CFA Institute

AI is delivering real productivity gains across data-rich sectors, yet today’s investment surge is unfolding through highly concentrated capital flows and unprecedented spending on chips, data centers, and cloud infrastructure. At the same time, a growing share of reported growth depends on circular financing loops between chipmakers, cloud providers, and AI developers. These practices — like those of past market bubbles — can inflate demand signals, distort revenue quality, and increase the fragility of a market driven by a small group of firms. For financial analysts, assessing how these forces shape cash-flow durability, valuations, and balance-sheet resilience is critical to distinguishing sustainable AI-driven performance from capital-fueled momentum. A Market Reshaped by Capital Concentration AI investment is reshaping financial and corporate sectors. By 2025, more than half of global VC funding is expected to flow into AI, supporting growth in the United States with large investments in data centers and cloud infrastructure. Although AI capital expenditure still makes up less than 1% of GDP, consistent with an early-stage development, AI’s impact on public markets is considerable. Nearly 50% of the S&P 500’s market cap (about US$20 trillion) is considered to have medium to high AI sensitivity. This concentration creates a tightly connected ecosystem of tech platforms, chipmakers, data-center operators, cloud providers, and financial firms. Inside the Circular Financing Engine Circular financing loops have become a defining feature of this investment cycle. In several major deals, leading chip and cloud companies — such as NVIDIA and Microsoft — take equity stakes, extend credit, or provide other financial support to AI startups and data-center operators like CoreWeave or Nscale. In return, these clients commit to multi-year contracts for GPUs, servers, and cloud capacity. The suppliers recognize revenue from these agreements, boosting their valuations, while the startups gain both credibility and guaranteed access to infrastructure. These long-term contracts also encourage banks and private lenders to extend additional credit, pulling more debt and equity into the same closed ecosystem. How Round-Tripped Revenue Inflates Growth Signals The pace and scale of these agreements are drawing significant market attention. Analysts estimate roughly US$1 trillion in related commitments across suppliers, cloud platforms, and developers. NVIDIA’s proposed US$100 billion pledge to support OpenAI’s 10-gigawatt data-center expansion illustrates the dynamic: it enhances OpenAI’s capacity while directly boosting NVIDIA’s hardware sales. Financial firms, especially G-SIBs, are increasingly flagging these circular arrangements, in which suppliers finance their clients, share ownership, and split revenues. The concern is that these interconnected deals can inflate demand signals, distort revenue and valuation metrics, and obscure underlying vulnerabilities. If conditions deteriorate, integration challenges, organizational delays, regulatory hurdles, or overestimated demand could erode confidence in the AI story, expose overbuilt infrastructure, strain financial relationships, and trigger a broader sector correction. Lessons from Telecom’s Vendor Financing Bubble The telecom surge of the late 1990s offers a useful parallel. Companies such as Lucent, Nortel, Alcatel, and Cisco provided generous vendor financing to carriers, who used the funds to purchase switches, routers, and optical equipment. On paper, sales and profits looked strong, but much of the demand was driven by vendor financing rather than sustainable, revenue-generating customers. When traffic growth and pricing failed to meet expectations, carriers struggled to manage their debt. Defaults became frequent, vendors wrote down large receivables and inventories, and the telecom bubble ultimately burst, exposing the fragility of these intertwined financial arrangements. The AI cycle follows a similar story: leading chipmakers and cloud providers are investing heavily in key AI clients, driving commitments for large infrastructure purchases, and creating “round-tripped” revenue. This dependence on a small group of firms raises meaningful risk. The notion of “limitless AI compute,” much like “infinite bandwidth” in the late 1990s, becomes problematic if GPU and data-center capacity grows faster than it can be monetized. Despite some similarities to past tech booms, several significant differences define the current AI investment scene. Today’s leading AI firms are generally more profitable and carry less debt than many telecom companies during the dot-com era. In addition, a larger share of spending now goes toward physical assets that often have alternative uses or resale value. Where Today’s Cycle Differs—and Why It Still Carries Risk There is also genuine demand from businesses and consumers who actively pay for AI services. Even so, the scale of investment in chips, data centers, and cloud infrastructure could create oversupply, shorten asset lifespans, and reduce returns, particularly since chip generations become obsolete quickly and data-center equipment may last only about five years. Circular financing is not inherently problematic, but it becomes a concern when supplier- or investor-driven demand outpaces sustainable end-user revenue. As a result, experts are now examining AI deal structures and capital plans with the same rigor that credit analysts once applied to telecom vendor financing. Operational and Labor Impacts: Early Productivity, Uneven Effects Beneath the surface of capital inflows, AI is already reshaping how firms and labor markets operate, though unevenly. Routine, rules-based roles remain the most vulnerable; the U.S. Bureau of Labor Statistics expects AI to “moderate or reduce (but not eliminate)” the need for workers such as claims adjusters and examiners. Larger, tech-savvy firms are better positioned to capture these efficiency gains, while smaller or slower adopters may struggle to keep pace. Predictable, task-focused roles face growing pressure to automate, even as demand and wage premiums rise for workers with AI skills. Productivity gains are emerging, but often at the expense of job quality, with greater oversight, faster work pace, fragmented tasks, and some degree of deskilling. Some workers in high-risk roles are already seeing stagnant or declining wages and downgraded positions, with responsibilities and pay shifting rather than disappearing. Yet studies show that only a small share of firms have seen a meaningful impact on profits; one report finds that 95% of organizations report “little to no P&L impact,” with most gains concentrated among major tech firms. Even so, there is a credible positive trajectory, especially over the medium term. Companies are already integrating AI into workflows by automating routine tasks, improving decision-making, and

Imagine steering your organization through a stormy sea, except the waves are now higher, the weather changes by the hour, and the maps you relied on are already outdated. Volatility spikes, rapid rate shifts, and evolving regulations are reshaping market risk faster than many investment teams can adapt. Waiting for quarterly reports or post-event analysis is no longer enough. By then, the damage is done. Key risk indicators (KRIs) are your radar, scanning ahead to detect trouble before it breaches your risk appetite or impacts performance. As a risk professional, I’ve seen well-designed KRIs transform how investment firms anticipate and manage threats. In this post, I’ll share the core principles for building effective KRIs, illustrated with investment-focused examples you can apply immediately. What Are Key Risk Indicators? KRIs are measurable metrics that help organizations identify, monitor, and manage risks that could derail their objectives. Acting as early warning signals, KRIs provide insights into emerging risks or shifts in exposure before they escalate. By tracking KRIs against control benchmarks, businesses can address vulnerabilities proactively, align risk management with strategic goals, and enhance decision-making. 5 Principles of Effective Key Risk Indicators KRIs are only as effective as their design. Below, I outline five essential principles, each paired with an investment risk example and a clear If-Then rule to make the concept immediately actionable. 1. Measurable and Relevant KRIs must target specific risks tied to organizational goals and be calculated consistently to ensure reliability. Overlapping KRIs waste resources and obscure insights. Example: In investment management, metrics like drawdowns, implied volatility, or historical volatility can all measure risk — but using more than one for the same purpose creates noise. For an unleveraged long-only equity portfolio consisting of public equities, historical volatility based on daily returns over one month may be suitable once aligned to the risk appetite of the firm, consistently reflecting investment risk. If–Then: If more than one KRI measures the same underlying risk, then select the single metric most relevant to the investment mandate and apply it consistently. 2. Predictive Unlike key performance indicators (KPIs), which measure past performance, KRIs must anticipate future risks to enable proactive action. Example: A $10M portfolio with 33% each in Apple, Meta, and Tesla had a historical volatility of 38.03%. After shifting to 50% Apple and 50% Meta, recalculating with the new allocation projects 45.71% annualized volatility, a critical forward-looking insight. If–Then: If portfolio holdings or allocations change materially, then recalculate the KRI using the new allocation to capture the updated risk profile. 3. Control Benchmarks KRIs must be actionable, using benchmarks within the organization’s control to secure buy-in and drive decisions. Example: Comparing a portfolio’s simulated volatility of 45.71% to the S&P 500’s 15.87% isolates portfolio-specific risk from market driven risks which are usually outside the control of an unleveraged long-only equity portfolio. If volatility exceeds the agreed multiple of the benchmark, the team can adjust holdings — for example, by adding a stable utility stock. Without a control benchmark, the KRI might flag risks the team can’t control, like market-wide volatility, reducing its usefulness. If–Then: If the KRI measurement design includes factors outside the organization’s control, consider whether enhancing the design of the KRI can minimize uncontrollable factors. 4. Proactive and Timely KRIs must trigger specific actions within set timelines, linking directly to risk mitigation strategies. Example: If portfolio volatility exceeds 2.5x the S&P 500’s level (e.g., 39.67%), the investment team might diversify within 48 hours to lower risk. Dynamic thresholds ensure that limits adjust with market conditions. If–Then: If a KRI breaches its dynamic threshold, then adjust portfolio composition to bring it back within limits using predefined actions within a fixed time frame to reduce risk before it escalates, such as stock or sector re-allocation. 5. Strategic Alignment KRIs must align with the organization’s strategic vision to secure leadership support and foster a risk-aware culture. Example: The risk team calibrates volatility thresholds to optimize the Sharpe Ratio, aligning the KRI with a KPI closely monitored by management. By back-testing to balance risk and return, the KRI’s value becomes clear to both leadership and front-line staff. If–Then: If back-testing shows a KRI misaligns with risk–return objectives, then recalibrate it with stakeholders to maintain both performance and strategic alignment. Overcoming Common KRI Challenges Implementing a robust set of KRIs can raise concerns about complexity, cost, and scalability. These challenges can be addressed with straightforward, investment-focused solutions: Challenge: Complexity of designing KRIs that fit the business unit.Solution: Start with one high-impact KRI for your most material risk exposure, using a clear If–Then rule. Expand gradually as processes mature. Challenge: High cost of implementation.Solution: Leverage existing portfolio data and widely available tools (e.g., Python’s Pandas library) to run simulations and calculations without expensive system upgrades. Challenge: Time-consuming manual updates.Solution: Automate KRI calculations in your portfolio management system or via scheduled scripts, ensuring data refreshes at set intervals without additional staff hours. Challenge: Lack of business unit buy-in.Solution: Tie KRIs directly to decision-making levers the unit controls — for example, linking volatility thresholds to reward metrics — so they see an immediate, tangible connection to performance outcomes. Turning KRI Theory Into Action The future of KRIs is predictive, data-driven, and embedded into real-time decision-making. But you don’t need to wait for the next wave of analytics tools to strengthen your portfolio oversight. Start now: Step 1: Identify your top three investment risk exposures. Step 2: Design one predictive, benchmarked KRI for each. Use metrics you can calculate consistently and that your team can act on. Step 3: Set dynamic thresholds tied to market conditions and agree on the specific portfolio actions to take when they’re breached. By taking these steps within the next quarter, you’ll not only improve your early warning capabilities but also demonstrate clear alignment between your risk framework and investment strategy, turning KRIs from a monitoring tool into a performance edge. source

Each year, CFA Institute Research and Policy Center publishes work that helps investment professionals navigate structural change, emerging risks, and innovation across global markets. This year’s most popular pieces reflect that mission. From the expanding role of artificial intelligence (AI) in asset management to capital formation in Africa, tokenization, pensions, and the ethics of private markets, these publications offer practical insight for leaders shaping investment decisions in a rapidly evolving landscape. Below is a selection of the Research and Policy Center’s top publications from 2025. Edited by Joseph Simonian, PhD, this book from CFA Institute Research Foundation and CFA Institute Research and Policy Center demonstrates how artificial intelligence (AI) is transforming asset management. Explore how AI and machine learning (ML) are being applied to portfolio design, risk oversight, and investment decision-making, with insights from leading practitioners and CFA Institute experts. CFA Institute Research and Policy Center convened net-zero thought leaders and investment luminaries to break down the big ideas around achieving net zero. These Voices of Influence provide practical guidance for investors, asset managers, investment professionals, and regulators. More than 50 authors from the United States, Europe, and Asia collaborated on 16 research projects. The initiative won the 2025 Silver Award for Excellence in Sustainable Development Goals Implementation from the Society for Scholarly Publishing. This report, edited by  Olivier Fines, CFA and Phoebe Chan,  offers a detailed assessment of the barriers to capital formation across 11 sub-Saharan African jurisdictions. It highlights how private markets, policy reforms, fintech innovation, and public–private partnerships can expand investment, strengthen infrastructure financing, and support economic resilience. Its country-level contributors, many of them CFA charterholders, provide deep local expertise that anchors the analysis in practical realities. Transparent, explainable AI is essential in finance, not only for regulatory compliance but also for institutional trust, ethical standards, and effective risk governance, as this report by Cheryll-Ann Wilson, PhD, CFA, explains. While automated tools can assist, the report underscores that human oversight and strong organizational alignment remain indispensable. Joseph Simonian, PhD, addresses the ethical concerns and risks of AI washing (AIW) in finance, providing crucial questions for stakeholders to evaluate managers’ AI claims and ensure transparency, integrity, and the genuine application of AI in investment strategies. The report examines what AIW is, why firms engage in it, and how it affects clients and the broader development of AI. It also offers guidance to asset owners on how to spot both genuine AI use and inflated claims in the marketplace. This report from Mercer and CFA Institute benchmark retirement income systems worldwide using more than 50 indicators. It rates global pension systems, recommending reforms to improve outcomes and participant trust in an era of aging populations and increasing government intervention. In part one of this report, Urav Soni, Olivier Fines, CFA, and Jinming Sun, CFA, examine tokenization’s transformative impact on traditional assets. It is a primer on tokenization in which we look at the technical process: what tokenization is, how it works, its value proposition, and current limitations. We also consider the impact this process could have on various asset classes. In part two, Giovanni Bandi, PhD, Olivier Fines, CFA, and Urav Soni examine the legal and regulatory changes needed for tokenization to grow responsibly while ensuring investor protection and market integrity. The report analyzes global regulatory regimes, international standards, and the need for legal clarity. This first of three modules of an updated CFA Institute Research Foundation guide—ETF Features and Evolving Landscape, written by Joanne M. Hill, PhD, Elisabeth Kashner, CFA, and Dave Nadig—explores the properties, benefits, mechanics, and history of exchange traded funds (ETFs). It describes the factors behind ETF’s exponential growth and evaluates the U.S. ETF landscape. This report from Stephen Deane, CFA, and Ken Robinson, CFA, CIPM, explores how continuation funds provide liquidity in sluggish private markets, examining their growth, benefits, and the conflicts of interest they present. The report provides an unbiased understanding of what continuation funds are, what has driven their dramatic growth, and what they tell us about private markets. It also explains both the heightened conflicts of interest arising in continuation funds and mechanisms to address them. Traditional investment wisdom holds that stocks and bonds tend to move in opposite directions, thus offering investors a natural hedge within balanced portfolios. But this longstanding negative correlation has shown signs of reversal. In this CFA Institute Research Foundation research brief, Friedrich Baumann, Abdolreza Nazemi, and Frank J. Fabozzi, CFA, discuss how machine learning can identify macroeconomic drivers of the shifting stock–bond correlation, offering actionable insights for asset allocation and risk management in this research brief. source

Ask investors how they manage risk, and many will give the same answer: tight stop-losses. Widely viewed as a cornerstone of disciplined risk management, tight stop-losses can sometimes work against investors’ long-term objectives. A stop-loss is a predefined rule that forces the exit of an investment position when its price moves against the investor by a specified amount. Its primary purpose is to limit downside losses on an individual position without requiring continuous monitoring. The rationale seems straightforward. By limiting losses on individual positions, investors believe they are exercising discipline and protecting the portfolio from severe drawdowns. More broadly, the issue touches on three related questions in risk management: the trade-off between precision and robustness, how trade-level rules aggregate into portfolio-level outcomes, and why controls designed for psychological comfort can impair long-term compounding. In practice, many who rigorously apply tight stop-loss rules experience a frustrating pattern: frequent small losses, occasional gains, and little progress toward durable capital growth. This raises a critical question for long-term investors, portfolio managers, and fiduciaries alike: can widely accepted stop-loss practices be structurally counterproductive? And what can they be replaced with? When Trade-Level Discipline Conflicts with Portfolio Outcomes Viewed in isolation, tight stop-losses appear prudent. By defining a small, predetermined loss, investors feel they have transformed uncertainty into something measurable and controllable. Each trade appears safe in isolation, and losses feel disciplined rather than accidental. This provides investors with a level of psychological comfort. Markets, however, do not reward isolated decisions. They reward sequences of decisions made under uncertainty. In trend-based or breakout strategies (e.g., when an asset or stock moves beyond its target price) profitable opportunities rarely develop smoothly. Early phases are often volatile, marked by reversals and false starts. Narrow stop-losses systematically remove investors during precisely this stage, not because the underlying signal is invalid, but because short-term price fluctuations exceed arbitrarily tight thresholds. Once stopped out, re-entry is difficult. Recent losses discourage recommitment to the same trade, and prices may have already moved away from the original entry point. The result is a portfolio that avoids large losses but also misses the handful of outsized gains that drive long-term returns. What looks like good risk control at the trade level can become opportunity destruction at the portfolio level. The Behavioral Appeal and Cost of Tight Stops The case against tight stop-losses has become stronger as markets themselves have changed. Modern markets are dominated by algorithmic trading, fragmented liquidity, and automated execution. Prices now move faster, liquidity is more conditional, and short-term volatility is often driven by order flow dynamics rather than information. In this environment, stop-losses behave differently than they did in slower, dealer-driven markets. The popularity of tight stop-losses reflects their psychological appeal. By defining a small, predetermined loss, investors feel a sense of control. Losses appear disciplined rather than accidental, and regret is minimized, at least in the short term. But this comfort comes at a cost. Tight stop-losses align closely with behavioral biases such as loss aversion and regret avoidance. They optimize for emotional relief rather than economic outcomes. Markets, however, reward sustained exposure to favorable return distributions, not psychological comfort. Risk Management is Also About Time in the Market Discussions about stop-losses often focus narrowly on loss size. But risk is not only about how much is lost when an investment fails, it is also about how long capital remains exposed to opportunity. Exposure persistence matters because capital growth is multiplicative. Long-term performance depends not only on avoiding losses but on remaining invested long enough to participate in sustained price movements. Truncating exposure too aggressively can be just as damaging as taking excessive losses. To examine this trade-off more clearly, it helps to move beyond individual trades and decompose performance into three components: Position size Win rate Payoff ratio (average gain relative to average loss) Stop-loss design directly affects both win rate and payoff ratio — often in opposing directions. What the Evidence Suggests To make these trade-offs concrete, it is useful to examine how stop-loss width affects portfolio outcomes when other variables are held constant. Specifically, consider a simple long-only trend-entry framework applied to a broad equity index. Positions are initiated when prices cross above a moving average. Position size is held constant, while stop-loss thresholds vary from very tight to relatively wide levels. Using daily S&P 500 (SPX) open, high, low, and close prices as a data source, I simulate 500 investors entering at random dates (2000–2005) and compare outcomes under different stop-loss widths and take-profit targets (15%–30%). Each curve summarizes the average result across investors (Figure 1). The objective is not to identify an optimal trading rule or maximize historical returns. Instead, the goal is to examine how stop-loss width structurally influences win rates, payoff ratios, and cumulative capital growth. As stop-losses widen, win rates increase. Trades are given more room to absorb short-term noise, reducing premature exits. Figure 1: Win Rate as a Function of Stop-Loss Width At the same time, when stop-losses are set farther away from the entry price, the average size of losses increases relative to the average size of gains. Figure 2: Payoff Ratio as a Function of Stop-Loss Width When these effects are combined at the portfolio level, cumulative returns plotted against stop-loss width reveal a striking asymmetry: a single peak surrounded by a broad, uneven plateau. Performance deteriorates sharply when stop-losses are too tight but declines only gradually when they are moderately widened beyond the optimal point. This asymmetry is especially evident when higher take-profit targets are considered. Figure 3: Cumulative Return as a Function of Stop-Loss Width Why Robustness Matters More Than Precision The existence of an optimal stop-loss level does not mean it must be identified with precision. Performance is highly fragile on the left side of the return curve, where stop-losses are too tight and small estimation errors, execution frictions, or regime shifts can have outsized negative effects. On the right side, cumulative returns form a broad plateau. Moderate increases in stop-loss width do

Defined contribution (DC) plans sit at the center of the US retirement system. As of the second quarter of 20251, US DC plans held $12.6 trillion, representing approximately 26% of all US retirement assets2. That concentration of capital places a significant fiduciary burden on plan sponsors, who must balance participant outcomes, regulatory expectations, cost pressures, and a rapidly evolving investment and technology landscape. Looking ahead to 2026 and beyond, incremental tweaks are unlikely to be enough. Technology is reshaping how participants engage with their plans. Education is shifting from generic communication to personalized, life-stage–based support. Investment lineups are being tested by higher volatility, new product structures, and renewed debate around alternatives. At the same time, litigation risk and regulatory change continue to redefine what “prudence” looks like in practice. Against this backdrop, plan sponsors are being asked to make more consequential decisions with less margin for error. The following sections highlight priority areas plan sponsors should consider as they evaluate and manage their retirement programs in the year ahead. Advancing Technology The expansion of personalized retirement solutions is increasingly driven by advancements in technology, particularly the rise of AI-powered participant engagement tools. These innovations promise guidance tailored to the unique financial behaviors, goals, and life circumstances of individual participants. A comprehensive retirement plan involves more than just looking at a single retirement plan account. A holistic approach considers the entirety of a participant’s financial life, including spending habits, debt, and savings outside of retirement accounts. This creates inherent scalability challenges. While human advisors offer depth and nuance, they can be costly. Conversely, robo-advisor or automated solutions, though efficient, may risk delivering generic or imprecise advice. There is no one-size-fits-all answer to this trend. Plan sponsors should carefully evaluate available solutions to determine the most appropriate fit for their participants, with particular attention to investment outcomes, service quality, and associated fees. Evolving Education Effectively supporting employees throughout the various phases of their lives and careers is a challenge many organizations face. A thoughtful yet flexible approach can make a meaningful difference, providing employees with the resources they need to grow their financial confidence and expand their knowledge. When employees feel empowered, they tend to be more engaged, collaborative, and productive. Empowerment comes not only from access to resources but from personalization — support that reflects both their career stage and current life circumstances. One-on-one education sessions offer an opportunity for employees to ask questions and explore retirement planning in a way that feels relevant and approachable. Continuity, such as recurring meetings with the same educator and building on previous conversations, can enhance the experience and reinforce progress. Personalized education is no longer considered optional; it is an expectation. Organizations that offer accessible, individualized support are better positioned to meet the evolving needs of their workforce and foster longer-term financial well-being. Resource Assessment When assessing tools and resources offered by recordkeepers, it is critical to evaluate how these platforms enhance participant engagement, foster financial wellness, and promote retirement readiness. Managed accounts and professional advice services represent a growing area of interest. These features provide customized investment strategies tailored to individual participant goals, risk tolerance, and financial situations. Many of these solutions combine algorithmic portfolio management with access to human advisors, enabling participants to make more informed and personalized decisions. Plan sponsors should consider usability, fee transparency, integration with plan data, and quality advice to meet both fiduciary standards and participant needs. In our role as advisor, while we do not recommend these solutions as the qualified default investment alternative (QDIA) for a plan, a well-vetted solution may serve as a valuable tool for participants seeking personalized investment support. Investment Strategy Alternative assets and target date funds The August 7, 2025, Executive Order “Democratizing Access to Alternative Assets for 401(k) Investors,” continues to generate ongoing public debate. While the Executive Order does not mandate that 401(k) plans offer alternative investments, it directs the Department of Labor and the Securities and Exchange Commission to reduce any regulatory barriers that may prevent fiduciaries from considering such options for their plans’ participants. Some commentators are focusing on integrating alternative investments into target date and target risk funds. As such funds are developed and considered by plan sponsors, fiduciaries must employ a prudent and well-documented process when evaluating these investment vehicles. Attention should be paid to how target date and target risk funds manage the traditionally unique characteristics of alternative investments such as limited liquidity, infrequent valuation, and higher fee structures. Active vs. passive fixed income strategies Investors are increasingly pivoting from passive fixed income strategies to active management. The shift may be driven by increased volatility in the bond markets over the last five years, as measured by the Merrill Lynch Option Volatility Estimate (MOVE) Index. Contributing factors driving increased volatility include economic uncertainty, greater geopolitical risk, and interest rate changes. When volatility increases, the stage is set for active bond managers to add value by dynamically adjusting the portfolio’s duration, credit exposure, and sector allocations, as well as locking in durable yields and navigating tightening credit spreads. Alternatively, passive fixed income managers seek to track broad fixed income indexes, which represent the entirety of a bond market’s sector(s) and are most heavily weighted in the most indebted issuers. Passive funds by their nature are unable to adapt quickly to bond market volatility and interest rate changes, limiting their ability to adapt in real time. Regulation and Compliance In 2025, the DC industry witnessed critical developments in litigation and regulation, reshaping the compliance requirements for plan sponsors. The Supreme Court’s 2025 decision in Cunningham v. Cornell University3 shifted the burden of proving exemptions in prohibited transaction cases to the defendants. This decision is expected to increase the number of lawsuits involving DC plans as it makes it easier for plaintiffs to withstand early motions to dismiss. The SECURE 2.0 regulation continues to significantly influence the retirement savings landscape for participants. In addition to the plan design changes spurred by SECURE 2.0, plan sponsors should be

When governments take equity stakes, investors should pay attention. The US sovereign wealth fund (SWF) announced in early 2025 is not a symbolic policy experiment or a passive reserve vehicle. It is emerging as an active investor in strategically critical supply chains, with direct implications for valuation, capital flows, and competitive dynamics across semiconductors, critical minerals, and AI infrastructure. Recent US investments in Intel, rare earth producer MP Materials, lithium developer Lithium Americas, and Canadian miner Trilogy Metals reveal a consistent strategy: deploy state capital to anchor domestic and allied supply chains, then use that signal to crowd in private investment. This approach blends industrial policy with market participation, reshaping how risk is shared between the public and private sectors in industries deemed vital to technological and economic sovereignty. The US sovereign wealth fund is not merely supporting national champions; it is redefining how strategic sectors are financed. For financial analysts and asset allocators, this marks a structural shift. Government balance sheets are becoming an explicit part of the capital stack, altering downside risk, return expectations, and the long-term investment case for companies embedded in the AI and advanced manufacturing supply chain. Anchoring Capital and Crowding In Private Investment The US government’s equity-for-grants investment in Intel illustrates how state capital is being used to reshape strategic markets in three important ways. First, it anchors expectations. By taking a direct equity stake, the government signaled long-term commitment to domestic chip manufacturing, reinforcing Intel’s role as the only advanced semiconductor manufacturer operating at scale on US soil. That signal matters for markets assessing execution risk and the durability of US onshoring efforts in a sector dominated by Taiwan Semiconductor Manufacturing Company and Korea’s Samsung. Second, it constrains strategic exit. In purely commercial terms, Intel faces pressure to retreat from capital-intensive manufacturing and focus on chip design, where returns are typically less volatile. From a supply-chain resilience perspective, however, a manufacturing exit would undermine US efforts to secure domestic capacity in advanced semiconductors. By embedding strategic objectives directly into the capital structure, government equity alters that calculus. Third, it crowds in private capital. Within days of the US investment, SoftBank committed $2 billion, followed by Nvidia’s $5 billion design and manufacturing partnership with Intel. Nvidia’s involvement, in particular, provided validation beyond public support. If the world’s dominant AI chip designer is willing to rely on Intel’s manufacturing capabilities, perceived execution risk falls, strengthening the investment case for additional private capital to follow. Government funding alone, however, is not sufficient to resolve Intel’s structural challenges. State capital does not eliminate execution risk or guarantee competitiveness against more established global foundries. Its role is catalytic rather than comprehensive: to reduce strategic uncertainty, stabilize long-term commitments, and create conditions under which private capital and commercial partnerships can scale. For investors, this distinction matters. The presence of government equity reshapes incentives and risk sharing, but it does not substitute for operational discipline or market validation. The same capital allocation logic is visible in the US government’s investment in MP Materials, the only fully integrated rare earth producer operating in the United States. As with Intel, the objective is not simply to support a domestic company, but to secure a strategically critical segment of the supply chain through direct equity participation. In July, the Department of Defense made a $400 million equity investment in MP Materials under the Defense Production Act. That stake signaled long-term government commitment to domestic rare earth processing and magnet manufacturing, an area where US supply remains heavily dependent on foreign production. As with Intel, the investment was designed to crowd in private capital and stabilize long-term demand. Following the government’s commitment, MP Materials secured $1 billion in private financing from JPMorgan Chase and Goldman Sachs to build its new “10X” magnet manufacturing facility in Texas. The Pentagon is positioned to become the company’s largest shareholder, supported by long-term offtake agreements that commit to purchasing the full output of the new facility. Rare earth magnets are critical inputs for advanced manufacturing, including defense systems, aerospace, and semiconductors, which helps explain why the Pentagon is positioned to become MP Materials’ largest shareholder, with a potential stake of up to 15% and long-term offtake agreements covering the facility’s full output. The same approach is evident in the US government’s investment in Lithium Americas, which is developing the Thacker Pass lithium project in Nevada. Through a combination of a restructured loan and a 5% equity stake in both the company and the project joint venture, the government is embedding itself directly in the capital structure of a resource critical to battery production and advanced manufacturing. As with semiconductors and rare earths, the objective is not short-term financial support but long-term supply assurance. By pairing equity participation with project-level financing, the investment reduces development risk, improves capital access, and increases the likelihood that domestic lithium production reaches commercial scale. The strategy is not confined to US borders. The US government’s 10% equity investment in Canadian mining company Trilogy Metals reflects a broader effort to secure access to critical minerals through allied supply chains, rather than relying exclusively on domestic production. Together, these investments suggest a repeatable model rather than a series of isolated interventions. Supply Chains Without Borders Trilogy Metals’ assets, which include copper deposits in Alaska, require substantial long-term capital to reach production. By taking an equity stake, the US government signals strategic interest while positioning itself to support future development alongside private investors. The investment underscores that supply-chain resilience, in practice, often depends on cross-border capital alignment with trusted partners. Overall, from semiconductors and rare earths to lithium and allied mining assets, the US SWF is operating less as a passive allocator and more as a strategic participant in the capital stack. Taken together, these investments point to a coherent effort to secure critical segments of the supply chain underpinning the US AI Action Plan, titled “Winning the Race,” through direct equity participation and capital coordination. By taking equity positions, pairing them with financing and

Public blockchains are moving toward practical use in regulated finance, supported by leading global institutions. Although early expectations assumed a longer development horizon, advancements in clearing and settlement integration suggest that blockchain-based systems are becoming increasingly relevant to the operational foundations of investment management. Just as SWIFT reshaped global transaction processing in the 1970s, blockchain-based settlement chain may play a similar role for tokenized financial instruments. For institutional allocators, chief investment officers, and risk professionals, these developments signal an inflection point in global banking infrastructure, even as important adoption challenges remain. From Pilot to Proof A key distinction in 2025 is the level of engagement from major financial institutions. Large organizations are collaborating on production-grade blockchain systems rather than conducting isolated pilots. This transition began in November 2023, when JPMorgan and the Monetary Authority of Singapore (MAS) executed the first binding interbank payment on a public blockchain, settling tokenized Singapore dollars on the Polygon network (a public, Ethereum-compatible blockchain optimized for low-cost, high-speed transactions). The transaction demonstrated that public blockchains can support transparent, final settlement for regulated payments which is an important milestone beyond early experimentation. MAS extended this work through Project BLOOM, an initiative to develop a scalable, multi-institution clearing framework for tokenized liabilities, including commercial bank deposits and regulated stablecoins. Designed to operate across both public and permissioned blockchains, BLOOM aims to improve interoperability and support coordinated issuance, clearing, and settlement. These developments suggest that core banking and investment systems will, over time, require the capability to interact with programmable, continuously available, and transparent ledgers as blockchain-based settlement gains traction. This blog explores three critical dimensions of this execution: emerging infrastructure, cross-border liquidity, and real-world adoption. Deterministic Settlement and Emerging Infrastructure The blockchain model introduces deterministic or “atomic” settlement, where payment and receipt occur simultaneously without intermediaries. This structure can reduce counterparty risk, streamline reconciliation processes, and shorten settlement cycles. To support these outcomes, infrastructure enhancements are taking shape, including: Unified token standards: improving interoperability and reducing operational complexity. Smart contract–based settlement: allowing regulatory requirements to be incorporated directly into transaction logic. Agentic payments: triggered automatically based on predefined conditions or real-world data inputs. Together, these features illustrate how tokenized settlement frameworks may modernize aspects of interbank payments while preserving the regulatory oversight and operational discipline required in traditional finance. Cross-Border Liquidity: Toward Continuous, Real-Time Capital Movement One of the most practical applications of blockchain-based settlement is the ability to move capital across jurisdictions in real time. Traditional cross-border transactions often involve multiple intermediaries, foreign exchange timing mismatches, and non-overlapping settlement windows, all of which contribute to liquidity fragmentation and increase operational costs. Potential benefits include: T+0 settlement: reducing settlement risk across time zones and improving cash availability. On-demand FX: enhancing execution certainty and automating aspects of currency management. Reduced capital requirements: including lower reliance on Nostro/Vostro accounts. However, challenges remain. These include data-input reliability (oracle risk), divergent regulatory frameworks across jurisdictions, and the need to embed compliance controls directly into automated workflows. Despite these considerations, the potential efficiency gains for fund managers and corporate treasuries, such as faster settlement, reduced liquidity buffers, and more automated operations, are meaningful. Real-World Adoption: Implications for Fiduciaries As blockchain-based settlement progresses from piloting to early adoption, fiduciaries and investment professionals will need to prepare for hybrid operating environments that incorporate both traditional and on-chain processes. Practical steps include: Assessing readiness: including custodians, fund administrators, and treasury partners. Building expertise: in smart contract risk, data governance, and operational controls. Equipping compliance/operations: manage workflows that interact with programmable settlement rails. While the transition will be gradual, these developments signal a modernizing shift in how financial institutions coordinate payments, data, and liquidity across markets. Looking Ahead: A Tokenized Settlement Environment For investment professionals, passive monitoring of blockchain developments is no longer sufficient. Firms will need to develop literacy around tokenized cash instruments, evaluate vendor readiness, and consider how blockchain-based settlement may affect operational efficiency, liquidity management, and risk oversight. As market infrastructure evolves, so must the fiduciary approach. Blockchain is no longer simply a ledger; it is emerging as part of the settlement process that may support the next generation of financial operations. source

Synthetic risk transfers (SRTs) have recently started raising eyebrows. First introduced in Europe in the early 2000s as a niche form of regulatory capital optimization, they have since evolved into one of the most important tools in modern bank balance sheet management.[1] Since 2016, banks have executed SRTs referencing more than $1.1 trillion in underlying assets, with annual issuance worth tens of billions of dollars. As activity has climbed, and as private credit funds have eagerly absorbed the contracts, regulators and financial journalists have grown increasingly vocal about their concerns. The question is whether this scrutiny is warranted. What are SRTs? SRTs are a form of synthetic securitization, often called “on-balance-sheet securitization,” in which a bank offloads a portion of a loan portfolio’s credit risk through a contract, typically a credit derivative or guarantee, without fully selling or removing the loans from its balance sheet. In Europe, where the market was born, the investor typically acquires mezzanine loan risk by selling (writing) a credit default swap (CDS) and, in the United States, through a credit-linked note (CLN). The primary protection sellers are public and private credit funds, which are attracted by competitive yields, access to high-quality diversified credit exposures, and the ability to tailor risk via tranches. Banks pay for this protection because it allows them to transfer part of their loan risk to investors, which in turn reduces their regulatory capital requirements and frees up capital for new lending at a lower cost than raising equity. The originating bank retains the first loss (junior) tranche[2]. The investor, who does not have specific knowledge of the pool’s underlying loans (only generic details like maturity, ratings, and industry) earns a fixed premium or coupon. If defaults in the portfolio occur, the bank absorbs the first loss while the investor covers losses up to the mezzanine tranche limit. The bank retains the client relationship, loan administration, and interest income to maintain “skin in the game,” which is a regulatory requirement. But since it shed a portion of the portfolio risk, the bank is permitted to reduce capital against the loans. SRTs are typically engineered for capital relief and risk management. On the former, Basel capital rules are widely viewed as excessively penalizing certain assets. For example, auto loans require disproportionately high capital despite extremely low default rates. SRTs allow banks to reduce risk-weighted assets (RWAs) by 50% to 80% in many transactions. In addition, by transferring risk without shrinking their balance sheets, banks can reduce geographic, borrower, or sector concentration risk. Where SRTs Are Growing and Why European banks remain the most active issuers, accounting for roughly 60% to 70% of global issuance. The market has its roots in Europe because it is a heavy bank-centric loan market with a stringent interpretation of post global financial crisis (GFC) capital regulations. A clear supervisory framework and a deep investor base in Europe have also supported its growth. Each SRT transaction undergoes European Central Bank/European Banking Authority review, and recent regulatory rules have rewarded high-quality structures with more efficient capital treatment. In the United States, following the Federal Reserve’s 2023 guidance recognizing direct CLN structures as eligible for capital relief, banks quickly entered the market. The United States now represents nearly 30% of global deal flow. In Asia, institutions in markets such as Australia and Singapore have experimented with SRT-like structures, often under different labels or pilot programs, though volumes are considerably smaller. Born of Overregulation, Yet Heavily Scrutinized Despite their benefits, SRTs continue to draw significant regulatory scrutiny. Supervisors are most focused on rollover risk, investor concentration, and back-leverage, all of which can become more pronounced as issuance grows. First, rollover risk arises because SRTs usually mature in three to five years, while the underlying loans often remain on the balance sheet for much longer. If market conditions worsen when an SRT comes up for renewal, banks may struggle to replace the protection, leading to a sudden increase in RWAs and potential pressure to deleverage. Second, this risk is amplified by investor concentration: a relatively small group of private credit funds dominate the mezzanine market. Their outsized role means that the entire SRT ecosystem depends on the willingness of a handful of players to refinance. In a stressed market, these funds could demand sharply higher spreads or pull back altogether, leaving banks with limited alternatives. Third, regulators are attuned to back-leverage. Under Basel III/IV and regional rules (e.g., the European Union’s Capital Requirements Regulation), a bank must prove that a material share of the portfolio has been transferred, that the transfer is real, and investors can be protected even under stressed market conditions. By requiring evidence of material risk transfer and bank skin in the game, the rules aim to prevent regulatory arbitrage through circular transactions and ensure that SRTs strengthen, rather than weaken, the resilience of the financial system. Finally, concerns about opacity persist. While SRTs are far more standardized and transparent than pre-2008 collateralized debt obligations, their bespoke nature and limited public disclosure still makes some observers uneasy about assessing the true distribution of risk. Eye on the Ball For banks, SRTs have become a strategic lever to manage capital, mitigate credit exposure, and keep lending volumes intact as the regulatory environment tightened after the GFC. The public skepticism that surrounds SRTs is, in my opinion, a result of PTSD from the financial crisis. The main difference this time is that moral hazard is meaningfully lower than in pre-2008. Banks retain first-loss exposure, investors hold real risk, and the overall market remains relatively small. Rather, SRT issuance is a response to overly conservative risk weights that, in the years following the crisis, pushed banks to limit lending. It is a rational approach to redistributing risk and freeing capital for investment, especially in Europe, where banks are by far the dominant player. To institutional investors, SRTs offer potentially differentiated credit exposure and compelling yield. [1] SRTs are also referred to as “Significant Risk Transfers.” The significant part refers to meeting regulatory criteria (like Basel rules) to get capital relief (reducing

The Financial Restructuring Tool Set: How to Fix Your Broken Balance Sheet. 2025. Mike Harmon. Columbia Business School Publishing. CFA charterholders might be startled by a statement that appears near the end of The Financial Restructuring Tool Set, by Mike Harmon: “Not once have any financial analysts in the history of time correctly forecasted the future cash flows of a business.” Reading on, charterholders will be relieved to find that the author is by no means criticizing their performance. His point is merely that it would be a mistake to regard financial modeling as a crystal ball. The outputs will never wind up being completely accurate, but the unquestionably valuable process enables analysts to “‘try on’ different capital structures under different scenarios,” says Harmon. Investors who specialize in distressed securities might cringe at Harmon’s mention of their characterization in some quarters as “bottom feeders.” He quickly notes, however, that “in nature, bottom fishers play a productive role in the ecosystem.” Lest these operators, also labeled “vultures,” feel totally absolved by that remark, Harmon proceeds to list certain ways in which they subtract rather than add value in restructuring situations. One example is premature default, which can occur when a distressed investor that seeks to obtain ownership of a company’s equity proves less willing than conventional investors to cooperate with a management that just needs a little more time to work out the company’s problems. Evenhanded to the end, Harmon also lists distressed investors’ positive impacts, such as infusing cash into viable companies that desperately need some but find conventional debt and equity investors less willing to provide it. Harmon maintains readers’ interest at a high level by sprinkling the book with facts and observations that are anything but mundane. He reports that recovery rates on leveraged loans have declined as a result of covenant-lite structures increasing from 4% of deals in 2008 to 96% in 2022. Over the 1984 to 2017 period, he adds, 20% of companies that emerged from Chapter 11 bankruptcy reorganization subsequently filed for bankruptcy at least one more time, with one company filing five times. Harmon also emphasizes that the company valuation determined by a financial restructuring plan does not necessarily equal the company’s true valuation. Rather, it is the product of high-stakes negotiations by the company’s various classes of creditors. He also points out that “big boy letters,” used by investors who receive material nonpublic information to get around securities laws prohibiting them from trading under such conditions, are not legally recognized as legitimate and remain largely untested in litigation. The Financial Restructuring Tool Set presents, in 352 pages, a comprehensive account of how distressed companies go about reducing the burden of their debts and other liabilities, both inside and outside bankruptcy. It covers such techniques as 363 asset sales, contract rejection, debt-for-equity swaps, and more. The book’s primary focus is practices in the United States, but one chapter is devoted to bankruptcy codes and practical experience in the United Kingdom, France, China, and Japan. An Oaktree Capital Management alumnus who now advises and invests in small- to medium-sized companies at Gaviota Advisors, Harmon is abundantly equipped to provide valuable insights even to experienced distressed debt practitioners. Readers who are less acquainted with the field may initially be daunted by a substantial volume of jargon, including such colorful terms as “zombie,” “freebie basket,” “blacklist” (not in its earlier labor practices sense), and “bondmail.” These are in addition to numerous acronyms unknown to neophytes, such as VERBO, NGRS, KERP, and ICERP. Harmon does an excellent job of explaining such unfamiliar phraseology, tacking on a 10-page Jargon Guide after the main text. The book’s Notes testify to his diligent study of scholarly research on his subject. Harmon makes a useful contribution to the field with suggestions for correcting the flaws in the existing U.S. insolvency regime. He maintains, for example, that too many small companies liquidate because many of the costs of reorganization in bankruptcy are fixed and too high for them. Potential solutions include creating greater awareness of Subchapter V’s pathway to lower-cost reorganization and using artificial intelligence to streamline bankruptcy-related documents as a means of further reducing costs. Just as analysts never hit companies’ financial projections on the nose, book authors rarely nail every single reference. The Financial Restructuring Tool Set illustrates that point by crediting baseball great Yogi Berra with this comically paradoxical statement: “Nobody goes [there] anymore. It’s too crowded.” Publishing house editors ought to know that attributions can easily be checked in the indispensable Quote Investigator website, which in this case reports that Berra appropriated the joke, but that its antecedents date back to 1882. Elsewhere, Harmon implies that BlackRock Chief Investment Officer Bob Doll is the originator of “No one rings the bell at the bottom.” It is actually an old Wall Street adage that I heard in the late 1970s. Such minor lapses do not alter the fact that The Financial Restructuring Tool Set is up to date and authoritative. It facilitates gaining an understanding of the many techniques for resolving financial distress with case studies involving such prominent companies as Chrysler, Frontier Communications, and J.C. Penney. Even practitioners interested in the topic who do not intend to read the book cover to cover should own it as a reference work that can be navigated through its highly detailed index. source

After years of low yields and rising macro volatility, investors are taking renewed interest in insurance-linked securities (ILS) for their very low correlation with traditional financial markets. Despite event-driven volatility, the first half of 2025 reaffirmed the market’s strength and growing scale. According to mid-year industry data, ILS issuance reached $17.2 billion across nearly 60 transactions, making 2025 the second-largest year in the market’s history, with half the year still to go. The total market size has now surpassed $56 billion, having expanded by more than 75% since 2020. This year alone has seen 10 new issuers and three wildfire bonds, signaling growing investor confidence alongside supportive market dynamics. Drivers of Growth The surge in issuance is being fueled by both sides of the equation: strong demand from sponsors seeking risk transfer and an equally strong appetite from investors looking for diversification. Elevated collateral yields and a wave of maturing bonds have created liquidity to reinvest. At the same time, diversification within the market has deepened, with new sponsors, new perils, and more sophisticated deal structures emerging. Recent issuances illustrate this breadth. US hurricane exposures still dominate, but there has also been $182 million of coverage for U.K. flood, $105 million for Canada earthquake and severe convective storms, and $100 million for French terrorism. Such variety highlights the maturing nature of the market and its widening relevance across geographies and perils. Performance and Investor Experience Performance has been another bright spot. The Swiss Re Global Cat Bond Index delivered a 9.89% return for the first ten months of 2025, even as global markets contended with tariffs, currency volatility, and other macro shocks. Looking further back, the consistency of returns stands out: since 2002, catastrophe bonds have produced positive monthly results nearly 90% of the time. Interestingly, inflation — typically a challenge for insurers — can have an indirect positive effect on the ILS market. Higher insured values at risk increase the need for risk transfer, which widens spreads and can enhance investor returns. Additionally, most catastrophe bonds pay floating-rate coupons tied to Treasury money market funds, meaning higher interest rates can directly benefit returns. For multi-asset allocators, the consistent return pattern of catastrophe bonds has made them a compelling complement to traditional fixed income in high-rate environments. Risk and Resilience The start of 2025 underscored the ever-present risks inherent in catastrophe-linked investments. The devastating wildfires in Los Angeles caused approximately $40 billion in insured losses, the largest wildfire-related loss on record. Severe convective storms across the United States added billions more in claims. More recently, Hurricane Melissa triggered a 100% payout of a $150 million World Bank Catastrophe Bond for Jamaica. Events like these are reminders that cat bonds are not risk-free. However, they also demonstrate the market’s resilience. While some structures were affected, in both cases the broader system absorbed the shocks without widespread disruption. The key lies in understanding and modeling the underlying risks accurately. Investors must know the exposures they are assuming, but they should also expect fair compensation through higher spreads and premiums as those risks increase. Institutions tend to access the market through specialist funds, with managers leveraging deep catastrophe modeling expertise to construct diversified portfolios. Re/insurers are well positioned in this space due to their access to proprietary data and scientific teams capable of analyzing complex risk factors. Institutional Adoption What was once a niche investment is increasingly finding its way into mainstream institutional portfolios. An open question remains: how should investors categorize ILS exposure? Some treat it as part of alternative fixed income, others within hedge fund allocations, and some view it as a standalone diversifier. Most institutions we speak to would allocate around 1% to 3% of portfolios to ILS. While that may seem modest, even small exposures can meaningfully enhance diversification and income. Modeling suggests that allocations of up to 10% could further improve portfolio metrics, though investors remain cautious and deliberate given the asymmetric risk profile and event-driven nature of returns. Looking Ahead The outlook for ILS remains constructive. Risk exposures are growing due to inflation, urbanization, and climate-related pressures, all of which increase the need for capital to absorb catastrophic losses. At the same time, innovation is expanding the range of available structures, including index-based solutions and parametric products that offer faster payouts and more efficient risk transfer. Continued institutionalization is also likely. As data quality and model transparency improve, investor confidence in the asset class should deepen. However, success will depend on maintaining rigorous risk assessment and disciplined portfolio construction. Catastrophe bonds and other insurance-linked securities are evolving from a specialist niche into a recognized source of diversification. Their appeal lies in their independence from economic cycles and their potential to provide steady returns even when traditional markets are under stress. For investors searching for uncorrelated returns, ILS can play a valuable role in portfolio resilience. source