Acquisition Prices and the Measurement of Intangible Capital

Investments by firms into intangible capital play a pivotal role in a firm’s strategy and value in today’s economy. However, because the benefits to these investments can be uncertain, current financial statements under U.S. GAAP mandate that firms record these investments as operating expenses, despite the long-term benefits these investments provide. The result is that the book value of equity and earnings may be misstated, leading to incorrect assessments based on a firm’s return on equity and potential misvaluations when judgments are based on multiples of book or earnings.1

To overcome these biases in corporate disclosures, academics and industry professionals have capitalized these expenditures to create intangible stocks and undo the bias to the accounting statements. To do this, they make assumptions regarding the depreciation parameters of Research and Development expenditures (R&D) that are capitalized into stocks of knowledge capital, as well as the fraction of Selling, General and Administrative expenses (SG&A) that are capitalized into long-lived organizational capital. Unfortunately, until now the parameters from which these estimates are based have had unknown origin or suffered from a lack of within-industry variation.

Our paper solves both issues by using market prices to estimate these capitalization parameters. Specifically, we focus on corporate investment activities that reveal the size of intangible assets: acquisitions. Since 2004, over 85% of public-to-public acquisitions recognize the purchase of either identifiable intangibles or goodwill. Acquisitions thus provide a direct valuation of off-balance sheet intangible assets, and allow us to use empirical data to justify our parameter estimates. We validate these new parameter estimates for depreciation and organizational capital in a variety of settings related to human capital, patent values, the investment-Q relationship, and the accounting-stock price relationship.

Our dataset spans 1996-2017 and comprises a large fraction of U.S. public firm acquisitions of U.S. public targets in SDC’s M&A database. We hand-collect over 1,500 acquisition events and retrieve two key numbers from the acquisition filings: goodwill and identifiable intangible asset allocations. Combined with the acquired firm’s trailing history of R&D and SG&A expenditures, we estimate the parameters for a depreciation model for intangible assets. The capitalization model closely follows existing work such as Corrado and Hulten (2014) and Peters and Taylor (2017), where the histories of R&D and SG&A are accumulated with separate depreciation rates to estimate the stock of knowledge and organizational capital, respectively. Our approach estimates these parameters using the acquisition price of intangibles, measured as the sum of goodwill and identifiable intangible assets. The new estimates imply an average 24% annual depreciation rate for knowledge capital, which is larger than the 15% benchmark rate commonly used in the empirical literature. Additionally, we find that an average of 22% of SG&A expenditures represent long-lived investments in organizational capital. This percentage varies dramatically across industries, from 12% in the consumer industry to 49% in the healthcare industry.

Our data shows that the sum of knowledge and organizational capital and scaled by total assets – intangible asset intensity – has increased from 35% in 1980 to 60% in 2016. Within industry, we find that the 60% intangible asset intensity in 2016 varies widely across industry, from as high as 80% in healthcare to as low as 40% in manufacturing. Organizational capital comprises the majority (over 80%) of all intangible capital across firm-years.

How well do our new parameter estimates perform when compared to previous approaches?  We first validate whether our new measure of organizational capital captures differences in human capital across firms. Following Eisfeldt and Papanikolaou (2013), we examine whether firms with high levels of organizational capital are more likely to disclose the risk of losing of key talent in their filings. To do so, we parse the management discussions of risk in over one hundred thousand 10-K filings from 2002-2017 and ask whether there is a mention of “personnel" or “key talent." Our measure of organizational capital outperforms the existing measures in all years: top quantile organizational capital firms are significantly more likely to mention these human capital risks than the bottom quantile. In contrast, the current method of capitalizing SG&A only produces significant differences across firms in 35% of the sample years.

In a similar vein to our first validation exercise, we next examine whether the new estimates of knowledge capital explain the Kogan, Papanikolaou, Seru, and Stoffman (2017) measure of patent value.  The authors provide such a measure using market reactions to patent grants. Our estimates of firm knowledge capital stocks explain a major part of patent valuations within and across firms. To our knowledge, this is one of the first direct measurements of intangible investment returns.

The third validation exercise takes the implied capital stocks to the expansive literature that tests dynamic investment models through the lens of the investment-q relation. For example, Peters and Taylor (2017), using intangible capital accumulation parameters derived from BEA-NSF macroeconomic data, show that incorporating measures of intangible capital stocks strengthen this relation. We find that investment-q regressions using our parameters perform similarly to Peters and Taylor (2017) capital stock measures based on BEA parameter estimates.

The final validation exercise confirms that the implied book value of intangible assets has meaningful explanatory power beyond the standard measures usually included in share price regressions used in accounting studies on financial statement informativeness such as in Ohlson (1995). Cross-sectional regressions of firm prices on book equity and earnings have a better fit when we include adjustments implied by our estimated stocks and flows of intangible assets.

In summary, we exploit public-to-public acquisitions – where market prices of intangibles are revealed during the process of the transaction.  These prices allow us to econometrically justify key parameters in models of intangible asset investment and accumulation used by economists, accountants and policy makers. The improved and refined parameter estimates are important in today’s economy for at least two reasons. First, any improvements to the depreciation rates of knowledge and organizational capital give us more accurate insights about the relative role of intangibles in the economy, while the rates themselves are crucial inputs for estimates of returns to intangible investment. Second, existing estimates of these depreciation rates lack within-industry variation and many parameter estimates are simply ad-hoc. The resulting estimates of intangible assets are thus difficult to compare, and it can be challenging to diagnose the key structural assumptions or data inputs. In contrast, our transparent, publicly-available data invites a methodology that rests on few structural assumptions.

References:

Corrado, C. A., & Hulten, C. R. (2014). Innovation accounting. In Measuring Economic Sustainability and Progress (pp. 595-628). University of Chicago Press.
Eisfeldt, A. L., & Papanikolaou, D. (2013). Organization capital and the cross‐section of expected returns. The Journal of Finance, 68(4), 1365-1406.
Kogan, L., Papanikolaou, D., Seru, A., & Stoffman, N. (2017). Technological innovation, resource allocation, and growth. The Quarterly Journal of Economics, 132(2), 665-712.
Peters, R. H., & Taylor, L. A. (2017). Intangible capital and the investment-q relation. Journal of Financial Economics, 123(2), 251-272.

1. The bias to earnings is most prominent when the firm is rapidly growing.