CFA Institute

Goodbye, Pitta It was a sunny afternoon when I dropped off the beautiful bird in a wooded park in the middle of a concrete jungle of a city. The pitta bird is a rare sight in tropical forests, even to the trained eyes of birdwatchers. Yet there she was just hours before, perched on the window of my condo, in a busy metropolis bustling with traffic and millions of pedestrians, miles away from home. Rather unoriginally, I named her Pitta. I hope she survives. Biodiversity loss ranks among the top five global risks. That’s according to “The Global Risks Report 2020” from the World Economic Forum. Of those top five risks, three were environmental in nature. The numbers are stark: The total populations of wildlife species have plummeted 68% between 1970 and 2016, and one million animal and plant species now face extinction. This deterioration of biodiversity and related ecosystem services is the combined result of land and sea use changes, direct exploitation, climate change, and pollution. Let’s explore why institutional investors should protect ecosystems and biodiversity and how sustainable investment strategies that offer risk mitigation and value creation opportunities can help accomplish this. The Case for Sustainable Investing 1. Institutional investors have a fiduciary responsibility to manage assets in the client’s best interest. Failure to consider long-term investment drivers, including financially material environmental, social, and governance (ESG) criteria, is a failure of fiduciary duty, according to the 2019 PRI Report. 2. The annual monetary value of ecosystem services is a whopping US$125 trillion to US$140 trillion. That’s more than one and a half times global GDP. A wide range of investable sectors rely on natural resources and ecosystem services and can have a potentially negative effect on biodiversity. These include agriculture, fisheries, extractives, fast-moving consumer goods (FMCG) companies, forestry, and utilities, among others. 3. Can sustainable investing reduce risk and enhance returns? Research says yes. Several studies and meta studies indicate ESG issues can be financially material to companies’ operational performance, lower the cost of capital, and potentially enhance alpha. Engaging with companies on ESG issues can create value for both investors and firms. What Investment Approaches, Asset Classes, and Strategies Are Available? Responsible investing strategies range from social investing with submarket returns to impact investing with market-driven return objectives to full ESG integration for long-term value creation. Sustainable investments now extend across the full range of asset classes that compose diversified investment portfolios. These include stocks, bonds, real estate, private equity, and venture capital. A growing number of exchange-traded funds (ETFs) with ESG tilts are available as well. Sustainable investing assets in Europe, the United States, Japan, Canada, and Australasia stood at US$35.3 trillion at the end of 2020, according to “Global Sustainable Investment Alliance Investment Review: 2020.” Alignment, Integration, and Engagement: A Necessary Paradigm Shift “A sustainable investment strategy consists of building blocks familiar to institutional investors: a balance between risk and return and a thesis about which factors strongly influence corporate financial performance.” — Sara Bernow, Bryce Klempner, and Clarisse Magnin, Mckinsey Thus, for a client seeking risk-adjusted returns with a biodiversity focus, the investment strategy should align with their objectives and timeframes and integrate these longer-term risks and factors into its investment processes. Full Integration extends investor objectives beyond risk mitigation to value capture and must occur across the financial system’s entire value chain. Time Frames: Pension and sovereign wealth funds, among other institutional investors, have long investment time horizons. Fund managers and investee companies, however, measure profitability on much shorter time tables — quarterly, for example. This misalignment of interests requires a shift in perspective. Explicit Costs of Natural Capital and Externalities: Understanding the value of both natural capital impacts and dependencies helps business and financial decision makers assess whether these issues affect their institutions and make more informed decisions. The Dasgupta Review from 2021 recommends valuing biodiversity as an economic asset rather than a free resource as a means of halting its depletion. The Cost of Externalities: On the other side of the coin, the environmental impacts of products or services that are not explicitly priced in — externalities — can influence the wider economy and potentially investors’ long-term total return. The solution? Internalize externalities through market-based instruments such as taxes, regulatory instruments like vehicle emission and safety standards, or such voluntary instruments as emission reduction agreements. The Value of Engagement: By opening a dialogue, investors and institutions can encourage companies to become more sustainable, more efficient in their use of natural resources, and ensure that their current earnings do not borrow from their future earnings. Policy Dialogue: Whether institutional investors generate sustainable returns and create value is influenced both by market efficiency and the effectiveness of public policy. The EU’s taxonomy for sustainable activities is a critical example. Investors can work with regulators, standard setters, stock exchanges, and other stakeholders to design a sounder and more stable financial system that better integrates ESG into financial decision making. Final Thoughts Let’s loop back to Pitta. What can be done? Various financing initiatives that leverage public sector and development finance for sustainable agriculture, biodiversity conservation, and the blue economy are emerging. Many of these are focused on vulnerable developing economies. The Asian Development Bank and the World Bank, among other such institutions, are creating innovative financing products that support these efforts. The World Bank’s five-year, $150 million Wildlife Conservation Bond, for example, is a form of biodiversity thematic investing that aims to protect South Africa’s black rhinos while offering investors a competitive return based on achieving conservation success indicators. So, efforts are under way. Let’s just hope they’re enough. Stay safe, Pitta. We will try our best. If you liked this post, don’t forget to subscribe to the Enterprising Investor. All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer. Image courtesy of Tahmeen Ahmad, CFA Professional Learning for

Introduction Mortgage rates have doubled and tripled in some countries since 2021. So, why aren’t residential real estate markets more distressed? For example, the average home price-to-income ratio in the United Kingdom is at an astounding 9x. This implies that most borrowers are spending more of their income on interest and amortization payments than ever before. The typical UK mortgage is five years, but the interest rate for a new loan has increased from 1.8% a year ago to 4.6% today. Many borrowers will not be able to refinance at this level and will be forced to default. The bank will then sell the home, putting more downward pressure on the housing market. Yet property markets continue to surprise. Many, including this author, thought that UK homes were already overpriced at an average home price-to-income ratio of 6x over the last decade. Then these homes became even more expensive. Perhaps governments will step in and support borrowers as the political pressure rises. Or maybe inflation will cool and central banks will lower interest rates. Since many variables influence housing prices, assessing residential real estate as an asset class is a complicated endeavor. So, what are the key drivers of the sector, what are some of the common misperceptions, and what is the long-term outlook? Supply and Demand Residential real estate prices are influenced by either fundamental supply and demand imbalances or simple speculation. The former is easy to understand: When demand outstrips supply, prices tend to appreciate. Supply could be constrained by natural population growth, immigration, urbanization, regulation, or some combination thereof. The trends tend to differ from countryside to city and even within cities, which makes it difficult to gain a clear picture of the true state of the housing markets. Differentiating between nominal and real post-inflation returns is critical when evaluating real estate investments. For example, residential real estate in China looks like it would have been a sure bet over the last two decades given the country’s phenomenal economic growth. But while that may be true for Shanghai and other cities, Chinese home prices only rose at a nominal rate of 3.5% per annum between 2005 and 2022. That compares to an annual GDP growth rate of 8%. So in real terms, residential real estate may not have been as great an investment as China’s economy overall. Nominal and Real House Price Growth Often Vary Sources: Bank for International Settlements (BIS) and Finominal That residential real estate will appreciate over time is a common assumption, but it is not always the case. When a housing market’s supply and demand balance is in equilibrium, prices can remain stable for decades. For example, Germany’s population rose only slightly from 78 million in 1970 to 83 million in 2022, and real house prices hardly budged over the entire period. House Prices Can Stay Flat for Decades Sources: Bank for International Settlements (BIS) and Finominal Based on fundamental demand, the long-term outlook for residential real estate in the world’s 10 largest economies looks pretty dismal. With only 4 of these nations expected to grow in population over the next 80 years, all 10 are expected to shrink by a cumulative 600 million people or so. Efforts to increase fertility rates by offering more childcare benefits or otherwise incentivizing population growth have largely failed. Increased immigration may help, but few countries have experience with the sort of large-scale immigration that will be required, and even those that do can often face internal resistance. Most of the decline is expected after 2050, but Japan will shrink by around 25 million people between now and then, according to UN estimates, and is already feeling the effects. Many rural areas have experienced rapid depopulation, and local municipalities have a hard time funding and staffing schools, hospitals, and other public infrastructure. Some towns now offer tax breaks to newcomers or just pay people outright to relocate there. Either way, there is less demand for housing, and that will ultimately mean lower prices. The Demographic Outlook Is Dismal in Many Large EconomiesEstimate Population Growth, 2023 to 2100 Sources: United Nations (UN) and Finominal Speculation Speculation is another key driver of housing prices and comes in many varieties. Sometimes prices rise because of a supply and demand imbalance. This persuades investors to pour their money in and creates a positive feedback loop. In some countries, entire generations have been raised on the concept of the property ladder. In the UK, that has meant buying a small flat after university, selling that once it has appreciated in value, buying something slightly bigger, and hopefully laddering up over the years to a large house in the countryside. Naturally, this assumes home prices appreciate forever. But as in any financial market, such feedback loops can lead to bubbles that are quite painful when they start to deflate. As an ascendant economic powerhouse in the 1980s, Japan experienced a significant boom in home prices during the 1980s, but the subsequent bear market lasted for almost three decades. Real Estate Bear Markets Can Be Long and Painful Sources: Bank for International Settlements (BIS) and Finominal Fiscal and monetary policy can also encourage real estate speculation. In the aftermath of the global financial crisis (GFC), the UK government adopted a help-to-buy program that offered interest-free mortgages, and quantitative easing (QE) and other accommodative measures by central banks provided a powerful tailwind for home prices. Interest rates had been on the decline since the 1980s in most developed countries, so both retail and professional investors came to see real estate as an alternative to bonds and shifted trillions in capital from fixed income. As a consequence, real estate yields reached record lows, with UK homes generating less than 2% per year in rental income before maintenance costs and taxes. As such, residential real estate made little sense as an investment — except when compared with equally low or even negative bond yields in some European countries. With the spike in interest rates over the last

Investors seeking to diversify their holdings away from traditional private equity may want to look at search funds. Although these funds debuted in the mid-1980s, they have gained traction in recent years as the number of funds has grown exponentially and returns have been consistently attractive. This blog looks at search funds — what they are, how they differ from private equity, and why they should be on the radars of some investors. What are Search Funds? A search fund is an investment vehicle formed to find, acquire, and operate a closely held business. The fund uses predetermined investment criteria, such as minimum EBITDA and revenue, industry, and geography. The funds were conceived in 1984 by Irv Grousbeck, the MBA Class of 1980 Adjunct Professor of Management at Stanford University’s Graduate School of Business. Since then, over 700 search funds have been launched, creating an entire ecosystem known as entrepreneurship through acquisition (ETA). There are now search funds operating in Europe, Latin America, and Asia. There are two primary types of search funds: the self-funded and the traditional model. A third, relatively new model, the independent sponsor model, is beginning to gain traction. In the self-funded model, an entrepreneur uses savings and family contributions to fund expenses such as marketing, subscriptions, and travel. Term loans and government-backed programs usually fund the acquisition, depending on the market in which the entrepreneur operates. However, most self-funded entrepreneurs partner with several investors to finance the equity portion of the deal. Under the traditional search fund model, the most prevalent, an entrepreneur raises capital by selling units to investors. These units represent an equity stake in the entrepreneur’s search fund. The capital covers search-related expenses for 24 to 36 months. Investors who purchase units at this stage receive the right but not the obligation to participate in financing the acquisition. They will have a right of first refusal to finance the entire equity portion of the acquisition before the entrepreneur approaches outside investors. A board of advisors provides the entrepreneur with guidance and support during the search phase and a full board of directors once the acquisition is made. The investment horizon post-acquisition ranges from four- to seven-years. Recently, however, search funds have adopted a long-term hold strategy to maximize value creation. The search fund ecosystem is being driven by leading business schools such as the University of Virginia’s Darden School of Business, Harvard, Stanford’s GSB, and the University of Chicago Booth School of Business. These schools identified search funds as a path graduates can take to become CEOs of small businesses. Search funds target small- to medium-sized businesses (SMBs) in underexplored markets, creating opportunities in areas often overlooked by private equity funds. Unlike private equity, which targets larger businesses with high competition, search funds operate in niches where valuations are lower, and deals are less contested. PE funds also invest in multiple companies while search funds are designed to invest in a single company. Many search funds tend to target businesses that serve local or regional markets, providing vital goods or services that can be scaled with proper management. Ideal acquisitions are companies that generate consistent positive cashflows, have recurring revenue, low customer churn, minimum EBITDA of $1 million, low exposure to external risks, and a strong management team. The opportunity lies in the value creation ability of the search fund. The newest type of search fund is the independent sponsor model. This model allows entrepreneurs to pursue acquisitions without raising a traditional search fund upfront. Instead of securing committed capital before searching, independent sponsors identify and negotiate deals first, then raise equity and debt financing from investors on a deal-by-deal basis. This approach offers flexibility, enabling searchers to leverage their networks and expertise while aligning investor interests with specific opportunities. The Value Proposition The Stanford Graduate School of Business 2024 Search Fund Study (Figure 1) analyzed the 681 search funds formed in the US and Canada since 1984. The funds reported an internal rate of return (IRR) of 35.1% and a return on investment (ROI) of 4.5x. The consistent performance across decades, despite changing macroeconomic conditions, underscores the resilience and long-term value-creation potential of the search fund model. Figure 1 | IRR and ROI by Year of Company Acquisition. Search funds offer a compelling investment model by aligning seamlessly with the long-term, strategic objectives of most investors who prioritize sustainable growth over quick exits. Unlike traditional private equity, search fund entrepreneurs emphasize operational value creation post-acquisition, dedicating themselves to hands-on management and value-add activities that enhance business efficiency and profitability, resulting in stronger operational performance. Search funds target undercapitalized small- to medium-sized businesses, unlocking unique opportunities in underexplored sectors with significant growth potential. This combination of alignment, operational focus, and access to untapped markets positions search funds as an attractive vehicle for investors seeking both financial returns and lasting impact. Given the role of business schools, there are opportunities for family offices and institutional investors to partner with MBA programs to help cultivate a pipeline of skilled operators while creating search fund accelerators, structured programs offering capital, mentorship, and networks could professionalize the ecosystem and reduce risk. The Future The search fund model is gaining momentum, with growing adoption in Europe, Latin America and Asia, alongside rising interest from institutional investors seeking alternatives to traditional private equity. This expansion reflects the model’s appeal: high potential returns from entrepreneurial talent in underserved markets. Technology is poised to accelerate this trend as AI and data-driven tools streamline the funds search process. Search funds will benefit from faster target identification, due diligence, and enhanced post-acquisition operations through predictive analytics and efficiency gains. Search funds stand out as a valuable alternative asset class, offering diversification, alpha potential, and operational upside in underserved markets. Their lower capital requirements, hands-on value creation, and alignment with long-term investor goals make them a compelling counterpoint to traditional private equity. In addition to their investment potential, search funds represent an opportunity to back entrepreneurial talent and reshape how value is

What makes financial capitalism so compelling is the idea that modern fund managers fully participate to the upside of their investment decisions with little exposure to the downside. This “Heads I Win, Tails You Lose” model helps maximize the economics of the trade. Certainly, private capital firms accumulate wealth regardless of the underlying portfolio’s risk–return trade-off. To recap, the performance of alternative asset managers is encapsulated in the following formula: Wealth = Controls + Economics We explored the techniques managers use to control investment outcomes in Part 1. Here, we outline the second component of the wealth equation: economics. Offloading Investment Risk How to diversify risk away is a vital piece of the economics puzzle for alternative managers. One way to accomplish this works like a game of roulette: The more numbers you bet on, the better your chances of winning. To improve their odds of making money, fund managers often invest in many corporations or start-ups that compete in the same sector. But the genius of alternative investments is that fund managers’ share of losses is restricted to only the portion of their annual bonuses — derived from annual management fees charged on their clients’ capital commitments — that they co-invest alongside their clients. This token participation gives the appearance of skin in the game and aligned interests, but the managers’ odds are much better than those of their LP investors: It works as a sort of call option that fund managers can exercise if the value of the portfolio asset rises or let expire if the value falls. The symbolic co-investment acts as an option premium. Another way private equity (PE) firms can tilt the balance in their favor is to finance buyouts with leverage. Higher leverage has the mechanical effect of lifting the internal rate of return (IRR), providing a shortcut to beat the hurdle rate. Of course, excess leverage amplifies the financial stress on the borrower and increases the likelihood of default. This, in turn, can lead creditors to seek control of the portfolio asset and provoke heavy capital losses for the fund managers’ clients. But as agents, the fund managers themselves simply lose out on future fee income. Management, Not Ownership Capitalism has moved away from its classical definition. It no longer depends on ownership rights and private property but on management rights and controls. We own our pension plans and other financial assets. But in Marxian terms, we are nonetheless “alienated” from them when we outsource their administration. Asset custody is indeed more relevant than ownership. The transfer of property rights doesn’t affect the fund managers’ ability to levy fees on capital commitments. These financial intermediaries have the “right to use” rather than the “right to own” their clients’ assets. The ingenuity of the custodial investment model is that, unlike banks and other traditional financial institutions, alternative managers do not pay for the privilege of administering other people’s money. Instead, they earn an abundance of fees, often irrespective of performance. The main consideration of the economics variable is, therefore, rent extraction engineered through quasi-unqualified, long-term contractual access to assets without being charged by the captive, fee-paying asset owners. Customary money management techniques, in contrast, rely on dividends and capital gains derived from equity instruments, or interest payments and coupons received from loans and bonds. Multi-Layered Charges The alternative fund manager’s fee-based model takes three tacks: First, annual management commissions (AMCs) can range from 1% to 2% of assets under management (AUMs) in PE and private debt (PD), and exceed 2.5% in smaller funds, particularly in venture capital (VC). What is most striking is how large management firms can keep drawing out AMCs in excess of 1%. Apollo Global Management, for instance, reaped “1.5% per annum of [its] Fund VIII Capital Commitments up to $7 billion, and . . . 1.0% per annum in excess of $7 billion,” according to the limited partnership agreement. Yet mega buyouts do not require proportionally higher involvement than mid-sized ones. At any rate, operational work is charged out separately in the form of advisory fees. But management commissions explain only part of the alternatives model’s profitability story. (Although some managers rely on them more than others. For example, over 80% of Bridgepoint’s operating income from 2018 to 2020 was from AMCs.) To complement their revenue stream, fund managers solicit performance fees — also called carried interest, or carry — which grant them the right to capital gains above a certain rate of return guaranteed to investors. This share of the upside varies widely: In PD, it is typically set at 10%; in PE, it is closer to 20%; for the most prestigious VC fund managers, it can exceed 30%. Importantly, the carry agreement never requires fund managers to share in the fund providers’ capital losses. This is a cornerstone of the private capital wealth equation. Besides, the guaranteed or preferred rate of return — the hurdle rate — is usually set at 8%, but managers with market power can negotiate much lower hurdle rates or forgo them altogether. KKR, for instance, raised two European PE funds in 2005 and 2008 without offering clients a hurdle rate, though it reversed course for its third European vintage in 2014. Finally, exceeding the hurdle rate is challenging. This makes carried interest neither dependable nor sufficient as a revenue source. For example, carry contributed only 5% to Bridgepoint’s operating income in the three years from 2018 to 2020. For that reason, ancillary charges can help top up earnings. Some of these are advisory in nature, such as monitoring, consulting, or director fees. Others have more prosaic labels, including completion, syndication, arrangement, or break-up fees. Many fund managers eventually return part or all of these advisory fees to their LPs. This fee-centric money machine relies on inertia: Because of a severe lack of liquidity, private capital firms will often hold onto assets through market downturns without facing the risk of redemption that afflicts hedge funds and open-ended mutual funds. Loose mark-to-market rules can

I’m the most important person in behavioral finance, because most of the behavioral finance is just the criticism of efficient markets. So, without me what do they got? Eugene Fama Gene has it all wrong. If it were not for Behavioral Finance, he and French would have had nothing to do for the past 25 years. He owes me everything. Richard Thaler After reading these quotes from Fama and Thaler, you may conclude that they are bitter rivals. But this is far from the case. Fama and Thaler are business school professors at the University of Chicago and well-documented golf buddies. But despite sharing the occasional 18 holes, there is very real underlying tension between the two. Fama is captain of Team Efficient Markets and Thaler is captain of Team Behavioral Finance. Each represents conflicting academic market philosophies that have been warring for years. It’s the academic equivalent of Lakers vs. Celtics. Team Efficient Markets believes that market prices reflect all available information and are therefore efficient. Its strongest proponents believe that risk-adjusted performance over long-time horizons isn’t possible. Over time, the philosophy expanded to include risk factors. Investors can be compensated by tilting their portfolios toward risk factors to achieve higher returns. This team believes that because these factor tilts represent increased risk, risk-adjusted performance over long-time periods isn’t possible. Market efficiency proponents argue that if empirical evidence shows long-term risk-adjusted performance was achieved, investors didn’t achieve it due to skill but by tilting their portfolios toward a previously unidentified risk factor, or by dumb luck. “Buffett’s Alpha” deconstructed Warren Buffet’s phenomenal track record at Berkshire Hathaway into different explanatory factors. The paper won the Graham and Dodd Award for best paper in 2018. The award recognizes excellence in research and financial writing in the Financial Analysts Journal. Although the authors conceded that Buffett’s track record was not due to luck, it’s hard to read the paper without coming away with the feeling that its purpose was to knock Buffett’s performance down a peg. Team Behavioral Finance, on the other hand, believes market prices reflect all available information most of the time, but that market participants are also influenced by behavioral biases. This behavior leads to market inefficiencies that can be exploited to achieve superior risk-adjusted performance, even over long-time horizons. Regarding factor investing, the behavioral camp believes that ‘risk factors’ represent price/value gaps due to behavioral biases rather than an increase in risk taking. As it pertains to Buffett, this camp is more likely to believe that his track record is due to his even-headed decision-making skill and access to unique information sources. Unfortunately, many issues arise when debating market anomalies. The main two issues stem from hypothesis testing difficulties (e.g., how would you test for behavioral biases?) and the subjective interpretation required when a market anomaly is discovered (e.g., increased risk, behavioral inefficiency, or spurious correlation). But fortunately, Fama and Thaler’s respective philosophies heavily influence two major asset management firms, Dimensional Fund Advisors (DFA) and Fuller & Thaler Asset Management (FullerThaler). DFA’s founder David Booth served as a research assistant under Fama while attending the University of Chicago in 1969. The firm’s investment underpinnings heavily rely on Fama’s academic research, leading it to tilt their portfolios toward small, cheap companies with higher-than-average profitability. Fama also serves as a director and consultant at DFA. As the name implies, Thaler co-founded FullerThaler with Russell Fuller. The firm seeks to exploit behavioral biases to outperform markets. Like DFA, the firm also tilts its portfolios toward value and size factors. Unlike DFA, the firm seeks to exploit the loss-aversion bias, believing that investors overreact to bad news and losses and underreact to good news. As the name implies, Thaler co-founded FullerThaler with Russell Fuller. The firm seeks to exploit behavioral biases to outperform markets. Like DFA, the firm also tilts its portfolios toward value and size factors. Unlike DFA, the firm seeks to exploit behavioral biases, believing that investors overreact to bad news and losses and underreact to good news. Both firms have an investment fund with a long track record and the same benchmark, The Russell 2000 Value Index. Figure 1 pits the competing philosophies against each other and the funds’ benchmark. Figure 1. DFA’s U.S. Small Cap Value Portfolio (DFSVX), FullerThaler’s Undiscovered Managers Behavioral Value Fund (UBVLX), and The Russell 2000 Value Index. Team Behavioral Finance outperformed Team Efficient Markets by an annualized 0.91% between December 1998 and July 25, 2024. But many readers may disagree that this proves Team Behavioral Finance’s victory, because the results don’t account for risk taken. Fair enough. To test this, I applied Jensen’s Alpha (Alpha) and only use The Russell 2000 Value Index as a benchmark. For the risk-free rate, I de-annualized the three-month treasury rate. Figure 2. After accounting for risk, Team Behavior still comes out on top. This is nearly confirmed unanimously throughout all risk-adjusted return metrics as shown below, apart from the Information Ratio. Despite the results implying that investors can exploit behavioral biases, even over long-time horizons, strong market efficiency believers may be hesitant to change their minds. If so, I encourage these individuals to check their own behavioral biases to ensure they exhibit the same rational traits that the market efficiency hypothesis assumes are true. source

In the world of finance, understanding and managing crises are crucial for maintaining robust portfolio performance. Significant drawdowns can erode years of accumulated gains. Therefore, identifying potential equity market drawdowns and understanding their economic implications is a key focus for asset managers. This post will explore a sophisticated identification methodology I developed in collaboration with Merlin Bartel and Michael Hanke from the University of Liechtenstein. The approach identifies equity drawdowns using advanced spatial modeling, which can be used as a dependent variable in predictive models. Understanding the Challenge: Drawdowns in Equity Markets Equity markets are inherently volatile, and periods of crises are an inevitable aspect of investing. A drawdown is not merely a temporary decline in an asset’s value; it represents a period during which investors may incur significant financial loss. The economic significance of avoiding drawdowns cannot be overstated. By minimizing exposure to severe market downturns, investors can achieve higher risk-adjusted returns, preserve capital, and avoid the psychological toll of significant losses. Traditional methods for identifying and managing drawdowns often rely on simplistic triggers, such as moving averages or volatility indicators. While these methods can provide some level of insight, they lack the depth and sophistication that is required to capture the complex, evolving nature of financial markets. This is where advanced techniques come into play. The Clustering and Identification Methodology Our approach begins by leveraging the concept of clustering to identify patterns in equity return sequences that may indicate the onset of a drawdown. Instead of using a binary approach (crisis vs. no crisis), we propose a continuous-valued method that allows for varying degrees of drawdown severity. This is achieved by employing advanced clustering methods, such as k-means++ clustering, to categorize sequences of equity returns into distinct clusters, each representing different market conditions and subsequently use spatial information to transform the classification into a continuous-valued crisis index, which can be used in financial modelling. Equity Return Sequences and Clustering: We utilize overlapping sequences of monthly equity returns to capture the dynamics of how crises develop over time. Rather than defining a crisis based on a single negative return, we identify a crisis as a sequence of returns that follow specific patterns. More recent returns in these sequences are weighted more heavily than older returns. Minimum Enclosing Ball and Spatial Information: To refine our identification process, we use the concept of a minimum enclosing ball for the non-crisis clusters. This involves identifying the smallest sphere that can enclose all the non-crisis cluster centers. Using the relative distances from the center of the ball and their direction, we can create a continuous measure of crisis severity. The approach provides a more nuanced understanding of crisis risks by incorporating both the distance and direction of return sequences. The Economic Significance of Avoiding Drawdowns The primary economic benefit of this advanced methodology is its ability to provide indications of potential drawdowns, thereby allowing investors to reduce or eliminate market exposure during these periods. By using a data-driven, continuous-valued crisis index, investors can better manage their portfolios, maintaining exposure during stable periods while avoiding severe downturns. This is because the crisis index is predictable, which significantly improves the risk-adjusted returns of investment strategies, as evidenced by empirical testing. Conclusion Identifying and avoiding equity drawdowns is essential for achieving superior long-term investment performance. In our joint research, Bartel, Hanke, and I introduce a sophisticated, data-driven methodology that enhances the identification and, subsequently, prediction of crises by incorporating spatial information through advanced techniques. By transforming hard clustering into a continuous variable, this approach offers a nuanced understanding of crisis severity, enabling investors to manage their portfolios more effectively with predictive modelling. The use of spatial information via the minimum enclosing ball concept is a significant advancement in financial risk management, providing a powerful tool for avoiding costly drawdowns and enhancing overall portfolio resilience. This methodology represents a step forward in the ongoing quest to combine academic insights with practical, actionable strategies in the field of finance. If you liked this post, don’t forget to subscribe to the Enterprising Investor. All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer. Image credit: ©Getty Images / Ascent / PKS Media Inc. Professional Learning for CFA Institute Members CFA Institute members are empowered to self-determine and self-report professional learning (PL) credits earned, including content on Enterprising Investor. Members can record credits easily using their online PL tracker. source

Among C-suite and financial executives at both for-profit and nonprofit organizations, 99% are committed to helping employees save for retirement and 84% believe they have made significant progress toward achieving their organization’s diversity, equity, and inclusion (DEI) goals. That’s according to a December 2021 PNC Survey on institutional social responsibility. Despite these commitments, many employees remain underprepared for retirement. Specifically, low- income workers, women, and people of color tend to have significantly less access to retirement plans, and when these groups do have access, they accumulate fewer retirement plan assets relative to other demographics. Thus, building a more equitable retirement program is essential to creating better retirement outcomes for employees and helping organizations achieve DEI-related goals. So, what does the current retirement landscape look like and how can we address these disparities? We propose three primary methods: automatic plan design features, creative matching contribution formulas, and innovative education strategies. The Current Retirement Landscape Workplace retirement savings vehicles, such as defined contribution (DC) plans, are one of the most common ways that US workers save for retirement. DC plan programs in the United States totaled $11 trillion in assets as of Q4 20211 and provide over 80 million participants with tax-deferred retirement accounts. As defined benefit plans — pensions — continue to decrease in number and with Social Security facing numerous funding-related headwinds, we believe DC plans will grow ever more critical to retirement outcomes. Yet statistics show that DC plans are not benefitting all demographic groups equally. Income level is a key first determinant of retirement readiness, and employees in lower wage groups struggle across the board, with lower access to, participation in, and take-up rates for DC plans. Defined Contribution Plan Access, Participation, and Take-Up Rate by Wage Percentile In terms of gender, a slightly greater percentage of women work for employers that offer retirement plans (69% vs. 65%), according to a 2020 National Institute on Retirement Security study, but a slightly greater percentage of men are eligible to participate in these plans (89% vs. 85%) and choose to do so (81% vs. 79%). This means men and women participate in DC plans at equal rates (47%). However, there is a significant gender gap in retirement income: Women aged 65 and older have a median household income of $47,244, or 83% of the $57,144 median household income of men aged 65 and over. What explains this retirement wealth gap? The gender pay gap and employment gaps for pregnancy, child care, and caregiving for elders or spouses all may play a role. Also, divorce can lead to worse financial outcomes for women than men. These and a host of other reasons may negatively impact women’s retirement outcomes. Household Retirement Plan Access, Participation, and Take-Up Rate by Race and Ethnicity Households with Access to Retirement Plans Households Participating in Retirement Plans Household Take-Up Rate Average Household Retirement Account Balance White 68% 60% 88% $50,000 Black 56% 45% 80% $20,000 Hispanic 44% 34% 77% $20,000 Other* 61% 54% 88% $34,000 *Defined as “a diverse group that includes those identifying as Asian, American Indian, Alaska Native, Native Hawaiian, Pacific Islander, other race, and all respondents reporting more than one racial identification.”Source: “Disparities in Wealth by Race and Ethnicity in the 2019 Survey of Consumer Finances,” Federal Reserve Bank, 28 September 2020 The numbers are even worse across race and ethnicity lines. The preceding table demonstrates the lower levels of access, participation, and average balances for households of color. The average account balance disparity is especially alarming. While plan sponsors strive to design plans that improve retirement outcomes, these statistics show that quite a lot more needs to be done. To address this, three strategies are worth considering. 1. Automatic Plan Design Features Automatic enrollment is a tried-and-true method to increase retirement assets. A company’s new hires automatically start contributing to the firm’s DC plan at a pre-set deferral rate. The contributions are invested in the plan’s qualified default investment alternative — often a target-date fund (TDF) — until the employees re-direct their investments. Auto-enrolled employees tend to remain enrolled — and at the deferral rate set by the plan’s automatic enrollment feature. Default enrollment helps overcome two key retirement savings challenges: lack of knowledge and inertia. Knowledge describes the various lifetime experiences and formal and informal education that leads an employee to employment with a particular company. While some people benefit from a background in which financial literacy was prominent, many do not. For example, low-to-moderate income communities are less likely to know or be solicited by financial advisers due largely to a perceived mismatch between the community’s expected need and the financial adviser’s expected opportunity. This may reduce the likelihood that members of such communities will be familiar with or prioritize saving for retirement. Inertia is a broad category, but our focus here is on two major types. Due to personal financial reasons — budget constraints, debt, etc. — many employees don’t believe they can set aside money for retirement. Other employees simply do not take the time to set up their retirement plan. They see it as “something to get to later” or otherwise delay enrolling in the retirement plan. What starts as “I’ll get to it tomorrow, next week, well definitely next month” can lead to months, years, or even a working lifetime of delayed retirement savings. While automatic enrollment doesn’t affect access, it can increase participation among eligible employees, according to a 2021 study. Indeed, 84% of workers cited the feature as a primary reason for earlier saving. This tracks with the significant rise in plan sponsor adoption over the past decade. In 2011, only 45.9% of plans featured automatic enrollment, according to the Plan Sponsor Council of America. In 2020, 62% of plans did. Automatic enrollment helps employees overcome knowledge and time-related barriers, so we expect more plans will adopt the feature. For plan sponsors that want to add or augment an automatic enrollment feature, these additional considerations may help maximize the impact: Setting the default automatic

“[O]ur mind is strongly biased toward causal explanations and does not deal well with ‘mere statistics.’” — Daniel Kahneman, Thinking, Fast and Slow The predictive power of the yield curve is a widely accepted causal narrative. But the history of the yield curve shows that the causal correlation between long and short rates is actually quite weak. While long and short rates tend to move in the same direction, they do so at varying rates. The debut of the Federal Reserve System in 1914 and the advent of modern central bank orthodoxy amid the Great Inflation of the late 1960s to early 1980s contributed to a divergence in how the market sets long and short rates. The yield curve’s predictive accuracy was decidedly mixed in the first half of the 20th century but was much more reliable in the second half — a shift that aligns with how the the US Federal Reserve has evolved over the years.  During the 19th century and the first three decades of the 20th, yields for four- to six-month commercial paper were higher on average than those of prime long-term bonds. As the US Civil War gave way to peace and deflation, interest rate levels exhibited a downward trend. But towards the turn of the century, gold discoveries increased the money supply and sent rates higher. During this period, the market set interest rates based primarily on the supply and demand of loanable funds. The low interest rates of the post–Civil War era did not prevent eight different NBER recessions between 1868 and 1900. But higher rates from 1900 to 1920 didn’t exert much of an influence over the economy either, with six different NBER recessions over the 20 years. A persistently inverted yield curve may have contributed to the high frequency of recession. After all, a negatively sloped interest rate term structure disincentivizes long-term investment. Only after 1930 did positive yield curves become more regular. The 1929 stock market crash, the resulting shift toward greater economic planning by the state, and the integration of Keynesian economic policies later in the 1930s certainly shifted the slope of the yield curve. As short rates came onto economic policymakers’ radar, they introduced a new causal force that broke the link between short and long rates. With the markets free to set long-term rates, the views of policymakers and the market on the state of the economy diverged. The Fed’s open market operations are, by their nature, countercyclical and lag the real economy. The market, on the other hand, is a forward-looking voting machine that represents the collective wisdom of the crowd. When the market thinks the Fed is too hawkish, long rates fall below short rates. When it perceives the Fed as too dovish, long rates rise well above their shorter counterparts. Market prices are the best indication we have of future market outcomes. Why? Because of the potential rewards available. If the future is in anyway knowable, prices in a free market are the most effective crystal ball: Resources will be directed to take advantage of any mispricings. Financiers in earlier eras would not recognize a connection between long-term and short-term rates. They saw short-term lending as primarily concerned with the return of principal and long-term lending on return on principal. But the combination of Keynesian economic policies and the market’s discounting mechanism made the yield curve the predictive tool that it is today. But it needs to be deployed with caution. It is not just the slope of the curve that matters but how it develops and how long the curve is inverted. Cumulative Days of Yield Curve Inversion Source: Federal Reserve Bank of St. Louis, NBER The yield curve has inverted from positive to negative 76 different times since February 1977 according to the preceding chart — sometimes for months at a time, at other times for just a day — but there have only been six recessions. So, inversion alone is hardly an accurate oracle. Only when the market and the Fed veer apart for an extended time period, when the market expects significantly lower growth than the Fed, does the market’s recession expectations tend to play out. Given the efficiency of the market voting machine, this should hardly come as a surprise. The yield curve is a popular recession indicator for good reason. But we need more proof of its efficacy, particularly when the signals suggest that Fed policy is too loose. If you liked this post, don’t forget to subscribe to the Enterprising Investor. All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer. Image credit: ©Getty Images/ ardasavasciogullari Professional Learning for CFA Institute Members CFA Institute members are empowered to self-determine and self-report professional learning (PL) credits earned, including content on Enterprising Investor. Members can record credits easily using their online PL tracker. source

The Bubble That Popped But Didn’t Deflate When financial bubbles burst, they usually, you know, burst. So, when the FTX crypto exchange collapsed last November, many crypto skeptics expected bitcoin prices to fall to where they believed they rightly belonged: roughly zero. Yet, as of this article’s writing, bitcoin is worth more than in the lead-up to FTX’s implosion. So, what can we make of all this? A key consideration is where crypto investors source their investment data. According to a 2021 study by the National Opinion Research Center (NORC) at the University of Chicago, crypto investors source 24% of their information from social media and only 2% from brokers and financial advisers. Trading platforms and crypto exchanges supply another 25% and 26%, respectively. So, just how does this reliance on social media drive crypto market behavior? To find out, we applied natural language processing (NLP) techniques to crypto-related comments on different forums, or subreddits, on the social media platform Reddit and explored how the resulting sentiment analysis correlated with bitcoin prices. Crypto Market Background Subreddit Subscribers(Millions) CryptoCurrency 6 Bitcoin 4.8 personalfinance 17.3 stocks 5.1 Economics 3.1 StockMarket 2.6 investing 2.2 finance 1.7 The topic-specific discussion boards to which Reddit users subscribe are capable of moving markets. The wallstreetbets subreddit ignited the GameStop short-squeeze in 2021, for example, and demonstrated the vast influence these channels can have on finance and investing. Given crypto investors’ ubiquitous presence on social media, we expected the influence of these subreddits to be especially pronounced. The most popular financial and crypto-related subreddits based on their total number of subscribers are listed in the accompanying chart. (wallstreetbets has banned discussion of crypto, so is not included in our analysis.) Each subreddit’s name gives a sense of its general focus, but the word clouds below, which correspond to our study period — 4 November 2022 to 15 January 2023 — provide a more granular picture and cover the lead-up to the 6 November FTX collapse through when we conducted our analysis. Subreddit Word Clouds, 4 November 2022 to 15 January 2023 Of the hundreds of thousands of comments on these subreddits over the examination period, we isolated those that implied a crypto sentiment based on seed words indicating a general rather than specific connection to cryptoassets. FTX, for example, might betray a sentiment bias given the surrounding controversy, so we excluded it. Crypto, bitcoin, ethereum, cryptocurrency, cryptocurrencies, BTC, and blockchain, on the other hand, are more neutral and thus were among the seed words that guided our analysis, the results of which are summarized in the following table. Subreddit Summary Statistics Subreddit Total Comments Average Crypto-RelatedComments per Day1 Number of Dayswith Crypto-Related Comments2 CryptoCurrency 130,055 1,782 73 Bitcoin 29,538 405 73 personalfinance 314 5 54 stocks 1,388 19 71 economics 1,583 22 67 StockMarket 2,747 38 72 investing 2,547 35 72 finance 487 11 27 1. Only comments with at least one seed word are included.2. Total number of days included in the analysis out of the 73-day examination period. Model Methodology We tested many open-source NLP models before selecting a fine-tuned RoBERTa model developed by students from the National University of Singapore (NUS-ISS) to conduct our sentiment analysis. The model was trained on 3.2 million comments from the StockTwits investing forum and was a natural choice given its similar domain and large training set. RoBERTa is based on the groundbreaking BERT model developed by Google’s artificial intelligence (AI) team in 2018. Through their ability to parse context, BERT models have increased the precision of NLP tasks by applying attention mechanisms, which determine how words relate to one another. These attention mechanisms are the same building blocks used in other large language models, such as ChatGPT by OpenAI. The RoBERTa model labeled each crypto-related Reddit comment as 0 or 1, meaning bearish or bullish, respectively, and generated a daily mean as a proxy for sentiment. A 0.5 score, for example, indicated equally bullish and bearish comments. Differences between the StockTwits and Reddit domains and how users comment on them led to some inaccurate labeling; we believe this would not materially impact the results, however, because we are more concerned with the impact on sentiment from the FTX collapse rather than the absolute measure of sentiment related to cryptoassets. Results For a more holistic picture, we combined all the non-crypto-related subreddits and plotted the five-day moving average of daily crypto sentiment in the crypto- and non-crypto-related subreddits as well as the price of bitcoin over the same interval. Below the first graph is the comment volume for each day. Crypto and Non-Crypto Subreddits: Sentiment Five-Day Moving Average vs. Bitcoin Close Price Sources: Yahoo! Finance, Reddit The three time series share some similarities: Each shows crypto sentiment growing more bearish around the FTX collapse and recovering not long after, with the non-crypto subreddits lagging their crypto-specific peers. When the non-crypto subreddits are broken out, the relationship looks a bit more tenuous. Economics Sentiment vs. Crypto Sentiment and Bitcoin Close Price investing Sentiment vs. Crypto Sentiment and Bitcoin Close Price StockMarket Sentiment vs. Crypto Sentiment and Bitcoin Close Price personalfinance Sentiment vs. Crypto Sentiment and Bitcoin Close Price finance Sentiment vs. Crypto Sentiment and Bitcoin Close Price stocks Sentiment vs. Crypto Sentiment and Bitcoin Close Price Sources for Six Preceding Charts: Yahoo! Finance and Reddit. There is no clear sentiment trend in the Economics, finance, and personalfinance subreddits, while StockMarket, stocks, and investing indicate increased bullishness a week or two before bitcoin prices resumed their ascent. The correlation matrices below, which describe the relationship between each subreddit’s daily mean sentiment and bitcoin prices, tell much the same story. For example, crypto sentiment on Economics has a -0.034 correlation with the price of bitcoin, highlighted by the cell outlined in purple. Crypto Sentiment Daily Mean Correlation Matrix Sources: Yahoo! Finance, Reddit So, how did each daily sentiment score relate to future bitcoin prices? To answer that question, we added three more datasets: one, two, and three days forward, or BTC-USD +1, +2,

Portfolio managers report their risk-adjusted performance using Sharpe, Treynor, information, and Sortino ratios, among other popular metrics. Of course, with various measures to choose among, might fund managers be tempted to cherry-pick those that reflect most favorably on their performance? Perhaps, but the potential for strategic selection only becomes a real problem if the performance metrics have weak or negative correlations. If they all have high positive correlations, then there really is no selection game to play. If a good, or bad, Sharpe ratio means similar Treynor, information, and Sortino ratios, then it hardly makes a difference which one (or two) is reported. So, how do these major performance metrics correlate, and have their correlations changed over time? To answer these questions, we pulled all active mutual fund manager returns for large-cap equity funds going back to the 1950s. We then calculated each fund’s Sharpe, Treynor, Sortino, and information ratio on a one-year rolling basis. With this data, we explored how the rank ordinal correlation between the metrics looks over each decade and over the full time period. First, over the full time period, Sharpe and Treynor ratios have high positive correlations as do the information and Sortino ratios. But both Sharpe and Treynor ratios are weakly correlated with the information and Sortino ratios. So, if a fund manager showcases their Sortino ratio and doesn’t highlight their Sharpe or Treynor ratio, it may signal that they are strategically selecting which measures to present. Performance Metric Correlations: All Periods, 1950 to 2023 Sharpe Ratio Treynor Ratio Information Ratio Sortino Ratio Sharpe Ratio 1 0.95 0.25 0.24 Treynor Ratio 0.95 1 0.24 0.23 Information Ratio 0.25 0.24 1 0.99 Sortino Ratio 0.24 0.23 0.99 1 Next, we examined the rank ordinal correlation of the four measures over each decade. The same pattern holds fairly steady from 1950 to 2020. We didn’t see any inordinate divergence in the correlations over the roughly 70 years under review. Performance Metric Correlations: 1950s Sharpe Ratio Treynor Ratio Information Ratio Sortino Ratio Sharpe Ratio 1 0.95 0.11 0.09 Treynor Ratio 0.95 1 0.01 -0.01 Information Ratio 0.11 0.01 1 0.99 Sortino Ratio 0.09 -0.01 0.99 1 Performance Metric Correlations: 1960s Sharpe Ratio Treynor Ratio Information Ratio Sortino Ratio Sharpe Ratio 1 0.97 0.35 0.32 Treynor Ratio 0.97 1 0.36 0.33 Information Ratio 0.35 0.36 1 0.98 Sortino Ratio 0.32 0.33 0.98 1 Performance Metric Correlations: 1970s Sharpe Ratio Treynor Ratio Information Ratio Sortino Ratio Sharpe Ratio 1 0.98 0.38 0.33 Treynor Ratio 0.98 1 0.37 0.32 Information Ratio 0.38 0.37 1 0.98 Sortino Ratio 0.33 0.32 0.98 1 Performance Metric Correlations: 1980s Sharpe Ratio Treynor Ratio Information Ratio Sortino Ratio Sharpe Ratio 1 0.97 0.25 0.23 Treynor Ratio 0.97 1 0.23 0.20 Information Ratio 0.25 0.23 1 0.98 Sortino Ratio 0.23 0.20 0.98 1 Performance Metric Correlations: 1990s Sharpe Ratio Treynor Ratio Information Ratio Sortino Ratio Sharpe Ratio 1 0.92 0.26 0.26 Treynor Ratio 0.92 1 0.22 0.21 Information Ratio 0.26 0.22 1 0.99 Sortino Ratio 0.26 0.21 0.99 1 Performance Metric Correlations: 2000s Sharpe Ratio Treynor Ratio Information Ratio Sortino Ratio Sharpe Ratio 1 0.97 0.27 0.25 Treynor Ratio 0.97 1 0.26 0.24 Information Ratio 0.27 0.26 1 0.99 Sortino Ratio 0.25 0.24 0.99 1 Performance Metric Correlations: 2010s Sharpe Ratio Treynor Ratio Information Ratio Sortino Ratio Sharpe Ratio 1 0.93 0.41 0.4 Treynor Ratio 0.93 1 0.44 0.43 Information Ratio 0.41 0.44 1 0.99 Sortino Ratio 0.40 0.43 0.99 1 Finally, we explored the correlations during recessions to see if they fell apart at the most critical moments. Of the seven recessions since the 1950s, again we found that the correlations stayed pretty similar to what they were during non-recession periods. In all, the results show that since Treynor and Sharpe ratios are highly correlated, whether a fund manager reports one and not the other is not especially material. The same holds with the information and Sortino ratios. But since the Treynor and Sharpe ratios are weakly correlated with the latter two metrics, managers could have the opportunity for strategic reporting. So, if a fund manager reports their Sortino or information ratio but goes silent on their Sharpe and Treynor ratios, it may reflect a strategic play and warrant further investigation. 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 / Uwe Krejci Professional Learning for CFA Institute Members CFA Institute members are empowered to self-determine and self-report professional learning (PL) credits earned, including content on Enterprising Investor. Members can record credits easily using their online PL tracker. source