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Temi Popoola, Group Managing Director and Chief Executive Officer of Nigerian Exchange Group, has called for continued collaboration among regulators, exchanges, and international partners to effectively channel sustainable capital flows across emerging markets.Speaking at the International Finance Corporation conference in Cairo during a panel session themed Capital Mobilization for Sustainability, Transition and Resilience, Popoola provided insights into the evolving landscape for developing economies.He acknowledged that emerging markets are navigating structural considerations, including the development of ESG data and reporting infrastructure, policy frameworks, funding costs, and market liquidity. He also noted a growing global investor appetite for sustainable assets, supported by innovation in labelled instruments and the ongoing enhancement of regulatory standards.“Emerging markets have a significant opportunity to contribute to the future of sustainable capital flows,” Popoola said. “Realizing this potential calls for constructive alignment, robust disclosure standards, policy consistency, and synergy across the capital market ecosystem.” He highlighted the importance of evolving disclosure frameworks, noting that stronger reporting standards can enhance transparency, support risk assessment, and help attract long-term investment.Drawing on Nigeria’s experience, he pointed to the country's green and sustainable bond market, which began with Africa’s first certified sovereign green bond in 2017. Since then, the market has expanded across sovereign, sub-national, and corporate issuers, with repeated oversubscription reflecting growing investor confidence. He also referenced Nigeria’s sovereign sukuk programme, including the most recent Series VII Sukuk, which recorded subscriptions significantly above the offer size, demonstrating sustained domestic demand for long-term infrastructure-linked instruments.According to Popoola, stock exchanges play a key role in advancing sustainable finance by providing platforms for impact-focused instruments, supporting disclosure standards, and aiding issuer capacity building. In this regard, he highlighted NGX’s Impact Board, launched in 2024 as a dedicated listing segment for green, social, and sustainability-linked instruments. He also discussed the NGX Net-Zero Programme, co-funded by DEG Impulse, which supports listed companies in developing science based transition plans and enhancing climate disclosures. The programme is projected to reduce or avoid approximately 20,000 tonnes of CO₂e emissions in its initial phase while positioning companies to access climate-aligned financing.On collaboration, Popoola emphasized the importance of alignment among policymakers and market operators, citing Nigeria’s first sovereign green bond, executed through coordination between the Exchange, the Ministry of Finance, Ministry of Environment and the Debt Management Office, as an example of effective public, private partnership.The conference convened policymakers, regulators, exchange leaders, and development finance institutions to explore pathways for mobilizing capital toward sustainability, resilience, and long-term economic growth across emerging markets.

Thank you for the invitation to speak to you today.1 Before I get into my main topic, I wanted to share my current views on the economy and monetary policy. Last week, we received the latest report on employment, and it provided further evidence that while the labor market slowed through last summer, it is now stabilizing. This stabilization is occurring with an unemployment rate that is broadly consistent with what many estimate is its long-run level, when the economy is in balance. That said, job creation has been near zero over the course of last year, as has labor force growth. With very low levels of job creation and also a low firing rate, there seems to be a tentative balance in labor supply and demand. But it is a delicate balance, and that means that the labor market could be especially vulnerable to negative shocks. Turning to the other component of our mandate, inflation based on personal consumption expenditures remains elevated at 3 percent, about where it was a year ago. Disinflation, which started in mid-2022, slowed last year, as goods price inflation picked up, in large part due to tariffs. That pattern appeared to continue in the inflation data released last week. Looking ahead, it is reasonable to forecast that tariff effects on inflation will begin to abate later this year, but there are many reasons to be concerned that inflation will remain elevated. I see the risk of persistent inflation above our 2 percent target as significant, which means we need to remain vigilant. The prudent course for monetary policy right now is to take the time necessary to assess conditions as they evolve. I would like to see evidence that goods price inflation is sustainably retreating before considering reducing the policy rate further, provided labor market conditions remain stable. Based on current conditions and the data in hand, it will likely be appropriate to hold rates steady for some time as we assess incoming data, the evolving outlook, and the balance of risks. I'll now turn to my main topic today. Artificial intelligence (AI)—and by this, I mean in particular the recent explosive growth of generative AI—looks increasing likely to become what technologists call a general-purpose technology. General-purpose technologies such as the steam engine, electricity, and personal computers are characterized by widespread adoption, continual improvement, and a cascade of downstream innovations in new goods or services, production processes, and business structures.2 In addition to the likelihood that AI becomes a general-purpose technology, it may also become an "invention in the method of invention," something that increases the efficiency of research and development (R&D) and thus drives further innovation and the attendant benefits. Personal computers qualify here because their widespread adoption, continuous improvement, and many applications over the past 50 years or so exponentially expanded our ability to invent things. And in the same way that computers were used to fundamentally improve the process of discovery in, for example, medicine, engineering, and physical sciences, generative AI and earlier forms of AI such as machine learning applications are already being used in R&D and yielding discoveries in domains such as drug discovery and materials science.3 Periods of rapid technological change are often accompanied by anxiety about the economic and social consequences of automation. Although new technologies often create winners and losers in the short run, history shows that in the longer run innovation leads to broadly shared increases in productivity and living standards that tend to support economic growth and a healthy labor market. As with other general-purpose technologies, the long-run effects of AI are likely to be profoundly positive. But in the short term, AI may deeply disrupt labor markets and harm some workers. The ultimate impact on workers will depend not only on the extent of the disruption and the length of time it takes for the long-term benefits to appear, but importantly on how we, as a society, navigate this transition. In the past, the type of work that was most amenable to automation, whether by machines or computer software, were routine tasks that followed explicit, codifiable rules—rules that were written by people. AI models, on the other hand, learn by example: An AI model doesn't need to be told exactly how to accomplish a certain task, only provided with the right training data to infer patterns. Consequently, AI can learn how to complete complex, nonroutine tasks that require knowledge that is difficult or impossible for humans to codify.4 Unlike a robot that follows necessarily human instructions to, say, bolt on a car fender over and over, this ability to implement complex tasks could vastly expand the set of tasks that AI is potentially capable of performing. That is especially true if one considers the integration of AI with other technologies such as robots, or cars. Moreover, agentic AI can accomplish more general goals with limited human supervision, mimicking human decision-making, reasoning, and implementation. Many economically valuable tasks can (or may soon) be feasible using AI.5 Developments in AI AdoptionThe capabilities of GenAI models have improved rapidly. In just a few years, we have seen AI models meet or surpass human performance on increasingly challenging benchmarks, including competition-level mathematics and Ph.D.-level science questions.6 Real-world applications abound. AI is already changing the speed of pharmaceutical drug discoveries, the efficiency of customer service, and the pace of computer coding, especially by the biggest tech firms themselves.7 The speed of AI adoption may be much faster than previous general-purpose technologies, boosting productivity growth, but also allowing less time for workers, businesses, and the economy to adapt to these changes. As of December 2025, 17 percent of businesses in the U.S. Census Business Trends and Outlook Survey (BTOS) report using AI in their business functions. While that may seem modest on the surface, the share is much higher among large firms and in tech-intensive sectors like information, finance and insurance, and professional and technical services. In the BTOS, about 30 percent of businesses with more than 250 employees report using AI. A recent survey of mostly large firms by McKinsey found that 88 percent report that AI has been used in at least one business function.8 The share using generative AI specifically rose from 33 percent in 2023 to 79 percent in 2025. Adoption of generative AI among both individuals and businesses has been very fast by historical standards. A 2024 St. Louis Fed paper estimates that generative AI adoption in the workplace following the release of ChatGPT in late 2022 was as fast as workplace computer adoption after the release of the IBM PC in 1984.9 Actual use of generative AI in the workplace may be even higher than reported by businesses since there is some evidence of workers using AI tools without their manager's knowledge.10 That said, the depth of AI adoption at this point remains unclear. McKinsey found that most businesses using AI remain in the experimentation or piloting phases of adoption. Some firms that have experimented with AI abandoned these trials.11 Like previous technology breakthroughs, effective use of AI will likely require fundamental changes in business practices and organization. Workers have to be retrained. Managers have to develop best practices. And obtaining the full range of productivity enhancements from new technology may require costly experimentation and further innovation. The productivity gains from electrification in the early 20th century reflected not only how factories were powered but also changes in how they were designed.12 This process took decades to play out. Within firms, there is evidence from the manufacturing sector that productivity follows a J-shape after technology adoption: adjustment costs lead to short-run losses before firms that ride it out are able to realize larger, longer-run gains.13 Within the Federal Reserve System, we have also been exploring the use of AI in our own operations and have established an AI program and governance framework for the use of AI technologies. One internal application of GenAI that shows considerable promise is technology modernization. Within clear guardrails, we are using GenAI tools to translate legacy code, generate unit tests, and accelerate cloud migration. So far, the result of this usage is faster delivery, improved quality, and an enhanced developer experience. In one recent project updating hundreds of databases, AI tools helped cut the time to complete this type of work by 50 percent, detected and resolved 30 percent more issues during testing compared to previous migrations, and enhanced team focus on higher-value coding work. My sense is that these are the kinds of uses and the scale of success that many businesses are experiencing. Implications for the Labor MarketPredictions about how generative AI will evolve, and in particular how it will affect the labor market, range from the utopian to the apocalyptic.14 In previous speeches, I have outlined a couple of scenarios as a way to think through the potential effects of AI on the economy, including the labor market.15 But as is the case for AI's technological advances, the debate about the possible effects of AI evolves quickly, so I will briefly revisit these scenarios and then discuss how new research is starting to bring the initial and potential labor market effects of AI into focus. Scenario of gradual adoptionUnder a first scenario, AI proceeds like other general-purpose technologies, perhaps diffusing a bit faster. This leads to strong productivity growth, comparable to what we saw in the late 1990s and early 2000s, or maybe even stronger than that. As was the case during earlier technological advances, some occupations are displaced while new ones emerge, as AI is increasingly integrated into many existing roles. But AI adoption occurs gradually enough that large and widespread joblessness is avoided. Unemployment might rise somewhat in the short term due to skill mismatch, but education and training choices adjust over time, and many workers successfully retrain and retain their jobs or find new ones. With strong productivity growth, the economy can sustain faster output growth and real wages rise. Scenario of rapid growth in AI capabilities and adoptionUnder a second scenario, AI capabilities grow exponentially and adoption is extremely rapid, ushering in a "jobless boom." AI agents replace or displace a range of professional and service occupations. Autonomous vehicles and robotics automate many manufacturing and transportation jobs, with labor increasingly concentrated in a few manual or highly skilled trades, or in roles where consumers put a premium on human interaction. AI-centric start-ups with radically new business models displace firms that are unable to adapt, and layoffs soar, leading to widespread unemployment in the short run and declines in labor force participation over time, as a large share of the population is essentially unemployable. It is understandable why many people would fear such a future, and it would present profound social and distributional challenges. With a vastly more productive economy, but much less demand for labor, society would have to rethink the social safety net to ensure that the gains from unprecedented economic growth are shared rather than concentrated among a small group of capital holders and AI superstars. And there would need to be profound changes in education, training, and workforce development. We should be clear-eyed about how painful these changes could be for affected workers and how challenging it would be for the government and the private sector to successfully manage the fallout. One thing that these two scenarios have in common is that AI's initial promise is borne out, and it transforms the economy—either gradually and in a more manageable way, or abruptly and to a much greater extent. Scenario of stalled growth in AI capabilities and adoptionA third option is that improvements in AI capabilities stall, perhaps owing to the exhaustion of training data, a shortage of electricity supply or distribution to satisfy the huge demands of data centers, or shortages of the capital required to build all this new infrastructure.16 One estimate is that AI investment will require the issuance of $1 trillion in new debt over the next five years, and other estimates are even higher. With questions about whether demand will grow sufficiently to utilize this investment, some have drawn comparisons to the overinvestment in the dot-com era.17 Timing mismatches in the investment and business integration process could lead to reduced realization of the potential of AI.18 The hard work of business process transformation takes time, which partly accounts for the J curve dynamics I mentioned earlier. Businesses that do not see immediate productivity improvements may lose interest. In a scenario of stalled growth in AI capabilities and adoption, some productivity improvements occur in easy-to-learn tasks, but AI proves incapable of completing hard-to-learn tasks or complex projects, or an AI bust occurs, abruptly ending needed investment. As a result, any boost that AI provides to aggregate productivity growth is modest and fades over time. It is possible that in this scenario, AI still ends up widely adopted. As is the case for social media or smartphones, AI applications may still generate significant value for consumers and many businesses. In the workplace, it might look much like email or search engines do now—tools that are ubiquitous, even indispensable, but not necessarily revolutionary by themselves. In a scenario where AI disappoints, the balance of risks shifts from the labor market to the financial sector. When anticipated demand falls short, the risk of financial stress increases, as happened following the expansion of the U.S. railroad network in the late 19th century.19 More recently, we saw these dynamics play out in a more limited way with the overbuilding of fiber optic telecommunications in the early 2000s, which contributed to stress in bond markets.20 Of course, these are stylized scenarios, and facts on the ground may play out differently. Or different scenarios might come to pass in different sectors of the economy in different ways and at different speeds. But a scenario-based approach helps ground our thinking about these potential outcomes. What Have We Learned about the Effects of AI So Far?In judging the prospects for the range of outcomes reflected in these scenarios, or other plausible scenarios, we can start with what we have learned about the effects of AI so far. Of course, ChatGPT was released only a bit over three years ago, and we are still in the very early stages of generative AI diffusion. So far, however, research seems to be more consistent with scenario 1: AI as a normal early-stage general-purpose technology, though that doesn't necessarily rule out more extreme scenarios going forward. ProductivityLet me focus on several aspects of the early economic effects of AI, starting with productivity. We have been in a period of elevated productivity growth for the past five years. This period of higher productivity growth began with the pandemic and the ensuing tight labor market, which led to investment in labor-saving technologies. Moreover, new business formation surged and has remained strong. New businesses that survive tend to be more productive than incumbents, and competition from new businesses spurs innovation among incumbents as well. While it is possible that AI has contributed to this strength more recently, GenAI has had relatively modest penetration thus far. Yet AI is very likely to have a profound positive impact on productivity growth in the long term. At the microlevel, there is increasing evidence that access to AI assistants improves worker efficiency, speed, and accuracy at various tasks.21 Aggregating the aforementioned task-level evidence, one recent study estimated that AI could contribute between 0.3 and 0.9 of a percentage point to annual total factor productivity growth over the next decade.22 The upper end of these estimates would make the productivity gains of AI comparable to those of internet communications technologies in the late 1990s, a period of strong productivity growth. Other studies point to much smaller or larger gains, underscoring how dependent these projections are on assumptions about the speed of technological progress and adoption of AI by businesses.23 But the forms these innovations will take and how long the benefits will take to accrue is hard to say. In 1987, for example, the economist Robert Solow famously quipped, "You can see the computer age everywhere but in the productivity statistics." As it turned out, firms had to learn how to integrate this technology into their business practices in order to fully realize the economic potential of personal computing. Of course, AI may also contribute to productivity growth not just by improving the efficiency of existing tasks, but also by increasing the efficiency of R&D. The potential of AI to boost the rate of innovation—to be an invention in the method of invention—is where we could see even greater economic benefits, though they may take some time to materialize.24 EmploymentSo far, the literature suggests that while AI has yet to have a substantial effect on aggregate employment or unemployment, it may be starting to adversely affect some groups, in particular young people who are just starting their careers in some sectors. On balance, this evidence so far is consistent with what we might expect under the gradual adoption scenario I previously described. One study uses data from the payroll provider ADP and finds that early-career workers in occupations highly exposed to AI—such as software developers and customer service representatives—have experienced a decline in employment relative to other early-career workers in less exposed fields and experienced workers in the same line of work.25 Some other research reaches a similar conclusion using resume and job-posting data.26 The long-run consequences of AI for recent cohorts of young workers is uncertain, but research shows that entering a weak labor market can have persistently adverse effects on workers' earnings. So, for these workers, the short run may have long-term consequences.27 More broadly, rather than laying off workers, there is evidence that AI adoption is so far leading to re-allocation within firms. One paper finds that although AI does substitute for labor at the task level, overall employment effects are small, as workers shift their time to complementary tasks and firms expand employment elsewhere.28 Consistent with this internal re-allocation, a recent survey by the New York Fed found that while some firms using AI did report reduced hiring plans and limited layoffs, a much larger share plan to retrain their existing workforce.29 At the same time, we should be prepared for the possibility that there might be serious short-term disruptions in the labor market, even if the long-term gains to society could be quite favorable. The extent of disruption will depend in part on whether society undertakes the investments needed in new job creation, worker training, connecting workers to new jobs, and other efforts to mitigate adverse labor market effects. The historical record on meaningful efforts to help workers in such a transition is not encouraging. 30 In my judgement, now is the time for society to begin to consider how to address these potential disruptions, while AI adoption is in its early stages. Income and InequalityAs with employment, there is little evidence that AI has had a meaningful impact on wage growth or the distribution of income gains, at least so far. Going forward, the effect of AI on wages and the distribution of income will depend on factors including whether AI complements or substitutes expertise within jobs that continue to exist, how AI changes relative demand for high-wage occupations, and who owns AI capital. On the one hand, research evaluating the effect of AI assistants in the workplace tends to find the largest productivity gains among the least-experienced workers.31 This suggests that AI could narrow gaps in productivity and wages. If AI facilitates worker learning, as some studies suggest, it might also help displaced workers to re-skill for new jobs, reducing the cost of job dislocation. On the other hand, recent research finds that GenAI is more commonly used by younger, highly educated, and high-income individuals.32 If high earners are better positioned to take advantage of AI, we could see wage inequality rise as the most productive workers pull even further ahead of their peers. AI can also affect the wage structure by shifting demand for different occupations. Whereas technological progress has historically favored occupations with higher wages and education requirements, one paper shows that AI has the potential to reverse this pattern, automating higher-paying information-based jobs while increasing relative demand for lower-paying jobs and those requiring less education, thus reducing aggregate wage inequality.33 As with our discussion of labor market disruptions, the effects of AI on inequality will depend in part on whether society undertakes the investments needed to mitigate adverse labor market effects. It is incumbent on us to begin thinking about these important questions now. Implications of AI for Monetary PolicyI am also thinking about the implications of AI for monetary policy. If AI causes a large and long-lasting dislocation of workers, permanently reducing demand for many kinds of jobs, it could imply higher rates of unemployment, even when the economy is healthy and operating close to its potential. Monetary policy is able to address cyclical conditions, like a downturn in the business cycle, but it cannot address the structural factors that determine the long-run rates of employment. While monetary policy is not suited to dealing with structural changes in the economy, it could be difficult for policymakers to assess in real time whether changes are structural or cyclical. Moreover, some components of the labor market may face structural changes, while others may not. As I noted earlier, it will be important for society to deal with the consequences of any structural changes in the economy because of AI, and policies beyond the purview of the central bank would certainly be needed to address a structural rise in the natural rate of unemployment. As a central banker, I see endeavoring to understand how AI is evolving and affecting labor markets as a crucial component of our work in the years ahead. I have noted that my base case foresees labor market disruptions as relatively short term, even if painful. Over the long term, the labor market would adjust in ways that create new jobs and augment the productivity of existing jobs, boosting real wages. But closely monitoring these developments and adapting, as needed, will be crucial. In the event that GenAI results in a long-lasting boost to productivity growth, wages and economic activity could grow more than would otherwise be the case without putting upward pressure on inflation. At the same time, demand for capital would rise because of the strong business investment required to take advantage of the technology, putting upward pressures on interest rates, and household savings could fall due to expectations of stronger real wage growth and thus higher lifetime earnings, also putting upward pressure on interest rates. All of this would imply a higher setting for the policy rate when the economy is at equilibrium, or what monetary economists call r*. Indeed, last year I raised my long-term estimate of r* modestly because of higher productivity. Moreover, in the short term, investment in AI could be inflationary—for example, if electricity supply constraints from inefficiencies in the power grid collide with strong energy demand from the building of data centers. For all of these reasons, I expect that the AI boom is unlikely to be a reason for lowering policy rates. ConclusionIn conclusion, I expect that AI will have a transformative effect on the economy and affect a large share of workers in ways that will challenge the ability of the private and public sectors to accommodate this adjustment. In the longer run, I expect AI will boost productivity and living standards, and it may even lead to new discoveries. Society will need to be nimble and bold to reduce the pain of short-term dislocations for workers and to ensure that the benefits are broadly shared. Widespread AI adoption will very likely lead to dramatic and sometimes difficult changes in the way many of us work and live, but the long-term benefits could be even more dramatic. 1. The views expressed here are my own and are not necessarily those of my colleagues on the Federal Reserve Board or the Federal Open Market Committee.  2. See Timothy F. Bresnahan and M. Trajtenberg (1995), "General Purpose Technologies 'Engines of Growth'?" Journal of Econometrics, vol. 65 (January), pp. 83–108.  3. See Martin Neil Baily, David M. Byrne, Aidan T. Kane, and Paul E. Soto (2025), "Generative AI at the Crossroads: Light Bulb, Dynamo, or Microscope?" Finance and Economics Discussion Series 2025-053 (Washington: Board of Governors of the Federal Reserve System, July).  4. See David H. Autor (2025), "Polanyi's Paradox and the Shape of Employment Growth," in Re-Evaluating Labor Market Dynamics: A Symposium Sponsored by the Federal Reserve Bank of Kansas City (Kansas City: Federal Reserve Bank of Kansas City, pp. 129–77).  5. Researchers typically measure exposure to AI at the occupation level by analyzing descriptions of job tasks and comparing them with assumptions about the tasks that AI might feasibly complete; see Kunal Handa, Alex Tamkin, Miles McCain, Saffron Huang, Esin Durmus, Sarah Heck, Jared Mueller, Jerry Hong, Stuart Ritchie, Tim Belonax, Kevin K. Troy, Dario Amodei, Jared Kaplan, Jack Clark, and Deep Ganguli (2025), "Which Economic Tasks Are Performed with AI? Evidence from Millions of Claude Conversations," working paper; Tyna Eloundou, Sam Manning, Pamela Mishkin, and Daniel Rock (2024), "GPTs Are GPTs: Labor Market Impact Potential of LLMs," Science, vol. 384 (6702), pp. 1306–08; Ed Felten, Manav Raj, and Robert Seamans (2023), "How Will Language Modelers Like ChatGPT Affect Occupations and Industries?" working paper; Michael Webb (2020), "The Impact of Artificial Intelligence on the Labor Market," working paper.  6. See Nestor Maslej, Loredana Fattorini, Raymond Perrault, Yolanda Gil, Vanessa Parli, Njenga Kariuki, Emily Capstick, Anka Reuel, Erik Brynjolfsson, John Etchemendy, Katrina Ligett, Terah Lyons, James Manyika, Juan Carlos Niebles, Yoav Shoham, Russell Wald, Toby Walsh, Armin Hamrah, Lapo Santarlasci, Julia Betts Lotufo, Alexandra Rome, Andrew Shi, and Sukrut Oak (2025), "The AI Index 2025 Annual Report," AI Index Steering Committee, Institute for Human-Centered AI, Stanford University (Stanford, Calif.: Stanford University, April).  7. See Economist (2026), "An AI Revolution in Drugmaking Is Under Way," January 5; Thomas Kwa, Ben West, Joel Becker, Amy Deng, Katharyn Garcia, Max Hasin, Sami Jawhar, Megan Kinniment, Nate Rush, Sydney Von Arx, Ryan Bloom, Thomas Broadley, Haoxing Du, Brian Goodrich, Nikola Jurkovic, Luke Harold Miles, Seraphina Nix, Tao Lin, Neev Parikh, David Rein, Lucas Jun Koba Sato, Hjalmar Wijk, Daniel M. Ziegler, Elizabeth Barnes, and Lawrence Chan (2025), "Measuring AI Ability to Complete Long Tasks," METR, March 19.  8. See Alex Singla, Alexander Sukharevsky, Bryce Hall, Lareina Yee, and Michael Chui (2025), "The State of AI in 2025: Agents, Innovation, and Transformation," McKinsey & Company, November 5.  9. See Alexander Brick, Adam Blandin, and David J. Deming (2024), "The Rapid Adoption of Generative AI," Working Paper Series 2024-027 (St. Louis: Federal Reserve Bank of St. Louis, September; revised October 2025).  10. See Conference Board (2023), "Majority of US Workers Are Already Using Generative AI Tools," press release, September 13.  11. See Kathryn Bonney, Cory Breaux, Cathy Buffington, Emin Dinlersoz, Lucia S. Foster, Nathan Goldschlag, John C Haltiwanger, Zachary Kroff, and Keith Savage (2024), "Tracking Firm Use of AI in Real Time: A Snapshot from the Business Trends and Outlook Survey," NBER Working Paper Series 32319 (Cambridge, Mass.: National Bureau of Economic Research, April).  12. See Paul A. David (1990), "The Dynamo and the Computer: An Historical Perspective on the Modern Productivity Paradox," American Economic Review, vol. 80 (May), pp. 355–61.  13. See Kristina McEleran, Mu-Jeung Yang, Zachary Kroff, and Erik Brynjolfsson (2025), "The Rise of Industrial AI in America: Microfoundations of the Productivity J-curve(s)," working paper.  14. See Mark A. Wynne and Lillian Derr (2025), "Advances in AI Will Boost Productivity, Living Standards over Time," Federal Reserve Bank of Dallas, June 24.  15. For example, see Michael S. Barr (2025), "Artificial Intelligence and the Labor Market: A Scenario-Based Approach," speech delivered at the Reykjavik Economic Conference 2025, Central Bank of Iceland, Reykjavik, Iceland, May 9.  16. For example, generation capacity aside, current inefficiencies in the U.S. electrical grid may not permit sufficient power to go where it is needed for rapid AI deployment.  17. A notable difference now is that most of the large tech companies making these investments are hugely profitable, in contrast to many of the profitless companies of that earlier boom.  18. One warning sign that the speed of adoption may not match the speed of AI infrastructure deployment is in what some firms are reporting about the depreciation of their investments. While computer chips have historically been depreciated over three years, some firms have stretched the depreciation of AI chips to five years or more in their disclosures to shareholders.  19. In the early 1890s, bankruptcies at a number of prominent railroads, as well as businesses connected directly and indirectly to the railroads, contributed to a deterioration in the quality of bank loan portfolios.  While this was not the trigger of the Panic of 1893, it was part of the backdrop that made the economy and the banking system more vulnerable; see Mark Carlson (2013), "Panic of 1893," in Randall E. Parker and Robert Whaples, eds., Routledge Handbook of Major Events in Economic History (London: Routledge), pp. 40–49.  20. See Jeff Hecht (2016), "OSA Centennial Snapshots: The Fiber Optic Mania," Optics & Photonics News, vol. 27 (October), pp. 46–53. For more information on the dynamics of the dot-com bubble and the effects on the bond market, see Patrick Lenain and Sam Paltridge (2003), "After the Telecommunications Bubble," OECD Economics Department Working Papers No. 361 (Paris: Organisation for Economic Co-operation and Development, June). According to Lenain and Paltridge, "Several large firms—including Worldcom and Global Crossing—filed for bankruptcy under Chapter 11 in the United States and AT&T Canada undertook a similar proceeding. This led to a wave of defaults on telecommunications corporate bonds and contributed to the largest cycle of defaults on bonds since the 1930s" (Lenain and Paltridge, 2003, p. 8).  21. On writing, see Shakked Noy and Whitney Zhang (2023), "Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence," Science, vol. 381 (6654), pp. 187–92; on customer service, see Erik Brynjolfsson, Danielle Li, and Lindsey Raymond (2025), "Generative AI at Work," Quarterly Journal of Economics, vol. 140 (May), pp. 889–942; on consultants, see Fabrizio Dell'Acqua, Edward McFowland III, Ethan Mollick, Hila Lifshitz-Assaf, Katherine C. Kellogg, Saran Rajendran, Lisa Krayer, Francois Candelon, and Karim R. Lakhani (2023), "Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality (PDF)," Working Paper 24-013 (Boston: Harvard Business School, September 22); on coders, see Sida Peng, Eirini Kalliamvakou, Peter Cihon, and Mert Demirer (2023), "The Impact of AI on Developer Productivity: Evidence from GitHub Copilot," working paper; Kevin Zheyuan Cui, Mert Demirer, Sonia Jaffe, Leon Musolff, Sida Peng, and Tobias Salz (2024), "The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers," working paper.  22. See Francesco Filippucci, Peter N. Gal, and Matthias Schief (2025), "Aggregate Productivity Gains from Artificial Intelligence: A Sectoral Perspective," working paper.  23. See Daron Acemoglu (2025), "The Simple Macroeconomics of AI," Economic Policy, vol. 40 (January), pp. 13–58; and Michael Chui, Eric Hazan, Roger Roberts, Alex Singla, Kate Smaje, Alex Sukharveksy, Lareina Yee, and Rodney Zemmel (2023), "The Economic Potential of Generative AI," McKinsey & Company (New York: McKinsey, June).  24. While AI may boost productivity growth relative to a counterfactual world without AI, this does not necessarily imply that AI will lead to productivity growth well above its long-run trend, as in the transformative scenario I described. Rather, as the growth effects of previous waves of innovation fade, new innovations, such as AI, might be needed just to keep productivity growth near its historical trend rather than slowing down.  25. See Erik Brynjolfsson, Bharat Chandar, and Ruyu Chen (2025), "Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence," working paper.  26. See Seyed M. Hosseini and Guy Lichtinger (2025), "Generative AI as Seniority-Biased Technological Change: Evidence from U.S. Resume and Job Posting Data," working paper.  27. See Philip Oreopoulos, Till von Wachter, and Andrew Heisz (2012), "The Short- and Long-Term Career Effects of Graduating in a Recession," American Economic Journal: Applied Economics, vol. 4 (January), pp. 1–29.  28. See Menaka Hampole, Dimitris Papanikolaou, Lawrence D.W. Schmidt, and Bryan Seegmiller (2025), "Artificial Intelligence and the Labor Market," NBER Working Paper Series 33509 (Cambridge, Mass.: National Bureau of Economic Research, February; revised September 2025).  29. See Jaison R. Abel, Richard Deitz, Natalia Emanuel, Ben Hyman, and Nick Montalbano (2025), "Are Businesses Scaling Back Hiring Due to AI?" Federal Reserve Bank of New York, Liberty Street Economics (blog), September 4.  30. See Lawrence F. Katz (2025), "Beyond the Race between Education and Technology (PDF)," paper prepared for "Labor Markets in Transition: Demographics, Productivity, and Macroeconomic Policy," an economic symposium sponsored by the Federal Reserve Bank of Kansas City, Jackson Hole, Wyoming, August 22.  31. See footnote 21.  32. See Jonathan Hartley, Filip Jolevski, Victor Melo, and Brendan Moore (2025), "The Labor Market Effects of Generative Artificial Intelligence," working paper.  33. See Huben Liu, Dimitris Papanikolaou, Lawrence D.W. Schmidt, and Bryan Seegmiller (2025), "Technology and Labor Markets: Past, Present, and Future; Evidence from Two Centuries of Innovation," Brookings Papers on Economic Activity, September 24. 

The Office of the Comptroller of the Currency (OCC) today requested comment on a proposal to establish revised procedures and policies for appeals by OCC-supervised entities of material supervisory determinations. The proposed changes reflect the OCC’s experience administering the bank appeals process and are intended to enhance the independence and efficiency of the appeals function. The proposal would make several changes to the current appeals process, including adding an “appeals board” to hear bank appeals, enhancing the role of the Ombudsman as a neutral liaison to help banks seek redress for grievances, establishing a de novo standard of review for appeals, and strengthening prohibitions against retaliation. Comments on the attached proposal are due 60 days after the date of publication in the Federal Register. Related Link Federal Register Notice (PDF)

The economic importance of synthetic risk transfer (SRT) markets has grown rapidly over the last decade. Compared with securitisations before the Great Financial Crisis (GFC), SRTs in use recently appear to be more prudently structured and managed. Risks associated with SRT use merit continued monitoring by supervisors. The Basel Committee on Banking Supervision today published a report on synthetic risk transfer (SRT) transactions. The economic importance of SRT markets has grown rapidly over the last decade and they have become an important source of capital relief for corporate credit risk. SRT transactions involve transferring all or a portion of the credit risk of a pool of assets to a counterparty while the bank retains ownership of the underlying assets. The investor base in SRTs is dominated by private investment funds, although public sector entities also play an important role in some jurisdictions. Capital and credit risk management are the main motivations for banks to engage in SRTs. Regulatory and supervisory reforms implemented since the 2008 Great Financial Crisis (GFC) make SRTs simpler and result in more scrutiny relative to credit risk transfer transactions in use before the GFC. Some jurisdictions and market participants believe there are blind spots related to disclosure and SRT financing activities. The total value of protected assets in Canada, the euro area, the United States and the United Kingdom, jurisdictions in which SRT markets are particularly vibrant, is estimated at about EUR 750 billion, or 1.1% of total bank assets. Risks associated with SRT use, such as banks' increased dependence on non-bank financial intermediaries (NBFIs), are acknowledged and, to some extent, actively managed by market participants. However, they merit continued monitoring by supervisors as SRT markets continue to grow. The report is part of the Committee's continued monitoring and investigation of the interconnections between banks and NBFIs. Background: The Basel Committee is the primary global standard setter for the prudential regulation of banks and provides a forum for cooperation on banking supervisory matters. Its mandate is to strengthen the regulation, supervision and practices of banks worldwide with the purpose of enhancing financial stability. The Committee reports to the Group of Central Bank Governors and Heads of Supervision and seeks its endorsement for major decisions. The Committee has no formal supranational authority, and its decisions have no legal force. Rather, the Committee relies on its members' commitments to achieve its mandate. The Group of Central Bank Governors and Heads of Supervision is chaired by Tiff Macklem, Governor of the Bank of Canada. The Basel Committee is chaired by Erik Thedéen, Governor of the Sveriges Riksbank. More information about the Basel Committee is available here.

Thank you, David [Woodcock], for your generous introduction. And good morning, ladies and gentlemen. I am delighted to be here, and grateful for this opportunity to share a few reflections. Let me begin by thanking our hosts at the Texas A&M University School of Law for convening today’s program. Though only in its second year, the symposium has already earned a reputation for rigor and insight. So, to address leading judges, scholars, and practitioners here, at the Federal Reserve Bank of Dallas, is a profound honor. And before I begin, let me add the customary disclaimer that the views I express here are my own as Chairman and not necessarily those of the SEC as an institution or of the other Commissioners. *** Now, some of you may recall that last fall, I addressed a conference at the University of Delaware’s Weinberg Center for Corporate Governance.[1] I spoke candidly about the declining number of public companies in our capital markets and the reforms that I believe are necessary to revitalize them. I also emphasized the important role that States play in these reforms, especially in the areas of litigation reform and shareholder proposals. That speech took place during a period when prominent firms were raising concerns about continuing to be domiciled in Delaware, with some moving elsewhere and encouraging others to follow suit.[2] Today, speaking at this symposium in Texas feels different. Indeed, Texas has begun to build something that could offer an interesting alternative to Delaware, through a framework designed to attract companies with shareholders who are eager to get back to basics, with less politicization, abusive litigation, and overall drama. That vision is rooted in a deeply American idea: that competition—among firms; among markets; and yes, among States—is the animating force behind a system that has produced more prosperity than any other in human history. Of course, Delaware is no stranger to competition, for it was not always the market leader for corporate domicile. That distinction once belonged to New Jersey.[3] However, in the early twentieth century, Delaware claimed that title and has held it ever since.[4] But competition does not pause for tradition, nor does it defer to incumbency. Over time, it compels systems, and States, to adapt—or to yield. Through competition, good ideas spread, poor ones fade, and the system itself grows stronger. My remarks this morning will first examine how Texas has entered that competition and what more the State might do to strengthen its position. I will then conclude with some ideas for reforming the SEC’s disclosure regime. *** During its 2025 legislative session, Texas took several significant steps to further its appeal as a destination for corporate domestication. Among the legislative actions was Senate Bill 29 (“SB 29”).[5] SB 29 made several important changes to enhance protections for Texas companies—and ultimately their shareholders—against abusive lawsuits. For example, litigants will no longer be able to recover their fees from a company in actions that result solely in “additional or amended disclosures…regardless of materiality.”[6] This amendment can help to deter lawsuits that are all too frequently filed—seemingly by rote—after a company releases its proxy materials for approval of a merger or an equity compensation plan. In these lawsuits, the litigants may not necessarily be seeking better proxy disclosure. Rather, the ulterior motive may be to seek a quick payout, knowing that companies wish to settle promptly and not delay their shareholder meeting. Texas’s consideration of the issue of attorneys’ fees in litigation signals that it recognizes their cumulative burden on capital formation. Another possible measure that many jurists and commentators have advanced is fee shifting[7]—the idea that the losing party in a litigation pays the winning party’s attorneys’ fees. Currently, companies can generally look only to Rule 11 of the Federal Rules of Civil Procedure[8] for fee shifting in the case of frivolous federal securities law claims. However, some jurisdictions have models for fee shifting beyond their civil procedure rules. This principle, often called the English Rule, prevails in many foreign jurisdictions, not just Great Britain, and they could serve as examples for Texas to follow. SB 29 also gave Texas companies more control over the venue and method of adjudicating lawsuits. For actions involving internal affairs claims, companies can now designate Texas courts as the exclusive forum for hearing those claims.[9] They may also waive jury trials for these actions.[10] These changes were significant first steps in rebalancing Texas’s process for resolving litigation. However, should litigation in a court—with or without a jury—be the only method available to companies for adjudicating shareholder disputes? Another possibility is arbitration. For many years, the SEC never clearly articulated its views on whether a mandatory arbitration provision in a company’s governing documents is inconsistent with the federal securities laws. The agency, in a very non-transparent manner, told companies on an ad hoc basis that including such a provision would mean that their IPO registration statement would not be declared effective. The most recent incident appears to have been in 2012 when the Carlyle Group—which was advised by top tier law firms and had its IPO underwritten by bulge bracket banks—sought to go public with such a provision.[11] I say “appears to be” because the Commission had never adopted any written principle to document this position. Instead, the SEC staff—likely at the direction of the then Chairman—advised Carlyle that its registration statement would not be declared effective as long as the mandatory arbitration provision remained. Carlyle removed the provision. To say the least, that is not the way that a United States government agency should operate. However, the situation changed last September when the Commission reviewed the law as enunciated by the courts, and concluded that, based on the Supreme Court’s decisions, mandatory arbitration provisions are not inconsistent with the federal securities laws.[12] The Commission voted three-to-one to direct its staff—and clarify to the public—that this unwritten, ad hoc practice would no longer govern SEC policy. The SEC has now done its part by making clear that it will not stand in the way of such provisions. However, before companies can adopt mandatory arbitration provisions, they must also consider the laws of their state of formation. Last summer, Delaware prohibited mandatory arbitration for federal securities law claims.[13] What will Texas do? Texas’s recent amendments to its corporate laws reflect the idea that competition amongst States for domiciling corporations is a healthy function of our capital markets. They remind us that state corporate law, working in tandem with the federal securities laws, matters profoundly to our economic strength as a country, and that through those laws, we can rigorously protect shareholders without needlessly paralyzing companies. If States function as laboratories, as Justice Brandeis famously remarked,[14] then the companies that operate within them often supply the ingredients for experimentation. *** Let me now turn to my second topic for this morning, SEC disclosure reform. Last December, I shared my vision for returning the Commission’s disclosure regime to its original intent of “protect[ing] the public with the least possible interference with honest business.”[15] At a high level, achieving this vision of having the “minimum effective dose of regulation” requires the Commission to follow two ideals. First, it must root its disclosure requirements, which are contained in Regulation S-K, in the concept of financial materiality. Second, it must scale these requirements with a company’s size and maturity. Today, I will share some details on the types of reform that I have instructed the Commission staff to explore. The SEC took its first step in reforming Regulation S-K last May by soliciting public comments and hosting a roundtable on its executive compensation disclosure requirements under Item 402.[16] In many ways, Item 402 epitomizes the problems with the SEC’s disclosure rules overall. The rules themselves are lengthy and complex, driven in part by piecemeal additions over the last two decades without a holistic review of how everything fits together. Having been chief of staff to then-Chairman Richard Breeden during the 1992 amendments to Item 402, I can say that the rule today has morphed into a Frankenstein monster beyond recognition. Furthermore, the rules sometimes drive corporate behavior, rather than reflect the outcome of corporate decisions. Preparing the required disclosure consumes significant time from boards and management and can impose substantial costs through the need for specialized lawyers, accountants, and consultants. Yet, the resulting information may not benefit or protect investors because of its volume, complexity, and lack of relevance. In short, disclosure intended to inform can instead overwhelm. So, it is no surprise that some of the reforms for Item 402 suggested by some commenters reflect principles that the SEC can apply throughout its rethinking of Regulation S-K. Specifically, I categorize these principles into the three buckets of rationalizing, simplifying, and modernizing the disclosure rules. First is rationalizing. Our rules should be sensible, with materiality as their north star. Today, companies must provide detailed compensation information—through both tabular and narrative formats—for up to seven executives in a given year.[17] A significant number of commenters have questioned whether that scope remains justified.[18] As one commenter explained, “with the exception of the [CEO], the volume of detailed information…about the remaining [executives] is often immaterial to investors and, if anything, tends to obscure the information that they genuinely seek.”[19] Requiring companies to devote extensive time and resources to prepare disclosure that can do more to obscure than illuminate is not rational. I agree with commenters that we should reconsider the number of executives for whom compensation information is provided to appropriately calibrate the level of disclosure with the cost. Second is simplifying. At the roundtable on executive compensation, one panelist described the Commission’s pay-versus-performance (“PvP”) rule[20] as “a very complex calculation” that is difficult not only to produce but also to interpret.[21] “It’s sort of disclosure written by economists for economists,” the panelist added.[22] This sentiment should give us pause as it reflects the opposite of what an SEC disclosure requirement ideally should be—intelligible by a reasonable investor and practical for a company to comply, without the need for a cottage industry of ultra specialized consultants. Unfortunately, all of the time and money spent on PvP disclosure has scarcely resulted in clear information to investors. Another commenter noted that the rule has “necessitate[d] further explanatory disclosure…to address any confusion…create[d] for investors.”[23] A regime that requires additional disclosure to explain the original disclosure is a signal that simplification is overdue. I agree with commenters that we should look for ways to make PvP disclosure simpler for companies to prepare and more straightforward for investors to understand. Finally, modernizing. Few areas exemplify the need for modernization more than the treatment of executive security as a perk. When the Commission last considered this issue in 2006, it concluded that security provided at an executive’s residence or during personal travel constituted a perk, while security provided at the office and during business travel did not.[24] At the time, the Commission reasoned that personal security services were not “integrally and directly related” to job performance.[25] But the world has changed. As one commenter noted, “[i]n today’s environment…comprehensive 24/7 protection is increasingly a necessity, not a luxury.”[26] Another stated that “[e]xecutive security is critical to the ability of many executives to perform their duties…”[27] I agree with commenters that the Commission should modernize its perks disclosure requirements to reflect how the world and security threats have evolved over the past twenty years. *** These are just some examples of how we can sensibly reform the SEC’s current executive compensation disclosure, which is a significant portion, but just one component, of Regulation S-K. Let me briefly address a couple of broader themes that should guide reform across the framework. First is the SEC’s attempt to indirectly regulate, or set expectations for, matters of corporate governance through “comply or explain” disclosure requirements. For example, if a company does not maintain a nominating or compensation committee, then it must explain why the board of directors believes that structure is appropriate.[28] If a company does not have a policy for considering director candidates recommended by shareholders, it must justify that decision.[29] And if a company has not established a formal process for shareholders to send communications to the board, it must disclose its reasons.[30] In theory, these are disclosure provisions. In practice, they can operate as mandates. When confronted with such requirements, a company may conclude that the most prudent course is to form the committee, adopt the policy, or establish the procedure—regardless of whether it suits their particular circumstances—rather than risk appearing deficient by explanation. Absent a congressional directive,[31] it is not the SEC’s role to enforce evolving notions of “best practice” governance standards through what I consider “regulation by shaming.” Our mandate is disclosure rooted in materiality, not governance orthodoxy enforced by embarrassment. A second theme involves disclosure requirements that are impractical. For example, if a company’s CEO departed in 2025, the company must still report his or her ownership of the company’s stock in a proxy statement filed in 2026.[32] Is it reasonable to require the company to track down the share ownership of its former CEO, who could have departed more than a year ago? Or consider the rules governing related-party transactions. Companies must disclose transactions between the company and an executive’s “immediate family members,” a term that extends well beyond spouses and children to encompass all of one’s in-laws.[33] The rule makes no distinction based on the closeness or continuity of a relationship. Perhaps a more workable standard for “immediate family members” is whether the executive has shared a Thanksgiving meal with them in the past year. *** The final area of disclosure that I wish to address this morning is risk factors. As a Commissioner in 2005, I supported a rulemaking to extend risk factor disclosure from prospectuses to annual and quarterly reports through what became Item 1A.[34] Based on conversations with my fellow commissioners and SEC staff at the time, we anticipated that companies would provide a concise discussion—perhaps enough to fill two or three pages—that describe “what keeps management up at night.” Today, however, the risk factors disclosure in a Form 10-K is on average one of the longest sections of the annual report. If PvP is disclosure written by economists for economists, then risk factors are disclosure written by lawyers for lawyers. Unlike other voluminous disclosure that may result from the SEC’s rulebook, lengthy risk factors are likely not the result of the SEC’s rule. Item 105 of Regulation S-K expressly requires disclosure of material risks and discourages disclosure of generic ones.[35] The Commission has previously recognized the problem with lengthy risk factors, and in 2020, amended Item 105 to require a summary if the section exceeds fifteen pages.[36] While this rule change may have resulted in some companies reducing their risk factors disclosure to under fifteen pages, many more elected to keep their lengthy disclosures and add a summary on top of it.[37] In fact, the overall length of many large companies’ risk factors section increased following the rule change.[38] How can we reduce the volume of risk factors so that only material risks are presented to investors? The answer may depend on how we view their primary purpose. Should risk factors be disclosure written by management for investors to convey “what keeps them up at night?” Or are they principally a tool for establishing liability defenses, such as the “bespeaks caution” doctrine,[39] or to qualify as “meaningful cautionary statements” under the statutory safe harbor for forward-looking statements?[40] What is clear is that effectively reducing the volume of risk factors requires some creative ideas and out-of-the-box thinking. If the primary purpose is for management to communicate to investors, then a novel idea could be to have an entity—perhaps the SEC or the company itself—maintain a set of risks, which could be published separately outside of the annual report, that broadly apply to most companies across most industries. For example, these could include impacts from U.S. legislative and regulatory developments, geopolitical issues, and natural disasters. These risks would serve as a form of “general terms and conditions” associated with any investment in securities. A company could refer to these risks, rather than prepare its own, and supplement them as necessary. This approach could result in a shorter risk factors section consisting of risks that are specific and material to the company. However, if the primary purpose of risk factors is litigation defense, then reforms should go straight to the heart of the issue—potentially offering a safe harbor from liability. The Commission could adopt a rule stating that failure to disclose impacts from publicized events that are reasonably likely to affect most companies will not constitute material omissions for purposes of some or all of the federal securities laws’ anti-fraud rules. Such a safe harbor could incentivize companies to include fewer generic risk factors by shielding them from liability for events related to those generic risks. After all, if companies are not compelled to catalogue nearly every conceivable contingency to guard against hindsight litigation, then they can focus on risks that are more distinctive to their business. *** I offer these ideas in the spirit of starting a conversation about the primary purpose of risk factors and rule-based corporate disclosure generally. How can we right-size their length and complexity without diminishing their value? Most importantly, I am eager to hear your ideas—and encourage you to be bold and creative. Beyond risk factors, I also welcome your views and feedback on the broader principles, themes, and ideas that I have shared today regarding executive compensation disclosure and the other parts of Regulation S-K. The SEC is currently accepting written comments on these topics, and I hope that you will submit yours as soon as possible.[41] For those of you more focused on Texas corporate law, this state has begun to build something that could have lasting ramifications. I am excited to see what happens over the next few years, including any further changes to the corporate law during next year’s legislative session. As baseball’s spring training begins, I am reminded that “if [Texas] build[s] it, [the companies] will come.”[42] It has been my pleasure speaking with you this morning. You have been a patient and indulgent audience. And you have my best wishes for a wonderful remainder of this conference. Thank you. [1] Paul S. Atkins, Keynote Address at the John L. Weinberg Center for Corporate Governance’s 25th Anniversary Gala (Oct. 9, 2025), available at https://www.sec.gov/newsroom/speeches-statements/atkins-10092025-keynote-address-john-l-weinberg-center-corporate-governances-25th-anniversary-gala. [2] See, e.g., Jai Ramaswamy, Andy Hill, and Kevin McKinley, We’re Leaving Delaware, And We Think You Should Consider Leaving Too (July 9, 2025), available at https://a16z.com/were-leaving-delaware-and-we-think-you-should-consider-leaving-too/. [3] See, generally, James D. Cox and Thomas Lee Hazen, Treatise on the Law of Corporations § 2:4 (4th ed). [4] Id. [5] Tex. S.B. 29, 89th Leg., R.S., available at https://legiscan.com/TX/text/SB29/id/3195811. [6] Tex. Bus. Orgs. Code Ann. § 21.561(c). [7] See, e.g., Jonathan T. Molot, Fee Shifting and the Free Market, 66 Vanderbilt Law Review 1807 (2013), available at https://scholarship.law.vanderbilt.edu/cgi/viewcontent.cgi?article=1322&context=vlr. [8] Fed. R. Civ. P. 11. [9] Tex. Bus. Orgs. Code Ann. § 2.115. [10] Tex. Bus. Orgs. Code Ann. § 2.116. [11] See, e.g., Carlyle Drops Arbitration Clause from I.P.O. Plans, Kevin Roose, The New York Times (Feb. 3, 2012), available at https://archive.nytimes.com/dealbook.nytimes.com/2012/02/03/carlyle-drops-arbitration-clause-from-i-p-o-plans/. [12] Acceleration of Effectiveness of Registration Statements of Issuers with Certain Mandatory Arbitration Provisions, Release No. 33-11389 (Sept. 17, 2025) [90 FR 45125 (Sept. 19, 2025)], available at https://www.federalregister.gov/documents/2025/09/19/2025-18238/acceleration-of-effectiveness-of-registration-statements-of-issuers-with-certain-mandatory. [13] 8 Del. C. § 115(c). [14] New State Ice Co. v. Liebmann, 285 U.S. 262 (1932) (Brandeis, L., dissenting) (“It is one of the happy incidents of the federal system that a single courageous State may, if its citizens choose, serve as a laboratory; and try novel social and economic experiments without risk to the rest of the country.”). [15] Paul S. Atkins, Revitalizing America’s Markets at 250 (Dec. 2, 2025), available at https://www.sec.gov/newsroom/speeches-statements/atkins-120225-revitalizing-americas-markets-250. [16] SEC Roundtable on Executive Compensation Disclosure Requirements, available at https://www.sec.gov/newsroom/meetings-events/sec-roundtable-executive-compensation-disclosure-requirements. [17] 17 CFR 229.402(a)(3). [18] See, e.g., American Bar Association (Oct. 6, 2025) (“ABA Letter”); Center On Executive Compensation (July 31, 2025) (“COEC Letter”); Davis Polk & Wardwell LLP (July 31, 2025); McGuireWoods LLP and Brownstein Hyatt Farber Schreck, LLP (June 2025); and Society for Corporate Governance (Aug. 27, 2025). [19] ABA Letter at 10. [20] 17 CFR 229.402(v). [21] Unofficial Transcript: SEC Roundtable on Executive Compensation Disclosure Requirements Panel (July 26, 2025) at 96, available at https://www.sec.gov/files/sec-roundtable-executive-compensation-disclosure-requirements-2025-06-26-transcript.pdf. [22] Id. [23] U.S. Chamber of Commerce (June 25, 2025) at 10. [24] Executive Compensation and Related Person Disclosure, Release No. 34-54302 (Aug. 29, 2006) [71 FR 53158, 53177 (Sept. 8, 2006)], available at https://www.federalregister.gov/documents/2006/09/08/06-6968/executive-compensation-and-related-person-disclosure. [25] Id. [26] COEC Letter at 9. [27] The Travelers Companies, Inc. (June 30, 2025) at 2. [28] 17 CFR 229.407(c)(1) and 17 CFR 229.407(e)(1). [29] 17 CFR 229.407(c)(2)(iii). [30] 17 CFR 229.407(f)(1). [31] See, e.g., 15 U.S.C. § 7265. [32] 17 CFR 229.403(b) and 17 CFR 229.402(a)(3)(i). See, also, Question 129.03, Regulation S-K Compliance & Disclosure Interpretations. [33] 17 CFR 229.404(a). [34] Securities Offering Reform, Release No. 33-8591 (July 19, 2005) [70 FR 44722 (Aug. 3, 2005)], available at https://www.federalregister.gov/documents/2005/08/03/05-14560/securities-offering-reform. [35] 17 CFR 229.105. [36] Modernization of Regulation S-K Items 101, 103, and 105, Release No. 33-10825 (Aug. 26, 2020) [85 FR 63726 (Oct. 8, 2020)], available at https://www.federalregister.gov/documents/2020/10/08/2020-19182/modernization-of-regulation-s-k-items-101-103-and-105. [37] SEC Risk Factor Disclosure Rules, Harvard Law School Forum on Corporate Governance, posted by Dean Kingsley and Matt Solomon, Deloitte & Touche LLP, and Kristen Jaconi, University of Southern California (Dec. 22, 2021), available at: https://corpgov.law.harvard.edu/2021/12/22/sec-risk-factor-disclosure-rules/. [38] Id. [39] See, generally, Safe Harbor for Forward-Looking Statements, Release No. 33-7101 (Oct. 13, 1994) [59 FR 52723, 52727 (Oct. 19, 1994)], available at https://archives.federalregister.gov/issue_slice/1994/10/19/52714-52743.pdf#page=10. [40] 15 U.S.C. § 77z-2(c)(1)(A)(i) and 15 U.S.C. § 78u-5(c)(1). [41] See Paul S. Atkins, Statement on Reforming Regulation S-K (Jan. 13, 2026), available at https://www.sec.gov/newsroom/speeches-statements/atkins-statement-reforming-regulation-s-k-011326. [42] Field of Dreams (Universal Pictures 1989).

A new report from advanced FX and cash management solutions provider, MillTech, has revealed that UK corporates lost an average of £6.71m in 2025 due to unhedged FX exposure, while US firms lost $9.85m, driving a renewed focus on currency protection in 2026. MillTech’s Q4 2025 Corporate Hedging Monitor includes findings from a survey of 250 senior finance decision-makers at UK and US corporates and reveals that four in five firms (80%) reported losses from unhedged risk in 2025, with nearly a fifth (19%) describing these as significant. Against this backdrop, corporate hedging activity rebounded from Q3 lows, signalling a renewed focus on risk reduction. The average hedge ratio rose from 46% to 49%, although it remains below levels seen before Q3 2025. Hedge tenors also lengthened, increasing from an average of 5.8 to 6.3 months, in line with levels recorded in the first half of 2025. In the UK, average hedge lengths marginally exceeded those of Q4 2024, highlighting a renewed willingness among corporates to lock in protection for longer and improve cash flow certainty. Central bank policy and inflation rates emerged as the most influential external factors shaping FX hedging decisions, each cited by 17% of corporates. Notably, this was the first time inflation rates ranked joint top, while central bank policy has consistently led the list since Q2 2025. Looking ahead, tariff-driven market uncertainty is prompting a more defensive outlook. Nearly two-thirds of corporates plan to increase hedge ratios (64%) and extend hedge tenors (59%), with UK firms more inclined to do so than their US counterparts. Only a small minority expect to reduce coverage, with 10% planning to lower hedge ratios and 9% hedge lengths. Eric Huttman, CEO of MillTech, commented: “Q4 2025 marked a clear shift back towards defensive FX management. While hedge ratios and tenors increased, they have not yet returned to early-2025 levels, suggesting firms continue to balance protection against cost and flexibility. However, with most corporates experiencing losses from unhedged exposure, 2026 is likely to see further increases in coverage as tariff and policy-driven uncertainty persists and major currencies recorded their largest swings in nearly a year.”

LeveL Markets, a U.S. registered broker-dealer and the operator of the LeveL ATS, today announced a strategic partnership with EDX Markets, a leading digital asset firm that combines an institutional-only trading venue with a central clearinghouse, to drive broader institutional participation in digital assets. The partnership will connect LeveL Markets’ institutional trading solutions with EDX Markets’ digital asset trading and clearing ecosystem, enabling institutional participants to trade digital assets through secure and efficient market infrastructure. By combining LeveL Markets’ expertise in low-latency execution, advanced order routing and institutional workflows with EDX’s institutional-grade trading venue, the integration will lower barriers to entry for traditional financial institutions exploring digital assets. Steve Miele, CEO of LeveL Markets, commented, “We’re excited to partner with EDX Markets to expand our institutional trading infrastructure into the digital asset space. This partnership reflects our shared commitment to applying the standards, transparency and performance expected in traditional markets to digital asset trading, creating a seamless and trusted path for institutions to engage with this asset class.” The partnership supports both LeveL Markets’ and EDX Markets’ missions to deliver institutional-grade access to digital assets, with a focus on market integrity, capital efficiency and alignment with evolving regulatory frameworks. Tony Acuña-Rohter, CEO of EDX Markets, said, “LeveL Markets brings deep expertise in institutional market access and execution. By working together, we’re strengthening the connectivity and infrastructure that institutions need to engage in digital assets with confidence.”

On 27 February 2026, ACER will open two public consultations on: economic input data to improve European electricity system modelling; and amendments to the Core region's long-term capacity calculation methodology. The consultation on economic input data for European electricity system modelling presents a new dataset outlining estimated investment and operational costs for various power generation technologies and demand-side flexibility at Member State level. Its aim is to ensure that the inputs used in European-level modelling are robust, transparent and properly grounded in national market realities. Stakeholders are invited to review the data relevant to their Member State(s) and provide feedback to help improve its accuracy and reliability. The consultation on amendments to the Core region’s long-term capacity calculation methodology aims to inform ACER’s decision on the proposed changes to enhance long-term capacity calculation. The transmission system operators’ proposal suggests enabling the implementation of the flow-based methodology and integrating the Ireland and Northern Ireland single electricity market – France bidding zone border into the Core region’s long-term capacity calculation process. Get involved! Both consultations will run from 27 February until 27 March 2026. Read more about the economic input data consultation. Read more about the Core region’s methodology consultation.

Legislation to facilitate changes to the UK prudential banking framework, including implementation of Basel 3.1 and the Small Domestic Deposit Takers (SDDT) regime Documents Applying the Financial Services and Markets Act 2000 model of regulation to the UK Capital Requirements Regulation: Policy Update 2025 PDF, 221 KB, 22 pages This file may not be suitable for users of assistive technology. Request an accessible format. If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email digital.communications@hmtreasury.gov.uk. Please tell us what format you need. It will help us if you say what assistive technology you use. Applying the Financial Services and Markets Act 2000 model of regulation to the UK Capital Requirements Regulation: Policy Update 2025 (Accessible) HTML Basel 3.1 Market Risk Transitional Provision - draft regulations PDF, 199 KB, 3 pages This file may not be suitable for users of assistive technology. Request an accessible format. If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email digital.communications@hmtreasury.gov.uk. Please tell us what format you need. It will help us if you say what assistive technology you use. Restatement of Capital Requirements Regulation definitions - draft regulations PDF, 331 KB, 12 pages This file may not be suitable for users of assistive technology. Request an accessible format. If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email digital.communications@hmtreasury.gov.uk. Please tell us what format you need. It will help us if you say what assistive technology you use. Applying the FSMA 2000 model of regulation to the Capital Requirements Regulation PDF, 203 KB, 22 pages This file may not be suitable for users of assistive technology. Request an accessible format. If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email digital.communications@hmtreasury.gov.uk. Please tell us what format you need. It will help us if you say what assistive technology you use. Revocation of CRR provisions to implement Basel 3.1 - draft regulations PDF, 311 KB, 11 pages This file may not be suitable for users of assistive technology. Request an accessible format. If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email digital.communications@hmtreasury.gov.uk. Please tell us what format you need. It will help us if you say what assistive technology you use. Restatement of provisions on capital buffers - draft regulations PDF, 233 KB, 10 pages This file may not be suitable for users of assistive technology. Request an accessible format. If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email digital.communications@hmtreasury.gov.uk. Please tell us what format you need. It will help us if you say what assistive technology you use. Revocation of CRR provisions on the definition of capital - draft regulations PDF, 204 KB, 6 pages This file may not be suitable for users of assistive technology. Request an accessible format. If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email digital.communications@hmtreasury.gov.uk. Please tell us what format you need. It will help us if you say what assistive technology you use. Details HM Treasury is progressing work to apply the UK’s established model of regulation for financial services, known as the FSMA model, to the law the UK inherited from the EU (known as assimilated law) on the capital framework for banks, building societies and investment firms. FSMA refers to the Financial Services and Markets Act 2000. This policy update confirms the legislative approach for implementing the final post-crisis reforms to banks’ capital requirements, known as Basel 3.1. It explains how HM Treasury will revoke certain parts of the Capital Requirements Regulation (“the CRR”, which is the central piece of assimilated law on bank capital requirements), which the Prudential Regulation Authority (PRA) will then replace with rules implementing the new Basel standards. The update also outlines the proposed legislative approach for revoking the remainder of the CRR and for revoking and restating with modifications the Capital Buffers Regulation. Completing the application of the FSMA model to this area of assimilated law will pave the way for a number of further reforms to the prudential regime proposed by the PRA, including a proportionate prudential regime for smaller banks and building societies. HM Treasury has published three pieces of draft legislation alongside the policy update and would welcome technical comments on the proposed legislative approach within the next 6 weeks. The PRA has also published a number of corresponding policy documents that set out the final Basel 3.1 package and further reforms the PRA proposes to make to the prudential regime. Update as of 17 January 2025: The Prudential Regulation Authority (PRA), in consultation with HM Treasury, has decided to delay the implementation of Basel 3.1 in the UK by one year until 1 January 2027. Read the press notice for more information. Update as of 19 March 2025: The PRA has published a Consultation Paper (CP 3/25), proposing a set of conditions for identifying recognised exchanges or assets traded on these exchanges. HMT will implement the necessary legislative changes to support the PRA’s proposals outlined in the consultation paper. Update as of 4 August 2025: On 15 July 2025, HM Treasury published a Policy Update inviting responses on the Overseas Recognition Regimes and Key CRR Definitions (outlined in Chapters 3 and 4 of the consultation document), as well as on the associated draft legislation. Responses were originally requested by 5 September 2025. In response to feedback that additional time would be helpful, HM Treasury has extended the consultation period as follows: Basel 3.1 transitional Statutory Instrument Basel 3.1 Market Risk Transitional Provision - Draft Regulations responses requested by 23:45 on 12 September 2025. Chapters 3 and 4 (Overseas Recognition Regimes and Key UK CRR Definitions) of the Policy Update 2025, and the draft Key UK CRR Definitions Statutory Instrument Restatement of Capital Requirements Regulation Definitions – Draft Regulations responses requested by 23:45 on 30 September 2025. Update as of 20 January 2026: The Prudential Regulation Authority (PRA) has published policy statements confirming its rules for implementing:  the Basel 3.1 standards (Basel 3.1 Rules). These will apply to firms from 1 January 2027, except for capital requirements for market risk under the internal models approach, which will apply from 1 January 2028   the relevant remaining provisions in the Capital Requirements Regulation (Restatement of CRR Requirements)  the simplified capital regime for Small Domestic Deposit Takers (SDDTs) (Small Domestic Deposit Takers Rules)  HM Treasury has made the necessary legislative changes to support the PRA’s rules and to enable their implementation: The Financial Services and Markets Act 2023 (Commencement No. 12 and Saving Provisions) Regulations 2026. HM Treasury will publish a full response to its policy update, Applying the Financial Services and Markets Act 2000 model of regulation to the UK Capital Requirements Regulation: Policy Update 2025, in due course. Update as of 17 February 2026: The FCA has published a Consultation Paper on securitisation, proposing simplifications to reporting, disclosure and due diligence requirements. The PRA has published a Consultation Paper, also relating to securitisation, setting out further proposals intended to reduce burdens on firms by making the current framework less prescriptive and by updating the capital treatment of loans under the Mortgage Guarantee Scheme. HMT will work with the FCA and the PRA on any legislative changes that HMT considers necessary.

The total value of transactions during the month in re view reached € 12,60 million, with an average of € 0,63 million per trading session. The Financials sector contrib uted 84,06% to the total value traded which was the high est among all other sectors. Investors primarily focused their interest on the shares of “Bank of Cyprus Holdings Plc” and also on shares of “Demetra Holdings Plc” with 72,76% and 5,16% of the total value respectively. Click here for full details.

LSEG and Bank of America today announced a multi year strategic partnership that will make LSEG’s data, analytics and workflow capabilities available across Bank of America’s platforms. The collaboration enhances Bank of America’s client proposition by making it easier and faster for clients to access trusted data, analyse markets more quickly, and uncover insights that support more confident decision making. The partnership will empower clients to uncover deeper insights and act on trusted, actionable intelligence. Under the agreement, LSEG’s data and analytics solutions will be integrated across key areas of Bank of America’s business. By providing governed access to high quality, rights cleared content, the partnership enables Bank of America to deliver tools that help clients analyse complex market conditions, interpret trends and make well informed decisions with confidence. LSEG will also supply AI ready content to accelerate analysis and enhance insight generation across client workflows. The partnership provides Bank of America with access to LSEG’s integrated workflow solutions, led by LSEG Workspace and complemented by APIs and enterprise platforms. At the same time, integrating LSEG’s World Check risk intelligence data will reinforce the consistency and reliability of Bank of America’s compliance, screening and monitoring processes across markets and jurisdictions. Fernando Vicario, CEO of Merrill Lynch International and UK Country Executive at Bank of America, said:“Trusted, high quality data is essential to how we support clients and manage risk. Partnering with LSEG provides a unified, governed source of intelligence that strengthens our solutions, empowers client decision making and enhances how we innovate and execute. Integrating LSEG’s capabilities across our workflows will enable faster insight generation and a more seamless experience for our clients.” Gianluca Biagini, Group Co-Head, Data & Analytics, LSEG, said:“By powering Bank of America’s ecosystem with our analytics and workflows, including LSEG Workspace, we are supporting the bank’s transformation ambitions while strengthening governance and risk management. This will equip Bank of America with the trusted data needed to deliver deeper insights, streamline decision making and drive new value across client workflows.” The new capabilities enabled by this partnership will bring significant value across Bank of America’s business propositions from advisory and investment workflows to trading, risk oversight and regulatory processes. By drawing on a unified framework of governed, high quality data, Bank of America will be delivering consistent and insight rich solutions for clients across a wide range of use cases.

ETFGI reports today that assets in the ETFs industry in Europe reached a new record of US$3.40 trillion at the end of January. During January the ETFs industry in Europe gathered record net inflows of US$58.67 billion, according to ETFGI's January 2026 European ETFs and ETPs industry landscape insights report, the monthly report which is part of an annual paid-for research subscription service. ETFGI is a leading independent research and consultancy firm with 14 years of experience, recognized for its expertise in subscription research, consulting services, industry events, and ETF TV, covering global ETF industry trends (All dollar values in USD unless otherwise noted.)Highlights Assets in Europe’s ETF industry reached a record $3.40 Tn at the end of January, surpassing the previous record of $3.22 Tn set in December 2025. Assets increased 5.5% year‑to‑date in 2026, rising from $3.22 Tn at the end of 2025 to $3.40 Tn. January saw record net inflows of $58.67 Bn, exceeding the previous January record of $32.93 Bn in 2025; the third‑highest January inflows were $29.12 Bn in 2022. January marked the 40th consecutive month of net inflows for the European ETF industry. “The S&P 500 rose 1.45% in January. Developed markets excluding the US gained 6.15% in January and are up 6.15%, with Korea (+26.73%) and Luxembourg (+18.64%) posting the strongest increases among developed markets. Emerging markets climbed 5.50% in January, led by Peru (+26.23%) and Colombia (+23.24%)”, according to Deborah Fuhr, managing partner, founder, and owner of ETFGI. Growth in assets in the ETFs industry in Europe as of the end of January The ETFs industry in Europe had 3,586 products, with 14,921 listings, assets of $3.40 Tn, from 146 providers listed on 30 exchanges in 25 countries at the end of January. iShares is the largest provider in terms of assets with $1.37 Tn, reflecting 40.2% market share; Amundi ETF is second with $421.90 Bn and 12.4% market share, followed by Xtrackers with $350.25 Bn and 10.3% market share. The top three providers, out of 146, account for 62.9% of European ETF AUM, while the remaining 143 providers each have less than 8% market share. During January, ETFs gathered a record $58.67 billion in net inflows. Equity ETFs generated $41.36 billion in net inflows, significantly higher than the $23.60 billion gathered in January 2025. Fixed income ETFs saw $13.09 billion in net inflows, up from $4.81 billion in January 2025. Commodities ETFs experienced $648.30 million in net outflows, compared to $2.42 billion in net inflows in January 2025. Active ETFs attracted $4.24 billion in net inflows, an increase from the $1.60 billion recorded in January 2025. Investors have tended to invest in Equity ETFs during January.

Transaction Network Services (TNS) today announced an upgrade to its TNS Data Usage Optimizer (DUO) with the launch of an interactive customer portal. Building on the launch of DUO in late 2024, this new interface offers buy-side and sell-side firms direct, on-demand access to the DUO portal to analyze market data subscriptions, identify unused services, and eliminate unnecessary spend.Market data is one of the largest operational expenses for financial firms, yet costs can be notoriously difficult to manage. The new DUO portal turns complex, raw data files from vendor entitlement systems into an actionable dashboard, allowing firms to quickly pinpoint and eradicate non-essential costs. TNS has used DUO with global banks and financial firms and in the case of one bank, TNS identified monthly savings of $60,000 by highlighting unused data feeds. "Last year we introduced DUO to solve a critical industry problem. Today, we're putting the power to control market data costs directly into our customers' hands," said Tom Lazenga, General Manager, TNS Financial Markets. "The DUO portal empowers data managers and desk heads to instantly identify and act on potential savings."The DUO portal is the second release of the software and provides a centralized, global view of data expenses. Key features include: Direct customer access: Firms can independently upload vendor files and generate savings reports on their own schedule, accelerating the optimization process. Advanced analytics: DUO’s data grid allows users to filter information by user, location, or data feed. Custom reports can be created and saved to generate an actionable list for immediate cost reduction. Global, multi-site management: A single, intuitive interface provides a holistic view to manage and analyze data costs across a firm’s portfolio of global offices. Customizable cost modeling: Users can input specific contract pricing, bulk discounts, and regional fee variations to build an accurate picture of potential savings. "Our goal with the DUO portal was to make identifying wasted spend as simple as possible," said Lazenga. "This portal lays the foundation for our product roadmap, which includes automated feed provisioning and support for additional data providers.”Find out more about building and managing market data services at https://tnsi.com/solutions/financial/market-data/

January 2026 opened with continued global market volatility, driven by lingering geopolitical tensions and the threat of trade tariffs. Despite the tumultuous start, the FTSE Mondo Visione Index climbed 3.2%, closing the month at 96,985.19 points, up from December's 93,979.05 points.   Top 5 Exchanges by Market Capitalisation (USD bn) CME Group – 104.16   Intercontinental Exchange – 99.84   Hong Kong Exchanges & Clearing – 70.16   London Stock Exchange Group – 57.67   Nasdaq – 55.63   The Tel Aviv Stock Exchange emerged as January's top performer, delivering a remarkable 30.1% capital return in U.S. dollars. Brazil's B3 followed with a 21.7% rise, while ASX rounded out the top three at 17%. On the other hand, the Bulgarian Stock Exchange declined 23.6%, ranking as the worst performer, followed by Croatia's Zagrebacka Burza (-20.6%) and Boursa Kuwait Securities (-14.7%).   Herbie Skeete, Managing Director of Mondo Visione, commented, "CME Group continues to dominate, driven by strong futures and options trading. With heightened macroeconomic uncertainty, we anticipate sustained trading activity, particularly in interest rate, energy, and metals contracts, which should further support CME's transaction-based revenues."   To explore the full details of January's market performance, click here to download the report.   1-YEAR PERFORMANCE CHART OF THE FTSE MONDO VISIONE EXCHANGES INDEX (USD CAPITAL RETURN) Source: FTSE Group, data as at 30 January 2026 Monthly FTSE Mondo Visione Exchanges Index Performance (Capital Return, USD) July 2014 3.1% August 2014 2.3% September 2014 -3.6% October 2014 2.8% November 2014 2.5% December 2014 -0.5% January 2015 -1.0% February 2015 8.5% March 2015 0.0% April 2015 10.7% May 2015 0.1% June 2015 -3.2% July 2015 -2.7% August 2015 -5.3% September 2015 -2.1% October 2015 7.6% November 2015 0.4% December 2015 -2.2% January 2016 -4,7% February 2016 -0.7% March 2016 6.7% April 2016 0.4% May 2016 1.8% June 2016 -2.2% July 2016 5.3% August 2016 2.3% September 2016 -1.6% October 2016 -1.6% November 2016 2.1% December 2016 0.1% January 2017 6.0% February 2017 -0.8% March 2017 1.4% April 2017 0.8% May 2017 1.6% June 2017 5.6% July 2017 2.7% August 2017 0.3% September 2017 3.6% October 2017 -0.7% November 2017 6.4% December 2017 -0.7% January 2018 10% February 2018 -0.5% March 2018 -1.6% April 2018 -1.0% May 2018 -1.5% June 2018 -0.8% July 2018 -0.7% August 2018 2.4% September 2018 -1.7% October 2018 1.0% November 2018 3.1% December 2018 -4.2% January 2019 5.4% February 2019 1.7% March 2019 -2.6% April 2019 4.6% May 2019 1.5% June 2019 4.3% July 2019 2.2% August 2019 3.7% September 2019 -0.8% October 2019 2.0% November 2019 -0.5% December 2019 1.6% January 2020 5.0% February 2020 -7.4% March 2020 -11.5% April 2020 8.0% May 2020 6.7% June 2020 2.3% July 2020 6.6% August 2020 4.9% September 2020 -5.2% October 2020 -6.7% November 2020 8.9% December 2020 7.2% January 2021 0.8% February 2021 1.4% March 2021 -2.7% April 2021 3.3% May 2021 2.5% June 2021 0.4% July 2021 0.4% August 2021 0.1% September 2021 -4.2% October 2021 5.9% November 2021 -5.6% December 2021 4.9% January 2022 -2.2% February 2022 -3.5% March 2022 3.5% April 2022 -8.6% May 2022 -5.1% June 2022 -0.7% July 2022 2.4% August 2022 -3.9% September 2022 -8.8% October 2022 -1.1% November 2022 11.5% December 2022 -2.9% January 2023 3.8% February 2023 -4.1% March 2023 5.0% April 2023 0.9% May 2023 -3.9% June 2023 3.8% July 2023 4.6% August 2023 -2.3% September 2023 -3.0% October 2023 -0.6% November 2023 7.7% December 2023 3.8% January 2024 -2.7% February 2024 4.3% March 2024 -0.1% April 2024 -3.8% May 2024 1.3% June 2024 -0.4% July 2024 3.2% August 2024 8.2% September 2024 4.7% October 2024 -1.2% November 2024 2.6% December 2024 -3.1% January 2025 4.3% February 2025 5.6% March 2025 2.2% April 2025 3.5% May 2025 4.4% June 2025 0.8% July 2025 -2.9% August 2025 -0.7% September 2025 -3.1% October 2025 -3.2% November 2025 3.6% December 2025 -0.4% January 2026 3.2%   About FTSE Mondo Visione Exchanges Index The FTSE Mondo Visione Exchanges Index, a joint venture between FTSE Group and Mondo Visione, was established in 2000. It is the first Index in the world to focus on listed exchanges and other trading venues. The FTSE Mondo Visione Exchanges Index compares performance of individual exchanges and trading platforms and provides a reliable barometer of the health and performance of the exchange sector. It enables investors to track 33 publicly listed exchanges and trading floors and focuses attention of the market on this important sector. The FTSE Mondo Visione Exchanges Index includes all publicly traded stock exchanges and trading floors: Australian Securities Exchange Ltd B3 SA Bolsa de Comercio Santiago Bolsa Mexicana de Valores SA Boursa Kuwait Securities BSE Bulgarian Stock Exchange Bursa de Valori Bucuresti SA Bursa Malaysia Cboe Global Markets CME Group Dar es Salaam Stock Exchange PLC Deutsche Bourse Dubai Financial Market Euronext Hellenic Exchanges SA Hong Kong Exchanges and Clearing Ltd Intercontinental Exchange Inc Japan Exchange Group, Inc Johannesburg Stock Exchange Ltd London Stock Exchange Group Multi Commodity Exchange of India Nairobi Securities Exchange Nasdaq New Zealand Exchange Ltd Philippine Stock Exchange Saudi Tadawul Group Singapore Exchange Ltd Tel Aviv Stock Exchange TMX Group Warsaw Stock Exchange Zagreb Stock Exchange The FTSE Mondo Visione Exchanges Index is compiled by FTSE Group from data based on the share price performance of listed exchanges and trading platforms.

Key takeaways: Fourth consecutive week of outflows, totalling US$173M, with US$3.74B withdrawn over the past 4 weeks. Sharp regional divergence, with US$403M in US outflows offset by US$230M of inflows across Europe and Canada Bitcoin and Ethereum led outflows, while XRP and Solana continued to attract fresh inflows The full research features in CoinShares’ weekly newsletter, which can also be found here.

The Bank of England held roundtable meetings with representatives from regulated firms on the responsible adoption of artificial intelligence and machine learning (AI and ML), to better understand the constraints that firms may be facing. Introduction As per the Bank’s approach to innovation in AI, DLT and quantum computing, we seek to engage with innovators and industry practitioners in various ways to better understand the latest technological developments and their implications for the financial sector. This includes via biennial AI surveys of the UK financial sector, the AI Consortium (a successor to the AI Public-Private Forum), the Cross Market Operational Resilience Group AI Taskforce, and the Bank’s Market Intelligence function. To complement these initiatives, and in line with the Bank’s secondary growth objective, in late 2025, the Bank of England hosted three roundtables with participants from regulated firms to better understand the constraints firms may be facing in adopting AI, and how the Bank and PRA can support responsible AI adoption. Each roundtable was held with representatives from a different PRA-regulated sector: (1) challenger banks and UK-focussed larger banks; (2) global systemically important banks; and (3) insurers. Observers from the FCA and HMT were also present. Below is a summary of the key points arising from the roundtable discussions, which were held under the Chatham House Rule. Summary of key points Across all three roundtables, participants from regulated firms expressed support for the PRA’s regulatory framework as it related to AI. Participants noted that the PRA’s principles-based, outcomes-based policy and supervisory statements gave firms sufficient space to innovate within clear regulatory guardrails. Supervisory Statement 1/23 on Model Risk Management in particular was noted by several participants as pragmatic in enabling responsible AI adoption. Most participants did not see the need yet for detailed AI-specific regulatory guidance or rules, and most couldn’t see a case for a Bank or PRA AI sandbox at this time; the FCA’s Supercharged Sandbox and AI Live Testing initiatives were seen as providing sufficient offerings for testing purposes. Second-line risk functions continue to approach the use of AI with caution, which may delay AI deployment pipelines. There were mixed views on whether this was an optimal or inevitable level of caution. Drivers could include both (a) bottlenecks in AI skills and expertise, given the dynamic and highly complex nature of the technology, and the range of uses to which it was being put; and (b) a desire to ensure compliance with supervisory expectations could be comprehensively demonstrated. As an example, several participants noted that firms’ traditional model risk management approach to validation wouldn’t be sustainable in its current form as generative AI and agentic systems proliferated. The traditional emphasis on understanding the inner workings of a model – i.e. how inputs mapped to outputs –wasn’t tenable or fully effective for increasingly complex AI models. The concept of having a ‘human-in-the-loop’ was also challenged by the rise of agentic AI. Several participants suggested that risk management needed to evolve to put greater emphasis on testing, monitoring and setting guardrails around the outcomes of broader AI systems. Some participants suggested there would be value in sharing supervisory observations on good and bad practice, or convening industry experts to define, agree and share best practice.footnote[1] Firms operating in multiple jurisdictions need to navigate different regulatory approaches to AI. Participants noted key differences between the UK’s regulatory approach, the US’s approach (e.g. Supervisory Letter SR11-7 on Guidance on Model Risk Management) and the EU AI Act. Fragmentation increases compliance costs, slowed AI adoption, and prevented firms from scaling AI use cases across borders. Several participants therefore encouraged the Bank to use its membership of various international fora to encourage global coordination and convergence. Procurement and contract negotiations with third-party AI providers were slowed by inconsistent familiarity with regulated firms’ compliance requirements. Some participants thought the market would eventually solve that problem i.e. minimum standards would emerge over time, but that there was an opportunity cost in the meantime. Several participants therefore noted that the Bank could explore convening financial and technology firms to agree minimum standards for third party AI providers to the regulated financial sector. Some participants noted that as AI models become embedded in agentic systems throughout their firm’s core business processes, substituting between AI providers may become more challenging. Data protection laws – along with emerging data sovereignty regimes in other jurisdictions – were a challenge to deploying and scaling AI use cases. Several participants noted that the legal requirement to complete Data Protection Impact Assessments in certain situations slowed their AI deployment pipeline.footnote[2] Participants noted that new data location requirements could prevent scaling AI solutions across borders. Data quality can also be a barrier to the use of AI, particularly in some areas of insurance. Some insurers have relatively little data on their individual customers, owing to the infrequency of customer engagement (e.g. annual policy renewal, when a claim is submitted), in contrast to banks’ visibility of their customers’ transactions. Therefore prospects of e.g. hyperpersonalised insurance products using AI were limited in some areas in the near term. To note, in November, the PRA published slides with supervisory observations on firms’ compliance with SS1/23 in the context of their use of AI and machine learning. To note, the ICO has published guidance on when firms are required to do a DPIA , as well as specific guidance on DPIAs and data protection law more broadly in the context of AI deployment.

Ahead of the Eurex Derivatives Forum Frankfurt on February 25-26, we sat down with Sushil Krishan, Managing Director at Goldman Sachs on recent growth in Buffer ETFs, the effect this rise might have on derivatives markets and its prospects in Europe. What is the current state of the Buffer ETF market in Europe? Over the last three years, the asset growth in Buffer ETFs has been almost exponential, moving from virtually zero to way above $100 billion1 in the US and in Europe. Initially there were all sorts of doubts as to whether these products would take off in Europe. People argued that the market was too small and couldn’t grow in the same way the US market did. It was very much a let's wait and see attitude. Now, within less than a year, the European market has already grown to more than $5 billion. The demand is clearly there, and the products are perceived advantageous to investors. These are not very new products - back in 2008/9, ETFs that essentially ran collar strategies were already being launched by some issuers, but they never grew above $20 million AuM. They were too far ahead of the curve as a product. Now, the dynamics have shifted quite fundamentally, and investors are looking at these products quite extensively as a tool for their overall portfolio construction. In addition, as the ETF space has grown, with more retail investors taking a passive approach, more products are moving in that direction. These products can be seen as an extension to the QIS offering which recorded tremendous growth in the past years. Banks have been very active in that market through OTC channels. Traditionally, their clients were actively managed or hedge funds, and it was not a very accessible space for retail investors. Now however, this market is starting to convert into passive investing models. What effect will these trends have on derivatives markets? Firstly, there will a much greater concentration in volume as the number of assets in Buffer ETFs keeps growing into the billions. The typical structure of these funds is selling a call and buying a put with some sort of variety in strikes and maturities. The call selling generates a premium for the ETF to buy a put again. It can be structured in different ways, by distributing trading over time, or using different maturities and strikes. It gives the investors some protection on the downside and captures some of the upside. It makes the product less volatile, which is the perfect match for a more conservative and income-oriented type of investing. What we are already observing in the US is that call selling can become so concentrated and account for such a large portion of activity that it starts influencing the market itself. In the past, call selling strategies have started to cap the market itself as more money flows into the strategy. As long as the underlying asset is increasing towards the call strike the dealer community becomes more long gamma, which caps the market with growing volumes. On the flip side of the trade, the put buying makes the dealer community that facilitates this downside protection short gamma. This dynamic reduces volatility on the upside but increases volatility on the downside. It is already observable in the US and given that volumes are increasing in Europe we should start to see this emerging in Europe, which is a much less liquid market. It is going to be very interesting to see how these dynamics unfold in the coming years and the effect they have on the derivatives ecosystem. What are the main challenges to growing the European market? There is always a big difference between any European market and its US equivalent. Europe is much more fragmented than the US, which is a very concentrated market for trading. There are also traditional differences in retail investing. The US is a much more independent and equity focused investment culture, while in Europe you need institutional interest to gain significant asset growth. For ETFs more specifically, there is also a significant difference in how these vehicles are constructed. In Europe, to be an ETF, issuers have to use the UCITS framework, which limits what a fund can do to a very high degree. In the US, we are seeing Buffer ETFs of high volatility and performing single stocks like Nvidia. It is very hard to do something like that in Europe, if not impossible, due to the diversification rules in the UCITS framework. Those restrictions don’t exist in the US. So, the spectrum of these products in Europe is set to center much more around overlay strategies on a diversified basket. The question remains though: will insurance companies, pension funds and corporate clients start to buy these ETFs where the overlay strategy is incorporated, instead of buying the assets and running an overlay strategy themselves? That brings us back to the different investor bases in Europe and the US, because in the end that is where the volume will be generated. I believe that in around a year this will start to accelerate. One and a half years ago, total assets in Buffer ETFs were less than a billion across all the funds in Europe. That has since grown by about 5-10 times[1] within a very short period and is clearly being driven by institutional adoption. The question then is what happens to the other overlay strategies? Will we see a reduction in the ones run bilaterally for institutional clients with the banks, as that activity transfers to ETFs? [1] Source: GS GIR as of Feb2026  Visit the panel at Derivatives Forum Frankfurt on 25 February from 17:15-18:00 CET Strategic Overlays with ETF Derivatives and Buffer ETFs: Enhancing Risk-Adjusted Returns This panel will explore how ETF derivatives and buffer ETFs are used to implement overlay strategies that improve portfolio efficiency, manage downside risk, and optimize capital. The conversation will highlight practical applications across asset classes, with insights from institutional investors and product specialists. How are ETF derivatives used to create tactical overlays in equity and fixed income portfolios? What role do buffer ETFs play in mitigating downside risk while maintaining upside participation? How do overlay strategies using ETF options and futures support liquidity and capital efficiency?   Moderator Radi Khasawneh, Derivatives Editor, FOW Speakers Sushil Krishan, Managing Director, Goldman Sachs Hamish Seegopaul, Global Head of Index Product Innovation, STOXX Alexandre Roubaud, EMEA Head ETF Secondary Markets & Liquidity Solutions, BlackRock Imanol Urquizu, Head of Derivatives, Santander Asset Management Further information  Derivatives Forum Frankfurt 2026

As Credit Index Futures continue to reshape the landscape of credit derivatives trading, Eurex is proud to spotlight the voices of our most influential partners. In this exclusive series, we sit down with leading market participants from firms that have played a pivotal role in the development, adoption, and evolution of Credit Index Futures at Eurex. From early product design to global market expansion, these conversations offer unique insights into how Credit Index Futures are being used across trading desks, what differentiates them from other instruments, and where the market is headed next. Jasper Jansen is the head of Fixed Income Trading at Flow Traders Europe and one of the earliest and biggest supporters of Eurex Credit Index Futures. He has been instrumental in establishing the product by providing pricing off- and on-screen since day one and has been most outspoken in his belief in Credit Index Futures. Following the launch of the Credit Derivatives Partnership Program, he sat down with us to discuss his and the firm’s involvement in Credit Index Futures and where he sees the market developing. Eurex: Jasper, you have been a staunch supporter of Credit Index Futures at Eurex from the start, establishing a market in these by providing executable prices in the orderbook and off-book markets. Please explain to us what excited you about the product and how your vision was able to come to fruition?   Jasper Jansen   Jasper Jansen: What immediately excited me about Credit Index Futures was the opportunity to bring an alternative to CDS and TRS instruments in credit markets. Credit is a core asset class, yet access to standardized, centrally cleared tools was relatively limited compared to rates or equities. From the outset, our vision was that Credit Index Futures could become a true benchmark instrument for trading and hedging credit risk, provided there was consistent liquidity, tight pricing, and confidence in execution. At Flow Traders, we believed that if we committed to providing executable prices both on-screen and off-book from day one, we could help accelerate that adoption cycle. Over time, that vision has come to fruition through close collaboration with Eurex and other market participants. By actively contributing to price formation and market depth, we helped demonstrate that these products can trade efficiently across market conditions, which in turn encouraged broader participation from our counterparties and other liquidity providers. Eurex: We were excited by your and Flow Traders enthusiastic support during the critical initial days of launching the product. Could you please share some considerations and feedback that your counterparties had in the early days and how has that changed over time? Jasper Jansen: In the early days, the feedback from market participants was understandably focused on liquidity, consistency, and transaction costs. Many of our counterparties are familiar with credit exposure through cash products such as ETFs and bonds and wanted to understand how Credit Index Futures would behave in terms of tracking, roll mechanics, and execution during periods of volatility. Over time, as liquidity improved and pricing became more reliable, the conversation shifted. Today, our counterparties are increasingly focused on scalability, execution efficiency, and portfolio applications. We see growing confidence in using Credit Index Futures not only for hedging but also for tactical positioning and relative-value strategies. Eurex: Credit futures markets are embedded in an ecosystem of other derivatives and cash products such as ETFs, Corporate Bonds, Credit-Default-Swaps and Total Return Swaps. How do you find your counterparties using the products and what are the biggest differentiators that you find for credit index futures compared to the other alternatives? Jasper Jansen: Our counterparties use Credit Index Futures in a variety of ways, from macro hedging and beta exposure to more granular relative-value and arbitrage strategies against ETFs, CDS indices, and cash bonds. What stands out is how naturally the product integrates into a broader multi-asset derivatives framework. The key differentiators are standardization, transparency, and capital efficiency. For many of our counterparties, Credit Index Futures serve as a bridging instrument, linking cash credit markets with derivatives in a way that is operationally efficient and easy to scale. That makes them particularly attractive in fast-moving markets where execution certainty matters. Eurex: Having witnessed this success story in Europe firsthand, how do you think the market will develop globally? Jasper Jansen: Europe has shown that with the right market structure, committed liquidity provision, and strong exchange support, Credit Index Futures can gain meaningful traction. I believe this success provides a clear blueprint for global expansion. Looking ahead, I expect increased adoption across regions as market participants continue to prioritize capital efficiency, transparency, and electronic execution. Regulatory developments and balance sheet considerations will further support the shift toward listed, cleared products. Ultimately, Credit Index Futures have the potential to become a core global risk-transfer instrument in credit markets, much like rate futures are today. As liquidity deepens and use cases expand, we expect them to play an increasingly central role in how credit risk is traded and managed worldwide. Further information  Credit Index Futures  Credit Index Derivatives Partnership Program  Download center Credit Index Futures

With great regret, this notice informs LME members and the broader metals community of the death of Dr Fred Demler. Download notice