Master theses: The Price of Patents, Liquidity, and information: Evidence from Acquisitions of Unlisted European High-Tech Targets

1. The Price of Patents, Liquidity, and Information:
Evidence from Acquisitions of Unlisted European
High-Tech Targets
Master’s Thesis in Finance
Antti Saari
Aalto University School of Economics
September 11, 2010

2. Acknowledgements
This thesis merits a great deal to the sponsoring firm and the questionnaire respondents. As the
author, I would like to especially thank LexFord Enterprises in Finland for sponsoring the thesis,
and for providing valuable insight as regards the theory and results of this thesis. Moreover,
Antti Kosunen and Matti Kanninen at LexFord provided invaluable comments on the survey
design and questions. I would also like to thank all of the participants at the LinkedIn discussion
concerning these questions. All of your comments were of great value, and helped improve the
final questionnaire significantly. Finally, I would also like to extend my sincerest gratitude to
all of the survey respondents. Without those responses, an important part of this study would
have been left unexplored, and a lot of the work mentioned above rendered moot.
Sincerely,
Antti Saari
M.Sc. (econ.) as of September, 2010, thanks to you

3. I
Abstract
This thesis explores the acquisition discounts of unlisted targets reported in US takeovers with
a European high-tech focused dataset, and a specific view on the determinants of that discount.
More specifically, I study the interrelatedness of patents, target shareholders’ demand for liq-
uidity, and the information asymmetry as explanatory measures of the acquisition discount.
To provide a more thorough view of the role of patents, liquidity, and information asymmetry in
acquisitions, I also study the determinants of the target having patented its innovations prior to
the acquisition announcement, and those of the acquirer abnormal announcement return. In the
former, I proceed with a specific focus on dimensions of information asymmetry as reasons for
a target having patents. In the latter, my focus is similar to the study of the acquisition discounts.
On the one hand, my results should provide validation for those found in the US, and on the
other, a more thorough understanding of the listing effect, and the role of patents, liquidity,
and information asymmetry in acquisitions of unlisted high-tech targets. Finally, I compliment
my empirical findings and applicable parts of theory with results from a questionnaire sent
to professionals in venture capital investments, and intellectual property management, both
dealing specifically with M&A transactions.
My results are consistent with my hypotheses that stem from literature and the survey results.
More specifically, I find that decreased availability of liquidity decreases value to both acquirer
and target owners. Moreover, both the survey responses and my empirical analyses suggest
that patents are valuable to target owners, and their quality dimensions are important as well.
Finally, I also find that the market’s perception of the economic rents to patents are attributable
to their assignee, or in this case, the target who owns them prior to the acquisition.

4. II
Contents
1. Introduction 1
1.1. Background and motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2. Research problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3. Contribution to existing literature . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.5. Main findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.6. Structure of the study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2. Theory and literature review 5
2.1. M&A deal valuation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1.1. The role of synergies . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1.2. Determinants of deal price . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2. Returns to bidders around the announcement date . . . . . . . . . . . . . . . . 11
2.3. Information asymmetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3.1. Information asymmetry, discount rates, and the value of the firm . . . . 14
2.3.2. Information asymmetry in acquisitions . . . . . . . . . . . . . . . . . 15
2.3.3. Information asymmetry and technology . . . . . . . . . . . . . . . . . 18
2.4. Acquirer preferences in and motivations behind technology-intensive takeovers 18
2.5. Patents and M&A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.6. The economics and value of patents . . . . . . . . . . . . . . . . . . . . . . . 21
2.6.1. Patent economics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.6.2. The value of patents . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.6.3. Patents as signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

5. III
3. Hypotheses and variables 24
3.1. Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.2. Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.2.1. Acquisition discounts . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.2.2. Acquisition announcement return . . . . . . . . . . . . . . . . . . . . 29
3.2.3. Patenting variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.2.4. Key explanatory variables in the regression models . . . . . . . . . . . 32
4. Data and empirical methodology 32
4.1. Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.1.1. Generalizability of the sample . . . . . . . . . . . . . . . . . . . . . . 34
4.1.2. Descriptive statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.1.3. Correlations between independent variables . . . . . . . . . . . . . . . 39
4.2. Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.2.1. Acquisition discounts . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.2.2. Appropriateness of ordinary least squares for the acquisition discount . 45
4.2.3. Acquirer announcement return . . . . . . . . . . . . . . . . . . . . . . 49
4.2.4. Appropriateness of ordinary least squares for the announcement return . 50
4.2.5. Covariance matrices and the wild bootstrap . . . . . . . . . . . . . . . 52
4.2.6. Patenting probability . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5. Results 57
5.1. Acquisition discounts and abnormal stock acquirer returns - do they exist in
Europe? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.1.1. Acquisition discount . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.1.2. Abnormal announcement returns of stock acquirers . . . . . . . . . . . 59

6. IV
5.2. What determines the acquisition discount? . . . . . . . . . . . . . . . . . . . . 61
5.2.1. Exploring the log-linearity of the distance-discount relation . . . . . . 61
5.2.2. Univariate results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.2.3. Multivariate results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
5.3. What determines the target’s probability to patent? . . . . . . . . . . . . . . . 71
5.4. What determines the announcement return? . . . . . . . . . . . . . . . . . . . 73
5.4.1. Univariate results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
5.4.2. Multivariate results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
6. Summary and conclusions 78
6.1. Summary of hypotheses and evidence . . . . . . . . . . . . . . . . . . . . . . 79
6.2. Discussion and conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
References 85
A. EPO global patent data coverage 90
B. Formulae and derivations 91
C. Design and results of the questionnaire 92

7. V
List of Figures
1. Scatter plot of acquisition discount residuals by observation . . . . . . . . . . . 46
2. Scatter plot of acquisition discount residuals by year . . . . . . . . . . . . . . 47
3. Error term distribution with untransformed dependent variable . . . . . . . . . 48
4. Error term distribution with transformed dependent variable . . . . . . . . . . 49
5. Scatter plot of the announcement return residual term by observation . . . . . . 51
6. Scatter plot of the announcement return residual term by year . . . . . . . . . . 52
7. Distribution of the (heteroskedasticity-consistent) ordinary least squares distur-
bance term . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
8. The impact of ln (Geographic distance) by distance in steps of 100km on D∗ . . 62
9. The impact of ln (Geographic distance) by ln (Geographic distance) in steps of
1 on D∗ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
10. The importance of patents with respect to other asset categories . . . . . . . . . 95
11. The impact of different factors on the value of a patent . . . . . . . . . . . . . 95

8. VI
List of Tables
1. Explanatory variables related to the regression models, and their expected signs 31
2. Raw acquisition multiple data from SDC Platinum. . . . . . . . . . . . . . . . 33
3. Are the unlisted targets with multiple data representative of the population? . . 35
4. Distribution of the sample by country . . . . . . . . . . . . . . . . . . . . . . 36
5. Distribution of the sample by industry . . . . . . . . . . . . . . . . . . . . . . 37
6. Summary statistics of relevant explanatory variables . . . . . . . . . . . . . . . 38
7. Correlations between explanatory variables . . . . . . . . . . . . . . . . . . . 40
8. T-test of difference in acquisition discount means between high-technology and
non-high-technology targets. . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
9. T-test of difference in abnormal acquisition announcement return means be-
tween stock acquirers of high-technology and non-high-technology targets. . . 60
10. Univariate results for the acquisition discount . . . . . . . . . . . . . . . . . . 64
11. Determinants of the acquisition discount. . . . . . . . . . . . . . . . . . . . . 69
12. Marginal effects on the acquisition discount . . . . . . . . . . . . . . . . . . . 70
13. What determines the probability of a target having patents? . . . . . . . . . . . 72
14. Univariate results for the announcement return . . . . . . . . . . . . . . . . . . 74
15. Determinants of the acquisition announcement return. . . . . . . . . . . . . . . 76
16. Hypotheses and empirical evidence. . . . . . . . . . . . . . . . . . . . . . . . 80
17. Jurisdictions covered in the EPO Worldwide patent database, and their abbrevi-
ations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
18. Means and standard deviations of responses to parts III-IV . . . . . . . . . . . 96
19. Means and standard deviations of responses to part V . . . . . . . . . . . . . . 96
20. Means and standard deviations of responses to part VI . . . . . . . . . . . . . . 96

9. 1
1. Introduction
1.1. Background and motivation
Officer (2007) finds that there is an acquisition discount of unlisted targets with respect to com-
parable industry transactions of listed targets in the US. Since the economic reality of lower
liquidity and less stringent disclosure requirements for unlisted versus listed firms persists in
Europe, the acquisition discount is likely to do so as well. If it did not, the feasibility of the
differences in these dimensions as an explanation for the acquisition discount would be debat-
able. Furthermore, Faccio et al. (2006) find that acquirers of unlisted targets earn a significant
positive abnormal return controlling for a multitude of variables. However, the authors state
that ’the fundamental factors that give rise to this listing effect, . . . , remain elusive’.
As already Akerlof (1970) notes, differential information between the buyer and seller of a good
leads (in his example in the used car markets) to the notion that a substantial part of the value of
the good disappears immediately after it has been taken into use. In the case of economic units,
such as companies, the distinction is not as straightforward. However, one can easily ascertain
that the direction, if not the magnitude, of influence related to the difference of information is
the same regardless of the goods being traded. If one was buying fruit randomly from a basket
with both oranges and lemons, one would surely not be willing to pay the same price for the fruit
as if the two were in separate baskets. Equally, if a company is planning to acquire another, they
will not be willing to pay the same price for one of which they know very little as they would
for one of which they know everything.
To the best of my knowledge, no author has previously studied the influence of patents on
the information asymmetries present in M&A transactions. While Officer (2007) finds little
statistical significance for his proxies for information asymmetry, he notes that it is ’notoriously
difficult to measure’, and is still a likely explanation to at least part of the acquisition discount.
Moreover, the sign of the information asymmetry proxy in Officer (2007) is expected, and the
coefficient is economically very significant.
In addition to the above, the reason why information asymmetries are likely to explain the
acquisition discount is that their presence is apparent in the acquisitions of unlisted targets
given the reduced disclosure requirements (Ekkayokkaya et al., 2009; Officer et al., 2009).
Whenever there is an additional risk present, the return requirement of that transaction must
go up. Suppose we have two similar companies, A and B, that we consider as targets. Let us
further assume that there is one difference between the two companies, namely that there is less
information available of company B. Since we know less about company B than company A,
we perceive it riskier and thus award it a higher discount rate. Given that the future cash flows
of both companies are equal (CFA,t = CFB,t , ∀ t), and that the case-specific cost of capital for

10. 2
company B is higher than for A (rB > rA ), company B would be acquired at a discount relative
to company A. (Merton, 1987; O’Hara, 2003; Easley and O’Hara, 2004) More formally, we
have:
T T
CFB,t CFA,t
∑ (1 + rB)t < ∑ (1 + rA)t , ∀rB < rA (1)
t=1 t=1
After Officer (2007) and Faccio et al. (2006), at least two attempts have been made to delve
deeper into the potential information asymmetry explanation of the anomalies related to ac-
quisitions of unlisted targets. One of them is a paper by Officer et al. (2009), who study the
returns to acquiring firms in the US utilizing an event study methodology. Another is a study
by Ekkayokkaya et al. (2009) that explores the long-term returns as well as the announcement
returns to acquirers of unlisted targets in the UK. The consensus of these authors is that there
is, in fact, an information asymmetry problem in acquisitions of private firms. Moreover, the
results from Officer et al. (2009) and Ekkayokkaya et al. (2009) indicate that the presence of
this asymmetry is very significant in both economical and statistical terms.
While Aboody and Lev (2000) find that information asymmetry is especially large in R&D-
intensive firms, it seems especially fruitful, with respect to information asymmetries, to study
some subset of targets that require a lot of R&D effort. One potential subset is technology-
intensive industries, as specified by for example Dessyllas and Hughes (2005a). Given that
patents are, among other things, a signal of the quality of the R&D output of the companies in
question, they can provide powerful evidence of the quality of the company as well, especially
in high-tech industries. When information is a scarce resource, and when there is potential for
easy, costless access to additional information, following the logic above, the additional infor-
mation should merit lower return requirements, and thus lower acquisition discounts. Moreover,
if the predominant source of information asymmetry is the R&D output or technology of the
firm, then patents should be an especially fruitful source of additional information. Further-
more, responses from the questionnaire presented in Appendix C show that practitioners feel
that patents are an important source of both risk and value in M&A transactions (in fact, the re-
spondents view patents to be more important than tangible assets, or other intellectual property),
and hence are an important factor contributing to both information and valuation.
1.2. Research problem
Given the discussion of the previous section, I arrive at the following three-fold research prob-
lem:
1. Is there an acquisition discount of unlisted firms in Europe?

11. 3
2. Are the disparities related to acquisitions of unlisted targets more prevalent in technology-
intensive industries?
3. Are these disparities fueled by asymmetric information and liquidity-needs of target own-
ers?
1.3. Contribution to existing literature
This thesis contributes to the existing literature by being, to the best of my knowledge, the
first to study the power of patents in reducing the information asymmetries related to mergers
and acquisitions. More specifically, I contribute to the work done by Officer (2007), Officer
et al. (2009), and Ekkayokkaya et al. (2009) by delving deeper into the information asymme-
try explanation of acquisitions of non-public targets. Also, I am the first to aim to confirm
the existence of the acquisition discount reported by Officer (2007) with a European data set.
Moreover, where Officer (2007) studies the acquisition discount as a supply-side phenomenon,
I also incorporate the approach of Officer et al. (2009) and Ekkayokkaya et al. (2009), and study
the demand-side determinants of the acquisition disparities1 , and the ’listing effect’ to which
Faccio et al. (2006) refer as the effect of positive abnormal returns to stock acquirers of unlisted
targets. Finally, I compliment my findings with the results of a questionnaire sent to Finnish
venture capital investors, and intellectual property professionals worldwide. The design and
results of the questionnaire are presented in Appendix C.
1.4. Terminology
Before proceeding with theory, methodology, and results, it is worthwhile defining some im-
portant terms concerning patenting.
Assignee
An assignee is a legal (person or non-person) entity to which the title to the intellectual
property included in a patent is transferred.
Citation
In the patent literature, and in the literature studying patents, citations refer to references
in more recent patents to the patent in question. For instance, if I’m granted a patent,
and then someone needs to utilize the solution documented in my patent to come up with
a new patentable technological solution, they will then refer to my patent in their patent
application. That reference will then, from the standpoint of my patent, be a citation.
1 Officer (2007) studies the owners’ need of cash as an explanation for the acquisition discount. My research
problem relates also to the lack of information on the buyers’ side, and mitigation thereof.

12. 4
Infringement
An infringement is the conduct of a breach of contract, law, right, or similar. The in-
fringement of a patent right includes the utilization of the protected technology without
the consent of the assignee (or inventor if he has no successor in title).
Inventor
An inventor is the person (or persons), who invented the technology included in the patent.
According to the European Patent Convention (EPC), Art. 60, the right to a patent belongs
to the inventor or his successor in title (assignee). An inventor may relinquish the title to
the patent, but he will always have the right to be mentioned before the European Patent
Office.
Jurisdiction
Jurisdiction in general refers to the practical authority granted to a formally constituted
legal body to administer justice in a given area of responsibility. In the context of patents,
a jurisdiction refers to a patent office.
Knowledge stock
A knowledge stock includes all the knowledge assets in possession of the firm (measured
in patents, or citation-weighted patents, accumulated R&D-expenses, etc).
Litigation
The conduct of a lawsuit is called litigation.
Patent
A patent is a set of exclusive rights granted by a jurisdiction to an inventor or an assignee
for a limited period of time in exchange for the public disclosure of an invention. Patent
applications are generally made public 18 months after they have been filed. Moreover,
in the European legal context, if two parties try to patent the same invention, the one who
applies for the patent first is considered to have title to all the rights vested in the patent.
Patent family
A patent family includes all the patents protecting the same (not similar, but exactly the
same) technologies in different jurisdictions. For instance, if a technology is protected
by a patent in Europe, the US, and Japan, the patents protecting that technology in those
jurisdictions form a patent family.
INPADOC patent family
Utilized in the European Patent Office (EPO) databases, the INPADOC patent family is an
extension of the usual patent family. More specifically, the INPADOC family includes all
patents linked directly or indirectly by a priority document. Also, the INPADOC family
includes all publications relating to one patent in one jurisdiction as separate members of
the family.

13. 5
Process industry
An industry in which raw materials are refined in a series of stages. Examples include oil
refining, food processing, etc.
1.5. Main findings
One of the most novel results in this thesis is the fact that the acquisition discount of un-
listed targets documented by Officer (2007) prevails over a sample of European firms, and
more importantly, that this discount is both statistically and economically significantly larger in
technology-intensive industries. Moreover, I find that the discount is fueled by both the need
for liquidity by target owners and the asymmetry in information between target and acquirer
owners. Furthermore, my results indicate that the number of patents assigned to a firm have a
both economically and statistically significant positive impact on the valuation of an unlisted
high-tech target amounting up to an average of $250, 000 per patent. Moreover, I find that the
probability that a high-tech target has patents is increasing in other dimensions of information
asymmetry, a finding consistent with the results from the questionnaire. Finally, my analysis
shows that managers of acquirers seemingly close to targets give no regard to the increase in
information asymmetry in distance between the two companies while valuing the deal, whereas
managers of more distant acquirers perceive the increase in information asymmetry resulting
from increased geographic distance.
1.6. Structure of the study
The rest of the thesis is structured as follows: Section 2. presents the existing literature and
theory relevant to my study. Section 3. presents the hypotheses and variables on which I base
the empirical analysis. Section 4. presents the data and methodology, Section 5. presents the
results of the empirical estimations, and Section 6. concludes.
2. Theory and literature review
I proceed with the theory and literature relevant to my topic as follows: first, in Section 2.1.,
I review the extant literature on the valuation of M&A deals, with a view on the specific case
of unlisted targets. Second, Section 2.2. explores the short-term acquirer returns around the
bid announcement date reported in the literature. Third, Section 2.3. explains the relevant
theory related to information asymmetries in the contexts of technology, and M&A-transactions.
Fourth, Section 2.4. reviews the extant literature concerning the preferences of acquirers of

14. 6
high-technology targets. Then, in Section 2.5., I briefly go through the relevant literature on the
interaction between patents and M&A-transactions. Finally, Section 2.6. explains the existing
theories related to the economics of patenting and the value of patents.
2.1. M&A deal valuation
Given that one part of my empirical analysis focuses on the value of M&A deals, or more
specifically, the relatively lower value of deals where the target is unlisted, it is crucial that I
also review the existing literature on those valuations. Of course, most of the literature on deal
pricing is focused on listed targets due to the ease with which information on such firms can
be obtained, but the majority of the economic determinants of value are still likely to have an
impact similar in direction, if not in magnitude.
2.1.1. The role of synergies
Practitioners tend to turn towards synergies when determining bid value. After all, they are the
very reason why a combination of two related firms should be more valuable than the sum of
the two separate firms. The instrumental role of synergies in corporate restructuring stems from
both simple economies of scale in certain corporate functions and the theory of corporate diver-
sification. Economies of scale suggest that a larger corporation can maintain certain functions
at a relatively lower cost than a smaller one. More specifically, a larger corporation can produce
a large amount of goods at a relatively lower price, thus making it more profitable. Diversifica-
tion theory, on the other hand, maintains that firms may have different needs for different types
of assets during the stages of the business cycle. Thus, merging two firms with such different
needs should theoretically lead to a more efficient use of assets throughout the cycle and thus
reduced opportunity costs of holding those assets.
Lang et al. (1989) find that the largest gains to bidders always occur when the bidder has a
wealth of positive return investment opportunities, and the target has none2 . Moreover, Servaes
(1991) posits that also low-q targets gain more the greater the dispersion between the Tobin’s
q’s of the acquirer and target. This also indicates that, adopting the definition of synergy from
Bradley et al. (1988) whereby synergy gains are the sum of increased wealth of the stockholders
of both the acquirer and the target3 , the potential for synergies is higher the larger the difference
in the amount of positive net present value (henceforth, NPV) investment opportunities to the
advantage of the bidder. The results of Lang et al. (1989) and Servaes (1991) may, as the authors
2 Lang et al. (1989) define a low-q firm as one with a Tobin’s q of less than one. With some assumptions, this
suggests that such firms only have investment opportunities with a negative Net Present Value (NPV).
3 As the authors themselves note, this definition assumes that claimants more senior to stockholders do not gain
in wealth as a result of a merger or acquisition.

15. 7
themselves note, be at least partly a result of the high-q acquirers having superior managerial
capabilities, and thus better abilities to utilize the assets of the low-q targets compared to the
target’s pre-acquisition management. However, it is highly unlikely that this is the sole expla-
nation. Other potential sources of synergy include, for instance, more efficient utilization of tax
shields, increased debt capacity, and internal capital markets where funds may be distributed
more efficiently.
2.1.2. Determinants of deal price
Extant literature includes a multitude of potential factors that may or may not influence the deal
premium. Instead of trying to test and list all of them exhaustively, I review the ones that are
most likely to be relevant in the specific case of unlisted technology-intensive targets. Betton
et al. (2008, 2009) discuss a multitude of these characteristics related to the target, the acquirer,
and the deal. However, some of these characteristics are impractical in the case of unlisted
targets, since they are either immeasurable or are unlikely to have similar significance. In the
following, I explain the variables and their expected signs of impact on deal value grouped into
acquirer, target, and bid characteristics as in for example Betton et al. (2008, 2009). Moreover,
I discuss any potential expected differences in impact between public and private targets. I
also explain here the macroeconomic variables that relate to the acquisition discount of unlisted
targets according to Officer (2007). It should be noted that since the final discount-related
regression has a transformed regressand whose value increases as the deal premium increases,
the expected signs stated here are the same as those in that regression, in Tables 10. and 11.
Also, even though I do test for the acquirer characteristics in unreported regressions, I do not
report them due to the significant decrease in sample size.
ACQUIRER CHARACTERISTICS
Market capitalization (+/−)
The market’s perception of the size of the firm. There are two opposite predictions for the direc-
tion of influence of acquirer market value on deal price. Agency theory, or more specifically the
empire building hypothesis, predicts that the managers of large acquirers have a motive to build
their own empire with little regard to the costs to their principals (Jensen, 1986). According to
this theory, it would thus stand to reason that larger firms have a tendency of paying too high
prices for corporate acquisitions, and thus the effect on the deal premium would be positive.
However, larger firms should have higher negotiating power, and it would thus also stand to
reason that they would be able to bargain the deal price down. Hence, the existing theory leads
still to ambiguous conclusions regarding the role of acquirer market value as a determinant of
deal premia.

16. 8
Price to book ratio (+)
A measure of the market’s perception of the positive NPV investment opportunities the firm
has. A price-to-book ratio greater than one indicates that the firm has investment opportunities
with a positive NPV. If the value is less than one, the firm only has negative NPV investment
opportunities.
Toehold ownership (+/−)
A measure of the bidders stake in the target prior to the bid. Betton et al. (2009) find that a larger
toehold decreases the offer premium. However, if the acquirer has a toehold in the target prior
to the acquisition, it is also likely to have some additional information a non-toehold acquirer
would not have. Such reduced information asymmetry might increase deal value assuming that
the target is a high-quality firm (see Section 2.3.). Hence, it is not entirely obvious whether a
toehold ownership increases or decreases the value of the deal.
TARGET CHARACTERISTICS
The vast majority of target characteristics reported in the literature to affect deal premia, for
example stock price run-up or market capitalization, are such that they cannot be measured for
unlisted targets. Moreover, if these variables cannot be measured, they can have no effect on
the deal price. There are a few, however, that are measurable.
Deal size (+)
A proxy for the size of the target. In the literature, target size is usually measured as the market
value of equity. However, as explained above, such a measure is impractical in the case of
unlisted targets. Furthermore, the utilization of deal size as an explanatory variable for the deal
premium generates some methodological issues, the mitigation of which is discussed in Section
4.2.
In the case of unlisted technology-intensive targets it stands to reason that a larger firm would be
relatively more valuable than a smaller one. Given that there is very little information available
on these firms, and that larger firms tend to be more established, it is likely that the insecurity
related to acquiring firms that are not minuscule is somewhat smaller. Even though the extant
literature is not unanimous on the impact of target size on deal premia, Stulz et al. (1990),
for example, do find a positive relation between target announcement return and market value.
Moreover, as stated above, the impact of the size of the deal on the premium in this specific case
is likely to be information-increasing and thus, positive.
Number of patents held (+, −)
It is clear from the existing literature that the number of patents held has a positive impact on the

17. 9
value of a firm (see e.g. Hall et al. (2005, 2007); Griliches (1981)). Moreover, Hussinger and
Grimpe (2007) find that patents also have a positive impact on acquisition premia. However,
firms with multiple patents are also more likely to be ones that need several patents to protect
one product. Moreover, given that patents also mitigate the information asymmetries related to
acquisitions of unlisted high-tech targets, the additional information contained in the marginal
patent is most definitely decreasing in the number of patents. Furthermore, the questionnaire
respondents made several notes with respect to the vast differences in patent properties. More
specifically, they note that one patent can cover anything from a minor part in a device to a
blockbuster drug, and obviously the two patents will merit very different valuations. Moreover,
the more a company has patents, the more likely those patents are to include such that cover
only minor parts of a product. Hence, I expect the marginal impact of a patent on deal value to
be decreasing in the number of patents.
Subsidiary target (−)
Officer (2007) finds a significantly higher acquisition discount for unlisted subsidiary targets
than he does for unlisted stand-alone targets (28% as opposed to 17%). Shleifer and Vishny
(1992) argue that during times of low availability of liquidity from the securities markets, the
peers of firms that need to liquidate some of their assets face the same needs themselves. This
leads to liquidity-distressed firms being forced to sell their assets at prices below their value in
best use. Officer (2007) further argues that this is likely to be the cause for the higher discounts
and thus lower valuations, of unlisted subsidiary targets relative to their stand-alone peers.
DEAL CHARACTERISTICS
Cash consideration (−)
As Officer (2007) states, one motivation for the acquisition discount of the unlisted firms is
their owners’ need for liquidity. Given that the assets of unlisted firms are not highly liquid,
their shareholders only have a few alternative sources of liquidity: loans or IPOs. It thus stands
to reason that the more liquid the method of payment, the higher the discount, and thus, the
lower the price of the deal.
Horizontal merger (+/−)
Once again, extant literature provides two potential, opposing directions of impact of horizontal-
ity of merger on deal premium. More specifically, the theory of corporate diversification would
suggest that non-horizontal mergers should be value adding, since they potentially reduce the
risks related to future cash flows. This explanation is consistent with the results of Betton et al.
(2009). On the other hand, agency theory predicts that since the actions of managers of a multi-
industry company are a lot harder to scrutinize than those of a company operating in a single

18. 10
industry, non-horizontal mergers should be especially value-destructive in that they increase the
potential for private managerial benefits (Jensen, 1986).
Geographic distance between acquirer and target (−)
Geographic proximity is a factor that increases information about the target in acquisitions.
The closer the target is to the acquirer, the more likely the acquirer is to know the target even
before starting the acquisition process. Thus, as Officer (2007) concludes that information
asymmetry is likely to explain a part of the acquisition discount related to unlisted targets,
anything that increases information asymmetry should increase the discount and thereby have a
negative impact on deal value.
However, Grote and Umber (2007) show that managers of acquiring firms are overconfident
about their own abilities to successfully negotiate deals at short distance. The authors further de-
velop an agency theory argument that managers of acquiring firms may seek private benefits by
seeking to acquire targets that are closer. For example, the acquiring managers’ local status may
be increased by the local acquisition. Moreover, the closer target also means, ceteris paribus4 ,
shorter traveling distances and a quieter life, which is in the managers’ preferences, according
to Bertrand and Mullainathan (2003). Thus, it is possible that in short distance transactions the
geographic distance has a smaller, or even negligible, impact on deal value. However, at least
at longer distances, the distance between acquirer and target should deter deal value.
MACROECONOMIC VARIABLES
Overall M&A activity (+)
Officer (2007) posits that one of the most important reasons for acquisition discounts of unlisted
firms is the need for liquidity. Overall M&A activity acts as a proxy for the availability of liq-
uidity. That is, it is a direct indicator of the demand for targets. Thus, when the demand is high,
it stands to reason that acquisition premia are higher as well. There is also a wealth of empirical
evidence supporting the fact that M&A valuations are higher during times of hot M&A mar-
kets. For example, Rhodes-Kropf and Viswanathan (2004) argue that a target will overweight
the firm-specific overvaluation when the market-wide overvaluation is high, and underweight it
when the market-wide overvaluation is low. Firms are thus more prone to accept offers during
market overvaluation than during market undervaluation, which conversely suggests that M&A
activity is higher during overall market overvaluation, which results in higher deal values.
IPO volume (+)
If the need for liquidity is one of the main reasons for the acquisition discount, then the increased
availability of any alternate sources of liquidity is expected to decrease the discount and increase
4 In this case, given that the firm is about to make some acquisition anyway.

19. 11
valuation. For the owners of privately held firms, the most obvious alternative to an M&A
transaction is an IPO. Hence, the hotter the IPO market, the lower a discount there should be
for unlisted firms, since the opportunity cost of selling at a discount increases.
Corporate loan spread (−)
The motivation for a negative impact of corporate loan spread on deal premia follows directly
from the liquidity explanation of unlisted target discounts argued by Officer (2007). Namely,
when alternative sources of liquidity are scarce, the value of those that remain increases. Thus,
when it is relatively more expensive for companies to obtain a loan, the opportunity cost of
obtaining liquidity through selling the firm obviously decreases.
2.2. Returns to bidders around the announcement date
Mergers may occur for several motives. The purest of those motives is to increase the wealth
of shareholders. However, agency theory suggests that this is not the whole story. Managers
may find it in their own self-interest to build their own empire at shareholders’ expense, and
thus enter into value-destroying activities, such as mergers. Moreover, Morck et al. (1990)
find that managerial motives may indeed lead to the destruction of bidder shareholder wealth.
More specifically, the authors contend that catering to managerial motives instead of those of
shareholders destroys shareholder wealth. If the only motive for mergers was to create value to
bidder shareholders, then efficient management should be able to do so on average. However, if
there are other motives, such as empire building, behind bids, the theoretical prediction of bid
announcement wealth effects becomes ambiguous.
Roll (1986) argues that managerial hubris leads to overbidding for targets and thus to the win-
ner’s curse in M&A bids. He posits that M&A bids are analogous to any bidding contest with
the specific property that the initial bid is made by the market. The author further proposes that
in fact there are no economic gains associated with M&A deals, but rather that any gains to the
targets are at least offset by losses to bidders. However, Jensen and Ruback (1983) make a com-
prehensive review of the evidence from US takeovers, and posit that takeovers do create value,
but that most of this value is attributed to target shareholders. Moreover, the authors find that
bidder shareholders do not lose either, on average, but rather win a little or break even. Franks
and Harris (1989) confirm these findings with a comprehensive, albeit already a bit outdated,
dataset of UK takeovers.
More recently, Andrade et al. (2001) also find that bidders that do not use stocks as consideration
gain a negligible return while stock bidders lose 1.5%. Furthermore, the authors find that targets
of both stock and non-stock bidders gain while the targets of stock bidders gain notably less.

20. 12
This finding is consistent with the notion that by using stock as consideration, the bidder dilutes
the impact of potential overpayment. The loss to stock bidders is likely due to the fact that, as
Shleifer and Vishny (2003) argue, by using stock as consideration, the bidder management also
signals that it views its stock to be overvalued. Thus, ceteris paribus, the signal of overvaluation
of the bidder more than offsets the value of stock consideration as a control mechanism.
There are some reasons why the hubris hypothesis is not directly applicable in the case of
unlisted targets. First of all, Roll (1986) relies on the notion that in takeover bids of public
targets, the valuation of a combination of assets for which a market value exists precedes the
bid. Moreover, he argues that if such a valuation results in a lower value than the market value,
the bid is abandoned. The lack of such a market price may indeed be one factor contributing to
the perceived discount in unlisted targets. Basically, the absence of a market price may lead to
the prevalence of some valuations that would have been deemed to be under that market price5 .
However, exploring this relation will be left for future studies. Secondly, bids often convey
other information about the bidder than simply their desire of combining with the target. For
example, Shleifer and Vishny (2003) argue that firms only use stock as a means of payment if
they are overvalued relative to the target. In that case, the method of payment in the bid does
convey additional information regarding the bidder, and thus the assumptions behind the hubris
hypothesis do not fully hold.
As ambiguous as the existing evidence is on returns to bidders in general, so it is on returns
to bidders of unlisted targets. For example, Chang (1998) finds no excess return to acquirers
of private targets while Fuller et al. (2002) find a small, yet significant, abnormal return to
acquirers of unlisted targets. However, even though the methodologies of the two studies differ
quite significantly, both find that while stock acquisitions of public firms are value-destructive,
the use of stock as consideration in bids for unlisted firms is value-creative. Furthermore, Faccio
et al. (2006) unambiguously find a listing effect in acquisitions of Western European unlisted
targets which leads to abnormal acquirer announcement returns. Moreover, Fuller et al. (2002)
find a negligible difference between returns on exclusive stock payment and mixed payment
deals, to the advantage of mixed payment deals. This finding is consistent with the notion that
even in small proportions, stock payments act as powerful monitoring mechanisms, when fair
value is ambiguous. It also indicates that mixed payment may even be preferable to full stock
payment, since it may be a smaller of a signal of overvaluation than the exclusive use of stock
as a means of payment. Also, Officer et al. (2009) find intuitively that the harder the target
firm is to value, the more beneficial the use of stock payment as a monitoring tool is. Hence,
the majority of evidence suggests that in acquisitions of private, hard-to-value firms, the use of
5 Of course, if managers are as apt to determine the fair value of assets as markets are, this type of a phenomenon
should not exists on average even in the absence of the invisible hand. However, if market efficiency is based on
the aggregation of irrational individuals into one rational market, then this aggregation will not exist in the absence
of those markets, and the valuations determined by management are not efficient.

21. 13
stock as a method of payment is clearly and unambiguously beneficial to bidder shareholders.
That is, in acquisitions of private firms, the benefits from monitoring far outweigh their costs6 ,
whereas the opposite is true concerning acquisitions of listed targets.
Betton et al. (2009) find that toehold acquisitions are associated with an economically, but not
statistically negligible negative abnormal announcement return to bidders. The authors also find
that compared to zero toehold acquisitions, the announcement returns are higher in those with
a positive toehold. Given that a pre-acquisition toehold in the target eases its monitoring, one
would expect the existence of a toehold to be associated with value creation to acquirer share-
holders. Also, as Betton et al. (2009) find that a toehold is associated with a lower acquisition
premium, then one could also deduce from this and Roll (1986) that the toehold is associated
with a wealth redistribution from target to acquirer shareholders. However, if the toehold is
associated with an all-cash bid, which is associated with lower returns to acquirers of unlisted
targets (see e.g. Chang (1998); Faccio et al. (2006); Officer et al. (2009); Ekkayokkaya et al.
(2009)), the acquirer is not able to monitor the target’s profitability post-bid, and such a case is
more likely to be associated with negative returns to the acquirer.
Moeller et al. (2005) find that during times of hot M&A markets, M&A transactions destroy ac-
quirer shareholder wealth. Moreover, they find that in the 1998 − 2001 US merger wave, share-
holders of successful bidders lost an average of 12 cents per dollar on the three-day event win-
dow centered around the announcement date of economically significant acquisitions7 . How-
ever, the authors conclude that the average losses to shareholders during the merger wave were
due to a few large loss deals, and that the exclusion of those (only 2% of their sample) would
have led to the notion that acquisitions generate wealth also during merger waves. Thus, it is not
obvious whether an increase in M&A activity has a positive or a negative impact on abnormal
acquirer announcement return.
To my knowledge, there is no empirical evidence regarding the impact of acquired patents on the
acquisition announcement return of the bidder. Hubris theory according to Roll (1986) suggests
that mergers are a zero sum game. Hence, if patents assigned to the target increase deal value to
target shareholders, they should, ceteris paribus, also decrease acquisition returns to the bidder.
Moreover, given that patents are an especially noisy measure of economic value (see e.g. Hall
et al. (2005)), they are obviously difficult to value and thus increase the uncertainty regarding
future profits. Hence, the inclusion of patents in an acquisition merits a higher discount rate
for that specific investment, and thus a lower announcement return to the bidder. On the other
hand, if patents do in fact mitigate information asymmetry in acquisitions of unlisted high-
tech targets, the investors, given rational behavior, perceive this effect, which would lead to
decreased uncertainty with respect to future profits, and hence, to a lower return requirement
6 Thecost here being the signal of overvaluation.
7 The definition of Moeller et al. (2005) includes acquisitions of assets totaling more than 1% of the bidders
pre-acquisition market value.

22. 14
for the acquisition. As there is, as of yet, no empirical evidence to support either conclusion,
and since both conclusions seem equally valid in light of economic theory, I expect patents
assigned to the target to have either a positive or a negative impact on deal value.
Servaes (1991), among others, finds that announcement returns to bidders are lower when there
are other bidders. Moreover, Servaes (1991) and Stulz et al. (1990) find that in such instances
the gains to targets are higher as well. Put together, the increased demand for the specific target
facilitates a wealth redistribution from bidder to target shareholders. While the challenged bid
variable is not related to my hypotheses in any way, it is an important factor to control for.
Finally, Lang et al. (1989) and Servaes (1991) find that tender offer bidders have lower acquisi-
tion returns if they have high Tobin’s q-values. Moreover, the authors also find that tender offer
bidders have higher acquisition returns if they have low Tobin’s q-values. While the tender offer
is of no significance with respect to my hypotheses, it is important to control for it.
2.3. Information asymmetry
Information asymmetries are central to this study in two aspects that are interlinked in my
thesis. First, information asymmetry is closely related to mergers and acquisitions. Moreover,
information asymmetries are higher when the firm in question is unlisted, since it does not
have to conform to as rigorous reporting standards as its listed peers (Officer, 2007; Officer
et al., 2009; Ekkayokkaya et al., 2009). Second, information asymmetries relate intensively to
firms with high levels of R&D (Aboody and Lev, 2000), a great deal of which are classified as
high-technology firms.
In what follows, I review the extant literature on information asymmetry starting with its impact
on firm value in Section 2.3.1. Then, in Section 2.3.2., I proceed to the theoretical framework re-
lating information asymmetries to mergers and acquisitions. Finally, in Section 2.3.3., I review
the literature on information asymmetries in the context of technology-intensive companies.
2.3.1. Information asymmetry, discount rates, and the value of the firm
Commonly used asset pricing models rely on market efficiency, and thus, also on the instan-
taneous dissemination of all publicly available information among investors (Merton, 1987).
While that assumption is a good theoretical baseline, it is not a universally exhaustive approach.
More specifically, as Merton (1987) argues, the return requirement of a firm of which few in-
vestors have enough information8 is higher than in the case of complete information. Thus, as
pointed out in Section 1.1., the present value of the future cash flows of such a firm is lower
8 Here, ’enough information’ is analogous to ’all publicly available information’.

23. 15
in the case of imperfect, or asymmetric, information than it would be in the case of perfect
information. This assertion is more recently confirmed by Easley and O’Hara (2004), who also
maintain that the cost of capital in a case of imperfect information is higher than in the case of
perfect information.
On the other hand, Hellwig (1980) and Grossman (1976) argue that markets that are large
enough relay information so perfectly that they may cancel the incentives to acquire costly
information. However, Grossman (1976) does further state that equilibria may occur in the
presence of incomplete information, and that when information is costly, equilibria most defi-
nitely occur in the presence of asymmetric information.9 Moreover, neither author specifically
defines ’large’. One can thus assume that markets for control over unlisted companies do not
fall into that category.
While Merton (1987) and Easley and O’Hara (2004) take no stand as to the origin of the infor-
mation imperfection as such, they do both include examples of cases where it is the asymmetry
that makes information imperfect. Following that logic, and the argumentation of Grossman
(1976), it is obvious that given two otherwise similar firms, the one of which there is little
information is less valuable to investors than the one of which they know a lot.
2.3.2. Information asymmetry in acquisitions
Leland (1979) shows that in markets with asymmetric information, the equilibrium will always
be attained at socially suboptimal levels of quality. Thus, there will be an over- or undersupply
of goods, which in turn will affect the equilibrium price. I will now shortly develop a simplistic
theoretical framework whereby it may be easier to understand why the balance in mergers and
acquisitions of unlisted targets weighs, on average, on the side of underpricing. The following
is essentially a simplification of the works of Akerlof (1970), Leland (1979), and more recently,
Lehto (2006), for the purposes of this analysis.
Consider the example of ’lemons’ versus good-quality cars in Akerlof (1970), where he argues
that in a worst case of information asymmetry, the goods of worse quality will drive out those
of little better quality in a process that will cause the market to disappear entirely. Obviously,
this is an extreme example, but it does provide an intuitive theoretical starting point for the
case of mergers and acquisitions. Consider a set of firms, T , that are being considered as
targets for acquisition. Let Q be the average quality of the firms. Moreover, let ’quality’ be the
exhaustive set of all characteristics that influence the value of the firm. Thus, in the following
9 When information is costly, and someone obtains it, they will do everything in their power not to signal that
information through their investment decisions, for example. Grossman (1976) maintains, that in such cases, either
equilibrium has to coexist with asymmetric information, or the incentive to acquire the information does not exist,
and thus no-one obtains the information, and it never becomes publicly available.

24. 16
analysis, quality includes not only characteristics of the specific target firm, but also those of
other potential companies, and every other determinant that may influence the valuation of an
acquisition10 .
Now, let us assume that a buyer A is buying firm t1 ∈ T that is of quality q1 > Q. In the presence
of perfect, symmetric information, the price would reflect the true quality of t1 , which also
defines the optimal supply curve for the target t1 as follows:
pS = pS (q1 )
1 1 (2)
The above would be optimal for targets of good quality, and suboptimal for targets of bad
quality11 . This is due to the fact that if all targets are valued according to the average quality of
potential targets, Q, then those of lower than average quality gain, and those of above average
quality lose. If there is no way for the acquirers to discern the true quality of the targets i, qi ,
they will only be willing to pay a price that reflects the average quality, Q, of the set of potential
targets, T . Thus, the demand curve for the target t1 would be defined by:
pD = pD (Q)
1 1 (3)
With no possibilities for monitoring, screening, or signaling, this could lead to the situation
described by Akerlof (1970). This is due to the fact that no owners of target ti of quality qi > Q
would be willing to sell at a price reflecting Q, unless the acquisition prices by definition include
a premium. However, the owners of any target t j of quality q j < Q would be happy to sell. Due
to this adverse selection problem, the market would disappear entirely. When information is
scarce, and the owners of the targets perceive that scarcity and have means to provide additional
information to acquirers, the demand curve for any target ti of quality qi reflects both the true
quality of that target, qi , multiplied by some parameter 0 ≤ λ ≤ 1, and the average quality Q of
the set of potential targets T multiplied by 1 − λ. Thus, the owners of the target are willing to
settle at a value lower than the true value of their firm so long as the premium over the settled
value at least covers the difference between the value of the firm and the value settled upon. The
equilibrium price is hence defined by equating:
pS (qi ) = pD (λqi , (1 − λ) Q) × (1 + P∗ )
i i (4)
10 Even though such an exhaustive definition of ’quality’ seems unrealistic, it is beneficial to the ease of under-
standing the analysis. Moreover, the characteristics of a good are often measured in relation to those of potential
substitutes rather than in absolute terms, which supports my definition.
11 Assuming that the bad quality targets’ trade off is between perfect and imperfect information, and thus, be-
tween the inclusion of average or true quality in the price equation.

25. 17
Where,
λ is the proportion of the true quality qi that can be discerned by the acquirers through a
combination of screening, monitoring, and signaling, as in Akerlof (1970), and
P∗ is the acquisition premium, that reflects potential synergies and other factors that make the
target more valuable to the acquirer than it is to the target shareholders.
In the case of acquisitions, one method of screening is the willingness of the sellers to take
equity in the merged entity as a consideration. One method of signaling for technology-intensive
firms, to which I will return in Section 2.6.3., is patenting the developed technologies.
Officer (2007) finds that private firms are valued at discounts as high as 30% with respect to
comparable public firms in acquisitions in the US. He explains part of the valuation discount
by the fact that information of private firms is less readily available than information of pub-
lic firms. Hence, the discount is partly an adjustment for asymmetric information. Although
the results found by Officer (2007) regarding the asymmetric information explanation are not
statistically strong, they are economically very significant. Moreover, the author also finds that
with his measures, information asymmetries seem to explain around a quarter of the acquisition
discount of unlisted targets. In his analysis, this translates to a 7.5% discount due to information
asymmetry alone.
Moreover, Officer (2007) uses the dispersion in analysts’ earnings forecasts for the parent of
subsidiary targets as a proxy for information asymmetry. He also notes that the subsidiaries
in his sample are relatively small with respect to their parents. Hence, the impact of any un-
certainty regarding the subsidiary’s future earnings is unlikely to be significant enough for the
parent to cause strong variation in analysts’ earnings estimates. Thus, although it may be the
best available proxy for the purposes of Officer (2007), parents’ earnings estimate dispersion is
unlikely to be an accurate proxy of the information asymmetry regarding the subsidiary. The
noise created by the inaccuracy of the proxy variable used may very well be the source of statis-
tical non-significance found for the actual phenomenon. Thus, as the author himself notes, the
explanation of information asymmetry regarding the valuation discount of non-public targets
merits future research.
According to Ekkayokkaya et al. (2009), information asymmetries in the acquisitions of private
targets do in fact result in positive short run and negative long run returns to acquirers. More-
over, the authors contend that the wealth generation effects of acquisitions of private targets are
significantly different from those of acquisitions involving public targets. Furthermore, Officer
et al. (2009) find that the information asymmetry is greatest when targets are the most diffi-
cult to value. Not entirely unlike my study or that of Aboody and Lev (2000), Officer et al.
(2009) try to delve deeper into technology-intensity as a source of information asymmetry.

26. 18
However, whereas they try to use notes to accounting statements, or more specifically, Securi-
ties Exchange Commission (SEC) filings, as indicators of technology-intensity, or intangibles-
intensity, I use industry classifications to specify those targets that are harder to value with
respect to their knowledge assets.
2.3.3. Information asymmetry and technology
Aboody and Lev (2000) show that insider gains are clearly more pronounced in R&D-intensive
firms than in other firms. Moreover, the authors attribute these insider gains to information
asymmetry arising from the uncertainty with respect to the quality of the R&D output on the
one hand, and the volume of the R&D input on the other. In their sample of 253,038 insider
transactions related to 10,013 publicly quoted US firms in the period of 1985 through 1997,
Aboody and Lev (2000) find that by going long on insider purchases of R&D-intensive firms
and short on those of non-R&D-intensive firms, an investor could make an excess return of
almost 1 percent over an average of 25 days, which compounds to an annual abnormal return of
approximately 10 percent.
Given that information asymmetries related to technology are this prevalent among listed firms
in the US, it seems reasonable to expect that there are clear information asymmetries related
to unlisted European high-technology firms as well. Moreover, from the analysis conducted
by Aboody and Lev (2000), it seems clear that technology-intensity is a substantial source of
information asymmetry, and that any potential means to mitigate this information asymmetry
are likely to prove to be valuable.
2.4. Acquirer preferences in and motivations behind technology-intensive
takeovers
After the discussion in Section 2.3., and the assertions of Akerlof (1970), Leland (1979) and
Lehto (2006), it is obvious that more information in a deal is always optimal to the acquirer,
and only suboptimal to the target if it is of poor quality, given that the opportunity cost of that
information does not surpass its value. Thus, when information in general is scarce, one would
expect potential buyers (or in this case, acquirers) to always prefer more information over less.
In this section, I review the empirical findings related to the preferences of acquirers of targets
in high-technology industries.
Among others, Uysal et al. (2008) and Böckerman and Lehto (2006) find that information
asymmetry increases with geographic distance. Also, Grote and Umber (2007) confirm this
finding and further show that the likelihood of deal success decreases with geographic distance.

27. 19
Therefore, it seems that those who acquire firms from further away should be interested in any
possible means of decreasing the information asymmetry, or conversely, in obtaining more in-
formation. This logic is confirmed by the results of Böckerman and Lehto (2006), who show
that this indeed is the case, at least for a sample of Finnish firms. Furthermore, Lehto (2006)
finds that any attribute of the target that eases monitoring increases its likelihood of becoming
targeted by a firm further away. Conversely, a firm that has become acquired by a distant ac-
quirer, is more likely to exhibit characteristics that ease monitoring than a firm that has not been
acquired from a distance.
One example of a relatively cheap source of information in technology-intensive takeovers is
patents12 . Indeed, Ali-Yrkkö et al. (2005) find, using a sample of Finnish unlisted firms, that
the number of patents increases the probability of being acquired across border. This finding
is also consistent with the views of the survey respondents, who, on average, posit that a firm
further away is a more feasible target if it has patents than if it did not. Interestingly, the
authors find little support for the claim that patents would increase the probability of becoming
acquired within borders. Even though the authors themselves provide no clear interpretation for
this result, one might posit that it is due exactly to the fact that geographic distance increases
information asymmetry, and patents are a way of mitigating that asymmetry. Moreover, it seems
intuitively reasonable that the closer the acquirer is to the target, the more it knows about the
R&D productivity of the target, and thus has less needs to find additional information with easy
access.
Dessyllas and Hughes (2005b) find, using a categorization similar to the one I employ, that the
likelihood of a high-tech firm becoming acquired increases with the citation-weighted patent
stocks they hold. Lehto and Lehtoranta (2004) confirm this finding more generally with all
knowledge stocks adding that in process industries accumulated technologies bear little or no
significance to the probability of becoming a target or an acquirer. Moreover, Dessyllas and
Hughes (2005b) find consistently with the findings of Officer (2007) that high-tech firms that
become targets are more liquidity-constrained, and consistently with acquirer rationality and the
findings of Servaes (1991), those firms are also likely to have a low Tobin’s q. Moreover, the
authors show that the targets are, despite a good past record, experiencing a low R&D-output
(i.e. low accumulation rate of their knowledge stock) at the time of the acquisition.
Lehto and Lehtoranta (2004) find that firms become acquirers more frequently, if they have
accumulated large knowledge stocks. Interestingly, however, Dessyllas and Hughes (2005a)
find that acquiring firms in high-tech industries are often in a phase where they experience a
decline in returns to their knowledge assets, use acquisitions as a substitute for in-house R&D
activity, and have accumulated a large knowledge stock prior to the takeover.
12 The cost of patents as a source of information is rather the cost of interpreting that information than that of
obtaining it.

28. 20
It is obvious from the above that acquirers generally prefer more information to less, and are
willing to trade off between alternate sources of information, for instance between distance and
patents. However, there are two variables that include some monitoring aspects whose direction
of influence on the existence of patents in the target is not entirely obvious. Namely, the size
of the target and toehold ownership. There is obviously some positive, albeit unlikely linear,
relation between firm size and the number of patents assigned to the firm (or even the existence
thereof). Since a larger firm can afford to spend more on producing and protecting innovations,
it is also more likely to have patents than a similar smaller firm.
One could easily be led to think that since patents provide additional information, and since
toehold ownership is a powerful pre-acquisition monitoring tool, acquirers might settle for one
at the expense of the other. However, there are some considerations that might lead to an
opposite conclusion. First of all, since patents are powerful competitive tools (Gilbert and
Newbery, 1982), a competitor might want to obtain a toehold in the target to strengthen their
relationships and potentially be less exposed to infringement litigation. Having strengthened
the relationship a priori, the firm may then decide to acquire the target. Also, it is possible
that the target perceives the interest of the competitor in obtaining shares in the target and thus
accelerates its innovative output to obtain a patent before becoming acquired in order to obtain
leverage for valuation negotiations. The above notions are consistent with the results from the
survey, which indicate, that when patenting firms are targeted in acquisitions, one of the key
drivers of them being targeted and their valuation is the existence and quality of their patent
portfolio. Finally, especially in non-horizontal acquisitions, the acquirer may lack the expertise
in the field of the patents of the target, and thus, in fact, require more monitoring due to the fact
that the target has a patent.
2.5. Patents and M&A
Patents and corporate restructuring have been studied separately to a great extent, but much
less so in conjunction (Schulz, 2007). The literature that does study the interrelatedness of
patents and M&A-transactions focuses more on the process whereby corporate restructuring
hinders innovation. For example, De Man and Duysters (2005) argue that the effect of M&A on
innovation is neutral or negative, but there are some scale economies brought about by M&A-
transactions that may result in lower costs of innovation.
Hussinger and Grimpe (2007) show that total asset-weighted patent stocks, patent citation rates,
and the blocking potential of patents determine partly the value of an M&A deal for corporate
acquirers. Intuitively, the authors also find that the blocking potential of patents is very sig-
nificant to corporate acquirers, but non-important at any statistically significant level to private
equity acquirers. This makes sense, since corporate acquirers can make better use of patents

29. 21
that can block competition, and thus allot more value to them. A private equity acquirer cannot
use the blocking potential of a patent to gain market share, whereas for a corporate acquirer,
such potential can be enormously valuable, given a large enough market, and a large enough
growth potential of the acquirer.
2.6. The economics and value of patents
This section covers the extant literature related to the economics of patents. More specifically,
Section 2.6.1. covers the general economics related to patents. Then, Section 2.6.2. discusses
the value of patents and some of the determinants of that value. Finally, Section 2.6.3. covers
the properties of patents as signals, with a specific view to the case of M&A transactions of
high-tech targets.
2.6.1. Patent economics
Patents are a powerful tool for protecting an innovation, provided that the invention is docu-
mented well enough and is, in fact, patentable. A valid patent essentially excludes everyone
else from utilizing the invention for a commercial purpose. As opposed to for instance a trade
secret, the protection provided by the patent is a lot stronger. If the invention is a trade secret
unprotected by a patent, anyone else may reverse-engineer the innovation from a product, and
utilize it for their own purposes.
Given the protective power of patents as opposed to trade secrets, it is optimal for an inventor to
apply for a patent as soon as possible (Hall et al., 2005; Reinganum, 1982). Also, as Reinganum
(1982) argues, a firm can never simply wait for the competitors to innovate even in the case
where the rewards to imitation are the same as those to innovation. This is due to the fact that
there is always a positive probability that none of the competitors will innovate. Moreover,
following the logic above, patenting an innovation can be considered a race to enter a market
with first-mover advantages of a large magnitude. Essentially, the advantage in this case is that
of a monopoly, or an oligopoly where the first mover can charge all of the economic gains from
the second movers through the licensing fees of the patented innovation13 . In the latter case,
the inventing firm can be considered similar to a monopoly with a scale greater than its own
production capacity.
13 Theoretically, this would be the case. However, in practice, there are conventions called reasonable licensing
fees, which are awarded by a court in case of an infringement. Also, there are organisations that try to force
the application of reason in charging licensing fees. Hence, in practice, the first mover can only charge some
reasonable part of the economic gain, not all of it.

30. 22
2.6.2. The value of patents
The interest of the economic literature in patents dates back to Griliches (1981). He is the
first to introduce a market value equation including patents as an explanatory variable. After
Griliches, several studies have been made into the relation between patents and firm value. The
most prominent and the widest in scope is that of Hall et al. (2005), where the authors study the
impacts of accumulated R&D stocks, patents, and citations on market value. More specifically,
Hall et al. (2005) factor in expectations of future citations, and account for the time value of
past and future patent, citation, and R&D stocks.
There are several sources from which patent value can originate. The most significant sources
of value are the right to exclude, the value of patents as strategic tools in business negotiations,
the pre-emption of competition, licensing revenue generation, and the pre-emption of potential
law suits (Gilbert and Newbery, 1982). There are also a few potential cases where patents may,
in fact, destroy value. One of these cases is the one argued by Hall (2005), where the increase in
the patenting rate of a company signals the increased threat of patent-related litigation. Another
potential channel of value destruction, although not as significant in magnitude, is one where
the firm simply patents all the innovations it makes irrespective of whether it is going to ever
need those technologies or not. Sadly, the survey respondents seem to feel that this is a fairly
common intellectual property (IP) management policy.
Academic studies show that patents indeed are a source of value to the firm, when firm value
is measured by the excess of market value over book value. Moreover, the number of patents a
firm has also bears significance on value over the mere existence of patents. Thus, the excess
of market over book value is partly explained by the fact that a firm has patents, but even
more so by the number of patents. (Griliches, 1981; Hall et al., 2005, 2007) Furthermore, Hall
et al. (2005, 2007) show that patents bear significant value to the firm even when past R&D
expenditure is controlled for.
Among others, Cotropia (2009) and Pakes (1986) take a view on patents as real options. While
Pakes (1986) estimates the different characteristics of options in three European countries,
Cotropia (2009) develops a more general, theoretical model of patents as real options. In
essence, he argues that patents can be viewed as call options on the commercialization of the
technology (or other non-obvious knowledge) underlying the patent. Cotropia (2009) further
explains that the post-grant R&D investment is thus viewed as the exercise price of the call,
whereas the pre-grant R&D investment and other costs pertaining to the receipt of the grant
should be viewed as the price of the call.
Both the private value and the market value of patents have been topics of increasing interest,
beginning as early as the 1960s. Recently, Hall et al. (2005, 2007) study the effect a patent has
on the market value of the firm in US and European contexts, respectively. In the US, Hall et al.

31. 23
(2005) find that an extra patent per million dollars of R&D boosts market value by about 2%,
and an extra citation per patent by about 3%. The authors also find that in explaining the market
value of a firm’s knowledge stocks, each of the variables, R&D/Assets, Patents/R&D, and Citations/Patents
adds to the explanatory power of the others with respect to Tobin’s q. That is, each of the three
variables have a both economically and statistically significant impact on market value when
the other two are controlled for.
Hall et al. (2007) find that in Europe EPO patent and citation stocks have an impact on market
value similar in magnitude and significance to that of US firms, but only if the EPO patents in
question have equivalents in the US.
Finally, the survey responses indicate that patent value can originate from multiple sources.
While some of those sources are impossible to measure with the data at hand, they do provide an
important insight into the value of patents. The most important sources of value (in descending
order of importance), according to the responses, are relatedness to the firm’s, or a competitor’s,
core business, importance for future technology, difficulty to invent around, remaining life,
scope, and importance for current technology. All of these scored above 4 on a scale of 1 − 5
in importance for patent value, where 5 = very important. Thus, a valuable patent creates
a competitive advantage either now or in the foreseeable future. Moreover, a patent is most
valuable, when it has a broad scope.
2.6.3. Patents as signals
Even though patents do have value in and of themselves, their most intriguing aspect related to
the current empirical setting is their role as signals of firm quality, to which Long (2002) refers
in his paper. In what follows, I will shortly discuss how patents behave as signals in light of the
framework described in Section 2.3.
Suppose that firms with patents are believed to be of quality qx > Q, and that λ is increasing in
the number of patents with some upper limit. Denoting the number of patents as PCount , we get
the following demand curve for target ti :
pD λ PCount qx , 1 − λ PCount
i Q × (1 + P∗ ) (5)
Recalling the equilibrium from equation 4, we get:
pS (qi ) = pD λ PCount qx , 1 − λ PCount
i i Q × (1 + P∗ ) (6)

32. 24
In order for patents to be credible signals of quality qx , it must hold that for any firm of quality
qi < qx , obtaining the marginal patent when the supply and demand curves intersect must be
more expensive than the increase in value. It must also hold that for companies of quality qi ,
obtaining patents up to the upper limit so that λ = 1 is less expensive than the increase in value
they experience. It must further hold for those firms that the acquisition premium (P∗ ) is large
enough to account for the pricing difference between the demand and supply curve, if patents
are the only signal of quality.
3. Hypotheses and variables
In this section, I present the hypotheses and variables I use to answer my research problem.
More specifically, I present and argue my hypotheses in Section 3.1., and review my variables
in Section 3.2.
3.1. Hypotheses
In this section, I develop my hypotheses with which I aim to answer my research problem.
All of the hypotheses are based on extant literature and theoretical frameworks, as discussed in
Section 2. I also recap the crucial parts of that literature in arguing for the hypotheses.
As Officer (2007) shows that there is an acquisition discount in unlisted US targets, there should
be one for European targets as well. This follows also directly from equation 1, and from the
reasoning presented by Easley and O’Hara (2004). Moreover, the acquisition discounts arise
due to the illiquidity of unlisted assets and relaxed disclosure requirements of unlisted firms.
H1 There is an acquisition discount of unlisted targets in Europe.
Given that part of the explanation for the acquisition discount offered first by Officer (2007),
and later by Officer et al. (2009) and Ekkayokkaya et al. (2009), includes information asymme-
try, and that Aboody and Lev (2000) show that information asymmetry is especially prevalent
among technology-intensive firms, the acquisition discount should also be more pronounced in
those firms.
H2 The acquisition discount is more prevalent in technology-intensive industries.
Officer et al. (2009), and Ekkayokkaya et al. (2009) argue that like the acquisition discounts,
the positive announcement returns earned by bidders who use stock to pay for unlisted targets

33. 25
are partly explained by information asymmetry. Given that Aboody and Lev (2000) find that the
information asymmetries are more prevalent in high-technology firms, the bidder announcement
return for stock bidders should also prevail across stock bidders of technology-intensive targets.
H3 The acquisition announcement returns to acquirers of unlisted targets in technology-
intensive industries are, ceteris paribus, higher for stock-swap transactions.
If the acquisition discount indeed is in part determined by the amount of information asymmetry
between the buyer and seller, it is reasonable, as above, to expect that the discount will increase
as the information asymmetry increases. As Uysal et al. (2008) find even within U.S. firms, the
information asymmetries increase with geographical distance. Following this logic, I arrive at
the following two-fold hypothesis:
H4a The acquisition discount of unlisted targets increases with the natural logarithm of geo-
graphic distance between the target and acquirer headquarters.
H4b The bidder acquisition announcement return decreases in the natural logarithm of the
geographic distance between acquirer and target headquarters.
When information is scarce, any additional source of information should provide additional
value. Lehto and Lehtoranta (2004); Lehto (2006); Böckerman and Lehto (2006) show that this
indeed is the case. For technology firms, one such source can be patents. Thus, the acquisition
discount should be reduced by the existence of patents.
H5 The existence of patents assigned to the target reduces the acquisition discount of unlisted
high-technology firms.
Analogously as in the case for H5, the accumulation of publicly accessible knowledge stock
prior to the acquisition provides useful information regarding the target. Hence, I arrive at the
following hypothesis:
H6a The number of patents assigned to the target reduces the acquisition discount of unlisted
high-technology firms.
If patents indeed are a source of information for the acquirer, it is likely that their value as a
source of information is not linearly increasing in their number. To see this, consider two similar
firms. One of those firms has ten patents that are a direct output of its R&D-efforts. The other
firm has also ten patents that are a direct output of its R&D-efforts, but it also has acquired

34. 26
another ten, and holds yet another ten patents that are not directly related to its business but
are a by-product of inventing the other ten. It is fairly obvious that the thirty patents held by
the other company are surely not three times as valuable as the ten held by the other. Even
without these assumptions, the case of patents as a source of information is analogous to the
case of screwdrivers in a garage. Without any, you’re lost. Owning one to five, you still gain
from having another, but beyond that you’re only drowning in screwdrivers. Furthermore, as
the questionnaire respondents note, a patent’s coverage can be anything from a small piece of a
product to an entire product. Given these differences, a firm with more patents is obviously more
likely to have several patents relating to one product than a firm with less patents. Following
this logic, I arrive at the following hypothesis:
H6b The marginal information value of patents is decreasing in the number of patents assigned
to the target.
Ali-Yrkkö et al. (2005) find that a small Finnish firm with patents is more likely to be targeted
in cross-border M&A transactions than a comparable firm with no patents. Moreover, the au-
thors find no statistically significant impact of patenting over domestic transactions. However,
the patenting variables used in Ali-Yrkkö et al. (2005) for the likelihood of domestic M&A
are economically significant. If the likelihood of becoming a cross-border target increases sub-
stantially when the firm has patents, it should also follow that a target further away from the
acquirer is more likely to have patents. Moreover, Lehto and Lehtoranta (2004); Lehto (2006);
Böckerman and Lehto (2006) find that acquirers that bid for firms further away, are interested
in such firms that have other means whereby the bidder can monitor them. Moreover, while the
questionnaire responses with respect to this point are somewhat volatile, the consensus seems
to indicate that distant targets are considered more feasible if they have patents. Thus, as infor-
mation asymmetry increases in one dimension, the acquirer will seek to decrease it in another.
H7 The likelihood of a target having patents increases with the geographic distance between
the target and the acquirer, and other factors contributing to information asymmetry.
As discussed in Section 2.4., it is likely that pre-negotiation competitive situation has driven the
acquirer management to obtain a toehold in the target due to the patent grant in order to im-
prove corporate relations and thus mitigate expected infringement suit costs. On the other hand,
the target may have perceived increased interest in its acquisition due to the obtained toehold,
and thus accelerated the patenting process. Finally, it is also possible that the acquirer lacks
the required expertise in the field of the patent, and hence, in fact requires the pre-acquisition
toehold monitoring to better ascertain the true value of the acquisition. Moreover, consistently
with the above notions, the questionnaire responses indicate that in several cases, the patent or

35. 27
intellectual property (IP) portfolio of a target may compliment that of the acquirer to an extent
where an acquisition becomes increasingly interesting. In such a case, it may be optimal for
the acquirer to obtain a toehold prior to the acquisition in order to better ascertain the value in
use of the target’s IP portfolio, as well as to facilitate a more friendly appearance of a takeover.
Hence, instead of a potential information trade off hypothesis, I hypothesize the following:
H8 A pre-acquisition toehold in the target increases the probability that the target has patents.
Beginning with Lerner (1994), authors have suggested that different means of assessing patent
quality increase their information content and value to the firm. The usual suspects in literature
are citations, references, scope, and family size. While citations and references receive little
support from the survey respondents as originators of patent value, the other two measures do
obtain significant support.
H9 The quality of the patents assigned to the target, as measured by citations, references,
scope, and the size of the INPADOC patent family, reduces the information asymmetries
related to acquisitions of unlisted high-technology firms.
Officer (2007) finds that a major factor contributing to the acquisition discount in the US is the
need for corporate liquidity. More specifically, he finds that the availability of liquidity has a
negative impact on the acquisition discounts. Thus, I arrive at the following hypothesis:
H10 Easy access to alternate sources of liquidity at the time of the acquisition reduces the
acquisition discount.
In Section 2.2., I discuss the theory related to abnormal acquirer returns around the announce-
ment date. Moreover, I explain that Moeller et al. (2005) find that even during times of hot
M&A markets acquirer shareholders do gain on average when large loss deals are excluded.
Given the small economic size of the transactions I analyze with a mean value of $54m, and
a peak at $984m, my sample does not include deals large enough to result in such enormous
losses. Moreover, as the acquired assets are illiquid by nature, and they are made liquid in
the transaction by pooling them into the assets of a listed company, it is more likely that during
times of high equity valuations (i.e. hot M&A markets), acquirer shareholders would gain more.
Furthermore, Harford (2005) finds that returns to merged firms during merger waves are higher
than prior to or after such waves. Thus, I hypothesize:
H11 High M&A activity at the time of the acquisition increases the acquirer announcement
return.

36. 28
However, the expectation with respect to the IPO market is quite the opposite. When IPO
activity is high in the industry and the target opts for becoming acquired instead of making
an IPO, it reveals to the market that the potential acquirer is willing to pay more for its assets
than it would receive from a public offering, even when demand for such offerings is plentiful.
Moreover, while IPO underpricing is higher in hot IPO markets, for instance Aggarwal et al.
(2002) find that IPO underpricing is not significantly related to IPO proceeds, and thus the
’temperature’ of the IPO market measures a shift of equilibrium in quantity, not in price. Thus,
I arrive at my final hypothesis:
H12 High IPO activity at the time of the acquisition in the industry of the target decreases the
acquirer announcement return.
3.2. Variables
In this section, I present the relevant variables pertaining to the acquisition discount, the likeli-
hood of patenting, and the bidder’s acquisition announcement return. There is an overwhelming
amount of literature related to announcement returns and deal value in acquisitions. I do not at-
tempt to control for all of these variables, since a sizable part of them are specific to acquisitions
of listed targets. However, I do control for the most relevant ones.
3.2.1. Acquisition discounts
Following Officer (2007), I define the acquisition discounts relative to book value of equity, net
income, earnings before interest payments and taxes (EBIT), and sales with respect to compa-
rable transactions in the industry as follows:
Multiple for company i
Di,m = 1 − (7)
Industry mean multiple
Where Di,m = the acquisition discount of firm i relative to multiple m.
More specifically, I define firms belonging to the same industry as ones with the same two-digit
Standard Industry Classification (SIC) code. Also, following Officer (2007), I center the three
year window of the comparable transactions to begin 18 months prior to and end 18 months
past the acquisition announcement date of the firm in question.
I then define the firm-specific acquisition discount as the equally weighted average of the dis-
counts related to each multiple as follows:

37. 29
1 M
Di = ∑ Di,m (8)
M m=1
Where,
M is the number of multiples available for firm i
Di,m is the acquisition discount of firm i relative to multiple m, and
Di is the equally-weighted acquisition discount of firm i relative to all multiples m available.
Here, I deviate from Officer (2007), and follow the logic in Officer et al. (2009) by defining
the acquisition discount as a positive number, when it indeed is a discount, and as a negative
number, when it turns out to be a premium. Hence, when a term has a negative impact on the
acquisition discount, it has a positive impact on deal value and v.v.
3.2.2. Acquisition announcement return
To define the abnormal acquisition announcement return, I first define normal return for firm i
relative to market M by regressing the return of that firm on the market as follows:
RP = α + βi,M ∗ RM
i (9)
Where,
RP is the normal (or predicted) return for firm i with respect to the market M
i
α is the intercept of the model
βi,M is the regression coefficient that describes the change in Ri for a unit-change in RM , or
Covi,M
βi,M = (10)
VarM
RM is the return for market M
To avoid potential anticipation effects of the deal being included in the predicted normal return,
I use a clean estimation period of 360 working days starting 390 working days before the deal
announcement, and ending 30 days before the deal announcement.

38. 30
I then define the abnormal acquisition announcement return, or cumulative abnormal return
(CAR[−t;t]) for some interval t before and after the acquisition announcement as follows:
CARi = Ri − RP
i
= Ri − (α + βi,M ∗ RM ) (11)
3.2.3. Patenting variables
The most important patenting variables I use are the patenting dummy, number of patents and
its square, number of citations, number of references, scope of patents, and the size of the
INPADOC patent family. For the count measures of patents and citations, I also experiment
with asset-weighted patent counts (Patents/ln (Total Assets)), and citation-weighted patent counts (see
e.g. Hall et al. (2005, 2007), and Hussinger and Grimpe (2007)). The measurement of all of the
variables above is unambiguous.
I also experiment with a compound patent portfolio quality measure, where the sums of the
relations between the quality measures and their respective sample means are used as weights
by which the patents are multiplied. So, if a patent has zero citations, then it’s citation-weighted
count is also zero. I arrive at the following measure for each dimension of quality:
P
∑ qi,p, j
p=1
Qi, j = n Y
(12)
1
nY ∑ ∑ qi,p
i=1 p=1
Where,
Qi, j is the quality weighted patent count for firm i for quality dimension j
p represents a patent
Y is the total number of patents in the whole sample
P is the total number of patents for firm i, and
n is the number of firms in the whole sample.
I do not have as extensive a sample as Hall et al. (2007), from which I could construct a compos-
ite quality measure utilizing factor analysis. Thus, my analysis is restricted to averaging across