CFA Institute

The Taiwan dollar’s rapid appreciation in the second quarter led to speculation of a “Plaza Accord 2.0” — a coordinated effort to weaken the US dollar — echoing the historic 1985 agreement among G5 nations. The original Plaza Accord was designed to address large US trade deficits by engineering a controlled depreciation of the dollar through joint currency intervention. It marked a rare and powerful example of global currency coordination. Any new Plaza-style agreement today would face far greater financial and geopolitical hurdles than it did 40 years ago. Indeed, if US policymakers seek to stimulate domestic manufacturing by depreciating the dollar, they must also account for the emerging costs and risks associated with global trade, capital flows, and market stability. This post examines the potential consequences of a coordinated dollar depreciation today — from FX volatility and insurance risk to broader macroeconomic impacts. A Weaker Dollar Could Heighten Global FX Volatility A weaker US dollar could have a dramatic effect on the FX market and, specifically, on Taiwanese life insurance companies. A January 2025 FT article pointed out that these companies hold assets equivalent to 140% of Taiwan’s GDP. A substantial portion of these holdings are in US-dollar-denominated bonds only partially hedged for FX volatility. Taiwan has enjoyed widening current account surpluses due in large part to strong demand for its semiconductors. To manage the resulting FX reserve growth and to maintain FX stability, the local monetary authority permitted life insurance companies to swap their Taiwan dollars for US dollars in the FX reserve. The insurers then swapped USD to buy US fixed-income assets to meet future (insurance policy) obligations. Despite shifting the bulk of their portfolio assets to US dollars, most of the insurance policies (firm liabilities) remain denominated in local currency. The result would be a significant currency mismatch where sharp declines in the US dollar would reduce the value of US-dollar-denominated bonds such as US Treasuries held by Taiwanese insurance companies, leaving the insurance companies with insufficient assets to match their liabilities. The original Plaza Accord signed by the G-5 countries in 1985 was agreed upon under the backdrop of a relatively benign macro environment. A hypothetical “Plaza Accord 2.0” to depreciate the US dollar would likely increase pressure on Taiwan’s insurers and their risk-management efforts. This vicious cycle would exacerbate pressure and magnify FX market volatility. Taiwanese insurance companies are also exposed to duration risks. The US dollar bonds held by Taiwanese insurance companies are longer-duration (with greater interest rate sensitivity than short-maturity debt). Sales of these assets would likely lift long-term US interest rates and transmit interest rate volatility across markets. Taiwanese insurers are not alone in their exposure to this type of risk. Similar carry-trade flows (sell local currency, buy US dollar and dollar-denominated assets) with the Japanese yen in the third quarter of 2024 triggered a brief-but-disruptive volatility surge across major asset markets. The US Trade Deficit’s Hidden Role  A “Plaza Accord 2.0” coming 40 years after the original accord would need to account for the US trade deficit as part of a circular currency flow to fund the US government. In 1985, the US deficit was at $211.9 billion. By 2024 it had risen to $1.8 trillion. Similarly, the US debt ballooned from $1.8 trillion in 1985 to $36.2 trillion in the second quarter this year. Non-US exporters reinvesting trade surplus dollars in US Treasuries (lending surplus dollars back to the US government) are a key source of liquidity in the US bond market: Under the present paradigm, a lower US trade deficit would likely disrupt the reinvestment of exporter dollar trade surpluses, which could reduce foreign demand at US Treasury auctions and negatively affect secondary market liquidity conditions. “Plaza Accord 2.0’s” Nuanced Impact On a Leaner US Manufacturing Sector The US manufacturing sector has evolved significantly over the past 40 years. According to BEA data, the US manufacturing sector’s share of nominal GDP fell to 9.9% in 4Q 2024 from 18.5% in 1985.The total number of workers in the manufacturing sector also declined. In April 1985, manufacturing employees as a share of total non-farm payrolls was 18.4%. By April 2025, that number had dropped to 8.0%. The reduction in manufacturing headcount (with improved productivity, until gains began to stagnate in the late 2000s) implies US manufacturing had become more efficient between 1987 and 2007: Thus, a changed manufacturing industry with relatively smaller payrolls now than in 1985 would likely benefit differently from impacts of Plaza style accords than four decades ago, when more households were directly participating in the industry. Assessing the Risk Reward of “Plaza Accord 2.0” Studies on the impact of the original Plaza Accord concluded that exchange rate shifts ultimately led to changes in trade balances with a lag of two years. A similar lag would likely apply today, raising questions about whether a new Plaza-style intervention could meaningfully support US manufacturing — now a leaner, smaller share of GDP — without triggering broader financial disruptions. Compared to 1985, today’s global system is more interconnected and more reliant on the dollar, particularly through foreign holdings of US debt. Any coordinated effort to weaken the dollar would need to balance potential industrial gains against risks to FX stability, institutional asset-liability mismatches, and the functioning of US debt markets. The cost-benefit equation for “Plaza Accord 2.0” is far more complex than it was four decades ago. Calls for a “Plaza Accord 2.0” reflect growing concern over US trade imbalances and industrial competitiveness. But unlike in 1985, the global economy today is more complex, with deeper interdependencies and more fragile financial linkages. A new Plaza-style agreement would carry unintended consequences — from FX volatility and insurance-sector risk in Asia to disruptions in US debt financing and monetary policy transmission. Under the original Plaza Accord, currency shifts took years to influence trade balances, underscoring the lag between intervention and impact. Policymakers must therefore assess whether the benefits to a leaner US manufacturing base would outweigh the risks to global markets, institutional stability,

Crypto is an uncorrelated asset class, according to its proponents, and as such ought to contribute to portfolio diversification. However, research has shown that this claim hasn’t held up particularly well. We examined the relationship between where bitcoin mining was conducted and how the performance of the local equity markets correlated with bitcoin prices and found that in the United States especially, the performance of the S&P 500 and bitcoin prices have exhibited a positive correlation over the last five years. Because the United States and China are where most bitcoin mining has been conducted over the last five years, we used the S&P 500, Hang Seng, and Shanghai Composite indices as our regional proxies. We calculated the rolling nine-month correlation between bitcoin returns and each of the three indices using weekly data. We then compared that with the bitcoin hash rate by country from September 2019—which is as far back as reliable records go—to January 2022.The bitcoin hash rate measures the computational power of the bitcoin blockchain and is a proxy for how much bitcoin mining is being done. Prior to November 2020, China accounted for more than 60% of bitcoin mining. But fast-forward to November 2021 and China’s share had plunged to about 15% in response to government steps to reduce bitcoin mining. Over the same time period, the United States climbed from representing about 10% of global bitcoin mining to more than 35%. Bitcoin Hash Rate Distribution by Country, Sept. 2019 to Jan. 2022 Bitcoin Mining Distribution by Country, 2019 to 2022 These trends make the November 2020 to November 2021 time frame an excellent window into how bitcoin-prices-to-index correlations adjust as bitcoin mining ebbs and flows. We found that as US crypto mining spiked between November 2020 and November 2021, bitcoin’s correlation with the S&P 500 rose to 0.39 from 0.28. But as crypto mining plummeted in China during the same period, bitcoin’s correlation with the two Chinese indices fell also. The Hang Seng’s and Shanghai Composite’s bitcoin correlation declined to -0.14 from 0.21 and to -0.44 from 0.09, respectively. Bitcoin Correlations with Equity Indices November 2020 November 2021 S&P 500 0.283 0.386 Hang Seng 0.213 –0.138 Shanghai Composite 0.085 –0.437 The results suggest the more bitcoin is mined in a particular country, the greater the correlation between the cryptocurrency and local equity markets. As bitcoin mining declines in a region, the correlation between bitcoin and local stock markets subsides as well. Regarding annual correlations between bitcoin prices and the indices in question, our hypothesis holds up there too. The more bitcoin mining in a locale, the greater the correlation between the price of bitcoin and local stock markets. This relationship was strongest with the S&P 500 and Shanghai Composite and less so with the Hang Seng over the full time period. Annual Correlations: Bitcoin and Equity Indices 2016 2017 2018 2019 2020 2021 2022 S&P 500 –0.174 –0.119 –0.045 0.064 0.155 0.186 0.747 Hang Seng –0.289 –0.378 0.010 0.011 0.148 –0.190 0.400 Shanghai Composite 0.014 0.253 0.096 0.122 0.169 –0.390 –0.040 In total, our results indicate that where bitcoin is mined may affect how it moves and which stock indices it moves with. And this affects what sort of diversification benefits bitcoin may or may not bring to a portfolio. 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/ photonaj 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

Cheaper, Faster Better: How We’ll Win the Climate War. 2024. Tom Steyer. Spiegel & Grau. In Cheaper Faster Better, Tom Steyer, co-executive chair of Galvanize Climate Solutions and co-founder of Farallon Capital, shares his own story and highlights the innovative work of other climate leaders in the clean-energy transition. He shows us how capitalism can be used to scale climate progress and calls on all of us to help stabilize our planet. As green technology, such as solar panels, green concrete, green steel, and green hydrogen, is fast becoming cleaner and cheaper, reshaping our planet’s future has never been more important. Steyer reminds us that natural disasters are devastating to economies. The toll includes the cost of rebuilding (borne by taxpayers), the cost of small businesses closed, the skyrocketing cost of insurance for homeowners and employees in a disaster’s path (or the inability to purchase insurance at any price), the loss of income of people who work outdoors who have to reduce their hours due to rising temperatures, and the human suffering and deaths that accompany these catastrophes. During the 2000s, the United States experienced an average of seven disasters per year that cost $1 billion or more to recover from. During the 2010s, that number jumped to thirteen billion-dollar disasters per year, and it has risen even higher during the 2020s. Reducing carbon pollution to achieve net zero can start with Steyer’s “five plus one” approach. The five areas where we will need to cut our emissions are electricity generation, transportation, manufacturing, agriculture, and buildings. As a real estate practitioner, I found his details on buildings to be insightful. Since most buildings leak, we need to ensure that what we are building today is net-zero emission. Since 80% of buildings in developed economies will still be in use in 2050, focusing on new construction is not enough. We need to retrofit old buildings so that they waste less energy and cost their owners less money in the process. The plus one is sequestration, which removes greenhouse gas from the air by techniques such as direct air capture. Natural solutions, such as planting trees or kelp beds that absorb carbon, can be useful strategies as well. Steyer, a capitalist, fundamentally disagrees with the premise behind two versions of a “green premium,” which assumes people will pay extra for products that are good for the planet, either out of kindness or in recognition of externalities. I agree with his sentiment that in a competitive world, selling more expensive products for any reason does not work and will not scale. Achieving net zero will require transitioning the entire world away from fossil fuels, making clean energy and cleantech the least expensive option. These green industries will need to compete on sticker price. For example, the cost of solar panels has fallen by 99% since 1977. Rooftop solar is not only cleaner than traditional power but also now far cheaper. The price gap is almost certain to keep growing because prices for new technologies tend to go down much faster than prices for things that have been around forever. Environmental justice is another reason we should care about reducing carbon emissions, and I am encouraged that Steyer stresses this point at the end of the book. Poorer countries will bear a disproportionate burden of climate change’s impact. In addition, in the United States, marginalized communities, such as coal miners in Appalachia, suffer the most from oil and gas-related pollution, even as their members are often the least able to protect themselves from the impact of climate change. Addressing these inequities is the correct thing to do. In summary, Cheaper Faster Better provides practical insights, including steps to transition to a clean energy economy. New technology is critical for this transition but once it breaks through, it can be cheaper, faster, and better, providing a better deal for people. source

“Our monetary policy deliberations and decisions build on what we have learned about inflation dynamics both from the high and volatile inflation of the 1970s and 1980s, and from the low and stable inflation of the past quarter-century. These lessons are guiding us as we use our tools to bring inflation down. . . . We will keep at it until we are confident the job is done.” — Jerome Powell, 26 August 2022 In “The Eye of the Storm: The Fed, Inflation, and the Ides of October,” I recommended that investors temper their enthusiasm in response to a strong equity market rally and not underestimate the US Federal Reserve’s resolve in its battle against inflation. On 26 August 2022, Fed chair Jerome Powell spoke at the annual Jackson Hole Economic Symposium. His forceful language and deliberate references to the lessons of history laid to rest any hope that the Fed will shift away from its tightening strategy. Equity markets responded with sharp declines. The Fed leadership has struggled over the last nine months to convince the markets that its dovish bias of the past 40 years no longer applies. What explains the communication challenge? Many investors simply do not understand that this is a rare and dangerous inflationary event. The inflation of 1919 to 1920 that followed World War I and the Great Influenza is the most relevant parallel. Although such major crises often lead to temporarily high inflation, the Fed still must act aggressively to contain it. Failure to do so could allow temporary inflation to transform into a repeat of the Great Inflation of the 1970s and early 1980s. In his speech, Powell emphasized three distinct lessons from financial history that explain the Fed’s approach. By framing the speech around these lessons, he showed that the Fed recognizes the severe danger if inflation persists at today’s elevated levels, that it accepts its unique responsibility to eliminate this risk, and that it is committed to avoiding its predecessors’ mistakes regardless of the short-term pain that will likely entail. 1. “The first lesson is that central banks can and should take responsibility for delivering low and stable inflation.” In the Fed’s 108-year history, the Great Inflation stands out among its gravest errors — rivaled only by the Great Depression. The flawed monetary policies of this period resulted, in part, from the common belief that the Fed was obligated to synchronize monetary and fiscal policy. When successive US presidents pursued overly expansionary fiscal policies, such as the Great Society and the Vietnam conflict, the Fed’s leadership hesitated to counterbalance them with contractionary monetary policy. In 1965, after the Fed pushed for higher interest rates (or cuts in spending), President Lyndon Johnson reportedly pinned the Fed chair, William McChesney Martin, Jr., against a wall at his Texas ranch and shouted, “Martin, my boys are dying in Vietnam and you won’t print the money I need.” When President Richard Nixon was asked whether he respected Fed chair Arthur F. Burns’s independence, he responded, “I respect his independence. However, I hope that independently he will conclude that my views are the ones he should follow.” Such coercion was not easy for the Fed to resist. But Powell has now made it clear that central banks can and should take responsibility for delivering low and stable inflation, thus signaling that the Fed will resist any potential political pressure. 2. “The second lesson is that the public’s expectations about future inflation can play an important role in setting the path of inflation over time.” Powell understands the enormous risk long-term high inflation poses to the US economy. The Fed’s experience during the Great Inflation is instructive. Under Martin, the Fed had the opportunity to extinguish inflation in the late 1960s. It failed to act, and its inaction did not go unnoticed: Market participants began incorporating higher inflation expectations into their future plans. Once higher inflation was entrenched in the economy, it became much more difficult to unwind. Indeed, Fed chair Paul Volcker had to raise interest rates all the way to 20% in 1981. History shows that lowering inflation expectations requires much more aggressive and sustained monetary tightening. That’s why it is critical to prevent higher inflation expectations from taking root in the first place. Powell’s statement shows the Fed is aware of this risk and recognizes that time is running out. 3. “That brings me to the third lesson, which is that we must keep at it until the job is done.” “Keep at it” evokes Paul Volcker, the Fed chair who triumphed over the longest lasting inflation crisis in the nation’s history. This reference reveals that Powell understands the severe consequences of the Fed’s half-hearted efforts to tighten monetary policy under Martin and Burns. The truth is that the Fed’s leadership in the 1960s and 1970s understood that inflation was damaging; they were just unable (or unwilling) to bear the costs of ending it. Each time they engaged in monetary tightening, they prematurely reversed course in response to rising unemployment. The public correctly interpreted the Fed’s lack of resolve as a sign that high inflation would continue. By the time Volcker announced a new strategy in October 1979, it required several years of pain to convince the public that he was serious. Powell’s recognition that the Fed “must keep at it until the job is done,” sends a clear message that a potential recession or uptick in unemployment will not stop the Fed from further monetary tightening. The Fed’s primary goal is to reduce inflation to its 2% target. An economic recession and job losses are, in Powell’s words, “unfortunate costs of reducing inflation.” These costs are worth it, however, because “a failure to restore price stability would mean far greater pain.” Those who recall the stagflation years of the 1970s can attest to the fact that one day we will be thankful for the Fed’s resolve. Future Outlook Powell’s statement at Jackson Hole reiterated that the Fed leadership understands why the

Risk is an inherent part of both investing and career growth. While being cautious can protect against losses, avoiding risk altogether comes with its own set of dangers — missed opportunities, underperformance, and stagnation. Historically, women are perceived as more risk-averse in investing and professional decision-making. Taking calculated risks is essential for success, however, whether it’s investing in alternative assets, negotiating compensation, or making bold career moves. In today’s competitive landscape, professionals who embrace risk thoughtfully are better positioned to drive innovation, achieve financial independence, and create lasting impact. Last month, I moderated a discussion at the Inspirational Women Forum & Leadership Awards, “The Risks of Being Risk Averse,” hosted by the Los Angeles Times. In this blog post, I’ll share insights (lightly edited) from two of the panelists, Amber Ortiz and Lara Shortz, about women and risk. Why have women historically been risk-averse? Barbara Stewart, CFA: We earned this stereotype, honestly. Historically, “risky” in the world of investing was defined by investing in equities versus fixed income or cash. Thirty years ago, over 60% of US men invested in stocks compared to only 40% of women, leaving an investment gap when it came to stocks. But times have changed. In the 2001 to 2008 period, 65% of men and 59% of women owned stocks. By 2009 to 2017, the gap narrowed further: 56% of men and 52% of women invested in equities. The 2024 Gallup data shows that in fact 63% of women own stocks versus 62% of men. In what situations do women continue to show an aversion to risk? Lara Shortz: One case in point is that many women executives are apprehensive about having tough conversations around pay increases/promotions. They often have a harder time with these conversations. Pay equity has improved for younger professionals, but at the senior level, particularly for women executives, the pay gap often widens over time and becomes much harder to bridge. This is not usually the result of intentional bias. It’s more about systemic factors. For example, women tend to change jobs less frequently, which limits opportunities to renegotiate pay. Yet, when hiring new talent, companies often offer higher salaries to attract candidates. The net effect is this: If women aren’t making career moves, they’re missing out on crucial pay discussions. Over time, this dynamic makes conversations about raises or promotions even more challenging. Amber Ortiz: Risk is multifaceted and can be influenced by several factors. I would argue that women are not risk averse but instead risk aware. Women take the time to understand the risk and evaluate all the potential drawbacks and how a decision might affect the those they care about most families, companies, and society.  What are the risks of being risk-averse? Barbara Stewart, CFA: In my most recent research “Women & Alts: A Global Perspective” commissioned by private equity firm Kensington Capital Partners, my key finding was that globally men have double the exposure to alternative investments than women. This is largely due to structural barriers in place such as lack of a network effect for women and macho-themed sales and marketing around alts. Whether low-alt exposure is actually due to risk aversion or other factors, what is clear is that underexposure to what is traditionally seen as a higher-risk asset class is a risk for female investors. Lara Shortz: In an increasingly competitive market, organizations need to foster a culture of innovation and experimentation. Why? Because in a competitive market you need to constantly think about ways to retain your talent. This requires a lot of creativity. It also depends on the environment. For example, in my business of law — a traditionally risk-averse business — having an entrepreneurial environment focused on professional development is not common for law firms. We are creating a very positive, forward-thinking cultural environment. In industries with fast-moving and highly creative environments, it is also important to provide clarity and stability for your team. In my view, being intentional and transparent about your business’s goals and the culture you’re building is key to helping people feel supported and motivated. Amber Ortiz: Underperformance and regret. This is true for both investment performance and business success. Risk is relative, and your tolerance continues to shift and shape as time passes. People evaluate potential risk and return differently. Risk can be highly subjective and dependent on the influences surrounding the decision. Not only should we spend time evaluating the negatives aspects of risk, but we also need to talk about how opportunities, creativity, growth, and resilience come from taking risk. There have been multiple studies that conclude that women generate better investment performance than men. Men tend to be more decisive and confident when making decisions due to their ability to compartmentalize, while women evaluate all angles and impacts an investment or decision might have on their family, business, liquidity, or even society. We need to remember that we face risk in almost every decision and as leaders we must inspire confidence and encourage risk-taking while also maintaining a prudent approach to decision-making. How can women replace risk-aversion with risk-awareness? Amber Ortiz: Take space and be involved. I suggest that you build a team of advisors you trust and respect and who listen and communicate effectively. Building a relationship with those who are advising you and your family is key. Surround yourself with like-minded women. I hold intimate all-women conversations (10-15 people) where we dive into a specific topic like estate planning, investing in real estate, and generational gaps. These all-women sessions provide space to ask questions, share stories of failure and success, and build a community of people that might have experienced a similar situation or have similar concerns. Lara Shortz: My best advice is to focus on what you do best and outsource the rest. For example, when it comes to negotiating compensation, having a lawyer handle it for you can create a completely different dynamic. It removes personal bias like questions about being too assertive and focuses attention on the terms

Demographics Unravelled: How Demographics Affect and Influence Every Aspect of Economics, Finance and Policy. 2021. Amlan Roy. Wiley. Demographics are destiny. This cliché will cause some investors to nod their heads in agreement, yet it provides little meaning. Demographics are often used to bolster an investment narrative, yet the rich details on their link with asset returns are often missing. Amlan Roy, an economist working on the intersection between demographics and investments, provides a comprehensive review for how the study of population affects many of our core investment and policy decisions. This volume covers all issues associated with population dynamics, from aging to geographical movement, and can serve as a comprehensive guide on how demographics affect asset pricing, pension management, health, retirement, and policy. Rather than just a problem of birth, deaths, and aging, Roy frames demographics as a driving factor on returns from the combination of tastes and numbers. Population numbers count, but tastes and the changing behaviors of different age groups drive markets. The book is divided into six major topic areas: core demographic foundations; population dynamics; the impact of demographics on the macroeconomic environment; the link between demographics and asset prices; problems of health and longevity across populations, pensions, and retirement; and the effect of demographics on quality of life, gender, governance, and sustainability. Each topic is linked to long-term returns and relative prices across asset classes and market sectors. The core population issues, which are the base for demographic analysis, are all well presented. Aging, life expectancy, fertility, and dependency generate economic problems that must be addressed by both investors and policymakers. Population changes generate headwinds and tailwinds for policy and asset prices that cannot be escaped and do not have simple solutions. Roy discusses how decisions made more than a generation ago will support or plague future generations, forcing countries to transition between population shortages and excesses. One country may face birth excesses while another grapples with aging. Each affects capital allocations and returns. Roy, through clear graphical analysis, highlights the dynamics of these core issues. The demographics and macroeconomics chapter drives home the core observation that population dynamics create market constraints. Demographics affect economic growth, living standards, inflation, public debt, capital flows, and exchange rates. The dynamics of population influence relative country growth as consumers age and move through their life cycles. The population mix sets policy preferences through voting and drives policy choice constraints. Bulges in population will constrain opportunities for both older and younger citizens. Roy dusts off the core consumption theory (the life-cycle and permanent income hypotheses) and links population changes with asset price behavior. As populations move through the aging process, their behaviors switch from spending to saving and thus influence the demand for risky and safe assets. Whether it be the equity premium or real interest rates, population dynamics will always pressure returns. As is well-documented for China and India, population dynamics coupled with tastes also drive commodity markets. Roy emphasizes the critical point that age by itself does not drive markets. The combination of population and tastes generates demand pressure on markets. Populations desire to survive and extend longevity, so health becomes a core focus with respect to expenditures. Just as fertility drives demographics, life extensions stretch the population with new demands. As incomes rise, there is a corresponding change in the composition of populations, and the demand for better health services increases. Longevity changes tastes and marches headlong into issues surrounding quality of life. Longevity and the aging of the population focus on the key investment issues of retirement and pensions. Flowing back to consumption models, Roy explains how if you expect to live longer, retirement planning and health care costs become even more important. When aggregated across generations, pension decisions weigh critically on returns and the asset management and insurance businesses. Trillion of dollars are being allocated to address a highly uncertain problem. Who will pay and at what costs are critical pension issues that are only exacerbated when the population structure bulges for older generations. The book ends with a discussion on such core issues as quality of life, gender, governance, and sustainability. Views toward gender and work upend many past demographic assumptions. Longevity shines a spotlight on happiness and life quality, while intergenerational transfers represent more than wealth and include the state of the world. These issues are hard to quantify, but Roy provides a holistic approach through connecting these topics to the core assumption that demographics coupled with tastes define our future. Demographics Unravelled provides an extensive and well-documented review of the finance and economic research influenced by demographics. This allows the reader to be exposed to the key topic research; however, it makes for a lengthier and less lively work that at times reads like an academic literature review with an author citation and conclusion approach. The graphics are extremely helpful in visually telling the demographic story, but these complex graphs are at times hard to read in their black and gray template. Roy does provide in one volume everything an investor should know about the impact of demographics on investing; nevertheless, connecting the research to core investment questions would have resulted in a more compelling story. Given the author’s long history of consulting in this area, it would have been helpful to show readers how to integrate the background research with investment decisions. For example, how should a pension investment committee use this information to improve allocation decisions? The answers are not immediately obvious. While demographics are destiny, our future can change with the right thinking. Demographics drive demand and pricing, but with the right lens, we can see these trends better and adapt to these headwinds and tailwinds. If a reader wants to be up to speed on demographics, this is the book to read. Demographics Unravelled should generate deeper discussions on the integration of demographics with investing, and if investment committees take the time to integrate this thinking, the result may be better performance. If you liked this

The debate about private market fund valuations and volatility has returned to center stage. To quote Mohamed El-Erian, some private equity managers believe “their asset class would avoid the reckoning that stocks and bonds have been exposed to this year because they were structurally immunised against disruptive changes in the investment landscape.” El-Erian says that this “may prove to be misplaced self-confidence,” while Cliff Asness describes it as “volatility laundering.” From a capital market perspective, how can investors price net asset value (NAV) valuations and efficiently transfer their eventual risk? We have developed an actionable framework. The best way to offer investment commentary is to walk the talk and take a side in a trade. If you think that a NAV’s valuation is low, you should buy at that price. If you think it’s high, you should sell. There should be a proper mechanism in place to reward such forward-looking, relative value trades. As a consequence, an investor could monetize a higher or lower return — a positive or negative risk premium — versus other allocations over a given time horizon. The Problem Private market valuations are still opaque, which makes it difficult for investors to determine the value of private assets. Unlike in listed markets, private market prices are not publicly available and the methodologies by which valuations are derived are often a mystery. Still, private market investments can’t ultimately conceal their true results. Their self-liquidating structures are intrinsically objective. Volatility can’t be laundered indefinitely. In the end, the total value produced over time will be converted to cash. Before liquidation, even when private market returns are measured with an accurate methodology, they are heavily influenced by the on-paper gains and losses of the estimated interim NAVs. General partners have different philosophies about what is a fair NAV valuation. Some have a mark-to-market outlook, while others take a less sensitive stance on market risk. Not all private market fund valuations are born equal. Indeed, the International Private Equity and Venture Capital Valuation (IPEV) Guidelines dictate several valuation methodologies for deriving the fair value of private funds. These run the gamut from comparable transaction multiples to discounted cash flow methodologies to quoted investment benchmarks. Nevertheless, the Financial Accounting Standards Board (FAS 157 – ASC 820) places the focus on fair value, with an emphasis on the exit value, or the expected proceeds from the sale of the given asset. While private market investments tend to be held for the long term, their fund’s liquidation mechanism gives their mark-to-market the final say. Only when portfolio assets are sold does the seller discover what the market is willing to pay. If the paper valuations of those assets do not reflect their corresponding secondary market price, the buyer may seek to negotiate a discounted price and thereby increase their probability of a positive risk premium. The Way Forward Our research has sought to explain and maximize the value of time-weighted metrics in private market investments. Why? Because private market assets should be comparable to all other asset classes and easier to comprehend. This will make the asset class more usable, improve portfolio and risk management, and reduce the idiosyncratic inefficiencies of the undrawn cash or overallocations. Our investigations have yielded many first-of-their-kind private market solutions. Valuation Transparency Through our duration-based calculation methodology, we measure the time-weighted performance of private market investments and establish a real-time valuation link with the public markets that makes volatility explicit and eliminates delays or lack of estimates. This rules-based probabilistic framework is grounded on a robust benchmarking approach. Investors can nowcast and objectively assess the mark-to-market quality of the NAV of their private market investments. Price Discovery With real-time, time-weighted indexing techniques, the duration-adjusted return on capital (DARC) methodology constructs a curve of forward returns for private market funds that ties ex-post performance to forward-looking expectations. Only time-weighted returns can be traded over time, and the DARC makes private funds tradable over future maturities. With our Private Fund Forward Exchange (PRIFFE), investors can test the potential of current NAVs to deliver equivalent cash in the future, anticipate the expected forward returns over the targeted time horizon, and manage the volatility of the mark-to-market. The premise behind our approach is that money on the table can take advantage of the staleness of misplaced private market NAVs — hence the PRIFFE acronym, which plays off of “priffe,” or money in the 19th-century Roman dialect, and priffe, a traditional Swedish card game with bids and contracts. Leveling the Playing Field for Private Market NAVs A conventional rationale for private market investments is that their “stale” valuation profile reduces the volatility of a typical multi-asset portfolio and provides return stability. But this is only true for short-term declines in valuations. Private market fund reporting has a lag of several months and may benefit from hindsight. Since the global financial crisis, we have yet to see a prolonged period of asset repricing. Hopefully, we won’t see one again, though that may be wishful thinking given the current economic framework. If such repricing occurs, private market investments have no way out. Market conditions will always influence the exit values and returns of private investment portfolios. Even assuming stable valuations, the liquidation process may take time, reducing returns. In uptrend cycles, like that of the last decade, duration and market risks are often neglected, but they track private market investments through the ups and downs. Mark-to-market just makes them more visible. Going forward we need to anticipate and manage the mark-to-market adjustments to increase transparency around private fund investments. Private market funds that adopt a mark-to-market approach may exhibit more volatility and seemingly even underperform in certain market conditions. But they offer investors three important advantages: Despite the usual reporting lag, investors can calculate more robust now-casted NAV estimates. The more consistent the starting point, the lower and more random the estimation error. Such NAV data makes investors’ balance sheets more resilient and eliminates the negative performance spiral that results from the artificial denominator

In today’s data-driven investment environment, the quality, availability, and specificity of data can make or break a strategy. Yet investment professionals routinely face limitations: historical datasets may not capture emerging risks, alternative data is often incomplete or prohibitively expensive, and open-source models and datasets are skewed toward major markets and English-language content. As firms seek more adaptable and forward-looking tools, synthetic data — particularly  when derived from generative AI (GenAI) — is emerging as a strategic asset, offering new ways to simulate market scenarios, train machine learning models, and backtest investing strategies. This post explores how GenAI-powered synthetic data is reshaping investment workflows — from simulating asset correlations to enhancing sentiment models — and what practitioners need to know to evaluate its utility and limitations. What exactly is synthetic data, how is it generated by GenAI models, and why is it increasingly relevant for investment use cases? Consider two common challenges. A portfolio manager looking to optimize performance across varying market regimes is constrained by historical data, which can’t account for “what-if” scenarios that have yet to occur. Similarly, a data scientist monitoring sentiment in German-language news for small-cap stocks may find that most available datasets are in English and focused on large-cap companies, limiting both coverage and relevance. In both cases, synthetic data offers a practical solution. What Sets GenAI Synthetic Data Apart—and Why It Matters Now Synthetic data refers to artificially generated datasets that replicate the statistical properties of real-world data. While the concept is not new — techniques like Monte Carlo simulation and bootstrapping have long supported financial analysis — what’s changed is the how. GenAI refers to a class of deep-learning models capable of generating high-fidelity synthetic data across modalities such as text, tabular, image, and time-series. Unlike traditional methods, GenAI models learn complex real-world distributions directly from data, eliminating the need for rigid assumptions about the underlying generative process. This capability opens up powerful use cases in investment management, especially in areas where real data is scarce, complex, incomplete, or constrained by cost, language, or regulation. Common GenAI Models There are different types of GenAI models. Variational autoencoders (VAEs), generative adversarial networks (GANs), diffusion-based models, and large language models (LLMs) are the most common. Each model is built using neural network architectures, though they differ in their size and complexity. These methods have already demonstrated potential to enhance certain data-centric workflows within the industry. For example, VAEs have been used to create synthetic volatility surfaces to improve options trading (Bergeron et al., 2021). GANs have proven useful for portfolio optimization and risk management (Zhu, Mariani and Li, 2020; Cont et al., 2023). Diffusion-based models have proven useful for simulating asset return correlation matrices under various market regimes (Kubiak et al., 2024). And LLMs have proven useful for market simulations (Li et al., 2024). Table 1.  Approaches to synthetic data generation. Method Types of data it generates Example applications Generative? Monte Carlo Time-series Portfolio optimization, risk management No Copula-based functions Time-series, tabular Credit risk analysis, asset correlation modeling No Autoregressive models Time-series Volatility forecasting, asset return simulation No Bootstrapping Time-series, tabular, textual Creating confidence intervals, stress-testing No Variational Autoencoders Tabular, time-series, audio, images Simulating volatility surfaces Yes Generative Adversarial Networks Tabular, time-series, audio, images, Portfolio optimization, risk management, model training Yes Diffusion models Tabular, time-series, audio, images, Correlation modelling, portfolio optimization Yes Large language models Text, tabular, images, audio Sentiment analysis, market simulation Yes Evaluating Synthetic Data Quality Synthetic data should be realistic and match the statistical properties of your real data. Existing evaluation methods fall into two categories: quantitative and qualitative. Qualitative approaches involve visualizing comparisons between real and synthetic datasets. Examples include visualizing distributions, comparing scatterplots between pairs of variables, time-series paths and correlation matrices. For example, a GAN model trained to simulate asset returns for estimating value-at-risk should successfully reproduce the heavy-tails of the distribution. A diffusion model trained to produce synthetic correlation matrices under different market regimes should adequately capture asset co-movements. Quantitative approaches include statistical tests to compare distributions such as Kolmogorov-Smirnov, Population Stability Index and Jensen-Shannon divergence. These tests output statistics indicating the similarity between two distributions. For example, the Kolmogorov-Smirnov test outputs a p-value which, if lower than 0.05, suggests two distributions are significantly different. This can provide a more concrete measurement to the similarity between two distributions as opposed to visualizations. Another approach involves “train-on-synthetic, test-on-real,” where a model is trained on synthetic data and tested on real data. The performance of this model can be compared to a model that is trained and tested on real data. If the synthetic data successfully replicates the properties of real data, the performance between the two models should be similar. In Action: Enhancing Financial Sentiment Analysis with GenAI Synthetic Data To put this into practice, I fine-tuned a small open-source LLM, Qwen3-0.6B, for financial sentiment analysis using a public dataset of finance-related headlines and social media content, known as FiQA-SA[1]. The dataset consists of 822 training examples, with most sentences classified as “Positive” or “Negative” sentiment. I then used GPT-4o to generate 800 synthetic training examples. The synthetic dataset generated by GPT-4o was more diverse than the original training data, covering more companies and sentiment (Figure 1). Increasing the diversity of the training data provides the LLM with more examples from which to learn to identify sentiment from textual content, potentially improving model performance on unseen data. Figure 1. Distribution of sentiment classes for both real (left), synthetic (right), and augmented training dataset (middle) consisting of real and synthetic data. Table 2. Example sentences from the real and synthetic training datasets. Sentence Class Data Slump in Weir leads FTSE down from record high. Negative Real AstraZeneca wins FDA approval for key new lung cancer pill. Positive Real Shell and BG shareholders to vote on deal at end of January. Neutral Real Tesla’s quarterly report shows an increase in vehicle deliveries by 15%. Positive Synthetic PepsiCo is holding a press conference to address the recent product recall. Neutral Synthetic Home

On February 27, 2024, Investing in U.S. Financial History was published, capping off my exhaustive four-year effort to document the financial history of the United States. The book begins with Alexander Hamilton’s brilliant financial programs in 1790 and ends with post-COVID-19 inflation in 2023. Now that the book promotion process is winding down, I am returning to my second passion, which is serving as an advisor to institutional investment plan trustees. This blog post draws from several chapters of my book, as well as on my more than 12 years’ experience as an investment consultant. It is framed around five quotes that relate to the fulfillment of a trustee’s fiduciary duties. If you serve as a trustee of an institutional investment plan, these quotes may help guide your decisions for the benefit of those who depend on your stewardship. Quote 1: “A trustee may only incur costs that are appropriate and reasonable in relation to the assets, the purpose of the trust, and the skills of the trustee…Wasting beneficiaries’ money is imprudent.” — Uniform Prudent Investor Act (1994) A trustee’s scarcest asset is rarely found in the portfolios they oversee. In fact, their scarcest asset is their time. Trustees typically convene quarterly for a few hours, which forces them to depend heavily on advice provided by investment consultants, professional staff, and asset managers. Over the past several decades, these advisors have encouraged trustees to add actively managed funds and expensive alternative asset classes. The Uniform Prudent Investor Act (UPIA) requires fiduciaries to evaluate whether these incrementally higher costs are worth it, but few pause to consider their obligation to make such determinations. Perhaps, reciting this quote before every decision — especially those that result in substantially higher fees — may serve as an inexpensive but powerful hedge against unintentional financial waste. Quote 2: “More often (alas), the conclusions can only be justified by assuming that the laws of arithmetic have been suspended for the convenience of those who choose to pursue careers in active management.” — Nobel Laureate William Sharpe (1991) Investment consultants and investment staff frequently recommend heavy use of active managers without considering the preponderance of evidence demonstrating that active management is highly unlikely to add value. Skeptics of this approach need only review the exceptional performance of the Nevada Public Employees’ Retirement System (PERS) to validate their concerns. Employing only two staff members and allocating roughly 85% of the portfolio to index funds, Nevada PERS boasts 10-, 15-, and 20-year returns that exceed roughly 90% of public pension plans with more than $1 billion in assets. When presented with these exceptional results, consultants and staff may deny the reality of the fundamental mathematical principles underpinning them or argue that they are exceptions to the rule. Trustees, in turn, often accept such explanations at face value even though the arguments are rarely backed by credible track records. This being the case, as a rule of thumb, if consultants or staff fail to demonstrate convincingly why they are uniquely capable of choosing the best fund managers repeatedly and sustainably for decades to come, the most prudent action is to assume that they are not. Quote 3: “You don’t want to be average; it’s not worth it, does nothing. In fact, it’s less than the market. The question is ‘How do you get to first quartile?’ If you can’t, it doesn’t matter what the optimizer says about asset allocation.” — Allan S. Bufferd, former treasurer Massachusetts Institute of Technology (2008) In 2000, David Swensen, the former CIO of the Yale Investments Office, published Pioneering Portfolio Management. The book detailed many techniques that he employed to produce returns that far exceeded those of his peers. The key to Yale’s success was the presence of an extremely talented CIO, stable and prudent governance, and a unique learning culture that enabled team members to replicate Swensen’s talents. The critical importance of these oft overlooked capabilities is covered in a subsection of Investing in U.S. Financial History entitled “Pioneering People Management.” Relying on this rare ecosystem, Yale repeatedly chose the best fund managers — especially in alternative asset classes like venture capital, buyout funds, and absolute return funds. After reading Pioneering Portfolio Management, rather than concluding that Yale’s ecosystem was exceptionally rare and difficult to replicate, investment staff, consultants, and OCIOs mistakenly assumed that mere access to alternative asset classes was a reliable ticket to Yale-like returns. The problem with that assumption is that even 15 years ago it was well established that Yale’s returns depended on consistent and sustainable selection of top-quartile fund managers. Without a Yale-like ecosystem in place, accomplishing this feat in the dangerous and expensive realm of alternative asset classes is highly unlikely, and failure to generate top-quartile returns is a recipe for mediocrity or worse. Therefore, before establishing or continuing to allocate to alternative asset classes, trustees should ask whether they and/or their advisors possess Yale’s capabilities. An honest answer in almost all cases is, “No.” Quote 4: “You either have the passive strategy that wins the majority of the time, or you have this very active strategy that beats the market…For almost all institutions and individuals, the simple approach is best.” – David Swensen, former CIO of Yale Investments Office (2012) Nobody understood the difficulty of outperforming ruthlessly efficient markets and dangerously opaque alternative asset classes better than Swensen himself. This is why he concluded that nearly all institutional and individual investors would produce better long-term outcomes by investing entirely in low-cost index funds. Sadly, the main reason this message never reaches boardrooms and investment committee meetings is because the people who advise trustees almost always suffer from a deep-seated fear that it will result in their own obsolescence. One of the greatest tragedies is that the opposite is true. Once advisors rid themselves of the hope and dream that they are among a tiny subset of investment professionals who can outwit the ruthless efficiency of markets, they can refocus trustees’ scarce time on addressing real financial challenges that are

These Are the Plunderers: How Private Equity Runs — and Wrecks — America. 2023. Gretchen Morgenson and Joshua Rosner. Simon & Schuster. In 1970, Milton Friedman penned an influential editorial in The New York Times stating that business had one social responsibility: to increase profits. The Friedman doctrine focuses on managers in their role as agents for owners. As Friedman points out, managers, as individuals, may have many responsibilities to their family, country, and community. However, in such cases, individuals are principals, not agents, and do not represent the interests of others. The exception to profits as the sole responsibility, Friedman points out, is when a group sets up a corporation for charitable purposes, such as a hospital or school. In These Are the Plunderers: How Private Equity Runs — and Wrecks — America, Gretchen Morgenson and Joshua Rosner attempt to pull back the curtain on the opaqueness of the private equity industry. Morgenson and Rosner contend that private equity (PE) has gone far beyond the Friedman doctrine and has even applied the goal of maximizing profits to formerly not-for-profit organizations. The book’s title indicates that the authors are not interested in presenting the industry’s good, bad, and ugly sides — just the latter two. Morgenson, a 2002 Pulitzer Prize winner, is the senior financial reporter for the NBC News Investigative Unit and has extensive experience in the financial markets, having worked as a stockbroker and reporter for the Wall Street Journal and the New York Times. Rosner, likewise, is a veteran of Wall Street and is the managing director of research at the consultancy Graham Fisher & Co. The two previously collaborated on a book on the 2008 financial crisis, Reckless Endangerment: How Outsized Ambition, Greed, and Corruption Led to Economic Armageddon. These Are the Plunderers is well researched and comprises 17 chapters and 52 pages of notes from the popular press, academic research from such sources as the NBER and the Journal of Financial Economics, court filings, legislative hearings, and author interviews. Although the book covers the private equity industry as a whole, much of it traces the misdeeds of Leon Black’s Apollo Fund. Other PE funds that receive significant coverage include Stephen Schwarzman’s Blackstone Group, Kohlberg Kravis Roberts (KKR), and the Carlyle Group. After a brief introduction to Michael Milken, junk bonds, and the art of leveraged buyouts, the book’s first half sets the stage for the rest of the book by focusing on the Apollo Group’s foray into the purchase of insurance company Executive Life. Although no one would view an insurance firm as one with charitable goals, insurance serves a more essential societal role than many other businesses. Much of this part of the book focuses on the victims — most notably, Vince and Sue Watson. The couple used a malpractice award for brain damage suffered by their toddler, Katie, to purchase a policy from Executive Life to fund her care. In painstaking detail, the authors describe how Black’s Apollo Fund acquired the firm, enriching Black and his partners and leaving policyholders with a fraction of what they were promised. Readers are likely to find this eye opening because most of us would expect that a structured settlement funded through an insurance annuity would provide guarantees to the recipient. However, the financial promises made by the original insurer do not apply to the acquirer. This calamity was made possible by the political ambitions or incompetence of California’s insurance commissioner at the time, John Garamendi. Garamendi chose to seize Executive Life even though many experts believed the firm would survive. In an affront to policyholders, Garamendi allowed Executive Life’s bond portfolio to be sold at fire sale prices to Black and his colleagues, even though Wall Street consultants believed the price was too low. Later research by Harry DeAngelo, Linda DeAngelo, and Stuart C. Gilson in the Journal of Financial Economics found that the company’s bond portfolio would have recovered in a year. To add insult to injury, a California judge approved a request to destroy all court documents and filings in the Executive Life case. The authors weave a compelling tale of greed and misdeeds throughout the book. We are introduced to a cast of characters on both sides of the issue. These stories dispel the myths about private equity that the profession promotes. That narrative holds that PE represents the best of capitalism, an industry that takes on the risks and receives the rewards for turning around companies on the verge of extinction. But Morgenson and Rosner offer examples of for-profit and not-for-profit organizations bled dry by PE, leaving employees, pension funds, taxpayers, and other stakeholders holding the bag. Readers might ask, “Did the authors cherry-pick a handful of egregious cases that do not represent the norm?” Throughout the book, the authors point out their attempts to obtain comments from PE funds that are discussed. In most instances, their requests were ignored; in others, they were given canned responses that painted the firm and industry in the best possible light. The PE playbook is always the same: Borrow money to acquire the firm, saddle it with debt, and extract exorbitant management fees. The fees sometimes continue long after the PE firm has already sold off the entity, a gambit that the authors call “money for nothing.” The authors illustrate that principle with the industry practice of charging pension funds for cash committed but not yet under management. In some instances, when the PE firm cannot identify a viable buyer for an exit, it may sell the entity to one of its other funds at an inflated price, leaving investors in the first fund with a nice profit and investors in the acquiring fund holding the bag. Elaborating further on plundering by private equity, Morgenson and Rosner provide cases of PE’s stranglehold on the health care industry. The authors recount stories of physicians and nursing home employees who were fired after speaking out about safety concerns and individuals who were banned from visiting loved