CFA Institute

Measuring corporate impact is time-consuming and resource intensive. Until recently, I worked at UN PRI and witnessed first-hand the significant challenges investors, employees, and customers face in finding trustworthy, comparable data to assess the net impact of companies. CFA Institute Research and Policy Center’s Climate Data in the Investment Process cites inconsistent and unreliable data as key challenges for stakeholders including investment professionals interested in assessing and managing the financial risks and opportunities posed by climate change. Upright Project – a Finnish impact data company — significantly influenced my perspective on data modeling, and I joined the company four months ago. Upright’s approach structured all scientific evidence in an organized manner and created a unique dataset that enabled comparisons of companies worldwide from an outside-in perspective. Upright’s net impact model classifies more than 150,000 products and services. This classification is used to define the business models of every company in its database. The model leverages more than 250 million academic articles to determine the science-based impact of each product and service. The data are aggregated at the firm and portfolio level to quantify the total material impact of an investment. Notably, a significant portion of this data is publicly accessible: more than 10,000 company impact data profiles are available on its platform using a free-use policy. With my academic background, I was inspired by a solution that not only leverages scientific evidence but also delivers practical applications for investment practitioners and investors. Applications Are Unfolding At Upright, we have learned a great deal from investors, but the potential applications of this data are still unfolding. Since the modeling approach is outside-in, private equity and venture capital investors have been early adopters of the data. In addition, the model’s transparency and objectivity make it useful for asset managers and asset owners — particularly for disclosure purposes — whether for fund-level requirements or to demonstrate the overall impact of their investments. Granular Data: The Challenges and Opportunities The full potential of this data is not yet clear. The granular nature of the data allows investors to pinpoint which business units of a company drive positive or negative financial and non-financial material impacts. This creates opportunities for risk assessment and stewardship. Furthermore, the model’s applicability to both private and public companies enables comparisons across all asset classes held by an investor. This can help identify high exposures to specific impact categories. While many investors have sought more detailed information, the use cases for this new, holistic approach to understanding and evaluating companies are still emerging. Because Upright’s modeling approach is new to most investors, I will illustrate how they can use the platform to evaluate a company’s impact. Step 1: Assess the business model of a company using a products- and services-based approach. Let’s use an example company, Siemens. Based on the latest publicly available version of the Upright model, Siemens sells more than 165 products and services. The total revenue of the company is 77,769 million euros, and it has 320,000 employees. Some 28% of its total revenue is generated by products and services within digital industries, which comprise electric motor control devices, gas turbines, generators, electric actuators, linear motors, and more. Details of the full product mix are visible on the Upright platform.  Siemens’ Digital Industry Products Source: List of Siemens’ products and services on the Upright platform. Step 2: Choose an impact category that you’re interested in. The Upright model currently covers four main impact categories: Society, Knowledge, Health, and Environment. Each category has sub-categories. For example, under Health, there exist physical diseases, mental diseases, nutrition, relationships, as well as meaning and joy. Impact categories can be negative and positive. In the case of Siemens, we can see that their products and services are creating both negative and positive impacts within the physical diseases sub-category. Siemens’ Health Impact Siemens’ public net impact profile on the Upright platform. Step 3: Choose whether you’re interested in upstream, internal, or downstream impact. Products and services do not exist in isolation. Often, one product is required to make another or for a user to create an impact using a product. The Upright model has mapped all products and services so that you can assess where in the value chain the associated impact occurs. In the case of Siemens, 94% of the positive impact on physical diseases or life years saved, associated with its products and services happens downstream from the company.  Siemens’ Downstream Impact Source: Upright platform. Step 4: Examine the products and services that are associated with the impact category you’ve chosen. In the case of Siemens, the products and services that contribute most to the positive impacts on physical health are radiation therapy machines, cardiac resynchronization therapy devices, private oncology diagnostics services, ultrasound machines, and mammography machines. Combined, these five products contribute the most to Siemens’ positive impact both because they compose a substantial proportion of the company’s revenue and because the latest scientific consensus suggests a high positive causal relationship between these products and services on physical health.  Upright’s Bayesian inference machine learning model finds causal relationships by classifying and translating from more than 250 million scientific articles, as well as from other sources. These insights form the foundation for defining whether the products and services that companies sell create negative or positive material outcomes, which together provide investors with a full view on the impact of their companies and portfolio. source

Will the son of a billionaire perpetuate his inherited wealth? Apparently not, if history is any guide. In fact, there is strong evidence that most “rich families” will be poorer after several generations. Some of the reasons for this are systemic. Taxes, for example, chip away at a family’s wealth. But most factors that diminish a family’s wealth over generations are the choices that heirs make. These include how they invest their inheritance, how many children they have, whether they get divorced, and other lifestyle choices. Figure 1. The 10 richest people in the world in 2013 and 2023. Source: Forbes As Figure 1 illustrates, six of the 10 richest people in the world were “created” in 10 years. And these were all men, which is why I use the term “patriarch” throughout this blog. Of course, this is too small a sample to be statistically significant. But at first glance, the Forbes Top10 List shows that capitalism has the capacity to create new billionaires and generate wealth. Another way to look at it is that capitalism replaces billionaires who either failed to increase their fortunes as quickly as others or lost it somehow. This raises an intriguing set of questions: what does it take for someone who was yesterday’s TOP10 billionaire to not be today’s TOP10 billionaire? Are the causes applicable to other affluent investors? If there is no single formula for getting rich, is there a single formula for losing a family’s wealth? When it comes to generational wealth, does the apple fall far from the tree? A Model to Explain Accumulation Capacity of an Affluent To test the capacity of an affluent person to perpetuate his or her wealth for the next five generations, we created a mathematical model that explains accumulation capacity in seven variables: Amount of heritage received (H) Number of heirs to split the wealth (Q) (i) Number of years of accumulation (N) Annual affluent’s expenditure, as a % of his family income (G) Divorce rate among affluents and, therefore, wealth split in the process (D) Wealth tax (T) Considering these variables, the future value that a patriarch will transmit to the second generation of their family will be: FV= [(H x (1+i)N) + ((H x i) x (1-G)/Q) x ((1+i)N – 1)/i)] x (1-T) And this cycle continues, from the second to the third generation, from the third to the fourth, and henceforth. Three factors in the accumulation process stand out: inheriting a lot of money, having more time in the accumulation phase, and realizing a higher return on investments. Conversely, four out of seven variables constrain accumulation: having more kids, spending too much, getting divorced, and living in a country with a high wealth tax. We test this question: Can an affluent family accumulate wealth for several generations, even if it has more kids, lives a lavish lifestyle, splits wealth in a divorce, and pays a wealth tax? You will notice that the variable “divorce” is not present in the basic formula. This is because it is random and binary. To test this effect in dynamic scenarios, we ran a Monte Carlo Simulation, considering 10,000 scenarios. We considered the following values and probability distributions: Amount of Inheritance received We begin at US$1 billion. This number was arbitrarily chosen and assumes that the family’s patriarch left $1 billion upon death and left all of it to his relatives (no philanthropy, no further donations, no relative denial nor exclusion of an heir). And consequently, we can determine the amount that his son would accumulate upon his death, the amount his grandson would inherit, and henceforth, until the family’s fifth generation. We acknowledge that each person will have his own propensity for leaving an inheritance, and that it varies according to cultural norms. It is not solely dependent on great wealth accumulation during a lifetime. The propensity to leave this inheritance also varies according to the type of heritage. Heritage can be tangible (buildings, cars, boats) or intangible (human values, personal branding, political power). We also know that a billionaire’s propensity to leave an inheritance doesn’t correlate with his wealth. Jeff Bezos and Elon Musk donate less than 1% of their wealth, and the more they enrich, the less they donate, in percentage terms. Number of heirs to split the wealth How many children does a billionaire have? Is it significantly different from an ordinary middle-class person? Elon Musk, for example, has nine children (when this article went to press) with three different women. According to Forbes, Elon Musk is an outlier, as the 700 richest people in America have on average of 2.3 kids, and only 22 of those  700 billionaires have seven or more children. Interpolating this and assuming a normal distribution, we reach a 2.39 standard deviation. Affluent’s annual net return This is probably the hardest variable to model. What is the average annual return of a billionaire? High returns are the variable that made Elon Musk go from anonymity to the top of the billionaire’s list in less than 10 years and Carlos Slim to fall from the top of the list to below number 20. In practice, we see that a billionaire’s return is volatile. First, many have leveraged returns. They own businesses that take on debt and some even leverage their own estates. Second, many of them allocate their wealth to private equities and venture capital, assets that may produce high returns or perform dismally. Using the Dimson-Marsh-Staunton database (2017), returns from 1900 to 2017 for the wealthiest segment of the population averaged 4.8% per annum with a 15.1% standard deviation. Number of years of accumulation How many years are necessary to accumulate the first million dollars? And the first billion? According to the financial planners Brian Preston and Bo Hanson, it takes approximately 27 years for a person to accumulate her first million (5.3 million Americans) and 14 more years to hit a billion (700 Americans). We know, however, that this probability of becoming a millionaire is

As defined contribution (DC) plans continue to evolve, plan sponsors face increasing complexity in managing retirement benefits. With $12.5 trillion in assets (3Q 2024) and accounting for one-third of all US retirement assets, DC plans carry significant responsibility for ensuring strong financial outcomes for participants​. In 2025, plan sponsors must focus on optimizing investment strategies, reducing costs, and enhancing participant education to improve retirement readiness. The top priorities for DC plans in 2025 include critical areas such as target date fund selection, fee transparency, investment lineup evaluation, and staying ahead of regulatory and litigation trends. Targeting Target Date Funds (TDFs) The Department of Labor’s guidance, Target Date Retirement Funds — Tips for ERISA Plan Fiduciaries, outlines best practices for TDF selection. Key takeaways include: Establishing a process for selecting and comparing TDFs and for periodic review Understanding the TDFs’ underlying investments and the glidepath Reviewing the TDFs’ fees and investment expenses Taking advantage of all available information in the review and decision-making process Documenting the process Developing effective employee communications. Implicit in this guidance are three key points to consider. First, as with any investment process, it is important to understand the purpose of the investments is to help your unique group of employees invest for retirement. Second, analyze the characteristics of the workforce by collecting workforce demographics, investment behavioral trends — commonly found in reports produced by the recordkeeper — and other workforce data. Finally, establish the plan sponsor’s goals for the plan and overall investment beliefs that will serve as a guide when evaluating various TDFs. Making prudent investment decisions requires these elements to drive the analysis and identify TDFs that are suitable for your workforce. Understanding Investment Fees and Share Classes We often see situations where the plan sponsor goes through the effort of finding a great investment strategy and then selects a less-than-optimal investment vehicle. For example, a plan sponsor or its advisor might select a mutual fund share class for which the expense ratio includes revenue-share dollars, which are paid to the advisor or collected by the recordkeeper to credit against its fees, rather than using a zero-revenue share class. In other cases, a plan might be eligible (meet the minimum investment threshold) for a collective investment trust (CIT) vehicle with a lower expense ratio than the mutual fund version(s) of the investment strategy. Often, these choices or oversights result in plan participants paying higher investment fees and recordkeeper fees than if the plan sponsor had optimized the choice of investment vehicle. We suggest plan sponsors consider the impact on participants of their current mutual fund share classes, if not zero revenue, and whether the plan qualifies for same CIT strategy. We recommend plan sponsors use zero-revenue share classes of mutual funds or collective investment trusts, as applicable, as they provide greater fee transparency and often lower overall fees, all else equal, than plans utilizing revenue-sharing share classes. Evaluating Investment Lineup Structure Most committees’ routine investment reviews follow a similar format: a look at the economy and capital markets followed by a review of the performance and risk metrics of the investment menu. If there are funds on watch or in need of replacement, changes are discussed. While routine reviews of plan fiduciaries are expected, we suggest supplementing with a periodic review of the investment lineup structure, meaning investment categories (Figure 1) and whether they are implemented with active management or passive management. We suggest this type of review at least every three years or earlier if workforce demographics change in a meaningful way. Figure 1: General Investment Structure. In Figure 1, we show a generic investment lineup structure. To evaluate the appropriateness of the lineup structure, plan sponsors should start by plotting the existing investment menu using the columns shown. This visualization can facilitate discussion about whether the current structure is appropriate or whether investment categories should be altered. Factors for the discussion could include participant group investment knowledge, age, demographics, and extent of retiree population in the plan. Offering Comprehensive Financial Education Resources In our 2024 Financial Wellness in the Workplace Study, employees reported spending at least three hours per week worrying about personal finances, with 68% stating that financial stress negatively impacts their mental health. And three out of four employers recognized that workers’ financial stress negatively affects workplace operations. We have seen firsthand how financial wellness benefits can help employees improve their financial health and reduce these challenges. While traditional group meetings have historically played a significant role — particularly for workforces where a large percentage of the population is not at a desk – there is a meaningful increase in the number of plan sponsors and their employees looking for individualized one-on-one meetings with financial educators. These private meetings enable employees to have candid conversations about their unique financial challenges. Examining Committee Structure and Responsibilities Employment trends from “the great resignation” to “the big stay” and “the great reshuffling” illustrate the mobility of today’s workforce. These changes also negatively impact a company’s retirement plan committee. Reasons might vary from changing positions to leaving the company or retirement. Committees should get back to the basics in 2025 by doing the following: Document the committee structure and responsibilities Build an onboarding education checklist for new committee members Maintain a calendar structure for fiduciary continuing education Confirm the fiduciary file is up to date, including the investment policy statement, executive summaries, and investment reporting Monitoring Trends in Litigation and Regulation With significant provisions of the 2017 Tax Cuts and Job Acts expiring at the end of 2025, there is the potential for new tax legislation. Changes to tax-advantaged retirement programs can come with tax legislation, so it will be important for plan sponsors to stay current on potential changes. From a litigation standpoint, two major trends shaped 2024: plan fees and usage of forfeiture assets. Plan fees remain a perennial focus. Has the committee fulfilled its fiduciary duty to monitor plan expenses so that they are reasonable for the services provided? It

Trust, in some form, is at the center of all financial transactions, and technology can enable and enhance that trust. How do we know? Because 50% of retail investors and 87% of institutional investors say greater use of technology in financial services has increased trust in their adviser/manager. That’s among the key findings of “Enhancing Investors’ Trust: 2022 CFA Institute Investor Trust Study,” the fifth edition in the biennial series. “Enhancing Investors’ Trust” zeroes in on the relationship between technology and trust in finance. It demonstrates that trust in financial services is both seen and unseen: It is the ever-present backbone of financial transactions and the outward interface through which those transactions are conducted.  Greater tech integration in finance helps establish two kinds of trust that are essential to investing: “execution trust” and “relationship trust.” The former refers to the knowledge that transactions are secure, accurate, and appropriately managed, while the latter describes the additive value better investing tools and product personalization create for investors. Technology improves access to financial markets and strengthens representative equality among different market participants. It drives the development of new products and services that open up the markets to more people and counteracts the trust divide, or the trust differential among retail and institutional investors, across geographies and demographics, and between retail investors with and without an adviser. Execution Trust and Fundamentals Execution trust encourages market participation, and all market participants, regardless of demography, require it. By fostering execution trust, technology bridges the trust divide among all types of investors and helps ensure a level playing field.   As the World Bank observes: “Fintech can democratize access to finance and the world can move closer to achieving financial inclusion. . . . Fintech has the potential to lower costs, while increasing speed and accessibility, allowing for more tailored financial services that can scale.” Globally, the first point of entry to financial services is often digital payment providers. In some markets, particularly those that lack traditional banking infrastructure, they are the primary mode of transaction. As such, trust in digital payment providers — Apple Pay, Venmo, Alipay, Zelle, etc. — was ranked highest among all financial services industry subsectors in most markets. Trust in Digital Payment Providers* Source: “Enhancing Investors’ Trust“Note: The exhibit shows the percentage of respondents selecting 4 or 5 on a scale of 1 (completely distrust) to 5 (completely trust).* “China” refers to Mainland China. Retail trading accounts and apps are further addressing the disparity in access to financial services. The survey found that 71% of respondents believe these tools improve their understanding of investing. Institutional investors are equally bullish: 89% say that they increase trust in financial information. These developments directly influence industry sentiment: Respondents with retail trading accounts are more than twice as likely to say they trust financial services than those without them.  Relationship Trust and Personalization Relationship trust is an additive value that builds on execution trust and describes what advisers can deliver when they understand, connect, and align with a client’s personal values and motivations. As with retail trading accounts, whether an investor has an adviser influences how much they trust financial services. Of those with an adviser, 69% have high or very high trust in financial services compared with 45% of those without an adviser. Technology can guide the form and frequency with which advisers communicate with clients and help them adapt accordingly to provide the right information at the right time for each client. It also can facilitate the development of more tailored products. Ultimately, technology-fueled personalization — direct indexing, AI investment strategies, etc. — strengthens the connection between investors and the investment industry. Demand for such products is high. The survey found that 78% of all retail investors and approximately 90% of those under age 45 are interested in more personalized investment products and services. Percentage of Respondents Who Want More Personalized Products/Services to Better Meet Their Investing Needs, by Age Group Source: “Enhancing Investors’ Trust“ Implications for the Future That financial technology adoption skews toward younger investors is no surprise, but as more assets are held by these “digital natives,” technology integration becomes ever more embedded in the client–adviser relationship. This influences how investors participate in the markets overall. For the first time in the Investor Trust series, access to the latest technology platforms and tools was cited as more important (56%) than having someone to navigate and execute the investment strategy (44%). As trust increases in financial technology, so too does the potential for new financial product and services providers to enter the market. The survey found that 56% of retail investors would be more interested in investing in financial products created by Amazon, Google, Alibaba, and other large technology firms than by financial institutions.  Of course, technology’s ubiquity in financial services creates certain challenges. Data privacy is a key consideration. More than one in four respondents (27%) say they are less willing to use online platforms that require inputting personal data than they were three years ago. Technology’s behavioral effect is another concern: Of survey participants with a retail trading account, 57% say it increased their trading frequency, while 74% say they believe acting upon digital “nudges” will improve their investment performance/decision making. Of course, such cautions are necessary reminders that unchecked technology can have unintended consequences. That’s why tech integration in finance must be approached with intent and oversight to maximize its trust-building effects on the industry. 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/Ilya Lukichev 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

Inflation expectations are skyrocketing. The University of Michigan Survey of Consumers[1] shows that median forecasts jumped to 6.5% in April from 3.3% in January, and professional forecasters have also revised their projections upward. But history shows that both groups frequently miss the mark. The gap between expected and actual inflation has been wide and persistent, making it difficult to anticipate when and how inflation will hit portfolios. For investors, this uncertainty underscores the value of real assets, which have historically helped hedge against the surprises that traditional assets often fail to absorb. Historically, realized inflation levels have often been quite different than consumer and forecaster expectations. This is a topic we tackle in some recent research, “Expecting the Unexpected With Real Assets.” In it, we document the historical correlation between expected inflation and actual inflation (one year later). From the third quarter of 1981 to first quarter of 2025, the correlation has been relatively low at 0.20 for consumers and only slightly higher for professional forecasters at 0.34. This piece explores the performance of real assets in different inflationary environments, with a particular focus on performance during periods of high expected and unexpected inflation. Historical evidence suggests that real assets, which include commodities, real estate, and global infrastructure, have been especially effective diversifiers for investors concerned with inflation risk. Therefore, maintaining allocations to real assets, regardless of inflation expectations, is an excellent way to prepare a portfolio for the unexpected. Expecting Inflation Expectations of future inflation vary both over time and among different types of investors. There are a variety of surveys that are used to gauge these expectations. For example, the Federal Reserve Bank of Philadelphia[2] has been conducting its “Survey of Professional Forecasters” quarterly since the second quarter of 1990.[3] Respondents, including professional forecasters who produce projections in fulfillment of their professional responsibilities, are asked to provide their one-year-ahead expectations of inflation (as measured by the CPI). In addition, the University of Michigan’s monthly survey of US households asks, “By about what percent do you expect prices to go up/down, on the average, during the next 12 months?”  There are also more aggregated models such as those by the Federal Reserve Bank of Cleveland[4]. Exhibit 1 includes inflation expectations for professional forecasters (defined as responses to the Federal Reserve Bank of Philadelphia survey) and consumers (from the University of Michigan survey) from January 1978 to May 2025. Exhibit 1: Inflation Expectations: January 1978 to May 2025 Source: Federal Reserve Bank of Philadelphia, the University of Michigan and Authors’ Calculations. We can see that inflation expectations have varied significantly over time. While expected inflation from forecasters and consumers is often similar, with a correlation of 0.49 over the entire period, there are significant differences over time. For instance, while inflation expectations from forecasters have been relatively stable, consumer expectations have exhibited a higher level of variability — especially recently. Expectations around inflation — like those for investment returns — play a critical role in portfolio construction. Inflation assumptions often serve as a foundational input in estimating asset return expectations (i.e., capital market assumptions). As a result, when inflation expectations are low, some investors may question the value of including real assets that are typically used to hedge inflation risk in their portfolios. A consideration, though, is that historically there has been a decent amount of error in forecasting inflation. For example, in June 2021, the expected inflation for the subsequent 12 months among professional forecasters was approximately 2.4%, while actual inflation during that future one-year period ended up being approximately 9.0%. This gap, or estimation error, of approximately 6.6% is called unexpected inflation. The correlation between expected inflation and actual inflation (one year ahead) has been 0.34 for forecasters and 0.20 for consumers, demonstrating the sizable impact unexpected inflation can have. Put simply, while forecasts of future inflation have been somewhat useful, there have been significant differences between observed inflation and expected inflation historically. Real Assets and Inflation Understanding how different investments perform in different types of inflationary environments, especially different periods of unexpected inflation, is important to ensure the portfolio is as diversified as possible. Real assets, such as commodities, real estate, and infrastructure are commonly cited as important diversifiers against inflation risk. They don’t always appear to be that beneficial, however, when the risk and returns of these assets are viewed in isolation. This effect is illustrated in Exhibit 3. Panel A shows the historical risk (standard deviations) and returns for various asset classes from Q3 1981 to Q4 2024. Panel B displays expected future returns and risk, based on the PGIM Quantitative Solutions Q4 2024 Capital Market Assumptions (CMAs). Exhibit 2: Return and Risk for Various Asset Classes Source: Morningstar Direct, PGIM Quantitative Solutions Q4 2024 Capital Market Assumptions and Authors’ Calculations. We can see in Exhibit 2 that real assets, which include commodities, global infrastructure, and REITs, appear to be relatively inefficient historically when compared to the more traditional fixed income and equity asset classes when plotted on a traditional efficient frontier graph (in Panel A).  However, while they may still be relatively less efficient when using forward-looking estimates (in Panel B), the expectations around lower risk-adjusted performance have narrowed. When thinking about the potential benefits of investments in a portfolio, though, it’s important to view the impact of an allocation holistically, not in isolation.  Not only do real assets have lower correlations with more traditional asset classes, but they also serve as important diversifiers when inflation varies from expectations (i.e. periods of higher unexpected inflation). This effect is documented in Exhibit 3, which includes asset class return correlations with both expected and unexpected inflation levels, based on professional forecasters’ expectations (Panel A) and consumer expectations (Panel B). Exhibit 3: Asset Class Return Correlations to Expected and Unexpected Inflation Levels: Q3 1981 to Q4 2024 Source: Morningstar Direct, Federal Reserve Bank of Philadelphia, the University of Michigan and Authors’ Calculations. We can see in Exhibit 3 that more traditional investments, such as cash and

Machine learning is set to transform investment management. Yet many investment professionals are still building their understanding of how machine learning works and how to apply it. With that in mind, what follows is a primer on machine learning training methods and a machine learning decision-making flowchart with explanatory footnotes that can help determine what sort of approach to apply based on the end goal. Machine Learning Training Methods 1. Ensemble Learning No matter how carefully selected, each machine learning algorithm will have a certain error rate and be prone to noisy predictions. Ensemble learning addresses these flaws by combining predictions from various algorithms and averaging out the results. This reduces the noise and thus produces more accurate and stable predictions than the best single model. Indeed, ensemble learning solutions have won many prestigious machine learning competitions over the years. Ensemble learning aggregates either heterogeneous or homogenous learners. Heterogeneous learners are different types of algorithms that are combined with a voting classifier. Homogenous learners, by contrast, are combinations of the same algorithm that use different training data based on the bootstrap aggregating, or bagging, technique. 2. Reinforcement Learning As virtual reality applications come to resemble real-world environments, trial-and-error machine learning approaches may be applied to financial markets. Reinforcement learning algorithms distill insights by interacting among themselves as well as from data generated by the same algorithm. They also employ either supervised or unsupervised deep neural networks (DNNs) in deep learning (DL). Reinforcement learning made headlines when DeepMind’s AlphaGo program beat the reigning world champion at the ancient game of Go in 2017. The AlphaGo algorithm features an agent designed to execute actions that maximize rewards over time while also taking the constraints of its environment into consideration. Reinforcement learning with unsupervised learning does not have either direct labeled data for each observation or instantaneous feedback. Rather, the algorithm must observe its environment, learn by testing new actions — some of which may not be immediately optimal — and reapply its previous experiences. Learning occurs through trial and error. Academics and practitioners are applying reinforcement learning in investment strategies: The agent could be a virtual trader that follows certain trading rules (actions) in a specific market (environment) to maximize its profits (rewards). Nevertheless, whether reinforcement learning can navigate the complexities of financial markets is still an open question. Machine Learning Decision-Making Flowchart Footnotes 1. Principal component analysis (PCA) is a proxy for the complexity of the prediction model and helps reduce the number of features, or dimensions. If the data has many highly correlated Xi features, or inputs, then a PCA can perform a change of basis on the data so that only the principal components with the highest explanatory power in regards to the variance of features are selected. A set of n linearly independent and orthogonal vectors — in which n is a natural number, or non-negative integer — is called a basis. Inputs are features in machine learning, whereas inputs are called explanatory or independent variables in linear regression and other traditional statistical methods. Similarly, a target Y (output) in machine learning is an explained, or dependent variable, in statistical methods. 2. Natural language processing (NLP) includes but is not limited to sentiment analysis of textual data. It usually has several supervised and unsupervised learning steps and is often considered self-supervised since it has both supervised and unsupervised properties. 3. Simple or multiple linear regression without regularization (penalization) is usually categorized as a traditional statistical technique but not a machine learning method. 4. Lasso regression, or L1 regularization, and ridge regression, or L2 regularization, are regularization techniques that prevent over-fitting with the help of penalization. Simply put, lasso is used to reduce the number of features, or feature selection, while ridge maintains the number of features. Lasso tends to simplify the target prediction model, while ridge can be more complex and handle multi-collinearity in features. Both regularization techniques can be applied not only with statistical methods, including linear regression, but also in machine learning, such as deep learning, to deal with non-linear relationships between targets and features. 5. Machine leaning applications that employ a deep neural network (DNN) are often called deep learning. Target values are continuous numerical data. Deep learning has hyperparameters (e.g., number of epochs and learning rate of regularization), which are given and optimized by humans, not deep learning algorithms. 6. Classification and regression trees (CARTs) and random forests have target values that are discrete, or categorical data. 7. The number of cluster K — one of the hyperparameters — is an input provided by a human. 8. Hierarchical clustering is an algorithm that groups similar input data into clusters. The number of clusters is determined by the algorithm, not by direct human input. 9. The K-nearest neighbors (KNN) algorithm can also be used for regression. The KNN algorithm needs a number of neighbors (classifications) provided by a human as a hyperparameter. The KNN algorithm can also be used for regression but is omitted for simplicity. 10. Support vector machines (SVMs) are sets of supervised learning methods applied to linear classification but which also use non-linear classification and regression. 11. Naïve Bayes classifiers are probabilistic and apply Bayes’s theorem with strong (naïve) independence assumptions between the features. References Kathleen DeRose, CFA, Matthew Dixon, PhD, FRM, and Christophe Le Lannou. 2021. “Machine Learning.” CFA Institute Refresher Reading. 2022 CFA Program Level II, Reading 4. Robert Kissell, PhD, and Barbara J. Mack. 2019. “Fintech in Investment Management.” CFA Institute Refresher Reading, 2022 CFA Program Level I, Reading 55. 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/Jorg Greuel 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

IFRS 18 Presentation and Disclosure in Financial Statements will usher in the most significant change to the statement of profit or loss since IFRS Accounting Standards were introduced more than 20 years ago to make the financial statements of public companies consistent and transparent.   The new Standard responds to investors’ concerns about challenges in comparing companies’ financial performance. Today, companies’ statements of profit or loss vary considerably in content and structure. IFRS 18 will give investors more transparent and comparable information about companies’ financial performance and support better investment decisions. IFRS 18 is not effective until 1 January 2027, but companies can apply the Standard early. Regardless, there are several steps they should take to prepare. Companies can assess necessary changes to internal systems and processes, for example. And they can consider how to communicate changes in reported information to investors. It is possible that early adopters of IFRS 18 will share some of this information with the market next year. IFRS 18 responds to market demand for greater comparability and transparency with a focus on information about financial performance in the statement of profit or loss. And all companies that apply IFRS around the world will be expected to use the new Standard beginning in 2027. IFRS 18 introduces three sets of new requirements, comprising: two new subtotals in the statement of profit or loss; disclosures about management-defined performance measures (MPMs); and enhanced guidance on the grouping of information in the financial statements. Subtotals in the Statement of Profit or Loss IFRS 18 improves the comparability of information in the statement of profit or loss by introducing: three new defined categories — operating, investing, and financing; and two new required subtotals to enable analysis — operating profit and profit before financing and income taxes. Among the challenges that investors face in comparing companies’ financial performance is the inconsistency in reporting operating profit. Operating profit is one of the most frequently used subtotals. However, companies apply various definitions to this subtotal because, until now, IFRS had not defined operating profit. For example, in a sample of 100 companies, 61 presented operating profit using at least nine different definitions. The structure of the statement of profit or loss set out in IFRS 18 requires companies to consistently classify their income and expenses as operating, investing, or financing. These requirements are illustrated in Figure 1 for a company that presents its operating expenses predominantly by function. The subtotals highlighted in dark grey are required by IFRS 18 and the subtotals in light grey are additional subtotals that are presented to provide a useful structured summary of the company’s income and expenses. Figure 1. Companies that present operating expenses predominantly by function. The operating category, together with the operating profit or loss subtotal: consists of all income and expenses not classified in the other categories; provides a complete picture of a company’s operations; and serves as a starting point for the statement of cash flows. The investing category: includes income and expenses from cash and cash equivalents and stand-alone investments, i.e., rentals from an investment property or dividends from shares in other companies; also includes shares of profits or losses from equity-accounted associates and joint ventures; and enables investors to analyse returns from these investments separately from a company’s operations. The financing category, together with the profit before financing and income taxes subtotal: includes income and expenses on financing liabilities such as bank loans and bonds; also includes interest expenses on any other liability, i.e., lease and pension liabilities; and allows investors to analyse the performance of a company before the effects of its financing. IFRS 18 also includes specific requirements to ensure that, for all companies, operating profit includes the income and expenses from a company’s main business activities. These requirements will mean that some companies like banks and insurers would otherwise classify some income and expenses in the operating category, rather than the investing or financing categories. Management-Defined Performance Measures Companies often provide company-specific measures, commonly referred to as alternative performance measures or non-GAAP measures. IFRS 18 requires companies to disclose company-specific measures related to the statement of profit or loss in the notes to their audited financial statements, along with accompanying explanations and reconciliations. Not all company-specific measures will be required to be disclosed in the financial statements. Only those measures that meet the definition of management-defined performance measures (MPMs) will be disclosed. MPMs are subtotals of income and expenses, such as adjusted operating profit, that are included in a company’s public communications outside financial statements and communicate management’s view of the company’s performance. Companies will be required to disclose information about MPMs in a single note. A crucial aspect of the disclosures is that each MPM will be required to be reconciled to the most directly comparable subtotal or total defined in IFRS Accounting Standards. Figure 2 illustrates the reconciliation of adjusted operating profit (MPM) to IFRS 18 operating profit and adjusted profit from continuing operations (MPM) reconciled to IFRS 18 profit from continuing operations. Figure 2. MPM disclosure. These reconciliations will increase investors’ understanding of how MPMs compare with subtotals defined by IFRS Accounting Standards. The package of disclosure about MPMs will bring transparency and discipline to these measures. Companies are also required to provide: explanations of why each MPM is reported and how it is calculated; for each adjusting item, the amount included in each line item in the statement of profit or loss together with the tax effect and effect on non-controlling interests; and explanations of any changes to reported MPMs. Companies welcome the disclosure requirements for MPMs because they can provide their view of performance in the financial statements, and investors like them because they expect greater transparency about management’s view. Grouping Information IFRS 18 introduces enhanced guidance on grouping information in the financial statements, otherwise known as aggregation and disaggregation. Companies will be required to reconsider how they group information in the financial statements. They will be required to consider: whether information

Editor’s Note: Our Enterprising Investor podcast features intimate conversations with some of the most influential people from the world of finance about the topics that matter most to investment professionals. This post summarizes the key talking points from a conversation between the show’s host, Mike Wallberg, CFA, MJ, and Meir Statman. In this episode of Enterprising Investor podcast, we delve into the relationship between money and happiness, and how your state of mind can influence investing success. Our esteemed guest, Meir Statman, a professor at the Levy School of Business at Santa Clara University and author of A Wealth of Well-Being: A Holistic Approach to Behavioral Finance, shared his insights on the broader aspects of financial well-being and its interconnection with life satisfaction. Statman emphasized that while money is necessary for supporting a family and ensuring financial stability, it is not sufficient for overall happiness. Life well-being encompasses various domains such as family, work, health, education, and religion, and it’s crucial to balance these to achieve a holistic sense of well-being. The conversation also touched upon the generational differences in risk tolerance and portfolio construction. Statman emphasized the importance of striving for long-term goals and taking calculated risks that can lead to rewards, such as investing in education or career changes. He advised against the pursuit of quick riches through speculative investments like Bitcoin or lottery tickets, advocating instead for a disciplined and science-based approach to investing. Statman shared his simple portfolio strategy, which is based on the dual goals of avoiding poverty and aspiring to be rich — not just in monetary terms, but in overall well-being. He discussed the benefits of diversification and the power of compounding over time, suggesting that investors should focus on being with the market rather than trying to beat it. In closing, Statman offered advice to his younger self and to the younger generation: sacrifice some present comfort for future well-being, take useful risks, and remember that enhancing the well-being of others can enhance your own well-being. 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 courtesy of Nick Webb. This file is licensed under the Creative Commons Attribution 2.0 Generic license. Cropped. source

The Price of Time: Interest, Capitalism, and the Curse of Easy Money. 2022. Edward Chancellor. Atlantic Monthly Press. Few areas of macroeconomic policy are as important and generate as much heat as monetary policy. Were a freshman economics major to inquire about the subject, I would tell them to start with the marvelously entertaining video called “Fear the Boom and Bust: The Original Keynes vs. Hayek Rap Battle.” I’d then hand the student a copy of Edward Chancellor’s The Price of Time. It is no secret that productivity growth is slowing worldwide; for example, in the United States, it fell from 2.8% per year between 1947 and 1973 to 1.2% after 2010. Things are worse in Europe and Japan, with productivity growing at less than 1% per year for a generation. Most famously, Robert Gordon of Northwestern University primarily blames the slowing pace of technological innovation. Professor Gordon and I must be exposed to different versions of the scientific literature, which to my reading bursts at the seams with evidence of technological progress. One unsexy, unremarked, but nonetheless momentous example: The Bosch–Haber process supplies most of the world’s fertilizer. This high-temperature chemical reaction consumes enormous amounts of fossil fuel, but the past decade has seen enormous advances in low-temperature catalysis that promise to both increase agricultural productivity and cut down on greenhouse gas emissions. Larry Summers (and before him, Alvin Hansen), however, blames “secular stagnation,” which ascribes falling productivity to an aging and thus less vigorous and intellectually nimble workforce. The problem with this explanation is that it does not fit the demographic data. Anecdotally, for example, the Roaring Twenties followed a long period of slowing population growth, and more systemic data show no relationship between population growth and the economic variety of growth. Chancellor provides a different, more compelling, and more frightening explanation of the world’s slowing economies: central banks’ now decades-long love affair with artificially low interest rates. He starts by discussing Swedish economist Knut Wicksell’s concept of the natural rate of interest, r* (r-star), below which inflation results and above which deflation occurs. While a skeptic might point out that r* is unobservable, it has been eminently clear for the past two decades that we are in monetary terra nova with prevailing rates well below r*. Chancellor’s central thesis, buttressed by extensive academic research, particularly from the Bank for International Settlements’ Claudi Borio, is that interest rates below r* promote a number of macroeconomic evils. Call them the “Four Horsemen of Cheap Money.” The first horseman is malinvestment. Rates below r* drive capital into projects with lower-than-normal expected returns; in other words, cheap money decreases the natural “hurdle rate” for investment. Think about the billions in investor cash that trained an entire generation of millennials that a crosstown ride should cost about $10 or, more generally, about the overinvestment in real estate, one of the least productive sectors of the economy. The second horseman is bloated asset prices. Again, think especially of the societally corrosive effects of unaffordable housing or, more generally, of the increasing concentration of financial assets in the upper percentiles of wealth, whose relatively low marginal propensity to consume further depresses economic growth. After all, if you direct income to poor people, they will only blow it on food and shelter. The third horseman, the financialization of the developed world’s economies, is perhaps the most insidious of all. Chancellor points out that by 2008 in the United States, “the output of the finance, insurance, and real estate sectors (FIRE) rose to be 50 per cent larger than manufacturing. The country possessed more [real estate] agents than farmers.” This financialization drove companies to load up on cheap debt, with disastrous unintended consequences. Prime among these were buybacks that starved ongoing operations, capital investment, and R&D. Additionally, debt-fueled acquisitions increase industry concentration, which, in turn, savages consumers. Moreover, the natural response to cheap debt is to incur more of it, thus guaranteeing an eventual conflagration. The fourth horseman of cheap money is the “zombification” of companies that in a normal interest rate environment would have gone bankrupt. One of the book’s most enjoyable and edifying sections compares properly functioning Schumpeterian creative destruction with a healthy forest. When forests are left to themselves, fires cull the least healthy trees and permit resilient young ones, whose growth would otherwise be stunted by bigger but diseased older ones, to flourish. For many decades, the US Forest Service aggressively fought fires, only to realize that this eventually resulted in giant conflagrations in acreages allowed to grow ecologically senile. Chancellor makes a convincing case that something similar has happened with monetary policy and that much of the fault for today’s low-productivity global economy can be laid at the feet of the overgrown forest of unhealthy zombie companies kept alive on low-interest life support. Perhaps the book’s most profound observation about low interest rates is that while their salutary effects on asset prices are plainly visible, the newly wealthy are far slower to perceive that the same thing has happened to the present value of their liabilities. Another fascinating observation: Low rates, by allowing manufacturers to push the production process further into the future, encourage the lengthening of global supply chains that can encompass multiple intercontinental voyages. If and when rates rise, globalization will of necessity go into a hard reverse. Chancellor, who well understands that Schumpeterian creative destruction requires a vigorous social welfare system, is no jumping-up-and-down libertarian. He approvingly quotes Tyler Cowen’s observation that “over the last few decades, we have been conducting a large-scale social experiment with ultralow savings rates, without a strong safety net beneath the high-wire act.” Chancellor follows Cowen’s observation with that of Michael Burry, lionized in Michael Lewis’s The Big Short: “The zero interest-rate policy broke the social contract for generations of hardworking Americans who saved for retirement, only to find their savings are not nearly enough.” Chancellor himself then observes that “an increasing number of Americans were forced to work beyond the traditional

The capital asset pricing model (CAPM) is one of the marvels of 20th century economic scholarship. Indeed, its creators took home Nobel Prizes for their efforts, and its insights have helped drive asset allocation decisions since the 1960s. To this day, many graduate school finance professors consider it the gospel on how to value equities. The problem, of course, is that it doesn’t always work in practice. So, we fixed it. Measuring the Equity Risk Premium (ERP) Correctly My team and I have spent the past five years studying the behavior of the US stock market over the past century and a half. Our efforts culminated in a new approach to equity and Treasuries valuations: We call it the Holistic Market Model. This model stretches well beyond the boundaries of traditional finance to include accounting, big data and analytics, history, and sociology. In developing it, we had to first re-engineer the CAPM to make it work both for the past 150 years and for the 2020s and beyond. The CAPM fails mainly because both components of the equity risk premium (ERP) are flawed. First, traditional earnings yields rely on inconsistent earnings figures. Second, risk-free rate calculations ignore the hidden risk premia embedded in US Treasury bonds. Therefore, to better understand the forces driving equity prices, we reconstruct these measures from the ground up. First, we determine which earnings figures are the best inputs to calculate equity earnings yields. We leverage the concept of “owner earnings,” which Warren Buffett originally devised for single stocks, and extend it to the S&P 500 Index, while accounting for investor personal taxes. Building on Buffett’s comparison of a stock index with a real perpetual bond, we convert the S&P 500’s earnings yield into its real perpetual bond yield equivalent. That requires us to address the fact that equities generally benefit from growth over time, but bonds do not. Second, we rethink the real risk-free rate, which is traditionally derived from nominal US Treasuries minus expected inflation. Our research shows that this measure is a poor approximation. Indeed, we uncover as many as 10 Treasury risk premia that most fixed-income investors don’t know about but should. These two steps allow us to calculate the ERP in a consistent fashion over the past 150 years by subtracting the real risk-free rate from Buffett’s real perpetual bond-equivalent earning yield. The resulting ERP is quite different and much more stable from that found in the Fed model and other traditional measures. Building an Explanatory Model of the ERP Because our ERP is consistent and reliable, we generate a CAPM that works in practice. Its variations can be explained by a four-factor model: The first factor is cyclical/sub-cyclical; the latter three are secular. They quantify often-referenced valuation drivers: Business cycle and sub-cyclical variations in economic and financial risk. Quantified levels of extreme inflation and deflation that are associated with poor equity performance. Intergenerational increases in risk aversion driven by long secular bear markets. Variations in the risk arbitrage between equities and Treasury bonds depending on real risk-free rate levels. To summarize, our re-engineered CAPM is based on the correctly calculated real risk-free rate and the four-factor ERP model and is a powerful explainer of equity valuations. The model has a single framework that covers the 150-year period: It indicates that the rules that govern stock prices have been surprisingly stable despite massive changes in the structure of the US economy. Re-Engineered CAPM Model: S&P 500 Real Price per Share, in US Dollars, January 1871 to December 2021 Source: S&P, Cowles Commission, Oliver Wyman What It Means for Managing Future Uncertainty The work has yielded numerous insights that have critical implications for portfolio construction and asset allocation, among them: Equity prices have been high in recent years not because of a bubble but rather because of highly favorable and unusual trends that have driven secular corporate profit margins to a 100-year high and the secular real risk-free rate to an all-time low. A crash is less likely now than if the ERP were unsustainably compressed due to a bubble. However, a financial crisis, large-scale geopolitical event, or natural disaster could trigger a crash if and when the fear of severe consequences from such an occurrence on the real economy and on inflation becomes overwhelming. Cyclical bull and bear markets are common. They are driven by the ever-changing dance between the economic cycle, the Fed cycle, and the mood of Mr. Market. As of this writing, we are already in a cyclical bear market if the 20% decline is measured in real terms, and on the verge of one if measured in nominal terms. Absent future P/E or margin expansion, secular forward-looking risk-adjusted returns are at an all-time low. But this is not enough to conclude that the 40-year secular bull market that started in 1982 is coming to an end. That also does not mean, however, that some new paradigm has rendered the current secular bull market immortal. Indeed, our work shows that this secular bull market will die for one or a combination of three reasons: The 30-year uptrend in corporate profit margins is unlikely to persist for another 40 years; neither is the post–global financial crisis (GFC) downtrend in the secular real risk-free rate; and even a milder form of 1970s-style inflation could also sound its death knell. There is a big difference between these three assassins, however. The first two are not yet within sight but will strike sometime in the next 40 years — the ballot box will largely determine when. The third, inflation, is in plain view right now, but it will kill the secular bull market only if it defeats the US Federal Reserve rather than the other way around. So, is there room for optimism in 2023 and beyond? Yes, because despite cyclical headwinds and gloomy headlines, the evidence to reliably call the end of the secular bull market has not yet appeared — and may not for many years. Until it does,