CFA Institute

I have a sense that the investing community in Sweden is more female friendly than most other markets. Lips da Cruz observed, “Women are an important factor here in the Nordics. They are bringing a longer-term and more sustainable ‘feminine energy’ to the investment ecosystem.” There is a growing network of women-focused investing groups like RadCap Ventures and Feminvest, which aim to increase female participation in the investment arena. I have great admiration for KvinnoKapital, a local women’s networking group that helps women in asset management build contacts, exchange experiences, and inspire others to strengthen women’s position in the Nordic asset management industry. Does Stockholm’s dynamism, including its access to capital and entrepreneurial opportunities, also translate into more IPOs and greater opportunity for women? The people I interviewed were skeptical as there is no clear data to back up my theory; however, the general consensus is that Sweden’s investing culture, social norms, and supportive system likely help the overall quality and depth of the talent pool. Maria Lindbom, owner and CEO at Lager & Partners, opined: “From my perspective as a headhunter specialising in senior finance roles — and with my own background in finance — Stockholm’s success reflects a combination of structural factors, one of which is the strong representation of women in capital markets. I’ve seen how Sweden’s ecosystem consistently produces broad and deep talent pools.” Long-term thinking, strong governance, and high institutional trust are core features of the market, Lindbom noted. “The fact that many women progress into decision-making roles is a natural outcome of this environment, rather than a policy-driven exception.” So, while women’s representation is not the reason Stockholm is outperforming, it is very much part of a broader, well-functioning capital-market ecosystem that attracts long-term capital and supports sustainable growth. source

Something fundamental is happening in wealth management. It is not a trend and it cannot be captured with a few new buzzwords. It reflects a structural shift away from advisory models built primarily around products, performance reporting, and periodic engagement toward advice that is continuous, contextual, and directly connected to how clients actually live their lives. Women and next-generation investors sit at the center of this shift. They are inheriting assets at unprecedented scale, building wealth through entrepreneurship and equity compensation, and engaging with financial advisors earlier, and with clearer expectations than previous generations. They are not looking for a modernized version of traditional advice. They are looking for advice that feels relevant, transparent, and aligned with how they define value, risk, and success. That reality became clear during the research for Wealth Management with a Difference, a book I co-authored with Nick Rice. Across conversations with more than 80 industry leaders worldwide and a review of more than 100 global research reports, one theme emerged consistently: the demographic profile of wealth is changing faster than advisory models are evolving to meet it. For wealth managers, the implication is straightforward. Technical excellence remains foundational, but relevance now depends on how effectively that expertise is applied to real client decisions, starting with women and rising-generation investors. Women Investors: Redefining the Advisory Relationship Women are rapidly becoming one of the most influential forces in wealth management, not simply because they control more wealth, but because they are changing how wealth is evaluated and how advice is delivered. As women come to control a growing share of wealth — in the United States alone forecasts show women will control about $34 trillion in investable assets by 2030 — many are challenging long-standing assumptions about risk, return, and what meaningful advice looks like. “Many women think about portfolios differently, and they are not looking for a light touch,” Margaret Franklin, CFA, CEO of CFA Institute, told us during our research. “They want to understand how these things work on a deep level. They take a much more ‘total portfolio’ or ‘balanced scorecard’ approach — and that is really going to challenge advisors.” For many women investors, success extends beyond returns alone to include long-term security, resilience, family priorities, philanthropy, and legacy. What Wealth Managers Need to Know Women are not seeking simplification; they are seeking understanding. Traditional risk–return conversations must expand to include outcomes, trade-offs, and long-term impact. A “total portfolio” mindset requires integrating investments with planning, tax strategy, governance, and purpose. What Wealth Managers Need to Do Redesign discovery to surface priorities early. Move beyond standard fact-finding to explicitly explore how clients define security, independence, flexibility, and legacy, and document those priorities as planning constraints, not side notes. Reframe portfolio discussions around outcomes, not just allocations. Explain how investment choices support specific life objectives over time, including downside protection, liquidity, and optionality, not only expected returns. Make education a visible and continuous part of the relationship. Use scenario modeling, decision frameworks, and plain-language explanations to help clients understand why strategies are recommended and how they evolve as circumstances change. Treat women as primary decision-makers by default. Address women directly in meetings, ensure equal access to information and planning tools, and design strategies that reflect longevity, career interruption, and independence rather than assuming shared or secondary roles. Next-Generation Investors: Where Values and Wealth Intersect Next-generation investors, primarily Millennials and Gen Z, are reshaping the advisory landscape not only because of the scale of wealth moving into their hands, but because of how they choose to engage with it. Over the next two decades, more than $80 trillion is expected to transfer to younger individuals, bringing with it a different set of expectations about what portfolios should do and represent. Scale matters, but expectations matter more. For younger investors, portfolios are not just financial tools, they are expressions of intent. Rather than rejecting performance or discipline, these investors are expanding the decision framework itself. Advisors are increasingly expected to balance traditional measures of risk and return with more explicit conversations about values, trade-offs, and real-world outcomes, and to explain not just what they recommend, but how those decisions are reached. That expectation places new weight on communication. Expertise will always matter, but the industry has not consistently done a good job translating that expertise for clients. The ability to communicate differently — to meet clients where they are, explain complexity clearly, and invite dialogue — will be essential. In this environment, “soft skills” are no longer optional. They are central to effective advice. What Wealth Managers Need to Know Values-based investing is a baseline expectation, not a niche offering. Younger investors want transparency, context, and dialogue—not black-box solutions. Trust is built through engagement and explanation, not credentials alone. What Wealth Managers Need to Do Integrate values into portfolio construction without sacrificing rigor. Clearly articulate how impact, sustainability, or values-based preferences affect risk, return, diversification, and associated trade-offs. Make the decision process visible. Walk clients through how recommendations are formed, what alternatives were considered, and why certain paths were chosen, reinforcing confidence through transparency. Adapt communication to support ongoing dialogue. Replace one-way reporting with interactive conversations that invite questions, challenge assumptions, and evolve as clients’ priorities change. Build relationships before assets transfer. Engage next-generation clients early with planning relevant to their lives: career development, equity compensation, cash flow, and first liquidity events, rather than waiting for formal wealth transitions. How to Use Relevance as a Growth Strategy For many firms, marketing remains a lagging indicator of change. Even as women and next-generation investors reshape wealth management, much of the industry’s marketing still reflects an older advisory model, one centered on products, performance, and credentials rather than decisions, context, and trust. The firms gaining traction are not creating campaigns “for women” or “for next gen.” They are changing what their marketing signals about how advice actually works. Traditional wealth management marketing answers a question few clients are asking: What do you offer? Women and younger investors

For decades, the dominant explanation for low investment participation and suboptimal portfolio choices has been a lack of information. Investors, we are told, do not invest well because they do not understand risk, returns, or financial products. The implied solution is therefore to provide more education, clearer disclosures, and better data. Yet despite significant investments in financial literacy programs, improved transparency, and broader access to markets, many of the same behavioral patterns persist. Investors remain overly conservative in their asset allocation, exit markets during periods of volatility, delay participation despite rising income, and display deep mistrust of financial institutions. These outcomes are observed not only among retail investors, but also among highly educated and financially sophisticated individuals. The consequences are measurable: investors hold excess cash during expansions, sell into drawdowns, and systematically erode long-term returns. This begs the question for all investment professionals serving retail investors: What if information, while necessary, is not sufficient to change behavior? Why Information Isn’t Enough Traditional financial theory assumes that once individuals are properly informed, they will act in a manner consistent with rational optimization. In practice, however, investment decisions are rarely made in neutral or controlled environments. They are made under uncertainty, emotional stress, social influence, and time pressure. When markets decline sharply, investors do not calmly reassess expected returns and correlations; they experience fear. When volatility rises, risk is not processed as a statistical distribution but as a psychological threat. In such contexts, additional information often fails to improve decision-making and can, in some cases, aggravate anxiety and inaction. Empirical evidence from behavioral finance supports this observation. Individuals are loss averse, overweight recent experiences, discount future outcomes, and rely on heuristics when faced with complexity. These tendencies persist even among financially literate investors. Firms that ignore this reality will continue to attribute client outcomes to behavior rather than to the systems that shape it. Behavior Follows Design One of the most robust insights from behavioral research is that behavior responds strongly to context. Defaults, framing, choice architecture, and institutional signals all influence decisions often more powerfully than information itself. For example, participation rates in retirement plans vary dramatically depending on whether enrollment is opt-in or opt-out, even when contribution options and disclosures are identical. Similarly, investors’ willingness to hold risky assets is affected by how performance information is presented, the frequency of feedback, and the perceived behavior of peers. These findings suggest that investment outcomes are shaped not only by what investors know, but by how investment systems are designed. Decisions are embedded in environments that either amplify or dampen behavioral biases. Despite this, many financial systems continue to assume high levels of self-control, foresight, and emotional resilience from participants. Products are designed with an implicit expectation of discipline. Advice frameworks assume follow-through. Regulation often assumes compliance once rules are clearly communicated. When outcomes fall short, the response is frequently to intensify education efforts rather than to reconsider the underlying design assumptions. From Education to Design Recognizing the limits of information does not diminish the role of investment professionals. It reframes it. The question shifts from “How much more can we explain?” to “How well are decisions being designed?” This reframing has important implications across the investment ecosystem: For asset managers, product success should not be evaluated solely on performance metrics. The behavioral journey of the investor such as how they enter, stay invested, and react to volatility is equally important. Products that are theoretically optimal but behaviorally fragile are unlikely to deliver intended outcomes at scale. For financial advisors, effectiveness depends not only on the quality of recommendations, but on when and how advice is delivered. Timing, framing, and emotional context shape whether advice is acted upon, particularly during periods of market stress. For policymakers and regulators, participation, trust, and inclusion are not primarily communication challenges. They are institutional design challenges. Rules and safeguards influence behavior not only through enforcement, but through the signals they send about trust, stability, and fairness. Designing for Real Investors A design-oriented approach to investment behavior does not reject rationality; it recognizes its limits. It acknowledges that humans operate with bounded rationality and predictable biases, and that systems should be built accordingly. This means asking different questions: Where can defaults support long-term behavior rather than short-term impulses? How can choice sets be simplified without reducing meaningful options? What forms of friction are helpful, and which are harmful? How do institutional rules affect trust and perceived legitimacy, especially in emerging markets? How do we reframe financial education as support, not a solution? These are not theoretical concerns. They are practical design questions with direct implications for asset allocation, market participation, and financial stability. Conclusion The persistent gap between investment knowledge and investment behavior suggests that the problem is not simply one of education. Information matters, but it operates within environments that shape decisions. If investment outcomes consistently fall short of intent, the critical question is not why investors fail to act rationally. It is whether the products, advice frameworks, and institutional rules they encounter are designed for real human behavior. Improving investment outcomes, therefore, requires a shift in focus from explaining more to designing better. From assuming rational agents to working with predictable behavior. From treating behavior as noise to recognizing it as a central feature of financial decision-making. This shift is not optional. It is increasingly essential for investment professionals seeking durable outcomes in an uncertain world. source

The CBOE Volatility Index (VIX) came on the scene in the 1990s as a way for investors to track expected risk in the market going forward. The Chicago Board Options Exchange’s VIX does something unique in that it uses 30-day options on the S&P 500 Index to gage traders’ expectations for volatility. In essence, it gives us a forward estimate of what the market thinks volatility in equities is going to be. But how accurate is this measure on a realized basis and when does it diverge from the market? We tackled this question by comparing the full spectrum of VIX data going back to 1990 to the realized volatility of the S&P 500 Index. We found that, on average, the market overestimated volatility by about 4 percentage points. But there were unique times when there were significant misestimations by the market. We tell this story in a series of exhibits. Exhibit 1 is an image of the full time series of data. It shows that, on average, the VIX overshot realized volatility consistently over time. And the spread was consistent as well, except for during spike periods (times when markets go haywire). Exhibit 1. In Exhibit 2, we summarize the data. The average S&P 500 Index realized volatility on a 30-day forward basis was 15.50% over the 35-year period. The average VIX (30-day forward estimate) was 19.59% over the same period. There is a 4.09% spread between the two measures. This implies that there is an insurance premium of 4.09 percentage points on expected volatility to be insulated from it, on average. Exhibit 2. Average (%) Median (%) S&P Volatility (forward 30 days) 15.50427047 13.12150282 VIX (30-day Estimate) 19.59102883 17.77 Difference (Actual Vs Estimate) -4.086758363 -4.648497179 Next, we turn toward a time when no major crisis happened: from 1990 to 1996. Exhibit 3 highlights how markets worked during these normal times. The VIX consistently overshot realized volatility by approximately five to seven percentage points. Exhibit 3. Exhibit 4 depicts a very different period: the 2008 global financial crisis (GFC), and we can see a very different story. In July 2008, realized volatility on a 30-day, forward-looking basis began to spike over the VIX. This continued until November 2008 when the VIX finally caught up and matched realized volatility. But then realized volatility fell back down and the VIX continued to climb, overshooting realized volatility in early 2009. Exhibit 4. This appears to be a standard pattern in panics. VIX is slow to react to the oncoming volatility and then overreacts once it realizes the volatility that is coming. This also says something about our markets: The Federal Reserve and other entities step in to quell the VIX once things look too risky going forward, thereby reducing realized volatility. In Exhibit 5, we saw this dynamic again during the COVID period. Exhibit 5. The Exhibits yield two interesting takeaways. One, investors, on average, are paying a 4% premium to be protected from volatility (i.e. the difference between the VIX and realized volatility). Two, the market is consistent in this premium; is slow to initially react to large, unexpected events like the GFC and COVID; and then overreacts. For those that are using VIX futures or other derivatives to protect against catastrophic events, these results highlight how much of a premium you can expect to pay for tail risk insurance as well as the risk you take in overpaying during times of market panic. 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

Quantitative finance continues to debate the reliability and limits of model-driven investment strategies. One central question is how much weight investors should place on backtesting. In The Factor Mirage: How Quant Models Go Wrong, Marcos López de Prado, PhD, and Vincent Zoonekynd, PhD, outline why investors should move beyond accepting historical performance at face value and focus on understanding why a model works. That is a valuable contribution to strengthening the rigor of quantitative investing — and one that invites further reflection on how that reasoning is structured. It may help to frame the issue not as a binary choice between correlation and causation, but as a layered problem in which different forms of reasoning play distinct roles. In practice, the choice is rarely between simple correlation and fully specified causality. Most investment research operates somewhere in between. Sometimes we can describe and test a mechanism directly. Sometimes we cannot. The system may move too quickly, key variables may be only partially observable, or the time and resources required to build a richer model may not be available. In those settings, association-based reasoning still has value. That is not a defect of finance; it is a general feature of decision-making under uncertainty. Association Under Constraint Human beings often rely on associations when there is no time to construct a full causal account. That is not necessarily irrational; it can be adaptive. A fast association can guide action before slower, more elaborate reasoning is possible. The same is true in investment practice. When relevant drivers cannot be directly observed or causal structure is only partly understood, associational signals may still contain useful information. Association is not explanation. The question is not whether association has value, but whether it is sufficient. For institutional investors, this distinction has practical implications for due diligence, including how managers justify the inclusion and exclusion of variables in systematic models. When stronger structural knowledge exists, ignoring it is not sophistication; it is a loss of information. Association has a place, but it should not become a stopping point. The call for greater causal discipline in finance is not new. The more interesting question is how to incorporate that discipline without oversimplifying the nature of markets themselves. Epidemiology as a Model of Structured Reasoning An epidemiologist would not analyze an epidemic as a purely statistical pattern detached from what is known about transmission. If susceptible individuals can become infected and infected individuals can recover or be removed, that knowledge becomes part of the model’s structure. Compartmental models such as SIR (susceptible, infected, recovered) and SEIR (susceptible, exposed, infected, recovered) formalize those transitions. Statistical methods remain essential for estimating parameters and testing fit. But the analysis does not begin from a blank slate; it begins from established causal structure. Finance can draw a similar lesson. Where durable mechanisms are reasonably well understood, they should be represented explicitly. If leverage amplifies forced selling, refinancing conditions shape default risk, inventories influence pricing power, passive flows affect demand, or network structures transmit distress, these are more than recurring correlations. They are mechanisms that can be modeled, tested, and challenged. Dynamic models can be especially useful here. A regression captures co-movement; a dynamic model represents stocks, flows, delays, and feedback. In finance, that may mean balance-sheet capacity, funding conditions, capital flows, or adoption dynamics. Such models help clarify how the state of the system evolves and how today’s conditions shape tomorrow’s outcomes. Reflexivity and Adaptive Markets Finance differs from epidemiology. Markets are reflexive. Beliefs influence prices, and prices in turn reshape beliefs, incentives, and financing conditions. A narrative can attract capital; capital flows can move prices; rising prices can reinforce the original narrative. What appears to be a durable relationship may, for a time, reflect a self-reinforcing loop. Causal reasoning remains essential, but the relevant structure may itself include feedback between beliefs, flows, and outcomes. A Three-Layered Framework Investment research can operate on three distinct but related layers: Association: What appears to predict, even imperfectly? Causal: What mechanism could plausibly generate that relationship? Reflexive: How might the use of the signal itself alter behavior, crowd the trade, change flows, or reshape the environment being modeled? Seen this way, the debate is not about choosing correlation over causation. It is about knowing when association is sufficient, when mechanisms must be modeled explicitly, and when reflexive feedback makes the system more adaptive than either approach assumes. Few serious quantitative researchers would defend correlation without scrutiny. Robust practice already includes stress testing, economic intuition, and structural reasoning. The question is not whether causality matters, but whether we are explicit about which layer is doing the work — and how those layers interact. Toward a More Disciplined Quantitative Practice We should use causal knowledge when it is available and test causal hypotheses when we have them. When a phenomenon involves accumulation, delay, or feedback, dynamic models may be more appropriate than static statistical fits. Association-based thinking retains an important role, especially under constraints of time and observability. But where established structure exists, ignoring it is not sophistication; it is a loss of information. The opportunity for quantitative finance is not to replace one methodological slogan with another. It is to become more disciplined and more transparent about how different forms of reasoning contribute to robust investment research — when patterns are enough, when mechanisms are required, and when reflexivity demands that we treat markets as adaptive systems shaped in part by our own participation. The future of investment research is therefore unlikely to be purely correlational or narrowly causal. It will be more plural, more dynamic, and more explicit about the difference between patterns that merely appear stable and mechanisms capable of sustaining them. References López de Prado, Marcos, and Vincent Zoonekynd. The Factor Mirage: How Quant Models Go Wrong. Enterprising Investor, CFA Institute, 30 October 2025. Delli Gatti D, Gusella F, Ricchiuti G. Endogenous vs exogenous fluctuations: unveiling the impact of heterogeneous expectations. Macroeconomic Dynamics. 2025;29:e125. doi:10.1017/S1365100525100345 Gigerenzer, Gerd, and Daniel G. Goldstein. “Reasoning the

Time to rethink AI exposure, deployment, and strategy This week, Yann LeCun, Meta’s recently departed Chief AI Scientist and one of the fathers of modern AI, set out a technically grounded view of the evolving AI risk and opportunity landscape at the UK Parliament’s APPG Artificial Intelligence evidence session. APPG AI is the All-Party Parliamentary Group on Artificial Intelligence. This post is built around Yann LeCun’s testimony to the group, with quotations drawn directly from his remarks. His remarks are relevant for investment managers because they cut across three domains that capital markets often consider separately, but should not: AI capability, AI control, and AI economics. The dominant AI risks are no longer centered on who trains the largest model or secures the most advanced accelerators. They are increasingly about who controls the interfaces to AI systems, where information flows reside, and whether the current wave of LLM-centric capital expenditure will generate acceptable returns. Sovereign AI risk “This is the biggest risk I see in the future of AI: capture of information by a small number of companies through proprietary systems.” For states, this is a national security concern. For investment managers and corporates, it is a dependency risk. If research and decision-support workflows are mediated by a narrow set of proprietary platforms, trust, resilience, data confidentiality, and bargaining power weaken over time.  LeCun identified “federated learning” as a partial mitigant. In such systems, centralized models avoid needing to see underlying data for training, relying instead on exchanged model parameters. In principle, this allows a resulting model to perform “…as if it had been trained on the entire set of data…without the data ever leaving (your domain).” This is not a lightweight solution, however. Federated learning requires a new type of setup with trusted orchestration between parties and central models, as well as secure cloud infrastructure at national or regional scale. It reduces data-sovereignty risk, but does not remove the need for sovereign cloud capacity, reliable energy supply, or sustained capital investment. AI Assistants as a Strategic Vulnerability “We cannot afford to have those AI assistants under the proprietary control of a handful of companies in the US or coming from China.” AI assistants are unlikely to remain simple productivity tools. They will increasingly mediate everyday information flows, shaping what users see, ask, and decide. LeCun argued that concentration risk at this layer is structural: “We are going to need a high diversity of AI assistants, for the same reason we need a high diversity of news media.” The risks are primarily state-level, but they also matter for investment professionals. Beyond obvious misuse scenarios, a narrowing of informational perspectives through a small number of assistants risks reinforcing behavioral biases and homogenizing analysis. Edge Compute Does Not Remove Cloud Dependence “Some will run on your local device, but most of it will have to run somewhere in the cloud.” From a sovereignty perspective, edge deployment may reduce some workloads, but it does not eliminate jurisdictional or control issues: “There is a real question here about jurisdiction, privacy, and security.” LLM Capability Is Being Overstated “We are fooled into thinking these systems are intelligent because they are good at language.” The issue is not that large language models are useless. It is that fluency is often mistaken for reasoning or world understanding — a critical distinction for agentic systems that rely on LLMs for planning and execution. “Language is simple. The real world is messy, noisy, high-dimensional, continuous.” For investors, this raises a familiar question: How much current AI capital expenditure is building durable intelligence, and how much is optimizing user experience around statistical pattern matching? World Models and the Post-LLM Horizon “Despite the feats of current language-oriented systems, we are still very far from the kind of intelligence we see in animals or humans.” LeCun’s concept of world models focuses on learning how the world behaves, not merely how language correlates. Where LLMs optimize for next-token prediction, world models aim to predict consequences. This distinction separates surface-level pattern replication from models that are more causally grounded. The implication is not that today’s architectures will disappear, but that they may not be the ones that ultimately deliver sustained productivity gains or investment edge. Meta, Open Platforms Risk LeCun acknowledged that Meta’s position has changed: “Meta used to be a leader in providing open-source systems.” “Over the last year, we’ve lost ground.” This reflects a broader industry dynamic rather than a simple strategic reversal. While Meta continues to release models under open-weight licenses, competitive pressure, and rapid diffusion of model architectures — highlighted by the emergence of Chinese research groups such as DeepSeek — have reduced the durability of purely architectural advantage. LeCun’s concern was not framed as a single-firm critique, but as a systemic risk: “Neither the US nor China should dominate this space.” As value migrates from model weights to distribution, platforms increasingly favor proprietary systems. From a sovereignty and dependency perspective, this trend warrants attention from investors and policymakers alike. Agentic AI: Ahead of Governance Maturity “Agentic systems today have no way of predicting the consequences of their actions before they act.” “That’s a very bad way of designing systems.” For investment managers experimenting with agents, this is a clear warning. Premature deployment risks hallucinations propagating through decision chains and poorly governed action loops. While technical progress is rapid, governance frameworks for agentic AI remain underdeveloped relative to professional standards in regulated investment environments. Regulation: Applications, Not Research “Do not regulate research and development.” “You create regulatory capture by big tech.” LeCun argued that poorly targeted regulation entrenches incumbents and raises barriers to entry. Instead, regulatory focus should fall on deployment outcomes: “Whenever AI is deployed and may have a big impact on people’s rights, there needs to be regulation.” Conclusion: Maintain Sovereignty, Avoid Capture  The immediate AI risk is not runaway general intelligence. It is the capture of information and economic value within proprietary, cross-border systems. Sovereignty, at both state and firm level, is central and that means a safety-first

Bitcoin is one of the most powerful technologies of our time and has delivered financial freedom to millions and disrupted established financial players. Yet, many of my fellow financial professionals remain deeply skeptical of its worth. This skepticism is starting to shift as seen in recent headlines. The rise of Bitcoin exchange traded funds (ETFs) and the marketing push from giants like BlackRock are softening attitudes. BlackRock’s IBIT has received $100bn worth of flows, making it one of the most successful ETFs in history, so clearly many investors are taking notice. JPMorgan said last week it would allow institutional clients to use Bitcoin as loan collateral. The Trump Administration is examining adding crypto to the list of approved 401-k investments. To be sure, challenges and resistance remain. And for many, everyday conversations with financial advisors still feel like hitting a wall. Young financial professionals tell me all the time, “If I mention Bitcoin at the office, people glaze over…” So why the resistance? Tech Friction                                                    With any shift from old to new, there will always be resistance. There is a learning curve to the internet, to artificial intelligence, or to any other breakthrough technology. These changes can be particularly challenging for older generations, but age alone is not the obstacle. Crypto’s user interface has presented additional challenges for the masses. Dealing directly with crypto assets onchain through hardware wallets and seed phrases is not particularly difficult but there are large swathes of the population that have neither the technical knowledge, nor the desire to up-skill sufficiently to feel safe enough to store significant portions of their net worth in these assets. The launch of ETFs in the US in January 2024 changes this dynamic, allowing anyone with a brokerage account to invest. I expect there will be other solutions which make self-custody security (security without a third-party intermediary) easier for non-technical users, allowing users to utilize the technology day-to-day, but it takes time for all these functionality layers to be built. We must also appreciate that there is a difference between using the internet to search for a product online or using AI to plan a business project, versus storing significant portions of one’s wealth in a new financial technology. The stakes are higher with crypto, and this could be hampering financial professionals’ approval. The higher stakes draw in some investors but are off-putting to others who would rather wait until the risks have declined and the technology is second nature. But financial professionals are smart, tech savvy people. Technical friction does not explain the visceral reaction when speaking to your resident economist. Economic Ideology Bitcoin is a non-state monetary asset. Its monetary policy is determined without a central bank. “Chancellor on the brink of second bailout” was embedded by its creator Satoshi Nakamoto into the blockchain’s first block, highlighting concern of overusing monetary and fiscal policy. The mindset required to understand its value and its unique proposition runs directly against economic orthodoxy. Source: The Times of London By contrast, traditional economists assume that central banks are necessary to set interest rates and manage inflation. In fact, many economists work at central banks, treasury departments, or private banks. They have a personal stake in maintaining the status quo. These same institutions dominate not just the profession, but also economic academia. As a result, this line of thinking is what gets taught to 95% of economics students around the world, which becomes the foundation for most financial professionals. Economic ideology is similar to political ideology and religion — it is deep-rooted and difficult to change. Once we have been taught that this is the way the world works and we have espoused the virtues of that school of thought, we are deeply entrenched in its continuity. Financial professionals probably have far stronger ideological bias than we would like to admit. Financial Valuation Investments are grounded in quantitative methods, and for good reason. We want substance behind these important decisions. As the field of finance has developed, a set of generally accepted valuation methodologies has emerged. That makes complete sense. For example, dividend discount models, discounted cash flow models, credit spreads, and option-adjusted spreads are all well-established approaches to valuing different asset classes. But Bitcoin doesn’t have earnings, dividends, yields, or interest rates. The many ways to think about valuing Bitcoin do not neatly fit into traditional methodologies. This asset requires more abstract thinking. You may need to question the long-term sustainability of the dollar monetary system or the inherent value of our current forms of money. This kind of conceptual thinking, and its clash with conventional valuation methods, fuels both ideological and technological friction. How do you explain to Warren Buffet that the valuation methods he relies on do not apply to this asset? It sounds suspicious. From his perspective, skepticism makes sense. Regulatory Restrictions Finance is a heavily regulated industry. Professionals have significant reporting requirements and are often mandated to hold specific approved assets. Regulators are almost always behind the ball when it comes to innovative technology, so it has taken them a long time to respond to Bitcoin. Bitcoin has been around for more than 15 years now and still regulated Bitcoin instruments are not available to many investors in various jurisdictions. Financial professionals are incentivized to promote the products that they manage and are licensed to sell. If Bitcoin is not on this list, then there is a major incentive misalignment. Even if a financial professional had a constructive view on Bitcoin in their personal capacity, their views might be tied when speaking to clients or in the media. With the advent of Bitcoin ETFs in the US and the GENIUS Act, which regulates stablecoins, regulatory restrictions are shifting. But regulations take time and they still serve as another barrier hindering support from the financial institutions. Career Risk Financial professionals spend years studying, achieving the Chartered Financial Analyst designation, PhDs, MBAs, CFPs, CPAs, and more. We have built a major barrier to entry for the powerful industry

How shifting correlations, volatility, and macro drivers undermine traditional diversification In March 2020, diversification broke down because liquidity disappeared. In 2022, it failed because inflation overwhelmed both stocks and bonds at the same time. Yet many institutional portfolios remained anchored to static allocation frameworks that assume risk relationships will eventually revert to historical norms, even as the underlying drivers of risk changed. This analysis examines why fixed portfolio structures struggle when regimes shift, and what portfolio managers must do differently when correlations, volatility, and macro forces no longer behave as expected. It is the first in a new series, Risk Regimes and Portfolio Resilience. Two Crises, Different Breakdowns March 16, 2020. The VIX hit 82.69, surpassing its 2008 crisis peak. Liquidity evaporated, correlations flipped, and diversification failed as markets moved from an initial flight to quality into widespread forced selling. In 2022, the breakdown looked very different. Inflation, not liquidity stress, became the dominant risk. Rising rates drove stocks and bonds lower together, producing the first simultaneous calendar-year loss for both asset classes since the Bloomberg Aggregate Bond Index was created in 1980. The classic 60/40 portfolio lost 16.7%, its worst calendar-year performance in modern history. The Question Every Portfolio Manager Should Ask Here’s the uncomfortable truth: most institutional portfolios operate under a dangerous fiction — that risk relationships remain stable enough to justify fixed allocation frameworks. We build models assuming correlations will revert to historical means, that volatility cycles predictably, and monetary policy acts as a reliable backstop. Then reality intervenes, regimes shift, and these assumptions unravel precisely when portfolios need them most. The question isn’t whether your portfolio can weather volatility. It’s whether it can recognize when the very nature of risk has fundamentally changed, and respond accordingly. What Actually Changed and Why It Matters Let’s be precise about what happened in the 2020 and 2022 regime shifts, because the details reveal why traditional approaches failed. In March 2020, we initially saw classic flight-to-quality dynamics. The S&P 500 lost a third of its value between February 20 and March 23. Treasury yields plummeted as investors stampeded into safe havens. The 10-year yield dropped below 0.71%, an unprecedented level. For roughly two weeks, the textbook negative stock-bond correlation held. Bonds rallied as stocks cratered. Then liquidity evaporated. Everything became a forced sale. Correlations flipped. The regime wasn’t just high volatility; it was a complete breakdown of market structure. Portfolio managers who relied on historical correlation matrices for their hedging strategies found themselves exposed on both sides. Fast forward to 2022. A completely different regime break. This time, the enemy was inflation, the dominant macro variable for the first time in decades. The Fed’s aggressive rate hiking cycle created a synchronized selloff across asset classes. Stocks and bonds declined together for 14 consecutive months, representing 31% of trading days. The 36-month stock-bond correlation spiked to 0.66 by December 2024, compared to a 20-year average of negative 0.10. Think about that: two profound market dislocations within 30 months, each requiring opposite defensive positioning. A portfolio optimized for the 2020 regime would have been decimated in 2022. And vice versa. The Tradeoffs Nobody Wants to Acknowledge This creates a genuine strategic dilemma for portfolio construction. You can’t build for both regimes simultaneously using traditional tools alone. Option 1: Optimize for the last crisis. This is the most common institutional response. After 2008, portfolios tilted heavily toward tail-risk hedging and liquidity buffers. These positions offered little protection in 2022 when the threat wasn’t deflation and financial contagion. It was persistent inflation and rising rates. Option 2: Stay perpetually defensive. Hold enough cash and short-duration bonds to weather any storm. But this comes at a massive opportunity cost. Over the past 20 years, equity risk premiums rewarded long-term holders handsomely. The price of permanent defensiveness is structural underperformance in non-crisis years, which are most years. Option 3: Accept the whipsaw. Build for average conditions, acknowledge you’ll get hurt in regime shifts, and trust in mean reversion to bail you out eventually. This works until it doesn’t — typically when client redemptions or regulatory capital requirements force you to lock in losses at precisely the wrong time. None of these are ideal responses. They’re just different ways of accepting static frameworks that can’t solve dynamic problems. What Adaptive Portfolio Management Looks Like The path forward requires acknowledging an uncomfortable reality: Effective risk management in modern markets demands regime-aware positioning. Not prediction recognition. The distinction matters. Consider what you actually need to identify regime shifts as they’re happening, not six months after the damage is done: Volatility isn’t a single number. Realized volatility and implied volatility can diverge dramatically during regime transitions. In early 2020, implied vol (VIX) spiked to 82 while many stocks showed relatively modest realized volatility in the weeks prior. The options market was screaming about a regime shift that backward-looking risk metrics hadn’t fully captured yet. You need frameworks that can synthesize these signals in real-time. Correlations are conditional, not constant. The relationship between stocks and bonds depends entirely on whether inflation or growth uncertainty dominates. When inflation expectations are anchored and growth drives markets, you get the classic negative correlation. When inflation becomes the primary concern, correlations flip positive. Monitoring the ratio of inflation volatility to growth volatility gives you advance warning of these shifts. Institutional flow matters more than most quantitative models acknowledge. In March 2020, the breakdown wasn’t just about fundamentals, it was about leveraged funds forced to deleverage, creating cascading liquidity crises. In 2022, the shift from QE to QT fundamentally altered the supply-demand dynamics for duration. Risk models that ignore these flow dynamics will consistently underestimate systemic stress. The operational challenge is integration. Most firms run separate models for volatility forecasting, correlation estimation, fundamental analysis, and flow monitoring. Each produces valuable signals. But they rarely communicate with each other in a coherent framework. A Framework for Thinking About Regime-Aware Positioning What would regime-adaptive portfolio management look like in practice? Start with regime identification that’s actually implementable.

Long-term investing is one of the most widely accepted principles in finance. The strategy is well supported: the data is clear, the logic is sound, and the outcomes are well documented. So, when clients hesitate, many financial advisors assume the reason is risk tolerance, lack of conviction, or insufficient understanding. In practice, stalled decisions often have little to do with any of these. Clients don’t necessarily disagree with the strategy, but committing early can feel internally misaligned. They understand the rationale. And still, when it comes time to move forward, momentum slows. Advisors may grow frustrated by the hesitation, but it helps to understand its source. The resistance is not about whether the strategy makes sense. It is about how the act of committing feels. For some clients, a decision is never just a choice — it is also a rejection of every other possibility. While the advisor points to the door labeled “long-term strategy,” the client’s attention lingers on all the other doors still open. Choosing one can feel like stepping onto ground that has not fully formed. This piece explores how to coach clients through that mental framework. A Decision That Feels Premature In conversations with clients, this often appears subtly: “I want to sit with it a bit longer.” “Let’s see how things evolve.” “I’m not against it — I just don’t feel ready yet.” Unless there is clear urgency, these clients experience a decision as acting too early. Advisors, on the other hand, often operate through a different mental filter. They approach long-term planning as an act of control: Decide early Reduce noise Remove future pressure For them, structure brings relief. For some clients, however, that same structure feels constraining. Planning and discipline can register as a loss of responsiveness — an obligation to follow a path even if conditions change. When advisors reinforce confidence with statements like “the data supports it” or “we’ve thought this through,” they address the logic but miss the lived experience. When advice sounds final, the client’s instinct is to slow the process. How to Spot It In conversation, you may notice that these clients: Use language that softens conclusions: “maybe,” “it depends,” “for now” Rarely reject your advice outright Ask “What if?” more often than “Which one is best?” Feel more comfortable when decisions “emerge” rather than when they are scheduled Coaching Shift #1: Reframe Commitment as Protection of Freedom Stop emphasizing what is “right.” Start showing clients how the decision protects future flexibility. Logic is not the missing ingredient. Many clients equate indecision with freedom. From their perspective, postponement preserves optionality. Their attention is anchored in the present, where future consequences feel abstract. In this case, the advisor’s role is to gently redirect attention toward how acting now preserves choice later. Language that helps: “Putting this in place now reduces the chance of being forced into a decision you don’t want.” “This keeps your options open when conditions are less favorable.” “Making a choice today protects your future freedom to choose.” The shift is subtle but powerful: the decision is no longer about being right today, but about preserving choice tomorrow. Coaching Shift #2: Reduce the Psychological Weight For clients who resist long-term commitment, the difficulty is rarely the goal itself. It is the perceived size and finality of the step required to reach it. Large, one-time decisions carry a heavy psychological burden and ruminating thoughts: What if this is the wrong moment? What if I regret acting now? Progress often improves when the decision is broken into smaller, sequential steps. Instead of proposing a single decisive allocation, structure the strategy as a series of intentional moves. The client is no longer deciding the entire future — only the next manageable step. Coaching Shift #3: Make Flexibility Visible in the Design For these clients, flexibility must be visible in the structure of the plan. One practical approach is to separate the portfolio in distinct sections rather than treating it as a single unified commitment. For example: A liquidity component for access and responsiveness A long-term component with a patient objective A more opportunistic component for optionality The exact structure will vary by client, but the principle remains: different parts of the portfolio follow different rules. This accomplishes two things: It reassures the client that not everything is locked in at once. It allows long-term capital to remain invested without triggering constant second-guessing. When flexibility is built into the design, commitment becomes easier. Framing Decisions Long-term investing often fails to gain traction not because clients lack discipline, but because the decision architecture does not match how they experience choice. When advisors adjust how decisions are framed — not just what is recommended — follow-through improves without pressure. This blog is part of the author’s series on behavioral investing. See more here: Managing Client Fear: The Cognitive Skill Every Financial Advisor Should Master Coaching Investors Beyond Risk Profiling: Overcoming Emotional Biases How Clients’ Investment Goals Reflect Risk Behavior and Hidden Biases source