CFA Institute

The following is derived from the 2022 Scroll Award-winning article “Which Corporate ESG News Does the Market React To?” by George Serafeim and Aaron Yoon, from the Financial Analysts Journal. Stock prices react only to financially material environmental, social, and governance (ESG) news and more so when the news is positive, receives more media coverage, and relates to social capital issues. That’s the conclusion of research I conducted with George Serafeim. We also find that based on their response to news that was likely to affect a company’s fundamentals, ESG investors are motivated by financial rather than nonpecuniary factors. Past Research Previous studies by Philipp Krüger and Gunther Capelle-Blancard and Aurélien Petit, for example, concluded that the market responds negatively to both positive and negative ESG news. However, which specific ESG news most moves the market is unclear as is whether any prior evidence would be generalizable today. Earlier research has tended to have small sample sizes, focus on periods when capital markets dismissed ESG issues through an agency-cost lens, and not differentiate ESG-related news that was likely to be material for a given industry. But now there is increasing buy-in that ESG issues use firm resources and therefore should affect shareholder value. Our Research The data sample we analyze is orders-of-magnitude larger than those in prior studies. It includes 109,014 unique firm-day observations for 3,109 companies with ESG news between January 2010 and June 2018. We divide our sample based on materiality classifications from the Sustainability Accounting Standards Board (SASB). FactSet TruValue Labs (TVL) tracks ESG-related information each day across thousands of companies, classifies news from different sources as positive or negative, and creates sentiment scores to gauge how positive or negative the news is for a firm-day and whether the news is financially material. TVL draws its data from many sources — including reports by analysts, media, advocacy groups, and government regulators — and its measures focus on vetted, reputable, and credible news sources that are likely to generate new information and insights for investors. Our primary research design is on a firm-day panel where the dependent variable is the daily market-adjusted stock return and our key independent variables are indicators of positive and negative news on that day based on TVL’s ESG news score. With this daily structure, we implement an event-study research design that measures short-term price reactions to ESG news every day. Our first set of analyses demonstrates that not all news events are associated with significant changes in stock price. Only financially material news translates into big price movements. For example, on firm-dates with at least three news articles — according to TVL, sentiment analysis requires at least three articles to be accurate — materially positive ESG news generated significant and positive price reactions. Negative news, however, did not generate similarly sized price swings. Our results increase in economic significance when we restrict the sample to material news that receives more than five ESG articles on a coverage day. Negative news sends stock prices lower. In contrast, there are no price movements for ESG news that is not material according to SASB standards, regardless of how we restrict our sample. When we evaluate ESG news themes, positive and negative news classified under social capital — that is, news about product impact on customers due to product safety, quality, affordability, and access issues — generates the largest and most significant market responses. This is particularly interesting given that ESG data and ratings contain little information about product impacts, with most metrics reflecting operational activities. We do see smaller but significant price movements associated with negative natural capital-related news and positive human capital and business model innovation-related news, among other themes. Finally, we examine how investors react to ESG news relative to expectations about a firm’s ESG activities. Using the MSCI ESG score as a proxy for investor expectations, we find that it predicts future ESG news. We then separate the positive and negative news into predicted and residual components as a function of a firm’s ESG performance score to determine whether unexpected news or news predicted by a firm’s ESG score influences stock prices. According to our results, the unexpected component of positive news drives investor behavior. This suggests that ESG performance scores have predictive power regarding future ESG news and that investors incorporate this predictive component in their stock price reactions. Our Results Our study paints a different picture of how investors respond to ESG news than its predecessors. We show that investors react positively to positive ESG news and much more strongly for positive than negative news. Why are our results different from those of earlier studies? Because we examine a period when ESG was much more prevalent and rely on technological advancements that systematically measure ESG news using natural language processing (NLP). This yields better measurement quality and less selection bias compared to studies that relied on human analysts subjectively codifying ESG news. Further, we extend our understanding of financial materiality of ESG issues. For example, in “Corporate Sustainability: First Evidence on Materiality,” Mozaffar Khan, Serafeim, and I determine that companies with good ratings on material sustainability issues exhibit superior long-term stock returns compared with companies with poor ratings. But firms with good ratings on immaterial issues did not outperform those with poor ratings. The market reacts to financially material information even during a short-term window by using data that provides daily ESG news data and classifies ESG news according to financial materiality. How can our results inform investment analysis? First, as more investors integrate ESG issues into their portfolio allocation decisions, related news should generate greater stock price movements. That said, we still know little about which specific issues create the most meaningful price swings when disseminated as news. Our results suggest that certain types of news lead to bigger swings. Second, we document that for much of our sample, corporate ESG news evokes little tangible response. This finding is intriguing. After all, if investors believe the market doesn’t appreciate the

If there was a Wild West in Private Equity (PE), it would be the Lower Middle Market (LMM) — the ecosystem of companies with revenues between $5 million and $50 million. The LMM offers lucrative opportunities but comes with unique risks that can derail even the most promising deals. For investment professionals, navigating this space requires a deep understanding of agency risk, an often-overlooked challenge stemming from the reliance on underqualified intermediaries and inexperienced sellers. Companies at this end of the market can vary greatly in terms of management quality, company infrastructure, and economic viability (post change of control). In addition, this end of the market is severely under advised, meaning that services given by the business brokers operating in this market are not as sophisticated as larger PE markets. Sellers often have little corporate or finance experience. Rather, they are technical and operating experts who often have built their businesses from scratch — without the help of institutional capital. A sale transaction is often a business owner’s first foray into the world of mergers & acquisitions (M&A). These business owners are selling their life’s work. The LMM Business Broker Profile Business brokers — the intermediaries in the lower middle market — are often not sophisticated M&A experts like investment bankers or attorneys. Yet, they have little trouble convincing sellers that they are. Brokers know enough about the M&A process to sound sophisticated to sellers. Given that brokers are usually the first point of contact with business owners considering M&A in this market, they quickly gain trust. This new trust, or acquiescence, quickly turns into an “advisory” relationship with a lengthy non-circumvention period with the broker squarely in the middle. At first blush, this arrangement does not raise any red flags. The broker helps the seller market the business — there is nothing wrong with that. The problem and the risk stems from the fact that the marketing relationship often turns into a de-facto financial advisory and/or legal advisory relationship. This is because often a seller isn’t sure if he or she wants to sell. Sellers are reluctant to spend money on appropriate advisors before they are certain of the viability of a sale. Brokers often step in to fill this void and are generally happy to negotiate letters of intent (LOI) on behalf of sellers and opine on deal terms. This is where significant agency risk[1] comes into play. There are three sub-categories of agency risk that LMM sellers and buyers should be aware of and attempt to mitigate: Anchoring: Brokers will sometimes anchor sellers to terms that are not market. Unlike investment banks that can see hundreds of deals a year, some brokers may work on five or fewer transactions a year. Worse, some or all these transactions may not close. However, this may not stop a broker from providing an opinion on what they believe are market terms for a particular part of the deal. We’ve had a broker anchor a seller to an interest rate that, when pressed, the broker admitted that they got from a term sheet on a transaction that did not close. Anchoring to terms that are non-market erodes trust by worsening what are already tight and emotional negotiations. Because brokers are good at convincing sellers that they are M&A experts, sellers might believe buyers are not being fair or forthcoming when a term comes in that is not in line with the anchor. Bad advice: Bad advice is an error of omission. It happens when a broker misses something that an attorney or a financial advisor would catch. This typically has to do with the details. For example, a broker often will help a seller negotiate an LOI while the buyer will have an attorney perform this task. You can imagine the mismatch. Once the LOI is signed and the seller finally engages an attorney, the attorney will look at the signed LOI and point out areas in which the seller is at a disadvantage. Situations like this can lead to bad optics — the seller will again think the buyer is trying to take advantage — leading to re-trading and wasted money. These circumstances erode trust by worsening what are already tight and emotional negotiations between a buyer and a seller. Telephone: Some brokers like to remain in the middle of the conversation, insisting that they are involved in calls or meetings, and some sellers give their brokers permission to negotiate on their behalf. The agency risk here is the potential for brokers to take liberties with negotiations. For example, a broker may neglect to vet an idea with the seller before offering it up as a term or a compromise. A broker can misinterpret or misrepresent a term from the buy-side to a seller, particularly if an agreed-upon term would make the broker look bad. We’ve had both situations happen and either can lead to frustration, re-trading, and eroded trust. Agency risk is a real problem and can make it significantly harder, if not impossible, to get a deal done. Knowing this, there are a few ways to control and partially mitigate agency risk: Speak candidly with the broker about anchoring. Brokers are incentivized to get deals done. If they are made aware of the anchoring impact that their words can have on sellers, it could make a difference. We had a good outcome regarding an anchoring situation where the broker acknowledged that he likely said too much, and it was a lesson learned. Mitigating this situation by having a conversation with the broker about anchoring to different deals or their own opinions can build trust and save a lot of pain later. Advise the seller to obtain advisory services. To us, a seller with counsel indicates a level of seriousness regarding the sale process. If a seller does not have legal counsel or financial advisory lined up pre-LOI, advise them to do so. It is important to note that, while the LOI is not legally binding, it does

Quarterly reporting is often blamed for corporate myopia, an overemphasis on meeting short-term earnings expectations at the expense of long-term value. Most US companies operate on investment cycles measured in years, not quarters, and investors often price stocks on even longer earnings horizons. In this context, changing reporting frequency does little to shift managerial behavior, while incentive structures — particularly executive compensation cycles — exert far greater pressure on short-term decisions. The question for financial analysts is whether reducing reporting frequency would improve long-term decision-making or simply weaken transparency and market efficiency. The evidence shows that it would not, and that such a shift would likely harm liquidity and reduce the reliability of information available to the market. Revisiting the Short-Termism Debate The debate is not new. The causes and consequences of short-termism have been examined for decades by academics, commentators, lawmakers, and practitioners. Prominent figures such as Jamie Dimon and Warren Buffett have publicly criticized the short-termism culture. Their concerns are reinforced by a 2004 survey of financial executives showing that half were willing to forgo positive NPV projects to avoid missing quarterly earnings expectations1. Although there is broad agreement that myopic corporate strategies harm investors and the market, it is not clear that ending quarterly reporting would solve the problem. Quarterly reporting and earnings guidance are associated with higher analyst coverage, greater liquidity, more transparent information, and lower volatility, all of which help cost of capital2, 3, 4, 5. When earnings releases become less frequent, information asymmetry rises and the risk of insider trading increases. The United Kingdom and Europe offer recent natural experiments. When regulators ended mandatory quarterly reporting in 2014, firms did not increase CapEx or R&D spending, contrary to what would be expected if quarterly earnings truly induced myopic management6. Furthermore, some practitioners and academics argue that companies would face less short-term pressure if more of their shareholder base consisted of long-term investors. From this perspective, firms seeking to attract such investors should reduce short-term guidance and place greater emphasis on long-term forecasts. Such a shift in strategic focus and disclosure toward longer-run performance creates a virtuous cycle—one in which companies that gain the interest and backing of investors with longer horizons end up reinforcing management’s confidence to undertake value-adding investments in their company’s future. Sarah Keohane Williamson and Ariel Babcock, FCLTGlobal (2020)7 Paradoxically, a 2016 study found no difference in long-term investment levels between firms that issued long-term forecasts and those that provided only short-term guidance8. This highlights the lack of consensus on how disclosure practices influence managerial horizons. A natural question follows: what constitutes a long-term horizon for corporate strategy? If the goal of reducing reporting frequency is to curb short-termism, it is reasonable to ask whether extending the reporting interval by three months would meaningfully influence managerial decision-making. When Investment Horizons Outrun Reporting Cycles As an initial way to approximate corporate investment horizons, I classified all US publicly traded companies using the industry classification benchmark (ICB) and used each sector’s two-year average ROIC turnover as a proxy for payback periods. This approach provides a practical, if simplified, measure of how long it takes firms to recover invested capital under steady-state conditions. Figure 1: ROIC, ROIC turnover & P/E analysis. Source: Bloomberg data and proprietary analysis (full table on appendix). My analysis shows that the average weighted ROIC turnover for US listed companies is roughly five years, with sector averages ranging from about three years in the lowest quartile to 22 years in the highest. The sample includes 3,355 publicly traded US companies, grouped into 42 ICB sectors and ranked by quartile. The longer the payback period (ROIC turnover), the less impact a three-month shift in reporting frequency is likely to have on corporate behavior. Managers would still face pressure to avoid near-term performance declines when initiating positive NPV projects; the definition of “short term” would simply move from three months to six months. Another lens on short-termism is the price-to-earnings (P/E) ratio. The P/E indicates how many years of current earnings it would take for investors to recoup their initial investment, assuming no change in earnings. A P/E of 10x, for example, implies a 10-year earnings horizon. High P/E ratios are common among growth companies, reflecting investor expectations for strong future performance through revenue expansion or margin improvement. Together with the ROIC-turnover results, P/E multiples help illustrate how investors weigh a firm’s long-term potential relative to near-term earnings. In general, companies with high P/E ratios face less pressure to deliver short-term results. Figure 2: ICB sector: ROIC & P/E ratio. Source: Bloomberg data and proprietary analysis (full table on appendix). US equities currently trade at an average P/E of 42.5x, with sector multiples ranging from 12.3x in life Insurance to 241x in automobile and parts. The highest-multiple companies are concentrated in the technology sector — such as Tesla (280x), Palantir (370x), Nvidia (45x), Apple (36x), Meta (21x), and Alphabet (34x) — reflecting strong investor expectations and the influence of AI-related optimism. Whether these valuations reflect a bubble or not, paying the equivalent of more than 40 years of earnings suggests that short-term results are not the primary driver of investor expectations. Taken together, the evidence indicates that quarterly earnings should not be blamed for corporate myopia. Several alternative approaches to reducing short-term pressures have been proposed that do not require eliminating quarterly reporting9. The Limits of Changing Disclosure Frequency One of the most effective ways to reduce short-term pressure would be to lengthen the duration of executive compensation, which is typically structured around a one-year performance cycle10. Such short horizons are misaligned with the multi-year payback periods implied by ROIC and P/E measures, and they can create incentives for managers to prioritize near-term results over positive NPV projects. When compensation is tied tightly to annual outcomes, deferring value-adding investments becomes a rational, though suboptimal, response. The central question is whether less-frequent disclosure would help or harm market participants. Reduced reporting is associated with lower liquidity, less transparency, higher volatility, and a

The investment industry is undergoing a transformation that is largely attributable to technological advancements. Investment professionals are integrating new technologies, such as machine learning (ML), across the investment process, including portfolio construction. Many asset managers are beginning to incorporate ML algorithms in the portfolio optimization process seeking more efficient portfolios than would be possible under traditional methods, such as mean-variance optimization (MVO). These trends necessitate a fresh look at how ML is altering the portfolio construction process. Investors will benefit from a basic understanding of ML algorithms and the impact these algorithms have on their portfolios. Ultimately, the strategies used by asset managers to construct client portfolios have a direct impact on the end investor. So investors should have sufficient awareness of these methods as they continue to gain in popularity. This article aims to provide an overview of the role ML algorithms play in the portfolio optimization process. Background The term ‘machine learning’ was first used by A.L. Samuel in 1959. Samuel conducted an experiment by training a computer to play checkers and concluded that the computer exhibited significant potential to learn. These results paved the way for further research on this topic and led to the development of increasingly powerful and sophisticated ML algorithms over the following decades. As a result, many industries, including investment management, have adopted these technologies in recent years. ML algorithms are particularly useful when it comes to analyzing high dimensional data or datasets with non-linear relationships, which is becoming increasingly common with the rise of unstructured data and other alternative data sources. The two main categories for ML are supervised learning and unsupervised learning. With supervised learning, the ML algorithm detects patterns between a group of features (i.e., input variables) and a known target variable (i.e., output variable)[1]. This is referred to as a labeled dataset because the target variable is defined. In unsupervised learning, however, the dataset is unlabeled, and the target variable is unknown. Thus, the algorithm seeks to identify patterns within the input data. Exhibit 1 describes some of the common ML algorithms currently used by investment professionals. Exhibit 1: Common Machine Learning Algorithms in Investment Management. ML Algorithm Description Least Absolute Shrinkage and Selection Operator (LASSO) A form of penalized regression that includes a penalty term for each additional feature included in the regression model. The goal of this regularization technique is to create a parsimonious regression model by minimizing the number of features and to increase the accuracy of the model. K-Means Clustering Divides data into k clusters. Each observation in a cluster should have similar characteristics to the other observations, and each cluster should be distinctly different from the other clusters. Hierarchical Clustering Two types: bottom-up hierarchical clustering, which aggregates data into incrementally larger clusters, and top-down hierarchical clustering, which separates data into incrementally smaller clusters. This results in alternative ways of grouping data. Artificial Neural Networks (ANNs) A network of nodes that contains an input layer, a hidden layer, and an output layer. The input layer represents the features, and the hidden layer is where the algorithm learns and processes the inputs to generate the output(s). These algorithms have many uses, including speech and facial recognition. Investment professionals expect new analytical methods to be highly disruptive to the investment industry in the coming years. Respondents to a 2022 survey of more than 2,000 CFA Institute members predicted that new analytical methods like ML will be the most significant disruptor to job roles in the next five to 10 years among respondents. Exhibit 2 displays this result, along with other expected disruptors to job roles. Exhibit 2. Factors Expected to Significantly Disrupt Job Roles in the Next 5 – 10 Years. Portfolio Optimization The development of neural networks in the 1960s laid the groundwork for many of the alternative methods to portfolio optimization using ML. In addition, the emergence of “expert systems”[2] has led investment professionals to rely increasingly on machines to help with solving complex problems. Some of the early uses of expert systems in finance include trading and financial planning expert systems. The use of ML algorithms in the portfolio construction process has grown in popularity in recent years as investment professionals seek additional ways to enhance portfolio returns and gain a competitive edge. In particular, integrating ML algorithms in the portfolio construction process can address the challenges and limitations of traditional portfolio optimization methods, such as MVO. One major limitation of MVO is that it only considers the mean and variance of returns when optimizing a portfolio and does not account for skewness in returns. In reality, however, investment returns tend to exhibit skewness. Specifically, research has shown that growth stocks have higher positive skewness in their returns, on average, than value stocks. To account for potential non-normality in investment returns, some investment professionals have opted to construct portfolios using mean-variance-skewness optimization models, or even mean-variance-skewness-kurtosis optimization models. These models, however, result in multi-objective optimization problems. ANNs can efficiently create mean-variance-skewness optimal portfolios to address this limitation. Another shortfall of MVO is that it prevents investors from expressing their views on future asset performance. An investor, for instance, might expect bonds to outperform equities in the next six months. The Black-Litterman (1992) model enables investors to incorporate these perspectives into the portfolio optimization process. An alternative approach is to integrate the Black-Litterman (1992) model with ANNs, which has the potential to generate high benchmark-relative returns without taking excess risk. The inputs in MVO are sensitive to measurement errors, which is especially true for expected return estimates.  Thus, MVO has the potential to produce “optimal” portfolios that perform poorly. Reverse optimization can be a useful alternative to develop more accurate expected return estimates. Investment professionals can then use these improved estimates as inputs in traditional MVO to generate more efficient asset allocations. Investment professionals can also use ML algorithms to predict stock returns and incorporate these estimates in MVO. Alternatively, a recent study developed an enhanced portfolio optimization approach, which consists of using a correlation shrinkage parameter to

Puttable bonds are often described as the mirror image of callable bonds: equal in theory, opposite in structure. Yet in modern capital markets, put bonds have quietly vanished. This blog explores the reason behind that disappearance, arguing that it stems not from mispricing but from structural misalignment. Investors hold the right to exit, but lack the power to influence outcomes, leading to a contract with symbolic protection and no strategic value. In this blog, I introduce the concepts of the Perception Gap and Power Asymmetry to explain why the put bond failed in practice. The lesson is clear: in finance, options only matter when the holder has control. Rights without power do not survive, and the market has already rendered its silent verdict. When Financial Theory Meets Market Reality In financial theory, symmetry is everything. For every call, a put. For every risk, a hedge. But the market doesn’t play by that symmetry. The call survives while the put disappears. This blog is not about the pricing formulas. They work. It’s about the deeper truth the market quietly reveals: the put bond failed not because it was mispriced, but because it offered rights without power. Investors were given an option they couldn’t enforce. Issuers were asked to pay for a feature they couldn’t control. The result? A contract that looks perfect on paper, but never found traction in practice. Theoretical symmetry. In the academic world, puttable and callable bonds are seen as elegant opposites. A callable bond is a straight bond minus a call option held by the issuer. A puttable bond is a straight bond plus a put option held by the investor. The symmetry. But giving investors a put option without control over the firm’s risk, leverage, or asset mix is like giving someone a parachute without a ripcord. Markets are not rejecting math. They’re rejecting a contract that fails the power test. The Perception Gap and Power Asymmetry If the call survives and the put disappears, the natural question is why. The pricing models don’t fail. The structures are sound. And in theory, the put offers value. But the market has rejected it anyway. This isn’t an inefficiency. It’s a lesson in control. Two forces drive the rejection: the Perception Gap and Power Asymmetry. The Perception Gap begins with trust, or the lack of it. Investors may hold the contractual right to sell the bond back to the issuer, but they don’t control what happens before that day comes. They don’t control leverage, asset sales, payout policy, or management risk. They don’t sit on the board. They don’t see behind the curtain. So even if the issuer appears healthy now, the investor must price the put as if things could deteriorate without warning and without recourse. From the issuer’s perspective, this creates a distorted cost. They’re being asked to insure against a pessimistic view they don’t share. The issuer may see the firm as stable, with no plans to increase risk. But the investor, lacking transparency, demands a premium for the unknown. The put option becomes expensive—not because of volatility, but because of mistrust. And deeper still is the Power Asymmetry. The call option held by an issuer is a tool. It allows for refinancing, redemption, strategic timing. It lives in the hands of the party that controls the asset. But the put? It offers no such leverage. The investor may “exit” the bond, but that exit doesn’t change the company’s behavior, structure, or value. The option to walk away is different from the power to act. In practice, this means the put is hollow. It lacks teeth. It offers a theoretical exit, not strategic influence. And because it resides with the weaker party — the one without visibility or control — it becomes a symbolic right, not a functional one. A Silent Verdict from the Market That’s why the put doesn’t trade. That’s why it doesn’t appear in portfolios. t’s about authority. The investor has a right but no power. The most powerful evidence against the put bond isn’t found in pricing spreads or volatility models. It’s found in what’s missing. There is no market. Puttable bonds are rarely issued, barely traded, and almost absent from portfolios –confirming their disappearance. This isn’t a failure of awareness. Investors know what a put does. Issuers can structure it easily. If the market believed the instrument had value, it would be everywhere. But it isn’t. Because markets, unlike models, have memory. They’ve seen how put bonds behave in the real world. Investors don’t trust that the option will matter when it’s needed most. Issuers don’t see the feature as worth its cost. Liquidity providers don’t want to hold something that might vanish when things get difficult. So the market moved on – quietly, without protest, without needing a correction. The silence isn’t apathy. It’s judgment. It tells us the models were too clean. The assumptions too optimistic. The contract too abstract. And it reminds us that financial products only survive when they serve real behavior, not just theoretical symmetry. Build structures that align with control, visibility, and action. Finance isn’t just about cash flows and optionality. It’s about who controls the narrative when things go wrong. That’s where value and survival are found. Rights Without Power Put bonds didn’t vanish because of faulty models. They vanished because the real world exposed their flaw. In theory, they offered investors control. In practice, they offered a one-time exit without any ability to shape outcomes. That disconnect between ownership and authority turned the put from a hedge into a hollow feature. The lesson is broader than just this instrument. In finance, as in law and governance, contracts only work when control matches optionality. Markets will not support structures that look fair but function weakly. The put bond failed not due to mispricing, but due to misalignment. And that is why the absence of put bonds is not a market failure. It is a market decision. A contract with no teeth, no

As regulators move to open private markets to a wider investor base, the question is not whether retail access should be allowed, but whether the structure of these markets can support it. Illiquidity, opaque performance reporting, and misaligned incentives between fund managers and investors already challenge institutional participants. With fee structures built for scale and governance mechanisms that provide limited accountability, extending the model to smaller investors risks amplifying those weaknesses rather than democratizing opportunity. New legislation seeks to grant retail investors universal access to private capital. In August, the Trump Administration issued an executive order entitled “Democratizing Access to Alternative Assets for 401(k) Investors.”[1] European authorities are not to be outdone. The British government has set the minimum to invest in long-term asset funds[2] as low as £10,000. The European Union’s Long-Term Investment Fund[3] product imposes no minimum. While illiquid or so-called “semi-liquid” private markets are now accessible for most retail investors, participating without understanding their limits could prove costly. Hazy Performance and Poor Liquidity Assessing the true performance of private markets is difficult. Reported returns are often opaque and cannot be precisely benchmarked.[4] The illiquid nature of these investments compounds the problem. Although private capital funds are typically structured with 10-year maturities, few distribute capital on schedule. A Palico analysis of 200 private equity (PE) funds found that more than 85% failed to return investors’ capital within that timeframe, and many successful venture funds take over a decade to reach a successful exit.[5] Secondary markets offer limited relief. While investors can sell stakes, transactions are sporadic and frequently completed at a discount to net asset value. The scale is also tiny compared with public markets: secondary trading represents less than 5% of the primary market in PE,[6] and less than 1% in private credit.[7] Once committed, investors cannot easily exit, and pricing transparency is minimal. The opacity endemic to private markets also raises a crucial question about performance. Whereas, on average, 1990s and early 2000s PE vintage funds did consistently deliver better returns than those of public markets, in the face of a massive inflow of capital allocated to the sector, outperformance has dwindled for recent vintages. Overallocation led to market saturation in developed economies,[8] inflating asset valuations and making it harder for fund managers to derive any sustainable angle, consistently and persistently, to beat their peers or even public markets. Performance Erosion Market saturation has steadily lowered performance targets in PE. Typical internal rate of return (IRR) goals have declined from about 25% in 2000 to roughly 15% today. To offset this, some firms have reduced or removed the traditional 8% hurdle rate and raised their share of capital gains above the historical 20% level, ensuring manager compensation is maintained even as returns compress. The industry’s profit engine has shifted from investment returns to asset accumulation. Large managers now channel more capital into scalable, lower-return strategies such as private credit and infrastructure. Apollo manages roughly $700 billion in private credit compared with $150 billion in PE, for instance. In other words, fund managers prioritize their own over their clients’ profitability. Management and advisory fees at Blackstone have exceeded performance fees in seven of the past 10 fiscal years, a pattern echoed across the sector. Unsurprisingly, recent 401(k) products offered by private capital firms to retail investors follow the same model, emphasizing predictable credit and real estate exposures rather than potentially higher-return but more competitive PE and VC.[9] With competition for deals intensifying, scale — not performance — has become the more reliable path to profitability.[10] And the focus for alternative asset managers to fundraising, even if it means moving away from their core competency.[11] Opacity Invites Audacity Eager to grow assets under management, private capital firms are actively lobbying governments and legislators to deregulate further.[12] This is a risky proposition. In the market euphoria that preceded the global financial crisis, private markets were the subject of numerous cases of alleged corruption and collusion, with regulators imposing heavy fines on several of the largest PE groups.[13] Beside the risk of fraudulent and questionable activity, private markets’ illiquid and opaque nature makes it hard for investors to gauge the competence of individual fund managers. In the UK, for instance, Neil Woodford, a seasoned asset manager in public equity, proved a poor allocator of funds across various private market asset classes.[14] Many of his PE and venture holdings underperformed, leading to the collapse of Woodford Equity Income in 2019, after that investment vehicle had lost over £5 billion in value. What should concern prospective retail investors further is the pervasiveness of agency problems in private markets. The asset management trade is primarily focused on the fund manager’s controls[15] and economics[16]. This default modus operandi, coupled with the lack of accountability and deficient supervision, contributes to a skewed outcome in favor of the fund manager. Institutional Failure Institutional limited partners (LPs) accept many of private markets’ inefficiencies because they too manage other people’s money. Pension funds, insurers, and endowments charge their own fees and often benefit from the same layering of costs (via multiple layers of fees)[17] that inflates fund managers’ earnings. As a result, few institutional investors are motivated to curb those practices. Oversight mechanisms are also weak. Replacing an underperforming or unethical general partner (GP) typically requires approval from 75% of investors – a high hurdle that leaves most managers entrenched. Meanwhile, personal and professional ties between LP executives and PE firms further blur accountability. Many senior LP representatives sit on advisory boards or attend networking events hosted by the GPs they are meant to oversee, creating subtle but powerful conflicts of interest. In theory, LP investors should hold private capital fund managers to the same fiduciary standards that the latter apply to their portfolio companies. In practice, the balance of power tilts heavily toward fund managers,  a structural flaw that perpetuates weak governance and limited investor protection. If Too Small to Play, Stay Away Institutional investors have realized their lack of influence in reining in the worst

Private ownership is gaining ground again across Europe as companies seek more control and relief from the pressures of public markets. Before delisting, however, managers often adjust reported earnings, sometimes to make the company appear less expensive or to smooth the path for a buyout. Yet once these plans become public, markets often respond favorably, viewing the move as a signal of future value. This shift toward going private began after the tech bubble burst in the early 2000s and accelerated following the 2008 financial crisis, as firms sought greater control and flexibility outside public markets. The expansion of private equity firms has reinforced the trend, offering new avenues to restructure and raise capital away from the glare of public disclosure. In Europe, where ownership is often concentrated, voluntary delistings through leveraged buyouts (LBOs), management buyouts (MBOs), or minority freeze-outs have become common. In this post, I share insights from my analysis of 526 European firms from 2005 to 2023. My goal was to understand how managers manage earnings in the year before these delistings and how markets react once those plans become public. This research, supervised by Wouter Creemers, PhD, CFA, won third prize in the 2024 CFA Society Belgium’s Master Thesis Awards. Earnings Management Before the Exit As voluntary delistings become more common in Europe, attention has turned to how managers handle earnings before these transactions. Accounting standards such as IFRS and US GAAP allow a degree of discretion, giving managers flexibility to influence reported results through accounting choices or real business decisions. This flexibility can make a firm’s performance appear better or worse than it really is, influencing decisions and contracts that depend on financial reports. When these actions comply with accounting standards and reflect genuine business activity, they are not fraudulent and can serve as a tool in corporate restructuring. Managers often engage in downward earnings management before voluntary delistings. In LBOs, lowering reported earnings can help reduce the takeover price, while in MBOs, it can secure a more favorable buyout price for managers themselves. In both cases, earnings management acts as a strategic tool, helping make delistings cheaper and smoother. The key questions, then, are whether managers in Europe manage earnings downward before voluntary delistings and whether markets recognize it before or around the announcement. Findings and Market Reactions My study examines 526 European firms — half that voluntarily delisted and half that remained public — using accounting and market data from 2005 to 2023. Abnormal current accruals were estimated following the DeFond and Park (2001) model to measure earnings management. An event study using stock prices measured cumulative abnormal returns (CARs) before and around each announcement date. T-tests and ordinary least squares regressions were then run to test the hypotheses. The results reveal clear patterns in firms’ behavior before delisting announcements: Firms manage earnings downward using negative abnormal current accruals in the year prior to the voluntary delistings via LBOs and MBOs. This pattern suggests managers may intentionally report lower earnings to support a lower deal price. These firms experience positive cumulative abnormal returns around the delisting announcement date, suggesting favorable market reactions to the voluntary delisting decision. For voluntarily delisting European firms via LBOs and MBOs, downward earnings management in the year prior to the delistings is influenced by the voluntary delisting decisions as well as firms’ ROA ratio, D/E ratio, age up until delisting, growth in revenue, MTB ratio, and the delisting years. In practice, stakeholders should factor in the influence these factors have on financial reporting practices to make better informed strategic decisions. Although consistent with prior research overall, this study did not find significant downward movements in stock prices before the announcements. Implications for Investors and Policymakers The results suggest several practical implications. Stakeholders should consider how voluntary delisting decisions affect financial reporting practices before announcements, to make more informed strategic decisions and better assess the reliability of financial statements. While the earnings management observed here, whether through accounting choices allowed under IFRS or real activity adjustments, is not illegal, it still reflects opportunistic managerial behavior in firms preparing to delist. Regulators may wish to strengthen disclosure standards to ensure financial reports more accurately reflect firms’ performance before delisting. Financial analysts and advisors can incorporate the impact of the delisting decisions on earnings management into their evaluations and client recommendations. Most previous studies on earnings management prior to voluntary delistings focus on the United States and the United Kingdom. By examining European firms, this research broadens the geographical scope of the literature and enhances the relevance of findings on earnings management. The analysis integrates perspectives from accounting, corporate finance, corporate governance, and law to provide a more comprehensive view of earnings management. Taken together, the findings highlight how managerial decisions shape financial reporting and market reactions in European voluntary delistings, offering both a broader understanding of earnings management and practical insights for investors and regulators. References Achleitner, A., Betzer, A., Goergen, M., & Hinterramskogler, B. (2013). Private equity acquisitions of continental European firms: The impact of ownership and control on the likelihood of being taken private. European Financial Management, 19(1), 72-107. https://doi.org/10.1111/j.1468-036X.2010.00569.x Christensen, T. E., Huffman, A., Lewis-Western, M. F., & Scott, R. (2022). Accruals earnings management proxies: Prudent business decisions or earnings manipulation? Journal of Business Finance & Accounting, 49(3-4), 536-587. https://doi.org/10.1111/jbfa.12585 DeFond, M. L., & Park, C. W. (2001). The reversal of abnormal accruals and the market valuation of earnings surprises. The Accounting Review, 76(3), 375-404. https://doi.org/10.2308/accr.2001.76.3.375 Fontana, S., Coluccia, D., & Solimene, S. (2019). VAIC as a tool for measuring intangibles value in voluntary multi-stakeholder disclosure. Journal of the Knowledge Economy,10(4), 1679-1699. https://doi.org/10.1007/s13132-018-0526-0 Graham, J. R., Harvey, C. R., & Rajgopal, S. (2005). The economic implications of corporate financial reporting. Journal of Accounting and Economics, 40(1-3), 3-73. https://doi.org/10.1016/j.jacceco.2005.01.002 Healy, P. M., & Wahlen, J. M. (1999). A review of the earnings management literature and its implications for standard setting. Accounting Horizons, 13(4), 365-383. https://doi.org/10.2308/acch.1999.13.4.365 Lerner, J. (2011). The future of private equity. European Financial Management, 17(3), 423-435.

Tokenized money market funds (MMFs) are emerging as a next‑generation “cash equivalent,” offering institutions faster settlement, collateral flexibility, and enhanced auditability. Recent industry pilots from Franklin Templeton, DBS, Ripple, Goldman Sachs, and BNY Mellon suggest that on-chain liquidity tools may soon play a major role in Treasury operations. Here’s what portfolio managers, treasurers, and risk officers need to know — and do — today. Potential Opportunity Money market funds have long served as the financial system’s go-to instrument for liquidity, capital preservation, and short-term yield. The market infrastructure for these products, in an increasingly digital and global marketplace, is lagging. Tokenized MMFs are regulated fund shares issued and settled as blockchain-based tokens. They offer a path to compress settlement cycles, optimize collateral, and redefine how institutions manage cash. Given the size and scale of market, a burgeoning market structure is in early stages. Total assets held in global money market funds stand at around $10 trillion while assets under management in tokenized funds are just a few billion dollars. However, recent implementations show serious momentum and real use cases. Recent Initiatives: From Proof to Pilot Piloted programs by some of the largest custodians and digital and financial institutions offer a guide. DBS, Franklin Templeton, and Ripple collaborated in Singapore to tokenize the sgBENJI MMF on the XRP Ledger. The tokenized version of this government-backed fund can now be held alongside stablecoins and used for on-chain settlement and collateral posting. Goldman Sachs and BNY Mellon, through BNY’s LiquidityDirect platform, are enabling institutional access to tokenized MMF shares on a private ledger. This structure maintains regulatory familiarity while introducing blockchain-based speed and interoperability. These initiatives suggest a rethinking of cash management. The idea isn’t to replace money, rather it’s to upgrade how money market fund shares move and settle in institutional workflows. Portfolio Impact: Why It Matters The promise of a more streamlined, efficient and timely process is at the core of these trials. 1. Faster Liquidity Deployment Tokenized MMFs allow fund shares to move across custodians or counterparties in near real time. That means faster execution for redemptions, trade settlements, or margin calls, without waiting for T+1 or T+2 cash wires. 2. Collateral That Works Harder Traditional MMFs are difficult to mobilize quickly across platforms. As tokens, they become programmable and composable, allowing for reuse, dynamic posting, and integration into real-time settlement engines. 3. Operational Efficiency On-chain records offer an immutable audit trail, reducing reconciliation errors and providing instant transparency into fund movements. That reduces reliance on manual compliance processes or delayed reports. 4. Yield Without Delay Tokenized MMFs preserve yield while offering faster settlement. That minimizes the “cash drag” of keeping idle balances tied up in pre-funded accounts or margin buffers. Measured Adoption While the technology is ready, structural challenges remain including: Custody and Interoperability: Different platforms use different ledgers. Without shared standards, liquidity could fragment, and collateral reuse becomes less viable across counterparties. Regulatory Ambiguity: Institutions remain cautious due to lack of clarity around how tokenized MMFs are classified and treated, especially under US law. Custody rules, transfer agent duties, and investor protections must be adapted for on-chain finance. Infrastructure Readiness: Most custodians, fund administrators, and auditors aren’t yet integrated with blockchain infrastructure. That slows internal approval and onboarding. A Break in the Clouds: The CLARITY Act In the background, pending US legislation such as the Digital Asset Market Structure and Clarity Act (or CLARITY Act) could help address some of the legal uncertainty around tokenized assets. While not specific to MMFs, the bill aims to delineate regulatory boundaries for digital securities, commodities, and stable-value instruments — potentially reducing legal tail risk for projects that tokenize regulated funds. Coinbase CEO Brian Armstrong has described the Clarity Act as “foundational” to unlocking institutional adoption and building trust in blockchain-based market structures. Should the bill become law, institutions may feel more confident piloting tokenized fund products at scale. What Portfolio Professionals Should Do Now Investment professionals can take these practical steps while regulation and infrastructure continue to mature: Pilot in Controlled Environments: Work with providers who offer sandboxed access to tokenized MMFs. Evaluate how these funds perform in daily treasury workflows or during high-volatility events. Review Legal Risk: Assess how tokens are structured. Is the instrument legally enforceable? How is redemption handled? What happens in a dispute? Engage Custodians and Administrators: Ask service providers about tokenization roadmaps. Will they support blockchain‑based custody or transfer? What controls are in place? Stress-Test Settlement Cycles: Model the impact of faster settlement on liquidity and risk metrics. Could tokenized MMFs reduce overnight balances or shorten working capital cycles? Monitor Regulation: Stay informed on developments like the Clarity Act. Changes to legal definitions could reduce friction or create new obligations. Conclusion Tokenized MMFs are not theoretical. They are regulated funds wrapped in a faster, more composable delivery mechanism and are on the cusp of development. If implemented well, they offer the promise of yield, transparency, and real-time mobility all within a structure institutional investors already know. For cash managers, CIOs, and risk officers, tokenized MMFs may soon become not just a portfolio option but a new operational standard. Comparative Table: Traditional MMFs vs Tokenized MMFs source

Capitalists once invested and administered their own money. But beginning in the late 19th century, independent managers progressively took charge, first of the businesses to which the capital was tied and later of the funds themselves. In what is now a core feature of financial capitalism, intermediaries run modern economies. Laissez-faireism has created a system wherein brokers and promoters drive the markets. Private markets, in particular, have come to exemplify this trend. Agency-Based Market Structure In the early stages of this transformation, one tier of financiers — typically institutional investors and fund managers — assumed control of the owners’ assets. Many more operators and representatives have since emerged with the financialization of the economy. Several layers of agents, as outlined in the following chart, are active in private markets, though the list is not exhaustive. The Agency Model of Private Capital Markets Capital Owners (Pensioners, Insurance Plan-Holders, Depositors, etc.)   Role Types of Participants Layer 1 Fund Providers, or Limited Partners (LPs) Pension Funds, Insurers, Banks,Endowments, Sovereign Wealth Funds,Wealth Managers, Family Offices, Secondary LPs Layer 2 Diversified LPs Funds of funds Layer 3 Fundraisers, Gatekeepers,Administrators Placement Agents,Portfolio Management Advisers, Offshore Fund Administrators and Custodians Layer 4 Fund Managers, or General Partners (GPs) Funds in Private Equity, Infrastructure,Real Estate, Venture Capital, etc. Layer 5 Loan Providers Banks, Private Debt Funds (GPs),Bond Investors Layer 6 Deal Brokers and Introducers Investment Banks,M&A Boutiques, Accountants Layer 7 Due Diligence Advisers Lawyers, Consultancies, Accountants,Executive Search Firms Layer 8 Business Managers Corporate Executives, Interim Managers,Turnaround Specialists Real Assets and Portfolio Companies Vertical Integration and Horizontal Diversification Fund management and advisory activities are lucrative sources of fees. To increase revenue, fund managers (Layer 4) at first went downstream, developing and pushing transactions (Layer 6) to guarantee proprietary deals. But fee generation in the M&A trade is highly unpredictable and fluctuates with the economic cycle. For that reason, deal origination is now essentially outsourced or intermediated. More dependable strategies focus on captive assets — those held in portfolios, or layers 7 and 8. Through the operational management of investee companies, agents charge various fees. In principle, private equity (PE) firms are contractually obligated to distribute most or all of these fees to LP investors, but not all comply. For example, KKR raised eyebrows for not dispersing to LPs the bulk of fees charged by its advisory unit, Capstone. KKR claimed Capstone was not an affiliate but an independent consulting firm, even though it worked exclusively on KKR’s portfolio assets. Eventually, PE firms moved up and across the supply chain, where ready access to capital has ensured the stream of commissions (layers 1, 2, and 5). Since the global financial crisis (GFC), the largest firms have acquired assorted LP entities and credit activities. Some have also devised internal liquidity solutions, such as Blackstone’s secondaries platform. Once capital is secured, PE firms can complement their fees by entering adjacent segments of the alternatives market. And when LP investors raised concerns about diminishing performance amid this expansion into real estate (RE), infrastructure, and even venture capital (VC), among other alternative sectors, PE firms weakened the LPs’ bargaining power by building permanent pools of capital. Closing the Transactional Loop Private markets are consolidating fast and from multiple angles, both at the fund manager and the capital provider ends, and through initiatives from the fund managers’ advisers. As GPs strengthened their market position, institutional investors tried to replicate the PE groups’ expertise. First, LPs with close GP relationships were granted the right to co-invest, accessing deals directly without having to build in-house origination capabilities while avoiding management and performance fees. But returns from co-investments were not on par with those from GP-funded transactions. Perhaps PE firms invite LPs to co-invest in only their most complex projects? That syndicated deals underperform GP-led ones indicates some form of adverse selection. In a more recent phase, LPs bypassed GPs entirely by building direct investment teams and adopting the GP model without the punishing fee structure. This should benefit the LPs’ clients, such as pensioners in the case of retirement plan administrators and taxpayers for sovereign wealth funds. LPs also moved downstream across several alternative segments. Infrastructure, RE, and PE are the most obvious targets: They provide the annual cash flows that institutions with regular capital calls require. BlackRock, Fidelity, and other asset managers have even entered the highly illiquid VC space with mixed results. Before this LP-GP standoff, other intermediaries had sought to boost commissions. Banks were already active as deal advisers and lenders. Some established fund management and administrative solutions. Lazard, for instance, provides private capital advice. Mizuho offers private placement services through its Capstone Partners subsidiary — no relation to KKR’s consulting division. And Goldman Sachs has long participated in direct equity funding via its principal investing division. Feasting on Fees Thanks to these vertical and horizontal expansion strategies, fee arrangements have branched out. What intermediaries can expect to earn in the PE sector, in particular, is outlined in the table below. Layers of Fees in Private Equity LP Management Typically 1% levied on assets under management (AUM). Pension funds charge 0.4% to 0.8% per annum, but charge more for allocations to alternative assets. Fund of Funds (Where Applicable) This second layer of LP fees can add up to 1% per year. Placement Agent* Up to 1.5% of total capital commitments is charged in fundraising years. GP Management Annual commissions range from 1% to 2.5% of AUM depending on fund size, track record, and brand. GP Performance Once returns exceed the hurdle rate, carried interest ranges from 10% to 30% of capital gains. Lending** Fees charged to structure and amend loans. Deal Brokerage Extract between 3% and 5% of the deal size for trade and financial sales and up to 7% on initial public offerings (IPOs). Due Diligence Commissions for financial, tax, legal, commercial, and other services range from 3% to 5% of the transaction value. GP Portfolio Monitoring Advisory fees charged by GPs directly to their investees during the monitoring phase can

Finance is fundamentally concerned with the future. For risk officers, strategists, and investment professionals, every decision — pricing assets, setting limits, allocating capital — rests on assumptions about how the world might evolve. Traditionally, those assumptions have drawn heavily on the past. But in an environment reshaped by technology, climate policy, geopolitics and social expectations, yesterday’s patterns no longer suffice. The most resilient institutions are learning not only about the future, but from multiple plausible futures. Learning from the futures means deliberately developing multiple, contrasting images of how the environment could plausibly unfold, and using them to illuminate the present. The emphasis is less on forecasting which path will occur and more on what reflection across several coherent plausibilities reveals about current assumptions, vulnerabilities and opportunities. From Forecasting to Foresight: Extending the Limits of Risk Models This is particularly important once you recognize the classical distinction between situations of risk, in which outcome distributions are reasonably stable and can be estimated from data, and situations of genuine uncertainty, in which the underlying structure of the game itself may change. Under risk, historical inference and probabilistic forecasting remain powerful tools. Under uncertainty, where novel policies, technologies, or political arrangements can reshape markets in discontinuous ways, past data are a less reliable guide and learning from structured imagination becomes more central. By “discontinuous,” I mean shifts that break with historical patterns rather than extend them — changes in rules, technology, or behavior that alter the status quo. For risk teams, strategists, and CIOs, the quantitative tradition in finance already offers a sophisticated way of learning from the future under risk: disciplined forecasting and calibration. However, many of the questions that financial institutions now face are not easily reducible to a single probability distribution. How will different combinations of technology and behavior reshape the cash flows of certain sectors? How might shifts in geopolitical alliances affect cross-border capital flows or the viability of particular financial centers? These are not questions for which a single true distribution can be estimated from the past. Instead, they lend themselves to scenario work in which several distinct, plausibly coherent futures are constructed and explored. In this context, learning from the futures means using qualitatively different narratives, backed by analysis of drivers, feedback, and constraints, to test how robust or fragile current strategies and positions are across a range of environments. Scenario-based learning operates through several mechanisms. First, it encourages decision-makers to hold more than one mental model of the environment at the same time. Rather than implicitly working with a single business as usual picture, they consider, for example, a world of rapid global coordination on climate policy, a world of fragmented, regionally differentiated approaches, and a world in which climate policy advances more slowly than technology and private innovation. Each of these contexts has its own logic, its own plausible patterns of prices, flows and behaviors. By comparing them, professionals can see more clearly which of their current beliefs are contingent on one storyline and which remain sensible under several. Second, building scenarios forces teams to articulate how change might actually propagate: through regulation, through shifts in client demand, through technological substitution, and through market sentiment. This integration of systems thinking and narrative detail surfaces hidden assumptions about causal structure that may not be visible in quantitative models alone. Applying Scenario Thinking: Strengthening Decisions Under Uncertainty For finance practitioners, the applications of this way of learning are tangible. In risk management, scenario work enriches stress testing by introducing structurally different worlds rather than merely scaling historical shocks. Instead of asking only how a portfolio behaves under “2008 plus 20%,” risk teams can explore, for example, a world in which certain assets lose their safe-haven status due to policy changes, a world in which a new technology compresses margins across an entire sector, or a world in which market infrastructures are disrupted. Assessing exposures, hedges, and liquidity profiles across such diverse contexts reveals concentrations and dependencies that may not appear in purely backward-looking metrics. The result is not a deterministic map of losses but a deeper understanding of where the institution is most sensitive to how futures that diverge from the past. In planning, learning from the futures can help firms evaluate the resilience of business models and growth plans. When leadership teams position existing and prospective activities against several plausible external environments, they can identify lines of business that are highly dependent on one policy or technological setting and others that are more adaptable. This in turn supports more informed capital allocation, investment in capabilities, and exit decisions. For example, a bank or asset manager may discover that certain products are attractive across all considered futures, while others are attractive only in those worlds where specific assumptions about market structure or client behavior hold. Thinking in this way does not eliminate commitment; rather, it allows commitments to be made with a clearer sense of the conditions under which they remain sound. Scenario work connects naturally with finance’s quantitative discipline. A practical approach is to derive from each scenario a small set of concrete, time-bound indicators that would tend to move in characteristic ways if that world were coming into being. These indicators can then become the basis for explicit forecasts and monitoring. As actual data arrive, discrepancies between expectations and outcomes provide further learning, they may suggest that some scenario logics are becoming more salient than others, or that certain assumptions need revision. In this way, narrative-based exploration and probabilistic calibration operates as a single learning loop, rather than treated as separate activities. For individual finance professionals, adopting a learning-from-the-futures mindset complements traditional analytical skills with strategic foresight. It encourages a broader awareness of contextual factors, a greater comfort with ambiguity, and a habit of asking “What else could plausibly happen?” before acting. It also encourages reflection on one’s own career and capabilities: considering futures in which certain functions become more automated, regulatory expectations evolve, or new types of clients emerge invites a proactive approach