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Volume 4, March 2019

DACH Capital Market Study 31 December 2018

Analysis of cost of capital parameters and multiples for the capital markets of Germany, Austria and Switzerland

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DACH Capital Market Study – 31 December 2018

Table of Contents Page

1. Preface & people 3

2. Executive summary 7

3. Risk-free rate 10

4. Market returns and market risk premium 16

a. Implied returns (ex-ante analysis) 16b. Historical returns (ex-post analysis) 24

5. Sector classification of the DACH region 32

based on sector indices

6. Betas 35

7. Sector returns 39

a. Implied returns (ex-ante analysis) 39b. Historical returns (ex-post analysis) 64

8. Trading multiples 67

Appendix 75

2

Preface & people1

DACH Capital Market StudyPreface

Dear business partners and friends of ValueTrust,

We are pleased to release our fourth edition of the ValueTrust DACH1)

Capital Market Study powered by and . The study waselaborated by ValueTrust Financial Advisors SE (ValueTrust) in cooperationwith and the Institute of Auditing and Sustainability Accountingat the Johannes Kepler University Linz . With this study, we providea data compilation of the capital market parameters that enables anenterprise valuation in Germany, Austria and Switzerland. It has thepurpose to serve as an assistant and data source as well as to show trendsof the analyzed parameters.

In this study, we analyze the relevant parameters to calculate the costs ofcapital based on the Capital Asset Pricing Model (risk-free rate, market riskpremium and beta). Additionally, we determine implied as well ashistorical market and sector returns. Moreover, this study includes capitalstructure-adjusted implied sector returns, which serve as an indicator forthe unlevered cost of equity. The relevered cost of equity can becalculated by adapting the company specific debt situation to theunlevered cost of equity. This procedure serves as an alternative to theCAPM.

Furthermore, we provide an analysis of empirical (ex-post) costs of equityin the form of total shareholder returns which consist of capital gains anddividends. The total shareholder returns can be used as a plausibility checkof the implied (ex-ante) returns. Lastly, trading multiples frame the end ofthis study.

We examine the before mentioned parameters for the German, Austrianand Swiss capital market (in form of the DAX Sector All Index2),WBI3) andSPI4)). These indices have been merged into twelve sector indices(so-called “super sectors") Banking, Insurance, Financial Services, RealEstate, Basic Materials, Consumer Goods, Telecommunication, Industrials,Consumer Service, Pharma & Healthcare, Information Technology andUtilities.

Historical data has been compiled between the reference dates31 December 2012 and 31 December 2018 and will be updated semi-annually, with the objective that historical, as well as current data, can beconsulted at the same time. Hence, we can understand changes in time,which allows to track the performance of all three capital markets.Additionally, further knowledge and information for financial decisionmaking is provided at www.finexpert.info.

The analyzed cost of capital data is accessible online atwww.firmvaluation.center by entering the reference date, the relevantsector and country.

1) D (Germany), A (Austria), CH (Switzerland). 2) All equities listed in Prime Standard, General Standard, Scale segment. 3) Vienna Stock Index.

4) Swiss Performance Index.

4

Prof. Dr. Christian AdersChairman of the Executive BoardValueTrust FinancialAdvisors SE

Prof. Dr. Ewald AschauerChair of Auditing and Sustainability Accounting, Johannes KeplerUniversity of Linz

Prof. Dr. Bernhard SchwetzlerChair of Financial Management, HHL Leipzig

http://www.finexpert.info/

http://www.firmvaluation.center/

DACH Capital Market Study People

Prof. Dr. Bernhard Schwetzler, CVA

Chair of Financial Management, HHL Leipzig

[email protected]

▪ Senior Advisor ValueTrust▪ Co-Founder and board member of the European Association of

Certified Valuators and Analysts (EACVA e.V.)

Prof. Dr. Christian Aders, CEFA, CVA

Chairman of the Executive Board, ValueTrust

[email protected]

▪ More than 25 years of experience in corporate valuation and financial advisory

▪ Previously Partner at KPMG and Managing Director at Duff & Phelps

▪ Honorary professor for "Practice of transaction-oriented company valuation and value-oriented management" at LMU Munich

▪ Member of the DVFA Expert Group "Fairness Opinions" and "Best Practice Recommendations Corporate Valuation“

▪ Co-Founder of the European Association of Certified Valuators and Analysts (EACVA e.V.)

Marion Swoboda-Brachvogel

Director

[email protected]

▪ Almost 15 years of project experience in financial advisory, investment banking and investment management

▪ Previously with McKinsey & Company, Unicredit, C.A. Cheuvreuxand B&C Industrieholding

▪ Extensive experience in the valuation of listed and private companies in various industries and in advising on strategic and financial issues

Prof. Dr. Ewald Aschauer

Chair of Auditing and Sustainability Accounting, University of Linz

[email protected]

▪ Senior Advisor ValueTrust▪ Member of the Working Group on Business Valuation of the

Austrian Chamber of Public Accountants and Tax Advisors▪ Nominated expert in valuation disputes

5

DACH Capital Market StudyDisclaimer

This study presents an empirical analysis which serves the purpose ofillustrating the cost of capital of Germany’s, Austria’s, and Switzerland’scapital markets. Nevertheless, the available information and thecorresponding exemplifications do not allow a complete exposure of aproper derivation of costs of capital. Furthermore, the market participanthas to take into account that the company specific costs of capital canvary widely due to individual corporate situations.

The listed information is not specified to anyone and, consequently, itcannot be directed to an individual or juristic person. Although we arealways endeavored to present information that is reliable, accurate, andcurrent, we cannot guarantee that the data is applicable to valuation inthe present as well as in the future. The same applies to our underlyingdata from the data provider S&P Capital IQ.

We recommend a self-contained, technical, and detailed analysis of theobserved situation and we dissuade from taking action based on theprovided information only.

ValueTrust and its co-authors do not assume any liability for the up-to-datedness, completeness or accuracy of this study or its contents.

6

Executive summary2

Executive Summary (1/2)

8

▪ Companies within the Consumer Service sector showed the highest unlevered sector specific betas with thearithmetic mean standing at 0.92 for the five-year period ending31 December 2018, while Utilities had the lowest unlevered beta (0.43) over the same period.

▪ The levered sector specific betas were highest for the Consumer Goods and Consumer Service sectors for the five-year period (arithmetic mean) as of 31 December 2018. If we consider the two year period, the InformationTechnology sector showed the highest levered and unlevered betas, followed by the Pharma & Healthcare. The RealsEstate sector had the lowest levered beta over both time horizons, followed by Utilities.

Betas

▪ In all analyzed markets we observed a significant increase in the implied market return. This is mainly due to aconsiderable decrease in market capitalization of the analyzed companies caused by market turbulences in thesecond half of 2018.

▪ The implied yearly market return (ex-ante) of the German market increased significantly from 8.9% as of 30 June2018 to 10.1% as of 31 December 2018 with a implied market risk premium of 7.7% and 9.0%, respectively.

▪ The implied market return of the Austrian market increased from 8.8% as of 30 June 2018 to 10.1% as of 31December 2018. The implied market risk premium amounts to 9.1% as of 31 December 2018.

▪ The implied market return of the Swiss market was the lowest in the DACH region with 8.2% as of 31 December 2018compared to 7.6% as of 30 June 2018. The implied market risk premium increased from 7.1% to 7.7%.

▪ Due to the before mentioned market turbulences the total shareholder returns for the past year were negative in allmarkets (Swiss market -7.0%, German market -18.3%, Austrian market -17.4%).

Market returns and market risk premium

▪ In comparison to 30 June 2018, the German risk-free rate decreased from 1.26% to 1.09% as of31 December 2018.

▪ The Austrian risk-free rate decreased from 1.12% as of 30 June 2018 to 0.95% as of 31 December 2018.

▪ The Swiss risk-free rate stayed stable and recorded only a slight decrease from 0.56% to 0.55% during the timeperiod from 30 June 2018 to 31 December 2018, hence showing a contrary trend compared to Germany andAustria. Overall, Switzerland has by far the lowest risk-free rate of the three analyzed markets.

Risk-free rate

Chapter 6

Chapter 4

Chapter 3

Executive Summary (2/2)

9

▪ The levered implied sector returns were in the range of 6.2% and 10.5%, the unlevered implied sector returns werebetween 3.9% and 6.6% (as of 31 December 2018).

▪ The ex-ante analysis of implied sector returns reveals that unlevered implied sector returns were the highest forcompanies in the Basic Materials sector at 6.6% (levered 9.3%).

▪ The ex-post analysis of historical sector returns based on total shareholder returns highlights that companies in theFinancial Services sector realized high total shareholder returns at 26.4% in the six and 15.1% in the three-yearaverage. Similarly, the Information Technology sector showed high returns at 18.6% in the six and 16.8% in thethree-year average. The lowest historical returns of the sectors were realized by the Banking sector at -5.4% in thethree-year average and only 1% in the six-year average.

Sector returns

(p.a.)

Trading Multiples

Chapter 7

Chapter 8

▪ As of 31 December 2018, the medians of the EV/Revenue, EV/EBIT, P/E and EqV/BV-Multiples were all lower than sixmonths ago when looking at the DACH region.

▪ The Real Estate sector had the highest median Revenue-Multiples compared to all other sectors: the median of theRevenue-Multiples amounted to 11.1x (LTM) and 13.8x (1yf). Opposed to that, the Consumer Goods sector showedthe lowest median Revenue-Multiples with values of 0.9x (LTM) and 1.0x (1yf).

▪ The Pharma & Healthcare sector represented the highest EqV/BV-Multiple with a median of 2.5x as of31 December 2018. On the other hand, the Banking sector showed the lowest EqV/BV-Multiple with a median ofonly 0.9x.

Risk-free rate3

Risk-Free RateBackground & approach

The risk-free rate is a return available on a security that the marketgenerally regards as free of default risk. It serves as an input parameter forthe CAPM and to determine the risk-adequate cost of capital.

The risk-free rate is a yield, which is obtained from long-term governmentbonds of countries with top notch rating. By using interest rate data ofdifferent maturities, a yield curve can be estimated for fictitious zerocoupon bonds (spot rates) for a period of up to 30 years. Therefore, theGerman Central Bank (Deutsche Bundesbank) and the Swiss National Bank(Schweizer Nationalbank) publish – on a daily basis – the parametersneeded to determine the yield curve using the Svensson method. Basedon the respective yield curve, a uniform risk-free rate is derived under theassumption of present value equivalence to an infinite time horizon.

The German bonds are internationally classified as almost risk-freesecurities due to its AAA rating according to S&P. As a result, the AustrianChamber of Public Accountants and Tax Consultants also recommendsderiving the risk-free rate from the yield curve using the parameterspublished by the German Central Bank.1) Likewise, bonds issued bySwitzerland enjoy a AAA rating and are also considered risk-free accordingto the Swiss National Bank.2) Hence, a similar approach as for Germanyand Austria is in our view appropriate for Switzerland with Swissparameters.3)

To compute the risk-free rate for a specific reference date, the Institute ofPublic Auditors (Institut der Wirtschaftsprüfer, IDW) in Germanyrecommends using an average value deduced from the daily yield curvesof the past three months (IDW S 1).

On the contrary, the Austrian Expert Opinion (KFS/BW 1) on companyvaluation recommends to derive the risk-free rate in line with theevaluated company's cash flow profile from the yield curve that is valid forthe reference date (reference date principle). Thus, the KFS/BW 1 and itscounterpart, the IDW S 1, differ from each other. Consequently, in thefollowing analyses, we depict the yield curve for Germany following IDW S1 while for Austria we adhere to the recommendations of KFS/BW 1.

For Switzerland, there is no generally accepted scheme to determine therisk-free rate. The most widely used risk-free rates in valuation practiceare the yield of a 10-year Swiss government bond as of the reference dateas well as the yield derived from the 3-month average of the daily yieldcurves (in accordance with IDW S 1).

Additionally, we illustrate the monthly development of the risk-free ratessince 2012 for all three capital markets.

1) www.bundesbank.de.

2) Swiss National Bank – Zinssätze und Renditen, p.11.

3) ibid., p.13.

11

Risk-Free Rate – DACHDetermination according to country specific recommendationsInterest rate curve based on long-term bonds (Svensson method)

12

1,09%

0,95%

0,55%

1,26%

1,12%

0,56%

-1,00%

-0,50%

0,00%

0,50%

1,00%

1,50%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Spo

t R

ate

Year

Risk-free rates as of 31 December 2018

Germany 31 December 2018 Austria 31 December 2018 Switzerland 31 December 2018

Germany 30 June 2018 Austria 30 June 2018 Switzerland 30 June 2018

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

Historical developement of the risk-free rate in % according to IDW S 1

2.37% 2.37%

2.75%

2.45%

1.86%

1.18%

1.41%

0.90% 0.95%

1.24% 1.29% 1.26%1.09%

Risk-Free Rate – GermanyDetermination following IDW S 1Historical development of the risk-free rate (Svensson method) since 2012

Note: Interest rate as of reference date using 3-month average yield curves in accordance with IDW S 1.

13

▪ The German risk-free rate decreased from1.26% as of 30 June 2018 to 1.09% as of31 December 2018.

▪ In the time period from 31 December 2012 to31 December 2018, the risk-free rate declinedfrom 2.37% to 1.09%.

▪ The German risk-free rate is the highest withinthe three DACH markets.

Risk-free rate January February March April May June July August September October November December2018 1.30% 1.34% 1.36% 1.34% 1.29% 1.26% 1.19% 1.13% 1.11% 1.14% 1.15% 1.09%2017 1.10% 1.20% 1.25% 1.22% 1.25% 1.24% 1.32% 1.32% 1.35% 1.33% 1.32% 1.29%2016 1.42% 1.26% 1.12% 1.00% 1.01% 0.90% 0.74% 0.57% 0.54% 0.62% 0.77% 0.95%2015 1.64% 1.36% 1.07% 0.86% 0.92% 1.18% 1.50% 1.53% 1.49% 1.41% 1.42% 1.41%2014 2.74% 2.69% 2.63% 2.56% 2.50% 2.45% 2.35% 2.22% 2.10% 2.00% 1.97% 1.86%2013 2.37% 2.42% 2.42% 2.36% 2.32% 2.37% 2.44% 2.54% 2.63% 2.72% 2.76% 2.75%2012 2.64% 2.59% 2.56% 2.55% 2.43% 2.27% 2.20% 2.23% 2.33% 2.38% 2.39% 2.37%

0,0%

1,0%

2,0%

3,0%

4,0%

5,0%

Historical developement of the risk-free rate in % according to KFS/BW1

1.12%

0.95%

2.19%

2.53%2.84%

2.30%

1.59%

1.67%

1.57%

0.49%

1.04%

1.33% 1.33%

Risk-Free Rate – AustriaDetermination following KFS/BW 1Historical development of the risk-free rate (Svensson method) since 2012

Note: Interest rate calculated using the daily yield curve in accordance with KFS/BW 1 (no 3-month average).

14

▪ The Austrian risk-free rate decreased from1.12% as of 30 June 2018 to 0.95% as of 31December 2018.

▪ In the time period from 31 December 2012 to31 December 2018, the risk-free rate morethan halved from 2.19% to 0.95%.

▪ The Austrian risk-free rate is the secondhighest within the three DACH markets.

Risk-free rate January February March April May June July August September October November December2018 1.37% 1.36% 1.23% 1.30% 1.17% 1.12% 1.15% 1.09% 1.15% 1.12% 1.08% 0.95%2017 1.33% 1.13% 1.24% 1.25% 1.29% 1.33% 1.45% 1.25% 1.38% 1.33% 1.25% 1.33%2016 1.13% 0.88% 0.91% 1.13% 1.02% 0.49% 0.45% 0.50% 0.48% 0.90% 0.89% 1.04%2015 1.10% 1.08% 0.71% 0.96% 1.18% 1.67% 1.47% 1.46% 1.39% 1.29% 1.38% 1.57%2014 2.55% 2.57% 2.55% 2.49% 2.36% 2.30% 2.15% 1.87% 2.00% 1.95% 1.79% 1.59%2013 2.43% 2.37% 2.27% 2.17% 2.41% 2.53% 2.54% 2.70% 2.65% 2.69% 2.70% 2.84%2012 2.59% 2.51% 2.55% 2.49% 1.89% 2.41% 2.30% 2.22% 2.32% 2.42% 2.35% 2.19%

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

Historical development of the risk-free rate in % according to IDW S 1

1.33%1.79% 1.61%

1.08%

0.57% 0.66%0.19% 0.23% 0.32% 0.39%

0.56% 0.55%

1.12%

Risk-Free Rate – SwitzerlandDetermination following IDW S 1Historical development of the risk-free rate (Svensson method) since 2012

Note: Interest rate as of reference date using 3-month average yield curves in accordance with IDW S 1.

15

▪ The Swiss risk-free rate decreased only slightlyfrom 0.56% as of 30 June 2018 to 0.55% as of31 December 2018.

▪ In the time period from 31 December 2012 to31 December 2018, the risk-free rate declinedsignificantly from 1.12% to 0.55%.

▪ The Swiss risk-free rate is by far the lowestwithin the three DACH markets.

Risk-free rate January February March April May June July August September October November December2018 0.40% 0.48% 0.58% 0.62% 0.59% 0.56% 0.51% 0.52% 0.54% 0.58% 0.59% 0.55%2017 0.33% 0.37% 0.37% 0.35% 0.36% 0.32% 0.36% 0.35% 0.37% 0.37% 0.40% 0.39%2016 0.60% 0.49% 0.36% 0.26% 0.25% 0.19% 0.09% -0.01% -0.04% -0.02% 0.08% 0.23%2015 0.85% 0.66% 0.54% 0.47% 0.47% 0.57% 0.72% 0.74% 0.73% 0.72% 0.71% 0.66%2014 1.81% 1.80% 1.75% 1.68% 1.63% 1.61% 1.56% 1.44% 1.33% 1.24% 1.20% 1.08%2013 1.16% 1.24% 1.31% 1.31% 1.28% 1.33% 1.46% 1.62% 1.72% 1.77% 1.78% 1.79%2012 1.35% 1.30% 1.30% 1.31% 1.26% 1.16% 1.10% 1.08% 1.10% 1.12% 1.13% 1.12%


a. Implied returns (ex-ante analysis)

4

For the following analysis, we use – simplified to annually – the formula ofthe Residual Income Valuation Model by Babbel:3)

with:

rt = Cost of equity at period t

NIt+1 = Expected net income in the following period t+1

MCt = Market capitalization at period t

BVt = Book value of equity at period t

g = Projected growth rate

Through dissolving the models to achieve the cost of capital, we obtainthe implied return on equity.4) Since Babbel's model does not need anyexplicit assumptions, except for the growth rate, it turns out to be robust.We source all data (i.e. the expected annual net income, the marketcapitalizations, and the company’s book value of equity, etc.) of theanalyzed companies from the data supplier S&P Capital IQ. Additionally,we apply the European Central Bank target inflation rate of 2.0% as atypified growth rate.

Henceforth, we determine the implied market returns for the entire DAX,ATX, and SMI. We consider these indices as a valid approximation for thetotal markets.5) The results build the starting points for the calculations ofthe implied market risk premiums of the three capital markets.

Implied Market Returns and Market PremiumBackground & approach

The future-oriented computation of implied market returns and marketrisk premiums is based on profit estimates for public companies andreturn calculations. This approach is called ex-ante analysis and allows tocalculate the “implied cost of capital”. It is to be distinguished from the ex-post analysis.

Particularly, the ex-ante method offers an alternative to the ex-postapproach of calculating the costs of capital by means of the regressionanalysis through the CAPM. The ex-ante analysis method seeks costs ofcapital which represent the return expectations of market participants.Moreover, it is supposed that the estimates of financial analysts reflectthe expectations of the capital market.

The concept of implied cost of capital gained in momentum recently. Forexample, it was recognized by the German Fachausschuss fürUnternehmensbewertung “FAUB”.1) It is acknowledged that implied costof capital capture the current capital market situation, and are thus able toreflect the effects of the current low interest rate environment.

Furthermore, recent court rulings with regards to appraisal proceedingsappreciate the use of implied cost of capital as they are forward-looking.2)

As of the reference date, it offers a more insightful perspective incomparison to the exclusive use of ex-post data.

1) cf. Castedello/Jonas/Schieszl/Lenckner, Die Marktrisikoprämie im Niedrigzinsumfeld – Hintergrund und Erläuterung der Empfehlung des FAUB (WPg, 13/2018, p. 806-825).

2) cf. Ruling of the regional court Cologne 02/2018.

3) cf. Babbel, Challenging Stock Prices: Stock prices und implied growth expectations, in: Corporate Finance, N. 9, 2015, p. 316-323, in particular p. 319.

4) cf. Reese, 2007, Estimation of the costs of capital for evaluation purposes; Aders/Aschauer/Dollinger, Die implizite Marktrisikoprämie am österreichischen Kapitalmarkt (RWZ, 6/2016, p. 195-202).

5) Approx. 75% of the total market capitalization (CDAX, WBI, SPI) is covered.

17

rt =NIt+1MCt

+ 1 −BVtMCt

∗ g

Implied Market ReturnsGerman market – DAX

18

▪ The implied market return of theGerman market shows an increasedmarket-value weighted mean of 10.1%as of 31 December 2018 vs. 8.9% as of30 June 2018 to .

▪ Since 30 June 2013 the implied marketreturn fluctuated between 8.3% and10.1%.

▪ In comparison to the Swiss market,the German market showed a higherreturn as of 31 December 2018, whileit is similar to the implied Austrianmarket return.

9.4%

8.6% 8.3% 8.9% 8.3% 8.5% 9.0% 8.6% 8.6% 8.7% 8.9%

10.1%

Min 7.5%

Max 10.1%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

Implied market returns - DAX

Range (10% - 90% quantile) Market-value weighted mean

Implied market returns - DAX

H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2017 H1 2018 H2 2018

30.06.2013 31.12.2013 30.06.2014 31.12.2014 30.06.2015 31.12.2015 30.06.2016 31.12.2016 30.06.2017 31.12.2017 30.06.2018 31.12.2018

Minimum 3.2% 4.8% 2.9% 5.1% 5.3% 5.2% 3.3% 3.4% 2.6% 4.2% 1.0% 2.7%

Lower quantile 4.3% 5.3% 5.2% 6.1% 5.6% 5.9% 5.0% 5.4% 5.3% 5.8% 5.4% 5.6%

Median 8.4% 7.7% 7.7% 7.8% 7.0% 7.9% 7.6% 7.6% 7.7% 8.0% 7.9% 9.1%

Arithmetic mean 8.6% 8.1% 7.6% 8.3% 7.9% 8.4% 8.7% 8.3% 8.4% 8.4% 8.8% 10.0%

Market-value weighted mean 9.4% 8.6% 8.3% 8.9% 8.3% 8.5% 9.0% 8.6% 8.6% 8.7% 8.9% 10.1%

Upper quantile 12.3% 10.5% 10.7% 11.4% 11.4% 12.0% 15.2% 12.4% 14.3% 13.0% 16.1% 17.5%

Maximum 16.2% 12.0% 11.9% 14.7% 17.0% 18.3% 24.2% 16.3% 16.7% 15.2% 21.5% 20.1%

Market-value weighted debt 231.6% 162.1% 167.5% 175.2% 154.5% 153.6% 200.8% 150.0% 137.0% 123.9% 123.2% 132.3%

Implied Market Risk PremiumGerman market – DAX

Knowing the implied market return and the daily measured risk-free rate (cf. slide 12 in this study) of the German capital market, we can determine theimplied market risk premium.

In the years from 2013 to 2018 the implied market returns were within the range of 8.3% to 10.1% (cf. slide 18 in this study). Subtracting the risk-free ratefrom the implied market return, we derive a implied market risk premium within the range of 5.8% to 9.0%.

The implied market return stands at 10.1% as of the reference date 31 December 2018. Taking the risk-free rate of 1.09% (cf. slide 13) into account, wedetermine a implied market risk premium of 9.0%. Due to the significant decline of the aggregate market capitalization of the DAX companies in the secondhalf of 2018, the implied market return reached its peak in December 2018. Starting in January 2019 the implied market return normalizes to levels seen inthe past years due to somewhat lower analyst earnings forecasts and a slightly increased market capitalization of the DAX companies. In situations like this,it is important to take into account the analysis of historical returns when determining the appropriate market risk premium.

19

Risk-free rate

Implied market return

MRP7.1%

5.9% 5.8%7.0% 7.1% 7.1%

8.1% 7.6% 7.3% 7.4% 7.7%9.0%

9.4%8.6% 8.3%

8.9%8.3% 8.5% 9.0%

8.6% 8.6% 8.7% 8.9%10.1%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

Implied market risk premium - DAX

Risk-free rate Implied market risk premium Market-value weighted mean

H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2017 H1 2018 H2 2018Market-value weighted mean 9.4% 8.6% 8.3% 8.9% 8.3% 8.5% 9.0% 8.6% 8.6% 8.7% 8.9% 10.1%Risk-free rate 2.4% 2.8% 2.5% 1.9% 1.2% 1.4% 0.9% 1.0% 1.2% 1.3% 1.3% 1.1%Implied market risk premium - DAX 7.1% 5.9% 5.8% 7.0% 7.1% 7.1% 8.1% 7.6% 7.3% 7.4% 7.7% 9.0%

Implied Market ReturnsAustrian market – ATX

20

▪ The implied market return of theAustrian market increased from8.8% as of 30 June 2018 to 10.1% asof 31 December 2018.

▪ Since 30 June 2013 it fluctuatedbetween 7.1% and 10.1%.

▪ The Austrian market represents thesame implied return as the Germanmarket as of 31 December 2018,while being significantly above theSwiss one.

Implied market returns - ATX

H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2017 H1 2018 H2 2018

30.06.2013 31.12.2013 30.06.2014 31.12.2014 30.06.2015 31.12.2015 30.06.2016 31.12.2016 30.06.2017 31.12.2017 30.06.2018 31.12.2018

Minimum 4.3% 0.0% 0.2% 2.0% 2.0% 3.6% 1.6% 2.1% 1.1% 4.0% 2.8% 4.3%

Lower quantile 4.5% 4.6% 1.6% 3.5% 3.9% 4.1% 5.2% 4.7% 5.1% 4.4% 4.6% 4.4%

Median 8.5% 7.4% 6.4% 7.2% 6.9% 7.7% 8.4% 7.7% 7.7% 7.7% 7.7% 9.8%

Arithmetic mean 8.6% 7.8% 6.4% 7.8% 6.9% 8.0% 8.1% 7.9% 7.5% 7.5% 7.7% 9.6%


Upper quantile 12.9% 11.9% 10.6% 13.2% 12.1% 12.3% 11.7% 10.9% 10.1% 9.5% 11.5% 14.4%

Maximum 13.9% 11.9% 10.7% 14.4% 13.4% 13.6% 12.3% 11.2% 13.0% 10.3% 12.3% 14.9%


9.5%

8.8%7.1%

8.4%7.3%

8.3% 8.1% 8.2% 8.3% 8.1%8.8%

10.1%

Min 6.4%

Max 10.1%

0.0%

4.0%

8.0%

12.0%

16.0%

Implied market returns - ATX


Implied Market Risk PremiumAustrian market – ATX

Knowing the implied market return and the daily measured risk-free rate (cf. slide 12 in this study) of the Austrian capital market, we can determine theimplied market risk premium.

In the years from 2013 to 2018 the implied market returns fell within the range of 7.1% to 10.1% (cf. slide 20 in this study). Subtracting the risk-free ratefrom the implied market return, we derive a market risk premium within the range of 4.8% to 9.1%.

The implied market return is at 10.1% as of the reference date 31 December 2018. Taking the risk-free rate of 0.95% (cf. slide 14) into account, wedetermine a impied market risk premium of 9.1%. Due to the significant decline of the aggregate market capitalization of the ATX companies in the secondhalf of 2018, the implied market return reached its peak in December 2018. Starting in January 2019 the implied market return normalizes to levels seen inthe past years due to somewhat lower analyst earnings forecasts and a slightly increased market capitalization of the ATX companies. In situations like this,it is important to take into account the analysis of historical returns when determining the appropriate market risk premium.

21

Risk-free rate


MRP7.0%

6.0%4.8%

6.8%5.6%

6.8%7.6% 7.2% 6.9% 6.8%

7.7%9.1%

9.5%8.8%

7.1%

8.4%

7.3%

8.3% 8.1% 8.2% 8.3% 8.1%8.8%

10.1%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

Implied market risk premium - ATX


H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2017 H1 2018 H2 2018Market-value weighted mean 9.5% 8.8% 7.1% 8.4% 7.3% 8.3% 8.1% 8.2% 8.3% 8.1% 8.8% 10.1%Risk-free rate 2.6% 2.9% 2.3% 1.6% 1.7% 1.6% 0.5% 1.0% 1.3% 1.3% 1.1% 0.9%Implied market risk premium - ATX 7.0% 6.0% 4.8% 6.8% 5.6% 6.8% 7.6% 7.2% 6.9% 6.8% 7.7% 9.1%

Implied Market ReturnsSwiss market – SMI

22

▪ The market-value weighted mean ofthe implied market return of theSwiss market increased from 7.6% asof 30 June 2018 to 8.2% as of 31December 2018.

▪ Since 30 June 2013 it fluctuatedbetween 6.8% and 8.2%. In total, itfollows a consistent trend.

▪ Compared to the German andAustrian market, the Swiss marketrepresents the lowest return as of31 December 2018.

Implied market returns - SMI

H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2017 H1 2018 H2 2018

30.06.2013 31.12.2013 30.06.2014 31.12.2014 30.06.2015 31.12.2015 30.06.2016 31.12.2016 30.06.2017 31.12.2017 30.06.2018 31.12.2018

Minimum 6.0% 6.2% 5.8% 5.7% 6.0% 5.4% 5.2% 4.5% 5.0% 5.4% 5.3% 5.9%

Lower quantile 6.4% 6.2% 5.9% 6.1% 6.2% 5.9% 5.7% 5.3% 5.3% 5.4% 5.4% 6.4%

Median 7.5% 7.7% 7.4% 7.9% 7.3% 7.7% 7.2% 7.4% 6.3% 7.0% 7.8% 8.1%

Arithmetic mean 8.2% 7.9% 7.5% 7.9% 7.6% 7.6% 7.5% 7.2% 6.8% 7.0% 7.6% 8.7%


Upper quantile 11.6% 10.7% 10.2% 10.6% 10.5% 9.6% 10.6% 9.1% 8.6% 8.7% 9.9% 12.4%

Maximum 12.9% 10.8% 10.4% 11.0% 10.6% 10.1% 11.0% 9.4% 8.7% 9.1% 10.8% 12.7%


8.1%7.9%

7.3% 7.6% 7.2% 7.4% 7.2% 7.4% 6.8% 7.2% 7.6%

8.2%

Min 6.8%

Max 8.2%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

Implied market returns - SMI


Implied Market Risk PremiumSwiss market – SMI

Knowing the implied market return and the daily measured risk-free rate (cf. slide 12 in this study) of the Swiss capital market, we can determine theimplied market risk premium.

In the years from 2013 to 2018 the implied market returns fell within a range of 6.8% to 8.2% (cf. slide 22 in this study). Subtracting the risk-free rate fromthe implied market return, we derive a implied market risk premium of 5.6% to 7.7%.

The implied market return is at 7.6% as of the reference date 30 June 2018. Taking the risk-free rate of 0.55% (cf. slide 15) into account, we determine amarket risk premium of 7.7%, the highest level in our observation period.

23

Risk-free rate


MRP

6.7%6.1% 5.6%

6.6% 6.6% 6.7% 7.0% 7.2%6.5% 6.8% 7.1% 7.7%

8.1% 7.9%7.3%

7.6%7.2% 7.4% 7.2% 7.4%

6.8%7.2%

7.6%8.2%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

9.0%

Implied market risk premium - SMI


H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2017 H1 2018 H2 2018Market-value weighted mean 8.1% 7.9% 7.3% 7.6% 7.2% 7.4% 7.2% 7.4% 6.8% 7.2% 7.6% 8.2%Risk-free rate 1.3% 1.8% 1.6% 1.1% 0.6% 0.7% 0.2% 0.2% 0.3% 0.4% 0.6% 0.5%Implied market risk premium - SMI 6.7% 6.1% 5.6% 6.6% 6.6% 6.7% 7.0% 7.2% 6.5% 6.8% 7.1% 7.7%


b. Historical returns (ex-post analysis)

4

Historical Market ReturnsBackground & approach

Besides analyzing the implied market returns through the ex-ante analysis,we also analyze historical (ex-post) returns. Once this analysis is performedover a long-term observation period, an expected return potential of theGerman, Austrian and Swiss capital markets is assessable. Therefore, theanalysis of historical returns can be used for plausibility checks of the costsof capital, more specifically return requirements, which were evaluatedthrough the CAPM.

To further enable a precise analysis of the historical returns of theGerman, Austrian and Swiss capital markets, we use the so-called returntriangle.1) It helps to present the annually realized returns from differentinvestment periods in a simple and understandable way. Especially thedifferent buying and selling points in time and the different annual holdingperiods are being illustrated comprehensively. To calculate the averageannual returns over several years, we use both the geometric andarithmetic mean.

In this study, we analyze the so-called total shareholder returns, whichinclude the returns on investments and the dividend yields. For ouranalysis, it is needful to focus on total return indices because they includethe price and dividend yields. Since DAX is a performance index, wealready have an index which includes the price and dividend yields. TheATX and SMI only include the price yields, hence we need their specifictotal return indices. The relevant total return index for Austria is called theATX Total Return and for Switzerland SMI Total Return. The composition ofboth indices are identical to the ATX and the SMI and compromise 20companies each.

The observation period amounts to 25 years. Therefore, the earliest dataof the DAX and the ATX Total Return is from the end of 1993. However,the data of the SMI Total Return starts from end of 1995. All ex-postreturns are being calculated by using the data as of the reference date 31December.

The following slides illustrate how the two calculation methods (arithmeticand geometric) differ from each other for the period between31 December 1991 and 31 December 2018:

▪ DAX:

• the arithmetic mean of the historical market returns is 9.2%

• the geometric mean of the historical market returns is 6.3%

▪ ATX:



▪ SMI:



1) The German Stock Institute e.V. (DAI) developed the return triangle for DAX and EURO STOXX.

25

Buy

-18.3% 2017

12.5% -2.9% 2016

6.9% 9.7% 0.4% 2015

9.6% 8.2% 9.6% 2.7% 2014

2.7% 6.1% 6.4% 7.9% 2.7% 2013 5

25.5% 14.1% 12.6% 11.1% 11.4% 6.5% 2012

29.1% 27.3% 19.1% 16.7% 14.7% 14.4% 9.7% 2011

-14.7% 7.2% 13.3% 10.6% 10.4% 9.8% 10.2% 6.6% 2010

16.1% 0.7% 10.1% 14.0% 11.7% 11.4% 10.7% 10.9% 7.7% 2009

23.8% 20.0% 8.4% 13.6% 16.0% 13.7% 13.1% 12.4% 12.4% 9.3% 2008 10

-40.4% -8.3% -0.2% -3.8% 2.8% 6.6% 6.0% 6.4% 6.5% 7.1% 4.8% 2007

15.0% Return higher than 13% 22.3% -9.0% 1.9% 5.5% 1.4% 6.0% 8.8% 8.0% 8.2% 8.1% 8.5% 6.3% 2006

10.0% Return between 8% and 13% 22.0% 22.1% 1.3% 6.9% 8.8% 4.9% 8.3% 10.5% 9.6% 9.6% 9.3% 9.6% 7.5% 2005

5.0% Return between 3% and 8% 27.1% 24.5% 23.8% 7.7% 11.0% 11.8% 8.0% 10.7% 12.3% 11.3% 11.2% 10.8% 10.9% 8.9% 2004

0.0% Return around zero (between -3% and +3%) 7.3% 17.2% 18.8% 19.7% 7.7% 10.4% 11.2% 7.9% 10.3% 11.8% 11.0% 10.9% 10.5% 10.7% 8.8% 2003 15

-5.0% Return between -3% and -8% 37.1% 22.2% 23.8% 23.4% 23.2% 12.6% 14.2% 14.4% 11.2% 13.0% 14.1% 13.1% 12.9% 12.4% 12.4% 10.5% 2002

-10.0% Return between -8% and -13% -43.9% -3.4% 0.2% 6.9% 9.9% 12.0% 4.5% 6.9% 7.9% 5.7% 7.8% 9.3% 8.8% 8.8% 8.7% 8.9% 7.3% 2001

-15.0% Return lower than -13% -19.8% -31.9% -8.9% -4.8% 1.5% 5.0% 7.4% 1.5% 3.9% 5.2% 3.4% 5.5% 7.0% 6.7% 6.9% 6.9% 7.2% 5.8% 2000

-7.5% -13.7% -23.8% -8.5% -5.4% 0.0% 3.2% 5.6% 0.5% 2.8% 4.0% 2.4% 4.5% 6.0% 5.8% 6.0% 6.1% 6.4% 5.1% 1999

39.0% 15.7% 3.9% -8.1% 1.0% 2.0% 5.6% 7.6% 9.3% 4.3% 6.1% 6.9% 5.3% 7.0% 8.2% 7.8% 7.9% 7.9% 8.1% 6.8% 1998 20

18.5% 28.7% 16.7% 7.5% -2.8% 3.9% 4.4% 7.2% 8.9% 10.2% 5.6% 7.1% 7.8% 6.2% 7.7% 8.8% 8.5% 8.5% 8.4% 8.6% 7.4% 1997

46.7% 32.6% 34.7% 24.2% 15.4% 5.5% 10.0% 9.7% 11.6% 12.6% 13.5% 9.0% 10.2% 10.6% 8.9% 10.2% 11.1% 10.6% 10.5% 10.4% 10.5% 9.2% 1996

27.4% 37.0% 30.9% 32.9% 24.8% 17.4% 8.6% 12.2% 11.6% 13.2% 14.0% 14.7% 10.4% 11.4% 11.7% 10.1% 11.2% 12.0% 11.5% 11.4% 11.2% 11.2% 9.9% 1995

7.8% 17.6% 27.3% 25.1% 27.9% 22.0% 16.0% 8.5% 11.7% 11.2% 12.7% 13.5% 14.1% 10.2% 11.2% 11.5% 9.9% 11.0% 11.7% 11.3% 11.2% 11.0% 11.1% 9.9% 1994

-7.5% 0.2% 9.2% 18.6% 18.6% 22.0% 17.8% 13.1% 6.7% 9.8% 9.5% 11.0% 11.9% 12.6% 9.1% 10.0% 10.3% 9.0% 10.0% 10.8% 10.4% 10.4% 10.2% 10.3% 9.2% 1993 25

Sell 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

5 10 15 20 25

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Investment period in years

Historical Market Returns (Arithmetic Mean) – German Market DAX Performance Index Return Triangle

26

Following: https://www.dai.de/files/dai_usercontent/dokumente/renditedreieck/2015-12-31%20DAX-Rendite-Dreieck%2050%20Jahre%20Web.pdf.

Reading example:An investment in the DAX Index at the endof the year 2008, when sold at the end ofthe year 2013, would have yielded anaverage annual return (arithmetic mean)of 16.0%. Other five-year investmentperiods are displayed along the blacksteps.

Buy

-18.3% 2017

12.5% -4.1% 2016

6.9% 9.7% -0.6% 2015

9.6% 8.2% 9.6% 1.9% 2014

2.7% 6.1% 6.3% 7.8% 2.0% 2013 5

25.5% 13.5% 12.2% 10.8% 11.2% 5.6% 2012

29.1% 27.3% 18.5% 16.2% 14.2% 14.0% 8.7% 2011

-14.7% 4.9% 11.4% 9.1% 9.2% 8.8% 9.3% 5.4% 2010

16.1% -0.5% 8.5% 12.5% 10.5% 10.3% 9.8% 10.2% 6.6% 2009

23.8% 19.9% 7.0% 12.2% 14.7% 12.6% 12.2% 11.5% 11.6% 8.2% 2008 10

-40.4% -14.1% -5.0% -7.5% -1.2% 2.9% 2.8% 3.6% 4.0% 4.8% 2.5% 2007

15.0% Return higher than 13% 22.3% -14.6% -3.3% 1.2% -2.2% 2.4% 5.4% 5.1% 5.6% 5.7% 6.3% 4.0% 2006

10.0% Return between 8% and 13% 22.0% 22.1% -3.8% 2.4% 5.0% 1.5% 5.0% 7.4% 6.8% 7.1% 7.1% 7.5% 5.3% 2005




-10.0% Return between -8% and -13% -43.9% -12.3% -6.2% 1.2% 5.0% 7.7% -1.0% 1.8% 3.3% 1.3% 3.6% 5.3% 5.1% 5.4% 5.5% 5.9% 4.3% 2001

-15.0% Return lower than -13% -19.8% -32.9% -14.9% -9.8% -3.4% 0.4% 3.3% -3.6% -0.9% 0.7% -0.8% 1.4% 3.1% 3.1% 3.5% 3.7% 4.2% 2.8% 2000

-7.5% -13.9% -25.4% -13.1% -9.4% -4.1% -0.8% 1.9% -4.0% -1.5% -0.1% -1.4% 0.7% 2.3% 2.3% 2.8% 3.0% 3.5% 2.2% 1999

39.0% 13.4% 1.0% -12.8% -4.6% -2.7% 1.1% 3.5% 5.4% -0.4% 1.6% 2.7% 1.3% 3.0% 4.4% 4.3% 4.6% 4.7% 5.1% 3.8% 1998 20

18.5% 28.3% 15.1% 5.1% -7.3% -1.0% 0.1% 3.1% 5.1% 6.7% 1.2% 2.9% 3.9% 2.4% 4.0% 5.2% 5.1% 5.3% 5.4% 5.7% 4.5% 1997

46.7% 31.8% 34.2% 22.3% 12.4% 0.1% 4.7% 5.0% 7.3% 8.6% 9.8% 4.4% 5.8% 6.5% 4.9% 6.3% 7.3% 7.0% 7.2% 7.2% 7.4% 6.1% 1996

27.4% 36.7% 30.3% 32.5% 23.3% 14.7% 3.6% 7.3% 7.3% 9.1% 10.2% 11.2% 6.0% 7.2% 7.7% 6.2% 7.4% 8.3% 8.0% 8.1% 8.0% 8.2% 6.9% 1995

7.8% 17.2% 26.3% 24.3% 27.1% 20.5% 13.7% 4.1% 7.3% 7.3% 9.0% 10.0% 10.9% 6.1% 7.2% 7.7% 6.3% 7.4% 8.3% 8.0% 8.1% 8.0% 8.2% 7.0% 1994

-7.5% -0.1% 8.3% 16.8% 17.2% 20.6% 16.1% 10.8% 2.7% 5.8% 5.9% 7.5% 8.6% 9.5% 5.1% 6.2% 6.8% 5.5% 6.6% 7.5% 7.2% 7.3% 7.3% 7.5% 6.3% 1993 25

Sell 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

5 10 15 20 25

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Historical Market Returns (Geometric Mean) – German MarketDAX Performance Index Return Triangle

27


Reading example:An investment in the DAX Index at the endof the year 2008, when sold at the end ofthe year 2013, would have yielded anaverage annual return (geometric mean)of 14.7%. Other five-year investmentperiods are displayed along the blacksteps.

Buy

-17.4% 2017

34.0% 8.3% 2016

12.5% 23.2% 9.7% 2015

11.6% 12.0% 19.4% 10.2% 2014

-11.4% 0.1% 4.2% 11.7% 5.9% 2013 5

5.3% -3.0% 1.9% 4.5% 10.4% 5.8% 2012

31.9% 18.6% 8.6% 9.4% 10.0% 14.0% 9.5% 2011

-32.3% -0.2% 1.6% -1.6% 1.0% 2.9% 7.4% 4.3% 2010

19.7% -6.3% 6.4% 6.2% 2.7% 4.1% 5.3% 8.9% 6.0% 2009

46.0% 32.9% 11.2% 16.3% 14.1% 9.9% 10.1% 10.4% 13.0% 10.0% 2008 10

-58.8% -6.4% 2.3% -6.3% 1.3% 2.0% 0.1% 1.5% 2.7% 5.9% 3.7% 2007

15.0% Return higher than 13% 0.8% -29.0% -4.0% 1.9% -4.9% 1.2% 1.8% 0.2% 1.4% 2.5% 5.4% 3.5% 2006

10.0% Return between 8% and 13% 26.0% 13.4% -10.7% 3.5% 6.8% 0.2% 4.8% 4.8% 3.0% 3.9% 4.7% 7.1% 5.2% 2005




-10.0% Return between -8% and -13% 3.7% 21.5% 34.0% 38.7% 36.1% 30.2% 17.5% 21.1% 20.9% 15.6% 17.1% 16.1% 14.0% 13.8% 13.7% 15.0% 13.1% 2001

-15.0% Return lower than -13% 9.2% 6.4% 17.4% 27.8% 32.8% 31.6% 27.2% 16.5% 19.8% 19.8% 15.0% 16.4% 15.6% 13.7% 13.5% 13.5% 14.7% 12.9% 2000

-9.1% 0.1% 1.3% 10.8% 20.4% 25.8% 25.8% 22.7% 13.6% 16.9% 17.1% 13.0% 14.5% 13.8% 12.1% 12.1% 12.1% 13.3% 11.7% 1999

8.9% -0.1% 3.0% 3.2% 10.4% 18.5% 23.4% 23.7% 21.2% 13.2% 16.2% 16.5% 12.7% 14.1% 13.5% 11.9% 11.9% 11.9% 13.1% 11.6% 1998 20

-12.3% -1.7% -4.1% -0.8% 0.1% 6.6% 14.1% 18.9% 19.7% 17.8% 10.9% 13.8% 14.2% 10.9% 12.3% 11.9% 10.5% 10.6% 10.7% 11.8% 10.4% 1997

17.7% 2.7% 4.8% 1.3% 2.9% 3.0% 8.2% 14.6% 18.8% 19.5% 17.8% 11.4% 14.1% 14.5% 11.4% 12.7% 12.2% 10.9% 10.9% 11.0% 12.1% 10.8% 1996

18.8% 18.2% 8.1% 8.3% 4.8% 5.5% 5.3% 9.5% 15.0% 18.8% 19.4% 17.9% 12.0% 14.4% 14.8% 11.8% 13.0% 12.6% 11.3% 11.3% 11.4% 12.4% 11.1% 1995

-6.2% 6.3% 10.1% 4.5% 5.4% 3.0% 3.9% 3.8% 7.8% 12.9% 16.5% 17.3% 16.0% 10.7% 13.0% 13.5% 10.8% 11.9% 11.6% 10.5% 10.5% 10.6% 11.6% 10.4% 1994

-7.3% -6.7% 1.8% 5.7% 2.1% 3.3% 1.5% 2.5% 2.6% 6.3% 11.1% 14.5% 15.4% 14.4% 9.5% 11.8% 12.2% 9.8% 10.9% 10.7% 9.6% 9.7% 9.8% 10.8% 9.7% 1993 25

Sell 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

5 10 15 20 25


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Historical Market Returns (Arithmetic Mean) – Austrian Market ATX Total Return Index Return Triangle

28


Reading example:An investment in the ATX Total Return atthe end of the year 2008, when sold at theend of the year 2013, would have yieldedan average annual return (arithmeticmean) of 14.1%. Other five-yearinvestment periods are displayed alongthe black steps.

Buy

-17.4% 2017

34.0% 5.2% 2016

12.5% 22.8% 7.6% 2015

11.6% 12.0% 18.9% 8.6% 2014

-11.4% -0.5% 3.6% 10.5% 4.2% 2013 5

5.3% -3.4% 1.4% 4.0% 9.4% 4.4% 2012

31.9% 17.9% 7.2% 8.3% 9.1% 12.9% 8.0% 2011

-32.3% -5.5% -2.0% -4.4% -1.4% 0.8% 5.0% 1.9% 2010

19.7% -9.9% 2.3% 3.0% 0.0% 1.8% 3.3% 6.7% 3.7% 2009

46.0% 32.2% 5.8% 11.8% 10.5% 6.5% 7.2% 7.8% 10.5% 7.3% 2008 10

-58.8% -22.4% -10.3% -16.4% -8.4% -6.3% -7.0% -4.9% -3.1% 0.1% -1.6% 2007

15.0% Return higher than 13% 0.8% -35.5% -15.3% -7.7% -13.2% -7.0% -5.3% -6.1% -4.3% -2.7% 0.2% -1.4% 2006

10.0% Return between 8% and 13% 26.0% 12.7% -19.4% -6.5% -1.8% -7.7% -2.8% -1.9% -3.0% -1.6% -0.4% 2.1% 0.4% 2005

5.0% Return between 3% and 8% 52.6% 38.6% 24.7% -5.5% 3.1% 5.7% -0.8% 2.8% 3.1% 1.5% 2.4% 3.2% 5.3% 3.5% 2004



-10.0% Return between -8% and -13% 3.7% 20.2% 32.0% 36.8% 34.6% 28.3% 9.1% 13.1% 13.8% 8.1% 10.0% 9.6% 7.9% 8.1% 8.4% 9.9% 8.0% 2001

-15.0% Return lower than -13% 9.2% 6.4% 16.4% 25.9% 30.8% 30.0% 25.3% 9.1% 12.7% 13.4% 8.2% 10.0% 9.6% 8.0% 8.2% 8.5% 9.8% 8.1% 2000

-9.1% -0.4% 1.0% 9.4% 17.9% 23.1% 23.5% 20.4% 6.9% 10.3% 11.1% 6.6% 8.4% 8.2% 6.7% 7.0% 7.3% 8.7% 7.1% 1999

8.9% -0.5% 2.6% 2.9% 9.3% 16.4% 21.0% 21.6% 19.1% 7.1% 10.2% 10.9% 6.8% 8.4% 8.2% 6.9% 7.1% 7.4% 8.7% 7.2% 1998 20

-12.3% -2.3% -4.6% -1.3% -0.3% 5.4% 11.8% 16.2% 17.3% 15.5% 5.2% 8.1% 8.9% 5.3% 6.9% 6.8% 5.6% 6.0% 6.3% 7.5% 6.2% 1997

17.7% 1.6% 4.0% 0.6% 2.2% 2.5% 7.1% 12.5% 16.4% 17.3% 15.7% 6.2% 8.8% 9.5% 6.1% 7.5% 7.4% 6.3% 6.5% 6.8% 8.0% 6.7% 1996

18.8% 18.2% 7.0% 7.5% 4.0% 4.8% 4.7% 8.5% 13.2% 16.6% 17.4% 15.9% 7.1% 9.5% 10.1% 6.8% 8.2% 8.0% 6.9% 7.1% 7.4% 8.5% 7.2% 1995

-6.2% 5.6% 9.5% 3.6% 4.6% 2.2% 3.2% 3.2% 6.7% 11.1% 14.3% 15.3% 14.1% 6.1% 8.4% 9.0% 6.0% 7.3% 7.2% 6.2% 6.4% 6.7% 7.8% 6.6% 1994

-7.3% -6.7% 1.1% 5.0% 1.3% 2.5% 0.8% 1.8% 2.0% 5.2% 9.3% 12.3% 13.3% 12.4% 5.1% 7.3% 8.0% 5.2% 6.5% 6.4% 5.5% 5.8% 6.1% 7.1% 6.0% 1993 25

Sell 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

5 10 15 20 25


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Historical Market Returns (Geometric Mean) – Austrian MarketATX Total Return Index Return Triangle

29


Reading example:An investment in the ATX Total Return atthe end of the year 2008, when sold at theend of the year 2013, would have yieldedan average annual return (geometricmean) of 10.5%. Other five-yearinvestment periods are displayed alongthe black steps.

Buy

-7.0% 2017

17.9% 5.4% 2016

-3.4% 7.3% 2.5% 2015

1.2% -1.1% 5.2% 2.2% 2014

12.9% 7.0% 3.6% 7.2% 4.3% 2013 5

23.9% 18.4% 12.7% 8.7% 10.5% 7.6% 2012

19.1% 21.5% 18.6% 14.3% 10.7% 11.9% 9.2% 2011

-4.6% 7.2% 12.8% 12.8% 10.5% 8.2% 9.6% 7.5% 2010

1.2% -1.7% 5.2% 9.9% 10.5% 8.9% 7.2% 8.5% 6.8% 2009

15.0% Return higher than 13% 22.1% 11.6% 6.2% 9.4% 12.3% 12.4% 10.8% 9.0% 10.0% 8.3% 2008 10

10.0% Return between 8% and 13% -32.8% -5.3% -3.2% -3.5% 1.0% 4.8% 6.0% 5.4% 4.4% 5.7% 4.6% 2007

5.0% Return between 3% and 8% -1.4% -17.1% -4.0% -2.7% -3.1% 0.6% 3.9% 5.1% 4.6% 3.8% 5.1% 4.1% 2006

0.0% Return around zero (between -3% and +3%) 18.0% 8.3% -5.4% 1.5% 1.4% 0.4% 3.1% 5.7% 6.5% 6.0% 5.1% 6.2% 5.2% 2005

-5.0% Return between -3% and -8% 36.0% 27.0% 17.5% 5.0% 8.4% 7.2% 5.5% 7.2% 9.1% 9.4% 8.7% 7.7% 8.5% 7.4% 2004

-10.0% Return between -8% and -13% 5.5% 20.7% 19.8% 14.5% 5.1% 7.9% 6.9% 5.5% 7.0% 8.7% 9.1% 8.4% 7.5% 8.3% 7.2% 2003 15

-15.0% Return lower than -13% 20.9% 13.2% 20.8% 20.1% 15.8% 7.7% 9.8% 8.7% 7.2% 8.4% 9.8% 10.1% 9.4% 8.5% 9.1% 8.1% 2002

-26.6% -2.8% -0.1% 9.0% 10.8% 8.7% 2.8% 5.2% 4.8% 3.8% 5.2% 6.8% 7.3% 6.8% 6.1% 6.9% 6.1% 2001

-20.2% -23.4% -8.6% -5.1% 3.1% 5.6% 4.6% -0.1% 2.4% 2.3% 1.6% 3.1% 4.7% 5.3% 5.0% 4.5% 5.3% 4.6% 2000

9.3% -5.5% -12.5% -4.2% -2.2% 4.1% 6.1% 5.2% 1.0% 3.1% 2.9% 2.3% 3.6% 5.0% 5.6% 5.3% 4.8% 5.5% 4.8% 1999

7.2% 8.2% -1.3% -7.6% -1.9% -0.7% 4.6% 6.3% 5.4% 1.6% 3.5% 3.3% 2.7% 3.8% 5.2% 5.7% 5.4% 4.9% 5.6% 5.0% 1998 20

15.7% 11.4% 10.7% 3.0% -2.9% 1.0% 1.7% 6.0% 7.3% 6.4% 2.9% 4.5% 4.2% 3.6% 4.6% 5.8% 6.2% 6.0% 5.5% 6.1% 5.5% 1997

60.9% 38.3% 27.9% 23.3% 14.6% 7.7% 9.6% 9.1% 12.1% 12.7% 11.4% 7.7% 8.8% 8.3% 7.4% 8.1% 9.1% 9.3% 8.9% 8.2% 8.7% 8.0% 1996

28.2% 44.5% 34.9% 28.0% 24.2% 16.8% 10.6% 11.9% 11.2% 13.7% 14.1% 12.8% 9.3% 10.2% 9.6% 8.7% 9.3% 10.1% 10.3% 9.8% 9.2% 9.6% 8.9% 1995

Sell 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

5 10 15 20

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Historical Market Returns (Arithmetic Mean) – Swiss MarketSMI Total Return Index Return Triangle

30


Reading example:An investment in the SMI Total Return atthe end of the year 2008, when sold at theend of the year 2009, would have yieldedan average annual return (arithmeticmean) of 12.3%. Other five-yearinvestment periods are displayed alongthe black steps.

Buy

-7.0% 2017

17.9% 4.7% 2016

-3.4% 6.7% 1.9% 2015

1.2% -1.1% 4.8% 1.7% 2014

12.9% 6.9% 3.3% 6.8% 3.9% 2013 5

23.9% 18.3% 12.3% 8.1% 10.0% 7.0% 2012

19.1% 21.5% 18.5% 13.9% 10.2% 11.5% 8.6% 2011

-4.6% 6.6% 12.1% 12.3% 10.0% 7.6% 9.0% 6.9% 2010

1.2% -1.8% 4.7% 9.2% 10.0% 8.4% 6.7% 8.0% 6.2% 2009

15.0% Return higher than 13% 22.1% 11.2% 5.6% 8.8% 11.7% 11.9% 10.3% 8.5% 9.5% 7.7% 2008 10

10.0% Return between 8% and 13% -32.8% -9.4% -6.0% -5.7% -1.2% 2.6% 4.0% 3.7% 2.9% 4.3% 3.2% 2007

5.0% Return between 3% and 8% -1.4% -18.6% -6.8% -4.9% -4.8% -1.2% 2.0% 3.3% 3.1% 2.4% 3.8% 2.8% 2006

0.0% Return around zero (between -3% and +3%) 18.0% 7.9% -7.9% -1.1% -0.7% -1.3% 1.3% 3.9% 4.9% 4.5% 3.8% 4.9% 3.9% 2005

-5.0% Return between -3% and -8% 36.0% 26.7% 16.5% 1.6% 5.4% 4.7% 3.3% 5.1% 7.1% 7.6% 7.0% 6.1% 7.0% 5.9% 2004

-10.0% Return between -8% and -13% 5.5% 19.8% 19.2% 13.7% 2.3% 5.4% 4.8% 3.6% 5.2% 6.9% 7.4% 6.9% 6.1% 6.9% 5.9% 2003 15

-15.0% Return lower than -13% 20.9% 12.9% 20.1% 19.6% 15.1% 5.2% 7.5% 6.7% 5.4% 6.7% 8.1% 8.5% 7.9% 7.1% 7.8% 6.8% 2002

-26.6% -5.8% -2.2% 6.2% 8.5% 6.8% 0.0% 2.5% 2.3% 1.6% 3.1% 4.7% 5.3% 5.0% 4.4% 5.2% 4.4% 2001

-20.2% -23.5% -10.9% -7.0% 0.3% 3.1% 2.4% -2.8% -0.3% -0.2% -0.6% 0.9% 2.5% 3.2% 3.1% 2.7% 3.5% 2.9% 2000

9.3% -6.6% -13.8% -6.2% -4.0% 1.8% 3.9% 3.3% -1.5% 0.6% 0.6% 0.2% 1.5% 3.0% 3.6% 3.5% 3.1% 3.8% 3.2% 1999

7.2% 8.2% -2.2% -9.0% -3.7% -2.2% 2.5% 4.3% 3.7% -0.7% 1.2% 1.2% 0.7% 1.9% 3.3% 3.8% 3.7% 3.3% 4.0% 3.4% 1998 20

15.7% 11.3% 10.7% 2.0% -4.5% -0.7% 0.2% 4.1% 5.5% 4.8% 0.7% 2.3% 2.2% 1.7% 2.8% 4.0% 4.5% 4.3% 3.9% 4.6% 4.0% 1997

60.9% 36.4% 25.9% 21.5% 11.7% 4.2% 6.4% 6.3% 9.2% 10.1% 9.0% 4.7% 5.9% 5.6% 4.9% 5.7% 6.7% 7.0% 6.7% 6.2% 6.7% 6.1% 1996

28.2% 43.6% 33.6% 26.5% 22.8% 14.3% 7.3% 8.9% 8.5% 11.0% 11.6% 10.5% 6.3% 7.4% 7.0% 6.2% 6.9% 7.8% 8.1% 7.7% 7.1% 7.6% 6.9% 1995

Sell 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

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Historical Market Returns (Geometric Mean) – Swiss MarketSMI Total Return Index Return Triangle

31


Reading example:An investment in the SMI Total Return atthe end of the year 2008, when sold at theend of the year 2013, would have yieldedan average annual return (geometricmean) of 11.7%. Other five-yearinvestment periods are displayed alongthe black steps.

Sector classification of the DACH region

based on sector indices

5

▪ Banking

▪ Insurance

▪ Financial Services

▪ Real Estate

▪ Basic Materials

▪ Consumer Goods

▪ Telecommunication

▪ Industrials

▪ Consumer Service

▪ Pharma & Healthcare

▪ Information Technology

▪ Utilities

DAX Sec. All 546

companies in 9

“Deutsche Börse super

sectors”

WBI 57

companies in 5

“ATX sector indices”

SPI204

companies in 10

“SPI sector indices”

Sector Indices of the DACH RegionMethodology & approach

The sector indices aim to cover the whole capital market of the DACH region. Therefore,this capital market study contains all equities of the German DAX Sector All Index1), Vienna StockExchange Index (WBI) and Swiss Performance Index (SPI). These three indices contain all shareslisted on the Official and Semi-Official Market.

The 807 public companies, which are listed in the mentioned indices as of 31 December 2018,build the base for the sector classification and the subsequent analyses:

▪ The German DAX Sector All Index includes 546 companies listed in the Prime Standard, GeneralStandard and Scale segment and is classified into nine “Deutsche Börse super sectors”.

▪ The Austrian ATX only has five indices, ValueTrust assigns the remaining companies of the WBIto the classified indices.

▪ The Swiss SPI contains ten sector indices that comprise 204 companies.

Eventually, merged all three market indices and the respective sector index classificationinto twelve sector indices, so-called “super sectors.”

The twelve sector indices for this study are defined as follows:

33

807 listed companies

Complete sector classification of the DACH region in 12 sector indices

classifies

Capital market of the DACH region

1) The DAX Sector All Index contains all equities listed in the Prime and General Standard as well as in the Scale segment of the Frankfurt stock exchange.

34

Sector Indices of the DACH Region as of 31 December 2018Sector distribution and number of companies

The chart shows the percentagedistribution of the 807 listed companies inthe twelve “super sectors” (the absolutenumber of companies is displayed inparenthesis).

The twelve defined sectors can beclassified in three different dimensions.▪ nine different sectors represent a

proportion of less than 10%,▪ two represent a share between 10%

and 20%,▪ and one represents a portion of more

than 20%.

Companies within the Industrials,Information Technology and ConsumerGoods sectors hence represent more than50% of the entire market.

1) Including asset managers, leasing firms and distribution companies for financial products.

1)

5% 2%

9%

7%

5%

12%

2%

27%

9%

9%

13%2%

Sector classification of the DACH Region

Banking ( 38 )

Insurance ( 14 )

Financial Services ( 69 )

Real Estate ( 59 )

Basic Materials ( 42 )

Consumer Goods ( 94 )

Telecommunication ( 15 )

Industrials ( 215 )

Consumer Service ( 72 )

Pharma & Healthcare ( 70 )

Information Technology ( 105 )

Utilities ( 14 )

1)

Betas6

BetasBackground & approach

36

Beta is used in the CAPM and is also known as the beta coefficient or betafactor. Beta is a measure of systematic risk of a security of a specificcompany (company beta) or a specific sector (sector beta) in comparisonto the market. A beta of less than 1 means that the security istheoretically less volatile than the market. A beta of greater than 1indicates that the security's price is more volatile than the market.

Beta factors are estimated on the basis of historical returns of securities incomparison to an approximate market portfolio. Since the companyvaluation is forward-looking, it has to be examined whether or whatpotential risk factors prevailing in the past do also apply for the future. Byvaluing non-listed companies or companies without meaningful shareprice performance, it is common to use a beta factor from a group ofcomparable companies (“peer group beta“), a suitable sector (“sectorbeta“) or one single listed company in the capital market with a similarbusiness model and a similar risk profile (“pure play beta“). Within thiscapital market study we have used sector betas which are computed asarithmetic means of the statistically significant beta factors of allcompanies of a particular sector.

The estimation of beta factors is usually accomplished through a linearregression analysis. We use the CDAX, WBI, and SPI as country specificreference indices.

Furthermore, it is important to set a time period for which the data iscollected (benchmark period) and whether daily, weekly or monthlyreturns (return interval) are analyzed. In practice, it is common to useobservation periods of two years with the regression of weekly returns or afive-year observation period with the regression of monthly returns. Bothalternatives are displayed in our study.

In the CAPM, company specific risk premiums include not only businessrisk, but also financial risk. The beta factor for levered companies(“levered beta”) is usually higher compared to a company with an identicalbusiness model but without debt (due to financial risk). Hence, changes inthe capital structure require an adjustment of the betas and therefore ofthe company specific risk premiums.

In order to calculate the unlevered beta, adjustment formulas have beendeveloped. We prefer to use the adjustment formula by Harris/Pringlewhich assumes a value-based financing policy, stock-flow adjustmentswithout time delay, uncertain tax shields and a so-called debt beta. Wecalculate the debt beta based on the respective company’s rating or theaverage sector rating (if a company’s rating is not available) through theapplication of the credit spread derived from the expected cost of debt.We do not adjust the credit spread for unsystematic risks. The capitalmarket data, in particular historical market prices, is provided by the datasupplier S&P Capital IQ.

BetasSector specific levered and unlevered betas as of 31 December 2018 (1/2)

37

1) Statistically not significant (t-test, confidence interval: 95%) beta factors are not being considered. As a consequence, the number of the companies decreased.

2) The debt ratio corresponds to the debt-to-total capital ratio.

3) No display of debt illustration for the sectors Banking, Insurance and Financial Services. We refrained from adjustments of the companies’ specific debt (unlevered) because indebtedness is part of the companies’ operational activities and economic risk. Therefore, a separation of operative and financial obligations is not possible. In addition, e.g. bank specific regulations about the minimum capital within financial institutions let us assume that the indebtedness degree is widely comparable. For that reason, it is possible to renounce the adaptation of beta factors.

Sector

Number of

companies1)

5-y. m. / 2-y. w. Aggregation

5-years

2018-2014

monthly

2-years

2018-2017

weekly

5-years

2018-2014

monthly

2-years

2018-2017

weekly

5-years

2018-2014

monthly

2-years

2018-2017

weekly

5-years

2018-2014

monthly

2-years

2018-2017

weekly

Median 0.72 0.78 n.a. n.a. n.a. n.a. n.a. n.a.

Arithmetic Mean 0.88 0.79 n.a. n.a. n.a. n.a. n.a. n.a.

Market-Value Weighted Mean 1.32 1.13 n.a. n.a. n.a. n.a. n.a. n.a.







Median 0.57 0.57 47% 44% 0.28 0.28 0.41 0.42

Arithmetic mean 0.68 0.60 57% 52% 0.29 0.29 0.47 0.48

Market-value weighted mean 0.54 0.54 47% 45% 0.25 0.25 0.40 0.42

Median 1.04 1.01 34% 30% 0.31 0.31 0.84 0.83

Arithmetic mean 1.04 1.05 40% 32% 0.32 0.33 0.80 0.84


Median 0.86 0.86 24% 24% 0.19 0.19 0.72 0.65

Arithmetic mean 1.05 0.89 37% 33% 0.20 0.20 0.75 0.69

Market-Value Weighted Mean 1.00 0.88 37% 40% 0.17 0.17 0.73 0.65

Debt ratio2)

Debt Beta Beta unleveredBeta levered1)

Basic Materials 30 / 32

Consumer Goods

Insurance3) 14 / 14

Financial Services3) 31 / 37

Real Estate 31 / 39

Banking3)

23 / 30

50 / 56

1) Statistically not significant (t-test, confidence interval: 95%) beta factors are not being considered. As a consequence, the number of the companies decreased.

2) The debt ratio corresponds to the debt-to-total capital ratio.

3) The market-value weighted mean of the levered beta for all DACH companies deviates slightly from 1 because statistically not significant betas were excluded.

BetasSector specific levered and unlevered betas as of 31 December 2018 (2/2)

38

Sector

Number of

companies1)

5-y. m. / 2-y. w. Aggregation

5-years

2018-2014

monthly

2-years

2018-2017

weekly

5-years

2018-2014

monthly

2-years

2018-2017

weekly

5-years

2018-2014

monthly

2-years

2018-2017

weekly

5-years

2018-2014

monthly

2-years

2018-2017

weekly

Median 0.71 0.72 16% 18% 0.19 0.19 0.61 0.61

Arithmetic mean 0.87 0.83 27% 28% 0.20 0.20 0.75 0.69


Median 0.90 1.01 19% 17% 0.30 0.30 0.75 0.83

Arithmetic mean 0.95 1.02 33% 27% 0.32 0.32 0.79 0.87


Median 0.82 0.88 12% 12% 0.32 0.32 0.74 0.71

Arithmetic mean 1.05 0.96 22% 22% 0.33 0.33 0.92 0.86


Median 0.99 1.05 12% 6% 0.21 0.19 0.85 1.02

Arithmetic mean 1.05 1.09 22% 14% 0.21 0.20 0.90 1.00


Median 0.99 1.13 10% 8% 0.19 0.19 0.76 0.99

Arithmetic mean 1.04 1.16 60% 16% 0.19 0.19 0.85 1.04


Median 0.68 0.71 53% 35% 0.19 0.19 0.45 0.55

Arithmetic mean 0.69 0.72 63% 55% 0.20 0.20 0.43 0.49


DACH3) Market-value weighted mean 0.96 0.97

Debt ratio2)

Debt Beta Beta unleveredBeta levered1)

Utilities 8 / 9

Telecommunication 12 / 13

Industrials 126 / 158

Consumer Service 36 / 41

Pharma & Healthcare 35 / 51

Information Technology 57 / 69

Sector returns

a. Implied returns (ex-ante analysis)

7

Implied Sector ReturnsBackground & approach

Besides the future-oriented calculation of implied market returns (cf. slide16 et seq.), we calculate implied returns for sectors. That offers analternative and simplification to the ex-post analysis of the company'scosts of capital via the CAPM. Using this approach, the calculation ofsector betas via regression analyses is not necessary.

The implied sector returns shown on the following slides can be used as anindicator for the sector specific levered costs of equity. Those alreadyconsider a sector specific leverage. Because of this, another simplificationis to renounce making adjustments with regards to the capital structurerisk.

Comparable to the calculation of the implied market returns, the followingreturn calculations are based on the Residual Income Valuation Model byBabbel.1) The required data (i.e. net income, market capitalization, andbook values of equity) are sourced from the data provider S&P Capital IQ.Regarding the profit growth, we assume a growth rate of 2.0%.

We unlever the implied returns with the following adjusting equation forthe costs of equity2) to take the specific leverage into account:3)

kEL = kE

U + kEU − Rf ∗

D

Ewith:

kEL = Levered cost of equity

kEU = Unlevered cost of equity

Rf = Risk-free rate

D

E= Debt 4) -to-equity ratio

The implied unlevered sector returns serve as an indicator for anaggregated and unlevered cost of equity for specific sectors. The processof relevering a company's cost of capital to reflect a company specific debtsituation (cf. calculation example on the next slide) can be worked outwithout using the CAPM.

40

1) cf. Babbel, Challenging Stock Prices: Share prices and implied growth expectations (Corporate Finance, n. 9, 2015, p. 316-323, especially p. 319); cf. Aders/Aschauer/Dollinger, Die implizite Marktrisikoprämie amösterreichischen Kapitalmarkt (RWZ, 6/2016, p. 195-202).

2) In situations in which the debt betas in the market are distorted, we would have to adjust these betas to avoid unsystematic risks. For simplification reasons, we deviate from our typical analysis strategy toachieve the enterprise value (Debt beta > 0) and assume that the costs of debt are at the level of the risk-free rate. This process is designed by the so-called Practitioners formula (uncertain tax shields, debt beta= 0), cf. Pratt/Grabowski, Cost of Capital, 5th ed., 2014, p. 253.

3) We assume that the cash and cash equivalents are used entirely for operational purposes. Consequently, we do not deduct excess cash from the debt.

4) “Debt” is defined as all interest bearing liabilities. The debt illustration of the companies in the Banking, Insurance and Financial Services sector only serves an informational purpose. We will not implement anadjustment to these companies’ specific debt (unlevered) because their indebtedness is part of their operational activities and economic risk.

Implied Sector ReturnsExemplary calculation to adjust for the company specific capital structure

Calculation example:

As of the reference date 31 December 2018, we observe a sector specific,unlevered cost of equity of 6.2% (market-value weighted mean) of anexemplary company X, which operates in the German Basic Materialssector. The following assumptions have been made:

▪ The debt-to-equity ratio of the exemplary company X: 40%

▪ The risk-free rate: 1.09% (cf. slide 12)

Based on these numbers, we calculate the relevered costs of equity ofcompany X with the adjustment formula:

𝐤𝐄𝐋 = 6.2% + (6.2% - 1.09%) * 40% = 8.2%

Thus, 8.24% is the company’s relevered cost of equity. In comparison, thelevered cost of equity of the Basic Materials sector is 9.3%.

41

7.7%*

9.6%*

6.1%*

3.6%

6.4%

5.6%

4.9%

5.7%

5.1%

6.6%

5.3%

4.6%

10.5%*10.3%*

7.4%*

3.9%

6.6%6.2%

4.6%

6.3%5.8%

6.4%6.1%

5.1%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

Implied Sector Returns (unlevered)*

Overview as of 31 December 2018 vs. 30 June 2018

42

*The returns for the sectors Banking, Insurance and Financial Services are levered sector returns. For all other sectors unlevered returns are displayed.

June 2018(transparent fill)

Dec 2018(darker fill)

The implied sector return (unlevered) of the BasicMaterials sector increased from 6.4% as of 30 June2018 to 6.6% as of 31 December 2018. It nowconstitutes the highest unlevered sector returnacross all sectors.

Implied sector returns (levered) - DACH - Banking

H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2017 H1 2018 H2 2018

30.06.2013 31.12.2013 30.06.2014 31.12.2014 30.06.2015 31.12.2015 30.06.2016 31.12.2016 30.06.2017 31.12.2017 30.06.2018 31.12.2018

Minimum -2.4% 5.2% 2.9% 6.2% 5.2% 4.8% 3.3% 3.5% 2.6% 4.1% 0.9% 2.7%

Lower quantile 2.9% 6.3% 5.1% 6.7% 5.6% 5.9% 5.0% 5.4% 5.1% 5.6% 5.3% 5.8%

Median 7.5% 8.1% 7.9% 8.8% 7.7% 8.4% 7.0% 6.6% 6.6% 7.2% 7.5% 10.1%

Arithmetic mean 7.3% 8.4% 9.1% 10.3% 8.4% 9.0% 8.5% 8.0% 7.6% 8.0% 8.1% 10.1%


Upper quantile 11.0% 10.8% 14.3% 13.2% 12.5% 10.6% 12.5% 11.3% 10.4% 10.2% 11.9% 14.3%

Maximum 11.1% 11.3% 35.3% 38.6% 21.8% 29.4% 24.3% 24.3% 23.2% 21.1% 22.4% 23.2%


Implied Sector ReturnsBanking

43

Note: The debt illustration of the companies in the Banking, Insurance and Financial Services sectors only serves an informational purpose. We will not implement an adjustment to thesecompanies’ specific debt (unlevered) because their indebtedness is part of their operational activities and economic risk (cf. slide 37 and 40).

▪ The implied market return in thebanking sector amounts to 10.5% asof 31 December 2018 and issignificantly higher than in June2018.

8.7% 9.0%

7.3%

9.5%

8.4% 8.7%

7.5% 7.2% 7.0%7.7% 7.7%

10.5%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

Implied sector returns - DACH - Banking

Range (10% - 90% quantile) Market-value weighted mean (levered)

Implied Sector ReturnsInsurance

44


▪ The implied market return in theinsurance sector increased from9.6% as of 30 Jun 2018 to 10.3% asof 31 December 2018.

▪ Over the course of time, the market-value weighted mean of the impliedsector return fluctuated between8.8% and 11.7%.

Implied sector returns (levered) - DACH - Insurance

H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2017 H1 2018 H2 2018

30.06.2013 31.12.2013 30.06.2014 31.12.2014 30.06.2015 31.12.2015 30.06.2016 31.12.2016 30.06.2017 31.12.2017 30.06.2018 31.12.2018

Minimum 9.0% 9.0% 7.9% 5.1% 1.8% 1.5% 4.8% 4.2% 4.3% 4.7% 5.4% 8.3%

Lower quantile 9.2% 9.1% 8.3% 6.3% 3.6% 3.6% 6.2% 5.7% 5.6% 5.5% 7.9% 8.4%

Median 11.2% 10.3% 10.3% 10.0% 10.0% 9.3% 9.3% 9.1% 8.5% 8.8% 9.0% 10.0%

Arithmetic mean 11.2% 10.2% 10.4% 10.0% 9.4% 9.2% 9.4% 9.0% 8.3% 8.4% 9.1% 9.9%


Upper quantile 12.9% 11.5% 12.3% 13.5% 12.5% 13.0% 11.2% 10.7% 9.8% 10.1% 10.9% 11.7%

Maximum 12.9% 11.7% 12.8% 14.4% 13.4% 13.6% 11.4% 10.8% 10.1% 10.4% 11.1% 12.0%


11.7%

10.4% 10.5%10.1% 9.9%

9.4%10.0% 9.1%

8.8% 8.8%9.6%

10.3%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

Implied sector returns - DACH - Insurance


Implied Sector ReturnsFinancial Services

45


▪ The implied market return in thefinancial services sector increasedfrom 6.1% as of 30 June 2018 to7.4% as of 31 December 2018.

▪ Over the course of time, the market-value weighted mean fluctuatedbetween 5.9% and 7.4%.

7.1% 7.3% 7.0% 7.0%6.3% 6.3% 6.1% 6.4%

5.9% 5.9% 6.1%

7.4%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

Implied sector returns - DACH - Financial Services


Implied sector returns (levered) - DACH - Financial Services

H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2017 H1 2018 H2 2018

30.06.2013 31.12.2013 30.06.2014 31.12.2014 30.06.2015 31.12.2015 30.06.2016 31.12.2016 30.06.2017 31.12.2017 30.06.2018 31.12.2018

Minimum 1,5% 4,4% 3,5% 0,4% 3,1% 4,6% 3,8% 5,1% 3,6% 4,6% 3,4% 1,9%

Lower quantile 2,6% 5,2% 4,2% 3,2% 4,4% 5,0% 4,0% 5,6% 4,8% 4,7% 4,3% 2,0%

Median 7,8% 7,6% 7,0% 7,8% 6,3% 6,4% 6,7% 7,1% 6,0% 6,7% 6,9% 7,2%

Arithmetic mean 7,7% 8,3% 8,8% 8,2% 8,8% 7,4% 7,1% 7,8% 7,4% 7,0% 7,1% 7,3%

Market-value weighted mean 7,1% 7,3% 7,0% 7,0% 6,3% 6,3% 6,1% 6,4% 5,9% 5,9% 6,1% 7,4%

Upper quantile 13,4% 14,5% 19,6% 14,5% 7,8% 11,3% 11,8% 12,5% 13,7% 9,1% 9,2% 11,6%

Maximum 18,6% 15,9% 32,4% 15,8% 55,8% 15,2% 12,0% 14,9% 14,2% 13,0% 16,9% 17,1%

Market-value weighted debt 66,5% 62,8% 72,8% 68,2% 58,3% 49,4% 58,3% 59,8% 55,4% 44,4% 46,3% 59,9%

Implied Sector ReturnsReal Estate (table)

46

Implied sector returns (levered) - DACH - Real Estate

H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2017 H1 2018 H2 2018

30.06.2013 31.12.2013 30.06.2014 31.12.2014 30.06.2015 31.12.2015 30.06.2016 31.12.2016 30.06.2017 31.12.2017 30.06.2018 31.12.2018

Minimum 2.6% 1.8% 2.7% 1.9% 2.5% 2.2% 1.6% 1.5% -0.2% 3.1% 2.9% 1.7%

Lower quantile 3.1% 4.0% 3.8% 3.2% 3.3% 3.6% 3.4% 3.2% 3.2% 3.8% 3.0% 3.3%

Median 5.9% 6.6% 6.2% 6.2% 5.9% 6.4% 6.4% 6.7% 6.5% 6.1% 5.2% 6.5%

Arithmetic mean 6.0% 6.9% 6.6% 7.9% 7.3% 7.9% 7.6% 7.9% 7.0% 7.0% 6.0% 7.8%


Upper quantile 8.9% 10.7% 10.2% 14.1% 16.6% 15.9% 15.3% 18.0% 12.4% 11.4% 10.1% 14.7%

Maximum 10.6% 14.2% 14.3% 32.5% 25.4% 17.2% 19.5% 32.4% 20.4% 21.8% 14.6% 23.5%


Implied sector returns (unlevered) - DACH - Real Estate

H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2017 H1 2018 H2 2018

30.06.2013 31.12.2013 30.06.2014 31.12.2014 30.06.2015 31.12.2015 30.06.2016 31.12.2016 30.06.2017 31.12.2017 30.06.2018 31.12.2018

Minimum 2.4% 2.5% 2.5% 1.9% 1.5% 2.1% 1.2% 1.6% 0.8% 2.3% 2.0% 1.4%

Lower quantile 2.5% 3.1% 3.0% 2.2% 2.2% 2.6% 2.3% 2.2% 2.5% 2.5% 2.2% 2.4%

Median 3.9% 4.4% 4.0% 3.7% 3.3% 3.8% 3.4% 3.4% 3.7% 3.6% 3.3% 3.9%

Arithmetic mean 3.8% 4.5% 4.1% 4.5% 3.8% 4.8% 4.4% 4.7% 4.6% 4.6% 3.7% 4.5%


Upper quantile 4.9% 5.4% 5.3% 7.5% 6.8% 7.9% 8.4% 7.1% 8.2% 6.8% 5.7% 7.9%

Maximum 5.0% 8.8% 8.0% 20.0% 7.4% 14.2% 17.4% 17.5% 16.8% 17.9% 6.1% 10.4%


4.0% 4.5% 4.0% 3.7% 3.3%4.0% 3.8% 4.0% 4.2% 4.1% 3.6% 3.9%

5.9% 6.5%5.9% 5.8% 5.5%

6.3% 6.1% 6.3% 6.2% 5.9%5.2%

6.2%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

Implied sector returns - DACH - Real Estate

Range (10% - 90% quantile) Market-value weighted mean (unlevered) Market-value weighted mean (levered)

Implied Sector ReturnsReal Estate (chart)

47

Note: The ranges refer to the implied sector returns (unlevered).

▪ The implied market return (unlevered) in theReal Estate sector increased from 3.6% as of30 June 2018 to 3.9% as of 31 December 2018.

▪ In comparison to the other sectors, the RealEstate sector had the lowest unlevered impliedsector return as of 31 December 2018.

Implied Sector ReturnsBasic Materials (table)

48

Implied sector returns (levered) - DACH - Basic Materials

H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2017 H1 2018 H2 2018

30.06.2013 31.12.2013 30.06.2014 31.12.2014 30.06.2015 31.12.2015 30.06.2016 31.12.2016 30.06.2017 31.12.2017 30.06.2018 31.12.2018

Minimum 2.2% -0.4% -1.8% 0.2% 1.3% 3.6% 0.4% 2.1% 0.6% 1.3% 1.9% 4.9%

Lower quantile 5.7% 2.9% 3.2% 3.8% 1.9% 4.7% 2.6% 4.1% 4.4% 3.3% 4.7% 5.9%

Median 8.3% 7.2% 6.2% 7.6% 6.2% 7.7% 7.6% 6.8% 6.8% 7.0% 7.7% 10.0%

Arithmetic mean 9.4% 8.1% 9.0% 7.8% 6.2% 8.1% 6.7% 7.3% 6.9% 7.6% 8.2% 10.1%


Upper quantile 11.2% 11.9% 10.1% 11.8% 9.1% 11.1% 9.2% 9.4% 10.6% 9.8% 12.9% 14.5%

Maximum 45.1% 41.7% 73.2% 20.4% 9.9% 20.4% 11.0% 23.3% 13.9% 31.3% 16.8% 19.6%


Implied sector returns (unlevered) - DACH - Basic Materials

H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2017 H1 2018 H2 2018

30.06.2013 31.12.2013 30.06.2014 31.12.2014 30.06.2015 31.12.2015 30.06.2016 31.12.2016 30.06.2017 31.12.2017 30.06.2018 31.12.2018

Minimum 2.3% 1.3% 0.9% 1.9% 1.2% 2.6% 0.5% 1.9% 0.9% 2.0% 1.7% 3.1%

Lower quantile 4.8% 2.8% 2.7% 3.6% 1.7% 3.3% 2.1% 2.4% 3.1% 3.7% 4.1% 4.3%

Median 6.4% 6.0% 5.4% 5.9% 5.0% 5.7% 5.4% 5.3% 5.2% 5.9% 5.6% 6.8%

Arithmetic mean 7.8% 5.8% 7.4% 5.9% 4.9% 5.7% 5.0% 5.4% 5.3% 6.0% 5.9% 6.6%


Upper quantile 9.5% 7.6% 7.5% 8.1% 7.1% 7.3% 6.8% 8.1% 7.9% 8.7% 8.1% 9.8%

Maximum 39.1% 12.9% 57.6% 12.6% 8.3% 12.1% 8.9% 15.0% 9.0% 15.3% 13.0% 11.1%


7.0% 6.7%6.2% 6.2%

5.6% 6.0% 5.6% 5.8% 5.9% 6.2% 6.4% 6.6%

8.5% 7.8%7.3%

7.7%7.0%

7.6% 7.5% 7.4% 7.4% 7.5%8.1%

9.3%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

Implied sector returns - DACH - Basic Materials


Implied Sector ReturnsBasic Materials (chart)

49


▪ The implied sector return (unlevered) of theBasic Materials sector slightly increased from6.4% as of 30 June 2018 to 6.6% as of 31December 2018.

▪ In comparison to other sectors, the BasicMaterials sector has the highest unleveredimplied return as of 31 December 2018.

Implied Sector ReturnsConsumer Goods (table)

50

Implied sector returns (levered) - DACH - Consumer Goods

H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2017 H1 2018 H2 2018

30.06.2013 31.12.2013 30.06.2014 31.12.2014 30.06.2015 31.12.2015 30.06.2016 31.12.2016 30.06.2017 31.12.2017 30.06.2018 31.12.2018

Minimum 1.5% 4.8% 1.3% 2.2% 0.5% 2.1% 2.6% 2.5% 2.0% 1.7% 1.8% 2.3%

Lower quantile 6.2% 6.1% 4.8% 4.5% 4.6% 4.4% 4.7% 4.8% 3.4% 4.7% 4.3% 4.8%

Median 9.0% 9.1% 8.0% 7.9% 7.6% 7.9% 7.5% 7.6% 6.9% 7.3% 7.3% 9.0%

Arithmetic mean 9.6% 9.4% 11.2% 8.0% 7.4% 7.8% 8.5% 8.1% 7.4% 7.7% 8.0% 10.0%


Upper quantile 14.2% 12.6% 11.8% 11.3% 10.2% 11.2% 14.7% 12.8% 12.2% 11.7% 14.6% 17.4%

Maximum 28.2% 31.1% 113.8% 16.6% 15.1% 13.4% 21.3% 16.8% 18.9% 16.7% 22.7% 25.0%


Implied sector returns (unlevered) - DACH - Consumer Goods

H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2017 H1 2018 H2 2018

30.06.2013 31.12.2013 30.06.2014 31.12.2014 30.06.2015 31.12.2015 30.06.2016 31.12.2016 30.06.2017 31.12.2017 30.06.2018 31.12.2018

Minimum 0.7% 1.0% 0.6% 0.6% 0.3% 0.4% 0.2% 0.4% 0.5% 1.3% 0.3% 1.1%

Lower quantile 4.8% 4.5% 3.8% 2.3% 2.5% 3.0% 3.0% 2.9% 2.1% 3.0% 1.3% 1.3%

Median 6.4% 6.6% 5.8% 5.7% 5.0% 5.2% 5.2% 5.1% 4.7% 5.5% 5.1% 5.6%

Arithmetic mean 7.5% 7.6% 7.6% 6.5% 5.8% 6.0% 6.1% 5.9% 5.6% 5.7% 5.8% 6.8%


Upper quantile 11.3% 12.0% 9.9% 10.1% 8.8% 9.0% 9.9% 9.6% 9.3% 8.1% 12.4% 13.2%

Maximum 16.7% 20.2% 44.8% 12.0% 11.1% 10.6% 13.3% 13.3% 14.6% 12.6% 15.1% 17.4%


Implied Sector ReturnsConsumer Goods (chart)

51


▪ The implied sector return (unlevered) of theConsumer Goods sector increased from 5.6%to 6.2% from 30 June 2018 to 31 December2018.

▪ We have observed a fluctuation between 5.2%and 7.0% of the market-value weighted mean(unlevered) since 30 June 2013.

▪ The levered implied return of the ConsumerGoods sector reached its highest level in ourobservation period.

Implied Sector ReturnsTelecommunication (table)

52

Implied sector returns (levered) - DACH - Telecommunication

H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2017 H1 2018 H2 2018

30.06.2013 31.12.2013 30.06.2014 31.12.2014 30.06.2015 31.12.2015 30.06.2016 31.12.2016 30.06.2017 31.12.2017 30.06.2018 31.12.2018

Minimum 1.8% 1.7% 5.1% -0.7% -1.8% 4.0% 2.8% 3.9% 3.4% 3.4% 3.5% 0.7%

Lower quantile 2.7% 4.0% 5.3% 2.8% 1.8% 4.3% 3.2% 4.4% 3.7% 4.8% 4.5% 3.1%

Median 8.6% 6.9% 5.8% 6.4% 6.7% 7.3% 5.9% 6.8% 4.6% 7.1% 7.6% 7.4%

Arithmetic mean 8.3% 6.9% 6.5% 5.9% 5.4% 6.8% 5.9% 6.5% 5.5% 6.5% 7.0% 7.8%


Upper quantile 13.4% 9.7% 8.5% 8.4% 7.6% 8.4% 8.9% 8.5% 7.8% 7.8% 8.6% 12.4%

Maximum 16.4% 10.0% 9.3% 9.2% 7.7% 9.1% 9.3% 8.7% 8.0% 7.8% 9.5% 12.7%


Implied sector returns (unlevered) - DACH - Telecommunication

H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2017 H1 2018 H2 2018

30.06.2013 31.12.2013 30.06.2014 31.12.2014 30.06.2015 31.12.2015 30.06.2016 31.12.2016 30.06.2017 31.12.2017 30.06.2018 31.12.2018

Minimum 1.9% 1.9% 3.5% 0.5% -0.1% 2.6% 1.6% 2.1% 2.3% 2.6% 2.7% 0.8%

Lower quantile 3.1% 3.3% 3.9% 2.5% 2.1% 3.3% 2.1% 2.4% 2.3% 3.3% 3.8% 2.2%

Median 6.6% 6.4% 5.2% 5.0% 4.5% 4.8% 4.0% 5.3% 4.0% 5.3% 5.3% 5.2%

Arithmetic mean 7.0% 5.8% 5.3% 4.8% 4.3% 4.9% 4.1% 4.7% 4.1% 4.7% 4.9% 5.2%


Upper quantile 11.8% 8.3% 7.0% 7.1% 6.2% 6.9% 5.4% 6.0% 5.5% 5.5% 5.8% 7.7%

Maximum 15.0% 8.8% 8.0% 8.0% 6.7% 7.1% 6.1% 6.7% 5.7% 5.7% 6.0% 9.5%


Implied Sector ReturnsTelecommunication (chart)

53


5.2% 4.9% 4.7% 4.5% 4.2% 4.4% 4.3% 4.5% 4.3% 4.8% 4.9% 4.6%

8.2%

6.7% 6.4% 6.6% 6.3% 6.7%7.3% 7.3%

6.7%7.5%

7.8%7.4%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

Implied sector returns - DACH - Telecommunication


▪ The implied sector return (unlevered) of theTelecommunication sector decreased from4.9% as of 30 June 2018 to 4.6% as of31 December 2018.

▪ In comparison to the other sectors, theTelecommunication sector had the secondlowest unlevered implied sector return as of31 December 2018.

Implied Sector ReturnsIndustrials (table)

54

Implied sector returns (levered) - DACH - Industrials

H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2017 H1 2018 H2 2018

30.06.2013 31.12.2013 30.06.2014 31.12.2014 30.06.2015 31.12.2015 30.06.2016 31.12.2016 30.06.2017 31.12.2017 30.06.2018 31.12.2018

Minimum 0.0% 0.3% 0.6% 0.2% -0.4% 1.6% 1.7% 2.1% 0.5% 2.7% 0.9% 0.9%

Lower quantile 4.6% 4.5% 3.5% 3.3% 2.9% 4.4% 4.3% 4.3% 3.2% 3.5% 3.1% 4.9%

Median 8.0% 7.6% 6.8% 8.0% 6.5% 7.2% 7.4% 7.2% 6.2% 6.7% 6.7% 8.0%

Arithmetic mean 8.0% 7.6% 6.5% 7.9% 6.4% 7.7% 7.8% 7.0% 5.7% 6.3% 6.2% 8.1%


Upper quantile 10.7% 10.3% 8.7% 10.6% 8.8% 10.3% 9.8% 9.4% 7.6% 8.3% 8.4% 11.2%

Maximum 31.9% 16.0% 12.3% 29.1% 12.8% 40.7% 42.5% 18.1% 9.5% 11.5% 10.9% 20.1%


Implied sector returns (unlevered) - DACH - Industrials

H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2017 H1 2018 H2 2018

30.06.2013 31.12.2013 30.06.2014 31.12.2014 30.06.2015 31.12.2015 30.06.2016 31.12.2016 30.06.2017 31.12.2017 30.06.2018 31.12.2018

Minimum 0.1% 0.3% 0.6% 0.6% 0.5% 1.5% 1.5% 1.7% 0.4% 2.4% 1.0% 1.0%

Lower quantile 4.2% 4.6% 3.0% 3.6% 2.4% 3.1% 2.4% 2.4% 2.3% 3.3% 2.1% 3.3%

Median 6.7% 6.9% 6.1% 6.4% 4.3% 5.2% 5.1% 5.2% 5.1% 5.4% 5.5% 6.1%

Arithmetic mean 6.2% 6.2% 5.5% 5.9% 4.5% 5.4% 5.1% 5.3% 4.5% 5.2% 4.8% 6.2%


Upper quantile 8.2% 8.2% 7.6% 7.7% 6.1% 7.9% 7.3% 7.4% 6.2% 6.9% 6.7% 8.6%

Maximum 9.2% 9.4% 8.4% 9.5% 8.0% 8.0% 7.6% 9.7% 6.8% 7.5% 7.7% 10.8%


Implied Sector ReturnsIndustrials (chart)

55


6.1% 6.2% 5.8% 6.1%5.4% 5.6% 5.5% 5.5% 5.3% 5.5% 5.7% 6.3%

7.8% 7.6% 7.2%7.8%

7.0% 7.3% 7.6%6.9% 6.8% 6.9% 6.7%

8.6%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

Implied sector returns - DACH - Industrials


▪ The implied sector return (unlevered) of theIndustrials sector increased from 5.7% as of30 June 2018 to 6.3% as of 31 December 2018.

▪ The current level of the implied sector return isat the highest level when looking at the past sixyears (both levered and unlevered).

Implied Sector ReturnsConsumer Service (table)

56

Implied sector returns (levered) - DACH - Consumer Service

H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2017 H1 2018 H2 2018

30.06.2013 31.12.2013 30.06.2014 31.12.2014 30.06.2015 31.12.2015 30.06.2016 31.12.2016 30.06.2017 31.12.2017 30.06.2018 31.12.2018

Minimum 2.3% 3.2% 3.6% 2.9% 2.7% 1.6% 1.8% 1.3% 2.1% 2.0% 2.0% 2.5%

Lower quantile 4.4% 4.3% 5.5% 4.3% 3.8% 3.0% 3.4% 3.8% 3.3% 2.9% 2.8% 4.6%

Median 7.9% 7.3% 6.6% 7.5% 5.9% 6.2% 6.4% 6.7% 6.0% 6.2% 6.3% 7.7%

Arithmetic mean 8.6% 8.1% 9.0% 9.8% 7.0% 6.7% 6.6% 6.4% 5.9% 6.1% 6.8% 8.3%


Upper quantile 13.7% 11.3% 11.3% 20.5% 10.5% 10.7% 8.9% 8.8% 8.2% 8.7% 10.4% 12.0%

Maximum 16.3% 23.5% 39.1% 35.1% 19.0% 14.7% 14.1% 9.5% 10.3% 11.0% 13.8% 17.0%


Implied sector returns (unlevered) - DACH - Consumer Service

H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2017 H1 2018 H2 2018

30.06.2013 31.12.2013 30.06.2014 31.12.2014 30.06.2015 31.12.2015 30.06.2016 31.12.2016 30.06.2017 31.12.2017 30.06.2018 31.12.2018

Minimum 2.3% 2.9% 3.6% 2.7% 2.1% 1.9% 1.3% 1.2% 2.1% 1.9% 1.7% 2.2%

Lower quantile 3.3% 4.8% 4.9% 3.6% 3.0% 3.2% 3.4% 2.5% 2.8% 2.8% 2.2% 3.7%

Median 6.3% 6.1% 5.8% 6.4% 5.2% 5.1% 5.0% 5.0% 5.0% 5.2% 5.1% 6.0%

Arithmetic mean 6.2% 6.6% 7.4% 7.9% 5.2% 5.6% 5.3% 5.1% 4.9% 4.9% 5.3% 6.0%


Upper quantile 7.9% 9.3% 7.9% 14.6% 7.5% 8.2% 8.4% 7.1% 6.5% 6.5% 8.0% 8.5%

Maximum 10.5% 10.3% 38.9% 34.6% 8.3% 11.4% 8.9% 8.3% 8.8% 7.2% 13.1% 9.6%


6.3% 6.2% 5.8%6.5%

4.8% 4.9% 5.1% 5.1% 4.7% 5.0% 5.1%5.8%

7.6%7.0% 6.7%

7.7%

5.9% 5.7%6.2% 6.2%

5.6%6.1%

6.6%

7.9%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

Implied sector returns - DACH - Consumer Service


Implied Sector ReturnsConsumer Service (chart)

57


▪ The implied sector return (unlevered) of theConsumer Service sector increased from5.1% as of 30 June 2018 and to 5.8% as of31 December 2018.

▪ We have identified a fluctuation between4.7% and 6.5% of the market-valueweighted mean (unlevered) since 30 June2013.

Implied Sector ReturnsPharma & Healthcare (table)

58

Implied sector returns (levered) - DACH - Pharma & Healthcare

H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2017 H1 2018 H2 2018

30.06.2013 31.12.2013 30.06.2014 31.12.2014 30.06.2015 31.12.2015 30.06.2016 31.12.2016 30.06.2017 31.12.2017 30.06.2018 31.12.2018

Minimum -0.4% 2.3% 0.8% 2.0% 1.4% 1.1% 1.4% 1.6% 2.0% 1.9% 1.9% 1.1%

Lower quantile 2.5% 5.4% 2.4% 3.5% 2.0% 3.7% 3.7% 2.7% 2.9% 2.4% 3.2% 3.3%

Median 6.5% 6.7% 6.1% 6.3% 5.8% 6.1% 6.0% 5.6% 5.8% 5.0% 5.2% 5.7%

Arithmetic mean 6.5% 7.0% 5.9% 6.9% 5.6% 6.6% 5.9% 7.9% 5.9% 5.5% 5.7% 5.8%


Upper quantile 9.0% 9.4% 8.3% 9.3% 7.5% 8.6% 7.9% 8.0% 7.3% 7.7% 7.9% 9.0%

Maximum 19.4% 11.0% 11.7% 22.2% 11.5% 24.8% 9.2% 76.3% 27.9% 20.2% 23.9% 13.2%


Implied sector returns (unlevered) - DACH - Pharma & Healthcare

H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2012 H1 2018 H2 2018

30.06.2013 31.12.2013 30.06.2014 31.12.2014 30.06.2015 31.12.2015 30.06.2016 31.12.2016 30.06.2017 31.12.2017 30.06.2018 31.12.2018

Minimum 0.8% 2.3% 1.3% 2.0% 1.4% 1.2% 1.3% 1.4% 1.5% 1.4% 1.2% 0.5%

Lower quantile 2.4% 4.6% 3.1% 3.8% 1.9% 3.2% 2.8% 2.1% 2.9% 2.2% 3.0% 0.5%

Median 5.7% 6.1% 5.6% 5.8% 5.1% 5.2% 4.9% 5.3% 4.8% 4.5% 4.6% 5.0%

Arithmetic mean 5.9% 6.3% 5.4% 5.7% 4.7% 5.0% 4.9% 5.3% 4.6% 5.1% 5.1% 4.2%


Upper quantile 7.7% 7.8% 7.3% 7.4% 6.3% 6.6% 6.5% 6.7% 5.9% 6.5% 6.6% 6.4%

Maximum 18.0% 10.6% 10.8% 9.2% 6.6% 7.7% 7.6% 18.6% 7.1% 20.1% 21.9% 12.1%


7.1% 7.1%6.5% 6.5%

6.0% 6.2% 6.0%6.6%

6.0% 6.1% 6.6% 6.4%

8.0% 7.9%7.1% 7.4%

6.8% 7.1% 7.1%7.7%

7.0% 7.1%7.7% 7.9%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

Implied sector returns - DACH - Pharma & Healthcare


Implied Sector ReturnsPharma & Healthcare (chart)

59


▪ The implied sector return (unlevered) of thePharma & Healthcare sector decreasedsomewhat from 6.6% as of 30 June 2018 to6.4% as of 31 December 2018.

Implied Sector ReturnsInformation Technology (table)

60

Implied sector returns (levered) - DACH - Information Technology

H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2017 H1 2018 H2 2018

30.06.2013 31.12.2013 30.06.2014 31.12.2014 30.06.2015 31.12.2015 30.06.2016 31.12.2016 30.06.2017 31.12.2017 30.06.2018 31.12.2018

Minimum 1.4% 1.3% 1.8% 0.9% -1.4% 1.0% 1.7% 0.2% 2.1% 0.5% 0.2% 1.3%

Lower quantile 5.1% 4.3% 4.2% 5.0% 4.6% 4.4% 3.8% 4.6% 3.6% 3.6% 3.4% 4.3%

Median 7.1% 7.3% 6.4% 7.6% 6.2% 6.5% 6.2% 6.6% 5.5% 5.5% 5.0% 7.3%

Arithmetic mean 7.5% 7.2% 6.4% 7.6% 6.4% 6.6% 6.3% 7.5% 5.7% 5.7% 5.6% 7.8%


Upper quantile 10.3% 9.8% 9.1% 10.8% 9.2% 8.7% 7.9% 10.1% 7.6% 6.9% 7.6% 12.1%

Maximum 17.3% 15.3% 11.9% 14.1% 11.7% 18.6% 16.5% 35.0% 15.8% 28.2% 28.2% 26.6%


Implied sector returns (unlevered) - DACH - Information Technology

H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2017 H1 2018 H2 2018

30.06.2013 31.12.2013 30.06.2014 31.12.2014 30.06.2015 31.12.2015 30.06.2016 31.12.2016 30.06.2017 31.12.2017 30.06.2018 31.12.2018

Minimum 1.8% 1.7% 2.7% 2.2% 1.3% 1.0% 1.2% 0.2% 2.0% 0.5% 0.7% 0.2%

Lower quantile 4.9% 3.8% 4.0% 4.8% 3.9% 4.2% 3.0% 3.9% 3.0% 3.3% 2.6% 2.9%

Median 6.5% 6.5% 5.8% 6.6% 5.4% 5.4% 5.3% 6.0% 5.0% 5.1% 4.9% 5.6%

Arithmetic mean 6.6% 6.5% 5.8% 6.7% 5.5% 5.7% 5.4% 6.6% 5.0% 4.9% 4.6% 5.7%


Upper quantile 8.8% 9.0% 7.7% 9.0% 7.1% 7.4% 7.3% 8.3% 6.3% 6.3% 6.6% 8.3%

Maximum 12.6% 10.9% 9.0% 10.6% 9.5% 17.6% 15.4% 33.4% 11.2% 9.5% 7.6% 10.1%


6.9% 6.9% 6.7% 6.7% 6.2% 6.2% 6.3% 6.1% 5.7% 5.8% 5.3%6.1%

7.3% 7.1% 7.0% 7.4%6.9% 6.7% 7.0% 6.6%

6.0% 6.1% 5.7%

7.0%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

Implied sector returns - DACH - Information Technology


Implied Sector ReturnsInformational Technology (chart)

61


▪ The implied sector return (unlevered) of theInformation Technology sector increased from5.3% as of 30 June 2018 to 6.1% as of31 December 2018.

▪ In comparison to other sectors, theInformation Technology sector had thesmallest spread between levered andunlevered market-value weighted mean. This isdue to the low market-value weighted debtratio.

Implied Sector ReturnsUtilities (table)

62

Implied sector returns (levered) - DACH - Utilities

H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2017 H1 2018 H2 2018

30.06.2013 31.12.2013 30.06.2014 31.12.2014 30.06.2015 31.12.2015 30.06.2016 31.12.2016 30.06.2017 31.12.2017 30.06.2018 31.12.2018

Minimum 5.6% 4.2% 3.7% 3.8% 3.9% 4.9% 5.2% 4.8% 5.5% 4.5% 4.8% 5.3%

Lower quantile 6.3% 5.5% 4.0% 4.9% 5.4% 5.2% 6.8% 5.7% 5.5% 5.2% 5.6% 5.7%

Median 7.4% 7.3% 7.0% 7.1% 6.5% 7.0% 7.5% 7.4% 7.5% 6.2% 6.6% 7.0%

Arithmetic mean 8.6% 7.5% 6.8% 7.0% 7.8% 7.0% 7.6% 7.7% 7.7% 6.8% 6.9% 7.2%


Upper quantile 11.6% 9.9% 8.9% 9.4% 11.5% 8.7% 8.9% 10.2% 9.7% 8.6% 8.6% 9.3%

Maximum 16.2% 10.1% 9.9% 10.1% 14.6% 9.7% 9.4% 10.5% 11.8% 10.7% 9.0% 9.6%


Implied sector returns (unlevered) - DACH - Utilities

H1 2013 H2 2013 H1 2014 H2 2014 H1 2015 H2 2015 H1 2016 H2 2016 H1 2017 H2 2017 H1 2018 H2 2018

30.06.2013 31.12.2013 30.06.2014 31.12.2014 30.06.2015 31.12.2015 30.06.2016 31.12.2016 30.06.2017 31.12.2017 30.06.2018 31.12.2018

Minimum 3.9% 3.5% 3.1% 2.8% 3.0% 2.6% 2.9% 2.6% 2.7% 3.0% 2.8% 3.1%

Lower quantile 4.1% 4.0% 3.3% 3.0% 3.0% 3.0% 3.1% 2.7% 3.5% 3.3% 3.6% 3.5%

Median 5.0% 4.8% 4.5% 4.2% 4.0% 3.8% 3.6% 4.1% 4.2% 4.1% 4.1% 4.6%

Arithmetic mean 5.3% 4.9% 4.5% 4.0% 4.2% 3.7% 3.7% 4.1% 4.5% 4.1% 4.4% 4.8%


Upper quantile 6.4% 5.8% 5.4% 4.7% 5.4% 4.6% 4.5% 5.5% 5.4% 5.0% 5.6% 6.5%

Maximum 8.2% 6.0% 5.6% 4.8% 7.2% 4.7% 4.9% 6.0% 6.9% 5.3% 5.6% 7.6%


6.1%5.1% 4.6% 4.1% 4.4%

3.8% 4.1% 4.1%4.8% 4.4%

4.6% 5.1%

10.5%

7.9%6.6% 6.9%

8.3%7.6%

8.3% 8.2% 8.6%

7.4% 7.3% 7.3%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

Implied sector returns - DACH - Utilities

Range (10%-90% quantile) Market-value weighted mean (unlevered) Market-value weighted mean (levered)

Implied Sector ReturnsUtilities (chart)

63


▪ The implied sector return (unlevered) of theUtilities sector increased from 4.6% as of30 June 2018 to 5.1% as of 31 December 2018.

▪ We have noticed a fluctuation between 3.8%and 6.1% of the market-value weighted mean(unlevered) since 30 June 2013.

Sector Returns

b. Historical returns (ex-post analysis)

7

Historical Sector ReturnsBackground & approach

In addition to the determination of historical market returns, we calculatehistorical sector returns. This option creates an alternative approach, likethe implied sector returns, to the ex-post analysis of the determination ofcosts of capital based on regression analyses following the CAPM.

Our analysis contains so-called total shareholder returns analogous to thereturn triangles for the German, Austrian and Swiss total return indices.This means, we consider the share price development as well as thedividend yield, whereas the share price development generally representsthe main component of the total shareholder return.

We calculate the annual total shareholder returns as of 31 December forevery DAX Sector All Index, WBI, and SPI listed company. Afterwards, weaggregate those returns market-value weighted to sector returns. Ourcalculations comprise the time period between 2013 and 2018. Sinceannual total shareholder returns tend to fluctuate to a great extent, theirexplanatory power is limited. Therefore, we do not only calculate the 1-year market-value weighted means, but furthermore calculate the 3-year(2016-2018) and the 6-year (2013-2018) averages.

65

1.0%

-5.4%

13.1%

7.2%

26.4%

15.1%15.8%

12.4%

7.1%

1.4%

8.6%

2.4%

12.0%

0.1%

11.1%

7.5%

13.4%

-5.3%

14.3%

2.4%

18.6%

16.8%

6.4%

18.7%

-10.0%

0.0%

10.0%

20.0%

30.0%

Banking

Insurance

Financial Services

Real Estate

Basic Materials

Consumer Goods

Telecommunication

Industrials

Consumer Service

Pharma & Healthcare

InformationTechnology

Utilities

Historical Sector ReturnsAnnual total shareholder returns as of 31 December 2018

66

6-year-average (2013-2018) 3-year-average (2016-2018)

Annual total shareholder returns by sector

Banking InsuranceFinancial

ServicesReal Estate

Basic

Materials

Consumer

Goods

Telecom-

municationIndustrials

Consumer

Service

Pharma &

Healthcare

Information

TechnologyUtilities

31 December 2013 11.5% 22.9% 26.3% 1.8% 22.7% 27.5% 41.1% 26.9% 48.6% 32.2% 18.6% -2.6%

31 December 2014 -4.7% 14.8% 28.6% 31.0% 5.6% 6.3% 8.5% 4.9% 9.3% 25.7% 6.3% 5.0%

31 December 2015 15.1% 19.3% 58.2% 24.7% 10.2% 10.5% 22.1% 12.1% 38.5% 20.7% 36.5% -20.4%

31 December 2016 -10.6% 5.3% 14.5% 10.0% 14.8% 2.7% -0.4% 19.3% -6.0% -1.4% 15.1% 2.0%

31 December 2017 17.5% 13.2% 38.9% 24.0% 17.6% 13.7% 2.5% 23.3% 10.7% 6.0% 41.1% 34.3%

31 December 2018 -23.1% 3.1% -8.2% 3.3% -28.3% -9.2% -1.7% -20.2% -20.8% 2.6% -5.8% 19.8%

Trading multiples8

Trading MultiplesBackground & approach

Besides absolute valuation models (earnings value, DCF), the multiplesapproach offers a practical way for an enterprise value estimation. Themultiples method estimates a company’s value relative to anothercompany’s value. Following this approach, the enterprise value resultsfrom the product of a reference value (revenue or earnings values arefrequently used) of the company with the respective multiples of similarcompanies.

Within this capital market study, we analyze multiples for the “super -sectors" as well as multiples for the DACH market consisting of theGerman, Austrian and Swiss capital markets (DAX Sector All Index, ATXand SPI). We will look at the following multiples:

▪ Revenue-Multiples (“EV1)/Revenue“)

▪ EBIT-Multiples (“EV1)/EBIT“)

▪ Price-to-Earnings-Multiples (“P/E“)

▪ Price-to-Book Value-Multiples (“EqV2)/BV“)

Multiples are presented for two different reference values. Firstly, thereference values are based on a company's realized trailing last 12months, which represent its financial performance for the past 12-monthperiod (so-called trailing-multiples, in the following “LTM”). Secondly, thereference values are based on one-year forecasts of analysts (so-calledforward-multiples, in the following “1yf”). Both approaches are typicallynot limited to the end of the fiscal year. The Price-to-Book Value-Multiplesare calculated with the book values as of the reference date (31 December2018).

We present historical multiples since 01 January 2013 in the appendix andwill update the applied multiples semi-annually at the predefinedreference date (as of 30 June and as of 31 December).

We provide a graphical, as well as a tabular illustration of the multiples asof 31 December 2018 on the following slides.

Additional to the arithmetic mean and median as essential average sizes,we show the minimum, the maximum, the standard deviation and thenumber of companies. For the purpose of simplification, we excludenegative multiples and multiples in the highest quantile (95%). Themultiples in the lowest quantile (5%) build the lower limit.

To calculate the multiples, we source the data (i.e. Market Cap., Revenue,EBIT, etc.) from the data provider S&P Capital IQ.

Additionally, we present a ranking table of the sector multiples. In a firststep, the sector multiples are sorted from highest to lowest for eachanalyzed multiple. The resulting score in the ranking is displayed in thetable and visualized by a color code that assigns a red color to the highestrank and a dark green color to the lowest rank. Thus, a red colored highrank indicates a high valuation level, whereas a green colored low ranksuggests a low valuation level. In a second step, we aggregate the rankingsand calculate an average of all single rankings for each sector multiple.This is shown in the right column of the ranking table. This average rankingindicates the overall relative valuation levels of the sectors when usingmultiples.1) Enterprise Value.

2) Equity Value.

68

EqV/BV

Sector LTM 1yf LTM 1yf LTM 1yf

Banking n.a. n.a. n.a. n.a. 14.5x 8.4x 0.9x

Insurance n.a. n.a. n.a. n.a. 12.7x 12.2x 1.0x

Financial Services n.a. n.a. n.a. n.a. 14.0x 13.9x 1.2x

Real Estate 11.1x 13.8x 28.7x 22.6x 13.9x 19.0x 1.2x

Basic Materials 1.2x 1.3x 14.9x 12.9x 12.6x 12.8x 1.3x

Consumer Goods 0.9x 1.0x 17.5x 15.1x 16.8x 15.3x 1.5x

Telecommunication 1.6x 2.0x 16.3x 15.0x 21.0x 16.5x 1.8x

Industrials 1.1x 1.2x 15.5x 15.0x 17.1x 16.1x 1.8x

Consumer Service 1.0x 1.0x 16.5x 14.7x 16.6x 14.0x 1.7x

Pharma & Healthcare 3.6x 3.9x 22.8x 21.2x 27.2x 22.9x 2.5x

Information Technology 1.6x 1.7x 20.4x 16.6x 23.1x 18.1x 2.1x

Utilities 1.9x 1.9x 21.0x 14.6x 15.4x 15.7x 1.4x

DACH 1.4x 1.5x 18.1x 15.8x 16.6x 16.3x 1.5x

EV/Revenue EV/EBIT P/E

Trading MultiplesSector multiples – MedianLTM and 1yf as of 31 December 2018

69

Reading example:

The weighted average of theIndustrials EV/EBIT ratiocalculated on the basis of thelast 12 months is 15.5x as of31 December 2018.

EUR 200m in EBIT over thelast twelve months wouldhence result in an enterprisevalue of EUR 3,100m.

Note: For companies in the Banking, Insurance and Financial Services sectors, Revenue- and EBIT-Multiples are not meaningful and thus are not reported.

EqV/BV Ø Ranking

Sector LTM 1yf LTM 1yf LTM 1yf

Banking n.a. n.a. n.a. n.a. 8 12 12 10.7

Insurance n.a. n.a. n.a. n.a. 11 11 11 11.0

Financial Services n.a. n.a. n.a. n.a. 9 9 9 9.0

Real Estate 1 1 1 1 10 2 10 3.7

Basic Materials 6 6 9 9 12 10 8 8.6

Consumer Goods 9 9 5 4 5 7 6 6.4

Telecommunication 5 3 7 6 3 4 3 4.4

Industrials 7 7 8 5 4 5 4 5.7

Consumer Service 8 8 6 7 6 8 5 6.9

Pharma & Healthcare 2 2 2 2 1 1 1 1.6

Information Technology 4 5 4 3 2 3 2 3.3

Utilities 3 4 3 8 7 6 7 5.4

P/EEV/Revenue EV/EBIT

Trading MultiplesSector multiples ranking based on median (LTM and 1yf as of 31 December 2018)

70

Note: Multiples are ranked from highest to lowest values: 1 – highest (red), 9/12 – lowest (dark green).

The EqV/BV-Multiple of the Utilities sector ranks 5th highest in a comparison of all sectors. Overall, the average ranking of the Utilities sector is 5.4, indicating a medium valuation level.

The Pharma &Healthcare sectorshows the highestmultiples on ave-rage, followed by theInformationTechnology sector.

The Banking andInsurance sectorsshow the leastexpensive valuationlevels of all sectors.

Trading MultiplesSector multiples detailed (1/4)LTM and 1yf as of 31 December 2018

Note: For companies in the Banking, Insurance and Financial Services sector, Revenues- and EBIT-Multiples are not meaningful and thus are not reported. For historical developments of the multiples please refer tothe appendix (cf. 83 et seq.).

71

Reading example:

The average (arithmeticmean) DACH EV/Revenue-ratio calculated on the basisof the last 12 months is 2.9xas of the reference date31 December 2018.

EUR 300m in revenues overthe last twelve monthswould result in an enterprisevalue of EUR 870m.

EV/Revenue EV/EBIT P/E EqV/BV

LTM 1yf LTM 1yf LTM 1yf

DACH Min 0.3x 0.4x 7.4x 8.1x 2.8x 6.8x 0.3x

Arithmetic mean 2.9x 2.8x 22.2x 17.3x 19.9x 17.9x 2.1x

Median 1.4x 1.5x 18.1x 15.8x 16.6x 16.3x 1.5x

Max 21.5x 19.2x 75.9x 38.8x 68.3x 44.8x 8.0x

Standard deviation 3.7x 3.6x 12.8x 6.6x 12.5x 8.0x 1.6x

Number of companies 600 362 479 316 549 317 642

Banking Min - - - - 3.2x 7.0x 0.3x

Arithmetic mean - - - - 15.2x 10.0x 0.9x

Median - - - - 14.5x 8.4x 0.9x

Max - - - - 59.7x 20.7x 1.7x

Standard deviation - - - - 9.5x 3.4x 0.3x

Number of companies - - - - 34 15 33

Insurance Min - - - - 10.3x 10.0x 0.7x


Median - - - - 12.7x 12.2x 1.0x

Max - - - - 62.7x 18.2x 1.6x



Financial Services Min - - - - 3.2x 9.1x 0.3x


Median - - - - 14.0x 13.9x 1.2x

Max - - - - 48.6x 40.6x 6.3x




72


Reading example:

The median Real EstateEV/EBIT ratio calculated onthe basis of the expectedEBIT (1-year forward) is 22.6xas of the reference date31 December 2018.

An expected EBIT of EUR 30mwould result in an enterprisevalue of EUR 678m.



Real Estate Min 0.4x 1.4x 2.8x 7.4x 2.8x 7.4x 0.3x


Median 11.1x 13.8x 28.7x 22.6x 13.9x 19.0x 1.2x

Max 21.5x 19.2x 52.6x 32.7x 52.6x 32.7x 3.2x



Basic Materials Min 0.3x 0.4x 9.1x 9.6x 3.4x 7.6x 0.4x

Arithmetic mean 2.1x 1.8x 21.7x 15.4x 15.0x 16.1x 1.7xMedian 1.2x 1.3x 14.9x 12.9x 12.6x 12.8x 1.3x

Max 11.6x 5.8x 75.3x 31.9x 62.9x 39.4x 6.4x



Consumer Goods Min 0.3x 0.5x 7.4x 8.1x 4.7x 8.0x 0.4x


Median 0.9x 1.0x 17.5x 15.1x 16.8x 15.3x 1.5x

Max 11.1x 8.4x 51.2x 27.3x 59.0x 33.7x 7.6x




73


Reading example:

The average (arithmeticmean) Industrials P/E ratiocalculated on the basis ofexpected earnings (1-yearforward) is 17.9x as of thereference date 31December 2018.

Expected earnings ofEUR 20m would result inan equity value ofEUR 358m.



Telecommunication Min 0.3x 1.1x 14.2x 13.1x 11.8x 9.6x 0.7x


Median 1.6x 2.0x 16.3x 15.0x 21.0x 16.5x 1.8x

Max 3.1x 2.9x 36.5x 32.3x 59.6x 39.9x 4.9x



Industrials Min 0.3x 0.5x 7.4x 8.5x 3.2x 7.1x 0.4x


Median 1.1x 1.2x 15.5x 15.0x 17.1x 16.1x 1.8x

Max 10.3x 8.6x 75.9x 35.1x 65.0x 44.8x 7.5x



Consumer Service Min 0.3x 0.4x 7.8x 8.2x 3.4x 7.3x 0.3x


Median 1.0x 1.0x 16.5x 14.7x 16.6x 14.0x 1.7x

Max 9.7x 9.7x 73.2x 35.9x 61.9x 26.7x 8.0x




74


Reading example:

The median Utilities EqV/BVis 1.4x as of the referencedate 31 December 2018.

A BV of EUR 100m wouldresult in an equity value ofEUR 140m.



Pharma & Healthcare Min 0.3x 0.5x 7.8x 9.6x 4.6x 11.5x 0.3x


Median 3.6x 3.9x 22.8x 21.2x 27.2x 22.9x 2.5x

Max 20.6x 19.1x 62.9x 38.8x 64.6x 44.5x 7.8x



Information Technology Min 0.3x 0.4x 7.9x 8.6x 5.9x 6.8x 0.3x


Median 1.6x 1.7x 20.4x 16.6x 23.1x 18.1x 2.1x

Max 10.8x 15.4x 69.0x 37.1x 68.3x 43.8x 7.7x



Utilities Min 0.6x 0.5x 10.3x 8.5x 6.8x 13.2x 0.6x


Median 1.9x 1.9x 21.0x 14.6x 15.4x 15.7x 1.4x

Max 10.2x 9.6x 36.2x 29.1x 43.1x 40.0x 4.1x



Appendix

Composition of the sectors of DAX Sector All Index, WBI and SPI as of 31 December 2018

AppendixComposition of each sector as of 31 December 2018

76

Banking Financial Services

Germany Germany

AAREAL BANK AG VALIANT BANK AG ADCAPITAL AG MUTARES AG

AUTOBANK AG VONTOBEL EUROPE AG ALBIS LEASING AG ÖKOWORLD AG

COMMERZBANK AG WALLISER KANTONALBANK AG BASIC RESOURCES AG OVB HOLDING AG

DEUTSCHE BANK AG ZUGER KANTONALBANK AG BERLINER EFFEKTENGESELLSCHAFT AG PEARL GOLD AG

DT.PFANDBRIEFBK AG Insurance CAPSENIXX AG PONGS & ZAHN AG

PROCREDIT HLDG AG Germany COMDIRECT BANK AG SCHERZER & CO. AG

QUIRIN BANK AG ALLIANZ SE creditsheld AG SHAREHOLDER VALUE BET. AG

WUESTENROT & WUERTTEMBERG AG HANNOVER RUECK SE DEUTSCHE BALATON AG SIXT LEASING

Austria MUENCHNER RUECK AG DEUTSCHE BETEILIGUNGS AG SM WIRTSCHAFTSBERATUNGS AG

BANK FUER TIROL UND VBG AG NUERNBERGER BET. AG DEUTSCHE CANNABIS AG SPARTA AG

BAWAG AG TALANX AG DEUTSCHE EFFECTEN-UND WECHSEL-BET. AG SPOBAG

BKS BANK AG ST Austria DEUTSCHE TECHNISCHE BET. AG THE NAGA GROUP AG

ERSTE GROUP BANK AG VIENNA INSURANCE GROUP AG DF DEUTSCHE FORFAIT AG TRADEGATE AG

OBERBANK AG BALOISE HOLDING AG DWS GROUP GMBH & CO KGAA VALUE MGMT & RESEARCH AG

RAIFFEISEN BANK INTERNATATIONAL AG Switzerland ERNST RUSS AG VARENGOLD BANK AG

Switzerland HELVETIA HOLDING AG FINLAB AG VDN AG

BANK CLER AG PARGESA HOLDING AG FINTECH GROUP AG VPE WERTPAPIERHANDELSBANK AG

BASELLAND. KANTONALBANK AG SWISS LIFE HOLDING AG FORIS AG WCM BET. & GRUNDBESITZ AG

BASLER KANTONALBANK SA SWISS RE AG FRITZ NOLS AG WEBAC HOLDING AG

BC DE GENEVE SA VAUDOISE ASSURANCES HLD. SA GRENKE AG Austria

BC DU JURA SA ZURICH INSURANCE AG GXP GERMAN PROEPRTIES AG BURGENLAND HOLDING AG

BC VAUDOISE SA HEIDELBERGER BET. HOLDING AG UNTERNEHMENS INVEST AG

BERNER KANTONALBANK AG HELIAD EQUITY PARTNERS GMBH & CO. KGAA WIENER PRIVATBANK SE

BK LINTH LLB AG HESSE NEWMAN CAPITAL AG Switzerland

CEMBRA MONEY BANK AG HYPOPORT AG BANQUE PROFIL DE GESTION SA

CREDIT SUISSE GROUP AG JDC GROUP AG BELLEVUE GROUP AG

EFG INTERNATIONAL AG KAP BETEILIGUNGS-AG CIE FIN. RICHEMONT AG

GLARNER KANTONALBANK AG KST BETEILIGUNGS AG GLOBAL ASSET MGMT AG

GRAUB KANTONALBANK AG LANG & SCHWARZ AG LEONTEQ AG

HYPOTHEKARBANK LENZBURG AG L-KONZEPT HOLDING AG PARTNERS GROUP HOLDING AG

JULIUS BAER EUROPE AG LLOYD FONDS AG PRIVATE EQUITY HOLDING AG

LUZERNER KANTONALBANK AG MAIER & PARTNER AG SPCE PRIVATE EQUITY AG

SCHWEIZERISCHE NATIONALBANK AG MAX21 AG SWISSQUOTE GROUP HOLDING LTD

ST GALLER KANTONALBANK GA MIC AG VALARTIS GROUP AG

THURGAUER KANTONALBANK AG MLP AG VZ HOLDING AG

UBS GROUP AG MPC CAPITAL AG


77

Real Estate Basic Materials

Germany S IMMO AG Germany CPH CHEMIE & PAPIER HOLDING AG

A.A.A. AG UBM DEVELOPMENT AG ASIAN BAMBOO AG EMS-CHEMIE AG

ACCENTRO REAL ESTATE AG WARIMPEX FINANZ- UND BETEILIGUNGS AG AURUBIS AG GIVAUDAN SA

ADLER REAL ESTATE AG Switzerland B.R.A.I.N. AG GURIT HOLDING AG

ALSTRIA OFFICE REIT-AG ALLREAL HOLDING AG BASF SE SCHMOLZ & BICKENBACH AG

CR CAPITAL REAL ESTATE AG ARUNDEL AG BAYER AG ZWAHLEN & MAYR SA

DEMIRE DT.MTS.RE AG BFW LIEGENSCHAFTEN AG COVESTRO AG

DEUTCSHE GRUNDSTÜCKSAKTIONEN AG CI COM SA DE RAJ GROUP AG

DEUTSCHE EUROSHOP AG Fundamental Real Estate Ltd. DECHENG TECHNOLOGY AG

DEUTSCHE KONSUM REIT-AG HIAG IMMOBILIEN HOLDING AG DEUTSCHE ROHSTOFF AG

DEUTSCHE REAL ESTATE AG INTERSHOP HOLDING AG EISEN- & HUETTENWERKE AG

DEUTSCHE WOHNEN AG INVESTIS HOLDING SA EVONIK INDUSTRIES AG

DIC ASSET AG MOBIMO AG FUCHS PETROLUB SE

EYEMAXX REAL ESTATE AG ORASCOM DEVELOPMENT HLD AG H & R GMBH & CO KGAA

FAIR VALUE REIT-AG PEACH PROPERTY GROUP AG K & S AG

GODEWIND IMMOBILIEN AG PLAZZA AG KHD HUMBOLDT WEDAG AG

GSW IMMOBILIEN AG PSP AG LANXESS AG

GWB IMMOBILIEN SWISS FIN & PROP INVESTMENT AG LINDE AG

HAEMATO AG SWISS PRIME SITE AG PETRO WELT TECHNOLOGIE AG

HAMBORNER REIT AG VARIA US PROPERTIES AG SALZGITTER AG

INCITY IMMOBILIEN AG WARTECK INVEST AG SGL CARBON SE

INSTONE REAL ESTATE GROUP N.V. ZUEBLIN AG SIMONA AG

LEG IMMOBILIEN AG ZUG ESTATES HOLDING AG SKW STAHL-METALLURGIE HOLDING AG

NORATIS AG SURTECO SE

PATRIZIA IMMOBILIEN AG SYMRISE AG

PRIMAG AG WACKER CHEMIE AG

PUBLITY AG YOUBISHENG GREEN PAPER AG

RCM BETEILIGUNGS AG Austria

SINNER AG AMAG AUSTRIA METALL AG

TAG IMMOBILIEN AG LENZING AG

TLG IMMOBILIEN AG OMV AG

UNIPROF REAL ESTATE HLD AG PORR AG

VERIANOS REAL ESTATE AG SCHOELLER-BLECKMANN AG

VONOVIA SE STRABAG SE

YMOS AG VOESTALPINE AG

Austria WIENERBERGER AG

ATRIUM EUROPEAN REAL ESTATE LTD Switzerland

CA IMMOBILIEN ANLAGEN AG CHAM PAPER GROUP HOLDING AG

IMMOFINANZ AG CLARIANT AG


78

Consumer Goods Telecommunication

Germany MINERALBR. UEBER. GMBH & CO. KGAA WOLFORD AG Germany

A.S.CREATION TAPETEN AG MING LE SPORTS AG Switzerland 11 88 0 SOLUTIONS AG

ADIDAS AG MONINGER HOLDING AG AIRESIS SA 3U HOLDING AG

ADLER MODEMAERKTE AG MUEHL PRODUKT & SERVICE AG ARYZTA AG DRILLISCH AG O.N.

ADM HAMBURG AG PARK U.BELLHEIMER AG AUTONEUM AG DT.TELEKOM AG NA

AGRARIUS AG PFERDEWETTEN.DE AG BARRY CALLEBAUT AG ECOTEL COMMUNICATION AG

AKASOL AG PORSCHE AUTOMOBIL HLD. SE BELL AG FREENET AG NA O.N.

ALNO AG PROGRESS-WERK OBERKIRCH AG BLACKSTONE RESOURCES LTD LS TELCOM AG

AUDI AG PUMA SE CALIDA HOLDING AG MVISE AG O.N.

BAWAG AG REGENBOGEN AG EMMI AG NFON AG

BAYERISCHE MOTOREN WERKE AG ROY CERAMICS SE GM SA STARDSL AG

BBS KRAFTFAHRZEUGTECHNIK AG SCHAEFFLER AG HOCHDORF HOLDING AG TELEFONICA DTLD HLDG AG

BEIERSDORF AG SCHLOSS WACHENHEIM AG LALIQUE GROUP SE Austria

BERENTZEN-GROUP AG SCHWAELBCHEN MOLKEREI J.B. AG LECLANCHE SA TELEKOM AUSTRIA AG

BERTRANDT AG SHW AG LINDT & SPRUENGLI AG Switzerland

BHS TABLETOP AG STEILMANN SE METALL ZUG AG SUNRISE N

BORUSSIA DORTMUND GMBH & CO. KGAA STO SE & CO. KGAA NESTLE SA SWISSCOM N

CEWE STIFTUNG & CO.KGAA STS GROUP AG ORIOR AG

CONTINENTAL AG SUEDZUCKER AG RESAPHENE SUISSE AG

DAIMLER AG TC UNTERHALTUNGSELEKTRONIK AG RICHEMONT SA

DIERIG HOLDING AG TOM TAILOR HOLDING AG SWATCH GROUP SA

EDAG ENGINEERING TONKENS AGRAR AG

EINHELL GERMANY AG ULTRASONIC AG

ELRINGKLINGER AG VERALLIA DTLD AG

FENGHUA SOLETECH AG VILLEROY & BOCH AG

FROSTA AG VOLKSWAGEN AG

GERRY WEBER INTERNATIONAL AG WASGAU PRODUNKTIONS & HANDELS AG

GRAMMER AG WESTAG & GETALIT AG

HELLA KGAA HUECK & CO. Austria

HELMA EIGENHEIMBAU AG AGRANA BETEILIGUNGS AG

HENKEL AG & CO. KGAA DO & CO AG

HUGO BOSS AG GURKTALER AG

HWA AG JOSEF MANNER & COMP. AG

IFA HOTEL & TOURISTIK AG KTM INDUSTRIES AG

JJ AUTO AG LINZ TEXTIL HOLDING AG

KAMPA AG OTTAKRINGER GETRAENKE AG

LEIFHEIT AG POLYTEC HOLDING AG

LEONI AG STADLAUER MALZFABRIK AG


79

Industrials (1/2)

Germany FRAPORT AG MBB SE SOFTING AG

2G ENERGY AG FRIWO AG MEDION AG SOLAR-FABRIK AG

7C SOLARPARKEN AG FROEHLICH BAU AG MS INDUSTRIE AG SOLARWORLD AG

A.I.S. AG GEA GROUP AG MTU AERO ENGINES AG STEICO SE

ADINOTEC AG GESCO AG MUEHLHAN AG STINAG STUTTGART INVEST AG

AIR BERLIN PLC & CO. LUFTV. KG HAMBURGER HAFEN & LOGISTIK AG MUELLER-DIE LILA LOGISTIK AG TECHNOTRANS AG

ALBA SE HANSEYACHTS AG M-U-T AG THYSSENKRUPP AG

AMADEUS FIRE AG HAPAG-LLOYD AG NABALTEC AG TURBON AG

ASKNET AG HEIDELBERG.DRUCKMASCHINEN AG NANOFOCUS AG UET AG

AUMANN AG HEIDELBERGCEMENT AG NANOGATE AG UTD POWER TECHNOLOGY AG

AVES ONE AG HELIOCENTRIS ENERGIE SOL. AG NESCHEN AG UZIN UTZ AG

BAUER AG HMS BERGBAU AG NORDEX SE VA-Q-TEC AG

BAUMOT GROUP AG HOCHTIEF AG NORDWEST HANDEL AG VARTA AG

BAVARIA INDUSTRIALS GROUP AG HOMAG GROUP AG NORMA GROUP SE VECTRON SYSTEMS AG

BAYWA AG IBU-TEC ADVANCED MATERIALS AG ORBIS AG VERBIO VEREINIGTE BIOENERGIE AG

BILFINGER SE IFA SYSTEMS AG OSRAM LICHT AG VISCOM AG

BLUE CAP AG INDUS HOLDING AG PFEIFFER VACUUM TECHNOLOGY AG VOLTABOX AG

BOEWE SYSTEC AG INFAS HLDG AG PHILIPP HOLZMANN AG VOSSLOH AG

BRENNTAG AG ITN NANOVATION AG PHOENIX SOLAR AG VTG AG

CENTROTEC SUSTAINABLE AG JENOPTIK AG PITTLER MA.FABR. AG WACKER NEUSON SE

CENTROTHERM PHOTOVOLTAICS AG JOST WERKE AG PLAN OPTIK AG WALTER BAU-AG

CHINA SPECIALITY GLASS AG JUNGHEINRICH AG PNE WIND AG WASHTEC AG

COREO AG KHD HUMBOLDT WEDAG AG PVA TEPLA AG ZHONGDE WASTE TECHNOLOGY AG

CROPENERGIES AG KION GROUP AG R. STAHL AG Austria

DALDRUP & SOEHNE AG KLOECKNER & CO: SE RATIONAL AG ANDRITZ AG

DATRON AG Knorr-Bremse AG RHEINMETALL AG CLEEN ENERGY AG

DELIGNIT AG KOENIG & BAUER AG RINGMETALL AG FACC AG

DEUTSCHE POST AG KROMI LOGISTIK S & O AGRAR AG FLUGHAFEN WIEN AG

DEUTZ AG KRONES AG SCHALTBAU HOLDING FRAUENTHAL HOLDING AG

DIEBOLD NIXDORF. KSB AG SCHUMAG AG HTI HIGH TECH INDUSTRIES AG

DISKUS WERKE AG KUKA AG SCY BETEILIGUNG AG MAYR-MELNHOF KARTON AG

DMG MORI AG KWS SAAT SE SFC ENERGY AG OESTER. STAATSDRUCKEREI HOLDING AG

DR. HOENLE AG LPKF LASER & ELECTRONICS AG SIEMENS AG OESTERREICHISCHE POST AG

DUERKOPP ADLER AG LUFTHANSA AG SINGULUS PALFINGER AG

DUERR AG MAN SE SINO-GERMAN UNITED AG ROSENBAUER INTERNATIONAL AG

ELEXXION AG MANZ AG SIXT SE SEMPERIT AG HOLDING

ENERGIEKONTOR AG MASCHINENFABRIK BERT.HER. AG SLM SOLUTIONS GROUP AG SW UMWELTTECHNIK AG

ENVITEC BIOGAS AG MASTERFLEX AG SMA SOLAR TECHNOLOGY AG ZUMTOBEL GROUP AG

FRANCOTYP-POSTALIA HOLDING AG MAX AUTOMATION AG SMT SCHARF AG


80

Industrials (2/2) Consumer Service

Switzerland Germany SENDR SE

ABB SCHWEIZ AG OC OERLIKON CORPORATION AG A.SPRINGER SE SLEEPZ AG

ADECCO GROUP AG PANALPINA WELTTRANSPORT AG AD PEPPER MEDIA N.V. SNOWBIRD AG

ADVAL TECH HOLDING AG PERFECT SA ARTNET AG SPL.MEDIEN AG

ARBONIA AG PERROT DUVAL HOLDING SA BASTEI LUEBBE AG STARAMBA SE

BELIMO AUTOMATION AG PHOENIX AG BEATE UHSE AG STROEER SE & CO. KGAA

BOBST GROUP SA POENINA HOLDING AG BET-AT-HOME.COM AG TAKKT AG

BOSSARD HOLDING AG RIETER MASCHINENFABRIK AG BIJOU BRIGITTE AG TELE COLUMBUS AG

BUCHER INDUSTRIES AG SCHAFFNER AG CECONOMY AG TMC CONTENT GROUP AG

BURCKHARDT AG SCHINDLER AUFZUEGE AG CLIQ DIGITAL AG TRAVEL24.COM AG

BURKHALTER HOLDING AG SCHLATTER HOLDING AG CONSTANTIN MEDIEN AG UHR.DE AG

BVZ HOLDING AG SCHWEITER TECHNOLOGIES AG CTS EVENTIM AG & CO. KGAA UNITED LABELS AG

CEVA LOGISTICS AG SENSIRION HOLDING AG DEAG DEUTSCHE ENTERTAINMENT AG WALLSTREET ONLINE AG

CICOR MANAGEMENT AG SFS GROUP AG DELIVERY HERO AG Westwing Group AG

COMET HOLDING AG SGS SA DELTICOM AG WIGE MEDIA AG

CONZZETA AG SIG Combibloc Group AG ECOMMERCE ALLIANCE AG WILD BUNCH AG

DAETWYLER HOLDING AG SIKA AG EDEL AG WINDELN.DE SE

DKSH HOLDING AG STARRAG GROUP HOLDING AG ELANIX BIOTECHNIK AG XIN RUI KE AG

DORMAKABA HOLDING AG SULZER AG ELUMEO SE YOUR FAMILY ENTERTAINMENT AG

ELMA ELECTRONIC AG TORNOS HOLDING AG ENERXY AG ZALANDO SE

FEINTOOL INTERNATIONAL HOLDING AG VAT GROUP AG FD GROUP AG ZOOPLUS AG

FISCHER AG VETROPACK HOLDING AG FIELMANN AG Switzerland

FLUGHAFEN ZUERICH AG VON ROLL HOLDING AG HAWESKO HOLDING AG APG SGA AG

FORBO HOLDING AG WALTER MEIER AG HELLOFRESH SE ASMALLWORLD AG

GAVAZZI HOLDING AG ZEHNDER GROUP AG HIGHLIGHT COMMUNICATIONS AG DUFRY AG

GEBERIT AG HOME24 SE GALENICA AG

IMPLENIA AG HORNBACH BAUMARKT AG HIGHLIGHT EVENT & ENTERTAINMENT AG

INFICON HOLDING AG HORNBACH HOLDING AG & CO. KGAA JUNGFRAUBAHN HOLDING AG

INTERROLL HOLDING AG INTERTAINMENT AG MOBILEZONE HOLDING AG

KARDEX AG KLASSIK RADIO AG OREL FUESSLI HOLDING AG

KLINGELNBERG LTD LOTTO24 AG TAMEDIA AG

KOMAX HOLDING AG LUDWIG BECK AG TITL BN BERG AG

KUEHNE & NAGEL INTERNATIONAL AG METRO AG VALORA AG

LAFARGEHOLCIM AG MYBET HOLDING SE

LANDIS+GYR GROUP AG ODEON FILM AG

LEM HOLDING AG PANTALEON ENTERTAIN. AG

MCH GROUP AG PROSIEBENSAT.1 MEDIA SE

MEYER BURGER AG ROCKET INTERNET SE

MIKRON SA SCOUT24 AG


81

Pharma & Healthcare Information Technology (1/2)

Germany STADA ARZNEIMITTEL AG Germany INVISION AG

4 SC AG STRATEC BIOMEDICAL AG ADESSO AG ISC BUSINESS TECHNOLOGY

AAP IMPLANTATE AG SYGNIS AG ADVA OPTICAL NETWORKING SE ISRA VISION

AGENNIX AG UMS INTERNATIONAL AG AIXTRON SE IVU TRAFFIC TECHNOLOGIE AG

BB BIOTECH AG VITA 34 AG ALL FOR ONE STEEB AG KPS AG

BIOFRONTERA AG WILEX AG ALLGEIER SE M & S ELEKTRONIK AG

BIOTEST AG. Austria AMALPHI AG MEDICAL COLUMBUS AG

CARL ZEISS MEDITEC AG VALNEVA SE AMATECH AG MENSCH UND MASCHINE SE

CO.DON AG Switzerland ARTEC TECHNOLOGIES AG METRIC MOBILITY SOLUTIONS AG

CURASAN AG ADDEX AG ATOSS SOFTWARE AG MEVIS MEDICAL SOLUTIONS AG

CYTOTOOLS AG AEVIS HOLDING SA B & S BANKSYSTEME AG MOBOTIX AG

DERMAPHARM HOLDING SE BACHEM HOLDING AG BECHTLE AG MUEHLBAUER HOLDING AG

DRAEGERWERK AG & CO. KGAA BASILEA PHARMACEUTICA AG BETA SYSTEMS SOFTWARE AG MYHAMMER HOLDING AG

ECKERT & ZIEGLER AG COLTENE HOLDING AG CANCOM SE MYNARIC AG

EPIGENOMICS AG EVOLVA HOLDING SA CENIT AG NEMETSCHEK SE

EVOTEC AG IDORSIA LTD CEOTRONICS AG NEXUS AG

FORMYCON AG IGEA Pharma N.V. COMPUGROUP MEDICAL SE NORCOM INFORMATION TECHN. AG

FRESEN.MED.CARE AG & CO. KGAA IVF HARTMANN AG CYAN AG OHB SE

FRESENIUS SE & CO.KGAA KUROS BIOSCIENCES AG DATA MODUL AG OPENLIMIT HOLDING AG

GERATHERM MEDICAL AG LONZA GROUP AG DATAGROUP SE OTRS AG

GERRESHEIMER AG MEDARTIS HOLDING AG DOCCHECK AG PA POWER AUTOMATION AG

HUMANOPTICS AG MOLECULAR PARTNERS AG EASY SOFTWARE AG PANAMAX AG

M1 KLINIKEN AG NOVARTIS AG ELMOS SEMICONDUCTOR AG PARAGON AG

MAGFORCE AG ObsEva SA EQS GROUP AG PSI AG

MATERNUS-KLINK AG POLYPHOR AG EUROMICRON AG QSC AG

MEDICLIN AG RELIEF THERAPEUTICS HOLDING AG FABASOFT AG REALTECH AG

MEDIGENE AG ROCHE AG FIRST SENSOR AG RIB SOFTWARE AG

MEDIOS AG SANTHERA PHARM. HOLDING AG FORTEC ELEKTRONIK AG S & T AG

MERCK AG & CO. KGAA SIEGFRIED HOLDING AG GBS SOFTWARE AG SAP SE

MOLOGEN AG SONOVA HOLDING AG GERM. STARTUPS GRP GMBH & CO. KGAA SCHWEIZER ELECTRONIC AG

MORPHOSYS AG STRAUMANN HOLDING AG GFT TECHNOLOGIES SE SECUNET SECURITY AG

MPH HEALTH CARE AG TECAN GROUP AG GIGASET AG SERVICEW ARE AG

NANOREPRO AG VIFOR PHARMA AG GK SOFTWARE SEVEN PRINCIPLES AG

PAION AG YPSOMED HOLDING AG HOLIDAYCHECK GROUP AG SHF COMMUNICATION TECHNOLOGIES AG

PAUL HARTMANN AG INFINEON TECHNIK AG SILTRONIC AG

RHOEN-KLINIKUM AG INIT INNOVATION SE SINNERSCHRADER AG

SANOCHEMIA PHARMAZEUTIKA AG INTERCARD AG SNP AG

SARTORIUS AG INTERSHOP COMMUNICATIONS AG SOFTMATIC AG

SIEMENS HEALTHINEER AG INTICA SYSTEMS AG SOFTWARE AG


82

Information Technology (2/2) Utilities

STEMMER IMAGING AG Germany

SUESS MICROTEC AG CAPITAL STAGE AG

SYZYGY AG E.ON SE

TDMI AG ENBW ENERGIE B./W. AG

TELES AG GELSENWASSER AG

TISCON AG INNOGY SE

TTL INFORMATION TECHN. AG MAINOVA AG

UMT UTD MOBILITY TECHN. AG MVV ENERGIE AG

USU SOFTWARE AG RWE AG

UTD. INTERNET AG UNIPER SE

VIVANCO GRUPPE AG Austria

WIRECARD AG EVN AG

XING AG VERBUND AG

Austria Switzerland

AT&S AUSTRIA TECH.& SYSTEMTECH. AG BKW ENERGIE AG

KAPSCH TRAFFICCOM AG EDISUN POWER EUROPE AG

MASCHINENFABRIK HEID AG ROMANDE ENERGIE HOLDING SA

RATH AG

Switzerland

AIROPACK TECHNOLOGY GROUP AG

ALSO HOLDING AG

AMS AG

ASCOM HOLDING AG

CREALOGIX HOLDING AG

HUBER+SUHNER AG

KUDELSKI SA

LOGITECH INTERNATIONAL SA

TEMENOS GROUP AG

U-BLOX HOLDING AG

WISEKEY INTERNATIONAL HOLDING AG

Appendix

Historical development of trading multiples since 2013

0.0x

5.0x

10.0x

15.0x

20.0x

25.0x

30.0x

EV/EBIT DACH

LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median

0.0x

0.5x

1.0x

1.5x

2.0x

2.5x

3.0x

3.5x

EV/Revenue DACH


Trading MultiplesDACH (1/2) – Revenue- and EBIT-MultiplesLTM and 1yf since 2013

Note: As of the reference date, the “FIRE” sector used in previous studies was divided into the sectors Banking, Financial Services, Insurance and Real Estate. The historical development of the DACH multiples is basedon the sector classification used in previous studies. Opposed to that, the DACH multiples as of the reference date 31 December 2018 correspond to the newly introduced sector classification (cf. 33).

84

2.9x

1.4x1.5x

2.8x

22.2x

18.1x

15.8x17.3x

Trading MultiplesDACH (2/2) – P/E- and EqV/BV-MultiplesLTM and 1yf since 2013

85

19.9x

16.6x16.3x

17.9x

2.1x

1.5x

Note: As of the reference date, the “FIRE” sector used in previous studies was divided into the sectors Banking, Financial Services, Insurance and Real Estate. The historical development of the DACH multiples is basedon the sector classification used in previous studies. Opposed to that, the DACH multiples as of the reference date 31 December 2018 correspond to the newly introduced sector classification (cf. 33).

0.0x

5.0x

10.0x

15.0x

20.0x

25.0x

30.0x

P/E DACH


0.0x

0.5x

1.0x

1.5x

2.0x

2.5x

3.0x

3.5x

EqV/BV DACH

Arithmetic mean Median

Trading MultiplesBanking (1/1) – P/E- and EqV/BV-MultiplesLTM and 1yf since 2013

86

15.2x14.5x

8.4x10.0x

0.9x0.9x

0.0x2.0x4.0x6.0x8.0x

10.0x12.0x14.0x16.0x18.0x

P/E Banking


0.0x

0.2x

0.4x

0.6x

0.8x

1.0x

1.2x

1.4x

EqV/BV Banking


Trading MultiplesInsurance (1/1) – P/E- and EqV/BV-MultiplesLTM and 1yf since 2013

87

16.6x12.7x

12.2x12.6x

1.1x

1.0x

0.0x

5.0x

10.0x

15.0x

20.0x

25.0x

P/E Insurance


0.0x

0.2x

0.4x

0.6x

0.8x

1.0x

1.2x

EqV/BV Insurance


0.0x

0.5x

1.0x

1.5x

2.0x

2.5x

3.0x

EqV/BV Financial Services


0.0x

5.0x

10.0x

15.0x

20.0x

25.0x

30.0x

P/E Financial Services


Trading MultiplesFinancial Services (1/1) – P/E- and EqV/BV-MultiplesLTM and 1yf since 2013

88

16.6x14.0x13.9x

17.8x

1.8x

1.2x

0.0x

5.0x

10.0x

15.0x

20.0x

EV/Revenue Real Estate


Trading MultiplesReal Estate (1/2) – Revenue- and EBIT-MultiplesLTM and 1yf since 2013

89

10.7x11.1x

13.8x12.0x

31.9x28.7x

22.6x21.6x

0.0x

5.0x

10.0x

15.0x

20.0x

25.0x

30.0x

35.0x

EV/EBIT Real Estate


Trading MultiplesReal Estate (2/2) – P/E-Multiples and EqV/BV-MultiplesLTM and 1yf since 2013

90

15.2x13.9x

19.0x19.3x

1.2x

1.2x

0.0x

5.0x

10.0x

15.0x

20.0x

25.0x

P/E Real Estate


0.0x

0.2x

0.4x

0.6x

0.8x

1.0x

1.2x

1.4x

1.6x

EqV/BV Real Estate


0.0x

5.0x

10.0x

15.0x

20.0x

25.0x

30.0x

EV/EBIT Basic Materials


0.0x

0.5x

1.0x

1.5x

2.0x

2.5x

3.0x

EV/Revenue Basic Materials


Trading MultiplesBasic Materials (1/2) – Revenue- and EBIT-MultiplesLTM and 1yf since 2013

91

2.1x

1.2x1.3x

1.8x

21.7x

14.9x12.9x

15.4x

0.0x

0.5x

1.0x

1.5x

2.0x

2.5x

3.0x

EqV/BV Basic Materials


0.0x

5.0x

10.0x

15.0x

20.0x

25.0x

30.0x

P/E Basic Materials


Trading MultiplesBasic Materials (2/2) – P/E-Multiples and EqV/BV-MultiplesLTM and 1yf since 2013

92

15.0x

12.6x12.8x

16.1x

1.7x

1.3x

0.0x

5.0x

10.0x

15.0x

20.0x

25.0x

EV/EBIT Consumer Goods


0.0x

0.5x

1.0x

1.5x

2.0x

EV/Revenue Consumer Goods


Trading MultiplesConsumer Goods (1/2) – Revenue- and EBIT-MultiplesLTM and 1yf since 2013

93

1.5x

0.9x1.0x

1.5x

20.2x

17.5x

15.1x15.1x

0.0x

0.5x

1.0x

1.5x

2.0x

2.5x

EqV/BV Consumer Goods


0.0x

5.0x

10.0x

15.0x

20.0x

25.0x

30.0x

P/E Consumer Goods


Trading MultiplesConsumer Goods (2/2) – P/E- and EqV/BV-MultiplesLTM and 1yf since 2013

94

18.3x16.8x

15.3x16.7x

1.8x

1.5x

0.0x

5.0x

10.0x

15.0x

20.0x

25.0x

30.0x

35.0x

EV/EBIT Telecommunication


0.0x

0.5x

1.0x

1.5x

2.0x

2.5x

3.0x

EV/Revenue Telecommunication


Trading MultiplesTelecommunication (1/2) – Revenue- and EBIT-MultiplesLTM and 1yf since 2013

1.7x1.6x

2.0x2.1x

19.3x

16.3x15.0x

17.3x

95

0.0x

0.5x

1.0x

1.5x

2.0x

2.5x

3.0x

EqV/BV Telecommunication


0.0x

5.0x

10.0x

15.0x

20.0x

25.0x

30.0x

35.0x

40.0x

P/E Telecommunication


Trading MultiplesTelecommunication (2/2) – P/E- and EqV/BV-MultiplesLTM and 1yf since 2013

96

26.5x

21.0x

16.5x18.5x

2.1x1.8x

0.0x

5.0x

10.0x

15.0x

20.0x

25.0x

30.0x

EV/EBIT Industrials


0.0x

0.5x

1.0x

1.5x

2.0x

2.5x

EV/Revenue Industrials


Trading MultiplesIndustrials (1/2) – Revenue- and EBIT-MultiplesLTM and 1yf since 2013

97

1.6x

1.1x1.2x

1.6x

18.3x

15.5x15.0x

16.1x

0.0x

0.5x

1.0x

1.5x

2.0x

2.5x

3.0x

3.5x

EqV/BV Industrials


0.0x

5.0x

10.0x

15.0x

20.0x

25.0x

30.0x

P/E Industrials


Trading MultiplesIndustrials (2/2) – P/E- and EqV/BV-MultiplesLTM and 1yf since 2013

98

19.3x

17.1x16.1x

17.9x

2.4x

1.8x

0.0x

5.0x

10.0x

15.0x

20.0x

25.0x

30.0x

EV/EBIT Consumer Service


0.0x

0.5x

1.0x

1.5x

2.0x

2.5x

3.0x

3.5x

EV/Revenue Consumer Service


Trading MultiplesConsumer Service (1/2) – Revenue- and EBIT-MultiplesLTM and 1yf since 2013

99

2.0x

1.0x1.0x

2.3x

20.8x

16.5x14.7x

16.6x

0.0x

0.5x

1.0x

1.5x

2.0x

2.5x

3.0x

3.5x

4.0x

EqV/BV Consumer Service


0.0x

5.0x

10.0x

15.0x

20.0x

25.0x

30.0x

P/E Consumer Service


Trading MultiplesConsumer Service (2/2) – P/E- and EqV/BV-MultiplesLTM and 1yf since 2013

100

21.3x

16.6x

14.0x15.8x

2.4x

1.7x

0.0x

5.0x

10.0x

15.0x

20.0x

25.0x

30.0x

35.0x

EV/EBIT Pharma & Healthcare


0.0x

1.0x

2.0x

3.0x

4.0x

5.0x

6.0x

EV/Revenue Pharma & Healthcare


Trading MultiplesPharma & Healthcare (1/2) – Revenue- and EBIT-MultiplesLTM and 1yf since 2013

101

4.9x

3.6x3.9x

4.8x

26.8x

22.8x

21.2x22.2x

0.0x

5.0x

10.0x

15.0x

20.0x

25.0x

30.0x

35.0x

P/E Pharma & Healthcare


Trading MultiplesPharma & Healthcare (2/2) – P/E- and EqV/BV-MultiplesLTM and 1yf since 2013

102

27.8x27.2x

22.9x25.3x

2.9x

2.5x

0.0x0.5x1.0x1.5x2.0x2.5x3.0x3.5x4.0x4.5x

EqV/BV Pharma & Healthcare


0.0x

5.0x

10.0x

15.0x

20.0x

25.0x

30.0x

35.0x

EV/EBIT Information Technology


0.0x

0.5x

1.0x

1.5x

2.0x

2.5x

3.0x

3.5x

EV/Revenue Information Technology


Trading MultiplesInformation Technology (1/2) – Revenue- and EBIT-MultiplesLTM and 1yf since 2013

103

2.3x

1.6x1.7x

2.7x

25.0x

20.4x

16.6x18.3x

0.0x0.5x1.0x1.5x2.0x2.5x3.0x3.5x4.0x4.5x

EqV/BV Information Technology


0.0x

5.0x

10.0x

15.0x

20.0x

25.0x

30.0x

35.0x

40.0x

P/E Information Technology


Trading MultiplesInformation Technology (2/2) – P/E- and EqV/BV-MultiplesLTM and 1yf since 2013

104

26.9x

23.1x

18.1x20.1x

2.5x

2.1x

0.0x

5.0x

10.0x

15.0x

20.0x

25.0x

30.0x

35.0x

EV/EBIT Utilities


0.0x0.5x1.0x1.5x2.0x2.5x3.0x3.5x4.0x4.5x

EV/Revenue Utilities


Trading MultiplesUtilities (1/2) – Revenue- and EBIT-MultiplesLTM and 1yf since 2013

105

3.1x

1.9x1.9x

3.1x

21.8x21.0x

14.6x16.4x

0.0x

0.5x

1.0x

1.5x

2.0x

2.5x

EqV/BV Utilities


0.0x

5.0x

10.0x

15.0x

20.0x

25.0x

30.0x

P/E Utilities


Trading MultiplesUtilities (2/2) – P/E- and EqV/BV-MultiplesLTM and 1yf since 2013

106

20.4x

15.4x15.7x18.9x

1.6x1.4x

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