBO a lot, which helps reduce trading costs for the rest of us, even retail and mutual funds trading off-exchange, so we show them adding the highest positive externalities (plus sign). Large algo brokers, as data indicates, trade a lot less on exchange than market makers and arbitrageurs. That means they need less data and likely a lot less co-location. Also, by working orders, they have a mix of both rebates and take fees. Depending on how their dark fills and order routing work, their net transaction fees could be slightly positive or negative. Because their customers are mostly doing one-sided trades, their contribution to the NBBO is likely smaller, too. However, brokers support research on companies, which makes markets more efficient. Small algo brokers, as data suggests, outsource fixed costs like co-location and routing infrastructure and may limit the proprietary data they buy if their algos are not latency sensitive. This saves them a lot of fixed costs with exchanges, as well as their own staffing and hardware. Retail trades mostly off-exchange, with the exception of non-marketable limit orders, which Rule 604 requires to be posted at the NBBO and tend to capture rebates. However, retail limit orders aren’t usually dynamic, which reduces co-location needs (and time at NBBO), and trading costs end up negative thanks to rebate capture. Data costs are also typically lower because human traders mostly only need SIP feeds or cheaper substitutes. In fact, we know that 99% of SIP customers don’t purchase direct feeds. Non-traders, meanwhile, are a number of businesses that profit from market data but don’t trade at all. Most of these can also use the SIP, so they don’t pay trade fees or even need co-location. All of this data blends into the “all in cost to trade” averages we have talked about before. But clearly each customer’s economic experience on the platform, as well as the value they add to the platform, is very different. And that’s before we take into account the implicit costs of different venues. Chart 1: Different customers of exchanges purchase the joint products [PM1] in different ways (and also contribute differently to market quality) Exchange customers choose to consume differently The data below, from a prior study, confirms that brokers with more volume typically chose to consume more robust data. That may be because of the types of customers they service, or the level of trading they do, and the commissions that they therefore earn. However, buying more hardware and data increases their fixed costs, especially compared to smaller brokers, which this data suggests don’t set up co-location at all. Chart 2: Customers choose different levels of consumption based on their own business needs That has important implications for the SEC’s current volume tiers proposal, too. That proposal would result in higher volume brokers that will end up paying the same variable trading costs on top of higher fixed costs that their different business models demand. Moreover, it has no regard for the economic benefits of competitive spreads across a broad range of stocks. It also fails to account for the benefits from exchanges themselves achieving economies of scale on their platforms, or the diseconomies to investors from higher search and spread costs in an even more fragmented market. Exchange customers also operate platforms Speaking of the volume tier proposal and platform economics, most brokers in the market run platforms, too. In general, the economics of trading tiers seem small compared to the scale of other aspects of their platforms. Importantly, we see above that small brokers may benefit from outsourcing trading to larger brokers, as it saves them fixed costs that are harder (for a smaller broker) to defray. Broker ATSs not only compete directly with exchanges for trades but also help brokers compete with each other. Our recent research found that most brokers bundle ATS services to their own clients while charging other brokers to interact, sometimes with negotiated pricing or volume discounts. But for a broker, part of the ATS platform is also the SIP revenues from printing trades, which we estimate are often larger than the costs of data needed for pegging dark trades. Notably, that cross-subsidy also grows larger with the size of the ATS. The quality and costs of algorithms and hardware are also important to some customers’ best executions, making that a point of competition, too. It’s also well known that brokers “bundle” research into commissions, with data suggesting research wallets account for around half of all commissions, likely adding to around $4 billion annually. Interestingly (speaking of positive network effects from cross-subsidies), regulators in Europe found that unbundling may have had market-wide harms, leading to less research coverage of small companies, which hurts capital formation and market efficiency. Some brokers also compete for hedge funds using prime services. Prime services have network effects for brokers, too, as hedge funds trade a lot more than mutual funds. That increases trading volumes, allowing for economies of scale at the broker on algo spend and staffing and maybe increases internalization (which lowers trading payments to exchanges). A recent study suggests prime services are offered by a small selection of larger banks, where financing costs might themselves offer a competitive edge. Hedge funds and prime services also create joint products for banks. Especially where hedge funds are multi-strategy, using stocks, bonds, futures and derivatives, they make broad use of the trading desks in a bank, as well as their financing and stock loan desks. That makes for complicated revenue allocation decisions for accountants at banks, as well as making allocating bank costs back to hedge fund customers difficult as well. This book has a fun example of this in the real world. Why is this important? If financial firms are all running platforms, it’s important for regulators to understand how they all compete. When some firms can bundle while others can’t, rules make it harder to compete. When economies of scale and market quality aren’t part of the regulators’ cost-benefit analyses, economic outcomes are going to make the market less efficient. Right now, this is relevant to a number of SEC proposals. Robust economic analysis that takes into account the negative impacts of over-regulation in any one area is essential to preventing multiple unintended consequences. Customers are different, but they pick products based on the overall profits (costs and benefits) they receive. It’s important to understand that markets are complex and that cost-benefits include implicit benefits and positive externalities — not just explicit costs. Making sure the U.S. capital markets remain the best in the world should rely on competition across platforms, not capping prices for specific fees and a subset of venues.