Company Valuation In The European Healthcare Pharma Sector

Published Date: 02 Nov 2017

Professional Thesis

Supervisor: Geoffroy Surbled

Thomas Neumann

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Postgraduate Programmes

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Contents

Summary

Business Valuation is a process used to estimate the economic value of an owner’s interest in a company. The observation of company valuation multiples, private and public, as a relative value model over the European Medical, Pharmaceutical and Cosmetics sector helps to answer the following question: To what extent do listed companies’ valuation multiples differ from private equity firms’ ratios in the European Healthcare Pharma sector? Further research is needed to answer the fundamental question: Is there an impact of being listed for the valuation of companies?

Data of 218 recorded M&A deals from 2007 until 2012 and their private market multiples as well as the public market multiples of 202 publicly listed firms have been analyzed for similarities and differences using descriptive statistics like boxplots and error bars as well as statistical hypothesis testing, taking into account the different nature of private and public market multiples due to control premiums and liquidity discounts, among others.

Using the Median for the calculation of average valuation multiples together with interquartile range and confidence intervals is of more relevance than using error bars with the arithmetic mean and standard deviation, since the median statistically can cope with outliers, while the mean cannot.

Enterprise value based multiples like EV/EBITDA are particularly relevant in mergers & acquisitions where the whole of the company’s stock and liabilities is acquired, while Equity price based multiples like P/B are more relevant where investors acquire minority positions in companies.

The Pepperdine Private Capital Markets Project report has shown in its Survey Report Winter 2011 a Median EBITDA deal multiple of 7,5xs for a company size with equity value of 50 million dollars. Compared to the EV=7.5*EBITDA average multiple of the ARGOS MID MARKET Index from December 2011, all three private sectors showed a higher average multiple, ranging from a 9,3xs average in the Medical sector up to a 10,6xs average in the pharmaceutical sector. Even the lowest Median with 8,4xs was clearly higher.

Investors are willing to pay a premium on the average cash flow multiple due to a comparatively high regulatory environment and the resulting complexities, intellectual property rights like patents and other risk minimizing facts associated to the Healthcare Pharma sector like the perpetual need for medical advice and body care. Additional reasons are high entry barriers for competition as well as synergies resulting from internally coherent and mutually supportive resources along the product discovery value chain.

Having a closer look at the private Healthcare Pharma sector one can find considerable differences in the firms’ valuations. For example, investors have been willing to pay a clearly and substantially higher EV/EBITDA valuation multiple for companies selling Pharmaceutical or Veterinary Pharmaceutical products than other Pharma associated firms.

It might be helpful to develop another industry specific valuation multiple for a more accurate and fair calculation of Enterprise Value. This specific valuation multiple would be calculated as EV divided by any operating metric that can be seen as an unlevered basis for competition within companies in the European Healthcare Pharma sector.

Statement of non-plagiarism

I declare that, apart from properly referenced quotations, this report is my own work and contains no plagiarism; it has not been submitted previously for any other assessed unit on this or other degree courses.

Date:

Signature:

(Thomas Neumann)

Acknowledgements

First I offer my sincerest gratitude to my supervisor, Geoffroy Surbled, who has supported me throughout my thesis with his knowledge whilst allowing me the room to work in my own way.

Grégoire Buisson, Founder and CEO of Epsilon Research, kindly provided me with the highly confidential private company data covered in this thesis. He offered advice and insight in private market multiples.

Finally, I would like to express my thankfulness to my family. I dedicate this work to my son Julian Samuel Neumann.

Context of the study

In finance, valuation is the process of estimating what something is worth. Business Valuation is a process and a set of procedures used to estimate the economic value of an owner’s interest in a company. Valuation is used by financial market participants to determine the price they are willing to pay or receive to achieve the sale of a whole business, which means controlling 51% to 100% of share capital. In addition to estimating the selling price of a firm, the same valuation tools are often used by business appraisers to resolve disputes related to estate and gift taxation, divorce litigation, allocate business purchase price among business assets, establish a formula for estimating the value of partners' ownership interest for buy-sell agreements, and many other business and legal purposes.

Databases with information about the value of companies are used by a wide variety of merger and acquisition professionals, including business appraisers, business brokers, investment bankers, and professionals who work in venture capital. Additionally, the data is used in price discovery by entrepreneurs, investors, advisors, and business owners who are considering a business purchase or sale. Two different databases were used for the purpose of this dissertation:

The Argos Mid-Market Index measures the evolution of European private mid market company valuations. Carried out by Epsilon Research for Argos Soditic and published every six months, it reflects median EV/EBITDA multiples, on a twelve-month rolling basis, of mid market M&A transactions in the euro zone. The Argos Mid-Market Index is intended to reflect the evolution of valuations of private mid market companies.

Infinancials.com is a database providing company valuation data of listed European companies including the Healthcare Pharma sector.

The comparison of EV/EBITDA multiples of private mid market companies and listed pharmaceutical companies in terms of M&A transactions via a cross analysis in the Healthcare-Pharma sector in Europe will show that there is a high variation in terms of valuation multiples within the Healthcare Pharma sector and lead to the main research questions: First "Are there subsectors within the Healthcare Pharma sectors that clearly show differences in the valuation multiples?" and second "To what extent do listed companies valuation multiples differ from private equity firms ratios in the European Healthcare Pharma sector"?.

In this dissertation the three sectors Pharmaceuticals, Medical and Cosmetics form the Healthcare Pharma sector. The pharmaceutical industry develops, produces, and markets drugs or pharmaceuticals licensed for use as medications. Pharmaceutical companies are allowed to deal in generic as well as brand medications and medical devices. The Medical industry provides goods and services to treat patients with curative, preventive, rehabilitative, palliative, or, at times, unnecessary care. The Cosmetics industry generally deals with mixtures of chemical compounds, some being derived from natural sources, many being synthetic.

The whole European Healthcare Pharma sector is subject to a variety of laws and regulations regarding the patenting, testing and ensuring safety and efficacy and marketing of the offered products. The modern health care sector is divided into many sub-sectors, and depends on interdisciplinary teams of trained professionals and paraprofessionals to meet health needs of individuals and populations.

Problem Statement

Is there an impact of being listed for the valuation of firms in the European Healthcare Pharma sector? To answer this question one is in need of a historical study over time in terms of absolute company value evolution of private as well as listed companies.

Even if the answer to this question is beyond this thesis, it works in the direction to answer this final question using a cross analysis of company valuation multiples, private and public, over the European Medical, Pharmaceutical and Cosmetics sector. This analysis helps to answer another question: To what extent do listed companies valuation multiples differ from private equity firms’ ratios in the European Healthcare Pharma sector?

The Healthcare Pharma sector private valuation multiples of the ARGOS MID-MARKET Index show a very high dispersion. That means that within this sector there might be more homogeneous subgroups forming the heterogeneous group of the Healthcare Pharma sector. Subsectors within the Healthcare Pharma sectors that clearly show differences in the valuation multiples are going to be studied.

This dissertation is going to have a look at subgroups of the Healthcare Pharma sector, in detail the three subgroups Pharma, Medical and Cosmetics as well as their subsectors. Additionally, the information about listed companies within the same sectors and its public market multiples are going to be taken into account and compared.

The market approach to business valuation is rooted in the economic principle of competition: In a free market the supply and demand forces will drive the price of business assets to a certain equilibrium. Buyers would not pay more for the business, and the sellers will not accept less, than the price of an exactly comparable business enterprise.

The market price of the stocks of publicly traded companies engaged in the same or a similar line of business, whose shares are actively traded in a free and open market, can be a valid indicator of value when the transactions in which stocks are traded are sufficiently similar to permit meaningful comparison. The difficulty lies in identifying a peer group: Public companies that are sufficiently comparable to the subject company for this purpose.

Determining a private company’s worth and knowing what drives its value is a prerequisite for deciding on the appropriate price to pay or receive in an acquisition or merger transaction.

Sales of privately held companies are neither actively traded or regularly reported to city or county recording offices, nor verified by these same local government offices. Sales of privately held companies are voluntarily reported by business brokers to data re-sellers or unscientifically accumulated by these same private, for profit data re-sellers. Consequently the company value data of private companies is in general hardly available for a relative valuation approach, by the very nature of the data collection process.

Objectives of the study

1 Company Value Market Multiples observation in the European Healthcare Pharma sector

A cross analysis over subsectors of the European Healthcare Pharma sector, for example the Medical, Cosmetics and Pharma sector, in terms of Private Market Multiples with the analysis of EV/sales, EV/EBITDA, EV/EBIT, EqV/PBT, P/E and P/B as well as Public Market Multiples with the analysis of P/E, EV/EBITDA, P/B and net debt situation, is one of the main objectives.

The Healthcare Pharma sector of the ARGOS MID MARKET Index is supposed to be highly heterogeneous. First, the objective is to find homogenous subsectors within the ARGOS MID MARKET Healthcare Pharma sector. Gregoire Buisson from Argos Soditic & Epsilon Research kindly agreed disclosing ARGOS MID MARKET Healthcare Pharma data. Second, it would be interesting to investigate in which subsector the December 2011 ARGOS MID MARKET multiple of EV=7,5*EBITDA (Source: ARGOS MID MARKET Index, Dec. 2011) works best as well as how big the difference of this model is in comparison to each subsector.

A comparison of listed as well as non-listed companies and their resulting Public Market Multiples versus Private Market Multiples is another purpose. Third, the objective is to find a correlation of these private company subsectors with publicly listed company subsectors using the INFINANCIALS database.

2 To what extent do listed companies valuation multiples differ from private equity firms’ ratios in the European Healthcare Pharma sector?

The valuation observation of companies in terms of absolute value and evolution over time would give an idea about the main research question: To what extent do listed companies valuation multiples differ from private equity firms’ ratios in the European Healthcare Pharma sector? This question leads to another question: "Is there a difference of being listed in comparison to being a private company?" However, these questions are beyond the range of this thesis.

A statistical correlation of private and listed companies in the Healthcare Pharma sector within the Euro zone at one time point each might provide first clues on the general question to what extent listed companies valuation multiples differ from private equity firms’ ratios. This research question is highly interesting since the data of listed companies is rather freely available, but the company value of private companies is hardly accessible.

Methodology

Absolute value models determine the present value of an asset's or a company’s expected future cash flows. These kinds of models take two general forms: Multi-period models such as discounted cash flow models or single-period models such as the Gordon model. These models rely on mathematics rather than price observation and are not used in this dissertation.

Valuation Using Multiples as a Relative Value Model

Relative value models determine value based on the observation of market prices of similar businesses. A total of 420 companies associated to the European Healthcare Pharma sector have been used for this analysis, of which 218 have been private firms and 202 were listed companies.

The Valuation using Multiples technique can be used to value public companies as well as to value private companies.

Valuation using multiples is a method of estimating the value of a business by comparing it to the values assessed by the market for similar or comparable businesses. The following ratios have been used for this dissertation: EV/EBITDA, P/E and P/B next to the information about net debt in terms of Public Market Multiples and EV/sales, EV/EBITDA, EV/EBIT, EqV/PBT, P/E and P/B in terms of Private Market Multiples.

The process of estimating the valuation multiples consists of three steps:

1) Identifying comparable businesses, which are called the peer group, and obtaining market values for these businesses.

A peer group is a set of companies which are selected as being sufficiently comparable to the company being valued. The reasoning is usually by having similar characteristics. In this dissertation similar characteristics are because of being in the same industry, the Healthcare Pharma sector. Other important characteristics would include the operating margin, company size, products, customer segmentation, growth rate, cash flow, number of employees, among others.

2) Converting these market values into standardized values relative to a key statistic, since the absolute prices cannot be compared. The process of standardizing is creating valuation multiples.

3) Applying the valuation multiple to the key statistic of the asset being valued, controlling for any differences between asset and the peer group that might affect the multiple.

A valuation multiple is an expression of market value of a business relative to a key statistic that is assumed to relate to that value. To be useful, that statistic – whether earnings, cash flow or some other measure – must bear a logical relationship to the market value observed; to be seen, in fact, as the driver of that market value.

Commonly Used Multiples

In stock trading, one of the most widely used multiples is the price-earnings ratio (P/E) which is popular in part due to its wide availability and to the importance ascribed to earnings per share as a value driver. However, the usefulness of P/E ratios is lessened by the fact that earnings per share is subject to distortions from differences in accounting rules and capital structures between companies, as well as because of different policies in the determination of earnings.

Other commonly used multiples are based on the enterprise value of a company, such as EV/EBITDA. These multiples reveal the rating of a business independently of its capital structure and its accounting rules in terms of tax, depreciation and amortization. They are of particular interest in mergers, acquisitions and transactions on private companies.

Not all multiples are based on earnings or cash flow drivers. The price-to-book ratio (P/B) is a commonly used benchmark comparing market value to the accounting book value of the firm's assets. The price/sales and EV/sales ratios measure value relative to sales. These multiples might be used with caution as both sales and book values might be less likely to be value drivers than earnings.

Some ratios are commonly used in certain industries due to their specificity. Using the same valuation multiple in a different industry sector one has to take into account the particular industry characteristics.

Private Market Multiples

An accurate valuation of privately owned companies largely depends on the reliability of the firm's historic financial information.

Epsilon Research (www.epsilon-research.com) is an independent research and financial analysis bureau specializing in the M&A/ private equity markets. It provides research, deal analysis and market intelligence on private company M&A and LBO transactions. Its key product, EMAT («Epsilon Multiple Analysis Tool™ »), is the largest database for European private company transaction multiples, with detailed analysis of more than 5000 M&A deals covering all industry sectors. It has become a reference source for its customers, which include investment funds, banks, accountants as well as M&A advisors. In 2011, 7% of this data, around 400 company deals, were associated to the Healthcare Pharma sector.

Sample

For the purpose of this dissertation only those M&A deals where the acquiring company acquired not less than 51 per cent of the target company, located in the European Union including Switzerland and Norway, were taken into account. The deals were announced in the time range of February 2007 until July 2012.

Following the Epsilon Research methodology the target company operated in the Healthcare Pharma sector. The M&A deal could be of the following types: Acquisition of Majority Stake, Exit, SBO, MBO, or LBO. The information about each deal included the equity value of the company, the enterprise value and the sales price and was sufficient to calculate at least one of the following ratios: EV/sales, EV/EBITDA, EV/EBIT, EqV/PBT, P/E and P/B.

Using this filter the author obtained a total of 218 M&A deals. 27 M&A deals in the Cosmetics sector, 139 M&A deals in the Medical sector as well as 52 deals in the Pharma sector. The Medical sector included Health Care Providers, Medical Equipment and Supplies.

The multiples were calculated either as current multiples as well as historic multiples. A historic multiple for EV/EBITDA would be calculated using for example the EBITDA of 2011 for a deal announced and paid in March 2012. In contrast, a current multiple in the same example would be calculated using an estimated EBITDA of 2012. For the purpose of this dissertation only historic multiples were taken into account.

When a peer group consists of companies or assets that have been acquired in mergers or acquisitions, this type of valuation is described as precedent transaction analysis, or "private market multiples".

The raw data of the ARGOS MID-MARKET Index is highly confidential and must be handled accordingly. Only the resulting multiples per sector and subsectors can be presented.

Public Market Multiples

For more than a decade, INFINANCIALS (www.infinancials.com) has been providing business valuation and equity research solutions to over 7,000 finance professionals worldwide. High-quality data and analytics on 80,000 publicly listed companies worldwide are provided via its database, including small- and mid-capitalizations.

Sample

On August 29th, 2012, data was exported from the INFINANCIALS database using the calculation setting of a 12 months average Price/Market Capitalization and the last annual net debt situation. Data from the year 2011 including the Market Capitalization, Enterprise Value, Net debt, EV/EBITDA ratio, P/E ratio and P/B ratio were extracted from 58 European pharmaceutical firms, 34 European cosmetics-related firms and 110 European companies from the medical sector including Health Care Providers, Medical Equipment and Supplies. On September 6th, 2012, the Cosmetics-related firms were recalculated from local currency into EUR.

When a peer group consists of public quoted companies, this type of valuation is often described as comparable company analysis, or "public market multiples".

The peer groups were found using the INFINANCIALS Premium "Multi-Criteria-Search" looking for the location "Eurozone". The keyword "Cosmetics" in the Chemicals sector was used for the cosmetic sector, while all companies from the Healthcare Pharmaceutical sector formed the Pharma sector. All companies from the Healthcare Providers, Medical Equipment and Medical Supplies formed the Medical sector, which was limited to 110 companies for the purpose of this dissertation.

A cross analysis over subsectors of the European Healthcare Pharma sector lead to tables displayed in the appendix.

Constraints

In practice, no two businesses are alike, and analysts will often make adjustment to the observed multiples in order to attempt to harmonize the data into more comparable format. These adjustments may be based on a number of factors, including:

Business environment factors: Business model, industry, geography, seasonality, inflation

Accounting factors: Accounting policies, financial year end

Financial: Capital structure

Empirical factors: Size

Additionally, the comparison of small and middle sized private companies after an M&A deal and listed middle to big sized companies is biased mainly due to three effects.

Control Premium

The first bias is because of the control premium. This premium is paid in the M&A deal but not taken into account in the value of the INFINANCIALS database. This is due to the fact that the INFINANCIALS database prices reflect the view of millions of marginal investors, which are minority stockholders, on a second to second basis. In contrast the ARGOS MID-MARKET Index database prices reflect M&A tender offer bids and achieved prices of M&A transactions. An average control bid premium might be assumed to be around 30 per cent with a high variation. (Surbled, G.; personal communication). Market commentators have traditionally quoted a range of 20 per cent to 40 per cent (RSM Bird Cameron’s Control Premium Study, 2010).

Liquidity Premium

The second bias is due to the liquidity or marketability premium. For a private company, the equity is less liquid than for a public company. In other words its stocks are less easy to buy or sell. Therefore, its value is considered to be slightly lower than such a market-based valuation of public firms would give.

The third bias is due to the company’s average size. This is a reason that the database takes into account the market capitalization of each public company and the equity value of each private firm.

Size Premium

Market capitalization is defined as the share price multiplied by the number of shares in issue, providing a total value for the company's shares outstanding. It represents the public consensus on the value of a private company's equity. In a public corporation, ownership interest is freely bought and sold through purchases and sales of stock, providing a market mechanism, which determines the price of the company's shares. As a private company’s equity, Market capitalization is based on a market estimate of a company's or its assets value, based on perceived future prospects, economic and monetary conditions.

Size contributes to the discount of the valuation since it reflects the industry. Smaller-sized businesses have considerably more risk than larger ones. A quite interesting analogy for the size premium is to be found in nature. Fossil records of 28 groups of mammals have shown that evolving larger takes about 10 times as long as evolving smaller (Evans A.R. et al. 2012). This reaffirms how much pure size is to be prized. In nature, this is because gaining girth for mammals does not only mean adding muscle and bones, but also includes reengineering organs and metabolism. Similarly, growing firms have to rethink their organizational structure.

The common stock of small firms have, on average, higher risk adjusted returns than the common stock of large firms (Banz, 1980).

Descriptive Statistics

The main features of the collected data are quantitatively portrayed using descriptive statistics. The aim of descriptive statistics is to summarize the samples, rather than use the data to learn about the population that the sample of data is thought to represent. Descriptive statistics provides simple summaries about the sample and about the observations that have been made. These summaries form the basis of the initial description of the data as part of a more extensive statistical analysis. Sometimes they have been sufficient in and of themselves for a particular investigation.

The peer groups Cosmetics, Medical and Pharmaceutical for both private and listed companies have either been summarized using univariate analysis describing the distribution of a single variable, for example Market Capitalization, including its central tendency, including the mean and median. While the median as a robust estimator is capable of coping with outliers, the mean is not. Additionally, the dispersion, including the range of the data-set and measures of spread such as the standard deviation or confidence interval has been prepared.

Statistical Analysis was performed with the programs Excel (Microsoft) as well as SPSS (IBM). For a first overview Box Plots have been prepared. After excluding outliers, error bars showing the mean and standard deviation or 95% confidence interval are presented.

Outliers can indicate erroneous procedures or areas where a certain theory might not be valid. However, in large samples, a small number of outliers are to be expected.

Box Plots

In its simplest form, the boxplot presents five sample statistics - the minimum, the lower quartile, the median, the upper quartile and the maximum - in a visual display. The box of the plot is a rectangle which encloses the middle half of the sample, with an end at each quartile. The length of the box is thus the interquartile range of the sample. The other dimension of the box does not represent anything in particular. A line is drawn across the box at the sample median. Whiskers sprout from the two ends of the box until they reach the sample maximum and minimum.

The hinges are at the top and bottom of the box - these generally match the upper and lower quartile. The distance between the upper and lower hinges is called the H-spread. The median is indicated by a thick black line inside the box.

The horizontal lines above and below the boxes mark the 'adjacent values.' These are the most extreme values in the sample that lie between the hinges and the 'inner fences'. Data points outside this range are considered as outliers. The 'inner fences' lay 1.5 times the H-spread above and below the hinges. The inner fences encompass about 99% of the distribution. Any outliers are marked with a circle and extreme cases with a star.

The vertical lines between the hinges and the adjacent values are called the 'whiskers'. The whiskers are the lines that extend out the top and bottom of the box. They represent the highest and lowest values that are not outliers or extreme values.

Outliers are values that are between 1.5 and 3 times the interquartile range. Extreme values are values that are more than 3 times the interquartile range. Outliers are represented by circles beyond the whiskers; extreme outliers are represented by stars beyond the whiskers.

Error bars

Being a graphical representation of the variability of data, error bars are used on graphs to indicate the error, or uncertainty in a reported measurement. They give a general idea of how accurate a measurement is, or conversely, how far from the reported value the true, or error free, value might be. Error bars can be used to compare visually two quantities if various other conditions hold. This can determine whether differences are statistically significant. Error bars can also show how good a statistical fit the data has to a given function.

Error bars in this dissertation represent one standard deviation of uncertainty or a 95 per cent confidence interval for the mean.

Confidence Intervals

A confidence interval (CI) is a kind of interval estimate of a population parameter and is used to indicate the reliability of an estimate. As an observed interval it is calculated from the observations. How frequently the observed interval contains the parameter is determined by the confidence level. More specifically, the meaning of the term "confidence level" is that, if confidence intervals are constructed across many separate data analyses of repeated and as such possibly different experiments, the proportion of such intervals that contain the true value of the parameter will match the confidence level; this is guaranteed by the reasoning underlying the construction of confidence intervals (Fisher, 1956).

Confidence intervals consist of a range of values that act as good estimates of the unknown population parameter. However, in rare cases, none of these values may cover the value of the parameter. The level of confidence of the confidence interval would indicate the probability that the confidence range captures this true population parameter given a distribution of samples. It does not describe any single sample. After a sample is taken, the population parameter is either in the interval made or not, there is no chance. The level of confidence is set by the researcher; it is not determined by the data. If a corresponding hypothesis test is performed, the confidence level corresponds with the level of significance, i.e. a 95% confidence interval reflects a significance level of 0.05, and the confidence interval contains the parameter values that, when tested, should not be rejected with the same sample. Greater levels of confidence give larger confidence intervals, and hence less precise estimates of the parameter. Confidence intervals of difference parameters not containing 0 imply that that there is a statistically significant difference between the populations.

Certain factors may affect the confidence interval size including size of sample, level of confidence, and population variability. A larger sample size normally will lead to a better estimate of the population parameter.

A confidence interval does not predict that the true value of the parameter has a particular probability of being in the confidence interval given the data actually obtained.

Standard Deviation

The standard deviation shows how much variation or "dispersion" exists from the average. The average might be the mean, or any expected value. A low standard deviation indicates that the data points tend to be very close to the mean, whereas a high standard deviation indicates that the data points are spread out over a large range of values. In addition to expressing the variability of a population, standard deviation is commonly used to measure confidence in statistical conclusions.

Statistical Hypothesis Testing

A statistical hypothesis test is a method of making decisions using data. In statistics, a result is called statistically significant if it is unlikely to have occurred by chance alone, according to a pre-determined threshold probability, the significance level.

Statistical Hypothesis tests are used in determining what outcomes of an experiment would lead to a rejection of the null hypothesis for a pre-specified level of significance; helping to decide whether experimental results contain enough information to cast doubt on conventional wisdom. It is sometimes called confirmatory data analysis.

Statistical hypothesis tests answer the question "Assuming that the null hypothesis is true, what is the probability of observing a value for the test statistic that is at least as extreme as the value that was actually observed?" (Duncan and Howitt, 2004) That probability is known as the P-value.

The so called critical region of a hypothesis test is the set of all outcomes which cause the null hypothesis to be rejected in favor of the alternative hypothesis.

Bivariate Analysis

Bivariate analysis is one of the simplest forms of the quantitative (statistical) analysis. It involves the analysis of two variables for the purpose of determining the empirical relationship between them (Babbie 2009). In order to see if the variables are related to one another, it is common to measure how those two variables simultaneously change together.

Bivariate analysis can be helpful in testing simple hypotheses of association and causality – checking to what extent it becomes easier to know and predict a value for the dependent variable if we know a case's value on the independent variable.

The major differentiating point between univariate and bivariate analysis, in addition to looking at more than one variable, is that the purpose of a bivariate analysis goes beyond simply descriptive: it is the analysis of the relationship between the two variables.

One way Analysis of Variance

The analysis of variance (ANOVA) is a collection of statistical models, and their associated procedures, in which the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether or not the means of several groups are all equal. For this reason, ANOVAs are useful in comparing two, three, or more means.

The analysis of variance can be presented in terms of a linear model, which makes the following assumptions about the probability distribution of the responses (Anderson et al, 1996):

Independence of observations – this is an assumption of the model that simplifies the statistical analysis.

Normality – the distributions of the residuals are normal.

Equality or Homogeneity of variances, called homoscedasticity — the variance of data in groups should be the same. Levene's test is an inferential statistic used to assess the equality of variances in different samples. It tests the null hypothesis that the population variances are equal. This is called homogeneity of variance. If the resulting p-value of Levene's test is less than some critical value (typically 0.05), the obtained differences in sample variances are unlikely to have occurred based on random sampling. Thus, the null hypothesis of equal variances is rejected and it is concluded that there is a difference between the variances in the population.

The separate assumptions of the ANOVA model imply that the errors are independently, identically, and normally distributed.

In this dissertation one way analysis of variance was used to test for differences among two or more independent means.

Statistical Significance

In statistical testing, a result is deemed statistically significant if it is unlikely to have occurred by chance, and hence provides enough evidence to reject the null hypothesis. The fundamental challenge is to find if any partial picture is subject to observational error.

If tests of significance give a p-value lower than the significance level, the null hypothesis is rejected. Such results are informally referred to as statistically significant. For example, if someone argues that "there's only one chance in a thousand this could have happened by coincidence", a 0.001 level of statistical significance is being implied. The lower the significance level chosen, the stronger the evidence required. The choice of significance level is somewhat arbitrary, but for many applications, a level of 5% is chosen, for no better reason than that it is conventional (Stigler 2008).

In this dissertation, a 1%, 5% as well as 10% significance level are used.

Results

Private Market Multiples

The tables 1-3 show the results from 218 Mergers and Acquisitions of pharmaceutical, medical and cosmetics-related companies: Within the number of companies with relevant information, the Median was found by arranging all the observations from lowest value to highest value and picking the middle one. If there was an even number of observations, then the median was defined to be the mean of the two middle values. The average is the arithmetic mean of the data set.

Medical

EV/sales

EV/EBITDA

EV/EBIT

EqV/PBT

P/E

Price to Book

Nb

95

60

57

33

32

75

Median

1,3 x

8,4 x

11,1 x

11,3 x

15,0 x

3,1 x

Average

1,5 x

9,3 x

13,3 x

14,2 x

20,6 x

5,1 x

Min.

0,2 x

3,3 x

3,5 x

6,0 x

7,0 x

0,4 x

Max.

5,5 x

28,3 x

44,0 x

37,8 x

64,2 x

35,6 x

SD

1,0 x

5,0 x

8,4 x

7,9 x

13,9 x

5,6 x

Table : Private Market Multiples of the Medical sector

Pharma

EV/sales

EV/EBITDA

EV/EBIT

EqV/PBT

P/E

Price to Book

Nb

46

29

19

13

17

34

Median

1,7 x

9,1 x

13,0 x

11,5 x

15,1 x

2,4 x

Average

3,9 x

10,6 x

16,6 x

17,0 x

23,3 x

4,4 x

Min.

0,1 x

4,4 x

4,9 x

4,3 x

7,5 x

0,2 x

Max.

67,8 x

34,9 x

67,3 x

65,9 x

93,9 x

20,8 x

SD

10,2 x

7,4 x

15,1 x

15,9 x

21,8 x

4,8 x

Table : Private Market Multiples of the Pharmaceutical sector

Cosmetics

EV/sales

EV/EBITDA

EV/EBIT

EqV/PBT

P/E

Price to Book

Nb

27

18

15

10

9

18

Median

1,8 x

9,2 x

12,4 x

12,3 x

18,9 x

2,8 x

Average

1,7 x

9,6 x

13,1 x

12,7 x

18,0 x

5,1 x

Min.

0,4 x

4,1 x

5,4 x

5,3 x

7,9 x

1,0 x

Max.

3,6 x

18,7 x

26,7 x

23,1 x

31,1 x

16,9 x

SD

0,9 x

3,4 x

5,2 x

5,4 x

7,6 x

4,9 x

Table : Private Market Multiples of the Cosmetics sector

Within the Pharma Healthcare private sector there is a high standard deviation of up to 21.8xs in terms of P/E multiples for the Pharmaceutical sector with an average value of 23.3xs. For the purpose of comparison of the three sectors, Boxplots and Error bars for each Valuation Multiple are presented. M is the abbreviation for Medical firms, P for Pharmaceutical companies and C stands for Cosmetics.

For the purpose of Error bars, all outliers have been removed before the calculation of mean, confidence interval and standard deviation. That means, for the purpose of these graphs all values higher or lower than 1.5xs the interquartile range have been removed.

EV/sales

Figure : Box Plot; Comparison of EV/sales multiples for the three private sectors Medical, Pharma and Cosmetics

This graph shows that the EV/sales multiples for the Cosmetics sector are centered on the Median of 1.8xs, with a minimum 0.4xs and a maximum of 3.6xs. There are neither extreme outliers nor outliers. For that reason it can be considered the best predictable sector in terms of the EV/sales multiple. The second best predictable sector is the Medical sector with 3 outliers and a median of 1.3xs (Min. 0.2, Max. 5.5). Within the Pharmaceutical sector there are 4 outliers, 3 of them extreme. The median of 1.7xs (Min. 0.1, Max 67.8) is quite far away from the average of 3.9xs, because of the very high maximum value of 67.8xs.

The highest Median is to be found in the Cosmetics sector with 1.8xs, followed by the Pharma sector with 1.7xs and the Medical sector with 1.3xs.

Figure : Error Bar SD; Comparison of EV/sales multiples for the three private sectors Medical, Pharma and Cosmetics

The lowest mean is to be found in the medical sector, the highest one in the Cosmetics sector. However, looking at the standard deviation, there is no obvious difference of the mean between the three sectors.

EV/EBITDA

Figure : Box Plot; Comparison of EV/EBITDA multiples for the three private sectors Medical, Pharma and Cosmetics

In this graph the Cosmetics sector has only one outlier. The Pharma sector has two outliers, both of them extreme, while the medical sector has 6 outliers, three of them extreme.

The 9.2xs Cosmetics Median (Min. 4.1, Max. 18.7) is followed by the Pharmaceutical Median with 9.1xs (Min. 4.4, Max. 34.9) and the Medical one with 8.4xs (Min. 3.3, Max. 28.3).

The interquartile range of medical companies is slightly lower than that of the Cosmetics sector firms, while the pharmaceutical sectors first quartile is about the value of the medical sector and its third quartile is about the value of the cosmetics sector.

Figure : Error Bar SD; Comparison of EV/EBITDA multiples for the three private sectors Medical, Pharma and Cosmetics

The lowest EV/EBITDA multiples mean is to be found in the medical sector, the highest one in the Cosmetics sector. However, looking at the standard deviation, there is no obvious difference.

The Cosmetics sector seems to have a slightly higher EV/EBITDA multiple, while the Pharma sector encompasses the whole range of the Healthcare Pharma sector in respect to this multiple.

Compared to the EV=7.5*EBITDA average multiple of the ARGOS MID MARKET Index from December 2011, all three sectors show a higher average multiple, ranging from a 9,3xs average in the medical sector up to a 10,6xs average in the pharmaceutical sector.

EV/EBIT

Figure : Box Plot; Comparison of EV/EBIT multiples for the three private sectors Medical, Pharma and Cosmetics

In this graph the Cosmetics and the Pharma sector are the best predictable ones with only 2 outliers, one of them extreme in terms of the Cosmetics sector and both of them extreme in terms of the Pharma sector. The Medical sector has 7 outliers, 2 of them extreme.

The 13xs Pharma Median (Min. 4.9, Max. 67.3) is followed by the Cosmetics Median with 12.4xs (Min. 5.4, Max. 26.7) and the Medical one with 11.1xs (Min. 3.5, Max. 44.0).

The third quartile is quite equal for all three sectors, while the first quartile is lower for the medical sector, but almost equal in terms of the Pharma and Cosmetics sector.

Figure : Error Bar SD; Comparison of EV/EBIT multiples for the three private sectors Medical, Pharma and Cosmetics

This graph shows that the EV/EBIT multiple is the highest one, the one for the pharmaceutical sector is the lowest. However, looking at the standard deviation, there is no obvious difference.

Using this multiple, the medical sector seems to be the least predictable, while the multiples of the Pharmaceutical and Cosmetics sector are rather centered on its mean.

EqV/PBT

Figure : Box Plot; Comparison of EqV/PBT multiples for the three private sectors Medical, Pharma and Cosmetics

In this graph three outliers are to be found, two outliers for the medical sector and one extreme outlier within the Pharma sector.

The highest median is the one of the Cosmetics sector with 12.3xs (Min. 5.3, Max. 23.1), followed by a multiple of 11.5xs (Min. 4.3, Max. 65.9) within the Pharmaceutical sector and the 11.3xs (Min. 6.0, Max. 37.8) Medical sector multiple.

The first and third quartiles of the Pharma sector are slightly higher than those of the medical sector. The Cosmetics sector is the one which values are centered the best.

Figure : Error Bar SD; Comparison of EqV/PBT multiples for the three private sectors Medical, Pharma and Cosmetics

The EqV/PBT multiple of the medical sector is the highest one. However, looking at the standard deviation, there is no obvious difference.

Looking at the standard deviation the Cosmetics sector is the best predictable one, the medical sector is the least predictable one.

P/E

Figure : Box Plot; Comparison of P/E multiples for the three private sectors Medical, Pharma and Cosmetics

In this graph we find two outliers within the medical sector, one of them in the extreme range. Three outliers are to be found in the Pharma sector, one of them extreme. Again, there are no outliers within the Cosmetics sector. However, the Cosmetics sector has only 9 relevant companies for that multiple.

The 18.9xs (Min. 7.9, Max. 31.1) Median of the Cosmetics sector is obviously the highest, followed by the 15.1xs (Min. 7.5, Max. 93.9) Pharma multiple and the 15.0xs (Min. 7.0, Max. 64.2) Medical sector.

The Median of the Cosmetics sector is rather associated with the third quartile, while the Medians of the Pharmaceutical and Medical sectors are closer to the first quartile.

Figure : Error Bar SD; Comparison of P/E multiples for the three private sectors Medical, Pharma and Cosmetics

The medical sector has the highest P/E valuation multiple mean. However, looking at the standard deviation, there is no obvious difference.

P/B

Figure : Box Plot; Comparison of P/B multiples for the three private sectors Medical, Pharma and Cosmetics

In this graph the Cosmetics sector has zero outliers. The Pharma sector has two outliers and the medical sector has four outliers, of which one each is an extreme outlier.

All three Medians are obviously closer to the first quartile than to the third quartile. The interquartile range of the Cosmetics sector is about double the size of the Pharma sector, starting at about the same value for the first quartile.

The 3.1xs (Min. 0.4, Max. 35.6) Medical multiples Median is the highest one. It is followed by the 2.8xs (Min. 1.0, Max. 16.9) Cosmetics sector. The 2.4xs (Min. 0.2, Max. 20.8) Pharma multiple Median is the lowest one.

Figure : Error Bar SD; Comparison of P/B multiples for the three private sectors Medical, Pharma and Cosmetics

The P/B valuation multiple is the largest one in the cosmetics sector and the smallest one in the pharmaceutical sector. However, looking at the standard deviation, there is no obvious difference.

This valuation multiple show its highest standard deviation in the Cosmetics sector.

Valuation multiples overview of the three private sectors Medical, Pharma and Cosmetics

Looking at the multiples means and standard deviations, there is no obvious difference between the three sectors Medical, Pharma and Cosmetics.

Median

EV/sales

EV/EBITDA

EV/EBIT

EqV/PBT

P/E

Price to Book

Pharma

2

2

1

2

2

3

Medical

3

3

3

3

3

1

Cosmetics

1

1

2

1

1

2

Table : Median of the three private sectors Pharma, Medical and Cosmetics 1=Highest, 2=Middle Value 3=Lowest

This table shows an overview of the Valuation multiples of the three sectors Medical, Pharma and Cosmetics. 1 is the highest Median of the three sectors, 3 is the lowest one and 2 is the value in-between the other two Medians.

The medical sector is valued by the lowest multiple for all calculated multiples except P/B, where it’s multiple is the highest one. The Cosmetics sector shows the highest value for all calculated multiples except P/B and EV/EBIT.

It is surprising that the Pharmaceutical sector is valued lower than the Cosmetics sector in EV/EBITDA but higher in the EV/EBIT multiple. This might be a hint that in the Pharma sector maintenance capital expenditure is of different importance for investors than in the Cosmetics sector.

Another finding is that the P/B multiple for the medical companies is the only one which is not the lowest one. This might be a hint that within the Medical sector in general assets are rather a core driver of earnings than in the Pharma or Cosmetics sector.

The medical sector might be a rather capital intensive industry. The companies might rely more on a large asset base to generate profits, while the firms within the Cosmetics and Pharma sector might rather count on innovation, patents, superior technology or marketing methods. Patents and other types of intellectual property are not represented in the book value. Dissimilar depreciation policies lead to divergent book values.

The Private Medical sector

Most of the private sector Companies have been associated with the Medical sector, 139 firms in total. This sector includes Health Care Providers, Medical Equipment as well as Supplies.

Three subsectors were created. The first subsector, 38 companies, consists of Medical Equipment, Medical Implants and Prostheses Manufacturers. 63 firms were associated with the second sector, all of them Home Care firms. The third sector consists of all other companies, like Specialist Care firms, Clinics and Hospitals and Medical Supplies, in total 38 companies.

Equity Value of the Private Medical subsectors

Figure : Box Plot; Median Comparison of the private Medical subsectors in terms of Equity Value

1: Medical Equipment, Medical Implants and Prostheses Manufacturers

2: Home Care Providers

3: Specialist Care, Clinics and Hospitals and Medical Supplies

This graph shows that the Median Equity Value of the Home Care Providers is clearly lower than those of the Medical Equipment, Medical Implants and Prostheses Manufacturers as well as those of sector 3, Specialist Care, Clinics and Hospitals and Medical Supplies, respectively.

Outliers should not be considered as Outliers in this case, since they have already been excluded one step ahead. However, the three highest Equity Values have obviously been from the first subsector. This subsector shows the highest data range up to 600 million EUR.

Figure : Error Bars with SD; Equity Value Means Comparison of the private Medical subsectors

1: Medical Equipment, Medical Implants and Prostheses Manufacturers

2: Home Care Providers

3: Specialist Care, Clinics and Hospitals and Medical Supplies

Comparing the Means of the Medical subsector’s Equity Value it is obvious that the first subsector has the highest one with 64 million EUR followed by the third subsector with 55 million EUR. The Home Care subsector has a mean Equity Value of only 28 million EUR.

Using a one way Analysis of Variance one can show that there is a statistically significant (5%) difference of at least one means. The Levene Test of Homogeneity of Variances is also positive.

ANOVA

Equity M

Value (K€)

Sum of Squares

df

Mean Square

F

Sig.

Between Groups

36503468421,885

2

18251734210,943

3,270

,041

Within Groups

759086710504,778

136

5581519930,182

Total

795590178926,664

138

Table : One way ANOVA results for the private Medical subsectors’ Equity Value

Figure : Error Bars with 95% Confidence Interval; Equity Value Means Comparison of the private Medical subsectors

1: Medical Equipment, Medical Implants and Prostheses Manufacturers

2: Home Care Providers

3: Specialist Care, Clinics and Hospitals and Medical Supplies

This graph shows Error Bars with a 95% Confidence Interval for the Equity Value means instead of the standard deviations. It is more obvious to see that the Home Care Equity Value mean is indeed significantly (5%) lower. This might be an indication that the value drivers of the Home Care subsector might be clearly different from those of the other two subsectors.

A statistical significant difference, neither at a 5% nor at a 10% level, in the EqV/PBT multiple could not be found. This might be due to the fact that for 139 companies the Equity Value was available, of which 3 values were excluded for the one factor ANOVA analysis. In contrast, the EqV/PBT multiple was only available for a total of 33 firms, 14 of them in the first subsector, 11 in the Home Care subsector, and only 8 in the third subsector.

P/B Valuation Multiples of the Private Medical subsectors

Figure : Box Plot; Median Comparison of the private Medical subsectors in terms of the P/B multiple

1: Medical Equipment, Medical Implants and Prostheses Manufacturers

2: Home Care Providers

3: Specialist Care, Clinics and Hospitals and Medical Supplies

This graph shows that the Median of the Home Care Providers in terms of the P/B multiple is clearly higher than those of the Medical Equipment, Medical Implants and Prostheses Manufacturers as well as those of sector 3, Specialist Care, Clinics and Hospitals and Medical Supplies, respectively.

Outliers should not be considered as Outliers in this case, since they have already been excluded one step ahead. However, the highest P/B Multiple Values can be observed in the Home care sector. This subsector shows the highest data range from 0.6xs up to 19.7xs.

Figure : Error Bars with SD; P/B Valuation Multiples Means Comparison of the private Medical subsectors

1: Medical Equipment, Medical Implants and Prostheses Manufacturers

2: Home Care Providers

3: Specialist Care, Clinics and Hospitals and Medical Supplies

Comparing the Means of the Medical subsector’s P/B valuation multiple it is obvious that Home Care subsector has the highest one with 6.1xs. The means of the first and the third subsector with 3.8xs and 4.0xs, respectively, are considerably lower.

Using a one way Analysis of Variance one can show that there is a statistically significant (10%) difference of at least one means. The Levene Test of Homogeneity of Variances is also positive.

ANOVA

Price to Book M

Sum of Squares

df

Mean Square

F

Sig.

Between Groups

85,638

2

42,819

2,417

,097

Within Groups

1258,017

71

17,719

Total

1343,655

73

Table : One way ANOVA results for the private Medical subsectors’ P/B Valuation Multiple

The P/B valuation multiple has been only available for a total of 74 firms, 26 companies within the first subsector, 27 firms within the Home Care subsector and 21 companies within the third subsector. One can assume that if there would have been more companies available with the relevant data, the 5% significance level might be achieved.

Figure : Error Bars with 95% Confidence Interval; P/B Valuation Multiples Comparison of the private Medical subsectors

1: Medical Equipment, Medical Implants and Prostheses Manufacturers

2: Home Care Providers

3: Specialist Care, Clinics and Hospitals and Medical Supplies

This graph shows Error Bars with a 95% Confidence Interval for the mean instead of the standard deviation. It is more obvious to see that the Home Care mean is indeed significantly (10%) higher. This might be an indication that the value drivers of the Home Care subsector might be clearly different from the other two subsectors.

The Private Pharma sector

The 52 M&A deals of the Pharmaceutical sector have been divided into two different subsectors. The first subsector consists of companies associated with Pharmaceuticals, including Veterinary Pharmaceuticals. The second subsector is completed by all other firms, including Specialty Chemicals, Laboratories, Laboratory Testing Service, Biotechnology, Personal Products, Animal Feed as well as Food Complements.

EV/EBITDA Valuation Multiples of the Private Pharma subsectors

Figure : Box Plot; Median Comparison of the private Pharma subsectors in terms of the EV/EBITDA multiple

1: (Veterinary) Pharmaceuticals

2: Specialty Chemicals, Laboratories, Laboratory Testing Services, Biotechnology, Personal Products, Animal Feed, Food Complements

Looking at this graph, the Median of subsector 2 is obviously lower than the Median of subsector 1. The median of subsector 1 is even higher than the third quartile of subsector 2. The interquartile range of subsector 2 is clearly lower than its counterparts’.

Outliers should not be considered as Outliers in this case, since they have already been excluded one step ahead.

Figure : Error Bars with SD; EV/EBITDA Valuation Multiples Means Comparison of the private Pharma subsectors

1: (Veterinary) Pharmaceuticals

2: Specialty Chemicals, Laboratories, Laboratory Testing Services, Biotechnology, Personal Products, Animal Feed, Food Complements

The mean of 9.8xs in the (Veterinary) Pharmaceuticals subsector is clearly higher than the 7.4xs one in the second subsector. However, an analysis of variance shows that there is only a significant difference at a 15% significance level. Additionally, Homogeneity of variances cannot be concluded due to the Levene test.

However, only 27 Multiples have been used for this analysis, 17 from subsector 1 and 10 from subsector 2.

Public Market Multiples

The following three tables show the results from 202 listed pharmaceutical, medical and cosmetics-related companies: Within the number of companies with relevant information, the Median was found by arranging all the observations from lowest value to highest value and picking the middle one. If there was an even number of observations, then the median was defined to be the mean of the two middle values. The average is the arithmetic mean of the data set.

For the purpose of these tables, all outliers have been removed before the calculation. That means, for the calculation of these tables as well as the following graphs all values higher or lower than 1.5xs the interquartile range have been removed. The original data for all public firms is to be found in the appendix.

Medical

EV/EBITDA

P/E

P/B

Nb

59

47

81

Median

8,3x

16,4x

1,6x

Average

12,7x

17,1x

2,3x

Min.

0,8x

4,1x

0,2x

Max.

179,9x

38,6x

6,5x

SD

23,2x

7,4x

1,5x

Table : Public Market Multiples of the Medical sector in the year 2011

Pharma

EV/EBITDA

P/E

P/B

Nb

27

25

33

Median

6,4x

13,4x

1,3x

Average

7,1x

15,2x

1,5x

Min.

1,1x

3,7x

0,5x

Max.

17,9x

32,2x

4,2x

SD

3,9x

7,1x

1,0x

Table : Public Market Multiples of the Pharmaceutical sector in the year 2011

Cosmetics

EV/EBITDA

P/E

P/B

Nb

24

22

27

Median

8,8x

16,7x

1,6x

Average

9,0x

19,6x

1,9x

Min.

4,2x

5,1x

0,4x

Max.

19,0x

41,7x

5,1x

SD

3,8x

11,5x

1,3x

Table : Public Market Multiples of the Cosmetics subsector in the year 2011

Within the Pharma Healthcare public sector there is also a very high standard deviation of up to 23.2 times in terms of the EV/EBITDA multiples for the Medical sector with an average value of 12.7 xs. For the purpose of comparison of the three sectors, Boxplots and Error bars for each Valuation Multiple are presented. M is the abbreviation for Medical firms, P for Pharmaceutical companies and C stands for Cosmetics.

EV/EBITDA

Figure : Box Plot; Comparison of EV/EBITDA multiples for the three public sectors Medical, Pharma and Cosmetics

The highest Median is to be found in the