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 MadridBusiness Administration

juliana.pavan.dornelles@gmail.com

Ayfer AliUniversidad Carlos III de Madrid

Business Administrationayfer.ali@uc3m.es

AbstractWe explore two different sources of potential patent value ? the legal right granted by the patent and the technicalinformation disclosure that serves as a signal of firm technological ability and value. We apply an event studymethodology to investigate market response to patent application publication and patent grant events, using theenactment 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 areassociated with abnormal returns generated by patent events.

Jelcodes:G14,O32

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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. Non-

rivalrous 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.

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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 re-

examinations 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,

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! !!!!!!!!!!!!!!!!!!!!!7According 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). 9However, 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 risk-

free 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

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! !!!!!!!!!!!!!!!!!!!!!"$!https://sites.google.com/site/patentdataproject/Home. Revised as of August 2010. Accessed: June 18

th, 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!

! "$!

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)

VARIABLES

(1) (2) (3) (4) (5) (6)

Pre-AIPA Grant Pre-AIPA Grant Post-AIPA

Publication

Post-AIPA

Publication

Post-AIPA

Grant

Post-AIPA

Grant

BCITES

-0.0000615

0.0000431

-0.0000828

(0.000048)

(0.0000674)

(0.0000568)

FCITES

0.0000928*

-0.0000981

-0.00000799

(0.0000472)

(0.000149)

(0.000104)

CLAIMS

-0.00000378

-0.0000423

0.000118

(0.0000578)

(0.0000943)

(0.0000846)

IPCNUM

0.00173

0.000298

-0.000233

(0.00133)

(0.00171)

(0.0016)

PATSTOCK

0.000000204

0.00000105

0.00000526*

(0.00000127)

(0.00000182)

(0.00000276)

FIRSTPAT

0.0018

0.000436

-0.00405

(0.0061)

(0.00849)

(0.00741)

DOM

-0.0024

-0.00753

0.00781

(0.00457)

(0.00948)

(0.00961)

SIZE

0.00519

-0.0192

0.0126

(0.00637)

(0.0146)

(0.0115)

TPF

0.000000198

-0.00658

0.00309

(0.00000127)

(0.00413)

(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* 0.205 -0.00846 0.123 -0.105 -0.238*

(0.128) (0.141) (0.0911) (0.177) (0.0732) (0.14)

Observations 12,205 12,194 5,177 5,177 5,221 5,113

R-squared 0.338 0.337 0.472 0.473 0.495 0.508

df 8245 8226 2802 2793 2859 2754

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 Source

BCITES Number of cited patents (backward citations) NBER

FCITES Number of forward citations received received by a patent, corrected

for trunction ( hjtwt ) NBER

IPCNUM Number of 4-digit IPC classes NBER

PATSTOCK Number of owned patents, from 1976, per Company at the focal

application publication/ patent grant event

own

calculations/NBER

FIRSTPAT Categorical variable = 1 if PATSTOCK=0

DOM Categorical variable = 1 if patent applicant is a US resident NBER

SIZE The logarithim of the number of outstanding shares CRSP

TPF 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

OECD18

Controls

IPC Categorical variables identifying each patent IPC class NBER

Companies Categorical variables identifying each patent company holder by

PERMNO Kogan et al. 2011

Event day Categorical variable identifying each event day

Kogan et al. 2011 /

NBER

SIC2 Categorical variable identifying 2-digits SIC codes CRSP

SIC4 Categorical variable identifing 4-digits SIC codes CRSP

!

!

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! !!!!!!!!!!!!!!!!!!!!!")!OECD Triadic Patent Families database, January 2013!

! #%!

SOTUB!"T!V!Q13@5?E0?I1!30/0?30?@3!

Pre-AIPA Grant

Variable Obs Mean Std. Dev. Min Max

CAR 12205 0.000 0.090 -0.812 1.532

CLAIMS 12205 21.376 17.836 1 374

BCITES 12205 16.994 25.838 1 541

FCITES 12194 19.578 32.652 0 758.470

DOM 12205 0.948 0.222 0 1

SIZE 12205 10.400 1.585 2.565 16.226

PATSTOCK 12205 193.599 1002.903 0 45,726

FIRSTPAT 12205 0.065 0.247 0 1

TPF 12205 0.363 0.481 0 1

IPCNUM 12205 1.531 0.994 1 14

COMPANIES 12205 1341.119 809.661 1 2,859

IPC 12205 308.526 177.052 1 547

EVENT 12205 258.446 134.544 1 567

Publication Post-AIPA

Variable Obs Mean Std. Dev. Min Max

CAR 5177 -0.001 0.079 -0.925 1.141

CLAIMS 5177 22.487 17.473 1 180

BCITES 5177 22.688 37.225 1 555

FCITES 5177 5.226 13.454 0 190.091

DOM 5177 0.961 0.193 0 1

SIZE 5177 10.692 1.546 3.332 16.201

PATSTOCK 5177 234.993 1183.566 0 31,621

FIRSTPAT 5177 0.064 0.245 0 1

TPF 5177 0.365 0.481 0 1

IPCNUM 5177 1.518 1.028 1 12

COMPANIES 5177 815.802 487.425 1 1,705

IPC 5177 236.184 131.097 1 411

EVENT 5177 120.270 68.640 1 278

!

!

!

!

!

!

! #&!

SOTUB!"T!LD20;!V!Q13@5?E0?I1!30/0?30?@3!

Grant Post-AIPA

Variable Obs Mean Std. Dev. Min Max

CAR 5221 -0.002 0.066 -0.900 0.938

CLAIMS 5221 23.091 18.296 1 265

BCITES 5116 21.709 33.215 1 532

FCITES 5218 6.482 14.656 0 264.325

DOM 5221 0.960 0.196 0 1

SIZE 5221 10.688 1.512 3.332 16.201

PATSTOCK 5221 184.269 921.626 0 25,406

FIRSTPAT 5221 0.073 0.260 0 1

TPF 5221 0.383 0.486 0 1

IPCNUM 5221 1.508 1.019 1 10

COMPANIES 5221 815.677 475.049 1 1,688

IPC 5221 237.608 136.922 1 429

EVENT 5221 164.429 70.425 1 278

!

! #'!

TABLE 1B - Spearman rank correlation matrix of explanatory variables. Pre-AIPA

patents: grant event

CLAIMS BCITES FCITES DOM SIZE PATSTOCK FIRSTPAT TPF IPCNUM

CLAIMS 1

BCITES 0.1738 1

FCITES 0.1324 0.1292 1

DOM 0.0244 0.0222 0.0824 1

SIZE 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

IPCNUM -0.0049 -0.0333 -0.0715 -0.0118 -0.0015 0.0228 -0.0167 0.1158 1

TABLE 1C - Spearman rank correlation matrix of explanatory variables. Post-AIPA

patents: publication event

CLAIMS BCITES FCITES DOM SIZE PATSTOCK FIRSTPAT TPF IPCNUM

CLAIMS 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 1

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

IPCNUM -0.0011 -0.02 -0.0315 -0.0176 0.0134 0.0371 -0.0184 0.123 1

TABLE 1D - Spearman rank correlation matrix of explanatory variables. Post-AIPA

patents: grant event

CLAIMS BCITES FCITES DOM SIZE PATSTOCK FIRSTPAT TPF IPCNUM

CLAIMS 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

Figure 2A – CARs Distribution. Pre-AIPA Grant event.

01

00

02

00

03

00

04

00

05

00

0

Fre

que

ncy

-1 -.5 0 .5 1 1.5Cumulative Abnormal Returns

! #)!

Figure 2B – CARs Distribution. Post-AIPA Publication event.

05

00

100

01

50

02

00

02

50

0

Fre

que

ncy

-1 -.5 0 .5 1Cumulative Abnormal Returns

! #*!

Figure 2B – CARs Distribution. Post-AIPA Grant event.

05

00

100

01

50

02

00

0

Fre

que

ncy

-1 -.5 0 .5 1Cumulative Abnormal Returns

! $+!

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) (2) (3) (4)

VARIABLES Post-AIPA

Publication Post-AIPA Publication Post-AIPA Grant Post-AIPA Grant

CLAIMS

-0.0003804

-0.0000727

(0.000351)

(0.000243)

BCITES

-0.0000618

-0.000184

(0.0002681)

(0.000171)

FCITES

-0.0002915

-0.000000546

(0.0005874)

(0.000299)

DOM

0.0095716

0.0106

(0.0368842)

(0.0376)

SIZE

-0.0207728

0.0186

(0.0427549)

(0.0306)

PATSTOCK

0.0000798

0.0000286

(0.0001774)

(0.00012)

FIRSTPAT

0.0129132

-0.00897

(0.0242197)

(0.0191)

TPF

-0.0186606

0.00441

(0.0186332)

(0.0112)

IPCNUM

-0.0059356

0.007

(0.0069219)

(0.00455)

IPC Class

Dummies Yes Yes Yes Yes

Company

Dummies Yes Yes Yes Yes

Event day

Dummies Yes Yes Yes Yes

Constant -0.00477 0.2326774 -0.166* -0.56

(0.189) (0.5340174) (0.931) (0.635)

Observations 2,257 2,257 2,257 2,257

R-squared 0.763 0.7676 0.811 0.814

df 452 443 445 436

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1