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

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

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    The price of patents liquidity and information   master's thesis by antti saari The price of patents liquidity and information master's thesis by antti saari Document Transcript

    • The Price of Patents, Liquidity, and Information: Evidence from Acquisitions of Unlisted European High-Tech Targets Master’s Thesis in Finance Antti Saari Aalto University School of Economics September 11, 2010
    • Acknowledgements This thesis merits a great deal to the sponsoring firm and the questionnaire respondents. As the author, I would like to especially thank LexFord Enterprises in Finland for sponsoring the thesis, and for providing valuable insight as regards the theory and results of this thesis. Moreover, Antti Kosunen and Matti Kanninen at LexFord provided invaluable comments on the survey design and questions. I would also like to thank all of the participants at the LinkedIn discussion concerning these questions. All of your comments were of great value, and helped improve the final questionnaire significantly. Finally, I would also like to extend my sincerest gratitude to all of the survey respondents. Without those responses, an important part of this study would have been left unexplored, and a lot of the work mentioned above rendered moot. Sincerely, Antti Saari M.Sc. (econ.) as of September, 2010, thanks to you
    • I Abstract This thesis explores the acquisition discounts of unlisted targets reported in US takeovers with a European high-tech focused dataset, and a specific view on the determinants of that discount. More specifically, I study the interrelatedness of patents, target shareholders’ demand for liq- uidity, and the information asymmetry as explanatory measures of the acquisition discount. To provide a more thorough view of the role of patents, liquidity, and information asymmetry in acquisitions, I also study the determinants of the target having patented its innovations prior to the acquisition announcement, and those of the acquirer abnormal announcement return. In the former, I proceed with a specific focus on dimensions of information asymmetry as reasons for a target having patents. In the latter, my focus is similar to the study of the acquisition discounts. On the one hand, my results should provide validation for those found in the US, and on the other, a more thorough understanding of the listing effect, and the role of patents, liquidity, and information asymmetry in acquisitions of unlisted high-tech targets. Finally, I compliment my empirical findings and applicable parts of theory with results from a questionnaire sent to professionals in venture capital investments, and intellectual property management, both dealing specifically with M&A transactions. My results are consistent with my hypotheses that stem from literature and the survey results. More specifically, I find that decreased availability of liquidity decreases value to both acquirer and target owners. Moreover, both the survey responses and my empirical analyses suggest that patents are valuable to target owners, and their quality dimensions are important as well. Finally, I also find that the market’s perception of the economic rents to patents are attributable to their assignee, or in this case, the target who owns them prior to the acquisition.
    • II Contents 1. Introduction 1 1.1. Background and motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2. Research problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3. Contribution to existing literature . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.4. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.5. Main findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.6. Structure of the study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2. Theory and literature review 5 2.1. M&A deal valuation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.1. The role of synergies . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.2. Determinants of deal price . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2. Returns to bidders around the announcement date . . . . . . . . . . . . . . . . 11 2.3. Information asymmetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.3.1. Information asymmetry, discount rates, and the value of the firm . . . . 14 2.3.2. Information asymmetry in acquisitions . . . . . . . . . . . . . . . . . 15 2.3.3. Information asymmetry and technology . . . . . . . . . . . . . . . . . 18 2.4. Acquirer preferences in and motivations behind technology-intensive takeovers 18 2.5. Patents and M&A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.6. The economics and value of patents . . . . . . . . . . . . . . . . . . . . . . . 21 2.6.1. Patent economics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.6.2. The value of patents . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.6.3. Patents as signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
    • III 3. Hypotheses and variables 24 3.1. Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.2. Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.2.1. Acquisition discounts . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.2.2. Acquisition announcement return . . . . . . . . . . . . . . . . . . . . 29 3.2.3. Patenting variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.2.4. Key explanatory variables in the regression models . . . . . . . . . . . 32 4. Data and empirical methodology 32 4.1. Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.1.1. Generalizability of the sample . . . . . . . . . . . . . . . . . . . . . . 34 4.1.2. Descriptive statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 4.1.3. Correlations between independent variables . . . . . . . . . . . . . . . 39 4.2. Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.2.1. Acquisition discounts . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.2.2. Appropriateness of ordinary least squares for the acquisition discount . 45 4.2.3. Acquirer announcement return . . . . . . . . . . . . . . . . . . . . . . 49 4.2.4. Appropriateness of ordinary least squares for the announcement return . 50 4.2.5. Covariance matrices and the wild bootstrap . . . . . . . . . . . . . . . 52 4.2.6. Patenting probability . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 5. Results 57 5.1. Acquisition discounts and abnormal stock acquirer returns - do they exist in Europe? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 5.1.1. Acquisition discount . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 5.1.2. Abnormal announcement returns of stock acquirers . . . . . . . . . . . 59
    • 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 of 1 on D∗ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 10. The importance of patents with respect to other asset categories . . . . . . . . . 95 11. The impact of different factors on the value of a patent . . . . . . . . . . . . . 95
    • VI List of Tables 1. Explanatory variables related to the regression models, and their expected signs 31 2. Raw acquisition multiple data from SDC Platinum. . . . . . . . . . . . . . . . 33 3. Are the unlisted targets with multiple data representative of the population? . . 35 4. Distribution of the sample by country . . . . . . . . . . . . . . . . . . . . . . 36 5. Distribution of the sample by industry . . . . . . . . . . . . . . . . . . . . . . 37 6. Summary statistics of relevant explanatory variables . . . . . . . . . . . . . . . 38 7. Correlations between explanatory variables . . . . . . . . . . . . . . . . . . . 40 8. T-test of difference in acquisition discount means between high-technology and non-high-technology targets. . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 9. T-test of difference in abnormal acquisition announcement return means be- tween stock acquirers of high-technology and non-high-technology targets. . . 60 10. Univariate results for the acquisition discount . . . . . . . . . . . . . . . . . . 64 11. Determinants of the acquisition discount. . . . . . . . . . . . . . . . . . . . . 69 12. Marginal effects on the acquisition discount . . . . . . . . . . . . . . . . . . . 70 13. What determines the probability of a target having patents? . . . . . . . . . . . 72 14. Univariate results for the announcement return . . . . . . . . . . . . . . . . . . 74 15. Determinants of the acquisition announcement return. . . . . . . . . . . . . . . 76 16. Hypotheses and empirical evidence. . . . . . . . . . . . . . . . . . . . . . . . 80 17. Jurisdictions covered in the EPO Worldwide patent database, and their abbrevi- ations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 18. Means and standard deviations of responses to parts III-IV . . . . . . . . . . . 96 19. Means and standard deviations of responses to part V . . . . . . . . . . . . . . 96 20. Means and standard deviations of responses to part VI . . . . . . . . . . . . . . 96
    • 1 1. Introduction 1.1. Background and motivation Officer (2007) finds that there is an acquisition discount of unlisted targets with respect to com- parable industry transactions of listed targets in the US. Since the economic reality of lower liquidity and less stringent disclosure requirements for unlisted versus listed firms persists in Europe, the acquisition discount is likely to do so as well. If it did not, the feasibility of the differences in these dimensions as an explanation for the acquisition discount would be debat- able. Furthermore, Faccio et al. (2006) find that acquirers of unlisted targets earn a significant positive abnormal return controlling for a multitude of variables. However, the authors state that ’the fundamental factors that give rise to this listing effect, . . . , remain elusive’. As already Akerlof (1970) notes, differential information between the buyer and seller of a good leads (in his example in the used car markets) to the notion that a substantial part of the value of the good disappears immediately after it has been taken into use. In the case of economic units, such as companies, the distinction is not as straightforward. However, one can easily ascertain that the direction, if not the magnitude, of influence related to the difference of information is the same regardless of the goods being traded. If one was buying fruit randomly from a basket with both oranges and lemons, one would surely not be willing to pay the same price for the fruit as if the two were in separate baskets. Equally, if a company is planning to acquire another, they will not be willing to pay the same price for one of which they know very little as they would for one of which they know everything. To the best of my knowledge, no author has previously studied the influence of patents on the information asymmetries present in M&A transactions. While Officer (2007) finds little statistical significance for his proxies for information asymmetry, he notes that it is ’notoriously difficult to measure’, and is still a likely explanation to at least part of the acquisition discount. Moreover, the sign of the information asymmetry proxy in Officer (2007) is expected, and the coefficient is economically very significant. In addition to the above, the reason why information asymmetries are likely to explain the acquisition discount is that their presence is apparent in the acquisitions of unlisted targets given the reduced disclosure requirements (Ekkayokkaya et al., 2009; Officer et al., 2009). Whenever there is an additional risk present, the return requirement of that transaction must go up. Suppose we have two similar companies, A and B, that we consider as targets. Let us further assume that there is one difference between the two companies, namely that there is less information available of company B. Since we know less about company B than company A, we perceive it riskier and thus award it a higher discount rate. Given that the future cash flows of both companies are equal (CFA,t = CFB,t , ∀ t), and that the case-specific cost of capital for
    • 2 company B is higher than for A (rB > rA ), company B would be acquired at a discount relative to company A. (Merton, 1987; O’Hara, 2003; Easley and O’Hara, 2004) More formally, we have: T T CFB,t CFA,t ∑ (1 + rB)t < ∑ (1 + rA)t , ∀rB < rA (1) t=1 t=1 After Officer (2007) and Faccio et al. (2006), at least two attempts have been made to delve deeper into the potential information asymmetry explanation of the anomalies related to ac- quisitions of unlisted targets. One of them is a paper by Officer et al. (2009), who study the returns to acquiring firms in the US utilizing an event study methodology. Another is a study by Ekkayokkaya et al. (2009) that explores the long-term returns as well as the announcement returns to acquirers of unlisted targets in the UK. The consensus of these authors is that there is, in fact, an information asymmetry problem in acquisitions of private firms. Moreover, the results from Officer et al. (2009) and Ekkayokkaya et al. (2009) indicate that the presence of this asymmetry is very significant in both economical and statistical terms. While Aboody and Lev (2000) find that information asymmetry is especially large in R&D- intensive firms, it seems especially fruitful, with respect to information asymmetries, to study some subset of targets that require a lot of R&D effort. One potential subset is technology- intensive industries, as specified by for example Dessyllas and Hughes (2005a). Given that patents are, among other things, a signal of the quality of the R&D output of the companies in question, they can provide powerful evidence of the quality of the company as well, especially in high-tech industries. When information is a scarce resource, and when there is potential for easy, costless access to additional information, following the logic above, the additional infor- mation should merit lower return requirements, and thus lower acquisition discounts. Moreover, if the predominant source of information asymmetry is the R&D output or technology of the firm, then patents should be an especially fruitful source of additional information. Further- more, responses from the questionnaire presented in Appendix C show that practitioners feel that patents are an important source of both risk and value in M&A transactions (in fact, the re- spondents view patents to be more important than tangible assets, or other intellectual property), and hence are an important factor contributing to both information and valuation. 1.2. Research problem Given the discussion of the previous section, I arrive at the following three-fold research prob- lem: 1. Is there an acquisition discount of unlisted firms in Europe?
    • 3 2. Are the disparities related to acquisitions of unlisted targets more prevalent in technology- intensive industries? 3. Are these disparities fueled by asymmetric information and liquidity-needs of target own- ers? 1.3. Contribution to existing literature This thesis contributes to the existing literature by being, to the best of my knowledge, the first to study the power of patents in reducing the information asymmetries related to mergers and acquisitions. More specifically, I contribute to the work done by Officer (2007), Officer et al. (2009), and Ekkayokkaya et al. (2009) by delving deeper into the information asymme- try explanation of acquisitions of non-public targets. Also, I am the first to aim to confirm the existence of the acquisition discount reported by Officer (2007) with a European data set. Moreover, where Officer (2007) studies the acquisition discount as a supply-side phenomenon, I also incorporate the approach of Officer et al. (2009) and Ekkayokkaya et al. (2009), and study the demand-side determinants of the acquisition disparities1 , and the ’listing effect’ to which Faccio et al. (2006) refer as the effect of positive abnormal returns to stock acquirers of unlisted targets. Finally, I compliment my findings with the results of a questionnaire sent to Finnish venture capital investors, and intellectual property professionals worldwide. The design and results of the questionnaire are presented in Appendix C. 1.4. Terminology Before proceeding with theory, methodology, and results, it is worthwhile defining some im- portant terms concerning patenting. Assignee An assignee is a legal (person or non-person) entity to which the title to the intellectual property included in a patent is transferred. Citation In the patent literature, and in the literature studying patents, citations refer to references in more recent patents to the patent in question. For instance, if I’m granted a patent, and then someone needs to utilize the solution documented in my patent to come up with a new patentable technological solution, they will then refer to my patent in their patent application. That reference will then, from the standpoint of my patent, be a citation. 1 Officer (2007) studies the owners’ need of cash as an explanation for the acquisition discount. My research problem relates also to the lack of information on the buyers’ side, and mitigation thereof.
    • 4 Infringement An infringement is the conduct of a breach of contract, law, right, or similar. The in- fringement of a patent right includes the utilization of the protected technology without the consent of the assignee (or inventor if he has no successor in title). Inventor An inventor is the person (or persons), who invented the technology included in the patent. According to the European Patent Convention (EPC), Art. 60, the right to a patent belongs to the inventor or his successor in title (assignee). An inventor may relinquish the title to the patent, but he will always have the right to be mentioned before the European Patent Office. Jurisdiction Jurisdiction in general refers to the practical authority granted to a formally constituted legal body to administer justice in a given area of responsibility. In the context of patents, a jurisdiction refers to a patent office. Knowledge stock A knowledge stock includes all the knowledge assets in possession of the firm (measured in patents, or citation-weighted patents, accumulated R&D-expenses, etc). Litigation The conduct of a lawsuit is called litigation. Patent A patent is a set of exclusive rights granted by a jurisdiction to an inventor or an assignee for a limited period of time in exchange for the public disclosure of an invention. Patent applications are generally made public 18 months after they have been filed. Moreover, in the European legal context, if two parties try to patent the same invention, the one who applies for the patent first is considered to have title to all the rights vested in the patent. Patent family A patent family includes all the patents protecting the same (not similar, but exactly the same) technologies in different jurisdictions. For instance, if a technology is protected by a patent in Europe, the US, and Japan, the patents protecting that technology in those jurisdictions form a patent family. INPADOC patent family Utilized in the European Patent Office (EPO) databases, the INPADOC patent family is an extension of the usual patent family. More specifically, the INPADOC family includes all patents linked directly or indirectly by a priority document. Also, the INPADOC family includes all publications relating to one patent in one jurisdiction as separate members of the family.
    • 5 Process industry An industry in which raw materials are refined in a series of stages. Examples include oil refining, food processing, etc. 1.5. Main findings One of the most novel results in this thesis is the fact that the acquisition discount of un- listed targets documented by Officer (2007) prevails over a sample of European firms, and more importantly, that this discount is both statistically and economically significantly larger in technology-intensive industries. Moreover, I find that the discount is fueled by both the need for liquidity by target owners and the asymmetry in information between target and acquirer owners. Furthermore, my results indicate that the number of patents assigned to a firm have a both economically and statistically significant positive impact on the valuation of an unlisted high-tech target amounting up to an average of $250, 000 per patent. Moreover, I find that the probability that a high-tech target has patents is increasing in other dimensions of information asymmetry, a finding consistent with the results from the questionnaire. Finally, my analysis shows that managers of acquirers seemingly close to targets give no regard to the increase in information asymmetry in distance between the two companies while valuing the deal, whereas managers of more distant acquirers perceive the increase in information asymmetry resulting from increased geographic distance. 1.6. Structure of the study The rest of the thesis is structured as follows: Section 2. presents the existing literature and theory relevant to my study. Section 3. presents the hypotheses and variables on which I base the empirical analysis. Section 4. presents the data and methodology, Section 5. presents the results of the empirical estimations, and Section 6. concludes. 2. Theory and literature review I proceed with the theory and literature relevant to my topic as follows: first, in Section 2.1., I review the extant literature on the valuation of M&A deals, with a view on the specific case of unlisted targets. Second, Section 2.2. explores the short-term acquirer returns around the bid announcement date reported in the literature. Third, Section 2.3. explains the relevant theory related to information asymmetries in the contexts of technology, and M&A-transactions. Fourth, Section 2.4. reviews the extant literature concerning the preferences of acquirers of
    • 6 high-technology targets. Then, in Section 2.5., I briefly go through the relevant literature on the interaction between patents and M&A-transactions. Finally, Section 2.6. explains the existing theories related to the economics of patenting and the value of patents. 2.1. M&A deal valuation Given that one part of my empirical analysis focuses on the value of M&A deals, or more specifically, the relatively lower value of deals where the target is unlisted, it is crucial that I also review the existing literature on those valuations. Of course, most of the literature on deal pricing is focused on listed targets due to the ease with which information on such firms can be obtained, but the majority of the economic determinants of value are still likely to have an impact similar in direction, if not in magnitude. 2.1.1. The role of synergies Practitioners tend to turn towards synergies when determining bid value. After all, they are the very reason why a combination of two related firms should be more valuable than the sum of the two separate firms. The instrumental role of synergies in corporate restructuring stems from both simple economies of scale in certain corporate functions and the theory of corporate diver- sification. Economies of scale suggest that a larger corporation can maintain certain functions at a relatively lower cost than a smaller one. More specifically, a larger corporation can produce a large amount of goods at a relatively lower price, thus making it more profitable. Diversifica- tion theory, on the other hand, maintains that firms may have different needs for different types of assets during the stages of the business cycle. Thus, merging two firms with such different needs should theoretically lead to a more efficient use of assets throughout the cycle and thus reduced opportunity costs of holding those assets. Lang et al. (1989) find that the largest gains to bidders always occur when the bidder has a wealth of positive return investment opportunities, and the target has none2 . Moreover, Servaes (1991) posits that also low-q targets gain more the greater the dispersion between the Tobin’s q’s of the acquirer and target. This also indicates that, adopting the definition of synergy from Bradley et al. (1988) whereby synergy gains are the sum of increased wealth of the stockholders of both the acquirer and the target3 , the potential for synergies is higher the larger the difference in the amount of positive net present value (henceforth, NPV) investment opportunities to the advantage of the bidder. The results of Lang et al. (1989) and Servaes (1991) may, as the authors 2 Lang et al. (1989) define a low-q firm as one with a Tobin’s q of less than one. With some assumptions, this suggests that such firms only have investment opportunities with a negative Net Present Value (NPV). 3 As the authors themselves note, this definition assumes that claimants more senior to stockholders do not gain in wealth as a result of a merger or acquisition.
    • 7 themselves note, be at least partly a result of the high-q acquirers having superior managerial capabilities, and thus better abilities to utilize the assets of the low-q targets compared to the target’s pre-acquisition management. However, it is highly unlikely that this is the sole expla- nation. Other potential sources of synergy include, for instance, more efficient utilization of tax shields, increased debt capacity, and internal capital markets where funds may be distributed more efficiently. 2.1.2. Determinants of deal price Extant literature includes a multitude of potential factors that may or may not influence the deal premium. Instead of trying to test and list all of them exhaustively, I review the ones that are most likely to be relevant in the specific case of unlisted technology-intensive targets. Betton et al. (2008, 2009) discuss a multitude of these characteristics related to the target, the acquirer, and the deal. However, some of these characteristics are impractical in the case of unlisted targets, since they are either immeasurable or are unlikely to have similar significance. In the following, I explain the variables and their expected signs of impact on deal value grouped into acquirer, target, and bid characteristics as in for example Betton et al. (2008, 2009). Moreover, I discuss any potential expected differences in impact between public and private targets. I also explain here the macroeconomic variables that relate to the acquisition discount of unlisted targets according to Officer (2007). It should be noted that since the final discount-related regression has a transformed regressand whose value increases as the deal premium increases, the expected signs stated here are the same as those in that regression, in Tables 10. and 11. Also, even though I do test for the acquirer characteristics in unreported regressions, I do not report them due to the significant decrease in sample size. ACQUIRER CHARACTERISTICS Market capitalization (+/−) The market’s perception of the size of the firm. There are two opposite predictions for the direc- tion of influence of acquirer market value on deal price. Agency theory, or more specifically the empire building hypothesis, predicts that the managers of large acquirers have a motive to build their own empire with little regard to the costs to their principals (Jensen, 1986). According to this theory, it would thus stand to reason that larger firms have a tendency of paying too high prices for corporate acquisitions, and thus the effect on the deal premium would be positive. However, larger firms should have higher negotiating power, and it would thus also stand to reason that they would be able to bargain the deal price down. Hence, the existing theory leads still to ambiguous conclusions regarding the role of acquirer market value as a determinant of deal premia.
    • 8 Price to book ratio (+) A measure of the market’s perception of the positive NPV investment opportunities the firm has. A price-to-book ratio greater than one indicates that the firm has investment opportunities with a positive NPV. If the value is less than one, the firm only has negative NPV investment opportunities. Toehold ownership (+/−) A measure of the bidders stake in the target prior to the bid. Betton et al. (2009) find that a larger toehold decreases the offer premium. However, if the acquirer has a toehold in the target prior to the acquisition, it is also likely to have some additional information a non-toehold acquirer would not have. Such reduced information asymmetry might increase deal value assuming that the target is a high-quality firm (see Section 2.3.). Hence, it is not entirely obvious whether a toehold ownership increases or decreases the value of the deal. TARGET CHARACTERISTICS The vast majority of target characteristics reported in the literature to affect deal premia, for example stock price run-up or market capitalization, are such that they cannot be measured for unlisted targets. Moreover, if these variables cannot be measured, they can have no effect on the deal price. There are a few, however, that are measurable. Deal size (+) A proxy for the size of the target. In the literature, target size is usually measured as the market value of equity. However, as explained above, such a measure is impractical in the case of unlisted targets. Furthermore, the utilization of deal size as an explanatory variable for the deal premium generates some methodological issues, the mitigation of which is discussed in Section 4.2. In the case of unlisted technology-intensive targets it stands to reason that a larger firm would be relatively more valuable than a smaller one. Given that there is very little information available on these firms, and that larger firms tend to be more established, it is likely that the insecurity related to acquiring firms that are not minuscule is somewhat smaller. Even though the extant literature is not unanimous on the impact of target size on deal premia, Stulz et al. (1990), for example, do find a positive relation between target announcement return and market value. Moreover, as stated above, the impact of the size of the deal on the premium in this specific case is likely to be information-increasing and thus, positive. Number of patents held (+, −) It is clear from the existing literature that the number of patents held has a positive impact on the
    • 9 value of a firm (see e.g. Hall et al. (2005, 2007); Griliches (1981)). Moreover, Hussinger and Grimpe (2007) find that patents also have a positive impact on acquisition premia. However, firms with multiple patents are also more likely to be ones that need several patents to protect one product. Moreover, given that patents also mitigate the information asymmetries related to acquisitions of unlisted high-tech targets, the additional information contained in the marginal patent is most definitely decreasing in the number of patents. Furthermore, the questionnaire respondents made several notes with respect to the vast differences in patent properties. More specifically, they note that one patent can cover anything from a minor part in a device to a blockbuster drug, and obviously the two patents will merit very different valuations. Moreover, the more a company has patents, the more likely those patents are to include such that cover only minor parts of a product. Hence, I expect the marginal impact of a patent on deal value to be decreasing in the number of patents. Subsidiary target (−) Officer (2007) finds a significantly higher acquisition discount for unlisted subsidiary targets than he does for unlisted stand-alone targets (28% as opposed to 17%). Shleifer and Vishny (1992) argue that during times of low availability of liquidity from the securities markets, the peers of firms that need to liquidate some of their assets face the same needs themselves. This leads to liquidity-distressed firms being forced to sell their assets at prices below their value in best use. Officer (2007) further argues that this is likely to be the cause for the higher discounts and thus lower valuations, of unlisted subsidiary targets relative to their stand-alone peers. DEAL CHARACTERISTICS Cash consideration (−) As Officer (2007) states, one motivation for the acquisition discount of the unlisted firms is their owners’ need for liquidity. Given that the assets of unlisted firms are not highly liquid, their shareholders only have a few alternative sources of liquidity: loans or IPOs. It thus stands to reason that the more liquid the method of payment, the higher the discount, and thus, the lower the price of the deal. Horizontal merger (+/−) Once again, extant literature provides two potential, opposing directions of impact of horizontal- ity of merger on deal premium. More specifically, the theory of corporate diversification would suggest that non-horizontal mergers should be value adding, since they potentially reduce the risks related to future cash flows. This explanation is consistent with the results of Betton et al. (2009). On the other hand, agency theory predicts that since the actions of managers of a multi- industry company are a lot harder to scrutinize than those of a company operating in a single
    • 10 industry, non-horizontal mergers should be especially value-destructive in that they increase the potential for private managerial benefits (Jensen, 1986). Geographic distance between acquirer and target (−) Geographic proximity is a factor that increases information about the target in acquisitions. The closer the target is to the acquirer, the more likely the acquirer is to know the target even before starting the acquisition process. Thus, as Officer (2007) concludes that information asymmetry is likely to explain a part of the acquisition discount related to unlisted targets, anything that increases information asymmetry should increase the discount and thereby have a negative impact on deal value. However, Grote and Umber (2007) show that managers of acquiring firms are overconfident about their own abilities to successfully negotiate deals at short distance. The authors further de- velop an agency theory argument that managers of acquiring firms may seek private benefits by seeking to acquire targets that are closer. For example, the acquiring managers’ local status may be increased by the local acquisition. Moreover, the closer target also means, ceteris paribus4 , shorter traveling distances and a quieter life, which is in the managers’ preferences, according to Bertrand and Mullainathan (2003). Thus, it is possible that in short distance transactions the geographic distance has a smaller, or even negligible, impact on deal value. However, at least at longer distances, the distance between acquirer and target should deter deal value. MACROECONOMIC VARIABLES Overall M&A activity (+) Officer (2007) posits that one of the most important reasons for acquisition discounts of unlisted firms is the need for liquidity. Overall M&A activity acts as a proxy for the availability of liq- uidity. That is, it is a direct indicator of the demand for targets. Thus, when the demand is high, it stands to reason that acquisition premia are higher as well. There is also a wealth of empirical evidence supporting the fact that M&A valuations are higher during times of hot M&A mar- kets. For example, Rhodes-Kropf and Viswanathan (2004) argue that a target will overweight the firm-specific overvaluation when the market-wide overvaluation is high, and underweight it when the market-wide overvaluation is low. Firms are thus more prone to accept offers during market overvaluation than during market undervaluation, which conversely suggests that M&A activity is higher during overall market overvaluation, which results in higher deal values. IPO volume (+) If the need for liquidity is one of the main reasons for the acquisition discount, then the increased availability of any alternate sources of liquidity is expected to decrease the discount and increase 4 In this case, given that the firm is about to make some acquisition anyway.
    • 11 valuation. For the owners of privately held firms, the most obvious alternative to an M&A transaction is an IPO. Hence, the hotter the IPO market, the lower a discount there should be for unlisted firms, since the opportunity cost of selling at a discount increases. Corporate loan spread (−) The motivation for a negative impact of corporate loan spread on deal premia follows directly from the liquidity explanation of unlisted target discounts argued by Officer (2007). Namely, when alternative sources of liquidity are scarce, the value of those that remain increases. Thus, when it is relatively more expensive for companies to obtain a loan, the opportunity cost of obtaining liquidity through selling the firm obviously decreases. 2.2. Returns to bidders around the announcement date Mergers may occur for several motives. The purest of those motives is to increase the wealth of shareholders. However, agency theory suggests that this is not the whole story. Managers may find it in their own self-interest to build their own empire at shareholders’ expense, and thus enter into value-destroying activities, such as mergers. Moreover, Morck et al. (1990) find that managerial motives may indeed lead to the destruction of bidder shareholder wealth. More specifically, the authors contend that catering to managerial motives instead of those of shareholders destroys shareholder wealth. If the only motive for mergers was to create value to bidder shareholders, then efficient management should be able to do so on average. However, if there are other motives, such as empire building, behind bids, the theoretical prediction of bid announcement wealth effects becomes ambiguous. Roll (1986) argues that managerial hubris leads to overbidding for targets and thus to the win- ner’s curse in M&A bids. He posits that M&A bids are analogous to any bidding contest with the specific property that the initial bid is made by the market. The author further proposes that in fact there are no economic gains associated with M&A deals, but rather that any gains to the targets are at least offset by losses to bidders. However, Jensen and Ruback (1983) make a com- prehensive review of the evidence from US takeovers, and posit that takeovers do create value, but that most of this value is attributed to target shareholders. Moreover, the authors find that bidder shareholders do not lose either, on average, but rather win a little or break even. Franks and Harris (1989) confirm these findings with a comprehensive, albeit already a bit outdated, dataset of UK takeovers. More recently, Andrade et al. (2001) also find that bidders that do not use stocks as consideration gain a negligible return while stock bidders lose 1.5%. Furthermore, the authors find that targets of both stock and non-stock bidders gain while the targets of stock bidders gain notably less.
    • 12 This finding is consistent with the notion that by using stock as consideration, the bidder dilutes the impact of potential overpayment. The loss to stock bidders is likely due to the fact that, as Shleifer and Vishny (2003) argue, by using stock as consideration, the bidder management also signals that it views its stock to be overvalued. Thus, ceteris paribus, the signal of overvaluation of the bidder more than offsets the value of stock consideration as a control mechanism. There are some reasons why the hubris hypothesis is not directly applicable in the case of unlisted targets. First of all, Roll (1986) relies on the notion that in takeover bids of public targets, the valuation of a combination of assets for which a market value exists precedes the bid. Moreover, he argues that if such a valuation results in a lower value than the market value, the bid is abandoned. The lack of such a market price may indeed be one factor contributing to the perceived discount in unlisted targets. Basically, the absence of a market price may lead to the prevalence of some valuations that would have been deemed to be under that market price5 . However, exploring this relation will be left for future studies. Secondly, bids often convey other information about the bidder than simply their desire of combining with the target. For example, Shleifer and Vishny (2003) argue that firms only use stock as a means of payment if they are overvalued relative to the target. In that case, the method of payment in the bid does convey additional information regarding the bidder, and thus the assumptions behind the hubris hypothesis do not fully hold. As ambiguous as the existing evidence is on returns to bidders in general, so it is on returns to bidders of unlisted targets. For example, Chang (1998) finds no excess return to acquirers of private targets while Fuller et al. (2002) find a small, yet significant, abnormal return to acquirers of unlisted targets. However, even though the methodologies of the two studies differ quite significantly, both find that while stock acquisitions of public firms are value-destructive, the use of stock as consideration in bids for unlisted firms is value-creative. Furthermore, Faccio et al. (2006) unambiguously find a listing effect in acquisitions of Western European unlisted targets which leads to abnormal acquirer announcement returns. Moreover, Fuller et al. (2002) find a negligible difference between returns on exclusive stock payment and mixed payment deals, to the advantage of mixed payment deals. This finding is consistent with the notion that even in small proportions, stock payments act as powerful monitoring mechanisms, when fair value is ambiguous. It also indicates that mixed payment may even be preferable to full stock payment, since it may be a smaller of a signal of overvaluation than the exclusive use of stock as a means of payment. Also, Officer et al. (2009) find intuitively that the harder the target firm is to value, the more beneficial the use of stock payment as a monitoring tool is. Hence, the majority of evidence suggests that in acquisitions of private, hard-to-value firms, the use of 5 Of course, if managers are as apt to determine the fair value of assets as markets are, this type of a phenomenon should not exists on average even in the absence of the invisible hand. However, if market efficiency is based on the aggregation of irrational individuals into one rational market, then this aggregation will not exist in the absence of those markets, and the valuations determined by management are not efficient.
    • 13 stock as a method of payment is clearly and unambiguously beneficial to bidder shareholders. That is, in acquisitions of private firms, the benefits from monitoring far outweigh their costs6 , whereas the opposite is true concerning acquisitions of listed targets. Betton et al. (2009) find that toehold acquisitions are associated with an economically, but not statistically negligible negative abnormal announcement return to bidders. The authors also find that compared to zero toehold acquisitions, the announcement returns are higher in those with a positive toehold. Given that a pre-acquisition toehold in the target eases its monitoring, one would expect the existence of a toehold to be associated with value creation to acquirer share- holders. Also, as Betton et al. (2009) find that a toehold is associated with a lower acquisition premium, then one could also deduce from this and Roll (1986) that the toehold is associated with a wealth redistribution from target to acquirer shareholders. However, if the toehold is associated with an all-cash bid, which is associated with lower returns to acquirers of unlisted targets (see e.g. Chang (1998); Faccio et al. (2006); Officer et al. (2009); Ekkayokkaya et al. (2009)), the acquirer is not able to monitor the target’s profitability post-bid, and such a case is more likely to be associated with negative returns to the acquirer. Moeller et al. (2005) find that during times of hot M&A markets, M&A transactions destroy ac- quirer shareholder wealth. Moreover, they find that in the 1998 − 2001 US merger wave, share- holders of successful bidders lost an average of 12 cents per dollar on the three-day event win- dow centered around the announcement date of economically significant acquisitions7 . How- ever, the authors conclude that the average losses to shareholders during the merger wave were due to a few large loss deals, and that the exclusion of those (only 2% of their sample) would have led to the notion that acquisitions generate wealth also during merger waves. Thus, it is not obvious whether an increase in M&A activity has a positive or a negative impact on abnormal acquirer announcement return. To my knowledge, there is no empirical evidence regarding the impact of acquired patents on the acquisition announcement return of the bidder. Hubris theory according to Roll (1986) suggests that mergers are a zero sum game. Hence, if patents assigned to the target increase deal value to target shareholders, they should, ceteris paribus, also decrease acquisition returns to the bidder. Moreover, given that patents are an especially noisy measure of economic value (see e.g. Hall et al. (2005)), they are obviously difficult to value and thus increase the uncertainty regarding future profits. Hence, the inclusion of patents in an acquisition merits a higher discount rate for that specific investment, and thus a lower announcement return to the bidder. On the other hand, if patents do in fact mitigate information asymmetry in acquisitions of unlisted high- tech targets, the investors, given rational behavior, perceive this effect, which would lead to decreased uncertainty with respect to future profits, and hence, to a lower return requirement 6 Thecost here being the signal of overvaluation. 7 The definition of Moeller et al. (2005) includes acquisitions of assets totaling more than 1% of the bidders pre-acquisition market value.
    • 14 for the acquisition. As there is, as of yet, no empirical evidence to support either conclusion, and since both conclusions seem equally valid in light of economic theory, I expect patents assigned to the target to have either a positive or a negative impact on deal value. Servaes (1991), among others, finds that announcement returns to bidders are lower when there are other bidders. Moreover, Servaes (1991) and Stulz et al. (1990) find that in such instances the gains to targets are higher as well. Put together, the increased demand for the specific target facilitates a wealth redistribution from bidder to target shareholders. While the challenged bid variable is not related to my hypotheses in any way, it is an important factor to control for. Finally, Lang et al. (1989) and Servaes (1991) find that tender offer bidders have lower acquisi- tion returns if they have high Tobin’s q-values. Moreover, the authors also find that tender offer bidders have higher acquisition returns if they have low Tobin’s q-values. While the tender offer is of no significance with respect to my hypotheses, it is important to control for it. 2.3. Information asymmetry Information asymmetries are central to this study in two aspects that are interlinked in my thesis. First, information asymmetry is closely related to mergers and acquisitions. Moreover, information asymmetries are higher when the firm in question is unlisted, since it does not have to conform to as rigorous reporting standards as its listed peers (Officer, 2007; Officer et al., 2009; Ekkayokkaya et al., 2009). Second, information asymmetries relate intensively to firms with high levels of R&D (Aboody and Lev, 2000), a great deal of which are classified as high-technology firms. In what follows, I review the extant literature on information asymmetry starting with its impact on firm value in Section 2.3.1. Then, in Section 2.3.2., I proceed to the theoretical framework re- lating information asymmetries to mergers and acquisitions. Finally, in Section 2.3.3., I review the literature on information asymmetries in the context of technology-intensive companies. 2.3.1. Information asymmetry, discount rates, and the value of the firm Commonly used asset pricing models rely on market efficiency, and thus, also on the instan- taneous dissemination of all publicly available information among investors (Merton, 1987). While that assumption is a good theoretical baseline, it is not a universally exhaustive approach. More specifically, as Merton (1987) argues, the return requirement of a firm of which few in- vestors have enough information8 is higher than in the case of complete information. Thus, as pointed out in Section 1.1., the present value of the future cash flows of such a firm is lower 8 Here, ’enough information’ is analogous to ’all publicly available information’.
    • 15 in the case of imperfect, or asymmetric, information than it would be in the case of perfect information. This assertion is more recently confirmed by Easley and O’Hara (2004), who also maintain that the cost of capital in a case of imperfect information is higher than in the case of perfect information. On the other hand, Hellwig (1980) and Grossman (1976) argue that markets that are large enough relay information so perfectly that they may cancel the incentives to acquire costly information. However, Grossman (1976) does further state that equilibria may occur in the presence of incomplete information, and that when information is costly, equilibria most defi- nitely occur in the presence of asymmetric information.9 Moreover, neither author specifically defines ’large’. One can thus assume that markets for control over unlisted companies do not fall into that category. While Merton (1987) and Easley and O’Hara (2004) take no stand as to the origin of the infor- mation imperfection as such, they do both include examples of cases where it is the asymmetry that makes information imperfect. Following that logic, and the argumentation of Grossman (1976), it is obvious that given two otherwise similar firms, the one of which there is little information is less valuable to investors than the one of which they know a lot. 2.3.2. Information asymmetry in acquisitions Leland (1979) shows that in markets with asymmetric information, the equilibrium will always be attained at socially suboptimal levels of quality. Thus, there will be an over- or undersupply of goods, which in turn will affect the equilibrium price. I will now shortly develop a simplistic theoretical framework whereby it may be easier to understand why the balance in mergers and acquisitions of unlisted targets weighs, on average, on the side of underpricing. The following is essentially a simplification of the works of Akerlof (1970), Leland (1979), and more recently, Lehto (2006), for the purposes of this analysis. Consider the example of ’lemons’ versus good-quality cars in Akerlof (1970), where he argues that in a worst case of information asymmetry, the goods of worse quality will drive out those of little better quality in a process that will cause the market to disappear entirely. Obviously, this is an extreme example, but it does provide an intuitive theoretical starting point for the case of mergers and acquisitions. Consider a set of firms, T , that are being considered as targets for acquisition. Let Q be the average quality of the firms. Moreover, let ’quality’ be the exhaustive set of all characteristics that influence the value of the firm. Thus, in the following 9 When information is costly, and someone obtains it, they will do everything in their power not to signal that information through their investment decisions, for example. Grossman (1976) maintains, that in such cases, either equilibrium has to coexist with asymmetric information, or the incentive to acquire the information does not exist, and thus no-one obtains the information, and it never becomes publicly available.
    • 16 analysis, quality includes not only characteristics of the specific target firm, but also those of other potential companies, and every other determinant that may influence the valuation of an acquisition10 . Now, let us assume that a buyer A is buying firm t1 ∈ T that is of quality q1 > Q. In the presence of perfect, symmetric information, the price would reflect the true quality of t1 , which also defines the optimal supply curve for the target t1 as follows: pS = pS (q1 ) 1 1 (2) The above would be optimal for targets of good quality, and suboptimal for targets of bad quality11 . This is due to the fact that if all targets are valued according to the average quality of potential targets, Q, then those of lower than average quality gain, and those of above average quality lose. If there is no way for the acquirers to discern the true quality of the targets i, qi , they will only be willing to pay a price that reflects the average quality, Q, of the set of potential targets, T . Thus, the demand curve for the target t1 would be defined by: pD = pD (Q) 1 1 (3) With no possibilities for monitoring, screening, or signaling, this could lead to the situation described by Akerlof (1970). This is due to the fact that no owners of target ti of quality qi > Q would be willing to sell at a price reflecting Q, unless the acquisition prices by definition include a premium. However, the owners of any target t j of quality q j < Q would be happy to sell. Due to this adverse selection problem, the market would disappear entirely. When information is scarce, and the owners of the targets perceive that scarcity and have means to provide additional information to acquirers, the demand curve for any target ti of quality qi reflects both the true quality of that target, qi , multiplied by some parameter 0 ≤ λ ≤ 1, and the average quality Q of the set of potential targets T multiplied by 1 − λ. Thus, the owners of the target are willing to settle at a value lower than the true value of their firm so long as the premium over the settled value at least covers the difference between the value of the firm and the value settled upon. The equilibrium price is hence defined by equating: pS (qi ) = pD (λqi , (1 − λ) Q) × (1 + P∗ ) i i (4) 10 Even though such an exhaustive definition of ’quality’ seems unrealistic, it is beneficial to the ease of under- standing the analysis. Moreover, the characteristics of a good are often measured in relation to those of potential substitutes rather than in absolute terms, which supports my definition. 11 Assuming that the bad quality targets’ trade off is between perfect and imperfect information, and thus, be- tween the inclusion of average or true quality in the price equation.
    • 17 Where, λ is the proportion of the true quality qi that can be discerned by the acquirers through a combination of screening, monitoring, and signaling, as in Akerlof (1970), and P∗ is the acquisition premium, that reflects potential synergies and other factors that make the target more valuable to the acquirer than it is to the target shareholders. In the case of acquisitions, one method of screening is the willingness of the sellers to take equity in the merged entity as a consideration. One method of signaling for technology-intensive firms, to which I will return in Section 2.6.3., is patenting the developed technologies. Officer (2007) finds that private firms are valued at discounts as high as 30% with respect to comparable public firms in acquisitions in the US. He explains part of the valuation discount by the fact that information of private firms is less readily available than information of pub- lic firms. Hence, the discount is partly an adjustment for asymmetric information. Although the results found by Officer (2007) regarding the asymmetric information explanation are not statistically strong, they are economically very significant. Moreover, the author also finds that with his measures, information asymmetries seem to explain around a quarter of the acquisition discount of unlisted targets. In his analysis, this translates to a 7.5% discount due to information asymmetry alone. Moreover, Officer (2007) uses the dispersion in analysts’ earnings forecasts for the parent of subsidiary targets as a proxy for information asymmetry. He also notes that the subsidiaries in his sample are relatively small with respect to their parents. Hence, the impact of any un- certainty regarding the subsidiary’s future earnings is unlikely to be significant enough for the parent to cause strong variation in analysts’ earnings estimates. Thus, although it may be the best available proxy for the purposes of Officer (2007), parents’ earnings estimate dispersion is unlikely to be an accurate proxy of the information asymmetry regarding the subsidiary. The noise created by the inaccuracy of the proxy variable used may very well be the source of statis- tical non-significance found for the actual phenomenon. Thus, as the author himself notes, the explanation of information asymmetry regarding the valuation discount of non-public targets merits future research. According to Ekkayokkaya et al. (2009), information asymmetries in the acquisitions of private targets do in fact result in positive short run and negative long run returns to acquirers. More- over, the authors contend that the wealth generation effects of acquisitions of private targets are significantly different from those of acquisitions involving public targets. Furthermore, Officer et al. (2009) find that the information asymmetry is greatest when targets are the most diffi- cult to value. Not entirely unlike my study or that of Aboody and Lev (2000), Officer et al. (2009) try to delve deeper into technology-intensity as a source of information asymmetry.
    • 18 However, whereas they try to use notes to accounting statements, or more specifically, Securi- ties Exchange Commission (SEC) filings, as indicators of technology-intensity, or intangibles- intensity, I use industry classifications to specify those targets that are harder to value with respect to their knowledge assets. 2.3.3. Information asymmetry and technology Aboody and Lev (2000) show that insider gains are clearly more pronounced in R&D-intensive firms than in other firms. Moreover, the authors attribute these insider gains to information asymmetry arising from the uncertainty with respect to the quality of the R&D output on the one hand, and the volume of the R&D input on the other. In their sample of 253,038 insider transactions related to 10,013 publicly quoted US firms in the period of 1985 through 1997, Aboody and Lev (2000) find that by going long on insider purchases of R&D-intensive firms and short on those of non-R&D-intensive firms, an investor could make an excess return of almost 1 percent over an average of 25 days, which compounds to an annual abnormal return of approximately 10 percent. Given that information asymmetries related to technology are this prevalent among listed firms in the US, it seems reasonable to expect that there are clear information asymmetries related to unlisted European high-technology firms as well. Moreover, from the analysis conducted by Aboody and Lev (2000), it seems clear that technology-intensity is a substantial source of information asymmetry, and that any potential means to mitigate this information asymmetry are likely to prove to be valuable. 2.4. Acquirer preferences in and motivations behind technology-intensive takeovers After the discussion in Section 2.3., and the assertions of Akerlof (1970), Leland (1979) and Lehto (2006), it is obvious that more information in a deal is always optimal to the acquirer, and only suboptimal to the target if it is of poor quality, given that the opportunity cost of that information does not surpass its value. Thus, when information in general is scarce, one would expect potential buyers (or in this case, acquirers) to always prefer more information over less. In this section, I review the empirical findings related to the preferences of acquirers of targets in high-technology industries. Among others, Uysal et al. (2008) and Böckerman and Lehto (2006) find that information asymmetry increases with geographic distance. Also, Grote and Umber (2007) confirm this finding and further show that the likelihood of deal success decreases with geographic distance.
    • 19 Therefore, it seems that those who acquire firms from further away should be interested in any possible means of decreasing the information asymmetry, or conversely, in obtaining more in- formation. This logic is confirmed by the results of Böckerman and Lehto (2006), who show that this indeed is the case, at least for a sample of Finnish firms. Furthermore, Lehto (2006) finds that any attribute of the target that eases monitoring increases its likelihood of becoming targeted by a firm further away. Conversely, a firm that has become acquired by a distant ac- quirer, is more likely to exhibit characteristics that ease monitoring than a firm that has not been acquired from a distance. One example of a relatively cheap source of information in technology-intensive takeovers is patents12 . Indeed, Ali-Yrkkö et al. (2005) find, using a sample of Finnish unlisted firms, that the number of patents increases the probability of being acquired across border. This finding is also consistent with the views of the survey respondents, who, on average, posit that a firm further away is a more feasible target if it has patents than if it did not. Interestingly, the authors find little support for the claim that patents would increase the probability of becoming acquired within borders. Even though the authors themselves provide no clear interpretation for this result, one might posit that it is due exactly to the fact that geographic distance increases information asymmetry, and patents are a way of mitigating that asymmetry. Moreover, it seems intuitively reasonable that the closer the acquirer is to the target, the more it knows about the R&D productivity of the target, and thus has less needs to find additional information with easy access. Dessyllas and Hughes (2005b) find, using a categorization similar to the one I employ, that the likelihood of a high-tech firm becoming acquired increases with the citation-weighted patent stocks they hold. Lehto and Lehtoranta (2004) confirm this finding more generally with all knowledge stocks adding that in process industries accumulated technologies bear little or no significance to the probability of becoming a target or an acquirer. Moreover, Dessyllas and Hughes (2005b) find consistently with the findings of Officer (2007) that high-tech firms that become targets are more liquidity-constrained, and consistently with acquirer rationality and the findings of Servaes (1991), those firms are also likely to have a low Tobin’s q. Moreover, the authors show that the targets are, despite a good past record, experiencing a low R&D-output (i.e. low accumulation rate of their knowledge stock) at the time of the acquisition. Lehto and Lehtoranta (2004) find that firms become acquirers more frequently, if they have accumulated large knowledge stocks. Interestingly, however, Dessyllas and Hughes (2005a) find that acquiring firms in high-tech industries are often in a phase where they experience a decline in returns to their knowledge assets, use acquisitions as a substitute for in-house R&D activity, and have accumulated a large knowledge stock prior to the takeover. 12 The cost of patents as a source of information is rather the cost of interpreting that information than that of obtaining it.
    • 20 It is obvious from the above that acquirers generally prefer more information to less, and are willing to trade off between alternate sources of information, for instance between distance and patents. However, there are two variables that include some monitoring aspects whose direction of influence on the existence of patents in the target is not entirely obvious. Namely, the size of the target and toehold ownership. There is obviously some positive, albeit unlikely linear, relation between firm size and the number of patents assigned to the firm (or even the existence thereof). Since a larger firm can afford to spend more on producing and protecting innovations, it is also more likely to have patents than a similar smaller firm. One could easily be led to think that since patents provide additional information, and since toehold ownership is a powerful pre-acquisition monitoring tool, acquirers might settle for one at the expense of the other. However, there are some considerations that might lead to an opposite conclusion. First of all, since patents are powerful competitive tools (Gilbert and Newbery, 1982), a competitor might want to obtain a toehold in the target to strengthen their relationships and potentially be less exposed to infringement litigation. Having strengthened the relationship a priori, the firm may then decide to acquire the target. Also, it is possible that the target perceives the interest of the competitor in obtaining shares in the target and thus accelerates its innovative output to obtain a patent before becoming acquired in order to obtain leverage for valuation negotiations. The above notions are consistent with the results from the survey, which indicate, that when patenting firms are targeted in acquisitions, one of the key drivers of them being targeted and their valuation is the existence and quality of their patent portfolio. Finally, especially in non-horizontal acquisitions, the acquirer may lack the expertise in the field of the patents of the target, and thus, in fact, require more monitoring due to the fact that the target has a patent. 2.5. Patents and M&A Patents and corporate restructuring have been studied separately to a great extent, but much less so in conjunction (Schulz, 2007). The literature that does study the interrelatedness of patents and M&A-transactions focuses more on the process whereby corporate restructuring hinders innovation. For example, De Man and Duysters (2005) argue that the effect of M&A on innovation is neutral or negative, but there are some scale economies brought about by M&A- transactions that may result in lower costs of innovation. Hussinger and Grimpe (2007) show that total asset-weighted patent stocks, patent citation rates, and the blocking potential of patents determine partly the value of an M&A deal for corporate acquirers. Intuitively, the authors also find that the blocking potential of patents is very sig- nificant to corporate acquirers, but non-important at any statistically significant level to private equity acquirers. This makes sense, since corporate acquirers can make better use of patents
    • 21 that can block competition, and thus allot more value to them. A private equity acquirer cannot use the blocking potential of a patent to gain market share, whereas for a corporate acquirer, such potential can be enormously valuable, given a large enough market, and a large enough growth potential of the acquirer. 2.6. The economics and value of patents This section covers the extant literature related to the economics of patents. More specifically, Section 2.6.1. covers the general economics related to patents. Then, Section 2.6.2. discusses the value of patents and some of the determinants of that value. Finally, Section 2.6.3. covers the properties of patents as signals, with a specific view to the case of M&A transactions of high-tech targets. 2.6.1. Patent economics Patents are a powerful tool for protecting an innovation, provided that the invention is docu- mented well enough and is, in fact, patentable. A valid patent essentially excludes everyone else from utilizing the invention for a commercial purpose. As opposed to for instance a trade secret, the protection provided by the patent is a lot stronger. If the invention is a trade secret unprotected by a patent, anyone else may reverse-engineer the innovation from a product, and utilize it for their own purposes. Given the protective power of patents as opposed to trade secrets, it is optimal for an inventor to apply for a patent as soon as possible (Hall et al., 2005; Reinganum, 1982). Also, as Reinganum (1982) argues, a firm can never simply wait for the competitors to innovate even in the case where the rewards to imitation are the same as those to innovation. This is due to the fact that there is always a positive probability that none of the competitors will innovate. Moreover, following the logic above, patenting an innovation can be considered a race to enter a market with first-mover advantages of a large magnitude. Essentially, the advantage in this case is that of a monopoly, or an oligopoly where the first mover can charge all of the economic gains from the second movers through the licensing fees of the patented innovation13 . In the latter case, the inventing firm can be considered similar to a monopoly with a scale greater than its own production capacity. 13 Theoretically, this would be the case. However, in practice, there are conventions called reasonable licensing fees, which are awarded by a court in case of an infringement. Also, there are organisations that try to force the application of reason in charging licensing fees. Hence, in practice, the first mover can only charge some reasonable part of the economic gain, not all of it.
    • 22 2.6.2. The value of patents The interest of the economic literature in patents dates back to Griliches (1981). He is the first to introduce a market value equation including patents as an explanatory variable. After Griliches, several studies have been made into the relation between patents and firm value. The most prominent and the widest in scope is that of Hall et al. (2005), where the authors study the impacts of accumulated R&D stocks, patents, and citations on market value. More specifically, Hall et al. (2005) factor in expectations of future citations, and account for the time value of past and future patent, citation, and R&D stocks. There are several sources from which patent value can originate. The most significant sources of value are the right to exclude, the value of patents as strategic tools in business negotiations, the pre-emption of competition, licensing revenue generation, and the pre-emption of potential law suits (Gilbert and Newbery, 1982). There are also a few potential cases where patents may, in fact, destroy value. One of these cases is the one argued by Hall (2005), where the increase in the patenting rate of a company signals the increased threat of patent-related litigation. Another potential channel of value destruction, although not as significant in magnitude, is one where the firm simply patents all the innovations it makes irrespective of whether it is going to ever need those technologies or not. Sadly, the survey respondents seem to feel that this is a fairly common intellectual property (IP) management policy. Academic studies show that patents indeed are a source of value to the firm, when firm value is measured by the excess of market value over book value. Moreover, the number of patents a firm has also bears significance on value over the mere existence of patents. Thus, the excess of market over book value is partly explained by the fact that a firm has patents, but even more so by the number of patents. (Griliches, 1981; Hall et al., 2005, 2007) Furthermore, Hall et al. (2005, 2007) show that patents bear significant value to the firm even when past R&D expenditure is controlled for. Among others, Cotropia (2009) and Pakes (1986) take a view on patents as real options. While Pakes (1986) estimates the different characteristics of options in three European countries, Cotropia (2009) develops a more general, theoretical model of patents as real options. In essence, he argues that patents can be viewed as call options on the commercialization of the technology (or other non-obvious knowledge) underlying the patent. Cotropia (2009) further explains that the post-grant R&D investment is thus viewed as the exercise price of the call, whereas the pre-grant R&D investment and other costs pertaining to the receipt of the grant should be viewed as the price of the call. Both the private value and the market value of patents have been topics of increasing interest, beginning as early as the 1960s. Recently, Hall et al. (2005, 2007) study the effect a patent has on the market value of the firm in US and European contexts, respectively. In the US, Hall et al.
    • 23 (2005) find that an extra patent per million dollars of R&D boosts market value by about 2%, and an extra citation per patent by about 3%. The authors also find that in explaining the market value of a firm’s knowledge stocks, each of the variables, R&D/Assets, Patents/R&D, and Citations/Patents adds to the explanatory power of the others with respect to Tobin’s q. That is, each of the three variables have a both economically and statistically significant impact on market value when the other two are controlled for. Hall et al. (2007) find that in Europe EPO patent and citation stocks have an impact on market value similar in magnitude and significance to that of US firms, but only if the EPO patents in question have equivalents in the US. Finally, the survey responses indicate that patent value can originate from multiple sources. While some of those sources are impossible to measure with the data at hand, they do provide an important insight into the value of patents. The most important sources of value (in descending order of importance), according to the responses, are relatedness to the firm’s, or a competitor’s, core business, importance for future technology, difficulty to invent around, remaining life, scope, and importance for current technology. All of these scored above 4 on a scale of 1 − 5 in importance for patent value, where 5 = very important. Thus, a valuable patent creates a competitive advantage either now or in the foreseeable future. Moreover, a patent is most valuable, when it has a broad scope. 2.6.3. Patents as signals Even though patents do have value in and of themselves, their most intriguing aspect related to the current empirical setting is their role as signals of firm quality, to which Long (2002) refers in his paper. In what follows, I will shortly discuss how patents behave as signals in light of the framework described in Section 2.3. Suppose that firms with patents are believed to be of quality qx > Q, and that λ is increasing in the number of patents with some upper limit. Denoting the number of patents as PCount , we get the following demand curve for target ti : pD λ PCount qx , 1 − λ PCount i Q × (1 + P∗ ) (5) Recalling the equilibrium from equation 4, we get: pS (qi ) = pD λ PCount qx , 1 − λ PCount i i Q × (1 + P∗ ) (6)
    • 24 In order for patents to be credible signals of quality qx , it must hold that for any firm of quality qi < qx , obtaining the marginal patent when the supply and demand curves intersect must be more expensive than the increase in value. It must also hold that for companies of quality qi , obtaining patents up to the upper limit so that λ = 1 is less expensive than the increase in value they experience. It must further hold for those firms that the acquisition premium (P∗ ) is large enough to account for the pricing difference between the demand and supply curve, if patents are the only signal of quality. 3. Hypotheses and variables In this section, I present the hypotheses and variables I use to answer my research problem. More specifically, I present and argue my hypotheses in Section 3.1., and review my variables in Section 3.2. 3.1. Hypotheses In this section, I develop my hypotheses with which I aim to answer my research problem. All of the hypotheses are based on extant literature and theoretical frameworks, as discussed in Section 2. I also recap the crucial parts of that literature in arguing for the hypotheses. As Officer (2007) shows that there is an acquisition discount in unlisted US targets, there should be one for European targets as well. This follows also directly from equation 1, and from the reasoning presented by Easley and O’Hara (2004). Moreover, the acquisition discounts arise due to the illiquidity of unlisted assets and relaxed disclosure requirements of unlisted firms. H1 There is an acquisition discount of unlisted targets in Europe. Given that part of the explanation for the acquisition discount offered first by Officer (2007), and later by Officer et al. (2009) and Ekkayokkaya et al. (2009), includes information asymme- try, and that Aboody and Lev (2000) show that information asymmetry is especially prevalent among technology-intensive firms, the acquisition discount should also be more pronounced in those firms. H2 The acquisition discount is more prevalent in technology-intensive industries. Officer et al. (2009), and Ekkayokkaya et al. (2009) argue that like the acquisition discounts, the positive announcement returns earned by bidders who use stock to pay for unlisted targets
    • 25 are partly explained by information asymmetry. Given that Aboody and Lev (2000) find that the information asymmetries are more prevalent in high-technology firms, the bidder announcement return for stock bidders should also prevail across stock bidders of technology-intensive targets. H3 The acquisition announcement returns to acquirers of unlisted targets in technology- intensive industries are, ceteris paribus, higher for stock-swap transactions. If the acquisition discount indeed is in part determined by the amount of information asymmetry between the buyer and seller, it is reasonable, as above, to expect that the discount will increase as the information asymmetry increases. As Uysal et al. (2008) find even within U.S. firms, the information asymmetries increase with geographical distance. Following this logic, I arrive at the following two-fold hypothesis: H4a The acquisition discount of unlisted targets increases with the natural logarithm of geo- graphic distance between the target and acquirer headquarters. H4b The bidder acquisition announcement return decreases in the natural logarithm of the geographic distance between acquirer and target headquarters. When information is scarce, any additional source of information should provide additional value. Lehto and Lehtoranta (2004); Lehto (2006); Böckerman and Lehto (2006) show that this indeed is the case. For technology firms, one such source can be patents. Thus, the acquisition discount should be reduced by the existence of patents. H5 The existence of patents assigned to the target reduces the acquisition discount of unlisted high-technology firms. Analogously as in the case for H5, the accumulation of publicly accessible knowledge stock prior to the acquisition provides useful information regarding the target. Hence, I arrive at the following hypothesis: H6a The number of patents assigned to the target reduces the acquisition discount of unlisted high-technology firms. If patents indeed are a source of information for the acquirer, it is likely that their value as a source of information is not linearly increasing in their number. To see this, consider two similar firms. One of those firms has ten patents that are a direct output of its R&D-efforts. The other firm has also ten patents that are a direct output of its R&D-efforts, but it also has acquired
    • 26 another ten, and holds yet another ten patents that are not directly related to its business but are a by-product of inventing the other ten. It is fairly obvious that the thirty patents held by the other company are surely not three times as valuable as the ten held by the other. Even without these assumptions, the case of patents as a source of information is analogous to the case of screwdrivers in a garage. Without any, you’re lost. Owning one to five, you still gain from having another, but beyond that you’re only drowning in screwdrivers. Furthermore, as the questionnaire respondents note, a patent’s coverage can be anything from a small piece of a product to an entire product. Given these differences, a firm with more patents is obviously more likely to have several patents relating to one product than a firm with less patents. Following this logic, I arrive at the following hypothesis: H6b The marginal information value of patents is decreasing in the number of patents assigned to the target. Ali-Yrkkö et al. (2005) find that a small Finnish firm with patents is more likely to be targeted in cross-border M&A transactions than a comparable firm with no patents. Moreover, the au- thors find no statistically significant impact of patenting over domestic transactions. However, the patenting variables used in Ali-Yrkkö et al. (2005) for the likelihood of domestic M&A are economically significant. If the likelihood of becoming a cross-border target increases sub- stantially when the firm has patents, it should also follow that a target further away from the acquirer is more likely to have patents. Moreover, Lehto and Lehtoranta (2004); Lehto (2006); Böckerman and Lehto (2006) find that acquirers that bid for firms further away, are interested in such firms that have other means whereby the bidder can monitor them. Moreover, while the questionnaire responses with respect to this point are somewhat volatile, the consensus seems to indicate that distant targets are considered more feasible if they have patents. Thus, as infor- mation asymmetry increases in one dimension, the acquirer will seek to decrease it in another. H7 The likelihood of a target having patents increases with the geographic distance between the target and the acquirer, and other factors contributing to information asymmetry. As discussed in Section 2.4., it is likely that pre-negotiation competitive situation has driven the acquirer management to obtain a toehold in the target due to the patent grant in order to im- prove corporate relations and thus mitigate expected infringement suit costs. On the other hand, the target may have perceived increased interest in its acquisition due to the obtained toehold, and thus accelerated the patenting process. Finally, it is also possible that the acquirer lacks the required expertise in the field of the patent, and hence, in fact requires the pre-acquisition toehold monitoring to better ascertain the true value of the acquisition. Moreover, consistently with the above notions, the questionnaire responses indicate that in several cases, the patent or
    • 27 intellectual property (IP) portfolio of a target may compliment that of the acquirer to an extent where an acquisition becomes increasingly interesting. In such a case, it may be optimal for the acquirer to obtain a toehold prior to the acquisition in order to better ascertain the value in use of the target’s IP portfolio, as well as to facilitate a more friendly appearance of a takeover. Hence, instead of a potential information trade off hypothesis, I hypothesize the following: H8 A pre-acquisition toehold in the target increases the probability that the target has patents. Beginning with Lerner (1994), authors have suggested that different means of assessing patent quality increase their information content and value to the firm. The usual suspects in literature are citations, references, scope, and family size. While citations and references receive little support from the survey respondents as originators of patent value, the other two measures do obtain significant support. H9 The quality of the patents assigned to the target, as measured by citations, references, scope, and the size of the INPADOC patent family, reduces the information asymmetries related to acquisitions of unlisted high-technology firms. Officer (2007) finds that a major factor contributing to the acquisition discount in the US is the need for corporate liquidity. More specifically, he finds that the availability of liquidity has a negative impact on the acquisition discounts. Thus, I arrive at the following hypothesis: H10 Easy access to alternate sources of liquidity at the time of the acquisition reduces the acquisition discount. In Section 2.2., I discuss the theory related to abnormal acquirer returns around the announce- ment date. Moreover, I explain that Moeller et al. (2005) find that even during times of hot M&A markets acquirer shareholders do gain on average when large loss deals are excluded. Given the small economic size of the transactions I analyze with a mean value of $54m, and a peak at $984m, my sample does not include deals large enough to result in such enormous losses. Moreover, as the acquired assets are illiquid by nature, and they are made liquid in the transaction by pooling them into the assets of a listed company, it is more likely that during times of high equity valuations (i.e. hot M&A markets), acquirer shareholders would gain more. Furthermore, Harford (2005) finds that returns to merged firms during merger waves are higher than prior to or after such waves. Thus, I hypothesize: H11 High M&A activity at the time of the acquisition increases the acquirer announcement return.
    • 28 However, the expectation with respect to the IPO market is quite the opposite. When IPO activity is high in the industry and the target opts for becoming acquired instead of making an IPO, it reveals to the market that the potential acquirer is willing to pay more for its assets than it would receive from a public offering, even when demand for such offerings is plentiful. Moreover, while IPO underpricing is higher in hot IPO markets, for instance Aggarwal et al. (2002) find that IPO underpricing is not significantly related to IPO proceeds, and thus the ’temperature’ of the IPO market measures a shift of equilibrium in quantity, not in price. Thus, I arrive at my final hypothesis: H12 High IPO activity at the time of the acquisition in the industry of the target decreases the acquirer announcement return. 3.2. Variables In this section, I present the relevant variables pertaining to the acquisition discount, the likeli- hood of patenting, and the bidder’s acquisition announcement return. There is an overwhelming amount of literature related to announcement returns and deal value in acquisitions. I do not at- tempt to control for all of these variables, since a sizable part of them are specific to acquisitions of listed targets. However, I do control for the most relevant ones. 3.2.1. Acquisition discounts Following Officer (2007), I define the acquisition discounts relative to book value of equity, net income, earnings before interest payments and taxes (EBIT), and sales with respect to compa- rable transactions in the industry as follows: Multiple for company i Di,m = 1 − (7) Industry mean multiple Where Di,m = the acquisition discount of firm i relative to multiple m. More specifically, I define firms belonging to the same industry as ones with the same two-digit Standard Industry Classification (SIC) code. Also, following Officer (2007), I center the three year window of the comparable transactions to begin 18 months prior to and end 18 months past the acquisition announcement date of the firm in question. I then define the firm-specific acquisition discount as the equally weighted average of the dis- counts related to each multiple as follows:
    • 29 1 M Di = ∑ Di,m (8) M m=1 Where, M is the number of multiples available for firm i Di,m is the acquisition discount of firm i relative to multiple m, and Di is the equally-weighted acquisition discount of firm i relative to all multiples m available. Here, I deviate from Officer (2007), and follow the logic in Officer et al. (2009) by defining the acquisition discount as a positive number, when it indeed is a discount, and as a negative number, when it turns out to be a premium. Hence, when a term has a negative impact on the acquisition discount, it has a positive impact on deal value and v.v. 3.2.2. Acquisition announcement return To define the abnormal acquisition announcement return, I first define normal return for firm i relative to market M by regressing the return of that firm on the market as follows: RP = α + βi,M ∗ RM i (9) Where, RP is the normal (or predicted) return for firm i with respect to the market M i α is the intercept of the model βi,M is the regression coefficient that describes the change in Ri for a unit-change in RM , or Covi,M βi,M = (10) VarM RM is the return for market M To avoid potential anticipation effects of the deal being included in the predicted normal return, I use a clean estimation period of 360 working days starting 390 working days before the deal announcement, and ending 30 days before the deal announcement.
    • 30 I then define the abnormal acquisition announcement return, or cumulative abnormal return (CAR[−t;t]) for some interval t before and after the acquisition announcement as follows: CARi = Ri − RP i = Ri − (α + βi,M ∗ RM ) (11) 3.2.3. Patenting variables The most important patenting variables I use are the patenting dummy, number of patents and its square, number of citations, number of references, scope of patents, and the size of the INPADOC patent family. For the count measures of patents and citations, I also experiment with asset-weighted patent counts (Patents/ln (Total Assets)), and citation-weighted patent counts (see e.g. Hall et al. (2005, 2007), and Hussinger and Grimpe (2007)). The measurement of all of the variables above is unambiguous. I also experiment with a compound patent portfolio quality measure, where the sums of the relations between the quality measures and their respective sample means are used as weights by which the patents are multiplied. So, if a patent has zero citations, then it’s citation-weighted count is also zero. I arrive at the following measure for each dimension of quality: P ∑ qi,p, j p=1 Qi, j = n Y (12) 1 nY ∑ ∑ qi,p i=1 p=1 Where, Qi, j is the quality weighted patent count for firm i for quality dimension j p represents a patent Y is the total number of patents in the whole sample P is the total number of patents for firm i, and n is the number of firms in the whole sample. I do not have as extensive a sample as Hall et al. (2007), from which I could construct a compos- ite quality measure utilizing factor analysis. Thus, my analysis is restricted to averaging across
    • 31 all weighted counts. I thus arrive at the following composite quality-weighted patent count for each firm: 4 1 CQi = ∑ Qi, j (13) 4 j=1 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 in equation 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 signs Explanatory variables related to the acquisition discount, the acquisition announcement return of the bidder, and the probability of having patents, and their respective expected signs. Patents held by 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 there is 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 with respect to those models, as discussed in Section 2. The expected signs related to the acquisition discount 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 geographic distance in detail. I utilize a dummy for deals over $20m in value to explain the acquisition discounts, since the deal size, even the natural logarithm of it, is not appropriate. This is due to the fact that the acquisition discount is included in the dollar value of the deal, and hence using one to explain the 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 in Section 4.1. Moreover, I describe the relevant methods, including the bootstrap technique for obtaining standard errors, and analyze the appropriateness of ordinary least squares, and the required 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 on M&A deals from the Thomson Reuters Securities Data Company (SDC) Platinum Worldwide Mergers & Acquisitions database. I collect data on all deals involving a European target and a European acquirer for the acquisition discount and acquirer return analyses. I further require that the deals involve a controlling stake in the target’s equity, and that the announcement date is between January 1, 1990 and December 31, 2006. Moreover, I only include transactions where the payment method is stock, cash, or a combination of the two, thereby excluding transactions where debt or preferred securities are used as consideration. After obtaining this data, I merge it with target patent data from the European Patent Office (EPO) free global databases, the contents of which are specified in Table 17. in Appendix A, and acquirer financial performance data from Thomson Reuters Datastream. Moreover, for the acquirer return analyses, I further require 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 and Commercial 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 Business Services, or SIC 87 Engineering, Accounting, Research, Management, and Related Services. From the SDC database, I gather the deal value, deal multiples, deal completion status, target public 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 sample consists 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 at least 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. In the sample where at least one multiple is required, technology-intensive firms account for 3,484 of the unlisted targets. To increase the robustness of my analysis and conclusions, I require that there are at least two multiples available for the unlisted target. Industry comparable multiples can 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, deal value to EBIT, deal value to sales, and deal value to net income as 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 unusable data points to avoid excessive use of time in this phase. As noted by Officer (2007), and as can be seen from Table 2., the data obtained from the SDC contain substantial noise14 . To get 14 Comparing the means and medians, one can easily see that especially in the Deal Value to Book Equity for listed targets, there are a couple of outliers that contribute significantly to the excessively high average. Looking at the data, I find at least two multiples in the order of thousands. Thus, as in Officer (2007), clearing the outliers seems to be the obvious choice.
    • 34 rid of the outliers, I adopt the logic utilized by Officer (2007) by not allowing the percentage difference of the acquisition multiples compared to the comparable industry transactions to exceed +100%. While the choice of this limit may seem arbitrary, it is in line with the implicit lower 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 that essentially searches for the patent families where at least one patent is granted within one of the EPO 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, I move 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 on each patent family by years from priority date. Similarly, I collect the number of references on the search report for each patent in the patent family. Although these algorithms are a lot more tedious, they are also a lot more accurate than the first one. Thus, the potential for error lies in the 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 obvious clusters 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 appear random enough that I can conclude that they indeed are random. Thus, there is no reason to believe that some underlying factor relevant to my models would be driving the availability of the data, and thus my results. There are, however, a couple of worrying years when the percentage of multiples data available is low relative to the sample mean. However, since the years of low percentages of multiple data coincide with periods of seemingly high M&A volumes reported in the literature and found in my analysis of the M&A volumes, the low data availability rate serves as a buffer against the concentration of the sample for those years, and thus reduces the possibility that the period of hot M&A markets would drive the results of this thesis. Furthermore, since the availability of multiples data does not appear to be linearly related with the number of transactions nor is it low always when the M&A market is hot, the low data availability in some years is more likely a result of random variation than a consistent lack of data from times of high M&A activity.
    • Table 3: Are the unlisted targets with multiple data representative of the population? Number of transactions, and percentage of transactions with acquisition multiple data for both high-tech and non-high-tech subsidiary and stand-alone targets grouped by year. As specified earlier in Section 4.1., I require that the target has at least two multiples available from the SDC database. Stand-alone targets Subsidiary targets High-Tech Non-High-Tech High-Tech Non-High-Tech Year Transactions % with multiples data Transactions % with multiples data Transactions % with multiples data Transactions % with multiples data 1990 134 32.8% 270 24.4% 171 21.6% 433 23.8% 1991 149 10.7% 423 9.5% 201 12.4% 475 13.7% 1992 156 19.2% 387 20.2% 191 19.4% 496 16.7% 1993 140 35.0% 387 34.1% 176 32.4% 501 31.3% 1994 182 31.9% 514 24.9% 194 29.9% 512 23.0% 1995 219 23.7% 536 24.1% 180 17.2% 541 15.2% 35 1996 226 21.2% 547 21.0% 215 6.5% 546 5.1% 1997 331 11.5% 680 9.9% 267 7.5% 786 6.9% 1998 369 6.5% 833 3.4% 291 7.6% 883 5.1% 1999 481 11.2% 879 8.2% 357 11.8% 937 13.3% 2000 617 10.0% 763 10.1% 387 12.7% 893 11.5% 2001 445 8.3% 582 9.5% 378 11.9% 683 12.7% 2002 295 9.2% 540 6.1% 290 5.9% 661 7.7% 2003 290 15.5% 551 16.9% 345 9.9% 661 12.9% 2004 339 26.0% 550 24.0% 307 20.5% 675 15.3% 2005 501 21.6% 692 18.8% 326 12.6% 745 12.9% 2006 520 20.6% 776 21.0% 323 18.3% 819 11.5% Full sample 5394 16.4% 9910 15.5% 4599 14.2% 11247 13.2% N 31150
    • 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. This indicates 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 and subsidiary targets are more likely to be a result of real differences between the categories than data availability. Of course, due to relatively low sample sizes (885 for stand-alone targets, and 653 for subsidiaries), the variation in the results may cause differences in statistical, and to some extent 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 of the descriptive statistics related to them for ease of interpretation. Moreover, the most important variables are only gathered for the subsample of unlisted high-tech targets due to the amount of work related to gathering them, and would thus have no reference point in other subsamples. Table 4: Distribution of the sample by country The number of high-tech targets in each geographic area included in the sample. 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 significant portion of the targets from the UK, with over 70% of observations coming from there. Even after that, the sample does not seem to be geographically different from other studies on the same general topic and geographic concentration. More specifically, excluding UK, most of the rest of the sample comes from France and Germany (totaling 12.2% of the sample), the Nordic countries (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. Perhaps the 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 does not change the qualitative results of this thesis. Furthermore, the subsamples of subsidiary and stand-alone targets do not exhibit any obvious clustering 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 significant factor in driving differences or similarities between those categories, and are more likely at- tributable to randomness. Table 5: Distribution of the sample by industry The 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 Computer 89 75 164 10.7% Equipment 36 - Electronics and Electrical Equipment 62 91 153 9.9% 37 - Transportation Equipment 45 44 89 5.8% 38 - Measuring, Analyzing and Controlling Instruments; 59 43 102 6.6% Photographic, Medical and Optical Goods 48 - Communications 58 43 101 6.6% 73 - Business Services 348 187 535 34.8% 87 - Engineering, Accounting, Research, Management, and 150 71 221 14.4% Related Services 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 bit worrying only due to the significance of patents in that industry. With respect to information asymmetry, one would only expect its magnitude to be greater, but the direction of impact, and its 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 thus its 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 Office Equipment) are classified as SIC 737 (Computer Processing & Data Processing). Thus, SIC 73 is an important part of the sample. Moreover, unreported tests, and the study of the industry fixed-effects dummies show that excluding or including SIC 73 does not have an impact on the qualitative results apart from the patenting variables15 , but it does influence the statistical significance of the tests due to its significant contribution to the sample. Table 6: Summary statistics of relevant explanatory variables Means, medians, standard deviations (σ), and range (min and max) of explanatory variables related 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 are measured 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 1477 Transformed discount 0.711 0.718 0.279 0.024 1.414 1477 CAR 1.18 0.15 6.02 −24.6 35.5 732 Patenting (0/1) 0.189 0 0.392 0 1 1538 Patent count 2.85 0 12.69 0 191 1538 References 6.05 0 40.01 0 1264 1538 Family 31.78 0 773.57 0 30215 1538 Scope 13.65 0 66.26 0 1244 1538 Citations 3.9 0 22.4 0 460 1538 Quality-weighted patents 3.07 0 27.57 0 996.96 1538 Geographic distance 274.4 158.3 362.4 0 2707.2 1538 % of stock used as consideration 6.44 0 17.41 0 100 1538 Divestiture (0/1) 0.394 0 0.489 0 1 1538 Relative size of the deal 0.38 0.09 1.13 0 13.65 738 ln (Sales) 11.79 11.58 2.29 4.78 19.33 774 ln (Total Assets) 11.89 11.8 2.29 3.5 18.35 763 Price-to-Book 3 2.4 22 −357.9 119.6 707 Market value 2529 146 12427 0 183834 745 M&A activity (0/1) 0.856 1 0.351 0 1 1538 Deal size > $20m (0/1) 0.38 0 0.485 0 1 1538 IPO volume 1.23 1.27 0.83 0.11 3.07 1538 Baa Spread 337 293 151 44 640 1538 Overnight 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 findings of Officer (2007), albeit somewhat higher, which may be explained simply by the fact that my sample focuses on high-tech firms, whereas his does not have an industry focus. The cumulative abnormal return in the interval [−1; 1] is in line with the findings of Officer et al. (2009), and Ekkayokkaya et al. (2009), and contributes to the finding that while acquirers of public targets do not gain on average, those of unlisted targets do. 15 This impact is expected, given that many jurisdictions, especially the ones most relevant to this study, are not eager to grant patents to software or service innovations. Moreover, Hall et al. (2007) find, that software patents are 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 number of patents for patenting firms is seriously positively skewed16 . Moreover, the patents of the targets in this sample average about a third over a citation per patent, which indicates that the average 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 single citation. 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 consideration on 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, are mostly 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 relatively less prone to acquire distant firms than large companies are. The Baa-loan spread has a strong negative correlation with the IPO market size indicator, which may 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 with seemingly unrelated variables, mostly with M&A and IPO activities, the deal size dummy and the log of deal size, and the mean acquisition discount. This indicates that while the central bank rate has been lower, the volume of equity-related transactions has been somewhat higher in the new millenium. 16 One needs no diagrams to see that a variable defined in the interval [0; ∞[ is positively skewed if it has a standard deviation higher than its mean. This is due to the fact that as the average observation deviates (positively or negatively) from the mean more than the mean, but the observation can never get negative values, the average observation that deviates positively from the mean will have to do so by more than the standard deviation, and the average observation that deviates negatively from the mean will do so by less than the standard deviation thereby producing a positive skew to the distribution. 17 This should be no surprise given that these transactions are, in part, defining the hot M&A market. 18 In other words, around the peak of the business cycle.
    • Table 7: Correlations between explanatory variables Correlation coefficients between explanatory variables related to the acquisition discount, the probability of patenting, and the acquirer announcement return. Market value, total assets, sales, and price-to-book ratio are those of the acquirers. Other firm-specific variables relate to the targets. Variable Number 1 2 3 4 5 6 7 8 9 10 11 12 Mean discount (1) 1 Patenting (0/1) (2) −0.088 1 Patent count (3) −0.039 0.472 1 Patent count2 (4) 0.022 0.193 0.851 1 Quality-weighted patents (5) −0.069 0.191 0.422 0.276 1 Patent count/ln (Total Assets) (6) −0.062 0.499 0.969 0.773 0.498 1 ln (Geographic distance) (7) −0.068 0.102 0.057 0.019 0.028 0.067 1 Cash deal (0/1) (8) 0.076 0.043 0.016 −0.015 −0.023 0.010 −0.004 1 Stock payment (0/1) (9) −0.051 −0.095 −0.086 −0.049 −0.043 −0.080 −0.151 −0.539 1 Stock acquisition, toehold (0/1) (10) −0.045 −0.038 −0.018 −0.007 −0.007 −0.019 0.030 −0.071 0.126 1 Stock acquisition, no toehold (0/1) (11) −0.044 −0.089 −0.083 −0.048 −0.042 −0.078 −0.157 −0.530 0.985 −0.049 1 40 Toehold, cash acquisition (0/1) (12) 0.064 0.020 −0.006 −0.006 −0.011 −0.019 −0.068 0.035 −0.155 −0.020 −0.153 1 ln(Deal Value) (13) −0.230 0.055 0.082 0.042 0.054 0.060 0.112 −0.024 −0.016 −0.012 −0.014 0.118 ln(Sales) (14) −0.054 0.077 0.124 0.088 0.014 0.066 0.120 0.162 −0.317 −0.049 −0.310 0.305 ln(Total Assets) (15) −0.062 0.041 0.101 0.080 −0.015 0.028 0.133 0.165 −0.301 −0.034 −0.297 0.239 Price-to-Book (16) 0.010 0.040 0.022 0.004 0.048 0.042 −0.018 0.031 −0.067 0.001 −0.068 −0.005 Market Value (17) −0.015 −0.025 0.008 0.006 −0.003 −0.002 0.028 0.029 −0.104 −0.014 −0.102 0.264 M&A-activity (0/1) (18) −0.021 −0.138 −0.144 −0.126 −0.122 −0.135 0.021 0.029 0.035 0.030 0.030 −0.050 Deal size > $20m (19) −0.145 0.014 0.105 0.079 0.079 0.090 0.061 0.021 −0.029 −0.016 −0.026 0.128 Aggregate IPO volume (20) 0.034 −0.063 −0.042 −0.046 −0.021 −0.049 0.043 0.012 0.067 0.009 0.066 0 Industry IPO volume (21) −0.010 −0.078 −0.040 −0.027 0.024 −0.043 0.089 0.031 0.018 0.045 0.010 −0.039 Baa spread (22) 0.089 −0.044 −0.045 0.002 −0.041 −0.046 −0.043 0.008 0.009 0.030 0.003 0.070 Overnight rate (23) −0.183 0.111 0.037 0.007 0.022 0.042 0.066 −0.024 −0.056 0.006 −0.058 −0.084 Deal made between 2000-2006 (0/1) (24) 0.233 −0.077 −0.053 −0.057 −0.050 −0.056 −0.040 −0.014 0.051 0.040 0.044 0.087 Continues on the next page
    • Table 7. continued Variable Number 1 2 3 4 5 6 7 8 9 10 11 12 Different industries (0/1) (25) −0.110 0.105 0.036 −0.011 0.041 0.052 0.014 0.075 −0.078 −0.001 −0.078 0.005 Challenged bid (0/1) (26) −0.021 −0.027 −0.013 −0.005 −0.005 −0.013 0.012 −0.050 0.027 0.351 −0.035 −0.014 Tender offer (0/1), acquirer q < 1 (27) 0.044 −0.027 −0.013 −0.005 −0.005 −0.013 −0.022 0.063 −0.035 −0.004 −0.035 0.226 Tender offer (0/1), acquirer q ≥ 1 (28) −0.020 −0.012 0.012 −0.005 −0.004 0.009 0.001 0.005 −0.017 0.129 −0.039 0.202 Variable Number 13 14 15 16 17 18 19 20 21 22 23 24 ln(Deal Value) (13) 1 ln(Sales) (14) 0.549 1 ln(Total Assets) (15) 0.504 0.938 1 Price-to-Book (16) −0.035 −0.009 −0.001 1 Market Value (17) 0.226 0.399 0.330 0.008 1 M&A-activity (0/1) (18) 0.086 0.019 0.031 0.005 0.046 1 41 Deal size > $20m (19) 0.770 0.485 0.442 −0.003 0.179 0.075 1 Aggregate IPO volume (20) 0.156 0.049 0.074 −0.056 0.001 0.256 0.144 1 Industry IPO volume (21) 0.108 0.039 0.074 −0.068 −0.020 0.181 0.091 0.677 1 Baa spread (22) −0.040 0.009 −0.021 0.056 0.036 −0.055 −0.033 −0.549 −0.325 1 Overnight rate (23) −0.154 −0.087 −0.021 0.010 −0.081 −0.364 −0.154 −0.143 0.046 −0.044 1 Deal made between 2000-2006 (0/1) (24) 0.203 0.124 0.048 −0.038 0.107 0.357 0.199 0.309 0.160 0.053 −0.701 1 Different industries (0/1) (25) −0.005 0.095 0.063 0.007 −0.073 −0.087 −0.002 −0.063 −0.067 0.005 0.157 −0.155 Challenged bid (0/1) (26) 0.068 0.014 0.019 0.002 −0.008 −0.064 0.077 −0.065 −0.034 0.028 0.082 −0.056 Tender offer (0/1), acquirer q < 1 (27) −0.066 0.131 0.092 −0.005 0.525 −0.064 0.018 −0.062 −0.035 0.072 −0.015 0 Tender offer (0/1), acquirer q ≥ 1 (28) 0.097 0.124 0.127 0.002 0.072 −0.081 0.104 −0.007 0.019 −0.009 0.046 −0.100 Variable Number 25 26 27 28 Different industries (0/1) (25) 1 Challenged bid (0/1) (26) 0.056 1 Tender offer (0/1), acquirer q < 1 (27) 0 −0.003 1 Tender offer (0/1), acquirer q ≥ 1 (28) 0.032 0.389 −0.008 1
    • 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 to use stock consideration in acquisitions. If size is an indication of the availability of liquidity, this is no surprise. Target and acquirer sizes are expectedly strongly correlated. Finally, it appears that high 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 models would 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 acquirer announcement 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 discount using 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 variant of the Kaplan and Ruback (1995) comparable industry transaction method. More specifically, I construct portfolios of transactions of public targets within the same two-digit SIC-code to which the acquisition multiples of the unlisted targets are compared. I further require that these transactions take place at most 18 months before or after the transaction to which they are compared. Also, the comparable 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 not make sense to require completely different portfolios for each transaction, any listed company can be 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, it does need motivation and some further scrutiny. Suppose we have two target companies, one listed and one unlisted, that are fully financed by equity. Suppose also that we know that acquisition prices always include, by custom, a premium over the fair market value of the target. Let the price paid for the listed company be PL , and the price paid for the unlisted one PU . Moreover, let the fair market values of the listed and unlisted companies be VL and VU , respectively19 . Further, let the fundamentals over which the multiples are calculated be FL and FU for the listed and unlisted targets, respectively. When analyzing the transaction multiples, we have: 19 Even though there is no market that would value the unlisted company, for the sake of this analysis, we assume that if such a market did exist, that value would be VU .
    • 43  M = PU U FU (14) M = PL L FL Where the subscripts U and L refer to the unlisted and listed targets, respectively, and Mi refers to the multiple for firm i. Thus, when we measure the acquisition discount of the unlisted target with respect to the listed target, we have: MU PU × FL 1− = 1− (15) ML FU × PL 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 the company, 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 at a premium (Officer, 2007). 3. If the listed targets are acquired at a premium, my acquisition discount measures overstate the discount and understate the premium. Although it is crucial to understand that discounts measured in this analysis are not actual discounts but rather proxies for it, it must also be noted that they are sufficiently accurate for the purposes of this thesis. Moreover, when the signs of the discounts are correct, so will be the signs of the coefficients of the explanatory variables. Acquisition discount regression model I regress my explanatory variables on two separate independent variables, namely the acquisition discount 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) + (16) φT × Liquidity + γT ×Control 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 INPADOC family size-weighted patent counts, as defined in equation 13, ln (Geographic distance) is the natural logarithm of the geographic distance between acquirer and target 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, aggregate IPO 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 quality indicator. The motivation behind adding the square of the patent count in addition to the plain patent count stems from the discussions in Sections 3.1. and 3.2., that is, the marginal effect of a patent is expected to be decreasing in the number of patents. Moreover, the coefficient of the square of the patent 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 explain the methodological corrections required for the appropriateness of the analysis. Gauss-Markov conditions Whenever OLS is applied, it is crucial to examine whether the Gauss-Markov conditions hold. If they do not, the coefficient estimates provided by ordinary least squares are no longer best linear unbiased estimators (BLUE). Thus, there are likely to be other estimates for the coefficients that would provide a better, less biased fit for the model. Jensen et al. (1975) reduce the Gauss-Markov conditions to the following form:  E(e) = 0 (17) V (e) = σ2 I n 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 the error terms are i.i.d., that is, whether they are distributed independently of and identically to each other. I also examine whether the disturbance terms are distributed independently of the explanatory variables. The first Gauss-Markov condition, that the disturbance term has a zero expected value, is met in my sample. More specifically, the mean value of the disturbance term in the regressions is of the order x × 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 term at the 1% level. Thus, I need to correct for heteroskedasticity. I use the HC3 covariance matrix estimator 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 any other property of the variables. Moreover, a correlation matrix of the explanatory variables and the disturbance 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 multicollinearity in the model. However, O’Brien (2007) discusses the use of rules of thumb and VIFs at length, and argues that more important than the specific VIF obtained by an independent variable i is whether the coefficient of i is statistically significant and of plausible magnitude in economic terms. Given that the variables of concern, namely the first and second order patent counts, obtain statistically significant 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 the regression that includes quality weighted patent counts instead of the first and second order patent counts, and a maximum VIF of 3, I maintain that multicollinearity does not have an adverse impact on the statistical plausibility of my results. Thus, the only Gauss-Markov condition that is violated by 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 term On top of the Gauss-Markov conditions discussed above, one also assumes that the disturbance term is normally distributed with a mean of zero20 . However, as can be seen from Figure 3., the distribution of the disturbance term is clearly skewed to the left. Since hypothesis tests assume a normally distributed disturbance term, no statistically valid inferences can be drawn from this data unless the dependent variable is transformed. Thus, introducing a simple transformation into the dependent variable improves the model to an extent where the normality assumption can be reasonably assumed to hold. Bartlett (1953), for example, proposes the square root transformation as a correction for skewness. Moreover, when the variable is negative skewed, the skewness may be corrected by subtracting the variable from its maximum, which in this case is one, and then taking a square root. The new dependent variable is hence: 20 Zero 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 can be 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- 21 Recall 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 economic discount, I will henceforth refer to it as the economic acquisition discount to ease the interpretation of my results. 4.2.3. Acquirer announcement return For the acquirer’s announcement event CAR, the model specification is the following (subscripts are again suppressed for notational convenience): CAR = α + β1 × ln (Geographic distance) + β2 × Stock acquisition dummy+ (19) β3 × Acquisition discount + φT × Liquidity + γT ×Control Where,
    • 50 CAR is the aquisition announcement event CAR for the period t − 1 to t + 1 where t denotes the announcement 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 made using 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, industry IPO 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 to explain 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 that those firms are acquired at a discount, and ceteris paribus, the acquirer would be gaining the relative discount. To avoid such interpretations making this analysis debatable, I control for the effects of the 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 explain the announcement return. Following the order in Section 4.2.2., I begin with the Gauss-Markov conditions, and then move on to normality. Gauss-Markov conditions As in the case of the acquisition discount, also in the case of acquirer announcement return the disturbance term does not have a mean significantly different from zero (now, it is of the order x × 10−11 , where x < 10). The Breusch-Pagan test rejects the null hypothesis of equal variances in the error terms, and thus I correct 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 disturbance terms are not dependent of the observation, or year. Finally, the VIFs do not rise above 3.2, and thus multicollinearity, 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 term In the case of announcement returns, the data do not support the assumptions underlying hypothesis testing of ordinary least squares coefficients. More precisely, the assumption of normality of the disturbance term is violated, as one can see from Figure 7. below. The empirical distribution of the disturbance term appears to be heavily leptokurtic, which implies under-rejection of the null hypothesis, if one would assume the data to be normal. Hence, to arrive at 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 matrices MacKinnon and White (1985) show that the original heteroskedasticity-consistent covariance es- timator proposed by White (1980) may perform even worse than the conventional OLS-estimate in finite, heteroskedastic samples. They move on to study estimates for the covariance matrix that exhibit 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 larger than those experimented on by MacKinnon and White (1985), I utilize the HC3 covariance matrix estimator instead of the often used White (1980) covariance matrix estimator, or HC0 . The use of the HC3 does have a marginally negative effect on the statistical significance of my variables, but it is also superior in terms of error in the rejection probability, and thus preferable. Bootstrapping heteroskedastic models Liu (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 and fitted 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 to the size of the original sample. Then, new values for the dependent variable are generated by: ybi = yt + at ut εt∗ ˆ ¯ (20) Where, ybi is the bootstrapped observation yi y is the OLS-fitted value of the dependent variable ˆ ut is the OLS residual estimate ¯
    • 54 εt∗ is a random variable generated by some distribution generating process, or DGP ˆ at is a value obtained from the diagonal matrix Ω of the heteroskedasticity-consistent covariance- matrix estimator. More specifically to HC3 : 1 at = , 1 − ht −1 ht = Xt X T X XtT (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 t th 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 as possible. Flachaire (2005) discusses the use of the HC3 covariance matrix estimator and the use of the Rademacher distribution 1 with probability 0.5 F : εt∗ = (22) −1 with probability 0.5 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 bootstrap replications, and α is the statistical significance level of interest. Hence, for example, any value of B such that (B + 1) mod 10x = 0 for any integer values of x ≥ 2 satisfies this definition for all two decimal values α ≥ 0.01. The percentile t-method Hall (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 of a 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 also higher-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, whereas the percentile t-method utilizes the bootstrapped distribution of statistics to obtain percentiles for given 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∗ at level 1 − α is obtained from: β∗ − se(β∗ )t1−α/2 ; β∗ − se(β∗ )tα/2 ∗ ∗ (23) Where, β∗ − β0 t∗ = (24) se (β∗ ) 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∗ )tα |; 0] ∗ (25) As explained above, the significant difference between the percentile t-test and the student’s t-test is that the percentile t-test assumes that the obtained sample represents the underlying population distribution of the coefficients, while the student’s t-test assumes that the population distribution is asymptotically the student’s t-distribution, a symmetric bell-shaped distribution. Hence, statistical significance 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 same t-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). 22 That is, in the BC method, one would compute the acceleration constant as one sixth of the sum of the skewness- a measures for each subsample j, where observation j is left out, following the approximation given by Efron (1987). 23 In the latter case, though, the significance levels corresponding to each t-value will be very close to each other given 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 estimate this model, I utilize the logit specification. Denote the probability that a target has patents by π(x). Thus, the model specification is the following: π(x) log = α + β1 × ln (Geographic distance) + β2 × Different industries+ (26) 1 − π(x) β3 × Acquirer is an investor + γT ×Control 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 have different 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 country fixed-effects. I choose the logit model over the probit model to facilitate the possibility to include fixed effects following, for instance, Lehto and Lehtoranta (2004). Furthermore, given the normality issues described in relation to my other two specifications, there really is little reason to expect the data to be 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 the variance-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 averaging the standard errors across j subsamples taken from the data, where for each subsample j, the jth observation from the original data is dropped out. More formally, 1 N N (xi − x)2 ¯ se j = N ∑ ∑ (N − 2) √N (27) j=1 i=1 i= j
    • 57 5. Results In this section, I present the empirical results pertaining to the validity of my hypotheses. Section 5.1. begins by examining whether there are acquisition discounts and abnormal returns to stock acquirers in acquisitions of unlisted European high-technology targets. Then, in Section 5.2., I discuss the univariate and multivariate results regarding acquisition discounts. Section 5.3. shows the results on the selection model related to the probability that a target has patents. Finally, Section 5.4. provides the univariate and multivariate results on the announcement returns of acquirers of unlisted high-tech targets in Europe. Also, in Sections 5.2., and 5.3., I compliment my findings with responses from the questionnaire. 5.1. Acquisition discounts and abnormal stock acquirer returns - do they exist 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-tech targets (H2), and whether stock acquirers of unlisted European high-tech targets gain more than non-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 the acquisition 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 yielding support for H1. Moreover, I find a statistically significant added acquisition discount for high-tech targets of over 10%-points on top of that for non-high-tech targets, and thus find support my second hypothesis as well. Table 8. also shows that this added acquisition discount for high-tech targets persists 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 and non-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 EBIT multiple. Also, the difference between high-tech and non-high-tech targets is the highest across categories 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.
    • Table 8: T-test of difference in acquisition discount means between high-technology and non-high-technology targets. Mean discounts and the difference in mean discounts between unlisted high-technology and non-high-technology targets for subsidiary and stand-alone target subsamples, as well as the entire sample. Figures in parentheses are the t-values for the difference in means. For the respective means of the high-tech and non-high-tech subsamples of each subsample, the figures in parentheses represent t-values with respect to the null hypothesis that the mean discounts are zero. Figures in brackets represent the sample size for each group. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. Stand-alone targets Subsidiary targets All targets High-tech Non-high-tech Difference High-tech Non-high-tech Difference High-tech Non-high-tech Difference Deal value to book equity 0.224∗∗∗ 0.146∗∗∗ 0.078∗ 0.326∗∗∗ 0.295∗∗∗ 0.032 0.267∗∗∗ 0.218∗∗∗ 0.049∗ (5.17) (5.93) (1.56) (6.42) (11.58) (0.56) (8.08) (12.23) (1.30) [189] [494] [683] [138] [467] [605] [327] [961] [1288] Deal value to EBIT 0.448∗∗∗ 0.389∗∗∗ 0.059∗∗∗ 0.488∗∗∗ 0.369∗∗∗ 0.119∗∗∗ 0.464∗∗∗ 0.379∗∗∗ 0.085∗∗∗ (24.65) (25.06) (2.46) (23.48) (23.78) (4.60) (33.83) (34.54) (4.84) [601] [913] [1514] [402] [914] [1316] [1003] [1827] [2830] Deal value to Sales 0.411∗∗∗ 0.344∗∗∗ 0.066∗∗∗ 0.473∗∗∗ 0.326∗∗∗ 0.147∗∗∗ 0.438∗∗∗ 0.335∗∗∗ 0.104∗∗∗ 58 (21.91) (21.13) (2.67) (23.28) (22.29) (5.87) (31.75) (30.69) (5.89) [714] [1087] [1801] [568] [1185] [1753] [1282] [2272] [3554] Deal value to Net Income 0.520∗∗∗ 0.354∗∗∗ 0.166∗∗∗ 0.561∗∗∗ 0.415∗∗∗ 0.146∗∗∗ 0.535∗∗∗ 0.383∗∗∗ 0.153∗∗∗ (25.13) (18.60) (5.92) (20.97) (22.12) (4.48) (32.68) (28.51) (7.22) [470] [720] [1190] [274] [635] [909] [744] [1355] [2099] Mean acquisition discount 0.393∗∗∗ 0.298∗∗∗ 0.095∗∗∗ 0.449∗∗∗ 0.329∗∗∗ 0.120∗∗∗ 0.417∗∗∗ 0.313∗∗∗ 0.103∗∗∗ (27.87) (25.33) (5.20) (27.81) (30.26) (6.15) (39.16) (39.12) (7.76) [848] [1348] [2196] [629] [1346] [1975] [1477] [2694] [4171]
    • 59 Table 8. shows that both the existence and the level of the acquisition discount persist across subsamples 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 to book 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 for subsidiary targets across multiples, with the exception of deal value to net income, where it is higher for stand-alone targets. All in all, the results presented in Table 8. are both statistically and economically significant, and robust across categories and subsamples. Unreported tests also 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 of the 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, the acquisition discount of unlisted targets is not a phenomenon unique to the US, or to the specific dataset employed by Officer (2007). Furthermore, the difference in the acquisition discount means between high-tech and non-high-tech targets supports the hypothesis that a significant proportion 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 results in Table 9. are consistent with this hypothesis. More specifically, the difference between returns to 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 be explained by the small number (30) of stock bids made of subsidiary targets, and hence, limited sample 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-stock bidders. 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 that category is merely a product of a limited sample size. Consistently with the works of Shleifer and Vishny (2003), and Officer et al. (2009) among others, 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 significantly positive averaging 2.3%, and on average (also statistically significantly) 3%-points greater than those to stock bidders of listed targets.
    • 60 Table 9: T-test of difference in abnormal acquisition announcement return 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 in parentheses are the respective standard errors, for the difference, figures in parentheses are the t-values for the difference in means. Figures in brackets 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, one cannot 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 maintain that stock consideration is used as a monitoring mechanism in acquisitions of unlisted targets. Unreported results show some economical but no statistical difference between announcement returns to stock bidders of unlisted high-tech and non-high-tech targets. Thus, although it may be 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 consideration drives the acquisition announcement returns of stock bidders, I cannot verify that it does so more than in the case of non-high-tech targets. Hence, given my results, it is more likely that it 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 information asymmetry, 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 model related 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 the interpretation 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 of geographic 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 distance variable 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 preference for 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 predictions of theory. Even if managers were overconfident about their abilities to value deals at short distances, 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 the linearity in univariate coefficients. Figure 8. shows the impact of the natural logarithm of geographic distance on the transformed acquisition discount grouped into steps of 100km by distance. The impact corresponding to each 100km in distance is that of a subsample ending at that 100km threshold, and beginning at a 100km shorter distance. So, for example the impact of the natural logarithm of geographic distance is approximately 0.5 in the subsample of deals where the distance is between 300 and 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 relation between geographic distance and acquisition value. Namely, the relation is not linear even in the 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 distance between 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 between integer 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 positive value of 1.4. However, at such short distances, the trade-off is so small that the coefficient is more likely in large part a result of randomness and the use of univariate analysis. Moreover, it does not appear that from this analysis one could exhaustively deduce some threshold where a change in signs would occur. Grote and Umber (2007) do use a seemingly arbitrary threshold of 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 the appropriateness of 470km for my data. It turns out that in the multivariate regressions, 470km is exactly the correct threshold, in terms of statistical significance. When distance exceeds that threshold, 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 set forward 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 short distances between the acquirer and the target. However, I must further posit that the existence and 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 variables on the transformed acquisition discount grouped by whether the target is a stand-alone firm or a subsidiary, and also the results for the full sample. The expected signs are reversed from Table 1., 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 has a positive sign, and that the liquidity and illiquidity measures have an impact whose direction is expected 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 discount √ Univariate 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 are different, and zero otherwise. 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 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 , - 1.1510∗∗ 0.4896 0.8941∗∗ < 470 (2.28) (0.82) (2.32) ln (Geographic distance) × 10−2 , - 3.58 −3.5431 0.0482 ≥ 470 (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 ? −0.1237∗∗∗ −0.1114∗∗∗ −0.1157∗∗∗ (0/1) (−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 the coefficients. Also, the fact that the square of the patent count does not have the expected sign for stand-alone targets in a univariate framework is no indication of rejection of my hypothesis H6b. The square is 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 first and second powers of the patent count are analysed separately, and not in conjunction, their coefficients 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 significant difference 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 between 2000-2006) dummy across categories. The former suggests that the demand for liquidity in its purest 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 of their firm at a discount in exchange for cash consideration, the owners of subsidiaries are more prone to demand even a higher price if the acquirer pays with cash. This analysis implies that the internal capital markets within groups work well enough for them to prefer less liquid means of payment, whereas the owners of stand-alone firms, whether they are private individuals or professional 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 so fully, 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 the expected 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 some reasons 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 2006 unlisted 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 information asymmetry in the new millenium thereby valuing firms lower when there is less information available. 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 is encouraging 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 away targets 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 the null hypothesis was that acquisition discounts increase in the natural logarithm of distance, then I might be able to provide support for the notion that managerial overconfidence indeed does drive overvaluation in distance to targets below 470km away from the acquirer (given a large enough 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 significantly positive 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, and the 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 of patents. That is, beyond a certain number of patents (around 125 in this case24 ), the additional value of a patent is in fact decreasing. Although the analysis is very specific, my sample does include 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 deal value. While my analysis is very restricted to only the number and some quality dimensions of target patents, it does support the view of the respondents. Unreported regressions where the patenting variables are replaced by a dummy variable that obtains 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 provide some support for the hypothesis that the mere existence of patents assigned to the target has an effect on deal value. However, in the final multivariate framework, simply having patents is not enough. Their number, on the other hand, clearly does matter, as does their quality. Indeed, the t-value of the patenting dummy variable is almost small enough to provide support for the null hypothesis 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 more in-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 received by a patent has a significant impact on market value, in my analysis the value of citations is left to lesser statistical significance. Again, a more thorough analysis of the characteristics of the patents in my sample shows that there are very few citations overall in the sample. More specifically, my sample includes only a little over one citation per patent whereas that in Hall et al. (2005), for instance, includes almost eight citations per patent. Moreover, the citations in my sample are clearly clustered into portfolios that are overwhelmingly large in size given the relatively small size of the sampled companies. Thus, in conjunction with the above analysis of decreasing marginal impact of patents, one might posit that in this particular sample patents with citations are likely to be pooled together in a portfolio with lower quality patents, or simply in portfolios where the marginal impact of a patent is already fairly small. Furthermore, given the lag in citations with respect to the patent grant, they are probably the hardest to access, least 24 From 2.319 the full sample regression, I obtain y = 0.002319x − 0.00001854x2 → ∂y ∂x = 0 ⇐⇒ x = 0.01854 ≈ 125
    • 67 likely, and most volatile source of additional information on the potential targets, and thus they may not mitigate information asymmetry at all. Indeed, Toivanen and Väänänen (2008) find that the real returns to inventors25 of patents only occur at a lag with respect to the patent grant when 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 additional information regarding the fundamental profitability of the target to which they are assigned in this particular sample. Finally, the result that citations and references are unimportant with respect to patent value is consistent with the survey responses, where the former obtained an average of 3.33, and the latter 2.90 in importance to patent value on a scale from 1 to 5, where 5 = very important. Moreover, the responses confirm the finding that scope and family size are extremely important. While the questionnaire responses clearly refer to the size of the patent family linked directly via a priority document, the EPO databases include the INPADOC family, where the members may be linked to the patent in question also indirectly. An encouraging finding in terms of future analysis is that the size of the INPADOC patent family, a variable not to my knowledge previously used in assessing the impact of patents on firm value, does bear very high statistical and economical significance. Thus, it is not only the patents that are directly linked with the original 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 that of the number of citations is easily explained by the fact that in looking at the information value of specific characteristics, only a priori information is relevant. Thus, the information value of all citations received after the acquisition announcement is by default zero. After the INPADOC family size, the scope-weighted patent count obtains the most significant coefficient, while the coefficient of references is equally negligible as that of citations. In addition to the information asymmetry explanation, Table 11. shows results consistent with my 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 acquisition discount, and thus a decrease in the value of the target. Also, when acquisition activity is above its time series median, the acquisition discounts are lower, indicating an increased demand, and thus 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 these firms. More strikingly, the sign of the IPO market effect is unexpected, and thus would indicate that acquisition discounts are higher when there are more IPOs than on average. An analysis of the correlations between explanatory variables provides no explanation for this. Moreover, in an unreported 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 25 Recall 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, the reduction in sample size and the fact that the HC3 covariance matrix is unobtainable makes the analysis dubious enough to warrant its exclusion from the reported results. Hence, the impact of the IPO market heat requires further inspection. Also, the sign and significance of the cash deal dummy are consistent with the interpretation that deals involving unlisted high-tech targets are 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 acquirer market 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 have a significant impact on the qualitative results, but it does reduce the sample size so much as to warrant its exclusion from the final reported analysis. Unreported results also show that toehold ownership does not have a statistically significant impact on the transaction value. It obtains a negative sign, but also |t| < 0.5, and thus the null hypothesis of no impact would not be rejected. Furthermore, the inclusion of toehold ownership does not have an impact on the qualitative results, nor does it increase the explanatory power of my tests. Hence, I omit it from the final analysis. Also, whether the bid was challenged or not has no statistical influence on the quantitative results, nor does it thus affect the qualitative results. Moreover, including the challenged bid dummy only decreases the explanatory power of my tests, and hence it is omitted. A study of the fixed-effects dummies in the full sample regressions indicates that the acquisition discounts 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 strong statistical significance of the Luxembourg dummy may be rather a result of randomness than that of Luxembourgian companies having significantly lower valuations than average European companies. Moreover, unlisted targets operating in SICs 37, 48, 73, and 8727 have a statistically significantly higher acquisition discount than firms operating in other high-tech industries. The exclusion or inclusion of country and industry fixed-effects has no impact on the qualitative results of my analysis. Finally, provided that I utilize time-varying explanatory variables, such as 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 the millenium, the results of which are explained earlier in this section. 26 Recall 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. 27 Transportation Equipment; Communications; Business Services; and Engineering, Accounting, Research, Management, and Related Services; respectively
    • Table 11: Determinants of the acquisition discount. √ Horse race regressions of patenting and non-patenting variables, as specified in Section 3.2. on the modified acquisition discount, or D∗ = 1 − D, as specified in Section 4.2.2. of unlisted European firms in technology-intensive industries. Models 1, and 2 are horse-races between the patenting variables using also the liquidity measures used by Officer (2007). Expected signs are next to the independent variables. d in the two rightmost columns refers to the distance between acquirer and target headquarters. Both of the rightmost models incorporate model 2. The numbers in parentheses are t-statistics. Standard errors are heteroskedasticity-consistent according to MacKinnon and White (1985). *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. Stand-alone targets Subsidiary targets Full sample Target-acquirer distance Independent variable Expected sign 1 2 1 2 1 2 d < 470km d ≥ 470km Intercept 0.8587∗∗∗ 0.8579∗∗∗ 0.6246∗∗∗ 0.6202∗∗∗ 0.7866∗∗∗ 0.7846∗∗∗ 0.8270∗∗∗ 1.1463∗∗∗ (13.85) (13.88) (9.15) (9.00) (17.70) (17.57) (16.84) (2.98) Patent count ∗ 10−2 + 0.1599 0.2356∗ 0.2319∗∗ (0.57) (1.52) (1.93) Patent count2 ∗ 10−4 - −0.1185 −0.1781∗ −0.1854∗∗ (−0.30) (−1.63) (−2.05) Quality-weighted patents ∗ 10−2 + 0.0286 0.0601 0.0313∗∗∗ 0.0176 0.3188∗ (0.86) (0.59) (2.51) (0.17) (1.54) ln (Geographic distance) - 0.0053 0.0053 0.0052 0.0056 0.0058∗ 0.0060∗ 0.0035 −0.0738∗ 69 (1.22) (1.21) (0.98) (1.07) (1.76) (1.84) (0.92) (−1.35) M&A-activity (0/1) + 0.1185∗∗∗ 0.1229∗∗∗ 0.0997∗∗∗ 0.0994∗∗∗ 0.1131∗∗∗ 0.1154∗∗∗ 0.0967∗∗∗ 0.2201∗∗∗ (3.04) (3.15) (2.43) (2.41) (4.11) (4.18) (3.17) (2.94) Deal size > $20m + 0.1447∗∗∗ 0.1443∗∗∗ 0.1503∗∗∗ 0.1509∗∗∗ 0.1427∗∗∗ 0.1429∗∗∗ 0.1505∗∗∗ 0.0981∗∗∗ (7.55) (7.57) (6.69) (6.68) (10.10) (10.10) (9.37) (2.66) IPO volume + −0.0174 −0.0179 0.0019 0.0012 −0.0093 −0.0102 −0.0178∗ 0.0309 (−1.20) (−1.24) (0.12) (0.08) (−0.90) (−0.98) (−1.53) (1.14) Baa spread - −0.0286∗∗∗ −0.0287∗∗∗ −0.0039 −0.0038 −0.0183∗∗∗ −0.0184∗∗∗ −0.0221∗∗∗ −0.0105 (−3.81) (−3.85) (−0.42) (−0.41) (−3.22) (−3.23) (−3.56) (−0.65) Deal made between 2000-2006 (0/1) ? −0.1223∗∗∗ −0.1211∗∗∗ −0.1368∗∗∗ −0.1351∗∗∗ −0.1298∗∗∗ −0.1283∗∗∗ −0.1221∗∗∗ −0.1673∗∗∗ (−5.67) (−5.67) (−5.21) (−5.14) (−7.98) (−7.91) (−6.57) (−4.58) Different industries (0/1) ? 0.0065 0.0063 0.0252 0.0281 0.0156 0.0166 0.0162 0.0312 (0.35) (0.34) (1.12) (1.24) (1.12) (1.19) (1.03) (0.82) Subsidiary (0/1) - −0.0752∗∗∗ −0.0746∗∗∗ −0.0837∗∗∗ −0.0292 (−5.27) (−5.23) (−5.15) (−0.83) Cash deal - −0.0407∗∗ −0.0399∗∗ 0.013 0.0218 −0.0175 −0.0161 −0.0146 −0.0489∗ (−2.18) (−2.14) (0.54) (0.49) (−1.23) (−1.13) (−0.92) (−1.40) Year fixed-effects No No No No No No No No Industry fixed-effects Yes Yes Yes Yes Yes Yes Yes Yes Country fixed-effects Yes Yes Yes Yes Yes Yes Yes Yes R2 0.22 0.23 0.21 0.21 0.21 0.20 0.22 0.26 N 848 848 629 629 1477 1477 1196 281
    • 70 Given that apart from the IPO volume, all of the coefficients obtain the same, expected sign across all subsamples, and given the analysis of correlations in Section 4.1., and that of the variance inflation factors in Section 4.2., my data does not present any apparent symptoms of multicollinearity. Moreover, as explained above, my results are robust against controlling for some 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 assumptions of OLS-regression, nor does it violate the normality assumption, and hence the coefficients are BLUE and the related hypothesis tests valid. Table 12: Marginal effects on the acquisition discount Marginal 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 for the 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 natural logarithm of geographic distance are derived from the regressions that are categorized according to the target-acquirer distance. Moreover, the effects of ln (Geographic distance), d < 470 are derived from the sample where the distance between target and acquirer headquarters is less than 470km, and the effects of ln (Geographic distance), d ≥ 470 from the sample where that distance is greater than or equal to 470km. −0.75 D =√ −0.5 D =√ −0.25 D =√ Independent variable Expected sign D∗ = 1.75 D∗ = 1.5 D∗ = 1.25 Intercept ? −2.0759 −1.9219 −1.7545 Patent count ∗ 10−2 -,+ −61.36 + 0.49x −56.81 + 0.45x −51.86 + 0.41x Quality-weighted patents ∗ 10−2 - −0.0827 −0.0765 −0.0699 ln (Geographic distance) , d < 470km - −0.0092 −0.0085 −0.0077 ln (Geographic distance) , d ≥ 470km + 0.1953 0.1808 0.1651 M&A-activity (0/1) - −0.3052 −0.2826 −0.2579 Deal size > $20m - −0.3782 −0.3502 −0.3196 IPO volume + 0.0269 0.0249 0.0227 Baa spread - 0.0487 0.0451 0.0412 Deal made between 2000-2006 (0/1) ? 0.3393 0.3142 0.2868 Different industries (0/1) ? −0.0440 −0.0407 −0.0372 Subsidiary (0/1) + 0.1975 0.1828 0.1669 Cash deal + 0.0426 0.0394 0.036 D=0 √ D = 0.25 √ D = 0.5 √ D = 0.75 √ Independent variable D∗ = 1 D∗ = 0.75 D∗ = 0.5 D∗ = 0.25 Intercept −1.5692 −1.3590 −1.1096 −0.7846 Patent count ∗ 10−2 −46.39 + 0.37x −40.17 + 0.32x −32.8 + 0.26x −23.19 + 0.19x Quality-weighted patents ∗ 10−2 −0.0625 −0.0541 −0.0442 −0.0313 ln (Geographic distance) −0.0069 −0.0060 −0.0049 −0.0035 ln (Geographic distance) 0.1477 0.1279 0.1044 0.0738 M&A-activity (0/1) −0.2307 −0.1998 −0.1631 −0.1154 Deal size > $20m −0.2859 −0.2476 −0.2022 −0.1429 IPO volume 0.0203 0.0176 0.0144 0.0102 Baa spread 0.0368 0.0319 0.0261 0.0184 Deal made between 2000-2006 (0/1) 0.2565 0.2222 0.1814 0.1283 Different industries (0/1) −0.0332 −0.0288 −0.0235 −0.0166 Subsidiary (0/1) 0.1493 0.1293 0.1056 0.0746 Cash 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 the transformation. More specifically, the statistical significance of the composite quality-weighted patent 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 some cases the use of the untransformed acquisition discount would have led to over-rejection of the null 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, the statistical significance is no longer overstated. To ease the interpretation of my results in Table 11., I calculate the marginal impacts of each explanatory 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. More specifically, consistently with for example Lehto (2006), Böckerman and Lehto (2006), and Ali-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 asymmetry increases in one dimension. Given previous empirical evidence, it is no surprise that the propensity of a target having patents increases 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 about the finer points of the business model than an acquirer operating in an industry that is close or similar 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 toehold dummy coefficient are supportive of my hypothesis H8. Namely, the motivation between the toehold-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 Section 2.4. Furthermore, the survey respondents clearly view that patents are an important tool in
    • 72 Table 13: What determines the probability of a target having patents? Logit-regression of explanatory variables on the patenting dummy variable of unlisted European technology-intensive targets. Standard errors are jackknifed standard errors, as explained in Section 4.2.6. *, **, and *** denote statistical 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 Yes Country fixed-effects Yes Industry fixed-effects Yes Pseudo-R2 0.13 Log-likelihood −641.49 N 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 have patents than other targets. While the high patenting rate in targets based in Luxembourg and the Czech 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 is an actual difference. Whether these differences are due to increased (or decreased) information asymmetry due to regulatory environments or merely an indicator of differences in patenting behavior 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 inclusion of country and industry fixed-effects has no impact on the qualitative results of my analysis. 28 Communications; 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 hypothesis tests 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 returns with a univariate analysis of its determinants in Section 5.4.1. I then move on to the multivariate analysis 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 multiplied by 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 univariate analysis. The former indicates that while information asymmetry increases with geographic distance, the market perceives this, and values acquisition announcements accordingly. The market 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, and thus merits a relatively higher valuation for the acquisition. The sign of the acquirer asset-scaled patent count provides preliminary support for the notion stemming from the hubris hypothesis (Roll, 1986) that while patents merit a higher valuation for the 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 accurately determined 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 seems to be unable to reject the null hypothesis related to H12. Thus, in a univariate setting, while the 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 hypothesis needs 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 acquirer price-to-book ratio, all obtain the expected signs, and their inclusion increases the explanatory power of the multivariate tests.
    • 74 Table 14: Univariate results for the announcement return Univariate OLS-regressions of the explanatory variables on the acquisition announcement returns of acquirers of unlisted European firms in technology-intensive industries. Explanatory variables are 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 acquisitions after the turn of the millenium significantly more generously than prior to it. Also, the results indicate 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, while the impact of non-horizontality is value increasing for target shareholders, it seems to increase value to acquirer shareholders only if the target is not a subsidiary. However, these results also need to be analyzed more carefully in light of the multivariate framework, since they are not statistically significant in the univariate setting.
    • 75 5.4.2. Multivariate results Table 15. reports the multivariate results pertaining to the acquirer announcement return. Given the unintuitive interpretation of the bootstrapped percentile t-values as discussed in Section 4.2.5., I report the corresponding p-values instead. The multivariate analysis yields support for my 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 (meriting a 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 small sample size, especially given that the sign of impact is the same, and that the p-value in the full sample regression is very close to that of the stand-alone target subsample. My hypothesis H3 regarding the increased announcement returns of stock acquirers is further supported 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 the multivariate 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 an unexpected negative one, which further refutes H3 with respect to unlisted high-tech subsidiary targets. Hence, it appears that the market does not perceive the use of stock consideration as a significant monitoring method when the target is a subsidiary. Moreover, this finding suggests that there is more information available of subsidiaries than of stand-alone targets, and hence the signaling effect of the use of stock consideration bears at least as much significance than its use as a monitoring mechanism. The combination of univariate and multivariate analyses supports my hypothesis H11, whereby the market rewards acquisitions of unlisted high-tech targets with a more positive return when those 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 statistically significant 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 the low statistical significance in the subsidiary subsample is at least partly due to small sample size. 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. The coefficients 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 the 5%-level. More curiously, though, the effect in the subsidiary target subsample is over twice as 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 multiplied by 10−2 (or alternatively, their coefficients by 102 ), except where otherwise indicated. The numbers in 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 Section 4.2.5. *’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 5.3483 0.08 0.07 0.01 ln (Geographic distance) - −0.4432 −0.3060 −0.3306 0.01 0.03 0.005 Stock acquisition, toehold (0/1) + 10.9244 0.5582 4.8734 0.00 0.18 0.05 Stock acquisition, no toehold (0/1) + 1.3315 −0.4392 1.0447 0.02 0.60 0.03 ln (Deal size) + 0.4192 0.4184 0.4444 0.04 0.05 0.01 Patent count/ln (Total Assets) ∗ 10−3 +/- −3.4284 −2.9445 −2.9209 0.22 0.23 0.06 ln(Sales) +/- −0.4794 −0.3592 −0.4362 0.02 0.16 0.01 Price-to-book ∗ 10−3 + 0.1119 0.3691 0.1196 0.10 0.09 0.08 M&A activity (0/1) + 2.2227 0.3271 1.2603 0.03 0.22 0.06 IPO volume - −0.1855 −0.4733 −0.2236 0.04 0.04 0.02 Baa spread - −0.1616 −0.5255 −0.2501 0.11 0.04 0.04 Acquisition discount + 0.5425 0.8166 0.9054 0.13 0.13 0.04 Deal made between 2000-2006 (0/1) ? 0.8330 1.1601 0.9125 0.13 0.16 0.07 Different industries (0/1) +/- 0.9468 −0.7124 0.4197 0.08 0.23 0.22 Tender offer (0/1), acquirer q < 1 + 6.5104 5.9367 0.03 0.01 Tender offer (0/1), acquirer q ≥ 1 - −1.2423 0.9028 −0.7363 0.13 0.17 0.15 Challenged bid (0/1) - −7.2512 −8.0876 0.06 0.03 Toehold, cash acquisition (0/1) +/- 1.0553 −4.6475 −1.5733 0.28 0.02 0.13 Year fixed-effects No No No Industry fixed-effects Yes Yes Yes Country fixed-effects Yes Yes Yes R2 0.11 0.23 0.11 N 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-tech subsidiary targets significantly more than acquisitions of stand-alone high-tech targets during times of high IPO activity in the industry. However, the difference in the coefficient means is by 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 market activity must be left for future study. As in the univariate regressions, the target patent count weighted by the log of acquirer assets obtains 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 indeed do 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 the acquirer 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 the multivariate framework. As in the univariate setting, the control variables obtain the expected signs. Moreover, their impacts are statistically significantly different from zero in the full sample regressions. My selection of control variables does divert slightly from the extant literature, mainly because the variables used in my regressions provide a better empirical fit than the ones suggested. More specifically, I use the natural logarithm of deal size instead of relative deal size, and the natural logarithm of sales instead of that of market value. While relative deal size and the natural logarithm of market value did obtain the same signs as my control variables, the explanatory power 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 of Jensen (1986), the market punishes larger acquirers statistically significantly more than smaller ones. That is, a unit increase in the natural logarithm of sales measured in $m is punished by a statistically significant loss close to 0.5% in market value. Furthermore, consistently with the theoretical expectations, acquirers with higher price-to-book ratios do earn a statistically significant added return. However, that increase is economically negligible, 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, tender offer 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 than one. Moreover, in economic terms, the gain to low-q acquirers is highly significant, while the loss to high-q acquirers is as well, but not equally so. A tender offer bidder with a low q gains more than 5% in market value, while a bidder with a high q loses slightly over 1%. Finally, the market 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 Italian firms are punished by a statistically significant loss in market value of approximately 2 and 4 percent, respectively. Moreover, acquisitions of Portuguese and Russian targets are rewarded by statistically significant respective approximate price run-ups of 7 and 5 percent. Other country fixed-effects variables do not obtain statistically significant coefficients. Finally, the industry dummy 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 to 1%29 . Other industry fixed-effects remain statistically insignificant. The exclusion or inclusion of 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. As discussed in Section 4.2.5., and given the significant number of bootstrap simulations run, the mean 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 with the 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 on previous literature. By employing the wild bootstrap, I obtain more robust results where the null 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 than the asymptotic normality assumption, given the discussion in Section 4.2.4. Also, the data does not suffer from such multicollinearity that would invalidate my results. Finally, as my results are robust controlling for a multitude of acquirer announcement return determinants utilized in previous 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 Section 6.1. Finally, in Section 6.2., I discuss the results and potential conclusions together with possible avenues for future research as well as answer my research problem. 29 Transportation 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 hypotheses specified in Section 3.1. Importantly, my data provides statistical support for almost all of my hypotheses. The sole exception is H5, which states that the simple existence of patents decreases the acquisition discount of unlisted European high-tech targets. Unreported results show very low t-values for the mean coefficient of the patenting dummy. However, as the rest of the patenting hypotheses do hold, it is obvious that the effect does exist, but that it is not exclusively 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 information asymmetry explanation explored by Officer et al. (2009), I find support for H2. Namely, that information asymmetry is greater in targets that are more difficult to value, at least in terms of the acquisition discount. Consistently with for instance Uysal et al. (2008), I find support for H4b. Namely, the returns to acquirers decrease in geographic distance between acquirers and targets. Unlike in the case of deal valuation, this effect is constant across short and long distances. I also find partial support for H4a. More specifically, at sufficiently long distances, the acquisition discount is increasing 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 discount is of special interest, and has to my knowledge only been previously explored in a working paper by Grote and Umber (2007). I can verify that there is a difference between close proximity transactions, 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 increase in the log of geographic distance. Moreover, the notion that the discount would first decrease in the log of distance is not statistically significantly consistent with my data. However, the data do show that the coefficient of geographic distance is not likely positive in short distance transactions. Finally, albeit also used by Grote and Umber (2007), the threshold of 470km seems arbitrary, and will require further validation from future studies.
    • Table 16: Hypotheses and empirical evidence. Hypotheses, as described in Section 3.1., and whether they are supported by empirical evidence described in Section 5. Column three spec- ifies the model with which the hypotheses are tested, the fourth column specifies the variables whose coefficients or means are used to test the hypotheses, and the final column specifies whether the hypothesis is supported to a statistically significant extent by the empirical results. Hypothesis Description Model Variables Support H1 The acquisition discount of unlisted targets persists across a data set of Euro- t-test; acquisition discounts acquisition discount Yes pean firms. H2 The acquisition discount is more prevalent in technology-intensive industries. t-test; acquisition discounts acquisition discount Yes H3 The acquisition announcement returns to acquirers of unlisted targets in t-test and Bootstrap OLS; CAR Yes technology-intensive industries are, ceteris paribus, higher for stock-swap CAR transactions. H4a The acquisition discount of unlisted targets increases with the geographical OLS; acquisition discount ln (Geographic distance) ≥ 470 Partial distance between the target and acquirer headquarters. H4b The bidder acquisition announcement return reduces in the natural logarithm Bootstrap OLS; CAR ln (Geographic distance) Yes of the geographic distance between acquirer and target headquarters. H5 The existence of patents assigned to the target reduces the acquisition discount OLS; acquisition discount, Patenting (0/1) No of unlisted high-technology firms. unreported H6a The number of patents assigned to the target reduces the acquisition discount OLS; acquisition discount Patent count Yes 80 of unlisted high-technology firms. H6b The marginal information value of patents is decreasing in the number of OLS; acquisition discount Patent count2 Yes patents assigned to the target. H7 The likelihood of a target having patents increases with the geographical dis- logit; patenting probability ln (Geographic distance) Yes tance between the target and the acquirer, and other factors contributing to information asymmetry. H8 A pre-acquisition toehold in the target increases the probability that the target logit; patenting probability Toehold (0/1) Yes has patents. H9 The quality of the patents assigned to the target, as measured by citations, OLS; acquisition discount Quality-weighted patents Yes references, scope, and the size of the INPADOC patent family, reduces the information asymmetries related to acquisitions of unlisted high-technology firms. H10 Easy access to alternate sources of liquidity at the time of the acquisition re- OLS; acquisition discount M&A-activity, Aggregate IPO vol- Yes duces the acquisition discount. ume, Baa spread H11 High M&A-activity at the time of the acquisition increases the acquirer an- Bootstrap OLS; CAR M&A-activity Yes nouncement return. H12 High IPO-activity at the time of the acquisition in the industry of the target Bootstrap OLS; CAR Industry IPO volume Yes decreases the acquirer announcement return.
    • 81 Not unlike studies by Hall et al. (2005, 2007), and Hussinger and Grimpe (2007), I find that the 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 has previously explored this explanation. I, however, do find that the marginal value of patents is decreasing 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 hubris hypothesis of M&A deals by Roll (1986), I find that while patents merit higher valuations for the target, their inclusion in the deal is value destructive for acquirer shareholders, albeit only to an economically minor extent. The responses from the questionnaire indicate that the most important sources of patent value are 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. The results also indicate that patents owned by the firm itself are more relevant to valuation than those owned by a competitor, while it may be in some instances that a competitor holds such a patent that disables the firm from operating in it’s core business area. Finally, a patent that generates revenue (through either licensing or through the product to which it relates) is more important 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 for my 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 prefer to 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 notion that 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 obtaining liquidity from other sources becomes increasingly expensive, the acquisition valuations adjust for this decreased opportunity cost. That is, when target shareholders find it difficult to obtain liquidity 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 supports hypothesis H11. More specifically, the data supports the notion that during peak times of M&A activity, 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 when M&A activity is higher than the time series median. Finally, the data is also consistent with my hypothesis H12. More precisely, when the IPO market is heated, an acquisition of an unlisted high-tech target generates less wealth than during a period of cold IPO markets. Thus, I find support for the notion that if a target opts for M&A instead of an IPO when the demand for IPOs is higher, the stock market punishes the acquirer for partaking in the deal. Moreover, the market perceives that the target is likely to have been a low quality IPO, and thus also a low quality target. Thus, becoming acquired during a time of a hot 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 Sections 5., 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 acquisition discount for unlisted European targets is of similar magnitude as that for unlisted US targets. 2. The acquisition discount is more pronounced for unlisted high-tech targets than for their peers in non-high-tech industries. However, I am unable to verify that the same applies to the abnormal stock acquirer announcement return. 3. The above disparities are clearly fueled by both information asymmetry and the target shareholders’ need for liquidity. Hence, the answer to the first and third parts is an unambiguous ’yes’, while the answer to the second 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 the threshold and the sign of the coefficient both require further research. Moreover, the theoretical rationale behind the non-linearity requires further work. One of the most promising avenues of future research that can be followed in light of my results is the impact of the INPADOC family size on the value of patents. As the EPO databases include also 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 family size 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 different quality dimensions of patents should prove to be very interesting. More specifically, exploring potential means to proxy for the difficulty to invent around a patent, and the importance of a patent for current of future technology, as well as the relatedness of the patent to both the firm’s and 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 a similar study as this one on the cross-section of European firms, with perhaps a less of a view on 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 in market 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 through M&A transactions will have to do so at an increased discount. Thus, value is destroyed from both the acquirer and the target. This avenue should prove to be extremely fruitful in terms of future 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 as my data allows. My data supports the notion that the economic rents resulting from innovation are attributable to the innovator. From the society’s point of view, this is the optimal allocation of wealth in M&A transactions. More specifically, as my findings suggest that the innovating firm 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 of assets measured in $m. Hence, the total gain from acquisitions involving patents in the target is positive. Furthermore, my results imply that there is a threshold above which the marginal patent has a negative value-contribution to the firm. While I do not argue that a threshold of 125 patents is valid for small and large corporations alike, it does give a hint on the relative number of patents beneficial to the firm, when compared to, for instance, the average transaction value of $54m in my sample. Furthermore, my results indicate that patent quality does matter. Hence, managing the patent portfolio and patenting strategy are clearly wealth generating activities. 30 Utilizing 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 averaging 41.7%. This discount is negatively (and thus deal value positively) affected by the general availability 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 avoid periods of high IPO activity, and be attracted to periods of high M&A activity and low corporate loan spreads to maximize shareholder wealth.
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    • 90 A. EPO global patent data coverage Table 17: Jurisdictions covered in the EPO Worldwide patent database, and their abbreviations Code Jurisdiction Code Jurisdiction AL Albania LI Liechtenstein AP ARIPO LT Lithuania AR Argentina LU Luxembourg AT Austria LV Latvia AU Australia MA Morocco BA Bosnia and Herzegovina MC Monaco BE Belgium MD Moldova BG Bulgaria ME Republic of Montenegro BR Brazil MK Former Yugoslav Republic of Macedonia CA Canada MN Mongolia CH Switzerland MT Malta CL Chile MW Malawi CN China MX Mexico CR Costa Rica MY Malaysia CS Czechoslovakia NI Nicaragua CU Cuba NL Netherlands CY Cyprus NO Norway CZ Czech Republic NZ New Zealand DD German Democratic Republic OA OAPI DE Germany PA Panama DK Denmark PE Peru DZ Algeria PH The Philippines EA Eurasia PL Poland EC Ecuador PT Portugal EE Estonia RO Romania EG Egypt RS Republic of Serbia EP European Patent Office RU Russia ES Spain SE Sweden FI Finland SG Singapore FR France SI Slovenia GB Great Britain SK Slovakia GC Gulf Cooperation Council SM San Marino GE Georgia SU Soviet Union GR Greece SV El Salvador HK Hong Kong S.A.R TJ Tajikistan HR Croatia TR Turkey HU Hungary TW Taiwan ID Indonesia UA Ukraine IE Ireland US United States of America IL Israel UY Uruguay IN India VN Vietnam IS Iceland WO World Intellectual Property Organization IT Italy YU Former Serbia and Montenegro JP Japan ZA South Africa KE Kenya ZM Zambia KR Korea (South) ZW Zimbabwe
    • 91 B. Formulae and derivations Haversine formula. The haversine of the relation of a distance between two coordinate points and the radius of the Earth (or more generally, the relation of the distance between two points on a sphere and the radius of that sphere) can be expressed as follows: d haversin = haversin (∆φ) + cos (φ1 ) cos (φ2 ) haversin (∆λ) (28) R 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 d d = R ∗ haversin−1 haversin = 2R ∗ arcsin haversin R R = 2R ∗ arcsin haversin (∆φ) + cos (φ1 ) cos (φ2 ) haversin (∆λ) (29) Recalling that haversin (θ) = sin2 θ 2 for any angle θ, we get: ∆φ ∆λ d = 2R ∗ arcsin sin2 + cos (φ1 ) cos (φ2 ) sin2 (30) 2 2
    • 92 Marginal effects The marginal effects on the acquisition discount are calculated by taking partial 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 that √ √ D ∈ [−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 dD ∂D∗ = ∂xi dD∗ ∂xi ∂D∗ = −2 (D∗ (X)) (32) ∂xi That is, the marginal impact of any variable xi on the acquisition discount depends on the level of the modified acquisition discount and the marginal impact of that variable on the modified acquisition 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 39 Finnish 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 in intellectual property management worldwide. Those groups totaled 38 respondents.
    • 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= 5= 2 3 4 Unimportant 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 5= No little or no 2 3 4 Very strong opinion impact impact 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= 5= Completely 2 3 4 Completely disagree 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= 5= Completely 2 3 4 Completely disagree 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)]? First-stage Second-stage Seed financing financing Non-exit M&A Exit Patents owned by the firm itself Competitors’ patents Page 1 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: Generating revenues by Excluding others from Obtaining the freedom to Protected technology relates to: licensing the technology utilizing the technology 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! Page 2 of 2
    • 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-IV Means and standard deviations of responses to "How much do you agree with the following statements?" Statement Mean Standard deviation My peers take patent portfolio risks and value into account when assessing M&A- 3.53 0.78 deals I take patent portfolio risks and value into account when assessing M&A-deals 4.38 0.73 Technology firms with patents are easier to value than firms with no patents operating 3.23 1.27 in the same industry A geographically distant technology firm with patents is a more feasible target than 3.58 1.10 one with no patents Table 19: Means and standard deviations of responses to part V Means (standard deviations) of responses to the question "How much 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?" 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 VI Means (standard deviations) of responses to: "How strong of an impact do patents in the following categories have on the value of a firm in an M&A-transaction?" CURRENT PRODUCTS FUTURE UNRELATED PRODUCTS IN R&D INDUSTRY MARKETS POTENTIAL The patent is used to generate 4.67 4.12 3.98 3.45 licensing revenues (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 the 4.24 3.81 3.67 2.60 freedom to operate in the market (0.83) (1.25) (1.24) (1.33)