The Price of Patents, Liquidity, and Information:Evidence from Acquisitions of Unlisted European

High-Tech TargetsMaster’s Thesis in Finance

Antti Saari

Aalto University School of Economics

September 11, 2010

Acknowledgements

This thesis merits a great deal to the sponsoring firm and the questionnaire respondents. As theauthor, 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 surveydesign and questions. I would also like to thank all of the participants at the LinkedIn discussionconcerning these questions. All of your comments were of great value, and helped improve thefinal questionnaire significantly. Finally, I would also like to extend my sincerest gratitude toall of the survey respondents. Without those responses, an important part of this study wouldhave been left unexplored, and a lot of the work mentioned above rendered moot.

Sincerely,

Antti SaariM.Sc. (econ.) as of September, 2010, thanks to you

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Abstract

This thesis explores the acquisition discounts of unlisted targets reported in US takeovers witha 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 inacquisitions, I also study the determinants of the target having patented its innovations prior tothe acquisition announcement, and those of the acquirer abnormal announcement return. In theformer, I proceed with a specific focus on dimensions of information asymmetry as reasons fora 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 theother, 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 complimentmy empirical findings and applicable parts of theory with results from a questionnaire sentto professionals in venture capital investments, and intellectual property management, bothdealing 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 acquirerand target owners. Moreover, both the survey responses and my empirical analyses suggestthat 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 attributableto their assignee, or in this case, the target who owns them prior to the acquisition.

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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

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 inEurope? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

5.1.1. Acquisition discount . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

5.1.2. Abnormal announcement returns of stock acquirers . . . . . . . . . . . 59

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

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 of1 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

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 andnon-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

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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 lowerliquidity and less stringent disclosure requirements for unlisted versus listed firms persists inEurope, the acquisition discount is likely to do so as well. If it did not, the feasibility of thedifferences 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 significantpositive abnormal return controlling for a multitude of variables. However, the authors statethat ’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 goodleads (in his example in the used car markets) to the notion that a substantial part of the value ofthe 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 ascertainthat the direction, if not the magnitude, of influence related to the difference of information isthe same regardless of the goods being traded. If one was buying fruit randomly from a basketwith both oranges and lemons, one would surely not be willing to pay the same price for the fruitas if the two were in separate baskets. Equally, if a company is planning to acquire another, theywill not be willing to pay the same price for one of which they know very little as they wouldfor one of which they know everything.

To the best of my knowledge, no author has previously studied the influence of patents onthe information asymmetries present in M&A transactions. While Officer (2007) finds littlestatistical significance for his proxies for information asymmetry, he notes that it is ’notoriouslydifficult 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 thecoefficient is economically very significant.

In addition to the above, the reason why information asymmetries are likely to explain theacquisition discount is that their presence is apparent in the acquisitions of unlisted targetsgiven 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 mustgo up. Suppose we have two similar companies, A and B, that we consider as targets. Let usfurther assume that there is one difference between the two companies, namely that there is lessinformation 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 flowsof both companies are equal (CFA,t = CFB,t , ∀ t), and that the case-specific cost of capital for

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company B is higher than for A (rB > rA), company B would be acquired at a discount relativeto company A. (Merton, 1987; O’Hara, 2003; Easley and O’Hara, 2004) More formally, wehave:

T

∑t=1

CFB,t

(1+ rB)t <

T

∑t=1

CFA,t

(1+ rA)t ,∀rB < rA (1)

After Officer (2007) and Faccio et al. (2006), at least two attempts have been made to delvedeeper 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 thereturns to acquiring firms in the US utilizing an event study methodology. Another is a studyby Ekkayokkaya et al. (2009) that explores the long-term returns as well as the announcementreturns to acquirers of unlisted targets in the UK. The consensus of these authors is that thereis, in fact, an information asymmetry problem in acquisitions of private firms. Moreover, theresults from Officer et al. (2009) and Ekkayokkaya et al. (2009) indicate that the presence ofthis 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 studysome 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 thatpatents are, among other things, a signal of the quality of the R&D output of the companies inquestion, they can provide powerful evidence of the quality of the company as well, especiallyin high-tech industries. When information is a scarce resource, and when there is potential foreasy, 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 thefirm, then patents should be an especially fruitful source of additional information. Further-more, responses from the questionnaire presented in Appendix C show that practitioners feelthat 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?

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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, thefirst to study the power of patents in reducing the information asymmetries related to mergersand acquisitions. More specifically, I contribute to the work done by Officer (2007), Officeret 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 confirmthe 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 studythe demand-side determinants of the acquisition disparities1, and the ’listing effect’ to whichFaccio et al. (2006) refer as the effect of positive abnormal returns to stock acquirers of unlistedtargets. Finally, I compliment my findings with the results of a questionnaire sent to Finnishventure capital investors, and intellectual property professionals worldwide. The design andresults 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.

AssigneeAn assignee is a legal (person or non-person) entity to which the title to the intellectualproperty included in a patent is transferred.

CitationIn the patent literature, and in the literature studying patents, citations refer to referencesin 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 witha new patentable technological solution, they will then refer to my patent in their patentapplication. That reference will then, from the standpoint of my patent, be a citation.

1Officer (2007) studies the owners’ need of cash as an explanation for the acquisition discount. My researchproblem relates also to the lack of information on the buyers’ side, and mitigation thereof.

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InfringementAn 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 withoutthe consent of the assignee (or inventor if he has no successor in title).

InventorAn 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 belongsto the inventor or his successor in title (assignee). An inventor may relinquish the title tothe patent, but he will always have the right to be mentioned before the European PatentOffice.

JurisdictionJurisdiction in general refers to the practical authority granted to a formally constitutedlegal body to administer justice in a given area of responsibility. In the context of patents,a jurisdiction refers to a patent office.

Knowledge stockA knowledge stock includes all the knowledge assets in possession of the firm (measuredin patents, or citation-weighted patents, accumulated R&D-expenses, etc).

LitigationThe conduct of a lawsuit is called litigation.

PatentA patent is a set of exclusive rights granted by a jurisdiction to an inventor or an assigneefor a limited period of time in exchange for the public disclosure of an invention. Patentapplications 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 whoapplies for the patent first is considered to have title to all the rights vested in the patent.

Patent familyA patent family includes all the patents protecting the same (not similar, but exactly thesame) technologies in different jurisdictions. For instance, if a technology is protectedby a patent in Europe, the US, and Japan, the patents protecting that technology in thosejurisdictions form a patent family.

INPADOC patent familyUtilized in the European Patent Office (EPO) databases, the INPADOC patent family is anextension of the usual patent family. More specifically, the INPADOC family includes allpatents linked directly or indirectly by a priority document. Also, the INPADOC familyincludes all publications relating to one patent in one jurisdiction as separate members ofthe family.

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Process industryAn industry in which raw materials are refined in a series of stages. Examples include oilrefining, 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, andmore importantly, that this discount is both statistically and economically significantly larger intechnology-intensive industries. Moreover, I find that the discount is fueled by both the needfor liquidity by target owners and the asymmetry in information between target and acquirerowners. Furthermore, my results indicate that the number of patents assigned to a firm have aboth economically and statistically significant positive impact on the valuation of an unlistedhigh-tech target amounting up to an average of $250,000 per patent. Moreover, I find that theprobability that a high-tech target has patents is increasing in other dimensions of informationasymmetry, a finding consistent with the results from the questionnaire. Finally, my analysisshows that managers of acquirers seemingly close to targets give no regard to the increase ininformation asymmetry in distance between the two companies while valuing the deal, whereasmanagers of more distant acquirers perceive the increase in information asymmetry resultingfrom increased geographic distance.

1.6. Structure of the study

The rest of the thesis is structured as follows: Section 2. presents the existing literature andtheory relevant to my study. Section 3. presents the hypotheses and variables on which I basethe empirical analysis. Section 4. presents the data and methodology, Section 5. presents theresults 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 caseof unlisted targets. Second, Section 2.2. explores the short-term acquirer returns around thebid announcement date reported in the literature. Third, Section 2.3. explains the relevanttheory 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

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high-technology targets. Then, in Section 2.5., I briefly go through the relevant literature on theinteraction between patents and M&A-transactions. Finally, Section 2.6. explains the existingtheories 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 morespecifically, the relatively lower value of deals where the target is unlisted, it is crucial that Ialso review the existing literature on those valuations. Of course, most of the literature on dealpricing is focused on listed targets due to the ease with which information on such firms canbe obtained, but the majority of the economic determinants of value are still likely to have animpact 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 thevery reason why a combination of two related firms should be more valuable than the sum ofthe two separate firms. The instrumental role of synergies in corporate restructuring stems fromboth 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 functionsat a relatively lower cost than a smaller one. More specifically, a larger corporation can producea 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 typesof assets during the stages of the business cycle. Thus, merging two firms with such differentneeds should theoretically lead to a more efficient use of assets throughout the cycle and thusreduced opportunity costs of holding those assets.

Lang et al. (1989) find that the largest gains to bidders always occur when the bidder has awealth 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’sq’s of the acquirer and target. This also indicates that, adopting the definition of synergy fromBradley et al. (1988) whereby synergy gains are the sum of increased wealth of the stockholdersof both the acquirer and the target3, the potential for synergies is higher the larger the differencein the amount of positive net present value (henceforth, NPV) investment opportunities to theadvantage of the bidder. The results of Lang et al. (1989) and Servaes (1991) may, as the authors

2Lang et al. (1989) define a low-q firm as one with a Tobin’s q of less than one. With some assumptions, thissuggests that such firms only have investment opportunities with a negative Net Present Value (NPV).

3As the authors themselves note, this definition assumes that claimants more senior to stockholders do not gainin wealth as a result of a merger or acquisition.

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themselves note, be at least partly a result of the high-q acquirers having superior managerialcapabilities, and thus better abilities to utilize the assets of the low-q targets compared to thetarget’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 taxshields, increased debt capacity, and internal capital markets where funds may be distributedmore efficiently.

2.1.2. Determinants of deal price

Extant literature includes a multitude of potential factors that may or may not influence the dealpremium. Instead of trying to test and list all of them exhaustively, I review the ones that aremost likely to be relevant in the specific case of unlisted technology-intensive targets. Bettonet 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 unlistedtargets, since they are either immeasurable or are unlikely to have similar significance. In thefollowing, I explain the variables and their expected signs of impact on deal value grouped intoacquirer, 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. Ialso explain here the macroeconomic variables that relate to the acquisition discount of unlistedtargets according to Officer (2007). It should be noted that since the final discount-relatedregression 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 notreport 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 theempire building hypothesis, predicts that the managers of large acquirers have a motive to buildtheir own empire with little regard to the costs to their principals (Jensen, 1986). According tothis theory, it would thus stand to reason that larger firms have a tendency of paying too highprices 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 toreason that they would be able to bargain the deal price down. Hence, the existing theory leadsstill to ambiguous conclusions regarding the role of acquirer market value as a determinant ofdeal premia.

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Price to book ratio (+)

A measure of the market’s perception of the positive NPV investment opportunities the firmhas. A price-to-book ratio greater than one indicates that the firm has investment opportunitieswith a positive NPV. If the value is less than one, the firm only has negative NPV investmentopportunities.

Toehold ownership (+/−)A measure of the bidders stake in the target prior to the bid. Betton et al. (2009) find that a largertoehold decreases the offer premium. However, if the acquirer has a toehold in the target priorto the acquisition, it is also likely to have some additional information a non-toehold acquirerwould not have. Such reduced information asymmetry might increase deal value assuming thatthe target is a high-quality firm (see Section 2.3.). Hence, it is not entirely obvious whether atoehold 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, forexample stock price run-up or market capitalization, are such that they cannot be measured forunlisted targets. Moreover, if these variables cannot be measured, they can have no effect onthe 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 marketvalue of equity. However, as explained above, such a measure is impractical in the case ofunlisted targets. Furthermore, the utilization of deal size as an explanatory variable for the dealpremium generates some methodological issues, the mitigation of which is discussed in Section4.2.

In the case of unlisted technology-intensive targets it stands to reason that a larger firm would berelatively more valuable than a smaller one. Given that there is very little information availableon these firms, and that larger firms tend to be more established, it is likely that the insecurityrelated to acquiring firms that are not minuscule is somewhat smaller. Even though the extantliterature 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 caseis 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

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value of a firm (see e.g. Hall et al. (2005, 2007); Griliches (1981)). Moreover, Hussinger andGrimpe (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 protectone product. Moreover, given that patents also mitigate the information asymmetries related toacquisitions of unlisted high-tech targets, the additional information contained in the marginalpatent is most definitely decreasing in the number of patents. Furthermore, the questionnairerespondents made several notes with respect to the vast differences in patent properties. Morespecifically, they note that one patent can cover anything from a minor part in a device to ablockbuster 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 coveronly minor parts of a product. Hence, I expect the marginal impact of a patent on deal value tobe decreasing in the number of patents.

Subsidiary target (−)Officer (2007) finds a significantly higher acquisition discount for unlisted subsidiary targetsthan 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, thepeers of firms that need to liquidate some of their assets face the same needs themselves. Thisleads to liquidity-distressed firms being forced to sell their assets at prices below their value inbest use. Officer (2007) further argues that this is likely to be the cause for the higher discountsand 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 istheir 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 standsto reason that the more liquid the method of payment, the higher the discount, and thus, thelower 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 wouldsuggest that non-horizontal mergers should be value adding, since they potentially reduce therisks 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

10

industry, non-horizontal mergers should be especially value-destructive in that they increase thepotential 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 evenbefore starting the acquisition process. Thus, as Officer (2007) concludes that informationasymmetry 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 anegative impact on deal value.

However, Grote and Umber (2007) show that managers of acquiring firms are overconfidentabout 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 byseeking to acquire targets that are closer. For example, the acquiring managers’ local status maybe 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, accordingto Bertrand and Mullainathan (2003). Thus, it is possible that in short distance transactions thegeographic distance has a smaller, or even negligible, impact on deal value. However, at leastat 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 unlistedfirms 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 empiricalevidence 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 overweightthe firm-specific overvaluation when the market-wide overvaluation is high, and underweight itwhen the market-wide overvaluation is low. Firms are thus more prone to accept offers duringmarket overvaluation than during market undervaluation, which conversely suggests that M&Aactivity 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 increasedavailability of any alternate sources of liquidity is expected to decrease the discount and increase

4In this case, given that the firm is about to make some acquisition anyway.

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valuation. For the owners of privately held firms, the most obvious alternative to an M&Atransaction is an IPO. Hence, the hotter the IPO market, the lower a discount there should befor 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 directlyfrom 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 ofobtaining 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 wealthof shareholders. However, agency theory suggests that this is not the whole story. Managersmay find it in their own self-interest to build their own empire at shareholders’ expense, andthus 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 ofshareholders destroys shareholder wealth. If the only motive for mergers was to create value tobidder shareholders, then efficient management should be able to do so on average. However, ifthere are other motives, such as empire building, behind bids, the theoretical prediction of bidannouncement 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 withthe specific property that the initial bid is made by the market. The author further proposes thatin fact there are no economic gains associated with M&A deals, but rather that any gains to thetargets 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 thatbidder shareholders do not lose either, on average, but rather win a little or break even. Franksand 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 considerationgain a negligible return while stock bidders lose 1.5%. Furthermore, the authors find that targetsof both stock and non-stock bidders gain while the targets of stock bidders gain notably less.

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This finding is consistent with the notion that by using stock as consideration, the bidder dilutesthe impact of potential overpayment. The loss to stock bidders is likely due to the fact that, asShleifer and Vishny (2003) argue, by using stock as consideration, the bidder management alsosignals that it views its stock to be overvalued. Thus, ceteris paribus, the signal of overvaluationof 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 ofunlisted targets. First of all, Roll (1986) relies on the notion that in takeover bids of publictargets, the valuation of a combination of assets for which a market value exists precedes thebid. 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 tothe perceived discount in unlisted targets. Basically, the absence of a market price may lead tothe 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 conveyother information about the bidder than simply their desire of combining with the target. Forexample, Shleifer and Vishny (2003) argue that firms only use stock as a means of payment ifthey are overvalued relative to the target. In that case, the method of payment in the bid doesconvey additional information regarding the bidder, and thus the assumptions behind the hubrishypothesis do not fully hold.

As ambiguous as the existing evidence is on returns to bidders in general, so it is on returnsto bidders of unlisted targets. For example, Chang (1998) finds no excess return to acquirersof private targets while Fuller et al. (2002) find a small, yet significant, abnormal return toacquirers of unlisted targets. However, even though the methodologies of the two studies differquite 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, Faccioet al. (2006) unambiguously find a listing effect in acquisitions of Western European unlistedtargets which leads to abnormal acquirer announcement returns. Moreover, Fuller et al. (2002)find a negligible difference between returns on exclusive stock payment and mixed paymentdeals, to the advantage of mixed payment deals. This finding is consistent with the notion thateven in small proportions, stock payments act as powerful monitoring mechanisms, when fairvalue is ambiguous. It also indicates that mixed payment may even be preferable to full stockpayment, since it may be a smaller of a signal of overvaluation than the exclusive use of stockas a means of payment. Also, Officer et al. (2009) find intuitively that the harder the targetfirm 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

5Of course, if managers are as apt to determine the fair value of assets as markets are, this type of a phenomenonshould not exists on average even in the absence of the invisible hand. However, if market efficiency is based onthe aggregation of irrational individuals into one rational market, then this aggregation will not exist in the absenceof those markets, and the valuations determined by management are not efficient.

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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 notstatistically negligible negative abnormal announcement return to bidders. The authors also findthat compared to zero toehold acquisitions, the announcement returns are higher in those witha positive toehold. Given that a pre-acquisition toehold in the target eases its monitoring, onewould 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 acquisitionpremium, then one could also deduce from this and Roll (1986) that the toehold is associatedwith a wealth redistribution from target to acquirer shareholders. However, if the toehold isassociated with an all-cash bid, which is associated with lower returns to acquirers of unlistedtargets (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 ismore 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 weredue to a few large loss deals, and that the exclusion of those (only 2% of their sample) wouldhave led to the notion that acquisitions generate wealth also during merger waves. Thus, it is notobvious whether an increase in M&A activity has a positive or a negative impact on abnormalacquirer announcement return.

To my knowledge, there is no empirical evidence regarding the impact of acquired patents on theacquisition announcement return of the bidder. Hubris theory according to Roll (1986) suggeststhat mergers are a zero sum game. Hence, if patents assigned to the target increase deal value totarget 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. Hallet al. (2005)), they are obviously difficult to value and thus increase the uncertainty regardingfuture profits. Hence, the inclusion of patents in an acquisition merits a higher discount ratefor that specific investment, and thus a lower announcement return to the bidder. On the otherhand, 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 todecreased uncertainty with respect to future profits, and hence, to a lower return requirement

6The cost here being the signal of overvaluation.7The definition of Moeller et al. (2005) includes acquisitions of assets totaling more than 1% of the bidders

pre-acquisition market value.

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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 patentsassigned 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 thereare other bidders. Moreover, Servaes (1991) and Stulz et al. (1990) find that in such instancesthe gains to targets are higher as well. Put together, the increased demand for the specific targetfacilitates a wealth redistribution from bidder to target shareholders. While the challenged bidvariable 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 offerbidders have higher acquisition returns if they have low Tobin’s q-values. While the tender offeris 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 mythesis. 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 nothave to conform to as rigorous reporting standards as its listed peers (Officer, 2007; Officeret al., 2009; Ekkayokkaya et al., 2009). Second, information asymmetries relate intensively tofirms with high levels of R&D (Aboody and Lev, 2000), a great deal of which are classified ashigh-technology firms.

In what follows, I review the extant literature on information asymmetry starting with its impacton 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 reviewthe 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, aspointed out in Section 1.1., the present value of the future cash flows of such a firm is lower

8Here, ’enough information’ is analogous to ’all publicly available information’.

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in the case of imperfect, or asymmetric, information than it would be in the case of perfectinformation. This assertion is more recently confirmed by Easley and O’Hara (2004), who alsomaintain that the cost of capital in a case of imperfect information is higher than in the case ofperfect information.

On the other hand, Hellwig (1980) and Grossman (1976) argue that markets that are largeenough relay information so perfectly that they may cancel the incentives to acquire costlyinformation. However, Grossman (1976) does further state that equilibria may occur in thepresence of incomplete information, and that when information is costly, equilibria most defi-nitely occur in the presence of asymmetric information.9 Moreover, neither author specificallydefines ’large’. One can thus assume that markets for control over unlisted companies do notfall 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 asymmetrythat 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 littleinformation 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 alwaysbe attained at socially suboptimal levels of quality. Thus, there will be an over- or undersupplyof goods, which in turn will affect the equilibrium price. I will now shortly develop a simplistictheoretical framework whereby it may be easier to understand why the balance in mergers andacquisitions of unlisted targets weighs, on average, on the side of underpricing. The followingis 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 arguesthat in a worst case of information asymmetry, the goods of worse quality will drive out thoseof 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 thecase of mergers and acquisitions. Consider a set of firms, T , that are being considered astargets for acquisition. Let Q be the average quality of the firms. Moreover, let ’quality’ be theexhaustive set of all characteristics that influence the value of the firm. Thus, in the following

9When information is costly, and someone obtains it, they will do everything in their power not to signal thatinformation through their investment decisions, for example. Grossman (1976) maintains, that in such cases, eitherequilibrium 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.

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analysis, quality includes not only characteristics of the specific target firm, but also those ofother potential companies, and every other determinant that may influence the valuation of anacquisition10.

Now, let us assume that a buyer A is buying firm t1 ∈ T that is of quality q1 > Q. In the presenceof perfect, symmetric information, the price would reflect the true quality of t1, which alsodefines the optimal supply curve for the target t1 as follows:

pS1 = pS

1 (q1) (2)

The above would be optimal for targets of good quality, and suboptimal for targets of badquality11. This is due to the fact that if all targets are valued according to the average quality ofpotential targets, Q, then those of lower than average quality gain, and those of above averagequality 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 potentialtargets, T . Thus, the demand curve for the target t1 would be defined by:

pD1 = pD

1 (Q) (3)

With no possibilities for monitoring, screening, or signaling, this could lead to the situationdescribed 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 includea premium. However, the owners of any target t j of quality q j < Q would be happy to sell. Dueto this adverse selection problem, the market would disappear entirely. When information isscarce, and the owners of the targets perceive that scarcity and have means to provide additionalinformation to acquirers, the demand curve for any target ti of quality qi reflects both the truequality of that target, qi, multiplied by some parameter 0≤ λ≤ 1, and the average quality Q ofthe set of potential targets T multiplied by 1−λ. Thus, the owners of the target are willing tosettle at a value lower than the true value of their firm so long as the premium over the settledvalue at least covers the difference between the value of the firm and the value settled upon. Theequilibrium price is hence defined by equating:

pSi (qi) = pD

i (λqi,(1−λ)Q)× (1+P∗) (4)

10Even 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 potentialsubstitutes rather than in absolute terms, which supports my definition.

11Assuming 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.

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Where,

λ is the proportion of the true quality qi that can be discerned by the acquirers through acombination of screening, monitoring, and signaling, as in Akerlof (1970), and

P∗ is the acquisition premium, that reflects potential synergies and other factors that make thetarget 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 takeequity in the merged entity as a consideration. One method of signaling for technology-intensivefirms, 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 tocomparable public firms in acquisitions in the US. He explains part of the valuation discountby 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. Althoughthe results found by Officer (2007) regarding the asymmetric information explanation are notstatistically strong, they are economically very significant. Moreover, the author also finds thatwith his measures, information asymmetries seem to explain around a quarter of the acquisitiondiscount of unlisted targets. In his analysis, this translates to a 7.5% discount due to informationasymmetry alone.

Moreover, Officer (2007) uses the dispersion in analysts’ earnings forecasts for the parent ofsubsidiary targets as a proxy for information asymmetry. He also notes that the subsidiariesin 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 theparent to cause strong variation in analysts’ earnings estimates. Thus, although it may be thebest available proxy for the purposes of Officer (2007), parents’ earnings estimate dispersion isunlikely to be an accurate proxy of the information asymmetry regarding the subsidiary. Thenoise 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, theexplanation of information asymmetry regarding the valuation discount of non-public targetsmerits future research.

According to Ekkayokkaya et al. (2009), information asymmetries in the acquisitions of privatetargets 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 aresignificantly different from those of acquisitions involving public targets. Furthermore, Officeret 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.

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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 withrespect 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-intensivefirms than in other firms. Moreover, the authors attribute these insider gains to informationasymmetry arising from the uncertainty with respect to the quality of the R&D output on theone hand, and the volume of the R&D input on the other. In their sample of 253,038 insidertransactions 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 firmsand short on those of non-R&D-intensive firms, an investor could make an excess return ofalmost 1 percent over an average of 25 days, which compounds to an annual abnormal return ofapproximately 10 percent.

Given that information asymmetries related to technology are this prevalent among listed firmsin the US, it seems reasonable to expect that there are clear information asymmetries relatedto unlisted European high-technology firms as well. Moreover, from the analysis conductedby Aboody and Lev (2000), it seems clear that technology-intensity is a substantial source ofinformation asymmetry, and that any potential means to mitigate this information asymmetryare likely to prove to be valuable.

2.4. Acquirer preferences in and motivations behind technology-intensivetakeovers

After the discussion in Section 2.3., and the assertions of Akerlof (1970), Leland (1979) andLehto (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 thatinformation does not surpass its value. Thus, when information in general is scarce, one wouldexpect 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 targetsin high-technology industries.

Among others, Uysal et al. (2008) and Böckerman and Lehto (2006) find that informationasymmetry increases with geographic distance. Also, Grote and Umber (2007) confirm thisfinding and further show that the likelihood of deal success decreases with geographic distance.

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Therefore, it seems that those who acquire firms from further away should be interested in anypossible 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 showthat 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 becomingtargeted 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 beenacquired from a distance.

One example of a relatively cheap source of information in technology-intensive takeovers ispatents12. Indeed, Ali-Yrkkö et al. (2005) find, using a sample of Finnish unlisted firms, thatthe number of patents increases the probability of being acquired across border. This findingis also consistent with the views of the survey respondents, who, on average, posit that a firmfurther away is a more feasible target if it has patents than if it did not. Interestingly, theauthors find little support for the claim that patents would increase the probability of becomingacquired within borders. Even though the authors themselves provide no clear interpretation forthis result, one might posit that it is due exactly to the fact that geographic distance increasesinformation asymmetry, and patents are a way of mitigating that asymmetry. Moreover, it seemsintuitively reasonable that the closer the acquirer is to the target, the more it knows about theR&D productivity of the target, and thus has less needs to find additional information with easyaccess.

Dessyllas and Hughes (2005b) find, using a categorization similar to the one I employ, that thelikelihood of a high-tech firm becoming acquired increases with the citation-weighted patentstocks they hold. Lehto and Lehtoranta (2004) confirm this finding more generally with allknowledge stocks adding that in process industries accumulated technologies bear little or nosignificance to the probability of becoming a target or an acquirer. Moreover, Dessyllas andHughes (2005b) find consistently with the findings of Officer (2007) that high-tech firms thatbecome targets are more liquidity-constrained, and consistently with acquirer rationality and thefindings of Servaes (1991), those firms are also likely to have a low Tobin’s q. Moreover, theauthors 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 haveaccumulated 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 adecline in returns to their knowledge assets, use acquisitions as a substitute for in-house R&Dactivity, and have accumulated a large knowledge stock prior to the takeover.

12The cost of patents as a source of information is rather the cost of interpreting that information than that ofobtaining it.

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It is obvious from the above that acquirers generally prefer more information to less, and arewilling to trade off between alternate sources of information, for instance between distance andpatents. However, there are two variables that include some monitoring aspects whose directionof influence on the existence of patents in the target is not entirely obvious. Namely, the sizeof 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 existencethereof). 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 sincetoehold ownership is a powerful pre-acquisition monitoring tool, acquirers might settle for oneat the expense of the other. However, there are some considerations that might lead to anopposite conclusion. First of all, since patents are powerful competitive tools (Gilbert andNewbery, 1982), a competitor might want to obtain a toehold in the target to strengthen theirrelationships and potentially be less exposed to infringement litigation. Having strengthenedthe relationship a priori, the firm may then decide to acquire the target. Also, it is possiblethat the target perceives the interest of the competitor in obtaining shares in the target and thusaccelerates its innovative output to obtain a patent before becoming acquired in order to obtainleverage for valuation negotiations. The above notions are consistent with the results from thesurvey, which indicate, that when patenting firms are targeted in acquisitions, one of the keydrivers of them being targeted and their valuation is the existence and quality of their patentportfolio. Finally, especially in non-horizontal acquisitions, the acquirer may lack the expertisein the field of the patents of the target, and thus, in fact, require more monitoring due to the factthat the target has a patent.

2.5. Patents and M&A

Patents and corporate restructuring have been studied separately to a great extent, but muchless so in conjunction (Schulz, 2007). The literature that does study the interrelatedness ofpatents and M&A-transactions focuses more on the process whereby corporate restructuringhinders innovation. For example, De Man and Duysters (2005) argue that the effect of M&A oninnovation 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 corporateacquirers. 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 privateequity acquirers. This makes sense, since corporate acquirers can make better use of patents

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that can block competition, and thus allot more value to them. A private equity acquirer cannotuse 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 enoughgrowth 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. discussesthe value of patents and some of the determinants of that value. Finally, Section 2.6.3. coversthe properties of patents as signals, with a specific view to the case of M&A transactions ofhigh-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 everyoneelse from utilizing the invention for a commercial purpose. As opposed to for instance a tradesecret, the protection provided by the patent is a lot stronger. If the invention is a trade secretunprotected by a patent, anyone else may reverse-engineer the innovation from a product, andutilize it for their own purposes.

Given the protective power of patents as opposed to trade secrets, it is optimal for an inventor toapply 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 casewhere the rewards to imitation are the same as those to innovation. This is due to the fact thatthere 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 marketwith first-mover advantages of a large magnitude. Essentially, the advantage in this case is thatof a monopoly, or an oligopoly where the first mover can charge all of the economic gains fromthe 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 ownproduction capacity.

13Theoretically, this would be the case. However, in practice, there are conventions called reasonable licensingfees, which are awarded by a court in case of an infringement. Also, there are organisations that try to forcethe application of reason in charging licensing fees. Hence, in practice, the first mover can only charge somereasonable part of the economic gain, not all of it.

22

2.6.2. The value of patents

The interest of the economic literature in patents dates back to Griliches (1981). He is thefirst to introduce a market value equation including patents as an explanatory variable. AfterGriliches, several studies have been made into the relation between patents and firm value. Themost prominent and the widest in scope is that of Hall et al. (2005), where the authors study theimpacts 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 ofpast and future patent, citation, and R&D stocks.

There are several sources from which patent value can originate. The most significant sourcesof 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 potentiallaw 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 inthe patenting rate of a company signals the increased threat of patent-related litigation. Anotherpotential channel of value destruction, although not as significant in magnitude, is one wherethe firm simply patents all the innovations it makes irrespective of whether it is going to everneed those technologies or not. Sadly, the survey respondents seem to feel that this is a fairlycommon intellectual property (IP) management policy.

Academic studies show that patents indeed are a source of value to the firm, when firm valueis measured by the excess of market value over book value. Moreover, the number of patents afirm has also bears significance on value over the mere existence of patents. Thus, the excessof market over book value is partly explained by the fact that a firm has patents, but evenmore so by the number of patents. (Griliches, 1981; Hall et al., 2005, 2007) Furthermore, Hallet al. (2005, 2007) show that patents bear significant value to the firm even when past R&Dexpenditure is controlled for.

Among others, Cotropia (2009) and Pakes (1986) take a view on patents as real options. WhilePakes (1986) estimates the different characteristics of options in three European countries,Cotropia (2009) develops a more general, theoretical model of patents as real options. Inessence, he argues that patents can be viewed as call options on the commercialization of thetechnology (or other non-obvious knowledge) underlying the patent. Cotropia (2009) furtherexplains 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 grantshould 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 hason the market value of the firm in US and European contexts, respectively. In the US, Hall et al.

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 marketvalue 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 threevariables have a both economically and statistically significant impact on market value whenthe other two are controlled for.

Hall et al. (2007) find that in Europe EPO patent and citation stocks have an impact on marketvalue similar in magnitude and significance to that of US firms, but only if the EPO patents inquestion 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 animportant insight into the value of patents. The most important sources of value (in descendingorder 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− 5in importance for patent value, where 5 = very important. Thus, a valuable patent createsa competitive advantage either now or in the foreseeable future. Moreover, a patent is mostvaluable, 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 tothe current empirical setting is their role as signals of firm quality, to which Long (2002) refersin his paper. In what follows, I will shortly discuss how patents behave as signals in light of theframework described in Section 2.3.

Suppose that firms with patents are believed to be of quality qx > Q, and that λ is increasing inthe number of patents with some upper limit. Denoting the number of patents as PCount , we getthe following demand curve for target ti:

pDi

(λ

(PCount

)qx,(

1−λ

(PCount

))Q)× (1+P∗) (5)

Recalling the equilibrium from equation 4, we get:

pSi (qi) = pD

i

(λ

(PCount

)qx,(

1−λ

(PCount

))Q)× (1+P∗) (6)

24

In order for patents to be credible signals of quality qx, it must hold that for any firm of qualityqi < qx, obtaining the marginal patent when the supply and demand curves intersect must bemore 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 valuethey experience. It must further hold for those firms that the acquisition premium (P∗) is largeenough to account for the pricing difference between the demand and supply curve, if patentsare 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 variablesin 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 inSection 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 shouldbe one for European targets as well. This follows also directly from equation 1, and from thereasoning presented by Easley and O’Hara (2004). Moreover, the acquisition discounts arisedue 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 prevalentamong technology-intensive firms, the acquisition discount should also be more pronounced inthose 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

25

are partly explained by information asymmetry. Given that Aboody and Lev (2000) find that theinformation asymmetries are more prevalent in high-technology firms, the bidder announcementreturn 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 asymmetrybetween the buyer and seller, it is reasonable, as above, to expect that the discount will increaseas the information asymmetry increases. As Uysal et al. (2008) find even within U.S. firms, theinformation asymmetries increase with geographical distance. Following this logic, I arrive atthe 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 thegeographic distance between acquirer and target headquarters.

When information is scarce, any additional source of information should provide additionalvalue. Lehto and Lehtoranta (2004); Lehto (2006); Böckerman and Lehto (2006) show that thisindeed is the case. For technology firms, one such source can be patents. Thus, the acquisitiondiscount should be reduced by the existence of patents.

H5 The existence of patents assigned to the target reduces the acquisition discount of unlistedhigh-technology firms.

Analogously as in the case for H5, the accumulation of publicly accessible knowledge stockprior to the acquisition provides useful information regarding the target. Hence, I arrive at thefollowing hypothesis:

H6a The number of patents assigned to the target reduces the acquisition discount of unlistedhigh-technology firms.

If patents indeed are a source of information for the acquirer, it is likely that their value as asource of information is not linearly increasing in their number. To see this, consider two similarfirms. One of those firms has ten patents that are a direct output of its R&D-efforts. The otherfirm has also ten patents that are a direct output of its R&D-efforts, but it also has acquired

26

another ten, and holds yet another ten patents that are not directly related to its business butare a by-product of inventing the other ten. It is fairly obvious that the thirty patents held bythe other company are surely not three times as valuable as the ten held by the other. Evenwithout these assumptions, the case of patents as a source of information is analogous to thecase of screwdrivers in a garage. Without any, you’re lost. Owning one to five, you still gainfrom having another, but beyond that you’re only drowning in screwdrivers. Furthermore, asthe questionnaire respondents note, a patent’s coverage can be anything from a small piece of aproduct to an entire product. Given these differences, a firm with more patents is obviously morelikely to have several patents relating to one product than a firm with less patents. Followingthis logic, I arrive at the following hypothesis:

H6b The marginal information value of patents is decreasing in the number of patents assignedto the target.

Ali-Yrkkö et al. (2005) find that a small Finnish firm with patents is more likely to be targetedin 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&Aare 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 theacquirer 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 interestedin such firms that have other means whereby the bidder can monitor them. Moreover, while thequestionnaire responses with respect to this point are somewhat volatile, the consensus seemsto 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 betweenthe 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 theacquirer 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 lacksthe required expertise in the field of the patent, and hence, in fact requires the pre-acquisitiontoehold monitoring to better ascertain the true value of the acquisition. Moreover, consistentlywith the above notions, the questionnaire responses indicate that in several cases, the patent or

27

intellectual property (IP) portfolio of a target may compliment that of the acquirer to an extentwhere an acquisition becomes increasingly interesting. In such a case, it may be optimal forthe acquirer to obtain a toehold prior to the acquisition in order to better ascertain the value inuse 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 patentquality increase their information content and value to the firm. The usual suspects in literatureare citations, references, scope, and family size. While citations and references receive littlesupport from the survey respondents as originators of patent value, the other two measures doobtain 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 asymmetriesrelated to acquisitions of unlisted high-technology firms.

Officer (2007) finds that a major factor contributing to the acquisition discount in the US is theneed for corporate liquidity. More specifically, he finds that the availability of liquidity has anegative 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 theacquisition 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 hotM&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, anda peak at $984m, my sample does not include deals large enough to result in such enormouslosses. Moreover, as the acquired assets are illiquid by nature, and they are made liquid inthe transaction by pooling them into the assets of a listed company, it is more likely that duringtimes 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 higherthan prior to or after such waves. Thus, I hypothesize:

H11 High M&A activity at the time of the acquisition increases the acquirer announcementreturn.

28

However, the expectation with respect to the IPO market is quite the opposite. When IPOactivity is high in the industry and the target opts for becoming acquired instead of makingan IPO, it reveals to the market that the potential acquirer is willing to pay more for its assetsthan 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 theacquirer 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 overwhelmingamount 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 acquisitionsof 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, netincome, earnings before interest payments and taxes (EBIT), and sales with respect to compa-rable transactions in the industry as follows:

Di,m = 1−Multiple for company iIndustry mean multiple

(7)

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-digitStandard Industry Classification (SIC) code. Also, following Officer (2007), I center the threeyear window of the comparable transactions to begin 18 months prior to and end 18 monthspast 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:

29

Di =1M

M

∑m=1

Di,m (8)

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 definingthe acquisition discount as a positive number, when it indeed is a discount, and as a negativenumber, when it turns out to be a premium. Hence, when a term has a negative impact on theacquisition 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:

RPi = α+βi,M ∗RM (9)

Where,

RPi is the normal (or predicted) return for firm i with respect to the market M

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

βi,M =Covi,M

VarM(10)

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 dealannouncement, and ending 30 days before the deal announcement.

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−RPi

= 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 andits square, number of citations, number of references, scope of patents, and the size of theINPADOC patent family. For the count measures of patents and citations, I also experimentwith asset-weighted patent counts (Patents/ln(Total Assets)), and citation-weighted patent counts (seee.g. Hall et al. (2005, 2007), and Hussinger and Grimpe (2007)). The measurement of all of thevariables above is unambiguous.

I also experiment with a compound patent portfolio quality measure, where the sums of therelations between the quality measures and their respective sample means are used as weightsby which the patents are multiplied. So, if a patent has zero citations, then it’s citation-weightedcount is also zero. I arrive at the following measure for each dimension of quality:

Qi, j =

P

∑p=1

qi,p, j

1nY

n

∑i=1

Y

∑p=1

qi,p

(12)

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

31

all weighted counts. I thus arrive at the following composite quality-weighted patent count foreach firm:

CQi =14

4

∑j=1

Qi, j (13)

Where,

CQi is the composite quality-weighted patent count for firm i, and

Qi, j is the quality weighted patent count for firm i for each dimension of quality, j specified inequation 12.

While this measure does not account for the differences in impact of the different quality di-mensions on the value of the deal, it does account for all of the hypothesized dimensions.

Table 1: Explanatory variables related to the regression models, and their expected signsExplanatory variables related to the acquisition discount, the acquisition announcement return ofthe bidder, and the probability of having patents, and their respective expected signs. Patents heldby the target includes all variations of the patent count used in regressions, except for the square.Values are left blank if the variable in question is not related to the model. Moreover, if thereis no expected sign, but the variable is included, there is a question mark (?). Finally, for un-clear expected signs for which theory yields support to both directions, the expected sign is +/−.

Expected signs with respect to

Independent variable Acquisition discount Announcement return Patenting probability

Patents held by target − +/−ln(Geographic distance) + − +M&A-activity − +Deal size > $20m −ln(Deal size) + +IPO volume − −Baa spread + −Non-horizontal merger +/− +/− +Target is a subsidiary + ?Acquisition discount +Acquirer is an investor +ln(Acquirer size) +/− +/−Stock consideration +Acquirer Price-to-book ratio +Tender offer, low-q acquirer +Tender offer, high-q acquirer −Toehold ownership +/− + +

32

3.2.4. Key explanatory variables in the regression models

Before moving on to describing data and methodology, and conducting empirical tests, Table 1.shows the explanatory variables related to the acquisition discount, the announcement return,and the probability of having patents, as well as the expected signs of their coefficients withrespect to those models, as discussed in Section 2. The expected signs related to the acquisitiondiscount are reversed in the final regressions, where the dependent variable is transformed,as per the discussion in Section 4.2.2. Appendix B describes the measurement of geographicdistance in detail.

I utilize a dummy for deals over $20m in value to explain the acquisition discounts, since thedeal size, even the natural logarithm of it, is not appropriate. This is due to the fact that theacquisition discount is included in the dollar value of the deal, and hence using one to explainthe other results in multicollinearity.

4. Data and empirical methodology

Before moving on to the empirical results of this thesis, I describe and analyze my dataset inSection 4.1. Moreover, I describe the relevant methods, including the bootstrap technique forobtaining standard errors, and analyze the appropriateness of ordinary least squares, and therequired corrections therein, for my analysis in Section 4.2.

4.1. Data

I merge three different sources of data to conduct my empirical analysis. First, I obtain data onM&A deals from the Thomson Reuters Securities Data Company (SDC) Platinum WorldwideMergers & Acquisitions database. I collect data on all deals involving a European target anda European acquirer for the acquisition discount and acquirer return analyses. I further requirethat the deals involve a controlling stake in the target’s equity, and that the announcement date isbetween January 1, 1990 and December 31, 2006. Moreover, I only include transactions wherethe payment method is stock, cash, or a combination of the two, thereby excluding transactionswhere debt or preferred securities are used as consideration. After obtaining this data, I mergeit with target patent data from the European Patent Office (EPO) free global databases, thecontents of which are specified in Table 17. in Appendix A, and acquirer financial performancedata from Thomson Reuters Datastream. Moreover, for the acquirer return analyses, I furtherrequire that data on returns for the acquirer are obtainable from Datastream.

Following Dessyllas and Hughes (2005a), I define technology-intensive firms as those having

33

their primary activities within SIC 28 Chemicals and Allied Products, SIC 35 Industrial andCommercial Machinery and Computer Equipment, SIC 36 Electronics and Electrical Equip-ment, SIC 37 Transportation Equipment, SIC 38 Measuring, Analyzing and Controlling Instru-ments; Photographic, Medical and Optical Goods, SIC 48 Communications, SIC 73 BusinessServices, or SIC 87 Engineering, Accounting, Research, Management, and Related Services.

From the SDC database, I gather the deal value, deal multiples, deal completion status, targetpublic status, payment method, target accounting data, M&A and IPO deal volumes, and vari-ables pertaining to the time, nature, and other dimensions of the acquisition. The initial sampleconsists of 35,307 bids of European targets by European companies for the selected time. Re-quiring at least one deal multiple reduces the sample to 9,521 unlisted targets, and requiring atleast two multiples further reduces the sample size to 4,558. Technology-intensive firms, fol-lowing the above definition, account for 1,538 of the unlisted targets in the latter category. Inthe sample where at least one multiple is required, technology-intensive firms account for 3,484of the unlisted targets. To increase the robustness of my analysis and conclusions, I require thatthere are at least two multiples available for the unlisted target. Industry comparable multiplescan be attained from any acquisition where at least one multiple is reported.

Table 2: Raw acquisition multiple data from SDC Platinum.Means (Medians) of deal value to book value of equity, dealvalue to EBIT, deal value to sales, and deal value to net incomeas reported by SDC. Numbers of observations are in brackets.

Unlisted targets

Multiple Listed targets Subsidiaries Stand-alone targets

Deal value to Book Equity 249.63 17.62 9.54(1.02) (1.74) (3.28)[3,262] [1,310] [1,755]

Deal value to EBIT 22.46 12.27 13.69(10.70) (9.00) (9.30)[2,160] [1,386] [1,624]

Deal value to Sales 1.04 1.10 1.28(0.63) (0.61) (0.71)[2,604] [1,825] [1,898]

Deal value to Net Income 74.33 202.50 60.65(18.24) (15.02) (17.60)[2,581] [1,121] [1,533]

Before moving on to patent data collection, I clean up the raw acquisition data from unusabledata points to avoid excessive use of time in this phase. As noted by Officer (2007), and ascan be seen from Table 2., the data obtained from the SDC contain substantial noise14. To get

14Comparing the means and medians, one can easily see that especially in the Deal Value to Book Equity forlisted targets, there are a couple of outliers that contribute significantly to the excessively high average. Lookingat the data, I find at least two multiples in the order of thousands. Thus, as in Officer (2007), clearing the outliersseems to be the obvious choice.

34

rid of the outliers, I adopt the logic utilized by Officer (2007) by not allowing the percentagedifference of the acquisition multiples compared to the comparable industry transactions toexceed +100%. While the choice of this limit may seem arbitrary, it is in line with the implicitlower bound of -100%. Furthermore, the qualitative results are not altered by this restriction.

I collect the patent data for the targets from the EPO free databases using an algorithm thatessentially searches for the patent families where at least one patent is granted within one of theEPO Worldwide patent database jurisdictions, and where the target is an assignee. I then man-ually check the data points to eliminate potential errors. After collecting the patent families, Imove on to collect the patent scope, defined as the number of International Patent Classification(IPC) codes quoted on the patent bibliography, for each patent. I then collect the citations oneach patent family by years from priority date. Similarly, I collect the number of references onthe search report for each patent in the patent family. Although these algorithms are a lot moretedious, they are also a lot more accurate than the first one. Thus, the potential for error lies inthe way by which the patents are assigned, which is why I check that data manually.

4.1.1. Generalizability of the sample

As can be seen from Table 3., the sample is representative of the data. There are no obviousclusters within or across categories, nor is there any clear tendency towards the end or beginning,or even the middle of the sample, in any category. Looking at the percentages, the figures appearrandom enough that I can conclude that they indeed are random. Thus, there is no reason tobelieve that some underlying factor relevant to my models would be driving the availability ofthe data, and thus my results.

There are, however, a couple of worrying years when the percentage of multiples data availableis low relative to the sample mean. However, since the years of low percentages of multiple datacoincide with periods of seemingly high M&A volumes reported in the literature and found inmy analysis of the M&A volumes, the low data availability rate serves as a buffer against theconcentration of the sample for those years, and thus reduces the possibility that the period ofhot M&A markets would drive the results of this thesis. Furthermore, since the availability ofmultiples data does not appear to be linearly related with the number of transactions nor is itlow always when the M&A market is hot, the low data availability in some years is more likelya result of random variation than a consistent lack of data from times of high M&A activity.

35

Tabl

e3:

Are

the

unlis

ted

targ

etsw

ithm

ultip

leda

tare

pres

enta

tive

ofth

epo

pula

tion?

Num

ber

oftr

ansa

ctio

ns,

and

perc

enta

geof

tran

sact

ions

with

acqu

isiti

onm

ultip

leda

tafo

rbo

thhi

gh-t

ech

and

non-

high

-tec

hsu

bsid

iary

and

stan

d-al

one

targ

ets

grou

ped

byye

ar.

As

spec

ified

earl

ier

inSe

ctio

n4.

1.,

Ire

quir

eth

atth

eta

rget

has

atle

ast

two

mul

tiple

sav

aila

ble

from

the

SDC

data

base

.

Stan

d-al

one

targ

ets

Subs

idia

ryta

rget

s

Hig

h-Te

chN

on-H

igh-

Tech

Hig

h-Te

chN

on-H

igh-

Tech

Yea

rTr

ansa

ctio

ns%

with

mul

tiple

sda

taTr

ansa

ctio

ns%

with

mul

tiple

sda

taTr

ansa

ctio

ns%

with

mul

tiple

sda

taTr

ansa

ctio

ns%

with

mul

tiple

sda

ta

1990

134

32.8

%27

024.4

%17

121.6

%43

323.8

%19

9114

910.7

%42

39.

5%20

112.4

%47

513.7

%19

9215

619.2

%38

720.2

%19

119.4

%49

616.7

%19

9314

035.0

%38

734.1

%17

632.4

%50

131.3

%19

9418

231.9

%51

424.9

%19

429.9

%51

223.0

%19

9521

923.7

%53

624.1

%18

017.2

%54

115.2

%19

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36

Finally, there are no obvious differences in the availability of data between high-tech and non-high-tech targets on the one hand, and subsidiary and stand-alone targets on the other. Thisindicates that first of all, my results are not driven by differential data availability for high-tech targets. Secondly, one can also conclude that the differences between stand-alone andsubsidiary targets are more likely to be a result of real differences between the categories thandata availability. Of course, due to relatively low sample sizes (885 for stand-alone targets, and653 for subsidiaries), the variation in the results may cause differences in statistical, and to someextent economical, significance. Nonetheless, the qualitative interpretations are not altered.

4.1.2. Descriptive statistics

In this section, I describe my sample related to the study of unlisted high-tech targets in Europe.Although a couple of tests do include non-high-tech and listed peers, I exclude the exploration ofthe descriptive statistics related to them for ease of interpretation. Moreover, the most importantvariables are only gathered for the subsample of unlisted high-tech targets due to the amount ofwork related to gathering them, and would thus have no reference point in other subsamples.

Table 4: Distribution of the sample by countryThe number of high-tech targets in each geographic area included in thesample.

Country Stand-alone Subsidiary Total % of sample

Austria 1 6 7 0.5%Belgium 10 12 22 1.4%Czech Republic 2 2 4 0.3%Denmark 17 11 28 1.8%Estonia 1 0 1 0.1%Finland 7 11 18 1.2%France 74 43 117 7.6%Germany 33 38 71 4.6%Greece 3 2 5 0.3%Hungary 1 0 1 0.1%Ireland-Rep 10 5 15 1%Italy 10 12 22 1.4%Luxembourg 2 0 2 0.1%Netherlands 15 16 31 2%Norway 8 12 20 1.3%Poland 4 4 8 0.5%Portugal 0 3 3 0.2%Russian Fed 1 2 3 0.2%Spain 23 12 35 2.3%Sweden 10 21 31 2%Switzerland 3 11 14 0.9%Turkey 1 0 1 0.1%United Kingdom 651 428 1079 70.2%

Total 887 651 1538 100%

37

Table 4. shows that as in most European M&A studies, my sample also includes a significantportion of the targets from the UK, with over 70% of observations coming from there. Evenafter that, the sample does not seem to be geographically different from other studies on thesame general topic and geographic concentration. More specifically, excluding UK, most of therest of the sample comes from France and Germany (totaling 12.2% of the sample), the Nordiccountries (6.3%), Mediterranean Europe (4.3%), and the Benelux countries (3.5%). The contri-butions of some Middle and Eastern European countries to the sample are insignificant. Perhapsthe most significant nuance in this sample is the inclusion of Russia, Turkey, and Estonia. How-ever, these countries account for a minor portion of the entire sample, and their exclusion doesnot change the qualitative results of this thesis.

Furthermore, the subsamples of subsidiary and stand-alone targets do not exhibit any obviousclustering nor clear differences in terms of their nationalities. There are, of course, some dif-ferences among countries, but again those differences cannot be interpreted to be a significantfactor in driving differences or similarities between those categories, and are more likely at-tributable to randomness.

Table 5: Distribution of the sample by industryThe number of high-tech targets in each industry included in the sample, defined according to its respective two-digit SIC-code.

Industry Stand-alone Subsidiary Total % of sample

28 - Chemicals and Allied Products 76 97 173 11.2%35 - Industrial and Commercial Machinery and ComputerEquipment

89 75 164 10.7%

36 - Electronics and Electrical Equipment 62 91 153 9.9%37 - Transportation Equipment 45 44 89 5.8%38 - Measuring, Analyzing and Controlling Instruments;Photographic, Medical and Optical Goods

59 43 102 6.6%

48 - Communications 58 43 101 6.6%73 - Business Services 348 187 535 34.8%87 - Engineering, Accounting, Research, Management, andRelated Services

150 71 221 14.4%

Total 887 651 1538 100%

Table 5. shows similar statistics of the sample by industry as Table 4. does by country. Nei-ther the stand-alone nor the subsidiary subsample, nor the entire sample seem to be clustered,apart from SIC 73 (Business Services), which accounts for little over a third of the whole sam-ple while there are a total of eight industries included in the sample. This clustering is a bitworrying only due to the significance of patents in that industry. With respect to informationasymmetry, one would only expect its magnitude to be greater, but the direction of impact, andits statistical significance to be similar, since services are by definition less tangible than man-ufacturing industries. Also, obtaining liquidity is likely to be harder for service firms, and thusits price higher. Although SIC 73 should, by definition, include primarily software firms, Des-

38

syllas and Hughes (2005a) also find that many firms active in SIC 357 (Computer and OfficeEquipment) are classified as SIC 737 (Computer Processing & Data Processing). Thus, SIC 73is an important part of the sample. Moreover, unreported tests, and the study of the industryfixed-effects dummies show that excluding or including SIC 73 does not have an impact onthe qualitative results apart from the patenting variables15, but it does influence the statisticalsignificance of the tests due to its significant contribution to the sample.

Table 6: Summary statistics of relevant explanatory variablesMeans, medians, standard deviations (σ), and range (min and max) of explanatory variablesrelated to hypothesis tests for the 1538 high-tech targets sampled. The Baa spread is mea-sured in basis points (100× percentage points), and the overnight rate and acquirer CAR aremeasured in per cent. The need for the transformed discount is motivated in Section 4.2.2.Patent quality measures are firm-specific stocks of each quality indicator, not, for example,patent-specific counts of that indicator.

Variable Mean Median σ Min Max N

Mean discount 0.417 0.484 0.409 −0.999 0.999 1477Transformed discount 0.711 0.718 0.279 0.024 1.414 1477CAR 1.18 0.15 6.02 −24.6 35.5 732Patenting (0/1) 0.189 0 0.392 0 1 1538Patent count 2.85 0 12.69 0 191 1538References 6.05 0 40.01 0 1264 1538Family 31.78 0 773.57 0 30215 1538Scope 13.65 0 66.26 0 1244 1538Citations 3.9 0 22.4 0 460 1538Quality-weighted patents 3.07 0 27.57 0 996.96 1538Geographic distance 274.4 158.3 362.4 0 2707.2 1538% of stock used as consideration 6.44 0 17.41 0 100 1538Divestiture (0/1) 0.394 0 0.489 0 1 1538Relative size of the deal 0.38 0.09 1.13 0 13.65 738ln(Sales) 11.79 11.58 2.29 4.78 19.33 774ln(Total Assets) 11.89 11.8 2.29 3.5 18.35 763Price-to-Book 3 2.4 22 −357.9 119.6 707Market value 2529 146 12427 0 183834 745M&A activity (0/1) 0.856 1 0.351 0 1 1538Deal size > $20m (0/1) 0.38 0 0.485 0 1 1538IPO volume 1.23 1.27 0.83 0.11 3.07 1538Baa Spread 337 293 151 44 640 1538Overnight rate 6.9 7 1.2 4.9 9.6 1538

Table 6. summarizes the relevant dependent and independent variables with respect to the anal-yses in Section 5. The mean and median of the acquisition discount are in line with the findingsof Officer (2007), albeit somewhat higher, which may be explained simply by the fact that mysample focuses on high-tech firms, whereas his does not have an industry focus. The cumulativeabnormal return in the interval [−1;1] is in line with the findings of Officer et al. (2009), andEkkayokkaya et al. (2009), and contributes to the finding that while acquirers of public targetsdo not gain on average, those of unlisted targets do.

15This impact is expected, given that many jurisdictions, especially the ones most relevant to this study, are noteager to grant patents to software or service innovations. Moreover, Hall et al. (2007) find, that software patentsare value-relevant to European firms only when they are granted in the US.

39

The summary statistics related to the patenting variables tell the usual story that very few (18.9%in this case) even of the high-tech firms do have patents, and that the distribution of the numberof patents for patenting firms is seriously positively skewed16. Moreover, the patents of thetargets in this sample average about a third over a citation per patent, which indicates that theaverage patent in the sample is neither especially novel, nor of especially poor quality. However,a closer look into the citation distribution reveals that the citations are clustered among good-quality patents, and that there are a sizable number of patents that have not received a singlecitation.

Furthermore, Table 6. shows that, consistently with Lehto and Lehtoranta (2004); Lehto (2006);Böckerman and Lehto (2006), high-tech acquirers prefer to acquire targets from medium dis-tance. Moreover, in the sampled transactions, only just over 6% of stock is used as considerationon average, and the transactions seem to occur at times of hot M&A markets17, average corpo-rate loan spreads, and fairly high interest rates. Also, the majority of the deals are less than 10%of the acquirer pre-acquisition market value18.

4.1.3. Correlations between independent variables

Table 7. shows the correlation coefficients between explanatory variables of each model. Corre-lations between variables that are not defined in a manner that would make them correlated, aremostly of the order |ρ|< 0.2. Acquirer size seems to be extraordinarily, albeit not highly signif-icantly, correlated with geographic distance, which indicates that small companies are relativelyless prone to acquire distant firms than large companies are.

The Baa-loan spread has a strong negative correlation with the IPO market size indicator, whichmay have an impact on the coefficients of those variables. Moreover, the UK overnight rate is,between 1990 and 2006, clearly lower after the turn of the millenium than before it. Fur-thermore, the millenium dummy variable has, partly by design, the highest correlations withseemingly unrelated variables, mostly with M&A and IPO activities, the deal size dummy andthe log of deal size, and the mean acquisition discount. This indicates that while the centralbank rate has been lower, the volume of equity-related transactions has been somewhat higherin the new millenium.

16One needs no diagrams to see that a variable defined in the interval [0;∞[ is positively skewed if it has astandard deviation higher than its mean. This is due to the fact that as the average observation deviates (positivelyor negatively) from the mean more than the mean, but the observation can never get negative values, the averageobservation that deviates positively from the mean will have to do so by more than the standard deviation, and theaverage observation that deviates negatively from the mean will do so by less than the standard deviation therebyproducing a positive skew to the distribution.

17This should be no surprise given that these transactions are, in part, defining the hot M&A market.18In other words, around the peak of the business cycle.

40

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42

Interestingly, the stock payment dummy seems to have a relatively strong negative correlation(≈−0.3) with the size of the acquirer. That is, smaller acquirers are somewhat more likely touse stock consideration in acquisitions. If size is an indication of the availability of liquidity, thisis no surprise. Target and acquirer sizes are expectedly strongly correlated. Finally, it appears thathigh market value acquirers have low Tobin’s q-values, and make tender offers.

Overall, the analysis of correlations between independent variables does not suggest that my modelswould suffer from multicollinearity. In the results section, I do check the variance inflation factors(VIFs) to be sure, but the present analysis is encouraging with regard to that problem.

4.2. Methodology

In this section, I review the relevant methods pertaining to the acquisition discount, the acquirerannouncement return, and the probability that the target has patents, respectively.

4.2.1. Acquisition discounts

As noted by Officer (2007), it is obviously impractical to attempt to measure the acquisition discountusing market prices, since no ex ante market valuation exists for the sample under scrutiny. Hence,following the methodology in Officer (2007), I measure the acquisition discount by using a variantof the Kaplan and Ruback (1995) comparable industry transaction method. More specifically, Iconstruct portfolios of transactions of public targets within the same two-digit SIC-code to whichthe acquisition multiples of the unlisted targets are compared. I further require that these transactionstake place at most 18 months before or after the transaction to which they are compared. Also, thecomparable acquisitions are required to have a deal value excluding assumed liabilities within 20%of the corresponding figure of the transaction that is compared to this portfolio. Since it does notmake sense to require completely different portfolios for each transaction, any listed company canbe in any number of comparable portfolios, given that the requirements above are fulfilled.

Given that I utilize a multiples instead of a market value approach in estimating the discounts, itdoes need motivation and some further scrutiny. Suppose we have two target companies, one listedand one unlisted, that are fully financed by equity. Suppose also that we know that acquisitionprices always include, by custom, a premium over the fair market value of the target. Let the pricepaid for the listed company be PL, and the price paid for the unlisted one PU . Moreover, let thefair market values of the listed and unlisted companies be VL and VU , respectively19. Further, letthe fundamentals over which the multiples are calculated be FL and FU for the listed and unlistedtargets, respectively. When analyzing the transaction multiples, we have:

19Even though there is no market that would value the unlisted company, for the sake of this analysis, we assume thatif such a market did exist, that value would be VU .

43

MU = PUFU

ML = PLFL

(14)

Where the subscripts U and L refer to the unlisted and listed targets, respectively, and Mi refers tothe multiple for firm i. Thus, when we measure the acquisition discount of the unlisted target withrespect to the listed target, we have:

1−MU

ML= 1− PU ×FL

FU ×PL(15)

Given that the true acquisition discount or premium would be measured over the fair market value,and that this value of the unlisted company is unavailable, we know three things:

1. The acquisition discounts measured in this context are not discounts over the fair value of thecompany, but rather discounts over multiples of fundamentals.

2. The sign of the acquisition discount will be correct as long as the listed targets are acquired ata premium (Officer, 2007).

3. If the listed targets are acquired at a premium, my acquisition discount measures overstate thediscount and understate the premium.

Although it is crucial to understand that discounts measured in this analysis are not actual discountsbut rather proxies for it, it must also be noted that they are sufficiently accurate for the purposesof this thesis. Moreover, when the signs of the discounts are correct, so will be the signs of thecoefficients of the explanatory variables.

Acquisition discount regression modelI regress my explanatory variables on two separate independent variables, namely the acquisitiondiscount as defined in Section 3.2., and the announcement event cumulative abnormal return (CAR)of the acquirer. In this section, I specify the regression model related to the acquisition discount.The model is of the form (subscripts are suppressed for notational convenience):

44

D = α+β1×Patent count+β2×Patent count2 +β3× ln(Geographic distance)+φT ×Liquidity+ γT ×Control

OR

D = α+β1×Patent quality+β2× ln(Geographic distance)+φT ×Liquidity+ γT ×Control

(16)

Where,

D is the acquisition discount for each firm, as specified in Section 3.2.,

α is the model intercept,

βi are the coefficients for the independent variables,

Patent count is the number of patents assigned to the target,

Patent count2 is the square of the number of patents assigned to the target,

Patent quality is an equally weighted average of the scope-, citations-, references-, and INPADOCfamily size-weighted patent counts, as defined in equation 13,

ln(Geographic distance) is the natural logarithm of the geographic distance between acquirer andtarget headquarters,

φT is the transpose of the vector of coefficients for the liquidity measures,

Liquidity is the vector of independent liquidity variables, including the Baa loan spread, aggregateIPO volume, and a liquidity index constructed of M&A activity.

γT is the transpose of the vector of coefficients for the control variables,

Control is the vector of independent control variables, including industry, and country fixed-effects.

To test my hypotheses, I run a horse-race between the patent counts and the equally weighted qualityindicator. The motivation behind adding the square of the patent count in addition to the plain patentcount stems from the discussions in Sections 3.1. and 3.2., that is, the marginal effect of a patent isexpected to be decreasing in the number of patents. Moreover, the coefficient of the square of thepatent count acts as a direct test of H6b.

45

4.2.2. Appropriateness of ordinary least squares for the acquisition discount

In this section I examine the appropriateness of OLS for determining the relation between the ex-planatory variables and the acquisition discount. More specifically, I examine whether the Gauss-Markov conditions and the normality assumption can be reasonably expected to hold. I also explainthe methodological corrections required for the appropriateness of the analysis.

Gauss-Markov conditionsWhenever OLS is applied, it is crucial to examine whether the Gauss-Markov conditions hold. Ifthey do not, the coefficient estimates provided by ordinary least squares are no longer best linearunbiased estimators (BLUE). Thus, there are likely to be other estimates for the coefficients thatwould provide a better, less biased fit for the model. Jensen et al. (1975) reduce the Gauss-Markovconditions to the following form:

E(e) = 0

V (e) = σ2In

(17)

Where,

E(e) is the expected value of the disturbance term,

V (e) is the variance-covariance matrix of the disturbance terms,

σ2 is the population variance of the disturbance term, and constant across all observations, and

In is an n×n identity matrix.

I explore the properties of the covariance matrix in more detail. Specifically, I examine whether theerror terms are i.i.d., that is, whether they are distributed independently of and identically to eachother. I also examine whether the disturbance terms are distributed independently of the explanatoryvariables.

The first Gauss-Markov condition, that the disturbance term has a zero expected value, is met in mysample. More specifically, the mean value of the disturbance term in the regressions is of the orderx×10−12 where x < 10.

Generally in econometric models, in terms of whether the disturbance terms are i.i.d., the usual sus-pect is heteroskedasticity. Not unlike the majority of financial models, mine is also heteroskedastic.Conducting the Breusch-Pagan test, I reject the null hypothesis of equal variances in the error termat the 1% level. Thus, I need to correct for heteroskedasticity. I use the HC3 covariance matrixestimator as specified in MacKinnon and White (1985), and discussed further in Section 4.2.5.

46

Figure 1: Scatter plot of acquisition discount residuals by observation

Figures 1. and 2. show that the data do not present any obvious clustering in terms of years, or anyother property of the variables. Moreover, a correlation matrix of the explanatory variables and thedisturbance term shows that it exhibits zero correlation with the independent variables.

Finally, a study of variance inflation factors reveals no concern for multicollinearity in the model.Even when both the first and second order patent counts are included, the VIFs do not rise above 5.Practitioners often consider a VIF of 5 or 10 a threshold value that reveals serious multicollinearityin the model. However, O’Brien (2007) discusses the use of rules of thumb and VIFs at length, andargues that more important than the specific VIF obtained by an independent variable i is whetherthe coefficient of i is statistically significant and of plausible magnitude in economic terms. Giventhat the variables of concern, namely the first and second order patent counts, obtain statisticallysignificant and expected coefficients, and that their magnitude seems plausible given previous re-search, multicollinearity does not pose a problem in my acquisition discount model. Moreover,given that the magnitude of the other regression coefficients is the same across categories in theregression that includes quality weighted patent counts instead of the first and second order patentcounts, and a maximum VIF of 3, I maintain that multicollinearity does not have an adverse impacton the statistical plausibility of my results. Thus, the only Gauss-Markov condition that is violatedby my model is that of heteroskedastic disturbance terms. Correcting for heteroskedasticity, the

47

Figure 2: Scatter plot of acquisition discount residuals by year

OLS-estimator is again BLUE.

Normality of the disturbance termOn top of the Gauss-Markov conditions discussed above, one also assumes that the disturbanceterm is normally distributed with a mean of zero20. However, as can be seen from Figure 3., thedistribution of the disturbance term is clearly skewed to the left. Since hypothesis tests assume anormally distributed disturbance term, no statistically valid inferences can be drawn from this dataunless the dependent variable is transformed.

Thus, introducing a simple transformation into the dependent variable improves the model to anextent where the normality assumption can be reasonably assumed to hold. Bartlett (1953), forexample, proposes the square root transformation as a correction for skewness. Moreover, whenthe variable is negative skewed, the skewness may be corrected by subtracting the variable from itsmaximum, which in this case is one, and then taking a square root. The new dependent variable ishence:

20Zero mean is included in the Gauss-Markov assumptions, but normality is not.

48

Figure 3: Error term distribution with untransformed dependent variable

D∗ =√

1−D (18)

Where,

D is the acquisition discount, as specified in Section 3.2.1.

D∗ is the new transformation of the acquisition discount.21

Meriting to this transformation, I obtain residuals that are distributed normally around zero, as canbe seen from Figure 4.

After correcting for heteroskedasticity and non-normality in the disturbance term, the coefficient es-timates obtained from ordinary least squares are BLUE, and the hypothesis tests related to those co-

21Recall that D ∈ [−1;1], so the transformed discount is defined for all possible values of D.

49

Figure 4: Error term distribution with transformed dependent variable

efficients are valid. While my acquisition discount measure is merely a proxy for the true economicdiscount, I will henceforth refer to it as the economic acquisition discount to ease the interpretationof my results.

4.2.3. Acquirer announcement return

For the acquirer’s announcement event CAR, the model specification is the following (subscriptsare again suppressed for notational convenience):

CAR = α+β1× ln(Geographic distance)+β2×Stock acquisition dummy+β3×Acquisition discount+φT ×Liquidity+ γT ×Control

(19)

Where,

50

CAR is the aquisition announcement event CAR for the period t− 1 to t + 1 where t denotes theannouncement date

α is the intercept,

βi are the coefficients for the independent information asymmetry variables,

ln(Geographic distance) is the geographic distance between the acquirer and target headquarters,

Stock acquisition dummy is a dummy variable equal to one if any part of the acquisition is madeusing stock as consideration, and zero otherwise,

Acquisition discount is the acquisition discount as specified in Section 3.2.1.

φT is the transpose of the vector of coefficients for the liquidity measures,

Liquidity is the vector of independent liquidity variables, including the Baa loan spread, industryIPO volume, and a liquidity index constructed of M&A activity.

γT is the transpose of the vector of coefficients for the control variables, and

Control is the vector of independent control variables, including the industry, and country fixed-effects, as well as Target Patents/Acquirer Total Assets, and some control variables that are known toexplain acuirer announcement returns, and are reviewed in Section 2.2.

It would be tempting to explain any excess returns in acquisitions of unlisted firms by the fact thatthose firms are acquired at a discount, and ceteris paribus, the acquirer would be gaining the relativediscount. To avoid such interpretations making this analysis debatable, I control for the effects ofthe acquisition discount.

4.2.4. Appropriateness of ordinary least squares for the announcement return

Similarly as for the acquisition discount, I explore the suitability of OLS-regression to explainthe announcement return. Following the order in Section 4.2.2., I begin with the Gauss-Markovconditions, and then move on to normality.

Gauss-Markov conditionsAs in the case of the acquisition discount, also in the case of acquirer announcement return thedisturbance term does not have a mean significantly different from zero (now, it is of the orderx×10−11, where x < 10).

The Breusch-Pagan test rejects the null hypothesis of equal variances in the error terms, and thus Icorrect for heteroskedasticity as specified in Section 4.2.5. Moreover, as can be seen from Figures

51

Figure 5: Scatter plot of the announcement return residual term by observation

5., and 6., the disturbance terms are all independently distributed. More specifically, the disturbanceterms are not dependent of the observation, or year. Finally, the VIFs do not rise above 3.2, and thusmulticollinearity, in light of the VIFs, is not a problem in the announcement return model. Again,the correlation vector of the disturbance term with each independent variable is a vector of zeros,and hence, apart from heteroskedasticity, the Gauss-Markov conditions are met.

Normality of the disturbance termIn the case of announcement returns, the data do not support the assumptions underlying hypothesistesting of ordinary least squares coefficients. More precisely, the assumption of normality of thedisturbance term is violated, as one can see from Figure 7. below.

The empirical distribution of the disturbance term appears to be heavily leptokurtic, which impliesunder-rejection of the null hypothesis, if one would assume the data to be normal. Hence, to arriveat more robust results, I turn to bootstrapping, which is explained in more detail in Section 4.2.5.below.

52

Figure 6: Scatter plot of the announcement return residual term by year

4.2.5. Covariance matrices and the wild bootstrap

Heteroskedasticity-consistent covariance matricesMacKinnon and White (1985) show that the original heteroskedasticity-consistent covariance es-timator proposed by White (1980) may perform even worse than the conventional OLS-estimatein finite, heteroskedastic samples. They move on to study estimates for the covariance matrix thatexhibit better qualities when N is small. Moreover, MacKinnon and White (1985) arrive at one,called the HC3, which always outperforms both the original and the two other improvements speci-fied, irrespective of sample size. Thus, given especially that my sample size is limited, albeit largerthan those experimented on by MacKinnon and White (1985), I utilize the HC3 covariance matrixestimator instead of the often used White (1980) covariance matrix estimator, or HC0. The use ofthe HC3 does have a marginally negative effect on the statistical significance of my variables, but itis also superior in terms of error in the rejection probability, and thus preferable.

Bootstrapping heteroskedastic modelsLiu (1988) originally proposed the wild bootstrap for use in models where the presence of het-

53

Figure 7: Distribution of the (heteroskedasticity-consistent) ordinary least squares disturbance term

eroskedasticity is suspected. The wild bootstrap is a procedure where first disturbance terms andfitted values of the dependent variable are generated from the OLS estimate of the underlying model.Second, observations are drawn with replacement from the original sample up to a number equal tothe size of the original sample. Then, new values for the dependent variable are generated by:

ybi = yt +at utε∗t (20)

Where,

ybi is the bootstrapped observation yi

y is the OLS-fitted value of the dependent variable

ut is the OLS residual estimate

54

ε∗t is a random variable generated by some distribution generating process, or DGP

at is a value obtained from the diagonal matrix Ω of the heteroskedasticity-consistent covariance-matrix estimator.

More specifically to HC3:

at =1

1−ht,

ht = Xt(XT X

)−1XT

t (21)

Where

X is the matrix of values of the independent variables xi for each observation t

Xt is the row vector of the values of the independent variables for observation t

ht is the tth element of the orthogonal projection matrix onto the span of the columns of X .

This procedure is then repeated, or simulated, B times in order to obtain as precise estimates aspossible.

Flachaire (2005) discusses the use of the HC3 covariance matrix estimator and the use of theRademacher distribution

F : ε∗t =

1 with probability 0.5−1 with probability 0.5

(22)

to generate the disturbance term for the wild bootstrap. Moreover, the number of bootstrap repli-cations should be chosen so as to make α(1+B) an integer, where B is the number of bootstrapreplications, and α is the statistical significance level of interest. Hence, for example, any value ofB such that (B+1) mod 10x = 0 for any integer values of x≥ 2 satisfies this definition for all twodecimal values α≥ 0.01.

The percentile t-methodHall (1988) posits that two estimates for confidence intervals emerge as higher-order efficient.Namely, the bias-corrected and accelerated, or BCa, and the percentile-t method. In a setting ofa skewed distribution, the BCa confidence interval -estimate is proposed by Efron (1987). However,since the distribution of the announcement returns does not suffer from skewness but rather from

55

kurtosis, these estimates are not optimal. Thus, I utilize the percentile-t estimate, which is alsohigher-order efficient, and hence preferable to the asymptotic normal or student’s t distributions.The essential difference between the BCa and percentile-t methods is that the BCa utilizes jack-knife22 to compute the acceleration constant and calculates the bias correction explicitly, whereasthe percentile t-method utilizes the bootstrapped distribution of statistics to obtain percentiles forgiven levels of α.

The percentile t-test is analogous to the student’s t-test, but it utilizes the (not necessarily symmetric)bootstrapped distribution of coefficients. More formally, the two-sided confidence interval of β∗ atlevel 1−α is obtained from:

[β∗− se(β∗)t∗1−α/2;β

∗− se(β∗)t∗α/2

](23)

Where,

t∗ =β∗−β0

se(β∗)(24)

and se(β∗) is the standard error of the coefficient β obtained from the bootstrap samples. Analo-gously, the one-sided confidence interval at level 1−α is obtained from:

|βα| ∈ [|β∗− se(β∗)t∗α|;0] (25)

As explained above, the significant difference between the percentile t-test and the student’s t-testis that the percentile t-test assumes that the obtained sample represents the underlying populationdistribution of the coefficients, while the student’s t-test assumes that the population distribution isasymptotically the student’s t-distribution, a symmetric bell-shaped distribution. Hence, statisticalsignificance of each coefficient in the percentile t-method is determined specifically to that coeffi-cient, and the percentile t-test may thus yield different levels of statistical significance for the samet-value for different coefficients, or even for different bootstrap simulation runs23.

For a more in-depth discussion on bootstrapping in the presence of heteroskedasticity, and the selec-tion of the covariance matrix, the reader is encouraged to turn to Flachaire (2005) and Liu (1988).For a more technical analysis of higher-order efficient bootstrap errors, see Hall (1988).

22That is, in the BCa method, one would compute the acceleration constant as one sixth of the sum of the skewness-measures for each subsample j, where observation j is left out, following the approximation given by Efron (1987).

23In the latter case, though, the significance levels corresponding to each t-value will be very close to each othergiven a large enough number of bootstrap simulations.

56

4.2.6. Patenting probability

Before moving on to the empirical results, I examine the methodology related to my selection model.More specifically, I run a logistic regression on the probability that the target has patents. To estimatethis model, I utilize the logit specification. Denote the probability that a target has patents by π(x).Thus, the model specification is the following:

log[

π(x)1−π(x)

]= α+β1× ln(Geographic distance)+β2×Different industries+

β3×Acquirer is an investor+ γT ×Control

(26)

Where,

α is the model intercept,

βi are the coefficients for the explanatory variables,

ln(Geographic distance) is the geographic distance between the acquirer and target headquarters,

Different industries is a dummy variable that obtains the value one if the acquirer and target havedifferent industries defined as their primary two digit SIC codes,

Acquirer is an investor is a dummy variable that obtains the value one if the acquirer is a non-industrial investor,

γT is the transpose of the vector of coefficients for the control variables, and

Control is the vector of independent control variables, including the industry, year, and countryfixed-effects.

I choose the logit model over the probit model to facilitate the possibility to include fixed effectsfollowing, for instance, Lehto and Lehtoranta (2004). Furthermore, given the normality issuesdescribed in relation to my other two specifications, there really is little reason to expect the data tobe normal, and hence the logit model is the optimal choice.

To facilitate more robust estimates for standard errors, and given that the HC3 estimate for thevariance-covariance matrix is unavailable in logistic regressions, I utilize the jackknife procedure.In essence, the jackknife is a resampling procedure which estimates the standard errors by averagingthe standard errors across j subsamples taken from the data, where for each subsample j, the jthobservation from the original data is dropped out. More formally,

se j =1N

N

∑j=1

N

∑i=1i 6= j

√(xi− x)2

(N−2)√

N(27)

57

5. Results

In this section, I present the empirical results pertaining to the validity of my hypotheses. Section5.1. begins by examining whether there are acquisition discounts and abnormal returns to stockacquirers in acquisitions of unlisted European high-technology targets. Then, in Section 5.2., Idiscuss the univariate and multivariate results regarding acquisition discounts. Section 5.3. showsthe results on the selection model related to the probability that a target has patents. Finally, Section5.4. provides the univariate and multivariate results on the announcement returns of acquirers ofunlisted high-tech targets in Europe. Also, in Sections 5.2., and 5.3., I compliment my findings withresponses from the questionnaire.

5.1. Acquisition discounts and abnormal stock acquirer returns - do theyexist in Europe?

In this section, I test my first three hypotheses. More specifically, I test whether there is an ac-quisition discount for unlisted European targets (H1), whether that discount is higher for high-techtargets (H2), and whether stock acquirers of unlisted European high-tech targets gain more thannon-stock acquirers (H3).

5.1.1. Acquisition discount

To test my first and second hypotheses, I conduct a simple t-test of differences in means for theacquisition discount of the target. Table 8. shows, consistently with the findings of Officer (2007),that the acquisition discount averages approximately 30% for non-high-tech targets thus yieldingsupport for H1. Moreover, I find a statistically significant added acquisition discount for high-techtargets of over 10%-points on top of that for non-high-tech targets, and thus find support my secondhypothesis as well. Table 8. also shows that this added acquisition discount for high-tech targetspersists across subsidiary and stand-alone target subsamples.

For every multiple analyzed, the acquisition discount is both statistically and economically signif-icant. It is the lowest for multiples of book value of equity (26.7% and 21.8% for high-tech andnon-high-tech targets, respectively), and highest for multiples of net income (53.5% and 38.8%)with the exception of stand-alone non-high-tech targets, where the highest discount is for the EBITmultiple. Also, the difference between high-tech and non-high-tech targets is the highest acrosscategories for the net income multiple. Unreported results verify, consistently with Officer (2007),that adding other multiples available from the SDC does not have an impact on these results.

58

Tabl

e8:

T-te

stof

diff

eren

cein

acqu

isiti

ondi

scou

ntm

eans

betw

een

high

-tec

hnol

ogy

and

non-

high

-tec

hnol

ogy

targ

ets.

Mea

ndi

scou

nts

and

the

diff

eren

cein

mea

ndi

scou

nts

betw

een

unlis

ted

high

-tec

hnol

ogy

and

non-

high

-tec

hnol

ogy

targ

ets

for

subs

idia

ryan

dst

and-

alon

eta

rget

subs

ampl

es,

asw

ell

asth

een

tire

sam

ple.

Figu

res

inpa

rent

hese

sar

eth

et-

valu

esfo

rth

edi

ffer

ence

inm

eans

.Fo

rth

ere

spec

tive

mea

nsof

the

high

-tec

han

dno

n-hi

gh-t

ech

subs

ampl

esof

each

subs

ampl

e,th

efig

ures

inpa

rent

hese

sre

pres

ent

t-va

lues

with

resp

ect

toth

enu

llhy

poth

esis

that

the

mea

ndi

scou

nts

are

zero

.Fi

gure

sin

brac

kets

repr

esen

tth

esa

mpl

esi

zefo

rea

chgr

oup.

*,**

,and

***

deno

test

atis

tical

sign

ifica

nce

atth

e10

%,5

%,a

nd1%

leve

ls,r

espe

ctiv

ely.

Stan

d-al

one

targ

ets

Subs

idia

ryta

rget

sA

llta

rget

s

Hig

h-te

chN

on-h

igh-

tech

Diff

eren

ceH

igh-

tech

Non

-hig

h-te

chD

iffer

ence

Hig

h-te

chN

on-h

igh-

tech

Diff

eren

ce

Dea

lval

ueto

book

equi

ty0.

224∗∗∗

0.14

6∗∗∗

0.07

8∗0.

326∗∗∗

0.29

5∗∗∗

0.03

20.

267∗∗∗

0.21

8∗∗∗

0.04

9∗

(5.1

7)(5.9

3)(1.5

6)(6.4

2)(1

1.58

)(0.5

6)(8.0

8)(1

2.23

)(1.3

0)[1

89]

[494]

[683]

[138]

[467]

[605]

[327]

[961]

[128

8]D

ealv

alue

toE

BIT

0.44

8∗∗∗

0.38

9∗∗∗

0.05

9∗∗∗

0.48

8∗∗∗

0.36

9∗∗∗

0.11

9∗∗∗

0.46

4∗∗∗

0.37

9∗∗∗

0.08

5∗∗∗

(24.

65)

(25.

06)

(2.4

6)(2

3.48)

(23.

78)

(4.6

0)(3

3.83)

(34.

54)

(4.8

4)[6

01]

[913]

[151

4][4

02]

[914]

[131

6][1

003]

[182

7][2

830]

Dea

lval

ueto

Sale

s0.

411∗∗∗

0.34

4∗∗∗

0.06

6∗∗∗

0.47

3∗∗∗

0.32

6∗∗∗

0.14

7∗∗∗

0.43

8∗∗∗

0.33

5∗∗∗

0.10

4∗∗∗

(21.

91)

(21.

13)

(2.6

7)(2

3.28)

(22.

29)

(5.8

7)(3

1.75

)(3

0.69)

(5.8

9)[7

14]

[108

7][1

801]

[568]

[118

5][1

753]

[128

2][2

272]

[355

4]D

ealv

alue

toN

etIn

com

e0.

520∗∗∗

0.35

4∗∗∗

0.16

6∗∗∗

0.56

1∗∗∗

0.41

5∗∗∗

0.14

6∗∗∗

0.53

5∗∗∗

0.38

3∗∗∗

0.15

3∗∗∗

(25.

13)

(18.

60)

(5.9

2)(2

0.97

)(2

2.12)

(4.4

8)(3

2.68

)(2

8.51

)(7.2

2)[4

70]

[720]

[119

0][2

74]

[635]

[909]

[744]

[135

5][2

099]

Mea

nac

quis

ition

disc

ount

0.39

3∗∗∗

0.29

8∗∗∗

0.09

5∗∗∗

0.44

9∗∗∗

0.32

9∗∗∗

0.12

0∗∗∗

0.41

7∗∗∗

0.31

3∗∗∗

0.10

3∗∗∗

(27.

87)

(25.

33)

(5.2

0)(2

7.81

)(3

0.26

)(6.1

5)(3

9.16

)(3

9.12

)(7.7

6)[8

48]

[134

8][2

196]

[629]

[134

6][1

975]

[147

7][2

694]

[417

1]

59

Table 8. shows that both the existence and the level of the acquisition discount persist acrosssubsamples and multiples. Moreover, the difference in means between high-tech and non-high-tech targets is statistically significant across subsamples, with the exception of deal value tobook equity in the subsidiary target subsample where the difference is both economically mi-nor, and statistically not significant even at the 10%-level. The difference is a bit higher forsubsidiary targets across multiples, with the exception of deal value to net income, where it ishigher for stand-alone targets. All in all, the results presented in Table 8. are both statisticallyand economically significant, and robust across categories and subsamples. Unreported testsalso show that the discount persists when the deal multiple is compared to the peer group me-dian multiples (as opposed to mean multiples). However, that discount is approximately 2/3 ofthe one found here.

All in all, a study of the results in Table 8. is consistent with my first two hypotheses. Moreover,these results are consistent with the findings from a US dataset in Officer (2007). Hence, theacquisition discount of unlisted targets is not a phenomenon unique to the US, or to the specificdataset employed by Officer (2007). Furthermore, the difference in the acquisition discountmeans between high-tech and non-high-tech targets supports the hypothesis that a significantproportion of the acquisition discount is, in fact, driven by information asymmetry. This expla-nation will be explored in more detail in Sections 5.2., 5.3., and 5.4.

5.1.2. Abnormal announcement returns of stock acquirers

In Section 3.1., I hypothesize that the acquisition announcement returns to stock acquirers of un-listed technology-intensive targets are higher than those to non-stock acquirers (H3). The resultsin Table 9. are consistent with this hypothesis. More specifically, the difference between returnsto stock and non-stock acquirers is economically significant across all subsamples. However,the difference in the subsidiary target subsample is not statistically significant. This may beexplained by the small number (30) of stock bids made of subsidiary targets, and hence, limitedsample size. However, the economical significance of the results cannot be dismissed. That is,stock bidders of unlisted high-tech targets gain an average of 1.8%-points more than non-stockbidders. Furthermore, the economic significance persists even in the subsidiary target sample,which provides further support for the hypothesis that the lack in statistical significance in thatcategory is merely a product of a limited sample size.

Consistently with the works of Shleifer and Vishny (2003), and Officer et al. (2009) amongothers, I also find that gains to stock bidders of listed targets are statistically significantly nega-tive. Moreover, I find that gains to stock bidders of unlisted targets are statistically significantlypositive averaging 2.3%, and on average (also statistically significantly) 3%-points greater thanthose to stock bidders of listed targets.

60

Table 9: T-test of difference in abnormal acquisition announcementreturn means between stock acquirers of high-technology and

non-high-technology targets.Mean abnormal announcement returns and the difference in mean abnor-mal announcement returns between stock and non-stock acquirers of un-listed high-technology targets. For the mean abnormal returns, figures inparentheses are the respective standard errors, for the difference, figuresin parentheses are the t-values for the difference in means. Figures inbrackets represent the sample size for each group. *, **, and *** de-note statistical significance at the 10%, 5%, and 1% levels, respectively.

Panel A: Stand-alone targets

Stock acquirers Non-stock acquirers Difference

Mean acquirer CAR 0.0257∗∗∗ 0.0077∗∗∗ 0.0181∗∗∗

(4.48) (2.71) (2.82)[177] [312] [489]

Panel B: Subsidiary targets

Stock acquirers Non-stock acquirers Difference

Mean acquirer CAR 0.0217∗ 0.0048 0.0169(1.63) (1.27) (1.22)[30] [213] [243]

Panel C: All unlisted targets

Stock acquirers Non-stock acquirers Difference

Mean acquirer CAR 0.0251∗∗∗ 0.0065∗∗∗ 0.0187∗∗∗

(4.77) (2.87) (3.25)[207] [525] [732]

Panel D: Listed vs. unlisted targets

Unlisted targets Listed targets Difference

Mean acquirer CAR 0.0234∗∗∗ −0.0069∗ 0.0303∗∗∗

(5.24) (−1.58) (4.85)[464] [266] [730]

My results indicate that while acquirers of listed targets only issue stock when it is overvalued,stock consideration has a dual role in acquisitions of unlisted targets. On the one hand, onecannot dismiss the possibility that acquirers issue stock only when it is overvalued also in ac-quisitions of unlisted targets. On the other hand, given the results in Table 9., I also maintainthat stock consideration is used as a monitoring mechanism in acquisitions of unlisted targets.Unreported results show some economical but no statistical difference between announcementreturns to stock bidders of unlisted high-tech and non-high-tech targets. Thus, although it maybe that especially in acquisitions of unlisted stand-alone high-tech targets the increased infor-mation asymmetry and thereby the strengthened monitoring effect of the stock considerationdrives the acquisition announcement returns of stock bidders, I cannot verify that it does somore than in the case of non-high-tech targets. Hence, given my results, it is more likely thatit is the fact that the target is unlisted, not its technology-intensity that drives the information

61

asymmetry between the bidder and the target. Technology-intensity may add to that informationasymmetry, but that addition is too volatile to be statistically significant in my sample.

5.2. What determines the acquisition discount?

The following sections discuss the determinants of the acquisition discount. More specifically,Section 5.2.1. discusses the linearity of the impact of distance, or the natural logarithm thereof,on the acquisition discount. Then, Section 5.2.2. discusses the univariate results for the acqui-sition discount. Finally, Section 5.2.3. discusses the multivariate results, and the full modelrelated to the acquisition discount. Moreover, Section 5.2.3. also discusses the marginal im-pact of the variables on the economic (instead of transformed) acquisition discount to ease theinterpretation and increase the clarity of my results.

5.2.1. Exploring the log-linearity of the distance-discount relation

In preliminary unreported regressions, it turns out that the impact of the natural logarithm ofgeographic distance obtains an unexpected positive sign with respect to the deal value, indepen-dently of whether country control variables are included or excluded, or whether the distancevariable is winsorized. It is difficult to find a theoretically valid interpretation for such a result.However, Grote and Umber (2007) do argue, that managerial overconfidence, and preferencefor quiet life may drive overvaluation of short-distance acquisitions. It does remain unclear,however, why in such a setting the effect of distance would be reversed from the predictionsof theory. Even if managers were overconfident about their abilities to value deals at shortdistances, it is not obvious that they would overvalue more those deals that are further away,provided that the distance is below a certain threshold. The following analysis explores thelinearity in univariate coefficients.

Figure 8. shows the impact of the natural logarithm of geographic distance on the transformedacquisition discount grouped into steps of 100km by distance. The impact corresponding toeach 100km in distance is that of a subsample ending at that 100km threshold, and beginningat a 100km shorter distance. So, for example the impact of the natural logarithm of geographicdistance is approximately 0.5 in the subsample of deals where the distance is between 300and 400 kilometers. The rightmost value is the pooled effect for all distances above 900km.Although the analysis is univariate, it does reveal an interesting characteristic of the relationbetween geographic distance and acquisition value. Namely, the relation is not linear even inthe logs. It also seems that, apart from an outlier at distances between 600 and 700 kilometers,the average impact of the log of distance gets the expected sign somewhere after a distancebetween the acquirer and the target of 400km. Since all of the other effects are excluded, one

62

Figure 8: The impact of ln(Geographic distance) by distance in steps of 100km on D∗

cannot deduce with full certainty the exact threshold from this analysis, but it does give a clue.

Moreover, Figure 9. reveals an identical story, now expressed in terms of coefficients betweeninteger values of the natural logarithms of distance. More specifically, at logs of distance be-tween 1 and 2 (i.e., 2.72, and 7.39 kilometers), the coefficient obtains a strikingly large positivevalue of 1.4. However, at such short distances, the trade-off is so small that the coefficient ismore likely in large part a result of randomness and the use of univariate analysis. Moreover, itdoes not appear that from this analysis one could exhaustively deduce some threshold where achange in signs would occur. Grote and Umber (2007) do use a seemingly arbitrary thresholdof 470km in their regressions, although the authors themselves argue for a threshold of 500km.Given that my analysis indicates that the threshold is somewhere above 400km, I test for theappropriateness of 470km for my data. It turns out that in the multivariate regressions, 470kmis exactly the correct threshold, in terms of statistical significance. When distance exceeds thatthreshold, the coefficient of its log obtains the expected sign at a statistically significant level.Although adjustments to this threshold can be made without loss of sign, the statistical signif-icance disappears almost instantly due to the vast loss in the number of observations. Hence,even though I do not argue that the exact threshold would be 470km, I do utilize it, and setforward the notion that there may be some proximity preference in acquirer management that

63

overpowers the increased information asymmetry in terms of distance, at some relatively shortdistances between the acquirer and the target. However, I must further posit that the existenceand exact specification of that threshold requires further study, and a significantly larger sample.

Figure 9: The impact of ln(Geographic distance) by ln(Geographic distance) in steps of 1 on D∗

5.2.2. Univariate results

Table 10. shows the univariate ordinary least squares regressions of the explanatory variableson the transformed acquisition discount grouped by whether the target is a stand-alone firm or asubsidiary, and also the results for the full sample. The expected signs are reversed from Table1., where they represent the expected direction of impact on the economic acquisition discount,as opposed to the transformed one.

Although there are some differences between categories, the coefficients in the univariate anal-ysis mostly have the expected signs. More importantly, the facts that the patenting dummy hasa positive sign, and that the liquidity and illiquidity measures have an impact whose direction isexpected give preliminary support for my hypotheses H5, H6a, and H9. Moreover, in a univari-ate analysis, one should not expect all (or any of) the coefficients to bear statistical significance,since their respective means and standard errors are defined in the final model instead of the

64

Table 10: Univariate results for the acquisition discountUnivariate OLS-regressions of the explanatory variables on the modified acquisition discount, or D∗ =

√1−D,

as specified in Section 4.2.2. of unlisted European firms in technology-intensive industries. Different in-dustries is an indicator variable that is equal to one if the acquirer and target primary SIC-codes aredifferent, and zero otherwise. Standard errors are heteroskedasticity-consistent according to MacKinnon andWhite (1985). *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

Independent variable Expected sign Stand-alone targets Subsidiary targets Full sample

Patenting (0/1) + 0.0314 0.0634 0.0422∗∗

(1.21) (2.28) (2.23)Patent count∗10−2 + 0.2629∗∗ 0.0694 0.0946

(1.91) (0.69) (1.12)Patent count2 ∗10−4 - 0.1877 −0.0125 −0.002

(0.88) (−0.14) (−0.02)Quality-weighted patents∗10−2 + 0.04913 0.1402 0.0568

(0.32) (1.25) (0.68)ln(Geographic distance)×10−2,< 470

- 1.1510∗∗ 0.4896 0.8941∗∗

(2.28) (0.82) (2.32)ln(Geographic distance)×10−2,≥ 470

- 3.58 −3.5431 0.0482(0.71) (−0.59) (0.01)

M&A-activity (0/1) + −0.0059 0.0147 0.0066(−0.21) (0.49) (0.32)

Deal size > $20m + 0.1067∗∗∗ 0.1545∗∗∗ 0.1187∗∗∗

(5.62) (7.09) (8.27)IPO volume∗10−2 + −0.8081 0.1894 −0.2378

(−0.67) (0.15) (−0.27)Baa spread - −0.0229∗∗∗ −0.0158∗∗ −0.0209∗∗∗

(−3.64) (−2.11) (−4.33)Overnight rate + 4.228∗∗∗ 1.870∗∗ 3.041∗∗∗

(5.54) (1.92) (5.01)Deal made between 2000-2006(0/1)

? −0.1237∗∗∗ −0.1114∗∗∗ −0.1157∗∗∗

(−6.68) (−5.03) (−8.12)Different industries (0/1) ? 0.0386∗∗ 0.0372 0.0327∗∗

(2.03) (1.59) (2.21)Cash deal - −0.0153 0.0264 −0.0076

(−0.8) (1.09) (−0.52)

univariate model. Furthermore, since the univariate analysis does not allow for the use of con-trol variables, their exclusion is likely to also influence the means and standard errors of thecoefficients.

Also, the fact that the square of the patent count does not have the expected sign for stand-alonetargets in a univariate framework is no indication of rejection of my hypothesis H6b. The squareis included in the analysis to model the decreasing returns to scale from patenting, and hence,in a univariate analysis, might not obtain the desired coefficient. Moreover, given that the firstand second powers of the patent count are analysed separately, and not in conjunction, theircoefficients in the univariate framework are expected to include some of the effects of the other.

Perhaps the most intriguing results from the univariate analysis are the seemingly significantdifference in the coefficients of the cash deal dummy between subsidiary and stand-alone tar-

65

gets, and the strongly significantly negative coefficient of the millenium (deal made between2000-2006) dummy across categories. The former suggests that the demand for liquidity in itspurest form is different to owners of subsidiaries from what it is to owners of stand-alone firms.Moreover, whereas the owners of stand-alone firms seem to be willing to relinquish control oftheir firm at a discount in exchange for cash consideration, the owners of subsidiaries are moreprone to demand even a higher price if the acquirer pays with cash. This analysis implies thatthe internal capital markets within groups work well enough for them to prefer less liquid meansof payment, whereas the owners of stand-alone firms, whether they are private individuals orprofessional investment organizations, clearly wish to exchange their investments in illiquid as-sets to very liquid assets, preferably cash. That is, when they are exiting, they wish to do sofully, even if it means that they need to sell at a relative discount.

A somewhat startling result is the fact that the log of geographic distance only obtains theexpected coefficient in the subsidiary subsample, when distance exceeds 470km. This is sug-gestive of the fact that in the case of unlisted high-tech targets in Europe, there may be somereasons why it would be beneficial for the distance to be greater, at least to some extent. How-ever, since this is a univariate analysis, I cannot draw definitive conclusions of it.

The clearly negative coefficient of the 2000-2006 dummy suggests that between 2000 and 2006unlisted high-tech targets were valued clearly lower in relation to their peers than they were be-tween 1990 and 1999. Thus, it is likely that acquirers have given a higher weight to informationasymmetry in the new millenium thereby valuing firms lower when there is less informationavailable. Also, it is possible that the supply of liquidity has been lower in the new millenium,which also would merit a lower relative valuation according to both the results in the next sec-tion, and those in Officer (2007).

All in all, the univariate analysis provides support for the analysis of the full model, and isencouraging with respect to the expected signs of most of the coefficients.

5.2.3. Multivariate results

The analysis of the results in Table 11. provides direct support for my hypotheses H4a, H6a,H6b, and H9. More specifically, I find statistically significant evidence that the further awaytargets are from acquirers, the greater the acquisition discount, provided that the distance be-tween the target and acquirer is at least 470km. Below that distance, Grote and Umber (2007)hypothesize that managerial overconfidence may drive the overvaluation of those targets. If thenull hypothesis was that acquisition discounts increase in the natural logarithm of distance, thenI might be able to provide support for the notion that managerial overconfidence indeed doesdrive overvaluation in distance to targets below 470km away from the acquirer (given a largeenough negative null coefficient). However, with the evidence at hand, and the null hypothesis

66

as zero impact, I cannot verify in statistically significant terms that this is the case. Interest-ingly, though, the short-distance coefficient is very likely to be non-negative and is significantlypositive in the univariate analysis, which does suggest that the hypothesis of managerial over-confidence might be supported in samples of sufficient size.

The results from the full sample regressions are consistent with hypotheses H6a and H6b, andthe responses from the questionnaire. Namely, the number of patents assigned to the target de-crease the information asymmetry and thus the acquisition discount of that target (H6a). More-over, the marginal impact of a patent on the acquisition discount is decreasing in the number ofpatents. That is, beyond a certain number of patents (around 125 in this case24), the additionalvalue of a patent is in fact decreasing. Although the analysis is very specific, my sample doesinclude firms with more than 125 patents, and is, at least from that point of view, valid. Finally,the survey responses clearly indicate that patents are extremely important in determining dealvalue. While my analysis is very restricted to only the number and some quality dimensions oftarget patents, it does support the view of the respondents.

Unreported regressions where the patenting variables are replaced by a dummy variable thatobtains the value one when Patent count > 0, and zero otherwise, fail to reject the null hypoth-esis corresponding to H5. However, the univariate regressions in Section 5.2.2. do providesome support for the hypothesis that the mere existence of patents assigned to the target has aneffect on deal value. However, in the final multivariate framework, simply having patents is notenough. Their number, on the other hand, clearly does matter, as does their quality. Indeed, thet-value of the patenting dummy variable is almost small enough to provide support for the nullhypothesis at the 10% level. Thus, I fail to provide statistical support for H5.

In addition to the above analysis on the impact of the number of patents on deal value, a morein-depth analysis of the composite quality measure is also quite intriguing. Whereas Hall et al.(2005, 2007), and Trajtenberg (1990) among others find that the number of citations receivedby a patent has a significant impact on market value, in my analysis the value of citations isleft to lesser statistical significance. Again, a more thorough analysis of the characteristics ofthe patents in my sample shows that there are very few citations overall in the sample. Morespecifically, my sample includes only a little over one citation per patent whereas that in Hallet al. (2005), for instance, includes almost eight citations per patent. Moreover, the citations inmy sample are clearly clustered into portfolios that are overwhelmingly large in size given therelatively small size of the sampled companies. Thus, in conjunction with the above analysisof decreasing marginal impact of patents, one might posit that in this particular sample patentswith citations are likely to be pooled together in a portfolio with lower quality patents, or simplyin portfolios where the marginal impact of a patent is already fairly small. Furthermore, giventhe lag in citations with respect to the patent grant, they are probably the hardest to access, least

24From the full sample regression, I obtain y = 0.002319x−0.00001854x2→ ∂y∂x = 0 ⇐⇒ x = 2.319

0.01854 ≈ 125

67

likely, and most volatile source of additional information on the potential targets, and thus theymay not mitigate information asymmetry at all. Indeed, Toivanen and Väänänen (2008) findthat the real returns to inventors25 of patents only occur at a lag with respect to the patent grantwhen the underlying citation-distribution of the patent begins to unravel. Following this logic,it seems reasonable that citations are not an especially fruitful and robust source of additionalinformation regarding the fundamental profitability of the target to which they are assigned inthis particular sample. Finally, the result that citations and references are unimportant withrespect to patent value is consistent with the survey responses, where the former obtained anaverage of 3.33, and the latter 2.90 in importance to patent value on a scale from 1 to 5, where5 = very important. Moreover, the responses confirm the finding that scope and family size areextremely important.

While the questionnaire responses clearly refer to the size of the patent family linked directlyvia a priority document, the EPO databases include the INPADOC family, where the membersmay be linked to the patent in question also indirectly. An encouraging finding in terms offuture analysis is that the size of the INPADOC patent family, a variable not to my knowledgepreviously used in assessing the impact of patents on firm value, does bear very high statisticaland economical significance. Thus, it is not only the patents that are directly linked with theoriginal patent that bear significant information, but also those linked to it indirectly via a prior-ity document. The clearly stronger significance of the INPADOC family size as opposed to thatof the number of citations is easily explained by the fact that in looking at the information valueof specific characteristics, only a priori information is relevant. Thus, the information value ofall citations received after the acquisition announcement is by default zero. After the INPADOCfamily size, the scope-weighted patent count obtains the most significant coefficient, while thecoefficient of references is equally negligible as that of citations.

In addition to the information asymmetry explanation, Table 11. shows results consistent withmy hypothesis H10. More specifically, an increase in the corporate loan spread (or baa spread),which indicates the general availability of corporate debt, leads to an increase in the acquisitiondiscount, and thus a decrease in the value of the target. Also, when acquisition activity is aboveits time series median, the acquisition discounts are lower, indicating an increased demand, andthus higher equilibrium price for the targets. Not unlike the results in Officer (2007), my anal-ysis fails to provide support for the role of IPOs as a competing source of liquidity for thesefirms. More strikingly, the sign of the IPO market effect is unexpected, and thus would indicatethat acquisition discounts are higher when there are more IPOs than on average. An analysis ofthe correlations between explanatory variables provides no explanation for this. Moreover, in anunreported regression where the sample is reduced to include only listed acquirers, the IPO mar-ket variable obtains a statistically significantly (at the 5%-level) negative coefficient of−0.0257

25Recall from Section 1.4. that an inventor is a person, not a company.

68

in model 2 of the full sample utilizing standard errors according to White (1980). However, thereduction in sample size and the fact that the HC3 covariance matrix is unobtainable makes theanalysis dubious enough to warrant its exclusion from the reported results. Hence, the impactof the IPO market heat requires further inspection. Also, the sign and significance of the cashdeal dummy are consistent with the interpretation that deals involving unlisted high-tech targetsare also partly motivated by the liquidity needs of the owners of the targets.

Unreported results do show a significant positive impact of the natural logarithm of acquirermarket value on the transformed acquisition discount, and hence a negative impact on the eco-nomic acquisition discount26. The inclusion of the log of acquirer market value does not havea significant impact on the qualitative results, but it does reduce the sample size so much as towarrant its exclusion from the final reported analysis.

Unreported results also show that toehold ownership does not have a statistically significantimpact on the transaction value. It obtains a negative sign, but also |t| < 0.5, and thus the nullhypothesis of no impact would not be rejected. Furthermore, the inclusion of toehold ownershipdoes not have an impact on the qualitative results, nor does it increase the explanatory powerof my tests. Hence, I omit it from the final analysis. Also, whether the bid was challenged ornot has no statistical influence on the quantitative results, nor does it thus affect the qualitativeresults. Moreover, including the challenged bid dummy only decreases the explanatory powerof my tests, and hence it is omitted.

A study of the fixed-effects dummies in the full sample regressions indicates that the acquisitiondiscounts are significantly higher for targets based in France, Ireland, Luxembourg, and Swe-den, while they are significantly lower in Italian targets compared to the average. The strongstatistical significance of the Luxembourg dummy may be rather a result of randomness thanthat of Luxembourgian companies having significantly lower valuations than average Europeancompanies. Moreover, unlisted targets operating in SICs 37, 48, 73, and 8727 have a statisticallysignificantly higher acquisition discount than firms operating in other high-tech industries. Theexclusion or inclusion of country and industry fixed-effects has no impact on the qualitativeresults of my analysis. Finally, provided that I utilize time-varying explanatory variables, suchas the M&A activity indicator, the Baa spread, and the like, I am unable to utilize year fixed-effects dummies. I do, however, control for different valuations before and after the turn of themillenium, the results of which are explained earlier in this section.

26Recall from Section 4.2., that the economic acquisition discount refers to the discount relative to peer multi-ples, while the transformed discount is its normally distributed transformation.

27Transportation Equipment; Communications; Business Services; and Engineering, Accounting, Research,Management, and Related Services; respectively

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70

Given that apart from the IPO volume, all of the coefficients obtain the same, expected signacross all subsamples, and given the analysis of correlations in Section 4.1., and that of thevariance inflation factors in Section 4.2., my data does not present any apparent symptoms ofmulticollinearity. Moreover, as explained above, my results are robust against controlling forsome additional explanatory variables of deal value suggested in the literature. Finally, as ex-plained in Section 4.2., the transformed discount model does not violate any of the assumptionsof OLS-regression, nor does it violate the normality assumption, and hence the coefficients areBLUE and the related hypothesis tests valid.

Table 12: Marginal effects on the acquisition discountMarginal effects of explanatory variables on the acquisition discount at some predefined levels of the ac-quisition discount, or equivalently, of the transformed acquisition discount, as specified in Appendix B.All of the marginal effects are derived from the full sample regression with patent counts, except forthe quality-weighted patent count, the marginal effect of whom is derived from the full sample regres-sion which includes the quality-weighted patent count measure. Also, the marginal effects of the naturallogarithm of geographic distance are derived from the regressions that are categorized according to thetarget-acquirer distance. Moreover, the effects of ln(Geographic distance), d < 470 are derived from thesample where the distance between target and acquirer headquarters is less than 470km, and the effectsof ln(Geographic distance), d ≥ 470 from the sample where that distance is greater than or equal to 470km.

D =−0.75 D =−0.5 D =−0.25Independent variable Expected sign D∗ =

√1.75 D∗ =

√1.5 D∗ =

√1.25

Intercept ? −2.0759 −1.9219 −1.7545Patent count∗10−2 -,+ −61.36+0.49x −56.81+0.45x −51.86+0.41xQuality-weighted patents∗10−2 - −0.0827 −0.0765 −0.0699ln(Geographic distance) , d < 470km - −0.0092 −0.0085 −0.0077ln(Geographic distance) , d ≥ 470km + 0.1953 0.1808 0.1651M&A-activity (0/1) - −0.3052 −0.2826 −0.2579Deal size > $20m - −0.3782 −0.3502 −0.3196IPO volume + 0.0269 0.0249 0.0227Baa spread - 0.0487 0.0451 0.0412Deal made between 2000-2006 (0/1) ? 0.3393 0.3142 0.2868Different industries (0/1) ? −0.0440 −0.0407 −0.0372Subsidiary (0/1) + 0.1975 0.1828 0.1669Cash deal + 0.0426 0.0394 0.036

D = 0 D = 0.25 D = 0.5 D = 0.75Independent variable D∗ =

√1 D∗ =

√0.75 D∗ =

√0.5 D∗ =

√0.25

Intercept −1.5692 −1.3590 −1.1096 −0.7846Patent count∗10−2 −46.39+0.37x −40.17+0.32x −32.8+0.26x −23.19+0.19xQuality-weighted patents∗10−2 −0.0625 −0.0541 −0.0442 −0.0313ln(Geographic distance) −0.0069 −0.0060 −0.0049 −0.0035ln(Geographic distance) 0.1477 0.1279 0.1044 0.0738M&A-activity (0/1) −0.2307 −0.1998 −0.1631 −0.1154Deal size > $20m −0.2859 −0.2476 −0.2022 −0.1429IPO volume 0.0203 0.0176 0.0144 0.0102Baa spread 0.0368 0.0319 0.0261 0.0184Deal made between 2000-2006 (0/1) 0.2565 0.2222 0.1814 0.1283Different industries (0/1) −0.0332 −0.0288 −0.0235 −0.0166Subsidiary (0/1) 0.1493 0.1293 0.1056 0.0746Cash deal 0.0322 0.0279 0.0228 0.0161

When compared against estimates on the untransformed acquisition discount, my estimates are

71

more robust. More specifically, the explanatory power(R2) of the regression is improved by

over 2%-points, which suggests that my results provide an improved fit in terms of the rela-tion between the dependent and independent variables. While the majority of the t-values re-main unaffected on average, those related to patenting variables are significantly affected by thetransformation. More specifically, the statistical significance of the composite quality-weightedpatent count is reduced by half due to the transformation. While the coefficient remains signifi-cant at the 5%-level even in the transformed regressions, this result clearly shows that in somecases the use of the untransformed acquisition discount would have led to over-rejection of thenull hypothesis. Provided that, as shown in Section 4.2.2., the normality assumption concern-ing the residuals is not violated in the transformed model but is in the untransformed model,the tests of hypotheses in the transformed model are valid. Moreover, as explained above, thestatistical significance is no longer overstated.

To ease the interpretation of my results in Table 11., I calculate the marginal impacts of eachexplanatory variable on my proxy for the economic acquisition discount (instead of the trans-formed one) using the chain rule as specified in Appendix B, and report them in Table 12.

5.3. What determines the target’s probability to patent?

The results from the selection model in Table 13. provide support for my hypothesis H7. Morespecifically, consistently with for example Lehto (2006), Böckerman and Lehto (2006), andAli-Yrkkö et al. (2005), the results indicate that acquirer appetite for additional means of miti-gating information asymmetry in other dimensions increases while the information asymmetryincreases in one dimension.

Given previous empirical evidence, it is no surprise that the propensity of a target having patentsincreases with the log of the geographic distance between the target and acquirer. Moreover,when the acquirer and target have different two-digit primary SIC-codes, the acquirer’s knowl-edge of the target’s business is lower than if they were operating in the same industry. Finally,when the acquirer is a non-industrial investor, it is likely to have less detailed knowledge aboutthe finer points of the business model than an acquirer operating in an industry that is close orsimilar to that of the target.

Consistently with my expectations, the impact of deal size on patenting probability is positive.Moreover, the economical and statistical significance and direction of impact of the toeholddummy coefficient are supportive of my hypothesis H8. Namely, the motivation between thetoehold-patenting relation has more to do with either fright of litigation by acquirer or facilita-tion of negotiating power by the target than with information trade-offs, as explained in Section2.4. Furthermore, the survey respondents clearly view that patents are an important tool in

72

Table 13: What determines the probability of a targethaving patents?

Logit-regression of explanatory variables on the patentingdummy variable of unlisted European technology-intensivetargets. Standard errors are jackknifed standard errors,as explained in Section 4.2.6. *, **, and *** denotestatistical significance at the 10%, 5%, and 1% levels.

Independent variable Expected sign Coefficient

Intercept −2.276∗∗∗

(−5.37)ln(Geographic distance) + 0.1316∗∗∗

(3.30)Different industries (0/1) + 0.3429∗∗

(2.05)Acquirer is an investor (0/1) + 0.4295∗∗

(2.02)Target is a subsidiary (0/1) ? 0.1742

(1.12)Toehold (0/1) + 0.6950∗∗

(2.25)ln(Deal value) + 0.0797∗∗

(1.78)

Year fixed-effects YesCountry fixed-effects YesIndustry fixed-effects Yes

Pseudo-R2 0.13Log-likelihood −641.49N 1495

negotiations and in obtaining competitive advantage. These views give further support for H8.

An analysis of the fixed-effects dummies shows that Czech, Finnish, Italian, and Luxembour-gian targets are significantly more likely to have patents than other targets in the sample. In-terestingly, targets based in Denmark and the Netherlands, are significantly less likely to havepatents than other targets. While the high patenting rate in targets based in Luxembourg and theCzech Republic may be a mere coincidence given that they total only 6 observations, the anal-ysis of the rest of the country fixed-effects is likely to be sufficiently robust to say that there isan actual difference. Whether these differences are due to increased (or decreased) informationasymmetry due to regulatory environments or merely an indicator of differences in patentingbehavior across countries cannot be said from the data at hand.

The data show no statistically significant differences across years sampled. There are, however,significant differences between industries. More specifically, the propensity to patent is signifi-cantly lower in SICs 48, 73, and 8328 compared to other industries. The exclusion or inclusionof country and industry fixed-effects has no impact on the qualitative results of my analysis.

28Communications; Business Services; and Engineering, Accounting, Research, Management, and Related Ser-vices , respectively.

73

Finally, while the use of jackknifed standard errors improves the robustness of the hypothesistests with respect to standard errors according to White (1980), the difference is minuscule.More specifically, the maximum difference between t-values is of the order |∆t|< 0.1.

5.4. What determines the announcement return?

Similarly as in Section 5.2., I begin my analysis of the bidder abnormal announcement returnswith a univariate analysis of its determinants in Section 5.4.1. I then move on to the multivariateanalysis in Section 5.4.2.

5.4.1. Univariate results

Table 14. shows the univariate effects of explanatory variables on the three-day cumulative ab-normal acquisition announcement return of the bidder. The independent variables are multipliedby 10−2, and thus the effects are those on the abnormal announcement return in percent.

Both the natural logarithm of the geographic distance and the stock acquisition dummy vari-able obtain the expected sign, and are strongly statistically significant already in the univariateanalysis. The former indicates that while information asymmetry increases with geographicdistance, the market perceives this, and values acquisition announcements accordingly. Themarket also perceives that using stock as consideration, when information asymmetry is rela-tively high, yields the acquirer an efficient tool for monitoring the true value of the target, andthus merits a relatively higher valuation for the acquisition.

The sign of the acquirer asset-scaled patent count provides preliminary support for the notionstemming from the hubris hypothesis (Roll, 1986) that while patents merit a higher valuation forthe target, their inclusion in a deal is value-destructive from the viewpoint of acquirer sharehold-ers. However, as the coefficients are not statistically significant and as they are more accuratelydetermined in the full model, the hypothesis has to be analyzed in the multivariate analysis.

While the univariate analysis does provide preliminary support for hypothesis H11, it seemsto be unable to reject the null hypothesis related to H12. Thus, in a univariate setting, whilethe general M&A activity has a positive, albeit not statistically significant, impact on the an-nouncement return, the direction of impact of the IPO market temperature is not unambiguous.However, the coefficients are by no standards statistically significant, and thus the hypothesisneeds to be analyzed in a multivariate setting. The control variables, including the tender of-fer and challenged bid dummies, acquisition discount, deal and acquirer size, and the acquirerprice-to-book ratio, all obtain the expected signs, and their inclusion increases the explanatorypower of the multivariate tests.

74

Table 14: Univariate results for the announcement returnUnivariate OLS-regressions of the explanatory variables on the acquisition announcement returnsof acquirers of unlisted European firms in technology-intensive industries. Explanatory variablesare multiplied by 10−2 (or alternatively, their coefficients by 102), except where otherwise in-dicated. Standard errors are heteroskedasticity-consistent according to MacKinnon and White(1985). *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

Independent variable Expected sign Stand-alone targets Subsidiary targets Full model

ln(Geographic distance) - −0.3500∗∗∗ −0.0771 −0.2752∗∗∗

(−2.44) (−0.55) (−2.52)Stock acquisition (0/1) + 1.807∗∗∗ 1.693 1.865∗∗∗

(2.81) (1.21) (3.25)ln(Deal size) + 0.1272 −0.0340 0.0188

(0.72) (−0.17) (0.14)Patent count/ln(Total Assets)∗10−3 +/- −0.1289 −0.2191 −0.2027

(−0.50) (−1.30) (−1.36)ln(Sales) +/- −0.3405∗∗ −0.2630 −0.3300∗∗∗

(−2.22) (−1.51) (−2.95)Price-to-book + 0.0040 0.0187 0.0047

(0.34) (0.57) (0.48)M&A activity (0/1) + 0.6159 0.3857 0.5732

(0.98) (0.56) (1.22)IPO volume∗10−2 - 0.1817 0.2526 0.2183

(0.59) (0.47) (0.79)Baa spread - −0.0717 −0.1376 −0.1042

(−0.41) (−0.59) (−0.75)Acquisition discount + 0.7557 0.0113 0.8033∗

(1.17) (1.08) (1.49)Deal made between 2000-2006 (0/1) ? 1.358∗∗∗ 0.6808 1.154∗∗∗

(2.46) (0.92) (2.59)Different industries (0/1) +/- 0.7150 −0.6495 0.2073

(1.29) (−0.87) (0.47)Tender offer (0/1), acquirer q < 1 + 4.849 4.331

(1.27) (1.14)Tender offer (0/1), acquirer q≥ 1 - −0.3197 −2.531∗∗∗ −2.228∗∗∗

(−0.20) (−2.39) (−2.53)Challenged bid (0/1) - −0.8138 −6.445∗∗∗ −4.797∗∗

(−1.13) (−11.13) (−1.85)Toehold acquisition (0/1) +/- 0.5261 −1.998∗ −0.9328

(0.39) (−1.95) (−1.11)

Finally, the univariate regressions indicate that in this setting the market rewards acquisitionsafter the turn of the millenium significantly more generously than prior to it. Also, the resultsindicate that while a non-horizontal acquisition of a stand-alone target is rewarded by the mar-ket, the acquirer is punished if it acquires a subsidiary from a different industry. Thus, whilethe impact of non-horizontality is value increasing for target shareholders, it seems to increasevalue to acquirer shareholders only if the target is not a subsidiary. However, these results alsoneed to be analyzed more carefully in light of the multivariate framework, since they are notstatistically significant in the univariate setting.

75

5.4.2. Multivariate results

Table 15. reports the multivariate results pertaining to the acquirer announcement return. Giventhe unintuitive interpretation of the bootstrapped percentile t-values as discussed in Section4.2.5., I report the corresponding p-values instead. The multivariate analysis yields support formy hypothesis H4b. More specifically, the impact of geographic distance on the acquirer an-nouncement return is both statistically (at the 1%-level) and economically significant (meritinga decrease in acquirer market value of 0.3%-points per a unit increase in the log of distance).The statistical insignificance in the subsidiary target subsample is most likely due to the smallsample size, especially given that the sign of impact is the same, and that the p-value in the fullsample regression is very close to that of the stand-alone target subsample.

My hypothesis H3 regarding the increased announcement returns of stock acquirers is furthersupported by the multivariate OLS regression analysis. This finding is consistent with the anal-ysis of Officer et al. (2009), although the framework is very different. Interestingly, in themultivariate framework, the null hypothesis of a zero coefficient in the subsidiary target sub-sample obtains statistically significant support. Furthermore, the sign of that coefficient is anunexpected negative one, which further refutes H3 with respect to unlisted high-tech subsidiarytargets. Hence, it appears that the market does not perceive the use of stock consideration as asignificant monitoring method when the target is a subsidiary. Moreover, this finding suggeststhat there is more information available of subsidiaries than of stand-alone targets, and hencethe signaling effect of the use of stock consideration bears at least as much significance than itsuse as a monitoring mechanism.

The combination of univariate and multivariate analyses supports my hypothesis H11, wherebythe market rewards acquisitions of unlisted high-tech targets with a more positive return whenthose acquisitions are made during hot M&A markets. Moreover, when M&A activity is high,acquirers of stand-alone targets are awarded an excess price increase of over 2%-points, signifi-cant at the 5%-level, whereas acquirers of subsidiary targets only gain slightly at no statisticallysignificant level. However, the full sample coefficient is statistically significant at the 5%-level,and economically significant meriting a 1.3% increase in market value, which suggests that thelow statistical significance in the subsidiary subsample is at least partly due to small samplesize.

Furthermore, the multivariate analysis unambiguously supports my final hypothesis H12, where-by the market punishes acquirers of unlisted targets during times of hot IPO markets. Thecoefficients in the full sample and the stand-alone target subsample amount up to around 0.2%-point decreases in abnormal returns to acquirers maintaining a statistical significance at the5%-level. More curiously, though, the effect in the subsidiary target subsample is over twiceas large (amounting up to a 0.48%-point decrease in returns) and statistically significant at

76

Table 15: Determinants of the acquisition announcement return.Bootstrap regressions of patenting and non-patenting variables on the announcement return of acquir-ers of unlisted European firms in technology-intensive industries. Explanatory variables are multipliedby 10−2 (or alternatively, their coefficients by 102), except where otherwise indicated. The numbersin italics are p-values corresponding to a one- or two-sided bootstrapped percentile t-test of each co-efficient, where the sidedness of the test depends on the unambiguity of the expected sign. Stan-dard errors are bootstrap standard errors from 99,999 simulations for each subsample. Statistical signifi-cance of the coefficients is determined using bootstrapped percentile t-distribution, as explained in Section4.2.5. *’s denoting statistical significance are suppressed given the intuitive interpretation of the p-values.

Independent variable Expected sign Stand-alone targets Subsidiary targets Full model

Intercept 4.3411 7.6541 5.34830.08 0.07 0.01

ln(Geographic distance) - −0.4432 −0.3060 −0.33060.01 0.03 0.005

Stock acquisition, toehold (0/1) + 10.9244 0.5582 4.87340.00 0.18 0.05

Stock acquisition, no toehold (0/1) + 1.3315 −0.4392 1.04470.02 0.60 0.03

ln(Deal size) + 0.4192 0.4184 0.44440.04 0.05 0.01

Patent count/ln(Total Assets)∗10−3 +/- −3.4284 −2.9445 −2.92090.22 0.23 0.06

ln(Sales) +/- −0.4794 −0.3592 −0.43620.02 0.16 0.01

Price-to-book∗10−3 + 0.1119 0.3691 0.11960.10 0.09 0.08

M&A activity (0/1) + 2.2227 0.3271 1.26030.03 0.22 0.06

IPO volume - −0.1855 −0.4733 −0.22360.04 0.04 0.02

Baa spread - −0.1616 −0.5255 −0.25010.11 0.04 0.04

Acquisition discount + 0.5425 0.8166 0.90540.13 0.13 0.04

Deal made between 2000-2006 (0/1) ? 0.8330 1.1601 0.91250.13 0.16 0.07

Different industries (0/1) +/- 0.9468 −0.7124 0.41970.08 0.23 0.22

Tender offer (0/1), acquirer q < 1 + 6.5104 5.93670.03 0.01

Tender offer (0/1), acquirer q≥ 1 - −1.2423 0.9028 −0.73630.13 0.17 0.15

Challenged bid (0/1) - −7.2512 −8.08760.06 0.03

Toehold, cash acquisition (0/1) +/- 1.0553 −4.6475 −1.57330.28 0.02 0.13

Year fixed-effects No No NoIndustry fixed-effects Yes Yes YesCountry fixed-effects Yes Yes Yes

R2 0.11 0.23 0.11N 434 203 637

the 5%-level, although the subsample consists of only 203 observations (less than half of the

77

stand-alone target subsample). Hence, the market appears to punish acquisitions of high-techsubsidiary targets significantly more than acquisitions of stand-alone high-tech targets duringtimes of high IPO activity in the industry. However, the difference in the coefficient means isby no standards statistically significant, and hence the study of the performance differences be-tween acquisitions of unlisted subsidiary and stand-alone targets with respect to the IPO marketactivity must be left for future study.

As in the univariate regressions, the target patent count weighted by the log of acquirer assetsobtains a marginally significant negative coefficient in the full sample regression. The persis-tence of the sign and level of impact across subsamples indicates that acquired patents indeeddo destroy relative value. However, the economic significance is not overwhelming. An ad-ditional acquired patent per ln($1m) in total assets only destroys less than 0.3%-points of theacquirer abnormal return. More specifically, to lose 0.3% in market value, an average acquirer(with ln(Total assets $m) ≈ 12) would have to acquire 12 patents. Moreover, unreported re-sults show that no other patent count measure obtains a statistically significant coefficient in themultivariate framework.

As in the univariate setting, the control variables obtain the expected signs. Moreover, theirimpacts are statistically significantly different from zero in the full sample regressions. Myselection of control variables does divert slightly from the extant literature, mainly because thevariables used in my regressions provide a better empirical fit than the ones suggested. Morespecifically, I use the natural logarithm of deal size instead of relative deal size, and the naturallogarithm of sales instead of that of market value. While relative deal size and the naturallogarithm of market value did obtain the same signs as my control variables, the explanatorypower of my tests is improved by using this specific mixture.

The market awards a unit increase in the log of deal size with an average of approximately 0.4%increase in the market value of the acquirer. For acquisitions of subsidiaries, the economic sig-nificance amounts up to an increase 0.5%. Given also that deals over $20m in size merit sta-tistically significantly higher valuations relative to their smaller peers, the results indicate that,among other things, deal size (or rather, the size of the target) does mitigate information asym-metry in acquisitions of unlisted companies. Consistently with the theoretical considerations ofJensen (1986), the market punishes larger acquirers statistically significantly more than smallerones. That is, a unit increase in the natural logarithm of sales measured in $m is punished by astatistically significant loss close to 0.5% in market value.

Furthermore, consistently with the theoretical expectations, acquirers with higher price-to-bookratios do earn a statistically significant added return. However, that increase is economicallynegligible, amounting only up to a 0.01% increase in value per unit increase in the price-to-book ratio. Moreover, as shown by Lang et al. (1989) and Servaes (1991) among others, tenderoffer acquirers gain if they have a Tobin’s q-value less than one, i.e. if the replacement value

78

of their assets exceeds their market value, and lose if they have a Tobin’s q-value greater thanone. Moreover, in economic terms, the gain to low-q acquirers is highly significant, while theloss to high-q acquirers is as well, but not equally so. A tender offer bidder with a low q gainsmore than 5% in market value, while a bidder with a high q loses slightly over 1%. Finally, themarket punishes winners of bid contests by a loss in market value in excess of 5%.

An analysis of unreported country fixed-effects indicates that acquisitions of Irish and Italianfirms are punished by a statistically significant loss in market value of approximately 2 and 4percent, respectively. Moreover, acquisitions of Portuguese and Russian targets are rewarded bystatistically significant respective approximate price run-ups of 7 and 5 percent. Other countryfixed-effects variables do not obtain statistically significant coefficients. Finally, the industrydummy variables indicate that while acquisitions of SIC 37 targets are rewarded by a price run-up of around 3%, those of SIC 87 are punished by a decrease in market value amounting up to1%29. Other industry fixed-effects remain statistically insignificant. The exclusion or inclusionof country and industry fixed-effects has no impact on the qualitative results of my analysis.

As discussed in Section 4.2., my regressions are robust with respect to the assumptions of OLS.Moreover, the hypothesis tests are valid, utilizing the bootstrapped percentile-t distribution. Asdiscussed in Section 4.2.5., and given the significant number of bootstrap simulations run, themean coefficients are essentially the same as they would be in an OLS-regression. However,the statistical significance of some hypothesis tests is substantially increased by the use of boot-strap. While the results would be consistent with my hypotheses even with the use of OLS withthe MacKinnon and White (1985) covariance matrix, such an analysis would lead to an under-rejection of the null hypotheses, especially concerning some of the control variables based onprevious literature. By employing the wild bootstrap, I obtain more robust results where thenull hypotheses are rejected or not rejected based on the assumption that my sample is ran-domly drawn from the underlying population, an assumption more reasonable in this case thanthe asymptotic normality assumption, given the discussion in Section 4.2.4. Also, the data doesnot suffer from such multicollinearity that would invalidate my results. Finally, as my resultsare robust controlling for a multitude of acquirer announcement return determinants utilized inprevious literature, this analysis appears to be valid.

6. Summary and conclusions

To finish my thesis, I summarize my hypotheses and the related empirical evidence in Section6.1. Finally, in Section 6.2., I discuss the results and potential conclusions together with possibleavenues for future research as well as answer my research problem.

29Transportation Equipment; and Engineering, Accounting, Research, Management, and Related Services; re-spectively.

79

6.1. Summary of hypotheses and evidence

Table 16. summarizes the results from the previous section with respect to the hypothesesspecified in Section 3.1. Importantly, my data provides statistical support for almost all ofmy hypotheses. The sole exception is H5, which states that the simple existence of patentsdecreases the acquisition discount of unlisted European high-tech targets. Unreported resultsshow very low t-values for the mean coefficient of the patenting dummy. However, as the restof the patenting hypotheses do hold, it is obvious that the effect does exist, but that it is notexclusively dependent on the existence of patents.

I am able to verify the results of Officer (2007) and Officer et al. (2009) with a European,high-tech focused data set in hypotheses H1 and H3. Also, partially related to the informationasymmetry explanation explored by Officer et al. (2009), I find support for H2. Namely, thatinformation asymmetry is greater in targets that are more difficult to value, at least in terms ofthe acquisition discount.

Consistently with for instance Uysal et al. (2008), I find support for H4b. Namely, the returnsto acquirers decrease in geographic distance between acquirers and targets. Unlike in the caseof deal valuation, this effect is constant across short and long distances. I also find partialsupport for H4a. More specifically, at sufficiently long distances, the acquisition discount isincreasing in the natural logarithm of geographic distance between the acquirer and the target.The separation between long and short distance transactions in terms of the acquisition discountis of special interest, and has to my knowledge only been previously explored in a working paperby Grote and Umber (2007). I can verify that there is a difference between close proximitytransactions, and transactions with relatively large distance between the acquirer and the target.However, it remains unclear why the acquisition discount would first decrease and then increasein the log of geographic distance. Moreover, the notion that the discount would first decreasein the log of distance is not statistically significantly consistent with my data. However, thedata do show that the coefficient of geographic distance is not likely positive in short distancetransactions. Finally, albeit also used by Grote and Umber (2007), the threshold of 470kmseems arbitrary, and will require further validation from future studies.

80

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tote

stth

ehy

poth

eses

,an

dth

efin

alco

lum

nsp

ecifi

esw

heth

erth

ehy

poth

esis

issu

ppor

ted

toa

stat

istic

ally

sign

ifica

ntex

tent

byth

eem

piri

cal

resu

lts.

Hyp

othe

sis

Des

crip

tion

Mod

elV

aria

bles

Supp

ort

H1

The

acqu

isiti

ondi

scou

ntof

unlis

ted

targ

ets

pers

ists

acro

ssa

data

seto

fE

uro-

pean

firm

s.t-

test

;acq

uisi

tion

disc

ount

sac

quis

ition

disc

ount

Yes

H2

The

acqu

isiti

ondi

scou

ntis

mor

epr

eval

enti

nte

chno

logy

-int

ensi

vein

dust

ries

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test

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uisi

tion

disc

ount

sac

quis

ition

disc

ount

Yes

H3

The

acqu

isiti

onan

noun

cem

ent

retu

rns

toac

quir

ers

ofun

liste

dta

rget

sin

tech

nolo

gy-i

nten

sive

indu

stri

esar

e,ce

teri

spa

ribu

s,hi

gher

for

stoc

k-sw

aptr

ansa

ctio

ns.

t-te

stan

dB

oots

trap

OL

S;C

AR

CA

RY

es

H4a

The

acqu

isiti

ondi

scou

ntof

unlis

ted

targ

ets

incr

ease

sw

ithth

ege

ogra

phic

aldi

stan

cebe

twee

nth

eta

rget

and

acqu

irer

head

quar

ters

.O

LS;

acqu

isiti

ondi

scou

ntln(G

eogr

aphi

cdi

stan

ce)≥

470

Part

ial

H4b

The

bidd

erac

quis

ition

anno

unce

men

tret

urn

redu

ces

inth

ena

tura

llog

arith

mof

the

geog

raph

icdi

stan

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ters

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oots

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OL

S;C

AR

ln(G

eogr

aphi

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stan

ce)

Yes

H5

The

exis

tenc

eof

pate

nts

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gned

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eta

rget

redu

ces

the

acqu

isiti

ondi

scou

ntof

unlis

ted

high

-tec

hnol

ogy

firm

s.O

LS;

acqu

isiti

ondi

scou

nt,

unre

port

edPa

tent

ing

(0/1

)N

o

H6a

The

num

ber

ofpa

tent

sas

sign

edto

the

targ

etre

duce

sth

eac

quis

ition

disc

ount

ofun

liste

dhi

gh-t

echn

olog

yfir

ms.

OL

S;ac

quis

ition

disc

ount

Pate

ntco

unt

Yes

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The

mar

gina

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form

atio

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lue

ofpa

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sis

decr

easi

ngin

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num

ber

ofpa

tent

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edto

the

targ

et.

OL

S;ac

quis

ition

disc

ount

Pate

ntco

unt2

Yes

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likel

ihoo

dof

ata

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havi

ngpa

tent

sin

crea

ses

with

the

geog

raph

ical

dis-

tanc

ebe

twee

nth

eta

rget

and

the

acqu

irer

,an

dot

her

fact

ors

cont

ribu

ting

toin

form

atio

nas

ymm

etry

.

logi

t;pa

tent

ing

prob

abili

tyln(G

eogr

aphi

cdi

stan

ce)

Yes

H8

Apr

e-ac

quis

ition

toeh

old

inth

eta

rget

incr

ease

sth

epr

obab

ility

that

the

targ

etha

spa

tent

s.lo

git;

pate

ntin

gpr

obab

ility

Toeh

old

(0/1

)Y

es

H9

The

qual

ityof

the

pate

nts

assi

gned

toth

eta

rget

,as

mea

sure

dby

cita

tions

,re

fere

nces

,sc

ope,

and

the

size

ofth

eIN

PAD

OC

pate

ntfa

mily

,re

duce

sth

ein

form

atio

nas

ymm

etri

esre

late

dto

acqu

isiti

ons

ofun

liste

dhi

gh-t

echn

olog

yfir

ms.

OL

S;ac

quis

ition

disc

ount

Qua

lity-

wei

ghte

dpa

tent

sY

es

H10

Eas

yac

cess

toal

tern

ate

sour

ces

ofliq

uidi

tyat

the

time

ofth

eac

quis

ition

re-

duce

sth

eac

quis

ition

disc

ount

.O

LS;

acqu

isiti

ondi

scou

ntM

&A

-act

ivity

,Agg

rega

teIP

Ovo

l-um

e,B

aasp

read

Yes

H11

Hig

hM

&A

-act

ivity

atth

etim

eof

the

acqu

isiti

onin

crea

ses

the

acqu

irer

an-

noun

cem

entr

etur

n.B

oots

trap

OL

S;C

AR

M&

A-a

ctiv

ityY

es

H12

Hig

hIP

O-a

ctiv

ityat

the

time

ofth

eac

quis

ition

inth

ein

dust

ryof

the

targ

etde

crea

ses

the

acqu

irer

anno

unce

men

tret

urn.

Boo

tstr

apO

LS;

CA

RIn

dust

ryIP

Ovo

lum

eY

es

81

Not unlike studies by Hall et al. (2005, 2007), and Hussinger and Grimpe (2007), I find thatthe number of patents (H6a) and their quality (H9) have an impact on both firm and deal value.However, even though previous authors have shown that patents exhibit certain characteris-tics whereby they may be suspected to present decreasing marginal value, no other author haspreviously explored this explanation. I, however, do find that the marginal value of patents isdecreasing in their number (H6b), and moreover, that beyond 125 patents in my specific sam-ple, the marginal patent destroys target shareholder value. Also, consistently with the hubrishypothesis of M&A deals by Roll (1986), I find that while patents merit higher valuations forthe target, their inclusion in the deal is value destructive for acquirer shareholders, albeit onlyto an economically minor extent.

The responses from the questionnaire indicate that the most important sources of patent valueare relatedness to the firms’, or a competitor’s, core business, importance for future technology,difficulty to invent around, remaining life, scope, and importance for current technology. Theresults also indicate that patents owned by the firm itself are more relevant to valuation thanthose owned by a competitor, while it may be in some instances that a competitor holds sucha patent that disables the firm from operating in it’s core business area. Finally, a patent thatgenerates revenue (through either licensing or through the product to which it relates) is moreimportant than one that is used to obtain the ability to exclude others, or the freedom to operate.Also, patents related to a current product are most valuable.

Consistently with Ali-Yrkkö et al. (2005), and Lehto and Lehtoranta (2004), I find support formy hypothesis H7. Namely, that the likelihood of a target having patents increases with infor-mation asymmetry. Moreover, my results are consistent with the notion that acquirers preferto know more of their target by any means accessible to them. In long distance transactions,patents assigned to the target are a valuable source of additional information, at least in tech-nology intensive industries. Perhaps somewhat surprisingly, I also find support for the notionthat a pre-acquisition toehold, although a powerful tool of mitigating information asymmetry,in fact increases the probability that the target has patents (H8). As explained in Section 3.1.,this is likely a result of strategic games between acquirer and target management.

My results are also consistent with hypothesis H10. More specifically, similarly as Officer(2007), I also find support for the hypothesis that one major reason for the lower relative ac-quisition prices of unlisted targets is their need for liquidity. Furthermore, while obtainingliquidity from other sources becomes increasingly expensive, the acquisition valuations adjustfor this decreased opportunity cost. That is, when target shareholders find it difficult to obtainliquidity elsewhere, they are more willing to relinquish control over the target at a lower price.

Consistently with my predictions and those of Harford (2005), my empirical evidence supportshypothesis H11. More specifically, the data supports the notion that during peak times of M&Aactivity, the announcement returns to acquiring firms are higher. Thus, an acquisition of an

82

unlisted high-tech target generates more wealth to both acquirer and target shareholders whenM&A activity is higher than the time series median.

Finally, the data is also consistent with my hypothesis H12. More precisely, when the IPOmarket is heated, an acquisition of an unlisted high-tech target generates less wealth than duringa period of cold IPO markets. Thus, I find support for the notion that if a target opts for M&Ainstead of an IPO when the demand for IPOs is higher, the stock market punishes the acquirerfor partaking in the deal. Moreover, the market perceives that the target is likely to have been alow quality IPO, and thus also a low quality target. Thus, becoming acquired during a time of ahot industry IPO market may signal that the target is of poor quality.

6.2. Discussion and conclusions

I set out to answer a three-fold research problem in Section 1. After the discussion in Sections5., and 6.1., I am able to answer my problem as follows:

1. There is an acquisition discount of unlisted firms in Europe. Moreover, the acquisitiondiscount for unlisted European targets is of similar magnitude as that for unlisted UStargets.

2. The acquisition discount is more pronounced for unlisted high-tech targets than for theirpeers in non-high-tech industries. However, I am unable to verify that the same appliesto the abnormal stock acquirer announcement return.

3. The above disparities are clearly fueled by both information asymmetry and the targetshareholders’ need for liquidity.

Hence, the answer to the first and third parts is an unambiguous ’yes’, while the answer to thesecond part depends on the perspective.

According to Grote and Umber (2007), the impact of the natural logarithm of geographic dis-tance on deal value may not be linear. While I cannot dismiss this notion, it is obvious that thethreshold and the sign of the coefficient both require further research. Moreover, the theoreticalrationale behind the non-linearity requires further work.

One of the most promising avenues of future research that can be followed in light of my resultsis the impact of the INPADOC family size on the value of patents. As the EPO databases includealso US patents, merging that database with the NBER patent citations master file by Hall et al.(2001) should not prove to be an impossible effort. Thus, the impact of the INPADOC familysize on firm value can be studied with both US and European data.

83

The survey responses indicate that a more in-depth research of the value-relevance of differentquality dimensions of patents should prove to be very interesting. More specifically, exploringpotential means to proxy for the difficulty to invent around a patent, and the importance of apatent for current of future technology, as well as the relatedness of the patent to both the firm’sand an important competitor’s core business would benefit several practical applications.

Finally, it seems fruitful to also study the differences between unlisted stand-alone and sub-sidiary targets with respect to the impact of liquidity on their valuation. Also, conducting asimilar study as this one on the cross-section of European firms, with perhaps a less of a viewon patents, should prove to be valuable.

From a macroeconomic perspective, becoming a target in acquisitions seems to be a wealth-destroying way of listing assets. More specifically, acquirers are punished by a decrease inmarket value when the target industry IPO market is above its time series median. Moreover,unreported, albeit somewhat non-robust30, regressions show that a similar effect is carried for-ward to deal value. That is, when the demand for IPOs is high, a firm listing its assets throughM&A transactions will have to do so at an increased discount. Thus, value is destroyed fromboth the acquirer and the target. This avenue should prove to be extremely fruitful in terms offuture research, especially if combined with a cross-sectional study of the acquisition discounts,for example in Europe.

Importantly, while there is to my knowledge no previous work related to the transfer and cre-ation of value in M&A transactions caused by patents, I explore that relation as thoroughly asmy data allows. My data supports the notion that the economic rents resulting from innovationare attributable to the innovator. From the society’s point of view, this is the optimal allocationof wealth in M&A transactions. More specifically, as my findings suggest that the innovatingfirm obtains all gains from the innovation, firms have an incentive to innovate instead of imitate,or acquire. However, the acquiring firms only lose in the order of 0.3% per patent per a log ofassets measured in $m. Hence, the total gain from acquisitions involving patents in the target ispositive.

Furthermore, my results imply that there is a threshold above which the marginal patent has anegative value-contribution to the firm. While I do not argue that a threshold of 125 patents isvalid for small and large corporations alike, it does give a hint on the relative number of patentsbeneficial to the firm, when compared to, for instance, the average transaction value of $54m inmy sample. Furthermore, my results indicate that patent quality does matter. Hence, managingthe patent portfolio and patenting strategy are clearly wealth generating activities.

30Utilizing standard errors according to White (1980) in an OLS-regression including a subsample of transac-tions where the acquirer is listed, and employing model 2, which includes the composite patent quality measure,in Table 11., I obtain a statistically significantly negative coefficient for the IPO market size variable on the trans-formed acquisition discount. However, the sample size is reduced so much that the result needs further validation,as discussed in Section 5.2.3.

84

To conclude, I find an acquisition discount to unlisted high-tech targets in Europe averaging41.7%. This discount is negatively (and thus deal value positively) affected by the generalavailability of liquidity from other sources, the number of patents held by the target, and geo-graphic proximity, to an extent. Moreover, both unlisted targets and their acquirers should avoidperiods of high IPO activity, and be attracted to periods of high M&A activity and low corporateloan spreads to maximize shareholder wealth.

85

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A. EPO global patent data coverage

Table 17: Jurisdictions covered in the EPO Worldwide patent database, and theirabbreviations

Code Jurisdiction Code Jurisdiction

AL Albania LI LiechtensteinAP ARIPO LT LithuaniaAR Argentina LU LuxembourgAT Austria LV LatviaAU Australia MA MoroccoBA Bosnia and Herzegovina MC MonacoBE Belgium MD MoldovaBG Bulgaria ME Republic of MontenegroBR Brazil MK Former Yugoslav Republic of MacedoniaCA Canada MN MongoliaCH Switzerland MT MaltaCL Chile MW MalawiCN China MX MexicoCR Costa Rica MY MalaysiaCS Czechoslovakia NI NicaraguaCU Cuba NL NetherlandsCY Cyprus NO NorwayCZ Czech Republic NZ New ZealandDD German Democratic Republic OA OAPIDE Germany PA PanamaDK Denmark PE PeruDZ Algeria PH The PhilippinesEA Eurasia PL PolandEC Ecuador PT PortugalEE Estonia RO RomaniaEG Egypt RS Republic of SerbiaEP European Patent Office RU RussiaES Spain SE SwedenFI Finland SG SingaporeFR France SI SloveniaGB Great Britain SK SlovakiaGC Gulf Cooperation Council SM San MarinoGE Georgia SU Soviet UnionGR Greece SV El SalvadorHK Hong Kong S.A.R TJ TajikistanHR Croatia TR TurkeyHU Hungary TW TaiwanID Indonesia UA UkraineIE Ireland US United States of AmericaIL Israel UY UruguayIN India VN VietnamIS Iceland WO World Intellectual Property OrganizationIT Italy YU Former Serbia and MontenegroJP Japan ZA South AfricaKE Kenya ZM ZambiaKR Korea (South) ZW Zimbabwe

91

B. Formulae and derivations

Haversine formula. The haversine of the relation of a distance between two coordinate pointsand the radius of the Earth (or more generally, the relation of the distance between two pointson a sphere and the radius of that sphere) can be expressed as follows:

haversin(

dR

)= haversin(∆φ)+ cos(φ1)cos(φ2)haversin(∆λ) (28)

Where,

d is the distance between two locations on a map

R is the mean radius of the Earth, or 6371km

∆φ is the latitude separation

φ1 is the latitude of the target company

φ2 is the latitude of the acquirer

∆λ is the longitude separation

Solving for d, we get:

d = R∗haversin−1(

haversin(

dR

))= 2R∗arcsin

(√haversin

(dR

))= 2R∗arcsin

(√haversin(∆φ)+ cos(φ1)cos(φ2)haversin(∆λ)

)(29)

Recalling that haversin(θ) = sin2 (θ

2

)for any angle θ, we get:

d = 2R∗arcsin

(√sin2

(∆φ

2

)+ cos(φ1)cos(φ2)sin2

(∆λ

2

))(30)

92

Marginal effects The marginal effects on the acquisition discount are calculated by takingpartial derivatives of the acquisition discount using the chain rule as follows:

Recall from 18., that we defined the transformed discount as:

D∗ =√

1−D

where D is the acquisition discount. First, we solve the equation in terms of D. Recalling thatD ∈ [−1,1], and thus

√1−D ∈

[0,√

2], we get:

D = 1−D∗2 (31)

Thus, for every independent variable xi, we get the marginal impact on D from:

∂D∂xi

=dDdD∗

∂D∗

∂xi

=−2(D∗ (X))∂D∗

∂xi(32)

That is, the marginal impact of any variable xi on the acquisition discount depends on the levelof the modified acquisition discount and the marginal impact of that variable on the modifiedacquisition discount.

C. Design and results of the questionnaire

This appendix includes the design of the patent value questionnaire in the following two pages,and some descriptive statistics related to the responses after that. The survey was sent to 39Finnish venture capital investors, of which 6 responded within the given time limit. Moreover,the survey was left to a number of LinkedIn groups the members of which are professionals inintellectual property management worldwide. Those groups totaled 38 respondents.

Page 1 of 2

State your professional opinion regarding the questions below.

PART I – IMPORTANCE OF PATENTS IN M&A Q: How important are the following assets in an M&A-transaction?

1= Unimportant

2 3 4 5=

Very important

Fixed tangible assets

Brands

Trademarks

Trade secrets

Patents

PART II – FACTORS IMPACTING PATENT VALUE IN M&A Q: How strong of an impact do the following factors have on the value of patents in M&A-transactions? Check the rightmost box if you have no opinion regarding the impact of the item (as opposed to being of the opinion that the item has no impact).

1 = Very little or no

impact 2 3 4

5 = Very strong

impact

No opinion

Remaining life of patent

Importance for future technology

Importance for current technology

Difficulty to invent around

Ease of proving infringement

Patent has been cited in the patent literature (newer technologies utilize the technological solution provided in the patent in question)

Patent refers to older patents

(the patent in question utilizes technological solutions provided in older patents)

Patent is related to the firm’s core business

Patent is related to a competitor’s core business

Patent has been confirmed in a court of law

Patent has not been opposed to by competitors

The technological scope of the patent (number of fields the patent relates to)

PART III – PEERS GROUP’S USE OF PATENTS Q: How much do you agree with the following statements?

1= Completely

disagree 2 3 4

5= Completely

agree

My peers take patent portfolio risks and value into account when assessing M&A-transactions

I take patent portfolio risks and value into account when assessing M&A-transactions

PART IV – PATENTS AS A SOURCE OF INFORMATION IN M&A-TRANSACTIONS Q: How much do you agree with the following statements?

1= Completely

disagree 2 3 4

5= Completely

agree

Technology firms with patents are easier to value than firms with no patents operating in the same industry

A geographically distant technology firm with patents is a more feasible target than one with no patents

PART V – PATENT VALUE-RELEVANCE Q: How strong of an impact do the firm’s own patents and patents owned by competitors (freedom to operate) have on the value of a firm in the following situations [On a scale of 1 to 5 (where 1=very little or no impact, and 5=very strong impact)]?

Seed

First-stage financing

Second-stage financing Non-exit M&A Exit

Patents owned by the firm itself

Competitors’ patents

Page 2 of 2

PART VI – STRATEGIC USE OF PATENTS Q: How strong of an impact do patents in the following categories have on the value of a firm in an M&A-transaction? [On a scale of 1 to 5 (where 1=very little or no impact, and 5=very strong impact)] E.g., consider a firm with a patent that is used to exclude others and protect a product in R&D. If you think that having such a patent makes the firm significantly more valuable, mark 5 in the box in the intersection of the second row and second column.

Patent is used for:

Protected technology relates to: Generating revenues by licensing the technology

Excluding others from utilizing the technology

Obtaining the freedom to operate in the market

Products currently in production

Products still in R&D

Potential future products in the industry

Related markets where the company does not operate or plan to operate

PART VII – PATENT VALUE DETERMINANTS IN M&A Q: Name some important determinants of patent value in an M&A-transaction. The items need not be in rank order.

PART VIII – DUE DILIGENCE Q: Do you conduct patent portfolio due diligence as part of the overall due diligence?

Yes No

If you answered yes, name some most important issues to consider in patent due diligence. If you answered no, name the reasons why you do not consider patent due diligence to be important. The items need not be in rank order.

PART IX – SUMMARY INFORMATION Q: Please respond to the following questions regarding the M&A-deals you have been involved in during the last five years

Number of deals

Average value of deals (in Euros)

Average number of patents in firms acquired

Average value of patents in firms acquired (in Euros) (Leave blank if you answered 0 to the previous question)

Average number of patents in portfolio companies involved in M&A-deals

Average value of patents in portfolio companies involved in M&A-deals (in Euros) (Leave blank if you answered 0 to the previous question)

Industries in which the portfolio companies operate

PART X – COMMENTS Q: Feel free to comment on these questions, this questionnaire, or other factors relating to patent-valuation:

Thank you for your participation!

95

Figure 10: The importance of patents with respect to other asset categories

Figure 11: The impact of different factors on the value of a patent

96

Table 18: Means and standard deviations of responses to parts III-IVMeans and standard deviations of responses to "How much do you agree with the following statements?"

Statement Mean Standarddeviation

My peers take patent portfolio risks and value into account when assessing M&A-deals

3.53 0.78

I take patent portfolio risks and value into account when assessing M&A-deals 4.38 0.73Technology firms with patents are easier to value than firms with no patents operatingin the same industry

3.23 1.27

A geographically distant technology firm with patents is a more feasible target thanone with no patents

3.58 1.10

Table 19: Means and standard deviations of responses to part VMeans (standard deviations) of responses to the question "How muchdo the firm’s own patents and patents owned by competitors (freedomto operate) have on the value of a firm in the following situations?"

Seed First-stage Second-stage Non-exit M&A Exit

Patents owned by the firm itself 4.02 4.31 4.26 3.95 4.05(1.03) (0.78) (0.88) (0.92) (0.96)

Competitors’ patents 3.60 3.63 3.55 3.51 3.38(1.34) (1.13) (1.11) (1.17) (1.29)

Table 20: Means and standard deviations of responses to part VIMeans (standard deviations) of responses to: "How strong of an impact do patentsin the following categories have on the value of a firm in an M&A-transaction?"

CURRENTPRODUCTS

PRODUCTSIN R&D

FUTUREINDUSTRYPOTENTIAL

UNRELATEDMARKETS

The patent is used to generatelicensing revenues

4.67 4.12 3.98 3.45(0.61) (1.09) (1.15) (1.31)

The patent is used to exclude others 4.26 3.98 3.79 2.74(0.73) (1.07) (1.16) (1.23)

The patent is used to obtain thefreedom to operate in the market

4.24 3.81 3.67 2.60(0.83) (1.25) (1.24) (1.33)