CFA Institute

Traditional investment approaches assume investors have equal access to market information and make rational, emotionless decisions. Behavioral finance, championed by Richard Thaler, Daniel Kahneman, and Amos Tversky, challenges this assumption by recognizing the role emotions play. But the ability to quantify and manage these emotions eludes many investors. They struggle to maintain their investment exposures through the ups and downs of market cycles. In this post, I introduce a holistic asset allocation process intended to manage the phenomenon of regret risk by considering each client’s willingness to maintain an investment strategy through market cycles. I also evaluate the suitability of a client’s expectations to determine if a strategy is a good match and is likely to be sustained. The upshot is a case for equally weighted investment strategies. The Importance of Maintaining an Investment Strategy Investors must maintain their strategy over a long period of time if they are to achieve the expected results. This requires rebalancing their portfolios periodically to maintain exposure in each segment of the strategy, especially across periods of high volatility. Investors whose emotions lead them to deviate from the strategy are effectively timing the market by making predictions about future returns. These actions present their own form of risk, adding to the existing risk of unpredictable markets. The Role of Knowledge We must acknowledge that we can’t predict the future with any certainty. Despite having data, analysis, and expert opinions, our forward-looking decisions are educated guesses. To manage the uncertainty of this knowledge gap, we must plan for the outcomes that may occur by holding investments that capitalize on favorable outcomes, combining these with other investments that mitigate the unfavorable ones. The investor can reasonably expect more stable returns from this more intuitive diversification approach. I evaluated my results using nearly a century of market data that cover the US economy across many of its market cycles and through times of both peace and extreme geopolitical stress. This analysis includes the types of regret-inducing events investors are likely to encounter. The Nature of Regret Regret is an emotional reaction to extreme events, whether the events produce losses or gains. When regret drives an investor to abandon an investment strategy, this adds the risk of a whipsaw effect: being wrong on both the exit from and re-entry into the investment markets. Over the past 95 years, the S&P 500 has returned 9.6% annually. Missing out on the 10 best years would have lowered that return to only 6%. However, avoiding the worst 10 years would have boosted the return to 13.4%. The investment markets provide ample opportunities for regret. This makes guarding against regret critical to helping investors maintain their investment strategies. Asset Allocation Through the Lens of Regret Harry Markowitz is called the father of Modern Portfolio Theory for his work in quantifying the benefits of diversification. Yet, in his own portfolio he divided his money equally between stocks and bonds, since he did not know which was likely to do better in any given year. This demonstrates the wisdom of splitting assets equally across investments. The case for equally weighted strategies is based on avoiding risk concentrations and equalizing each asset’s marginal contribution to return and risk. This is a fundamental driver of efficiency. We see many examples of equally weighted indexes outperforming their capitalization-weighted counterparts. We used a 70/30 mix of large-cap and small-cap stocks for the US equity market, and a 50/50 mix of 10-year and 20-year Treasuries for the bond market. We expect these investments to have complementary, if not opposite reactions to market conditions, making them ideal diversifiers. We also prepared for a third scenario — the most stressful and regret-inducing — the likelihood of intense geopolitical turmoil. When markets become unsettled, economies are distressed, and currencies lose much of their value. During these times, investors turn to real assets as a more secure store of wealth and liquidity. We created a category of reserves comprising gold and Treasury bonds. Following our naïve diversification approach, we split the reserves allocation equally between bonds and gold. Figure 1: Regret-managed strategy Evaluating the Diversification of the Regret-Managed Strategy Over 95 Years We found that equities, bonds, and reserves were uncorrelated with each other. Within reserves, the gold and Treasuries were also uncorrelated to each other. While gold and Treasuries earned the same return, their combination earned a significantly higher return. Table 1: Correlation of assets within regret-managed portfolio Figure 2: Growth of reserves portfolio Performance Results Our goal was to minimize regret and the likelihood of abandoning the asset allocation. I found that the regret-managed portfolio performed well in the context of traditional efficiency. The portfolio return is higher than the average of its components, and its risk is nearly as low as its lower-volatility reserves. Table 2: Returns over 95 years Figure 3: Efficiency of regret-managed strategy Regret-Managed Strategy Versus Classic 60-40 Benchmark The regret-managed strategy outperformed the familiar 60-40 benchmark (S&P 500 + Aggregate bonds) since the benchmark’s inception nearly 50 years ago. This shows that my efforts to minimize regret did not come at the cost of efficiency. The 60-40 investor also experienced greater severity and frequency of regret. Figure 4: Regret-managed strategy vs 60-40 strategy Quantifying Regret The first step in measuring regret is to assign a limit to the returns that qualify as regret-inducing. Perceptions of regret are unique to each client, recognizing that investors respond more strongly to losses than to gains. Some suggest that the response to losses is twice that of similar-sized gains. We developed our upside and downside regret targets with negative values at about half the positive target. Our base case sets the targets at -12% and 25%. Any returns beyond this range are regret-inducing. The next step is to determine the magnitude and the likelihood of upside and downside regret experiences. We calculated the average of the returns exceeding the regret targets, along with their percentage occurrence. These produce an expected regret penalty in the same units as the expected

Evaluating investment managers is a challenging endeavor. Why else would asset owners expend so much time and resources, often with the aid of consultants, to conduct manager searches? Proper manager selection and evaluation requires thorough due diligence, but a relatively simple filter can serve as a helpful initial screen of potential investment managers. There are three basic questions that asset owners should ask of any quantitative manager before initiating their due diligence process with that manager. If a manager does not provide adequate responses, they may not merit further consideration. Though our focus is quantitative managers, the same questions also work for fundamental managers, especially in regards to the quantitative screens or signals they use in their investment processes. 1. What are the drivers of your investment process? Investment managers should be able to explain what factors they consider most important to their investment decision making and provide some conceptual justification for them. For example, their equity factors ought to be economically intuitive and understandable rather than opaque or synthetic. As a case in point, consider the definition of the Value factor. A single understandable metric like price-to-book has advantages over hybrids such as a “Value” factor comprised of some combination of price-to-book and price-to-earnings. Why avoid such hybrid approaches? First, the evidence that price-to-earnings is a rewarded risk factor has far weaker empirical support relative to price-to-book. Second, even if we were to use both metrics, a hybrid that combines the two individual metrics in some way, say 50% price-to-book and 50% price-to-earnings, does not make any economic sense. That is, what is the return stream of the hybrid “factor” a return stream of? Third, combining different metrics may give us exposures that we do not want. Finally, even if we combine factors as above, we will have to apply some form of weighting scheme, whether static or dynamic. But then we have to provide a justification for our weighting scheme. If our only justification is that it worked well in a backtest, then we are succumbing to the most fundamental error in both investing and statistics: We are basing what is supposed to be a generalizable investment strategy on an overfitted metric. Thus, using a clear set of factors that makes economic sense and can be defended on conceptual grounds is critical to evaluating whether a manager has a firm and well-constructed investment process or is making investment decisions based on a flimsier set of considerations. An important additional component of equity factor strategies is controlling the potential negative interaction effect among the various equity factors. For example, the stocks in a Value strategy have at least some exposure to Momentum and Size, among other factors. If the exposure is large and negative, then the strategy could wash away the premia that is being harvested from the Value exposure. Thus, managers must have a procedure in place that allows for factor tilts but controls for these negative interaction effects. If not, then a given strategy will stray from its stated mandate. Managers should be able to explain how their process ensures their intended exposures in the presence of interaction effects. Finally, an important aspect of gauging a manager’s answers to our first question is their consistency. What if different members of an investment team, say the head of research and the senior portfolio managers, have divergent views on what the most important factors are in their investment process? Then maybe their strategy is not fully developed. This “inconsistency risk” can plague both quantitative and fundamental managers but is perhaps more common among fundamental managers who often have less disciplined investment processes relative to their quantitative peers. 2. What evidence is there that your investment process will be effective? A well-constructed investment process should be validated through a large body of empirical evidence and a comprehensive array of statistical tests. For example, a quantitative process should be supported by very large data sets, tests that use different subsamples, and various types of simulations. All these validation methods should be documented, ideally in peer-reviewed journals. For example, the investment team at Scientific Beta has collectively published dozens of papers over the years that articulate its views and back up its approach to equity factor investing with evidence. Why is publishing papers in journals useful? Because it gives the wider investment community the opportunity to evaluate an investment team’s ideas. And because the evaluators share no business interests with the authors, their assessments are more objective. Publishing research helps establish the legitimacy of quantitative investment processes. Not only does it provide a view into a manager’s investment methodology, but it also aligns a manager’s research efforts with genuine scientific practice. In science, answers to questions are derived from consensus. That is, different research teams operating independently come to similar conclusions. Because of this, their results reinforce each other. If a manager cannot explain or provide any support, empirical or otherwise, why their process works, asset owners should take it as a red flag. Of course, some investment firms do not publish their research because they say they want to protect the proprietary elements of their investment process, their ”secret sauce.” But that is not convincing. After all, other firms do publish their research without fear of misappropriation. Either way, a firm’s methodologies should be supported by both proprietary manager research and research external to the firm. 3. What risk controls are part of your investment process? Ensuring that a strategy is delivering what it is supposed to and is not exposing itself to undesirable risks is integral to effective investment processes. For example, in an equity factor strategy, the goal is often providing focused exposure to one or more factors. So, a Value strategy’s return should be primarily driven by exposure to the Value factor. If a factor strategy’s return stream comes from other factors or the idiosyncratic risk of individual stocks, then unwanted risk exposures are creeping in. Thus, lack of risk control may lead to unintended consequences. Model

Liquidity risk is often misunderstood, yet it plays a crucial role in financial stability and market confidence. The collapse of Silicon Valley Bank (SVB) highlighted how perceptions of liquidity risk — often mistaken for solvency issues — can rapidly escalate into a full-blown crisis. For financial analysts, understanding bank liquidity risk is essential not just for assessing individual banks but also for evaluating broader market conditions. Whether analyzing balance sheet structures, stress-testing funding sources, or identifying potential market liquidity disruptions, analysts must recognize how liquidity risk influences asset pricing, creditworthiness, and systemic risk. The primary cause of the SVB failure is often cited as a failure to manage liquidity risk.  But what is liquidity risk?  Did SVB fail solely because it was unable to fully meet the redemptions of all its depositors? Why couldn’t SVB simply sell its loans and Treasury assets to cover the redemption request of its depositors?  These questions illustrate the typical confusion between liquidity and solvency. In SVB’s case, it was unclear whether the marketable value of its assets (mostly highly liquid Treasury bills and bonds) would have been enough to satisfy its liabilities given the huge unrealized losses it was sitting on. Simply put, the value of its liabilities exceeded the value of its assets at a given moment in time. It was insolvent, not illiquid. The depositors realized that if they were first in line, they would get back 100% of their deposits. Wait too long, and SVB would have simply run out of funds, unless the remaining deposits were insured by the Federal Deposit Insurance Corporation (FDIC). Many of the deposits were not FDIC-insured. On the surface it might have looked like a pure bank run. Only, it was not.  What Is Bank Liquidity Risk? Structural Liquidity Risk Structural liquidity refers to the risks a bank has on its balance sheet because of maturity transformation. The bank pools short, liquid liabilities and buys or issues longer-term, illiquid debt or loans. The liquidity risk here arises from balance sheet structure due to maturity mismatch. Term Liquidity Risk Term liquidity refers to a mismatch between the timing of a bank’s cash inflows from its assets and the cash outflows to fund its liabilities. Structural and term liquidity are related because asset portfolio cash inflows are typically contractual in nature and do not always align with liabilities cash outflows. These deposit and short-term borrowing cash flows are largely behavioral, non-contractual, in nature. Contingent Liquidity Risk Contingent liquidity risk refers to the risk of having insufficient funds to meet sudden or unexpected short-term obligations. Contingent liquidity is related to structural and term liquidity in a sense that there is always a possibility of a mismatch. Banks always need a way to plug the gap in short-term cash. On any given day, there might be an unusually large deposit withdrawal or many of the borrowers may decide to draw down on their line of credit. The Federal Reserve discount window, the repo market, or the Federal Home Loan Bank (FHLB) credit line are a few contingent credit facilities that banks can draw on. Banks should ensure that they always have access to these secured lines. Banks must also ensure that they have high-quality, unencumbered assets to use as collateral to secure the credit. Market Liquidity Risk Market liquidity risk is the risk that arises from the inability to sell assets into the market at “fair value” due to temporary market disruptions. This disruption usually manifests itself in very large bid-ask spreads. What Is Bank Liquidity Risk Management? Banks rely on several forms of liquidity risk management. Tactical Liquidity Risk Management There are two fundamental ways of assessing tactical liquidity risk: Net cash position and maturity mismatch approach. Net cash position measures the bank’s ability to fund its assets on a fully collateralized basis. It looks at the ratio or the difference between highly liquid securities (unencumbered, repo eligible) and unsecured, short term rating-sensitive funding. Basel LCR, NSFR is an example of such an assessment. This approach is simple and intuitive but says nothing about the timing.  In other words, it tells you the banks can survive but not for how long. Maturity mismatch approach matches the inflows and outflows of cash based on residual maturity (whole loans, for example.), liquidation period (AFS and investments, for example), short-term contingent outflows (line of credit and guarantees, for example) and behavioral maturities (NMDs and prepayments, for example). These flow-based approaches are the Fed’s method for assessing and reporting liquidity risk. Strategic Liquidity Risk Management Strategic liquidity risk management refers to predicting and managing how news and information about a bank’s net worth, its creditworthiness, or its overall credit or market risk position will affect its ability to borrow or to attract or maintain its depositors and investors.  There are three questions the banks must address with regards to strategic liquidity risk management: Funding sources:  Are the CD/CP’s, repo, securitization, and reliance on backup lines and the Fed all back-tested and reliable? Does the bank have a contingency plan? Scenario analysis:  How stable are the behavioral models and assumptions under various scenarios, and how are the net cash or mismatch gap assessments impacted? Has the bank tested the model assumptions under stress scenarios? Internal funds transfer pricing: How quickly will those who lend to banks pull out given certain set of events? What is the cost of raising additional liquidity or attracting new deposits or investors? And are those costs being allocated to the right business lines? Key Takeaways Liquidity risk is more than just the ability to access cash — it is about managing uncertainty in timing, availability, and cost of funds. The distinction between liquidity and solvency is critical, as seen in high-profile banking failures like SVB where asset values failed to cover liabilities. Effective liquidity risk management requires banks to address structural mismatches, anticipate contingent liquidity needs, and maintain reliable funding sources. Without a robust strategy, even well-capitalized banks can face destabilizing crises. Understanding these dynamics is essential

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