Analysis of corporate valuation theories and a...

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MSc. Finance and International Business

Master Thesis

Authors: Doreen Nassaka and Zarema Rottenburg

Advisor: Baran Siyahhan

Analysis of corporate valuation theories

and a valuation of ISS A/S

1st August 2011

Aarhus School of Business and Social Sciences

Aarhus University

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Acknowledgements

We would like to thank our advisor, Baran Siyahhan, for his helpful and constructive guidance.

We would also like to thank our friends and family for their support and patience.

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Executive summary

In this paper various corporate valuation theories are analysed, and selected corporate valuation

theories are applied to the case company International Service Systems, ISS A/S (ISS).

ISS is a Danish founded multinational, multibusiness company, which operates within the facility

services industry. The company was public until 2005 when it was acquired by two equity funds:

Swedish EQT Partners and American Goldman Sachs Capital Partners. In the period 2005-2010,

ISS has been acquiring many companies in order to expand geographically. In the future ISS

expects to focus on organic growth, and limit their growth through acquisitions to emerging

markets. ISS has suffered operating losses in the whole period when it was private and has obtained

high amounts of debt. This has lead to high financial expenses for the company and an inability to

pay off its debt on time. In order to improve its capital structure ISS decided to go public again on

the Copenhagen stock exchange. The initial public offering (IPO) was planned in March 2011, but

was postponed due to the instability of the financial markets.

The corporate valuation theories analysed in this paper are the discounted cash flow (DCF) model,

the dividend discount model (DDM), the residual income model (RIM), real options valuation

(ROV) and valuation using multiples. Furthermore, two methods for determining the expected rate

of return on a company‘s stock are evaluated, these are: the capital asset pricing model (CAPM)

and the Fama and French three factor model. Based on the analysis, the DCF model is selected as

the primary corporate valuation model in this thesis, and the capital asset pricing model (CAPM) is

chosen to estimate the cost of equity for the company. Additionally, real options analysis is applied

as an extension of the DCF model, in order to capture the value of the managers‘ flexibility in

relation to ISS‘ expansion in emerging markets. Lastly, the valuation using multiples is conducted

in order to evaluate whether the results obtained from the DCF and real options valuation are

reasonable.

In the valuation process ISS‘ corporate environment is studied by means of a strategic analysis.

Additionally, the company value from the DCF model is calculated based on the forecasted free

cash flow in the explicit forecasting period and the estimated continuing value after the explicit

forecast period.

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In the real options analysis a five year option to expand in South Korea is valued and added to the

value obtained from the DCF model. The value of the real option is estimated by using the binomial

model.

The valuation using multiples is conducted based on ISS‘ identified peer group of seven similar

companies, and two multiples EV/EBITDA and EV/S are used in the analysis.

The valuation process is finished off with a discussion of the results and a comparison of these

results to equity values of ISS published by professional analysts and to the expected IPO share

price published by ISS.

ISS‘ firm value from the DCF model is DKK 55,173 million, the equity value is DKK 39,049

million and the share price is DKK 167.59. The enterprise value including the real options is DKK

56,279 million, the equity value is DKK 40,154 million and the share price is DKK 172.34.

From the multiples analysis, ISS‘ enterprise value, equity value and share price are estimated within

the range DKK 40,641 - 48,959 million, DKK 24,516 - 32,834 million and DKK 105 – 141,

respectively.

The equity value obtained from the DCF and the real options analysis is compared to the published

equity value estimates from professional analysts that are within the range of DKK 39.4-49 billion.

The estimated share price of DKK 172.34 is compared to the IPO share price that is within the

range of DKK 100 – DKK 135. The difference in the share price may be due to the fact that it is a

second-time IPO and that the company has high debt levels.

It is concluded that the enterprise value for ISS of DKK 56,279 million is reasonable, and that the

share price of DKK 172.34 represents the fair market value of the company‘s shares.

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Table of contents

Executive summary .............................................................................................................................. 1

1. Introduction ...................................................................................................................................... 8

2. Description of ISS ............................................................................................................................ 9

3. Problem statement .......................................................................................................................... 10

4. Definitions ...................................................................................................................................... 11

5. Delimitations .................................................................................................................................. 11

6. Data collection and validity ........................................................................................................... 12

7. Analysis of the corporate valuation theories .................................................................................. 12

7.1 The discounted cash flow method ................................................................................................ 13

7.1.1 Step 1: The calculation of free cash flow .............................................................................. 13

7.1.2 Step 2: The weighted average cost of capital ........................................................................ 14

7.1.3 Step 3: Identifying the continuing value ............................................................................... 23

7.1.4 Step 4: Calculating the company value ................................................................................. 24

7.1.5 Evaluation of the DCF method ............................................................................................. 25

7.2 Dividend discount model (DDM) ................................................................................................ 25

7.2.1 Advantages and disadvantages of the DDM ......................................................................... 27

7.2.2 Evaluation of the DDM ......................................................................................................... 28

7.3 Residual income model (RIM) ..................................................................................................... 28

7.3.1 Advantages and disadvantages of the RIM ........................................................................... 29

7.3.2 Evaluation of RIM ................................................................................................................ 32

7.4 Real Options Valuation (ROV) .................................................................................................... 32

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7.4.1 Advantages and disadvantages of real options ..................................................................... 33

7.4.2 The binomial model .............................................................................................................. 34

7.4.3 The Black and Scholes model ............................................................................................... 36

7.4.4 Evaluation of real options ..................................................................................................... 37

7.5 Valuation using multiples ............................................................................................................ 37

7.5.1 Advantages and disadvantages of multiples ......................................................................... 40

7.5.2 Evaluation of the multiples method ...................................................................................... 41

8. Valuation of multibusiness companies .......................................................................................... 41

8.1 Challenges of valuing multibusiness companies ..................................................................... 42

8.2 Summary .................................................................................................................................. 43

9. The choice of corporate valuation theories .................................................................................... 43

10. Structure of the valuation process ................................................................................................ 44

11. Strategic analysis of ISS .............................................................................................................. 45

11.1 PESTEL analysis........................................................................................................................ 46

11.2 Porter‘s five forces analysis ....................................................................................................... 49

11.3 Core competencies ..................................................................................................................... 52

11.4 SWOT analysis .......................................................................................................................... 53

12. Valuation of ISS using the DCF model ....................................................................................... 56

12.1 Step 1: Choice between using aggregated or disaggregated numbers ................................... 56

12.2 Step 2: Currency choice ......................................................................................................... 57

12.3 Step 3: Calculation of free cash flow ..................................................................................... 57

12.3.1 The reformulated statement of shareholders‘ equity (SE) .................................................. 58

12.3.2 The reformulated balance sheet .......................................................................................... 58

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12.3.3 The reformulated income statement .................................................................................... 60

12.3.4 Trend analysis ..................................................................................................................... 60

12.3.5 Profitability analysis ........................................................................................................... 62

12.3.6 Summary of the financial statement analysis ...................................................................... 68

12.3.7 The amount of free cash flow ............................................................................................. 69

12.3.8 Forecasting free cash flow .................................................................................................. 69

12.4 Step 4: Estimating the weighted average cost of capital ........................................................ 77

12.5 Step 5: Calculating the continuing value ............................................................................... 83

12.6 Step 6: Calculating the company value .................................................................................. 83

12.7 Scenario analysis .................................................................................................................... 84

12.8 Sensitivity analysis ................................................................................................................. 86

13. Valuation of ISS using real options ............................................................................................. 87

13.1 Step 1: The value of ISS without flexibility .......................................................................... 90

13.2 Step 2: Event tree for ISS ....................................................................................................... 90

13.3 Step 3: Decision tree for ISS .................................................................................................. 92

13.4 Step 4: The value of ISS with flexibility................................................................................ 93

14. Valuation of ISS using multiples ................................................................................................. 94

15. Discussion of the results .............................................................................................................. 95

16. Conclusion ................................................................................................................................... 99

References ........................................................................................................................................ 102

List of appendices ............................................................................................................................ 111

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1. Introduction

The purpose of this master thesis is to analyse and discuss different theories of corporate valuation,

and to apply the most appropriate ones to the case company International Service Systems, ISS A/S

(ISS).

Corporate valuation has been of great interest to the authors throughout the master course; however,

this topic has not been covered in details in the corporate finance classes attended by the authors. It

was therefore decided to focus on this topic in the final master dissertation.

The intention of this assignment is to apply theoretical knowledge about company valuation to a

real life case by using external publically available data such as annual reports and published

articles. It is preferred to analyse the company from an external point of view because obtaining

internal data from the company often makes the analysis process slower because the authors will

have to depend on data provided by the company‘s employees.

ISS has been chosen as the case company due to its interesting financial history, the large media

coverage at the time the thesis topic was chosen, and the fact that it is a challenge to value this

company with the traditional financial valuation tools. The financial history of ISS is considered to

be interesting because it started out as a small private company in Copenhagen in 1901, became a

public company in 1977 and was acquired through a leveraged buyout by two private equity funds

in 2005. Burdened by huge amounts of debt, ISS was considering another private sale or a second

IPO in 2010/2011. Valuation of ISS is challenging because ISS is a very large, private,

multinational, multibusiness company, which offers different facility services to private and public

customers. However, due to the rumours about a sale or an IPO at the end of 2010 and the

beginning of 2011 numerous articles about ISS were published stating factors such as the estimated

corporate value of the company, its expected growth rate and the expected share price. Furthermore,

due to the size of ISS and its past as a public company, all the annual reports for ISS are published

for all the years. So even though performing an external valuation of ISS is a challenge, it is not

unachievable with the publically available information, and ISS was therefore chosen as the case

company.

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2. Description of ISS

ISS was founded in 1901 as a Danish security firm by 20 night watchmen in Copenhagen, Denmark

and in 1934 the company began offering cleaning services. In 1977, ISS was listed on the

Copenhagen Stock Exchange, and in 2005 the company was bought by FS Invest S.À R.L, which is

located in Luxembourg, and de-listed from the Copenhagen Stock Exchange (issworld.com).

Today, ISS is one of the world‘s largest private companies owned by FS Invest S.À R.L, a company

that is owned by two private equity funds: Swedish EQT Partners and American Goldman Sachs

Capital Partners, whereby each fund owns 54% and 44% of the share capital, respectively. The

remaining 2% of the shares are owned by some employees at the top management level of the

company (ISS Annual report, 2010).

ISS operates under a decentralized organizational structure. ISS‘ headquarter is currently located in

Copenhagen, where they assist their regional and local management in the different countries (ISS

Annual report, 2010). By applying decentralized decision making, ISS focuses on strong local

leadership and independent decision making, in order to quickly respond to customers‘ demands

and needs (issworld.com). In 2010, ISS had revenues of DKK 74 billion and a net loss of DKK 532

million. This loss is an improvement from the net loss of DKK 1,629 million in 2009 (ISS Annual

report, 2010).

With its 522,700 employees, ISS provides facility services to over 200,000 public- and private-

sector customers in the business to business (B2B) market, that are located in 53 countries in

Europe, Asia, North America, South America and Pacific. Facility services includes the following

six services (the numbers in the parenthesis represent the percentage of the total revenue in 2010 for

the ISS group): cleaning services (52%), property services (20%), catering services (9%), support

services (8%), security services (7%) and facility management services (4%) (ISS Annual report

2010). The abovementioned services can be delivered separately or as integrated solutions

(issworld.com).

The cleaning services include daily cleaning (e.g. dust control and wash room services), periodical

cleaning (e.g. carpet and window cleaning), special cleaning (e.g. telephone and computer cleaning,

laundry services) and segment cleaning (e.g. office, health care and retail cleaning). To maintain the

value of the company‘s customers‘ buildings and surroundings, ISS offers property services that

include building maintenance (e.g. painting services, repairs and replacements), ground

maintenance (e.g. road services and landscaping), environment maintenance (e.g. pest control and

waste management), energy (e.g. water supply, lighting and power) and damage control (e.g.

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dehumidification). The catering services include contract catering (e.g. hospitality services and

staff dining), vending machines, event catering, and confectionery services (e.g. coffee shops and

fruit service).

Additionally, the support services consist of activities related to the front office (e.g. reception and

hostess services), back office (e.g. mail handling, call centers and office supplies), welfare facilities

(e.g. sports facilities and fruit services) and labor supply (e.g. temporally workers and recruitment).

ISS focuses on keeping its customers‘ employees and properties safe, thus security services contain

these activities: physical security (e.g. manned guarding and emergency response), surveillance

(e.g. monitoring and alarm response), technical installations (e.g. car park management, fire and gas

detection), work place emergency management (e.g. first aid services and emergency evacuation)

and consulting services (e.g. security training). Lastly, the company offers its customers Integrated

Facility Services, whereby the different services can be combined into one solution in order to

reduce overhead costs as well as allocate resources more efficiently (issworld.com).

At the end of 2010, ISS‘ owners were considering to change the capital structure of the company

once again either by selling ISS to another private owner or to let it go public (Webb and Espana,

2010). Even though several equity funds were preparing bids for ISS, the owners decided to go

company public (www.altassets.net). The IPO was planned for March 2011 on the Copenhagen

stock exchange, but was postponed due to the instability of the financial markets

(www.issworld.com). For the purpose of the calculations in this paper, it is assumed that ISS will

go public in 2012.

3. Problem statement

The purpose of this master thesis is to analyse and discuss different theories of corporate valuation,

determine which theories are relevant for valuing ISS, and apply them to estimate ISS‘ firm value.

The theories that will be examined include the discounted cash flow (DCF) model, the dividend

discount model (DDM), the residual income model (RIM), real options valuation (ROV) and

valuation using multiples. In relation to the DCF model, two methods for determining the expected

rate of return on a company‘s stock will be evaluated, these are: the capital asset pricing model

(CAPM) and the Fama and French three factor model. The corporate valuation theories will be

analysed based on a literature review that for example includes a discussion of advantages and

disadvantages of the different theories.

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In relation to valuing ISS, a strategic and a financial analysis of the company will be performed. In

the strategic analysis, ISS‘ corporate environment will be studied using the PESTEL analysis,

Porter‘s five forces, and the SWOT analysis. Furthermore, ISS‘ core competences will be identified.

In the financial analysis, the relevant corporate valuation theories will be applied to ISS. As part of

the analysis, ISS‘ financial statements will be reformulated in order to identify the company‘s main

value drivers. Based on the financial analysis, ISS‘ enterprise value, equity value and share price

will be determined.

Additionally, the equity value of ISS, estimated based on the strategic and financial analysis will be

compared to official equity value estimates published by professional analysts, and the estimated

share price will be compared to the expected IPO share price published by ISS. If there is a

considerable difference between the results, possible reasons for the difference will be discussed.

The structure of this paper is as follows: sections 1 to 2 include an introduction and the description

of ISS, in sections 7 to 9 corporate valuation theories are discussed and the relevant theories are

identified, in sections 11 to 14 the strategic and financial analysis of ISS are performed, and finally

sections 15 and 16 contain a discussion of the results and a conclusion of the study. Appendix O

contains an overview of the thesis structure.

4. Definitions

Due to the fact that formulas are stated with different notations in the literature, the notations of

some formulas are changed when necessary in order to achieve notation consistency in this paper.

Furthermore, due to the fact that there are several abbreviations used throughout this paper, a list of

abbreviations has been made in appendix P.

5. Delimitations

This section states the delimitations relevant for this paper. Only financial theories will be analysed

in depth and discussed based on a literature review. Marketing models will not be discussed, but

just applied to the case company due to the fact that this paper focuses on a financial valuation of

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the company. Corporate valuation theories will not be derived1 in this thesis, because the main

focus is on the possible application of these theories to a real life company.

6. Data collection and validity

All the information that is used in this thesis whether it is from news papers, the internet or books is

publically available. The main source of financial information about ISS is from ISS‘ annual

reports. However, some of the information from ISS is considered to be subjective and providing a

more positive picture of ISS and has thus been used with precaution. The data from other sources is

considered to have an objective opinion about ISS.

7. Analysis of the corporate valuation theories

In general the value of an asset equals the present value of the cash flows that it will generate in the

future. This methodology can be used to value single projects, investments and also whole

companies. What matters in relation to the present value of the cash flows is the timing of the cash

flow and the risk level (Benninga and Sarig, 1997). According to Damodaran (1996), valuation

methods can be generally grouped into the following three categories: discounted cash flow

valuation, relative valuation and contingent claim valuation. Discounted cash flow methods

forecast future cash flows of an asset, and discount them at a given rate in order to get the asset‘s

present value. Relative valuation methods determine the value of an asset by comparing variables

such as earnings, cash flows, book value or sales, and contingent claim valuations apply option

pricing models to measure the value of an asset. These methods can lead to different results

depending on the assumptions used in each method.

Benninga and Sarig (1997) advise to use more than just one valuation method to estimate the firm

value. It is advised to use more than one method because there is a great deal of uncertainty in

relation to value estimation as it involves predicting future returns of the company, and if the

different methods give similar results it implies that the estimated value is sensible. Due to the

advice from Benninga and Sarig (1997) various valuation models will be discussed in this section

and the relevant methods will be used to analyse ISS in sections 12-14

1 For example the underlying assumptions of the models will not be discussed

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7.1 The discounted cash flow method

In the discounted cash flow (DCF) method, the value of an asset is calculated based on the present

value (PV) of future cash flows generated by the asset. These cash flows are discounted by using a

rate that represents their risk (Damodaran, 2010 ―3‖). According to Miller and Modigliani (1961),

the DCF approach can be used to value a whole company, (Cooper and Argyris et al., 1998)

whereby the company is considered as a lot of projects combined. To determine the firm value, the

PV of future cash flows from all the projects in the firm‘s operations are identified (Penman, 2010).

Furthermore, the cash flows in the DCF method can be estimated using different cash flow proxies

such as dividends, free cash flow (FCF) or accounting earnings (Koller et. al, 2005). The DCF

model using the dividends proxy is discussed in section 7.2, the DCF model using the FCF proxy is

discussed in section 7.1 and the DCF model using accounting earnings, i.e. the residual income

model is discussed in section 7.3. Given very strict consistent assumptions, valuation using the

DCF method with the various cash flow proxies should result in similar firm value estimates.

However, empirical evidence shows that the different proxies lead to different firm value estimates

(Torrez et al. 2006).

There are two approaches to the DCF analysis: one is to value the firm as if it was only equity

financed: i.e. the equity valuation and the other is to value the whole firm including all its

claimholders: i.e. the firm or enterprise valuation (Damodaran, 1996). Since the focus of this paper

is on firm valuation, the latter method is discussed further. The DCF model can be set up by using

four steps suggested by Penman (2010). In the first step, a company‘s free cash flow is estimated to

a given year. The second step involves determining the weighted average cost of capital (WACC)

and discounting the free cash flows using this discount rate so as to determine their net present

values (NPV). Additionally, the continuing value is identified in the third step, and the

determination of the company value is explained in step four. Lastly, an evaluation of the DCF

method is made. Appendix A includes an overview of the steps in the DCF model.

7.1.1 Step 1: The calculation of free cash flow

Free cash flow is “the difference between cash flow from operations and cash investment in

operations” (Penman, 2010 p. 341). It is the cash flow that is available to investors after

investments in fixed assets and working capital (Brealey and Myers et al., 2007). FCF is also

independent of leverage (Koller et. al, 2005) and it determines a company‘s capability to pay off its

debt and equity claims (Penman, 2010). Additionally, FCF is a good indicator of the company‘s

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ability to generate cash and therefore also profit. A negative FCF does not mean that the company‘s

operations are unprofitable, but it could be a sign that the company is growing fast and is therefore

making large investments. Fast growth is good for the company as long as it is earning more than

the cost of capital on its investments (Brealey and Myers et al., 2007).

In order to forecast a company‘s future cash flows, it is necessary to evaluate its past financial

performance first. This way the main drivers of a company‘s value: the return on invested capital

(ROIC), the growth rate and free cash flow can be identified. Moreover, it is also possible to

conclude whether the company has generated value, if it has grown as well as compare its

performance to competitors. However, ROIC and FCF cannot be calculated directly from a

company‘s reported financial statements, thus it is necessary to reformulate these statements so as

to identify a company‘s operating items, non-operating items and financial structure (Koller et. al,

2005). The limitations of free cash flow include its inability to identify value created that does not

involve cash flows, the fact that it evaluates investments as loss of value, and the option to increase

the FCF by e.g. investing less (Penman, 2010).

7.1.2 Step 2: The weighted average cost of capital

The weighted average cost of capital is the rate of return that investors expect from investing in a

given company instead of other companies with similar risk (Brealey & Myers et al., 2007). As

mentioned earlier, WACC is used to discount the free cash flow. It is one of the most important

features of the DCF model, because a small change in WACC can lead to major changes in firm

value (Steiger, 2010). To successfully apply WACC, it is important to have uniformity between the

inputs of WACC and free cash flow regarding factors such as duration and the risk of financial

securities. WACC and FCF must both be calculated on an after-tax basis and in the same currency

(Koller et. al, 2005). If a firm is considered as a combination of projects, as mentioned earlier, then

WACC is the suitable discount rate representing the riskiness of the cash flows from all the projects

(Penman, 2010). WACC can be calculated by determining its three components: the after-tax cost

of debt, the cost of equity and the company’s target capital structure (Koller et. al, 2005). Thus, the

formula for WACC is (Brealey & Myers et al., 2007):

WACC =

Where D/V = Target level of debt to enterprise value using market values

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E/V = Target level of equity to enterprise value using market values

= cost of debt

= cost of equity

= company‘s marginal income tax rate

The following sections include an explanation of how to determine the after-tax cost of debt and the

cost of equity in the WACC formula.

The cost of debt

The cost of debt is the rate that a company pays to borrow money (Damodaran, 2009). There are

three factors needed to calculate the cost of debt: the risk free rate, the default spread and the tax

rate (Damodaran, 2010). The risk free rate is discussed below in relation to the CAPM. The second

factor, which is the default spread2 can be determined in three ways, which are chosen depending

on the company to be evaluated: a) if a company has outstanding bonds, then the cost of debt can be

calculated by applying the current market interest rate (yield to maturity, YTM) on the company‘s

long-term bonds, b) if a firm has bond ratings from rating agencies such as Moody‘s or Standard

and Poor (S&P) , the default spread can be determined based on the ratings (Steiger, 2010), and c) if

the firm is not rated, an artificial rating can be made based on the firm‘s interest coverage ratio

(EBIT/ Interest expense) (Damodaran, 2009)3.

The last part for determining the cost of debt is the tax rate. Interest payments on debt are

subtracted from income before tax is determined, thus taking on debt can act as a tax shield

(Brealey & Myers et al., 2007). The formula for the after tax cost of debt is (Damodaran, 2010):

After tax cost of debt = (Risk free rate + Default spread) (1- marginal tax rate)

The advantages of debt besides the tax shield include committing managers to operate efficiently in

order to fulfill principal and interest payments and encouraging lenders to monitor the firm

2Borrowers of the firm bear the risk of not getting their expected payments (interest and principal). To compensate for

this risk, the lenders add a default or credit spread to the risk free rate (Damodaran, 2010). A credit spread is the

difference between the risk free rate and the interest rate that a company pays to borrow money (Steiger, 2010).

3 In option one, long-term bonds are applied because short term bonds do not match the duration of the company‘s free

cash flow forecasts (Koller et. al, 2005). Option one is suitable if the firm has liquid bonds that represent the overall

debt of the firm. In option three, the higher the interest coverage ratio the higher the ratings.

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(Binsbergen et al., 2010). Even though interest expenses are tax deductible, companies do not

borrow to the maximum, because of the disadvantages of extreme borrowing such as financial

distress (Scholze, 2010). There is also an explicit cost and implicit cost to debt financing; the

explicit cost is the interest rate that bond holders require from the company and the implicit cost

relates to the fact that when a firm borrows more money, its equity becomes more risky, thus equity

holders demand a higher return to compensate for this risk (Brealey & Myers et al., 2007).

The cost of equity

The cost of equity can be calculated by using asset pricing models that help to determine the

expected rate of return on a company‘s stock. There are three asset pricing models: the capital asset

pricing model (CAPM), Fame and French three factor model and the arbitrage pricing theory

(APT). The main difference between these models is the way they identify a stock‘s risk. The

CAPM, which is the most widely used model, states that a stock‘s risk depends on its sensitivity to

the stock market, Fame and French‗s model claims that a stock‘s risk is based on its sensitivity to

three factors: the stock market, a portfolio based on firm size and a portfolio based on book-to-

market ratios, and the APT extends Fame and French‗s model, by arguing that a security‘s rick

depends on even more factors (Koller et. al, 2005; Bartholdy and Paula, 2003). However, the APT

does not specify these factors, and for this reason it will not be discussed further in this paper.

Instead the two main models: the CAPM and Fame and French‘s model are discussed below.

The capital asset pricing model

The capital asset pricing model, which was introduced by Sharpe (1964), Linter (1965) and Black

(1972) based on Markowitz‘s (1952) portfolio theory, explains the relationship between risk and

expected return and it is stated as (Torrez et al. 2006):

Where: = expected return on security i

= risk free rate

= sensitivity of the stock‘s return to the return on the market portfolio

= expected return of the market

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Based on the CAPM model, a stock‘s expected return is determined by two things: a) the time value

of money and b) the risk premium. The time value of money is represented by the risk free rate (rf),

i.e. investors are compensated for putting their money in any investment over a period of time.

Additionally, the extra return that investors demand for taking on risk is called the risk premium

i.e. , this return depends on the risk measure , and the market risk

premium (Brealey & Myers et al., 2007).

In addition, risk can be divided into unique risk (unsystematic or firm-specific risk) and market risk

(systematic risk). Unique risk is specific to the individual firm and possibly its direct competition

and it can be diversified away as the investor increases the number of securities in his portfolio.

Market risk, which is caused by macroeconomic factors such as changes in interest rates, oil prices

and foreign exchange rates that affect the whole stock market, cannot be diversified away (Brealey

& Myers et al., 2007).

According to the CAPM, the risk free rate and the market risk premium are the same for all

companies; it is only beta that is different for each company (Koller et. al, 2005), thus the main idea

of the CAPM is that the variance of a stock by itself is not an important determinant of the stock‘s

expected return, instead it is the covariance of the stock‘s return with the return on a given market

index that is important. Thus, the CAPM is developed as a method to evaluate market risk

(Mukherji, 2011; Hillier et al., 2008). The following sections consist of a discussion of the inputs to

the CAPM: the risk free rate, beta, and the market risk premium as well as an evaluation of this

theory.

Determining the risk free rate

Risk is determined as the possibility that an investment's actual return will differ from the expected

return, (Damodaran, 2008) whilst for a risk free investment, the actual return will always be equal

to the expected return (Vučković, 2010). This will occur only if these two conditions are fulfilled;

that there is no default risk and there is no reinvestment risk. No default risk means that there should

be no chance that the issuer of the security will not accomplish his contract, and no reinvestment

risk implies that there ought to be no possibility that interest earned from an investment may have to

be reinvested at a lower interest rate. In conclusion, if the requirements for a financial instrument to

be risk free are the nonexistence of default risk and reinvestment risk, then the risk free rate can

only vary, depending on the investment period (Damodaran, 2008).

http://www.investopedia.com/terms/i/investment.asp

http://www.investopedia.com/terms/r/return.asp

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The risk free rate can be determined by looking at the long-term government default-free bonds4.

Government bonds have different maturities ranging from for example one months to 20 years (e.g.

for U.S. Treasury issue bonds). Considering the fact that different bonds have different maturities

that can lead to different yields to maturity, a company‘s cash flows must be discounted by using a

bond with a similar maturity. For companies in the U.S., the most frequent applied proxy is a 10-

year government bond. For European firms, the 10 year Germany Eurobond can be used, because it

is believed to have a higher liquidity and lower credit risk compared to bonds of other European

countries. Moreover, as mentioned earlier, in order to consider inflation consistency, the cash flows

and the cost of capital should be stated in the same currency (Koller et al., 2005).

Estimating beta

As stated earlier, a stock‘s expected return depends on its beta, which is a measure of how much the

stock price fluctuates in relation to the market (the stock‘s volatility) (Koller et. al., 2005). The beta

value for the market is 1.0, stocks with a beta greater than 1.0 are sensitive to market fluctuations

whilst stocks with a beta less than 1.0 are less sensitive to market fluctuations (Brealey and Myers

et al., 2007). Unfortunately, beta cannot be directly observed, thus it must be estimated. This is

often done by using the market model, whereby the returns on a stock are regressed against a

market‘s return over a given time period. The market model can be stated as (Damodaran, 1999):

Where: Ri is the return on stock i, Rm is the return on the market index and beta (ß) is the slope of

the regression. While the market model seems good in theory, there are some practical issues

related to estimating the beta because this theory does not specify the market index, time period and

return interval5 to be used. Therefore, the betas estimated by different analysts for the same firm

will be different depending on the time period, return interval and market index applied

(Damodaran, 1999).

According to the CAPM, investors are risk averse and they demand a higher return for taking on

additional risk as stated earlier. The advantage of the beta, which is the mostly used measure of an

4 Koller et. Al (2005) defines the risk free rate as a return on a portfolio or security that does not correlate with the

market ( i.e. with a CAPM = 0). In theory, a zero-beta portfolio can be calculated; however the process will be

expensive and complicated. This is why long-term government default- free bonds are often used. These bonds are not

entirely risk-free and have very low betas. 5 Stock returns can be determined daily, weekly, monthly, quarterly or yearly.

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asset‘s riskiness (Shalit and Yitzhaki, 2002) is that it provides a quantifiable way of evaluating the

required return on a risky investment as well as helping investors recognize attractive stocks based

on their risk preferences. Additionally, beta is a good standard for discussing market efficiency, and

for evaluating a stock‘s performance compared to the market (Liang, 2006). Beta is a very

important part in the CAPM model and the usefulness of CAPM mostly depends on the accuracy of

beta (Gürsoy and Rejepova, 2007).

As stated earlier, beta measures market risk by evaluating the volatility of a stock to a given index.

One of the disadvantages of beta is the fact that it only considers the risk caused by macroeconomic

factors on a company‘s stocks, and firm-specific risk is not fully evaluated by the beta. Beta is

therefore an unreliable measure that does not compute the total risk of a firm, because a given firm

may have a high level of firm-specific risk but a low level of market risk (Gunlaugsson, 2007).

Another problem with betas from a regression analysis is that they are based on historical data and

companies tend to change over time6. Thus historical betas are hardly good representatives of the

firm‘s current and future structure (Damodaran, 1999). Moreover, beta estimates are ―noisy‖ since

―they are statistical estimates, with standard errors” (Damodaran, 2011, p.5). In Fama and French

(1992)‘s paper about the trustworthiness of past betas, they concluded that the CAPM beta does not

explains the last 50 years of average stock returns.

Damodaran (2011) suggests improving the accuracy of the regression betas by using bottom up

betas. This refers to a beta that is estimated by using the average betas of similar firms in the

industry. This beta should also be adjusted for variations in financial leverage.

Furthermore, unlike public firms, private firms do not have past price information to use in a linear

regression. Private firm owners, e.g. private equity funds often choose to invest money in just one

or few companies and therefore they do not have very diversified portfolios compared to public

firm owners7, thus estimating beta for private firms needs to be done in a different way in order to

get a reliable beta. To get a beta for private firms, the analysts can for example 1) regress

accounting earnings against changes in earnings for a market index to get accounting betas, 2)

regress the betas of a given market index to a private firm‘s ratios such as book debt/equity and

6 Firms for example: terminate businesses, invest in new businesses and acquire firms. They also change their financial

leverage by issuing or paying off debt as well as pay dividends and buy back shares. Firms also grow over time. All

these events will change a firm‘s beta (Damodaran, 1999). 7 One of the assumptions of the CAPM is that investors have well diversified portfolios, in which they diversify all

firm-specific risk, thus only market risk is relevant as mentioned earlier. For private firms, beta understates the effect of

market risk.

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book value of assets to get fundamental betas or 3) look at average betas for similar public firms to

get bottom-up betas (Damodaran, 2009).

Calculating the market risk premium

The market risk premium (MRP) is a challenging measure to estimate, because the

expected return on the market cannot be directly observed (Bowman, 2001). Therefore, a

universally accepted model for estimating the MRP does not exist. It is common practice to

determine the market risk premiums by using past risk premiums, this refers to premiums that

investors have earned over long periods for example 75 years. An alternative option for determining

the MRP involves calculating a forward looking premium from current stock price levels and

expected future cash flows (Koller et al., 2005).

Assuming that investors make their expectations based on their past experiences, the average of

historical returns can be expected to have the main influence on investors‘ future expectations

(Officer and Bishop, 2008). If investors who are believed to be risk averse, haven‘t changed their

attitude towards risk in the past 75 years, then historical returns are a sensible proxy for future

expectations (Koller et. al, 2005). Bowman (2001) claims that historical estimates are not suitable to

be used in the CAPM, which is a forward looking model that relies on investors future expectations.

However, due to the lack of reliability in forecast methods, historical estimates are often applied in

the CAPM (Officer and Bishop, 2008).

Evaluation of the CAPM

Having introduced the CAPM in the previous sections, this section will include an evaluation of this

theory. The main benefit of the CAPM is that, it is helpful in explaining the relationship between

risk and return of a given investment (Fame and French, 2004). Unfortunately, empirical evidence

shows that the CAPM is not completely valid in explaining stock returns, instead there are other

factors besides beta that give better reasons (Gunlaugsson, 2007) such as firm size and book-to-

market ratios (Fame and French, 1992). Moreover, Roll (1977) argued that since a true market

portfolio cannot be identified, or replaced by a proxy such as a stock market index, the CAPM can

never be truly tested (Gürsoy and Rejepova, 2007). There is also the challenge of testing the market

model, i.e. the joint hypothesis problem, where there is simultaneous testing of both the model and

the market, and this makes the results of the test inconclusive (Hillier et al., 2008). In conclusion,

the CAPM is the most applied and criticized model in finance (Damodaran, 2010).

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Fama-French three-factor model

As already mentioned, the CAPM model is useful in explaining portfolios that are related to the

market. However, if a portfolio is not closely related to the market, the CAPM is less helpful in

explaining its returns. For this reason, Fama and French (1992; 1993) developed the CAPM further

by suggesting that besides market risk, a stock portfolio‘s risk also depends on the firm size and the

book-to-market ratio (Fama, 2007). In the Fama-French model, the excess returns on a given stock

are regressed on: the excess returns on the market (like in the CAPM), the excess returns on small

minus big stocks (SMB) and the excess returns on high book-to-market stocks minus low book-to-

market stocks (HML) (Koller et al., 2005). SMB represents the size premium, i.e. the fact that

stocks of companies with small market capitalization (cap) tend to result in higher returns than large

cap stocks. HML represents the value premium, i.e. the tendency that companies with high book-to-

market ratios (value stocks) generate higher returns than those with low book-to-market ratios

(growth stocks) (Womack et al., 2003). When calculating the Fame-French regression analysis,

returns for three portfolios: the market portfolio, the SMB portfolio and the HML portfolio are

needed (Koller et al., 2005; Bundoo 2008).The regression can be stated as (Koller et al., 2005):

– +

Where: is the return on portfolio, is a constant, is the risk free rate, is the coefficient for

the excess return of the market portfolio over the risk free rate, is the return of the stock

market, is the coefficient for the excess average return of portfolios with SMB,

represents SML, is the coefficient for the excess average return of HML portfolios,

represents HML, and is the error term.

The three-factor model implies that investors can target a given level of return by weighting their

portfolios as desired on each of the three risk factors (Womack et al., 2003). Furthermore, small cap

stocks yield a higher expected return because they are believed to be more risky (Fama, 2007). This

is for example due to the fact that they offer fewer opportunities for diversification and they are less

capable of dealing with negative financial results (Womack et al., 2003). In their 1995 study, Fama

and French (1995) concluded that companies with high book-to-market ratios are often distressed

compared to those with low book-to-market ratios. For this reason, investors of firms with high

book-to-market ratios achieve greater expected returns for holding less beneficial and more risky

stocks (Bundoo 2008).

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Evaluation of the Fama-French three-factor model

In conclusion, empirical evidence shows that the Fama-French model has a higher explanatory

power than the CAPM with its R2 values of 0.95 and above (Womack et al., 2003). In other words a

combination of beta, size, and value explains 95% of a diversified portfolio‘s return. Unfortunately,

similar to the CAPM, the Fama-French model also has some practical issues regarding for example

the amount of data to be used for estimating the risk premium, the return interval and the time

period for the regression. Koller et al. (2005) suggests that these practical issues exist because the

Fama-French model is relatively new.

Additionally, several studies (Bartholdy and Peare, 2003; Lam, 2005) have compared the results

from the one-factor model (CAPM) and the three-factor model, and have concluded that neither

model is better than the other.

Overview of the WACC formula

As stated earlier WACC is the expected rate of return on a firm‘s securities (e.g. equity, debt and

preferred stock), whereby each security is weighted proportionally on the firm‘s market value. The

WACC formula is generally written as if the company only has two securities: debt and equity in its

capital structure. The cost of debt is the current market interest rate that is required by a firm‘s

bondholders and the cost of equity is the expected return on a firm‘s stocks. If a firm‘s capital

structure changes, the rates of return demanded by bondholders and stockholders will change; for

instance if a company issues more debt, this will increase the risk of both its debt- and stockholders,

who will require a higher return to compensate for this risk as mentioned earlier. However, this does

not automatically indicate that the WACC will increase. Research by Miller and Modigliani, claims

that by assuming that the company does not pay taxes, WACC will remain the same regardless of

whether the amount of debt and equity changes (Brealey & Myers et al., 2007)

Furthermore, in order to determine the appropriate capital structure in the WACC formula, it is

important to use target weights instead of the company‘s current capital structure. This is because a

company‘s capital structure may change in the coming years, as mentioned earlier, and using

today‘s structure may lead to an overestimation (or underestimation) of the value of tax shields for a

firm whose debt level may increase (or decrease) (Koller et. al, 2005). Koller et. al, (2005) suggest

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determining a company‘s target capital structure by first examining its current capital structure and

then forming some expectations about the future.

In order to calculate the WACC, the after tax cost of debt, the cost of equity, and target value

weights are inserted in the WACC formula. This rate is used to discount the free cash flows and the

continuing value to the present. Thus, the next section discusses the calculation of the continuing

value.

7.1.3 Step 3: Identifying the continuing value

A company‘s value can be determined by dividing the expected cash flows into two periods as

stated below (Copeland et al., 2000; Russell, 2007; Jennergren, 2008;):

Value = PV of cash flows during the + PV of cash flows after the

explicit forecast period explicit forecast period

The explicit forecast period is the period, in which detailed forecasts of a company‘s cash flows are

made for a given period up to a specific year, the horizon year (H). The second part of the formula

is the continuing value (terminal value or horizon value), which is the value of the firm after the

explicit forecast period. According to Brealey & Myers et al. (2007), a firm‘s continuing value can

be stated as:

Where: PVH is the value at horizon, i.e. the value of FCF in periods H+1, H+2 etc.

The continuing value can be determined in two ways: one way is to assume that the firm will be

liquidated in the horizon year and estimate the value of its assets in that year (Damodaran, 2010

―2‖). The other method is to use the constant-growth formula, by assuming that the firm is going

concern, i.e. it will continue to grow up to infinity after the horizon year (Brealey & Myers, 1991;

Russell, 2007). Since the analysis of ISS will be based on the assumption that it is a going concern

company, the constant-growth formula is discussed below.

In a going concern firm, it is not realistic to precisely forecast free cash flow to the year infinity,

thus the horizon year, which is the year when the firm‘s business is expected to have a stable

growth rate, is often used (Brealey and Myers, 1991; Morris, 1994; Russell, 2007).

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To use the constant-growth formula, the analyst needs the FCF for the first year after the horizon

year (year H+1), a long-run growth rate (g) and WACC as the discount rate (r), thus the formula for

the PV at horizon is (Brealey and Myers, 1991):

Even though theoretically the computation of the continuing value seems easy, in reality it is

challenging to estimate the development of a company between the horizon year and infinity

(Financial Times, 2005). Several researches claim that continuing value calculations often account

for more than half of the total firm value, and that a small change in the perpetual growth rate leads

to major changes in firm value (Morris, 1994; Brealey & Myers et al., 2007; Steiger, 2010). The

large impact of the continuing value can be due to the fact that a firm‘s cash outflows in the explicit

forecast period are caused by investments that are expected to generate cash inflows after the

explicit forecast period (Copeland et al., 2000).

7.1.4 Step 4: Calculating the company value

Finally, the value of the firm can be determined as the discounted free cash flow up to a horizon

year (H) plus the forecasted value of the firm at the horizon, that is (Brealey & Myers et al., 2007):

Once the company value is estimated, the equity value can be calculated by subtracting net debt

and minority interest from the company value (Penman, 2010). As mentioned earlier, the DCF

analysis involves predicting free cash flows for the next five to fifteen years. For this reason, it is

necessary to make assumptions about a company‘s future situation (Steiger, 2010). Predicting the

future always involves uncertainty and risk (Koller et. al, 2005) but methods such as scenario

analysis, sensitivity analysis, decision trees and simulations can help in analyzing the uncertainty

related to the valuation results, as well as ensure whether the assumptions used are realistic

(Damodaran, 2007). Scenario and sensitivity analysis will be used in this paper in order to analyze

the effect of different assumptions about both macroeconomic and asset specific variables.

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7.1.5 Evaluation of the DCF method

After looking at the procedure of the discounted cash flow analysis, this section includes an

evaluation of the method's strengths and weaknesses. One of the advantages of the DCF method is

that it is intuitively easy to understand; the value of a company depends on its future cash flows

(Morris, 1994). This method focuses on cash flows, which is a ―real‖ measure that is simple to

explain. The DCF method also works regardless of a company‘s accounting principles (Penman,

2010). When analyzing a company using this method, the analyst performs a useful exercise by

identifying a company‘s value drivers as well as examining its growth and risk (Damodaran, 2010

―3‖). In general, the DCF method is perceived to be the best method for company valuations, but

only if the company is profitable (Russell, 2007).

The limitations of the DCF method include its large dependency on WACC and continuing value

assumptions, this is because small changes in these values have a considerable impact on firm value

as stated earlier ( Steiger, 2010; Copeland et al., 2000). For this reason, the DCF method can be

easily manipulated by the analyst in order to achieve a given result. Additionally, it requires a lot of

information to determine a company‘s future cash flows, growth rates and discount rates. Similar to

any other analytical tools, the DCF must be used with caution. The results from any model depend

on the model‘s inputs: ―it is garbage in, garbage out”(Damodaran, 2010 ―3‖ p. 11).

7.2 Dividend discount model (DDM)

According to Brealey et al., (2009), DDM is a stock valuation tool that is used to determine stock

prices as the present value of future expected dividends discounted back at a certain rate. Michaud

and Davis (1982), state that one of the main objectives of the DDM is to improve the process of

stock valuation. The simplest DDM model is the DDM with no growth, where the discount rate (r)

equals the rate of return demanded by investors investing in other stocks at the same risk level,

is the value of the stock and is next year‘s dividend. The DDM with no growth can be stated

as (Brealey et al., 2009):

A more advanced DDM is the DDM with constant growth. This model assumes that a company‘s

dividend payouts grow over the years. Because it is too time consuming to forecast dividends for

every single year in the future, the calculations are simplified by forecasting dividends for the next

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period, and then forecasting a single growth rate with which the dividends will grow in all the

following periods. It is assumed that there is an infinite number of periods in the model and the

dividend growth rate (g) has to be less than the discount rate (r) (Brealey et al., 2009). The DDM

with constant growth can be written as:

If the growth is not constant the formula above cannot be used. Additionally, for mature industries,

growth is fairly stable and constant growth DDM is a good model. According to Penman (1998), it

is common in practice to forecast dividends for a limited number of years and then to calculate a

terminal value at the horizon; this corresponds to the DDM with non-constant growth. In order to

use the DDM with non-constant growth, it is necessary to set the investment horizon (H). Until the

investment horizon the dividends should be estimated for each period individually and after the

investment horizon the company‘s growth is expected to settle down. In order to estimate the stock

price, the dividends until the investment horizon year are discounted back to present value and at

the end the terminal value is added. The terminal value is the estimated present value of the stock

price at the horizon (Brealey et al., 2009). The formula for the DDM with non-constant growth is:

Moreover, the DDM can also be used to determine the cost of equity for a company. In the simple

models, it is assumed that the only cost of equity a company has is the dividend payout. If the

market price of the firm is known, the rate of return can just be isolated and it equals to the

company‘s cost of equity (Mills and Robertson, 2004).

According to Penman (1998), it is often claimed in the literature that the DDM does not perform

very well when company value with a finite horizon has to be estimated. The calculation of terminal

value is considered to be problematic in the financial literature, and many different formulas for

calculating terminal value have been developed over the years. Whilst using alternative valuation

models such as discounted cash flow model or residual income model often results in a firm value

that is similar to the firm value estimated using the dividend discount model, it is the different ways

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of calculating terminal value that cause the largest variations (Penman, 1998). Terminal value

basically consists of an estimate of all future cash flows, or in the case of DDM, all future

dividends, and these are discounted back and summed up to a single number. According to Penman

(1998), a calculation of terminal value is only needed if the discounted payoffs up to the horizon do

not capture the total value of the firm. A more detailed discussion of terminal value is been given in

section 7.1.3.

7.2.1 Advantages and disadvantages of the DDM

According to Hand and Landsman (2005), there are various issues related to the use of the present

value of expected future dividends as an estimate for the company value. In short, Hand and

Landsman (2005) argue that dividends are often positively priced even when they should not be due

to the financial situation of the company, and when these dividends are used to estimate the firm

value the result is often wrong. Hand and Landsman (2005) suggest that dividends are positively

priced because they are based on public information that is trying to predict a company‘s future

abnormal earnings. This means that the price of the dividends is correlated with the analytical

forecasts in the market, which can often be wrong, and not based on internal measures for a firm‘s

ability to achieve abnormal earnings. Hand and Landsman (2005) find in their analysis that

investors often misprice the current earnings and the equity value of a firm which leads to positively

priced dividends. In contrast to the Hand and Landsman (2005) study, Fama and French (1998) and

Akbar and Stark (2003), find a positive relation between firm value and dividends. The results of

their analysis show that dividends are based on information about future expected cash flows and

can therefore be used to estimate firm value. However, Michaud et al. (1983), argues that the DDM

contains little market valuation information and the firm value estimated using the DDM is

therefore subject to error.

According to Hand and Landsman (2005), a main problem is information asymmetry between the

information available to the managers of the company and the information available to the

shareholders. The managers can pay out a certain amount of dividends to send a certain signal out to

the shareholders and the market. For example managers can pay out high dividends to signal that

they are doing a very good job and are expecting high abnormal returns in the future. This is a

situation where the principal-agency problem may arise, i.e. managers who act as agents for the

principals (the shareholders), will act in their own best interest (Grossman and Hart, 1983).

According to Hand and Landsman (2005), under the agency cost hypothesis, it is argued that

dividends are a more reliable measure if the current earnings of the company are positive and the

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company has free cash flow. On the other hand, under the profitability signalling hypothesis, it is

argued that dividends are more reliable as a measure, if the company is currently suffering from

negative earnings, because if the company can afford to pay out dividends in such a state it must be

because the managers are almost certain of future positive earnings. Penman (2010) states that

dividends are not a good measure to estimate firm value, because the company can obtain loans to

finance the dividend payouts, and in this case dividends do not represent the earnings the company

generates.

According to Miller and Modigliani (1961), the dividends are irrelevant to the calculation of firm

value. They show in their article that firm value depends on the firm‘s earnings and level of

investment. Miller and Modigliani (1961) specifically point out the irrelevance of the dividends in

relation to an acquisition. If an investor is interested in acquiring a firm, the investor is free to

determine the level of the dividends in the future and the firm value calculated based on future

expected dividends does not make sense in that case. The firm value important to the acquirer is

based on the expected earnings of the firm and its investment opportunities, which will provide

additional earnings in the future.

7.2.2 Evaluation of the DDM

The DDM is a type of discounted cash flow model as mentioned earlier. The essence of the DDM is

the fact that it uses dividends as the proxy for cash flows. Using dividends to estimate firm value is

widely criticized in the literature mainly because dividends are more often than not determined by

the management, so they can be subject to manipulation and are therefore not as reliable as other

proxies for cash flows. Additionally, using the DDM to estimate the value of private companies is

problematic since private companies do not pay out dividends in the same way as public companies.

7.3 Residual income model (RIM)

Ohlson (1991) argues that due to the ―dividend policy irrelevance‖ concept presented in Miller and

Modigliani (1961), the value of a firm should not be calculated based on dividends, but based on a

more fundamental variable which does not depend on dividends. Based on the analysis Ohlson

(1991) concludes that the variable ―earnings‖ is a good replacement for dividends because earnings

do not depend on dividends and could be used to estimate company value. As a continuation of his

research from 1991, Ohlson (1995) defines a valuation model, which is based on residual income.

However, according to Xiaoquan and Bon-Soo (2005), the idea of a residual income model can be

traced back to Preinreich (1938) and is thereby not a new discovery.

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According to Ohlson (1995), residual income is the amount by which a company‘s net income

exceeds the required return on the firm‘s equity. The residual income is thereby a measure of the

additional value created for the shareholder, which is also known as abnormal earnings or economic

value added (EVA). Ohlson (1995) derives the RIM from the DDM using among other things the

clean surplus relation to prove his point. The clean surplus relation states that the current book value

equals beginning book value with an addition of current earnings and a subtraction of current

dividends. Mathematically the clean surplus relation is defined as:

Where, B is the book value for period (t and t-1), E is the earnings for period t and D represents the

dividends for period t. Ohlson (1995) derives the formula for the residual income model by isolating

the dividends in the clean surplus relation, and replacing the dividend term in the dividend discount

model with the obtained equation. After replacing the dividend term, Ohlson (1995) gets a formula

for calculating firm value, which does not contain dividends. The residual income model is given

by:

The RIM calculates the firm value by adding two parts: the current book value, plus the present

value of future residual income. Where Bt is the book value in period t, RIt is the residual income in

future periods (RIt = Et – rBt-1)8, and re is the required rate of return on equity.

The residual income model, unlike other valuation models, puts emphasis on accounting data

instead of financial data. All the numbers except for the required rate of return on the firm‘s equity

are accounting numbers, which can be obtained from the firms‘ financial statements (Ohlson, 1995).

The book value used in the model is a sum of the book value of owners‘ equity and the book value

of operating net assets, which can be obtained from the balance sheet. And the residual income is

based on operating earnings which can be obtained from the income statement (Skogsvik, 2002).

7.3.1 Advantages and disadvantages of the RIM

According to Ohlson (1995), the residual income model moves the focus away from the well-

known dividend discount model and instead the value of the firm is calculated as a sum of current

8RI is equal to net income in period ( Et) minus the cost of equity in currency i.e re * . In other words, residual

income is net income minus a charge for the use of shareholders‘ capital.

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book value and present value of expected future abnormal earnings. The fact that the RIM is based

on book value and abnormal earnings is a major advantage according to Xiaoquan and Bon-Soo

(2005) because they believe that these variables contain more important information in relation to

firm value than dividends alone, which are used in the DDM. Additionally, by estimating earnings

instead of dividends, it is necessary for the analysts to investigate the factors explaining the firm‘s

performance which leads to a deeper understanding of the company‘s activities (Bernard, 1995;

Penman, 2007). Furthermore, Xiaoquan and Bon-Soo (2005) find the fact that the RIM does not use

dividends to calculate the firm value very beneficial because it can easily be applied to companies,

which do not pay out dividends on a regular basis.

One of the disadvantages of using earnings in firm valuation is that future earnings are affected by

external events, which cannot be predicted at the time of estimation. Thus, valuations based on

earnings can result in imprecise value calculations (Bernard, 1995). Xiaoquan and Bon-Soo (2005)

state that the dividends are present in the RIM, they are just defined broadly as the difference

between earnings and changes in book value, and these dividends include both regular dividends

and other types of cash payouts (e.g. share repurchases) to the shareholders.

According to Penman (2007), the fact that the RIM relies on accounting numbers can both be seen

as an advantage and a disadvantage. It is an advantage that the already recognized book values can

be used in the valuation model. On the other hand, accounting numbers can be manipulated and this

will affect the calculated firm value. For this reason, it is important to evaluate the quality of the

numbers in the financial statements before applying them in order to obtain a useful result (Rees

,1997).

Even though the RIM is sensitive to accounting manipulation, there are some types of manipulation

it is actually protected from. If accrual accounting is used to create earnings by for example

recording lower book values at present and recognising higher income in the future, it looks like the

company is earning more, but in reality no additional value is created. In the RIM, this type of

accounting will not result in a higher firm value because the beginning book value will be used in

the calculation (Penman, 2007). An important observation made by Skogvik (2002) in relation to

the RIM is the fact that it is not correct to treat the required rate of return on the firm‘s equity as a

constant if the firm‘s capital structure is expected to change over time. However, applying different

rates of return to the model based on the expected capital structure makes the model much more

complicated.

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When applying the RIM, the forecast horizon should also be considered like in the other valuation

models. The RIM can be constructed in a way that is similar to the DDM model, where residual

earnings for a number of years are forecasted and discounted back and a continuing value is added

at the end to capture the added value from residual earnings at the horizon. The formula for the

residual income model with a continuing value calculation can be stated as (Penman, 2007):

Where RIt+1 is the residual income for the first the year after the horizon.

In case it is forecasted that the company will experience constant growth at horizon, the continuing

value term can be modified to:

Where g is the growth rate and g should always be less that re .

According to Penman (2007), there is often more weight on the continuing value compared to the

value calculated up to horizon in the DDM and the DCF model. But in the RIM when the

continuing value term is added, there is more emphasis on the value created up to horizon, which

can be estimated with more certainty than the value at horizon. Penman (2007) argues that because

of this feature the results from the RIM are more certain compared to the results obtained using the

DDM and the DCF. Additionally, previous researches undertaken by various authors have

compared the performance of the DDM, RIM and the DCF model and concluded that the RIM gives

more accurate value estimates and explains more of the variation in stock prices (Xiaoquan and

Bon-Soo, 2005).

According to Rees (1997) it can be difficult to apply the RIM in practice because it contains

expected abnormal earnings, which can be difficult to estimate. He states, that it is therefore

important to use valid forecasts to predict reliable future abnormal earnings. Additionally, Rees

(1997) claims that it might not be enough to look at book values and expected abnormal earnings to

calculate firm value in practice, additional factors such as financial management of the firm,

dividend payouts, debt levels and capital expenditure should also be considered.

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To prove his statements, Rees (1997) performs a statistical analysis on a number of public limited

UK companies, excluding financial companies, property companies and investment trusts. He tests

whether dividends, amount of debt and capital investments have an impact on firm value. His

analysis shows that earnings paid out as dividends have a higher impact on firm value than retained

earnings, and that capital investments have a positive impact on firm value in contrast to debt which

has a negative impact. Ohlson (2001) states that all other factors that can affect company value

should also be investigated in addition to the value calculated using the RIM. Therefore the RIM is

not sufficient on its own to capture the actual value of the company.

7.3.2 Evaluation of RIM

Based on the analysis performed in this section, it can be seen that the RIM is a relatively new

model compared to the other popular valuation models such as the DDM and the DCF model. One

of the important features that distinguish the RIM from the other valuation models is the fact that it

is based on accounting numbers. Whether the use of accounting numbers is an advantage or a

disadvantage is widely discussed in the literature, and there is no final conclusion on the discussion

because there are both pros and cons.

7.4 Real Options Valuation (ROV)

An option is the right, but not the obligation to buy or sell an underlying asset under the terms that

are specified in the contract (Damodaran, 1994). Options can be exercised before the expiration date

(American options), at the expiration date (European options), or not at all. Additionally, options

are especially valuable in environments that are characterized by uncertainty, because they give the

opportunity to make a decision after seeing how the events turned out. Originally options have been

used in the financial theory, but have later been extended to cover non-financial assets; these are

known as real options (Amram and Kulatilaka, 2000).

A real option is the right, but not the obligation to take action at an agreed price within the options

life (Copeland and Antikarov, 2003). In general real options are mainly used by managers to

evaluate single investments or project decisions in a company (Koller et al., 2005).

In order to value options, methods such as the Black-Sholes model and the binomial model can be

applied (Damodaran, 2010 ―3‖). These two methods are discussed further in sections 7.4.2 and

7.4.3.

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Brovles (2003) claims that methods such as DCF are too simplistic to capture the actual value of the

company and a better value estimation is achieved if the future options of the company are

considered using a real options framework. Moreover, Damodaran (1994) states that even in cases

where the traditional DCF method gives a sensible result; real options give another perspective on

valuation which can be useful. According to Koller et al. (2005) the standard DCF method does not

consider managerial flexibility, that is, the fact that managers react to changes in the economic

environment and thereby adjust the company‘s plans and strategies. According to Damodaran

(2002), real options include for example the options to expand, delay or abandon a project these are

all examples of managerial flexibility which creates value.

The DCF method often understates the firm value because it only considers future cash flows and

not the firm‘s future options to expand or invest and thereby achieve unexpected success. These

options can be accounted for when using the real options frame work (Damodaran, 2001).

The analysis conducted by Andrés-Alonso et al. (2006) shows that the total market value of a firm

consists of two parts, these are: the value of the firm‘s existing assets and the value of the firm‘s

real options. In order to apply the real options method to company valuation, it is therefore

necessary to first determine the value of the underlying asset using another method such as DCF, if

the asset‘s price cannot be directly observed in the market, the actual method of applying ROV is

discusses further in section 7.4.2. For this reason the ROV does not replace the traditional DCF

method, but is a complementary method (Koller et al., 2005).

7.4.1 Advantages and disadvantages of real options

Damodaran (2001), discusses some of the limitations of the real options pricing models, he

mentions that the variance of the underlying asset might change over the life of the option which

makes the option valuation more complicated. Furthermore, it might not be possible to exercises a

real option instantly which affects the option‘s value. According to Koller et al. (2005), the ROV

depends on precise estimates of the value and variance of the underlying asset; otherwise it will lead

to incorrect estimates of the flexibility value. There are therefore some issues with the application of

real options theory in practice. According to Damodaran (2005), another danger is the fact that the

required inputs of the ROV can be manipulated which makes the resulting value biased.

According to Kemna (1993), when applying ROV to real life cases it is beneficial to limit the

number of options to the most important ones, because having too many options makes the model

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more difficult and confusing and is not necessarily adding more value. Furthermore, when there is

some sort of political uncertainty in the macroeconomic environment the relevance of ROV

increases because more companies choose to postpone their investments in order to wait and see

what happens (Copeland and Antikarov, 2003).

7.4.2 The binomial model

The binomial option pricing model, which is a discrete model for valuing European and American

options, was created by Cox, Ross, and Rubinstein in 1979. This model, separates the time to

maturity of an option into discrete intervals, whereby during each interval, the price of an asset for

example a stock follows a binomial process moving from its original value, S, either to Su (with

probability p) or to Sd (with probability 1-p). Afterwards the value of the option can be determined

by working backwards from maturity (Cox et al., 1979).

According to Koller et al. (2005), real options valuation can be done in four steps as illustrated in

figure 7.1.

Figure 7.1 The four step process to estimate value of real options

Source: Adapted from Koller et al. (2005, p. 560)

Step 1: The value without flexibility

In the first step of the real options valuation, the present value of the underlying asset without

flexibility is determined (Koller et al., 2005). If the valued asset is a whole company, then the

company is valued first using another method, e.g. DCF model, and this value is then used as the

value of the underlying asset.

Step 2: Event tree

According to Koller et al. (2005) it is necessary to model the different possible values of the

underlying asset in an event tree in the second step. The values can either be modelled in a

1.Estimate NPV of the underlying asset without

flexibility

2.Model uncertainty in an

event tree

3.Model flexibility in a decision tree

4.Estimate the NPV with flexibility

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geometric event tree9 or an arithmetic event tree

10. Koller et al. (2005) recommend using a

geometric event tree rather than an arithmetic event tree due to the fact that a geometric event tree

does not produce negative values.

The up- and downward movements in the geometric event tree can be determined as (Koller et al.,

2005):

Where: σ is the annualized volatility of the underlying asset, and T is the number of years per

upward movement.

According to Damodaran (2002), there are three different approaches to estimating annual

volatility: 1) use the variance in cash flows from the company‘s earlier similar investments, 2) use

simulation to estimate possible cash flows, find the present values, and then calculate the variance

across the present values, or 3) use the variance in values of similar firms in the industry as a proxy.

After the up- and downward movements are determined, the probabilities of these movements can

be calculated as (Koller et al., 2005):

Where: r is the cost of capital of the underlying asset.

According to Koller et al. (2005), the values in the event tree can be verified using the estimated

probabilities pu and pd. The values can be calculated as:

Step 3: Decision tree

9Geometric event tree: The asset value at time t+1 is determined by multiplying the asset value at time t with a factor

10 Arithmetic tree: The asset value at time t+1 is determined by adding a value to the asset value at time t

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In order to model flexibility, decision points are added to the event tree from the second step and

thereby a decision tree is obtained. The decision points are added by estimating a percentage

increase in company value due to the expansion and the costs of expansion. If the value with

expansion is higher than the value without expansion the company chooses to expand and this value

is thus stated in the decision tree at a given time (Koller et al., 2005).

Step 4: The value with flexibility

The value of the real option is determined in the fourth step. One of the methods to determine the

value is to use risk-neutral valuation. In this method the probabilities are adjusted to risk-neutral

probabilities using the formulas (Koller et al., 2005):

Then by working from right to left the values estimated at each point in time in the decision tree can

be discounted back at the risk-free rate. This way the total asset value including the value of the

option is estimated.

The binomial model is helpful in valuing American options, thereby providing the owner with an

intermediate decision making tool at a given point in time until expiration. This model is

mathematically simple compared to the Black and Scholes model (Cox et al. 1979). The major

drawback of the binomial model is its quite slow process, especially when calculating many prices

in a short period of time (Subramani, 2009) as well as the large number of required inputs, which

are the expected future prices at each node (Damoradan, 2005).

7.4.3 The Black and Scholes model

The Black-Scholes model from 1972 is not an alternative to the binomial model; rather, it is one

limiting case of the binomial model (Damodaran, 1994). Both models have similar assumptions

except that the Black-Scholes model only follows a continuous process, which does not allow for

price jumps of the assets‘ prices (Damodaran, 2005).

The Black and Scholes model for valuing a call option can be stated as (Damodaran, 1994):

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Where

Additionally, S is the current value of the underlying asset, K is the strike price of the option, t is the

time to expiration of the option, rf is the risk free rate for the life of the option, and is the

variance in the ln(value) of the underlying asset.

The above stated model was originally created for valuing European options, on securities that do

not pay dividends. This means that this model does not consider an early exercise and dividend

payments, both of which affect the value of options. Recognizing the limitations of the Black-

Scholes Model, the binomial model is considered to be more accurate because it includes several

factors such as the possibility of early exercise and other factors like dividends (Cox et al. 1979).

7.4.4 Evaluation of real options

Based on the literature review, it is found that real options is a useful tool to capture the value of

managers‘ flexibility, i.e. their future options, in relation to company valuation. In general, real

options analysis is not an independent method, but rather an extension of the DCF model. The two

popular models for option valuation, discussed in this section, are the Black-Scholes model and the

binomial model. According to the recommendations from the literature, the binomial model is the

preferred choice, when valuing real options.

7.5 Valuation using multiples

Valuation using multiples is a broadly used supplementary method to the well-known discounted

cash flow method, when it comes to company valuation (Benninga and Sarig, 1997), according to

Yoo (2006), the popularity of this method is mainly caused by its simplicity. In general, multiples

are the average price divided with a certain performance measure; therefore many different

multiples can be calculated for a firm. The primary ratio which is generally used to estimate value is

the price/earnings (P/E) ratio (Benninga and Sarig, 1997) and the two other ratios which are

commonly used are the price/book value (P/B) ratio, and the price/sales (P/S) ratio (Damodaran,

1994).

According to the analysis performed by Lie and Lie (2002), where they test the accuracy of ten

different multiples through an empirical study of 8621 companies, the P/B ratio generates more

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exact and unbiased results than the other multiples including the P/E and P/S ratios. According to

Koller et al. (2005) P/E ratio can generate imprecise results because it is systematically affected by

the capital structure of the company, and, furthermore, the earnings used in the ratio are affected by

non-operating revenues and expenses. For example deduction of a non-cash expense which is a

non-operating item, results in lower earnings, but does not affect the firm value. The P/S ratio gives

the least accurate results both according to Lie and Lie (2002) and Dragos (2009). In contrast, Liu et

al. (2002) state that the P/E ratio performs best based on their empirical analysis. Cheng and

McNamara (2000) analyse the performance of P/E and P/B ratios in relation to valuation based on

30,310 observations of company data over 20 years and conclude that a combination of these two

ratios gives the most exact results, however if one ratio has to be chosen then the P/E ratio is found

to be superior to the P/B ratio.

Besides the abovementioned multiples that focus on the price, i.e. the equity, there are also those,

that focus on enterprise value. A commonly used enterprise value multiple is the enterprise-value-

to-EBITDA (EV/EBITDA), where EBITDA stands for earnings before interest, taxes, depreciation

and amortization (Koller et al., 2005). Increasing P/E is often associated with company growth by

investors, however, Koller et al. (2005) argue that it is more correct to draw conclusions about a

company‘s growth based on increases in the EV/EBITDA multiple because it also considers the

return on invested capital (ROIC). And according to Koller et al. (2005), it is only possible to

conclude that a company is growing, based on increase in a multiple, when the company‘s ROIC is

higher than the cost of capital. Additionally, Koller et al. (2005) recommend using the EV/EBITDA

multiple because it is independent of capital structure, and can thus be easily applied to comparable

companies that have different capital structure. Koller et al. (2005) also discuss the sales multiple

and state that this multiple can be used when the valued company has small or negative profits,

however they recommend using the EV/S ratio instead of the P/S.

Benninga and Sarig (1997) state that valuation using multiples often requires several average ratios

in order to be applicable in practice. According to Yoo (2006), if the multiples are calculated based

on historical numbers, then it is advised to use several multiples for valuation in order to improve

the accuracy of the result. If the multiples are calculated based on a mixture of historical data and

forecasted earnings then no improvements in the estimated firm value is observed, and it is thus

enough to limit the valuation procedure to multiples that are based on forecasted earnings (Yoo,

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2006). This means that forecasted earnings should be used rather than historical data whenever

possible (Koller et al., 2005).

Damodaran (1994) states that there are at least two approaches to multiples valuation; one is the

fundamentals approach, where the multiples are related to the fundamentals of the firm such as

growth rates in earnings or cash flows, and the second one is the comparables approach, where

multiples are estimated from the comparable firms. Alford (1992) discusses how to identify

comparable firms, and states that choosing comparable firms based on several factors such as risk

levels, growth rates and industry provides the most precise valuation results, however several firms

that are so similar can be difficult to find in one industry. Therefore, using just one factor for

identifying a comparable firm can be suggested. Selecting comparable firms based on risk levels or

earnings growth is supported by the literature, but better results are achieved when the comparable

firms are chosen based on the industry, and especially when the industry is defined narrowly

(Alford, 1992).

This view is also supported by Benninga and Sarig (1997) who write, that a clear definition of

which industry the firm operates in is necessary to identify comparable firms. Based on the research

conducted by Alford (1992) selecting comparable firms based both on industry and size does not

improve the results compared to a selection based purely on industry. Cheng and McNamara

(2000), find that industry is the most important factor for selecting comparable firms when P/E and

P/B ratios are used for valuation. Cheng and McNamara (2002) recommend to use at least six

comparable firms to estimate the average industry multiples, and in general they state that the larger

the number of firms in a given industry, the better value estimates can be obtained.

According to Benninga and Sarig (1997) the procedure of multiples valuation starts with selecting

comparable firms. There are two main goals in the selection of comparable firms‘ process and these

are: selecting firms that are very similar to the valued company and selecting a relatively large

sample of firms in order to get unbiased average multiples. Once the sample is in place, it is

necessary to choose the relevant bases for multiples and then calculate the average ratios for the

peer group. The estimated average ratios are then multiplied by the valued firm’s actual accounting

numbers, whereby a value estimate is obtained (Cheng and McNamara, 2000).

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7.5.1 Advantages and disadvantages of multiples

According to Damodaran (1994), it is beneficial to apply multiples, because they are easy to relate

to and quick to calculate compared to other valuation methods. However, multiples are also easy to

misuse and manipulate, particularly when the comparable approach is used, because the analyst can

choose which firms to compare to in order to obtain a desired result. Additionally, valuation using

multiples requires that there is a large number of comparable firms being traded in the market, and

that these firms are priced correctly, which is not always the case in reality (Damodaran, 1994).

Moreover, in the multiples method, the firm value is not obtained through the analysis of the firm

like it is in for example the DCF model, but through a comparison to similar companies, which can

easily lead to errors in the estimated value (Damodaran, 1994). Yoo (2006) also acknowledges the

weaknesses of the multiples method and calls the obtained value and approximation rather than

exact value estimation. Taking the weaknesses of the multiples method into account, it can be used,

as suggested by Benninga and Sarig (1997), not as a primary method to value a company, but as a

secondary method to verify that the result obtained from the primary valuation model is realistic.

According to Copeland et al. (2000), valuation using multiples is a very limited method because it is

often only earnings from the current year or the next year that are considered in the valuation. In

relation to this, Liu et al. (2002) suggest that forecasted future earnings for several years should be

used for value estimation, because they contain more value-relevant information than historical

data.

Copeland et al. (2000) state that the value estimated using multiples, which is based on earnings is

not essential to investors, because in reality it is cash flows that matter and not earnings. Using

multiples from comparative companies can thereby be misleading because companies can have

similar levels of earnings, but different levels of cash flows, which lead to different company

values, but this is ignored by the multiples approach (Copeland et al, 2000). However, in situations

when earnings do reflect the actual cash flow level of the company, the earnings multiple performs

well according to Copeland et al. (2000). In order to make the valuation using multiples more

accurate Copeland et al. (2000) suggests including the investments required to generate the earnings

and the risk associated with these investments into the equation.

Alford (1992) finds that the value calculated using the multiples method is more accurate for large

companies. This is explained by the fact that large companies generate their earnings from a higher

number of different projects, which gives more diversification in their earnings, so that on average

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extraordinary profits and losses offset each other. This finding is also supported by the results from

the analysis made by Lie and Lie (2002). According to Alford (1992), valuation using the P/E ratio

is often applied in case of initial public offering (IPO). Kim and Ritter (1999) analyse the valuation

using multiples method in relation to IPOs, and find that valuation based on P/E and P/S ratios

provides satisfying results only if future forecasted numbers are used. Additionally, Kim and Ritter

(1999) conclude that multiples analysis gives more accurate results for older firms than for young

firms. Bhojraj and Lee (2002) state that using the firm‘s own historical multiples provides the best

results for public firms, however, because this data is not available for private firms, multiples

obtained from comparable firms is a good tool to value private firms.

7.5.2 Evaluation of the multiples method

Based on the literature review, it can be concluded that valuation using multiples is a simple method

that can be used to obtain approximate firm values, and verify the results obtained using other

valuation methods. There seems to be a disagreement in the literature about which ratio performs

best. According to some sources the P/E ratio gives the best results and according to other sources

the P/B ratio is the most accurate. However, the fact that the P/S ratio performs worse to the P/E

and P/B ratios is proved in various analyses. Moreover, Koller et al. (2005) suggest using the

EV/EBITDA multiple to deal with differences in capital structure of the comparable companies, and

to use EV/S multiple for firms with negative profits. The multiples method is most often applied by

estimating the ratios from the comparable firms. There is much discussion in the literature about

how to identify the comparable firms, but many authors conclude that choosing the comparable

firms based on industry provides satisfying results. It appears that multiples valuation is most

effective when it is applied to large, old companies and when the multiples are calculated using

forecasted earnings. Finally, some researchers claim that valuation using multiples is useful to value

private companies and IPOs.

8. Valuation of multibusiness companies

According to Koller et al. (2005), many large companies operate in multiple industries. Operating in

different industries usually leads to the net income and growth being very diverse for the different

operating units of the company. Damodaran (2009, ―2‖) states that when a company operates in

multiple businesses and in multiple countries the valuation of the whole company becomes much

more challenging than valuation of a single business company. When a company operates in several

industries, Koller et al. (2005) recommend valuing each operating unit individually and then

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summing up the values of the different units to obtain an overall value for the entire firm. However,

Damodaran (2009, ―2‖) does not agree with this suggestion and states that the value of the entire

firm could be more or less than just the sum of its parts. The company value could be more than the

sum, if all the business units of the company create valuable synergies. On the other hand, the

company value could be less than the sum of its parts, if the business units being one company

results in lower efficiency than if these business units were individual companies.

Technically to value a multibusiness company using the DCF method, separate financial statements

and individual cost of capital estimates are needed for each business unit. Furthermore, the

determination of levered beta is necessary to value the company, here again it is advised to estimate

separate betas for each business unit rather than one beta for the whole company, as this practice

has proved to give more exact results. When all the necessary input variables including cash flow

estimates for each business unit are obtained, the whole firm can be valued as a sum of the business

unit values (Koller et al., 2005).

8.1 Challenges of valuing multibusiness companies

One of the challenges in valuing multibusiness companies as a sum of its parts is that there are

usually high centralized costs in the company, which cannot be spread out to the various business

units, this challenge is mentioned by both Koller et al. (2005) and Damodaran (2009, ―2‖ ). The

complexity of valuing multibusiness firms increases, if the firms have many intercompany

transactions where the different business units for example sell goods to each other or lend each

other money (Damodaran, 2009, ―2‖). In relation to that, Koller et al. (2005) suggests treating the

intercompany transactions like transactions with external partners and consider them in the

valuation process. Another challenge that Damodaran (2009, ―2‖) mentions in relation to valuation

of multinational companies is that the cash flows from the firm‘s operating activities are most likely

in different currencies. This creates a challenge because the different currencies can have different

inflation rates, discount rates and risk free rates.

According to Damodaran (2009, ―2‖) there are also issues that arise if valuation using multiples is

applied to a multinational, multibusiness company. The main challenge is that, it is very difficult to

find comparable firms that have exactly the same product mix. One solution could be to find

comparable companies for each business unit; however this solution does not necessarily give

correct results.

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8.2 Summary

It is obvious from the literature review, that valuation of multibusiness companies that operate

across different countries is much more complicated than valuing a single business domestic

company, regardless of which valuation approach (consolidated or separate) is used. Valuing a

multibusiness company is more challenging, however not impossible according to Damodaran

(2009, ―2‖).

9. The choice of corporate valuation theories

The discounted cash flow analysis is chosen as the primary method to value ISS. Even though this

analysis provides better estimates for companies with positive earnings as stated in section 7.1, it

will still be applied to value ISS, because of the method‘s wide application and in-depth analysis of

the company‘s value drivers, sources of growth and risk.

The cash flows in the discounted cash flow analysis can be estimated by using for example

dividends (DDM), residual income (RIM) or free cash flows (DCF) as discussed in sections 7.1 –

7.3.

It is decided not to apply the DDM to value ISS, because dividends are often determined by a firm‘s

management and do not necessarily reflect a given year‘s earnings. For example when ISS was

public they paid out DKK 88 million in dividends from a profit of DKK 286 million in 2003. In

2004 the dividends were DKK 177 million, while the profit was DKK 131 million (ISS Annual

report, 2004). However, when ISS became private in 2005 they paid out DKK 7.2 billion in

dividends and in 2006 they paid out DKK 1.3 billion, whereas their actual profit for the year was

DKK 948 million in 2005 and DKK 911 million in 200611

(ISS Annual report, 2006).

Therefore, dividends are assumed not to be a reliable measure for valuation of a private company

like ISS. The RIM model will also not be used to value ISS, because it is mainly based on

accounting numbers and there is no agreement in the literature as to whether this is a benefit or not.

Instead the DCF model with free cash flows will be applied, because the company has had positive

cash flows even though there was a loss on the income statement (ISS Investor meeting, 2011). For

this reason it is decided that the DCF model with free cash flows can be successfully applied to ISS.

11

These numbers are different from the numbers presented in appendix H, because these numbers are from the annual

reports of ISS, i.e. the daughter company. The numbers in appendix H are from the consolidated income statements in

the parent company‘s, i.e. FS Funding‘s, annual reports. FS Funding was renamed to ISS A/S in 2007.

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In the WACC formula, which is used for discounting the free cash flows to the present, the CAPM

is chosen instead of the Fame and French three-factor model because empirical evidence concludes

that neither of the two models performs better than the other, but CAPM is more often used in

practice.

Due to the fact that the DCF model is better for analyzing a profitable company and ISS has had

negative earnings in the last five years, the DCF method will be supplemented with the real options

analysis. The real options analysis will be used to capture the value of the flexibility that the

company‘s managers have. To estimate the value of ISS‘ real options the binomial model will be

used, because it is the recommended choice in relation to real options.

Lastly, a multiples analysis will be made in order to verify the value calculated using the DCF

method and the real options analysis. The P/E ratio is meaningless when a firm‘s earnings are

negative, instead the P/B ratio or P/S ratio can be used. However, book values are more meaningful,

when valuing the assets of a firm that has tangible assets. This means that for service firms, the P/S

ratio, which is based on revenues and profit margins, may be more appropriate (Damodaran, 1996).

However, based on the literature review in section 7.5, the P/S ratio is not practical to use and

Koller et al. (2005) suggest using the EV/S ratio instead. The EV/S is recommended for companies

with low or negative profits, which is the case for ISS as can be seen in appendix H. Thus the EV/S

and the EV/EBITDA multiples will be applied to ISS.

10. Structure of the valuation process

According to Benninga and Sarig (1997) a standard valuation process of a company consists of five

stages:

1. Study of the corporate environment

Benninga and Sarig (1997) write that this stage is necessary to understand the firm‘s operations and

market conditions, and to form some expectations about the future development which can be used

in the forecasting stage. In this paper it is done by performing a strategic analysis of ISS in section

11.

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2. Examination of the firm’s expected financial performance

After understanding the corporate environment that the valued firm operates in, it is necessary to

analyse the firm‘s historical financial performance and forecast the firm‘s future expected financial

performance (Benninga and Sarig, 1997). The historical performance of ISS is analysed by

reformulating the company‘s financial statements in sections 12.3.1 – 12.3.3, and in section 12.3.8

ISS‘ future FCF are forecasted based on the strategic analysis and the analysis of the historical

performance.

3. Conversion of the firm’s expected financial performance to values

In the third stage, the forecasted cash flows of the firm are discounted back to present values and a

firm value is obtained (Benninga and Sarig, 1997). For ISS the forecasted FCF are discounted back

by using WACC and added to the discounted continuing value in section 12.6. Furthermore, an

option to expand is applied in section 13, and added to the value estimated with the DCF method,

and thereby a total firm value is found.

4. Exploration of alternative valuation techniques

The fourth stage considers alternative valuation techniques to verify the firm value obtained in stage

3 (Benninga and Sarig, 1997). In this thesis valuation using multiples is applied to ISS in section

14, to verify the results from the DCF and real options analysis.

5. Consideration of the implications of the estimated values

Finally, the results should be considered in a relevant context, i.e. discussed in relation to the

purpose of the valuation (Benninga and Sarig, 1997). This is done in section 15, where the obtained

results are discussed in relation to the IPO and officially published value estimates for ISS.

11. Strategic analysis of ISS

As mentioned in section 10 the first stage in the company valuation process is a study of the

corporate environment that the valued firm operates in (Benninga and Sarig, 1997). In order to

analyse the corporate environment a three stage structure suggested by Hollensen (2007) is applied.

The three stage structure divides the analysis of the corporate environment into macro, meso and

micro levels (Hollensen, 2007). At the macro level the external macroeconomic factors that affect

ISS are examined, for this purpose a PESTEL analysis is used. At the meso level ISS‘ interaction

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with its environment is analysed by applying Porter’s five forces analysis. Finally, at the micro

level ISS‘ core competences are identified. Additionally, in order to give an overview of the

different aspects of the external and internal corporate environment a SWOT analysis for ISS is

conducted.

11.1 PESTEL analysis

The purpose of the PESTEL analysis is to examine various macro-environment aspects that can

have an influence on a company‘s business. Specifically, the factors analysed in this model are the

political, economic, social, technological, environmental and legal (Kotler and Keller, 2006). The

most important factors of the macroeconomic environment that ISS operates are discussed below.

Political and legal

The political and legal parts are often combined into one section in a PESTEL analysis due to the

similarities of these two factors (Hollensen, 2007). The points mentioned in this part are considered

to be relevant for both the political and legal aspects, and it is therefore decided to combine the two

sections. Being a multinational company, ISS has to operate under legislations of 53 different

countries. The different legislations can set some limitations for ISS‘ development of its global

business activities and cause economic losses if any legislation is violated. An example of

limitations that ISS can experience due to legislation is the restrictions on how many acquisitions

ISS can make in a country, where they already have strong market presence without violating the

competition law (ISS, 2011, ―3‖). Making sure that national and international legislations are

followed in the different locations requires a lot of resources from ISS and is thus very costly (ISS,

2011, ―1‖).

Another issue that most multinational companies have to deal with is the tax issue. According to

Fischer (2006), it has been shown in many studies that multinational companies are sensitive to

differences in the income taxes of the different countries. Since ISS operates in 53 countries they

also have to use many resources on following the different tax rules. ISS recognizes that changes in

tax levels and tax rules are important factors that can affect their business, especially given their

high activity level in emerging markets where legislation and politics are often less stable than in

Europe (ISS, 2011, ―3‖).

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The issue of differences in legislation and trade restrictions becomes much less significant when a

company operates across EU countries. The European Union has created favourable conditions of

free movement of people, products, services and capital that make it easier and cheaper for

multinational companies to operate across several EU countries and to enter new EU markets

(Hollensen, 2007). The EU policy is very beneficial to ISS because Western Europe is their largest

market where ISS generates 53% of its revenue (ISS Annual report, 2010).

Economic

An economic crisis in either one of the countries that ISS operates in, or a global economic crisis

would affect ISS negatively (ISS, 2011, ―3‖). During an economic crisis, customers are less inclined

to purchase facility services, and ISS risks suffering operating losses, this is discussed further in

section 11.4.

As a multinational company, ISS is exposed to exchange rate risks, interest rate risks and inflation

rate risks. ISS has mainly local transactions in the different countries and it carries out its local

transactions in local currencies, which protects the different subsidiaries from exchange rate risk on

a daily basis. However, the consolidated financial statements are completed in DKK and ISS is

thereby exposed to exchange rate risks because 95% of ISS‘ revenue is generated in other

currencies than DKK (ISS, 2011, ―3‖). Furthermore, ISS‘ local business units obtain the necessary

loans in local countries and ISS is thereby exposed to changes in interest rates in 53 countries.

Finally, ISS is exposed to changes in inflation rates in all the countries that they operate in. In

general exchange rates, interest rates and inflation rates are more volatile in emerging markets so

ISS‘ increasing investments in emerging markets makes them more sensitive to these factors (ISS,

2011, ―3‖).

Social

In order to withstand competition and be able to get large contracts within the private and public

sector, it is important for ISS to maintain a good reputation and brand name. In order to maintain a

good reputation ISS has to deliver services at expected quality levels, fulfil their contractual

obligations, make sure that the salaries they pay to their staff are in accordance with the local

requirements and respond to eventual customer complaints in order to ensure customer satisfaction

(ISS, 2011, ―3‖).

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The success of ISS depends on the demand for facility services that ISS offers. According to one of

ISS‘ managers at the investor meeting,12

increasing welfare in the world leads to increasing demand

for facility services (ISS investor meeting, Aarhus, 2011). Increasing welfare is therefore an

important social factor for ISS.

Technological

52% of ISS‘ revenue is generated through the cleaning services (ISS, 2010, ―4‖). Cleaning often

requires a lot of manual labour; however, in order to make the cleaning process more effective

advanced technologies are also used. For example in 2010 ISS started using a special

environmentally friendly technology named ―ec-H2O‖ which eliminates the use of chemicals and

reduces the amount of water used in daily cleaning (ISS, 2010, ―4‖).

Another technological factor that is important to ISS is advanced IT systems which allow them to

have an overview of all their operations in the 53 countries, and to align the various reporting

procedures from all their subsidiaries (ISS, 2011, ―3‖).

Environmental

The problems of using too much energy from non-sustainable energy sources such as coal13

or gas,

and the effect on the environment from extensive use of various chemicals which can lead to

problems such as global warming14

are general environmental problems that have received a lot of

attention in the media. The mentioned environmental issues are all relevant to ISS because they for

example need to use many different chemicals in their cleaning service (ISS, 2010, ―4‖), and energy

is always used for all types of activities, especially cleaning during night time. In order to minimize

the negative effects on the environment, ISS is following an environment policy which amongst

other things includes energy saving programs, minimization of cleaning chemicals and water used,

and reduction of waste (ISS, 2011, ―3‖). Furthermore, ISS is offering their customers ―ISS Green

Cleaning‖ where environmentally friendly chemicals are used, and the cleaning is done during the

day hours in order to save energy otherwise needed for lighting up the buildings during the evenings

and nights (ISS, 2010, ―4‖).

12

The investor meeting with ISS took place in Aarhus on the 08.03.2011. The purpose of the meeting was to tell

potential investors about ISS and their upcoming IPO. 13

http://www.ucsusa.org/clean_energy/coalvswind/c02c.html 14

http://www.globalwarming.org/

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Summary of the PESTEL analysis

The external environmental factors which are mostly relevant for ISS‘ operations have been

identified and examined in this analysis. The factors that are most uncontrollable for ISS are

considered to be the political and economic factors, because ISS is not able to affect a country‘s

politics, changes in legislation or what is happening to the macro economy. For the social factors, it

is for example possible for ISS to affect their reputation in the market by working in accordance

with the standards that they set. For the technological factors, it is possible for ISS to invest in new

technologies and thereby achieve more efficiency, because technology is under constant

development. And finally for the environmental factors, it is possible for ISS to focus on doing their

business in an environmentally friendly way.

11.2 Porter’s five forces analysis

According to Porter (1985), a company‘s profitability is mainly determined by the attractiveness of

the industry in which it operates. Porter (1985) suggests five forces for evaluating the potential for

profitability in an industry. These are: the competitive rivalry among existing firms, the bargaining

power of suppliers, the bargaining power of buyers, the threats of substitute products and services

and the threat of new entrants. The combined strengths of the five forces determine a firm‘s ability

to generate rates of return on investments above the cost of capital (Porter, 1985). ISS operates in

many countries and in different industries such as commercial cleaning, security services and the

catering industry. For this reason, ISS‘ main competitors can be divided into three groups: the small

local companies that offer one or more facility services to the local customers, the international

facility service companies such as Compass, Sodexo, G4S, Securitas (ISS, 2011, ―3‖) Ecolab Inc

and ABM Industries Incorporated (see appendix B), and companies that handle their facility

services internally. In order to determine the attractiveness of the facility services industry for ISS,

Porter‘s five forces analysis is applied.

The competitive rivalry among existing firms

Due to the fact that the facility services industry includes many different types of services, the

rivalry between the different competitors is less intense, because the different companies have

different facility services as their primary operations. For example ISS‘ main areas: cleaning and

property services generated 52% and 20 % of the revenues in 2010, respectively, with the rest of the

services (catering, support, security and facility management) accounting for the remaining 28% of

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the revenue (ISS Annual report, 2010). In comparison, Compass mainly offers food and support

services, Sodexo primarily focuses on food and facility management services, Rentokil mostly

generates revenues from its hygiene services and products, while G4S and Securitas are security

service companies. ABM generates most of its revenues from cleaning services, but it mainly

operates in the countries, where ISS has either no or a low presence (see appendix B).

Additionally, the intensity of competitor rivalry is reduced for ISS because the company has long-

term contracts15

with its customers (ISS investor meeting, Aarhus, 2011). This way, the customers

incur costs if they decide to switch service suppliers before the end of their contract. However, ISS

is also subject to penalty costs if it does not fulfill the agreed contracts with its customers and

suppliers and this can be considered as an exit barrier. In general, however, the facility services

industry does not involve high investments in fixed assets such as (plants or machinery) (ISS, 2011,

―3‖), thus there are low exit barriers and competitors will engage in more rivalry.

The global facility services market has grown with about 6% per year from 2001 to 2006, and then

the growth rate has decreased from 2006 to 2009 due to the global economic downturn. The growth

rates depend on the geographical area with higher growth rates in Asia, South America and Eastern

Europe and lower growth rates in Western Europe, the Nordic, the Pacific and North America

(www.issworld.com). This development is also reflected in the development of the revenues

generated by ISS services in the period 2006 to 2010, for example the percentage of the revenues

from cleaning services, property services, and facility management out of the total revenue have

decreased by 5%, 2% and 3%, respectively, in this period (Annual reports, ISS, 2006 – 2010) (see

appendix C). In order to grow in this period, ISS has been acquiring other companies and so has its

competitors. An increasing level of growth through acquisitions instead of organic growth may

indicate that rivalry in the facility services industry is intense.

In summary, ISS operates in different markets worldwide and there is no other company offering

exactly the same combination of services. However, ISS still faces competition both at the national

and international levels in the different markets.

15 According to Jakob Stausholm (CFO), ISS has long term contracts up to 10 years with some customers, and 80% of

the company‘s revenue depends on long term contracts (ISS investor meeting, Aarhus, 2011). For example in 2011 ISS,

signed a 5 year contract with telecoms operator TDC (www. issworld.com )

http://www.hoovers.com/company/Rentokil_Initial_plc/sfycki-1-1njdap.html

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The bargaining power of suppliers

When the suppliers in a given industry have high bargaining power, the industry is assumed to be

less attractive to operate in (Porter, 1985). ISS uses about 5500 suppliers every year for providing

items such as cleaning products and vehicles, and in 2009 the company purchased products for

DKK 1.3 billion from the different suppliers (Innologics, 2009). Due to the fact that ISS is a large

and thereby attractive customer to the suppliers, it is assumed that it is relatively easy for ISS to find

new suppliers. On the other hand, ISS may incur penalty costs in case it switches suppliers before a

contract is fulfilled. In conclusion, the suppliers have low bargaining power over ISS and the

industry is attractive for ISS to buy its supplies.

The bargaining power of buyers

ISS‘ customers range from multinational companies such as The UK Foreign Commonwealth

office, Hewlett-Packard and Sony Erikson that request integrated facility services, to small

customers that demand single services. The company‘s 10 largest customers accounted for about

6% of the revenues in 2009 (ISS, 2011, ―3). The bargaining power of ISS‘ buyers is assumed to be

low especially for buyers, who mainly demand single services. On the other hand, the buyers who

demand integrated facility services in one or several countries may have some bargaining power

and for example demand a discount due to their larger purchase of services. Overall, ISS has a

relatively high bargaining power compared to the buyers due to the company‘s large customer base.

The threats of substitute products and services

The existence of substitute products can lower the attractiveness of a given industry by limiting

price levels (Porter, 1985). In the facility services industry, there is currently no real substitute for

the services that ISS offer because they require human involvement. However, in an economic

recession companies may choose to perform activities such as cleaning and catering themselves to

cut the costs down on their non-core activities.

The threat of new entrants

New entrants in an industry can increase the level of competition, thus reducing its attractiveness

(Porter, 1985). The basic facility services such as cleaning services can be delivered with a few

resources, this means that the entry barriers are low, and as a result there will always be small, local

companies that enter this market. On the other hand, it requires more resources to deliver integrated

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facility services in several countries, and the threat of potential entrants at an international level,

that offer various facility services, is therefore significantly lower (ISS, 2011, ―3‖).

Conclusion of the five forces

Based on the above analysis, it can be concluded that the competition in the facility services

industry and the threat of new entrants is relatively high, while the threat of substitutes and the

bargaining power of suppliers and buyers is relatively low. Thus, it is moderately attractive for ISS

to operate in the facility services industry.

11.3 Core competencies

Core competencies arise from a company‘s technical and production expertise within its area of

business. A combination of a firm‘s core competencies and distinct capabilities such as unique

business processes leads to competitive advantage (Kotler, 2000). Hamel and Prahalad (1990)

suggest three characteristics for identifying a company‘s core competencies: that is, whether a

company‘s skills: 1) create a significant contribution to the perceived customer benefits 2) are

difficult to imitate and 3) have a breadth of applications to different potential markets. In order to

identify ISS‘ core competences, these characteristics are discussed below.

The perceived customer benefits for ISS‘ customers lie in the fact that the company offers

customized integrated facility services. This way ISS makes its customers‘ non-core jobs its main

business and the customers can achieves benefits such as cost reductions16

and deal with one

supplier of services. Additionally, ISS‘ strategy, ‖The ISS Way‖ is difficult to imitate for

competitors because it includes a unique delivery of services through an integration of people,

processes and management (ISS, 2011, ―5‖). ISS claims that the implementation of its strategy is

supported by business growth; either organic or through acquisitions of different companies.

Business growth has helped ISS to achieve a great customer base, knowledge, and an increase in its

service capabilities and capacity (such as trained employees and consistent quality). This has then

led to satisfied customers, who renewed their long-term contracts with ISS (issworld.com).

16

E.g. reception services can be combined with security services when an employee switches roles from the front desk

to guard (www. issworld.com). A cleaning employee can also perform tasks such as watering plants, filling up vending

machines or assist at the reception at peak hours (ISS, 2011, ―5‖).

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Lastly, ISS‘ skills can be applied in a wide variety of markets because they are centered on

facilitating the customer‘s needs. The services ISS provides, such as cleaning, security and food

services are not related to the core activities of their customers, but are still necessary for all

companies (www.issworld.com).

In conclusion, the company has achieved its core competences through customization and an

integration of the different facility services as well as its great emphasis on management of people

and employee training. Furthermore, it is concluded that ISS‘ strategy ‖The ISS Way‖ is a

differentiation and growth strategy.

11.4 SWOT analysis

SWOT analysis is an overall evaluation of a company‘s internal environment (strengths and

weaknesses) and external environment (opportunities and threats). This simple marketing analysis

can help a company identify ways for matching its strengths with its opportunities, converting its

weaknesses into strengths as well as respond to the threats in the external environment (Kotler and

Keller, 2006). For a large, multinational company like ISS numerous factors can be mentioned for

each of the categories in this analysis, however, due to space limitations and the fact that this thesis

focuses on the financial valuation of ISS, only the factors that are most important to company

valuation are included in this section.

Strengths

ISS specializes within different types of services which are all described in section 2, and is thereby

able to offer its customers individual contracts with the required service mixture, all delivered from

ISS and managed by one person. This solution is often cheaper for customers compared to receiving

all the services from individual suppliers as mentioned in section 11.3. ISS offers single services, a

selection of two or more services which they call multi services, or the company can be in charge of

all or most of the facility functions for a customer, which they call Integrated Facility Services. The

fact that ISS can offer this flexibility of different service products to match their customers‘ needs is

recognized as a major strength and competitive advantage by the company (ISS, 2011, ―1‖; ISS,

2011, ―2‖).

By being a multinational company, ISS is able to offer their multinational customers the same

services in different countries. This way ISS‘ multinational customers are able to centralise their

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service purchases and use the same supplier in various countries, while ISS gets major international

contracts and thereby outcompetes the local service providers (ISS, 2011, ―1‖).

Weaknesses

ISS is a very large and diverse multinational company which operates within different industries,

this make ISS‘ organizational structure very complex. Additionally, ISS is exposed to different

types of risk in the different industries as mentioned in the PESTEL analysis. It is therefore a

challenge to manage this very complex organization so that the different business units create

synergy, and also to manage all the different risks that the company as a whole is facing (ISS, 2011,

―1‖). Another weakness in relation to the organizational structure of ISS is the decentralisation,

where the managers of the different subsidiaries are responsible for their subsidiary‘s performance.

Even though ISS has many rules and guidelines regarding how their subsidiaries are expected to

operate, it is difficult to fully control operations in 53 countries. Therefore, ISS depends on the

managers‘ ability to manage the subsidiaries that they are responsible for in a satisfying way. Due

to large cultural differences especially between European markets and emerging markets, ISS can

be subjected to for example lower quality performance or violation of local legislation in some

countries which can affect the company‘s results negatively (ISS, 2011, ―3‖).

Since 2000 ISS has been buying and selling companies, and it has bought 660 companies in 48

countries, that it integrates in its organization. The sale of companies has mainly occurred in areas

that ISS considers to be unfit with its strategy (ISS, 2011, ―3‖). By achieving growth through

acquisitions, ISS has been “aggressively pursuing an inorganic growth strategy17

‖ in the past years.

The fast inorganic growth has led to the fact that ISS faces challenges related to integrating the

various businesses as well as challenges in decreasing operational costs (RocSearch, 2011).

As shown in appendix F ISS has very large amounts of debt. In fact the debts are so high that ISS

has difficulties paying them off. If the company is not able to change the contract terms for its debts

or refinance it in some way it will lead to major financial problems for ISS (ISS, 2011,‖1‖).

Opportunities

ISS has achieved high growth partly due to many acquisitions of smaller companies in the different

countries as stated in Porter‘s five forces analysis. According to ISS there is an opportunity to

17

http://www.marketresearch.com/product/display.asp?productid=1585206 (23.06.2011)

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continue with the growth strategy through acquisitions; however, their main focus is now on

acquisitions in emerging markets18

(ISS, 2011,‖1‖).

Additionally, ISS has very high debt levels which can be seen in appendix F and in an article

published in June 201119

ISS informs about their request for an extension of their debt maturities.

The largest lenders have already agreed to extend the debt maturities, which will give ISS some

financial flexibility and thereby some opportunities to adjust their capital structure before launching

their second time IPO. This way ISS hopes to get a better share price than the one offered in 2011

and also raise more capital to pay off some of the debt.

Threats

As mentioned in section 2, ISS‘ offers services such as cleaning services, catering services etc.

However, when the economy is going bad, as has been experienced during the financial crisis which

started in 200720

, services like those that ISS offer are often what companies cut down on first

(Hollensen, 2007). Thus a weakening economy in a single country where ISS operates or a globally

distressed economy is a major threat to ISS (ISS, 2011, ―1‖). The fact that another financial crisis is

a threat to ISS was also mentioned at the investor meeting with ISS (ISS investor meeting, Aarhus,

2011).

Since ISS is a global company that operates across 53 countries, its overall financial results are very

sensitive to changes in the exchange rates, interest rates and inflation rates, as described in the

PESTEL analysis. According to ISS, large and unpredicted changes in any of these factors can

affect their business very negatively and are therefore another significant threat (ISS, 2011, ―1‖).

Since the entering barriers are low within the service sector that ISS operates in, as discussed in

Porter‘s five forces analysis, ISS is subjected to heavy competition. ISS is therefore constantly

threatened by losing contracts to competitive firms or by losing contracts if ISS‘ customers decide

to take care of the facility services themselves without using external suppliers (ISS, 2011, ―3‖).

18

Emerging markets are defined by ISS as: Asia, East Europe, Latin America, Turkey, Israel and South Africa (ISS,

2011, ―1‖) 19

http://www.issworld.com/press/press_releases/archive2011/Pages/ISS_Announcement_Amend_Extend.aspx 20

http://www.globalissues.org/article/768/global-financial-crisis

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Summary of the SWOT analysis

In this section the most important aspects of ISS‘ internal and external marketing environment were

evaluated. ISS is good at differentiating themselves by offering a selection of services and

customized offers, but are threatened by major competition. Thus, to facilitate its strengths of

providing consistent high quality services worldwide, ISS can work on making their employee

management policies and worldwide operations more effective and thereby outperform competitors.

Additionally, by being multinational ISS has an advantage of servicing their multinational

customers in different countries, but is on the other hand threatened by both global and local

economic risk factors in all the markets. To deal with some of these risks the company can

thoroughly examine the tradeoffs associated with operating in each country.

Finally, an important negative factor that is currently very relevant for ISS is their major debt levels,

which they have difficulties paying off. Besides extending the debt maturities, the company can

raise more capital to decrease the debt by becoming listed on the stock market and divesting in

unprofitable operations.

12. Valuation of ISS using the DCF model

The DCF method for a company that offers one type of business in one industry can be set up by

using the four steps that are discussed in section 7.1. Due to the fact that ISS is a multibusiness and

a multinational company, it has been decided to add two additional steps to the DCF method: These

are: the choice between using aggregated or disaggregated numbers and the currency choice. The

following sections include a DCF analysis of ISS using the six steps.

12.1 Step 1: Choice between using aggregated or disaggregated numbers

As the first step in valuing a multibusiness, multinational company Damodaran (2009 ―2‖) suggests

to choose between valuing the company as a whole using consolidated financial statements or

valuing the different business units separately. The first determinant of the decision is the

availability of information for the company. ISS provides consolidated financial statements for the

whole corporation in the annual report for 2010, there is however no accounting information

available for the different subsidiaries. The second point that is important to consider is how

different the various business units are. If the differences in terms of for example risk and growth

are not very big, the company should be valued as consolidated (Damodaran, 2009 ―2‖). Even

though, there are some differences in terms of risk and growth in the business units located in

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developed and developing countries, ISS‘ revenues from developing countries are at a relatively

low level compared to the revenues generated from businesses in developed countries. For this

reason country specific risk and growth are assumed not to have a significant effect on firm value.

Additionally, there is insufficient information to determine risk and growth rates for each country.

Lastly, it should be considered how many business units the company has and how many countries

it operates in. If this leads to an unreasonably high number of valuations, it is more beneficial to

value the overall company. ISS offers 6 different services, as described in section 2, and operates in

53 countries. Not all services are provided in all countries, but if each service in each country is

treated as a separate business unit it amounts up to 644 business units21

that need to be valued. Due

to the fact that there is no financial information available for the different subsidiaries and that

valuing 644 business units requires too many resources compared to the potential gain. Based on the

discussion above, it is decided to value ISS using consolidated financial statements.

12.2 Step 2: Currency choice

The second step in the multinational, multibusiness company valuation is the choice of currency. It

is necessary to choose one currency to work with when the company is valued as a whole. In

practice, it is often chosen to use the currency in which the company‘s consolidated financial

statements are reported (Damodaran, 2009 ―2‖). ISS‘ consolidated financial statements are reported

in DKK, and it is therefore decided that the valuation of ISS will be carried out in DKK in this

paper.

12.3 Step 3: Calculation of free cash flow

As mentioned in section 7.1.1, the main drivers of a company‘s value: the return on invested capital

(ROIC)22

, the growth rate and free cash flow cannot be directly determined from a company‘s

annual report. Thus, it is necessary to reformulate these statements so as to identify a company‘s

operating items, non operating items and financial structure (Koller et. al, 2005).

The reformulated statement of shareholders’ equity provides the overall profitability measure: the

return on shareholders‘ common equity (ROCE) which together with growth determines the

company‘s value. The reformulated balance sheet and income statement provide more details about

21

Based on the information provided on p. 163 in ISS Annual report, 2010 22

Also known as: return on net operating assets (RNOA), which is the notation used by Penman (2010), and which will

also be used throughout this paper

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the sources of profitability and growth, i.e. the drivers of ROCE and growth, which will be used for

forecasting the free cash flow and valuing the company (Penman, 2010).

The quality of financial statements

ISS‘ consolidated financial statements, which are used in the analysis of the company, are prepared

according to International Financial Reporting Standards (IFRS) in the analyzed period. Even

though, ISS switched from Danish accounting policies to IFRS in 2005, the information that was

stated according to the Danish accounting policies has been changed to comply with IFRS (ISS,

Annual report, 2005). The financial statements are assumed to be reliable (provide a true and fair

view of the company‘s financial position) because they are signed by the board of directors, the

executive group management and an independent auditor (KPMG) (ISS, Annual report, 2005).

12.3.1 The reformulated statement of shareholders’ equity (SE)

The reformulated statement of shareholders‘ equity shows all the transactions that affect equity in a

company. This statement also corrects the fact that the earnings in the reported income statement are

not complete, by identifying comprehensive income (Penman, 2010). Furthermore, the profitability

of the owners‘ investment for the period (ROCE) and the growth in equity from business activities

can be calculated from the data in the SE statement (Penman, 2010).

The formula for ROCE for the current year (period t) can be stated as:

Where: is the average common shareholders‘ equity for the year.

The analysis of the drivers of ROCE is called the profitability analysis, and it is discussed further in

section 12.3.5. The original statements of shareholders‘ equity for the years 2005-2010 are enclosed

in appendix D and the reformulated statements of shareholders‘ equity are enclosed in appendix E.

12.3.2 The reformulated balance sheet

According to Penman (2010), the balance sheet is reformulated by categorizing the different

balance sheet items into operating assets (OA), financial assets (FA), operating liabilities (OL) and

financial obligations (FO). Afterwards, the net operating assets (NOA) are calculated as:

NOA = OA – OL

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And the net financial assets/obligations (NFA/NFO) are calculated as:

NFA = FA – FO or NFO = FO – FA

Finally, common shareholders‘ equity (CSE) is found as:

CSE = NOA – NFO – minority interest

It is decided to analyse the financial statements of ISS for the years 2005-2010, because ISS was

acquired in 2005 and as a consequence its financial structure has changed since 2005. The original

balance sheets for the years 2005-2010 are enclosed in appendix F and the reformulated balance

sheets are enclosed in appendix G. As it can be seen from the reformulated balance sheets, ISS has

net financial obligations rather than net financial assets for all the years. This means that ISS is

financing its operations mainly through debt rather than equity. For example in 2010 the company

had DKK 32,955 million in net operating assets, DKK 30,329 million (92%) were financed through

debt (NFO) and DKK 2,626 million (8%) were financed through equity. In general for the period

2006-2010 over 80% of NOA were financed by NFO.

The main part of operating assets for ISS are the intangible assets (e.g. goodwill, brands, customer

contracts), which account for DKK 35,358 million (69%) out of the total DKK 51,520 million in

2010. In comparison, fixed tangible assets (property, plant and equipment) account only for a small

share DKK 2,055 million (4%). This finding is consistent with the strategic analysis of ISS in

section 11, which stated that there are no requirements for major investments in tangible fixed

assets in the facility service industry. The intangible fixed assets are specified in note 15 in ISS‘

annual report from 2010, and it can be seen that DKK 27,747 million is goodwill; this means that

most of the intangible fixed assets are generated from acquisition of other companies. This confirms

the fact stated in section 11 that ISS has been focused on growth through acquisitions. The last

noticeable item in the operating assets is the trade receivables, which account for DKK 10,896

million (31%) in 2010. This means that ISS sells a large amount of services on credit to its

customers; compared to the total revenue from 2010 which was DKK 74,073 million, 15% of

services are sold on credit. According to the annual report (2010), DKK 241 million (2%) of the

trade receivables are written off as uncollectable receivables. Having such high trade receivables

makes the company more risky, because there is always a risk that the receivables will be

uncollectable, and it also reduces the cash flow to the firm. The trade payables are low relative to

trade receivables. Trade payables account for DKK 2,830 million (15%) of the total operating

liabilities, which are DKK 18,565 million. This shows that ISS does not purchase nearly as much on

credit as it sells. It is not very good for the shareholders, because it means that more purchases have

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to be financed through either debt or equity, and it also reduces the cash flow to the firm. If the

money was not paid to the suppliers immediately, it could have been temporary invested in e.g.

financial securities to generate more income.

12.3.3 The reformulated income statement

Penman (2010) states that it is necessary to reformulate the company‘s income statement by

grouping the different items presented in the original income statement into operating items and

financing items. The operating items are divided into operating income from sales and other

operating income in order to identify the profitability from trading with customers. Furthermore, the

reformulated income statement includes items from the reformulated statement of owners‘ equity

and the obtained result from the reformulated income statement is therefore comprehensive income.

A last important change in the reformulated income statement is the allocation of taxes. Taxes are

allocated to the operating and financing items, so that the operating income from sales after taxes is

not affected by the tax shield that financial expenses generate. Penman (2010) recommends using

the marginal tax rate rather than the effective tax rate for the tax allocation purposes. For ISS, the

marginal tax rate is the Danish corporate tax rate, which was 28% in the years 2005-2006 and 25%

in the following years. The reformulated income statement for ISS in appendix I, shows the

company‘s operating income from sales after taxes which is DKK 3,318 million. After accounting

for other income and expenses which are not related to trade with customers, the after tax result is

DKK 2,133 million. This income is reduced by ISS‘ net financial expenses of DKK 2,368 million

(DKK 1,776 million after tax) which are caused by the large net financial obligations, identified in

the reformulated balance sheet, amounting up to DKK 34,244 million. This leaves the company

with a comprehensive income of DKK 357 million at the end of 2010.

12.3.4 Trend analysis

Penman (2010) suggests performing a trend analysis of the valued company‘s historical financial

statements in order to have an overview of how the different items have changed over time. Thus, a

trend analysis is made for ISS for the years 2005-2010 with 2005 as the base year. A deeper

analysis of the changes is performed in section 12.3.5.

The trend analysis for selected items from the reformulated income statement is shown in table

12.1.

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Table 12.1 Trend analysis of selected items from the reformulated income statement

2010 2009 2008 2007 2006 2005 Base: 2005 in

DKK million

Operating revenues 233.37 217.40 216.85 201.39 175.71 100.00 31,741

Gross margin 236.16 217.66 221.97 207.26 174.53 100.00 8,352

Operating income from sales

(before tax) or Core operating

income

214.73 198.99 206.88 196.39 166.97 100.00 2,383

Operating income from sales

(after tax)

229.54 206.10 231.98 243.34 177.80 100.00 1,445

Operating income (after tax) 784.58 162.06 35.37 606.99 334.31 100.00 272

Financial income

215.22 242.39 263.04 216.30 250.00 100.00 92

Net financial expense

143.33 139.70 165.30 182.61 136.61 100.00 1,239

Source: Authors, 2011, based on the reformulated income statement

It can be seen from table 12.1 that operating revenues have grown in all the years in the analyzed

period, and the total growth in operating revenues from 2005 to 2010 is 133.37%. The gross margin

has in general grown at a rate that is close to the growth rate of operating revenues, which means

that cost of sales have been relatively stable over the years. Operating income has grown

substantially over the years, with an increase of 684.58% from 2005 to 2010, however, this positive

trend has been reduced by the high net financial expenses, resulting in a negative comprehensive

income in 2010 (see appendix I).

Looking at the trend analysis for the reformulated balance sheet presented in table 12.2, an

increasing level of trade receivables can be observed, this means that ISS have been allowing its

customer an increasing level of credit, which is not very good for the company as it reduces the

FCF to shareholders.

Table 12.2 Trend analysis of selected items from the reformulated balance sheet

2010 2009 2008 2007 2006 2005 Base: 2005

in DKK

million

Trade receivables 144.05 133.92 133.49 133.71 122.70 100.00 7564

Trade payables 144.98 134.43 145.24 140.88 132.94 100.00 1952

Net operating assets 112.32 111.83 111.49 117.66 109.35 100.00 29362

Cash and cash equivalents 199.89 186.47 164.14 143.07 122.84 100.00 1804

Total financial assets 186.70 178.21 156.65 137.96 119.89 100.00 2097

Total financial obligations 138.72 139.19 131.61 129.31 116.02 100.00 24685

Net financial obligations 134.27 135.57 129.29 128.51 115.66 100.00 22588

Common shareholders' equity

(CSE)

39.11 32.62 52.10 81.31 88.13 100.00 6714

Source: Authors, 2011, based on the reformulated balance sheet

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Trade payables have been increasing with almost the same percent rate as trade receivables;

however, given the substantially lower base amount in 2005 for the trade payables, the actual

increase in trade payables is much lower than the actual increase in trade receivables, therefore the

FCF to shareholders has not increased that much. Net operating assets and net financial obligations

have both increased in the 5 years period, however NFO have increased at a higher rate than NOA.

The growth in the NOA indicates that ISS is investing in its operations over the years, and the high

growth in net financial obligations indicates that the company is mainly financing its operations

through loans.

Common shareholders‘ equity has been decreasing over the years, and in total from 2005 to 2010

the CSE has decreased by 60.89%. This indicates that the owners have reduced their investment in

the company significantly since the acquisition in 2005, probably due to the planned sale of the

company.

Moreover, total financial assets are increasing continuously over the years, with a total increase of

86.7% from 2005 to 2010. This means that ISS has also been focusing on generating income by

investing in financial assets; this is reflected in the income statement because the financial income

increases overall during the period, with a total increase of 115.22% in 2010 compared to 2005.

12.3.5 Profitability analysis

According to Penman (2010), the analysis of the drivers of ROCE, i.e. the profitability analysis can

be performed at three levels: 1) the analysis of financial and operating liability leverage, 2) the

analysis of the drivers of operating profitability and 3) the analysis of the profit margin and turnover

drivers (see appendix J). Through the profitability analysis, it can be determined where the firm is

now financially, and by understanding the present ROCE of the firm, the analyst can predict

whether the future ROCE will be different from the current ROCE (Penman, 2010).

The profitability analysis is based on the years 2006 - 2010 because some of the formulas for the

ratios involve using prior years and for 2005, the prior year 2004 is not comparable to the other

years, because ISS was a public company in 2004 and it had amongst other things a different capital

structure.

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Level 1: The analysis of financial and operating liability leverage

The first level break down of ROCE separates the profitability from operating activities and

financing activities. Additionally, the effect of leverage, that levers ROCE upwards or downwards

through liabilities is also analyzed at this level (Penman, 2010).

Financial leverage

Financial leverage (FLEV) refers to the degree to which a business finances its net operating assets

(NOA) with net financial obligations (NFO) or by common equity. As shown in appendix J, ROCE

can also be stated as (Penman, 2010):

The formula above implies that ROCE depends on the return on net operating assets, financial

leverage and net borrowing costs (NBC23

). If a company does not have financial leverage, then

ROCE is equal to its return on net operating assets (RNOA24

). Moreover, favorable leverage is

achieved when a company generates a RNOA higher than its after tax NBC (Penman, 2010).

As mentioned in section 12.3.2, ISS is financing its net operating assets mainly through debt rather

than equity. The effect from the use of debt financing can be seen in table 12.3, which shows that in

the period 2006 – 2009, the debt had a negative influence on ROCE, and during this period ISS was

not able to generate a RNOA higher than its net borrowing cost, which led to an unfavourable

leverage and a negative spread, with a huge impact especially in 2009 and 2008. However in 2010,

ISS‘ RNOA increased from 1.35% in 2009 to 6.49% in 2010 (an increase of 5.14%), and in the

same period the company achieved a positive spread. This improvement in RNOA resulted in a

positive ROCE of 14.84%25

. The reasons for this development will be discussed in the level 2 and

level 3 breakdowns below.

23

NBC = NFE (after tax) / NFO 24

RNOA = OI (after tax) / NOA = OI after tax / (OA – OL) 25

The results for the same ratio using different formulas might not be identical due to rounding errors

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Table 12.3 ROCE first level break down, financial leverage

2010 2009 2008 2007 2006

Average (NFO) 30,476 29,913 29,116 27,577 24,357

Average equity 2,408 2,844 4,479 5,688 6,316

Financial leverage (FLEV) 12.66 10.52 6.50 4.85 3.86

Net financial expense after tax

(NFE)

1,754 1,890 2,156 1,978 1,466

RNOA 6.49% 1.35% 0.29% 4.96% 2.96%

Net borrowing cost (NBC) 5.83% 5.79% 7.03% 8.21% 6.95%

SPREAD (RNOA-NBC) 0.66% -4.44% -6.75% -3.25% -3.99%

ROCE 14.84% -45.37% -43.59% -10.80% -12.43%

Source: Authors, 2011

As shown in table 12.3 under FLEV, ISS is highly leveraged with an increasing financial leverage

in the period 2006-2010 and the higher leverage a company has the higher is its risk. As seen in

appendix H, ISS has been making losses in all the years partly due to the high financial expenses in

table 12.3. To compensate for the losses, the owners have been borrowing more money, and this is

illustrated by the increasing amounts of NFO in table 12.3.

RNOA shows how efficient a firm‘s management is at using its operating assets to generate profit

(Penman, 2010). Table 12.3 shows that the RNOA decreased from 4.96% in 2007 to its lowest level

of 0.29% in 2008 and then it started increasing until the year 2010 as mentioned earlier. An increase

in the RNOA can be due to higher profit margins or a more efficient use of operating assets and

operating liabilities in generating revenue. The RNOA is discussed further in the level 2 breakdown.

Operating liability leverage

Operating liability leverage (OLLEV) refers to the degree to which a firm applies operating

liabilities (OL) to lever up the RNOA through a reduction in net operating assets (Penman, 2010).

As mentioned in section 11.4, there was a financial crisis that started in 2007. The financial crisis

may be the main reason for the downturn development for ISS‘ operating income from DKK 1,652

million in 2007 to DKK 96 million in 2008, which is a decrease of 94% as shown in table 12.4. In

this period ISS also had huge losses on hedges and foreign exchange adjustments of subsidiaries

and non-controlling interests and its lowest RNOA 0.29%.

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Table 12.4 ROCE first level break down, operating liability leverage

2010 2009 2008 2007 2006

Total operating income (OI) after tax 2,135 441 96 1,652 910

Total operating assets 50,617 50,320 52,455 49,739 44,359

Total operating liabilities 18,540 17,782 17,583 17,909 17,633

Net operating assets ( OA-OL) 32,908 32,786 33,642 33,326 30734

RNOA (OI after tax/ average NOA) 6.49% 1.35% 0.29% 4.96% 2.96%

Operating income from sales (after tax)

or core OI

3,318 2,979 3,353 3,517 2,570

RNOA from sales or core RNOA 10.08% 9.09% 9.97% 10.55% 8.36%

OLLEV 55.19% 53.93% 52.75% 53.32% 53.08%


Despite the economic downturn, ISS managed to gradually improve its operating income from

DKK 441 million in 2009 to DKK 2,135 million in 2010 (an increase of 384%). Looking at ISS‘

core OI in table 12.4, ISS has generally been able to generate a higher operating income from sales

in the period 2006-2010, with an increase of 29 % from 2006 to 2010, despite a 15% decrease in

core operating income from 2007-2009. This has lead to a higher core RNOA relative to RNOA.

However, ISS has also incurred high operating expenses over the years, which reduced its total

operating income. These expenses have resulted in relatively low RNOA levels, which are lower

than the net borrowing cost, and thus wealth was not created for ISS‘ shareholders in the period

2006 -2009.

In general, ISS‘ NOA are high relative to its operating liabilities as shown in table 12.4 in the years

2006 to 2010, which results in relatively high OLLEV over 50%26

. This means that it has been

necessary for ISS to invest a lot of capital in operating assets as discussed in section 12.3.2. By

investing a lot of cash in operating assets, ISS does not create value for the shareholders because the

company could for example put the money to an alternative use by letting the suppliers carry the

investment in e.g. inventory.

Level 2: The analysis of the drivers of operating profitability

At this level the RNOA, is analyzed further. The two drivers of RNOA are the operating profit

margin (PM) and the asset turnover (ATO). The PM measures how much a company makes on each

DKK of sales, and it is calculated as: (Penman, 2010)

26

According to Penman (2010) a typical OLLEV is around 40%

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PM = OI (after taxes)/sales

The ATO measures the amount of sales generated for every DKK of NOA. The ATO is determined

as: ATO = Sales/NOA

In general, there is a tradeoff between ATO and PM, companies with businesses that require low

investments tend to yield a high ATO and a low PM (Penman, 2010). As table 12.5 shows, ISS‘ PM

has been relatively low throughout the period, with 2010 showing the best results.

Table 12.5 ROCE second level break down, operating profitability analysis

2010 2009 2008 2007 2006

Operating revenues 74,073 69,004 68,829 63,922 55,772

Operating income (after tax) 2,135 441 96 1,652 910

PM 2.88% 0.64% 0.14% 2.58% 1.63%

Average NOA 32,908 32,786 33,642 33,326 30,734

ATO 2.25 2.10 2.05 1.92 1.81

Inverse value ATO 0.44 0.48 0.49 0.52 0.55

Source: Authors, 2011, based on the financial statement analysis

The PM increased from 2006 to 2007, then it decreased in 2008 due to the financial crisis and the

costs incurred during this period. The generally low PM implies that ISS operates in competitive

markets. A low PM also indicates that there is a possibility for making high ATO and there is a low

investment requirement to enter a given market. Due to the low entry barriers in the facility services

industry as mentioned in section 11.2, this will attract competition and ISS may be forced to lower

its sales prices in order to maintain the turnover.

Furthermore the asset turnover measures a firm's efficiency at using its assets in generating sales or

revenue, the higher the number the better. ISS‘ ATO has increased by 24% from 2006 to 2010; this

is possibly due to the increase in revenues over the period. This may imply a slight improvement in

the ATO, that can also be seen in ISS‘ decreasing inverse value of ATO, which shows how much

money is tied up in operating assets in order to generate 1 DKK of revenue; for example in 2006

ISS used 0.55 per DKK for every 1 DKK of revenue, but in 2010, ISS used a lesser amount of DKK

0.44, (a decrease of 20%).

Level 3: The analysis of the profit margin and turnover drivers

Level three involves an analysis of the drivers of the profit margin and turnover.

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The profit margin drivers

The profit margin consists of the sales profit margin and other items profit margin. The sales profit

margin is increased by reducing a company‘s costs of sales, by adding income from other items and

by reducing costs per dollar of sales (Penman, 2010). For ISS cost of sales consist of staff cost and

consumables. Table 12.6 shows that the consumables account for a relatively low percentage of

revenue which has been stable from 2006-2007 and thereby not improved the PM ratio.

Table 12.6 ROCE third level break down, PM and ATO drivers

2010 2009 2008 2007 2006

Profit margin drivers

Operating revenues 74,073 69,004 68,829 63,922 55,772

Staff cost (% of revenue) 64.79% 64.90% 64.15% 64.14% 65.06%

Consumables (% of revenue) 8.58% 8.76% 8.91% 8.78% 8.81%

Other operating expense (% of revenue) 19.72% 19.47% 19.77% 19.76% 19.00%

Turnover drivers

Total operating working capital -1,378 -1,584 -2,307 -1,617 -2,291

Operating working capital / Sales -0.019 -0.023 -0.034 -0.025 -0.041

Total long-term NOA 34,358 34,419 35,044 36,163 34,397

Long-term net operating asset / Sales 0.46 0.50 0.51 0.57 0.62


Moreover, ISS‘ staff costs account for the largest part of its cost of sales, and these costs have also

not changed considerably from 2006-2010. According to ISS (2011, ―3‖) one of the reasons for the

lack of reduction in staff costs is the fact that employment in the facility services industry is often

considered as a temporally job or a second job by many people, and ISS incurs recruitment and

training costs every time a new employee is hired. ISS has been trying to reduce staff costs by for

example providing varying work tasks and training courses, in hope that the employees can become

more efficient (ISS, 2011, ―3‖).

Additionally, the staff costs are also related to the revenue in the sense that when the company gets

a new contract they can hire new employees and thereby also increase their revenue. ISS can also

fire the employees if they for example lose a big contract or do not have work for them. Overall,

there has been no great decrease in these costs and thereby no added improvement to the PM.

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Furthermore, another reason for the low PM ratio is the fact that ISS has high other operating

expenses during this period.

The turnover drivers

The turnover drivers can be grouped into the operating working capital27

driver and the long-term

net operating asset driver28

(Penman, 2010) (see the electronic appendix 1, working capital & long-

term NOA). As table 12.6, illustrates, ISS‘ operating working capital was negative from 2006-2010,

this means that during the whole period ISS was unable to meet its short-term liabilities with its

current assets. This may indicate that ISS was not operating in the most efficient way because e.g.

money that is tied up in current assets e.g. accounts receivables cannot be used to pay off the

company's obligations as discussed in section 12.3.2.

The long-term net operating assets to sales ratio has decreased over the years from 0.62 in 2006 to

0.46 in 2010 (a reduction of 26%), this means that ISS has improved at using its long-term NOA in

generating sales.

12.3.6 Summary of the financial statement analysis

The financial statement analysis has shown that ISS finances its operations through debt rather than

equity, and this has had a negative effect on the company‘s ROCE. This is because ISS was not able

to generate a RNOA higher than its net borrowing cost in the years 2006 to 2009. Additionally,

there was a downward development in ISS‘ performance in the years 2008 and 2009 mainly due the

financial crisis; however ISS has managed to improve its performance in the year 2010.

For all the years, ISS‘ net operating assets have been high relative to its operating liabilities, and

this means that it has been necessary for ISS to invest a lot of capital in operating assets, and

thereby not create a lot of value for the shareholders. ISS‘ relatively low PM ratios over the years

are mainly due to intense competition in the industry and high operating costs, which have not been

reduced considerably in the period 2006-2010. Despite the slight improvement in the assets

turnover over the years, ISS has not been operating in the most effective way over the years, this

may be due to the challenge of integrating the operations of the different acquired businesses as

mentioned in section 11.4.

27

Operating working capital = operating current assets – operating current liabilities 28

Long-term operating assets usually consist of property, plant and equipment, intangibles and investments in equities

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12.3.7 The amount of free cash flow

In order to achieve a correct result from the DCF analysis, it is necessary to obtain the free cash

flow (FCF) from the reformulated financial statements. This can be done in three different ways:

1) FCF = operating income – change in NOA

where: operating income is gathered from the reformulated income statement and change in NOA is

calculated from the reformulated balance sheet for the last two years.

2) FCF = net financial expense – change in NFO + net dividends + minority interest in income

– change in minority interest in the balance sheet

where: net financial expense is obtained from the reformulated income statement, the net dividends

are from the reformulated statement of owners‘ equity and the last three items are obtained from the

reformulated balance sheets.

3) Find FCF by reformulating the cash flow statement

If one of the first two methods is used to obtain the FCF, it is not necessary to reformulate the cash

flow statement. Furthermore, it is more useful to forecast the expected free cash flows that are

necessary for the DCF valuation by forecasting reformulated balance sheets and income statements,

instead of forecasting the reformulated cash flow statements (Penman, 2010). Therefore it is

decided to use method 1 to calculate the FCF for the DCF model, and to forecast the FCF in section

12.3.8. The FCF for the years 2006-2010 calculated using method 1 is illustrated in table 12.7.

Table 12.7 ISS’ FCF in the years 2006-2010 (numbers in DKK million)

2010 2009 2008 2007 2006 2005

Operating income 2135 441 96 1652 910 272

NOA 32,980 32,835 32,737 34,546 32,106 29,362

FCF 1,990 343 1,905 -788 -1,834

Source: Authors, 2011, based in the reformulated financial statements

Table 12.7 shows that the FCF for ISS has been negative in the years 2006-2007 and positive in the

period 2008-2010.

12.3.8 Forecasting free cash flow

In order to forecast FCF, it is necessary to forecast its elements first. Therefore NOA and operating

income are forecasted and used to calculate the FCF. The forecasts are made for a ten year period

from 2011-2020, as recommended by Koller (2005). Selected forecasts up to year 2018 are

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presented in the following sections whilst the full forecasts can be found in the electronic appendix

1, valuation of ISS. It is decided only to include forecasts up to year 2018 in the presented tables

due to the limited table space.

Forecasting sales

In order to forecast sales for ISS three main areas that are suggested in Penman (2010) are

investigated, these are: the company’s strategy, the market conditions and the company’s marketing

plan. In order to discuss and evaluate the effect of the different factors on sales, the strategic

analysis performed in section 11 is used.

In relation to the company’s strategy, ISS follows a differentiation strategy where it focuses on

creating a unique delivery of services which is difficult for the competitors to imitate, as discussed

in section 11.3. Furthermore, ISS is following a growth strategy. The company has grown mostly

through acquisitions and partly organically over the past years, and their strategy involves

continuing growth through acquisitions mainly in the emerging markets, and increasing organic

growth. The strategic analysis shows that business growth helps ISS to increase its capacity and

service capabilities which enable the company to serve more customers and to achieve higher

customer satisfaction which for example leads to renewals of long-term contracts as mentioned in

section 11.3 According to ISS (2011, ―3‖) the key factors for sales increase are organic growth and

growth through acquisitions, whilst an important factor that can reduce sales is divestment in

subsidiaries.

In relation to the market conditions for the facility services, it is expected that the demand for these

services increases in an upward economic development as discussed in section 11.1.

Gross domestic product (GDP) is an indicator of the level of the economic activity, e.g. ISS states

that when GDP is growing in the countries where ISS operates, the demand for ISS‘ products also

grows. According to Eurostat the GDP in EU, which is the main market for ISS as mentioned in

section 11, is expected to increase in the coming years, which is beneficial for ISS. The

development in the GDP in the EU is presented in table 12.8.

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Table 12.8 Real GDP growth rate in EU

2006A 2007A 2008A 2009A 2010A 2011E 2012E

European Union (25 countries) 3.20% 2.90% 0.40% -4.20% 1.90% 1.80% 1.90%

Source: Eurostat29

Furthermore, ISS is investing in emerging markets and as these markets pick up economically, the

demand for ISS‘ services is expected to increase accordingly (ISS, 2011, ―3‖). However, in 2010

ISS only generated 18.3% of their revenue from emerging markets (ISS, 2011, ―3‖) and the rest was

generated from established markets (Europe and North America), so the impact from emerging

markets is considered to be relatively low. In general, economic conditions mainly affect the single

services that ISS offers, whilst the integrated facility services (IFS) are less sensitive to economic

crises, this trend was experienced by ISS during the financial crisis which started in 2007. However,

single services accounted for 64.8% of the total revenue in 2010, whilst IFS accounted for 18.7%

and the remaining 16.5% was generated by multiservices (ISS, 2011, ―3‖), therefore overall, ISS is

evaluated to be sensitive to economic crises.

ISS‘ marketing plan is a part of their ―The ISS way‖ strategy, where the company amongst other

things focuses on increasing their sales of integrated facility services which is one of their major

strengths (section 11.4), and which is expected to generate more profit than single services in the

future.

In order to forecast ISS‘ sales in the future, historical growth levels in sales are analyzed first. From

2005 to 2010 the sales have increased continuously except for year 2009, the growth in sales is

illustrated in table 12.9.

Table 12.9 Annual growth in sales for ISS

2010 2009 2008 2007 2006 2005

Operating revenues (in

million DKK)

74.073 69.004 68.829 63.922 55.772 31.741

Annual growth 7.35% 0.25% 7.68% 14.61% 75.71%


The major growth in sales in 2006 is most likely caused by the fact that ISS was acquired in 2005

and the growth and differentiation strategies discussed above started to be implemented by the new

owners. 2008 and 2010 show a lower and more stable level of growth compared to the first years

29

http://epp.eurostat.ec.europa.eu/portal/page/portal/eurostat/home/ (Real GDP growth rate)

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after acquisition. The low growth in 2009 is most likely caused by the financial crisis that started in

2007. In 2010 the growth in sales picked up again, probably due to the diminishing effect of the

financial crisis. As the financial crisis is fading away, ISS‘ level of sales is expected to continue

increasing in the coming years.

ISS‘ differentiation and growth strategies that have lead to increases in sales in the past years are

expected to increase the sales further in the future, assuming that there is no second financial crisis.

Overall, it is expected that ISS‘ sales will grow in the coming years, but the growth rate is expected

to decrease slowly towards a stable long-term growth rate by the end of the forecast period. ISS

experienced a growth in sales of 8% in the first quarter of 2011 compared to the first quarter of

2010, but for the whole 2011 the expected growth in sales is 4% (www.issworld.com). As

mentioned in section 11.2, the global market for the facilities services has grown with

approximately 6% per year in the period 2001-2006, and then at a slower rate in the period 2006-

2009 mainly due to the economic downturn.

Even though, the financial crisis temporarily reduced growth rates in the facility service industry, it

is expected that this industry will improve with the recovery in world economy. This is because an

economic downturn enforces the need for companies to reorganize their business operations in a

cost-effective way in order to remain competitive in the market. The facility service industry is

expected to increase at a moderate level in mature markets such as Europe and United States, and at

a higher rate in emerging markets as the standard of living increases (Global Industry Analysts, Inc,

2011).

As illustrated in table 12.10, it is decided to set the growth rate for 2011 to 4% to follow ISS‘

expectations.

Table 12.10 Forecasted operating revenues

2010A 2011E 2012E 2013E 2014E 2015E 2016E 2017E 2018E

Operating revenues

(in DKK million) 74,073 77,036 81,658 88,191 93,482 99,091 104,541 109,768 114,708

Revenue growth 7.35% 4.00% 6.00% 8.00% 6.00% 6.00% 5.50% 5.00% 4.50%


In 2012 the growth in sales is mainly expected to be due to the company‘s organic growth and

partly due to acquisitions in the emerging markets. In 2013 the growth is expected to increase up to

8% due to improved operational efficiency and continued organic growth. In 2014 and 2015 the

growth rate is set to 6% due to increased competition and a stabilized, lower level of organic

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growth. From 2016 the growth rates are expected to decrease slowly until they reach a stable level

of 3.5% in 2018 (electronic appendix 1, valuation of ISS).

Forecasting ATO and calculating NOA

When forecasting the NOA, Penman (2010) suggests first to forecast the ATO and then to calculate

the NOA as:

NOA = Sales/ATO

An accurate estimate of the ATO is achieved by forecasting the different elements of ATO

individually (Penman, 2010).

In order to determine which elements of ATO should be forecasted separately for ISS, the ratios of

all the different OA to sales and OL to sales are calculated for the years 2006-2010, selected items

are presented in table 12.11, whilst the full dataset can be found in the electronic appendix 1, ATO

drivers.

Table 12.11 1/ATO drivers

2010 2009 2008 2007 2006

Average Intangible assets/sales 47.80% 51.77% 53.14% 57.24% 61.59%

Average PPE/sales 2.74% 3.10% 3.27% 3.43% 3.69%

Average trade receivables/sales 14.19% 14.66% 14.68% 15.17% 15.10%

Other average OA/sales 4.21% 3.61% 3.57% 4.09% 3.97%

Average deferred tax liabilities/sales 3.15% 3.52% 3.84% 4.66% 5.80%

Average trade payables/sales 3.68% 3.96% 4.06% 4.18% 4.08%

Average other liabilities/sales 14.46% 15.17% 15.22% 16.08% 16.30%

Other average OL/sales 3.23% 2.98% 2.66% 2.88% 3.08%

1/ATO (OA/sales - OL/sales)

44.43% 47.51% 48.88% 52.14% 55.11%


It is decided to forecast the ratios that are higher than 2% separately, whilst the ratios that are lower

than 2% are summed up and forecasted together as other average OA/sales.

Based on the figures presented in table 12.11, it can be seen that the ratios with: intangible assets,

property, plant and equipment and trade receivables are higher than 2%. Similarly, for operating

liabilities: deferred tax liabilities, trade payables and other liabilities are forecasted individually

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and the rest are forecasted together as other operating liabilities. All the forecasts are enclosed in the

electronic appendix 1, valuation of ISS, whilst the resulting 1/ATO, and the calculated ATO and

NOA are presented in table 12.12.

Table 12.12 Forecasted 1/ATO and calculated NOA

2010A 2011E 2012E 2013E 2014E 2015E 2016E 2017E 2018E

1/ATO 44.43% 42.80% 41.67% 40.45% 39.35% 38.50% 37.70% 37.90% 37.90%

ATO 2.25 2.34 2.40 2.47 2.54 2.60 2.65 2.64 2.64

NOA =

sales/ATO 32908 32971 34027 35673 36785 38150 39412 41602 43474


The ATO drivers in the electronic appendix 1, valuation of ISS are forecasted based on a

combination of their historical performance, available published information about ISS and the

authors‘ future assumptions about the given drivers.

For example ISS, is planning to continue growing through acquisitions, but only to a limited extent

(section 11). Intangible assets are mainly affected by acquisitions because 77% of intangible assets

consist of goodwill (ISS Annual report, 2010), since no major acquisitions are planned by ISS, it is

expected that the decreasing trend of intangible assets/sales ratio will continue. With regard to PPE

in the years 2011-2020, it is decided to increase the PPE for the years 2012-2013 because additional

PPE might be needed when the level of operations increases.

Some of the items such as deferred tax liabilities/sales, other OA/sales and other OL/sales, which

have not been subject to major changes in the past years, are set to constant growth rates.

As it can be seen from table 12.12, ATO and NOA are calculated to increase gradually up to year

2018 based on the forecasted ATO drivers. After 2018 the growth is expected to stabilize, this effect

will be captured by the continuing value which is calculated in section 12.5.

Forecasting operating income

In order to forecast operating income which is needed for the FCF calculation, it is necessary to

forecast the different parts of operating income, these are: core operating income and other

operating income/expense. The operating income is forecasted through the operating PM from both

the core OI and other OI, using the formula (Penman, 2010):

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OI = sales * operating PM

Table 12.13 contains the different components of the operating PM for ISS. As it can be seen from

the table, all the costs directly related to sales have been quite stable in the period 2006-2010 as

discussed in section 12.3.5. Thus, it is assumed that these costs will continue to be stable in the

future.

The costs which are directly related to sales, i.e. staff costs, consumables and operating expenses

are forecasted as an average of the historical costs, and the rates are set to be constant for the whole

forecast period due to the very small historical changes. The calculated forecasts for year 2011 are

shown in table 12.13, and the full forecasts can be found in the electronic appendix 1, valuation of

ISS.

Table 12.13 Operating income to sales ratio and its components

2006A 2007A 2008A 2009A 2010A 2011E

Operating revenues 55,772 63,922 68,829 69,004 74,073 77,036

Staff cost/sales 65.06% 64.14% 64.15% 64.90% 64.79% 64.78%

Consumables/sales 8.81% 8.78% 8.91% 8.76% 8.58% 8.65%

Gross margin/sales 26.14% 27.08% 26.93% 26.34% 26.63% 26.57%

Operating expenses/sales 19.00% 19.76% 19.77% 19.47% 19.72% 19.42%

Core operating income (before tax)/sales 7.13% 7.32% 7.16% 6.87% 6.91% 7.15%

Tax on operating income/sales 2.53% 1.82% 2.29% 2.55% 2.43% 2.43%

Core operating income from sales (after

tax)/sales

4.61% 5.50% 4.87% 4.32% 4.48% 4.72%

Other operating expense (after tax)/sales 3.02% 2.65% 2.81% 4.17% 2.80% 2.70%

Other operating income without tax

effect/sales

0.05% -0.26% -1.92% 0.49% 1.20% 1.00%

Total operating income (after tax)/sales 1.63% 2.58% 0.14% 0.64% 2.88% 3.02%


Additionally, other operating income for ISS consists of other operating expenses and other

operating income without the tax effect. These ratios are also illustrated in table 12.13. The

forecasted rate for other operating expense (after tax)/sales is set to 2.70% from 2011 to 2013,

which is slightly lower than in 2010, and then this ratio is expected to decrease gradually as

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illustrated in the electronic appendix 1, valuation of ISS. The ratio is forecasted in this way because

the main part of these expenses is caused by goodwill impairment and amortization of brands

(electronic appendix 1, PM drivers) and ISS expects to be able to lower these costs, partly because

fewer acquisitions are expected in the coming years (ISS, 2011, ―3‖). The forecasted rate for other

operating income without tax effect/sales is set to 1% because this ratio has been increasing and

decreasing over the years as illustrated in table 12.13, and since the main part of this income is

caused by foreign exchange rate adjustments which will continue to exist due to ISS‘ international

operations, it is expected to have an effect in the future.

As illustrated in table 12.14, the resulting operating PM is 3.02% in the years 2011-2013, 3.12% in

the years 2014-2016 and then slowly increasing up to the long-term rate of 3.72% (electronic

appendix 1, valuation of ISS). In general, the forecasted PM is slightly higher than in the past years,

however it matches well with ISS‘ expectations to increase its profit margin in the future (ISS,

2011, ―3‖). Based on the forecasted operating PM and operating revenues, expected operating

income is calculated for ISS, and the results up to year 2018 are presented in table 12.14.

Table 12.14 Forecasted operating income

2010A 2011E 2012E 2013E 2014E 2015E 2016E 2017E 2018E

Operating revenues 74,073 77,036 81,658 88,191 93,482 99,091 104,541 109,768 114,708

Operating PM30

2.88% 3.02% 3.02% 3.02% 3.12% 3.12% 3.12% 3.32% 3.32%

OI 2,135 2,328 2,468 2,665 2,918 3,094 3,264 3,646 3,811


Calculating future FCF

Based on the forecasted NOA and OI, it is possible to calculate the expected FCF for the forecast

period. The expected FCF is calculated using the first method described in section 12.3.7.

The resulting FCF up to year 2018 is presented in table 12.15, and the full forecasts up to year 2020

are enclosed in the electronic appendix 1, valuation of ISS.

30

Operating PM = Total operating income (after tax)/sales

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Table 12.15 Future FCF (in DKK million)

2010A 2011E 2012E 2013E 2014E 2015E 2016E 2017E 2018E

NOA 32,980 32,971 34,027 35,673 36,785 38,150 39,412 41,602 43,474

OI 2,135 2,328 2,468 2,665 2,918 3,094 3,264 3,646 3,811

FCF 1,990 2,337 1,412 1,019 1,806 1,729 2,002 1,456 1,938


12.4 Step 4: Estimating the weighted average cost of capital

According to Damodaran (2009 ―2‖), it is often necessary to adjust the risk levels for multinational

companies that are incorporated in a developed country located in e.g. Europe or United States and

operate in emerging markets. This is because the risk of operating in an emerging market is usually

higher compared to operating in a developed market, discount rates assigned to the multinational

company based on where it is incorporated do not represent the actual operating risk, and this will

lead to an overvaluation of the company. ISS‘ headquarter is in Denmark as mentioned in section 2

and the company does operate in developing countries, however ISS‘ revenue from emerging

markets accounts for only 18% of the total revenue (ISS Annual report, 2010) and this issue is

therefore considered to be insignificant in case of ISS and will not be taken into account in the

valuation process.

The weighted average cost of capital (WACC) for ISS

As stated in section 7.1.2, WACC can be calculated by determining its three components: the after-

tax cost of debt, the cost of equity and the company’s target capital structure (Koller et. al, 2005).

Thus, the formula for WACC is (Brealey & Myers et al., 2007):

WACC =

Determining the risk free rate

As mentioned in section 7.1.2, it is common practice to use the YTM from the German Eurobond as

the risk free rate for European firms, because it is assumed to have higher liquidity and lower credit

risk compared to bonds of other European countries. The 10-year German Eurobond with maturity

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in 2021 has currently a YTM of 2.8%31

and this rate is used as the risk free rate in the WACC

calculation.

ISS’ cost of debt

As mentioned in section 7.1.2 a company‘s cost of debt can be determined by the following three

factors: the risk free rate, the default spread and the tax rate.

As stated above, ISS‘ risk free rate is 2.8%.

The default spread will be determined by using option a) from section 7.1.2 i.e. using the market

interest rate on ISS‘ long-term bonds. According to ISS‘ annual report (2010) the company‘s

market interest rate on long-term bonds is 5.01%. Thus, the default spread is determined as:

By using ISS‘ marginal tax rate of 25%, the company‘s after tax cost of debt is determined as

After tax cost of debt = (Risk free rate + Default spread) (1- marginal tax rate) = 2.8%+2.21%*(1-25%) =3.76%.

The after tax cost of debt of 3.76% is used in ISS‘ WACC calculation below.

ISS’ cost of equity

ISS‘ cost of equity can be determined by the CAPM, which is discussed in section 7.1.2. The inputs

to the CAPM: the risk free rate, beta and the market risk premium for ISS are determined in this

section. ISS‘ risk free rate is already determined to 2.8%, the beta and the market risk premium are

determined below.

Estimating beta for ISS

As mentioned in section 7.1.2, the beta for private firms can be estimated by using: 1) accounting

earnings, 2) using a private firm‘s ratios to get fundamental betas or 3) using average betas for

similar public firms to get bottom up betas. Due to the fact that ISS has negative earnings in the

analyzed period, option 1 is not used. According to Damodaran (2009), fundamental betas often

have a low R2, therefore option 2 is not used. It has been decided to apply option 3, whereby bottom

31

http://www.bloomberg.com/markets/rates-bonds/government-bonds/germany/ ( 13-07-2011)

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up betas are determined by using the market model regression that is stated in section 7.1.2. In order

to determine the bottom up beta, returns from ISS‘ competitors are regressed against the returns

from the market. Five competitors32

are chosen for the regression, these are specified in table 12.16,

and the market returns are determined based on MSCI world index. The regression results from the

market model are stated in appendix K and table 12.16 shows ISS‘ resulting bottom up beta (the

calculations for table 12.16 can be seen in the electronic the electronic appendix 1, ISS‘ bottom up

beta).

Table 12.16 Calculations for ISS’ bottom up beta

Levered Beta R2 Debt/Equity Unlevered beta

(Market model)

Compass 0.92 51% 0.06 0.88

Sodexo 0.92 29% 0.09 0.87

Rentokil 1.68 53% 0.58 1.19

Ecolab 0.71 49% 0.05 0.69

ABM 0.91 34% 0.02 0.90

Average beta 1.028 43% 0.16 0.90

Average unlevered beta Debt/Equity Levered beta

ISS 0.90

0.59 1.30

Source: Authors, Datastream, 2011

Table 12.16 shows the levered betas, the debt/equity ratio and the unlevered betas for the chosen

peer group companies. In order to consider the differences in financial and operating leverage

between ISS and the other firms in the market, the unlevered beta is adjusted so as to get a more

accurate beta estimate. The adjustment can be made by using the average peer group D/E ratio or

the private firm‘s D/E ratio if it is stated by the management (Damodaran, 2009). Due to the fact

that ISS has a high leverage compared to the peer group, it is decided to use the firm‘s target D/E

ratio of 0.59.

32

Due to the fact that the authors want to focus on companies that offer a wide variety of services like ISS in the

regression analysis, it is decided not to include the companies G4S and Securitas because they offer security services.

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Thus, ISS‘ levered beta is determined as (Damodaran, 2009):

Levered beta = Unlevered beta * (1 + (1 – Tax rate) * (Debt / Equity))

= 0.90*(1+ (1- 0.25)*(0.59) =1.30

ISS has a rather high beta compared to the market, this means that the company is sensitive to

market fluctuations and thereby has more operational and financial risk than the average firm in the

market. This may be due to the company‘s high leverage.

Additionally, table 12.16 shows that the average R2 from the regression analysis is relatively low

(43%), which means that on average only 43% of the variation in the peer group's returns is

explained by the market, while the rest is due to firm specific factors. This fact confirms the

discussion in section 7.1.2, which states that the CAPM does not explain a lot of the variation in

stock returns. Therefore, the results should be interpreted with caution.

Calculating the market risk premium (MRP)

As stated in section 7.1.2, the extra return that investors demand for taking on risk i.e. the risk

premium, depends on the risk measure, beta, and the market risk premium .

In order to calculate the MRP, the historical return index for the MSCI world was retrieved from

Datastream for the period 1970 – 2010 as shown in the electronic appendix 2, MSCI historical Rm.

Additionally, the historical risk free rate for the same period was retrieved from www.forecast-

chart.com and afterwards the MRP was calculated by using the arithmetic average (Koller et al.,

2005) as shown in the electronic appendix 2, market risk premium. As shown in this appendix, the

resulting MRP is calculated to be 5.86%. However, according to a report by

PriceWaterHouseCoopers (2010) about corporate valuation methods in practice and the level of

MRP used by companies, it is concluded that companies use an MRP within the interval 4.0% to

7.2% with the average MRP of 4.9%.

The calculated MRP of 5.86% is considered to be high relative to the average MRP of 4.9%

presented in the report. The calculated MRP is based on 40 observation which might not be a long

enough period, because Koller et al. (2005) suggest analyzing the past 75 years. However, the data

on Datastream only reported observations for the past 41 years. Therefore, there is no reason to

believe that the calculated MRP is more correct than the MRP reported by

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PriceWaterHouseCoopers (2010). Since changes in MRP have direct effect on the resulting WACC,

and small changes in WACC can have a large effect on the resulting company value, as illustrated

in section 12.8, it is decided to use a MRP of 4.9%.

Calculating the cost of equity for ISS

Having calculated the inputs for the CAPM formula, ISS‘ cost of equity can now be determined as:

ISS‘ cost of equity will be used in the WACC calculation.

ISS’ target capital structure

After estimating the cost of equity and the cost of debt, the next step towards calculating ISS‘

WACC is to identify the target weights of debt and equity by using market values as mentioned in

section 7.1.2.

Estimating ISS’ target capital structure

In order to identify ISS‘ target debt and equity level, the company‘s NFO and equity are forecasted

in the period 2011 to 2020. Table 12.17 shows the forecasts for the period 2011-2018, and the

forecasts for the last two years can be seen in the electronic appendix 1, WACC and CAPM.

Table 12.17 Forecasts of ISS’ NFO33

and equity

2011E 2012E 2013E 2014E 2015E 2016E 2017E 2018E

NFO (debt) 30,632 17,332 16,639 16,140 15,817 15,501 15,191 14,887

Percentage change NFO 1.00% -43.42% -4.00% -3.00% -2.00% -2.00% -2.00% -2.00%

Equity 2,651 15,951 19,141 21,055 22,108 22,550 23,001 23,461

Percentage change equity 0.00% 501.70% 20.00% 10.00% 5.00% 2.00% 2.00% 2.00%

Source: Authors, 2011.

As mentioned in section 12.3.5, ISS‘ current capital structure consists mainly of debt financing and

this has resulted in high financial costs and less value created for the shareholders. Thus, ISS is

planning to go public in order to obtain cash to pay off some of the debt (ISS, 2011,―3‖).

33

ISS‘ book value of debt is assumed to be approximately equal to the market value of debt.

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Table 12.17 shows that in 2011, ISS‘ NFO are assumed to increase by 1% from the 2010 debt level,

due to the costs associated with extending the maturities on loans due in 2012 and 2013 by two

years (Javed, 2011). For the rest of the forecast period, ISS‘ debt levels are expected to decrease

with the largest decrease occurring in 2012, because it is assumed that in this year ISS will go

public, and use all of the proceeds from the IPO to reduce its debt level34

(this leads to a reduction

of 43.42% of debt). In the following years, ISS‘ debt is expected to decrease gradually at lower

percentages as the principal is being paid off.

In relation to the forecasts for the equity level, ISS is expected to issue shares worth DKK 13,300

million, and this will result in a market value of equity of DKK 15,951 million in 2012 (an increase

of 501.70%) as shown in table 12.17. In 2013, it is assumed that ISS will issue more shares, in

order to get more money to improve operations e.g. by integrating companies in order to grow

organically and acquire companies, and this will result in a 20% increase in equity. Additionally, in

2014, it is assumed that the share price will increase or more shares will be issued and this will

result in a 10% increase in equity value. For the rest of the years, it is assumed that the equity value

will generally increase at lower percentages up to 2020.

In conclusion, ISS‘ is expected to have a target debt level of DKK 14,297 million and target equity

level of DKK 24,409 million in 2020, as shown in the electronic appendix 1, WACC and CAPM.

These levels are used in the WACC formula.

Calculating ISS’ WACC

After determining the inputs of WACC in the previous sections, WACC can be calculated as:

WACC = (D/V*rdebt) + (E/V*requity) = (14,297/38,706 )* 3.76% + (24,409 /38,706 )* 9.19% = 7.18%

According to Penman (2010), there is a lot of uncertainty and speculation related to estimating the

WACC inputs, thus a sensitivity analysis of the WACC can help in analyzing how this uncertainty

affects the valuation results. For this reason a sensitivity analysis is performed in section 12.8.

34

By reducing the debt level, it is expected that ISS will achieve a better credit rating in the future and this will make it

possible for ISS to borrow money at lower interest rates (ISS, 2011, investor meeting). ISS‘ current credit rating is B2

by Moody‘s and BB- by S&P which is a junk bond, and thereby not worth investing in

(http://www.business.dk/service/kreditbureauer-overvejer-hoejere-karakter-til-iss).

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12.5 Step 5: Calculating the continuing value

The continuing value for ISS is calculated by using the constant-growth formula, which is discussed

in section 7.1.3 To apply the constant-growth formula, it is assumed that ISS will go concern and

grow at a constant, long-term rate after the horizon year. In order to determine a reasonable long-

term growth rate for ISS, the growth rate for the world economy is analyzed. In table 12.18 the

growth rate for the world economy in the years 2005A-2011E is presented, and an average growth

rate of 3.91% is calculated.

Table 12.18 Real GDP growth rate for the world economy

2005A 2006A 2007A 2008A 2009A 2010A 2011E Average

World 4.90% 4.70% 5.30% 5.20% 3.10% -0.70% 4.90% 3.91%

Source: Indexmundi35

In order for a company‘s long-term growth rate to be sensible it should be below the growth rate of

the world economy, otherwise the company will outgrow the world at some stage which is not a

realistic assumption (Koller et al., 2005). The long-term growth rate for ISS is thus set to be 3.5%

which is assumed to be a reasonable long-term growth rate under which the company will not

outgrow the world economy.

The FCF for year (H+1) i.e. 2021 is calculated in the electronic appendix 1, valuation of ISS, to be

DKK 3,119 million, and the r is the WACC that is calculated in section 12.4. Thus, the present

value of ISS‘ continuing value at the horizon year i.e. year 2020 can be determined as:

Afterwards, the obtained continuing value is discounted back to the end of 2010 by 10 years:

12.6 Step 6: Calculating the company value

As stated in section 7.1.4, the firm value can be calculated as:

35

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The PV of ISS‘ FCF in the forecast period up to the horizon i.e. 2011-2020 is DKK 12,735 million

and the PV of the company‘s continuing value is DKK 42,438 million, and these result in a firm

value of DKK 55,173 million.

Additionally, ISS‘ equity value can be calculated by subtracting debt36

and minority interest (MI)

from the company value as stated in section 7.1.4.

Equity value = firm value – debt - MI =55,173 – 16,100 - 25 = 39,049

ISS‘ market value of equity is thus DKK 39,049 million. The company‘s share price of DKK

167.59 is then determined as: the equity value of DKK 39,049 million divided by the number of

shares of 233 million37

(ISS‘ company value calculation is shown in the electronic appendix 1,

valuation of ISS).

Furthermore, since forecasting involves making assumptions about the company‘s future, and

predicting the future involves uncertainty as discussed in section 7.1.4, a scenario analysis is

performed in section 12.7. The scenario and sensitivity analysis will help to analyze the uncertainty

related to the valuation results.

12.7 Scenario analysis

According to Damodaran (2007), it is a good idea to perform a scenario analysis in order to see how

the estimated value of the asset changes under different scenarios. Since it is not possible to predict

the future, a scenario analysis can give an idea of e.g. how low or how high the asset value can

become based on different possible future cash flows. It is then possible to determine the expected

value of the asset by either applying weights to the different scenarios and estimate a weighted

average value, or by choosing one of the scenarios as the expected outcome.

It is decided to perform a scenario analysis to analyse how ISS‘ firm value and the corresponding

share price change depending on different assumptions about the future growth rates. The scenario

analysis contains a base case, a best case and a worse case scenario. All the calculations for the

36

As mentioned in section 12.4 ISS is expected to go public in 2012, thus it is assumed that there will be a major change

in the capital structure whereby ISS‘ debt level will be reduced. For this reason the 2012 debt level (NFO) have been

discounted back (using the cost of debt) to 2010 in order to get the debt level, which is relevant for the equity value and

the share price calculations i.e. 17,332/ (1+ 3.76%)2 = 16,100

37

ISS has 100 million existing shares and it plans to issue 133 million new shares, and these result in 233 (100+133)

million shares (ISS, 2011, "3").

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different scenarios can be found in the electronic appendix 1. The base case is the expected

outcome which is used to estimate the firm value of ISS in section 12.6, and is therefore not

discussed further here. The best case and worse case scenarios are discussed below.

Best case scenario

In the best case it is assumed that the economy will grow in all the markets that ISS operates in,

which will affect the company‘s growth positively. ISS will therefore achieve its goal of at least 6%

organic sales growth (ISS, 2011, ―3‖) in the forecast period, and grow at a rate that is higher than

6% in the years 2012-2018 for example due to growth through acquisitions, improved operations

and successful integration of the acquired subsidiaries. The expected growth rates and

corresponding operating revenues until year 2018 are presented in table 12.19; the full forecasts are

enclosed in the electronic appendix 1, scenario analysis best.

Table 12.19 Expected growth in the best case scenario

2010A 2011E 2012E 2013E 2014E 2015E 2016E 2017E 2018E

Operating revenues

(in DKK million)

74,073 78,517 84,014 90,735 97,812 105,148 112,298 119,710 127,251

Revenue growth 7.35% 6.00% 7.00% 8.00% 7.80% 7.50% 6.80% 6.60% 6.30%


Furthermore, in the best case scenario the growth rate until infinity is expected to be 3.80%, this is

slightly below the average growth in the world economy of 3.91% as discussed in section 12.5.

Worse case scenario

In the worse case it is assumed that the economy will grow slowly, and ISS will not reach its goal of

a 6% organic growth rate at any stage. Furthermore, already in year 2018 ISS will reach its long-

term growth rate of 2% due to strong, increasing competition in all the markets that ISS operates in.

The annual growth rates and corresponding operating revenues under the worse case scenario are

outlined in table 12.20, whilst the full forecasts can be found in the electronic appendix 1, scenario

analysis worse.

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Table 12.20 Expected growth in the worse case scenario

2010A 2011E 2012E 2013E 2014E 2015E 2016E 2017E 2018E

Operating revenues

(in DKK million)

74,073 77,036 80,503 84,528 88,754 92,304 95,073 97,450 99,399

Revenue growth 7.35% 4.00% 4.50% 5.00% 5.00% 4.00% 3.00% 2.50% 2.00%


Table 12.21 summarizes the resulting enterprise value, equity value and share price under the

different scenarios. The resulting values in the best case scenario are also affected by minor changes

in the forecasted other operating expenses and other operating income, these changes are specified

in the electronic appendix 1, scenario analysis best. In the worse case scenario other operating

expenses are set at constant rate of 2.70% and other operating income is set at a constant rate of 1%

as illustrated in the electronic appendix 1, scenario analysis worse.

Table 12.21 Company value of ISS under the different scenarios

PV of FCF up to

2020

PV of continuing

value

Enterprise value Market value of

equity

Price per

share

Best case 12,721 55,212 67,993 51,808 221

Base case 12,735 42,438 55,173 39,049 167

Worse case 14,919 23,230 38,149 22,024 94


As illustrated in table 12.21, in the best case scenario, the increased growth results in a price per

share of DKK 222, which is 32% higher than in the base case. In the worse case scenario the share

price is estimated to be DKK 94, which is 44% lower than in the base case. This shows that the

company value of ISS and the corresponding share price are sensitive to changes in growth rates

and costs. In case ISS achieves a performance level similar to the one forecasted in the best case

scenario, the share price will increase significantly compared to the IPO price, which is of course

very beneficial for investors. However, in case ISS does not manage to do so well and will grow at

a level similar to the worse case scenario, the investors can risk that the share price drops below the

lowest IPO price of DKK 100, and investors will thereby suffer a loss.

12.8 Sensitivity analysis

Koller et al., (2005) and Brealey and Myers et al., (2007) suggest performing a sensitivity analysis

in order to evaluate the forecast model‘s robustness under different assumptions, for example by

stating optimistic and pessimistic values for WACC and revenue growth. The changes in revenue

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growth are discussed in section 12.7, thus this section will include a discussion of the optimistic and

pessimistic values for the WACC.

Optimistic and pessimistic values for WACC

In order to evaluate the effect of changes in ISS‘ expected WACC of 7.18% on the enterprise value,

it is assumed that the optimistic value represents a 1% decrease in WACC and the pessimistic value

represents a 1% decrease in WACC as shown in table 12.22 (the calculations can be seen in the

electronic appendix 1, sensitivity analysis).

Table 12.22 Optimistic and pessimistic values for WACC

Expected

outcome

Optimistic

value

% change Pessimistic

value

% change

WACC 7.18% 6.18% -1.00% 8.18% 1.00%

Total PV of FCF up to 2020 12,735 13,379 5.05% 12,138 -4.69%

PV of continuing value 42,438 64,033 50.88% 30,402 -28.36%

Enterprise value 55,173 77,411 40.30% 42,540 -22.90%

Price per share 167.59 263.03 56.95% 113.37 -32.35%


In table 12.22, it can be seen that small percentage changes in ISS‘ WACC have a large effect on

the enterprise value, for instance for the optimistic value, a 1% decrease in WACC leads to a

50.88% increase in the PV of the continuing value and a 5.05% increase in the total PV of FCF up

to 2020. This results in a 40.30% increase in ISS‘ enterprise value and a 56.95% increase in the

company‘s price per share. Additionally, for the pessimistic value table 12.22 illustrates that a 1%

increase in WACC lead to a 22.90% decrease in firm value and a 32.35% decrease in the

company‘s share price.

In conclusion, ISS‘ estimated enterprise value is considerably sensitive to small changes in the

WACC, thus this value should be interpreted with caution.

13. Valuation of ISS using real options

As discussed in section 7.4, using the DCF method alone to estimate the value of a company is too

simplistic and it does not capture the value of the company‘s future options, the value obtained from

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the DCF analysis may therefore be too low. For this reason real options valuation is applied to ISS

as an extension of the DCF analysis.

ISS focuses on organic growth and growth through acquisitions as discussed in section 11. ISS‘

increase in operating revenue which leads to an increase in firm value is often caused by the

company‘s growth, therefore an option to expand is found to be relevant for ISS. As mentioned in

section 11, ISS plans to acquire companies in emerging markets in the future due to the expected

high demand in these markets as discussed in section 11. Therefore, it is decided to estimate the

value of the flexibility that ISS has in relation to expanding in emerging markets. The company‘s

option to expand is discussed in the next section.

Option to expand

ISS has an option to expand in the emerging markets; however, emerging markets is a very broad

term which covers over many countries on different continents. In order to estimate the value of an

option to expand, a more narrow definition of the option is needed. To define a specific option for

ISS the countries in the emerging markets where ISS does not currently have any operations are

identified first. Next the demand for facilities services in these countries is evaluated. One of the

countries that ISS is not currently present in, is South Korea. Based on a market potential index,

developed by GlobalEdge, for countries in emerging markets, South Korea is ranked 7 out of 26

countries based on the market size. A list of top 10 countries based on the market size is illustrated

in table 13.1. The other rankings for South Korea in table 13.1 are also relatively high compared to

other countries in emerging markets, which indicates that South Korea is in general a favourable

market to enter.

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Table 13.1 Top 10 countries in emerging markets based on market size

Countries Market

Size

Market

Growth

Rate

Market

Intensity

Market

Consumption

Capacity

Commercial

Infrastructure

Economic

Freedom

Market

Receptivity

Country

Risk

Overall

Index

Rank Rank Rank Rank Rank Rank Rank Rank Rank

China 1 1 26 13 21 25 23 8 2

India 2 2 24 11 25 18 20 13 9

Russia 3 26 20 16 8 24 22 15 21

Brazil 4 11 17 23 13 14 25 12 11

Indonesia 5 4 19 10 20 15 24 22 12

Mexico 6 22 8 21 16 9 7 11 13

Korea,

South

7 16 6 1 4 4 9 4 4

Turkey 8 10 5 9 15 12 13 17 10

South

Africa

9 19 18 25 24 11 3 14 24

Pakistan 10 3 9 4 26 21 26 26 22

Source: GlobalEdge, 201138

Furthermore, ISS is currently present in all the other countries shown in table 13.1, except for

Pakistan (ISS Annual report, 2010), which should make it easier for ISS to enter South Korea as the

company must have experience in operating in emerging markets. The facilities service market in

South Korea is expected to achieve a growth rate of 4%-6% in the years 2012-2016, as South

Korean companies choose to outsource their facilities management. And it is expected that it is the

foreign facilities management companies that will be providing these services39

. This makes South

Korea an attractive market for ISS.

An option to expand for ISS is therefore defined as an option to enter the South Korean market

through acquisition of one or more local companies. The option is expected to expire in five years

when the growth rate of the facilities service market in South Korea will slow down. If ISS enters

the South Korean market before the option expires, it is assumed that its operating revenues will

increase by 0.80% up to year 2016, then by 0.30% in the period 2017-2020 and finally the long-

term growth rate will increase by 0.05%. These increases in operating revenues are expected to

cause an increase in the enterprise value of 2.33% as shown in the electronic appendix 1, increase in

EV with ROV. ISS stated that they are planning to spend around DKK 500 million a year on

acquisitions mainly in the emerging markets (ISS, 2011, ―3‖), it is thus assumed that DKK 250

38

http://globaledge.msu.edu/resourcedesk/mpi/index.asp?year=2010&sort=1#mpiGrid 39

http://www.reuters.com/article/2011/04/20/idUS18937+20-Apr-2011+BW20110420

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million will be spent on acquisition and integration of a subsidiary in South Korea, which is the cost

of the option.

13.1 Step 1: The value of ISS without flexibility

As discussed in section 7.4.2, when valuing a whole company with ROV the company value from

e.g. the DCF analysis can be used as the value of the underlying asset. The value of ISS without

flexibility, which is used in the ROV, is DKK 55,173 million as calculated in the DCF analysis in

section 12.6.

13.2 Step 2: Event tree for ISS

The second step of the ROV analysis involves determining the up- and downward movements in the

geometric event tree using the annual volatility. The annual volatility can be determined in three

different ways as discussed in section 7.4.2. Due to the fact that the cash flows from ISS‘ similar

expansion options are not provided in the annual report it is decided to use option 3, i.e. the

variance in values of similar firms in the industry to determine the variance of the expansion option.

This variance is estimated by using the market model, which has already been applied in section

12.4, and is stated as:

Two public, South Korean companies similar to ISS are found in Datastream, these companies are:

Korean Environment Technology Co. Ltd and Y-Entec Co. Ltd. A short description of these

companies is enclosed in appendix L. Thus, the stock returns for the Korean Environment

Technology Co.,Ltd. and the Y-Entec Co., Ltd., are regressed on the MCSI world index in Eviews.

The regression outputs for the market model are stated in appendix M. The data for the regressions

is retrieved from Datastream and can be seen in the electronic appendix 3. Table 13.2 shows the

resulting annual volatility. The betas and standard errors in table 13.2 are obtained from the

regression results, calculations of var(ei) and var(Rm) are shown in electronic appendix 4, annual

volatility. The monthly variance is determined as (Grinblatt and Titman, 1998):

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Table 13.2 Volatility

Korean Environment

Technology

Y-Entec Average

β 1.31 1.44 1.375

std.error 0.1765 0.1579 0.1672

var(ei) 0.0312 0.0249 0.0280

var(Rm) 0.00442

Monthly variance (σ2

i) 0.0364

Monthly volatility 0.1908

Annual volatility 0.6609

Authors, 2011

The monthly and annual volatility are calculated based on the monthly variance as shown in the

electronic appendix 4, annual volatility. The annual volatility is found to be 66.09%, and this is used

in determining the up and down movements in the event tree as:

The number of years per upward movement is set to 1 year. By using the calculated factors for the

up- and downward movements in the event tree, and the enterprise value from the DCF analysis as

the value of the underlying asset, the possible values of ISS for the next five years are modelled in

an event tree. The obtained event tree is illustrated in figure 13.1.

Figure 13.1 Event tree for ISS’ expansion option in South Korea

T 0 1 2 3 4 5

1,502,617

775,933

400,682

400,682

206,907

206,907

106,844

106,844

106,844

55,173

55,173

55,173

28,491

28,491

28,491

14,712

14,712

7,597

7,597

3,923

2,026


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The probabilities of the up- and downward movements in the event tree are determined as:

where the WACC (7.18%) from the DCF analysis is used as the cost of capital, r.

The values in the event tree can thus be verified using the probabilities pu and pd, as mentioned in

section 7.4.2. As an example the value in the upper branch in year 4 is calculated as:

The same calculation can be done for all the other values in the event tree, confirming that the tree

is correct.

13.3 Step 3: Decision tree for ISS

In order to make the decision tree, it is assumed that the cost of expanding in South Korea is DKK

250 million, the expansion is assumed to increase ISS‘ enterprise value by 2.33% and the option can

be exercised at any time during the next five years as discussed in the description of the expansion

strategy. The decision tree for ISS‘ expansion option is illustrated in figure 13.2.

Figure 13.2 The decision tree

T 0 1 2 3 4 5

1,537,378

793,769

409,781

409,768

211,498

211,485

109,121

109,097

109,084

56,279

56,246

56,215

28,986

28,946

28,904

14,903

14,857

7,648

7,597

3,923

2,026


Looking at for example the uppermost branch in period 5, on the upward limb, the payout without

the expansion option is DKK 1,502,617 million as stated in figure 13.2, but with the expansion, it is

(1+ 2.33%) *1,502,617 - 250 = 1,537,378. Due to the fact that the value with expansion is higher

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than the value without expansion, ISS would choose to expand. On the other hand, looking at the

lowest branch in period 5, the value without expansion is DKK 2,026 million, whilst the value with

expansion is (1 + 2.33%) * 2,026 – 250 = 1,823. In this case the value without expansion is higher,

and thus ISS would not choose to expand.

These calculations are performed for all the values in year 5 to decide whether or not to expand.

When the value with flexibility is higher than the value without flexibility, the company chooses to

expand. The values for which ISS would choose to expand in year 5 are marked bold in figure 13.2.

13.4 Step 4: The value of ISS with flexibility

In step 4 the value of the real option is determined by working backwards from right to left through

the decision tree by using risk neutral probabilities at each node for the remaining 4 years. The risk

neutral probabilities are determined as:

For example the value of the option in the uppermost branch in period 4 is determined as:

Option = Max((pu* upper value with flexibility year 5+(1 - pu*) down value with flexibility year 5) / (1

+ rf); value without flexibility year 4)

= Max (793,769 ; 775,933) = 793,769

The rest of the calculations can be seen in electronic appendix 4, event and decision tree.

After determining the value for all the nodes, the enterprise value with the expansion option is DKK

56,279 million, subtracting this value from the enterprise value without the option DKK 55,173

million, results in an option value of DKK 1,106 million (56,279 - 55,173). The enterprise value,

equity value and share price for ISS with and without the expansion option are summarized in table

13.3.

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Table 13.3 Value of ISS with and without the expansion option

Enterprise value Equity

value

Share price

Without expansion option 55,173 39,049 167.59

With expansion option 56,279 40,154 172.34


As illustrated in table 13.3, ISS‘ share price with the real option is DKK 172.34, which is an

increase of 2.8% compared to the share price without the option. This shows that the expansion

option in South Korea does add value to ISS, and thereby creates value for ISS‘ shareholders. The

firm value of ISS is therefore determined to be DKK 56,279 million in this paper. The fair share

price for ISS is then DKK 172.34, and is discussed further in section 15.

14. Valuation of ISS using multiples

The multiples analysis is performed by determining an average ratio of a given multiple based on

comparable firms as mentioned in section 7.5. In section 9, it was decided that the EV/EBITDA and

the EV/S multiples are suitable for ISS‘ peer group analysis, and ISS‘ peer group was determined in

section 11.2, based on the industry criterion. Table 14.1 shows the EV/EBITDA and EV/S multiples

of ISS‘ comparable companies (the calculations can be seen in electronic appendix 5, Peer group).

Table 14.1 Peer group analysis

EV/EBITDA EBITDA margin EV/S

Company 2010A 2011E 2012E 2010A 2011E 2012E 2010A 2011E 2012E

Compass group 8.52 8.60 7.63 8.53% 8.64% 8.86% 0.73 0.74 0.68

ABM 8.09 8.32 7.43 4.46% 4.52% 4.84% 0.36 0.38 0.36

Rentokil 5.77 5.65 4.98 18.11% 17.17% 17.89% 1.05 0.97 0.89

Sodexo 7.97 7.95 6.89 6.63% 6.93% 7.16% 0.53 0.55 0.49

G4S 8.06 7.62 6.89 8.90% 8.98% 9.06% 0.72 0.68 0.62

Securitas 6.85 6.91 6.12 7.39% 7.08% 7.34% 0.51 0.49 0.45

Ecolab 10.34 10.41 9.06 19.08% 19.11% 19.84%

Average 7.94 7.92 7.00 10.44% 10.35% 10.71% 0.65 0.64 0.58

ISS

6.91% 7.15% 7.15%

Source: Authors, www.4-traders.com, Companies’ Annual reports, 2010

The EV/EBITDA multiple shows a company‘s operating profitability. Looking at table 14.1, it can

be seen that ISS‘ EBITDA margin is around 3.5% lower than the average EBITDA margin for the

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peer group companies in the period 2010-2012, this means that ISS has relatively higher operating

expenses compared to its peers, and this results in a lower EBITDA margin.

It is decided not to include the EV/S ratios for Ecolab, because they are almost twice as high as the

ratios for the other companies in the peer group, which can be verified in the electronic appendix 5,

Peer group. Ecolab‘s EV/S ratio is therefore not found to be representative of the industry, and is

thus excluded from the average.

Table 14.2 shows ISS‘ enterprise values from the multiples analysis and the corresponding share

prices.

Table 14.2 ISS’ multiple analysis (numbers in DKK million)

EV/EBITDA

EV/S

ISS 2010A 2011E 2012E 2010A 2011E 2012E

Enterprise value 40,641 43,648 40,870 47,948 48,959 47,538

Debt 16,100 16,100 16,100 16,100 16,100 16,100

Minority interest 25 25 25 25 25 25

Market value of Equity 24,516 27,523 24,745 31,823 32,834 31,413

Share price 105 118 106 137 141 135

Source: Authors, www.4-traders.com, Companies’ Annual reports, 2010

The enterprise values in table 14.2 are all between 13% and 28%40

lower than the enterprise value

obtained from the DCF and real options analysis, which is DKK 56,279 million. The share price

estimated based on the multiples analysis is within the range of DKK 105 – 141 compared to the

DCF and real options analysis result of DKK 172.34 per share. One of the reasons for this may be

that the share prices based on the multiples analysis represent the value that investors are willing to

pay for stocks of a facilities service company at the moment (Martínez & Perron, 2004), and it does

not include the long term value of ISS. The results from multiples analysis are discussed further in

section 15.

15. Discussion of the results

This section includes a discussion of the results that are found by using the DCF model, the real

options valuation and the multiples analysis.

40

(56,279-40,641)/56,279 = 0.28 and (56,279-48,959)/56,279 = 0.13

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The DCF model

ISS‘ firm value from the DCF model is DKK 55,173 million, with an equity value of DKK 39,049

million and a share price of DKK 167.59. These values should be interpreted with caution because

the DCF method is largely dependent on WACC and continuing value assumptions as concluded in

the scenario and sensitivity analysis; for example in the continuing value at horizon calculation,

ISS‘ expected growth rate is assumed to be 3.5% and this results in a continuing value at horizon of

DKK 84,858 million, however in the scenario analysis best case when this growth rate is assumed

to be 3.8% (an increase of 0.3%), the continuing value at horizon is DKK 110,400 million (an

increase of 30%).

Another illustration of the DCF model‘s dependency on the given assumptions is illustrated in the

sensitivity analysis in which it is shown that a 1% change in the WACC results in large changes in

firm value and share prices.

These examples show that the results from the DCF model can be relatively subjective, because the

DCF model is largely dependent on the analyst‘s belief about the future direction of the company,

the results should therefore be interpreted with caution.

Real options

Due to the fact that the DCF method does not include the value of managers‘ flexibility, it is

supplemented with a real options analysis. The enterprise value of ISS with the 5 year expansion

option in South Korea is DKK 56,279 million, the equity value is DKK 40,154 million and the

share price is DKK 172.34 as stated in section 13. Thus, ISS‘ enterprise value and share price with

the real option increase by 2% and 2.8% respectively compared to the enterprise value and share

price without the option. This means that the expansion option creates value for ISS under the given

assumptions.

However, the value of the option to expand depends on accurate estimates of the value and variance

of the underlying asset as discussed in section 7.4.1. The underlying value of the asset is the value

of ISS obtained from the DCF model, which, as mentioned, can be relatively subjective. The

variance is estimated based on the variance in market returns and the variance in returns of two

local South Korean competitors. Since ISS might have different competences and experience than

the local South Korean companies, it might have different firm specific variance, thus the estimated

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variance is only an approximation for ISS. For this reason the value of the expansion option is

approximate, rather than exact.

Multiples analysis

As discussed in section 7.5, the multiples analysis only gives an approximate company value, which

can be used to determine whether the company value calculated using another method e.g. DCF is

reasonable. Additionally, the value from the multiples represents the market‘s perception of the

current market value of a facility services company‘s stock as mentioned in section 14.

ISS‘ enterprise and equity value from the multiples analysis are within the range DKK 40,641 -

48,959 million and DKK 24,516-32,834 million respectively. The enterprise and equity values from

the multiples analysis are between 13%-28% and 18%-39% lower than the enterprise and equity

value obtained from the DCF and real options analysis. The share price estimated based on the

multiples analysis is within the range of DKK 105-141 compared to the DCF and real options

analysis result of DKK 172.34 per share.

The values from the multiples analysis are generally lower than the values from the DCF analysis

with real options. Since the multiples analysis is used to verify the results obtained from other

models it can be discussed whether the value obtained from the DCF and real options analysis is

overvalued. However, before drawing any conclusions it is necessary to evaluate the quality of the

multiples analysis. According to section 7.5, several multiples should be used in the analysis. In this

paper two multiples are determined to be relevant for ISS. It is recommended to use at least six

comparable companies in section 7.5, and a peer group of seven comparable companies has been

identified for the EV/EBITDA multiple, whilst six companies were used in the EV/S multiple, so

this criterion is assumed to be fulfilled. However, there are not many companies that are similar to

ISS in all respects, e.g. in relation to the range of services offered and the range of countries in

which these services are offered. Thus, the peer group includes e.g. two security companies

(Securitas and G4S) and a company which mainly operates in the US (ABM). For this reason the

values of ISS obtained from the multiples analysis are not exact and a larger variation between these

values and the fair value can be acceptable. It is therefore decided not to conclude that the value of

ISS obtained from the DCF and the real options analysis is too high purely based on the comparison

to the multiples valuation. Instead, the obtained value is compared to value estimates published by

professional analysts and to the IPO share price published by ISS.

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The official equity value estimates published by professional analysts41

range from DKK 39.4-49

billion. Comparing these estimates to the equity value of DKK 40,154 million estimated in this

paper, it can be concluded that the estimated equity value of ISS is within this range, but in the low

end. It should be noted that the goal of this thesis is not to obtain the same value as other analysts,

because all valuations are based on different assumptions, future expectations and different methods

of estimating the various inputs such as beta42

, the risk free rate and the market risk premium. It is

therefore not possible to determine one right answer. However, comparing the obtained value to

values of other analysts can give a hint about whether the estimated value is reasonable just like the

multiples analysis. Based on the comparison to other value estimates of ISS, it is concluded that the

equity value of ISS of DKK 40,154 million is not overvalued, since it is in the low end of the range.

Lastly, the share price of ISS estimated to be DKK 172.34 per share is compared to the IPO share

price published by ISS, which is given as a range of DKK 100 – DKK 135 per share (ISS, 2011,

―3‖). The share price published by ISS is 22% - 42% lower compared to the share price estimated in

this paper.

In general, IPO prices have a tendency of increasing on the first day of trading and this results in the

common trend that IPOs are underpriced at an average of 15% (Martínez & Perron, 2004). One of

the explanations for the difference between the initial and the first day trading prices is that firms

going public are considered to be risky. This means that investors require a discount on the IPO

share price, mainly due to the uncertainty of how the stock price of the newly listed company will

develop after the IPO. Otherwise, if the share price of a company at the IPO is similar to the share

prices of its competitors, investors may choose to invest in competitors‘ shares because they know

how the development of the share prices has been in the past, and can base their future expectation

on that.

This can partly explain and justify the lower IPO share price published by ISS compared to the

estimated share price, however, the difference is somewhat higher than the common 15% discount,

therefore further possible reasons for the difference in share prices are investigated.

Assuming that the fair price for ISS‘ shares is DKK 172.34 as estimated in this paper, a reason for a

higher than usual discount on the IPO share price for ISS can be the fact that this is a second IPO,

since the company has already been public before 2005. According to Lian and Wang (2009), a

41

www.ugebrev.dk; www.jyskebank.dk; www.fyens.dk; www.businessweek.com 42

The results of the beta vary depending on the different market index, time period and return interval used.

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company‘s second-time IPO often sells at a major discount compared to its first-time IPO. This is

because the market considers second-time IPOs to be riskier than first time IPOs, and it includes the

negative information associated with the previous withdrawal when determining the price for the

second-time IPOs.

Furthermore, it is possible that investors require an additional discount in case of ISS, because of

the company‘s high debt levels, which make the company riskier than average.

Based on the above discussion it is concluded that the enterprise value obtained using the DCF and

real options analysis of DKK 56,279 million and the equity value of DKK 40,154 million are

reasonable estimates for ISS, despite the flaws of the models and the lower results obtained from

the valuation using multiples. Furthermore, the estimated share price of DKK 172.34 is assumed to

represent a fair value of ISS‘ share price, since a higher than average discount for the IPO is found

to be reasonable in this case.

16. Conclusion

In this thesis the following corporate valuation theories have been discussed; the discounted cash

flow model, the dividend discount model, the residual income model, real options valuation and

valuation using multiples. Furthermore, the capital asset pricing model and the Fama and French

three factor model have been evaluated as alternative methods for determination of the expected

rate of return on a company‘s stock.

The corporate valuation theories have been discussed in sections 7.1-7.5, and specifically

advantages and disadvantages have been identified for each model. Based on the literature review it

was decided in section 9 to use the DCF analysis with free cash flows as the primary method to

value ISS and the CAPM was chosen instead of the Fame and French three factor. Furthermore, it

was decided to supplement the DCF model with a real options analysis. An expansion option was

found relevant for ISS, due to their growth through acquisitions strategy in the emerging markets.

Based on a short review of emerging markets a five year option to expand in South Korea was

defined. To verify the valuation results it was decided to apply a multiples analysis to ISS and to

value the company based on a peer group of seven similar companies and two different multiples

the EV/S and the EV/EBITDA.

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Before the financial valuation of ISS, a strategic analysis that includes a study of ISS‘ corporate

environment was performed by using the PESTEL analysis, Porter‘s five forces, and the SWOT

analysis as well as an identification of the company‘s core competences.

From the PESTEL analysis, the factors that were most uncontrollable for ISS were the political and

economic factors, because ISS is not able to affect a country‘s politics, changes in legislation or

what is happening to the macro economy. Based on Porter’s five forces analysis it was concluded

that the facility services industry is moderately attractive for ISS to operate in due to the relatively

low threat of substitutes and the relatively low bargaining power of suppliers and buyers. On the

other hand, the competition in the facility services industry and the threat of new entrants is

relatively high.

Additionally, ISS has achieved its core competences through customization and an integration of

the different facility services internationally.

In the SWOT analysis, it was concluded that ISS‘ main strength lies in its core competences but on

the other hand the company is threatened by both global and local economic risk factors in all the

markets. ISS‘ major weakness is its high debt levels that have resulted in high financial expenses

over the years. This indicates that there is a need for a change in the capital structure.

In the financial analysis the DCF method, the real options valuation and the multiples were applied.

As part of the DCF analysis ISS‘ financial statements for the period 2005-2010 were reformulated

and analyzed and the historical amounts of the free cash flow were found. Based on the analysis of

the historical financial statements and the strategic analysis of ISS, the company‘s future FCF were

forecasted in the explicit forecast period 2010-2020, and the PV of these FCF summed up to DKK

12,735 million. In order to capture the value of the free cash flows after the explicit forecast period

a continuing value was estimated. The PV of the continuing value was estimated to DKK 42,438

million. This resulted in an enterprise value of DKK 55,173 million, an equity value of DKK

39,049 million and a share price of DKK 167.59. In order to account for the uncertainty of forecasts

a scenario analysis with a best, base and worse case scenarios was conducted. In the best case

scenario the share price was calculated to be DKK 221 due to the increased growth and in the worse

case scenario the share price was calculated to be DKK 94 due to reduced growth and increased

expenses. The WACC used in the DCF analysis was estimated to be 7.18%, but due to the

uncertainties in relation to the inputs of the WACC formula, a sensitivity analysis of WACC was

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carried out. In the sensitivity analysis a 1% lower optimistic value of WACC and a 1% higher

pessimistic value of WACC were analyzed. The result was a share price of DKK 263.03 in the

optimistic case and a share price of DKK 113.37 in the pessimistic case.

In the real options valuation in section 13, it was assumed that the expansion option in South Korea

will increase the enterprise value of ISS with 2.33% and will come at a cost of DKK 250 million.

The real options analysis was performed using the binomial model, which resulted in an option

value of DKK 1,106 million. Thus, the enterprise value of ISS from the DCF analysis and the real

options analysis combined was determined to be the fair value of the company found in this thesis,

and it was estimated at DKK 56,279 million, with the equity value of DKK 40,154 million and a

share price of DKK 172.34.

In section 14 valuation using multiples was applied to ISS based on actual numbers from 2010 and

forecasted numbers for 2011-2012. The share prices estimated using the EV/EBITDA multiple were

in the range DKK 105 - DKK 118, and the share prices estimated using the EV/S multiple were in

the range DKK 135 – DKK 141.

In section 15 the obtained results from all the models were compared and discussed. The enterprise

and equity value obtained from the DCF and real options analysis was compared to the enterprise

and equity value from the multiples analysis, and it was found that in general the values from the

multiples analysis were lower. Due to the weaknesses of the multiples method the values from the

DCF and the real options analysis were also compared to the published equity value estimates from

professional analysts that are within the range of DKK 39.4-49 billion, and ISS‘ published IPO

share price that is within the range of DKK 100 – DKK 135. It was concluded that the equity value

of DKK 40,154 million is at the low end, but still within the range. The IPO share price is 22% -

42% lower compared to the estimated share price of DKK 172.34. This difference is explained by

the fact that investors may require additional discounts in case of ISS due to the fact that it‘s a

second-time IPO and the company has high amounts of debt.

It is therefore concluded that the enterprise value for ISS of DKK 56,279 million is reasonable, and

that the share price of DKK 172.34 represents the fair market value of the company‘s shares.

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Appendices

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List of appendices

Electronic appendices

Electronic appendix 1: The DCF model

Electronic appendix 2: Data for CAPM

Electronic appendix 3: Data for real options

Electronic appendix 4: Real options valuation

Electronic appendix 5: Multiples valuation

Printed appendices

Appendix A: Overview of the DCF model

Appendix B: ISS‘ competitors

Appendix C: Percentage change of ISS‘ revenues from 2006 to 2010 per service type

Appendix D: Original statements of shareholders‘ equity for the years 2005-2010

Appendix E: The reformulated statements of shareholders‘ equity

Appendix F: Original balance sheets

Appendix G: The reformulated balance sheets

Appendix H: Original income statements

Appendix I: The reformulated income statements

Appendix J: The drivers of ROCE

Appendix K: Regression results for the market model

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Appendix L: South Korean competitors

Appendix M: Regression results for real options

Appendix N: Notes to the electronic appendix 2

Appendix O: Structure of the thesis

Appendix P: List of abbreviations

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