The emergence and performance of the
Indian mergers and acquisitions market
and the impact of partner location
Replication of Dolfsma & McCarthy (2017)
RESEARCH PAPER
By
Nadi Dijksterhuis
University of Groningen
Faculty of Economics and Business
Pre-MSc Strategic Innovation Management
June, 2020
Nadi Dijksterhuis Kaap De Goede Hoop 24 9642 AV Veendam (06) 37152699
Abstract
The rapid growing Chinese market for mergers and acquisitions and the literature on its performance raise considerable interest in the targets where Chinese firms can best do
business with. Recently, Dolfsma & McCarthy (2017) reported that domestic Chinese mergers and acquisitions deals outperform international deals, because international deal makers cope with the liabilities of ‘distance’, ‘foreignness’ and ‘outsideness’. The Indian market for mergers and acquisitions is experiencing a similar growth. Therefore, with the objective of extending this knowledge in a different geographical and industrial context, the study of Dolfsma and McCarthy is replicated based on mergers and acquisitions deals by Indian acquirers in high-technology industries. OLS regression techniques were used in order to predict the performance of domestic mergers and acquisitions deals by Indian acquirers in high-technology industries in comparison to the performance of international Indian deals. My findings do not reveal sufficient evidence to support that domestic mergers and
acquisitions deals outperform international Indian deals. In addition, I highlight the limitations of the generalizability of samples from one country to another. Finally, the underlying
Introduction
The world is influenced by forces of globalization and rapid technological changes. As a result, the degree of competition among firms is fierce. In order to cope with these
challenging trends, and moreover, to explore the opportunities, an increasing number of firms choose for inorganic growth through a strategic alternative such as mergers and acquisitions (Pillania, Kumar, & Bansal, 2008). The Chinese merger market, for instance, is booming as Chinese acquirers spent $38 million on mergers and acquisitions in 1990, and $661.3 billion in 2016 (McCarthy, Dolfsma, & Weitzel, 2016). According to Dolfsma & McCarthy (2017), the Chinese merger market and the literature on the performance of Chinese mergers and acquisitions increased simultaneously. However, a limited number of studies considered whether the location of the target has an impact on performance or not (e.g. McCarthy & Aalbers, 2016). In order to address this gap, Dolfsma and McCarthy published an article on the emergence and performance of the Chinese merger market and the impact of partner location. The aim of the paper was to address the emergence of the Chinese merger market, examine whether partner choice in terms of physical location has an impact on performance and to respond to the increasing demand of scholars to examine the generalizability of the existing literature on companies situated in emerging markets (Dolfsma & McCarthy, 2017).
Dolfsma & McCarthy (2017) tested their targets using a final sample of 1,542 large (>$10 m) acquisitions by stock-listed acquirers, for which they could measure performance. This paper has provided insightful hypotheses with regard to theoretical literature which suggests that international deals are more expensive. By introducing the terms of the liabilities of
‘distance’, ‘foreignness‘ and ‘outsideness’, Dolfsma & McCarthy (2017) highlight the costs associated with doing business abroad. Furthermore, Dolfsma & McCarthy (2017) argued that domestic Chinese mergers and acquisitions will outperform international deals and that
international deals which become subject to more of these liabilities would underperform those that experienced less. The result demonstrated that dissimilar to Western firms, Chinese acquirers create value through mergers and acquisitions and the literature applies only partly to Chinese acquirers (Dolfsma & McCarthy, 2017). The study of Dolfsma & McCarthy (2017) was the first study, to the best of my knowledge, to examine whether the location of the target has an impact on Chinese overseas acquisitions. The study has provided useful insights with regard to the Chinese merger market and the impact of partner locations.
The mergers and acquisitions activity in India, for instance, has reached a peak with an annual volume of 35.9 USD billion and a total of 409 deals in 2016 (Pandya, Street, & Street, 2018). As the Indian market for mergers and acquisitions experienced a substantial growth, the literature available on the performance increased as well (e.g. Kumar & Bansal, 2008; Sardana & Zhu, 2017). Despite the fact that the Indian mergers and acquisitions market is emerging, a limited number of studies have examined this particular market. In addition, it is notable that the major part of the executed literature applied descriptive statistics (e.g.
Chidambara, Krishnakumar, & Sethi, 2018, p. 11). Furthermore, none of these studies
specifically examined the emergence and performance of the Indian mergers and acquisitions market and the impact of partner location based on acquisitions by firms operating in high-technology industries in India. As a result, there exists a research gap with regard to the way in which the Indian mergers and acquisitions market operates. It is essential to embrace more samples involving emerging markets and find out whether the results based on samples from one single country (e.g. Dolfsma & McCarthy, 2017) could be generalizable to another country (Deng & Yang, 2015). Therefore, the objective of this research paper is to replicate the study of Dolfsma & McCarthy (2017) examining how partner choice in terms of physical location impacts performance using a different geographical location and industrial context. In conclusion, this study will focus on the performance of domestic mergers and acquisitions deals by Indian acquirers operating in high-technology industries in comparison to the performance of international Indian deals.
Theoretical background
Cross-border mergers and acquisitions
With companies acquiring targets all over the world, mergers and acquisitions can be described as universal. Today, international mergers and acquisitions are a well-known activity in the business world (Chatterjee & Aw, 2000). Through cross-border mergers and acquisitions, firms create the opportunity to obtain competitive assets from domestic firms, such as advanced technologies and reputable brands. Furthermore, cross-border mergers and acquisitions can be used to realize strategic objectives such as fast entry into rapidly growing markets or consolidation of market power in concentrated sectors (Chen & Zeng, 2004). In conclusion, international investment provides large diversification opportunities (Sarkissian & Schill, 2009).
By means of contrast, cross-border deals are also described as the ‘problematic’ part of mergers and acquisitions (e.g. Chatterjee & Aw, 2000; Sarkissian & Schill, 2009). Compared to domestic mergers and acquisitions, cross-border mergers and acquisitions involve
additional layers of uncertainty regarding the cultural, legal, and business environment of the host country (Xu, 2017). For instance, differences in legal systems and financial standards between the acquirer and target countries can make it more problematic to detect value-enhancing targets. In addition, both cultural differences and potential nationalism can be sources of uncertainty for a successful foreign investment (Ahern et al., 2014).
Therefore, international deal-makers have added the liabilities of ‘distance’ (Boeh &
Beamish, 2012), ‘foreignness’ (Zaheer, 1995) and ‘outsideness’ (Johanson & Vahlne, 2002) to the performance equation of mergers and acquisitions (Dolfsma & McCarthy, 2017). First of all, the liabilities of ‘distance’ are the costs associated with undertaking business in distant markets. Distance causes transportation costs (Capron, Dussuage, & Mitchell, 1998) and monitoring costs (Böckermann & Lehto, 2003, cited in Dolfsma and McCarthy, 2017, p. 4). Furthermore, distance thwarts the flow of information and it increases information
The Indian M&A market and high-technology industries
Regardless of the serious complications with corruption and poverty, the economic growth of India is clearly visible. The leadership in software and IT in high-technology industries enables India to focus on innovation during the further developments of the country (Gupta & Wang, 2009). As a result, the Indian mergers and acquisitions market has experienced a rapid growth as both domestic and cross-border acquisition activity have increased (Chidambara, Krishnakumar, & Sethi, 2018).
The research is addressed by means of addressing the applicability of the literature on cross-border mergers and acquisitions to Indian acquirers. Both culture and cultural components such as different languages, regulations and legal systems complicate the performance of a foreign acquisition based on acquisitions by firms operating in India. While these components predict the greater performance for domestic acquisitions, the liabilities of ‘distance’ (Boeh & Beamish, 2012), ‘foreignness’ (Zaheer, 1995) and ‘outsideness’ (Johanson & Vahlne, 2002) are the impediments of international deal makers. In addition, empirical evidence suggests that in comparison to domestic mergers and acquisitions, cross-border mergers and
acquisitions underperform (Chatterjee & Aw, 2010, cited in Dolfsma and McCarthy, 2017, p. 4). At this moment, there is no motive to suggest that Indian acquirers in high-technology industries are more capable of managing these liabilities. As a result, I follow Dolfsma and McCarthy by suggesting that the current literature applies. Therefore, the following
hypothesis is composed:
Hypothesis: “Domestic mergers and acquisitions deals by Indian acquirers operating in
Methodology Sample and data Empirical context
The aim of this research paper is to examine the emergence and performance of the Indian mergers and acquisitions market and the impact of partner location based on acquisitions by firms operating in high-technology industries in India with a sample of all mergers and acquisitions deals announced between Jan 01, 2011 and April 01, 2020. The Indian mergers and acquisitions market experienced a rapid growth as both domestic and cross-border acquisition activity strongly increased (Chidambara, Krishnakumar and Sethi, 2018). Supported by the liberalization of outward investment policies, the total amount of Indian overseas acquisitions reached a value of 13.4 billion in 2018. The high-technology industries in India accounted for 16.2% of the market share (Indiablooms, 2019). Nowadays, high-technology industries and external acquisition of new technologies are becoming more important (Kang & Kang, 2015). The intensive technological development is the defining characteristic of the second half of the 20th century (Romanova, Korovin, and Kuzmin, 2017).
Data sources and sample
This paper is connecting with high-technology industries in order to address innovation. The original paper of Dolfsma and McCarthy does not take the industrial context into
consideration. Following Cloodt et al. (2006), high-technology industries are defined to mean the aerospace and defence codes 372 and 376), computers and office machinery (SIC-code 357, 35 and 737), pharmaceuticals (SIC-(SIC-code 283) and electronics and communications (SIC-code 36 and 48) industries. In addition, the hypotheses behind the impact of partner location were tested using data from Zephyr. The Zephyr database contains information with regard to mergers and acquisitions deals and stock market data from 2011 onwards.
The sample of the hypothesis consists of all merger and acquisition deals with either Indian or international targets, and moreover, meets the following requirements: (1) acquired by Indian firms (2) announced between Jan 01, 2011 and April 28, 2020; (3) with a minimum
Model and analysis Deal performance
By following a well-known approach of mergers and acquisitions performance studies,
Dolfsma and McCarthy measured performance using an event study methodology. By making a comparison of the ‘forecast’ with ‘actual’ data on the firm’s stock price, an indicator of the firms’ ‘abnormal’ return is provided after the event.
This study differs from the research of Dolfsma and McCarthy as the abnormal return is calculated in a different manner due to data restrictions.
In the equation, Ri,t is the actual stock return on a given day, whereas 𝑅̅𝑖,𝑗 is the average return on the stock. Equation (1) shows the mean-adjusted returns model (MAR) of abnormal return of a security i;
𝐴𝑅𝑖,𝑡 = 𝑅𝑖,𝑡− 𝑅̅𝑖,𝑗
In order to calculate Ri,t and 𝑅̅𝑖,𝑗 , the compound annual growth rate (CAGR) is used. CAGR is defined as:
𝐶𝐴𝐺𝑅(𝑡0, 𝑡𝑛) = ( 𝑉(𝑡𝑛) 𝑉(𝑡0) ) 1 𝑡𝑛−𝑡0 − 1
Where 𝑉(𝑡0) is the initial value, 𝑉(𝑡𝑛) is the end value and 𝑡𝑛 − 𝑡0 is the number of years. In comparison to the approach of Dolfsma and McCarthy, I calculated MAR using the event estimation window of the acquirer stock price measured from three months before each event and the acquirer stock price prior to the announcement. The event window is calculated by the acquirer stock price at completion date and the acquirer stock price one month after
completion.
The stock-market data necessary to complete the event study is retrieved from Zephyr. The data availability reduces the initial samples of available deals to 67.
Target location
By making use of the locations of the firms involved, I follow Dolfsma and McCarthy by measuring target location using indicator variables.
In order to test the hypothesis, the following indicators were created; (1) an India indicator that identifies Indian targets;
Control variables
A number of factors are known to impact the dependent variable, i.e. deal performance. I follow Dolfsma and McCarthy by using OLS regression techniques and I control for the following variables. First of all, Acquirer Size, measured in pre-deal acquirer total number of assets because larger acquirers make worse deals (Moeller, Schlingemann, & Stulz, 2004,
cited in Dolfsma & McCarthy, 2017, p. 8). Secondly, the Deal Size (Deal value in EUR), because larger deals underperform (Moeller, Schlingemann, & Stulz, 2004, cited in Dolfsma & McCarthy, 2017, p. 8). It is important to note I did not include the control variables per cent of the deal financed by cash (Per cent Cash) and stock (Per cent Stock) as these variables contained a large amount of missing values in Zephyr.
All the necessary data is collected from Zephyr. Finally, I follow Dolfsma and McCarthy by including year dummies, to account for year specific effects.
Results
Table 1 illustrates the descriptive statistics, including means and standard deviations.
Table 1
Table 2 shows the results of the OLS regression analysis predicting the performance of domestic M&A deals by Indian acquirers in high-technology industries in comparison to the performance of international Indian deals. The coefficient for the hypothesis Indian target (dummy) shows whether the performance of domestic M&A deals will significantly
outperform international Indian deals. While I have identified and removed potential outliers, the coefficient did not show a significant effect, and therefore, there is not enough evidence to support:
Hypothesis: “Domestic M&A deals by Indian acquirers operating in high-technology
industries will outperform international Indian deals”.
Table 2 – results replication paper Table 3 – results original study Dolfsma & McCarthy
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
However, it is important to notice the significant, but weak (p=0,097), relationship between the abnormal return (𝐴𝑅𝑖,𝑡) and the control variable Dealsize (Deal value in EUR). The coefficient is positive (1.32e-06), which illustrates that Dealsize (Deal value in EUR) is positively related to the performance of M&A deals (𝐴𝑅𝑖,𝑡). In short, the bigger the deal size, the better the performance of M&A deals in high-technology industries.
Variable Obs Mean Std.Dev. Min Max Deal performance 78 -1.213 5.761 -45.20 4.892 Deal size 135 236057 939565 7655 1.030e+07 Acquirer size 115 6.369e+06 1.640e+07 4295 9.780e+07
(1)
VARIABLES Deal performance
Acquirer size 1.25e-08
(1.97e-08)
Deal size 1.32e-06*
(7.83e-07)
Domestic target (dummy) -1.205
(0.761)
Year dummies Included
Constant -1.483
(2.719)
Observations 66
R-squared 0.237
(1)
VARIABLES Deal performance
Acquirer size -0.003
(0.002)
Deal size 0.008***
(0.003)
Domestic target (dummy) 0.026***
(0.007)
Year dummies Included
Constant -0.022
(0.062)
Observations 1542
Discussion
By suggesting that international deals are more expensive in terms of the liabilities of
‘distance’, ‘foreignness‘ and ‘outsideness’, Dolfsma & McCarthy (2017) found that domestic Chinese M&A deals outperform international deals. This research is addressed by means of addressing the applicability of the literature on cross-border mergers and acquisitions to Indian acquirers. While I have identified and removed potential outliers, the sequenced
reexamination fails to find sufficient evidence to support Dolfsma and McCarthy’s conclusion that domestic deals outperform international deals. Rather than realizing results fully
consistent with the above findings, there is insufficient evidence to state that domestic M&A deals by Indian acquirers operating in high-technology industries will outperform
international Indian deals.
More specifically, I find that my replication, by following the regression and the related formulas of the Chinese target of Dolfsma and McCarthy, does not yield any identical coefficient as compared to the coefficient of the Indian target. In addition, the p-values associated with the obtained coefficients are high, making it difficult to infer that domestic Indian M&A deals outperform international deals. As a result, I am unable to reject the null hypothesis of domestic M&A deals by Indian acquirers operating in high-technology
industries outperforming international Indian deals, and I do not find that the acquirer size is an important contextual influence on the performance of M&A deals in high-technology industries. However, I did find that deal size is of important contextual influence, meaning that the bigger the deal size, the better the performance of M&A deals in high-technology industries.
It is important to explain the difference between this study and the findings of Dolfsma and McCarthy (see table 2 & 3). First of all, the difference between the small sample I was able to obtain and the significantly larger sample of Dolfsma and McCarthy is raising some questions with regard to the reliability of the research. According to Schmidt (2009), it is difficult to learn how to conduct a sensitive experiment on the basis of the description of the Methods section as there are usually key aspects missing. From this point of view, it is possible that there were several aspects and analyses absent in the data collection of Dolsfma and
McCarthy. As a result, it is likely that the results might differ as it is complicated to obtain a similar dataset. In addition, the database Zephyr did not contain all the necessary data in order to get a similar sample size as compared to the study of Dolfsma and McCarthy. According to Sanz-Alonso (2018), larger sample sizes allow researchers to enhance the determination of the average values of their data and to avoid errors as a result from examining a small number of possible atypical samples. By pointing out the concerns over replication failures, also referred to as the “replication crisis” (Quiggin, 2019), it is possible that the replication of significant results fail.
The divergence between this study and the study of Dolfsma and McCarthy is disconcerting. This raises questions about whether the results based on samples from China could be generalizable to India (Deng & Yang, 2015). Dealing with the government and overcoming bureaucratic barriers have become essential when doing business in India. While, the
In addition, India ranks 60th behind the neighboring nation China (15th) among 79
developing countries in the inclusive development index (Sardana & Zhu, 2017). Despite the fact that India’s GDP per capita and labor productivity growth has been strong, the debt-to-GDP ratio is high. This raises some questions about the sustainability of government spending in India (PTI, 2017). In conclusion, the above-mentioned facts illustrate important differences between China and India with regard to ease of doing business in the world and the inclusive development index. Therefore, it is difficult to determine whether the results based on samples from China could be generalizable to India. In conclusion, the deviation of China from India, could have influenced the final results of this paper.
Limitations
The findings of this paper are subject to a number of important limitations. First of all, I followed Dolfsma and McCarthy by solely considering large acquisitions, meaning above $10 million. This could have influenced the results, as smaller deals were excluded. Secondly, the control variables of Dolfsma and McCarthy; per cent of the deal financed by cash (Per cent Cash) and stock (Per cent Stock) were excluded as these contained a large amount of missing values in Zephyr. Thirdly, I was unable to follow Dolfsma and McCarthy by measuring the acquirer size by means of the total number of employees. As a result, pre-deal acquirer total number of assets was used instead of the total number of employees as the data in Zephyr with regard to the total number of employees turned out to be incomplete. In addition, this
replication study deviates from the original as performance was measured by means of the mean-adjusted returns model (MAR) and the compound annual growth rate (CAGR). Besides that, both the estimation window and event window were defined differently due to choice restrictions in Zephyr. Furthermore, it is possible that the findings of the study of Dolfsma and McCarthy do not hold for high-technology industries. Finally, the variables derived from
Zephyr contained additional data gaps which reduced the, relatively small, initial sample size
even more. Therefore, the final sample size was not representative as it was significantly smaller in comparison to the sample size of the study of Dolfsma and McCarthy.
Implications for further research
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