How exchanges are becoming more than just marketplaces

The days when exchanges could rely purely on transaction and listing revenues are slowly dwindling, particularly as it pertains to traditional exchange-traded assets like equities. While these revenue streams won’t simply disappear, their contribution to exchanges’ overall revenue will potentially be smaller in quarters and years to come. 

For example, in its most recent earnings results, London Stock Exchange Group (LSEG) recorded a decline in its equities business by 10.9% to £116 million ($148 million) during the first half of 2023, owing to a slowdown in primary and secondary activity. FX trading also recorded lower market volumes. However, its fixed income and derivatives business grew 5.8% to £515 million. 

Similarly, Nasdaq saw a decline in trading services revenue to $250 million in the second quarter due to “unfavorable foreign currency rates on flat organic revenues.”  

The Intercontinental Exchange (ICE), too, recorded lower revenue for its listings business and cash equities and equity options. Listings revenue was down by 5%, while the cash equities and equity options business fell by 3%. 

Exchanges have realized—or are realizing—they need to diversify their offerings to include wider asset classes and, more importantly, to focus on data and analytics products. Other ways to grow revenues, particularly data revenues, are by increasing market data fees—always a hard pill to swallow for firms that pay to subscribe to the data—or finding new ways to bundle existing datasets. 

Eggs and baskets

During ICE’s earnings call presentation on August 3, Benjamin Jackson, president and chair of ICE Mortgage Technology, noted that the fixed income and data services business grew during the first half of its earnings year, a “testament to the strategic diversification” of its business and ability to grow through varying macroeconomic conditions. He stressed that ICE intentionally diversified across asset classes and geographies, so the growth is not tied to cyclical trends. 

ICE is also further expanding its mortgage business, about which Jackson said, from a long-term perspective, the exchange feels “great” about its position.  

Exchanges are also reaching beyond their traditional roles as marketplaces and to clients beyond their member firms. For example, Deutsche Börse’s buy of SimCorp gives it exposure to the coveted buy side, while Nasdaq’s acquisition of Adenza expands the exchange’s reach into market and trading risk.  

But let’s return to the heart of the exchange business as it operates today—data. 

David Schwimmer, LSEG’s chief executive, said during LSEG’s earnings call: “Demand for data is greater than ever, and that’s only going to grow from here. Customers want to automate workflows, manage financial and reputational risk, optimize capital and trade electronically and cross-border.” As an exchange, LSEG is at the fulcrum of all these trends.

He also added that LSEG is transforming its business to match its customers’ needs. One way in which it’s doing that is by turning its data infrastructure into a “scalable machine” where it can ingest data once and distribute it an unlimited number of times more cost effectively. This transformation also includes its initiative to build next-generation workflow tools with its partner Microsoft.

To that data and analytics end, most big exchanges have signed multi-year partnership deals with the big cloud providers. CME’s partnership with Google is already yielding new data and analytics products faster than it would have if it had used traditional technology. (To read about how Big Tech is, in turn, eating more of the capital markets pie, click here.) 

The pursuit for new data and analytics products, as well as the capability to distribute data more efficiently, is core to many of these partnerships. But those fruits don’t necessarily come easy—despite the vast amount of available data—especially if an exchange decides to build them alone. 

Having the right resources

Not all exchanges have the budget and appetite to bid billions of dollars on small, niche vendors, and their IP and client rosters, in order to quickly boost data revenues. Sometimes, it requires working with third parties or entering partnerships that could bring some value.

The Johannesburg Stock Exchange’s (JSE) director of information services, Mark Randall, who I spoke with recently, explains that the traditional exchange market data business is a wholesale data delivery operation—distributing low-latency feeds for trading, end-of-day prices, reference data, and corporate actions, either directly to clients or via data distributors.

The JSE, like other exchanges, started thinking about the data it has at source, the trend toward cloud computing, and how to pull these two closer together—whether that’s in the form of analytics or different delivery mechanisms—so that end-users don’t have to recreate these datasets and analytics by themselves. 

“In our conversation with brokers, we quickly realized that an exchange has got excellent access to the brokers and the market to understand the business use cases to understand the decision-makers, understand the actual hunger for what analytics and where [they] are valuable,” Randall says. 

But—and this is a crucial point—an exchange may not (and often doesn’t) have the technical capabilities to build and deliver those products. 

“You could sit in your armchair and say, ‘Well, this is easy. You’ve just got to get an engineer and a math PhD and a data and visualization engineer, and then build it. How hard can it be?’ And the reality is that it’s actually pretty difficult to get it right the first time, and then there is a point about getting it done as quickly as possible,” Randall says. 

Other exchanges have either tried or are trying to expand their data services. One such example is the Australian Securities Exchange, which launched DataSphere, an initiative meant to capture more of its internal data and commercialize it for the investment community. For others, defining their data strategy is taking a little more time. 

For the JSE, this led to its partnership with data analytics provider Big XYT, after the pair worked together for three years. They formed a joint venture called Big XYT Ecosystems to provide other trading venues with a platform where they could build their own analytics and deliver them to clients. Together they built Trade Explorer, a white-label data analytics platform, which launched in South Africa earlier this year. The platform, and Big XYT Ecosystems, are an equal investment by the exchange and Big XYT. 

The platform allows trading venues to distribute data analytics solutions to their clients, including trading firms, issuers, and investors. The analytics are delivered directly to the users through web-hosted services under the JSE’s brand.

So far, Trade Explorer provides 12 metrics that help users understand market liquidity and flows, market share, business concentration, and execution performance. Later, Big XYT Ecosystems will also offer “DataShop” and private cloud functionality to help trading venues promote their unique datasets. 

The JSE understands the exchange ecosystem and typical business model, and it has strong relationships with other exchanges. Meanwhile, Big XYT has the expertise in building and hosting analytics and in knowing how those analytics are consumed and used. 

“Those two things just seemed to mesh for us pretty seamlessly and pretty naturally,” Randall says. 

Richard Hills, Big XYT’s head of client engagement and business development lead on the joint venture, notes there are around 120 substantial exchanges around the world, including alternative venues. 

If these exchanges and venues had to build analytics capabilities themselves, the cost redundancy to the overall industry would be huge.

“From my old broker days, these infrastructure costs from a broker’s perspective—if you were going to do it globally—they cost tens of millions. So we’re doing it at a fraction of that cost, and the same goes for the exchanges,” Hills says. 

He adds that Trade Explorer could act as an additional layer on top of exchanges’ cloud data infrastructures. For example, brokers might download data from the exchanges via their cloud services, but they still have to curate that data and perform computations. They would still need mathematicians and then to deliver computations to the front end, whether they’re for a sales trading desk, an electronic desk, or an algorithm. 

“Where we’ve come in is to actually help exchanges deliver on their own cloud strategies,” Randall adds. “In the same way that some of this has been solved for cloud data delivery, how do we solve the analytics-as-a-service piece?”

Expanding horizons

According to Keiren Harris, co-founder of MarketData.Guru, a market data strategy consultancy, the joint venture between the JSE and Big XYT emphasizes the need for exchanges to expand their information services business beyond price data. 

How exchanges grow beyond those traditional information services businesses—whether by acquisition, or by partnerships, or by use of an emerging technology like generative AI (I’ll leave this topic for another time)—will be something to keep an eye on.  

Through all this, one thing remains true: No matter the size or stature of an exchange, these trading venues can no longer sit by and rely on the revenue streams of a traditional marketplace. They might have gotten by despite cyclical trends and macroeconomic changes in the past, but things have changed. 

Firms—on both the buy and the sell side—want more data to help them increase their margins and optimize their costs. But it doesn’t stop there. More data doesn’t equal better data or useful data. Instead, firms want their suppliers—including exchanges—to also provide context around that data, and analytics that they can use almost immediately without having to get too involved in making it user-ready. 

It certainly won’t be long before we see other partnerships at exchanges—or ventures, initiatives, efforts, projects, or whatever they want to call them—dominate the revenue diversification conversation.