CFA Institute

With government contracting surpassing $700 billion annually, the US federal procurement system represents one of the world’s largest and most complex marketplaces. For financial analysts evaluating companies with significant government exposure, financial statements are not merely compliance artifacts. They are core inputs into assessing earnings quality, margin sustainability, contract risk, and valuation. Because so much depends on the accuracy of contractors’ financial information, the external financial statement audit plays an essential role. Independent assurance over reported revenue, costs, and margins directly shapes how analysts interpret performance in businesses where pricing, reimbursement, and profitability are influenced by regulation as well as market forces. Companies with meaningful government contracts operate across sectors such as aerospace and defense, engineering and construction, information technology services, healthcare, and pharmaceuticals. In these industries, specialized accounting requirements, including cost allowability, indirect cost allocation, and contract-specific revenue recognition, materially affect reported earnings. This elevated risk increases the importance of auditor expertise. Auditors with deep experience in government contracting are better positioned to evaluate complex accounting judgments, identify potential misstatements, and support timely, credible reporting. For financial analysts, that expertise translates into more reliable earnings information and greater confidence in reported performance for companies with significant public-sector exposure. In a recent study, I examined how auditor specialization in government contracting affects audit quality and the market’s assessment of reported earnings. The findings point to a clear takeaway for financial analysts: as reporting complexity increases, the value of task-specific audit expertise becomes economically meaningful, not merely procedural. The Case for Specialized Auditor Expertise Government contractors operate in a regulatory environment that is far more complex than that faced by most corporate issuers. They must comply with detailed cost standards, contract-specific revenue recognition requirements, and oversight from multiple agencies, increasing the difficulty of assessing reported performance. Since revenue and expense recognition in this setting is subject to requirements that extend beyond US GAAP, such as specific procedures for indirect cost rate calculations and limitations on cost allowability, financial reporting risk is inherently higher for government contractors. As a result, specialized auditor expertise in government contracting becomes increasingly important. Such expertise helps address contract-specific reporting risks and gives financial analysts greater confidence in the earnings information they rely on when evaluating companies with significant government contract exposure. Audit firms build this expertise by employing personnel who are closely familiar with the rules and regulations that apply to government contractors and by developing experience through repeated engagements. Over time, this accumulation of specialized knowledge differentiates audit approaches in ways that matter for reporting quality. Why Auditor Specialized Expertise Matters for Market Integrity Strong external audits foster transparent capital markets. In government procurement, the stakes are even higher, as contracting adds another layer of complexity that directly affects the reliability of reported financial information. Government contractors must navigate a set of requirements that increase accounting judgment and reporting risk, including: Errors or misinterpretations in these areas can produce material analyst risks; notably, billing disputes that signal contract performance problems, financial restatements, delayed filings that impede timely forecasting, and greater uncertainty around reported contract economics and cash flows. To navigate this complexity effectively, specialist auditors who work extensively with government contractors build proficiency across three domains: technical accounting rules unique to government contracting (e.g., Cost Accounting Standards), contract-specific pricing, reimbursement, and cost allocation requirements, and compliance standards and legal frameworks such as the FAR and DCAA audit guidance. This combination of skills is specialized and valuable, and it can make the difference between a reporting process that runs smoothly and one that results in delays, disputes, or restatements. Identifying Government Contract Specialist Auditors While there is no explicit dataset on auditor specialization, it can be assessed through observable patterns. In my study, I measure specialization using audit fee data from Audit Analytics, focusing on audit firms that hold a substantial and sustained share of government contractor engagements within specific industries. These firms are classified as national specialists. In practice, investors and analysts can assess auditor specialization by: • reviewing whether audit firms maintain dedicated government contracting practices, and• examining peer companies within aerospace and defense, pharmaceuticals, and other sectors to identify audit firms that serve multiple major contractors in the same industry. Fewer Restatements, Faster Filings, Higher Credibility My research findings suggest that national government contract specialists deliver higher audit quality for government contractors. These specialists are associated with: fewer revenue- and expense-related restatements, more timely financial filings, and higher perceived credibility of earnings. These findings demonstrate that the effects of auditor expertise in government contracting extend beyond compliance and contribute to the overall quality of financial reporting. How Auditor Expertise Shapes Earnings Valuation For financial analysts, financial reporting quality is central to assessing performance, risk, and valuation. Companies with significant government contract exposure operate in environments where accounting issues can trigger material downside risk. Specialist auditors help reduce these risks by improving the accuracy, reliability, and timeliness of reported performance. The market recognizes these benefits: investors place greater trust in earnings audited by government contract specialists, as evidenced by higher value relevance compared with earnings audited by non-specialists. For those analyzing government contractors, audit firm specialization should be treated as a key informational signal that provides insight into the reliability of financial reporting. Implications for Regulators and Policymakers The findings are also relevant for public authorities. Complex regulatory environments require auditor expertise that matches clients’ reporting needs. This is crucial in sectors where the government is the primary customer and taxpayers bear part of the cost of accounting errors. When granting contracts, government agencies should consider whether the financial statements submitted were attested by an audit firm specializing in government contracts. Auditor expertise is a mechanism that builds trust and reduces information asymmetry, underscoring the need for specialized audit practices in areas with high compliance demands. What This Means for Audit Committees Audit committees of government contractors face a critical decision in selecting an auditor. The evidence reveals several key insights: specialized expertise should be a key consideration in auditor selection, fee premiums for specialists may represent value rather

“Nothing is easier than self-deceit. For what every man wishes — that he also believes to be true.” —DEMOSTHENES (349 BCE) As we begin 2026, the belief that private markets represent the next durable opportunity is deeply entrenched. This post argues that such confidence is misplaced. Private markets are not only exhibiting clear signs of late-cycle behavior; they now display the same structural conditions that have preceded past financial crises. Three defining attributes stand out: segmented risk creation, near-perfect incentive alignment across an expansive supply chain, and a deeply rooted but flawed assumption about the nature of private markets themselves. Drawing on more than 200 years of financial history, with the 2008–2009 global financial crisis (GFC) as a reference point, I examine the private markets supply chain end to end to show how institutional allocators, consultants, fund managers, wealth advisors, trade associations, trade media, and academics can each act rationally in isolation while collectively amplifying systemic risk. Tracing these dynamics upstream reveals that the rapid growth of evergreen and semi-liquid private-market vehicles reflects not financial innovation, but a late-cycle mechanism for warehousing illiquid assets, delaying price discovery, and sustaining the appearance of stability. The warning is not about bad actors, but about a system whose incentives have become so tightly aligned that even modest stress could produce severe damage. Retail investors, positioned at the end of this speculative supply chain, must be especially vigilant. The Speculative Supply Chain After studying multiple financial crises over the past 235 years, I developed a deep respect for an unsettling reality: the most damaging crises are rarely caused by a small group of bad actors. This insight exposes a common but flawed instinct to seek simple explanations after the fact, often by assigning blame to a handful of villains. While emotionally satisfying, such narratives are usually incomplete. Far more often, crises emerge from millions of actors taking billions of small, incentive-driven actions across an expansive and siloed system. Each participant responds rationally to local incentives that feel defensible within their immediate role, yet few can see how those actions compound when undertaken simultaneously and without meaningful accountability. The tragic irony is that this pattern of rational behavior has historically proven more dangerous than the actions of a small group of bad actors. It preceded the panics of the 1810s, the 1830s, 1907, 1929, 1999, and 2008–2009. Those same conditions are now visible in private markets. 3 Key Attributes of Speculative Supply Chains One way to understand speculative episodes is to view them as manufacturing supply chains. Across past financial crises, three core attributes consistently emerge. These are outlined below using the GFC as a reference point. 1. Risk Segmentation Segmentation of risk across an assembly line-like system is a defining feature of systemic financial crises. Each segment adds risk to the process, yet no single participant has sufficient visibility to understand how that risk compounds as it moves through the system. During the GFC, independent mortgage originators relaxed underwriting standards to increase loan volume. Those loans were sold to investment banks, repackaged into mortgage-backed securities, distributed to institutional investors, pooled into funds, and ultimately sold to both institutional and retail investors. At each station, participants may have recognized incremental risk locally, but few could see how those risks were being amplified elsewhere in the chain or how they compounded collectively. Figure 1: The GFC Speculative Supply Chain[i]. Source: Investing in U.S. Financial History  (2024). The relative isolation of each segment is what makes systemic crises so difficult to identify in real time. Almost no participant has sufficient visibility. In The Big Short, what distinguishes figures like Michael Burry and Steve Eisman is not intelligence alone, but vantage point. Many equally capable participants failed to recognize the danger simply because they lacked the same line of sight. 2. Incentive Alignment The second attribute required for a systemic financial crisis is the near-perfect alignment of incentives among all participants. In many cases, alignment extends well beyond direct participants. During the GFC, mortgage originators, investment banks, and fund managers all shared a common incentive to increase the volume of mortgage production and the issuance of mortgage-backed securities. But the alignment did not stop there. Additional risk amplifiers included ratings agencies, specialized insurers, and prominent voices in the financial media. Each benefited directly or indirectly from higher origination volumes, greater securitization activity, and expanding asset pools. Critically, no major participant had a strong economic incentive to slow the assembly line. Fee structures, compensation models, market share dynamics, and political pressures all leaned heavily against restraint. Had even one systemically important segment been incentivized to reduce production volume or tighten underwriting standards, the crisis may have been averted, or at least rendered less catastrophic. 3. Deeply Rooted But Flawed Assumption “There is no national price bubble [in real estate]. Never has been; never will be.”[ii] —DAVID LEREAH, chief economist, National Association of Realtors (2004) At the core of every speculative episode lies a nearly universal assumption that later proves to be fundamentally incorrect. In the 1810s, Americans purchased farmland aggressively thinking that wheat prices would remain elevated indefinitely. In the late 1920s, Americans believed it was safe to purchase stocks on margin because they assumed equity prices would never suffer sustained declines. During the GFC, people assumed that residential real estate prices would never decline on a national level. The presence of a widely held but fundamentally flawed assumption allows participants in a speculative supply chain to systematically underestimate the incremental risks that they add to the system. Because the flawed assumption is rarely questioned and instead reinforced by recent experience, it provides the psychological comfort necessary to allow risks to remain unchecked. The Private Markets Supply Chain Historically, these three attributes have been identifiable ahead of major financial crises. It is therefore concerning that all three are now present in private markets. Across the supply chain, participants operate under incentives that are closely aligned to expand production while overlooking the erosion of underwriting discipline. Indirect participants, including

Index fund investors have various choices when selecting the weighting style of the funds they hold. There are market cap-weighted indices like the S&P 500 and the Russell 2000/3000, stock price-weighted indices like the Dow Jones Industrial Average, as well as equally weighted indices. But to our knowledge, there is no index constructed at the US country level that weights holdings by each sector’s underlying GDP. So, how would we construct such an index and how would it compare to the S&P 500 in terms of performance and risk? To create our US GDP-weighted index, we broke the S&P 500 down into its 11 underlying sectors and pulled the data for each sector’s corresponding Vanguard exchange-traded fund (ETF) going back to 2005. Next, we took each sector’s contribution to GDP at the start of each quarter and calculated each sector’s GDP contribution over the subsequent quarter and multiplied that by the sector’s relative GDP weight at the start of the quarter. That gave us the sector’s contribution to the index’s overall return over that quarter. For instance, if Financials contributed 10.95% to US GDP in the first quarter of 2015 and the Vanguard Financials ETF (VHF) declined 0.81% that quarter, then by our calculation — 10.95% * –0.81% — the Financials industry contributed –0.089% to the overall GDP-weighted index during that particular quarter. Adding up all 11 sectors’ contributions yields the index’s overall return in the first quarter of 2015. Comparing this GDP-weighted index to the S&P 500 over time highlights some interesting differences in performance. The graph below charts the relative performance of the two indices during our 2005 to 2023 time period. Total Returns of US GDP-Weighted vs. SPX Based on their total returns, the two indices tracked with statistical similarity from 2005 to mid-2009. But after 2009, the GDP-weighted index outperformed the S&P 500 by over half a percentage point each year up until 2023. The summary statistics reflect these results as well. The US GDP-weighted index averaged an annualized return of 10.11% compared to 9.61% for the S&P 500 over the sample period. The US GDP-weighted index also had a lower average beta — 0.98 — over the sample period.   GDP Index SPX Mean Total Return 10.11% 9.61% Max. Total Return 35.23% 32.39% Min. Total Return –35.33% –36.99% Std. Dev. Total Teturn 18.45 18.00 Mean Skewness –0.27 –0.22 All in all, the results indicate that a US GDP-weighted index may offer the potential for excess returns with similar levels of risk compared to its benchmark. To be sure, our results occur over a limited time period of 18 years. So while it is too early to make a definitive statement about what such an index can deliver relative to a value-weighted index like the S&P 500, this is definitely an area worthy of further study and analysis. 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 / Peach_iStock 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

After more than a decade of US market dominance, 2025 may have marked a turning point for global investors. International equities have surged ahead of their US counterparts, evidenced by strong earnings growth and supported by policy reform momentum and a reassessment of “American exceptionalism.” This broad-based outperformance across Europe, Japan, and emerging markets has prompted investors to ask whether the tide is turning in favor of global diversification. Is this the start of a new structural cycle in market leadership, or simply a short-term correction after years of imbalance? Since the global financial crisis (GFC), US equities have been the centerpiece of global portfolios, benefiting from a powerful mix of dollar strength, technological innovation, and economic resilience. This “only game in town” narrative has been reinforced by a record bull market in both the dollar and the technology sector, drawing unprecedented capital inflows and leaving investors structurally overweight US assets. This post is the first in a series exploring whether this outperformance marks the start of a structural trend or merely a temporary shift, and how global investors can position for it. A Historical Perspective History reminds us that market leadership is cyclical, not permanent. Each decade brings its own defining theme—from the Nifty Fifty boom of the 1960s and early 1970s, when a handful of blue-chip growth stocks traded at extreme valuations before dramatically underperforming—to emerging markets and commodities in the 2000s. Dominant markets often give way to new sources of growth and value once the cycle turns. In 2025, that cyclical pattern appeared to reassert itself. International equities outperformed US stocks by roughly 17 points, with broad-based gains across Europe, Japan, and emerging markets, based on the MSCI indices and Bloomberg. While such dispersion may seem abrupt or transient after years of US dominance, it reflects a combination of narrowing growth differentials, improving corporate fundamentals internationally, and renewed policy momentum in key economies. The question now confronting global allocators is whether this shift marks the beginning of a sustained leadership transition or merely a temporary recalibration within a long-running US bull cycle. The US Has Faced Challengers Analysis going back 75 years shows that the dominant investing theme changes each decade, from the 1960s to 1970s boom to US technology in the 1990s and to emerging markets and commodities in the 2000s. In fact, a given investment theme (early technology, for example), often reverses sharply in the next (see Chart 1 below). Chart 1: Investment Themes (Cumulative, % Return) Source: Bloomberg, Breakout Capital Recent memory ends up playing a role in shaping narratives, and thus the United States’ 8% annualized out-performance since the GFC seems a given. However, history shows that US market outperformance is not the norm. Since the 1900s, US equities have lagged international peers about half the time per UBS research and DMS database (Chart 2). Looking at more high frequency Bloomberg data, US annualized returns were broadly similar to the international markets in the four decades, pre GFC. Chart 2: Average Annual Stock Market Returns by Decade, US vs Rest of World Source: UBS, DMS Database, 2024, Breakout Capital Calculations. Note: Expressed in real USD terms Pay Attention to Fundamentals Based on the latest Bloomberg data, US stocks are trading at more than 22 times forward 12-month earnings, slightly short of the extreme levels last observed during the dotcom bubble and post pandemic. This compares with 13 times for emerging markets, and 15 times for international markets outside US. Investor sentiment mirrors this valuation gap:  Per EPFR fund flow data, more than three-fourths of equity fund flows in this decade have gone into US assets, even though the United States represents 65% of the MSCI global equity index and less than 50% of global earnings based on data from MSCI and Bloomberg. Such an extreme valuation differential affords little margin for safety if fundamentals weaken, even if relatively. US fundamental outperformance now shows signs of normalization. A key driver of prior dollar strength and earnings growth was US economic momentum, which outpaced about half of emerging markets over the past five years. International Monetary Fund projections indicate this advantage is fading as more than 80% of major emerging markets are expected to grow faster than the US over the next five years. Consensus forecasts echo this trend: emerging markets are projected to deliver 17% earnings growth in US dollar terms over 2024-2026, compared with 12% for the US, and just 8% for the US equal weight index (Chart 3). Chart 3: Annualized Earnings Growth, USD Source: MSCI, Bloomberg, Breakout Capital Calculations Can the US Defend its Exceptionalism? There are many elements of US Exceptionalism including a free market-based economy, strength of institutions, and an innovation ecosystem that provides it a structural advantage. However, financial markets move in cycles as investor sentiment gets overstretched. US equities’ dominance over the last 15 years was helped by procyclical loop between attractive post crisis valuations for stocks and US dollar and balance sheet clean-up for private as well as public sector. We believe we are in a new regime where there will be an increased recognition that international markets are on the mend and offer strong earnings growth and policy improvement at much cheaper valuations. The strong cyclical advantages that the US offered 15 years ago are increasingly being chipped away creating the conditions for a multi-year tailwind in favor of international markets. Role of US Dollar: International market outperformance has historically aligned with periods of US dollar weakness. While much commentary focuses on the dollar’s reserve status, history shows it has endured several multi-year bear markets, typically lasting around seven years and averaging a 40% decline (see the DXY Index from Bloomberg in Chart 4). After a 13-year bull run and amid softer fundamentals and rising debt, the likelihood of another sustained dollar upswing appears low. Chart 4: US Dollar Index Source: Bloomberg US has become one big bet on AI now: Artificial intelligence has become the dominant driver of US equity performance, accounting for

A Dollar for Fifty Cents: Proven Strategies to Outperform the Market with Closed-End Funds. 2025. Michael Joseph. IW$ Press   Closed-end funds (CEFs) are “chronically mispriced by the market,” writes Michael Joseph, CFA, but for investors hoping to capitalize on that inefficiency, “simply buying a closed-end fund trading at a discount isn’t enough.” Just picking the funds with the deepest discounts to net asset value (NAV) or the highest yields, adds Joseph, is a “recipe for disaster.”   He further cautions that investing in a CEF in hopes that an activist investor will swoop in and close the gap between NAV and market price is “risky” and “speculative.” Furthermore, says the Deputy Chief Investment Officer at Stansberry Asset Management, purchasing a CEF when it is initially offered is “irrational.” He also points out that when the Fed aggressively raised interest rates in 2022, several leveraged municipal bond CEFs’ valuations were slashed nearly in half.  By thus dispelling expectations of easy money, the author of this 89-page book corrects any misapprehensions that might be induced by his title, A Dollar for Fifty Cents. That phrase also appears in a subheading of a section recounting how Warren Buffett and Charlie Munger’s purchase of 20 percent of the shares of Source Capital after the 1969-1970 market downturn drove the CEF nearly 50 percent below the value of its underlying assets.   Buffett and Munger ultimately doubled their money, but as Joseph remarks in an understatement about discounts to NAV, they “aren’t always as steep as 50%.” In a fairer representation of the actual opportunity set, he cites research showing that the best CEF strategy is to buy at a 20 percent discount, with the objective of selling when the discount narrows to 15 percent.  A Dollar for Fifty Cents is written to be accessible to nonprofessional investors but provides information and insights that can benefit professionals who are not already intimately familiar with CEFs. Joseph summarizes the extensive literature on what academics view as the puzzle of why any CEF would ever trade at less than the value of its holdings. He discusses the comparatively recent emergence of CEFs with specified termination dates. That structure is designed to ensure that holders can cash in at the NAV at a time known in advance, but Joseph notes that the termination dates “can often be extended for a variety of reasons.” He also informs investors about free screening sites that can aid CEF selection. Helpful, too, are his warnings about funds with names that do not accurately describe their actual holdings, as well as the misleading distribution rates shown on some CEF factsheets.  As for the book’s subtitle, Proven Strategies to Outperform the Market with Closed-End Funds, Joseph references several studies that found superior returns for CEFs. Readers hoping to see a contemporary, attested, index-beating management record built exclusively on CEFs, however, will be disappointed. They must settle for the statement of foreword writer Rich Bello of Blue Ridge Capital that his firm “achieved great returns” and “invested in more than a few CEFs.”  Many money managers would agree, though, that closed-end funds can play a constructive role in investment portfolios. One important application is providing diversification within an income-focused portfolio that also contains assets such as bonds, preferred stocks, and REITs. CEFs that increase their distributions over time help income-focused investors to keep pace with inflation despite substantial allocations to fixed-income securities. Investors pursuing such a strategy will benefit greatly from Michael Joseph’s balanced account of CEFs’ virtues and pitfalls.  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 / Ascent / PKS Media Inc. 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

Markets move up and down — that’s a fact. Emotional reactions to those movements, however, are optional. But even the most analytical, financially literate clients are not immune to anxiety, fear, or regret. When emotions take hold, investors tend to lose perspective. They start zeroing in on recent losses, alarming headlines, or isolated data points rather than the big-picture goal or why they started initially investing. To appease clients, financial advisors often respond with more information like additional charts, statistics, and explanations. Yet when a client is emotionally activated, more detail fuels the fire, further pushing the client toward the very thing that triggered them. As I’ve noted in previous blogs, it’s important for advisors to address clients’ emotional triggers, lest they manifest as risk aversion in portfolio design and undermine long-term returns. That’s where chunking up comes in. This technique, drawn from cognitive psychology and widely used in athletic coaching, allows investors to reconnect with long-term reasoning, reduce emotional stress, and make decisions aligned with their goals rather than their fears. What follows is a practical framework for financial advisors, supported by client–advisor dialogues, illustrating how to guide clients toward steadier thinking amid inevitable market swings. Chunking Up for the Win Chunking involves grouping information into more meaningful patterns to make sense of more complex ideas. But when markets turn volatile, it’s easy for clients to get lost in the details, or chunk down. Hence: Chunking down: focusing on specifics Chunking up: redirecting attention to broader intentions, values, or goals An advisor “chunks up” by steering clients away from emotionally charged details and back to the higher-level purpose behind their investments, restoring balance and strengthening long-term decision-making. A parallel example appears in sports. When an athlete misses a shot or loses a match, their attention often narrows to the mistake itself, a classic example of chunking down. A skilled coach reframes the moment by shifting the athlete’s attention from the error to the broader objective, such as the team’s overall strategy. This chunking up process diffuses emotional reactivity and promotes mental clarity. Under stress, investors behave similarly. They magnify a short-term loss, a colleague’s poor experience, or a negative headline, losing sight of the broader plan. Chunking up reverses this effect. It draws attention away from the immediate trigger and back to strategy. Its power lies in how it reshapes mental processing, encouraging clients to re-engage in long-term reasoning and escape the cognitive traps that lead to poor strategizing. A Practical Framework Advisors can use the following process to move clients from emotional reactivity to goal-aligned reasoning. Each step builds on the last, guiding the conversation from detail to direction. Identify the emotional anchor: Pinpoint the detail dominating the client’s attention: a recent loss, a worrying headline, or a peer’s negative experience. Recognizing the anchor informs what’s driving the reaction. Chunk up with one question: Introduce a higher-level question that reframes perspective, such as: What was the purpose behind this choice? What long-term goal does this relate to? What were we trying to achieve originally?This simple pivot interrupts the emotional loop and opens the path to broader, more rational reasoning. Connect to values and objectives: Link the discussion back to what truly matters, the client’s long-term goals, priorities, and values. Re-centering on long-term plans (retirement security, independence, family legacy) reactivates purpose and steadies perspective. Reevaluate through the higher frame: With emotions quelled, you can guide clients to reassess choices through this broader lens. Urgency tends to fade once context is restored. Fear often dissolves at this stage. Then return to specifics: With perspective regained, revisit allocations, timing, risk level, and implementation. Clients are calmer and better equipped to make decisions aligned with their long-term objectives. This sequence transforms reactive moments into opportunities for clarity, trust, and insight. In an environment defined by uncertainty, chunking up is one of the most valuable skills an advisor can master. In Practice: Two Client Dialogues Case 1: Fear of Regret (Regret Aversion) Client: I’m afraid of making the wrong call. What if we allocate to equities now and markets drop?Advisor (chunking up): I understand. Let’s step back for a moment. What’s the bigger purpose you’re trying to serve with this allocation?Client: To make my money work better than it currently does.Advisor: And is the goal to avoid temporary declines, or to grow capital over 10–15 years?Client: Growing capital.Advisor: So which choice supports that purpose more: staying fully safe, or taking measured risk?Client: Taking some risk.Advisor: Exactly. From there, we can explore how much risk feels appropriate. Key takeaway: The client’s fear wasn’t about equities; it was about regret. Chunking up surfaced the deeper intention behind the emotion. Case 2: Recency Bias After a Negative Headline Client: I’ve read another article predicting a recession. We should pause all contributions.Advisor (chunking up): Totally understand that instinct. Let me ask, what’s your primary goal with these monthly contributions?Client: To build enough for financial independence.Advisor: And is financial independence something that depends on one quarter or on decades?Client: Decades.Advisor: So if your goal is decades-long independence, how does stopping contributions after one article support or hinder that?Client: …It might actually hurt it.Advisor: Exactly. Shall we look at how disciplined contributions have performed historically during volatile periods? Key takeaway: The advisor avoided debating the headline, likely a losing game, and reconnected the strategy to the client’s true anchor: financial independence. Turning Anxiety into Insight In a profession where uncertainty is constant, the ability to reframe emotion is invaluable. By mastering chunking up, advisors can transform anxious reactions into meaningful dialogue, allowing clients to follow a plan grounded in purpose rather than panic. A single well-timed question can be the bridge between fear and focus and is the mark of an advisor who truly leads with clarity. source

Conventional retirement planning often treats delaying Social Security until age 70 as a default best practice, citing the value of delayed retirement credits and higher guaranteed lifetime income. For high-net-worth households, however, Social Security represents a relatively small component of overall wealth. Once taxes, opportunity cost, and realistic longevity probabilities are incorporated, delaying benefits often functions less as a superior investment decision and more as a form of longevity insurance, one that may come at a measurable cost to after-tax wealth and liquidity. For financial advisors, the question is therefore not whether delaying Social Security is “right” or “wrong,” but how to frame the trade-offs for affluent clients whose portfolios already bear most of the longevity and income risk. Maximum Benefits and the Economic Cost of Waiting Using Social Security Administration (SSA) projections for a maximum‑earning worker reaching eligibility in the mid‑2020s, approximate monthly benefits are: Claiming Age Estimated Monthly Benefit 62 (Early) $3,000 67 (FRA) $4,200 70 $5,300 Ignoring taxes and investment returns, the cumulative breakeven age, total benefits from delaying equal those from early claiming, generally falls in the early 80s. These breakeven points occur later once taxes and investment returns are considered. For high-net-worth individuals who continue to earn meaningful income from employment or active businesses, claiming Social Security at the earliest eligibility age is often impractical. Prior to full retirement age (FRA), Social Security applies an earnings test to wage and self-employment income (not investment income), and the threshold is relatively low. As a result, benefits may be partially or fully withheld. In practice, many higher earning income individuals choose to delay claiming until benefits can be collected without any earned-income limitations and with the added advantage of higher lifetime benefits. For advisors, this reframes Social Security claiming as a capital-allocation decision within the retirement balance sheet, not a standalone income optimization exercise. Early Claiming as a Capital-Allocation Decision An alternative approach is to claim earlier, at age 62 or at full retirement age (FRA), and invest the proceeds conservatively. The asset mix would typically emphasize high-quality fixed income, such as Treasuries, municipals, or diversified low-risk strategies. Long-term nominal return: approximately 4–5% pre-tax. After-tax return for top-bracket investors on taxable assets: approximately 3%, depending on asset location and tax management. Under these assumptions, an individual claiming at age 62 can accumulate a substantial pool of liquid capital by age 70, while the individual who delays has received no benefits during that period. Importantly, this capital remains fully liquid and available for spending, reinvestment, gifting, or estate planning. For advisors, this reframes Social Security claiming as a capital-allocation decision within the retirement balance sheet, rather than a standalone income optimization exercise. Longevity Risk, Quantified The strongest argument for delaying Social Security is longevity insurance: higher guaranteed income if an individual lives well beyond average life expectancy. That benefit, however, must be weighed against the after-tax economic value of benefits received earlier and invested. Early Claiming and a Potential $220,000 After-Tax Capital Advantage If benefits are claimed at age 62 and invested through age 70, the early claimant can accumulate a meaningful pool of capital before the delayed claimant receives any benefits. Using illustrative assumptions: Maximum benefit at age 62: $3,000 per month. After-tax benefit, assuming approximately 68.5% retained after federal tax (37%*0.85): about $2,055 per month. After-tax investment return: approximately 3.15% annually, equivalent to roughly 5% pre-tax for top-bracket taxable investors. Monthly compounding. Under these assumptions, the cumulative value of invested benefits at age 70 is approximately $220,000. By contrast, the individual who delays claiming until age 70 has accumulated no Social Security benefits during this period. Importantly, the $220,000 represents liquid, investable capital, not an annuity equivalent, and therefore constitutes the initial advantage of the early-claiming strategy. Even if the after-tax investment return is reduced to half the illustrative assumption, the cumulative value at age 70 remains approximately $210,000. At twice the assumed return, cumulative invested benefits rise to approximately $255,000. Over very long horizons, investment returns matter more, but the payoff profile is asymmetric: higher returns have a greater impact on outcomes than lower returns. Net Advantage by Age at Death The table below shows the estimated net after-tax advantage of claiming earlier versus delaying to age 70. Net advantage reflects: After-tax Social Security benefits received After-tax value of invested early claiming The higher monthly benefit received by the delayed claimant. Positive values favor earlier claiming; negative values favor delaying to age 70. Age at Death Male Survival Probability Female Survival Probability Net Advantage: Claim at 62 vs. Delay to 70 Net Advantage: Claim at FRA (67) vs. Delay to 70 70 70% 81% $220,000 $110,000 80 48% 62% $90,000 $55,000 90 17% 28% -$90,000 -$20,000 95 5% 11% -$200,000 -$65,000 100 1% 2% -$330,000 -$120,000 Survival probabilities are approximate cumulative survival from age 62 (for the 62 vs. 70 comparison) and from age 67 (for the FRA vs. 70 comparison), based on SSA period life tables. Figures are rounded for clarity. How to read the Table: Age 70: The early claimant’s advantage is almost entirely the accumulated benefits invested, approximately $220,000. Ages 75 to 85: The advantage declines as the delayed claimant’s higher monthly benefit begins to narrow the gap. Around age 88 to 90: The two strategies typically converge. Extreme longevity (95 to 100): Delaying to age 70 eventually produces higher cumulative after-tax benefits, but only in low-probability scenarios. When outcomes are weighted by survival probabilities rather than extreme endpoints, claiming at age 62 or at full retirement age often produces higher expected after-tax wealth for high-net-worth retirees. Bottom Line For financial advisors working with high-net-worth clients: Claiming Social Security at age 62 or at full retirement age and investing conservatively can often maximize expected after-tax wealth. Delaying benefits until age 70 is best understood as a form of longevity insurance, rather than a universally superior financial return. The appropriate strategy depends on client-specific factors, including health, tax profile, portfolio structure, spousal considerations, and preferences for liquidity versus guaranteed income. Because no client can know ex ante which

Momentum investing remains a cornerstone of systematic equity strategies, and our recent research shows it is one deserving of allocators’ full attention. In our latest review (forthcoming, 2026), we provide a comprehensive update on its empirical foundations and practical evolution. Drawing on more than 150 years of data and thousands of portfolio specifications, we reaffirm momentum’s resilience while highlighting its transformation into a multidimensional phenomenon. The momentum premium is not a statistical fluke or a product of data mining; rather, it is a consistent and sizable return spread that has endured across eras, geographies, and portfolio construction choices. For institutional investors, however, our findings are both a validation and a challenge: momentum is robust, but its implementation and risk profile have changed in ways that demand careful attention. 150 Years of Persistence….and Counting Momentum’s long-term persistence is perhaps its most defining feature and the primary reason it remains relevant for investors. Exhibit 1 illustrates this long-term performance, showing the cumulative returns of a simple long-short momentum strategy from 1866 to 2024. Over this 150-year sample, a simple long–short strategy that buys past winners and sells past losers turns an initial $1 into more than $10,000, reflecting annualized returns of roughly 8–9%. These returns are not only sizable, but also highly statistically significant, with t-statistics far above the thresholds typically used to determine whether a result is real or due to chance. Importantly, this finding is not sensitive to how the portfolios are constructed. Whether we use value-weighted or equal-weighted returns, adjust the definition of momentum, or alter the time period examined, the premium persists. Such robustness across specifications and sample windows strengthens the conclusion that momentum is not an artifact of a particular methodology. For institutional investors, the message is straightforward: momentum has endured across eras, market conditions, and portfolio designs, indicating that it reflects a structural feature of financial markets rather than a fleeting anomaly. Exhibit 1: Long-Term Performance of Momentum This exhibit shows the cumulative returns of a long-short momentum strategy (winner-minus-loser portfolio) in US equities from 1866 to 2024. Performance is gross of transaction costs in USD. Both value-weighted and equal-weighted portfolios are displayed, highlighting the remarkable growth and resilience of momentum over more than 150 years. Chart represents a snapshot of the data which is fully accounted for through 2024. Source: Baltussen, Dom, Van Vliet & Vidojevic (2026). Momentum factor investing: Evidence and evolution, forthcoming in Journal of Portfolio Management. Yet momentum should not be viewed as a single, uniform strategy. Its performance depends heavily on how the portfolio is built. Design choices such as whether returns are value-weighted or equal-weighted, where breakpoints are set, industry neutralization, and microcap stock inclusion can all affect both the level of returns and the amount of risk taken. To quantify this sensitivity, we create more than 4,000 variations of momentum portfolios. All of them generate positive Sharpe ratios, indicating that the momentum premium is broadly robust. However, the performance range is substantial: the median Sharpe ratio is 0.61, but individual specifications span from 0.38 to 0.94. This indicates that reported returns can vary depending on how the factor is built. For practitioners, it underscores the importance of rigorous specification checks and transparency in factor design, especially when benchmarking or reporting results. In recent decades, momentum research has broadened well beyond simple price trends. New forms of momentum capture different ways in which returns continue over time. Fundamental momentum, based on earnings surprises, analyst revisions, or news sentiment, reflects investors’ tendency to underreact to new information. Residual momentum focuses on firm-specific return patterns, isolating company-level news and typically producing smoother, higher-Sharpe results. Anchor-based momentum, such as the distance to a stock’s 52-week high, exploits behavioral biases like anchoring and the reluctance to sell at a loss. Industry and network momentum capture both top-down forces (sector trends, macro cycles) and bottom-up relationships (product-market linkages, analyst attention spillovers), while factor momentum reflects slow-moving capital flows into styles and persistent macro environments favoring certain characteristics. These alternative signals are imperfectly correlated with traditional price momentum and with one another, providing meaningful diversification. The multidimensional composite (EW_ALL), which equally weights price momentum and ten alternative signals, delivers higher average returns, stronger t-statistics, and substantially improved drawdown characteristics relative to price momentum alone. Exhibit 2 illustrates the cumulative performance of this composite versus traditional price momentum since 1927, making the diversification benefits and risk-efficiency gains readily apparent. Exhibit 2: Multidimensional Momentum vs. Price Momentum This exhibit compares the cumulative returns of traditional price momentum and the multidimensional momentum composite (EW_ALL) since 1927. Performance is gross of transaction costs in USD. All underlying signal portfolios are equal-weighted. The equal-weighted composite combines price momentum with ten alternative momentum signals, demonstrating superior returns and risk-adjusted performance relative to price momentum alone. Chart represents a snapshot of the data which is fully accounted for through 2024. Source: Baltussen et al. (2026). Momentum factor investing: Evidence and evolution, forthcoming in Journal of Portfolio Management. The Blind Spot The Achilles heel of momentum, however, remains its crash risk. Momentum strategies are vulnerable to sharp reversals, particularly during market regime shifts. We document maximum drawdowns as large as –88% for traditional price momentum, accompanied by left-skewed and fat-tailed return distributions. However, many alternative momentum signals are less volatile, and the multidimensional composite meaningfully reduces risk relative to price momentum alone. Building on prior work, we implement volatility-scaling at both the portfolio and stock levels, dramatically reducing drawdowns and improving Sharpe ratios. The resulting risk-managed momentum strategy (RM_MOM) delivers annualized returns of nearly 18% at volatility comparable to standard momentum, with drawdowns cut nearly in half. Diversify the Signals For institutional investors, the implications are clear. Factor construction matters, and robustness checks across portfolio designs are critical. Diversifying momentum signals can deliver superior risk-adjusted returns. Managing crash risk through volatility scaling and multidimensional portfolios is essential for sustainable momentum exposure. While risk-based theories may explain some of the premium, behavioral biases and limits to arbitrage remain central to momentum’s persistence. We consider

The 60/40 equity–bond portfolio remains a widely used benchmark for long-term asset allocation, despite ongoing debate about its optimality (Pham et al., 2025). For many households, however, the challenge lies not in the framework itself but in the amount of capital required to implement it. Limited investable assets, a desire to avoid explicit borrowing, significant exposure to residential real estate, and the need to maintain liquid reserves often constrain the ability to fully fund a traditional allocation. Leveraged ETFs offer an alternative. Rather than increasing risk, they allow households to achieve a desired risk exposure with less deployed capital, improving the management of liquidity, real-estate leverage, and broader balance-sheet constraints. As illustrated below, leveraged ETFs combined with cash holdings can approximate the risk characteristics of a traditional 60/40 portfolio while avoiding margin accounts, personal credit lines, or other forms of household-level leverage. By separating market exposure from capital commitment, this framework preserves liquidity and financial flexibility while maintaining a familiar asset allocation profile. While many practitioners view leveraged ETFs as unsuitable for long-term use, this analysis is aimed at financial advisors willing to examine that assumption in the context of capital-constrained household portfolios. Motivation: Asset Allocation at the Household Level For most retail investors, portfolio construction takes place within the constraints of the household balance sheet, where housing exposure, mortgage leverage, employment income risk, and liquidity needs shape feasible investment choices. Many households are already structurally leveraged through real estate. Over recent decades, rising home values in developed economies have increased net worth while simultaneously concentrating risk in illiquid assets. As a result, investors often find themselves overweight real assets and underweight liquid financial capital. Traditional forms of financial leverage introduce additional risks that many retail investors are unwilling or unable to bear, including margin calls during drawdowns, fixed repayment obligations on credit lines, and behavioral pressures that can lead to poorly timed de-risking or forced liquidation during periods of heightened volatility. In contrast, when used thoughtfully, leveraged ETFs—whose leverage is contained at the fund level rather than the household balance sheet—allow investors to separate market exposure from capital deployment, providing greater flexibility in household portfolio construction. Methodology and Portfolio Construction The following analysis evaluates whether a portfolio constructed from leveraged equity and bond ETFs combined with cash can approximate the return and volatility characteristics of a traditional 60/40 equity–bond portfolio, without relying on margin, personal borrowing, or other forms of household-level leverage[1]. Benchmark and Instruments The target allocation is a conventional 60/40 portfolio consisting of: 60% exposure to the S&P 500 40% exposure to US Treasuries, represented by a duration of approximately seven years To implement these exposures, the analysis employs the following instruments: A hypothetical ETF providing three times the daily return of the S&P 500 A hypothetical ETF providing three times the daily return of long-duration US Treasuries (20+ year maturity; duration ≈16), with position size scaled to achieve the target portfolio duration Cash earning the overnight rate Although the leveraged Treasury instrument has a longer underlying maturity, its portfolio weight is scaled such that the resulting effective duration of the combined portfolio approximates the seven-year target. Cost and Financing Assumptions To better approximate real-world performance, the following assumptions are incorporated: Annual management expense ratio (MER): 1% Fund-level borrowing cost: overnight rate + 50 basis points Cash earns the overnight rate Portfolio Construction Process Rather than fixing nominal portfolio weights, the strategy targets stable effective market exposures: An equity exposure equivalent to approximately 60% of the S&P 500 A Treasury duration of approximately seven years At each month-end, portfolio weights are adjusted to maintain these exposure targets. Equity and bond ETF allocations are scaled to achieve the desired equity exposure and portfolio duration, with residual capital allocated to cash. Monthly rebalancing is required to offset exposure drift arising from the daily reset nature of leveraged ETFs. Due to the daily reset nature of leveraged ETFs, effective exposures drift over time, necessitating periodic rebalancing. Over the sample period, the resulting average portfolio weights are approximately 20% in the leveraged equity ETF, 15% in the leveraged Treasury ETF, and 65% in cash. Observed Outcomes and Comparison to 60/40 The strategy is back tested using monthly data from 31 December 2002 through 31 December 2024 and evaluated against a traditional 60/40 benchmark (Table 1). Over the sample period, the leveraged ETF plus cash portfolio delivers cumulative returns broadly comparable to the benchmark. More importantly, realized volatility closely tracks that of the traditional 60/40 portfolio, indicating that the exposure-targeting framework is effective in replicating first-order risk characteristics. Table 1 (Summary Statistics) Tracking Differences Periods of divergence between the two portfolios are primarily driven by: Daily leverage reset effects during volatile markets Embedded financing costs within leveraged ETFs Monthly rebalancing frequency The prevailing cash yield environment These factors introduce tracking error but do not materially alter the portfolio’s overall risk profile. Figure 1 (Annual Returns) Figure 2 (Allocation %) Distributional Effects While mean returns and volatility are comparable, the leveraged portfolio exhibits fatter tails relative to the traditional 60/40 portfolio. This reflects the nonlinear return dynamics introduced by daily leveraged instruments, especially during periods with high volatility. Figure 3 (Return Distribution) Practical Risks and Limitations While the framework illustrates a capital-efficient approach to exposure management, it involves important trade-offs that warrant careful consideration. Leveraged ETFs are designed to track multiples of daily index returns; over longer holding periods, their performance becomes path-dependent due to daily leverage resets, with volatility drag increasing nonlinearly as leverage rises (Pessina and Whaley, 2021). In addition, the analysis relies on hypothetical leveraged ETFs, and realized performance of actual products may deviate from modeled results, particularly during periods of market stress. Finally, although average volatility may align with a traditional 60/40 portfolio, the use of leverage increases tail risk, implying a higher likelihood of extreme outcomes. Figure 4 (Drawdown) Capital Efficiency as Portfolio Design Leveraged ETFs are frequently dismissed as unsuitable for long-term investors due to volatility drag and path dependency. This analysis shows that, when employed

The active asset management industry has reached a breaking point. After decades of thriving on high fees and growing assets, active managers now face relentless margin pressure. Passive investing has eroded revenues, while the cost of producing alpha remains stubbornly high due to large teams, complex data needs, and heavy infrastructure. While some firms have managed to trim absolute costs through traditional cuts, these savings rarely keep pace with the relentless margin compression. With additional burdens from regulation, cybersecurity, and technology upkeep, firms are caught in a structural squeeze: falling fees and weak inflows on one side, rising or inflexible costs on the other. The battleground is no longer performance alone, but the cost of alpha. Technology was supposed to solve this, but in many cases it has done the opposite. Years of investment in AI and automation have failed to reduce costs because most firms remain trapped in a legacy architecture that consumes resources and imposes a growing complexity tax. Much of today’s tech spend simply maintains existing systems (often 60% to 80% of total technology budgets), leaving little room for innovation. Even when modern tools are introduced, human resistance often limits their impact, as portfolio managers and analysts fear loss of control or job relevance. For CIOs, the real transformation is cultural: success comes when AI is used to empower experts, not replace them, freeing teams to focus on the highest-value decisions. Blueprint for a Cost-Effective Alpha Factory   There’s a steep opportunity cost of having highly compensated portfolio managers spend time on manual data gathering rather than high-value judgment. The industry is full of talk, but short on actual, working blueprints. So, how can asset managers escape the fee-cost claw, generate sustainable alpha, break free of the legacy trap, and bring their people along? The solution is to reimagine the investment process itself to build a new kind of alpha factory that is highly efficient and scalable yet keeps human expertise at its core. Drawing on over 20 years of experience managing institutional portfolios (over €1.6bn AUM) and architecting Human+AI investment processes, I have designed and tested a specific end-to-end blueprint that cuts the cost of alpha by addressing these root causes. For instance, during a live run at the beginning of October 2025, the model highlighted an unusual valuation dislocation in the Japanese company IHI Corporation that a traditional factor screen failed to detect. The alert prompted an immediate review of the company’s fundamentals. Within hours, the portfolio manager validated the underlying drivers, judged the mispricing to be genuine, and initiated a position. This trade was part of a live model portfolio designed to test the full Human+AI blueprint in real time and to measure its impact on the cost of alpha. Here’s what the new alpha factory looks like: The New IP: License Models, Build PromptsThe edge today no longer comes from building proprietary AI models — it comes from how firms use them. Instead of sinking capital into in-house development, CIOs should license multiple best-in-class external models and focus on the true differentiator: implementation. That means knowing which models to use, where to deploy them in the investment process, and how to combine their outputs effectively. A firm’s real intellectual property now lies in its prompt library — the tailored workflows that embed its investment philosophy into general-purpose models. This Human+AI approach shifts spending from heavy CapEx to flexible OpEx, often at a modest cost of roughly $500 to $5,000 per model per month and requires continuously monitoring the AI landscape so new and better models can be tested and integrated as they emerge. The New Process: A Four-Stage Human+AI FunnelThe traditional linear research process needs to become a multi-stage system in which humans and machines work together from the top down. In a global equity example (equally applicable to fixed income or multi-asset), AI first supports regime-aware allocation decisions, such as steering cash levels based on market signals and adding a critical layer of risk management before individual stock work begins. From there, portfolio management runs through a four-stage Human+AI funnel: Stage 1: Pre-Screening (e.g., 17,000 → 5,000 stocks)This first step is purely quantitative and requires no AI. It involves screening the global developed-markets universe—roughly 17,000 stocks—against essential criteria such as minimum liquidity and market capitalization. The goal is to narrow the field to a more manageable universe of approximately 5,000 companies that meet basic investability standards. Stage 2: Idea Generation (e.g., 5,000 → 500 stocks)This is where AI’s strength truly comes into play. Machine learning and generative AI models are applied to the 5,000-stock universe to surface new investment ideas aligned with the current market environment. Unlike static screening, this process is adaptive: AI can dynamically shift focus between value and growth styles, identify emerging sector trends, and flag outliers that traditional methods might overlook, like the IHI Corporation example. Stage 3: Deep Analysis (e.g., 500 → 100 stocks)Now you can deploy generative AI functions as a team of junior analysts. Leveraging the firm’s proprietary prompt library, AI reads and analyzes corporate filings, management tone, technical indicators, sentiment data, competitive positioning and much more across the 500 companies that advanced from the prior stage. The AI handles the mechanical workload, while the human analyst or portfolio manager provides the critical interpretation. Together, they distill a high-conviction shortlist of roughly 100 candidates. In the IHI Corporation example, the manager used AI’s deep-dive analysis to validate the firm’s balance-sheet strength and moat, moving from idea to conviction in a fraction of the usual time. Stage 4: Portfolio Construction (e.g., 100 → 70 stocks)Finally, the portfolio manager takes full control, using AI as a co-pilot in the construction phase. With the 100-stock shortlist in hand, the manager employs AI-driven tools to optimize position sizing and manage portfolio-level risk exposures. As detailed in my previous post, this final step—where human judgment meets machine precision—can significantly enhance risk-adjusted performance and ensure that alpha generation is both scalable and cost-effective. This funnel compresses portfolio management cycles, strengthens process