CFA Institute

Asset Allocation: From Theory to Practice and Beyond, Second Edition. 2021. William Kinlaw, CFA, Mark Kritzman, CFA, and David Turkington, CFA. Wiley. To build a robust investment process, asset allocators must address a long list of issues, including: which assets to choose, how to forecast risk and return, and how to manage currency risk. William Kinlaw, CFA, Mark Kritzman, CFA, and David Turkington, CFA, offer advice on these and a wide range of other topics in asset allocation, backing up their recommendations with solid quantitative analysis. Along the way, they dispel a few myths and tackle some of the most challenging aspects of investing. The authors identify seven essential characteristics of every asset class: Their composition must be stable (not static). They are directly investable. The components are similar to one another. The asset class is dissimilar to other asset classes. Investing in the asset class raises the expected utility of the portfolio. Selection skill is not a requirement for investing. Investors can access the asset class in a cost-effective way. (I would add an eighth: Investors must be able to come up with credible forecasts of return, risk, and correlations to other assets, to implement inclusion in an optimization process. This requirement would exclude, for example, cryptocurrencies.) What do these criteria mean in practice? Global equities are not internally homogeneous and therefore cannot be considered a single asset class. Instead, the authors identify three equity asset classes: domestic equities (meaning US equities for the authors), foreign developed market equities, and foreign emerging market equities. Excluded from the authors’ defined asset classes are art (not accessible in size), momentum stocks (unstable composition), and — more unconventionally — high-yield bonds, which are not externally heterogeneous because they are similar to investment-grade bonds and therefore form part of the corporate bond asset class. Ironically, the first myth that the book tackles is the importance of asset allocation. A much-cited 1986 article by Gary P. Brinson, L. Randolph Hood, and Gilbert L. Beebower found that asset allocation determines more than 90% of performance. This book argues, however, that the methodology of that study is flawed because it assumes a starting point of an uninvested portfolio. In practice, the authors show, once investors have made the decision to invest, asset allocation and security selection are likely to be equally important (depending, of course, on the investment approach taken). “In the absence of any skill, effort, or careful consideration,” they write, “investors can simply default to a broadly diversified portfolio such as 60–40 stocks and bonds.” The outputs from mean–variance optimizers are hypersensitive to small changes in inputs. Yet the authors dispel the myth that this sensitivity leads to error maximization. It is true that small changes in estimates between assets with similar risk and return characteristics can lead to big shifts in allocations between them. Because the assets in question are close substitutes, however, these reallocations have little impact on the portfolio’s return distribution. By contrast, pronounced sensitivity to changes in inputs is not observed with assets that have dissimilar characteristics. In particular, small changes in estimates for equities and bonds do not lead to large swings in the optimal allocation between them. Asset Allocation covers all the key ingredients of its subject, such as forecasting returns, optimization, and currency hedging. The chapter on rebalancing provides a good flavor of what practitioners will find: a mix of detailed quantitative analysis and practical advice, with scope to draw one’s own conclusions. Investors must evaluate the trade-off between the cost of rebalancing their portfolios to target against the cost of sticking with a suboptimal mix. A section on a dynamic programming methodology concludes that this approach is computationally impossible. The authors then present an optimal rebalancing methodology, the Markowitz–van Dijk heuristic approach. Its costs (5.4 bps) are compared with the costs for calendar-based rebalancing (5.5 bps to 8.9 bps), tolerance band rebalancing (5.8 bps to 6.9 bps), and no rebalancing (17.0 bps). This detailed analysis supports a simpler conclusion for those of us who deal with individual clients, for whom behavioral biases present the biggest threat to long-term success: Have a long-term plan, rebalance your portfolio to that plan, but don’t trade too often. The book presents high-level quantitative analysis to explore some of the most challenging aspects of asset allocation. For example, the authors assess the probability of forward-looking scenarios using a technique originally developed by Indian statistician P.C. Mahalanobis to characterize human skulls. They employ a hidden Markov model to develop a regime-shifting approach. Additionally, they identify the fundamental drivers of stock–bond correlations using statistically filtered historical observations. Notwithstanding its reliance on such sophisticated techniques, this new edition of Asset Allocation is accessible to those of us who work with quant teams rather than in them. Each chapter offers a stand-alone analysis of one of 24 aspects of asset allocation. I find myself regularly returning to this book for its framing of the issues I face, the authors’ analysis, and their concise presentation of the bottom line. If you liked this post, don’t forget to subscribe to the Enterprising Investor. All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer. Professional Learning for CFA Institute Members CFA Institute members are empowered to self-determine and self-report professional learning (PL) credits earned, including content on Enterprising Investor. Members can record credits easily using their online PL tracker. source

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

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

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

Anticipating and riding short squeezes has grown in popularity as an investment tactic in recent years. The GameStop short squeeze, galvanized by motivated retail investors on internet message boards, is a vivid example of this phenomenon. The ideal outcome for a prospective short-squeezer is what we’ll call the short-squeeze trifecta: They must identify the short squeeze before it happens, successfully ride the stock as its value soars on the way up, and bail out before the price falls back down to earth. Stocks that end up in a short squeeze tend to exhibit two well-known determinants: They have high short interest and are thinly traded. But do other factors come into play? We wondered whether certain macro conditions might correlate with greater numbers of short squeezes or if short squeezes were more common in particular sectors. Our analysis indicates two additional factors are associated with increased short squeeze activity: elevated market uncertainty and speculative technologies with yet-to-be-determined long-term value. Strict and Loose Short Squeezes To study short squeezes over time, we first had to develop a methodology to establish whether they actually took place. Using data from all publicly listed US companies from 1972 to 2022, we defined two distinct categories of short squeezes: “strict” and “loose.” A strict short squeeze is when a stock’s price rises by 50% to 500% and then falls back down to between 80% and 120% of its previous value in the course of one month. The same pattern occurs in a loose squeeze but over two months. This approach identified 1,051 strict short squeezes and 5,969 loose short squeezes during the study period. The results for strict short squeezes are presented below. The loose method demonstrated qualitatively similar results. Strict Short Squeezes by Year The number of strict short squeezes varied considerably over time. Many years had close to zero while others had more than 100. The five most active short squeeze months, normalized by the total number of contemporary equity listings, were February 2021, May 2020, October 2008, February 2000, and October 1974. Tumultuous Times What do all these months have in common? They fell amid periods of extreme market uncertainty. Inflation and COVID-19 infections were resurgent in February 2021, for example. In May 2020, the pandemic had upended life as we know it. The global financial crisis (GFC) and the associated panic were in full swing in October 2008. In February 2000, the dot-com bubble was approaching its speculative peak before beginning its subsequent downward spiral. High inflation, oil price shocks, and a severe recession were all center stage in October 1974, and the US Federal Reserve would soon start slashing interest rates, prioritizing economic growth over reducing inflation. So tough times for the markets and the larger economy tend to be good times for short squeezes. Yet-to-Be-Proven Technology How did strict short squeezes vary by sector? They occurred most often in biotech, with 20 in 2000 and 23 in 2020. These were the top years for short squeezes for any sector. Software and computing was the second most common short-squeezed sector. Strict Short Squeezes by Sector The biotech and software and computing sectors share a heavy reliance on new and often unproven technology. This makes them more prone to speculation, more difficult to value, and, as our data show, likelier targets for short squeezes. By contrast, the least short-squeezed sectors are railroads, lodging, life insurance. These all have established, well understood business models and little uncertainty around their valuations. They have little appeal for potential short-squeezers. So to determine whether a stock might become the target of a short squeeze, there are four criteria to keep in mind: Is the stock being shorted? Is it thinly traded? Does it rely on unproven technology? Are macro conditions especially unstable? To be sure, short squeezes are not especially common phenomena, so even if all four conditions apply, the odds of predicting one are still very long. And as GameStop demonstrates, there are always outliers. Moreover, even if these four factors help identify short squeezes before they happen, their trajectories — how quickly they crest and crash — will always be fraught and uncertain. Which is why short squeezes are waves we shouldn’t stake too much on catching and riding. 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/ cnsphotography 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

Financial Statement Analysis for Value Investing. 2025. Stephen Penman and Peter Pope. Columbia University Press. The discipline of value investing has had a tough time of late. The relentless ascent of passive investment strategies, the prolonged outperformance of growth stocks since the Global Financial Crisis, and the soaring overall valuations in developed markets (where time-tested valuation principles no longer seem to apply), to name but a few, have all contributed to its struggles. As a result, the heirs of the Graham and Dodd tradition are numbered these days and relegated to deep-value strategies in emerging markets or Japan. Is this merely a temporary aberration, or does the tradition need some refinements to remain relevant in today’s financial landscape? Against this backdrop, Stephen Penman, the George O. May Professor Emeritus at Columbia Business School, and Peter Pope, Professor Emeritus of Accounting at the London School of Economics, have published a 432-page tome entitled Financial Statement Analysis for Value Investing, a work firmly rooted in the Graham and Dodd value investing tradition. The book also expands on the framework developed by Penman in his 2011 work, Accounting for Value. In both books, readers will encounter classic value investing concepts, such as negotiating with Mr. Market or the importance of a margin of safety, and some insights from modern portfolio theory, such as the neutrality of dividends or a company’s capital structure in creating value for shareholders. Practitioners will find this surprising and eclectic combination of ideas refreshing and enlightening. As the authors succinctly state in the introduction: You will find the book contrasts with many investment books. The ubiquitous beta is not of highest priority by far. The common discounted cash flow (DCF) is put aside. Indeed, the book is skeptical about valuation models in general. Perhaps surprisingly, the book takes the position that it is best to think that “intrinsic value” does not exist. For a value investor that sounds like heresy, but intrinsic value is just too hard to pin down. That requires an alternative approach to be put on the table, one that challenges the market price with confidence. Some investors see the alternative as trading on multiples, smart beta investing, factor investing, and more. The book brings a critique to these schemes. So, what do the authors propose? The cornerstone of the book is the residual income model. First formalized in the 1980s[1] and 1990s[2], much later than other valuation frameworks such as the dividend discount model, the residual income model was popularized in the 1990s by the consulting firm Stern Stewart and briefly adopted by the management teams of several large U.S. corporations to gauge whether their investment decisions were creating value for their shareholders. However, despite numerous academic papers on the model, its adoption by practitioners has remained limited, lagging behind more widely used approaches such as valuation multiples and the free cash flow model. As a quick refresher, the residual earnings model instructs us to think about valuation through the lens of the future residual (or economic) earnings that a business is expected to generate. Residual earnings are simply accounting earnings after taking into account a cost of capital charge. These future residual earnings must then be discounted back to the present and added to the company’s current book value to arrive at a valuation for the equity. Notably, if a company’s return on equity matches its cost of capital, it will generate accounting earnings but no residual earnings, meaning that its shares should trade at book value. The elegance of the model lies in the seamless integration of business fundamentals with accounting figures, which in turn produce a valuation for the investor. Although the three valuation frameworks (dividends, free cash flows, and residual income) are mathematically equivalent, the residual income stands out for its ability to capture the true sources of value creation for shareholders. Companies that do not pay dividends or reinvest in profitable growth opportunities would be hard to value using the dividend discount or the free cash flow model, respectivel, but they do not obstruct the residual income framework. The reason this model captures value creation more accurately (and earlier) is rooted in the accruals that govern current accounting systems. While so-called “cash accounting” is often favored by practitioners over accrual accounting on the oft-touted premise that cash is closer to “hard and cold facts” whereas unscrupulous management teams can easily manipulate accruals, Penman and Pope show that this conventional wisdom is simply misguided. First, cash flows themselves can also be manipulated by management teams. Second, there are a plethora of transactions that do not involve cash flows yet still shift value between stakeholders, with stock compensation being probably the most prominent example. But most importantly, earnings are usually recognized earlier than cash flows under the “realization principle.” For instance, sales on credit are recognized before the company gets the cash, capital investments are depreciated over time (increasing earnings at the onset of the investment), and pension obligations are accounted for immediately, even though cash will not flow out of the company to pay the promises until decades later. The important implication for investors valuing stocks in the real world, where the future is uncertain, is that “[w]ith this earlier recognition of value added, there is less weight on a terminal value in a valuation.” In summary, an accounting system based on accruals and the realization principle inherently reflects sound thinking about how firms create value for investors, as well as some guidelines for understanding risk and return. Value is capitalized on the balance sheet only when the certainty of the investment is high, and subsequent earnings are added to book value only when they are realized. From this standpoint, alternative forms of “carrying” the accounting book, such as fair value accounting, fail to uphold these principles. Throughout the book, Penman and Pope criticize fair value accounting for encouraging speculative behavior by placing uncertain values on the balance sheet, which ultimately contributes to investor speculation — as was exemplified during

Resistance Money: A Philosophical Case for Bitcoin. 2024. Andrew M. Bailey, Bradley Rettler, and Craig Warmke. Routledge. “Bitcoin is for criminals. It’s a tool for terrorists, drug dealers, and hackers, and a plaything for degenerate speculators.” “Compared to physical cash, bitcoin enables some wrongdoing more easily over longer distances.” “Perhaps in the long run, bitcoin could destroy the international order by making sanctions less effective.” “Even if bitcoin intrinsically has no serious problems, it is surrounded by a culture rife with scams.” “Bitcoin does involve significant carbon emissions. This is bad.” “…bitcoin benefits North Korea’s totalitarian government. This is bad.” “…bitcoin does not automatically provide users with significant financial privacy.” “Throughout its history, bitcoin has shown enormous volatility.”   “It might even go to zero.” The preceding excerpts from Resistance Money will likely strike readers of this review as puzzling in view of the book’s subtitle, “A Philosophical Case for Bitcoin” (emphasis added).  In reality, authors Andrew M. Bailey (Associate Professor of Humanities, Yale-NUS College, Singapore), Bradley Rettler (Associate Professor of Philosophy, University of Wyoming), and Craig Warmke (Associate Professor of Philosophy, Northern Illinois University) are forthrightly stating the case against bitcoin in the course of arguing that on balance, one should prefer to live in a world with bitcoin rather than one without it. The book’s evenhanded approach is a welcome contrast to the extreme comments regularly heard from both bitcoin’s zealous proponents and its frequently ill-informed opponents.   High among the positives that, in the authors’ view, outweigh bitcoin’s negatives is its users’ ability to defend themselves against financial censorship. They point out that people with dissident political views who depend on conventional finance are vulnerable to shutdown of their bank accounts, blocking of their transactions, and even seizure of their funds. Bailey, Rettler, and Warmke note that such tactics are not employed solely by dictatorial governments.  From 2013 to 2017, the US Department of Justice and Federal Deposit Insurance Corporation’s “Operation Checkpoint” pressured banks to deplatform individuals and companies involved in fully legal businesses, including ATM operators, coin dealers, dating services, pawnshops, and payday lenders. In 2022, 22 rights groups including the American Civil Liberties Union and the Freedom of the Press Foundation asked PayPal to stop shutting down accounts under a new user agreement which gave the company sole discretion to confiscate up to $2,500 from customers it deemed to be publicly spreading misinformation. Bitcoin is not censorship-proof, say the authors, but it is censorship-resistant.    Resistance Money also pleads on behalf of the world’s billions of unbanked individuals. Bitcoin requires no minimum balance, charges no fees for opening an account, and does not exclude people with problematic credit histories. It is accessible to immigrants who lack documents to verify their identities and financial histories and the poor who lack the resources to obtain them. Bitcoin users need not worry about being surprised by a hidden charge, being discriminated against on the basis of their ethnicity, or living too far from a branch bank to obtain access to banking services. All they need to enter the bitcoin network is a mobile phone or a laptop. Eighty-five percent of Americans currently own smartphones, up from thirty-nine percent 10 years ago.  Masters of argumentation by virtue of their training as philosophers, the authors also tackle in a reasoned manner such standard objections to bitcoin as its high price volatility and the sizable quantity of energy consumed in mining bitcoins. Happily, the scenario presented by a 2017 Newsweek headline, “Bitcoin Mining on Track to Consume All of the World’s Energy by 2020,” did not come to pass. Bailey, Rettler, and Warmke even address several criticisms of bitcoin that many well-informed financial practitioners have probably never previously heard. These include complaints that bitcoin is divisible into unduly small subunits (one bitcoin equals 100 million satoshis, each of which was worth about $0.00025 when the book was written), the objection that bitcoin is very unequally distributed (about 7.9 billion people on earth own none), and the allegation (disputed by the authors) that although bitcoin is purposely designed to operate without makers, mediators, or managers, bitcoin miners are in fact mediators. The last point touches on a problem that many readers are likely to encounter in reading Resistance Money: Following certain of its arguments requires a deep immersion in the technical details of bitcoin’s design and operation. Nonspecialists may, for example, find the lengthy description of bitcoin’s failed predecessors a slog and somewhat beside the point. Along with most other books that Enterprising Investor reviews, Resistance Money is not completely free of error. The text refers at one point to the “Great Recession of 2007-2009.”  In reality, the National Bureau of Economic Research dates the beginning of that economic contraction to January 2008.  None of these difficulties or imperfections should deter practitioners from reading this authoritative examination of a controversial asset with a current aggregate value of $1.3 trillion. The book comes much closer to a CFA Institute-style ideal of rational, evidence-based analysis than most comments on bitcoin’s merits, or lack thereof. With clients asking their advisors either to add bitcoin to their portfolios or to provide a good reason for not doing so, Resistance Money will immensely help advisors reach a firmly grounded decision on which way to go.     source

Artificial Intelligence (AI) and big data are having a transformative impact on the financial services sector, particularly in banking and consumer finance. AI is integrated into decision-making processes like credit risk assessment, fraud detection, and customer segmentation. These advancements raise significant regulatory challenges, however, including compliance with key financial laws like the Equal Credit Opportunity Act (ECOA) and the Fair Credit Reporting Act (FCRA). This article explores the regulatory risks institutions must manage while adopting these technologies. Regulators at both the federal and state levels are increasingly focusing on AI and big data, as their use in financial services becomes more widespread. Federal bodies like the Federal Reserve and the Consumer Financial Protection Bureau (CFPB) are delving deeper into understanding how AI impacts consumer protection, fair lending, and credit underwriting. Although there are currently no comprehensive regulations that specifically govern AI and big data, agencies are raising concerns about transparency, potential biases, and privacy issues. The Government Accountability Office (GAO) has also called for interagency coordination to better address regulatory gaps. In today’s highly regulated environment, banks must carefully manage the risks associated with adopting AI. Here’s a breakdown of six key regulatory concerns and actionable steps to mitigate them. 1. ECOA and Fair Lending: Managing Discrimination Risks Under ECOA, financial institutions are prohibited from making credit decisions based on race, gender, or other protected characteristics. AI systems in banking, particularly those used to help make credit decisions, may inadvertently discriminate against protected groups. For example, AI models that use alternative data like education or location can rely on proxies for protected characteristics, leading to disparate impact or treatment. Regulators are concerned that AI systems may not always be transparent, making it difficult to assess or prevent discriminatory outcomes. Action Steps: Financial institutions must continuously monitor and audit AI models to ensure they do not produce biased outcomes. Transparency in decision-making processes is crucial to avoiding disparate impacts. 2. FCRA Compliance: Handling Alternative Data The FCRA governs how consumer data is used in making credit decisions Banks using AI to incorporate non-traditional data sources like social media or utility payments can unintentionally turn information into “consumer reports,” triggering FCRA compliance obligations. FCRA also mandates that consumers must have the opportunity to dispute inaccuracies in their data, which can be challenging in AI-driven models where data sources may not always be clear. The FCRA also mandates that consumers must have the opportunity to dispute inaccuracies in their data. That can be challenging in AI-driven models where data sources may not always be clear. Action Steps: Ensure that AI-driven credit decisions are fully compliant with FCRA guidelines by providing adverse action notices and maintaining transparency with consumers about the data used. 3. UDAAP Violations: Ensuring Fair AI Decisions AI and machine learning introduce a risk of violating the Unfair, Deceptive, or Abusive Acts or Practices (UDAAP) rules, particularly if the models make decisions that are not fully disclosed or explained to consumers. For example, an AI model might reduce a consumer’s credit limit based on non-obvious factors like spending patterns or merchant categories, which can lead to accusations of deception. Action Steps: Financial institutions need to ensure that AI-driven decisions align with consumer expectations and that disclosures are comprehensive enough to prevent claims of unfair practices. The opacity of AI, often referred to as the “black box” problem, increases the risk of UDAAP violations. 4. Data Security and Privacy: Safeguarding Consumer Data With the use of big data, privacy and information security risks increase significantly, particularly when dealing with sensitive consumer information. The increasing volume of data and the use of non-traditional sources like social media profiles for credit decision-making raise significant concerns about how this sensitive information is stored, accessed, and protected from breaches. Consumers may not always be aware of or consent to the use of their data, increasing the risk of privacy violations. Action Steps: Implement robust data protection measures, including encryption and strict access controls. Regular audits should be conducted to ensure compliance with privacy laws. 5. Safety and Soundness of Financial Institutions AI and big data must meet regulatory expectations for safety and soundness in the banking industry. Regulators like the Federal Reserve and the Office of the Comptroller of the Currency (OCC) require financial institutions to rigorously test and monitor AI models to ensure they do not introduce excessive risks. A key concern is that AI-driven credit models may not have been tested in economic downturns, raising questions about their robustness in volatile environments. Action Steps: Ensure that your organization can demonstrate that it has effective risk management frameworks in place to control for unforeseen risks that AI models might introduce. 6. Vendor Management: Monitoring Third-Party Risks Many financial institutions rely on third-party vendors for AI and big data services, and some are expanding their partnerships with fintech companies. Regulators expect them to maintain stringent oversight of these vendors to ensure that their practices align with regulatory requirements. This is particularly challenging when vendors use proprietary AI systems that may not be fully transparent. Firms are responsible for understanding how these vendors use AI and for ensuring that vendor practices do not introduce compliance risks. Regulatory bodies have issued guidance emphasizing the importance of managing third-party risks. Firms remain responsible for the actions of their vendors. Action Steps: Establish strict oversight of third-party vendors. This includes ensuring they comply with all relevant regulations and conducting regular reviews of their AI practices. Key Takeaway While AI and big data hold immense potential to revolutionize financial services, they also bring complex regulatory challenges. Institutions must actively engage with regulatory frameworks to ensure compliance across a wide array of legal requirements. As regulators continue to refine their understanding of these technologies, financial institutions have an opportunity to shape the regulatory landscape by participating in discussions and implementing responsible AI practices. Navigating these challenges effectively will be crucial for expanding sustainable credit programs and leveraging the full potential of AI and big data. source

Few people have managed to avoid noticing the increasing popularity of cannabis consumption. However, if you walk down the street and catch a whiff of that grassy, pungent smell, you are witnessing a dying breed of cannabis users. While more people consume cannabis, they are increasingly shifting away from the raw flower. For investors in the space, the changing cannabis consumption landscape opens challenges and opportunities in product creation, marketing, and targeting new consumer groups. Cannabis is Becoming a Commodity While cannabis is famously a weed, its connoisseurs will let you know if you get your strains mixed up. Different strains have varying amounts of THC, the cannabinoid that gets you high, as well as other non-psychoactive cannabinoids and terpenes, several of which have been found to have beneficial effects on inflammation, stress, and more. Cannabis strains all have their own individual look, smell, taste, and experience. Cannabis is becoming increasingly commoditized, however, and consumption is moving farther away from the raw flower. A growing segment of consumer products are infused with THC and other cannabinoids, thus removing the experience of a flower strain and its characteristic cannabinoid and terpene combinations. Cultivating cannabis is increasingly a large-scale commercial affair, with the size of cannabis “grows” limited in part by their market size because the United States does not allow interstate commerce of the crop. Many larger operations belong to so-called multi-state operators — companies structured to operate in multiple states. If you have ever invested in a cannabis exchange-traded fund (ETF), these are the companies you bought. Because of their wide reach, their market capitalization is large enough to make it into cannabis ETFs. Yet, no cannabis company can ship across state lines. What you grow in a state is also what you sell in that state. More People in the US Now Consume Cannabis Than Alcohol The commoditization of cannabis parallels an important trend that sees cannabis use spreading across age groups and social spheres. More cannabis consumers are now college kids and middle-aged women, as opposed to your traditional “potheads.” We see more distilled and infused products, such as gummies, drinks, and vapes, and less flower for examining, grinding, and smoking. Surveys of consumption in the United States reveal that cannabis consumption has surpassed alcohol drinking. This has long been the case among younger cohorts. Among Millennials and Gen Zs, alcohol consumption has been on a slow and steady decline while cannabis use has been increasing. This is partly due to a realization of the damage that alcohol does, and partly because of the softer effect of cannabis. Cannabis is now recognized as a more benign drug than alcohol and has lost the heavy stigma it previously carried. Growing Product Categories For investors, a wider demographic of cannabis use opens a field of new investment opportunities. Product categories are broadening, creating opportunities for expanding branding, and marketing. Smoking is increasingly recognized as unhealthy and unappealing. Familiar products that do not require inhalation such as chocolates, cookies, and drinks are becoming more popular. There are powders and concentrates for making cannabis cocktails. Edibles such as gummies and drinks are particularly popular among consumers aged 55 and over, according to a report by New Frontier (2023). The same report also finds that those aged 18 to 24 are the least likely to smoke cannabis exclusively, pointing to the trend of a new generation of cannabis users replacing alcohol and not wanting to smoke. Smoking in some form, flower or vape, is still the dominant form of cannabis consumption, but the trend toward other types of products creates opportunities for new entrants to claim niche spaces. There is an increasing popularity in lower-dose products, according to New Frontier. Their consumer report notes a decrease in popularity and frequency of use of blunts, bongs, and water pipes, as well as dabbing and concentrates. Meanwhile, we are seeing an increasing use of edibles, which is now the most frequently used form of cannabis. Also rising are vapes, drinks, topicals, and to some extent tinctures. While cannabis has become increasingly common at student parties, we anticipate that it will eventually make its way into bars and other social settings. Twelve states currently allow cannabis consumption lounges in some form. Some states allow for purchase of cannabis on site, while in other states, lounges can only offer spaces for consumption. While lounges still mostly come in the form of simple cafes, this category of hospitality establishments may well soon evolve into pleasant speakeasies and dance clubs. This field is wide open for entrepreneurs. New Consumer Groups As cannabis becomes more mainstream, new target groups are emerging. As mentioned, young people are dominant users of cannabis. But the fastest growing group of cannabis users is senior citizens over the age of 65. Another important and quickly growing consumer group is middle-aged women, using it to alleviate symptoms of menopause. Pain relief and sleep aid are common applications of cannabis for all cohorts but especially for these groups. And as with any health remedy, whatever humans use on themselves they also provide their pets, making the pet market an important focus for investors in cannabis. Understanding how to reach these new consumer groups offers great opportunities for novel branding and product categories. Investment Opportunities As weed becomes increasingly commoditized, opportunities emerge outside crop cultivation. Prices of flower are falling in most states, making it increasingly attractive to be a buyer of biomass instead of its producer. By contrast, on an international level, markets outside the United States are increasingly integrated via cannabis trade, opening opportunities to grow cannabis in low-cost and climate-appropriate places for export, with southern Africa and Colombia attracting a growing stream of investments. Cannabis remains underdeveloped as a consumer-packaged goods (CPG) category. Most packaging remains rudimentary and brand building is still in its infancy. This may well be due to entrepreneurs in cannabis not entering from a CPG angle but out of a passion for the plant and its health benefits. While this

1. Fama and French: The Five-Factor Model Revisited How well has Eugene F. Fama and Kenneth R. French’s five-factor model explained returns? Derek Horstmeyer, Ying Liu, and Amber Wilkins share their analysis. 2. Tell Me a Story: Aswath Damodaran on Valuing Young Companies When valuing companies, “You don’t have to be right to make money,” Aswath Damodaran says. “You just have to be less wrong than everybody else.” Roger Mitchell considers Damodaran’s insights. 3. The Elephant in the Room: The ESG Contradiction The inherent conflict between the “E,” the “S,” and the “G” in ESG investing can no longer be ignored. As much as we might wish otherwise, the goals embedded in these initials don’t always align with one another, Andrea Webster, Paul Smith, CFA, and Kübra Koldemir contend. 4. How Long Can Russia Withstand the Sanctions? The toll of the economic embargo on Russia will be enormous, Joachim Klement, CFA, predicts. He goes on to calculate just how enormous. 5. Investing’s First Principles: The Discounted Cash Flow Model Brian Michael Nelson, CFA, explains why the DCF model is not only relevant to today’s market, but remains an absolute necessity. 6. Retirement Income: Six Strategies How can we mitigate sequence of returns risk (SoRR)? Krisna Patel, CFA, shares half a dozen strategies to safeguard clients’ retirement portfolios. 7. Building a CAPM That Works: What It Means for Today’s Markets “The capital asset pricing model (CAPM) is a marvel of economic scholarship,” Jacques Cesar writes. “The problem is that it doesn’t always work in practice. So, we fixed it.” 8. Equity Risk Premium Forum: Don’t Bet Against a Bubble? Cliff Asness, Rob Arnott, Roger G. Ibbotson, and other luminaries explore the nature of bubbles and the momentum factor. Paul McCaffrey provides a synopsis of their dialogue. 9. A Tale of Two Suits: The Three Capitals of Career Success According to Eric Sim, CFA, human capital, financial capital, and social capital helped build his career in finance. Paul McCaffrey considers Sim’s compelling personal story and how we can apply the lessons to our own careers. 10. From No to Yes: Persuading Clients with the 3Ps Method In this adaptation from Small Actions: Leading Your Career to Big Success, Eric Sim, CFA, and Simon Mortlock discuss the 3Ps method — perseverance, perspective, and positivity — and how to use it to transform rejection into approval. 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