finexpert report capital market data

Transcription

1 finexpert report capital market data Volume 4 Content 1 Preface & People 3 Multiples: Procedure & How to read our charts 4 Multiples: EV/EBIT 12 Multiples: EV/EBITDA 2 Multiples: EV/Sales 23 Multiples: P/E 32 Multiples: P/Sales 3 CAPM Beta Factors 37 Term Structure of listed German Federal Securities 38 Current Research: Inorganic Growth Strategies in Private Equity

2 Preface Dear finexpert members, we are pleased to release the Q1 214 finexpert capital market data update. Our current issue focuses on the development of trailing and forward EV/EBIT, EV/EBITDA, EV/Sales, P/E and P/Sales multiples. It also contains Q1 214 capital market data from our website including industry betas, cost of capital and our estimate for the German risk-free yield curve (Svensson). Finally, the research corner - section presents a recent working paper from our chair dealing with add-on acquisitions of Private Equity Sponsors. Please note that we have slightly changed the structure and timing of our PE corner; it will appear from now in semi-annual sequence. Sincerely yours, Prof. Dr. Bernhard Schwetzler, Chair of Financial Management HHL - Leipzig Graduate School of Management 1

3 People Jun.-Prof. Dr. Alexander Lahmann Capital market data, Yield Forecasts Research Interests: Asset Pricing & Corporate Valuation Benjamin Hammer, M.Sc. Capital Market Data, Current Research Research Interests: Private Equity, Entrepreneurial Finance Johanna Stein, cand. B.Sc. Technical editing, Current Research Research Interests: Mathematical & Financial Economics Daniel Schönekäs, B.A. Technical editing Research Interests: Applied Econometrics and Financial Economics 2

4 Multiples: Procedure We estimated industry multiples based on industry indices provided by Deutsche Börse AG Time frame: January 29 January 214 We calculated trailing and 1 year forward EV/EBIT, EV/EBITDA, EV/Sales, P/E, P/Sales Earning estimates for forward-multiples have been taken from I/B/E/S Data bases on quarterly estimates; Industry composition changes over time In each estimation period we excluded outliers multiples beyond the limit of the upper %-quantile Multiples: How to read our Charts In the following charts you will find forward multiples (blue) and trailing mutiples (green) combined in one chart. 3

5 Multiples: EV/EBIT Executive Summary EV/EBIT multiples have predominantly increased since last year; only exception is the food & beverages industry Significant increase especially in technology and software industry EBIT growth expectations are predominantly positive, except for the food & beverages industry again Analysis Year-on-year (y-o-y) comparison of the median EV/EBIT multiples 1 shows considerably improved valuation of the Prime All Share Standard (from 11.3x to 13.8x). In part, this holds also true for the most important sub-indices, among which the TecDAX 3 exhibits the by far highest y-o-y increase (from 12.4x to 17.9x). DAX3 (from 13.3x to 14.4x) and MDAX (from 13.x to 14.8x) display smaller but still significant valuation gains. On industry level, the y-o-y comparison draws a familiar picture: the highest EV/EBIT multiple still appears in the Telecommunication industry (last year 1.7x vs. current year 26.9x); the lowest one in the Food & Beverages Branch (last year 6.2x vs. current year 3.2x). Interesting is the significant dispersion among the current EV/EBIT multiples in the Telecommunication sector. While companies such as Drillisch (44.1x) or Telefonica Deutschland (49.x) have rather high EV/EBIT valuations, others such as Telegate (.3x) show very low ones. Such high dispersion also leads to differing industry multiples for different aggregation methods: using the harmonic mean for aggregation, for example, yields a Telecommunication industry multiple of just 1.x, being substantially lower than the multiples basing on the arithmetic mean or median 2. This should be taken into account when applying the aggregate industry multiple for valuation purposes. Besides the telecommunication industry, many other branches have gained in valuation too, most notably the sectors basic resources (from 7.x to 1.8x) 3, technology (from 7.8x to 13.8x) and software (from.9x to 16.x). The opposite holds true for only few branches. The only considerable decline in EV/EBIT valuation is apparent in the food & beverages industry (from 6.2x to 3.2x). 1 Respective due dates are January 1, 213 and Among the presented aggregation methods, the harmonic mean is generally the most conservative one. It mitigates the impact of large outliers but augments the impact of small ones. 3 Please note that both the basic resources trailing and forward multiple traditionally base on very few data points so that small changes in sector composition or data availability can significantly affect the aggregated industry multiple 4

6 Multiples: EV/EBIT When looking at the quarterly 4 development since last year, some of the y-o-y changes seem rather exceptional. In the basic resources industry, for example, we observe a remarkably high compounded annual growth rate (CAGR) of roughly 21% since last year. This growth rate is predominantly driven by the recent valuation increase from Q4 213 to Q1 214 (from 7.x to 1.8x); during the first three quarters of 213, however, the EV/EBIT multiple in the basic resources branch even declined twice. A more sustainable, yet smaller, valuation increase is the case for the technology branch (CAGR 1%) where the EV/EBIT multiple increased consistently since Q Remarkable is also the very flat EV/EBIT development in the construction (variance =.16), industrials (variance =.2) and utilities sector (variance =.27). Comparing the current trailing and forward EV/EBIT multiples allows for conclusions about EBIT growth expectations: lower forward than trailing multiples indicate optimistic EBIT projections. Accordingly, the outlook is positive for the Prime All Share Standard (Trailing 13.8x vs. Forward 13.x) and its most important sub-indices, the DAX3 (Trailing 14.4x vs. Forward 13.9x), TecDAX3 (Trailing 17.9x vs. Forward 17.8x) and MDAX (Trailing 14.8x vs. Forward 13.2x). Several industries exhibit positive growth expectations too, for example the sectors telecommunication (Trailing 26.9x vs. Forward 13.x), basic resources (Trailing 1.8x vs. Forward 12.4x), and utilities (Trailing 11.8x vs. Forward 9.6x). Lower trailing than forward multiples in the food & beverages (Trailing 3.2x vs. Forward 4.6x) and retail industry (Trailing.4x vs. Forward 11.8x) indicate pessimistic growth expectations though. For the analysis of the trailing and forward EV/EBIT multiples we used cut off values of 1.13x and 8.42x, respectively, to avoid bias through outliers. Both values equal the upper % quantile. This led to the exclusion of 11 out of 213 companies for the trailing multiple and 11 out of 29 for the forward multiple. Last year s cut-off values have been 4.28x and 36.6x, respectively, which led to the exclusion of 13 out of 233 companies for the trailing multiple and 14 out of 222 for the forward multiple. 4 Respective due dates are: January 1, 213 (Q1 213), April 1, 213 (Q2 213), July 1, 213 (Q3 213), October 1, 213 (Q4 213) and January 1, 214 (Q1 214).

7 Prime All Share Industries, DAX 3, TecDAX 3, MDAX : EV/EBIT as of Arithm. mean Median Trailing EV/EBIT 1 YR Forward EV/EBIT Harm. mean Variance n Arithm. mean Median Harm. mean Variance n Automobiles 13, 13,9 12,7 9,1 13, 12, 12,6,7 Basic Resources 1,8 1,8 8,2 142,9 3 11,6 12,4 4,1 73,3 4 Chemicals 16,3 16, 13, 43, 12 18,7 1,9 1,2 11,6 Construction 11,9 12,6 11, 14,3 3 16,3 13, 12,8 6,9 4 Consumer 14,9 13,1 6, 8, ,6 13,9 6,2 29, 18 Food & Beverages 3,2 3,2,2 19,7 2 4,6 4,6,4 38,9 2 Industrial 13,3 12,4 1,9 36, 6 14,3 12,6 1,1 76,1 8 Media 1, 13,6 12,1 48,2 1,3 13,6 11,6 47,7 9 Pharma & Healthcare 17,2 17,2 7, 79, 19 14, 14,8 11,1 31,8 18 Retail,4,4,7 19, ,4 11,8 8, 17,3 11 Software 18,9 16, 1,7 91, ,9 14,7 14,8 73, 23 Technology 1, 13,8 12,3 6,1 1 16,6 14, 13, 66,3 16 Telecommunication 27, 26,9 1, 43,9 14,3 13, 12,6 39,1 4 Transport. & Logistics 12,4 14, 11,6 9, 9 12, 12,8 11,4,6 8 Utilities 11,6 11,8 11, 1,1 3,1 9,6 9, 9, 3 Prime All Share 14,9 13,8 3,4 6, ,6 13, 2,8 9,9 198 DAX 3 1, 14,4 13,2 4, 21 13,9 13,9 12,9 1, 23 TecDAX 3 21,2 17,9 8,4 143, ,6 17,8 14,9 1,9 22 MDAX 1,1 14,8 12,7 3,1 3 1,7 13,2 13,1 84, 36 Prime All Share Industries 6

8 finexpert capital markets data Vol. 4 Development of Multiples EV/EBIT - Indices 3 Multiples Prime All Share Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBIT +SD EV/EBIT SD EV/EBIT EV/EBIT 1YF +SD EV/EBIT 1YF SD EV/EBIT 1YF Fig. 1: EV/EBIT - Prime All Share 4 Multiples DAX Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBIT +SD EV/EBIT SD EV/EBIT EV/EBIT 1YF +SD EV/EBIT 1YF SD EV/EBIT 1YF Fig. 2: EV/EBIT - DAX Multiples TecDAX Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBIT +SD EV/EBIT SD EV/EBIT EV/EBIT 1YF +SD EV/EBIT 1YF SD EV/EBIT 1YF Fig. 3: EV/EBIT - TecDax3 3 Multiples MDAX Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBIT +SD EV/EBIT SD EV/EBIT EV/EBIT 1YF +SD EV/EBIT 1YF SD EV/EBIT 1YF Fig. 4: EV/EBIT - MDAX 7

9 finexpert capital markets data Vol. 4 Development of Multiples EV/EBIT - Per Industry I/IV 6 Multiples Automobiles Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBIT +SD EV/EBIT SD EV/EBIT EV/EBIT 1YF +SD EV/EBIT 1YF SD EV/EBIT 1YF Fig. : EV/EBIT - Automobiles 7 Multiples Basic Resources Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBIT +SD EV/EBIT SD EV/EBIT EV/EBIT 1YF +SD EV/EBIT 1YF SD EV/EBIT 1YF Fig. 6: EV/EBIT - Basic Resources 6 Multiples Chemicals Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBIT +SD EV/EBIT SD EV/EBIT EV/EBIT 1YF +SD EV/EBIT 1YF SD EV/EBIT 1YF Fig. 7: EV/EBIT - Chemicals 3 Multiples Construction Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBIT +SD EV/EBIT SD EV/EBIT EV/EBIT 1YF +SD EV/EBIT 1YF SD EV/EBIT 1YF Fig. 8: EV/EBIT - Construction 8

10 finexpert capital markets data Vol. 4 Development of Multiples EV/EBIT - Per Industry II/IV 3 Multiples Consumer Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBIT +SD EV/EBIT SD EV/EBIT EV/EBIT 1YF +SD EV/EBIT 1YF SD EV/EBIT 1YF Fig. 9: EV/EBIT - Consumer 3 Multiples Industrial Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBIT +SD EV/EBIT SD EV/EBIT EV/EBIT 1YF +SD EV/EBIT 1YF SD EV/EBIT 1YF Fig. : EV/EBIT - Industrial 3 Multiples Media Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBIT +SD EV/EBIT SD EV/EBIT EV/EBIT 1YF +SD EV/EBIT 1YF SD EV/EBIT 1YF Fig. 11: EV/EBIT - Media 4 3 Multiples Pharma Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBIT +SD EV/EBIT SD EV/EBIT EV/EBIT 1YF +SD EV/EBIT 1YF SD EV/EBIT 1YF Fig. 12: EV/EBIT - Pharma & Healthcare 9

11 finexpert capital markets data Vol. 4 Development of Multiples EV/EBIT - Per Industry III/IV 2 Multiples Retail 2 1 Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBIT +SD EV/EBIT SD EV/EBIT EV/EBIT 1YF +SD EV/EBIT 1YF SD EV/EBIT 1YF Fig. 13: EV/EBIT - Retail 3 Multiples Sofware Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBIT +SD EV/EBIT SD EV/EBIT EV/EBIT 1YF +SD EV/EBIT 1YF SD EV/EBIT 1YF Fig. 14: EV/EBIT - Software 6 Multiples Technology Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBIT +SD EV/EBIT SD EV/EBIT EV/EBIT 1YF +SD EV/EBIT 1YF SD EV/EBIT 1YF Fig. 1: EV/EBIT - Technology Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Fig. 16: EV/EBIT - Telecommunication Multiples Telco EV/EBIT +SD EV/EBIT SD EV/EBIT EV/EBIT 1YF +SD EV/EBIT 1YF SD EV/EBIT 1YF

12 finexpert capital markets data Vol. 4 Development of Multiples EV/EBIT - Per Industry IV/IV 4 Multiples Transportation Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBIT +SD EV/EBIT SD EV/EBIT EV/EBIT 1YF +SD EV/EBIT 1YF SD EV/EBIT 1YF Fig. 17: EV/EBIT - Transport & Logistics 2 Multiples Utilities 2 1 Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBIT +SD EV/EBIT SD EV/EBIT EV/EBIT 1YF +SD EV/EBIT 1YF SD EV/EBIT 1YF Fig. 18: EV/EBIT - Utilities 11

13 Multiples: EV/EBITDA Executive Summary EV/EBITDA valuations have improved since last year The quarterly development of EV/EBITDA since Q1 213 shows consistency EBITDA growth expectations are rather conservative Analysis Year-on-year (y-o-y) comparison of the median EV/EBITDA multiples 1 confirms the results of the EV/EBIT analysis: operating valuations have improved since last year (Prime All Share Standard from 6.9x to 9.x). For some of the major sub-indices, valuation improvements are even greater, e.g. for the DAX3 (from 7.6x to 8.6x) and MDAX (from 8.6x to.7x). The valuation gain of the TecDAX3 (from 9.3x to 11.9x) is less pronounced but still remarkable. On industry level, the software industry (12.x) has the highest multiple whereas the food & beverages branch (2.x) exhibits the lowest one. The telecommunication sector (6.2x) is among the industries with the lowest EV/EBITDA valuation too, being in sharp contrast to the results of the EV/EBIT analysis where it displayed both the currently highest multiple and one of the largest y-o-y gains. These differences show how even two operating multiples can yield contradicting valuation results depending on the specifics of the industry. Other industries such as automobiles or software display almost similar developments though. The basic resources (from.x to 9.1x) 2, consumer (from 6.1x to.x), and pharma & healthcare industries (from 11.2x to 11.6x) show the largest relative gains in EV/EBITDA valuation, mainly driven by Salzgitter (from 6.x to 9.x), Puma (from 7.8x to 16.9x) and Geratherm Medical (from 6.8x to 12.1x), respectively. The food & beverages (from 4.9x to 2.x), construction (from 7.3x to 6.4x) 3 and utilities sectors (from 7.2x to 6.4x), in contrast, show the largest y-o-y declines, mainly driven by Haikui Seafood (from 2.x to.1x), Hochtief (from.x to 3.7x) and Eon (from 16.6x to 6.1x). 1 Respective due dates are January 1, 213 and Please note that both the basic resources trailing and forward multiple traditionally base on very few data points so that small changes in sector composition or data availability can significantly affect the aggregated industry multiple. 3 Please note that both the construction trailing and forward multiple traditionally base on very few data points so that small changes in sector composition or data availability can significantly affect the aggregated industry multiple. 12

14 Multiples: EV/EBITDA A closer look at the quarterly development since Q1 213 reveals that the pharma & healthcare industry has long had the highest EV/EBITDA multiple, losing its spot in Q1 214 for the first time to the software industry. Interesting is also that many industries have slightly improved (e.g. automobiles, technology) or kept up (e.g. telecommunication) their valuation since Q1 213 without great fluctuation. The dispersion among the EV/EBITDA multiples over the past quarters 4 is therefore smaller than among the EV/EBIT multiples. The construction sector is the only industry with consistent EV/EBITDA decline since Q Other industries with y-o-y decline have either lost in valuation just recently (e.g. food & beverages) or have already started to rebound from lower valuations (e.g. utilities). Comparing the current trailing and forward multiples allows for analyzing expected EBITDA development. Accordingly, growth expectations are almost unchanged for the Prime All Share (Trailing 9.x vs. Forward 8.9x), and DAX3 (Trailing 8.6x vs. Forward 8.7x). Far more optimistic is the outlook for the TecDAX3 (Trailing 11.9x vs. Forward 11.3x), MDAX (Trailing.7x vs. Forward 9.6x) and some industries, e.g. pharma & healthcare (Trailing 11.6x vs. Forward.1x) or utilities (Trailing 6.4x vs. Forward.7x). There are also several industries with negative EBITDA growth expectations, most notably the sectors food & beverages (Trailing 2.x vs. Forward 3.3x), retail (Trailing 7.x vs. Forward 9.2x) and construction (Trailing 6.4x vs. Forward 7.6x). For the analysis of the trailing and forward EV/EBITDA multiples we used cut off values of 27.19x and 2.93x, respectively, to avoid bias through outliers. Both values equal the upper % quantile. This led to the exclusion of 12 out of 223 companies for the trailing multiple and 11 out of 218 for the forward multiple. Last year s cut-off values have been 18.32x and 16.1x, respectively, which led to the exclusion of 14 out of 244 companies for the trailing multiple and 14 out of 227 for the forward multiple. 4 Respective due dates are: January 1, 213 (Q1 213), April 1, 213 (Q2 213), July 1, 213 (Q3 213), October 1, 213 (Q4 213) and January 1, 214 (Q1 214). 13

15 Prime All Share Industries, DAX 3, TecDAX 3, MDAX : EV/EBITDA as of Arithm. mean Median Trailing EV/EBITDA 1 YR Forward EV/EBITDA Harm. mean Variance n Arithm. mean Median Harm. mean Variance n Automobiles 8,3 8,1 7,8 4,8 8,3 8,3 8,1 2, Basic Resources 8,4 9,1 6, 13,8 4 8,6 9, 2,9 36,4 Chemicals, 8,3 8,8 24,9 13 9,9 9,3 9,3 6, 11 Construction 6,4 6,4,6 6,8 3 7,3 7,6 6,4 6, 4 Consumer,4,,4 37,1 19 8,8,, 18,1 18 Food & Beverages 2, 2,,2 11,8 2 3,3 3,3,4 19,6 2 Industrial 9,3 9, 1,7 22,3 6 8,8 8,4 1, 14,7 62 Media 9,,1, 27,,8,2 7,8 39,1 Pharma & Healthcare 11, 11,6 6, 18,9 2,2,1 8,8 11,1 18 Retail 7,6 7, 3,8 14,3 12, 9,2 7,8 33,4 13 Software 12, 12,,8 23,1 2 12,8 11,8,9 27,8 22 Technology 7,9 7,7 7,1,4 16 8, 8,1 7,3,6 16 Telecommunication 7,4 6,2 1,2 28,7 9,2 6, 7, 38,1 Transport. & Logistics 6,4 6,,8 4,3 9 6,3 6,,7 4,4 8 Utilities 6, 6,4 6,,2 3,8,7,7 1,6 3 Prime All Share 9,4 9, 2,9 21, ,3 8,9 2,3 18,7 27 DAX 3, 8,6 8,4 18,7 22 8,9 8,7 8, 9,1 23 TecDAX 3 11,7 11,9 6, 3, ,7 11,3,8 2,1 23 MDAX,,7 8,8 16,8 38,1 9,6 8,9 12,3 38 Prime All Share Industries 14

16 finexpert capital markets data Vol. 4 Development of Multiples EV/EBITDA - Indices Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Fig. 19: EV/EBITDA - Prime All Share Multiples Prime All Share EV/EBITDA +SD EV/EBITDA SD EV/EBITDA EV/EBITDA 1YF +SD EV/EBITDA 1YF SD EV/EBITDA 1YF Multiples DAX Jan 8 Jul 8 Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBITDA +SD EV/EBITDA SD EV/EBITDA EV/EBITDA 1YF +SD EV/EBITDA 1YF SD EV/EBITDA 1YF Fig. 2: EV/EBITDA - DAX 3 2 Multiples TecDAX Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBITDA +SD EV/EBITDA SD EV/EBITDA EV/EBITDA 1YF +SD EV/EBITDA 1YF SD EV/EBITDA 1YF Fig. 21: EV/EBITDA - TecDAX Multiples MDAX Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBITDA +SD EV/EBITDA SD EV/EBITDA EV/EBITDA 1YF +SD EV/EBITDA 1YF SD EV/EBITDA 1YF Fig. 22: EV/EBITDA - MDAX 1

17 finexpert capital markets data Vol. 4 Development of Multiples EV/EBITDA - Per Industry I/IV 2 Multiples Automobiles 2 1 Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBITDA +SD EV/EBITDA SD EV/EBITDA EV/EBITDA 1YF +SD EV/EBITDA 1YF SD EV/EBITDA 1YF Fig. 23: EV/EBITDA - Automobiles 2 Multiples Basic Resources 2 1 Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBITDA +SD EV/EBITDA SD EV/EBITDA EV/EBITDA 1YF +SD EV/EBITDA 1YF SD EV/EBITDA 1YF Fig. 24: EV/EBITDA - Basic Resources 2 Multiples Chemicals 2 1 Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBITDA +SD EV/EBITDA SD EV/EBITDA EV/EBITDA 1YF +SD EV/EBITDA 1YF SD EV/EBITDA 1YF Fig. 2: EV/EBITDA - Chemicals 16 Multiples Construction Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBITDA +SD EV/EBITDA SD EV/EBITDA EV/EBITDA 1YF +SD EV/EBITDA 1YF SD EV/EBITDA 1YF Fig. 26: EV/EBITDA - Construction 16

18 finexpert capital markets data Vol. 4 Development of Multiples EV/EBITDA - Per Industry II/IV Multiples Consumer Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBITDA +SD EV/EBITDA SD EV/EBITDA EV/EBITDA 1YF +SD EV/EBITDA 1YF SD EV/EBITDA 1YF Fig. 27: EV/EBITDA - Consumer Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Fig. 28: EV/EBITDA - Industrial Multiples Industrial EV/EBITDA +SD EV/EBITDA SD EV/EBITDA EV/EBITDA 1YF +SD EV/EBITDA 1YF SD EV/EBITDA 1YF Multiples Media Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBITDA +SD EV/EBITDA SD EV/EBITDA EV/EBITDA 1YF +SD EV/EBITDA 1YF SD EV/EBITDA 1YF Fig. 29: EV/EBITDA - Media Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Fig. 3: EV/EBITDA - Pharma Multiples Pharma EV/EBITDA +SD EV/EBITDA SD EV/EBITDA EV/EBITDA 1YF +SD EV/EBITDA 1YF SD EV/EBITDA 1YF 17

19 finexpert capital markets data Vol. 4 Development of Multiples EV/EBITDA - Per Industry III/IV Multiples Retail Jan 9 Apr 9 Jul 9 Oct 9 Jan Apr Jul Oct Jan 11 Apr 11 Jul 11 Oct 11 Jan 12 Apr 12 Jul 12 Oct 12 Jan 13 Apr 13 Jul 13 Oct 13 Jan 14 EV/EBITDA +SD EV/EBITDA SD EV/EBITDA EV/EBITDA 1YF +SD EV/EBITDA 1YF SD EV/EBITDA 1YF Fig. 31: EV/EBITDA - Retail 2 Multiples Software 2 1 Jan 9 Apr 9 Jul 9 Oct 9 Jan Apr Jul Oct Jan 11 Apr 11 Jul 11 Oct 11 Jan 12 Apr 12 Jul 12 Oct 12 Jan 13 Apr 13 Jul 13 Oct 13 Jan 14 EV/EBITDA +SD EV/EBITDA SD EV/EBITDA EV/EBITDA 1YF +SD EV/EBITDA 1YF SD EV/EBITDA 1YF Fig. 32: EV/EBITDA - Software 2 Multiples Technology 2 1 Jan 9 Apr 9 Jul 9 Oct 9 Jan Apr Jul Oct Jan 11 Apr 11 Jul 11 Oct 11 Jan 12 Apr 12 Jul 12 Oct 12 Jan 13 Apr 13 Jul 13 Oct 13 Jan 14 EV/EBITDA +SD EV/EBITDA SD EV/EBITDA EV/EBITDA 1YF +SD EV/EBITDA 1YF SD EV/EBITDA 1YF Fig. 33: EV/EBITDA - Technology Jan 9 Apr 9 Jul 9 Oct 9 Jan Apr Jul Oct Jan 11 Apr 11 Jul 11 Oct 11 Jan 12 Apr 12 Jul 12 Oct 12 Jan 13 Apr 13 Jul 13 Oct 13 Jan 14 EV/EBITDA +SD EV/EBITDA SD EV/EBITDA EV/EBITDA 1YF +SD EV/EBITDA 1YF SD EV/EBITDA 1YF Fig. 34: EV/EBITDA - Telecommunication Multiples Telco 18

20 finexpert capital markets data Vol. 4 Development of Multiples EV/EBITDA - Per Industry IV/IV 12 Multiples Transportation Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBITDA +SD EV/EBITDA SD EV/EBITDA EV/EBITDA 1YF +SD EV/EBITDA 1YF SD EV/EBITDA 1YF Fig. 3: EV/EBITDA -Transportation Multiples Utilities Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 EV/EBITDA +SD EV/EBITDA SD EV/EBITDA EV/EBITDA 1YF +SD EV/EBITDA 1YF SD EV/EBITDA 1YF Fig. 36: EV/EBITDA - Utilities 19

21 Multiples: EV/Sales Executive Summary TecDAX3 shows largest year-on-year EV/Sales increase and most optimistic sales growth expectations Automobiles industry with largest y-o-y EV/Sales increase; food & beverages branch with largest y-o-y decline Pharma & healthcare industry with highest EV/Sales valuation for all quarters since Q1 213 Analysis At first glance, year-on-year (y-o-y) analysis shows almost similar median EV/Sales multiples 1 : neither the Prime All Share Standard (from.8x to.9x), nor the DAX3 (from 1.2x to 1.4x) or MDAX (from.9x to 1.x) exhibit significant changes in EV/Sales valuation. Only the TecDAX3 multiple shows substantial improvement (from 1.2x to 1.8x), confirming the results from the EV/EBIT and EV/EBITDA analysis. On industry level, the pharma & healthcare industry (1.8x) has the highest EV/sales valuation; the basic resources sector (.4x) has the lowest one. The automobile industry displays the largest relative increase since last year (from.8x to 1.1x), mainly due to substantially improved multiples of Leoni (from.3x to.6x), Continental (from.8x to 1.2x) and SHW (from.6x to.9x). The opposite holds true for the food & beverages branch exhibiting the largest relative decline (from.8x to.4x). Responsible for that have been the decreases of the Hakui Seafood (from.x to.2x) and Südzucker (from 1.1x to.8x) multiples. The spread between the minimum and maximum EV/Sales multiples is small. This holds true across both industries as well as quarters. Even small changes in valuation can therefore change the rank order of the different industries with the lowest or highest multiple. Since Q1 213, for example, the industry with the lowest EV/Sales multiple has changed every quarter (transportation & logistics in Q1 213 with.4x, basic resources in Q2 213 with.4x, retail in Q3 213 with.4x, food & beverages in Q4 213 with.x and currently basic resources with.4x) 2. In light of these rather frequent changes, it is remarkable that the pharma & healthcare industry has consistently had the highest multiple among all industries since Q Respective due dates are January 1, 213 and Respective due dates are: January 1, 213 (Q1 213), April 1, 213 (Q2 213), July 1, 213 (Q3 213), October 1, 213 (Q4 213) and January 1, 214 (Q1 214). 2

22 Multiples: EV/Sales A comparison of the current trailing and forward multiples confirms the conclusions from the y-o-y analyses: the TecDAX3 shows the only significant difference between trailing (1.8x) and forward multiple (1.x) indicating positive sales growth expectations. Close-to-zero differences are the case for the Prime All Share Standard, DAX3 and MDAX. In line with this, few industries exhibit very positive or negative growth outlook. Worth mentioning are only the optimistic sales projections for the telecommunication industry (trailing.8x vs. forward.4x) and the rather pessimistic growth projections for the transportation & logistics sector (trailing.x vs. forward.7x). For the analysis of the trailing and forward EV/Sales multiples we used cut off values of.2x and 4.63x, respectively, to avoid bias through outliers. Both values equal the upper % quantile. This led to the exclusion of 13 out of 29 companies for the trailing multiple and 13 out of 244 for the forward multiple. Last year s cut-off values have been 3.6x and 3.11x, respectively, which led to the exclusion of 16 out of 273 companies for the trailing multiple and 1 out of 22 for the forward multiple. 21

23 Prime All Share Industries, DAX 3, TecDAX 3, MDAX : EV/Sales as of Arithm. mean Median Trailing EV/Sales 1 YR Forward EV/Sales Harm. mean Variance n Arithm. mean Median Harm. mean Variance n Automobiles 1,1 1,1,9,2 1,1 1,1,9,2 Basic Resources 1,,4,4 1,4,9,4,4,9 Chemicals 1, 1,3,9,8 13 1, 1,4 1,,6 12 Construction,7,,4,3 4,7,,4,2 4 Consumer 1,,8,6,6 2 1,1,8,7,6 18 Food & Beverages,4,4,,3 2,4,4,1,3 2 Industrial 1,,8,3, 74,9,8,3,4 71 Media 1,6 1,4,8 1,6 11 1,8 1,,8 2,1 11 Pharma & Healthcare 2,1 1,8 1,7 1,3 23 1,9 1,7 1,,9 21 Retail,9,7,4,7 1,9,6,4,7 14 Software 1,8 1, 1,1 1,7 28 1,8 1, 1,1 1, 2 Technology 1,1,7,7 1,1 2,9,7,6,3 19 Telecommunication 1,2,8, 1,9 8,8,4,,4 7 Transport. & Logistics 1,,,4 1,1 1,,7,4 1,1 9 Utilities,6,7,6, 3,6,6,6, 3 Prime All Share 1,2,9, 1, ,2,9,4,8 231 DAX 3 1,6 1,4 1,1 1, 23 1, 1,4 1,1,9 23 TecDAX 3 2,1 1,8 1,3 1,7 24 1,7 1, 1,2 1, 22 MDAX 1,3 1,,7,8 37 1,3 1,,7 1, 38 Prime All Share Industries 22

24 Multiples: P/E Executive Summary Year-on-year analysis reveals positive overall market development P/E ratio of Prime All Share Standard has continuously improved since Q1 213 Forward multiples indicate positive earnings growth expectations for the overall market and the major sub-indices Analysis Year-on-year (y-o-y) comparison of the median P/E multiples 1 reveals a very positive overall market development (Prime All Share Standard from 1.x to 2.2x), from which all the major sub-indices could clearly benefit (DAX3 from 14.9x to 2.2x; TecDAX3 from 16.9x to 26.4x; MDAX from 17.x to 21.x). Within this positive market environment, the transportation & logistics multiple stands out with the currently highest valuation on industry level (2.x). The food & beverages branch exhibits the lowest P/E multiple (.9x). Relative to the previous year, the banking industry provides the highest P/E increase, although this increase is difficult to interpret due to the very small number of available firms in this Prime All Share sub-index and the reduced data availability since last year 2. More reliable are the increases of the transportation & logistics (from 1.9x to 2.x), and technology (from 14.7x to 23.x) multiples, which are mainly driven by improved valuations of Fraport (from 14.9x to 2.x) and Paragon (from 7.1x to 27.1x), respectively. The telecommunication industry shows the largest y-o-y decline (from 14.8x to 7.x). This decline is mainly caused by the substantially reduced P/E multiple of Telefonica Deutschland (from 4.4x to.6x). Compared to the rather stable EVmultiples, the P/E ratio fluctuates generally much more across industries and also over time. Static y-o-y comparisons can therefore be misleading. The TecDAX3 index, for example, exhibits a compounded annual growth rate (CAGR) of roughly 12% from Q1 213 (16.9x) to Q1 214 (26.4x) although the P/E ratio even reduced to 14.8x in the meantime (Q3 213). 1 Respective due dates are January 1, 213 (Q1 213) and 214 (Q1 214). 2 The current banks multiple solely bases on the valuation of Aareal Bank. 23

25 Multiples: P/E Nevertheless, few examples for continuous improvement (e.g. automobiles, banks, industrials) are still available and the Prime All Share Standard itself is probably the best example (Q1 213: 1.x; Q2 213: 1.3x; Q3 213: 1.8x; Q4 213: 17.1x; Q1 214: 2.2x) 3. A continuous P/E decline since Q1 213 is not observable for any industry, which is not surprising though given the overall positive market development. The current P/E forward multiples are below the respective trailing multiples for the Prime All Share Standard (Trailing 2.2x vs. Forward 17.9x), DAX3 (Trailing 2.2x vs. Forward 17.6x), TecDAX3 (Trailing 26.4x vs. Forward 24.9x) and MDAX (Trailing 21.x vs. Forward 19.x) indicating positive earnings growth expectations for the overall market and its major sub-indices. Many industry indices exhibit similarly positive or even better expectations too, for example basic resources (Trailing 2.2x vs. Forward 11.9x) 4, utilities (Trailing 1.9x vs. Forward 11.6x), transportation & logistics (Trailing 2.x vs. Forward 19.6x) as well as technology (Trailing 23.x vs. Forward 18.x). Although there are few branches with negative earnings growth expectations, these few branches largely display forward multiples that are far above the trailing ones. This is especially the case for the telecommunication (Trailing 7.x vs. Forward 14.x) and food & beverages industry (Trailing.9x vs. Forward 7.9x). For the analysis of the trailing and forward P/E multiples we used cut off values of 4.4x and 7.69x, respectively, to avoid bias through outliers. Both values equal the upper % quantile. This led to the exclusion of 12 out of 231 companies for the trailing multiple and 13 out of 244 for the forward multiple. Last year s cut-off values have been 6.x and 4.3x, respectively, which led to the exclusion of 12 out of 239 companies for the trailing multiple and 13 out of 23 for the forward multiple. 3 Respective due dates are: January 1, 213 (Q1 213), April 1, 213 (Q2 213), July 1, 213 (Q3 213), October 1, 213 (Q4 213) and January 1, 214 (Q1 214). 4 Please note that both the basic resources trailing and forward multiples traditionally base on very few data points so that small changes in sector composition or data availability can significantly affect the aggregated industry multiple Please note that both the utilities trailing and forward multiples traditionally base on very few data points so that small changes in sector composition or data availability can significantly affect the aggregated industry multiple. 24

26 Prime All Share Industries, DAX 3, TecDAX 3, MDAX : P/E as of Arithm. mean Median Trailing P/E 1 YR Forward P/E Harm. mean Variance n Arithm. mean Median Harm. mean Variance n Automobiles 14, 12,9 12, 33,2 14,3 13,8 13,4 14,7 Banks 17,2 17,2 17,2 1 2,3 18,6 19,6 28, 3 Basic Resources 1,6 2,2,9 134,7 3 13, 11,9 3, 131,7 Chemicals 2, 2, 1,1 117,1 8 2,7 18,6 18,8 2,9 9 Construction 41, 22, 22,7 1733, 3 21,7 21,4 2, 37,9 3 Consumer 24,4 2,2 7,7 376, ,4 19,6 7,8 81,2 19 Financial Services 22,3 18, 12,2 36, ,7 16,9 12,,2 26 Food & Beverages,9,9 3,8 23,8 2 7,9 7,9 6,1 28,1 2 Industrial 2,1 18,9 13,4 98, ,9 17,4 12,2,6 7 Insurance,1,1,,4 4 9,3 9,2 9,3,2 4 Media 37,2 23,7 28,2 67,4 7 21,7 19,6 19,8 6,8 8 Pharma & Healthcare 2,3 22,1 1,8 237,4 18 2,1 19,9 1,6 49,8 18 Retail 26, 18,2 19,2 61,2 17,9 16,8 9,8,9 12 Software 29,1 24,7 22,4 273,1 28 2,4 21,7 21, 112, 24 Technology 21,8 23, 18, 62, 14 18, 18, 14, 76, 16 Telecommunication,8 7, 7,9 6,9 13, 14, 11,8 24,9 4 Transport. & Logistics 24,7 2, 19,6 2,1 7 19, 19,6 18,6 18,7 8 Utilities 1,9 1,9 1,2 23,1 2 13,7 11,6 11, 63,4 3 Prime All Share 22,9 2,2 13,2 23, ,2 17,9 12,1 81,8 231 DAX 3 19,9 2,2 16, 78, ,3 17,6 1,6 66,1 28 TecDAX 3 26,3 26,4 14, 3, 23 2,2 24,9 17,4 138,3 23 MDAX 24,7 21, 18, 323, ,7 19, 18,1 31, 42 Prime All Share Industries 2

27 finexpert capital markets data Vol. 4 Development of Multiples P/E - Indices 4 4 Multiples Prime All Share Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Fig. 37: P/E - Prime All Share 4 4 Multiples DAX Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Fig. 38: P/E - DAX 3 6 Multiples TecDAX Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Fig. 39: P/E - TecDAX 3 6 Multiples MDAX Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Fig. 4: P/E - MDAX 26

28 finexpert capital markets data Vol. 4 Development of Multiples P/E - Per Industry I/V 6 Multiples Automobiles Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Fig. 41: P/E - Automobiles 7 Multiples Banks Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Fig. 42: P/E - Consumer 7 Multiples Basic Resources Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Fig. 43: P/E - Basic Resources 7 Multiples Chemicals Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Fig. 44: P/E - Chemicals 27

29 finexpert capital markets data Vol. 4 Development of Multiples P/E - Per Industry II/V 9 8 Multiples Construction Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Fig. 4: P/E - Construction Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Fig. 46: P/E - Consumer Multiples Consumer 7 Multiples Financial Services Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Fig. 47: P/E - Financial Services 4 4 Multiples Industrial Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Fig. 48: P/E - Industrial 28

30 finexpert capital markets data Vol. 4 Development of Multiples P/E - Per Industry III/V 3 Multiples Insurance Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Fig. 49: P/E - Insurance 7 Multiples Media Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Fig. : P/E - Media 7 Multiples Pharma Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Fig. 1: P/E - Pharma 6 Multiples Retail Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Fig. 2: P/E - Retail 29

31 finexpert capital markets data Vol. 4 Development of Multiples P/E - Per Industry IV/V Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Fig. 3: P/E - Software Multiples Software 6 Multiples Technology Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Fig. 4: P/E - Technology Multiples Telco Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Fig. : P/E - Telecommunication 6 Multiples Transportation Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Fig. 6: P/E - Transportation & Logistics 3

32 finexpert capital markets data Vol. 4 Development of Multiples P/E - Per Industry V/V 7 Multiples Utilities Jan 9 Jul 9 Jan Jul Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Fig. 7: P/E - Utilities 31

33 Multiples: Price/Sales Executive Summary Substantially increased overall market valuation Financial services (construction) industry with highest (lowest) P/Sales valuation for every quarter since Q1 213 Very optimistic sales growth expectations for the retail sector; very pessimistic ones for the banks and financial services industries Analysis Year-on-year (y-o-y) comparison of the median P/Sales multiples 1 reveals substantially increased valuation of the Prime All Share Standard (from.7x to.8x), DAX3 (from.7x to.9x), TecDAX3 (from 1.x to 1.9x) and MDAX (from.8x to 1.x) although, overall, the relative valuation gains are smaller than those obtained from the P/E analysis. The construction (.2x) 2 and financial services industry (2.3x) exhibit the currently lowest and highest valuation, which is contrary to the P/E analysis where food & beverages as well as transportation & logistics displayed the lowest and highest multiples. The largest y-o-y increase is observed in the telecommunication industry (from.7x to 1.x) whereas the food & beverages branch exhibits the largest y-o-y decline (from.7x to.4x). Compared to the results of the P/E analysis, especially the remarkable increase of the telecommunication multiple stands out 3. The different conclusions result from different data availability though: for the P/Sales multiple, data is available for the entire subindex whereas the P/E ratio only bases on a subset of telecommunication shares. The same subset of shares would have yielded a decline in telecommunication P/Sales multiple too. P/Sales multiples are generally more stable than P/E ratios thus showing smaller fluctuation across industries and over time. The financial services industry, for example, has consistently showed the highest P/Sales multiple since Q1 213 with a variance of just.7. The fact that a variance of just.7 constitutes the largest one across all industries over the same time span 4 underpins the stability of the P/Sales multiple. Similarly, the construction sector has consistently displayed the smallest valuation since Q Respective due dates are January 1, 213 (Q1 213) and 214 (Q1 214). 2 Please note that both the construction trailing and forward multiple traditionally base on very few data points so that small changes in sector composition or data availability can significantly affect the aggregated industry multiple. 3 The P/E analysis shows the largest y-o-y decline for the telecommunication industry. 4 Respective due dates are: January 1, 213 (Q1 213), April 1, 213 (Q2 213), July 1, 213 (Q3 213), October 1, 213 (Q4 213) and January 1, 214 (Q1 214). 32

34 Multiples: Price/Sales Some industries, e.g. the technology or insurance sector, even show close-to-zero fluctuation. A comparison of the current trailing and forward multiples yields ambiguous results. While it indicates slightly positive sales growth expectations for the TecDAX3 (trailing 1.9x vs. forward 1.8x), growth expectations are slightly negative for the DAX3 (trailing.9x vs. forward 1.x) and the Prime All Share Standard (trailing.8x vs. forward.9x). There is no significant difference between the MDAX trailing and forward multiple. On industry level, the very optimistic growth expectations for the retail sector stand out (trailing.8x vs. forward.4x). The banks (trailing.8x vs. forward 1.7x) and financial services industry (trailing 2.3x vs. forward 3.3x) show pessimistic sales growth expectations. For the analysis of the trailing and forward P/E multiples we used cut off values of 6.83x and 6.84x, respectively, to avoid bias through outliers. Both values equal the upper % quantile. This led to the exclusion of 16 out of 36 companies for the trailing multiple and 1 out of 291 for the forward multiple. Last year s cut-off values have been 6.18x and.18x, respectively, which led to the exclusion of 16 out of 316 companies for the trailing multiple and 12 out of 293 for the forward multiple. 33

35 Prime All Share Industries, DAX 3, TecDAX 3, MDAX : Price/Sales as of Arithm. mean Median Trailing Price/Sales 1 YR Forward Price/Sales Harm. mean Variance n Arithm. mean Median Harm. mean Variance n Automobiles,8,7,6,2,8,7,6,2 Banks 1,,8,9,2 3 1,9 1,7 1,7,6 3 Basic Resources,8,3,3 1,4 6,7,3,3,8 6 Chemicals 1,2 1,,,7 13 1,3 1,2,7,6 12 Construction,3,2,2,1 4,3,2,2,1 4 Consumer,9,6,4,7 22 1,,6,,7 19 Financial Services 2,6 2,3,6 3,2 26 3,2 3,3,6 3,2 23 Food & Beverages,4,4,3, 2,,,, 2 Industrial 1,,7,2 1,1 76 1,,7,3,9 73 Insurance,,,4, 4,,,4, 4 Media 1,9 1,2,9 2,7 13 1,8 1,1,9 2, 13 Pharma & Healthcare 2,3 1,9 1,4 2,9 23 2,2 1,7 1, 2,2 22 Retail,9,8,4,9 1,8,4, 1, 1 Software 2,1 1,9 1,2 2,3 31 2,1 1,9 1,2 2,2 28 Technology 1,1,6,6 1, 21 1,1,7,6 1,8 22 Telecommunication 1,1 1,,6 1,2 8 1,2,9,6 1,6 8 Transport. & Logistics,6,4,2,4,6,4,2,4 9 Utilities,3,3,3, 3,3,3,3, 3 Prime All Share 1,4,8,4 1,9 29 1,4,9,1 1,9 276 Prime All Share Industries DAX 3 1,3,9,7 1,6 28 1,4 1,,7 1,6 28 TecDAX 3 2,4 1,9 1,4 3,1 27 2,3 1,8 1,4 2,8 28 MDAX 1,6 1,,6 2,3 47 1, 1,,6 1,

36 Prime All Share Industries, DAX 3, TecDAX 3, MDAX : Betas and Debt-to-Equity Ratios as of year Equity Beta R² n Cost of Equity Debt - Equity Ratio (Market Values) Asset Beta Asset Beta Miles Ezzell Debt Beta =.3 Net-Debt - Equity Ratio (Market Values) Operating Asset Beta Automobiles 1,26, ,7% 1,19,71,77,96,77,83 Banks 1,39, 3 9,% Basic Resources,99,3 6 7,3%,98,6,67,6,71,7 Chemicals 1,8, ,8%,28,91,93,23,94,96 Construction 1,12,43 4 8,%,89,71,77,6,81,8 Consumer,84,8 26 6,4%,9,79,8 -,1,84,84 Financial Services,71,1 34,8% Food & Beverages,47,4 2 4,4%,8,34,4,43,37,42 Industrial,91,7 78 6,9%,36,74,77,18,82,83 Insurance 1,18,76 4 8,3% Media,49,2 13 4,%,19,44,46,12,46,47 Pharma & Healthcare,48, ,%,31,4,43,24,41,44 Retail,74,31 18,9%,1,6,6,22,6,67 Software,83,4 34 6,4%,8,79,8 -,,86,8 Technology 1,13, ,1%, 1,7 1,7 -,1 1,26 1,2 Telecommunication,87,39 8 6,6%,78,8,64,71,6,6 Transport. & Logistics 1,6,9 11 7,6%,44,82,8,2,94,9 Utilities,8,2 4 6,% 1,32,46, 1,,,8 Prime All Share 1, 1, 32 7,3%,7,73,77,42,78,82 DAX 3 1,6,99 3 7,7%,9,77,81,46,82,8 TecDAX 3,77,62 3 6,1%,12,71,72,,77,77 MDAX,77,78 6,1%,3,64,67,12,72,73 Operating Asset Beta Miles Ezzell Debt Beta =.3 Prime All Share Industries 3

37 Prime All Share Industries, DAX 3, T ecdax 3, M DAX : Cost of Capital as of ROE (Return on Equity) Non-cash ROE (Return on Equity) ROC (Return on Capital) Non-cash ROC (Return on Capital) Capex / Depr. Dividend payout Automobiles,21,14,8,9,7,17 Banks,73 2,1 Basic Resources,1,,2,2,98 -,61 Chemicals,24,19,1,16 1,,4 Construction,,3,,6 1,13,49 Consumer,23,16,18,23 1,32,32 Financial Services,,4 13,26,9 Food & Beverages,32,24,18,21 1,24,33 Industrial,12,7,8,9 1,1,68 Insurance 1,38,39 Media,31,21,17,21,4 1,16 Pharma & Healthcare,2,16,13,1,74,26 Retail,1 -,,4,4 1, 3, Software,3,2,23,32,66,46 Technology,,3,,8,64,97 Telecommunication,,4,,,7-2,14 Transport. & Logistics,4,2,4, 1,3,43 Utilities,2,13,12,12 1,76,98 Prime All Share,17,12,9,,96, DAX 3,19,13,,11,91,62 TecDAX 3,6,6,6,11,9 1,77 MDAX,12,8,9,11 1,33,4 Prime All Share Industries 36

38 Term Structure of listed German Federal Securities as at Source: Deutsche Bundesbank Term Structure of listed german federal securities yields with residual maturities of years,2, for listed federal securities,19,189,12,14,2,42,63,8,7,129,1 discrete yields,19,16,12,14,2,42,63,8,8,13,11 years for listed federal securities,169,18626,218,21,227,237,246,23,28,263,266 discrete yields,17,188,24,218,23,24,249,26,262,266,27 years for listed federal securities,268,26997,277,271,27,269,268,267,26,263 discrete yields,272,274,274,27,274,273,272,27,268,266 Yield Curve: Svensson (1994) 3 2, 2 1, Yield in % 1,

39 Inorganic Growth Strategies in Private Equity by Benjamin Hammer and Johanna Stein This edition s current research focuses on a recent study of the Chair of Financial Management at HHL Leipzig Graduate School of Management dealing with add-on acquisitions of Private Equity portfolio companies 1. The growing importance of such acquisitions has been linked to the quest for new value creation strategies in a maturing Private Equity industry. Fierce competition, saturated markets and increasing return pressure, amongst others, have brought so-called buy & build strategies, which rely on an initial platform acquisition and a set of smaller subsequent add-ons, into vogue. To put it in the words of the 212 Bain Global Private Equity Report: When done right, buy-and-build can be a portfolio management strategy ideally suited for markets like today s, when PE firms can no longer ride in the tailwinds of multiple expansion and leverage but need to generate alpha to earn their carry. 2 Despite the seemingly growing importance of add-on acquisitions, academic evidence is still scarce. The few existing studies have furthermore examined intermediate M&A activity as a mere return driver, whereas no study has highlighted the determinants of add-on activity itself. Thus, the Chair of Financial Management s study Inorganic Growth Strategies in Private Equity: Empirical Evidence on Add-on Acquisitions addresses the need for empirical analysis of: the overall add-on relevance in the PE market the reasons for add-on acquisitions and the way they are executed the determinants of add-on acquisition strategies. Besides shedding light on a hitherto unexplored but evidentially important component of PE value creation, the study also sets standards in terms of market coverage. With a total of 9,48 buyouts and 4,973 associated addon acquisitions, it truly constitutes of the largest studies of the PE market in recent years. The findings of the study are threefold. First, it shows that add-on acquisitions generally move along the same cycles as the overall buyout market although the global financial crisis has affected add-on activity slightly less (see figure). 1 The research paper Inorganic Growth Strategies in Private Equity: Empirical Evidence on Add-on Acquisitions by Benjamin Hammer, Alexander Knauer, Magnus Pflücke and Bernhard Schwetzler is available at 2 The 212 Bain Global Private Equity Report is available at: 38

40 The fact that around 12% of involved PE sponsors account for 8% of add-on acquisitions furthermore shows that relatively few PE firms rely on inorganic growth strategies. These few PE firms are predominantly large and successful PE sponsors indicating that only such sponsors have access to addon acquisitions. Regarding reasons for add-ons, secondly, the results suggest that PE firms aim at exploiting synergy affects since most of the subsequent add-on acquisitions occur in the same industry as the platform acquisition. Portfolio firms furthermore conduct add-on acquisitions at a rather early stage of the holding period which might account for the pressure of PE sponsors to realize these synergies before exit. Finally, the study finds several determinants that increase add-on likeliness: large buyouts, public-to-private and financial buyouts as well as those sponsored by large and successful PE firms are found to be significantly more likely to pursue inorganic growth strategies. Buyouts in less fragmented industries exhibit high add-on likeliness too. Indexed development of buyouts and add-on acquisitions 39

41 The study of the Chair of Financial Management provides a good first overview on the add-on market and enhances understanding for such acquisitions and the way PE firms make use of it. In light of the very few existing studies, however, much work is still to come. In that sense, spadework was also done by Mariela Borel and Diana Heger from the Centre for European Economic Research (ZEW) with their recent discussion paper Sources of Value Creation Through Private Equity- Backed Mergers and Acquisitions: The Case of Buy-and-Build Strategies. 3 This study adds insight into the operating characteristics of platform and add-on targets and the impact of buy & build strategies upon their performance. Therewith, the ZEW discussion paper complements the findings of the Chair of Financial Management s add-on study. Both research papers together hopefully enhance the decision-making abilities of Private Equity professionals and investors as well as the understanding for contemporary value creation strategies of PE firms. 3 The research paper Sources of Value Creation Through Private Equity-Backed Mergers and Acquisitions: The Case of Buy-and-Build Strategies by Mariela Borel and Diana Heger is available at 4