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ESSAYS ON CORPORATE STRATEGY: EVOLUTIONOF CORPORATE CAPABILITIES ANDTHE ROLE OF INTANGIBLE ASSETS

DISSERTATION

Presented in Partial Fulfillment of the Requirements for

the Degree Doctor of Philosophy in the Graduate Schoolof The Ohio State University

By

Asli Musaoglu Arikan, MBA

*****

The Ohio State University

2004

Dissertation Committee: Approved by

Professor Jay Barney, Adviser

Professor Karen Wruck

Professor David Hirshleifer _________________________

Professor Anita McGahan Adviser

Professor Konstantina Kiousis Business Administration Graduate Program

Professor Oded Shenkar


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Copyright by

Asli Musaoglu Arikan

2004


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ABSTRACT

This dissertation is comprised of in depth analysis on the broader topic of

corporate strategy with emphasis of the role of intangible assets. The first chapter looks at

the performance implications of acquiring firms that have highly intangible assets

structures. The second essay looks at dynamic characteristics as well as outcomes of

developing intangible yet valuable corporate level capabilities in relation to managing

alliances and acquisitions. The final section looks at the role of intangible assets in

contracting between and within firms by utilizing property rights theory and the resource

based view.

A consistent finding regarding mergers and acquisitions (M&A) is that: on

average shareholders of target firms earn significant economic gains whereas

shareholders of acquiring firms break-even (Jensen and Ruback, 1983; Jarrell et. al.,

1988). Despite this general finding M&A activity has persisted, increasing in number and

transaction value because, managers often perceive M&A activity as a mechanism for

growth (e.g. Penrose, 1959). Therefore, it is natural to ask, `What type of assets are worth

buying?'

This paper investigates the long-run performance effects of acquiring intangible

versus tangible targets. Intangibility of target is proxied by multiple measures based on


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R&D, advertisement and human capital stocks, and the Tobin's q 1-year prior to the

corporate event. Using a sample of M&A transactions spanning a 4 year period (1988-

1991), long-run-buy-and-hold expected returns are calculated by constructing portfolios

of cohort firms that pursue M&A activity and tracked for 5 years. Each firm's long-run-

abnormal performance is calculated as the excess return to the benchmark portfolio.

Results show that on average, acquirers of intangible targets earn negative abnormal

returns, whereas acquirers of tangible targets break-even. However, for the whole sample,

there is no evidence of long-run abnormal returns.

Existence of asymmetric misvaluation between M&As of intangible versus

tangible targets is tested by regressing short-run returns on the buy-and-hold long-run

returns. Results provide evidence for market overreaction to the announcements that

involve highly intangible targets. Overall, findings suggest that on average, ownership

claim to the target's intangible assets via M&A does not transfer the associated economic

value.

In the second section I investigate how long it takes for publicly traded firms

within the United States to develop corporate capabilities for conducting alliances and

acquisitions effectively. The development of corporate capabilities has been difficult to

study directly because little information has been available on the accumulation at the

corporate level of performance-enhancing knowledge. The research reported here relies

on a dataset that tracks the behavior of the 3,595 firms that went through an initial public


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offering (IPO) between 1988 and 1999 to show how quickly corporate capabilities

developed from the earliest years of firm formation.

In particular, we conduct an event-study analysis to investigate how the abnormal

returns to alliance and acquisition announcements changed as the firms accumulated

experience in conducting deals of each type. The results suggest that firms accumulated

capabilities for executing and managing both alliances and acquisitions, and that

investors came to expect that firms would continue to exploit their specialized

capabilities into the future.

Finally I provide discussion of the theoretical implications of the empirical

findings and contribute to the literature on corporate strategy and resources based view

by incorporating insights from the property rights literature which can be considered as

the recent development extension of transaction cost economics.


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Dedicated to my grandmother, Guzide Egilmez


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ACKNOWLEDGMENTS

I wish to thank my adviser Jay Barney, and my committee members David

Hirshleifer, Konstantina Kiousis, Anita McGahan, Oded Shenkar, and Karen Wruck for

their intellectual support, encouragement, and enthusiasm which made this thesis

possible.

I am grateful to Ilgaz Arikan for his continued support and stimulating discussions

on all aspects of my research interests.

I wish to also thank to Laurence Capron, Russ Coff, Ken Hatten, Anne-Marie

Knott, Harbir Singh, Ralph Walkling, Julie Wulf, and Bernard Yeung for their helpful

comments. The author also benefited from discussions with Josh Lerner, Dan Levinthal,

Jan Rivkin, Anju Seth, Jamal Shamsie, Scott Shane and Sid Winter. All the errors remain

mine.


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VITA

December 1, 1972Born Izmir, Turkey

1994.BS Istanbul technical University, Istanbul Turkey

1997 MBA University of North Carolina

2003-Current ..Instructor, Boston University

PUBLICATIONS

Barney J.B., Arikan A.M. 2002. The resource-based view. Origins and implications. InHitt M.A., Freeman R.E., Harrison J.S. (eds.), Handbook of StrategicManagement. Blackwell Publishers: Oxford, UK; 124-188.

Arikan, A.M. 2002. Does it pay to capture intangible assets through mergers andacquisitions? Academy of Management Meetings Best Paper Proceedings

Arikan, A.M. 2003. Does it pay to capture intangible assets through mergers andacquisitions? In Strategic Management Society Book Series on M&A Summit

Arikan, A.M. 2003. Cross-border mergers and acquisitions: What have we learned? InB.J. Punnett, and O. Shenkar (Eds.) 2nd Edition ofHandbook of InternationalManagement Research, University of Michigan Press

FIELDS OF STUDY

Major Field: Business AdministrationMinor Field: Financial Economics


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TABLE OF CONTENTS

PageAbstract ............................................................................................................................... iiDedication .......................................................................................................................... viAcknowledgments.............................................................................................................. viVita........................................................................................................ viiList of Tables ...................................................................................................................... x

List of Figures ................................................................................................................... xii

Chapters:

1. Introduction............................................................................................................. 12. What Type Of Assets Is Worth Buying Through Mergers & Acquisition? ........... 5

2.1 Resource-Based View And Competitive Advantage........................................ 82.2 Controls For Other Factors ............................................................................. 11

2.2.1 Agency Motives............................................................................... 122.2.2 Information Asymmetry And Financing Of Intangible Assets........ 142.2.3 Market Over- Or Under-Valuation Of Growth Opportunities......... 17

2.3 Methodology And Data................................................................................... 202.3.1 Valuation Of Intangible Assets........................................................ 212.3.2 Which Measure Of Performance?.................................................... 242.3.3 Why Not Traditional Event Methodology? ..................................... 262.3.4 Long Run Buy & Hold Abnormal Returns...................................... 292.3.5 Calculating Reference Portfolios ..................................................... 31

2.4 Data ................................................................................................................. 322.5. Results............................................................................................................ 362.6 Discussion And Conclusion............................................................................ 44

3. How Long Does It Take To Build Corporate Capabilities For ConductingAlliances And Acquisitions?................................................................................. 503.1 Antecedents..................................................................................................... 513.2 Theory And Hypothesis .................................................................................. 53

3.2.1 Industry And Time Effects............................................................... 583.3 Data ................................................................................................................. 593.4 Descriptive Statistics....................................................................................... 613.5 Methods........................................................................................................... 643.6 Results............................................................................................................. 66


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3.7 Conclusion ...................................................................................................... 724. Why Do We Observe Heterogeneous Governance Choices For Similar

Transactions? Theoretical Issues In Corporate Strategy....................................... 734.1 Overview......................................................................................................... 744.2 Theoretical Background.................................................................................. 78

4.2.1 Transaction Cost Economics............................................................ 784.2.2 Property Rights Theory.................................................................... 804.2.3 Capabilities View Of The Firm And Agency Costs ........................ 824.2.4 Hybrid Forms As Real Options........................................................ 83

4.3 Model Setup And Intuition ............................................................................. 844.4 A Real World Example................................................................................... 92

4.4.1 Who Should Own What? ................................................................. 944.4.2 Case 1: Agent jB1 (Manufacturing Division Of Pfizer) Owns T

Target B2 'S (Arqule's) Assets........................................................ 95

4.4.3 Case 2: Target B2 (Arqule) Continues To Own Its Assets ........... 964.5 Discussion....................................................................................................... 97

List Of References ............................................................................................................ 99


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LIST OF TABLES

Table Page

1. Measures of Intangibility Part 1........................................................................... 116

2. Measures of Intangibility Part 2............................................................................ 117

3. Variable Descriptions.............................................................................................. 118

4. Descriptive Statistics............................................................................................... 119

5. Correlation Matrix for Target Variables ................................................................. 120

6. Correlation Matrix .................................................................................................. 121

7. Test of Median Equality for the 60-Month Average Buy-and-Hold AbnormalReturns .................................................................................................................... 122

8. Descriptive Statistics for the Average Monthly Buy-and-Hold Abnormal Returns

[BHARjt

]............................................................................................................... 123

9. Sample Descriptio................................................................................................... 130

10. Number of Alliance for each IPO year ................................................................... 131

11. Number of M&A for each IPO year ....................................................................... 132

12. Mortality Rates of Firms and Deal Frequency........................................................ 133

13. Average number of M&A deals per firm for each year following the IPO event .. 134

14. Descriptive Statistics for CARs per deal over -5,,+5 days around the dealannouncements........................................................................................................ 136

15. Descriptive Statistics for CARs per M&A deal over -5,,+5 days around the dealannouncements........................................................................................................ 137


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16. Descriptive Statistics for CARs per Alliance deal over -5,,+5 days around the dealannouncements........................................................................................................ 138

17. Descriptive Statistics for CARs-5,,+5 following the IPO year ............................... 139

18. Logit Analysis of Deal Type Choice (Alliance=1, M&A=0) and Past experience inthe same-type deals ................................................................................................. 140

19. Logit Analysis of Deal Type Choice (M&A=1, Alliance =0) and Past experience inthe same-type deals ................................................................................................. 141

20. Logit Analysis of Deal Type Choice and Market Reaction to the same-type deals 142


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LIST OF FIGURES

1. Cumulative Abnormal Returns Around the Announcement Day, t=0.................... 124

2. Average Monthly Abnormal Returns to the Acquirers with announcement year of1988......................................................................................................................... 125




6. Average Monthly Abnormal Returns to the Pooled Acquirers with announcementyears in 1988-1991.................................................................................................. 129

7. Model Payoffs......................................................................................................... 143

8. Corporate Capability as a System of Governance Mechanisms ............................. 144


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CHAPTER 1

INTRODUCTION

Firms can be viewed as bundles of resources (Rumelt, 1984) that can be broadly

partitioned into tangible and intangible resources. Intangible resources are less likely to

be redeployable in a second-best use without losing value. Therefore investments in

building/acquiring intangible assets are riskier than building/acquiring tangible assets.

``Given the role of both tangible and intangible assets of the firm, a strategist should

choose projects that are within the firm's area of expertise and appropriate to its skills''

(Itami, 1987: 159).

However, firms intending to grow are more likely to create deviations from this

ideal fit to accumulate intangible assets by, for example, following an overextension

strategy. First, firms that overextend know that they will not be able to do the new

business effectively when they enter the new market; second, they know that they will

eventually have to get into this new area; and third, those firms make sure that the

intangible assets accumulated will be applicable beyond the segment that they were

initially accumulated. M&A activity serves as an investment mechanism to achieve


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growth while possibly accumulating intangible assets1.

A consistent finding regarding mergers and acquisitions (M&A) is that: on

average shareholders of target firms earn significant economic gains whereas

shareholders of acquiring firms break-even (Jensen and Ruback, 1983; Jarrell et al.,

1988). Despite this general finding M&A activity has persisted, increasing in number and

transaction value because, managers often perceive M&A activity as a mechanism for

growth (e.g. Penrose, 1959). It is natural ask, `What type of assets are worth buying?'

Lang and Stulz (1994) suggest that firms with valuable future growth

opportunities have highly intangible assets. Intangible assets, such as managerial talent,

corporate culture, R&D expertise, and brand capital have also been identified as sources

of competitive advantage (e.g. Veblen, 1908; Grabowski and Mueller, 1978; Prahalad

and Bettis, 1986; Barney, 1991). Thus, target firms with such assets appear very

attractive to buyers. Can a buyer extract economic value associated with its target's

intangible assets?

This chapter investigates the long-run performance effects of acquiring intangible

targets versus tangible targets. Using a sample of M&A transactions spanning a 4 year

period (1988-1991), long-run-buy-and-hold expected returns are calculated by

constructing portfolios of cohort firms that pursue M&A activity in the 5-year post-event

period. Each firm's long-run-abnormal performance is calculated as the excess return to

the benchmark portfolio. Intangibility of the target's assets is proxied by Tobin'sq

1-

1Strategic alliances can be another external method to accumulate intangible assets and create growthopportunities. However, the economic value associated with such growth opportunities is endogenous. Thefirm's commitment to the alliance-related activities affects the value created. In the case of M&As,ownership and control rights of the buyer are more closely aligned.


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year prior to the corporate event. This classification is robust to other measures of

intangibility, such as R&D and advertising stock, and human capital intensity. Results

show that on average, acquirers of intangible targets earn negative abnormal returns,

whereas acquirers of tangible targets break-even. The average long-run abnormal

performance of a buyer in the sample confirms the stylized fact that acquirers break-even

at best.

The evidence on overconfidence is such that the individuals tend to be more

confident in decision making situations where the feedback is delayed or inconclusive

(Einhorn, 1980). The performance implications of M&As involving highly intangible

targets are more likely to have delayed feedback or be inconclusive. Also the expected

returns to such corporate events are harder to forecast. Thus, M&As of highly intangible

targets are more likely to create situations where overconfidence can play a role in

forming expectations. Moreover the behavioral model of Daniel, et. al. (1998) asserts that

investors are more confident about their private signals and overreact to such

information. In the same spirit with this model and above explanations, one would expect

the M&A activity involving targets with intangible assets to trigger misvaluation due to

overreaction.

Existence of asymmetric misvaluation between M&As of intangible versus

tangible targets is tested by regressing short-run returns on the buy-and-hold long-run

returns. Results provide evidence for market overreaction to the announcements that

involve highly intangible targets. The market overreacts to the announcements regarding

intangible targets and corrects its initial response over time. On the other hand, there is no

evidence of a misvaluation regarding the M&As of highly tangible targets. However this


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is not sufficient evidence to say that the overall market is inefficient. Market efficiency

suggest that there are classes of events that might be priced based on overreactions or

underreactions to information, but on average these effects are cancel each other out

(Fama, 1998). On the other hand, it is fair to say that the long-run underperformance of

buyers of intangible targets imply that firms that develop an expertise to manage M&As

of such targets can create competitive advantage.

The second chapter is organized as follows. In the first section, the theoretical

background and the hypotheses are presented. In the second section methodology used

and the data are discussed. In the third section, the main results are presented. Fourth

section includes the theoretical discussion of and empirical tests for other confounding

effects. In the fifth section, the relevant robustness tests and their results are presented. In

the final section theoretical and managerial implications of the findings are discussed

.


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CHAPTER 2

WHAT TYPE OF ASSETS IS WORTH BUYING THROUGH MERGERS &

ACQUISITION?

Assets fall into three categories (Lindenberg and Ross, 1981): i) those that are

sold in the market and constitute what is traditionally known as the capital stock, ii)

special factors of production which lower its costs relative to those of competitive or

marginally competitive firms2, and iii) special factors of production that the firm

possesses, which act as barriers to entry and generate abnormal returns. Intangible assets

are more likely to fall into the third category; such assets would have different economic

value for different owner-firms, thus creating resource heterogeneity and

nonredeployability. Intangible assets are information-based resources such as technology,

know-how, innovativeness, patents, brand equity, employee motivation and commitment,

customer service, corporate culture, and management skills. Tangible assets, such as the

plant, equipment, raw materials, and financial capital, have to be present for the business

operations to take place whereas intangible assets are necessary for competitive success

2Such resources are valued at their cost-reducing abilities (e.g. a river whose water acts as a naturalcoolant).


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(e.g. Prahalad and Bettis, 1986). It is also the case that well-managed firms with

highly intangible assets have unrealized growth potential for future. Well-managed

bidders (high Tobin's q ) benefit positively from tender offers especially if the targets

were poorly managed (low Tobin's q ) (Lang et al., 1989). However, well-managed

targets benefit less than the poorly managed targets from a tender offer. Two possible

explanations for this finding are offered (Lang et al., 1989). First, already well-managed

targets cannot be improved further through takeovers. Second, the fact that the bidder

succeeds in acquiring such a high q target may mean that the target is not as valuable as

the bidder initially thought. In both explanations, there is the underlying assumption that

the motivation for the takeover is to improve the quality of the management of the target

firm. Based on this assumption, the ``surprise'' factor of announcing a takeover would be

less pronounced since the market also would most likely predict that the target is not

well-managed and who the potential buyers would be.

Another possible reason why firms would want to buy targets with high

intangibles is to internalize the target firms' growth potential. Bidder firms can grow

through buying highly intangible targets ( 1>q ) by funding, otherwise not funded,

positive net present value (NPV) projects3. Acquirers that buy targets with less

3If highq

measures the growth opportunities stemming from intangible assets, above and beyond thetangible assets, why would the target firm be willing to sell the firm? For target firms with high intangibles,

as the degree of nonredeployability increases, it will be inefficient for debt holders to finance newinvestments because the increasing risk of default, coupled with high uncertainty regarding the flow ofproject cash-flows, would lead expected value of the debt holders' claims to decline. In such cases targetfirms would have to forego some of the positive NPV projects because of financing. Where projects facemarket breakdowns it is efficient to finance it through equity. Therefore equity financing is an endogenousresponse to governance needs of suppliers of finance (in this context the bidder firms) who invest innonredeployable projects. These suppliers are the residual claimants who are awarded `control' over theboard of directors.


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deployable assets foresee some positive NPV projects that only the merged company

could undertake. In this case, the information is most likely to be private to the buyer

firm. Therefore the ``surprise'' factor of an announcement of a takeover of a well-

managed target (high Tobin's q ) would be greater since the market becomes aware of

new information.

Hypothesis 1a: Abnormal returns to highly tangible targets in the pre-announcement

period would be higher than the returns to highly intangible targets.

Hypothesis 1b: Announcement-day abnormal returns to highly intangible targets would

be higher than the returns to highly tangible targets.

Intangible assets of a firm, such as R&D projects, patent stocks, and human

capital are more likely to be undervalued by the market when they are bought by another

company. Such assets would generally have high target-firm specificity and therefore

lower second-best use, which in turn leads to the undervaluation. This expected

undervaluation is common to both the market and the potential buyers. Given this adverse

setup, if the market observes a bid for a highly intangible target, in theory it should signal

the buyer's expectations to redeploy the target's intangible assets and create new growth

opportunities. Potentially, buying a firm with high intangibles is a more noisy way to

obtain a particular subset of intangible assets. Even though successful post-event

integration of targets with highly intangible assets as opposed to targets with highly

tangible targets is more problematic, this is also expected by the market participants as


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well as the buyers. Therefore, this difference should have less bearing on the post-event

long-run abnormalperformance of buyers4.

Overall, intangible sources of firm value are of a differential character, in that the

advantage of those firms who own them may lead to competitive disadvantage of those

who do not (Veblen, 1908). Conversely, tangible resources would not lead to competitive

advantage over firms that lack such resources5. The main reason is that the price charged

by the owners of those tangible resources in factor markets would be equal to the income

that would be generated by the buyers of those resources in product markets. Also if the

assets of the target have high redeployability (high ratio of tangibles), then acquiring such

targets would be, on average, equivalent to internally developing the same resources

because the costs associated with both methods would be approximately the same.

2.1 Resource-Based View and Competitive Advantage

There has been a systematic effort to distinguish the types of assets (tangible or

intangible) and their effect on the firm's competitiveness (e.g. Coff 1999a, 1999b; Delios

and Beamish, 2001; Finkelstein and Haleblian, 2002; Hall 1992, 1993; Hitt et al., 1990,

4What could make a difference is if managers' and the market's expectations of post-event integration ofhighly intangible targets differ significantly. Managers may either have favorable private information thatjustifies the acquisition of highly intangible target, or act in self-interest as a result of agency conflicts(Jensen, 1986) specific to the context of buying highly intangible targets. These two factors affect long-runabnormal firm performance in the opposite directions. However, the related theories are less explicit about

the aggregate direction.

5What about the highly synergistic acquisitions even though the target's assets are highly intangible? Thereis no theoretical reason to believe that the probability and the magnitude of post-event synergies wouldsystematically differ in cases where the target is highly tangible versus intangible. The only assumptionrequired to follow through with this logic is the following: thepotentialfor synergies is equally likely toexist for both the acquirers of highly intangible targets as well as highly tangible targets.


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1991a, 1991b; Mowery et al., 1998). For example, Prahalad and Bettis (1986)

emphasized a ``dominant logic'' as an intangible asset that could be shared between firms

through diversification to create economic value. Firms that develop their core

competency, defined as ``the collective learning in the organization, especially how to

coordinate diverse production skills and integrate multiple streams of technologies'', are

more likely to have a strategic advantage over their competitors (Prahalad and Hamel,

1990:82).

According to the resource-based logic, resources that are rare, valuable, and

inimitable are the real sources of competitive advantage (Barney, 1991a, 1991b; Barney,

1986; Conner, 1991; Rumelt, 1984; Wernerfelt, 1984). Of these firm-specific resources,

intangible assets are more likely to be the source of sustainable competitive advantage6

because they are harder and more time-consuming to accumulate, provide simultaneous

uses, and are both inputs and outputs of business activities. Another characteristic of

these intangible assets is that they are likely to be causally ambiguous (Dierickx and

Cool, 1989) making them less likely to be imitated by competitors (Barney, 1991a).

Therefore firms that seek to internalize intangible assets through acquiring highly

intangible targets are, on the one hand, trying to internalize new growth opportunities, but

on the other hand more likely to suffer from potential pricing, integration and

maintenance problems of the targets due to causal ambiguity, complexity and tacitness of

the very same intangible assets.

6Villalonga (1999) tested this assertion by using the predicted value from a hedonic regression of Tobin's qas a measure of resource intangibility and found supporting results.


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Knowledge, one of the most important firm-specific intangible assets, has been

developed as a reason for a firm's existence (Liebeskind, 1996; Spender, 1996)7. Highly

firm-specific knowledge would be harder to transmit because fewer parties other than the

innovator can benefit from the application of that knowledge (Henderson and Cockburn,

1996; McEvily and Chakravarthy, 2002). If the firm is an accumulation of idiosyncractic

knowledge that is valuable, what are the methods of developing that firm-specific

knowledge base? One of the direct methods is to pursue M&A activity and try to

internalize knowledge intensive targets. Such target firms are necessarily the ones with

highly intangible asset stocks. Buying a firm with high intangibles is a more noisy way to

obtain a particular subset of intangible assets8.

Although resource-based view and other related approaches to defining sources of

competitive advantage favor the accumulation and utilization of intangible assets, one

cannotextend these arguments to suggest any systematic differences between the two

M&A strategies: buying highly tangible versus intangible targets. If one argues that

7Leibeskind conceptualized firms as structures to keep knowledge proprietary (1996). This assumes, inessence, that there is a fully efficient market for knowledge, and that without the firm the knowledge wouldhave been diffused which in essence is similar to Porter's idea of entry barriers (1980). Conner andPrahalad (1995) developed a resource-based theory of the firm based on knowledge as a valuable asset. Themain argument is that, absent opportunism, firm organization would provide a better mechanism to allowan owner to provide his/her knowledge as input in the team production setting with higher value than in amarket setting. Information and knowledge are factors of production that could be sources of competitiveadvantage. However, these factors of productions are also very hard to price; moreover their value iscontext- and owner-specific. Given this, how would a strategic factor market for knowledge, and moregenerally intangible assets, work? I argue that the firm is an internal market for knowledge that decreasesthe inefficiencies of the external market for knowledge. Once an individual offers his/her knowledge to the

team production, the internal processes would translate it into a firm-specific knowledge base (Kogut andZander, 1992).

8An alternative and more precise way would be to develop intangible assets internally through firm-specificprocesses such as employee training and R&D. In this case, because the direct method of internaldevelopment would be more precise and less risky, the expected rate of return would more likely be lowerwhen compared to the expected rate of return to the acquirers of highly intangible targets.


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buying highly intangible targets would more likely be a source of competitive advantage,

then as a corporate strategy it constitutes a ``rule for riches'' and generates no sustainable

competitive advantage (abnormal returns). This is analogous to the performance

implications of related versus unrelated acquisitions (Seth, 1990a; Singh and

Montgomery, 1987; Clark and Ofek, 1994). Empirical evidence supports the theoretical

argument that both related and unrelated acquisition strategies can create significant

synergies.

As mentioned above, targets, on average, appropriate most of the economic value

associated with the acquisition synergies, while buyers on average breakeven (e.g. Jensen

and Ruback, 1983; Lubatkin, 1983, 1987; Agrawal et. al., 1992). Buyers can create

sustainable competitive advantages only if there are unique, valuable and inimitable

synergies with the targets (neither the target nor other bidders have this information) at

the time of the acquisitions (Barney, 1988). However, this condition can equally apply for

acquisition strategies of both highly tangible and intangible targets.

Hypothesis 2: On average, there is no systematic above-normal performance differences

between buying intangible versus tangible targets.

Hypothesis 3:On average, corporate strategies of buying intangible or tangible targets

cannot be a source of systematic competitive advantage.

2.2 Controls for Other Factors


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There are three alternative explanations9 that could affect the performance of

M&A strategies: agency motives, financing and tax treatments, and behavioral

explanations regarding market reactions. Controlling for these alternative explanations

also serves as robustness checks for the tests of the above hypotheses.

2.2.1 Agency Motives

It has been widely discussed that M&A strategies could be motivated by self-

interested managers. The relevant question in this context is would agency motives, such

as the use of free cash flow (or cash reserves) to increase the size of the firm (Jensen,

1986), systematically lead managers to pursue targets with highly intangible assets as

opposed to targets with highly tangible assets? Managers, who want to be viewed

favorably, have an incentive to delay or advance the project resolution. This type of

manipulation of information arrival can be achieved by greater investment in execution

projects (which tend to resolve early) than exploratory projects (which tend to resolve

late) (Hirshleiferet al., 2001). High ability managers are more likely to choose execution

projects and low ability managers are more likely to choose exploratory projects.

M&A activity of highly tangible targets would be similar to the execution projects

in the sense that the project resolution (realization of synergies) is less likely to be

delayed. However, M&A of highly intangible targets would be akin to exploratory

projects where the resolution of outcomes arrive later. Therefore, if the agency motives

play a differential role in target selection, then low ability managers are more likely to

9I would like to thank Anita McGahan and Karen Wruck for providing useful insights.


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pursue highly intangible targets to take advantage of the longer horizon of project

resolution. Also cash reserves increases the likelihood of engaging in M&A activity and

cash-rich bidders destroy 7 cents in value for every excess dollar of cash reserves held.

(Harford, 1999).

Based on the above discussion, the buyers that pursue targets with highly

intangible targets are expected to be firms with low ability managers and higher free cash

flows or cash reserves prior to the acquisition when compared to the buyers of highly

tangible targets. As a result, buyers of highly intangible targets are expected to

underperform in the long-run. However, this is only a necessary but not sufficient

condition to cause a systematic abnormal performance between the two types of

acquisition strategies. For such inefficient capital allocations to persist over time, there

have to be other systematic factors that impede corrections to irrational expectations. If

there are such impediments, than it is plausible to entertain the existence of irrational

expectations of firm performance when M&A of intangible targets are concerned.

For the purposes of testing for agency motives in this context, the following

hypotheses are developed:

Hypothesis 4a: If buyers of target firms with highly intangible assets have significantly

higher levels of pre-event free-cash-flows or cash reserves then such targets tend to

attract firms with costly agency problems.

Hypothesis 4b: If the market does not expect agency motives to systematically drive the

acquisition of targets with highly intangible versus tangible assets then there should


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be no significant differences between the market reactions to the two types of M&A

announcements.

2.2.2 Information Asymmetry and Financing of Intangible Assets

There are two main concerns that might affect the long-run performance of buyers

adversely. The first one is related to the increased debt burden of the buyers to finance

large transactions, such as M&A of highly intangible targets that also have large market

capitalizations. The second one is related to the adverse effects of information

asymmetries. If the highly intangible targets are already overvalued then the buyers of

such targets end up paying an excessive amount of premium.

Target firms that have highly intangible targets have unrealized but valuable

growth opportunities. How can these firms finance their growth opportunities, such as

R&D projects? First, retained earnings can be used. Second, the firm can seek external

financing from debt and/or equity financial markets. Since governance is costly, the

general rule is to reserve complicated forms of financing for complicated investments

(Williamson, 1991, 1988). `Expressed in terms of asset specificity, fungible assets can be

leased, semi-specific assets can be debt financed, and equity is the financial form of last

resort to be used for assets of a very nonredeployable kind' (Williamson, 1991: 84)10

.

Nonredeployability also suggests that the value of the assets in its first-best use is

significantly higher than the value of that same asset in its second-best use. Therefore, we

would expect nondeployable assets that are financed by equity to be intangible assets.

10Emphasis added.


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15

Overall, firms with highly intangible assets have a lower concentration of debt financing.

Titman and Wessel (1988) find a negative relationship between the measures of

uniqueness (e.g. high R&D expenditure) and its debt ratio (Debt/Equity). Specifically,

firms with low employee turnover and large R&D expenditures have relatively low debt

ratios. R&D intensive firms receive higher returns to firm shares following new debt

issues (Alam and Walton, 1995; Zantout, 1997).

There are two methods of equity financing: a target firm can either issue new

equity (seasoned equity offering-SEO) or be bought out by another company. There are

differences between the two methods of financing. First, there is well-documented

negative stock market reaction to announcements of SEOs for the issuing firm. The

dominant explanation for this empirical regularity is based in information asymmetry

between the firm's insiders and outsiders. Myers and Majluf (1984) show that with

information asymmetry, insiders have an incentive to issue new equity when the firm is

overvalued. The stock market knows this, and therefore discounts the firm that issues

SEO. Second, especially for firms with highly intangible assets, financing through SEO is

not preferred because of adverse effects of disclosing proprietary information about the

firm's projects that were to be financed with the proceedings. The only other method of

equity financing is through M&As.

Hypothesis 5: Buyers of highly intangible targets will decrease debt ratio when compared

to the buyers of highly tangible targets in the post-M&A period.

According to Modigliani-Miller theorem (1958, 1961), firms should be indifferent


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16

between internal and external sources of financing for the marginal R&D project since in

both cases the cost of capital would be the same. However, as widely researched, this

theorem fails in practice due to several reasons. As discussed in detail by Hall (2002), the

divergence between the internal and the external cost of capital is due to the information

asymmetries between the inventor and the investor, moral hazard on the part of the

inventor due to the separation of ownership and control, and tax considerations.

Asymmetric information creates a ``lemons'' market (Akerlof, 1970) for R&D

project financing because the investors have a hard time distinguishing good projects

from the bad ones when the projects are long-term R&D projects (Leland and Pyle,

1977). Therefore investors require a ``lemons'' premium (Hall, 2002). In the case of

highly intangible targets, buyers would require a premium for the ``lemons'' problem

associated with the information asymmetry between the target (inventor) and the acquirer

(investor). On the other hand, a takeover would decrease the moral hazard problems that

would have been present for the other potential investors if the target firm had issued

equity or new debt instead of being acquired.

Empirically there seem to be limits to leveraging strategy in R&D intensive

industries such that the leveraged buy-outs in the 1980s that were financed by high levels

of debt were almost exclusively in industries where R&D intensity was insignificant

(Hall, 2002, 1994, 1990; Opler and Titman, 1994). Tax treatment of R&D lowers the

required rate of return, because the effective tax rate on R&D assets is lower than that on

other types of tangible assets (Hall, 2002). On average, although these two effects act in

opposite directions, the rate of return expected by the buyers of highly intangible targets

would be higher than the expected rate of return expected by the buyers of highly


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17

tangible targets.

Moreover, buyer's of highly intangible targets expect to have post-event

integration problems (e.g. Greenwood et al., 1994). This expectation increases the

riskiness of fully realizing the expected synergies. As a result, buyer's expected rate of

return increases, and the price (premium) to be paid decreases. The effective tax rate on

R&D assets is lower than tangible assets such as plant, property and equipment because

R&D is expensed as it is incurred (Hall, 2002). This would mean that the rate of return

for such investment would be lower.

In comparing the acquisition of highly tangible targets versus highly intangible

targets, while moral hazard, post-event integration, and the ``lemons'' problems are more

severe for the case of intangible assets, tax considerations are more favorable. As the

riskiness of a project increases, the expected rate of returns increases. As the expected

rate of return increases the investor is willing to pay less.

Hypothesis 6: Buyers of highly intangible targets will pay a lowerpremium in

comparison to buyers of highly tangible targets.

2.2.3 Market Over- or Under-Valuation of Growth Opportunities

Firms are producers of tangible and intangible information about themselves.

Investors utilize firm-generated as well as other sources of information to decide on a


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course of action in both financial and labor markets11. Tangible information is explicit

performance measures such as book-to-market ratio of equity, earnings, and sales,

therefore any information generated by using financial statements will be tangible

(Daniel and Titman, 2001). However, this definition of tangible information may not

correspond one-to-one to the tangibility of the asset base. In essence, the validity of this

tangible information is more in question when the firm's asset base is highly intangible. A

firm with highly tangible assets is more likely to provide all the relevant information

about its nature as tangible information in the form of financial statements.

On the other hand, a firm with highly intangible assets, has a harder time

reporting information about itself in the form of financial statements; rather, it is more

likely to produce intangible information such as reputation or corporate culture (e.g.

Louis et al., 2001). Moreover a common ratio, such as book-to-market, will be downward

biased due to the lack of a book value of intangible assets in the numerator. This bias

taints its validity as a measure of tangible information. Investors are more likely to

overreact to intangible information, but rationally react to the tangible information

(Daniel and Titman, 2001). Distinctions between public versus private information follow

a similar logic (Daniel et al., 1998). Private information, such as the growth opportunities

of a firm, would be more ambiguously defined and is heterogeneous among investors12.

11For example, public firms, by law, produce more tangible information which is coded in financial

statements. On the other hand private firms do not produce as much tangible information and yet theinvestors might benefit from other types of intangible information like reputation to form their expectationsabout the firm's performance.

12The behavioral model of Daniel et al. asserts that investors are more confident about their private signalsand overreact to such information (1998).


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If we allow for the possibility of investor irrationality in the form of

overconfidence, stock prices reflect both systematic risk and misperceptions of firm's

prospects (Daniel et al., 2001)13

. Therefore, it is reasonable to expect mispricings due to

overconfidence to be more severe for firms with highly intangible assets, such as R&D

firms with relatively long-run projects (Daniel et al., 2001; Chan et al., 1999; Leland and

Pyle, 1977).

The mispricing will be equivalent to the divergence between the market's initial

reaction to the announcement and the post-event long-run stock market performance of

the buyers

14

.

Hypothesis 7: Market is more likely to correctly evaluate and price the buyer's synergies

with the highly tangible target.

Hypothesis 8: Market is less likely to correctly evaluate and price the buyer's synergies

with the highly intangible targets.

13The evidence on overconfidence is such that the individuals tend to be more confident in decision makingsituations where the feedback is delayed or inconclusive (Einhorn, 1980).

14However this divergence could be due to the revelation ofunexpectedbut negative news after the event,which could not have been incorporated into the market's reaction at the time of the announcement. Thiscase also requires the assumption that the unexpected but negative reaction to bad news is much moresevere than the unexpected but positive reaction to good news. Conversely, holding the severity of thereaction equal for both cases, the likelihood of unexpected negative news should be significantly higherthan the likelihood of unexpected positive news.


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Hypothesis 8a: Negative deviation between the market's initial response and the long-run

performance of the buyers represent marketoverreaction.

Hypothesis 8b: Positive deviation between the market's initial response and the long-run

performance of the buyers represent marketunderreaction.

2.3 Methodology and Data

Management and financial economics literature consist of many event studies that

detect abnormal stock returns following major corporate events or decisions, such as

earning announcements, acquisitions, stock splits, or seasoned equity offerings. However,

studies that are concerned with long-run abnormal returns in the context of M&A are

fewer in number15. Modified Tobin's q is used as a proxy for the measure of intangible

assets in the target firms which will be discussed in detail. The analysis is concerned with

measuring abnormal economic performance. The most important step in measuring

abnormal performance is to define a theoretically sound benchmark to proxy the expected

performance. First, a brief discussion of modified Tobin's q will be provided. Second, the

traditional method of calculating long-run abnormal returns will be discussed. Second,

15The main concern in these event studies is to determine whether there are abnormal returns associatedwith the firm-specific events. There is considerable variation among these studies regarding the calculationof abnormal returns and the statistical tests carried out to detect the presence of abnormal returns. Refer to

McWilliams and Siegel (1997) for a detail discussion of event studies. Some representative studies areSeth, (1990a, 1990b), Lahey and Conn (1990), Conn et al. (1991), Haleblian and Finkelstein (1999).


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the shortcomings of this method will be presented. Barber and Lyon (1997) provide

evidence that the common techniques used to calculate short-run abnormal returns, when

applied over a longer horizon, are conceptually flawed and/or lead to biased test statistics.

Finally, the data and the methodology used in this study will be discussed.

2.3.1 Valuation of Intangible Assets

In this paper, we use 1-year pre-event Tobin's q (Tobin, 1969) as an indicator of

the target's intangible assets (Daniel et al., 2001; Klock and Megna, 2000; Loughran and

Vijh, 1997; Lang et al., 1989). True q ratio of i th firm is defined as the market value

of all financial claims on the firm, iMV , relative to the firm's total assets calculated as

the sum of the i th firm's replacement values of tangible assets iT and intangible assets

iI .

ii

ii

IT

MVq

+

In competitive markets with linear homogeneous production technology that

employs optimal level of capital stock16, the i th firms true equilibrium q ratio will be

16The assumption on homogeneous production technology allows us to study firms in equilibrium. Both

acquirers and targets are incumbents in their industries, and every firm has an existing base of capital.


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equal to 1 (Megna and Klock, 1993). The iMV of the target firm is measured as the

sum of all outstanding claims on the firm including book value of debt iD and preferred

stock iP , and market value of common equity iC . Book value of total assets is denoted

as iA .

Debtterm-LongsInventorieofBook value

AssetsCurrentsLiabilitieCurrent

where,

++

++

i

iiii

D

CPDMV

Since in reality we do not have the theorized homogeneity due to differences in

tax provisions, depreciation schedules, heterogeneous production functions given firm-

specific resources and capabilities, etc., the true equilibrium value of i th firm's q

defined by 'q is unobservable. Therefore one observes

i

i

iT

MVq

The replacement cost of tangible assets of i th firm is approximated by iA ,

book value of total assets, because the replacement value of intangible assets is not

reflected in the iA . This approximation follows Chung and Pruit (1994). Although it is

not as accurate as the Lindenberg and Ross's (1981) algorithm, due to differences in the

calculation of the replacement value of assets, both are highly correlated (Lee and

Tompkins, 1999; Bharadwaj et al., 1999). Also, the advantage of this approximation is


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23

that bidder firms and the investors are more likely to use this simpler formula that

requires publicly available financial and accounting data. It is reasonable to assume that

the observed q values will approximate the portion of market value of the firm

explained by the firm's tangible assets. If q is greater than 1 then there are firm-

specific valuable intangible assets contributing above and beyond the firm's tangible

assets.

A q that is less than 1 would suggest that the firm's tangible resources and

capabilities are underutilized or that there are value destroying intangible resources (e.g.

bad management). Such intangible resources, in theory, would have negative replacement

value. If we can fully explain the market value of a firm based on its tangible resources

then the firm's q is equal to 1 . In the mean time, the equilibrium value of q for any

firm will change from year to year as there are changes in the mix of the old capital, new

capital, and intangible capital, as well as changes in the macroeconomic and regulatory

environment (Megna and Klock, 1993). Another source of change in q is M&As that

are most likely to alter the mix of a firm's asset base and its economic value.

Accounting based measures of intangibility are based on R&D and advertisement

expenditures (Lev and Sougiannis, 1996; Louis et al., 2001), and labor costs (Qian,

2001). Each firm's R&D (advertisement) capital is estimated from its pre-event history of

R&D (advertisement) expenditures based on Lev and Sougiannis (1996) and Louis et. al.,

(2001) as follows17:

17The financial information is taken from the COMPUSTAT/CRSP merged database provided by WhartonResearch Database Services. R&D expenditure is annual data item 46; sales is annual data item 12; netincome is annual data item 172; dividends and book value of common equity are measured as annual dataitems 21 and 60.


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4321

4321

2.04.06.08.0

2.04.06.08.0

++++=

++++=

itititititit

itititititit

ADVADVADVADVADVADVC

RDRDRDRDRDRDC

where itRDC and itADVC are the R&D and advertisement capital respectively

for firm i in year .t These estimates of R&D and advertisement capital measure the

proportion of past spending that is still productive in a given event-year }0,..,5{=t

based on current and past R&D and advertisement expenditures of itRD and itADV .

This approximation assumes that the productivity of each dollar of spending declines

linearly by 20% a year. As a robustness check the approximations are recalculated by

using a 15% capital amortization rate that is used by Hall et. al. (1988) for the database

compiled on R&D activity. The results are qualitatively unaffected. The intangibility of

the target firm is measured by the estimated R&D (advertisement) expense as a

percentage of either total sales, and cost of goods sold. These ratios are recalculated using

R&D (advertisement) capital. Other measurers include Tobin's q , 4 -digit SIC

adjusted leverage ratio, total intangibles (R&D and advertisement capital) as a percent of

cost of goods sold, and cash as a percent of sales. As discussed earlier as the intangibility

of a firm's assets increases, its debt ratio decreases whereas its cash reserves increases to

fund projects internally. In Table-1 the descriptive statistics (in Panels A and B) and

between-group equality tests are reported.

2.3.2 Which measure of performance?

Strategic management is concerned with improving firm performance. Thus any

strategy such as M&A activity, is assumed to have an effect on firm performance, which


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is the main concern of this paper. Performance can be measured (accounting versus

economic performance) in multiple ways over various lengths of time (short-run versus

long-run). In M&A what is the relevant performance measure that would allow detection

of sustainable competitive advantage? This line of inquiry allows whether or not

acquiring firms internalize the economic value associated with the intangible assets of the

target firm.

Intangible assets of a firm are akin to latent assets (Brennan, 1990) in the sense

that they cause a potential bias in the firm's market value mainly because they are

expensed in the accounting statements. It would be the case that the accounting measures

would understate the true return in the early years for investments in capacity, new

product R&D, etc.18

. On the other hand, firm value can be viewed as being generated by

its tangible assets and intangible assets. Accounting measures that use book values of the

firm's assets would cause a downward bias in the performance measures as the

concentration of firm-specific productive intangible assets increases. This downward bias

would be most severe in the short-run because those development projects would not

generate any income in the early years. Overall, based on the extensive literature on the

drawbacks of using accounting measures, economic measures of performance based on

stock returns will be employed in this paper.

18This is especially critical for the valuation of growth opportunities of firms. Although, by conventionalaccounting measures a highly intangible target may appear to be trading at a premium, for the buyercompany the price paid can be justifiable.


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2.3.3 Why not traditional event methodology?

The traditional approach in corporate event studies is to calculate Cumulative

Abnormal Returns ( CAR ). A security's performance can only be considered `abnormal'

relative to a particular benchmark (Brown and Warner, 1980). Therefore a model

generating normalreturns (exante expected returns) has to be specified. Almost

exclusively all event studies of this kind apply the Capital Asset Pricing Model (CAPM)

or market model19

to estimate the normal returns. This method focuses on average market

model residuals of the sample securities for a number of periods around the event date.

The null hypothesis is such that if there are no significant effects associated with the

corporate event, CAR s will be a random-walk.20

The operationalization is as follows.

First, we define itR as the month t simple return on a sample firm i , )( itRE as the

month t expected return for the sample firm, and itAR as the abnormal return in

month t . To calculate ),( itRE we regress itR on the market portfolio MtR over an

estimated period of kt = L1 daysprecedingthe event as Mtiiit RbaR += , where

ia and ib are the ordinary least square parameter estimates.21 Then

19Refer to Chatterjee et al. (1999) for a discussion of the CAPM and the strategic theory of risk premium.

20The average residual in the event time are independent and identically distributed, with a mean of zero.

21It is important to notice that the coefficient ia is in fact the systematic risk factor, in the CAPM.


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27

Mtiiit RbaRE +=)( is calculated for L0=t periods after the announcement.

L0for)( == tRERAR ititit

Cumulating across periods yields a CAR :

it

t

it ARCAR

1==

Generally these studies identify 5= or some other shorter window of

analysis around the event date of 0=t . The main argument behind this identification is

that the stock prices reflect the discounted economic value of all future expectations. As

discussed earlier, M&A activity is an event that is much more complex than any other

corporate event such as an earnings announcement. Also the nature of the event is

conducive to exacerbate any potential investor biases such as overconfidence. Therefore

studies as early as 1974 have started looking at longer post-event periods to gauge long-

run performance effects of M&A activity.22

All of these studies used CAR and overall

document negative CAR for mergers and positive CAR for tender offers. Also most of

these studies document a less than %3 CAR in magnitude for combined M&A activity

with varying signs.

22Refer to Mandelker (1974), Dodd and Ruback (1977), Langetieg (1978), Asquith (1983), Bradley et. al.(1983), Malatesta (1983), Agrawal et. al. (1992), Loderer and Martin (1992), Gregory (1997), Loughranand Vijh (1997), Rau and Vermaelen (1998). These studies are reviewed in detail by Bruner (2001).


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There are four major types of biases documented when using CAR methodology

to determine long-run abnormal returns (Barber and Lyon, 1997). First, CAR s (summed

monthly) ignore monthly compounding, which leads to a measurementbias in the

calculation of long-run abnormal returns. Consequently, the t-statistics would be

negatively biased. Second, new listings andsurvivorbiases occur because sampled firms

are tracked for a long post-event period, but firms that constitute the index (e.g. S&P500,

equally-weighted or value-weighted market indexes) typically include firms that begin

trading subsequent to the event month or firms that are delisted subsequently.

Especially with the new listed firms, which are mainly IPOs, the index

incorporates the underperformance of these firms and results in a downwardly biased

estimate of the long-run return from investing in a passive (not rebalanced) reference

portfolio in the same time period. Third, rebalancingbias arises because the compound

returns of a reference portfolio (proxied by a market portfolio, e.g. equally weighted

market index), is generally calculated assuming periodic (e.g. monthly) forced

rebalancing to maintain equal weights. This rebalancing inflates long-run return on the

reference portfolio. Skewness bias arises because the distribution of long-run abnormal

stock returns is positively skewed, which also contributes to the misspecified test

statistics. An alternative method for calculating long-run buy-and-hold abnormal returns (

BHAR ) that eliminate these biases will be briefly discussed next.

Two other sources of bias that affect the test statistics in long-run performance

studies of both types ( CAR vs. BHAR ) are due to the cross-sectional dependence

between firms and the validity of the asset pricing model used to estimate abnormal

returns. In this paper the expected returns are not estimated by a model such as CAPM;


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therefore, the validity of the model is an irrelevant issue. However cross-dependence

might be a source of bias in BHAR studies. The main problem is that cross-sectional

dependence inflates test statistics because the number of sample firms overstates the

number of independent observations. It is especially problematic if there is calendar date

clustering (e.g. a high number of announcements per a specific event date) or industry

clustering23.

As long as there is no additional industry clustering or unusual pre-event return

performance, the approach used in this paper also eliminates the biases due to cross-

sectional dependence and `bad model'. Moreover reference portfolios are formed in each

sample-year, which also accounts for any cross-sectional dependence due to the event

year (e.g. hot versus cold periods for M&A activity). The method used in this paper to

calculate returns to reference portfolios formed by similar firms in terms of their size, as

well as their book-to-market ratios as a proxy of long-run expected returns to a firm that

engage in M&A will be discussed further in detail.

2.3.4 Long Run Buy & Hold Abnormal Returns

The first issue is to decide on an unbiased measure of abnormal returns that

reflects the investor behavior accurately, because CAR s and BHAR s answer two

23In this sample, there are more than 75 industries with no significant clustering of M&A activity, as well as

no significant calendar-date clustering.


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different questions (Barber and Lyon, 1997). For example, a test of null hypothesis that

the 12 -month CAR is zero is equivalent to a test of the null hypothesis that the mean

monthly abnormal return of sample firms during the 1 -year post-event period is equal to

zero. On the other hand, the null hypothesis that 1 -year BHAR is equal to zero would

test whether the mean annualabnormal return is equal to zero. Of course, for

detecting/testing long-run abnormal returns the second null hypothesis is relevant.

itBHAR are calculated as the difference between the return on buy-and-hold investment

in the sample firm and the return on a buy-and-hold investment in an asset portfolio with

an appropriate expected return. As shown below the calculation of itBHAR , unlike

CAR, takes into account the monthly compounding (Barber and Lyon, 1997; Lyon et al.,

1999) .

[ ] [ ])(1111

it

t

it

t

it RERBHAR ++===

This method eliminates the measurementbias due to the realistic compounding. It

is also imperative to state that this difference in compounding makes CAR a biased

estimator of itBHAR 24.

24Let's compare 1-year CAR and 1-year BHAR for a random sample of 10,000 observations (Barber

and Lyon, 1997). Assume that both CAR and BHAR are calculated using equally weighted market

index. The annual CAR and BHAR per portfolio are calculated for 100 portfolios, each of which has100 stocks. Since, on average, the returns on individual securities are more volatile than the return on the

market index, CAR is understated when BHAR is above zero and overstated when BHAR is belowzero (Barber and Lyon, 1997).


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2.3.5 Calculating Reference Portfolios

Expected returns are proxied by forming size/book-to-market reference

portfolios

25

. Fifteen reference portfolios are formed based on acquirer firm size and book-

to-market ratios in the sixth event month of each sample-year (1988-1991) to eliminate

new listings and rebalancingbiases (Lyon et al., 1999). First, we calculate firm size

(market value of equity calculated as price per share multiplied by shares outstanding) in

the sixth event month of each year (for the entire event period of 1988-1992 for the

acquirers in 1988, and 1989-1993 for the acquirers in 1989, etc.) Second, in the sixth

month of year t , we rank all the sample firms that engaged in M&A in each sample

year, for example, 1988, on the basis of firm size and form 5 size portfolios based on

these rankings. Third, in each size group we rank the firms based on their book-to-market

ratio (book value of common equity (COMPUSTAT data item 60) reported on the firm's

balance sheet in year t divided by the market value of common equity in the end of year

t ) and further partition each size group into 3 book-to-market subgroups. Finally, the

returns to the 15 size/book-to-market portfolios are tracked for the period of 60=

months.

25Reference portfolios based in industry membership could also be considered to proxy expected returns tothe buyer firms (e.g. Dess et al., 1990). This assumes that the expected returns vary systematically acrossindustries. However industry membership has no power in explaining stock return, whereas market value of

equity and book-to-market ratio of equity do have explanatory power (Fama and French, 1993). In otherwords industry membership is not priced while equity size and book-to-market ratio of equity are priced bythe market, which warrants for controlling those systematic effects. Also BHARs are biased (Lyon et al.,1999) only if there is an industry clustering which is not relevant for this sample.


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Long-run returns on each reference portfolio are calculated by first compounding

the returns on securities constituting the portfolio and then summing across securities:

s

it

s

st

i

n

ps

bh

n

R

Rs

1)1(

1

+

=

+

=

=

where sn is the number of sampled securities traded in month s , the beginning

period for the return calculation. The return on this portfolio represents a passive equally

weighted investment in all securities constituting the reference portfolio in that period s

. With this method there are no new listings and rebalancingbiases because there are no

out-of-sample new firms added subsequent to period s .

itBHAR is calculated using (), and the )( itRE is proxied by () which were in

essence formed by matched control firm technique. The use of reference portfolios based

on a control firm approach yields well-specified test statistics in both random and size

samples (Lyon et. al., 1999). Also if size/book-to-market portfolios are formed by an

increased number of groupings (thus with a lower number of firms in each group) the

precision of the reference portfolios increases; in fact, this technique can approach the

rule of matching a control firm with 70-130% of its size and/or book-to-market ratio26

.

2.4 Data

26There are 15 size/book-to-market portfolios in this study. The range of number of firms in each portfoliois between 5 and 7 and the firms in each portfolio are within 70-130% range of their size and book-to-market ratios.


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For the analysis, recapitalizations, self-tender offers, and repurchasing of common

shares, are excluded from the sample. Buyers should have stock returns for the preceding

60-months after the announcement date and target firms should have available

information to calculate q . A total of 413 M&As during 1988-1991 were identified

from the Thompson Financial Securities Data (SDC Mergers and Acquisitions Database)

that fit the selection criteria27

. Financial and accounting data are obtained from

COMPUSTAT and the stock returns data is extracted from CRSP tapes using WRDS

database. Missing returns of acquirers are replaced with a mean monthly reference

portfolio return that the company belonged to based on its last reported return. It is, in a

sense, reinvesting the returns from the delisted stock, for that month, on the reference

portfolio that the stock belongs to.

After excluding the acquirers of target firms for which q could not be

calculated due to nonreported balance sheet item, there were 109 acquirers in 1988=y

. After calculating itBHAR using (), the acquirers were divided into two groups based

on the type (high or low q ) of targets they bought. Of the 109 acquirers, there are,

1gn , 75 acquirers that bought target firms with 1q (group 1=j ), and, 2gn , 34

27This might potentially limit us to draw inferences conditional on the survival of the acquirers. Howeverbecause the abnormal returns are not calculated by using a regression model, the tests of equality as well astest to detect abnormal returns are not affected. Moreover the benchmark performance for each firm in thesample is also formed within the sample, eliminating any survivorship bias in the calculation of abnormal

returns.


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acquirers that bought target firms with 1>q (group 2=j ). For each group j , the

average monthly jtBHAR is calculated for 60 post-event months as:

2,1and601for1 ==

= = jtn

BHAR

BHARgj

it

n

ijt

gj

L

The calculation of itBHAR for the other sample-years is carried out in the same

manner. The resulting pooled dataset has 780,24 firm month observations. The

frequency statistics per-sample year is as follows:

157,256,413

29,70,991991

48,47,951990

46,64,1101989

34,75,1091988

21

21

21

21

21

====

====

====

====

====

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In Tables 3-5 various additional descriptive statistics are presented for the

variables used in the analysis as well as some representative variables such as

advertisement capital as a percent of sales ( )advstsls ; R&D capital as a percent of sales

( )rdstsls ; plant, property, and equipment capital as a percent of sales )(ppesales ;

and plant, property, and equipment capital as a percent of total assets( )ppeasset . The

descriptions of the variables are provided in Table-2. Descriptive statistics for the

variables are presented in Table-3. If we look at the correlation matrix in Table-4, we

observe that the advertisement capital as a percent of sales is positively correlated


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,130.0( )008.0=p with the group membership, rankq . We can conclude that it is

more likely to have a highly intangible target when that target has a higher ratio of

advertisement expenditure accounting for the net sales. It is reasonable to assume that

much of advertisement expenditure directly affects brand equity. Moreover, plant,

property, and equipment capital as a percent of total assets is equally and negatively

correlated with advertisement capital as a percent of sales ,242.0( )002.0=p and

R&D capital as a percent of sales ,241.0( )002.0=p . This is also in line with the

argument that firms that have higher advertisement and R&D capital as a percent of sales,

such as consulting firms or high-technology firms, would be less likely to have highly

tangible assets such as plant, property, and equipment.

In the theoretical setup, we argued that the type of asset-base of the target would

have a differential effect on the abnormal returns to the acquirers in the long-run. It is

important to stress the need to look at the long-run performance to gauge for performance

differences between acquirers of highly intangible versus highly tangible targets, because

even if we have similar synergies between the two types, the processes which firms need

to go through to realize those synergies would be different across these two types of

M&A. In the case of highly tangible targets there is very little that is unknown. However,

in the case for highly intangible targets, such as high-technology firms, it is a matter of an

acquirer's firm-specific capability even to pinpoint the source of economic value because

it would not necessarily be in the accounting statements.

The most important result in Table-5 is the relationship between q of the target

and the itBHAR of the acquirer. There is a negative and significant relationship between


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the level of intangibles of the target before the acquisition and the buy-and-hold long-run

abnormal returns, itBHAR , to the acquirer of that target ( ,065.0 0001.0


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significance tests reject the null hypothesis that there is no significant difference between

the pre-event CARs to highly tangible versus intangible targets (test-statistic 36.3= ,

001.0= valuep , and the median target CARs for both groups is negative) with

001.0= significance level. This finding supports the expectation that the bids for

highly intangible targets constitute more of an unexpected event than the bids for highly

tangible targets.

In the same line of argument, announcement-day returns to highly intangible

targets are expected to be higher than the returns to highly tangible targets (Hypothesis

1b ). The null hypothesis of no significant differences between the announcement-day

returns to the buyers or highly intangible versus tangible targets is rejected (test-statistic=

-2.49, 01.0= valuep ), providing evidence that announcement median CARs to

buyers of highly intangible targets (median 01.0=CAR ) is statistically higher than

median CARs to buyers of highly tangible targets (median 0018.0=CAR ).

The BHAR methodology provides the abnormal returns to an acquiring firm

above and beyond the expected returns that would be generated by firms with same

size/book-to-market ranking that employ the same strategy of M&A. Figures 2-6 show

striking differences between the monthly average abnormal returns for each group of

acquirers (buyers of highly intangible versus tangible targets). In each figure, there is a

significant downward trend in the average buy-and-hold abnormal returns to the acquirers

of highly intangible targets in the 60-month period after the merger announcement28

. In

28However in Figure-4 there seems to be an outlier month that corresponds to 1996. When the t -statistics

are recalculated for 1991 to test the equality betweentMed1 and tMed2 with a restricted sample of


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Table-6, the following null Hypothesis 2, regarding the equality of medians across two

time series in each year is tested:

pooledyBHARBHARH

pooledyBHARBHARH

yya

yy

,19911988for:

,19911988for:

212

2120

L

L

=

==

where yBHAR1 is the median of the 60-month time series of average buy-and-

hold abnormal returns to the firms that acquired highly tangible targets )1( q in the

year y . Similarly, yBHAR2 is the median of the 60-month time series of average buy-

and-hold abnormal returns to the firms that acquired highly intangible targets )1( >q in

the year y .

Buy-and-hold long-run abnormal returns, generally, have positively skewed

distributions that would violate the normality assumptions. This violation introduces a

downward bias in the t-statistics (Barber and Lyon, 1997). Therefore nonparametric tests

of equality are carried out for the median value rather than the mean value for the

series29

. The key test-statistic reported is one of Wilcoxon signed-ranks nonparametric

50-months the null hypothesis is still rejected at the 01.0= significance level. Similar results are alsoobtained when the rest of the t-tests are run based on 50-month samples.

29Overall nonparametric tests are more powerful for detecting abnormal performance when compared to theparametric test (Barber and Lyon, 1996).


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test30, although other equality tests31 are also reported in Table-6. Based on the t-statistics

and the associated probabilities, we can reject the null hypothesis at the 05.0=

significance level, for each sample year as well as the pooled series, that the abnormal

returns, on average, would be the same for both strategies of buying highly tangible and

intangible targets. This result fails to support the theoretical argument that there are no

systematic differences between the acquisition strategies of buying highly tangible versus

intangible targets.

According to Hypothesis 3 , we expect that the abnormal returns, on average,

would be equal to zero for both of sets of acquirers. The related test is as follows:

2,1and,19911988for0:

2,1and,19911988for0:

3

30

==

===

jpooledyBHARH

jpooledyBHARH

jya

jy

L

L

30Suppose that we compute the absolute value of the difference between each observation and the mean,and then rank these observations from high to low. The Wilcoxon test is based on the idea that the sum of

the ranks for the samples above and below the median should be similar. P-value for the normalapproximation to the Wilcoxon T-statistic is reported after being corrected for both continuity and ties.

31``Kruskal-Wallis one-way ANOVA by ranks'' is a generalization of the Mann-Whitney test to more thantwo subgroups. The test is based on a one-way analysis of variance using only ranks of the data. Kruskal-Wallis test statistic is calculated by the chi-square approximation (with tie correction). Under the nullhypothesis, this statistic is approximately distributed with [(Number of Groups)-1] degrees of freedom.``Van der Waerden'' (normal scores test) is analogous to the Kruskal-Wallis test, except the ranks aresmoothed by converting them into normal quantiles. The reported statistic is approximately distributed with[(Number of Groups)-1] degrees of freedom under the null hypothesis. ``Chi-square'' test for the median isa rank-based ANOVA t

corp stratg capablility intangible asset

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