CFA Institute

Some high yield market participants claim the days of +1,000-basis-point (bps) spreads are behind us. Citing improved credit quality, aggressive Fed intervention, and a persistent shortage of supply, they argue that future recessions will not push high yield spreads to the extremes seen in 2020 or in prior downturns. But a closer, data-driven examination reveals that these arguments are overstated or flawed. In fact, conditions remain firmly in place for spreads to breach +1,000 bps again in the next recession, making it a mistake to dismiss that possibility too easily. During the most recent US recession, the risk premium over default-risk-free Treasury bonds tripled in a period of less than three months. The ICE BofA US High Yield Index’s option-adjusted spread versus (OAS) widened from +360 bps on December 31, 2019 to +1,087 bps on March 23, 2023. Over that interval, the high yield index posted a dispiriting -20.56% total return, which compared unfavorably with fixed income alternatives such as the investment grade ICE BofA US Corporate Index at -9.97% and the ICE BofA US Mortgage Backed Security Index at 1.71%. With that experience still fresh in institutional investors’ minds, some high yield managers are understandably talking down expectations that the OAS will widen to as much as +1,000 bps during the next recession. Times have changed, they say, since the recessions of 1990 to 1991 and 2001, when the spread also surpassed +1,000 bps. Barring an economic contraction as severe as the 2008 to 2009 Great Recession, a repeat of that rough patch’s +2,147 bps peak spread is unlikely. Institutional gatekeepers quite reasonably expect something more than a mere assertion that things will be different this time around. Accordingly, high yield marketers have devised three rationales for proclaiming that the OAS will max out at 600 to 800 bps in the next recession: Better high yield index credit quality than in the past. Fed intervention aimed at keeping the spread well below past maximum levels — the “Fed put” hypothesis. Effect of a persistent shortage of supply of high yield bonds. All three are plausible on their face, but none hold up well upon close examination. Better Quality Than in Past This argument’s underlying notion is correct. Bonds with the top-tier speculative-grade rating, BB, have narrower spreads and widen out less during recessions than those rated lower: B, CCC, CC, and C.[1] Therefore, if the high yield index is more concentrated in BBs than in a given past recession, it follows that in a future recession of equivalent magnitude the index’s overall spread should widen by less than in the earlier instance. The ICE BofA US High Yield Index does indeed have a larger BB component than in the past, defining the past as the average from the inception date of the index’s rating subindexes, December 31, 1996 through December 31, 2024. The BB share of total market value averaged 44.53% over that period. By contrast, the figure stood at 53.55% on April 17, 2025, the observation date I used in a recent analysis. The April 17, 2025 BB share was only slightly higher than on March 23, 2020, the date of the maximum high yield OAS during the most recent recession. Basing the analysis on the most recent recession avoids comparability problems that could arise from changes that may have occurred in the rating agencies’ standards over a longer period. For each rating category in the index, I calculated a weighted-average OAS based on the spreads of the bonds within the category. Then, assuming a recession of comparable quality to the 2020 downturn, I applied the weighted-average OAS to the April 17, 2025 ratings mix. The projected index spread came to not +600 bps or +800 bps, but +1,093 bps. That projection’s small excess over the last recession’s +1,087 bps maximum was attributable to higher concentrations in the two lowest rating categories, CC and C, than on March 23, 2020. The key point, however, is that the index’s much talked about improvement in ratings mix is not substantial enough to prevent a widening to +1,000 bps or more during the next recession. Fed Put Although the Fed’s legislative mandate is to maintain stable prices consistent with full employment — rather than to manage the spread-versus-Treasuries on high yield bonds — the central bank does pay attention to whether debt financing is available to companies that lack top credit ratings. The historical record displayed in the table shows, however, that intervention in the form of an initial reduction of the Fed funds rate does not stop spread-widening dead in its tracks. Fed easing may prevent the high yield spread from widening as much as it would have without the intervention, but not, judging by experience, from widening to at least +1,000 bps. Persistent Supply Shortage Like the others, the persistent supply shortage rationale contains a kernel of truth. On our April 17, 2025 observation date, the ICE BofA US High Yield Index’s total face amount was $1.4 trillion, unchanged from 10 years earlier. To a substantial extent, new issuance has migrated to leveraged loans and, in the last few years, to private credit. A large new issue volume is required just to keep outstandings from declining as bonds mature, get called, default, and rise to investment grade. Stagnant supply in the face of growing investable wealth is a recipe for chronic overvaluation that could curtail spread-widening in a recession. On the face of it, the supply-shortage argument is supported by the recent history of actual spreads on the high yield index, compared with fair values estimated by my econometric model of the spread. Historically, the actual spread frequently swung back and forth from cheaper then to richer and then fair value. From October 2022 through March 2025, however, the actual spread was less than fair value in every single month, in some cases by upwards of 200 basis points. It would not seem unreasonable, therefore, to contend that when fair value next widens to +1,000 bps, the actual spread

Artificial intelligence is transforming how investment decisions are made, and it is here to stay. Used wisely, it can sharpen professional judgment and improve investment outcomes. But the technology also carries risks: today’s reasoning models are still underdeveloped, regulatory guardrails are not yet in place, and overreliance on AI outputs could distort markets with false signals. This post is the second installment of a quarterly reflection on the latest developments in AI for investment management professionals. It incorporates insights from a team of investment specialists, academics, and regulators who are collaborating on a bi-monthly newsletter for finance professionals, “Augmented Intelligence in Investment Management.” The first post in this series set the stage by introducing AI’s promise and pitfalls for investment managers, while this post pushes further into risk frontiers. By examining recent research and industry trends, we aim to equip you with practical applications for navigating this evolving landscape. Practical Applications Lesson #1: Human + Machine: A Stronger Formula for Decision Quality The fusion of human and machine intelligence strengthens consistency, which is a key marker of decision quality. As Karim Lakhani of Harvard Business School summarized: “It’s not about AI replacing analysts—it’s about analysts who use AI replacing those who don’t.” Practical Implication: Investment teams should design workflows where human intuition is complemented, not replaced, by AI-driven reasoning aids, ensuring more stable decision outcomes. Lesson #2: Humans Still Own the Uncertainty Frontier Current limitations of large reasoning models (LRM), which can think through a problem and create calculated solutions, mean it is up to investment managers to decipher the impact of less structured imperfect markets. Frontier reasoning models collapse under high complexity, reinforcing that AI in its current form remains a pattern‑recognition tool. While the new generation of reasoning models promise marginal performance improvements such as better data processing or forecasting, the results do not live up to the promises. In fact, the less structured a market phenomenon, the more failure-prone the models’ outcomes. Practical Implication: Transparency around benchmark sensitivity and prompt design is vital for consistent use in investment research. Lesson #3: Regulators Enter the AI Arena Supervisory authorities are piloting Generative AI (GenAI) for process automation and risk monitoring, offering case studies for industry adoption. Regulators are quickly identifying a bevy of vulnerabilities pertaining to AI that could negatively impact financial stability. A report issued by the Financial Stability Board (FSB) which was established after the 2008 financial crisis to promote transparency in financial markets, pointed out a number of potential negative implications. GenAI can be used to spread disinformation in financial markets, the group said. Other possible issues include third-party dependencies and service provider concentration, increased market correlation due to the widespread use of common AI models, and model risks, including opaque data quality. Cybersecurity risks and AI governance were also on the FSB’s list. To wit, regulators are on alert, working on their own integration of AI applications to address the systemic risks explored. Practical Implication: Adaptive regulatory frameworks will shape AI’s role in financial stability and fiduciary accountability. Lesson #4: GenAI as a Crutch: Guarding Against Skill Atrophy GenAI can boost efficiency, particularly for less-experienced workers, but it also raises concerns about metacognitive laziness, or the tendency to offload critical thinking to a machine/AI, and skill atrophy. Structured AI‑human workflows and learning interventions are critical to preserving deep industry engagement and expertise. GenAI firm Anthropic’s analysis of student AI use shows a growing trend of outsourcing high-order thinking, like analysis and creation, to GenAI. For investment professionals, this is a double-edged sword. While it can boost productivity, it also risks atrophy of core cognitive skills critical for contrarian thinking, probabilistic reasoning, and variant perception. Practical Implication: Investors must ensure that AI tools do not become a crutch. Instead, they should be embedded in structured decision-making and workflows that preserve and even sharpen human judgment. In this new environment, developing metacognitive awareness and fostering intellectual humility may be just as valuable as mastering a financial model. Investing in AI literacy and piloting AI‑human workflows that preserve critical human judgment will serve to foster and perhaps amplify, cognitive engagement. Lesson #5: The AI Herd Effect Is Real Being contrarian in seeking alpha means understanding the models everyone else is using. Widespread use of similar AI models introduces systemic risk: increased market correlation, third-party concentration, and model opacity. Practical Implication: Investment professionals should: Diversify model sources and maintain independent analytic capabilities. Build AI governance frameworks to monitor data quality, model assumptions, and alignment with fiduciary principles. Stay alert to information distortion risks, especially through AI-generated content in public financial discourse. Use AI as a thinking partner, not a shortcut—build prompts, frameworks, and tools that stimulate reflection and hypothesis testing. Train teams to challenge AI outputs through scenario analysis and domain-specific judgment. Design workflows that combine machine efficiency with human intent, especially in investment research and portfolio construction. Conclusion: Navigate the AI Risk Frontier with Clarity Investment professionals cannot rely on the overly confident promises made by artificial intelligence firms, whether they come from LLM providers or related AI agents. As use cases grow, navigating emerging risk frontiers with mindfulness of what they can and cannot add in improving the investment decision quality are of paramount importance. Appendix & Citations: Fagbohun, O., Yashwanth, S., Akintola, A. S., Wurola, I., Shittu, L., Inyang, A., . . . Akinbolaji, T. (2025). GreenIQ: A deep search platform for comprehensive carbon market analysis and automated report generation. arXiv. Handa, K., Bent, D., Tamkin, A., McCain,  ., Durmus,  ., Stern, M., . . . Ganguli, D. (2025, April 8). Anthropic Education Report: How university students use Claude. Retrieved from Anthropic: https://www.anthropic.com/news/anthropic-education-report-how-university-students-use-claude van Zanten, J. (2025). Measuring Companies’ Environmental and Social Impacts: An Analysis of ESG Ratings and SDG Scores. Organization and Environment. Brynjolfsson, E., Li, D., & Raymond, L. (2025). Generative AI at Work. The Quarterly Journal of Economics. Pérez‑Cruz, F., & Shin, H. (2025). Putting AI agents through their paces on general tasks. Bank for International Settlements (BIS). Ren, Y., Deng,

The Four Job Categories When it comes to artificial intelligence (AI), many anticipate a rosy future with various sources of new revenue, reduced expenses, and ultimately increased profits. Others worry about the jobs that might be lost to machines. So, does AI steal human jobs? Or put another way, should we replace humans with AI? Before answering these questions, we first need to categorize different types of jobs. I’ve devised a table below that divides them into four categories based on yes or no answers to two questions. The four cells describe who or what should perform a particular task that falls into that specific category. Jobs can be described as roles, and the tasks are the problems that need to be solved within those roles. To be sure, the table is simplified for illustrative purposes and not mutually exclusive, collectively exhaustive (MECE). That said, it should give financial, technology, and management professionals plenty of food for thought. Do we have (almost) zero- or low-tolerance for any error in a job? Yes No Can we solvethe problem in an automated mannerbased only on objective factsand simple rulesand principles? Yes No 1. Traditional Computer Programs and Other Technologies Mainly for Process Automation 3. Humans 2. AI, Traditional Computer Programs, and Other Technologies 4. AI and Humans 1. Traditional Computer Programs and Other Technologies Mainly for Process Automation This category includes but is not limited to certain trading, money wiring, settlement, clearing, and other operations at banks, trading venues, and investment management firms. In a strict sense, humans often have to be involved for technical, economic, and legal and regulatory reasons, among others. Some humans might resist streamlined processes without human intervention all the way down the line. They will be inclined to cling to jobs that can be done by machine. 2. AI, Traditional Computer Programs, and Other Technologies Some jobs that may fall into this category include recommending web content or applications based on user preferences and past web or app behavior. AI results can leave room for interpretation. The consequences of decision making are not that critical or significant. Even traditional computer programs and other technologies can be applied. Results from such applications often provide more and better results than humans and at scale. 3. Humans The jobs of corporate executives, politicians, or any other person who makes decisions based not only on objective facts and simple rules and principles but also on long-term perspectives and human values are among those in this category. Decision-making processes are usually one-off, non-automatic, and often have irreversible consequences. Human decisions are not necessarily based only on short-term, economic, and rational reasons. What look like knee-jerk or irrational responses at first glance may in fact be based on subtle calculations. Moreover, humans can have subjective opinions, applying varying time scales, and acting on complicated rules and principles that cannot be reduced to relatively simple algorithms. Unlike machines, humans can take responsibility for a result and understand the legal and ethical obligations. 4. AI and Humans This is an area where humans and AI (machines) compete for the job. Humans can be replaced by machines if all the following conditions are met: Machines offer a better solution than humans based on costs, output quantity and quality, and so forth. There are no legal restrictions. It is appropriate according to normal social conventions and there is no ethical obligation to do otherwise. In other cases, humans and machines can work together. We can solve problems by referring to the (past) data and envisioning an often complex future state. Humans should be good at the latter: We are “teachers” who know and can define what is a correct or incorrect answer, or future state. We can also assume responsibility for decision making and its results. AI has mastered many things and solved various problems standardized by human beings, but in other ways it can be outthought by a toddler. It requires frequent human intervention. Stock selection, portfolio management, client services, sales, and other jobs with human interaction can fall into this category. The artistic realm is another area where this human-machine collaboration has worked well, in the form of, say, AI-assisted computer graphics. The Solution: Focus on What Only Humans Can Do and Do Well To avoid losing our jobs to machines, we humans need to identify and focus on what only we humans can do and excel at. We need to remember that only humans can define each job, what it does or does not require, and whether it can be assigned to machines. Dividing jobs into sub-jobs and then categorizing these into these groups is something that only humans can do and should be good at. Furthermore, humans can transform a job, redefining it and moving it from one category to another. This way, humans can and should maximize the value of machines so that we can focus on more meaningful, productive, and enjoyable activities. In the end, humans have feelings: These are often unstable and seemingly irrational. Machines, thankfully, do not have them and will do only the tasks that we humans can assign them. Of course, AI — “machines” — are only as intelligent as the data it learns from, the models and techniques that are deployed, and the humans that are associated with it. Raw data itself, data cleaning, and knowledge and experience about how the data is generated, collected, processed, stored, and analyzed, do matter. Selecting an appropriate model is also important as is understanding the objective of the analysis. The role of even subjective expert human judgment based on knowledge and experience is critical as well. For various legal, ethical, and economic reasons, not all human jobs should be replaced by machines. But humans equipped with machines, by using a combination of AI and human intelligence, will replace some jobs. AI may transform our businesses, but it is not the existential threat to human jobs that many of us fear. Rather, those human teams that successfully adapt to

Biologists have long debated the mysterious role of mate selection in evolution. Investors can learn much from their findings. Mate selection, after all, is the competitive process by which scarce reproductive resources are allocated.[1] And what is financial selection, or investing, if not the competitive process by which scarce productive resources are allocated? Thus, mate selection and financial selection are similar evolutionary processes.[2] But first, what is financial selection? We define it as “any capital allocation decision.”[3] Capital allocators (i.e., investors) are thus the agents of financial selection. They are the filter through which capital passes, and their preferences dictate who gets capital and who doesn’t. Accordingly, seekers of capital adapt to their preferences. The more widespread a preference among investors, the more capital its satisfaction will unlock and the more influential the preference will be. This evolutionary process of adaptation is financial selection. It does not shape our commercial world alone, however. It operates alongside consumer selection. Consumers select products with superior value propositions. A product must have differentiating traits, or “premes,” to be superior. Firms that produce such a product tend to be more profitable, grow faster, and survive longer. They are fit, and their differentiated products are copied by less-fit competitors. Consumer selection shapes the investor preferences behind financial selection much like natural selection shapes the mating preferences behind mate selection. Mating preferences at odds with natural selection, for example, produce unfit offspring unable to survive. Likewise, investor preferences at odds with consumer selection finance unfit firms producing inferior products. Thus, “as [mate] selection is to natural selection,” I concluded elsewhere, “financial selection is a byproduct of, and an aid to, consumer selection.” It is, in other words, “nested within consumer selection.” But is this always true? Perhaps not. As we will see, biologists are unsure whether mate selection is always nested, and under certain conditions it may only be quasi-nested. If the same is true of financial selection, the implications are material. Nested or Not? When Selection Favors Fashion over Fitness The evolutionary role of mate selection is an old mystery. Evolutionary biologist Charles Darwin thought mate selection is not necessarily subservient to or contained within, that ruthless economizer he called natural selection.[4] It can become unnested and produce harmful traits with negative survival value. Alfred Wallace, Darwin’s contemporary, disagreed. He thought mate selection must be subservient to natural selection since mating preferences are themselves subject to natural selection.[5] The peacock’s elaborate train is a classic case that divided the two camps. Such an elaborate train must make the peacock more obvious to predators and therefore must harm its survival, said Darwin. Wallace disagreed. He said it must somehow signal survival fitness.[6] Wallace’s view has since been vindicated in part. Peafowl, as the species is known, suffer from parasitism, but immune resistance is hard for females, or peahens, to observe.[7] Peahens can, however, observe an elaborate train, and only those males, or peacocks, with strong immune resistance can bear the cost of such an ornament.[8] In this way, the peacock’s train is an honest signal of survival fitness, but its size and vibrance seems like overkill to many. Why, then, has natural selection allowed mate selection to favor such an extreme ornament? British mathematician, statistician, biologist, and geneticist Ronald Fisher provided an explanation – the “sexy son hypothesis.”[9] Once a preference for elaborate trains is dominant among peahens, the choosy sex, every female must select males with elaborate trains to have sexy sons.[10] Mom’s genes won’t pass to later generations if her sons survive but don’t seduce.[11] The mating preferences of peahens therefore have a powerful herding tendency thanks to the “sexy son” effect. This sparked an evolutionary arms race among males, or peacocks, whose trains became ever more elaborate in their effort to seduce.[12] The peacock’s train evolved towards a costly extreme, however, as the “sexy son” effect swamped the honest signal effect.[13] At this point, “[the] sexy son effect will continue even if the peacock’s ornaments themselves are giving no reliable information about the quality of the male in other respects. Once [a] female preference is established, the females are slaves to fashion. They dare not choose differently lest they have unsexy sons.”[14] In fact, John Maynard Keynes foreshadowed this idea when he observed,  “[P]rofessional investment may be likened to those newspaper competitions in which the competitors have to pick out the six prettiest faces from a hundred photographs, the prize being awarded to the competitor whose choice most nearly corresponds to the average preferences of the competitors as a whole; so that each competitor has to pick, not those faces which he himself finds prettiest, but those which he thinks likeliest to catch the fancy of the other competitors, all of whom are looking at the problem from the same point of view.”[19] (emphasis added) We now have a quasi-nested explanation for the peacock’s train. Natural selection allows this mating preference to persist so long as the trait’s positive reproductive value outweighs its negative survival value.[15] It is, however, a suboptimal outcome. The species’ mating market is stuck in an evolutionary disequilibrium.[16] It is a market failure, so to speak, where “[a] mating preference has driven the entire species down a hazardous evolutionary path.”[17] Weighing vs. Voting: Why Investor Preferences Can Distort Value If mate selection is not always subservient to, or “nested” within, natural selection, then surely the same is true of financial selection. It may only be “quasi-nested” within consumer selection under certain conditions. And why not? If “reproduction of the sexiest [can] trump survival of the fittest,” as Matt Ridley says, then promotion of the popular can trump survival of the economical.[18] Thus, financial selection may cause firms to evolve objectively harmful traits with negative value.  Most investors, in other words, are trying to anticipate their peers’ preferences, not a firm’s value, since preferences dictate money flows and money flows dictate short-run stock prices. And, as we all know, poor short-run performance damns money-raising. Like mating preferences in peahen,

Purpose and Profit: How Business Can Lift up the World. 2022. George Serafeim. HarperCollins Leadership. In Purpose and Profit: How Business Can Lift up the World, George Serafeim, the Charles M. Williams Professor of Business Administration at Harvard Business School, provides a roadmap and best practices for businesses to achieve the long-term competitive advantage that can emerge when they prioritize environmental, social, and corporate governance (ESG) goals, such as climate change mitigation, diversity and inclusion, and sustainability, alongside the pursuit of profit. The importance of ESG factors has been accelerated by the COVID-19 pandemic, making this book essential reading for all investors. Once considered “soft” and outside the scope of what a serious investor should be thinking about, ESG issues are now not only important in society but also critical in business. Today, it is incumbent on asset managers to incorporate all long-term drivers of value, including ESG factors, as part of their fiduciary duty to investors. Over the past decade, Serafeim has found that purpose-driven firms that improve performance on material ESG issues outperform their competitors by more than 3% annually in terms of stock returns, based on a sample of more than 2,300 companies. In addition, based on a sample of 3,078 global companies, the author found that firms that responded to the COVID-19 pandemic with significant efforts to protect customers, employees, and suppliers outperformed their peers by about 2.2% in the one month covering the March 2020 stock market collapse. Over the past five decades, since Milton Friedman argued in 1970 that the “business of business is business” and his agency theory was widely accepted, there has been an evolution on the importance of ESG issues. The stakeholder theory, which emerged in the 1980s, provided support for the ESG movement. Serafeim found that in the 1990s, firms with strong ESG performance received more pessimistic analyst recommendations than peer companies, because their sustainability initiatives were considered a waste of shareholder resources. By the end of 2008, however, this correlation was zero, and by the mid-2010s, firms with strong ESG performance attracted more positive analyst recommendations than other companies. The UN Principles for Responsible Investment (PRI) started in 2005, and by 2020, assets under management (AUM) by PRI signatories surpassed $100 trillion. This evolution included financial education; CFA Institute started to incorporate ESG topics into its curriculum in 2018 and more recently created the CFA Institute Certificate in ESG Investing program.  ESG investing started out with negative screening, which proved to have minimal positive impact. According to the Serafeim, companies need to understand which ESG issues are financially material in their industry and how to focus on them. Firms that improve their performance on nonmaterial ESG issues in their industry exhibited little performance differential from their competitors. Financially material ESG issues for commercial banks include access to finance for underserved populations, customer data privacy, incorporation of environmental risks in loans originated, and strong anti-corruption practices. For agricultural product companies, material ESG issues include greenhouse gas emissions, water management, the physical safety of employees, and crop-related risks emerging from climate change. Focusing on ESG issues that matter to a particular industry can make the difference between success and failure.  I found Serafeim’s most insightful example to be the $1.6 trillion Japanese Government Pension Investment Fund. Since this fund owns the “universe,” it has sought to make the universe more sustainable rather than attempting to outperform the universe. Since pension funds have long time horizons, they need the earth to be viable 100 years from now to be able to pay out their obligations. As “stewards of the commons,” the largest investors are important to sustainability, because they hold numerous positions across industries that face a significant number of material threats. The final chapter is the most important one for the “Impact Generation,” which seeks alignment between values and work. Because alignment is not static, it might be appropriate to take a position at a currently misaligned company, provided one has the agency to bring about change, rather than a currently aligned company. It is the slope of alignment, rather than the current level of alignment, that determines the potential reward. The decision comes down to patience or one’s own personal discount rate. If you liked this post, don’t forget to subscribe to the Enterprising Investor. All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer. Professional Learning for CFA Institute Members CFA Institute members are empowered to self-determine and self-report professional learning (PL) credits earned, including content on Enterprising Investor. Members can record credits easily using their online PL tracker. source

Bank runs are among the most destabilizing events in financial markets, capable of turning liquidity fears into full-blown crises. At the heart of this phenomenon is the Diamond-Dybvig Model, a foundational framework that explains how banks’ role in transforming illiquid assets into liquid liabilities makes them inherently vulnerable. While this role provides significant economic value, it also relies heavily on depositor confidence. If expectations shift — whether due to real or perceived risks — a self-fulfilling crisis can emerge. This blog explores the mechanics of bank runs — why they happen even in the absence of fundamental financial distress, and how central banks can intervene to stabilize the system. A good starting point is to look to the research of Douglas Diamond, the Merton H. Miller Distinguished Service Professor of Finance at the University of Chicago, who was awarded the Nobel Prize in Economic Sciences in 2022.[1]  Diamond is primarily known for his research into financial intermediaries, financial crises, and liquidity, and his research agenda has been dedicated to explaining what banks do, why they do it, and the consequences of these arrangements.  He is perhaps best known for the Diamond-Dybvig Model[2], which precisely explains how the role of banks in creating liquid liabilities (deposits) to fund illiquid assets (such as business loans) makes them fundamentally unstable and gives rise to bank runs. It also shows why banks may need a government safety net more than they need other borrowers. Diamond-Dybvig Model is elegant in its simplicity and intuitiveness; it precisely describes how bank failures like Silicon Valley Bank (SVB) in 2023 can happen and, indeed, even the greater liquidity crisis and bank failures that occurred during the Great Financial Crisis. Moreover, the model prescribes how such events can be avoided. Simple Diamond-Dybvig Model One of the key functions of banks in the economy is the transformation of illiquid asset into liquid liability. This brilliant feat of financial engineering adds a lot of value to the economy but exposes banks to liquidity risk of their own and makes them inherently unstable. Assume that there exists an illiquid asset that an investor can hold directly. You can invest in this asset at t=0 for $1.00. It can either be liquidated at t=1 for $1.00 or held until t=2 for a $2.00 payoff. Each investor in this economy faces uncertain future liquidity needs. Each knows that he or she will need cash either at t=1 (Type 1) or at t=2 (Type 2), but without certainty when at t=0. To be more precise, we can assume that each individual investor has a 25% probability of cash need at t=1 and a 75% probability of cash need at t=2.   Each investor has a simple risk-averse consumption utility function U(C)=110-(100/C). The Type 1 investor consumes $1.00 at t=1 and the Type 2 investor consumes $2.00 at t=2.  Each investor’s expected utility at t=0 is 0.25*U(1) + 0.75*U(2)=47.50. What if a more liquid asset is available in this economy? Instead of $1.00 at t=1 and $2.00 at t=2, the more liquid asset pays off $1.28 at t=1 and $1.81 at t=2.  Then the investor’s expected utility at t=0 would be 0.25*U(1.28) + 0.75*U(1.81)=49.11. This second, more liquid asset does not yet exist. But can a bank create one?  Suppose a bank collects $1.00 from 100 investors and invests in the first illiquid asset and promises to pay $1.28 at t=1 for those who withdraw at t=1 and $1.81 to those who withdraw at t=2.  At t=1, the bank’s portfolio is only worth $100. If 25 investors withdraw as expected, then 32% of the portfolio must be liquidated to pay the investors (25*($1.28) = $32). The remaining 68% of portfolio value is worth $68. At t=2, the remaining 75% of the investors can now receive $1.81 ($68*$2.00)/75.  If fraction c receives a at t=1, then each of the remaining can receive (1-c*a)*$2.00/(1-c). This is the optimal contract a bank can write given the payoff structure of the illiquid asset, the investor’s utility function, and the proportion of investor types. This risk pooling and sharing and liquidity transformation is one of the most important functions a bank can perform. It is an impressive feat of financial engineering that adds a lot of value to the economy. Unstable Equilibrium But this financial alchemy is not without its costs. In the above example, 25 of the 100 investors withdraw at t=1 and 75 withdraw at t=2. This is the equilibrium given everyone’s expectation at t=0.  But this is not the only possible equilibrium. What if a future Type 2 investor did not know how many investors were Type 1 at t=0 and expects a higher percentage of withdrawals at t=1? If, for example, 79 of the 100 investors withdraw at t=1, the bank’s portfolio is worth at most $100. If 79 of the investors receive 1.28%, then the bank is expected to fail (79*$1.28=$101.12 > $100). Given this new expectation, a rational response would be for the Type 2 investor to withdraw at t=1 to get something as opposed to nothing. In other words, an expectation of 100% at t=1 is as self-fulfilling as an expectation of 25% at t=1 and 75% at t=2. The bottom line is that the anticipation of liquidity problems (real or perceived) lead to current real liquidity problems, and investors’ expectations can change based on no fundamental changes in the balance sheet.  Applications The Diamond-Dybvig Model of liquidity is robust enough for analyzing all types of “runs” that a complex dealer bank can face — flight of short-term financing, flight of prime brokerage clients, flight of derivative counterparties, loss of cash settlement privileges, among others. It also serves as a useful framework for analyzing the economic consequences of a liquidity crisis and policy responses. Panicked investors seeking liquidity at the same time impose serious damage to the economy because they force liquidation of productive longer-term investments and interrupt financing of the current productive projects.  Financing by central banks as lender of last resort might be needed

In the early days of the COVID-19 pandemic, the Federal Open Market Committee (FOMC) announced primary and secondary market corporate bond purchase programs as part of its response to the severe market and economic dislocations. These initiatives were aimed at supporting corporations’ access to credit and improving liquidity in the primary and secondary corporate bond markets. The programs had an almost immediate impact on liquidity and valuations in the investment grade market, where the purchases were concentrated. And even though the US Federal Reserve bought only token amounts of fallen angels and high-yield exchange-traded funds (ETFs), these actions also helped stabilize the high-yield market. Over the course of the program, investment grade (IG) and high-yield (HY) companies could access primary markets, doing so in record amounts to refinance their debt at historically low interest rates. The Fed backstop also boosted investor confidence in the corporate bond market, leading spreads on IG and HY indices to quickly retrace to pre-pandemic levels. The programs were so successful in restoring investor confidence that ultimately, out of a secondary market purchase commitment of up to $250 billion, the Fed only bought $13.7 billion of corporate bonds and ETFs. While these and other Fed responses to the pandemic prevented much worse market and economic outcomes, the corporate bond purchase program has generated criticism. Some believe corporate bond market interventions have permanently altered price discovery as investors may now assume corporations are ring-fenced from future economic shocks. Having crossed a longstanding red line and purchased credit instruments, the Fed will most certainly do so again during future recessions or financial crises. Or so the logic goes. Even if this turns out not to be the case, the expectation of future intervention can still affect corporate credit valuations, at least until that expectation is disappointed. The lower cost of credit for corporations could thus encourage excessive leverage, which could very well sow the seeds of a future crisis. Fed Intervention Stabilizes Financial Markets As of 31 December 2021Source: Bloomberg Other investors may believe there is a higher hurdle to Fed intervention in credit markets; that is, it would take a tail event, such as a major financial crisis, for the central bank to bring back the corporate purchase facilities. Even so, this expectation could impact compensation for bearing long-term credit risk even during normal times, resulting in a new, lower equilibrium for credit risk compensation. In addition to financial stability concerns, the perception of a Fed backstop for corporate credit may have implications for investment strategy. And these implications are of immediate import, given that recessions in both the United States and eurozone are likely over the next year. For example, investors who typically underweight corporate credit markets late in the economic cycle on expectations of spread widening may instead discover that the activation of a corporate purchase program prevents spreads from widening as much as they otherwise would as the economy weakens. Alternatively, such investors stand to benefit if market assumptions of a “Fed put” in credit markets turn out to be incorrect. Thus, understanding the extent to which market valuations currently reflect expectations of future central bank interventions, and the conditions under which the Fed might indeed intervene during future shocks, will remain important to credit investors. In this series, we first review corporate bond purchase activity under the Fed’s credit programs during the pandemic. In the second installment, we will discuss corporate bond purchases in the euro area, where the European Central Bank’s (ECB’s) authority to purchase corporate bonds is clearer and more independent of the political process. Comparisons with bond purchases in the euro area are also useful in our analysis of spreads, model-based valuations, and options pricing. For example, if investors now assume a permanent Fed backstop of corporate credit, US credit might be permanently repriced relative to euro-area credit, where a corporate backstop has been in place for longer. We will also provide a legal framework for corporate credit purchases by the Fed, as well as the political context of purchases, because these considerations will influence the potential for future interventions in credit markets. By way of contrast, our analysis will also include some discussion of the legal framework for ECB corporate bond purchases. Following our review of corporate bond purchase activity in the United States and the euro area, we will move on to the heart of our analysis: the search for evidence that credit market interventions have left an enduring “footprint” on corporate debt valuations. Our focus is on spread levels, pricing of credit indices relative to model valuations, and options pricing. Comparison of current spreads to valuation models, as well as options skew, can help us understand whether Fed and ECB purchases of credit instruments continue to influence pricing. Finally, we will summarize our findings and determine whether there is clear evidence that the Fed’s and ECB’s purchases of corporate bonds have permanently altered the pricing of corporate credit risk. A Review of the Corporate Purchases: The Fed Asset purchase programs as we know them became a staple of US monetary policy in 2008, in response to the housing and resulting financial crisis. On 25 November 2008, the Fed announced that it would purchase up to $600 billion in agency mortgage-backed securities (MBS) and agency debt. On 1 December 2008, then-Fed chair Ben Bernanke provided the public with details on the program, which was formally launched later that month on 16 December 2008. On 18 March 2009, the FOMC announced it would expand purchases of MBS and agency debt by an additional $850 billion and purchase $300 billion of US Treasury debt. These announcements resulted in a substantial decline in the yields of various assets, as the table below demonstrates, including those not on the Fed’s buy list. Option-adjusted spreads (OAS), however, generally widened on the news. This was likely due to expectations of an economic downturn and probable increase in default risk, or at a minimum, impaired liquidity conditions at the time. The Fed followed up

The latest earnings results for banks include words like “record,” “outstanding,” and “doubles.” So far, 2023 has been a banner year for the sector, at least from an earnings perspective. But bank stock prices have yet to eclipse their previous highs. The KBW NASDAQ Global Bank Index, which tracks global banks, has barely grown since the current rate-hiking cycle began in early 2022 and generally has not exceeded its pre-COVID-19 peaks. Other bank indexes haven’t outperformed either. The S&P Regional banks index is trading at 2016 levels. Banking is a complex sector with many influences. So, to understand the mid- to long-term outlook, we need to understand the three key drivers at work in the industry today. 1. The Transition to a Higher Rate Environment The US Federal Reserve’s hiking cycle has been the fastest in decades, and the banking sector has profited from it. As rates rise, a bank’s assets tend to reprice faster than its liabilities and thus a bank’s net interest income, which constitutes the bulk of its earnings, increases. That is what has happened in the current rate cycle, which has created a tailwind for the industry’s financials. But higher interest rates are a double-edged sword. Many banks loaded up on sizable portfolios of long-duration securities during the easy money era, and their prices have plunged as rates have risen. Held-to-maturity — or hide-’til-maturity — accounting has shielded bank financials from the impact, but should these portfolios be unwound, the losses will materialize and the bank’s capital will take a hit. This is a sector-wide concern, as W. Blake Marsh and Brendan Laliberte observe in “The Implications of Unrealized Losses for Banks.” Indeed, the switchover from a low or negative rate environment to one with a positive but inverted yield curve occurred quite quickly. Could this spell trouble for banks? According to financial theory, banks engage in term transformation — they borrow in the short term to lend over the long term — so the answer to the question may very well be yes, theoretically. But in practice, banks borrow and lend at different points on the curve, and the average maturities of loans and securities tend to be below five years. Additionally, assets and liabilities are well matched, so the banks may still make money with an inverted yield curve. In fact, in “How Have Banks Responded to Changes in the Yield Curve?” Thomas King and Jonathan Yu find evidence that banks actually increase their net interest margin with a flat curve. 2. Reduced Competition from Neobanks Neobanks and fintechs are the offspring of low rates and technological disruption. Low rates forced banks to look for alternative sources of income amid historically low spreads on their bread-and-butter products, which meant charging higher fees for credit cards, cash transfers, etc., to generate non-interest income. This combined with old technology stacks and start-ups financed with cheap money created fierce competition for traditional banks. That is, until the fintech winter settled in. With easy financing rounds a thing of the past, most neobanks will have trouble surviving. The vast majority have yet to achieve profitability, and they won’t have cheap funding to fill the gap any longer. Moreover, as banks revitalize their reliance on conventional sources of revenue — interest income — the pressure to increase service fees will fall. For all the hype about customer experience and digital disruption, neobanks will have a hard time retaining customers if their fees are more or less the same as traditional banks. Some banks may even be tempted to go on the offensive and cut their commissions now that their interest income offers a financial cushion. 3. Market Multiples So, how are the market variables moving for banks? Not very well. The sector is still underpriced relative to other industries. Price-to-book is banking’s universal multiple, and many banks are still below the magic value of 1. There are several reasons for this. Even though earnings are improving, clouds are gathering on the horizon. Unilateral government action through direct taxes as in Italy, increased regulation, and additional capital requirements are all possibilities. Bank compliance departments are growing ever larger and constituting an ever greater drag on profitability. A further headwind is the unrealized losses on securities portfolios. How large are they? Large enough to trigger a liquidity event? We don’t know, and that poses an additional risk for the sector. New production — slower credit growth due to tighter conditions and a deteriorating economy — is another challenge. Germany and Holland are already in technical recession, and whether the United States can avoid one in a higher rate environment is unclear. The latest GDP readings have been robust, and the labor market is resilient, which helps explain why US banks trade at a higher price-to-book ratio than their more-subdued European peers. But even in the United States, credit card and auto loan delinquency rates have started to swing upwards, and the housing market’s outlook appears cloudier the longer rates stay elevated. Looking Forward The banking sector is in better shape now than during the last decade of low or negative rates. The fintech winter will ease competitive pressure and give some banks the opportunity to buy out neobanks and appropriate their technology stack. However, latent losses in banks’ securities portfolios, the political temptation to overtax and overregulate the sector, and the damage higher rates may inflict on the economy could take a toll on an otherwise bullish outlook. So, the next few quarters should present both considerable challenges and opportunities. If you liked this post, don’t forget to subscribe to Enterprising Investor. All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer. Image credit: ©Getty Images / sakchai vongsasiripat Professional Learning for CFA Institute Members CFA Institute members are empowered to self-determine and self-report professional learning (PL) credits earned, including content on Enterprising Investor. Members can record credits easily using

Now that individual investors can access direct real estate investments, what should they keep in mind regarding the main US commercial real estate (CRE) sectors and their respective outlooks? To conclude our series, we analyze the prevailing perspectives on the US CRE market and four of its key segments, specifically residential — multifamily, industrial, retail, and office.* Residential — Multifamily  The United States faces a significant housing unit shortfall. Pre-COVID-19, Fannie Mae data estimated a shortage of 3.8 million homes. New estimates range from 2 to 3 million. While construction starts rose through most of 2021, according to Green Street analysis, the estimated influx of 1.3 million net units after subtracting obsolete properties will not be enough to accommodate the projected 4.7 million household formations. Real wages have increased across the wealth spectrum, but suitable, affordable housing that costs less than 30% of household income is still out of reach for much of the US population, particularly in leading primary markets. At 63.1%, the US homeownership rate is at a record 53-year low, as millennials, who are of prime age to start families and purchase homes, face far steeper costs than prior generations.  Given the recent surge in housing prices as well as the high (mortgage) interest rate environment and ongoing economic uncertainty, the affordable housing shortage should further fuel near-term demand for rental properties. This could benefit sub-asset classes, such as single-family rental, as an alternative to ownership and, at the most affordable end of the spectrum, manufactured housing. The US population today is also more mobile than previous generations. Remote and hybrid work and changing family and community structures have prompted greater geographic movement. Knowledge workers may relocate to secondary metros, suburbs, and exurbs at increasing rates in search of lower rent and lower cost of living as well as more space and more favorable tax regimes.  Tech hubs have emerged outside of San Francisco, Seattle, Boston, and other knowledge capital strongholds. With their robust educational institutions, affordability, and business-friendly climates, Salt Lake City, Utah; Phoenix, Arizona; Memphis, Tennessee; Raleigh, North Carolina; and other ascendant cities are attracting knowledge workers and tech businesses. These trends will provide fertile ground for multifamily investors. Demand for affordable rental housing will grow given the underlying scarcity and elevated inflation. This dynamic accounts for recent growth in real rents — 14% nationally and 20% to 30% in some markets. Since residential leases are usually of shorter duration — often one year — relative to other asset classes, they better capture a portion of inflation, and rents recalibrate more quickly. Despite an estimated 20% decline in apartment values compared with 2022, according to Green Street’s Commercial Property Pricing Index (May 2023), as rent growth normalizes in the near term, residential units in robust markets may still see additional rent growth. Industrial Industrial today has diverse and persistent demand drivers. The COVID-19 consumption boom spurred e-commerce sales growth of almost 40% in 2020, generated nearly 250 million square feet in warehouse demand, and led to global supply chain disruptions. As a result, US industrial is coming off the best two years in its history. Thanks to COVID-19 quarantines, e-commerce experienced perhaps decades of evolution in two or three years. In the new normal, e-commerce has greater penetration than traditional brick-and-mortar retail and requires three times the square footage, according to Green Street estimates. As such, national market rents grew by more than 40% in the last two years, more than in the previous seven years combined.  Industrial has had historically low vacancy rates — below 5% since 2016 — and sustained elevated demand: Retail sales are up 17% over pre-COVID-19 levels despite inflation, according to Green Street. These strong fundamentals augur well for future performance. Geographically, coastal markets, particularly on the East Coast and Gulf Coast, should have the most valuable investments. Thanks to port expansions and supplier diversification, they have gained 8% in market share over the last five years, according to the American Association of Port Authorities, and US imports are almost evenly divided between both coasts. Many importers shifted volume from West Coast to Gulf Coast and East Coast ports during the pandemic, to the benefit of the latter. But population growth in secondary West Coast markets, California’s large population base, and continued market capture of e-commerce mean there is still significant opportunity for industrial operators in certain West Coast markets. Orange County and the Inland Empire were both in the top five markets for revenue per available square foot (RevPAF) growth in 2022. This was driven by per capita industrial square footages for Amazon fulfillment centers that still lag other key markets throughout the United States. Southern California markets, in particular, also benefit from more stringent barriers to entry for new supply.  Fundamentally, the current capital-constrained market has reduced new construction, with 15% fewer deliveries in 2024 and 2025, according to Green Street estimates. That adds up to approximately 100 million square feet. The sector should be on pace to produce enough new supply to roughly match new demand, with occupancy remaining stable, and otherwise support continued rent growth. Real e-commerce sales remain 50% higher over year-end 2019, and firms are building out traditional and last-mile warehouse facilities to meet increased online sales. This should further help demand keep pace with supply. While industrial values declined by an estimated 15% compared with 2022, according to Green Street’s Commercial Property Pricing Index (May 2023), industrial investors should look for appealing assets in robust coastal markets with strong rent growth potential. Among the in-demand sub-categories are third-party logistics and last-mile industrial facilities that cater to e-commerce. Lease structures that index to CPI/inflation could become more common — again, following a prolonged period of low inflation resulting in fixed rent steps — and offer investors a means to offset inflation. The cold storage sub-sector is worth paying attention to as consumers trend towards fresher, healthier, and better-quality foods delivered in shorter timeframes and as food producers continue to ramp up their production

Investors often choose diversified, multi-factor strategies to overcome the limitations of traditional cap-weighted benchmarks. These benchmarks are overly concentrated on companies with the largest market capitalization and expose investors to idiosyncratic risks that are not rewarded over the long term. Moreover, cap-weighted benchmarks incorporate no explicit objective to capture exposure to those risk factors that have been documented in the academic literature to offer a long-term reward. Significant deviations from the traditional cap-weighted benchmark are required, therefore, to deliver stronger risk-adjusted performance over the long term. Namely, choosing stocks that target explicit exposures to rewarded factors and applying a well-diversified weighting scheme to manage stock specific risks. However, deviations from the benchmark create unintentional exposure to economic risks. For example, if a factor portfolio is too heavily tilted toward low volatility stocks, it may behave in an overly “bond-like” manner and accordingly exhibit strong sensitivity to Treasury yields and movements in the yield curve. Ideally, your factor portfolio will deliver factor premia in a systematic and reliable fashion without such undue sensitivity to economic risks that create additional tracking error for no additional long-term reward. In this article, I outline a methodology — which we call EconRisk — to mitigate economic risks of factor-driven equity strategies and eliminate unnecessary tracking error by keeping strong exposures to rewarded factors and preserving diversification benefits. Getting Exposures to Rewarded Factors There are six consensus rewarded factors that emerge from academic literature and that have passed sufficient hurdles to be considered robust, namely size, value, momentum, volatility, profitability, and investment. Their long-term reward is justified by economic rationale. Investors require compensation for additional risks brought by factor exposures in bad times when assets that correspond to a given factor tilt tend to provide poor payoffs (Cochrane, 2005). For instance, to build the value factor sleeve of our multi-factor index, we first select stocks with the highest book-to-market ratio adjusted for unrecorded intangibles to acquire the desired exposure. When doing so, we might select value stocks with negative exposures to other rewarded factors such as profitability, for example (Fama and French, 1995), Zhang (2005). This could be problematic when assembling the different factor sleeves into a multi-factor portfolio, since it will lead to factor dilution. To account for this effect, we screen out from the value selection the stocks with poor characteristics to other rewarded factors. This approach enables us to design single-factor sleeves with strong exposure to their desired factor but without negative exposures to other rewarded factors. The goal is to build multi-factor portfolios with strong and well-balanced exposure to all rewarded factors. Reducing Idiosyncratic Risks The second objective is the diversification of idiosyncratic risks. Indeed, we want to avoid the performance of our multi-factor indices, which should be driven by exposure to the market and rewarded factors, being significantly impacted by stock-specific shocks, since they can be mitigated by holding diversified portfolios. Typically, an investor would not want the performance of their multi-factor portfolio to be negatively affected by a profit warning made by a single company. The reasons is this unexpected shock is not related to the premium of the market of rewarded factors and is only company specific. Hence, we combine four different weighting schemes that are proxies of the mean-variance optimal portfolio (Markowitz, 1952). Each weighting scheme implies some trade-offs between estimation and optimality risks. For example, one of the four weighting schemes that we use is the Max Deconcentration. This has no estimation risks because it assumes that volatility, correlations, and expected returns are all identical across stocks. Given this strong assumption, this weighting scheme will be far from the mean-variance optimality. To mitigate the estimation and optimality risks of each weighting scheme, we simply average them together into a diversified multi-strategy weighting scheme. Unintentional Economic Risks Both sources of deviations discussed above are necessary to achieve the objective of long-term risk-adjusted performance improvement compared to the cap-weighted benchmark. Nonetheless, they create implicit exposures to economic risks that can affect the short-term performance of factor strategies. A low-volatility factor portfolio, for example, tends to overweight utilities companies, which are more sensitive to interest rate risks than the stocks in the cap-weighted benchmark. This is illustrated in Table 1. The sensitivity of each single-factor sleeve of our Developed Multi-Factor Index to each of the economic risk factors that we have in our menu. Each factor sleeve has different sensitivity to the factors.    Table 1. As of June 2024 Single-Factor Sleeves of Developed Multi-Factor Size Value Momentum Low Volatility Profitability Investment Supply Chain 0.08 0.13 0.09 0.05 0.06 0.09 Globalization -0.16 -0.17 -0.05 -0.22 -0.08 -0.19 Short Rate 0.02 0.13 0.13 0.04 0.05 0.07 Term Spread -0.01 0.07 0.07 -0.11 -0.02 0.00 Breakeven Inflation 0.12 0.14 0.14 0.02 0.03 0.07 The sensitivity of a factor sleeve to a given economic risk factor is the weighted average (using the stock weights within the sleeve) of underlying stock-level betas. These stock-level economic risk betas capture the sensitivity of stock returns more than the cap-weighted reference index to the returns of five market-beta neutral long-short portfolios that capture the five economic risks. Our menu of economic risk factors is designed to capture recent economic disruptions that are likely to continue in the future, such as increased supply chain disruptions, surging trade tensions between Western countries and China, changes to monetary policy by central banks to manage growth and inflation risks, and increasing geopolitical risks such as the war in Ukraine or tensions in the Middle East. Given that these economic risks are not rewarded over the long term, investors might benefit from trying to get more neutral exposures to them relative to the cap-weighted benchmark, while still trying to maximize the exposures to consensus rewarded factors. EconRisk to mitigate unintentional economic risks To preserve the benefits of our diversified multi-factor strategy, we introduced a weighting scheme we call EconRisk. The weighting scheme is implemented separately on each factor sleeve. Weights of each single factor sleeve are allowed to move away from the diversified multi-factor strategy