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Indian Journal of Finance and Banking; Vol. 4, No. 2; 2020 
                                       ISSN 2574-6081   E-ISSN 2574-609X 

Published by CRIBFB, USA 

99 

 

Impact of Cash Deals and Related Industry Merger on Synergies Gains: A 
Case of Indian M&A  

 
Anjala Kalsie PhD 
Assistant Professor 

Faculty of Management Studies 
 University of Delhi, India  

E-mail: kalsieanjala@gmail.com 
 

Neha Singh 
Research Scholar 

Faculty of Management Studies 
University of Delhi, India 

E-mail: nehasingh.usms@gmail.com 

 
 
Received: August 17, 2020        Accepted: August 30, 2020      Online Published: September 11, 2020 
 
doi: 10.46281/ijfb.v4i2.760       URL: https://doi.org/10.46281/ijfb.v4i2.760 
 
 
Abstract 
A firm's financial attributes play an essential part in the merger decision. The present paper attempts to improve the existing 
literature on assessing M&A activity in Indian corporate. This research paper aims primarily to analyze the (a) Synergies 
realized when the mode of payment in the merger deal is cash, (b) impact on bidder liquidity when payment is made in cash 
(c) Synergies realized when both target and acquirer in the deal belong to related industry, i.e. the merger is horizontal and 
(d) assess the impact on bidder leverage when payment is made in equity. The paper has analyzed a panel of 120 major 
Indian M&A deals from 2005 to 2015, having three years of data pre and post-merger. Instrument Variable Probit 
Regression analysis has been employed in the study. The key results from the analysis show that in case of payment method 
in the deal being cash, M&A appears financially favorable for the bidder companies. The results of the empirical analysis of 
the study do support the generation of synergies in the case of horizontal mergers. The combined firm has also found to 
have lower liquidity for Indian Mergers & Acquisitions. Significant results have also been obtained for the leverage variables 
indicating fewer borrowings for the merged firm. 
 
Keywords: M&A Activity, Synergies, Variable, Regression Analysis, Cash Deals, Industry Relatedness.     
 
JEL Classification Codes: G34, C35, M41.                    
 
1. Introduction  
Corporate restructuring involves any change in the assets or capital structure of a company or its ownership through an 
inorganic route (Godbole, 2013). Such a change can be effected through either acquisition of a company, merger or 
demerger of/into two or more companies, delisting or selling off a company, or its important assets. Mergers & 
Acquisitions is the primary mean of corporate restructuring. A merger can be defined as the consolidation of the resources, 
liabilities , and operations of two or more firms into one, where payment is made in the form of the merger company's 
equity shares or debentures or cash or else a hybrid of the payment methods listed above (Beena, 2000). Some of the main 
objectives to undergo a merger are expanding into new markets, considerable cost savings, and knowledge sharing as well as 
risk-sharing. However, the prime objective of undertaking any form of restructuring is to gain synergies generated out of the 
combination. Synergy is the potential benefit that is achieved post the amalgamation.  Apart from the lure of quantum 
growth associated with the mergers, there are many other motives for which companies resort to M&A, financial and 
operating synergies being the most important out of them, which add to the enterprise valuation (Sudarsanam, Holl, & 
Salami, 1996). 

     Synergies are of two types – revenue-generating and cost reduction with the former being more difficult to achieve 
(Cullinan, Le Roux, & Weddigen, 2004). Financial synergies involve combining both target and acquirer companies’ 
balance sheets to achieve improved financial parameters (Godbole, 2013). Operating synergies are the ones that are 
generated due to improved operating efficiencies of merged entities, which is majorly due to improved tax benefits or 
investment cutbacks (Huyghebaert & Luypaert, 2013; Hamza, Sghaier, & Thraya, 2016; Loukianova, Nikulin, & 

mailto:kalsieanjala@gmail.com
mailto:nehasingh.usms@gmail.com
https://doi.org/10.46281/ijfb.v4i2.760


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Vedernikov, 2017). The synergies generated are not always positive, and firms can even experience negative synergies, which 
create the exact opposite outcome to that of positive synergies. In the case of negative synergies, the sum is less than its parts 
due to value erosion. The theories of diseconomies of scale and scope are used to explain the adverse effects of negative 
synergies (Harding & Rovit, 2004). 

    The M&A deals have become common in India in the last two decades. In the post-liberalization period, though they 
had not been uncommon before, but the frequency was less (Bhoi, 2000). The liberal economic policy by the Government 
post-1990s incentivized companies to undergo expansion, diversification, up-gradation of technology, and entering into 
newer geographical areas. Several firms deemed it necessary to combine with related business units and subsidiaries to 
achieve cost efficiency and improved production. The quantum of deals in India has seen a steady increase since 2013 with a 
similar increase in the total value of deals undertaken. In 2015, companies announced over 1200 transactions with a total 
value over 51 billion USD. The number of transactions increased moderately by 15.6 percent compared to 2014, while the 
value has increased by 63 percent (M&A Statistics by Countries-Institute for Mergers, Acquisitions , and Alliances (IMAA), 
2019).  

     The financial performance and also the assessment of mergers & acquisitions have dwelled well in the field of 
financial and industrial economics. Despite this, there is debate if mergers & acquisitions boost corporate efficiency. The 
present study explores the performance of the acquirer and whether synergies are achieved in the post-merger time period, 
when the mode of payment for the transaction is cash. Similar gains are analyzed for horizontal mergers as well. The present 
study analyses 120 deals of Mergers & Acquisitions which took place between 2005 and 2015 for the Indian Corporate. To 
estimate the relationship, the Instrument Variable Probit Regression model is applied in the study. 

     The paper is organized into six sections, which are as follows. Literature review of the different methodologies used 
in the existing studies and their findings have been discussed in Section 2. Section 3 gives the objective and hypothesis of 
the present study. Section 4comprises the research design, variables, data source, and methodology employed in the research. 
Section 5 pertains to the results based on the econometric analysis. The paper ends with the conclusion and implications 
presented in Section 6. 
 
2. Literature Review 
Majority of studies to date in Mergers & Acquisitions relate to economic costs & benefits accrued to acquirers and targets in 
the post-merger scenario. Few studies deal with the stock market returns and misvaluations. Majorly,  the methodology of 
event study has been used in the existing literature, which assesses the impact of the merger in the short-run ([-1,+1],[-
5,+5] ), i.e. to investigate the implications of the announcement of M&As on the wealth of the shareholder. They have 
concluded either significantly negative abnormal returns or insignificant abnormal returns in the short-run (Andrade, 
Mitchell, & Stafford, 2001; Bruner & Mullins, 1987; Bradley, Desai, & Kim, 1988; Byrd & Hickman, 1992; Kaplan & 
Weisbach, 1992; Healy, Palepu, & Ruback, 1992; Lang, Stulz, & Walkling, 1989; Mulherin & Boone, 2000; Servaes, 1991; 
Smith & Kim, 1994). Whereas the result of the long-run event studies studying post-merger returns after three years have 
pointed out that firms experience negative abnormal returns (Andrade, Mitchell, & Stafford 2001; Lahey & Conn, 1990; 
Limmack, 1991; Loughran & Vijh, 1997; Mitchell & Stafford, 2000; Rau & Vermaelen, 1998). 

     Despite the strength of the research in this area, there is a lack of consensus on the stimulus mergers and acquisitions 
have on the economies of the countries in which they occur, especially on the targeted corporations. In the past, several 
studies have sought to examine the costs and gains of mergers and acquisitions. However, the conclusions of these 
investigations are so varied that it is impossible to arrive at a clear consensus. Although some research supports the benefits 
accrued to the acquired firm, no consensus can be derived on the benefits obtained by acquiring companies' shareholders 
(Cummins & Weiss, 2004; Mohanty & Mishra, 2011). 

     There are studies, which assess the shareholder wealth by accounting performance through performance measures 
like operating cash flows to sales, operating cash flows to total assets, return on assets and operating income over total 
assets; and they come to a variety of conclusions. While some studies like Andrade, Mitchell, and Stafford (2001); 
Ramaswamy and Waegelein (2003) shows the gain in accounting performance post-acquisition; there are studies like 
Ravenscraft and Scherer (2011)which show retrogression of performance post-M&A.  

     Studies undertaking the assessment of synergies in terms of operating performance after undertaking Merger & 
Acquisition have shown mixed results. While studies like Linn and Switzer (2001); Moeller and Schlingemann (2004); 
Switzer (1996); Parrino and Harris (1999); Powell and Stark (2005) looking into pretax cash flows have shown an increase 
in post-acquisition cash flows; there are studies which shows an overall decline in cash flow (Kruse, Park, & Suzuki, 2003), 
lower profitability (Meeks, 1977), a significant decline in the ROA (Yeh & Hoshino, 2002; Dickerson, Gibson, & 
Tsakalotos, 1997)and insignificant improvement in operational efficiency following the acquisition by the acquirer (Ghosh, 
2001; Herman & Lowenstein, 1988; Lev & Mandelker, 1972; Sharma & Ho, 2002). 

     Existing literature in financial synergies studying the existence and extent of financial synergies have suggested 
deterioration in post-M&A profitability measure in respect of EPS (Hogarty, 1970), Return on capital equity (Harris, 
Franks, & Mayer, 1987), ROE (Yeh & Hoshino, 2002), liquidity, profitability, and solvency ratios (Pazarskis, 
Vogiatzogloy, Christodoulou, & Drogalas, 2006). The results suggest that the result of the acquisition of the profitability 
of the firm is detrimental (Dickerson, Gibson, & Tsakalotos, 1997). However, an analysis of the financial efficiency of 
selected Indian financial institutions showed that long-term value was created and financial performance improved for the 
acquired firm post-acquisition; but not on all parameters (Sinha, Kaushik, & Chaudhary, 2010). 



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     The mode of payment is also one of the determinants of the synergies gained in the post-acquisition. Linn and 
Switzer (2001) suggest that the operating efficiency of the combined firm in the US have a propensity to be greater in cases 
where cash was the key mode of payment. Acquisitions financed by stocks are associated with smaller synergy changes than 
when the payment method is in cash (Carline, Linn, & Yadav, 2005; Ghosh, 2001). Statistical findings have consistently 
shown that the target and the acquirer's share prices react more favorably to a cash proposition than to a stock purchase at 
the time of the initial announcement of the bid (Peterson & Peterson, 1991; Carnes, Black, & Jandik, 2001; Bouwman, 
Fuller, & Nain,  2009). 

     Another critical issue in the literature is how the horizontal acquisition affects the efficacy of the acquirer, by 
influencing synergies within the combined organization. It is believed that horizontal acquisitions provide substantial 
synergy opportunities, because of the similar institutional climate of the acquirer and the target (Farjoun, 1994; Barai & 
Mohanty, 2014). At the same time, vertical acquisitions are hypothesized to offer lesser potential for synergy (Chatterjee, 
1986). It is increasingly being recognized that the complementary resources of varied industries may also provide the major 
potential for synergies (Barkema, Baum, & Mannix, 2002; Harrison, Hitt, Hoskisson, & Ireland, 2000; Tanriverdi & 
Venkatraman, 2005). Meta-analysis results, however, suggest no significant association between the performance of the 
acquirer and similarity of the industry of the acquired firm (King , Dalton, Daily, & Covin, 2004). 
 
An overview of the prominent studies showing the reported variables and the methodology adopted is given in Table 1. 
 
Table 1. Summary of Literature Review 
 

Existing Literature Methodology 
adopted 

Key Variables Used in the study 

Varaiya and Ferris (1987), Lang, Stulz and Walkling 
(1989), Bradley, Desai, and Kim (1988), Asquith, 
Bruner, and Mullins (1990), Healy, Palepu, and 
Ruback (1992), Byrd and Hickman (1992), Mulherin 
and Boone (2000), Kaplan and Weisbach (1992),  
Andrade, Mitchell, and Stafford (2001), Kuipers, 
Miller and Patel (2002), Chari, Ouimet and Tesar 
(2004), Huyghebaert and Luypaert (2013), Barai and 
Mohanty (2014) 

Event Study Acquirer Return, Free Cash Flow, Premium, 
Leverage, Relative Size, Announcement 
Returns, Leverage, Return On Asset, Sales, 
Market Capitalisation, EBITDA To Sales, 
Cumulative Average Abnormal Returns 

Hogarty (1970), Philippatos, Choi, and Dowling 
(1985), Ramaswamy and Salatka (1996), Ravenscraft 
and Scherer (2011) 

Univariate 
Regression 
Analysis 

Operating Cash Flow Return On Assets, 
Earning Per Share, Operating Expense Ratio, 
Operating Income Over Assets 

Ghosh (2001), Morag (2011), Tanriverdi and 
Uysal(2011), Barai and Mohanty (2014) 

Multivariate 
Regression 
Analysis 

Integration Effectiveness, 
Relatedness, Organizational Culture, 
Synergy Potential, M&A Success, Cash 
Flows To Total Assets, Profitability, Return 
On Asset, Leverage, Growth Of Net Assets, 
Leverage, Free Cash Flow, Relative Size, 
Method Of Payment, Book Leverage, IT 
Capability Of Acquirer, Relative Acquisition 
Size 

Cudd and Duggal (2000), Kumar and Rajib (2007), 
Basu, Dastidar, and Chawla, (2008), Ismail (2011), 
Bena and Li (2014), Ismail, Dbouk, and Azouri 
(2014), Fich Nguyen, and Officer (2018) 

Logit Analysis Liquidity Ratio, Growth Rate, Market To 
Book Ratio, Total Assets Ratio, Sales, Cash 
Flow, Price To Earnings Ratio, Leverage, 
Tobin Q, Cash Payment, Relative Size, Log 
Assets  

Harris (1982), Pastena and Ruland (1986), Harford 
(1999), Bernile (2005),  Mooney and Shim, (2015), 
Chira, García-Feijóo, & Madura (2017), Tremblay 
(2017), Bernile and Lyandres (2019) 

Probit Analysis Size, Liquidity, Leverage, Profitability, 
Growth, Price/Earnings ratio, Dividend 
policy 
 

Source: Authors’ representation based on the previous literature 
 
3. Objective and Hypothesis 
The present paper aims to assess if synergies are gained post the merger for the acquirer. The study examines 120 M&A 
deals for the Indian Corporate, which took place between 2005 and 2015. Certain parameters have been selected to 
effectively represent the synergies gained (Appendix B). The mergers have been selected from a broad period to ensure 
representation from different business cycles. The primary objective of the present study is to analyze (a) the Synergies 
realized when the mode of payment in the merger deal is cash, (b) impact on bidder liquidity when payment is made in cash, 



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(c) Synergies realized when both target and acquirer in the deal belong to related industry, i.e. the merger is horizontal and 
(d) impact on bidder leverage when payment is made in equity. 
 
The models developed in the paper are listed in the table below. 
 
Table 2. Models employed in the study 
 

Objectives Hypothesis Dependent Variable 
(Binary Variable) 

Equations for each model 

Model 1 H1: When payment 
is made in cash, 
more synergies are 
generated. 

Mode of Payment is 
Cash 

Payment_Cashit= α +  𝛽𝑗
𝑗
𝑗=1 𝑋𝑖𝑡

𝑗
+  γitZit+Ԑit 

 

Model 2 H2: When bidder 
liquidity is high, 
payment is made in 
cash.  

Mode of Payment is 
Cash  

Payment_Cashit= α +  𝛽𝑗
𝑗
𝑗=1 𝑋𝑖𝑡

𝑗
+  γitZit+Ԑit 

 

Model 3 H3: When merger & 
acquisition take 
place in the related 
industry sectors, 
more synergies are 
generated.  

Relatedness of 
Industry 

Industry_Relatednessit= α +  𝛽𝑗
𝑗
𝑗=1 𝑋𝑖𝑡

𝑗
+  γitZit+Ԑit 

 

Model 4 H4: When bidder 
leverage is high, 
payment is made in 
equity. 

Mode of Payment is 
Equity 
 

Payment_Equityit= α +  𝛽𝑗
𝑗
𝑗=1 𝑋𝑖𝑡

𝑗
+  γitZit+Ԑit 

 

Where X is the independent variable and Z is our instrument variable. 
 
4. Data and Methodology 
4.1 Data Description 
The study considers an unbalanced panel data of the 120 Mergers& Acquisition deals which took place in India from 2005 
to 2015. Data of seven years (3 years post-Merger, year of Merger, 3 years pre-Merger) has been taken for each deal. Hence, 
the period of data used in the study is from 2002 to 2018. The study has excluded non-listed acquirer firms, and also 
financial and banking companies because they have distinct accounting, operational, and risk-based features. The highest 
representation for the acquirer in the deals under consideration is in the industrial sector with 30 deals. The basic Materials 
sector has been most represented for the target (Appendix A). 

     Accounting and financial data, which is used as regressors and dependent variables for the probit analysis, i.e. Mode 
of Payment (cash or equity) and Relatedness of Industry have been compiled from Bloomberg. Appendix B defines the 
variables that the study uses. For the present study, the combined entity's performance in the post-acquisition period has 
been equated with that of target and acquirer (A+T) entities. For an appropriate comparison, each variable is deflated by 
tangible assets of the considered firms and thus eliminating the size effect (Healy, Palepu, & Ruback, 1992). (Note 1)  

     The paper has also measured operating cash flow returns on assets to assess operational efficiency changes, as 
suggested by Healy, Palepu, and Ruback (1992). Conceptually, cash flows have been concentrated as they reflect the real 
economic gains that are generated by assets. Operating cash flows were defined as the addition of sales, goodwill expenses, 
and depreciation; followed by deduction of the selling and administrative expenses and cost of goods sold. As the amount of 
economic gains is influenced by the assets used, cash flows have been scaled by total assets to form a measure of return that 
can be measured over time and even across the firms. 
 
4.2 Methodology 
The present study uses the two-step Instrument Variable Probit regression for empirical analysis instead of conventional 
multivariable regression analysis. Binary data models that are of a dichotomous type assume a binomial distribution for the 
dependent variable which are well described by Awogemi and Oguntade (2012); Gujarati, (2004); Krzanowski (1998); 
Hollander and Wolfe (1973). The assumptions of normality such as disturbance terms and observations are normally 
distributed; homogeneity of variance; normality measures are null. The dichotomous quality of dependent variables collapses 
the assumptions of Ordinary Least Square (OLS).  

     Variables used in the study for the firms are correlated to each other. This can be observed from the fact that if we 
have included Net Income in the model, we cannot say that a firm’s Net income is not impacted by the EBITDA or total 
assets of the firm which has not been considered in the equation employed. It suggests that in such a model EBITDA or 
total assets will be represented in the error term. And hence the error term shows a correlation with the model's independent 



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variables. This kind of problem is distinctive in such type of studies. Hence, instead of employing Probit Regression, we 
have used the Instrument Variable Probit Regression methodology in the study to tackle the issue of endogeneity. 

     IV-probit suits those probit models in which one or more of the regressorare determined endogenously. This is used 
when you consider that the error term is associated with one or more of the regressor. The estimator of minimum chi-
squared is invoked with the option two-step (Newey, 1987). It relies on the assumption of the continuous endogenous 
regressors and unsuitable to use with endogenous regressors which are discrete.The model used in IV probit estimation is: 

 y1i*=y2iγ + x1iδ + µi (1) 
 

 y2i=x1iΦ1 + x2iΦ2 +νi (2) 

 
     Where i = 1, . . . , N,y2i is a 1×n vector of endogenous variables, x1i is a 1×m1 vector of exogenous variables, x2i is a 

1 × m2 vector of additional instruments, and the y2i is reduced form of the equation. 

     For a typical Probit model, it is presumed that the error term has one variance. However, we presume that (µi, νi) is 
multivariate normal with a matrix of covariance, in the case of a Probit model with an endogenous regressor. Consequently, 

γ and δ results would not be yielded by the estimator of Newey and two-step estimators of probit. Instead, γ/σ and δ/σ 

estimates are generated, where σ is defined as the square root of Var (µi|νi). Therefore a direct comparison of the estimates 
obtained from estimator of Newey with those obtained from probit or maximum likelihood is not possible. However, the 
two-step estimator is still beneficial. The maximum likelihood estimator can struggle to converge, particularly with multiple 
endogenous variables; but the convergence of the two-step estimator is most definite. Furthermore, while the coefficients 
from the two models are not comparable precisely; it is still possible to use the two-step calculations to check for 
statistically significant relations. In two-step IV probit estimation, Wald test of the null hypothesis H0  (of no endogeneity) 
works as the exogeneity test. 
 
4.3 Robustness Test 
Weak IV identification test has been conducted to check that the weak-IV concern is not present in the instruments. The 
weak IV test of STATA module is performed on the endogenous variable(s) in an instrumental variables (IV) model to 
verify the validity of the instrument used, and create confidence sets for these coefficients. These confidences and tests are 
robust to weak instruments, in the context that the coefficients are not believed to be known. Weak IV test can be used to 
estimate linear models (including fixed panel effects and dynamic panel data), probit, and Tobit IV (Finlay, Magnusson, & 
Schaffer, 2014). In the case of IV probit, the two-step estimator (Newey's, 1987) is required. 

The weak IV test for IV probit in stata reports the Anderson-Rubin test (AR). AR test is a joint test of the structural 
parameter (beta=b0, which represents the coefficient of endogenous regressor) and the exogeneity of the instruments (E 
(Zu) =0, where u indicate the disturbance in the structural equation and Z indicate the instruments).  
 
5. Empirical Analysis and Results 
5.1 Stationary Test and Correlation Matrix 
Fisher-type (Choi, 2001) for an unbalanced panel is applied to all variables used in the models to check for stationarity. 
The study found that a majority of the variables is commonly stationary at their first difference. Out of 41 variables, 39 
variables were found stationery at their first difference. 

     Appendix C, D, E, and F exhibit of correlation among the significant variables of the models. There is a weak 
correlation reported between the majorities of variables. 
 
5.2 Results of IV Probit Regression and Interpretation 
Table 3 reports the findings of IV- Probit model 1 results. The paper has estimated three equations for model 1with 
Payment_Cash as the dependent variable. The probit model estimates involve reverse causality and possible biases, which 
raises concerns of endogeneity. To mitigate this concern for endogeneity, two-step Instrumented Variable (IV) probit 
regressions (Newey, 1987; Rivers & Vuong, 1988) has been employed. Specifically, in the first stage, the paper has 
estimated the current market cap as the selected instrument. In the second stage, using the predicted values of relative 
current market cap and other variables as regressors, the study estimates the IV probit regression. The second stage results 
of the IV-probit model, provided in table 3 columns 2, 4, and 6, indicate that the current market cap is negative and 
significant at the level of < 1 percent. As can be observed from table 3, net income has a positive and statistically significant 
relationship with payment dummy for all the three reported models, indicating that if payment for the deal is made in cash 
then synergy realized in the form of net income is more in the post-acquisition stage. Similar positive and statistically 
significant outcomes have also been observed for R&D expense to Net sales in our model, indicating spillover of technology 
in the post-merger period. Significant results have also been obtained for the leverage variables like Net debt (EQ 3) and 
Net Debt to Ffcf (EQ 1 & 2), indicating fewer borrowings for the merged firm. Thus, the results of the empirical analysis 
of the study support hypothesis 1, which states “When payment is made in cash, more synergies are generated”. Prob> 
chi2-is the probability of achieving this chi-square statistic if collectively independent variables do not influence the 
dependent variable (UCLA: Statistical Consulting Group).This p-value is compared to a critical value, i.e. at 10 percent, 5 
percent, and 1 percent to determine the statistical significance of the overall model.  In this case, all the three models are 



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statistically significant at less than 1 percent level. A Wald test of the exogeneity of the instrumented variables is reported at 
the bottom of the results. On that basis, we refute the null hypothesis that there is no endogeneity. 
 
Table 3. Results of Instrument Variable IV Probit estimation for Mode of Payment in Cashis the dependent variable. This 
table reports the coefficient estimates and p-statistics from Instrument Variable Probit MODEL 1.  
 

 EQ 1 EQ 2 EQ 3 

 First stage 
(1) 

Two-step probit 
with endogenous 
regressors (2) 

First stage 
(3) 

Two-step probit 
with endogenous 
regressors (4) 

First stage(5) Two-step probit 
with endogenous 
regressors (6) 

Cur_Mkt_Cap  -1.9114 
(0.7866)*** 

 -1.8289 
(0.5748)*** 

 -1.4760 
(0.4863)*** 

Net_Income 6.4955 
(0.570) 
*** 

    11.6607 
(5.5305) 
** 

6.6359 
(0.627) 
*** 

11.1299 
(4.2499) 
*** 

7.679 
(0.62) 
*** 

10.4152 
(4.1947) 
** 

Personnel_Expn_
Per_Employee 

  -1.68E-
09(3.69E-
09) 

-1.54E-08 
(1.63E-08) 

  

Net_Debt_To_F
fcf 

-0.00003 
(0.0001) 

-0.0011 
(0.0016) 

-0.0002 
(0.0003) 

-0.0037 
(0.00233)* 

  

Risk_Premium -0.0208 
(0.020) 

-0.1561 
(0.0499)*** 

-0.0165 
(0.023) 

-0.1383 
(0.0499)*** 

-0.00565 
(0.0198) 

-0.1157 
(0.03873)*** 

Opcfroa_Healy -0.0028 
(0.004) 

-0.0708 
(0.0304)** 

-0.0028 
(0.009) 

-0.08294 
(0.0364)** 

-0.00372 
(0.0083) 

-0.0534 
(0.0291)* 

Rd_Expend_To_
Net_Sales 

0.1342 
(0.037) 
*** 

0.2185 
(0.1277) 
* 

0.1316 
(0.039) 
*** 

0.2012 
(0.1040) 
* 

0.1193 
(0.03748)*** 

0.1801 
(0.0876) 
** 

Net_Debt     -1.2330 
(0.16332)*** 

-1.2993 
(0.6302) 
** 

Goodwill_Assets
_ 

    0.016086 
(0.0032)*** 

0.0276 
(0.0098) 
*** 

constant 0.4326 
(0.127) 
*** 

1.3274 
(0.5524) 
** 

0.3159 
(0.148) 
** 

1.3175 
(0.445) 

 1.1969 
(0.4517) 

Wald test of 
exogeneity 

 16.78***  22.07***  14.60*** 

Anderson-Rubin 
test 

 17.82***  26.47***  18.11  *** 

Wald chi2(9)  17.95  23.02  23.59 

Prob> chi2 0 
 

0.0064 0 0.0017 0 0.0027 

Number of obs 660 660 529 529 577 577 

Robust standard errors in parentheses.***p<0.01, **p<0.05, * p<0.1 
Source: Authors’ estimation 

 
     Table 4 presents the findings of IV-Probit for model 2“When payment is made in cash, bidder liquidity is high”. 

Probit regression model has been run with Payment_Cash as the dependent variable and liquidity variables such as free cash 
flow and operating cash flow return on assets along with other variables as part of the independent variables. This model 
estimate Net Assets as the selected instrument in all three reported equations.  As per the results Operating cash flow return 
on asset, Working Capital, Cash and Cash Equivalent and Quick ratio are statistically significant but negative in all the 
three models when the payment is made in cash. This indicates that the combined firm has lower liquidity for Indian 
Mergers & Acquisitions, and it is more likely the company will struggle with paying debts when payment is made in cash, 
thereby negating the hypothesis 2. However, free cash flow was found to have an opposite effect in our estimation. Prob> 
chi2 indicates that the EQ 1  and 2 are statistically significant at less than 10 percent level and EQ 3 at less than 5 percent 
level. Based on Wald's test of the exogeneity of the instrumented variables, we refute the null hypothesis of no endogeneity. 
 
 



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105 

 

Table 4. Results of Instrument Variable Probit estimation for Mode of Payment in Cash as the dependent variable. This 
table reports the estimates of coefficient and p-statistics from Instrument Variable Probit Model 2. 
 

 EQ 1 EQ  2 EQ 3 

 First stage 
(1) 

Two-step 
probit with 
endogenous 
regressors (2) 

First stage 
(3) 

Two-step 
probit with 
endogenous 
regressors (4) 

First 
stage(5) 

Two-step 
probit with 
endogenous 
regressors (6) 

Net_Assets  22.553 
(8.457) 
*** 

 19.506 
(8.604) 
** 

 20.024 
(8.849) 
** 

Working_Capital 0.367 
(0.046)*** 

-9.051 
(3.435) 
*** 

0.519 
(0.061) 
*** 

-10.739 
(4.778) 
** 

0.527 
(0.062) 
*** 

-10.876 
(4.987) 
** 

Cf_Free_Cash_Flow -0.079 
(0.090) 

2.315 
(2.395) 

-0.289 
(0.122) 
** 

6.01 
(3.505) 
* 

-0.280 
(0.122) 
** 

6.338 
(3.554) 
* 

Opcfroa_Healy 0.001 
(0.0004) 

-0.060 
(0.0269) 
** 

-0.00005 
(0.0007) 

-0.052 
(0.028) 
* 

-0.00005 
(0.001) 

-0.0464 
(0.0274) 
* 

Invent_Turn 0.0001 
(0.0001) 

-0.018 
(0.017) 

0.0004 
(0.0002) 
** 

-0.019 
(0.014) 

0.0004 
(0.00015) 
** 

-0.02114 
(0.01525) 

Cce_And_Sti_Detailed 0.034 
(0.0173)** 

-1.347 
(0.657) 
** 

0.0254 
(0.0235) 

-1.002 
(0.649) 
* 

0.0262 
(0.0235) 

-0.953 
(0.6674) 

Quick_Ratio 0.0149 
(0.0047) 
*** 

-0.604 
(0.232) 
*** 

0.0191 
(0.0064)**
* 

-0.634 
(0.262) 
** 

0.0192 
(0.0064) 
*** 

-0.585 
(0.2709) 
** 

Ebitda 0.396 
(0.1005) 
*** 

-8.2799 
(3.5739) 
** 

0.7466 
(0.1349)**
* 

-13.72 
(6.179) 
** 

0.7493 
(0.135) 
**** 

-12.83 
(6.377) 
** 

Revenue_Sequential_Gro
wth 

1.17E-
06(3.83E-
06) 

-0.0013 
(0.0017) 

3.55E-
07(5.21E-
06) 

-0.0013 
(0.0017) 

0.0000004 
(0.000005) 

-0.0017 
(0.0017) 

Net_Fixed_Assets_5_Ye
ar_Growth 

-
0.00004(0
.00008) 

-0.0014 
(0.0023) 

0.0001(0.
00011) 

-0.0041 
(0.0027) 

0.000093 
(0.00014) 

-0.00423 
(0.0028) 

Cash_Flow_To_Net_Inc 0.0026 
(0.0004) 
*** 

-0.075 
(0.025) 
*** 

0.0058 
(0.0005)**
* 

-0.129 
(0.0522) 
** 

0.0058 
(0.0005) 
*** 

-0.132 
(0.0536) 
** 

Total_Debt_And_Prefer
red_Equity 

-0.524 
(0.0419)**
* 

12.560 
(4.517) 
*** 

-0.169 
(0.0525)**
* 

4.343 
(1.8002) 
** 

-0.1725 
(0.0527) 
*** 

4.128 
(1.877) 
** 

Capitalization_Ratio 0.00075(0
.0003)** 

-0.016 
(0.0107) 

-0.00107 
(0.0004)**
* 

0.0212 
(0.0117) 
* 

-0.0011 
(0.0004) 
*** 

0.0214 
(0.0121) 
* 

Cons  5.235 
(2.3851)** 

 -6.994 
(2.991) 

0.3828 
(0.034) 
**** 

-7.148 
(3.073) 
** 

Wald test of exogeneity  36.08***  33.58***  34.16*** 

Anderson-Rubin test  32.21***  31.63***  32.28*** 

Wald chi2(9)  23.07  19.81  25.80 

Prob> chi2 0 0.0591 0 0.0999 0 0.0275 

Number of obs 577 577 577 577 577 577 

Robust standard errors in parentheses.***p<0.01, **p<0.05, * p<0.1 
Source: Authors’ estimation 

 



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     Table 5 reports the findings of IV probit regression model run for the Model 3 with Industry relatedness as the 
dependent variable. This model estimates the current market cap as the selected instrument. The second stage results of the 
IV-probit model, presented in columns 2 and 6 of Table 5, reflects that the instrumented current market cap is negative and 
significant at the less than 1 percent level and less than 10 percent level in EQ 3 (column 4). It has been highlighted by the 
results that significant variables like Return on capital, Net income growth, total operating expense as a percentage of sales, 
asset turnover, and free Cash flow yield have a positive and statistically significant relationship with the relatedness of the 
industry of the reported EQ 2 and 3, indicating that if acquirer undertakes a horizontal merger then synergy in the form of 
said variables are gained post the merger. Similar positive and statistically significant results have also been observed for 
Inventory turnover in all the reported models. Thus, the results of the study's empirical analysis do support hypothesis 3, 
which states “When mergers & acquisition take place in the related industry sector, more synergies are generated”. However, 
Financial Leverage and Net debt of the combined firm were observed to have a positive and statistically significant 
relationship with the relatedness of the industry as depicted in EQ 1; indicating an increase in borrowing post-merger for 
the combined firm. The rise in financial leverage is the result of an increase in debt capacity (Ghosh & Jain, 2000). Prob> 
chi2 indicates that all the models are statistically significant at less than 1 percent level. We refute the null hypothesis of no 
endogeneity, based on Wald's test.  
 
Table 5. Results of Instrument Variable Probit estimation for Relatedness of Industry as the dependent variable. This table 
reports the estimates of the coefficient and p-statistics from Instrument Variable Probit MODEL 3. 
 

 EQ 1 EQ  2 EQ 3 

 First stage 
(1) 

Two-step 
probit with 
endogenous 
regressors (2) 

First stage 
(3) 

Two-step 
probit with 
endogenous 
regressors (4) 

First 
stage 
(5) 

Two-step probit 
with endogenous 
regressors (6) 

Cur_Mkt_Cap  0.2758 
(0.106) 
*** 

 1.636 
(0.905) 
* 

 0.368 
(0.126) 
*** 

Invent_Turn 0.0033 
(0.0008) 
*** 

0.0025 
(0.0019) 
*** 

0.001 
(0.0006) 
* 

0.006 
(0.004) 
* 

0.001 
(0.001) 

0.0069 
(0.0032) 
** 

Ebitda_To_Revenue -0.0009 
(0.0007) 

0.0179 
(0.004) 

  -0.0035 
(0.0025) 

0.0253 
(0.0129) 
** 

Oper_Margin -0.0023 
(0.0015)* 

-0.0176 
(0.0042) 
** 

    

Total_Opex_As_A_Perc
entage_Sales 

-0.0043 
(0.0014)**
* 

-0.0049 
(0.0027) 
*** 

-0.00304 
(0.00098)*** 

-0.0115 
(0.0045) 
*** 

-0.0049
 (0.00
16) 
*** 

-0.0196 
(0.0078) 
** 

Quick_Ratio 0.0447 
(0.0306) 

-0.125 
(0.0459) 

-0.0463
 (0.02039
)** 

-0.0143 
(0.0657) 

0.0234 
(0.0313) 

-0.047 
(0.0465) 

Return_Com_Eqy 0.0007 
(0.0012) 

-0.0144 
(0.0031) 

    

Asset_Turnover 0.0292 
(0.0905) 

0.2066 
(0.125) 

-0.1164 
(0.0604) 
* 

0.6911 
(0.2234) 
*** 

-0.205 
(0.098) 

0.3964 
(0.154) 
*** 

Free_Cash_Flow_Yield -0.0014 
(0.0005)**
* 

0.0052 
(0.0015) 
*** 

-0.00011 
(0.00035) 

0.002733 
(0.00142) 
* 

-0.0013 
(0.00057
) 

0.00163 
(0.00125) 

Ebitda   -1.724 
(1.2352) 

-2.4798 
(2.00431) 

  

Fncl_Lvrg   -0.02543 
(0.00967)*** 

0.0191 
(0.0331) 

  

Capitalization_Ratio   -0.00595 
(0.0012) 
*** 

0.0143 
(0.00665)** 

  

Bs_Long_Term_Investm   0.33778 -1.0551   



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107 

 

ents (0.16034)** (0.5077) 
** 

Ebitda_To_Revenue   -0.000014 
(0.00060) 

0.0489 
(0.01196)*** 

  

Return_On_Cap   0.00473 
(0.00240)** 

-0.0211 
(0.00780)*** 

0.0192 
(0.004) 
*** 

-0.0219 
(0.0066) 
*** 

Tobin_Q_Ratio   0.8315 
(0.0288) 
*** 

-1.4132 
(0.7662) 
* 

  

Normalized_Net_Incom
e_Growth 

  -
0.000038(0.
00002)* 

0.00025 
(8.27E-05) 
*** 

-0.00003 
(0.00004
) 

0.00022 
(0.00007) 
*** 

Oper_Margin   -0.00445 
(0.0011) 
*** 

-0.043 
(0.01091) 
*** 

-0.0005 
(0.002) 

-0.041 
(0.01186) 
*** 

Net_Debt     0.0453 
(0.229) 

0.9648 
(0.338) 
*** 

Net_Fixed_Assets_5_Ye
ar_Growth 

    -0.0001 
(0.0005) 

0.00742 
(0.0023) 
*** 

Mkt_Cap_To_Revenue     0.0098 
(0.007) 

-0.0232 
(0.0134)* 

Constant 0.6927 
(0.181) 
*** 

0.758 
(0.318) 
** 

0.263 
(0.1303) 
** 

1.275 
(0.466) 
*** 

0.659 
(0.212) 
*** 

1.4216 
(0.8551) 
* 

Wald test of exogeneity  8.43***  5.51**  11.00*** 

Anderson-Rubin test  7.10***  5.20  **  9.16*** 

Wald chi2(9)  44.50  40.97  51.77 

Prob> chi2 0 0.0000 0 0.0003 0 0.0000 

Number of obs 744 744 725 725 578 578 

Robust standard errors in parentheses.***p<0.01, **p<0.05, * p<0.1 
Source: Authors’ estimation 

 
     Lastly, IV Probit model has been estimated for model 4" When payment is made in equity, bidder leverage is high", 

for which results are presented in table 6. Payment_Equity is the dependent variable and leverage variables along with other 
variables form the part of the independent variables. The current market cap is the selected instrument in EQ 1 and 2, along 
with other variables in the outcome regression in the first stage. For the estimation of EQ 3, the total asset has been used as 
the selected instrument. As per the results, EQ 1 suggests long term borrowing to have a statistically significant relationship 
with payment equity. EQ 2 suggests short and long term debt and EQ 3 suggests net debt to have a negative and statistically 
significant relationship with the payment equity. This suggests lower borrowings post the merger when the payment is made 
in equity, thus not supporting our hypothesis 4. Prob>chi2indicates that all the models are statistically significant at less 
than 1 percent level. We reject the null hypothesis of no endogeneity, based on Wald's test. 
 
Table 6. Results of Instrument Variable Probit estimation for Mode of Payment in Equity as the dependent variable. This 
table reports the estimates of the coefficient and p-statistics from Instrument Variable Probit MODEL 4.  
 

 EQ 1 EQ  2 EQ 3 

 First stage 
(1) 

Two-step 
probit with 
endogenous 
regressors (2) 

First stage (3) Two-step 
probit with 
endogenous 
regressors (4) 

First stage(5) Two-step probit with 
endogenous 
regressors (6) 

Cur_Mkt_Cap  1.33 
(0.67) 
** 

 1.169 
(0.467) 
** 

  

Bs_Lt_Borrow 0.318 
(0.283) 

-1.985 
(0.51) 
*** 

    

Net_Debt_To_Ffcf -0.00001 0.003     



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(0.00006) (0.0021) 

Tot_Debt_To_Tot_Asset -0.014 
(0.0024) 
*** 

0.02 
(0.0104) 
* 

    

Free_Cash_Flow_Yield -0.0014 
(0.0006) 
** 

0.004 
(0.0014) 
*** 

    

Net_Income 6.667 
(0.572) 
*** 

-8.3663 
(4.7636) 
* 

    

Quick_Ratio -0.0733 
(0.03099) 
** 

0.347 
(0.112) 
*** 

    

Short_And_Long_Term_
Debt 

  2.284 
(0.32442)*** 

-5.126 
(1.628) 
*** 

  

Total_Debt_And_Preferre
d_Equity 

  -3.004 
(0.354) 
*** 

4.935 
(1.838) 
*** 

  

Is_Oper_Inc   7.534 
(1.653) 
*** 

-12.648 
(4.101) 
*** 

  

Ebitda   -1.652 
(1.551) 

5.391 
(2.5356) 
** 

  

Rd_Expend_To_Net_Sal
es 

  0.1308 
(0.03553) 
*** 

-0.15142 
(0.08326) 
* 

  

Bs_Tot_Asset      3.2273 
(0.976) 
*** 

Net_Debt     0.505 
(0.033) 
*** 

-2.166 
(0.563) 
*** 

Degree_Financial_Leverag
e 

    0.00014 
(0.00025) 

-0.004 
(0.0025) 
*** 

Totaldebttototalequity     1.00E-
06(1.61E-
06) 

1.42E-05 
(1.63E-05) 

Gross_Fix_Asset_Turn     0.00052 
(0.00216) 

0.0702 
(0.0309) 
** 

Cf_Free_Cash_Flow     0.0939 
(0.0652) 

-0.818 
(0.464) 
* 

Pretax_Margin     0.00004 
(0.00015) 

0.0015 
(0.00091) 
* 

Constant 1.0775 
(0.259) 
*** 

-0.781 
(0.9823) 

0.5242 
(0.1062) 
*** 

-0.03086 
(0.3362) 

0.8347 
(0.02295)*** 

-2.4354 
(0.8841) 
*** 

Wald test of exogeneity  7.76***  13.40***  13.74*** 

Anderson-Rubin test  8.09***  12.90***  13.94*** 

Wald chi2(9)  32.95  28.42  38.70 

Prob> chi2 0 0.0001 0 0.0001 0 0.0000 

Number of obs 712 712 732 732 795 795 

Robust standard errors in parentheses.***p<0.01, **p<0.05, * p<0.1 
Source: Authors’ estimation 

 
 
 



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109 

 

Summary of result of hypothesis testing has been detailed below in the table. 
 
Table 7.  Summary results of hypothesis testing 
 

Objectives Hypothesis Expected 
Sign 

Test Result 

Model 1 H1: When payment is made in cash, more synergies are generated. + Supported 

Model 2 H2: When bidder liquidity is high, payment is made in cash. + Not 
Supported 

Model 3 H3: When merger & acquisition take place in the related industry sectors, 
more synergies are generated. 
 

+ Supported 

Model 4 H4: When bidder leverage is high, payment is made in equity. + Not 
Supported 

 
     Thus, this study contributes to the literature in two ways. The main finding of the present study is the generation of 

synergies if the mode of payment for merger and acquisition is cash for Indian Corporate. Also, horizontal mergers generate 
greater value for the Indian corporate. The robustness of the results was checked further by estimating the weak IV 
instrument robustness test as has been discussed in the sub section 5.3 below. 
 
5.3 Weak Instrument Robustness Test for the Instrument Variable (Weak IV Test)  
The Anderson-Rubin statistics, as reported in Table 3,4,5,6 for each Model specified in the study, are significant at less 
than 1 percent significance level. It refutes the null hypothesis, which states that the coefficient is zero on the endogenous 
variable. To put it another way, the instruments developed are not weak. These findings indicate that the instrument is 
strongly related to the endogenous variable and does not suffer from the weak IV problem. The weak-instrument-robust 
inference tests are also significantly varied from zero, suggesting that the predicted effects are robust to weak IV problems if 
any.  
 
6. Conclusion 
A firm's financial assets play a significant part in the decision-making phase of a merger. The present paper aims to improve 
the existing literature on assessing M&A activity in Indian corporate. This research paper aims primarily to analyze the (a) 
Synergies realized when the mode of payment in the merger deal is cash, (b) impact on bidder liquidity when payment is 
made in cash (c) Synergies realized when both target and acquirer in the deal belong to related industry, i.e. the merger is 
horizontal and (d) assess the impact on bidder leverage when payment is made in equity. A panel of 120 major Indian M&A 
deals from 2005 to 2015, each having 3 years of data pre and post-merger (seven years of data in totality including the year 
of the merger), i.e. data from 2002 to 2018 has been used in the analysis for the considered firms. The study employs 
Instrument Variable Probit Regression analysis to tackle the issue of endogeneity. 

     Summary of results for the models employed in the present study has been detailed in the table below. 
 
Table 8. Significant variables and their relationship with the dependent variable as per the present study  
 

Objectives Hypothesis Significant variables as per the study 
conducted 

Relationship with the 
dependent variable 

Model 1 H1: When payment is made in cash, 
more synergies are generated. 

Net Income Positive 

R&D Expenditure To Net Sales Positive 

Model 2 H2: When bidder liquidity is high, 
payment is made in cash. 

Operating Cash Flow Return On Asset Negative 

Working Capital Negative 

Cash And Cash Equivalent Negative 

Quick Ratio Negative 

Model 3 H3: When merger & acquisition take 
place in the related industry sectors, 
more synergies are generated. 
 

Return On Capital Positive 

Net Income Growth Positive 

Total Operating Expense As A Percentage Of 
Sales 

Positive 

Asset Turnover Positive 

Free Cash Flow Yield Positive 

Model 4 H4: When bidder leverage is high, 
payment is made in equity. 

Long Term Borrowing Negative 

Net Debt Negative 

Short And Long Term Debt Negative 



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110 

 

     From the above table 8, it is indicated that in the case payment method in the deal is cash; M&A appears to be 
financially profitable for the bidder companies and synergies are realized for the Indian corporate. The major contribution 
of the present research is in the identification of the sources of synergy creation for the India M&A deals. The study 
highlight that net income has a positive and statistically significant relationship with cash payment of deal. Significant 
results have also been obtained for the leverage variables like net debt and net debt to free cash flow, indicating fewer 
borrowing for the merged firm in the post-merger period. Similar positive and statistically significant results have also been 
observed for R&D expense to net sales in our model, indicating spillover of technology post the merger. These results align 
with existing research such as Ghosh (2001), Megginson, Morgan, and Nail (2005), Ismail (2011). Secondly, we show that 
there is a significant association between realization of synergies and similarities of industry of target and acquirer firm. It 
was found that net income growth, return on capital, total operating expense as a percentage of sales, asset turnover, and free 
Cash flow yield contributes to the value creation in case of relatedness of industry in the merger deal. Thus, the results of 
the empirical analysis of the study do support the generation of synergies in the case of horizontal mergers. These results are 
found to be in resonance with Barai and Mohanty (2014), Rozen-Bakher (2018) but in dissonance with Mooney and Shim 
(2015). 

     The results also suggested an increase in borrowing post-merger for the combined firm. The improvement in 
financial leverage is the result of the rise in debt capacity (Ghosh & Jain, 2000). Operating cash flow return on asset, 
working capital, cash and cash equivalent and quick ratio is statistically significant but negative when the payment is made in 
cash. This indicates that the combined firm has lower liquidity for Indian Mergers & Acquisitions, and it is more likely the 
company will struggle with paying debts when payment is made in cash. This is in contrast with Jensen's theory of free cash 
flow (Jensen, 1986). Our results also indicate that leverage has a negative relationship with equity mode of payment. It 
implies the lowering of borrowings post the merger when the payment is made in equity.  
 
6.1 Implications for the Indian Market 
From the results of this analysis can be extracted some significant implications. Firstly, it supports the results of previous 
research that adhere to the point of view that bidder firms in India have achieved better financial performance post the 
merger and acquisition. The nature and trend of the Indian companies' mergers & acquisitions strategies show more 
horizontal mergers. This lends support to the argument that Indian firms are concentrating on their core areas and growing 
further into similar strength areas that are helping to realize synergistic benefits. The major contribution of the study lies in 
determining various sources of value creation or destruction for Indian mergers and acquisitions. It supports the hypothesis 
that M&A generates synergy for Indian M&As when Indian corporate focus on undertaking merger & acquisitions in similar 
industries, to gain economies of scale and create value post the merger. Secondly, the decision to use stock or cash often 
sends signals about the acquirer's estimate of the risk of failing to achieve the synergies anticipated from the acquisition. 
Repeated empirical research shows that the market responds far more favorably to cash-deal announcements than to stock-deal 
announcements. Synergy has been shown to be created post the merger in case cash is the preferred mode of payment for the 
merger deal in Indian scenario. Managers in the merger deal should emphasize on cash payment for the deal to generate higher 
value creation. 
 
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Notes  
Note 1. Tangible Asset is defined as a total fixed asset. (Source: Bloomberg Terminal)  
Note 2. Due to the paucity of space, the result is not mentioned here. The Result of the stationary test is available on 
request.  
 
Appendix A. Distribution of sample across sectors. 
 

Sectors Acquirer Industry Sector Target Industry Sector 

Industrial 30 23 

Basic Materials 28 29 

Consumer, Cyclical 24 27 

Consumer, Non-cyclical 17 21 

Technology 9 7 

Energy 4 2 

Utilities 4 4 

Communications 2 6 

Diversified 2 1 

Source: Bloomberg Terminal 
 

Appendix B. Definition of the Variables 
 

S.No. Variable Symbol Definition of the variable 

1 Mode of Payment 
dummy 

Payment_Cash (Dependent 
Variable) 

Value 1 if cash is the method of payment for the deal 
and 0 otherwise. 

2 Mode of Payment 
dummy 

Payment_Equity  (Dependent 
Variable) 

Value 1 if the method of payment is equity and 0 
otherwise. 

3 Relatedness of industry 
dummy 

Industry_Relatedness 
(Dependent Variable) 

Value 1 if the acquisition is horizontal and 0 
otherwise. 

4 Free Cash Flow 
 

Cf_Free_Cash_Flow It is the cash that a firm may yield after outlining the 
capital necessary to sustain or extend its assets. 

5 Earnings before 
interest, taxes, 
depreciation, and 
amortization 

Ebitda (Net income + taxes+ depreciation+ interest  
+amortization) 
It is used to evaluate and equate profitability among 
firms since the consequences of accounting and 
financing resolutions are excluded by it. 

6 Current Market Cap 
 

Cur_Mkt_Cap The total current market value of all the outstanding 
shares of the firm. 

7 Working Capital Working_Capital Current Assets  minus  Current Liabilities 

8 Short and Long Term 
Debt 

Short_And_Long_Term_Debt Summation of Short and Long Term Debt. 
 

9 Net Debt Net_Debt Indicates the company's overall debt. Net of liabilities 
and debts along with cash and other similar liquid 
assets.  

10 Financial Leverage 
 

Fncl_Lvrg Average assets/Average equity 

11 Degree of Financial 
Leverage 
 

Degree_Financial_Leverage The affect a given amount of financial leverage has on 
a firm's earnings. 

12 Net Fixed Assets 5 
Year Growth 

Net_Fixed_Assets_5_Year_Gro
wth  

The geometric growth rate over five years in net fixed 
assets. 

13 Gross Fixed Asset 
Turnover 

Gross_Fix_Asset_Turn Net sales / gross fixed assets.  

14 Personnel Expenses per 
Employee 

Personnel_Expn_Per_Employee Personnel expenses/number of employees. 

15 R & D Expenditure to 
Net Sales 

Rd_Expend_To_Net_Sales Research and development (R&D) expenditures as a 
percentage of the net sales.   

16 Goodwill to Assets % Goodwill_Assets_ Goodwill / total assets. 



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17 EBITDA Margin Ebitda_To_Revenue The ratio of EBITDA to revenue. 

18 Cash Flow to Net 
Income 

Cash_Flow_To_Net_Inc A firm's total net income that is accessible as cash for 
investing and financing the current business. 

19 Operating Margin Oper_Margin Operating Income (Losses) / Total Revenue * 100 

20 Total Operating 
Expenses as a 
Percentage of Sales 

Total_Opex_As_A_Percentage_
Sales 

Measures the total operating expenses (including the 
cost of goods sold and selling, general, and 
administrative expenses) as a percent of sales. 

21 INVENT_TURN Invent_Turn 
 

The ratio shows the number of times a firm's inventory 
is sold and gets replaced over a period. 

22 NET INCOME Net_Income Amount of profit of the firm after settling all of its 
expenses.  

23 Net Debt to FCFF 
(Free Cash Flow to 
Firm) 

Net_Debt_To_Ffcf It is a leverage ratio indicating a firm's ability to pay 
off its debts after deducting the cash outlays necessary 
to maintain its current operation. 

24 Risk Premium 
 

Risk_Premium An investor requires an average incremental return as 
compensation for investing in equities rather than as a 
risk-free instrument. 

25  Quick ratio Quick_Ratio Cash and Near Cash+ Account Receivables + Short 
Term Investments / Current Liabilities 

26 Total Assets 
 

Bs_Tot_Asset 
 

Sum of short and long-term assets. 

27 Operating Income or 
Losses 

Is_Oper_Inc (Net Sales + Other Operating Income)  – (Cost of 
Goods Sold + Other Operating Expenses)  

28 Free Cash Flow Yield Free_Cash_Flow_Yield Return expected per share. 

29 Return on Common 
Equity 

Return_Com_Eqy Measure how much income a corporation earns, in 
percentage, with the money shareholders invested.   

30 Asset Turnover Asset_Turnover Amount of sales or revenues generated per assets.   

31 Capitalization Ratio 
 

Capitalization_Ratio Long-term debt as a percentage of total equity and 
long-term debt, including preferred equity and 
minority share. 

32 Long Term 
Investments 

Bs_Long_Term_Investments Includes long-term investments.  

33 Return on Capital 
 

Return_On_Cap Measures, in percentage, the return generated by an 
investment for capital contributors. 

34 Tobin's Q Ratio 
 

Tobin_Q_Ratio The ratio of a firm's market value to the cost of 
replacement of its assets. 

35 Normalized Net 
Income Growth 

Normalized_Net_Income_Grow
th 

Year over year growth in normalized net income. 

36 Market Cap To Net 
Revenue 

Mkt_Cap_To_Revenue Market Value of Equity/Trailing 12 Month Net 
Revenue. 

37 Long Term Debt 
 

Bs_Lt_Borrow All interest-bearing financial obligations which are not 
due within a year. 

38 Total Debt to Total 
Assets 

Tot_Debt_To_Tot_Asset The total amount of debt relative to assets. 

39 Total Debt and 
Preferred Equity 
 

Total_Debt_And_Preferred_Eq
uity 

Sum of short term borrowing, long term borrowing, 
and preferred equity at the end of the period end date. 

40 Total Debt to Total 
Equity 

Totaldebttototalequity Total debt/total shareholders' equity. 

41 PRETAX MARGIN Pretax_Margin Earnings before tax for a firm as a proportion of 
overall income or profits. 

42 Net Asset Net_Assets Total Assets - Current Liabilities - Long-term 
Borrowings - Other Long-term Liabilities 

43 Cash and Cash 
Equivalents 

Cce_And_Sti_Detailed Cash in vault + Deposits in banks + short term 
investments having a maturity of less than 90 days. 

44 Revenue Sequential 
Growth 

Revenue_Sequential_Growth Period to period sequential growth rate in revenue. 

Source: Bloomberg terminal 



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Appendix C. Correlation Matrix for Model 1 
 

  Cur_Mkt
_Cap 

Net_Inco
me 

Net_Deb
t_To_Ffc
f 

Risk_Premi
um 

Opcfroa_H
ealy 

Rd_Expend
_To_Net_S
ales 

Net_De
bt 

Goodwill
_Assets_ 

Cur_Mkt_Ca
p 

1               

Net_Income 0.182 1             

Net_Debt_T
o_Ffcf 

-0.053 -0.067 1           

Risk_Premiu
m 

-0.060 -0.078 0.009 1         

Opcfroa_Hea
ly 

0.016 0.025 -0.009 -0.021 1       

Rd_Expend_
To_Net_Sale
s 

0.135 0.024 -0.017 -0.081 -0.024 1     

Net_Debt -0.120 0.034 0.037 0.025 -0.059 -0.098 1   

Goodwill_As
sets_ 

0.134 0.051 -0.034 -0.026 0.020 -0.044 0.122 1 

Source: Authors’ estimation 
 

Appendix D. Correlation Matrix for Model 2 
 

  Net_Ass
ets 

Workin
g_Capit
al 

Cf_Free
_Cash_
Flow 

Opcfroa
_Healy 

Invent_
Turn 

Cce_And_
Sti_Detaile
d 

Quick_
Ratio 

Ebitda Cash_Flo
w_To_N
et_Inc 

Net_Assets 1                 

Working_Ca
pital 

0.264 1               

Cf_Free_Cas
h_Flow 

0.004 0.018 1             

Opcfroa_He
aly 

-0.021 0.017 0.093 1           

Invent_Turn 0.162 0.095 0.011 -0.008 1         

Cce_And_Sti
_Detailed 

0.178 0.164 0.004 -0.025 0.029 1       

Quick_Ratio 0.170 0.139 -0.039 -0.010 0.109 0.149 1     

Ebitda 0.144 0.080 0.140 0.002 0.088 -0.141 -0.025 1   

Cash_Flow_
To_Net_Inc 

0.154 0.006 -0.020 -0.016 0.005 0.057 0.048 -0.156 1 

Source: Authors’ estimation 
 
Appendix E. Correlation Matrix for Model 3 
 

  Cur_Mk
t_Cap 

Invent_
Turn 

Total_O
pex_As_
A_Percen
tage_Sales 

Free_Cash_Fl
ow_Yield 

Return_O
n_Cap 

Normalized
_Net_Inco
me_Growth 

Oper_Mar
gin 

Cur_Mkt_Cap 1             

Invent_Turn 0.197 1           



Copyright © CC-BY-NC 2020, CRIBFB | IJFB 
 

www.cribfb.com/journal/index.php/ijfb                 Indian Journal of Finance and Banking                          Vol. 4, No. 2; 2020 

117 

 

Total_Opex_As_A_Per
centage_Sales 

-0.054 -0.023 1         

Free_Cash_Flow_Yield -0.158 -0.03 -0.134 1       

Return_On_Cap 0.230 0.166 -0.078 -0.0546 1     

Normalized_Net_Inco
me_Growth 

-0.070 0.097 0.114 -0.005 -0.005 1   

Oper_Margin 0.039 0.013 -0.273 0.144 0.080 -0.054 1 

Source: Authors’ estimation 
 
Appendix F. Correlation Matrix for Model 4 
 

  Cur_M
kt_Cap 

Bs_Lt_B
orrow 

Free_Ca
sh_Flow
_Yield 

Net_In
come 

Quick_
Ratio 

Short_An
d_Long_
Term_De
bt 

Total_De
bt_And_
Preferred
_Equity 

Rd_Exp
end_To
_Net_S
ales 

Bs_T
ot_As
set 

Degree_Fi
nancial_Le
verage 

Cur_Mkt_
Cap 

1                   

Bs_Lt_Bor
row 

-0.19 1                 

Free_Cash
_Flow_Yie
ld 

-0.151 0.129 1               

Net_Inco
me 

0.172 -0.127 -0.091 1             

Quick_Rat
io 

0.006 -0.026 -0.058 0.074 1           

Short_And
_Long_Te
rm_Debt 

-0.122 0.327 0.130 -0.091 -0.108 1         

Total_Deb
t_And_Pre
ferred_Equ
ity 

-0.29 0.787 0.171 -0.19 -0.14 0.205 1       

Rd_Expen
d_To_Net
_Sales 

0.137 -0.149 -0.034 0.055 0.029 -0.124 -0.15 1     

Bs_Tot_As
set 

0.162 0.130 -0.073 0.02 0.013 0.162 0.240 -0.028 1   

Degree_Fi
nancial_Le
verage 

-0.02 0.069 -0.001 -0.04 -0.02 0.060 0.069 0.021 0.05 1.0000 

Source: Authors’ estimation 
 
 
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Copyright for this article is retained by the author(s), with first publication rights granted to the journal. This is an open-
access article distributed under the terms and conditions of the Creative Commons Attribution license 
(http://creativecommons.org/licenses/by/4.0/). 
 

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