DACH Capital Market Study 30 June 2017

Transcription

1 powered by and DACH Capital Market Study 30 June 2017 Analysis of cost of capital parameters and multiples for the capital markets of Germany, Austria and Switzerland Volume 1, October 2017

2 DACH Capital Market Study 30 June 2017 Table of Contents Page 1. Preface & people 3 2. Executive summary 7 3. Risk-free rate Market returns and market risk premium 16 a. Implied returns (ex-ante analysis) 16 b. Historical returns (ex-post analysis) Sector classification of the DACH region 32 based on sector indices 6. Betas Sector returns 38 a. Implied returns (ex-ante analysis) 38 b. Historical returns (ex-post analysis) Trading multiples 61 Appendix 68 2

3 1 Preface & people

4 DACH Capital Market Study Preface Dear addressee, We are pleased to release our first edition of the DACH 1) Capital Market Study powered by and. The study was elaborated by ValueTrust Financial Advisors SE (ValueTrust) in cooperation with and the Institute of Auditing and Sustainability Accounting at the Johannes Kepler University Linz. With this study, we provide a data compilation of the capital market parameters that enables an enterprise valuation in Germany, Austria, and Switzerland. It has the purpose to serve as an assistant and data source as well as to show trends of the analyzed parameters. In this study, we analyze the relevant parameters to calculate the costs of capital with assistance of the Capital Asset Pricing Model (risk-free rate, market risk premium and beta). Additionally, we determine implied, as well as historical market and sector returns. Moreover, this study includes capital structure adjusted implied sector returns, which serve as an indicator for the unlevered cost of equity. The relevered cost of equity can be calculated by adapting the company specific debt situation to the unlevered cost of equity. This procedure serves as an alternative to the CAPM. Lastly, trading multiples frame the end of this study. We examine the before mentioned parameters for the German, Austrian and Swiss capital market (in form of the CDAX 2), WBI 3), and SPI 4) ). These indices have been merged into nine sector indies (so-called super-sectors"): FIRE (Finance, Insurance and Real Estate), Basic Materials, Consumer Goods, Telecommunication, Industrials, Consumer Service, Pharma & Healthcare, Information Technology and Utilities. The historical data from the reference dates between 2011 and 30 June 2017 has been compiled and will be updated semi-annually, with the objective that historical, as well as current data, can be consulted at the same time. Furthermore, we can comprehend changes in time and this allows to track the performance on all three capital markets. Additionally, further knowledge and information for financial decision making is provided at The analyzed cost of capital data is accessible online at by simply entering the reference date, the relevant sector and country. We would be pleased, if this study aroused your interest. It would be our pleasure to answer all the questions you might have and discuss the results of our analysis with you. 1) D (Germany), A (Austria), CH (Switzerland) 2) Composite German Stock Index 3) Vienna Stock Index 4) Swiss Performance Index Prof. Dr. Christian Aders Chairman of the Executive Board ValueTrust Financial Advisors SE Prof. Dr. Ewald Aschauer Chair of Auditing and Sustainability Accounting, University of Linz Prof. Dr. Bernhard Schwetzler Chair of Financial Management, HHL Leipzig 4

5 DACH Capital Market Study 30 June 2017 People Prof. Dr. Christian Aders, CEFA, CVA Chairman of the Executive Board, ValueTrust Almost 25 years of experience in corporate valuation and corporate finance consulting 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 Recommendation Company Assessment Co-Founder of the European Association of Certifies Valuators and Analysts (EACVA e.v.) Florian Starck, Steuerberater Member of the Executive Board, ValueTrust Almost 20 years of project experience in corporate valuation and corporate finance consulting Previously employed in leading positions at KPMG and Duff & Phelps Extensive experience in complex company evaluations for business transactions, financial restructuring, court and arbitration proceedings and value based management systems Prof. Dr. Bernhard Schwetzler, CVA Chair of Financial Management, HHL Leipzig Senior Advisor, ValueTrust Co-Founder and board member of the European Association of Certifies Valuators and Analysts (EACVA e.v.) Prof. Dr. Ewald Aschauer Chair of Auditing and Sustainability Accounting, University of Linz 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

6 DACH Capital Market Study Disclaimer This study presents an empirical analysis, which serves the purpose of illustrating the cost of capital of Germany s, Austria s, and Switzerland s capital markets. Nevertheless, the available information and the corresponding exemplifications do not allow a complete exposure of a proper derivation of costs of capital. Furthermore, the market participant has to take into account that the company specific costs of capital can vary widely due to individual corporate situations. The listed information is not specified to anyone, and consequently, it cannot be directed to an individual or juristic person. Although we are always endeavored to present information that is reliable, accurate, and current, we cannot guarantee that the data is applicable to valuation in the present as well as in the future. The same applies to our underlying data from the data provider S&P Capital IQ. We recommend a self-contained, technical, and detailed analysis of the observed situation, and we dissuade from taking action based on the provided information only. ValueTrust and its co-authors do not assume any liability for the up-todatedness, completeness or accuracy of this study or its contents. 6

7 2 Executive summary

8 Executive Summary Risk-free rate In comparison to 31 December 2016, the German risk-free rate increased from 0.95% to 1.24% as of 30 June The Austrian risk-free rate increased from 1.04% as of 31 December 2016 to 1.33% as of 30 June The Swiss risk-free rate recorded an increase from 0.23% to 0.32% during the time period 31 December 2016 to 30 June Overall, Switzerland had by far the lowest risk-free rate in comparison to Germany and Austria. Chapter 3 Market returns and market risk premium Beta The implied yearly market return of the German market remained constant at 8.6% as of 30 June 2017 compared to 31 December 2016 whereas the market risk premium decreased from 7.6% to 7.4%. The implied market return of the Austrian market increased slightly from 8.2% as of 31 December 2016 to 8.3% as of 30 June 2017 and was below the German level. The market risk premium decreased from 7.2% to 6.9%. The implied market return of the Swiss market was the lowest in the DACH region with 6.8% as of 30 June 2017 compared to 7.4% as of 31 December The market risk premium decreased from 7.2% to 6.5%. In Germany and Austria the market risk premium decreased mainly due to an increase of the risk-free rates. Companies within the Pharma & Healthcare sector showed the highest unlevered sector specific betas as of 30 June 2017 with the arithmetic mean standing at 0.93 for the five year period and at 0.77 for the two year period. Companies within the Utilities sector had the lowest unlevered betas at 0.42 (two year period). The levered sector specific betas were highest for the Consumer Goods sector at 1.06 (market-value weighted mean) for the five year period as of 30 June If we consider the two year period, the FIRE sector showed the highest levered beta at 1.06 and the Consumer Service sector showed the lowest at Chapter 4 Chapter 6 8

9 Executive Summary Sector returns (p.a.) The development of the implied sector returns shows rather varied results. The ex-ante analysis of implied sector returns reveals that unlevered implied sector returns were highest for companies in the Pharma & Healthcare sector at 6.0% (levered 7.0%) of 30 June The ex-post analysis of historical sector returns based on total shareholder returns highlights that especially companies of the Information Technology sector realized high total shareholder returns at 21.5% in the sixand 26.5% in the three-year average. By far the lowest historical returns of the sectors were realized by the Utilities sector at -3.1% in the six- and -5.4% in the three year average. Chapter 7 Multiples At the reference date 30 June 2017, every illustrated multiple, EV/Revenue, EV/EBIT, P/E and EqV/BV (LTM as well as 1yf) reached its highest level compared to the past six years in the aggregate view of the DACH market. The Revenue- and EBIT-Multiples of the DACH market increased from 1.2x in 2011 to 2.5x in 2017 and from 14.4x in 2011 to 24.5x in 2017 (LTM, arithmetic mean), respectively. Chapter 8 9

10 3 Risk-free rate

11 Risk-Free Rate Background & approach The risk-free rate is a return available on a security that the market generally regards as free of risk of default. It serves as an input parameter for the CAPM and to determine the risk-adequate cost of capital. The risk-free rate is a yield, which is obtained from long-term government bonds of countries with top notch rating. By using interest rate data from different maturities, a yield curve can be estimated for fictitious zero coupon bonds (spot rates) for a period of up to 30 years. Therefore, the German Central Bank (Deutsche Bundesbank) and the Swiss National Bank (Schweizer Nationalbank) publish on a daily basis the parameters needed to determine the yield curve using the Svensson method. Based on the respective yield curve, a uniform risk-free rate is derived under the assumption of present value equivalence to an infinite time horizon. The German bonds are internationally classified as almost risk-free securities due to its AAA rating according to S&P. As a result, the Austrian Chamber of Public Accountants and Tax Consultants also recommends deriving the risk-free rate from the yield curve using the parameters published by the German Central Bank. 1) Likewise, bonds issued by Switzerland enjoy a AAA rating and are also considered as risk-free according to the Swiss National Bank. 2) Hence, a similar approach like for Germany and Austria is in our view also appropriate for Switzerland with Swiss parameters. 3) To compute the risk-free rate for a specific reference date, the Institute of Public Auditors (Institut der Wirtschaftsprüfer, IDW) in Germany recommends using an average value deduced from the daily yield curves of the past three months (IDW S 1). On the contrary, the Austrian Expert Opinion (KFS/BW 1) on company valuation recommends that the risk-free rate is to be derived in line with the evaluated company's cash flow profile from the yield curve that is valid for the reference date (reference date principle). Thus, the KFS/BW 1 and its counterpart, the IDW S 1, differentiate from each other. Consequently, in the following analyses we depict the yield curve for Germany following IDW S 1 and for Austria we adhere to the recommendations of KFS/BW 1. For Switzerland, there is no generally accepted scheme to determine the risk-free rate. The most widely used risk-free rates in the valuation practice are the yield of a 10-year Swiss government bond as of the reference date as well as the yield derived from the 3-month average of the daily yield curves (in accordance with IDW S 1). Both will be shown in the following slides. Additionally, we illustrate the monthly development of the risk-free rates since 2011 for all three capital markets. 1) 2) Swiss National Bank Zinssätze und Renditen, p.11 3) ibid., p.13 11

12 Risk-Free Rate DACH Determination according country specific recommendations Interest rate curve based on long-term bonds (Svensson-Method) 1.50% 1.00% Risk-free rates as of 30 June 2017 and 31 December % 1.24% 1.04% 0.95% Spot Rate 0.50% 0.00% % 0.23% -0.50% -1.00% Germany 30 June 2017 Austria 30 June 2017 Switzerland 30 June 2017 Germany 31 December 2016 Austria 31 December 2016 Switzerland 31 December 2016 Year 12

13 Risk-Free Rate Germany Determination following IDW S 1 Historical development of the risk-free rate (Svensson-Method) since 2011 Historical developement of the risk-free rate in % according to IDW S 1 5.0% 4.0% 3.0% 3.78% 2.77% 2.37% 2.37% 2.75% The German risk-free rate increased from 0.95% as of 31 December 2016 to 1.24% as of 30 June In the time period between 30 June 2011 and 30 June 2017 the risk-free rate declined from 3.78% to 1.24%. The German risk-free rate is the second highest within the three DACH markets. 2.0% 1.0% 0.0% 2.28% 2.45% 1.87% 1.17% 1.41% 0.91% 0.95% 1.24% Risk-free rate January February March April May June July August September October November December % 1.19% 1.25% 1.23% 1.25% 1.24% % 1.27% 1.12% 1.00% 1.01% 0.91% 0.74% 0.58% 0.54% 0.61% 0.76% 0.95% % 1.37% 1.08% 0.87% 0.92% 1.17% 1.49% 1.53% 1.50% 1.41% 1.42% 1.41% % 2.69% 2.64% 2.57% 2.50% 2.45% 2.35% 2.22% 2.11% 2.00% 1.97% 1.87% % 2.42% 2.42% 2.37% 2.32% 2.37% 2.44% 2.53% 2.63% 2.72% 2.76% 2.75% % 2.59% 2.56% 2.55% 2.44% 2.28% 2.20% 2.22% 2.33% 2.38% 2.39% 2.37% % 3.57% 3.68% 3.80% 3.81% 3.78% 3.60% 3.44% 3.27% 3.03% 2.87% 2.77% Note: Interest rate as of reference date using 3-month average yield curves in accordance with IDW S 1 13

14 Risk-Free Rate Austria Determination following KFS/BW 1 Historical development of the risk-free rate (Svensson-Method) since 2011 Historical developement of the risk-free rate in % according to KFS/BW1 5.0% 4.0% 3.0% 2.0% 3.86% 2.42% 2.41% 2.19% 2.53% 2.84% 2.30% 1.67% The Austrian risk-free rate increased from 1.04% as of 31 December 2016 to 1.33% as of 30 June In the time period between 30 June 2011 to 30 June 2017 the risk-free rate declined from 3.86% to 1.33%. The Austrian risk-free rate is the highest within the three DACH markets. 1.57% 1.33% 1.0% 1.59% 1.04% 0.0% 0.49% Risk-free rate January February March April May June July August September October November December % 1.13% 1.24% 1.25% 1.29% 1.33% % 0.88% 0.91% 1.13% 1.02% 0.49% 0.45% 0.50% 0.48% 0.90% 0.89% 1.04% % 1.08% 0.71% 0.96% 1.18% 1.67% 1.47% 1.46% 1.39% 1.29% 1.38% 1.57% % 2.57% 2.55% 2.49% 2.36% 2.30% 2.15% 1.87% 2.00% 1.95% 1.79% 1.59% % 2.37% 2.27% 2.17% 2.41% 2.53% 2.54% 2.70% 2.65% 2.69% 2.70% 2.84% % 2.51% 2.55% 2.49% 1.89% 2.41% 2.30% 2.22% 2.32% 2.42% 2.35% 2.19% % 3.63% 3.87% 3.84% 3.66% 3.86% 3.48% 3.27% 2.80% 2.99% 3.06% 2.42% Note: Interest rate calculated using the daily yield curve in accordance with KFS/BW 1 (no 3-month average) 14

15 Risk-Free Rate Switzerland Determination following IDW S 1 Historical development of the risk-free rate (Svensson-Method) since 2011 Historical development of the risk-free rate in % according to IDW S 1 5.0% 4.0% 3.0% 2.0% 1.0% 0.0% 2.25% 1.45% 1.16% 1.12% 1.33% 1.79% 1.61% 1.08% 0.57% The Swiss risk-free rate increased from 0.23% as of 31 December 2016 to 0.32% as of 30 June In the time period between 30 June 2011 to 30 June 2017 the risk-free rate declined from 2.25% to 0.32%. The Swiss risk-free rate is the lowest within the three DACH markets. 0.66% 0.19% 0.23% 0.32% -1.0% Risk-free rate January February March April May June July August September October November December % 0.37% 0.37% 0.35% 0.36% 0.32% % 0.49% 0.36% 0.26% 0.25% 0.19% 0.09% -0.01% -0.04% -0.02% 0.08% 0.23% % 0.66% 0.54% 0.47% 0.47% 0.57% 0.72% 0.74% 0.73% 0.72% 0.71% 0.66% % 1.80% 1.75% 1.68% 1.63% 1.61% 1.56% 1.44% 1.33% 1.24% 1.20% 1.08% % 1.24% 1.31% 1.31% 1.28% 1.33% 1.46% 1.62% 1.72% 1.77% 1.78% 1.79% % 1.30% 1.30% 1.31% 1.26% 1.16% 1.10% 1.08% 1.10% 1.12% 1.13% 1.12% % 2.15% 2.16% 2.21% 2.25% 2.25% 2.17% 1.98% 1.78% 1.60% 1.52% 1.45% Note: Interest rate as of reference date using 3-month average yield curves in accordance with IDW S 1 15

16 4 Market returns and market risk premium a. Implied returns (ex-ante analysis)

17 Implied Market Returns and Market Premium Background & approach The future-oriented computation of implied market returns and market risk premiums is based on profit estimates for public companies and return calculations. This approach is called ex-ante analysis and allows to calculate the Implied Costs of Capital. It is to be distinguished from the ex-post analysis. Particularly, the ex-ante method offers an alternative to the ex-post approach of calculating the costs of capital by means of the regression analysis through the CAPM. The ex-ante analysis method seeks costs of capital which represent the return expectations from market participants. Moreover, it is supposed that the estimates of financial analysts reflect the expectations of the capital market. Three basic models can be used for the calculation of implied costs of capital: Dividend Discount Model Residual Income Valuation Model Earnings Capitalization Model Through dissolving the models to achieve the cost of capital, we obtain the implied return on equity. 1) Furthermore, various model specifications were developed based on the three basic models. For the following analysis, we use simplified to annually the formula of the Residual Income Valuation Model by Babbel: 2) with: r t = Cost of equity at period t NI t+1 = Expected net income in the following period t+1 MC t BV t g r t = NI t+1 MC t + 1 BV t MC t = Market capitalization at period t = Book value of equity at period t = Projected growth rate g Since Babbel's model does not need any explicit assumptions, except for the growth rate, it turns out to be robust. We source our data (i.e. the expected annual net income, the market capitalizations, and the company s book value of equity, etc.) of the analyzed companies from the data supplier S&P Capital IQ. Additionally, we apply the European Central Bank target inflation rate of 2.0% as a typified 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 the total markets. 3) The results build the starting points for the calculations of the implied market risk premiums of the German, Austrian, and Swiss capital markets. 1) cf. Reese, 2007, Estimation of the costs of capital for evaluation purposes. 2) cf. Babbel, Challenging Stock Prices: Stock prices und implied growth expectations, in: Corporate Finance, N. 9, 2015, p , in particular p ) Approx. 75% of the total market capitalization (CDAX, WBI, SPI) is covered. 17

18 Implied Market Returns German market DAX Implied market returns - DAX H H H H H H H H H H H H Minimum 6.0% 3.2% 3.8% 3.2% 4.8% 2.9% 5.1% 5.3% 5.2% 3.3% 3.4% 2.6% Lower quantile 7.3% 6.5% 5.5% 4.3% 5.3% 5.2% 6.1% 5.6% 5.9% 5.0% 5.4% 5.3% Median 10.5% 10.0% 9.4% 8.4% 7.7% 7.7% 7.8% 7.0% 7.9% 7.6% 7.6% 7.7% Arithmetic mean 11.7% 10.5% 9.4% 8.6% 8.1% 7.6% 8.3% 7.9% 8.4% 8.7% 8.3% 8.4% Market-value weighted mean 11.8% 11.2% 10.0% 9.4% 8.6% 8.3% 8.9% 8.3% 8.5% 9.0% 8.6% 8.6% Upper quantile 17.1% 15.3% 13.0% 12.3% 10.5% 10.7% 11.4% 11.4% 12.0% 15.2% 12.4% 14.3% Maximum 23.9% 17.9% 14.6% 16.2% 12.0% 11.9% 14.7% 17.0% 18.3% 24.2% 16.3% 16.7% Market-value weighted debt 333.2% 323.2% 252.0% 231.6% 162.1% 167.5% 175.2% 154.5% 153.6% 200.8% 150.0% 137.0% 16.0% 14.0% 12.0% 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% 11.8% 11.2% 10.0% 9.4% Implied market returns - DAX 8.6% 8.3% 8.9% 8.3% 8.5% 9.0% Max 11.8% 8.6% 8.6% Min 7.5% The implied market return of the German market showed a pretty constant market-value weighted mean of 8.6% between 31 December 2016 to 30 June Since 31 December 2011 the implied market return fluctuated between 7.5% and 11.8%, overall it follows a declining trend. The German market showed the highest return in comparison to the two other markets as of 30 June Range (10% - 90% quantile) Market-value weighted mean 18

19 Implied Market Risk Premium German market DAX Knowing the implied market return and the daily measured risk-free rate (cf. slide 13 in this study) of the German capital market, we can determine the implied market risk premium. In the years from 2011 to 2017 the implied market returns fell within a range of 8.3% to 11.8% (cf. slide 18 in this study). Subtracting the risk-free rate from the implied market return, we derive a market risk premium within the range of 5.8% to 9.0%. The implied market return is at 8.6% as of the reference date 30 June Taking the risk-free rate of 1.24% (cf. slide 12) into account, we determine a market risk premium of 7.4%. 14.0% Implied market risk premium - DAX 12.0% 10.0% 8.0% 6.0% 4.0% 2.0% 11.8% 11.2% 9.0% 9.0% 10.0% 9.4% 7.6% 7.1% 8.6% 8.3% 5.9% 5.8% 8.9% 8.3% 8.5% 7.0% 7.1% 7.1% 9.0% 8.6% 8.6% 8.1% 7.6% 7.4% Implied market return Risk-free rate MRP 0.0% Risk-free rate Implied market risk premium Market-value weighted mean H H H H H H H H H H H H Market-value weighted mean 11.8% 11.2% 10.0% 9.4% 8.6% 8.3% 8.9% 8.3% 8.5% 9.0% 8.6% 8.6% Risk-free rate 2.8% 2.3% 2.4% 2.4% 2.8% 2.5% 1.9% 1.2% 1.4% 0.9% 1.0% 1.2% Implied market risk premium - DAX 9.0% 9.0% 7.6% 7.1% 5.9% 5.8% 7.0% 7.1% 7.1% 8.1% 7.6% 7.4% 19

20 Implied Market Returns Austrian market ATX Implied market returns - ATX H H H H H H H H H H H H Minimum -1.2% 3.0% 3.0% 4.3% 0.0% 0.2% 2.0% 2.0% 3.6% 1.6% 2.1% 1.1% Lower quantile 5.3% 5.2% 4.9% 4.5% 4.6% 1.6% 3.5% 3.9% 4.1% 5.2% 4.7% 5.1% Median 9.8% 8.9% 8.2% 8.5% 7.4% 6.4% 7.2% 6.9% 7.7% 8.4% 7.7% 7.7% Arithmetic mean 10.2% 10.1% 8.6% 8.6% 7.8% 6.4% 7.8% 6.9% 8.0% 8.1% 7.9% 7.5% Market-value weighted mean 11.6% 11.3% 9.5% 9.5% 8.8% 7.1% 8.4% 7.3% 8.3% 8.1% 8.2% 8.3% Upper quantile 16.8% 16.4% 13.8% 12.9% 11.9% 10.6% 13.2% 12.1% 12.3% 11.7% 10.9% 10.1% Maximum 18.0% 16.8% 14.5% 13.9% 11.9% 10.7% 14.4% 13.4% 13.6% 12.3% 11.2% 13.0% Market-value weighted debt 240.2% 215.1% 190.1% 173.4% 161.2% 136.6% 177.3% 141.8% 149.9% 147.7% 122.7% 101.0% 20.0% 16.0% 12.0% 8.0% 4.0% 0.0% 11.6% 11.3% 9.5% 9.5% Implied market returns - ATX 8.8% 8.4% 7.1% 7.3% Max 11.9% 8.3% 8.1% 8.2% 8.3% Min 6.4% The implied market return of the Austrian market shows a relatively constant market-value weighted mean with 8.2% as of 31 December 2016 and 8.3% asof 30 June It fluctuated since 31 December 2011 between 6.4% and 11.9%, overall it follows a declining trend. The Austrian market represents the second highest implied return in comparison to the two other markets as of 30 June Range (10% - 90% quantile) Market-value weighted mean 20

21 Implied Market Risk Premium Austrian 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 the implied market risk premium. In the years from 2011 to 2017 the implied market returns fell within a range of 7.1% to 11.6% (cf. slide 20 in this study). Subtracting the risk-free rate from the implied market return, we derive a market risk premium within the range of 4.8% to 9.2%. The implied market return is at 8.3% as of the reference date 30 June Taking the risk-free rate of 1.33% (cf. slide 14) into account, we determine a market risk premium of 6.9%. 14.0% Implied market risk premium - ATX 12.0% 11.6% 11.3% 10.0% 8.0% 6.0% 4.0% 2.0% 9.2% 8.9% 9.5% 9.5% 7.3% 7.0% 8.8% 6.0% 7.1% 4.8% 8.4% 6.8% 7.3% 5.6% 8.3% 8.1% 8.2% 8.3% 6.8% 7.6% 7.2% 6.9% Implied market return Risk-free rate MRP 0.0% Risk-free rate Implied market risk premium Market-value weighted mean H H H H H H H H H H H H Market-value weighted mean 11.6% 11.3% 9.5% 9.5% 8.8% 7.1% 8.4% 7.3% 8.3% 8.1% 8.2% 8.3% Risk-free rate 2.4% 2.4% 2.2% 2.6% 2.9% 2.3% 1.6% 1.7% 1.6% 0.5% 1.0% 1.3% Implied market risk premium - ATX 9.2% 8.9% 7.3% 7.0% 6.0% 4.8% 6.8% 5.6% 6.8% 7.6% 7.2% 6.9% 21

22 Implied Market Returns Swiss market SMI Implied market returns - SMI H H H H H H H H H H H H Minimum 6.8% 6.3% 6.5% 6.0% 6.2% 5.8% 5.7% 6.0% 5.4% 5.2% 4.5% 5.0% Lower quantile 6.9% 6.4% 6.9% 6.4% 6.2% 5.9% 6.1% 6.2% 5.9% 5.7% 5.3% 5.3% Median 9.5% 9.2% 8.4% 7.5% 7.7% 7.4% 7.9% 7.3% 7.7% 7.2% 7.4% 6.3% Arithmetic mean 9.9% 9.4% 8.9% 8.2% 7.9% 7.5% 7.9% 7.6% 7.6% 7.5% 7.2% 6.8% Market-value weighted mean 9.9% 9.4% 8.9% 8.1% 7.9% 7.3% 7.6% 7.2% 7.4% 7.2% 7.4% 6.8% Upper quantile 14.7% 12.5% 11.7% 11.6% 10.7% 10.2% 10.6% 10.5% 9.6% 10.6% 9.1% 8.6% Maximum 16.7% 15.7% 13.6% 12.9% 10.8% 10.4% 11.0% 10.6% 10.1% 11.0% 9.4% 8.7% Market-value weighted debt 202.2% 193.0% 144.8% 107.7% 87.0% 81.0% 85.7% 78.3% 74.1% 87.7% 79.4% 71.3% 16.0% 14.0% 12.0% 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% 9.9% 9.4% 8.9% 8.1% 7.9% Implied market returns - SMI 7.3% 7.6% 7.2% 7.4% 7.2% 7.4% Max 9.9% Min 6.8% 6.8% The market-value weighted mean of the implied market return of the Swiss market decreased from 7.4% as of 30 December 2016 to 6.8% as of 30 June It fluctuated since 31 December 2011 between 6.8% and 9.9%, overall it follows a declining trend. The Swiss market represents the lowest return, compared to the German and Austrian market as of 30 June Range (10% - 90% quantile) Market-value weighted mean 22

23 Implied Market Risk Premium Swiss 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 the implied market risk premium. In the years from 2011 to 2017 the implied market returns fell within a range of 6.8% to 9.9% (cf. slide 24 in this study). Subtracting the risk-free rate from the implied market return, we derive a market risk premium of 5.6% to 8.5%. The implied market return is at 6.8% as of the reference date 30 June Taking the risk-free rate of 0.32% (cf. slide 15) into account, we determine a market risk premium of 6.5%. 12.0% Implied market risk premium - SMI 10.0% 8.0% 6.0% 9.9% 9.4% 8.9% 8.1% 7.9% 7.3% 7.6% 7.2% 7.4% 7.2% 7.4% 6.8% Implied market return 4.0% 2.0% 8.5% 8.3% 7.8% 6.7% 6.1% 5.6% 6.6% 6.6% 6.7% 7.0% 7.2% 6.5% Risk-free rate MRP 0.0% Risk-free rate Implied market risk premium Market-value weighted mean H H H H H H H H H H H H Market-value weighted mean 9.9% 9.4% 8.9% 8.1% 7.9% 7.3% 7.6% 7.2% 7.4% 7.2% 7.4% 6.8% Risk-free rate 1.4% 1.2% 1.1% 1.3% 1.8% 1.6% 1.1% 0.6% 0.7% 0.2% 0.2% 0.3% Implied market risk premium - SMI 8.5% 8.3% 7.8% 6.7% 6.1% 5.6% 6.6% 6.6% 6.7% 7.0% 7.2% 6.5% 23

24 4 Market returns and market risk premium b. Historical returns (ex-post analysis)

25 Historical Market Returns Background & approach Besides analyzing the implied market returns through the ex-ante analysis, we also analyze historical (ex-post) returns. Once this analysis is performed over a long-term observation period, an expected return potential of the German, Austrian, and Swiss capital market is assessable. Therefore, the analysis of historical returns can be used for plausibility checks of the costs of capital, more specifically return requirements, which were evaluated through the CAPM. To further enable a precise analysis of the historical returns of the German, Austrian, and Swiss capital market, we use the so-called return triangle. 1) It helps to present the annually realized returns from different investment periods in a simple and understandable way. Especially the different buying and selling points in time, and different annual holding periods are being illustrated comprehensively. For calculating the average annual returns over several years, we use both the geometric and arithmetic mean. In this study, we analyze the so-called total shareholder returns, which include the returns on investments and the dividend yields. For our analysis, it is needful to focus on total return indices because they include the price and dividend yields. Since the DAX is a performance index, we already have an index which includes the price and dividend yields. The ATX and SMI only include the price yields, hence we need their specific total return indices. The relevant total return index for Austria is called the ATX Total Return and for Switzerland SMI Total Return. The composition of both indices are identical to the ATX and the SMI and compromise 20 companies each. 1) The German Stock Institut e.v. (DAI) developed the return triangle for DAX and EURO STOXX The observation period amounts to 26 years. Therefore, the earliest data of the DAX and the ATX Total Return is from the beginning of However, the data of the SMI Total Return starts from All ex-post returns are being calculated by using the data as of the reference date 30 June. The following slides illustrate how the two calculation methods (arithmetic and geometric) differ from each other for the period between 30 June 1991 and 30 June 2017: DAX: the arithmetic mean of the historical market returns is 10.5% the geometric mean of the historical market returns is 8.1% ATX: the arithmetic mean of the historical market returns is 9.2% the geometric mean of the historical market returns is 6.4% SMI: the arithmetic mean of the historical market returns is 10.1% the geometric mean of the historical market returns is 8.1% 25

26 Historical Market Returns (Arithmetic Mean) German Market DAX Performance Index Return Triangle Buy Reading example: An investment in the DAX Index mid of the year 2009, when sold mid of the year 2014, would have yielded an average annual return (arithmetic mean) of 16.5%. Other five-year investment periods are displayed along the black steps. 27.3% % 7.9% % -0.1% 9.0% % 17.4% 7.8% 12.7% % 23.8% 19.6% 11.8% 14.9% % 5.5% 11.5% 11.5% 6.9% 10.3% % 5.3% 11.6% 14.6% 13.9% 9.7% 12.2% % 23.9% 11.6% 14.7% 16.5% 15.6% 11.7% 13.7% % Return greater than 13% -25.1% -0.5% 7.5% 2.4% 6.7% 9.5% 9.8% 7.1% 9.4% % Return between 8% and 13% -19.8% -22.5% -7.0% 0.7% -2.0% 2.3% 5.3% 6.1% 4.1% 6.4% % Return between 3% and 8% 40.9% 10.5% -1.3% 5.0% 8.7% 5.1% 7.8% 9.8% 10.0% 7.8% 9.6% % Return between -3% and +3% 23.9% 32.4% 15.0% 5.0% 8.8% 11.3% 7.8% 9.8% 11.4% 11.3% 9.3% 10.8% % Return between -3% and -8% 13.2% 18.5% 26.0% 14.5% 6.6% 9.5% 11.5% 8.5% 10.2% 11.5% 11.5% 9.6% 11.0% % Return between -8% and -13% 25.8% 19.5% 21.0% 26.0% 16.8% 9.8% 11.8% 13.3% 10.4% 11.8% 12.8% 12.7% 10.8% 12.0% % Return lower than -13% -26.5% -0.3% 4.2% 9.1% 15.5% 9.6% 4.6% 7.1% 8.9% 6.7% 8.3% 9.6% 9.7% 8.2% 9.4% % -27.1% -9.4% -3.8% 1.7% 8.3% 4.3% 0.6% 3.2% 5.2% 3.6% 5.3% 6.7% 7.0% 5.8% 7.1% % -19.9% -22.1% -10.1% -5.5% -0.6% 5.4% 2.2% -0.8% 1.7% 3.7% 2.3% 3.9% 5.3% 5.7% 4.7% 6.0% % 8.0% -3.9% -9.5% -2.5% 0.2% 3.5% 8.2% 5.1% 2.1% 4.1% 5.7% 4.3% 5.7% 6.9% 7.1% 6.0% 7.2% % 10.2% 2.7% -4.9% -9.2% -3.4% -1.0% 2.1% 6.4% 3.8% 1.2% 3.1% 4.7% 3.4% 4.8% 5.9% 6.3% 5.3% 6.4% % 23.6% 25.1% 15.8% 7.1% 1.5% 5.0% 6.0% 8.0% 11.3% 8.5% 5.7% 7.1% 8.3% 6.8% 7.9% 8.8% 9.0% 7.9% 8.9% % 50.8% 31.2% 30.5% 22.0% 13.7% 7.9% 10.2% 10.5% 11.9% 14.5% 11.6% 8.8% 9.9% 10.8% 9.3% 10.2% 10.9% 11.0% 9.8% 10.7% % 34.8% 41.6% 29.2% 29.0% 22.1% 15.0% 9.8% 11.6% 11.8% 12.9% 15.2% 12.5% 9.8% 10.8% 11.6% 10.1% 10.9% 11.6% 11.6% 10.5% 11.2% % 13.2% 24.3% 32.0% 24.0% 24.7% 19.5% 13.6% 9.1% 10.8% 11.0% 12.1% 14.3% 11.9% 9.4% 10.3% 11.1% 9.8% 10.5% 11.2% 11.2% 10.1% 10.9% % 11.2% 15.2% 23.0% 29.4% 23.2% 23.9% 19.4% 14.2% 10.1% 11.5% 11.7% 12.6% 14.6% 12.3% 10.0% 10.8% 11.5% 10.2% 10.9% 11.5% 11.5% 10.5% 11.2% % 8.0% 6.4% 10.6% 17.8% 24.0% 19.4% 20.5% 16.9% 12.4% 8.9% 10.3% 10.5% 11.5% 13.4% 11.4% 9.2% 10.0% 10.8% 9.6% 10.3% 10.9% 10.9% 10.0% 10.6% % 2.5% 8.0% 6.8% 10.1% 16.2% 21.7% 18.0% 19.1% 16.0% 12.0% 8.8% 10.1% 10.4% 11.3% 13.1% 11.2% 9.2% 9.9% 10.6% 9.5% 10.2% 10.7% 10.8% 9.9% 10.5% 1991 Sell Investment period in years Investment period in years Following: 26

27 Historical Market Returns (Geometric Mean) German Market DAX Performance Index Return Triangle Buy Reading example: An investment in the DAX Index mid of the year 2009, when sold mid of the year 2014, would have yielded an average annual return (geometric mean) of 15.4%. Other five-year investment periods are displayed along the black steps. 27.3% % 6.1% % -0.8% 7.8% % 17.3% 6.7% 11.6% % 23.8% 19.5% 10.8% 13.9% % 3.9% 10.1% 10.4% 5.6% 8.9% % 3.7% 10.1% 13.3% 12.9% 8.4% 10.9% % 23.9% 10.1% 13.4% 15.4% 14.7% 10.5% 12.5% % Return greater than 13% -25.1% -3.6% 4.7% 0.0% 4.4% 7.4% 7.9% 5.3% 7.5% % Return between 8% and 13% -19.8% -22.5% -9.3% -2.0% -4.3% -0.1% 3.0% 4.0% 2.1% 4.4% % Return between 3% and 8% 40.9% 6.3% -5.4% 1.2% 5.4% 2.0% 4.9% 7.1% 7.6% 5.5% 7.3% % Return between -3% and +3% 23.9% 32.1% 11.9% 1.2% 5.4% 8.2% 4.9% 7.1% 8.8% 9.1% 7.0% 8.6% % Return between -3% and -8% 13.2% 18.4% 25.5% 12.2% 3.5% 6.7% 8.9% 5.9% 7.8% 9.3% 9.5% 7.5% 8.9% % Return between -8% and -13% 25.8% 19.3% 20.8% 25.6% 14.8% 6.9% 9.2% 10.9% 8.0% 9.5% 10.7% 10.7% 8.8% 10.1% % Return lower than -13% -26.5% -3.8% 1.5% 6.7% 12.8% 6.6% 1.3% 3.9% 6.0% 3.9% 5.6% 7.0% 7.3% 5.8% 7.1% % -27.1% -12.5% -6.7% -1.3% 4.8% 0.8% -2.8% -0.2% 2.0% 0.5% 2.3% 3.8% 4.3% 3.2% 4.5% % -20.3% -22.4% -12.5% -7.8% -3.2% 2.2% -0.9% -3.9% -1.4% 0.6% -0.6% 1.1% 2.6% 3.1% 2.1% 3.5% % 6.1% -6.6% -12.0% -5.5% -2.6% 0.8% 5.1% 2.0% -1.1% 0.9% 2.7% 1.4% 2.8% 4.1% 4.5% 3.5% 4.7% % 8.7% 1.2% -6.9% -11.2% -5.9% -3.4% -0.3% 3.6% 0.9% -1.8% 0.2% 1.8% 0.7% 2.1% 3.3% 3.8% 2.8% 4.0% % 19.5% 22.3% 12.6% 3.1% -2.6% 1.1% 2.5% 4.7% 7.8% 5.0% 2.1% 3.6% 4.9% 3.6% 4.8% 5.8% 6.1% 5.1% 6.1% % 50.8% 27.9% 28.0% 18.7% 9.3% 3.3% 5.9% 6.6% 8.3% 10.9% 7.9% 4.9% 6.2% 7.3% 5.9% 6.9% 7.7% 7.9% 6.9% 7.8% % 34.3% 40.9% 26.7% 27.0% 19.4% 11.2% 5.6% 7.6% 8.2% 9.5% 11.8% 9.0% 6.1% 7.2% 8.2% 6.8% 7.7% 8.5% 8.6% 7.6% 8.4% % 12.8% 23.1% 30.4% 21.6% 22.7% 17.0% 10.2% 5.3% 7.2% 7.7% 9.0% 11.2% 8.6% 5.9% 7.0% 7.9% 6.6% 7.5% 8.2% 8.4% 7.4% 8.2% % 10.9% 14.8% 22.0% 28.0% 21.2% 22.2% 17.2% 11.1% 6.6% 8.2% 8.6% 9.7% 11.7% 9.3% 6.7% 7.7% 8.5% 7.2% 8.0% 8.7% 8.8% 7.9% 8.6% % 7.4% 6.0% 10.0% 16.5% 22.2% 17.4% 18.7% 14.8% 9.6% 5.7% 7.2% 7.7% 8.8% 10.7% 8.5% 6.1% 7.0% 7.9% 6.7% 7.5% 8.2% 8.3% 7.4% 8.1% % 2.3% 7.6% 6.5% 9.6% 15.1% 20.1% 16.2% 17.4% 14.1% 9.5% 5.9% 7.3% 7.7% 8.7% 10.5% 8.4% 6.2% 7.1% 7.9% 6.8% 7.5% 8.1% 8.3% 7.4% 8.1% 1991 Sell Investment period in years Investment period in years Following: 27

28 Historical Market Returns (Arithmetic Mean) Austrian Market ATX Total Return Return Triangle Buy Reading example: An investment in the ATX Total Return mid of the year 2009, when sold mid of the year 2014, would have yielded an average annual return (arithmetic mean) of 8.4%. Other five-year investment periods are displayed along the black steps. 52.7% % 20.9% % -6.3% 13.4% % 7.1% 1.1% 14.0% % 15.8% 10.0% 4.8% 14.4% % -5.3% 1.7% 0.9% -1.5% 7.6% % -0.7% 4.8% 7.5% 5.7% 2.9% 10.0% % 18.5% 3.5% 6.6% 8.4% 6.8% 4.2% 10.3% % Return greater than 13% -44.5% -16.2% -2.5% -8.5% -3.6% -0.4% -0.6% -1.9% 4.2% % Return between 8% and 13% -17.2% -30.9% -16.6% -6.2% -10.2% -5.9% -2.8% -2.7% -3.6% 2.1% % Return between 3% and 8% 32.3% 7.6% -9.8% -4.3% 1.5% -3.1% -0.4% 1.6% 1.2% 0.0% 4.8% % Return between -3% and +3% 24.9% 28.6% 13.4% -1.1% 1.5% 5.4% 0.9% 2.7% 4.2% 3.6% 2.3% 6.5% % Return between -3% and -8% 55.8% 40.4% 37.7% 24.0% 10.3% 10.6% 12.6% 7.7% 8.6% 9.4% 8.4% 6.8% 10.3% % Return between -8% and -13% 55.0% 55.4% 45.2% 42.0% 30.2% 17.7% 16.9% 17.9% 13.0% 13.3% 13.5% 12.2% 10.5% 13.5% % Return lower than -13% 7.3% 31.1% 39.4% 35.8% 35.1% 26.4% 16.2% 15.7% 16.7% 12.4% 12.7% 13.0% 11.9% 10.2% 13.1% % 6.4% 22.6% 30.9% 29.7% 30.1% 23.4% 14.9% 14.6% 15.6% 11.8% 12.1% 12.4% 11.4% 9.9% 12.6% % 7.7% 7.6% 19.4% 26.7% 26.4% 27.3% 21.7% 14.3% 14.1% 15.1% 11.6% 11.9% 12.2% 11.3% 9.9% 12.4% % 1.9% 3.1% 4.1% 14.3% 21.2% 21.8% 23.1% 18.6% 12.3% 12.3% 13.3% 10.3% 10.7% 11.0% 10.2% 9.0% 11.4% % -10.8% -3.9% -1.6% 0.2% 9.3% 16.0% 17.1% 18.8% 15.2% 9.8% 10.0% 11.1% 8.4% 8.9% 9.3% 8.7% 7.6% 10.0% % 0.2% -1.9% 1.0% 1.9% 2.8% 10.3% 16.0% 17.0% 18.5% 15.3% 10.3% 10.4% 11.4% 8.9% 9.4% 9.7% 9.1% 8.1% 10.3% % 19.0% 7.5% 4.1% 5.3% 5.3% 5.6% 11.8% 16.7% 17.5% 18.8% 15.8% 11.2% 11.2% 12.2% 9.8% 10.1% 10.4% 9.8% 8.8% 10.8% % 15.1% 15.4% 7.7% 4.9% 5.7% 5.7% 5.9% 11.4% 15.8% 16.6% 17.9% 15.2% 11.0% 11.0% 11.9% 9.7% 10.0% 10.3% 9.7% 8.7% 10.7% % 3.5% 9.7% 11.3% 5.9% 3.9% 4.8% 4.9% 5.1% 10.1% 14.3% 15.2% 16.5% 14.1% 10.2% 10.3% 11.1% 9.1% 9.4% 9.7% 9.2% 8.3% 10.2% % 12.4% 11.0% 13.8% 14.2% 9.2% 7.1% 7.4% 7.2% 7.2% 11.6% 15.2% 16.0% 17.2% 14.9% 11.2% 11.2% 12.0% 9.9% 10.2% 10.5% 10.0% 9.0% 10.9% % 8.8% 5.5% 6.2% 9.4% 10.5% 6.7% 5.1% 5.7% 5.7% 5.8% 9.9% 13.4% 14.3% 15.5% 13.4% 10.0% 10.1% 10.9% 9.0% 9.4% 9.7% 9.2% 8.3% 10.1% % -11.1% 1.2% 0.6% 2.1% 5.5% 7.0% 4.2% 3.0% 3.7% 3.9% 4.2% 8.1% 11.5% 12.4% 13.6% 11.8% 8.7% 8.9% 9.7% 7.9% 8.3% 8.6% 8.2% 7.4% 9.2% 1991 Sell Investment period in years Investment period in years Following: 28

29 Historical Market Returns (Geometric Mean) Austrian Market ATX Total Return Return Triangle Buy Reading example: An investment in the ATX Total Return mid of the year 2009, when sold mid of the year 2014, would have yielded an average annual return (geometric mean) of 6.7%. Other five-year investment periods are displayed along the black steps. 52.7% % 16.7% % -6.4% 10.2% % 6.7% 0.5% 11.6% % 15.8% 9.7% 4.1% 12.4% % -7.7% -0.4% -0.7% -2.8% 4.8% % -4.1% 2.1% 5.4% 3.9% 1.3% 7.4% % 18.3% 1.0% 4.5% 6.7% 5.2% 2.8% 8.0% % Return greater than 13% -44.5% -21.2% -8.1% -13.1% -7.9% -4.3% -4.0% -4.9% 0.3% % Return between 8% and 13% -17.2% -32.2% -19.9% -10.5% -13.9% -9.5% -6.3% -5.7% -6.3% -1.6% % Return between 3% and 8% 32.3% 4.7% -15.3% -9.2% -3.2% -7.5% -4.5% -2.2% -2.1% -3.0% 1.1% % Return between -3% and +3% 24.9% 28.6% 11.0% -6.7% -3.2% 1.0% -3.4% -1.2% 0.5% 0.3% -0.8% 2.9% % Return between -3% and -8% 55.8% 39.5% 37.1% 20.9% 3.4% 4.8% 7.5% 2.5% 3.9% 5.0% 4.4% 3.0% 6.2% % Return between -8% and -13% 55.0% 55.4% 44.5% 41.4% 27.0% 10.6% 10.8% 12.5% 7.3% 8.1% 8.8% 7.9% 6.3% 9.1% % Return lower than -13% 7.3% 29.0% 37.4% 34.1% 33.8% 23.5% 10.2% 10.4% 11.9% 7.3% 8.1% 8.7% 7.9% 6.4% 9.0% % 6.4% 20.6% 28.6% 27.8% 28.6% 20.7% 9.6% 9.8% 11.2% 7.2% 7.8% 8.4% 7.7% 6.3% 8.8% % 7.7% 7.5% 17.8% 24.6% 24.7% 25.7% 19.3% 9.6% 9.8% 11.1% 7.4% 8.0% 8.5% 7.8% 6.6% 8.8% % 1.6% 2.9% 4.0% 12.6% 18.9% 19.7% 21.2% 16.2% 7.9% 8.3% 9.6% 6.3% 6.9% 7.5% 6.9% 5.8% 7.9% % -10.9% -4.5% -2.1% -0.3% 7.3% 13.2% 14.6% 16.5% 12.5% 5.5% 6.1% 7.4% 4.5% 5.3% 5.9% 5.4% 4.5% 6.6% % -1.0% -2.8% 0.3% 1.3% 2.3% 8.5% 13.5% 14.8% 16.4% 12.9% 6.4% 6.8% 8.0% 5.3% 5.9% 6.5% 6.0% 5.0% 7.0% % 19.0% 6.1% 2.9% 4.3% 4.5% 4.9% 10.1% 14.5% 15.5% 16.9% 13.6% 7.5% 7.8% 8.9% 6.3% 6.8% 7.3% 6.8% 5.8% 7.7% % 14.9% 15.3% 6.6% 4.0% 4.9% 5.0% 5.3% 9.9% 13.8% 14.8% 16.2% 13.2% 7.5% 7.8% 8.8% 6.4% 6.9% 7.3% 6.9% 5.9% 7.7% % 3.4% 9.3% 10.9% 5.0% 3.1% 4.0% 4.2% 4.6% 8.8% 12.4% 13.4% 14.7% 12.1% 6.9% 7.3% 8.2% 5.9% 6.4% 6.9% 6.5% 5.6% 7.3% % 11.6% 10.4% 13.3% 13.8% 8.3% 6.1% 6.6% 6.4% 6.5% 10.2% 13.4% 14.3% 15.5% 13.0% 8.0% 8.3% 9.1% 6.9% 7.3% 7.7% 7.3% 6.4% 8.0% % 7.5% 4.5% 5.4% 8.6% 9.8% 5.7% 4.2% 4.8% 4.9% 5.1% 8.5% 11.6% 12.5% 13.7% 11.5% 7.0% 7.3% 8.1% 6.1% 6.5% 6.9% 6.5% 5.8% 7.3% % -11.2% -0.2% -0.4% 1.2% 4.4% 6.0% 3.0% 2.0% 2.7% 3.0% 3.3% 6.6% 9.5% 10.5% 11.8% 9.8% 5.7% 6.0% 6.9% 5.0% 5.5% 5.9% 5.6% 4.9% 6.4% 1991 Sell Investment period in years Investment period in years Following: 29

30 Historical Market Returns (Arithmetic Mean) Swiss Market SMI Total Return Index Return Triangle Buy Reading example: An investment in the SMI Total Return mid of the year 2009, when sold mid of the year 2014, would have yielded an average annual return (arithmetic mean) of 13.6%. Other five-year investment periods are displayed along the black steps. 14.9% % 4.7% % 0.2% 5.1% % 10.3% 5.0% 7.5% % 22.6% 17.0% 11.4% 12.1% % 16.0% 15.6% 13.2% 9.4% 10.3% % 3.1% 12.2% 12.8% 11.4% 8.6% 9.5% % 10.6% 7.6% 13.3% 13.6% 12.3% 9.8% 10.4% % Return greater than 13% -19.8% -1.5% 0.5% 0.8% 6.7% 8.1% 7.7% 6.1% 7.1% % Return between 8% and 13% -22.3% -21.0% -8.4% -5.2% -3.8% 1.9% 3.7% 4.0% 2.9% 4.1% % Return between 3% and 8% 23.0% 0.3% -6.4% -0.6% 0.4% 0.6% 4.9% 6.1% 6.1% 4.9% 5.8% % Return between -3% and +3% 24.6% 23.8% 8.4% 1.4% 4.5% 4.5% 4.1% 7.4% 8.2% 7.9% 6.7% 7.4% % Return between -3% and -8% 13.6% 19.1% 20.4% 9.7% 3.8% 6.0% 5.8% 5.3% 8.1% 8.7% 8.5% 7.3% 7.9% % Return between -8% and -13% 18.6% 16.1% 19.0% 20.0% 11.5% 6.3% 7.8% 7.4% 6.7% 9.1% 9.6% 9.3% 8.2% 8.6% % Return lower than -13% -17.3% 0.6% 5.0% 9.9% 12.5% 6.7% 2.9% 4.7% 4.6% 4.3% 6.7% 7.4% 7.3% 6.3% 6.9% % -16.9% -5.1% -0.4% 4.6% 7.7% 3.4% 0.5% 2.3% 2.5% 2.4% 4.8% 5.5% 5.6% 4.8% 5.5% % -11.0% -13.1% -5.2% -1.4% 2.9% 5.8% 2.3% -0.2% 1.5% 1.8% 1.8% 4.0% 4.8% 4.8% 4.2% 4.8% % 4.3% -2.6% -6.3% -1.3% 1.2% 4.5% 6.8% 3.6% 1.3% 2.7% 2.8% 2.7% 4.7% 5.4% 5.4% 4.8% 5.3% % 1.4% -0.9% -4.8% -7.3% -3.0% -0.6% 2.6% 4.8% 2.1% 0.1% 1.5% 1.7% 1.7% 3.6% 4.3% 4.4% 3.9% 4.5% % 15.4% 14.9% 9.8% 4.6% 0.9% 3.4% 4.7% 6.9% 8.5% 5.7% 3.6% 4.6% 4.6% 4.4% 6.0% 6.6% 6.5% 5.9% 6.3% % 47.2% 27.8% 24.3% 18.4% 12.6% 8.3% 9.6% 10.0% 11.5% 12.5% 9.6% 7.4% 8.0% 7.8% 7.4% 8.8% 9.1% 8.9% 8.2% 8.5% % 47.4% 45.6% 31.4% 27.9% 22.4% 16.8% 12.5% 13.2% 13.3% 14.3% 15.0% 12.1% 9.9% 10.3% 10.0% 9.5% 10.6% 10.9% 10.6% 9.8% 10.1% 1995 Sell Investment period in years Investment period in years Following: 30

31 Historical Market Returns (Geometric Mean) Swiss Market SMI Total Return Index Return Triangle Buy Reading example: An investment in the SMI Total Return mid of the year 2009, when sold mid of the year 2014, would have yielded an average annual return (geometric mean) of 13.2%. Other five-year investment periods are displayed along the black steps. 14.9% % 4.2% % 0.0% 4.7% % 10.2% 4.7% 7.2% % 22.4% 16.6% 10.6% 11.5% % 15.1% 15.0% 12.6% 8.8% 9.8% % 3.1% 11.5% 12.3% 11.0% 8.0% 9.0% % 10.4% 7.4% 12.8% 13.2% 11.9% 9.2% 9.9% % Return greater than 13% -19.8% -3.2% -0.7% -0.1% 5.3% 6.9% 6.7% 5.1% 6.1% % Return between 8% and 13% -22.3% -21.0% -10.0% -6.6% -5.0% 0.1% 2.1% 2.6% 1.6% 2.9% % Return between 3% and 8% 23.0% -2.2% -8.5% -2.7% -1.3% -0.8% 3.1% 4.5% 4.7% 3.6% 4.6% % Return between -3% and +3% 24.6% 23.8% 6.0% -1.1% 2.2% 2.6% 2.5% 5.6% 6.6% 6.5% 5.4% 6.1% % Return between -3% and -8% 13.6% 19.0% 20.3% 7.9% 1.7% 4.0% 4.1% 3.8% 6.5% 7.3% 7.1% 6.0% 6.7% % Return between -8% and -13% 18.6% 16.1% 18.9% 19.9% 9.9% 4.3% 6.0% 5.8% 5.3% 7.6% 8.2% 8.0% 6.9% 7.5% % Return lower than -13% -17.3% -1.0% 3.7% 8.6% 11.3% 4.8% 0.9% 2.8% 2.9% 2.8% 5.1% 5.8% 5.8% 5.0% 5.6% % -16.9% -6.5% -1.8% 3.0% 6.1% 1.5% -1.5% 0.4% 0.8% 0.9% 3.1% 3.9% 4.1% 3.4% 4.1% % -11.1% -13.3% -6.2% -2.5% 1.5% 4.4% 0.6% -1.9% -0.2% 0.2% 0.3% 2.4% 3.2% 3.4% 2.8% 3.5% % 3.8% -3.4% -7.1% -2.5% 0.0% 3.2% 5.5% 2.0% -0.4% 1.0% 1.3% 1.3% 3.2% 3.9% 4.0% 3.4% 4.1% % 0.6% -1.4% -5.4% -7.9% -4.0% -1.6% 1.3% 3.5% 0.6% -1.5% 0.0% 0.3% 0.4% 2.2% 2.9% 3.1% 2.6% 3.2% % 12.3% 12.9% 8.0% 2.6% -1.1% 1.5% 3.0% 5.2% 6.8% 3.8% 1.6% 2.7% 2.8% 2.7% 4.3% 4.9% 4.9% 4.3% 4.8% % 47.1% 24.4% 21.7% 15.7% 9.6% 5.3% 6.8% 7.6% 9.2% 10.4% 7.2% 4.8% 5.6% 5.5% 5.3% 6.6% 7.1% 7.0% 6.3% 6.7% % 47.3% 45.5% 28.6% 25.5% 19.8% 13.7% 9.3% 10.3% 10.6% 11.8% 12.7% 9.5% 7.1% 7.7% 7.5% 7.2% 8.4% 8.7% 8.5% 7.8% 8.1% 1995 Sell Investment period in years Investment period in years Following: 31

32 5 Sector classification of the DACH region based on sector indices

33 Sector Indices of the DACH Region Methodology & 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 Composite German Stock Index (CDAX), Vienna Stock Exchange Index (WBI), and Swiss Performance Index (SPI). These three indices contain all shares listed on the Official and Semi-Official Market. Capital market of the DACH region 796 listed companies The 796 public companies, which are listed in the mentioned indices as of 30 June 2017, build the base of the sector classification and the subsequent analyses: The German CDAX includes 542 companies listed in the Prime Standard and General Standard and is classified into nine Deutsche Börse super sectors. The Austrian ATX only has five indices, ValueTrust positions the remaining companies of the WBI into the classified indices. The Swiss SPI contains ten sector indices that comprise 191 companies. Eventually, merged all three market indices and the respective sector index classification into nine sector indices, so-called super-sectors. These nine sector indices for this study are defined as follows: FIRE Basic Materials Consumer Goods Telecommunication Industrials sector indices Consumer Service Pharma & Healthcare Information Technology Utilities CDAX 542 companies in 9 Deutsche Börse super sectors WBI 63 companies in 5 ATX sector indices classifies SPI 191 companies in 10 SPI sector indices Complete sector classification of the DACH region in 9 sector indices 33

34 Sector Indices of the DACH Region as of 30 June 2017 Sector distribution and number of companies Sector classification of the DACH Region 2% 13% 22% 8% 5% 8% 12% 2% 27% FIRE ( 178 ) Basic Materials ( 43 ) Consumer Goods ( 96 ) Telecommunication ( 14 ) Industrials ( 212 ) Consumer Service ( 67 ) Pharma & Healthcare ( 64 ) Information Technology ( 107 ) Utilities ( 15 ) The chart shows the percentage distribution of the 796 listed companies in the nine super-sectors (behind the sector names is the numerical amount listed). The nine defined sectors can be classified in three different dimensions. Five different sectors represent a proportion of less than 10%, two represent a share between 10% and 20%, and another two represent a portion of more than 20%. Companies within the Industrials and FIRE sectors represent almost 50% of the entire market. 34

35 6 Betas

36 Betas Background & approach Beta is used in the CAPM and is also known as the beta coefficient or beta factor. Beta is a measure of systematic risk of a security of a specific company (company beta) or a specific sector (sector beta) in comparison to the market. A beta of less than 1 means that the security is theoretically less volatile than the market. A beta of greater than 1 indicates that the security's price is more volatile than the market. Beta factors are estimated on the basis of historical returns of securities in comparison to an approximate market portfolio. Since the company valuation is forward-looking, it has to be examined whether or what potential risk factors prevailing in the past do also apply for the future. By valuing non-listed companies or companies with no meaningful share price performance, it is common to use a beta factor from a group of comparable companies ( peer group beta ), a suitable sector ( sector beta ) or one single listed company in the capital market with a similar business model and a similar risk profile ( pure play beta ). Within this capital market study, we have used sector betas which are computed as arithmetic means of the statistically significant beta factors of all companies of a particular sector. The estimate of beta factors is usually accomplished through a linear regression analysis. We use the CDAX, WBI, and SPI as country specific reference indices. Furthermore, it is important to set a time period, in which the data is collected (benchmark period) and whether daily, weekly or monthly returns (return interval) are analyzed. In practice, it is common to use observation periods of two years with the regression of weekly returns or a five year observation period with the regression of monthly returns. In the CAPM, company specific risk premiums include besides the business risk also the financial risk. The beta factor for levered companies ( levered beta ) is usually higher compared to a company with an identical business model but without debt (due to financial risk). Hence, changes in the capital structure require an adjustment of the betas and therefore of the company specific risk premiums. In order to calculate the unlevered beta adjustment formulas have been developed. We prefer to use the adjustment formula by Harris/Pringle which assumes a value-based financing policy, stock-flow adjustments without time delay, uncertain tax shields, and a so-called debt beta. We calculate the debt beta based on the respective company rating or the average sector rating (if a company rating is not available) through the application of the credit spread derived from the expected cost of debt. The capital market data, in particular historical market prices, is provided by the data supplier S&P Capital IQ. 36

37 Betas Sector specific levered and unlevered betas as of 30 June 2017 Sector FIRE 3) 81 / 95 Basic Materials 30 / 32 Consumer Goods 43 / 46 Telecommunication 8 / 11 Industrials 121 / 137 Consumer Service 23 / 33 Pharma & Healthcare 29 / 37 Information Technology 45 / 60 Utilities 8 / 8 Number of companies 1) 5-y. m. / 2-y. w. Aggregation Beta levered 1) Debt ratio 2) Debt Beta Beta unlevered 2-years 5-years 2-years 5-years 2-years 5-years weekly monthly weekly monthly weekly monthly 5-years monthly 2-years weekly Median n.a. n.a. n.a. n.a. n.a. n.a. Arithmetic mean n.a. n.a. n.a. n.a. n.a. n.a. Market-value weighted mean n.a. n.a. n.a. n.a. n.a. n.a. Median % 31% Arithmetic mean % 36% Market-value weighted mean % 26% Median % 24% Arithmetic mean % 38% Market-value weighted mean % 39% Median % 29% Arithmetic mean % 33% Market-value weighted mean % 40% Median % 19% Arithmetic mean % 31% Market-value weighted mean % 28% Median % 10% Arithmetic mean % 25% Market-value weighted mean % 17% Median % 4% Arithmetic mean % 12% Market-value weighted mean % 15% Median % 10% Arithmetic mean % 15% Market-value weighted mean % 11% Median % 49% Arithmetic mean % 51% Market-value weighted mean % 51% ) 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 debt illustration of the companies of the "FIRE" sector only serves an informational purpose. We will not implement an adjustment to the company's specific debt (unlevered) because a bank's indebtedness is part of its operational activities and economic risk. Therefore, a separation of operative and financial obligations is not possible. In addition, 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 the beta factors. 37

38 7 Sector returns a. Implied returns (ex-ante analysis)

39 Implied Sector Returns Background & approach Besides the future-oriented calculation of implied market returns (cf. slide 17 et seq.), we can calculate implied returns for sectors. That offers an alternative and simplification to the ex-post analysis of the company's costs of capital via the CAPM. Using this approach the calculation of sector betas via regression analyses are not necessary. The implied sector returns shown on the following slides, can be used as an indicator for the sector specific levered costs of equity. Those already consider a sector specific leverage. Because of this, another simplification is to renounce making adjustments with regards to the capital structure risk. We unlever the implied returns with the following adjusting equation for the costs of equity 2) to take the specific leverage into account 3) : with: k E L = k U E = R f = D E = Levered cost of equity k E L = k E U + k E U R f Unlevered cost of equity Risk-free rate Debt 4) -to-equity ratio D E Comparable to the calculation of the implied market returns, the following return calculations are based on the Residual Income Valuation Model by Babbel. 1) The required data (i.e. net income, market capitalization, and book 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%. The implied unlevered sector returns serve as an indicator for an aggregated and unlevered cost of equity for specific sectors. The process of relevering a company's cost of capital to reflect a company specific debt situation (cf. calculation example on the next slide) can be worked out without using the CAPM. 1) cf. Babbel, Challenging Stock Prices: Share prices and implied growth expectations (Corporate Finance, n. 9, 2015, p , especially p. 319). 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 to achieve the enterprise value (Debt beta>; 0) and assume that the costs of capital 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 ) We assume that the cash and cash equivalents are used entirely for operative purposes. Consequently, we do not deduct excess cash from the debt. 4) The debt illustration of the companies of the "FIRE" sector only serves an informational purpose. We will not implement an adjustment to the company's specific debt (unlevered) because a bank's indebtedness is part of its operational activities and economic risk. 39

40 Implied Sector Returns Exemplary calculation to adjust for the company specific capital structure Calculation example: As of the reference date 30 June 2017, we observe the sector specific, unlevered cost of equity of 5.9% (market-value weighted mean) of the exemplary company X, which operates in the German Basic Materials sector. The following assumptions have been made: The debt-to-equity ratio of the exemplary company X: 40% The risk-free rate: 1.24% (cf. slide 12) Based on these numbers, we can calculate the relevered costs of equity of company X with the adjustment formula: k E L = 5.9% + (5.9% %) * 40% = 7.8% Thus, 7.8% is the company s relevered cost of equity. In comparison, the levered cost of equity of the Basic Materials sector is 7.4%. 40

41 Implied Sector Returns FIRE Implied sector returns (levered) - DACH - FIRE H H H H H H H H H H H H Minimum 2.2% 2.0% -1.5% 1.5% 3.8% 1.4% 0.4% 1.8% 1.5% 1.6% 1.5% 2.6% Lower quantile 4.8% 3.8% 3.3% 4.3% 4.8% 4.5% 4.2% 4.1% 4.6% 4.4% 4.3% 4.2% Median 10.1% 9.0% 8.3% 7.5% 7.9% 7.6% 8.0% 6.8% 7.9% 7.1% 7.2% 6.9% Arithmetic mean 10.7% 9.8% 8.8% 7.8% 9.2% 8.6% 9.3% 8.7% 8.9% 8.0% 8.1% 7.8% Market-value weighted mean 13.1% 11.6% 10.2% 9.8% 9.3% 8.6% 9.3% 8.6% 8.6% 8.3% 7.9% 7.7% Upper quantile 16.8% 14.8% 13.6% 11.6% 10.9% 11.4% 13.1% 11.5% 14.4% 12.0% 11.3% 11.6% Maximum 40.6% 45.5% 21.4% 18.6% 89.5% 54.1% 60.7% 55.8% 60.1% 24.3% 32.4% 33.6% Market-value weighted debt % % 756.7% 624.1% 468.9% 466.2% 482.2% 401.2% 376.4% 512.1% 390.8% 340.3% Implied sector returns - DACH - FIRE 16.0% 14.0% 12.0% 10.0% 8.0% 6.0% 4.0% 13.1% 11.6% 10.2% 9.8% 9.3% 8.6% 9.3% 8.6% 8.6% 8.3% 7.9% 7.7% The implied market return in the FIRE sector decreased from 7.9% as of 31 December 2016 to 7.7% as of 30 June Over the course of time, the marketvalue weighted mean of the implied sector return decreased noticeably since 30 June 2011 (13.1%). 2.0% 0.0% Range (10% - 90% quantile) Market-value weighted mean (levered) Note: The debt illustration of the companies of the "FIRE" sector only serves an informational purpose. We will not implement an adjustment to the company's specific debt (unlevered) because a bank's indebtedness is part of its operational activities and economic risk (cf. slide 37 and 39). 41

42 Implied Sector Returns Basic Materials (table) Implied sector returns (levered) - DACH - Basic Materials H H H H H H H H H H H H Minimum -1.2% 3.6% 2.6% 2.2% -0.4% -1.8% 0.2% 1.3% 3.6% 0.4% 2.1% 0.6% Lower quantile 6.7% 4.9% 4.5% 5.7% 2.9% 3.2% 3.8% 1.9% 4.7% 2.6% 4.1% 4.4% Median 10.8% 8.7% 9.2% 8.3% 7.2% 6.2% 7.6% 6.2% 7.7% 7.6% 6.8% 6.8% Arithmetic mean 11.4% 10.0% 9.5% 9.4% 8.1% 9.0% 7.8% 6.2% 8.1% 6.7% 7.3% 6.9% Market-value weighted mean 10.5% 10.0% 8.9% 8.5% 7.8% 7.3% 7.7% 7.0% 7.6% 7.5% 7.4% 7.4% Upper quantile 17.5% 15.2% 13.1% 11.2% 11.9% 10.1% 11.8% 9.1% 11.1% 9.2% 9.4% 10.6% Maximum 33.0% 30.0% 32.7% 45.1% 41.7% 73.2% 20.4% 9.9% 20.4% 11.0% 23.3% 13.9% Market-value weighted debt 42.3% 42.3% 36.1% 35.9% 29.7% 33.0% 38.3% 35.2% 34.9% 43.5% 35.0% 31.9% Implied sector returns (unlevered) - DACH - Basic Materials H H H H H H H H H H H H Minimum 1.0% 3.0% 2.5% 2.3% 1.3% 0.9% 1.9% 1.2% 2.6% 0.5% 1.9% 0.9% Lower quantile 5.2% 4.0% 3.4% 4.8% 2.8% 2.7% 3.6% 1.7% 3.3% 2.1% 2.4% 3.1% Median 8.3% 7.4% 7.2% 6.4% 6.0% 5.4% 5.9% 5.0% 5.7% 5.4% 5.3% 5.2% Arithmetic mean 8.6% 7.8% 7.8% 7.8% 5.8% 7.4% 5.9% 4.9% 5.7% 5.0% 5.4% 5.3% Market-value weighted mean 8.3% 7.8% 7.3% 7.0% 6.7% 6.2% 6.2% 5.6% 6.0% 5.6% 5.8% 5.9% Upper quantile 11.2% 10.3% 10.1% 9.5% 7.6% 7.5% 8.1% 7.1% 7.3% 6.8% 8.1% 7.9% Maximum 29.5% 27.3% 29.6% 39.1% 12.9% 57.6% 12.6% 8.3% 12.1% 8.9% 15.0% 9.0% Market-value weighted debt 42.3% 42.3% 36.1% 35.9% 29.7% 33.0% 38.3% 35.2% 34.9% 43.5% 35.0% 31.9% 42

43 Implied Sector Returns Basic Materials (chart) 16.0% 14.0% Implied sector returns - DACH - Basic Materials The implied sector return (unlevered) of the Basic Materials sector increased slightly from 5.8% as of 31 December 2016 to 5.9% as of 30 June In comparison to other sectors, the Basic Materials sector showed the second highest unlevered implied return as of 30 June Overall, we could identify a fluctuation between 5.6% and 8.3% of the market-valued weighted mean (unlevered) since 31 December % 10.0% 8.0% 6.0% 4.0% 10.5% 10.0% 8.3% 7.8% 8.9% 8.5% 7.8% 7.3% 7.0% 6.7% 7.3% 7.7% 6.2% 6.2% 7.0% 5.6% 7.6% 7.5% 7.4% 7.4% 6.0% 5.6% 5.8% 5.9% 2.0% 0.0% Range (10% - 90% quantile) Market-value weighted mean (unlevered) Market-value weighted mean (levered) Note: The ranges refer to the implied sector returns (unlevered). 43

44 Implied Sector Returns Consumer Goods (table) Implied sector returns (levered) - DACH - Consumer Goods H H H H H H H H H H H H Minimum 4.1% 4.6% 5.5% 1.5% 4.8% 1.3% 2.2% 0.5% 2.1% 2.6% 2.5% 2.0% Lower quantile 6.7% 6.1% 7.1% 6.2% 6.1% 4.8% 4.5% 4.6% 4.4% 4.7% 4.8% 3.4% Median 9.7% 9.1% 9.5% 9.0% 9.1% 8.0% 7.9% 7.6% 7.9% 7.5% 7.6% 6.9% Arithmetic mean 11.5% 10.9% 10.8% 9.6% 9.4% 11.2% 8.0% 7.4% 7.8% 8.5% 8.1% 7.4% Market-value weighted mean 11.0% 11.0% 10.1% 9.6% 8.8% 8.4% 8.8% 8.4% 8.4% 9.1% 8.8% 8.8% Upper quantile 18.9% 17.9% 16.1% 14.2% 12.6% 11.8% 11.3% 10.2% 11.2% 14.7% 12.8% 12.2% Maximum 22.0% 26.7% 28.8% 28.2% 31.1% 113.8% 16.6% 15.1% 13.4% 21.3% 16.8% 18.9% Market-value weighted debt 73.7% 81.1% 69.4% 68.6% 54.7% 58.0% 60.6% 59.8% 61.4% 79.6% 70.0% 67.9% Implied sector returns (unlevered) - DACH - Consumer Goods H H H H H H H H H H H H Minimum 0.8% 0.5% 0.7% 0.7% 1.0% 0.6% 0.6% 0.3% 0.4% 0.2% 0.4% 0.5% Lower quantile 4.2% 4.1% 4.4% 4.8% 4.5% 3.8% 2.3% 2.5% 3.0% 3.0% 2.9% 2.1% Median 6.8% 6.1% 6.3% 6.4% 6.6% 5.8% 5.7% 5.0% 5.2% 5.2% 5.1% 4.7% Arithmetic mean 8.7% 8.2% 8.1% 7.5% 7.6% 7.6% 6.5% 5.8% 6.0% 6.1% 5.9% 5.6% Market-value weighted mean 8.1% 7.3% 7.6% 7.0% 7.0% 6.3% 6.6% 6.6% 6.5% 6.1% 5.2% 5.5% Upper quantile 13.5% 14.6% 14.5% 11.3% 12.0% 9.9% 10.1% 8.8% 9.0% 9.9% 9.6% 9.3% Maximum 21.0% 18.2% 19.5% 16.7% 20.2% 44.8% 12.0% 11.1% 10.6% 13.3% 13.3% 14.6% Market-value weighted debt 73.7% 81.1% 69.4% 68.6% 54.7% 58.0% 60.6% 59.8% 61.4% 79.6% 70.0% 67.9% 44

45 Implied Sector Returns Consumer Goods (chart) 16.0% 14.0% Implied sector returns - DACH - Consumer Goods The implied sector return (unlevered) of the Consumer Goods sector increased from 5.2% to 5.5% from 31 December 2016 to 30 June In comparison to the other sectors, the Consumer Goods sector had the highest levered implied sector return as of 30 June Overall, we could identify a fluctuation between 5.2% and 8.1% of the market-valued weighted mean (unlevered) since 31 December % 10.0% 11.0% 11.0% 10.1% 9.6% 8.8% 8.4% 8.8% 8.4% 8.4% 9.1% 8.8% 8.8% 8.0% 6.0% 4.0% 8.1% 7.3% 7.6% 7.0% 7.0% 6.3% 6.6% 6.6% 6.5% 6.1% 5.2% 5.5% 2.0% 0.0% Range (10% - 90% quantile) Market-value weighted mean (unlevered) Market-value weighted mean (levered) Note: The ranges refer to the implied sector returns (unlevered). 45

46 Implied Sector Returns Telecommunication (table) Implied sector returns (levered) - DACH - Telecommunication H H H H H H H H H H H H Minimum 5.0% 3.4% 2.4% 1.8% 1.7% 5.1% -0.7% -1.8% 4.0% 2.8% 3.9% 3.4% Lower quantile 6.6% 3.9% 4.9% 2.7% 4.0% 5.3% 2.8% 1.8% 4.3% 3.2% 4.4% 3.7% Median 9.1% 7.7% 10.0% 8.6% 6.9% 5.8% 6.4% 6.7% 7.3% 5.9% 6.8% 4.6% Arithmetic mean 9.3% 8.4% 9.2% 8.3% 6.9% 6.5% 5.9% 5.4% 6.8% 5.9% 6.5% 5.5% Market-value weighted mean 9.1% 8.7% 8.5% 8.2% 6.7% 6.4% 6.6% 6.3% 6.7% 7.3% 7.3% 6.7% Upper quantile 12.3% 12.1% 13.2% 13.4% 9.7% 8.5% 8.4% 7.6% 8.4% 8.9% 8.5% 7.8% Maximum 12.8% 15.3% 13.2% 16.4% 10.0% 9.3% 9.2% 7.7% 9.1% 9.3% 8.7% 8.0% Market-value weighted debt 115.7% 119.2% 103.1% 107.8% 84.6% 78.7% 79.0% 68.8% 71.3% 80.5% 75.9% 73.1% Implied sector returns (unlevered) - DACH - Telecommunication H H H H H H H H H H H H Minimum 5.0% 3.3% 2.4% 1.9% 1.9% 3.5% 0.5% -0.1% 2.6% 1.6% 2.1% 2.3% Lower quartile 5.0% 4.0% 3.3% 3.1% 3.3% 3.9% 2.5% 2.1% 3.3% 2.1% 2.4% 2.3% Median 7.4% 6.1% 6.9% 6.6% 6.4% 5.2% 5.0% 4.5% 4.8% 4.0% 5.3% 4.0% Arithmetic mean 7.1% 6.3% 6.6% 7.0% 5.8% 5.3% 4.8% 4.3% 4.9% 4.1% 4.7% 4.1% Market-value weighted mean 5.8% 5.4% 5.3% 5.2% 4.9% 4.7% 4.5% 4.2% 4.4% 4.3% 4.5% 4.3% Upper quantile 9.0% 8.8% 9.9% 11.8% 8.3% 7.0% 7.1% 6.2% 6.9% 5.4% 6.0% 5.5% Maximum 9.9% 10.1% 10.2% 15.0% 8.8% 8.0% 8.0% 6.7% 7.1% 6.1% 6.7% 5.7% Market-value weighted debt 115.7% 119.2% 103.1% 107.8% 84.6% 78.7% 79.0% 68.8% 71.3% 80.5% 75.9% 73.1% 46

47 Implied Sector Returns Telecommunication (chart) 16.0% Implied sector returns - DACH - Telecommunication The implied sector return (unlevered) of the Telecommunication sector decreased from 4.5% as of 31 December 2016 to 4.3% as of 30 June In comparison to the other sectors, the Telecommunication sector had the lowest unlevered implied sector return as of 30 June Overall, the fluctuation of the market-value weighted mean (unlevered) since 31 December 2011 is quite small (4.3% to 5.8%). 14.0% 12.0% 10.0% 9.1% 8.7% 8.5% 8.2% 8.0% 6.7% 6.4% 6.6% 6.3% 6.7% 7.3% 7.3% 6.7% 6.0% 4.0% 5.8% 5.4% 5.3% 5.2% 4.9% 4.7% 4.5% 4.2% 4.4% 4.3% 4.5% 4.3% 2.0% 0.0% Range (10% - 90% quantile) Market-value weighted mean (unlevered) Market-value weighted mean (levered) Note: The ranges refer to the implied sector returns (unlevered). 47

48 Implied Sector Returns Industrials (table) Implied sector returns (levered) - DACH - Industrials H H H H H H H H H H H H Minimum 0.0% 0.3% 0.1% 0.0% 0.3% 0.6% 0.2% -0.4% 1.6% 1.7% 2.1% 0.5% Lower quantile 5.3% 3.6% 4.7% 4.6% 4.5% 3.5% 3.3% 2.9% 4.4% 4.3% 4.3% 3.2% Median 10.1% 9.0% 8.9% 8.0% 7.6% 6.8% 8.0% 6.5% 7.2% 7.4% 7.2% 6.2% Arithmetic mean 10.5% 10.0% 9.2% 8.0% 7.6% 6.5% 7.9% 6.4% 7.7% 7.8% 7.0% 5.7% Market-value weighted mean 10.2% 9.5% 8.4% 7.8% 7.6% 7.2% 7.8% 7.0% 7.3% 7.6% 6.9% 6.8% Upper quantile 14.0% 13.1% 13.2% 10.7% 10.3% 8.7% 10.6% 8.8% 10.3% 9.8% 9.4% 7.6% Maximum 31.3% 61.6% 27.5% 31.9% 16.0% 12.3% 29.1% 12.8% 40.7% 42.5% 18.1% 9.5% Market-value weighted debt 49.2% 54.5% 48.8% 48.2% 37.6% 39.1% 40.3% 41.7% 42.6% 47.4% 39.0% 37.4% Implied sector returns (unlevered) - DACH - Industrials H H H H H H H H H H H H Minimum 0.0% 1.5% 0.7% 0.1% 0.3% 0.6% 0.6% 0.5% 1.5% 1.5% 1.7% 0.4% Lower quantile 5.0% 4.2% 4.6% 4.2% 4.6% 3.0% 3.6% 2.4% 3.1% 2.4% 2.4% 2.3% Median 7.5% 7.1% 7.2% 6.7% 6.9% 6.1% 6.4% 4.3% 5.2% 5.1% 5.2% 5.1% Arithmetic mean 7.5% 9.8% 7.1% 6.2% 6.2% 5.5% 5.9% 4.5% 5.4% 5.1% 5.3% 4.5% Market-value weighted mean 7.4% 6.9% 6.4% 6.1% 6.2% 5.8% 6.1% 5.4% 5.6% 5.5% 5.5% 5.3% Upper quantile 10.2% 10.2% 9.9% 8.2% 8.2% 7.6% 7.7% 6.1% 7.9% 7.3% 7.4% 6.2% Maximum 12.5% 61.5% 11.4% 9.2% 9.4% 8.4% 9.5% 8.0% 8.0% 7.6% 9.7% 6.8% Market-value weighted debt 49.2% 54.5% 48.8% 48.2% 37.6% 39.1% 40.3% 41.7% 42.6% 47.4% 39.0% 37.4% 48

49 Implied Sector Returns Industrials (chart) 16.0% 14.0% Implied sector returns - DACH - Industrials The implied sector return (unlevered) of the Industrials sector decreased from 5.5% as of 31 December 2016 to 5.3% as of 30 June Overall, we could identify a fluctuation between 5.3% and 7.4% of the market-valued weighted mean (unlevered) since 31 December % 10.0% 8.0% 10.2% 9.5% 8.4% 7.8% 7.6% 7.2% 7.8% 7.0% 7.3% 7.6% 6.9% 6.8% 6.0% 4.0% 7.4% 6.9% 6.4% 6.1% 6.2% 5.8% 6.1% 5.4% 5.6% 5.5% 5.5% 5.3% 2.0% 0.0% Range (10% - 90% quantile) Market-value weighted mean (unlevered) Market-value weighted mean (levered) Note: The ranges refer to the implied sector returns (unlevered). 49

50 Implied Sector Returns Consumer Service (table) Implied sector returns (levered) - DACH - Consumer Service H H H H H H H H H H H H Minimum 3.0% 2.2% 2.9% 2.3% 3.2% 3.6% 2.9% 2.7% 1.6% 1.8% 1.3% 2.1% Lower quantile 6.2% 5.0% 6.7% 4.4% 4.3% 5.5% 4.3% 3.8% 3.0% 3.4% 3.8% 3.3% Median 9.7% 9.7% 9.1% 7.9% 7.3% 6.6% 7.5% 5.9% 6.2% 6.4% 6.7% 6.0% Arithmetic mean 10.9% 9.8% 10.1% 8.6% 8.1% 9.0% 9.8% 7.0% 6.7% 6.6% 6.4% 5.9% Market-value weighted mean 10.0% 9.3% 9.2% 7.6% 7.0% 6.7% 7.7% 5.9% 5.7% 6.2% 6.2% 5.6% Upper quantile 15.1% 15.0% 13.5% 13.7% 11.3% 11.3% 20.5% 10.5% 10.7% 8.9% 8.8% 8.2% Maximum 35.9% 21.6% 25.7% 16.3% 23.5% 39.1% 35.1% 19.0% 14.7% 14.1% 9.5% 10.3% Market-value weighted debt 36.6% 35.2% 35.3% 28.4% 23.2% 22.8% 20.3% 17.4% 21.5% 25.0% 26.0% 23.9% Implied sector returns (unlevered) - DACH - Consumer Service H H H H H H H H H H H H Minimum 3.0% 2.9% 2.8% 2.3% 2.9% 3.6% 2.7% 2.1% 1.9% 1.3% 1.2% 2.1% Lower quantile 5.1% 4.8% 5.5% 3.3% 4.8% 4.9% 3.6% 3.0% 3.2% 3.4% 2.5% 2.8% Median 8.2% 7.6% 7.3% 6.3% 6.1% 5.8% 6.4% 5.2% 5.1% 5.0% 5.0% 5.0% Arithmetic mean 9.0% 8.1% 7.6% 6.2% 6.6% 7.4% 7.9% 5.2% 5.6% 5.3% 5.1% 4.9% Market-value weighted mean 7.9% 7.3% 7.3% 6.3% 6.2% 5.8% 6.5% 4.8% 4.9% 5.1% 5.1% 4.7% Upper quantile 11.0% 10.3% 9.9% 7.9% 9.3% 7.9% 14.6% 7.5% 8.2% 8.4% 7.1% 6.5% Maximum 35.7% 20.9% 12.4% 10.5% 10.3% 38.9% 34.6% 8.3% 11.4% 8.9% 8.3% 8.8% Market-value weighted debt 36.6% 35.2% 35.3% 28.4% 23.2% 22.8% 20.3% 17.4% 21.5% 25.0% 26.0% 23.9% 50

51 Implied Sector Returns Consumer Service (chart) 16.0% 14.0% Implied sector returns - DACH - Consumer Service The implied sector return (unlevered) of the Consumer Service sector decreased from 5.1% as of 31 December 2016 to 4.7% as of 30 June In comparison to other sectors, the Consumer Service sector showed the lowest levered implied return as of 30 June Overall, we can identify a fluctuation between 4.8% and 7.9% of the market-valued weighted mean (unlevered) since 31 December % 10.0% 8.0% 6.0% 4.0% 10.0% 9.3% 9.2% 7.6% 7.0% 6.7% 7.9% 7.3% 7.3% 6.3% 6.2% 5.8% 7.7% 6.5% 5.9% 6.2% 6.2% 5.7% 5.6% 4.8% 4.9% 5.1% 5.1% 4.7% 2.0% 0.0% Range (10% - 90% quantile) Market-value weighted mean (unlevered) Market-value weighted mean (levered) Note: The ranges refer to the implied sector returns (unlevered). 51

52 Implied Sector Returns Pharma & Healthcare (table) Implied sector returns (levered) - DACH - Pharma & Healthcare H H H H H H H H H H H H Minimum 0.2% -0.4% 1.5% -0.4% 2.3% 0.8% 2.0% 1.4% 1.1% 1.4% 1.6% 2.0% Lower quantile 4.3% 3.6% 3.6% 2.5% 5.4% 2.4% 3.5% 2.0% 3.7% 3.7% 2.7% 2.9% Median 8.0% 7.4% 7.3% 6.5% 6.7% 6.1% 6.3% 5.8% 6.1% 6.0% 5.6% 5.8% Arithmetic mean 8.0% 9.9% 7.4% 6.5% 7.0% 5.9% 6.9% 5.6% 6.6% 5.9% 7.9% 5.9% Market-value weighted mean 10.1% 9.7% 9.3% 8.0% 7.9% 7.1% 7.4% 6.8% 7.1% 7.1% 7.7% 7.0% Upper quantile 11.6% 14.3% 10.4% 9.0% 9.4% 8.3% 9.3% 7.5% 8.6% 7.9% 8.0% 7.3% Maximum 14.7% 73.9% 12.9% 19.4% 11.0% 11.7% 22.2% 11.5% 24.8% 9.2% 76.3% 27.9% Market-value weighted debt 26.6% 27.0% 24.3% 18.1% 16.7% 15.7% 18.2% 16.8% 18.5% 20.3% 20.6% 20.2% Implied sector returns (unlevered) - DACH - Pharma & Healthcare H H H H H H H H H H H H Minimum 1.1% 0.4% 1.8% 0.8% 2.3% 1.3% 2.0% 1.4% 1.2% 1.3% 1.4% 1.5% Lower quantile 4.1% 3.1% 3.0% 2.4% 4.6% 3.1% 3.8% 1.9% 3.2% 2.8% 2.1% 2.9% Median 6.4% 6.2% 6.3% 5.7% 6.1% 5.6% 5.8% 5.1% 5.2% 4.9% 5.3% 4.8% Arithmetic mean 6.8% 8.0% 6.2% 5.9% 6.3% 5.4% 5.7% 4.7% 5.0% 4.9% 5.3% 4.6% Market-value weighted mean 8.5% 8.1% 7.8% 7.1% 7.1% 6.5% 6.5% 6.0% 6.2% 6.0% 6.6% 6.0% Upper quantile 9.7% 13.6% 8.9% 7.7% 7.8% 7.3% 7.4% 6.3% 6.6% 6.5% 6.7% 5.9% Maximum 14.5% 52.3% 9.5% 18.0% 10.6% 10.8% 9.2% 6.6% 7.7% 7.6% 18.6% 7.1% Market-value weighted debt 26.6% 27.0% 24.3% 18.1% 16.7% 15.7% 18.2% 16.8% 18.5% 20.3% 20.6% 20.2% 52

53 Implied Sector Returns Pharma & Healthcare (chart) 16.0% 14.0% Implied sector returns - DACH - Pharma & Healthcare The implied sector return (unlevered) of the Pharma & Healthcare sector decreased from 6.6% as of 31 December 2016 to 6.0% as of 30 June In comparison to other sectors, the Pharma & Healthcare sector had the highest unlevered implied return as of 30 June Overall, we could identify a fluctuation between 6.0% and 8.5% of the market-valued weighted mean (unlevered) since 31 December % 10.0% 8.0% 10.1% 9.7% 9.3% 8.5% 8.1% 7.8% 8.0% 7.9% 7.1% 7.4% 6.8% 7.1% 7.1% 7.7% 7.0% 6.0% 4.0% 7.1% 7.1% 6.5% 6.5% 6.0% 6.2% 6.0% 6.6% 6.0% 2.0% 0.0% Range (10% - 90% quantile) Market-value weighted mean (unlevered) Market-value weighted mean (Levered) Note: The ranges refer to the implied sector returns (unlevered). 53

54 Implied Sector Returns Information Technology (table) Implied sector returns (levered) - DACH - Information Technology H H H H H H H H H H H H Minimum 1.0% 0.6% 1.3% 1.4% 1.3% 1.8% 0.9% -1.4% 1.0% 1.7% 0.2% 2.1% Lower quantile 6.8% 5.0% 4.3% 5.1% 4.3% 4.2% 5.0% 4.6% 4.4% 3.8% 4.6% 3.6% Median 9.7% 8.7% 8.5% 7.1% 7.3% 6.4% 7.6% 6.2% 6.5% 6.2% 6.6% 5.5% Arithmetic mean 10.5% 8.8% 8.6% 7.5% 7.2% 6.4% 7.6% 6.4% 6.6% 6.3% 7.5% 5.7% Market-value weighted mean 8.8% 8.1% 7.3% 7.3% 7.1% 7.0% 7.4% 6.9% 6.7% 7.0% 6.6% 6.0% Upper quantile 16.7% 13.5% 11.9% 10.3% 9.8% 9.1% 10.8% 9.2% 8.7% 7.9% 10.1% 7.6% Maximum 20.9% 17.9% 19.1% 17.3% 15.3% 11.9% 14.1% 11.7% 18.6% 16.5% 35.0% 15.8% Market-value weighted debt 9.2% 8.0% 7.6% 8.0% 6.5% 7.1% 16.3% 14.1% 10.7% 11.0% 8.5% 6.8% Implied sector returns (unlevered) - DACH - Information Technology H H H H H H H H H H H H Minimum 1.0% 0.9% 1.8% 1.8% 1.7% 2.7% 2.2% 1.3% 1.0% 1.2% 0.2% 2.0% Lower quantile 7.3% 5.9% 6.1% 5.6% 5.6% 5.0% 5.5% 4.7% 4.9% 4.7% 5.2% 3.7% Median 8.8% 7.5% 7.7% 6.5% 6.5% 5.8% 6.6% 5.4% 5.4% 5.3% 6.0% 5.0% Arithmetic mean 9.3% 7.6% 7.5% 6.6% 6.5% 5.8% 6.7% 5.5% 5.7% 5.4% 6.6% 5.0% Market-value weighted mean 8.3% 7.6% 6.9% 6.9% 6.9% 6.7% 6.7% 6.2% 6.2% 6.3% 6.1% 5.7% Upper quantile 10.5% 9.1% 9.3% 7.9% 7.6% 6.8% 8.0% 6.1% 6.3% 6.0% 6.8% 5.9% Maximum 18.8% 16.3% 13.0% 12.6% 10.9% 9.0% 10.6% 9.5% 17.6% 15.4% 33.4% 11.2% Market-value weighted debt 9.2% 8.0% 7.6% 8.0% 6.5% 7.1% 16.3% 14.1% 10.7% 11.0% 8.5% 6.8% 54

55 Implied Sector Returns Informational Technology (chart) 16.0% 14.0% Implied sector returns - DACH - Information Technology The implied sector return (unlevered) of the Information Technology sector decreased from 6.1% as of 31 December 2016 to 5.7% as of 30 June In comparison to other sectors, the Information Technology sector had the smallest spread between levered and unlevered market-value weighted mean. This is due to the lowest market-value weighted debt ratio. Overall, we could identify a fluctuation between 5.7% and 8.3% of the market-valued weighted mean (unlevered) since 31 December % 10.0% 8.0% 6.0% 4.0% 8.8% 8.1% 8.3% 7.6% 7.3% 7.3% 7.1% 7.0% 7.4% 6.9% 6.9% 6.9% 6.7% 6.7% 6.9% 6.7% 7.0% 6.6% 6.2% 6.2% 6.3% 6.1% 6.0% 5.7% 2.0% 0.0% Range (10% - 90% quantile) Market-value weighted mean (unlevered) Market-value weighted mean (levered) Note: The ranges refer to the implied sector returns (unlevered). 55

56 Implied Sector Returns Utilities (table) Implied sector returns (levered) - DACH - Utilities H H H H H H H H H H H H Minimum 7.0% 3.4% 4.6% 5.6% 4.2% 3.7% 3.8% 3.9% 4.9% 5.2% 4.8% 5.5% Lower quantile 7.2% 6.1% 5.5% 6.3% 5.5% 4.0% 4.9% 5.4% 5.2% 6.8% 5.7% 5.5% Median 9.5% 8.3% 8.9% 7.4% 7.3% 7.0% 7.1% 6.5% 7.0% 7.5% 7.4% 7.5% Arithmetic mean 9.9% 8.3% 8.5% 8.6% 7.5% 6.8% 7.0% 7.8% 7.0% 7.6% 7.7% 7.7% Market-value weighted mean 11.4% 9.7% 10.3% 10.5% 7.9% 6.6% 6.9% 8.3% 7.6% 8.3% 8.2% 8.6% Upper quantile 12.6% 10.7% 11.1% 11.6% 9.9% 8.9% 9.4% 11.5% 8.7% 8.9% 10.2% 9.7% Maximum 16.6% 13.0% 13.3% 16.2% 10.1% 9.9% 10.1% 14.6% 9.7% 9.4% 10.5% 11.8% Market-value weighted debt 111.5% 100.7% 116.7% 108.4% 113.1% 87.8% 118.4% 107.9% 158.5% 124.5% 139.9% 101.6% Implied sector returns (unlevered) - DACH - Utilities H H H H H H H H H H H H Minimum 4.5% 2.7% 3.2% 3.9% 3.5% 3.1% 2.8% 3.0% 2.6% 2.9% 2.6% 2.7% Lower quantile 5.0% 4.0% 3.9% 4.1% 4.0% 3.3% 3.0% 3.0% 3.0% 3.1% 2.7% 3.5% Median 6.1% 5.3% 5.1% 5.0% 4.8% 4.5% 4.2% 4.0% 3.8% 3.6% 4.1% 4.2% Arithmetic mean 6.1% 5.3% 5.1% 5.3% 4.9% 4.5% 4.0% 4.2% 3.7% 3.7% 4.1% 4.5% Market-value weighted mean 6.6% 5.9% 5.9% 6.1% 5.1% 4.6% 4.1% 4.4% 3.8% 4.1% 4.1% 4.8% Upper quantile 6.9% 6.4% 6.3% 6.4% 5.8% 5.4% 4.7% 5.4% 4.6% 4.5% 5.5% 5.4% Maximum 8.0% 7.4% 6.9% 8.2% 6.0% 5.6% 4.8% 7.2% 4.7% 4.9% 6.0% 6.9% Market-value weighted debt 111.5% 100.7% 116.7% 108.4% 113.1% 87.8% 118.4% 107.9% 158.5% 124.5% 139.9% 101.6% 56

57 Implied Sector Returns Utilities (chart) 16.0% 14.0% Implied sector returns - DACH - Utilities The implied sector return (unlevered) of the Utilities sector increased from 4.1% as of 31 December 2016 to 4.8% as of 30 June In comparison to other sectors, the Utilities sector had the highest spread between levered and unlevered market-value weighted mean. Overall, we could identify a fluctuation between 3.8% and 6.6% of the market-valued weighted mean (unlevered) since 31 December % 11.4% 9.7% 10.3% 10.5% 10.0% 8.0% 7.9% 6.6% 6.9% 8.3% 7.6% 8.3% 8.2% 8.6% 6.0% 4.0% 2.0% 6.6% 5.9% 5.9% 6.1% 5.1% 4.6% 4.1% 4.4% 3.8% 4.1% 4.1% 4.8% 0.0% Range (10% - 90% quantile) Market-value weighted mean (unlevered) Market-value weighted mean (levered) Note: The ranges refer to the implied sector returns (unlevered). 57

58 7 Sector Returns b. Historical returns (ex-post analysis)

59 Historical Sector Returns Background & approach In addition to the determination of historical market returns (cf. slide 26 et seq.), we are able to calculate the historical sector returns. This option creates an alternative approach, like the implied sector returns, for the expost analysis of the determination of costs of capital based on regression analyses following the CAPM. Our analysis contains so-called total shareholder returns analogous to the return triangles for the German, Austrian and Swiss total return indices This means, we consider the share price development as well as the dividend yield, whereas the share price development generally represents the main component of the total shareholder return. We calculate the annual total shareholder returns as of 30 June for every CDAX, WBI, and SPI listed company. Afterwards, we aggregate those returns market-value weighted to sector returns. Our calculations comprise the time period between 2012 and Since annual total shareholder returns tend to fluctuate to a great extent, their explanatory power is limited. Therefore, we do not only calculate the 1-year marketvalue weighted means, we additionally calculated the 3-year ( ) and the 6-year ( ) averages. 59

60 Historical Sector Returns Annual total shareholder returns as of 30 June % FIRE 50.0% 47.8% Basic Materials 40.0% 30.0% 20.0% 10.0% 14.1% 4.9% 44.0% 40.6% 34.9% 31.2% 28.3% 30.0% 28.0% 27.4% 27.7% 25.9% 24.0% 23.3% 23.8% 21.7% 22.9% 13.3% 11.1% 32.6% 26.2% 24.0% 23.4% 19.8% 17.4% 14.6% 12.0% 3.9% 11.8% 42.8% 32.0% 28.7% 19.7% 17.5% 11.7% 9.6% 9.7% Consumer Goods Telecommunication Industrials Consumer Service Pharma & Healthcare 0.0% -10.0% -20.0% 0.0% -0.7% -4.0% -1.6% -4.7% -5.5% -4.8% -6.3% -10.2% -11.2% -11.9% -13.5% -13.3% -12.5% -13.6% -15.6% Information Technology Utilities FIRE Basic Materials Consumer Goods Telecommunication Industrials Consumer Service Pharma & Healthcare Information Technology 3-year-average ( ) 16.5% 17.1% 9.1% 8.4% 16.0% 14.1% 13.5% 26.5% -5.4% 6-year-average ( ) 13.1% 19.0% 13.5% 12.6% 14.4% 15.6% 18.9% 21.5% -3.1% Utilities 60

61 8 Trading multiples

62 Trading Multiples Background & approach Besides absolute valuation models (earnings value, DCF), the multiples approach offers a practical way for an enterprise value estimation. The multiples method estimates a company s value relative to another company s value. Following this approach, the enterprise value results from the product of a reference value (revenue or earnings values are frequently used) of the company with the respective multiples of similar companies. Within this capital market study, we analyze multiples for the super - sectors" as well as multiples for the DACH market consisting of the German, Austrian, and Swiss capital market (CDAX, ATX, and SPI). We will look at the following multiples: Revenue-Multiples ( EV 1) /Revenue ), EBIT-Multiples ( EV 1) /EBIT ), Price-to-Earnings-Multiples ( P/E ) Price-to-Book Value-Multiples ( EqV/BV ) We present historical multiples since 31 December 2011 in the appendix and we will update the applied multiples semi-annually at the predefined reference date (as of 30 June and as of 31 December). We provide a graphical, as well as a tabular illustration of the multiples as of 30 June 2017 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 the number of the companies. For the purposes of simplification, we exclude negative multiples and multiples in the highest quantile (95%). The multiples in the lowest quantile (5%) build the lower limit. For calculating the multiples, we source the data (i.e. Market Cap., Revenue, EBIT, etc.) from the data provider S&P Capital IQ. Multiples are presented for two different reference values. Firstly, the reference values are based on a company's realized trailing last 12 months, which represent its financial performance for the past 12-month period (so called trailing-multiples, in the following LTM ). Secondly, the reference values are based on one-year forecasts of analysts (so called forward-multiples, in the following 1yf ). Both approaches are typically not limited to the end of the fiscal year. The Price-to-Book-Value-Multiples are calculated with the book values as of reference date (30 June 2017). 1) Enterprise Value 2) Equity Value 62

63 Multiples Executive summary (1/2) DACH At the reference date 30 June 2017, every illustrated multiple EV/Revenue, EV/EBIT, P/E and EqV/BV (LTM as well as 1yf) reached its highest level compared to the past six years. The median of the Revenue- and EBIT-Multiples increased in a constant manner from of 0.9x in 2011 to 1.5x in 2017 (LTM) and from 12.1x in 2011 to 20.8x in 2017 (LTM). FIRE For financial institutions no Revenue- and EBIT-Multiples were calculated since revenues and EBIT are not meaningful for those companies as for their operations mainly interest payments/expenses are critical. For that reason, only P/E- and EqV/BV-Multiples were calculated for the FIRE sector. The median of the P/E-Multiples (1yf) was 16.2x as of the reference date 30 June Basic Materials Compared to the past six years, the Revenue-Multiples were at their highest level with an median of 1.7x (1yf) as of 30 June The median of the EBIT-Multiples based on the expected EBIT (1yf) of the Basic Materials sector amounted to 16.0x as of 30 June Consumer Goods The median of the EBIT-Multiples increased from 15.9x (LTM) as of 30 June 2016 to 17.8x (LTM) as of 30 June The sector had the lowest Revenue-Multiples with an arithmetical mean of 1.4x (LTM) and a median of 1.1x (LTM) compared to the other sectors. Telecommunication The sector Telecommunication reached its highest Revenue- and EBIT-Multiples compared to its past six years with a median of 2.0x and 20.0x (LTM), respectively, as of 30 June Its P/E-Multiples showed a relatively high fluctuation over the past six years with the median varying from 9.4x to 29.8x (LTM) and from 10.9x to 24.0x on a 1yf-basis. 63

64 Multiples Executive summary (2/2) Industrials All displayed multiples reached their highest value for the past six years. The median of the Revenue-Multiples increased from 1.1x (LTM) and (1yf) as of 30 June 2016 to 1.3x (LTM) and 1.5x (1yf) as of 30 June EV/EBIT multiples averaged 17.4x (1yf,median) as of 30 June Consumer Service The median of the Revenue-Multiples increased from 1.1x (LTM) and (1yf) as of 30 June 2016 to 1.3x (LTM) and 2.2x (1yf) as of 30 June The median of the EBIT-Multiples increased from 19.5x (LTM) and 15.9x (1yf) as of 30 June of 2016 to 23.4x (LTM) and 18.3x (1yf) as of 30 June of Pharma & Healthcare The sector had the highest median EBIT- and P/E-Multiples compared to all other sectors as of 30 June 2017: It had an EV/EBIT median of 25.2x (LTM) and 21.8x (1yf) and a P/E median of 29.6x (LTM) and 23.8x (1yf). Its Revenue-Multiples represented the highest values with an arithmetic mean of 5.3x (LTM) or 3.5x (1yf) and a median of 3.6x (LTM) and 3.2x (1yf) as of 30 June Information Technology The Information Technology sector represented the highest EqV/BV-Multiples with an arithmetic mean of 3.8x and a median of 3.0x as of 30 June The sector had the highest EBIT- and P/E-Multiples compared to all the other sectors. It had an EBIT-Multiples arithmetic mean of 27.7x (LTM) or 22.2x (1yf) and P/E-Multiples arithmetic mean of 32.4x (LTM) or 26.6x (1yf). Utilities The Utilities sector multiples showed high volatility in the past six years, compared to other sectors. However, the Revenue-Multiples reached their highest median of 1.9x (LTM) as of the reference date compared to 1.8x (LTM) as of 31 December The arithmetic mean of the EBIT-Multiples increased from 22.3x (LTM) and 15.1x (1yf) as of 30 June of 2016 to 27.7x (LTM) and 16.0x (1yf) as of 30 June of

65 Trading Multiples Sector multiples (1/3) LTM and 1yf as of 30 June 2017 DACH FIRE Basic Materials Consumer Goods EV/Revenue EV/EBIT P/E EqV/BV LTM 1yf LTM 1yf LTM 1yf Min 0.4x 0.5x 8.9x 11.5x 4.1x 10.9x 0.7x Arithmetic mean 2.5x 2.2x 24.5x 19.7x 24.3x 22.0x 2.7x Median 1.5x 1.6x 20.8x 17.8x 20.8x 20.2x 2.0x Max 21.9x 9.3x 71.3x 44.2x 81.8x 52.0x 10.9x Standard deviation 3.0x 1.7x 12.9x 6.6x 13.7x 8.4x 2.1x Number of companies Min x 10.9x 0.7x Arithmetic mean x 18.7x 1.8x Median x 16.2x 1.2x Max x 43.8x 10.7x Standard deviation x 7.2x 1.6x Number of companies Min 0.4x 0.6x 9.7x 11.9x 5.9x 11.6x 0.7x Arithmetic mean 2.3x 2.2x 21.9x 17.5x 23.1x 19.4x 2.2x Median 1.5x 1.7x 19.9x 16.0x 19.2x 17.1x 1.8x Max 11.7x 7.9x 59.8x 29.4x 57.2x 36.0x 5.3x Standard deviation 2.3x 1.6x 10.9x 4.7x 11.9x 6.4x 1.2x Number of companies Min 0.4x 0.6x 9.0x 11.8x 7.0x 10.9x 0.7x Arithmetic mean 1.4x 1.4x 21.9x 17.8x 22.3x 21.3x 2.2x Median 1.1x 1.1x 17.8x 15.9x 18.8x 17.7x 1.9x Max 9.1x 3.8x 71.0x 34.5x 67.9x 52.0x 9.5x Standard deviation 1.2x 0.8x 12.0x 5.5x 11.9x 10.0x 1.4x Number of companies Reading example: The average (arithmetic mean) Utilities P/E ratio calculated on the basis of the reported revenues of the last 12 months is 2.3x as of the reference date 30 June EUR 300m in revenues over the last 12 months would result in an enterprise value of EUR 690m. For companies in the FIRE sector Revenues- and EBIT-Multiples are not meaningful and thus are not reported. For historical developments of the multiples please refer to the appendix (cf. 76 et seq.) 65

66 Trading Multiples Sector multiples (2/3) LTM and 1yf as of 30 June 2017 Telecommunication Industrials Consumer Service EV/Revenue EV/EBIT P/E EqV/BV LTM 1yf LTM 1yf LTM 1yf Min 0.4x 1.6x 14.9x 14.1x 10.7x 14.3x 0.7x Arithmetic mean 2.3x 2.6x 25.5x 21.9x 27.2x 26.5x 1.9x Median 2.0x 2.1x 20.0x 17.3x 26.0x 18.5x 1.8x Max 5.3x 5.2x 52.6x 34.1x 48.3x 47.5x 3.4x Standard deviation 1.4x 1.2x 13.0x 8.0x 14.0x 14.2x 0.8x Number of companies Min 0.4x 0.5x 8.9x 11.6x 4.1x 13.1x 0.7x Arithmetic mean 2.0x 1.9x 24.0x 19.0x 25.7x 21.9x 2.9x Median 1.3x 1.5x 20.7x 17.4x 22.1x 20.8x 2.3x Max 15.7x 8.9x 71.3x 43.9x 75.6x 42.5x 10.4x Standard deviation 2.1x 1.5x 12.2x 5.7x 12.8x 6.2x 2.0x Number of companies Min 0.4x 0.6x 9.8x 12.3x 5.0x 13.7x 0.8x Arithmetic mean 3.0x 2.8x 24.7x 21.0x 23.8x 23.5x 3.4x Median 1.3x 2.2x 23.4x 18.3x 21.0x 19.6x 2.3x Max 21.7x 8.6x 66.1x 39.8x 81.8x 49.6x 10.9x Standard deviation 3.9x 2.3x 13.5x 7.4x 14.1x 9.1x 2.8x Number of companies Reading example: The median Consumer Service EV/EBIT ratio calculated on the basis of the expected EBIT (1 year forward) is 18.3x as of the reference date 30 June An expected EBIT of EUR 30m would result in an enterprise value of EUR 549m. For companies in the FIRE sector Revenues- and EBIT-Multiples are not meaningful and thus are not reported. For historical developments of the multiples please refer to the appendix (cf. 76 et seq.) 66

67 Trading Multiples Sector multiples (3/3) LTM and 1yf as of 30 June 2017 Pharma & Healthcare Information Technology Utilities EV/Revenue EV/EBIT P/E EqV/BV LTM 1yf LTM 1yf LTM 1yf Min 0.7x 0.8x 9.8x 11.5x 15.1x 14.3x 0.7x Arithmetic mean 5.3x 3.5x 26.8x 22.7x 32.0x 26.1x 3.5x Median 3.6x 3.2x 25.2x 21.8x 29.6x 23.8x 3.2x Max 21.9x 9.3x 68.3x 43.1x 57.3x 46.1x 7.9x Standard deviation 5.2x 1.9x 13.0x 8.1x 10.9x 8.8x 2.0x Number of companies Min 0.4x 0.6x 9.9x 11.6x 4.3x 12.8x 0.7x Arithmetic mean 2.8x 2.6x 27.7x 22.2x 32.4x 26.6x 3.8x Median 2.1x 2.3x 23.6x 21.6x 27.6x 23.1x 3.0x Max 10.2x 7.5x 66.8x 44.2x 68.2x 50.9x 10.6x Standard deviation 2.3x 1.7x 13.8x 7.4x 16.1x 9.7x 2.5x Number of companies Min 0.8x 0.8x 12.7x 12.0x 9.6x 13.4x 0.8x Arithmetic mean 3.9x 1.6x 27.7x 16.0x 23.1x 17.1x 1.9x Median 1.9x 1.0x 22.1x 15.1x 15.8x 16.7x 1.6x Max 14.0x 3.4x 62.9x 23.2x 78.9x 20.8x 5.6x Standard deviation 4.2x 0.9x 16.6x 3.5x 19.0x 3.0x 1.3x Number of companies Reading example: The average (arithmetic mean) Utilities P/E ratio calculated on the basis of expected earnings (1 year forward) is 17.1x as of the reference date 30 June An expected revenue of EUR 50m would result in an equity value of EUR 855m. For companies in the FIRE sector Revenues- and EBIT-Multiples are not meaningful and thus are not reported. For historical developments of the multiples please refer to the appendix (cf. 76 et seq.) 67

68 Appendix Composition of the sectors of CDAX, WBI and SPI as of 30 June 2017

69 Appendix Composition of each sector as of 30 June 2017 FIRE A.A.A. AG FRITZ NOLS AG PUBLITY AG ERSTE GROUP BANK AG INTERSHOP HOLDING AG AAREAL BANK AG GREENWICH BETEILIGUNG QUIRIN BANK AG IMMOFINANZ AG INVESTIS HOLDING SA ACCENTRO REAL ESTATE AG GRENKE AG RCM BETEILIGUNGS AG OBERBANK AG JULIUS BAER EUROPE AG ADCAPITAL AG GSW IMMOBILIEN AG RINGMETALL AG RAIFFEISEN BANK INTERNAT. AG LEONTEQ AG ADLER REAL ESTATE AG GWB IMMOBILIEN SCHERZER & CO. AG S IMMO AG LUZERNER KANTONALANK AG ALBIS LEASING AG GXP GERMAN PROERTIES AG SHAREHOLDER VALUE BET. AG UBM DEVELOPMENT AG MOBIMO AG ALLIANZ SE HAEMATO AG SINNER AG UNIQA INSURANCE GROUP AG ORASCOM DEVELOPMENT HLD AG ALSTRIA OFFICE REIT-AG HAMBORNER REIT AG SINO-GERMAN UNITED AG UNTERNEHMENS INVEST AG PARGESA HOLDING AG AUTOBANK AG HANNOVER RUECK SE SIXT LEASING VIENNA INSURANCE GROUP AG PARTNERS GROUP HOLDING AG BASIC RESOURCES AG HEIDELBERGER BET.HOLDING AG SM WIRTSCHAFTSBER WARIMPEX FINANZ- UND BETEIL. AG PAX AG BERLINER EFFEKTENGESELLSCHAFT AG HELIAD EQUITY PAR. GMBH & CO. KGAA SPARTA AG WIENER PRIVATBANK SE Peach Property GROUP AG COMDIRECT BANK AG HESSE NEWMAN CAPITAL AG SPOBAG ALLREAL HOLDING AG PLAZZA AG COMMERZBANK AG HYPOPORT AG TAG IMMOBILIEN AG Arundel AG PSP AG CR CAPITAL REAL ESTATE AG INCITY IMMOBILIEN AG TALANX AG BALOISE HOLDING AG SSCHWEIZER NATIONALBANK DEMIRE DT.MTS.RE AG JDC GROUP AG TLG IMMOBILIEN AG BANQUE PROFIL DE GESTION SA ST. GALLER KANTONALBANK DT. AG DEUTSCHE BALATON AG KAP-BETEILIGUNGS-AG TRADEGATE AG BASELLANDSCHAFT. KANTONALBANK AG SWISS FIN&PROP INVESTMENT AG DEUTSCHE BANK AG KST BETEILIGUNGS AG UNIPROF REAL ESTATE HLD AG BASLER KATONALBANK SA SWISS LIFE HOLDING AG DEUTSCHE BOERSE AG LANG & SCHWARZ AG VALUE MGMT & RESEARCH AG BC DE GENEVE SA SWISS PRIME SITE AG DEUTSCHE EUROSHOP AG LEG IMMOBILIEN AG VARENGOLD BANK AG BC DU JURA SA SWISS RE AG DEUTSCHE WOHNEN AG L-KONZEPT HOLDING AG VDN AG BC VAUDOISE SA SWISSQUOTE GROUP HLD. SA DF DEUTSCHE FORFAIT AG LLOYD FONDS AG VERIANOS REAL ESTATE AG BERNER KATONALBANK AG THURGAUER KANTONALANK AG DIC ASSET AG MAIER & PARTNER AG VONOVIA SE BELLEVUE GROUP AG UBS GROUP AG DEUTSCHE BETEILIGUNGS AG MARS ONE VENTURES AG VPE WERTPAPIERHANDELSBANK AG BFW LIEGENSCHAFTEN AG VALARTIS GROUP AG DEUTSCHE CANNABIS AG MAX21 AG WALLSTREET:ONLINE AG BANK COOP AG VALIANT BANK AG DT.EFF.U.WECH.-BET. AG MIC AG WCM BET. & GRUNDBESITZ AG BK LINTH LLB AG VARIA US PROPERTIES AG DT.GRUNDST.AUKT. AG MLP AG WEBAC HOLDING AG CEMBRA MONEY BANK AG VAUDOISE ASSURANCES HLD. SA DT.PFANDBRIEFBK AG MPC CAPITAL AG WUESTENROT & WUERTTEMBERG. AG CI COM SA VONTOBEL EUROPE AG DEUTSCHE REAL ESTATE AG MUENCHENER RÜCK AG YMOS AG CIE FIN. RICHEMONT AG VZ HOLDING AG DEUTSCHE TECHNISCHE BET. AG MUTARES AG ATRIUM EUROPE REAL ESTATE AG CREDIT SUISSE GROUP AG WALLISER KANTONALBANK AG DVB BANK SE NUERNBERGER BET. AG BANK FUER TIROL UND VBG AG EFG INTERNATIONAL AG WARTECK INVEST AG ERNST RUSS AG OVB HOLDING AG BKS BANK AG GLOBAL ASSET MGMT AG ZUEBLIN AG EYEMAXX REAL ESTATE AG PATRIZIA IMMOBILIEN AG BURGENLAND HOLDING AG GLARNER KANTONALBANK AG ZUG ESTATES HOLDING AG FAIR VALUE REIT-AG PEARL GOLD AG BUWOG AG GRAUB KANTONALBANK AG ZUGER KANTONALBANK AG FINLAB AG PONGS & ZAHN AG CA IMMOBILIEN ANLAGEN AG HELVETIA HOLDING AG ZURICH INSURANCE AG FINTECH GROUP AG PRIMAG AG CONWERT IMMOBILIEN INVEST SE HIAG IMMOBILIEN HOLDING AG FORIS AG PROCREDIT HLDG AG C-QUADRAT INVESTMENT AG HYPOTHEKARBANK LENZBURG AG Note: Sorted by DACH 69

70 Appendix Composition of each sector as of 30 June 2017 Basic Materials ASIAN BAMBOO AG CPH CHEMIE & PAPIER HOLDING AG AURUBIS AG EMS-CHEMIE AG B.R.A.I.N. AG GIVAUDAN SA BASF SE GURIT HOLDING AG BAYER AG SCHMOLZ & BICKENBACH AG COVESTRO AG SYNGENTA AG DECHENG TECHNOLOGY AG ZWAHLEN & MAYR SA DEUTSCHE ROHSTOFF AG EISEN- & HUETTENWERKE AG EMQTEC AG EVONIK INDUSTRIES AG FUCHS PETROLUB SE H & R GMBH & CO. KGAA K & S AG KHD HUMBOLDT WEDAG AG LANXESS AG LINDE AG PETRO WELT TECHNOLOGIE AG SALZGITTER AG SGL CARBON SE SIMONA AG SKW STAHL-METALLURGIE HOLDING AG SURTECO SE SYMRISE AG WACKER CHEMIE AG YOUBISHENG GREEN PAPER AG AMAG AUSTRIA METALL AG LENZING AG OMV AG PORR AG SCHOELLER-BLECKMANN OILFIELD EQUIPMENT AG STRABAG SE VOESTALPINE AG WIENERBERGER AG CHAM PAPER GROUP HOLDING AG CLARIANT AG Consumer Goods A.S.CREATION TAPETEN AG LEIFHEIT AG OTTAKRINGER GETRAENKE AG ADIDAS AG LEONI AG PANKL RACING SYSTEMS AG ADLER MODEMAERKTE AG MINERALBR.UEBERK. GMBH&CO.KGAA POLYTEC HOLDING AG ADM HAMBURG AG MING LE SPORTS AG SCHLUMBERGER AG AGRARIUS AG MONINGER HOLDING AG STADLAUER MALZFABRIK AG AHLERS AG MUEHL PRODUKT & SERVICE AG WOLFORD AG ALNO AG PARK & BELLHEIMER AG ACCU HOLDING AG AUDEN AG PELIKAN AG AIRESIS SA AUDI AG PFERDEWETTEN.DE AG ARYZTA AG BAYRISCHE MOTOREN WERKE AG PORSCHE AUTOMOBIL HLD. SE AUTONEUM AG BBS KRAFTFAHRZEUGTECHNIK AG PROGRESS-WERK OBERKIRCH AG BARRY CALLEBAUT AG BEIERSDORF AG PUMA SE BELL AG BERENTZEN-GROUP AG REGENBOGEN AG CALIDA HOLDING AG BERTRANDT AG ROY CERAMICS SE EMMI AG BHS TABLETOP AG SCHAEFFLER AG GM SA BORUSSIA DORTMUND GMBH&CO.KGAA SCHLOSS WACHENHEIM AG HOCHDORF HOLDING AG CEWE STIFTUNG & CO.KGAA SCHWAELBCHEN MOLKEREI J.B. AG HUEGLI HOLDING AG CONTINENTAL AG SHW AG LECLANCHE SA DAIMLER AG STEILMANN SE LINDT & SPRUUENGLI AG DIERIG HOLDING AG STO SE & CO METALL ZUG AG EDAG ENGINEERING SUEDZUCKER AG NESTLE SA EINHELL GERMANY AG TC UNTERHALTUNGSELEKTRONIK AG ORIOR AG ELRINGKLINGER AG TOM TAILOR HOLDING AG RICHEMONT SA FEIKE AG TONKENS AGRAR AG SWATCH GROUP SA FENGHUA SOLETECH AG ULTRASONIC AG FROSTA AG VERALLIA DTLD AG GERRY WEBER INTERNATIONAL AG VILLEROY & BOCH AG GRAMMER AG VOLKSWAGEN AG HELLA KGAA HUECK & CO. WASGAU PRODUKTIONS & HANDELS AG HELMA EIGENHEIMBAU AG WESTAG & GETALIT AG HENKEL AG & CO. KGAA AGRANA BETEILIGUNGS-AG HUGO BOSS AG DO & CO AG HWA AG GURKTALER AG IFA HOTEL & TOURISTIK AG JOSEF MANNER & COMP. AG JJ AUTO AG KTM INDUSTRIES AG KAMPA AG LINZ TEXTIL HOLDING AG Note: Sorted by DACH. 70

71 Appendix Composition of each sector as of 30 June 2017 Industrials 2G ENERGY AG FROEHLICH BAU AG NANOFOCUS AG VA-Q-TEC AG DORMAKABA HOLDING AG 7C SOLARPARKEN AG GEA GROUP AG NANOGATE AG VECTRON SYSTEMS AG ELMA ELECTRONIC AG A.I.S. AG GESCO AG NESCHEN AG VERBIO VEREINIGTE BIOENERGIE AG FEINTOOL INTERNATIONAL HOLDING AG ADINOTEC AG GFK SE NORDEX SE VISCOM AG FISCHER AG AIR BERLIN PLC & CO. LUFTV. KG HAMBURGER HAFEN & LOGISTIK AG NORDWEST HANDEL AG VOSSLOH AG FLUGHAFEN ZUERICH AG ALBA SE HANSEYACHTS AG NORMA GROUP SE VTG AG FORBO HOLDING AG ALPHAFORM AG HAPAG-LLOYD AG ORBIS AG WACKER NEUSON SE GAVAZZI HOLDING AG AMADEUS FIRE AG HEIDELBERG. DRUCKMASCHINEN AG OSRAM LICHT AG WALTER BAU-AG GEBERIT AG ASKNET AG HEIDELBERGCEMENT AG PFEIFFER VACUUM TECHNOLOGY AG WANDERER-WERKE AG IMPLENIA AG AVES ONE AG HELIOCENTRIS ENERGIE SOL. AG PHILIPP HOLZMANN AG WASHTEC AG INFICON HOLDING AG BABCOCK-BSH AG HMS BERGBAU AG PHOENIX SOLAR AG ZHONGDE WASTE TECHNOLOGY AG INTERROLL HOLDING AG BASLER AG HOCHTIEF AG PITTLER MA.FABR. AG ANDRITZ AG KARDEX AG BAUER AG HOMAG GROUP AG PLAN OPTIK AG BWT AG KOMAX HOLDING AG BAUMOT GROUP AG IFA SYSTEMS AG PNE WIND AG CLEEN ENERGY AG KUEHNE + NAGEL INTERNATIONAL AG BAVARIA INDUSTRUALS GROUP AG INDUS HOLDING AG PVA TEPLA AG FACC AG LAFARGEHOLCIM AG BAYWA AG INFAS HLDG AG R. STAHL AG FLUGHAFEN WIEN AG LEM HOLDING SA BILFINGER SE ITN NANOVATION AG RATIONAL AG FRAUENTHAL HOLDING AG MCH GROUP AG BLUE CAP AG JENOPTIK AG RHEINMETALL AG HTI HIGH TECH INDUSTRIES AG MEYER BURGER AG BOEWE SYSTEC AG JUNGHEINRICH AG ROPAL EUROPE AG MAYR-MELNHOF KARTON AG MIKRON SA BRENNTAG AG KHD HUMBOLDT WEDAG INT. AG S & O AGRAR AG OESTERREICHISCHE POST AG OC OERLIKON CORPORATION AG CENTROTEC SUSTAINABLE AG KION GROUP SCHALTBAU HOLDING OESTERR. STAATSDRUCKEREI HLDG AG PANALPINA WELTTRANSPORT AG CENTROTHERM PHOTOVOLTAICS AG KLOECKNER & CO SE SCHUMAG AG PALFINGER AG PERFECT SA CHINA SPECIALITY GLASS AG KOENIG & BAUER AG SCY BETEILIGUNG AG RHI AG PERROT DUVAL HOLDING SA CLERE AG KROMI LOGISTIK SFC ENERGY AG ROSENBAUER INTERNATIONAL AG PHOENIX AG COREO AG KRONES AG SIEMENS AG SEMPERITHOLDING AG RIETER MASCHINENFABRIK AG CROPENERGIES AG KSB AG SINGULUS SW UMWELTTECHNIK AG SCHAFFNER AG DALDRUP & SOEHNE AG KUKA AG SIXT SE ZUMTOBEL GROUP AG SCHINDLER AUFZÜGE AG DATRON AG KWS SAAT SE SLM SOLUTIONS GROUP AG ABB SCHWEIZ AG SCHLATTER HOLDING AG DELIGNIT AG LPKF LASER & ELECTRONICS AG SMA SOLAR TECHNOLOGY AG ADECCO GROUP AG SCHWEITER TECHNOLOGIES AG DEUTSCHE POST AG LUFTHANSA AG SMT SCHARF AG ADVAL TECH HOLDING AG SFS GROUP AG DEUTZ AG MAN SE SOFTING AG ARBONIA AG SGS SA DIEBOLD NIXDORF MANZ AG SOLAR-FABRIK AG BELIMO AUTOMATION AG SIKA AG DISKUS WERKE AG MASCHIENENFABRIK BERT.HER. AG SOLARWORLD AG BEAT BUCHER AG STARRAG GROUP HOLDING AG DMG MORI AG MASTERFLEX AG STEICO SE BOBST GROUP SA SULZER AG DR. HOENLE AG MAX AUTOMATION AG STINAG STUTTGART INVEST AG BOSSARD HOLDING AG TORNOS HOLDING AG DUERKOPP ADLER AG MBB SE STRABAG AG BURCKHARDT AG VAT GROUP AG DUERR AG MEDION AG TECHNOTRANS AG BURKHALTER HOLDING AG VETROPACK HOLDING AG ELEXXION AG MS INDUSTRIE AG THYSSENKRUPP AG BVZ HOLDING AG VON ROLL HOLDING AG ENERGIEKONTOR AG MTU AERO ENGINES AG TRIPLAN AG CICOR MANAGEMENT AG WALTER MEIER AG ENVITEC BIOGAS AG MUEHLHAN AG TURBON AG COMET HOLDING AG ZEHNDER GROUP AG FRANCOTYP-POSTALIA HOLDING AG MUELLER-DIE LILA LOGISTIK AG UET AG CONZZETA AG FRAPORT AG M-U-T AG UTD POWER TECHNOLOGY AG DAETWYLER HOLDING AG FRIWO AG NABALTEC AG UZIN UTZ AG DKSH HOLDING AG Note: Sorted by DACH. 71

72 Appendix Composition of each sector as of 30 June 2017 Information Technology ADESSO AG INTICA SYSTEMS AG SINNERSCHRADER AG ADVA OPTICAL NETWORKING SE INVISION AG SNP AG AIXTRON SE ISC BUSINESS TECHNOLOGY SOFTMATIC AG ALL FOR ONE STEEB AG ISRA VISION SOFTSHIP AG ALLGEIER SE IVU TRAFFIC TECHNOLOGIE AG SOFTWARE AG AMALPHI AG KONTRON AG SUESS MICROTEC AG AMATECH AG KPS AG SYZYGY AG ARTEC TECHNOLOGIES AG M & S ELEKTRONIK AG TDMI AG ATOSS SOFTWARE AG MEDICAL COLUMBUS AG TELES AG B & S BANKSYSTEME AG MENSCH UND MASCHINE SE TISCON AG BECHTLE AG METRIC MOBILITY SOLUTIONS AG TTL INFORMATION TECHN. AG BETA SYSTEMS SOFTWARE AG MEVIS MEDICAL SOLUTIONS AG UMT UTD MOBILITY TECHN. AG CANCOM SE MOBOTIX AG USU SOFTWARE AG CENIT AG MSG LIFE AG UTD INTERNET AG CEOTRONICS AG MUEHLBAUER HOLDING AG VIVANCO GRUPPE AG COMPUGROUP MEDICAL SE MYHAMMER HOLDING AG VTION WIRELESS TECHNOLOGY AG DATA MODUL AG NEMETSCHEK SE WIRECARD AG DATAGROUP SE NEXUS AG XING AG DOCCHECK AG NORCOM INFORMATION TECHN. AG AT&S AUSTRIA TECH.& SYSTEMTECH. AG EASY SOFTWARE AG OHB SE KAPSCH TRAFFICCOM AG ELMOS SEMICONDUCTOR AG OPENLIMIT HOLDING AG MASCHINENFABRIK HEID AG EQS GROUP AG OTRS AG RATH AG EUROMICRON AG PA POWER AUTOMATION AG AIROPACK TECHNOLOGY GROUP AG FABASOFT AG PANAMAX AG ALSO HOLDING AG FIRST SENSOR AG PARAGON AG AMS AG FORTEC ELEKTRONIK AG PSI AG ASCOM HOLDING AG GBS SOFTWARE AG QSC AG CREALOGIX AG GERM. STARTUPS GRP GMBH&CO.KGAA REALTECH AG GOLDBACH GROUP AG GFT TECHNOLOGIES SE RIB SOFTWARE AG HUBER & SUHNER AG GIGASET AG S & T AG KUDELSKI SA GK SOFTWARE SAP SE LOGITECH INTERNATIONAL SA HOLIDAYCHECK GROUP AG SCHWEIZER ELECTRONIC AG MYRIAD GROUP AG INFINEON TECHNIK AG SECUNET SECURITY AG TEMENOS GROUP AG INIT INNOVATION SE SEVEN PRINCIPLES AG U-BLOX GROUP AG INTERCARD AG SHF COMMUNICAT. TECHNOLOGIES AG WISeKey INTERN. HOLDING AG INTERSHOP COMMUNICATIONS AG SILTRONIC AG Pharma & Healthcare 4 SC AG SARTORIUS AG AAP IMPLANTATE AG STADA ARZNEIMITTEL AG ABWICKLUNGSG. ROESCH AG STRATEC BIOMEDICAL AG AGENNIX AG SYGNIS AG BB BIOTECH AG UMS INTERNATIONAL AG BIOFRONTERA AG VITA 34 AG BIOTEST AG WILEX AG CARL ZEISS MEDITEC AG VALNEVA SE CO.DON AG ACTELION PHARMA SCHWEIZ AG CURASAN AG ADDEX AG CYTOTOOLS AG AEVIS HOLDING SA DRAEGERWERK AG & CO. KGAA BACHEM HOLDING AG ECKERT & ZIEGLER AG BASILEA PHARMACEUTICA AG EPIGENOMICS AG COLTENE HOLDING AG EVOTEC AG EVOLVA HOLDING AG FORMYCON AG IVF HARTMANN AG FRESEN.MED.CARE AG & CO. KGAA Kuros BIOSCIENCES AG FRESENIUS SE & CO. KGAA LIFEWATCH AG GERATHERM MEDICAL AG LONZA GROUP AG GERRESHEIMER AG MOLECULAR PARTNERS AG HUMANOPTICS AG NOVARTIS AG M1 KLINIKEN AG ROCHE AG MAGFORCE AG SANTHERA PHAMA. HOLDING AG MATERNUS-KLINK AG SIEGFRIED HOLDING AG MEDICLIN AG SONOVA HOLDING AG MEDIGENE AG STRAUMANN HOLDING AG MEDIOS AG TECAN GROUP AG MERCK AG & CO. KGAA YPSOMED HOLDING AG MOLOGEN AG MORPHOSYS AG MPH MITT. PHARMA HOLDING AG NANOREPRO AG PAION AG PAUL HARTMANN AG RHOEN-KLINIKUM AG SANOCHEMIA PHARMAZEUTIKA AG Note: Sorted by DACH 72

73 Appendix Composition of each sector as of 30 June 2017 Consumer Service A.SPRINGER SE AD PEPPER MEDIA N.V. ARCANDOR AG ARTNET AG BASTEI LUEBBE AG BEATE UHSE AG BET-AT-HOME.COM AG BIJOU BRIGITTE Ag BMP HOLDING AG CHANG RUN CHINA INV. AG CLIQ DIGITAL AG CONSTANTIN MEDIEN AG CTS EVENTIM AG & CO. KGGAA DEUTSCHE ENTERTAINMENT AG DELTICOM AG ECOMMERCE ALLIANCE AG EDEL AG ELANIX BIOTECHNIK AG ELUMEO SE ENERXY AG FD GROUP AG FIELMANN AG HAWESKO HOLDING AG HIGHLIGHT COMMUNICATIONS AG HORNBACH BAUMARKT AG HORNBACH HOLDING AG & CO. KGAA INTERTAINMENT AG KLASSIK RADIO AG LOTTO24 AG LUDWIG BECK AG M4E AG METRO AG MYBET HOLDING SE ODEON FILM AG PANTALEON ENTERTAIN.AG PRAKTIKER AG PROSIEBENSAT.1 MEDIA SE ROCKET INTERNET SE SCOUT24 AG SENDR SE SNOWBIRD AG SPL.MEDIEN AG STARAMBA SE STROEER SE & CO TAKKT AG TELE COLUMBUS AG TMC CONTENT GR.AG TRAVEL24.COM AG UHR.DE AG UNITED LABELS AG WIGE MEDIA AG WILD BUNCH AG WINDELN.DE SE YOUR FAMILY ENTERTAINMENT AG ZALANDO SE ZOOPLUS AG APG SGA AG DUFRY AG GALENICA AG HIGHLIGHT EVENT & ENTERTAINMENT AG JUNGFRAUBAHN HOLDING AG MOBILEZONE HOLDING AG ORELL FUESSLI HOLDING AG TAMEDIA AG TITL BN BERG AG VALORA HOLDING AG VILLARS HOLDING SA Telecommunications SOLUTIONS AG 3U HOLDING AG DRILLISCH AG DEUTSCHE TELEKOM AG ECOTEL COMMUNICATION AG FREENET AG LS TELCOM AG MVISE AG STARDSL AG TELEFONICA DT HOLDING AG YOC AG TELEKOM AUSTRIA AG SUNRISE COMMUNICATION AG SWISSCOM AG Utilities CAPITAL STAGE AG CHORUS CLEAN ENERGY AG E.ON SE ENBW ENERGIE B./W. AG GELSENWASSER AG INNOGY SE MAINOVA AG MVV ENERGIE AG RWE AG UNIPER SE EVN AG VERBUND AG BKW ENERGIE AG EDISUN POWER EUROPE AG ROMANDE ENERGIE HOLDING SA Note: Sorted by DACH (No Austrian company represented inthe Consumer Service sector). 73

74 Appendix Historical development of trading multiples since 2011

75 Trading Multiples DACH (1/2) Revenue- and EBIT-Multiples LTM and 1yf since x 2.5x 2.0x 1.5x 1.0x 0.5x 0.0x EV/Revenue DACH 2.5x 2.2x 1.6x 1.5x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 30.0x 25.0x 20.0x 15.0x 10.0x 5.0x 0.0x EV/EBIT DACH 24.5x 20.8x 19.7x 17.8x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 75

76 Trading Multiples DACH (2/2) P/E- and EqV/BV-Multiples LTM and 1yf since x 25.0x 20.0x 15.0x 10.0x 5.0x 0.0x P/E DACH 24.3x 22.0x 20.8x 20.2x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 3.2x 2.8x 2.4x 2.0x 1.6x 1.2x 0.8x 0.4x 0.0x EqV/BV DACH 2.7x 2.0x Arithmetic mean Median 76

77 Trading Multiples FIRE (1/1) P/E- and EqV/BV-Multiples LTM and 1yf since x 15.0x 10.0x P/E FIRE 18.7x 18.0x 16.2x 15.2x 5.0x 0.0x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 2.0x 1.6x 1.2x 0.8x 0.4x 0.0x EqV/BV FIRE 1.8x 1.2x Arithmetic mean Median 77

78 Trading Multiples Basic Materials (1/2) Revenue- and EBIT-Multiples LTM and 1yf since x 2.5x 2.0x 1.5x 1.0x 0.5x 0.0x EV/Revenue Basic Materials 2.3x 2.2x 1.7x 1.5x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 30.0x 25.0x 20.0x 15.0x 10.0x 5.0x 0.0x EV/EBIT Basic Materials 21.9x 19.9x 17.5x 16.0x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 78

79 Trading Multiples Basic Materials (2/2) P/E-Multiples and EqV/BV-Multiples LTM and 1yf since x 25.0x 20.0x 15.0x 10.0x 5.0x 0.0x P/E Basic Materials 23.1x 19.4x 19.2x 17.1x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 2.4x 2.0x 1.6x 1.2x 0.8x 0.4x 0.0x EqV/BV Basic Materials 2.2x 1.8x Arithmetic mean Median 79

80 Trading Multiples Consumer Goods (1/2) Revenue- and EBIT-Multiples LTM and 1yf since x 1.4x 1.2x 1.0x 0.8x 0.6x 0.4x 0.2x 0.0x EV/Revenue Consumer Goods 1.4x 1.4x 1.1x 1.1x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 25.0x 20.0x 15.0x 10.0x 5.0x 0.0x EV/EBIT Consumer Goods 21.9x 17.8x 17.8x 15.9x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 80

81 Trading Multiples Consumer Goods (2/2) P/E- and EqV/BV-Multiples LTM and 1yf since x 25.0x 20.0x 15.0x 10.0x 5.0x 0.0x P/E Consumer Goods 22.3x 21.3x 18.8x 17.7x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 2.4x 2.0x 1.6x 1.2x 0.8x 0.4x 0.0x EqV/BV Consumer Goods 2.2x 1.9x Arithmetic mean Median 81

82 Trading Multiples Telecommunication (1/2) Revenue- and EBIT-Multiples LTM and 1yf since x 2.5x 2.0x 1.5x 1.0x 0.5x 0.0x EV/Revenue Telecommunication 2.6x 2.3x 2.1x 2.0x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 30.0x 25.0x 20.0x 15.0x 10.0x 5.0x 0.0x EV/EBIT Telecommunication 25.5x 21.9x 20.0x 17.3x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 82

83 Trading Multiples Telecommunication (2/2) P/E- and EqV/BV-Multiples LTM and 1yf since x 35.0x 30.0x 25.0x 20.0x 15.0x 10.0x 5.0x 0.0x P/E Telecommunication 27.2x 26.5x 26.0x 18.5x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 3.2x 2.8x 2.4x 2.0x 1.6x 1.2x 0.8x 0.4x 0.0x EqV/BV Telecommunication 1.9x 1.8x Arithmetic mean Median 83

84 Trading Multiples Industrials (1/2) Revenue- and EBIT-Multiples LTM and 1yf since x 2.0x 1.5x 1.0x 0.5x 0.0x EV/Revenue Industrials 2.0x 1.9x 1.5x 1.3x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 30.0x 25.0x 20.0x 15.0x 10.0x 5.0x 0.0x EV/EBIT Industrials 24.0x 20.7x 19.0x 17.4x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 84

85 Trading Multiples Industrials (2/2) P/E- and EqV/BV-Multiples LTM and 1yf since x 25.0x 20.0x 15.0x 10.0x 5.0x 0.0x P/E Industrials 25.7x 22.1x 21.9x 20.8x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 3.2x 2.8x 2.4x 2.0x 1.6x 1.2x 0.8x 0.4x 0.0x EqV/BV Industrials 2.9x 2.3x Arithmetic mean Median 85

86 Trading Multiples Consumer Service (1/2) Revenue- and EBIT-Multiples LTM and 1yf since x 3.0x 2.5x 2.0x 1.5x 1.0x 0.5x 0.0x EV/Revenue Consumer Service 3.0x 2.8x 2.2x 1.3x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 30.0x 25.0x 20.0x 15.0x 10.0x 5.0x 0.0x EV/EBIT Consumer Service 24.7x 23.4x 21.0x 18.3x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 86

87 Trading Multiples Consumer Service (2/2) P/E- and EqV/BV-Multiples LTM and 1yf since x 25.0x 20.0x 15.0x 10.0x 5.0x 0.0x P/E Consumer Service 23.8x 23.5x 21.0x 19.6x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 3.6x 3.2x 2.8x 2.4x 2.0x 1.6x 1.2x 0.8x 0.4x 0.0x EqV/BV Consumer Service 3.4x 2.3x Arithmetic mean Median 87

88 Trading Multiples Pharma & Healthcare (1/2) Revenue- and EBIT-Multiples LTM and 1yf since x 5.0x 4.0x 3.0x 2.0x 1.0x 0.0x EV/Revenue Pharma & Healthcare 5.3x 3.6x 3.5x 3.2x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 30.0x 25.0x 20.0x 15.0x 10.0x 5.0x 0.0x EV/EBIT Pharma & Healthcare 26.8x 25.2x 22.7x 21.8x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 88

89 Trading Multiples Pharma & Healthcare (2/2) P/E- and EqV/BV-Multiples LTM and 1yf since x 30.0x 25.0x 20.0x 15.0x 10.0x 5.0x 0.0x P/E Pharma & Healthcare 32.0x 29.6x 26.1x 23.8x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 4.0x 3.6x 3.2x 2.8x 2.4x 2.0x 1.6x 1.2x 0.8x 0.4x 0.0x EqV/BV Pharma & Healthcare 3.5x 3.2x Arithmetic mean Median 89

90 Trading Multiples Information Technology (1/2) Revenue- and EBIT-Multiples LTM and 1yf since x 2.5x 2.0x 1.5x 1.0x 0.5x 0.0x EV/Revenue Information Technology 2.8x 2.6x 2.3x 2.1x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 30.0x 25.0x 20.0x 15.0x 10.0x 5.0x 0.0x EV/EBIT Information Technology 27.7x 23.6x 22.2x 21.6x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 90

91 Trading Multiples Information Technology (2/2) P/E- and EqV/BV-Multiples LTM and 1yf since x 30.0x 25.0x 20.0x 15.0x 10.0x 5.0x 0.0x P/E Information Technology 32.4x 27.6x 26.6x 23.1x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 4.4x 4.0x 3.6x 3.2x 2.8x 2.4x 2.0x 1.6x 1.2x 0.8x 0.4x 0.0x EqV/BV Information Technology 3.8x 3.0x Arithmetic mean Median 91

92 Trading Multiples Utilities (1/2) Revenue- and EBIT-Multiples LTM and 1yf since x 4.0x 3.5x 3.0x 2.5x 2.0x 1.5x 1.0x 0.5x 0.0x EV/Revenue Utilities 3.9x 1.9x 1.6x 1.0x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 35.0x 30.0x 25.0x 20.0x 15.0x 10.0x 5.0x 0.0x EV/EBIT Utilities 27.7x 22.1x 16.0x 15.1x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 92

93 Trading Multiples Utilities (2/2) P/E- and EqV/BV-Multiples LTM and 1yf since x 25.0x 20.0x 15.0x 10.0x 5.0x 0.0x P/E Utilities 23.1x 17.1x 16.7x 15.8x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 2.4x 2.0x 1.6x 1.2x 0.8x 0.4x 0.0x EqV/BV Utilities 1.9x 1.6x Arithmetic mean Median 93

94 ValueTrust is the sole financial advisory firm in the German speaking countries that focuses on the core competencies of business valuation and corporate finance advisory. ValueTrust advises management, boards and investors in acquisitions, mergers, restructurings, disputes and litigations as well as value management. ValueTrust offers its clients solution oriented financial advisory services combining both client focus and independence with highest standards of quality. ValueTrust s advisory approach is based on years of project experience, the skills of its professionals, a trustful cooperation with its clients and the support of industry experienced senior advisors. Corporate Transactions Buy side advisory and carve out services Fairness opinions Takeover and delisting advisory Purchase price allocation and impairment tests Valuation opinions regarding the determination of fair market values for legal valuation purposes Dispute and Litigation Damage analysis Party related valuation opinions Financial and economic advice in proceedings Expert determination (as arbitrators) and mediation consulting Valuations as court appointed expert Restructuring Valuation reports for reorganizations and tax purposes Valuation opinions for financial restructurings Valuation of debt and mezzanine capital Capital structure analysis and optimization Value Management Portfolio and value analysis Business planning and evaluation of corporate strategies Value based controlling systems Cost of capital optimization CFO and financial expert advice ValueTrust Financial Advisors SE Theresienstrasse Munich Germany trust.com Prof. Dr. Christian Aders Chairman of the Executive Board christian.aders@value trust.com Florian Starck Steuerberater Member of the Executive Board florian.starck@value trust.com