CFA Institute

We are bearing witness to a remarkable demographic revolution in developed nations — the unprecedented aging of our populations. One in six Americans were over the age of 65 at the 2020 US Census. By 2034, for the first time in history, US adults aged 65+ are projected to outnumber children 18 years of age and younger. In the coming decades, retirement needs and goals will change profoundly in the US and other developed nations, creating significant societal and economic challenges that call for an ideological shift in both policy and retirement planning. The populations of developed nations are rapidly aging during an era marked by economic uncertainties, climatic disruptions, mounting national debts, historically low savings rates, escalating personal financial responsibilities, ballooning inflation, and the cessation of declining interest rates. The practice of relying on passive investments like S&P 500-based exchange-traded funds (ETFs) to fund retirement is gradually losing appeal amidst this volatile environment. The need for expert financial advice is becoming paramount, on the scale of seeking a diagnosis from a family doctor. Consequently, we are seeing a push for more active investment management, along with the design of tailored retirement strategies that accommodate the requirements of different socioeconomic groups and generations.  These demographic shifts will have far-reaching impacts on areas like healthcare, caregiving, and housing — responsibilities that younger generations will inevitably help shoulder. We’re Living Longer Longer lifespans mean that individuals are increasingly surpassing their first retirement plan projections, creating the need to revisit and modify financial roadmaps. More older adults are staying in the workforce beyond the retirement age, either on a full-time or part-time basis. More than half of these older adults hold a college degree or higher, enabling them to pursue jobs that demand less physical exertion yet stimulate their mental faculties. These educational milestones serve as a safety net, allaying worries about inadequate retirement funds and paving the way for intriguing possibilities in entrepreneurship during their mature years. But not all Baby Boomers (60-78) are well-prepared for their golden years. A significant segment lacks both adequate retirement savings and the necessary qualifications for continuous employment. These individuals rely on governmental programs like Social Security and Medicare. These programs’ financial health and future are uncertain, presenting a dire situation for Baby Boomers. This circumstance also puts immense pressure on the younger generations—Generation X (44-59), Millennials (28-43), and Generation Z (12-27). These groups are dealing with their own set of challenges as they try to arrange for their later years, particularly in a panorama fraught with uncertainty about the sustainability of these government-supported financial protections. Governments Need to Rethink Existing Policies The rising old-age population in the United States and other developed nations calls for immediate attention and the development of new policies. The US federal government is behind the eightball when it comes to adopting a comprehensive approach. California is an exception. By 2030, the number of Californians over the age of 60 is projected to double, reaching 10.8 million and making up a quarter of the state’s population. The pioneering 2019 Master Plan on Aging set forth by Governor Gavin Newsom seeks to foster equal aging opportunities across various sectors. This plan, albeit exploratory, is a crucial step towards combating ageism and discrimination, with the goal of reducing anxiety across generations. As we adjust to significant demographic shifts, it’s anticipated that both Baby Boomers and Gen X will enter retirement financially stronger than Millennials and Gen Zs. This forecast is rooted in current trends that hint at a reduction in household debt as the older generations refocus their financial strategies toward debt settlement and bolstering retirement nest eggs. Things look less favorable for Gen X and younger Baby Boomers, however, as high debt levels threaten their net worth. The escalating costs of healthcare and the extension of our life expectancy could result in an uptick in retirees needing to lean on debt resolutions like reverse mortgages, ultimately undermining the potential value of their assets. Reassessing Retirement Strategies Looking ahead to the 2030s, Baby Boomers will hold a significant portion of household wealth. Such a shift demands a rebalance of established risk tolerance patterns, calling financial advisors and asset managers to reassess their strategies. Innovations such as artificial intelligence (AI) and blockchain technology could reshape asset management. With enhanced efficiency, these intentional technological strides could significantly aid in formulating investment strategies that accommodate the individual preferences and needs of aging investors. It’s crucial that financial advisors and asset managers ensure that as retirees set their financial goals, they have a variety of solutions at hand that fit comfortably within their personalized risk tolerances. By 2030, the wealth of US households is forecast to skyrocket to $120 trillion, accentuating the need for apt shifts in financial institutions’ strategies, pushing them to pioneer innovative measures to leverage these modifications. Customer categorization will no longer be a choice. It will be necessary for effective engagement and sustainable profit. Thus, it is imperative that financial organizations strategically place themselves amidst this evolving environment, primarily because they cater to an increasingly diverse and aged demographic. Successful financial advisors will deliver personalized strategies that incorporate fintech and AI approaches. Cutting-edge educational subscription services could potentially connect with a broader section of the population. With advanced AI technology, it is possible to compute, model, and foresee every financial aspect of an individual’s life through countless scenarios. This is a game-changer, especially for those who currently cannot access conventional financial advisement. Imagine a multitude of top-class finance professionals using powerful tools like Asset-Map to algorithmically plot clients’ financial terrain and explain it in simple terms. Generational Divides But here’s a sobering reality: The economic forces defining the lives of Gen Zs and Millennials are more unpredictable and drastically different to those older generations encountered. Unprecedented changes in employment trends, lower homeownership rates, an increase in individuals with negative net worth, and the big hex of soaring student loan debt all conspire to make wealth accumulation feel just out of reach for

Franklin J. Parker, CFA, is the author of Goals-Based Portfolio Theory, published by Wiley. Last summer, I was having lunch on the shores of Lake Maggiore in Italy with a few other investment professionals, one of whom I had known for several years. A former CIO for a family office, he had left that gig to start some cryptocurrency projects, including a fund and a venture or two in the non-fungible token (NFT) space. “Man, I’m excited to ask you something I’ve not been able to get an answer to,” I told him. “I’ve followed bitcoin since 2011, I read Satoshi Nakamoto’s original white paper, and I really think blockchain will be an important piece of the future, but I never did invest.” “Why not?!” he asked with a smirk. He had made quite a bit of money, and he had only been in crypto for a few years. “I remember being really excited about bitcoin in 2011,” I said. “But the price had already gone from $3 a coin to $17 a coin. I had no idea if the move was over or not.” “It wasn’t over,” he quipped. “I know!” I said. “And that is what I am upset about. I could have given $100 to a guy in a parking lot, and I would’ve had $300 grand a decade later.” “Why didn’t you?” he asked. “For the same reason I haven’t invested at $25,000 a coin,” I said. “I really believe that blockchain will be a big deal, but I have no idea how to value bitcoin. Is bitcoin the future? And is it the future at $25,000, $60,000, or $1,500,000 per coin? That’s why I’ve wanted to talk to you. You’re not a crypto bro — you’re a professional. How do you value it?” I was earnest. I genuinely wanted to know how he went about making buy/sell decisions in an asset class that I knew precious little about. Needless to say, I was disappointed. After about 10 minutes of the standard, “It’s the future,” “It’s digital gold,” “It’s a storehouse of value,” and the inevitable, “You just have to believe it,” I lost patience. I agree. Crypto, blockchain, and (maybe) bitcoin are the future . . . but at what price? I cannot consume in bitcoin. At some point, I have to convert it into goods and services so I can live my life. That makes it an investment. And when investing, price matters. Maybe it is digital gold, but at least with gold, we have some pricing models to lean on. And a “storehouse of value”? Come on. It loses 15% in a day. Not only is that a terrible currency; it is also no “storehouse of value.” In the end, I just sighed, leaned back in my chair, and watched the boats drift across the stunning mountain scenery. He just didn’t know. He didn’t know how to value the asset he had made a career trading. But who is the smart one? I have been dramatically wrong about bitcoin for over a decade now. And that upsets me. I want to participate in this asset class. But as a professional investor subject to a fiduciary standard (and my own rationality), I have to have an intelligible reason. So, here I am, over a year later, and I still have no crypto investment. Yes, I can brag about how I missed a $60,000 to $20,000 per coin drawdown. But that is cold comfort when I could have invested at $1,000 or less. Which reminds me of a two-hour presentation from a successful and respected pension fund manager I sat through at Old Parkland, the most exclusive office space in Dallas, Texas. The audience was mostly family office managers and staff. I did not expect a bitcoin pitch. But that’s what we got. In retrospect, I should have seen it coming. The speaker began with a very cogent analysis of how the US dollar has been mismanaged, a point I agree with, and how that demonstrated the need for an alternative. And then, there it was: bitcoin. Often people bury their argument beneath the evidence, but the argument was there all the same: Bitcoin will be successful because the US dollar will falter. Will it, though? If the US dollar collapses — an outcome I don’t anticipate — why would bitcoin come out the big winner? As a counterexample, what happened when the Venezuelan bolivar collapsed? Bitcoin didn’t emerge as the replacement of choice. People much preferred physical gold. At the Dallas presentation, I asked the question I always ask of crypto enthusiasts: At what price? I didn’t mean to be contentious, but the presenter felt otherwise and went on a long diatribe. Apparently, I hadn’t been listening, he said, and recapped his previous evidence. I stopped listening. All of which brings me to my point: Professional investors need a pricing model — any model at all — if we are to include this asset class in our portfolios. We don’t include it because, as my experience on Lake Maggiore and at Old Parkland demonstrates, no one knows what any of it is worth. At this point a ballpark, back-of-the-envelope, rough rule-of-thumb would do. But I am tired of the hand-wavy, don’t-ask-too-many-questions approach. Therefore, on behalf of the professional investment community, I am respectfully asking any crypto expert to put together some cogent, coherent concept of how to make buy and sell decisions in a cryptocurrency portfolio. Please don’t forget the sell decisions part. Without sell discipline, we are fanatics not investors. Then, maybe, I will finally have the answer to that question I’ve been asking since 2011: At what price? For more from Franklin J. Parker, CFA, check out Goals-Based Portfolio Theory and follow him at Directional Advisors. 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

The Worth of Art: Financial Tools for the Art Markets. 2023. Arturo Cifuentes and Ventura Charlin. Columbia University Press. “Sorry, but we do not have a magical equation for predicting which artists will be hot next year or whether Andy Warhol’s Marilyns will outperform the S&P 500 in the next five years.” So write Arturo Cifuentes and Ventura Charlin in The Worth of Art: Financial Tools for the Art Markets. What, then, can readers hope to accomplish with the financial tools for navigating the art market that the book’s subtitle promises? The answer: Objectives that are actually achievable, such as determining how the market arrives at values for different works by a given artist and estimating returns on the artist’s overall body of work. There is no more reliable way of predicting the short-term price performance of a painting, the authors maintain, than there is for a common stock. The quantitative techniques described by Cifuentes and Charlin, research associates at CLAPES-UC (Catholic University of Chile ) who also reside in New York, yield fascinating findings, such as the following, which refer to the oeuvre of Pierre-Auguste Renoir: If the artist’s Femme après le bain had been 10% bigger, its 1985 auction price of $2,865,892 would have been 6.5% higher. Auction prices for a painter’s work generally increase with size. Above a certain square footage, however, prices decline because extremely large works can be displayed only in museums or palaces, which limits the number of potential bidders. All else being equal, inclusion of one or more persons in the composition positively affects a Renoir’s price, whereas the inclusion of nudes and landscapes reduces the price per square centimeter (cm2) — the “crude but useful” metric used throughout the book. A Renoir landscape in a vertical frame will sell for more per cm2 than one in a horizontal frame (otherwise known, ironically, as “landscape”) format. A Renoir will likely fetch a higher price if sold in New York rather than elsewhere. This finding, the authors note, defies the law of one price, implying some inefficiency in the Renoir market. Estimating returns is tougher for artworks, which typically change hands infrequently, than for securities, for which, in many cases, daily transaction prices are available. Quantifying art’s diversification effect within a portfolio composed primarily of securities and commodities similarly represents a formidable challenge. Cifuentes and Charlin address the difficulties with highly sophisticated mathematics. They acknowledge that terms such as “heteroscedasticity-consistent covariance matrix estimates” lie outside many readers’ knowledge base, but they provide an “appendix for poets” that explains their methodology’s underlying concepts. The Worth of Art also deals with art-secured lending and the risk of guaranteeing minimum prices at auctions. Separately, Cifuentes and Charlin report that artificial intelligence (AI) has not outperformed experienced appraisers in predicting auction prices. They see potential for AI, however, in helping museums and scholars classify artworks by style or movement. Finally, the authors show how their quantitative techniques can also be applied to certain other collectibles, namely, violins, wine, and classic cars. I found The Worth of Art both illuminating and riveting, but I do have one quibble. The authors write that “if you have never been moved by a painting . . . this book is not for you.” It is fine for individuals, if they so choose, to skew their purchases toward their esthetic preferences. Such behavior would not be appropriate for a fiduciary, however. A money manager tasked with investing in art on behalf of a client can benefit from reading this book as an aid to making objective decisions aimed solely at maximizing risk-adjusted returns. The manager would be no more disadvantaged than a counterpart dealing in commodities who has never actually seen a bushel of wheat or barrel of oil. Furthermore, even investment professionals who have no plans of ever becoming involved in art or collectibles would do themselves a favor by grappling with Cifuentes and Charlin’s criticisms of conventional securities analysis. For example, they point out that the standard calculation of diversification benefits is dubious in view of the pronounced time dependency of correlations, “a dirty secret in financial analysis, which most financial textbooks and almost all academics hate to acknowledge or even discuss.” In the art realm, they report that, depending on which rolling seven-year period from 2004 to 2020 one selects, the return correlation between Old Masters and the Case–Shiller home price index ranges from –20.54% to +27.98%. Asset allocators dealing in more mainstream asset classes may be surprised by the instability of the correlations on which they have been basing their decisions. The authors also reject as deficient the widely followed practice of equating risk with standard deviation of returns when calculating risk–reward ratios. They argue that such exercises should, instead, incorporate value at risk, defined as the maximum possible loss within time period x at confidence level y. In addition, Cifuentes and Charlin fault standard reports of historical asset performance for insufficient attention to real, as opposed to nominal, returns. No text of just 241 pages could do justice to every aspect of art investment. The authors very responsibly state that thorough explanations of certain topics they touch upon lie beyond the book’s scope. One point that might have been worthy of greater prominence, however, is the disappointment that may befall investors who acquire a contemporary artist’s work with Warren Buffett’s favorite holding period (forever) in mind. The price progressions over the decades that the authors detail for various artists’ works are not invariably representative. For example, in 2015, analyst and author Zac Bissonnette recounted that Jean-Louis-Ernest Meissonier (1815–1891) was described by his fellow artist Eugène Delacroix as “the incontestable master of our epoch. . . . Amongst all of us, surely it is he who is most certain to survive,” according to James Grant in “Attention, Larry Fink,” from the 15 May 2015 issue of Grant’s Interest Rate Observer. Meissonier’s painting Friedland sold in 1876 for what the Metropolitan Museum of Art, the painting’s current home, describes

Private equity (PE) investments have expanded significantly across sectors such as industrials, education, logistics, and technology. As PE firms continue to optimize companies for profitable exits, strategic buyers must scrutinize deals more carefully. What looks financially healthy on paper may conceal operational vulnerabilities and sustainability risks. For investment professionals evaluating these opportunities, this is not just about valuation, it’s about vigilance. The following framework brings together lessons from finance, operations, and governance to help strategic buyers protect value and drive long-term performance after a PE exit. Why PE-Backed Deals Require Special Attention PE-backed deals often look impressive on the surface. Many exit-ready businesses are structured with lean operations, aggressive working capital models, and optimized tax strategies designed to boost short-term returns. But what benefits the seller can complicate life for the acquirer. Strategic buyers are not just acquiring a company, they are inheriting years of decisions optimized for exit, not permanence. Unlike financial buyers, they must think about long-term integration, capability building, and stakeholder alignment. That requires going beyond headline numbers to examine the operational DNA of the business: its systems, culture, and true earning power. Key Risk Areas When Acquiring from Private Equity To move from surface-level diligence to true insight, acquirers need to understand where short-term engineering can distort long-term value. 1. Adjusted EBITDA vs. Real EarningsPE sellers often present inflated EBITDA through excessive add-backs, sometimes labelling recurring costs as “one-offs.” For example, a tech firm reported USD 15 million in adjusted EBITDA but excluded USD 4 million in platform support costs that would recur annually. To separate sustainable earnings from presentation effects, finance teams should build a bottom-up model validated through department-level interviews and benchmark results against peer data. This recasts EBITDA to reflect true ongoing performance. 2. Deferred Capex and Investment GapsIn the race to show high free cash flow, PE owners may delay critical investments in infrastructure, maintenance, or IT systems. The short-term optics can be impressive—but the long-term costs can be steep. A logistics company that deferred fleet modernization, for example, faced sharply higher maintenance expenses post-acquisition. Analyzing historical capex-to-depreciation ratios and conducting technical due diligence on asset quality can help buyers uncover hidden reinvestment needs before they turn into surprises. 3. Sale-Leaseback StructuresSale-leasebacks often release capital upfront but create future obligations. Buyers inherit long-term leases with inflation-linked escalators that can squeeze margins in downturns. In one case, a retail chain was acquired with above-market lease rates, eroding profitability as consumer demand softened. Finance leaders should run lease sensitivity models and evaluate occupancy alternatives before finalizing valuation to ensure apparent liquidity doesn’t mask future constraints. 4. Working Capital Management GamesWorking capital can be another area of distortion. PE-backed firms sometimes stretch payables or accelerate receivables to inflate cash conversion metrics before exit. To identify manipulation, buyers should normalize net working capital over a rolling 12-month cycle and speak directly with key vendors to confirm true payment terms. Transparency here can reveal whether “efficiency” is real or engineered. 5. Management and Organizational DepthLean management structures make companies look efficient but can leave thin leadership benches. Middle managers who carry institutional knowledge may depart post-transaction, leaving critical capability gaps. Strategic buyers should assess management continuity early and build retention and onboarding plans into the integration phase. Sustaining performance requires leadership depth, not just financial efficiency. 6. Non-Recurring Commercial GainsShort-term pricing actions, temporary promotional pushes, or early revenue recognition can inflate top-line growth right before an exit. Analyzing revenue at the contract level helps distinguish one-time effects from ongoing trends. This analysis supports more realistic revenue forecasts and helps determine how much growth is repeatable versus engineered. 7. Tax, Legal, and Compliance OverhangsFinally, optimized holding structures may conceal contingent liabilities or unresolved regulatory risks. Complex entity charts, related-party arrangements, or untested tax positions can pose hidden exposure. Finance diligence teams should deploy integrated legal-tax reviews to identify transfer pricing risks, structure unwind costs, or potential disputes that may resurface after closing. Valuation Challenges in PE Exits Valuation in PE-backed exits often becomes a negotiation between deal optics and underlying fundamentals. Multiples may appear consistent with peers but often rest on inflated earnings or deferred investments. Strategic buyers should approach valuation through a forensic lens that links financial performance to sustainability. Here are some techniques: Recasted EBITDA: Adjust for normalized personnel costs, recurring vendor contracts, and hidden support functions previously absorbed by the PE sponsor. Cash Conversion Reality: Review multi-year cash flow data to identify distortions from one-off working capital plays or timing adjustments. Capex Benchmarking: Compare historic and forecasted capex-to-sales or capex-to-depreciation ratios against industry norms to model true reinvestment needs. Integration Adjustments: Layer in post-deal costs such as system integration, shared service migrations, or rebranding, which are often omitted from PE forecasts. Exit Multiple Sensitivities: Build conservative scenarios reflecting slower growth and margin normalization to stress-test returns. A robust valuation process triangulates several methods: adjusted EV/EBITDA on normalized earnings, discounted cash flow models with integration overlays, and public comparable ranges discounted for private market opacity and liquidity risk. Valuation should capture not only what the company has been but how resilient and future-ready it is likely to be under strategic ownership. Financial Lessons and Diligence Enhancements Across transactions, one pattern is clear: thorough diligence and financial scrutiny often determine post-acquisition success. The most effective acquirers don’t stop at validating earnings; they test the durability of the business model, culture, and governance. Commissioning quality-of-earnings reports that integrate operational realities, rather than focusing only on accounting reclassifications, helps uncover recurring costs hiding in temporary classifications. Scenario planning tools can then stress-test lease obligations, debt refinancings, and other contingent risks. Strategic buyers should also ensure that post-acquisition reporting structures, governance processes, and system integrations are mapped before the deal closes. Scrutinizing the board composition and oversight culture inherited from PE owners is equally vital. Recasting valuation models with a bottom-up lens — rather than relying solely on PE-crafted projections — adds transparency and reduces surprises. These practices shorten the time to value realization and strengthen confidence across stakeholders,

Artificial intelligence (AI)-based strategies are being increasingly applied in investing and portfolio management. Their contexts, utility, and results vary widely, as do their ethical implications. Yet for a technology that many anticipate will transform investment management, AI remains a black box for far too many investment professionals. To bring some clarity to the subject, we zeroed in on one particular AI equity trading model and explored what it can bring in terms of benefits and risk-related costs. Using proprietary data provided by Traders’ A.I., an AI trading model run by our colleague Ashok Margam and team, we analyzed its decisions and all-around performance from 2019 to 2022. Traders’ A.I. has few constraints on the market positions it takes: It can go both long and short and flip positions at any point in the day. By each day’s closing bell, however, it completely exits the market, so its positions are not held overnight.  So how did the strategy fare over different time periods, trading patterns, and volatility environments? And what can this tell us about how AI might be applied more broadly in investment management? Traders’ A.I. outperformed its benchmark, the S&P 500, over the three-year analysis period. While the strategy was neutral with respect to long vs. short, its beta over the time frame was statistically zero. Traders AI Model vs. S&P 500 Monthly Equity Curve ($10k Investment) Traders’ A.I. leveraged moments of higher skewness to achieve these results. While the S&P 500 had negative skewness, or a strong left tail, the AI model displayed the opposite: right skewness, or a strong right tail, which means Traders’ A.I. had few days where it generated very high returns. AI Model S&P 500 Mean 0.00111881 Mean 0.00064048 Standard Dev. 0.005669 Standard Dev. 0.01450605 Kurtosis 11.1665 Kurtosis 13.1015929 Skewness 1.59167732   Skewness -0.62582387 So, where was the model most successful? Was it better going long or short? On high or low volatility days? Does it choose the right days to sit out the market? On the latter question, Traders’ A.I. actually avoided trading on high return days. It may anticipate high risk premium events and opt not to take a position on which direction the market will go. Traders’ A.I. performed better on a market-adjusted basis when it went short. It made 0.13% on average on its short days while the market lost 0.52%. So the model has done better predicting down days than it has up days. This pattern is reflected in bear markets as well, where Traders’ A.I. generated excess performance relative to bull markets. AI Model’s Average Return S&P 500’s Average Return When Model Is Active 0.1517% -0.0201% When Model Sits Out 0% 0.8584% When Model Is Long 0.1786% 0.6615% When Model Is Short 0.1334% -0.5215% When Model Is Long andShort in a Day 0.1517% -0.0201% On High-Volatility Days 0.1313% -0.0577% On Low-Volatility Days 0.0916% 0.1915% In Bull Markets (Annual) 17.0924% 46.6875% In Bear Markets (Annual) 20.5598% -23.0757% In Bull Markets 0.0678% 0.1853% In Bear Markets 0.0816% -0.0916% Finally, the AI model performed better on high-volatility days, beating the S&P 500 by 0.19% a day on average while underperforming on low-volatility days. AI Model’s Return Percentage vs. VIX Percentage Change All in all, Traders’ A.I.’s results demonstrate how one particular AI equity trading model can work. Of course, it hardly serves as a proxy for AI applications in investing in general. Nevertheless, that it was better at predicting down days than up days, succeeded when volatility was high, and avoided trading all together before big market-moving events are critical data points. Indeed, they hint at AI’s vast potential to transform investment management. For more on this topic, don’t miss “Ethics and Artificial Intelligence in Investment Management: A Framework for Professionals,” by Rhodri Preece, CFA. 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 / Svetlozar Hristov 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

A Wealth of Well-Being: A Holistic Approach to Behavioral Finance. 2024. Meir Statman. John Wiley & Sons. In A Wealth of Well-Being, Meir Statman, the Glenn Klimek Professor of Finance at Santa Clara University and a prominent thought leader in behavioral finance, explores how financial well-being can lead to life well-being through the third generation of behavioral finance. The first generation describes people as “irrational,” whereas the second generation describes them as “normal.” Statman likewise describes people as “normal” but broadens the lens of finance to see them as whole persons and show them in life well-being domains, including dating/marriage, family, friends, health, work, education, religion, and society. Financial well-being is a critical element (domain) of life well-being, but it is life well-being that people ultimately want. The book combines scientific findings by scholars in various fields, such as finance, economics, medicine, psychology, and sociology, with practical stories that provide insights into those findings. This important book informs financial advisers, financial planners, financial academics, and investors about the third generation of behavioral finance’s focus on well-being as people’s primary want and on ensuring that finances are integrated into lifestyle to achieve both financial and life well-being. According to Statman, having more money (greater financial well-being) is correlated with higher levels of life well-being, but money alone is not everything and money is not sufficient when we assess people. Social status, for example, matters for life well-being. The main goal of the book is to help readers reflect on what goes into their life well-being, including what makes life worth living, as well as insights into how managing financial well-being can optimize the portfolio of life choices. I found Statman’s conclusions on education, health, and work to be most insightful and relevant for myself. Although education costs money in tuition and living expenses, it enhances well-being by the utilitarian benefits of better employment and higher income and by the expressive and emotional benefits of increased knowledge, lifelong friendships, and high social status. People experiencing high life well-being enjoy high perceived health, low self-reported pain, and low medical risks, with mental illness being the best single predictor of low evaluative well-being. According to Statman, we derive utilitarian benefits from our work in the form of earnings, but we also derive expressive and emotional benefits from our work through identity, meaning, community, dignity, and pride. Work enhances well-being, and well-being enhances work prospects and income. People who work longer live longer. High employee well-being leads to high employee productivity, high customer loyalty, and increased profitability. Unfortunately, few people are fortunate enough to enjoy well-being in all the domains. One way wealth advisers can benefit from this book is through Statman’s suggestion that they evolve into well-being advisers if they are to compete for today’s clients and clients of the future because many of the traditional services of financial advisers are now generic. Financial advisers provide advice on asset allocation and rebalance portfolios, but so do robo-advisers at a lower cost. Robo-advisers, however, cannot serve as well-being advisers, which is a potential competitive advantage for financial advisers. By becoming friends with their clients, financial advisers obtain an understanding of what is going on in the lives of their families and children. In summary, A Wealth of Well-Being is a thoughtful and practical book with research backing much of Statman’s advice. Finance practitioners can benefit from his challenge to make finance an “afterthought” and spend more time thinking about our life well-being. The domain of finances is only one of the many domains of life well-being, yet it has a uniquely important place because it underlies all other domains, since money is needed to pay for food, shelter, education, and religious contributions and to maintain our health. source

In 1688, Joseph de la Vega wrote, “Profits on the exchange are the treasures of goblins. At one time they may be carbuncle stones, then coals, then diamonds, then flint stones, then morning dew, then tears.” He was writing about the trading of shares on the Amsterdam Stock Exchange of his day. He could have been writing about modern-day alpha — that extra portion of return investors clamor for. Academics can’t define it rigorously for lack of an agreed-upon market (asset-pricing) model. Empirically, and owing to statistical noise, it can be difficult to pin down, even when we use the returns-generating process of our choosing. Yet, many investors seem to think they can spot this element of return in advance. So, large numbers of them eagerly pursue alpha. Alpha is elusive. Michael Jensen, who wrote about mutual fund performance in 1967 and is responsible for coining the term “alpha,” observed, “…the mutual fund industry … shows very little evidence of an ability to forecast security prices. Furthermore, there is surprisingly little evidence that indicates any individual funds in the sample might be able to forecast prices.” S&P Global continues this work, showing that 88% of large-cap mutual funds underperformed the S&P 500 for the 15 years ended 2023. My own work, which focuses on the performance of institutional portfolios, indicates that none of the 54 public pension funds that I track have outperformed market index benchmarks by a statistically significant margin since the Global Financial Crisis of 2008 (GFC). Endowments do no better. Moreover, alpha is short-lived. As investors attempt to exploit it, it begins to disappear. This element of extra return is as difficult to capture as it is to locate. The cost of active investing is a different matter altogether. Investment expenses, whether in the form of management fees or carry, are factual, exactly measurable, and don’t fade away. But no one, it seems, wants to talk about them. In my studies of public pensions and endowments, I have identified just a handful that regularly make full disclosure of their investment expenses, including carry. CEM Benchmarking has observed that public pension funds in the United States underreport the cost of investing by more than half. My own work confirms this finding. And endowments do not report their costs. An NBER study shows that balanced mutual funds underperform market-index benchmarks by an amount just equal to their cost, on average. I find the same perverse equality holds true for public pension funds and endowments. I estimate that the average expense ratio of public pension funds, with more than 30% in alternative investments, is 1.3%. The corresponding figure for large endowments, with more than 60% alts, is 2.5%. These are also the typical margins of underperformance. For institutions, cost appears to be directly proportional to the percentage allocation to alternative investments. I estimate that Harvard University, with about 80% in alternative investments, spends three full percentage points of endowment value on money management annually, including the operation of its investment office. I estimate Harvard underperformed a tailored blend of market indexes by a like amount since the GFC. Harvard spends more on money managers than it takes in in tuition each year. It’s no wonder institutional investors are reluctant to talk about their investment expenses. There is every reason to believe that both public and private markets will become steadily, ineluctably more efficient, making alpha even harder to come by. That puts the spotlight on cost for active investors. Gentle reader, understand that the cost of institutional investing has become an impossible burden. Here are suggestions for reconciling elusive alpha and corrosive costs: Know the cost of your investment program from top to bottom. It takes work to compile this information. Make it known throughout your organization. Make cost-awareness, rather than cost-denial, part of your investing culture. Rethink portfolio design to reflect the realities of contemporary institutional investing. Conduct an asset class triage. For example, research — mine and others’ — indicates that non-core private real estate equity and hedge funds, in particular, have been a serious drag on performance since the GFC. It’s no surprise: These competitively traded asset classes can cost more than 3% of invested capital annually and provide precious little diversification. Do you really want them in your portfolio? Passive investments, at next to no cost, will play an increasingly important role in successful investment programs. You may have a fancy risk budget. Consider establishing an old-fashioned expense budget. Having such wouldn’t rule out active investing and might make it more selective. Evaluate your performance relative to a simple passive benchmark, sometimes called a reference portfolio. This is a combination of a few stock and bond indexes that reflects your risk tolerance and taste for international diversification. The so-called custom benchmarks typically used by institutional investors, which are opaque and generally paint a rosy picture, only mask reality. When all is said and done, which would you prefer: a conventional portfolio with all manner of costly esoterica that underperforms a legitimate reference portfolio by 100 bps or more per year? Or, one that is 80% passive with far fewer, carefully chosen active strategies that outperform by 10 bps or more per year?   Reduce costs. Give alpha a chance. source

Private equity (PE) ownership fundamentally reshapes a company’s financial profile, but understanding the true implications requires a deeper dive into balance sheet mechanics. This final installment in my three-part series explores critical nuances in how PE-backed firms report their financials, particularly regarding intangible asset amortization and shareholder loans. These accounting distinctions can significantly impact leverage ratios, profitability measures, and overall financial interpretation, making them essential knowledge for investment professionals navigating the PE landscape. Nuances in PE-Owned Company Balance Sheets One important nuance in PE-owned company balance sheets is reported assets, and particularly the mechanical amortization of their intangible assets over time. When a group has grown by acquisition, its balance sheet may include intangible assets that reflect any difference between the price paid for assets and their book value. These assets are then amortized over time through non-cash charges in the income statement. When an acquisition is made at a premium to book value, the group’s total assets will be understated over time relative to the actual capital that has been invested. The reverse holds for acquisitions made at a discount to their book value. Naturally, this amortization process can have a considerable impact on a group’s profitability and leverage ratios, where the denominator is often the group’s total assets. That is, if total assets are understated, profitability and leverage ratios will rise. How serious an issue it may be will reflect the proportion of total assets which are represented by intangible assets and the speed at which intangibles are amortized.[1] The higher these are, the greater the distortion in total assets. To underline how this can affect PE-backed targets’ assets — and consequently impact any accounting ratios – in a recent study, I closely examine the financial structure of PE-backed groups in the UK over the last two decades. Figure 1 shows the median and interquartile percentage difference between the PE target group’s net and gross intangible assets in each year, post-buyout. Median gross intangible assets are around 10% larger than net intangible assets in the first year, post-buyout. This difference increases by about 40% after five years. Figure 1: Percentage difference between gross and net intangible assets during the PE holding period. Note: Figure 1 shows the median and interquartile range of the difference between PE portfolio companies’ gross and net intangible assets during the PE holding period, from the consolidated group accounts. The dot shows the median for each year relative to the buyout, and the bars show the interquartile range. The second important nuance in PE-owned company balance sheets is how PE investors invest in target groups. They often invest through a combination of ordinary equity alongside shareholder loans. Shareholder loans are loans made from the PE investor to the company which they are acquiring. Interest on these loans is often rolled up and paid at exit when the business is sold. The rationale behind using these instruments may reflect tax considerations, seniority, and incentivizing management. They typically sit between junior debt and equity on the capital structure. It may be argued that these shareholder loans ought to be excluded from the total debt figure (and therefore leverage ratios) of PE targets as they often require minimal contractual cash payments, and a lender who is a shareholder is unlikely to take legal actions in the event of financial distress. Nevertheless, it is debatable as to whether shareholder loans should be treated as debt or as equity. Figure 2 shows that these shareholder loans typically represent a considerable portion of liabilities for PE-backed firms during the PE ownership years. At the median, shareholder debt represents between 35% and 40% of total liabilities of the target group’s balance sheet in each year during the PE ownership period. Figure 2: Shareholder debt as a percentage of total liabilities during the PE holding period. Note: Figure 2 shows the median and interquartile range of PE portfolio companies’ shareholder debt as a percentage of total liabilities during the PE holding period, from the consolidated group accounts. The dot shows the median for each year relative to the buyout, and the bars show the interquartile range. Figure 3 is an illustrative example of the impact of shareholder debt on leverage ratios. In panel B of Figure 3, if we compute the leverage ratio (total debt divided by total assets) of the consolidated group entity, Viola Holdco Limited, and include shareholder debt within the total debt figure, the group would have a leverage ratio of 86% in 2018 and rising to 96% in 2022. However, if we classify shareholder debt as equity instead of debt, the leverage ratio would instead be calculated as 36% in 2018 and falling to 30% in 2022.[2] It is perhaps unlikely that the PE investor, Inflexion Private Equity Partners LLP, would report Xtrac’s leverage to LPs and to third-party lenders including shareholder loans. Figure 3: Consolidated and Operating Firm Accounts. Together, being able to identify shareholder debt on the balance sheet and being able to account for gross intangible assets allows for a cleaner and more detailed analysis of PE-backed targets. Figures 4 and 5 show the median and interquartile range of PE portfolio companies’ leverage (as measured by total debt divided by total assets) and return on assets (as measured by EBITDA divided by total assets) from the year prior to the buyout to five years following the buyout, comparing between operating entity accounts, and consolidated group accounts. Figure 4: Leverage during the PE ownership period. Note: Figure 4 shows the median and interquartile range of PE portfolio companies’ leverage, as measured by total debt divided by total assets, from the year prior to the buyout to five years following the buyout. The dot shows the median for each year relative to the buyout, and the bars show the interquartile range. There are considerable differences between leverage when calculated based on financials reported in the operating entity accounts, compared to leverage based on consolidated group financials. Median leverage is around three to four times larger when

Institutional investors are operating in an environment where traditional approaches are under strain and long-held assumptions no longer hold. Yet within these challenges lies an opportunity to rethink strategy, sharpen focus, and build more resilient, forward-looking portfolios. This curated selection of Enterprising Investor posts reflects both sides of that equation. Some contributions examine the cracks — underperformance, governance gaps, and structural inefficiencies. Others offer practical ideas for adaptation — integrating investment teams more strategically, adopting HR practices that help retain top talent, and aligning portfolios with long-term sustainability goals. Performance Pressure & Strategy Reassessment Many institutional portfolios are facing a performance reckoning. Big Funds, Small Gains: Rethinking the Endowment Playbook shines a spotlight on endowment underperformance, citing return smoothing, structural underperformance, and allocations to alternative investments. Are Institutional Investors Meeting Their Goals? Spotlight on Earnings Objectives questions whether institutions are achieving their investment goals, highlighting the use of custom benchmarks that may obscure true performance. The Alternative View: 401(k) Plans Are Better off Without Private Investments challenges assumptions around the promise of private equity in defined contribution (DC) plans — especially when simulations mask access challenges and cost realities. On the other hand, The 60/40 Portfolio Needs an Alts Infusion explores the theoretical basis for going beyond the 60/40 portfolio and considers the market conditions that could make alternative portfolio allocations useful to institutional and individual investors alike. Governance & Decision-Making Strong governance is foundational to investment success. The Unspoken Conflict of Interest at the Heart of Investment Consulting raises concerns about advisor incentives. From the archives, Investment Governance for Fiduciaries: The How and the Why is a timeless exploration of the principles of sound oversight. Great investment governance provides a defensible, repeatable, and documented process that places our beneficiary at the heart of all we do, the authors write. In another evergreen post, Choosing Investment Managers: A Guide for Institutional Investors delves into the complexities of manager selection and ongoing diligence. Structural and Operational Issues Market structure and portfolio mechanics matter. Rebalancing’s Hidden Cost: How Predictable Trades Cost Pension Funds Billions explores how transparency around trading patterns leads to value leakage. Predictable rebalancing policies expose large pension funds to front-running, resulting in billions of dollars in annual losses, the author reports. Volatility Laundering: Public Pension Funds and the Impact of NAV Adjustments exposes the gap between private asset net asset values (NAVs) and their real market value. This phenomenon is known as volatility laundering, and it can give misleading impressions of private asset volatility. Looking at the big picture, Aging Populations Demand Urgent Pension Reforms: Are We Prepared? points to the challenges and opportunities created by the world’s aging population. The author raises a red flag for governments, policymakers, fund managers, pension plans, and financial advisors. From the archives, Global Pension Funds: The Coming Storm draws on global events at the time to illustrate the implications of unrealistic return expectations and government inertia. Constructive Paths Forward Retirement Readiness in Focus: Key Actions for DC Plan Success in 2025 calls for DC plans to focus on optimizing investment strategies, reducing costs, and enhancing participant education to improve retirement readiness. The authors identify the top priorities for DC plans in 2025: target date fund selection, fee transparency, investment lineup evaluation, and staying ahead of regulatory and litigation trends. The Enterprise Approach for Institutional Investors makes the case for treating investment teams as strategic arms of the institution — not separate, siloed units. Organizations that implement investment programs in the context of their broader financial measures of success may benefit from sound investment discipline years into the future, the author suggests. What’s the secret sauce behind the Canadian pension plan system’s track record of robust returns and resilience? Retaining Top Investment Talent: Lessons Learned by Large Canadian Pension Plans outlines how leading funds have modified their HR strategies to attract and retain world-class investment talent. Two additional pieces highlight the growing emphasis on impact and long-term value: Finally, Five Quotes from Financial History to Guide Trustees provides enduring wisdom for decision-makers in an era of constant disruption. Final Thought With long-held assumptions under strain — amid demographic shifts, volatile markets, and rising stakeholder scrutiny — this is a pivotal moment for institutional investors to reassess their mandates. The traditional approach is showing its limits, from underperformance to governance gaps and operational risks. The insights in this collection highlight not just where recalibration is needed, but how institutions can lead. For investment committees, trustees, asset owners, and the investment professionals serving them, the imperative is clear: take stock, ask tough questions, and ensure that today’s strategy is built for tomorrow’s demands. source

Liability-Driven Investing (LDI) is often associated with developed markets, where deep liquidity and a wide range of derivatives allow investors to hedge with precision and meet long-term obligations confidently. Products such as inflation-linked securities, interest rate swaps, and long-duration corporate bonds make it easier to align portfolios with actuarial forecasts and regulatory requirements. In frontier and emerging markets, however, the same philosophy operates under tighter constraints. When market depth is limited and policy shocks are frequent, as in Nigeria, LDI becomes less about instruments and more about discipline. It relies on timing, currency alignment, and interest rate sensitivity rather than on complex financial instruments. The goal is the same everywhere: to meet cashflow obligations reliably. However, in frontier markets, like Nigeria, success depends on adaptability, patience, and structural foresight. Matching Timing with Obligations In practice, applying LDI in emerging markets means translating familiar principles into a far less forgiving environment. The objectives are the same, matching timing, currency exposure, and interest rate sensitivity to future obligations, but the execution relies on discipline rather than derivatives. Investors must work within a narrow set of instruments and use judgment where models and hedges fall short. For Nigerian insurers, particularly those managing life or annuity products, this discipline provides stability amid frequent liquidity shocks, currency devaluations, and shifting regulations. LDI keeps liabilities — not returns — at the center of decision-making. In my experience across actuarial and investment functions in Nigeria’s insurance sector, the strongest balance sheets consistently maintained this liability alignment, even when data infrastructure is weak and market liquidity thin. The following sections outline how Nigerian institutions have applied LDI principles in practice — lessons that hold value for other frontier and emerging markets as well. Mapping the Liability Terrain Nigerian insurance liabilities come in several forms: life obligations with actuarially predictable timing, general insurance reserves with higher variance in cashflow timing, and embedded guarantees with interest sensitivity. Three primary dimensions define the liability structure: Timing: Life and annuity obligations often extend across five-to-30 years. General insurance liabilities may require settlement within six-to-24 months. Cashflow projections must distinguish between these timelines and adjust for reinsurance recoveries and expense provisions. Currency: Currency alignment remains a foundational principle. The Central Bank of Nigeria’s exchange rate management framework experienced a series of adjustments between 2020 and 2025, including a move from a managed peg to a more market-reflective rate. The naira depreciated from ~₦380/USD in 2020 to above ₦1,500/USD by Q1 2025, a decline of over 290% (source: CBN, 2025). For insurers with foreign-currency liabilities, holding naira assets introduces unrecoverable mismatches. Interest Rate Sensitivity: Duration, convexity, and key rate duration (KRD) tools help estimate how liabilities will reprice under shifting yield curves. KRD has been instrumental in identifying exposures to specific tenors, such as the five-year or 10-year points. This granularity is essential in Nigeria, where non-parallel curve shifts are common. Navigating Nigeria’s Market Architecture Nigeria’s yield curve is not a smooth continuum of maturities and pricing. Rather, it behaves as a segmented curve, shaped by government borrowing patterns, institutional demand, and central bank policy actions. Federal Government of Nigeria (FGN) bonds, issued by the Debt Management Office (DMO), dominate the fixed-income space. These instruments offer tenors between two and 30 years, but issuance is often clustered. The secondary market is shallow. As of mid-2025, pension funds held over 60% of outstanding FGN bonds, and a substantial portion were marked as “held to maturity” (PenCom, 2025). Insurance companies, facing similar regulatory treatment under Nigeria’s National Insurance Commission (NAICOM) rules, also maintain low trading activity. This limits portfolio rebalancing flexibility. Monetary policy changes frequently introduce short-term volatility. Open market operations (OMOs), cash reserve debits, and sudden benchmark interest rate changes have led to 200-to-300-basis points yield spikes over a single week. For example, in this year’s first quarter, the 10-year FGN bond yield rose from 16.8% to 22.6% following a surprise monetary policy rate hike and liquidity sterilization campaign (BusinessDay, 2025). These dynamics have three implications for LDI strategy: Parallel duration matching strategies can produce unintended mismatches during non-parallel curve shifts. Active KRD management, even in the absence of derivatives, allows better immunization. Segmenting portfolios between matching and return-seeking buckets improves resilience. Building the LDI Portfolio Under Constraint Constructing an LDI-aligned portfolio in Nigeria requires practical creativity. Portfolio architecture depends on instrument availability, regulatory constraints, and realistic trading liquidity. Core instruments for Nigerian LDI include: Asset Class Key Role in LDI Observations FGN Bonds Matching long-term liabilities Most liquid and regulatory-compliant, but clustered issuance Treasury Bills / Short-Term Deposits Matching short-term reserves High yield variability; useful for P&C claims buffers Corporate Bonds Yield enhancement Scarce issuance, low liquidity; requires strong credit analysis Subnational / Infrastructure Bonds Long-term exposures Offers tenor extension; often illiquid post-issuance Equities Return-seeking only Highly volatile; not relevant for matching unless insurer writes index linked products Alternatives (PE, Infrastructure Debt) Enhancing long-dated portfolios Useful for illiquid liabilities; governance-dependent Duration alignment is most effective when structured around key tenors. In practice, an allocation with similar average duration to liabilities may still result in NAV instability if the asset portfolio is concentrated in short-dated bonds while liabilities peak at the 10-year mark. Insurers with foreign obligations, such as those paying offshore reinsurers, benefit from maintaining US dollar reserves or instruments with US dollar-linked cashflows. Given Nigeria’s limited FX hedging instruments, currency mismatches often introduce downside risks that are unable to be hedged. Managing Volatility Through Structured Scenario Analysis Scenario testing has become a core risk management tool in Nigerian insurance asset and liability practices. Volatility in yields, FX, and inflation is both frequent and severe. Each episode, whether from policy, geopolitical, or supply-side shocks, tests an institution’s positioning. Incorporating regular stress testing into investment governance cycles produces tangible advantages. The most effective institutions model quarterly scenarios across: Interest rate shocks: +300bps parallel and non-parallel shifts, with attention to short-end dislocations. FX devaluations: Simulated 20–30% shifts, benchmarked against historical CBN adjustments. Liquidity events: Disruptions in the repo market or increased capital call requirements. Inflation