Valuing firms within the utilities sector using regression analysis1450574/...EXAMENSARBETE INOM...

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Valuing firms within the utilities sector using regression analysis:

An empirical study of the US and European market

LUDVIG HARTING

NILS ÅKESSON

KTHSKOLAN FÖR TEKNIKVETENSKAP

Valuing firms within the utilities sector using regression analysis: An empirical study of the US and European market Ludvig Harting Nils Åkesson ROYAL

Degree Projects in Applied Mathematics and Industrial Economics (15 hp) Degree Programme in Industrial Engineering and Management (300 hp) KTH Royal Institute of Technology year 2020 Supervisor at KTH: Mykola Shykula Examiner at KTH: Sigrid Källblad Nordin

TRITA-SCI-GRU 2020:115 MAT-K 2020:016

Royal Institute of Technology School of Engineering Sciences KTH SCI SE-100 44 Stockholm, Sweden URL: www.kth.se/sci

Abstract

Valuing a company is an important task in finance, especially before a potential mergeror acquisition of a company. It is then of great importance for both parties in a dealto make an accurate estimate of the value of the company. The goal of this paperis to investigate how well regression analysis can be applied in this matter and ifit can perform at par or better than more frequently used methods in the industrytoday. The study was conducted within the utilities sector in the US and Europe,with data collected from historic public transactions dating back to 2009. The studyconcludes that a regression model as a valuation tool can generate several advantagesas it identifies key value drivers and is based on core mathematical concepts. However,the model created in this thesis underperforms compared to the prominent methods inplace today. For further research this thesis may provide useful insight into differentareas to consider when creating a valuation model.

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Abstract

Att vardera ett foretag ar en viktig uppgift inom finanssektorn, sarskilt innan en po-tentiell sammanslagning eller forvarv av ett foretag. Det ar da av stor vikt for badaparter i en affar att gora en exakt uppskattning av foretagets varde. Malet med dennastudie ar att undersoka hur val regressionsanalys kan tillampas i denna fraga och omden kan generera samma eller battre resultat an mer anvanda varderingsmetoder inombranschen idag. Studien genomfordes inom el-, gas- och vattensektorn i USA och Eu-ropa, med data som samlats in fran historiska offentliga transaktioner som gar tillbakatill 2009. Studien drar slutsatsen att en regressionsmodell som ett varderingsverktygkan generera flera fordelar eftersom den identifierar viktiga faktorer som driver envardering och baseras pa grundlaggande matematiska begrepp. Modellen som skap-ats i denna avhandling underpresterar dock jamfort med de framstaende metodernasom finns idag. For ytterligare forskning kan denna studie ge anvandbar insikt i olikaomraden att beakta nar man skapar en varderingsmodell.

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Contents

1 Introduction 61.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.2 Research Question . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.3 Limitations & Feasibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.4 Purpose & aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2 Financial & Economical Theory 82.1 Enterprise Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.2 Valuation multiples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.3 Comparable Companies Valuation . . . . . . . . . . . . . . . . . . . . . . . 82.4 Discounted Cash Flow analysis . . . . . . . . . . . . . . . . . . . . . . . . . 9

3 Mathematical background 103.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113.2 Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113.3 Diagnostics and handling of outliers, leverage and influential observations . 12

3.3.1 Cook’s distance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123.4 Treatment of influential observations . . . . . . . . . . . . . . . . . . . . . . 133.5 Multicollinearity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.5.1 Variance inflation factor (VIF) . . . . . . . . . . . . . . . . . . . . . 143.5.2 Variable selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4 Methods 144.1 Data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

4.1.1 Response variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154.1.2 Covariates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

4.2 Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164.2.1 Initial model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

5 Results 175.1 Initial model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

5.1.1 Analysis of initial model . . . . . . . . . . . . . . . . . . . . . . . . . 185.1.2 Improvement of initial model . . . . . . . . . . . . . . . . . . . . . . 21

5.2 Second model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225.2.1 Analysis of second model . . . . . . . . . . . . . . . . . . . . . . . . 225.2.2 Improvement of second model . . . . . . . . . . . . . . . . . . . . . . 25

5.3 Third model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255.3.1 Summary of the third model . . . . . . . . . . . . . . . . . . . . . . 27

6 Analysis 27

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7 Discussion 297.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297.2 Covariates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317.4 Practical usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327.5 Further research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

8 Conclusion 34

9 Appendix 379.1 Collected data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

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1 Introduction

1.1 Background

Valuing a company is often described as more of an art than a science. How could one pos-sibly estimate the value of a large, dynamic corporation with a great amount of intangibleand tangible assets accurately? Add to the task an ever changing market where the com-petition and consumer preferences changes by the day. Furthermore, predicting the futurecash flows is an art itself since they heavily depend on immeasurable factors such as thestaff’s skill set, the company culture, the brand recognition, technological advancements,etc. The common notion is that accurately valuing a company is in most cases impossible.However, there are several techniques and methods which yield a range of values in whichone predicts the true value of the business to lie.

Notwithstanding the fact that the process is complex and arduous, valuing a companyis a very important part of finance. A common and important use is in the investmentbanking profession when advising a client through a company transaction such as a mergeror acquisition. During this task, it is of great importance for the acquirer, target com-pany, and underwriter to estimate the value of the target company accurately. Thus, theinvestment bank often charges a fee for providing expertise through this process, as well asadditional equity earned through the deal. Apart from the valuation, the investment bankmay also consult in the strategy of the acquisition.

An important distinction is the difference between a strategic and financial acquisition.A strategic acquisition is an acquisition of a company that is active in the same industry,which often is a supplier or customer to the company. The Purpose of such acquisitionsis often to gain synergy effects, e.g. economies of scale. This added value for the buyerthen also tends to increase the transaction value financial acquisitions, and thus generatea premium. Financial acquisitions are characterized by actors acquiring the company withthe belief that the value of the company will increase in the future and generate a returnon investment with or without active management involvement. The actor then sells thecompany or the stake that it has acquired in order to make a profit. Generally, this isdone by institutional buyers with the most common being private equity firms, venturecapitalists, and holding companies[11].

Naturally, there exist several different valuation methods, two of which will be discussedin this paper include the discounted cash flow analysis (DCF) and comparable companiesvaluation (CCV). The idea of this research is to extend on the existing CCV method inorder to improve its applicability and precision. Corporate executives often reason for sim-plicity using a single multiple, which is ill advised among corporate finance professionalsdue to the uncertainty of relying on a single metric[2]. The use of several regressors may

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solve this issue in a simple manner.

1.2 Research Question

The research questions that will be covered in this paper are

• Is it possible for an regression analysis model to estimate utility companies’ enterprisevalue within North America more accurate than the existing comparable companiesvaluation approach?

• How well does this type of valuation method fit the need of investors today?

1.3 Limitations & Feasibility

The research has been limited to utility companies in the United States and Europe duringthe years 2009-2020. The utilities industry is particularly interesting from a regressionanalysis perspective, as the sector is characterized by large material assets and stable cashflows. Firms within the sector are likely not to be regarded as growth companies, whichmakes comparable company analysis more reliable and financial measurements such asEBITDA a relevant multiple factor. Furthermore, the stable cash flows enables the DCFmethod to achieve greater precision. [10]

The sector is particularly interesting from a valuation standpoint as the current busi-ness climate allows for great synergy effects. Economies of scale, push towards growthopportunities and a consolidation within renewable energy as an effect of decreasing oilreserves and prices generates a great market for mergers and acquisitions. [9]

The study will limit data collection to the year of 2009-2020, as a longer time framecharacterized by large disruptive events will undermine the reliability of the results byskewing the data.

1.4 Purpose & aim

The aim of this paper is to create a valuation model for US and European utilities com-panies based on regression analysis and examine if it provides a more accurate result thancomparative companies valuation (CCV).[4] In order to accomplish this, one must distin-guish explanatory covariates of a company within the given sector and region’s enterprisevalue. The significance level of the covariates’ explanatory value has been set at 5%.

The purpose of the thesis is to analyse the potential applicability of a regression modelas a valuation tool for financial institutions such as investment banks, and discuss itsadvantages and disadvantages compared to the currently more widely used methods ofcomparable companies valuation (CCV) and discounted cash flow analysis (DCF).

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2 Financial & Economical Theory

2.1 Enterprise Value

When valuing a company one must first formally define the measurement used for saidevaluation. One simple measure is a company’s market capitalisation, otherwise known asEquity Value. This is the total value held by shareholders, and thus the theoretical priceone must pay to acquire the company’s total outstanding shares. [10]

Another valuation measurement that is often regarded as more realistic is enterprise value(EV), also called asset value or firm value. Unlike equity value, enterprise value capturesthe company’s total value to all stakeholders, not only equity owners [10]. The formaldefinition of enterprise value is the following:

EV = (share price× number of shares) + total debt - cash

Thus, enterprise value encompasses the total value of the assets of the company excludingcash held by equity owners as well as debt owners [1]. Enterprise value is thus the theoreticalprice one would have to pay to acquire the whole company, i.e. its equity as well as itsdebt and cash. [10]

2.2 Valuation multiples

An effective way of valuing a company is the relative approach, using multiples of previoustransactions of a similar company. The valuation theory is based on the assumption thatsimilar assets should be priced similarly, and thus that one can extract valuation multiplesfor comparable companies with regards to a specific financial metric.

For example, consider the multiple EV/EBITDA. According to theory, two companiesin the same sector and with other similarities should trade at the same multiple. Enter-prise value multiples and equity value multiples are most common, with a distinguishabledifference in that equity value multiples unlike EV multiples can be affected by the firm’scapital structure but are more easily computed as market capitalization quicker to extract.

The most common multiples are: EV/EBITDA, EV/EBIT, EV/Sales, Price/Earnings,Price/Earnings to growth.

2.3 Comparable Companies Valuation

Comparable companies valuation (CCV), or comparable company analysis is a very com-mon valuation method among particularly investment banks. The technique builds on thetheoretical foundation presented above, where similar companies should trade at similarvaluation multiples such as EV/EBITDA, EV/Sales and Price/Earnings.

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An important part of CCV is establishing a ”peer group” according to a set of criteria,often business industry classification, region, size, growth rate, and profitability. Establish-ing an efficient peer group can be difficult as it is very subjective and requires experience.A peer group with several companies that closely resembles the target company will allowthe comparable valuation method to be precise, and thus this step is highly important. [10]

As the peer group has established, a table of financial information for each company such asmarket capitalization, net debt, EV, Sales, and EBITDA is set up. Subsequently, multiplesfor each comparable company within the peer group is calculated and analysed, data fromwhich the target company valuation can be calculated. Most often, one takes the medianor mean of the multiples of the peer group and apply them to the target company with acertain interval. [10]

CCV is widely used in the financial industry because of its simplicity, where data is easilyextractable and the results easily explained in a pitch to the target company’s owners.Another positive aspect of CCV is that it reflects the current market sentiment and is notsensitive to assumptions like the DCF method. CCV provides a highly realistic valuationmethod if the companies in the peer group are very similar, but as no two companiesare exactly alike there will always exist discrepancies that CCV may not capture but willultimately affect the real valuation. Furthermore, comparables are highly sensitive to tem-porary market conditions and non fundamental factors which can alter the extracted valuedisproportionally in the short term and leave an over- or undervaluation of the wholeindustry unnoticed. [5]

2.4 Discounted Cash Flow analysis

While CCV is based on relative measurements, discounted cash flow analysis is an intrin-sic valuation method where the net present value of future cash flows is estimated. Thisvaluation is the most theoretically correct method of estimating the enterprise value of afirm, as it computes the present value of future free cash flows to all both equity- and debtholders [1].

The formula for discounted cash flows is

DCF =

t=n∑t=1

CFt(1 + r)t

where CFt is free cash flow at time t and r is the used discount rate. The discount rate isalso called WACC (Weighted Average Cost of Capital, and is defined as the average rate ofreturn investors expects for a given year [1]. The expected cash flows are usually estimatedfor a period of 5 years into the future, and is complemented with a terminal value. The

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terminal value is calculated either through an exit multiple, where the business is assumedto be acquired, or perpetual growth at a fixed rate. The exit multiple is usually extractedfrom a CCV analysis, for example extracting the mean EV/EBITDA multiple of similarcompanies, whereas the perpetual growth rate is a realistic but conservative growth rateof 2-4%. [10]

The DCF analysis provides the advantage of estimating the intrinsic value of a firm basedon its fundamentals, not by comparing to other companies. The method thus serves asthe foundation on which other valuation methods are built by capturing the underlyingdrivers of value, and comes closest to the real intrinsic value of a company. Unlike theCCV method, DCF is not blind to sector under- and overvaluations.

The greatest disadvantage of the DCF method is that it is very sensitive to assumptionsregarding WACC and perpetual growth rate. Small changes in these assumptions will causegreat fluctuations in the estimated value, and thus an effective valuation requires preciseassumptions that are very hard to make without a high degree of confidence. This is notvery likely, as perpetual growth rate is almost impossible to estimate accurately.

3 Mathematical background

Regression analysis is a powerful and widely used technique to examine the influence of aselected set of prediction variables on a response variable. The research will be based on alinear regression model, which general form can be described by

yi = β0 + x1,iβ1 + ...+ xn,iβn + εi

where y is the response variable, βj are the regression coefficients, xi are the observed valuesand εi is the error term. The model can conveniently be described in vector notation as

y = Xβ + ε

where

y =

y1y2...yn

X =

1 x11 x12 · · · x1p1 x21 x22 · · · x2p...

.... . .

1 xn1 xn2 · · · xnp

β =

β0β1...βn

; ε =

ε1ε2...εn

The model is fitted by the ordinary least squares approach on a data set containing nobservations of the response variable and the corresponding p prediction variables. That

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is, find the parameter β which minimizes the squared sum of the residuals, ei. The residu-als are the distances from the observed values y and the model’s predicted values y. Thus,the parameter β that minimizes the squared sum is calculated by

β = argminβ

(y −Xβ)′(y −Xβ)

where β is the estimate of the true regression coefficient β.

y = β0 + β1x1 + β2x2 + ...+ βpxp

3.1 Assumptions

In regression analysis there a a few underlying assumptions regarded the fit of the modelthat deserve great care in the analysis, since a violation of these may make the modelmisleading or erroneous. The assumptions are:

1. There is a linear, or at least approximately linear, relationship between the responsey and the regressors.

2. The error term ε has zero mean.

3. The error term has constant variance σ2.

4. The errors are uncorrelated.

5. The errors have a normal distribution.

It is important to conduct analyses to examine the validity of these assumptions as grossviolations can lead to an unstable and unreliable model. The methods of diagnosing thebasic regression assumptions are primarily based on studying the model’s residuals anddifferent residual plots. This form of model adequacy checking must be done for everymodel that is under consideration to be used in practice. If a violation is detected, onemust alter the model in order for the assumptions to be true, which is often done by datatransformation.

3.2 Transformations

Plots of the residuals are a very powerful tool for detecting violations of the basic regres-sion assumptions. When a violation is present one must perform a transform in order tocorrect for this model inadequacy. It it not unusual that the regressors are expressed in theincorrect scale of measurement or metric causing inequality of variance. Ideally, the choiceof metric should be made by an analyst with knowledge of the subject that is examined.However in many cases the this information is not available and a data transform may thenbe chosen by a trial and error approach or by some analytical procedure.

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Furthermore, one may may realize that there is not a linear relationship between theresponse variable and the regressors which is a critical assumption for the model. It is thenappropriate to linearize the model by a suitable transformation, when this is possible themodel is said to be intrinsically linear. An example of an intrinsically linear model is theexponential function, where a logarithmic transformation yields a straight line.

3.3 Diagnostics and handling of outliers, leverage and influential obser-vations

An observation with an unusual x and y value may have a great influence over the model’sfit. This situation is undesirable because the model is supposed to be representative of theall the observations and not be overly influenced by a few extreme cases. It is thereforeimportant to detect and handle these odd observations.

Although an observation may have an unusual x-value, its corresponding y-value mayfall near the regression line. This is called a leverage point. It will not affect the fit of theregression, but will affect the model summary statistics such as the R2 and the standarderrors of the regression coefficients. An influence point on the other hand will also havean unusual y-value, causing it to alter the regression line in its direction. The amount ofleverage depends on its location in x space and can be determined from the hat matrix,H = X(X ′X)−1X ′. The elements hij can be interpreted as the leverage exerted by the ithobservation yi on the jth fitted value yj . The further away the observation is in regardsof the centroid, the greater the leverage that point has. A point is remote enough to beconsidered a leverage point if the hat diagonal exceeds 2p/n, given that 2p/n > |1|. Anoften used method for measuring the observations’ influence and the model’s fit is Cook’sdistance which will be further explained in this section.

3.3.1 Cook’s distance

As described above it is important to take both the location of the point in x space andits response variable into account when determining the observation’s influence. Cooksuggested a way of dealing with this by a measure between the least square fitted coefficientsβ and the least square fitted coefficients with the ith observation deleted, ˆβ(i). This measurecan be calculated by

Di =r2ip

V ar(yi)

V ar(ei)=r2ip

hii1− hii

Generally, a good criteria for determining influential points as outliers is the thresholdD(i) > 4/n.

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3.4 Treatment of influential observations

Diagnostics of the observations leverage and influence is an important tool in order toidentify important observation that deserve more attention. It can be difficult to decidewhether or not to discard these observation and it is important to make that decision withcare since they have a large influence on the model. Generally, if there was an error whilecollecting the data if the data point is not a part of the population that was intended tobe sampled, the observation may be invalid and can be discarded. However, if the datacollected can not be proven invalid there is no justification for having it removed, even ifit has a large influence on the model.

3.5 Multicollinearity

In a perfect model, there exists no linear relationship between the prediction variables,and they are then said to be orthogonal. On the contrary, if the prediction variables areperfectly linear combinations of each other, problems arise when trying to solve for β inthe OLS equation and one quickly realizes that there exists no solution.

The problem of multicollinearity is said to exist if there is near linear relationships be-tween regressors in the model. The presence of multicollinearity may lead to misleadingor inaccurate inferences, which if the regressors were orthogonal would easily be avoided.Such inferences include:

1. Identifying e relative effects of the regressor variables

2. Prediction and/or estimation

3. Selection of an appropriate set of variables for the model

The multiple regression model is described in section 2 as

Xβ + ε

where y is an n x 1 vector of the response variables, X is an n x p matrix of the regressorvariables, and ε is a n x 1 vector of the normally distributed randos. Let Xj be the jthcolumn of the X matrix. The vectors of Xj are said to linearly dependent if there is a setof constants kj , not all zero, such that

p∑j=1

kjXj = 0

If the equation above holds exactly for a subset of X, the rank of X’X is less than p, andits inverse does not exist. However, if the equation holds approximately, multicollinearityis said to exist.

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In order to adjust for multicollinearity and analyze the data correctly, it’s important toidentify and understand its sources. There are three primary sources of multicollinearity:

1. The data collection method2. Constraints on the model or population3. Model specification

3.5.1 Variance inflation factor (VIF)

One method of detecting multicollinearity is by analyzing the Variance inflation factor(VIF). The diagonal elements of the matrix C = (X′X)−1 can be written as Cjj =(1− R2

j )−1, where R2

j is the coefficient of determination obtained when xj is regressed onremaining the p − 1 regressors. Thus, as xj goes towards orthogonality to the remainingp−1 regressors, R2

j goes towards zero and consequently Cjj to unity. If xj is almost linearly

dependent on some subset of the remaining regressors, R2j is closer to unity and Cjj very

large. The variance inflation factor (VIF) is thus defined as

V IFj = Cjj = (1−R2j )−1

If one or more larger VIFs are identified, it indicates multicollinearity within the model.Usually, a VIF that exceeds 5 or 10 indicate that associated regressor coefficients may bepoorly estimated, while smaller VIFs indicate smaller multicollionearity.

3.5.2 Variable selection

Among the most common corrective techniques for multicollinearity is variable selection,yet it does not guarantee full elimination as there are cases where several regressors arerelated but some subset of them still belongs in the model. Selecting the ”best” regressionmodel is the process of compromising the trade-off between having as many regressors aspossible and thus having more information affecting the predicted variable y, and havingas few regressors as possible and thus minimizing the variance of the prediction value y.

4 Methods

The method used for this research was largely based on the framework for regressionmodel building presented by D. Montgomery et al in Introduction to Linear Regression[8].After having collected the relevant data, the basic approach is first to fit the full modelwith all covariates and then perform a thorough analysis of the model, including residualanalysis, investigating possible multicollinearity and handling outliers. If one then detectsviolations of the basic assumptions, a transformation would be needed to correct for themodel inadequacies. Then, one needs to test the significance of the covariates and select

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the optimal subset with regards to a given criteria. Lastly, the final model needs to bethoroughly analyzed in order to determine its adequacy and performance.

4.1 Data collection

Finding relevant data is a difficult task, especially when dealing with financial data asmany firms rarely report more than the are obliged to. Thus, the supply of financial datawithin the utilities sector in the US and Europe is limited. The data collected for thisproject was downloaded from Merger Markets, which is a financial database containing arecord of historic MA transactions and some financial information of the target companies.Although the financial data is quite limited from this source, it is believed to still be suffi-cient for this thesis.

In addition to the data collected from Merger Markets, the Dow Jones Utility Average(DJUA) of the time of transaction was added to as it can reflect the macro economic envi-ronment during the time of transaction. The daily close of the index was downloaded fromyahoo finance, dating back to 2009.

In total, 249 observations with disclosed financial information were obtained from transac-tions between 2009 and 2020, with six covariates that were relevant for the model.

4.1.1 Response variable

Enterprise value

The response variable, i.e the variable which is to be estimated by the regression model,is enterprise value (EV). This metric is often used to valuate a company for a potentialacquisition. EV measures the total value of the company and is often used as a morecomprehensive alternative to market capitalization.

4.1.2 Covariates

Revenue

Revenue, or sales, is the total income of a company generated by its business operations.Since revenue include the income from all business activities, it is a good indicator of thesize of the company.

A frequent reason for using sales as a value driver is that earnings and cash flows maybe negatives and in these cases the revenue multiple will convey more information thanthe negative earnings [7]. Although revenue has its advantages in these situations, it mayoften be an inconsistent value driver as the value of a company is heavily dependent on

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future cash flows which can vary greatly among firms that have similar incomes.

EBITDA

Earnings Before Interest, Taxes, Depreciation and Amortization, or EBITDA, is a measureof a company’s overall financial performance and profitability. EBITDA is a widely usedmetric for valuation as it is a precise measure of corporate performance since it shows thecompany’s earnings before being influenced by accounting and financial deductions. How-ever, one has to keep in mind that in some occasions it may be misleading as it does notinclude the cost associated with capital investments such as property, plant and equipment.

EBIT

Earnings before interest and taxes (EBIT) is, as one’s intuition would suggest, simplycalculated by the revenue subtracted with expenses excluding interest and tax. EBIT isoften also referred to as operating earnings or operating profit.

Earnings

Earnings, or net income, is the bottom line on the income statement and is the finalprofit after having paid all expenses, including interest and tax. Earnings is the most reli-able indicator of a company’s success and therefore an important covariate for this analysis.

Dow Jones Utility Average

Dow Jones Utility Average (DJUA) is a stock index that aims to represent the perfor-mance of large utility companies in the US by keeping track of 15 prominent firms. AddingDJUA to the model is believed to adjust the valuation according to the overall marketconditions at the time of transaction.

4.2 Data Processing

The data set is processed in R which is a free software environment for statistical computing.It is widely used among statisticians for developing statistical software and analysis. It waschosen for this project as it implements a wide variety of statistical techniques for linearmodeling.

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4.2.1 Initial model

A multi linear regression model was initially fitted with all regressors under consideration.The linear model is on the form

Y = β0 + β1X1 + β2X2...+ β5X5 + ε

The variables are presented in table 1 below.

Y Enterprise ValueX1 RevenueX2 EBITDAX3 EBITX4 EarningsX5 Dow Jones Utility Average

Table 1: Variables for initial model

5 Results

5.1 Initial model

Estimate Std. Error t value Pr(>|t|)(Intercept) -3947.1311 1271.9292 -3.10 0.002141

Revenue -0.1090 0.0618 -1.76 0.079155EBITDA 11.8079 0.9430 12.52 <2e-16

EBIT -7.2290 1.7189 -4.21 3.67e-05Earnings 8.2292 1.8881 4.36 1.93e-05

DJUA close 8.8664 2.2482 3.94 0.000105

Table 2: Summary of initial model

R2 0.74R2adj. 0.7347

Table 3: Initial model

A brief summary of the initial model is presented in table 2. One can clearly see thatall regressors except for revenue have a significant explanatory value of the enterprisevalue of a utility company. The R2 of the initial model was computed to 0.7519 meaningthat these regressors can explain 75% of the variance of the response variable. Althoughthese initial results are promising, a major flaw is the negative signs of the revenue- and

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EBIT-coefficients, which imply a reverse relationship between enterprise value and the twocovariates. A common reason for a coefficient having the wrong sign is that the modelsuffers from multicollinearity which will be further analysed later in this section.

5.1.1 Analysis of initial model

Presented below is an analysis of the initial model in accordance with the theories pre-sented in section 3.

Fitted Values

Figure 1: Initial model - fitted values

As can be seen from the plot above, the model generates negative company valuation inseveral cases. A negative enterprise value is not necessarily a problem, and can be thecase when the company has little debt and the market value is below the value of its cashequivalent. This happens especially in bear markets. As data was imported in descendingorder based on date, it can be seen that most negative valuations are on transactions be-tween 2009-2010 from the extracted data. This brings potential explanation, as the yearsfollowing the financial crisis of 2008 were characterised by a bear market, but the relativelyhigh number of negative valuations still poses a problem as it should be rather unusual forthe market to value a company with low debt below its cash equivalents. Thus, this willbe corrected in subsequent models.

Normality

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Figure 2: Initial model - Normal QQ plot

Shown in the Q-Q-plot above is a distribution with considerably lighter tails than a normaldistribution. This poses a problem as an assumption of Ordinary Least Squares is for thestandardized residuals to follow a normal distribution.

Multicollinearity

Ind. variable VIF

Revenue 2.738671EBITDA 6.812031

EBIT 10.516609DJUA 1.108537Region 1.097706

Table 4: VIF for independent variables

The general rule is that a VIF factor above 10 indicates multicollinearity. As can beseen, the model is not characterized by high multicollinearity, except for EBIT. This isperhaps rather surprising, as Revenue, EBITDA, and EBIT are derived from a unified andcorrelated financial statement.

Influential points

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Figure 3: Initial model - Cook’s distance

The plot above shows the Cook’s distance for the extracted data points according to thedescription in 3.3.1. As can be seen, there are several outliers, but observation #46 standsout with a value above 30. Thus, there are several influential points which can skew themodel and need to be dealt with.

After investigating each influential observation a common theme was that several of theseobservation had negative earnings and some even negative EBIT. Consequently, since themodel acts similar to a multiple valuation the results of having negative metrics will havecause negative valuations on the companies. The issue was solved by limiting the dataset to only include observations with positive margins. In effect, the model will only beapplicable for companies meeting this criteria.

Variance

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Figure 4: Initial model - Residuals vs fitted values

As presented in under 3.1, one of the assumptions of Ordinary Least Squares is that theerror term has constant variance. The plot above of the residuals vs fitted values shows thatthe said assumption probably is not the case. One can distinguish a fairly clear ”outwardscone”, which suggests the residual variance is linearly correlated with the magnitude of thefitted values. For the assumption to hold, this must be corrected in a subsequent model.

5.1.2 Improvement of initial model

As has been analysed, several improvements need to be made to the model, in part tofulfill the assumptions of OLS, and in part to provide a better performing model. Firstly,one need to adjust the model to fulfill the assumption of normally distributed residuals.This is done by performing a square root transformation of the dependent variable, whichalso hypothetically would stabilize the variance. Secondly, all data points with negativecovariate values, most often EBIT, are removed as the model performs poorly in theseinstances and is heavily influenced by these points. Lastly, a no-intercept restrictions isimposed as it is assumed that a company with a value of zero for all covariates also willbe valued at zero. This adjustment is viable as the intercept of the initial model is fairlyclose to zero. Furthermore, Region is removed from the model as it is deemed unrealisticto add a value to all US companies regardless of size. Lastly, the Dow Jones Utility Indexis weighted in the model to provide a more dynamic and realistic affect of the macroenvironment on individual companies, rather than just a set mark-up or mark-down for allentities regardless of size.

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5.2 Second model

In the second model, a square root transformation was performed, zero-intercept restrictionimposed, and reduction of data set to positive only covariates set. These adjustments weremade in order to achieve greater normality among the standardized residuals, correct fornegative enterprise values, stabilize the variance, and correct for heavily influential points.Furthermore, Region was removed from the model and the dependent variable divided bythe absolute value of the DJUA index to provide a weighted affect on the companies.

Estimate Std. Error t value Pr(>|t|)Revenue -0.0012 0.0006 -1.97 0.050411

EBITDA 0.0799 0.0104 7.67 7.17e-13EBIT -0.0637 0.0189 -3.38 0.000869

Earnings 0.0444 0.0115 3.87 0.000147DJUA close 0.0487 0.0035 13.81 < 2e-16

R2 0.8942R2adj. 0.8916

Table 5: Second model

5.2.1 Analysis of second model

Fitted Values

Figure 5: Second model - Fitted values

As can be seen, the fitted values of the dependent variable are now positive, which will

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yield a better performing model for those companies with positive covariates.

Normality

Figure 6: Second model - Normal QQ plot

The square root transformation of the dependent variable improved the fit of the residualsto the normal distribution curve significantly, which can be seen in the Q-Q plot above.This fulfills the OLS assumption and allows for further analysis of the model.

Multicollinearity

Ind. variable VIF

Revenue 3.705776EBITDA 39.154518

EBIT 46.589236Earnings 6.182972

DJUA 1.305640

The adjustments in the model increased the VIF of EBITDA and EBIT significantly. This isnot surprising, as they both are highly correlated and originate from the income statementof the company. Apart from inflating the estimates and variances, the presence of highmulticollinearity also tends to cause the confidence interval for the regression estimates tobecome very wide and the statistics very small. Thus, it becomes difficult to reject the nullhypothesis of any study where multicollinearity is present in the data under study. It canbe seen in table 6 that the confidence interval of the EBIT coefficient estimate is indeedvery wide. This multicollinearity and poor estimate is handled in the third model.

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2.5 % 97.5 %

Revenue -8.942938e-05 1.011914e-05EBITDA 2.358830e-03 4.129233e-03

EBIT -4.045037e-03 -8.408365e-04Earnings 1.128717e-03 3.077828e-03

Table 6: Confidence intervals of the regression coefficients

Influential Points

Figure 7: Second model - Cook’s distance

It can be seen that the most influential points of the initial were those that provided anegative fitted value. When removed, there are still several influential points, but to amuch lesser degree which can be seen as an improvement of the model. Variance

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Figure 8: Second model - Residuals vs fitted values

As can be seen from the plot above, the residuals follow a pattern that resembles that ofa constant variance much more than the initial model. The square root transformation ofthe dependent variable thus succeeds to fulfill another assumption necessary for OLS.

5.2.2 Improvement of second model

The second model presented high multicollinearity in EBITDA and EBIT. In order tocombat this, EBIT will be removed from the model with the hypothesis that it will notgreatly affect R2

adj..

5.3 Third model

In the third and last model, EBIT was removed as covariate in order to reduce the multi-collinearity within the model.

Estimate Std. Error t value Pr(>|t|)Revenue -0.0001 0.0000 -2.02 0.04436

EBITDA 0.0021 0.0002 9.37 < 2e-16Earnings 0.0015 0.0005 3.20 0.00157

DJUA close 0.0019 0.0002 12.38 < 2e-16

R2 0.8894R2adj. 0.8873

Table 7: Third model

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The third and final model include Revenue, EBITDA, Earnings and the closing price of theDow Jones Utility Average Index on the day of the announcement of the transaction. TheR2adj. was reduced marginally when removing EBIT, and is still 0.8873 which is deemed

fairly high in relation to the high variance of enterprise values in the data set. Furthermore,all independent variables contribute to the model with a significance level less than .05.

Multicollinearity

Ind. variable VIF

Revenue 3.616314EBITDA 9.021762Earnings 4.992659

DJUA 1.303615

As can be seen in the table above, removing EBIT from the model reduced the VIF ofEBITDA to below the threshold of 10. This is not surprising, as EBITDA and EBIT bothstem from the income statement of the entity with the difference being depreciation andamortization, and thus are highly correlated. With all VIF factors being less than thegeneral threshold level of 10, the model is not considered to suffer from excessive multi-collinearity.

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5.3.1 Summary of the third model

Neither the distribution of the standardized residuals, the variance of the residuals, andfitted values are altered significantly with the third model. The Cook’s Distance increasedsomewhat for a number of observations, as the removal of EBIT might have caused someobservations that drove the EBIT regressor to become more influential. Still, no valueexceeds 1 and the model is thus not considered to contain highly influential outliers. Inconclusion, the third model fulfills the assumptions of OLS as the standardized residualsfollows the normal distribution curve with marginal errors, and presents what can beinferred as a constant residual variance. Furthermore, the fitted values provides a fairlyrealistic view of the span in which the observed entities might fall within Enterprise value.

6 Analysis

In order to test the predictability of the model, a new data set from Merger Markets wasretrieved on which the model estimated the enterprise values of 25 different companies.The results are found in table 7. Noted is a fairly large deviance from the true enterprisevalues and the mean absolute error is roughly 36%.

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Target Company Enterprise Value (Millions e) EV-estimate (Millions e) ∆ |∆|Energy Transfer Partners, L.P. 51605.2082 86155.0887 67% 0.669503752Williams Partners L.P. 46954.3028 36873.01802 -21% 0.214704174MPLX LP 27782.8918 11786.98906 -58% 0.575746501Spectra Energy Partners, LP 23758.6453 10704.8076 -55% 0.54943527Spectra Energy Partners, LP 21812.6808 9253.555069 -58% 0.575771765Calpine Corporation 14495.2486 15057.80474 4% 0.038809692Andeavor Logistics LP 12564.9829 19715.49793 57% 0.56908275Andeavor Logistics LP 11843.3546 19715.49793 66% 0.664688645Western Midstream Operating, LP 10616.7703 14695.59193 38% 0.384186671EnLink Midstream Partners LP 9111.0438 9709.571395 7% 0.065692538Enbridge Energy Partners LP 8965.6369 15050.1884 68% 0.678652456EQGP Holdings, LP 8383.238 8558.102358 2% 0.020858809Energen Corporation 8009.5238 8872.949376 11% 0.107799864RSP Permian, Inc. 7665.9708 7545.603234 -2% 0.015701542Great Plains Energy Inc 7229.2192 11895.83314 65% 0.645521157Antero Midstream Partners LP 6330.18 8918.37652 41% 0.408866181WGL Holdings Inc. 6227.4827 7402.434725 19% 0.188672066Cheniere Energy Partners LP Holdings, LLC 6184.5572 4745.941718 -23% 0.232614144Antero Midstream Partners LP 6171.337 8918.37652 45% 0.445128749Pattern Energy Group Inc. 5469.3804 4918.582976 -10% 0.100705635Tallgrass Energy Partners, LP 4418.6613 5481.23412 24% 0.240473924Dominion Energy Midstream Partners 4400.0974 5430.596152 23% 0.234199077Peoples Natural Gas Company, LLC 3728.0893 4491.20503 20% 0.204693522Anadarko Petroleum Corporation 3518.2264 5586.801701 59% 0.587959689KLX Inc. 3471.1622 5813.267229 67% 0.674732235Average 0.363768032

Table 8: Performance of final model

There has been extensive research of the accuracy of different valuations methods. PeterHarbula conducted a research for multiple valuation and evaluated the accuracy of differentmultiples and concluded that the EV/EBITDA-multiple shows the most consistent results.Within the energy sector, Harbula estimated the mean absolute error of the EV/EBITDA-mulitple at 21% [2]. Although it may be difficult to determine an absolute value for theaccuracy of CCV as it is heavily dependent on the occasion, the number presented abovemay still be used as an indication of how well one would expect a CCV to perform. Addi-tionally, the more extensive approach, DCF analysis, have also been thoroughly analyzed.The use of a DCF analysis in valuation purposes provide even better accuracy. Shahedet al estimated the mean absolute error of an DCF valuation at 16.88%[3], by conduct-ing a comprehensive content analysis of equity research reports for most of the firms onthe components list of the Dow Jones EuroStoxx 50 Index. Comparing the results of theregression model with the prior research of CCV and DCF, it is clear that the regressionmodel in this case performs poorly in comparison.

Also noted from the results of the regression model is that it performs poorly amongsmaller firms as it has a large relative deviation from the true enterprise value. The simplereason for this lies within the fundamental assumptions of the regression model - That theerror term ε has constant variance. Thus, the errors are of the same magnitude in absoluteterms among the larger and smaller firms, which causes the relative deviation to be inflatedamong the smaller companies. In order to mitigate for this problem, a limited interval ofthe company size would have been appropriate. However, the limited data available for thisproject made just that unfeasible since it would cause more problems with the reliabilityof the model.

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Furthermore, the data consists of transactions from both the US and Europe. The reasonfor having data on two different markets was simply due to the fact that there was notenough data available for a single market. A problem arising with this is that the differentmarket conditions in which the companies operate within will potentially have an effect onthe valuation of the companies. A market characterized by stability and prosperity willprobably have a positive effect on the value of the companies in that market. There arealso different rules and regulations among different countries which will affect its actorsdifferently. For example the corporate tax rate varies notably among countries which willhave a direct affect on the companies financial. The regression model does not take thesegeographical factors into account, unlike the traditional CCV valuation where it is crucialto only compare companies which operate under the same market conditions.

The greater weaknesses from multiples valuation are inevitable inherited into the regressionmodel. The underlying assumption of the model that two companies can be compared inan absolute manner is incorrect. Every company is unique and has its own competitiveadvantages and disadvantages. The fact that the regression model is a generalized modelof an entire industry inflates this issue. Additionally, Marc Goedhart et al argues in ”Theright role for multiples in valuation” to only use peers with similar prospects for ROICand growth [5]. Finding the right companies is challenging and usually starts with an ex-tensive examination of the industry. Although being very time consuming it is importantfor the accuracy of the valuation. This critical component of valuing a company preciselywith multiples is disregarded when creating a regression model across an entire industry,naturally this follows with poor performance of the model. Lastly, the data from which themodel was based does not specify if it is a strategic- or a financial acquisition. This addsanother level of discrepancy as strategic acquisition are often accepted at i higher premiumdue to the synergy benefits achieved.

7 Discussion

7.1 Data

As of writing this report, the market has experienced a tumble amid the Covid-19 crisis.Most companies have decreased in implied value by the market, and the situation is inmany aspects unique. The data on the transactions was collected from the fiscal year of2009 to end of march 2020. Thus, the data set incorporates many macro economic fluctu-ations, but might fail to encompass the wide uncertainty and new reality that individualsand companies face in in the midst and post Covid-19. Subsequently, the results may beskewed and the model not perform as well as hopes in the near future. However, it shouldadapt somewhat as the the DJUA covariate reflects the current general market sentiment,and thus adjusts the value accordingly even for companies that manage to endure this crisis

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with a relatively intact income statement.

When deciding to extract data from a longer time period, the risk of developing a modelthat could not perform due to the highly changing global environment during that timewas discussed. On the other hand, it was observed that the greater number in data pointsthat the longer period provided lead to a more precise model with lower significance levelfor all covariates. The larger sample size allowed for better estimations in the regressionmodel, and thus the longer time frame was with the inclusion of the Dow Jones UtilityAverage index as a covariate in order to identify the current general utility market sen-timent. This should theoretically mean that the model is not static but rather dynamic,and thus provide useful results not only during the given time period but afterwards as well.

In the initial model, the standardized residuals did not follow a normal distribution. Inorder to solve for this, two solutions were proposed: gather a larger samples size or trans-form the dependent variable. The first was not possible, as only 173 transactions disclosedthe full financial information needed for the model. Thus, a square root transformationwas made which also solved for the constant variance of the residuals.

7.2 Covariates

The final model included the covariates Revenue, EBITDA, Earnings, and Dow Jones Util-ity Average index. All covariates except for DJUA are derived from the income statement,which implies that there is large correlation between the covariates as they are derivedfrom each other. However, the most important tools in order to value a company are thefinancial statements, from which one can calculate the firms cash flow and project futurecash flows from which the fundamental value of a company is derived. Thus, the covariatesfrom the income statement are very important in order to determine the entity’s enterprisevalue, and indeed belong in the model.

EBIT was removed from the model as it drove the multicollinearity to unacceptable lev-els which would make the results unreliable and skewed. When removed, the model lostvery little in R2

adj. which was expected as EBIT and EBITDA contain mostly the sameinformation. Region was removed as a dummy variable as it was deemed unrealistic thata company, regardless of size, would have a set premium added if it originated from aspecific continent. It is not the value-add per se that was a problem, but that the sameaddition was made on all companies, which especially would make the model overvaluecertain smaller companies greatly.

Lastly, it would have been very interesting to include more covariates in the model suchas Total Assets and Leverage. Unfortunately, it was not possible to gain that kind of dataefficiently enough through given sources, and it was ruled out. However, if future revision

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of the model is made, Total Assets and Leverage in particular would two very interest-ing variables to investigate. Utility companies often have large material assets on theirbooks and the stable cash flows allow for high leverage. Thus, including these variables ascovariates can hypothetically make the model better performing.

7.3 Methodology

The resulting regression model and its performance is a direct determinant of the method-ology used to achieve it. In this project, a set of hypothetical covariates was determinedbeforehand and data gathered from Merger Market to form an initial model. Subsequently,updated versions of the model were iterated, were covariates were removed based on VIF-tests and assumptions fulfilled through transformations on the dependent variable. Theiterative process might have yielded a slightly different model if exclusion tests such as BICor AIC were applied, but it was deemed enough to recognize a high VIF value and con-clude that the R2

adj. was left practically unchanged when, for example, EBIT was removed.Thus, EBIT contributed little to the model but drove multicollinearity as it was heavilycorrelated with EBITDA.

It became evident that the initial set of covariates were not enough, and that data onfor example total assets and leverage would hypothetically have provided a better modelwith more explanatory value and nuance. The final model in this thesis was very similarto a simple comparative company analysis, and a larger set of covariates may have allowedfor performance that would capture some of the aspects of a valuation that CCV misses.However, there is always a trade off between having many covariates and thus a large ex-planatory value with the risk of suffering from large multicollinearity.

Lastly, a shortcoming is the wide range of companies that was used in this model. The util-ities sector is very varied with many distinct sub-sectors with completely different businessmodels and thus foundations for valuation. A solar panel producer with private consumersas clientele might depend heavily on a strong brand entity and marketing, while an oldoil refinery instead create value through supply chain efficiency and client relationships.Furthermore, there was no limit of company size when extracting data, which proved tolimit the result of the model as the deviation from the true enterprise value was very largerelatively for small companies while small for larger companies. The model might havebeen more effective if the scope of companies in relation to size was more limited, whichwould have reduced the relative deviation even if the total squared residual was the same.However, a specialization in a specific sub-segment of the utilities sector as well as the sizeof the companies would have reduced the amount of data available and made the modelless statistically significant.

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7.4 Practical usage

The purpose and aim of this thesis was to evaluate if a regression model could provide avaluation method that outperformed CCV and furthermore to be used by professionals inthe financial industry. In particularly the investment banking industry, which involves ininvesting and advisory on transactions, such a model could be of great use.

It is important to remember that valuing a company is more than a strict science, andrequires vast experience to be done efficiently. Included in the valuation methodology ismuch more than information on the income statement and the current macroeconomic envi-ronment, which this model tries to encompass. An interview with an unnamed investmentbanker at Morgan Stanley confirms this and states that oftentimes the most difficult partof a valuation is valuing a company’s intangibles such as a strong brand identity, in-housetechnology systems, loyal clientele, a thriving company culture etc. These are highly rele-vant factors for an entity’s future success and thus its value, but is hard to account for in amathematical model such as the regression model this thesis proposes and CCV. In prac-tice, if a company is identified to have a higher value than what is captured in a model basedon the financial statement, a premium is applied which might stem from raw valuation ofthe intangibles, but most often by comparing it to the highest performers of the peer group.

The interviewee noted that the regression model might be more suitable for industriesnot characterized by high intangibles, and that this makes the utilities sector especiallyinteresting. Companies withing the utilities sector are not as dependent on brand entityand other intangible asset such as for example the technology and media industry. Thesector in general is characterized by high tangible assets and low intangible assets, whichmight make it well suited for a regression model. However, an old and very large oil andgas refinery differ greatly from a startup reinventing hydroelectric power generation butstill in the RD state. Thus, the industry provides a wide spectrum of different businessmodels, why the regression model might fail to perform consistently on its own withoutindividual tampering on a stand-alone basis.

Furthermore, a big part of the investment banker’s job is to present the valuation andits foundation in an explainable manner for the client. The most positive aspect of CCV isits simplicity to explain to any company executive regardless of experience and education.A regression model might, as the interviewee put it, ”fly above the clients head”, whichcan potentially be a deal breaker. If the regression model is just marginally better thanCCV, the latter might still be chosen because of its simplicity and the fact that the impliedvaluation often is presented in a range. However, it was noted that professionals might usethe regression model internally where a greater complexity is accepted and will provide asanity-check for other valuation methods.

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7.5 Further research

The regression model that was developed in this thesis can be used as a complementaryvaluation tool, if analysed by individuals with proper knowledge about regression analysisand statistics. However, if it were to be applied as a primary valuation tool, further re-search is required to investigate if the model could be improved and yield better results.

Firstly, more covariates may be included in the model to increase its explanatory value.In order to limit the multicollinearity when introducing new covariates, one should avoidadditional variables stemming from the income statement and focus on other endogenousvariables. As brought up earlier in the discussion, the balance sheet can provide interestingvariables such as total assets and leverage, but other covariates such as the crude oil priceper barrel or the Household Energy Price Index may also be of value in the utilities sector.Furthermore, a larger sample size would perhaps be the most effective way of yielding bet-ter results. However, this would require a larger database, perhaps by merging data fromMerger Markets, Pitchbook, and Orbis.

Another interesting point of further research is if the model performs better in specificsub-sections of an industry with a limit on company size. This restriction would make thesample size of the companies more similar to a peer group in CCV and thus reduce theapplication width of the model, but perhaps the credibility and precision of its results. Oneexample would be to create a model specifically for oil and gas companies with an enter-prise value between 1 and 5 billion EUR. This would hypothetically increase the modelsprecision as the companies should engage in more comparable value driven activities, butwill reduce the sample size which may pose some issues regarding statistical significance.

The interview with an investment banking professional concluded that the model maynot be very useful in her daily tasks, as it proved hard to account for intangible assets on acompany-specific basis but most importantly fails to be simple enough for clients to under-stand. However, the model may be of great use for in-house corporate finance departmentsor institutional investors such as hedge funds, mutual funds, endowments or private equityfirms.

In this thesis, the regression model’s average deviation from the true enterprise value wascompared to CCV, specifically with the EBITDA multiple as it is deemed the most reliablein the utilities sector. An interesting continuation of the research would be to comparethe model to valuations through DCF and precedent transactions. These are heavily usedvaluation techniques and are thus relevant to include if one were to investigate the relativeprecision and feasibility of a regression model. However, DCF is generally a highly timeconsuming task and relies on a variety of assumptions regarding cost of capital and futurecash flow. This would make the process very inefficient and dependent on these assump-

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tions.

Lastly, an interesting aspect to further study is the application of a regression model as avaluation tool in other industries. It was previously touched upon that the model wouldperform optimally in industries characterized by low intangible assets. The utilities fallsinto this category [6], with a low average net PPE (percentage of physical assets ownedas compared to total assets known). Other interesting industries with the same charac-teristics include minerals, transportation, and industrial services. It might be of value toexplore the models performance in such industries, but also in sectors with the oppositecharacteristics regarding intangible assets. Building a regression model in industries suchas health technology, consumer durables, and commercial services might not provide ausable valuation tool, but serve as a great indicator of the importance of incorporatingintangible assets in company valuations.

8 Conclusion

The research and analysis performed in this thesis does not confirm that regression analysisprovides a more accurate valuation tool than comparable companies valuation. The finalmodel generated a fairly high explanatory value of R2

adj = 0.89 in general, but the relativedeviation proved to be very large for smaller companies. However, the model performedquite well for larger companies and identified Revenue, EBITDA, Earnings as key driversto company value, as well as the current market sentiment represented here as the DowJones Utility Average.

As mentioned, the model tend to underperform compared to CCV in specifically smallercompanies, but yields a higher accuracy for larger companies. This provides an interestingbase for further research with a more narrow spectrum of companies in regards of size.Furthermore, one may include more covariates such as total assets and leverage in furtherstudies, the utilities industry generally is characterized by large material assets and stablecash flows which allows for a very leveraged capital structure.

The main drawback for using regression model in the investment banking industry is itscomplexity and difficulty to explain to a client without the relevant educational background.An important part of the investment banker’s task is to sell its advisory services during amerger or acquisition to such clients, which often is easier when pitching a company valuethat can be explained and easily motivated. Thus, regression analysis may not be suitedfor such tasks, but can serve as a sanity-check for internal use and as a primary valuationtool in buy-side firms not relying as heavily on client sales.

In conclusion, regression analysis may not be suited as a direct replacement for other

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valuation methods such as CCV, but may provide an efficient complementary tool for in-ternal use. Thus, firms will be able to triangle a value that may more accurately portraythe true enterprise value of a firm.

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References

[1] Jonathan B. Berk and Peter DeMarzo. Corporate finance. Pearson, 2020.

[2] Peter Harbula. “Valuation Multiples: Accuracy and Drivers Evidence from the Eu-ropean Stock Market”. In: Business Valuation Review 28.4 (2009), pp. 186–200. doi:10.5791/0882-2875-28.4.186.

[3] Shahed Imam, Jacky Chan, and Syed Zulfiqar Ali Shah. “Equity valuation modelsand target price accuracy in Europe: Evidence from equity reports”. In: InternationalReview of Financial Analysis 28 (2013), pp. 9–19. doi: 10.1016/j.irfa.2013.02.008.

[4] Mimi James and Zane Williams. Not enough comps for valuation? Try statisticalmodeling. Aug. 2012. url: https://www.mckinsey.com/business-functions/strategy- and- corporate- finance/our- insights/notenough- comps- for-

valuation-try-statistical-modeling.

[5] Koller et al. The Right Role for Multiples in Valuation. Sept. 2005. url: https:

//papers.ssrn.com/sol3/papers.cfm?abstract_id=805166.

[6] Barry Libert, Megan Beck, and Yoram. Investors Today Prefer Companies with FewerPhysical Assets. Oct. 2017. url: https://hbr.org/2016/09/investors-today-prefer-companies-with-fewer-physical-assets.

[7] Jing Liu, Doron Nissim, and Jacob Thomas. “Equity Valuation Using Multiples”.In: Journal of Accounting Research 40.1 (2002), pp. 135–172. doi: 10.1111/1475-679x.00042.

[8] Douglas C. Montgomery, Elizabeth A. Peck, and G. Geoffrey Vining. Introduction tolinear regression analysis. Wiley-Blackwell, 2013.

[9] PricewaterhouseCoopers. North American power and utilities deals insights Year-end 2019. url: https://www.pwc.com/us/en/industries/power-utilities/library/quarterly-deals-insights.html.

[10] Joshua Rosenbaum and Joshua Pearl. Investment Banking. Wiley, 2018.

[11] Strategic vs. Financial Buyers: Maximizing MA Transaction Value. Oct. 2019. url:https://investmentbank.com/strategic-vs-financial-buyers/.

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9 Appendix

9.1 Collected data

Announced Date Currency Enterprise Value Revenue EBITDA EBIT Earnings Region DJUA close1 2020-04-02 EUR 3252.29 3820.09 348.98 165.49 45.98 Europe 729.362 2020-04-02 EUR 3143.54 3820.09 348.98 165.49 45.98 Europe 729.363 2020-03-05 EUR 3674.29 2321.80 673.30 403.50 302.40 Europe 908.144 2020-01-16 EUR 41120.86 35434.00 2267.00 794.00 -42.00 Europe 900.425 2019-12-23 EUR 964.59 127.05 24.77 -5.36 -18.09 USA 870.156 2019-11-25 EUR 4383.00 4100.00 415.00 162.00 121.00 Europe 848.887 2019-10-08 EUR 12808.18 78176.00 1371.00 -161.00 -452.00 Europe 868.178 2019-09-26 EUR 637.05 65.75 2.13 -4.58 -13.12 USA 879.119 2019-06-03 EUR 3794.58 788.42 234.37 150.27 73.57 USA 791.77

10 2019-05-10 EUR 9070.16 3584.57 598.95 363.87 -51.51 USA 777.4611 2019-04-02 EUR 5548.58 2435.70 472.04 311.77 164.38 USA 775.4412 2019-03-29 EUR 8471.54 731.25 409.96 281.98 167.51 USA 778.7213 2018-10-23 EUR 3728.09 670.91 229.14 164.38 49.08 USA 737.0414 2018-10-11 EUR 42.32 9.03 1.83 1.18 0.69 Europe 723.3715 2018-09-18 EUR 8965.64 2023.33 1240.83 872.50 166.67 USA 738.3916 2018-09-03 EUR 564.98 42.72 28.02 21.24 5.87 Europe 726.4117 2018-08-24 EUR 23758.65 1625.00 757.50 469.17 200.00 USA 731.0818 2018-08-14 EUR 8009.52 800.87 523.76 120.95 255.69 USA 725.6819 2018-06-19 EUR 84.31 25.06 8.17 5.87 5.19 Europe 687.9620 2018-06-01 EUR 34.41 6.41 0.76 0.52 0.65 Europe 684.7421 2018-05-17 EUR 46954.30 6675.00 2277.50 860.83 725.83 USA 668.5622 2018-04-23 EUR 6609.83 2214.42 495.50 265.33 180.00 USA 691.3423 2018-04-18 EUR 2607.41 1966.28 142.70 102.06 51.88 Europe 697.8224 2018-04-04 EUR 3000.00 231.80 162.20 80.50 49.70 Europe 690.6525 2018-03-17 EUR 2564.95 1035.21 241.23 148.80 92.13 Europe 691.8226 2018-03-15 EUR 895.50 89.21 43.07 28.52 20.88 USA 684.2327 2018-03-12 EUR 36520.31 43139.00 4331.00 2816.00 778.00 Europe 675.3428 2018-03-07 EUR 43.95 2.52 2.02 1.65 1.25 Europe 664.8029 2018-02-22 EUR 34247.10 23306.00 3760.00 2112.00 1237.00 Europe 668.2030 2018-02-08 EUR 636.76 42.96 75.17 75.17 72.31 USA 647.9031 2018-01-22 EUR 21812.68 1625.00 757.50 469.17 200.00 USA 684.8632 2018-01-03 EUR 11844.14 3672.50 381.67 63.33 -99.17 USA 706.5233 2017-12-20 EUR 46.40 41.22 9.01 5.27 5.21 Europe 728.9834 2017-12-19 EUR 15.99 0.15 -1.88 -1.97 -1.93 Europe 733.8435 2017-12-18 EUR 3056.58 10136.25 -21.26 -137.55 -227.62 Europe 745.8436 2017-12-18 EUR 3056.58 10136.25 -21.26 -137.55 -227.62 USA 745.8437 2017-12-15 EUR 11050.82 1575.29 785.13 418.28 -15.67 Europe 753.3838 2017-12-13 EUR 3971.18 315.32 168.44 84.80 -15.37 Europe 750.5039 2017-12-11 EUR 321.26 81.28 9.40 1.75 0.93 USA 762.5940 2017-11-22 EUR 43.99 12.65 4.22 2.31 1.67 Europe 757.7341 2017-11-08 EUR 810.45 1715.77 85.60 31.01 -4.18 USA 759.5942 2017-11-01 EUR 537.52 520.81 62.03 -39.48 -90.12 USA 748.9543 2017-10-23 EUR 431.42 230.33 179.98 141.24 -24.21 Europe 749.5544 2017-10-13 EUR 263.00 373.44 20.23 11.33 6.67 Europe 737.2545 2017-10-05 EUR 111.77 201.61 12.84 8.91 6.31 Europe 731.6246 2017-09-26 EUR 9688.12 67788.00 2122.00 -3963.00 -3217.00 Europe 731.6747 2017-08-31 EUR 58.91 28.71 3.67 1.83 1.29 USA 743.2448 2017-08-18 EUR 14495.25 6589.79 1424.64 796.32 87.32 USA 738.3849 2017-07-25 EUR 134.61 35.00 6.54 3.61 1.73 Europe 715.8650 2017-07-21 EUR 82.89 177.09 11.82 5.13 3.53 Europe 725.4851 2017-07-10 EUR 7229.22 2539.86 1013.48 587.41 259.59 USA 701.4352 2017-05-02 EUR 1902.07 360.84 157.22 90.94 36.12 Europe 701.6853 2017-04-28 EUR 11.68 16.43 1.17 0.79 0.51 Europe 704.3554 2017-02-21 EUR 245.03 57.70 16.06 10.29 4.97 USA 679.8055 2017-02-08 EUR 1467.99 269.25 150.39 93.89 58.36 Europe 669.9956 2017-02-01 EUR 21634.76 8464.75 1617.97 1249.17 1012.50 USA 656.0857 2017-01-25 EUR 6227.48 2095.80 386.14 267.86 150.21 USA 655.3858 2017-01-10 EUR 49610.24 7109.91 2863.52 1231.02 409.07 USA 651.1459 2017-01-09 EUR 50812.53 7109.91 2863.52 1231.02 409.07 USA 653.1960 2016-12-30 EUR 31.13 55.52 4.00 3.09 1.90 Europe 659.61

37

Announced Date Currency Enterprise Value Revenue EBITDA EBIT Earnings Region DJUA close61 2016-12-23 EUR 1175.26 5459.92 196.52 137.85 62.97 Europe 660.8162 2016-12-06 EUR 406.35 719.49 42.69 41.44 35.74 Europe 631.3863 2016-11-22 EUR 118.17 102.00 36.50 20.55 9.89 Europe 636.0964 2016-11-16 EUR 156.27 29.07 14.28 8.25 5.85 Europe 630.4765 2016-10-21 EUR 144.80 29.07 14.28 8.25 5.85 Europe 655.0066 2016-10-17 EUR 9667.35 4559.58 1922.81 1382.49 547.90 Europe 655.4167 2016-10-17 EUR 9348.21 4559.58 1922.81 1382.49 547.90 Europe 655.4168 2016-10-17 EUR 9010.81 1354.85 859.62 642.52 416.37 Europe 655.4169 2016-10-11 EUR 169.42 102.85 11.21 4.58 1.07 USA 640.7970 2016-08-01 EUR 4718.19 268.69 -440.40 -592.94 -53.39 USA 710.7771 2016-07-29 EUR 4924.73 2917.32 620.46 386.47 174.99 Europe 711.4272 2016-07-28 EUR 1300.00 247.18 127.12 86.05 33.09 Europe 706.7873 2016-07-20 EUR 324.13 109.99 49.82 35.77 30.87 Europe 707.2774 2016-06-30 EUR 509.38 97.40 81.75 51.16 32.34 Europe 716.5275 2016-06-29 EUR 5133.09 1098.00 742.00 469.00 340.00 Europe 701.1276 2016-06-17 EUR 40.29 51.38 3.16 2.94 1.86 Europe 686.5877 2016-06-13 EUR 47.02 13.86 7.50 4.96 3.25 Europe 680.0478 2016-05-31 EUR 10875.91 2250.95 852.71 568.42 267.21 USA 659.4479 2016-05-27 EUR 161.08 60.55 4.59 3.68 2.41 Europe 656.2880 2016-05-02 EUR 468.81 120.62 -29.34 -39.72 -41.06 USA 659.9981 2016-04-18 EUR 358.41 56.84 28.25 18.82 10.61 Europe 664.4282 2016-03-17 EUR 10684.24 1221.88 611.44 483.39 281.46 USA 663.1983 2016-03-04 EUR 265.37 550.00 77.10 27.90 4.40 Europe 633.0584 2016-03-04 EUR 7869.96 6476.00 814.60 323.80 35.15 Europe 633.0585 2016-02-09 EUR 2117.79 554.30 161.88 88.15 51.80 USA 626.7786 2016-02-05 EUR 8200.75 6476.00 814.60 323.80 35.15 Europe 624.6287 2016-02-01 EUR 5609.91 1038.81 547.28 342.70 191.03 USA 616.4388 2016-01-28 EUR 374.59 444.69 68.46 34.70 30.52 Europe 599.1989 2016-01-08 EUR 111.27 202.32 9.87 7.89 5.74 Europe 578.8290 2015-12-18 EUR 1327.20 1079.04 53.81 5.11 -4.77 Europe 567.9491 2015-11-03 EUR 10807.07 7121.07 826.28 539.92 386.53 USA 581.3292 2015-10-27 EUR 568.96 1795.66 213.16 82.73 38.19 USA 590.5393 2015-10-26 EUR 5941.43 1165.82 245.23 142.65 114.05 USA 592.0594 2015-10-16 EUR 2320.62 9977.95 162.93 156.99 183.60 Europe 596.9895 2015-10-15 EUR 6.66 12.20 0.71 0.62 0.40 Europe 596.0496 2015-09-28 EUR 49940.35 6311.57 2268.59 1296.69 1929.75 USA 567.3697 2015-09-18 EUR 898.08 140.93 106.48 93.06 91.77 USA 564.9998 2015-09-17 EUR 304.21 161.87 88.94 17.70 -7.61 Europe 567.7299 2015-09-04 EUR 9262.97 2120.99 700.25 417.69 170.58 USA 541.97

100 2015-08-24 EUR 10485.58 4450.41 1219.01 904.96 464.46 USA 573.22101 2015-08-12 EUR 14737.84 10075.76 1190.13 949.44 752.31 USA 599.14102 2015-06-14 EUR 823.41 91.27 -198.34 -307.26 -319.62 USA 560.53103 2015-06-02 EUR 11172.17 1114.05 504.46 406.28 222.07 USA 580.12104 2015-05-21 EUR 597.11 246.45 -166.50 -237.23 -291.21 USA 589.22105 2015-05-13 EUR 45006.42 1139.62 598.70 338.57 354.85 USA 572.55106 2015-04-22 EUR 2013.60 180.80 149.20 114.70 10.30 Europe 587.50107 2015-04-16 EUR 264.73 60.05 29.59 19.44 10.45 Europe 585.29108 2015-04-01 EUR 89.91 18.64 12.78 7.20 3.56 Europe 588.01109 2015-02-26 EUR 4134.34 1348.71 331.73 204.47 90.56 USA 593.83110 2015-01-31 EUR 113.52 433.38 19.27 16.75 12.44 Europe 637.20111 2014-12-31 EUR 502.89 666.51 172.28 86.46 55.83 Europe 618.08112 2014-12-15 EUR 8929.99 6749.98 118.12 -307.67 -1506.31 Europe 592.61113 2014-12-03 EUR 122.89 196.26 24.70 9.33 4.04 Europe 602.69114 2014-11-12 EUR 173.70 90.10 11.67 3.94 1.16 Europe 592.72115 2014-11-04 EUR 61.43 25.19 1.65 -1.37 -1.68 USA 596.64116 2014-11-03 EUR 1297.00 1147.00 67.00 37.00 33.00 Europe 601.07117 2014-10-20 EUR 3698.47 796.62 340.95 223.96 116.72 USA 570.63118 2014-10-13 EUR 4534.98 1530.36 54.27 -68.20 -110.18 USA 560.87119 2014-09-12 EUR 990.85 319.60 106.30 93.06 63.80 Europe 549.64120 2014-08-10 EUR 47421.77 9101.47 3395.80 2345.46 2383.24 USA 542.69121 2014-07-30 EUR 5.94 9.81 1.98 1.42 1.04 Europe 548.58122 2014-06-23 EUR 6635.79 4092.83 606.01 412.36 257.72 USA 565.44123 2014-05-26 EUR 73.51 57.14 6.55 2.26 1.88 Europe 534.02124 2014-04-30 EUR 8783.67 3389.26 828.79 485.22 79.90 USA 553.58125 2014-04-14 EUR 2420.72 8.45 -6.71 -6.88 184.79 Europe 537.70126 2014-04-07 EUR 1164.65 387.40 126.93 95.04 41.69 USA 529.65127 2014-03-11 EUR 75.98 59.35 14.50 -3.60 -5.11 Europe 510.21128 2014-02-26 EUR 29.65 96.58 3.82 3.64 3.03 Europe 517.70129 2014-01-23 EUR 222.88 562.97 141.52 66.19 29.05 Europe 496.88130 2013-12-17 EUR 584.30 834.00 15.70 15.40 15.00 Europe 481.35131 2013-12-11 EUR 3117.63 1106.81 324.74 190.66 68.84 USA 478.30132 2013-12-06 EUR 2117.00 467.14 279.93 161.71 46.71 Europe 490.29133 2013-11-29 EUR 11452.50 9013.30 1157.00 -466.60 -539.00 Europe 487.13134 2013-11-11 EUR 48.77 41.68 4.68 3.82 2.75 Europe 502.04135 2013-11-08 EUR 1604.58 133.84 93.35 60.33 -23.51 Europe 502.46136 2013-09-10 EUR 5085.95 1155.49 656.70 377.73 158.51 Europe 479.41137 2013-08-13 EUR 3108.80 2325.85 445.27 33.97 -4.23 Europe 497.21138 2013-08-01 EUR 797.62 266.50 164.24 118.45 88.71 Europe 509.06139 2013-07-16 EUR 160.99 38.24 16.68 15.35 14.22 Europe 503.27140 2013-06-26 EUR 554.29 764.24 120.53 71.30 56.80 Europe 482.86

38

Announced Date Currency Enterprise Value Revenue EBITDA EBIT Earnings Region DJUA close141 2013-06-10 EUR 418.00 540.00 103.17 64.40 25.73 Europe 485.37142 2013-06-03 EUR 1936.44 1701.73 170.36 118.64 106.37 Europe 482.71143 2013-05-29 EUR 8010.24 2255.76 880.37 594.43 243.77 USA 484.95144 2013-04-15 EUR 1591.81 11889.25 173.59 -70.60 -90.10 Europe 516.31145 2013-04-05 EUR 2400.00 355.25 233.71 163.88 80.11 Europe 514.73146 2013-03-07 EUR 27385.30 5428.00 3628.00 2143.00 1012.00 Europe 486.89147 2013-02-04 EUR 360.81 68.59 35.33 19.23 3.51 Europe 471.36148 2013-01-29 EUR 67.91 33.64 1.47 -1.15 -3.48 Europe 472.88149 2012-12-20 EUR 74.43 35.81 5.57 2.69 0.48 Europe 460.14150 2012-12-01 EUR 35.98 198.61 27.52 8.51 2.88 Europe 454.12151 2012-10-18 EUR 34.41 169.00 24.65 19.90 16.61 Europe 487.36152 2012-09-28 EUR 185.47 297.56 29.73 19.26 7.05 Europe 475.75153 2012-09-14 EUR 312.84 377.21 30.82 17.12 10.70 Europe 472.13154 2012-08-28 EUR 552.35 542.74 115.97 52.56 18.03 Europe 472.86155 2012-08-03 EUR 14.60 4.73 3.79 2.98 2.01 Europe 491.08156 2012-07-25 EUR 2498.72 421.05 164.39 78.71 -143.39 Europe 481.08157 2012-07-17 EUR 5020.33 1205.53 710.29 439.31 176.07 Europe 485.55158 2012-07-10 EUR 1979.79 1660.27 160.43 97.83 91.89 Europe 478.35159 2012-06-25 EUR 787.23 69.52 50.34 28.38 -41.53 Europe 470.21160 2012-06-13 EUR 12.69 16.38 2.11 1.81 1.36 Europe 477.37161 2012-06-08 EUR 4415.45 436.62 268.47 163.41 149.91 USA 478.48162 2012-05-30 EUR 22666.30 3539.00 2612.00 1958.00 790.00 Europe 465.84163 2012-05-16 EUR 2899.86 1150.33 289.40 217.66 317.11 Europe 467.28164 2012-05-16 EUR 5.38 127.61 5.54 5.44 4.00 Europe 467.28165 2012-05-11 EUR 397.57 45.94 30.59 22.07 14.91 Europe 472.01166 2012-02-22 EUR 3688.64 798.00 464.95 283.19 120.59 Europe 452.34167 2012-02-22 EUR 3870.20 798.00 464.95 283.19 120.59 Europe 452.34168 2012-02-21 EUR 1109.93 760.37 105.75 76.84 35.73 USA 452.28169 2012-01-30 EUR 42.63 30.11 6.32 2.78 1.73 Europe 446.56170 2012-01-20 EUR 15810.85 1836.92 1124.38 679.27 254.87 Europe 448.54171 2012-01-20 EUR 42.85 100.07 5.72 0.95 0.15 Europe 448.54172 2011-12-27 EUR 22.88 9.84 1.08 0.92 0.68 Europe 466.58173 2011-12-22 EUR 29199.01 14170.74 3613.00 2063.00 1235.00 Europe 459.66174 2011-11-23 EUR 23.49 21.53 5.43 2.80 1.43 Europe 423.96175 2011-11-16 EUR 231.00 138.34 15.53 5.62 -1.71 Europe 442.33176 2011-10-17 EUR 17.19 23.91 2.76 2.38 1.92 Europe 438.14177 2011-10-16 EUR 26972.57 3448.89 2452.93 978.78 566.35 USA 438.76178 2011-10-11 EUR 389.29 361.10 44.18 17.21 15.61 Europe 435.93179 2011-10-05 EUR 427.14 113.97 54.89 21.05 7.70 Europe 422.27180 2011-08-29 EUR 101.98 142.86 4.91 4.89 1.14 USA 432.24181 2011-08-02 EUR 85.84 22.83 10.46 8.47 5.80 Europe 425.31182 2011-08-02 EUR 5482.71 835.33 470.57 344.27 201.90 Europe 425.31183 2011-07-20 EUR 5724.48 3175.81 555.89 431.63 150.70 USA 434.61184 2011-07-12 EUR 4479.43 1520.00 802.80 550.00 175.50 Europe 433.06185 2011-06-24 EUR 690.14 1291.54 126.90 93.81 69.59 Europe 423.99186 2011-06-20 EUR 2467.11 1974.23 184.57 126.10 18.99 Europe 429.17187 2011-06-17 EUR 31858.81 19630.00 4609.00 2893.00 1415.00 Europe 426.79188 2011-06-16 EUR 6358.00 1860.37 514.98 344.15 181.30 USA 424.33189 2011-06-10 EUR 815.95 269.80 150.75 137.60 85.83 Europe 423.83190 2011-06-06 EUR 14.39 14.34 1.36 -0.40 -0.48 Europe 424.98191 2011-05-07 EUR 11.09 0.23 -0.18 -0.22 1.26 Europe 429.81192 2011-04-28 EUR 7023.12 10714.29 -541.99 -928.72 -696.20 USA 428.42193 2011-04-22 EUR 2846.45 223.37 107.70 28.55 45.13 Europe 418.37194 2011-01-21 EUR 21.14 48.51 2.00 -0.26 -0.59 Europe 413.34195 2011-01-12 EUR 147.75 27.27 10.71 7.54 2.83 USA 408.63196 2011-01-10 EUR 19938.66 7613.57 2343.84 1534.67 644.80 USA 405.41197 2010-12-31 EUR 25092.64 14170.74 3613.00 2063.00 1235.00 Europe 404.99198 2010-12-20 EUR 64.08 159.26 14.24 13.94 9.92 Europe 403.75199 2010-12-16 EUR 1129.95 198.49 -84.05 -113.46 58.81 Europe 400.51200 2010-12-07 EUR 2290.63 1846.09 289.64 153.35 94.32 USA 395.69201 2010-10-28 EUR 55.13 11.10 3.60 2.30 1.30 Europe 403.98202 2010-10-26 EUR 4786.70 2396.61 627.41 306.34 218.60 Europe 404.26203 2010-09-17 EUR 10.93 19.50 1.98 1.55 1.06 Europe 391.12204 2010-09-07 EUR 7192.08 17757.83 1896.77 1290.96 793.75 USA 397.44205 2010-09-01 EUR 57.77 22.47 3.56 2.61 0.96 Europe 397.36206 2010-08-09 EUR 1414.91 1079.82 201.58 118.39 39.26 USA 395.02207 2010-07-28 EUR 120.00 277.66 18.44 18.25 14.27 Europe 393.12208 2010-07-28 EUR 2089.49 1031.44 187.71 150.06 120.64 Europe 393.12209 2010-07-01 EUR 2755.67 2349.91 337.82 240.34 200.95 Europe 356.46210 2010-06-28 EUR 76.06 40.67 6.80 3.62 2.05 Europe 367.92211 2010-06-15 EUR 128.50 269.50 20.60 15.60 10.20 Europe 377.23212 2010-05-25 EUR 8860.20 6620.17 1178.04 504.89 152.93 Europe 355.99213 2010-05-24 EUR 296.67 130.03 41.94 17.03 7.77 Europe 358.50214 2010-04-26 EUR 311.46 133.78 66.97 48.27 18.18 Europe 386.87215 2010-04-15 EUR 28403.20 34551.00 3810.20 2020.10 884.70 Europe 383.63216 2010-04-13 EUR 4.21 16.77 2.93 2.65 1.88 Europe 384.34217 2010-03-03 EUR 310.18 146.94 2.10 -8.24 2.82 USA 373.56218 2010-02-26 EUR 27.77 64.42 4.90 2.20 1.43 Europe 367.39219 2010-02-01 EUR 127.96 41.53 13.60 7.49 4.61 Europe 381.12220 2010-01-13 EUR 900.06 1374.87 199.71 128.13 103.47 Europe 401.59

39

Announced Date Currency Enterprise Value Revenue EBITDA EBIT Earnings Region DJUA close221 2009-12-30 EUR 26.06 588.67 0.56 -1.94 -3.40 USA 404.12222 2009-12-18 EUR 53.76 39.38 3.86 3.75 2.64 Europe 402.48223 2009-12-17 EUR 107.15 19.97 14.08 7.49 5.82 Europe 401.16224 2009-10-23 EUR 2900.00 380.98 70.52 57.47 335.01 Europe 377.43225 2009-10-22 EUR 4881.50 2950.45 620.17 439.20 235.28 Europe 383.68226 2009-10-22 EUR 4889.78 2950.45 620.17 439.20 235.28 Europe 383.68227 2009-10-16 EUR 6.40 8.69 1.10 0.89 0.78 Europe 382.03228 2009-09-30 EUR 47.80 107.70 11.30 3.60 -0.40 Europe 377.23229 2009-08-31 EUR 205.83 354.63 9.03 7.31 4.01 Europe 373.35230 2009-07-27 EUR 839.26 231.19 133.13 109.51 106.65 USA 378.56231 2009-07-23 EUR 22.33 58.80 3.60 2.41 2.01 Europe 373.85232 2009-07-06 EUR 9.17 0.12 -4.35 -4.72 -4.55 USA 353.90233 2009-06-29 EUR 4278.96 9686.41 271.81 181.39 138.54 USA 359.95234 2009-06-23 EUR 1274.90 1136.50 157.36 83.23 39.39 Europe 349.10235 2009-06-08 EUR 14.32 10.10 1.23 0.90 0.57 Europe 342.49236 2009-05-29 EUR 15.64 19.58 0.66 0.06 0.01 Europe 340.99237 2009-05-29 EUR 14.00 10.55 1.74 1.55 0.98 Europe 340.99238 2009-05-29 EUR 1844.60 307.10 160.70 63.40 17.30 Europe 340.99239 2009-05-13 EUR 2505.22 5863.20 274.95 214.50 192.60 Europe 340.07240 2009-05-05 EUR 618.06 631.83 59.30 21.12 2.41 Europe 346.04241 2009-04-23 EUR 13439.64 7189.00 2280.10 1674.50 1269.60 Europe 327.67242 2009-04-20 EUR 56.52 120.64 10.16 3.79 2.50 USA 328.39243 2009-04-15 EUR 1389.96 1136.50 157.36 83.23 39.39 Europe 331.44244 2009-02-23 EUR 8500.00 4685.00 590.00 401.00 497.00 Europe 327.35245 2009-02-12 EUR 2588.20 303.57 237.00 172.40 82.93 Europe 366.53246 2009-02-12 EUR 4206.00 781.00 461.00 323.00 228.00 Europe 366.53247 2009-01-28 EUR 19.60 81.16 1.35 1.33 0.81 Europe 380.62248 2009-01-14 EUR 10715.53 14016.00 1740.00 946.00 337.00 Europe 359.50249 2009-01-05 EUR 11503.43 14016.00 1740.00 946.00 337.00 Europe 382.00

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