THE IMPACT OF MERGERS AND ACQUISITIONS ON INNOVATION AS MEASURED BY RESEARCH AND DEVELOPMENT INTENSITY WITHIN THE HIGH

TECH SECTOR, 1990-2014

Sam Boutin

Abstract

Mergers between competing firms, also known as "horizontal" mergers, are a significant

dynamic force in the American economy. In the last decade alone, over 20,000 intentions to

merge were filed with antitrust authorities. The Federal Trade Commission and the

Department of Justice have 30 days to forecast the competitive effects of the proposed

merger with the goal of stopping those mergers that may harm competition and consumers.

Therefore, the methods used to predict these effects are of the utmost importance. In this

project I use an event study method to examine the abnormal stock market returns of close

competitors of the merging parties as a possible method to determine the effects of mergers

and acquisitions. I particularly focus on mergers in the research-intensive industries, as

they account for a disproportionate contribution to overall innovation in the U.S. economy

and as such, the costs of making the wrong decision in these sectors are larger. Using more

than 250 mergers occurring during the period between 1990-2014, I present evidence that

event studies can help antitrust agencies when considering mergers in any of the industries

where their decision, whether affirmative or negative, is likely to have repercussions for

future innovation. This study, in addition to assessing the public policy implications of

large mergers and acquisitions in research-intensive industries, also provides guidance on

how to define rivals, construct portfolios, and use event studies in merger evaluations.

I. Introduction

In the United States there were roughly 10,000 mergers per year from 1985 to 2011, and

the value of these transactions ranged anywhere from $250 billion to $2.85 trillion a year.

Mergers and acquisitions as seen above are very numerous and coupled with the fact that

these transactions deal with millions sometimes billions of dollars, they pose a large risk to

not only the individual consumer but society as a whole. The United States has enacted

several antitrust laws in order to prevent monopolistic mergers and acquisitions within the

US. Charged with protecting consumers from these possible harmful mergers and

acquisitions are the Department of Justice (DOJ) and the Federal Trade Commission

(FTC). These agencies were granted this responsibility via the Hart-Scott-Rodino Antitrust

Improvements Act of 1976, which dictates that the DOJ and FTC have 30 days, 15 for cash

offers, to gauge whether or not the filing requires further inquiry.

During this 30-day interim the DOJ and FTC must do their best to determine the

motives behind the merger or acquisition. Motives for a firm to merge with or acquire

another firm include two main theories, the “productive efficiently”rtheory, stating firms

merge in order to utilize economies of scale, and “market power”atheory, asserting that

firms merge solely to increase their market share. Depending on the acquiring firm’s

motivation consumers and rival firms can experience either positive or negative effects. In

the case of the “productive efficiency”rtheory, the acquiring firm benefits because it can

produce goods for less because of the utilization of economies of scale and in turn they can

pass these savings onto consumers. Rival firms suffer because they are now competing with

a more efficient company. With respect to “market power”atheory, the acquiring firm

gains market share which allows it, as well as its rivals, to raise prices ultimately hurting

consumers.

For these reasons that it is so important for regulatory agencies to have the appropriate

tools in order to determine whether a merger is benefiting society by increasing innovation

or if in fact the merger is harmful towards competition and the consumer. Initially Robert

Stillman (1983) and Espen Eckbo (1983) proposed observing stock market reactions of

competing firms to merger and acquisition publications to help forecast possible impacts of

these mergers. The general hypothesis was that positive cumulative abnormal returns

(CARs) suggest that the merger lessens competition allowing rival firms to increase in

value, while negative CARs propose that the merger enhances competition in turn lowering

rivals’rvalues. However, this method of analysis was all but phased out, likely in part to the

fact that there is a lack of evidence suggesting CARs help predict merger outcomes on

which antitrust agencies are focused, for example, impact on market prices, output and

innovation.

This Paper seeks to explain the effect that announcements of mergers and acquisitions

have on a companies, particularly in the four most innovative sectors of the economy;

consumer products and services, high tech, industrial and telecommunications, and those

consisting of transaction values no less than $500 million. This will be done through the use

of cumulative abnormal returns (CARs) that companies and their competitors receive from

a merger. These allow us to understand the effect that a merger or acquisition have on a

company’s stock price over time. This method has been used most recently by Filson, Olfati

and Radoniqui (2015) and this study, as well as others that utilized this method, is

explained in the review of literature in order to provide us with a history of the use of event

studies as well as their accuracy in predicting the effects of mergers and acquisitions.

This study uses the event study methodology which can be broken down into three

steps. First we look at a firm’s data, specifically its stock price and R&D intensity before an

announcement of a merger or acquisition is made. Then we analyze the firm at the point

the new information is made public. Finally, we look at the firm’s data in the trailing time

of the announcement to understand how price and R&D intensity are affected. Using the

event study methodology, we look not only at the acquiring firm’s data but also the data of

the firm being acquired as well as its major competitors in the industry.

The paper proceeds as follows. In Section II, we review the related literature. Section

III presents the empirical specifications, details the data, econometric model used, and the

variable construction used in the analysis. Section IV discusses the estimation results, and

section V discusses conclusions on the subject.

II. Review of the Relevant Literature

Mergers and acquisitions and their effects on the economy as well as on competing

firms have been analyzed quite extensively in past studies. The first study to look at the

effects of horizontal mergers was a study done by Espen Eckbo (1983), who tested the

hypothesis that horizontal mergers generated positive abnormal returns to stockholders

because of an increase in the probability of successful collusion among rival producers.

Under this hypothesis Eckbo postulates that because of collusion rival firms can limit

output and raise prices which in turn would have an adverse affect on consumers and the

economy. Furthermore, Eckbo looks at firms that were targeted and challenged by the

government for violating antitrust laws, finding that antitrust agencies systematically pick

reasonably profitable mergers for persecution, regardless of the fact that there is little

evidence signifying these mergers would have had anticompetitive effects. Being the

seminal work in this area, Eckbo not only answered important questions but laid the frame

work for future research questions. Most notably, how the release of new information of a

possible merger can be used in event based studies to track the variation in price of both

the acquirer stock and the stocks of its competitors and compared to what prices were

predicted to be had this information not been released.1

Robert Stillman (1983) conducted a study in which he stipulated that to be

anticompetitive or socially wasteful, a horizontal merger must result in higher product

prices to the consumer. Stillman stated that higher product prices would result in increased

profits for rival firms so if a horizontal merger was to cause detriment to consumer

welfare, competitor firms would have to rise in value at the time of the event. His study

used daily stock return data from a sample of rivals of 11 horizontal mergers between 1964

and 1972 which had been challenged by antitrust agencies. His hypothesis was if not for

government intervention these mergers would have resulted in higher product prices. His

conclusions, in fact, supported this theory confirming that had government not intervened

these mergers would have caused adverse affect on consumer welfare.2

Another Study done by Eckbo (1985) examined the classical market concentration

doctrine which predicts that horizontal mergers are more likely to have collusive,

anticompetitive effects the greater the merger-induced change in industry concentration.

1 Espen B. Eckbo, 1983 2 Robert Stillman, 1983

He tested this proposal by examining the cross-sectional relationship between the industry

wealth effect of merger proposal announcements and industry characteristics. His

conclusions are quite revealing in that they reject the antitrust doctrine which for the three

decades prior to him conducting this study had been considered critical by antitrust

agencies in determining the likelihood that a horizontal merger would have had

anticompetitive effects.3 This Study created more questions than answers because it called

into question everything that we thought had been known about accurately targeting

mergers believed to have anticompetitive effects.

Bronwyn Hall (1990) used data on R&D to observe the impact that acquisitions had on

industrial research and development. Hall found evidence of declines in R&D intensity as

measured by the ratio of R&D to sales after acquisitions were completed, but these declines

seemed to be statistically non-significant.4 Hall (1999) furthered her work by stratifying

potential bidders based on their inclination to acquire. She concluded that firms with a

high acquiring proclivity that actually follow through with an acquisition had a significant

increase in their R&D.5

Gautam Ahuja and Riitta Katila (2001), discussed the impact of acquisitions on

subsequent innovation performance of acquiring firms looking primarily at the chemicals

industry. They were also careful to distinguish between technological acquisitions in which

technology was the component of the acquired firm’s asset as opposed to non-technological

acquisitions in which a technological element was not involved. They discovered that in

technological acquisitions, absolute size of the acquired base knowledge enhances

innovation, while relative size of acquired knowledge base actually reduces innovation

output. Furthermore, they concluded that the relatedness of acquiring and acquired

knowledge base has a nonlinear impact on innovation output.6

Xinlei Zhao (2009), examined whether or not technological innovation is a motivation

factor in a firm’s decision to merge with or acquire another firm and the affects this has on

technological innovation in following years. The findings follow as such, firms engaging in

acquisition bids are less innovative and experience declines in innovation in years leading

3 B. Espen Eckbo, 19854 Bronwyn H. Hall, 19905 Bronwyn Hall, 1999 6 Gautam Ahuja and Riitta Katila, 2001

up to the bid; however, if the bid is successful these firms benefit greatly from completing a

merger or acquisition. Zhao also determined that relatively more innovative firms were

much less likely to agree to and complete any form of merger and acquisition. Additionally,

she observes that firms that attempted a bid but failed significantly underperformed their

non bidding peers. All of these findings suggest that technological innovation affects firms’

decisions in terms of mergers and acquisitions and consequently acquisitions help

firms’eoverall innovation.7

Panos Desyllas and Alan Hughes (2010), explored whether acquirers become more

innovative and the factors that can improve their absorptive and financial capacity to

benefit from the acquisition. The study consisted of a three-year post acquisition window

with a sample of 2,624 high technology US acquisition records. They found that in related

acquisitions a large knowledge base tends to increase R&D intensity which is consistent

with an improved ability to select and absorb targets. With unrelated acquisitions the

opposite is shown to be true.8

Darren Filson, Saman Olfati, and Fatos Radoniqi (2015), conducted a study in which

they observed the effects on R&D intensity after a merger took place by looking at the

cumulative abnormal returns of rival firms before and after the merger. This was

accomplished by looking at large pharmaceutical mergers. They used an event study

approach and looked at a three day window of pharmaceutical stock activity, as well as the

changes in price pre merger announcement and R&D intensity of the firm both before and

after the merger was announced and completed. They found that if rivals of the merging

firm reacted positively the acquiring firm exhibited a lower R&D intensity. 9

III. Data, Econometric Model, and Variable Construction

Data was gathered from four major sources, Thomson One, Securities and Exchange

Commission, Hoovers, and Wharton. Thomson One was used to gather merger and

acquisition announcement data that involved high tech, telecommunications, industrial and

consumer services and products consisting of transaction values no less than $500 million.

7 Xinlei Zhao, 20098 Panos Desyllas and Alan Hughes, 20109 Filson, Darren, Saman Olfati, and Fatos Radoniqi. 2015

The Securities and Exchange Commissions’lwebsite was used to gather a list of the firm’s

rivals by reading their 10-k. The Claremont McKenna Hoovers data base was used to find

any competitors not listed in the SEC’s 10-k filings. Wharton School of Business database

allowed us access to compustat, which consisted of quarterly R&D data as well as the

Center for Research and Security Prices (CRSP), which provided us with daily stock data.

All of the information utilized fell between the years of 1990 and 2014.

Employing the above resources, and the criteria of high tech industries with a transaction

cost of more than $500 million, 212 mergers were found to be suitable for this study. Of

these 212 firms, only 156 of these mergers had compustat data on acquirer’s past and

future R&D intensity. R&D intensity is derived by dividing the R&D by the total revenues

of the firm. After consolidating these 156 mergers we determined which mergers saw a

positive change in R&D intensity. Of the 156, 102 mergers saw a positive change in R&D

intensity. From this point on we will focus only on these positive changes in R&D intensity

and look at how CARs relate to them. The main objective is to look at the sign of the CARs

not the magnitude, meaning is the CAR negative or positive, not big or small.

The first task of our analysis was to ascertain the research and development intensity as

well as the CARs for four months prior to the acquisition announcement. This allowed us

to understand the condition of the company, as well as what to expect had no

announcement been made. We then analyzed the R&D intensity and the CARs at the time

the announcement was made. The data was looked at a third time in order to ascertain

whether or not the changes in R&D intensity or CARs were large enough to be the outcome

of the merger announcement. These changes could only be spotted because of the use of the

event study method which enabled us to forecast the stock price for a three-day window

based on previous stock prices.

The model we utilized for this study was, as mentioned above, the event study method

which is based on the market model, which had been shown to be useful in past studies.

This method employs market data to understand how an event such as a merger

announcement, is used in order to understand the affect on the firm’s value. Additionally,

we are interested in discovering whether or not the event study methodology would be an

effective tool that antitrust agencies could use in order to detect mergers that could be

possibly harmful to social welfare.

Our analysis makes use of the short-horizon model, which prompted us to create a four-

month window pre merger announcement and another four-month window post merger

announcement. Within these windows we studied the the stock prices of the merging firms

and their competitors. Then using a three-day event window, we used the market model to

develop a relationship between the S&P 500 and the stock price of the companies. We then

took expected returns and subtracted those from actual returns in order to determine the

cumulative abnormal returns. Furthermore, we added the research and development of a

firm in the first four-month window as well as in the second four-month window and

divided that by the firm’s total sales in order to determine a firms R&D intensity.

IV. Estimation Results

The results section focuses on understanding the effects that the announcements, as well

as the actual mergers themselves, had on the acquirer’s stock prices, the reaction of the

target’s stock prices, and the reactions of the acquirer’s rivals. For these results we chose to

focus on the sign of the CARs rather than their magnitude because the size of the CARs

can be altered by several factors which can interfere with the results.

The results for the acquirer’s stock prices were quite surprising; 31.1% of the

acquirers’ CARs reacted positively, while 69% of them reacted negatively. We found this

to be significant at 1%. These results are surprising because they seem to indicate that

investors are reacting negatively to the announcement of mergers even though they result

in an increase in R&D intensity. This could possibly mean that investors are not optimistic

about the outcome of the merger for reasons such as the firm having to spend a large

amount of money in paying a premium price for the firm they acquired.

The reaction of target stock prices is less surprising and for the most part expected. We

found that 91.3% of the target firms had positive CARs, while only 8.7% of target firms

experienced negative CARs. This was found to be significant at 1%. As noted, this was

expected because the firm is being acquired by a larger firm at a premium price. In light of

this finding, these results are not that useful of a measure to delineate between innovation

improving mergers and those that do not lead to an improvement in innovation.

The most important reaction to analyze however is that of the acquirers’hrivals. We

determined that 37.9% of acquirers’irivals had positive CARs, while 63.1% of these firms

demonstrated negative CARs. This was found to be significant at 1%. Comparing the

reaction of acquirers’ rivals, we can see that most of them (63%) react negatively to merger

announcements of their rivals that lead to increases in R&D intensity. This result is very

important as it can be used by antitrust authorities to assess the innovation impact of

mergers because it shows that rivals will react negatively as long as innovation is increased

in the acquirer’s firm.

V. Conclusions

From this study we can conclude that authorities should utilize event studies as one of

their tools in order to help them determine whether or not mergers are likely to have

repercussions for future innovation. Our findings demonstrate that acquirers react

negatively to mergers which while surprising does not help antitrust agencies, nor do the

results of the target companies who always seem to react positively. Nonetheless it is the

acquirers’arivals’ results that provide us with the most information because they

demonstrate that if R&D intensity in the acquiring firm increases, rivals’ results should

decrease, consequently, if there is an increase in rival R&D intensity this could be cause for

concern. Thus, if rivals react positively to a merger, antitrust agencies should take action

and investigate the merger in order to ascertain if it poses a threat to consumer welfare.

While this method is not optimal for detecting short term impacts, it can and should be

utilized in accessing long run repercussions to the market.

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