CFA Institute

For more on artificial intelligence (AI) in investment management, check out The Handbook of Artificial Intelligence and Big Data Applications in Investments, by Larry Cao, CFA, from the CFA Institute Research Foundation. Performance and Data Despite its seemingly “magical” qualities, ChatGPT, like other large language models (LLMs), is just a giant artificial neural network. Its complex architecture consists of about 400 core layers and 175 billion parameters (weights) all trained on human-written texts scraped from the web and other sources. All told, these textual sources total about 45 terabytes of initial data. Without the training and tuning, ChatGPT would produce just gibberish. We might imagine that LLMs’ astounding capabilities are limited only by the size of its network and the amount of data it trains on. That is true to an extent. But LLM inputs cost money, and even small improvements in performance require significantly more computing power. According to estimates, training ChatGPT-3 consumed about 1.3 gigawatt hours of electricity and cost OpenAI about $4.6 million in total. The larger ChatGPT-4 model, by contrast, will have cost $100 million or more to train. OpenAI researchers may have already reached an inflection point, and some have admitted that further performance improvements will have to come from something other than increased computing power. Still, data availability may be the most critical impediment to the progress of LLMs. ChatGPT-4 has been trained on all the high-quality text that is available from the internet. Yet far more high-quality text is stored away in individual and corporate databases and is inaccessible to OpenAI or other firms at reasonable cost or scale. But such curated training data, layered with additional training techniques, could fine tune the pre-trained LLMs to better anticipate and respond to domain-specific tasks and queries. Such LLMs would not only outperform larger LLMs but also be cheaper, more accessible, and safer. But inaccessible data and the limits of computing power are only two of the obstacles holding LLMs back. Hallucination, Inaccuracy, and Misuse The most pertinent use case for foundational AI applications like ChatGPT is gathering, contextualizing, and summarizing information. ChatGPT and LLMs have helped write dissertations and extensive computer code and have even taken and passed complicated exams. Firms have commercialized LLMs to provide professional support services. The company Casetext, for example, has deployed ChatGPT in its CoCounsel application to help lawyers draft legal research memos, review and create legal documents, and prepare for trials. Yet whatever their writing ability, ChatGPT and LLMs are statistical machines. They provide “plausible” or “probable” responses based on what they “saw” during their training. They cannot always verify or describe the reasoning and motivation behind their answers. While ChatGPT-4 may have passed multi-state bar exams, an experienced lawyer should no more trust its legal memos than they would those written by a first-year associate. The statistical nature of ChatGPT is most obvious when it is asked to solve a mathematical problem. Prompt it to integrate some multiple-term trigonometric function and ChatGPT may provide a plausible-looking but incorrect response. Ask it to describe the steps it took to arrive at the answer, it may again give a seemingly plausible-looking response. Ask again and it may offer an entirely different answer. There should only be  one right answer and only one sequence of analytical steps to arrive at that answer. This underscores the fact that ChatGPT does not “understand” math problems and does not apply the computational algorithmic reasoning that mathematical solutions require. The random statistical nature of LLMs also makes them susceptible to what data scientists call “hallucinations,” flights of fancy that they pass off as reality. If they can provide wrong yet convincing text, LLMs can also spread misinformation and be used for illegal or unethical purposes. Bad actors could prompt an LLM to write articles in the style of a reputable publication and then disseminate them as fake news, for example. Or they could use it to defraud clients by obtaining sensitive personal information. For these reasons, firms like JPMorgan Chase and Deutsche Bank have banned the use of ChatGPT. How can we address LLM-related inaccuracies, accidents, and misuse? The fine tuning of pre-trained LLMs on curated, domain-specific data can help improve the accuracy and appropriateness of the responses. The company Casetext, for example, relies on pre-trained ChatGPT-4 but supplements its CoCounsel application with additional training data — legal texts, cases, statutes, and regulations from all US federal and state jurisdictions — to improve its responses. It recommends more precise prompts based on the specific legal task the user wants to accomplish; CoCounsel always cites the sources from which it draws its responses. Certain additional training techniques, such as reinforcement learning from human feedback (RLHF), applied on top of the initial training can reduce an LLM’s potential for misuse or misinformation as well. RLHF “grades” LLM responses based on human judgment. This data is then fed back into the neural network as part of its training to reduce the possibility that the LLM will provide inaccurate or harmful responses to similar prompts in the future. Of course, what is an “appropriate” response is subject to perspective, so RLHF is hardly a panacea. “Red teaming” is another improvement technique through which users “attack” the LLM to find its weaknesses and fix them. Red teamers write prompts to persuade the LLM to do what it is not supposed to do in anticipation of similar attempts by malicious actors in the real world. By identifying potentially bad prompts, LLM developers can then set guardrails around the LLM’s responses. While such efforts do help, they are not foolproof. Despite extensive red teaming on ChatGPT-4, users can still engineer prompts to circumvent its guardrails. Another potential solution is deploying additional AI to police the LLM by creating a secondary neural network in parallel with the LLM. This second AI is trained to judge the LLM’s responses based on certain ethical principles or policies. The “distance” of the LLM’s response to the “right” response according to the judge AI is fed back into the LLM as part of its training process. This way, when the LLM considers its choice of

Hedge funds promise sophisticated strategies and the potential for market-beating returns, but do they deliver enough value to justify their high fees? Research reveals a mixed picture. While some hedge fund managers demonstrate impressive skills in stock-picking or market timing, their overall performance often falls short of standard indices. For investment professionals, the challenge lies in identifying the few managers who combine skill, performance, and persistence. This is the first in a series of three blog posts that explore the hedge fund literature. Skill I found mixed evidence that hedge fund managers have investment skills. In fact, their investment outcomes are not much better than what you could expect from mere luck. However, several papers indicate that the best managers stand out. Kosowski et al. (2007) found that top hedge fund performance cannot be explained by luck. Chen and Liang (2009), looking at a sample of 227 market-timing hedge funds from 1994 to 2005, found evidence of market timing skill, especially during bear markets and volatile market conditions. Nohel et al. (2010) compared the returns of mutual funds run by managers who also manage hedge funds with the returns of other mutual fund managers, finding the former significantly outperforming the latter. More recently, Aiken and Kang (2023) found that hedge fund managers have stock-picking skills that diminish over time but do not find evidence of market timing skills. Barth et al. (2023) found that hedge funds not listed in commercial databases generated up to $600 billion in value-added (before fees) returns from 2013 to 2019. Other studies identify characteristics that may help pick out skilled hedge fund managers. Agarwal et al. (2009) found that hedge funds with greater managerial incentives, higher levels of managerial ownership, and the inclusion of high-water-mark provisions are associated with superior performance. They also found that funds with a higher degree of managerial discretion, proxied by more extended lockup notice and redemption periods, deliver superior performance. Sun et al. (2012) devised a “Strategy Distinctiveness Index.” Funds with a higher index were associated with better subsequent performance. After adjusting for risk, funds in the highest SDI quintile outperformed funds in the lowest quintile by 3.5% in the following year. Cao et Al. (2021) found that start-up hedge funds launched during periods of low demand for this type of fund outperformed those launched in high-demand periods. Performance On balance, research does not suggest impressive performance from hedge funds. Ackermann et al. (2002) found that hedge funds consistently outperform mutual funds but not standard market indices. They also found hedge funds to be more volatile than mutual funds. Kosowski et al. (2007) reported that hedge funds generate statistically insignificant alphas in five of the six categories reviewed: long/short, directional, multi-process, security selection, and funds-of-funds. The authors also mentioned that long/short equity funds’ residuals are negatively skewed, and relative value funds exhibit high kurtosis, or higher than normal frequency of extreme outcomes. By contrast, Newton et al. (2019), studying 5,500 North American hedge funds that followed 11 different strategies from 1995 to 2014, found that all but two hedge fund strategies outperformed the market as stand-alone investments, although their manager skill level was low. Sullivan (2021) analyzed the performance of hedge funds during the 1994–2019 period, dividing his data into two subsamples. From 1994 to 2008, he found an alpha of 3.4% annually. However, for the more recent 2009 to 2019 period, he found a ‑1.0% alpha. The author concludes that hedge fund performance may have declined over time due to reduced exposure to active management risk. Two other studies, Eksi and Kazemi (2022) and Amir-Ghassemi et al. (2022), confirmed the fading of hedge fund performance since 2009. By contrast, Barth et al. (2023) claimed that from 2013 to 2019, non-listed hedge funds produced, on average, positive alphas. However, Swedroe (2024) has challenged this claim, arguing that while the average non-listed fund may have added value, the median fund (a more representative statistical figure) does not. Persistence A key measure of whether the best hedge fund managers outperform by luck or by skill is persistence. Do the best-performing hedge funds tend to repeat their outperformance in subsequent periods? Unfortunately, with one notable exception, most studies find significant hedge fund persistence over short periods that vanishes at longer horizons. Baquero et al. (2005) reported positive persistence in hedge fund quarterly returns after correcting for investment style, with weakly significant annual persistence. Kosowski et al. (2007) also found that the best hedge funds persisted at annual horizons. Agarwal et al. (2009) found maximum persistence at the quarterly horizon, indicating that persistence among hedge fund managers is short-lived. Sun et al. (2018) reported evidence that hedge fund performance is persistent following weak hedge fund markets but is not persistent following strong markets. Aiken and Kang (2023) found weak evidence that managers exhibit persistence in selectivity skills. In a noteworthy study, Barth et al. (2023) found that, in contrast to vendor-listed funds, significant persistence existed over all horizons among non-listed hedge funds in 2013–2019, providing hope that outperforming hedge funds can be identified in advance. Key Takeaway Overall, research suggests skill and alpha are scarce and difficult to obtain in the hedge fund market, especially among those listed in commercial databases. Furthermore, most studies report that outperformers fail to repeat their feats over long periods. Investors considering hedge funds should not overlook unlisted funds. In my next post, I will discuss hedge fund risk and diversification properties. source

For most investment managers, ChatGPT represents the starting whistle in a tech arms race many had hoped to avoid. The post ChatGPT: The Origins, the Hype, the Opportunity appeared first on CFA Institute Enterprising Investor. source

Introduction Around 90% of US drivers rate themselves as safer and more skillful than average. Obviously, such perceptions do not reflect reality. After all, 9 out of 10 people can’t all be above average. Nevertheless, the results are compelling: They illustrate an innate human tendency to overrate our own talents and skills and underrate those of others. Equity mutual fund managers likely have a similarly distorted view of their ability to generate alpha by outperforming the stock market. Otherwise, how would they justify their jobs? But perhaps we’re missing the point. Maybe most drivers do drive safely and most fund managers outperform, with only a very few accounting for a disproportionate share of traffic tickets and accidents and major capital losses, respectively. Unfortunately not. The majority of fund managers do underperform their benchmarks: Only 17% of US large-cap mutual fund managers beat the S&P 500 over the last 10 years, according to the latest S&P SPIVA Scorecard. Moreover, there is no consistency among those few who did outperform. This all implies that successful manager selection is almost impossible. But research shows that factors rather than skill explain out- and underperformance. Therefore, outperformance and alpha are not exactly the same thing. So, how do we explain the difference? Outperformance While fund managers emphasize their ability to create alpha for clients, fund factsheets compare their performance to a benchmark. For example, the Invesco S&P 500 Pure Value exchange-traded fund (ETF, RPV) generated a return of 0.7% over the last 12 months, while its benchmark, the S&P 500, yielded –10.2%. The S&P 500 Value index might be a better point of comparison for RPV, but relative to the broad index, the ETF has delivered significant value — pun intended — to its investors. RPV Smart Beta ETF Outperformance = Alpha? Source: FactorResearch Factor Exposure Analysis Since the RPV ETF selects roughly the 100 cheapest S&P 500 stocks, it is a value-focused strategy. A regression analysis with a one-year lookback validates this. RPV has high betas relative to the S&P 500 — it is a long-only strategy — as well as to the value and quality factors.  The value factor exposure and the quality factor negative beta are both intuitive because cheap companies tend to rank poorly on quality metrics. Stocks trading at low valuations tend not to be highly profitable and often have excessive leverage or other issues. Factor Exposure Analysis — RPV Smart Beta ETF: Betas, Last 12 Months Source: FactorResearch Contribution Analysis With the factor betas, we can create a contribution analysis. RPV had a high beta compared with the S&P 500 — 0.90 — which was down 10.2% over the last 12 months. Therefore, the broad market contributed –9.1% to RPV’s returns. Save for the value factor, which contributed 12.5%, other equity factors had a marginal impact. Factor Contribution Analysis: RPV Smart Beta ETF, Last 12 Months Source: FactorResearch Alpha Calculation Since we know how much the stock market and equity factors contributed to RPV’s performance, we can also calculate the residual. Theoretically, this represents the manager’s skill, or whatever market beta and factors are not responsible for. Stated differently, it is the alpha. For RPV, the alpha was negative. But how can the alpha be negative when the ETF outperformed its benchmark? The implication is that the value-focused strategy was implemented poorly. Management fees, market impact, and transaction costs must also be taken into account. While there will always be slippage, that only explains a fraction of the –5.7% result. Based on this analysis, investors would have been better off avoiding RPV and buying the S&P 500 and the factor exposures through a zero-cost ETF and risk premia indices, respectively. Alpha Calculation: RPV Smart Beta ETF, Last 12 Months Source: FactorResearch The alpha calculation may be a little confusing since RPV is a smart beta ETF that provides exposure to the value factor and we’re using a factor exposure analysis to measure the contributions. But we can replicate this approach with Fidelity Contrafund (FCNTX), one of the most well-known equity mutual funds. FCNTX has a long track record going back more than 40 years and manages close to $100 billion. The fund holds a concentrated equity portfolio that is dominated by Amazon, Microsoft, Apple, and other growth stocks. But over the last 12 months, this strategy hasn’t worked well either: FCNTX has declined by more than 20% due to beta and factor exposure. According to the contribution analysis, the S&P 500 and equity factors can’t fully explain the negative performance, that is, alpha was negative. As such, the fund manager must take responsibility for at least some of the losses. Alpha Calculation: Fidelity Contrafund (FCNTX), Last 12 Months Source: FactorResearch Outperformance vs. Alpha By running contribution analyses for 13 US stock market equity mutual funds and ETFs, we can demonstrate the significant difference between outperformance and alpha. In only one case — the Davis Select US Equity ETF (DUSA) — were outperformance and alpha almost identical at –0.5%. The ETF does have exposure to factors, but the contributions netted themselves out. That means the loss can only be attributed to fees or lack of skill. As for the ARK Innovation ETF (ARKK), much of the recent criticism may be overstated. According to our calculations, Cathie Wood, ARKK’s fund manager, has created alpha. The ETF is down 61.8% over the last 12 months, but the market accounted for –17.7% of that and factors for another –53.0%. So, there was 8.9% of alpha. ARKK is highly concentrated with a few growth names — Tesla, for example. This results in betas to the S&P 500 of 1.7 and to the value factor of –1.35. Since factor exposure analysis reveals all this, investors have only themselves to blame if such bets go south. Active Fund Managers: Outperformance vs. Alpha Source: FactorResearch Different Input, Different Output Though contribution analysis is the most meaningful alpha calculation methodology, the data that is used matters. So far, we have employed FactorResearch factors. These apply industry-standard definitions

What are the keys to a successful finance career? Eric Sim, CFA, has thought about that question a lot. As a university lecturer and author, he had to give his students and readers a logical framework to follow, and 20-odd unconnected tips were not going to do it. So he looked back on his professional life: How had he gone from a teenager in Singapore washing bowls at his father’s prawn noodle stand to serving as a managing director at UBS Investment Bank and achieving financial independence? What were the common threads that tied the seemingly random twists and turns, the unexpected setbacks and equally unanticipated strokes of good fortune, into a comprehensible narrative? It all came down to capital, Sim concluded: specifically, human capital, financial capital, and social capital. Where he succeeded, such capital had been essential. Where he failed, a lack of capital in some form had been determinative. So, what are these three forms of capital, and how has Sim’s career demonstrated their influence? Human Capital “So human capital is our knowledge and skills,” Sim said. “We need to have human capital so that we are useful to people. And when we are useful to people, we can then build other forms of capital.” We gain human capital through education and experience. The first in his family to go to college, Sim graduated from the National University of Singapore with a degree in engineering. Though his bachelor’s in science was not an obvious fit, he parlayed it into a corporate sales position at DBS Bank thanks to a well-told anecdote about what he learned serving drinks at a crowded nightclub. Sim spent the next two years working in foreign exchange (FX) before deciding to roll the dice and take a big chance. And that’s where financial capital came in. Financial Capital: Not Rolex, But Timex With his blue collar background, Sim had little experience with the bonus culture in banking and finance. “When I got my bonus when I was a junior banker, a lot of my peers went on big holidays,” he recalled. “Some bought watches and spent $30,000.” It didn’t make sense. “Who looks at your hand?” Sim said. “I don’t look at people’s watches, and I assume nobody will look at mine. So why spend the money?” So, rather than blow his windfall on a fancy Rolex, Sim bought a Timex and invested the rest in stocks. And when he was ready to make a bet on his future, he had $30,000 to invest in himself and a master’s in finance program at Lancaster University in the United Kingdom. Sim had done the research. The 10-month program was the most economical and the only one he could afford. Similar programs in London were way out of his price range, but Sim thought if he did well at Lancaster, it would open up some doors and expand his horizons. “I’ll spend $30,000,” he thought. “I can then work in London, make money, and change my fate a little bit.” Culture Shock It didn’t work out quite as well as he expected. First there was adjusting to a new culture on the other side of the world. Novelty was everywhere. Growing up in Singapore, Sim had never even seen sheep. Now they were grazing outside his bedroom window. He hadn’t anticipated how pricey food and housing costs would be either. “Everything was three or four times more expensive than what I thought,” he said, “I couldn’t eat anything, except a slice of pizza for each meal. . . . One slice of pizza is not a full meal.” But the culture shock went beyond that: As a non-native English speaker, Sim was automatically at a disadvantage. He wasn’t wealthy or well-traveled, and now for the first time, he was far away from home, from family and friends. He was also insecure. The legacy of colonialism in Singapore affected his confidence. Indeed, one of his first big doubletakes in the United Kingdom was seeing Caucasians laying bricks and mixing cement at building sites. “That blew me away. White people doing construction work — never in my life. So I was shocked,” he said. “I was hit by this colonial mindset,” Sim continued. “I come from selling food and never read widely and didn’t think I could compete.” Whatever doubts he had, he quickly put them to rest in his financial mathematics course. Sitting next to Cambridge-educated peers, Sim outperformed everyone, earning the top score in the class. That was a pattern he repeated throughout his course of study. After 10 months in Lancaster, Sim was confident and ready to take the next step. He had bonded with his classmates, built friendships, and excelled in his coursework. He had no doubt he would find a finance job in London and embark on the next phase of his career. But first he needed a suit. The Chicken Suit While Sim’s studies may have improved his human capital, the same could not be said for his financial capital. All his savings were gone, and he had to somehow finance a new business suit as well as several days’ stay in London to sit for interviews. London wasn’t like Singapore where the heat made a collared shirt, tie, and dress pants acceptable business attire. So Sim went clothes shopping, and Savile Row was not an option. Money was so tight that he had to go to the Oxfam charity where the secondhand suit selection was decidedly limited. “Of course they didn’t have my size,” Sim said. “I’m already on the small side in Asia. Then in the UK, I’m like extra small. So I bought a suit so big that I could have hidden a chicken inside.” The ill-fitting double-breasted suit did not make a good impression on the well-tailored London bankers he interviewed with. “It was ridiculous. How could I expect to pass an interview with Goldman or Morgan Stanley?” Sim said. “All the interviews ended early, and none

Abraham Lincoln, a lawyer and the sixteenth president of the United States, is an oft-idealized and highly quoted leader with good reason. He made wartime decisions with patience, communicated sincerely with his cabinet, and showed altruism in wanting to educate people. Lincoln’s example offers valuable lessons for investors, especially in passive investing, where balancing profit with integrity is central. His many monikers stand as an ode to greatness: from his humble beginnings as “The Rail-Splitter” (a name given to one who cuts wood to fasten into fences), to “Honest Abe” (because of his ethics and bias for truth in his law practice), and finally as “The Great Emancipator” (for ending slavery in the United States). Lincoln’s self-reflective leadership style has been studied and imitated throughout the ages by astute politicians, pioneering attorneys, and even captains of finance. His trademark beliefs — patience, discipline, integrity, and education — mirror the central tenets of passive investing, and professionals in the field can learn from his example and the quotes throughout this blog. It’s not about chasing the latest trends or reacting to market noise; it is about building with purpose, conviction, and perseverance. As Honest Abe would put it, leadership and investing call for character and consistency to achieve enduring success. Patience: Lincoln’s Strategic Vision and the Long-Term Power of Passive Investing “We shall sooner have the fowl by hatching the egg than by smashing it.” Patience is one quality that guided Lincoln’s decision-making in both his legal practice and political career. During his presidency, for example, he strategically delayed the Emancipation Proclamation, an executive order which abolished slavery, until the ideal moment. As the great American poet and Lincoln scholar Carl Sandburg noted in The War Years, this judicious sense of timing amplified the impact of Lincoln’s message both at home and abroad.1 Passive investment strategies, especially those that focus on diversified asset classes, are less impacted by timing the markets. Investors must understand that the value of “time in the market” is greater than “timing the market.” Lincoln did not waver in the dynamic and fickle political climate of his time and stayed the course with his long-term decisions. Investors, too, can avoid the pitfalls of chasing short-term returns in favor of better long-term outcomes through focused and disciplined portfolio rebalancing. Discipline: Lincoln’s Strategic Planning and Investment Precision “Always bear in mind that your own resolution to succeed is more important than any one thing.” Lincoln’s legal training helped inform his renowned communication skills. He carefully crafted each phrase to produce the desired effect in every speech, letter, and policy draft and an economy of language pervaded his most important works. For example, each of the 272 words of The Gettysburg Address, served a purpose.2 Likewise, passive investment strategies depend on clear communication and a methodical approach that must be translated effectively to investors. Communication and asking the right questions around risk tolerance, diversification, and individual financial goals is pertinent to the creation and management of a long-term portfolio. A successful, passively invested financial plan requires prudence in clarity and accuracy. As Lincoln needed to adapt and evolve his political and military strategies in the face of new challenges, so goes rebalancing which requires constant evaluations and adjustments to market fluctuations. Investors need to consistently fine-tune and adapt their approach while staying true to the fundamental principles and objectives of the investment. An advisor must always be up to date with their clients and ask them probing questions regarding any updates to their life or overall financial picture.  Integrity: The Basis of Lincoln’s Ethical Leadership and Financial Integrity “Truth is generally the best vindication against slander.” Lincoln was often referred to as Honest Abe, especially with regard to his practice of law. The name reflected the key to his reputation — integrity, trustworthiness, and reliability. As a lawyer, president, and commander-in-chief during the U.S. Civil War, Lincoln’s commitment to truth and honor did not waver. Passive investing aligns closely with this virtue. A passive investment strategy is inclined towards lower fees, clarity, transparency, and reduced conflicts of interest with the advisor. The core tenet of passive investing is that markets are efficient and any attempt to time or outguess the market is futile. This contrasts with hidden costs, kickbacks, and speculative risks involved in active management strategies. Passive investment advisors give ethical responsibility, client education, and transparent disclosure due consideration. This ensures recommendations are made in the best interest of clients, not the advisor’s bottom line. These practices are in line with how Lincoln lived and led. Lincoln always believed that trust was built through honesty. This reassures investors that a particular venture or advisor is a trustworthy partner through their financial journey. Education: Lincoln’s Relatable Communication and Empowering Investors “Whatever you are, be a good one.” Lincoln could easily break down complex issues into relatable anecdotes that appealed to the listener. He took care to craft any public communication with strategic clarity to reach a broad range of the population. As Harry Jaffa noted in Crisis of the House Divided, Lincoln made a deliberate choice to translate legal and constitutional matters into the moral language used by common citizens.3 Such devotion to education and accessibility has important implications in investment management as well. Although the concept of passive investing is based on technical principles, it can — and should — be made available and explained to ordinary investors. The emergence of affordable index funds and online learning tools has given millions of people the confidence to invest in markets without the need for a strong financial background, and modern investors expect clear, transparent communication from investment professionals. In managing clients’ portfolios, financial advisors play a similar role to that of Lincoln in his approach to public leadership. During uncertain times, advisors must increase communication and focus on coherent and precise language rather than technical jargon. Market downturns often trigger fear, leading to poor decision-making. Advisors who communicate with clarity help clients stay invested, reducing the risk of emotional

Wise investors should keep their eyes fixed on India and its booming startup ecosystem, which is the third largest as of 2024. Since Prime Minister Shri Narendra Modi began the Startup India initiative in January 2016, funding for the country’s startups has increased 15-fold. The coming year promises even more growth for this ecosystem, with valuation projections of more than $450 billion by 2025. These startups cover a vast range of emerging sectors and are vital contributors to India’s transformation into a fully developed nation. Combined with strong government and corporate support and increased ease of business, India has created well-developed investment opportunities with big returns and significant impacts. Already the world’s most populous country and largest democracy, India is on track to become the most extensive startup ecosystem outside the United States, with expected year-over-year growth pegged at 12% to 15%. Investors would do well to learn about this ecosystem, from its critical industries to its risks and challenges. This post covers some of the most essential information for investors to consider. Emerging Sectors and Positive Change Those investing in India have more options than ever before. More than 100,000 startups are registered with India’s Department for Promotion of Industry and Internal Trade (DPIIT). These span a wide range of industries, but two with excellent market potential are technology and renewable energy. The technology sector covers many bases, but fintech and deep tech are the most prominent players. According to a Hindu Business Line report, India possesses the third-highest fintech count globally, with more than 9,000 as of 2023, accounting for 14% of current startup funding. In the same report, Elevation Capital partner Mridul Aroroa states that India’s “fast-growing digital population, world-class digital public infrastructure, and proactive regulators” will help the fintech sector expand to $400 billion in total value by 2030. Savvy investors are already making significant investments in India’s fintech startups, with the city of Bengaluru alone receiving $949 million in fintech funding in 2023. Deep tech is a fast-growing sector that encompasses hot global markets: AI, blockchain, and quantum computing. Venture capital funding has doubled over the past decade, with investments of $100 million or more becoming increasingly common. Investors can rest assured that India is already ahead of the curve in this highly relevant sector, with 3,000 deep tech startups growing at a 53% CAGR since 2013. Like fintech, deep tech is poised for exponential growth this decade. Ramkumar Narayanan, Chairperson of Nasscom’s DeepTech Council, predicts more than 10,000 deep tech startups will exist in India by 2030. India is more than prepared to meet the demands of investors looking to make reliable profits in the age of AI and blockchain. The other major sector, renewable energy, is very pertinent for India. The country is the third largest in total energy consumption and comes fourth in renewable power capacity additions. Because it aims to achieve an ambitious goal of 500 gigawatts in renewable capacity by 2030, as well as net-zero carbon emissions by 2070, it is no surprise that India is providing support to clean and renewable energy startups. The Clean Energy International Incubator Centre (CEIIC), a joint venture between the nonprofit Tata Trusts and the Indian government that was launched in 2018, has “incubated 25 startups”, according to the International Energy Agency, supporting those that “could effectuate deep and lasting social and environmental impacts.” Investors can provide support in this sector, knowing that India is committed to delivering a green future with the help of its startup ecosystem. These and other sectors are rich markets for investors, and they are a vital piece of the Viksit Bharat, Prime Minister Modi’s vision to make India a fully developed nation by 2047, the 100th year of its independence. The Prime Minister’s website states that fostering India’s startup ecosystem is “contributing to an environment that encourages innovation, entrepreneurship, and global connectivity, thereby propelling India’s standing as a thriving hub for startups,” a significant step toward its path to complete development. By investing in India’s startups, investors are not only making smart profits but becoming valuable players in the country’s future. A Solid Foundation for Business The Prime Minister’s website also highlights a significant factor in India’s blooming startup economy: an increased ease of business and greater support for startups. The website states, “Since 2016, the government has undertaken over 50 regulatory reforms…facilitating capital raising and reducing compliance burdens within the startup ecosystem.” Such reforms have included greater protections for intellectual property, a streamlined process for procurement, and a three-year exemption for income tax. These have led India to jump to 14th place (from a previous ranking of 63rd) in ease of doing business, according to the 2020 Doing Business study from World Bank Group. The same research placed India in its top 10 improvers for the third consecutive year, a remarkable feat highlighting India’s dedication to its startup ecosystem. In addition to reforms, the government is providing support to startups via government initiatives. There is the previously mentioned Startup India, but other initiatives exist, such as the Credit Guarantee Scheme, which provides credit guarantees for startup loans recognized by the DPIIT. Indian startups are also receiving help from corporate connections and India’s network of accelerators and incubators. Prominent companies are throwing weight behind startups; Facebook has partnered with Startup India to disburse cash grants of $50,000 each to five handpicked startups. Microsoft has thrown its hat in the ring as well, aiding 16 startups through its Venture Accelerator program. These corporate partnerships offer mutual benefits, furnishing startups with essential connections, expanded market reach, innovative opportunities, and access to fresh talent. India also has a wide network of startup incubators and accelerators, which total a combined portfolio of 5,420 companies. Incubators provide startups with solid guidance during the early stages and connect them to a network of angel investors and venture capital funds. Accelerators take on the role of intense mentorship, usually lasting for no more than a year, facilitating rapid growth through education and networking in

Investors who want to target exposure to macroeconomic risks in their equity investments can enhance the robustness of those portfolios with a new strategy that delivers more consistent exposures to macroeconomic factors. That’s the critical takeaway of Graham and Dodd Award of Excellence-winning research from Mikheil Esakia and Felix Goltz. In “Targeting Macroeconomic Exposures in Equity Portfolios: A Firm-Level Measurement,” which earned the 2023 Graham and Dodd Top Award, Esakia and Goltz demonstrate how investors can more precisely target stock portfolios’ economic risk exposure than with strategies that allocate across sectors or equity-style factors. I spoke with Esakia, a senior quantitative research analyst at Scientific Beta and a PhD candidate at EDHEC Business School, for CFA Institute Research and Policy Center for insights on their research findings and to produce an In Practice summary of the study. Below is a lightly edited and condensed transcript of our conversation. CFA Institute Research and Policy Center: What motivated you to conduct the research and author the paper? Mikheil Esakia: Investors would typically use sector and style factor portfolios to manage the macroeconomic risks, and what really wasn’t there in the literature was an explicit attempt to try to improve this type of measure. One of the reasons we don’t see such equity products is because it’s very challenging to make portfolios that out of sample can give you the exposure that you want. What is new or novel about your research? I would say the contribution from our side is to have a focus on measurement of the link between equities and macroeconomic risks that allows you to maintain or predict the sensitivity out of sample in a proper way. The study demonstrates how investors can more precisely target stock portfolios’ economic risk exposure than strategies that allocate across sectors or equity-style factors. In contrast to popular practice, we propose a systematic approach that is transparent and replicable. We also go beyond analyzing sector differences and instead exploit the firm-level heterogeneity of risk exposures. I think it’s novel when it comes to how macro risks are managed in practice. What are the key innovations in the study? The methodology to measure these exposures, including the selection of right macro variables, as well as building portfolios from stock-level rather than allocating across existing portfolios, makes our approach quite unique. Our approach is systematic and is intended to harvest both the long-term equity premium and to protect the portfolio from sudden changes in economic conditions. What is the study’s key finding? It is possible to construct equity portfolios that possess out of sample exposure that facilitate more precise targeting of levels of macroeconomic risk exposure. How does your approach perform? The long-term performance of dedicated macro strategies is very similar to that of the broad market portfolio. The stand-alone returns of eight macro exposure strategies as well as their Sharpe ratios are not significantly different from the market portfolio in the study’s sample. They also do not come with negative alphas in a multifactor model that includes the usual style factors. In what ways can practitioners apply the findings? Investors can use the construction methodology for a variety of applications, including tilting long-only portfolios to target desired macroeconomic sensitivities. They can build equity portfolios that hedge undesired macroeconomic risks with reliable measurement of how different stocks are exposed to macroeconomic risks. To whom do the paper’s findings apply? Who should be interested, and why? Our methodology allows designing equity portfolios that would react to changes in investors’ expectations about economic conditions, such as short-term interest rates, the term spread, the credit spread, and breakeven inflation in portfolios. The approach should help investors whose portfolios may come with substantial exposures to such macroeconomic risks to better manage them. For more on this research, check out the full article, “Targeting Macroeconomic Exposures in Equity Portfolios: A Firm-Level Measurement,” from the Financial Analysts Journal. If you liked this post, don’t forget to subscribe to Enterprising Investor and the CFA Institute Research and Policy Center. All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer. Image credit: ©Getty Images / Kunakorn Rassadornyindee Professional Learning for CFA Institute Members CFA Institute members are empowered to self-determine and self-report professional learning (PL) credits earned, including content on Enterprising Investor. Members can record credits easily using their online PL tracker. source

There is an uncomfortable truth every investor must confront: your capital is never truly safe. The twin threats to long-term wealth are inflation and stock market crashes. Preserving and growing capital requires balancing these two major risks. The Million Dollar Question Imagine receiving $1 million today, along with the responsibility to safeguard and grow it over the next decade. Your primary goal: preserve its real value — and ideally increase it. As a careful steward, how would you allocate this capital? At first glance, the answer seems straightforward: keep it safe, perhaps in a savings account. But on closer inspection, the choice is far from simple. History shows that even the most secure-seeming options can carry hidden risks. Capital at Risk, Always To understand this, let’s turn to history. Using US financial data from January 1900 to December 2024, we find that inflation averaged 3.0% per year.[1] This means hiding cash under the mattress would have been disastrous: over a century, one dollar eroded to less than four cents — a >96% loss in purchasing power. Inflation will eat it slowly and silently. Put it in a savings account? That gives you interest and also feels safer. Over the past century, savings accounts in countries like the United States and other western countries have on average kept pace with inflation. The average short-term saving rate, proxied by short-term US T-bills, averaged 3.0% per year. Averages mask significant losses, however. In the 1940s and early 1950s, during a period of financial repression, interest rates were held artificially low while prices crept higher. This was done to bring down the debt of the governments that were heavily indebted after World War II. Savers witnessed a real loss in purchasing power of more than 40%. Exhibit 1 serves as a wake-up call to savers. It shows the peak-to-bottom real return of US T-bills. It has a maximum lookback window of 10 years (otherwise recovery would be even slower). This is a picture which is counterintuitive. Your savings are not safe when you have a 10-year horizon and correct for inflation. Exhibit 1. Real Drawdowns US T-Bills Sources: McQuarrie (2024) and Robeco As of 2025, a new era of financial repression appears to be underway. The inflation spike of 2022, combined with interest rates lagging behind, caused a real loss in value of nearly 20%. Over time, this negative real rate has compounded. Savers are still down about 10% relative to 2010 levels, and with real interest rates near zero in 2025, catching up will be difficult. The Illusion of Safety These episodes underscore a fundamental truth: even assets that feel safe — like savings accounts — can expose investors to real, lasting losses. That brings us to a broader point: capital is always at risk. Whether you choose to save or invest, you’re making a bet. Inflation and market volatility are ever-present forces. Over longer horizons, the line between saving and investing begins to blur. What feels stable in the short term may fail to preserve value in the long term. Government Bonds: Safer—But Safe Enough? For many investors, the next step beyond saving is government bonds. They typically offer about 1% more yield than a savings account and are often viewed as a safer alternative to equities. But safe from what? Bond investors have faced challenging periods since 1900. After World War I, a postwar economic boom led to rising inflation, which eroded the purchasing power of government bonds issued during the war. These so-called Liberty Bonds came with low fixed interest rates, which quickly became unattractive in the new inflationary environment. The Federal Reserve responded by raising rates, and bond prices fell sharply, amplifying losses during the recession and deflation that followed in the early 1920s. A similar pattern followed World War II: artificially low interest rates and a prolonged bond bear market. The experience of the 1970s is even more familiar. During that “bond winter,” bondholders lost nearly 50% in real terms. That’s not just volatility, that’s wealth destruction. Remember: it takes a 100% gain to recover from a 50% loss. As of 2025, investors are once again in a “bond winter,” facing a cumulative real loss of around 30%, driven by the high inflation of the early 2020s and the subsequent rise in bond yields. Exhibit 2. Real Drawdowns US Bonds Sources: McQuarrie (2024) and Robeco Stocks: Long-Term Gain, Long-Term Pain An investor is always either at an all-time high or in a drawdown. Most stock market investors are aware of this. Stocks can really disappoint in both the short-term and the long-run. The Great Depression wiped out nearly -80% of real wealth invested in the US stock market. Even after a strong recovery, it took many years, even decades, for most investors to fully heal. Not every dip is followed by a swift recovery. Inflation, often overlooked, can further erode real returns, even when stock markets go up in nominal terms.  Exhibit 3 shows that history is full of market corrections of -20% or more. The 21st century alone had three drawdowns of more than -30% in real terms. These huge and frequent losses are a feature of stock markets. Because losses tend to occur suddenly, most investors are well aware of the short-term risks. Exhibit 3. Real Drawdowns US Stock Market Sources: McQuarrie (2024) and Robeco Over the long-term equities deliver returns higher than bonds. Yet over multi-decade horizons, equities can still disappoint. Recent research by Edward McQuarrie suggests that even in the 19th century, stocks did not consistently outperform bonds, challenging the idea that equities are always the safest long-term investment.[2] Comparing Asset Classes We examine real losses — the decline in purchasing power — across four key asset classes: savings accounts, government bonds, gold, and equities. We look at both short-term (one-year) and long-term (10-year) risk using the conditional value at risk (CVar) — a measure of average losses in the worst periods — also known as the first lower partial moment (LPM1). This

Despite their recent struggles, small caps aren’t dead — they’re just misunderstood. After eight consecutive years of underperformance relative to large caps, some investors are ready to write them off entirely, even calling for exclusion from portfolios. But declaring the death of US small-cap equities is premature. History, valuation metrics, and macro conditions suggest a different story – one that points to an approaching comeback. That’s why it’s critical to reassess their role in a modern portfolio — not just through the lens of recent performance, but through the structural forces now working in their favor. In this post, I explore the case for maintaining a strategic allocation to small caps across three dimensions: market cycle timing, interest rate dynamics, and relative value. US small caps still play a critical role in a total portfolio strategy for three key reasons: All cycles end Interest rates are favorable for small caps Small caps are where to find value All Cycles End It is not unusual for small-cap stocks to experience prolonged periods of underperformance relative to large-cap stocks. Prior to the recent cycle, small-cap stocks underperformed large-cap stocks during the periods 1955 to 1962, 1977 to 1978, and 1989 to 2005, all seen in Exhibit 1. On average, the small-cap vs. large-cap cycle lasts about nine years. We are now in the 12th year of the current cycle, which is historically extended.   As trade tensions and geopolitical risks continue to pressure large, globally exposed firms, domestically focused small caps stand to benefit. These dynamics suggest the current cycle of small-cap underperformance may soon give way to a period of relative strength. Source: Bloomberg. Northern Trust Global Asset Allocation Quantitative Research. Data from January 1, 1930–December 31, 2024. Note: 10-year return spread is calculated as rolling 10-year annualized total return spread between Russell 2000 and Russell 1000 Indices. Prior to 1979, return data is based off S&P 500 Index and US Small Caps (bottom decile) total return time series downloaded from New York University. Interest Rates Are Favorable For Small Caps My analysis found a significant positive long-term correlation (0.6) between interest rates and small caps moving up or “migrating” to large caps as their market capitalization increases. In higher interest rate environments, small caps tend to migrate at an increased rate, as seen in Exhibit 2. This is important for two reasons: (1) small caps that migrate tend to be higher performers; and (2) higher migration rates tend to improve overall small-cap Index performance. Unfortunately, small- cap migration rates have declined since 2001, which also coincided with declining small-cap performance. What caused the migration rate to decline? There is a key fundamental backdrop behind this trend: the decade-long easy money policy following the global financial crisis. During this period, the US Federal Reserve set the funds rate near zero between 2008 and 2015 and again from 2020 to 2021. Ultra-low interest rates fueled acquisition activity, and many small-cap firms were acquired by larger public firms or private equity investors rather than migrating into the large-cap space. This trend is reversing – we are observing an uptick in the migration rate in recent years. This trend is likely to continue under the new fed funds rate regime, which is expected to maintain interest rates above 3%, over the next decade. Source: Bloomberg; Congressional Budget Office (CBO). Northern Trust Global Asset Allocation Quantitative Research. Data from January 1, 1990 to December 31, 2024, with projection to 2035. Migration rate is calculated as the percentage of market cap moving from Russell 2000 Index into Russell 1000 Index each quarter. There is no assurance that any estimate, forecast or projection will be realized. Small Caps Are Where to Find Value My analysis indicates small cap stocks are a good place to find value and quality in the equity universe. I compared these factors and historical performance between small caps and the bottom subset of large caps ranked by quality and size, which are relatively close in market capitalization to small caps. Small-cap stocks have exhibited higher quality, as measured by an average return on assets (ROA) of 0.9, versus -2.3% for the bottom quintile of large-cap stocks ranked by ROA since 1990. Small caps had more attractive valuations, with an average price-to-book (P/B) ratio of 1.66, compared to 2.59 for their large-cap counterparts. This analysis runs contrary to the views of some investors, who argue that only the weakest companies remain in the small-cap space, while large-cap indices contain higher-quality companies.  My analysis further disputes this view if we compare performance between small caps and the bottom tercile of large caps, as seen in Exhibit 3. Small caps consistently outperformed the smallest large-cap stocks since 1990.   1-year 3-year 5-year 10-year 35-year Russell 2000 11.5% 1.2% 7.4% 7.8% 8.9% Bottom Tercile of Russell 1000 by Market Cap 5.5% -0.3% 4.9% 5.2% 6.3% Source: Bloomberg, FactSet. Northern Trust Global Asset Allocation Quantitative Research. Return data is from January 1, 1990, to December 31, 2024. Index performance returns do not reflect any management fees, transaction costs or expenses. It is not possible to invest directly in any index. Key Takeaways Small-cap underperformance has historical precedent — but cycles turn. We’re in the 12th year of a small-cap lagging cycle, longer than average. Historical data suggests a reversal is near. Higher interest rates are reigniting migration. With rates expected to stay elevated, small-cap stocks are more likely to graduate to large caps — boosting overall performance potential. Valuation and quality favor small caps. Compared to the weakest segment of large caps, small-cap stocks offer stronger return on assets and more attractive price-to-book ratios, contradicting the view that only low-quality names remain in the space. References [1] Evans, Garry, Xiaoli Tang, Juan Correa-Ossa, Felix-Antoine Vezina-Poirier, Chen Xu, Peter Berezin (2024).  The Great Small Caps Heist:  How Venture Capital and Big Tech Stole America’s best small companies. BCA Research.  [2] Baltussen, Guido, Abhishek Gupta, Daniel Fang (2024). Why Small Caps are Attractive.  Northern Trust White Paper. [3] Fama,