CFA Institute

Risk management is so simple a concept and so central to financial analysis that it feels superfluous to even mention it. Yet when it comes to climate change and sustainability, efforts are under way across the United States to impede our ability as investors to conduct simple risk management. Policymakers have proposed and even passed laws that make it more difficult, if not illegal, for investors to consider the financial risks of climate change. These efforts are misguided. The freedom to invest responsibly and the principle of risk management must be defended, and that requires us to go back to basics. Does climate change pose financial risk? The answer is clear. Drought, heat waves, and extreme weather all exact a signficant toll from infrastructure, supply chains, facilities, and people. Indeed, the United States recorded $165 billion in losses from climate disasters just last year. But the climate crisis also presents enormous opportunity. The Inflation Reduction Act has driven a clean energy boom across the country. Investors should not have to sit it out. Informed by these facts, investors have increasingly integrated climate considerations into their decision making precisely because the financial effects are so clear. They are acting on sound, rational logic, and governments should not interfere with that process. Yet some states have instituted new laws forbidding investors from taking climate change impacts into account when assessing bond issuances, pension fund management, and other government contracts. In effect, they are penalizing risk management. Ignoring a financial risk does not make it go away; it only makes it worse. Whether on individual balance sheets or across a national economy, failing to account for and address potential threats has a significant downside. Investors need data to assess these risks and the freedom to act on that data based on their business considerations. Their fiduciary duty requires it. When investors lack these essentials, markets are less efficient and less effective, and everyone invested in those markets suffers. If there are fewer financial institutions competing in the marketplace, states will be forced to pay millions more in extra interest payments. And if states work only with institutions that do not consider climate- and sustainability-related risks, they will expose their pension funds, beneficiaries, and taxpayers to the downsides of those risks. Most investors understand the threat and are responding as they should: by studying the data, following the trends, and keeping a watchful eye out for risks and opportunities. But being rational market actors isn’t enough. That’s why investors and private and public sector leaders have joined together to urge policymakers to protect every investor’s right to incorporate climate and sustainability risks into their decision making. They are making a clear statement that executing their fiduciary duty should not be subject to government interference. Such interference will only make it harder for them to do their jobs and serve their clients. That is why we all need to stand up, speak out, and demand the freedom to invest responsibly. If you liked this post, don’t forget to subscribe to Enterprising Investor. All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer. Image credit: ©Getty Images / trekandshoot 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

This article is derived from “Ensemble Active Management – AI’s Transformation of Active Management” and “Methodology, Design, and Data Integrity Validation Study of Turing Technology’s 2024 Ensemble Active Management” white paper. Numerous studies have evaluated active US equity managers’ ability to outperform index funds and exchange-traded funds (ETFs). While time horizons vary, the results tend to converge on the same result: Active managers outperform standard benchmarks less than half the time. Adding to the headwinds, active investments are structurally more expensive than their passive counterparts.  Investors don’t want to pay more for equivalent returns and are voting with their wallets. As of year-end 2023, actively managed US equity funds have experienced 18 consecutive years of net outflows totaling more than $2.5 trillion, according to Morningstar Direct. The required leap for active to once again outperform passive cannot be accomplished through incremental gains. The gap is simply too large. For active management to acquire sufficient alpha to achieve a step-change improvement, a paradigm shift driven by new technologies and new methods is required. That’s where Ensemble Active Management (EAM) comes in. EAM is built on critical new technologies and employs a stock-selection approach mirroring other industries’ best practices for conducting complex decision making. It pivots from a single manager to a multi-manager approach. In short, EAM represents the paradigm shift necessary to revitalize active management. EAM is not an academic concept. It was first introduced in 2018 and EAM portfolios launched later that year. There are now dozens of EAM track records that range in age from two to five years.  This paper lays out EAM’s construction mechanics and presents three critical validation pillars that support EAM’s results to date and explain its future potential. The data shows that as of year-end 2023, live EAM portfolios represented the country’s strongest lineup of actively managed US equity portfolios. Ensemble Active Management Defined EAM must harness substantial added alpha to outperform both traditional active and passive management. To accomplish that, we apply the proven mathematics of Ensemble Methods to portfolio management. Ensemble Methods feature a multiple-expert system that improves the accuracy of single-expert predictive algorithms or engines. This is accomplished by mathematically integrating multiple predictive models based on consensus agreement. The end result is a stronger predictive engine. Ensemble Methods are thus an artificial intelligence (AI) version of the “wisdom of experts.” For clarity, EAM does not employ Ensemble Methods to design a “smarter” portfolio manager. In fact, a defining principle of Ensemble Methods is its use of multiple predictive engines. Instead, EAM generates active security selection by integrating a multi-investment-manager platform through Ensemble Methods.  Actively managed mutual funds work within an Ensemble Methods environment because they effectively operate as predictive engines wherein managers try to “predict” which stocks will outperform. Further, substantial research shows that managers’ highest conviction stock picks do reliably outperform. EAM’s breakthrough came from the discovery of how to extract a fund’s “dynamic predictive engine” from its real-time holdings and weights. Turing Technology accesses this data through its machine learning-based fund replication technology, Hercules.ai. Launched in 2016, Hercules.ai provides real-time replication of actively managed funds. It houses data representing more than $4 trillion in assets and achieves a 99.4% correlation between the replicated fund returns and the actual fund returns. To build EAM portfolios, 10 to 12 quality mutual funds are selected from a similar investment category. Turing extracts each fund’s predictive engine by accessing its real-time holdings and weights, and then maps that data against the benchmark’s weights. The relative over- or underweight positions reflect the funds’ predictive engines. Turing then deploys these extracted predictive engines within the Ensemble Methods mathematical “engine” to generate the EAM portfolio. The final result is a portfolio of up to 50 stocks, with no derivatives, no leverage, and all holdings represented in the benchmark. EAM therefore constitutes the “consensus top picks of a dozen quality managers.” Further Understanding of Ensemble Methods The subset of machine learning known as Ensemble Methods is the key to creating new sources of alpha. Ensemble Methods are integral to nearly every major computational challenge in the world, and Giovanni Seni and John F. Elder have described them as “the most influential development in Data Mining and Machine Learning in the past decade.” There are more than 250,000 published applications of Ensemble Methods, including facial recognition, early autism detection, MRI-based tumor detection, cyber threat detection, and many more. Scaled Research:  2024 EAM White Paper The following data are excerpted from “Ensemble Active Management – AI’s Transformation of Active Management,” the largest study ever conducted to measure the performance potential of EAM. 60,000 randomly constructed portfolios of 12 funds each were built. 60,000 EAM portfolios were constructed based upon the sets of 12 underlying funds. Results were evaluated over 2016 to 2022. 333 underlying funds were used from more 140-plus fund companies representing more than $3 trillion in AUM. These funds account for more than 60% of the assets of the active US equity universe. The study covered Large Value, Large Blend, Large Growth, Small Value, Small Blend, and Small Growth style boxes, or 10,000 EAM portfolios per style box. To put the scale of this research effort into perspective, 420,000 discrete calendar year performance returns were generated (seven years each, from 60,000 portfolios). This is 20 times larger than the number of discrete calendar year returns delivered by the entire active US equity industry for the past 25 years. The results are statistically significant, and were subjected to an independent academic review, verifying the study’s methodology and results. Performance Comparison vs. Standard Benchmarks The study compared the performance of the 60,000 EAM portfolios versus their corresponding benchmark (the Russell Indexes), based on rolling one-, three- and five-year periods, as well as the full seven-year window. The results, derived from more than 560 million total data points are presented in the chart below. Two of the key metrics were Success Rates and average annual excess returns. The former measures the percentage of rolling time periods that the EAM portfolio outperformed the benchmark, with the average annual excess return reflecting the average of all rolling period relative

The NASDAQ 100 index hit rock bottom during intra-day trading on 10 October 2002, down 77% from its all-time high on 10 March 2000. An estimated 100 million individual investors lost $5 trillion in the stock market. It took more than 15 years for the tech-heavy index to revisit its peak. Between such dot-bombed firms as Webvan and eToys and uniconned start-ups like Theranos and FTX, when it comes to venture capital (VC), the New Economy of the late 1990s and today’s gig economy share a few commonalities. New Lexicon, Old Tricks Unlike public markets, VC is all about inside information. Proprietary deals are recipes for success. At the same time, early stage investors usually follow one trend after another instead of pursuing predictable performance. The current craze for generative artificial intelligence (AI) — 44% of minted unicorns last year operated in AI and machine learning — follows the mad but short-lived dash into non-fungible tokens (NFTs) or the metaverse, which came soon after the race into anything vaguely related to blockchain and cryptocurrencies, which likewise came on the heels of huge investments in augmented reality and electric vehicles (EVs). Two Venture Capital Bubbles    1994 to 2003 2014 to 2023 Defining the Era Dot-coms Unicorns Performance Limited or no revenues,ubiquitous losses Large revenues, larger losses Launch Locale Garage or dorm room Accelerator, co-working space,work from home (WFH) Buzzwords Eyeballs, B2B, B2C,click-and-mortar, anything “e”(eCommerce, eBanking, etc.), New Economy Big data, clickbait, blockchain, deepfake,anything “tech”(edtech, fintech, proptech, etc.),machine learning, mobile apps,gig economy Hot Sectors Online advertising, e-tailing,web portals, search engines,Internet Protocol, dark fiber Electric vehicles, virtual/augmented reality,cybersecurity, anything as a service (XaaS),crypto, artificial intelligence (AI) Ownership  Publicly listed Privately owned as well asICOs and SPACs Piling Up Losses to Scale Up  Beyond the buzzwords, one distinction between the recent tech bubble and its predecessor is a new dimension of loss-making. Today’s valuations and deal sizes exceed those of the dot-com era. In 1999, the collective losses of the 200 largest dot-coms were $6.2 billion on total annual sales of $21 billion. That year, Amazon reported a $720 million loss on sales of $1.6 billion. Twenty years later, Uber alone lost $8.5 billion on $14 billion in revenue. The bets have scaled up, yet they do not provide better odds of success. Both dot-coms and unicorns sought to establish market dominance by outspending rivals, even if they employed different tactics. In 2000, as companies were being “Amazoned,” they were losing to smaller, nimbler rivals. By contrast, when they are “Uberized,” incumbents are now losing to larger competitors. Start-ups have become better at driving sales, not at turning a profit. Ad-hoc profits are also easier to manufacture — as WeWork did with its community-adjusted EBITDA, for instance — than positive, recurring operating margins. The Unicorn Generation  Unicorns follow a market strategy first tested in the dot-com days: launch innovative business ideas and grow the top line exponentially while racking up huge losses. The trick is to ensure almost unlimited access to financing.  Thanks to unprecedented money-printing throughout the 2010s, the number of unicorns rose from fewer than 200 in 2015 to more than 600 in 2020. They passed the 1,000 mark in 2022 and now exceed 1,200. Advocates of such richly valued enterprises point to the pioneers of the internet revolution — Amazon and Google, for example — that are now among the most valuable companies in the world. They rarely mention previous market darlings like AOL and Netscape. That a handful of companies become extremely successful does not imply that a long tail of market participants will justify such august hopes.  Overcapacity is another major risk. Numerous multi-billion-dollar food delivery services emerged during the pandemic, just as dark fiber was overbuilt during the internet’s early days. As the 2015 to 2021 vintages turn into vinegar, many start-ups will meet a similar, humbling fate. While they remain under private ownership, their true worth is unclear. Stock markets can be temporarily mispriced, but eventually they offer a reality check to companies seeking to float. At its initial public offering (IPO), Instacart sold at a 75% discount to its 2021 private valuation.  Yet despite regulations introduced after the dot-com crash, stock markets remain easy to manipulate, as the initial coin offerings (ICOs) of the late-2010s and the more recent glut of special-purpose acquisition companies (SPACs) illustrate. Unfortunately for those unicorn backers hoping to attract unsophisticated punters, the blank-check bubble quickly fizzled out.  The Big Long  Until two years ago, historically low interest rates artificially turbocharged the valuation of illiquid, risky assets. Central banks’ reluctance to turn off the quantitative easing (QE) spigot amplified this trend. With easy access to cheap capital, financiers and entrepreneurs adopted behaviors distinct from those of the dot-com era. Then VC backers engineered artificially inflated valuations by introducing portfolio companies to public markets and creating “first-day pops” with the cooperation of unscrupulous underwriters. Nowadays, price jumps for newly listed tech stocks are quite tame compared with their NASDAQ counterparts in the 1990s, when open-source developer VA Linux’s stock soared 733% on 9 December 1999. Transaction volumes are also much lower. In 2019, there were 159 US IPOs, one third as many as in 1999. It is not for lack of public appetite. Rather, venture capitalists realized that by exiting early they left too much money on the table. Apple listed in 1980, almost four years after its inception, at a $1.8 billion market capitalization. Amazon’s 1997 IPO valued it at $438 million less than three years after the company launched.  Today, funding is driven by the VC firms’ desire to hold onto start-ups longer. They breed unicorns in-house, which requires bankrolling portfolio companies for several more rounds of financing. They profit by inflating valuations in the years leading up to their exit, keeping most of the value expansion under wraps. The bad news for public investors is that it is much harder to register a 100%-plus price increase if a company floats or markets itself for tens of billions of dollars, as Facebook,

Skewness in asset returns is a perplexing phenomenon and evokes different behavior from investors. Some show a preference for stocks with significant right skewness, which much like playing the lottery, hit the jackpot every once in a while and deliver outsized returns. Other investors try to steer clear of such volatility and opt for stocks that have no skewness or even demonstrate left skewness. But how does skewness in returns relate to other factors in asset pricing? Might investors be betting on particular factors precisely because they want lottery-like skewness in their returns? To answer these questions, we constructed cross-sectional growth and value portfolios and examined the distribution of monthly returns over five-year periods. From an investing universe of all the equities traded on the NYSE and NASDAQ since 1975, we created our growth and value portfolios out of the quintile of stocks with the highest and lowest P/E ratios, respectively.  Our growth portfolio exhibited more right skewness in its returns, on average, than our value portfolio did. This held true over 6 of the 10 time periods. Growth Stocks: Monthly Returns Mean Median Volatility Skewness 1975 to 1980 3.02% 0.78% 53.24% 8.92 1980 to 1985 1.33% 0.02% 44.26% 1.10 1985 to 1990 2.04% 0.85% 55.99% 20.44 1990 to 1995 1.88% 0.38% 59.80% 10.51 1995 to 2000 3.44% 1.44% 67.22% 8.99 2000 to 2005 1.43% 0.01% 71.05% 2.54 2005 to 2010 0.71% 0.02% 48.44% 2.14 2010 to 2015 1.50% 0.90% 41.30% 7.30 2015 to 2020 6.94% 0.57% 50.22% 9.97 2020 to 2022 1.22% 0.28% 59.21% 5.10 Average 2.35% 0.52% 55.07% 7.70 Value Stocks: Monthly Returns Mean Median Volatility Skewness 1975 to 1980 2.44% 0.00% 47.26% 2.07 1980 to 1985 1.66% 0.01% 44.25% 1.94 1985 to 1990 1.26% 0.02% 48.23% 14.73 1990 to 1995 1.26% 1.02% 55.05% 2.55 1995 to 2000 1.23% 0.00% 52.13% 5.62 2000 to 2005 2.43% 1.15% 18.08% 9.31 2005 to 2010 0.68% 0.00% 48.75% 2.24 2010 to 2015 1.70% 1.02% 38.59% 1.85 2015 to 2020 0.86% 0.56% 36.92% 1.45 2020 to 2022 1.38% 0.53% 82.10% 9.30 Average 1.49% 0.43% 47.13% 5.10 So, what can we glean from these results? Our theory is that skewness tends to move based on investor preferences. That is, when a particular factor is en vogue, skewness significantly increases while it’s in fashion. For instance, growth stocks were all the rage as the dot-com bubble inflated from 1995 to 2000, and they demonstrated significant skewness while value stocks showed a distinct lack of it. Growth Stocks: Monthly Returns, 1995 to 2000 Growth’s popularity took off again in the 2010 to 2020 period, while value underperformed and again showed a lack of skewness in returns. Value Stocks: Monthly Returns, 2010 to 2015 Now, these results don’t tell us which direction the association goes, only that an association exists. The data suggest to us that when a particular asset pricing style is popular among investors, returns for that style exhibit greater skewness. In sum, investors in growth stocks may be pursuing lottery-like payouts, especially when such stocks are in style. 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/piotr_malczyk 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

Trailblazers, Heroes, and Crooks: Stories to Make You a Smarter Investor. 2024. Stephen R. Foerster, CFA. John Wiley & Sons. In 2021, the Washington Post reported that five-time FIFA world player of the year Cristiano Ronaldo triggered a $4 billion plunge in Coca-Cola’s stock market valuation. He replaced two bottles of Coke with a bottle of water at a press conference held in conjunction with a tournament sponsored by the soft drink maker. As Stephen Foerster, CFA, documents in Trailblazers, Heroes, and Crooks: Stories to Make You a Smarter Investor, however, Coca-Cola’s stock price decline actually preceded Ronaldo’s snub and was at least partly related to the shares going ex-dividend. Foerster uses the Washington Post’s fact-check failure as a jumping-off point for illustrating how investors can be led astray if they mistake correlation for causation. Relatively recent events such as the Ronaldo/Coca-Cola affair combine with venerable ones already well known to students of financial history in this highly readable and instructive book. Foerster, a professor of finance at the Ivey Business School at Western University in London, Ontario, furnishes insights that will be new to most readers, even on a story as familiar as the South Sea Bubble. He cites Andrew Odlyzko’s fresh findings on Isaac Newton’s involvement in the notorious bubble, published in 2019. Readers who are acquainted with the outlines of the Panic of 1907 and Bernie Madoff’s Ponzi scheme will gain by learning important details from Foerster’s account. Similarly, many investors who lived through the US stock market crash of 19 October 1987 are probably unaware of the comparatively obscure Major Market Index’s pivotal role in the subsequent rebound. They may also not know that Fed chairman Alan Greenspan was aboard a flight from Washington, DC, to Houston as the market plummeted. Greenspan breathed a sigh of relief when told (upon deboarding and after the close) that the Dow Jones Industrial Average was down “five-oh-eight,” thinking the speaker meant 5.08 rather than 508 points. As its title indicates, the book deals not only with famous scams and debacles but also with such innovators as Warren Buffett, Charles Ellis, and Hetty Green. They appear alongside less heralded but perhaps equally colorful characters, such as Leo “The Moon” Rugendorf and John “Donkey Ears” Wolek, bit players in the 1963 American Express salad oil scandal. Foerster’s heroes include Paul Revere, whose claim to fame in financial history is that he engraved the border of inflation-indexed bonds issued by Massachusetts in 1780. The author is a CFA charterholder and calls the designation “the gold standard of credentials in the investment industry.” Note, however, that Foerster seeks to make Trailblazers, Heroes, and Crooks accessible to the general reader by defining such basic terms as “short selling,” “IPO,” and “buying on margin.” He counsels non-professionals to consider owning passive index funds, likely to the displeasure of many active managers in his potential audience. At the same time, Foerster points out the pitfalls of abdicating responsibility by relying on the widely used target-date funds. He describes them as “autopilots” because they never update asset allocations in light of new information about individuals’ risk preferences or tolerances. (As recently noted by Elizabeth O’Brien in Barron’s, one solution is to “off-date” by choosing a fund that corresponds to one’s preferred asset mix, rather than on the basis of an intended retirement year. Neither does the author let the objective of aiding non-professionals deter him from highlighting issues that veteran analysts and portfolio managers can benefit from exploring. For example, he points out how a confounding variable may trip up efforts to adjust a portfolio’s geographic mix through use of country indexes. A switch from a US to a Canadian equity index, for instance, also produces a major shift in industry concentration. In summary, Stephen Foerster extracts lessons from exceptional episodes in financial history that ordinary investors and seasoned professionals alike can put to profitable use. The bonus is that Trailblazers, Heroes, and Crooks is immensely entertaining from start to finish. If you liked this post, don’t forget to subscribe to the Enterprising Investor. All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer. Professional Learning for CFA Institute Members CFA Institute members are empowered to self-determine and self-report professional learning (PL) credits earned, including content on Enterprising Investor. Members can record credits easily using their online PL tracker. source

Global technology leaders including Alphabet, Amazon, Apple, Meta, and Microsoft are increasingly integrating artificial intelligence (AI) technologies into their product offerings. The substantial energy consumption associated with AI training and operation has raised concerns about the environmental impact, particularly regarding GHG emissions[1]. Should investors demand these companies disclose their energy consumption to calculate Scope 3 GHG Emissions? From a sustainable investor’s perspective, the carbon emissions of a company can have implications on its discount factor (i.e., cost of capital). Companies with higher emissions may face increased regulatory scrutiny, potential carbon taxes, and reputational risks, all of which could increase their Weighted Average Cost of Capital (WACC). On the other hand, companies which have made long-term commitments, for example to clean energy, might enjoy a lower discount rate due to lower environmental risks. Carbon footprint is a measure of the total amount of carbon emissions that is directly and indirectly created by an activity or over the life of a product[2]. Carbon footprint could also be used by investors as a proxy for the sustainability of companies’ operations. Companies with efficient energy use may signal to investors that they are more resilient to energy price fluctuations and regulatory changes, as well as the feasibility of success in achieving Net-Zero pledges. For the technology leaders whose energy consumption has very significantly increased due to AI operations and yet whose reported carbon footprint may not seem as greatly increased, investors might question the integrity of the company’s overall carbon neutrality[3]. Big Tech Investment in Private AI Companies Microsoft’s AI efforts have historically been somewhat fragmented, compared to the more focused strategies of competitors like Alphabet and Amazon. By investing heavily in OpenAI (~$10B), Microsoft aimed to catch up and potentially surpass its competitors[4]. OpenAI’s models, integrated into Microsoft’s Azure cloud platform, have positioned Microsoft as a formidable player in the AI space[5]. Another case of significant investment in a private AI company by mega technology companies is Anthropic. Amazon has announced a $4B investment[6]. Prior to that, Alphabet committed to investing up to $2B in Anthropic[7]. This combined stake is still thought to be in the region of 30%, putting their scale and timing a distant second to Microsoft from an investment point of view[8]. How Amazon and Alphabet will report their investment in Anthropic is yet to be seen in the upcoming financial reports and sustainability disclosures. All these large-scale corporate investments add substantially more complexity to an already-difficult problem of assessing and reporting correctly total GHG emissions. This issue of complexity and a lack of agreed approach has been explored in detail in a recent Financial Times report[9], “Big Tech’s bid to rewrite the rules on net zero,” which describes where potential loopholes are and how large energy users might be able to hide their true emissions. Our paper examines these issues and considers the broader implications for disclosures where companies have substantial corporate investments in AI-focused ventures. Challenges and Implications The Greenhouse Gas Protocol, which supplies the world’s most widely used greenhouse gas accounting standards and guidance, introduced three “Scopes” (Scope 1, Scope 2, and Scope 3) for GHG accounting and reporting purposes[10]: Scope 1: Direct GHG emissions. Direct GHG emissions occur from sources that are owned or controlled by the company. Scope 2: Electricity-indirect GHG emissions. Scope 2 accounts for GHG emissions from the generation of purchased electricity consumed by the company. Scope 2 emissions physically occur at the facility where electricity is generated. Scope 3: Other indirect GHG emissions. Scope 3 is an optional reporting category that allows for the treatment of all other indirect emissions. Scope 3 emissions are a consequence of the activities of the company, but they occur from sources not owned or controlled by the company. “Technical Guidance for Calculating Scope 3 Emissions” provided by the Greenhouse Gas Protocol recommends that companies should account for the proportional Scope 1 and Scope 2 emissions of the investments that occur in the reporting year[11]. As such, disclosing investee company’s Scope 1 and 2 in the investor company’s Scope 3 emissions, proportionally to the ownership, aligns with global sustainability goals and guidance, but there are several challenges: Accurately measuring and reporting indirect emissions requires robust data-collection and verification processes. Detailed disclosures may reveal sensitive information about operational efficiencies and competitive strategies. Integrating GHG emissions data from partners, such as OpenAI, for example, into Microsoft’s reporting framework involves significant logistical and technical challenges, and possible double counting. Understanding Carbon Neutrality and Net Zero To evaluate a company’s environmental commitments, it is important to distinguish between “carbon neutrality” and “net-zero” emissions. Carbon neutrality refers to the reduction of a company’s emissions through credits or other measures without necessarily reducing the emissions at the source. In contrast, achieving net zero means that a company is reducing its overall emissions across its supply chain and operations to as close to zero as possible, using offsets only to cover unavoidable emissions. The Science-Based Targets Initiative (SBTi)[12] defines net zero as “a state of balance between anthropogenic emissions and anthropogenic removals.” To stabilize global temperatures, net-zero GHG emissions must be achieved worldwide, and targets under the SBTi Net-Zero Standard must cover all emissions defined by the United Nations Framework Convention on Climate Change (UNFCCC)/Kyoto Protocol[13]. The SBTi’s Corporate Net-Zero Standard guides companies on how to align with global net-zero goals[14]. It requires rapid, deep emission cuts, with a 50% reduction by 2030 and at least 90% by 2050 to limit global warming to 1.5°C above pre-industrial levels. Companies claiming carbon neutrality may offset CO2 without reducing emissions to the levels needed for net-zero or covering all GHGs. Renewable Energy Certificates Furthermore, current GHG accounting standards allow companies to use “Renewable Energy Certificates” (RECs) to report reductions in emissions from purchased electricity (Scope 2) as progress towards meeting their science-based targets[15]. A renewable energy certificate is a market-based instrument that represents the property rights to the environmental, social, and other non-power attributes of renewable electricity generation. One REC is issued when one megawatt-hour (MWh) of electricity is generated

In Agatha Christie’s mystery novel Hallowe’en Party, a young guest who may have witnessed a murder drowns in an apple-bobbing basket. In the wrong place at the wrong time, the unfortunate partygoer’s fate is a metaphor for that of the unlucky investor who bites into a losing stock and tastes the consequences. Taking a page from Benjamin Graham and David Dodd, Howard Marks, CFA, co-chair and co-founder of Oaktree Capital Management, describes fixed-income investing as “a negative art”: Success depends not on finding winners but on avoiding losers, on not buying those companies likely to default on loans and drag down returns. In Winning the Loser’s Game, Charles D. Ellis, CFA, draws a similar parallel between professional money management and tennis and golf. In tennis and golf, the winner tends to be the player who makes the fewest errors, not necessarily the one who makes the best shots. Small-cap investing is a similarly “negative art.” But in addition to steering clear of losers — avoiding mistakes — small-cap investors have to demonstrate the “positive art” of finding winners. By achieving that equilibrium and, importantly, selecting a smaller subset of very big winners, small-cap investors stand the best chance of harvesting alpha. Investing in smaller, early-stage companies has specific pitfalls that make risk control paramount. Many such firms have unproven business models and inexperienced management teams. They often lack sufficient financial resources, which could lead to significant dilution as they seek to raise funds for operations. In some cases, the value of the enterprise could go to zero and investors could experience total capital loss. That’s why prudens investor should avoid these types of companies just as they would invitations to Christie’s Hallowe’en party. By ignoring the “bad apples,” investors can focus on that subset of companies that are likely to do well, potentially so well they become the drivers of great long-term returns. Indeed, research demonstrates that almost 40% of stocks lose money, while only 20% account for most returns. So, is there a recipe for finding such a stellar investment, say, a stock that returns $100 for every $1 invested and joins the so-called “100-Bagger Club”? Yes, there is, and while it may be simple, it is far from easy. The 100-Bagger Recipe Multiple Growth + Earnings/Intrinsic Value + (Earnings Growth of 25x) x (Multiple Expansion 4x) = 100x Return But there are other important attributes to screen for. So, remember: Smaller is better. Why? Because smaller companies tend to adapt more quickly to changing market conditions and often have faster growth rates. Prioritize companies with differentiated products and services. Don’t underrate the value of a long runway and a large addressable market. A proven, long-term-focused management team whose incentives are aligned with investors. Focus on underfollowed firms. Avoid crowded trades to obtain greater value than what you pay. When an investor finds a subset of these companies, history has shown it pays to hold on for as long as earnings are increasing. Taking profits is standard operating procedure for investors because no one wants to experience the regret of seeing significant paper gains dissipate. Yet, as Marks pointed out in his memo, the investor who held onto Apple stock from its split-adjusted cost of $0.37 in 2003 would have enjoyed a 500-fold return by 2023. When bobbing for tasty investments, we have to focus just as much on avoiding the sour ones as we do on snagging the winners. Over time, the winners will take care of themselves. If you liked this post, don’t forget to subscribe to Enterprising Investor. All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer. Image credit: ©Getty Images / andyh 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

People who are extremely successful in business or investing tend to think of themselves as more skilled and hard working than the average person. No doubt they are on some level, but the more extreme their success, the greater the role luck plays in achieving it. Luck is so critical to extreme success, in fact, that those who attain it do so almost entirely due to luck. No offense intended to any readers, it’s just a matter of math. Let me explain. We are all enthralled by the most successful people in the world. Jeff Bezos and Bill Gates are inspirations to many aspiring business leaders, and Elon Musk has become a rock star thanks to both his enormous business achievements and his personal antics. In the investment world, we look up to all-time legends like Warren Buffett as well as star fund managers with a string of good returns like Cathie Wood in 2020. We all know that a combination of luck and skill determines the performance of investors and business leaders alike. But what we don’t realize is that even if luck plays a minor role in general, it dominates at the extreme tails of the distribution. To see how this works, I simulated the performance of 10,000 investors, with their skill randomly distributed between 0% and 100%. At the same time, these investors had varying degrees of luck, with that attribute also randomly distributed between 0% and 100%. Overall, total success in this model is driven 95% by skill and just 5% by luck. If luck plays such a minor role in success, becoming a top investor should mostly be a matter of skill. But it isn’t. The chart below illustrates the average luck score of our 10,000 investors as their performance moves upward from the mean to greater and greater success. Average Luck of Investors as Their Performance Improves, When Luck = 5% of Performance Source: Liberum Of course, the average luck for all investors is 50%. Those who end up in the top quartile or in the top 10% tend to have slightly better luck than average. But the investors who end up in the top 1% or 0.1% have an awful lot of luck. Even though luck plays only a 5% role in determining success, to end up in the top 1% or top 0.1%, investors have to be very lucky indeed. That also implies that the common approach of emulating the most successful investors or business leaders likely means following less-skilled individuals. The following graphic inverts the process and explores the likelihood that those in the top 25% really have top 25% skill. Among the top quartile investors in our simple model, 97% have top quartile skill, while 94% of top 10% performers have top 10% skill. However, only half of the top 1% performers truly have top 1% skill, and out of the top 0.1% performers, only one in 10 truly has top 0.1% skill. Share of Investors with Skill Corresponding to Performance, When Luck = 5% of Performance Source: Liberum And again, these numbers are based on a model in which skill accounts for 95% of success. In real life, or at least in the investment world, I suspect luck plays a much larger role, probably somewhere close to 50%. The chart below shows the share of investors with skill corresponding to their performance when skill accounts for 55% of total performance and luck for 45%. Only six out of 10 top quartile managers truly have top quartile skills. And only one of seven top 1% investors truly have top 1% skills. Oh, and on average, none of the top 0.1% investors have top 0.1% skills. They are all there because they got very, very lucky. Share of Investors with Skill Corresponding to Performance, When Luck = 45% of Performance Source: Liberum And now remember that most, if not all, of the people who read this are in the top 1% of some sort. If you earn more than £50,000 a year, you are in the top 1% of global income. If you live in the United Kingdom and earn more than £58,300 a year (before taxes), you are in the top 10% in the UK, and if you earn more than £180,000 a year, you are in the top 1%. That is, you are in the top 1% of a country in the top 10% of all countries. And whatever that is, it’s probably more the result of luck than skill. For more from Joachim Klement, CFA, don’t miss Risk Profiling and Tolerance and 7 Mistakes Every Investor Makes (and How to Avoid Them) and sign up for his regular commentary at Klement on Investing. 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/RomoloTavani 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

ESG investing was built for a world that mostly behaved. The idea was simple: channel capital to climate-conscious companies, inclusive workplaces, and ethical supply chains, and the planet — not just your portfolio — would benefit. And for a while, it worked. ESG scores became a badge of honor. Funds slapped leaves on their logos. Boardrooms started sounding like climate summits. Everyone relaxed, like we had found the formula for saving the world and feeling good about our quarterly reports. This is not a rejection of ESG but a recognition that good intentions need backup plans. The world has reminded us that cooperation isn’t a constant; it’s a convenience. And lately, it’s been anything but convenient. Supply chains have broken down like cheap umbrellas. Ransomware attacks have shut off pipelines and exposed just how vulnerable critical infrastructure is. Energy supplies have turned into geopolitical poker chips. Semiconductors have sold out faster than an IPO with “AI” somewhere in the name. It has become clear that volatility isn’t the exception; it is the architecture. So, the question for asset managers and analysts is no longer just: Does this company have a solid climate pledge? It is now: Can this company still function if its cloud provider ends up on a sanctions list? Can it keep delivering products if its key supplier sits on the wrong side of a border dispute? What happens when the grid fails or data leaks? When “free trade” starts to unravel enough to make David Ricardo roll over in his grave? In short, the market has stopped applauding good intentions and started testing whether companies can withstand the world’s mess. From Virtue to Viability That shift — from idealism to viability — makes it clear that we need a new approach. So, I’m proposing ARMOR, which is short for Allocation for Resilient Markets and Operational Readiness. It borrows from how the US government frames national security objectives — not just as military defense, but as economic resilience, supply chain security, and infrastructure continuity. ARMOR gives institutional investors a practical way to evaluate ESG. It doesn’t reject ESG, it extends it. ESG asks if a company is sustainable in principle. ARMOR pushes further, asking if it’s built to survive in practice. Resilience Isn’t an Appendix Item That’s how ARMOR shifts the conversation. In this framework, resilience isn’t about having a perfunctory mention of cybersecurity buried in an appendix — the place where essential topics are acknowledged, then quickly forgotten. It’s about whether operations continue when energy is rationed. It’s about whether a company’s data are stored in a jurisdiction that might suddenly become adversarial, or whether its suppliers are all parked along a trade route that turns into a geopolitical flashpoint. ARMOR asks those questions up front, not after the fact. When Models Miss the Real Risk Value-at-Risk doesn’t blink when global tensions rise. Sharpe ratios don’t care if a company ends up on a sanctions list. A company might look great on paper — low beta, smooth returns, maybe even a shiny ESG report — and still get blindsided by a geopolitical punch it didn’t see coming. That’s the blind spot ARMOR is designed to fill. It doesn’t just ask whether a company is financially healthy or ethically branded, it asks whether the lights stay on when the grid flickers, whether a business can still access its cloud provider if legal jurisdictions shift, and whether it has a plan B when trade routes turn into flashpoints or critical suppliers end up on a watchlist. Building Portfolios That Survive the Mess ARMOR blends portfolio strategy with geopolitical foresight. It’s not a vibe check — it’s a real-world stress test. Instead of optimizing for sunny days, it prepares for storms. And let’s be clear: this isn’t just about dodging risk for safety. It’s about staying in the game. Because when fragility hits, the companies that survive — not just look good surviving — are the ones that end up leading. That’s not just resilience. That’s performance with staying power. In this world, real diversification isn’t just spreading across sectors or regions. It’s about asking deeper questions. Are all your holdings relying on the same chip supply? The same cloud jurisdiction? The same energy corridor? If so, your “diversification” might be an illusion waiting to crack. ARMOR flips the script. It says to stop measuring what looks efficient and start measuring what endures. That doesn’t mean throwing away Sharpe ratios or ESG filters. It means adding a layer that checks for durability when the rules of the game change, and lately, they have changed fast. ARMOR won’t appear on your Bloomberg terminal yet. It’s a mindset — and increasingly, a toolkit — for navigating an asset management future where geopolitical shockwaves, infrastructure bottlenecks, and cross-border data fights aren’t rare. They’re becoming regular fixtures in headlines, earnings calls, and risk memos. Resilience Is the Future of Performance The world in which investors operate has changed, and the playbook needs updating. ARMOR is a step in that direction — not as a replacement for ESG or traditional models — but as a necessary add-on for a world where supply chains tangle, cloud access can vanish overnight, and resilience isn’t a luxury, it’s a survival strategy. In an era when stability can’t be assumed, asset managers must look beyond performance metrics and ask more complex questions about continuity, jurisdiction, and control. This new reality is not just about which companies perform but which ones endure. source

“Every individual . . . intends only his own gain; and he is in this . . . led by an invisible hand to promote an end which was no part of his intention . . . By pursuing his own interest, he frequently promotes that of the society more effectually than when he really intends to promote it.” — Adam Smith, The Wealth of Nations In a book nearly 400,000-words long, the above quote is Adam Smith’s sole reference to the “invisible hand.” Nevertheless, his metaphor inspired the belief, particularly over the last half century, that laissez-faireism fosters economic development. But contrary to the orthodoxies of classical and neoliberal economics, free markets do not, and never did, create perfect competition. Indeed, perfect competition is an urban legend that is easily debunked. Demystifying the Theory What assumptions underlie a perfectly competitive landscape? 1. Products and services are homogeneous, substitutable, and interchangeable. Oddly, if true, this argument would justify market concentration, because product standardization increases the potential for economies of scale. A few major players often dominate industries with broadly indistinguishable products. The four ABCD firms — Archer Daniels Midland (ADM), Bunge, Cargill, and (Louis) Dreyfus — largely direct the global grain trade, and four major players exert a similar influence over the palm oil sector. 2. Firms cannot set their own prices. “The price of monopoly is upon every occasion the highest which can be got,” Smith explains. “The natural price, or the price of free competition, on the contrary, is the lowest which can be taken, not upon every occasion indeed, but for any considerable time together.” Yet many firms proactively influence prices. In retail distribution, supermarkets counterbalance the pricing power of Coors, Heinz, and other large brands by making access to consumers conditional. Even when circumstances may not favor price-setting, market participants may still try to set them illegitimately. For example, energy trader Marc Rich + Co cornered the world aluminum market in 1988 and attempted to repeat the feat with zinc four years later. 3. The market is fragmented. On the contrary, extreme concentration is common. Sectors as diverse as grocery stores, digital operating systems, social media, automotive, and audit all have only a few major players. Even consolidation-averse creative industries are far from immune: The five largest advertising agencies account for the bulk of the global market. 4. Consumers and producers have perfect information about products, substitutes, and prices. We may know where in our neighborhoods to purchase cheaper bread or movie tickets, but in a digital and global economy with increasingly diverse sources of supply, there is simply too much data for us to sift through and too many variables for us to consider. Comparison websites can help us bridge the gap, but they only operate in utilities and such commoditized services as energy, travel, and insurance. 5. Barriers and costs to market entry and exit are low. For perfect competition, suppliers must have easy access to an industry as well as an easy out. But such conditions are rarely met. Think of sectors that require heavy capital commitments, such as semiconductors and aerospace — Airbus and Boeing; those that benefit from network effects, including social platforms; or those where a strong brand is nurtured over several decades of advertising spend, which gave us Coke and Apple. Opening Up to Competition The economist Léon Walras formulated the concepts of perfect competition and market equilibrium a full century after the publication of The Wealth of Nations. Smith himself never framed his treatise in those terms, even if his views inspired many to do so in his name. His reference point was drastically different. The 18th-century marketplace was organized locally around farming communities and controlled by individual landlords as well as small textile and machine tool concerns established by craftsmen, alongside monopolies of artisans and merchants sometimes still operating as guilds. The Industrial Revolution was in its infancy and hardly noticeable — the phrase would first be recorded in 1799. Corporations were government-backed agencies such as British East India Company and its European counterparts. State policies sought to guarantee domestic supply. In 1665, France’s first Minister of State Jean-Baptiste Colbert established a factory to manufacture mirrors, a popular luxury item of the day. That national monopoly would later become Saint Gobain. In short, free markets did not exist in Smith’s time. But by the time Walras had enhanced the theory, they were meant to evolve, somewhat magically, towards an equilibrium with a set price for a given quantity of goods. Market Equilibrium under Perfect Competition Visible Sleight of Hand According to modern economic theory, in an unregulated landscape, many buyers meet many sellers, and neither side of a transaction can unduly affect the price discovery process. “Although Adam Smith could never prove his theory, he did have a point. Modern economists now know that there is a sense in which people’s selfish actions are led as if by an invisible hand toward a harmonious final result,” Paul Samuelson and William Nordhaus observe in Economics. “[A]n economy driven by perfect competition leads to an efficient level and allocation of inputs and outputs.” But such an economy has never existed. In the 19th century, telegraphy, railroads, and other emerging industries quickly consolidated as small and local operators gave way to national juggernauts. Indeed, by 1900, seven railway companies controlled the US market, and Western Union had monopolized telegraphy, bypassing the postal monopoly. In a free market, even corporations that have been broken up because of their monopolistic positions tend to reconsolidate. AT&T dominated the telecom industry in the United States for most of the 20th century. US regulators split it into seven independent regional operators, the “Baby Bells,” in the 1980s. Four decades later, after further market liberalization, the sector reconcentrated around three players: Verizon, T-Mobile, and AT&T, which had re-aggregated several Baby Bells. It is a standard progression: Dismantled monopolies often reconstitute themselves. After the 1911 dissolution of Standard Oil into 34 separate companies, the surviving entities