CFA Institute

Through his examinations of how uncertainty influences asset prices, Nobel laureate Myron Scholes has helped revolutionize our understanding of the financial markets. His development of the Black–Scholes options pricing model with Fischer Black more than half a century ago redefined how investment professionals do their jobs and opened up a new era in the world of finance. Even though he is one of the most influential living economists, Scholes is not resting on his laurels. His explorations of the inner workings of the financial markets continue, with a particular focus on both artificial intelligence (AI) and carbon credits and how they compare with options, among other phenomena. He recently participated in a wide-ranging fireside chat arranged by Janus Henderson,hosted by CFA Society Hong Kong, and moderated by Alvin Ho, PhD, CFA. The conversation, which took place on 3 July 2023 in Hong Kong, covered both the continued relevance of the Black–Scholes model 50 years after its unveiling as well as Scholes’s current research interests. Below is a lightly edited transcript of the discussion. The Black–Scholes Revolution CFA Society Hong Kong: It has been 50 years since you published the famous Black–Scholes model, and it remains one of the most popular readings among financial professionals. How did that happen? Myron Scholes: The model was really about explaining how to price options, but I’m happy that it has changed the banking landscape from an agency-only to a principal business. Now, if you think about it, uncertainty is the most important thing in your life. The mean is nothing! Having options to deal with uncertainties and risks is so important. If life were unchanging, then options would not be as valuable, but life is always changing, which makes options and the ability to deal with uncertainties very precious. With the Black–Scholes technology, we can help clients figure out what exactly they want and how to offset the delta and risks associated with it. Essentially, I see the options market as a crowd-sourcing place to determine what level of risk the market is signaling and subsequently help business owners to make decisions. Decarbonization and Portfolio Construction Going into your decarbonization and portfolio theory, how does the work that you have done in the options space help here? I have done a lot of risk–return portfolio theory. To me, understanding constraints is of the utmost importance. You do not need to be a better forecaster than everyone else, but you do need to understand the constraints of others. For example, if people are constrained, if they trust you, they would be willing to pay you to take their constraints off. That’s when your options are valuable. This ability to unconstrain the constrained also happens in parenting and M&A. If you want to make money in your life, being “boring” is important. You wouldn’t want the choppiness of your life affecting your returns, but you would want to smooth the volatility of returns and cut the tails. If you managed to do that, your compounded return would be so much better. My options theory is really meant to help understand the tail. If you think about decarbonization, we also want to smooth the path to decarbonization, and one way to do that is to create more paths to achieve it, and to some extent, it’s quite like a put option. Myron, to dive deeper into the same topic, I want to ask a three-part question. First, how should investors determine the fair value of carbon credits? Market efficiency is my core belief, and I do think it’s a good way to determine fair value for carbon credits. However, the problem is when we have cheaters coming into the market. We need teams and infrastructure to sort out the good and bad credits. Like the fixed-income market, we will have the whole hierarchy in the system. We have a credit rating agency to rate corporate fundamentals and allow investors to choose what level of risk and credit they would like to be involved in. After all, I’m not saying market price should always equal the fair value, but the market price usually gives you a good anchor point to determine that. Speaking of the origin of the option formula that helps price options: People kept saying to me, “You should keep it to yourself.” I said to myself that I could have made more money doing other things. Hence, I decided to share it with everyone. Some guys said they had a solution before you did. Yes, they said that, but they could never prove that. You see: Every successful idea has a thousand fathers, and every bad idea is mine. Are you in the camp that every carbon credit is different, or does the quality of the forest also matter? Decarbonization is about taking carbon out of the system. We shouldn’t care about where the carbon came from or where it is being taken away from. Eventually, all we need to know is what is the net carbon and how much it can contribute to decarbonization. The way I think about a carbon credit is that it is a commodity to me. I don’t care where it comes from; just get it graded, and that’s my credit. We should commoditize it just like any other commodity in the market. It should just be a matter of time before carbon credits become a commodity. As portfolio managers, how should we determine the optimal allocation or risk budget for carbon credits? Do you think that should be a decision made by the asset owners themselves? From what I devised in my paper and through a reference, it is a mechanism for individual choice. It puts in place the separation of the carbon problem from the portfolio problem. You can tell your client so that individuals can make their own decisions based on the two different portfolios — a regular portfolio and another one with carbon net zero. Not everyone should be doing the valuations of carbon credits.

Commemorating Warren Buffett’s legacy and the Financial Analysts Journal’s 80th Anniversary through the lens of the award-winning article, “Buffett’s Alpha.” The post Two Enduring Legacies, One Oracle’s Exit, and “Buffett’s Alpha” appeared first on CFA Institute Enterprising Investor. source

Introduction Playing the stock market should be easy: When the economy is booming, buy equities. When it’s deteriorating, short them.  Stock selection shouldn’t take much effort either — we just need to apply metrics from factor investing literature. In bull markets, that might mean focusing on cheap, low-risk, outperforming, small, or high-quality stocks, and in bear markets it might mean the inverse. Of course, in practice, equity investing is neither easy nor effortless. First, not even economists can really pinpoint when an economy goes from boom to bust. Economic data isn’t released in real time and is often revised. It may take quarters if not years to determine precisely when the tide turned. Second, in the recent, long-running bull market, buying stocks with high factor loadings has not been a winning formula. For example, the Goldman Sachs ActiveBeta U.S. Large Cap Equity ETF (GSLC) — the largest multi-factor product, with almost $11 billion in assets under management (AUM) — has underperformed the S&P 500 by 10% since its launch in September 2015. But what about shorting stocks? How has that worked as a strategy? Let’s explore. Shorting Stocks with Lousy Features To identify what stocks to short, we focused on five factors: value, quality, momentum, low-volatility, and growth. The first four of these are supported by academic research, and while the growth factor is not, we included it in our analysis given its popularity among investors. We created five indices composed of the top 10% of the most expensive, low-quality, low-momentum, high-volatility, and low-growth stocks in the S&P 500 and shorted them. To determine whether the strategy generated any excess returns, we added a long position in the stock market. We rebalanced our portfolios each month and added 10 basis points (bps) to simulate transaction costs. From 2005 to 2022, shorting low-growth and low-momentum stocks effectively delivered zero excess returns, while doing the same for low-quality and high-volatility stocks yielded negative returns. Bets against low-growth stocks worked well until about a year ago, when Amazon, Meta, and other high-growth companies started to underperform. Three portfolios crashed when the stock market recovered from the global financial crisis (GFC) in 2009. Why? Because the stock prices of Citigroup and other overleveraged and unprofitable financial firms had been sputtering and highly volatile, but when governments and central banks stepped in to ensure these companies didn’t fail, their share prices soared. Excess Returns: Shorting Stocks with Lousy Features Source: Finominal Breakdown by Factors Although some of these portfolios followed similar trajectories, the underlying portfolios were quite varied. Tech and health care dominated the expensive and high-volatility portfolios over the 17 years under review. Real estate stocks tend to be highly leveraged, so screen poorly on quality metrics. Consumer discretionary companies made up the largest contingent in our portfolio of underperforming stocks. Real estate, financials, and energy stocks all demonstrated comparatively poor sales and earnings growth. Shorting Stocks with Lousy Features: A Sector Breakdown, 2005 to 2022 Source: Finominal Correlation Analysis Stocks with poor features shared certain relationships. The excess returns of low-quality, low-momentum, high-volatility, and low-growth stocks were all highly correlated. Expensive stocks had low but positive correlations with the other four portfolios. Correlations of Stocks with Lousy Features, 2005 to 2022 Source: Finominal Shorting Stocks with Multiple Lousy Features While high correlations among stocks with lousy features do not bode well for a portfolio composed of stocks with multiple lousy features, we applied the intersectional model to build a portfolio of expensive, low-quality, high-volatility, low-momentum, and low-growth stocks. This portfolio had substantially different sector weights compared to the S&P 500. Health care, technology, and real estate dominated, while utilities and staples were underrepresented. Shorting Stocks with Multiple Lousy Features: A Sector Breakdown, 2005 to 2022 Source: Finominal But what about the portfolio’s fundamental and technical metrics? We compared the rankings of the top 10 stocks in our portfolio with those of the S&P 500. Snap scored the worst, followed by cruise line operators and biotech companies. These stocks do not rank poorly on all metrics. For example, they exhibited relatively high sales growth. Naturally, the more features used in the stock-selection process, the fewer stocks fulfill all criteria. Fundamental Metrics: Top 10 Stocks with Multiple Lousy Features vs. S&P 500Best Score = 100 Source: Finominal So, what sort of excess returns did combining all these features in the stock-selection process deliver? We began with our expensive stock portfolio and added the other metrics one by one. Performance did not improve. Shorting these stocks would not have been a good bet between 2009 and 2021, though it would have worked before the GFC and again in 2022. Excess Returns: Shorting Stocks with Multiple Lousy Features Source: Finominal Further Thoughts Why is shorting stocks so difficult? Research from Robeco indicates that factor investing primarily works on the long side, so investors can generate excess returns by buying cheap or outperforming stocks but not much from shorting expensive or underperforming stocks. Research from AQR finds just the opposite, that shorting such stocks can be profitable. The challenge of short selling may lie in the asymmetry between making money on the long and short sides. Losses on long positions top out at 100% since stock prices can’t go negative. Losses on short positions, on the other hand, are theoretically infinite. Famed short seller Jim Chanos shorted Tesla for years. In 2020, the electric automaker’s stock had truly abysmal fundamental metrics and was trading at an excessive valuation. Nevertheless, shares rose by more than 2000% thereafter. Lousy stocks are sometimes great investments. For more insights from Nicolas Rabener and the Finominal team, sign up for their research reports. 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 / wildpixel Professional Learning for CFA Institute Members CFA Institute members are empowered to self-determine and self-report

As we celebrate International Women’s Day, it is essential to reflect on the strides made by women in various sectors, particularly in wealth management, a field that has historically been male-dominated. The narrative is changing, and there are countless opportunities for women to thrive and lead in this dynamic industry. This blog explores the current landscape, highlights the challenges, and underscores the immense potential for women to excel in wealth management. The financial services industry has seen a gradual but noticeable shift toward inclusivity. The statistic that women constitute approximately 20% of the global investment management workforce is often cited in reports by CFA Institute, particularly in its research on gender diversity within the finance sector. One key publication is “CFA Institute Gender Diversity in Investment Management” report, which examines the state of gender diversity in the investment profession and provides insights into the barriers women face in the industry. While this figure indicates progress, it also reveals that there is significant room for growth. This International Women’s Day, we must recognize that the wealth management sector is not only a viable career path for women but also one ripe with opportunities for advancement and leadership. What is the driving force behind the increasing presence of women in wealth management? Research consistently shows that companies with diverse leadership teams outperform their peers. A McKinsey report found that organizations in the top quartile for gender diversity are 21% more likely to experience above-average profitability. This statistic underscores the importance of women’s perspectives and leadership styles in shaping investment strategies and client relationships. Meeting the Needs of a Diverse Clientele Moreover, the wealth management industry is evolving to meet the needs of a diverse clientele. Women control an ever-increasing share of global wealth, with estimates suggesting that by 2025, women will hold nearly 30% of global wealth. As financial service providers recognize this demographic shift, the demand for female advisors who can relate to and understand the unique challenges faced by women investors will continue to grow. This presents an unparalleled opportunity for women to carve out successful careers in wealth management, leveraging their insights to better serve clients. Despite these promising trends, women in wealth management still face challenges that can hinder their progress. A study conducted by FA Institute revealed that women are often less likely to pursue careers in finance due to a lack of role models and mentorship opportunities. As we celebrate International Women’s Day, it is crucial to emphasize the importance of mentorship and sponsorship in helping women navigate their careers. Organizations should prioritize initiatives that connect emerging female talent with experienced professionals who can provide guidance and support. Work-Life Balance Furthermore, the issue of work-life balance cannot be overlooked. Many women in finance cite the demanding nature of the industry as a barrier to entry and advancement. However, the COVID-19 pandemic has accelerated the adoption of remote work and flexible schedules, providing an opportunity to reshape the traditional work environment. Wealth management firms that embrace flexibility and support work-life balance will not only attract female talent but also enhance employee satisfaction and retention. Education and Professional Development In addition to mentorship and flexible work arrangements, education and professional development play a pivotal role in empowering women in wealth management. CFA Institute’s commitment to education and professional standards equips individuals with the necessary skills and knowledge to excel in the industry. Women should be encouraged to pursue certifications such as the Chartered Financial Analyst (CFA) designation, which not only enhances their credibility but also expands their professional networks. As we look to the future, it is vital for wealth management firms to prioritize diversity and inclusion at all levels. This commitment should extend beyond hiring practices to encompass leadership development, succession planning, and creating an inclusive company culture. By fostering an environment where women feel valued and empowered, firms can tap into the full potential of their talent pool. Key Takeaway International Women’s Day serves as a reminder of the progress made and the work that remains in promoting gender equality in wealth management. The opportunities for women in this field are abundant, driven by the demand for diverse perspectives, the changing demographics of wealth, and the push for inclusive workplace cultures. As we acknowledge the challenges women face, let us also celebrate their resilience and determination. By championing mentorship, education, and flexible work environments, we can create a future where women not only participate in wealth management but thrive as leaders. As we reflect on this special day, let us commit to fostering a more inclusive industry that empowers women to seize the opportunities that lie ahead. Together, we can shape a wealth management landscape that reflects the diversity of the clients we serve and drives success for all. source

Institutional investing often elicits images of ivy-clad walls, multi-billion-dollar endowments, and investment committees comprised of professionals from the largest and most well-known firms. That is certainly one component of the institutional market. However, there is a much larger segment that garners less attention. There are almost two million nonprofit organizations in the United States, many of which have endowments or board-designated funds, often with balances that are far smaller than those of the largest institutions. While these two market segments differ in many ways, they usually share a similar investment goal. Most nonprofit portfolios are in place to balance the current and future needs of the parent organization. Spending policies of around 4% to 5% are common across the spectrum of institutional investors. Yet despite shared goals and broadly similar mandates, nonprofit investment portfolios consistently underperform. This blog explores the drivers of that underperformance — manager selection, committee behavior, and structural inefficiencies — and proposes a remedy: the adoption of a clearly articulated investment philosophy statement. Institutional Investment Performance There are many studies showing systemic underperformance across the institutional investment market, but perhaps the broadest was written by Sandeep Dahiya and David Yermack in 2019.  The study gathered data on 28,000 institutional investment portfolios and their returns. What it found was that: Endowments badly underperform market benchmarks, with median annual returns 5.53 percentage points below a 60-40 mix of US equity and Treasury bond indexes, and statistically significant alphas of -1.01% per year. Smaller endowments have less negative alphas than larger endowments, but all size classes significantly underperform. Higher education endowments, most of the $0.7 trillion asset class, do significantly worse than funds in other sectors. Why have larger institutions performed worse? Most likely because of their allocations to alternative investments. Smaller organizations may not have access to the biggest and best hedge funds and private equity deals, but studies suggest that may be a good thing.  Richard Ennis recently observed: Alternative investments, or alts, cost too much to be a fixture of institutional investing…Alts bring extraordinary costs but ordinary returns — namely, those of the underlying equity and fixed income assets. Alts have had a significantly adverse impact on the performance of institutional investors since the Global Financial Crisis of 2008 (GFC). Private market real estate and hedge funds have been standout under-performers. Ennis shows that the largest investors don’t necessarily have an advantage over smaller portfolios and haven’t benefited from their size.  Who is to Blame? It is no secret that the investment industry has generally failed to generate benchmark-beating alpha.  The biannual SPIVA (SPIVA U.S. Scorecard Year-End 2024) study shows that active managers across asset classes largely fail to add value above their passive benchmarks. Clearly, the investment industry bears some responsibility for the nonprofit performance shortfall.  Still, there is plenty of blame to share for the systemic failure of institutional investors. Investment committees also need to reexamine their behavior and composition. While it may be easy for Harvard University to fill the seats of its investment committee with some of the smartest, best resourced, and most experienced investors, that is not universally true.  Often committees for smaller organizations are staffed with savvy businesspeople, attorneys, accountants, and stockbrokers (who generally are sales professionals rather than investment professionals), but how many of them truly understand the nuances of how to build or assess efficient portfolios for the long term?  Additionally, I have noted the cycle of hiring an outside investment manager through an RFP process where past performance is the primary consideration. In such instances, the manager with the best recent track record is hired, then underperforms, prompting yet another RFP. This effectively locks in the process of selling low (at least on a relative basis) and buying high. Not the best approach.  More formal evidence of this has been shown in studies, including a CFA monograph by Scott Stewart back in 2013 (rf-v2013-n4-1-pdf.pdf) and “The Selection and Termination of Investment Management Firms by Plan Sponsors” written by Amit Goyal and Sunil Wahal.  Worse still, there may be perverse incentives at some organizations that lock in long-term underperformance. The aforementioned Ennis blog notes: CIOs and consultant-advisors, who develop and implement investment strategy, have an incentive to favor complex investment programs. They also design the benchmarks used to evaluate performance. Compounding the incentive problem, trustees often pay bonuses based on performance relative to these benchmarks. This is an obvious governance failure. Even if an organization is fortunate enough to have a qualified committee that implements a robust long-term investment program, membership turnover hurts consistency. It is not unusual for committee members to rotate in and out every year or so. Without some documented philosophy to adhere to, committees can rush from one shiny object to the next in search of investment outperformance, even if the academic literature largely suggests that is a fool’s errand unlikely to yield positive excess returns.  A Way Forward: Establishing an Investment Philosophy What’s to be done? How do organizations break out of the cycle of systematic underperformance? It can’t be through better committee selection since in most communities there simply aren’t enough qualified volunteer committee members. It is also unlikely to come from a change in the investment industry, as its conflicts and problems have been well documented for over a century. Organizations must instead adopt a deliberate, long-term investment philosophy. Almost all nonprofit organizations have investment policy statements. These layout investment considerations and the basics of the portfolio including time horizon, liquidity needs, asset allocation targets and ranges, and benchmarks. However, most investment policy statements I’ve seen still leave a lot of discretion to outsourced investment managers. While flexibility may benefit a skilled manager, evidence suggests that most underperform, especially when given broad tactical discretion. That suggests committees should have more formality and constraints in how they run their investment portfolios. But there is a lot missing in most investment policy statements. Most investment policy statements lack a robust articulation of long-term philosophy, something that could help committees commit to a consistent strategy over

The Federal Reserve’s rate cuts in 2024 reignited a debate familiar to investors: do easing cycles extend expansions or signal looming recession? With inflation still a threat, the Fed’s next move carries real consequences for portfolios. History offers a guide. Past cycles reveal how monetary shifts have influenced recessions, bear markets, and investment styles leadership, lessons investors can use as they navigate today’s late-cycle environment. Echoing Milton Friedman’s observation regarding the “long and variable lags” of monetary policy, this post examines historical Fed rate cycles to assess their relationship across a variety of market dynamics. By analyzing past data, we aim to provide insights into how monetary policy actions have historically influenced yield curves, style leadership, and economic outcomes — insights that can help investors interpret today’s cycle. KEY OBSERVATIONS Rate Cut Cycles Two out of 10 previous rate cut cycles avoided a recession, with the 2024 cut cycle marking the 3rd out of 11, if the recession is avoided in the current cycle. Equity style performance has been extremely mixed after cuts across both recessionary and non-recessionary periods. Rate Hike Cycles Across 12 rate hike cycles since 1965, we have experienced 10 yield curve inversions and eight recessions, if the current inversion continues to avoid a recession. The only hike cycle that included an inversion but avoided a recession was 1966, (similar to current period) coincided with a ~3% deficit/GDP fiscal expansion, like the ~3% fiscal expansion over the past four years. Yield Curve Inversions The range of time of a yield curve inversion to market peak was two to 15 months for the eight out of nine yield curve inversions that preceded a recession. Currently, we sit at 35 months. One previous yield curve inversion (1966) avoided a recession, and we saw growth, high beta, and quality styles leading performance as the curve normalized, like today. Figure 1 presents equity market performance across three distinct periods following the Fed’s initial rate cut: months one to12, 13 to 24, and 25 to 36. While returns tend to be broadly positive, the lack of a consistent pattern across cycles indicates that outcomes are largely influenced by the specific macroeconomic environment in which each easing cycle occurs. Figure 1: Top 1000 Returns After Rate Cuts. Disclosures: Past performance is no guarantee of future results. All the returns in the chart above are in reference to unmanaged, hypothetical security groupings created exclusively for analytical purposes. Please see appendix for definitions and citations. Figure 2 illustrates the historical relationship between Fed rate-cutting cycles, recessions, and bear markets. Analysis of 12 distinct cycles reveals that in 10 instances, the Fed initiated rate cuts only after equity markets had already peaked, suggesting a lag in policy responsiveness. Furthermore, recessions have typically been identified by the National Bureau of Economic Research (NBER) with a delay of four to 21 months following their actual onset. Notably, since the highly volatile monetary environment of the 1970s, the Fed has more frequently begun rate cuts prior to the formal recognition of a recession. Figure 2: Federal Reserve Rate Cut Cycles. Disclosures: Please see appendix for definitions and citations.  Figure 3 shows the performance of various investment styles following the initiation of Fed rate-cutting cycles. The data revealed a mixed pattern of returns, underscoring the idiosyncratic nature of each cycle. One plausible explanation for this variability is that monetary easing does not consistently align with equity market cycles, sometimes resulting in divergent investment style behavior. There just doesn’t seem to be a connection between rate-cut cycles, recessions, and market risk behavior, making style persistence impossible to anticipate. Figure 3: Style Excess Returns 1-Year After First Rate Cut. Disclosures: Past performance is no guarantee of future results. All the returns in the chart above are in reference to unmanaged, hypothetical security groupings created exclusively for analytical purposes. These are hypothetical styles based on describing characteristics. Please see appendix for definitions and citations. Excess Return is Annualized Return over the Top 1000 Portfolio. Since 1965, there have been 12 distinct rate-hiking cycles, of which eight culminated in recessions, 10 were preceded by yield-curve inversions, and nine coincided with bear markets (Figure 4). The median duration of these cycles is 18 months, ranging from 12 to 39 months, while the median increase in the federal funds rate was 3.75%, with a range between 1.75% and 13%. The median time from the start of a hiking cycle to the market peak preceding a recession was 22 months, with a range of four to 51 months. Figure 4: Federal Reserve Rate Hike Cycles. Disclosures: Please see appendix for definitions and citations.  In the majority of rate-hiking cycles, the Fed continued to tighten monetary policy even after equity markets had reached their peak. This pattern reinforces the long-held adage that bull markets are not ended by old age, but by the actions of the Fed. While this aggressive stance often contributes to economic contraction, there are instances where the Fed has attempted to preemptively mitigate recessionary pressures. In five of the eight recessions observed since 1965, the Fed began cutting interest rates prior to the official onset of economic contraction, indicating a proactive policy shift aimed at cushioning the economy. However, as these five episodes illustrate, preemptive rate cuts do not always succeed in averting recessions, underscoring the limitations of monetary policy once broader economic momentum begins to deteriorate. The performance of investment styles in the year following the end of rate-hiking cycles has been mixed, reflecting the cycle-specific nature of monetary policy and market dynamics. This variability likely stems from monetary cycles not consistently aligning with equity market cycles. In the 1970s, for example, the Fed often transitioned directly from hiking to cutting rates, making post-hike and post-cut return profiles effectively indistinguishable. One historical pattern that continues is that high beta stocks are typically among the best or worst performers and value and quality stocks are often better than average and rarely amongst the worst. This observation is also persistent following the end of hiking cycles. Figure 5: Style Excess Returns 1 Year After Last Rate Hike. Disclosures: Past performance is no guarantee

Climate risk has emerged as one of the most formidable challenges of our time, affecting economies, financial systems, and societies at large. From rare catastrophic physical events to sudden shifts in policy and consumer behavior, the uncertainties inherent in climate risk make it incredibly difficult to model accurately. In this post, I explore the complexities of modeling climate risk, focusing on both physical risks and transition risks that arise from societal and political changes. Moreover, I consider the implications for financial risk management and economic resource allocation. Regime Change and the Data Problem At the heart of physical climate risk modeling is the challenge of dealing with a rapidly changing climate regime. Historically, risk models have relied on extensive datasets that describe past events. However, with climate change, the evidence of future risk events is not yet present in the historical record. In addition, modeling the “left tail” of the probability distribution: the region that represents rare but catastrophic losses, is challenging even without assuming any regime change. By definition, extreme events are underrepresented in historical data, yet they are precisely the outcomes that could have devastating consequences. For example, flood defenses, urban planning, and agricultural investments might be based on historical climate patterns. However, as climate change alters weather patterns and increases the frequency and severity of extreme events, historical data becomes an unreliable guide for future risk. Without accurate data for these new regimes, the models may underestimate the likelihood and impact of such events, leaving communities and financial institutions exposed to unforeseen shocks. The Butterfly Effect The inherent difficulty in modeling climate risk is further exacerbated by what meteorologist Edward Lorenz famously termed the “butterfly effect.” This phenomenon highlights the extreme sensitivity of complex systems — like the Earth’s climate — to initial conditions. A minute error in input data can result in drastically different outputs. For instance, small discrepancies in temperature, humidity, or wind speed inputs can lead to entirely divergent climate projections when extended decades into the future. In practical terms, climate models that forecast weather or climate trends for 2030 or 2040 must contend with a high degree of uncertainty. The chaotic nature of the climate system means that even state-of-the-art models, when fed slightly imperfect data, can yield unreliable predictions. This “chaos” propagates into financial risk management, where the outputs of climate models serve as inputs to financial models. As a result, uncertainties compound, potentially rendering the final predictions for physical risk worthless. The Complexity of Transition Risk While physical risks stem from direct impacts like extreme weather, transition risk refers to the economic and financial repercussions of the shift towards a low-carbon economy. This includes a variety of factors: political restrictions on emissions, shifts in consumer demand, technological changes, and even geopolitical tensions. Transition risk is characterized by a high degree of uncertainty, often driven by so called “unknown unknowns:” unforeseen events for which we have no historical experience. In other words, we don’t even realize we should be considering these risks when modelling or making decisions. For example, consider policies aimed at curbing carbon emissions. While well-intentioned, these policies can disrupt industries that rely heavily on fossil fuels. Companies in these sectors might see sudden drops in stock value, and regions dependent on these industries may experience economic downturns. Moreover, consumer preferences are rapidly evolving, and market forces may accelerate or decelerate the pace of transition in unpredictable ways. All these second- and third-order effects might not be obvious at the policy inception date. Financial risk management traditionally relies on statistical models that work well under conditions of relative stability. However, when faced with transition risk, these models struggle because the future does not resemble the past. The events that drive transition risk are often unprecedented, and their effects can be both systemic and nonlinear. In the realm of transition risk, the advice of risk management thinkers like Nassim Nicholas Taleb becomes particularly relevant. Taleb, known for his work on “black swan” events, argues that when facing unknown unknowns, it is more prudent to adopt strategies that account for extreme uncertainty. His approach suggests that instead of trying to predict every possible outcome with precision, risk managers should focus on building resilient systems that can absorb shocks. This involves: Diversification: Avoiding overconcentration in any single asset or sector. Redundancy: Building in extra capacity or safety margins to handle unforeseen events. Flexibility: Designing policies and financial instruments that can adapt to changing circumstances. Stress Testing: Regularly simulating extreme scenarios to evaluate how systems respond under duress. Adopting these strategies can help mitigate the impact of transition risk, even when the underlying drivers are difficult to predict. The relevance of this approach has been highlighted in the recent wildfires in California. While the general trend toward more wildfires might have been predictable from a statistical standpoint given the increased temperatures, drought conditions, and rain patterns, the timing, location and severity of the event were not. As risk managers, it is the severity of the event what we want to predict, not just the occurrence of a wildfire. That’s why financial institutions need to incorporate climate risk into their risk management frameworks, although the compounded uncertainties pose significant challenges, leading to potential mispricing of risk and misallocation of capital. What Next? The data scarcity problem and prediction problem might be solved up to a point. One promising avenue to improve climate risk modeling is the integration of multidisciplinary insights. Advances in data science, machine learning, and complexity theory offer tools that may enhance the predictive capabilities of traditional climate and financial models. For example, ensemble modeling, where multiple models are run in parallel to provide a range of outcomes, can help capture the uncertainty inherent in each individual model. Moreover, incorporating real-time data from sensors, satellites, and IoT devices can provide more granular inputs, potentially reducing some of the errors that lead to divergent outcomes in climate modeling. These technological advances, however, must be integrated with a keen awareness of their limitations. As

Introduction A falling stock market is not bad for everyone. Sure, many investors lose out as their portfolios decline in value, but those who are just starting to invest or have underweighted equities can benefit from lower valuations, which tend to deliver higher returns over the long term.  Naturally, equity markets do not fall without reason. As the economic environment changes, so do expectations. The positive feedback loop that sends valuations rising eventually reverses course and turns negative. But at some point, economic and business conditions stabilize and valuations come down enough to attract new investors and lure old ones back in. For instance, companies with anticyclical business models can increase their appeal by raising their dividend payments. But not all securities markets exhibit the same dynamic as that of equities. For example, the Italian lira consistently lost value against the Deutsche Mark for decades before both currencies were merged into the euro, and currencies can effectively become worthless when hyperinflation sets in. So, what about cryptocurrency tokens? Critics have long raised concerns about their intrinsic value, or lack thereof, and there doesn’t seem to be a relationship between a token’s price and the product for which it is supposed to serve as a medium of exchange. But with nearly 10,000 cryptocurrencies available, security selection should matter. So, does it? Can token pickers demonstrate differentiated performance? Probability of Making Money in Cryptocurrencies One of the more profitable approaches to cryptocurrencies is to invest in the private seed round of a start-up seeking token financing. The early price tends to be heavily discounted relative to the public sale price, which is comparable to pre-IPO investing. But more than four out of five tokens trade below their initial trading price, according to an analysis of nearly 10,000 cryptocurrencies by Jackdaw Capital, a London-based asset manager. Crypto Tokens: Current Price vs. Initial Trading Price Sources: Jackdaw Capital, FactorResearch Types of Tokens Such odds — less than 20% that a token traded on an exchange will eclipse its initial listing price — make token investing challenging. But there are different kinds of tokens. Some categories might still offer investors the prospect of attractive returns via security selection. To find out, we constructed a universe of the more than 3,500 tokens trading today and divided them into 17 categories. The largest category — non-fungible token (NFT) and collectibles — had 585 constituents, while the smallest — move to earn — had only 19. These token varieties represent different crypto products that ought to be relatively uncorrelated. Token Types: By the Numbers Source: FactorResearch Token Performance Next, we created equal-weighted indices for each of the 17 token categories. The majority of our categories have only a few years of trading history, but NFTs and masternodes go back to 2013 with track records of almost a decade. Most of these indices generated such abnormally high performance that we needed a logarithmic scale to measure them. This explains much of crypto’s appeal: The potential for 1,000% annual returns can be tough to resist. Token Performance by Type Source: FactorResearch Cryptocurrency Volatility But the crypto market hit a rough patch over the last few months. Its total market capitalization decreased from nearly $3 trillion to less than $1 trillion, while bitcoin declined from an all-time high of $69,000 in November 2021 to $20,000 as of this writing. Still, the log charts hardly register the cryptocurrency crash of 2022 since token indices use the mean return and equal weighting for index calculations. Tokens exhibited such a high positive skew that the average return moved up significantly more than down. For example, Terracoin (TRC) skyrocketed from $52 to $2,535 in just a few days in 2013. The maximum a token can lose is 100%, but the upside could be parabolic. Cryptocurrency Volatility: Performance of TRC Source: FactorResearch Token Performance Adjusted for Reality Since the average investor cannot participate in every token sale, however, the mean return is not an accurate measure of a token index’s performance. The median return is a better metric. And it tells a much different story. All 17 token types have lost money for their investors since the inception of the indices. The performance between 2013 and 2018 — the peak of the first crypto bull market — was differentiated, although only a few tokens traded. Some token types — governance, for example — did well relative to, say, NFTs. From 2017 into 2018, however, hundreds of initial coin offerings (ICOs) took place. Many of these were, at best, speculative; others were outright scams. Since 2018, all token varieties have been in a consistent decline. Despite their different purposes and ostensible business models, all types of tokens followed the same downward trajectory. This implies that security selection does not matter in the crypto space. Furthermore, our universe is composed of tokens that are still trading and thus includes some survivorship bias. So, the returns are slightly overstated, which makes the perspective even more negative. Token Performance by Type: Median Returns Source: FactorResearch Inflationary vs. Deflationary Tokens But maybe these bearish results are not as bad as they look. What happens if we differentiate between cryptocurrencies with a limited supply, like bitcoin, and those, like Ethereum, that have no supply constraints? Bitcoin and other limited-supply tokens could have a deflationary effect, especially when the issuer buys back tokens, while unlimited tokens could be inflationary as more and more tokens put downward pressure on token price. We divided the 550 DeFi tokens in our universe along these lines and found little difference between these two varieties from 2018 to the present. The supposedly deflationary limited-supply tokens actually performed worse. Performance of DeFi Tokens: Limited vs. Unlimited Token Supply Source: FactorResearch Further Thoughts Fund managers have had a hard time creating value through security selection in equities and other traditional markets. Alpha generation has been low to negative over the last few decades. Theoretically, the new and complicated world of cryptocurrencies should offer plenty of information asymmetries that sophisticated investors

Introduction Alternative investments accounted for $13 trillion in assets under management (AUM) in 2021, nearly twice what it was 2015. By 2026, that figure is expected to reach more than $23 trillion, according to Preqin research. Boom times are here for venture capitalists, private equity (PE), and hedge fund managers. Although 2022 hasn’t been kind to venture capital, among other alternatives, some fund managers are faring better than others. Why? Because they can finesse their investments’ valuations. PE funds have no daily mark-to-market accounting, so they can smooth losses out across several quarters. The ingenuity of this practice is that even though they have similar risk exposure PE returns appear uncorrelated to equities. On paper, everything looks great. Correlations are the hallmark of alternative investments. Generating uncorrelated returns in a year when the traditional 60/40 equity-bond portfolio has posted double digit losses is a quick way to capture investor interest and capital. However, correlations are like icebergs floating in the sea, there is a lot hiding beneath the surface. So just what are the pitfalls of using correlations to choose alternative strategies? The Alternative Champions To find out, we selected seven well-known strategies from the hedge fund universe that have attracted billions from capital allocators. Our data is sourced from HFRX, which has daily returns going back to 2003. This nearly 20-year period covers several market cycles when alternative strategies should have demonstrated their value by providing diversification benefits. We calculated these hedge fund strategies’ correlations to traditional asset classes. Three of these strategies — equity hedge, merger arbitrage, and event-driven — have S&P 500 correlations in excess of 0.5. It wouldn’t make much sense to add these to an equity portfolio given their similar risk profiles. However, three strategies demonstrated low stock market correlations without high correlations to US investment-grade bonds. This suggests they may offer some value for investors. Hedge Fund Strategies: Correlations to Stocks and Bonds, 2003 to 2022 Source: Finominal Quantifying Diversification’s Benefits When presented with a range of alternative strategies, a capital allocator should select those with the lowest correlations to stocks and bonds since they demonstrate the highest diversification potential. To test this hypothesis, we sorted the seven hedge fund strategies by their average correlations to stocks and bonds and ran simulations that added a 20% allocation to each strategy to a 60/40 equity-bond portfolio and then rebalanced on a quarterly basis. Contrary to expectations, adding an alternative allocation did not improve the Sharpe ratios for the 2003 to 2022 period. What’s even more unusual is that there does not seem to be any relationship among the correlations. For example, merger arbitrage had a higher average correlation to stocks and bonds than equity market neutral. Yet adding the latter to a traditional portfolio did not lead to a significantly higher Sharpe ratio. 60/40 Portfolio Plus 20% Alternative Allocation: Sharpe Ratios, 2003 to 2022 Source: Finominal We next calculated the maximum drawdowns for all the portfolios. These all occurred during the global financial crisis (GFC) in 2009. Both stocks and bonds fell, much like they have this year. Our equity-bond portfolio plunged 35%, while our diversified portfolios all declined between 31% and 39%. Such risk reduction is not particularly impressive. But as with our earlier Sharpe ratio analysis, the maximum drawdowns did not fall further when more diversifying alternative strategies were added. We would expect a linear relationship between decreasing correlations and drawdowns, at least until correlations reach zero. If they become too negative, as with a tail risk strategy, then diversification benefits deteriorate again. We’re expecting an unhappy smile, but no one is smiling. So, are correlations failing investors in their efforts to identify useful alternative strategies? 60/40 Portfolio Plus 20% Alternative Allocation: Max Drawdowns, 2003 to 2022 Source: Finominal Fair Weather Correlations One partial explanation for our results is that correlations are deceiving. Even if they are close to zero on average, there still may be periods of high correlation. Unfortunately, correlations often spike exactly when investors require uncorrelated returns.  Take merger arbitrage as an example. The strategy is typically uncorrelated to equities, but when stock markets crash, mergers fall apart. A portfolio with long positions in acquirable companies and short positions in acquiring firms can be constructed beta-neutral. But that does not negate economic cycle risk, which is also inherent in stocks.  All our seven alternative strategies lost money during the global financial crisis between 2008 and 2009. Convertible arbitrage lost even more than equities. That’s quite an accomplishment since the S&P 500 declined by 53%.  Hedge Fund Strategy Performance during the Global Financial Crisis (GFC), 2008 and 2009 Source: Finominal Why else have alternatives failed to improve Sharpe ratios and reduce drawdowns? Because, frankly, they’re terrible at making money. They may generate attractive returns before fees, but their net returns for investors have been poor over the last 19 years. The S&P 500 produced a CAGR of 9.5% for the 2003 to 2022 period, but that isn’t the right benchmark for hedge fund strategies. Beating bonds is a more reasonable goal, and only merger arbitrage achieved it. And that strategy is too strongly correlated to equities to offer much in the way of diversification. Inflation was roughly 2% for this period, so CAGRs below that imply negative real returns. Inflation is much higher today, so the goalposts for these strategies have moved much further away. Hedge Fund Strategy Performance: CAGRS and Correlations, 2003 to 2022 Source: Finominal Further Thoughts Correlations alone are not enough to identify alternative strategies. A more nuanced approach is required. Specifically, investors should measure correlations when stocks are falling. This will weed out merger arbitrage and other strategies that have inherent economic risk. If calculated correctly, this should reveal that most private asset classes — PE, VC, and real estate — provide the same risk. They therefore offer limited diversification benefits. We need better tools to measure the diversification potential of alternative strategies. Of course, this does not change the underlying issue: Many strategies no longer

As global economies evolve, infrastructure debt emerges as a unique asset class with promising opportunities for investors. Offering low correlation with business cycles and appealing yields, it’s fueling critical sectors like renewable energy and AI infrastructure. Public and private sectors are converging to meet the growing global demand for modern infrastructure. While governments lay the groundwork with strategic investments, private capital is increasingly stepping in to drive innovation and address funding gaps, shaping the future of essential projects. This post touches upon some of the topics that will be the scope of an upcoming CFA Institute Research Foundation Brief centered on infrastructure debt. Within the investment landscape, besides having low correlation with the business cycle, infrastructure debt has historically been a source of relatively stable returns and a high degree of differentiation within a portfolio. Infrastructure loans finance capital-intensive, tangible assets like transportation systems, energy facilities, and data centers. These loans are generally provided by private funds, either alone or in combination with public funding. Private infrastructure debt typically invests in regulated assets (more rarely in a company involved in infrastructure services or operations) with inelastic demand, in either monopolistic or quasi-monopolistic markets.  The debt is typically secured against the cash flows generated by the project itself. The loans are tailored on the project’s specific risks and revenue-generating capabilities. While most debt issued is senior, some transactions also include junior tranches to offer more attractive yields to less risk-averse investors. The asset class has historically grown at a steady pace but has been expanding more rapidly in recent years, largely fueled by a favorable macroeconomic environment — including pandemic-driven fiscal expansion and post-GFC financial regulation that limited commercial banks’ ability to hold long-term debt on their balance sheets. Since 2017, the global private infrastructure investment market has more than doubled, reaching over $1 trillion annually. Geographically, infrastructure debt is heavily concentrated, with the United States and Europe leading the way. A Favorable Macro Environment A significant boost to infrastructure debt came with the post-pandemic increase in government spending across the developed world. The US Congress approved a large infrastructure package in 2021 with bi-partisan support aimed at modernizing the country’s ageing bridges, tunnels, and railroads, as well as building new high-speed internet connections. A year later, the Inflation Reduction Act (“IRA”) added more funds for large infrastructure projects, with potential co-investment opportunities for the private sector. In the European Union, the post-pandemic NextGEN EU fund also provided grants and loans for member states to spend on infrastructure projects. Finally, the UK Infrastructure Bank — launched in June 2021 as a “substitute” for the European Investment Bank as the UK exited the European Union and recently renamed National Wealth Fund — supports a range of sustainable infrastructure projects in the United Kingdom, especially in the underdeveloped northern part of the country. This recent push to revive infrastructure in developed economies was mainly driven by the desire to reverse decades of underinvestment in the space from the public sector. But it also spurred a flurry of private sector activity due to attractive co-investment opportunities and, in some cases, government spending was seen as de-risking. It remains to be seen whether the macro environment will remain supportive going forward. A reduction in government spending — perhaps to control ballooning deficits — may cause a slowdown in infrastructure debt growth. However, in theory, it can equally spark more interest from the private sector amidst potential higher yields in the context of decreased supply. The Push for Renewable Energy Projects Despite the recent backlash against environmental, social, and governance (ESG) investing, so-called “green” investments in clean energy, climate mitigation, and resilience continue to increase. Of course, the backlash against ESG may be attributable to a lack of clarity in ratings criteria and attempts to overregulate disclosures, leading to large companies gaming the system. With more clarity on ratings criteria, public opinion about ESG investing may reverse. And the pressure to reduce carbon emissions has led to a strong demand for infrastructure investment in renewable energy, electrification, and public transport, to name a few. And financing for wind and solar projects, energy storage, and electrification infrastructure is becoming a focal point for investors. Infrastructure debt could be a way for investors to gain exposure to potentially attractive yields, while at the same time fulfilling an “impact” mandate by climate-conscious asset owners, especially in Europe. Building Infrastructure for the AI Revolution With the rapid rise of artificial intelligence (AI), the need for new types of infrastructure has become apparent. Data centers, essential for AI processing and cloud computing, are one of the latest drivers of infrastructure spending. Infrastructure debt offers a unique way to participate in the AI-driven future by funding the physical backbone supporting this technology. In addition, AI energy consumption is emerging as a serious issue that some companies are already addressing by building small nuclear reactors to power their data centers. These new facilities demand significant capital and sophisticated management skills and can produce attractive investment opportunities because debt can be issued to complement equity investments, such as the ones from this recently created AI infrastructure fund. Why Infrastructure Debt is an Attractive Asset Class Aside from cyclical macroeconomic tailwinds, infrastructure debt appeals to investors for several reasons. First, there is the unique risk-return profile. Infrastructure debt typically shows low levels of correlation not only with publicly traded bonds, but even with direct lending or consumer lending opportunities in private markets. Somewhat overlooked is the fact that infrastructure debt has shown diversification from the business cycle, too. Another prominent factor is the potential exposure to an illiquidity premium. Infrastructure debt often exhibits lower liquidity than corporate debt, but, as argued here previously, this is not necessarily a negative. While the jury is still out on whether investors are compensated for giving up liquidity, a fair argument is that illiquidity limits opportunities for investors’ knee-jerk reactions to broader market moves.    Finally, the sector’s default risk has been historically low compared to similarly rated