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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

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Acknowledgements

The authors of this paper would like to acknowledge the following persons for making it possible to complete this thesis.

First, we would like to express our gratitude to our tutors; Per-Olof Bjurgren and Louise Nordström for their support and commitment during this thesis’ entire process.

We would also like to take the opportunity to thank Per Forsberg, accountant, KPMG, Marcus Eriksson, senior analyst, Nordea, and Erik Sellstedt, Danske Bank, for their professional insights and perspectives in general.

Finally, we would like to show our gratefulness towards our fellow colleagues at Jönköping International Business School for their inputs, comments, and support.

Peter Eriksson Tobias Forsberg Nicklas Gustavsson

International Management Program 2008 - 2011 Jönköping International Business School 2011

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Bachelor Thesis within Business Administration, 15 ECTS-credits

Title: Fundamental Stock Analysis

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

Authors: Peter Eriksson, Tobias Forsberg, Nicklas Gustavsson

Tutors: Per-Olof Bjurgren, Louise Nordström

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

Abstract

The interest for stocks and stock-trading has grown tremendously during the last decade. The challenge for small private investors is how to use and filter the most relevant information in the stock selection process. As a result, this thesis investigates the accuracy of the Gordon Growth, Discounted Cash Flow (Free Cash Flow to Equity), P/E multiple, EV/EBITDA multiple, and Net Asset Valuation in relation to the target prices set by financial analysts. This in order to create an understanding how target prices are set, and which models that are useful for a specific firm or industry.

The research covers twelve companies, divided in four industries: telecom, retail, construction and oil, over the time period of 2008 - 2011. All companies are listed on NASDAQ OMXS, Large or Mid Cap. In order to determine the most suitable models, several analyses were conducted in form of two interval tests (10% and 15%), hit ratio test, and multiple regressions test.

From the results, it can be concluded that there exist no universal valuation models. However, this research showed that the estimations generated from EV/EBITDA- and P/E multiples outperformed the other investigated valuation models. The more complex models: Gordon Growth and Discounted Cash Flow performed poorly. In this case, the forecasted growth rate is believed to have had an impact on the results, since it was based upon historical data only. Due to lack of results, the estimations from the Net Asset Valuation indicated that none of the firms hold any substantial proportions of tangible net asset in relation to their market value.

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Kandidat uppsats inom företagsekonomi, 15 ECTS-poäng

Titel: Fundamental Stock Analysis

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

Författare: Peter Eriksson, Tobias Forsberg, Nicklas Gustavsson

Handledare: Per-Olof Bjurgren, Louise Nordström

Nyckelord: Fundamental Analys, Aktiepris, Aktievärdering, Gordon

tillväxtmodel, Fri kassaflödesanalys, P/E multipel, EV/EBITDA multipel, Substansvärde, Riktkurs

Abstrakt

Intresset för aktier och aktierelaterad handel har ökat kraftigt under det senaste decenniet. Den största utmaningen för småsparare är hur man använder och filtrerar den mest relevanta informationen i valet av aktier. Denna forskning undersöker träffsäkerheten för Gordons tillväxtmodell, Kassaflödesvärdering, P/E multipel, EV/EBITDA multipel, och Substansvärde i förhållande till finansanalytikers riktpriser. Detta för att skapa en förståelse hur riktpriser fastställs och vilka modeller som är och kan vara användbara för en specifik bransch eller företag.

Undersökningen omfattar tolv bolag, vilka är uppdelade i fyra branscher: telekom, detaljhandel, bygg och olja under tidsperioden, 2008 - 2011. Alla företag är noterade på NASDAQ OMXS, Large eller Mid Cap. För att bestämma de mest lämpliga modellerna, har ett flertal analyser genomförts i form ut av två olika intervallundersökningar (10% respektive 15%), ”hit ratio” undersökning och multipel regressionsanalys.

Vi kan via resultatet dra slutsatsen att det inte förekommer någon universell värderingsmodell. Undersökning visade dock att de estimeringar som genererats ifrån EV/EBITDA- och P/E multiplar överträffade de övriga undersökta värderingsmodellerna. Vi observerade också att de mer komplexa modellerna: Gordons tillväxtmodell och Kassaflödesvärdering emellertid ger ett sämre resultat. Den prognostiserade tillväxten tros ha haft en inverkan på resultaten, eftersom de enbart var baserade på historisk data. På grund utav bristande resultat, indikerade Substansvärderingsresultaten att inget utav företagen förfogar över någon betydande andel materiella nettotillgångar i förhållande till deras marknadsvärde.

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Table of Contents

Disposition ... 1

1

Introduction ... 2

1.1 Background ... 2

1.2 Problem Discussion ... 2

1.3 Purpose and Research Questions... 3

1.4 Delimitation ... 4 1.5 Literature Review ... 4

2

Previous Research ... 6

3

Frame of Reference ... 8

3.1 Market Efficiency ... 8 3.2 Growth ... 8

3.3 Dividend Discount Models ... 9

3.3.1 Gordon Growth Model ... 9

3.4 Discounted Cash Flow Model ... 10

3.4.1 Two-Stage FCFE Model ... 11

3.5 Valuation Multiplies ... 12

3.5.1 P/E Multiple ... 12

3.5.2 EV/EBIDTA Multiple ... 13

3.6 Net Asset Valuation ... 14

4

Methodology ... 16

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

4.2 Research Type: Descriptive, Explanatory, and Exploratory ... 16

4.3 Data Collection: Quantitative Primary and Secondary Data ... 16

4.4 Choice of Valuation Models ... 17

4.5 Sample Choice: Choice of Stocks ... 17

4.6 Test Period ... 18

4.7 Calculations ... 18

4.8 Interpretation and Data Analysis ... 19

4.9 Non-Statistical Method ... 19

4.10 Hit Ratios and Total number of hits ... 19

4.11 Statistical Method ... 20 4.12 Hypotheses ... 20 4.13 Empirical Assumptions ... 22 4.14 Reliability ... 23 4.15 Validity ... 23 4.16 Critiques of Method ... 24

5

Empirical Tables ... 25

6

Empirical Presentation and Analysis ... 29

6.1 Empirical Presentation ... 29

6.2 Firm-specific Analysis ... 32

6.2.1 Gordon Growth Model ... 32

6.2.2 FCFE ... 33

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6.2.4 EV/EBITDA to Target Prices ... 35

6.2.5 Net Asset Valuation ... 37

6.3 Industrial Analysis ... 38

6.4 Final Analysis... 40

7

Conclusion ... 43

8

Discussion and Recommendations ... 45

List of references ... 47

Appendices ... 51

Appendix A – Compilation of Analysts’ target prices ... 51

Appendix B – Practical Calculations ... 52

Appendix B Continued – Practical Calculations ... 53

Appendix B continued – Practical Calculations ... 54

Appendix C – Industry Average for P/E and EV/EBITDA 2004 - 2010 ... 55

Appendix D – Annual Price/Earnings Multiples 2004 - 2010 ... 56

Appendix E – Annual EV/EBITDA Multiples 2004 – 2010 ... 57

Appendix F – SPSS Statistics ANOVA tables ... 58

Appendix G – SPSS Statistics Coefficients ... 59

Appendix H – 10% and 15% intervals: Telecom Industry ... 60

Appendix H continued – 10% and 15% intervals: Retail Industry ... 61

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

Appendix H continued – 10% and 15% intervals: Oil Industry... 63

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Disposition

Introduction Chapter

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

thesis, including research questions.

Previous Research

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

Frame of References

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

paper.

Methodology

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

Empirical Tables

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

Empirical Presentation &

Analysis

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

Conclusion

•Chapter Seven discuss the results from the analysis. The final conclusions regarding Fundamental Stock Analysis are stated in order to answer the research questions stated in chapter one and thereby conduct the purpose of

this thesis.

Discussion and Recommendations

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

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1

Introduction

1.1

Background

The stock market is characterized and affected by the general economic environment, the flow of information, and psychology. The importance of each element has become more evident, especially during the recent financial crisis that started in 2007-2008 and the IT-crash in 2001. During these crises the stock prices have shown to be more volatile than normal.

In the last decade, the interest for stocks and stock-trading has grown tremendously. Data from World Federation of Exchanges (2010) shows that, during the last ten years, the total number of trades has increased by 700% globally. One reason could be that the average value per trade has dropped with 85%, while the number of stock listings has increased with 41%. This could also be explained by the rapidly increased usage of Internet. According to Internet World Stats (2010a; 2010b), the number of Internet users has increased with 444% worldwide, while the Internet usage in Sweden has grown to 92.5% of its population. The rapidly development of Internet has revolutionized the financial sector where the majority of all transactions take place online and in real-time. The Nordic stock exchange operator, NASDAQ OMX Nordic (which includes NASDAQ OMX Stockholm) has also increased its annual turnover drastically over the last decade. According to World Federation of Exchanges (2010), the most drastic changes at the OMX Nordic occurred between 2008 and 2009. This is believed to be a result of the financial crisis. The average daily turnover-value decreased by 45.19% from $US 5268.4M to $US 2887.4M. These changes were common for the majority of the world’s stock markets as an effect of the instability and insecurity in the financial market.

People in Sweden have a broad interest in the stock market. According to statistics from SCB (2010), 16.5% of the Swedish population own stocks through direct ownership. The availability of information and its flows are essential to make justified valuations of firms. However, the major problem is no longer to access the information but rather how to filter the most relevant. It is important to know how the information should be interpreted and used, especially for small private investors who choose to invest a share of their savings into the stock market.

1.2

Problem Discussion

When information is available, the biggest issue for small private investors is how to understand and apply the relevant information in their stock selection processes. There are three broad approaches that determine the value of a stock, and when to sell or buy. These are technical analysis, sentiment analysis, and fundamental analysis.

Technical analysis (TA) is used to analyze the historical patterns within the stock market in order to predict future prices. TA tells you, according to a set of parameters, when to buy or when to sell a stock. If the patterns can be correctly understood, it can

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relative strength index (RSI), moving average, or stochastic, are a few ways of doing a TA (Liu & Lee, 1997). TA requires constant surveillance which makes this approach less relevant for small private investors since the majority of them are “passive” and has a longer investment horizon. TA is therefore more suitable for traders, who buy and sell on a daily basis.

Sentiment analysis (SA) is about the psychology and behavior by the investors in the financial market. By analyzing the investors’ subconscious activities and psychological preferences, the actions and general pattern of the herd can be determined (Fontanills, Gentile & Cawood, 2001). Using these indications as a tool, one can take a forward position in order to beat the market (Sincere, 2003). Psychology and herd behavior are more common among small private investors, especially in times of crises. SA is also time-consuming and therefore the approach is less relevant for the majority of the small private investors.

Fundamental analysis (FA) is emphasizing figures and numbers (fundamentals) of a company’s financial reports. Actual earnings, equity, dividends, risk and growth are examples of commonly used fundamentals. Financial analysts that are using fundamental analysis argue that the valuation of a company can be calculated (Lev & Thiagarajan, 1993). This method could be applied by using one or several different valuation models and theories such as; Gordon Growth, Discounted Cash Flow (DCF), P/E ratio and EV/EBITDA ratio (Multiples) and Net Asset Valuation (NAV). However, even if analysts are starting off using the same valuation model, the valuation in the end often differs. One of the reasons is that some models, such as DCF requires a number of assumptions. Compared to TA and SA, the FA can be used to calculate a motivated value of a firm. We therefore believe that this approach is more interesting for small private investors, as it is possible to screen and choose potential “low-valued” stocks to invest in.

As there are a large number of different valuation models, the aim of this thesis is to increase the knowledge of small private investors regarding FA and enable them to filter relevant information. It is desirable to create an objective understanding in the work of financial analysts and how target prices are calculated. This is relevant because if small private investors know what models that provide similar estimations as the financial analysts’. The small private investors will through this paper create a greater understanding of the issues regarding subjectivity within financial reporting that fundamental analysis deals with. However, using one valuation model alone might not give the whole picture of the investment potential of the firm that is being valued. Therefore stock estimations should be treated cautiously.

1.3

Purpose and Research Questions

The purpose of this paper is to investigate the accuracy of five commonly used valuation models, included in fundamental analysis, in relation to the target prices set by professional financial analysts. The investigated valuation models are the Gordon Growth, Discounted Cash Flow (Free Cash Flow to Equity), P/E and EV/EBITDA (Multiples), and Net Asset Valuation.

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The models used will be applied on twelve different companies divided in four industries at the NASDAQ OMXS over the time period 2008-2011. The following research questions will be applied throughout the thesis in order to fulfill the purpose and contribute to the authors’ final conclusions;

 Do the estimations, generated from the investigated valuation models, provide similar results to analysts’ target prices?

 Is it possible to determine a more reliable valuation model, relative to the others, for an industry in general?

 Which of the investigated valuation models are the most appropriate for the chosen companies?

1.4

Delimitation

The research will focus on the comparison between the empirical results of the investigated models, and the analysts’ target prices. Therefore, the actual stock prices will be excluded, even though they are included in the models’ calculations.

The four chosen industries are telecom, retail, construction, and oil. The companies used in this research are listed at NASDAQ OMXS’ (Stockholm, Sweden) Large Cap or Mid Cap.

TA and SA will not be analyzed in this research, but instead FA will be in focus. However, FA normally includes external factors such as macroeconomic aspects, management board valuation etc., but this thesis will be limited to the numerical fundamentals.

Moreover, the aim of this paper is to investigate the market as a whole by conducting the analysts’ target prices in form of averages. Therefore, the large deviations from the individual analysts are not taken into considerations.

1.5

Literature Review

The literature review had three major aims: first, to create a structure and framework for the topic. Second, to collect the relevant information needed within this framework. And third, in order to provide an understanding of previous research. Information was gathered in form of scholarly articles, research papers and books.

Internet has been an important tool in the search for literature and in order to provide access to databases. JSTOR and SCOPUS are the databases that were mainly used, and have been available through Jönköping’s University Library. Through these databases books and articles were found, which provided us with additional sources and references that could be of value for our thesis.

The key words that were used in the search at these databases were Stock price, Stock price valuation, Fundamental analysis, P/E ratio, Gordon Growth, Discounted Cash

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

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2

Previous Research

According to Goedhart, Koller and Wessels (2005), the most accurate and flexible valuation method is the discounted cash flow model. However, the accuracy of the model depends on the forecasts on which it is based upon. Potential errors are the company’s return on investment, the growth rate, and the weighted average cost of capital, which will affect the valuation. Goedhart et al. (2005) further state that, using industry averages for the calculations of multiples are insufficient. Even though the valuated companies are operating within the same industry, the differences can be remarkable between firms in terms of the expected growth rates, returns on investment, and the capital structure.

Kaplan and Ruback (1995) investigated the relationship between the market value and the forecasted discounted cash flow. As a result, the average discounted cash flow estimations (out of 51 samples) showed to be within a 10% range from the current market price. Kaplan and Ruback mean that the discounted cash flow approach individually performs similar or better than multiples. However, the authors also argue that comparable valuation methods in form of multiples are useful, especially when they are used in combination with the discounted cash flow valuation approach.

Fernández (2001) argues that multiple valuation of companies’ equity is target for critics and is highly debatable. The major problem when using multiple valuations is the broad dispersions they cause. However, as a second-stage (combination) of the valuation, the multiples are useful. This is, after the valuation has been performed using another model, multiples are used with advantages for comparisons of comparable firms and thereby identify differences between the valuations of firms. He states that the P/E and EV/EBITDA multiples are the most useful to use for the building and construction, and the clothing industry, while P/E is the best multiple for the oil industry.

Lie and Lie (2002) state that the asset multiples provide more accurate and less biased estimates compared to sales and earnings multiples. In the meanwhile, the EBITDA multiple generally yields a better estimation than the EBIT multiple. Lie and Lie also say that using forecasted earnings for estimating company value is better than using trailing earnings. This is, even though adjustments for companies’ cash levels do not improve the estimation. The company size, profitability, and the extent of intangible value in the company affect the estimated value and the performance of the multiples. Liu, Nissim and Thomas (2002) investigated in their research, the performance of a number of value drivers in several valuation models, e.g. forecasted earning and historical earnings. The performance was evaluated by examining the deviation between the actual stock price and the predicted stock price. The result showed that forward earning multiplies performed remarkable well and about half of the sample was within 15% of the current stock price. Historical earnings multiples performed second best, followed by cash flow and book value of equity tied on third place.

Olbert (1992) investigated, in a survey-based study whether any valuation factors among professional financial analysts were more or less important. The analysts were about to rank several factors on a 1 to 5 scale for each industry (1 = most important, 5 =

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most important factor in most industries. However, Olbert found it difficult to generalize the other valuation factors, as some showed to be more important for specific industries. For instance, the result showed that the net asset valuation approach is more useful for real estate-, wood- and investment firms, while employee skills is more valuable for firms operating within the retail- and service industry.

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3

Frame of Reference

3.1

Market Efficiency

According to Fama (1970) market efficiency could be explained as an ideal market where stock prices at anytime fully reflect all available information. Market efficiency is based upon a number of assumptions (1) no transaction costs involved when trading securities, (2) all information is free and available to all investors and (3) all investors are assumed to be rational. This means that everyone agrees on the same implication based on the market information, current and future prices of each security. Market efficiency can be measured in three different subsets:

The weak form focuses on the fundamental analysis which is based on historical information. Investors seek to earn profit by studying financial statements to determine whether a particular stock is under- or overvalued.

Semi-strong form states that all information should be reflected in the market and therefore investors have no use of TA or FA. Only non-public information can benefit investors to abnormal returns.

The third subset is called strong form and means that all information, both public and private, are fully reflected in the market. Therefore, no further research can benefit investors to gain abnormal returns.

3.2

Growth

The growth rate is one of the most vital parameter in several stock valuation models. For instance, it is used to forecast future revenues and earnings. Evans (1987) argues that the growth rate is assumed to decrease with the firm’s age and size. In addition, Damodaran (2002) means that it is easier for small firms to have high growth rates compared to large firms as the growth rate is expressed in percentage terms. The historical growth rate is therefore less reliable in small firms.

Estimating the growth rate of a firm can be done in several ways. The most common way is to use the historical organic growth rate of either revenues or earnings. In addition, the market potential and the total estimated market value can, and should be used as complements (Damodaran, 2002). However, the historical growth rate is not always a good indicator of how a firm will continue to grow in the future (Cragg & Malkiel, 1968; Little, 1960). Little (1960) argues that there is almost no relationship (correlation of 0.02) between historical and future growth. However, the growth rates based on revenues are more likely to be persistent and more predictable than earnings growth. According to Damodaran (2002), the correlation is stronger between historical revenue growth and future growth compared to historical earning growth and future growth.

The two main approaches for estimating future growth are arithmetic average and geometric average, which both are based on historical data. The arithmetic average is

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average includes the compounding growth that occurs from period to period. For firms with volatile earnings, the result from the two approaches can vary widely (Damodaran, 2002).

The following formula is used to estimate the future growth rates

Growth rates are largely subjective and, as a rule of thumb, stable growth refers to a growth rate which cannot exceed the growth rate of the economy in which the firm operates. However, the stable growth rate can during periods exceed the economic growth rate with maximum 1-2% (Damodaron, 2002). Forecasted growth also requires considerations of how well the company is running, and the outlook for its product development, but also the general economic and political risks the markets serves (Gordon, 1962; Barker, 2001).

3.3

Dividend Discount Models

There are several different dividend discount models, such as; Gordon Growth model, two-stage growth model, and three-stage growth model. The growth rate is essential in which model to choose.

3.3.1 Gordon Growth Model

The Gordon Growth model is a fundamental approach that focuses on growth of dividends over time. By using the discounted present value of future dividend payments, the stock prices and the market value can be estimated (Gordon, 1962; Heaton & Lucas, 1999). The Gordon Growth model relates the value of a stock to its expected dividend in the next time period, the cost of equity, and the expected growth rate in dividends (Damodaran, 2002).

The interpretation of the model is straightforward. If both the shareholders’ expected rate of return and the level of future dividends are assumed to be fixed, the stock price should remain constant. However, the model is sensitive to small changes in the parameters since they are assumed to be relevant over a lifetime of a firm (Barker,

Formula 1

DPS1 = expected dividends next year

ke = cost of equity

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2001). There is a trade-off for firms whether it should increase the dividends or not. It is not for sure that the stock price will increase with raised dividends, but instead harm the growth of the company. This is because they are paying their shareholders instead of doing new investments (Fernández, 2007).

Even though the model is useful in estimating stock prices, it has some limitations. For instance, the model is not able to deal with corporations that pay zero dividends at the end of the first year. The model suits relatively stable and established companies better than start-ups or companies in distress. In addition, if the growth rate exceeds the cost of capital, the model will not work. Profits and dividends might be possible to forecast in the very near future. However, the model does not explain the underlying determinants of why dividend is growing (Gordon, 1962; Barker, 2001). The model is limited to stable growth since the earnings and dividends are expected to grow at the same rate in infinity (Fuller & Hsia, 1984, Gordon, 1962). If earnings grow faster or slower than dividends, the dividend payout ratio will over time converge towards zero or dividends will over time exceed earnings (Damodaran, 2002).

3.4

Discounted Cash Flow Model

The originally discounted cash flow (DCF) model assumes that the only cash flow shareholders can receive is the dividends. Until now, a large number of modified versions of the model have been established. More frequently, the fundamentals of a firm are used to estimate future cash flows discounted to the present value (PV). This is a useful tool to determine the investment potential of a particular firm (Damodaran, 2002).

According to Damodaran (2002) there are three main paths of discounted cash flows. (1) Equity Valuation: This approach determines the equity stake in the business, (2) Firm Valuation: Focuses on the valuation of the entire firm including equity, debt and other claim holders such as bonds, preferred shares, and (3) Adjusted Present Value (APV) Valuation: The last method is used to evaluate the firm in pieces. As long as the same set-up of assumptions is used, the three approaches will yield consistent estimations. However, mismatches between cash flows are important to avoid in order for the estimations not to be biased.

Formula 2

n = life of the asset

CFt = cash flow generated by the company in period i

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DCF models should, on a more individual basis, provide more accurate estimations than other models. The reason why is because forecasted cash flows, discount rates etc. are direct related to the firm being valued, or the industry that the firm operates within (Baker & Ruback, 1999). However, the model is highly sensitive to small changes in variables (Barker, 2001). The discount rate is determined by e.g., risk and historical volatilities. The cost of capital will also vary from asset to asset (Damodaran, 2002; Fernández, 2007). This makes small and young firms harder to estimate. This since there are a lot of uncertainty involved which make it hard to forecast the future cash flow as well as the appropriate discounted rates.

The market does make mistakes. It is therefore possible that stock prices can deviate from its intrinsic value even though this is assumed to be adjusted over time. There are also a number of scenarios where the uncertainty of DCF estimations increases. A few examples are: firms in distress, cyclical firms, firms with utilized assets, firms with patents-portfolios, firms in process or restructuring, firms involved in acquisitions and private firms. Such scenarios require an extension of the framework, and that is a challenge (Damodaran, 2002).

Free cash flow to equity (FCFE) is one of the most widely used approaches in DCF (Damodaran, 2002). The free cash flow excludes non-cash affecting items such as depreciation, non-distributed profits in associations, capital gains/losses and provisions (SFF, 2009). In this approach, the discount rate equals the required return by the investors, which is determined by the CAPM. The model is relevant for investors as it deals with the residual future cash flows. The residual cash flow is the amount of cash a firm can pay to its shareholders after all expenditures, re-investments, tax and debt payments etc. It estimates the potential dividends or share buybacks that will benefit the shareholders (Damodaran, 2002).

3.4.1 Two-Stage FCFE Model

The model has been developed since the constant growth FCFE model is limited to firms in stable growth. The two-stage model fits a wider range of firms and can estimate the value of a firm that initially is growing much faster than the rest of the economy for a limited time of period. However, the model cannot deal with companies in extremely high-growth period. The firm is after the high-growth phase assumed to jump to stable growth rate (Damodaron, 2002).

Formula 3

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

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The implication with the DCF approach is that it requires several assumptions. One important input is the growth rate, which is discussed in the Growth section (3.2). Other implications related to the model are e.g., whether a firm is cyclical or not, and whether to include or exclude restructuring costs or capital gains in the FCFE. For instance, if a firm is cyclical it is important to identify where in the cycle the firm is, and how this might affect the potential revenues and earnings in the future. Therefore, the model has to be customized and dealt with on a firm-specific basis (SFF, 2009).

3.5

Valuation Multiplies

Using multiples to valuate firms is a popular, simple, and the most commonly used way of measuring financial and operational performance (Damodaran, 2002). These multiples can be used independently or as complement to other valuation methods, such as the discounted cash flow approach. Multiple valuation comparisons require investors to assume that the comparable firms have equally proportional expectations about e.g., cash flows and risks as the company being valued. Hence can multiplies in theory provide accurate estimations. However, in reality comparable firms can be very different (Baker & Ruback, 1999).

3.5.1 P/E Multiple

The P/E ratio explains the relationship between the market value of a firm and its net profit (SFF, 2009). This approach is the most widely used, but also misused of all multiples. It has become an attractive method because of its simplicity and can be used for making judgments on relative value to pricing initial public offerings (Damodaran, 2002). The ratio is used by both investors and analysts to determine if individual stocks are reasonable priced (Shen, 2000). The P/E ratio can be measured as (Copeland, Koller & Murrin, 2000): Formula 4

FCFEt = Free cash flow to equity in period t

Pn = Price at the end of the extraordinary growth period

kn = Cost of equity in high growth (hg) and stable growth (st) periods

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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

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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

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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.

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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

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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:

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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).

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(=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

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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,

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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

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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

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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

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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.

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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.

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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.

Figure

Table 1. Company overview. The table above shows the companies and stocks that will be investigated  and  evaluated  through  this  paper
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
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
Table 5. EV/EBITDA – Stock Prices. For extended practical calculations see Appendix B(4)
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References

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