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1 Investor Questions and Answers: November 5, 2025 We encourage current shareholders, potential shareholders, and other interested parties to send questions to us in writing and we make written responses available on a periodic basis. The following answers respond to selected questions received primarily through September 30, 2025. We retain the discretion to combine answers for duplicate or similar questions into one comprehensive response. If you would like to submit a question, please send an e-mail to investors@morningstar.com or write us at the following address: Morningstar, Inc. Investor Relations 22 W. Washington St. Chicago, IL 60602 Use of Non-GAAP Measures These Investor Questions and Answers reference non-GAAP financial measures, including but not limited to, adjusted operating income and free cash flow. These non-GAAP measures may not be comparable to similarly titled measures reported by other companies. Reconciliation of non-GAAP financial measures can be found at https://shareholders.morningstar.com/financials- stock-info/key-financials/default.aspx. We have received a number of queries related to Artificial Intelligence in the recent period, including questions regarding the opportunity it creates for Morningstar and potential changes to the competitive landscape. Our CEO Kunal Kapoor addressed a number of these topics in his Q3 25 shareholder letter. We have consolidated responses to other outstanding questions here. Competitive Advantage 1. Over the past year, what has Morningstar done to widen its moat? I'd appreciate if your response includes the economic moat source that was impacted. How is Morningstar responding to competitive challenges from successful fintech startups in order to maintain (and ideally increase) its elevance with its wealth and asset management clients? Through the first 10 months of 2025, we have taken a number of deliberate steps designed to fortify our economic moat by strengthening core sources of competitive advantage, particularly intangible assets, switching costs, and network effects. - Deepening Intangible Assets Through Data Expansion and Ratings Innovation Our teams are focused on continually expanding our data and analytical frameworks, including those that bring transparency to historically opaque private markets. For example, PitchBook has nearly doubled its private company coverage over the course of 2025 through early November and continues to expand its credit data with the expansion of its collateralized loan obligation (CLO) data. Earlier this year, our manager research team introduced Medalist Ratings for Semiliquid Funds, which provide qualitative assessments on vehicles in a fast-growing segment that sits between mutual funds and private markets. These ratings are now being used by advisors and asset managers to evaluate interval and tender-offer funds with the same rigor as traditional vehicles. 2 We also announced our plan to acquire The Center for Research in Securities Prices (CRSP), which brings access to more than 65 years of high-quality equity data. This acquisition is expected to enhance our ability to deliver AI-ready, structured datasets to clients, whether directly, in Morningstar products, or through third parties. - Increasing Switching Costs Through Workflow Integration and Platform Upgrades We recognize the appeal of niche fintech platforms with targeted functionality. Our response has been to double down on user- first design, delivering intuitive, workflow-specific tools that combine the depth of our data with the usability of modern software. We have made significant progress on the rollout of our Direct Advisory Suite (DAS), which now features a modernized interface and advanced analytics built on a common Direct Platform foundation. This unified experience allows advisors to seamlessly analyze public and private exposures, benchmark portfolios, and generate proposals within a single application. DAS is more than a user interface refresh—it is a strategic move to embed Morningstar deeper into advisor workflows, making it harder to switch away without losing efficiency and insight. Our acquisition of Lumonic has enabled us to digitize covenant reviews and compliance workflows in private credit, a notoriously complex asset class. By combining Lumonic’s granular monitoring tools with PitchBook’s market data, we are expanding into more parts of the workflow and positioning PitchBook as a more comprehensive analytical environment for general partners, wealth managers, and institutional consultants. - Building Network Effects Through Public-Private Convergence Morningstar is focused on equipping investors of all kinds, from individuals to institutions and the intermediaries who serve them, with the tools they need to navigate the convergence of public and private markets. Drawing on data from PitchBook, including Leveraged Commentary & Data (LCD) datasets, and Morningstar Credit, we aim to deliver transparency and comparability across asset classes to support better portfolio decisions. Our approach integrates four connected capabilities: actionable data, including expanded data coverage on private companies and semiliquid funds; standardized analytics including standardized categories, liquidity-adjusted risk scores, and percent-private exposure; differentiated intellectual property (IP) such as the new Medalist Rating for Semiliquid Funds and benchmarks like the Morningstar PitchBook Unicorn 30 and the Morningstar PitchBook Modern Market 100 Index, the first to bridge public and private equity exposure in a single index; and unified workflow tools like DAS and the addition of Lumonic’s private credit monitoring alongside PitchBook’s best- in-class private markets reference data and analytical suite. Together, these capabilities can enable professionals to construct, monitor, and explain portfolios with greater precision, supporting our mission of empowering investor success across the investing ecosystem. Finally, we’d note that we provide data to many fintech firms and that client segment has been a source of growth for our business. 3 2. What makes it difficult to synthesize Morningstar’s data into a competitive product? Why have competitors (including startups) failed to match the depth and breadth of Morningstar data? How does Morningstar protect its research, IP, and data from competitive disintermediation? Please also address what you are doing to protect MORN from AI as a competitive risk. We believe that Morningstar’s data moat is built around several key factors: - Credibility and Industry Acceptance: We set the industry’s shared language of investing for managed investments and are working to do the same for private market investments. Our proprietary frameworks—including categorization, calculation methodologies, the Style Box, and the Morningstar Rating— are deeply embedded in our products and widely recognized across the industry. We believe that these systems transform raw data into actionable insights that investors can understand and trust, and that would be difficult for competitors to recreate. These widely used research-driven tools serve as “benchmark-like” standards that can enhance our clients’ ability to find, evaluate, and communicate investment ideas. - Systematic Data Collection: We believe that we have an edge in systematic, scalable data collection. While collecting data is relatively easy, doing it in systematic, consistent ways with scalable technology systems is not. We believe that we have an edge in ingesting high volumes of data and processing and categorizing it into systems, languages, and products that investors can understand. This process has been refined over decades and is supported by robust technology that we believe makes it difficult for others to match our depth and breadth. We also collect data that is not always required to be disclosed by regulation, making it difficult to obtain and highly differentiated. Finally, Morningstar’s datasets—especially survivor-bias-free fund data—aim to enable more credible back testing and long-term analysis. This depth of information is difficult to replicate and becomes embedded in client workflows, making products including Morningstar Direct indispensable for rigorous investment research. - Data Enrichment & Differentiated IP: We continue to invest in what differentiates us: our unique, highly valuable IP layered on top of our data. While we agree that basic reference data and investment details will be further commoditized in an AI-first world, we believe that our differentiation comes from the data that we collect and enrich with our standard-setting categorizations, calculations, and methodologies. We have built a broad and deep collection of differentiated IP including company- and portfolio-level ratings, sustainability and risk scores, proprietary modeling, scenario analyses, and tools such as the VC Exit Predictor. We continue to expand that edge, including with the CRSP acquisition, which brings us a first-rate market database with more than 65 years of high-quality equity data. As markets evolve, our analyst teams are producing new research and IP designed to support investment decision-making which can further expand our moat. - Strong Governance: We have established a dedicated Responsible AI Council and a governance framework designed to protect our data in client agreements and are regularly reviewing how legal action is evolving related to AI. We are focused on implementing contractual safeguards to protect our data and proactively adapting to new legal standards, especially as they relate to AI and internet scraping. While AI presents new challenges and uncertainties, we believe our proprietary frameworks, systematic and scalable data processes, unique IP, and governance practices create a high barrier to entry. We believe these elements collectively make it difficult for 4 competitors to synthesize a truly competitive product or replicate the breadth of our research and data. We continue to invest in innovation and work to safeguard our data contractually with the goal of keeping our data secure and protecting our moat. Please see a response dated Sept. 2025 for a detailed discussion of more specifics related to PitchBook’s moat. 3. How much of Morningstar's revenue is driven by proprietary data? What proportion of data in PitchBook is publicly available/sourced on the internet? In Morningstar Data, what is the proportion of sales from ratings (where data is used to form an opinion or analysis), or pure data? License-based data accounted for 71.4% of Morningstar revenue in 2024. At this time, we don’t disclose the percent of revenue directly tied to proprietary data: however, we would share the following observations about the value of the data that we provide: - Data powers the full Morningstar Direct Platform product suite (including our Morningstar Data key product area). This includes data, calculations, and analytics based on Morningstar IP as well as returns data, holdings data, and portfolio statistics that are collected, processed, normalized, and derived using consistent Morningstar methodologies, and which enable clients to compare investments on an apples-to-apples basis and often come in historical time series. We believe that this is not easy to replicate. - PitchBook’s data moat is built on years of investment in proprietary infrastructure, analyst expertise, and rigorous verification processes. Our network effects and trusted relationships across the private markets enable us to source non-public, hard-to- access information directly from market participants through rigorous primary research and self-reported processes. - Many of the leading large language models (LLMs) and AI-powered tools recognize the strength of our data and are working with us to provide this data to their users. For example, Morningstar Direct Platform has entered into a relationship with Anthropic that enables entitled users access to our content within Claude for Financial Services. In late October, we built on this relationship with PitchBook’s announcement that its private market data is now available within Claude for licensed PitchBook users. PitchBook users are now able to query proprietary data directly using natural language prompts while maintaining high standards for data security, accuracy, and compliance. This launch builds on PitchBook’s growing network of collaborations with leading enterprise LLMs and AI-powered tools including Perplexity, Rogo, Hebbia, Finster, Model ML, and Farsight. For more on Morningstar’s data moat, please see a related question this month and a similar question specific to PitchBook’s moat from Sept. 2025.

5 Artificial Intelligence 4. AI’s potential impact on information services businesses is a key focus area for investors. We would like to know how management thinks Morningstar scores on an AI framework around both threats & opportunities to the business, including in the following areas: quality of data, industry structure, workflow/mission criticality, revenue model, ability to use AI to improve products and operations, and impact to end market. Please specifically address your more desktop or workflow-based products like PitchBook or Morningstar Direct, and explain what it is that you think still provides advantages and relevance for these products as AI competition rises. In his Q3 25 shareholder letter, CEO Kunal Kapoor shared his thoughts on what we believe AI will mean for Morningstar and our use of AI technologies in three important areas: delivering our data and IP; accelerating speed-to-insights in our products; and boosting the operational excellence of our teams. Kunal addressed many of the issues raised in your question, including the quality of our data, industry structure, revenue model, and ability to use AI to improve products and operations. We also address our data moat and revenue model in separate questions filed as part of this 8-K response. We share a few additional thoughts below on topics not covered elsewhere. - Workflow/mission criticality Software products such as Morningstar Direct, Morningstar Advisor Workstation and its successor, DAS, provide clients with key client reporting, due diligence, and proposal capabilities that are essential parts of the workflow. Please see a related question this month that addresses Morningstar Direct Platform’s “stickiness” with clients. We believe that PitchBook is deeply embedded in the daily operations of private market professionals, supporting use cases from deal sourcing and due diligence to benchmarking and portfolio monitoring. Our acquisition of Lumonic expands our capabilities in private credit portfolio monitoring, while our integration of LCD and DealX data and analytics strengthens our credit analytics and structured finance coverage. These moves position us to serve a broader set of workflows and deepen our value across client segments. More broadly, our strategy is to deliver data, research, and technology across multiple distribution channels, including the PitchBook platform, direct data feeds, and emerging LLM interfaces, supporting client workflows wherever they operate. - Impact on end market AI is emerging as both a disruptor and a source of competitive advantage in our industry. We expect that customers will license and build AI tools to accelerate time and resource-consuming administrative tasks, while executing core workflows and tasks using AI, allowing them to deliver against their business objectives more quickly and efficiently. LLMs can process large data streams and produce complex deliverables and artifacts, but accuracy and provenance remain critical. We believe that those who harness AI responsibly, with defined use cases and trusted partners (technology, data, research, etc.) are positioned to lead. Finally, it is our view that the combination of AI and human insight can deliver superior outcomes. Indeed, we see many builders of AI tools increasingly focused on incorporating authoritative data and research from respected sources into their products. 6 5. Is there a level of usage-based revenue within Morningstar Direct Platform? Does AI pose a risk to a seat-based revenue model? How much of your revenue is seat-based vs enterprise license? Does it make sense to focus more on enterprise revenue and reduce seat- based exposure over time? “Usage-based revenue” refers to income a company earns by charging customers based on how much they actually use a product or service. Currently, we do not follow a “usage based” model in Morningstar Direct Platform. We follow varying revenue models in different areas of the business. For example, Morningstar Data is predominantly priced by use case, as opposed to how much clients use either the software or data. Morningstar Direct is licensed on a per-seat basis; Morningstar Advisor Workstation follows a hybrid seat-based/enterprise approach. For the PitchBook platform, we utilize seat-based licenses as a foundational pricing model. Over time, many of our clients move to an enterprise-wide model. This gives us elastic upside for high- value users/clients and positions us for strategic bundling of data products. Overall, we’d note that commercial models are still evolving with the growth of AI, but we’re confident that the pairing of our IP with data – including hard-to-access data – offers a compelling value proposition. That means experimenting with flexible consumption- and seat-based revenue models as we seek to match price to value. 6. How much are you spending on AI and can you maintain/improve margins while concurrently spending on AI? We are using AI technologies in three important ways in our business: to deliver data and IP; to accelerate speed-to-insights in our products; and to boost the operational excellence of our teams. We will allocate investment towards AI where we see high potential for positive returns. While these returns may not always materialize in the first year, we believe that we can continue to expand our adjusted operating income (AOI) and AOI margins over time, building on the progress we have made in recent years. We don’t disclose AI costs separately, but they are included within compensation, professional fees, and infrastructure & other categories as shown in the Adjusted Operating Income walk of our quarterly supplemental deck available on our IR website: https://shareholders.morningstar.com/financials-stock-info/key-financials/default.aspx. 7. How is Morningstar incorporating AI into the Direct Platform? What has been the customer feedback thus far? How many desktops is “Mo” deployed on today? AI has been integrated across key product lines within the Direct Platform business to enhance both internal operations and client- facing solutions. We were early to launch in-product AI features in 2023 with Mo, our digital research assistant offering conversational responses from our extensive research library in products like Morningstar Investor and Morningstar DAS. Based on continual review of usage data and user feedback we have evolved the Morningstar Intelligence Engine—the brains behind Mo—to remove friction from product workflows, including assisting advisors in creating investment proposals, supplying investor-ready talking points for proposed portfolio changes, populating and running investment screens, and answering product support questions. These features enable clients to leverage AI-driven insights and tools directly within their workflows. Mo is available to all users of DAS, Direct Compass, the new Direct experience, and Morningstar.com Premium. (Morningstar Direct’s new experience lets users create, edit, and analyze investment collections, all backed by independent research, in a workspace that’s designed to be intuitive and unified and streamline workflows. Clients can also access Morningstar Direct’s new experience via their web browser, making it easy to work from any computer, anywhere.) Additionally, we offer AI solutions through our Direct Web Services APIs, which are marketed under the Direct AI Solutions umbrella and included in Morningstar Data revenue. These include: 7 - Morningstar Agent - Morningstar MCP Server - Direct AI Insights These solutions can be licensed independently to power the AI applications our clients are building, or the AI tools our clients’ employees are using. Many of our clients use third-party AI platforms for building and/or using AI applications, so we are additionally working with these platforms to provide turnkey access to Morningstar’s AI-ready data and research. 8. Which LLMs, or new AI driven financial analysis subscriptions, such as Claude for Financial Services or Hebbia, do you supply data to? How do you minimize the risk of clients who are consuming your bulk/ API data from accidentally leaking (e.g., loose permissions on buckets) your proprietary databases and other assets? What is the data sharing model with your AI platform partnerships (e.g., Claude)? That is, (i) do you share entire databases for them to query or (ii) do they send specific queries (potentially AI generated) into Morningstar’s data warehouse and Morningstar respond with aggregated results? Morningstar is focused on making our AI-ready data, research, and capabilities available in the AI platforms our clients are using to build applications, and the AI tools their employees are using. Examples include Morningstar and PitchBook’s relationships with LLM providers like Anthropic, which enable entitled users to access Morningstar’s content within Claude for Financial Services through our investment in AI-optimized Model Context Protocol (MCP), and Perplexity, where users will find the quality and accuracy of Morningstar and PitchBook reports. In addition, PitchBook has also announced strategic collaborations with leading enterprise LLMs and AI-powered tools including Rogo, Hebbia, Finster, Model ML, and Farsight. We are focused on implementing contractual safeguards to protect our data and proactively adapting to new legal standards, especially as they relate to AI and internet scraping. We have formed multi-disciplinary forums that include representatives from legal, compliance, and product teams to assess the latest trends, risks, and legal precedents arising from the rapid growth of AI. We have always sought to protect our data in client agreements and have adapted to include additional restrictions and protections related to AI use cases over the past two years. In AI-oriented agreements and third-party AI platform integrations, we’re focusing on MCP, Agentic, and traditional API delivery methods. These delivery mechanisms provide robust entitlement, observability, and usage reporting mechanisms that support our agreement frameworks. Importantly, these delivery methods provide AI-ready data for specific queries rather than requiring full copies of entire data sets. Use cases tied to traditional data feed delivery are subject to additional restrictions and requirements in our agreements. Morningstar Direct Platform 9. Could you please describe how the Direct Platform is “sticky” with existing customers? What % of the data or revenue for Direct segment is proprietary to Morningstar or intertwined/commingled with customers’ data that might drive higher levels of stickiness? What % is proprietary or difficult to source? We believe that Morningstar Direct Platform’s “stickiness” stems from three factors: our differentiated data, the depth of client integration, and the high switching costs associated with established workflows. These elements combine to make Morningstar Direct a deeply embedded and difficult-to-replace solution for investment professionals. 8 Morningstar’s data, analytics, and IP provide an essential foundation for both the platform’s value and its role in clients’ workflows. Morningstar Direct Platform products leverage data that is largely our own, including our databases on managed products (funds), equity fundamentals, sustainability, and private markets. While we supplement with third-party data (such as fixed income securities, equity estimates, and third-party indexes), the platform’s core value is driven by data that we collect, process, normalize, derive, and calculate using consistent Morningstar methodologies as well as our signature IP. Morningstar Data For clients of our Morningstar Data products distributed via data feed or API, our clients are generally bringing our data into their ecosystems with the intention of displaying it alongside their own data in software products or tools, or for use in creating fact sheets or other materials. We believe that our clients value data points that represent our proprietary methodologies as well as the supporting data that underlies those methodologies. Morningstar Direct We assess stickiness based on how clients engage with Morningstar Direct, revealing three groups that reflect workflow depth and switching friction: - Data Users: These users primarily access Morningstar data and IP through tools like Workspace, Data Discovery, and exporting to Excel for further analysis. While this group is more at risk for churn, many remain due to the ease of use, especially among smaller firms without the infrastructure to manage bulk data feeds. - Capability Users: These users leverage Direct’s analytical tools—Portfolio Analysis, Scorecarding, Investment Discovery, Dashboards, and Interactive Research— to complete various jobs based on routine processes or task-specific usage based on internal or market demands. Their reliance on these capabilities increases switching friction. - Scaled Users: These clients have deeply embedded Direct into their operations through use of monthly performance reporting, Presentation Studio template creation and distribution, custom portfolio import and analysis, and Excel add-in access. Even if time spent within Direct is minimal, the value delivered is high and difficult to replicate elsewhere. Data users represent the smallest group of Direct users. Our strategic focus is on moving more users into the second and third tiers, where retention is strongest. This behavioral depth is reflected in our high net revenue renewal rates, as reported annually in our 10-K. From a reporting standpoint, Direct clients who use Presentation Studio primarily rely on Morningstar data, but they also have the flexibility to import their own custom datasets. This capability enables them to blend proprietary insights with internal data when producing reports, deepening the integration of our tools within their decision-making workflows We continue to invest in features that deepen integration and reduce friction, including: - Bulk portfolio import tools - Customizable reporting in Direct’s Presentation Studio Morningstar Advisor Workstation/Direct Advisory Suite In addition to the quality and breadth of our independent data and research, we believe that Advisor Workstation/DAS’s stickiness comes from: - The trust that investors have in the Morningstar brand and IP such as the Star Rating, Medalist Rating, and Portfolio Risk Score, which advisors leverage in their client conversations.

9 - Our compliant reporting, which we believe sets the industry standard for highly visual, client-facing reporting and is more extensive than our competitors’. Taken together, we believe what differentiates Morningstar Direct Platform is not just our differentiated data, but how seamlessly our IP is embedded across the entire investment lifecycle—from data selection and analysis to reporting and client communication. This end- to-end integration reinforces product stickiness and positions Direct Platform as a vital part of our clients’ decision-making processes. PitchBook 10. Has competition for PitchBook increased due to new competitors using AI tools to gather and structure data? Has the threat of new competition diminished pricing power for PitchBook? As we noted in our Q3 25 shareholder letter, we are observing longer sales cycles, due in part to firms seeking to do more with fewer resources and ongoing efforts to clarify their strategies for data and artificial intelligence (AI). That said, net revenue renewal rates have remained strong and we’re seeing expansion opportunities, especially with large clients. 11. There has been tremendous growth in private debt funds. Are there ways Morningstar/PitchBook can capitalize on this trend? We believe we are well-positioned to capitalize on the tremendous growth in private debt funds across multiple parts of our business including PitchBook, Morningstar Credit, and Morningstar Direct Platform. For PitchBook specifically, firms that are primarily identified as private credit and lenders represent a small, but growing client segment, while investors, general partners (GPs), and investment banks value our private credit data and capabilities. For example, private credit capabilities have supported the expansion of our relationship with certain large asset management clients. We continue to build these capabilities including comprehensive insights into direct lending, business development companies (BDCs), and CLOs. Our platform now enables clients to track both broadly syndicated loan (BSL) and private credit trends, reflecting the increasing complexity and opportunity within the market. Furthermore, PitchBook has deepened its coverage of the private debt asset class and has introduced tracking of semiliquid and evergreen fund structures, many of which are within the private credit category, enhancing our offerings for wealth advisors and the growing wealth market. Through these enhancements, we’re providing institutional investors, GP fundraisers, wealth advisors, and private credit managers and lenders access to timely, granular data needed for research, due diligence, and portfolio construction. Complementing these capabilities, Lumonic provides advanced private credit portfolio monitoring tools. Within Morningstar Direct Platform, we’re expanding our private credit capabilities to address important use cases for asset managers and advisors and wealth managers. We launched our Morningstar Medalist Rating for Semiliquid Funds, including coverage of private credit funds. In late June, we introduced new capabilities in DAS designed to help advisors evaluate, compare, and communicate the role of private investments within a broader portfolio context. This includes the addition of a new private capital fund universe to screen, compare, and monitor private investments, the enhancement of the Morningstar Risk Model to account for private capital funds, and new portfolio transparency tools. 10 Finally, Morningstar Credit has significant exposure to private ratings activity, with roughly a quarter of 2024 transaction-based revenue related to private credit ratings. This is an area where we see continued growth opportunities. 12. What are the incentives for PitchBook’s sales force to drive incremental new business wins, grow customer count, incremental seats/usage, and drive higher NPS scores? How has the PitchBook go-to-market sales engine changed if at all? What is Morningstar PitchBook’s NPS score YTD? While PitchBook is focused on driving incremental new business wins, growing customer count, increasing seat usage, and achieving a strong NPS score, we do not publicly disclose specific sales incentives. Our commitment remains centered on delivering exceptional value for our clients and continuously improving both our product and customer experience. PitchBook’s NPS score was 49 as of September 2025. We have not made significant changes to our go-to-market sales engine. 13. What has gross new logo and net new logo growth been for PitchBook in 2024 through 1H’2025? What has been price vs units for sales growth YTD? From January 2024 through June 2025, the trend in PitchBook net logos was slightly negative, as churn in existing logos (especially in the corporate sector) was mostly offset by the addition of new logos. During this same period, annual contract value (ACV) per logo has increased. 14. Could you please break down the updated TAM for PitchBook by price, seats, customer type/end market? Which use cases or end markets are most ripe for incremental growth/opportunity within the $11 billion total addressable market (TAM)? How do you drive incremental penetration? As of April 2025, we estimated PitchBook’s TAM at $11.1 million. The TAM is comprised of roughly 60% corporate logos, roughly 20% investor logos, with the remaining TAM roughly equally divided between limited partners and service providers. We’d note that in 2024, we adjusted our TAM definition to include corporates with one use case, rather than the minimum of three use cases under the previous definition. This contributed to growth in the number of companies in our TAM and the size of the TAM in 2024. Growth in 2024 and 2025 was also driven by the expansion of private markets globally as well as the growing demand for integrated data solutions across corporates, service providers, and financial sponsors. Market penetration is highest in our core segments including private equity, venture capital, and investment banking. We expect future growth to come from a combination of new logo acquisition and expansion with existing customers. Even at large firms where PitchBook is well-penetrated, our footprint often covers only certain teams or geographies. We believe there is a meaningful opportunity to broaden adoption across functions, business units, regions, and asset classes (including private credit), as well as to expand usage through new products, data sets, and AI-driven workflows. At the same time, we continue to see new logo opportunities across segments, particularly in underpenetrated corporate and international markets and with commercial banks. In short, new logos remain an important driver, but expansion at existing clients, especially large global firms, also represents a substantial and durable growth opportunity. Please see our Q3 25 shareholder letter for more on how we’re thinking about PitchBook’s growth strategy. 11 Capital Allocation 15. Data and analytics companies/info services have fallen out of favor with investors. Morningstar and similar high- quality companies are trading at historical discounts to their long-term trading multiples. How has this changed your approach to capital allocation? Does it make sense to flex up buybacks opportunistically in the current environment? How did management think through the tradeoff between allocating capital to this acquisition compared with share buybacks given the valuation of Morningstar’s business? How does management think through the tradeoff between allocating capital to acquisitions (such as CRSP) compared with other uses of capital (e.g. buybacks, etc.)? What is the funding for the CRSP acquisition (cash/debt)? As you saw in our recently reported Q3 25 results, we agree with your thesis that it has been a good time to accelerate share repurchases. For the year-to-date through Oct. 28, 2025, we repurchased 1,873,729 shares for a total of $487.0 million, completing the share repurchases authorized under the three-year $500 million program effective Jan. 1, 2023. Our Board of Directors has approved a new, three-year $1 billion repurchase program effective Oct. 31, 2025. Similarly, when we make acquisitions, we consider the return potential of those acquisitions relative to other uses of capital. In the first 10 months of 2025, we acquired Lumonic and DealX and announced our pending acquisition of CRSP. More broadly, our long-term capital allocation priorities remain consistent: - We seek to preserve a strong balance sheet to maintain flexibility; - We prioritize funding organic growth and consider acquisitions where we see opportunities to generate long-term shareholder value; - We aim to maintain and increase our dividend over time commensurate with company financial results; and - We look to repurchase shares when we have available cash and will accelerate that activity opportunistically when we believe that our shares are undervalued. Finally, we plan to finance the CRSP acquisition primarily through our new credit facility. On Oct. 31, we announced that we had entered a new Credit Agreement with borrowing capacity of up to $1.5 billion, including a five-year, $750 million revolving credit facility, a five- year term facility of up to $375.0 million, and a three-year $350.0 million term facility. For more detail, please see the 8-K filing dated Oct. 31, 2025. 16. It seems that stock you buy back is not cancelled and remains as treasury stock. Why have you decided to keep treasury stock? Are you planning to cancel it? Our share repurchase activity is guided by our focus on long-term value creation for our shareholders. Consistent with common practice, we treat our repurchased stock as treasury shares, which are not considered outstanding. 12 M&A 17. Please provide more detail on the announced acquisition of CRSP, including the rationale. Specifically, where do you see operational synergies, from a cost, efficiency, and revenue cross-sell potential, between CRSP and Morningstar Indexes. How would you rank the different revenue synergy opportunities by revenue and near-term probability? What were the contractual limitations of the business historically and what potential is there in terms of growth and profitability as part of an enlarged Indexes division under Morningstar? How does CRSP differ from your existing Indexes business and how will it be integrated into the existing portfolio? How valuable/replicable is the CRSP data set? How is Morningstar maintaining the Vanguard relationship and how much does that represent of the $55 million annual sales base? Our planned acquisition of CRSP represents an important step in scaling our Morningstar Indexes business and will position us as one of the largest index providers for public US equity index funds. CRSP brings market share and scale in US beta equity indexes, strengthening our overall global offering. These core beta benchmarks are often a competitive differentiator in the business and complement our current capabilities, which include significant assets tracking dividend and strategic beta capabilities Finally, CRSP is known for its high-quality and comprehensive historical US equity and index data, which it started building more than 65 years ago. Its data capabilities are well-known and well-respected in the investment and academic communities, and its indexes are designed to be replicable for use in passive products. As we integrate CRSP into Morningstar Indexes, its products will be supported by our marketing, product, and sales function and our broader, commercially focused global enterprise. Morningstar Indexes is excited to expand the distribution and use cases for the CRSP US Market Series. Morningstar currently has a strong relationship with Vanguard, and we look forward to continuing to expand that relationship across an even broader set of capabilities and connection points. 18. Please discuss the expected financial impact of the CRSP transaction. What is the revenue breakdown today between AUM-based fees, subscription fees, and other? How profitable has CRSP been historically and what are your expectations for future profitability? How much of your rationale for acquiring the business is about improving operating margins prospectively? Do you expect the transaction to be accretive or dilutive? The incremental sales pro forma takes Indexes from $87.5 million LTM to $142.5 million – theoretically providing incremental scale to your US public equity indexes. How much operating cost is Morningstar taking on for CRSP? We have shared that CRSP generates $55 million in revenue annually. We believe we can bring a stronger commercial focus to grow the franchise over time and to support the scaling of Morningstar Indexes. We expect the CRSP transaction to be accretive to adjusted operating margin in 2026. We do not disclose detailed revenue breakdowns beyond the key product area (revenue) or adjusted operating expense beyond the reportable segment level. 19. What will your approach to pricing be with the acquisition of CRSP? Do you anticipate any meaningful shifts in the strategy from how the business operates today? We have positioned Morningstar Indexes as a disruptor in the index space for a number of years and intend to continue to play this role. We seek to provide value-based pricing for our core index capabilities.

13 Compensation and Benefits 20. Morningstar equity analysts have long used ROIC to evaluate a company’s economic moat and management’s capital allocation effectiveness. In 2024, executive bonuses were based on revenue growth and adjusted operating income, but ROIC was not included as an incentive metric. As a shareholder, it would be encouraging to see management held accountable for capital use through ROIC. Could you explain why ROIC is not currently used in the bonus matrix? Our annual company-level incentive program, which also applies to executives, is designed to reflect our focus on increasing revenue and adjusted operating income (AOI) as a driver of long-term value creation. In 2025, we have assigned a 50% weight to revenue performance and a 50% weight to AOI performance relative to targets. We would highlight that our executive compensation program is designed to align our executives’ interests with long-term shareholder interests. This is accomplished through, among other items, a significant portion of CEO and NEO compensation being driven by Total Shareholder Return (TSR) through our market stock units (MSUs), as well as meaningful executive stock ownership requirements which are described in more detail at shareholders.morningstar.com/governance. MSUs are earned based on Morningstar’s “absolute” TSR, which includes the impact of both stock price appreciation and dividends and is not determined relative to a peer group. If Morningstar's TSR increases over the performance period, that translates into increased value to our shareholders and increased shares ultimately earned by MSU participants (and the inverse is true). We believe this motivates value creation, including accretive capital allocation, and further aligns executive compensation with the shareholder experience. We actively monitor our return on invested capital (ROIC) and view it as a useful measure of long-term capital efficiency and value creation. We’d note that outside of acquisitions our business is not capital-intensive so increases in margins and profitability have a direct impact on ROIC. Overall, we believe our compensation philosophy supports accountability and performance across the business, with a focus on driving revenue growth and profitability in the annual bonus and ultimately enhancing shareholder value through the long-term MSU incentive. Morningstar Founder 21. Is there a target % ending ownership for Joe Mansueto’s stake each year? What will Joe Mansueto’s pro forma stake be at the end of 2025 based on publicly disclosed data? Joe Mansueto does not have a target percent ownership in Morningstar stake each year. Joe Mansueto historically has entered into 10b5-1 plans on an annual basis to sell a portion of his Morningstar shares to diversify his investments. These annual plans have allowed for trading to begin on May 1st, several months after the release of our 10-K and year- end earnings. The plans have been designed to spread sales evenly over the next four quarters. His current sales plan covers 400,000 shares, or sales of 100,000 shares per quarter. In our Q3 25 10-Q, Morningstar reported 41,129,198 shares outstanding as of Oct. 24, 2025. Also as of that date, Joe Mansueto’s ownership stake in Morningstar was 36.5%. Using the number of shares outstanding as of Oct. 24, and assuming that 100,000 shares are sold under Joe Mansueto’s 10b5-1 plan in Q4 25, his year-end ownership stake would be 36.3%.