PATENT INFORMATION AND CORPORATE CREDIT RATINGS: AN EMPIRICAL STUDY OF PATENT VALUATION BY CREDIT RATING AGENCIES

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1 PATENT INFORMATION AND CORPORATE CREDIT RATINGS: AN EMPIRICAL STUDY OF PATENT VALUATION BY CREDIT RATING AGENCIES Peter Neuhaeusler, Carl Benedikt Frey, Knut Blind Patent Statistics for Decision Makers Conference 2011 November 17 th 2011 Image Source: istockphoto.comcom

2 Contact at Fraunhofer ISI Peter Neuhäusler Phone Competence Center "Policy and Regions" Fraunhofer Institute for Systems and Innovation Research ISI Breslauer Strasse Karlsruhe Germany Seite 2

3 Overview and Motivation Capital markets are often deprived when assessing the value of technologydriven firms. They only have few tangible assets-in-place (e.g. Chan et al. 2001). Associated with a great deal of uncertainty resulting from asymmetric information Leads to difficulties in pricing intangible information compared to information on tangible assets (Daniel/Titman 2001) however Seite 3

4 Overview and Motivation Seite 4

5 One could say easy way: Use Patents as an information i source Benefits Downsides Value relevant Strong correlation with the market value (Ramb/Reitzig 2005a; 2005b, Hall 2005, Neuhäusler et. al 2011) Forward-looking filed 2-3 years before revenues are generated td (Ernst, 2001) and widely available 80 percent of all technical knowledge is disclosed in public patent databases (Ehrat, 1997) Highly skewed distribution ib i of patent value (e.g. Gambardella et al., 2008) with many low value patents.associated with an increase in strategic t patenting ti (Blind et al., 2006, 2009) Noise in the patent data, requires specific knowledge about the patent system Seite 5

6 Credit Rating Agencies Credit Rating Agencies (CRA) could help to overcome these difficulties As information intermediaries they reduce the complexity of information by giving opinions on the creditworthiness of corporate debt issuers (evaluate investment risk) and therefore have an important function at capital markets (lower costs, and increase the supply of risk capital in the economy) Seite 6

7 Research Questions Research on whether CRA consider patent information in their assessments is relatively limited. Czarnitzky/Kraft (2004) find an inverse U-shaped relationship between innovation indicators and credit ratings for german firms No indicators on patent value in the study Did not control for the companies asset base and debt structure We aim to bridge the gap between the research on patent information and corporate credit ratings. Do CRA take into account the firms technology portfolio in terms of patents? Do CRA differentiate between the value of patent portfolios? Seite 7

8 Hypotheses Based on the literature of patent value indicators we derive the following hypotheses: H1: A larger patent output leads to a higher rating. H2a: A higher average number of forward citations is associated with a higher rating (e.g. Hall et al. 2005; Harhoff et al., 2003; Narin/Noma 1987; Trajtenberg 1990). H2b: A larger average family size leads to a higher rating (e.g. Harhoff et al., 2003; Lerner, 1994). Seite 8

9 Data & Sample Firm-level Panel based on the DTI-Scoreboard (British Department for Innovation, Universities & Skills (DIUS); Department for Business, Enterprise & Regulatory Reform (BERR)) from 1990 to 2007 comprised of 479 firms Ranking of international companies according to their R&D-expenditures Financial data (e.g. rating, assets, debt): Standard & Poor s COMPUSTAT Global and North America databases Patent indicators (including quality indicators): EPO Worldwide Patent Statistical Database (PATSTAT) including gpatent information on the companies subsidiaries Information on credit rating available only for North American companies restriction of the data to USPTO granted patents from 1990 to 2001 In total: 2292 observations from 191 US companies Seite 9

10 Independent Variables and Controls Independent Variables: Patent flows of a company (annual USPTO grants) Average number of forward citations to firms patents (4-year citation window) Average family size (average number of distinct patent offices, at least one USPTO member, no singletons ) Control variables Firm size (sales in billions) Return on Assets (EBIT/total assets) R&D expenditures (in billions) Debt (Risk and Amortization): Leverage (Total debt/total t assets) Cash flow adequacy (Net debt/ebit) Subordinated debt (Dummy) Interest rate (Net interest/total debt) Short term debt (Debt due in 1 year/total debt) Seite 10

11 The Model Standard & Poor's Credit Rating: Categorical variable ranging from AAA (highest rating) to D (lowest rating) Specified with a one period lead for reasons of causality (Czarnitzky/Kraft 2004). Ordered probit models with maximum likelihood (ML) estimation Distribution of the Corporate Credit Rating Variable we cluster by companies allows us to control for unobserved heterogeneity in our models. and add time- and industry dummies...to account for period- and industry-specific effects. in % D C B B B+ A A A+ Seite 11

12 Results: Ordered Probit Models DV = S&P Rating M1 M2 M3 Coef. S.E. Coef. S.E. Coef. S.E. Return on assets *** *** *** No effect of R&D Subordinated debt dummy *** *** *** Net Interest/Total Debt * Strong impact of patent Sales (in billions) ** *** *** flows R&D flows Patent flows ** ** Average family size * Quantity rather than quality Average # of forward cit * Time Dummies YES YES YES Negative impact of forward Id Industry Dummies YES YES YES citations ti Observations Nr. of companies Pseudo R² Confirms H1 and H2b AIC BIC Significance Level: ***p<0.01, **p<0.05, *p<0.1 Only Significant Variables shown Seite 12

13 Conclusions CRA seem to evaluate patents differently from stock markets strong impact of patent flows (quantity rather than quality). negative impact of forward citations. i Possible Explanation: Although associated with higher market value and survival rates, high value patents post a higher litigation risk (Lanjouw/ Schankerman, 2001; Harhoff et al. 2003). If we regard patents as collateral or insurance against lawsuits, CRA might be more likely to determine risks rather than potential future economic benefits. CRA at least partially differentiate between high and low value patents Is that enough? Seite 13

14 Implications Implications for the financing of technological innovation by means of debt firms either face costs to build and uphold a large patent portfolio as insurance or they will eventually face higher costs of capital (increased interest payments due to lower rating when funding via debt). Increased costs of innovation especially ill troublesome for smaller technology-driven di firms. Is debt unsuitable for the financing of technological innovation? especially against the background of an increase in strategic patenting Seite 14

15 Thank you for your attention Peter Neuhäusler Fraunhofer ISI Breslauer Strasse Karlsruhe Germany Seite 15