Unveiling the Nexus: Exploring the Impact of Corporate Governance on the Financial Performance of Acquiring Companies in the Indian Context

Abstract

This study investigates the effect of corporate governance characteristics on the financial performance of 124 listed Indian companies that have undergone mergers and acquisitions between 2014 and 2020. It employs several performance measures, such as short-term capital market performance, long-term capital market performance, accounting- and market-based measures, and firm-level control factors. The study finds board size to be a positive and significant factor affecting short-term market performance. Furthermore, it also documents weak linkages with other corporate governance variables, such as board independence and CEO duality. Regarding control variables, leverage, company age, price-to-book ratio, and research and development expenses significantly impact acquiring companies’ financial returns. The findings add to our understanding of corporate governance’s impact on performance in cases such as mergers and acquisitions.

1. Introduction

Corporate merger and acquisition (thereafter M and As) decisions are critical to business growth and financial development (Hitt et al. 1990). M and As are considered successful if they lead to higher efficiency for the new entity or the acquiring firm (Kumar and Bansal 2008). Increased efficiency can be attributed to several important factors, including managerial skills, cost efficiency, financial resources, technology, and better marketing skills, etc. (Buckley et al. 2022; Capron 1999; Kang and Johansson 2000; Rahman and Lambkin 2015). The acquiring firm’s corporate governance (thereafter CG) also plays an important role when a firm goes for merger and acquisition deals (Holmstrom and Kaplan 2001). It has been discovered that board independence and CEO duality affect acquisition performance (Pham et al. 2015; Teti et al. 2017). Consistent with the majority of CG literature, which suggests that independent directors aid businesses in making better judgments, more independent boards support businesses in pursuing value-adding acquisitions in M and A policies (Dutta and Kumar 2009; Tarighi et al. 2023; Teti et al. 2017).

The presence of CEO duality helps large boards with coordination and communication issues, while simultaneously enhancing information flow and decision-making quality. When it comes to intricate strategic responsibilities such as M and As, combining the two jobs might enhance the board’s decision-making ability. Under stronger and more cohesive leadership, companies with sizable boards can gain from an expanded pool of directors and generate value through merger transactions (Alshabibi 2021; Tampakoudis et al. 2022).

Agency theory is used in most research to examine how board characteristics affect acquisition performance. Novel techniques utilizing alternative frameworks, such as Resource Dependency Theory, have been essential, as they demonstrate that directors can affect value creation in M and As in ways other than monitoring (Redor 2016).

Previous research has primarily focused on either M and A s or CG as distinct disciplines of study. CG and M and A are among the most important fields of finance research. Specifically, this study aims to determine the impact of CG variables on shareholder stock market performance and accounting returns while controlling for various firm characteristics when acquiring a company.

CG parameters, such as board size, board independence, and CEO duality, were examined in the Indian scenario. This study examined the influence of CG mechanisms present in bidder firms on their performance. Most studies in this field have focused on accounting- and market-based measures. However, this study examines the impact of calculating cumulative abnormal returns both in the short and long run for acquiring companies.

As dependent variables, accounting-based methodologies, such as the return on assets and return on equity, have favored much of the research on the relationship between CG and performance in industrialized countries. Numerous studies on the performance of acquiring companies regarding CG elements have yielded mixed results. Limited research on market-based performance has been conducted in India. Consequently, to make a significant conclusion, it is necessary to evaluate the performance of acquiring a company using a battery of indicators.

The study found that acquiring companies’ short-term capital market performance was positively impacted by board size. The results are consistent with earlier research in this area (Dahya et al. 2016; Teti et al. 2017), suggesting that larger boards may be in a better position to offer management more insightful strategic counsel and help them make better M and A choices.

The remainder of this paper is organized as follows. Section 2 summarizes earlier research, and Section 3 discusses the empirical model and data collection methodology. The results of this study are discussed in Section 4. Finally, Section 5 provides conclusions, implications, and limitations.

2. Literature Review

Among the earliest studies, Desai et al. (2003) examined the relationship between CEO duality and acquisition performance using a sample of 149 publicly traded US firms. The author finds evidence that CEO duality negatively impacts firm performance, supporting agency theory.

Similarly, De Jong et al. (2007) used CG variables such as the takeover defense index, percentage of insider ownership, board size, and percentage of block shareholders for listed firms in the Netherlands and found limited evidence of CG and acquirer performance. Further, Brewer et al. (2010) investigated the relationship between firm-level CG variables and M and As in the banking sector from to 1990–2004 and found CG variables such as independent directors, independent block holders, and managerial share ownership play a crucial role in increasing the wealth generated by M and A transactions.

Amar et al. (2011) investigated CEO attributes, board composition, and governance characteristics of the acquiring companies over 273 M and A s in Canada during the period from to 1998–2002. The author concluded that, while board size has a negative impact on the performance of the acquiring firm, CEO ownership and board independence have a positive impact.

Another study by Afza and Nazir (2012) examined the connection between the CG profile of acquiring companies and the changes in operating performance brought about by M and As in Pakistan and found that board size and CEO duality have a negative relationship with firm performance. Similarly, Dahya et al. (2016) examined the impact of CG variables, such as the effect of outsider directors on acquiring companies’ performance on UK mergers and concluded that director representation is related to better acquirer returns in deals involving listed targets, but not with respect to private firms. Teti et al. (2017) investigated whether CG mechanism variables such as CEO duality and board independence influence M and A performance in the US context from to 2009–2013. The results indicate that board independence and CEO duality have a positive impact on the return on acquisitions.

Similarly, Tampakoudis et al. (2018) evaluated the impact of M and As from 2003 to 2017, using a sample size of 349 M and A s across all sectors in the European context. They use different CG proxies, such as board size, voting rights, and anti-takeover provisions, and investigate their effects on acquiring firm market performance. This result suggests that a large board is negatively related to the announcement return to the acquiring firm.

More recently, Awan et al. (2020) examined the role of CG in acquiring firm performance in Pakistan from 2004 to 2017. The findings of the study indicate that CG variables such as CEO duality and the presence of a board of directors are important for acquiring firm performance.

To summarize, earlier research has explored the impact of CG factors on firm performance using alternate proxies of performance and governance across diverse sectors and countries (Awan et al. 2020; Golubov et al. 2015; Teti et al. 2017; Kumar Soni 2023; Singh and Soni 2022; Arora and Soni 2023). The relationship with several factors, including Board Size (Amar et al. 2011) and CEO separation (Desai et al. 2003; Awan et al. 2020; Pham et al. 2015; Teti et al. 2017), board independence (Brewer et al. 2010; Dahya et al. 2016; Liu and Zhao 2012; Golubov et al. 2015; Masulis et al. 2007; Miletkov et al. 2015), board size (Cheng et al. 2008; Shekhar and Torbey 2005; Teti et al. 2017; Soni and Arora 2021), and CEO compensation ( Singh and Soni 2022; Agyei-Boapeah et al. 2019) affect firm performance has been confirmed. However, the literature on the role of CG in influencing acquiring firms’ futures is yet to be studied extensively in transition economies.

Further, previous research has primarily focused on either M and A s or CG as distinct disciplines of study. CG and M and A are among the most important fields of finance research. Furthermore, studies in this field have focused on accounting- or market-based measures. Accounting-based methodologies, such as return on assets and return on equity, have favored much research on the relationship between CG and performance in industrialized countries. Numerous studies on the performance of acquiring companies regarding CG elements have yielded mixed results. Limited research on market-based performance has been conducted in India (Appendix B). Consequently, to make a significant conclusion, it is necessary to evaluate the performance of acquiring a company using a battery of indicators.

In light of this, this study examines the linkage between CG and the firm performance of 124 acquiring companies from 2014 to 2020. It employs both market- and accounting-based indicators after controlling for firm-specific characteristics to investigate the effects of “internal corporate governance” on the performance of acquiring firms.

3. Methodology

The sample data of acquiring firms were collected from Prowess, a database developed by the Center for Monitoring Indian Economy (CMIE). The event date was taken as the first public announcement date collected from the Prowess. Data relating to the adjusted closing price of the sample acquiring companies and the index-adjusted closing price were taken from Prowess. The BSE Sensex closing price is used as a proxy for computing the market returns obtained from Prowess. Furthermore, the financial accounting data required for the study were taken from Prowess. The company’s annual report was used to collect data related to the CG variables.

Table 1. Industry-wise distribution of sample firms.

Industry-Wise Distribution of Firms	Number of Firms	Number of Firms (In Percentage)
Manufacturing
Food and Agro-Based product	7	5.65%
Textiles	8	6.45%
Chemicals and Chemical products	26	20.97%
Consumer Goods	4	3.23%
Construction Material	6	4.84%
Metals and metal products	11	8.87%
Machinery	13	10.48%
Transport Equipment	7	5.65%
Misc. Manufacturing	2	1.61%
Service	33	26.61%
Financial Service	7	5.65%
Whole Total	124	100

Source: Authors own compilation.

presents the 124 M and A announcement cases considered in the present study. We evaluated a total sample of 124 firms over seven years. A final sample size of 124 listed firms across various industries was considered for 2014–2020.

3.1. Description of Various Performance Measures Used in the Study

This study used different performance measures, such as short-term capital market-based measures, long-term capital market-based measures, accounting-based measures, and market-based measures. The various performance measures used in this study are described below and summarized in

Table 2. Description of variables used in the study.

Variables	Measurement	Hypothesized Relationship
Independent Variables
Board size	The number of directors on the board before the merger announcement year	Negative
Board Independence	The ratio of independent directors on the board before the merger announcement	Positive
CEO duality	The dummy variable if the CEO is chairman “1”, otherwise “0” before the merger announcement year	Negative
Control Variables
Size	Total assets of the acquiring firm by taking natural log before the one-year announcement, a proxy for acquiring companies’ size	Negative
Age	Age of the acquiring firm at the time of the merger announcement	Positive
SD return	The volatility of stock return of the last year before the merger announcement	Negative
Leverage	Debt-equity ratio average past one year before the announcement year	Positive
Beta	The beta of the acquiring company stock at the time of the merger announcement	Negative
Sales Growth	Indicator of average growth in sales before the announcement of the merger averaged over the past three years before the announcement year	Positive
R&D	Research and development as a percentage of sales before the one-year merger announcement	Positive
PB ratio	Dummy for growth If PB ratio is more than one the value is taken as ‘1’ or otherwise ‘0’ before the one-year merger and acquisition announcement	Positive
Cash reserve	Cash and cash equivalent divided by total assets before the merger announcement	Positive
Dependent Variable
CAR [−5, +5]	An indicator of cumulative abnormal return for the window period [−5, +5]	NA
CAR [−2, +2]	An indicator of cumulative abnormal return for the window period [−2, +2]	NA
BHAR [0, +12]	An indicator of long-term abnormal return for 12 months after the announcement	NA
BHAR [0, +24]	An indicator of long-term abnormal return for 24 months after the announcement	NA
Tobin’s Q ratio	The market value of equity is divided by total assets before the merger announcement	NA
ROA	An Indicator of profitability measure calculated EBIT divided by the total assets of the acquiring firm	NA
ROE	EBIT divided by the total net worth of the acquiring company before the announcement year	NA
Stock Return	Stock return is the average of an individual year’s stock return over three years before the announcement year	NA

.

3.1.1. Cumulative Abnormal Return (CAR)

The short-term performance of acquiring companies was analyzed around the announcement period using different window periods. The study used two window periods, namely, [−2, +2] and [−5, +5] of cumulative abnormal return, to investigate the effect of CG characteristics on selected companies. This study employs event study methods to assess short-term capital market performance. Event analysis allows us to predict how asset prices, such as stock prices, react to economic event announcements that include new information that affects the value of underlying assets. According to the financial theory, all further information is promptly reflected in asset prices in an efficient capital market. Assuming that the market is efficient and that no other events occur on a given day, the change in the price of an asset is attributable to the reaction of a specific occurrence, which can be called the price effect of that event. The difference between the realized and predicted returns in the absence of an event is the firm’s abnormal return. The use of abnormal returns to assess the impact of an event is critical because it separates the influence of the event from general market movement. The selection of an appropriate model is an essential aspect of event investigation. According to Brown and Warner (1985), the market model is the standard model for estimating returns after an announcement, and it produces good results. The study used a market model to calculate the abnormal returns of acquiring companies.

The event date is when information on M and A s is publicly available for the first time.

Event Window: The period considered to capture the complete short-term consequences of the occurrence is known as the event window. We assess abnormal returns over a 41-day window surrounding merger announcements. To analyze the abnormal returns for the sample companies, we calculate the abnormal returns 20 days before and 20 days after the information announcements. A 41 day interval was chosen to reduce the impact of abnormal returns on the estimation period while increasing the likelihood of accurately capturing short-term market performance.

Benchmark: The discrepancy between the realized return and benchmark or normal return is abnormal. The normal return can be calculated using various models. We used the market model to attribute two components of stock returns: systematic risk, which is represented by a linear relationship between stock returns and market returns as assessed by the beta coefficient, and abnormal risk, which is described by the error term.

Estimation Window: The period in which the benchmark is calculated varies among investigations. The model parameters were estimated across a time interval of (−121, −20) in these studies. To evaluate the parameters of our benchmark model, we used the 121-day stock price 20 days before the announcement day. The daily stock returns are calculated as follows:

$$r_{i,t}=In\left(P_{i,t}\right)-In\left(P_{i,t-1}\right)$$

(1)

$$A{R}_{i,t}=R_{i,t}-E (R_{i,t})$$

(2)

${AR}_{i,t}$ is the abnormal return of the firm, R_i, is the actual return of the firm, and E (R_{i, t}) is the expected return in the absence of the event. α and β coefficients are calculated using a market index using ordinary least square regression (OLS) over a window period of (−121, −20).

Then, the cumulative abnormal return is calculated as follows:

$$CA{R}_{i,T1,T2}={{\displaystyle \sum }}_{t=T1}^{T2}A{R}_{i,t}$$

(3)

This study calculates cumulative abnormal returns over a given window period. It is used as a short-term market performance indicator to evaluate the effect of CG features on the selected companies.

3.1.2. Buy-and-Hold-Abnormal-Return (BHAR)

Buy-and-hold-abnormal return (BHAR) is the most common method for analyzing long-term market performance. This study uses the BHAR technique to examine Indian bidders’ long-term performance in CG and firm-specific qualities. According to Lyon et al. (1999), the BHAR approach is one of the most extensively utilized and ideal methods because it measures investor experience in particular.” The long-term abnormal return (BHAR) was calculated by taking the difference between the buy-and-hold returns of the acquiring firm with an appropriate expected return. The expected return is calculated with the benchmark, that is, using the market index return:

$$BHA{R}_{i(T_1{T}_{2)}}=\prod \left(1+R_{i,t}\right)-\prod (1+R_{benchmark},t)$$

(4)

The expected return is calculated with the benchmark, that is, using the market index return. Buy and hold abnormal returns are determined for 12 and 24 months following the M and A announcement for acquiring companies.

3.1.3. Measures of Accounting Performance Measure

Return on Assets (ROA)

ROA was used in this study as an accounting-based measure of a company’s performance. We divided earnings before interest and taxes on non-recurring transactions by total assets to determine the return on assets. The return on assets represents how well the company’s assets have been capitalized to generate shareholder value for acquiring firms. Several academics, such as Bansal and Sharma (2013), Mishra and Kapil (2018), and Boussaada and Karmani (2015), have utilized ROA as a reliable performance indicator in their research.

Return on Equity (ROE)

Another way to evaluate a company’s performance is to examine its return on equity. The return on equity (ROE) is a critical metric that shows how well a company has managed its owners’ resources. This ratio represents the degree to which the goal of maximizing shareholder wealth is achieved. A high return on equity (ROE) for the acquiring firm implies that the company’s management is effective and works to minimize agency conflicts while considering the interests of shareholders. Several academics, such as Haldar et al. (2018) and Syriopoulos and Tsatsaronis (2012), have utilized ROE as a reliable performance indicator in their research. In this study, ROE was estimated by dividing the net worth.

3.1.4. Market-Based Measures

Tobin’s Q Ratio: The Tobin’s Q ratio measures an acquiring firm’s growth potential and internal governance quality. Servaes (1991) and Lang and Stulz (1994) suggest that higher valuations yield higher abnormal returns when a merger is announced. Tobin’s Q ratio was used as a proxy for management quality. A ratio greater than one indicates that the financial market favorably perceives firms’ investment decisions. Scholars argue that Tobin’s Q is a superior measure to capture the effect of managerial action on performance. Several academics, for example, Golubov et al. (2015), Cheng et al. (2008), and Das and Dey (2016), have utilized reliable performance indicators in their research. This study defines Tobin’s Q ratio as the ratio of the market value of equity divided by total assets.

Stock Return: This is a measure of a manager’s effectiveness. Stock returns of the purchasing corporation were used to assess managerial performance. Companies with high financial performance are good indicators for investors (Kurniati 2019). A favorable stock return would boost management’s confidence in pursuing M and A. Before the announcement year, we calculated the stock returns and averaged them over the previous three years.

Governance Characteristics: An attempt has been made to determine the impact of governance elements, such as board size, number of independent directors, and CEO duality, on the performance of acquiring firms using a variety of accounting-based returns, as well as market-based returns. A detailed description of the variables is provided below:

Board size: Board size is calculated as the number of directors on the board. Small board size is expected to have a positive correlation with acquiring firm performance. As the board expands in size, one would anticipate the board’s aggregate expertise and talent to grow. Larger panels are more likely to boost cognitive diversity, which leads to increased decision-making creativity and the appearance of new options for firm development (Shapiro et al. 2015). Larger boards have a broader body of knowledge and information, including product marketplaces, technology, and legislation (Defrancq et al. 2021). Thus, larger panels may be better positioned to provide management with more qualitative strategic advice, potentially leading to better M and A decisions.

Board independence: Board independence characterizes the percentage of independent directors on board size. Board independence is calculated as the ratio of independent directors to the overall board size. Board independence is expected to have a positive correlation with acquiring firm performance. In M and A s, studies on the impact of independent boards on value generation are often mixed (Chi et al. 2011). Outside directors are believed to be more observant of CEO decisions than insiders are. Consequently, boards with a high percentage of independents are considered more cautious when voting on acquisitions, resulting in improved M and A success (Teti et al. 2017). According to previous studies, independent directors on the board of directors play a vital role in reviewing managers’ decision-making processes (Fama and Jensen 1983). For a long time, agency theorists have maintained that good CG necessitates more outsiders on the board of directors. The fundamental notion is that independent outsiders are better equipped to defend and promote shareholder interest.

CEO duality: CEO duality is a dummy variable that values “1” if the board chairman is the same person as The CEO or managing director or otherwise “0”. It is expected that CEO duality has negatively correlated with firm performance. When it comes to M and A deals, CEOs who are also chairpersons of the board of directors are supposed to have more freedom to pursue their interests. In the governance literature, duality, or a situation in which a single person serves as a CEO and Board chair, has been linked to poor governance. In a short-run market-based assessment, Masulis and Mobbs (2011) find a negative link between dualism and acquisition performance.

3.1.5. Control Variables

The acquiring firm’s performance is influenced by some of the characteristics of the acquiring firm that control the variable. The control variables used in the study include firm size, risk of the acquiring firm, age and leverage of the firm, volatility of the firm, sales growth, the PB ratio, research and development expenses, and cash reserves.

Firm size: Prior studies have stated that acquiring firm performance can be influenced by firm size. According to Moeller et al. (2004), a small firm size leads to better performance, as they pay less than large firms do. Large-sized firms experienced negative abnormal returns because of the hubris hypothesis proposed by Moeller et al. (2005). In line with previous studies, the present study measured size as the logarithm of the acquiring firm’s total assets before the merger announcement.

Leverage: Masulis and Mobbs (2011) stated that a higher level of debt leads to better market performance. Thus, a positive correlation is expected between the acquiring firm and higher leverage. Leverage is calculated by dividing total liabilities by the total assets of the acquiring firm before the merger announcement year.

The standard deviation of return: Risk is related to future events, as M and As influence firms’ risk in the long run. This study takes the standard deviation of stock returns as a measure of risk. This is calculated as firm volatility in terms of the standard deviation of stock returns before the acquiring firm’s 12 months of the merger announcement.

Age: Because of its increased expertise and capabilities, a firm’s period is crucial in decision-making. Consequently, the company can make investment decisions and compete effectively with other companies. Older companies gain from the impact of the learning curve on critical strategic choices such as acquisition (Awan et al. 2020).

Beta: Different studies have sought to evaluate the relationship between systematic risk and corporate profitability on the assumption that ‘the larger the risk, the higher the return.’ The results were mixed. The beta of the stock, which is a stock market metric, is included as a company-specific risk indicator.

Sales Growth: The growth potential may inspire firms to be optimistic about the future and overpay for the target, resulting in fewer gains for the bidding firm. The average annual compounded growth rate in sales for the three years before each firm’s acquisition was used to determine sales growth.

PB ratio: It has been suggested that companies with a high price-to-book value ratio, dubbed “glamour firms,” are more likely to overestimate their acquisition management abilities. It has been suggested that a higher price-to-book value ratio negatively influences an acquiring firm’s return. As proposed by Roll (1986), Hubris’s hypotheses have an impact on them (Roll 1986). We use the price-to-book value ratio as a proxy for growth companies.

Cash Reserve: According to previous studies, cash flow may lead to agency problems in a firm. Compared with non-cash corporations, cash-rich companies are projected to undertake more acquisitions. The cash reserve is calculated by dividing cash and cash equivalent items by total assets in the year before the M and A announcement.

Research and Development Expenses: R&D costs increase the bidding firm’s charges, whereas acquiring firms are motivated to develop new technology and innovative enterprises to grow. Research and development costs were calculated as the proportion of revenue before the announcement year.

To address the impact of CG characteristics on various measures of acquiring companies’ financial returns, eight econometric models were developed. The eight models consider different proxies of firm performance: $CA{R}_{\left[-5,+5\right]},CA{R}_{\left[-2,+2\right]}, BHA{R}_{\left[0,+12\right]}, BHA{R}_{\left[0,+24\right]}, ROA,ROE,$  $Tobin,s Q ratio \mathrm{and} stockreturn$.

Additionally, to account for heterogeneity in firm performance over the years and across industries we take year and industry dummy in our model. The generic form of the eight tested models is presented in Equation (5).

$$\begin{array}{c}FP=\alpha +{\beta }_1\mathrm{Board} \mathrm{size}+{\beta }_2\mathrm{Board} \mathrm{Independence}+{\beta }_3\mathrm{CEO} \mathrm{Duality}+{\beta }_4\mathrm{SD} \mathrm{of} \mathrm{returns}+{\beta }_5\mathrm{Firm} \mathrm{size}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\beta }_6\mathrm{firm} \mathrm{age}+{\beta }_7\mathrm{beta}+{\beta }_8\mathrm{leverage}+{\beta }_9\mathrm{price} \mathrm{to} \mathrm{book} \mathrm{value} \mathrm{ratio}+{\beta }_{10}\mathrm{R}\&\mathrm{D}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\beta }_{11}\mathrm{sales} \mathrm{growth}+{\beta }_{12}\mathrm{Cash} \mathrm{reserve}+\mathrm{Industry} \mathrm{Dummy}+\mathrm{Year} \mathrm{Dummy}+ϵ\end{array}$$

(5)

4. Results

This section discusses the descriptive summaries, correlation results, and regression results to analyze the effects of the selected CG variables and control variables on various performance measures.

Table 3. Summary statistics.

Variable	N	Mean	SD	Minimum	Maximum
CAR [−5, +5]	124	−0.014	0.130	−0.59	0.394
CAR [−2, +2]	124	−0.008	0.092	−0.385	0.310
BHAR [0, +12]	124	−0.003	0.070	−0.259	0.311
BHAR [0, +24]	124	0.007	0.186	−0.391	1.020
ROA	124	3.150	21.217	−166.67	45.610
ROE	124	0.182	0.210	−0.910	0.800
Tobin’s Q ratio	124	2.573	3.138	0.00	19.51
Board Size	124	9.008	3.051	1.00	19.00
Board independence	124	0.513	0.155	0.00	0.800
CEO Duality	124	0.419	0.542	0.000	3.000
Firm size	124	8.653	2.062	−0.223	13.829
Firm age	124	21.234	26.179	0.000	146.000
BETA	124	0.690	0.530	0.000	1.690
Leverage	124	0.863	1.007	0.000	5.380
SDRET	124	0.164	0.077	0.036	0.398
PB	124	2.310	2.471	−4.070	13.430
RD	124	0.009	0.044	0.000	0.460
Sales growth	124	35.686	143.318	−45.360	1562.940
Cash reserve	124	0.077	0.122	0.000	0.947

Source: Author’s own compilation.

reports the descriptive statistics for all 124 listed acquiring firms from 2014 to 2020. The first set of variables CAR [−5, +5], CAR [−2,+ 2] are related to short-term market performance measure; the minimum abnormal returns for the window period CAR [−5, +5] is −0.59, which is a negative return of 59% and maximum returns for the window period is 39.4%. The average abnormal return for CAR [−5, +5] is −0.014, generating a negative abnormal return to the shareholder. The second window period selected for the study is [−2, +2], having a maximum value of 0.310, and the average return is around 0.008. The BHAR [0, +12] [0, +24], is used to measure long-term market performance. The average return of the BHAR [0, +12] is −0.003, and BHAR [0, +24] for the window period is 0.007. It is important to note that except for BHAR [0, +24], the mean return is negative, giving preliminary evidence of lower performance of M and A deals in the short and medium term. The sample firm’s average ROA ratio is 3.15 and 0.18 for ROE. The ROA ratio appears on the higher side which is due to high variation in the profitability of the companies during the period of M and A’s. The same is reflected in high standard deviation, minimum and maximum values. The mean of Tobin’s Q ratio is around 2.573. The average board size is around nine directors, with a maximum board size of 19 and a minimum of 1, roughly in line with the size of directors for Indian acquiring firms.

Further, according to the Indian Companies Act of 2013, one-third or more of the directors of the board must be independent. The average proportion of independent directors on the board was approximately 51.3%. CEO duality is a binary variable that measures CEO duality used in the study. The values of the control variables used in the study for the selected acquiring firms from 2014 to 2020 are also listed in

Table 4. Correlation table for the selected variable in the study.

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)	(15)	(16)	(17)	(18)	(19)
(1) CAR [−5, +5]	1.000
(2) CAR [−2, +2]	0.738 *	1.000
(3) BHAR [0, +12]	0.658 *	0.882 *	1.000
(4) BHAR [0, +12]	−0.050	−0.014	0.013	1.000
(5) ROA	−0.010	−0.040	−0.063	−0.037	1.000
(6) ROE	−0.080	−0.097	−0.082	−0.036	0.265 *	1.000
(7) Tobin’s Q	0.109	−0.077	−0.035	0.258 *	0.190 *	0.158	1.000
(8) Board Size	0.224 *	0.199 *	0.232 *	0.147	0.290 *	0.176	0.158	1.000
(9) BIND	0.022	0.094	0.157	−0.036	0.113	−0.074	0.040	−0.061	1.000
(10) CEO Duality	0.086	0.033	0.004	−0.050	0.168	0.108	−0.044	0.126	0.059	1.000
(11) FIRM SIZE	0.121	0.135	0.132	0.070	0.329 *	0.045	0.103	0.544 *	−0.011	0.127	1.000
(12) Firm age	0.146	0.165	0.188 *	0.007	0.038	0.106	0.174	0.185 *	−0.088	0.041	0.178 *	1.000
(13) BETA	0.183 *	0.252 *	0.280 *	0.035	0.130	−0.004	−0.046	0.225 *	0.143	0.027	0.544 *	0.150	1.000
(14) Leverage	−0.008	0.047	0.116	−0.142	−0.029	−0.104	−0.199 *	0.115	0.091	−0.155	0.118	0.089	0.165	1.000
(15) SDRET	−0.038	0.058	−0.003	−0.189 *	−0.120	−0.060	−0.247 *	−0.231 *	−0.047	−0.084	−0.259 *	−0.162	0.037	0.127	1.000
(16) PB	−0.071	−0.132	−0.138	0.089	0.339 *	0.312 *	0.663 *	0.165	−0.073	0.042	0.197 *	0.128	0.029	−0.147	−0.125	1.000
(17) RD	−0.082	−0.132	−0.164	0.001	0.211 *	0.016	0.087	−0.086	0.041	0.139	−0.151	−0.064	−0.092	−0.101	−0.062	0.131	1.000
(18) Sales growth	−0.322 *	−0.262 *	−0.173	−0.002	0.024	0.051	−0.019	−0.014	−0.068	−0.071	−0.130	−0.084	−0.124	0.086	0.138	0.105	−0.018	1.000
(19) Cash reserve	0.044	0.056	−0.006	−0.086	0.017	0.097	0.057	0.044	−0.066	−0.104	−0.003	−0.036	−0.123	−0.088	0.017	0.099	−0.001	−0.043	1.000

Notes: * p < 0.1.

. Firm size is defined as the natural log of the value of a firm’s total assets. The average firm size is 8.653.

Furthermore, leverage is the ratio of total debt to equity with an average of 8.63%. The maximum leverage value is 5.38%, with acquiring firms accounting for most of this high leverage. The maximum age of acquiring firms was 146 years, indicating the existence of public firms in the study. The average beta of acquiring firms in the study was 0.69, with a maximum beta of 1.69. The standard deviation of returns used to capture the volatility of the study was found to be, on average 16.4%. The average price-to-book value ratio, used as an indicator for growth firms, was an average of 2.3 for Indian acquiring firms. The average R&D expenses were 0.009 for Indian acquiring firms. On average, the sales growth for the selected acquiring firms was 35.68%. The cash equivalent to total assets was 7.77% for acquiring firms.

Table 4 reports the correlation table of various performance measures and firm-specific factors. The board size is significantly correlated with CAR [−2,+ 2], BHAR [0, +12], BHAR [0, +24], and Tobin’s Q ratio. Board independence has also been negatively correlated with BHAR [0, +24], the ROE of the acquiring firms. Firm age significantly relates to the acquiring firms’ BHAR [0, +12]. Beta is statistically insignificant with CAR [−5,+5], CAR [−2, +2], and BHAR [0,+12] performance measures. Leverage is correlated and significant with market performance measures, that is, Tobin’s Q ratio. The market performance measures BHAR [0, +12], and Tobin’s Q ratio is inverse and statistically significant with a standard deviation of stock returns. The price-to-book ratio is correlated with ROA, ROE, and Tobin’s Q ratio. Research and development have also been found positive and statistically significant with ROA. Sales growth significantly correlates with CAR [−5,+5] and CAR [−2,+2].

Before proceeding to the multivariate cross-sectional regression analysis we tested the variance inflation factor (VIF) for our independent variables have been presented in Appendix A. The results of VIF indicate that the issue of multicollinearity is not present. We then investigate the impact of CG factors and firm-specific factors that influence short- and long-term market performance. We then present the results of the multivariate cross-sectional regression analysis to investigate the impact of CG factors and firm-specific factors that influence short- and long-term market performance. Model 1 in

Table 5. Linkage between short/long term firm performance and CG.

Variables	Model 1 $\mathit{C}\mathit{A}{\mathit{R}}_{\left[-5,+5\right]}$	Model 2 $\mathit{C}\mathit{A}{\mathit{R}}_{\left[-2,+2\right]}$	Model 3 $\mathit{B}\mathit{H}\mathit{A}{\mathit{R}}_{\left[0,+12\right]}$	Model 4 $\mathit{B}\mathit{H}\mathit{A}{\mathit{R}}_{\left[0,+24\right]}$
Board Size	0.739 *	0.586 *	0.712 *	1.182 *
	(0.418)	(0.334)	(0.338)	(0.377)
Board Independence	−0.203	3.094	−5.139	−7.285
	(7.062)	(5.645)	(8.625)	(13.024)
CEO Duality	0.108	−1.038	−0.064	−4.256
	(1.965)	(1.570)	(2.400)	(3.623)
SD of returns	0.358 **	0.354 **	0.047	−0.157
	(0.171)	(0.136)	(0.209)	(0.315)
Firm Size	−0.431	−0.260	−1.042	−0.917
	(0.735)	(0.587)	(0.898)	(1.356)
Firm Age	−0.021	0.002	−0.058	−0.110
	(0.043)	(0.034)	(0.053)	(0.079)
Beta	0.482	1.651	−0.731	1.387
	(2.624)	(2.097)	(3.205)	(4.839)
Leverage	0.265	−0.002	−1.473	−2.058
	(1.088)	(0.870)	(1.329)	(2.007)
Price-to-book value ratio	0.035	−0.199	0.046	0.724
	(0.452)	(0.362)	(0.552)	(0.834)
R&D	0.350 **	0.358 **	0.045	−0.157
	(0.171)	(0.138)	(0.208)	(0.315)
Sales growth	−0.023 ***	−0.011 *	−0.003	−0.002
	(0.007)	(0.006)	(0.009)	(0.013)
Cash reserve	0.115	0.066	−0.041	−0.177
	(0.116)	(0.093)	(0.142)	(0.214)
Year Dummy	Y	Y	Y	Y
Industry Dummy	Y	Y	Y	Y
Constant	−13.383 *	−14.460 **	4.303	4.688
	(7.755)	(6.199)	(9.472)	(14.303)
R2	0.264	0.252	0.141	0.163
N	124	124	124	124

Source: Author’s own compilations. Notes: Standard errors in parentheses, * p < 0.10, ** p < 0.05, *** p < 0.01.

shows the effect of CG measured on the CAR [−5, +5] window period. Board size has a significant and positive impact on the abnormal returns of acquiring firms. Other CG variables such as board independence and CEO duality seem insignificant when measured using the CAR [−5, +5] window period. For the control variables, R&D expenses to total sales are negative and statistically significant for acquiring firms’ performance with the announcement period measured by CAR [−5, +5]. A possible explanation for the positive impact of board size on acquiring firms’ performance can be linked to the effective monitoring and decision-making skills of diversified and larger boards. Firms will benefit from increased experience, ideas, proposals, and assistance from a larger board of directors, providing them with essential resources and substantial investment opportunities. This increases the performance of businesses and benefits the shareholders.

Model 2 in Table 5 shows the analysis of CG measured on cumulative abnormal return, which measures short-term capital market performance with a CAR [−2, +2] window. In line with the results of Model 1, board size has a significant and positive effect on acquiring firms’ abnormal returns. Further, board independence and CEO duality are insignificant predictors when the CAR [−2, +2] performance period is considered. For the control variable R&D expenditure, the SD of returns negatively influences the acquiring firm’s performance in the short-run window period. However, the relationship is positive and significant for sales growth.

Model 3 in Table 5 shows the relationship between BHAR as a measure of long-term capital market performance with the BHAR [0, +12] window period and the CG variables. The study finds that board size has a significant and positive effect on the long-run abnormal returns of acquiring firms. However, board independence and CEO duality were insignificant factors when studying the relationship with BHAR one year after the event. The relationship between R&D expenses and long-term profitability seems negative and significant when measured through the BHAR period [0, +12].

Model 4 in Table 5 reports the relationship between CG and firm performance, measured through BHAR [0, +24], which measures long-term capital market performance. Except for board size, both board independence and CEO duality are insignificant. In the case of control variables, the firm’s R&D was negative and statistically significant.

Model 5 in

Table 6. Linkage between firm performance (accounting and market-based measures) and CG.

	Model 5 $\mathit{R}\mathit{O}\mathit{A}$	Model 6 $\mathit{R}\mathit{O}\mathit{E}$	Model 7 Tobin’s Q Ratio	Model 8 $\mathit{S}\mathit{t}\mathit{o}\mathit{c}\mathit{k} \mathit{R}\mathit{e}\mathit{t}\mathit{u}\mathit{r}\mathit{n}$
Board Size	0.016	0.077	0.132 *	0.008
	(0.061)	(0.066)	(0.341)	(0.008)
Board Independence	2.678 **	1.726	1.297 **	0.050
	(1.024)	(1.107)	(1.033)	(0.134)
CEO Duality	−0.012	−0.521 *	2.379	0.036
	(0.285)	(0.308)	(3.014)	(0.037)
SD of returns	−1.815	−2.721	−3.244	−0.222
	(2.476)	(2.677)	(2.192)	(0.323)
Firm Size	−0.327 ***	0.010	−0.310 ***	−0.013 ***
	(0.107)	(0.115)	(1.128)	(0.104)
Firm Age	0.015 **	0.003	0.001	0.002 **
	(0.006)	(0.007)	(0.066)	(0.001)
Beta	0.902 **	−0.238	−3.441	0.036
	(0.380)	(0.411)	(4.026)	(0.050)
Leverage	−0.305 *	−0.143	−2.595	−0.033
	(0.158)	(0.171)	(1.669)	(0.021)
Price-to-book value ratio	0.138 **	0.757 ***	2.477 ***	0.023 ***
	(0.066)	(0.071)	(0.694)	(0.009)
R&D	0.018 **	0.003	0.003	0.003 **
	(0.008)	(0.007)	(0.069)	(0.002)
Sales growth	−0.002	−0.001	−0.003	0.000
	(0.001)	(0.001)	(0.011)	(0.000)
Cash reserve	0.003	0.006	−0.363 **	0.002
	(0.017)	(0.018)	(0.178)	(0.002)
Year Dummy	Y	Y	Y	Y
Industry Dummy	Y	Y	Y	Y
Constant	3.636 ***	−0.508	−15.269	0.155
	(1.125)	(1.216)	(11.897)	(0.147)
R2	0.365	0.696	0.394	0.276
N	124	124	124	124

Source: Author’s own compilations. Notes: Standard errors in parentheses, * p < 0.10, ** p < 0.05, *** p < 0.01.

presents the results for the relationship between a firm’s CG and ROA, a measure of accounting-based performance. Except for board independence, board size and CEO duality were not statistically significant and therefore had no impact on firm performance. The relationship between the price-to-book value ratio, R&D, and beta is positive and significant, whereas firm size and leverage are negative.

Similarly, Model 6 in Table 6 reports the relationship between CG and ROE, which is used to measure accounting-based performance. In line with previous results, board size and board independence were statistically insignificant and therefore had no impact on acquiring firm performance. In the case of CEO duality, this study found the relationship to be negative and significant. The price-to-book value ratio is positive and significant and influences firm performance.

Model 7 examines the effects of CG and Tobin’s Q ratio, which is used to measure market-based performance. Board size is positive and significant, with a coefficient value (0.132) with firm performance. Board independence also has a statistically significant positive coefficient of 1.297. The price-to-book value ratio was positive and significant.

Model 8 presents the results for the effect of CG on stock returns. Board size, board independence, and CEO duality have no impact on performance, as measured through stock returns. Firm size was negative and significant, with a coefficient of −0.013 for the stock return performance measurement. R&D expenses have also been found to have a positive and significant effect on acquiring firms’ stock returns.

5. Discussion and Conclusions

This study empirically examines the effect of CG characteristics on acquiring companies’ financial performance using a sample of 124 companies in the Indian context. This study uses various alternate proxies of firm performance, such as accounting-based measures and market-based measures, to analyze the various important CG characteristics, such as board size, board independence, and CEO duality, which influence acquiring companies’ financial performance in M and A. The study found that board size has a significant impact on the short-term capital market performance of acquiring companies. These results are similar to those of De Jong et al. (2007), Brewer et al. (2010), Dahya et al. (2016), Awan et al. (2020), and Defrancq et al. (2021). Furthermore, the results also contradict previous studies by Amar et al. (2011), Afza and Nazir (2012), and Tampakoudis et al. (2018), who found a negative relationship between board size and capital market performance. Further, the study found limited evidence of the effect of board independence and CEO duality in both the short and long terms, as these were found to be insignificant factors when studying the relationship between accounting and market-based measures. The results contradict the findings of Desai et al. (2003); Masulis and Mobbs (2011); Teti et al. (2017) which have found significant relationship between board independence and firm performance post-merger. With regard to the other variables, (study) found that the price-to-book value ratio (Roll 1986) and R&D expenses positively influence acquiring companies’ performance and firm size to be negatively related to firms’ performance, which is in line with Moeller et al. (2004) and Masulis and Mobbs (2011).

A possible explanation for the positive impact of board size on acquiring firms’ performance can be linked to the effective monitoring and decision-making skills of diversified and larger boards. Firms will benefit from increased experience, ideas, proposals, and assistance from a larger board of directors, providing them with essential resources and substantial investment opportunities, thus increasing the performance of businesses and benefiting shareholders. Larger boards tend to have a broader diversity of talent, business relationships, and experience than smaller boards, which means they have a better chance of securing vital resources. Second, larger boards of directors expand the knowledge base, which boosts managers’ ability to make significant and better business decisions, thereby increasing their effectiveness. Finally, it has been established that the monitoring capacity of a corporate board is positively connected to the size of the board, as more people with a diverse range of expertise are better positioned to subject managerial choices, such as mergers and acquisitions, to more examination and supervision.

The findings suggest that independent perspectives on the board of directors have a bearing on firm performance both in the short and long term, and can help companies generate benefits when pursuing M and A transactions. However, existing literature states that independent directors can help companies make better decisions, and having more independent directors helps companies avoid disasters and failures. Numerous examples of CG failures that have resulted in the collapse of firms exist worldwide and in India. This is not reflected in our results, where we find limited evidence that board independence is linked to higher firm performance. Therefore, emphasis should be placed on improving the role of independent board members as watchdogs who make decisions in the interests of stakeholders, which have a bearing on the long-run performance of the company. Consequently, it is critical to build a strategy to implement the nomination and training of independent directors who are efficient and successful in providing independent opinions.

To conclude, while the current research focuses primarily on acquiring firms’ performance, a more in-depth investigation at the country level may yield additional insights into the relationship between diversification, value, and governance. This study excluded empirical observations on local and foreign acquisition characteristics/financial performance in the given country, which might be investigated further. Future studies should examine board connections in countries with weak investor protection and the influence of this connectedness on merger value, given the likelihood that businesses with more independent directors have more linked directors than companies with more inside directors.