CFA Institute

Editor’s Note: This is the second in a series of articles that challenge the conventional wisdom that stocks always outperform bonds over the long term and that a negative correlation between bonds and stocks leads to effective diversification. In it, Edward McQuarrie draws from his research analyzing US stock and bond records dating back to 1792. CFA Institute Research and Policy Center recently hosted a panel discussion comprising McQuarrie, Rob Arnott, Elroy Dimson, Roger Ibbotson, and Jeremy Siegel. Laurence B. Siegel moderated. The webinar elicits divergent views on the equity risk premium and McQuarrie’s thesis. Subscribe to Research and Policy Center, and you will be notified when the video airs. Edward McQuarrie: My inaugural post on the equity risk premium presented a new historical account of US stock and bond returns that tells a different, more nuanced story than the account offered by Siegel in his seminal book, Stocks for the Long Run, now in its 6th edition. This blog series stems from my Financial Analysts Journal article, “Stocks for the Long Run? Sometimes Yes, Sometimes No,” which is open for all to read on Taylor & Francis. A reader of my first post objected to my conclusions, arguing that the 19th century US data presented was just too far in the past to be meaningful to investors today. I anticipated that objection at the end of my last post. Here, I refute that notion with the help of recent international data. New International Data is Available When Siegel began his work in the early 1990s, international market history was more terra incognita than 19th century US market history. In recent years, Elroy Dimson and his colleagues have shed light on historical returns. In 2002, they published Triumph of the Optimists, an account of 15 markets outside the United States, replete with historical returns on stocks and bonds dating back to 1900. The Dimson-led effort was not the only expansion of the international record. Bryan Taylor at Global Financial Data, and Oscar Jorda and colleagues at macrohistory.net, have also developed historical databases of international returns, stretching back in some cases to the 1700s. Indeed, many financial historians, including William Goetzmann, Editor of the Financial Analysts Journal, have spent entire careers digging into historical data to extract insights that shape our evolving understanding of markets and their role in shaping society. A few years after Triumph‘s publication, the Dimson team began to update and expand their database on an annual basis, producing a series of yearbooks, most recently the 2024 edition. Along the way, they’ve expanded the markets covered. Triumph had been criticized for survivorship bias, i.e., including only the markets that fared reasonably well and excluding markets that went bust, such as Russia in 2017 and those that fizzled, such as Austria after the war. Most important, the Dimson team began to calculate a world ex-US index of stock and bond performance, allowing a better assessment of the differences between US stock returns and returns elsewhere. None of this data had been compiled when Jeremy Siegel started out. I presented portions of it in my paper as an out-of-sample test of the Stocks for the Long Run thesis. The United States in Context The 120-year annualized real return on world stocks ex-US is now estimated by the Dimson team to be approximately 4.3%. Siegel estimated real long-term returns of 6% to 7%. That difference does not sound like much, but Dimson and colleagues note: “A dollar invested in US equities in 1900 resulted in a terminal value of USD 1937 … An equivalent investment in stocks from the rest of the world gave a terminal value of USD 179…less than a tenth of the US value.” We might say that international investors suffered a 90% shortfall in wealth creation. Regime Switching A key concept in my paper is the idea of regime switching, when asset returns fluctuate through phases that can last for decades. In one phase, bonds may perform terribly, as seen in the United States after World War II. In another phase, stocks may languish, as seen in the United States before the Civil War. Because returns are not stationary in character, it may not be useful to calculate asset returns over centuries and sum these up by offering one single number. In my view, there’s too much variance for one number to offer investors meaningful guidance, or to set expectations for what might happen over their unique horizons. The Range of Returns: the Good, the Bad, and the Ugly Here is an analogy to highlight the problem. Let’s say that the 100 students who attended my lecture this morning had their shoes ruined. The carpet cleaner last night used a solvent rather than the intended cleaning solution. This caused the carpet to lift in patches, which bonded to the students’ shoe soles. The University wishes to make amends by purchasing a new pair of shoes for each student. As an academic educated in statistics, I suggest to administrators that they simplify their task by buying 100 pairs of shoes all in the average shoe size, because the mean gives the best linear unbiased estimate. How many students will be happy with their new shoes? Returning to market history, what investors need to understand is the range of returns, not the all-sample average. Investors need to grasp how much returns can vary over long time horizons that correspond to the periods over which they might seek to accumulate wealth, such as 10-, 20-, 30-, or 50-year spans. The accepted approach for doing so is to calculate rolling returns. Thus, we can look at the set of 20-year returns: 1900 to 1919 inclusive, 1901 to 1920, 1902 to 1921, etc. Rolls allow us to examine how investors fared across all available starting points: the good, the bad, and the ugly. In my paper I looked at 20-, 30-, and 50-year returns for 19 markets outside the US, using data as far back as were available. First, however, we need to deal with an objection that quickly

In times of geopolitical tension, market volatility, and economic uncertainty, emotions can become a hidden liability for investors. The temptation to react, often hastily, can lead to decisions that erode long-term returns. Understanding and managing emotional biases isn’t just good practice; it’s essential to stay grounded when the headlines are anything but. Emotional biases aren’t new. Examples date back centuries and more recently have been well-documented by behavioral economists including the late Nobel laureate Daniel Kahneman. For example, when something happens in the stock market, we instinctively want to take action. We are overconfident. We have a fear of missing out. We confuse correlation with causation. And we’re enticed by tantalizing yet unattainable high expected returns. By understanding and learning from history, investors can avoid the emotional biases and mistakes that others have made. In light of the uncertainty dominating today’s markets and headlines, it’s worth revisiting some of the behavioral pitfalls that have tripped up investors for centuries, many of which I explore in my recent book, Trailblazers, Heroes, and Crooks: Stories to Make You a Smarter Investor. Emotional Bias #1: Feeling the need to sell when there is a large drop in the stock market When there is a large drop in the stock market, investors may feel the need to sell. Yet this is often the worst time to sell. Instead, a better strategy is known as “masterly inactivity,” or the art of knowing when not to act. It dates back to the Second Punic War (218–201 BCE), when Roman dictator Quintus Fabius defeated Carthaginian Hannibal Barca, one of the greatest military commanders in history. When Hannibal initially tried to engage Fabius in a battle, Fabius did nothing, biding his time until he was able to build up his army. In 1974, in Zaire, Africa Muhammad Ali used masterly inactivity in the form of his famous rope-a-dope strategy to defeat George Foreman in an epic boxing match known as the Rumble in the Jungle. In 1975, trailblazer Jack Bogle founded The Vanguard Group and introduced the first mutual fund index fund, designed as a long-term buy-and-hold vehicle. According to Bogle, “When you hear news that moves the market and your broker calls up and says, ‘Do something,’ just tell him my rule is ‘Don’t do something, just stand there!’” Let’s look at what would have happened if an investor panicked following large stock market drops. The worst 10 US stock market days occurred in 1987, 1997, 2008, and 2020. The one-day drops ranged from -20% to -7.0%. The median (or mid-range) daily loss was -8.9%. Panic selling would have locked-in these losses. Alternatively, how would masterly inactivity have played out? Over the subsequent 10 trading days, in seven of the 10 cases, the market was up, in one case the market was flat, and on only two cases the market continued downward. In both of those continued downturns, the market corrected soon after the 10 trading days. Overall, the average median short-term rebound was 5.5%. So, related to extreme negative events, on average, masterly inactivity pays off. Emotional Bias #2: Overconfidence in investing abilities Emotions and behavioral biases tend to lead to underperformance. A major bias identified by numerous studies is that investors tend to be overconfident in their abilities. Overconfidence can lead to excessive trading. In a classic study, academics Brad Barber and Terrence Odean from the University of California, Davis examined the discount broker accounts for more than 66,000 households between 1991 and 1996. While the overall market annual returns were 17.9%, those investors who traded the most underperformed by 6.5%. In 1998, Charley Ellis wrote the best-selling book, Winning the Loser’s Game. He used the analogy of amateur tennis players who tried to play like the pros but ended up losing. The same goes for investing. Instead of trading excessively and trying to beat the market, it can pay off to simply buy and hold an index fund. Emotional Bias #3: Fear of missing out One of the worst emotional reactions for an investor is FOMO or fear of missing out. It’s not a new investment phenomenon. It dates back at least three centuries. That’s when famed mathematician and physicist Sir Issac Newton made a huge gain in 1720 by investing in South Sea stock and sold out. He then watched the stock continue to rise, and afraid of what he was missing out on, got back in — right near the peak. He ended up losing the equivalent of millions of dollars today. As he purportedly observed, “I can calculate the motion of heavenly bodies, but not the madness of people.” More recently, many investors were burned by FOMO in meme stocks like GameStop. Once an investor sells a security, they shouldn’t look back. Emotional Bias #4: Assuming correlation implies causation Correlation doesn’t imply causation. This headline from The Washington Post, in 2021 is a good example of getting that wrong: “Cristiano Ronaldo snubbed Coca-Cola. The company’s market value fell $4 billion.”  At a European Football Championship press conference, Ronaldo proceeded to remove two bottles of Coke that were prominently displayed on the table in front of him. This was shocking because Coca-Cola was one of the tournament’s official sponsors. He replaced them with a bottle of water, saying, “Agua. No Coca-Cola.” But the stock price drop had nothing to do with Ronaldo. Rather, the stock fell as expected that day on a technicality, on its ex-dividend date. Here’s another example that shows correlation doesn’t imply causation. In 20 out of the first 22 Super Bowls, when an original NFL team won, the stock market was up that year, and vice versa when an AFC team won. That’s when the Super Bowl Indicator became huge news. But what could possibly suggest the winner of a football game could cause the outcome of the stock market in the subsequent year? Simple answer: nothing. Not surprisingly, since the Super Bowl Indicator appeared in the popular press, it’s been debunked. It’s been a virtual coin

Defined contribution (DC) plans, among other retirement savings vehicles, are the most common ways that US workers save for retirement. DC plan programs in the United States totaled $8.9 trillion in assets as of Q3 2022 and represent 22% of total retirement assets in the country. Plan sponsors thus have a tremendous responsibility to provide and manage retirement benefits on behalf of their employees. To help plan sponsors, we curated seven topics that we believe are top priorities for retirement programs in 2023. 1. Saving for Retirement: Lower for Longer Investment Expectations Setting aside the 2022 bear market for equities and most other fixed-income types, capital market assumptions about investment performance over 10-year and 30-year horizons are lower than their historical averages. All else equal, this implies that retirement savers need to save more to build their desired retirement nest egg. This is especially concerning for retirement savers who are unaware of the changing expectations or the resulting need to up their savings rates. Because retirement savers don’t always know about the dichotomy between past and expected future investment performance, plan sponsors should maximize their communications and prioritize educational methods that encourage increased savings rates. Two specific approaches have succeeded with our clients. The first is high-quality, one-on-one or group financial education. The second is assessing whether a plan’s automatic enrollment and automatic increase deferral percentages are set to appropriate levels given lower-for-longer investment expectations. Reviewing tools, such as retirement calculators, can also be useful to help ensure their settings reflect lower expected returns. 2. Examining the Investment Menu Review Process Creating and maintaining an investment menu that empowers plan participants to select and build a diversified investment portfolio is among DC plan sponsors’ most important duties. Reviewing the menus should be a regular, well-documented, and ongoing exercise — and not just during or following challenging years like 2022. In particular, we’ve noticed more plan sponsors want to reaffirm their target date fund (TDF) suite selection or consider a change. As participant demographics evolve over time, does the current TDF remain appropriate? That is a critical question to evaluate. We encourage plan sponsors to integrate guidance from the Department of Labor’s (DOL’s) “Target Date Retirement Funds — Tips for ERISA Plan Fiduciaries” into the review and document the process and outcome. We recommend regular reviews, at least every three-to-five years, and potentially more often when there are material changes to the composition or characteristics of the participant group or to the glide path or composition of the TDF. 3. Driving Employee Engagement through Plan Advocates/Plan Champions Labor trends and the war for talent are forcing employers to highlight the value and quality of theirretirement benefits. We work with clients to analyze how competitive their plans’ key features are within their industry. With that in mind, even the most competitive DC plan is only as effective as the degree to which employees engage with it. To bring more employees in, we recommend customizing messaging and communications based on their different knowledge levels and backgrounds. As the Baby Boomer generation nears retirement and Gen Z enters the workforce, workforce demographics are changing — and communication strategies need to adapt to stay relevant. We also encourage empowering “plan advocates” outside of the HR team who can help champion the plan to other employees. This works especially well when hiring managers are among the plan advocates. They can leverage their plan knowledge both in their recruiting efforts and to retain the teams they manage. One final note: Statistics show that not all demographic groups are benefiting equally from their DC plans. Better communication methods can help close that gap. Generic, one-size-fits-all messages won’t. Plan advocates with diverse backgrounds, experience, and career levels can help customize messaging in a way that resonates across the organization. 4. Delayed Retirements Due to 2022 Market Downturn The 2022 market downturn led some individuals to delay or consider delaying retirement. Those who chose to delay need to re-examine and re-affirm their asset allocation or TDF vintage. Industry surveys show that participants have a general misunderstanding about TDFs, particularly around equity risk at retirement age and the protection of principal. Plans sponsors need to clear up this confusion for those at or near retirement or who might be 10 to 15 years away from their planned retirement age. To this end, plan sponsors in 2023 should consider communications and participant education focused on planning for retirement. This education should familiarize participants with adjusting asset allocation based on expected retirement date, adequacy of savings, risk tolerance, and general financial planning, among other topics. Further, we believe this education is best delivered by unbiased, non-commissioned educators who are not driven by rollovers or commissions. The programs should be available at different times, including early morning and at night, to fit all employees’ schedules. These efforts together can not only help those near or at retirement get back on course; they can also improve employee morale over the long term. 5. Legislative and Regulatory Activity Congress and the DOL have been actively revising DC plan rules and regulations over the past couple of years. Late in 2022, President Joseph Biden signed the omnibus spending package, which includes the Setting Every Community Up for Retirement Enhancement (SECURE) 2.0 Act. The Act expands on SECURE Act 1.0 themes and concepts intended to expand retirement plan access and make saving for retirement easier for employers and employees alike. It also introduced provisions impacting plan distributions, among other initiatives. The Act has widespread implications for the industry and will increase many Americans’ saving potential. Some SECURE 2.0 provisions took effect on 1 January 2023. The required minimum distribution age rose to 73, for example. Other aspects, such as requiring automatic enrollment for new 401(k) and 403(b) plans, will start in 2025. Most plan sponsors are not required to amend the plan to comply with the Act until the end of the 2025 plan year. There is no doubt that plan sponsors will be focusing on the

That local mood affects local stock returns is a longstanding truism of the financial markets. Numerous behavioral studies back this up. When a sports teams loses, for example, the stocks of local firms tend to fall as well. Similar patterns have emerged around weather and election results. That is, sunny weather in a particular market is correlated with outperformance of the corresponding stocks, and equities associated with particular causes or candidates do well when elections seem to result in their favor. But what has the COVID-19 era revealed about this local phenomenon? Specifically, since 2020, have COVID-19 case counts had any correlation with stock returns in certain regions? To study this premise, we identified four sectors that are associated with specific geographies. We homed in on the communications, energy, technology, and finance industries and the corresponding US regions they are often associated with: Los Angeles, Houston, the San Francisco Bay Area, and New York City, respectively. We used exchange-traded funds (ETFs) as rough proxies for each industry and region, with the Communication Services Select Sector SPDR Fund (XLC) standing in for Los Angeles/communications, the Energy Select Sector SPDR Fund (XLE) for Houston/energy, the Technology Select Sector SPDR Fund (XLK) for the Bay Area/tech, and the Financial Select Sector SPDR Fund (XLF) for New York City/finance. In each sector/region, we looked at how the case count in that particular metropolitan area correlated with returns in the associated industry from February 2020 through February 2022.  So, what did we find? Median Weekly Abnormal Returns Sector/Region Low COVID-19 Case Count25th Percentile and Below High COVID-19 Case Count75th Percentile and Above Communications (Los Angeles, XLC) 0.0017 0.0001 Energy (Houston, XLE) –0.0108 0.0217 Technology (San Francisco Bay Area, XLK) 0.0046 –0.0015 Finance (New York City, XLF) –0.0006 –0.0026 Across the four areas, we did not identify any major difference in abnormal returns in either a high or low COVID-19 case month across the full two years of data. But the worst month for COVID-19 case counts was a different story. In the months where COVID-19 cases were at their highest, there was a negative correlation between cases and returns. In other words, as the case counts spiked in these regions, the prices of the ETFs associated with the local industry fell. Highest Case Month: Correlation between Stock Returns and Cases Communications (Los Angeles, XLC) –0.049 Energy (Houston, XLE) –0.572 Technology (San Francisco Bay Area, XLK) –0.050 Finance (New York City, XLF) –0.231 Our results suggest that only the worst COVID-19 months had an effect on returns in localized areas and industries. In particular, as cases spiked in Houston, XLE prices plummeted. Of course, correlation is not causation, and the financial performance of these industries and regions is hardly explained by any one single variable.  Nevertheless, the results suggest that COVID-19 may have had an outsized effect on localized returns — but only when the local case counts were sufficiently high. If you liked this post, don’t forget to subscribe to the Enterprising Investor. All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer. Image credit: ©Getty Images/Avalon_Studio 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

“There can be few fields of human endeavor in which history counts for so little as in the world of finance. Past experience, to the extent that it is part of memory at all, is dismissed as the primitive refuge of those who do not have the insight to appreciate the incredible wonders of the present.” — John Kenneth Galbraith Who are the greatest investors of all time? Andrew Mitchell, founder of Ophir Asset Management, recently asked ChatGPT to name the top 10. The AI responded with the list below, which the manager then posted to LinkedIn. It prompted a lively discussion. I was intrigued by both the question and ChatGPT’s response. I’d just finished the manuscript for Investing in U.S. Financial History, and so many legendary investors were on my mind. While ChatGPT’s list was not terrible, it included four individuals who I believe were undeserving and excluded several more who were very much worthy. So where did ChatGPT go wrong? There were four problems in my view. First, by only including US men with 20th- and 21st-century track records, ChatGPT displayed three biases: nationality, gender, and recency. It also did not explain its selection criteria. In fairness, Mitchell did not ask for ChatGPT’s rationale, but the lack of transparency still presented a problem. ChatGPT’s List of the Greatest Investors 1. Warren Buffett 2. Peter Lynch 3. Benjamin Graham 4. George Soros 5. Ray Dalio 6. Jim Simons 7. Philip Fisher 8. John Paulson 9. Charlie Munger 10. Jesse Livermore The absence of standard criteria got me thinking about the fundamental factors that differentiate the best investors of all time. To my mind, the first criterion must be the duration of the individual’s investment track record. Given the ruthless and ever-increasing efficiency of securities markets, only investors with persistent success over an extended period warrant consideration. Further, to ensure that skill rather than luck drove that outperformance, they ought to have excelled in different market environments. A track record that depended upon a few windfalls is not enough to qualify. This initial screen disqualifies Jesse Livermore, John Paulson, and Peter Lynch. Livermore’s career ended in bankruptcy in the wake of the Great Crash of 1929. Paulson made billions in the global financial crisis (GFC) but has had mixed results since. Lynch’s heyday lasted only 13 years or so, and his strategy benefited from a strong tailwind thanks to prevailing market forces of the day. Finally, I had to exclude Philip Fisher. While my knowledge of Fisher’s techniques is more limited, his name struck me as the least compelling left on the list, and room had to be made for J. Pierpont Morgan. Timeless Investing Virtues So, why have the other individuals identified by ChatGPT earned their positions? And who should occupy the three spots that are still open after the addition of Morgan? I selected individuals based on the assumption that great investing depends on four key premises. The first is that the only way for investors to achieve sustained outperformance relative to the market and their peers is if they have a unique ability to uncover material facts that are almost completely unknown to everybody else. Second, once such investors act on these facts, they must often hold unpopular positions for a long time before they realize a profit. Third, they must sustain their competitive advantage as markets evolve. Finally, the rarest talent among the greatest investors is creating a legacy and passing their talents on to the next generation. The best investors in US history all meet the first three requirements, but only a very select few have achieved the fourth. What follows are my revisions to ChatGPT’s rankings. The brief summary of each investor’s qualifications is also accompanied by a distinct virtue in which they excelled. An important caveat is that the proposed revisions to ChatGPT’s selections suffer from some of the same limitations: They are US-centric and overwhelmingly male. For this reason, this is more a list of the best investors in “US history.” Nevertheless, this list helps explain why truly exceptional investors are such rarities. 1. Discovering Hidden Truths The wisdom of crowds is the most underappreciated principle in investing. It explains why securities markets are so unforgiving and why just about all investors should stick with traditional asset classes and index the vast majority of their portfolio. Still, some individuals do outperform market indexes and peers by uncovering truths that are overlooked by almost everybody else. Virtues that assist them in this effort include skepticism, persistence, and creativity. Charlie Munger: Skepticism “Invert, always invert: Turn a situation or problem upside down. Look at it backwards.” — Charlie Munger Unearthing valuable, unseen facts is only possible when we question conventional thinking. Charlie Munger elevates this quality to an art form by using the practice of inversion. His 13 June 1986 commencement address at the Harvard School in Los Angeles demonstrates this. Rather than advise graduates on how to achieve success, Munger turned things upside down and discussed what vices they could embrace if they wanted to live a miserable life. He suggested being unreliable in relationships, refusing to learn from the mistakes of others, and always giving up in the face of adversity. Rather than tell the graduates what to do, he told them what not to do. Munger applies the same inversion techniques in his evaluation of investments and credits many of his best decisions to his willingness to examine problems from an unconventional perspective. Recommended Reading: Poor Charlie’s Almanack by Charlie Munger Ray Dalio: Persistence “There is almost always a good path that you just haven’t figured out yet, so look for it until you find it rather than settle for the choice that is then apparent to you.” — Ray Dalio Former Bridgewater Associates CIO Ray Dalio generated consistent outperformance over nearly three decades, a feat even more impressive when adjusted for risk and fees. Core to Dalio’s achievements was his relentless and often painful pursuit of truth. This forced Bridgewater’s investment teams to confront uncomfortable

Quebec — La Belle Province — has experienced a significant uptick in mergers and acquisitions (M&A) deal activity among small-cap companies since early autumn. To date, private equity firms and strategic investors have acquired several Quebec-based companies at healthy premiums. What do they know that other investors don’t? For some time, my colleagues and I have been beating the drum in our commentaries and webinars about the value that the current gulf between the intrinsic value and market prices of some of these Quebec-based companies represents. There are appealing risk/reward attributes and the potential for high future returns at bargain prices. The list of recent transactions spans sectors and industries from semiconductors (OpSens) to water treatment (H2O Innovation) and marine terminals (Logistec). Why the sudden interest from investors? Two key drivers have propelled the surge in dealmaking, and we don’t anticipate them easing up anytime soon. 1. Mind the (Valuation) Gap The divergence between small- and large-cap companies reached historic levels. In November 2023, the S&P 500 was up 17% for the year compared with the Russell 2000, which had only risen 2%. Investors noticed the difference and the premium underlying it. 2. Buyer, Meet Seller Pent-up demand created a more favorable match-up between motivated buyers and sellers. Private equity funds have $2.5 trillion in dry powder, and sellers are slowly realizing that it’s 2023, not 2020, and company valuations should be adjusted accordingly. Indeed, frustrated shareholders have increasingly taken an activist stance and called on company boards to unlock value at the current market price. Investors have capitalized on this environment. For example, in the completed acquisition of Magnet Forensics and current offers for H2O Innovation and Q4 Inc., private equity–led management buyouts and insiders rolled their interest into the privatized company. Aimia Inc. is also in the midst of a hostile takeover from its largest shareholder, Mithaq Capital, amid a contentious battle among insiders. Such conditions constitute a favorable environment for small-cap-focused equity funds. Companies are trading at deep discounts to their intrinsic or private market value. This presents a favorable tailwind for arbitrage funds since M&A activity in the small-cap universe tends to drive performance in this space. Several additional market dynamics make small-cap M&A particularly compelling right now and particularly in Quebec: Smaller companies have a larger pool of potential suitors, including strategic buyers, management buyouts, private equity funds, pension/sovereign funds, and industry consolidators. The end-market for small-cap businesses is often domestic or transborder. Amid geopolitical uncertainty and governments promoting reshored supply chains, these are appealing characteristics. It’s not 2021 when it comes to financing conditions either. Borrowing rates are much higher and large-cap mergers and leveraged buyouts (LBOs) require large syndicates of financiers. Smaller acquisitions are easier to finance with cash on hand and more flexible funding options. Many companies that went public in 2020 and 2021 are trading well below their initial public offering (IPO) price. Even with positive growth and good fundamentals, many of these businesses will find it challenging to gain new public market investors because of anchoring bias, among other reasons. Once bitten, many investors are twice shy. These companies can be attractive insider buyout targets. The regulatory environment in both Canada and the United States is more restrictive when it comes to mergers. Smaller mergers may avoid the regulatory pushback. In the current economic environment, well-heeled strategic buyers looking to leverage scale and synergies by acquiring competitors have more leeway to negotiate favorable conditions. While these conditions may not be unique to Quebec, recent M&A activity suggests the province has more than its share of opportunities. We believe investors should pay attention. If you liked this post, don’t forget to subscribe to Enterprising Investor and the CFA Institute Research and Policy Center. All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer. Image credit: ©Getty Images / naibank 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

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