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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
i
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
ii
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.
iii
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.
iv
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
v
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
1
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.
P. Eriksson, T. Forsberg & N. Gustavsson
2
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
P. Eriksson, T. Forsberg & N. Gustavsson
3
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.
P. Eriksson, T. Forsberg & N. Gustavsson
4
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
P. Eriksson, T. Forsberg & N. Gustavsson
5
Flow, Earnings multiples, EV/EBITDA, Book value, and Net asset valuation, among
several others.
P. Eriksson, T. Forsberg & N. Gustavsson
6
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
P. Eriksson, T. Forsberg & N. Gustavsson
7
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.
P. Eriksson, T. Forsberg & N. Gustavsson
8
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
P. Eriksson, T. Forsberg & N. Gustavsson
9
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
P. Eriksson, T. Forsberg & N. Gustavsson
10
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
P. Eriksson, T. Forsberg & N. Gustavsson
11
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)
P. Eriksson, T. Forsberg & N. Gustavsson
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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
P. Eriksson, T. Forsberg & N. Gustavsson
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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
P. Eriksson, T. Forsberg & N. Gustavsson
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
P. Eriksson, T. Forsberg & N. Gustavsson
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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.
P. Eriksson, T. Forsberg & N. Gustavsson
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).
P. Eriksson, T. Forsberg & N. Gustavsson
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:
P. Eriksson, T. Forsberg & N. Gustavsson
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
P. Eriksson, T. Forsberg & N. Gustavsson
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
P. Eriksson, T. Forsberg & N. Gustavsson
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
P. Eriksson, T. Forsberg & N. Gustavsson
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
P. Eriksson, T. Forsberg & N. Gustavsson
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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
P. Eriksson, T. Forsberg & N. Gustavsson
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
P. Eriksson, T. Forsberg & N. Gustavsson
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.
P. Eriksson, T. Forsberg & N. Gustavsson
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.
P. Eriksson, T. Forsberg & N. Gustavsson
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.
P. Eriksson, T. Forsberg & N. Gustavsson
27
Company Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
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.
Company Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
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
P. Eriksson, T. Forsberg & N. Gustavsson
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.
P. Eriksson, T. Forsberg & N. Gustavsson
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).
P. Eriksson, T. Forsberg & N. Gustavsson
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
P. Eriksson, T. Forsberg & N. Gustavsson
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
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.
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
P. Eriksson, T. Forsberg & N. Gustavsson
32
Figure 4a and 4b. Units of total hits. 10% and 15% respectively for the Construction 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.
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
P. Eriksson, T. Forsberg & N. Gustavsson
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).
P. Eriksson, T. Forsberg & N. Gustavsson
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
P. Eriksson, T. Forsberg & N. Gustavsson
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.
P. Eriksson, T. Forsberg & N. Gustavsson
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).
P. Eriksson, T. Forsberg & N. Gustavsson
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
P. Eriksson, T. Forsberg & N. Gustavsson
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
P. Eriksson, T. Forsberg & N. Gustavsson
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).
P. Eriksson, T. Forsberg & N. Gustavsson
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
P. Eriksson, T. Forsberg & N. Gustavsson
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
P. Eriksson, T. Forsberg & N. Gustavsson
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.
P. Eriksson, T. Forsberg & N. Gustavsson
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
P. Eriksson, T. Forsberg & N. Gustavsson
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.
P. Eriksson, T. Forsberg & N. Gustavsson
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
P. Eriksson, T. Forsberg & N. Gustavsson
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.
P. Eriksson, T. Forsberg & N. Gustavsson
47
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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
Industry Average 88,74
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
Industry Average 7,35
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
Industry Average 7,92
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
Industry Average 6,34
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
Industry Average 7,97
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
p-value (Gordon Growth, FCFE) 0,877 0,477 0,371
alpha value 0,10 0,10 0,10
decision accept accept accept
p-value (P/E, EV/EBITDA, NAV) 0,013 0,024 0,054
alpha value 0,10 0,10 0,10
decision reject reject reject
ANOVA TELECOM INDUSTRY
HM B KAHL NEWA B
p-value (Gordon Growth, FCFE) 0,085 0,285 0,041
alpha value 0,10 0,10 0,10
decision reject accept reject
p-value (P/E, EV/EBITDA, NAV) 0,002 0,481 0,815
alpha value 0,10 0,10 0,10
decision reject accept accept
ANOVA RETAIL INDUSTRY
NCC B PEAB B SKA B
p-value (Gordon Growth, FCFE) 0,531 0,791 0,557
alpha value 0,10 0,10 0,10
decision accept accept accept
p-value (P/E, EV/EBITDA, NAV) 0,035 0,229 0,658
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