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The Financial Crisis of 2008: A History of US Financial Markets 2000–2012. 2021. Barrie A. Wigmore. Cambridge University Press. Barrie Wigmore analyzes an extremely complex topic, the financial crisis of 2008, with wide-ranging and deep analysis. He brings to bear a richly experienced point of view, based on working “in the trenches” as an investment banker over multiple cycles. For Wigmore, shocking levels of leverage sounded the main alarm about the mounting crisis. This was represented most dramatically by the Federal National Mortgage Association (Fannie Mae) and the Federal Home Loan Mortgage Corporation (Freddie Mac) operating with leverage of 100 to 1. These government-sponsored enterprises had to make subprime loans and hold private residential mortgage-backed securities (RMBSs) because the US Department of Housing and Urban Development (HUD) had mandated that these institutions increase the number of low-income homeowners, beginning with the Community Reinvestment Act in 1992. In November 2004, HUD set additional low-income lending goals for Fannie Mae and Freddie Mac. Fannie Mae exceeded these aggressive goals in 2005 and again in 2006. At this point in the narrative, the author tells the story in such an exciting way that you can smell the credit danger lurking around the corner. Not only do subprime borrowers represent a disturbingly high percentage of total borrowers, but also Wigmore presents astonishing data directly out of Fannie Mae’s “credit book” cited in its 2006 10-K. The data suggested that both Fannie Mae and Freddie Mac were exposed, beyond HUD’s mandates, to the weakest credit sectors. While this was occurring, state and local government pension funds, insurance companies, and the commercial and investment banking intermediaries that serviced Fannie Mae and Freddie Mac continued to fund them despite their unlimited information resources, their attention to financial markets, and their own stakes in the outcome. There was also the parallel challenge of seeking higher investment returns in a declining interest rate environment — not only for retail investors but also for institutional investors, the so-called smart money. This stretch for yield is presented in Table 2.5, which sums up in simple terms the $11 trillion apocalypse to come. Wigmore cogently presents the setting for the crisis. It visibly began in the second half of 2007, with house prices leveling off after huge runs in such places as Los Angeles, Phoenix, and Las Vegas. The US Federal Reserve noted that consumers’ debt servicing capability was deteriorating from traditional levels, even with the low interest rates prevailing at the time. Consumer liabilities rose from 15% to 22% of net worth between 2000 and 2007, due in particular to growth in residential mortgage debt. Yet, the Fed evidenced no major concern at that time, believing that consumer strength would support a further rise in consumer spending. Subprime mortgages were beginning to default at high rates. The value of asset-backed securities and private RMBSs sank. Mortgage originators with large sub-prime exposure, such as New Century and Fremont General, lost their lenders. Countrywide Financial, IndyMac, and Washington Mutual faced unprecedented disruptions. Their published balance sheets did not keep up with the rapid deterioration in the quality of their loans. The institutional collapses that occurred had a common narrative: extreme leverage; complicated, if not unexplainable, real-time balance sheets; and poor-quality assets, in the case of investors, or liabilities, in the case of lenders. The author methodically explains the collapses, with numerous graphs to underscore the severity of the strains, both individually and systemwide. In the chapter titled “Epilogue 2012–2016,” Wigmore cites many instructive indicators of market and economic recovery. Security markets’ recovery preceded recovery in the economy, based on anticipated recovery in S&P 500 Index earnings forecasts. In 2012, equity valuations stretched in a way never before seen, as the S&P 500’s dividend yield and the 10-year Treasury rate converged for the first time since 1957. Housing prices and commercial real estate sales rebounded. Consumer confidence rose. Federal debt to GDP was still high; however, the Fed’s balance sheet was huge, interest rates were artificially low, and the status of Fannie Mae and Freddie Mac remained to be determined. In reading this masterful book, I was initially impressed by its structure in addressing such a complex time in history. It analyzes the market and economic environment preceding the crisis, during the crisis, and over a number of years that followed it. The book delves deeply into the institutions and the securities. The author differentiates opinion from fact, relying on extrapolation from actual reported numbers. I found it impressive that he uses the analyst’s most trusted original sources, corporate 10-Ks and 10-Qs. Neatly rendered graphics and tables assist the analytical narrative. Wigmore cites Federal Reserve Economic Data (FRED) frequently and appropriately. The Financial Crisis of 2008 is essential reading for banking, investment, and insurance firm leadership but also for investors, analysts, economists, and students of financial and investment history. It depicts how widespread risk-taking at the firm level can morph into systemwide near collapse and how the mantra of homeownership for all must be considered in light of the associated financial risks and undisciplined creation of asset-backed securities. The book is required reading for a generation. 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

What is the current outlook for investors in today’s credit market? Interest rates had been in secular decline since the 1980s. In the aftermath of the global financial crisis (GFC), rates hovered near zero as central banks embraced quantitative easing (QE) and flooded markets with liquidity. Among other effects, these monetary policies elevated the valuations of most assets, including private and public debt. This trend came to an end in 2022 when central banks began to raise rates and tighten credit conditions to tame inflation. Today, investors must navigate this transition. In terms of economic expression — and to take a page from Thomas Piketty — we have shifted from an r > g to an i > g world, from one where the real rate of return exceeds the rate of economic growth to one where nominal interest rates outpace the rate of economic growth. This has significant implications for debtors whose earnings are likely to grow slower than the interest accumulated on borrowed funds. As our parents might say, this is likely to “end in tears.” Simply put, many businesses and investments have not been tested. Since 2009, save for a brief period in early 2020, nominal growth has outpaced nominal rates. Warren Buffett famously said, “You only find out who is swimming naked when the tide goes out.” Well, the tide is going out and as businesses refinance at higher rates, default rates and distressed exchanges are likely to increase concomitantly. When revenue grows more slowly than the cost of financing, especially over an extended period, businesses feel the pinch. Add to this the large amount of US corporate fixed-rate debt coming due in the next couple years and banks and other traditional lenders getting cold feet, among other factors, and many businesses will be left vulnerable. Some are rolling over debt early, even at higher rates, to avoid potentially not being able to do so at all later on. Costs for high-yield borrowers are hovering near 9%. For investors, the risk focus has shifted from the rising cost of capital to refinancing, period. Year to date, total US corporate bankruptcies have been at their highest level since 2010. The pace of defaults is expected to continue if not increase in 2023 and 2024 due to the lagged impacts of higher rates, slower economic growth, and inflation. This is not “business as usual.” Investors’ risk appetite has also changed. While they may have felt compelled to venture further out on the risk continuum to capture yield, as the risk-free rate has increased, investors have less need to do so. The tumult in the US regional banking sector, with the March collapse of Silicon Valley Bank and Signature Bank and the failure of First Republic in May, has cast a pall over lending. A recent report on US economic activity showed a slowdown in job growth and a near-term deterioration of business prospects. Where does that leave asset allocation in public and private credit? Rising rates have pushed bond prices down. Notwithstanding the ongoing love affair with private debt, there is an overlooked and growing opportunity set in the public debt markets that appears mispriced relative to risk and return. In 2020 and 2021, public and private debt was priced at par (or above) with private debt offering a liquidity premium in the form of a fat coupon. Today, the situation is different, with the edge going to the public markets. There are several reasons for this. In the public debt market: Pricing is determined in the open market and adjusted to changing market conditions. There is greater price transparency. This brings more price volatility and more opportunities to acquire assets below par to increase the margin of safety. Greater liquidity makes exiting a position easier should the risk/reward balance change or a better prospect for deploying capital develop. Companies that issue public bonds have proven their business models in the market. There is greater diversification of bonds in the public markets. Public debt has corrected more than private debt in the rising interest rate environment. In every economic cycle, some businesses with solid growth profiles will nevertheless carry some debt. For example, starting in 2015, the energy sector was severely stressed while other areas — hospitality, for example — were not. In 2020, amid peak COVID, hotels, movie theaters, and automobile rental services were struggling, but bakeries were doing fine. At some point, the prices in stressed sectors fell far enough that investors were compensated for the risk. Selective investors could find companies with high quality assets and strong competitive advantages. The occasional price volatility in publicly traded bonds offers the potential to exploit mispricing. In the four previous default cycles, the average drawdown of lower-rated high yield was about 30% and the average recovery approximately 80% over the ensuing two years. With the high-yield bond market down roughly 18% in 2022, investors are beginning to see good opportunities developing in the eventual recovery in lower quality credits. Investors looking to diversify their portfolios and take advantage of the valuation gap between public and private bonds should consider an allocation to public credits. Among an assortment of small to mid-sized companies lies an attractive risk-reward proposition. Due to their size, these companies experience greater capital scarcity and investors face lower competition from other capital providers. Further, as credit conditions remain tight and refinancing costs increase, more quality businesses will need to raise capital. If you liked this post, don’t forget to subscribe to the Enterprising Investor. All posts are the opinion of the author(s). 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 / Tatomm 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

Public pension funds allocate on average 30% of their assets to expensive alternative investments and as a result have underperformed passive index benchmarks by 1.2% per year since the Global Financial Crisis of 2008 (GFC). Large endowments, which allocate twice as much on average to alternatives, underperformed passive index benchmarks by 2.2% per year since the GFC. These unfortunate results typically get little attention because the overseers of public pension funds and endowments often use performance benchmarks of their own devising that give an unduly favorable impression of performance. They should use passively investable benchmarks that reflect the funds’ average market exposures and risks over time. Their “custom” benchmarks are complex, opaque combinations of indexes, often nebulous and invariably subjective in their design, that lower the bar by 1.4 to 1.7 percentage points per year compared to simple, sound index benchmarks.[1] In this post, I examine institutional investment performance from a different perspective. My focus is on whether institutions are meeting their investment goals. For public pension funds, I compare industrywide returns with the average actuarial earnings assumption prevailing since the GFC. For endowments, I compare the return earned by NACUBO’s large-fund cohort to a common goal for colleges and universities. That goal is to enjoy a typical rate of spending from the endowment, increasing over time at the rate of price inflation. In both cases, I seek to determine whether institutions have met their earnings objectives, rather than how well they have performed relative to market benchmarks.[2] Public pension plans generate public liabilities. Actuaries for the plans estimate the value of those liabilities and prescribe an amount of annual contribution that would eventually lead to funding the liabilities. Their work includes identifying an earnings rate on invested funds that makes the pension funding math work over the long run. Public pension trustees often state that their top investment priority is to achieve the actuarial earnings assumption. Doing this affords them peace of mind that they are doing their part to see that pension liabilities do not go unmet. The Center for Retirement Research at Boston College reports the average actuarial earnings assumption of large pension plans. That figure averages 7.4% per year between fiscal years 2008 and 2023. Colleges and universities typically seek to spend a sustainable percentage of their endowment fund in support of the institutional program. Spending percentages vary among schools and over time, recently averaging 4.5% of endowment value among large endowments, according to NACUBO. The cost of conducting higher education has risen faster than consumer prices historically. Accordingly, a separate measure of price inflation, the Higher Education Price Index (HEPI), is typically used to estimate cost increases for colleges and universities. Taken together, a target spending rate plus inflation (as measured by HEPI) is often used as an indication of the endowment earnings requirement. “HEPI + 4.5%” has amounted to 7.0% per year since fiscal year 2008. Investment Policy Choices Investment overseers have an important choice to make when establishing investment policy. They can use index funds (at next to no cost) in proportions compatible with their risk tolerance and taste for international diversification. Alternatively, they can use active managers — including for alternative assets — deemed to be exceptionally skillful in the hope of garnering a greater return than available through passive investment. If it chooses index funds, the institution relies on theory and evidence regarding the merit of active and places its trust in the capital markets to generate sufficient returns to meet financial requirements. If it chooses active management, the institution bets that markets are meaningfully inefficient, and that the institution would be among the minority of active investors that can exploit presumed market inefficiency. And most try to do so with inefficient, clumsy, diversification: many institutions use 100 or more active managers jumbled together. Active versus passive is the most important investment policy choice institutions face in determining how to meet their financial requirements. In recent decades, institutions have opted overwhelmingly for active management, with particular emphasis on private-market assets. How well has the active strategy served institutions during the 15 years since the GFC? As with most studies of this type, the results are sensitive to the period selected. I believe the post-GFC era offers a fair representation of circumstances having a bearing on the evaluation of investment strategy.[3] Exhibit 1 analyzes rates of return for public pension funds and large school endowments from fiscal year 2008 to fiscal year 2023. The return objective in the case of public pension funds is the actuarial earnings assumption described above. For the endowments, it is HEPI + 4.5%. The “actual return” for public pensions is that of an equal-weighted composite of 54 large funds. The “actual return” for the endowments is that of the NACUBO large fund cohort composite. In both cases, the indexed strategy is a combination of indexes with the same market exposures and risks as their respective composites — a kind of best-fitting, hybrid market index.[4] Both types of institutions failed to meet their institutional investment objectives since the GFC: public funds fell short by 1.3 percentage points per year, and endowments fell short by 0.6 of a percentage point. The indexed strategy, however, essentially met the public plan requirement and handily outpaced that of the endowments. Exhibit 1. Actual Returns and Indexed Strategy vs. Objectives2008–2023.   Public Endowment Return Objective 7.4% 7.0% Actual Return 6.1 6.4 Indexed Strategy Return 7.3 8.7 Exhibits 2 and 3 illustrate the results graphically. The investment objective in both cases is represented by the horizontal line with the constant value of 1.00. The other lines represent cumulative earnings for the active and passive strategies relative to the objective. For both types of institutions, the low-cost indexed strategies generated sufficient earnings to meet the objective. In neither case, however, did the actual active strategies do so. Their high cost of investing proved to be too great a drain. Exhibit 2. Public Funds: Investment Returns vs. Actuarial Earnings Assumption. Exhibit 3. Large Endowments: Investment

Last year was humbling for economists and investment strategists alike. It began with an “imminent” recession and ended with equity markets near all-time highs. Historic rate hikes fueled a compelling narrative that, at best, anticipated both a weak economy and disappointing returns. To be sure, legitimate concerns underpinned this narrative. Post-COVID-19, amid resurgent inflation, the world was still emerging from an era of “unprecedented everything.” But the inherent pressure to take a stance on the economic trajectory led many investors to find comfort in collective concern and embrace the prevailing storyline. For many investors, human nature took the wheel. So, what can we learn from this scenario? Investors crave a compelling, rational narrative. Economic data, which is more detailed and accessible than ever, helps us paint those narratives. But with great amounts of data comes great responsibility. We not only have to keep our convictions, goals, and time horizons in perspective; we must also remember that the economy and financial markets are not the same thing. That is easy to forget. In the rational, well-ordered world of economic theory, various pieces of economic data fit together like a puzzle that visualizes the ever-evolving interplay between businesses, consumers, investors, governments, and central banks. Of course, in reality, these pieces of data are often lagged and revised and have varying and evolving impacts on financial markets. Moreover, this data is often cherry picked for clickbait headlines and political talking points. And with economic projections shifting with the wind, investors struggle to identify clear, actionable insights. So, what are we to do? The economy deserves its fair share of attention, but we shouldn’t let it steal the spotlight. The financial markets themselves provide considerable insight. Here are five questions to ask to better understand the markets without having to speculate about the larger economy: 1. How Has Market Composition Evolved? What forces are working beneath the surface and churning the financial markets? How concentrated are market-cap-weighted indexes? How have sector weights adjusted over time? Which stocks are newly listed or jumping across the market-cap and style spectrums? To understand the recipe, we have to understand the ingredients. 2. Which Companies Are Contributing the Earnings? Are the markets giving credit where it is due? Comparing a stock’s earnings weight with that of its market cap indicates what is moving the stock and whether that movement is temporary or sustainable over the long term. Closer examination of earnings trends across sectors, sizes, and factors offers critical context that surface-level data simply doesn’t. 3. Which Stocks Are Contributing the Returns? Stock prices reflect collectively evolving opinions. What are investors rewarding? Fundamentals? Narratives? Narrow or broader segments of the market? Does a 360-degree analysis support these returns into the future? Last year presented quite the riddle for investors. The “Magnificent Seven” lifted the S&P 500 for most of the year. But should we always count on a handful of players to carry the team? Proactive risk management requires that we understand the source of our returns. 4. What Are the “Fundamental Technicals” Saying? Just as doctors render their diagnoses after batteries of tests and exams, so too must investors. A cursory examination of market data is not enough context. We need to know what’s going on beneath the surface. “Fundamental technicals” are critical gauges of the underlying health of financial markets. They measure what’s really going on under the hood. Market breadth, relative strength, put–call ratios, equal-weighted indexes, and volume, among other metrics, can shed light on risks and opportunities alike. 5. Where Are the Asset Flows Going? Expressing a view of the market is one thing, but committing actual investment capital to that thesis is quite another. Do we have the courage of our convictions? Asset flows measure consensus as well as the extremes and outliers. They reflect real choices with real consequences. From a behavioral perspective, the sentiments they uncover can be both entertaining and insightful. Conclusion The economy matters, but it matters differently to different investors depending on their distinct objectives, timelines, and asset allocation. And it’s not the only thing that matters. As humans, we have an innate tendency toward groupthink. The more we follow the headlines, the more our own perceptions will correlate with them and lure us away from our investment process right at the moment when sticking to it matters most. Ultimately, we must exercise the discipline to convert our analysis into actionable insight. We have to relentlessly ask ourselves, “What does this mean in the context of my strategy?” If you liked this post, don’t forget to subscribe to Enterprising Investor and the CFA Institute Research and Policy Center. 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. The opinions expressed are those of John W. Moore, CFA, CAIA, as of the date stated on this article and are subject to change. This material does not constitute investment advice and is not intended as an endorsement of any specific investment or security. Please remember that all investments carry some level of risk, including the potential loss of principal invested. Indexes and/or benchmarks are unmanaged and cannot be invested in directly. Returns represent past performance, are not a guarantee of future performance and are not indicative of any specific investment. Diversification and strategic asset allocation do not assure profit or protect against loss. Image credit: ©Getty Images / Peter Hansen 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

Default: The Landmark Court Battle over Argentina’s $100 Billion Debt Restructuring. 2024. Gregory Makoff. University of Georgetown Press. In his autobiography, preeminent financier William R. Rhodes notes a phrase inscribed on the gold Cross pen used by Nicaraguan authorities to sign off on their 1980s debt restructuring with private creditors: “Firmar me harás. Pagar jamás.” The phrase translates to “You can make me sign, but you’ll never make me pay” — a prescient warning that proved true. Gregory Makoff continues in the tradition of Rhodes with his book Default: The Landmark Court Battle over Argentina’s $100 Billion Debt Restructuring. He has penned the authoritative take on the most important debt restructuring (other than Greece) in the history of global finance. A physicist by training, Makoff worked as a banker for more than two decades, advising developing nations on debt management policy. He then moved on to scholarly pursuits at the Centre for International Governance Innovation and at the Mossavar-Rahmani Center for Business and Government at the Harvard Kennedy School. Perhaps it took the transactional experience of a banker, combined with a physicist’s training to derive complexity, to establish this historical narrative for posterity. Argentina’s debt restructuring tests the theoretical limits of chaos theory. As Henri Poincaré famously noted, “An accumulation of facts is no more a science than a heap of stones is a house.” Makoff’s feat is to build his narrative as a thriller without losing the detailed facts valued by specialists. I read the book in 48 hours. Readers will inevitably develop empathy for Judge Thomas Griesa, who serves as a central actor in his role overseeing the case for the US District Court for the Southern District of New York. Griesa ultimately broke through a decade of “uniquely recalcitrant” behavior from Argentina with a legal interpretation that prevented Argentina from paying interest on new bonds before settling amounts owed to holdout creditors from its earlier debt restructuring. The author avoids a simplified hero-versus-villain narrative. Makoff demonstrates how court cases were steered by rationalized self-interest on both sides and deterministic properties governed by the initial conditions of international lending agreements. Given this pragmatic and apolitical approach, investors, scholars, and policymakers alike will find value in Default. For investors, Makoff provides a healthy reminder to read the terms of one’s bond documentation. Only by understanding the lessons of history can investors navigate the current generation of sovereign debt distress. The author explains how the regrettable decision to not include exit consents in the original debt restructuring allowed some minority creditors to engage in an eventually successful holdout strategy. For students and professors, Makoff sticks the landing in authoring both a scholarly and practical history. Much ink has been spilt in academic circles on how sovereign debt markets work in theory. It took a practitioner like Makoff to explain how the world is rather than how it is supposed to be. For policymakers, this historical narrative is well timed as newly contemplated reforms are being reviewed in both multilateral (Global Sovereign Debt Roundtable) and legislative (proposed legislation in New York state) forums. The international bond market can be a positive force in developing economics, allowing countries to navigate from their present to their future by pulling forward investment. As scholar Barry Eichengreen reminds us, however, sovereign debt is a “Janus-faced” asset class. If mismanaged, sovereign borrowing can lead to default and an arduous process to manage through an evolving debt resolution architecture (sovereign nations cannot file for bankruptcy). Default is ultimately an origin story for enhanced collective action clauses (CACs), a modernization of international lending agreements that bind majority agreements for debt restructuring onto the minority. This approach prevents a repeat of the contentious holdout creditor dynamic in Makoff’s Argentina saga. As the US Court of Appeals for the Second Circuit stated in its review of Judge Griesa’s ruling, “It is highly unlikely that in the future sovereigns will find themselves in Argentina’s predicament.” Thanks to CACs, one can hope this will be the last book that is necessary to describe a decade-long debt restructuring. If you liked this post, don’t forget to subscribe to the Enterprising Investor. All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer. Image credit: ©Getty Images / Ascent / PKS Media Inc. Professional Learning for CFA Institute Members CFA Institute members are empowered to self-determine and self-report professional learning (PL) credits earned, including content on Enterprising Investor. Members can record credits easily using their online PL tracker. source

Today’s spiking inflation, rising interest rates, and billion-dollar investment frauds are good reminders that a clear understanding of the past helps us better anticipate the future. Indeed, it can provide a reliable foundation on which we can develop portfolio-allocation decisions. That’s why we need to have a firm grasp on different equity markets’ past relative and absolute performance. After all, what drives the market cycle if not the relative under- and outperformance of different assets? With that in mind, the equity market universe is variously categorized by region, country, sector, market capitalization, and style. The well-established geographic segmentation divides the world into frontier, emerging, and developed markets. What determines a country’s designation? The MSCI Market Classification Framework focuses on three criteria: economic development, size and liquidity, and market accessibility. The latter is a function of three components: openness to foreign ownership, ease of capital inflows and outflows, and the efficiency and stability of the institutional framework. It hasn’t always been easy or cheap to invest at the regional or country level, but thanks to technological advances and the development of exchange-traded funds (ETFs), retail investors now have much better, if not limitless, access to the various segments of the equity market universe. So, how have these geographic segments performed relative to one another in recent market cycles? Benchmark Investability Market Index Investable ETF MSCI ACWI and Frontier Markets Index Not Available MSCI ACWI iShares MSCI ACWI ETF (ACWI) MSCI World Index iShares MSCI World ETF (URTH) MSCI Emerging Markets iShares MSCI Emerging Markets ETF (EEM) MSCI Frontier Markets Not Available MSCI Frontier & Select Emerging Markets iShares MSCI Frontier and Select EM ETF (FM) Introduced on 7 April 2003, the iShares MSCI Emerging Markets ETF (EEM) had a cumulative return of 381% through 31 December 2010. By comparison, the iShares Core S&P 500 ETF (IVV) generated a 66% return over the same sample period. So, in that particular market cycle, emerging markets did much better than their developed counterparts. EEM vs. IVV 7 April 2003 to 31 December 2010 But let’s take the examination one step further and use intermarket analysis to compare the relative performance, or relative strength, of EEM and IVV. This way we can differentiate among distinct trend periods by identifying the major inflection points on their price charts. The relative strength of EEM to IVV over the sample period shows a key pivot in early 2011, as detailed in the following chart. Relative Strength of EEM vs. IVV12 April 2003 to 7 December 2022 Emerging markets as a category did better than the S&P 500 until that 2011 inflection point. Since then, the downward slope demonstrates their underperformance. Frontier markets generated better returns than other geographic segments at various times as well. For example, from 2002 to 2007, the MSCI Frontier Markets Index beat both the MSCI EM and MSCI World Indexes. Some frontier markets may again eclipse both developed and emerging markets. This kind of intermarket analysis has applications well beyond geographic segments. We can use it to identify similar inflection points relative to the S&P 500 in the energy, technology, and commodities sectors. For example, the Energy Select Sector SPDR Fund (XLE) lagged the S&P 500 for years. But the relationship pivoted in early 2021, and the energy sector proxy has since outpaced the benchmark index. The technology sector’s relationship with the S&P 500 flipped in the opposite direction. After years of beating the S&P 500, our tech proxy, the Technology Select Sector SPDR Fund (XLK), began to fall behind early in 2022. As for commodities, the S&P GSCI Commodity-Indexed Trust ETF went from lagging to outperforming the S&P 500 in late April 2020. Analysis like this can inform tactical asset allocation decisions. We can adjust the percentage of the portfolio held in an asset class or across asset classes based on these changing market opportunities. Of course, we have to understand both the index and the relative performance of its underlying constituents. Country factors, for example, may have more influence on returns than industry factors, while returns in a frontier market country may result more from country-specific than global factors. As such, active investors should examine the individual country characteristics rather than simply allocate to a broad frontier or emerging market index. Single country indexes are becoming more accessible through ETFs, but not all are equally investable. For example, Saudi Arabia’s stock exchange, the Tadawul, operated long before the iShares MSCI Saudi Arabia ETF (KSA) began trading in 2015. There is a larger lesson to these inflection points (when for whatever reason, the switch is flipped and the dynamic between geographic segments changes): Relationships in the global equity market universe are not static. There will be critical pivots in the future just as there were in the past. One segment or another will have periods of sustained outperformance relative to their peers. That’s why adjusting our allocations to emerging or frontier markets may at times enhance risk-adjusted returns. 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/ Luigi Masella / EyeEm 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

Pause to Think: Using Mental Models to Learn and Decide. 2024. Jaime Lester. Columbia Business School Publishing. Pause to Think offers a perfect balance of light and serious reading. It is direct, delightful, and articulate in suggesting specific ways to improve approaches to anything we attempt to do. The author presents mental models in accessible, entertaining ways that prove both memorable and immediately useful. Jaime Lester is a hedge fund industry veteran and an adjunct professor at Columbia Business School. He has divided this powerful book into two sections — Part I: Important Concepts and Part II: Important Frameworks — with an interlude. He suggests we take a slow rather than fast approach to getting things thoughtfully done. Being human means being imperfect. Using this awareness, we can counteract impetuous actions and biases. Consistency and that overused word, “mindfulness,” should reduce flawed outcomes. The identification of many of the cognitive biases discussed by Lester is rooted in investment research, particularly in the behavioral finance discipline that emerged in the 1970s. According to behavioral finance, human psychology and emotion cause fluctuations in securities prices, which are often determined irrationally. The rise of robo-advisors is frequently described as a solution to these biases, intended to compel investors to assess their risk, determine their most suitable allocation, and stick to the program. At the conclusion of each section in Part I, the author offers some exercises to try. They involve excellent thought questions on topics such as investing and science, economics and business, and probability and statistics. I found that these reinforced the concepts discussed and enabled me to evaluate my own biases. Some of the exercises also assess basic mathematical literacy. Try them and you will find that the lessons are worthy of mastering. Have a calculator at hand if your mental math is rusty. If your eyes are too tired to read, or you choose to give Lester’s excellent narrative a break, turn to the adorable illustrations by Albertus Ang Hartono, aka Everwinter, that emphasize the intent of the book. You will capture the book’s sense of porpoise! See the illustration on p. 127, in connection with efficient learning, to understand this seabound-mammal reference. In Part II of the book, Lester is unreservedly opinionated in addressing important frameworks for investing. Most readers will fully agree with his five keys to successful investing but will argue with his recommendations, such as ignoring the value of the investment portfolio, setting up automatic savings and investment options, and avoiding all high-fee investment products. Micromanaging or over-policing values does not make sense, but periodic review of the asset allocation and holdings certainly does. Setting up automatic savings and investment options must include some flexibility associated with risk tolerance and liquidity requirements that can change when one least expects. “Set it and forget it” does not fulfill the needs of responsible, prudent investors. And lastly, fees of considerable size could be justified, based on the investment product and its objectives. One would not expect to pay much at all for a passive exchange traded fund but would expect to pay a premium fee for a special-purpose hedge fund. Lester repeats and emphasizes that “you will make more money choosing stocks randomly than paying a professional investor to choose them for you” (p. 160). Many clients of investment advisors are nonetheless required to employ professional managers, or else they are unskilled or time-constrained and admit they need help from a professional. The investment professional coaches and converses with the investor, provides a sounding board for investment concerns, manages expectations, and consistently delivers performance reports to ensure that the investment program is on track with its agreed-upon objective. But fear not! Lester brings the book to a truly bullish conclusion, addressing the framework of happiness. His focus is on professional happiness that evolves into happiness, period. He encourages us all, young and aging, to reduce time and energy devoted to activities that are unlikely to make us happy, including seeking the perfect job. Increase time and energy, the author urges, toward activities that are likely to make us happy. My favorite of Lester’s prescriptions for maximizing happiness is to maintain an appropriate context and perspective by incorporating mindfulness and gratitude in the daily routine. As I pause to think why this is so important, I turn to our universal pandemic experience and its outcome for each of us, personally and professionally. I think that Lester has struck gold in delivering a unique book that touches the minds and hearts of investment professionals — one that we all should pause to read. source

Passive environmental, social, and governance (ESG) investing has become increasingly active. It’s time for a rethink. Assets have poured into passive ESG indexes in recent years. As more investors look to align sustainability goals with their investments, global ESG assets under management (AUM) have soared from $2.2 trillion in 2015 to $18.4 trillion in 2021. Yet ESG assets aren’t all that’s on the rise. With ever-changing methodologies and expanding exclusion lists, the active risk of popular ESG indexes is also climbing. This begs the question: Does the term “passive” even apply to ESG investing? Given the lack of consensus on sustainability criteria and the evolving data sets and exclusions, the answer is no. ESG investing is inherently active and investors should evaluate ESG strategies accordingly. That means focusing on the drivers of risk and return. But just what is propelling the rise in index tracking errors and how can investment managers integrate factors and deliver risk-efficient portfolios with proven sources of return that aren’t found in “passive” offerings? Passive indexes are popular because they track broad market benchmarks at low cost. With similar inclusion criteria and construction methodologies, such capitalization-weighted indexes share common features and are built around a broad consensus. Passive ESG indexes do not exhibit the same characteristics. Indeed, four popular ESG indexes display vastly different levels of ESG uplift and tracking error, as Exhibit 1 demonstrates. While the drivers of active risk vary among indexes, most are sourced from idiosyncratic risk. Exhibit 2, which lists the top five overweights and underweights of each index, illustrates this. The MSCI US ESG Leaders Index has a 5.3% overweight to Microsoft and -7.0% underweight to Apple. Such active weights resemble traditional active management more than passive investing. Similar divergence even occurs within the same index family. The MSCI US ESG Leaders and MSCI US ESG Universal indexes, for example, share only Nvidia among their top five overweights. Alphabet, with combined Class A and Class C shares, meanwhile, comprises the second largest overweight in Leaders and the third largest underweight in Universal even though the indexes use the same ESG ratings. The differences are even more dramatic across index providers. Three of the top five overweights in the S&P 500 ESG Index are among the top five underweights in the MSCI US ESG Leaders Index. Two of the latter’s top holdings — Tesla and Johnson & Johnson — are underweights in the former. Apple is the most extreme example, with an absolute difference between the two indexes of nearly 10% — -7.0% vs. +2.8%. By comparison, Apple’s weight in the cap-weighted MSCI US and S&P 500 indexes differs by fewer than 10 basis points (bps) over the last five years. No wonder the MSCI US ESG Leaders and S&P 500 ESG indexes have a 2.5% relative tracking error, three times more than their cap-weighted counterparts. The lack of overlap among ESG indexes and the wide dispersion in risk levels means that investors must be diligent in aligning their ESG objectives with their chosen strategy. They must also ensure that the level of ESG is commensurate with the active risk taken. And they have to remember that the evolving ESG landscape requires constant oversight. Less ESG for More Risk? While higher ESG content has always necessitated higher active risk, recent trends have exacerbated this trade-off. Exhibit 3 plots the aggregate cap-weighted ESG ratings of the MSCI World and MSCI US indexes along with the number of exclusions for the MSCI World ESG and US ESG Leaders indexes. The broad-based ESG rating improvements over the past five years suggest that companies are addressing their ESG risks. But over this same time period, more and more companies are being excluded from ESG indexes. These trends are difficult to reconcile, as higher aggregate ESG ratings ought to lead to fewer exclusions not more. In practice, the exclusions have proven easy to add yet difficult to remove. These changes, along with numerous methodology revisions, have increased “passive” ESG index tracking error levels over the past five years. Exhibit 4 demonstrates the increasingly active nature of these passive ESG benchmarks. While the COVID-19 crisis is a factor in this development, it does not fully account for the rise in tracking error. Tracking error jumped in late 2018, well before the onset of pandemic-related market turmoil in 2020. The ESG rating uplifts also declined in response to the higher ratings of the underlying indexes, as shown in Exhibit 3. What it all adds up to is more concentrated portfolios with lower ESG uplifts and more active risk. The irony of course is that the integration of ESG data is often promoted as a way to decrease portfolio risk. The key to capturing ESG benefits, and capturing them efficiently, lies in modern portfolio construction techniques. Improving ESG Efficiency How can we improve a portfolio’s efficiency when integrating ESG content? First, we need to establish a baseline. In Exhibit 5, we plot the achievable active risk levels at varying degrees of ESG uplift based on each index provider’s unique ESG ratings and after excluding those companies flagged by MSCI controversy criteria. As we can see within the chart, each ESG index falls well outside of what would be considered efficient. The distance of each index from the efficient frontier is a product of two developments: additional business involvement exclusions and suboptimal portfolio construction. To quantify the effect of each, we plotted a hypothetical ESG portfolio within the MSCI World ESG frontier in Exhibit 6. The ESG Portfolio excludes the same set of companies as the MSCI World ESG Leaders Index, while targeting a higher (20%) ESG rating uplift and similar levels of carbon reduction. Given the distance of the ESG Portfolio from the efficient frontier, we see that multiple ESG objectives can be achieved with a minimal increase in active risk. The ESG Portfolio has less than half the active risk of the MSCI World ESG Leaders Index. We attribute that 76 bps vs. 198 bps divergence to portfolio construction.

The Role of Low Volatility Strategies in Investment Portfolios  Low volatility equity strategies appeal to investors for many reasons. First, they help keep our portfolios invested in equities during periods of market turmoil. Second, when well-constructed, they often exhibit higher risk-adjusted returns than their higher volatility counterparts. While general surveys of low volatility strategies show that they do indeed shield investors from market-driven risk, what is frequently overlooked is that these same strategies can be insufficiently diversified or risk controlled. To that end, we will examine the critical components of an effective low volatility portfolio construction process. These elements enable the construction of low volatility portfolios with more diversification and significantly better risk-adjusted returns than the standard low volatility strategy. Low Volatility Strategies: Three Potential Drawbacks   Low volatility stocks can deliver a premium over the longer term. And while they may provide both volatility reduction and capital protection in bear markets relative to cap-weighted indices, not all low volatility strategies accomplish this to the same degree. Indeed, many commercially available low volatility strategies suffer from common drawbacks. 1. A Lack of Diversification Inverse volatility and minimum variance optimization are two common methodologies in low volatility strategies. In inverse volatility portfolios, a stock’s portfolio weight is proportional to its risk. Such portfolios penalize high volatility stocks and reward their low volatility counterparts. They can also be highly concentrated. The same criticism applies to the minimum variance optimization technique, which, without various constraints, can also unduly overweight the portfolio in one or more stocks. 2. Negative Exposure to Other Rewarded Factors Value, Momentum, High Profitability, and Low Investment, in particular, are among the factors that have rewarded investors over the years, but low volatility strategies can underweight such factors and constitute a drag on the long-term risk adjusted performance. 3. Excess Risk through Sector and Regional Exposures Low volatility portfolios may have persistent sector or regional exposures that can open them up to undo macroeconomic risks.  A Better Way to Build Low Volatility Portfolios  There are several remedies to these diversification- and risk-related challenges in low volatility portfolios. To address the excess weighting issue, we can build more diversified low volatility portfolios by selecting weights based on several optimization frameworks and introducing robust weight constraints. Every model has parameter estimation risks due to its particular architecture. By averaging across multiple models, we can reduce much of the model risk that comes with relying on a single framework. In addition, without a considerable amount of at times ad hoc constraints, such as min-max weights on stocks or sectors, a given model may produce overly concentrated or otherwise insufficiently diversified portfolios. To address this issue, we use so-called norm weight constraints that avoid concentration better than ad-hoc, sample-dependent constraints. (We also employ principal component analysis — PCA, a statistical technique — to de-noise the covariance matrices with which we construct our portfolios.) Another way to address diversification in a low volatility strategy is to increase a portfolio’s factor intensity. This measure, when applied to a single stock, is simply the sum of individual factor exposures, or betas, in a portfolio. So, if we are selecting stocks for a low volatility portfolio, we prefer those with high exposure to the low volatility factor, but we also want to filter out stocks with significant negative exposure to other rewarded factors. By implementing such filtering, our low volatility stocks will have, to the maximum degree possible, positive exposure to Value, Momentum, and other rewarded factors. As a result, in environments where the low volatility factor is underperforming, the other factors may be able to “pick up the slack” and shield the portfolio from some of the damage that the portfolio might incur without such filtering. Every rewarded equity factor has exposure to macroeconomic factors. Which factor loads on the most macroeconomic risk will depend, of course, on the macroeconomic environment, or regime. Country- or region-specific drivers explain much of a portfolio’s macro risk, so we can mitigate that risk by constructing portfolios that are geographically neutral relative to a cap-weighted benchmark. Because macro risks are also often sector driven, selecting low volatility stocks within sectors can mitigate macro risk. Sectors are important considerations since low volatility strategies can overweight specific sectors, such as Utilities, that are sensitive to interest rate and other forms of risk. In terms of empirical results, the exhibit below shows that a low volatility portfolio with factor intensity filters delivers a significant risk-adjusted return compared with both cap-weighted and standard low volatility indexes. This holds for both US and Developed Markets low volatility strategies. Low Volatility Equity Strategy Performance and Risk Measures  US Statistics 21 June 2002 to 30 September 2023(RI/USD)  Cap-Weighted  Robust Low VolatilityStrategy  MSCI MinimumVolatility Annualized Returns  9.41%  9.85%  8.92% Annualized Volatility 19.35% 15.81%  16.17% Sharpe Ratio 0.42 0.54 0.47  Maximum Drawdown 54.6% 43.0% 46.6% Developed Market Statistics  21 June 2002 to 30 September 2023(RI/USD) Cap-Weighted Robust Low VolatilityStrategy MSCI MinimumVolatility Annualized Returns 8.32%  9.45%  7.96%  Annualized Volatility 16.16%  12.79%  12.09%  Sharpe Ratio 0.43  0.63  0.55  Maximum Drawdown 57.1%  45.6%  47.7%  The process described above results in significantly higher factor intensities for both US and Developed Market portfolios, as the following charts demonstrate. Factor Intensity in Low Volatility Equity Strategies  US Factor Intensities  21 June 2002 to30 September 2023(RI/USD) Robust Low VolatilityStrategy MSCI MinimumVolatility Factor Intensity (Int) 0.43  0.21  Developed Market Factor Intensities  21 June 2002 to30 September 2023(RI/USD)  Robust Low VolatilityStrategy MSCI MinimumVolatility Factor Intensity (Int)  0.47  0.25  This approach also reduces macro exposures across geographies as the tables below indicate.  Macro Exposures in Low Volatility Strategies  US Exposures 21 June 2002 to 30 September 2023(RI/USD)  Robust Low VolatilityStrategy MSCI MinimumVolatility Short Rates  –1.23 –1.43 Term Spread  –3.16 –3.16 Default Spread  1.35 1.41 Breakeven Inflation  –3.75 –4.17 Developed Market Exposures 21 June 2002 to30 September 2023(RI/USD) Robust Low VolatilityStrategy MSCI Min Vol Short Rates –1.21 –1.95 Term Spread –3.17 –4.00 Default Spread 1.62 2.28 Breakeven Inflation –4.21 –6.04 Conclusion  Low volatility equity portfolios can be valuable additions to investor portfolios. They

Investors across the globe use the Sharpe Ratio, among other risk-adjusted metrics, to compare the performance of mutual fund and hedge fund managers as well as asset classes and individual securities. The Sharpe Ratio attempts to describe the excess return relative to the risk of the strategy or investment — that is, return minus risk-free rate divided by volatility — and is among the primary gauges of fund manager performance. But hidden within the Sharpe Ratio is the assumption that volatility — the denominator of the equation — captures “risk” in its entirety. Of course, if volatility fails to entirely reflect the investment’s risk profile, then the Sharpe Ratio and similar risk-adjusted measures may be flawed and unreliable.  What are the implications of such a conclusion? A common one is that the distribution of returns must be normal, or Gaussian. If there is significant skewness in the returns of the security, strategy, or asset class, then the Sharpe Ratio may not accurately describe “risk-adjusted returns.” To test the metric’s effectiveness, we constructed monthly return distributions for 15 global stock market indices to determine if any had such exacerbated skewness as to call into question the measure’s applicability. The distribution of returns went as far back as 1970 and were calculated on both a monthly and annual basis. The monthly return distributions are presented blow. Annual return results were qualitatively similar across the various indices studied. We ranked all 15 indices by their skewness. The S&P 500 came in close to the middle of the pack on this measure, with an average return of 0.72% and a median return of 1% per month. So, the S&P distribution skews just a bit to the left. S&P 500 Monthly Return Distributions, Since 1970 The complete list of indices ranked by their skewness is presented in the chart below. Ten of the 15 indices exhibit left skewness, or crash risk: They are more prone to pronounced nose-dives than they are to steep upward climbs. The least skewed distributions were those of France’s CAC 40 and the Heng Seng, in Hong Kong, SAR. Monthly Returns by Global Index Index Mean Median Min. Max. STD Skewness ASX 200 0.58% 1.01% -42.3% 22.4% 0.048 -1.3 TSX 0.60% 0.88% -22.6% 16% 0.044 -0.77 FTSE 0.53% 0.91% -27.6% 13.7% 0.045 -0.73 Russell 2000 0.84% 1.60% -21.9% 18.3% 0.055 -0.55 S&P 500 0.72% 1.00% -21.8% 16.3% 0.044 -0.45 DAX 0.67% 0.74% -25.4% 21.4% 0.056 -0.39 Nikkei 0.54% 0.91% -23.8% 20.1% 0.055 -0.37 MXX 1.23% 1.16% -29.5% 20.4% 0.066 -0.34 MOEX 1.29% 1.63% -30% 33% 0.079 -0.29 CAC 40 0.64% 0.98% -22.3% 24.5% 0.056 -0.11 Hang Seng 1.17% 1.23% -44.1% 67.3% 0.090 0.33 NSE 1.50% 1.05% -24% 42% 0.076 0.53 KRX 0.90% 0.49% -27.3% 50.7% 0.074 0.80 BVSP 5.63% 1.94% -58.8% 128.6% 0.184 2.51 SSE 1.65% 0.63% -31.2% 177.2% 0.151 6.26 The Shanghai Composite has exhibited the greatest degree of right skewness over time, tending to crash up more than down, and otherwise generating average returns of 1.65% per month and median returns of 0.63% per month. Shanghai Composite (SSE) Monthly Return Distribution, Since 1990 On the opposite end of the spectrum is the Australian ASX. The ASX has the most left skewness of all the indices, with an average monthly return of 0.58% and median monthly return of 1.01% since 1970. Australian Stock Exchange (ASX) Monthly Return Distributions, Since 1970 In the end, the BSVA in Brazil, the Shanghai Composite in China, and, to a lesser extent the ASX in Australia just have too much skewness in their returns to validate the Sharpe Ratio as an appropriate measure for their risk-adjusted performance. As a consequence, metrics that account for skewness in returns may be better gauges in these markets. Of the other indices, seven had fairly symmetrical distributions and five had moderately skewed ones. All told, this suggests that the Sharpe Ratio still has value as a performance metric and that it may not be as obsolete or ineffective as its critics contend. If you liked this post, don’t forget to subscribe to the Enterprising Investor. All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer. Image credit: ©Getty Images/NPHOTOS 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