The motives and short-term
performance of cross-border bidders
from India
Student number: S2307391 Name: Yanjun Deng
Study Program: Msc Finance Supervisor: Dr.Halit Gonenc
Subject: Motives and performance of cross-border M&As University of Groningen
ABSTRACT
During the recent decades, increasing participation in cross-border merger and acquisitions of the firms from emerging markets has gained great attention in the extant literature. This study investigates the motives and short-term performance of Indian acquirers in the context of cross-border M&As deals. Results show that Indian shareholders are more likely to benefit from targeting developed-economy firms. Moreover, Indian bidders can gain from the cross-border expansion of companies that belong to IT industry. Nevertheless, Indian acquirers do not consider the advantage of geographically close to host countries as an accelerating factor in the race for accessing foreign markets and strategic resources. My sample consists of 125 completed deals by Indian bidders over the period 2000-2012 and the results hold after controlling for various firm-level and deal-level characteristics.
Keywords: Cross-border mergers and acquisitions, India, Shareholder value,
Motives.
1. Introduction
Source: World Investment Report 2003, 2005 and 2012.
Fig.1. Number of cross-border deals by Indian firms from 2000 to 2011.
Source: World Investment Report 2003, 2005 and 2012.
Fig.2. Value of cross-border deals by Indian firms from 2000 to 2011.
Despite the thriving trend of cross-border M&As from emerging countries, researchers find that the motives for EM companies to conduct foreign acquisitions
are fundamentally different from those of developed- economy companies (Luo & Tang, 2007; Rui & Yip, 2008; Gubbi et al., 2010). Luo and Tang (2009) claim that EM companies undertake cross-border M&As actually for asset seeking instead of asset exploiting. Therefore, the motives for cross-border deals by EM bidders are worth studying on. Furthermore, the issues whether cross-border M&As undertaken by EM firms can generate additional value for the shareholders and which factors play an important role in the M&A performance also are worth paying attention to. Consequently, this paper uses a standard event study methodology and multivariate analysis to investigate the motives for cross-border M&As undertaken by Indian firms and put forward the possible explanations for the impact of such motives on the bidder’s short-term performance.
In this study, I focus on 125 completed cross-border deals by Indian bidders between 2000 and 2012. The results reveal that Indian acquirers gain significantly positive abnormal returns when they target developed-economy companies and when the acquiring firms belong to IT industry, respectively. This implies that the pursuit of strategic resource is an important motivation for cross-border M&As from Indian bidders, especially seeking for more advanced resources (e.g. technological resources). Moreover, I find negative effect of geographical distance between India and target countries on the value of shareholders but fail to prove its significance. This indicates that Indian bidders do not recognize the advantage of geographically close to target countries as a factor that could ease and speed up the access to foreign markets and strategic assets.
The proposed contribution of this paper is twofold. First, this paper invokes the main motivations of Indian companies that undertake cross-border M&As. These factors help us understand the underlying reasons that have led to overseas acquisition by Indian firms and identify the conditions under which acquisitions contribute to obtain superior performance. Second, in order to test fast entry hypothesis I introduce the geographical distance as a factor that might have impact on the motivation of a fast entry into foreign market for Indian bidders, although numerous scholars use cultural distance between the acquirer’s and target’s country to measure the cultural (dis)advantage of the bidders (Drogendijk & Slangen, 2006; Nicholson & Salaber, 2013).
2. Literature review
2.1. Performance of cross-border M&As
With respect to the cross-border M&As, numerous researches have focused on the foreign investment of developed-economy companies but the evidence is mixed. The short-term performance of American bidders acquiring foreign companies has been found to be either positive (Moeller & Schlingemann, 2005; Francis et al., 2008; Kim et al., 2011; Klossek et al., 2012) or negative (Eckbo & Thorburn, 2000; Ghemawat, 2001). Researchers have proposed several reasons for the gains from cross-border M&As. Morck and Yeung (1992) point to the internalization benefits in the cross-border M&As. Acquiring firms get profits from foreign investment by internalizing imperfections from host country market when their specific assets fail to find comparable values elsewhere. Ayban and Ficici (2009) argue that the resulting rents originated from internalization are expected to be capitalized into a higher value of the firm. Conn et al. (2005) state that geographical diversification by direct investment in overseas subsidiary helps firms expand the boundary of the firm. This will lead to the value creation for the acquiring firm. Baldwin and Caves (1991) point out that the gains of cross-border M&As may be from diversifications when bidders search for synergies arising from intangible and information-based assets such as R&D expertise, new technology and brand name.
takeover premium paid for target firms may negatively impact the value of acquiring firms.
As one of the largest developing country, India companies are more and more involved in cross-border activities especially after a report published in October 2003, which is called ―Dreaming with BRICs: The Path to 2050‖ written by Goldman Sachs. However, research on emerging-economy bidders is limited and the results are also mixed .Aybar and Ficici (2009) investigate cross-border M&As from 13 emerging countries and find that bidders earn negative abnormal returns. Gubbi et al. (2010) state that Indian acquiring firms earn positive abnormal returns from cross-border M&As during the period 2000-2007, and partly explain this by the advanced economy and institution of the target country. Bhagat et al. (2011) study 698 cross-border deals from emerging markets and show that these bidders have a positive and significant market response around the announcement date.
2.2. Motives for cross-border M&As
illustrate, internationalization of developed-economy multinational enterprises (MNEs) typically aims to leverage their existing competitive strengths in foreign markets. For hitting the mark, they need to transfer advanced knowledge, technologies, and other valuable resources to the target firms. Thus, the performance and competitiveness of the targets will be enhanced after absorbing these resources (Dunning, 1993). In contrast, firms from emerging markets face a less supportive institutional environment and lack important resources such as cutting-edge technologies, operating systems and brands when compared with their developed counterparties. Therefore, EM companies tend to consider the internationalization not as an approach to leverage their existing resources, but as a ―springboard‖ to acquire valuable resources (Luo & Tung, 2007).
unavailable domestically, such as superior management and skills, EM firms should be expected to acquire the targets from developed countries (Makino et al., 2002; Gubbi et al., 2010). Furthermore, Harrison et al. (2001) suggest that when combined low-cost back-end capabilities existing in emerging markets with the high-value front-end capabilities available in developed countries, the combination can be a uniquely valuable resource and helps EM bidders achieve higher market value and globalization realization.
The bidders of cross-border M&As from emerging markets are also motivated by the fast entry into foreign markets. As compared to internally generated product development and new business, acquisition is an effective way for MNEs, especially EM firms to rapidly expand their size and consumer markets internationally (Li, 2007; Wang & Boatend, 2007; Deng, 2009). Datta and Puia (1995) state that cross-border M&As provide acquiring firms the opportunity for quick access to a market with established sales volume. Moreover, cross-border M&As also become the fastest means for outward expansion compared to greenfield investment or joint venture (UNCTAD, 2000). Shimizu et al. (2004) draw a similar conclusion in their research on entry mode choice. They suggest that if the investor does not have much time to penetrate a foreign market, the only available choice will be buying an existing firm. In contrast, the greenfield entries require a more moderated approach. Hennart and Park (1993) state that if the target markets enjoy a high growth rate, the cross-border M&As allow the bidders to penetrate the markets more quickly.
appraisals of such information problematic if the targets are far away (Chetty, 1999; Dow, 2000; Li and Liu, 2009; Grote and Umber, 2006). Moreover, Grote and Rucker (2007) state that the wealth effects experienced by German firms acquiring foreign targets vary greatly based upon the proximity of their investments. Specifically, the authors show that German bidders experience positive cumulative abnormal returns of 6.5 percent when buying a firm in a neighboring country, while they lose an average of 4.4 percent in other deals. Hu and Wu (2011) also identify that the cross-border M&As generate additional value for the shareholders of Chinese acquiring firms when their targets are in neighboring countries.
2.3. Other factors affecting bidder’s performance
In order to genuinely reflect the performance of cross-border M&A deals from India, some other factors which are likely to influence any domestic or cross-border bidder’s gains must be considered (both from developed and emerging countries). According to the literature on M&A performance (Moeller & Schlingermann, 2005; Sapienza et al., 2006; Gubbi et al., 2010; Mann & Kohli, 2011; Barbopoulos et al., 2012; Danbolt & Maciver, 2012;). I use several control variables which have been proved to impact bidder’s abnormal returns. These factors can be fallen into two parts: bidder characteristics (firm size and firm age) and deal characteristics (mode of payment, relative deal size and target status). I present here the literature related to these control variables.
2.3.1. Size of acquiring firm
in order to improve their position in the industry and to become an attractive target for being acquired in the future. Therefore, smaller acquirers tend to be more profitable than larger ones (Gorton et al., 2009).
2.3.2. Firm age
Age of firm indicates that extent of experience a firm owns. The perspective of company learning theory is that firms can learn from their own history or other’s experience (Cyert & March, 1964). Based on this theory, a number of studies related to the impact of pre-acquisition experience of a firm on the acquisition performance were carried out by scholars. Hitt et al. (1998) show that, firms that have acquiring experience or reshuffle tend to be more flexible in management and hence make post-acquisition restructuring more smoothly. Wu and Xie (2010) find that Chinese firms with more internationalization experiences perform better after acquisition than those with limited experiences. Furthermore, on the aspect of asymmetric information, Zhang (2006) suggest that investors will have more information on firms with longer histories. That is, the longer the firm’s history, the lower the information asymmetry.
2.3.3. Mode of payment
stock if the acquirer wants to retain the control in target firm during the post-acquisition period. However, an alternative tax-based hypothesis argues that stock offers are more favorable. If an acquirer pays cash, the target shareholders will pay taxes immediately; but in the case of stock payment, taxes will be deferred (Fuller et al., 2002).
2.3.4. Relative deal size
It is generally believed that, by expanding capital investments in related fields, the firms are able to obtain economies of scale in their marketing, distribution, production management, etc. (Nicholson & Salaber, 2013). The same logic can apply to cross-border M&As: synergies generated by successful cross-border deals leading to large size may make the post-acquisition firm value exceed the value of simple addition of both firms (Lamacchia, 1997). Aybar and Ficici (2009) suggest that emerging-economy companies may also benefit from the presence of expanding market and the efficiency of capital exploration globally, which eventually enhance their profitability. Nevertheless, some scholars find several shortcomings associated with investment size. Information asymmetry may lead to misidentification of synergies and overstatement of post-acquisition value (Aybar & Ficici 2009). Large deals may result in negative market reactions (Mulherin & Boone, 2000). Empirically, numerous researchers identify that relative deal size is a statistically significant factor for bidding firms (Moeller & Schlingemann, 2005; von Eije & Wiegerinck, 2010).
2.3.5. Status of target
firm generates the free-rider problem, which increases the payment price (Grossman & Hart, 1980). Acquiring an unlisted firm does not generate as much competition. Moreover, there may well be a liquidity discount in the price for buying an unlisted firm considering that it is more difficult to sell unlisted firms than listed ones (Officer, 2007). Fuller et al. (2002) and Conn et al. (2006) find that the positive abnormal returns are related to the actual impact of listing when an unlisted firm is acquired. They obtain negative abnormal returns when the target firm is listed. Gubbi et al. (2010) do not find the status of the target to be a significant factor for Indian cross-border M&As.
3. Hypotheses development
Based on the previous theory background and evidence, I propose three hypotheses related to the respective motives of Indian bidders acquiring abroad, and the impact of such motives on the bidder’s performance.
3.1. Developed target country and performance
emerging markets with the high-value front-end capabilities available in developed countries, the combination can be a uniquely valuable resource and helps EM bidders achieve higher market value and globalization realization (Harrison et al., 2001).
Thus, driven by the strategic asset seeking, Indian bidders would gain more if targets are in developed countries as these transactions would help them obtain strategic resources unavailable domestically (Gubbi et al., 2010). Hence, I expect that acquisitions into developed countries are valued positively by Indian shareholders.
H1. Indian acquirers enjoy higher abnormal returns when they target more advanced
economies.
3.2. Competitive advantage and performance
Indian firms invest abroad in order to obtain more advanced and valuable resources such as cutting-edge technologies and knowledge-based abilities (Buckley et al., 2012). These foreign technological assets are used to improve the performance and competitive advantage of the firm (Tsang et al., 2008). The industry of information technology in India has obtained a brand identity as a knowledge-based economy because of its IT and ITES sector. The IT–ITES industry includes two major components: IT Services and business process outsourcing (BPO). The IT-ITES sector has led huge growth in the service sector in India and contributed substantially to increase in GDP, employment, and exports. For instance, the sector helps India's GDP rise from 1.2% in FY1998 to 7.5% in FY2012. Moreover, human resources in IT firms are mobile and easily re-trainable for alternative use (Arora et al., 2001). Thus the benefits of cross-border acquisitions are potentially higher for IT MNEs compared to non-IT MNEs.
3.3. Impact of geographical distance on fast entry
Finally, I test how the other main motivation for cross-border M&As (fast entry to foreign markets) is affected by the geographical distance between the bidder and target countries. Corporations from emerging market take the cross-border acquisitions as an convenient, quick and easy way to obtain new marketing network and consumers (Chen & Findlay, 2003; Wang & Boateng, 2007), the process can be tough and costly relying on company-specific and country- specific advantages or disadvantages (Boateng & Glaister, 2003; Luo & Tung, 2007). Though acquiring an existing company can easily achieve the goal of fast entry, buying a firm with cultural difference would force a bidder to engage in cultural turnaround of the unit which are toilsome and time-consuming (Hofstede, 1989). Indeed, diversified cultural systems are involved in multinational deals and cultural cognition becomes an important factor that influence international transactions (Djankov et al., 2002). For this reason, many enterprises tend to make direct foreign investment in the countries which have similar social and cultural background before entering the countries with big differences in cultural background (kogut & Singh, 1988). Li and Liu (2009) state that the shorter the geographical distance from the host country to the home country is, the similar the institutional environment at the level of cultural cognition would be. Even though cultural differences still exist, it is easier for host countries to accept the cultural shock due to the short distance from the home country. Moreover, Grote and Umber (2006) claim that the farther you are from your target, the more difficult it will be to appraise the target’s key resources (e.g., human capital, key technologies, brands, growth prospects and relationship with other firms and customers). Firms favor countries that are closer in geographic distance to their home country because this lowers economic and management costs in expanding to international markets (Chetty, 1999; Dow, 2000).
H3. The closer the geographical distance from the country of target firm to India is,
3.4. Measurement of predictor variables
The first group of independent variables is related to the hypothesis of strategic resource seeking of Indian bidders. According to Buckley et al. (2007) who use the membership of Organization for Economic Cooperation and Development (OECD) and International Monetary Fund (IMF) as proxies for developed countries, I set the first dummy variable, DEVELOPED, taking value one if the country of target firm is a member of OECD and/or in the list of advanced economies from IMF, zero otherwise. Generally, developed-economy targets possess more advanced tangible and intangible assets that help EM bidders achieve higher market value and globalization realization (H1). Furthermore, what I focus on is whether there is a positive relationship between the abnormal returns for shareholders and the competitive advantage of Indian bidders. The second dummy, IT INDUSTRY, takes the value one if the Indian acquirer’s main activity is in the IT industry, zero otherwise. This measure captures the competitive advantage of Indian bidders (H2).
In order to test the fast entry hypothesis (H3), I use the geographical distance between the bidder’s and target’s country to measure the geographical advantage or disadvantage of Indian bidders. The geographical distance is measured by taking the distance between their capital cities. The Google Earth provides this measure in miles. As emphasized in Section 3.3, I expect the short-term performance of Indian cross-border M&As to be negatively affected by the geographical distance, i.e. Indian bidders experience more favorable abnormal returns when they target countries with a shorter geographical distance to India.
4. Data and methodology
4.1. Data selection
and Thomson Datastream. I screen these databases according to the following criteria: (i) Indian acquiring firms must be alive and listed on Bombay Stock Exchange and announced cross-border M&As during the period 2000-2012, with the target firms being listed or unlisted in any other country in the world.
(ii) Deals status must be complete.
(iii) The bidder holds more than 50% of the shares of the target (to focus only on the majority-stock acquisitions).
(iv) Acquiring firms are non-financial firms (financial firms are subject to special accounting and regulatory requirements, making them difficult to compare with other firms).
(v) Deal value needs to be at least €1 million (to avoid the potential implications of very small deals).
(vi) Stock price of all bidding firms can be obtained from Thomson Datastream and their daily returns must be available on Datastream from 160 trading days prior to the takeover announcement.
Furthermore, I keep only the deals for which the data on all of the control variables can be found. Finally, the sample of cross-border M&A announcements consists of 125 transactions involving targets firms from 28 countries, with a total deal value of over €11 billion and an average of over €88 million.
4.2. Sample description
Table 1
Regression sample description.
Panel A: Year distribution Panel B: Country distribution Panel C: Industry distribution
Year Number of deals Country Number of deals Industries Number
of deals
2000 2 United States 46 Software & Computer Services 53
2001 1 Great Britain 20 Industrial Engineering 14
The table demonstrates the deal characteristics of Indian cross-border deals between January 2000 and December 2012. The regression sample includes cross-border acquisitions for which information is available for all control variables.
Panel A of Table 1 illustrates the distribution of cross-border M&As in India by different years. From 2001 to 2007, the distribution shows a growing trend and reaches the peak at 23 deals in 2007. In 2008, there are 15 cross-border transactions undertaken by Indian firms. However, after 2008, the number of deals drops dramatically maybe due to the global financial crisis during recent years. Regarding the target country (in Panel B), the U.S. has the priority for Indian bidders, followed by the U.K., Germany, Singapore and Australia. Three of the five top ranking target countries are traditional Western industrialized countries. It seems that Indian acquirers specially favor the developed countries for their cross-border transactions. Finally, Panel C reports the industry distribution of Indian acquiring firms. The Software & Computer sector accounts for 42% of all the deals, which demonstrates the importance of IT industry in the Indian economy. The second to fifth places are taken by the industry of Industrial Engineering (11%), Pharmaceuticals & Biotechnology (9%), Chemicals (9%) and Food Producers (4%) respectively.
2003 13 Singapore 8 Chemicals 11
2004 14 Australia 4 Food Producers 5
2005 14 France 4 Construction & Materials 5
2006 20 Egypt 3 Personal Goods 5
2007 23 Korea (South) 3 Industrial Metals & Mining 4
2008 15 South Africa 3 Health Care Equipment & Services 3
2009 3 Switzerland 3 Automobiles & Parts 2
2010 7 Others 23 Others 12
2011 5
2012 2
Fig.3. Year distribution of cross-border deals by Indian firms from 2000 to 2012.
Fig.4. Industry distribution of cross-border deals by Indian firms from 2000 to 2012.
0 5 10 15 20 25 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Number of deals 0 10 20 30 40 50 60 Automobiles & Parts
Chemicals Construction & Materials Electronic & Electrical Equipment Fixed Line Telecommunications Food Producers General Industrials Health Care Equipment & Services Household Goods & Home Construction Industrial Engineering Industrial Metals & Mining Media Mobile Telecommunications Oil & Gas Producers Personal Goods Pharmaceuticals & Biotechnology Real Estate Investment & Services Software & Computer Support Services
Fig.5. Country distribution of cross-border deals by Indian firms from 2000 to 2012.
4.3. Event study
In order to assess the short-term performance of a particular deal, I adopt a standard event study methodology to calculate bidder-announcement effects – abnormal returns (ARs) and cumulative abnormal returns (CARs) – around the acquisition announcement date. Following MacKinlay (1997), I first estimate expected returns of each stock in line with the market model:
Rjt = αj + βjRmt +εjt (1) Where Rjt is the daily return of acquiring firms and Rmt is the daily return on the market. The estimation period runs from 150 to 30 days prior to the announcement date. I then use the coefficient αj and βj to forecast the abnormal returns which are defined as the difference between the bidder returns and the expected returns from the market model.
ARjt = Rjt - α̅j - β̅jRmt (2) Finally, I compute the cumulative abnormal return by summing up the daily abnormal returns. For each deal, the cumulative abnormal return is calculated as:
CARj(t2, t1) = ∑t2t=t1ARjt (3) United States(36.8%) Great Britain (UK)(16.00%) Germany(6.40%) Singapore(6.40%) Australia(3.20%) France(3.20%) Egypt(2.40%) Korea (South)(2.40%) South Africa(2.40%) Switzerland(2.4%) Other(18.4%)
4.4. Cross-sectional model specification
In order to test all the hypotheses, a series of multivariate analysis were carried out with cumulative abnormal returns (CARs) as dependent variables for Indian. I employ the following regression model:
CARj = β0 + β1DEVELOPEDj + β2IT INDUSTRYj + β3GEOGRAPHICAL DISTANCEj + β4CASHj + β5RELATIVESIZEj + β6PRIVATEj + β7FIRMSIZEj + β8FIRMAGEj + β9Year dummy +β10Industry dummy + εj (4)
DEVELOPEDj is a dummy variable equal to one if the target is in developed country, zero otherwise. IT INDUSTRYj is a dummy variable taking the value one if the bidder’s main activity is in IT industry, zero otherwise. GEOGRAPHICAL DISTANCEj represents the geographical distance between India and target countries. CASHj is a dummy variable taking the value one if the acquisition is an all-cash deal, zero otherwise. RELATIVESIZEj is the deal value divided by the acquiring company’s pre-acquisition market value. PRIVATEj is a dummy variable equal to one if the target is an unlisted firm, zero otherwise. FIRMSIZEj indicates the acquiring firm’s size measured by the log of firm’s market capitalization one month prior to the announcement of the bid. FIRMAGEj represents the age of the bidder, measured by the log of the number of days between the day of bid announcement and the date of firm’s firs record in Datastream.
5. Results and analysis
Table 2
Average abnormal returns (AAR) and cumulative average abnormal returns (CAAR).
N = 125 Mean s.t. t-Stat
AAR(-10) -0.0025 0.0305 -0.9317
AAR(-9) -0.0019 0.0219 -0.9684
AAR(-7) 0.0012 0.0221 0.6244 AAR(-6) 0.0002 0.0227 0.1058 AAR(-5) 0.0005 0.0293 0.2067 AAR(-4) 0.0005 0.0238 0.2579 AAR(-3) 0.0026 0.0215 1.3736 AAR(-2) 0.0017 0.0297 0.6279 AAR(-1) 0.0073 0.0308 2.6474*** AAR(0) 0.0119 0.0384 3.4738*** AAR(+1) 0.0029 0.0290 1.1064 AAR(+2) 0.0019 0.0272 0.7858 AAR(+3) -0.0031 0.0235 -1.4919 AAR(+4) 0.0011 0.0281 0.4476 AAR(+5) -0.0028 0.0232 -1.3422 AAR(+6) -0.0071 0.0279 -2.8239*** AAR(+7) 0.0003 0.0233 0.1623 AAR(+8) 0.0008 0.0344 0.2462 AAR(+9) 0.0013 0.0211 0.6721 AAR(+10) -0.0005 0.0239 -0.2365 CAAR(-1,+1) 0.0221 0.0550 4.4935*** CAAR(-2,+2) 0.0257 0.0701 4.0936*** CAAR(-3,+3) 0.0252 0.0723 3.8966*** CAAR(-5,+5) 0.0246 0.0944 2.9185*** CAAR(-10,+10) 0.0160 0.1305 1.3736
N indicates the number of observations while s.t. denotes the standard deviation. One sample t-test statistics for the significance of AAR and CAAR are calculated.
* indicates statistical significance at 10% level. ** indicates statistical significance at 5% level. *** indicates statistical significance at 1% level.
Table 3
Variables’ correlation matrices.
1 2 3 4 5 6 7 8 9 1 CAR(-2, +2) 1.00 2 DEVELOPED 0.02 1.00 3 IT INDUSTRY 0.11 0.13* 1.00 4 GEOGRAPHICAL DISTANCE 0.03 0.24*** 0.16* 1.00 5 CASH -0.02 -0.02 -0.09 -0.12* 1.00 6 RELATIVESIZE 0.48*** -0.07 -0.1 0.07 -0.19** 1.00 7 PRIVATE 0.1 0.18** 0.14* 0.13* -0.07 0.12* 1.00 8 FIRM SIZE -0.12* 0.03 0.01 -0.1 0.07 -0.26*** -0.19** 1.00 9 FIRM AGE 0.32*** -0.06 0.05 -0.02 0.12* -0.21** -0.09 0.28*** 1.00
CAR, cumulative abnormal return; DEVELOPED, developed target country; CASH, cash-financed deals; PRIVATE, unlisted target; RELATIVESIZE, deal value divided by market value of bidder; FIRMAGE, the age of acquiring firm; FIRMSIZE, acquiring firm size; GEOGRAPHICAL DISTANCE, geographical distance between the bidder’s and target’s country.
* indicates statistical significance at 10% level. ** indicates statistical significance at 5% level. *** indicates statistical significance at 1% level.
In order to explain the variance in CARs of Indian bidders obtained from the event study, ordinary least squares (OLS) regressions are carried out on the explanatory variables. Table 3 provides the correlations and their level of significance for all variables used in the analysis for Indian cross-border deals. The CAR for the (-2 to +2) period is positively and significantly correlated with the relative deal size and firm’s age at 1% level. Variable that has negative and significant correlation with CAR is acquiring firm’s size.
Table 4
as the deal value divided by the bidder’s pre-acquisition market value. PRIVATE takes the value one if the target is privately held, zero otherwise. FIRM SIZE is calculated by taking the log of bidder’s market capitalization one month prior to the acquisition. FIRM AGE equals to the log of the number of days between the day of bid announcement and the date of firm’s firs record in Datastream. T-Statistics are reported in parenthesis.
Independent variable: CAR(-2, +2) Model 1 Model 2 Model 3 Model 4
DEVELOPED 0.0248** (2.24) IT INDUSTRY 0.0256** (2.30) GEOGRAPHICAL DISTANCE -0.0003 (-1.02) CASH 0.0111 0.0113 0.0126 0.0113 (0.92) (0.96) (1.07) (0.92) RELATIVESIZE 0.0042*** 0.0045*** 0.0045*** 0.0041*** (4.69) (5.07) (5.05) (4.61) PRIVATE 0.0072 0.0033 0.0112 0.0068 (0.24) (0.12) (0.39) (0.23) FIRM SIZE -0.0024 -0.0018 -0.0019 -0.0023 (-0.78) (-0.59) (-0.63) (-0.75) FIRM AGE 0.01756** 0.019** 0.0184** 0.0173** (2.16) (2.34) (2.30) (2.07) Constant 0.1411* 0.1321* 0.1223 0.1419 (1.80) (1.71) (1.58) (1.04)
Year dummy Yes Yes Yes Yes
Industry dummy Yes Yes Yes Yes
Number of observations 125 125 125 125
R-squared 0.3129 0.3422 0.3435 0.3131
Adjusted R-squared 0.2526 0.2782 0.2796 0.2395
Note.
* indicates statistical significance at 10% level. ** indicates statistical significance at 5% level. *** indicates statistical significance at 1% level.
cross-border transactions in developed markets, Model 3 includes the competitive advantage dummy (IT industry for India), and Model 4 tests for the fast entry hypothesis. Over the four model specifications, most control variables are not statistically significant except for the variable RELATIVESIZE and the variable FIRM AGE, they are positive and significant at 1% and 5% level respectively. Moreover, according to Nicholson and Salaber (2013) who state that M&A event studies in developed markets usually use a 3-day event window around the announcement date but emerging markets are not as efficient (due to the insider trading, imperfect regulatory environment, etc.). Therefore, I use a 5-day event window (-2, +2) as the dependent variable to test the market response in India (a longer event window could dilute the announcement effect).
5.1. Developed target country hypothesis
The coefficient of the variable DEVELOPED in Table 4 Model 2 is positive and significant at the 5% level, indicating that shareholders of acquiring firms from India are able to obtain more abnormal returns if the target countries enjoy higher level of development. This value creation amounts 2.5% for Indian bidders. Developed markets possess creditable institutional regulations and more advanced tangible and intangible resources. Through cross-border M&As, EM firms can penetrate the developed market and use these advantages to increase the value for their shareholders (Buckley et al., 2012). In the 125 observations, developed target countries account for 76% and significantly positive outcome is obtained. This reflects that complementary capacity and higher-level institutional environment offered by developed countries have impacted and helped companies from emerging markets to expand globally (Gubbi et al., 2010).
5.2. Competitive advantage hypothesis
coefficient at 5% level as well. This result reveals that Indian firms in the IT industry enjoy a competitive advantage both within borders and across borders. This is in line with the policy of Indian government that promoting IT industry as one of the top priorities of the country and creating better brand equity in the global IT market. Therefore, investors strongly believe that cross-border M&As from Indian multi-national IT enterprises, in the pursuit of strategic resources (primarily advanced technology resources), can strengthen their competitive advantage and thus their post-acquisition performance.
5.3. Fast entry and geographical distance hypothesis
Firms favor geographically close countries because this decreases the economic and management costs in expanding to international markets (Chetty, 1999; Dow, 2000). Hu and Wu (2011) find negative and statistically significant effect of geographical distance between China and target countries on the value of Chinese acquiring firms. However, as can be seen in Table 4 Model 4, I just find negative coefficient of the variable GEOGRAPHICAL DISTANCE but fail to prove its significance. This means that Indian bidders do not recognize the advantage of geographically close to target countries as a factor that could ease and speed up the access to foreign markets and strategic assets. The insignificant result is possibly caused by the fact that India was colonized by Britain and has experienced long-term influence from western countries. For instance, English becomes the official language in India. The international transaction has less hurdles and more probability of rapid success for Indian firms as they have high multinational communication skills, especially with the increasing use of English in global business (Buckley et al., 2012). Moreover, as a consequence of being a former British colony, Indian bidders are more likely to undertake foreign acquisitions and get benefits from a fast entry into markets of Britain or other western countries rather than those of neighboring countries (Ghemawat, 2001).
hypotheses. Regarding H1, Indian bidders tend to acquire targets in more developed economies and obtain higher cumulative abnormal returns. With respect to H2, acquiring firms from India create significant abnormal returns when they expand their competitive advantage globally. These results imply that the pursuit of strategic resource is an important motivation for cross-border M&As from Indian bidders, especially seeking for more advanced resources (e.g. technological resources). Nevertheless, the third hypothesis, i.e. Indian bidders are able to benefit from a fast entry into geographically close countries, cannot be proved through the multivariate regression analysis.
6. Conclusion and discussion
With the remarkable economic development made by India in the recent decades, MNEs from this fast-growing economy has been playing a major role in the global M&As activity. In line with this trend, issues such as the performance, motivation and other aspects of overseas acquisitions from Indian enterprises are gaining interest in the academic literature, although not as comprehensive as researches about firms from developed economies. This paper enriches the extant literature by formally testing two motivations for cross-border deals from India and examining their impact on acquirer’s performance.
rapidly (Accenture, 2006).
This paper is not without some limitations, summarized below along with some suggestions for future areas of study. First, the sample of Indian cross-border M&As is still very limited. Originally, there are 457 cross-border deals undertaken by listed Indian acquirers can be found in Zephyr. However, after applying the criteria mentioned in Section 4, 186 deals are left. Eventually, I was able to match only l25 acquisition deals with Thomson Datastream. The criterion to keep only publicly listed firms with daily stock returns available around the announcement date is necessary to apply the event study methodology. Therefore, the results in this study possibly not hold for private firms who might have different motives for undertaking cross-border M&As. Second, by analyzing the short-term reaction of the shareholders of the acquiring firms, I do not make any forecasts about the long-term performance of theses cross-border deals. A positive short-term performance does not necessarily implicate that the international transaction can enjoy long-term success. The objective of this study was to investigate how shareholders of Indian acquiring firms identify and value the intrinsic motivations behind overseas acquisitions. Future research can be done to investigate the motives of private firms from emerging markets to expand globally or to analyze the long-term performance of these cross-border M&As.
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