Don’t box me in – is style vs growth still relevant?

Patrick Lee
Vice President, Strategy Distribution, Vaughan Nelson Investment Management

For decades, the investment industry has organized equity portfolios into two dominant categories: growth and value. These labels, popularized largely by the mutual fund industry in the 1990s and later institutionalized by the Russell and S&P indices, provided investors with a simple framework for understanding what they owned and why. “Growth” stocks were those exhibiting above-average earnings expansion, typically with premium valuations; “Value” stocks were identified by low price-to-earnings and price-to-book ratios, often accompanied by dividend payments. The two camps were treated as opposing, binary poles of the investment universe.

While useful in a simpler era, this framework has not kept pace with the structural transformations reshaping global capital markets. The rise of tech-dominant mega caps, the volatility of interest rate cycles, and the proliferation of algorithmic and factor-based investing has rendered the classic growth-versus-value dichotomy not merely imprecise, but misleading.

The origins and logic of style-box investing

The empirical foundation for value investing traces back to Benjamin Graham and David Dodd’s Security Analysis (1934) and was reinforced by decades of academic research, most notably the Fama-French three-factor model. Growth investing, meanwhile, drew from Philip Fisher and later Peter Lynch, who argued that identifying companies with durable competitive advantages and long runways for expansion would reward patient investors far more than bargain-hunting.

The growth-versus-value framework was born of practical necessity. As mutual funds proliferated in the 1980s and 1990s, investors and advisors needed a shorthand for comparing funds and constructing diversified portfolios. Introduced in 1992, Morningstar’s style box became the industry standard, placing funds on a grid of market capitalization and valuation characteristics. The intuition was sound: companies with low current valuations and steady cash flows – the so-called value stocks – would tend to behave differently from high-multiple, rapidly expanding growth companies.

Both philosophies had merit and could coexist because they described genuinely distinct types of businesses operating in a relatively stable, industry-defined competitive landscape.

Why the framework has broken down

The most fundamental challenge to the classic framework is the emergence of companies that are simultaneously the fastest-growing, most profitable, and most cash-generative entities – ‘mature growth’ companies. This cohort of companies – formerly FAANG and more recently dubbed the “Magnificent 7” – often defy categorization.

Take, for example, Alphabet (née Google), a company that generates more than $100 billion in annual free cash flow, issues investment-grade bonds at sub-market rates, pays dividends, and repurchases stock aggressively – all behaviors traditionally associated with mature value companies – while also growing its core cloud and AI businesses at double-digit annual rates. Forcing such a company into either a “growth” or “value” box produces a distorted picture of its core fundamental attributes.

Perhaps unsurprisingly, this has led to overlap between index constituents. As of May 2026, there were 248 companies listed in both the Russell 1000 Value and Russell 1000 Growth index. If nearly 30% of the Russell 1000 Value and 60% of the Russell 1000 Growth is in this “mixed” camp, there’s clearly confusion on category constitution. Perhaps we should question category relevance, as well.

The AI disruption variable

No force has more thoroughly scrambled traditional categorization than artificial intelligence. AI is not merely a tech sector phenomenon; it is a cross-industry disruptor. AI rapidly transforms a stable, cash-generating “value” business into an obsolescence story. Alternatively, it can supercharge the productivity (and margins) of a company already classified as “classic” growth.

Nvidia is an instructive example: initially a cyclical semiconductor manufacturer – a borderline value candidate during downturns – Nvidia became the most valuable company in the world in a span of two years, driven by AI demand that no conventional valuation framework had predicted nor priced. Meanwhile, traditional media, software and data companies – many that recently seemed to be reliable value investments – suddenly faced existential threats from large language models. In this environment, the growth-versus-value labels tell investors virtually nothing useful about future return, risk, or long-term viability.

Should we replace the style box?

Abandoning the growth-versus-value framework does not mean abandoning analytical discipline. It means replacing a blunt two-category system with a richer set of variables that better reflect how value is created and destroyed in the modern economy. This adaptation also helps diminish the style-driven, mean-reversion performance whipsaw many have experienced in the post-COVID years.

Professional investors need latitude to organize their approach around business quality and durability. Attractive return on invested capital and return on equity (ROIC & ROE); strong free cash flow conversion; resilient competitive moats; management teams who allocate future capital to return-accretive projects: these are hallmarks of successful companies.

Meanwhile, factor analysis helps pinpoint exposure to momentum, profitability, and volatility drivers, among others. A keen focus on disruption risk – a qualitative, yet increasingly quantifiable assessment of a business’s exposure to tech shifts, regulatory change, or structural demand swings – helps managers attain more consistent and reliable predictive power than simply a “growth” vs. “value” paradigm. These are crucial research inputs in the modern economy.

Conclusion

The growth/value framework served a generation of investors well. It provided structure, enabled comparison, and introduced millions of retail participants to the concept of assessing “price vs. anticipated return.” But frameworks are tools, not truths, and tools must evolve as the work they perform changes.

In today’s economy, the most consequential investment decisions cannot be made through a binary lens. Whether to own AI infrastructure, how to evaluate and price cyclical industries like Energy, whether a dividend-paying technology giant belongs in an income portfolio or growth portfolio – these are nuanced and complex topics. The companies that generate the greatest wealth over the next decade will be those who combine growth, profitability, and resilience in ways that the classic framework was never designed to capture.

Investors who cling to the style box do so at their own peril. The future belongs not to those who ask, “is this a growth stock or a value stock?” but rather “is this a superior business trading at a sensible price? And can this company withstand the inevitable disruptions ahead?”