CFA Institute

Convertible Securities: A Complete Guide to Investment and Corporate Financing Strategies. 2022. Tracy V. Maitland, F. Barry Nelson, CFA, and Daniel G. Partlow. McGraw Hill. Professionals who contemplate investing in, hedging, or issuing investment-grade or speculative-grade convertible bonds or preferreds in the public or private market in North America, Europe, or Asia will find just about everything they need to know in Convertible Securities: A Complete Guide to Investment and Corporate Financing Strategies. Pointers on such matters as using convertibles to diversify a portfolio or to optimize a capital structure are meticulously supported with empirical data and amplified with case studies. If, on certain subjects, readers desire more details than even the book’s 560 pages could accommodate, they can follow up on handy references to material on the website of Advent Capital Management, where Tracy V. Maitland, F. Barry Nelson, CFA, and Daniel G. Partlow apply their expertise in managing convertibles. In addition, the book recounts the asset class’s evolution from its 19th century origins right through the investment implications of the Tax Cuts and Jobs Act of 2017 and recent modifications of accounting standards for convertible issuers. The authors address a broad audience. Lay investors can apply basic financial theory, presented by way of background, to activities well outside the convertible market’s confines. At the same time, the book presents quantitatively sophisticated valuation methods and trading strategies, invoking terms of art that will be new even to many seasoned practitioners — for example, “ASCOTs,” “zomma,” “nuking,” and “happy meal.” It is incumbent on the reader to pay strict attention to the authors’ carefully considered wording throughout. Recollecting his introduction to financial markets in the 1980s, Advent founder Tracy Maitland mentions in his preface “long-term returns from convertibles that were equivalent to the returns from common equities, but with significantly less risk.” Bringing the story up to date in the main text, the authors state that “convertibles historically have returned approximately as much as common stocks over the long-term.” Careful to avoid overstating matters, they write at another point, “Convertibles typically provide less volatility than stocks.” Equally circumspect is this comment: “The record of convertible indices essentially matching the returns of equity indices over the decades may partly reflect the superior growth of convertible issuers relative to the growth of companies found in the equity indices” (italics added in the preceding sentences). One message that comes through clearly is convertibles’ asymmetric behavior, capturing much of the associated stocks’ upside while cushioning the downside via the bond side of their nature. Among many useful observations that are tangential to the main topic, two call for a bit of annotation. First, the authors state that “because risk increases with respect to time, longer-term securities tend to have wider credit spreads than shorter-term securities.” Records from ICE Indices, LLC, confirm that except from December 2007 to March 2009, the option-adjusted spread (OAS) on 10- to 15-year US investment-grade corporate bonds has consistently exceeded the OAS on 3- to 5-year issues. For high-yield bonds, however, the 3- to 5-year OAS has usually exceeded the 10- to 15-year OAS. Second, the authors state that “entities that have the ability to print money are considered to be completely risk-free because under any circumstances they can repay their debt with currency that they alone can create.” Actually, control of a currency is a necessary but not a sufficient condition for posing zero risk of default. History records a number of sovereign defaults on debt denominated in the home currency, such as Russia’s 1998 default on its ruble debt. Also worth keeping in mind in this connection is the fact that the US Treasury has a Standard & Poor’s rating of only AA+, not the agency’s highest rating (AAA). “Busted” (out-of-the-money) convertibles represent another time-honored topic in fixed-income circles. Some bond salesmen have promoted the belief that these issues invariably get neglected once they cease to be of interest to convertible investors, consequently becoming bargains with yields higher than the yields on comparable straight (nonconvertible) bonds. Maitland, Nelson, and Partlow judiciously state that convertibles priced at discounts to par merely “have the potential to significantly outperform non-convertible bonds” (italics added). As with most books, a few minor items in Convertible Securities bear cleaning up in a future edition. The book refers to the ICE BofA US High Yield Corporate Index by its former name, the “High Yield Master II Index.” Other editorial slips include mentions of the BlackRock “Alladin” fund, the “Capital Assets Pricing Model,” and the “Discounted Dividend Model.” These stylistic peccadillos do not detract from the many delights awaiting readers of Convertible Securities. One does not expect to discover in a weighty tome on finance the Latin antecedent of the saying, coined by Shakespeare, “It’s Greek to me.” Similarly serendipitous is a Talmudic commentary on the symbolism of the Hebrew analogues of the Greek letters gamma and delta. Most important, though, are the original research contributions that enrich the coverage of every aspect of the convertible ecosystem. York Capital Management CEO Jamie Dinan is right to call Convertible Securities a “remarkably comprehensive book.” Full disclosure: The reviewer is mentioned in this book’s acknowledgements and in an endnote. If you liked this post, don’t forget to subscribe to the Enterprising Investor. All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer. Professional Learning for CFA Institute Members CFA Institute members are empowered to self-determine and self-report professional learning (PL) credits earned, including content on Enterprising Investor. Members can record credits easily using their online PL tracker. source

Our understanding of financial markets is inherently constrained by historical experience — a single realized timeline among countless possibilities that could have unfolded. Each market cycle, geopolitical event, or policy decision represents just one manifestation of potential outcomes. This limitation becomes particularly acute when training machine learning (ML) models, which can inadvertently learn from historical artifacts rather than underlying market dynamics. As complex ML models become more prevalent in investment management, their tendency to overfit to specific historical conditions poses a growing risk to investment outcomes. Generative AI-based synthetic data (GenAI synthetic data) is emerging as a potential solution to this challenge. While GenAI has gained attention primarily for natural language processing, its ability to generate sophisticated synthetic data may prove even more valuable for quantitative investment processes. By creating data that effectively represents “parallel timelines,” this approach can be designed and engineered to provide richer training datasets that preserve crucial market relationships while exploring counterfactual scenarios. The Challenge: Moving Beyond Single Timeline Training Traditional quantitative models face an inherent limitation: they learn from a single historical sequence of events that led to the present conditions. This creates what we term “empirical bias.” The challenge becomes more pronounced with complex machine learning models whose capacity to learn intricate patterns makes them particularly vulnerable to overfitting on limited historical data. An alternative approach is to consider counterfactual scenarios: those that might have unfolded if certain, perhaps arbitrary events, decisions, or shocks had played out differently To illustrate these concepts, consider active international equities portfolios benchmarked to MSCI EAFE. Figure 1 shows the performance characteristics of multiple portfolios — upside capture, downside capture, and overall relative returns — over the past five years ending January 31, 2025. Figure 1: Empirical Data. EAFE-Benchmarked Portfolios, five-year performance characteristics to January 31, 2025. This empirical dataset represents just a small sample of possible portfolios, and an even smaller sample of potential outcomes had events unfolded differently. Traditional approaches to expanding this dataset have significant limitations. Figure 2.Instance-based approaches: K-nearest neighbors (left), SMOTE (right). Traditional Synthetic Data: Understanding the Limitations Conventional methods of synthetic data generation attempt to address data limitations but often fall short of capturing the complex dynamics of financial markets. Using our EAFE portfolio example, we can examine how different approaches perform: Instance-based methods like K-NN and SMOTE extend existing data patterns through local sampling but remain fundamentally constrained by observed data relationships. They cannot generate scenarios much beyond their training examples, limiting their utility for understanding potential future market conditions.  Figure 3: More flexible approaches generally improve outcomes but struggle to capture complex market relationships: GMM (left), KDE (right).   Traditional synthetic data generation approaches, whether through instance-based methods or density estimation, face fundamental limitations. While these approaches can extend patterns incrementally, they cannot generate realistic market scenarios that preserve complex inter-relationships while exploring genuinely different market conditions. This limitation becomes particularly clear when we examine density estimation approaches. Density estimation approaches like GMM and KDE offer more flexibility in extending data patterns, but still struggle to capture the complex, interconnected dynamics of financial markets. These methods particularly falter during regime changes, when historical relationships may evolve. GenAI Synthetic Data: More Powerful Training Recent research at City St Georges and the University of Warwick, presented at the NYU ACM International Conference on AI in Finance (ICAIF), demonstrates how GenAI can potentially better approximate the underlying data generating function of markets. Through neural network architectures, this approach aims to learn conditional distributions while preserving persistent market relationships. The Research and Policy Center (RPC) will soon publish a report that defines synthetic data and outlines generative AI approaches that can be used to create it. The report will highlight best methods for evaluating the quality of synthetic data and use references to existing academic literature to highlight potential use cases. Figure 4: Illustration of GenAI synthetic data expanding the space of realistic possible outcomes while maintaining key relationships. This approach to synthetic data generation can be expanded to offer several potential advantages: Expanded Training Sets: Realistic augmentation of limited financial datasets Scenario Exploration: Generation of plausible market conditions while maintaining persistent relationships Tail Event Analysis: Creation of varied but realistic stress scenarios As illustrated in Figure 4, GenAI synthetic data approaches aim to expand the space of possible portfolio performance characteristics while respecting fundamental market relationships and realistic bounds. This provides a richer training environment for machine learning models, potentially reducing their vulnerability to historical artifacts and improving their ability to generalize across market conditions. Implementation in Security Selection For equity selection models, which are particularly susceptible to learning spurious historical patterns, GenAI synthetic data offers three potential benefits: Reduced Overfitting: By training on varied market conditions, models may better distinguish between persistent signals and temporary artifacts. Enhanced Tail Risk Management: More diverse scenarios in training data could improve model robustness during market stress. Better Generalization: Expanded training data that maintains realistic market relationships may help models adapt to changing conditions. The implementation of effective GenAI synthetic data generation presents its own technical challenges, potentially exceeding the complexity of the investment models themselves. However, our research suggests that successfully addressing these challenges could significantly improve risk-adjusted returns through more robust model training. The GenAI Path to Better Model Training GenAI synthetic data has the potential to provide more powerful, forward-looking insights for investment and risk models. Through neural network-based architectures, it aims to better approximate the market’s data generating function, potentially enabling more accurate representation of future market conditions while preserving persistent inter-relationships. While this could benefit most investment and risk models, a key reason it represents such an important innovation right now is owing to the increasing adoption of machine learning in investment management and the related risk of overfit. GenAI synthetic data can generate plausible market scenarios that preserve complex relationships while exploring different conditions. This technology offers a path to more robust investment models. However, even the most advanced synthetic data cannot compensate for naïve machine learning implementations. There is no safe

“Complexity is like energy. It cannot be created or destroyed, only moved somewhere else. When a product or service becomes simpler for users, engineers and designers have to work harder. [Donald A.] Norman writes, ‘With technology, simplifications at the level of usage invariably result in added complexity of the underlying mechanism.‘ For example, the files and folders conceptual model for computer interfaces doesn’t change how files are stored, but by putting in extra work to translate the process into something recognizable, designers make navigating them easier for users.” — Shane Parrish, Farnam Street Fitch Ratings’ downgrade of US sovereign credit from AAA to AA+ last week highlights a latent principal–agent problem in modern financial markets: Investors have outsourced much of their risk management to the rating agencies. But the problem goes beyond just risk management and the rating agencies. Before Standard & Poor’s reduced its US credit rating in 2011, financial contracts referred to “risk-free” or liquid assets as AAA-rated securities. Considered “good collateral,” these assets were a requirement in most financial transactions. When US credit became split-rated, the risk of forced liquidation of US Treasuries after another downgrade emerged as a clear and present danger. As Jim Bianco writes, “In the subsequent 12 years, most of these financial contracts have been rewritten to include ‘debt backed by the US Government’ or words to this effect.” But the financial position of the United States has deteriorated over the past decade, which explains why the Fitch downgrade was not a huge surprise. To be sure, some disagreed with the decision, while others felt it didn’t come soon enough, but most market participants greeted the news with a collective shrug. A strict financial analysis of US sovereign credit ignores the country’s strong geopolitical position. Its enviable geography and singular influence over global shipping lanes ensure a prominent place in the world economy and are vital inputs to its creditworthiness. This is the dilemma that Fitch and other rating agencies face when distilling a phenomenon as complex as a sovereign nation’s creditworthiness down to a simple designation. Such labels help keep the gears of commerce turning, but what they actually mean is growing cloudier and losing their informational value. Before 2011, two rating agencies could initiate a deleveraging and spark a panic in the financial markets. But thanks in part to the re-wording of financial contracts in the intervening years, Fitch’s decision failed to catalyze such an event. That counts as a present good. But what about the restorative effects a deleveraging could have on balance sheets or the fiscal discipline it could engender? What if policymakers need to be reminded that ongoing debt accumulation comes with a cost? In the past, the markets imposed that discipline. Market-imposed discipline meant greater financial market volatility and less financial intermediation. Of course, while that may have made for healthier balance sheets, it also meant less growth and lower living standards. The rating agencies and other financial market actors provide a form of third-party oversight. They apply a loose system of checks and balances to counter outsized risk accumulation. The Commodity Futures Trading Commission (CFTC) imposes position limits on investment firms, the US SEC fights securities fraud, and the US Federal Reserve regulates the banking system. These are all worthwhile functions. The question is: Does the increased functionality these efforts bring to the financial markets come with any hidden costs? This is the principal–agent problem in its purest form. Financial innovations increase intermediation, which makes capital cheaper and more readily available. This leads to economic growth and higher standards of living. Lower barriers to entry and seemingly reduced complexity encourage people to invest their savings in the markets. But beneath the surface, the underlying market complexity has never gone away; it has just been moved somewhere else. If the complexity of our financial system is constant, then where is it hiding and who is managing it? The dependency paradox suggests that as principals delegate responsibilities to others, they may inadvertently reduce their own capacity to make informed decisions, understand complex issues, and retain the necessary skills to perform those tasks well. Innovations like exchange-traded funds (ETFs) have opened up the financial markets in a cost- and tax-efficient way. Investors can now buy a well-diversified portfolio with the click of a mouse. But in the not-too-distant past, such an endeavor would have required teams of professionals to accomplish, and today the mechanism that transforms that mouse click into a portfolio remains a mystery to most. The complex algorithms, order routing, payment for order flow, and execution occurring behind the scenes go largely unnoticed until we read about the outsized profits that certain firms make by providing liquidity to the market. In a way, financial innovations are creating two classes of investors: those who merely consume the products and those who understand how the system that creates these products works. This goes to the heart of the principal–agent problem. Knowledge gaps between principals and agents can lead to, but don’t necessitate, conflicts of interest. With rating agencies, the conflict arises from the risk that they could pose to the financial system. On the one hand, if they stray from their disciplined analytical approach, their value as a market referee drops, but if they follow too strictly, they could cause a meltdown. To bridge the markets’ inevitable knowledge gap, we have to accept that complexity can only be transformed and that agents must be empowered to manage this complexity to increase the functionality of the markets. It is not enough for these agents to be transparent and accountable. It is on us, the principals, to monitor and participate in the financial markets and educate ourselves on how they work. While investing has gotten “easier,” beneath the simple mouse clicks and user-friendly interfaces lies a complex world that we cannot lose sight of or ignore. That complexity will inevitably reveal itself, and when it does, instead of panicking or assigning blame, we should look to understand it for what

Markets in Chaos: A History of Market Crises around the World. 2024. Brendan Hughes, CFA. Business Expert Press. Should history teach or merely inform? This question lies at the heart of Markets in Chaos, a broad yet succinct historical overview of macroeconomic crises around the world and across time. No mere exposition of financial market ructions in such places as Weimar Germany or Japan in the 1990s, this volume studies the mechanics of market disruptions in depth with a view toward educating the reader and investor. The writing is clear, and the case studies are well researched. The author’s discussion and analyses are not only instructive but also relevant. Many of the events reviewed will be familiar to finance and investment professionals. Nonetheless, this volume should be a part of any (aspiring) practitioner’s library, if for no other reason than to provide invaluable perspective on the context in which financial decisions are made and guidance on how best to navigate macroeconomic dislocations in the service of clients. Additionally, the case studies could supplement the CFA Program curriculum on the topics of macroeconomics and risk management. Financial market regulators, economists, policymakers, portfolio managers, and risk officers will find this book a timely and welcome refresher. As the author, Brendan Hughes, CFA, makes clear throughout, some decision makers will need to pay more heed than others. Market history, for want of a less hackneyed turn of phrase, often does rhyme, if not repeat, as the events analyzed between these pages make clear. Common themes arise time and again. Central banks print money, economies are financialized, fiat currencies hamper productivity growth, and government corruption places fragile economies in a doom loop. Cheap money leads to financial excess. Does no one learn? Throughout the narrative, Hughes draws frequent parallels between macroeconomic events, comparing and contrasting policy decisions and market outcomes, emphasizing the missteps and the lessons learned. Some governments learn from their mistakes; others, not so much. Chile’s experience in the 1980s stemmed from a dearth of oversight and a surfeit of credit creation. The government’s lack of adequate foreign exchange reserves and overreliance on copper extraction for export revenue led the economy into recession once the commodity boom subsided. Similar circumstances were present in Iceland prior to the global financial crisis (GFC) and Indonesia in the late 1990s during the Asian financial crisis. Market shocks lay bare undiversified economies’ problems. Whereas Chile subsequently demonstrated fiscal probity and supported free trade, proper oversight, and more prudent bank lending policies that made its economy relatively stable in a region whose other economies are not, Iceland and Indonesia continue more or less on a path of economic fragility and volatility. The frequent cross-references in the book tie the narrative together and help reinforce critical concepts. While the chapters may be read in isolation, the discussion and analysis make for a smooth progression between them. Separated by time and region, the macroeconomic dislocations of the economies and markets that form the events in this volume share experiences and teachable moments. To paraphrase George Orwell, all markets and economies are alike — some more so than others. Hughes’s approach to the subject matter is instructive and well organized, allowing for ease of reference and understanding of how concepts interrelate. With the exception of the chapters that explore market dislocations in the farther reaches of history (18th century France, 19th century America and Europe, and Ancient Rome), where a lack of data from the time periods in question would preclude an assessment of how companies were affected, each chapter provides a background and market impact, the impact on businesses, and a review of how the disruptions affected the macroeconomy. The closing chapter offers useful, if familiar, guidance on how to apply financial history to everyday investment decisions: Stay invested, but prudently; diversify across both asset classes and countries, avoid market timing, do not invest in businesses that require leverage to achieve good returns, do invest in those that require little capital to operate and have pricing power. Thousands of years of history can improve investors’ understanding of possible outcomes and better inform their investment decisions. Hughes does not merely examine the historical record but weighs in on the implications of recurring themes at both a macroeconomic level and a microeconomic level. In his estimation, the US Federal Reserve has strayed from its mandate, for years creating cheap money that stokes inflation and provides fodder for speculation and inflated property markets. Fractional reserve banking only compounds the problem. A return to the gold standard in some measure — the author undertakes an informative and critical examination of its history — along with a link between money supply and GDP growth and full-reserve banking, which would better align the goals of central banks and private commercial banks, would alleviate the risks attached to fiat currencies and create conditions propitious for a more stable economy less prone to inflation. European monetary union, itself due in part to the eventuality of the United States’ abandonment of the price link to gold more than two decades earlier, presents a problem of mismatch between a shared currency and member countries’ economic and political circumstances. The disparate experiences of the Greek and German economies in the 2010s are telling. The euro’s longer-term prospects would appear questionable. Hughes puts the current state of financial services under the microscope. Banking has its rewards and risks, the latter giving rise in very recent history to new financial technologies that offer financial services with banking features — think Venmo, SoFi, and Credit Karma — but which lack proper surveillance. Regtech is still catching up to financial innovation. The jury is still out on the hidden risks that the emergence of shadow banking has created. Designed as an alternative to the risks of centralized finance, cryptocurrencies labor under flaws similar to those of commercial banks, as the fate of many cryptocurrency exchanges attests. Stablecoins look to be anything but stable. If we have learned nothing else, it is that the

In today’s investment landscape, the debate between active and passive management often misses a critical point: they aren’t mutually exclusive. Incorporating passive funds into actively managed portfolios can enhance diversification, reduce risk, and optimize returns. By leveraging a hybrid, “team of funds” approach, investors can capitalize on the strengths of both strategies, creating portfolios that achieve financial goals more efficiently. This post explores how passive investments complement active strategies, driving better results for clients and portfolios alike. Robust Portfolio Construction With a Team of Funds Approach A robust portfolio construction process should be customized to the client’s asset allocation and target excess return. It should also maximize alpha diversification across the portfolio. This approach provides these benefits: Minimizing tracking error by aligning the portfolio’s long-term effective exposures to its benchmark Equalizing each fund’s contribution to portfolio alpha and tracking error Capitalizing on the relationship between the portfolio’s active return and market return. Here is a case study that illustrates this approach. It tests results using all active funds versus using a combination of active and passive funds. I chose four asset allocation strategies across global equity and bonds to represent a set of client risk tolerances. I excluded real assets and illiquid investments in favor of publicly available funds that represent the core of most investment portfolios. Portfolio parameters: Strategies range from 50-50 to 80-20 mixes of equity and bonds Alpha targets range from a minimum 50 basis points (bps) to a maximum of 175 bps in 25 bps increments. This produced a set of 24 teams of funds. I maintained diversification and style characteristics across strategies, with global, developed equity balanced between value and growth styles. This produced a total of 11 minor asset segments. Table 1: Diversification across asset strategies. All-Active Fund Results The all-active performance results over a 12-year period are grouped by investment strategy. The hybrid, team of funds approach delivered enhanced returns with lower volatility than their benchmarks. This result was driven by two factors. First, alpha diversification removed most individual fund tracking error. Second, the slightly negative correlation of each portfolio’s excess return to the portfolio’s total return causes a portion of its tracking error to be subtracted from its volatility, given this relationship: Contribution to Volatility = Weight * Volatility * Correlation with Portfolio Return Chart 1: Team of Funds vs. Benchmarks. I repeated this approach, this time allowing passive funds into the mix. Each portfolio was free to hold any funds on our platform and an unlimited allocation of passive funds. The goal was to earn the target excess return while minimizing volatility risk. The surprising result — across all strategies and alpha targets — is that portfolios that held substantial exposure to passive investments replicated the returns of the portfolios that held all active funds. This result corrects the prevailing wisdom that passive funds dilute excess return. The passive-active hybrid portfolios had an average of 40% passive exposure and a range of about 10% to 65%, depending on the strategy and the alpha target. Chart 2: All-Active Portfolios vs Hybrid Portfolios. Passive Exposures Across Strategies and Alpha Targets Passive funds allow us to be more selective in our use of active investments, choosing only the best of the best. They eliminate asset allocation constraints that limit efficiency in the selection of active funds. This drives greater “alpha diversification” and lowers active risk. Table 2: Passive Exposures. Key insight: Including passive funds drives a more efficient selection of active funds. Effect of Passive Investments on Active Risk Chart 3 compares the relationship between alpha and tracking error for all-active and hybrid portfolios. Tracking error increases modestly with total volatility in the all-active portfolios, until reaching an inflection point, when risk begins increases rapidly. The hybrid portfolios are dramatically more efficient. Active risk across the strategies is nearly identical, with differences only at the highest alpha targets. The return-to-risk line is nearly linear. Chart 3: Active results for All-Active and Hybrid Portfolios. Benefits of Lowering Active Risk Alpha diversification, the selective use of passive investments, and an unconstrained active fund team create a combination of factors that produce superior active results. These benefits are consistent across the strategies, with lower active risk increasing high-confidence minimum alpha in the hybrid portfolios. Chart 4 illustrates the 95% confidence level alpha across all portfolios. The trend lines for the all-active and the hybrid strategies summarize the improvement that passive funds contribute. On average, this is between 15 bps and 20 bps of excess return. Chart 4: High-Confidence alpha for All-Active and Hybrid Portfolios. Evaluating Hybrid Portfolio Performance I selected the 60-40 strategy with 100 bps target alpha to illustrate my hybrid performance evaluation approach. My decision-based approach focuses on an active component plus a passive component, in a hierarchical framework: Active vs. passive allocation Major asset segments Minor asset segments Funds My passive allocation is close to an 80-20 mix of stocks and bonds, while the active allocation is a nearly-even mix. This creates substantially different long-term allocation performance effects. Chart 5: Hybrid Portfolio Allocation to Asset Classes Within Passive and Active Components. The active and passive components also differ in their allocations within equity and bonds. This is primarily driven by the alpha opportunities found in the active funds. It is also influenced by alpha diversification across the active funds we include. Chart 6: Allocation to Major Asset Segments within Passive and Active Components. My most detailed passive and active allocations (at the style level) fully explain my allocation to more than 40% of the portfolio’s assets to passive investments. The total exposure in each asset segment matches the benchmark allocation. Table 3: Style-Level Allocations Across Passive and Active Components. Key Drivers of Hybrid Portfolio Return Chart 7 illustrates total return and volatility for each performance component. Relative to the benchmark return, the active allocation detracted from excess return while the passive allocation contributed to excess return. Chart 7: Return and Risk for Hybrid Portfolio Performance Components. The selection effect is evaluated by comparing

Our 4×4 Asset Allocation philosophy approaches every asset or strategy based on how it contributes to — or detracts from — four goals: Growth, Income, Preservation, and Liquidity. In particular, under the 4×4 Goal Parity framework, each of these goals has equal weight. So, what does a goals-based approach to equity factors look like from this perspective? The literature on equity factors, particularly Eugene F. Fama and Kenneth R. French’s extended family of factors, is deep and extensive. These include Small Minus Big (SMB) and High Minus Low (HML), which respectively describe the difference between returns of small- and large-cap stocks and high book-to-market and low book-to-market stocks. Among the factors of more recent vintage are Robust Minus Weak (RMW) and Conservative Minus Aggressive (CMA), or the difference in returns among companies with robust and weak operating profitability and between those that invest conservatively and aggressively. The Kenneth R. French Data Library also features Momentum (Mom), or the return spread between winners and losers over the last 12 months, as well as Short-Term and Long-Term Reversal factors (ST_Rev, LT_Rev). While many researchers focus on each factor’s stand-alone performance metrics, we are more concerned with the relationships among the factors themselves, their (non-linear) relationships with larger market and macroeconomic conditions, and ultimately each factor’s role in a goals-based, investor-specific portfolio. How do the simple correlations between factors compare? The market’s excess return (Mkt-RF) is negatively correlated with Mom, HML, and RMW, a frequent proxy for Quality. Mkt-RF is most negatively correlated, however, with CMA, which may be a “management behavior quality” factor and perhaps a quality or defensive factor. That is, aggressively investing executives engaged in empire-building activities may do well when capital is flowing but suffer in market downturns. Conservative firms, on the other hand, save cash for rainy days and rely less on external financing. HML and CMA have a 68% correlation. This could be because investors place higher valuation multiples on firms with fast-growing assets than on their slower-asset-growing peers. In any case, based on empirical correlations, HML is a defensive factor as well since Value tends to do well in recessions. Fama–French Factor Correlations, July 1963 to December 2022 Methodology: Standard Pearson correlation coefficients computed with monthly returns. But what does a non-linear analysis of the factors reveal when we compare their skewness and convexity with respect to major risk factors, such as Mkt-RF, monthly changes in 10-year Treasury yields, monthly changes in money-market or “risk-free” rates (RF), and month-on-month changes of the CPI-U index? Convexity reflects the co-skewness coefficient between a Fama–French factor and two instances of a risk factor squared. In particular, the co-skewness of Mkt-RF with itself is simply Mkt-RF skewness. If a Fama–French factor has positive co-skewness with a risk factor, it is convex with respect to that risk factor. If it has negative co-skewness, it is concave. For example, Mkt-RF is concave with respect to CPI MoM while CMA is convex with respect to Mkt-RF. Fama–French Factor Skewness and Convexity, July 1963 to December 2022 Red cells represent negative and blue cells positive co-skewness values.Skewness and co-skewness coefficients computed with monthly returns and bounded by [-0.5,0.5]. Convexity relative to a major risk factor indicates better performance in crises driven by rapid changes in interest rates, inflation, or sharp market declines. Heuristically, convexity should contribute to our (real capital) Preservation goal. Conversely, concave, negatively skewed assets and strategies may behave like income-generating corporate bonds and equities, delivering their best performance in placid environments but underperforming in crises. Convexity and Concavity: CMA vs. Mkt-RF and Mkt-RF vs. CPI MoM In 4×4 Goal Parity we quantify this intuition with two investor-specific parameters: strategic horizon and loss tolerance. In particular, we look at Fama–French factors from 1963 to 2022. Given a 10-year strategic horizon and a 15% loss tolerance, our methodology demonstrates the following: Value (HML) makes a significant Preservation contribution, providing some protection in recessions. CMA and LT_Rev factors are even better Preservation factors. In particular, HML, CMA and LT_Rev did very well in 2022 when both stocks and bonds declined. RMW overlaps with Quality and High Dividend equities and contributes more to Income. Twelve-month window Mom contributes to Income as well. Faster Mom would deliver more Preservation. 4×4 Asset Map: Investor Goals and Fama–French Factors, July 1963 to December 2022 Strategic horizon 𝑇=10 years, “substantial loss” barrier B=85%. The eight-factor portfolio includes equal weights of Mkt-RF, SMB, HML, RMW, CMA, Mom, ST_Rev, and LT_Rev. Sources: 4x4invest’s proprietary methodology; Kennneth R. French’s Data Library From our goals-based perspective (or a macro lens perspective), Fama–French factors play quite different roles. So, should investors build diversified factor portfolios balanced across all of our 4×4 goals? To start answering this question, we built an equal-weighted portfolio of eight Fama–French factors — Mkt-RF, SMB, HML, RMW, CMA, Mom, ST_Rev, and LT_Rev — and rebalanced it monthly. From 1963 to 2022, the eight-factor portfolio lags Mkt-RF during bull markets but does much better during bear markets and with lower volatility. The portfolio achieves a Sharpe ratio of 1.16 versus 0.42 for Mkt-RF without accounting for transaction costs. Perhaps excess equity market returns reflect GDP growth in the large and relatively closed US economy. From this perspective, the eight-factor portfolio’s performance pattern more resembles that of US nominal GDP, with a much lower “tracking error.” Mkt-RF and Eight-Factor Portfolio vs. US Nominal GDP Growth, July 1963 to December 2022 The eight-factor portfolio includes equal weights of Mkt-RF, SMB, HML, RMW, CMA, Mom, ST_Rev, and LT_Rev. 4x4invest’s calculations for illustration and educational purposes only. Past performance is not indicative of future results. The 4×4 Asset Map above shows that the equal-weighted eight-factor portfolio comes pretty close to a 4×4-optimal Goal Parity portfolio, with equal weight on Growth, Income, Preservation, and Liquidity. The relationships among the different factors vary over the six-decade examination period. Nevertheless, “powering” all four goals may have delivered the diversification benefits necessary to achieve resilient performance across the bear markets of 1972 to 1974, 2000 to 2002, and

Quantitative Risk and Portfolio Management: Theory and Practice. 2024. Kenneth J. Winston. Cambridge University Press. The field of textbooks on quantitative risk and portfolio management is crowded, yet there is a problem matching the right book with the appropriate audience. Like Goldilocks, there is a search for a book that is neither too technical nor too simple to reach a broad audience and have the most significant reader impact. The perfect quant text should be a mix of explaining concepts clearly with the right level of intuition and enough practicality, combined with mathematical rigor, so the reader can know how to employ the right tools to solve a portfolio problem. Although textbooks are not often reviewed for CFA readers, it is useful to highlight a book that fills a unique gap between the CFA curriculum and the growing demand to find model-driven investment management solutions. Quantitative Risk and Portfolio Management: Theory and Practice achieves that critical balance by providing an apt mix of intuition and applied math. Author Ken Winston, the author of Quantitative Risk and Portfolio Management, has had a distinguished career moving between industry and academic positions. He is well-placed to provide readers with the necessary tools to be an effective quant or a professional who needs to digest the output from quants. Winston’s book fills a niche between theory and practice; nevertheless, it is not the ideal text for every CFA charterholder. It places greater emphasis on the math and programming of solutions than most practical portfolio management books. Programming is currently a “hidden curriculum” item in investment risk and portfolio management education that goes beyond theory and research. Brad De Long, the University of California Berkeley economic historian, has conjectured that programming skills are like the fine chancery hand of medieval university graduates. Programming goes beyond the classic liberal arts or business education, showing your distinction as an educated man. In today’s world, it is not enough to say you know portfolio or risk management; you must be able to “do” it. Winston closely links quant concepts with Python programming to make the hidden curriculum of quant finance transparent and accessible. You will not become a quant programmer from studying this book, but Quantitative Risk and Portfolio Management enables you to more easily bridge the link between theory and critical quantitative analysis through programming. Quantitative Risk and Portfolio Management integrates Python code snippets throughout the text so that the reader can learn a concept and the foundational math and then see how Python code can be integrated to build a model with output. While this is not a financial cookbook, the close integration of code distinguishes it from others. That makes the book useful for sitting on the shelf as a reference for analysts and portfolio managers. For example, the reader can learn about fixed-income yield curves and then see how the code can generate output for different models. If you want to build a simple model, creating the basic code is not a trivial exercise. Exposure to Winston’s code snippets allows the reader to move more quickly from a risk and portfolio management learner to a doer. The book is divided into twelve chapters that cover all the basics of quantitative risk and portfolio management. The emphasis for many of these chapters, however, is significantly different from what many readers may expect. Winston often focuses on concepts not covered in more traditional or advanced texts by building on core math foundations. For example, there is a chapter on how to generate convex optimizations following the discussion on the efficient frontier. If you are going to run an optimization, this is critical knowledge, yet it is the first time I have seen an extensive review of optimization techniques in a finance text. At times, the chapter order may seem odd to some readers. For example, optimization and distributional properties come after equity modeling. However, this sequencing is not problematic and does not take away from the book. Winston begins with the basic concepts of risk, uncertainty, and decision-making, which are central issues facing any investor. Before discussing individual markets, the book focuses on risk metrics based on no-arbitrage models and presents the often-overlooked Ross Recovery Theorem. Quantitative Risk and Portfolio Management then focuses on valuation measurements for equity and bond markets. The author takes a unique presentation approach to discuss these core markets, which is a critical difference between this book and its competitors. For fixed income, he starts with classic discounting of cash flows but then layers in greater degrees of complexity so that readers can learn how more complex models are developed and extend their earlier thinking. I have not seen this done as effectively in any other portfolio management book, even ones that focus solely on fixed income. The same technique is used with the equity markets section. From a simple presentation of Markowitz’s efficient frontier, Winston adds complexities to show how the problem of uncertain expected returns is addressed to improve model results. He also effectively presents the complexities of factor models and the arbitrage pricing theorem. Again, this is not generally the approach presented in other texts. Quantitative Risk and Portfolio Management presents a focused chapter on distribution theory and a section on simulations, scenarios, and stress testing. These are important risk concepts, especially when the problem of risk management is placed in the context of controlling for uncertainty. The book then explains time-varying volatility measurement through current modeling techniques, the extraction of volatility from options, and the measurement of relationships across assets based on correlation relationships. While it is neither a math book nor one on econometrics, Quantitative Risk and Portfolio Management strikes a nice balance between the core concepts on measuring volatility and covariance with more advanced issues concerning risk forecasting. The book ends with a chapter on credit modeling and one on hedging, and in both cases follows Winston’s approach of layering in greater modeling complexity. Given his clear discussion of the difference between risk and uncertainty,

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