CFA Institute

We’re witnessing a dramatic transformation of the US utility sector, driven largely by climate change and the swift advancement of technologies such as artificial intelligence (AI). Rising infrastructure costs and the push toward renewable energy are shaking up traditional investment models that depend on fossil fuels. Institutional investors face potential harm to their reputations caused by the slow adoption of climate risk measures and a fall in coal asset values. This uncertainty casts a shadow over dividend stability, pushing investors to seek higher returns and driving up capital costs. At the same time, utility companies are being asked to provide more clarity on sustainability in their climate risk reports. They have an obligation to build resilience against climate impacts and secure their long-term financial sustainability. AI to the Rescue: The Path to Net Zero The path to attaining zero emissions by 2050 calls for a daunting overhaul of the global power grid, with the cost now estimated to be about $21 trillion. However, energy transition faces a complex web of regulatory and financial obstacles. Electricity grid operators in the United States have begun to use AI and other digital tools to analyze vast amounts of data and tackle complex problems. This is a practical alternative to overhauling the entire electricity grid infrastructure. Through public and private funding, it offers a financially feasible pathway to achieve net-neutral targets by 2050. Over the next 25 years, AI and other digital strategies will be deployed to substantially reduce the cost of revamping the US Power Grid. Integrating AI into the grid is critical for precise power forecasting and agile responses to challenges like equipment malfunction and fluctuating weather patterns. Regardless of the evident improvements in system reliability brought about by the integration of AI, broadening its application for all-encompassing control over the grid continues to confront resistance from traditional utilities and governing entities. Leaders in the US utility sector face several complex challenges including aged infrastructure, tighter regulations, and a broader shift to a digital, environmentally conscious economy. As they rise to these challenges, they will help mold an evolving operating environment. Reliable data regarding utility firms’ investment in AI and other digital tools for climate risk mitigation is sparse. But there is a significant rise in AI and machine learning applications in various operations in the sector. The US federal government, aware of AI’s potential to minimize costs and enhance efficiency, has taken decisive steps. The Department of Energy has committed $3 billion for AI-centric smart grid programs, for example. AI is a powerful tool for managing grid operations, providing real-time data and predictive analytics, and expediting routine planning tasks. Importantly, AI also lends a hand in estimating power interruptions by evaluating weather patterns and demographic data. AI also optimizes the physical maintenance of the grid, enabling utility companies to orchestrate infrastructure supervision efficiently and plan timely repairs. This growing reliance on AI underscores its pivotal role in the journey to update and administer the US power grid. Regulatory Spearheads Key regulatory bodies such as the North American Electric Reliability Corporation (NERC), the Federal Energy Regulatory Commission (FERC), and various Public Utilities Commissions (PUCs) are spearheading the transition to renewable energy. Their role is quintessential in sanctioning the deployment of digital technologies like AI in the utility sector, simultaneously scrutinizing cost-effectiveness, openness, and the potential impact on end consumers. Playing a pivotal role in the incorporation of AI to mitigate emissions is the National Energy Technology Laboratory (NETL). The NETL operates under the auspices of the Department of Energy and is dedicated to introducing improved technologies related to coal, natural gas, and oil that are in harmony with sustainability goals and climate resilience. No Walk in the Park Transitioning to renewable energy in the utility sector isn’t a walk in the park. The quest to ditch fossil fuel dependency faces opposition to rate increases and water shortages. These are reasons why embracing novel ideas to meet sustainability goals and improve grid robustness is crucial The economic repercussions of climate change are clear. The bankruptcy of Pacific Gas and Electric Company (PG&E) is just one example. The primary cause of the utility’s downfall was the enormous financial burden caused by 2019 wildfires. Natural disasters such as these underscore the need to integrate AI and other digital technologies as strategic measures to mitigate the effects of climate change.  In response to PG&E’s staggering $30 billion in wildfire-related liabilities, California orchestrated a novel wildfire insurance policy. The innovative approach involved the creation of a $21 billion fund and stipulated a compulsory $5 billion investment toward safety by utilities, highlighting the gravity of these expenses. Notably, the policy allows for the disruption of power supply as a preventive measure against wildfire threats. This, of course, presents its own set of complexities, particularly for vulnerable sectors of the population.  The marketplace tends to assume that ratepayers and insurers will shoulder the burden of costs associated with climate-related disasters. But, because climate threats are inherently unpredictable, calculating the risk is tricky. PG&E is participating in a pilot program through EPRI Incubator Labs that illustrates the future of AI-powered wildfire detection. The technology integrates data from various channels that include live camera broadcasts and satellite imagery to detect fires and prevent potential devastation. The growing adoption of AI in the utility sector is a striking contrast to 2019, when the absence of advanced technologies resulted in considerable loss of life in California and significant financial costs to investors in PG&E. The incorporation of AI serves as a turning point in PG&E’s commitment to boosting the safety and effectiveness of operations. The Changing Face of Utility Stocks Investors’ perspective on utility stocks in the United States has been shifted by the growing frequency of climate disasters. Once known as secure and profitable investments due to their rich dividends, utilities are now viewed as enterprises fraught with financial risks. Investors should favor utilities that employ AI and other digital strategies to minimize damage from natural disasters. The case of Hawaiian Electric, which is

Tariffs have reclaimed the economic spotlight. But with their timing and magnitude uncertain, investors are on edge. A fascinating history of tariffs and their effects on investment returns is provided by Baltussen et al in a recent Enterprising Investor blog. This blog takes a complementary approach to exploring their possible implications for returns. Tariffs change relative prices. Just as large changes in oil prices pushes up energy costs compared to other goods, tariffs make imports relatively more expensive. In economics’ parlance, tariffs are “supply shocks.” And because price adjustment is costly to firms in the short run, import prices rise in response to large tariffs while other prices don’t immediately change despite possibly softening demand (see Romer 2019 for the modern macro explanation of “nominal rigidities”). This causes the average price level to rise. That is, tariffs cause the headline (all items) inflation rate to go up. This post offers a framework for thinking about the effect of tariffs on major asset class returns by estimating asset classes’ response to supply shocks. By separating inflation’s “signal,” or trend component (determined by fundamental forces) from its shock-driven “noise” component, we can estimate the past response of major asset classes to the latter. This may suggest lessons about the possible response of asset classes to one-time tariffs. Quantifying Inflation Shocks Using Core and Median CPI Economic theory and a little analysis allow us to guess at how asset classes might respond to the inflation-shock effect of tariffs. As for theory, modern macroeconomics describes inflation using a “Phillips curve” framework, named after the economist who first noted that economic slack and inflation were negatively related (Phillips used unemployment and wages). Phillips curves can be specified in various ways. Generally, they explain inflation with three variables: inflation expectations (consumer, business, or professional forecaster), an output gap (for example, the unemployment rate or the vacancy-to-unemployment ratio), and a shock term. This blog uses a Phillips curve approach to separate inflation’s signal or trend, driven by inflation expectations and the output gap, from noise or the fleeting factors that come and go. This sidesteps two issues: that tariff shocks pass through to trend inflation by raising inflation expectations and costs of production as well as other channels. There is in fact already evidence that consumer inflation expectations are rising. Incorporating these effects would make this analysis considerably more complicated, however, and so they are ignored for now. The Phillips Curve tells us that we can decompose inflation into trend and shock components. Typically, this is done by subtracting the trend in inflation from headline (all items) inflation. This blog instead uses the median consumer price index (CPI) inflation rate as calculated by the Federal Reserve Bank of Cleveland as its proxy for trend inflation because of median CPI’s attractive properties.[1] And instead of using headline CPI inflation as its starting point, it uses core CPI inflation, which excludes food and energy (XFE CPI). XFE CPI is preferred because the difference between XFE and median CPI yields a measure of shocks purged of large changes in the relative price of food and energy. This measure is referred to as “non-XFE shocks.” The charts in the panels of Exhibit 1 give a sense of the frequency and size of non-XFE shocks. The scatterplot shows monthly XFE versus median inflation. When they’re equal, points lie on the 45-degree line. Pairs above the 45-degree line are positive non-XFE shocks and vice versa. (The R-code used to produce charts and perform analysis presented in this blog can be found on an R-Pubs page). The histogram shows the distribution of these shocks. Large disturbances are rare. Exhibit 1. Top panel shows median vs. XFE CPI from 1983 to 2025:3. Bottom panel shows the distribution of the shocks (the distance from the 45-degree line in the top panel); frequencies for each of the 11 “bins” appear on the bars. Source: FRED Asset-Class Sensitivity to Inflation Surprises Having defined non-XFE shocks, we can estimate how major asset classes have responded to them. This may provide a preview of how these asset classes might react to inflation shocks resulting from tariffs. Relationships are estimated in the customary way: by regressing asset-class returns on non-XFE shocks. The resulting estimated coefficient is the left-hand-side variable’s non-XFE shock “beta.” This approach is conventional, and mirrors that taken in my Enterprising Investor blog Did Real Assets Provide an Inflation Hedge When Investors Needed it Most? Regressions use monthly percentage changes for non-XFE shocks as the right-hand side variable, monthly returns for the S&P 500 total return (S&P 500) index, Northern Trust Real Asset Allocation total return (real assets) index, Bloomberg Commodities Total Return (BCI) index, Bloomberg TIPS index, and 1–3-month Treasury bill return (T-bills) index as dependent variables. Inflation data comes from FRED and index returns from YCharts. Because sample size varies by asset class regressions are run over the longest available sample period for each asset class, which ends in March 2025 in each case. One caveat before discussing results. Non-XFE shocks could be due to any large relative price change, except of course changes in food and energy. That is, supply shocks include more than supply-chain shocks. Unfortunately, there’s no obvious way to isolate the disturbances we’re most interested in using public inflation data. But since we can’t know exactly what form such tariff-induced inflation disturbances will take, an examination of asset class response to non-XFE shocks is a reasonable place to start. With that said, results are shown in Exhibit 2. Exhibit 2. Regression results. Dep. variable TIPS BCI T-bills S&P 500 Real assets   Begin date 1998:5 2001:9 1997:6 1989:10 2015:12   Non-XFE shock “beta” 0.545 4.440* -0.248*** 2.628 1.365   95% CI (-1.191, 2.280) (-0.585, 9.465) (-0.432, -0.064) (-1.449, 6.704) (-4.015, 6.745)   Observations 323 283 334 426 112   R2 0.001 0.011 0.021 0.004 0.002   Notes: *p<0.1; **p<0.05; ***p<0.01; standard errors are adjusted as indicated by residual behavior. Sources: FRED, YCharts, Author’s regressions. A positive, significant estimate for the “non_xfe_shock” coefficient suggests that an asset class hedges against non-XFE

“It often happens that a player carries out a deep and complicated calculation, but fails to spot something elementary right at the first move.” — Alexander Kotov, Chess Grandmaster Introduction The FX impact on corporate earnings and guidance should be front of mind for both corporates and the analyst community. Indeed, more than 45% of revenues in S&P 500 companies originate internationally. But last year, the hedging performance of many US multinational corporations (MNCs) was well off the mark, and few CFOs explained their hedging decisions on earnings calls. Why such poor hedging performance? After all, treasury management system (TMS) providers claim to offer “push-button” capabilities for limiting the FX impact within $0.01 of earnings per share (EPS). The answer may not be as elusive as some of us may imagine. Though hedging earnings has its challenges, including exposure estimation and accounting-driven issues, very few corporates actually hedge earnings risk to the consolidated income. Around 60% of companies cite earnings volatility mitigation as a key risk management objective, but less than 15% actually hedge their earnings translation exposure, according to a Citibank survey. This raises an intriguing behavioral finance question: Could the varied financial accounting treatments of hedging transaction risk at the subsidiary level and translation risk at the consolidated income level be unduly influencing prudent decision making, resulting in a transference of financial accounting to mental accounting? Key questions to consider include: Are CFOs and corporate treasurers making effective hedging decisions? Are they substituting expediency for substance, making decisions based on financial accounting considerations? Is there too much career risk in putting on fair value hedges? On a broader level, how beneficial is it to categorize FX risk? Is it counterproductive to pigeon-hole FX exposures in neat boxes — transactional, translational, or structural? The Fungibility of FX: One Risk, Three Forms FX’s fungibility is easy to underestimate. For example, to better match client revenue to production costs, EU-based firms can reduce their structural risk by relocating production facilities to the United States. But they will just be substituting one core risk for another: transactional for translational. Moreover, if a subsidiary reinvests its earnings instead of upstreaming dividends to its parent, then the unrealized transactional risk over the corresponding will accumulate to match the translational risk to the consolidated income. The difference between transactional and translational risks is not fundamental but an issue of timing. Hedging vs. Accounting Accounting rules provide for three types of hedges: fair value, cash flow, and net investment hedges. Fair value hedges result in the recognition of derivatives gains or losses in the current-period income statement. With cash flow and net investment hedges, current-period derivatives gains or losses are deferred through other comprehensive income (OCI), which is recorded on the shareholders’ equity section of the balance sheet. Under IFRS, intercompany dividends can only be transactionally hedged once they are declared. This provides protection for the period between the declaration and payment, which is usually too short to significantly reduce the risk. If corporates are more inclined to execute cash flow hedges rather than fair value hedges — which can cover longer periods under an estimated exposure but must be dragged through the income statement — then adverse FX impacts should not come as a surprise whenever macro conditions deteriorate or during bouts of rapid USD appreciation.  There are accounting hacks: One way corporates address unfavorable accounting treatment around earnings hedges is to classify them as net investment hedges whenever possible, since they have similar recognition mechanics as cash flow hedges. Through holding companies or regional treasury centers, some MNCs deploy such accounting-friendly solutions to manage genuine timing issues, which can also potentially incorporate economic and structural hedges. Despite such methods, the broader questions remain: Why are publicly traded companies “routinely” blindsided by FX volatility? Do financial accounting rules influence hedging decisions? Do corporate treasurers and CFOs tend to avoid fair value hedges and, in the process, overlook earnings exposures? Is the tail wagging the dog? While the topic may receive limited attention in academia, sell-side practitioners catering to corporates know that accounting considerations often have an outsized influence on the types of “accounting exposures” that are hedged. Boardroom Dynamics: Holding the CFO Accountable Boardrooms need to do a better job of holding CFOs accountable. All too frequently, discussions regarding FX’s impact on EPS tend to trade the prosaic for the poetic. No asset class is better than FX for rhapsodizing on all things macro — from fundamentals, flows, institutional credibility, to geopolitical dynamics — but the elemental questions underlying the rationale for what is being hedged (or not hedged) are seldom, if ever, posed. Similarly, debates on technology can become a canard that distracts from the underlying issues. While firms need systems that “talk to each other” and provide gross and net exposures across the company, flawless visibility is not a panacea in and of itself. As Laurie Anderson put it, “If you think technology will solve your problems, you don’t understand technology — and you don’t understand your problems.” Smart hedging policies address a firm’s level of risk aversion relative to its market risks. A firm’s choice of risk measures and benchmarks is intricately linked to its specific circumstances: shareholder preferences, corporate objectives, business model, financial standing, and peer group analysis. “Know thyself” is a useful precept in this regard. For instance, if an MNC in the fast-moving consumer goods (FMCG) industry wants to maximize earnings while preserving its investment grade rating, then consolidated earnings-at-risk (EaR) ought to be among the appropriate risk-based measures. It’s essential that the right risk measures and benchmarks are pursued, regardless of accounting considerations. Conclusion To summarize, effective corporate hedging begins with understanding FX’s fungibility: Risk cannot be “categorized” away. Furthermore, there is no substitute for thoughtful hedging policies and selecting performance indicators that define success and ensure consistent interpretation and pricing of risk across the firm. These policies must also address the tension between the core hedging objectives and financial accounting considerations. If you liked this post, don’t forget to subscribe to Enterprising Investor. All posts are the opinion of the author. As such, they

Buffett’s Early Investments: A New Investigation into the Decades When Warren Buffett Earned His Best Returns. 2024. Brett Gardner. Harriman House. I became aware of Warren Buffett in the early 1980s when a graduate school classmate encouraged me to read John Train’s The Money Masters. At the time, Buffett was unknown to the public and even to many in the business community. Some four decades later, perhaps more has been written about him than any other businessperson or investor. The writings include biographies by journalists, friends, and former employees. There have been books detailing his investment strategies and words of wisdom, as well as magazine and academic journal articles. The question is, what can Brett Gardner offer about Buffett’s investments that has not been written before? Fortunately, Gardner, a value investor and analyst at Discerene Group, a private investment partnership, has taken a different path from the authors of other investment books. Rather than scour through Buffett’s shareholders’ letters at Berkshire Hathaway, he digs into Buffett’s early, pre-Berkshire investments. The result is a fresh look into the origins of Buffett’s investment approach. We have previously read about Buffett’s transformation from a value investor who picked investments simply because they were cheap, “cigar butt” investing, to an investor who sought out great businesses at fair prices. Gardner takes us through this journey by examining 10 stocks from Buffett’s early investment years. Of the 10, only American Express and Disney are household names. Most others are likely little known to even the most devoted Buffett followers. The book is divided into the Pre-Partnership Years and the Partnership Years, with each section highlighting five stocks. In attempting to provide a deeper understanding of Buffett’s methods, Gardner takes a unique approach to glimpsing into Buffett’s mind. Rather than simply looking for clues in his words, Gardner uses financial information available to Buffett when he made the investments. Three criteria drove the author’s choice of the 10 investments he selected. First, could he obtain the relevant financial documents, such as Moody’s Industrial Manual and company annual reports? Second, he wanted to add value by not rehashing investments that had been widely written about. Finally, how interesting was the story behind the investment? Did its price embed misconceptions that he could correct? Gardner begins with Buffett’s 1950 purchase of Marshall-Wells Company, North America’s largest hardware wholesaler. Going back in time, Gardner pulls information from Moody’s manuals and tries to discern the value in Marshall-Wells that Buffett might have perceived. Gardner asks, “Why did Buffett invest in the company?” In his early years as an investor, Buffett focused on Benjamin Graham’s philosophy of seeking cheap stocks. Marshall-Wells’s valuation metrics, e.g., P/E and EV/EBIT, which are presented in the book, likely piqued Buffett’s interest in Marshall-Wells, and the fact that its hard assets offered downside protection and a margin of safety. Although the company would struggle and eventually be acquired, Gardner points out that investors who bought the stock at Buffett’s purchase price likely earned respectable returns. As the author moves through the Pre-Partnership Years, we get a glimpse into the model that Buffett would follow in transforming Berkshire Hathaway from a New England textile firm into one of America’s largest conglomerates. The lesson comes from Micky Newman, the son of Benjamin Graham’s partner Jerome Newman. The 1954 purchase of shares in Philadelphia and Reading Railroad (P&R) was the beginning of a model Buffett would follow of using cash from a moribund company to acquire profitable businesses. Newman, who later became P&R’s president, used the cash from liquidating inventories at P&R for such acquisitions. He preferred businesses where management would stay on to run the subsidiaries, a hallmark of Buffett’s acquisitions with Berkshire. One of the more interesting investments is Buffett’s purchase of American Express shares in 1964. The chapter begins with an entertaining look at the famous Salad Oil Scandal, which provided an opportunity to purchase American Express at a compelling price. Although Gardner does not have much information about Buffett’s thinking, he attempts to piece together Buffett’s logic in acquiring American Express. The biggest concern for investors was the salad oil liability. Going beyond simply purchasing the stock because it was cheap, Gardner points out, Buffett recognized the importance of American Express’s reputation. To determine if the scandal impacted American Express’s core businesses of Travelers Cheques and credit cards, he surveyed local restaurants to gauge credit card usage. Buffett even contacted American Express CEO Clark to praise him for honoring the subsidiary’s liabilities rather than using bankruptcy to divest the problem. This appears to be the beginning of Buffett’s evolution from a passive investor to an activist shareholder. In Buffett’s Early Investments, Gardner dispels the myth that Buffett succeeded simply by sitting in a room with Moody’s Industrial Manuals. Buffett’s analysis went well beyond the financials. His purchase of Studebaker presents an example of his hands-on approach to investing. Studebaker, an automobile company successful enough to be included in the Dow in 1916, had fallen into hard times. In 1965, the company’s single-digit price-to-earnings ratio and tax-loss carryforward made the stock intriguing to Buffett. At the time, Studebaker had 10 divisions, but Buffett and Sandy Gottesman, founder of First Manhattan, believed that the STP motor oil additive was the most important. To estimate the demand for STP, Buffett traveled to Kansas City to count railcars of STP. In another example of Buffett’s exhaustive leg work, he and Charlie Munger used family visits to Disneyland to evaluate the profitability of rides. The book is not just about Buffett’s successes but also looks at less successful ventures such as Cleveland Worsted Mills Co. and retailer Hochschild, Kohn & Co., which produced lessons that shaped Buffett’s investment philosophy. Complementing his meticulous analysis, Gardner writes in a fluid and engaging style that makes Buffett’s Early Investments an enjoyable read, even for those who may not wish to delve deeply into Buffett’s strategies. His insights into companies like Disney make his historical overviews well worth the read. Examining Buffett’s early

Market risk is the potential for losses in securities due to fluctuations in market factors like interest rates, currency values, FX/commodity spot rates, and equity prices. These risks are inherent in all traded securities, from corporate bonds to commodities. Each type of security may face multiple risks simultaneously, making market risk a crucial consideration for investors and financial institutions. Compounding these risks is model risk, which refers to the risk inherent with the development and usage of a model to determine financial outputs and decision making. An inefficient or incorrect modelling technique can sometimes lead to drastic repercussions for the firm. Understanding and managing this risk is therefore essential for making informed financial decisions and safeguarding against potential losses. More on Market Risk Various risk factors in the security’s structure determine the type and extent of the market risk it carries. The most widely studied and observed market risk types include interest rate risk, credit risk, foreign exchange risk, equity risk, and commodity risk. A single security can exhibit just one or more of these risks. A corporate bond, for example, exhibits not just credit risk but also interest rate risk, and if it is denominated in a foreign currency, it also carries FX risk. Broadly, we can think of market risk as the fluctuation in the value of a security due to the market-related risk factors such as interest rates and equity price movements. However, it has far-reaching impacts since these security valuations are utilized to make more decisions such as investments, regulatory compliance, and portfolio optimization, among others, depending on the profile of the company or risk manager. More on Model Risk A model has various components, namely the inputs/data, assumptions, logic/process, and final output. An inefficient or incorrect modelling technique along any of these process components can sometimes lead to drastic repercussions for the firm. The SR11-7 regulatory framework defines how model risk should be managed by banks, and it is relevant for other financial firms. Market Risk and Model Risk: Dependencies Although market and model risk represent different dimensions of riskiness, they are interweaved in a sequential way. This is evident since quantification or determination of market risk by a firm and all resulting decisions are usually represented as an output of financial models. Whenever corporate managers are focused on managing market risk proficiently, the process involves managing model risk equally efficiently. Thus, it makes sense to view these two risks in conjunction with each other when estimating costs, time, and resources to manage a firm’s investment -or market-related risks. An example would be the use of a financial model to determine the value of a securities portfolio which in turn would determine a buy/sell decision. If the valuation model makes incorrect assumptions by not considering diversification/hedging effects in the portfolio, this might lead to incorrect decision making which may lead to not just financial impact for the firm but also reputational and regulatory risks. Model risk is a crucial risk that needs to be managed effectively by financial institutions, not just to ensure sound market risk management decisions or comply with regulatory requirements but also to survive and thrive. In cases in which firms use third-party vendors for pricing and valuations, model risk is compounded because most vendors also use models to determine their numbers. In such cases, clients must conduct due diligence to ensure third-party vendor models are validated and/or audited. Regulatory Use Case The Fundamental Review of Trading Book (FRTB) is a market risk regulatory framework with a lot of quantitative techniques enlisted by the regulator to quantify market risk carried on banks’ trading books in the form of capital charges. One crucial change in this regulatory framework is a shift from existing value at risk (VaR) based techniques to expected shortfall-based market risk metrics calculations. This shift requires modifying existing market risk models or in some cases rebuilding these from scratch to efficiently carry out these FRTB customized calculations. This gives rise to a wide amount of model-related risk from new assumptions, input data, modifying codes/software programs, and output metric customization. If FRTB model assumptions are changed, the capital charge numbers may vary considerably. Application of this framework to manage market risk more efficiently introduces extra costs and complexities to manage model risk inherent in new or updated custom models to carry out these FRTB specific calculations. Key Takeaway Risk managers must look at market and model risk through a single lens to see the complete picture of their market-related investment and trading risks, as well as management costs, complexities, time, and regulatory requirements. References [1] https://www.bis.org/bcbs/publ/d457.htm [2] https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm source

Risk is not simply a matter of volatility. In his new video series, How to Think About Risk, Howard Marks — Co-Chairman and Co-Founder of Oaktree Capital Management — delves into the intricacies of risk management and how investors should approach thinking about risk.  Marks emphasizes the importance of understanding risk as the probability of loss and mastering the art of asymmetric risk-taking, where the potential upside outweighs the downside. Below, with the help of our Artificial Intelligence (AI) tools, we summarize key lessons from Marks’s series to help investors sharpen their approach to risk. Risk and Volatility Are Not Synonyms One of Marks’s central arguments is that risk is frequently misunderstood. Many academic models, particularly from the University of Chicago in the 1960s, defined risk as volatility because it was easily quantifiable. However, Marks contends that this is not the true measure of risk. Instead, risk is the probability of loss. Volatility can be a symptom of risk but is not synonymous with it. Investors should focus on potential losses and how to mitigate them, not just fluctuations in prices. Asymmetry in Investing Is Key A major theme in Marks’s philosophy is asymmetry — the ability to achieve gains during market upswings while minimizing losses during downturns. The goal for investors is to maximize upside potential while limiting downside exposure, achieving what Marks calls “asymmetry.” This concept is critical for those looking to outperform the market in the long term without taking on excessive risk. Risk Is Unquantifiable Marks explains that risk cannot be quantified in advance, as the future is inherently uncertain. In fact, even after an investment outcome is known, it can still be difficult to determine whether that investment was risky. For instance, a profitable investment could have been extremely risky, and success could simply be attributed to luck. Therefore, investors must rely on their judgment and understanding of the underlying factors influencing an investment’s risk profile, rather than focusing on historical data alone. There Are Many Forms of Risk While the risk of loss is crucial, other forms of risk should not be overlooked. These include the risk of missed opportunities, taking too little risk, and being forced to exit investments at the bottom. Marks stresses that investors should be aware of the potential risks not only in terms of losses but also in missed upside potential. Furthermore, one of the greatest risks is being forced out of the market during downturns, which can result in missing the eventual recovery. Risk Stems from Ignorance of the Future Drawing from Peter Bernstein and philosopher G.K. Chesterton, Marks highlights the unpredictable nature of the future. Risk arises from our ignorance of what’s going to happen. This means that while investors can anticipate a range of possible outcomes, they must acknowledge that unknown variables can shift the expected range. Marks also cites the concept of “tail events,” where rare and extreme occurrences — like financial crises — can have an outsized impact on investments. The Perversity of Risk Risk is often counterintuitive. To illustrate this point, Marks shared an example of how the removal of traffic signs in a Dutch town paradoxically reduced accidents because drivers became more cautious. Similarly, in investing, when markets appear safe, people tend to take greater risks, often leading to adverse outcomes. Risk tends to be highest when it seems lowest, as overconfidence can push investors to make poor decisions, like overpaying for high-quality assets. Risk Is Not a Function of Asset Quality Contrary to common belief, risk is not necessarily tied to the quality of an asset. High-quality assets can become risky if their prices are bid up to unsustainable levels, while low-quality assets can be safe if they are priced low enough. Marks stresses that what you pay for an asset is more important than the asset itself. Investing success is less about finding the best companies and more about paying the right price for any asset, even if it’s of lower quality. Risk and Return Are Not Always Correlated Marks challenges the conventional wisdom that higher risk leads to higher returns. Riskier assets do not automatically produce better returns. Instead, the perception of higher returns is what induces investors to take on risk, but there is no guarantee that these returns will be realized. Therefore, investors must be cautious about assuming that taking on more risk will lead to higher profits. It’s critical to weigh the possible outcomes and assess whether the potential return justifies the risk. Risk Is Inevitable Marks concludes by reiterating that risk is an unavoidable part of investing. The key is not to avoid risk but to manage and control it intelligently. This means assessing risk constantly, being prepared for unexpected events, and ensuring that the potential upside outweighs the downside. Investors who understand this and adopt asymmetric strategies will position themselves for long-term success. Conclusion Howard Marks’ approach to risk emphasizes the importance of understanding risk as the probability of loss, not volatility, and managing it through careful judgment and strategic thinking. Investors who grasp these concepts can not only minimize their losses during market downturns but also maximize their gains in favorable conditions, achieving the highly sought-after asymmetry. source

Introduction Much of the crypto world is, by definition, cryptic and difficult to understand. But two crypto trends are crystal clear: Both talent and money are flooding into the digital currency market. Almost every day brings a fresh announcement of software developers from Google or financiers from JPMorgan joining crypto start-ups that are about to revolutionize something. Indeed, while the total market capitalization of cryptocurrencies has fallen from its previous heights, it is still above the $2 trillion threshold. That’s the equivalent in value of the entire German stock market, which includes such blue-chip companies as Siemens, BMW, and Volkswagen. It is as easy to invest in crypto today as it is in equities, but what is actually being bought is not as clear. When investors purchase Shiba Inu — a token with a $15 billion market capitalization and a Shiba Inu hunting dog mascot — SHIB tokens are deposited into their digital wallets. But what do they really own? And what drives SHIB’s performance? Theoretically, the more popular the token, the higher the price. But does that relationship hold up in practice? Let’s investigate. Tokens vs. Coins Before diving in, we first need to define some basic crypto terminology: A token is a smart contract based on a blockchain, and a crypto coin is the native token of a particular blockchain. For example, ETH is the coin of the Ethereum blockchain, but SHIB is a token based on Ethereum. While all coins are tokens, not all tokens are coins. The number of tokens has exploded over the last couple of years, and tokens now outnumber coins by a factor of eight. Ethereum and Binance Smart Chain account for a combined 85% or so of the market share of the blockchain infrastructure layer where tokens are bought and sold. This raises the question of whether all of the 1,000 or so coins currently available are necessary. Over the long term, they probably aren’t. Cryptocurrencies: Number of Tokens and Coins Sources: CoinMarketCap, FactorResearch Token Financing Crypto start-ups are financed through equity and tokens. Raising capital via equity means issuing shares that are privately held by angel investors, venture capitalists, and the like. These shares represent an ownership stake that entitles the recipients to dividends and proceeds when the company is sold. Token financing is very different: It gives investors no legal claim to the underlying business. As a consequence, token and equity investing are not really comparable. Naturally, start-ups pursuing token financing need to convince investors there is value to be gained by participating in the token sale. The typical pitch is that the start-up’s product requires the use of tokens. This can create rather complex ecosystems that resemble small economies with their assorted stakeholders: The start-up is the equivalent of the government, the product a stand-in for goods, the users for consumers, and the token for the currency or medium of exchange. Since each token represents a currency, demand and supply should determine its price. Token and coin issuers can influence supply: Bitcoin, for example, limits the total number of tokens to 21 million, and Ethereum has bought back ETH tokens and “burned” them. Since the tokens represent cryptocurrencies, their demand should be influenced by their popularity. What’s the Correlation between Token Price and Token Volume? The relationship between the product of the start-up and the underlying token is not straightforward, however, and is thus hard to evaluate. Stockholders would love to own shares in a booming, revenue-generating business. But token investors have no claim on such cash flows. Worse, token investors face an information deficit since start-ups release little to no financial data on the underlying business. This puts them at a major disadvantage relative to equity investors. The best way for token investors to understand the value of their holding is to interpret the change in token volume as a proxy for the demand of the associated product. The more popular the product, the higher the demand for the token, which should reflect an increasing volume of the token on the exchange. But that relationship doesn’t hold up under scrutiny. The rolling correlation between changes in token volume and token price across all tokens between 2014 and 2022, on both a monthly and annual basis, is close to zero. This indicates that there is no positive relationship between the business of the start-up and the price of its token. Token Price to Token Volume Correlations Source: FactorResearch But what about the correlation between token volume and the price for all tokens? The crypto space has its share of bad actors, and some token issuers may be more interested in fleecing underinformed investors than in building long-term businesses. So, what if we limit our universe to only the most successful tokens by market capitalization: the top 1,000, the top 100, the top 50, and the top 10? The last of these categories has a combined market cap of approximately $100 billion and includes Chainlink and Uniswap. These tokens are associated with products that have some of the largest user bases in the crypto community. If they were normal companies, their equity would be quite valuable. Again, the correlation between volume and price is negligible no matter how it’s measured. So, perhaps product and token have no bearing on one another in the crypto space. But if product utility doesn’t drive token performance, what does? The obvious answer is speculation. In cases like Shiba Inu, this is pretty obvious. SHIB is a meme token with no underlying product. At best, it is a gamble on other investors piling in and driving up the price. This represents speculation in its purest form. Investors are simply playing a game of musical chairs and betting that they will find a seat before the music stops. Top Tokens Price and Volume Correlations, 2017 to 2022 Source: FactorResearch Axie Infinity provides a good case study of how this dynamic plays out. An online game in which players battle each other to earn tokens called

Consumer and business sentiment affect everything from momentum in stock markets, to elections, to purchasing decisions. But what factors drive consumer and business sentiment? To answer that question, we looked at measures of sentiment — also known as confidence — and their underlying determinants going back to the 1980s. We found that the factors that have historically accurately signaled the direction of sentiment are no longer reliable. We examined the University of Michigan Consumer Sentiment Index (UMCSENT), the Consumer Confidence Index (CCI), and the Business Confidence Index (BCI). We then pulled data on various macro factors. These included unemployment, interest rates (Fed funds rate), inflation, GDP growth, loan delinquency rates, personal savings rates, stock market returns, and labor force participation rates. Next, we regressed each of our consumer and business sentiment measures against each of the macro variables, partitioning the sample by decade. Figure 1 presents the results for our model using UMCSENT as the dependent variable. Figure 2 uses CCI, and Figure 3 uses BCI. In the tables, a “+” symbol denotes that the coefficient in our model was significant and in the correct direction, (i.e., based on historical expectations). An “x” symbol denotes that the coefficient was either insignificant or in the incorrect direction (i.e., not what we have seen historically). Figure 1. University of Michigan Consumer Sentiment Index (UNCSENT) Figure 2. Consumer Confidence Index (CCI) Figure 3. Business Confidence Index (BCI) The first interesting finding is that in our consumer sentiment measures during the 1980s, almost all the variables were significant and in the direction you would expect. GDP growth led to great consumer confidence; greater unemployment led to lower consumer confidence; greater inflation led to less consumer confidence, etc.  But as time went on, our model became less predictive. By the post-COVID period, an increase in GDP did not lead to an increase in consumer sentiment. An increase in unemployment also had no impact on sentiment. In fact, only two variables out of eight had significant power in predicting the direction of consumer sentiment: inflation and the stock market returns. To put some numbers to the coefficients in our model, during the 1980s a one percentage point increase in inflation led to a 3.4-point drop in the Michigan index, and a 1% increase in unemployment led to a 3.6 drop in the Michigan index. Indeed, during the post-COVID period our model has become much more muted. From 2020 forward, a 1 percentage point increase in inflation led to just a 1.1-point drop in the Michigan index, and a 1% increase in unemployment led to just a 2.3 drop in the index. Further, the strength of our model (i.e. the predictive power) has also decreased over time. The Adjusted-R^2 was 0.88 in the 1980s and dropped to 0.72 in the present day.  We see similar results in the BCI model as well but not to the same degree that we see in our consumer sentiment results. What may be the underlying cause of all this? There are likely many factors, but one highlighted by past literature could be partisanship. Individuals have noted that individuals switch their views on the economy and sentiment to a much greater extent in the present day based on who holds political office. The upcoming US presidential election could be one of the underlying factors that we omitted in our study. Whatever the case, unemployment, labor force participation, and GDP growth no longer explain how consumers are feeling about their prospects. The root causes of this phenomenon deserve more careful study. source

Women are reshaping the world of alternative investments, driven by growing wealth, increased financial fluency, and expanding access to new platforms. From healthcare innovations to blockchain-powered opportunities, women are not only investing more but also redefining what success looks like in alts. The future is clear: women are transforming the alternative investment landscape. This post is the second in a two-part series sharing insights about women and alternative investing today through the lens of more than 50 finance industry leaders from around the world. Here, I share some of the key highlights and select quotes, edited for clarity. When we talk about women and alternative investments, everything we talk about is part of a broader context of economics, monetary policy, regulatory environments, politics, and culture. This is especially true for “money culture.” These elements are often interrelated, and they vary considerably by country or region, as illustrated by the following insights. Women’s Wealth Is Growing. So Is Their Need for Financial Fluency The ongoing generational wealth transfer is accelerating, with women poised to play a leading role. Women are living longer, inheriting family assets, and becoming key decision-makers in their financial futures. “Women are breadwinners and earning more for their families while taking greater control of their finances,” emphasized Alicia Syrett, Founder and CEO at Pantegrion Capital and Founder of Madam Chair, New York. Caroline Miller, an independent corporate director based in Montreal, Canada, noted, “Women are transitioning from the high-spend phase of child-rearing to managing aging parents’ finances and sustaining their lifestyle amid rising costs. Their goal isn’t just financial literacy, it’s financial fluency.” This shift is creating a new wave of female investors prepared to navigate complex financial landscapes with confidence and long-term strategies. New Platforms and Tokenization: A Boon for Women Digital transformation has democratized investment opportunities, making alternative investments more accessible than ever. “Fintech tools like robo-advisors and AI-driven platforms simplify the process, offering transparency and ease for retail investors,” expressed Sofia Beckman, Co-Founder and Partner at North House in Stockholm. Diana Biggs, Partner at 1kx in Zug, Switzerland, remarked, “Tokenization is a game-changer — it eliminates traditional barriers like cheque size and gender, allowing smaller investments and expanding access to private equity for women.” Hanna Pri-Zan, Chairperson of Israel Experience in Tel Aviv, highlighted Israel’s progress: “In 2010, only 30% of women had securities accounts. Today, that number has risen to 42%, thanks to improved ease of account setup and digital platforms.” Platforms like Moonfare and Crowdcube are enabling retail investors to enter the private equity space with smaller stakes, breaking the long-standing exclusivity of the ultra-wealthy, noted Callum Woodcock CEO of WineFi in London. Healthcare Is the #1 Sector of Interest HealthTech and FemTech have emerged as leading sectors of interest for female investors, driven by women’s recognition of gaps in healthcare innovation. “Women know how to invest in health and wellness sectors because they understand these needs firsthand,” said Alice Tang, Chief Operating Officer at MA Asset Management in Sydney. Charlotte Beyer, Founder and Principle of Quest Foundation and Founder of Institute for Private Investors in New York, shared her excitement: “I invested in a venture working on a male birth control pill. Women are driving groundbreaking innovations that challenge traditional healthcare norms.” Anna Pearson, Co-Founder of Harriet in Singapore, highlighted the struggles within FemTech: “The market is expected to hit $60 billion by 2027, yet many companies still struggle to secure funding. This highlights the need for greater support in this critical sector.” Investing Culture Cultural dynamics play a significant role in women’s engagement with alternative investments. In male-dominated regions like Switzerland, women can be cautious and won’t invest in what they don’t understand, while men sometimes jump in with overconfidence, observed Peter Wüthrich, Consulting Investor at Gehrenholz GmbH in Zürich. In contrast, Singapore, Malaysia, Indonesia, Taiwan, Australia, and Turkey show greater gender parity, according to my interviewees in the region. Metin Aslantaş, Partner & TMT Country Leader at Deloitte in Istanbul, commented, “Turkish women invest strategically, focusing on less-risky products and staying in the game longer. They often outperform male counterparts in long-term gains.” JoAnn Fan, Venture Capitalist and Board Director at Cheng-An Investment Company in Taipei, added, “Many women here are second-generation family business leaders. They actively enhance their portfolios with private equity and private credit, showing strong engagement with alternative assets.” Regulatory Frameworks and Policies Regulations heavily influence accessibility to alternative investments. Anna Jonsson, CEO, Storebrand Asset Management in Stockholm, noted, “Strict rules around marketing illiquid products require exhaustive onboarding processes, which can deter potential investors, especially women.” In India, Hansi Mehrotra, Founder, The Money Hans in Bengaluru, pointed to innovative solutions like gold bonds: “They offer a 2.5% yield and exposure to gold without the hassle of physical storage, making them attractive for conservative investors.” Meanwhile, in Australia, Anna Shelley, Chief Investment Officer at AMP in Melbourne, highlighted the country’s value-driven culture: “Our superannuation system focuses on low-fee, high-performing products. High-fee fund managers don’t even bother entering this market.” Geopolitics and Alternative Investments Geopolitical factors are influencing investment trends, particularly in Ukraine and Lithuania. Olga Burenko, Vice-President of Investment Banking at Dragon Capital  in Kyiv, shared, “War memorabilia has become an investment in resilience — it tells the story of our brave people and their sacrifices.” Nora Laurinaityte,Green Finance Expert at INVEGA in Vilnius, Lithuania, emphasized the shift in perception: “Defense tech, like drones and radar systems, is no longer seen as macho. These investments are practical tools for resilience and security.” Pension Systems and Tax Policies Pension systems and tax policies vary greatly across regions, shaping women’s investment behaviors. Judith Sanders, Sustainable Investment Strategist at ABN AMRO Bank N.V. in  Amsterdam, observed, “Our pension system reduces the need for aggressive private capital investments, but as social costs rise, this may change.” In Eastern Europe, tax-incentivized retirement plans are encouraging long-term investments. Kateřina Bendová, a financial advisor in Prague, noted, “These plans are great opportunities, but many older generations remain hesitant to embrace them fully.” Lack of

Risks and Returns: Creating Success in Business and Life. 2024. Wilbur Ross. Skyhorse Publishing. President of Faulkner, Dawkins & Sullivan Securities Corporation at age 29. Head of investment banking at Rothschild, Inc. Founder of a private equity firm. Board president of The Dakota cooperative apartment building when resident John Lennon was shot to death there. Vice chairman of the Brooklyn Museum. Chairman of the Smithsonian Institution’s National Board. President of the Japan Society. Trustee of Sarah Lawrence College. US Secretary of Commerce. Wilbur Ross has excelled in a wide variety of leadership roles. His memoir Risks and Returns emphasizes both the qualities that contributed to his success and the lessons he has drawn from his experiences. This highly readable book also contains numerous entertaining anecdotes about, and descriptions of, the lifestyles of the rich and famous. Its educational value to CFA charterholders primarily involves the following topics. Bankruptcy Process Ross earned the soubriquet “King of Bankruptcy” as an investment banker advising parties in negotiations to settle claims of companies that defaulted on their debts — as well as a principal who acquired and revived several such companies. Understanding bankruptcy resolution and turnrounds is essential for investors in lower-rated and distressed bonds, and most commentary on the subject is written by lawyers and securities analysts. That literature is quite informative, but Ross is able to provide a fresh and valuable perspective by drawing on his involvement in such prominent bankruptcies as Drexel Burnham Lambert, Federated Department Stores, Pan Am, Texaco, Trump Taj Mahal, and Trans World Airlines (TWA). Especially noteworthy is how Ross highlights the importance of labor relations in resurrecting failed businesses. He notes that the unprofitable steel subsidiaries of Ling-Temco-Vought (LTV), which WL Ross & Co. acquired, were hampered by the existence of 32 separate job classifications. The rolling mill operators and the maintenance workers were able to perform each other’s jobs. When the mill was running, however, the maintenance team sat around playing pinochle, and when the mill broke down, the operators were idled. Ross induced the plants’ union to reduce the job classifications to five, which greatly increased efficiency. Trade Policy Issues of international trade have important investment implications both at the macroeconomic level and for corporations that export goods, source parts offshore, or compete with imported products. Many readers undoubtedly share this reviewer’s free trade orientation, and they will find their certitude challenged by Ross’s defense of protectionist measures to counter trade barriers erected by other countries. He notes that ardent free trader Lawrence Kudlow supported the Trump administration’s tough stance against China, saying that country’s behavior regarding trade puts it in a category by itself. Cryptocurrency Ross’s skepticism about Bitcoin and its brethren extends beyond the most common criticisms. “Having seen hackers penetrate our most secret federal agencies,” he writes, “the notion that only the pseudonymous founder of cryptocurrency, Satoshi Nakamoto, could figure out the algorithm that creates it seem[s] highly improbable” (p. 336). He also declares unproven the assumption that sufficient demand exists for the supply of Bitcoin that can ultimately be created. It does not detract from the overall excellence of Risks and Returns to surmise that some investment professionals who read it will remain unpersuaded by Ross’s arguments on certain controversial topics. So much the better if his willingness to wade into such issues sparks lively debate. Here is just one example of the potential for productive dialogue that the book creates: Outside the financial sphere, Ross hails former New York mayor Rudolph Giuliani’s success in “drastically reducing crime.” On the contrary, argues the Poynter Institute — a journalism school and research organization judged by public benefit corporation AllSides Technologies to show no political bias toward either the left or the right — independent studies generally have found no link between the Giuliani administration’s tactics and the drop in the crime rate. Violent crime had already been declining in New York for three years before Giuliani took office in 1994, according to US Justice Department records. Moreover, crime fell sharply throughout the country in the 1990s, with San Francisco leading other major cities. Many criminologists attribute the nationwide drop in crime to a complex combination of causes, including a waning of the crack cocaine epidemic. In addition to thought-provoking commentary on a wide variety of topics, Risks and Returns provides fascinating glimpses of financial history. Readers learn about the end of fixed commissions on the New York Stock Exchange and the birth of the loan-to-own strategy. Ross also recounts his early career as an innovative airline analyst. He distinguished his team’s efforts from the competition by using weekly takeoff and landing data to forecast carriers’ monthly earnings, which the Civil Aeronautics Board required them to report. Ross describes his encounters with financial luminaries such as Carl Icahn, Victor Posner, and Paul Singer, as well as confrontations with American communists and Neapolitan organized crime lords. Readers’ interest will never flag as they glean the chunks of wisdom that they can productively apply to investment analysis. source