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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
acquisitions are worth half billion dollars each, with four worth a billion dollars each.
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
19
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
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.
22
References
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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%)
1996 • Pharmaceutical preparations (33%)
• 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,
not elsewhere classified (11%)
29
2001 • Pre-packaged software (24%) • Computer programming services (18%)
• Pre-packaged software (24%) • Information retrieval services (18%)
2002 • Pharmaceutical preparations (17%)
• 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%)
• Pharmaceutical preparations (22%)
• Pre-packaged software (17%)
• Computer related services, not elsewhere classified (17%)
2003 • Pharmaceutical preparations (11%)
• Motor vehicle parts and accessories (8%)
• Pharmaceutical preparations (8%)
• Pre-packaged software • Computer related services,
not elsewhere classified (5%)
• 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%)
• Computer facilities management services (5%)
2004 • Pre-packaged software (21%) • Pharmaceutical preparations (14%)
• Computer related services, not elsewhere classified (12%)
• Pharmaceutical preparations (10%)
• Pre-packaged software (10%)
2005 • 2834 Pharmaceutical preparations (17%)
• 7376 Computer facilities management services (11%)
• 2834 Pharmaceutical preparations (12%)
• Pre-packaged software (9%)
2006 • 2834 Pharmaceutical preparations (16%)
• 7372 Pre-packaged software (10%)
• 2834 Pharmaceutical preparations (12%)
• Pre-packaged software (10%)
2007 • Pre-packaged software (14%) • Pharmaceutical preparations (9%)
• Pre-packaged software (9%) • Pharmaceutical preparations (7%)
• Computer facilities management services (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.
37
38
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
Acquirer announcement period returns
Low Government effectiveness
Medium Government effectiveness
High Government effectiveness
Mean .013 .028 .036
Median .005 .020 .028
Sub-sample size 95 97 97
Panel C: Acquirer announcement period returns categorized by per capita GDP in target country
Acquirer announcement period returns
Low per capita GDP Medium per capita GDP High per capita GDP
Mean .007 .033 .030
Median .006 .026 .019
Sub-sample size 76 102 111
Panel D: Acquirer announcement period returns categorized by PDI (proxy for cultural difference between acquirer and target countries)
Acquirer announcement period returns
Low PDI Medium PDI High PDI
Mean .035 .030 .011
Median .027 .020 .003
Sub-sample size 103 103 95
Panel E: Acquirer announcement period returns categorized by relative size of the transaction
Acquirer announcement period returns
Low relative size Medium relative size High relative size
Mean .014 .019 .048
Median .010 .017 .045
Sub-sample size 76 77 76
Panel F: Acquirer announcement period returns categorized by acquirer size measured as total assets
Acquirer announcement period returns
Low acquirer size Medium acquirer size High acquirer size
Mean .041 .028 .009
Median .025 .019 .001
Sub-sample size 104 105 105
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
Sub-sample size 211 43 35
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