Unlocking Hidden Value: How Corporate Language Reveals the Future of Intangible Investment

The article “Unlocking Hidden Value: How Corporate Language Reveals the Future of Intangible Investment” was originally published on Alpha Architect blog.

Intangible assets—things like brand reputation, proprietary knowledge, and organizational capabilities—have become more valuable than physical factories and equipment with many studies estimating that intangibles now account for roughly one third to one half of corporate capital. However, measuring these assets remains one of finance’s great challenges. Andrea Eisfeldt, Barney Hartman-Glaser, Edward Kim, and Ki Beom Lee, authors of the November 2025 study “Intangible Intensity,” tackled this problem by using artificial intelligence to decode what companies are really telling us about their intangible investments.

What the Researchers Examined

The research team developed a novel approach to measure intangible investment by analyzing the language companies use in their annual 10-K filings. Rather than relying solely on accounting data, which often obscures or incompletely captures intangible spending, they built a sophisticated text-processing framework that identifies when companies discuss investments in knowledge, customer relationships, and organizational capabilities.

Their method combines modern AI tools with traditional transparency. They used a large language model to isolate relevant passages from regulatory filings, then employed advanced embedding techniques to identify patterns across roughly 10,000 frequently-used phrases. The result is an “intangible intensity” score that captures what proportion of a company’s narrative focuses on intangible-related activities. Their data covered nearly the entire universe of public firms from 2002–2023.

Critically, they broke this overall score into three distinct components: knowledge capital (R&D, innovation, technical expertise), customer capital (marketing, brand building, customer acquisition), and organization capital (internal processes, management systems, operational capabilities).

Key Findings

The results reveal striking patterns about how companies invest and grow:

High intangible intensity signals a distinct growth phase. Companies with elevated intangible intensity scores are smaller, younger, and deeply engaged in investment activities, with high R&D and SG&A intensity, substantial liquidity needs, and elevated labor costs—but command significantly higher valuations. Their average Tobin’s Q (a measure of market value relative to assets) reaches 3.15 compared to just 1.34 for low-intensity firms. These firms resemble classic high-growth firms that have yet to reach sustained profitability. These firms also rely more heavily on liquidity (measured as cash-to-assets) to support sustained “cash burn.” While valuation ratios indicate that market expectations of future free cash flows are high, profitability (defined as the ratio of income before extraordinary items to book value of assets) exhibits the opposite relationship across quintiles—consistent with high-intensity firms entering a high-growth, high-expenditure phase, moving from quintile 1 (the lowest) to quintile 5 (the highest), profitability deteriorated from 0.059% to –0.258%. This pattern reflects the asymmetry in accounting treatment: tangible investment is capitalized, while intangible investment is expensed, so book assets and earnings both understate the true scale of investment for high-intangible firms.

Each type of intangible capital tells a different story. The three components map to economically distinct firm types.

• Knowledge-intensive firms resemble classic high-growth technology companies with substantial R&D spending and highly skilled workforces. Knowledge scores are highest for technology and platform firms such as Microsoft, Alphabet, Nvidia, Tesla, and Meta, which also exhibit elevated R&D to sales ratios.
• Customer-intensive firms look more like mature, profitable businesses focused on maintaining market position through advertising and customer relationships—profitability is highest for quintile 5 firms (high customer intensity), implying that customer-intensive firms generate steady earnings from an established customer base. In addition, their spending mix differs sharply with the R&D to sales ratio being lowest for Q5 firms, while the advertising to sales ratio is highest. Customer scores are highest for consumer-facing franchises such as Procter & Gamble, Coca-Cola, and PepsiCo, where advertising to sales ratios are large and sustained.
• Organization-intensive firms are large, operationally complex incumbents that rely on scale and established processes rather than frontier innovation—their physical capital to assets ratio and their use of leverage increases. They also exhibit more modest valuations. Organization scores are relatively high for operationally intensive retailers such as Walmart and Home Depot, which have low R&D and modest customer scores but rely heavily on logistics and scale.

Corporate language predicts future performance beyond accounting numbers. Perhaps most striking, the intangible intensity measure predicts future profitability even after controlling for traditional metrics like the ratio of selling, general, and administrative expenses to sales. This suggests managers embed forward-looking signals about their investment strategy in their narrative disclosures—information that gets lost in aggregated financial statements.

AI as a Game-Changer for Valuation Analysis

This research exemplifies a broader transformation in how artificial intelligence is reshaping financial analysis. Traditional valuation methods relied on structured data—balance sheets, income statements, and standardized ratios—leaving vast amounts of qualitative information untapped. Modern AI tools, particularly large language models and natural language processing techniques, can systematically analyze the narrative portions of corporate filings that human analysts could never process at scale. By extracting meaning from thousands of pages of text across thousands of companies, AI reveals patterns and signals that would otherwise remain hidden in plain sight. This capability extends beyond intangible asset measurement to other critical areas: detecting subtle shifts in management tone, identifying emerging risks before they hit financial statements, and comparing strategy execution across competitors. The implication is profound: the unstructured data that companies disclose may contain some of the most valuable predictive information available to investors. As AI tools become more sophisticated and accessible, the competitive advantage will increasingly belong to those who can harness both quantitative and qualitative data in their valuation frameworks.

Their findings led the authors to conclude:

“These findings demonstrate that text-based measures provide an economically meaningful and empirically powerful lens through which to study intangible investment, opening new possibilities for research on firm growth and valuation in settings where accounting data are incomplete or silent.”

Investor Takeaways

Look beyond the income statement. When evaluating growth companies, current profitability can be misleading. Firms heavily investing in intangibles will show depressed earnings because accounting rules expense these investments rather than capitalizing them. The research confirms that high intangible intensity today predicts lower near-term profitability—but potentially stronger long-term prospects reflected in market valuations.

Understand the type of intangible story. Not all intangible investment is created equal. A company emphasizing knowledge capital is making a different bet than one focused on customer acquisition or organizational scale. Knowledge-intensive firms require patience and tolerance for volatility as they pursue innovation. Customer-intensive firms may offer more stable returns from established market positions. Organization-intensive firms often represent mature businesses optimizing operations rather than pursuing transformative growth.

Pay attention to the narrative, not just the numbers. The finding that textual disclosures contain predictive information absent from accounting ratios has important implications. Investors who carefully read management’s discussion of their cost drivers and investment priorities may gain insights that aren’t captured in standard financial metrics. When management devotes substantial discussion to building capabilities, they’re signaling strategic priorities that traditional ratios may obscure.

Consider coverage gaps in traditional metrics. The research highlights that R&D and advertising expenses—the traditional proxies for knowledge and customer capital—are only reported by 70% and 40% of firms respectively. Organization capital has no accounting proxy at all. This text-based approach provides a more comprehensive view across the full universe of public companies, helping investors avoid systematic blind spots.

Reassess valuation frameworks for intangible-intensive firms. Companies with high intangible intensity trade at much higher market-to-book ratios, but this may be justified given their investment in future productive capacity. The research suggests the market appropriately recognizes this distinction, with intangible-adjusted book-to-market ratios showing more modest premiums.

The Bottom Line

As the economy continues its shift toward intangible assets, investors need better tools to identify and value these investments. This research demonstrates that companies telegraph their intangible investment strategies through their narrative disclosures in ways that complement and sometimes surpass traditional financial metrics.

For investors, the message is clear: in an intangible economy, reading between the lines isn’t just useful—it’s essential.

Implications for Market Efficiency

The democratization of AI-powered textual analysis has important implications for market efficiency. Historically, deep analysis of narrative disclosures was the province of well-resourced institutional investors with teams of analysts capable of reading thousands of pages of filings. This informational advantage allowed sophisticated investors to identify intangible investment strategies that less-equipped market participants might miss. As AI tools become more accessible and widespread, this information asymmetry should diminish. When more investors can systematically extract and interpret the signals embedded in corporate language, prices should more rapidly and accurately reflect companies’ true intangible investment profiles. This doesn’t eliminate the need for skilled analysis—interpreting what intangible intensity means for a specific company still requires judgment—but it does level the playing field for information processing. The result should be markets that more efficiently price the intangible-intensive businesses that increasingly dominate the modern economy, reducing the gap between a company’s disclosed strategy and its market valuation.

Larry Swedroe is the author or co-author of 18 books on investing, including his latest Enrich Your Future.