High-tech acquisitions, firm specific characteristics and the role of investment bank advisors

Journal of High Technology Management Research 16 (2005) 101 – 120

High-tech acquisitions, firm specific characteristics and the role of investment bank advisors Georgina Benou a, Jeff Madura b,T a

BHD Bank S.A, Dominican Republic Florida Atlantic University, United States

b

Available online 25 July 2005

Abstract The valuation effects of US acquirers of high-tech targets are dependent on factors specific to the acquirers or targets. Specifically, acquisitions of public high-tech targets yield negative valuation effects on average, while acquisitions of private high-tech targets yield favorable valuation effects. When acquisitions of public high tech targets are examined separately, we find that deals advised by top-tier banks elicit a more favorable share price response than those advised by either mid or third-tier banks. Acquisitions of private high-tech targets that are certified by an investment bank of any tier experience the most favorable announcement effects. Furthermore, the valuation effects are more favorable when the target has less intangible assets and when the targets receive more media exposure prior to the acquisition announcement. D 2005 Elsevier Inc. All rights reserved. Keywords: High-tech acquisitions; Underwriter reputation; Investment bank advising

1. Introduction Recent years have been marked by an unprecedented pace in the rate of technological change and innovation which, in turn, has forced companies to manage their assets aggressively. Since 1990, there has been a substantial increase in mergers and acquisitions (M&A) of high-tech companies. Most of these acquisitions involved the takeover of small, relatively young start-up companies and were motivated by the acquirers’ need to obtain highly developed technical expertise and T Corresponding author. E-mail address: [email protected] (J. Madura). 1047-8310/$ - see front matter D 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.hitech.2005.06.006

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capabilities. As was stated by Rauft and Lord (2000): bAcquiring firms may not have the ability to develop these valuable knowledge-based resources internally or, alternatively, internal development may take too long or be too costlyQ. Kohers and Kohers (2000) state: bThe high-growth nature of technology-based industries distinguishes them from other types of industries. In addition to their high-growth potential, however, another distinctive feature of high-tech industries is the inherent uncertainty associated with companies whose values rely on future outcomes or developments in unproven, uncharted fieldsQ [p. 40]. In fact, many bpureQ technology stocks are young companies, underfunded and without prospects for generating any cash flows in the near future. Nevertheless, despite the inherent uncertainty of high-tech industries, investors seemed to disregard most equity fundamentals when valuing technology stocks, especially during the market upturn in the late 1990s. As a result, even though high-tech stocks were in general extremely volatile, many of them were trading at remarkable premiums. The exploding rate of growth of M&A activity in high-tech industries can be partly attributed to those overly optimistic valuations (see Puranam, 2001). Many of these acquisitions were also motivated by the acquirer’s need to obtain critical technologies and expertise in order to quickly enhance their own technological competence. According to the McKinsey Quarterly Report (2002) (Frick & Torres, 2002), btransactions and consolidations can often fill holes in a product line, open new markets, and create new capabilities in less time than it would take to build businesses internally. Such moves may be prerequisites to achieving a dominant position—the best assurance for survivalQ. The burst of the tech bubble has lead to lower overall valuations, making more targets affordable. Given the volatile environment in which high-tech stocks operate, as well as the recent developments in the M&A marketplace, the use of investment bank advisors in such deals should be especially valuable. Specifically, if top-tier investment banks have valuable experience and specialized insight to offer, then high-tech companies using them as advisors in their M&A deals should be able to create shareholder value. Although many acquirers still choose to identify a potential target on their own, investment banks may be able to conduct the search for acquisition candidates more efficiently than the firm itself. Superior expertise in the market for acquisitions and lower search costs due to economies of scale are two of the most common cited reasons. Furthermore, an experienced investment bank, whether representing the bidder or target, should be able to negotiate the offer at more favorable terms, increasing shareholder gains. The investment bank’s role in mergers and acquisitions is controversial, partly because the contingent fee payments to the investment bank contracts may force the investment bank to focus only on completing the deal. McLaughlin (1990, 1992) examined the nature of investment-banking contracts in tender offers. He indicated that most fee contracts are substantially contingent on offer outcome, offering an average of $7.96 million for a completed acquisition but only $1.56 million for no transaction. He argued that contingent fees, as well as other provisions in the contracts, give investment banks substantial incentives to complete a transaction and create potential conflicts of interest. Studies have found that targets enjoy most of the expected merger and acquisition gains, while acquiring firms experience zero or even negative announcement effects. Acquirers are sometimes motivated to acquire for reasons that maximize managerial benefits rather than firm value (see Amihud, Kamin, & Ronen, 1983; Lloyd, Hand, & Modani, 1987; Malatesta, 1983; Morck, Shleifer, & Vishny, 1990). In addition, managers may be overly optimistic about the value of their targets, act on the presumption that their valuations are correct and fail to recognize that there are really no gains in takeovers (Roll, 1986).

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Despite the volume, magnitude and importance of high-tech acquisitions, research in the finance literature has been limited. Kohers and Kohers (2000) examined the value creation potential of hightech mergers and found that upon announcement acquirers of high-tech targets experience significantly positive abnormal returns (ARs) regardless of whether the merger is financed with cash or stock. However, in a following study (2001) they conclude that the high expectations regarding the future merits of these investments do not seem to be justified. Over a 3-year period following the merger, the bidders significantly underperform both industry-matched and size/book to market matched control portfolios. That lead them to conclude that the market tends to exhibit excessive enthusiasm toward the expected benefits of certain high-tech mergers but many of these benefits do not materialize. Their results are further supported by a recent PriceWaterhouseCoopers’ report, which estimates that approximately 80% of the technology acquisitions during the time period of 1994–1997 have failed to achieve their objectives. As Puranam (2001) stated: bSimply acquiring a technologically sophisticated start-up does not automatically guarantee that a firm will attain proficiency at the new technology and will be able to use it as a platform for future product development. In fact as many as 60% of such acquisitions suffer from severe problems in the postmerger integration stageQ. Our study is intended to build on the research on high-tech acquisitions, by addressing tech-specific factors that can explain the variation in valuation effects. We examine how the use of an investment bank as an advisor is associated with the valuation effects of high-tech acquisitions. We also test the impact of a target’s level of R&D expenditures and media exposure on valuation effects of acquisitions. These two factors do not normally receive attention in acquisitions research, but play a bigger role for high-tech acquisitions. We also consider the impact of other variables that have been used in previous studies on acquisitions, not only as a control, but also to determine whether their impact is altered when applied to high-tech acquisitions.

2. Hypotheses 2.1. Impact of investment bank advisor presence High-tech targets are especially difficult to value due to their intangible assets and growth opportunities. Given the high levels of information asymmetry and a need to provide a quality signal to shareholders, acquirers may benefit from hiring investment banks to serve as advisors in high-tech acquisitions. Many investment banks often advertise their superior expertise and unique knowledge in valuing high-tech companies. For example, according to Morgan Stanley Dean Witter b. . .our technology group has been serving the needs of leading technology companies worldwide for the past 18 years. Morgan Stanley combines its global presence with thorough technology industry knowledge to provide world-class financial services tailored specifically to the technology industry. Morgan Stanley is committed to building lasting relationships with the world’s highest quality technology companies—from today’s established leaders to tomorrow’s emerging starsQ. Specifically, we hypothesize that the valuation effects will be more favorable when an investment bank is involved as an adviser. If investment banks acting as advisors in high-tech M&As are able to reduce transaction costs, minimize asymmetric information problems and lower contracting costs, then there should be a positive relation between wealth effects of a high-tech acquisition and the reputation of the investment banker.

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2.2. Impact of investment bank advisor tier If top-tier investment banks have better experience and superior knowledge of the high-tech industry, then they may provide a more credible signal to the financial markets by certifying an acquisition, which in turn would translate into higher valuation effects in response to the announcement. Therefore, we hypothesize that acquisitions using the services of a top-tier investment bank will be viewed more favorably. Servaes and Zenner (1996) counter that managers seeking to make value-destroying acquisitions in order to satisfy their personal interests may want the involvement of an investment bank as protection against shareholder lawsuits. Moreover, if investment banks face strong deal completion incentives in the fee structure of their contracts (McLaughlin, 1990, 1992) they may be only interested in completing the deal. These arguments lead to the alternative hypotheses of no relationship between acquirers involved in high-tech acquisitions and the reputation of the investment banker used. 2.3. Impact of research and development (R&D) High-tech targets are especially difficult to value due to their intangible assets and other growth opportunities that need to be considered. Most high-tech acquisitions are associated with a high degree of information asymmetry, which in turn can make investors skeptical about the future prospects of the deal. In a comprehensive study of high-tech acquisitions by Prospectus (2002), most managers identified uncertainty as a key failure factor. They felt that existing uncertainty did not let the markets recognize the value in their acquisitions. The use of R&D expenditures is also based on the argument that most acquirers in the high-tech market are motivated by their desire to acquire an R&D capability. Since R&D is mostly an intangible asset with inherent uncertainty, it becomes difficult for technology companies to ensure that they deliver value from an R&D based acquisition. Therefore, we hypothesize that acquisitions of high-tech targets with a large amount of R&D expenditures will be more difficult to value and therefore should be associated with lower announcement period ARs than acquisitions of targets with a smaller amount of R&D expenditures. Following Markides and Oyon (1998), we use R&D expenditures as a proxy for the high-tech target’s intangible assets in order to capture the degree of difficulty in valuing a high-tech target. 2.4. Impact of media hype (buzz hypothesis) A recent study on the Internet IPO Underpricing by DuCharme, Rajgopal, and Sefcik (2001) argues that media exposure just prior to the offering provides relatively more price run-up on the actual day of the offer. The authors find support for their hypothesis since post-offer return performance is worse for hot Internet IPOs that receive more media attention prior to the IPO date. To the extent that media attention of high-tech firms can affect the pricing of an IPO, it may also affect the image and potential benefits that a target can offer to the acquirer. That is, media attention may generate more enthusiasm about the merging of the two entities, which would translate into a more favorable market reaction for the acquirers at the time of the acquisition announcement. Following DuCharme et al. (2001) we measure media exposure in the popular print and electronic media as the sum of the number of articles published about a firm in the bMajor NewspapersQ database

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and the number of features in the bTelevision and Radio News Broadcast TranscriptsQ database of Lexis/ Nexis for 6 months prior to the acquisition announcement for each firm. We hypothesize that the media attention on the high-tech target company prior to the acquisition announcement will favorably affect the acquirer’s announcement period ARs. 2.5. Impact of payment method and ownership status Chang (1998) discovered that acquisitions of privately held targets that are financed with stock exhibit positive ARs for the acquirer’s shareholders. He argued that in those cases, the target shareholders have an incentive to assess the bidding firm’s prospects carefully because they will end up holding a substantial amount of the bidding firm’s stock after the merger. Since most high-tech acquisitions involve privately held targets, the method of payment combined with the ownership status of the target may have driven the positive ARs that were found over the short-run by Kohers and Kohers (2000). We hypothesize that acquirers of privately held high-tech targets that use stock as a method of payment experience more favorable announcement period ARs. 2.6. Impact of hostile versus friendly high-tech acquisitions It has been argued that disciplinary takeovers are more likely to be bhostileQ and synergistic takeovers are more likely to be bfriendlyQ (see Morck, Shleifer, & Vishny, 1988). As a result, top management turnover should be higher following hostile rather than friendly takeovers. This view is supported by Walsh (1989), who found out that a target company’s managers seem to turn over at a higher rate following hostile, rather than friendly, negotiations. However, in high-tech acquisitions, targets are evaluated on intangible assets such as intellectual property, skilled employees and specialized development teams. As Prentice and Fox (2002) argue: bIt is the knowledge and creativity of people that drives success in high-tech firms to a much greater extent than in other industriesQ (p. 329). John Chambers, CEO of CISCO once commented: bMost people forget that in a high-tech acquisition, you really are only acquiring people. That’s why so many fail. At what we pay, $500,000 to $2 million an employee, we are not acquiring current market share. We are acquiring futuresQ. Chaudhuri and Tabrizi (1999) try to determine the key success factors of high-tech acquisitions and among their main findings they report that beffective acquirers usually keep the new people together in a separate division, and they try to keep the leader of the purchased company in charge thereQ (p. 5). On a similar note, Prentice and Fox (2002) argue that in high-tech acquisitions even the symbolic retention of higher-level executives helps keep the organizations stable and functioning. Since hostile takeovers are associated with increased top-management turnover, we hypothesize that these types of acquisitions will be less successful in high-tech industries. 2.7. Impact of the acquirer’s past experience A recent KPMG Report (2001) on the types of M&A deals that create shareholder value, as well as a McKinsey Quarterly Report (2001) (Bieshaar, Knight, & van Wassenaer, 2001) on the same subject, both conclude that there is no correlation between experience and success as those companies that are involved in a high number of transactions do not necessarily have a better track record in creating shareholder value.

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However, given the unique nature of high-tech deals and the difficulties that acquirers may face in extracting the desired synergies, experienced acquirers should be more skilled at completing the acquisitions as well as at managing the post-merger integration process. Therefore, we hypothesize that experienced acquirers involved in high-tech acquisitions should earn higher announcement period ARs than acquirers with less experience. The experience is measured as the number of high-tech deals in which the acquirer is previously involved. 2.8. Impact of the relative size of the target Academic research has found that larger transactions are associated with larger bidder returns (see Asquith, Bruner, & Mullins, 1983; Bruner, 1988; Song & Walkling, 1993). Kohers and Kohers (2000) found a positive relationship between bidder excess returns and the size of the transaction relative to the acquirer’s size. They concluded that high-tech target companies which are large relative to their acquirers are better able than small targets to provide synergies in a merger. One could also expect that acquirers involved in large deals would invest significant resources in forming an appropriate corporate strategy, identifying the target, structuring the deal, and communicating the deal to the market. Furthermore, large acquisitions tend to attract more attention and therefore more scrutiny from the market. In contrast, a McKinsey Quarterly Report on high-tech deals (2002) (Frick & Torres, 2002) which assessed the performance of 485 large high-tech companies found that successful acquirers tend to undertake transactions that are small compared with their own market value. Deals in which the purchase of the target was 50% or more of the acquirer’s market capitalization were rare. The report argued that bsmaller transactions lend themselves to simpler, more disciplined structuring and integration, thereby minimizing the negotiations and infighting that, in larger deals, can defeat the logic of the original planQ (p. 116). Given the above mixed findings we attempt to further investigate the size effects of high-tech acquisitions on acquirer wealth. Due to the unique nature of high-tech acquisitions, we hypothesize an inverse relation between acquirer CARs and the size of the acquisition. An alternative measurement is the size (log of market capitalization) of the acquirer. The potential impact of an acquisition is smaller for large acquirers. Furthermore, Ang and Kohers (2001) suggest that larger bidders may be more likely to pay relatively high premiums for targets. 2.9. Impact of the time period Information asymmetry problems were especially pronounced for high-tech firms during the techbubble period when many investors refused to use common sense while valuing tech companies and relatively verifiable indicators of corporate worth, such as current profitability were ignored. Therefore, even though an environment of increased information asymmetry would normally imply that buyers are not willing to pay full price, it is possible that a combination of media praise, euphoria and a pressure to act as a bherdQ, left investors unaware of the serious lack of information they were facing. According to a Business Week/Harris Poll (August 27, 2001) b. . . most Americans think that btechnophiliaQ got out of hand. By a 74% to 24% margin, they agree that people got too carried away with the promise of technology to improve their livesQ (p. 78). Yet, valuations on the seller’s side created many opportunities for buyers who had cash. The McKinsey Quarterly Report (2002) (Frick & Torres, 2002) on high-tech deals argued that companies

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able to move quickly can still profit in such volatile markets: bSuccessful deal-makers recognize that volatility gives them opportunities by affecting their own valuations in relation to the valuations of target companiesQ (p. 122). Given those contrasting arguments, we compare valuation effects of bidding companies at the beginning and at the end of the of the tech-bubble period. We hypothesize that acquirers involved in high-tech acquisitions after the end of the tech-bubble period will be viewed with skepticism, thus resulting to zero or negative announcement period ARs for bidding firm shareholders. 2.10. Impact of the specific high-tech sector High-tech companies cover a broad spectrum of sectors such as biotechnology, computer software and hardware, electronics, communications, pharmaceuticals and internet. Although their high-tech nature is a common characteristic for all these sectors that separates them from other industries, investors may perceive certain within-industry differences. Consider the following comments provided by Business Week that distinguish among the tech sectors when assessing their future prospects: bComputers and telecommunications are general-purpose tools. That means they will continue to grow in importance, because they will be put to uses that nobody today can imagineQ (August 27, 2001, p. 84). bBig investors are focusing on software, semiconductors, IT services and outsourcing companies they think can sidestep tech’s worst problems and produce healthy profit or revenue rebounds in the coming yearQ (December 31, 2001, p. 98). bDespite the Nasdaq’s bad performance and Silicon Valley’s gloom, business spending on IT gear and software is about 75% higher today than it was in early ’95Q (July 15, 2002, p. 40). bFurthermore, most investors would now agree that the bdot-com mania, especially towards the end was clearly unconnected to economic or business realitiesQ (July 15, 2002, p. 40). To control for the different sectors in the tech industry, we classify high-tech companies as (1) Biotechnology and Health Care, (2) Computer Software and Hardware, Telecommunications and Electronics, and (3) Internet.

3. Data and sample description To test the valuation effects of domestic high-tech acquisitions a preliminary sample was obtained from the SDC M&A database. SDC provides data on domestic acquisitions dating as far back as 1979. To be included in the final sample each acquisition has to satisfy the following criteria: (i) The takeover announcements refer to completed acquisitions in the period from 1980 to 2001. (ii) Each target has to belong to a high-tech industry as identified by the SDC database classifications. (iii) The acquisition bid is for at least 50% of the equity of the target in order to ensure that effective control of the target’s assets passes to the management of the bidder.

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(iv) The acquirers’ stock is traded either on the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX) or the Nasdaq. This last requirement ensures that the stock returns of the acquirer can be retrieved from the CRSP database. Furthermore, ADRs, utilities, financial companies and undisclosed M&As are excluded from the sample. The merger announcement dates and other merger-related information, including the names of the investment bank advisors involved in each deal, are retrieved from the SDC’s M&A database. The stock return data, as well as other financial information, are retrieved from the CRSP and Compustat databases. The SDC search yields an initial sample of 3816 high-tech acquisitions. Table 1 provides a detailed breakdown of the SDC initial sample by years. Clearly, the majority of these acquisitions take place during the last 10 years from 1992 to 2001, with year 2000 alone accounting for 18% of all high-tech acquisitions. This is hardly surprising given the increasing importance of high-tech industries and the continuous development of new high-tech fields. The time period from 1981 to 1991 collectively accounts for 350 acquisitions or 9% of the total sample. Table 2 provides further descriptive information on the high-tech sample of acquisitions. 1845 hightech acquisitions, or 48.35% of the sample, involve targets in the Computer Equipment subsector. 882 targets or 23.11% of the sample belong in the Communications subsector, while 15.83% of the targets are part of the Biotech sector. Electronics account for 11.22% of the sample and the rest 1.49% of targets fall under the Others category. It should be noted that Internet companies belong to the Communications subsector and make up for almost half of the targets in it. More specifically, from the total of 882 target companies in the Communications sector, 438 are denoted as Internet Services and Software companies. Table 1 SDC data description by year Year

Number of deals

% of Total

2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 1986 1985 1984 1981 Total

359 689 625 483 371 308 242 166 117 106 68 39 52 53 55 48 25 7 3 3816

9.41 18.06 16.38 12.66 9.72 8.07 6.34 4.35 3.07 2.78 1.78 1.02 1.36 1.39 1.44 1.26 0.66 0.18 0.08 100.00

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Table 2 Sample description for all high-tech targets Industries

Number of deals

Computer equipment Communications Biotech Electronics Other Total

1845 882 604 428 57 3816

% of Total 48.35% 23.11% 15.83% 11.22% 1.49% 100%

Target ownership structure Number of high-tech private targets Number of high-tech public targets Other

2770 887 159

Of all high-tech transactions only 887 or 23% involve publicly traded targets, while 2770 or approximately 72% involve privately-held targets. The sample of targets in the Computer Equipment subsector include IBT Group, Management Software Inc., Spectron Microsystems, Sunward Technologies Inc., while the sample of targets in the Communications subsector include Chase Telecom, Broadcast International Inc., Call Communications Inc. The sample of targets in the Biotechnology subsector include Squibb Corp., Clotech Laboratories Inc., Integrated Genetics Inc., while the sample of targets in the Internet Services and Software subsector include Musicvideos.com, Promedix.com, Ubarter.com Inc., Egghead.com Inc. Finally, Table 3 provides some key descriptive statistics for the acquirers of high-tech targets and the acquisitions themselves. According to SDC, the merger transactions in this sample have a mean value of $433.2 million and a median value of $26.77 million. Apparently, many of these acquisitions involve relatively small high-tech companies, a finding consistent with what Kohers and Kohers (2000) had observed for their sample. The mean market value of the bidder was $7.19 billion on the year of the acquisition and had followed a steady upward trend during the past 2 years. However, both the mean ROA and ROI for the bidders were negative on the year of the acquisition and, although not reported here, have been negative during the previous 2 years as well. In contrast, Kohers and Kohers (2000) report a positive ROA for their sample of bidders which may be indicative of the sample period used.

Table 3 Descriptive statistics for acquirers of high-tech targets Key variables

Mean

Median

Value of merger transaction ($mil) Market value of bidder ($mil) Market value of bidder 1 year before ($mil) Market value of bidder 2 years before ($mil) ROA for bidder ROI for bidder Bidder 2-year sales growth

433.2 7195.6 6842.0 5862.6
9.14%
27.73% 27.60%

26.77 558.03 529.39 411.53 2.58% 3.71%

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More specifically, their sample ends in 1996 and therefore does not capture the more volatile years of 1999, 2000 and 2001. To measure investment bank reputation and prestige, we use the rankings constructed by Rau (2000) as well as Hunter and Jagtiani (2003). Rau (2000) classifies a total of 30 investment banks into a top-tier, a mid-tier and a third-tier bracket. However, their sample of acquisitions only covers the period from 1980 to 1994. Since our sample period expands from the early 1980s to 2001 we supplement his rankings with those reported by Hunter and Jagtiani (2003). Their rankings are based on mergers that were announced during the 1995–2000 time period. If an investment bank advisor in our sample does not belong to any of the three-tier brackets reported by Rau, we check the rankings provided by Hunter and Jagtiani and use the same classification that they have. If none of the known rankings contain the specific advisor, then it is classified as botherQ. It should be noted that the high-tech acquisitions in our sample are classified as having been advised by a bank of a particular tier on the basis of the most senior bank advising the acquirer.

4. Methodology The impact of announced high-tech acquisitions on the shareholder wealth of acquiring firms is estimated using a standard event-study methodology. For acquirers and publicly traded targets cumulative abnormal returns (CARs) are calculated for various intervals around the announcement day per acquirer. The average CARs across acquisitions are calculated over several subperiods and are then compared for statistical significance against a test statistic of zero. A multivariate model is finally applied to capture all other variables that may affect valuation effects of high-tech acquisitions. CARj ¼ b0 þ b1 ADVISORj þ b2 TIERj þ b3 R&Dj þ b4 MEDIAj þ b5 PUBLICj þ b6 PAYMENT þ b7 HOSTILE þ b8 EXPERIENCEj þ b9 SIZEj þ b1 TIMEj þ b11 INTERNETj þ b12 BIO&HEALTHj þ ej where: CARj = cumulative abnormal returns for the acquirer j over the event window (
1, 1) surrounding the announcement day t = 0. ADVISORj = a dummy variable equal to 1 for all acquisitions advised by an investment bank, 0 for all others. TIER1 = a dummy variable equal to 1 if the investment bank advisor to the acquisition is a top-tier bank, and 0 if it is either a mid- or a third-tier bank. An alternative measurement is also used—TIER2 = a dummy variable equal to 1 if the investment bank advisor to the acquirer is a top-tier or mid-tier bank, and 0 otherwise. R&Dj = the $ amount of R&D expenditures for the target firm j as obtained from Compustat during the year prior to the acquisition. MEDIAj = number of hits in the bMajor NewspapersQ and bTelevision and Radio News Broadcast TranscriptQ database of Lexis/Nexis during the 6 months prior to the acquisition announcement of firm j. PUBLICj = a dummy variable equal to 1 when the target is public and 0 otherwise. PAYMENTj = a dummy variable equal to 1 when cash is used and 0 otherwise. HOSTILEj = a dummy variable equal to 1 if the acquisition is hostile, and 0 otherwise. EXPERIENCEj = a variable that captures the number of high-tech deals in which the acquirer is previously involved. SIZEj = a variable that accounts for the relative size of the acquisition calculated with data provided by the SDC database. An alternative measurement for size is LOGMV, which is the log of the market capitalization of the acquirer, as the

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potential impact of an acquisition on larger acquirers is expected to be smaller. TIMEj = a dummy variable used to capture the end of the tech-bubble period. It takes the value of 1 for acquisitions announced after the month of March 2000, and 0 otherwise. INTERNETj = a dummy variable equal to 1 if the target is an internet company, and 0 otherwise. BIO&HEALTHj = a dummy variable equal to 1 if the target is a biotechnology or health care company, and 0 otherwise.

5. Empirical results 5.1. Valuation effects for targets and acquirers Table 4 reports the valuation effects for the sample of 786 publicly traded high-tech targets for which CRSP return data is available. High-tech targets experience an increase in valuation of approximately 18% over the 2-day event period (
1, 0) statistically significant at the 0.1% level. The pre-event period mean CAR is 5.83% significant at the 0.1% level which seems to indicate the presence of information leakage. The post-event period mean CAR is also positive and statistically significant at the 0.1% level. Overall, the results for the high-tech targets are consistent with those of existing research which document favorable valuation effects for target shareholders upon announcement. From a bidder’s perspective, in order to examine the stock price reaction to the announcement of a high-tech acquisition, CARs are calculated surrounding the event date t = 0. Table 5 reveals that the sample of acquirers is almost equally divided between firms that experience positive versus negative CARs upon announcement. It also shows that U.S. acquirers of high-tech targets experience positive but insignificant CARs averaging 0.35% for the 2-day event period (
1, 0) and 0.40% for the 2-day period (0, + 1). However, CARs are negative averaging
0.98% and
1.56% over the windows (
10, + 10) and (+1, + 10), statistically significant at the 1% and 0.1% level, respectively. Although these results are in line with past findings on the wealth effects of acquiring firms, they are in contrast with the results reported by Kohers and Kohers (2000). Their study on high-tech targets revealed positive and significant CARs for the overall sample of acquirers, averaging 1.26% over the 2-day period (0, + 1). Such differences in the reported results may be attributed to the time period used by each study. Kohers and Kohers earlier study is restricted over the 1987 to 1996 time period and thus fails to capture the most recent, and also more critical, years of the tech industry’s rise and, at least temporary, downfall. 5.2. Results partitioned by privately-held versus publicly-traded tech targets The full sample of acquisitions for which data are available is further separated into two distinct subsamples. The first subsample involves acquisitions of privately-held tech targets, while the second Table 4 Cumulative ARs for publicly traded high-tech targets, N = 786 Window

CAR (%)

Z-stat

Positive/negative

Event period (
1, 0) Pre-event period (
10,
2) Post-event period (1, +10)

18.38 5.83 3.56

90.06**** 12.01**** 7.13****

641:145 524:262 422:364

**** denote statistical significance at the 10%, 5%, 1% and 0.1% levels, respectively.

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Table 5 Cumulative ARs for all acquirers of high-tech targets, N = 3400 Window

CAR (%)

Z-stat

Positive/negative

(
1, 0) (0, + 1) (
1, + 1) (
10, + 10) (+ 1, + 10)

0.35 0.40 0.56
0.98
1.56

1.548 0.712 1.684*
3.066***
5.674****

1650:1749 1662:1738 1677:1723 1584:1816 1509:1891

*, ***, **** denote statistical significance at the 10%, 5%, 1% and 0.1% levels, respectively.

subsample includes publicly-traded high-tech targets. Table 6 shows that acquisitions of privately-held targets dominate the high-tech acquisition market with a reported number of 2770 deals. In contrast, there are only 887 deals in this sample that involve acquisitions of publicly traded targets. Despite the preponderance of acquisitions that target privately-held companies, the mean transaction (merger) value for this sample is $75.116 million. The comparable figure for acquisitions of public high-tech targets is almost $1.6 billion. Furthermore, the difference between these two figures is statistically significant at the 0.1% level. Given the mean transaction (merger) value figures, the total value of high-tech mergers amounts to $208 billion when private firms are targeted and approximately $1.4 trillion when publicly traded targets are involved. The mean relative size of the merger is computed as the mean of total transaction (merger) value divided by the sum of the acquirer’s market value and the total transaction value (see Ang & Kohers, 2001). Table 6 shows that the relative size of the deal is 8.8% for acquisitions of private high-tech targets and 19% for acquisitions of public high-tech targets. The differences in relative sizes of the deals are statistically significant at the 0.1% level. In addition, as can be seen from Table 6 the mean size of the acquirer that purchases a publicly traded high-tech target is substantially larger than the mean size of an acquirer that buys a private tech target. More specifically, the mean market value of acquirers of privately-held targets is $6.9 billion while the comparable figure for acquirers of public tech targets is

Table 6 Descriptive statistics for acquisitions of privately-held and publicly traded high-tech targets, time period: 1981–2001

Total number Mean transaction value ($mil) Median transaction value ($mil) Total merger value ($mil) Mean relative size of merger Mean MV of bidder ($mil) Mean acquirer total assets ($mil) Number of cash-only deals Number of stock-only deals Mixed offers

Acquisitions of private targets

Acquisitions of public targets

2770 75.116 16.2 208,071.32 8.80% 6931.17 1770.69 750 1102 648

887 1581.26 193.21 1,402,577.62 19.0% 19,050.99 6161.42 201 416 106

T-stat and p-value for test in means
7.277, [0.00]****


13.90, [0.00]****
5.94, [0.00]****
5.88, [0.00]****

The third column provides T-stat and associated p-values for two-tailed t-tests. **** denote statistical significance at the 0.1%, 1%,5% and 10% levels.

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$19 billion. The acquirer’s market value is computed as the price per share 1 month prior to the bid announcement times the number of common shares outstanding. When size is captured by the acquirer’s total assets the reporting results are similar. In acquisitions of privately-held high-tech targets the acquirers involved have mean total assets of $1.77 billion, whereas acquirers of public targets have mean total assets of $6.16 billion. The difference in size for acquirers involved in private versus public target acquisitions is statistically significant at the 0.1% level independent of the measure of size used. Table 6 also shows that acquirers of private companies appear to favor stock-only deals. One possible explanation may be that in cases where there is high uncertainty about the target firm’s value, the bidder may choose to offer stock, forcing target shareholders to share part of the risk if the bidder ends up overpaying for the target (Hansen, 1987). The CARs are measured separately for acquirers of privately-held high-tech targets and publicly traded high-tech targets. Tables 7 and 8 show strikingly different findings for acquisitions of private versus public high-tech targets. More specifically, acquirers of privately-held targets enjoy positive and statistically significant CARs in all windows surrounding the event date. The mean CAR for the 2-day event period (
1, 0) is 1.08%, statistically significant at the 0.1% level. Similarly, for the (
10, + 1) window CARs are 1.65% statistically significant at the same level. As a result, it appears that acquisitions of privately-held targets are associated with positive and significant shareholder wealth effects. However, immediately after the acquisition is announced ARs become negative and the mean CAR over the post-event period (+ 1, + 10) is
1.35%, statistically significant at the 0.1% level. For the sample of takeovers that target publicly traded companies, the results are notably different. Table 8 shows that, upon the announcement of the acquisition, acquirers experience a mean negative AR of
1.96% statistically significant at the 0.01% level. The 2-day CAR on days t =
1 and t = 0 is

Table 7 ARs and CARs for acquirers of privately-held high-tech targets, N = 2523 Day

Mean AR (%)

Z-stat


4
3
2
1 0 1 2 3 4 5

0.01 0.12 0.11 0.21 0.88 0.52
0.06
0.14
0.40
0.40


0.826 1.396 0.034 1.830* 9.672**** 4.983**** 0.179
1.081
2.67***
4.21****

Window

Mean CAR (%)

Z-stat

(
1, 0) (
1, 1) (
10, + 1) (1, + 10)

1.08 1.60 1.65
1.35

8.15**** 9.42**** 4.41****
3.63****

*, ***, **** denote statistical significance at the 10%, 5%, 1% and 0.1% levels, respectively. The mean AR and Z-stat may differ in sign because the former assigns uniform weights to each observation and the latter assigns non-uniform weights (Mikkelson & Partch, 1988).

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Table 8 ARs and CARs for acquirers of public high-tech targets, N = 799 Day

Mean AR (%)

Z-stat


4
3
2
1 0 1 2 3 4 5

0.29 0.14 0.03
0.05
1.96
0.74
0.15
0.19
0.13
0.13

0.279 0.971
1.175
0.078
16.07****
5.866**** 0.126
1.291
1.579
1.617

Window

Mean CAR (%)

Z-stat

(
1, 0) (
1, 1) (
10, + 10) (1, + 10)


2.01
2.75
3.04
2.15


11.43****
12.70****
5.401****
4.810****

**** denote statistical significance at the 10%, 5%, 1% and 0.1% levels, respectively.


2.01% also statistically significant at the 0.01% level. Furthermore, CARs over the post-event window (+1, + 10) and the 21-day window (
10, + 10) are also negative and significant averaging
2.15% and
3.04%, respectively.1

6. Results of multivariate analysis A cross-sectional analysis is applied to assess the impact of firm-specific factors on valuation effects, while controlling for other factors. Given the apparent differences between public and private high tech acquisitions, cross-sectional analyses are also performed separately for each subsample. All regressions, whenever necessary, are corrected for heteroskedasticity. 6.1. Results for all high-tech acquisitions The cross-sectional analysis for the complete sample of high-tech acquisitions are presented in Table 9, based on six different models. All six models are statistically significant at the 0.1% level. The media and R&D variables are not included here since the data were not available for private targets.

1 In order to ensure that the results are not due to size differences between the two samples, a size-adjusted sample is formed. Specifically, acquisitions of public high-tech targets with a reported transaction value that falls between one standard deviation of the mean transaction value for the sample of private target takeovers are investigated separately. This sample includes 504 public target takeovers and the results of its analysis are reported in Table 9. Although announcement period ARs and CARs are slightly smaller for the sample of size-adjusted acquisitions, the results are similar to those of the full sample of public target takeovers. CARs over all reported windows are still negative and statistically significant at the 0.1% level. As a result, any differences in the announcement period wealth effects for acquirers of public versus private target takeovers do not appear to be attributed to deal size differences.

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Table 9 Regression analysis of bidder CARs (
1, + 1) on various target and bidder characteristics, sample includes all high-tech targets Variables

1

2

3

4

5

6

INTERCEPT

0.06491 (4.70)****

0.06438 (4.51)****

0.01294 (4.12)****

0.06478 (4.66)**** 0.0057 (0.84)

0.06378 (4.59)****

0.0614 (4.41)**** 0.0172 (2.32)**

ADVISOR TIER2 PUBLIC ADVISOR  PUBLIC TIER2  PUBLIC EXPERIENCE


0.0384 (
6.33)****


0.039 (
6.27)****

0.00118 (1.94)*

0.00117 (1.93)*


0.01705 (
3.55)****
0.0147 (
2.53)**


0.01688 (
3.47)****
0.0145 (
2.52)**
0.0018 (
0.22) 0.00288 (0.46)

RELSIZE LOGMV TIME INTERNET BIO&HEALTH ADVISOR  TIME Number of observations R2 F-statistics


0.0406 (
6.21)****

0.0069 (0.76)
0.026 (
3.46)****

0.00134 (2.22)**


0.0271 (
2.03)** 0.00117 (1.94)*

0.00133 (2.22)**


0.0193 (
3.30)****


0.0176 (
3.67)****
0.0172 (
2.81)***


0.0169 (
3.52)****
0.0149 (
2.57)**


0.0174 (
3.61)****
0.0155 (
2.67)***

2487

2487

0.039 16.88****

0.041 18.08****


0.0615 (
8.55)****


0.000054 (
0.14) 0.093 (3.75)****

2487

2487

2423

0.00839 (0.60) 2487

0.0378 24.38****

0.0379 16.28****

0.0447 28.29****

0.0388 16.67****


0.0209 (
2.69)***
0.0396 (
3.21)***

*, **, ***, **** denote statistical significance at the 10%, 5%, 1% and 0.1% levels, respectively. TIER2 is a dummy that takes the value of 1 for top- and mid-tier advised acquisitions, 0 for all other deals. ADVISOR is a dummy that takes the value of 1 for acquisitions that used an I.B, 0 for all remaining deals.

Acquisitions that used an I.B. of any tier were viewed more favorably than similar acquisitions with no I.B., as indicated by the positive and statistically significant ADVISOR coefficient. However, the different tier classifications do not appear to have any effect on shareholder’s wealth as shown by the insignificant TIER2 coefficient. The PUBLIC dummy variable is negative and statistically significant in all 6 models, suggesting that acquisitions of tech publicly traded targets result in a negative market response from bidder shareholders. This is consistent with the findings reported earlier for subsamples. The coefficient of the LOGMV variable is also negative and statistically significant at the 0.1% level. Larger bidders may have less to gain from acquisitions. The EXPERIENCE variable which captures the number of high-tech deals the acquirer has previously completed is positive and statistically significant in most models, which supports the hypothesis that bidder-shareholders are generally more optimistic when an experienced acquirer is involved.

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The TIME variable which takes the value of one for acquisitions announced after March 2000 is constructed in order to capture the end of the tech-bubble period and its effects on shareholder wealth. Its coefficient is negative and statistically significant in all 6 models, suggesting that acquirers involved in high-tech acquisitions after the end of the tech-bubble period were viewed with more skepticism. When the INTERNET and BIO&HEALTH dummy variables are introduced in the cross-sectional analysis, their coefficients are not statistically significant. Consequently, within-industry differences do not affect the valuations of high-tech acquisitions. The interaction variables TIER2  PUBLIC as well as ADVISOR  PUBLIC are both negative and significant in Models 5 and 6, respectively. As a result, acquisitions of publicly traded tech companies that used the services of an I.B advisor experienced a less favorable market reaction. 6.2. Cross-sectional results for public target acquisitions Table 10 presents the results of the cross-sectional analysis of acquisitions of public high-tech target acquisitions. The coefficient is positive for the TIER1 variable, which suggests that for public tech target acquisitions, deals advised by top-tier banks perform better than those advised by either mid- or third-tier banks. The R&D variable is consistently negative and statistically significant, which supports the hypothesis that acquirers will experience weaker valuation effects when they acquire high-tech targets with more intangible assets (as measured by research and development). The media variable is consistently positive and significant, supporting the hypothesis that valuation effects are more favorable for acquisitions of high-tech targets that received more media coverage. The coefficient of the EXPERIENCE variable is typically positive and significant, as hypothesized. In contrast, the TIME variable is negative and significant, indicating that tech acquisitions announced during the 2000 and 2001 tech correction period perform worse than earlier announced tech acquisitions. Table 10 Regression analysis of bidder CARs (
1, + 1) on various target and bidder characteristics, high-tech target is public Variables

1

2

3

4

INTERCEPT TIER1 TIER2 R&D MEDIA HOSTILE EXPERIENCE RELSIZE TIME INTERNET BIOHEALTH Number of observations R2 F-statistics


0.03889 (
2.98)***


0.087 (
6.10)****


0.1144 (
6.42)**** 0.02424 (1.88)*


0.0754 (
4.70)****

0.00528 (0.19) 0.00402 (2.41)** 0.01168 (0.25)
0.01786 (
1.43)
0.01612 (
0.63) 0.00002 (0.23) 403


0.000105 (
2.419)*** 0.0153 (6.231)**** 0.0193 (0.445) 0.00388 (2.062)** 0.02651 (1.869)*
0.0208 (
1.948)** 0.00119 (0.058)
0.0078 (
0.635) 331


0.0001 (
3.44)**** 0.0165 (5.07)**** 0.0138 (0.27) 0.00299 (1.54) 0.04901 (1.59)
0.04413 (
2.27)**
0.00498 (
0.15) 0.006288 (0.43) 187


0.20525 (
1.94)*
0.000092 (
2.81)*** 0.01526 (6.27)**** 0.01711 (0.72) 0.003733 (2.2)** 0.02935 (0.75)
0.0249 (
1.99)** 0.004636 (0.19)
0.007712 (
0.69) 331

0.018 1.24

0.145 6.836****

0.206 5.103****

0.154 6.272****

*, **, ***, **** denote statistical significance at the 10%, 5%, 1% and 0.1% levels, respectively. TIER1 is a dummy that takes the value of 1 for top-tier advised acquisitions, 0 for mid- and third-tier advised deals. TIER2 is a dummy that takes the value of 1 for top- and mid-tier advised acquisitions, 0 for all other deals.

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Finally, it should be noted that the attitude of the acquisition (hostile versus friendly) as well as dummy variables designed to capture the different tech sectors is not significant. 6.3. Cross-sectional results for private target acquisitions The regression analysis results for the sample of acquirers targeting private high-tech companies are presented in Table 11. Since there are no available data on R&D expenditures and media coverage for the acquisitions of private target firms, these variables are not included in the results. Nevertheless, all 5 models are statistically significant at the 0.1% level and several interesting results arise from the crosssectional analysis. Specifically, the TIER1 variable is not significant. Alternative definitions of the TIER variable, not shown here, yield similar results. Bidder-firm shareholders do not react more favorably to private target acquisitions based on the tier of their investment bank advisor. However, they do seem to value whether the acquirer uses an investment bank advisor as indicated by the ADVISOR variable. More specifically, acquisitions of high-tech private targets that are certified by an investment bank of any

Table 11 Regression analysis of bidder CARs (
1, + 1) on various target and bidder characteristics, high-tech target is private Variables

1

2

3

4

5

INTERCEPT


0.0037 (
0.67)

0.0798 (4.43)****

0.0002 (0.03)


0.0035 (
0.26)

0.0773 (4.44)**** 0.018853 (2.51)**

0.01038 (1.95)**
0.00002 (
0.04) 0.2259 (3.96)****


0.0088 (
0.64) 0.03346 (2.22)** 0.0004 (0.21) 0.2009 (3.47)****

ADVISOR TIER1 PAYMENT EXPERIENCE RELSIZE

0.01076 (2.03)**
0.00004 (
0.09) 0.2268 (3.95)****


0.01658 (
2.59)***
0.00158 (
0.18) 0.00217 (0.28)


0.0229 (
3.81)****
0.01332 (
2.05)**
0.00075 (
0.09) 0.00222 (0.28)

2020 0.057 20.41****

LOGMV TIME INTERNET BIO&HEALTH

0.0009 (0.16) 0.00127 (1.88)*

Number of observations R2 F-statistics


0.02417 (
4.12)****
0.01346 (
2.08)**

2015


0.01652 (
2.57)***
0.002 (
0.24) 0.00404 (0.54)
0.00592 (
0.92) 2019

509

2015

0.024 8.436****

0.057 17.57****

0.054 4.790****

0.028 11.66****

SAMESECTOR


0.0079 (
0.5)
0.0203 (
0.98)

0.00338 (0.59) 0.00158 (2.37)**

*, **, ***, **** denote statistical significance at the 10%, 5%, 1% and 0.1% levels, respectively. TIER1 is a dummy that takes the value of 1 for top-tier advised acquisitions, 0 for mid- and third-tier advised deals. ADVISOR is a dummy that takes the value of 1 for acquisitions that used an I.B, 0 for all remaining deals.

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tier enjoy higher announcement period ARs than all other acquisitions. The ADVISOR variable coefficient is positive and statistically significant at the 5% level. Despite the acquirer’s preference for stock-financed acquisitions, we find that the PAYMENT variable which is designed to capture all cash-financed acquisitions is typically positive and significant, indicating that announcement period effects for bidder shareholders are smaller in stock and mixed offers than in cash offers. The RELSIZE variable is positive and statistically significant at the 0.1% level suggesting that there is a positive relation between bidder excess returns and the size of the acquisition. This is consistent with the findings of Kohers and Kohers (2000) as well as Asquith et al. (1983), Bruner (1988) and Song and Walkling (1993). The log of the acquirer’s market value (LOGMV) is negative and statistically significant at the 0.1% level. This finding supports the hypothesis that larger acquirers experience less favorable valuation effects, perhaps because they have less to gain or may be more likely to pay higher than average premiums for targets. There is modest evidence that the EXPERIENCE coefficient is positive and significant, suggesting that the valuation effects are more favorable when an experienced acquirer is involved. The TIME variable, which captures the end of the tech-bubble period, is negative and statistically significant in most models. Investors appear to view acquisitions after the end of the tech-bubble period with skepticism, perhaps questioning the potential growth that will result from acquisitions. The INTERNET and BIO&HEALTH variables introduced to capture any potential differences between the various tech subsectors are not significant in any of the regression models. As a result, the sector designation of technology does not affect the valuations of high-tech acquisitions.

7. Conclusions US acquirers of high-tech targets experience positive but insignificant announcement period CARs averaging 0.35% for the 2-day event period (
1, 0) and 0.40% for the (0, +1) 2-day period. Furthermore, over the post-event window (1, + 10) CARs become negative and statistically significant at the 0.1% level. The results are dependent on whether the acquisition involves a private versus a public high-tech target. Specifically, investors regard acquisitions of publicly traded high-tech companies as value-decreasing events, while acquisitions of similar privately-held companies yield significant gains and are considered value-enhancing transactions. We also find that acquirers of private companies appear to favor stock-only deals. One possible explanation may be that in cases where there is high uncertainty about the target firm’s value, the bidder may choose to offer stock, forcing target shareholders to share part of the risk if the bidder ends up overpaying for the target (Hansen, 1987). The cross-sectional analysis results on the sample of all high-tech deals indicate that acquisitions using an investment bank of any tier are viewed more favorably than similar acquisitions with no investment bank. Overall, the findings on the role of investment bank advisors appear to largely depend on whether the acquisition involves a public versus a privately-held target. Separate crosssectional regressions on the sample of public high-tech target acquisitions show that deals advised by top-tier banks perform better than those advised by either mid- or third-tier banks. These results suggest that top-tier investment banks may provide expertise in valuing high-tech assets, and serve as a valuable endorsement of a deal. The cross-sectional results on the sample of privately-held high-tech

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target acquisitions indicate that the tier has no impact on valuation effects, but acquisitions that are certified by an investment bank of any tier enjoy higher announcement period ARs than other acquisitions. Furthermore, despite the acquirer’s preference for stock-financed acquisitions, announcement period effects for bidder shareholders are smaller in stock and mixed offers than in cash offers. The results support the expected inverse relationship between R&D and valuation effects of the bidder. They also support the expected positive relationship between the target’s MEDIA exposure prior to the acquisition announcement and the valuation effects of the bidder. It appears that the more media attention the tech targets receive prior to the acquisition, the more enthusiastic bidder shareholders become about the prospects of these companies and the more favorably they react to news about their acquisition.

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