CFA Institute

The financial markets are experiencing a sea change marking the end of a long period of accommodative central bank monetary policy, and there is little hope of ultra-low interest rates returning anytime soon, legendary investor Howard Marks, CFA, explained in a virtual conversation with Margaret “Marg” Franklin, CFA, president and CEO of CFA Institute, at the Asset and Risk Allocation Conference last month. Marks believes this represents the beginning of a new era in the financial markets that will force many investors to rethink how they approach investing, use different risk/reward assumptions, and adjust to more difficult conditions that many practitioners are seeing for the first time in their careers. “I’m not saying interest rates are going to go back up. I just think they’re done coming down,” Marks said. “One of the basic tenets of my thesis is that in the next five to 10 years, interest rates will not be constantly coming down or constantly ultra-low. And if that’s true, I think we’re in a different environment, and that’s a sea change.”  As co-chair and co-founder of Oaktree Capital Management, an investment firm with more than $170 billion in assets under management (AUM), Marks has earned a reputation as one of the world’s most prominent value investors. As he sees it, this sea change — the third he has witnessed in his 54-year career — doesn’t necessarily spell a “financial cataclysm . . . but financing, avoiding default, making money will not be as easy, and borrowing will not be as cheap,” he said. The market has rotated from a period that was bad for lenders and great for borrowers to one now that is better for lenders and less positive for borrowers, according to Marks. “So, this is a great time to be investing in credit. It’s better than it has been for a long time,” he said. “Might it get better? Yes; interest rates could go higher, in which case the fixed-income investor could have a chance later to invest at even higher rates. But this is a good time. I think the most powerful statement I can make is that today you can get equity-like returns from fixed income or credit.” Previous Market Sea Changes The first sea change Marks experienced was the arrival of non-investment-grade bonds in the primary markets in the 1970s. He discovered in 1978 that “unsafe” non-investment grade bonds could actually yield enviable returns. “Michael Milken and others made it possible for companies to issue non-investment grade bonds, and for investors to invest in them prudently if the bonds offered sufficient interest to compensate for their risk of default,” he explained. The sea change here was that responsible bond investing previously meant buying only presumedly safe investment grade bonds, but now investment managers could buy low-grade bonds if they felt the potential return adequately compensated for the attendant credit risk.  “Risk-return thinking is extremely important,” Marks said. He explained that when he entered high yield bond investing in 1978, Moody’s defined a B-rated bond as one that “fails to possess the characteristics of a desirable investment.” In that environment, Marks said, there were only good investments and bad investments, and a fiduciary could not properly invest in a “bad investment,” such as a B-rated bond. The concept of a good or bad investment is anachronistic. “These days we say, ‘It is risky? What’s the prospective return? And is the prospective return enough to compensate for the risk?’” Marks said. The second sea change, he said, was driven by macroeconomics and the OPEC oil embargo of 1973 and 1974. As the price of a barrel of oil more than doubled within a year, it sent the cost of many other goods soaring as well and ignited rapid inflation. The year-over-year increase in the Consumer Price Index (CPI) leaped to 11.0% in 1974 from 3.2% in 1972, before reaching 13.5% in 1980. It took the appointment of Paul Volcker as chair of the US Federal Reserve in 1979, and hiking the federal funds rate to 20% in 1980, to extinguish inflationary pressures, as inflation receded to 3.2% by the end of 1983. Marks said Volcker’s success in bringing inflation under control allowed the Fed to reduce the federal funds rate to the high single digits and keep it there throughout the 1980s, before dropping it to the mid-single digits in the 1990s. “[Volcker’s] actions ushered in a declining-interest-rate environment that prevailed for four decades,” he said. “I consider this the second sea change in my career.” Contributors to the Current Sea Change Several events have contributed to the current sea change, which has caused investor pessimism to balance optimism in the financial markets, according to Marks. Stocks that seemed fairly priced in a low-interest-rate environment have in recent months fallen to somewhat lower P/E ratios that are more commensurate with higher interest rates. Likewise, he said, the massive increase in interest rates has had a depressing effect on bond prices. Amid declining stock and bond prices, the fear of missing out (FOMO) has dried up and fear of loss has replaced it. Because the tighter monetary policies last year were designed to slow the economy, investors focused on the difficulty the Fed faces in achieving a soft landing and thus the strong potential of a recession. The anticipated effect of a recession on earnings dampened investors’ spirits. Thus, the S&P 500’s decline over the first nine months of 2022 rivaled the greatest full-year declines of the last century, Marks said. (Markets have since recovered considerably.) Risk and Return Outlook Franklin asked Marks about his expectations regarding risk and return and interest rates, as well as the more granular risks and opportunities the current market presents. One of Marks’s hallmarks is his deep research and analysis seeking outsized returns, paying close attention to the risk characteristics. “So maybe you could provide some perspective on those two levers or dimensions as well?” Franklin asked. “We had the tech bubble burst in 2000, and the stock market continued to decline in 2001 and 2002,”

Are public pension funds truly delivering the returns they claim? The gap between private asset net asset values (NAVs) and their real market value, a phenomenon known as volatility laundering, reveals significant implications for institutional investors. With private assets often overstated by as much as 12%, public pension funds may face greater underperformance than reported. This post explores how the practice of volatility laundering distorts returns and why transparency in private asset valuation is more critical than ever for public pension funds in the United States. State of Play By convention, private assets like unlisted real estate and private equity are carried at their NAV in the valuation of institutional funds and in the calculation of their rates of return. NAV is a figure arrived at by the general partners (GPs) of private asset funds and reviewed by their accountants.[1] In recent years, a gap opened between private-asset values in the secondary market and their NAVs. The gap persists today.[2] The marketplace is telling us that those private assets are not worth what the GPs and their accountants say they are worth. Cliff Asness coined the term volatility laundering to describe the practice of not marking private assets to market. Public Fund Performance with Reported Returns I acquired rates of return for a sample of 50 large US public pension funds for the 16 fiscal years ended June 30, 2024. The sources are the Center for Retirement Research at Boston College (CRR) and the funds’ annual reports. I included only funds reporting returns net of fees. I then created an equal-weighted composite of fund returns and developed a Market Index to evaluate the performance of the composite. The Market Index has the same effective stock-and-bond market exposures and the same risk (standard deviation of total return) as the composite. The Market Index blends returns of US- and non-US stock indexes with those of an investment-grade US bond index to form a single, hybrid index.[3] The composite has an annualized return of 6.88% for the 16 years, and the Market Index return is 7.84%. The difference between the two series, or annual excess return (ER), is -0.96%. See Exhibit 1. Exhibit 1. Historical Returns Fiscal Years 2009 to 2024.   Fiscal Year   Public Fund Composite   Market Index   Excess  Return 2009 -19.8 -17.5% -2.2% 2010 13.7 13.0 0.7 2011 21.5 22.6 -1.1 2012 1.1 1.7 -0.6 2013 12.0 13.9 -1.9 2014 16.8 18.2 -1.5 2015 3.3 4.% -1.0 2016 0.6 0.9 -0.3 2017 12.7 13.6 -0.9 2018 8.8 9.1 -0.3 2019 6.4 7.3 -0.9 2020 2.2 5.2 -3.0 2021 27.1 29.4 -2.3 2022 -3.8 -13.3 9.5 2023 6.7 12.2 -5.5 2024 9.4 15.4 -6.1 Annualized 6.88% 7.84% -0.96% Secondary Market Pricing In fiscal year 2022, an unusually large gap — 950 basis points (bps) — between the public fund composite return and that of the Market Index appeared. The average ER in the prior 13 years was just -1.2%. See Exhibit 1. Stock and bond markets experienced a sharp decline late in fiscal year 2022. NAVs reported by GPs of private asset partnerships, however, typically lag public market reporting by a quarter or more. The lag in reporting NAVs produced large positive returns for private assets in fiscal year 2022, despite the sell-off in stocks and bonds. This unleashed a series of NAV adjustments by fund managers in the years following to bring marks into conformance with marketplace realities. (See fiscal years 2023 and 2024 in Exhibit 1.) The marketplace, however, believes the GPs and their accountants have more work to do in marking private assets to market. This observation is based on data from the secondary market for private asset transactions. The data in Exhibit 2 were compiled by Jeffries’s Private Capital Advisory unit. Exhibit 2 summarizes the discounts from NAV for various categories of private assets during the first half of 2024. Exhibit 2. NAV Discounts for Private Assets.       Asset Type   First Half of 2024 Buyout 6% Credit 15 Real Estate 26 Venture 30 All 12% Source: Jeffries Private Capital Advisory In the analysis that follows, I incorporate the overall discount of 12% for private asset transactions in the first half of 2024 in estimating pension fund returns that reflect fair market pricing. The Center for Retirement Research reports that public funds allocated an average of 24% to private assets (private equity and real estate, only) through fiscal year 2022. I multiply the private asset percentage of 24% by the average NAV discount of 0.12, which produces a figure of 2.9%. Assuming Jeffries’s overall discount applies, this indicates that the funds, in the aggregate, were over-valued by approximately 3% relative to the market. I apply this adjustment to the excess return figure of -0.96%. I do this by dividing 3% by 16 (years), producing a 0.2% (18 bps, to be precise) haircut to excess return. (If we spread the haircut over the most recent 10 years, it amounts to 0.3% per year. The period chosen for applying the haircut is arbitrary. This results in an adjusted excess return (AER) of -1.14% per year since fiscal year 2009. See Exhibit 3. The calculations are rough and ready but good enough to get the idea across. Exhibit 3. Recap of Calculation of Adjusted Excess Return. Measure Annualized Returns Reported Return 6.88%   Market Index -7.84   Excess Return (ER) -0.96% -0.96% Private Assets Haircut   -0.18 Adjusted Excess Return (AER)   -1.14% Key Takeaway Public pension funds have underperformed a public market index by approximately one percentage point per year since the Global Financial Crisis. I attribute this to their high cost of operation and inefficient diversification. Volatility laundering — the practice of not marking private assets to market — obscures another dimension of economic underperformance of these funds. Were public funds to mark private assets to market, it would bring about a two- or three-tenths of a percentage point per year worsening of their long-term performance — a hit they can ill afford.

Many of the businessmen I know are well-versed in economics, but none uses the science in their daily work. No other science is so thoroughly ignored by its practitioners. The reason is that economics went astray by borrowing ideas from physics. This mischaracterizes commerce as a “closed equilibrium system.” Commerce is instead a complex, adaptive, and open system in constant disequilibrium. Economics should instead borrow ideas from evolutionary biology. After all, early economists were the first to recognize evolutionary processes. The political economist Thomas Malthus spoke elegantly about the “struggle for existence” in 1798. Charles Darwin even credits Malthus with his concept of “natural selection,” or “survival of the fittest,” which was his core insight in Origin of Species. Applying Malthus’s concept to biology, Darwin rightly noticed: [C]an we doubt (remembering that many more individuals are born than can possibly survive) that individuals having any advantage, however slight, over others, would have the best chance of surviving and of procreating their kind? On the other hand, we may feel sure that any variation in the least degree injurious would be rigidly destroyed. This preservation of favourable individual differences and variations, and the destruction of those which are injurious, I have called Natural Selection, or the Survival of the Fittest. Charles Darwin The same is true of commercial firms. Many more firms are born than can survive. Advantaged firms, however slight the advantage, have the best chance to survive and expand while others die. Favorable variations are thereby preserved while injurious variations are destroyed. This is “natural selection.” Accordingly, commerce is evolutionary, and economics should recognize this reality.   To say commerce evolves is no metaphor. It is true in a technical sense. Any population subject to “cumulative selection” pressure will evolve, which is true if the population’s agents (1) replicate with fidelity, (2) have variable and heritable traits, and (3) replicate at rates based on their variable traits. Commercial products undoubtedly possess these characteristics:      Products are reproduced with great fidelity by firms, which means they replicate. Products also possess variable traits, and those traits influence a product’s replication rate. Ford, for example, cannot sustain, much less expand, the F-150 product line if consumers do not select the F-150 over substitutes, and consumer selection hinges on the F-150’s differentiating traits. This is not debatable. Furthermore, the focus should be on products, not firms, which is a Neo Darwinian view. Neo Darwinism revolutionized biology. The theory says the proper unit of evolutionary analysis is the gene, not the organism as Darwin had thought. Genes are the true “replicators,” in other words, and organisms are merely their “survival machines.”   A similar hierarchy exists in commerce. A product, whether a good or a service, is a firm’s DNA, and products comprise many sub-units, or “premes.” The preme is the gene of commerce; they are the “units of heredity” differentiating product-lines. Accordingly, premes are the primary “replicators” of commerce, and firms, like organisms, are merely their “survival machines.” The Firm Is a Commercial Organism A firm, like an organism, is “an open system that survives through some form of exchange with its environment.” It requires energy to sustain itself. Without energy, a firm will surrender to the forces of entropy and dissolve into its surroundings. Like any organism, therefore, a firm must “make a living” by earning an energy surplus absent external infusions of resources. To earn an energy surplus, a firm’s energy intake, or revenues, must exceed its energy expenditures, or costs, including its cost of capital. That is, a firm must produce a product consumers deem more valuable than the resources employed by the firm in production. If achieved, a firm will earn an energy surplus, or profit, and survive. If not, a firm will earn an energy deficit, or loss, and die. The more profitable a firm is, the more value it creates, and value creation determines the “fitness” of a firm. Thus, a firm that earns a 20% profit is “fitter” than a competitor earning 5%. The former is better adapted to the demands of its niche. “Fitter” firms will have higher survival rates and grow faster. Their products will thereby gain market share. This is how a species of industry evolves. Investors should therefore prefer “fit” firms, or those earning large profits. Large profits attract competition, however. It signals to entrepreneurs an opportunity to create value of their own. To do so, entrepreneurs will replicate the differentiating traits of a “fit” firm’s product. How, then, can a “fit” firm sustain its economics? This is where an evolutionary perspective becomes most useful. The Preme-Product-Firm Hierarchy: A New Model Evolutionary theory is the best tool for assessing the sustainability of profits. Excess profits cannot be sustained without durable competitive advantages, and durable competitive advantages are best understood through an evolutionary lens. Such a lens must be properly focused, however, on the proper unit of evolutionary analysis. In commerce, this is the product and its “premes.” A firm depends on consumers for nourishment. Consumer selection occurs at the product level, however, which means products, not firms, are the proper units of evolutionary analysis. More specifically, since the value proposition of a product (e.g., Ford’s F-150) depends on its various sub-units (e.g., engine, brand, style), the proper unit of analysis is best described as the preme. Products, in other words, are like DNA. They are complex structures of subunits called premes, and premes, like genes within DNA, battle for inclusion in products. A preme is any attribute impacting a product’s value proposition. It can be as minor as employees saying, “My pleasure,” at Chick-fil-A or as major as iOS for Apple products. Premes are thus the “premetic material” of products and their firms, and premetic material is all around us in the form of ideas. It floats about like pollen ready to fertilize a receptive entrepreneur’s mind. As such, premetic material mutates, or changes, at warp speed. It takes only a new idea. And mutations alter products quickly as entrepreneurs adopt

The financial world thrives on timely insights, accurate analysis, and forward-looking strategies. Over the years, natural language processing (NLP) has emerged as a precious tool for interpreting vast amounts of financial text, aiding investors and analysts in making informed decisions. From basic sentiment lexicons to advanced large language models (LLMs) like BERT and FinBERT, the field has made significant progress. However, domain-specific challenges in financial news analysis persist. We homed in on a popular LLM, ChatGPT, to analyze Bloomberg Market Wrap news using a two-step method to extract and analyze global market headlines. By generating a sentiment score and converting it into an investment strategy, we assessed the performance of the NASDAQ market. Our findings are promising, indicating the potential for forecasting NASDAQ returns and potentially designing investible strategies. This post outlines a two-step sentiment extraction process from financial summaries, a method for converting sentiment into actionable allocations, and an evaluation demonstrating outperformance against a passive investment strategy. After a short review of related work, we detail our prompt engineering approach, describe the conversion to investment strategies, and present evaluation results. An in-depth analysis of our study is available on ssrn: “Sentiment Score of Bloomberg Market Wraps with ChatGPT.” Other Resources Recent research has highlighted ChatGPT’s applications in finance and economics. Hansen and Kazinnik [8] showed its utility in interpreting Federal Reserve communications, and Lopez-Lira and Tang [16] demonstrated effective prompting for stock predictions. Cowen and Tabarrok [3] and Korinek [13] explored its use in economics education, while Noy and Zhang [20] focused on productivity benefits. Yang and Menczer [31] examined its credibility assessments for news, though Xie et al. [30] noted that its numerical predictions align with linear regression, and Ko and Lee [12] faced challenges in portfolio selection. Our study extends this literature by using a multi-step ChatGPT approach to predict NASDAQ trends, reducing noise and enhancing accuracy. Prompt Engineering The first step in prompt engineering is data collection. We collected daily summaries from Bloomberg Global Markets, known as Market Wraps, from 2010 to October 2023. We excluded summaries with fewer than 1200 characters or those that did not mention at least two of the following market types: equities, fixed income, foreign exchange, commodities, or credit. In addition, we included only summaries that had widespread online distribution to ensure significant public impact. This process yielded a dataset of over 70,000 articles, each averaging 1000 words and approximately 6000 characters. Naïve Approach Initially, our prompt directive was to provide a sentiment score from the text as follows: This straight approach similar in spirit to Romanko et al. [25] or Kim et al. [11] turned out to be disappointing as it led to correlations close to zero with major stock indexes like NASDAQ and S&P500, most likely because of random model hallucinations. Shift to Two-Step Approach We then opted to decompose the instructions into simpler and more straightforward tasks. In accordance with the recommendations posited in [16], we devised two prompts to refine the objectives for ChatGPT, focusing on tasks empirically demonstrated to align well with ChatGPT’s capabilities. Our first prompt consisted of summarizing the text into titles or headlines as follows: Our second prompt consisted of determining a sentiment score on each headline. For the two prompts, we used the gpt-3.5-turbo version of ChatGPT. The overall idea of this two-step approach is to ease the task of ChatGPT and leverage its amazing capacity to make summaries and in a second step find the tone or sentiment. We can now devise an enhanced and more pertinent “Global Equities Sentiment Indicator” as follows: Definition 1. Daily Sentiment Score: Let us denote hi as the ith headline scanned from the daily news n and have two scoring functions that are consistent, a positive one p(hi) which returns 1 if hi is positive, 0 otherwise and a negative one n(hi) which returns 1 if hi is negative, 0 otherwise. The sentiment score S for a day with N headlines is given by: The sentiment score S measures the relative dominance of positive versus negative sentiments in a day’s headlines. It satisfies a couple of simple properties that are trivial to prove. Proposition 1. The sentiment score S satisfies some canonical properties: Boundedness: S is bounded as −1 ≤ S ≤ 1. Symmetry: If sentiments of all headlines are reversed, then S changes its sign. Neutrality: S=0 if there are equal numbers of positive and negative headlines. Monotonicity: S increases as the difference between positive and negative headlines increases. Scale Invariance: S remains the same if we multiply the number of both positive and negative headlines by a constant. Additivity: The combined S for two sets of headlines is the weighted average of the individual S values. Figure 1 shows the raw signal and highlights that the signal is very noisy. Using the raw sentiment score for daily news headlines of 10 results in noisy and less-interpretable results. To address this, we propose a cumulated sentiment score over a specified period. This score aggregates news sentiments over a duration, offering a more comprehensive measure of the news impact during that period. T. Figure 1. Raw Signal: It Exhibits Significant Noise. Definition 2. Cumulated Sentiment Score: We defined a monthly (d=20) Cumulative score as follows. Given: hi,t as the ith headline on day t. p(hi,t) and n(hi,t) as functions returning 1 for positive and negative sentiments of hi,t respectively, 0 otherwise. d as the duration (we use d = 20 business days, approximating a month). The cumulated sentiment score Sd over period d is: Figure 2. Cumulative Sentiment Score. The mathematical properties, that is boundedness, symmetry, neutrality, monotonicity, scale invariance remains for the Cumulated Sentiment Score. Figure 2 illustrates how the cumulated process diminishes the noise within the signal. Converting to an Investment Strategy Removing noise is key. Given the cumulated sentiment score (see definition 2), it is crucial to de-trend this score to identify more actionable trading signals. We compute the trend of the sentiment score by calculating the difference between the cumulated sentiment score and its

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