Paper to be presented at the
DRUID Society Conference 2014, CBS, Copenhagen, June 16-18
Patent value: exclusivity or signal of research productivity?
Juliana Pavan Dornelles
Charles III University of Madrid
Business Administration
juliana.pavan.dornelles@gmail.com
Ayfer Ali
Universidad Carlos III de Madrid
Business Administration
ayfer.ali@uc3m.es
Abstract
We explore two different sources of potential patent value ? the legal right granted by the patent and the technical
information disclosure that serves as a signal of firm technological ability and value. We apply an event study
methodology to investigate market response to patent application publication and patent grant events, using the
enactment of AIPA as a natural experiment. The American Inventors Protection Act (AIPA), enacted in November 2000,
introduced a new stage to patent disclosure process. Patents applications filed after AIPA are disclosed after 18 months,
instead of when the patent is granted. We also evaluate how patent characteristics and firm characteristics are
associated with abnormal returns generated by patent events.
Jelcodes:G14,O32
PATENT VALUE: EXCLUSIVITY OR SIGNAL OF RESEARCH PRODUCTIVITY?
Abstract
We explore two different sources of potential patent value – the legal right granted by the patent and
the technical information disclosure that serves as a signal of firm technological ability and value.
We apply an event study methodology to investigate market response to patent application
publication and patent grant events, using the enactment of AIPA as a natural experiment. The
American Inventors Protection Act (AIPA), enacted in November 2000, introduced a new stage to
patent disclosure process. Patents applications filed after AIPA are disclosed after 18 months,
instead of when the patent is granted. We also evaluate how patent characteristics and firm
characteristics are associated with abnormal returns generated by patent events.
Key words: patent value, exclusivity rights, quality signal, stock market returns
1. INTRODUCTION
The patent system aims to circumvent the resource misallocation problem in knowledge
production by transforming a public good1 into a private good (Arrow, 1962). It aims to encourage
innovation by bestowing inventors a temporary exclusive right to practice and sell their invention in
return for its public disclosure. Thus, the intellectual property (IP) system’s objective is to boost
knowledge diffusion, increasing knowledge stock in the economy, by providing a mechanism that
allows inventors to appropriate the benefits of their invention.
Through this process, detailed description of the invention contained in the patent application
document, required by the patent office, conveys information about the direction and output of the
research and development (R&D) carried by the applicant firm and about the firm’s technological
competencies (Long, 2002). As a result, patents act as a signal of other non-observable firm
characteristics such as knowledge capital. This in turn can help generate investments by attracting
external financing (Haeussler at al., 2009; Hsu and Ziedonis, 2008).
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
"!According
to Arrow (1962), knowledge has characteristics of public good: non-rivalrous and non-excludable. Nonrivalrous means that the use of a particular innovation by a producer does not preclude its use by others. Knowledge is a
non-excludable good because the innovator is not always able to prevent others from using it without authorization once
it has been disclosed and in the absence of patents. (Kanwar and Evenson, 2003). --- Kanwar and Evenson were not the
first ones to say this. I think Arrow says it as well. !
!
"!
For many firms, patents have a strategic importance beside IP protection and signaling. For
example, firms use patents as defensive weapons in cross-licensing deals, to block competitors from
entering the market, and as signal of R&D productivity and quality (Macdonalds, 2004; Cohen et al.,
2000; Hall and Ziedonis, 2001). Moreover, Gambardella (2013) argues that patents are significant
assets in generating cash flows through licensing contracts and in determining technological
alliances or in establishing technology standards through patent pools. Besides some attempts to find
some value indicators of patent quality as, e.g., forward citations, patent family size and, renewal
rates (van Zeebroeck, 2009) researchers still struggle to evaluate patents’ innovative content, trying
to separate wheat from chaff by pinpointing outstanding patents within firms’ patent portfolio.
Intensive patenting behavior has led to what is known as the patent paradox (Hall and Ziedonis,
2001; Parchomovsky and Wagner, 2005). It refers to the increasing number of patent applications to
the Unites States patent and trademark office (USPTO) and to the European patent office (EPO) even
when inventors argue that patents are becoming less effective as an instrument for protecting
innovation and, therefore, less valuable (Cohen et al, 2000; Levin et al. 1987). For that reason, the
simple patent count as a measure of innovation productivity is deemed problematic as the value of
patents is rather skewed – most patents are worth very little to their inventors whereas, a few patents
are very valuable and yield high economic benefits to their owners (Schankerman and Pakes, 1985).
However, even though patents are an imperfect measure of innovative performance, as
acknowledged by Griliches (1990), they remain important in accounting for firm research activities
because of their wide availability. The challenge remains to be able to distinguish their quality in
some way.
We contribute to the patent valuation literature by analyzing the underlying sources of patent
value - the intellectual property right and the signaling value through information disclosure.
Considering the enactment of the American Inventors Protect Act (AIPA) in November 29, 2000, we
are able to disentangle the IP rights effect from the signaling effect of patents. Before AIPA the
technological content of a patent document was revealed when the patent was issued2, while after
AIPA the patent application document is published 18 months after filing date. Although the applied
patent document is not the same as the granted patent3, the publication of a patent application
conveys information that may act as a signal about the innovative activity that has been taking place
in the firm. The AIPA enactment is an external event, unrelated to patent value that enables us to
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
2
Since the difference between patent grant and patent issuance are merely related to the USPTO patent prosecution
process, we shall use the two terms interchangeably.
$!Usually the patent document is modified through the examination process, where examiners might modify or rule out
some claims and add prior art citations.
!
#!
deep down on the patent value problem investigating where does it lies, separating the signaling
value of the patent application disclosure from the IPR value enabled by patent grant.
To assess the value impact of information revealing stages, publication and grant, we estimate
the stock market reaction to the arrival of information, before and after AIPA. Sood and Tellis
(2009) argue that stock market returns to innovation may be crucial in assessing rewards to
innovation, and, consequently the value conveyed by the patent.
The reminder of the paper is structured as follows. Section two presents a literature review on
patent valuation, followed by our hypotheses. Section four presents the methodology and section five
the data. Part six presents the findings and part seven discusses the results and points out some of the
limitations of our study.
2. PATENT VALUE: LITERATURE REVIEW
Van Zeebroeck and van Pottelsberghe de la Potterie (2011) define two broad categories of patent
value indicators, patent-based indicators and market-based indicators. The former refer to those
indicators that come directly from the patent system, while the latter refer to those indicators that
come from outside the patent system. Both measures are briefly reviewed in this section.
2.1 Patent-based indicators
There is a vast literature addressing the patent valuation issue that has been surveyed by Dixon
and Greenhalgh (2002), Reitzig (2004), van Zeebroeck (2009) and, van Zeebroeck and van
Pottelsberghe de la Potterie (2011). In order to assess the market value of a patent, empirical studies
used practitioners’ surveys or secondary data from patent offices to identify potential value
determinants.
Analyzing the value of individual patents Harhoff et al. (1999) interviewed holders of patents
applied for in Germany. They concluded that patents that have a higher private value are more likely
to be cited in subsequent patents. Furthermore, Harhoff et al. (2003) correlated patent characteristics
with patent holders’ valuation and found that both the number of forward and backward citations are
positively related with patent value. On the other hand, patent scope, measured as the number of
different IPC classification, was not significant in determining patent value. Trajtenberg (1990),
Lanjow and Shankerman (1999) and, Hall et al. (2000, 2005) also found forward citations to be
strongly positively correlated with patent value.
!
$!
Another patent value indicator widely applied in the literature is the renewal rate because it is
costly to maintain a patent by paying the regular maintenance fees to keep the invention protected
(Pakes, 1984; Schankerman and Pakes, 1985; Bessen, 2008). Therefore, the protected invention must
be more valuable than the renewal costs.
Court decisions, i. e., whether a patent has been upheld in opposition or not, was found to be
positively correlated with patent value (Harhoff et al., 2003; Reitzig, 2004). Broadly speaking, an
opposed patent is one that has had its validity challenged. Therefore, being upheld means that the
patent has been deemed valid.
2.2 Market-based value related indicators
Another stream of research looks at the association between firm market value and patent
indicators, Hall et al. (2005) used the Tobin’s q measure of firm value and three measures of
knowledge stock as explanatory variables. Results showed that the three proposed complementary
measures have a significant impact on Tobin’s q, confirming that R&D inputs, R&D output
measured by patents, and further “high-quality” R&D output measured by patent citation intensity
are valued by the market. Moreover, the findings revealed that highly cited patents command a
market-value premium. The authors also argue that the relevance of forward citations to market
value endorses the forward-looking characteristic of equity markets, as market value premiums are
associated with future received citations. Lanjow and Schankerman (2004) found a positive and
significant relation between patents´ mean quality and Tobin’s q indicating that investors may access
information that enables them to distinguish the quality differences of patents across firms.
Another measure of patent value and the value of the technological content protected by a patent
is the stock market price variation due to patent events, as well as the correlation between stock
prices and innovative activity indicators (e. g. R&D). Hirshleifer et al. (2013), measuring innovative
efficiency by a firm’s ability to generate patents and patent citation per dollar of R&D, found
individual firms’ excess of return to be positive related to innovative efficiency. They concluded that
stock market recognizes the value of innovative efficiency and accords higher valuations to more
efficient firms.
According to Fama (1990) variations in stock returns reflect shocks to the expected cash flow
stream, then a successful innovation may increase a firm’s revenue, carrying information that might
be incorporated by the stock market and reflected in stock prices. However, innovation activity takes
time and its outcome is highly uncertain. Innovative efficiency reflects the firm’s ability to transform
innovative efforts, usually measured by R&D expenses, in innovative and marketable products.
!
%!
Therefore, the stock price may correlate with the firm’s technological capability and innovative
efficiency. Moreover, Hirshleifer et al. (p. 633, 2013) argue that “firms with high innovative
efficiency tend to be more profitable and have higher returns on assets.” Furthermore, Pakes (1985)
argues that from a management point of view resources will be allocated to an R&D program to
maximize expected net cash flows and it will be considered in the market valuation.
Austin (1993) estimates patent value and the effect of a patent event on rival firms in the
biotechnology industry through an event study. Results show significant abnormal returns due to
patent grant event. Moreover, patents linked to products are more valuable. Exploring the stock
return response arising from a patent event, Erturk et al. (2004) applying an event study methodology
investigated 3,520 patent-related events in the manufacturing and service sectors. The results
indicate that positive announcements as patent filings, notice-of allowances, approval and upholds4
are associated with stock price increases. On the other hand, negative announcements as reexaminations and denials are associated with stock price decreases. Notwithstanding, the paper does
not provide further explanation on the association between patent characteristics and abnormal
returns.
3. HYPOTHESIS DEVELOPMENT
The aforementioned patent value indicators focus on patent characteristics that may reflect the
economic value of a patent, usually on the basis of ex-post information. However, these indicators do
not provide an explanation for the underlying sources of such value. We argue that there are
fundamentally two value sources engendered in a patent: the right to exclusive use of the patented
and the information conveyed by the patent about the research abilities of the firm.
The exclusivity rights – conferred by the patent title – to make, use and, sell the patented
invention grants temporary monopoly over the invention in exchange for detailed5 information about
the technology patented. Therefore, the patent holder bears the right to exclude others and, further,
to enforce patent rights against infringers who do not have an authorization to exploit the technical
knowledge embodied in the patent.
The knowledge characteristic of a public good, non-rivalrous and non-excludable, is the main
argument in favor of private property over an invention. It implies that frequently the inventor is not
able to reap all the benefits from the invention, i.e., to appropriate the economic rents derived from
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
4
Patents which are upheld, are the ones that went through an opposition but were declared valid by the patent office or
by the court (Harhoff et al., 2003).
&!“(…) full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it
is most nearly connected, to make and use the same” 35 U.S.C. § 112.!
!
&!
the invention, resulting in resource misallocation (Arrow, 1962). Hence, patents are instruments
designed to correct this market failure. To Verspagen, (1999) the patent system pursues a dual and
contradictory objective: i) protecting inventors against imitations to stimulate inventive activity, and
ii) disseminating information technology as a mechanism to facilitate the invention and innovation
for the benefit of the whole society.
Yet, according to Verspagen, patents, more than other forms of IPRs - such as copyrights and
trademarks - are important to the dynamic performance of the economy. The technological
knowledge contained in a patent application is not only useful for the patent applicant, but also for
the inventors of the same field. This knowledge described in a patent increases the stock of general
knowledge and allows certain aspects of technological knowledge from being exploited by other
inventors (Verspagen, 1999).
From the technical knowledge conveyed by the patent stems the second value source of a patent.
The detailed information disclosed in a patent document can reduce information asymmetries,
between the patenting firm and an outsider investor, on a firm’s innovation activity and generates
value through facilitating a firm’s financing. Therefore, some researchers (Czarnitzki et al., 2014,
Hsu and Ziedonis, 2008, Long, 2002) argue that patents work as quality signals of firms’
technological capabilities.
According to the signaling theory differentially costly actions taken by the party subject to
uncertainty can act as quality signals6 to external observers (Spence, 1973). Long (2002) argues that
patent counts are correlated with other unobservable or difficult-to-measure variables of firm
innovation, e.g., R&D expenses productivity and knowledge capital of the firm. Furthermore, patents
can be seen as credible signals as claims have been reviewed by the patent granting authority and
misstatements are public, verifiable and, impose high costs to the patentee (Long, 2002). Some of the
value of a patent or a patent’s economic returns emerges from this view. As a publically available
document, patents may minimize information cost to potential investors about firm capabilities even
if that is not directly related to the firm’s patented inventions. Thus, a part of a patent´s value rests on
its function as a signal attracting potential investors.
The American Inventors Protection Act of 1999, enacted in November 29, 2000, provides a
quasi-experimental setup allowing us to estimate the role of the legal right and information conveyed
by the patent as sources of value generation. Before the enactment of the AIPA patent applications
were published only after the patent was granted. Ergo, the patent content was unknown until the
patent grant, when the actual protection started. Thereby, the legal right and knowledge signal effects
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
'!Hsu
and Ziedonis (p. 8, 2007) define quality signal as “information capable of altering an observer’s probability
distribution of unobserved variables”.
!
'!
could not be disentangled as both happened at the same time. The non-publication rule stood on two
main arguments. First, the earlier publication would harm mainly small inventors discouraging the
flow of new inventions. Second, while the disclosure of patent information enhances knowledge
stock and promotes information diffusion it may enable competitors to copy or invent around the
applied patent invention. Then, frightened by the possibility of being copied many inventors would
prefer to keep major inventions secret, slowing knowledge diffusion (Johnson and Popp, 2000; Aoki
and Spiegel, 1998).
AIPA established that US patent applications have to be published 18 months after the earliest
filing date. In exchange of earlier disclosure, the applicant can recover damages from an infringer
starting from the publication date (Johnson and Popp, 2001). At application publication, the market
will have access to the text of the patent that has been applied for, including the technological merit
of the invention. However, uncertainty about patent grant will still remain (Gans, Hsu and Stern,
2007). As the patent is issued, it will be published on the USPTO website as well as in the USPTO
Official Gazette which is available to the public. Patent application publication can work as a signal
of the firm’s knowledge and research activities, without guarantees that the patent application will be
granted7. At the grant event the legal right is bestowed to the applicant mitigating the uncertainty
over the patent8.
Ideally, we would estimate a “differences-in-differences” setup by estimating the difference
between a treatment group of patent application affected by the law and a control group of patent
applications which would not have been affected by the law. Since we do not have a control group of
patents that were not affected9 by the legal change, we compare the information disclosure effects by
comparing patents before and after AIPA enactment. The implementation of AIPA rules was an
external “shock”, independent of the patent itself, aiming to harmonize the U.S. patent system to the
patent system established in other developed countries.
By estimating stock market responses to patent events before and after AIPA we aim to capture
market assessment of patents’ underlying value. Patent grant, before AIPA, encompassed both the
the intellectual property and knowledge disclosure as a signal of firm capability. On the other hand,
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
7
According to Clarke (2003) U.S. patent applicant has about a 75% probability of success in obtaining a patent.
Moreover, the granted patent document is most likely to be different from the published patent application. The
application may include some claims that later are determined not patentable (Warren and Cobern, 2000).
)!Uncertainty is not totally mitigates by the patent grant as, after grant, U.S. patents can be challenged by litigation or by
a re-examination request of the patent by the USPTO (Graham et al., 2002).
9
However, patent applicants can opt-out by certifying that the invention disclosed in the application will not be subject of
application in another country, or under an international multilateral agreement that requires publication 18 months after
filing day (35 U.S.C. § 122). Nevertheless, the ones who forgo pre-grant publication self-selected themselves.
!
(!
after AIPA, the patent application publication discloses the information of firm capability but does
not assure the exclusivity right which is conferred when the patent is granted.
Therefore, we test the following hypotheses:
Hypothesis 1a: Patent grant, before AIPA (intellectual property protection plus
knowledge disclosure effects) and after AIPA (intellectual property protection,) generates
abnormal returns.
Following the literature reviewed in the previous section, which identifies some patent
characteristics that are correlated with the value of a patent, we also hypothesize that:
Hypothesis 1b: Patent characteristics previously associated with patent value are related
to abnormal returns at grant:
a) Forward citations
b) Backward citations
c) Number of claims (indicator of patent scope)
d) Number of IPC classes (indicator of patent scope)
Additionally we hypothesize that information disclosed in a patent application publication
conveys information about firm capabilities.
Hypothesis 2: Patent t application publicationgenerates abnormal return through revealing
information about firm capabilites.
4. METHODOLOGY
In order to assess the market response to a patent event, publication or grant, we adopt an event
study methodology. Although an event study methodology is most popular in finance it has been
employed in management to evaluate the stock market reaction to the arrival of information related
to a firm’s performance. The main assumption is that the market is efficient and stock prices adjust
instantaneously as the new information arrives (Fama et al., 1969).
McWilliams and Siegel (1997) argue that as stock prices reflect the discounted value of future
cash flows incorporating all relevant information they may reflect the true value of the firm. Thus,
an event study, first proposed by Fama et al. (1969), assesses the market reaction to a new
information arrival through the “unusual” behavior of the stock returns induced by the information.
Therefore, the release of information about the company or government actions through the media
!
)!
channels characterizes an event and an event study evaluates whether a specific event generates
abnormal returns (Park, 2004).
Austin (1993) and Erturk et al. (2004) argue that a patent announcement is a surprise. Even
though the market can in some instances anticipate the announcement, the actual timing is uncertain.
Then, an announced patent event, publication or grant, will cause investors to respond to this new
information and value the expected future cash flow that this patent can generate.
Following McWilliams and Siegel’s (1997) steps for an event study, we identified the event of
interest, the publication day and the grant day, and defined the event window – days surrounding the
event date that may be affected by the event and may also generate abnormal returns. Then, we
computed daily abnormal returns, cumulative abnormal returns, and tested the statistical significance
of abnormal returns generated due to the patent event.
In order to estimate the abnormal returns two steps are undertaken. First, the normal returns
during the estimation window are estimated. Normal returns are the returns that would have been
observed if the event had not taken place. They are measured for a period of 60 trading days
preceding the event10, from 70 days to 10 days prior to the event.
Normal returns are estimated following the Fama and French (1993) three-factor model. Kolari
and Pynnöen (2010) argue that a factor model extracts as much as possible of the common residual
cross-sectional correlation, reducing cross-correlation in abnormal returns to a minimum. Therefore,
the Fama and French estimated model is:
!!" ! !!" ! !! ! !!! !!" ! !!" ! !!! !"#! ! !!! !"#! ! !!"
(1)
Where, !!" is the stock return of firm i over the time t, !!" is the rate of return attributed to a riskfree investment at time t, usually the interest rate on a three-month U.S. Treasury Bill. !!" accounts
for the return on all firms in NYSE, AMEX and NASDAQ at time t, SMBt is the index of small
versus big capitalization portfolios at time t and HMLt is the index of high versus low book/price
ratio portfolios at time t.
Next, the abnormal returns (ARi) are computed by calculating the difference between the actual
observed returns over the event window and the returns expected by the above benchmark model.
Thus,
!"!! ! !!" ! !!!!" !
(2)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
"+!There
is no estimation window length standard as a variety of lengths have been used in prior studies (Campbell et al.,
2003). In this study we chose a 60 days period in order to retain the most observations in our sample.!
!
*!
Where !"!" is the abnormal return, !!" is the observed return and ! !!" is the expected normal
return over the event window t. To test the null hypothesis of zero abnormal returns we compute the
cumulative abnormal returns (CARi) aggregating the returns over the event window for each
security.
It is assumed that there is no confounding event (other than the one of interest) during the event
window. According to McWilliams and Siegel (1997) the longer the event window, the more
difficult it is to control confounding events that may have an effect on returns. Evaluating the impact
of a patent event on a firm’s market value, Johnson and Scowcroft (2013) test four different event
windows, 1 day before to 1 day after, 1 day before to 7 days after, 7 days before to 1 day after, and 7
days before and 7 days after. However, the most common approach is to consider one day before and
one after (Sears and Hoetrer, 2013; Alexy and George, 2011; Park and Mezias, 2005). Thus, we
defined the event window as a 3 day window, one day before to one day after the event (-1,1). One
day before the event accounts for anticipation effects, whereas, including the day after the event
captures announcement effects on price that may arise after stock market closing on the event day.
Hence, CARs are computed aggregating ARs over the 3 day event window as follow:
,$-!
!!!!
!"#! !
!"!" !!!!!!!
!!!!
where t1 and t2, respectively, denotes the beginning and the end of the event window.
Further, we investigated how patent and firm characteristics might affect the CARi by using a
cross-sectional regression model:
!"#! ! !! ! !!! !"#$%&! ! !!! !!"#$%! ! !!! !"#$%&! !
,%-!
!!!! !"#$%&! !!!! !"#$#%&'! ! !!! !"#$%!"#! ! !!! !"#! ! !!! !"#$! !
!!! !"#! ! !! !"#$%"&'! ! !!
Table 1A presents the definition and the sources of the explanatory variables used to estimate
equation (4).
Abnormal stock returns from a patent event may be explained by variables that reflect the
technological relevance of a patent to the firm by looking at the patent as a firm asset. Accordingly,
!
"+!
the literature has looked at the importance of some patent characteristics such as the number of
patents cited (Xie and Giles, 2011; Cohen et al., 2013; Van Zeebroeck, 2009) and number of claims
(Suzuki, 2011; Xie and Giles, 2011; Lanjouw and Schankerman, 2004). Number of cited patents and
number of claims delimit the rights protected by the patent; the first refers to prior art11 and the
second delimits what the patent protects (Xie and Giles, 2011). The number of IPC classes is related
to the innovation’s technological complexity and to the patent’s technological scope (Guellec and
van Pottelsberghe de la Potterie, 2000; van Zeebroeck and van Pottelsberghe de la Potterie, 2011).
Trajtenberg (1990) highlights the role of the number of citations received by a patent as an
indicator of the value of the innovations, whereas, Lanjow and Shankerman (2004) and Bloom and
Van Reenen (2002) proxy patent value and quality by the number of forward citations. Although
forward citations are not known by the time the patent is granted, Hall et al. (2005) found a positive
and significant association between forward citations and the firm’s market value confirming the
forward-looking nature of stock market. The quality of a patent is also captured by the number of
countries the patent has been applied for, i. e., the family size. Nagaoka et al. (2010) argue that the
triadic patent family12 database covers higher quality patents, since filing a patent application in all
three patent offices implies significant costs and as a results works as an indicator of the firm’s
evaluation of the its own patent. Additionally, controlling for domestic applicants aims to capture the
home bias effect as domestic applicants tend to file disproportionately more patents in their home
office (Criscuolo, 2006).
Besides patent characteristics, firms’ patenting experience is also considered through two
variables, the patent stock and a dummy variable taking value 1 if the patens stock is equal 0,
identifying inexperienced patentees. Equation 4 also considers the firms’ size (Gambardella et al.,
2012; Bessen, 2008). Even though there is no consensus in the literature if larger or smaller firms are
more innovative, it is suggested that “the size of a firm is an important structural variable that affects
the market returns on innovation” (Sood and Tellis, p. 445, 2009).
It is well recognized in the literature that the use and the effectiveness of patents as a tool to
protect and to harvest returns from innovation varies drastically across industries (Mansfield 1986,
1994, 1995; Orsenigo and Sterzi, 2010). Mainly it varies according to the technology embodied in
the innovation (complex or descriptive) and the efficiency of complementary assets (e.g.
manufacturing capabilities, distribution channels, marketing, etc.) to create competitive advantage.
Cockburn and Griliches (p. 13, 1987) found “evidence of an interaction between industry level
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
11
Defined in 35 U.S.C. §§ 102, 103 (2006), prior art refers “to anything negating patentability requiements of novelty
and nonobviousness in a particular case” (Worrel, 2010, p. 834).
"#!./01203!45/2016!78!091!:;<;!./0120!=>>?@1!/26!>?A16!/0!091!BC5DE1/2!./0120!=>>?@1!,B.=-!/26!F/E/2131!./0120!
=>>?@1!,F.=-;!
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measure of the effectiveness of patents and the market’s valuation of a firm’s past R&D and
patenting performance.” So, dummy variables accounting for the IPC technology class of each patent
as well as firm dummies are included in the model. Moreover, robustness checks include industry
dummies by including SIC codes.
Finally, USPTO patent grants are published through the Official Gazzette for Patents, every
Tuesday, including all issued patents during that week. It generates time-clustered events rendering
“the independence assumption for abnormal returns in the cross-section incorrect” (Kothari and
Werner, 2004, p. 14). To overcome this we control for the event dates.
5. DATA
Sample
The patent data comes from the NBER patent data project13 (Hall et al., 2001), which contains
USPTO granted patents from 1976 to 2006. Patent data was matched with CRSP unique firm code14
(permno), and patents with missing “permnos” were eliminated. To evaluate the patent event impact
on stock market before and after America Inventors Protection Act (AIPA) two samples were
selected. The first sample, considering the filing date, ranges from June 8, 1995, when the patent
term extension introduced by the TRIPS agreement was enacted in the US, to November 28, 2000,
one day before AIPA entered into force. The second sample includes patents filed from November
29, 2000 up to the final grant day contained in the NBER database (December 26, 2006).
From the patent dataset multiple patent events in the same day for the same firm were eliminated
and for each firm we dropped patents that were granted within an interval of 90 days from each
other. This significantly lowered the number of observations in our sample but is important as the
existence of patents applied for by the same firm, within the estimation period, would have
confounded our results when we measure normal returns.
Besides the patent data, observations with missing stock returns information were also dropped as
described below.
The two periods and AIPA enactment yielded three samples accounting for different types of
patent events. For the pre-AIPA sample we selected all patents for which applications were not
published before grant resulting in 566,969 patents. After dropping observations we ended up with
12,205 patent grant events for 2,859 different firms. For the post-AIPA period we started with a
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Revised as of August 2010. Accessed: June 18th, 2013.
"%!Kogan, L., Papanikolaou, D., Seru, A., and Stoffman, N. (2011) Technology allocation, resource allocation, and
growth. Working Paper, available at: http://ssrn.com/abstract=2193068!
"$!https://sites.google.com/site/patentdataproject/Home.
!
"#!
population of 333,440 patents, and created a dataset for the event study of the aoolication publication
event and for the event study for the grant event. The two are not fully matched because of the
requirements for constructing the normal returns windows. One sample accounts for publication
event, 5,177 events by 1,784 different firms, and the other for grant event, 5,221 events by 1,741
different firms. However, we have a third dataset of 2,257 patents, which have both the application
publication event and the grant event. For the patents in this last group, the normal return window in
both the publication and the grant is uncontaminated by other firm patent events.
Dependent Variable
In equation 4, the dependent variable is the cumulative abnormal returns calculated as described
above. Following the three-factor model proposed by Fama and French (1993) stock returns data
come from the Center for Research in Security Prices (CRSP). Based on stock returns data, we
dropped securities with less than 30 days return information for the normal returns during the
estimation window or no return data during the event window. The other factors in the model,
market returns (all firms in NYSE, AMEX and NASDAQ) minus the risk-free asset return, SMB
(small minus big) portfolio returns and HML (high minus low) portfolio returns, are available at
French’s data library15.
The explanatory variables are displayed in the appendix, table 1A. Tables 1B-1D contain
descriptive statistics and Spearman rank correlations of the explanatory variables, pre and post
AIPA. Appendix 2 displays the CARs distribution for each event sample.
6. RESULTS
Table 2 presents the event study results. The models include company dummies and IPC class
dummies to account for firm and technology heterogeneities. Event day dummies were included
because calendar event clustering may generate contemporaneous covariance between residuals of
different firms (Henderson Jr, 1990). Campbell et al. (1997) suggest that one approach to solve the
covariance between individual sample CARs problem, due to event clustering, is to include dummy
variables for the each event date.
-----------------------------Insert Table 1 about here
-----------------------------!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
15
!
Available at http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.
"$!
Besides its advantages, an event study applied to patent events has some limitations, the most
important of which is sample selection. To that end, our results should be interpreted with that caveat
in mind.
First, as the dependent variable is the firms’ stock cumulative abnormal returns, only patents by
companies listed in the NYSE, AMEX or NASDAQ are selected. It excludes patents applied for by
individual inventors and small, unlisted firms. Second, as explained in section 4, many observations
were dropped to avoid confounding effects. Therefore, firms that patent intensively were most
affected, especially discrete technology industries that rely on numerous separate patents to protect
one product or process (Cohen et al., 2000)16. Consequently, the patents included in the sample were
biased towards not patenting intensive firms, while patenting intensive companies are
underrepresented.
With these caveats in mind we consider the results depicted in table 1. Model 1 presents
estimation result for patents filed between June 1995 and November 2000, before AIPA enactment.
It shows that, on average, a patent grant event generated positive cumulative abnormal returns
(positive and significant constant coefficient), meaning that overall investors evaluated patented
innovations as potential future cash flow generators.
Columns 3 and 5, present baseline estimations for patents filed from November 29, 2000
onwards, after AIPA entered into force. In these two regressions the constant is not significant
meaning that we cannot reject the null hypothesis of zero abnormal returns. Therefore, on average,
after AIPA enactment there was no market response to patent events. The two models report the two
steps of information disclosure established by AIPA, application publication 18 months after the
earliest filing date and the patent grant.
Estimations 2, 4 and 6 present the extended model where some patent and firm characteristics are
included to explain cumulative abnormal returns. Equation 2 displays the results for the pre-AIPA
grant event, where the only significant variable is forward citations. Model 4 shows the estimation
results for the CARs generated by the post-AIPA publication event and model 6 the results regarding
the grant event of patents filed after AIPA. Equation 6 indicates that a firm’s patent stock correlates
positively with CARs generated by the focal patent.17
As mentioned above, two groups of dummies control for companies and technological
characteristics. Companies develop idiosyncratic characteristics which make them more innovative
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
"'!Cohen
et al. (2000) differentiate discrete and complex technologies. The first characterizes products or processes that
comprise many separate patents versus relatively few. I DON’T UNDERSTAND WHAT HE SAYS. THIS SENTENCE
DOESN’T MAKE SENSE.!
"( !<DG1! 5D7C302133! 130?G/013! H151! E15>D5G16! /26! /51! /I/?A/7A1! CED2! 51JC130;! BJC/0?D2! #! H/3! 130?G/016!
@D205DAA?24!>D5!K.LM!<KL!%!6?4?03M!/26!<KL!#!6?4?03;!N13CA03!@D2>?5G!091!ED3?0?I1!/26!3?42?>?@/20!@D551A/0?D2!710H112!
>D5H/56!@?0/0?D23!/26!LON3;!
!
"%!
and patenting intensive. Apart from a firm’s capabilities to innovate, the sector in which it operates
may determine how the market perceives a patent event as bringing economic benefits to the firm.
Heger and Zaby (2013) argue that the invention disclosure required in patent applications imposes
heterogeneous costs to firms which generates heterogeneous propensity to patent.
For the sample of patents filed post-AIPA, we matched the patents that were included in both
event samples, publication and grant. The results are exhibited in appendix 3. Besides grant event
which generated negative CARs, on average, results are not significant, meaning that we cannot
reject the hypothesis of zero abnormal return for this subsample.
7. DISCUSSION AND CONCLUSION
We found partial support for hypotheses 1a and 1b and, no support for hypothesis 2. Patent grant
events generated, on average, positive abnormal returns for patents granted before AIPA and no
effect for patent grant events of patent applications filed after AIPA. Conversely, patent application
publication did not generate abnormal returns. Furthermore, no patent characteristics were
significantly correlated with CARs for patent application events after AIPA.
Traditionally the literature has focused on the exclusivity right conferred by the patent in
exchange of knowledge disclosure (Machlup, 1958). Patents were deemed, mainly, as mechanisms to
rewards innovators and, therefore, spur the innovation activity. Lately, researchers have noted that
patents play an important role as signals for firm technological capabilities. This signaling is
especially relevant for small firms that seek to attract external financing, as they are more dependent
on external capital to support innovative activity (Long, 2002; Hsu and Ziedonis, 2008).
In answering the question where does the value of a patent come from, we investigate the IP
rights effect and signaling effect. Our results suggest that the market considers both value sources,
combined. Taken alone, neither legal rights nor knowledge disclosure generates abnormal returns, as
in the post-AIPA samples. On the other hand, pre-AIPA patent grant event stands for knowledge
disclosure and IP rights entitlement simultaneously which allows the market to reevaluate the firm’s
stock price.
Regarding hypothesis 1b, we find that for the pre-AIPA sample, the only variable that was
significant in determining CARs is forward citations received by the patent. In fact, the results show
that it is patents with a higher number of forward cites that are responsible for the positive abnormal
returns. Citations to the patent in subsequent patent applications have been proved to be correlated
with the value of a patent (Haroff et al., 1999; Gambardella et al., 2008). The more a patent is cited,
the more influential it is to the technological field it belongs to, the higher the value associated with
!
"&!
this patent. Although, forward citations are observed later through the patent life, a positive
correlation between citations received and market response indicates that the market can recognize
the quality of a patent, in line with Hall et al. (p. 35, 2005) who argue that “the market “already
knows” more about the value of particular innovations”.
Further, an interesting finding is that the patent stock is positively correlated with the cumulative
abnormal returns generated due to the patent grant event for the post-AIPA sample. An additional
patent granted means that the patent portfolio increases and, moreover, increases the strength of the
patent portfolio enhancing the firm’s strategic position in the industry. Parchomovsky and Wagner
(2005) argue that the value of a patent lies in the firm’s patent portfolio, as individual patents are
seldom valuable, whereas increasing the patent portfolio increase the rents the firm can collect from
it.
This paper contributes to the patent value literature by investigating the sources of patent value.
Taking advantage of the AIPA which entered into force in November, 2000, we analyze stock
market responses to different knowledge disclosure stages, before and after AIPA. Findings suggest
that patent value rests, jointly, on the legal control and on the signaling role. Conversely, as imposed
by AIPA, patent application publication does not generate value and seems to reduce the abnormal
returns generated to the patent grant event. Additionally, the relevance of the patent stock to explain
the abnormal returns to patent grant event point towards the patent portfolio theory as an explanation
to firms’ patenting behavior even when individual patents by themselves have low value.
Practical implications to managers and policymakers may arise from our results. We show that
market reactions to patent events are based on the exclusivity right as well as the signal contained in
the patent information. When the right grant and the information disclosure happen at the same time,
abnormal returns may indicate a valuable patent.
Nonetheless, these results should be considered with caution. The first limitation regards to the
sample construction, as due to the methodology, patenting intensive firms may be under represented.
Also, the leakage of the patent content through firms’ reports may undermine stock market response
as patents are allowed before the patent grant and firms may choose to disclose this information.
Unfortunately, patent allowance data is not easily available and needs to be manually gathered and
we are in the process of procuring it. Given our results, future research may benefit from a sample
assembling that allows the inclusion of more patenting intensive firms.
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!
#"!
TABLE 1 – OLS estimation. Dependent variable: Cumulative abnormal returns (CARs)
(1)
(2)
VARIABLES
Pre-AIPA Grant Pre-AIPA Grant
BCITES
FCITES
CLAIMS
IPCNUM
PATSTOCK
FIRSTPAT
DOM
SIZE
TPF
(3)
(4)
(5)
(6)
Post-AIPA
Publication
Post-AIPA
Publication
Post-AIPA
Grant
Post-AIPA
Grant
-0.0000615
(0.000048)
0.0000928*
(0.0000472)
-0.00000378
(0.0000578)
0.00173
(0.00133)
0.0000431
(0.0000674)
-0.0000981
(0.000149)
-0.0000423
(0.0000943)
0.000298
(0.00171)
-0.0000828
(0.0000568)
-0.00000799
(0.000104)
0.000118
(0.0000846)
-0.000233
(0.0016)
0.000000204
0.00000105
0.00000526*
(0.00000127)
0.0018
(0.0061)
-0.0024
(0.00457)
0.00519
(0.00637)
0.000000198
(0.00000127)
(0.00000182)
0.000436
(0.00849)
-0.00753
(0.00948)
-0.0192
(0.0146)
-0.00658
(0.00413)
(0.00000276)
-0.00405
(0.00741)
0.00781
(0.00961)
0.0126
(0.0115)
0.00309
(0.00338)
IPC Class
Dummies
Yes
Yes
Yes
Yes
Yes
Yes
Company
Dummies
Yes
Yes
Yes
Yes
Yes
Yes
Event day
Dummies
Yes
Yes
Yes
Yes
Yes
Yes
Constant
0.239*
(0.128)
0.205
(0.141)
-0.00846
(0.0911)
0.123
(0.177)
-0.105
(0.0732)
-0.238*
(0.14)
Observations
R-squared
12,205
0.338
8245
12,194
0.337
8226
5,177
0.472
2802
5,177
0.473
2793
5,221
0.495
2859
5,113
0.508
2754
df
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
!
!
!
!
!
!
##!
O..BPQKR!"!
!
SOTUB!"O!V!W/5?/7A13!C316!0D!130?G/01!091!@5D33X31@0?D2!GD61A!
Dependent
Cumulative abnormal returns (CARS)
Independent
BCITES
Number of cited patents (backward citations)
Number of forward citations received received by a patent, corrected
FCITES
for trunction ( hjtwt )
IPCNUM
Number of 4-digit IPC classes
Number of owned patents, from 1976, per Company at the focal
PATSTOCK
application publication/ patent grant event
FIRSTPAT
Categorical variable = 1 if PATSTOCK=0
DOM
Categorical variable = 1 if patent applicant is a US resident
SIZE
The logarithim of the number of outstanding shares
Categorical variable = 1 if the patent was filed at the European Patent
Office (EPO), the Japanese Patent Office (JPO) and granted at the
U.S. Patent Office (USPTO). Triadic patent families
TPF
Controls
IPC
Companies
Event day
SIC2
SIC4
Categorical variables identifying each patent IPC class
Categorical variables identifying each patent company holder by
PERMNO
Categorical variable identifying each event day
Categorical variable identifying 2-digits SIC codes
Categorical variable identifing 4-digits SIC codes
Source
NBER
NBER
NBER
own
calculations/NBER
NBER
CRSP
OECD18
NBER
Kogan et al. 2011
Kogan et al. 2011 /
NBER
CRSP
CRSP
!
!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
")!OECD
!
Triadic Patent Families database, January 2013!
#$!
SOTUB!"T!V!Q13@5?E0?I1!30/0?30?@3!
Pre-AIPA Grant
Variable
Mean
Std. Dev.
Min
Max
12205
CAR
12205
CLAIMS
12205
BCITES
12194
FCITES
12205
DOM
12205
SIZE
12205
PATSTOCK
12205
FIRSTPAT
12205
TPF
12205
IPCNUM
12205
COMPANIES
12205
IPC
12205
EVENT
Publication Post-AIPA
Obs
Variable
0.000
21.376
16.994
19.578
0.948
10.400
193.599
0.065
0.363
1.531
1341.119
308.526
258.446
0.090
17.836
25.838
32.652
0.222
1.585
1002.903
0.247
0.481
0.994
809.661
177.052
134.544
-0.812
1
1
0
0
2.565
0
0
0
1
1
1
1
1.532
374
541
758.470
1
16.226
45,726
1
1
14
2,859
547
567
Mean
Std. Dev.
Min
Max
5177
5177
5177
5177
5177
5177
5177
5177
5177
5177
5177
5177
5177
-0.001
22.487
22.688
5.226
0.961
10.692
234.993
0.064
0.365
1.518
815.802
236.184
120.270
0.079
17.473
37.225
13.454
0.193
1.546
1183.566
0.245
0.481
1.028
487.425
131.097
68.640
-0.925
1
1
0
0
3.332
0
0
0
1
1
1
1
1.141
180
555
190.091
1
16.201
31,621
1
1
12
1,705
411
278
CAR
CLAIMS
BCITES
FCITES
DOM
SIZE
PATSTOCK
FIRSTPAT
TPF
IPCNUM
COMPANIES
IPC
EVENT
Obs
!
!
!
!
!
!
!
#%!
SOTUB!"T!LD20;!V!Q13@5?E0?I1!30/0?30?@3!
Grant Post-AIPA
Variable
Obs
Mean
Std. Dev.
Min
Max
CAR
CLAIMS
BCITES
FCITES
DOM
SIZE
PATSTOCK
FIRSTPAT
TPF
IPCNUM
COMPANIES
IPC
EVENT
5221
5221
5116
5218
5221
5221
5221
5221
5221
5221
5221
5221
5221
-0.002
23.091
21.709
6.482
0.960
10.688
184.269
0.073
0.383
1.508
815.677
237.608
164.429
0.066
18.296
33.215
14.656
0.196
1.512
921.626
0.260
0.486
1.019
475.049
136.922
70.425
-0.900
1
1
0
0
3.332
0
0
0
1
1
1
1
0.938
265
532
264.325
1
16.201
25,406
1
1
10
1,688
429
278
!
!
#&!
TABLE 1B - Spearman rank correlation matrix of explanatory variables. Pre-AIPA
patents: grant event
CLAIMS
CLAIMS
BCITES
FCITES
DOM
SIZE
FIRSTPAT
TPF
IPCNUM
1
BCITES
0.1738
1
FCITES
0.1324
0.1292
1
DOM
0.0244
0.0222
0.0824
1
SIZE
PATSTOCK
0.0053
0.0278
-0.079
-0.0438
1
PATSTOCK
-0.0435
-0.0175
-0.0876
-0.0082
0.4263
1
FIRSTPAT
0.0045
-0.0015
0.0184
-0.0134
-0.1083
-0.4275
1
TPF
0.0396
0.1071
0.0159
-0.0324
-0.0097
0.0575
-0.0429
1
-0.0049
-0.0333
-0.0715
-0.0118
-0.0015
0.0228
-0.0167
0.1158
IPCNUM
1
TABLE 1C - Spearman rank correlation matrix of explanatory variables. Post-AIPA
patents: publication event
CLAIMS
CLAIMS
BCITES
FCITES
DOM
SIZE
PATSTOCK
FIRSTPAT
TPF
IPCNUM
1
BCITES
0.113
1
FCITES
0.033
0.074
1
DOM
0.0548
0.0905
0.0007
1
SIZE
-0.006
-0.0354
-0.0005
-0.0131
PATSTOCK
-0.0521
-0.0177
0.0412
-0.0015
0.43
1
FIRSTPAT
0.0166
-0.0022
-0.0316
-0.013
-0.0948
-0.4245
1
TPF
0.0206
0.1306
0.0321
-0.0125
-0.0648
0.0319
-0.0478
1
-0.0011
-0.02
-0.0315
-0.0176
0.0134
0.0371
-0.0184
0.123
IPCNUM
1
1
TABLE 1D - Spearman rank correlation matrix of explanatory variables. Post-AIPA
patents: grant event
CLAIMS
CLAIMS
BCITES
FCITES
DOM
SIZE
PATSTOCK
FIRSTPAT
TPF
IPCNUM
1
BCITES
0.1226
1
FCITES
0.062
0.0879
1
DOM
0.0609
0.0926
0.0173
1
SIZE
-0.0015
-0.0259
0.0449
-0.0268
1
PATSTOCK
-0.0318
-0.0257
0.1202
-0.0157
0.3784
1
FIRSTPAT
-0.0036
0.0189
-0.0731
0.0026
-0.0905
-0.4517
1
TPF
0.0159
0.1065
0.028
0.0083
-0.0469
0.0285
-0.0209
1
IPCNUM
0.0089
-0.0129
-0.0089
0.0112
-0.0068
0.013
-0.0042
0.1162
!
1
#'!
APEENDIX 2
Cumulative Abnormal Returns – Distribution
3000
2000
0
1000
Frequency
4000
5000
Figure 2A – CARs Distribution. Pre-AIPA Grant event.
-1
!
-.5
0
.5
Cumulative Abnormal Returns
1
1.5
#(!
1500
1000
0
500
Frequency
2000
2500
Figure 2B – CARs Distribution. Post-AIPA Publication event.
-1
!
-.5
0
.5
Cumulative Abnormal Returns
1
#)!
1000
0
500
Frequency
1500
2000
Figure 2B – CARs Distribution. Post-AIPA Grant event.
-1
!
-.5
0
Cumulative Abnormal Returns
.5
1
#*!
APPENDIX 3
Post-AIPA estimations for matched patents included in the publication and grant samples.
TABLE 3A – OLS estimation. Dependent variable: Cumulative abnormal returns
(1)
VARIABLES
(2)
Post-AIPA
Publication
CLAIMS
FCITES
DOM
SIZE
PATSTOCK
FIRSTPAT
TPF
IPCNUM
Observations
R-squared
df
!
(4)
Post-AIPA Publication Post-AIPA Grant Post-AIPA Grant
-0.0003804
(0.000351)
-0.0000618
(0.0002681)
-0.0002915
(0.0005874)
0.0095716
(0.0368842)
-0.0207728
(0.0427549)
0.0000798
(0.0001774)
0.0129132
(0.0242197)
-0.0186606
(0.0186332)
-0.0059356
(0.0069219)
BCITES
IPC Class
Dummies
Company
Dummies
Event day
Dummies
Constant
(3)
-0.0000727
(0.000243)
-0.000184
(0.000171)
-0.000000546
(0.000299)
0.0106
(0.0376)
0.0186
(0.0306)
0.0000286
(0.00012)
-0.00897
(0.0191)
0.00441
(0.0112)
0.007
(0.00455)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-0.00477
(0.189)
0.2326774
(0.5340174)
-0.166*
(0.931)
-0.56
(0.635)
2,257
0.763
452
2,257
2,257
0.7676
0.811
443
445
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
2,257
0.814
436
$+!