CFA Institute

Capital markets in Hong Kong SAR are buzzing with activity in 2024. Mainland China’s largest freshly made bubble tea chain, Mixue Bingcheng, applied for an initial public offering (IPO) on the Hong Kong Stock Exchange (HKEX) and is looking to raise US$500 million to US$1 billion. Mainland China internet giant Alibaba Group continues to list its smart logistics arm Cainiao on the HKEX. This is the first spin-off listing totaling more than US$1 billion since August 2022 and could be among the hottest IPOs in Asia this year, according to Bloomberg. In fact, Alibaba Group is neither the first nor likely the last to engage in subsidiary spin-offs and subsequent IPOs. From 2018 to August 2022, 664 companies IPOed in Hong Kong SAR, and of these, 64, or almost 10%, went public through spin-off listings. So, what’s behind the appeal of spin-offs in general and in Mainland China and Hong Kong SAR, in particular? I sat down with KPMG partner Mike Tang, CFA, CPA, for his perspective. A full video of our conversation is available in Cantonese and Mandarin. Unlocking Potential Value So many listed companies are keen on spin-off IPOs in Hong Kong SAR because they deliver value to shareholders. “One of the most attractive aspects of spin-off listings lies in the ability to unlock the potential value of related — sometimes secondary — businesses and maximize shareholder value,” Tang says. Conglomerates with multiple business lines identify the business segment with the highest growth potential — often these are asset-light businesses — and then look to list them separately through the spin-off. Through the valuation process, the market helps realize the potential value of these businesses. Sometimes the spin-off leads to an interesting phenomenon wherein the market capitalization of the spin-off subsidiaries, due to higher price-to-earnings (PE) ratios, surpasses that of the parent company. In other words, the parts come to be worth more than the whole, which perfectly illustrates the appeal of spin-off listings. The same rationale applies to spin-off listings on the A-share market in Mainland China. These involve highly sought-after concept stocks or emerging industries. The same business segment, when listed on the domestic A-share market, benefits from higher valuation. Meanwhile, the parent company retains its ownership and control over the newly listed subsidiary, sharing the commercial benefits brought by the listing and further driving up its own stock price. In the case of Alibaba, the group retains ownership of over 50% of Cainiao’s shares. This win–win scenario appeals to both the listed companies and the major shareholders. “More than 30 Hong Kong–listed companies have successfully landed their business segments on the A-share market via spin-off listings since 2018,” Tang says. Diversifying Financing Channels  Spin-off listings also help diversify a company’s financing channels. For example, even with immense growth opportunities, biotechnology companies often lack access to funding during their research and development stages. This can leave them pressed for cash. The spin-off listing opens an independent financing channel for the subsidiary. It establishes clearer and more attractive positioning and gives the parent company added flexibility in its capital operations. Having both onshore and offshore financing channels is a huge benefit, according to Tang. “The effect of diversification is especially evident when the group has independent financing platforms both domestically and internationally,” he says. “It helps mitigate the impacts of individual market volatilities on the group’s overall financing capabilities and resilience.” Enhancing Operational Efficiency and Competitiveness  Spin-off listings can help companies reassess their businesses so that both the parent company and the subsidiary can focus on their core segments. This, in turn, improves operational efficiency and overall competitiveness. In addition, the equity incentive introduced by the spin-off motivates the subsidiary’s management team and employees to achieve better performance.  Making Hong Kong SAR a Capital-Raising Hub  For Hong Kong SAR specifically, the emergence of spin-off listings has boosted its competitiveness by increasing the number of new economy listings, especially large, innovative platform companies. However, regulatory safeguards help strike a balance between enhancing Hong Kong SAR’s competitiveness and protecting investors. Hong Kong SAR–listed companies seeking to spin off their businesses into separate listings have to apply to the HKEX in accordance with the Listing Rules Practice Note 15 (PN15). Tang identifies three key areas that the exchange focuses on when reviewing spin-off listing applications:  1. Will the Parent Company Still Meet the Listing Requirements after the Spin-Off?  The HKEX examines whether the parent company will retain sufficient assets post-spin-off and whether the remaining business will conform to the exchange’s listing criteria around profitability and market capitalization, among other requirements. 2. Does the Spin-Off Listing Serve the Interests of Current Shareholders?  Not only does HKEX consider the nature of the spin-off business itself, but it also examines how a spin-off listing will impact existing shareholders. For businesses with promising returns, the HKEX focuses on how the parent company can derive commercial benefits by retaining control over the subsidiary.  3. Will the Spin-Off Be Independent from the Parent Company?  PN15 explicitly requires that newly listed subsidiaries be independent from the parent company in terms of business, finance, and administrative management. “Connected transactions between the newly listed subsidiary and the parent company are of particular concern,” Tang says. “As the two become separate listed entities with their own shareholders, HKEX will have to make sure there are no suspicions of transferring benefits to major shareholders through connected transactions.”  Proceed with Caution  Companies seeking to spin off parts of their business into separate listings should conduct a comprehensive review beforehand. They should analyze market sentiment as well as the scope of the deal and what it may mean for the diversification of their business. They should also consider the potential obstacles that a spin-off listing could create. That requires developing a concrete strategy and a long-term plan that takes into account the principles that the HKEX laid out. If the spin-off necessitates restructuring, the companies should engage with the relevant intermediaries early on to ensure a smooth listing process. If you liked

The Bank of Japan (BOJ) widens the 10-year yield trading range. The BOJ announced its latest yield curve control (YCC) change on 19 December, raising the 10-year yield cap from 25 basis points (bps) to 50 bps. Some interpreted the shift as the first in a forthcoming series of hawkish moves from the BOJ, and the yen rallied from 137.41 to 130.58 before paring gains. Previously, when Japanese government bond (JGB) yields rose toward the BOJ cap, the yen weakened. But the recent policy shock briefly restored the traditional macro-dynamic: The higher the yields, the stronger the currency in expectation of capital inflows. Nevertheless, there is reason to be cautious about the nascent yen rally. While the market expects the BOJ to loosen YCC further, the bank’s next move in that direction, barring any policy surprises, may still be months away. Amid the yen’s renewed strength, rebounding global long-term interest rates may again exert upward pressure on JGB yields. This is consistent with the framework of co-movements between global long-term sovereign bonds that are “close substitutes,” as outlined by Governor Lael Brainard of the US Federal Reserve. Co-Movement in Global Long-Term Interest Rates Should global yields spike, the BOJ may have no choice but to defend its new 50 bps yield cap by creating new cash reserves to buy 10-year JGBs and reestablish curve control. That would come with a cost: The yen would weaken as short USD/JPY momentum unwinds, even if the BOJ shifts further later in the year. This isn’t the first time the BOJ has revised its 10-year trading band. After the central bank inaugurated quantitative and quality easing (QQE) with YCC in September 2016, it established a precedent with two policy shifts. On 31 July 2018, the Policy Board expanded the 10-year trading range from +/–10 bps to +/–20 bps, and then to +/– 25 bps on 19 March 2021. BOJ intervention weakened the yen when the 10-year JGB yield tested the policy ceiling in 2022. Until YCC ends, there is nothing to keep that from happening again. Japan 10-Year Yield vs. Yield Curve Control “Ceiling” Potential Triggers for Renewed BOJ Yield Curve Defense As the global economy continues to evolve beyond pandemic-related disruptions, revived overseas growth and greater demand for energy commodities, among other factors, may offset the demand destruction dynamics. In the United Kingdom, fiscal stimulus has supplanted fiscal austerity, as the government plans to extend former prime minister Liz Truss’s energy subsidy plan into spring 2024. Japan’s economy is sensitive to global commodity prices, and a price spike could lift domestic inflation expectations and exert upward pressure on the 10-year JGB yield. Thus, the anticipated timeline of BOJ hawkishness may become decoupled from market developments. If the next BOJ policy shift is expected in the second quarter of 2023, what happens if rising yields test the BOJ’s yield curve defense early in the first quarter? The BOJ may transform the JGB rout into a weaker yen, printing money to finance yield defense at its 50 bps line in the sand. Conversely, softer-than-expected global growth, a return to fiscal austerity among major economies, easing geopolitical tension, and falling commodity prices could lower the 10-year JGB yield and reduce the likelihood of forceful BOJ interventions. In effect, the yen remains sensitive to the spread between the 10-year JGB and the BOJ policy cap. In other words, moving the goalposts further down the field doesn’t mean the ball won’t get there. So long as there are goalposts, they will have to be defended, and the BOJ has yet to signal its readiness to abandon yield curve control altogether. 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/ Hiro_photo_H 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

Understanding the historical context of financial markets is crucial for investment professionals seeking to make informed decisions in today’s complex landscape. This exploration of historical data stretching back more than 230 years reveals how markets have evolved and how continuity and change shape investment opportunities. From the dominance of railroads in the 19th century to the emergence of multi-sector indexes, this historical lens offers invaluable insights for analysts working with older data. By integrating this knowledge into modern strategies, professionals can better navigate market cycles, understand long-term trends, and refine their investment approaches. This post – part II of a three-part series – is intended for investment analysts who plan to work with older data and need to know more about the historical context. My first post dated and defined the fully modern era and then traced the roots of the modern era to the 1920s. This post pushes the history back further. The audience again is the analyst who plans to work with this older data and needs to know more about the historical context. Continuity and Change Only a few elements of today’s financial markets can be shown to be continually present from the 1790s: The joint stock limited liability company — as a legal structure with reasonable liquidity for buying and selling — has been available to US investors from that time. And a stockholder has always been a remainder man, junior in the capital structure, and last in line to be paid in the event of firm dissolution. A government bond market, sometimes with only sub-sovereign issues (state and city bonds) has also been in continuous operation since the 1790s. In short, a US stock and bond return series can be constructed that extends more than 230 years back in time. I do have to acknowledge that despite decades of effort, these data are still not as good as post-1925 data. Nonetheless, I believe the record is good enough for many purposes. To trace how the stock and bond markets of the 1790s evolved toward their modern form, it will again be desirable to work backward. From the Civil War to World War I If you read enough historical analyses produced on Wall Street, you will encounter such phrases as “since 1871 stocks have …” or “this was the best [worst] return seen over the past 150 years.” Admittedly, these phrases appear less often than you hear “since 1926,” but you will find them. What happened in 1871? Nothing. Like 1926, it is once again an arbitrary date set by the needs and preferences of later data compilers and not any real historical juncture. The true point of beginning for the early modern period was the end of the Civil War. In addition to being a notable hinge point in history, from 1865 we have in hand the equivalent of the Wall Street Journal and a Moody’s manual, with contemporaneous publication of stock prices, share counts, dividends, and earnings, and information on bond prices, coupons, issue amounts, maturities and terms. That source, the Commercial & Financial Chronicle, has been made available online by the St. Louis branch of the Federal Reserve. Stocks Statements anchored in 1871 typically use data from Robert Shiller’s web site. Shiller reproduces the price, dividend, and earnings data compiled by Alfred Cowles in the 1930s. Cowles had data from 1917 forward already compiled by Standard Statistics, the predecessor of Standard & Poor’s. His unique contribution was to push the stock record back by five decades. What did Cowles find, there at the beginning of his data in 1871? The New York Stock Exchange had already achieved national predominance. Cowles felt he could safely ignore stocks trading on regional exchanges or over the counter (in those days described as trading “on the curb”). He found 80% or more of market cap on the NYSE—about the same proportion of total US market cap as represented by the S&P 500 in our day. There was one key difference, however. A single sector dominated the NYSE of this era: railroads, which accounted for about 90% of NYSE cap at the outset, and still almost 75% by 1900. Only in the 1880s did gas and electric utilities begin to appear in Cowles’ record, and only after 1890 were there industrials — one reason why the Dow Jones Industrial Average dates only to 1896. In fact, that’s why Cowles postponed his start date to 1871. He was committed to constructing a multi-sector index, as had become possible for Standard Statistics from 1917. Only by 1871 could he scrounge a few stocks which he could deem “utilities,” which in his case included canals and “industrials,” which meant coal mines and shipping services. The analyst today should not be fooled: for all intents and purposes, the Shiller-Cowles stock index is a single sector index of railroads until after 1900, when sectors did begin to proliferate, approaching modern levels of diversity by World War I. Of course, business enterprises from diverse sectors long predate 1900, but these businesses either did not have traded stock or did not trade on the NYSE. In fact, banks and financial services firms had ceased to trade on the NYSE from even before the Civil War. This sector is absent from Cowles’ indexes throughout. The final point of difference concerns the number of stocks available: just under 50 stocks were in Cowles’ index at the outset. There were not 100 stocks until 1899 and a count of 200 was not achieved until World War I. Nonetheless, setting aside counts and sector concentration, the differences between the US stock market in the 1870s, relative to the market in the 1920s, are not substantially greater than the differences that separate the 1920s from 1970s. There is meaningful continuity. With these caveats in mind, the analyst can append the Cowles-Shiller data to post-1925 data to construct a monthly series of stock returns that spans over 150 years. Price return can be distinguished from total return, dividend yields and

Elite endowments with heavy allocations to alternative investments are underperforming, losing ground to simple index strategies. High costs, increased competition, and outdated perceptions of superiority are taking a toll. Isn’t it time for a reset? Endowments with large allocations to alternative investments have underperformed comparable indexed strategies. The average return among the Ivy League schools since the Global Financial Crisis of 2008 was 8.3% per year. An indexed benchmark comprising 85% stocks and 15% bonds, the characteristic allocation of the Ivies, achieved 9.8% per year for the same 16-year period. The annualized difference, or alpha, is -1.5% per year. That adds up to a cumulative opportunity cost of 20% vis-à-vis indexing. That is a big chunk of potential wealth gone missing.[1] “Endowments in the Casino: Even the Whales Lose at the Alts Table” (Ennis 2024), shows that alternative investments, such as private equity, real estate, and hedge funds, account for the full margin of underperformance of large endowments. Why do some endowments continue to rely heavily on what has proven to be a losing proposition? Endowment managers with large allocations to alternative investments suffer from what I call the Endowment Syndrome. Its symptoms include: (1) denial of competitive conditions, (2) willful blindness to cost, and (3) vanity. Competitive Conditions Alternative investment markets were comparatively small and inchoate when David Swensen (Yale) and Jack Meyer (Harvard) worked their magic in the 1990s and early 2000s. Since then, many trillions of dollars have poured into alternative investments, increasing aggregate assets under management more than tenfold. More than 10,000 alternative asset managers now vie for a piece of the action and compete with one another for the best deals. Market structure has advanced accordingly. In short, private market investing is vastly more competitive than it was way back when. Large endowment managers, however, mostly operate as if nothing has changed. They are in denial of the reality of their markets. Cost Recent studies offer an increasingly clear picture of the cost of alternative investing. Private equity has an annual cost of at least 6% of asset value. Non-core real estate runs 4% to 5% per year. Hedge fund managers take 3% to 4% annually.[2] I estimate that large endowments, with 60%-plus in alts, incur a total operating cost of at least 3% per year. Now hear this:A 3% expense ratio for a diversified portfolio operating in competitive markets is an impossible burden. Endowments, which don’t report their costs and don’t even discuss them as far as I can tell, seem to operate in see-no-evil mode when it comes to cost. Vanity There exists a notion that the managers of the assets of higher education are exceptional. A dozen or so schools cultivated the idea that their investment offices were elite, like the institutions themselves. Others drafted on the leaders, happy to be drawn into a special class of investment pros. Not long ago, a veteran observer of institutional investing averred: Endowment funds have long been thought to be the best-managed asset pools in the institutional investment world, employing the most capable people and allocating assets to managers, conventional and alternative, who can and do truly focus on the long run. Endowments seem particularly well suited to [beating the market]. They pay well, attracting talented and stable staffs. They exist in close proximity to business schools and economics departments, many with Nobel Prize-winning faculty. Managers from all over the world call on them, regarding them as supremely desirable clients.[3] That is heady stuff. No wonder many endowment managers believe it is incumbent upon them –either by legacy or lore — to be exceptional investors,  or at least to act like they are. Eventually, though, the illusion of superiority will give way to the reality that competition and cost are the dominant forces. [4] The Awakening The awakening may come from higher up, when trustees conclude the status quo is untenable.[5] That would be an unfortunate denouement for endowment managers. It could result in job loss and damaged reputations. But it doesn’t have to play out that way. Instead, endowment managers can begin to gracefully work their way out of this dilemma. They could, without fanfare, set up an indexed investment account with a stock-bond allocation of, say, 85%-15%. They could then funnel cash from gift additions, account liquidations, and distributions to the indexed account as institutional cash flow needs permit. At some point, they could declare a pragmatic approach to asset allocation, whereby they periodically adjust their asset allocation in favor of whichever strategy — active or passive — performs best. Or, as Senator James E. Watson of Indiana was fond of saying, “If you can’t lick ‘em, jine ‘em.” To which, I would add, “And do it as quietly as you please.” References Ben-David, Itzhak and Birru, Justin and Rossi, Andrea. 2020. “The Performance of Hedge Fund Performance. NBER Working Paper No. w27454, Available at SSRN: https://ssrn.com/abstract=3637756. Bollinger, Mitchell A., and Joseph L. Pagliari. (2019). “Another Look at Private Real Estate Returns by Strategy.” The Journal of Portfolio Management, 45(7), 95–112. Ennis, Richard M. 2022. “Are Endowment Managers Better than the Rest?” The Journal of Investing, 31 (6) 7-12. —— . 2024. “Endowments in the Casino: Even the Whales Lose at the Alts Table.” The Journal of Investing, 33 (3) 7-14. Lim, Wayne. 2024. “Accessing Private Markets: What Does It Cost? Financial Analysts Journal, 80:4, 27-52. Phalippou, Ludovic, and Oliver Gottschalg. 2009. “The Performance of Private Equity Funds.” Review of Financial Studies 22 (4): 1747–1776. Siegel, Laurence B. 2021. “Don’t Give Up the Ship: The Future of the Endowment Model.” The Journal of Portfolio Management (Investment Models), 47 (5)144-149. [1] I corrected 2022-2024 fund returns for distortions caused by lags in reported NAVs. I did this by using regression statistics for the prior 13 years combined with market returns for the final three. (The corrected returns were actually 45 bps per year greater than the reported series.) I created the benchmark by regressing the Ivy League average return series on three market indexes.

Governments worldwide have a long and storied history of directing capital flows to and within private markets to meet public policy objectives. But encouraging private capital is preferable. When generous tax breaks are awarded to wealthy investors and capital commitments are made to subpar fund managers, public accountability requirements and performance targets are rarely met. At the extreme, state capitalism rests control of capital flows with the state. The state chooses the winners, full stop. This is antithetical to the principles of free markets and private enterprise. Yet, Western democracies have adopted a hybrid version of state capitalism since the Great Depression. Origins of Modern State Intervention Modern state intervention can be traced back to Franklin Delano Roosevelt’s New Deal, which launched with unemployment relief programs and then progressed to investments in public works and infrastructure. Across the Atlantic, the UK government in 1931 explored the economic and social impact of the Great Depression by forming the Committee on Finance and Industry. Chaired by Lord Macmillan, the Committee identified a chronic shortage — dubbed the Macmillan gap — of long-term investment capital for small- and medium-sized enterprises (SMEs). Immediately after World War II, the Industrial and Commercial Finance Corporation was created. It would later be renamed Investors in Industry, or 3i Group, which listed on the London Stock Exchange 30 years ago. The US Government launched the Small Business Administration (SBA) in 1953 to support entrepreneurs and small businesses by providing low-interest loans. State subsidies often make the headlines when federal governments or individual states offer incentives for large corporations to base their headquarters or manufacturing facilities within their borders. One well-known example is the $5 billion government support for Elon Musk’s companies — including Tesla and SpaceX. This support included commercial contracts, grants, loans, and tax breaks. Subsidies to smaller market participants, particularly in the private sector, are easily overlooked. They are often channeled through universities in the form of research grants. There is a growing ecosystem of science parks that includes a combination of universities, research institutes, incubators, and start-ups. Although some of these innovation centers are private initiatives, such as Xerox’s Palo Alto Research Center, many were born out of government initiatives. Examples include the Research Triangle Park in North Carolina, which saw the light in 1959, Sophia Antipolis in southern France launched 10 years later, followed by the Cambridge Science Park  in the UK in 1970. This formula has been adopted around the world. Daedeok Innopolis in South Korea, and China’s Zhanjiang Hi-Tech in Shanghai are among the world’s largest clusters. Governments often use two economic tools — agencies and subsidies — as part of a policy remit to oversee strategic sectors and promote emerging industries. Railroads, telecoms, and aircraft manufacturing have benefited from these policies historically. But in recent decades, particularly in the wake of the global financial crisis (GFC), governments have expanded investment policies to encompass practically all sectors of the economy. Sovereign wealth funds are the most wide-reaching tools. Although their investments are not restricted to private markets, several — including Singapore’s GIC and Abu Dhabi’s ADIA — have long been active contributors to private equity (PE), real estate, infrastructure, and private credit funds. Norway’s sovereign wealth fund is a well-known exception, taking a negative stance on PE. State Banks and Investment Funds Other instruments applied by governments to influence private markets are more recent additions. In most cases, the GFC triggered their implementation. In the UK, Prime Minister Gordon Brown and his successor David Cameron commissioned a report from Chris Rowlands, a former senior executive at 3i Group, to investigate a likely “funding gap” in the small- and medium-sized enterprises (SME) segment. Rowlands duly identified a capital shortage, even though the UK has the largest supply of private capital in proportion to its economy among European countries. The “need” led to the launch of several new entities that, over the following decade, flooded private markets with capital. The Business Growth Fund (BGF), the British Business Bank (BBB), the Enterprise Capital Funds (ECFs), and other programs saw the light and have been operating ever since. Their performance is far from impressive and their reporting is opaque. Several major European economies adopted similar approaches. France’s Banque Publique d’Investissement (BPI) and Italy’s Fondo Strategico Italiano (FSI) launched at about the same time. BPI France and BGF frequently top the league tables of dealmakers in Europe. And an Irish government agency turned out to be the most active investor in European VC last year. In truth, European governments have always been far more interventionist than the United States. The Netherlands emulated the Macmillan initiative when it launched NPM Capital in 1948, for instance. And during the dotcom bubble, the French State launched the Fonds National d’Amorçage (FNA) to support fund managers sponsoring early-stage enterprises. Post-GFC, the FNA was reintroduced, reportedly as a one-off. However, yet another vintage was launched in 2016. Perhaps nothing encapsulates more the French State’s love-in with market intervention than the fact that BPI’s parent company is the Caisse des Dépôts et consignations, the country’s investment arm enacted under Louis XVIII in 1816. The European Investment Bank and the European Investment Fund were established to commit capital to venture and buyout funds as well as to take equity stakes in, and provide loans to, private enterprises across the continent. Their establishment was a natural evolution of state intervention in private markets. Tax Credits and Avoidance Schemes Complementing this panoply of government programs, tax credits were instituted to encourage research and development in technology. These credits have been extended to cover all capital injections that support young and innovative enterprises, often but not exclusively in the tech sector. In the United States, university endowment funds and ultra-high-net-worth individuals who can set up foundations receive tax incentive to reinvest part of their accumulated capital back into the economy. These are government-sponsored tax avoidance schemes. In Europe, such schemes took a different path. Since the 1990s, successive UK governments have prioritized investments by individual investors.

Key themes in the most-read blogs published on Enterprising Investor between July 1 and September 30 include warnings signs in private markets, positioning for Fed pivots, testing new AI tools in research and portfolio construction, and reinforcing governance and philosophy to stay resilient through uncertainty. Resilience Over Prediction: Whether in response to Fed timing, inflation expectations, or market cycles, this quarter’s most popular blogs emphasize portfolio durability, diversification, and structural strength amid uncertainty. A Smarter Use of Metrics and Tools: From capital deployment factors in private equity to ML-driven portfolio construction and private GPTs for research, investors are rethinking how they measure, analyze, and act on information. Integrating Macro, Technology, and Governance: Today’s investment edge comes from connecting macro context, technological innovation, disciplined governance, and coherent philosophy to achieve consistent long-term results. The warning signs are piling up. From valuation inflation to fee extraction on unrealized gains, today’s market bears striking resemblance to the late stages of past financial manias, writes Mark J. Higgins, CFA, CFP. This post draws on financial history to show how those patterns are resurfacing in private markets. Bill Pauley, CFA, Kevin Bales, CFA, Adam Schreiber, CFA, CAIA, and Ty Painter review Fed hiking and easing cycles since 1965 to show why policy pivots don’t provide a simple playbook. Out of 12 hiking cycles, 10 saw yield-curve inversions and eight ended in recessions. Even preemptive rate cuts do not always avoid a recession. Cash, bonds, and gold have their perks, but the downside can be severe, writes Pim van Vliet, PhD. Shares of low-volatility companies with earnings that can grow with inflation may lag in bull markets but historically cushion drawdowns and may deliver long-term returns. When blended well into a portfolio, they can improve downside risks without relying solely on bonds. Baridhi Malakar, PhD, outlines how to set up a practical, privacy-preserving AI research assistant in an open-source environment. The benefit is a secure, cost-effective, and fast way to parse thousands of pages in seconds as part of the research process while maintaining governance and IP protection. Xavier Pintado, PhD, and Jérôme Spichiger, CIIA, argue that private equity firms’ performance metrics do not include idle capital, which can be substantial. More precise metrics are the capital deployment factor (CDF), and the Orbital Assets Method (OAM), which treats the investor capital holistically with outcomes comparable to public markets. Forecasts and surveys show that both professionals and consumers get it wrong when predicting inflation, write David Blanchett, PhD, CFA, CFP, and Jeremy Stempien. Real assets (commodities, listed infrastructure, REITs) may look inefficient when inflation is low, but their portfolio value appears when inflation surprises to the upside. Riding out volatility is often critical to achieving long-term success in the markets and history provides a lesson to that end, write Bill Pauley, CFA, Kevin Bales, CFA, Adam Schreiber, CFA, CAIA, and Ty Painter. After evaluating 15 bear markets using the S&P 500 since 1950, they conclude that low volatility and dividend investment styles endure irrespective of recessionary conditions. Winston Ma, CFA, Esq, explores how the emergence of a US sovereign wealth fund could upend markets, unearthing both risks and opportunities, particularly as it reshapes strategic sectors like semiconductors, artificial intelligence, and rare earths. Mark Armbruster, CFA, examines the reasons for underperformance among nonprofit and endowment portfolios. Among them: costly alternatives and governance issues. His suggested remedies include adopting a deliberate, long-term investment philosophy and setting limits on certain asset classes. Investment management firms who adopt and train machine learning (ML) tools will maintain a competitive edge over their peers in portfolio construction and performance, argues Michael Schopf, CFA. ML methods better capture non-linear risks and can more quickly assess a group of stocks under various market conditions and improve diversification. Looking Ahead Together, these Q3 blogs show how investors are adapting to a fast-changing environment, learning from past rate cycles, experimenting with AI and machine learning in research and portfolio design, and reinforcing the value of resilient, well-governed investment approaches. In world shaped by policy shifts and technological disruption, adaptability grounded in sound philosophy remains investors’ best advantage. source

Digital Assets: Pricing, Allocation and Regulation 2025. Edited by Reena Aggarwal and Paolo Tasca. Cambridge University Press. www.cambridge.org Digital Assets delivers an extensive array of provocative articles in a compact format. From presenting methods for valuing the assets and demonstrating the impact of their inclusion on portfolio performance to dealing with rapidly evolving regulations of crypto assets, it is full of novel and sometimes complex concepts that begin with a simple question: Are digital assets a colossal bubble or will their underlying technology, blockchain, transform the world of finance? A reader such as me, a traditional fundamental analyst, then inquires: Are digital assets, such as cryptocurrencies, true investment assets? How is their value determined? Is blockchain an investment or simply a tool to facilitate faster, complex digital bookkeeping? This volume inspires institutional investors to evaluate for themselves the risks and rewards associated with investing in digital assets and the appropriateness of such investments in portfolios. The editors wisely selected specialists in key areas of interest including defining and evaluating digital assets, determining their suitability as institutional investments, reviewing regulations and compliance, and addressing monetary policy and central bank digital currency (CBDCs). They also presented a handy reference for dozens of digital asset-related acronyms. The conclusions and lengthy bibliographies included in each chapter serve to solidify conceptual understanding and build upon it. There is a “voice” associated with each chapter, to the point where you want to read more of  selected contributors’ work. Each reader will linger on some sections more than others, based on their level of interest in the topics. The initial chapter, “Institutionalization of Digital Assets,” provides a comprehensive overview of the composition of digital assets. The single largest is Bitcoin, which represents 75% of the total market capitalization as of the chapter’s writing. Bitcoin is but a subset of the cryptocurrency asset class that uses encryption to conduct monetary transactions rather than a bank or third party. The Chicago Mercantile Exchange (CME) successfully introduced regulated Bitcoin futures contracts in 2017 and now ranks as the world’s largest venue for USD Bitcoin transactions. There are also digital asset exchange traded funds (ETFs), both physical-based and futures-based. The major deterrents to widespread institutionalization are related to inefficiencies surrounding valuation, volatility, regulatory clarity, and the introduction of custodians and prime brokers. In addition, most cryptocurrency trading is executed on unregulated exchanges. These points of concern are addressed through subsequent chapters in the book. On the positive side, cryptocurrency’s low correlation with most investable asset classes could make a strong case for it as a diversifier in portfolios. “How and When Are Cryptocurrency Predictable?” This inquiry, the focus of Chapter 2, fleshes out the back-testing of the portfolio economic value attributed to cryptocurrency. Spoiler alert: With the evidence presented in this section, readers will understand why cryptocurrencies display large monthly average returns but also massive volatilities. The authors have utilized cryptocurrency-specific factors in their predictive exercises. They conclude that based on their evidence, Bitcoin may give a first-order contribution to portfolio diversification but “will need further scrutiny before calling Bitcoin or any other companion digital currency a new asset class.” (p. 40) How does one value a digital asset? Using a valid methodology presented in Chapter 3, “DeFi versus TradFi: Valuation Using Multiples and Discounted Cash Flows,” the authors apply conventional valuation analysis comparisons to DeFi (decentralized finance) tokens and provide a comparison with the valuation of stocks of publicly listed firms. The methodology seems quite simple, but is actually extremely complex, incorporating various components of the cryptocurrency ecosystem. The authors analyze decentralized exchanges (DEXs), protocols for loanable funds (PLFs), and yield aggregators (yield farmers and liquidity miners, viewed as return maximizers), which are compared with exchanges, banks, and asset managers, respectively. Another spoiler alert: The authors conclude that DeFi tokens have been overpriced relative to the equity of financial services firms. “Regulations and Compliance of Digital Assets,” Part III of Digital Assets, should be compulsory reading for regulators, bankers, and asset managers globally. This large section is so well-written and presented that it serves as a compliance and regulatory blueprint for digital assets. Issues that are forefront and directly addressed in this section include KYC (Know Your Customer), AML (anti-money laundering), financing terrorism, security risk, tax evasion, transparency, and custody. The total picture cries out for global rather than fragmented regulation, especially because cryptoassets run on the internet, which has no national boundaries. Space in this review for critiques of individual chapters is limited, but a final one must be highlighted: “Monetary Policy in a World with Cryptocurrencies, Stablecoins, and Central Banks Digital Currency (CBDC),” Chapter 10. How could digital currencies influence monetary policy? As a general matter, the balance sheet of the central bank would not change. Even if new forms of money and new currencies are introduced, the central bank does not lose its ability to control short-term interest rates and implement monetary policy. If, in the case of the US Federal Reserve, however, a foreign currency is “dollarized,” as in a stablecoin, monetary policy would lose its influence. The author argues for regulation similar to that on existing banks and financial market infrastructures to avoid runs on stablecoin issuers. There are few criticisms to lodge against this excellent book. Through no fault of the authors, the articles are already a bit out-of-date, due to the long lead time required to produce a reference work of this quality. The latest data employed dates to 2022. The digital asset ecosystem is constantly changing, if not transforming, so anything anyone writes will instantly be outdated. Still, the concepts presented in Digital Assets remain intact. source

For more on artificial intelligence (AI) in investment management, check out The Handbook of Artificial Intelligence and Big Data Applications in Investments, by Larry Cao, CFA, from the CFA Institute Research Foundation. Performance and Data Despite its seemingly “magical” qualities, ChatGPT, like other large language models (LLMs), is just a giant artificial neural network. Its complex architecture consists of about 400 core layers and 175 billion parameters (weights) all trained on human-written texts scraped from the web and other sources. All told, these textual sources total about 45 terabytes of initial data. Without the training and tuning, ChatGPT would produce just gibberish. We might imagine that LLMs’ astounding capabilities are limited only by the size of its network and the amount of data it trains on. That is true to an extent. But LLM inputs cost money, and even small improvements in performance require significantly more computing power. According to estimates, training ChatGPT-3 consumed about 1.3 gigawatt hours of electricity and cost OpenAI about $4.6 million in total. The larger ChatGPT-4 model, by contrast, will have cost $100 million or more to train. OpenAI researchers may have already reached an inflection point, and some have admitted that further performance improvements will have to come from something other than increased computing power. Still, data availability may be the most critical impediment to the progress of LLMs. ChatGPT-4 has been trained on all the high-quality text that is available from the internet. Yet far more high-quality text is stored away in individual and corporate databases and is inaccessible to OpenAI or other firms at reasonable cost or scale. But such curated training data, layered with additional training techniques, could fine tune the pre-trained LLMs to better anticipate and respond to domain-specific tasks and queries. Such LLMs would not only outperform larger LLMs but also be cheaper, more accessible, and safer. But inaccessible data and the limits of computing power are only two of the obstacles holding LLMs back. Hallucination, Inaccuracy, and Misuse The most pertinent use case for foundational AI applications like ChatGPT is gathering, contextualizing, and summarizing information. ChatGPT and LLMs have helped write dissertations and extensive computer code and have even taken and passed complicated exams. Firms have commercialized LLMs to provide professional support services. The company Casetext, for example, has deployed ChatGPT in its CoCounsel application to help lawyers draft legal research memos, review and create legal documents, and prepare for trials. Yet whatever their writing ability, ChatGPT and LLMs are statistical machines. They provide “plausible” or “probable” responses based on what they “saw” during their training. They cannot always verify or describe the reasoning and motivation behind their answers. While ChatGPT-4 may have passed multi-state bar exams, an experienced lawyer should no more trust its legal memos than they would those written by a first-year associate. The statistical nature of ChatGPT is most obvious when it is asked to solve a mathematical problem. Prompt it to integrate some multiple-term trigonometric function and ChatGPT may provide a plausible-looking but incorrect response. Ask it to describe the steps it took to arrive at the answer, it may again give a seemingly plausible-looking response. Ask again and it may offer an entirely different answer. There should only be  one right answer and only one sequence of analytical steps to arrive at that answer. This underscores the fact that ChatGPT does not “understand” math problems and does not apply the computational algorithmic reasoning that mathematical solutions require. The random statistical nature of LLMs also makes them susceptible to what data scientists call “hallucinations,” flights of fancy that they pass off as reality. If they can provide wrong yet convincing text, LLMs can also spread misinformation and be used for illegal or unethical purposes. Bad actors could prompt an LLM to write articles in the style of a reputable publication and then disseminate them as fake news, for example. Or they could use it to defraud clients by obtaining sensitive personal information. For these reasons, firms like JPMorgan Chase and Deutsche Bank have banned the use of ChatGPT. How can we address LLM-related inaccuracies, accidents, and misuse? The fine tuning of pre-trained LLMs on curated, domain-specific data can help improve the accuracy and appropriateness of the responses. The company Casetext, for example, relies on pre-trained ChatGPT-4 but supplements its CoCounsel application with additional training data — legal texts, cases, statutes, and regulations from all US federal and state jurisdictions — to improve its responses. It recommends more precise prompts based on the specific legal task the user wants to accomplish; CoCounsel always cites the sources from which it draws its responses. Certain additional training techniques, such as reinforcement learning from human feedback (RLHF), applied on top of the initial training can reduce an LLM’s potential for misuse or misinformation as well. RLHF “grades” LLM responses based on human judgment. This data is then fed back into the neural network as part of its training to reduce the possibility that the LLM will provide inaccurate or harmful responses to similar prompts in the future. Of course, what is an “appropriate” response is subject to perspective, so RLHF is hardly a panacea. “Red teaming” is another improvement technique through which users “attack” the LLM to find its weaknesses and fix them. Red teamers write prompts to persuade the LLM to do what it is not supposed to do in anticipation of similar attempts by malicious actors in the real world. By identifying potentially bad prompts, LLM developers can then set guardrails around the LLM’s responses. While such efforts do help, they are not foolproof. Despite extensive red teaming on ChatGPT-4, users can still engineer prompts to circumvent its guardrails. Another potential solution is deploying additional AI to police the LLM by creating a secondary neural network in parallel with the LLM. This second AI is trained to judge the LLM’s responses based on certain ethical principles or policies. The “distance” of the LLM’s response to the “right” response according to the judge AI is fed back into the LLM as part of its training process. This way, when the LLM considers its choice of

Hedge funds promise sophisticated strategies and the potential for market-beating returns, but do they deliver enough value to justify their high fees? Research reveals a mixed picture. While some hedge fund managers demonstrate impressive skills in stock-picking or market timing, their overall performance often falls short of standard indices. For investment professionals, the challenge lies in identifying the few managers who combine skill, performance, and persistence. This is the first in a series of three blog posts that explore the hedge fund literature. Skill I found mixed evidence that hedge fund managers have investment skills. In fact, their investment outcomes are not much better than what you could expect from mere luck. However, several papers indicate that the best managers stand out. Kosowski et al. (2007) found that top hedge fund performance cannot be explained by luck. Chen and Liang (2009), looking at a sample of 227 market-timing hedge funds from 1994 to 2005, found evidence of market timing skill, especially during bear markets and volatile market conditions. Nohel et al. (2010) compared the returns of mutual funds run by managers who also manage hedge funds with the returns of other mutual fund managers, finding the former significantly outperforming the latter. More recently, Aiken and Kang (2023) found that hedge fund managers have stock-picking skills that diminish over time but do not find evidence of market timing skills. Barth et al. (2023) found that hedge funds not listed in commercial databases generated up to $600 billion in value-added (before fees) returns from 2013 to 2019. Other studies identify characteristics that may help pick out skilled hedge fund managers. Agarwal et al. (2009) found that hedge funds with greater managerial incentives, higher levels of managerial ownership, and the inclusion of high-water-mark provisions are associated with superior performance. They also found that funds with a higher degree of managerial discretion, proxied by more extended lockup notice and redemption periods, deliver superior performance. Sun et al. (2012) devised a “Strategy Distinctiveness Index.” Funds with a higher index were associated with better subsequent performance. After adjusting for risk, funds in the highest SDI quintile outperformed funds in the lowest quintile by 3.5% in the following year. Cao et Al. (2021) found that start-up hedge funds launched during periods of low demand for this type of fund outperformed those launched in high-demand periods. Performance On balance, research does not suggest impressive performance from hedge funds. Ackermann et al. (2002) found that hedge funds consistently outperform mutual funds but not standard market indices. They also found hedge funds to be more volatile than mutual funds. Kosowski et al. (2007) reported that hedge funds generate statistically insignificant alphas in five of the six categories reviewed: long/short, directional, multi-process, security selection, and funds-of-funds. The authors also mentioned that long/short equity funds’ residuals are negatively skewed, and relative value funds exhibit high kurtosis, or higher than normal frequency of extreme outcomes. By contrast, Newton et al. (2019), studying 5,500 North American hedge funds that followed 11 different strategies from 1995 to 2014, found that all but two hedge fund strategies outperformed the market as stand-alone investments, although their manager skill level was low. Sullivan (2021) analyzed the performance of hedge funds during the 1994–2019 period, dividing his data into two subsamples. From 1994 to 2008, he found an alpha of 3.4% annually. However, for the more recent 2009 to 2019 period, he found a ‑1.0% alpha. The author concludes that hedge fund performance may have declined over time due to reduced exposure to active management risk. Two other studies, Eksi and Kazemi (2022) and Amir-Ghassemi et al. (2022), confirmed the fading of hedge fund performance since 2009. By contrast, Barth et al. (2023) claimed that from 2013 to 2019, non-listed hedge funds produced, on average, positive alphas. However, Swedroe (2024) has challenged this claim, arguing that while the average non-listed fund may have added value, the median fund (a more representative statistical figure) does not. Persistence A key measure of whether the best hedge fund managers outperform by luck or by skill is persistence. Do the best-performing hedge funds tend to repeat their outperformance in subsequent periods? Unfortunately, with one notable exception, most studies find significant hedge fund persistence over short periods that vanishes at longer horizons. Baquero et al. (2005) reported positive persistence in hedge fund quarterly returns after correcting for investment style, with weakly significant annual persistence. Kosowski et al. (2007) also found that the best hedge funds persisted at annual horizons. Agarwal et al. (2009) found maximum persistence at the quarterly horizon, indicating that persistence among hedge fund managers is short-lived. Sun et al. (2018) reported evidence that hedge fund performance is persistent following weak hedge fund markets but is not persistent following strong markets. Aiken and Kang (2023) found weak evidence that managers exhibit persistence in selectivity skills. In a noteworthy study, Barth et al. (2023) found that, in contrast to vendor-listed funds, significant persistence existed over all horizons among non-listed hedge funds in 2013–2019, providing hope that outperforming hedge funds can be identified in advance. Key Takeaway Overall, research suggests skill and alpha are scarce and difficult to obtain in the hedge fund market, especially among those listed in commercial databases. Furthermore, most studies report that outperformers fail to repeat their feats over long periods. Investors considering hedge funds should not overlook unlisted funds. In my next post, I will discuss hedge fund risk and diversification properties. source

For most investment managers, ChatGPT represents the starting whistle in a tech arms race many had hoped to avoid. The post ChatGPT: The Origins, the Hype, the Opportunity appeared first on CFA Institute Enterprising Investor. source