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

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PAT E N T I N F O R M AT I O N A N D C O R P O R AT E C R E D I T

R AT I N G S : A N E M P I R I C A L S T U D Y O F PAT E N T

P e t e r N e u h a e u s l e r C a r l B e n e d i k t F r e y K n u t B l i n d

R AT I N G S : A N E M P I R I C A L S T U D Y O F PAT E N T

VA L U AT I O N B Y C R E D I T R AT I N G A G E N C I E S

P e t e r N e u h a e u s l e r , C a r l B e n e d i k t F r e y , K n u t B l i n d

P a t e n t S t a t i s t i c s f o r D e c i s i o n M a k e r s C o n f e r e n c e 2 0 1 1 N o v e m b e r 1 7t h _{2 0 1 1}

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C o n t a c t a t F r a u n h o f e r I S I

Peter Neuhäusler

Phone +49 721 6809-335

peter.neuhaeusler@isi.fraunhofer.de

Competence Center "Policy and Regions" Fraunhofer Institute for Systems and

Innovation Research ISI

B l S 48

Breslauer Strasse 48 76139 Karlsruhe Germany

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O v e r v i e w a n d M o t i v a t i o n

Capital markets are often deprived

when assessing the value of

technology-driven firms.

• They only have

few tangible assets-in-place

(e.g. Chan et al. 2001).

i

d

i h

d

l f

i

l i

f

i

• 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)

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O n e c o u l d s a y … e a s y w a y : U s e P a t e n t s a s a n

i f

i

Benefits

i n f o r m a t i o n s o u rc e …

Downsides

Benefits

Value relevant Downsides Hi hl k d di ib i f

• Strong correlation with the market value (Ramb/Reitzig 2005a; 2005b, Hall 2005, Neuhäusler et. al 2011)

• Highly skewed distribution of

patent value (e.g. Gambardella et al., 2008)

Forward-looking

• filed 2-3 years before revenues are

t d ( )

• …with many “low value

patents”

• ….associated with an increase in

t t i t ti ( l d l

generated (Ernst, 2001)

and widely available

strategic patenting (Blind et al., 2006, 2009)

• “Noise” in the patent data requires

• 80 percent of all technical knowledge is disclosed in public patent

databases (Ehrat, 1997)

Noise in the patent data, requires specific knowledge about the patent system

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C re d i t R a t i n g A g e n c i e s

Credit Rating Agencies (CRA) could help to overcome these difficulties… As information intermediaries they…y

reduce the complexity of information by giving opinions on the creditworthiness

… 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)

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R e s e a rc h Q u e s t i o n s

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

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 take into account the firms technology portfolio in terms of patents?

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H y p o t h e s e s

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).

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D a t a & S a m p l e



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 dataFinancial data (e.g. rating, assets, debt): Standard & Poor’s COMPUSTAT Global(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 patent information on the companies’ g p p

subsidiaries

•

Information on credit rating g available only for North American companies y p  restriction of the data to USPTO granted patents from 1990 to 2001

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I n d e p e n d e n t Va r i a b l e s a n d C o n t ro l s

Independent Variables:

•

Patent flows of a company (annual USPTO grants)

•

Average number of g forward citations to firms’ patents (4-year citation window)p ( y )

•

Average family size (average number of distinct patent offices, at least one USPTO member, no “singletons”)

Control variables Debt (Risk and Amortization):

L (T t l d bt/t t l t )

•

Firm size (sales in billions)

•

Return on Assets (EBIT/total assets)

•

R&D expenditures (in billions)

•

Leverage (Total debt/total assets)

•

Cash flow adequacy (Net debt/EBIT)

•

Subordinated debt (Dummy)

•

Interest rate (Net interest/Total debt)

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T h e M o d e l

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).

20 25

Ordered probit models with maximum likelihood (ML) estimation

Distribution of the Corporate Credit Rating Variable

10 15 20 in %

•

we cluster by companies

…allows us to control for

0 5

D C B‐ B B+ A‐ A A+ unobserved heterogeneity in our

models.

D C B‐ B B+ A‐ A A+

•

and add time- and industry dummies

...to account for period- and industry-specific effects.y p

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R e s u l t s : O rd e re d P ro b i t M o d e l s

DV = S&P Rating M1 M2 M3

• No effect of R&D

• Strong impact of patent Coef. S.E. Coef. S.E. Coef. S.E.

Return on assets 3.500 *** 0.998 3.486 *** 1.109 3.414 *** 1.134 Subordinated debt dummy -1.030 *** 0.296 -0.901 *** 0.255 -0.902 *** 0.231

Net Interest/Total Debt 0 353 * 0 204 0 313 0 226 0 324 0 218 • Strong impact of patent flows

• Quantity rather than quality

Net Interest/Total Debt -0.353 0.204 -0.313 0.226 -0.324 0.218 Sales (in billions) 0.023 ** 0.011 0.023 *** 0.008 0.020 *** 0.008 R&D flows 0.000 0.000 0.000 0.000

Patent flows 0.427 ** 0.187 0.491 ** 0.193

• Quantity rather than quality

• Negative impact of forward it ti

Average family size 0.055 * 0.032

Average # of forward cit. -0.049 * 0.028 Time Dummies

I d t D i YES YES YES YES YES YES

citations… • Confirms H1 and H2b Industry Dummies Observations 688 649 632 Nr. of companies 135 133 129 Pseudo R² 0.165 0.179 0.192

YES YES YES

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C o n c l u s i o n s

CRA seem to evaluate patents differently from stock markets

…strong impact of patent flows (quantity rather than quality).

i i f f d i i

…negative impact of forward citations.

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

• 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?

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I m p l i c a t i o n s

Implications for the financing of technological innovation by means of debt

…firms either face costs to build and uphold a large patent portfoliop g p p 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

i ll t bl f ll t h l d i fi

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Thank you for

your attention

Peter Neuhäusler Fraunhofer ISI Fraunhofer ISI Breslauer Strasse 48 76139 Karlsruhe Germany

Peter Neuhaeusler@isi fraunhofer de Peter.Neuhaeusler@isi.fraunhofer.de