Fundamental Stock Analysis - diva-portal.org432648/FULLTEXT01.pdf · regarding Fundamental Stock Analysis are stated in order to answer the research questions stated in chapter one

Fundamental Stock Analysis A study of the fundamental analysis for practical use at the

Swedish Stock Exchange

Bachelor’s thesis within Business Administration

Authors: Peter Eriksson

Tobias Forsberg

Nicklas Gustavsson

Tutors: Per-Olof Bjurgren

Louise Nordström

Jönköping May 2011

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Acknowledgements The authors of this paper would like to acknowledge the following persons for making it

possible to complete this thesis.

First, we would like to express our gratitude to our tutors; Per-Olof Bjurgren and Louise

Nordström for their support and commitment during this thesis’ entire process.

We would also like to take the opportunity to thank Per Forsberg, accountant, KPMG,

Marcus Eriksson, senior analyst, Nordea, and Erik Sellstedt, Danske Bank, for their

professional insights and perspectives in general.

Finally, we would like to show our gratefulness towards our fellow colleagues at Jönköping

International Business School for their inputs, comments, and support.

Peter Eriksson Tobias Forsberg Nicklas Gustavsson

International Management Program 2008 - 2011 Jönköping International Business School 2011

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Bachelor Thesis within Business Administration, 15 ECTS-credits

Title: Fundamental Stock Analysis

Subtitle: A study of the fundamental analysis for practical use at the Swedish stock exchange

Authors: Peter Eriksson, Tobias Forsberg, Nicklas Gustavsson

Tutors: Per-Olof Bjurgren, Louise Nordström

Key words: Fundamental Analysis, Stock Price, Stock Price Valuation, Gordon Growth, Free Cash Flow to Equity, P/E ratio, EV/EBITDA, Net Asset Valuation, Multiples, Target Prices.

Abstract

The interest for stocks and stock-trading has grown tremendously during the

last decade. The challenge for small private investors is how to use and filter

the most relevant information in the stock selection process. As a result, this

thesis investigates the accuracy of the Gordon Growth, Discounted Cash Flow

(Free Cash Flow to Equity), P/E multiple, EV/EBITDA multiple, and Net

Asset Valuation in relation to the target prices set by financial analysts. This in

order to create an understanding how target prices are set, and which models

that are useful for a specific firm or industry.

The research covers twelve companies, divided in four industries: telecom,

retail, construction and oil, over the time period of 2008 - 2011. All companies

are listed on NASDAQ OMXS, Large or Mid Cap. In order to determine the

most suitable models, several analyses were conducted in form of two interval

tests (10% and 15%), hit ratio test, and multiple regressions test.

From the results, it can be concluded that there exist no universal valuation

models. However, this research showed that the estimations generated from

EV/EBITDA- and P/E multiples outperformed the other investigated valuation

models. The more complex models: Gordon Growth and Discounted Cash

Flow performed poorly. In this case, the forecasted growth rate is believed to

have had an impact on the results, since it was based upon historical data only.

Due to lack of results, the estimations from the Net Asset Valuation indicated

that none of the firms hold any substantial proportions of tangible net asset in

relation to their market value.

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Kandidat uppsats inom företagsekonomi, 15 ECTS-poäng

Titel: Fundamental Stock Analysis

Under rubrik: A study of the fundamental analysis for practical use at the Swedish stock exchange

Författare: Peter Eriksson, Tobias Forsberg, Nicklas Gustavsson

Handledare: Per-Olof Bjurgren, Louise Nordström

Nyckelord: Fundamental Analys, Aktiepris, Aktievärdering, Gordon tillväxtmodel, Fri kassaflödesanalys, P/E multipel, EV/EBITDA multipel, Substansvärde, Riktkurs

Abstrakt

Intresset för aktier och aktierelaterad handel har ökat kraftigt under det senaste

decenniet. Den största utmaningen för småsparare är hur man använder och filtrerar

den mest relevanta informationen i valet av aktier. Denna forskning undersöker

träffsäkerheten för Gordons tillväxtmodell, Kassaflödesvärdering, P/E multipel,

EV/EBITDA multipel, och Substansvärde i förhållande till finansanalytikers

riktpriser. Detta för att skapa en förståelse hur riktpriser fastställs och vilka modeller

som är och kan vara användbara för en specifik bransch eller företag.

Undersökningen omfattar tolv bolag, vilka är uppdelade i fyra branscher: telekom,

detaljhandel, bygg och olja under tidsperioden, 2008 - 2011. Alla företag är noterade

på NASDAQ OMXS, Large eller Mid Cap. För att bestämma de mest lämpliga

modellerna, har ett flertal analyser genomförts i form ut av två olika

intervallundersökningar (10% respektive 15%), ”hit ratio” undersökning och multipel

regressionsanalys.

Vi kan via resultatet dra slutsatsen att det inte förekommer någon universell

värderingsmodell. Undersökning visade dock att de estimeringar som genererats

ifrån EV/EBITDA- och P/E multiplar överträffade de övriga undersökta

värderingsmodellerna. Vi observerade också att de mer komplexa modellerna:

Gordons tillväxtmodell och Kassaflödesvärdering emellertid ger ett sämre resultat.

Den prognostiserade tillväxten tros ha haft en inverkan på resultaten, eftersom de

enbart var baserade på historisk data. På grund utav bristande resultat, indikerade

Substansvärderingsresultaten att inget utav företagen förfogar över någon betydande

andel materiella nettotillgångar i förhållande till deras marknadsvärde.

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Table of Contents

Disposition ................................................................................. 1

1 Introduction .......................................................................... 2

1.1 Background ................................................................................... 2 1.2 Problem Discussion ....................................................................... 2 1.3 Purpose and Research Questions................................................. 3 1.4 Delimitation ................................................................................... 4

1.5 Literature Review .......................................................................... 4

2 Previous Research ............................................................... 6

3 Frame of Reference .............................................................. 8

3.1 Market Efficiency ........................................................................... 8

3.2 Growth ........................................................................................... 8 3.3 Dividend Discount Models ............................................................. 9 3.3.1 Gordon Growth Model ................................................................... 9

3.4 Discounted Cash Flow Model ...................................................... 10 3.4.1 Two-Stage FCFE Model .............................................................. 11

3.5 Valuation Multiplies ..................................................................... 12 3.5.1 P/E Multiple ................................................................................. 12 3.5.2 EV/EBIDTA Multiple .................................................................... 13

3.6 Net Asset Valuation ..................................................................... 14

4 Methodology ....................................................................... 16

4.1 Research Approach: Inductive vs. Deductive .............................. 16

4.2 Research Type: Descriptive, Explanatory, and Exploratory ........ 16 4.3 Data Collection: Quantitative Primary and Secondary Data ........ 16

4.4 Choice of Valuation Models ......................................................... 17 4.5 Sample Choice: Choice of Stocks ............................................... 17

4.6 Test Period .................................................................................. 18 4.7 Calculations ................................................................................. 18

4.8 Interpretation and Data Analysis ................................................. 19 4.9 Non-Statistical Method ................................................................ 19 4.10 Hit Ratios and Total number of hits ............................................. 19

4.11 Statistical Method ........................................................................ 20 4.12 Hypotheses ................................................................................. 20

4.13 Empirical Assumptions ................................................................ 22 4.14 Reliability ..................................................................................... 23 4.15 Validity ......................................................................................... 23 4.16 Critiques of Method ..................................................................... 24

5 Empirical Tables ................................................................. 25

6 Empirical Presentation and Analysis ................................ 29

6.1 Empirical Presentation ................................................................ 29 6.2 Firm-specific Analysis .................................................................. 32 6.2.1 Gordon Growth Model ................................................................. 32 6.2.2 FCFE ........................................................................................... 33 6.2.3 P/E to Target Prices .................................................................... 34

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6.2.4 EV/EBITDA to Target Prices ............................................. 35

6.2.5 Net Asset Valuation .......................................................... 37 6.3 Industrial Analysis ............................................................. 38 6.4 Final Analysis.................................................................... 40

7 Conclusion .......................................................................... 43

8 Discussion and Recommendations .................................. 45

List of references ..................................................................... 47

Appendices .............................................................................. 51

Appendix A – Compilation of Analysts’ target prices .................................... 51 Appendix B – Practical Calculations ............................................................. 52 Appendix B Continued – Practical Calculations ........................................... 53 Appendix B continued – Practical Calculations ............................................ 54

Appendix C – Industry Average for P/E and EV/EBITDA 2004 - 2010 ......... 55 Appendix D – Annual Price/Earnings Multiples 2004 - 2010 ........................ 56 Appendix E – Annual EV/EBITDA Multiples 2004 – 2010 ............................ 57

Appendix F – SPSS Statistics ANOVA tables .............................................. 58 Appendix G – SPSS Statistics Coefficients .................................................. 59 Appendix H – 10% and 15% intervals: Telecom Industry ............................. 60 Appendix H continued – 10% and 15% intervals: Retail Industry ................. 61

Appendix H continued – 10% and 15% intervals: Construction Industry .................................................................................................... 62

Appendix H continued – 10% and 15% intervals: Oil Industry...................... 63 Appendix I – Geometric Growth ................................................................... 64

P. Eriksson, T. Forsberg & N. Gustavsson

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Disposition

Introduction Chapter

•Chapter One presents the background to the chosen subject: Fundamental Stock Analysis, together with the problem discussion and the purpose of this

thesis, including research questions.

Previous Research

•Chapter Two presents previous research within the subject Fundamental Stock Analysis, and is highly connected to chapter three: Frame of References.

Frame of References

•Chapter Three consists of the theoretical framework which will be the foundation for this thesis. It will work as a guidance throughout the whole

paper.

Methodology

•Chapter Four describes the methodology and scientific approaches used for this paper.

Empirical Tables

•Chapter Five shows the empirical findings in table formats which have been used for the presentation and analysis in chapter six.

Empirical Presentation &

Analysis

•Chapter Six presents the analysis based upon the empirical findings in the previous chapter.

Conclusion

•Chapter Seven discuss the results from the analysis. The final conclusions regarding Fundamental Stock Analysis are stated in order to answer the

research questions stated in chapter one and thereby conduct the purpose of this thesis.

Discussion and Recommendations

•Chapter Eight discuss additional findings and reflections, and states recommendations for further research within Fundamental Stock Analysis.


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1 Introduction

1.1 Background

The stock market is characterized and affected by the general economic environment,

the flow of information, and psychology. The importance of each element has become

more evident, especially during the recent financial crisis that started in 2007-2008 and

the IT-crash in 2001. During these crises the stock prices have shown to be more

volatile than normal.

In the last decade, the interest for stocks and stock-trading has grown tremendously.

Data from World Federation of Exchanges (2010) shows that, during the last ten years,

the total number of trades has increased by 700% globally. One reason could be that the

average value per trade has dropped with 85%, while the number of stock listings has

increased with 41%. This could also be explained by the rapidly increased usage of

Internet. According to Internet World Stats (2010a; 2010b), the number of Internet users

has increased with 444% worldwide, while the Internet usage in Sweden has grown to

92.5% of its population. The rapidly development of Internet has revolutionized the

financial sector where the majority of all transactions take place online and in real-time.

The Nordic stock exchange operator, NASDAQ OMX Nordic (which includes

NASDAQ OMX Stockholm) has also increased its annual turnover drastically over the

last decade. According to World Federation of Exchanges (2010), the most drastic

changes at the OMX Nordic occurred between 2008 and 2009. This is believed to be a

result of the financial crisis. The average daily turnover-value decreased by 45.19%

from $US 5268.4M to $US 2887.4M. These changes were common for the majority of

the world’s stock markets as an effect of the instability and insecurity in the financial

market.

People in Sweden have a broad interest in the stock market. According to statistics from

SCB (2010), 16.5% of the Swedish population own stocks through direct ownership.

The availability of information and its flows are essential to make justified valuations of

firms. However, the major problem is no longer to access the information but rather

how to filter the most relevant. It is important to know how the information should be

interpreted and used, especially for small private investors who choose to invest a share

of their savings into the stock market.

1.2 Problem Discussion

When information is available, the biggest issue for small private investors is how to

understand and apply the relevant information in their stock selection processes. There

are three broad approaches that determine the value of a stock, and when to sell or buy.

These are technical analysis, sentiment analysis, and fundamental analysis.

Technical analysis (TA) is used to analyze the historical patterns within the stock

market in order to predict future prices. TA tells you, according to a set of parameters,

when to buy or when to sell a stock. If the patterns can be correctly understood, it can

give an indication of how the market will move in the future (Roberts, 1959). The


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relative strength index (RSI), moving average, or stochastic, are a few ways of doing a

TA (Liu & Lee, 1997). TA requires constant surveillance which makes this approach

less relevant for small private investors since the majority of them are “passive” and has

a longer investment horizon. TA is therefore more suitable for traders, who buy and sell

on a daily basis.

Sentiment analysis (SA) is about the psychology and behavior by the investors in the

financial market. By analyzing the investors’ subconscious activities and psychological

preferences, the actions and general pattern of the herd can be determined (Fontanills,

Gentile & Cawood, 2001). Using these indications as a tool, one can take a forward

position in order to beat the market (Sincere, 2003). Psychology and herd behavior are

more common among small private investors, especially in times of crises. SA is also

time-consuming and therefore the approach is less relevant for the majority of the small

private investors.

Fundamental analysis (FA) is emphasizing figures and numbers (fundamentals) of a

company’s financial reports. Actual earnings, equity, dividends, risk and growth are

examples of commonly used fundamentals. Financial analysts that are using

fundamental analysis argue that the valuation of a company can be calculated (Lev &

Thiagarajan, 1993). This method could be applied by using one or several different

valuation models and theories such as; Gordon Growth, Discounted Cash Flow (DCF),

P/E ratio and EV/EBITDA ratio (Multiples) and Net Asset Valuation (NAV). However,

even if analysts are starting off using the same valuation model, the valuation in the end

often differs. One of the reasons is that some models, such as DCF requires a number of

assumptions. Compared to TA and SA, the FA can be used to calculate a motivated

value of a firm. We therefore believe that this approach is more interesting for small

private investors, as it is possible to screen and choose potential “low-valued” stocks to

invest in.

As there are a large number of different valuation models, the aim of this thesis is to

increase the knowledge of small private investors regarding FA and enable them to filter

relevant information. It is desirable to create an objective understanding in the work of

financial analysts and how target prices are calculated. This is relevant because if small

private investors know what models that provide similar estimations as the financial

analysts’. The small private investors will through this paper create a greater

understanding of the issues regarding subjectivity within financial reporting that

fundamental analysis deals with. However, using one valuation model alone might not

give the whole picture of the investment potential of the firm that is being valued.

Therefore stock estimations should be treated cautiously.

1.3 Purpose and Research Questions

The purpose of this paper is to investigate the accuracy of five commonly used

valuation models, included in fundamental analysis, in relation to the target prices set by

professional financial analysts. The investigated valuation models are the Gordon

Growth, Discounted Cash Flow (Free Cash Flow to Equity), P/E and EV/EBITDA

(Multiples), and Net Asset Valuation.


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The models used will be applied on twelve different companies divided in four

industries at the NASDAQ OMXS over the time period 2008-2011. The following

research questions will be applied throughout the thesis in order to fulfill the purpose

and contribute to the authors’ final conclusions;

Do the estimations, generated from the investigated valuation models, provide

similar results to analysts’ target prices?

Is it possible to determine a more reliable valuation model, relative to the others,

for an industry in general?

Which of the investigated valuation models are the most appropriate for the

chosen companies?

1.4 Delimitation

The research will focus on the comparison between the empirical results of the

investigated models, and the analysts’ target prices. Therefore, the actual stock prices

will be excluded, even though they are included in the models’ calculations.

The four chosen industries are telecom, retail, construction, and oil. The companies used

in this research are listed at NASDAQ OMXS’ (Stockholm, Sweden) Large Cap or Mid

Cap.

TA and SA will not be analyzed in this research, but instead FA will be in focus.

However, FA normally includes external factors such as macroeconomic aspects,

management board valuation etc., but this thesis will be limited to the numerical

fundamentals.

Moreover, the aim of this paper is to investigate the market as a whole by conducting

the analysts’ target prices in form of averages. Therefore, the large deviations from the

individual analysts are not taken into considerations.

1.5 Literature Review

The literature review had three major aims: first, to create a structure and framework for

the topic. Second, to collect the relevant information needed within this framework.

And third, in order to provide an understanding of previous research. Information was

gathered in form of scholarly articles, research papers and books.

Internet has been an important tool in the search for literature and in order to provide

access to databases. JSTOR and SCOPUS are the databases that were mainly used, and

have been available through Jönköping’s University Library. Through these databases

books and articles were found, which provided us with additional sources and

references that could be of value for our thesis.

The key words that were used in the search at these databases were Stock price, Stock

price valuation, Fundamental analysis, P/E ratio, Gordon Growth, Discounted Cash


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Flow, Earnings multiples, EV/EBITDA, Book value, and Net asset valuation, among

several others.


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2 Previous Research

According to Goedhart, Koller and Wessels (2005), the most accurate and flexible

valuation method is the discounted cash flow model. However, the accuracy of the

model depends on the forecasts on which it is based upon. Potential errors are the

company’s return on investment, the growth rate, and the weighted average cost of

capital, which will affect the valuation. Goedhart et al. (2005) further state that, using

industry averages for the calculations of multiples are insufficient. Even though the

valuated companies are operating within the same industry, the differences can be

remarkable between firms in terms of the expected growth rates, returns on investment,

and the capital structure.

Kaplan and Ruback (1995) investigated the relationship between the market value and

the forecasted discounted cash flow. As a result, the average discounted cash flow

estimations (out of 51 samples) showed to be within a 10% range from the current

market price. Kaplan and Ruback mean that the discounted cash flow approach

individually performs similar or better than multiples. However, the authors also argue

that comparable valuation methods in form of multiples are useful, especially when they

are used in combination with the discounted cash flow valuation approach.

Fernández (2001) argues that multiple valuation of companies’ equity is target for

critics and is highly debatable. The major problem when using multiple valuations is the

broad dispersions they cause. However, as a second-stage (combination) of the

valuation, the multiples are useful. This is, after the valuation has been performed using

another model, multiples are used with advantages for comparisons of comparable firms

and thereby identify differences between the valuations of firms. He states that the P/E

and EV/EBITDA multiples are the most useful to use for the building and construction,

and the clothing industry, while P/E is the best multiple for the oil industry.

Lie and Lie (2002) state that the asset multiples provide more accurate and less biased

estimates compared to sales and earnings multiples. In the meanwhile, the EBITDA

multiple generally yields a better estimation than the EBIT multiple. Lie and Lie also

say that using forecasted earnings for estimating company value is better than using

trailing earnings. This is, even though adjustments for companies’ cash levels do not

improve the estimation. The company size, profitability, and the extent of intangible

value in the company affect the estimated value and the performance of the multiples.

Liu, Nissim and Thomas (2002) investigated in their research, the performance of a

number of value drivers in several valuation models, e.g. forecasted earning and

historical earnings. The performance was evaluated by examining the deviation between

the actual stock price and the predicted stock price. The result showed that forward

earning multiplies performed remarkable well and about half of the sample was within

15% of the current stock price. Historical earnings multiples performed second best,

followed by cash flow and book value of equity tied on third place.

Olbert (1992) investigated, in a survey-based study whether any valuation factors

among professional financial analysts were more or less important. The analysts were

about to rank several factors on a 1 to 5 scale for each industry (1 = most important, 5 =

least important). The investigation showed that the earnings per share were the single


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most important factor in most industries. However, Olbert found it difficult to

generalize the other valuation factors, as some showed to be more important for specific

industries. For instance, the result showed that the net asset valuation approach is more

useful for real estate-, wood- and investment firms, while employee skills is more

valuable for firms operating within the retail- and service industry.


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3 Frame of Reference

3.1 Market Efficiency

According to Fama (1970) market efficiency could be explained as an ideal market

where stock prices at anytime fully reflect all available information. Market efficiency

is based upon a number of assumptions (1) no transaction costs involved when trading

securities, (2) all information is free and available to all investors and (3) all investors

are assumed to be rational. This means that everyone agrees on the same implication

based on the market information, current and future prices of each security. Market

efficiency can be measured in three different subsets:

The weak form focuses on the fundamental analysis which is based on

historical information. Investors seek to earn profit by studying financial

statements to determine whether a particular stock is under- or overvalued.

Semi-strong form states that all information should be reflected in the market

and therefore investors have no use of TA or FA. Only non-public information

can benefit investors to abnormal returns.

The third subset is called strong form and means that all information, both

public and private, are fully reflected in the market. Therefore, no further

research can benefit investors to gain abnormal returns.

3.2 Growth

The growth rate is one of the most vital parameter in several stock valuation models.

For instance, it is used to forecast future revenues and earnings. Evans (1987) argues

that the growth rate is assumed to decrease with the firm’s age and size. In addition,

Damodaran (2002) means that it is easier for small firms to have high growth rates

compared to large firms as the growth rate is expressed in percentage terms. The

historical growth rate is therefore less reliable in small firms.

Estimating the growth rate of a firm can be done in several ways. The most common

way is to use the historical organic growth rate of either revenues or earnings. In

addition, the market potential and the total estimated market value can, and should be

used as complements (Damodaran, 2002). However, the historical growth rate is not

always a good indicator of how a firm will continue to grow in the future (Cragg &

Malkiel, 1968; Little, 1960). Little (1960) argues that there is almost no relationship

(correlation of 0.02) between historical and future growth. However, the growth rates

based on revenues are more likely to be persistent and more predictable than earnings

growth. According to Damodaran (2002), the correlation is stronger between historical

revenue growth and future growth compared to historical earning growth and future

growth.

The two main approaches for estimating future growth are arithmetic average and

geometric average, which both are based on historical data. The arithmetic average is

calculated by taking a simple average of the past growth rates, while the geometric


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average includes the compounding growth that occurs from period to period. For firms

with volatile earnings, the result from the two approaches can vary widely (Damodaran,

2002).

The following formula is used to estimate the future growth rates

Growth rates are largely subjective and, as a rule of thumb, stable growth refers to a

growth rate which cannot exceed the growth rate of the economy in which the firm

operates. However, the stable growth rate can during periods exceed the economic

growth rate with maximum 1-2% (Damodaron, 2002). Forecasted growth also requires

considerations of how well the company is running, and the outlook for its product

development, but also the general economic and political risks the markets serves

(Gordon, 1962; Barker, 2001).

3.3 Dividend Discount Models

There are several different dividend discount models, such as; Gordon Growth model,

two-stage growth model, and three-stage growth model. The growth rate is essential in

which model to choose.

3.3.1 Gordon Growth Model

The Gordon Growth model is a fundamental approach that focuses on growth of

dividends over time. By using the discounted present value of future dividend

payments, the stock prices and the market value can be estimated (Gordon, 1962;

Heaton & Lucas, 1999). The Gordon Growth model relates the value of a stock to its

expected dividend in the next time period, the cost of equity, and the expected growth

rate in dividends (Damodaran, 2002).

The interpretation of the model is straightforward. If both the shareholders’ expected

rate of return and the level of future dividends are assumed to be fixed, the stock price

should remain constant. However, the model is sensitive to small changes in the

parameters since they are assumed to be relevant over a lifetime of a firm (Barker,

Formula 1

DPS1 = expected dividends next year ke = cost of equity g = growth rate in dividends forever


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2001). There is a trade-off for firms whether it should increase the dividends or not. It is

not for sure that the stock price will increase with raised dividends, but instead harm the

growth of the company. This is because they are paying their shareholders instead of

doing new investments (Fernández, 2007).

Even though the model is useful in estimating stock prices, it has some limitations. For

instance, the model is not able to deal with corporations that pay zero dividends at the

end of the first year. The model suits relatively stable and established companies better

than start-ups or companies in distress. In addition, if the growth rate exceeds the cost of

capital, the model will not work. Profits and dividends might be possible to forecast in

the very near future. However, the model does not explain the underlying determinants

of why dividend is growing (Gordon, 1962; Barker, 2001). The model is limited to

stable growth since the earnings and dividends are expected to grow at the same rate in

infinity (Fuller & Hsia, 1984, Gordon, 1962). If earnings grow faster or slower than

dividends, the dividend payout ratio will over time converge towards zero or dividends

will over time exceed earnings (Damodaran, 2002).

3.4 Discounted Cash Flow Model

The originally discounted cash flow (DCF) model assumes that the only cash flow

shareholders can receive is the dividends. Until now, a large number of modified

versions of the model have been established. More frequently, the fundamentals of a

firm are used to estimate future cash flows discounted to the present value (PV). This is

a useful tool to determine the investment potential of a particular firm (Damodaran,

2002).

According to Damodaran (2002) there are three main paths of discounted cash flows.

(1) Equity Valuation: This approach determines the equity stake in the business, (2)

Firm Valuation: Focuses on the valuation of the entire firm including equity, debt and

other claim holders such as bonds, preferred shares, and (3) Adjusted Present Value

(APV) Valuation: The last method is used to evaluate the firm in pieces. As long as the

same set-up of assumptions is used, the three approaches will yield consistent

estimations. However, mismatches between cash flows are important to avoid in order

for the estimations not to be biased.

Formula 2

n = life of the asset CFt = cash flow generated by the company in period i r = discount rate


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DCF models should, on a more individual basis, provide more accurate estimations than

other models. The reason why is because forecasted cash flows, discount rates etc. are

direct related to the firm being valued, or the industry that the firm operates within

(Baker & Ruback, 1999). However, the model is highly sensitive to small changes in

variables (Barker, 2001). The discount rate is determined by e.g., risk and historical

volatilities. The cost of capital will also vary from asset to asset (Damodaran, 2002;

Fernández, 2007). This makes small and young firms harder to estimate. This since

there are a lot of uncertainty involved which make it hard to forecast the future cash

flow as well as the appropriate discounted rates.

The market does make mistakes. It is therefore possible that stock prices can deviate

from its intrinsic value even though this is assumed to be adjusted over time. There are

also a number of scenarios where the uncertainty of DCF estimations increases. A few

examples are: firms in distress, cyclical firms, firms with utilized assets, firms with

patents-portfolios, firms in process or restructuring, firms involved in acquisitions and

private firms. Such scenarios require an extension of the framework, and that is a

challenge (Damodaran, 2002).

Free cash flow to equity (FCFE) is one of the most widely used approaches in DCF

(Damodaran, 2002). The free cash flow excludes non-cash affecting items such as

depreciation, non-distributed profits in associations, capital gains/losses and provisions

(SFF, 2009). In this approach, the discount rate equals the required return by the

investors, which is determined by the CAPM. The model is relevant for investors as it

deals with the residual future cash flows. The residual cash flow is the amount of cash a

firm can pay to its shareholders after all expenditures, re-investments, tax and debt

payments etc. It estimates the potential dividends or share buybacks that will benefit the

shareholders (Damodaran, 2002).

3.4.1 Two-Stage FCFE Model

The model has been developed since the constant growth FCFE model is limited to

firms in stable growth. The two-stage model fits a wider range of firms and can estimate

the value of a firm that initially is growing much faster than the rest of the economy for

a limited time of period. However, the model cannot deal with companies in extremely

high-growth period. The firm is after the high-growth phase assumed to jump to stable

growth rate (Damodaron, 2002).

Formula 3

Free Cash Flow to Equity = Net Income – (Capital expenditure – Depreciation) - (Change in working capital) + (New debt issued - Debt repayments)


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The implication with the DCF approach is that it requires several assumptions. One

important input is the growth rate, which is discussed in the Growth section (3.2). Other

implications related to the model are e.g., whether a firm is cyclical or not, and whether

to include or exclude restructuring costs or capital gains in the FCFE. For instance, if a

firm is cyclical it is important to identify where in the cycle the firm is, and how this

might affect the potential revenues and earnings in the future. Therefore, the model has

to be customized and dealt with on a firm-specific basis (SFF, 2009).

3.5 Valuation Multiplies

Using multiples to valuate firms is a popular, simple, and the most commonly used way

of measuring financial and operational performance (Damodaran, 2002). These

multiples can be used independently or as complement to other valuation methods, such

as the discounted cash flow approach. Multiple valuation comparisons require investors

to assume that the comparable firms have equally proportional expectations about e.g.,

cash flows and risks as the company being valued. Hence can multiplies in theory

provide accurate estimations. However, in reality comparable firms can be very

different (Baker & Ruback, 1999).

3.5.1 P/E Multiple

The P/E ratio explains the relationship between the market value of a firm and its net

profit (SFF, 2009). This approach is the most widely used, but also misused of all

multiples. It has become an attractive method because of its simplicity and can be used

for making judgments on relative value to pricing initial public offerings (Damodaran,

2002). The ratio is used by both investors and analysts to determine if individual stocks

are reasonable priced (Shen, 2000). The P/E ratio can be measured as (Copeland, Koller

& Murrin, 2000):

Formula 4

FCFEt = Free cash flow to equity in period t Pn = Price at the end of the extraordinary growth period kn = Cost of equity in high growth (hg) and stable growth (st) periods gn =Growth rate after the terminal year forever


13

There are several different kinds of P/E ratios: (1) Current P/E ratio which uses current

earnings, (2) Trailing P/E ratio which uses the trailing earnings for the last twelve

months, and (3) Forward P/E ratio which uses the expected earnings (Damodaran,

2002). Ou and Penman (1989) argue that P/E-ratios comparisons have shown to be a

relatively good predictor to value companies.

There could be a number of reasons why comparable firms are assigned different P/E

ratios, but a low P/E ratio is normally more attractive to investors than a high P/E ratio.

Investors’ combined opinions concerning a firm´s potential prospects and its riskiness is

most likely represented by the P/E ratio. This means that investors often overprice more

favorable viewed firms, which are assigned a higher P/E ratio relative to less attractive

firms that receive lower P/E ratios (Goodman & Peavy, 1983; Graham, 1949).

However, Nicholson (1960) means that this “overreaction” will adjust over time and

those stocks with low P/E ratios tend to outperform the ones with high P/E ratios, but

also to beat the market in the long run. Investors should therefore include stocks with

low P/E ratio in their investment strategy in order to earn abnormal returns even if this

contradicts with the efficient market hypothesis.

3.5.2 EV/EBIDTA Multiple

EBITDA (Earnings Before Interests, Taxes, Depreciation, and Amortization) is a

measurement that is independent of capital structure where the non-cash flow expenses

of depreciation and amortization are added back. This makes EBITDA a more reliable

earnings measurement, but also more flexible to respond to changing market conditions.

The EBITDA multiple could therefore be used to estimate total enterprise value for

firms with different capital structure without creating any bias (Barker, 2001; Lie & Lie,

2002). EBITDA has during the last two decades become more frequently used among

financial analysts. This because of three major reasons: First, there are fewer firms

which have negative EBITDA than have negative earnings per share. Second, since

there are different depreciation methods used by different companies it can cause

differences in operating income or net income but will not affect EBITDA. Third,

EBITDA can easily be compared across firms with different financial leverage

(Damodaran, 2002).

A limitation of the measurement might include potential distortion due to subjectivity in

calculations of depreciation and amortization. Moreover, it is not clear why some

Formula 5

Formula 6


14

accruals should be revised and some left unchanged since all can be seen subjectively

(Barker, 2001).

Goedhart, Koller, and Wessels (2010) describe the EV/EBITDA multiple with the

following calculation:

Lie and Lie (2002) argue that the EBITDA multiple provides more accurate estimations

than other similar measures such as EBIT when the ratio is used at companies in the

same industry or at companies with similar transactions. The ratios are generally used

by a simple mean or median of the multiples within an industry of comparable firms to

estimate the enterprise values (Baker & Ruback, 1999). Similarly, Damodaran (2002)

means that the EBITDA multiple is useful within capital-intensive firms with heavy

infrastructure. It is also extra useful when depreciation methods differ across firms.

3.6 Net Asset Valuation

An alternative approach of valuating a firm is by calculating the net asset value (NAV).

One of the conditions is that the firm is assumed to continue to operate: “a going

concern” (PWC, 2008). First, the method gives a fairly stable measurement that can be

compared to the market price. Second, the accounting standards across firms are

reasonably consistent which make it possible for the book value to be compared with

similar firms in order to detect signs of under- or overvaluation. Third, firms that have a

negative earning can be evaluated by using price-book value methods (Damodaran,

2002).

The NAV is calculated by subtracting the total liabilities from the total assets from the

official balance sheet, which goes under the term shareholders equity. The real market

value of the net asset can be estimated in two ways; (1) Replacement value/Market

value and (2) Liquidation value (Isaksson, Martikainen and Nilsson, 2002; PWC, 2008).

Formula 8

Net Asset Value = Value of Total Assets – Value of Total liabilities

Formula 7


15

Isaksson et al. (2002) argue that the NAV is best suited for small and private firms,

mainly during acquisitions. However, the approach might also be relevant for listed

firms that have detailed information available about its assets. For firms that are

expected to have a high proportion of assets compared to its market value, such as real

estate-, investment- and shipping firms. Olbert (1992) have investigated the importance

of several valuation methods. Olbert concludes that the NAV approach is most suitable

for wood-, real estate- and investment firms. Further, his investigation also showed that

the NAV were less important in service- and retail firms e.g., HM and ERIC. In such

firms intangible assets (such as employees) are of more value and will be valued way

over the NAV.


16

4 Methodology

4.1 Research Approach: Inductive vs. Deductive

According to Jacobsen (2002) there are two different research approaches regarding

data collection. The first one is the deductive approach, which can be explained by

moving from theory to empiricism. This is done by determine assumptions in

beforehand, and thereafter collect empirical data to see if the results are consistent with

the assumptions. The other approach is called inductive and goes the other way around:

from empiricism towards theory. This is, empirical data are collected with barely any

assumptions, and the theories are then formed based on the results (Jacobsen, 2002).

Jacobsen (2002) further states that, while the deductive approach is target for critiques

for limiting relevant information, the inductive research approach is more open for new

information.

Since this thesis aims to investigate the accuracy of five common valuation methods,

theories and models are used to consolidate the empirical data. Therefore, a deductive

viewpoint is applied. However, no predetermined hypotheses are formulated and the

theories themselves are not to be tested, but the research will rather be conducted

through an “open mind” without any stated assumptions. It can therefore be argued that

some inductive elements are included, similar to the combination of deductive and

inductive approaches as Jacobsen (2002) argues.

4.2 Research Type: Descriptive, Explanatory, and Exploratory

Anderson (2004) states three different research types. The descriptive research is trying

to profile situations or events, and focuses on the questions what, when, where, and

who. The quantitative and qualitative data used in the descriptive research are then used

to draw relevant conclusions. The explanatory research is aiming for explaining a

situation or problem. The focus is on why and how of a relationship between different

variables. The last research type, exploratory research, is a qualitative approach trying

to obtain new insights and find out what is happening.

In this paper, the authors are working with a mix of both descriptive and exploratory

research. The major part will use descriptive research, which includes analyzing

quantitative data and perform statistical tests. From this analysis conclusions will be

drawn. However, the authors also apply an exploratory research in the sense that they

will gain new insights about the role of valuations methods versus financial analysts’

target prices.

4.3 Data Collection: Quantitative Primary and Secondary

Data

According to Jabobsen (2002), qualitative method deals with words, while quantitative

method deals with numbers. The quantitative approach is of interest for this research

since it provides information in form of numbers (in this case, numerical fundamentals).


17

Furthermore, Jacobsen (2002) argues that there are two different types of data: primary

data and secondary data. The primary data means that the researcher is using the

primary information sources where the data collection is tailored for a specific research

area. The secondary data, on the other hand, use existing information which will be

adjusted to a topic.

The data collection for the empirical study of this research is based upon quantitative

data received from primary and secondary sources. The data collected consist of

financial reports, analysts’ target prices, and closing prices for stocks, which provide

information in form of numbers and are used in statistical methods. The financial

reports are gathered from primary sources in form of each company’s website archives.

The analysts’ target prices are, however, gathered from a secondary source in form of

Avanza Bank’s database. Furthermore, one could argue that the closing prices for the

stocks gathered are secondary data since the data is collected through the program

Avanza Online Trader in order to reach the NASDAQ OMXS’ database. However, the

authors of this research argue that the closing prices are consolidated and collected for

the purpose of this thesis, and that the Avanza Online Trader is just a “door-opener” to

NASDAQ OMXS. Therefore, the authors states that the data is classified as primary

data.

4.4 Choice of Valuation Models

The choice of valuation models in this thesis in based on discussion with professional

analysts from Danske Bank, Nordea, Nordnet and KPMG. We have from those

conversations received recommendations regarding commonly used valuation models.

Based on this, we have selected a number of models to investigate further.

4.5 Sample Choice: Choice of Stocks

This research covers totally twelve stocks; all defined on either Large Cap or Mid Cap

at NASDAQ OMX Stockholm. The stocks are divided into four categories based on the

industry the companies are operating within: Telecom, Retail, Construction (and

Building), and Oil. In this paper, the stock names will from now on be used, i.e., the

abbreviations under “Stock name” in the table below. Table 1 shows the chosen stocks:


18

COMPANY STOCK NAME INDUSTRY MARKET

Ericsson ERIC Telecom Large Cap

Tele2 TEL2 Telecom Large Cap

TeliaSonera TLSN Telecom Large Cap

Hennes & Mauritz HM Retail Large Cap

KappAhl KAHL Retail Mid Cap

New Wave NEWA Retail Mid Cap

NCC NCC Construction Large Cap

PEAB PEAB Construction Large Cap

Skanska SKA Construction Large Cap

Alliance Oil Company AOIL Oil Large Cap

Lundin Petroleum LUPE Oil Large Cap

PA Resources PAR Oil Mid Cap Table 1. Company overview. The table above shows the companies and stocks that will be investigated

and evaluated through this paper. As can be seen, the chosen stocks belong to four different industries,

and both Large Cap and Mid Cap are represented.

The reason why these specific companies have been chosen is because their stocks are

under more surveillance compared smaller firms on e.g. Small Cap. This means that

more analysts are following these companies with target prices and recommendations

on a more regular basis, and thereby increasing the transparency.

4.6 Test Period

The test period for this paper covers the period 2008 – 2011. The time frame is divided

on a quarterly basis. This means that each stock is evaluated and analyzed twelve times

for each valuation model, except for the Gordon Growth model and Free Cash Flow to

Equity, which are investigated on a yearly basis.

4.7 Calculations

In order to test whether the five models provide accurate estimations or not, all models

must be measured in value per share. Both P/E ratio and EV/EBITDA are multiplies

that are used for comparisons and do not, originally, provide any values in form of stock

prices but rather need to be converted. Therefore, the average multiple will be

calculated for each industry and used as a benchmark. Both the models will therefore be

reversed in order to estimate the market value of the firm, where the multiple is given

from start.

The geometric average growth is calculated according to the formula in Section 3.2

Growth. For practical results, see Appendix I. For the DCF approach, the two-stage

FCFE model is calculated according to Formula 3 and Formula 4. The industry averages

for P/E and EV/EBITDA multiples are calculated according to Appendix B(6).

The paper deals with the expressions models and multiples (= ratios) regarding the

results of P/E and EV/EBTIDA. “Model” refers to the calculated target prices


19

(=calculated stock prices in SEK), while multiple refer the fundamental numbers that

are calculated in the first stage

All calculations are adjusted for dividends and splits.

4.8 Interpretation and Data Analysis

The method used for this research is divided into two parts: non-statistical and

statistical. The non-statistical part is what the authors in this thesis call, a table-analysis,

which test the empirical findings on two different intervals. This analyze model is

customized for the investigation that this research is aiming for. In addition, hit ratios

are used as a part of the non-statistical method. The statistical analyze method is, on the

other hand, a more traditional one, using SPSS (Statistical Package for the Social

Sciences) as a tool. The aim of using several methods is to provide a more complete

picture and ensure to cover the whole topic, and thereby answer the previously stated

research questions.

4.9 Non-Statistical Method

The non-statistical analysis is performed by using a customized model, which uses two

intervals, 10% and 15% respectively. This model will test whether the empirical

findings are “in line” with the financial analysts’ target prices or not. The intervals will

be based on average target prices because the authors of this thesis want to determine if

any models can provide accurate estimations relative to the analysts’ target prices.

When any of our calculations are within the interval, it will be considered as a “hit” (see

Section 4.10 Hit Ratios and Total Number of hits). The number of hits will be

summarized in a table to create an overview of the final result.

Many of the models require a number of assumptions, and the more variables the model

have, the more the final result can differ. Therefore, the 10% and 15% intervals were

chosen because it is more or less impossible to end up at exactly the same value even if

the initial approach is the same. Moreover, we also choose to have two intervals (10% is

the main interval) because we want to see whether there are any significant differences

in the result when the interval is increased with 5%.

At the same time, it is important to know that the analysts’ underlying calculations

regarding their target prices were not available for this thesis’ authors. The results will

therefore be treated cautiously.

4.10 Hit Ratios and Total number of hits

In order to provide a clearer picture of our analysis of empirical findings, the second

part of the analysis is working with hit ratios and total number of hits. The hit ratios are

basically the percentage of hits for the whole industry in relation to the maximum

possible hits. This is a method to describe to what degree the fundamental valuation

methods generate accurate results relative to the analysts’ target prices. The total

number of hits is simply the number of hits that each valuation method generates for


20

respectively company. Just as described in Section 4.9, both the hit ratios and the total

number of hits are presented separately for 10% and 15% intervals.

4.11 Statistical Method

For the statistical part, the statistic program SPSS is used in order to perform multiple

regressions, which will determine whether a valuation method will be accepted as

appropriate or not.

The ANOVA table indicates whether there exist a relationship between the chosen

variables and the analysts’ average target prices. The chosen alpha level is 0.10 for all

the statistical tests. If the significant value (p-value) is below 0.10 the null hypotheses

will be rejected. There is statistical evidence that there is a relationship between the

chosen variables and the analysts’ average target prices. Once we conclude that a

relationship exists, we need to conduct separate tests to determine which of the

parameters are different from zero.

From the coefficient table the parameters’ significant value can be found. The

significant value for each parameter tests against the alpha value of 0.10. If significant

value is less than alpha value (i.e., <0.10) the null hypothesis will be rejected. For the

parameter that rejects the null hypothesis there is statistical evidence that there is a

relationship between the parameter and the financial analysts’ average target prices.

4.12 Hypotheses

The hypotheses are testing if there exist a linear relationship between the selected

valuation methods, and the financial analysts’ average target prices. The hypotheses are

tested against a significant level of 90% (alpha level 0.10).

Hypotheses for the models: P/E, EV/EBITDA, and NAV:

H0: β1 β2 β3 = 0 (there is no linear relationship between P/E model, EV/EBITDA model, NAV model and the financial analysts’ target prices H1: Not all the βi are zero (there is a linear relationship between P/E model, EV/EBITDA model, NAV model, and the financial analysts’ target prices Hypotheses for the models: Gordon Growth and FCFE: H0: β1 β2 = 0 (there is no linear relationship between Gordon Growth model, FCFE model and the financial analysts’ target prices


21

H1: Not all the βi are zero there is a linear relationship between Gordon Growth model, FCFE model, and the financial analysts’ target prices The hypotheses are testing which valuation method that can be used to determine the financial analysts’ average target prices. These hypotheses are:

(1) H0: β1 = 0 (there is no relationship between P/E model and financial analysts’ target prices

H1: β1 ≠ 0 there is a relationship between P/E model and financial analysts’ target prices)

(2) H0: β2 = 0 (there is no relationship between EV/EBITDA model and financial analysts’ target prices

H : β2 ≠ 0 there is a relationship between EV EBITDA model and financial analysts’ target prices

(3) H0: β3 = 0 (there is no relationship between NAV model and financial analysts’ target prices

H1: β3 ≠ 0 there is a relationship between NAV model and financial analysts’ target prices)

From another coefficient table, Gordon Growth model and FCFE model can be interpreted:

(1) H0: β1 = 0(there is no relationship between Gordon Growth model and financial analysts’ target prices

H0: β1 = 0 (there is no relationship between Gordon Growth model and financial analysts’ target prices

(2) H0: β2 = 0 (there is no relationship between FCFE model and financial analysts’ target prices

H1: β2 ≠ 0 there is a relationship between FCFE model and financial analysts’ target prices


22

4.13 Empirical Assumptions

The foundation of this research is based on the valuation models presented in the frame

of references. However, in order to submit all empirical results, a number of

assumptions have been required. This is because of four major reasons:

insecurity of the right model - we do not know what valuation models the

analysts’ have used in their valuations

different versions of the same models - although it is possible that we have used

the same valuation models as the analysts, there are still different versions of

how the models can be applied

average target prices - averages do not provide the whole picture of an industry,

since companies within the same industry can differ heavily and therefore

averages can provide a misleading guidance

forecasted versus trailing - in this thesis, the majority of calculations are using

trailing numbers rather than forecasted

The following assumptions and adjustments have been made:

1. Stable growth rate – the stable growth rate that has been used in the calculations,

and for the analyses, is the Swedish economic growth rate that is estimated by

Riksbanken (2011) to be 2.50 %.

2. Risk-free rate – From Riksbanken the annual average risk-free rate has been

acquired for each year.

3. Cost of equity – cost of equity = risk free rate + company beta value * risk

premium.

4. Risk premium – Pinto, Henry, Robinson, and Stowe (2010) measured the

Swedish risk premium to 5.8% based on historical equity risk premium 1900-

2007. The risk premium is assumed to be the same in both high and stable

growth period.

5. Return on equity (ROE) – The ROE is set to 10 % in stable growth. According

to Damodaran (2002) ROE should be higher than the cost of capital but not too

high, normally lower than industry average. This ROE is used in the FCFE

calculations.

6. Beta – the beta value for each firm has been retrieved from Avanza Bank’s

database. However, when the firms is assumed to move into stable growth

periods, the beta is assumed to move towards 1, therefore a beta value of 1 has

been used in the calculations.

7. $US exchange rate – a few of the companies that have been analyzed have their

financial reports in $US. In order to convert the currency to SEK, the exchange

rates used are taken the same date as the financial reports were published. The

historical exchange rates were retrieved from the Swedish Riksbanken.

8. Industry averages – industry averages are used for P/E and EV/EBITDA in order

to provide a benchmark for respectively industry

9. Geometric average growth rate – the growth rate is not adjusted for organic

growth, i.e., growth caused by acquisitions


23

4.14 Reliability

Hussey and Hussey (1997) mean that the reliability is a measurement of trustworthiness

of a study and its conclusion. A high reliability means that, if someone would repeat the

study, the result should be the same. This research will be conducted by using

established valuation models, such as Gordon Growth, DCF, Multiples, and NAV. The

approaches are straightforward, but could be interpreted in different ways. Moreover,

some models require the practitioners to make a number of assumptions, to which some

extent, can affect the result. Hence small changes can have large impacts on the

estimations. The study could therefore be considered to have high reliability, even

though the final result can differ.

Furthermore, there are four different measuring scales: nominal, ordinal, interval, and

ratio scale (Lundahl & Skärvad, 1996). According to Arbnor and Bjerke (2008) the

differences in the scale is the sensitivity, precision, and reliability. The nominal scale

result is the least precision one, while ratio scale gives a more accurate result. It is

possible to shift the whole scale (scale formations) without making it less useful.

Therefore, our research is based on the interval scale that gives a high precision in the

measurement and a high reliability.

4.15 Validity

There are two main validity techniques: analytical approach and system approach. The

most important factor in the analytical approach is to question: What the study is

intended to measure? and Does the study reflect and measure the reality? The

systematic approach does not to the same extent focus to be consistent with existing

theories but rather whether the results reflect as many angles as possible through

interviews or secondary materials. One should however be careful to “accept” or state

that the result is “correct” as emotional involvement is the underlying determinant for

the decision (Arbnor and Bjerke, 2008). Bad practices, selections and inaccurate

measurements are frequent explanations for low validity (Lundal & Skärvad, 1996).

From the analytical approach, it is of high importance that our estimations really reflect

the underlying value of the firm. At the same time, the system approach deals with

comparisons between the results and the secondary material to determine whether the

results are correct or not. By using both approaches, the validity will increase.

This study is focused on the fundamental analysis only (historical fundamentals), and

one can argue that the validity is weak as macroeconomic outlook, market size and

market potential etc. is not included in the study. Therefore, it is hard to determine

whether the chosen variables capture the whole picture (Hussey and Hussey, 1997). On

the other hand, it is possible through the limitations to reduce the size of the study and

thus, increase the validity of the defined area of research.

Although one could argue that the sample size for this research is small, for each

industry investigated, the whole population is gathered. Because, focusing on the


24

NASDAQ OMXS’ Large Cap and Mid Cap, the three companies within each industry

represent, in most of the cases, the whole population. Furthermore, what increases the

validity of this thesis is the fact that the analysis is performed by using several different

analysis methods.

4.16 Critiques of Method

The analysts’ target prices are all taken from Avanza Bank’s database and it is possible

that a larger group of analysts’ target prices would have been taken into account if more

sources were used for the collection.

The ROE set on 10 % for a firm in stable growth in the calculations for FCFE could

have been assumed as 12 % or 14 % or any other number. The thoughts here was to set

a ROE that was higher than cost of capital but at the same time not to high. The

preferences in the calculations were to be rather pessimistic than optimistic. Moreover,

the choice of risk premium of 5.8% is also debatable. Even if the investigation behind is

robust; the current risk premium in the market for each calculated period could probably

have generated different results.

As already mentioned, one could argue that the sample size used is too small, even

though the sample size almost equal the population. Furthermore, critiques could be

leveled against the specific choice of companies investigated, especially regarding

NEWA in the retail industry, and ERIC in the telecom industry. For the retail industry,

with focus on clothing companies, NEWA could be seen as a mismatch since their

operations consist of more than just clothing. In this case, a company such as BORG

(Björn Borg) or FIX B (Fenix Outdoors), both on Mid Cap, could be seen as better

alternatives. However, the reason for chosen NEWA is because the existing information

(in form of analysts’ target prices) was more complete for the time period used in this

thesis. Similar situation is for ERIC where MIC SDB on Large Cap (Millicom

International Cellular SDB) could be a better alternative. This because, in fact, ERIC is

not counted as a pure telecom company, but rather belongs to the information

technology industry. However, we argue that ERIC does provide operations that are

similar and highly related to the telecom industry, and once again, the information

available for ERIC regarding target prices and recommendation were more complete

than for MIC SDB.


25

5 Empirical Tables

The empirical study of this research is based upon financial data (fundamentals) from

192 interim reports on a quarterly basis and 96 annual reports. From each company, 16

quarterly reports were gathered for the time period 2007 Q2 - 2011 Q1, which are the

foundation to the fundamental analysis presented in this paper. Furthermore, the annual

reports conducted for the time period 2002 - 2010 are used in order to construct industry

averages, calculate the average growth rates, and also in order to create comparable

numbers of our own valuation models and thereby enable an analysis.

Table 2 shows the result for the Gordon Growth. As can be seen, no numbers are

printed for the companies within the oil industry as a result of no dividends were paid

out for these companies.

Company 2008 2009 2010 2011

ERIC 35.60 28.95 33.15 34.58

TEL2 44.86 54.77 63.81 92.20

TLSN 56.96 28.17 37.29 42.26

HM 99.68 121.27 132.59 145.99

KAHL 156.65 70.41 20.72 49.94

NEWA 13.25 2.60 3.81 14.23

NCC 156.65 62.59 94.44 153.67

PEAB 32.04 35.21 41.43 39.96

SKA 74.76 82.15 87.01 88.36

AOIL - - - -

LUPE - - - -

PAR - - - -

Gordon Growth Model - Value of Equity

Table 2. Gordon Growth Model – Value of Equity. For extended practical calculations see Appendix

B(1).

Table 3 below shows the FCFE value per share. For all companies, the FCFE

estimations are changing heavily between the different years. In addition, the table

shows negative results for several of the companies, there among the oil company

LUPE which presents negative results 2007 – 2009.


26

Company 2007 2008 2009 2010

ERIC 23.57 37.52 66.11 23.88

TEL2 75.82 89.33 52.06 43.72

TLSN 75.82 89.33 52.06 43.72

HM 288.91 342.05 532.33 284.19

KAHL 82.67 54.27 148.69 62.59

NEWA 24.28 18.57 -136.03 2.82

NCC 168.82 225.41 -41.16 145.71

PEAB -90.91 61.43 43.22 87.21

SKA -26.54 128.81 21.13 110.62

AOIL 4.24 16.55 3163.83 1301.51

LUPE -16.51 -121.28 -882.64 441.14

PAR 1354.03 250.30 272.81 -97.38

Free Cash Flow to Equity - Value per share

Table 3. Free Cash Flow to Equity – Value per share. For extended practical calculations see Appendix

B(2).

Table 4 below shows the P/E multiple converted to target prices. As can be seen in the

table, the P/E multiple-to-target prices for the companies within the oil industry are

highly volatile. Furthermore, the calculated target prices for the oil companies differ

significant from the analysts’ target prices. However, the majority of the target prices

calculated for the companies within the telecom-, retail-, and construction industries are

on relatively stable levels.

Company Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

ERIC 14.27 16.59 24.40 34.89 39.41 35.38 27.57 14.03 12.20 16.10 26.96 42.09

TEL2 -26.47 -15.13 40.63 66.25 63.68 96.75 120.9 124.93 142.13 155.92 178.26 190.81

TLSN 49.17 50.02 48.31 51.61 51.61 52.58 53.31 51.24 51.97 54.05 56.61 57.71

HM 279.34 288.60 291.69 300.30 293.15 298.19 300.79 321.75 344.66 313.46 287.14 220.19

KAHL 145.93 88.89 96.04 94.74 89.28 80.60 69.88 68.25 82.23 82.23 82.88 87.10

NEWA 49.24 50.54 55.58 34.94 25.51 21.29 7.31 20.96 35.75 35.26 43.88 52.98

NCC 247.53 210.74 187.1 186.09 150.08 137.81 136.92 129.79 142.39 132.46 143.28 156.32

PEAB 52.80 63.56 67.57 70.47 62.44 57.76 59.54 51.07 48.84 45.6 45.38 44.82

SKA 116.41 117.86 116.85 82.84 71.03 73.81 72.12 96.89 120.20 100.46 96.89 107.71

AOIL 27.89 50.14 67.24 -5.06 -1.80 146.83 327.87 1040.25 1207.98 1156.61 1223.61 768.62

LUPE 317.69 337.21 378.03 155.30 155.30 63.01 -68.33 -748.08 -247.21 476.79 560.98 1567.03

PAR 693.06 651.35 486.30 563.50 381.58 276.87 255.57 7.99 -2.66 -87.85 -82.53 91.40

2008 2009 2010

Price/Earnings-to-Target Prices

Table 4. Price/Earnings-to-Target Prices. For extended practical calculations see Appendix B(3).

The EV/EBITDA multiple-to-target prices in Table 5 below are calculated in two steps

according to Appendix B(4). The target prices in Table 5 are changing on a relatively

normal level. Remarkable is the time period 2009 Q3 – 2010 Q3 for the oil company

LUPE that presents negative EV/EBITDA target prices. Also, 2008 Q1 for ERIC shows

a small number compared to the following quarters.


27


ERIC 16.14 69.06 52.60 62.60 49.97 68.09 43.46 38.34 34.88 41.50 74.90 60.39

TEL2 144.64 122.80 115.81 107.77 81.71 98.66 87.37 134.34 129.94 119.71 117.02 125.88

TLSN 55.39 52.89 45.60 51.39 48.91 53.82 59.57 59.81 49.90 59.09 56.15 63.42

HM 226.97 220.35 226.53 241.27 239.56 235.28 241.36 260.85 281.83 143.08 149.35 147.67

KAHL 73.11 66.22 63.87 65.66 55.64 54.36 52.38 52.23 56.51 58.30 58.08 57.99

NEWA 14.64 17.10 20.27 12.36 16.68 15.83 5.31 16.21 11.99 11.46 17.06 24.51

NCC 183.32 128.33 135.14 128.66 91.66 88.76 102.65 145.53 152.05 133.95 136.44 150.8

PEAB 51.34 54.55 50.43 15.62 21.20 46.43 34.32 41.80 47.51 34.55 33.93 31.47

SKA 126.49 111.80 112.23 94.52 83.19 85.49 91.05 117.98 113.12 103.39 100.37 109.52

AOIL 6.08 32.28 20.89 7.95 9.59 135.86 87.79 117.42 108.44 107.59 110.58 109.64

LUPE 40.49 41.53 61.54 28.91 21.31 12.87 -6.16 -85.72 -83.70 -73.74 -60.47 70.32

PAR 80.12 78.23 80.82 60.74 42.93 48.50 36.46 40.76 43.76 13.77 10.60 11.03

2008 2009 2010

EV/EBITDA-to-Target Prices

Table 5. EV/EBITDA – Stock Prices. For extended practical calculations see Appendix B(4).

Table 6 below, shows the NAV per share. For the majority of the companies the values

are on stable levels.


ERIC 8.42 40.91 42.47 44.33 45.78 44.57 43.40 43.85 37.73 43.75 43.28 45.64

TEL2 64.82 57.25 60.12 67.66 68.93 66.63 64.82 68.55 68.11 64.76 67.17 70.23

TLSN 23.89 25.04 27.64 31.50 32.90 31.35 30.44 31.73 31.57 30.28 29.30 29.54

HM 42.06 29.45 37.25 44.65 49,60 38.24 41.83 49.08 52.86 21.79 23.78 26.69

KAHL 13.62 3.53 5.26 7.09 9.75 4.18 4.21 5.05 7.69 7.21 8.53 9.90

NEWA 18.68 19.14 23.63 26.58 26.76 27.33 25.77 26.83 26.53 27.77 26.93 28.70

NCC 113.62 52.89 58.02 63.33 128.58 62.17 66.13 70.89 66.76 64.64 69.56 75.01

PEAB 21.24 20.42 21.31 22.48 23.54 28.33 24.20 25.71 25.42 23.29 24.38 25.97

SKA 49.58 44.7 46.35 46.18 46.91 44.98 46.32 49.26 49.91 43.64 43.81 50.57

AOIL 6.17 6.44 4.27 3.24 2.48 53.90 58.23 67.56 78.28 68.70 70.59 67.41

LUPE 34.3 67.51 40.26 40.93 42.31 41.02 38.88 27.69 29.83 19.37 19.65 20.21

PAR 23.48 24.97 26.83 32.58 31.96 16.00 29.07 27.65 26.07 9.53 8.36 8.24

201020092008

Net Asset Value per share

Table 6. Book Value – NAV per share. For extended practical calculations see Appendix B(5).

Below in Table 7 and Table 8, compilations of the ANOVA tables constructed in SPSS

are shown. These two tables show whether the hypotheses stated in Section 4.10 are

accepted or rejected. If the ANOVA table state reject the individual valuation methods

will be tested separately, which is summarized in Table 9, according to the previously

stated hypotheses. Both the ANOVA and coefficient are tested on a significant level of

90%. For full computations and presentations of p-values, see Appendix F and G.

ANOVA ERIC TEL2 TLSN HM KAHL NEWA

accept accept accept reject accept reject

reject reject reject reject accept accept

Telecom Industry Retail Industry

GORDON GROWTH, FCFE

P/E, EV/EBITDA, NAV


28

Table 7. ANOVA table for the telecom and retail industry. For calculations and p-values from SPSS see

Appendix F.

In Table 8 above, it is shown that the statistical investigation done in SPSS generates 13

“reject”, i.e., when in 13 cases some valuation method works for some

company/companies.

ANOVA NCC PEAB SKA AOIL LUPE PAR

accept accept accept accept accept reject

reject accept accept reject accept reject

Construction Industry Oil Industry

GORDON GROWTH, FCFE

P/E, EV/EBITDA, NAV

Table 8. ANOVA tables for the construction and oil industry. For calculations and p-values from SPSS

see Appendix F.

ERIC TEL2 TLSN HM NEWA NCC AOIL PAR

- - - reject reject - - -

- - - reject reject - reject reject

accept reject accept accept - reject accept accept

accept reject accept accept - reject reject accept

reject accept accept accept - accept - reject

EV/EBITDA

NAV

Coefficient

Gordon Growth

FCFE

P/E

Table 9. Coefficient table. For calculations and p-values from SPSS Appendix G.


29

6 Empirical Presentation and Analysis

6.1 Empirical Presentation

In Table 10, the number of hits for the 10% and 15% intervals are presented. For each

company, the number of hits is out of maximum 4 (TOTAL = 12 per industry) for

Gordon Growth and FCFE, while the maximum for P/E, EV/EBITDA, and NAV is 12

(TOTAL = 36 per industry). The actual intervals that are used can be seen in Appendix

H. The next table, Table 11, is an overview of the statistical findings from SPSS. It is a

simplified table of Table 7 – 9 presented in the previous section, and states which of the

investigated valuation models that are suitable according to the statistics in form of

multiple regression.

Company Gordon FCFE P/E EV/EBITDA NAV

AOIL SDB 0/0 0/0 0/0 2/2 0/0

LUPE 0/0 0/0 0/0 0/0 1/2

PAR 0/0 0/0 0/0 1/2 1/3

TOTAL 0/0 0/0 0/0 3/4 2/5

ERIC B 0/0 0/0 1/1 5/6 0/0

TEL2 B 1/1 0/0 6/6 6/8 0/0

TLSN 1/1 1/2 7/8 5/6 0/0

TOTAL 2/2 1/2 14/15 16/20 0/0

HM B 0/0 2/2 4/5 0/0 0/0

KAHL 0/0 1/1 0/1 2/4 0/0

NEWA B 0/0 0/0 3/4 1/1 1/1

TOTAL 0/0 3/3 7/10 3/5 1/1

NCC B 0/1 0/0 3/3 3/5 1/1

PEAB B 1/1 1/1 4/6 4/5 2/2

SKA B 0/1 0/0 3/4 1/5 0/0

TOTAL 1/3 1/1 10/13 8/15 3/3

Hit Table

Table 10. Hit Table. Shows the compilation of the empirical findings from the 10% and 15% intervals.

The first number represents the 10% interval, while the later number represents the 15% interval. See

Appendix H for the intervals used. For each company, the number of hits is out of maximum 4 (TOTAL =

12 per industry) for Gordon Growth and FCFE, while the maximum for P/E, EV/EBITDA, and NAV is 12

(TOTAL = 36 per industry).


30

Company Gordon FCFE P/E EV/EBITDA NAV

AOIL SDB - - - OK OK

LUPE - - - - -

PAR - OK - - -

ERIC B - - - - OK

TEL2 B - - OK OK -

TLSN - - - - -

HM B OK OK - - -

KAHL - - - - -

NEWA B OK OK - - -

NCC B - - OK OK -

PEAB B - - - - -

SKA B - - - - -

Statistical Overview

Table 11. Statistical overview. Shows a compilation of the empirical findings from the statistical

investigation in SPSS. OK = the model is functioning according to the stated hypothesis. Dash (-) = the

model is not functioning according to the stated hypothesis.

Figure 1 – 4 will clarify the hit table in graphical form. The figures show the total units

of hits for the intervals 10% and 15% for the investigated firms. The data alone is

however not sufficient to determine whether some of the valuation models are suitable

for any of the firms.

Figure 1a and 1b show that EV/EBITDA is the only model that generates hits for AOIL.

For LUPE, NAV provides hits in both intervals. Seen to both intervals, NAV provides

best estimations for PAR.

Figure 1a and 1b. Units of total hits. 10% and 15% respectively for the oil industry. For each company,

the number of hits is out of maximum 4 for Gordon Growth and FCFE, while the maximum for P/E,

EV/EBITDA, and NAV are 12.

0

0,5

1

1,5

2

2,5

Go

rdo

n

FCFE P/E

EV/E

BIT

DA

NA

V

AOIL

LUPE

PAR0

0,5

1

1,5

2

2,5

3

3,5

Go

rdo

n

FCFE P/E

EV/E

BIT

DA

NA

V

AOIL

LUPE

PAR


31

Figure 2a and Figure 2b show that EV/EBITDA is the model that probably suits ERIC

and TEL2 best. On the other hand, P/E provides best result for TLSN.

Figure 2a and 2b. Units of total hits. 10% and 15% respectively for the Telecom industry. For each

company, the number of hits is out of maximum 4 for Gordon Growth and FCFE, while the maximum for

P/E, EV/EBITDA, and NAV is 12.

Figure 3a and Figure 3b show that the FCFE provides the best estimation for HM (even

though P/E has more hits but that sample is larger). For KAHL, EV/EBITDA is

probably the best suited model. The P/E model generates the most hits for NEWA.

Figure 3a and 3b. Units of total hits. 10% and 15% respectively for the Retail industry. For each



In the constructing industry, EV/EBITDA is according to Figure 4a and 4b the best

model for NCC. For PEAB and SKA, P/E provides the best estimations.

0

1

2

3

4

5

6

7

8

Go

rdo

n

FCFE P/E

EV/E

BIT

DA

NA

VERIC

TEL2

TLSN0

1

2

3

4

5

6

7

8

9

Go

rdo

n

FCFE P/E

EV/E

BIT

DA

NA

V

ERIC

TEL2

TLSN

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

Go

rdo

n

FCFE P/E

EV/E

BIT

DA

NA

V

HM

KAHL

NEWA0

1

2

3

4

5

6

Go

rdo

n

FCFE P/E

EV/E

BIT

DA

NA

V

HM

KAHL

NEWA


32

Figure 4a and 4b. Units of total hits. 10% and 15% respectively for the Construction industry. For each



6.2 Firm-specific Analysis

6.2.1 Gordon Growth Model

GORDON GROWTH ERIC TEL2 TLSN HM KAHLNEWANCC PEAB SKA AOIL LUPE PAR

Interval 10%/15% 0/0 1/1 1/1 0/0 0/0 0/0 0/1 1/1 0/1 0/0 0/0 0/0

Statistics - - - OK - OK - - - - - -

Table 12. Overview Gordon Growth. The table shows the number of hits for the 10% and 15% intervals,

and whether the valuation model is functioning according to the stated hypothesis.

Telecom

ERIC and TLSN have both had an annual average growth rate between 4.18% - 6.18%

and 7.56% - 10.13%, respectively. The number of hits is low as expected (0/0 and 1/1),

since the growth rate exceeds the stable growth rate for both firms. The reason for this is

that the model only works for firms in stable growth (Gordon, 1962). TEL2 has had an

annual average growth of 1.04% - 4.14%, which is within the defined range of stable

growth. We are therefore surprised that TEL2 does not generate any hits within the

interval at all. The statistical evidences do not help to explain the result of TEL2 either.

Statistically, we can confirm that the model is not a suitable valuation model for any of

the companies within the industry (H0 is accepted in the ANOVA table).

Retail

The case for HM and NEWA is similar to the one for ERIC and TLSN. Both firms have

had an annual average growth rate exceeding the stable growth (11.45% - 12.11% and

12.22% - 16.08%). This resulted in no hits for either of the firms, as expected. When it

comes to KAHL, the average annual growth rate is close to or slightly above the stable

growth (2.95% - 3.54%). Seen to the growth rate alone, we anticipated more accurate

estimations. However, KAHL is a relatively small firm (Mid Cap) that time to time has

been struggling with the profitability. We believe that this is a possible explanation to

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

Go

rdo

n

FCFE P/E

EV/E

BIT

DA

NA

V

NCC

PEAB

SKA0

1

2

3

4

5

6

7

Go

rdo

n

FCFE P/E

EV/E

BIT

DA

NA

V

NCC

PEAB

SKA


33

the poor result, because both Gordon (1962) and Barker (2001) argue that the model is

less reliable when valuating companies in distress. However, the statistical results show

the opposite. Statistically, the model should be suitable for HM and NEWA but not for

KAHL (Coefficient for H0 is rejected for HM and NEWA while accepted for KAHL).

This outcome is surprising, since the statistical result is against (Gordon, 1962)

assumption that the firm being valued must be in stable growth, as well as our results.

Construction

PEAB’s annual average growth rate exceeds the stable growth rate (8.49% - 9.48%).

The number of hits is therefore low, as expected (1/1). Instead of high growth rates,

SKA has been struggling with negative growth (-2.16% - -0.22%) and its number of hits

are very low (0/1). In Gordon’s (1962) model, the dividend is assumed to grow at the

same rate into infinite. Since the growth rate for SKA is negative, this means that the

dividends are expected to decrease forever. We see this as an unrealistic situation.

Compared to SKA, the growth rate for NCC has been relatively close to stable growth

(1.13% - 5.27%). Nevertheless, the estimations do not generate any hits at all for NCC,

to which we are unable to find any potential explanation for. The statistical evidences

show that the model is not suitable for any of these firms in the construction industry

(H0 is accepted in the ANOVA table.)

Oil

The firms in the oil industry did not generate any estimations at all. The explanation is

that none of the firms pays dividends. The results are as expected; as Gordon (1962)

states that the model cannot be used for companies that pay out zero dividends at the

end of the first year.

6.2.2 FCFE

FCFE ERIC TEL2 TLSN HM KAHLNEWANCC PEAB SKA AOIL LUPE PAR

Interval 10%/15% 0/0 0/0 1/2 2/2 1/1 0/0 0/0 1/1 0/0 0/0 0/0 0/0

Statistics - - - OK - OK - - - - - OK

Table 13. Overview FCFE. The table shows the number of hits for the 10% and 15% intervals, and

whether the valuation model is functioning according to the stated hypothesis.

Telecom

Unlike, the Gordon Growth model, the FCFE model is a more complex model that can

deal with high growth firms (Damodaran, 2002). As a result, we expect that the high-

growth firms will generate better estimations with this model. As TLSN has been

growing faster (7.56% - 10.13%) than stable growth, the model is expected to generate

relatively accurate estimations. TLSN is the only firm generating hits (1/2). In the

meanwhile, we find it strange that ERIC does not generate a single hit although its

growth rate has been higher than the stable growth (4.18% - 6.18%) as well. On the

other hand, TEL2 has been growing with rates relatively close to stable growth (1.04% -

4.14%). Since the growth is not high enough (even if it was on the verge in 2007), we

have reasons to believe that the model is not suitable for TEL2. This is also reflected in

the number of hits. Moreover, there are no statistical evidences that the model is

suitable for any of the firms in the telecom industry (H0 is accepted in the ANOVA

table).


34

Retail

HM is the firm that provides best estimations relative to the analysts’ target prices (2/2),

followed by KAHL (1/1). NEWA does not provide any hits at all. Both HM and NEWA

have remarkable higher growth rates than stable growth (11.45% - 12.11% and 12.22%

- 16.08%) and should therefore generate better estimations. KAHL is, based on its

growth history, assumed to be in stable growth rate (2.95% - 3.54%). Therefore, there is

no surprise that the estimations for KAHL generate zero hits. The statistical evidences

further add strength to the above discussion. The model is accepted for both HM and

NEWA. As discussed, both firms have been growing at high rates. However, KAHL

cannot be statistically accepted. (H0 Coefficient for HM and NEWA is rejected while H0

for KAHL is accepted in the ANOVA table).

Construction

When interpreting the data, PEAB is the only firm that generates hits (1/1). The growth

rates reveal that PEAB also is the only firm in high growth (8.49% - 9.48%). NCC is in

stable growth (1.13% - 5.27%), while SKA struggles with negative growth rates (-

2.16% - -0.22%). Neither of NCC or SKA are therefore not expected to generate large

number of hits. Statistically, the model cannot be accepted as a suitable valuation model

for the construction companies either (H0 is accepted in the ANOVA table). This adds

strength to the above discussion. PEAB should theoretically generate more hits which is

the case. However, one hit cannot be considered as good enough to say the model is

suitable.

Oil

The estimations for the oil industry show zero hits for each company for the both

intervals. All firms in this industry have extremely high growth rates (between 45.28% -

118.48%). We therefore believe that these firms might require an extension of the

framework. This as Damodaran (2002) argues that the model will not work for firms in

extremely high growth. Statistically, the model is only suitable for PAR, (as H0

Coefficient for PAR is rejected while H0 for AOIL and LUPE is accepted in the

ANOVA table).

6.2.3 P/E to Target Prices

P/E ERIC TEL2 TLSN HM KAHLNEWANCC PEAB SKA AOIL LUPE PAR

Interval 10%/15% 1/1 6/6 7/8 4/5 0/1 3/4 3/3 4/6 3/4 0/0 0/0 0/0

Statistics - OK - - - - OK - - - - -

Table 14. Overview P/E-to-Target Prices. The table shows the number of hits for the 10% and 15%

intervals, and whether the valuation model is functioning according to the stated hypothesis.

Telecom

TLSN and TEL2 generate relatively accurate estimations in relation to the analysts’

target prices (7/8 and 6/6), while the number of hits for ERIC is very low (1/1). We

have reasons to believe that a possible explanation is that ERIC historically has been

assigned a higher P/E multiple. Goodman and Peavy (1983) and Graham (1949) mean

that favorable stocks are assigned higher P/E multiples. The implication of this is that, a


35

firm’s P/E multiple can deviate largely from the industry average. This is exactly what

happens in this case. The average P/E multiple in the telecom industry is 12.4, while

ERIC’s average for the same period is 22.98. This will impact the estimations for sure.

Because, when the multiple doubles, the value per share also doubles. Both TLSN and

TEL2 have a P/E multiple closer to the industry average multiple, which is also

reflected to the number of hits. Moreover, the statistics can only confirm the suitability

of the model for TEL2 (H0 Coefficient is rejected while H0 Coefficient for ERIC and

TLSN is accepted).

Retail

HM generates the most accurate estimations within the retail industry (4/5). Similarly to

ERIC, both HM and KAHL’s estimations are negatively affected by the lower or higher

industry average. The industry average for the retail industry is 16.25, while HM and

KAHL’s averages are 22 and 8.13, respectively. Unlike HM, KAHL generates very low

number of hits (0/1). It is possible to see that KAHL’s estimations are deviating largely

from the analysts’ target prices. For NEWA, the average P/E multiple (18.62) is close to

the industry average and therefore they are not negatively affected (3/4). However, the

statistical results cannot confirm the suitability of the model for any of the firms in the

industry (H0 in the ANOVA table is accepted).

Construction

In the construction industry, NCC, PEAB, and SKA generate similar amount of hits to

each other (3/3, 4/6, 3/4). Unlike other industries, the spread between the average P/E

multiple for the construction industry (11.15) and the individual firm’s average P/E

multiples are much smaller (9.14 – 13.16). Noticeable is that the statistical evidences

show that the model is suitable for NCC (H0 for Coefficient is rejected) but not for

PEAB or SKA (H0 for ANOVA is accepted). This even though PEAB and SKA

generate a more accurate results than NCC in the non-statistical investigation.

Oil

None of firms in the oil industry generated hits within any of the two intervals. Of what

we can see from the firms’ financial statements, revenues and earnings are fluctuating

heavily from quarter to quarter. This implies that the P/E target prices also become very

unstable and unreliable. The spread of the firms’ average P/E multiples vary between

15.72 – 190.38, while the industry average is 88.74, which are relatively large numbers.

Using the industrial average multiple in this case makes the estimation ends up far away

from the analysts’ target prices. The statistical evidences confirm the non-statistical

investigation; that the model is not suitable for any of the firms in the oil industry (H0

for LUPE is accepted in the ANOVA table while H0 Coefficient for AOIL and PARE

are accepted).

6.2.4 EV/EBITDA to Target Prices

EV/EBITDA ERIC TEL2 TLSN HM KAHLNEWANCC PEAB SKA AOIL LUPE PAR

Interval 10%/15% 5/6 6/8 5/6 0/0 2/4 1/1 3/5 4/5 1/5 2/2 0/0 1/2

Statistics - OK - - - - OK - - OK - -

Table 15. Overview EV/EBITDA-to-Target Prices. The table shows the number of hits for the 10% and

15% intervals, and whether the valuation model is functioning according to the stated hypothesis.


36

Telecom

The firm that has the largest amount of hits is TEL2 (6/8), closely followed by ERIC

and TLSN with same number of hits (5/6). More interesting is that ERIC generates

significantly more hits when using the EV/EBITDA model compared to when the P/E

model was used. Multiples based on pure earnings can be misleading because it is

affected by depreciation and amortization. According to Barker (2001) and Lie and Lie

(2002), EBITDA is a more reliable earnings measure since it is independent of capital

structure, depreciation and amortization. We can therefore say that EV/EBITDA is

better suitable for ERIC. Further, in this model ERIC is not assigned a higher

EV/EBITDA multiple compared to its competitors like in the P/E model. The statistical

evidences can only confirm EV/EBITDA as a suitable model for TEL2 (H0 Coefficient

for TEL2 is rejected while H0 Coefficient ERIC and TLSN is accepted) even though the

number of hits is close to 50% for both ERIC and TLSN.

Retail

HM and NEWA generate poor estimations (0/0 and 1/1), while KAHL is better (4/5).

The retail industry is not known for heavy infrastructure or the capital intensity as other

industries. The reason why is because, it is common in the retail industry to rent instead

of owning the store spaces. As expected, the number of hits is low for HM and NEWA.

However, KAHL generates better estimations and therefore surprises us. The suitability

is however confirmed by the statistical investigation. The EV/EBITDA model is not

useful for any of the firms within this industry (H0 for KAHL and NEWA are accepted

in the ANOVA table while H0 for Coefficient HM is accepted).

Construction

The EV/EBITDA model shows a similar result within all three firms (NCC 3/5, PEAB

4/5, and SKA 1/5). The EV/EBITDA model neither generates better or worse

estimations compared to the P/E model. Therefore, it is unclear whether the

construction industry is more capital intensive or not. Otherwise, we believe that the

EV/EBITDA should have generated more hits compared to the P/E model. The

statistical evidences show that the EV/EBITDA model is suitable for NCC. (H0

Coefficient for NCC is rejected while H0 for PEAB and SKA are accepted in the

ANOVA table). Noticeable is that PEAB provides better estimations than NCC in the

non-statistical investigation, but is still not suitable statistically.

Oil

For the firms in the oil industry, AOIL and PAR generate a few hits (2/2 and 1/1) unlike

LUPE, with zero hits (0/0). Based on the result, we have reasons to believe that the oil

industry is more capital intensive and/or with more infrastructure. Even though the

number of hits is low, the EV/EBITDA model generates a better estimation than the P/E

model. Statistically, AOIL is the only firm that can be valued by the EV/EBITDA

according to the statistics (H0 Coefficient for AOIL is rejected, H0 Coefficient for PAR

accepted while H0 for LUPE accepted in ANOVA table).


37

6.2.5 Net Asset Valuation

NAV ERIC TEL2 TLSN HM KAHLNEWANCC PEAB SKA AOIL LUPE PAR

Interval 10%/15% 0/0 0/0 0/0 0/0 0/0 1/1 1/1 2/2 0/0 0/0 1/2 1/3

Statistics OK - - - - - - - - OK - -

Table 16. Overview NAV. The table shows the number of hits for the 10% and 15% intervals, and

whether the valuation model is functioning according to the stated hypothesis.

Telecom

The NAV did not provide any hits in any of the two intervals in the telecom industry.

However, there are statistical evidences that prove that the model is only suitable for

ERIC (H0 Coefficient for ERIC is rejected while H0 Coefficient for TEL2 and TLSN are

accepted). Therefore, the results are in line with our expectations. This because we did

not anticipate the industry, in particular, to hold any substantially amounts of tangible

net assets. According to Olbert (1992) it is more likely that service firms do not have

any large amount of net assets, which is necessary to generate good estimations. Hence,

such firms relies more on intangible assets, such as employees.

Retail

In the retail industry, NEWA is the only firm that generates hits (1/1). The result is in

line with our expectation, as the retail industry is more service-oriented, and therefore

does not hold large capital. For instance, it is more common to lease store spaces instead

of owning it. In addition, intangible assets in form of employees are more important for

the operations than assets (Olbert, 1992). For NEWA, we can see that NAV per share

over the investigated period has been relatively stable. But in the first quarter of 2009,

the analysts started to lower their target prices. As a result, our estimations happen to

fall inside the interval. We have therefore no reasons to believe that the analysts use

NAV for the valuation of NEWA. The lack of hits within the retail industry can be

strengthen by the statistic results, which show that the model is not suitable for any of

the firms (H0 for ANOVA KAHL and NEWA are accepted while H0 for Coefficient HM

is rejected).

Construction

For the construction companies, SKA generates no hits. NCC has only one hit (1/1) and

that happens to be in the period Q1-2008. The NAV per share has been relatively stable

over the investigated period (slowly rising from ~53SEK – 75SEK). However, during

Q1-2008 and Q1-2009 the NAV per share has peaked to the double only to then fall

back. And the only hit for NCC happens to be during those peaks. PEAB generates the

largest number of hits (2/2). Even here, the NAV per share has been very stable over the

investigated period (~21SEK-26SEK). However, in Q3-2008 the analysts started to cut

the target prices from 60SEK to 20SEK. In Q4-2008, the analysts increased the target

price to 25SEK. During these two quarters, our estimations are in line with the analysts’

target prices. But the evidences are not strong enough to determine whether the analysts

use NAV as their valuation model for PEAB, or not. This is a similar as discussed for

NEWA in the retail industry. Further, the statistical evidences show that NAV is not a

suitable valuation model for any of the firms in the construction industry, and hence

strengthen the non-statistical results above. (H0 for PEAB and SKA are accepted in the

ANOVA table while H0 for Coefficient NCC is accepted). Based on the result we have


38

reasons to believe that the construction industry do not holds substantial amounts of

tangible net assets.

Oil

The results in the oil industry show that AOIL generates zero hits, while LUPE and

PAR provide more hits (1/2 and 1/3). The most of the hits for LUPE and PAR are in the

15% interval, this indicate that some estimations are close to the analysts’ average target

prices. Remarkable is that, in eight out of twelve quarters, PAR has a higher NAV per

share compared to the analysts’ target prices. This means that the firm has more net

assets than the market value of the firm. Fama (1970) argues that in the weak form of

market efficiency, investors can study fundamentals to determine if a stock is under- or

overvalued. Therefore, theoretically it is possible to say that PAR is undervalued. The

statistical evidence does not, however, agree with the non-statistical investigation.

Statistically, NAV should only be suitable for AOIL which is opposite to the non-

statistical results (H0 for LUPE is accepted in the ANOVA table, H0 Coefficient for

PARE is accepted while H0 Coefficient for AOIL is rejected).

6.3 Industrial Analysis

TELECOM

Based on the result from the 10% interval in Figure 5, EV/EBITDA is the single most

accurate valuation method with a hit ratio of 44%, closely followed by the P/E model

with a hit ratio of 39%. Even when the interval increases to 15%, EV/EBITDA and P/E

still perfoms best. As both multiple models are superior, the findings can therefore be

confirmed with previous research done by Liu et al. (2002). They found that multiples

based on historical earnings were the second best alternative to value firms. Forward

earnings multiples qualified as the best valuation. Neither Gordon Growth model, FCFE

model or NAV model generates satisfying results. Overall, it is shown that the firms in

the telecom industry are in high growth periods. Therefore, we can confirm that the

Gordon Growth model is not suitable (hit ratio of 16.67%). Instead, the FCFE model

should, theoretically, provide better estimations. However, as the result vary from firm

to firm, it is hard to say whether the model is appropirate for the industry or not, but

also since we cannot accept the model statistically. Moreover, as none of the firms have

any substantially amounts of tagible net assets in relation to the market, we can

therefore say that NAV is not a suitable valution model for telecom firms (hit ratio of

0%).

RETAIL

Figure 5 shows that the two models with the highest hit ratios are FCFE (25%) and P/E

(19%). If the interval increases to 15%, FCFE’s hit ratio remains the same while the hit

ratio for P/E increases to 28%. Goedhart et al. (2005) argue that the DCF model should

provide accurate estimations. Similarly, Kapplan and Ruback (1995) found that

estimations from DCF were deviating with 10% from the market price. Both HM and

NEWA are in high growth periods. The non-statistical results are mixed. But statistially,

we can verify Goedhart et al. (2005) and Kapplan and Ruback’s (1995) theories that the

DCF model is a better estimator than other models in the retail industry. In addition, the

results are partially in line with the research by Fernández (2001). Fernández argues that

P/E and EV/EBITDA are useful in the clothing industry, especially in combination with


39

other models. On the other hand, the retail industry is not capital intensive, which we

believe is the reason why the EV/EBITDA model does not provide any sufficient results

(8.33%). Further, the Gordon Growth model is, in general, not suitable for the retail

industry as the growth rates for the firms exceed the stable growth (hit ratio of 0%). The

hit ratio for NAV is 2.77% which is, as anticipated, very low. Similar as in the telecom

sector, firms in the retail industry do not hold any large amounts of net tangible assets in

relation to the market values, and therefore the appropriateness of NAV is poor.

CONSTRUCTION

In the construction industry, the valuation models with the highest hit ratios are the

same as in the telecom industry. Figure 5 shows that the P/E model has the highest hit

ratio (28%), followed by the EV/EBITDA model (22%). In the 15% interval,

EV/EBITDA generates a better result than P/E (42% vs. 36%). Our result is similar to

Fernández’s (2007) theory that the most useful valuation models for the construction

industry are P/E and EV/EBITDA. When it comes to the discount models, Gordon

Growth and FCFE, the results are poor (8.33% for both). We believe that one of the

reasons is related to the large variety of growth rates for the individual firms. SKA has

negative growth rate, PEAB has high growth rate, and NCC has stable growth rate.

Therefore, the result does not reflect the construction industry as a whole and the

suitability of the models are uncertain. Based on the results from NAV, we cannot see

that any of the firms in the industry have any high proportions of net assets in relation to

their market value. This is also confirmed by the statistical investigation.

OIL

As can be seen in Figure 5, EV/EBITDA and NAV have low hit ratios (9% and 6%)

while remaining models do not provide any reliable results as the hit ratios are zero. In

the 15% interval, NAV (14%) and EV/EBITDA (11%) are still the only models that

generate any hits. However, it is not surprising that NAV and EV/EBITDA performs

best. This as Damodaran (2002) states that it is better to use EBITDA for capital-

intensive firms. Based on this we have reason to believe that the oil firms are more

capital intense, since the EV/EBITDA model provides better estimations relative to the

P/E model which is based on pure earnings (0%). Moreover, the research by Isaksson et

al. (2002) further strengthens the discussion. Isaksson et al. (2002) argue that NAV is

relevant for firms that are expected to have a high proportion of tangible net assets of its

market value. Based on the result, we can see that the investigated firms within the oil

industry hold a high proportion of tangible net assets. As the growth rates are extremely

high for the investigated firms, it is possible to exclude the Gordon Growth model and

the FCFE model as suitable valuation methods for the oil industry. In addition, none of

the oil firms pays dividends, which is necessary to apply the Gordon Growth model

(Gordon, 1962).


40

Figure 5. Total hit ratios: 10% and 15% intervals. This figure measures the percentage of how accurate

estimations each valuation model provides for respective industry.

6.4 Final Analysis

The growth rate is one of the more important variables for Gordon Growth model and

FCFE model. In both cases, historical growth has been used instead of forecasted

growth. We believe that this can be a reasonable explanation to the insignificant

estimations that the models have provided. This as both Cragg and Malkie (1968) and

Little (1960) argue that the correlation between historical and future growth is close to

zero. The use of historical average growth can also overemphasize the calculations of

last year’s growth rate and thereby create a misleading picture. This as the both model

are sensitive to small changes in the different variables Barker (2001) argues. Therefore,

factors such as market size and potential, product development, and the economic

outlook should have been included in order to enhance the estimations.

The Gordon Growth model has shown to be insufficient as a valuation method for most

of the investigated firms. We believe that one of the reasons is that many of the firms

have had high- or negative growth rates instead of stable growth. This creates

implications since the chosen growth rate is assumed to go on forever (Damodaran,

2002). In addition, this might be the reason why Fuller and Hsia (1984) and Gordon

(1962) argue that the model is best suited for stable firms. We can conclude, at an early

stage, that for some of the firms (e.g. TLSN, HM, and PEAB) it is possible to exclude

the Gordon Growth model as a suitable valuation approach. The reason is because many

of the firms are in high growth periods. However, for some firms it is unclear whether

they are in stable growth or not. This since the growth rate is allowed to exceed the

stable growth with a maximum of 1-2% for the model to work (Damodaran, 2002). In

these cases, it has been hard to indentify whether this is a reasonable explanation why

the model does not generate good estimations.

16,67% 16,67% 8,33%25,00%

8,33% 16,67%

25,00% 25,00% 8,33%

8,33%

38,89%41,67%

19,44% 27,78%27,78%

36,11%

44,44%

55,56%

8,33%13,89% 22,22%

41,67%

8,33% 11,11%

2,77%

2,77% 8,33%

8,33%

5,56%13,89%

Hit Ratios

Gordon Growth FCFE P/E EV/EBITDA NAV


41

Baker and Ruback (1999) mean that the DCF model should generate more accurate

estimations compared to other valuation models. Similarly, SFF (2009) means that

customization is one of the strengths in the model and should therefore lead to more

accurate estimations. This since, the variables are adjusted for each specific firm that is

being valued. Similar to the Gordon Growth model, FCFE has shown to be insufficient

as a valuation method for the majority of the firms since the number of hits is low, even

though there are some exceptions. We can see that there is a tendency that the firms in

high-growth period generate more hits than the firms in, or close to, stable growth. We

can therefore confirm Damodaran’s (2002) theory that the FCFE model is more suitable

for firms in high-growth periods rather than for firms in low or stable growth. However,

as the overall results of the model are not convincing, we believe that forecasted growth

rates should have generated better estimations than historical growth rate.

For the two models consisting of multiples, P/E and EV/EBITDA, the calculations have

been based upon industry averages. Using industry averages as guidance are argued to

provide insufficient estimations (Goedhart et al. 2005), especially for firms that have

significant higher or lower multiples relative to the industry averages. Even though this

method could be questioned, industry averages are commonly used in the creation of

industry benchmarks (Baker and Ruback, 1999). Based on our result, we can see that

the spread between the firms’ average EV/EBITDA multiple and the industry average

multiple is small. This should therefore not affect the estimations significantly. The case

is similar when using the P/E model. However, unlike the EV/EBITDA model, there are

some firms (e.g., ERIC, HM and KAHL) that deviate large from the industry multiple,

which will affect the accuracy of these estimations significantly. We can, out of this,

argue that EV/EBIDTA is more reliable as there were smaller deviations between the

firms’ average and the industry average.

Another important implication for both models is whether to use trailing or forecasting

earnings. Lie and Lie (2002) mean that forecasted earnings will provide more accurate

estimations than using trailing earnings. Similarly, Liu et al. (2002) argue that

forecasted earnings will increase the accuracy even though trailing earnings will

generate relatively good estimations as well. Our results show that trailing earnings are

relatively good, especially for the telecom industry. However it reasonable to believe

that a use of forecasted earnings can probably enhance the estimations over the whole

line.

More specifically on the EV/EBITDA results, we can confirm Damodaran’s (2002)

theory which states that EBITDA earnings are more useful within capital intensive

firms or those with a heavy infrastructure. Our findings indicate that both the telecom-

and the oil industry are more capital intensive compared to the construction-, and retail

industry. This as several of the firms in the oil industry have poor performances using

the P/E model which is based on pure earnings, but generate better results when using

the EV/EBITDA model.

Furthermore, the overall results for NAV show that the model is not suitable for the

invested firms or industries. Nevertheless the result is not surprising since, Isaksson et

al. (2002) and Olbert (1992) argue that the NAV is useful for firms that generally have a

high proportion of tangible net assets, such as real estate- and investment firms. As none

of the firms are operating within any of those industries, we can easily confirm their


42

theory. However, we can see a tendency that the firms operating within the oil- and

construction industries hold substantially more assets than the firms in the telecom- and

retail industries. The results also confirm Olbert’s (1992) theory, that the NAV is less

important for service firms. For instance, ERIC and HM rely more in intangible assets.

When summarizing the results, we can actually say that P/E- and EV/EBITDA models

generate best estimations relatively to the analysts’ target prices. We have therefore

reasons to believe that both models could be used by analysts’ when valuating firms,

with the oil sector as an exception. The results for FCFE are disappointing, even though

the model generates the highest hit ratio in the retail industry. For the Gordon Growth

model and NAV, the results are also weak and are therefore not suitable as valuation

approaches. However, as the result for most of our models are weak (except for P/E and

EV/EBITDA), we have reasons to believe that the analysts are using several valuation

models in combinations. This argument is strengthen by Fernández (2001), who means

that valuation models cause dispersion and should therefore be used in combination

with other valuation models. Also, Kaplan and Ruback (1995) argue that the FCFE

model is the most accurate model, but it should be used in combination with e.g.,

multiples.


43

7 Conclusion

The intention with this paper was to investigate the suitability of five commonly used

fundamental valuation models, and how accurate estimations these can provide in

relation to the financial analysts’ target prices.

AOIL LUPE PAR ERIC TEL2 TLSN HM KAHL NEWA NCC PEAB SKA

Model EV/EB NAV EV/EB EV/EB EV/EB EV/EB FCFE EV/EB EV/EB EV/EB EV/EB EV/EB

Model - - NAV P/E P/E P/E P/E FCFE P/E P/E P/E P/E

Model - - - - - - - - NAV - - -

Most appropriate firm-specific models

Table 17. Models on firm-specific level. Shows the most appropriate models based on a firm-specific

level. EV/EB = EV/EBITDA. Dash (-) = no alternative model. The third row states the model, in case it

has generated the same number of hits as another, i.e., two models are equally appropriate.

Table 18. Models on industry level. Shows the most appropriate models based on a industry level.

The empirical findings illustrate (Table 17 and 18) that our estimations are similar on

firm-specific level, as on an industry-specific level. We can conclude that the

estimations based on EV/EBITDA and P/E multiples, are the two most suitable

valuation models for the investigated firms, relative to the analysts’ target prices. This is

further strengthen by the results on the industry level where both EV/EBITDA and P/E

are, in overall, are superior valuation models in the telecom-, retail- and construction

industry. Due to lack of results (in both intervals) in the oil industry, EV/EBITDA and

NAV are the only models that generate sufficient estimations relative to the analysts’

target prices.

We believe that, the major reason why the models based on multiples generate the best

results is because of its simplicity and the low number of variables used in the

calculations. In comparison, EV/EBITDA and P/E are more straightforward relative to

FCFE and Gordon Growth. We can also conclude that estimations based on the

EBTIDA provide better predictions than pure earnings (net income). This is because

EBITDA has shown to be a more stable measurement of earnings, while the net income

is more volatile. In addition, EBITDA ignores the capital structure, depreciation and

amortization.

On the other hand, Gordon Growth and FCFE are more complex models, that require a

number of assumptions e.g., forecasted growth rate and cost of capital. Based on our

results, we can see that FCFE is qualified as one of the two best models for the retail

industry. Besides that none of the two models are able to generate estimations close to

the analysts’ target prices. However, we can confirm the theories that the FCFE model

at least performs better estimations for high-growth firms than for firms in stable

growth. Furthermore, since the forecasted growth rate is the single most important

variable, we have reasons to believe that this has affected the accuracy of our

OIL TELECOM RETAIL CONSTRUCTION

Model EV/EBITDA EV/EBITDA FCFE P/E

Model NAV P/E P/E EV/EBITDA

Most appropriate industry models


44

estimations. This as several authors argue that forecasted growth should be based on

external macroeconomic factors, and not on historical growth rates.

When it comes to NAV, we can, based on our results conclude that this model is not

useful as an estimator. We can therefore say that the investigated industries do not hold

any substantial amounts of tangible net assets. However, there is a tendency that the

firms in the oil industry hold more tangible net assets.

Although we have been able to identify the most suitable valuation models for

respective firm and industry, it is hard to determine on what scale the results can be

classified as accurate. However, as our purpose states, the research concerns the five

investigated models, the classification is therefore set relatively to these models. Based

on our findings, the final conclusion is that the EV/EBITDA and P/E models generate

the most accurate results relative to the financial analysts’ target prices.


45

8 Discussion and Recommendations

The application of the different models generates varying results. For most of the

industries it is possible to determine if there are any models that provide more accurate

results in comparison to the others, except for the oil industry. During this research it

has therefore become clear that in order to be able to evaluate firms within the oil

industry; other aspects have to be taken into consideration. As the research has been

limited to historical information, and very few external variables, the impact of the

financial crisis (in the investigated period) has not been taken into consideration. When

comparing our target prices with the financial analysts’ target prices, we can see a

pattern. Our target prices are more stable over the whole period for most of the firms,

while the analysts are starting to drastically cut the target prices for the majority of the

firms. We therefore believe that the analysts often include more external factors in their

estimations. In addition, the financial analysts’ subjectivity regarding financial reporting

should be taken into considerations, e.g., the net income is target for questioned

objectivity and could be highly debatable of how it is calculated. This could be because

of different accounting standard that has been applied etc.

In our conclusion we state that estimations from EV/EBITDA and P/E generate the

most accurate results in relation to the financial analysts. However, in Fama’s (1970)

theory regarding weak form of market efficiency, investors can identify under- and

overvalued stocks by studying financial statements. This means that even though the

estimations from our calculations are not close to the financial analysts’ target prices, it

does not mean that they are wrong. Instead, it might be possible to say that a stock is

over- or undervalued. This phenomenon would become even clearer if estimations were

compared to the actual stock prices.

For small private investors it is hard to determine which valuation models to use. We

have through our research and empirical findings experienced that the choice of

valuation model should be based on the variables itself. For instance, a firm in high

growth requires a model that can deal with high-growth variables, and vice versa.

One potential implication with the Gordon Growth model is that, the previous research

is not dealing with the Swedish market in particular. The perceptions of dividend

payouts can differ between countries, and it is not known whether or how, it affects the

growth of a company. There are other aspects involved in this discussion e.g. principal-

agent problem and asymmetric information. This was not seen as an area of high

relevance for this paper. However, it is an interesting topic to cover in future studies.

When it comes to the NAV model, it is important to understand that the original model

does not reflect the market value of the assets or the hidden assets in a company. We

believe that in order to valuate these assets, a deep and specialized knowledge is

required of the specific firm to provide better estimations.

We have through our findings experienced that some of the models do not provide

sufficient results. However, there are several authors (Fernández, 2001; Kaplan &

Ruback, 1995) that argue that valuation models often are, or should be, used in

combination in order to provide more reliable valuations. This as some models alone

can provide too optimistic or too pessimistic estimations. Suggestions for further studies


46

would therefore be to investigate whether a combination of several models can generate

more accurate estimations relative to the financial analysts’ target prices.

In addition, at an early stage, the intention of this paper was to investigate whether the

analysts or the investigated valuation models could generate the most accurate

estimations relative to the actual stock prices. This is, instead of analyzing two variables

against each other, it would be of interest to include a third variable, in form of the

actual stock prices and thereby construct a triangular analysis. This would either

strengthen or weaken the appropriateness and accuracy of each individual model.

We see this research as a broad and robust investigation. This since it deals with five

different valuation models, which have been applied to twelve different companies

divided in four different industries. One of the strengths is that the chosen sample

basically corresponds to the whole population at Large- and Mid Cap. In order to,

answer the chosen research questions, several different kinds of independent methods

have been used to interpret the data. The methods that have been used are a non-

statistical approach that includes intervals (10% and 15%) and hit ratios, and a statistical

approach in form of multiple regressions.

On the other hand, there are several ideas that come to mind what could have been done

differently. The number of observations in the sample could have been more, since this

would further have increased the credibility and validity of this paper. The fact that it

was a financial crisis within the investigated period, can eventually have affected our

final results. Therefore, the investigated period could have been extended, or changed.

Furthermore, the assumptions should have been more individually customized for each

firm in order to enhance the accuracy of the estimations.


47

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Sincere, M. (2003). Understanding stocks. USA: McGraw-Hill Companies.

Skanska. (2011). Interim reports. Retrieved 2011-03-04, from

http://www.skanska.com/en/Investors/Reports/Interim-reports/

Tele2. (2011). Reports. Retrieved 2011-03-10, from http://www.tele2.com/explore-our-

financials/2162.html

Teliasonera. (2011). Reports and presentations. Retrieved 2011-03-10, from

http://www.teliasonera.com/investors/reports-and-presentations/

World Federation of Exchanges. (2010). 10 Years in Review (2000-2009). Retrieved

2011-02-15, from http://www.world-exchanges.org/statistics



http://www.skanska.com/en/Investors/Reports/Interim-reports/

http://www.tele2.com/explore-our-financials/2162.html

http://www.tele2.com/explore-our-financials/2162.html

http://www.teliasonera.com/investors/reports-and-presentations/

http://www.world-exchanges.org/statistics

Appendix

51

Appendices

Appendix A – Compilation of Analysts’ target prices

2011

Company Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1

ERIC 15.46 61.80 63.75 58.5 55.00 62.00 79.38 73.62 85.57 89.00 77.53 83,67 87.70

TEL2 140.80 129.80 91.70 90.00 91.64 98.47 117.13 129.71 138.33 146.78 155.17 162.63 158.90

TLSN 56.57 51.24 40.08 38.38 40.62 44.56 50.45 56.63 57.25 59.24 57.90 60.95 54.18

HM 403.00 328.33 312.00 335.00 346.25 395.22 445.00 464.00 519.83 249.71 261.23 211.00 206.11

KAHL 84.00 54.00 48.50 35.00 26.75 32.50 47.60 54.13 74.00 80.00 70.00 66.00 43.10

NEWA 50.00 35.00 12.00 10.00 16.00 22.50 27.00 34.00 36.33 44.75 48.00 45.00 57.40

NCC 124.75 88.33 70.00 96.67 70.00 78.33 99.43 118.67 146.00 140.33 158.25 200.00 193.50

PEAB 55.00 60.00 20.00 25.00 45.00 49.33 51.25 48.00 45.5 48.33 51.75 55.50 52.00

SKA 115.14 89.20 73.57 75.17 94.83 105.11 122.67 136.25 132.67 132.00 141.50 149.00 143.94

AOIL 9.79 11.00 3.65 4.10 9.10 127.00 115.32 143.86 140.7 145.44 139.00 147.13 147.44

LUPE 94.50 87.67 47.33 44.20 58.20 56.50 66.00 64.40 54.60 59.92 70.42 105.00 96.50

PAR 83.00 90.00 30.00 25.00 25.00 28.50 28.50 24.25 6.65 7.17 7.23 5.33 5.70

2008 2009 2010

Compilation of Analysts taget prices (average, in SEK)

Appendix

52

Appendix B – Practical Calculations

1 Gordon Growth

2 Free Cash Flow to Equity

FCFEt = Free cash flow to equity in period t (see Formula 3, Section 3.4) Pn = Price at the end of the extraordinary growth period kn = Cost of equity in high growth (hg) and stable growth (st) periods

gn =Growth rate after the terminal year forever

Appendix

53

Appendix B Continued – Practical Calculations

3 Price/Earnings Ratio to Target Prices (Model)

*EPS = Earnigns Per Share

** P/E Annual Industry Average – See Appendix D

4 EV/EBITDA to Target Prices (Model)

EV = EBITDA (for four trailing quarters) * EV/EBITDA Annual Industry Average MV = EV – Value of debt + cash Value per share =MV / Total outstanding shares

*EV/EBITDA Annual Industry Average – See Appendix D

Appendix

54

Appendix B continued – Practical Calculations

5 Net Asset Valuation (NAV)

6 Industry Averages (for P/E and EV/EBITDA Multiples)

MY = Multiple Year (for P/E or EV/EBITDA)

*See Appendix C for the final results of Industry Average (P/E and EV/EBITDA Multiples)

*See Appendix D and E for company multiples for each year

Appendix

55

Appendix C – Industry Average for P/E and EV/EBITDA 2004 - 2010

22.98 9.33

0.29 5.99

13.33 6.73

12.20 7.35

22.00 7.16

8.13 4.58

18.62 11.74

16.25 7.92

9.14 5.81

11.14 6.85

13.16 6.36

11.15 6.34

190.38 10.50

15.72 7.56

60.1 5.84

88.74 7.97

KAHL

NEWA

Industry Average

NCC

ERIC

TEL2

TLSN

Industry Average

HM

Industry Average

P/E Industry Average

Period 2004 - 2010

ERIC

TEL2

TLSN

Industry Average

HM

KAHL

NEWA

PEAB

SKA

Industry Average

AOIL

LUPE

PAR

LUPE

PAR

Industry Average

EV/EBITDA Industry Average

Period 2004 - 2010

Industry Average

NCC

PEAB

SKA

Industry Average

AOIL

Appendix

56

*Annual P/E multiples for the period 2004 – 2010; See Appendix E ** Annual EV/EBITDA for the period 2004 – 2010; See Appendix E

Appendix D – Annual Price/Earnings Multiples 2004 - 2010

Company 2010 2009 2008 2007 2006 2005 2004 Average

Ericsson 22,59 57,81 16,39 10,95 16,73 17,37 19,01 22,98

Tele2 8,89 10,39 12,48 -37,00 -19,49 16,02 10,72 0,29

TeliaSonera 11,27 12,32 9,03 14,97 14,88 16,64 14,22 13,33

Industry Average 12,20

Hennes & Mauritz 21,00 21,00 16,00 24,00 24,00 23,00 25,00 22,00

KappAhl 8,49 9,90 7,70 7,40 12,40 11,00 8,13

New Wave 12,03 20,54 2,87 19,36 22,26 27,33 25,93 18,62

Industry Average 13,84 17,15 8,86 16,92 19,55 20,44 16,98 16,25

NCC 11,00 8,00 10,00 3,00 7,00 12,00 13,00 9,14

PEAB 14,00 10,00 3,00 14,00 13,00 10,00 14,00 11,14

Skanska 13,97 14,06 10,42 12,47 15,55 13,05 12,60 13,16

Industry Average 12,99 10,69 7,81 9,82 11,85 11,68 13,20 11,15

Alliance Oil Company 11,94 6,98 22,73 26,29 37,70 1222,26 4,73 190,38

Lundin Petroleum 7,60 -6,07 17,50 23,38 29,69 21,85 16,12 15,72

PA Resources -12,36 295,22 1,81 7,81 43,18 37,12 48,06 60,12


closing price 31 dec respectively year

Annual P/E ratios for the period 2004-2010

Appendix

57

Appendix E – Annual EV/EBITDA Multiples 2004 – 2010

Company 2010 2009 2008 2007 2006 2005 2004 Average

Ericsson 9,46 11,65 7,37 6,11 10,11 10,69 9,94 9,33

Tele2 6,01 5,18 3,72 8,58 7,19 5,71 5,54 5,99

TeliaSonera 6,47 6,34 5,21 8,54 7,83 6,63 6,10 6,73


Hennes & Mauritz 6,49 6,45 5,52 7,98 8,38 7,46 7,84 7,16

KappAhl 5,07 4,11 3,86 5,94 5,28 4,85

New Wave 10,59 14,67 6,91 1,46 16,60 22,10 9,82 11,74


NCC 6,23 5,26 3,27 5,03 7,35 7,18 6,36 5,81

PEAB 10,30 8,41 8,31 6,91 3,89 5,57 4,57 6,85

Skanska 7,23 6,15 4,72 5,76 8,27 6,82 5,56 6,36


Alliance Oil Company 0,67 6,56 1,56 6,45 14,01 15,10 22,72 10,50

Lundin Petroleum 6,12 5,13 2,78 7,31 8,79 7,85 7,61 7,56

PA Resources 3,75 3,11 0,95 3,60 21,29 1,75 2,85 5,84


Average EV/EBITDA for the period 2004 - 2010

Annual EV/EBITDA for the period 2004 - 2010

Appendix

58

Appendix F – SPSS Statistics ANOVA tables

AOIL SDB LUPE PAR

p-value (Gordon Growth, FCFE) 0,533 0,202 0,084

alpha value 0,10 0,10 0,10

decision accept accept reject

p-value (P/E, EV/EBITDA, NAV) 0,000 0,337 0,012

alpha value 0,10 0,10 0,10

decision reject accept reject

ANOVA OIL INDUSTRY

ERIC B TEL2 B TLSN


alpha value 0,10 0,10 0,10

decision accept accept accept


alpha value 0,10 0,10 0,10

decision reject reject reject

ANOVA TELECOM INDUSTRY

HM B KAHL NEWA B


alpha value 0,10 0,10 0,10

decision reject accept reject


alpha value 0,10 0,10 0,10

decision reject accept accept

ANOVA RETAIL INDUSTRY

NCC B PEAB B SKA B


alpha value 0,10 0,10 0,10

decision accept accept accept


alpha value 0,10 0,10 0,10

decision reject accept accept

ANOVA CONSTRUCTION INDUSTRY

Appendix

59

Appendix G – SPSS Statistics Coefficients

AOIL SDB alpha Decision LUPE alpha Decision PAR alpha Decision

p-value Gordon Growth - 0,10 - 0,10 - - 0,10 -

p-value FCFE - 0,10 - 0,10 - 0,034 0,10 reject

p-value P/E 0,723 0,10 accept - 0,10 - 0,193 0,10 accept

p-value EV/EBITDA 0,057 0,10 reject - 0,10 - 0,284 0,10 accept

p-value NAV 0,002 0,10 reject - 0,10 - 0,285 0,10 accept

COEFFICIENT OIL INDUSTRY

ERIC B alpha Decision TEL2 B alpha Decision TLSN alpha Decision

p-value Gordon Growth - 0,10 - 0,10 - 0,10

p-value FCFE - 0,10 - 0,10 - 0,10

p-value P/E 0,178 0,10 accept 0,044 0,10 reject 0,456 0,10 accept

p-value EV/EBITDA 0,514 0,10 accept 0,012 0,10 reject 0,125 0,10 accept

p-value NAV 0,003 0,10 reject 0,532 0,10 accept 0,241 0,10 accept

COEFFICIENT TELECOM INDUSTRY

NCC B alpha Decision PEAB B alpha Decision SKA B alpha Decision

p-value Gordon Growth - 0,10 - 0,10 - 0,10

p-value FCFE - 0,10 - 0,10 - 0,10

p-value P/E 0,035 0,10 reject - 0,10 - 0,10

p-value EV/EBITDA 0,006 0,10 reject - 0,10 - 0,10

p-value NAV 0,904 0,10 accept - 0,10 - 0,10

COEFFICIENT CONSTRUCTION INDUSTRY

HM B alpha Decision KAHL alpha Decision NEWA B alpha Decision

p-value Gordon Growth 0,090 0,10 reject - 0,10 0,026 0,10 reject

p-value FCFE 0,059 0,10 reject - 0,10 0,066 0,10 reject

p-value P/E 0,188 0,10 accept - 0,10 - 0,10

p-value EV/EBITDA 0,150 0,10 accept - 0,10 - 0,10

p-value NAV 0,796 0,10 accept - 0,10 - 0,10

COEFFICIENT RETAIL INDUSTRY

Appendix

60

Appendix H – 10% and 15% intervals: Telecom Industry

15% 17,779 71,07 73,313 67,275 63,25 71,3 91,287 84,663 98,406 102,35 89,16 96,2205

10% 17,006 67,98 70,125 64,35 60,5 68,2 87,318 80,982 94,127 97,9 85,283 92,037

ERIC Average 15,46 61,8 63,75 58,5 55 62 79,38 73,62 85,57 89 77,53 83,67

-10% 13,914 55,62 57,375 52,65 49,5 55,8 71,442 66,258 77,013 80,1 69,777 75,303

-15% 13,141 52,53 54,188 49,725 46,75 52,7 67,473 62,577 72,735 75,65 65,901 71,1195

15% 161,92 149,27 105,46 103,5 105,39 113,24 134,7 149,17 159,08 168,8 178,45 187,025

10% 154,88 142,78 100,87 99 100,8 108,32 128,84 142,68 152,16 161,46 170,69 178,893

TEL2 Average 140,8 129,8 91,7 90 91,64 98,47 117,13 129,71 138,33 146,78 155,17 162,63

-10% 126,72 116,82 82,53 81 82,476 88,623 105,42 116,74 124,5 132,1 139,65 146,367

-15% 119,68 110,33 77,945 76,5 77,894 83,7 99,561 110,25 117,58 124,76 131,89 138,236

15% 65,056 58,926 46,092 44,137 46,713 51,244 58,018 65,125 65,838 68,126 66,585 70,0925

10% 62,227 56,364 44,088 42,218 44,682 49,016 55,495 62,293 62,975 65,164 63,69 67,045

TLSN Average 56,57 51,24 40,08 38,38 40,62 44,56 50,45 56,63 57,25 59,24 57,9 60,95

-10% 50,913 46,116 36,072 34,542 36,558 40,104 45,405 50,967 51,525 53,316 52,11 54,855

-15% 48,085 43,554 34,068 32,623 34,527 37,876 42,883 48,136 48,663 50,354 49,215 51,8075

Appendix

61

Appendix H continued – 10% and 15% intervals: Retail Industry

15% 463,45 377,58 358,8 385,25 398,19 454,5 511,75 533,6 597,8 287,17 300,41 242,65

10% 443,3 361,16 343,2 368,5 380,88 434,74 489,5 510,4 571,81 274,68 287,35 232,1

HM Average 403 328,33 312 335 346,25 395,22 445 464 519,83 249,71 261,23 211

-10% 362,7 295,5 280,8 301,5 311,63 355,7 400,5 417,6 467,85 224,74 235,11 189,9

-15% 342,55 279,08 265,2 284,75 294,31 335,94 378,25 394,4 441,86 212,25 222,05 179,35

15% 96,6 62,1 55,78 40,25 30,76 37,38 54,74 62,25 85,1 92 80,5 75,9

10% 92,4 59,4 53,35 38,5 29,425 35,75 52,36 59,543 81,4 88 77 72,6

KAHL Average 84 54 48,5 35 26,75 32,5 47,6 54,13 74 80 70 66

-10% 75,6 48,6 43,65 31,5 24,075 29,25 42,84 48,717 66,6 72 63 59,4

-15% 71,4 45,9 41,225 29,75 22,738 27,625 40,46 46,011 62,9 68 59,5 56,1

15% 57,5 40,25 13,8 11,5 18,4 25,875 31,05 39,1 41,78 51,463 55,2 51,75

10% 55 38,5 13,2 11 17,6 24,75 29,7 37,4 39,963 49,225 52,8 49,5

NEWA Average 50 35 12 10 16 22,5 27 34 36,33 44,75 48 45

-10% 45 31,5 10,8 9 14,4 20,25 24,3 30,6 32,697 40,275 43,2 40,5

-15% 42,5 29,75 10,2 8,5 13,6 19,125 22,95 28,9 30,881 38,038 40,8 38,25

Appendix

62

Appendix H continued – 10% and 15% intervals: Construction Industry

15% 143,46 101,58 80,5 111,1705 80,5 90,08 114,34 136,47 167,9 161,38 181,99 230

10% 137,23 97,163 77 106,337 77 86,163 109,37 130,54 160,6 154,36 174,08 220

NCC Average 124,75 88,33 70 96,67 70 78,33 99,43 118,67 146 140,33 158,25 200

-10% 112,28 79,497 63 87,003 63 70,497 89,487 106,8 131,4 126,3 142,43 180

-15% 106,04 75,081 59,5 82,1695 59,5 66,581 84,516 100,87 124,1 119,28 134,51 170

15% 63,25 69 23 28,75 51,75 56,73 58,938 55,2 52,325 55,58 59,513 63,825

10% 60,5 66 22 27,5 49,5 54,263 56,375 52,8 50,05 53,163 56,925 61,05

PEAB Average 55 60 20 25 45 49,33 51,25 48 45,5 48,33 51,75 55,5

-10% 49,5 54 18 22,5 40,5 44,397 46,125 43,2 40,95 43,497 46,575 49,95

-15% 46,75 51 17 21,25 38,25 41,931 43,563 40,8 38,675 41,081 43,988 47,175

15% 132,41 102,58 84,606 86,4455 109,05 120,88 141,07 156,69 152,57 151,8 162,73 171,35

10% 126,65 98,12 80,927 82,687 104,31 115,62 134,94 149,88 145,94 145,2 155,65 163,9

SKA Average 115,14 89,2 73,57 75,17 94,83 105,11 122,67 136,25 132,67 132 141,5 149

-10% 103,63 80,28 66,213 67,653 85,347 94,599 110,4 122,63 119,4 118,8 127,35 134,1

-15% 97,869 75,82 62,535 63,8945 80,606 89,344 104,27 115,81 112,77 112,2 120,28 126,65

Appendix

63

Appendix H continued – 10% and 15% intervals: Oil Industry

15% 11,259 12,65 4,1975 4,715 10,465 146,05 132,62 165,44 161,81 167,26 159,85 169,2

10% 10,769 12,1 4,015 4,51 10,01 139,7 126,85 158,25 154,77 159,98 152,9 161,843

AOIL Average 9,79 11 3,65 4,1 9,1 127 115,32 143,86 140,7 145,44 139 147,13

-10% 8,811 9,9 3,285 3,69 8,19 114,3 103,79 129,47 126,63 130,9 125,1 132,417

-15% 8,3215 9,35 3,1025 3,485 7,735 107,95 98,022 122,28 119,6 123,62 118,15 125,061

15% 108,68 100,82 54,43 50,83 66,93 64,975 75,9 74,06 62,79 68,908 80,983 120,75

10% 103,95 96,437 52,063 48,62 64,02 62,15 72,6 70,84 60,06 65,912 77,462 115,5

LUPE Average 94,5 87,67 47,33 44,2 58,2 56,5 66 64,4 54,6 59,92 70,42 105

-10% 85,05 78,903 42,597 39,78 52,38 50,85 59,4 57,96 49,14 53,928 63,378 94,5

-15% 80,325 74,52 40,231 37,57 49,47 48,025 56,1 54,74 46,41 50,932 59,857 89,25

15% 95,45 103,5 34,5 28,75 28,75 32,775 32,775 27,888 7,6475 8,2455 8,3145 6,1295

10% 91,3 99 33 27,5 27,5 31,35 31,35 26,675 7,315 7,887 7,953 5,863

PAR Average 83 90 30 25 25 28,5 28,5 24,25 6,65 7,17 7,23 5,33

-10% 74,7 81 27 22,5 22,5 25,65 25,65 21,825 5,985 6,453 6,507 4,797

-15% 70,55 76,5 25,5 21,25 21,25 24,225 24,225 20,613 5,6525 6,0945 6,1455 4,5305

Appendix

64

Appendix I – Geometric Growth

Company 2010 2009 2008 2007

ERIC 4.18% 5.1% 6.18% 5.19%

TEL2 1.21% 1.04% 1.37% 4.14%

TLSN 7.56% 9.06% 9.69% 10.13%

HM 11.45% 12.11% 11.78% 11.55%

KAHL 3.54% 3.33% 3.01% 2.95%

NEWA 12.22% 13.47% 16.08% 12.74%

NCC 1.13% 1.98% 4.1% 5.27%

PEAB 8.49% 8.53% 9.48% 9.34%

SKA -2.16% -0.88% -0.22% -0.95%

AOIL 64.37% 73.01% 98.4% 118.48%

LUPE 45.28% 54.78% 67.4% 79.8%

PAR 57.09% 66.3% 85.16% 114.88%

Annual Geometric Average Growth

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