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Review of Accounting Studies

23.11.2023

Differences in the value relevance of identifiable intangible assets

verfasst von: Zachary King, Thomas J. Linsmeier, Daniel D. Wangerin

Erschienen in: Review of Accounting Studies

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Abstract

Motivated by investor criticisms of current accounting standards, this study investigates whether differences exist in how acquired identifiable intangible assets relate to investors’ expectations about the entity’s cash flow prospects. Some investors assert that all acquired intangibles should be subsumed within goodwill, while others prefer separate recognition of identifiable intangibles only when they are strategically important sources of future cash flows. Still other investors call for separate recognition from goodwill only when identifiable intangibles are separable from the business, have defined useful lives, and have identifiable revenue streams (i.e., “wasting” intangibles). Consistent with some investor views, we find cross-sectional variation in the value relevance of identifiable intangibles based on differences in underlying asset characteristics. Our primary findings suggest that strategically important and wasting intangibles provide information different from that provided by goodwill. These findings inform standard setters as they evaluate recognition and disclosure alternatives for identifiable intangible assets.

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Fußnoten
1
For that asset class, we fail to find consistent evidence that coefficients differ from zero, which suggests expensing rather than capitalizing those intangibles.
 
2
For example, see Aboody and Lev (1998), Barth et al. (1998), Barth and Clinch (1998), Chalmers et al. (2011), Bauman and Shaw (2018), Kallapur and Kwan (2004), Kimbrough (2007), Sahut et al. (2011), Tsoligkas and Tsalavoutas (2011), and Wangerin (2019).
 
3
Investors’ dependence on information sources beyond accounting is increasing with the growing availability of timelier sources of forward-looking information. Still investors have noted that does not negate the value of accounting information if it confirms the information used to set security prices (Papa 2018). If investors use other timelier sources of information, then the confirmatory value of financial reporting information increases relative to predictive value. Investors also have reported that this confirmatory value enables them to have governance over other timelier sources of information by enabling assessments of its reliability and consistency across various management communication platforms both non-GAAP and GAAP (Papa 2018). Therefore it is not necessary for financial statement disclosures to have a direct causal effect on investors’ judgments (i.e., provide decision-relevant information) to conclude accounting information is relevant.
 
4
This research relies on extensive evidence in the accounting and finance literatures that equity markets efficiently impound publicly available information in prices. Prior research shows that, when potential market inefficiencies exist, any bias to parameter estimates in price-level regressions is not economically significant. Therefore those estimates support reliable inferences (Aboody et al. 2002; Barth 2007).
 
5
Prior research also operationalizes the value relevance of asset values in alternative ways, including studies that associate recognized asset values with future income (e.g., Aboody and Lev 1998) and operating cash flows (e.g., Deng and Lev 2006). However, it is important to recognize that there is significant heterogeneity in the pattern over which future cash flows and earnings arise from different types of intangible assets. For example, Clarke (1976) estimates the benefits to marketing-related intangibles (e.g., tradenames, brands, etc.) are realized between three and 15 months. In contrast, Healy et al. (2002) propose that a commercialized pharmaceutical can generate cash inflows while under patent for upwards of 10 years.
 
6
Failing to find differences between coefficients on certain types of intangibles and goodwill also could be due to a lack of statistical power.
 
7
According to the measurement guidance in SFAS 157 (FASB 2006; codified and currently active in ASC Topic 820), fair values for identifiable intangibles are generally Level 2 or Level 3 estimates because there are rarely identical assets traded in active markets. Most fair value estimates for intangible assets use Level 3 inputs, but there are certain cases where transactions involving similar (but not identical) assets are observable. In these cases, Level 2 inputs are most commonly available for internet domain names, FCC licenses, and carbon emission rights (AICPA 2012).
 
8
We use the term “outcome uncertainty” to be consistent with the IASB’s Conceptual Framework, which defines it as uncertainty arising from unknown amounts, timing, or both of future cash inflows and outflows from an asset or liability (IASB 2018). This differs from measurement uncertainty in the IASB’s Conceptual Framework, which is defined as arising when prices are unobservable in an active market and therefore must be estimated. However, to be consistent with how measurement uncertainty often is described in prior research, outcome uncertainty also could be viewed to be an additional dimension of measurement uncertainty.
 
9
Easton and Sommers (2003) show that biased coefficients in price-levels regressions can arise due to the effects of outliers concentrated among large firms. Therefore we also follow the recommendation of Easton and Sommers (2003) to use market capitalization as an alternative scalar in price-level regressions (untabulated). Regression diagnostic statistics indicate outliers are concentrated among the largest firms in our sample. This suggests that scaling by market capitalization (rather than shares outstanding) within our sample of acquirers also is not sufficient to produce unbiased coefficients.
 
10
Leone et al. (2019) recommend examining studentized residual and DFIT statistics to identify potentially influential observations in the data. Such an examination using equation (1) (described in the next section) identifies 65 potentially influential observations in our data with absolute studentized residuals greater than 2.0 and 93 potentially influential observations in our data with absolute DFIT statistics greater than 2*(p/n)1/2, where p is the number of parameters (24) and n is the number of observations (2,560). Robust regression helps ensure that our results are not driven by outliers.
 
11
We also validate our classification of wasting and organically replaced intangibles by regressing changes in future sales and future advertising expenses on these variables. We expect that both wasting and organically replaced intangibles will be associated with future sales but only organically replaced intangibles will be associated with future advertising expenses because only organically replaced intangibles require future expenditures to maintain or enhance their value. In untabulated validation tests, we find results that confirm our expectations.
 
12
To provide evidence on whether the recent financial crisis affects inferences, we re-ran our primary analyses on firm-years that Compustat assigns an FYEAR of 2010 or higher. Inferences are unchanged in this analysis (untabulated).
 
13
There are 633 firm-year observations reporting negative earnings in the acquisition year, which represents approximately 25% of our sample. Therefore we re-estimated our primary tests allowing the coefficient on earnings to vary for loss firms. Inferences are unchanged.
 
14
When using the industry-based (asset-type) classification, there are 708 (829) observations with no strategically important intangibles and 508 (496) observations with more than one strategically important intangible (untabulated).
 
15
We do not report adjusted R2 statistics because they are not meaningful goodness-of-fit measures in robust regressions (Leone et al. 2019). When estimating our models in weighted least squares using weights obtained from robust regressions, we obtain adjusted R2 statistics of 0.66 or better (untabulated).
 
16
We show, in Appendix 2, that the identifiable intangibles management discloses as strategic are the same as those identified by our measures, suggesting both approaches may consistently identify the same assets.
 
17
We omit industry fixed effects because our changes specifications examine first differences of all dependent and independent variables of interest. Therefore factors that do not vary over time, such as industry, are implicitly controlled for (Greene 2012, pp. 355–356; Wooldridge 2013, pp. 459–474).
 
18
We limit our analyses to the strategically important cross-section because our previous cross-sectional results indicate that nonstrategic organically replaced intangibles are not consistently associated with equity prices and, therefore not relevant to investors.
 
19
Prior to the release of the purchase price allocation disclosure, we expect that some investors may have formed expectations about the amounts allocated to intangibles based on other publicly available information. However, without some observable measure of such investor expectations, it is not possible to determine whether a particular recognized amount should have a positive or negative effect on signed returns over short windows surrounding the release of purchase price allocations.
 
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Metadaten
Titel
Differences in the value relevance of identifiable intangible assets
verfasst von
Zachary King
Thomas J. Linsmeier
Daniel D. Wangerin
Publikationsdatum
23.11.2023
Verlag
Springer US
Erschienen in
Review of Accounting Studies
Print ISSN: 1380-6653
Elektronische ISSN: 1573-7136
DOI
https://doi.org/10.1007/s11142-023-09810-8