Intangible Assets & the Value Premium: Evidence From U.S Industries

Abstract

The value strategy, an investment strategy focused on purchasing undervalued stocks based on fundamental metrics, has faced prolonged underperformance over the past decade. This thesis investigates whether incorporating intangible assets into the book-to-market ratio can improve the value premium. Specifically, it aimed to capture an intangible value premium across different U.S. industries and integrate an intangible-adjusted high-minus-low HML INT factor into the Fama & French (2015) five-factor model framework. To conduct the analysis, a sample of U.S.-domiciled stocks was investigated in the time period 1999–2024.The results indicate that accurately defining and measuring intangible assets requires careful calibration of capitalization rates for R&D and SGA expenses, tailored to each industry, and that a holistic method combining multiple data sources and approaches could enhance the precision of these estimations. Incorporating HML INT into the five-factor model partially mitigated the negative value premium but did not fully reverse it. Overall, the factor models struggled to capture any significant alpha and explain the return variation of the sample investigated. An investment strategy that went long HML INT and short the traditional HML generated significantly positive returns in most industries, along with a robust risk-adjusted return in the Capital Goods and Pharmaceutical industries. The findings of this thesis support recent critiques of the five-factor model's relevance in modern asset pricing. It establishes the presence of an intangible value premium and the possible returns it can generate. This thesis further contributes to the literature by highlighting the importance of integrative thinking and industry-specific calibration when quantifying intangible assets, constructing factor models, and implementing value investment strategies.