CFA Institute

In both public and private markets, AI’s rise has been extraordinary: fewer than a dozen technology stocks now account for roughly 40% of the S&P 500, while AI-driven startups dominate venture inflows and valuations (see Figures 1 and 2). Assessing fund quality now means distinguishing not only among managers but also among emerging technologies at varying stages of maturity. The central challenge remains: How can investors separate a signal from noise, and identify real, lasting value in AI-focused venture portfolios? Figure 1 Figure 2 The following framework can help LPs and advisors cut through the noise and evaluate AI venture funds with greater precision. A Simple Framework LPs, advisors, and investors interested in AI-focused funds should start by asking themselves the following questions: Am I just investing in generative pre-trained transformer (GPT) wrappers that will not withstand a new feature release from OpenAI? How saturated are the verticals into which I would be deploying capital? Is there value in reinventing legacy software-as-a-service (SaaS) with AI, even as incumbent enterprise SaaS companies (like ServiceNow) move fast to secure market share? Once those initial questions are addressed, two additional factors can help investors assess the durability and scalability of AI-focused companies. First, do these companies operate in areas with high barriers to entry, and are they well-positioned to take advantage of concurrent innovation waves? If so, they are more likely to have defensible staying power and deliver outsized returns as the market matures. Startups with high barriers to entry have wider and longer lasting moats that provide some protection from the next OpenAI keynote or Google I/O event. The notetaking apps or coding assistants that emerge overnight will likely face challenges moving forward if they are not insulated from broader technological advancements. In addition, one of the highest barriers to entry is, oftentimes, trust in the company. Trust is vital in product adoption and is built over time through relationships, expertise, and empathy. The best companies can harness trust and deepen relationships with targeted, rather than blanket, AI use. In these cases, AI acts as a supercharger for shorter development cycles to deliver in response to client feedback. AI augments, rather than replaces, and that augmentation builds client trust and supports the overall growth of the business. This is in contrast to “vibe coding,” where AI writes all the code in the interest of shipping with speed rather than focusing on delivering quality outputs or solving for real needs.     Second, positioning around multiple innovative supercycles improves both the durability of a startup and its ability to scale its go-to-market strategy. Rather than investing exclusively in AI companies with AI-only use cases, expanding the aperture to include adjacent use cases raises the chances of building a competitive moat with multiple points of entry for customers. Examples include a logistics startup using physical sensors alongside AI agents to manage shipyards autonomously, or a healthcare company leveraging AI for practice management functions such as scheduling, billing, and document sharing, delivering those capabilities seamlessly to patients via an app. Wiz as a VC Case Study A clear example of how these two factors come together is Wiz, a cloud-security startup founded in 2021, which Google intends to purchase for $32 billion. Cloud security has significant barriers to entry. It is a segment built on a high degree of operational trust, given the sensitive nature of storing enterprise data and preventing leaks. Wiz grew its business with early proof-of-concepts, recruiting top engineering talent and embedding teams with clients to build trust. Customers who initially adopted Wiz for early cloud migration faced new security challenges associated with enterprise AI development, and Wiz capitalized on that business as well. By building trust around their products and simultaneously selling into both the cloud and AI waves, Wiz attracted Google’s attention and delivered strong returns for investors. Cutting Through the Noise The proliferation of AI-focused VC funds demands sharper due diligence from investors and advisors. Applying this simple framework can help distinguish managers backing companies with real barriers to entry and long-term strategic positioning from those chasing hype. The investors who can tell the difference will be the ones who thrive in the years ahead. For disclaimers, visit: https://www.optoinvest.com/disclaimers source

Artificial intelligence has advanced rapidly in recent years, raising expectations across the investment industry for meaningful gains in research efficiency, reporting, and risk management. Yet emerging academic and industry research offers a more sober view of this fast-moving technology. Recent findings point to persistent reliability gaps, the continued need for human judgment and oversight, and limits on near-term value creation, suggesting that AI’s impact may be more measured than early enthusiasm implied. For investors, the message is clear: AI remains a powerful long-term opportunity, but one best realized through disciplined, evidence-driven adoption rather than early-stage exuberance. This post is the third installment of a quarterly reflection on the latest developments in AI for investment management professionals. Drawing on insights from investment specialists, academics, and regulators contributing to the bi-monthly newsletter Augmented Intelligence in Investment Management, it builds on earlier articles that explored AI’s promise and pitfalls and risk management techniques. This installment moves toward a more pragmatic understanding of its potential. A close review of recent papers reveals three common themes that may temper the industry’s optimism. 1. The Reliability Challenge Despite impressive advances, AI’s reliability remains a primary barrier to deployment in high-stakes financial environments. A recent analysis by NewsGuard (2025) documents a sharp rise in false or misleading statements from leading AI chatbots, with error rates climbing from roughly 10% to nearly 60%. This expansion of “hallucinations” is not merely a statistical anomaly: an internal OpenAI study (2025) finds that hallucinations are often a structural feature of model training, as current benchmarks reward confident answers over calibrated uncertainty, incentivizing plausible but incorrect statements. Concerns also extend to ethical alignment. In a financial decision-making simulation inspired by governance failures at cryptocurrency exchange and hedge fund FTX, Biancotti et al. (2025) show that several leading models carry a substantial probability of recommending ethically or legally questionable actions when facing trade-offs between personal gain and regulatory compliance. For investment professionals, whose work depends on precision, transparency, and accountability, these studies collectively underscore that AI is not yet reliable enough to operate autonomously in many regulated financial workflows. 2. Premium on Human Judgement A second theme in the research is that AI appears to augment rather than replace human expertise and may even increase the importance of high-quality human oversight. Neuroscience research from MIT (Kosmyna et al., 2025) finds that participants interacting with LLMs exhibit reduced brain activity in regions associated with memory retrieval, creativity, and executive reasoning. Although AI may accelerate initial analyses, heavy reliance on these systems may dull the cognitive capabilities that underpin robust investment judgment. AI adoption also does not diminish the need for human presence in client-facing contexts. Yang et al. (2025) show that clients perceive AI-generated investment advice as significantly more trustworthy when accompanied by a human advisor, even when the human adds no analytical value. Similarly, Le et al. (2025) find that customer satisfaction improves when human–AI collaboration is made explicit rather than concealed. Automation remains limited as well. In large-scale task benchmarking, Xu et al. (2025) observe that advanced AI agents autonomously complete only about 30% of complex, multi-step tasks. A separate study by Tomlinson (2025), analyzing more than 200,000 Copilot interactions, shows that in roughly 40% of cases model actions diverge meaningfully from user intent. Taken together, these findings suggest that investment firms should view AI as a tool for augmenting humans rather than replacing them, with a continual need to fact-check the quality of machine-generated output. This ongoing and structured oversight reduces the value added by the machine and increases complexity and costs, particularly because AI output often appears plausible even when incorrect. The literature also highlights the importance of organizational policies to prevent cognitive deskilling. 3. Structural and Economic Constraints Finally, macroeconomic constraints also temper expectations. Acemoglu (2024) suggests that even under optimistic assumptions, aggregate productivity gains from AI over the next decade are likely modest. Much of the initial evidence comes from tasks that are “easy to learn,” while harder, context-dependent tasks show a more limited scope for automation. Regulation adds further friction. Foucault et al. (2025) and Prenio (2025) note that AI adoption in financial intermediation introduces new concentration risks, infrastructure dependencies, and supervisory challenges, prompting regulators to move cautiously. This increases compliance costs and may slow industry-wide adoption. These structural factors indicate that AI’s impact may be more incremental and less disruptive than commonly assumed. Monitoring AI Advancements AI’s promise is real, but its impact will hinge on how thoughtfully and responsibly the industry integrates it. It will play a central role in the industry’s future, but its trajectory will likely be more complex and dependent on effective human stewardship than early expectations suggested. References Acemoglu, D. The Simple Macroeconomics of AI, National Bureau of Economic Research, Working Paper 32487, May 2024 Biancotti et al., Chat Bankman-Fried: an Exploration of LLM Alignment in Finance, arXiv, 2024 Foucault, T, L Gambacorta, W Jiang and X Vives (2025), Barcelona 7: Artificial Intelligence in Finance, CEPR Press, Paris & London. Kosmyna, et al. Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task, MIT Media Lab, June 2025 Le et al., The Future of Work: Understanding the Effectiveness of Collaboration Between Human and Digital Employees in Service, Journal of Serivce Research, vol. 28(I) 186-205, 2025 NewsGuard, Chatbots Spread Falsehoods 35% of the Time, September 2025 Prenio, J., Starting with the basics: a stocktake of gen AI applications in supervision, BIS, June 2025 Tomlinson, et al., Working with AI: Measuring the Applicability of Generative AI to Occupations, Microsoft Research, 2025 Xu et al, TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks, ArXiv, December 2024 Yang, et al., My Advisor, Her AI and Me: Evidence from a Field Experiment on Human-AI Collaboration and Investment Decisions, ArXiv, June 2025 source

The question of whether quarterly earnings reporting helps or harms long-term value creation has returned to the US policy agenda. As a former fund manager, I can appreciate the appeal, but as someone who currently spends her days analyzing investor decision-making data, I see the implications of a shift to semi-annual reporting as far broader than the familiar short-termism argument suggests. Reducing the cadence of earnings releases would amount to a major behavioral intervention in how market practitioners learn, recalibrate, and compete. While proponents argue that quarterly disclosure causes both companies and investors to fixate on short-term results (McKinsey research links short-term focus to lower ROIC[1]), the market consequences for investment professionals are more complex and subtle than this suggests — with different implications for different parties. From a big picture perspective, moving to a semi-annual earnings cycle would likely slow feedback loops, widen the dispersion in investment decision quality, shift informational advantage, and increase uncertainty for quantitative models and benchmarks. Having been a portfolio manager in the United Kingdom when companies reported only twice a year, I recall how much more enjoyable fundamental investing was under that structure. We genuinely thought longer-term, and the administrative burden was lighter for everyone involved, so I can appreciate the argument for making the change. However, as someone who now spends her days distilling useful insights from data, my instinct is that removing quarterly earnings would reduce transparency in a way the industry can ill afford. For all its flaws, quarterly reporting remains one of the few structured feedback mechanisms available to public investors. It anchors accountability and gives practitioners a regular opportunity to recalibrate expectations, test hypotheses, and revisit assumptions. Eliminating that rhythm would lengthen the feedback cycle and weaken the industry’s collective learning mechanism. Essentia’s data show that decision-making quality improves most when feedback is timely, structured, and specific, precisely the qualities quarterly reporting delivers. Winners, Losers, and Unintended Consequences Moving from quarterly to semi-annual earnings reports would be a significant behavioral intervention, designed to reduce short-termism but certain to carry a range of intended and unintended consequences. For regulators such as the SEC, the Fed, and other monitors of systemic risk, eliminating quarterly earnings would mean a 50% reduction in a data source they rely on heavily. Less frequent corporate information would slow feedback loops and could delay the detection of emerging risks, a concerning dynamic in an era of index funds, algorithmic trading, and rapid capital movement. Perhaps the biggest winner from a lengthening of the cadence of earnings reports would be the fundamental active fund management industry. It is also hard to imagine company management being anything other than pleased by the prospect of less-frequent public reporting. It would feel like a windfall to decision-makers who want more room to focus on long-term strategy rather than on managing the share price each quarter. It might even help revive the ailing IPO market, where the reporting burden associated with quarterly earnings remains a meaningful deterrent to going public. Corporate governance advocates would argue (and I would agree) that reduced transparency increases the risk of poor management or even malfeasance going unnoticed. That said, with the infrastructure already in place for quarterly internal reporting, there is little reason to think that well-intentioned management teams would neglect governance; they simply would not face the burden of reporting it publicly every three months. Quant and systematic strategies that depend on a continuous flow of reported fundamentals to recalibrate factor exposures, forecast risk, and validate machine-learning inputs would face clear challenges. That said, many are likely already running scenarios and adjusting their factor construction and risk-monitoring practices in anticipation of such a shift. Perhaps the biggest winner from a lengthening of the cadence of earnings reports would be the fundamental active fund management industry. Less frequent public information means more room for alpha generation: more space for expertise to make a difference, whether that expertise comes in the form of a human, a computer or, increasingly, a mix of both. This is an environment where fundamental analysts and PMs must adjust their research cycles and model inputs to a more extended timeline, prioritizing proprietary research. Quant and systematic strategies that depend on a continuous flow of reported fundamentals to recalibrate factor exposures, forecast risk, and validate machine-learning inputs would face clear challenges. That said, many are likely already running scenarios and adjusting their factor construction and risk-monitoring practices in anticipation of such a shift. Anyone whose product relies on frequent disclosures to evaluate governance, compensation alignment, and ESG progress would likely suffer. Alternative data providers would likely see an acceleration in demand as firms redeploy the time and resources currently devoted to earnings processing into data that can illuminate the gaps left by less-frequent disclosure. By contrast, providers whose products rely on regular filings to evaluate governance, compensation alignment, and ESG progress would face clear challenges. It is less clear whether the sell-side would be a net winner or loser. Much of equity research, sales, and corporate broking activity is anchored around earnings season, and without that event, trading catalysts would diminish. Halving the frequency of formal results would mean fewer opportunities to publish notes, host calls, and capture client attention. The financial media would also lose a key driver of readership and engagement. A slower cadence would shift narrative power from reported data to speculation, potentially reducing accountability for both journalists and analysts. Could fewer public earnings calls help preserve the roles of equity research analysts? The threat of AI to junior analysts remains, but the expertise within the seasoned sell-side community could become more valuable. Knowing which questions to ask and which data to analyze between formal earnings announcements is an experienced analyst’s stock-in-trade, and a slower cadence could reinforce the importance of that skill set. In a similar vein, less frequent and standardized disclosures would create challenges for the passive investment ecosystem, which depends on regular, standardized reporting to maintain index accuracy and benchmark integrity. Allocators and institutional managers using

Ever since the economic reform and opening of Chinese Mainland markets in the 1980s, Chinese Mainland enterprises have long desired to raise funds via equity and bond issuance to foreign investors. Even amidst the peak of onshore domestic growth, Chinese Mainland firms have been actively engaged in offshore listings to access foreign capital pools backed by hard (fully-convertible) currencies, such as the US dollar. This post builds on my earlier analysis of Hong Kong SAR market’s IPO resurgence. In this piece, I examine the broader forces behind the phenomenon, including how shifting regulations, US–China tensions, and Hong Kong Exchanges and Clearing Limited (HKEX) reforms are reshaping global capital flows and channeling Chinese Mainland listings back to Hong Kong SAR. Up until 2025, more than 300 Mainland Chinese companies had listed overseas and raised hundreds of billions of US dollars in total. In 2020, during the COVID-19 pandemic, which marked the last peak of IPOs, companies listed on the HKEX raised around $50 billion from IPO proceeds, driven primarily by secondary listings in the Hong Kong market from already US-listed Chinese Mainland tech giants like JD and NetEase. From Wall Street to Central: How China’s Capital Flows Are Rebalancing For decades, global IPO activity has been dominated by the NYSE and NASDAQ, exchanges that together account for over $50 trillion in market capitalization. Ranked #1 and #2, these US exchanges surpass the total market cap of the rest of the top 10 stock exchanges in the world. Indeed, for decades, the NYSE and Nasdaq have dominated the global IPO market. The United States possesses a combination of structural, economic, and institutional advantages that attract global companies, including those from the Chinese Mainland, which have consistently demonstrated a strong appetite for US listings. The HKEX, despite being outranked by the US market in both issuance volume and proceeds, remains among the major stock exchanges globally, frequently ranking among the top three exchanges worldwide in terms of IPO proceeds, and is undoubtedly the regional gateway for the Greater China market. Chinese Mainland companies seeking offshore capital have typically faced a binary choice: The United States (NYSE/Nasdaq) or Hong Kong SAR (HKEX). The US market was often preferred, especially for tech and growth companies, due to its global visibility, valuation premiums, and deep liquidity. Chinese Mainland firms rarely consider major stock exchanges in other markets, such as the United Kingdom, Continental Europe, India, or Japan, because of a mix of factors, including a lack of investor familiarity, valuation disadvantages, cultural barriers, and political factors. Source: SEC, HKEX, LSEG. Notes: 1. The US consists of both the NYSE and the Nasdaq; 2. Proceeds include only IPO issuances, excluding transfer and introduction. For global investors, this rebalancing means new access points to Chinese Mainland growth — but through a market more tightly linked to domestic policy and liquidity cycles. Regulation, Risk, and Realignment Chinese Mainland’s path to overseas capital has fundamentally changed over the past decade, shaped by deepening US–China tensions and new layers of regulation. Chinese Mainland companies are now facing more stringent requirements to access US capital markets. Consequently, the number of new listings from Chinese  Mainland companies on US exchanges has almost halved from 19 in 1H23 to 11 in 1H25. The passage of the Holding Foreign Companies Accountable Act (HFCAA)[1] in the United States in 2020 was a landmark, which forces mandatory delisting from the US market if a foreign company fails to comply with the PCAOB’s inspection of its audit papers. Chinese  Mainland national security laws prohibit the sharing of certain financial and operational information with foreign entities, however. For instance, Chinese Mainland Data Security Law[2] imposes strict controls on cross-border data transfers, which directly collide with US requirements. The combined impact of regulatory barriers, delisting waves, and geopolitical uncertainty has led to a structural realignment in global capital markets. In addition, the increasing popularity of private market capital raising in the United States further diminished the appeal for public listings. Global PE funds raised $424.6 billion in 1H2025, already more than the total in 2024. To date, only a minor portion of delistings of Chinese Mainland firms have been driven by PE acquisition compared to the forced delistings. However, greater flexibility, confidentiality, fewer disclosure requirements, and strategic control render the private market an emerging attractive alternative. This shift is not temporary. It’s a structural recalibration of how companies list, how investors evaluate, and where capital flows. As US–China decoupling deepens, HKEX is positioning itself as the new gateway for Chinese Mainland’s global ambitions. Investors must adapt as the investable universe of Chinese Mainland equities shifts from ADRs to Hong Kong SAR listings, reshaping liquidity, governance, and valuation dynamics. Company Industry Delisting Date Main Reason Voluntary or forced Luckin Coffee Food and Beverage June 2020 Fraud Scandal; $864M lost by U.S. investors Forced China Telecom, China Mobile, China Unicom Telecom Jan 2021 Executive order citing their ties to the Chinese military Forced CNOOC Ltd. Oil and gas Oct 2021 National security concerns Forced Didi Ride-hailing June 2022 Data security concerns Forced ChinData Data Service Dec 2023 Strategic acquisition by a PE firm Voluntary Table: Notable delistings of Chinese Corps in the US exchanges. Source: SEC, NYSE, Nasdaq. The Gateway Reinvented: HKEX’s Structural Advantage HKEX’s recent reforms build on a long-held advantage: proximity and policy alignment that make it the natural destination for Chinese Mainland listings. The Stock Connect was developed and launched by HKEX, Chinese exchanges, and ChinaClear in 2014 to build a mutual market access system between Chinese Mainland and Hong Kong SAR, allowing Chinese Mainland investors to trade Hong Kong SAR stocks via local brokers, largely boosting liquidity and valuation potential and maintaining domestic coverage for Hong Kong SAR-listed Chinese Mainland firms. These changes make HKEX not only the listing venue of choice for issuers, but an increasingly important conduit for investors seeking diversified exposure to Chinese Mainland’s innovation economy. For a long time, Chinese Mainland firms preferred U.S. exchanges for dual-class share structures that allow them to

GenAI is reshaping investment workflows faster than most firms can adapt. The  release of Claude for Financial Services is the latest step in applying GenAI in the investment industry. Its focus on domain knowledge and specialized workflows distinguishes it from generalized frontier LLMs and raises important questions about how financial workflows will evolve, how tasks will be divided between humans and machines, and which skills will be needed to succeed in the future of finance. Financial firms are contending with the most significant overhaul of technology capabilities in a generation. AI-driven digital transformation is reshaping job roles and investment processes, prompting professionals to reconsider the boundaries between human and machine cognition, while firms work to upgrade their technology stacks and human capital to remain competitive. Amid this shift, firms and professionals must reevaluate the skills needed for success. Projecting how AI will change workflows and job roles is challenging given the pace of technological progress and uncertainty around transition pathways. Even so, this assessment is necessary for strategic planning, both for industry leaders and for individuals considering their career paths. CFA Institute  continually monitors and interprets AI developments and provides guidance and education to help financial professionals navigate the changing landscape and build the career skills they need to succeed. To advance this mission, we are embarking on an ambitious project to analyze the structural implications of AI for the investment profession. We will explore scenarios for how AI will affect professional practice, judgment, trust, accountability, and career paths, building on our research to date.[1] In this context, two questions often arise: Will AI replace human professionals? And what is the relevance of the CFA Program in a future environment where AI can perform most technical tasks?[2] As we’ve noted elsewhere, we  believe  the future will be defined by the complementary cognitive capabilities of humans and machines, characterized by the “AI + HI” paradigm and the continued importance of professional competence. To  understand what this combination looks like, it is first necessary to  assess the current extent of AI adoption in investment workflows, before identifying possible transition pathways to future  scenarios characterized by differing mixes of human and machine interaction. Current Landscape Early last year, CFA Institute published a survey-based study, “Creating Value from Big Data in the Investment Management Process: A Workflow Analysis.” In it, we analyzed the extent of technology adoption across different workflow tasks performed in categories of job roles including advisory, analytical, investment and decision-making, leadership, risk, and sales and client management. A key takeaway of this work is that investment professionals adopt a multihoming strategy, in which they use  multiple platforms and/or technologies to complete a task. In the Analytical job role category, three example workflows—valuation, industry, and company analysis, and preparing research reports—illustrate this pattern. The table shows the proportion of respondents that use different technologies for each of these tasks. Unsurprisingly, traditional tools like Excel and market databases continue to be the most heavily used, but respondents also report integrating tools such as Python and GenAI alongside traditional software. For example, while 90% of respondents expressed using Excel for valuation tasks, 20% also indicated using Python in this workflow. For analytical roles, GenAI was most used to assist in the preparation of research reports, cited by 27% of respondents.[3] Source: Wilson, C-A, 2025, Creating Value from Big Data in the Investment Management Process: A Workflow Analysis: https://rpc.cfainstitute.org/research/reports/2025/creating-value-from-big-data-in-the-investment-management-process. GenAI in Practice: A Workflow Example Let’s consider conducting industry and company analysis, where, at the time our survey was conducted in 2024, 16% of respondents acknowledged using GenAI in this workflow. Our Automation Ahead content series, in the installment RAG for Finance: Automating Document Analysis with LLMs, provides a concrete example of how GenAI can enhance this  workflow.. The case study is supplemented with Python notebooks in our RPC Labs GitHub repository. It shows how RAG can  extract executive compensation and governance details from corporate proxy statements across portfolio companies and  present the results in a structured table,  one of several tasks performed in this workflow. Such a task is traditionally manual and time-intensive, with the effort required largely driven by the number of  portfolio holdings. With GenAI, the process can be scaled efficiently with only marginal additional compute, freeing the analyst from manual data extraction and preparation of a tabular comparison. With the tasks of data extraction and information presentation outsourced to the GenAI model, the analyst can focus on  data interpretation rather than preparation. Instead of crunching the numbers, the analyst focuses on evaluating the output by interrogating the model, checking data validity, understanding the limitations of the analysis, correcting  errors, supplementing the output with additional information or insights from other sources, all toward the goal of identifying potential governance risks across portfolio holdings. Far from eliminating the need for a human analyst, this example shows how greater value can be unlocked from human input by providing more time and capacity for critical thinking and decision-making. It also illustrates the limitations of AI (such tasks have imperfect accuracy scores), and the enduring need for human oversight and judgment. Evolution Agentic AI has emerged as a powerful tool that can further enhance workflows and deepen the human-machine interaction. These tools build on some of the limitations of RAG and incorporate chain-of-thought reasoning and external function calling (see our article, “Agentic AI For Finance: Workflows, Tips, and Case Studies“). AI agents expand the scope of tasks machines can perform and may shape the future direction of human-machine interaction. Source: Pisaneschi, B., 2025, Agentic AI For Finance: Workflows, Tips, and Case Studies: https://rpc.cfainstitute.org/research/the-automation-ahead-content-series/agentic-ai-for-finance. In many ways, this evolution simply extends the multihoming strategy, combining multiple tools and platforms into a single user interface. Claude for Financial Services reflects this approach, connecting with market databases and traditional platforms like Excel to produce reports and analyses for the user. In this way, AI functions as an application layer  on top of other software tools, interfacing with the human analyst who retains oversight and accountability. Professional judgment remains

The blogs that resonated most with readers in 2025 were those that used historical evidence to illuminate present-day dynamics. Across topics — market concentration, small-cap cycles, private-equity stress, geopolitical shifts, AI, and even foundational valuation tools like discounted cash flow — practitioners consistently sought analysis that connected current signals to the longer arcs that shape them. And the Most Read blog published in 2025, built entirely around historical quotes, shows how powerful distilled insight can be. Mark J. Higgins, CFA, CFP, and Rachel Kloepfer take us on a compact tour through centuries of market wisdom, highlighting behavioral tendencies that repeat across cycles and helping investors recognize them in current conditions. Daniel Fang, CFA, CAIA, reviews the structural and cyclical forces that shape relative performance between small and large caps. He outlines the conditions that have marked turning points in past cycles. Bill Pauley, CFA, Kevin Bales, CFA, and Adam Schreiber, CFA, CAIA, examine historical concentration regimes that resulted in “lost decades,” highlighting how elevated dependence on a small group of stocks can reshape risk, diversification, and forward return expectations. Mark J. Higgins, CFA, CFP, breaks down seven indicators that private-market risks may be rising, providing investors with a practical lens for evaluating structural and late-cycle vulnerabilities. Michael Schopf, CFA, presents a head-to-head comparison of AI models and human analysts. The results show where machines now outperform, where analysts retain an edge, and how this evolving division of strengths is reshaping research teams. In this review of prior Fed cutting cycles, Bill Pauley, CFA, Kevin Bales, CFA, Adam Schreiber, CFA, CAIA, and Ty Painter highlight the market patterns, sector rotations, and risk dynamics that tend to follow policy pivots, giving investors a clear framework for interpreting today’s rate environment. Written in April, this piece offered a forward-looking analysis of how tariff shifts and geopolitical tensions were expected to influence the global economy through 2025. Kanan Mammadov framed his outlook with macro context around growth, inflation, and evolving regional market conditions. Markus Schuller, Michelle Sisto, PhD, Wojtek Wojaczek, PhD, Franz Mohr, Patrick J. Wierckx, CFA, and Jurgen Janssens offer a practical look at how investment teams are adopting AI across research, portfolio construction, and workflow design. They distill five lessons from early front-line deployment and the operational changes the technology is driving. Sandeep Srinivas, CFA, reviews the challenges of applying discounted cash flow models, underscoring their sensitivity to assumptions and the practical complexities that arise in real-world analysis. Paul Lavery, PhD, explains why private-equity buyouts rely on multi-entity structures, showing how acquisition vehicles and layered financing shape deal mechanics and affect risk and portfolio company outcomes. Understanding this architecture is essential for evaluating modern PE transactions. source

Gold’s spectacular rally in 2025 has entered a more volatile phase. After topping $4,300 an ounce and gaining more than 50% for the year, the metal has now fallen sharply. The correction underscores what many investors suspected: even a structural bull market can stumble when sentiment overshoots. The question is no longer simply why gold has risen, but whether its newfound prominence as a portfolio cornerstone can withstand stress. For investors, this latest swing is a reminder that gold’s evolution from hedge to strategic signal is a story still being written. Geopolitical Anxiety and the Safe-Haven Reflex Conflict and political dysfunction remain powerful motivators for gold demand. Ongoing wars in Ukraine and Gaza, persistent regional instability, and US fiscal uncertainty have reinforced the impulse to seek protection in real assets. As Nigel Green of deVere Group noted, “political promises do not equate to financial security.” When faith in institutions wavers, gold’s lack of counterparty risk becomes its greatest asset. But the pullback highlights that even fear has limits. As short-term risks ebb or markets regain confidence, the safe-haven trade can unwind quickly. Professional investors increasingly view gold as a strategic holding rather than a panic hedge, a nuanced shift that explains both the strength of the rally and the speed of its correction. Central Banks: Still the Quiet Accumulators Behind the headlines, central banks continue to anchor demand. Since 2022, they have collectively purchased about 1,000 tons of gold annually, the fastest pace in decades. The freezing of Russia’s reserves was a turning point, prompting emerging-market central banks to diversify away from the dollar and into politically neutral reserves. A World Gold Council survey found that 95% of central banks expect global gold holdings to rise further over the next year. These official purchases remain a stabilizing force even amid market volatility. For private investors, they signal that diversification into tangible stores of value is not a short-term fad but part of a longer-term realignment of monetary strategy. Policy Shifts and the Dollar Dynamic The macro backdrop also continues to matter. Earlier in the year, expectations of US rate cuts had propelled gold higher by lowering the opportunity cost of holding non-yielding assets. But as the dollar rebounded and traders pared back bets on further easing, gold’s tailwind briefly turned into a headwind. For portfolio managers, this reinforces the lesson that gold’s sensitivity to policy and currency expectations can be as important as its role as an inflation or crisis hedge. The same flows that lift prices can retreat just as quickly when macro narratives change. Investor Flows and Momentum Reversal ETF inflows were a major accelerant of the rally, with record-setting September inflows supporting the strongest quarter on record. Yet those same flows may now be amplifying the downside. As the price dropped, profit-taking by speculative positions cascaded through futures and ETF markets, illustrating how liquidity can magnify both directions of movement. Still, the underlying investor interest remains intact. Compared with digital assets and many commodities, gold’s liquidity and perceived stability continue to attract strategic reallocations, particularly from institutions reassessing long-term diversification. A Test of Conviction The correction doesn’t negate gold’s structural appeal, it tests it. The same drivers that propelled the rally (geopolitical tensions, central-bank diversification, and fiscal strain) are still in place. But the pace of gains had outstripped fundamentals, and the pullback is a reminder that no “safe haven” is immune to volatility. For professional investors, the key takeaway is balance. Gold’s new role is not to outperform equities or replace bonds but to signal shifts in trust, liquidity, and policy credibility. Its latest slide shows that the market is still calibrating how much of that signal belongs in portfolios, and at what price. source

Time in the market is better than timing the market, the adage says. Likewise, to see “quality” shares outperform over time, investors must be patient. Quality stocks are defined as stocks of companies with high returns on equity, stable earnings, and low debt. They’re known among investors for outperforming broader markets over the long run, as seen in Figure 1. Figure 1: Stock market performance (31 December 1998-30 September 2025). Over the long term, quality shares have significantly outperformed the broader stock market. Source: CCLA, Bloomberg, MSCI (returns net of withholding tax, in local currency). The above data is not annualized. Past performance is not a reliable indicator of future returns. The value of investments may fall as well as rise. Clients often ask us: “How has my portfolio performed this quarter?” or “What do you expect markets to do next quarter?” They’re right to ask that question, but single quarters aren’t always the most helpful way of gauging long-term success. In 2025, for example, quarterly returns fluctuated, showing how unpredictable short-term outcomes can be. When US President Donald Trump took office in January, he implemented company-friendly tax cuts and deregulated key industries, moves that typically create market tailwinds. However, during the first quarter, the MSCI World Index fell 3.6%. In April, President Trump announced tariffs that were, by many estimates, negative for the US economy. But the index rose 9.5% in the second quarter. And between 1 July and 1 October this year, the index rose another 7%, despite more tariffs. Now, some “star” investors claim that they can time the stock market. But most evidence shows that trying to time the market usually ends with poor returns. When we look at the data, systematic stock market patterns have mainly played out over the longer term. And over that longer term, quality shares have historically outperformed other types of shares. Payoff Takes Time Adhering to any investment style, including quality, usually means that a manager mixes periods of outperformance with periods of underperformance. Figure 2 and Table 3 below show the MSCI World Index (currently 1,320 companies from 23 countries) with its smaller sub-indices the MSCI World Quality Index (300 highest-quality companies from those same countries) and the MSCI World Growth Index (603 highest-growth companies) over the time periods stated. Figure 2: Quarterly, annual, five-year and 10-year returns of the MSCI World Quality Index, relative to the MSCI World Index (31 December 2008-30 September 2025). The longer the timeframe, the more quality has outperformed the MSCI World Index. Source: MSCI, CCLA. The above data is not annualized. Past performance is not a reliable indicator of future returns. The value of investments may fall as well as rise. The data for Figure 2 above is represented in Table 3 below. Column 1 of that Table shows the performance, in absolute terms, of the MSCI World Quality Index, which is made up of companies with high returns on equity, stable year-on-year earnings growth, and low debt levels, for quarters ending on the dates shown. Banking giant JPMorgan, for example, isn’t in the MSCI World Quality Index because, like many banks, it has high debt levels. Column 2 shows the relative performance of the MSCI World Quality Index versus the MSCI World Index. Column 3 shows the relative performance of the MSCI World Quality Index versus the MSCI World Growth Index. The MSCI Growth Index captures shares with high growth rates in revenues, earnings per share and in retained earnings. It includes, for example, Nvidia and Microsoft, but not Facebook parent Meta, because Meta’s growth is comparatively low. Columns 4 through 6 of Table 3 show the same absolute and relative performance, but for the one-year period ending on the date shown. Columns 7 through 12 show the same data for, respectively, five-year timeframes and 10-year timeframes. Table 3: Quarterly, annual, five-year and 10-year performance (2008-2025). The longer the timeframe, the more quality shares have outperformed the broader stock market and growth shares. The left-hand side of Table 3 is a patchwork of reds and greens, as quality shares underperform and outperform in a pattern that is hard to predict from quarter-to-quarter. By contrast, the right-hand side is mostly green, demonstrating that over the longer time horizon, quality shares have outperformed the broader market. The bottom row of Column 11 in Table 3 above shows that the MSCI World Quality Index has outperformed the broader MSCI World Index over all 10-year timeframes since 1998. That’s a remarkably consistent performance. Figure 4 shows this performance in a line chart. Figure 4: Historical outperformance of the MSCI World Quality Index over the MSCI World Index (31 December 1998-30 September 2025). Over longer periods, quality shares have increasingly outperformed the broader stock market. Source: CCLA, MSCI. Past performance is not a reliable indicator of future results. The value of investments may fall as well as rise. Quality Over Growth Quality shares have also outperformed (currently popular) growth stocks the longer you have held them, in 85% of the quarters over a 10-year horizon. Only infrequent, structural crises have upset that regularity. For example, quality shares underperformed growth shares for six quarters in 2021 to 2022, when investors piled into growth stocks such as Peloton and Zoom during the Covid pandemic and lockdown. For the quarters during which the 10-year performance of quality shares lagged growth shares, quality shares had 10-year absolute returns between 178% and 335%, hardly a major concern in performance terms. The bottom row of Column 3 in Table 3 is particularly interesting. The 49% (circled) demonstrates that growth shares outperformed quality shares slightly more often on a quarterly basis. Nevertheless, using the same returns over a longer run, e.g., five years or 10 years, quality outperformed growth 69% of the time (column 9) or 85% of the time (column 12), respectively. In the Long Run Why this paradox between marginal underperformance in the short run and substantial outperformance in the long run? Principally, during market crises in the last 25 years, prices

The development of digital financial assets has fundamentally changed the financial ecosystem, challenging traditional valuation methodologies and introducing new complexities for both analysts and investors. Digital assets — which include cryptocurrencies, stablecoins, non-fungible tokens (NFTs), and tokenized securities — are now used in business transactions, investment portfolios, and capital formation. Even with their growing use, valuation remains clouded with uncertainty due to the absence of standardized valuation frameworks and methods, a market infrastructure that is often fragmented, and limited technological transparency. For financial analysts, this evolution presents both an opportunity and a challenge. Traditional valuation concepts still apply, but they must be adapted to a market where observable inputs, governance structures, and trading conventions differ sharply from established asset classes. This post explains how to approach fair value measurement for digital tokens under ASC 820 and IFRS 13, highlighting key areas of professional judgment such as identifying principal markets, determining exit prices, and assessing discounts for illiquidity or lock-ups. The discussion is organized into five steps that mirror the valuation process: from identifying the token to determining its fair value under varying market and liquidity conditions. Unlike traditional financial assets, many digital instruments often lack established market oversight, observable market inputs, or common and consistent rights of ownership. Tokenized securities may represent beneficial interests in special purpose vehicles, fractional equity, or synthetic exposures, each with distinct legal and economic implications. Cryptocurrencies and NFTs, by contrast, are traded across decentralized exchanges with varying degrees of price transparency and custody risk, and can be susceptible to manipulation. These factors complicate the application of established valuation methods such as those described in ASC 820 and IFRS 13 Fair Value Measurements, which rely on market participant assumptions and observable inputs. These criteria may be absent or unreliable with digital assets. Even with these significant challenges, the traditional valuation approaches still apply to the valuation of digital assets. Tokens that generate cash flows to their holder may lend themselves to the use of a discounted cash flow method of valuation. Certain digital assets are actively traded on certain exchanges, which may be useful to provide inputs for relative valuation methodologies. Finally, developers commonly track the costs to tokenize a security, which can be useful in applying methods of valuation under the cost approach. This post explores the valuation challenges posed by digital assets, with a focus on fair value measurement, marketability discounts, legal structure, and technological risk. It proposes a structured approach to valuation that integrates traditional financial principles with emerging practices in blockchain analytics and decentralized finance. Through practical examples and a methodological analysis of tokens that are traded on major digital exchanges such as Coinbase and Binance, it aims to equip financial analysts with the tools necessary to navigate the valuations within this evolving asset class with rigor and clarity, with a focus on the market approach. Depending on trading volume and market characteristics, these tokens would typically qualify as Level 1 or Level 2 assets under the ASC 820/IFRS 13 fair value standards. We conclude with some notes on Simple Agreement for Future Tokens (SAFTs) as a type of contract (Level 3) that is becoming increasingly common in token-based fund raising as an alternative to actual token issuance for early-stage projects. Step 1: Identify the Token You’re Valuing As a first step in the valuation process, it is critical to identify the key technical features of the digital asset being valued. Some common types include: Cryptocurrencies (ex: Bitcoin, Ethereum, Solana). Cryptocurrencies typically have a dedicated blockchain and are used for peer-to-peer payments. Stablecoins (ex: Theter’s USDT and USDC). Stablecoins are used as a step in the conversion of other digital tokens into a fiat currency such as the US dollar or the Euro. They typically trade at a price close to par (1 USDT = 1 USD), but, similarly to certain money market funds, this parity should not be taken for granted, as it can break in periods of market disruption and may affect the proceeds at exit in an underlying digital token sale. Utility tokens (for example, Ethereum’s Ether, Solana’s Chainlink). Utility tokens operate above an underlying primary blockchain. They may be used to pay for services provided by the issuing platform (Service Tokens), exercise voting rights in the operations of the underlying business (Government Tokens), or for a variety of other functions. They could also be purchased as an investment to gain exposure to the underlying platform. While a token does not provide equity participation rights, the traded price of a utility token will typically benefit from progress made in the development of the underlying platform’s business plan and, more generally, from improvements in the underlying platform’s operations. An understanding of the token’s technical features is critical to assess the token’s risk profile, identify comparable tokens, and identify the drivers of supply and demand which ultimately determine the token’s market performance. Tokens that operate on the same blockchain may belong to different layers. Native Layer-1 tokens are the primary cryptocurrencies of independent blockchain networks, such as Bitcoin (BTC) and Ethereum (ETH). Layer 2 tokens strive to extend the capabilities of the underlying base layer network. Tokens on the same blockchain may also differ based on their use of standards. For instance, Binance USD (BUSD) operates using the ERC-20 standard on Ethereum, while NTFs typically use ERC-721. Other important features to consider include the total supply of tokens and number of tokens in circulation, the characteristics of the initial coin offering, and the token’s regulatory background. The token’s whitepaper will provide relevant information on the project behind the token’s issuance and will help identify its key technical features. Step 2: Determine the Principal Market According to ASC 820 and IFRS 13, the fair value of an asset should be measured based on pricing information obtained from its “principal market,” defined as “the market with the greatest volume and level of activity for an asset or liability.” It is common for digital tokens to trade on multiple exchanges. For example, based

Enrich Your Future: The Keys to Successful Investing. 2024. Larry E. Swedroe. Wiley. Before you reach the introduction to Enrich Your Future: The Keys to Successful Investing, you will be thrown a curveball in the foreword by Cliff Asness, managing and founding partner at AQR Capital. He lures us into a trap by suggesting a number of best investment practices. For instance, he recommends beating the stock market through timing and stock picking, using fire and hire decisions on money managers that add value in the long term, and retaining oversized holdings as a prudent and low-risk strategy. Surprise! These approaches are the opposite of what Larry Swedroe recommends in Enrich Your Future. Indeed, they are the opposite of what Swedroe has practiced for decades as head of economic and financial research at Buckingham Strategic Wealth and what he has expressed in his numerous books and articles. He explains that the tactics illustrated in the foreword can be highly damaging to long-term financial health. This engaging book is simultaneously memorable and humorous. The numerous sports analogies between investing and success in playing or betting on basketball, American football, and golf will have you smiling as you absorb the lessons. Swedroe presents unforgettable investment precepts in four parts: (1) How Markets Work: How Security Prices Are Determined and Why It’s So Difficult to Outperform; (2) Strategic Portfolio Decisions; (3) Behavioral Finance: We Have Met the Enemy and He Is Us; and (4) Playing the Winner’s Game in Life and Investing. The themes repeated throughout each part are, first, the necessity of having an investment plan that focuses on objectives and risk tolerance; and second, implementing that plan using passive investments. It is as simple as that. With such a plan in place, investors need only to rebalance as necessary or to shift allocations if their objective or risk tolerance changes. Swedroe provides an abundance of entertainment with sports analogies related to probabilities of success in betting — and to investing in an efficient market. In the sports world, there exists a collective knowledge, analogous to the efficient market, which reflects everything known about each team and all the players in it. It is extremely difficult to achieve an “excess return” in sports betting absent a surprise, such as the 64th-ranked NCAA basketball team moving into the Elite Eight or better. The price-to-earnings and book-to-market ratios act like point spreads. Swedroe’s argument is that beating the market is almost impossible to achieve on an ongoing basis because of the market’s efficiency, and that everything known about an individual stock is incorporated into its price — until a surprise occurs, such as an earnings blowup or a blowout forecast. At the end of each chapter, Swedroe supplies “The Moral of the Tale,” succinctly summarizing the preceding topics and items he implores investors to address. With these “morals” in hand, readers will come away with no doubt about his recommendations for smart investing and letting the market work for the investor. For example, the competition is just too tough for any one investor or fund manager to outperform consistently. Just take par. Do not be greedy for birdies and eagles. Another lesson, from Chapter 16, “All Crystal Balls Are Cloudy,” is never make the mistake of treating even the highly likely as if it were certain. My favorite chapter is Chapter 34, “Bear Markets.” In it, Swedroe recommends that you create and sign an investment plan, complete with an asset allocation plan, and stick with it. Be certain that it considers bear markets so that you do not freak out when they occur. Change the plan only if your assumptions about risk change. This simple though highly charged “moral” summarizes the book perfectly and applies to both individual and institutional investors. Value-oriented, conservatively motivated, or risk-averse investors may cringe as they read Chapter 30, “The Economically Irrational Investor Preference for Dividend-Paying Stocks.” I suggest readers keep in mind that risk assessment is one of the key elements of asset allocation. Many investors may prefer a preservation objective, with an overweight in fixed-income assets and dividend-producing stocks from companies that are fairly priced and have a clear dividend policy. Swedroe makes a strong case for avoiding dividends, however. He cites the 1961 paper by Merton Miller and Franco Modigliani, “Dividend Policy, Growth, and the Valuation of Shares,” which established that dividend policy should be irrelevant to stock returns. He also acknowledges Warren Buffett’s comments on the same point when Berkshire Hathaway announced a share buyback in September 2011. Swedroe further points out that 60% of US stocks and 40% of international stocks do not pay dividends. Therefore, investors who must include dividends in their investment portfolios are far less diversified than they could be, he maintains. Swedroe states that investors should sell stock rather than receive dividends. It is a matter of how the “payout” problem is framed. For some institutional and individual investors, the selling strategy may be suitable, but for others it may be inadvisable. I am reminded of years when portfolio distributions have become severely depleted due to market declines, as in 2022, when the S&P 500 Index fell by 19.4%, and 2008, when it collapsed by 38.5%. Swedroe’s “enriched future” goes beyond achieving successful returns on investment from a well-allocated passive portfolio. He devotes Chapter 40, “The Big Rocks,” to the effects that applying modern portfolio theory, the efficient market hypothesis, and passive investing have on personal and professional lives. Don’t sweat the small stuff and hear all the market’s noise. Focus on what matters in life: family, faith, and causes. The appendix presents a selection of passive funds by asset class, and this list goes well beyond the expected iShares and SPDRs. Well-detailed chapter notes are also provided. Yet, this expansive book lacks an index. I found myself wanting specific direction to the work of prominent scholars and practitioners such as Asness, Modigliano, Peter Bernstein, Aswath Damodaran, Charles Ellis, Eugene Fama, Andrew Lo, Jeremy Siegel, and Nassim Nicholas Taleb,