To generalize or specialize?
From the vantage point of 11 June 1947, when four financial analyst societies joined forces to create the federation now known as CFA Institute, that question may have had a different answer than it does for investment professionals today.
Concentrated in New York and London, finance was hardly the world-spanning sector of 2022. Frankfurt, Hong Kong SAR, Mumbai, Shanghai, Singapore, Tokyo, Toronto — such cities were a long way from emerging as the global investment hubs they are now.
Of course, the differences between finance then and now aren’t just geographical. The financial theories, asset classes, products, and technologies we take for granted — the capital asset pricing model (CAPM), private equity, index funds, online trading, etc. — were still years away or at least in their infancy in 1947. So, while specialization was an option, generalization was the order of the day.
But what about today? Seventy-five years after CFA Institute was established, how should investment professionals and aspiring investment professionals approach the choice?
The Case for Specialists
Adam Smith describes the benefits of specialization in The Wealth of Nations. He attributes “[t]he greatest improvements of the productive powers of labor, and the greater part of the skill, dexterity, and judgement” to “the effects of the division of labor.” Labor economists generally agree with this assessment: Specialization will continue to increase because it is in all our interests.
The modern-day investment profession demonstrates how this process can transform an industry. When Warren Buffett started his investment partnership in the 1950s, he was a one-person team with a limited investment universe. This was the common experience for the founders of CFA Institute and the investors of their era. The institutionalization of the investment business and the rise of various types of mutual funds and investment trusts in the 1970s initiated an era of more formal specialization.
Today, global multi-asset managers may invest in hundreds if not thousands of (underlying) investment instruments across a dozen or more asset classes in scores of countries and markets around the world. Specialization has become a necessity rather than an option.
If we measured professional investors’ degree of specialization on a continuum, those in the 1940s and 1950s were at or near zero; most were generalists, and investing was arguably more art than science. As the profession has evolved in the decades since, so too have the skill needs.
In modern finance, most industry roles now involve some form of specialization. Investment professionals are assumed to have domain expertise, whether in an asset class, industry, or geography, or otherwise possess role-specific knowledge so that they can, for example, differentiate between a European REIT analyst and an Asian emerging market bond portfolio manager.
Over time, as Smith’s division of labor theory predicted, the optimal skills mix in finance has moved rightward from the zero-specialization end of the continuum. Four investment industry factors have helped propel that shift:
In recent decades, major asset owners, financial advisers, and retail brokers, with their model portfolios, have increased their international allocations. When Dennis Stattman, CFA, proposed a 40% international allocation for the Merrill Global Asset Allocation portfolio in the late 1980s, it was a revolutionary idea. Such an allocation to international stocks and bonds is far more common for US investors today as well as among international investors given the more limited size of their home markets.
New markets require more distinctive knowledge. For example, access to the onshore renminbi (RMB) bond market demands expertise in local market conventions and dynamics, whether policy orientation or industry and company fundamentals. It also requires the ability to communicate that knowledge to a global investor base. Such attributes are often difficult to find.
2. New Asset Classes and Products
Alternatives may be the most significant “new” asset class to emerge in the last 75 years. The endowment model pioneered by Yale’s long-time chief investment officer David Swensen was key to their ascent. His approach included a significant allocation to less-liquid assets like private equity, real estate, and absolute return strategies.
Again, an investment team needs focused expertise if it is going to access these assets. For example, private equity investors need to understand deal structures and term sheets as well as the industries and companies they plan to invest in.
This proliferation of new products further incentivizes specialization. Such innovations as exchange-traded funds (ETFs) have been investor-friendly, lowering fund management fees and improving liquidity for investors. Others — collateralized debt obligations (CDOs), for example — may have been ill conceived or misused. But whatever their strengths or faults, they require more than a generalist’s knowledge to master.
3. Industry Concentration
The asset management sector has consolidated over the years. That trend isn’t going away. The Willis Towers Watson 2021 report found that the 20 largest asset managers controlled 44% of the industry’s assets under management (AUM), compared with only 29% in 1995. As firms grow, their product lines often expand as well. That requires new and more distinctive talent to manage. The size of these firms also helps provide the resources to support an army of specialists.
The fund industry’s maturity in a market and its overall AUM correlates with its degree of concentration. The US fund industry is more concentrated than Europe’s, which is more concentrated than the Asia-Pacific region’s.
4. Quantitative Investing
Quants began to join the investment profession en masse in the 1980s. They apply supreme mathematical rigor to price derivatives, measure and forecast risk, and even predict investment returns.
The Black–Scholes model was a harbinger of the quant revolution. According to Myron Scholes, who developed the model with Fischer Black, quant investing requires much more specialized training in mathematics, science, and statistics than business majors received at the time. But no matter the depth of the underlying skills, quant investing is hardly an error-free discipline.
Overall, the more factors that an investment team must consider, the more it will need team members with distinctive expertise, both at present and in the future.
The Case for Generalists
Despite specialization’s allure, professionals on an investment team must collaborate with fellow team members as well as other stakeholders to be effective individually and collectively. There are still many generalists in the investment business, and they are often integral to the investment process.
Generalists dominate boutique shops where broad skill differentiation may not be an option. Buffett may have built up a formidable investment empire, but many small investment managers are still solo operations. Given the cost of managing independent investment shops today, they are likely to further dwindle in number, but some will survive and continue to deliver idiosyncratic value to their investor base.
Of course, those who endure are not “generalists without specialization.” Boutique firms tend to be unique in some way that defines their value proposition.
In extreme cases, if specialists on a team fail to collaborate, generalists have to step in. Our field research on artificial intelligence (AI) and big data adoption projects at financial institutions demonstrates that generalists often coordinate and lead the efforts of investment and data science specialists who come from entirely different educational backgrounds. Fostering their collaboration can be a tremendous challenge. Those generalists with investment and data science skills can straddle both sides and thus have exceptional value. They are very “special” even if they are classified as generalists in this context.
Of course, investment and data science specialists also play critical roles: They’re the ones who get the work done. The generalists facilitate that work and bridge the gap between their specialties. Hence, both roles are integral to the AI and data science adoption process.
The different modes of specialization in today’s investment management industry have myriad implications for whether generalists or specialists will be most in demand. To acquire the optimal skillset for their defined roles on an investment team, investment professionals must understand where their team operates on the specialization spectrum now and where it will operate in the future.
Academic researchers largely agree with this assessment. For example, as Florenta Teodoridis, Michael Bikard, and Keyvan Vakili write in Harvard Business Review, “. . . generalists appear to be relatively successful as long as the pace of change is not too rapid, but their productivity decreases when the pace of change increases [and] specialists appear to perform better when the pace of change accelerates.”
However, we place more emphasis on the development stage. In an emerging sector, generalists are more in demand. The same is true when it comes to AI and big data adoption in investing today. But as the sophistication and the pace of change increases over time, so too does the demand for specialists.
And that’s something for future generations of investment professionals as well as those of us working in the field today to keep in mind. Accelerating change has been the story of the investment industry in the years since CFA Institute was founded. And it’s likely to be the story of the next 75 as well.
The above was adapted from the forthcoming CFA Institute Report The Future of Skills and Learning.
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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.
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Larry Cao, CFA, senior director of industry research, CFA Institute, conducts original research with a focus on the investment industry trends and investment expertise. His current research interests include multi-asset strategies and FinTech (including AI, big data, and blockchain). He has led the development of such popular publications as FinTech 2017: China, Asia and Beyond, FinTech 2018: The Asia Pacific Edition, Multi-Asset Strategies: The Future of Investment Management and AI Pioneers in Investment management. He is also a frequent speaker at industry conferences on these topics. During his time in Boston pursuing graduate studies at Harvard and as a visiting scholar at MIT, he also co-authored a research paper with Nobel laureate Franco Modigliani that was published in the Journal of Economic Literature by American Economic Association. Larry has more than 20 years of experience in the investment industry. Prior to joining CFA Institute, Larry worked at HSBC as senior manager for the Asia Pacific region. He started his career at the People’s Bank of China as a USD fixed-income portfolio manager. He also worked for US asset managers Munder Capital Management, managing US and international equity portfolios, and Morningstar/Ibbotson Associates, managing multi-asset investment programs for a global financial institution clientele. Larry has been interviewed by a wide range of business media, such as Bloomberg, CNN, the Financial Times, South China Morning Post and the Wall Street Journal.