A Study of Financial Performance Post-Merger and Acquisition in the Pharmaceutical Sector: ACACA Approach
Ashima Verma
Research Scholar,
Department of Management Studies
J.C.Bose University of Science and Technology,
YMCA, Faridabad
Rachna Agrawal
Associate Professor,
Department of Management Studies
J.C.Bose University of Science and Technology,
YMCA, Faridabad
Abstract
The purpose of this paper is to analyse the financial performance of the companies in the pharmaceutical sector post-merger and acquisition. To study the financial performance of the companies, a company-wise clubbed average comparative analysis (CACA) has been used. The study will focus on 6*6*6 i.e., six companies with six years (3 Pre years and 3 post years) and with in-depth six major-interpreted variables. The comparative analysis of the various ratios also shows fruitful interpretations in the research. With all-inclusive ratio analysis, PH5 gains most from the merger and acquisition (M&A) among the six companies. It will be a profound aid in decision making for the managers of companies, policymakers in the respective sector, investors and other stakeholders in analyses and deciding for M&A as a form of restructuring.
Keywords: Merger and acquisition, financial performance, clubbed average comparative analysis (CACA), pharmaceutical sector
The world is witnessing a major crisis in the present scenario. It is on the verge of unfathomable recession. With corona pandemic affecting the lives of millions of people around the globe, temporary shutdown of all manufacturing units, abridged businesses etc. is bringing life to a standstill. Many organisations are already terminating their operations and a spike in the unemployment rate is witnessed. The world output is likely to drop to -3% as the world lockdown is bringing economic activity to a stoppage. There is a 6.8% fall in the projected growth rate of 2020 (World Economic Outlook, 2020). With such turbulent circumstances, the primary goal of any business is to survive. Merger and Acquisition (M&A) is one of the profound ways to keep themselves alive. The concept of restructuring through M&A has evolved since its inception. Earlier the only aim of considering M&A was to make sick units profitable again (Bose, 2006). This has transformed into getting new technologies, entering new and unexplored markets and gaining synergies. This cycle is coming back to its initial position for pharmaceutical sector, whose survival has become imperative in the current scenario. Pharmaceutical sector has become extremely significant in the present scenario. Therefore the study selects this pivotal sector to understand the performance of companies post-M&A and explains the relevance of M&A as a mode of restructuring in Indian context.
The world is now looking up to Indian Pharmaceutical Industry (IPI) which is unique because, first, branded generics account for 70% to80% of the retail market segment. Second, the Indian Pharmaceutical Sector has revolutionised from being a generic drug provider to an investor in research and development (R&D)to a caterer to the world market. More than 50% of the generic drug demand of the world is served by India. This estimate has reached 40% and 25% for the U.S. and the U.K. respectively (IBEF, 2019). Third, it ranks third in position for production of drugs by volume on the global scale (McKinsey & Company, 2020). It is growing very well and giving shoulder to shoulder competition to the other pharmaceutical companies across the globe. Recently, 15 new drugs are permitted for production in India and 42 applications are approved by USFDA for their production in India which is equivalent to the applications from the USA. This has made M&A a necessary tool for the pharmaceutical sector to grow and prosper. It is evidenced from the fact that 25 new M&A deals were finalised in this sector in 2019 (Grant Thorton Annual Health Check, 2019).Minister of Chemicals and Fertilizers at India Pharma and India Medical Device 2020 event said that Indian Pharmaceutical sector will become a $ 100 Billion sector by 2025 (Pharma Express, 2020).
Analysing the financial performance of the companies undergoing M&A is highly demanded (Majumdar & Rahman, 2011; Shahnia & Endri, 2020). Park, Kim and Lee (2017)use knowledge acquisitions to study the alliance performance. One of the indispensable ways is through ratio analysis (Novyarni & Ningsih, 2019). Various studies in the past conclude that the analysis through ratio measurement forms an appropriate way to predict their performances. It is also a very persistent measure of bankruptcy. Altman (1968) suggests that it is difficult to find out the generalised order of relative importance of each ratio. Ratio analysis helps in forecasting the future trends by determining the troubling areas and taking requited actions (Lessambo, 2018).
Significance of the study
The role of pharmaceutical sector cannot be de-emphasised in the current global scenario and their survival and growth becomes particularly significant. Companies embrace M&A for the need of resources, synergistic benefits, new technologies, entering new markets and so on(Xiao, 2020). And the need for better technologies and resources has never been more reflective. Therefore, financial analysis is essential with regards to the companies’ financial position pre and post-M&A (Fadel & Parkinson, 2012). Financial ratio analysis forms a link between each element of the financial statements (Endri, et al., 2020). It helps in predicting how to work with assets and liabilities to have a balanced position of the organisation in the future on the basis on the current ratios (Rahaman, 2014). It is one of the oldest yet the most helpful in determining the financial performance of any organisation. The present study focusses on a new element: clubbed average comparative analysis (CACA)blended with the performances of pharmaceutical companies’ pre and post-M&A. It will be of a magnanimous help to the companies by comparing the actual financial operations.
Moreno and Casillas (2007);Yim and Mitchell (2005)distinguish between growths of different companies where they use solvency, liquidity, asset turnover and other financial ratios.The investors can take advantage of the change in prices of stocks of the companies undergoing M&A.Barreda, et al. (2017);Ferrier,et al. (2002)conceptualize that the financial ratios foretell the future status of the company.
Beaver (1966);Courtis(1978) are of the opinion that financial ratios help in determining the illness of a company. Theyarea guide forfinancial analysts to take corrective actionsin order to save a company from failure. According to O’Conner(1973);Castagna (1976) financial ratios have been used for analysis of company data since the 1880s but its usage in decision making is brought to light only in the 1960s.Van Horne (1990); Parasanna(2004)consider them as one of the most important instruments of evaluation of a company’s performance. Ocal, et al., (2007); Innocent, Mary and Matthew (2013)believe that a company wise analysis in a sector and a sector wise comparison can be done with financial ratios. They say that a positive or a negative picture about a company’s performance cannot be concluded with only one ratio, but a combination of many ratios. Christopoulos, et al. (2018)prepares three models viz. liquidity model - on the basis of liquidity ratios, profitability model - on the basis of profitability ratios and the third model is the one that blends the first two. They find that profitability is a better predictor of the future success or failure of any firm. Mateos-Ronco and Mas (2011);Imhanzenobe (2020) consider various financial ratios to achieve a correct prediction of failure and financial sustainability while stating the profoundness of efficiency, liquidity and solvency ratios. Liang (2016) uses financial ratios namely solvency ratios, liquidity ratios and cash flow ratios to develop a model to predict financial instability and bankruptcy.
The literature review shows that it is worthwhile and pertinent to use ratio analysis in almost every field to help the management find a fault in the performance of a company and rectify it. These tools are used for checking bankruptcy, success or failure and health of companies. The use of CACA for analysing the post-M&A financial performance of a company is not found anywhere to the best of researchers’ knowledge. Therefore, the present study employs CACA to understand the impact of M&A on the financial performance of the companies.
The present study tries to analyse the usefulness of M&A to the companies in the pharmaceutical sector by comparing their financial performances pre and post-M&A. The research analyses six ratios namely return on net worth (RNW), return on total assets (RTA), current ratio (CR), interest coverage ratio (IC), total income/total assets (TITA) and total income/compensation to employees (TICE). These ratios are from the broader heads of profitability, liquidity and efficiency. This is followed by a company wise evaluation of each ratio and their comparative assessment. Zenzerovic (2009) uses these 6 ratios to get better results in the performance of the companies post-M&A.
RNW is an estimate that presents financial performance to the shareholders. RTA measures the impressive use of assets given the returns. Okoye et.al. (2017) check sustainability through this ratio. CR is a representation of liquidity (Bowlin, 1963). It implies how well a company is able to pay off its short term debts. Imminent authors consider it a very meaningful ratio as short term liabilities need to be paid in a year, so it measures the urgency to pay debts. IC explains the ability of the company to pay interest expenses. A falling interest coverage ratio is not good for the company as it shows the hardship the company will incur in future to pay off its debt expenses. TITA and TICE are efficiency ratios that depict the optimal utilization of income in buying assets and paying to the employees.
Figure1: Conceptual Framework
According to Wang, Dennis and Tu (2007), the health of a human body is on the same lines as the financial health of a company. Just as one system of human body cannot work without the other system and are dependent upon each other, similarly the financial health cannot be assessed by one financial ratio but a combination of them. They form a connect with the financial parameters that help stakeholders including the managers, investors and employees to understand the financial performance of the company and take requited actions respectively. According to Barnes (1990), the most integral part of prediction for decision making by the managers is the accounting data. Hence, to actually come to a conclusion to the success or failure of the M&A financial ratio analysis becomes imperative.
The study follows analytical research design to understand how well these ratios predict the companies’ performance pre and post-M&A. This ratio analysis is used to understand how the performances of the pharmaceutical companies that have undergone M&A have changed. It is followed by a CACA of the 6 ratios of the companies. Following this research design will be a lot of help to the stakeholders in this sector as it follows an all-inclusive analysis of the financial performance and gives coherent conclusions.
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PH5 performed best post-M&A |
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Post- M&A |
The present study uses secondary data for analysis. The main data source used is PROWESS of Centre for Monitoring Economy (CMIE) which is an internationally accepted database for academicians. Alongside, the annual reports of the companies are also taken for data collection. Research and Development (R&D) cost is taken as the basis for selecting the companies in pharmaceutical sector (Mahajan, Nauriyal and Singh, 2014). R&D is the heart and soul of this sector. The domestic sector understood the essentiality of R&D for this sector with The Patents (Amendment) Act (2005) when India moved from process patents to product patents (Mahajan, 2020). The research takes 2015-16 as the base year for considering the companies that are R&D intensive and undergo M&A. Out of a total of 17 companies which underwent M&A in 2015-16, six companies that were research intensive, are considered for this study.The study focuses on three years pre and post-M&A for the research (Sil and Saha, 2013).Therefore, the period of study is from 2012-13 to 2018-19.
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Table 3.1List of variable with their definitions and abbreviations |
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S.No. |
Variable |
Definition |
Abbreviation |
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1. |
Return on Net Worth |
Net Income/Shareholders’ Equity |
RNW |
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2. |
Return on Total Assets |
EBIT/Total Assets |
RTA |
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3. |
Current Ratio |
Current Assets/Current Liabilities |
CR |
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4. |
Interest Coverage Ratio |
EBIT/Interest Expense |
IC |
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5. |
Total income o Total Assets ratio |
Total Income/Total Assets |
TITA |
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6. |
Total Income to Compensation to Employees |
Total Income/Compensation to Employees |
TICE |
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7. |
Biocon Ltd. |
Pharmaceutical Company 1 |
PH1 |
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8. |
Cadila Healthcare Ltd. |
Pharmaceutical Company 2 |
PH2 |
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9. |
Lincoln Pharmaceuticals Ltd |
Pharmaceutical Company 3 |
PH3 |
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10. |
Lupin Ltd. |
Pharmaceutical Company 4 |
PH4 |
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11. |
Natco Pharmaceutical Ltd |
Pharmaceutical Company 5 |
PH5 |
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12. |
Torrent Pharmaceutical Ltd |
Pharmaceutical Company 6 |
PH6 |
A company-wise clubbed and comparative ratio analyses (CCRA)has been applied with six pharmaceutical companies’ pre and post-M&A with the help of IBM SPSS Statistics 25 and Microsoft Excel (2013). This study with the help of six ratios tries to explain if the companies performed better because of M&A and improved their financial performance or not. Six variables namely two profitability ratios i.e. RNW, RTA, two liquidity ratios i.e. CR and IC, two efficiency ratios i.e. TITA and TICE are taken. Skogsvik (1990)states the importance of the ratios mentioned above.
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Table 4.1 Group statistics |
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Groups |
|
Mean |
Standard Deviation |
Maximum value |
Minimum value |
|
Pre-M&A |
RNW |
0.384 |
0.157 |
0.565 |
0.214 |
|
|
RTA |
0.226 |
0.107 |
0.407 |
0.096 |
|
|
CR |
0.031 |
0.015 |
0.053 |
0.016 |
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|
IC |
2.124 |
3.07 |
7.190 |
0.061 |
|
|
TITA |
0.014 |
0.003 |
0.014 |
0.012 |
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|
TICE |
0.168 |
0.052 |
0.269 |
0.115 |
|
Post-M&A |
RNW |
0.234 |
0.106 |
0.410 |
0.079 |
|
|
RTA |
0.159 |
0.085 |
0.316 |
0.069 |
|
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CR |
0.030 |
0.011 |
0.046 |
0.016 |
|
|
IC |
1.103 |
1.222 |
2.930 |
0.054 |
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|
TITA |
0.009 |
0.003 |
0.016 |
0.005 |
|
|
TICE |
0.116 |
0.009 |
0.125 |
0.102 |
This table 4.1represents about the descriptive statistics for all ratios over a period of six years. It is observed that the mean values for all the ratios decrease post-M&A. It cannot be stated about the standard deviation values that show mixed results post-M&A. The standard deviation value fall for all the ratios except TITA where is remains the same post-M&A.
4.2
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Table 4.2.1 Descriptive Statistics (in $ Millions) Table 4.2.1 (a) RNW |
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PH1 Pre Post |
PH2 Pre Post |
PH3 Pre Post |
PH4 Pre Post |
PH5 Pre Post |
PH6 Pre Post |
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|
Mean |
0.23 |
0.07 |
0.43 |
0.22 |
0.21 |
0.25 |
0.56 |
0.20 |
0.3 |
0.41 |
0.56 |
0.23 |
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SD |
0.07 |
0.072 |
0.07 |
0.05 |
0.01 |
0.013 |
0.20 |
0.06 |
0.41 |
0.02 |
0.11 |
0.05 |
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Median |
0.23 |
0.06 |
0.44 |
0.23 |
0.21 |
0.25 |
0.54 |
0.13 |
0.28 |
0.44 |
0.61 |
0.22 |
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Max Value |
0.23 |
0.12 |
0.5 |
0.27 |
0.22 |
0.27 |
0.64 |
0.34 |
0.33 |
0.50 |
0.68 |
0.31 |
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|
Min Value |
0.23 |
0.05 |
0.33 |
0.15 |
0.20 |
0.24 |
0.48 |
0.13 |
0.28 |
0.28 |
0.40 |
0.16 |
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Table 4.2.1 (b) RTA |
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|
PH1 Pre Post |
PH2 Pre Post |
PH3 Pre Post |
PH4 Pre Post |
PH5 Pre Post |
PH6 Pre Post |
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|
Mean |
0.17 |
0.06 |
0.23 |
0.12 |
0.09 |
0.16 |
0.41 |
0.16 |
0.17 |
0.31 |
0.27 |
0.11 |
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|
SD |
0.004 |
0.02 |
0.04 |
0.02 |
0.01 |
0.016 |
0.05 |
0.07 |
0.02 |
0.06 |
0.06 |
0.03 |
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|
Median |
0.17 |
0.05 |
0.23 |
0.13 |
0.09 |
0.15 |
0.38 |
0.11 |
0.15 |
0.35 |
0.31 |
0.08 |
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|
Max Value |
0.18 |
0.11 |
0.29 |
0.16 |
0.11 |
0.18 |
0.48 |
0.28 |
0.20 |
0.36 |
0.33 |
0.16 |
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|
Min Value |
0.17 |
0.04 |
0.17 |
0.09 |
0.08 |
0.14 |
0.35 |
0.11 |
0.14 |
.023 |
0.18 |
0.07 |
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Table 4.2.1 (c) CR |
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|
PH1 Pre Post |
PH2 Pre Post |
PH3 Pre Post |
PH4 Pre Post |
PH5 Pre Post |
PH6 Pre Post |
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|
Mean |
0.04 |
0.04 |
0.19 |
0.15 |
0.02 |
0.03 |
0.05 |
0.06 |
0.01 |
0.03 |
0.03 |
0.02 |
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|
SD |
0.004 |
0.01 |
0.005 |
0.004 |
0.003 |
0.001 |
0.011 |
0.004 |
0.008 |
0.01 |
0.001 |
0.01 |
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|
Median |
0.04 |
0.04 |
0.019 |
0.017 |
0.018 |
0.02 |
0.05 |
0.052 |
0.02 |
0.04 |
0.03 |
0.01 |
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MaxValue |
3.09 |
3.83 |
1.17 |
1.39 |
1.58 |
2.15 |
3.88 |
4.39 |
1.03 |
3.34 |
2.29 |
2.96 |
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|
Min Value |
2.29 |
2.66 |
1.02 |
0.65 |
0.99 |
1.76 |
2.01 |
3.39 |
0.82 |
1.53 |
1.85 |
1.13 |
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The table 4.2.1 (a) discusses about Return on Net worth of six pharmaceutical companies and their respective comparison pre and post-M&A. RNW is the ratio between net income and Shareholders’ equity. It is also known as return on equity. In the table above, the average values of this ratio falls for PH1, PH2, PH4 and PH6 post-M&A. Except for PH2, PH4, PH5 and PH6, the deviation for all other companies rise post-M&A. The median values follow the same trend average values. The minimum return on net worth value throughout all years of the six pharmaceutical companies is 0.05 and maximum value for the same group is 0.68.
The table 4.2.1 (b) depicts the descriptive statistics of RTA ratio of six pharmaceutical companies and their comparison pre and post-M&A. This ratio keeps a check on the usage of the assets for the generation of revenue. It is estimated by dividing EBIT (Earnings before Interest and Taxes) by total assets. In other words, this ratio analyses the effective utilisation of assets by the companies to produce the revenues. The mean values of this ratio fall for PH1, PH2, PH4 and PH6. The deviation falls for PH2 and Ph6 post-M&A. The median values also witness a similar trend as the average values and they rise only for PH3 and PH5 post-M&A. Where we see PH4 with a maximum Return on total assets ratio pre-M&A to be 0.48, we also have a minimum return on total assets ratio for PH6 post-M&A i.e.0.07.
Finally the table 4.2.1 (c)discusses descriptive statistics of CR for six pharmaceutical companies and their comparison pre and post-M&A. Current ratio analyses the ability of a company to pay its short term obligations. It is calculated by dividing current assets with the current liabilities. This ratio provides the short term solvency position of a company at a snapshot. Looking into the table, the average values for PH2 and PH6 fall post-merger and acquisition can be observed. The median values rise for PH3, PH4 and PH5. The standard deviation values are contrasting and fall only for PH2, PH3 and PH4 post-M&A. The maximum current ratio value is seen for PH4 post-M&A with 4.39and the minimum value is for PH2 post-M&A with 0.65.
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Descriptive statistics (in $ Millions) Table 4.2.1 (d) IC |
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PH1 |
|
PH2 |
PH3 |
PH4 |
PH5 |
PH6 |
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|
Pre |
Post |
Pre |
Post |
Pre |
Post |
Pre |
Post |
Pre |
Post |
Pre |
Post |
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|
Mean |
7.19 |
2.93 |
0.37 |
0.41 |
0.06 |
0.17 |
4.7 |
2.3 |
0.08 |
0.71 |
0.31 |
0.05 |
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|
SD |
1.4 |
1.2 |
0.25 |
0.40 |
0.01 |
0.04 |
4.2 |
0.77 |
0.01 |
0.16 |
0.16 |
0.01 |
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Med |
7.5 |
2.1 |
0.2 |
0.4 |
0.06 |
0.17 |
2.4 |
2.04 |
0.08 |
0.64 |
0.42 |
0.04 |
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Max.Value |
8.7 |
4.7 |
0.72 |
0.44 |
0.06 |
0.23 |
10.6 |
3.4 |
0.10 |
0.94 |
0.45 |
0.07 |
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Min.Value |
5.2 |
1.9 |
0.1 |
0.3 |
0.05 |
0.1 |
0.9 |
1.5 |
0.08 |
0.5 |
0.4 |
0.04 |
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Table 4.2.1 (e) TITA |
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|
PH1 |
|
PH2 |
|
PH3 |
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PH4 |
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PH5 |
|
PH6 |
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|
Pre |
Post |
Pre |
Post |
Pre |
Post |
Pre |
Post |
Pre |
Post |
Pre |
post |
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|
Mean |
0.01 |
0.005 |
0.012 |
0.006 |
0.017 |
0.015 |
0.018 |
0.009 |
0.01 |
0.01 |
0.014 |
0.007 |
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SD |
0.0003 |
0.0002 |
0.0003 |
0.0009 |
0.0008 |
0.001 |
0.001 |
0.0015 |
0.0006 |
0.002 |
0.002 |
0.001 |
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|
Med |
0.01 |
0.005 |
0.01 |
0.007 |
0.01 |
.015 |
0.01 |
0.008 |
0.009 |
0.01 |
0.01 |
0.006 |
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Max.Value |
0.0128 |
0.0057 |
0.013 |
0.007 |
0.018 |
0.016 |
0.019 |
0.01 |
0.01 |
0.014 |
0.017 |
0.009 |
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Min.Value |
0.0121 |
0.0051 |
0.012 |
0.005 |
0.016 |
0.014 |
0.016 |
0.008 |
0.009 |
0.007 |
0.011 |
0.006 |
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Table 4.2.1 (f) TICE |
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PH1 |
PH2 |
PH3 |
PH4 |
PH5 |
PH6 |
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|
Pre |
Post |
Pre |
Post |
Pre |
Post |
Pre |
Post |
Pre |
Post |
Pre |
post |
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Mean |
0.15 |
0.10 |
0.14 |
0.12 |
0.26 |
0.12 |
0.17 |
0.11 |
0.11 |
0.12 |
0.15 |
0.10 |
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SD |
0.01 |
0.012 |
0.006 |
0.01 |
0.016 |
0.012 |
0.012 |
0.012 |
0.01 |
0.018 |
0.012 |
0.01 |
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Med |
0.15 |
0.1 |
0.14 |
0.12 |
0.26 |
0.11 |
0.18 |
0.1 |
0.11 |
0.12 |
0.15 |
0.1 |
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Max.Value |
0.17 |
0.11 |
0.15 |
0.13 |
0.26 |
0.14 |
0.18 |
0.13 |
0.12 |
0.14 |
0.17 |
0.12 |
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Min.Value |
0.14 |
0.08 |
0.14 |
0.1 |
0.24 |
0.11 |
0.15 |
0.1 |
0.1 |
0.1 |
0.14 |
0.09 |
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The table 4.2.1 (d)represents descriptive statistics of IC for six pharmaceutical companies’ pre and post-M&A. This ratio signifies the interest expenses paying capacity of the companies. The average values for PH1, PH4 and PH6 fall contrary to the other companies. Standard deviation follows the same path and decline for PH1, PH4 and PH6. The median values show a similar trend to the mean values. The maximum and minimum values are 10.6 for PH4 pre-M&A and 0.08 for PH5 pre-M&A respectively.
The table 4.2.1 (e) shows descriptive statistics of TITA for six pharmaceutical companies’ pre and post-M&A. The mean values are decreasing for PH1, PH2, PH4 and PH6. The standard deviation is falling for PH1, PH3 and PH6. The middle values are on the same lines as mean values. The maximum value is for PH4 pre-M&A, while the minimum value is for PH2 post-M&A.
Finally, table 4.2.1 (f)depicts descriptive statistics of TICE for six pharmaceutical companies. The average values increase for only PH5 and no other company. The standard deviation values fall only for PH3 and no other company. The median values increase only for PH5. The maximum values and minimum values are 0.26 for PH3 pre-M&A and 0.08 for PH1 post-M&A.
|
Table 4.3 Clubbed average analysis (in $ Million) |
|||||||
|
|
PH1 Mean S.D. |
PH2 Mean S.D. |
PH3 Mean S.D. |
||||
|
Sales |
Pre |
377.6 |
29.6 |
664.2 |
107.1 |
35.9 |
0.8 |
|
|
post |
393.1 |
14.9 |
772.7 |
202.8 |
48.9 |
2.15 |
|
Total Expenses |
Pre |
334.02 |
39.4 |
655.2 |
58.5 |
34.5 |
0.62 |
|
|
Post |
368.2 |
31.4 |
698.5 |
139.1 |
44.3 |
1.3 |
|
PAT |
Pre |
54.4 |
21.1 |
95.1 |
42.9 |
2.03 |
0.2 |
|
|
Post |
61.6 |
17.6 |
165.7 |
53.3 |
5.2 |
1.1 |
|
Total Liabilities/Total Assets |
Pre |
545.5 |
45.6 |
1135.7 |
91 |
35.8 |
2.8 |
|
|
Post |
1173.3 |
36.5 |
1998.3 |
153.4 |
50.9 |
4.1 |
|
|
|
PH4 Mean S.D. |
PH5 Mean S.D. |
PH6 Mean S.D. |
||||||
|
Sales |
Pre |
1469.8 |
121.5 |
108.8 |
7.12 |
544.5 |
26 |
|||
|
|
Post |
1690.5 |
151.1 |
304.1 |
17.6 |
717.7 |
68.9 |
|||
|
Total Expenses |
Pre |
1199.8 |
64.3 |
94.6 |
2.8 |
475.4 |
41.5 |
|||
|
|
Post |
1444.5 |
54.5 |
223.7 |
9.15 |
683.8 |
69.9 |
|||
|
PAT |
Pre |
337.7 |
75 |
19.2 |
4.4 |
109.4 |
11.7 |
|||
|
|
Post |
299 |
119.8 |
92.5 |
14.2 |
102.9 |
21.6 |
|||
|
Total Liabilities/Total Assets |
Pre |
1530.8 |
212.5 |
206.3 |
15.7 |
806.7 |
192.8 |
|||
|
|
Post |
2819.6 |
94.8 |
517.8 |
119.9 |
1682.4 |
301.7 |
|||
Table 4.3 shows a comparative analysis of major heads in balance sheet of six pharmaceutical companies. Here, values of sales, total expenses, PAT and Total Liabilities/Total assets of the pre and post three years of M&A is taken. The average values of all six companies under four heads is increasing post-M&A. This increase in Sales and PAT and Total Assets definitely is positive for the companies and this indicates their efficiency and good financial performance post-M&A. But it can be observed that an increase in the mean values of total expenses and total liabilities that adds a little worry for the management. The same cannot be stated for standard deviation values. With an increase in mean values majorly all pharmaceutical companies also see a rise in deviations. Looking at PH4, where even though there is an increase in mean of total expenses, the deviations from the mean decrease post-M&A. It signifies a better control on total expenses by PH4. But a major matter of concern is the fall in mean values for PAT and a simultaneous increase in deviations. A very interesting observation is for PH1, here Sales, Total Expenses, Total liabilities/Total assets show a fall in deviations post-M&A with an increase in mean values for sales and PAT, which is definitely an applauding change brought out due to M&A. Similarly, PH5 has also tried to increase mean and reduce deviations for Sales post-M&A.
4.4 Comparative analysis among Companies
Comparative analysis represents the absolute change and percentage change of the main heads in balance sheet of six pharmaceutical companies. The absolute change is the difference in values post-M&A. First the average of three years of the values have been taken under each head post-M&A and then subtract with the average value of three years pre-M&A under the chosen head. It gives us the absolute change. In this way, it can be calculated the percentage change of the heads in various companies.
|
Table 4.4.1 Comparative analysis among companies |
||||||
|
PH1 |
PH2 |
|
PH3 |
|||
|
|
Absolute Change |
Percentage Change |
Absolute Change |
Percentage Change |
Absolute Change |
Percentage Change |
|
Sales |
15.5 |
4.1 |
108.4 |
16.3 |
12.9 |
36.1 |
|
Change in Stock |
6.2 |
197.7 |
14.1 |
215.3 |
0.6 |
88.7 |
|
Total Expenses |
34.4 |
10.2 |
43.29 |
6.6 |
9.8 |
28.4 |
|
Raw Material, Stores, Spares |
15.5 |
10.7 |
34.9 |
22.8 |
2.4 |
51.1 |
|
Power, Fuel and Water Charges |
(1.4) |
(5.4) |
3.4 |
18.3 |
0.6 |
198.5 |
|
Compensation to employees |
18.4 |
44.1 |
5.2 |
5.4 |
3.7 |
160.1 |
|
Interest Expenses |
0.19 |
118.6 |
(2.3) |
(22.6) |
(0.3) |
(40.25) |
|
Depreciation |
0.95 |
4.6 |
18.5 |
72.1 |
0.2 |
83.6 |
|
PAT |
7.16 |
13.1 |
70.5 |
74.2 |
3.24 |
159.2 |
|
Total Liabilities |
627.7 |
115.1 |
862.5 |
75.9 |
15.13 |
42.2 |
|
Shareholder’s Funds |
604.1 |
147.8 |
523.6 |
83.7 |
16.3 |
96.1 |
|
Paid up Equity Capital |
17.6 |
103.5 |
(2.23) |
(12.7) |
0.19 |
6.9 |
|
Reserves and Funds |
585.8 |
149.3 |
533.5 |
87.7 |
16.1 |
113.6 |
|
Non-Current Liabilities |
1.07 |
3.2 |
197.3 |
102.6 |
2.4 |
170.8 |
|
Long Term Borrowings |
5.6 |
124.9 |
81.04 |
63.5 |
0.4 |
128.9 |
|
Current Liabilities |
23.1 |
22.4 |
133.8 |
42 |
(3.6) |
(20.9) |
|
Net Worth |
604.1 |
147.8 |
523.6 |
83.7 |
16.3 |
96.1 |
|
Tangible Net Worth |
603.5 |
148.2 |
509.2 |
8109 |
16.3 |
96.9 |
|
Total Assets |
627.7 |
115 |
862.5 |
75.9 |
15.1 |
42.2 |
|
Non-Current Assets |
552 |
197.9 |
732.9 |
98.4 |
11.1 |
97.3 |
|
Net Fixed Assets |
(14.9) |
(9.7) |
124.6 |
45.4 |
6.24 |
110.5 |
|
Long Term Investments |
534.3 |
2370.7 |
377 |
163.6 |
2.04 |
102.4 |
|
Current Assets |
75.7 |
28.4 |
129.5 |
33.1 |
4.01 |
16.4 |
In the table 4.4.1, we have comparative analysis starting with PH1. The Power, Fuel and Water charges under the main head of Total Expense witness a fall post-M&A. There is a decline of 5.4% in comparison to the pre period. The shareholder’s funds increase by 147.8%. Both these changes show positive sign for the company. There is a fall of almost 22% in interest expenses. We also see that there is an increase in stock by 215.3%. It is appreciable for a company whose major proportion of the revenue comes through sales. There is a 159.2% increase in PAT post-M&A shows a favourable impact of M&A on the profits of the company. The net fixed assets increase by 110.5% which is a good sign for the company. The current assets increase by 16.4% and the current liabilities decline by 20.9%, which again depicts a very good short term solvency of the company.
|
Table 4.4.2 Comparative analysis among companies |
||||||
|
|
PH4 |
|
PH5 |
|
PH6 |
|
|
|
Absolute Change |
Percentage Change |
Absolute Change |
Percentage Change |
Absolute Change |
Percentage Change |
|
Sales |
220.6 |
15 |
195.1 |
179.2 |
173.1 |
31.8 |
|
Change in Stock |
(12.8) |
(52) |
0.6 |
24.9 |
3.04 |
31.2 |
|
Total Expenses |
244.6 |
20.3 |
129.1 |
136.3 |
208.4 |
43.8 |
|
Raw Material, Stores, Spares |
7.5 |
2.1 |
(31.4) |
(33.1) |
14.4 |
10.7 |
|
Power, Fuel and Water Charges |
3.3 |
6.1 |
(24.9) |
(75.9) |
4.2 |
33.9 |
|
Compensation to employees |
70.3 |
47.3 |
21.4 |
128.4 |
45.3 |
71.8 |
|
Interest Expenses |
(0.2) |
(19.8) |
(2.5) |
(53.9) |
35.8 |
300.2 |
|
Depreciation |
23.2 |
80.4 |
4.8 |
97.1 |
42 |
258 |
|
PAT |
(38.7) |
(11.4) |
73.3 |
381.7 |
(6.5) |
(5.9) |
|
Total Liabilities |
1288.8 |
84.1 |
311.5 |
150.9 |
876.2 |
108.6 |
|
Shareholder’s Funds |
1193.2 |
101.6 |
294.2 |
242.6 |
321.2 |
85.6 |
|
Paid up Equity Capital |
(1.8) |
(12.1) |
(0.17) |
(3.12) |
0.7 |
6.2 |
|
Reserves and Funds |
1195 |
103.1 |
294.9 |
254.8 |
320.4 |
88.2 |
|
Non-Current Liabilities |
37.7 |
55.4 |
(17.9) |
(67.9) |
393.7 |
175.6 |
|
Current Liabilities |
57.9 |
20.1 |
34.7 |
59.3 |
161.2 |
77.8 |
|
Net Worth |
1323.4 |
112.7 |
294.2 |
242.6 |
320.7 |
85.4 |
|
Tangible Net Worth |
1192.3 |
101.7 |
294.1 |
244.3 |
(149.7) |
(54.8) |
|
Total Assets |
1288.8 |
84.1 |
311.5 |
150.9 |
876.2 |
108.6 |
|
Non-Current Assets |
785.9 |
117.6 |
107.6 |
74.5 |
748.8 |
196.4 |
|
Net Fixed Assets |
128.8 |
36.3 |
10.1 |
11.3 |
668.1 |
272.3 |
|
Long Term Investments |
561.5 |
28.9 |
(1.6) |
(9.6) |
(4.12) |
(16.1) |
|
Current Assets |
502.9 |
58.2 |
203.8 |
329.1 |
127.4 |
29.9 |
The table 4.4.2 shows the comparative analysis where the PH4 witnesses a fall of 11.4% in the PAT of the company which does not represent a favourable impact of M&A on the company. The paid up capital see a decline post-M&A that signify the lack of confidence of the shareholders in the company. There is a fall in the expenditure of raw material, stores and spares by 33.1% and Power, fuel and water charges by 75.9% and interest expenses by 53.9%. All these form a substantial part of the total expenses and such a change is very advantageous for the company. The tangible net worth of the PH6 has fallen by 54.8% which is definitely not an appreciative change post-M&A. This reduces the liquidity of the company and a cause of concern for the management. Again the long term investment shows a negative trend post-M&A.
CACA forms a comprehensive test for evaluating future survival of a company. Many premier authors were of the opinion that ratios related to efficiency, assets, liquidity, profitability and capital structure move in tandem with the economic changes in the country (Endri & Rinaldo, 2020). A company doing CACA will be able to analyse their own faults and issues in decision making. It can thereby help in rectifying the actions taken by the management. Therefore, financial ratio analysis is a significant tool for the decision makers of the companies to study their current position and come up with the required corrective actions.
5.2 Concluding Remarks
The present study showcases the analysis of financial performance of companies in the pharmaceutical sector pre and post-M&A. It evaluates the positive impact of M&A on the financial performance of the company. Therefore, this study aims to understand it as a blueprint of a company’s performance management tool.
The comparative analysis among companies show varied results. PH5 shows a commendable increase in PAT post-M&A with an increase of 381.7%. The same cannot be stated about PH4 that shows11.4% and PH6 with 5.9% fall post-M&A. Again PH5 witnesses the highest percentage increase in sales and net worth. Therefore, all in all PH5 has gained and performed the best post-M&A. It gains the most from its M&A. While calculating CACA descriptive statistics of major heads in the financial statement are calculated. While there is a noteworthy increase in the mean sales value for PH5 post-M&A, this increase is accompanied with a considerable rise in total expenses and standard deviation values. All other companies do not witness such a profound increase in sales. Hence, out and out PH5 benefits the most due to M&A.
Overall, it can be interpreted that CACA is indispensable for the evaluation of financial performance of a company. Ratio Analysis has been used as a tool to find out bankruptcy, financial distress, success or failure of the company. It works as a diagnostic tool to assess the financial performance of any company and the probable existence of the company in the future.Today, when the economy is so dynamic and robust, companies always have an eye on alternatives to survive and ultimately grow profitably, financial ratios provide a pragmatic view of a company’s financial position (Subramaniyam, Wild & Yanti, 2010). Right now is the period of opportunity for the pharmaceutical companies to gather resources and synergise their operations for collaborative profits. This study will be the most beneficial for the managers of such companies that are wary of going in for M&A. Since the study articulates about the ratios that are highly predictive of the financial performance of the companies, it will guide them to take correct decisions for restructuring the companies. This will positively impact the nation as a whole at a time when the pharmaceutical companies’ contribution to the society is pivotal.
The present study considers financial ratios to analyse the performance of companies in pharmaceutical sector post-M&A. The study may increase the number of ratios under the existing three heads of ratios or may expand the heads of ratios for bringing out in-depth results. More companies can be added to existing companies for giving enriched conclusions about the research. Furthermore, the study is one sector specific i.e. pharmaceutical sector; a similar evaluation may also be done with other industries like textile sector, manufacturing sector, service sector etc. that could be useful for the dynamic Indian economy. Another interesting area of research will be an inter-sector analysis where two competitive sectors may be selected and a comparison may be done that will analyse the success rate of M&A specific to the sector. The present study will help in forming a base to form a theory that finds out the impact of M&A on the financial performance with and ratio analysis. This theory will then be replicated to other sectors as well.
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