CFA Institute

Tax season in the United States shouldn’t be confined to March and April alone. Our clients’ taxes and the tax-savings strategies we can devise for them should be on our minds year-round. Unfortunately, too often tax planning advice is almost entirely about identifying deductions. That’s a mistake that can lead to clients leaving money on the table. I’ve worked with many high-net-worth individuals (HNWIs) — including the CEOs of some of the largest publicly traded and privately held companies in the United States — and too many of them fail to minimize their taxes. It may be because they are time constrained or lack a strategic tax advisory team. But I’ve also found HNWIs tend to think about investments in terms of immediate returns: They don’t consider the fees and expenses, tax costs, and long-term returns. And it’s in these areas where investment advisers and wealth managers can add the most value. Constant Tax Code Changes Necessitate Constant Tax Planning The tax code has been revised or amended almost 6,000 times since 2001. The Tax Cuts and Jobs Act passed in 2017, for example, is the largest revision of the tax code in 30 years. If you add in the SECURE Act, the proposed SECURE Act 2.0, and the related changes to retirement plan rules, the complexity can be overwhelming. The original SECURE Act, which came into effect in 2020, has a number of implications for HNWIs. It pushed back required minimum distributions (RMDs) from age 70½ to 72 and removed the age limit on IRA contributions. The SECURE Act 2.0, which passed the House of Representatives and is now before the Senate, would increase the RMD age to 75 and allow for additional planning time in pre-RMD years. So, however we look at them, taxes are always complicated and always in flux. To help our clients navigate them, here are my top five tax-planning and saving strategies. 1. Maximize Employer Benefits Clients with earned income should take advantage of employer benefits early and often. While 401(k) options are pretty standard these days, high-income earners need to maximize mega-backdoor Roth options, health savings accounts (HSAs), and other valuable offerings. Let’s do the math: If a client contributes $7,300 — the 2022 maximum for families — to an HSA each year, they will accumulate $146,000 in 20 years. If those funds grow at an annual rate of 7%, they will have $320,000. If clients don’t use these funds for medical expenses, they can distribute them penalty-free after age 65, though the distributions will be subject to standard income tax. If they spend them on assisted living, unreimbursed medical bills, or other health care, at a 35% blended tax rate, they will still save over $110,000 in income taxes. 2. Leverage Charitable Gift Planning Options To further maximize their tax saving, clients can also use appreciated, long-term securities instead of cash; donor-advised funds (DAFs); and charitable trusts. They can gift these securities without recognizing gains and also time the income tax deduction to occur in a high-tax-rate year. For example, say a client makes a $250,000 cash donation to a charity but later that year needs that $250,000 for lifestyle expenses. To facilitate that, they sell $250,000 of investments with a cost basis of $100,000. Had they made the donation in stock and covered the personal expenses in cash, they could have saved almost $50,000. The lesson here: Charitable gift planning should include long-term, appreciated stock. Clients might also want to bunch their charitable contributions into a high-tax-rate year. That can provide serious, permanent tax savings. 3. Tax Loss Harvest in Down Markets Clients don’t like to look at their investment account statement and see unrealized losses or an investment that is worth less than what they paid for it. But if they have to pay capital gains now or in the not-so-distant future, they may want to sell those positions to generate a capital loss and access the related tax benefits. For example, let’s say a client has a stock investment that lost $100,000 and the client also sold a real estate investment this year that generated a $100,000 profit. If they sold the stock position and realized the loss — essentially monetizing a paper loss — they could offset the real estate gain and save on taxes. And if they reinvested the stock sale’s proceeds into a similar security, their overall investment position would be the same. (That is, so long as they didn’t put money in the same investment. That would violate the wash sale rule and make the loss unusable.) Because capital losses carry forward indefinitely, this strategy could add value even if the client didn’t expect the subsequent gains to be generated for many years. 4. Convert Pre-Tax IRAs to Roth IRAs Clients should convert their pre-tax IRA to a Roth IRA during down markets and low-income years. Roth IRAs don’t have an upfront tax break, but the contributions and earnings grow and are ultimately distributed tax-free. On the other hand, a pre-tax IRA provides a tax benefit when initially funded, but income is taxable at ordinary rates when distributed. With careful marginal rate tax planning, converting pre-tax IRAs to Roth IRAs can minimize the overall tax paid on the distributions. While this is always a great tax-planning strategy, it may be an especially smart move in the present environment. The current tax to convert will be based on current value and should be significantly less than it would have been last winter due to 2022 market declines. When the market rebounds, clients could harvest that additional growth tax-free since they already paid the tax at conversion. 5. Coordinate Estate Planning and Income Tax Planning Clients should consider gifting income-producing assets and assets with unrealized gains to family members in lower tax brackets, while keeping the “kiddie tax” rules in mind. If a parent in the highest tax bracket has long-term stock worth $32,000 with an unrealized gain of $20,000, they can gift the annual exclusion amount to their offspring. Instead of selling the stock

Mark J. Higgins, CFA, CFP, and Raphael Palone, CFA, CFP, will be presenting at the Planejar Annual Conference in Sao Paulo, Brazil, on 24 October 2022. Their program compares the US Federal Reserve’s response to post-COVID-19 inflation with its policies following the Great Influenza and World War I in 1919 and 1920. “I think the major impediments [to international coordination of monetary policy] are that it sounds fine in theory, but when the exchange rate objective seems to conflict with domestic urgency, domestic urgency wins out. It’s very difficult politically to appear to be subordinating domestic policy to international exchange rate stability, even though in the long run that may be a desirable thing to do.” — Paul Volcker The US Federal Reserve’s aggressive monetary tightening is at a scale that the world has not seen since the early 1980s. Over the past year, US securities markets have suffered substantial losses, yet the US economy and financial system remain on reasonably solid ground. The situation abroad is more precarious. Higher US interest rates and a strong dollar are disrupting cross-border capital flows and straining the finances of countries holding large amounts of dollar-denominated debt. The impact of Fed policy on the global financial system is yet another feature of the COVID-19 pandemic that caught investors off guard. But much like post-pandemic inflation, it is hardly unprecedented. Ever since World War I ended, US monetary policy has shaped cross-border capital flows, central bank policies, and debt-servicing sustainability throughout the world. This is a power that the United States assumed when it became the world’s largest creditor after World War I and the world’s primary reserve currency issuer after World War II. Fed policies will undoubtedly rattle the world again over the coming months. In fact, the United Nations Conference on Trade and Development issued an ominous report earlier this month warning of potentially severe ramifications in some of the most vulnerable nations. Beyond these generalities, however, how Fed policy will play out across the globe is difficult to predict. But one question is worth pondering: Will the Fed adjust its policies in the interest of global financial stability? There are two scenarios from history that may help answer this question. Ben Strong and the Roaring ’20s The Fed tightened monetary policy aggressively in 1920 for a familiar reason: to tame inflation. That led to a sharp but relatively short depression. The economy recovered in 1922 only to start overheating in the mid-1920s. This put the Fed in a difficult position. Blamed in part for having caused the depression of 1920 to 1921, Fed leaders feared repeating their mistake and were biased against raising rates prematurely. Complicating matters further, the Fed was under intense pressure from European central bankers to keep rates low. Why? Because if the Fed raised rates, gold would flow from Europe to the United States, as investors sought higher returns on capital. This would threaten post-war reconstruction by reducing the European money supply and forcing European central banks to raise interest rates to stem the outflow of gold. The Fed’s commitment to European reconstruction was first tested by the United Kingdom in 1925. After World War I, the pound sterling had largely forfeited its reserve currency status to the US dollar. But the UK’s political leadership wanted to restore it. Amid calls from leaders of the Bank of England and his Conservative Party to reestablish the gold standard, Winston Churchill, serving as chancellor of the exchequer, caved to the pressure. The pound, he announced, would return to the pre-war fixed ecxhange rate of $4.86. This substantially overvalued the pound, instantly rendering UK exports uncompetitive. That increased gold shipments from the UK to the United States and created problems for both countries: The UK suffered a painful recession, while the US money supply went through a rapid and unwanted expansion. In spring 1927, fearing the Fed would again raise interest rates amid increasing inflation and speculation, central bankers from the United Kingdom, Germany, and France traveled to the United States to lobby in favor of easy monetary policy. New York Federal Reserve Bank Governor Ben Strong helped convince his fellow Fed leaders to accede to the Europeans’ demands. But they went a step further: Instead of holding rates steady, they cut them. The Federal Reserve Bank of New York reduced the rediscount rate from 4.0% to 3.5%. The cut was approved with only one dissenter, Adolph C. Miller, whose words proved prescient. He described the decision as “The greatest and boldest operation ever undertaken by the Federal Reserve System, and . . . one of the most costly errors committed by it or any other banking system in the last 75 years!” This was not an exaggeration. The Fed’s overly accommodative monetary policy fueled rampant speculation in the late 1920s. This concluded with the catastrophic crash in October 1929, which triggered the Great Depression. The Depression, in turn, created the harsh economic conditions that enabled the rise of the Nazi party and Japanese militarists. Paul Volcker and the Great Inflation Fed chair Paul Volcker announced his famous monetary tightening program on 6 October 1979. Volcker understood it would have enormous consequences outside of the United States. But he didn’t let that affect his policy decisions. His priority was taming US inflation first and then dealing with the consequences, both foreign and domestic, as they emerged. Volcker’s monetary tightening persisted for nearly two years. As inflation moderated and the US economy could no longer sustain the austerity, the Fed began easing rates in July 1981. The US slowly emerged from the severe recession of 1981 to 1982, and the subsequent price stability helped fuel nearly two decades of prosperity. Other nations did not fare as well. The situation in Latin America was especially painful. Indeed, the 1980s are often considered Latin America’s lost decade. The sharp and sudden increase in US interest rates caused the dollar to appreciate substantially against many foreign currencies. Many Latin American countries had loaded

Interest rates move markets worth trillions of dollars, influence politics, impact the value of currencies, and even affect our grocery bills. Central bank press conferences announcing rate decisions attract large audiences and make captivating headlines such as, “Rates Lift Off.” And pundits use jargon such as “soft landing” and “hard landing” to describe the expected consequences of central bank policy decisions. But in a perfect world, where exactly should we be landing? Economists and practitioners alike have been wondering about this since the 19th Century, when Swedish economist Knut Wicksell came up with the idea of the natural rate of interest, also known as the neutral interest rate, the equilibrium rate, and r* (r-star). It is the rate at which monetary policy is not stimulating or restricting economic growth. It is important because central bankers use it to set monetary policy, primarily by raising, lowering, or maintaining interest rates. The neutral rate is compatible with stable price levels and maximum employment. If current interest rates are higher than r*, the implication is that we are in a restrictive monetary environment in which inflation will tend to fall. Prevailing rates that are lower than r* imply that we are likely to experience higher inflation. The idea of r* is extremely attractive. We have a rate that equates to all savings and investments in the economy while keeping output at its full potential without inflation. This is a place where we want to land the economy. No wonder so much research has been done in the area. The neutral rate can be considered the Holy Grail of central banking: the rate that promises low inflation without impacting employment. However, just like the Holy Grail itself, r* is remarkably difficult to find. It is elusive because it is not observable. With Federal Reserve Chair Jerome Powell’s semiannual address to the Senate Banking Committee this week fresh in mind, it is an ideal time to consider the drivers of r*. It is important to remember that the Fed’s response to changing financial conditions has subsequent impacts on financial conditions.  The Forces that Drive R* R* is widely believed to be determined by real forces that structurally affect the balance between savings and investment in an economy. This includes potential economic growth, demographics, risk aversion, and fiscal policy, among others. It is the rate that will prevail in an equilibrium once the effects of short-term perturbations have petered out. All of this makes r* unobservable, and therefore analysts and economists must resort to models to derive an approximation of the rate. Each model has its pros and cons, and the resulting estimated rate is model dependent and never the true r*. Central banks estimate the natural rate of interest regularly using differing models. The Federal Reserve Bank of New York, for example, uses the Laubach-Williams (LW) and Holston-Laubach-Williams (HLW) models. The latter is represented in Exhibit 1. Exhibit 1. Source: Federal Reserve Bank of New York. Is Money Really Neutral? Despite the challenges associated with relying on different models to derive r*, there has been a clear trend shared by each model: rates were in a secular decline for four decades. This decline resulted from structural forces driving rates ever lower. Factors like China’s rising savings rate and strong appetite for US securities, an ageing population pushing savings up and investments down, globalization, and low productivity growth played a role in reducing the neutral rate of interest. But there is another, less-discussed driver of r*. That is monetary policy. Most of the macroeconomic research assumes that money is neutral with no impact over real variables and that r* is determined by real variables. Therefore, in theory, monetary policy is irrelevant in the search for r*. In practice, however, monetary policy is not irrelevant. The importance of monetary policy is patent when we consider the decades-long effort by the major central banks to lower rates, in fact pushing interest rates well below r*. When this happens, several “evils” take hold of an economy, and these evils impact both real and nominal variables, explained Edward Chancellor in his book The Price of Time: The Real Story of Interest. One evil is faulty investment assessment. Artificially low rates reduce the hurdle rate for evaluating projects and, therefore, capital is directed to sectors and projects with lower-than-normal expected returns. Another is the “zombification” of the economy. When rates are low and debt financing is plentiful, companies that should have gone bankrupt continue to operate at ever higher levels of debt. This puts the Schumpeterian mechanism of creative destruction on hold, allowing non-viable companies to continue in existence. Third is the lengthening of supply chains. Low rates promote unsustainable expansion of supply chains as manufacturers push their production process further into the future. This implies that when rates rise, globalization trends will reverse, as we are already starting to observe. The fourth evil is fiscal imprudence. For politicians, it is tempting to spend money on popular policies to win elections. If interest rates are low and bond “vigilantes” are nowhere in sight, then the temptation is impossible to avoid. This is reflected in the ever-red US fiscal balance. The fact that the US deficit stands at 6% of GDP is a worrying trend for the United States. Exhibit 2. Federal Surplus or Deficit as a Percent of GDP. Source: Federal Reserve Bank of St. Louis. Remaining consistently below r* will not only drive up inflation but will also create a host of other imbalances throughout the economy. These imbalances will need to be corrected at some point with considerable pain and impact over real variables. The fact is that monetary policy has not been neutral, and central bankers have not been seeking the rate of equilibrium. Rather, they have pushed rates ever lower under the assumption that this is the way to achieve maximum employment, regardless of the imbalances accumulating throughout the economy. Where Do We Go From Here? To find the future trajectory of the neutral rate, we must project

What does it take to thrive in finance over three decades of disruption and reinvention? For Kam Shing Kwang, CFA — CEO, Hong Kong, and Chairwoman for North Asia at J.P. Morgan — the answer is adaptability. In a conversation with CFA Society Hong Kong’s Alvin Ho, CFA, the J.P. Morgan executive reflects on her journey and shares hard-won lessons for succeeding in a fast-changing industry. This post highlights some of the key talking points. Her story begins with a pragmatic choice: accounting. But Kam Shing soon realized her aspirations lay elsewhere. Conversations with portfolio managers revealed a new possibility: portfolio management. With its blend of analytical rigor and strategic vision, it was her ideal fit. It marked a turning point in her career and it was when she first decided to pursue a CFA charter. The CFA Charter: Building a Disciplined Foundation All success begins with mastery of fundamentals. Kam Shing emphasized the CFA program’s role in shaping her career. In her early career, most learning happened on the job, and she absorbed knowledge gradually, piece by piece. Fortunately, the CFA program curriculum provided her with a comprehensive, structured, and disciplined way to delve into the investment world. She highlighted how the CFA program has equipped her with knowledge of different asset classes, risk management, and ethical investing, all of which remain vital in today’s financial environment. For young professionals, she advocates embracing the process to pursue the CFA designation. What matters more than passing is the discipline instilled by preparing for the CFA exams. Finance today is exponentially more complex; this rigor prepares you. The Accidental Banker Initially focused on portfolio management, she thought her career would primarily revolve around managing investments. To her surprise, she discovered a passion for client-facing roles, even as an introvert. Her exposure to client interactions at J.P. Morgan revealed a natural aptitude — and genuine enjoyment — for relationship management. Her career progression, from managing teams to overseeing Southeast Asian markets and later Hong Kong, were also unplanned. Each new opportunity emerged organically as she proved herself in previous roles. Kam Shing also credited lateral moves like her Singapore-Hong Kong transition as a great growth opportunity, stressing that adaptability requires valuing sideways steps equally with upward climbs. Each experience builds on the last and learning fosters growth. Hong Kong’s Private Banking Landscape: Agility in a Shifting Arena Hong Kong’s private banking sector is intensely competitive. Kam Shing, however, expressed optimism despite the challenges posed by economic cycles and increasing competition. Hong Kong retains enormous wealth creation potential, she asserted, pointing to Asia’s dominance in global wealth growth and Hong Kong’s unique advantages. These include its financial infrastructure, proximity to mainland China, and role as a gateway for cross-border capital. Yet, Hong Kong faces stiff competition from global cities vying for a share of international financial activity. Many cities are vying for the same opportunities as the international financial landscape is becoming increasingly crowded. Kam Shing sees this competition as a positive sign — spaces with little competition often signal unattractive or highly challenging opportunities. Amid this competitive landscape, Kam Shing highlighted the critical importance of talent. While financial institutions must adapt their offerings to meet ever-changing client demands, the ultimate solution lies in cultivating sophisticated talent. While the firm makes experienced hires, Kam Shing believes the key to success also hinges on cultivating in-house talents, people who are equipped to understand clients’ needs and utilize a combination of products to create effective solutions. Equally important is the ability to deliver solutions in ways that resonate — tailoring not just the product, but also the approach to each client. By doing so, institutions can better navigate the complexities of the market and provide meaningful value to their clients. Singapore vs. Hong Kong: Complementary Rivals Kam Shing is Singaporean and raised in both Singapore and Hong Kong, making her extremely qualified to assess the relative merits of the sister cities. According to her, both cities share the same DNA as international financial hubs — strong legal systems, tax efficiency, and talent pools — but their paths diverge. Kam Shing stressed that the two cities’ interplay balances rivalry and synergy. They compete intensely yet simultaneously complement one another. This relationship has been a catalyst for innovation and market advancement. Asia’s abundance of opportunities is sufficient to sustain both hubs; the collaborative growth between the two will strengthen both cities’ global standing. The Secret Recipe for Gen Z: The Three Cs Adaptability is rooted in the mindset. Kam Shing distilled her philosophy into three principles: Curiosity: “You always have to learn.” Kam Shing emphasized the importance of embracing continuous learning, especially in today’s fast-paced environment. Curiosity is a powerful tool that enables professionals to adapt and thrive. Courage: “Be brave enough to try different things, sometimes ignorance is bliss.” Kam Shing encouraged young professionals to take risks and push boundaries. Stepping out of one’s comfort zone can lead to unexpected growth opportunities. “Ask for a promotion,” Kam Shing prompted everyone, and be prepared to accept that you don’t always get what you want. Stay put and fight, rather than jumping ship; her actions speak for themselves. Thirty years at one firm have rewarded her with numerous transitions, all the way to the helm. Competence: Focus on excelling in your current role. “If you do your job well, opportunities will come,” she advised. Building a strong foundation through dedication and hard work is essential for future success. On Leading as a Woman in Finance As a female executive, Kam Shing rejected perfectionism. She advises women juggling multiple roles to optimize (not maximize) efforts. She also shared a personal tip: “Delegate shamelessly. For example, I relied on family, domestic help, even my friends to stay connected with my daughters.” Her metaphor? “Life is like portfolio management. Learn to diversify energy and you will be amazed how synergy does the magic.” Would You Hire Your Younger Self? Kam Shing closed with a conviction. Having seen many cycles, she would choose

Posted In: Behavioral Finance, Drivers of Value, Economics, Leadership, Management & Communication Skills, Portfolio Management Editor’s Note: In memory of Daniel Kahneman, we have reposted this Enterprising Investor article which shares insights from his presentation at the 2018 CFA Institute Annual Conference. Nobel laureate Daniel Kahneman transformed the fields of economics and investing. At their most basic, his revelations demonstrate that human beings and the decisions they make are much more complicated — and much more fascinating — than previously thought. He delivered a captivating mini seminar on some of the key ideas that have driven his scholarship, exploring intuition, expertise, bias, noise, how optimism and overconfidence influence the capitalist system, and how we can improve our decision making, at the 71st CFA Institute Annual Conference in Hong Kong. “Optimism is the engine of capitalism,” Kahneman said. “Overconfidence is a curse. It’s a curse and a blessing. The people who make great things, if you look back, they were overconfident and optimistic — overconfident optimists. They take big risks because they underestimate how big the risks are.” But by studying only the success stories, people are learning the wrong lesson. “If you look at everyone,” he said, “there is lots of failure.” The Perils of Intuition Intuition is a form of what Kahneman calls fast, or System 1, thinking and we often base our decisions on what it tells us. “We trust our intuitions even when they’re wrong,” he said. But we can trust our intuitions — provided they’re based on real expertise. And while we develop expertise through experience, experience alone isn’t enough. In fact, research demonstrates that experience increases the confidence with which people hold their ideas, but not necessarily the accuracy of those ideas. Expertise requires a particular kind of experience, one that exists in a context that gives regular feedback, that is effectively testable. “Is the world in which the intuition comes up regular enough so that we have an opportunity to learn its rules?” Kahneman asked. When it comes to the finance sector, the answer is probably no. “It’s very difficult to imagine from the psychological analysis of what expertise is that you can develop true expertise in, say, predicting the stock market,” he said. “You cannot because the world isn’t sufficiently regular for people to learn rules.” That doesn’t stop people from confidently predicting financial outcomes based on their experience. “This is psychologically a puzzle,” Kahneman said. “How could one learn when there’s nothing to learn?” That sort of intuition is really superstition. Which means we shouldn’t assume we have expertise in all the domains where we have intuitions. And we shouldn’t assume others do either. “When somebody tells you that they have a strong hunch about a financial event,” he said, “the safe thing to do is not to believe them.” Noise Alert Even in testable domains where causal relationships are readily discernible, noise can distort the results. Kahneman described a study of underwriters at a well-run insurance company. While not an exact science, underwriting is a domain with learnable rules where expertise can be developed. The underwriters all read the same file and determined a premium. That there would be divergence in the premium set by each was understood. The question was how large a divergence. “What percentage would you expect?” Kahneman asked. “The number that comes to mind most often is 10%. It’s fairly high and a conservative judgment.” Yet when the average was computed, there was 56% divergence. “Which really means that those underwriters are wasting their time,” he said. “How can it be that people have that amount of noise in judgment and not be aware of it?” Unfortunately, the noise problem isn’t limited to underwriting. And it doesn’t require multiple people. One is often enough. Indeed, even in more binary disciplines, using the same data and the same analyst, results can differ. “Whenever there is judgment there is noise and probably a lot more than you think,” Kahneman said. For example, radiologists were given a series of X-rays and asked to diagnose them. Sometimes they were shown the same X-ray. “In a shockingly high number of cases, the diagnosis is different,” he said. The same held true for DNA and fingerprint analysts. So even in cases where there should be one foolproof answer, noise can render certainty impossible. “We use the word bias too often.” While Kahneman has spent much of his career studying bias, he is now focused on noise. Bias, he believes, may be overdiagnosed, and he recommends assuming noise is the culprit in most decision-making errors. “We should think about noise as a possible explanation because noise and bias lead you to different remedies,” he said. Hindsight, Optimism, and Loss Aversion Of course, when we make mistakes, they tend to skew in two opposing directions. “People are very loss averse and very optimistic. They work against each other,” he said. “People, because they are optimistic, they don’t realize how bad the odds are.” As Kahneman’s research on loss aversion has shown, we feel losses more acutely than gains. “Our estimate in many situations is 2 to 1,” he said. Yet we tend to overestimate our chances of success, especially during the planning phase. And then whatever the outcome, hindsight is 20/20: Why things did or didn’t work out is always obvious after the fact. “When something happens, you immediately understand how it happens. You immediately have a story and an explanation,” he said. “You have that sense that you learned something and that you won’t make that mistake again.” These conclusions are usually wrong. The takeaway should not be a clear causal relationship. “What you should learn is that you were surprised again,” Kahneman said. “You should learn that the world is more uncertain than you think.” So in the world of finance and investing, where there is so much noise and bias and so little trustworthy intuition and expertise, what can professionals do to improve their decision making? Kahneman proposed four simple strategies for better decision making that can be applied to both finance and life. 1. Don’t Trust

The efficient market hypothesis (EMH) says that active equity management is a waste of time. Because stock prices incorporate all relevant information, it is not possible to consistently beat the market, according to EMH true believers. That’s because EMH is based on the concept of the wisdom of crowds. Behavior of crowds is a superior lens for viewing market activity. With this lens, you can replace “active management delusion” with stock market opportunity. Wisdom of Crowds Simply put, the wisdom of the crowds maintains that the average of the estimates provided by many individuals is more accurate than are the individual estimates themselves. A popular example of the wisdom of crowds is asking a group of individuals to estimate the number of jellybeans in a large jar at the front of the room. It is most often the case that the average is more accurate than any of the individual estimates. The statistical equivalent is the law of large numbers — the larger the number of individual estimates, the more accurate is the resulting average. Portraying active equity management as being driven by the wisdom of crowds seems to make sense. Afterall, millions of investors are competing by placing billions of trades for stocks worth trillions of dollars. Mark J. Higgins, CFA, CFP, makes that argument in a recent Enterprising Investor post. Citing extensive evidence of underperformance, Higgins contends that active equity is doomed to fail because it is underpinned by the wisdom of crowds. He characterizes the $6 trillion in actively managed funds (from a total $12 trillion invested in US equity mutual funds), as “active management delusion.” Behavior of Crowds A superior lens for viewing market activity is the behavior of crowds. Stock prices gyrate wildly, often with no new information or for no obvious reason. The average stock sports an annual return standard deviation of 50%. This implies a 95% return confidence range of +/- 100%. This level of price chaos cannot be explained by the wisdom of crowds. Stock fundamentals do not change enough during the year to justify this craziness. It is better to view the stock market as a collection of ever-evolving emotional crowds, each of which is made up of individuals keenly aware of what the other crowds are doing. Emotionally driven behavior takes over in such situations. Because large sums of money are involved and the market moves rapidly, our ancestral fight or flight and herding instincts come to the fore. The result is rampaging emotional crowds with little or no self-control. When a stock price begins rising, even for no obvious fundamental reason, the prospect of making large sums of money pushes us to join the herd pricing frenzy. The opposite happens when the stock’s lofty price begins to decline: chaotic selling ensues. The result is a market in which stock prices are always wrong, to one degree or another, relative to underlying fundamentals. Emotional crowds coalesce around random bits of information, often broadcast by the largest “megaphone.” Prices are determined by the herd, not by averaging independent individual price estimates, such as in the jellybean example. An individual’s price estimate depends on what the herd thinks it should be with little or no consideration of fundamentals.   Active Equity Despite the prevalence of emotionally driven pricing, there exists underlying weak trading pressure that inevitably moves prices back in line with fundamentals. Consequently, stocks gyrate erratically around their fundamental value, visiting the correct price only briefly before moving away again. The challenge for active equity managers is to identify measurable and persistent emotional pricing patterns in the sea of noise that is the market. This can be accomplished by a range of analysis including fundamental, technical, and behavioral. The few resulting stocks selected for inclusion in a portfolio are “best idea stocks.” It is not enough simply to identify the best idea stocks. It is equally critical to manage the portfolio with an eye toward avoiding emotional errors, some of which mimic those being harnessed by the manager’s stock picking efforts. The evidence implies that most active equity managers are either failing at stock picking, failing at portfolio management, or both. Behavioral Crowds: A Stock Picker’s Friend Studies confirm that active equity managers can identify attractive investment opportunities. “Best Ideas” is the most compelling of these studies. Authors Miguel Anton, Randolph B. Cohen, and Christopher Polk find that the top 10 stocks held by active equity mutual funds — as measured by portfolio weights relative to index weights — significantly exceed their benchmarks. As relative weights decline, however, performance fades and at some point, probably around the 20th stock, a stock’s performance falls below the benchmark’s. Applying a variation of the “Best Ideas” relative weight methodology, my firm rates stocks by the fraction held by the best active equity funds. We define the best funds as those that consistently pursue a narrowly defined strategy while taking high-conviction positions. We update our objective fund and stock ratings based on monthly data. The best and worst idea stocks are, respectively, those most and least held by the best US active equity funds. We derive each stock’s rating from the collective stock-picking skill of active equity funds, each pursuing a distinct investment strategy. Exhibit 1 presents the annual net returns of best idea and “filler stocks” from 2013 to 2022, distilled from more than 400,000 stock month observations. The two best-idea categories eclipse their benchmarks by 200 and 59 basis points (bps), respectively, as measured by the average stock return net of the equally weighted S&P 500. The filler stocks — as in “fill out the portfolio” — by contrast, underperform. These results would have been even more dramatic had we excluded large-cap stocks since stock-picking skill decreases as market cap increases. The smallest market-cap quintile best idea returns far outpace those of the large-cap top-quintile best ideas. Individual stock outperformance declines as the best funds hold less and less of the stock. Those held by fewer than five funds — the category

Asset Allocation: From Theory to Practice and Beyond, Second Edition. 2021. William Kinlaw, CFA, Mark Kritzman, CFA, and David Turkington, CFA. Wiley. To build a robust investment process, asset allocators must address a long list of issues, including: which assets to choose, how to forecast risk and return, and how to manage currency risk. William Kinlaw, CFA, Mark Kritzman, CFA, and David Turkington, CFA, offer advice on these and a wide range of other topics in asset allocation, backing up their recommendations with solid quantitative analysis. Along the way, they dispel a few myths and tackle some of the most challenging aspects of investing. The authors identify seven essential characteristics of every asset class: Their composition must be stable (not static). They are directly investable. The components are similar to one another. The asset class is dissimilar to other asset classes. Investing in the asset class raises the expected utility of the portfolio. Selection skill is not a requirement for investing. Investors can access the asset class in a cost-effective way. (I would add an eighth: Investors must be able to come up with credible forecasts of return, risk, and correlations to other assets, to implement inclusion in an optimization process. This requirement would exclude, for example, cryptocurrencies.) What do these criteria mean in practice? Global equities are not internally homogeneous and therefore cannot be considered a single asset class. Instead, the authors identify three equity asset classes: domestic equities (meaning US equities for the authors), foreign developed market equities, and foreign emerging market equities. Excluded from the authors’ defined asset classes are art (not accessible in size), momentum stocks (unstable composition), and — more unconventionally — high-yield bonds, which are not externally heterogeneous because they are similar to investment-grade bonds and therefore form part of the corporate bond asset class. Ironically, the first myth that the book tackles is the importance of asset allocation. A much-cited 1986 article by Gary P. Brinson, L. Randolph Hood, and Gilbert L. Beebower found that asset allocation determines more than 90% of performance. This book argues, however, that the methodology of that study is flawed because it assumes a starting point of an uninvested portfolio. In practice, the authors show, once investors have made the decision to invest, asset allocation and security selection are likely to be equally important (depending, of course, on the investment approach taken). “In the absence of any skill, effort, or careful consideration,” they write, “investors can simply default to a broadly diversified portfolio such as 60–40 stocks and bonds.” The outputs from mean–variance optimizers are hypersensitive to small changes in inputs. Yet the authors dispel the myth that this sensitivity leads to error maximization. It is true that small changes in estimates between assets with similar risk and return characteristics can lead to big shifts in allocations between them. Because the assets in question are close substitutes, however, these reallocations have little impact on the portfolio’s return distribution. By contrast, pronounced sensitivity to changes in inputs is not observed with assets that have dissimilar characteristics. In particular, small changes in estimates for equities and bonds do not lead to large swings in the optimal allocation between them. Asset Allocation covers all the key ingredients of its subject, such as forecasting returns, optimization, and currency hedging. The chapter on rebalancing provides a good flavor of what practitioners will find: a mix of detailed quantitative analysis and practical advice, with scope to draw one’s own conclusions. Investors must evaluate the trade-off between the cost of rebalancing their portfolios to target against the cost of sticking with a suboptimal mix. A section on a dynamic programming methodology concludes that this approach is computationally impossible. The authors then present an optimal rebalancing methodology, the Markowitz–van Dijk heuristic approach. Its costs (5.4 bps) are compared with the costs for calendar-based rebalancing (5.5 bps to 8.9 bps), tolerance band rebalancing (5.8 bps to 6.9 bps), and no rebalancing (17.0 bps). This detailed analysis supports a simpler conclusion for those of us who deal with individual clients, for whom behavioral biases present the biggest threat to long-term success: Have a long-term plan, rebalance your portfolio to that plan, but don’t trade too often. The book presents high-level quantitative analysis to explore some of the most challenging aspects of asset allocation. For example, the authors assess the probability of forward-looking scenarios using a technique originally developed by Indian statistician P.C. Mahalanobis to characterize human skulls. They employ a hidden Markov model to develop a regime-shifting approach. Additionally, they identify the fundamental drivers of stock–bond correlations using statistically filtered historical observations. Notwithstanding its reliance on such sophisticated techniques, this new edition of Asset Allocation is accessible to those of us who work with quant teams rather than in them. Each chapter offers a stand-alone analysis of one of 24 aspects of asset allocation. I find myself regularly returning to this book for its framing of the issues I face, the authors’ analysis, and their concise presentation of the bottom line. 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

Regulators are cognizant of the disruptive impact and security threats posed by weak data governance (DG) and data management (DM) practices in the investment industry. Many investment firms are not developing comprehensive DG and DM frameworks that will keep pace with their ambitious plans to leverage new technologies like machine learning and artificial intelligence (AI). The industry must define legal and ethical uses of data and AI tools. A multidisciplinary dialogue between regulators and the financial industry at the national and international levels is needed to home in on legal and ethical standards. Steps Toward Data Efficiency and Effectiveness First, establish multiple and tangible goals in the short-, mid-, and long-term. Next, set an initial timeline that maps the effort in manageable phases: a few small pilot initiatives to start, for example. Without clear targets and deadlines, you’ll soon be back to your day-to-day jobs, with that outdated refrain from the business side, “The data governance and management thing is IT’s job, isn’t it?” It is extremely important to begin with a clear vision that includes milestones with set dates. You can think about how to meet the deadlines along the way. As you are defining and establishing the DG and DM processes, you should think about future-proofing systems, processes, and results. Does a specific data definition, procedure, and policy for decision-making tie back to an overall company strategy? Do you have management commitment, team involvement, and clients? As I pointed out in my first post on this topic, organizations having the most success with their DG and DM initiatives are those that take a T-shaped team approach. That is, a business-led, interdisciplinary technology team-enabled partnership that includes data science professionals. Setting realistic expectations and showing achievements will be essential disciplines, because DG and DM frameworks cannot be established overnight. Why are DG and DM Important in Financial Services? For investment professionals, turning data into complete, accurate, forward-looking, and actionable insights is more important than ever. Ultimately, information asymmetry is a great source of profit in financial services. In many cases, AI-backed pattern recognition abilities make it possible to acquire insights from esoteric data. Historically, data were mainly structured and quantitative. Today, well-developed natural language processing (NLP) models deal with descriptive data as well, or data that is alphanumerical. Data and analytics are also of importance in ensuring regulatory compliance in the financial industry, one of the world’s most heavily regulated areas of business. No matter how sophisticated your data and AI models are, in the end, being “human-meaningful” can significantly affect the users’ perception of usefulness of the data and models, independent of the actual objective results observed. The usefulness of the data and techniques that do not operate on “human-understandable” rationale are less likely to be correctly judged by the users and management teams. When intelligent humans see correlation without cause-and-effect links identified as patterns by AI-based models, they see the results as biased and avoid false decision-making based on the result. Data- and AI-Driven Initiatives in Financial Services As financial services are getting more and more data- and AI-driven, many plans, projects, and even problems come into play. That’s exactly where DG and DM come in. Problem and goal definition is essential because not all problems suit AI approaches. Furthermore, the lack of significant levels of transparency, interpretability, and accountability could give rise to potential pro-cyclicality and systemic risk in the financial markets. This could also create incompatibilities with existing financial supervision, internal governance and control, as well as risk management frameworks, laws and regulations, and policymaking, which are promoting financial stability, market integrity, and sound competition while protecting financial services customers historically based on technology-neutral approaches. Investment professionals often make decisions using data that is unavailable to the model or even a sixth sense based on his or her knowledge and experience; thus, strong feature capturing in AI modelling and human-in-the-loop design, namely, human oversight from the product design and throughout the lifecycle of the data and AI products as a safeguard, is essential. Financial services providers and supervisors need to be technically capable of operating, inspecting data and AI-based systems, and intervening when required. Human involvements are essential for explainability, interpretability, auditability, traceability, and repeatability. The Growing Risks To properly leverage opportunities and mitigate risks of increased volumes and various types of data and newly available AI-backed data analytics and visualization, firms must develop their DG & DM frameworks and focus on improving controls and legal & ethical use of data and AI-aided tools. The use of big data and AI techniques is not reserved for larger asset managers, banks, and brokerages that have the capacity and resources to heavily invest in tons of data and whizzy technologies. In fact, smaller firms have access to a limited number of data aggregators and distributors, who provide data access at reasonable prices, and a few dominant cloud service providers, who make common AI models accessible at low cost. Like traditional non-AI algo trading and portfolio management models, the use of the same data and similar AI models by many financial service providers could potentially prompt herding behavior and one-way markets, which in turn may raise risks for liquidity and stability of the financial system, particularly in times of stress. Even worse, the dynamic adaptive capacity of self-learning (e.g., reinforced learning) AI models can recognize mutual interdependencies and adapt to the behavior and actions of other market participants. This has the potential to create an unintended collusive outcome without any human intervention and perhaps without the user even being aware of it. Lack of proper convergence also increases the risk of illegal and unethical trading and banking practices. The use of identical or similar data and AI models amplifies associated risks given AI models’ ability to learn and dynamically adjust to evolving conditions in a fully autonomous way. The scale of difficulty in explaining and reproducing the decision mechanism of AI models utilizing big data makes it challenging to mitigate these risks. Given today’s complexity and interconnectedness

As we settle into a new year, the global initial public offering (IPO) landscape presents a fascinating tapestry of challenges and opportunities. The year 2024 was marked by geopolitical uncertainties, macroeconomic headwinds, and evolving regulatory frameworks, all of which have reshaped the investment terrain. In a recent fireside chat, industry experts Mike Tang, CFA, CPA, and Grace Yeung, CFA, CPA delved into the key trends that defined the IPO market last year. From the impressive growth in the Americas and EMEIA (Europe, Middle East, India, and Africa) regions to the surprising ascent of the National Stock Exchange of India, the dynamics of global capital flows are shifting, the conversation — facilitated by Phoebe Chan — revealed. The year of 2024 presented a mixed bag of challenges and opportunities. Tang and Yeung discussed several key trends and observations during their fireside chat.   Globally, the total capital raised through IPOs in 2024 reached $119.1 billion, a 10% decline from the previous year. The number of listings also contracted, falling from 1,371 in 2023 to 1,159 in 2024. While the overall market experienced a downturn, a closer look reveals a more nuanced picture. Excluding Mainland China’s A-share market, global IPO fundraising experienced an uptick, driven primarily by robust activities in the Americas and the EMEIA region, with both recording impressive growth rates of approximately 50% and 40%, respectively. This divergence suggests a potential recalibration of global capital flows, with investors seeking opportunities beyond traditional markets. A standout development in 2024 that surprised both Tang and Yeung was the National Stock Exchange of India (NSE) ascending to the top of the global IPO rankings, having raised $17.3 billion. This remarkable climb reflects India’s burgeoning economy, recent capital market reforms that have successfully attracted foreign investment, and a growing trend of multinational corporations spinning off their Indian subsidiaries. Hyundai’s $3.3 billion IPO of its Indian operations exemplifies this trend, underscoring India’s growing importance as a hub for multinational operations, Tang and Yeung agreed. Meanwhile, Nasdaq and the New York Stock Exchange (NYSE) solidified their positions in the global rankings, demonstrating the enduring appeal of the US capital markets. Nasdaq benefited from the year’s largest IPO, Lineage Inc., which raised $5.1 billion. The exchange’s total fundraising reached $16.5 billion, up from $12.5 billion in 2023. Driven by access to deep capital pools, the resurgence of Chinese companies seeking US listings also contributed to activities on US exchanges — with 61 Chinese firms going public in the US in 2024, compared to 36 in 2023. Focusing on Chinese firms, the Shanghai and Shenzhen stock exchanges, which led the global IPO market in 2023, saw fundraising plummet in 2024 due to China Securities Regulatory Commission’s (CSRC) periodic tightening of new listings. According to Tang, this policy shift indirectly benefited the Hong Kong market as Chinese companies sought alternative listing venues. The Hong Kong Stock Exchange (HKEX) made a notable comeback in 2024, climbing back into the top five global rankings. With 63 new listings, including many from Mainland China, the HKEX raised HK$82.9 billion, an 80% surge from 2023. Midea’s landmark HK$35.7 billion IPO, the second largest globally, was a key contributor to this rebound. The HKEX’s ongoing efforts to enhance its market attractiveness, including a recent consultation on IPO price discovery and open market requirements, demonstrate its commitment to continued growth. CFA Institute and CFA Society Hong Kong contributed to the consultation process, and we will provide a more detailed analysis of these proposed changes and their implications in a future article. Navigating today’s dynamic global IPO market presents both exciting opportunities and inherent risks. While there is the potential for lucrative returns, investors should approach new listings with a discerning eye. Thorough due diligence is essential, including a comprehensive analysis of a company’s financial health, competitive positioning, and growth trajectory – carefully weighing the allure of potential rewards with the inherent uncertainties that accompany early-stage ventures. Regulators play a critical role in fostering market stability and investor confidence. Rapidly developing markets like India, where the NSE has recently seen remarkable growth, call for a robust regulatory framework. This includes not only clear listing requirements and transparent disclosure practices, but also effective enforcement mechanisms to ensure market fairness and investor protection. The 2024 IPO market was defined by its dynamism. Exchange rankings shifted dramatically, with the rise of the NSE, the continued dominance of US exchanges, and the rebound of Hong Kong all telling a story of evolving market forces. This evolving landscape will continue to require both investors and regulators to remain vigilant, adaptable, and informed to capitalize on opportunities while mitigating risks.   It is essential to approach new listings with a discerning eye. Thorough due diligence, including a comprehensive analysis of a company’s financial health, competitive positioning, and growth trajectory, is crucial. By carefully weighing the allure of potential rewards against the inherent uncertainties of early-stage ventures, investors can make informed decisions. source

Before the Civil War, the US financial markets operated in a world far removed from today’s fast-paced trading floors. Auctions were held only twice daily and newspapers served as a primary source of trade reports. Understanding these early market behaviors, from the rise of railroads to the impact of the Panic of 1837, sheds light on the risks and opportunities that shaped the foundation of today’s financial systems. This historical narrative uncovers lessons crucial for modern analysts navigating an ever-changing landscape. It is the final in a three-part series (Part I, Part II). Step Back in Time When we go back in time before the Civil War, the stock market appears very different from today. There was exchange trading, but there was no specialist at a post, nor was trading continuous. Rather, auctions were held twice a day. The names of listed stocks were called in turn. The announcer paused to see if a bid or an ask, or more than one, was shouted out, and if any were matched they were recorded in the books as a trade. Most stocks did not trade every day in this era. When the offers ceased to be shouted or in the absence of any offers, the announcer continued down the list to the next stock. In many cases neither the bid nor ask, if any, were matched at the auction. Instead, bids and asks served only as starting points, an anchor to set expectations, with the actual trade taking place later, in the street. These trades may have been reported in the newspapers but were not found in the NYSE records. Fortunately for historical analysis, stock trades were reported in the daily newspapers from the beginning. “Prices of Stocks,” as these sections were sometimes labelled, have always been newsworthy. In fact, some years ago a team led by Richard Sylla of New York University was able to compile a vast archive of newspaper price quotes before the Civil War. You might be astonished to learn just how many stocks have trading records that extend back to the War of 1812 and earlier. It is only before 1800 that the number of quoted stocks thins to a handful. New York Was Not the Epicenter of Finance Another key point of difference: the New York Stock Exchange did not achieve national predominance until after the 1840s. To obtain reasonable coverage of total market capitalization, a stock market index for this period must include stocks traded in Boston, Philadelphia, and Baltimore. In fact, at the outset of this period, Philadelphia was the financial center of the United States. New York did not take the lead until the Panic of 1837, and consolidation of its leading role was still in process at the beginning of the Civil War. There were rival exchanges in New York city itself, as well as other cities, through the 1860s. True predominance for the NYSE awaited the post-war knitting together of the nation by railroad, telegraph, and ticker. The non-dominance of New York was not well understood before Richard Sylla’s work. Jeremy Siegel’s path-breaking compilation of stock returns to 1802 used exclusively stocks listed in New York for most of the antebellum period. This is true for the Goetzmann, Ibbotson and Peng dataset back to 1815. I believe using exclusively stocks listed in New York introduces considerable survivorship bias. There’s a reason that the NYSE ultimately rose to national dominance. Economic, political, and financial conditions were more favorable for wealth accumulation through investing in New York City than anywhere else. I found much lower stock returns in Philadelphia and Baltimore, with more failures and busts, which had the effect of substantially lowering the stock returns reported in my paper in the Financial Analysts Journal, relative to those reported in Jeremy Siegel’s book, Stocks for the Long Run. Nonetheless, from 1793 onward there is a US stock market, with multiple stocks listed and trading, with a good historical record. For stocks, this period can be divided into two, with the Panic of 1837 serving as the hinge. From 1793  to the Panic of 1837 As of January 1793 I could find one bank each trading in New York, Boston, and Philadelphia, along with the 1st Bank of the United States (traded on all exchanges), each with a price record and information on share count and dividends. There are quotes in the Sylla database from before 1793, including during the first market panic in 1792, but I could not extract a price and dividend record that I judged trustworthy before January 1793. For the first dozen years almost all of stock market capitalization consisted of commercial banks. There was no other traded sector. By the War of 1812, there had appeared multiple insurance companies and a handful of turnpike stocks, but banks still dominated. After the war, marine and fire insurance companies proliferated, especially in New York, so that for the first time the market contained two sectors of roughly equal weight; or perhaps only one sector, the financial sector, if bank and insurance stocks are lumped together. The collective capitalization of the financial services sector vastly exceeded the handful of transportation and manufacturing stocks that traded before 1830. In 1830, railroad stocks began to be traded in New York and soon came to dominate trading volume. Even a small railroad would have capitalization the size of a large bank. As the Panic of 1837 began, total railroad cap was approaching that of the insurance sector. By the end of the depression that followed, in 1843, after the failure of numerous banks and insurance firms, the still-expanding railroad sector had a market cap about the same as the entire traded financial sector. By the end of the period, banks and insurance firms had moved off-exchange. From 1845 until near the end of the century, the US stock market — evaluated in terms of capitalization, and focusing on the NYSE — became almost entirely a market of railroad stocks. From the Panic of