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Intangible investment and the importance of firm-specific factors in the determination of earnings

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Abstract

We examine the effect of intangible investment on earnings noncommonality, defined as the extent to which a firm’s earnings performance is determined by firm-specific factors versus market and industry factors. Such insight is important in determining the appropriate weighting of these factors when forecasting a firm’s earnings. For a sample of US firms over the 1980–2006 period, we find that earnings noncommonality is positively associated with intangible asset intensity. This finding is consistent with the resource-based view of the firm, which posits that intangible investments allow firms to differentiate themselves economically from their rivals. We also find that separable recognized intangibles contribute more to earnings noncommonality than do either goodwill or R&D, perhaps because separable recognized intangibles are more likely to arise from contractual or legal rights and thus are less susceptible to expropriation by rival firms. Finally, we find that the positive impact of R&D on earnings noncommonality is significantly greater for those industries where patents and other legal mechanisms are most effective in protecting R&D. This result suggests that the success of intangible investment as a differentiation strategy depends largely on the effectiveness of mechanisms used to protect intangible investments from expropriation.

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Notes

  1. As discussed in Sect. 3.1, we adjust reported earnings and asset measures for implicit R&D capitalization when calculating quarterly ROA.

  2. The nonrival attribute of intangibles refers to the originating firm’s ability to use such resources without impairing the potential usefulness (or scarcity) of the same resource to external firms (Romer 1990). Intangibles are non-excludable in that external firms can rarely be precluded from enjoying some of the benefits of these resources.

  3. Prior studies document that earnings commonality is an important determinant of several accounting and market phenomena such as stock return comovement (Morck et al. 2000; Piotroski and Roulstone 2004; Elgers et al. 2004; Ball et al. 2009), management disclosure (Gong et al. 2009; Kimbrough and Wang 2010), the structure of analyst research portfolios and analyst forecast accuracy (De Franco et al. 2011; Kini et al. 2009), and the structure of institutional investors’ stock portfolios (Engelberg et al. 2009).

  4. See FASB (1985, para. 26) for a discussion of controllability as related to asset recognition.

  5. Relatedly, Fairfield et al. (2009) provide evidence suggesting that industry-level information is closely associated with analysts’ forecasts of firm-specific sales growth, while market-wide information is more closely related to forecasts of firm-specific return on equity.

  6. See Chap. 2, p. 9.

  7. Relatedly, Shiller (1989) implies that firms operating in intangible intensive industries could exhibit a greater degree of commonality in firm performance due to imitation during R&D or technological races.

  8. The most extensive evidence on the existence of spillovers of intangible resources can be found in the literature on R&D spillovers (see, e.g., Arrow 1962; Jaffe 1986; Levin et al. 1987; Cockburn and Griliches 1988; Davis 2001).

  9. Arrow (1962, p. 615) notes “mobility of personnel among firms provides a way of spreading information.” Consistent with this observation, Bhide (2000) finds that 71 percent of the firms included in the Inc 500 (a group of young, fast growing firms) were established by managers who exploited an innovation created by their previous employer. Prior evidence also suggests that intangible-intensive firms view employee mobility as a competitive threat. For instance, Moen (2005) finds that high technology firms pay lower wages to knowledge workers in anticipation that such workers will expose the firm’s innovative activities once they leave. Erkens (2010) finds that the use of stock options as a retention tool is greater for R&D-intensive firms, consistent with such firms being concerned about the threat of spillover due to employee turnover. Lastly, prior studies document innovative firms’ use of non-competition agreements to prevent spillovers due to employee mobility (Gilson 1999; Marx et al. 2009).

  10. Competitive intelligence is the “methodical acquisition, analysis, and evaluation of information about competitors, both known and potential” (Von Hoffman 1999, p. 3), which is predicated on the notion that firms can successfully profit from knowledge of other firms’ resources and that these resources are exploitable. The competitive intelligence literature documents that firms actively attempt to learn about the innovative activities of their rivals using such sources as patent disclosures, publications, trade shows, government records, discussions with employees and sales-people of the competing firm, and reverse engineering of competitors’ products (see Prescott and Bhardwaj 1995; Kahaner 1997; Lavelle 2001). Survey-based studies (e.g., Levin et al. 1987; Mansfield 1985; Cockburn and Griliches 1988; Cohen et al. 2002) corroborate that managers seek out information about their rivals’ R&D efforts. Mansfield (1985) finds that development decisions are in the hands of rivals within 12–18 months and that information about new products or processes leaks out within a year. Cohen et al. (2002) report that 16% (44%) of surveyed firms in the US (Japan) are aware of their rivals’ R&D projects before the development stage.

  11. We acknowledge that recognized goodwill could overstate the value of potential synergistic benefits due to the firm’s possible overpayment during the acquisition process.

  12. This argument is also consistent with Matolcsy and Wyatt (2008), who find that appropriability conditions surrounding the firm’s intangible investments have a significant impact on the firm’s future earnings growth and, in turn, its market value of equity.

  13. This methodology is similar to that used in prior studies to estimate comovement or noncommonalities in stock returns (see Morck et al. 2000: Durnev et al. 2004; Piotroski and Roulstone 2004). We also use this methodology to construct our measure of stock return noncommonality as outlined in Sect. 5.2.1.

  14. This treatment is also consistent with Lev and Sougiannis (1996), who report that the useful life of R&D capital is, on average, 5–7 years for most industries.

  15. We obtain quarterly R&D expenditures from Compustat, when available. In cases where actual quarterly R&D expenditures are not available due to the sparseness of quarterly R&D data in Compustat, we estimate the quarterly expenditures by assuming that the annual R&D expenditures as reported in the annual Compustat file occur evenly across all four quarters within the fiscal year. That is, for each quarter, we calculate quarterly R&D expenditures as annual R&D expenditures divided by four.

  16. Under the assumption that the implicit amortization of R&D expenditures under a capitalization regime occurs evenly throughout the year, we estimate quarterly R&D amortization as the estimate of annual amortization (based on the 20% amortization rate applied to historical R&D expenditures) divided by four.

  17. Our results and inferences are unchanged when we use four-digit SIC codes as well as the Fama–French industry categories to classify industries.

  18. Our earnings noncommonality measure is qualitatively similar to that used in De Franco et al. (2011) and Gong et al. (2009). De Franco et al. (2011) and Gong et al. (2009) construct their measure using the average pair-wise correlation between a firm’s earnings and the earnings of each of its industry peers. However, we choose not to use this methodology because it excludes explicit controls for the systematic correlation between firm-level earnings and the earnings across all firms in the market as documented in prior research (Ball and Brown 1967; Magee 1974). Finally, we note that prior studies find no difference in their results when (non)commonality measures are constructed based on pair-wise correlations of individual firm performance as opposed to correlations with average industry performance (see Morck et al. 2000 and Gong et al. 2009).

  19. We do not examine advertising as a separate class of intangibles for the following reasons: First, the data for quarterly advertising expenditures are even sparser in Compustat. Second, prior studies report that the benefits of advertising are short-lived, lasting for only a few months or 1 year (Peles 1970; Lev and Sougiannis 1996).

  20. Similar to the closely related Tobin’s Q measure, the market-to-book ratio is not a perfect proxy for unrecorded intangibles to the extent that it reflects the market’s upward revaluations of recorded tangible and intangible assets as well as the effect of accounting conservatism on the net book values of recorded assets.

  21. Bradley et al. (1984) also posit that intangible intensive firms are less likely to issue debt since the full expensing of unrecognized intangible investments such as R&D serves as a nondebt tax shield, thereby decreasing the tax advantage of debt financing.

  22. Given the Law of Large Numbers, measures of earnings noncommonality will by default decrease with the number of firms within the industry (see Morck et al. 2000; Durnev et al. 2003 for further details).

  23. The 1994 Carnegie Mellon Survey builds and improves on the 1983 Yale Survey of industry appropriability conditions conducted by Levin et al. (1987). We do not use the 1983 Yale Survey results given the improvements in the structure and sampling strategy of the 1994 Carnegie Mellon Survey. In a limited comparison of the 1983 and 1994 survey results, Cohen et al. (2000) find that the effectiveness of patents for product innovations have increased slightly for large firms, while the effectiveness of patents for process innovations remains stable across all firms.

  24. The data in Cohen et al. (2000) are reported at the industry level using ISIC codes. We thank David Erkens for providing information to re-classify the ISIC codes into the appropriate SIC codes.

  25. As discussed in Sect. 5.1.1, we further correct for serial correlation using the two-way clustering approach suggested by Petersen (2009). A similar clustering approach is used in prior research on stock return comovement (see Jin and Myers 2006). In robustness tests (see Sect. 5.2.4), our inferences are unchanged when we conduct our empirical tests using non-overlapping subsamples where each firm-year observation is 20 calendar quarters apart.

  26. We find similar evidence after eliminating those observations with a negative book value of equity as well as observations with a market value of equity that is less than the book value of equity. These additional data restrictions attempt to control for firms with possible asset impairments.

  27. Our results are robust to the exclusion of observations with absolute value of studentized residuals greater than 3.

  28. This evidence is consistent with Gong et al. (2009), who report that, on average, 88% of firm-level earnings are not explained by market- and industry-wide factors. Similarly, Kimbrough and Wang (2010) report a mean earnings noncommonality measure of 71% for a smaller sample of firms.

  29. When the dependent and independent variables are both log transformed, the estimated coefficients can be interpreted in terms of percent change or elasticity (Wooldridge 2002). Therefore, from Table 4, the estimated coefficient of 0.362 for log(1 + INTANGINT) represents a 3.5% increase for every 10% increase in (1 + INTANGINT), i.e., 1.100.362 = 1.0351.

  30. In supplemental tests (see Table 6), we find a significantly negative association between REG and stock return noncommonality, consistent with prior studies. As discussed in Sect. 5.2.1, this differential result likely reflects differences in the time horizons captured by earnings- versus returns-based noncommonality measures.

  31. We acknowledge the modest explanatory power of our earnings noncommonality regression despite the fact that we have included all determinants of which we are aware based on the existing literature. However, given that research on the determinants of earnings noncommonality is in its infancy, it is likely that our list of determinants is not comprehensive. The low explanatory power of our regression model could also reflect that our earnings noncommonality measure captures correlations in realized performance over short horizons whereas the differentiating or commonality-inducing effects of intangibles likely take place over longer horizons. To address this issue, we replicate our regressions using a stock return-based measure of noncommonality, which captures not only realized performance but anticipated future performance (see Sect. 5.2.1 for further details). Consistent with prior research (e.g., Piotroski and Roulstone 2004), we find that the R2s from the returns-based models are substantially higher (between 25 and 30%), indicating that a noncommonality measure that incorporates both short- and anticipated long-term effects leads to regression models with better explanatory power.

  32. We do not interact LEGALRIGHTS with SEPRBLINT or GDWLINT since the survey data relates only to the appropriability conditions surrounding R&D investments. However, in robustness tests, our inferences are unchanged when we interact LEGALRIGHTS with both SEPRBLINT and GDWLINT.

  33. Specifically, we again retain the fourth calendar quarter of each firm-year and then form separate non-overlapping subsamples using observations that are 5 years or 20 calendar quarters apart. This procedure yields five separate non-overlapping subsamples beginning in each year from 1980 to 1984. For example, the subsample beginning in 1980 contains observations for the six calendar years: 1980, 1985, 1990, 1995, 2000, and 2005. The subsample beginning in 1981 follows a similar five-year pattern. Note that the subsamples beginning in 1982, 1983, and 1984 contain observations for only five calendar years since our sample period ends in 2006.

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Acknowledgments

We thank Bill Baber, Ted Christensen, David Erkens, Jennifer Francis, Bjorn Jorgensen, Partha Mohanram, Tatiana Melguizo, Darren Roulstone (discussant), Stephen Ryan (editor), two anonymous referees, Tatiana Sandino, and workshop participants at Columbia Business School, George Mason University, Georgetown University, the 2010 Review of Accounting Studies Conference at the University of Notre Dame, and the Information, Markets, and Organization Conference at Harvard Business School for helpful comments and suggestions. We thank Marquita Barnes for excellent research assistance.

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Authors and Affiliations

  1. J. Mack Robinson College of Business, Georgia State University, Atlanta, GA, 30303, USA

    Nerissa C. Brown

  2. Robert H. Smith School of Business, University of Maryland, College Park, MD, 20742-1871, USA

    Michael D. Kimbrough

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  1. Nerissa C. Brown
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Correspondence to Michael D. Kimbrough.

Appendix: Variable definitions

Appendix: Variable definitions

UNEXPLAINED = 1 minus the R2 obtained from estimating the following model over the 20 calendar quarters (requiring a minimum of 10 observations) preceding and including quarter t for firm i:

$$ ROA_{i,t} = \alpha_{0} + \alpha_{1} MKTROA_{i,t} + \alpha_{2} INDROA_{i,t} + \varepsilon_{i,t} $$

where:

  • ROA i,t  = return on assets for firm i during calendar quarter t, measured as reported income before extraordinary items (data item IBQ) plus quarterly R&D expense (data item XRDQ) less the estimated R&D amortization expense in calendar quarter t, scaled by the sum of total recognized assets (ASSETS, data item ATQ) and estimated R&D capital (RDCAP) as of the beginning of calendar quarter t. RDCAP is a self-constructed measure of the unamortized cost of R&D investment using current and past R&D expenditures amortized at an annual rate of 20% (i.e., assuming a five-year useful life and straight-line depreciation).

  • MKTROA i,t  = the weighted average ROA (adjusted for R&D capitalization) during calendar quarter t for all Compustat firms excluding those in the same two-digit SIC code as firm i, measured as the sum of adjusted income before extraordinary items for all Compustat firms excluding those in the same two-digit SIC code as firm i in calendar quarter t scaled by the sum of total recognized assets and estimated R&D capital as of the beginning of calendar quarter t for all Compustat firms excluding those in the same two-digit SIC code as firm i;

  • INDROA i,t  = the weighted average ROA (adjusted for R&D capitalization) during calendar quarter t for all Compustat firms excluding firm i in the same two-digit SIC code, measured as the sum of adjusted income before extraordinary items for all Compustat firms in the same two-digit SIC code excluding firm i scaled by the sum of total recognized assets and estimated R&D capital as of the beginning of calendar quarter t for all Compustat firms in the same two-digit SIC code excluding firm i.

$$ {\varvec {NONCOMMON}} = log\left( {{\frac{{UNEXPLAINED_{i,t} }}{{1 - UNEXPLAINED_{i,t} }}}} \right) $$

UNEXPLAINED_RET = 1 minus the R2 obtained from estimating the following model over the 60 calendar months (requiring a minimum of 40 observations) preceding and including month t for firm i:

$$ RET_{i,t} = \alpha_{0} + \alpha_{1} MKTRET_{i,t} + \alpha_{2} INDRET_{i,t} + \varepsilon_{i,t} $$

where:

  • RET i,t  = the market return for firm i in month t

  • MKTRET i,t  = the value-weighted average RET for all CRSP firms during calendar month t (excluding the RET of those firms in the same two-digit SIC code as firm i);

  • INDRET i,t  = the value-weighted average RET for all CRSP firms in the same two-digit SIC code as firm i during calendar month t (excluding the RET of firm i).

$$ {\varvec {NONCOMMON\_RET}} = log\left( {{\frac{{UNEXPLAINED\_RET_{i,t} }}{{1 - UNEXPLAINED\_RET_{i,t} }}}} \right) $$

INTANGINT = the average intangible intensity for the firm, where the average is calculated over the number of quarters with nonmissing data (N) comprising the estimation period used to calculate UNEXPLAINED and NONCOMMON. The average intangible intensity measure is calculated as:

$$ {\frac{{\sum\nolimits_{q = - 19}^{0} {\left( {{\frac{{INTANG_{i,t + q} }}{{ASSETS_{i,t + q} + RDCAP_{i,t + q} }}}} \right)} }}{N}} $$

where INTANG = (SEPRBL + GDWL + RDCAP), and N = the number of nonmissing observations over the 20 quarter period. SEPRBL is the amount of separable recognized intangible assets (excluding goodwill, data item INTANQ); GDWL is the amount of recognized goodwill (data item GDWLQ); RDCAP is the estimated unamortized cost of R&D investment; ASSETS is total recognized assets (data item ATQ).

SEPRBLINT = the average asset intensity for separable recognized intangibles for the firm, where the average is calculated over the number of quarters with nonmissing data (N) comprising the estimation period used to calculate UNEXPLAINED and NONCOMMON:

$$ {\frac{{\sum\nolimits_{q = - 19}^{0} {\left( {{\frac{{SEPRBL_{i,t + q} }}{{ASSETS_{i,t + q} + RDCAP_{i,t + q} }}}} \right)} }}{N}} $$

GDWLINT = the average goodwill intensity for the firm, where the average is calculated over the number of quarters with nonmissing data (N) comprising the estimation period used to calculate UNEXPLAINED and NONCOMMON:

$$ {\frac{{\sum\nolimits_{q = - 19}^{0} {\left( {{\frac{{GDWL_{i,t + q} }}{{ASSETS_{i,t + q} + RDCAP_{i,t + q} }}}} \right)} }}{N}} $$

RDINT = the average R&D capital intensity for the firm, where the average is calculated over the number of quarters with nonmissing data (N) comprising the estimation period used to calculate UNEXPLAINED and NONCOMMON:

$$ {\frac{{\sum\nolimits_{q = - 19}^{0} {\left( {{\frac{{RDCAP_{i,t + q} }}{{ASSETS_{i,t + q} + RDCAP_{i,t + q} }}}} \right)} }}{N}} $$

MB = the average quarterly market-to-book ratio, where the average is calculated over the number of quarters with nonmissing data (N) comprising the estimation period used to calculate UNEXPLAINED and NONCOMMON.

MVE = the average market value of equity, where the average is calculated over the number of quarters with nonmissing data (N) comprising the estimation period used to calculate UNEXPLAINED and NONCOMMON.

MKTSHARE = the average market share over the number of quarters with nonmissing data (N) comprising the estimation period used to calculate UNEXPLAINED and NONCOMMON, where the market share for each quarter is calculated as the firm sales (data item SALEQ) divided by the total sales of the two-digit SIC code in which the firm operates.

STDROA = the standard deviation of return on assets (ROA) measured over the number of quarters with nonmissing data (N) comprising the estimation period used to calculate UNEXPLAINED and NONCOMMON.

DIVERS = the average quarterly revenue-based Herfindahl index of firm diversification using the reported business segments of the firm, where the average is measured using the number of quarters with nonmissing data (N) comprising the estimation period used to calculate UNEXPLAINED and NONCOMMON.

HERF = the average quarterly revenue-based Herfindahl index of industry-level concentration, where the average is calculated over the number of quarters with nonmissing data (N) comprising the estimation period used to calculate UNEXPLAINED and NONCOMMON.

LEVERAGE = the average quarterly ratio of long-term debt (data item DLTTQ) to the sum of long-term debt (data item DLTTQ) and book value of equity (data item CEQQ) of the firm, where the average is calculated over the number of quarters with nonmissing data (N) comprising the estimation period used to calculate UNEXPLAINED and NONCOMMON.

NIND = the average number of firms used to estimate the quarterly industry ROA index (INDROA), where the average is calculated over the number of quarters with nonmissing data (N) comprising with the estimation period used to calculate UNEXPLAINED and NONCOMMON.

REG = 1 if the firm operates in a regulated industry, defined as the two-digit SIC codes 62 (financial institutions) and 49 (utilities) and 0 otherwise.

NREV = the average annual number of forecast revisions of one-year ahead earnings, where the average is calculated over the number of quarters with nonmissing data (N) comprising the estimation period used to calculate UNEXPLAINED and NONCOMMON.

∆INST = the average of the annual absolute value of the change in the number of shares held by institutional owners scaled by annual trading volume, where the average is calculated over the number of quarters with nonmissing data (N) comprising the estimation period used to calculate UNEXPLAINED and NONCOMMON.

TRADES = the average of the annual absolute value of the total shares purchased by insiders less total shares sold by insiders scaled by annual trading volume, where the average is calculated over the number of quarters with nonmissing data (N) comprising the estimation period used to calculate UNEXPLAINED and NONCOMMON.

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Brown, N.C., Kimbrough, M.D. Intangible investment and the importance of firm-specific factors in the determination of earnings. Rev Account Stud 16, 539–573 (2011). https://doi.org/10.1007/s11142-011-9151-x

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