Copyright © CC-BY-NC 2020, CRIBFB | IJFB 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 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 100 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). 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 101 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, 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 102 (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 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 103 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 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 104 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. 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 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 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 106 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 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 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 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 108 (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 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 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 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 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. References Andrade, G., Mitchell, M., & Stafford, E. (2001). New evidence and perspectives on mergers. Journal of Economic Perspectives, 15(2), 103–120.American Economic Association.https://doi.org/10.1257/jep.15.2.103 Asquith, P., Bruner, R. F., & Mullins, D. W. (1990).Merger returns and the form of financing. Awogemi, C . A., & Oguntade, E. S. (2012). Element of Statistical Methods.USA: LAMBERT. Academic Publishing. Barai, P., & Mohanty, P. (2014). Role of industry relatedness in performance of Indian acquirers-Long and short run effects. Asia Pacific Journal of Management, 31(4), 1045–1073.https://doi.org/10.1007/s10490-014-9372-1 Barkema, H. G., Baum, J. A., & Mannix, E. A. (2002). Management challenges in a new time. Academy of Management Journal, 45(5), 916-930. Basu, D., Dastidar, S. G., & Chawla, D. (2008). Corporate Mergers and Acquisitions in India: Discriminating between Bidders and Targets. Global Business Review, 9(2), 207– 218.https://doi.org/10.1177/097215090800900203 Beena, P. (2000). An analysis of mergers in the private corporate sector in India. Bena, J., & Li, K. (2014). Corporate Innovations and Mergers and Acquisitions.Journal of Finance, 69(5), 1923– 1960.https://doi.org/10.1111/jofi.12059 Bernile, G. (2005). The information content of insiders’ forecasts : analysis of the gains from mergers in the 90s. November 2003. Bernile, G., & Lyandres, E. (2019). The effects of horizontal merger operating efficiencies on rivals, customers, and suppliers.Review of Finance, 23(1), 117–160. Bhoi, B. K. (2000). Mergers and Acquisitions: An Indian Experience, 21(1). Retrieved from https://rbidocs.rbi.org.in/rdocs/Publications/Pdfs/18577.pdf Bouwman, C. H. S., Fuller, K., & Nain, A. S. (2009). Market valuation and acquisition quality: Empirical evidence. Review of Financial Studies, 22(2), 633–679. https://doi.org/10.1093/rfs/hhm073 https://doi.org/10.1257/jep.15.2.103 https://doi.org/10.1007/s10490-014-9372-1 https://doi.org/10.1111/jofi.12059 https://rbidocs.rbi.org.in/rdocs/Publications/Pdfs/18577.pdf https://doi.org/10.1093/rfs/hhm073 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 111 Bradley, M., Desai, A., & Kim, E. H. (1988). Synergistic gains from corporate acquisitions and their division between the stockholders of target and acquiring firms. Journal of financial Economics, 21(1), 3-40. Bruner, R., & Mullins, D. J. (1987). Merger returns and the form of financing.Harvard University. Byrd, J. W., & Hickman, K. A. (1992). Do outside directors monitor managers?. Evidence from tender offer bids. Journal of Financial Economics, 32(2), 195–221. https://doi.org/10.1016/0304-405X(92)90018-S Carline, N. F., Linn, S. C., & Yadav, P. K. (2005). The Influence of Managerial Ownership on the Real Gains in Corporate Mergers and Market Revaluation of Merger Partners: Empirical Evidence.SSRN Electronic Journal.https://doi.org/ 10.2139 ssrn.302606 Carnes, T. A., Black, E. L., & Jandik, T. (2001). The long-term success of cross-border mergers and acquisitions.Available at SSRN 270288. Chari, A., Ouimet, P., & Tesar, L. L. (2004, March). Cross border mergers and acquisitions in emerging markets: The stock market valuation of corporate control. In EFA 2004 Maastricht Meetings Paper (No. 3479). Chatterjee, S. (1986). Types of synergy and economic value: The impact of acquisitions on merging and rival firms. Strategic Management Journal, 7(2), 119–139.https://doi.org/10.1002/smj.4250070203 Chira, I., García-Feijóo, L., & Madura, J. (2017). When do managers listen to the market? Impact of learning in acquisitions of private firms. Review of Quantitative Finance and Accounting, 49(2), 515– 543.https://doi.org/10.1007/s11156-016-0599-4 Choi, I. (2001). Unit root tests for panel data. Journal of International Money and Finance 20(2), 249–272. Cudd, M., & Duggal, R. (2000). Industry distributional characteristics of financial ratios: An acquisition theory application. Financial Review, 35(1), 105–120. https://doi.org/10.1111/j.1540-6288.2000.tb01409.x Cullinan, G., Le Roux, J. M., & Weddigen, R. M. (2004). When to walk away from a deal. Harvard business review, 82(4), 96-105. Cummins, J. D., & Weiss, M. A. (2004). Consolidation in the European Insurance Industry: Do Mergers and Acquisitions Create Value for Shareholders? Brookings-Wharton Papers on Financial Services, 2004(1), 217–258. https:// doi.org/ 10.1353/pfs.2004.0001 Dickerson, A. P., Gibson, H. D., & Tsakalotos, E. (1997). The impact of acquisitions on company performance: Evidence from a large panel of UK firms. Oxford Economic Papers, 49(3), 344–361. https://doi.org/10.1093/oxfordjournals.oep.a028613 Farjoun, M. (1994). Beyond industry boundaries: Human expertise, diversification and resource-related industry groups.Organization science, 5(2), 185-199. Fich, E. M., Nguyen, T., & Officer, M. (2018). Large Wealth Creation in Mergers and Acquisitions.Financial Management, 47(4), 953–991. https://doi.org/10.1111/fima.12212 Finlay, K., Magnusson, L., & Schaffer, M. (2014). WEAK IV: Stata module to perform weak-instrument-robust tests and confidence intervals for instrumental-variable (IV) estimation of linear, probit and to bit models.https:// ideas.repec.org/ c/ boc/bocode/s457684.html. Ghosh, A. (2001). Does operating performance really improve following corporate acquisitions? Journal of Corporate Finance, 7(2), 151–178. https://doi.org/10.1016/S0929-1199(01)00018-9 Ghosh, A., & Jain, P. C. (2000). Financial leverage changes associated with corporate mergers. Journal of Corporate Finance, 6(4), 377–402. https://doi.org/10.2469/dig.v31.n4.964 Godbole, P. (2013). Mergers, acquisitions and corporate restructuring.Vikas Publishing House Pvt Ltd. Gujarati, D. (2004). Basic Econometrics. (4 th edtn) The McGraw− Hill Companies. Hamza, T., Sghaier, A., & Thraya, M. F. (2016). How do Takeovers Create Synergies? Evidence from France.Studies in Business and Economics, 11(1), 54–72. https://doi.org/10.1515/sbe-2016-0005 Harding, D., & Rovit, S. (2004). Mastering the merger: Four critical decisions that make or break the deal. Harvard Business Review Press. Harford, J. (1999). Corporate cash reserves and acquisitions.Journal of Finance, 54(6), 1969– 1997.https://doi.org/10.1111/0022-1082.00179 Harris, R. S., Franks, J., & Mayer, C. (1987). Means of Payment in Takeovers: Results for the UK and US (No. w2456). National Bureau of Economic Research. Harris, R. S., Stewart, J. F., Guilkey, D. K., & Carleton, W. T. (1982). Characteristics of Acquired Firms: Fixed and Random Coefficients Probit Analyses. Southern Economic Journal, 49(1),164-184. Harrison, J. S., Hitt, M. A., Hoskisson, R. E., & Ireland, R. D. (2000). Resource Complementarity in Business Combinations: Extending the Logic to Organizational. Journal of Management, 27(6), 679–690. https:// doi.org/ 10.1177/ 014920630102700605 Healy, P. M., Palepu, K. G., & Ruback, R. S. (1992). Does corporate performance improve after mergers?. Journal of financial economics, 31(2), 135-175. Herman, E., & Lowenstein, L. (1988). The efficiency effects of hostile takeovers. Oxford University Press. Hogarty, T. (1970). The profitability of corporate mergers.The Journal of Business.https://www.jstor.org/stable/2351781 Hollander, M., & Wolfe, D. A. (1973). Nonparametric Statistical methods. London: John Wiley and Sons. https://doi.org/10.2139/ssrn.302606 https://doi.org/10.1002/smj.4250070203 https://doi.org/10.1007/s11156-016-0599-4 https://doi.org/10.1111/j.1540-6288.2000.tb01409.x https://doi.org/10.1093/oxfordjournals.oep.a028613 https://doi.org/10.1111/fima.12212 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 112 Huyghebaert, N., & Luypaert, M. (2013). Sources of Synergy Realization in Mergers and Acquisitions: Empirical Evidence from Non-Serial Acquirers in Europe.International Journal of Financial Research, 4(2).https://doi.org/10.5430/ijfr.v4n2p49 Ismail, A. (2011). Does the management’s forecast of merger synergies explain the premium paid, the method of payment, and merger motives? Financial Management, 40(4), 879–910.https://doi.org/10.1111/j.1755- 053X.2011.01165.x Ismail, A., Dbouk, W., & Azouri, C. (2014). Does industry-adjusted corporate governance matter in mergers and acquisitions? Corporate Ownership and Control, 11(4 Continued 7), 642–656. Jensen, M. (1986). Agency costs of free cash flow, corporate finance, and takeovers. The American Economic Review.https://www.jstor.org/stable/1818789 Kaplan, S. N., & Weisbach, M. S. (1992). The Success of Acquisitions: Evidence from Divestitures. The Journal of Finance, 47(1), 107–138. https://doi.org/10.1111/j.1540-6261.1992.tb03980.x King, D. R., Dalton, D. R., Daily, C. M., & Covin, J. G. (2004). Meta-analyses of Post-acquisition Performance: Indications of Unidentified Moderators. Strategic Management Journal, 25(2), 187– 200.https://doi.org/10.1002/smj.371 Kruse, T. A., Park, H. Y., & Suzuki, K. (2003).The Value Of Corporate Diversification: Evidence From Post-Merger Performance In Japan.https://papers.ssrn.com/sol3/papers.cfm?abstract_id=344560 Krzanowski, W. J. (1998). An Introduction to Statistical Modelling. London: Arnold Publishers. Kuipers, D., Miller, D., & Patel, A. (2002). Shareholder wealth effects in the cross-border market for corporate control. Working Paper. Lahey, K., & Conn, R. (1990). Sensitivity Of Acquiring Firms’returns To Alternative Model Specifications And Disaggregation.Journal of Business Finance & Accounting. Retrieved from http://www.academia.edu/download/49517093/j.1468-5957.1990.tb01195.x20161010-6175-rqvzly.pdf Lang, L., Stulz, R., & Walkling, R. (1989). Managerial Performance, Tobin’s Q, And The Gains From Successful Tender Offers.Journal of Finance, 24, 137–154. https://www.researchgate.net/publication/242423169 Lev, B., & Mandelker, G. (1972). The microeconomic consequences of corporate mergers.Journal of Business,85- 104.https:// www.jstor.org/stable/2351600 Limmack, R. J. (1991). Corporate Mergers and Shareholder Wealth Effects: 1977-1986. Accounting and Business Research, 21(83), 239–252. https://doi.org/10.1080/00014788.1991.9729838 Linn, S. C., & Switzer, J. A. (2001). Are cash acquisitions associated with better post combination operating performance than stock acquisitions?. Journal of Banking and Finance, 25(6), 1113–1138. https://doi.org/10.1016/S0378- 4266(00)00108-4 Loughran, T., & Vijh, A. M. (1997). Do long-term shareholders benefit from corporate acquisitions?. Journal of Finance, 52(5), 1765–1790. https://doi.org/10.1111/j.1540-6261.1997.tb02741.x Loukianova, A., Nikulin, E., & Vedernikov, A. (2017). Valuing synergies in strategic mergers and acquisitions using the real options approach. Investment Management and Financial Innovations, 14(1), 236– 247.https://doi.org/10.21511/imfi.14(1-1).2017.10 M&A Statistics by Countries-Institute for Mergers, Acquisitions and Alliances (IMAA). (2019). Retrieved from https://imaa-institute.org/m-and-a-statistics-countries/ Meeks, G. (1977). Disappointing marriage: A study of the gains from merger. Megginson, W. L., Morgan, A., & Nail, L. A. (2005). The Determinants of Positive Long-Term Performance in Strategic Mergers: Corporate Focus and Cash. SSRN Electronic Journal, 8501(205).https://doi.org/10.2139/ssrn.321449 Mitchell, M. L., & Stafford, E. (2000). Managerial decisions and long-term stock price performance. Journal of Business, 73(3), 287–329. https://doi.org/10.1086/209645 Moeller, S., & Schlingemann, F. (2004). Are cross-border acquisitions different from domestic acquisitions? Evidence on stock and operating performance for US acquirers.Journal of Banking and Finance.https:// papers.ssrn.com/ sol3/ papers.cfm? abstract_id=311543 Mohanty, P., & Mishra, S. (2011). Run-up in Stock Prices Prior to Merger & Acquisitions Announcements: Evidence from India. NSE Working Paper., January, 8–9. Mooney, T., & Shim, H. (2015). Does Financial Synergy Provide a Rationale for Conglomerate Mergers? Asia-Pacific Journal of Financial Studies, 44(4), 537–586. https://doi.org/10.1111/ajfs.12099 Morag, O. (2011). The Role of Speed of Integration in the Integration Effectiveness and Mergers & Acquisitions Success. Retrieved from http://pea.lib.pte.hu/bitstream/handle/pea/1163/Omri Morag - tezisek.pdf?sequence=2 Mulherin, J. H., & Boone, A. L. (2000). Comparing Acquisitions and Divestitures. Journal of Corporate Finance.https://www.sciencedirect.com/science/article/pii/S0929119900000109 Newey, W. K. (1987). Efficient estimation of limited dependent variable models with endogenous explanatory variables.Journal of Econometrics, 36(3), 231–250. Parrino, J. D., & Harris, R. S. (1999). Takeovers, Management Replacement, and Post-acquisition Operating Performance: Some Evidence from the 1980s. Journal of Applied Corporate Finance, 11(4), 88– 96.https://doi.org/10.1111/j.1745-6622.1999.tb00518.x https://doi.org/10.5430/ijfr.v4n2p49 https://doi.org/10.1111/j.1755-053X.2011.01165.x https://doi.org/10.1111/j.1755-053X.2011.01165.x https://www.jstor.org/stable/1818789 https://doi.org/10.1111/j.1540-6261.1992.tb03980.x https://doi.org/10.1002/smj.371 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=344560 http://www.academia.edu/download/49517093/j.1468-5957.1990.tb01195.x20161010-6175-rqvzly.pdf https://www.researchgate.net/publication/242423169 https://doi.org/10.1080/00014788.1991.9729838 https://doi.org/10.1016/S0378-4266(00)00108-4 https://doi.org/10.1016/S0378-4266(00)00108-4 https://doi.org/10.1111/j.1540-6261.1997.tb02741.x https://doi.org/10.21511/imfi.14(1-1).2017.10 https://imaa-institute.org/m-and-a-statistics-countries/ https://doi.org/10.2139/ssrn.321449 https://doi.org/10.1086/209645 https://doi.org/10.1111/ajfs.12099 https://www.sciencedirect.com/science/article/pii/S0929119900000109 https://doi.org/10.1111/j.1745-6622.1999.tb00518.x 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 113 Pastena, V., & Ruland, W. (1986). The Merger/Bankruptcy Alternative.The Accounting Review, 61(2), 288-301. Pazarskis, M., Vogiatzogloy, M., Christodoulou, P., & Drogalas, G. (2006). Exploring the improvement of corporate performance after mergers-the case of Greece.Int. Res. J. Financ. Econ. Retrieved from http://www.drogalas.gr/uploads/publications/Exploring_the_improvement_of_corporate_performance_after_ mergers_-_the_case_of_Greece.pdf Peterson, D., & Peterson, P. (1991). The medium of exchange in mergers and acquisitions.Journal of Banking & Finance.https://www.sciencedirect.com/science/article/pii/037842669190074V Philippatos, G., Choi, D., & Dowling, W. (1985). Effects of mergers on operational efficiency: A study of the S&L industry in transition. Northeast Journal of Business & Economics, 11, 1-14. Powell, R. G., & Stark, A. W. (2005). Does operating performance increase post-takeover for UK takeovers? A comparison of performance measures and benchmarks.Journal of Corporate Finance, 11, 293– 317.https://doi.org/10.1016/j.jcorpfin.2003.06.001 Rajeshkumar, B., & Rajib, P. (2007). Characteristics of merging firms in India: An empirical examination. Vikalpa, 32(1), 27–44. https://doi.org/10.1177/0256090920070103 Ramaswamy, K., & Salatka, W. (1996). Impact of Mergers on Long Term Operating Performance of the Combined Firm.Working Paper. Ramaswamy, K. P., & Waegelein, J. F. (2003). Firm financial performance following mergers.Review of Quantitative Finance and Accounting, 20(2), 115–126. https://doi.org/10.1023/A:1023089924640 Rau, P. R., & Vermaelen, T. (1998). Glamour, Value and the Post-acquisition Performance of Acquiring Firms.Journal of Financial Economics, 49, 223–253. https://www.researchgate.net/publication/228839525 Ravenscraft, D., & Scherer, F. (2011). Mergers, sell-offs, and economic efficiency. Rivers, D., & Vuong,Q. H. (1988). Limited information estimators and exogeneity tests for simultaneous probit models.Journal of Econometrics 39(3), 347–366. Rozen-Bakher, Z. (2018). Comparison of merger and acquisition (M&A) success in horizontal, vertical and conglomerate M&As: industry sector vs. services sector. Service Industries Journal, 38(7–8), 492–518. https://doi.org/10.1080/02642069.2017.1405938 Servaes, H. (1991). Tobin’s Q and the Gains from Takeovers. The Journal of Finance, 46(1), 409–419. https:// doi.org/ 10.1111/j.1540-6261.1991.tb03758.x Sharma, D. S., & Ho, J. (2002). The impact of acquisitions on operating performance: Some Australian evidence. Journal of Business Finance and Accounting, 29(1–2), 155–200. https://doi.org/10.1111/1468-5957.00428 Sinha, D. N., Kaushik, D. K. ., & Chaudhary, T. (2010). Measuring Post Merger and Acquisition Performance: An Investigation of Select Financial Sector Organizations in India. International Journal of Economics and Finance, 2(4), 190–200. https:// doi.org/10.5539/ijef.v2n4p190 Smith, R. L., & Kim, J. H. (1994). The Combined Effects of Free Cash Flow and Financial Slack on Bidder and Target Stock Returns.The Journal of Business, 67(2), 281.https://doi.org/10.1086/296633 Sudarsanam, S., Holl, P., & Salami, A. (1996). Shareholder wealth gains in mergers: Effect of synergy and ownership structure. Journal of Business Finance and Accounting, 23(5–6), 673–698. https://doi.org/10.1111/j.1468- 5957.1996.tb01148.x Switzer, J. (1996). Evidence on real gains in corporate acquisitions.Journal of Economics and Business.https://www.sciencedirect.com/science/article/pii/S0148619596000331 Tanriverdi, H., & Uysal, V. B. (2011). Cross-business information technology integration and acquirer value creation in corporate mergers and acquisitions. Information Systems Research, 22(4), 703– 720.https://doi.org/10.1287/isre.1090.0250 Tanriverdi, H., & Venkatraman, N. (2005). Knowledge relatedness and the performance of multibusiness firms. In Strategic Management Journal, 26(2), 97–119. https://doi.org/10.1002/smj.435 Tremblay, A. (2017). Cultural Differences, Synergies and Mergers and Acquisitions.SSRN Electronic Journal.https://doi.org/10.2139/ssrn.2895110 Ucla: Statistical Consulting Group. (n.d.). Logistic Regression Analysis | Stata Annotated Output. Retrieved May 14, 2020, from https://stats.idre.ucla.edu/stata/output/logistic-regression-analysis/ Varaiya, N. P., & Ferris, K. R. (1987). Overpaying in Corporate Takeovers: The Winner’s Curse.Financial Analysts Journal, 43(3), 64–70.https://doi.org/10.2469/faj.v43.n3.64 Yeh, T., & Hoshino, Y. (2002). Productivity and operating performance of Japanese merging firms: Keiretsu-related and independent mergers. Japan and the World Economy, 14(3), 347–366. https://doi.org/10.1016/S0922- 1425(01)00081-0 http://www.drogalas.gr/ https://www.sciencedirect.com/science/article/pii/037842669190074V https://doi.org/10.1111/1468-5957.00428 https://doi.org/10.1086/296633 https://doi.org/10.1111/j.1468-5957.1996.tb01148.x https://doi.org/10.1111/j.1468-5957.1996.tb01148.x https://doi.org/10.1287/isre.1090.0250 https://stats.idre.ucla.edu/stata/output/logistic-regression-analysis/ https://doi.org/10.2469/faj.v43.n3.64 https://doi.org/10.1016/S0922-1425(01)00081-0 https://doi.org/10.1016/S0922-1425(01)00081-0 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 114 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. 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 115 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 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 116 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 Copyrights 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/). http://creativecommons.org/licenses/by/4.0/