Profitability of Mergers and Acquisitions: Evidence from India’s High-Tech Industries

Abstract

This article analyses the post-merger profitability of acquirers for a sample of mergers and acquisitions (M&As) that did take place during the period 1999–2011 in certain high technology industries in India. Taking the performance of median firm(s) from the three-digit industry category of acquirer(s) as the benchmark, this study deploys difference-in-differences (DID) method to evaluate acquirer performance using both parametric and non-parametric tests. Results of the analysis show that an overwhelming majority of acquirers have performed better than the benchmark firms in their respective industries. Interaction of acquirer characteristics such as size, types of M&As undertaken and industry origin, among others, impacts the outcome. Horizontal M&As are more successful than the other types. Smaller firms, being more inclined to go for horizontal M&As, have fared better than their larger counterparts. Similarly, firms from the drugs & pharmaceuticals and electronics industries have performed better than those from chemicals, electrical and non-electrical industries. Here too, better performing industries have higher sprinkling of horizontal M&As.

Keywords

Mergers acquisitions firm size profitability industry

JEL Codes: G34, L2

I. Introduction

Reform measures of the 1990s, by removing barriers to new entry, exposed domestic firms to an unprecedented level of competition, both internal and external, where in, for sheer survival, they needed to improve their efficiency level by cutting costs and acquiring optimal scale. Abolition of merger control provisions, along with other measures to develop market for corporate control, provided firms with an opportunity to use mergers and acquisitions (M&As) as a device to improve efficiency by restructuring and consolidating their business. In the early 1990s, large business houses in India indeed took to M&As in a big way to bring under direct control enterprises that they had held nominally as independent units during the monopoly and restrictive trade practices act (MRTPA) era. (Agarwal & Bhattacharjea, 2006). Similarly, relaxed norms for foreign equity holding opened up M&As as the faster route of entry for multinational companies (MNCs) wherein they could leverage their superior intangible assets to location-specific advantages of the incumbents. MNCs responded not only by raising equity in their existing subsidiaries, but also by purchasing controlling stakes in firms they previously partnered with, and even those that were owned domestically, raising, for the first time, the spectre of hostile takeovers in India’s market for corporate control (Bhattacharjea, 2016). Domestically owned firms responded, especially those belonging to the business groups, by raising equity stakes in their affiliated firms in a bid to retain control (Beena, 2008; Kaur, 2012); other firms that operated with an over-diversified portfolio of business hived-off peripheral businesses to concentrate on their core competencies (Bhattacharjea, 2016). Consequently, by mid-1990s, India’s market for corporate control witnessed a flurry of activity, first in the manufacturing sector, dominated by domestically owned firms, and, thereafter, in the services sector, due probably to liberalisation of its financial sector, where MNCs participated more vigorously (Beena, 2008; Kaur, 2012). Some of the specificities of M&A space in India have been a rather high incidence of within the ‘Business Group’ mergers, preponderance of horizontal type M&As, participation of both large and small businesses and active involvement of MNCs (Beena, 2008; Kaur, 2012).

A number of studies came up in the wake of increased M&As activities seeking to analyse their nature, motives and consequences for the economy. In regard to the motives of M&As, analysts generally believe that firms have attempted to optimise shareholders’ wealth/value of firm rather than pursue other goals such as empire building and pursuit of managerial hubris. They, however, differ in their assessment of the strategies the firms might have been pursuing to achieve this. Preponderance of within the ‘Business Group’ as well as intra-industry (horizontal) mergers in the post-reform era has led some of them to infer that M&As may have been used as a device by large firms to improve efficiency/reduce costs by eliminating intra-group competition, and by acquiring scale and focus in business (Agarwal, 2003; Basant, 2000; Kaur, 2012; Kumar, 2000). Others have inferred that large MRTP firms by actively indulging in horizontal type M&As have succeeded in raising industry concentration level in a bid to raise their price cost margin (Beena, 2014; Kumar, 2000).

Profitability of M&As, nonetheless, has remained an unsettled issue both in theory and empirics. While the results of theorisation have depended on specific assumptions made with regard to firm’s costs and industry demand conditions, results of empirical studies have shown great amount of variation across country, industry, time period and methodology chosen for the analysis. It has also been observed that the potentially beneficial effect of M&A in any particular case may get more than nullified by various forms of X-inefficiency emanating from contrasting managerial styles and work cultures of the combining entities. The contingent nature of outcome of the analysis has made this issue a recurrent theme of research.

This article is an addition to the body of research pertaining to private profitability of M&As. It proceeds by picking a sample of M&As that occurred between the years 1998 and 2012 in certain high-tech segments of Indian industry, namely chemicals (NIC 201, 202), drugs & pharmaceuticals (NIC 210), electronics (NIC 261, 262, 265, 266), electrical (NIC 271, 273, 275, 279) and non-electrical (NIC 281, 282) machinery.¹ The period 1999–2011, chosen for the analysis, witnessed intense M&A activity within these industry segments. Firms in high-tech industries are able to generate more merger-specific synergies through transfer of technology and other intangibles; as such M&As in these industries are likely to be more successful. At the same time, these were the industries in India which, in the aftermath of policy of liberalisation, have had to undergo a painful restructuring process.

This article is divided into six sections. Section II presents review of relevant literature—both theoretical and empirical. Section III elaborates the empirical methodology. Section IV describes data, their sources and measurement. Section V presents the findings of the empirical analysis. The final section (Section VI) concludes.

II. Review of Literature

Early theoretical discussions of welfare implications of M&As often implicitly assumed that firms invariably had an incentive to merge; by doing so, firms could increase profits by raising price or by reducing cost or both. Theoretical justification for such a presumption came from industry models such as the symmetric Cournot model that exhibited higher prices and profit per firm as the number of firms in the industry declined following a merger. Such a presumption also led to an erroneous belief among the analysts that merger waves and their subsequent ebbing away in the past—for instance, in the United States—had always been the outcome of antitrust policy stance of the government, rather than a lack of profitable opportunities on the part of merging firms (Jesus Seade, 1980; Perry, 1984).

Stigler (1950) was among the earliest dissenters who pointed out that this may not be the case, simply because the merger participants may not be able to partake of all the profits that may potentially ensue from their merger. Salant et al. (1983), using Cournot–Nash framework, demonstrated formally that a horizontal merger, under the twin assumptions of a linear industry demand and identical constant average costs for each firm, both before and after the merger, while raising overall industry profit, may, at the same time, cause the profit of the merged firm to remain below the combined pre-merger profit levels of the merging entities. From this, they concluded that mergers normally would be unprofitable, the exception to this may occur only when there is an industry-wise merger. This, indeed, represented a sharp about turn in analysts’ belief regarding the profitability of a horizontal merger.

It was soon recognised, however, that the results of the above model depended critically on certain assumptions, which also violated one’s intuitive notions of what a merger was all about. Merger of two firms from a symmetric equilibrium of (n + 1) firms should result in an equilibrium situation with (n – 1) old firms and one new that should be ‘larger’ in some sense than the others as the new firm must possess the combined productive capacity/product lines of both the merging partners. This was simply assumed away in the model of Salant et al. (1983), where the merged firm continued to have the same constant average cost as the non-merging ones. Deneckere and Davidson (1985) and Perry and Porter (1985), by introducing two different notions of ‘size’, were able to reverse the results of Salant et al. (1983).

Deneckere and Davidson (1985) adopted a framework where price-setting firms produced differentiated products. Here, the notion of size was introduced from demand side by allowing the merged firm to continue to produce all the products of its constituent firms. Under these assumptions, the results of Salant et al. (1983) got reversed.

Perry and Porter (1985) attacked the problem from cost side by allowing the newly created firm to control the assets belonging to each of its constituents. Besides, while the long-run technology was assumed to exhibit constant returns to scale, due to the presence of the fixed factor, the short-run marginal-cost curve of firms was allowed to slope upward. A large firm with more (fixed) capital could produce the same output at lower cost than a small firm. With increasing marginal costs of firms, the incentive to merge usually remained, and the model by Salant et al. (1983) turned out to be a special case of that of Perry and Porter with firms assumed to have flat marginal cost curves.

A somewhat different route was adopted by Dowel (1984) who argued that in the presence of industry-specific non-salvageable assets, it was possible for pre- and post-merger industry output and price to be the same, leaving profits, either for the industry as a whole or for the firms in the industry, unchanged.

Clearly, predictions about profitability of M&As, in theory, depended on the assumptions made in regard to costs and industry demand conditions. Moreover, it was also probable that the likely beneficial effects of a merger, in any one particular case, might get more than nullified by various forms of internal inefficiencies, including managerial and financial types. Considering these possibilities, analysts veered round the idea that the resolution of the issue of private profitability of mergers essentially belonged to the arena of empirical research.

Extant empirical works on profitability of M&As, undertaken mostly for the developed countries, have thrown up conflicting evidence. The results of these studies vary along a host of factors, namely the time period chosen, the nature of sample selected, the country of study and the methodology deployed. Rest of this section briefly reviews the findings of some of the important studies undertaken for both India and abroad. The review leaves out studies that follow ‘event study’ approach, focusing mostly on those that use accounting data.

A number of studies on profitability of horizontal mergers undertaken during the 1970s and 1980s for the United Kingdom (Cowling et al., 1980; Hughes, 1989; Meeks, 1977; Singh, 1971; Utton, 1974), for Continental Europe (Mueller, 1980; Peer, 1980; Ryden & Edberg, 1980) and for the United States (Mueller, 1985; Reid, 1968) showed that M&As, on average, either had no effect or led to slight decrease in profitability. Ravenscraft and Scherer’s (1989) celebrated work for the United States for the period 1957–1977 that covered a large number of lines of business data reported, on average, a fall in the profitability of participating firms. Given the unlikelihood of mergers reducing the market power of firms, such a result was interpreted as being indicative of a decline in their efficiency. Healy et al. (1992), on the other hand, with a sample of 50 large US firms undergoing M&As in the period 1979–1984, reported an improvement in the post-merger performance of acquirers. Differences in the size and nature of sample selected and time period chosen mostly accounted for the diametrically opposite findings of the two studies. While the former study covered the period of ‘conglomerate merger wave’, the latter pertained to an era when the firms pursued the strategy of focusing on their core business.

A number of other works followed, which were essentially a modification of Healy et al.’s (1992) in some respect or the other. Switzer (1996), while sticking to Healy et al.’s (1992) methodology, worked with a larger sample (327 firms) covering a longer time span (1967–1987). Her findings, nevertheless, concurred with those of Healy et al. She also observed positive association between ‘abnormal return’ on equity around the time of announcement of merger events and the long-term performance of the merged firms. Taking Healy et al.’s (1992) measure of performance, Ramaswamy and Waegelein (2003) also observed a significant improvement in post-merger financial performance using a sample of 162 US firms that merged during the period 1975–1990.

Replicating the methodology of Healy et al. (1992), Ghosh (2001) analysed 315 US mergers completed during the period 1981–1995 and found improvement in firms’ financial performance. However, the results got reversed when he performed his calculations by switching to firms matched on performance and size of total assets as controls. Sharma and Ho (2002), in their study of 36 acquisitions undertaken during 1986–1991 in Australia’s manufacturing sector, deployed both Healy et al.’s (1992) methodology, as also one that used control category formed using matched sample based on asset size and industry group. With the latter, no significant improvement in the post-acquisition performance of firms was observed. Manson et al. (2000), using a sample of 44 takeovers that occurred in the United Kingdom during the period 1985–1987, reported significant positive return on assets deployed. In a study of 20 acquisitions during 1987–1992 in Taiwan, Yeh and Hoshino (2000) examined both post-merger accounting measures of profitability and the movement of the stock price of acquirers at the time of the announcement of the acquisition. They discovered that even when abnormal returns on acquirers stock were positive, their post-acquisition accounting measure of profitability showed a decline.

Following the methodology of Healy et al. (1992), Gugler et al. (2003) analysed the effects of M&As undertaken in the years 1981–1998 in a number of countries on the post-merger profitability and net sales of firms. The authors concluded that, across countries, mergers tended, in general, to increase profitability but led firms to reduce net sales. From this, they inferred that M&As, in general, tended to increase market power of acquiring firms.

Studies that investigate profitability of M&As using data from Indian market for corporate control are fewer in number. Several of these are based either on small sample, or take shorter pre- and post-merger time duration, or choose not to control for variables other than the act of merger that might impact firm performance.

Using the methodology adopted by Cosh et al. (1998) and Mueller (1986), Pawaskar (2001) analysed post-merger financial performance of 36 mergers that occurred during 1992–1995, but he found no significant improvement in firms’ profitability. Ramakrishanan (2008) with a sample of 87 domestic mergers that took place during 1996–2002, however, reported an improvement in post-merger operating performance of firms. Mishra and Chandra (2010) explored the impact of 52 M&As that occurred during 2000–2001 to 2007–2008 in the pharmaceutical industry by estimating the profit function of firms. They found no significant impact of M&A activity on firms’ long-term profitability. Rani et al. (2013) with a large sample of 305 firms across all industries that merged/acquired between 2003 and 2008 concluded that firms showed an improvement in post-merger operating performance. Using Du Pont analysis, the study also pointed out that the improvement in performance is due to improvement in operating margins.

A review of both theoretical and empirical studies leads one to believe that the benefits of mergers are not self-evident, either from the point of view of the shareholders or of the society as a whole. A general presumption in favour of mergers, therefore, does not appear to be justified, thereby warranting a study for each individual industry/country and time period.

III. Methodology

It is evident from the review undertaken above that empirical studies examining the issue of profitability of mergers have moved broadly on two different methodological trajectories: ‘event study’ approach and approach that uses accounting data.

Event-study approach as applied to the study of M&As typically measures any abnormal returns (i.e. returns in excess of the market portfolio) accruing to the acquirer’s/target’s share price up on announcement of an M&A and treats it as market’s way of assessing the event. A positive (negative) excess return is taken to be an indication that the market views the event favourably (unfavourably). More specifically, an acquisition is considered as wealth enhancing and hence successful if the target firm’s shareholders gain and that of the bidding firm’s shareholders do not lose (Jensen & Ruback, 1983). The methodology is premised on the efficient financial market hypothesis.

Proponents of this analysis assert that the financial gains are for real as it arises out of a more efficient redeployment of assets through the agency of takeovers. In the absence of a credible defence of this contention, critics continue to hold the view that premium earned by target firms are essentially a redistribution of wealth away from other stakeholders such as the employees (as takeover often results in reneging of contract by the new masters), the consumers (due to rise in acquirers market power), bondholders and the government (in the form of lower taxes). The controversy has also generated a lively debate about whether market for corporate control has turned firm management myopic in the United States.

Studies that use accounting data compare actual post-merger profits of acquiring firms/combined entities with those of their projected profits which, in turn, is calculated using the profits of a suitably chosen counter factual as benchmark. This allows comparison of pre–post performance of acquiring firms/combined entities over a longer time horizon, until the time all the forces set in motion play themselves out. It also controls for factors such as size and industry which impact firms’ profit. The choice of a suitable counterfactual, however, has been a bone of contention in this approach. Most studies that use accounting data, nevertheless, have settled for the median firm (in terms of profitability) from the industry of the merging firms to be the benchmarks, with the implicit assumption that their performance, in the absence of merger, would have been the same as those of their respective median firms (Gugler et al., 2003; Healy et al., 1992). This study has settled for the accounting data approach and uses algorithms of Gugler et al. (2003). Taking a long-term view, the study tracks changes in profits of acquiring firms for the full five post-merger years.

Determining the Effects of M&As on Profitability

Identification of the median firm for each of the merging/acquiring/acquired firms is done at the three-digit industry level of aggregation so as to ensure availability of good number of candidates from among whom to choose. This has the additional advantage of the chosen benchmark firms’ profit figure not getting contaminated by an act of merger itself. Although a three-digit industry’s performance will track performance at five-digit industry with some error, one can safely assume away the possibility of any systematic bias arising out of this choice. The control group so formed will exclude firms involved in an act of M&A in the period t – 1 to t + 5, where t is the year of the merger.

Now, supposing that M&A among firms takes place in the year t, the projected profits of the combined entity in the post-merger year t + n under the hypothetical situation that no such event has taken place will be the sum of the following components: actual profits of the acquirer in t – 1, predicted growth in its profits from t – 1 to t + n, actual profits of the acquired firm in t and the predicted growth in its profit from t to t + n. Note that while the profit of the acquirer is taken for the year t – 1, that of the acquired is for the year t. The predicted growth in profits of acquirer/acquired requires, in turn, projection of two variables: predicted level of its total assets and the predicted change in the return on total assets over the relevant period. The former could be obtained by scaling up the total assets of acquirer/acquired using the ratio of the total assets of the median firm in the three-digit industry of the firm between the years t + n and t – 1 for the acquirer, and between the years t + n and t for the acquired; the latter could be had by taking the difference in the return on assets (i.e. profit/total assets) of the median firm between the years t + n and t – 1 for acquirer and t + n and t for acquired.

Letting ΔR_{IAt–1, t + n} to be the projected difference in the returns on the total assets of the acquiring firm during the time period t – 1 to t + n, we have

Δ R_{I A t - 1, t + n} = (\frac{P_{I A t + n}}{K_{I A t + n}}) - (\frac{P_{I A t - 1}}{K_{I A t - 1}})

(1)

where P_{IA t + n} and K_{IA t + n} represent profits and total assets, respectively, of the median firm from the industry of the acquirer in time t + 1. Defining ΔR_{IB t, t + n} as the projected difference in the returns on the total assets of the acquired firm analogous to ΔR_{IA t–1, t + n}, the projected profits of the combined entity in time t + n can be calculated as follows:

\begin{array}{l} P_{C t + n} = P_{A t - 1} + (\frac{K_{I A t + n}}{K_{I A t - 1}}) K_{A t - 1} Δ R_{I A t - 1, t + n} + P_{B t} \\ + (\frac{K_{I B t + n}}{K_{I B t}}) K_{B t} Δ R_{I B t, t + n} \end{array}

(2)

where P_{C t + n} stands for predicted profits of the combined entity in time t + n; P_{A t + n} for the profits of the acquirer in time t + n; K_{A t + n} for total assets of the acquirer in time t + n; P_{B t} for the profits of the acquired firm in time t; K_{IB t + n} for the total assets of the median firm in the industry of the acquired company in year t + n and K_{B t} for the total assets of the acquired firm in time t.

Additional acquisitions or spin-off undertaken by the same firm could easily be incorporated in Equation (2). Suppose a firm makes two successive acquisitions in years t and t + 2 and spins or splits off in year t + 3, the modified form of Equation (2) would be as follows (Gugler et al., 2003):

\begin{array}{l} P_{C t + 1} = P_{A t - 1} + (\frac{K_{I A t + n}}{K_{I A t - 1}}) K_{A t - 1} Δ R_{I A t - 1, t + n} + P_{B t} + (\frac{K_{I B t + n}}{K_{I B t}}) K_{I B t} Δ R_{I B t, t + n} + P_{B t + 2} \\ + (\frac{K_{I B t + n}}{K_{I B t + 2}}) K_{B t + 2} Δ R_{I B t + 2, t + n} - P_{S t + 3} - (\frac{K_{I S t + n}}{K_{I S t + 3}}) K_{s t + 3} Δ R_{I S t + 3, t + n} \end{array}

(3)

where P_{S t + 3} is spun- or sold-off profits in time t + 3, K_{IS t + n} are assets of the median firm in the industry of the spun- or sold-off firm in time t + n, K_{S t + 3} are total assets of the spun- or sold-off firm in time t + 3 and ΔR_{IS t + 3, t + n} is the projected difference in the returns on the spun- or sold-off firm’s assets from time t + 3 to t + n.

The estimator above is subject to biases emanating from missing data on divestures (which they are any way) and unreported additional acquisitions undertaken between times t and t + 5. Assuming spun- or sold-off profits to be positive, missing data on divestures will lead to overestimation of predicted profits; on the other hand, missing data on additional merger, if any, assuming that such taken-over profits are positive, will lead to underestimation. As these work in opposite directions, it is assumed here that they would cancel each other.

Finally, the effect of M&As on profitability would involve comparing the predicted profits of the merged entities in a given post-merger year with that of its actual profits in the same year applying t-test for paired sample.

Wilcoxon Matched-Pairs Signed Rank Test

To check the robustness of the results, the study deploys Wilcoxon matched-pairs signed rank test as a non-parametric alternative to t-test for related samples. While the t-test is based on the assumption of normality of the underlying population distribution, Wilcoxon matched-pairs signed rank test stands on somewhat weaker assumptions of randomness of paired data and symmetry of underlying population distribution only. Besides, it is a test of the population median value.

In this analysis, actual and predicted profits for each of the acquiring firms are treated as observations of two different but related samples, because, as explained earlier, computation of predicted profits for various post-merger years uses actual profits of acquirers and targets in t – 1 and t years, respectively.

The empirical analysis will be, first, for the entire sample and subsequently for various subsets of it based on industry origin and size of acquirers, and type of M&As, to see whether mergers on average have increased profits or reduced them with any distinct change in pattern across various subsamples.

IV. Data: Sources, Description and Measurement

The principal source of data for this study is Prowess database version 4.2 compiled by the Centre for Monitoring Indian Economy (CMIE). Other supplementary sources such as company reports and newspaper reporting of M&As have been used to access detailed information on specific M&As. Prowess database 4.2 reports data on M&As in two broad categories of M&As. However, this study clubs data on both mergers and acquisitions together on the premise that in the ultimate analysis both involve transfer of control. For data on acquisitions, CMIE’s categories ‘Substantial Acquisition of shares’ (when 15% or more stakes is purchased) and ‘Minority Acquisition of shares’ (when 5% or more shares are purchased) have been included. The data also include cases of purchase of small quantities of shares that eventually resulted in substantial acquisitions of shares.

For the year t + 1 (i.e. the year following the year of the merger), the final sample consists of 156 acquiring firms that make 201 acquisitions providing a total of 638 observations (Table A.1). It should be noted that as the time distance from the date of merger increases, sample size gets smaller because acquiring firms drop out of the sample; in the terminal year t + 5, for instance, the total number of observations falls to 96. Of the total of 156 firms in the year t + 1, 50 belong to chemicals, 35 to drugs & pharmaceuticals, 15 to electronics, 20 to electrical and 35 to non-electrical industries. Clearly, the sample is dominated by observations from the chemicals and non-electrical machinery, with electronics industry showing the least presence. The differing industry-wise distribution of the sample may be the outcome of (non)availability of data rather than an indication of industry intensity of M&As. Of the total, 58 observations (38 per cent) are of horizontal type M&As and 98 observations (62 per cent) are of ‘others’ (non-horizontal) type. Higher incidence of non-horizontal M&As is prevalent in four out of five industry categories, and non-electrical machinery having the highest figure of 80 per cent. Only in the case of drugs & pharmaceuticals, majority of firms in the sample are of horizontal type M&As. Within the non-horizontal type M&As, in a large number of cases, firms have gone in for acquisition in related industries or to acquire downstream businesses. Some are also results of consolidation attempts on the part of large business groups. Moreover, relatively larger size firms have shown greater inclination to go in for non-horizontal type M&As, and smaller ones preferring to go the horizontal way so as to reap the benefits of scale and scope. As it turns out, post-merger performance of firms is strongly impacted by the characteristics such as their size, industry origin and type of M&As they undertake.

Data on profits and total assets are measured in millions of rupees and have been collected for all the acquiring and acquired firms for relevant years. Profit figures included are earnings before the payment of depreciation, interest and taxes and they have been deflated using GDP deflator (2004–2005 as base year). Total assets constitute of the following: net fixed assets, net preoperative expenses pending allocation, capital work-in-progress, loans and advances by finance companies, investments, current assets and loans and advances, and they have been deflated using Asset deflator (2004–2005 as base year). Table A.2 provides a brief summary of firm characteristics—average of profits, total assets and profit rates—for both acquirer and acquired. Taking total assets as measure of size, acquirer firms are, on average, 8.7 times those of the target firms. In terms of profitability, however, the two groups of firms are not very different, the former having a negligible advantage over the later.

V. Results of the Study

Overall Results

Table A.3 depicts the effects of M&As on the post-merger profitability of acquiring firms. Note that the size of the sample declines with increase in the distance between the year of analysis (t + i) and the year of merger (t) because fewer observations are available as one gets closer to the terminal year of analysis. For the entire sample, for all the years following M&As, more than two-third of the acquiring firms has reported profits which is in excess of the profits of the median firms in their respective three-digit industry categories. Proportion of firms registering positive outcomes increases as distance from the year of merger increases so much so that in the year t + 4 as many as 69 per cent of such results is positive (Table A.3, column 5); the decline in this number in the final year could be due to relatively more successful firms dropping out of the sample.

As the acquiring firms vary significantly in terms of size, the sample has been divided into two equal halves by taking the median of total assets of acquiring firms in the year prior to the year of merger as the dividing line. This has been done to find out whether size-related factors do have any bearing on the behaviour and performance of firms.

In terms of percentage of positive outcomes, small firms seem to be doing as well as or even better than their larger counterparts. In the year t + 4, 71 per cent of smaller firms perform better than their benchmark firm as against the figure of 67 per cent for the large firms (Table A.3). Smaller firms, however, appear to be slow in reaping the benefits of M&As. One possible reason for this may lie in their preference to go more for horizontal type M&As which opens up the possibility of extensive yet time-consuming process of asset reorganisation of combining entities.

The results of the t-test for the mean of difference between actual and projected profits of the combined entities for the entire sample show the p-value to be statistically significant for all the five post-merger/acquisition years at 5 per cent level (Table A.3, column 4). By the criteria of profitability, M&A, on an average, has been a resounding success. A measure of the practical significance of results can be had by looking at the figure for difference in actual and projected profit in the terminal year of the analysis. The difference of ₹434.72 million of profits in the year t + 5 comes to 5.04 per cent of the total assets/33.83 per cent of profits (in the year t – 1) of the average acquirer (Table A.3, column 3). The results remain more or less the same even when the analysis is carried on after dividing the sample into small and large firms.

Results by Type of M&As

Table A.4 presents firm performance by type of M&As, i.e. horizontal vis-a-vis ‘others’ (non-horizontal) as well as by firm size within these. In terms of percentage of firms showing above average performance, an equal percentage (62%) from both the categories—horizontal and others—demonstrates above average outcomes in the year immediately following M&As, i.e. year t + 1. Overtime, however, with the exception of year t + 2, horizontal type shows a much higher rate of success than the ‘others’ category so much so that in the terminal year t + 5, 78.38 per cent of firms under the former category displays above average outcomes as against 54.24 per cent of the latter category (Table A.4, column 6).

The same pattern prevails for the profitability analysis (Table A.4, column 5). As indicated by the p-value of mean difference in profits, both categories of M&As have performed well, and the difference, if any, lies in the time pattern of successful outcomes. The category of ‘others’ (non-horizontal) shows mean of difference in profits to be statistically significant (at 5% level) in the very first post-acquisition year and continues to show that until the fourth year of the event. The performance is alike for both the small and large firms within this category. By contrast, for the first 2 years after the merger, the results of horizontal type M&As, though positive, are statistically insignificant. This is largely due to inferior performance of larger firms undergoing horizontal merger. Perhaps, as compared with their smaller counterparts, these firms take more time to integrate their businesses. As such, the statistically significant outcomes at the aggregate level for the first 2 years are almost entirely on account of superior performance of firms undertaking non-horizontal M&As.

Performance of firms undertaking horizontal type M&As, however, improves dramatically from the third year onwards and remains statistically significant (at 5 per cent level) until the terminal year of the analysis. Both small and large firms within this category report significant outcomes at 5/10 per cent level—smaller firms benefiting more out of horizontal integration of businesses than the larger ones. By contrast, the non-horizontal category—small and large firms alike within it—report positive but insignificant results in the terminal year of the analysis. The delayed but solid performance of horizontal M&As indicates the time-taking yet enduring nature of gains arising out of economies of scale and scope. Non-horizontal M&As, on the other hand, allow firms to exploit the low hanging fruits quickly but prove to be transient in nature.

Results by Industry, Firm Size and Type of M&As

Reorganisation of data on industry lines brings out marked industry-specific characteristics of the results (Table A.5). In terms of the number of positive observations, out of the five two-digit industry groups, firms in the drugs & pharmaceuticals (NIC 21), electronics (NIC 26) and electrical (NIC 27) have performed the most. In the terminal year of analysis, 83 per cent of acquiring firms from drugs & pharmaceuticals have reported above average performance, and the highest for electronics and electrical being 77 and 81 per cent. More generally, for all the industries over all the post-acquisition years, more than half of the acquiring firms have performed better than the median firms in their respective three-digit industry grouping. Firms in the non-electrical machinery appear to benefit the least though (Table A.5, column 7).

Analysis of profit figures of acquiring firms corroborates the pattern described earlier (Table A.5, column 6). For the drugs & pharmaceutical industry, results of a t-test of mean of difference between actual and projected profits are significant at 5 per cent for 4/5 post-acquisition years. In 1 year, it is significant at 10 per cent level. Results for the electronics industry are significant at 10 per cent level except in 1 year. The outcomes for chemicals and electrical machinery are erratic though—for some years p-values are low enough to make these statistically significant (years t + 1 and t + 2 for chemicals and t + 2 for electrical), but not in others. Non-electrical machinery represents the other end of the spectrum in which outcomes, though positive for all the years, are statistically significant for none of them.

Analysing the data by type of M&As and firm size within each industry group throws further light on the nature of industry-specific outcomes of M&A activity by firms (Table A.5).

A somewhat mixed performance of the chemicals industry at the aggregate level, for instance, turns out to be the outcome of different sets of firms—by type of M&As and size performing differently in various post-merger years. Profit gains are significant in years (t + 1 and t + 2) in which the lowest percentage of firms record above average gains (just about 60% of all). This result is the outcome of a strong showing on the part of larger firms undertaking non-horizontal type M&As in the immediate aftermath of merger. For subsequent years, while the percentage of firms reporting above average performance goes up significantly, it fails to show up favourably in the p-values of profit analysis. In fact, in the last 2 years of the analysis horizontal types have given statistically significant results, but since the horizontal type M&As are mostly undertaken by smaller firms their better showing is unable to impact significantly the overall outcome.

A similar pattern of performance by type of M&As and firm size emerges for the drugs & pharmaceutical industries as well-better showing by category ‘others’ in the initial years of merger followed by an above average performance by horizontal types in the latter years. This is evident both in terms of the number of firms showing positive outcomes and also the analysis of mean difference in profits. A special feature of this industry, as evidenced in the sample, is the preference on the part of majority of firms, both small and large, to go in for horizontal M&As. Due to this, at the aggregate level, this industry presents strong showing for all the post-merger years. For the purposes of illustration, while in the year t + 3, 82 per cent of ‘others’ and 58 per cent of horizontal type M&As show above average results, the corresponding figures, for the terminal year t + 5, are 63 and 93 per cent, respectively. With regard to analysis of post-merger profitability by type of M&As, results of t-test are statistically significant (at 5/10% level) for both horizontal and ‘others’ categories for three out of five years. However, while the results are significant for only the ‘others’ category in the very first (t + 1) year, it is significant for only the horizontal type in the closing year (t + 5)—reflecting somewhat the delayed but robust pattern of outcome for the latter. Overall, for the drugs & pharmaceutical industry, good showing for all the years has been the outcome of impressive performance on the part of larger firms undertaking both horizontal and others type M&As. Only in the fifth year, smaller firms undertaking horizontal type M&As have shown significant results.

Compared with other industries included in the analysis, the sample size of the electronics industry is small and constitutes largely of smaller firms (11 out of 15). Majority of firms—both small and large—have settled for non-horizontal type combinations; horizontal combination has been undertaken only by smaller firms. Well above two-third of acquirers have shown above average performance—77 per cent of firms in year t + 3 being the highest and 70 per cent being the lowest in the terminal year. A falling proportion of successful M&As over time, as pointed out earlier, has been the outcome of larger firms opting for non-horizontal M&As failing to put their acts together. As reported earlier, at the aggregate level, mean difference in profits is statistically significant at 10 per cent level for 4/5 post-merger years. Segregation of the sample on the basis of type of M&As, however, reveals that the results are significant in none of the years for either of the types horizontal or others. Given the dominance of the others category, this can be attributed largely to a lacklustre performance by firms—both small and large—undertaking non-horizontal type M&As.

Within the electrical industry, again, ‘others’ type M&As dominate with 60 per cent share. Both categories receive an equal sprinkling of small and large firms. The number of observations showing above average performance has a large range from a low of 50 per cent in year t + 1 to a high of 81 per cent in t + 4. Besides, there is a marked disjointedness in the performance of different categories of M&As—horizontal and others—in different years. These facts together explain the lacklustre performance of the industry at the aggregate level. As such, the mean of profit difference at the aggregate level, though positive for 4 out of 5 post-merger years, nonetheless, is statistically significant only in the year t + 2 due to a strong showing by larger firms irrespective of type of M&As in that year (Table A.5). Smaller firms continue to underperform throughout the period of analysis.

For the non-electrical industry, in the aggregate, percentage of firms showing increase in profits ranges from 48 to 72 per cent, and it is the lowest among all industry groups included in the sample. Two-third of all the firms and all of the larger ones in the sample have opted for non-horizontal M&As path to growth and consolidation. Small firms, as in the case of other industries, have gone in for horizontal M&As, but unlike in other industries, they have witnessed deterioration in their performance over time—only 33 per cent of such cases shows above average performance in the terminal year of analysis. For the non-horizontal category, a much lower percentage as compared with other industries has shown above average profitability. All these have led to a situation where for none of the years in the aggregate, or by type of M&As, performance in terms of profitability of merging firms has shown statistically significant results. By resorting to M&As, firms in this industry may have attempted to consolidate business so as to cut losses.

Results of Wilcoxon Test: Overall and By Type of M&As

The results of the Wilcoxon test of profitability are in agreement with those arrived at using the t-test. At the aggregate level, for all the five post-merger years, p-value of the Z statistics is significant at 5 per cent level leading to the conclusion that on an average M&As have been a profitable proposition for firms (Table A.6). Results remain unchanged when the sample is divided between small and large firms leading to the conclusion that both small and large firms have profited albeit with a difference in strategy—smaller ones preferring to go horizontal while large firms choosing to gain from vertical integration and consolidation of business (Table A.6).

Results of the analysis of profitability by type of M&As are also broadly in agreement with the results of those of the t-test (Table A.7). As in the previous analysis, non-horizontal type M&As start bearing fruits from the very first post-merger year, but the gains appear to get dissipated by the fifth year of the merger. On the contrary, horizontal M&As start generating above average gains with a time lag but continue to do so till the terminal year of the analysis (Table A.7, column 5). The pattern of gains, perhaps, reflects the differing nature and extent of post-merger business restructuring that under lay these two types of M&As.

Results of Wilcoxon Test: By Industry and Type of M&As

Analysis of firm performance by industry category is also broadly in sync with the previous analysis (Table A.8, column 6). M&As in drugs & pharmaceutical industry have been an unqualified success, followed by those in the electronics and chemical industries, though to a lesser extent. Success of drugs & pharmaceutical industry has been brought about by good showing on the part of firms going in for both types of M&As. For chemicals and electronics industries, both the tests report significant gains in two out of five years; the years reported, though, are different. As in the previous analysis, somewhat uneven performance of chemicals could be attributed to disjointedness across time in the performance of firms opting for two different types of M&As within it (Table A.8). Wilcoxon test paints a better picture of gains of M&A activity in electrical and non-electrical machinery industries than does the t-test, as the former shows above average performance by merging firms in some years.

VI. Conclusion

This study examines the question whether M&As that occurred between 1998 and 2012 in certain technology intensive industries have succeeded in generating profits higher than a carefully chosen benchmark group of firms by deploying difference-in-differences (DID) approach and using both parametric and non-parametric tests. It finds that with profitability as the criterion of success, an overwhelming number of acquiring/merging firms appear to pass muster. For all the five post-merger years, majority of firms have displayed above average performance. Firms undertaking both horizontal and non-horizontal type M&As have done well; however, the success rate of the former is higher. Relatively smaller firms too have shown higher success rate owing to their propensity for choosing horizontal type M&As. Firm performance have marked industry orientation; those from the drugs & pharmaceuticals, and to a lesser degree, chemicals and electronics have done much better as compared with firms in the electrical and non-electrical machinery segments. The findings of the analysis concur with those of Ramakrishanan (2008) and Rani et al. (2013). This study, however, has not identified the sources of additional profit gains accruing to acquirers/merged entities, i.e. whether these have come about primarily due to improvement in their efficiency or their market power—at the cost of consumers—or both.

Footnotes

Acknowledgement

I would like to thank Prof V. K. Kaul, Dean, Faculty of Applied Social Sciences and Humanities and Head, Department of Business Economics, South Campus, University of Delhi and Prof Aditya Bhattacharjea, Delhi School of Economics, for their detailed comments on an earlier draft which have helped improve the final version of this article, and Pilu C. Das, Assistant Professor, Kidderpore College, Calcutta University, for excellent research assistance. None of them is responsible for any remaining shortcomings.

Appendix A

Table A1. Summary Statistics

No. of Years After the M&As	Type of M&As	No. of Observations	% of Horizontal/Others Type M&As in Total	% of Positive Observations in All/Horizontal/Others Type	% of Small/Large Firms by Type of M&As
t + 1	ALL	156		62
	HORIZONTAL	58	37.18	62.07
	Small	36			62.07
	Large	22			37.93
	OTHERS	98	62.82	61.22
	Small	42			42.86
	Large	56			57.14
t + 2	ALL	140		67
	HORIZONTAL	55	39.23	58.18
	Small	32			58.18
	LARGE	23			41.82
	Others	85	60.71	72.94
	Small	38			44.71
	Large	47			55.29
t + 3	ALL	130		66
	HORIZONTAL	54	41.54	66.67
	Small	31			57.41
	Large	23			42.59
	OTHERS	76	58.46	65.79
	Small	34			44.74
	Large	42			55.26
t + 4	ALL	115		69
	HORIZONTAL	45	39.13	73.33
	Small	26			57.78
	Large	19			42.22
	OTHERS	70	60.87	65.71
	Small	32			45.71
	Large	38			54.29
t + 5	ALL	96		64
	HORIZONTAL	37	38.54	78.38
	Small	22			59.46
	Large	15			40.54
	OTHERS	59	61.46	54.24
	Small	26			44.07
	Large	33			55.93

Source: Author’s calculation based on CMIE database.

Notes: M&As, mergers and acquisitions.

Table A2. Characteristics of Acquiring and Acquired Companies

Firm Type	Profits (Average) (₹ Million)	Total Assets (Average) (₹ Million)	Profit Rate (per cent)
Acquirer	1,383.985	9,153.751	15.12
Acquired	155.781	1,052.08	14.8

Source: Author’s calculation based on CMIE database.

Table A3. Effects of M&As on Acquirers’ Profits Relative to Median Firm

Years After the M&As	No. of Observations	Difference in Profits (Mean) (₹ Million)	p-Value	Per cent of Positive Observations
t + 1
All firms	156	243.2953	0.0208^**	62.0
Small firms	78	26.99192	0.5133	60.0
Large firms	78	459.5987	0.0257^**	63.0
t + 2				0.0
All firms	140	205.1052	0.0353^**	67.0
Small firms	70	68.18263	0.0019^*	67.0
Large firms	70	342.0279	0.0778^***	67.0
t + 3				0.0
All firms	130	351.6737	0.0048^*	66.0
Small firms	65	152.3836	0^*	66.0
Large firms	65	1561.91	0.0001^*	66.0
t + 4				0.0
All firms	115	324.1168	0.001^*	69.0
Small firms	58	70.52565	0.0044^*	71.0
Large firms	57	582.1569	0.0029^*	67.0
t + 5				0.0
All firms	96	434.7209	0.0074^*	64.0
Small firms	48	101.814	0.0212^*	63.0
Large firms	48	767.6279	0.0166^**	65.0

Source: Author’s calculation based on CMIE data.

Notes: *, **, *** indicate p-values at 1%, 5% and 10% level of significance. M&As, mergers and acquisitions.

Table A.4. Effects on Acquirers’ Profits by Type of M&As

Years After the M&As	Type of M&As	No. of Observations	Mean Difference in Profits (₹ Million)	p-Value	Positive Observations (%)
t + 1	ALL firms	156	243.30	0.021**	62.0
	HORIZONTAL	58	166.99	0.169	62.1
	Small firms	36	15.71	0.859
	Large firms	22	414.55	0.151
	OTHERS	98	288.45	0.058***	61.2
	Small firms	42	36.66	0.034**
	Large firms	56	477.30	0.073***
t + 2	ALL firms	140	205.11	0.035**	65.7
	HORIZONTAL	55	1.84	0.987	58.2
	Small firms	32	76.03	0.058***
	Large firms	23	–101.38	0.692
	OTHERS	85	336.63	0.020**	72.9
	Small firms	38	61.57	0.008*
	Large firms	47	559.01	0.032**
t + 3	ALL firms	130	351.67	0.005*	66.0
	HORIZONTAL	54	271.13	0.052**	66.7
	Small firms	31	68.45	0.090***
	Large firms	23	544.31	0.094***
	OTHERS	76	408.90	0.031**	65.8
	Small firms	34	47.03	0.068***
	Large firms	42	701.85	0.040**
t + 4	ALL firms	115	324.12	0.001*	69.0
	HORIZONTAL	45	362.62	0.031**	73.3
	Small firms	26	94.94	0.046**
	Large firms	19	728.93	0.064***
	OTHERS	70	299.36	0.014**	65.7
	Small firms	32	50.69	0.033**
	Large firms	38	508.77	0.023**
t + 5	ALL firms	96	434.72	0.007*	64.0
	HORIZONTAL	37	826.00	0.005*	78.4
	Small firms	22	88.38	0.039**
	Large firms	15	1,907.84	0.005*
	OTHERS	59	189.34	0.316	54.2
	Small firms	26	113.18	0.128
	Large firms	33	249.35	0.458

Source: Author’s calculation based on CMIE database.

Notes: *, ** and *** indicate p-values at 1%, 5% and 10% level of significance. M&As = mergers and acquisitions.

Table A.5. Effects on Acquirers’ Profits by Industry, Firm Size and Type of M&As

Industry	Years After the M&As	M&A Type/Firm Size	No. of Observations	Mean Difference in Profits(₹ Million)	p-Value	Per cent of Positive Observation
Chemicals(NIC 20)	t + 1	All	50	446.7	0.0634***	62
		HORIZONTAL	19	250.6453	0.4492	58
		Small	10	–295.864	0.2185
		Large	9	857.8778	0.1842
		OTHERS	31	566.858	0.1861	65
		Small	18	41.89641	0.1861
		Large	13	1,293.728	0.1075
	t + 2	All	49	266.5	0.1881	57
		HORIZONTAL	18	–355.95	0.0772***	44
		Small	8	51.94377	0.6227
		Large	10	–682.265	0.1881
		OTHERS	31	322.2821	0.3373	65
		Small	17	79.3264	0.0772***
		Large	14	617.2997	0.3679
	t + 3	All	44	73.14	0.2793	61
		HORIZONTAL	18	232.602	0.1791	67
		Small	9	111.0158	0.1958
		Large	9	354.1882	0.3373
		OTHERS	26	357.6165	0.1094	58
		Small	14	50.92328	0.2006
		Large	12	715.4253	0.439
	t + 4	All	40	306.4742	0.2135	70
		HORIZONTAL	16	206.7358	0.1884	69
		Small	8	118.6859	0.2793
		Large	8	294.7858	0.1791
		OTHERS	24	306.3481	0.0194**	71
		Small	13	40.42009	0.3141
		Large	11	620.6268	0.141
	t + 5	All	29	52.815	0.7088	59
		HORIZONTAL	12	661.6274	0.0109**	75
		Small	6	157.9089	0.1094
		Large	6	1,165.346	0.1068
		OTHERS	17	–376.934	0.6495	47
		Small	8	66.05561	0.5112
		Large	9	–770.703	0.2135
Drugs & pharmaceuticals (NIC 21)	t + 1	All	35	268.0226	0.0999***	69
		HORIZONTAL	20	149.9113	0.9765	65
		Small	11	239.4784	0.1884
		Large	9	40.44042	0.8877
		OTHERS	15	425.5043	0.026**	73
		Small	4	–31.011	0.5656
		Large	11	591.5098	0.0194**
	t + 2	All	30	484.9994	0.0014*	70
		HORIZONTAL	18	298.0357	0.0388**	67
		Small	8	99.10578	0.1271
		Large	10	457.1797	0.0756***
		OTHERS	12	765.4449	0.0151**	75
		Small	3	–9.07865	0.7088
		Large	9	1,023.619	0.0109**
	t + 3	All	30	508.404	0.0987***	67
		HORIZONTAL	19	477.9569	0.1868	58
		Small	8	64.25175	0.4038
		Large	11	778.8333	0.2191
		OTHERS	11	560.9946	0.3482	82
		Small	2	51.46367	0.6495
		Large	9	674.2237	0.3624
	t + 4	All	24	762.3968	0.0173**	79
		HORIZONTAL	16	819.2485	0.0721***	81
		Small	7	153.3559	0.118
		Large	9	1,337.165	0.0999***
		OTHERS	8	648.6934	0.0788***	75
		Small	2	0.881058	0.9765
		Large	6	864.6308	0.0749***
	t + 5	All	23	1,274.472	0.0064*	83
		HORIZONTAL	15	1,478.464	0.0259**	93
		Small	7	166.7	0.0608***
		Large	8	2,626.258	0.0286**
		OTHERS	8	891.9872	0.1264	63
		Small	1	–121.864
		Large	7	1,036.823	0.1187
Electronics (NIC 26)	t + 1	All	15	213.4406	0.0963***	73
		HORIZONTAL	4	52.53438	0.1561	75
		Small	4
		Large	0
		OTHERS	11	198.7788	0.2646	73
		Small	6	90.14103	0.1809
		Large	5	329.1441	0.4328
	t + 2	All	14	103.3808	0.0932***	71
		HORIZONTAL	6	–9.97446	0.8276	50
		Small	6
		Large	0
		OTHERS	8	–136.556	0.704	88
		Small	5	81.77891	0.0635***
		Large	3	–500.448	0.6666
	t + 3	All	13	240.2734	0.0883***	77
		HORIZONTAL	6	59.40428	0.1926	83
		Small	6
		Large	0
		OTHERS	7	395.3041	0.1353	29
		Small	5	59.30441	0.4283
		Large	2	1,235.303	0.134
	t + 4	All	11	246.5278	0.1423	73
		HORIZONTAL	4	57.32463	0.4453	75
		Small	4
		Large	0
		OTHERS	7	354.6439	0.1852	71
		Small	5	71.24652	0.3192
		Large	2	1,063.137	0.3544
	t + 5	All	10	69.90719	0.0987***	70
		HORIZONTAL	4	40.41482	0.214	75
		Small	4
		Large	0
		OTHERS	6	–133.589	0.5918	67
		Small	5	93.50109	0.2284
		Large	1
Electricals(NIC 27)	t + 1	All	20	–146.231	0.568	50
		HORIZONTAL	8	185.3272	0.3411	63
		Small	6	–32.4409	0.6633
		Large	2	838.6315	0.3578
		OTHERS	12	–367.269	0.3742	42
		Small	4	5.873294	0.7893
		Large	8	–553.84	0.3839
	t + 2	All	18	258.3381	0.0217**	78
		HORIZONTAL	7	111.5463	0.3257	71
		Small	5	55.83722	0.7138
		Large	2	250.8191	0.0173**
		OTHERS	11	351.751	0.0417**	82
		Small	5	22.05273	0.7888
		Large	6	626.4995	0.033**
	t + 3	All	18	268.5652	0.2884	67
		HORIZONTAL	6	51.08706	0.7347	67
		Small	4	–110.738	0.5194
		Large	2	374.7362	0.1553
		OTHERS	12	377.3043	0.3202	67
		Small	5	33.62345	0.7975
		Large	7	622.7907	0.3523
	t + 4	All	16	485.6511	0.1756	81
		HORIZONTAL	4	–90.5165	0.6563	75
		Small	2	90.48593	0.3658
		Large	2	–271.519	0.5936
		OTHERS	12	677.7069	0.1537	83
		Small	5	66.39925	0.4665
		Large	7	1,114.355	0.1786
	t + 5	All	15	371.8456	0.3548	53
		HORIZONTAL	3	205.0832	0.4237	67
		Small	3
		Large	0
		OTHERS	12	413.5362	0.4141	50
		Small	5	–145.893	0.0552***
		Large	7	813.1283	0.3625
Non-electrical (NIC 28)	t + 1	All	35	193.6851	0.4567	57
		HORIZONTAL	7	33.2063	0.7791	57
		Small	5	174.9101	0.1269
		Large	2	–321.053	0.1977
		OTHERS	28	233.8049	0.473	57
		Small	9	39.77019	0.0488**
		Large	19	325.716	0.5025
	t + 2	All	29	244.2523	0.2749	72
		HORIZONTAL	6	70.47201	0.6506	67
		Small	5	201.0648	0.0681***
		Large	1
		OTHERS	23	289.5863	0.3033	74
		Small	8	62.40668	0.017**
		Large	15	410.7488	0.3478
	t + 3	All	24	375.2417	0.1467	67
		HORIZONTAL	5	142.0171	0.2127	80
		Small	4	173.8242	0.2334
		Large	1
		OTHERS	19	436.6166	0.1826	63
		Small	7	43.0477	0.1424
		Large	12	666.1984	0.2044
	t + 4	All	23	–92.7584	0.5239	48
		HORIZONTAL	5	7.023672	0.959	60
		Small	5
		Large	0
		OTHERS	18	–120.476	0.5138	44
		Small	6	73.27812	0.1231
		Large	12	–217.353	0.4374
	t + 5	All	18	335.8386	0.3314	56
		HORIZONTAL	3	–110.482	0.4826	33
		Small	3
		Large	0
		OTHERS	15	425.1028	0.3066	60
		Small	6	482.0463	0.0791***
		Large	9	387.1405	0.5788

Source: Author’s calculation based on CMIE PROWESS database.

Notes: *, ** and *** indicate p-values at 1%, 5% and 10% level of significance, respectively. M&As, Mergers and acquisitions.

Table A.6. Effects on Acquirers’ Profits: Results of Wilcoxon Test

Years After the M&As	No. of Observations	Z Value	p-Value of IZI
t + 1
All firms	156	3.024	0.003*
Small firms	78	2.568	0.010**
Large firms	78	2.134	0.033**
t + 2
All firms	139	4.323	0.000*
Small firms	70	3.263	0.001*
Large firms	69	3.04	0.002*
t + 3
All firms	130	4.102	0.000*
Small firms	65	2.794	0.0052*
Large firms	65	3.356	0.0008*
t + 4
All firms	115	3.848	0.000*
Small firms	58	3	0.003*
Large firms	57	2.856	0.004*
t + 5
All firms	96	2.755	0.006*
Small firms	48	2.246	0.025**
Large firms	47	1.852	0.064***

Source: Author’s calculation based on CMIE database.

Notes: *, ** and *** indicate p-values at 1%, 5% and 10% level of significance. M&As, mergers and acquisitions.

Table A.7. Effects on Acquirers’ Profits by Type of M&As: Results of Wilcoxon Test

Years After the M&As	Type of M&As	No. of Observations	Z Value	p-Value of IZI
t + 1	ALL	156	3.02	0.003*
	Horizontal	58	1.80	0.072***
	Others	98	2.40	0.017**
t + 2	ALL	140	4.14	0.000*
	Horizontal	55	1.56	0.119
	Others	85	3.99	0.000*
t + 3	ALL	130	4.10	0.000*
	Horizontal	54	2.41	0.016**
	Others	76	3.33	0.001*
t + 4	ALL	115	3.85	0.000*
	Horizontal	45	2.87	0.004*
	Others	70	2.57	0.010**
t + 5	ALL	96	2.76	0.006*
	Horizontal	37	4.07	0.000*
	Others	59	0.47	0.640

Source: Author’s calculation based on CMIE database.

Notes: *, ** and *** indicate p-values at 1%, 5% and 10% level of significance. M&As, Mergers and acquisitions.

Table A.8. Effects on Acquirers’ Profits by Industry and Type of M&As: Results of Wilcoxon Test

Industry	Years After the M&As	Type of M&As	No. of Observations	Z Value	p-Value of IZI
Chemicals(Industry code 20)	t + 1	ALL	50	1.53	0.126
		Horizontal	19	0.36	0.717
		Others	31	1.65	0.100
	t + 2	ALL	49	0.91	0.363
		Horizontal	18	–0.72	0.472
		Others	31	1.65	0.100
	t + 3	ALL	44	2.25	0.024**
		Horizontal	18	1.85	0.064***
		Others	26	1.31	0.191
	t + 4	ALL	40	2.55	0.011**
		Horizontal	16	2.02	0.044**
		Others	24	1.71	0.087***
	t + 5	ALL	29	0.49	0.627
		Horizontal	12	2.59	0.010**
		Others	17	–1.25	0.210
Drugs & pharmaceuticals(Industry code 21)	t + 1	ALL	35	2.74	0.006*
		Horizontal	20	1.61	0.108
		Others	15	2.33	0.020**
	t + 2	ALL	30	3.12	0.002*
		Horizontal	18	2.11	0.035**
		Others	12	2.28	0.229
	t + 3	ALL	30	2.15	0.032**
		Horizontal	19	1.09	0.277
		Others	11	1.96	0.051**
	t + 4	ALL	24	2.80	0.005*
		Horizontal	16	2.07	0.039**
		Others	8	1.68	0.093***
	t + 5	ALL	23	3.32	0.001*
		Horizontal	15	3.07	0.002*
		Others	8	1.26	0.208
Electronics(Industry code 26)	t + 1	ALL	15	1.59	0.112
		Horizontal	4	1.46	0.144
		Others	11	1.25	0.213
	t + 2	ALL	14	1.41	0.158
		Horizontal	6	0.11	0.917
		Others	8	1.40	0.161
	t + 3	ALL	13	1.99	0.046**
		Horizontal	6	1.57	0.116
		Others	7	1.52	0.128
	t + 4	ALL	11	1.96	0.051**
		Horizontal	4	0.73	0.465
		Others	7	1.52	0.128
	t + 5	ALL	10	1.07	0.285
		Horizontal	4	1.46	0.144
		Others	6	0.31	0.753
Electricals(Industry code 27)	t + 1	ALL	20	0.08	0.941
		Horizontal	8	0.84	0.401
		Others	12	–0.39	0.695
	t + 2	ALL	18	2.46	0.014**
		Horizontal	7	1.01	0.311
		Others	11	2.31	0.021**
	t + 3	ALL	18	0.89	0.372
		Horizontal	6	0.31	0.753
		Others	12	0.94	0.347
	t + 4	ALL	16	1.91	0.056***
		Horizontal	4	0.37	0.715
		Others	12	2.04	0.041**
	t + 5	ALL	15	0.28	0.776
		Horizontal	3	0.54	0.593
		Others	12	0.08	0.938
Non-electrical(Industry code 28)	t + 1	ALL	35	0.59	0.555
		Horizontal	7	0.34	0.735
		Others	28	0.48	0.633
	t + 2	ALL	29	1.96	0.050**
		Horizontal	6	0.73	0.463
		Others	23	1.73	0.083***
	t + 3	ALL	24	2.00	0.046**
		Horizontal	5	1.48	0.138
		Others	19	1.57	0.117
	t + 4	ALL	23	–0.55	0.584
		Horizontal	5	–0.14	0.893
		Others	18	–0.63	0.528
	t + 5	ALL	18	0.68	0.500
		Horizontal	3	–1.07	0.285
		Others	15	0.91	0.364

Source: Author’s calculation based on CMIE database.

Notes: *, ** and *** indicate p-values at 1%, 5% and 10% level of significance. M&As, mergers and acquisitions.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author received no financial support for the research, authorship and/or publication of this article.

Note

References

Agarwal

(2003). Analyses of mergers in India (unpublished MPhil dissertation). Delhi School of Economics, University of Delhi.

Agarwal

Bhattacharjea

(2006). Mergers in India: A response to regulatory shocks. Emerging Markets Finance and Trade , 42(3), 46–65.

Basant

(2000). Corporate response to economic reforms. Economic & Political Weekly , 35(10), 813–822.

Beena

P. L.

(2008). Trends and perspectives on corporate mergers in contemporary India. Economic and Political Weekly , 43(39), 48–56.

Beena

P. L.

(2014). Mergers and acquisitions: India under globalization . Routledge.

Bhattacharjea

(2016). Recent trends in mega-mergers in India and lessons for competition policy, insights from merger cases under the competition act in international trade and industrial development in India. In Veeramani

Nagaraj

(Eds.), International trade and industrial development in India: Emerging trends, patterns and issues. New Delhi: Orient Blackswan.

Cosh

Hughes

Lee

Singh

(1998). Takeovers, institutional investment and the persistence of profits. In Begg

Henry

(eds.). Applied economics and public policy (pp. 107–144). http://www.cbr.cam.ac.uk/pdf/wp030.pdf

Cowling

(1980). Mergers and economic performance . Cambridge University Press.

Deneckere

Davidson

(1985). Incentives to form coalitions with Bertrand competition. The RAND Journal of Economics , 16(4), 473–486.

10.

Dowell

(1984). Asset salvage ability and the potential for trade restraint through merger. The RAND Journal of Economics , 15(4), 537–545.

11.

Ghosh

(2001). Does operating performance really improve following corporate acquisitions. Journal of Corporate Finance , 7(2), 151–178.

12.

Gugler

Mueller

D. C.

Yurtoglu

B. B.

Zulehner

(2003). The effects of mergers: An international comparison. International Journal of Industrial Organization , 21(5), 625–653.

13.

Hall

B. H.

Vopel

(1996). Innovation, market share, and market value. Prepared for The international conference on the economics and econometrics of innovation. The European Parliament.

14.

Healy

P. M.

Palepu

K. G.

Ruback

R. S.

(1992). Does corporate performance improve after mergers. Journal of Financial Economics , 31(2), 135–175.

15.

Hughes

(1989). The impact of merger: A survey of empirical evidence for the UK. In Fairbum

Kay

(Eds.). Mergers and merger policy . Oxford University Press.

16.

Jensen

M. C.

Ruback

R. S.

(1983). The market for corporate control. Journal of Financial Economics , 11(1–4), 5–50. https://doi.org/10.1016/0304-405X(83)90004-1

17.

Kaur

(2012). Mergers in India: Exploiting financial synergies . Academic Press.

18.

Kumar

(2000). Mergers and acquisitions by MNEs. Economic & Political Weekly , 35(32), 2852–2858.

19.

Manson

Powell

Stark

A. W.

Thomas

H. M.

(2000). Identifying the sources of gains from takeovers. Accounting Forum , 24(4), 319–343.

20.

Meeks

(1977). Disappointing marriage: A study of the gains from merger . Cambridge University Press.

21.

Mishra

Chandra

(2010). ‘Mergers, acquisitions and firms’ performance: Experience of Indian pharmaceutical industry. Eurasian Journal of Business and Economics , 3(5), 111–126.

22.

Mueller

D. C.

(1980). The determinants and effects of mergers: An international comparison . Oelgeschlager, Gunn & Hain.

23.

Mueller

D. C.

(1985). Profit in the long run . Cambridge, MA: Cambridge University Press.

24.

Pawaskar

(2001). Effect of mergers on corporate performance in India. Vikalpa , 26(1), 19–32.

25.

Peer

(1980). The Netherlands, 1962–1973. In Mueller

D.C.

(eds.), The determinants and effects of mergers: An international comparison (pp. 163–191). Oelgeschlager, Gunn & Hain.

26.

Perry

M. K.

(1984). Scale economies, imperfect competition and public policy. Journal of Industrial Economics , 32(3), 313–333.

27.

Perry

M. K.

Porter

R. H.

(1985). Oligopoly and the incentive for horizontal merger. The American Economic Review , 75(1), 219–227.

28.

Ramakrishanan, (2008). Long-term post-merger performance of firms in India. Vikalpa , 33(2), 47–63.

29.

Ramaswamy

K. P.

Waegelein

J. F.

(2003). Firm financial performance following mergers. Review of Quantitative Finance and Accounting , 20(2), 115–126.

30.

Rani

Yadav

S. S.

Jain

P. K.

(2013). Post-M&A operating performance of Indian acquiring firms: A Du Pont analysis. International Journal of Economics and Finance , 5, 65–73. http://dx.doi.org/10.5539/ijef.v5n8p65

31.

Ravenscraft

D. J.

Scherer

(1989). The profitability of mergers. International Journal of Industrial Organization , 7(1), 101–116.

32.

Reid

(1968). Mergers, managers and the economy . McGraw-Hill.

33.

Ryden

Edberg

J. O.

(1980). Large mergers in Sweden, 1962–1976. In Mueller

D. C.

(eds.), The determinants and effects of mergers: An international comparison (pp. 193–226). Oelgeschlager, Gunn & Hain.

34.

Salant

S. W.

Switzer

Reynold

R. J.

(1983). Losses from horizontal merger: The effects of an exogenous change in industry structure on Cournot–Nash equilibrium. The Quarterly Journal of Economics , 98(2), 185–199.

35.

Seade

J. K.

(1980). On the effects of entry. Econometrica , 48(2), 479–489.

36.

Sharma

D. S.

(2002). The impact of acquisitions on operating performance: Some Australian evidence. Journal of Business Finance and Accounting , 29(1–2), 155–200.

37.

Singh

(1971). Takeovers . Cambridge University Press.

38.

Stigler

G. J.

(1950). Papers and proceedings of the sixty-second annual meeting of the American economic association. The American Economic Review , 40(2), 23–34.

39.

Switzer

J. A.

(1996). Evidence on real gains in corporate acquisitions. Journal of Economics and Business , 48(5), 443–460.

40.

Utton

M. A.

(1974). On measuring the effects of industrial mergers. Scottish Journal of Political Economy , 21(1), 13–28. https://doi.org/10.1111/j.1467-9485.1974.tb00173.x.

41.

Yeh

Hoshino

(2000). The effects of mergers and acquisitions on Taiwanese corporations. Review of Pacific Basin Financial Markets and Policies , 3, 183.