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Impact of Mergers and Acquisitions on the Financial Performance of UK Firms

Info: 10944 words (44 pages) Dissertation
Published: 4th Oct 2021

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Tagged: Business



Over the decades gone by, we have seen companies like Amazon, Facebook, Google and Cisco that have been able to grow in a drastic manner and have generated revenue by means of aggressive mergers and acquisition strategies. Experienced executives of companies have always looked for ways in which they can generate revenue and gain market share in an efficient and profitable manner. The most common ways in which one can facilitate strategic growth are: organic, inorganic and external means. Hiring new salespeople, developing new products and expanding geographically are examples of organic growth. On the other hand, a classic example of inorganic growth is the acquisition of another firm, this is commonly done with the following motives in mind: gaining a new product line, customer segment or geographical expansion. External means of growth typically consist of franchising, licensing, joint ventures, as well as the appointment of overseas distributors. These are available to growing companies as an alternative to mergers and acquisitions as a source of growth (Sherman, 2018).

Existing theories suggest that companies go through with mergers and acquisitions for a lot of reasons, some of which include, diversification into a new type of market (one that is different which the company already operates in), capturing synergistic resource exchanges among firms that are merging as well as overcoming barriers to entry in a new market environment, among other reasons (Hitt, 2001; Jensen and Ruback, 1983). Moreover, acquisitions can lead to reduction in competitive rivalry and facilitate collusion among competitors, both of these create a competitive context that is favourable to the acquirer (Kim and Singal, 1993; Chatterjee, 1986).

Mergers and Acquisitions are an important source of strategy for an organisation to be able to compete on a global level. In the year 2015 more than 10,000 mergers took place in the United States of America alone, which amounted to more than 2 trillion dollars (Sinclair, 2015). It has been recorded that 70%-90% of the mergers actually fail to generate value for the acquirer (Christensen, 2011). Mergers and Acquisitions turned into a ‘trend’ during the years of 1960s and 1980s (Lev, 1993), these were the years where the first merger ‘waves’ came into existence. During this period, a lot of firms were involved in the merger activities, these firms were not only constricted to the United States and Europe, but also in Australia as well as Japan (Lubatkin and Lane, 1996). The reason because of which these merger waves became a prominent phenomenon was that mergers and acquisitions played an important role in the business environment in the 1960s. This was because they were seen not only as part of the financial activities but also as an investment opportunity.

Research has found that the results of acquisitions are often negative (King, 2004) and has identified the cause of the negative results as the various internal costs and the challenges firms face in the acquisition process. Negative results revolve around the takeover premium paid to the acquired firms’ shareholders, costs of integration after the acquisition which were not expected to arise and managers’ aim for personal gain from the acquisition (Hitt, 1990). Moreover, certain situations might come up where rivals are involved in a pre-acquisition bidding war on a common target in order to increase the price of the target, this leads to reduction in return on the acquisition (Brandenburg and Nalebuff, 1995). Ghemawat and Ghadar (2000) have said that mergers and acquisition may also lead to rivals engaging in bold moves to attain the distraction of acquirer management during the course of a merger process.

Motivation for Study

I have always been intrigued by the companies that opt for merger and acquisitions. Why they do it? Is it a strategic step? Does it improve or decline their financial performance? If so, by how much? These were some of the questions that I have, and my aim is to understand this topic as I might be faced by a situation in my career where I need to make a decision regarding merging with another company and I want to have a complete understanding of the situation before making that decision.

Aims of Study

This dissertation aims to study the impact of mergers and acquisitions on the financial performance of the firms that operate in the United Kingdom in the short run by comparing the financial ratios of the companies involved as well as performing a regression analysis on those financial ratios to draw further conclusions.


Causes of Mergers and Acquisitions

There are some theories that can be classified into seven groups which explain the causes of mergers and acquisitions. The seven groups can be put into three different categories depending upon their level of plausibility (Trautwein, 1990). The first category that contains the Valuation theory, the Empire Building theory and the Process theory has the highest plausibility. The second category has the Efficiency and the Monopoly theory and has lesser plausibility compared to the first category. Finally, the third and last category has the Raider theory and the Disturbance theory and can be considered to be implausible at the least.

It is said in the Valuation theory that the acquirers of a firm have more and better information about a target company as compared to others in the sector or market. This is the reason why they are able to make estimates about the target firm that are superior compared to others. Thus, making it achievable to gain from the firms with lesser value (Holderness and Sheehan, 1985). On the other hand, the Empire building theory says that the mergers are initiated my managers whose aim is to improve their utility in place of the shareholders’ value (Trautwein, 1990). The Process theory argues that the decision to merge comes into existence by a strategic decision process that is facilitated within a firm. It can also be said that in practice, it is possible that a decision process might not be completely based on the reasonable choices, therefore, it can also be formulated on the basis of political factors, agency issues, organisational routines as well as limited information accessibility (Herbert, 1957).

Coming to the second category, the Efficiency theory states that mergers and acquisitions can be considered to be planned and executed to attain a strong alliance or synergies. These synergies can further be classified into three different sectors. Firstly, the financial synergies, that can present advantage in the form of lower cost of capital. Secondly, the operational synergies, wherein the parties involved can work together to lower the cost as well as to come up with a new and different product or service. Thirdly, the managerial synergies, according to which the high-level managers of the acquiring firm can help in improving the planning as well as monitor the target firm in question (Trautwein, 1990). The Monopoly theory as the name suggests considered mergers and acquisitions as way to achieve market power with a horizontal or vertical merger or acquisition.

During their study of influence of mergers and acquisitions on the firm’s performance, Healy, Palepu and Ruback (1992) concluded that the merged firms display drastic improvements in the productivity of assets that are corresponding to their industry, that in turn become the cause of higher operating cash flow returns within the target company. These improvements are mainly highlighted by the change in the book values of asset disposals. Prior to the merger the value of asset disposal was 0.9% which changed to 1.3%. Comparing these numbers to those of their industry rivals, they found them to be of 0.1% and 0.6% respectively. According to Andrade, Mitchell and Stafford (2001) they conclude the following statement, “mergers create value for stockholders of the combined firms, with the majority of gains accruing to the stockholders of the target.” by this they Avg. that mergers and acquisitions in general are profitable and have a positive impact on the stockholders of the target or merged company. The studies of Healy (1992) and Andrade (2001) also show that the main causes to merger or acquire another firm are to improve profitability and also maximise the wealth of the shareholders. When we compare these finding to the theory of Trautwein we can link the Efficiency theory to Healy’s (1992) research as it points towards the improvement in productivity and we can also link the Valuation theory to Andrade’s (2001) research as it highlights the increase in the value of the shareholders.

The theories that Trautwein (1990) came up with can provide a substantial explanation as to why firms are interested in merging but only gives us an internal perspective. However, it can be said that the internal actions can be facilitated by some external reasons, some of which can be the importance of the industry structure and the economic environment. A merger can be a result of a technological and supply shock, which can result in excess production capacity in many industries (Jensen, 1993). A merger or an acquisition can be a solution to this problem because it opens up a route that could make it possible to prevent the contraction of the company and perhaps to continue the use of full production facilities. Other than the technological and supply shocks, there can be other major shocks that might result in the emergence of a merger. These shocks could be in the form of deregulation, increased foreign competition, financial innovations and oil price shocks (Mitchell and Mulherin, 1996 and Harford, 2005). A study of the most recent merger wave pointed out that in this wave the low financing rates as well as the companies’ rich cash balances played a massive role in the merger and acquisitions decision (Alexandridis, Mavrovitis and Travlos, 2012). Over the years, mergers have occurred in different patterns. Some of the periods had the involvement of very intense merger activities while other had lesser merger activities. These included both successful mergers as well as the ones that turned out to be huge failures. Merger and Acquisitions specialists and historians have identified five merger patterns in the history of USA and each of them had their own features. These patterns came to be known as merger waves (https://www.cleverism.com/lexicon/merger-waves/).

Impact of Mergers and Acquisitions on financial performance

A number of studies have been carried out to show the the positive as well as the negative impacts of mergers and acquisitions on the financial performance of a company. These impacts can be placed on the profitability, leverage and the liquidity of a firm.

Positive Impacts of Mergers and Acquisitions

According to Pandit and Srivastava (2016) they have said that the valuation of a merger deal is a crucial point when it comes to comparing the performance of difference mergers. They argue that the valuation method is important for effective negotiation. Commonly, when a company is being valued for acquisition, there are two methods that are used. The first one being the liquidation value of the company and the second one is the value of the target firm as a going concern. In most merger and acquisition transactions the acquired firm is valued on a going concern basis unless the target company is in distress (https://www.lskfirm.com/publications/mergers-acquisitions-valuation-methods/). The authors interviews across ten executives of formerly merged firms and then went on to analyse the financial ratios of the companies involved. They found out that only the fair valuation is proved to create alliance and positive effects on the firm’s performance after a merger is completed.

It has been proven by Arikan and Stulz (2016) that newer companies are able to build more valuable and well-diversified mergers and acquisitions as compared to older firms. Their study has revealed that the acquiring firms had a better performance and were also able to create wealth through the acquisition of nonpublic firms. Also, their research has shown the emergence of agency theories as the older firms tend to have negative stock price reactions for public firms. The agency theory deals with solving problems that may occur because of asymmetrical goals or different levels of risk (https://www.investopedia.com/terms/a/agencytheory.asp).

By using joint ventures, mergers and acquisitions and alliances as data Drees (2014) performed a meta-analysis on 204 studies to examine the corporate strategies. Thereon, he concluded that joint ventures and mergers and acquisitions boost substantial performance. He was also able to conclude that mergers and acquisitions had a more positive effect on the accounting based as well as market-based performance of the firms compared to joint ventures and alliances. Furthermore, Leespa and Mishra (2012) looked into the financial performance of the companies in the manufacturing sector of India after they were involved in a merger. Their study was conducted in a four year period wherein they used an accounting based approach and used three different criteria which were liquidity, profitability and leverage. They compared the average of the post and prior financial ratios and examined any noticeable change in the financial performance of the companies involved. They saw that the liquidity position of the firms was bettering, and the same effect was seen on the profitability of the firms in the form of return on capital, but decreased in the form of return on net worth of the companies. All in all, they saw an improvement in the financial position of the companies after the mergers and acquisitions in terms of liquidity that includes, current ratio and quick ratio and profitability in terms of return on capital. Equivalently, this study also revealed an advancement in the leverage which includes the coverage ratio.

Ghatak (2012) conducted a study on the mergers and acquisitions in the pharmaceutical industry in India. He took 52 listed drugs and pharmaceutical firms as a sample in his study. In his research, he found out that the size, the selling efforts, the exports and the imports intensities of the company influence the profitability of the company in a positive manner.

Ramaswamy and Waegelein (2003) looked into the financial position and used the financial data of 162 firms which had merged and considered industry adjusted cash flow returns as a criteria taking 5 year pre and post merger period. They concluded that after a merger the performance of the firms was negatively related with the size of the acquired firm and have a positive relationship with the long term motivation plans. Even the firms that were in different industries showed improvements in their financial performance. The authors also made use of regression analysis to check if there were any enhancements in performance after the merger had gone through compared to the financial performance after the merger.

Negative Impacts of Mergers and Acquisitions

Mergers have proven to be beneficial for many firms but for many others they have shown negative or insignificant results as well. Al-Hroot (2016) tried to analyse the impact of mergers on the financial performance of some of the companies in Jordan. He considered 7 merged companies and used ratio analysis. In his study he concluded that there was insignificant improvement in the financial performance of the companies that were involved after the merger had taken place. He also concluded that companies in different industries had different results for the effects of mergers and acquisitions.

Huh (2015) also inspected the impacts of acquisition deals on the companies in the steel industry. He concentrated on the technical efficiency and the Price Earning Ratio (PER) of the acquiring firms. The results of his study show that the operating performance of the companies that were acquired by financial institutions has had a negative result whereas, the price earning ratio has had an insignificant increase.

This trend was also noticed by Ahmed and Ahmed (2014) in their research when they took merged manufacturing companies of Pakistan as a sample in their study. Similar to the other studies, their results showed insignificant improvement in their profitability, liquidity and capital situation. Also, the tests for efficiency showed some deteriorating performance.

In the case of Kandzija (2014), he considered the Croatian merged companies and noticed that the success as well as the failure of the merger depends on the industry that the companies are operating in as well as the structure of the given companies. He concluded that the performance of the target firm is majorly dependant on the concentration ratio of the industry in which the target firm operates. He found out that the performance of the acquired firm would be higher if the concentration ratio is lower.

Leespa and Mishra (2014) tried to approach this in a scientific manner. They thought that this would help them to analyse the multiple financial ratios that pertain to the pre as well as the post merger period. Their aim was to explore post merger factors of manufacturing companies in India. To do this they came up with a composite index score. To achieve they used Principal Components Analysis for the pre and post merger period. After analysing the financial ratios such as return on capital, size of acquirer, quick ratio, industry relatedness and interest coverage ratios they made a conclusion that these are the determinants of the success or failure of a merger deal that has been undertaken.

Singh and Mogla (2010) drew a comparison between the pre and post merger operating performance of 153 merged firms in India between the years of 1994 to 2002. In this case they took the accounting approach for their analysis. Their research revealed that the profitability of the merged companies has decreased significantly after the merger took place. Moreover, profitability of other similar firms displayed a significant decrease in profitability as well over the same time period. They argued that the decline in profitability could not the because of the mergers only. To back this argument, they used regression equation on the same data and were able to say that the current ratio, the debt equity ratio as well as the size of the company are negatively linked to the profitability of the firm. It was also concluded that firms which work in groups perform better as compared to the firms that do no operate in groups.

Sharma (2016) investigated the performance of some of the metal companies in India after a merger. She considered nine metal companies between the period of 2009 and 2010. To carry out this investigation she applied the sample t-test to the data both before and after the merger. An independent sample t-test is used to draw a comparison between the means of the two data sets to find out if there is any statistical evidence that the means of both the sets of data involved have a significant difference (https://libguides.library.kent.edu/SPSS/IndependentTTest). Her results display negligible and insignificant improvements in the liquidity as well as the leverage position of the metal industry after the merger. On the other hand, the profitability in this study went down in terms of the Return on Net Worth (RONW) and Return on Asset (ROA). She has also said that synergy by means of mergers is possible, but only when you have the adequate resources and using them efficiently. She went on to say that the success of a merger depends on the integration process and monitoring the process carefully.

After carefully reviewing the literature of mergers and acquisitions we have observed that the mergers and acquisitions have different effects on the financial performance of the firms that have been merged or acquired. In some cases, we saw that mergers and acquisitions lead to significant improvement in the profitability, liquidity and the solvency of the firms. Whereas, in other cases that mergers and acquisitions may have a negative effect on the overall financial performance of the firms. There are several studies in which we were able to see that the performance after the merger or acquisition have mixed results, for example, the profitability of a firm improves but the liquidity position doesn’t.


The methodology is aimed at examining if the financial performance of the acquiring firm improves after it has been involved in a Merger or an Acquisition. The use of secondary data has been made to perform this study. The source for this study is the FAME database. The study has made use of five years’ data of the acquiring companies involved.

The sample for this study consists of acquiring data of the companies involved in Mergers and Acquisitions during the year of 2014/2015, depending on whether the company prefers to display its data in the form of a calendar year or the financial year. The reason behind selecting the five-year period is the examine the performance of the company in the short term, so that we can the immediate improvements in the financial performance in the company after it has gone through with a merger or an acquisition. The reference period for this study includes the two years after the merger or acquisition and two years prior to the merger or acquisition.

Data and Ratio Analysis

Ratio analysis is a form that is most commonly used to analyse the financial performance of the company. By making use of financial ratios we can explain the good and the bad circumstances that a company goes through. This study makes use of three types of financial ratios which further contain different ratios that indicate different effects or trends of the company. The ratios used are as follows:

Profitability Ratios

  1. Return on Capital Employed (%) =

    Operating ProfitCapital Employed*100

  2. Return on Shareholder’s Fund (%) =

    Income after Tax and Interest Total average Shareholders Equity*100

  3. Return on Total Assets (%) =

    Earnings before Interest and TaxTotal Net Assets *100

  4. Profit Margin (%) =

    Net IncomeNet sales or Net Revenue*100

Operational Ratios

  1. Net Assets Turnover =

    Net Sales Average Total Assets

  2. Debtors Turnover =

    Net Credit Sales Average Accounts Receivables

Liquidity Ratios

  1. Current Ratio =

    Current AssetsCurrent Liabilities

  2. Quick Ratio / Acid Test Ratio =

    Current Assets – InventoryCurrent Liabilities

The assessment of the ratios is done to obtain the description of the financial development of the company, so that we can observe the changes that occur in the financial performance of the company after the merger or acquisition has been completed. Extreme values have been excluded from the data to avoid the heavy influence of an abnormal event. After the collection of the data, the values of the ratios have been considered till two decimal places. Only those firms have been selected for which we could obtain the in pairs of pre and post Mergers and Acquisitions. The average of different pairs has been analysed to produce conclusive results. The pairs are as follows:

  1. One year before and after the Merger or Acquisition (-1, 1).
  2. One year before and two years after the Merger or Acquisition (-1, 2).
  3. Two years before and two years after the Merger or Acquisition (-2, 2).
  4. Two years before and three years after the Merger or Acquisition (-2, 3).

This sample consists of Mergers and Acquisitions of the companies that operate within the Technological Sector of the UK. The study uses short term data to analyse the short-term performance of the companies after a Merger or an Acquisition. Data for two years prior and three years post Merger has been taken into consideration and the year in which the Merger or Acquisition takes place has been named the 0 year, this year has been excluded from the study, as the involvement of this year might lead to unnecessary fluctuation in the results. The study focuses on comparing the pre and post Merger financial performance of the acquiring company in terms of profitability, operations as well as liquidity. An improvement in the profitability after the merger or acquisition can be associated with various reasons such as- higher asset productivity, better operating margins, reduced costs, enhanced market performance, etc. This is the reason why the number of profitability ratios is higher than the number of operating ratios and structure ratios.

Regression Analysis

Regression analysis is a way of mathematically sorting out which variables have an impact on the dependant variable. The main questions answered by regression analysis is that which factor matters the most when contributing toward the dependant variable. It is mainly used for forecasting, time series modelling and also to find the casual effect relationship between variables. A good example for a case where regression analysis can be used is to determine the relationship between rash driving and the number of car accidents by a driver. There are seven types of regression analysis that exist. They are- Linear Regression, Logistic Regression, Polynomial Regression, Stepwise Regression, Ridge regression, Lasso Regression and lastly, Elastic Net Regression. I have made use of multiple linear regression to investigate the relation between profit margin of the firms and a number of different variables. Linear regression builds a relationship among the dependant variable (Y) and one or more independent variable(s) (X) using a best fit straight line which is also called the regression line. I have made use of multiple independent variables hence the term ‘Multiple Linear Regression’. The equation of regression is the following:

Y = C + β1x1 + β2x2 + ……. + βnxn

Table IX represents the value of the variable that are a part of the regression equation.

Variable Value
x1 Fixed Asset Turnover (FAT)
x2 Liquidity Ratio (LR)
x3 Return on Shareholder’s Fund (ROSF)
x4 Return on Total Assets (ROTA)
x5 Net Assets Turnover (NAT)
x6 Return on Capital Employed (ROCE)
x7 Gross Margin (GM)
x8 Debtor’s Turnover (DT)
x9 Current Ratio (CR)

Table IX: Values of the Variables in the Regression Equitation.

There are nine unknown parameters in this regression model, the unknown parameters will be estimated using the least square estimation method. The least square estimation method revolves around estimating parameters by minimising the squared discrepancies in the sample set of data that has been obtained by the assessor in one place and their estimated values in the other. This will also be done in the software used. C is the y intercept, this represents the mean value of Profit Margin (PM), provided all the other variables are 0. β represents the slope of the regression line that is, the effect of the unit change in the corresponding variable on the Profit Margin. Profit Margin is the response variable (dependant variable) whereas, Fixed Assets Turnover (FAT), Liquidity Ratio (LR), Return on Shareholder’s Fund (ROSF) Return on Total Assets (ROTA), Net Assets Turnover (NAT), Return on Capital Employed (ROCE), Gross Margin (GM), Debtor’s Turnover (DT) and Current Ratio (CR) are the co-variates or the explanatory variables (independent variable). The aim of my study is to work out if there is any relationship between dependant variable (PM) and the number of independent variables. This will be done by investigating and carrying out the analysis on the results of the regression model. The software that I have used to carry out the regression analysis is E-Views 9. I have also made use of confidence intervals after performing the regression analysis so that I can further back my result. To calculate the t-value during further analysis we used the statistical software R.

Results and Findings

Ratio Analysis

The objective of this research was to find out if a merger or acquisition leads to any improvements in the financial performance of a firm. In this part of the research we made use of financial ratios and checked if there is any change in the ratios of the company after the merger be it negative of positive.

Profitability Ratios

Return on Capital Employed

Most businesses (excepting very small ones) regard Return on Capital Employed as their key measure for total performance. It combines the profit made by the company with the capital it currently sits on. More capital means that the company has a scope to earn more profits. This ratio mainly shows how the profit produced stands up to the capital that is being used to generate it (https://link.springer.com/chapter/10.1007%2F978-1-349-07472-3_18). Consistency plays an important role when it comes to Return on Capital Employed. This is the reason why we have considered the Return on Capital Employed for five years. This has been done to ensure that we generate conclusive results for our analysis. It is evident from Table I that the Return on Capital Employed has increased overall when we compared it to the pre merger time period. We can just see an improvement in the Return on Capital Employed all the way through, except for the second paired sample where it turns negative. We can also see a massive improvement in this ratio in the immediate year after the merger took place. This indicates that the company is generating large portion of profits, which can be used to invest back into the company for the wellbeing and benefit of the shareholders.

Paired Sample (Before, After) Avg. Ratio after M&A Avg. Ratio before M&A Difference
(-1, 1) 63.21 46.31 16.9
(-1, 2) 40.73 46.31 -5.58
(-2, 2) 40.73 37.07 3.66
(-3, 2) 40.73 19.42 21.31
Total     36.29%

Table I: Findings from Return on Capital Employed

Return on Equity

In the world of corporate finance, the Return on Equity (ROE) is considered to be a determinant of the profitability of the business in relation to the book value of the shareholder equity. Return on Equity is a measure of how well a company uses the investments made in it to generate earnings growth. In other words, Return on Equity expresses the capability on the management to generate income from the equity that is available to it (Visconti, 2018). It is also worth mentioning that the reason behind growth in the Return on Equity can be a decrease in the value of the shareholders’ equity. A sudden increase in Return to Equity can be caused by write-downs as well as share buybacks. Also, a high level of debt can be the cause of sudden boosts in the ROE, this is true because the more debt a firm has, the less shareholders’ equity it has (http://www.investinganswers.com/financial-dictionary/financial-statement-analysis/return-equity-roe-916). In this case however, we can see from Table II that the return on Equity is negative, which tells us that the shareholders are losing their value rather than gaining it. From the table we can conclude that the performance of the company in terms of Return on Equity has declined in the second year after the merger. Managers usually try to avoid negative ROE as much as possible.

Paired Sample (Before, After) Avg. Ratio after M&A Avg. Ratio before M&A Difference
(-1, 1) 58.34 44 14.34
(-1, 2) 30.64 44 -13.36
(-2, 2) 30.64 42.2 -11.56
(-3, 2) 30.64 33.75 -3.11
Total     -13.69%

Table II: Findings from Return on Equity

Return on Total Assets

The Return on Total Assets (ROTA) ratio is mainly used to measure a firm’s earnings before interest and tax (EBIT) against its total net assets. This ratio tells us how effectively a company uses its assets to bring out earnings before any interest or taxes must be paid. A low Return on Total Assets ratio indicates that the company is overinvesting in its assets which in turn are not contributing to the net income. We can see from Table III there is an overall increase in the Return on Total Assets, this means that the firm is earning more than it is spending. The first two paired samples are negative whereas the next two are positive, which is the indicator that the performance of the company has improved as compared to the data from more than two years before the merger.

Paired Sample (Before, After) Avg. Ratio after M&A Avg. Ratio before M&A Difference
(-1, 1) 14.47 32.30 -17.83
(-1, 2) 23.34 32.30 -8.96
(-2, 2) 23.34 9.07 14.27
(-3, 2) 23.34 -5.70 29.04
Total     16.52%

Table III: Findings from Return on Total Assets

Profit Margin

Profit Margin can be calculated as the net income divided by the revenue of a business. It can alternatively be calculated when the net profits are divided by the sales of the company. On a basic level, a low Profit Margin can be the indicator that the company’s profitability is not very secure. A decline in the sales of a company can be the cause of decline in the profit margin ratio as well. This ratio also tells us about the industry in which the firm operates. If the profit margin of a company is low, it can mean that the said company’s performance is lower to that of its competitors or that the industry itself is suffering. According to Table IV, we can conclude that there is an overall increase in the profit margin ratio which means that the sales of the company have increased. The mean difference in the first two paired samples is seen to be negative but it turns positive in the next two paired samples. This tells us that there is an improvement in the performance of the company and that more promising results can be seen in the long run. An increase in the price of the units sold could also be the reason behind the improvement in this ratio.

Paired Sample (Before, After) Avg. Ratio after M&A Avg. Ratio before M&A Difference
(-1, 1) 16.02 17.88 -1.86
(-1, 2) 17.77 17.88 -0.11
(-2, 2) 17.77 11.85 5.92
(-3, 2) 17.77 -3.57 21.34
Total     25.29%

Table IV: Findings from Profit Margin

Operational Ratios

Net Assets Turnover Ratio

The Net Assets Turnover Ratio is used to evaluate the value generated in terms of the sales or the revenue of a company and how it is relative to the assets. It is also often used as an indicator of the level of efficiency with which the firm is using its assets to generate sales or revenue. It is evident form Table V that the net assets turnover ratio for the sample selection is negative after the companies have completed the merger. It is evident from the table that the performance or the firm in terms of Net Assets Turnover was better prior to the merger. This goes to show that the Net Assets Turnover might decrease in the short term post merger. A negative Net Assets Turnover ratio means that the current liabilities of the firm are higher than the current assets. Furthermore, the net sales of a company can not be a negative figure, this means that the ratio becomes negative when the firm’s working capital is negative. This can be improved by increasing sales, improving efficiency, selling assets and accelerating collections from the customers.

Paired Sample (Before, After) Avg. Ratio after M&A Avg. Ratio before M&A Difference
(-1, 1) 1.47 2.84 -1.37
(-1, 2) 1.38 2.84 -1.46
(-2, 2) 1.38 3.75 -2.37
(-3, 2) 1.38 1.63 -0.25
Total     -5.45

Table V: Findings from Net Assets Turnover

Debtors Turnover Ratio

The Debtors Turnover ratio is also known as the Trade Receivables Turnover ratio. It is the number of times per year that the firm collects its average accounts receivable or the cash that is owed by the customers. It is used to reflect a firm’s effectiveness and capabilities to extend credit and then collecting the amount that is due. In other words, this ratio shows us the efficiency with which the company collects the credit that is issued to its customers. As reflected in Table VI, the average ratio for the sample is positive overall. We can see that the majority of the paired samples have a positive impact except for (-2, 2), this is the reason why the comprehensive result is positive. This means that the companies have strengthened their credit collection policies after a merger and perform well when it comes to the collection of cash from the customers.

Paired Sample (Before, After) Avg. Ratio after M&A Avg. Ratio before M&A Difference
(-1, 1) 20.72 14.57 6.15
(-1, 2) 14.91 14.57 0.34
(-2, 2) 14.91 18.27 -3.36
(-3, 2) 14.91 11.09 3.82
Total     6.95

Table VI: Findings from Debtors Turnover Ratio

Liquidity Ratios

Current Ratio

The Current Ratio mainly measures the firm’s ability to pay any short term and long term obligations. To obtain this ratio we divide the total current assets with the total current liabilities of the firm. It tells us if the company is in a position to pay the liabilities that it has with it assets that are currently in possession of the firm. When considering the data mentioned in Table VII, we can see that in the first two paired samples, the mean difference is a negative figure which indicates that the firms is unable to pay its current liabilities with its current assets but in the later stages it has shown to have improved that leads to an overall improvement in the ratio.

Paired Sample (Before, After) Avg. Ratio after M&A Avg. Ratio before M&A Difference
(-1, 1) 3.55 4.17 -0.62
(-1, 2) 3.50 4.17 -0.67
(-2, 2) 3.50 2.29 1.21
(-3, 2) 3.50 2.44 1.06
Total     0.98

Table VII: Findings from Current Ratio

Quick Ratio

This ratio is also known as the Acid Test Ratio. The Quick Ratio is a measure of the short term liquidity of the company and also tells us the ability of the firm to fulfil its short term obligations with the most liquid assets in its possession. Highly liquid assets are those that can be converted into cash within a time period of 90 days. Inventories being a part of current assets is not considered to be a highly liquid asset as it cannot be sold quickly. Table VIII displays a similar trend as seen in the case of Current Ratio. This tells us that the company, in a time of need can dissolve its highly liquid assets to pay off their liabilities.

Paired Sample (Before, After) Avg. Ratio after M&A Avg. Ratio before M&A Difference
(-1, 1) 3.25 4.11 -0.86
(-1, 2) 3.45 4.11 -0.66
(-2, 2) 3.45 2.23 1.22
(-3, 2) 3.45 2.33 1.12
Total     0.82

Table VIII: Findings from Quick Ratio

Regression Analysis

Pre-Merger Regression Equation

PM = 15.122 + 0.059 FAT – 7.92 LR + 0.15 ROSF + 0.44 ROTA – 2.52 NAT – 0.01 ROCE – 0.33 GM + 0.11 DT + 6.96 CR

Post-Merger Regression Equation

PM = 6.1302 – 0.03 FAT – 39.94 LR + 0.24 ROSF – 1.74 ROTA – 2.60 NAT + 0.75 ROCE – 0.92 GM +0.89 DT + 49.70 CR

A hypothesis test at 95% significant level was carried out to investigate if there is concrete evidence which points that the particular variables [Return on Total Assets (ROTA), Net Assets Turnover (NAT), Return on Capital Employed (ROCE), Gross Margin (GM) and Current Ratio (CR)] from the variables in Table X, have a significant effect on the Profit Margin (PM). The Null hypothesis in our case is that β is equal to 0, whereas the alternative hypothesis will be that β is not equal to 0. A Null hypothesis is that there is no significance in the sample set. It attempts to show that there is no variation between variables or that an individual variable is not different compared to its mean. The logic behind this is that the null hypothesis is indicating that the slope of the remaining variables which are- Fixed Assets Turnover (FAT), Liquidity Ratio (LR), Return on Shareholder’s Fund (ROSF) as well as Debtor’s Turnover (DT) is actually 0. This suggests that it will have no effect on the Profit Margin that is out dependant variable, which means that, no relationship exists between the above mentioned independent variables and the dependant variable. On the other hand, the alternative hypothesis is that when β is not equal to 0, there exists a relationship between the Profit Margin (dependant variable) and the significant independent variable (example: ROCE, NAT, etc.). The probability of significance (α) in a 95% significance level hypothesis test is 0.05. Any probability less than this value is deemed significant, this means that any probability less than 0.05 can provide enough evidence to accept that alternative hypothesis which is, β is not equal to 0. Any probability that is greater than 0.05 is deemed to be insignificant. These probabilities are given in Table X and Table XI. From these tables we can see that:

In the pre merger period the Fixed Asset Turnover had a probability of 0.6743 that is higher than 0.05 which is the significance level, Therefore, we can say that it was insignificant in the pre merger period. However, the results of the post merger period also say that the fixed asset turnover is insignificant because the probability of this variable is still higher than the significance level. The p-value of this variable post merger is 0.3188. In addition to that, the coefficient of FAT post merger is -0.038433 which tells us that it has a negative effect on the Profit Margin.

In the case of Liquidity ratio, during the pre merger period, the probability was 0.7389 which means that it was insignificant, this is because the p-value of this variable before the merger is higher than 0.05. It is clear that the p-value of liquidity ratio after the merger is also higher than 0.05. It’s value is 0.0864 which is better than the pre merger value but is still insignificant. Also, the coefficient of liquidity ratio in the post merger period is -39.94426 which tells us that it also has a negative impact on the profit margin just like fixed asset turnover.

For return on shareholder’s fund, the pre merger probability is 0.4311. Again, this is higher than the level of significance 0.05 which means than this variable was insignificant prior to the merger. The variable underwent a change in the post merger period and has become 0.0932 which makes it insignificant yet again. In this case though, the coefficient has changed from 0.151783 in the pre merger period to 0.245339 in the post merger period. This shows that there is improvement, but the variable still remains insignificant.

Coming onto return on total assets, we see that in the pre merger time, the variable is significant with a p-value of 0.0239. Since this value is less than the significance level of 0.05, we can conclude that the variable was significant before the merger. When we look at the post merger time, the p-value is 0.0126 which is better than the pre merger. The variable continues to be significant in the post merger period as well with the coefficient changing from 0.441053 to -1.746691. This means that the variable has a negative but significant impact on the profit margin after the merger has been completed.

Net Assets Turnover was insignificant in the pre merger period, with a p-value of 0.2452 being higher than the level of significance that is 0.05. In the post merger period though, the p-value shifted to 0.0105 which is less than 0.05, making this variable a significant one. The coefficient like the previous variable is a negative figure that is -2.606137 which means that it has a negative impact on the profit margin.

Return on Capital Employed (ROCE) was insignificant in the pre merger period with the p-value being 0.8712 which again is higher than 0.05 making it insignificant. When the merger was complete, the p-value of this variable changed to 0.0184 that turned it significant. The coefficient of this variable also changed from -0.016521 before the merger to 0.756704 in the post merger. This tells us that the variable had a negative impact in the pre merger time but now has a positive impact in the post merger period.

Gross Margin (GM) represents the total revenue generated from the sales of the company that it retains after the deduction of the direct costs that is associated with the production of the goods and services it deals in. We can see that the gross margin was also insignificant before the merger with its p-value being 0.0747 but after the merger that has now changed to 0.0039 which is lesser than the level of significant which is 0.05 hence making it significant in the post merger period. Its coefficient is also negative which means that it has a negative but significant impact on the profit margin of the company.

Debtors Turnover is insignificant in the pre merger time and remains to be insignificant in the post merger time as well. Even though the p-value has changed from 0.6303 to 0.0844, which is a huge change, it is still higher than the significance level which makes it insignificant. The coefficient however, has improved from 0.116816 to 0.893721 which tells us that the impact on this variable on the profit margin has increased after the merger.

Current Ratio was insignificant before and had a p-value of 0.7703 which is higher than the level of significance which is 0.05. We can see a drastic increase in the coefficient of this variable which has jumped from 6.966706 to 47.70289. This shows that there is a huge impact on the profit margin as the merger has been completed. The p-value after the merger is 0.0493 which is less that the significance level and is therefore turns significant.

It can be seen that some variables have significant probabilities while other do not, this suggests that there is enough evidence to accept the hypothesis which is β is not equal to 0. Moreover, we can see that the value of the adjusted R-squared has increased from 0.428331 to 0.725118 in the ore merger and post merger period respectively. The adjusted R-squared only increases if there is an improvement in the model that is more than what was expected in the first place (Kvålseth, 1985).

Post-Merger Regression Results

Dependent Variable: PM (Y)    
Method: Least Squares    
Date: 04/22/18   Time: 19:45  
Sample: 1 23      
Included observations: 23    
Variable Coefficient Std. Error t-Statistic Prob.
C 6.130291 5.361145 1.143467 0.2735
FIXED_ASSETS_TURNOVER -0.038433 0.037074 -1.036643 0.3188
LIQUIDITY_RATIO -39.94426 21.53417 -1.854925 0.0864
RETURN_ON_SF 0.245339 0.135437 1.811454 0.0932
RETURN_ON_TA -1.746691 0.603656 -2.893520 0.0126
NET_ASSETS_TURNOVER -2.606137 0.872953 -2.985424 0.0105
ROCE 0.756704 0.280762 2.695183 0.0184
GM -0.921463 0.263162 -3.501501 0.0039
DEBTORS_TURNOVER 0.893721 0.478364 1.868286 0.0844
CURRENT_RATIO 47.70289 22.00032 2.168281 0.0493
R-squared 0.837570     Mean dependent var 18.76354
Adjusted R-squared 0.725118     S.D. dependent var 18.09899
S.E. of regression 9.489148     Akaike info criterion 7.637195
Sum squared resid 1170.571     Schwarz criterion 8.130888
Log likelihood -77.82774     Hannan-Quinn criter. 7.761357
F-statistic 7.448273     Durbin-Watson stat 2.009914
Prob(F-statistic) 0.000727      

Table X: Regression Summary Post-Merger

Pre-Merger Regression Results

Dependent Variable: PM    
Method: Least Squares    
Date: 04/22/18   Time: 19:51  
Sample: 1 23      
Included observations: 23    
Variable Coefficient Std. Error t-Statistic Prob.
C 15.12266 10.37841 1.457126 0.1688
FIXED_ASSETS_TURNOVER 0.059334 0.138013 0.429918 0.6743
LIQUIDITY_RATIO -7.921639 23.26387 -0.340513 0.7389
RETURN_ON_SF 0.151783 0.186787 0.812599 0.4311
REETURN_ON_TA 0.441053 0.172515 2.556603 0.0239
NET_ASSETS_TURNOVER -2.524338 2.073935 -1.217173 0.2452
ROCE -0.016521 0.099882 -0.165407 0.8712
GM -0.332479 0.171584 -1.937707 0.0747
DEBTORS_TURNOVER 0.116816 0.237004 0.492888 0.6303
CURRENT_RATIO 6.966706 23.37107 0.298091 0.7703
R-squared 0.662196     Mean dependent var 10.26490
Adjusted R-squared 0.428331     S.D. dependent var 22.33132
S.E. of regression 16.88444     Akaike info criterion 8.789682
Sum squared resid 3706.095     Schwarz criterion 9.283375
Log likelihood -91.08135     Hannan-Quinn criter. 8.913845
F-statistic 2.831535     Durbin-Watson stat 2.148770
Prob(F-statistic) 0.043437      

Table XI: Regression Summary Pre-Merger
This result can further be authenticated using the 95% interval on the β of each variable. To add to this analysis and verify the outcomes even further, we will also calculate the confidence intervals for each variable on the 95% significance level.

The Upper Limit and the Lower Limit confidence levels will be calculated using the following equation:

In the above equation, β is the coefficient value that was calculated during the regression. All β values are given in post and pre merger regression summary tables above (Table X and Table XI). The t-value in the equation is different to what is mentioned in the regression summary tables. This t-value is calculated at (n – 9) degrees of freedom. Degree of freedom is nothing but the number of observations (n = 23 companies) minus the number of parameters estimated (9 ratios). This is to take into account the fact that the variables being used for β are estimates. The degree of freedom in our case is 23 – 9 = 14. The t-value was calculated to be 2.144787. The standard error is a part of the regression summary tables that are given above for both pre and post merger and acquisition. This is basically the measure of deviation of each variable from the estimated value. The way this interval can be interpreted is that there is a 95% chance that the values within this interval; are the values that the coefficient will take. All the confidence intervals for each variable are present in the table below (Table XII):

β ± [t-value x Std Error (β)]

Variables                           Confidence Intervals 
  Pre-Merger Post-Merger
Fixed Assets Turnover (-0.236, 0.349) (-0.12, 0.041)
Liquidity Ratio (-57.817, 41.974) (-86.196, 6.308)
Return on Shareholder’s Fund (-0.248, 0.4006) (-0.045, 0.535)
Return on Total Assets (0.071, 0.370) (-3.041, -0.451)
Net Assets Turnover (-6.972, 1.923) (-4.47, -0.733)
Return on Capital Employed (-2.158, 2.142) (0.154, 1.358)
Gross Margin (-0.7009, 0.352) (-1.485, -0.377)
Debtors Turnover (-0.391, 0.508) (-0.132, 1.919)
Current Ratio (-43.159, 50.125) (0.516, 94.888)

Table XII: Confidence Intervals of Variables Pre and Post Merger

It can be seen from the table above that only the significant vales from our initial hypothesis test do not contain 0 in their interval. Thus, reaffirming our earlier finding.


This study finds that most merger and acquisition deals that took place in the year 2014/15 in the United Kingdom result in a better performance of the acquiring company. However, it also shows that mergers and acquisition do not always have a positive impact towards all the factors of the firm.

In this study we have exclusively examined the impact of mergers and acquisitions on the financial performance of an acquiring firm of the United Kingdom in the short term. In order to assess the impact of mergers and acquisition on the profitability, structure as well as the liquidity of the firm, we carried out financial ratio analysis as well as multiple linear regression analysis using the data on the merger deals that took place between the year 2014/15 depending on how the firms like to present their data (i.e. financial year or calendar year). After the use of multiple linear regression, we also calculated the confidence intervals to further strengthen our results and findings.

By using the financial ratio analysis, we were able to target specific paired samples of different number of years pre and post merger or acquisition. This helped us to take a look at the change in the different ratios from different years, both before and after the merger. The final total of the difference in averages of the ratios gave us an overall verdict on the performance of the company in the post merger period. This is how we were able to come up with conclusive results by using the financial ratios.

We were also able to verify our alternative hypothesis by using the regression analysis. There we saw that some variables are still insignificant after the merger but many of them turned significant, this shows us that the mergers actually helped the firms to improve their financial performance by engaging in a merger. Also, the change in the adjusted R-square helped us to conclude that the post merger results are better than the pre merger ones. By using the confidence intervals, we were further able to back our results and make them more strong. Since none of the intervals corresponding to the significant variables had a possibility of having the outcome where β turns 0, this showed that our regression is actually correct and successful.

We also came across some limitations in this study. The results would have been more promising and prominent if the data size was bigger than what was considered. This could not be done because there are not many mergers and acquisitions that take place in the United Kingdom, and for the ones that actually happen, it is difficult to find the data. Sometimes databases like FAME don not have the complete information regarding the ratios, balance sheet as well as cash flow statements of the firms that are to be taken into consideration. During the time of data collection, we encountered some issues where the FAME database did not have the ratios for some of the firms which were involved in mergers and acquisitions. We had to take those firms out of our study, simply because of the lack of data.

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