Measuring Intangible Capital with Market Prices

Author:

Ewens Michael12ORCID,Peters Ryan H.3,Wang Sean4ORCID

Affiliation:

1. Department of Finance, Columbia Business School, New York, New York 10027;

2. National Bureau of Economic Research, Cambridge, Massachusetts 02138;

3. Department of Finance, Tulane University, New Orleans, Louisiana 70118;

4. Department of Accounting, Southern Methodist University, Dallas, Texas 75275

Abstract

Accounting standards prohibit internally created knowledge and organizational capital from being disclosed on firm balance sheets. As a result, balance sheets exhibit downward biases that have become exacerbated by increasing levels of intangible investments. To offset these biases, researchers must estimate the value of these off-balance sheet intangibles by capitalizing prior flows of research and development (R&D) and selling, general, and administrative (SG&A). In doing so, a set of capitalization parameters must be assumed (i.e., the R&D depreciation rate and the fraction of SG&A that represents a long-lived asset). We estimate these parameters using market prices from firm exits and use them to capitalize intangibles for a comprehensive panel of firms from 1978 to 2017. We then use a series of validation tests to examine the performance of our intangible capital stocks versus those developed from commonly used parameters. On average, our estimates of intangible capital are 15% smaller than estimates from status quo parameters while exhibiting larger variation across industry. Intangible capital stocks derived from exit price parameters outperform existing measures when explaining market enterprise values and identifying human capital risk. Adjusting book values with exit-based intangible capital stocks markedly attenuates well-documented biases in market-to-book and return on equity ratios while increasing the precision of the high-minus-low asset pricing factor. We conclude that our capitalization parameters create intangible stocks that perform equal to or better than status quo measures in various applications. This paper was accepted by Victoria Ivashina, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.02058 .

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

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