Developing Country Acquirers: The Case of India Sanjai...

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Developing Country Acquirers: The Case of India

Sanjai Bhagat

Shavin Malhotra

Peng Cheng Zhu

Abstract

We find during the early (1994-1996) and latter (2002-2007) parts of our sample period, a significant proportion of Indian acquirers and targets in cross-border acquisitions are from the pharmaceutical industry. Second, during this decade (2000-2007) the software companies and computer-related services companies have significant representation among acquirers and targets. The largest number of targets are from the U.S. followed by U.K. In the 1990s, target values tended to be small (by U.S. transaction value measures) averaging about $10 million (in 2006 dollars). However, during this decade ten acquisitions are worth half billion dollars each, with four worth a billion dollars each.

Indian acquirers experience an average market response of 1.48% on the announcement day; this return is statistically significant. The positive announcement return is consistent with Martynova and Renneboog’s (2008) bootstrapping hypothesis: The acquirer voluntarily bootstrapping itself to the higher governance standards of the target – resulting in a positive valuation impact on the acquirer. In addition, we find that smaller acquirers experience a more positive return. Acquirers that pay for the acquisition with cash experience a more positive return compared to acquisitions paid for by the acquirer’s stock. Acquisitions of privately-held targets generate more positive returns for acquirers than acquisition of publicly-held targets. Finally, acquirer returns are positively correlated with the relative size of the acquisition.

August 2008

Please send correspondence to [email protected], or at Leeds School of Business, University of Colorado, Boulder CO 80309.

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1. Introduction

“India's Global M&A Boom: Indian corporations, established at home and

seeking new markets, are flush with cash and spending it abroad. But have they gone

overboard?” Business Week, May 15, 208.

“Indian-Style Mergers: Buy a Brand, Leave It Alone,” Wall Street Journal, March

22, 2008, page A9.

“India M&A push undeterred by credit woes,” Reuters, December 6, 2007.

“Tata May Bid For Ford’s Luxury Brands,” Wall Street Journal, August 27, 2007,

page A6.

During this decade the popular financial media has prominently featured cross-

border acquisitions by Indian acquirers. These articles have provided an interesting

and sometimes colorful description of the transaction and the principals involved. In

contrast, to the best of our knowledge, there is not a sinle academic paper that

focuses on the financial impact on the Indian companies of these cross-border

acquisitions. For example, what is the stock market’s response to the acquisition

announcement? What are the cross-sectional determinants of this market response?

What can we learn about the economic motivation of these acquiring companies? This

paper focuses on the above questions.

We find during the early (1994-1996) and latter (2002-2007) parts of our sample

period, a significant proportion of acquirers and targets are from the pharmaceutical

industry. Second, during this decade (2000-2007) the software companies and

computer-related services companies have significant representation among acquirers

and targets. The largest number of targets are from the U.S. followed by U.K. In the

1990s, target values tended to be small (by U.S. transaction value measures)

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averaging about $10 million (in 2006 dollars). However, during this decade ten

acquisitions are worth half billion dollars each, with four worth a billion dollars each.

Indian acquirers experience an average market response of 1.48% on the

announcement day; this return is statistically significant. The positive announcement

return is consistent with Martynova and Renneboog’s (2008) bootstrapping hypothesis:

The acquirer voluntarily bootstrapping itself to the higher governance standards of the

target – resulting in a positive valuation impact on the acquirer. In addition, we find that

smaller acquirers experience a more positive return. Acquirers that pay for the

acquisition with cash experience a more positive return compared to acquisitions paid

for by the acquirer’s stock. Acquisitions of privately-held targets generate more

positive returns for acquirers than acquisition of publicly-held targets. Finally, acquirer

returns are positively correlated with the relative size of the acquisition.

The remainder of the paper is organized as follows. The next section reviews

the returns to acquirers; most of this literature focuses on acquirers from developed

countries. Section 3 reviews the cross-sectional determinants of the returns to these

acquirers from developed countries. Section 4 describes our data and sample. Section

5 discusses our results on the cross-sectional determinants of returns to Indian

acquirers. The final section concludes.

2. Returns to Acquirers

2.1. Returns to Acquirers in Domestic (U.S.) Acquisitions

Shareholder returns in acquisitions is a topic of significant interest to corporate

finance scholars, corporate managers, and policy makers. Andrade, Mitchell and

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Stafford (2001) summarize the extant literature and evidence. They consider a sample

of 3,688 acquisitions over 1973-1998 and find that, on average, acquirers earned

returns statistically indistinguishable from zero. However, for acquisitions financed with

stock, they document a small but statistically significant negative market response.

Moeller, Schlingemann and Stulz (2005) contains a more recent and comprehensive

evidence on the returns to acquirers. They consider 12,023 acquisitions during 1980-

2001. They document a small but significant positive market response for the acquirer.

However, this positive response is driven by the acquisitions of small acquirers; large

acquirers, typically, experience a negative market response, especially those financed

with stock. Interestingly, they find that when the target is privately held, both large and

small acquirers experience a small but statistically significant positive return regardless

of the method of financing. On the other hand, for public targets, large acquirers

experience a small but statistically significant negative response. Furthermore, small

acquirers of public targets using cash (stock) experience a significant positive

(negative) response. Table 1 summarizes the above results. The above two studies,

similar to the vast majority of papers in this literature, focus on U.S. acquirers of

primarily U.S. targets.

2.2. Returns to Acquirers in Cross-Border Acquisitions

Table 2 summarizes a subset of papers that provide acquirer returns in cross-

border acquisitions. Four studies report a significant positive return to the acquirer, two

report a significant negative return, and two report returns insignificantly different from

zero. There are no obvious differences in sample (acquirer/target from

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developed/developing country) or sample period between the studies that report such

qualitatively different returns to acquirers. In these studies, while the targets are from

developed and developing countries, the acquirers are largely from developed

countries.

3. Determinants of Returns to Acquirers in Cross-Border Acquisitions

The classical theories on the determinants of returns to acquirers in cross

border acquisitions focused on diversification, operational efficiency and market power

as sources. The neoclassical literature has focused on the changes in shareholder

rights and changes in other corporate governance features implicit when acquirers and

targets are from substantially different governance regimes. Finally, management

scholars have focused on the cultural determinants of acquirer returns in cross-border

acquisitions.

3.1. Determinants of Returns to Acquirers: The Classical Theories

Mergers and takeovers is an extensively studied topic by corporate

finance scholars, eclipsed only by the even more extensive literature on capital

structure. The early literature on the determinants of acquirer returns in cross-border

acquisitions is based on the corresponding literature for domestic (U.S.) acquisitions.

Two broad types of such determinants have been considered, to wit, value creation

and wealth transfer.

Value creation is the initial focus of scholars studying shareholder wealth effects

in domestic (U.S.) acquisitions; Jensen and Ruback (1983) and Brickley, Jarrell and

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Netter (1991) survey some of this literature. Following are the hypothesized sources of

value creation; these are neither exhaustive nor mutually exclusive:

Diversification: If the cash flows of the acquirer and target are less than perfectly

correlated, the combined company’s cash flow will have a smaller variance. While the

reduction in variance may not reduce systematic risk, it may lower the cost of debt;

acquiring and target shareholders can ultimately capture this benefit.

Better use of target’s assets: There are two versions of this hypothesis. Under the first

version, target managers are doing as well with the target’s assets as possible given

their understanding of the target’s production and investment possibilities. Acquiring

managers have a different, perhaps “better”, understanding of the target’s production

and investment possibilities. These could include increases, decreases, or different

kinds of capital expenditures, R&D investments, marketing expenditures, and human

resource investments. Under the alternate version of the above hypothesis, target

management is maximizing its own welfare at the expense of shareholder value. For

example, target management may increase its expenditures on a pet project beyond

the firm value-maximizing level because management derives psychic or pecuniary

benefits or income from such increased expenditures. Conversely, target management

may decrease its expenditures (in capital items, R&D, marketing, human resources)

from the value-maximizing level, perhaps because this lessens their effort and stress.

After the acquisition, under either version of the hypothesis, the acquiring

management implements a superior production and investment strategy with the

target’s assets.

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Synergy between acquirer and target assets: There are possible scale economies if

the acquirer and target are producing very similar products or services. The acquirer

(target) can also leverage its technology and brand name to the target’s (acquirer’s)

products or services.

Reduction of tax liability: It is possible under certain circumstances for the tax liability

of the combined company to be less than the sum of the tax liabilities of the target and

acquirer operating independently.

Some scholars have suggested that wealth effects in acquisitions reflect wealth

transfers, rather than value creation. Such wealth transfers could occur from the

exercise of market power by the acquirer and/or target on their customers and

suppliers. To the extent acquirer and/or target shareholders are benefiting from the

exercise of market power, the policy implications for regulators are quite different than

for acquisitions in which value is being created; see Kim and Singal (1993). Some

authors have focused on wealth transfers from target and acquirer employees to target

and/or acquirer shareholders; for example, see Bhagat, Shleifer and Vishny (1991).

3.1. 1. Determinants of Negative Returns to Acquirers

Up through the 1980s, most studies focus on the acquisitions of publicly-held

U.S. acquirers of publicly-held U.S. targets. The average market response for

acquiring shareholders is a small negative return. Roll (1986) suggests that the

negative market response is a result of acquirers overpaying for targets; in other words,

the negative response on acquiring shareholders is merely a wealth transfer from

acquiring to target shareholders.

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Several other explanations for the negative returns to acquirers have been

noted in the literature: The classic paper by Myers and Majluf (1984) argues that firms

issuing equity signal to the market that their equity is overvalued. McCardle and

Vishwanathan (1994) and Jovanovic and Braguinsky (2002) suggest that firms first

fund their internal projects; if they have no attractive internal investment opportunities,

they look to the outside for growth. Hence, the acquisition is a signal that internal

growth opportunities have been exhausted, and the market interprets this signal as

negative information about the acquirer management’s ability to grow the company.

Jensen (1986) suggests that acquisitions reflect empire-building by acquiring

managers who are engaging in acquisitions instead of paying out the free cash flow to

their shareholders. In all of the above scenarios, the negative market response at the

announcement of the acquisition is not due to the acquisition per se, but to the stand-

alone value of the acquirer; see Bhagat, Dong, Hirshleifer and Noah (2005).

Why are returns to large acquirers particularly negative? Demsetz and Lehn

(1985) suggest that incentives of small firm managers are better aligned with

shareholder interests, perhaps because of greater stock ownership. Following up on

Roll’s (1986) hubris hypothesis, large firm managers may be more prone to hubris,

given their past success in growing the company. Large firms may also have more

resources (of both cash and stock) to pay for the acquisition. Large firms may also be

further along in their life cycle; such firms are more likely to have exhausted internal

growth opportunities. Finally, arbitrageurs are more likely to establish a short position

for a large firm involved in acquisition, because of the lower cost of establishing such a

position. Moeller et al (2004) provide a more comprehensive discussion and analysis.

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Why are acquirer returns more negative when targets are publicly-held,

compared to acquisitions when targets are privately-held? The Grossman and Hart

(1980) type free-rider problem allows for greater bargaining power for public-company

shareholders. Private company owners may face greater liquidity constraints, hence,

might accept a lower price.

3.2. Determinants of Returns to Acquirers: The Neo-classical Approach

In a series of influential papers, La Porta et al. (1997, 1998, 1999, 2000, 2002)

analyze the role a country’s legal system has in protecting investor rights. They argue

(2000, p.4): “Such diverse elements of countries’ financial systems as the breadth and

depth of their capital markets, the pace of new security issues, corporate ownership

structures, dividend policies, and the efficiency of investment allocation appear to be

explained both conceptually and empirically by how well the laws in these countries

protect outside investors.” La Porta et al. (1998) draw on the work of David and

Brierley (1985) and Zweigert and Kotz (1987) to postulate that the commercial legal

codes of most countries are based on four legal traditions: the English common law,

the French civil law, the German civil law, and the Scandinavian law. They find that

common law countries provide the most protection to investors (La Porta et al. 1998),

and that they have the deepest stock markets and most dispersed corporate

ownership structures (La Porta et al., 1997, 1999) . They also document that countries

develop substitute mechanisms for poor investor protection, such as mandatory

dividends and greater ownership concentration. In a follow-up paper, La Porta et al.

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(2002) find that investor protection is positively correlated with valuation across

countries.

In their most recent work, La Porta et al. (2003) construct two indices

measuring the quality of securities regulation representing the strength of public and

private enforcement mechanisms (the former consists of powers of the national

securities regulator, the latter, private litigation regime features such as the burden of

proof), to examine the effect of securities regulation on stock markets. As in the case

of their investor protection measure, which they refer to as a shareholder rights or

antidirector rights index, the public and private enforcement measures have higher

values in nations with common law traditions. La Porta et al. find that the private

enforcement measure is more significant than either the public enforcement measure

or the shareholder rights index for the development of a stock market.

The overarching theme of the influential and extensive La Porta et al. corpus is

that “law matters.” The cluster of countries associated with the common-law legal

tradition, which is identified with stronger investor protection and securities regulation,

have deeper stock markets, less concentrated ownership of public firms, and in their

view, given those nations’ higher level of financial development, offer better

opportunities for economic growth and prosperity. Their work has generated

considerable discussion. Some scholars have disagreed with the construction of the

investor protection measure (e.g., Vagts, 2002; Berglof and von Thadden, 1999).

Others have sought to offer alternative explanations of why common law systems are

associated with higher financial development. However, this criticism notwithstanding,

it cannot be denied that their work has had a major impact –international institutions

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such as the International Monetary Fund and World Bank focus on corporate

governance as a key plank in their policy toward emerging market nations -- and that

their corporate law index captures an important element driving cross-national

differences in financial development, despite nuances of legal regime differences

among nations that are grouped together in their legal categorization (see, e.g.,

Cheffins, 2001, distinguishing between the corporate law and institutions of the United

States and United Kingdom, which are grouped together in La Porta et al.’s analysis).

Another sign of the influence of La Porta et al.’s research agenda is the large body of

literature that has developed using the La Porta et al. variables to investigate a variety

of other cross-national differences. These studies also provide evidence that legal

rules matter in important ways for national economies; for a review see Denis and

McConnell (2003).

Rossi and Volpin (2004) use the differential investor protection characterization

across countries developed by La Porta et al. to study the volume and characteristics

of cross-border acquisitions. They find that targets are typically in countries with poorer

investor protection than acquirers. They conclude that cross-border acquisitions may

be partially motivated by enhancement of investor protection in target firms. To the

extent investors value such protection, this would be reflected in positive returns to the

acquirer at the time of the announcement. Martynova and Renneboog (2008) (MR)

characterize this as the positive spillover by law hypothesis. Correspondingly, if the

acquirer has less demanding governance standards than the target, this would have a

negative valuation impact on the acquirer; MR note this as the negative spillover by

law hypothesis. However, MR also suggest the possibility of the acquirer voluntarily

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bootstrapping itself to the higher governance standards of the target – resulting in a

positive valuation impact on the acquirer; MR refer to this as the bootstrapping

hypothesis.

3.3. Determinants of Returns to Acquirers: The Cultural Perspective

In an influential book Hofstede (1980) reports perhaps the most comprehensive

study of how values in the workplace are influenced by culture. Hofstede administers

questionnaires to 88,000 employees of IBM across 40 countries between 1968 and

1972 (it involved running the questionnaires twice after a four year time interval).

Based on his analysis of these questionnaires, he reports four measures of national

culture. These are labeled as uncertainty avoidance, individuality, power distance, and

masculinity-femininity. He also finds strong reliability for the four dimensions over the

four year interval. Subsequent studies validating the earlier results include commercial

airline pilots and students in 23 countries, civil service managers in 14 counties, 'up-

market' consumers in 15 countries and 'elites' in 19 countries.

Although researchers have criticized aspects of the study, nevertheless,

Hofstede’s study has some appealing attributes, namely, the size of the sample, the

codification of cultural traits along a numerical index, and its emphasis on attitudes in

the workplace. For these reasons, the four Hofstede dimensions is widely used to

measure culture in research.

Many studies have found culture to be a strong determinant of foreign activities

by multinational firms. One such activity is the choice of entry mode, which has been

explained by cultural and national factors (Gatignon and Anderson, 1988; Kogut and

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Singh, 1988; Chang and Rosenzweig, 2001). Kogut and Singh (1988) found that

differences in culture between home and host countries increases the level of risk in

post-acquisition integration, and that it leads firms to choose less risky entry modes.

Studies in M&A point out that the integration of resources, particularly human

resources, is critical for the success of acquisitions (DePamphilis, 2005, 31–21). Thus,

the high levels of cultural difference may increase post-merger management costs and

lower the performance of acquisitions. This suggests that greater the cultural distance,

the more a foreign investor may prefer greenfield investments over acquisitions,

leading to lower premiums (Weitzel and Berns, 2006). Similarly, Gatignon and

Anderson (1988) find that high socio-cultural distance is associated with partial

ownership rather than full ownership.

Given the above concern about cultural match, it is appropriate to consider

cultural difference between India and the target country as a determinant of acquirer

returns. We use Hofstede’s power distance index as a simple proxy for cultural

difference. We use this proxy as several studies have reported this measure to be the

strongest predictor within Hofstede’s (1980) cultural index (Husted, 1999; Weitzel and

Berns, 2006).

Hofstede defines power distance as “the power distance between a boss B and

a subordinate S in a hierarchy is the difference between the extent to which B can

determine the behavior of S and to which S can determine the behavior of B”. In high

power-distance countries there is considerable dependence of subordinates on their

superiors in the form of paternalism (defined as a system by which superiors provide

favors to subordinates in return for their loyalty [Kogut and Singh, 1988]). As the

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decisions are not made on the basis of merit, but on the basis of a balance of favors

and loyalty, it leaves considerable room for corruption.

Hofstede (1980) computes the power distance index (PDI) on the basis of the

country mean scores for the following three factors: 1. Non-managerial employees’

perception that employees are afraid to disagree with their managers. 2. Subordinates’

perception that their boss tends to take decisions in an autocratic or

persuasive/paternalistic way. 3. Subordinates’ preference for anything but a

consultative style of decision-making in their boss: that is for an autocratic, a

persuasive/paternalistic, or a democratic style.

4. Sample and Data Description

Our sample consists of all cross-border acquisitions by Indian companies; this

sample was obtained from the SDC Thomson Financial database. Our sample period

is from January 1994 through July 2007. The initial sample consists of 383 acquisitions.

Given the motivation of this study, we need stock market data around the

announcement date for these acquiring companies. We can obtain stock return data

for firms listed on the Bombay Stock Exchange. Hence, we restrict our sample to these

firms; this reduces our sample to 327 acquisitions.

Table 3, Panel A, notes a secular increase in the number of cross-border

acquisitions by Indian companies from January 1994 through July 2007. Table 3,

Panel B, notes the industries of the target and acquiring companies. There are, at

least, four noteworthy features of the industry composition of the target and acquiring

companies. First, during the early (1994-1996) and latter (2002-2007) parts of the

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sample period, a significant proportion of acquirers and targets are from the

pharmaceutical industry. Second, during this decade (2000-2007) the software

companies and computer-related services companies have significant representation

among acquirers and targets. Third, several acquirers and targets from the early part

of the sample period are in the consumer food and clothing industries. Finally, during

1996-1998, a significant number of target companies are from the financial services

sector.

Table 4 notes the country of the target companies. The largest number of

targets are from the U.S. (126 targets), followed by U.K. (55 targets), Singapore (19),

Germany (18), and Australia (15). Table 5 notes the descriptive statistics of the

transaction values. In the 1990s target values tended to be small (by U.S. transaction

value measures) averaging about $10 million (in 2006 dollars); the largest was only

$74 million. 2000 witnessed the acquisition of Tetley Group by Tata Tea; the

transaction value is over half billion dollars; see Table 6. During this decade ten


The largest acquisition in our sample is of Corus Group by Tata Steel; see Table 6.

Not surprisingly, given the small size of most acquisitions, the vast majority (215

of 383 transactions) of targets are privately-held companies; see Table 7. Only 21

targets are publicly-held companies. 133 transactions involve the acquisition of a

subsidiary.

4.1. Governance

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La Porta et al (2000) define corporate governance as the set of policies and

procedures that provide outside investors with a fair return on their investment. There

are two dimensions to governance, one is country specific, and the other is company

specific. Country specific items include the corruption of government officials in a

country, the effectiveness and integrity of the judiciary system, the access of new and

mid-size firms to the country’s capital markets, the concentration of stock ownership in

the country, and whether or not the country allows shareholders to mail their proxy

vote. Company specific governance measures include the anti-takeover provisions in

the company’s charter, manager and director compensation policy, board structure,

and board governance policies; Bhagat, Bolton, and Romano (2008) provide a detailed

discussion of company specific governance measures and their pros and cons.

Ideally we would like to consider data on country specific and company specific

governance measures. However, Doidge, Karolyi and Stulz (2007) suggest that the

inter-country differences are much greater than differences across companies within a

country. Hence, we choose to focus on country specific governance measures; these

data are from La Porta et al (2003), and are described in Table 9.

4.2. Cultural Difference

Power distance is measured on a scale from 0 to 100. In our research India has

a power distance value of 77, while US has 40, meaning that in India, superiors and

subordinates consider each other as unequal. The values for power distance are

obtained from www.geert-hofstede.com. For this study we use the absolute difference

in the power distance values between India and target countries as the proxy for

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cultural difference; we denote this variable as APDI.

5. Acquirer Returns

5.1. Announcement Period Abnormal Returns

Table 8 summarizes the announcement period abnormal returns for Indian

acquirers during 1994-2007. Day 0 is the announcement day. Indian acquirers

experience an average market response of 1.48% on the announcement day. This

return is statistically significant at the .01 level considering both parametric and non-

parametric tests. This is consistent with the findings of Char, Ouimet and Tesar (2004),

Burns and Moya (2006), Cakici, Hessel and Tandon (1997), and Martynova and

Renneboog (2008); these authors also document a small but significant positive return

to acquirers in cross-border acquisitions – see Table 2. However, two comments are

worth noting. First, the acquirers in all of the above four studies are from developed

economies (and the targets are also mostly from developed economies). Second, as

detailed in Table 2, two studies document an insignificant return and two document a

small but significant negative return to acquirers in cross-border acquisitions; this

contrasts with our evidence of a significant positive market response to Indian

acquirers in cross-border acquisitions.

The positive announcement return is consistent with MR’s bootstrapping

hypothesis: The acquirer voluntarily bootstrapping itself to the higher governance

standards of the target – resulting in a positive valuation impact on the acquirer. Also,

the positive announcement return is inconsistent with MR’s negative spillover by law

hypothesis which applies to situations when an acquirer has less demanding

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governance standards than the target. To further test MR’s bootstrapping hypothesis

we consider the correlation between announcement returns and the difference in

governance between the target and acquirer. As noted above we use differences in

country specific governance measures as proxies for differences in acquirer and target

governance.

5.2. Cross-sectional Determinants of Acquirer Returns

Table 11 notes acquirer announcement period returns categorized by various

acquirer and target characteristics. Smaller acquirers experience a more positive

announcement period return (Panel A). Acquirers that pay for the acquisition with cash

experience a more positive return compared to acquisitions paid for by the acquirer’s

stock (Panel B). Acquisitions of privately-held targets generate more positive returns

for acquirers than acquisition of publicly-held targets (Panel C). Acquirer returns are

positively correlated with the relative size of the acquisition (Panel D). All of these

findings are consistent with the literature.

We next categorize returns of Indian acquirers by governance and other

characteristics of the target’s country. To the best of our knowledge, none of the

following findings have yet been documented in the literature: Acquisitions of targets in

higher per capita GDP countries is correlated with higher acquirer returns (Panel E).

However, this relationship is not monotonic; acquirer returns for targets in medium per

capita GDP countries are larger than for targets in high per capita GDP countries.

Acquirer returns from acquisition of targets in English legal origin countries are higher

than French legal origin countries (Panel F). Acquirer returns are positively correlated

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with target country government effectiveness (Panel G), and target country ant-

corruption index (Panel H). Government effectiveness and a culture of anti-corruption

are positively correlated with better corporate governance at the country level; see La

Porta et al (2000). Our tentative conclusion based on the results in Panels F, G and H:

Returns of Indian acquiring companies is positively correlated with the target countries’

quality of corporate governance. Our conclusion is tentative since we have not

controlled for the other cross-sectional determinants of acquirer returns.

Table 12 summarizes regression results for the cross-sectional determinants of

returns to Indian acquirers. We include SIGMA, idiosyncratic volatility of acquirer

returns, given the evidence in Moeller, Schlingemann and Stulz (2007) who conclude

that there is no difference in cross-sectional acquirer returns “…between cash offers

for public firms, equity offers for public firms, and equity offers for private firms…” after

controlling for idiosyncratic volatility.

Consistent with the literature, notably Moeller et al (2004), we find a significant

negative correlation between acquirer size (LVMV) and acquirer return in all

regression specifications.

We observe a positive relation between acquirer return and acquirer Tobin’s Q;

however this relation is significant in only one specification – Model 3. This positive

relation is consistent with the findings in Servaes (1991), and Lang et al (1989), but

inconsistent with the results in Moeller et al (2004) and Bhagat et al (2005).

We have data on transaction values for 230 observations in our sample. Model

4 notes a significant positive relation between acquirer returns and transaction size.

Model 5 documents a significant positive relation between acquirer return and relative

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size of the acquisition; this result is consistent with the findings in Moeller et al (2004)

and Bhagat et al (2005).

Model 6 focuses on the relation between method of payment (1=cash, 0=mixed)

and acquirer returns. We have method of payment data on only 92 acquisitions.

Consistent with the literature, we document a positive relation between acquirer return

and acquisitions paid for with cash.

In Models 7 through 14, we focus on various target country specific governance

variables. Model 7 indicates a significant negative relation between target country

ownership concentration and acquirer return. Model 8 indicates a significant positive

relation between anti-director rights and acquirer return. Models 9, 12, and 13 suggest

a positive relation between the effectiveness of a country’s judiciary, the government

effectiveness, the country’s stance on anti-corruption and acquirer return. These

results are consistent with MR’s bootstrapping hypothesis: the acquirer return is more

positive when there is greater improvement in acquirer governance as a consequence

of better target governance.

Model 10 (Model 11) suggests a significant positive (negative) relation between

target’s in countries with an English (French) legal origin and acquirer return. Since

shareholders are treated more favorably under English origin laws than French origin

laws, this evidence is also consistent with MR’s bootstrapping hypothesis.

Finally, Model 14 provides support for the notion that when Indian companies

acquire targets in countries with similar cultural traditions, their returns are more

positive.

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6. Summary and Conclusions

We find during the early (1994-1996) and latter (2002-2007) parts of our sample period,

a significant proportion of Indian acquirers and targets in cross-border acquisitions are

from the pharmaceutical industry. Second, during this decade (2000-2007) the software

companies and computer-related services companies have significant representation

among acquirers and targets. The largest number of targets are from the U.S. followed by

U.K. In the 1990s, target values tended to be small (by U.S. transaction value measures)

averaging about $10 million (in 2006 dollars). However, during this decade ten


Indian acquirers experience an average market response of 1.48% on the

announcement day; this return is statistically significant. The positive announcement

return is consistent with Martynova and Renneboog’s (2008) bootstrapping hypothesis:

The acquirer voluntarily bootstrapping itself to the higher governance standards of the

target – resulting in a positive valuation impact on the acquirer. In addition, we find that

smaller acquirers experience a more positive return. Acquirers that pay for the acquisition

with cash experience a more positive return compared to acquisitions paid for by the

acquirer’s stock. Acquisitions of privately-held targets generate more positive returns for

acquirers than acquisition of publicly-held targets. Finally, acquirer returns are positively

correlated with the relative size of the acquisition.

22

References

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23

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24

Officer, M.S.,“Termination Fees in Mergers and acquisitions, Journal of Financial Economics 69, 2003, 431-468. Roll, R., 1986, The Hypothesis of Corporate Takeovers. Journal of Business 59, 197-216. Rossi, S. and P. Volpin (2004), “Cross-Country Determinants of Mergers and Acquisitions”, Journal of Financial Economics 74: 277-304. Vagts, D. (2002), “Comparative Company Law – The New Wave”, in R. Schweizer, H. Burkert and U. Gasser, eds., Festschrift für Jean Nicolas Druey zum 65. Geburtstag (Schulthess, Zurich) 595-605. Weitzel, U. and Berns, S. (2006) ‘Cross-border takeovers, corruption, and related aspects of governance’, Journal of International Business Studies 37: 786-806. Zweigert, K. and H. Kotz (1987), An Introduction to Comparative Law (Clarendon, Oxford).

25

Table 1: Acquiring Company’s Announcement Period Abnormal Returns

in U.S. acquisitions from Moeller et al (2005) categorized by method of payment, and target status (public or private), * indicates significance at .01 level

Stock Cash All

Full Sample

(12,023 acquisitions) .15%* 1.38%* 1.10%*

Small Acquirers 2.03%* 2.17%* 2.32%*

Large Acquirers -.96%* .69%* .08%

Stock Cash All

Publicly-held Targets

(2,642 acquisitions) -2.02%* .36% -1.02%*

Small Acquirers -.75%* 2.84%* .92%*

Large Acquirers -2.45%* -.42%* -1.70%*

Stock Cash All

Privately-held Targets

(5,583 acquisitions) 1.49%* 1.21%* 1.50%*

Small Acquirers 2.70%* 1.52%* 2.14%*

Large Acquirers .50%* .81%* .70%*

26

Table 2: Cross-Border Acquirer Returns in the Literature

Paper Sample period

Sample size

Bidder from these

countries

Target from these

countries

Bidder return

Bidder return Z-statistic or (sig. level)

Bidder return positively related

to

Bidder return

negatively related to

Chari-Ouimet-Tesar (2004)

1988-2002 346 Developed market

Emerging market

2.43% (.05) Majority control ---

Burns-Moya (2006)

1988-2004 1,129 U.S. 26 developed countries,

20 emerging countries

.83% (n=755)

2.41% (n=153)

4.19

4.44

Private targets

---

Cakici-Hessel-Tandon (1997)

1983-1992 195 Developed countries

(UK, Canada,

Germany, Japan, )

U.S.

.63%

4.69

--- ---

Martynova-Renneboog

(2008)

1993-2001 2,419 European countries

European countries

0.47% 2.25 Bidder/target same language, Bidder/target common border, Bidder shareholder rights improvement, Target shareholder rights improvement,

Bidder size, Hostile bid

Benou-Gleason-Madura (2007)

1985-2001 503 U.S. 22 developed,

18 less-developed countries

.29%

.79

Target media visibility,

IB reputation

Cash offers,

Tech bubble period

Mueller-Turtoglu (2007)

1981-2002 9,733 Developed countries

Developed countries

.006% -- Separate regressions for positive and negative acquirer returns.

Separate regressions for positive and negative acquirer returns.

Kuipers-Miller-Patel

(2003)

1982-1991 181 Developed countries

U.S. -0.92% 5.82 Level of shareholder rights, and rule of law in acquirer’s country.

Level of creditor rights in acquirer’s country.

Bris-Cabolis (2008)

1989-2002 506 Developed and

emerging countries

Developed and emerging countries

-1.12% (.03) --- ---

27

Table 3, Panel A: Number of Cross-border acquisitions by Indian companies by year

Year # of Completed Acquisitions by

Publicly Listed Indian Firms 1994 3 1995 8 1996 3 1997 7 1998 5 1999 10 2000 27 2001 17 2002 18 2003 38 2004 42 2005 66 2006 82 2007 57 Total 383

28

Table 3, Panel B: Industries (based on 4-digit SIC) of target and acquiring companies in cross-border acquisitions by Indian companies during 1994-2007, and percent of all such acquisitions for the year Year Acquirer industry (most

frequent), (%) Acquirer industry (next most frequent), (%)

Target industry (most frequent) (%)

Target industry (next most frequent) (%)

1994 • Malt beverages (33%) • Cigarettes (33%) • Pesticides and agricultural

chemicals, not elsewhere classified (33%)

- • Vegetable oil mills, except corn, cottonseed, and soybean (33%)

• Pesticides and agricultural chemicals, not elsewhere classified (33%)

• Drinking places (alcoholic beverages) (33%)

-

1995 • Pharmaceutical preparations (25%)

• Cigarettes (13%) • Crude petroleum and

natural gas (13%) • Broadwoven fabric mills,

cotton (13%) • Textile goods, not

elsewhere classified (13%)

• Security brokers, dealers, and flotation companies (13%)

• Motion picture and video tape production (13%)

• Pharmaceutical preparations (38%)

• Crude petroleum and natural gas (13%)

• Broadwoven fabric mills, cotton (13%)

• Women's, misses', and juniors' blouses and shirts (13%)

• Wood products, not elsewhere classified (13%)

• Electronic parts and equipment, not elsewhere classified (13%)


• Malt beverages (33%) • Computer programming

services (33%)

- • Pharmaceutical preparations (33%)

• Malt beverages (33%) • Insurance agents, brokers,

and service (33%)

-

1997 • Computer programming services (57%)

• Malt beverages (14%) • Lead and zinc ores

(14%) • Switchgear and

switchboard apparatus (14%)

• Life insurance (57%) • Copper ores (14%) • Power, distribution, and

specialty transformers (14%)

• Groceries and related products, not elsewhere classified (14%)

1998 • Computer programming services (20%)

• Pesticides and agricultural chemicals, not elsewhere classified (20%)

• Surgical and medical instruments and apparatus (20%)

• Investment advice (20%) • Engineering services (20%)

- • Life insurance (20%) • Heavy construction, not

elsewhere classified (20%) • Industrial inorganic chemicals,

not elsewhere classified (20%)

• Investors, not elsewhere classified (20%)

• Management consulting services (20%)

-

1999 • Footwear, except rubber, not elsewhere classified (20%)

• Household audio and video equipment (20%)

• Investors, not elsewhere classified (20%)

• Investment advice (10%) • Paints, varnishes,

lacquers, enamels, and allied products (10%)

• Computer peripheral equipment, not elsewhere classified (10%)

• Computer facilities management services (10%)

• Computer related services, not elsewhere classified (20%)

• Inorganic pigments (10%) • Air and gas compressors

(10%) • Computer terminals (10%) • Air-conditioning and warm

air heating equipment (10%) • Personal credit institutions

(10%) • Heavy construction

equipment rental and leasing (10%)

• Pre-packaged software • Information retrieval

services (10%) 2000 • Prepackaged software (24%) • Information retrieval

services (6%) • Pre-packaged software (15%) • Computer related services,


29

2001 • Pre-packaged software (24%) • Computer programming services (18%)

• Pre-packaged software (24%) • Information retrieval services (18%)


• Schools and educational services, not elsewhere classified (17%)

• Computer programming services (11%)

• Paints, varnishes, lacquers, enamels, and allied products (11%)

• Motor vehicles and passenger car bodies (11%)


• Pre-packaged software (17%)



• Motor vehicle parts and accessories (8%)


• Pre-packaged software • Computer related services,


• Paints, varnishes, lacquers, enamels, and allied products (5%)

• Information retrieval services (5%)

• Motor vehicle parts and accessories (5%)

• Computer programming services (5%)

• Steel works, blast furnaces (including coke ovens) (5%)

• Copper ores (5%) • Perfumes, cosmetics, and

other toilet preparations (5%)


2004 • Pre-packaged software (21%) • Pharmaceutical preparations (14%)




2005 • 2834 Pharmaceutical preparations (17%)

• 7376 Computer facilities management services (11%)

• 2834 Pharmaceutical preparations (12%)


2006 • 2834 Pharmaceutical preparations (16%)

• 7372 Pre-packaged software (10%)

• 2834 Pharmaceutical preparations (12%)


2007 • Pre-packaged software (14%) • Pharmaceutical preparations (9%)

• Pre-packaged software (9%) • Pharmaceutical preparations (7%)


30

Table 4: Country of target companies in cross-border acquisitions by Indian companies during 1994-2007

Target Nation # of transactions United States 126 United Kingdom 55 Singapore 19 Germany 18 Australia 15 France 9 Thailand 8 Italy 7 United Arab Emirates 7 Belgium 6 Canada 6 Indonesia 6 Sri Lanka 6 Brazil 5 China 5 Romania 5 South Africa 5 Spain 5 Czech Republic 4 Egypt 4 New Zealand 4 Switzerland 4 Denmark 3 Hong Kong 3 Malaysia 3 Norway 3 Zambia 3 Argentina 2 Bermuda 2 Fiji 2 Finland 2 Ireland-Rep 2 Myanmar(Burma) 2 Netherlands 2 Oman 2 Philippines 2 Russian Fed 2 South Korea 2 Sweden 2 Uzbekistan 2 Bahamas 1 Bosnia 1 Chile 1 Hungary 1 Israel 1 Mauritius 1 Monaco 1 Morocco 1 Nepal 1 Pakistan 1 Poland 1 Portugal 1 Sudan 1 Total 383

31

Table 5, Panel A: Transaction values in cross-border acquisitions by Indian companies during 1994-2007

Year Sum of Nominal Value of Transaction ($mil USD)

Sum of Nominal Value of Transaction (mil Indian

Rupee)

Sum of Real Value of Transaction ($mil

USD 2006)

Sum of Real Value of Transaction (mil Indian

Rupee 2006)

1994 17.67 554.46 23.89 1,176.49

1995 36.45 1,282.21 48.02 2,468.86

1996 11.00 395.45 14.10 690.95

1997 67.17 2,639.62 83.63 4,231.29

1998 2.68 113.95 3.26 170.40

1999 5.95 258.80 7.13 341.86

2000 686.46 32,092.10 804.77 40,489.38

2001 103.70 5,005.70 117.61 6,072.59

2002 849.31 40,766.93 936.70 47,645.33

2003 466.73 21,259.60 506.72 23,822.27

2004 853.56 36,933.58 906.13 39,870.54

2005 1,494.43 67,174.54 1,545.14 69,861.52

2006 3,242.26 143,016.09 3,242.26 143,016.09

2007 4,190.65 169,218.45 4,055.46 163,126.58

32

Table 5, Panel B: Transaction value (in 2006 US$ millions) descriptive statistics in cross-border acquisitions by Indian companies during 1994-2007

Year N Min. Max. Mean Std. Dev.

1994 2 2.15 21.74 11.95 13.85

1995 6 0.55 20.60 8.00 8.93

1996 1 14.10 14.10 14.10 -

1997 2 10.17 73.46 41.81 44.75

1998 1 3.26 3.26 3.26 -

1999 3 0.94 4.31 2.38 1.74

2000 17 0.08 506.42 47.34 121.65

2001 11 1.22 40.15 10.69 11.00

2002 11 0.30 846.76 85.15 252.76

2003 20 0.49 109.98 25.34 33.14

2004 28 0.02 301.35 32.36 62.93

2005 35 1.03 299.40 44.15 64.42

2006 46 1.01 571.39 70.48 113.54

2007 36 0.15 1,258.06 112.65 279.02

33

Table 6: 50 largest transactions in cross‐border acquisitions by Indian companies during 1994‐2007

Announcement Date Target Company Name Target Country Acquiring Company Name CAR (-1,+1)

Real Value of Transaction ($mil

USD 2006) 10/17/2006 Corus Group PLC United Kingdom Tata Steel UK Ltd -0.97% $14,748.80 2/10/2007 Novelis Inc United States AV Aluminum Inc -14.58% $5,601.86 3/30/2007 Kaltim Prima Coal PT Indonesia Tata Power Co Ltd 0.13% $1,258.06 5/16/2007 Whyte & Mackay Ltd United Kingdom United Spirits Ltd 19.79% $1,138.43

10/30/2002 Greater Nile Petroleum Sudan ONGC 3.13% $846.76 12/30/2006 Sinvest ASA Norway Aban International Norway AS 23.32% $657.56 2/16/2006 Betapharm Arzneimittel GmbH Germany Dr Reddys Laboratories Ltd 10.60% $571.39 3/17/2006 Eve Holding NV Belgium AE-Rotor Techniek BV -2.83% $566.93

2/9/2007 REpower Systems AG Germany Suzlon Windenergie GmbH -9.25% $534.93 6/16/2006 Sinvest ASA Norway Aban Loyd Chiles Offshore Ltd 15.06% $446.87 2/27/2000 Tetley Group Ltd United Kingdom Tata Tea Ltd 6.45% $506.42 3/29/2006 Terapia SA Romania Ranbaxy Laboratories Ltd 12.49% $324.00 8/16/2004 NatSteel Asia Pte Ltd Singapore Tata Iron & Steel Co Ltd 5.23% $301.35 6/28/2005 Thomson SA-CAthode Ray Tube France Videocon International Ltd 1.95% $299.40 3/13/2007 SLI Sylvania Netherlands Havells India Ltd 5.00% $290.70 7/17/2007 Yipes Communications Inc United States FLAG Telecom Group Ltd 2.01% $290.32 7/16/2007 Yipes Communications Inc United States Reliance Communications Ltd -1.92% $290.32

5/3/2007 Negma Lerads SAS France Wockhardt Ltd -1.89% $256.45 6/25/2006 Eight O Clock Coffee co United States Tata Coffee Ltd 2.52% $220.00

10/16/2003 FLAG Telecom Group Ltd Bermuda Reliance Gateway Net Pvt Ltd 2.02% $211.51 6/19/2005 Docpharma NV Belgium Matrix Laboratories Ltd 6.30% $196.62 7/25/2005 Teleglobe Intl Hldg Ltd Bermuda Videsh Sanchar Nigam Ltd 4.71% $182.96 2/21/2006 BC-10 Offshore Block,Brazil Brazil ONGC Nile Ganga NV 2.81% $170.00 1/18/2007 Syndesis Ltd Canada Subex Azure Ltd -0.26% $159.19 10/3/2006 Pinewood Laboratories Ireland-Rep Wockhardt Ltd 2.49% $150.00

11/14/2006 Cerexagri Inc United States Uniphos Enterprises Ltd 8.99% $142.26 4/25/2006 Azure Solutions Ltd United Kingdom Subex Systems Ltd 20.71% $140.00

8/1/2006 ColCarbon SA Colombia Global Steel Holdings Ltd -2.62% $140.00 2/26/2007 LASON Inc United States HOV Services Ltd 11.31% $143.23 11/1/2004 Tyco Global Network United States Videsh Sanchar Nigam Ltd 6.83% $138.01 4/30/2007 INTERMET Europe GmbH & Co KG Germany Sakthi Auto Component Ltd 3.10% $125.12 2/14/2006 Advanta Netherlands Holdings Netherlands Biowin Corp Ltd -3.13% $120.22

3/2/2006 AIP/GLC Holdings LLC Canada Rain Commodities(USA)Inc 11.17% $108.59 11/24/2005 Brunner Mond Group Ltd United Kingdom Tata Chemicals Ltd 4.42% $112.70 11/5/2003 Daewoo Commercial Vehicle Co South Korea Tata Motors Ltd -6.20% $109.98 6/24/2004 Trevira GmbH Germany Reliance Industries Ltd -0.08% $106.16 2/19/2007 eSys Technologies Pte Ltd Singapore Teledata Informatics Ltd 4.22% $101.67 9/27/2006 Bergen Offshore Logistics Pte Singapore Sical Logistics Ltd 7.55% $96.90 8/18/2005 INCAT International PLC United Kingdom Tata Technologies Inc -0.14% $99.06 1/24/2003 Straits(Nifty)Pty Ltd Australia Hindalco Industries Ltd 2.74% $95.99

4/7/1994 Occidental Chemical-Florida United States Tata Chemicals(Tata Group) 3.35% $165.47 7/24/2006 Tashkent-Toyetpa Tekstil Ltd Uzbekistan Spentex Industries Ltd 16.11% $81.00

12/15/2005 Millennium Steel PCL Thailand Tata Steel Ltd -0.49% $75.85 10/31/2006 TKS-Teknosoft SA Switzerland Tata Consultancy Services Ltd 1.09% $80.45 12/8/2003 Ceylon Petroleum-Stations(100) Sri Lanka IOC 0.64% $81.43 5/24/2006 Amcis AG Switzerland Dishman Pharm & Chem Ltd -8.63% $74.50 12/1/2006 Be-Tabs Pharmaceuticals Ltd South Africa Ranbaxy Laboratories Ltd 0.08% $70.00

10/12/2004 Cymbal Corp United States Patni Computer Systems Ltd 0.90% $72.19 1/30/2006 Dunlop Tyres International Ltd South Africa Apollo Tyres Ltd 0.55% $65.96

3/7/2006 Brunner Mond Group Ltd United Kingdom Tata Chemicals Ltd 0.73% $65.50

34

Table 7, Panel A: Listing status of target companies in cross-border acquisitions by Indian companies during 1994-2007

Target Firm Public Status # of transactions Private 215 Subsidiaries 133 Public 21 Joint Ventures 13 Govt. 1

Grand Total 383

Table 7, Panel B: Industry relatedness (based on 4-digit SIC code) of target companies in cross-border acquisitions by Indian companies during 1994-2007

Related # of transactions No 254Yes 129Total 383

Industry relatedness (based on 2-digit SIC code) of target companies in cross-border acquisitions by Indian companies during 1994-2007

Related # of transactions No 151Yes 232Grand Total 383

35

Table 8: Announcement period abnormal returns for days -10 through +10 in cross-border acquisitions by Indian companies during 1994-2007

Panel A. Daily Abnormal Returns

Day N Mean Abnormal

Return Median Abnormal

Return Positive : Negative Patell Z Signed Rank

-10 327 -0.24% -0.18% 139:174 -1.274 -2884.5$

-9 327 0.17% -0.07% 155:163 0.329 373.5

-8 327 -0.10% -0.19% 141:172 -0.884 -3118.5$

-7 327 0.19% 0.03% 158:157 1.029 -764

-6 327 -0.38% -0.28% 130:181< -2.786** -4127.0**

-5 327 -0.43% -0.34% 135:180( -2.450** -4211.0**

-4 327 -0.14% -0.39% 136:180( -1.348$ -3097.0$

-3 327 -0.25% -0.09% 147:170 -1.299$ -1472.5

-2 327 0.30% -0.01% 158:162 1.360$ 1960

-1 327 0.64% 0.20% 169:147>> 3.802*** 4295.00**

0 327 1.48% 1.18% 210:113>>> 9.429*** 12351.0***

1 327 0.44% 0.13% 164:154> 2.210* 1989.5

2 327 0.06% -0.12% 143:169 0.449 -1281

3 327 0.07% -0.18% 148:159 0.206 -1143

4 327 -0.12% -0.24% 139:170 -1.17 -3270.5*

5 327 -0.17% -0.12% 148:163 -0.549 -2181

6 327 -0.32% -0.24% 132:176( -3.624*** -2972.0$

7 327 0.33% -0.01% 159:160 2.004* 417

8 327 0.13% -0.01% 154:156 1.844* -478.5

9 327 -0.05% -0.05% 150:160 -0.893 -1457.5

10 327 -0.20% 0.02% 157:152) -1.07 -356.5 The symbols $,*,**, and *** denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a 1-tail test. The symbols (,< or ),> etc. correspond to $,* and show the significance and direction of the generalized sign test.

Panel B. Cumulative Abnormal Returns

Days N Mean Cumulative Abnormal Return

Median Cumulative Abnormal Return

Positive : Negative Patell Z Signed Rank

(-1,+1) 327 2.51% 1.38% 210:117>>> 9.104*** 11889.0***

(-2,+2) 327 2.86% 1.97% 209:118>>> 7.797*** 11608.0***

(-5,+5) 327 1.86% 0.81% 181:146>>> 3.096*** 5289.00**

(-10,-1) 327 -0.21% -0.65% 155:172 -1.101 -446

(0,+10) 327 1.64% 0.93% 174:153>> 2.647** 4513.00** The symbols $,*,**, and *** denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a 1-tail test. The symbols (,< or ),> etc. correspond to $,* and show the significance and direction of the generalized sign test.

36

Table 9: Variable Definition

Variables Variable Definition Continuous Variables

CAR (-1, +1) Cumulative abnormal return in (-1, +1) days around the M&A announcement dates based on market model LNMV Log transformed market capitalization (at the latest fiscal year end before acquisition) Q Tobin's Q (at the latest fiscal year end before acquisition) SIGMA Unsystematic risk of acquiring firms' stock (using 120 days daily returns in the estimation period of the market

model) LNTRANS Log transformed real value of transaction values RELSIZE Relative size of transaction (transaction values divided by acquiring firms' total assets) ACCESS Index of the extent to which business executives in a country agree with the statement “Stock markets are open to

new firms and medium-sized firms”. Scale from 1 (strongly agree) though 7 (strongly disagree). ANTIDIR This index of Anti-director rights is formed by adding one when: (1) the country allows shareholders to mail their

proxy vote; (2) shareholders are not required to deposit their shares prior to the General Shareholders’ Meeting; (3) cumulative voting or proportional representation of minorities on the board of directors is allowed; (4) an oppressed minorities mechanism is in place; (5) the minimum percentage of share capital that entitles a shareholder to call for an Extraordinary Shareholders’ Meeting is less than or equal to ten percent (the sample median); or (6) when shareholders have preemptive rights that can only be waved by a shareholders meeting. The range for the index is from zero to six.

CONCENTR Average percentage of common shares not owned by the top three shareholders in the ten largest non-financial, privately-owned domestic firms in a given country. A firm is considered privately-owned if the State is not a known shareholder in it. Source: La Porta et al. (1999), African equities for Kenya, Bloomberg and various annual reports for Ecuador, Jordan, and Uruguay.

Corruption Corruption Perception Index -- Source: Transparency International -- 2000. EFF_JUD Assessment of the “efficiency and integrity of the legal environment as it affects business, particularly foreign

firms” produced by the country risk rating agency International Country Risk (ICR). It may be “taken to represent investors’ assessment of conditions in the country in question.” Average between 1980 and 1983. Scale from 0 to 10, with lower scores representing lower efficiency levels. Source: International Country Risk Guide.

Gov_effect Average of the government effectiveness scores for the period 1996, 1998, and 2000. LNGDP Logarithmic of per capita Gross Domestic Product (in US dollars) in 2000. APDI Power dimension index, a proxy of cultural distance between the acquiring and target country. Binary Variables RELATED Within 4-digit SIC code industry M&As (1 is yes, 0 is no) CONTROL Controlling ownership (>50%) after acquisition (1 is yes, 0 is no) PUBLIC Public listing status of target firms (1 is Public, 0 is Private) PAYMENT Payment method of the transaction (1 is cash payment, 0 is mixed cash and stock payment) UK_LO 1 if English legal origin. FR_LO 1 if French legal origin. GE_LO 1 if German legal origin. SC_LO 1 if Scandinavian legal origin.

Table 11: Bivariate analysis of acquirer announcement period returns in cross-border acquisitions by Indian companies during 1994-2007

Panel A: Acquirer announcement period returns categorized by Anti-corruption index of target country

Acquirer announcement period returns

Low Anti-corruption index

Medium Anti-corruption index

High Anti-corruption index

Mean .012 .030 .034

Median .006 .020 .019

Sub-sample size 95 97 97

Panel B: Acquirer announcement period returns categorized by Government effectiveness in target country


Low Government effectiveness

Medium Government effectiveness

High Government effectiveness

Mean .013 .028 .036

Median .005 .020 .028


Panel C: Acquirer announcement period returns categorized by per capita GDP in target country


Low per capita GDP Medium per capita GDP High per capita GDP

Mean .007 .033 .030

Median .006 .026 .019


Panel D: Acquirer announcement period returns categorized by PDI (proxy for cultural difference between acquirer and target countries)


Low PDI Medium PDI High PDI

Mean .035 .030 .011

Median .027 .020 .003


Panel E: Acquirer announcement period returns categorized by relative size of the transaction


Low relative size Medium relative size High relative size

Mean .014 .019 .048

Median .010 .017 .045


Panel F: Acquirer announcement period returns categorized by acquirer size measured as total assets


Low acquirer size Medium acquirer size High acquirer size

Mean .041 .028 .009

Median .025 .019 .001


Panel G: Acquirer announcement period returns categorized by target status

Acquirer announcement period returns Public Subsidiary Private Mean 0.013 0.027 0.025 Median 0.006 0.019 0.013 Sub-sample size 21 111 180

Panel H: Acquirer announcement period returns categorized by method of payment

Acquirer announcement period returns Only Cash Cash and Stock Only Stock Mean 0.031 0.020 -0.026 Median 0.021 0.022 -0.031 Sub-sample size 74 13 7

Panel I: Acquirer announcement period returns categorized by target country’s legal origin

Acquirer announcement period returns English legal origin French legal origin Others

Mean 0.031 0.002 0.022

Median 0.020 0.001 0.008


Table 12: Cross-sectional analysis of acquirer announcement period returns in cross-border acquisitions by Indian companies during 1994-2007

Model 1 Model 2 Model 3

β t stats p value β t stats p value β t stats p value (Constant) 0.021 3.290 0.001 *** 0.145 3.880 0.000 *** 0.161 4.252 0.000 *** SIGMA 5.722 2.539 0.012 ** 5.226 2.304 0.022 ** 4.164 1.807 0.072 * RELATED ‐0.004 ‐0.604 0.546 ‐0.005 ‐0.689 0.491 ‐0.004 ‐0.558 0.577 PRIVATE ‐0.001 ‐0.138 0.890 ‐0.001 ‐0.181 0.857 ‐0.002 ‐0.332 0.740 LVMV ‐0.005 ‐3.393 0.001 *** ‐0.006 ‐3.792 0.000 *** Q 0.001 2.498 0.013 ** LNTRANS RELSIZE PAYMENT N 327 312 311 Adjusted R2 0.012 0.051 0.067 F 2.305 * 5.202 *** 5.476 ***

model 4 Model 5 Model 6

β t stats p value β t stats p value β t stats p value (Constant) 0.121 2.142 0.033 ** 0.138 2.760 0.006 *** 0.223 2.421 0.018 ** SIGMA 2.896 1.064 0.288 2.321 0.860 0.391 2.607 0.679 0.499 RELATED ‐0.012 ‐1.442 0.151 ‐0.013 ‐1.530 0.128 ‐0.010 ‐0.818 0.415 PRIVATE 0.001 0.166 0.868 0.001 0.089 0.930 0.006 0.427 0.671 LVMV ‐0.009 ‐4.112 0.000 *** ‐0.004 ‐1.753 0.081 * ‐0.011 ‐2.747 0.007 *** Q 0.002 1.208 0.228 0.000 0.185 0.854 0.003 1.352 0.180 LNTRANS 0.006 2.387 0.018 ** RELSIZE 0.007 3.288 0.001 *** 0.006 2.542 0.013 ** PAYMENT 0.043 2.313 0.023 ** N 230 230 92 Adjusted R2 0.075 0.096 0.206 F 4.107 *** 5.031 *** 4.409 ***

*** 1% significant, ** 5% significance, * 10% significance.

Model 7 Model 8 Model 9 Model 10 β t stats p value β t stats p value β t stats p value β t stats p value

(Constant) 0.186 3.157 0.002 *** 0.135 2.130 0.034 ** 0.120 1.752 0.081 * 0.161 2.639 0.009 ***

SIGMA 3.423 1.091 0.277 3.808 1.209 0.228 3.234 1.034 0.303 3.091 0.989 0.324

RELATED ‐0.013 ‐1.361 0.175 ‐0.013 ‐1.406 0.161 ‐0.013 ‐1.445 0.150 ‐0.014 ‐1.467 0.144

PRIVATE 0.001 0.153 0.879 0.001 0.115 0.908 0.003 0.366 0.715 0.002 0.208 0.836

NEWERA 0.018 1.582 0.115 0.019 1.685 0.094 * 0.016 1.454 0.148 0.019 1.637 0.103

CONTROL ‐0.014 ‐1.116 0.266 ‐0.013 ‐1.068 0.287 ‐0.011 ‐0.884 0.378 ‐0.013 ‐1.080 0.281

LVMV ‐0.006 ‐2.506 0.013 ** ‐0.006 ‐2.551 0.012 ** ‐0.007 ‐2.610 0.010 ** ‐0.007 ‐2.693 0.008 ***

Q 0.001 0.517 0.606 0.001 0.664 0.508 0.001 0.389 0.698 0.001 0.730 0.466

RELSIZE 0.007 3.371 0.001 *** 0.007 3.383 0.001 *** 0.007 3.380 0.001 *** 0.007 3.457 0.001 ***

CONCENTR ‐0.064 ‐2.000 0.047 **

ANTIDIR 0.008 2.256 0.025 **

EFF_JUD 0.005 1.919 0.057 *

UK_LO 0.019 1.817 0.071 *

N 201 201 201 201

Adj. R2 0.14 0.145 0.139 0.137

F 4.625 *** 4.769 *** 4.583 *** 4.532 ***

Model 11 Model 12 Model 13 Model 14 β t stats p value β t stats p value β t stats p value β t stats p value

(Constant) 0.177 2.994 0.003 *** 0.154 2.366 0.019 ** 0.127 1.859 0.065 * 0.158 2.844 0.005 ***

SIGMA 3.948 1.254 0.211 3.144 0.993 0.322 3.840 1.199 0.232 2.648 0.870 0.385

RELATED ‐0.014 ‐1.501 0.135 ‐0.014 ‐1.483 0.140 ‐0.013 ‐1.455 0.147 ‐0.014 ‐1.614 0.108

PRIVATE 0.002 0.250 0.802 0.003 0.369 0.713 0.004 0.421 0.674 0.001 0.094 0.925

NEWERA 0.017 1.532 0.127 0.015 1.316 0.190 0.014 1.278 0.203 0.015 1.415 0.159

CONTROL ‐0.010 ‐0.870 0.385 ‐0.011 ‐0.928 0.355 ‐0.011 ‐0.913 0.362 ‐0.010 ‐0.895 0.372

LVMV ‐0.007 ‐2.696 0.008 *** ‐0.007 ‐2.517 0.013 ** ‐0.006 ‐2.392 0.018 ** ‐0.007 ‐2.919 0.004 ***

Q 0.001 0.566 0.572 0.001 0.466 0.641 0.000 0.288 0.774 0.000 0.344 0.731

RELSIZE 0.007 3.339 0.001 *** 0.007 3.422 0.001 *** 0.007 3.379 0.001 *** 0.007 3.372 0.001 ***

FR_LO ‐0.029 ‐2.400 0.017 **

Gov_effect 0.010 1.247 0.214

Corruption 0.005 1.759 0.080 *

APDI ‐0.001 ‐2.680 0.008 ***

N 201 201 201 201

Adj. R2 0.148 0.128 0.136 0.149

F 4.858 *** 4.301 *** 4.505 *** 4.992 *** *** 1% significant, ** 5% significance, * 10% significance.

Figure 1: Average Cumulative Abnormal Returns for the 10 days before and after the announcement of cross-border acquisitions by Indian companies during 1994-2007

Developing Country Acquirers: The Case of India Sanjai...

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