London Stock Exchange Group plc

Good morning and welcome to the investor and analyst call for LSEG's third quarter 2025 trading update. At this time, all participants are in listen-only mode. Later, we will conduct a question and answer session through the phone lines and instructions will follow at that time. I would like to remind all participants that this call is being recorded. I will now hand over to David Schwimmer, CEO of LSEG, to open the presentation. Please go ahead.

Good morning, everyone. Thanks for joining the call. I'm here with Map and Peregrine as usual, and we are also joined by Daniel McGuire, our head of markets, to talk about the post-trade transaction that we announced this morning. For this quarter, we're going to take a slightly different approach from a normal Q3, given the intense debate in recent months around our business and AI. I'll cover some key aspects of our AI strategy and the excitement we have about the current opportunities before Map goes through the Q3 numbers and Dan covers the post-trade transaction. Then, of course, we'll be happy to take your questions. It has been a really busy quarter with great progress on several fronts. Group organic growth continues to be very healthy at 6.4%, with DNA growing at 4.9%, similar to the first half. ASV growth came in at 5.6%, a little better than expected, and we anticipate it being better again in Q4. We're raising our margin guidance to the top of the original range at around 100 basis points of improvement, reflecting strong operating leverage and cost control. As you may have seen, we've launched a number of AI-related partnerships involving our data, which is valued and relied on by partners old and new as industry standard. We've announced an important transaction today that creates a strong partnership and aligned incentives for the adoption of post-trade solutions, while also increasing our revenue share from Swapclear and extending the profit sharing arrangement with our partner banks by 10 years. More on this in a few minutes. And on the share buyback that we announced at our half-year results, the original intention was to complete that by mid-December. But we've taken advantage of a lower share price and accelerated the £1 billion buyback to finish by the end of this month. And we're today announcing a further £1 billion buyback to be completed by our full-year results in February of next year. Our strong cash generation gives us the firepower and the flexibility to invest organically, to make important strategic moves, and to be active in returning cash to our shareholders. On the next slide, we have summarized our LSEG Everywhere AI strategy under three key pillars, trusted data, transformative products, and intelligent enterprise. We'll talk more about those second two at the Innovation Forum in November. But let me take a minute or two to dive into our data and the critical and valuable role it plays now and will play in an AI-rich world. The easiest way to think about our data is that the content itself and access to it is effectively financial markets infrastructure, something we know a lot about. It is industry standard, deeply trusted, embedded in highly regulated customer workflows, and supported by processes and infrastructure that are extremely hard to replicate. And we are and always have been open. We deliver data to wherever our customers want it, their screens, their servers, their cloud, and of course, through third-party providers. Let's unpack this over the next few slides. Data and feeds accounts for a little over one fifth of group revenues. On this slide, we've broken down these by data type. But before we get into that, I want to remind you of the scale of our data. It is the largest pool in the industry, both in terms of breadth and depth. We have over 33 petabytes of data. That is over three times the so-called common crawl, the data set formed from the public internet, which is used to train many LLMs. Let's begin with the 45% of our data and feeds revenue derived from real time. This is a business built on physics, not probability. We've built connections to 575 exchanges and execution venues globally with our own infrastructure. In the blink of an eye, we standardize and translate the exchange outputs into a single common language and deliver them directly into the world's financial institutions. Millions of hard facts per second, not probabilistic algorithms. In a nutshell, AI cannot replicate or replace our real-time data. Then we have 25% of our data and feeds revenue, which is specialized and enhanced by our own enrichment. By specialized, we mean proprietary. Think trade web, fixed income pricing. or exclusive like the Reuters news agreement or contributed like our deals database. So an LLM could not access these data sets through public sources. And then on top of that, we are enriching this data with value-added enhancements and augmentation by our data experts. That is our additional value add. And then that all comes with the LSEG curation standards, accuracy, normalization and tagging. So think of this data as protected by three modes. It is either proprietary or exclusive. It is enriched by our own intellectual property and it is curated applying the LSEG standards which have often become the industry standard. Let me give you an example to bring this to life. Our deals league tables are highly valuable to banks, advisors, and law firms. These league tables are widely considered the industry standard with LSEC data obtained daily from thousands of sources commingled with data sourced from nearly 2000 financial and legal advisors actively contributing their deal flow. We get up to 25,000 of these contributions per month. This input, which is from humans, is crucial to the quality, accuracy and completeness of this data. These contributions clarify and correct deal details that appear in the press. They also add additional information to public deals and supply information on other deals that are not reported anywhere. So a data set built solely on public disclosures would be both inaccurate and incomplete. We further enrich this data with our proprietary calculation of rank value, which sets the standard for deal comps, market share and pitch books around the world. We refine this methodology each year through round tables with advisory firms, So in case anyone is missing the point, no LLM can gather this data from public sources. Three moats, LSEG proprietary or exclusive data enriched by LSEG IP and curated by LSEG applying the LSEG standards. Let's move on to the next bucket representing 10% of data and feeds revenues. It is almost exactly identical to the previous bucket. It is specialized data, proprietary, exclusive, or contributed with LSEG standards applied. So not accessible by an LLM through public sources, our aftermarket research, for example. And to carry on the analogy with the moats, this is data protected by two powerful moats. Next is another 10% of revenue from data that is indeed public, but to which we apply our enrichment and analysis, similar to what I was talking about with customer contributions on the league tables. And we also applied the LSAT curation standards. Examples here would be earnings estimates and sent to mine analytics applied to earnings calls and other sources. So can an LLM access it? Yes, but the data will be incomplete. Here it is two modes applied on public data. So 90% of our revenue is from data that is non-replicable by an LLM. That leaves us with the last 10% of data and feeds revenue. which represents the data derived from public sources for which we apply LSEG curation standards, data like company filings or economic metrics. This data is rarely sold on a standalone basis. And here there is still one moat, a powerful and important one, and that is our standards, which I will cover on the next slide. Now that we've established that 90% of data and feeds revenue is from data that is simply out of reach or inaccessible to an AI model trawling for public data, let me take a minute to explain very concretely what I mean by that third mode, the LSEG data curation standards. There are five major processes in the curation of LSEG's high quality trusted data, which are simply non-negotiable for our customers in regulated activities. These five processes are the foundations of what we call the LSEG standards. Let's look at them in a little bit more detail. We do not build our data sets on probabilistic models. We have constructed them from decades of hard data, much of which is no longer retrievable. We source them from our customer community, with over 40,000 customers contributing regularly. And in many cases, our own analysts and experts generate them internally. So that is sourcing. We then extensively cleanse and validate this data to ensure quality, for example, verifying its accuracy and completeness. Publicly sourced data is not reliable without this step. The third step, normalizing and mastering, means creating a single source of the truth, consistent from year to year and from security to security, factoring in corporate actions, for example, or restatements or perimeter changes. And then, concordance and tagging, which is a critical and differentiated step, This is where the universal symbology of the RIC or Reuters instrument codes and our use of perm IDs to tag each piece of data are so powerful. They allow full interoperability across the data estate and create logical semantic relationships between related data. For example, between a company and its directors or a bond it has issued. And the fifth step, distribution. Irrespective of technology platform, data format, or channel, the data we distribute to customers is consistent and authoritative. I'll talk more about our distribution strategy in a couple of minutes. So to summarize, for those who think AI models can scoop up so-called public data from the internet and displace us, That just does not reflect how this industry works and fundamentally ignores the non-replicable nature of the vast majority of our data. There's also been a lot of focus on our workflows business. We have driven a lot of change here over the last four years and now have our customers in a modern, modular, customizable platform where we enhance functionality week in and week out. And we're doing more and more. As we said at H1, it is not AI or a desktop. It is AI in the desktop, fully embedded in financial markets workflow. Workspace is now integrated with Microsoft Teams. We'll be launching Open Directory in the coming weeks and the full Workspace AI platform in the first half of 26 with agentic tools coming as well. You'll see all of this at the innovation forum in a couple of weeks. So let's look at our workflows revenue the same way we did for data and feeds. 50% of workflows revenue comes from traders who are deeply engaged with the platform to execute their roles. They need real-time data, a network community, and integration with a range of pre- and post-trade tools. Further, 20% of workflows revenue comes from ancillary trading services, such as trade routing and order execution and management. Another 15% comes from investment banking, where we have specialized content across deals, corporate actions and research, as well as integrated productivity tools. That leaves 5% of workflows revenue from wealth and 10% from investment management. These customers benefit from our unrivaled data, exclusive Reuters news and portfolio analytics. But in these groups, there are lighter users who are mainly doing desktop research and basic charting, perhaps like many people on this call. Whether someone is a power user deep in trading workflow or a lighter user, all workspace users will benefit from the significant AI and collaboration enhancements coming over the next few months. They will have the full functionality of some of the newer applications out there, but embedded in their existing workflow and based on data they can trust. Now, over the last couple of months, you can see the pace of execution on LSEG everywhere, delivering our data to where our customers are working as the partner of choice for financial markets data. This is no change in strategy. We have long provided data to and distributed data through our competitors and partners. For example, we are the number one data provider to Aladdin. The industry now has new entrants, building new applications and functionality, which we believe can expand our reach and drive additional consumption of our trusted high quality data. The economics of these deals support our growth aspirations through data licensing, new channels, and the potential for usage-based revenue over time. Rogo is a specialist provider of applications to investment banking and private equity. Customers with workspace licenses can access certain LSEG data sets through Rogo. The construct with Databricks is similar. These are attractive new distribution channels for our data. Just last week, we took a major step forward in our partnership with Microsoft, introducing certain data sets into Copilot for any Copilot subscriber and more valuable data sets, both into Copilot and Copilot Studio for LSEG licensees. This will allow customers to build their own agents working with our data. You should expect the list of partners to continue to grow as we look to distribute our data through other major channels. That's the fundamental premise of LSEG Everywhere. A key part of many of these partnerships has been our ongoing build out of MCP servers as we make more and more data sets available over time. Before I hand over to Matt, it has also been a very busy quarter in other parts of our business. Just to highlight a couple of significant developments. With Microsoft, we have fully replatformed our trade routing network, Autex, in Azure, with Autex now connecting 1600 brokers and asset managers via the cloud. As a result, it's faster, has much greater capacity, and is even more resilient. And we have executed the first transaction on our digital markets infrastructure, which is positioned to become an important new capability for trading and settlements. we're preparing to launch our private securities market. More on that at the Innovation Forum. And in Risk Intelligence, we have launched WorldCheck on Demand with all our critical data and insight now updated in real time. That takes me appropriately to our Innovation Forum in a couple of weeks. In the first part of the event, Map and I will cover our unique positioning, our end markets, and execution to date. Irfan Hussain, our CIO, and Emily Prince, our head of AI, will cover our AI strategy and engineering transformation. And then Ron Lefferts and Gianluca Biagini will talk about product strategy and monetization in DNA. We'll then have specific product walkthroughs and demos across the group. We're looking forward to showing you both the present and the future. And just to be clear, this is not a traditional capital markets day. Don't expect any new guidance or anything along those lines. So with that, let me hand it over to Map to talk about our Q3 performance in more detail.

Thanks, David. So just a few words on our financial performance. We have delivered another quarter of strong growth across the group. Organic growth for the quarter was 6.4%, with all divisions contributing well. We had a benefit of 30 bps from the ICD acquisition of last year and a headwind of 190 bps from FX, which together translates into our reported growth of 4.8%. Within DNA growth of 4.9%, workflows and data and feeds saw very similar growth to Q2, with only a slight impact from the new UBS contract that I mentioned at the H1 results. Analytics continue to grow strongly. The competitive environment is stable and we are excited about the product pipeline. Our expectation for pricing into 2026 is for the yield to be similar to the last three years in the 3.5% range. FTSE Russell, as I indicated at H1, saw slightly slower growth in subscriptions, with fewer account reviews in the period. But on the other hand, asset-based fee growth was strong as we lapped the loss of a contract last year. Risk intelligence had another strong quarter, driven by both world check and digital identity and fraud. So overall, these subscription businesses delivered 6.5% growth in Q3, ahead of our expectation of 6% for the second half of the year. ASV growth came in at 5.6%, a bit ahead of the 5.4% we had anticipated. Good sales momentum partially offset the expected impact of the final Credit Suisse impact wrapped into the new long-term partnership with UBS. As I have said before, I expect this to pick up again to 5.8% as we exceed the year. The market's business continued to grow well, though at a slightly slower pace than H1 as volatility was lower and comps got tougher. Looking at the two main lines, OTC derivative was up 9.2%, driven by continued strength in client clearing volumes in SwapClear. And fixed income was up 9.9% as TradeWeb continued to drive growth through its innovative trading protocols and an uncertain macroeconomic outlook. Elsewhere, we have seen the IPO pipeline pick up in the equities business with more to come heading into 2026. And we are seeing the final headwinds to growth in securities and reporting from the Euronext exit. Moving now to our delivery against guidance. we are absolutely on track and in some respects ahead of our original plan. Year-to-date organic growth is 7.3% comfortably within our guidance range, and this remains unchanged. On margin, the natural operating leverage in our business give us confidence to raise our margin guidance to the top of the range at around 100 bps improvement year on year. This is a big step up for a 9 billion revenue business and it factors significant ongoing investment in AI and new products. we are very confident of hitting our 2026 guidance of 250 bps over three years, taking us to 50% plus. Obviously, before the impact of the post-trade transaction, which I will cover in a moment. On CapEx, we will invest at a rate of 10% of revenue this year, as planned, and expect that intensity to come down in future years. 102 in the market have asked whether we will need to invest more in an AI future. The answer is clearly no. We have been investing at a double-digit CapEx intensity for several years, and we are now switching the mix over time from technology debt payback towards more investment for growth, obviously, including AI. And finally, we have good visibility of hitting our free cash flow guidance of at least $2.4 billion. And finally, let's look at how we are allocating this cash flow. Overall, we are deploying more this year than what we are generating. That reflects the opportunities we see in front of us. So we expect to spend around 3.5 billion versus free cash flow of 2.4 billion. We are financing the difference with new borrowings of 1.1 billion. Total dividends for the year are just over 700 million, representing a 35 payout of adjusted earnings. In addition, we are deploying 700 million net on the post-trade transaction announced today, where we expect returns to be very attractive. And finally, as David mentioned, you may have noticed that over recent weeks, we significantly accelerated the 1 billion buyback announced with the H1 result, and we have nearly completed it. Given our strong cash generation, low leverage, and the enhanced returns we believe we will generate at this share price level, we are today committing to a further $1 billion. This will start shortly and complete by the full year result in February 2026. We plan to execute $500 of this billion in-year. This is a further demonstration of the flexibility and optionality our strong cash flow generation gives us and our very active capital allocation decision making. Taking all this together, our leverage at the end of this year should be around 1.9 times EBITDA, so in the middle of our 1.5 to 2.5 times net debt to EBITDA range. Let's now look at the rationale of the transaction in our post-trade business that we announced this morning. First, A group of 11 leading global banks is taking a 20% stake in our post-trade solution business. The perimeter of PTS includes the recent acquisition, Quantile and Acadia, plus businesses we have grown organically, mainly swap agents. This transaction deepens our partnership with institutions that can benefit significantly from PTS services and allows them to help share its future and share in its growth. Second, we have agreed to alter the terms of the revenue share paid to the partner banks from Swapclear. Historically, and up to 2024, this sat at 30%, reflected in our cost of sales. We are taking this down to 15% for 2025, applied across the whole year, and 10% for 2026 and beyond. And finally, we are extending it from 2035 to 2045. Again, this is strategically important and it improves our economics at a fair valuation and extends the deep relationship with our partner banks into the long term. Daniel will cover the strategic value in more detail in a moment. But the financial effects of this transaction are very positive. The impact of reducing the revenue share from 30% to 15%, which again is retroactive across the whole of 2025, will add around 250 bps to the market's divisional EBITDA margin and 100 bps to the group margin this year. While obviously there are some financing costs, overall, this transaction is two to 3% accretive to EPS this year onwards. But beyond these financials, and even more importantly, we expect this transaction to accelerate the long-term growth in PTS. Let me hand over to Daniel to recap on the playbook that has been so successful. Thank you, Matt.