CFA Institute

When I watch expert investors giving interviews from home on a Zoom call, I always hope to get a glimpse of the books on the shelves behind them. I’ll pause the video and try to decipher the titles in their personal libraries. Maybe, just maybe, reading what they read will help me (and you) think a little more like they do. Recently, I spoke with prominent investors and asked them a simple question: What books should someone read if they want to become a better investor? Their answers were wide-ranging and practical. What follows are their recommendations, edited for clarity. Start with the Basics: Numbers and Clear Thinking David Abrams, Founder, Abrams Capital, recommends Innumeracy, a short book by John Allen Paulos. “People don’t understand how numbers work,” he says. For Abrams, “the first step” in investing is to become more fluent with numbers. Without that, he argues, “you aren’t going to make a lot of progress in finance.” You do not need to be “a brilliant mathematician,” but you do need to understand “something about numbers and how math works.” With that foundation, he adds, “the financial stuff then becomes easier.” He also recommends Black Box Thinking  by Matthew Syed. The title refers to the black box in airplanes. Abrams’s point is that the airline industry records and studies its mistakes, in contrast to many industries that bury them, such as medicine. For those interested in self-improvement, he says it is a valuable idea to consider. The book also argues that sometimes looking at the data that is not apparent is as important, or more so, than the data that is obvious. Reflect on Human Behavior  William Bernstein, Co-Founder, Efficient Frontier Advisors, recommends two books. One is Joe Henrich’s The Secret of Our Success. “It’s about human beings—how we operate, how our brains work, and how different societies function.”  The other is Expert Political Judgment by Philip Tetlock, which examines what separates good forecasters from poor ones. “What you really learn is that there are almost no good forecasters,” he observes.   Wisdom From “The Oracle” Himself  Abrams and Tobias Carlisle, Founder, Acquirers Funds, recommend reading Warren Buffett’s Letters to the Shareholders of Berkshire Hathaway. They are available for free on the internet and reading them is like getting an MBA, says Carlisle.  “I think that a lot of the stuff that they teach in the MBA is silly—and I did a business degree,” he quips. “They taught me a lot of silly stuff that sort of put me on the wrong path. But I was fortunate that I had read Buffett’s letters when I was about 17 years old.”  Ric Dillon, Founder, Vela Investment Management, also recommends Buffett’s letters but a curated version. “For people who are really interested in investments, the best book is The Essays of Warren Buffett: Lessons for Corporate America,” he notes. Lawrence Cunningham, the book’s author, compiled decades of Buffett’s letters into a coherent roadmap for sound investing and strong corporate governance.   “It is priceless,” he says, adding, that even though that’s what he did, “you don’t have to read it cover to cover.” At one point he went to Barnes & Noble bookstore, bought all the copies, and gave them to his board members and executives. “It is by far the best book I’ve ever read in finance generally, and in investments in particular.”   Adapt to Complex, Shifting Markets  Bernard Horn, Founder, Polaris Capital Management, suggests Andrew Lo’s book Adaptive Markets. Investing is like sailing, and the winds are always shifting, he says. “The conditions and the environment that you are investing in are constantly changing and becoming more sophisticated over time. We’re living in a world where things are changing very rapidly.” Advancements in technology and science are moving very quickly, he points out.  “If you don’t keep getting better educated throughout your career, somebody else may take advantage of you. It is a competition. You have to constantly keep evolving.”  On Cognitive Behavior, Discipline, and Strategy  Barry Ritholtz, Founder, Ritholtz Wealth Management, says Daniel Kahneman’s Thinking, Fast and Slow is the first book he recommends to anybody who asks for a book about investing. “You realize your brain is part of the problem. It isn’t the Federal Reserve; it isn’t the secret cows controlling the market. It is your brain. You weren’t built for this—you were built for surviving on the Savannah.”  A second recommendation, Charlie Ellis’s Winning the Loser’s Game, compares investing to playing tennis. Ninety-nine-point nine percent of people who play tennis are amateurs; only a tiny fraction are pros, he says. “And pros win in very specific ways—they serve aces, hit with power, paint the lines, and pull off elegant drop shots.”  This contrasts with how amateurs play and win, he notes. “We double fault. We hit the ball into the net. We attempt a fancy shot and miss. Most amateur matches aren’t won by scoring points—they’re lost through unforced errors.”  If you focus on staying within your limits, returning the ball, and avoiding mistakes, you’ll do well in tennis—and even better in investing. Trouble arises when investors believe they can consistently pick winning stocks or superior fund managers. Most can’t.  Cautionary Tales Every Investor Should Know  Roger Lowenstein’s When Genius Failed, is a fascinating book, says Tom Sosnoff, Founder, thinkorswim and tastytrade. “It is about Long-Term Capital Management and the Nobel Prize winners who wrote the Black Scholes model and then almost blew up the markets.”  He also recommends Where Are the Customers’ Yachts?  by Fred Schwed. It’s essentially about a tour of the old Merrill Lynch offices in Battery Park, overlooking the Hudson River. A Merrill guy is showing a visitor all the Wall Street guys’ yachts. The visitor looks out and asks, “Well, where are the customers’ yachts?” The Merrill guy replies, “Yeah… there aren’t any of those around here.”  It’s a reminder that intelligence, models, and prestige can’t protect you from reality. It’s an absolute Wall Street classic.  Stay Curious, Humble, and Agile  Taken together, the recommendations point to a simple idea: becoming a better investor requires stronger judgment, intellectual curiosity, humility, and a willingness to learn from history.   source

The artificial intelligence (AI) revolution, with its expansion into neural networks and other novel fields, marks a dramatic shift away from traditional innovation models. And like all revolutions, it comes with challenges as rapid technological advancement gives rise to concurrent risks. Market volatility and convoluted regulations are significant hurdles, especially for generative AI and large language models (LLMs). But previous market bubbles provide valuable lessons for investors and emphasize the need for a clear-sighted, cautious approach. New Boss Same as the Old Boss? Today’s AI trends are influencing both the macroeconomic outlook as well as our investment strategies. With their enormous influence, Google, Microsoft, Meta, IBM, Amazon, Nvidia, and other technology giants are setting the pace for the rapidly evolving sector. By nurturing specialized AI start-ups and continuously innovating and delivering new AI products, these companies are laying the foundation for the industry’s future. While progress is substantial, especially in graphic processing units (GPUs), the slow pace of mass adoption is a concern. By deploying open AI models, however, big tech could help bring stability to the market. AI has had a relatively small direct impact on big tech’s revenues but contributed a projected $2.4 trillion increase to the sector’s overall value. Generative AI has an undeniable appeal. ChatGPT and other platforms have made remarkable strides, with their undeniable conversational prowess. Yet they betray a surprising lack of depth. They build sentences based on statistical patterns not deep comprehension. Such a flaw could contribute to the spread of misinformation. Buckle Up? Despite such shortcomings, investment capital continues to flood into these systems, propelled as much by AI’s buzzword appeal as its evidence-based results. The disparity between public perception and practical utility is marked, but generative AI is poised to up its game in the years ahead and address its limitations, Few sectors are immune to generative AI’s potential benefits. As the technology is honed and deployed at scale for commercial use, the productivity gains across the global economy could be astronomical. While generative AI is shaping market trends, significant regulatory impediments are coming into focus, particularly around the transparency of algorithms, and underscore the inherent risks. That’s why AI investors should be on the lookout for companies with solid fundamentals and pragmatic valuations as a hedge against the uncertainties embedded in the market. As AI investors, we must be discerning. Not all AI start-ups are sound investments. For example, Lede AI’s venture into AI-generated news articles was a disappointment. AI-generated journalism missed critical details, injected inaccuracies into its stories, damaged the reputations of storied news organizations, and underscored AI’s quality and consistency issue. iTutorGroup applied AI to its recruitment processes and subsequently had to settle an age discrimination lawsuit, emphasizing why AI applications require robust guardrails to avoid such financial and reputational traps. Reality is creeping into the AI sector in the wake of the ChatGPT boom. Jasper and other emerging companies have grappled with dwindling user engagement and workforce cutbacks. Platforms like Midjourney and Synthesia have seen diminished traffic as they have dialed back their ambitions for market dominance. Now, many AI applications would be satisfied with proficient functionality. The strong positions of tech giants like Microsoft and Google have also given investors pause. A stark gap has emerged between high-flying investor aspirations and genuine market conditions. The enthusiasm that spurred the initial wave of AI commercialization is giving way to disillusionment and doubt. The high cost of AI model training and the lack of a transparent and viable business blueprint have contributed to the growing frustration as have a host of legal and ethical debates. Given such difficulties and despite a significant influx of capital and widespread public anticipation, AI start-ups may be hazardous investments. Regulations Cometh? President Joseph Biden’s 31 October 2023 executive order signals an imperative shift in the control of generative AI. It seeks to position the United States at the forefront of AI development and emphasizes safety, security, and addressing algorithmic bias. The order requires AI developers to conduct safety tests and publicly share their findings. It holds the US Department of Commerce and other entities accountable for defining and regulating AI standards. While these mandates will help ensure AI’s safe and ethical application, they could also further increase execution costs, slow research and development, and impose new standards on data privacy and management. Such regulation could limit AI’s application, particularly among smaller firms and start-ups, potentially stunting their growth. Finding the right balance between AI development and the essential supervisory role of public policy will be an ongoing challenge for US and global regulators. Beware the Bubble? In today’s high-speed, tech-driven investment world, bubbles are both more frequent and more intense. The main accelerant? The pervasive influence of the internet and social media. This dynamic ensures the rapid flow of capital into developing trends and fuels the cyclical fervor of AI investment. What are the implications of this? A likely procession of booms and busts within the AI sector that resemble generational shifts, with each surge and downturn shaping and propelling the industry’s evolution. Does this mean investors ought to pull back? Certainly not. Rather, it underscores how crucial an intelligent investment strategy in emerging AI technology could be. We must exercise thorough due diligence and keep a keen eye on cash flow and other solid value indicators. Exposure to investments rooted in unrealized and unproven potential should be carefully controlled. Technology bubbles are nothing new, From Railway Mania in the United Kingdom to the dot-com bubble in the United States, they underscore the interplay between economic theory and speculative fervor. Bubbles can end in swift, dramatic market implosions or gradual deflations, and they can transform entire industries. Despite the excessive speculation, many present-day tech leviathans emerged out of the dot-com bubble and went on to reshape our world. The dot-com boom reminds us of the dangers of unchecked optimism when investing in technology. But we must also remember the tech industry adapted and refocused on the intrinsic value of its investments.

The anchoring of inflation expectations is a cornerstone of modern macroeconomic theory and a key measure of central bank credibility. When investors believe inflation will remain close to target over the long term, central banks can influence economic activity effectively by adjusting interest rates in line with the Taylor principle (Bauer, 2015). But if long-term expectations become unstable, markets may doubt the bank’s commitment or capacity to control inflation, diminishing the power of policy decisions. This issue has come to the forefront in Europe. The European Central Bank’s (ECB) primary, medium-term mandate is to ensure inflation remains stable at 2%. Aggressive monetary tightening by the ECB including rate hikes and quantitative tightening, brought inflation down to 2.5% by June 2024 after it surged to a record 10.7% in October 2022 amid post-COVID supply shocks and energy price spikes. Yet even this level sits slightly above the ECB’s 2% goal, leaving markets and policymakers to ask: has the ECB successfully preserved the anchoring of inflation expectations, or has recent turbulence eroded its credibility? This blog outlines a broader award-winning thesis by the author who won first prize in the 2024 CFA Society Belgium’s Master Theses Awards and addresses this question by examining how euro-area inflation expectations, measured through inflation-linked swap (ILS) rates, responded to monetary policy shocks between 2013 and 2024. This period spans two critical phases: the pre-COVID years of persistently low inflation and the post-COVID spike. Understanding investor reactions across this timeline sheds light on whether the ECB’s forward guidance, rate adjustments, and quantitative easing (QE) have reinforced or undermined confidence in its inflation target. What sets this study apart While earlier research has examined high-frequency market surprises around policy announcements (e.g., Bernanke & Kuttner, 2005; Gurkaynak, Sack & Swanson, 2005; Altavilla et al., 2019), this study introduces new innovations: It extends the timeline to 2013 to 2024, capturing both the pre-COVID period of low inflation and the post-COVID surge that most prior analyses overlook. It examines the full-term structure of inflation expectations by analyzing spot and forward ILS rates up to ten-year maturities (García & Werner, 2021; Miccoli & Neri, 2019), providing a more comprehensive view across short, medium, and long-term horizons. It applies local projections with external instruments, a method shown by Plagborg-Møller & Wolf (2022) to be more robust than traditional Vector Autoregression (VAR) models for shorter samples and horizons. Finally, it separates pure monetary policy effects from information effects using methodologies inspired by Jarociński & Karadi (2020) and Andrade & Ferroni (2021), distinguishing news about Odyssean shocks, which refer to future policy from Delphic shocks, which are signals about the economic outlook. What we found was that for the ECB, the results argue for cautious use of forward guidance. While it can shape market expectations effectively, poorly calibrated guidance risks generating Delphic shocks that undermine policy goals. Conventional rate moves and quantitative easing (QE) influence expectations more predictably. Overreacting with overly restrictive policy, however, is unnecessary. The anchoring of long-term expectations suggests that inflation can be steered back to target without jeopardizing growth. Short-Term Uncertainty, Long-Term Stability We took the analysis in three parts: First, we measured how ILS rates respond to four identified types of monetary shocks: target rate set by policy changes, short-term guidance/timing, medium term forward guidance, and quantitative easing (QE). The immediate response of ILS rates to these shocks is muted, but significant movements emerge after 10 to 15 days, a lag consistent with the low liquidity of the euro-area inflation swap market (Miccoli & Neri, 2019). Restrictive target rate and QE shocks lower near-term inflation expectations up to two years, as theory predicts. By contrast, short-term timing and forward guidance shocks yield weaker, sometimes counterintuitive effects, echoing earlier observations by Altavilla et al. (2019) and Andrade & Ferroni (2021). To address these anomalies, the second stage of this thesis separates Odyssean and Delphic components. By analyzing co-movements between two-year overnight index swaps (OIS) and the Euro STOXX 50 around policy announcements, we classify each shock type (Odyssean future policy and Delphic economic outlook) and in doing we see some surprising reactions of inflation expectations are responses to economic news, not monetary policy per se. Still, splitting events this way shortens the sample and increases estimation noise. To mitigate this, the final stage applies a new identification strategy treating each event as a mix of three factors: Odyssean timing, Odyssean forward guidance, and Delphic path. This refined model produces responses consistent with macroeconomic theory: restrictive Odyssean shocks depress near-term expectations by up to 10 basis points, while Delphic shocks raise them. Importantly, the model underscores that forward guidance carries the risk of triggering Delphic shocks if markets misinterpret signals as news about the economic outlook, potentially offsetting its intended effects. This makes conventional measures and QE safer alternatives. Across all models, five- to-10-year inflation expectations remain unaffected by policy surprises. Even during the extreme volatility of 2022 to 2023, investors did not revise their long-term outlook for euro-area inflation in a way that would suggest de-anchoring. This is strong evidence that, despite the ECB’s delayed response to soaring prices, its 2% target remains credible. Implications for Investors and Policymakers For market participants, these findings offer two takeaways: First, near-term inflation pricing can be sensitive to communication missteps. Investors should consider not only the size and direction of policy moves but also the tone and context of ECB statements, particularly during volatile periods when distinguishing between Odyssean and Delphic signals is difficult. Second, the persistence of anchored long-term expectations suggests that inflation expectations remain firmly anchored. This credibility helps stabilize financial markets and temper risk premiums even when short-term price movements are volatile. In sum, even during the recent post-COVID period of high inflation, monetary policy announcements did not lead to a de-anchoring of long-term inflation expectations in the euro area. Consequently, the ECB’s inflation target of 2% appears credible to financial markets, indicating that the ECB may not need to adopt an overly restrictive monetary stance to guide inflation back

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.