DACH Capital Market Study 31 Dec 2017

Transcription

1 powered by and DACH Capital Market Study 31 Dec 2017 Analysis of cost of capital parameters and multiples for the capital markets of Germany, Austria and Switzerland Volume 2, March 2018

2 DACH Capital Market Study 31 December 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 second 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 indices (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 2012 and 31 December 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 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 Recommendations Corporate Valuation 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 (1/2) Risk-free rate In comparison to 30 June 2017, the German risk-free rate increased from 1.24% to 1.29% as of 31 December The Austrian risk-free rate remained constant and amounted to 1.33% as of 30 June 2017 and 31 December ) The Swiss risk-free rate recorded an increase from 0.32% to 0.39% during the time period 30 June 2017 to 31 December Overall, Switzerland has 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 relatively constant at 8.7% as of 31 December 2017 compared to 8.6% as of 30 June 2017 with a market risk premium of 7.4% and 7.3% respectively. The implied market return of the Austrian market 2) decreased slightly from 8.3% as of 30 June 2017 to 8.1% as of 31 December 2017 and was below the German level. The market risk premium amounted to 6.8% as of 31 December 2017 and 6.9% as of 30 June The implied market return of the Swiss market was the lowest in the DACH region with 7.2% as of 31 December 2017 compared to 6.8% as of 30 June The market risk premium increased from 6.5% to 6.8%. Companies within the Pharma & Healthcare sector showed the highest unlevered sector specific betas as of 31 December 2017 with the arithmetic mean standing at 0.93 for the five-year period and at 0.86 for the two year period. Companies within the Utilities sector had the lowest unlevered betas at 0.36 (two year period). The levered sector specific betas were highest again for the Pharma & Healthcare sector for the five-year period (arithmetic mean) as of 31 December If we consider the two year period, the Basic Materials and the Pharma & Healthcare sectors showed the same highest levered beta (arithmetic mean) and the Utilities sector was again the lowest. Chapter 4 Chapter 6 1) 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; this approach differs from the methodology in the ValueTrust Austria Capital Market Study. 2) Basis: ATX 8

9 Executive Summary (2/2) Sector returns (p.a.) The development of the implied sector returns shows a range for the levered implied sector returns of 6.1% to 8.9% and 4.4% to 6.2% for the unlevered implied sector returns. The ex-ante analysis of implied sector returns reveals that unlevered implied sector returns were highest for companies in the Basic Materials sector at 6.2% (levered 7.5%) as of 31 December 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 26.7% in the six- and 30.9% in the three-year average. The lowest historical returns of the sectors were realized by the Utilities sector at 3.1% in the six- and 5.3% in the three-year average. Chapter 7 Multiples At the reference date 31 December 2017, the medians of the illustrated multiples EV/Revenue (1yf), EV/EBIT (1yf), P/E (1yf) and EqV/BV reached their highest level compared to the past six years when looking at all analyzed companies in the DACH region. The Pharma & Healthcare sector had the highest median EBIT-Multiples compared to all other sectors as of 31 December The median of the EBIT-Multiples amounted to 27.0x (LTM) and 24.1x (1yf). The Information Technology sector represented the highest Price-to-Book Value-Multiples with a median of 3.3x as of 31 December The P/E-Multiples (1yf) increased in a constant manner from 13.2x (arithmetic mean) and 12.6x (median) in 2012 to 24.3x (arithmetic mean) and 21.7x (median) in the DACH region as of 31 December The high total shareholder returns combined with comparably low implied returns point to the current high valuation levels and even a possible over-valuation of the market. 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). Additionally, we illustrate the monthly development of the risk-free rates since 2012 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 to country specific recommendations Interest rate curve based on long-term bonds (Svensson method) 1.50% 1.00% Risk-free rates as of 31 December % 1.33% 1.29% 1.24% Spot Rate 0.50% 0.39% 0.32% 0.00% % -1.00% Germany 31 December 2017 Austria 31 December 2017 Switzerland 31 December 2017 Germany 30 June 2017 Austria 30 June 2017 Switzerland 30 June 2017 Year 12

13 Risk-Free Rate Germany Determination following IDW S 1 Historical development of the risk-free rate (Svensson method) since 2012 Historical developement of the risk-free rate in % according to IDW S 1 5,0% 4,0% 3,0% 2,0% 1,0% 2.77% 2.28% 2.37% 2.37% 2.75% 2.45% 1.87% 1.17% 1.41% The German risk-free rate increased from 1.24% as of 30 June 2017 to 1.29% as of 31 December In the time period from 31 December 2011 to 31 December 2017 the risk-free rate declined from 2.77% to 1.29%. The German risk-free rate is the second highest within the three DACH markets. 0.91% 0.95% 1.24% 1.29% 0,0% 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% 1,32% 1,32% 1,35% 1,33% 1,32% 1,29% % 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% 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 2012 Historical developement of the risk-free rate in % according to KFS/BW1 5,0% 4,0% 3,0% 2,0% 1,0% 2.42% 2.41% 2.19% 2.53% 2.84% 2.30% 1.59% 1.67% 1.57% The Austrian risk-free rate amounted to 1.33% as of 31 December 2017 and hence remained unchanged vs. 30 June In the time period from 31 December 2011 to 31 December 2017 the risk-free rate declined from 2.42% to 1.33%. The Austrian risk-free rate is the highest within the three DACH markets. 0.49% 1.04% 1.33% 1.33% 0,0% 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% 1,45% 1,25% 1,38% 1,33% 1,25% 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% 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% 1.45% 1.16% 1.12% 1.33% 1.79% 1.61% 1.08% 0.57% 0.66% The Swiss risk-free rate increased from 0.32% as of 30 June 2017 to 0.39% as of 31 December In the time period from 31 December 2011 to 31 December 2017 the risk-free rate declined from 1.45% to 0.39%. The Swiss risk-free rate is the lowest within the three DACH markets. 0.19% 0.23% 0.32% 0.39% 0,0% -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% 0,36% 0,35% 0,37% 0,37% 0,40% 0,39% % 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% 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 Cost 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 3.2% 3.8% 3.2% 4.8% 2.9% 5.1% 5.3% 5.2% 3.3% 3.4% 2.6% 4.2% Lower quantile 6.5% 5.5% 4.3% 5.3% 5.2% 6.1% 5.6% 5.9% 5.0% 5.4% 5.3% 5.8% Median 10.0% 9.4% 8.4% 7.7% 7.7% 7.8% 7.0% 7.9% 7.6% 7.6% 7.7% 8.0% Arithmetic mean 10.5% 9.4% 8.6% 8.1% 7.6% 8.3% 7.9% 8.4% 8.7% 8.3% 8.4% 8.4% Market-value weighted mean 11.2% 10.0% 9.4% 8.6% 8.3% 8.9% 8.3% 8.5% 9.0% 8.6% 8.6% 8.7% Upper quantile 15.3% 13.0% 12.3% 10.5% 10.7% 11.4% 11.4% 12.0% 15.2% 12.4% 14.3% 13.0% Maximum 17.9% 14.6% 16.2% 12.0% 11.9% 14.7% 17.0% 18.3% 24.2% 16.3% 16.7% 15.2% Market-value weighted debt 323.2% 252.0% 231.6% 162.1% 167.5% 175.2% 154.5% 153.6% 200.8% 150.0% 137.0% 123.9% 16.0% 14.0% 12.0% 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% 11.2% 10.0% 9.4% Implied market returns - DAX 8.6% 8.9% 8.3% 8.3% 8.5% 9.0% Max 11.2% 8.6% 8.6% 8.7% Min 7.5% The implied market return of the German market shows a relatively constant market-value weighted mean with 8.6% as of 30 June 2017 and 8.7% as of31 December Since 30 June 2012 the implied market return fluctuated between 7.5% and 11.2%, overall it follows a declining trend. The German market showed the highest return in comparison to the two other markets as of 31 December 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 12 in this study) of the German capital market, we can determine the implied market risk premium. In the years from 2012 to 2017 the implied market returns were within a range of 8.3% to 11.2% (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 8.7% as of the reference date 31 December Taking the risk-free rate of 1.29% (cf. slide 13) into account, we determine a market risk premium of 7.4%. Implied market risk premium - DAX 12.0% 10.0% 11.2% 10.0% 9.4% 8.6% 8.3% 8.9% 8.3% 8.5% 9.0% 8.6% 8.6% 8.7% 8.0% 6.0% Implied market return 4.0% 2.0% 9.0% 7.6% 7.1% 5.9% 5.8% 7.0% 7.1% 7.1% 8.1% 7.6% 7.3% 7.4% 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 11.2% 10.0% 9.4% 8.6% 8.3% 8.9% 8.3% 8.5% 9.0% 8.6% 8.6% 8.7% Risk-free rate 2.3% 2.4% 2.4% 2.8% 2.5% 1.9% 1.2% 1.4% 0.9% 1.0% 1.2% 1.3% Implied market risk premium - DAX 9.0% 7.6% 7.1% 5.9% 5.8% 7.0% 7.1% 7.1% 8.1% 7.6% 7.3% 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 3.0% 3.0% 4.3% 0.0% 0.2% 2.0% 2.0% 3.6% 1.6% 2.1% 1.1% 4.0% Lower quantile 5.2% 4.9% 4.5% 4.6% 1.6% 3.5% 3.9% 4.1% 5.2% 4.7% 5.1% 4.4% Median 8.9% 8.2% 8.5% 7.4% 6.4% 7.2% 6.9% 7.7% 8.4% 7.7% 7.7% 7.7% Arithmetic mean 10.1% 8.6% 8.6% 7.8% 6.4% 7.8% 6.9% 8.0% 8.1% 7.9% 7.5% 7.5% Market-value weighted mean 11.3% 9.5% 9.5% 8.8% 7.1% 8.4% 7.3% 8.3% 8.1% 8.2% 8.3% 8.1% Upper quantile 16.4% 13.8% 12.9% 11.9% 10.6% 13.2% 12.1% 12.3% 11.7% 10.9% 10.1% 9.5% Maximum 16.8% 14.5% 13.9% 11.9% 10.7% 14.4% 13.4% 13.6% 12.3% 11.2% 13.0% 10.3% Market-value weighted debt 215.1% 190.1% 173.4% 161.2% 136.6% 177.3% 141.8% 149.9% 147.7% 122.7% 101.0% 86.7% 20.0% 16.0% 12.0% 8.0% 4.0% 11.3% 9.5% 9.5% 8.8% Implied market returns - ATX 8.4% 7.1% 7.3% Max 11.3% 8.3% 8.1% 8.2% 8.3% 8.1% Min 6.4% The implied market return of the Austrian market declined slightly from 8.3% as of 30 June 2017 to 8.1% as of 31 December Since 30 June 2012, it fluctuated between 6.4% and 11.3%, overall it follows a declining trend. The Austrian market represents the second highest implied return in comparison to the two other markets as of 31 December % 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 2012 to 2017 the implied market returns fell within a range of 7.1% to 11.3% (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 8.9%. The implied market return is at 8.1% as of the reference date 31 December Taking the risk-free rate of 1.33% (cf. slide 14) into account, we determine a market risk premium of 6.8%. 12.0% 11.3% Implied market risk premium - ATX 10.0% 8.0% 6.0% 9.5% 9.5% 8.8% 7.1% 8.4% 7.3% 8.3% 8.1% 8.2% 8.3% 8.1% Implied market return 4.0% 2.0% 8.9% 7.3% 7.0% 6.0% 4.8% 6.8% 5.6% 6.8% 7.6% 7.2% 6.9% 6.8% 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 11.3% 9.5% 9.5% 8.8% 7.1% 8.4% 7.3% 8.3% 8.1% 8.2% 8.3% 8.1% Risk-free rate 2.4% 2.2% 2.6% 2.9% 2.3% 1.6% 1.7% 1.6% 0.5% 1.0% 1.3% 1.3% Implied market risk premium - ATX 8.9% 7.3% 7.0% 6.0% 4.8% 6.8% 5.6% 6.8% 7.6% 7.2% 6.9% 6.8% 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 6.3% 6.5% 6.0% 6.2% 5.8% 5.7% 6.0% 5.4% 5.2% 4.5% 5.0% 5.4% Lower quantile 6.4% 6.9% 6.4% 6.2% 5.9% 6.1% 6.2% 5.9% 5.7% 5.3% 5.3% 5.4% Median 9.2% 8.4% 7.5% 7.7% 7.4% 7.9% 7.3% 7.7% 7.2% 7.4% 6.3% 7.0% Arithmetic mean 9.4% 8.9% 8.2% 7.9% 7.5% 7.9% 7.6% 7.6% 7.5% 7.2% 6.8% 7.0% Market-value weighted mean 9.4% 8.9% 8.1% 7.9% 7.3% 7.6% 7.2% 7.4% 7.2% 7.4% 6.8% 7.2% Upper quantile 12.5% 11.7% 11.6% 10.7% 10.2% 10.6% 10.5% 9.6% 10.6% 9.1% 8.6% 8.7% Maximum 15.7% 13.6% 12.9% 10.8% 10.4% 11.0% 10.6% 10.1% 11.0% 9.4% 8.7% 9.1% Market-value weighted debt 193.0% 144.8% 107.7% 87.0% 81.0% 85.7% 78.3% 74.1% 87.7% 79.4% 71.3% 68.7% 16.0% 14.0% 12.0% 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% 9.4% 8.9% 8.1% 7.9% 7.3% 7.6% Implied market returns - SMI 7.2% 7.4% 7.2% 7.4% Max 9.4% 6.8% 7.2% Min 6.8% The market-value weighted mean of the implied market return of the Swiss market increased from 6.8% as of 31 June 2017 to 7.2% as of 31 December Since 30 June 2012 it fluctuated between 6.8% and 9.4%, overall it follows a declining trend. The Swiss market represents the lowest return, compared to the German and Austrian market as of 31 December 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 2012 to 2017 the implied market returns fell within a range of 6.8% to 9.4% (cf. slide 22 in this study). Subtracting the risk-free rate from the implied market return, we derive a market risk premium of 5.6% to 8.3%. The implied market return is at 7.2% as of the reference date 31 December Taking the risk-free rate of 0.39% (cf. slide 15) into account, we determine a market risk premium of 6.8%. Implied market risk premium - SMI 10.0% 9.0% 8.0% 7.0% 6.0% 5.0% 4.0% 3.0% 2.0% 9.4% 8.3% 8.9% 7.8% 8.1% 7.9% 7.3% 6.7% 6.1% 5.6% 7.6% 7.2% 7.4% 7.2% 7.4% 6.6% 6.6% 6.7% 7.0% 7.2% 6.8% 7.2% 6.5% 6.8% Implied market return Risk-free rate MRP 1.0% 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 9.4% 8.9% 8.1% 7.9% 7.3% 7.6% 7.2% 7.4% 7.2% 7.4% 6.8% 7.2% Risk-free rate 1.2% 1.1% 1.3% 1.8% 1.6% 1.1% 0.6% 0.7% 0.2% 0.2% 0.3% 0.4% Implied market risk premium - SMI 8.3% 7.8% 6.7% 6.1% 5.6% 6.6% 6.6% 6.7% 7.0% 7.2% 6.5% 6.8% 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 markets 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 markets, 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 the 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 27 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 31 December. The following slides illustrate how the two calculation methods (arithmetic and geometric) differ from each other for the period between 31 December 1990 and 31 December 2017: DAX: the arithmetic mean of the historical market returns is 11.3% the geometric mean of the historical market returns is 8.6% ATX: the arithmetic mean of the historical market returns is 10.9% the geometric mean of the historical market returns is 7.0% SMI: the arithmetic mean of the historical market returns is 9.6% the geometric mean of the historical market returns is 7.6% 25

26 Historical Market Returns (Arithmetic Mean) German Market DAX Performance Index Return Triangle Buy Reading example: An investment in the DAX Index beginning of the year 2009, when sold beginning of the year 2014, would have yielded an average annual return (arithmetic mean) of 16.0%. Other five-year investment periods are displayed along the black steps. 12.5% % 9.7% % 8.2% 9.6% % 6.1% 6.4% 7.9% % 14.1% 12.6% 11.1% 11.4% % 27.3% 19.1% 16.7% 14.7% 14.4% % 7.2% 13.3% 10.6% 10.4% 9.8% 10.2% % 0.7% 10.1% 14.0% 11.7% 11.4% 10.7% 10.9% % 20.0% 8.4% 13.6% 16.0% 13.7% 13.1% 12.4% 12.4% % -8.3% -0.2% -3.8% 2.8% 6.6% 6.0% 6.4% 6.5% 7.1% % -9.0% 1.9% 5.5% 1.4% 6.0% 8.8% 8.0% 8.2% 8.1% 8.5% % 22.1% 1.3% 6.9% 8.8% 4.9% 8.3% 10.5% 9.6% 9.6% 9.3% 9.6% % 24.5% 23.8% 7.7% 11.0% 11.8% 8.0% 10.7% 12.3% 11.3% 11.2% 10.8% 10.9% % Return greater than 13% 7.3% 17.2% 18.8% 19.7% 7.7% 10.4% 11.2% 7.9% 10.3% 11.8% 11.0% 10.9% 10.5% 10.7% % Return between 8% and 13% 37.1% 22.2% 23.8% 23.4% 23.2% 12.6% 14.2% 14.4% 11.2% 13.0% 14.1% 13.1% 12.9% 12.4% 12.4% % Return between 3% and 8% -43.9% -3.4% 0.2% 6.9% 9.9% 12.0% 4.5% 6.9% 7.9% 5.7% 7.8% 9.3% 8.8% 8.8% 8.7% 8.9% % Return between -3% and +3% -19.8% -31.9% -8.9% -4.8% 1.5% 5.0% 7.4% 1.5% 3.9% 5.2% 3.4% 5.5% 7.0% 6.7% 6.9% 6.9% 7.2% 2001 Investment period in years -5.0% Return between -3% and -8% -7.5% -13.7% -23.8% -8.5% -5.4% 0.0% 3.2% 5.6% 0.5% 2.8% 4.0% 2.4% 4.5% 6.0% 5.8% 6.0% 6.1% 6.4% % Return between -8% and -13% 39.0% 15.7% 3.9% -8.1% 1.0% 2.0% 5.6% 7.6% 9.3% 4.3% 6.1% 6.9% 5.3% 7.0% 8.2% 7.8% 7.9% 7.9% 8.1% % Return lower than -13% 18.5% 28.7% 16.7% 7.5% -2.8% 3.9% 4.4% 7.2% 8.9% 10.2% 5.6% 7.1% 7.8% 6.2% 7.7% 8.8% 8.5% 8.5% 8.4% 8.6% % 32.6% 34.7% 24.2% 15.4% 5.5% 10.0% 9.7% 11.6% 12.6% 13.5% 9.0% 10.2% 10.6% 8.9% 10.2% 11.1% 10.6% 10.5% 10.4% 10.5% % 37.0% 30.9% 32.9% 24.8% 17.4% 8.6% 12.2% 11.6% 13.2% 14.0% 14.7% 10.4% 11.4% 11.7% 10.1% 11.2% 12.0% 11.5% 11.4% 11.2% 11.2% % 17.6% 27.3% 25.1% 27.9% 22.0% 16.0% 8.5% 11.7% 11.2% 12.7% 13.5% 14.1% 10.2% 11.2% 11.5% 9.9% 11.0% 11.7% 11.3% 11.2% 11.0% 11.1% % 0.2% 9.2% 18.6% 18.6% 22.0% 17.8% 13.1% 6.7% 9.8% 9.5% 11.0% 11.9% 12.6% 9.1% 10.0% 10.3% 9.0% 10.0% 10.8% 10.4% 10.4% 10.2% 10.3% % 19.6% 15.7% 18.6% 24.2% 23.3% 25.5% 21.4% 16.8% 10.7% 13.1% 12.6% 13.8% 14.3% 14.9% 11.4% 12.1% 12.4% 10.9% 11.8% 12.5% 12.1% 11.9% 11.7% 11.8% % 22.3% 12.4% 11.2% 14.5% 19.8% 19.6% 22.1% 18.8% 14.9% 9.6% 11.9% 11.5% 12.6% 13.2% 13.8% 10.6% 11.4% 11.6% 10.3% 11.2% 11.8% 11.4% 11.4% 11.2% 11.2% % 5.4% 19.2% 12.5% 11.6% 14.2% 18.8% 18.8% 21.0% 18.2% 14.7% 9.8% 11.9% 11.6% 12.6% 13.2% 13.8% 10.7% 11.4% 11.7% 10.4% 11.3% 11.9% 11.5% 11.4% 11.2% 11.3% 1991 Sell 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 beginning of the year 2009, when sold beginning of the year 2014, would have yielded an average annual return (geometric mean) of 14.7%. Other five-year investment periods are displayed along the black steps. 12.5% % 9.7% % 8.2% 9.6% % 6.1% 6.3% 7.8% % 13.5% 12.2% 10.8% 11.2% % 27.3% 18.5% 16.2% 14.2% 14.0% % 4.9% 11.4% 9.1% 9.2% 8.8% 9.3% % -0.5% 8.5% 12.5% 10.5% 10.3% 9.8% 10.2% % 19.9% 7.0% 12.2% 14.7% 12.6% 12.2% 11.5% 11.6% % -14.1% -5.0% -7.5% -1.2% 2.9% 2.8% 3.6% 4.0% 4.8% % -14.6% -3.3% 1.2% -2.2% 2.4% 5.4% 5.1% 5.6% 5.7% 6.3% % 22.1% -3.8% 2.4% 5.0% 1.5% 5.0% 7.4% 6.8% 7.1% 7.1% 7.5% % 24.5% 23.8% 3.1% 7.0% 8.4% 4.8% 7.5% 9.4% 8.7% 8.8% 8.6% 8.9% % Return greater than 13% 7.3% 16.8% 18.5% 19.4% 3.9% 7.0% 8.3% 5.1% 7.5% 9.2% 8.6% 8.7% 8.5% 8.8% % Return between 8% and 13% 37.1% 21.3% 23.2% 22.9% 22.8% 8.8% 10.9% 11.5% 8.2% 10.2% 11.5% 10.7% 10.6% 10.3% 10.5% % Return between 3% and 8% -43.9% -12.3% -6.2% 1.2% 5.0% 7.7% -1.0% 1.8% 3.3% 1.3% 3.6% 5.3% 5.1% 5.4% 5.5% 5.9% % Return between -3% and +3% -19.8% -32.9% -14.9% -9.8% -3.4% 0.4% 3.3% -3.6% -0.9% 0.7% -0.8% 1.4% 3.1% 3.1% 3.5% 3.7% 4.2% 2001 Investment period in years -5.0% Return between -3% and -8% -7.5% -13.9% -25.4% -13.1% -9.4% -4.1% -0.8% 1.9% -4.0% -1.5% -0.1% -1.4% 0.7% 2.3% 2.3% 2.8% 3.0% 3.5% % Return between -8% and -13% 39.0% 13.4% 1.0% -12.8% -4.6% -2.7% 1.1% 3.5% 5.4% -0.4% 1.6% 2.7% 1.3% 3.0% 4.4% 4.3% 4.6% 4.7% 5.1% % Return lower than -13% 18.5% 28.3% 15.1% 5.1% -7.3% -1.0% 0.1% 3.1% 5.1% 6.7% 1.2% 2.9% 3.9% 2.4% 4.0% 5.2% 5.1% 5.3% 5.4% 5.7% % 31.8% 34.2% 22.3% 12.4% 0.1% 4.7% 5.0% 7.3% 8.6% 9.8% 4.4% 5.8% 6.5% 4.9% 6.3% 7.3% 7.0% 7.2% 7.2% 7.4% % 36.7% 30.3% 32.5% 23.3% 14.7% 3.6% 7.3% 7.3% 9.1% 10.2% 11.2% 6.0% 7.2% 7.7% 6.2% 7.4% 8.3% 8.0% 8.1% 8.0% 8.2% % 17.2% 26.3% 24.3% 27.1% 20.5% 13.7% 4.1% 7.3% 7.3% 9.0% 10.0% 10.9% 6.1% 7.2% 7.7% 6.3% 7.4% 8.3% 8.0% 8.1% 8.0% 8.2% % -0.1% 8.3% 16.8% 17.2% 20.6% 16.1% 10.8% 2.7% 5.8% 5.9% 7.5% 8.6% 9.5% 5.1% 6.2% 6.8% 5.5% 6.6% 7.5% 7.2% 7.3% 7.3% 7.5% % 16.5% 13.5% 16.8% 22.3% 21.6% 24.0% 19.5% 14.3% 6.5% 8.9% 8.8% 10.1% 10.9% 11.6% 7.4% 8.3% 8.7% 7.3% 8.3% 9.1% 8.8% 8.8% 8.7% 8.9% % 19.9% 10.0% 9.4% 12.8% 17.8% 17.9% 20.4% 16.9% 12.6% 5.7% 8.0% 7.9% 9.2% 10.0% 10.7% 6.8% 7.7% 8.1% 6.8% 7.8% 8.5% 8.3% 8.3% 8.3% 8.4% % 5.1% 17.5% 10.7% 10.1% 12.8% 17.1% 17.3% 19.5% 16.5% 12.6% 6.2% 8.3% 8.3% 9.4% 10.2% 10.9% 7.1% 7.9% 8.3% 7.1% 8.0% 8.7% 8.5% 8.5% 8.4% 8.6% 1991 Sell Investment period in years Following: 27

28 Historical Market Returns (Arithmetic Mean) Austrian Market ATX Total Return Index Return Triangle Buy Reading example: An investment in the ATX Total Return beginning of the year 2009, when beginning mid of the year 2014, would have yielded an average annual return (arithmetic mean) of 14.1%. Other fiveyear investment periods are displayed along the black steps. 34.0% % 23.2% % 12.0% 19.4% % 0.1% 4.2% 11.7% % -3.0% 1.9% 4.5% 10.4% % 18.6% 8.6% 9.4% 10.0% 14.0% % -0.2% 1.6% -1.6% 1.0% 2.9% 7.4% % -6.3% 6.4% 6.2% 2.7% 4.1% 5.3% 8.9% % 32.9% 11.2% 16.3% 14.1% 9.9% 10.1% 10.4% 13.0% % -6.4% 2.3% -6.3% 1.3% 2.0% 0.1% 1.5% 2.7% 5.9% % -29.0% -4.0% 1.9% -4.9% 1.2% 1.8% 0.2% 1.4% 2.5% 5.4% % 13.4% -10.7% 3.5% 6.8% 0.2% 4.8% 4.8% 3.0% 3.9% 4.7% 7.1% % 39.3% 26.4% 5.1% 13.3% 14.4% 7.7% 10.7% 10.1% 8.0% 8.3% 8.7% 10.6% % Return greater than 13% 59.0% 55.8% 45.8% 34.6% 15.9% 20.9% 20.8% 14.1% 16.1% 15.0% 12.6% 12.5% 12.5% 14.1% % Return between 8% and 13% 39.4% 49.2% 50.3% 44.2% 35.5% 19.8% 23.6% 23.1% 16.9% 18.4% 17.2% 14.9% 14.6% 14.5% 15.8% % Return between 3% and 8% 3.7% 21.5% 34.0% 38.7% 36.1% 30.2% 17.5% 21.1% 20.9% 15.6% 17.1% 16.1% 14.0% 13.8% 13.7% 15.0% % Return between -3% and +3% 9.2% 6.4% 17.4% 27.8% 32.8% 31.6% 27.2% 16.5% 19.8% 19.8% 15.0% 16.4% 15.6% 13.7% 13.5% 13.5% 14.7% 2001 Investment period in years -5.0% Return between -3% and -8% -9.1% 0.1% 1.3% 10.8% 20.4% 25.8% 25.8% 22.7% 13.6% 16.9% 17.1% 13.0% 14.5% 13.8% 12.1% 12.1% 12.1% 13.3% % Return between -8% and -13% 8.9% -0.1% 3.0% 3.2% 10.4% 18.5% 23.4% 23.7% 21.2% 13.2% 16.2% 16.5% 12.7% 14.1% 13.5% 11.9% 11.9% 11.9% 13.1% % Return lower than -13% -12.3% -1.7% -4.1% -0.8% 0.1% 6.6% 14.1% 18.9% 19.7% 17.8% 10.9% 13.8% 14.2% 10.9% 12.3% 11.9% 10.5% 10.6% 10.7% 11.8% % 2.7% 4.8% 1.3% 2.9% 3.0% 8.2% 14.6% 18.8% 19.5% 17.8% 11.4% 14.1% 14.5% 11.4% 12.7% 12.2% 10.9% 10.9% 11.0% 12.1% % 18.2% 8.1% 8.3% 4.8% 5.5% 5.3% 9.5% 15.0% 18.8% 19.4% 17.9% 12.0% 14.4% 14.8% 11.8% 13.0% 12.6% 11.3% 11.3% 11.4% 12.4% % 6.3% 10.1% 4.5% 5.4% 3.0% 3.9% 3.8% 7.8% 12.9% 16.5% 17.3% 16.0% 10.7% 13.0% 13.5% 10.8% 11.9% 11.6% 10.5% 10.5% 10.6% 11.6% % -6.7% 1.8% 5.7% 2.1% 3.3% 1.5% 2.5% 2.6% 6.3% 11.1% 14.5% 15.4% 14.4% 9.5% 11.8% 12.2% 9.8% 10.9% 10.7% 9.6% 9.7% 9.8% 10.8% % 27.2% 16.1% 16.7% 16.9% 12.1% 11.6% 9.0% 9.0% 8.5% 11.3% 15.3% 18.2% 18.7% 17.5% 12.8% 14.7% 15.0% 12.5% 13.5% 13.1% 12.0% 12.0% 12.0% 12.9% % 22.6% 12.6% 7.9% 10.1% 11.4% 8.0% 8.1% 6.2% 6.5% 6.2% 9.0% 12.8% 15.7% 16.4% 15.4% 11.0% 13.0% 13.3% 11.0% 12.0% 11.7% 10.7% 10.8% 10.8% 11.7% % -13.7% 11.4% 6.8% 4.2% 6.6% 8.2% 5.6% 6.0% 4.5% 4.9% 4.8% 7.5% 11.2% 13.9% 14.7% 13.9% 9.8% 11.7% 12.1% 10.0% 11.0% 10.8% 9.8% 9.9% 10.0% 10.9% 1991 Sell Investment period in years Following: 28

29 Historical Market Returns (Geometric Mean) Austrian Market ATX Total Return Index Return Triangle Buy Reading example: An investment in the ATX Total Return beginning of the year 2009, when sold beginning of the year 2014, would have yielded an average annual return (geometric mean) of 10.5%. Other fiveyear investment periods are displayed along the black steps. 34.0% % 22.8% % 12.0% 18.9% % -0.5% 3.6% 10.5% % -3.4% 1.4% 4.0% 9.4% % 17.9% 7.2% 8.3% 9.1% 12.9% % -5.5% -2.0% -4.4% -1.4% 0.8% 5.0% % -9.9% 2.3% 3.0% 0.0% 1.8% 3.3% 6.7% % 32.2% 5.8% 11.8% 10.5% 6.5% 7.2% 7.8% 10.5% % -22.4% -10.3% -16.4% -8.4% -6.3% -7.0% -4.9% -3.1% 0.1% % -35.5% -15.3% -7.7% -13.2% -7.0% -5.3% -6.1% -4.3% -2.7% 0.2% % 12.7% -19.4% -6.5% -1.8% -7.7% -2.8% -1.9% -3.0% -1.6% -0.4% 2.1% % 38.6% 24.7% -5.5% 3.1% 5.7% -0.8% 2.8% 3.1% 1.5% 2.4% 3.2% 5.3% % Return greater than 13% 59.0% 55.7% 45.1% 32.5% 4.9% 10.8% 12.1% 5.2% 7.9% 7.6% 5.8% 6.2% 6.7% 8.5% % Return between 8% and 13% 39.4% 48.9% 50.1% 43.7% 33.8% 10.0% 14.5% 15.2% 8.6% 10.7% 10.2% 8.2% 8.5% 8.8% 10.3% % Return between 3% and 8% 3.7% 20.2% 32.0% 36.8% 34.6% 28.3% 9.1% 13.1% 13.8% 8.1% 10.0% 9.6% 7.9% 8.1% 8.4% 9.9% % Return between -3% and +3% 9.2% 6.4% 16.4% 25.9% 30.8% 30.0% 25.3% 9.1% 12.7% 13.4% 8.2% 10.0% 9.6% 8.0% 8.2% 8.5% 9.8% 2001 Investment period in years -5.0% Return between -3% and -8% -9.1% -0.4% 1.0% 9.4% 17.9% 23.1% 23.5% 20.4% 6.9% 10.3% 11.1% 6.6% 8.4% 8.2% 6.7% 7.0% 7.3% 8.7% % Return between -8% and -13% 8.9% -0.5% 2.6% 2.9% 9.3% 16.4% 21.0% 21.6% 19.1% 7.1% 10.2% 10.9% 6.8% 8.4% 8.2% 6.9% 7.1% 7.4% 8.7% % Return lower than -13% -12.3% -2.3% -4.6% -1.3% -0.3% 5.4% 11.8% 16.2% 17.3% 15.5% 5.2% 8.1% 8.9% 5.3% 6.9% 6.8% 5.6% 6.0% 6.3% 7.5% % 1.6% 4.0% 0.6% 2.2% 2.5% 7.1% 12.5% 16.4% 17.3% 15.7% 6.2% 8.8% 9.5% 6.1% 7.5% 7.4% 6.3% 6.5% 6.8% 8.0% % 18.2% 7.0% 7.5% 4.0% 4.8% 4.7% 8.5% 13.2% 16.6% 17.4% 15.9% 7.1% 9.5% 10.1% 6.8% 8.2% 8.0% 6.9% 7.1% 7.4% 8.5% % 5.6% 9.5% 3.6% 4.6% 2.2% 3.2% 3.2% 6.7% 11.1% 14.3% 15.3% 14.1% 6.1% 8.4% 9.0% 6.0% 7.3% 7.2% 6.2% 6.4% 6.7% 7.8% % -6.7% 1.1% 5.0% 1.3% 2.5% 0.8% 1.8% 2.0% 5.2% 9.3% 12.3% 13.3% 12.4% 5.1% 7.3% 8.0% 5.2% 6.5% 6.4% 5.5% 5.8% 6.1% 7.1% % 22.4% 12.0% 13.7% 14.5% 9.5% 9.4% 6.9% 7.2% 6.8% 9.4% 12.9% 15.5% 16.3% 15.2% 8.0% 9.9% 10.4% 7.6% 8.7% 8.6% 7.6% 7.8% 7.9% 8.9% % 16.2% 7.7% 4.1% 6.9% 8.6% 5.3% 5.8% 4.0% 4.5% 4.4% 7.0% 10.3% 12.9% 13.7% 12.9% 6.4% 8.3% 8.8% 6.3% 7.4% 7.3% 6.4% 6.6% 6.8% 7.8% % -13.7% 6.4% 2.8% 0.9% 3.7% 5.6% 3.2% 3.8% 2.4% 3.0% 3.1% 5.5% 8.6% 11.1% 12.0% 11.3% 5.3% 7.2% 7.8% 5.4% 6.5% 6.4% 5.6% 5.9% 6.1% 7.0% 1991 Sell Investment period in years Following: 29

30 Historical Market Returns (Arithmetic Mean) Swiss Market SMI Total Return Index Return Triangle Reading example: An investment in the SMI Total Return beginning of the year 2009, when sold beginning of the year 2014, would have yielded an average annual return (arithmetic mean) of 12.3%. Other fiveyear investment periods are displayed along the black steps. Buy 17.9% % 7.3% % -1.1% 5.2% % 7.0% 3.6% 7.2% % 18.4% 12.7% 8.7% 10.5% % 21.5% 18.6% 14.3% 10.7% 11.9% % 7.2% 12.8% 12.8% 10.5% 8.2% 9.6% % -1.7% 5.2% 9.9% 10.5% 8.9% 7.2% 8.5% % Return greater than 13% 22.1% 11.6% 6.2% 9.4% 12.3% 12.4% 10.8% 9.0% 10.0% % Return between 8% and 13% -32.8% -5.3% -3.2% -3.5% 1.0% 4.8% 6.0% 5.4% 4.4% 5.7% % Return between 3% and 8% -1.4% -17.1% -4.0% -2.7% -3.1% 0.6% 3.9% 5.1% 4.6% 3.8% 5.1% % Return between -3% and +3% 18.0% 8.3% -5.4% 1.5% 1.4% 0.4% 3.1% 5.7% 6.5% 6.0% 5.1% 6.2% % Return between -3% and -8% 36.0% 27.0% 17.5% 5.0% 8.4% 7.2% 5.5% 7.2% 9.1% 9.4% 8.7% 7.7% 8.5% % Return between -8% and -13% 5.5% 20.7% 19.8% 14.5% 5.1% 7.9% 6.9% 5.5% 7.0% 8.7% 9.1% 8.4% 7.5% 8.3% % Return lower than -13% 20.9% 13.2% 20.8% 20.1% 15.8% 7.7% 9.8% 8.7% 7.2% 8.4% 9.8% 10.1% 9.4% 8.5% 9.1% % -2.8% -0.1% 9.0% 10.8% 8.7% 2.8% 5.2% 4.8% 3.8% 5.2% 6.8% 7.3% 6.8% 6.1% 6.9% % -23.4% -8.6% -5.1% 3.1% 5.6% 4.6% -0.1% 2.4% 2.3% 1.6% 3.1% 4.7% 5.3% 5.0% 4.5% 5.3% % -5.5% -12.5% -4.2% -2.2% 4.1% 6.1% 5.2% 1.0% 3.1% 2.9% 2.3% 3.6% 5.0% 5.6% 5.3% 4.8% 5.5% % 8.2% -1.3% -7.6% -1.9% -0.7% 4.6% 6.3% 5.4% 1.6% 3.5% 3.3% 2.7% 3.8% 5.2% 5.7% 5.4% 4.9% 5.6% % 11.4% 10.7% 3.0% -2.9% 1.0% 1.7% 6.0% 7.3% 6.4% 2.9% 4.5% 4.2% 3.6% 4.6% 5.8% 6.2% 6.0% 5.5% 6.1% % 38.3% 27.9% 23.3% 14.6% 7.7% 9.6% 9.1% 12.1% 12.7% 11.4% 7.7% 8.8% 8.3% 7.4% 8.1% 9.1% 9.3% 8.9% 8.2% 8.7% % 44.5% 34.9% 28.0% 24.2% 16.8% 10.6% 11.9% 11.2% 13.7% 14.1% 12.8% 9.3% 10.2% 9.6% 8.7% 9.3% 10.1% 10.3% 9.8% 9.2% 9.6% 1996 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 Reading example: An investment in the SMI Total Return beginning of the year 2009, when sold beginning of the year 2014, would have yielded an average annual return (geometric mean) of 11.7%. Other fiveyear investment periods are displayed along the black steps. Buy 17.9% % 6.7% % -1.1% 4.8% % 6.9% 3.3% 6.8% % 18.3% 12.3% 8.1% 10.0% % 21.5% 18.5% 13.9% 10.2% 11.5% % 6.6% 12.1% 12.3% 10.0% 7.6% 9.0% % -1.8% 4.7% 9.2% 10.0% 8.4% 6.7% 8.0% % Return greater than 13% 22.1% 11.2% 5.6% 8.8% 11.7% 11.9% 10.3% 8.5% 9.5% % Return between 8% and 13% -32.8% -9.4% -6.0% -5.7% -1.2% 2.6% 4.0% 3.7% 2.9% 4.3% % Return between 3% and 8% -1.4% -18.6% -6.8% -4.9% -4.8% -1.2% 2.0% 3.3% 3.1% 2.4% 3.8% % Return between -3% and +3% 18.0% 7.9% -7.9% -1.1% -0.7% -1.3% 1.3% 3.9% 4.9% 4.5% 3.8% 4.9% % Return between -3% and -8% 36.0% 26.7% 16.5% 1.6% 5.4% 4.7% 3.3% 5.1% 7.1% 7.6% 7.0% 6.1% 7.0% % Return between -8% and -13% 5.5% 19.8% 19.2% 13.7% 2.3% 5.4% 4.8% 3.6% 5.2% 6.9% 7.4% 6.9% 6.1% 6.9% % Return lower than -13% 20.9% 12.9% 20.1% 19.6% 15.1% 5.2% 7.5% 6.7% 5.4% 6.7% 8.1% 8.5% 7.9% 7.1% 7.8% % -5.8% -2.2% 6.2% 8.5% 6.8% 0.0% 2.5% 2.3% 1.6% 3.1% 4.7% 5.3% 5.0% 4.4% 5.2% % -23.5% -10.9% -7.0% 0.3% 3.1% 2.4% -2.8% -0.3% -0.2% -0.6% 0.9% 2.5% 3.2% 3.1% 2.7% 3.5% % -6.6% -13.8% -6.2% -4.0% 1.8% 3.9% 3.3% -1.5% 0.6% 0.6% 0.2% 1.5% 3.0% 3.6% 3.5% 3.1% 3.8% % 8.2% -2.2% -9.0% -3.7% -2.2% 2.5% 4.3% 3.7% -0.7% 1.2% 1.2% 0.7% 1.9% 3.3% 3.8% 3.7% 3.3% 4.0% % 11.3% 10.7% 2.0% -4.5% -0.7% 0.2% 4.1% 5.5% 4.8% 0.7% 2.3% 2.2% 1.7% 2.8% 4.0% 4.5% 4.3% 3.9% 4.6% % 36.4% 25.9% 21.5% 11.7% 4.2% 6.4% 6.3% 9.2% 10.1% 9.0% 4.7% 5.9% 5.6% 4.9% 5.7% 6.7% 7.0% 6.7% 6.2% 6.7% % 43.6% 33.6% 26.5% 22.8% 14.3% 7.3% 8.9% 8.5% 11.0% 11.6% 10.5% 6.3% 7.4% 7.0% 6.2% 6.9% 7.8% 8.1% 7.7% 7.1% 7.6% 1996 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 785 listed companies The 785 public companies, which are listed in the mentioned indices as of 31 December 2017, build the base of the sector classification and the subsequent analyses: The German CDAX includes 531 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 194 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 531 companies in 9 Deutsche Börse super sectors WBI 60 companies in 5 ATX sector indices classifies SPI 194 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 31 December 2017 Sector distribution and number of companies Sector classification of the DACH Region 2% 13% 22% 8% 8% 27% 2% 12% 5% FIRE ( 178 ) Basic Materials ( 41 ) Consumer Goods ( 94 ) Telecommunication ( 14 ) Industrials ( 210 ) Consumer Service ( 65 ) Pharma & Healthcare ( 63 ) Information Technology ( 107 ) Utilities ( 13 ) The chart shows the percentage distribution of the 785 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 without 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 estimation 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 31 December 2017 Sector FIRE 3) 86 / 93 Basic Materials 29 / 31 Consumer Goods 46 / 45 Telecommunication 7 / 9 Industrials 118 / 128 Consumer Service 24 / 28 Pharma & Healthcare 31 / 32 Information Technology 45 / 59 Utilities 6 / 6 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 % 29% Arithmetic mean % 31% Market-value weighted mean % 26% Median % 19% Arithmetic mean % 30% Market-value weighted mean % 39% Median % 29% Arithmetic mean % 35% Market-value weighted mean % 40% Median % 16% Arithmetic mean % 26% Market-value weighted mean % 27% Median % 18% Arithmetic mean % 30% Market-value weighted mean % 31% Median % 8% Arithmetic mean % 14% Market-value weighted mean % 16% Median % 9% Arithmetic mean % 15% Market-value weighted mean % 10% Median % 46% Arithmetic mean % 52% Market-value weighted mean % 55% ) 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 is 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 31 December 2017, we observe the sector specific, unlevered cost of equity of 6.2% (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: 1.29% (cf. slide 13) Based on these numbers, we can calculate the relevered costs of equity of company X with the adjustment formula: k E L = 6.2% + (6.2% %) * 40% = 8.16% Thus, 8.16% is the company s relevered cost of equity. In comparison, the levered cost of equity of the Basic Materials sector is 7.5%. 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 2.0% -1.5% 1.5% 3.8% 1.4% 0.4% 1.8% 1.5% 1.6% 1.5% 2.6% 3.1% Lower quantile 3.8% 3.3% 4.3% 4.8% 4.5% 4.2% 4.1% 4.6% 4.4% 4.3% 4.2% 4.6% Median 9.0% 8.3% 7.5% 7.9% 7.6% 8.0% 6.8% 7.9% 7.1% 7.2% 6.9% 7.1% Arithmetic mean 9.8% 8.8% 7.8% 9.2% 8.6% 9.3% 8.7% 8.9% 8.0% 8.1% 7.8% 7.5% Market-value weighted mean 11.6% 10.2% 9.8% 9.3% 8.6% 9.3% 8.6% 8.6% 8.3% 7.9% 7.7% 7.8% Upper quantile 14.8% 13.6% 11.6% 10.9% 11.4% 13.1% 11.5% 14.4% 12.0% 11.3% 11.6% 10.3% Maximum 45.5% 21.4% 18.6% 89.5% 54.1% 60.7% 55.8% 60.1% 24.3% 32.4% 33.6% 21.9% Market-value weighted debt % 756.7% 624.1% 468.9% 466.2% 482.2% 401.2% 376.4% 512.1% 390.8% 340.3% 295.8% Implied sector returns - DACH - FIRE 16.0% 14.0% 12.0% 10.0% 8.0% 6.0% 4.0% 11.6% 10.2% 9.8% 9.3% 8.6% 9.3% 8.6% 8.6% 8.3% 7.9% 7.7% 7.8% The implied market return in the FIRE sector slightly increased from 7.7% as of 30 June 2017 to 7.8% as of 31 December Over the course of time, the marketvalue weighted mean of the implied sector return decreased noticeably since 30 June 2012 (11.6%). 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 ispart 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 3.6% 2.6% 2.2% -0.4% -1.8% 0.2% 1.3% 3.6% 0.4% 2.1% 0.6% 1.3% Lower quantile 4.9% 4.5% 5.7% 2.9% 3.2% 3.8% 1.9% 4.7% 2.6% 4.1% 4.4% 3.3% Median 8.7% 9.2% 8.3% 7.2% 6.2% 7.6% 6.2% 7.7% 7.6% 6.8% 6.8% 7.0% Arithmetic mean 10.0% 9.5% 9.4% 8.1% 9.0% 7.8% 6.2% 8.1% 6.7% 7.3% 6.9% 7.6% Market-value weighted mean 10.0% 8.9% 8.5% 7.8% 7.3% 7.7% 7.0% 7.6% 7.5% 7.4% 7.4% 7.5% Upper quantile 15.2% 13.1% 11.2% 11.9% 10.1% 11.8% 9.1% 11.1% 9.2% 9.4% 10.6% 9.8% Maximum 30.0% 32.7% 45.1% 41.7% 73.2% 20.4% 9.9% 20.4% 11.0% 23.3% 13.9% 31.3% Market-value weighted debt 42.3% 36.1% 35.9% 29.7% 33.0% 38.3% 35.2% 34.9% 43.5% 35.0% 31.9% 29.2% Implied sector returns (unlevered) - DACH - Basic Materials H H H H H H H H H H H H Minimum 3.0% 2.5% 2.3% 1.3% 0.9% 1.9% 1.2% 2.6% 0.5% 1.9% 0.9% 2.0% Lower quantile 4.0% 3.4% 4.8% 2.8% 2.7% 3.6% 1.7% 3.3% 2.1% 2.4% 3.1% 3.7% Median 7.4% 7.2% 6.4% 6.0% 5.4% 5.9% 5.0% 5.7% 5.4% 5.3% 5.2% 5.9% Arithmetic mean 7.8% 7.8% 7.8% 5.8% 7.4% 5.9% 4.9% 5.7% 5.0% 5.4% 5.3% 6.0% Market-value weighted mean 7.8% 7.3% 7.0% 6.7% 6.2% 6.2% 5.6% 6.0% 5.6% 5.8% 5.9% 6.2% Upper quantile 10.3% 10.1% 9.5% 7.6% 7.5% 8.1% 7.1% 7.3% 6.8% 8.1% 7.9% 8.7% Maximum 27.3% 29.6% 39.1% 12.9% 57.6% 12.6% 8.3% 12.1% 8.9% 15.0% 9.0% 15.3% Market-value weighted debt 42.3% 36.1% 35.9% 29.7% 33.0% 38.3% 35.2% 34.9% 43.5% 35.0% 31.9% 29.2% 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 from 5.9% as of 30 June 2017 to 6.2% as of 31 December In comparison to other sectors, the Basic Materials sector showed the highest unlevered implied return as of 31 December Overall, we could identify a fluctuation between 5.6% and 7.8% of the market-value weighted mean (unlevered) since 30 June % 10.0% 8.0% 10.0% 8.9% 8.5% 7.8% 7.3% 7.7% 7.0% 7.6% 7.5% 7.4% 7.4% 7.5% 6.0% 4.0% 7.8% 7.3% 7.0% 6.7% 6.2% 6.2% 5.6% 6.0% 5.6% 5.8% 5.9% 6.2% 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 4.6% 5.5% 1.5% 4.8% 1.3% 2.2% 0.5% 2.1% 2.6% 2.5% 2.0% 1.7% Lower quantile 6.1% 7.1% 6.2% 6.1% 4.8% 4.5% 4.6% 4.4% 4.7% 4.8% 3.4% 4.7% Median 9.1% 9.5% 9.0% 9.1% 8.0% 7.9% 7.6% 7.9% 7.5% 7.6% 6.9% 7.3% Arithmetic mean 10.9% 10.8% 9.6% 9.4% 11.2% 8.0% 7.4% 7.8% 8.5% 8.1% 7.4% 7.7% Market-value weighted mean 11.0% 10.1% 9.6% 8.8% 8.4% 8.8% 8.4% 8.4% 9.1% 8.8% 8.8% 8.9% Upper quantile 17.9% 16.1% 14.2% 12.6% 11.8% 11.3% 10.2% 11.2% 14.7% 12.8% 12.2% 11.7% Maximum 26.7% 28.8% 28.2% 31.1% 113.8% 16.6% 15.1% 13.4% 21.3% 16.8% 18.9% 16.7% Market-value weighted debt 81.1% 69.4% 68.6% 54.7% 58.0% 60.6% 59.8% 61.4% 79.6% 70.0% 67.9% 67.4% Implied sector returns (unlevered) - DACH - Consumer Goods H H H H H H H H H H H H Minimum 0.5% 0.7% 0.7% 1.0% 0.6% 0.6% 0.3% 0.4% 0.2% 0.4% 0.5% 1.3% Lower quantile 4.1% 4.4% 4.8% 4.5% 3.8% 2.3% 2.5% 3.0% 3.0% 2.9% 2.1% 3.0% Median 6.1% 6.3% 6.4% 6.6% 5.8% 5.7% 5.0% 5.2% 5.2% 5.1% 4.7% 5.5% Arithmetic mean 8.2% 8.1% 7.5% 7.6% 7.6% 6.5% 5.8% 6.0% 6.1% 5.9% 5.6% 5.7% Market-value weighted mean 7.3% 7.6% 7.0% 7.0% 6.3% 6.6% 6.6% 6.5% 6.1% 5.2% 5.5% 5.3% Upper quantile 14.6% 14.5% 11.3% 12.0% 9.9% 10.1% 8.8% 9.0% 9.9% 9.6% 9.3% 8.1% Maximum 18.2% 19.5% 16.7% 20.2% 44.8% 12.0% 11.1% 10.6% 13.3% 13.3% 14.6% 12.6% Market-value weighted debt 81.1% 69.4% 68.6% 54.7% 58.0% 60.6% 59.8% 61.4% 79.6% 70.0% 67.9% 67.4% 44

45 Implied Sector Returns Consumer Goods (chart) 16.0% Implied sector returns - DACH - Consumer Goods The implied sector return (unlevered) of the Consumer Goods sector decreased from 5.5% to 5.3% from 30 June 2017 to 31 December In comparison to the other sectors, the Consumer Goods sector had the highest levered implied sector return as of 31 December Overall, we could identify a fluctuation between 5.2% and 7.6% of the market-value weighted mean (unlevered) since 30 June % 12.0% 11.0% 10.0% 10.1% 9.6% 8.8% 8.4% 8.8% 8.4% 8.4% 9.1% 8.8% 8.8% 8.9% 8.0% 6.0% 4.0% 7.3% 7.6% 7.0% 7.0% 6.3% 6.6% 6.6% 6.5% 6.1% 5.2% 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). 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 3.4% 2.4% 1.8% 1.7% 5.1% -0.7% -1.8% 4.0% 2.8% 3.9% 3.4% 3.4% Lower quantile 3.9% 4.9% 2.7% 4.0% 5.3% 2.8% 1.8% 4.3% 3.2% 4.4% 3.7% 4.8% Median 7.7% 10.0% 8.6% 6.9% 5.8% 6.4% 6.7% 7.3% 5.9% 6.8% 4.6% 7.1% Arithmetic mean 8.4% 9.2% 8.3% 6.9% 6.5% 5.9% 5.4% 6.8% 5.9% 6.5% 5.5% 6.5% Market-value weighted mean 8.7% 8.5% 8.2% 6.7% 6.4% 6.6% 6.3% 6.7% 7.3% 7.3% 6.7% 7.5% Upper quantile 12.1% 13.2% 13.4% 9.7% 8.5% 8.4% 7.6% 8.4% 8.9% 8.5% 7.8% 7.8% Maximum 15.3% 13.2% 16.4% 10.0% 9.3% 9.2% 7.7% 9.1% 9.3% 8.7% 8.0% 7.8% Market-value weighted debt 119.2% 103.1% 107.8% 84.6% 78.7% 79.0% 68.8% 71.3% 80.5% 75.9% 73.1% 70.8% Implied sector returns (unlevered) - DACH - Telecommunication H H H H H H H H H H H H Minimum 3.3% 2.4% 1.9% 1.9% 3.5% 0.5% -0.1% 2.6% 1.6% 2.1% 2.3% 2.6% Lower quantile 4.0% 3.3% 3.1% 3.3% 3.9% 2.5% 2.1% 3.3% 2.1% 2.4% 2.3% 3.3% Median 6.1% 6.9% 6.6% 6.4% 5.2% 5.0% 4.5% 4.8% 4.0% 5.3% 4.0% 5.3% Arithmetic mean 6.3% 6.6% 7.0% 5.8% 5.3% 4.8% 4.3% 4.9% 4.1% 4.7% 4.1% 4.7% Market-value weighted mean 5.4% 5.3% 5.2% 4.9% 4.7% 4.5% 4.2% 4.4% 4.3% 4.5% 4.3% 4.8% Upper quantile 8.8% 9.9% 11.8% 8.3% 7.0% 7.1% 6.2% 6.9% 5.4% 6.0% 5.5% 5.5% Maximum 10.1% 10.2% 15.0% 8.8% 8.0% 8.0% 6.7% 7.1% 6.1% 6.7% 5.7% 5.7% Market-value weighted debt 119.2% 103.1% 107.8% 84.6% 78.7% 79.0% 68.8% 71.3% 80.5% 75.9% 73.1% 70.8% 46

47 Implied Sector Returns Telecommunication (chart) 16.0% 14.0% 12.0% Implied sector returns - DACH - Telecommunication The implied sector return (unlevered) of the Telecommunication sector increased from 4.3% as of 30 June 2017 to 4.8% as of 31 December In comparison to the other sectors, the Telecommunication sector had the second lowest unlevered implied sector return as of 31 December Overall, the fluctuation of the market-value weighted mean (unlevered) since 30 June 2012 is quite small (4.2% to 5.4%). 10.0% 8.0% 8.7% 8.5% 8.2% 6.7% 6.4% 6.6% 6.3% 6.7% 7.3% 7.3% 6.7% 7.5% 6.0% 4.0% 2.0% 0.0% 5.4% 5.3% 5.2% 4.9% 4.7% 4.5% 4.2% 4.4% 4.3% 4.5% 4.3% 4.8% 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 0.3% 0.1% 0.0% 0.3% 0.6% 0.2% -0.4% 1.6% 1.7% 2.1% 0.5% 2.7% Lower quantile 3.6% 4.7% 4.6% 4.5% 3.5% 3.3% 2.9% 4.4% 4.3% 4.3% 3.2% 3.5% Median 9.0% 8.9% 8.0% 7.6% 6.8% 8.0% 6.5% 7.2% 7.4% 7.2% 6.2% 6.7% Arithmetic mean 10.0% 9.2% 8.0% 7.6% 6.5% 7.9% 6.4% 7.7% 7.8% 7.0% 5.7% 6.3% Market-value weighted mean 9.5% 8.4% 7.8% 7.6% 7.2% 7.8% 7.0% 7.3% 7.6% 6.9% 6.8% 6.9% Upper quantile 13.1% 13.2% 10.7% 10.3% 8.7% 10.6% 8.8% 10.3% 9.8% 9.4% 7.6% 8.3% Maximum 61.6% 27.5% 31.9% 16.0% 12.3% 29.1% 12.8% 40.7% 42.5% 18.1% 9.5% 11.5% Market-value weighted debt 54.5% 48.8% 48.2% 37.6% 39.1% 40.3% 41.7% 42.6% 47.4% 39.0% 37.4% 34.1% Implied sector returns (unlevered) - DACH - Industrials H H H H H H H H H H H H Minimum 1.5% 0.7% 0.1% 0.3% 0.6% 0.6% 0.5% 1.5% 1.5% 1.7% 0.4% 2.4% Lower quantile 4.2% 4.6% 4.2% 4.6% 3.0% 3.6% 2.4% 3.1% 2.4% 2.4% 2.3% 3.3% Median 7.1% 7.2% 6.7% 6.9% 6.1% 6.4% 4.3% 5.2% 5.1% 5.2% 5.1% 5.4% Arithmetic mean 9.8% 7.1% 6.2% 6.2% 5.5% 5.9% 4.5% 5.4% 5.1% 5.3% 4.5% 5.2% Market-value weighted mean 6.9% 6.4% 6.1% 6.2% 5.8% 6.1% 5.4% 5.6% 5.5% 5.5% 5.3% 5.5% Upper quantile 10.2% 9.9% 8.2% 8.2% 7.6% 7.7% 6.1% 7.9% 7.3% 7.4% 6.2% 6.9% Maximum 61.5% 11.4% 9.2% 9.4% 8.4% 9.5% 8.0% 8.0% 7.6% 9.7% 6.8% 7.5% Market-value weighted debt 54.5% 48.8% 48.2% 37.6% 39.1% 40.3% 41.7% 42.6% 47.4% 39.0% 37.4% 34.1% 48

49 Implied Sector Returns Industrials (chart) 16.0% 14.0% 12.0% Implied sector returns - DACH - Industrials The implied sector return (unlevered) of the Industrials sector increased from 5.3% as of 30 June 2017 to 5.5% as of 31 December Overall, we could identify a fluctuation between 5.3% and 6.9% of the market-value weighted mean (unlevered) since 30 June % 8.0% 9.5% 8.4% 7.8% 7.6% 7.2% 7.8% 7.0% 7.3% 7.6% 6.9% 6.8% 6.9% 6.0% 4.0% 6.9% 6.4% 6.1% 6.2% 5.8% 6.1% 5.4% 5.6% 5.5% 5.5% 5.3% 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). 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 2.2% 2.9% 2.3% 3.2% 3.6% 2.9% 2.7% 1.6% 1.8% 1.3% 2.1% 2.0% Lower quantile 5.0% 6.7% 4.4% 4.3% 5.5% 4.3% 3.8% 3.0% 3.4% 3.8% 3.3% 2.9% Median 9.7% 9.1% 7.9% 7.3% 6.6% 7.5% 5.9% 6.2% 6.4% 6.7% 6.0% 6.2% Arithmetic mean 9.8% 10.1% 8.6% 8.1% 9.0% 9.8% 7.0% 6.7% 6.6% 6.4% 5.9% 6.1% Market-value weighted mean 9.3% 9.2% 7.6% 7.0% 6.7% 7.7% 5.9% 5.7% 6.2% 6.2% 5.6% 6.1% Upper quantile 15.0% 13.5% 13.7% 11.3% 11.3% 20.5% 10.5% 10.7% 8.9% 8.8% 8.2% 8.7% Maximum 21.6% 25.7% 16.3% 23.5% 39.1% 35.1% 19.0% 14.7% 14.1% 9.5% 10.3% 11.0% Market-value weighted debt 35.2% 35.3% 28.4% 23.2% 22.8% 20.3% 17.4% 21.5% 25.0% 26.0% 23.9% 32.9% Implied sector returns (unlevered) - DACH - Consumer Service H H H H H H H H H H H H Minimum 2.9% 2.8% 2.3% 2.9% 3.6% 2.7% 2.1% 1.9% 1.3% 1.2% 2.1% 1.9% Lower quantile 4.8% 5.5% 3.3% 4.8% 4.9% 3.6% 3.0% 3.2% 3.4% 2.5% 2.8% 2.8% Median 7.6% 7.3% 6.3% 6.1% 5.8% 6.4% 5.2% 5.1% 5.0% 5.0% 5.0% 5.2% Arithmetic mean 8.1% 7.6% 6.2% 6.6% 7.4% 7.9% 5.2% 5.6% 5.3% 5.1% 4.9% 4.9% Market-value weighted mean 7.3% 7.3% 6.3% 6.2% 5.8% 6.5% 4.8% 4.9% 5.1% 5.1% 4.7% 5.0% Upper quantile 10.3% 9.9% 7.9% 9.3% 7.9% 14.6% 7.5% 8.2% 8.4% 7.1% 6.5% 6.5% Maximum 20.9% 12.4% 10.5% 10.3% 38.9% 34.6% 8.3% 11.4% 8.9% 8.3% 8.8% 7.2% Market-value weighted debt 35.2% 35.3% 28.4% 23.2% 22.8% 20.3% 17.4% 21.5% 25.0% 26.0% 23.9% 32.9% 50

51 Implied Sector Returns Consumer Service (chart) 16.0% 14.0% 12.0% Implied sector returns - DACH - Consumer Service The implied sector return (unlevered) of the Consumer Service sector increased from 4.7% as of 30 June 2017 to 5.0% as of 31 December Overall, we can identify a fluctuation between 4.7% and 7.3% of the marketvalue weighted mean (unlevered) since 30 June % 9.3% 9.2% 8.0% 6.0% 4.0% 7.3% 7.3% 7.6% 7.0% 6.7% 6.3% 6.2% 5.8% 7.7% 6.5% 5.9% 6.2% 6.2% 5.7% 6.1% 5.6% 4.8% 4.9% 5.1% 5.1% 4.7% 5.0% 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). 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 -0.4% 1.5% -0.4% 2.3% 0.8% 2.0% 1.4% 1.1% 1.4% 1.6% 2.0% 1.9% Lower quantile 3.6% 3.6% 2.5% 5.4% 2.4% 3.5% 2.0% 3.7% 3.7% 2.7% 2.9% 2.4% Median 7.4% 7.3% 6.5% 6.7% 6.1% 6.3% 5.8% 6.1% 6.0% 5.6% 5.8% 5.0% Arithmetic mean 9.9% 7.4% 6.5% 7.0% 5.9% 6.9% 5.6% 6.6% 5.9% 7.9% 5.9% 5.5% Market-value weighted mean 9.7% 9.3% 8.0% 7.9% 7.1% 7.4% 6.8% 7.1% 7.1% 7.7% 7.0% 7.1% Upper quantile 14.3% 10.4% 9.0% 9.4% 8.3% 9.3% 7.5% 8.6% 7.9% 8.0% 7.3% 7.7% Maximum 73.9% 12.9% 19.4% 11.0% 11.7% 22.2% 11.5% 24.8% 9.2% 76.3% 27.9% 20.2% Market-value weighted debt 27.0% 24.3% 18.1% 16.7% 15.7% 18.2% 16.8% 18.5% 20.3% 20.6% 20.2% 19.6% Implied sector returns (unlevered) - DACH - Pharma & Healthcare H H H H H H H H H H H H Minimum 0.4% 1.8% 0.8% 2.3% 1.3% 2.0% 1.4% 1.2% 1.3% 1.4% 1.5% 1.4% Lower quantile 3.1% 3.0% 2.4% 4.6% 3.1% 3.8% 1.9% 3.2% 2.8% 2.1% 2.9% 2.2% Median 6.2% 6.3% 5.7% 6.1% 5.6% 5.8% 5.1% 5.2% 4.9% 5.3% 4.8% 4.5% Arithmetic mean 8.0% 6.2% 5.9% 6.3% 5.4% 5.7% 4.7% 5.0% 4.9% 5.3% 4.6% 5.1% Market-value weighted mean 8.1% 7.8% 7.1% 7.1% 6.5% 6.5% 6.0% 6.2% 6.0% 6.6% 6.0% 6.1% Upper quantile 13.6% 8.9% 7.7% 7.8% 7.3% 7.4% 6.3% 6.6% 6.5% 6.7% 5.9% 6.5% Maximum 52.3% 9.5% 18.0% 10.6% 10.8% 9.2% 6.6% 7.7% 7.6% 18.6% 7.1% 20.1% Market-value weighted debt 27.0% 24.3% 18.1% 16.7% 15.7% 18.2% 16.8% 18.5% 20.3% 20.6% 20.2% 19.6% 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 increased slightly from 6.0% as of 30 June 2017 to 6.1% as of 31 December In comparison to other sectors, the Pharma & Healthcare sector had the second highest unlevered implied return as of 31 December Overall, we could identify a fluctuation between 6.0% and 8.1% of the market-value weighted mean (unlevered) since 30 June % 10.0% 8.0% 6.0% 4.0% 9.7% 9.3% 8.1% 7.8% 8.0% 7.9% 7.1% 7.1% 7.1% 7.4% 6.5% 6.5% 6.8% 7.1% 7.1% 6.0% 6.2% 6.0% 7.7% 6.6% 7.0% 7.1% 6.0% 6.1% 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 0.6% 1.3% 1.4% 1.3% 1.8% 0.9% -1.4% 1.0% 1.7% 0.2% 2.1% 0.5% Lower quantile 5.0% 4.3% 5.1% 4.3% 4.2% 5.0% 4.6% 4.4% 3.8% 4.6% 3.6% 3.6% Median 8.7% 8.5% 7.1% 7.3% 6.4% 7.6% 6.2% 6.5% 6.2% 6.6% 5.5% 5.5% Arithmetic mean 8.8% 8.6% 7.5% 7.2% 6.4% 7.6% 6.4% 6.6% 6.3% 7.5% 5.7% 5.7% Market-value weighted mean 8.1% 7.3% 7.3% 7.1% 7.0% 7.4% 6.9% 6.7% 7.0% 6.6% 6.0% 6.1% Upper quantile 13.5% 11.9% 10.3% 9.8% 9.1% 10.8% 9.2% 8.7% 7.9% 10.1% 7.6% 6.9% Maximum 17.9% 19.1% 17.3% 15.3% 11.9% 14.1% 11.7% 18.6% 16.5% 35.0% 15.8% 28.2% Market-value weighted debt 8.0% 7.6% 8.0% 6.5% 7.1% 16.3% 14.1% 10.7% 11.0% 8.5% 6.8% 5.5% Implied sector returns (unlevered) - DACH - Information Technology H H H H H H H H H H H H Minimum 0.9% 1.8% 1.8% 1.7% 2.7% 2.2% 1.3% 1.0% 1.2% 0.2% 2.0% 0.5% Lower quantile 4.4% 4.6% 4.9% 3.8% 4.0% 4.8% 3.9% 4.2% 3.0% 3.9% 3.0% 3.3% Median 7.5% 7.7% 6.5% 6.5% 5.8% 6.6% 5.4% 5.4% 5.3% 6.0% 5.0% 5.1% Arithmetic mean 7.6% 7.5% 6.6% 6.5% 5.8% 6.7% 5.5% 5.7% 5.4% 6.6% 5.0% 4.9% Market-value weighted mean 7.6% 6.9% 6.9% 6.9% 6.7% 6.7% 6.2% 6.2% 6.3% 6.1% 5.7% 5.8% Upper quantile 11.1% 10.1% 8.8% 9.0% 7.7% 9.0% 7.1% 7.4% 7.3% 8.3% 6.3% 6.3% Maximum 16.3% 13.0% 12.6% 10.9% 9.0% 10.6% 9.5% 17.6% 15.4% 33.4% 11.2% 9.5% Market-value weighted debt 8.0% 7.6% 8.0% 6.5% 7.1% 16.3% 14.1% 10.7% 11.0% 8.5% 6.8% 5.5% 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 slightly increased from 5.7% as of 30 June 2017 to 5.8% as of 31 December 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 7.6% of the market-value weighted mean (unlevered) since 30 June % 10.0% 8.0% 6.0% 4.0% 8.1% 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% 6.1% 5.7% 5.8% 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 3.4% 4.6% 5.6% 4.2% 3.7% 3.8% 3.9% 4.9% 5.2% 4.8% 5.5% 4.5% Lower quantile 6.1% 5.5% 6.3% 5.5% 4.0% 4.9% 5.4% 5.2% 6.8% 5.7% 5.5% 5.2% Median 8.3% 8.9% 7.4% 7.3% 7.0% 7.1% 6.5% 7.0% 7.5% 7.4% 7.5% 6.2% Arithmetic mean 8.3% 8.5% 8.6% 7.5% 6.8% 7.0% 7.8% 7.0% 7.6% 7.7% 7.7% 6.8% Market-value weighted mean 9.7% 10.3% 10.5% 7.9% 6.6% 6.9% 8.3% 7.6% 8.3% 8.2% 8.6% 7.4% Upper quantile 10.7% 11.1% 11.6% 9.9% 8.9% 9.4% 11.5% 8.7% 8.9% 10.2% 9.7% 8.6% Maximum 13.0% 13.3% 16.2% 10.1% 9.9% 10.1% 14.6% 9.7% 9.4% 10.5% 11.8% 10.7% Market-value weighted debt 100.7% 116.7% 108.4% 113.1% 87.8% 118.4% 107.9% 158.5% 124.5% 139.9% 101.6% 89.8% Implied sector returns (unlevered) - DACH - Utilities H H H H H H H H H H H H Minimum 2.7% 3.2% 3.9% 3.5% 3.1% 2.8% 3.0% 2.6% 2.9% 2.6% 2.7% 3.0% Lower quantile 4.0% 3.9% 4.1% 4.0% 3.3% 3.0% 3.0% 3.0% 3.1% 2.7% 3.5% 3.3% Median 5.3% 5.1% 5.0% 4.8% 4.5% 4.2% 4.0% 3.8% 3.6% 4.1% 4.2% 4.1% Arithmetic mean 5.3% 5.1% 5.3% 4.9% 4.5% 4.0% 4.2% 3.7% 3.7% 4.1% 4.5% 4.1% Market-value weighted mean 5.9% 5.9% 6.1% 5.1% 4.6% 4.1% 4.4% 3.8% 4.1% 4.1% 4.8% 4.4% Upper quantile 6.4% 6.3% 6.4% 5.8% 5.4% 4.7% 5.4% 4.6% 4.5% 5.5% 5.4% 5.0% Maximum 7.4% 6.9% 8.2% 6.0% 5.6% 4.8% 7.2% 4.7% 4.9% 6.0% 6.9% 5.3% Market-value weighted debt 100.7% 116.7% 108.4% 113.1% 87.8% 118.4% 107.9% 158.5% 124.5% 139.9% 101.6% 89.8% 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 decreased from 4.8% as of 30 June 2017 to 4.4% as of 31 December In comparison to other sectors, the Utilities sector had the lowest unlevered implied return as of 31 December Overall, we could identify a fluctuation between 3.8% and 6.1% of the market-value weighted mean (unlevered) since 30 June % 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% 7.4% 6.0% 4.0% 2.0% 5.9% 5.9% 6.1% 5.1% 4.6% 4.1% 4.4% 3.8% 4.1% 4.1% 4.8% 4.4% 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 25 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 31 December 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 31 December ,0% FIRE 50,0% 48,6% Basic Materials 40,0% 33,9% 32,4% 42,5% 41,1% 32,2% 38,5% 36,5% 41,1% 34,3% Consumer Goods 30,0% 20,0% 10,0% 0,0% -10,0% -20,0% 27,8% 23,4% 18,3% 17,0% 7,3% 0,2% 27,5% 22,7% 16,4% 26,9% 18,6% -2,6% 25,7% 9,3% 8,6% 8,5% 6,3% 6,3% 5,6% 4,9% 5,0% 22,1% 20,6% 20,7% 10,5% 12,1% 10,2% -20,4% 19,3% 14,8% 15,1% 2,7% 2,0% 0,6% -0,4% -1,4% -6,0% 23,3% 18,3% 17,6% 13,7% 10,7% 6,0% 2,5% Telecommunication Industrials Consumer Service Pharma & Healthcare Information Technology Utilities -30,0% FIRE Basic Materials Consumer Goods Telecommunication Industrials Consumer Service Pharma & Healthcare Information Technology 3-year-average ( ) 13,2% 14,2% 9,0% 8,1% 18,2% 14,4% 8,4% 30,9% 5,3% 6-year-average ( ) 15,4% 17,5% 15,5% 13,5% 18,3% 19,9% 16,7% 26,7% 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 markets (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 2) /BV ) We present historical multiples since 31 December 2012 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 31 December 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 (31 December 2017). 1) Enterprise Value 2) Equity Value 62

63 Multiples Executive summary (1/2) DACH At the reference date 31 December 2017, the medians of the illustrated multiples EV/Revenue (1yf), EV/EBIT (1yf), P/E (1yf) and EqV/BV reached their highest level compared to the past six years. The median of the Revenue- and EBIT-Multiples increased in a constant manner from of 1.0x in 2012 to 1.5x in 2017 (LTM) and from 13.1x in 2012 to 20.7x 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) increased from 16.2x as of 30 June 2017 to 17.3x as of 31 December Basic Materials Compared to the past six years, the Revenue-Multiples were at their highest level with the median amounting to 1.8x (1yf) as of 31 December The median of the EBIT-Multiples based on the expected EBIT (1yf) of the Basic Materials sector increased slightly from 16.0x as of 30 June 2017 to 16.3x as of 31 December Consumer Goods The median of the EBIT-Multiples increased from 16.9x (LTM) as of 31 December 2016 to 18.3x (LTM) as of 31 December The sector had the lowest Revenue-Multiples with a median of 1.2x (LTM and 1yf) compared to the other sectors. Telecommunication The sector Telecommunication reached its highest EBIT-Multiples compared to its past six years with a median of 22.0x (LTM) as of 31 December The median of the one-year forward EBIT-Multiples amounted to 17.8x as of 31 December Its P/E-Multiples showed a relatively high fluctuation over the past six years with the median varying from 10.4x 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 except the EBIT-Multiples reached their highest value for the past six years. The median of the Revenue-Multiples increased from 1.2x (LTM) and (1yf) as of 31 December 2016 to 1.4x (LTM) and 1.6x (1yf) as of 31 December EBIT-Multiples averaged 18.9x (1yf,median) as of 31 December Consumer Service The median of the Revenue-Multiples increased from 1.1x (LTM) and 1.4x (1yf) as of 31 December 2016 to 1.3x (LTM) and 1.9x (1yf) as of 31 December The median of the EBIT-Multiples increased from 19.8x (LTM) and 15.4x (1yf) as of 31 December 2016 to 20.1x (LTM) and 20.5x (1yf) as of 31 December Pharma & Healthcare The Pharma & Healthcare sector showed the highest median EBIT-Multiples compared to all other sectors as of 31 December 2017: It had EV/EBIT median multiples of 27.0x (LTM) and 24.8x (1yf). Its Revenue-Multiples represented the highest values, too, with a median of 3.1x (LTM) and 3.7x (1yf) as of 31 December Information Technology The Information Technology sector represented the highest EqV/BV-Multiples with a median of 3.3x as of 31 December The sector also showed the highest P/E-Multiples compared to all other sectors. It had P/E median multiples of 27.2x (LTM) and 29.4x (1yf). Utilities The Utilities sector multiples showed high volatility in the past six years, compared to the other sectors. However, the Revenue-Multiples reached their highest median of 1.9x (LTM) and (1yf) as of the reference date compared to 1.8x (LTM) and 0.9x (1yf) as of 31 December

65 Trading Multiples Sector multiples (1/3) LTM and 1yf as of 31 December 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 8.6x 11.3x 3.7x 11.6x 0.7x Arithmetic mean 2.4x 2.4x 25.0x 21.4x 24.4x 24.3x 2.9x Median 1.5x 1.7x 20.7x 19.0x 21.0x 21.7x 2.1x Max 13.4x 10.3x 89.8x 58.1x 83.1x 65.9x 11.7x Standard deviation 2.3x 2.0x 14.2x 8.4x 15.2x 10.2x 2.2x Number of companies Min x 11.6x 0.7x Arithmetic mean x 20.9x 1.8x Median x 17.3x 1.3x Max x 56.2x 11.3x Standard deviation x 9.4x 1.5x Number of companies Min 0.4x 0.6x 8.9x 12.1x 9.5x 12.3x 0.7x Arithmetic mean 1.9x 2.3x 21.3x 18.1x 23.7x 21.0x 2.5x Median 1.4x 1.8x 18.6x 16.3x 18.3x 18.3x 2.0x Max 6.9x 7.3x 52.8x 27.1x 79.1x 36.0x 8.6x Standard deviation 1.5x 1.6x 10.9x 5.2x 15.1x 7.1x 1.7x Number of companies Min 0.4x 0.6x 9.2x 11.4x 7.5x 14.1x 0.7x Arithmetic mean 1.5x 1.4x 22.6x 17.4x 21.4x 22.0x 2.2x Median 1.2x 1.2x 18.3x 16.0x 18.5x 19.8x 1.8x Max 8.0x 4.2x 89.8x 36.0x 59.4x 38.6x 5.7x Standard deviation 1.4x 0.9x 14.5x 5.0x 10.2x 6.8x 1.2x 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 1.9x as of the reference date 31 December EUR 300m in revenues over the last 12 months would result in an enterprise value of EUR 570m. 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 31 December 2017 Telecommunication Industrials Consumer Service EV/Revenue EV/EBIT P/E EqV/BV LTM 1yf LTM 1yf LTM 1yf Min 0.4x 1.7x 14.2x 14.5x 7.4x 15.7x 0.7x Arithmetic mean 1.8x 2.6x 25.9x 20.9x 23.5x 19.3x 2.5x Median 1.8x 2.0x 22.0x 17.8x 14.1x 17.3x 1.9x Max 3.7x 4.4x 56.6x 33.8x 68.6x 29.0x 8.8x Standard deviation 1.0x 0.9x 13.9x 6.8x 21.0x 5.0x 2.1x Number of companies Min 0.4x 0.5x 8.7x 12.1x 4.0x 13.3x 0.7x Arithmetic mean 2.1x 2.1x 24.1x 20.9x 28.4x 24.1x 3.2x Median 1.4x 1.6x 20.6x 18.9x 24.1x 21.8x 2.4x Max 12.6x 9.8x 72.4x 56.7x 83.1x 65.9x 11.7x Standard deviation 2.2x 1.7x 11.6x 7.3x 16.7x 8.5x 2.3x Number of companies Min 0.4x 0.5x 8.7x 11.8x 3.7x 12.3x 0.7x Arithmetic mean 2.4x 3.1x 26.7x 21.9x 21.3x 25.7x 3.8x Median 1.3x 1.9x 20.1x 20.5x 19.4x 24.9x 2.4x Max 9.9x 10.1x 86.3x 53.0x 46.8x 65.8x 10.8x Standard deviation 2.3x 2.6x 19.0x 10.4x 11.7x 12.1x 3.0x Number of companies Reading example: The median Consumer Service EV/EBIT ratio calculated on the basis of the expected EBIT (1-year forward) is 20.5x as of the reference date 31 December An expected EBIT of EUR 30m would result in an enterprise value of EUR 615m. 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 31 December 2017 Pharma & Healthcare Information Technology Utilities EV/Revenue EV/EBIT P/E EqV/BV LTM 1yf LTM 1yf LTM 1yf Min 0.4x 0.7x 8.9x 11.3x 4.3x 13.8x 0.7x Arithmetic mean 3.8x 3.9x 29.0x 26.1x 29.7x 27.2x 3.8x Median 3.1x 3.7x 27.0x 24.1x 27.2x 24.8x 2.9x Max 11.9x 10.2x 68.0x 58.1x 68.5x 56.9x 10.0x Standard deviation 2.8x 2.2x 15.8x 12.0x 14.9x 10.8x 2.5x Number of companies Min 0.4x 0.7x 8.6x 14.8x 4.9x 16.4x 0.7x Arithmetic mean 3.0x 3.1x 27.7x 25.8x 31.0x 33.4x 4.0x Median 2.1x 2.3x 23.5x 23.2x 27.2x 29.4x 3.3x Max 13.4x 10.3x 82.7x 49.8x 73.4x 62.9x 11.7x Standard deviation 2.5x 2.3x 14.5x 8.8x 15.9x 13.1x 2.5x Number of companies Min 0.8x 0.9x 12.8x 11.3x 6.0x 12.0x 0.8x Arithmetic mean 3.1x 1.9x 27.4x 16.0x 19.6x 17.4x 1.6x Median 1.9x 1.9x 21.3x 14.5x 18.4x 16.0x 1.3x Max 10.9x 3.7x 81.1x 24.4x 37.4x 24.8x 4.1x Standard deviation 3.1x 1.0x 18.3x 4.6x 9.5x 4.3x 0.8x Number of companies Reading example: The average (arithmetic mean) Utilities P/E ratio calculated on the basis of expected earnings (1-year forward) is 17.4x as of the reference date 31 December An expected revenue of EUR 50m would result in an equity value of EUR 870m. 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 31 December 2017

69 Appendix Composition of each sector as of 31 December 2017 FIRE A.A.A. AG GRENKE AG RCM BETEILIGUNGS AG IMMOFINANZ AG INVESTIS HOLDING SA AAREAL BANK AG GSW IMMOBILIEN AG RINGMETALL AG OBERBANK AG JULIUS BAER EUROPE AG ACCENTRO REAL ESTATE AG GWB IMMOBILIEN SCHERZER & CO. AG RAIFFEISEN BANK INTERNATIONAL AG LEONTEQ AG ADCAPITAL AG GXP GERMAN PROEPRTIES AG SHAREHOLDER VALUE BET. AG S IMMO AG LUZERNER KANTONALBANK AG ADLER REAL ESTATE AG HAEMATO AG SINNER AG UBM DEVELOPMENT AG MOBIMO AG ALBIS LEASING AG HAMBORNER REIT AG SINO-GERMAN UNITED AG UNIQA INSURANCE GROUP AG ORASCOM DEVELOPMENT HLD AG ALLIANZ SE HANNOVER RUECK SE SIXT LEASING UNTERNEHMENS INVEST AG PARGESA HOLDING AG ALSTRIA OFFICE REIT-AG HEIDELBERGER BET. HOLDING AG SM WIRTSCHAFTSBERATUNGS AG VIENNA INSURANCE GROUP AG PARTNERS GROUP HOLDING AG BASIC RESOURCES AG HELIAD EQUITY PAR. GMBH & CO. KGAA SPARTA AG WARIMPEX FINANZ- UND BETEIL. AG Peach Property GROUP AG BERLINER EFFEKTENGESELLSCHAFT AG HESSE NEWMAN CAPITAL AG SPOBAG WIENER PRIVATBANK SE PLAZZA AG COMDIRECT BANK AG HYPOPORT AG TAG IMMOBILIEN AG ALLREAL HOLDING AG PRIVATE EQUITY HOLDING AG COMMERZBANK AG INCITY IMMOBILIEN AG TALANX AG Arundel AG PSP AG CR CAPITAL REAL ESTATE AG JDC GROUP AG THE NAGA GROUP AG BALOISE HOLDING AG SCHWEIZERISCHE NATIONALBANK AG DEMIRE DT.MTS.RE AG KAP BETEILIGUNGS-AG TLG IMMOBILIEN AG BANK CLER AG SPCE PRIVATE EQUITY AG DEUTCSHE GRUNDSTÜCKSAKTIONEN AG KST BETEILIGUNGS AG TRADEGATE AG BANQUE PROFIL DE GESTION SA ST GALLER KANTONALBANK AG DEUTSCHE BALATON AG LANG & SCHWARZ AG UNIPROF REAL ESTATE HLD AG BASELLAND. KANTONALBANK AG SWISS FIN & PROP INVESTMENT AG DEUTSCHE BANK AG LEG IMMOBILIEN AG VALUE MGMT & RESEARCH AG BASLER KANTONALBANK SA SWISS LIFE HOLDING AG DEUTSCHE BETEILIGUNGS AG L-KONZEPT HOLDING AG VARENGOLD BANK AG BC DE GENEVE SA SWISS PRIME SITE AG DEUTSCHE BOERSE AG LLOYD FONDS AG VDN AG BC DU JURA SA SWISS RE AG DEUTSCHE CANNABIS AG MAIER & PARTNER AG VERIANOS REAL ESTATE AG BC VAUDOISE SA SWISSQUOTE GROUP HOLDING LTD DT. EFF. UND WECH.-BET. AG MARS ONE VENTURES AG VONOVIA SE BELLEVUE GROUP AG THURGAUER KANTONALBANK AG DEUTSCHE EUROSHOP AG MAX21 AG VPE WERTPAPIERHANDELSBANK AG BERNER KANTONALBANK AG UBS GROUP AG DEUTSCHE KONSUM REIT-AG MIC AG WALLSTREET ONLINE AG BFW LIEGENSCHAFTEN AG VALARTIS GROUP AG DEUTSCHE REAL ESTATE AG MLP AG WCM BET. & GRUNDBESITZ AG BK LINTH LLB AG VALIANT BANK AG DEUTSCHE TECHNISCHE BET. AG MPC CAPITAL AG WEBAC HOLDING AG CEMBRA MONEY BANK AG VARIA US PROPERTIES AG DEUTSCHE WOHNEN AG MUENCHNER RUECK AG WUESTENROT & WUERTTEMBERG AG CI COM SA VAUDOISE ASSURANCES HLD. SA DF DEUTSCHE FORFAIT AG MUTARES AG YMOS AG CIE FIN. RICHEMONT AG VONTOBEL EUROPE AG DIC ASSET AG NORATIS AG ATRIUM EUROPEAN REAL ESTATE LTD CREDIT SUISSE GROUP AG VZ HOLDING AG DT.PFANDBRIEFBK AG NUERNBERGER BET. AG BANK FUER TIROL UND VBG AG EFG INTERNATIONAL AG WALLISER KANTONALBANK AG ERNST RUSS AG OVB HOLDING AG BAWAG AG GLARNER KANTONALBANK AG WARTECK INVEST AG EYEMAXX REAL ESTATE AG PATRIZIA IMMOBILIEN AG BKS BANK AG GLOBAL ASSET MGMT AG ZUEBLIN AG FAIR VALUE REIT-AG PEARL GOLD AG BURGENLAND HOLDING AG GRAUB KANTONALBANK AG ZUG ESTATES HOLDING AG FINLAB AG PONGS & ZAHN AG BUWOG AG HELVETIA HOLDING AG ZUGER KANTONALBANK AG FINTECH GROUP AG PRIMAG AG CA IMMOBILIEN ANLAGEN AG HIAG IMMOBILIEN HOLDING AG ZURICH INSURANCE AG FORIS AG PROCREDIT HLDG AG C-QUADRAT INVESTMENT AG HYPOTHEKARBANK LENZBURG AG FRITZ NOLS AG PUBLITY AG ERSTE GROUP BANK AG INTERSHOP HOLDING AG Note: Sorted by DACH. 69

70 Appendix Composition of each sector as of 31 December 2017 Basic Materials Consumer Goods ASIAN BAMBOO AG EMS-CHEMIE AG A.S.CREATION TAPETEN AG LEIFHEIT AG OTTAKRINGER GETRAENKE AG ST AURUBIS AG GIVAUDAN SA ADIDAS AG LEONI AG PANKL RACING SYSTEMS AG B.R.A.I.N. AG GURIT HOLDING AG ADLER MODEMAERKTE AG MINERALBR. UEBER. GMBH & CO. KGAA POLYTEC HOLDING AG BASF SE SCHMOLZ & BICKENBACH AG ADM HAMBURG AG MING LE SPORTS AG STADLAUER MALZFABRIK AG BAYER AG ZWAHLEN & MAYR SA AGRARIUS AG MONINGER HOLDING AG WOLFORD AG COVESTRO AG AHLERS AG MUEHL PRODUKT & SERVICE AG AIRESIS SA DECHENG TECHNOLOGY AG ALNO AG PARK U.BELLHEIMER AG ARYZTA AG DEUTSCHE ROHSTOFF AG AUDEN AG PFERDEWETTEN.DE AG AUTONEUM AG EISEN- & HUETTENWERKE AG AUDI AG PORSCHE AUTOMOBIL HLD. SE BARRY CALLEBAUT AG EVONIK INDUSTRIES AG BAYRISCHE MOTOREN WERKE AG PROGRESS-WERK OBERKIRCH AG BELL AG FUCHS PETROLUB SE BBS KRAFTFAHRZEUGTECHNIK AG PUMA SE CALIDA HOLDING AG H & R GMBH & CO KGAA BEIERSDORF AG REGENBOGEN AG EMMI AG K & S AG BERENTZEN-GROUP AG ROY CERAMICS SE GM SA KHD HUMBOLDT WEDAG AG BERTRANDT AG SCHAEFFLER AG HOCHDORF HOLDING AG LANXESS AG BHS TABLETOP AG SCHLOSS WACHENHEIM AG HUEGLI HOLDING AG LINDE AG BORUSSIA DORTMUND GMBH & CO. KGAA SCHWAELBCHEN MOLKEREI J.B. AG LECLANCHE SA PETRO WELT TECHNOLOGIE AG CEWE STIFTUNG & CO.KGAA SHW AG LINDT & SPRUENGLI AG SALZGITTER AG CONTINENTAL AG STEILMANN SE METALL ZUG AG SGL CARBON SE DAIMLER AG STO SE & CO. KGAA NESTLE SA SIMONA AG DIERIG HOLDING AG SUEDZUCKER AG ORIOR AG SKW STAHL-METALLURGIE HOLDING AG EDAG ENGINEERING TC UNTERHALTUNGSELEKTRONIK AG RICHEMONT SA SURTECO SE EINHELL GERMANY AG TOM TAILOR HOLDING AG SWATCH GROUP SA SYMRISE AG ELRINGKLINGER AG TONKENS AGRAR AG WACKER CHEMIE AG FEIKE AG ULTRASONIC AG YOUBISHENG GREEN PAPER AG FENGHUA SOLETECH AG VAPIANO SE AMAG AUSTRIA METALL AG FROSTA AG VERALLIA DTLD AG LENZING AG GERRY WEBER INTERNATIONAL AG VILLEROY & BOCH AG OMV AG GRAMMER AG VOLKSWAGEN AG PORR AG HELLA KGAA HUECK & CO. WASGAU PRODUNKTIONS & HANDELS AG SCHOELLER-BLECKMANN OILFIELD EQUIPMENT AG HELMA EIGENHEIMBAU AG WESTAG & GETALIT AG STRABAG SE HENKEL AG & CO. KGAA AGRANA BETEILIGUNGS AG VOESTALPINE AG HUGO BOSS AG DO & CO AG WIENERBERGER AG HWA AG GURKTALER AG CHAM PAPER GROUP HOLDING AG IFA HOTEL & TOURISTIK AG JOSEF MANNER & COMP. AG CLARIANT AG JJ AUTO AG KTM INDUSTRIES AG CPH CHEMIE & PAPIER HOLDING AG KAMPA AG LINZ TEXTIL HOLDING AG Note: Sorted by DACH. 70

71 Appendix Composition of each sector as of 31 December 2017 Industrials 2G ENERGY AG GEA GROUP AG NANOFOCUS AG VERBIO VEREINIGTE BIOENERGIE AG FLUGHAFEN ZUERICH AG 7C SOLARPARKEN AG GESCO AG NANOGATE AG VISCOM AG FORBO HOLDING AG A.I.S. AG HAMBURGER HAFEN & LOGISTIK AG NESCHEN AG VOLTABOX AG GAVAZZI HOLDING AG ADINOTEC AG HANSEYACHTS AG NORDEX SE VOSSLOH AG GEBERIT AG AIR BERLIN PLC & CO. LUFTV. KG HAPAG-LLOYD AG NORDWEST HANDEL AG VTG AG IMPLENIA AG ALBA SE HEIDELBERG.DRUCKMASCHINEN AG NORMA GROUP SE WACKER NEUSON SE INFICON HOLDING AG AMADEUS FIRE AG HEIDELBERGCEMENT AG ORBIS AG WALTER BAU-AG INTERROLL HOLDING AG ASKNET AG HELIOCENTRIS ENERGIE SOL. AG OSRAM LICHT AG WANDERER-WERKE AG KARDEX AG AUMANN AG HMS BERGBAU AG PFEIFFER VACUUM TECHNOLOGY AG ZHONGDE WASTE TECHNOLOGY AG KOMAX HOLDING AG AVES ONE AG HOCHTIEF AG PHILIPP HOLZMANN AG ANDRITZ AG KUEHNE & NAGEL INTERNATIONAL AG BABCOCK-BSH AG HOMAG GROUP AG PHOENIX SOLAR AG CLEEN ENERGY AG LAFARGEHOLCIM AG BASLER AG IBU-TEC ADVANCED MATERIALS AG PITTLER MA.FABR. AG FACC AG LANDIS+GYR GROUP AG BAUER AG IFA SYSTEMS AG PLAN OPTIK AG FLUGHAFEN WIEN AG LEM HOLDING AG BAUMOT GROUP AG INDUS HOLDING AG PNE WIND AG FRAUENTHAL HOLDING AG MCH GROUP AG BAVARIA INDUSTRIALS GROUP AG INFAS HLDG AG PVA TEPLA AG HTI HIGH TECH INDUSTRIES AG MEYER BURGER AG BAYWA AG ITN NANOVATION AG R. STAHL AG MAYR-MELNHOF KARTON AG MIKRON SA BILFINGER SE JENOPTIK AG RATIONAL AG OESTERREICHISCHE POST AG OC OERLIKON CORPORATION AG BLUE CAP AG JOST WERKE AG RHEINMETALL AG OESTER. STAATSDRUCKEREI HOLDING AG PANALPINA WELTTRANSPORT AG BOEWE SYSTEC AG JUNGHEINRICH AG S & O AGRAR AG PALFINGER AG PERFECT SA BRENNTAG AG KHD HUMBOLDT WEDAG AG SCHALTBAU HOLDING ROSENBAUER INTERNATIONAL AG PERROT DUVAL HOLDING SA CENTROTEC SUSTAINABLE AG KION GROUP AG SCHUMAG AG SEMPERIT AG HOLDING PHOENIX AG CENTROTHERM PHOTOVOLTAICS AG KLOECKNER & CO: SE SCY BETEILIGUNG AG SW UMWELTTECHNIK AG POENINA HOLDING AG CHINA SPECIALITY GLASS AG KOENIG & BAUER AG SFC ENERGY AG ZUMTOBEL GROUP AG RIETER MASCHINENFABRIK AG COREO AG KROMI LOGISTIK SIEMENS AG ABB SCHWEIZ AG SCHAFFNER AG CROPENERGIES AG KRONES AG SINGULUS ADECCO GROUP AG SCHINDLER AUFZUEGE AG DALDRUP & SOEHNE AG KSB AG SIXT SE ADVAL TECH HOLDING AG SCHLATTER HOLDING AG DATRON AG KUKA AG SLM SOLUTIONS GROUP AG ARBONIA AG SCHWEITER TECHNOLOGIES AG DELIGNIT AG KWS SAAT SE SMA SOLAR TECHNOLOGY AG BELIMO AUTOMATION AG SFS GROUP AG DEUTSCHE POST AG LPKF LASER & ELECTRONICS AG SMT SCHARF AG BOBST GROUP SA SGS SA DEUTZ AG LUFTHANSA AG SOFTING AG BOSSARD HOLDING AG SIKA AG DIEBOLD NIXDORF. MAN SE SOLAR-FABRIK AG BUCHER INDUSTRIES AG STARRAG GROUP HOLDING AG DISKUS WERKE AG MANZ AG SOLARWORLD AG BURCKHARDT AG SULZER AG DMG MORI AG MASCHINENFABRIK BERT.HER. AG STEICO SE BURKHALTER HOLDING AG TORNOS HOLDING AG DR. HOENLE AG MASTERFLEX AG STINAG STUTTGART INVEST AG BVZ HOLDING AG VAT GROUP AG DUERKOPP ADLER AG MAX AUTOMATION AG TECHNOTRANS AG CICOR MANAGEMENT AG VETROPACK HOLDING AG DUERR AG MBB SE THYSSENKRUPP AG COMET HOLDING AG VON ROLL HOLDING AG ELEXXION AG MEDION AG TURBON AG CONZZETA AG WALTER MEIER AG ENERGIEKONTOR AG MS INDUSTRIE AG UET AG DAETWYLER HOLDING AG ZEHNDER GROUP AG ENVITEC BIOGAS AG MTU AERO ENGINES AG UTD POWER TECHNOLOGY AG DKSH HOLDING AG FRANCOTYP-POSTALIA HOLDING AG MUEHLHAN AG UZIN UTZ AG DORMAKABA HOLDING AG FRAPORT AG MUELLER-DIE LILA LOGISTIK AG VA-Q-TEC AG ELMA ELECTRONIC AG FRIWO AG M-U-T AG VARTA AG FEINTOOL INTERNATIONAL HOLDING AG FROEHLICH BAU AG NABALTEC AG VECTRON SYSTEMS AG FISCHER AG Note: Sorted by DACH. 71

72 Appendix Composition of each sector as of 31 December 2017 Information Technology Pharma & Healthcare ADESSO AG INTICA SYSTEMS AG SINNERSCHRADER AG 4 SC AG STRATEC BIOMEDICAL AG ADVA OPTICAL NETWORKING SE INVISION AG SNP AG AAP IMPLANTATE AG SYGNIS AG AIXTRON SE ISC BUSINESS TECHNOLOGY SOFTMATIC AG ABWICKLUNGSG. ROESCH AG UMS INTERNATIONAL AG ALL FOR ONE STEEB AG ISRA VISION SOFTSHIP AG BB BIOTECH AG VITA 34 AG ALLGEIER SE IVU TRAFFIC TECHNOLOGIE AG SOFTWARE AG BIOFRONTERA AG WILEX AG AMALPHI AG KONTRON AG SUESS MICROTEC AG BIOTEST AG. VALNEVA SE AMATECH AG KPS AG SYZYGY AG CARL ZEISS MEDITEC AG ADDEX AG ARTEC TECHNOLOGIES AG MEDICAL COLUMBUS AG TDMI AG CO.DON AG AEVIS HOLDING SA ATOSS SOFTWARE AG MENSCH UND MASCHINE SE TELES AG CURASAN AG BACHEM HOLDING AG B & S BANKSYSTEME AG METRIC MOBILITY SOLUTIONS AG TISCON AG CYTOTOOLS AG BASILEA PHARMACEUTICA AG BECHTLE AG MEVIS MEDICAL SOLUTIONS AG TTL INFORMATION TECHN. AG DRAEGERWERK AG & CO. KGAA COLTENE HOLDING AG BETA SYSTEMS SOFTWARE AG MOBOTIX AG UMT UTD MOBILITY TECHN. AG ECKERT & ZIEGLER AG EVOLVA HOLDING SA CANCOM SE MSG LIFE AG USU SOFTWARE AG EPIGENOMICS AG IDORSIA LTD CENIT AG MUEHLBAUER HOLDING AG UTD. INTERNET AG EVOTEC AG IVF HARTMANN AG CEOTRONICS AG MYHAMMER HOLDING AG VIVANCO GRUPPE AG FORMYCON AG KUROS BIOSCIENCES AG COMPUGROUP MEDICAL SE MYNARIC AG VTION WIRELESS TECHNOLOGY AG FRESEN.MED.CARE AG & CO. KGAA LONZA GROUP AG DATA MODUL AG NEMETSCHEK SE WIRECARD AG FRESENIUS SE & CO.KGAA MOLECULAR PARTNERS AG DATAGROUP SE NEXUS AG XING AG GERATHERM MEDICAL AG NOVARTIS AG DOCCHECK AG NORCOM INFORMATION TECHN. AG AT&S AUSTRIA TECH. & SYSTEMTECH. AG GERRESHEIMER AG RELIEF THERAPEUTICS HOLDING AG EASY SOFTWARE AG OHB SE KAPSCH TRAFFICCOM AG HUMANOPTICS AG ROCHE AG ELMOS SEMICONDUCTOR AG OPENLIMIT HOLDING AG MASCHINENFABRIK HEID AG M1 KLINIKEN AG SANTHERA PHARM. HOLDING AG EQS GROUP AG OTRS AG RATH AG MAGFORCE AG SIEGFRIED HOLDING AG EUROMICRON AG PA POWER AUTOMATION AG AIROPACK TECHNOLOGY GROUP AG MATERNUS-KLINK AG SONOVA HOLDING AG FABASOFT AG PANAMAX AG ALSO HOLDING AG MEDICLIN AG STRAUMANN HOLDING AG FIRST SENSOR AG PARAGON AG AMS AG MEDIGENE AG TECAN GROUP AG FORTEC ELEKTRONIK AG PSI AG ASCOM HOLDING AG MEDIOS AG VIFOR PHARMA AG GBS SOFTWARE AG QSC AG CREALOGIX HOLDING AG MERCK AG & CO. KGAA YPSOMED HOLDING AG GERM. STARTUPS GRP GMBH & CO. KGAA REALTECH AG GOLDBACH GROUP AG MOLOGEN AG GFT TECHNOLOGIES SE RIB SOFTWARE AG HUBER+SUHNER AG MORPHOSYS AG GIGASET AG S & T AG KUDELSKI SA MPH HEALTH CARE AG GK SOFTWARE SAP SE LOGITECH INTERNATIONAL SA NANOREPRO AG HOLIDAYCHECK GROUP AG SCHWEIZER ELECTRONIC AG MYRIAD GROUP AG PAION AG INFINEON TECHNIK AG SECUNET SECURITY AG TEMENOS GROUP AG PAUL HARTMANN AG INIT INNOVATION SE SEVEN PRINCIPLES AG U-BLOX HOLDING AG RHOEN-KLINIKUM AG INTERCARD AG SHF COMMUNIC. TECHNOLOGIES AG WISEKEY INTERNATIONAL HOLDING AG SANOCHEMIA PHARMAZEUTIKA AG INTERSHOP COMMUNICATIONS AG SILTRONIC AG STADA ARZNEIMITTEL AG Note: Sorted by DACH. 72

73 Appendix Composition of each sector as of 31 December 2017 Consumer Service Telecommunication Utilities A.SPRINGER SE SLEEPZ AG SOLUTIONS AG CAPITAL STAGE AG AD PEPPER MEDIA N.V. SNOWBIRD AG 3U HOLDING AG ENBW ENERGIE B./W. AG ARCANDOR AG SPL.MEDIEN AG DEUTSCHE TELEKOM AG GELSENWASSER AG ARTNET AG STARAMBA SE DRILLISCH AG INNOGY SE BASTEI LUEBBE AG STROEER SE & CO. KGAA ECOTEL COMMUNICATION AG MAINOVA AG BEATE UHSE AG TAKKT AG FREENET AG MVV ENERGIE AG BET-AT-HOME.COM AG TELE COLUMBUS AG LS TELCOM AG RWE AG BIJOU BRIGITTE AG TMC CONTENT GROUP AG MVISE AG UNIPER SE CECONOMY AG TRAVEL24.COM AG STARDSL AG EVN AG CONSTANTIN MEDIEN AG UHR.DE AG TELEFONICA DEUTSCHLAND HOLDING AG VERBUND AG CTS EVENTIM AG & CO. KGAA UNITED LABELS AG YOC AG BKW ENERGIE AG DEAG DEUTSCHE ENTERTAINMENT AG WIGE MEDIA AG TELEKOM AUSTRIA AG EDISUN POWER EUROPE AG DELTICOM AG WILD BUNCH AG SUNRISE COMMUNICATIONS AG ROMANDE ENERGIE HOLDING SA ECOMMERCE ALLIANCE AG WINDELN.DE SE SWISSCOM AG EDEL AG YOUR FAMILY ENTERTAINMENT AG ELANIX BIOTECHNIK AG ZALANDO SE ELUMEO SE ZOOPLUS AG ENERXY AG APG SGA AG FD GROUP AG DUFRY AG FIELMANN AG GALENICA AG HAWESKO HOLDING AG HIGHLIGHT EVENT & ENTERTAINMENT AG HIGHLIGHT COMMUNICATIONS AG JUNGFRAUBAHN HOLDING AG HORNBACH BAUMARKT AG MOBILEZONE HOLDING AG HORNBACH HOLDING AG & CO. KGAA OREL FUESSLI HOLDING AG INTERTAINMENT AG TAMEDIA AG KLASSIK RADIO AG TITL BN BERG AG LOTTO24 AG VALORA AG LUDWIG BECK AG VILLARS HOLDING SA M4E AG ZUR ROSE GROUP AG METRO AG MYBET HOLDING SE PANTALEON ENTERTAIN. AG PRAKTIKER AG PROSIEBENSAT.1 MEDIA SE SCOUT24 AG SENDR SE Note: Sorted by DACH (No Austrian company represented inthe Consumer Service sector). 73

74 Appendix Historical development of trading multiples since 2012

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 2.4x 2.4x 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 DACH 25.0x 21.4x 20.7x 19.0x 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 24.4x 24.3x 21.7x 21.0x 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 2.9x 2.1x Arithmetic mean Median 76

77 Trading Multiples FIRE (1/1) P/E- and EqV/BV-Multiples LTM and 1yf since x 20.0x 15.0x 10.0x 5.0x 0.0x P/E FIRE 20.9x 18.4x 17.3x 14.7x 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 1.3x 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 1.9x 1.8x 1.4x 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 21.3x 18.6x 18.1x 16.3x 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 23.7x 21.0x 18.3x 18.3x LTM arithmetic mean LTM median 1yf arithmetic mean 1yf median 2.8x 2.4x 2.0x 1.6x 1.2x 0.8x 0.4x 0.0x EqV/BV Basic Materials 2.5x 2.0x Arithmetic mean Median 79

80 Trading Multiples Consumer Goods (1/2) Revenue- and EBIT-Multiples LTM and 1yf since x 1.6x 1.4x 1.2x 1.0x 0.8x 0.6x 0.4x 0.2x 0.0x EV/Revenue Consumer Goods 1.5x 1.4x 1.2x 1.2x 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 22.6x 18.3x 17.4x 16.0x 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 21.4x 22.0x 19.8x 18.5x 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 1.8x 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 2.0x 1.8x 1.8x 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 25.9x 22.0x 20.9x 17.8x 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 23.5x 19.3x 17.3x 14.1x 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 2.5x 1.9x 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 2.1x 2.1x 1.6x 1.4x 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 24.1x 20.9x 20.6x 18.9x 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 28.4x 24.1x 24.1x 21.8x 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 Industrials 3.2x 2.4x 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 3.1x 2.4x 1.9x 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 26.7x 21.9x 20.5x 20.1x 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 25.7x 24.9x 21.3x 19.4x 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 Consumer Service 3.8x 2.4x 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 3.9x 3.8x 3.7x 3.1x 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 Pharma & Healthcare 29.0x 27.0x 26.1x 24.1x 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 29.7x 27.2x 27.2x 24.8x 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 3.8x 2.9x Arithmetic mean Median 89

90 Trading Multiples Information Technology (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 Information Technology 3.1x 3.0x 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 25.8x 23.5x 23.2x 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 35.0x 30.0x 25.0x 20.0x 15.0x 10.0x 5.0x 0.0x P/E Information Technology 33.4x 31.0x 29.4x 27.2x 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 4.0x 3.3x 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 3.1x 1.9x 1.9x 1.9x 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 27.4x 21.3.x 16.0x 14.5x 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 19.6x 18.4x 17.4x 16.0x 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 1.6x 1.3x 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