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Uppsala university Department of Economics D-level thesis Supervisor: Sebastian Arslanogullari Spring term 2006

What Characterises Successful Stocks? -A case study of Swedish companies between 1995 and 2005.

Gabriel Forss 800718-4735 Djäknegatan 40 018-25 80 19 [email protected]

Abstract This paper discusses the indicators of financial success for Swedish companies from 1995

until 2005. Quarterly data on 42 Swedish companies were collected from the Datastream data

base and analysed by using both portfolio analyses and parametric analysis. In this study,

financial success is measured by using the acclaimed concepts of the Sharpe ratio and the

Jensen’s Alpha. The Sharpe ratios of the companies are studied between 1995-2005 and this

discussion is complemented by analysis of the Jensen’s Alpha in the second half of that time

period i.e. 2000-2005. The relationship between these performance metrics and certain

company-characteristics such as the book-to-market ratio, the ROA measure and capital

structure is studied. The conclusion is that companies that have a high degree of profitability

and maintain high book-to-market ratios outperform other companies in terms of generating

excess returns to shareholders. Another interesting observation is the fact that company size

does not have any significant relationship to company performance.

Keywords: financial success, Swedish companies, profitability, stock returns, book-to-market,

Sharpe ratio, Jensen’s Alpha

Table of Contents

Chapter 1. Introduction .............................................................................................................. 1

1.1 Aim................................................................................................................................... 2 1.2 Limitations ....................................................................................................................... 3 1.3 Methodology and data ...................................................................................................... 4 1.4 Previous research.............................................................................................................. 5 1.5 Structure ........................................................................................................................... 6

Chapter 2. Independent Variables .............................................................................................. 7

2.1 The book-to-market ratio ................................................................................................. 7 2.2 Size ................................................................................................................................... 9 2.3 Return on Assets (ROA) ................................................................................................ 11 2.4 Capital Structure............................................................................................................. 12 2.5 Liquidity ......................................................................................................................... 14 2.6 Cash Conversion Cycle .................................................................................................. 15

Chapter 3. Proxies for financial success................................................................................... 17

3.1 The Sharpe Ratio............................................................................................................ 17 3.2 The Jensen’s Alpha ........................................................................................................ 18

Chapter 4. Results and discussion............................................................................................ 20

4.1 Portfolio Analyses .......................................................................................................... 20 4.1.1 ROA ........................................................................................................................ 20 4.1.2 Book-to-market ratio ............................................................................................... 21 4.1.3 Size .......................................................................................................................... 23 4.1.4 Liquidity .................................................................................................................. 24 4.1.5 Capital structure and the cash conversion cycle...................................................... 26

4.2 Parametric analysis......................................................................................................... 26 4.2.1 Reliability ................................................................................................................ 28

Chapter 5. Concluding remarks................................................................................................ 30

5.1 Suggestions for future research ...................................................................................... 31

Bibliography............................................................................................................................. 32

Appendices ............................................................................................................................... 34 Appendix A: List of Companies........................................................................................... 34 Appendix B: Additional portfolio analyses.......................................................................... 35 Appendix C: Complete regression analyses......................................................................... 37 Appendix D: Tests of Heteroskedasticity and autocorrelation............................................. 39

Chapter 1. Introduction

Why are some companies more successful than others in terms of generating returns on

stocks? That is undoubtedly a question that many an investor has contemplated over the years.

The question is as simple as it is intriguing. Indeed, this problem seems quite straightforward

but at the same time it is obvious that there is no easy answer to it. Being able to firmly assert

which companies would outperform others in terms of stock returns of course on the outset

seems quite difficult. Nevertheless, the area opens up for much interesting research and

innovative approaches. The central question contemplated is whether or not there are certain

indicators of successful companies. Can success be broken down into different measurable

factors and scrutinised? Are there in fact certain company-specific characteristics that would

add up to a statement on what constitutes a so-called “winning bet” on the stock market?

These thought-stimulating postulations certainly provide scholars with many interesting

starting points of analysis. Different frameworks of analysing success-related variables are

also possible to set up. To be able to create and evaluate such frameworks is indeed an

enticing thought to many scholars of financial economics and indeed to investors everywhere.

Such models are of course immensely difficult to construct, especially bearing in mind that

these models hopefully in the end should serve somewhat of a predictive function. Predictive

in the sense that one from such a model should be able to conclude that companies of a certain

kind would yield larger returns than others. Obviously, reaching firm conclusions in such a

matter is quite difficult a task. Nevertheless, the usefulness of a reliable model of this kind

would undoubtedly be great.

Recently, the work of economists interested in company valuation has also been facilitated

through the greater availability and user friendliness of comprehensive data bases which

constitutes valuable sources of information. This evolution has to a great extent simplified the

process of data collection of many researchers and has contributed greatly to the production of

interesting articles and theses within the academic community.

Every rational investor is obviously interested in achieving the highest possible return on his

investment. Consequently much attention within this field is aimed at determining what

factors drive above stock market performance of certain stocks. Simply by quickly glancing

through an economic journal it is evident that some stocks fare better than other and create

returns that are in excess of the average on the stock market. Therefore it has for a long time

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been of interest not just to the financial economics academic community but the actual

financial practitioners, who are doing business and working in different consulting firms.

Building on this interest in achieving financial success, much research has been performed

with regards to this phenomenon. Economists and financial advisors are constantly searching

for winning stocks and this search has spurred a continuously growing field of research.

Therefore, there is certainly a huge bulk of literature to benefit from and to also be able to

make a contribution into.

1.1 Aim

This study aims at investigating Swedish companies and determining what factors are key in

terms of generating returns to shareholders. More specifically, the objective is to assert what

impact certain variables have in terms of generating above-average stock returns. A

conclusion will thus be made as to which are the indicators of successful companies

(throughout this paper, success of a company will refer to the notion of creating excess wealth

for shareholders in relation to other companies). Therefore it is assumed that the success of a

company is not due to chance but that there is a relationship between some important

company-specific factors and shareholder wealth. Perhaps these factors will not completely

explain the variation in stock returns but certainly to a great extent. Great effort has therefore

been made in choosing the explanatory variables and this will be discussed in greater detail

later on. At the moment it suffices to simply mention which variables will be tested in terms

of explaining the financial success of companies. As such, the variables have been chosen to

reflect certain important financial aspect of the companies and the variables are: size, book-to-

market ration, return on assets, capital structure, liquidity, cash conversion cycle. These

concepts thus serve as potential indicators of superior financial performance. Indeed, these

variables must also be assumed to illustrate important financial features of different

companies and these should also be sustainable in the sense that they apply equally well

across industries and types of businesses. This is important considering the wide range of

businesses that are investigated in the study.

On the other side of the analysis one should at this stage mention that the variables that will

define success are the Sharpe Ratio, and the Jensen Alpha. Financial success will thus be

measured using these two different methods. These concepts will be dealt with in great detail

later on but should nevertheless at least be mentioned now. As with the explanatory variables,

these variables have been chosen from practice developed within earlier studies and they

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serve as widely recognised and renowned measures of accumulation of excess shareholder

wealth. All of these variables have been computed from information found in the Datastream

data base.

1.2 Limitations

The companies used in this study are all companies that are registered on the Swedish stock

market. Data for the companies are without exception taken from the data base called

Datastream.1 For the selection of companies the starting point was all the companies that were

registered at the stock exchange at the first of January 2005, which amounted to 271. The next

step was to choose a year from which to start the study. Many aspects were considered,

mainly time specific events such as tax reforms, regulations of the financial markets and the

presence of the financial crisis of Sweden in the beginning of the 90s. All of these instances

would surely affect the financial data of the companies but in the end the year 1995 was

chosen as a good time of departure since many of the above-mentioned events had taken place

and been internalised into the market. Thus the time period selected for the study ranges from

1995 until 2005. Selecting this time period undoubtedly had some precise implications for the

availability of data and the selection of units to be studied. It was contemplated to choose a

somewhat shorter time period. Surely, such an outlook would have simplified the matters of

finding complete data sets, but in the end it was nevertheless decided to stick with the original

period and instead cope with having fewer companies present in the study. Recognising that

many overwhelming changes occurred within the financial system and the capital market in

the early years of the 1990s, for the reasons mentioned above 1995 was chosen as a good year

of departure for this study.

With regards to the selection of companies, it was essentially restricted by the availability of

data. Finding complete data sets for the companies was necessary albeit tedious work.

Evidently the companies had to exist for the entire time period and complete sets of data on

the variables in the study were needed .Working backwards from 2005 to 1995, many lapses

and instances of missing data in the data sets in Datastream narrowed the selection down to 42

companies. This process of natural selection so to speak, although not optimal is nevertheless

a factor which shouldn’t hamper the study to any great extent, especially not statistically

anyway.

1 Datastream available at Uppsala university.

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1.3 Methodology and data

This paper is an empirical analysis of a selection of Swedish companies. Being quantitative in

its design, the study uses quarterly data and assesses the importance of six company-specific

characteristics on the ability of companies to generate shareholder wealth. More specifically,

the study is divided into two parts. A more practically-oriented part consisting of investment

strategies will be accompanied with a parametric method of regression analyses.

The first part is based on a method commonly used within the business of stock valuation.

The procedure is as follows. At time t, the companies will be sorted into seven different

portfolios according to their score on one of the six indicators used to affect financial success.

This means that the companies with the highest book-to-market ratio (for instance) will be

placed in portfolio number one whereas companies with lower book-to-market ratios will be

divided into corresponding groups with portfolio number seven consisting of the companies

with the lowest ratios. At t+1 the objective is then to study how these different groups of

companies fare with regards to generating shareholder returns (measured by the Sharpe Ratio

and the Jensen’s Alpha). At time t+1 the procedure is repeated with new groups of companies

being constructed due to their book-to-market ratios. Similarly the creation of shareholder

returns is studied over the subsequent time period and so on. This process will be repeated

with all of our indicator variables. A difference in shareholder returns should hopefully be

distinguishable, at least between portfolio number one and portfolio number seven. Albeit

quite straightforward and investment-based, this valuation method suffers from not being able

to ascertain whether the results are statistically robust or not. Therefore further statistical

analysis needs to be incorporated into the study.

To accompany this broad method of company valuation a parametric analysis will follow.

Thus, the relationship between the indicator variables and the two measures of financial

success will be studied. The method used to evaluate this relationship will be a plain and

simple ordinary least squares (OLS) regression model. The intuition and workings of such an

estimation is well-known and there is no need to further dwell on it here. The use of it is

indeed quite conventional and should be familiar to all scholars of financial economics.

Consequently, the x-variables will thus contain the quarterly data collected for the 40

companies on the six indicator variables. These are subsequently regressed on the measures of

financial success, first on the Sharpe Ratio and then on the Jensen’s Alpha. Computing the

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quarterly numbers for the Sharpe Ratio is easily done but generating time series of Jensen

Alphas is a bit trickier. Essentially, the Jensen Alpha is the alpha that appears in the Capital

Asset Pricing Model (CAPM) equation2 which means that if one is supposed to compute time

series on alpha, there is a need to know the beta for the company. Therefore, the beta values

for the different companies during 1995-2005 have been estimated using the CAPM equation.

This estimation of beta has then been used to generate the company-specific values of the

Jensen Alpha on a quarterly basis.

Furthermore, in order to make the regression analysis interesting from an investing point of

view, the indicator variables have been lagged by one time period. This means that for

instance the ROA value at time t will be coupled with the Sharpe Ratio of time t+1. This will

produce a model where the predictive functions of the indicator variables will be thoroughly

tested.

1.4 Previous research

This paper benefits greatly from an earlier study by Johnson and Soenen (2003). In fact their

article is the chief instance of motivation for this present paper. In that article the authors

perform a study of 478 American companies during a time period of 16 years and investigate

what are good and reliable indicators of success of those companies. Both in terms of

reference literature and other practical issues this study has been of great inspiration to me.

Nevertheless, due to certain restrictions, such as time and scope and what not, I have not been

able to perform as comprehensive a study as Johnson and Soenen. The similarities between

our work should however be acknowledged. As to my knowledge no previous study of this

kind has been performed on Swedish companies and therein lays the contribution of this paper

to the scientific community. Hopefully further insights into the characteristics of Swedish

companies and the Swedish stock market will thus be generated.

The explanatory variables, which will be discussed in the following chapter, have been

selected from those used by Johnson and Soenen and from other similar studies. They

represent a broad spectrum of company-specific characteristics and thoroughly portray the

financial situations of the different companies. A couple of other studies could be mentioned

as more frequently cited in this paper and among these the influential work of Fama and

French (1992) and (1998a) is greatly used. Their work on the importance of the much 2 Ri-Rf=alpha+beta(Rm-Rf)

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publicised book-to-market ratio has been quite important for this study. Also the work of

Sharpe (1994) and Jensen (1969) have been used to a great extent for the calculation of the

different measures used to operationalise financial success.

1.5 Structure

The structure of this paper is quite straightforward. This introduction will be followed by two

chapters that comprehensively describe and discuss and the choice of variables in the study.

The fourth chapter will contain the results of the OLS regression analysis and will also

contain a discussion of the results obtained. Finally, a concluding chapter will round off the

paper with a summary of the conclusions reached by the study. Ideas of further research will

also be mentioned.

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Chapter 2. Independent Variables

This chapter will give a thorough description of the choice of independent variables for the

study. These variables have been selected from the vast literature of previous studies on this

subject. The discussion in this chapter will also mention the ways in which these variables

have been operationalised.

2.1 The book-to-market ratio

In a much renowned article Fama and French (1992) investigate the importance of company

size and the book-to-market ratio in order to explain the returns of individual stocks. One of

their important conclusions is that the book-to-market ratio is indeed quite instrumental in

explaining average stock returns. In fact they state that there is “a strong cross-sectional

relationship between average returns and book-to-market equity.”3 The usefulness of the

book-to-market ratio in order to explain and predict the success of a company is further

acknowledged by other studies. For example Rosenberg, Reid and Lanstein (1985) show that

average returns on U.S. stocks are positively related to the companies’ ratio of book values of

common equity to their market value. This observation holds for the Japanese firms as well as

Chan, Hamao and Lakonishok (1991) report that the book-to-market ratio in fact is equally

important in explaining the financial success of the companies registered on the Japanese

stock exchange. Nevertheless, the emergence of the book-to-market ratio as an important

determining factor in order to predict stock returns was firmly established by Fama and

French (1992) and (1998a,b).

The book-to market ratio is also extensively used as an indicator of a firm’s growth

opportunities. Kothari and Shanken (1997) study the significance of the book-to-market ratio

and the notion of dividend yield as indicators to predict stock returns. Their investigation

show that the book-to-market ratio indeed provides a good measure of financial success of the

Dow Jones Industrial Average for the time period 1926-1991. Even though dividend yield

holds a slightly stronger explanatory position in certain sub periods, the book-to-market ratio

remains the stronger of the two variables for the entire period. They thus provide reliable

evidence that the book-to-market ratio track time series variation in stock returns.

3 Fama and French (1992), p. 440.

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Further evidence of the usefulness of the book-to-market ratio as an indicator of stock returns

is given by Pontiff and Schall (1998). In a similar vein as Kothari and Schanken (1997) they

investigate the book-to-market ratios of the Dow Jones Industrial Average. Their conclusion

is that the book-to-market ratio indeed serves as a good forecast of future market return on

stocks. In their study they also include and control for other variables that have previously

been assumed to predict market returns, such as default spreads, interest rates, term structure

slopes, and dividend yields. These indicators do not share the same predictive and forecasting

power of the book-to-market ratio however and the usefulness of the book-to-market ratio as

an indicator of stock return is once again emphasised.

Fama and French (1998b) ascertain that investors classify stocks that have high ratios of

earnings to price, cash flow to price or indeed book-to-market as value stocks. A value stock

is a stock that tends to trade at a lower price in relation to its fundamentals; such a stock is

therefore considered undervalued by investors. Value stocks are often contrasted against

growth stocks. Growth stocks (sometimes referred to as ‘glamour stocks’) are found in

companies that are growing. These stocks do not usually generate dividends as the company

prefers to put their retained earnings into new investments. Stocks from growing companies

are often overvalued, having low book-to-market ratios.

Nevertheless, interested in the book-to-market ratio a value investor would thus believe that

the market is inefficient and that it is possible to find stocks that are traded for less than they

are worth. Indeed several studies, including Fama and French (1998b) and Lakonishok,

Schleifer and Vishny (1994) contend that there is a significant value premium for U.S. stocks.

Value stocks, i.e. stocks with high ratios of book-to-market (B/M), earnings to price (E/P),

and cash flow to price (C/P) do create higher returns on the average than stocks with low

B/M, E/P, C/P. There is also evidence that this value premium exists in other markets as well,

for instance Chan, Hamao, and Lakonishok (1991) reports corresponding findings is Japan.

Fama and French (1998b), studying the returns generate by value stocks on the one hand and

growth stocks on the other conclude that “value stocks tend to have higher returns than

growth stocks in markets around the world” and “sorting on book-to-market equity, value

stocks outperform growth stocks in twelve of thirteen different major markets during the

1975-1995 period.”4

4 Fama and French (1998b), p. 1997.

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In this study, the book-to-market ratio is reported as an indicator of value stocks. The book-to-

market ratio is calculated in a straightforward manner following Fama and French (1992),

taking the book value of common equity in the firm divided by their corresponding market

value.

2.2 Size

As Johnson and Soenen (2003) states, company size is “the second most publicized variable

to explain stock returns.”5 The preoccupation of company size as a proxy to depict stock

returns, hence creating shareholder value, is thus evident in much research. Fama and French

(1992) maintain that in the presence of rational pricing of assets, stock risks are

multidimensional. An aspect of this multidimensionality is consequently that of company size.

Since Fama and French (1992) were very much interested in deriving the relationship between

financial leverage and security returns, they excluded financial firms in their selection of

sample firms. Because high leverage is quite normal for financial firms and not necessarily a

signal of financial distress (as it typically is for non-financial firms), financial firms were left

out of their study. The Fama and French study did however single out the variables of

company size and the book-to-market ratio as being the most important ones in terms of

generating security returns. The chief finding of their study is that stock returns are negatively

related to size but positively related to the book-to-market ratio.

In subsequent research performed by Barber and Lyon (1997) the robustness of the results of

Fama and French (1992) are tested by examining the applicability of size and book-to-market

ratio as proxies explaining the stock returns of financial firms which, as noted, were excluded

in the previous study. Barber and Lyon’s conclusion is that financial and non-financial firms

indeed have very similar return patterns and that “both financial and nonfinancial firms

exhibit a significant size and book-to-market premium.”6 Small companies with high book-to-

market ratios tend to achieve higher stock returns. Barber and Lyon are also unable to reject

the hypothesis that financial and non-financial firms would differ in terms of their premium

on the variables of size and book-to-market ratio. Therefore, “firm size and book-to-market

ratios have similar meanings for financial and nonfinancial firms – at least as they relate to

security returns.”7

5 Johson and Soenen (2003), p. 365. 6 Barber and Lyon (1997), p. 876. 7 Id.

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Laporta et al. (1997) find similar evidence of the similarity between financial and non-

financial companies with regards to the usefulness of size and book-to-market ratio as

indicators of superior stock returns. Evidently, these two variables explain the stock returns of

companies in an economically meaningful way, both financial and non-financial ones.

Furthermore, Laporta et al. (1997) discusse the role of expectational errors by investors as a

possible explanation why value stocks outperform growth stocks. These behaviouristic

explanations are undoubtedly gaining ground in the world of finance these days. There is no

doubt however, that the size and book-to-market ratio of a company represent two core

fundamentals that are highly correlated to stock returns.

Rouwenhoust (1999) is also preoccupied with size and the distinction between value stocks

and growth stocks. As has already been noted, numerous studies confirm that American

stocks exhibit a significant relationship both to size and the book-to-market ratio.

Rouwenhoust reports that similar studies have been performed on developed equity markets

outside of the United States with findings similar to those of American stocks. In an attempt

to extend this analysis to emerging markets, Rouwenhousts study shows that the many of the

characteristics of the developed equity markets are prevalent in emerging markets as well.

More importantly, Rouwenhoust concludes that “averaged across all emerging markets,

stocks exhibit momentum, small stocks outperform large stocks, and value stocks outperform

growth stocks.”8

It is interesting to note that, whereas Fama and French (1992), Rouwenhoust (1999), and

Barber and Lyon (1997) report a negative relationship between size and stock returns,

Johnson and Soenen (2003) find just the opposite. Johnson and Soenen in their study find that

size indeed shows a significant positive relationship to stock returns. This positive

relationship is also documented in Shefrin and Statman (1995).

There are several different ways of measuring company size. There is the possibility of

computing company size by taking the price per share times the shares outstanding or simply

using the market value of the firm as estimation where such information is available. In this

study, the market value of the company will be used as a proxy for company size.

8 Rouwenhoust (1999), p.1441.

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2.3 Return on Assets (ROA)

As a measure of profitability the concept of return on assets (ROA) is commonly used. ROA

is thus a proxy for how profitable a company is relative to its total assets. As Johnson and

Soenen (2003) state, ROA “is an asset utilization ratio that indicates how effectively or

efficiently a firm uses its assets.”9 Thus, ROA indeed gives an idea of how efficient

management is at generating earnings in relation to the firm’s total assets. Their study also

reports a significant positive relationship between stock returns and ROA.

Furthermore, as highlighted by Johnson and Soenen, the “effectiveness with which a fixed

capital, working capital and other assets are employed obviously is a driver of growth.”10

Therefore the profitability of the firm, exemplified by a greater level of ROA, gives an idea of

the growth possibilities of the firm. With regards to investment strategies, the profitability of

the firm indeed is an important characteristic which is important to pay attention to.

In this study, the ROA is computed as the ratio between the net income of the firm relative to

their total assets. Thus it is displayed as a percentage which shows what earnings are

generated from invested capital (assets). The ROA ratio is thus a good indication of whether

or not management are continuing to earn increasing profits on investments. Investors would

thus expect good management to strive for a greater ROA ratio, since that would mean that

greater profit on each invested assets is being extracted.

Another common indicator of profitability which is commonly used by financial analysts is

the return on equity (the ROE). Though still perhaps being the most widely employed metric

of profitability there is evidently some shortcomings to it. Evidently, the ROE does not tell

investors anything about whether or not a company has excessive debt or is using debt to

drive returns. Such factors are obviously important to take into considerations when planning

investments. In such situations, it is important to note that the ROA is computed with total

assets in the denominator. Total assets are of course calculated as the sum of liabilities and

shareholders equity. Therefore, the debt situation of the firm is incorporated into this measure

and thus resolves one of the problems typically associated with the ROE measure.

9 Johnson and Soenen (2003), p.365. 10 Id.

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2.4 Capital Structure

Ross, Westerfeld and Jaffe (2005) suggest that “the theories of capital structure are among the

most elegant and sophisticated in the field of finance.”11 This bold statement hints at the

importance of a firm’s capital structure for achieving greater financial success for the

company. In an influential paper Bradley, Jarrell, and Kim (1984) assert that the notion of

finding an optimal capital structure for the firm has been “one of the most contentious issues

in the theory of finance during the past quarter century.”12 This interest and preoccupation

with capital structure has been huge and it is obvious that this field of study has continued to

generate great interest in recent years. Nevertheless, as highlighted by Ross, Westerfeld and

Jaffe it is difficult to prescribe any exact formula for computing the optimal rate of leverage

for the firm. Evidently, the debt-to-equity ratio is quite an elusive concept. Nevertheless, an

intriguing relationship between the leverage and the profitability of a firm seems to exist.

In this study the capital structure of the firms refer their employment of leverage; therefore it

is measured as the ratio of long-term debt to total assets. Financial leverage is often believed

to comprise many advantages. If there are investment opportunities available to a firm which

would generate a higher return than the required interest rate for borrowed funds, additional

leverage would thus be beneficial. These differences generate greater profits for shareholders

and increase the return on equity. As Johnson and Soenen (2003) points out, this argument “is

largely based on the tax deductibility of interest expenses making borrowing a ‘cheaper’

source of financing than equity.”13

Using debt as a tool to drive returns hinges on the assumptions behind the so-called ‘trade-off

theory’ of capital structure. This theory posits that there is a trade-off between the “tax

advantages of debt and various leverage related costs.”14 Such costs are those often associated

with financial distress and include both direct and indirect costs. The trade-off theory thus

implies that there is an optimal level of debt to every company which consequently should be

pursued by financial managers.

11 Ross, Westerfeld and Jaffe (2005), p. 461. 12 Bradley, Jarrell, and Kim (1984), p. 857. 13 Johnson and Soenen (2003), p. 365. 14 Bradley, Jarrell, and Kim (1984), p. 857.

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A line of thought which holds several objections to the trade-off theory of capital structure is

the one commonly attributed to the work of S.C. Myers15, namely the pecking-order theory.

The main point of difference compared to the trade-off theory is that a target level of leverage

for the firm does not exist and that profitable firms use less debt. Therefore a negative

relationship between profitability and leverage is implied. As described by Ross, Westerfeld

and Jaffe profitable firms generate internal funds which can be used to finance projects. This

type of financial slack is often quite beneficial and desirable for the firm. The source of

financing available to firms can therefore be ranked in order, hence the name pecking-order

theory.

As described by Leary and Roberts (2004) internal funds are at the bottom of this pecking

order with equity being at the top. The firms’ preference order of financing sources is due to

asymmetric information between managers and investors with timing being an essential

attribute of financial managers. Leary and Roberts (2004) assert that “since internal funds

avoid informational problems entirely, they are at the bottom of the pecking order.”16

Accordingly, equity holds the largest adverse selection costs and “when internal funds are

insufficient to meet financing needs (i.e. financing deficit), firms turn first to risk-free debt,

then risky debt, and finally equity, which is at the top of the pecking order.”17 Leary and

Roberts also do point out that the empirical evidence concerning the pecking-order theory is

conflicting, with several studies suffering from statistical power problems. Therefore it is

subject to extensive research which will determine which way to go concerning capital

structure.

Nevertheless, with regards to the capital structure of firms there seems to be contradictory

results stemming from different empirical studies. Bhandari (1988) finds evidence that “the

expected returns on common stocks are positively related to the debt/equity ratio (DER),

controlling for the beta and firm size.”18 Fama and French (1998a) on the other hand, provide

conclusions which are at odds with the generally accepted view of the relationship between

debt and firm value. Instead of the assumed positive relationship between debt and firm value,

they find no reliable evidence of the supposed tax effect. Their results point to a negative

15 Myers,S.C. (1984) The Capital Structure Puzzle, Journal of Finance 39. 16 Leary and Roberts (2004), p.5. 17 Id. 18 Bhandari (1988), p.527.

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relationship between these two notions exposing the fact that increased leverage is bad news

for firm value.

2.5 Liquidity

A notion that is undoubtedly related to the previous discussion on capital structure is that of

company liquidity (previously referred to as financial slack). Measuring the liquidity of a

company is usually done by taking the most liquid assets, i.e. cash and other marketable

securities, as a fraction of the total assets. This method is also employed in this study where

liquidity is computed as the ratio between common assets and total assets. The term common

assets is used in the Datastream data base referring to notions such as cash and short term

securities. This term is deemed to be an adequate measure of the liquid resources of the

companies.

The liquidity of a company is important in several respects. First and foremost, having a stock

of cash at hand greatly facilitates the process of being able to perform new investments.

Obviously, a greater degree of liquidity means that the company is likely to have sufficient

funds ready for upcoming investment projects. When projects with positive net present values

emerge it is often desirable to act quickly and in such situations internally generated funds are

of the utmost importance. Johnson and Soenen (2003) highlight this aspect of liquidity by

stressing that having cash at hand is “most valuable to firms with plenty of positive-NPV

growth opportunities.”19 Therefore, the ability of being able to use internal funds as the chief

resources of funding for new investments is the most important advantage of having a high

degree of liquidity within the company.

However, there are also some documented downsides to having a large amount of liquid

resources at the company’s disposal. An excessive amount of liquid resources is not always

beneficial for the firm. According to Johnson and Soenen such a situation might signal “slack

management practises.” From the onset there is obviously already an agency problem between

management and share holders which can be augmented by an increased amount of cash at the

management’s disposal. The relationship between shareholders and management can be

described as a principal – agent problem from which certain typical features can be discerned.

Jensen (1984) describes the relationship between shareholders and corporate managers as one

‘fraught with conflicting interests.’ It can be assumed that both the shareholders (the 19 Johnson and Soenen (2003), p.366.

14

principals) and management (the agents) share some fundamental interests, that the company

is successful etc. Be that as it may, there are undoubtedly some diverging views with regards

to deal with any potential financial slack. The directors of the company might use such funds

for expanding their on-the-job consumption, having a greater spending account or other job

perks and requisites. The shareholders would perhaps rather have the financial slack returned

to them in form of dividends or invested in new projects. Situations as the one described

above is undoubtedly quite frequent in the business world. Jensen (1984) discusses the

negative aspects of having plenty of free cash flow and stresses that managers in such

situations tend to make bad acquisitions. Managers might thus be encouraged to take it easy

when the free cash flow situation is excessive.

2.6 Cash Conversion Cycle

Johnson and Soenen (2003) assert that “efficient working capital management is an integral

part of overall corporate strategy to create shareholder value.”20 Working capital refers to the

capital that a company needs in order to handle its day-to-day operations. It is commonly

accepted as an indicator of a company’s efficiency and its short term financial health. The

concept of the cash conversion cycle typically refers to “the continuing flow of cash from

suppliers to inventory to accounts receivables and back into cash.”21 Originally, the notion of

the cash cycle stems from the work of Gitman (1974). In this article Gitman provides a

complement to original cash flow analysis for establishing the minimum liquidity

requirements of the company. The technique he derives makes it possible to make quick

estimates of company specific liquidity preferences.

Nevertheless, building on the work of Gitman (1974), the concept of the cash conversion

cycle was further developed by Richards and Laughlin (1980). Essentially, Richards and

Laughlin were concerned with the quite static view of handling the balance sheet liquidity

ratios in order to calculate the company’s liquidity position. In their view, the cash conversion

cycle was best conceptualised as “reflecting the net time interval between actual cash

expenditures on a firm's purchase of productive resources and the ultimate recovery of cash

receipts from product sales.”22 It was also necessary to adopt a payables turnover concept in

20 Johnson and Soenen (2003), p.366. 21 Id. 22 Richards and Laughlin (1980), p.34.

15

order to extend “the operating cycle analysis to incorporate both the relevant outflow and

inflow components.”23

In this study the cash conversion cycle is measured following Johson and Soenen (2003) and

is computed as (inventories + accounts receivables - accounts payables) *360 / total sales.

Their calculation is very much based on the concept established by Gitman (1974) and

Richards and Laughlin (1980).

23 Richards and Laughlin (1980), p.36.

16

Chapter 3. Proxies for financial success

This chapter deals with the two variables defining financial success in the study, namely the

Sharpe Ratio and the Jensen’s Alpha. A general background to the functioning of these

variables will be accompanied with a description of the computations used in order to derive

them.

Before we turn to the measures of financial success used in this study it is of interest to say a

few words of the selection of these concepts. Evidently, there are several measures available

which are used to measure financial success. Examples of such concepts are for instance the

Treynor ratio, and the method of computing the Economic Value Added (EVA). The Treynor

ratio is quite similar to the Sharpe ratio but differs in the respect that the Treynor ratio uses

the beta of the portfolio as a measure of its volatility whereas the Sharpe ratio uses the

portfolios standard deviation. Taking the market risk into account obviously reveals much

information, and it should be accounted for in some way. In this study, the market risk aspect

of the portfolios is captured in the use of the Jensen’s Alpha as a complement to the Sharpe

ratio.

The EVA measure has received a lot of attention in recent years.24 In essence this concept

describes what amount of shareholder value the company is creating. Thus, this performance

metric is computed by subtracting the cost of a company’s capital (both equity and debt) from

its operating profits. Johnson and Soenen (2003) use the EVA as a performance metric of

financial success in their study and also provide an outline of how to compute it using data

from the Compusat data base. Though, similar to the data base used in this study of Swedish

companies, the concepts used in Compusat are not completely comparable to the ones used in

Datastream. For that matter it was also much a question of availability of data. Therefore, the

EVA was not incorporated into this study and the two metrics of performance that are used

are consequently that of the Sharpe ratio and the Jensen’s Alpha.

3.1 The Sharpe Ratio

The Sharpe ratio is essentially a measure of a portfolio’s risk-adjusted return. In fact the ratio

measures performance relative to total risk. It is a very practical proxy because it reveals

information whether or not the excess returns of a portfolio come with much additional risk.

24 see for example Hecking (2002)

17

Therefore, the Sharpe ratio is often used to distinguish ‘good investments’ from ‘bad

investments’ because although a portfolio may generate greater returns than its peers it is

important to determine if those excess returns do not come with too much extra risk. In

Sharpe (1994) the user friendliness of the Sharpe ratio is discussed and the ratio is calculated

by using the following formula.

In order to compute the Sharpe ratio it is thus necessary to take the risk free interest rate into

account. In this study, the risk free rate is calculated as the average of the three months market

interest rate of the period. Data on the three months rate were taken from the SCB25 and

computed accordingly.

Evidently, the Sharpe ratio represents a useful method for evaluating the financial

performance of companies. It does, however, have some shortcomings which highlight the

need to complement it with an additional performance metric.

3.2 The Jensen’s Alpha

The Jensen measure is based on the Capital Asset Pricing Model (CAPM). More specifically,

it is a risk-adjusted performance measure that represents the average return above the return

predicted by the CAPM, given the portfolio’s beta and the average market return. This rate of

return is the portfolios alpha, hence the name Jensen’s alpha.

25 www.scb.se

18

The interesting aspect conveyed by Jensen’s Alpha, as compared to the Sharpe Ratio is the

fact that that Jensen’s Alpha brings the market risk into consideration. Therefore, the measure

accounts how well portfolio manager is at dealing with the systematic risk. Essentially, the

Jensen’s Alpha gives an indication of the degree to which the portfolios are earning

significant returns after accounting for market risk, as measured by beta. If the portfolio is

earning a fair return, for the given market risk, then alpha would be zero. A positive value of

the Jensen’s Alpha is thus a sign of a portfolio earning greater returns than would be expected

by the CAPM.

With regards to the computation of the Jensen’s Alphas in this study the market return has

been computed by using the general index. It was thus decided to use a broad index for the

bench-mark portfolio and the general index was a top candidate for that position. Also, for

this study to work, it was necessary to calculate time series of the Jensen measure to comply

with the other collected data. In order to generate series of Jensen Alphas on a quarterly basis

it was thus necessary to determine the value of beta for the different companies used in the

study. It was contemplated to use the measures found in the publication “Börsguiden” but this

idea was discarded due to lack of available information. Instead, it was necessary to calculate

these beta values using regression analyses. A problem that emerged was the fact that it was

impossible to calculate statistically correct beta values for the entire time period 1995-2005.

In order to avoid the statistical problem of “errors in variables”26 it was needed to limit the

values of Jensen Alpha to the latter part of the time period. More specifically this was done by

dividing the sample companies into two different parts with respect to two different time

periods. The first group consists of the time period 1995-2000 and the second group thus

deals with the time period 2000-2005. Subsequently, using regression analyses on the first

time period, beta values were obtained for the companies. These beta values were

consequently used to generate quarterly time series of alpha for the second time period.

Therefore, analysing the indicator variables in connection with the Jensen Alphas will only be

performed for the time period 2000-2005.

26 see Campbell, Lo, and Mackinley, (1997) for a discussion of this phenomenon.

19

Chapter 4. Results and discussion

This section conveys the results of the analysis of what are the indicators of financial success

for Swedish companies during the time period 1995-2005. The method of portfolio analysis

which was introduced earlier constitutes a point of departure for presenting the outcome of the

investigation.

4.1 Portfolio Analyses

4.1.1 ROA

The first portfolio analysis concerns the profitability measure ROA. Not surprisingly, the

portfolios that exhibited higher ratios of ROA generated superior returns in the following

quarter. The results are presented in the table below.

Table 1

This table presents the average returns (provided by the Sharpe Ratio) of seven different portfolios which are ranked according to their score on the ROA measure. Portfolios range from 1995-2005 and are reformed every quarter.

Portfolio 1 (high ROA) 0,067319

Portfolio 2 0,011477



Portfolio 5 -0,02084


Portfolio 7 (low ROA) -0,26444

The Sharpe ratios of the different portfolios exhibit a clear and visible pattern with the top

four portfolios generating positive values of the Sharpe ratio whereas the bottom three

perform very poorly. Especially, the portfolio consisting of the least profitable companies

achieve low returns on investment. By comparing the two extreme portfolios it is obvious that

the companies with a high degree of profitability outperform the low-profitability companies.

This result is in line with the conclusions drawn by Johnson and Soenen (2003) who find that

the ROA measure is the variable which has the greatest impact on the performance measures

for financial success. They report a relatively strong, statistically significant (at the 99% level)

relationship between ROA and the Sharpe ratio and Jensen’s Alpha respectively.

20

Turning now to the additional portfolio analysis, which illustrates the performance of the

portfolios in relation to the Jensen Alpha, the same pattern as in the Sharpe ratio discussion is

distinguishable. Portfolios with greater levels of ROA, do fare better than less profitable

companies. The results from the portfolio analysis concerning Jensen’s Alpha are posted in

the following table.

Table 2

This table presents the average returns (provided by Jensen’s Alpha) of seven different portfolios which are ranked according to their score on the ROA measure. Portfolios range from 2000-2005 and are reformed every quarter.

Portfolio 1 (high ROA) 0,032716




Portfolio 5 0,00507


Portfolio 7 (low ROA) -0,03173

When comparing the results from the Sharpe ratio analysis with the Jensen Alpha measures

from the second half of the time period it is obvious that the Sharpe ratios are more

differentiated. Compared to the Jensen Alphas, the Sharpe ratios span over a wider spectrum,

ranging all the way down to quite large negative returns. Obviously, the Sharpe ratios are

measured over a longer time period which make them more susceptible to different sorts of

time specific events and other historic occurrences. The financial development of the

companies on the Stockholm stock exchange might quite plausibly have been somewhat

volatile during the latter half of the 1990’s. The start of the 20th century does similarly not

contain such stocks as was prevalent during the 1990’s. Such matters are of course of the

utmost importance when comparing time series of this kind.

4.1.2 Book-to-market ratio

Similar to the ROA measure the same pattern is visible when analysing the impact of the

book-to-market ratio on financial success. The top portfolio, which consists of the companies

with the largest proportion of common equity in relation to their market value, generates a

21

Sharpe ratio of 0,046 whereas the portfolio with the lowest book-to-market value performs

worse. The results are presented in the following table.

Table 3

This table presents the average returns (provided by the Sharpe Ratio) of seven different portfolios which are ranked according to their book-to-market ratio. Portfolios range from 1995-2005 and are reformed every quarter.

Portfolio 1 (high book-to-market ratio) 0,046

Portfolio 2 -0,097

Portfolio 3 0,022

Portfolio 4 0,0003

Portfolio 5 -0,07

Portfolio 6 -0,083

Portfolio 7 (low book-to-market ratio) -0,0006

As in Fama and French (1992), the book-to-market ratio exhibits a positive relationship to the

Sharpe ratio. It is somewhat peculiar that portfolio number two records such a surprisingly

bad return. It should be noted, however, that there are studies that predict a negative

relationship between the book-to-market ratio and financial success. Johnson and Soenen

(2003) and Shefrin and Statman (1995) actually find a negative relationship between the

book-to-market ratio and financial success.

By contrast to Shefrin and Statman, the Jensen’s Alpha analysis suggests that companies with

larger ratios of common equity in relation to market value give greater returns than companies

with low book-to-market ratios. The numbers are presented in the following table.

Table 4

This table presents the average returns (provided by Jensen’s Alpha) of seven different portfolios which are ranked according to their book-to-market ratio. Portfolios range from 2000-2005 and are reformed every quarter.

Portfolio 1 (high book-to-market ratio) 0,027096


22





Portfolio 7 (low book-to-market ratio) -0,01381

These numbers show a clear pattern of a positive relationship between the book-to-market

ratio and the Jensen’s Alphas for the companies. Whereas the top portfolio yields positive

returns, the bottom portfolio does not reproduce the same positive numbers. These results are

in support of the Fama and French (1992) study and suggest that Swedish firms exhibit a

positive relationship between book-to-market and financial success.

4.1.3 Size

A much studied relationship is that between financial success and firm size. Several studies,

including Shefrin and Statman (1995) and Johnson and Soenen (2003), report that large

companies fare better than small ones in terms of generating excess returns to shareholders.

Their results, albeit statistically significant, are at odds with previous investigations. Fama and

French (1992), for example, find that stock returns are negatively related to size. Because

several studies point to a robust relationship between size and financial success, it was

expected to find similar patterns in the analysis of Swedish companies. The results are

somewhat inconclusive, however, as is evident from the following table.

Table 5

This table presents the average returns (provided by the Sharpe Ratio) of seven different portfolios which are ranked according to their market value. Portfolios range from 1995-2005 and are reformed every quarter.

Portfolio 1 (high market value) -0,014

Portfolio 2 -0,036

Portfolio 3 0,0042

Portfolio 4 0,053

Portfolio 5 -0,074

23

Portfolio 6 0,0003

Portfolio 7 (low market value) -0,12

Evidently, the portfolio analysis does not present any evidence of company size being either

positively or negatively correlated to financial success. The numbers are quite random, with

middle-sized companies being the best bet for investment. Nevertheless, it is evident that no

robust relationship can be observed.

When contemplating the results from the Jensen’s Alpha analysis, the same uncertain pattern

as in the Sharpe ratio investigation is discernable. Even though the portfolio consisting of the

biggest companies outperform the portfolios with the low market value companies, the top

return-yielding companies seem to be the middle-sized ones. Table 6 presents the numbers.

Table 6

This table presents the average returns (provided by Jensen’s Alpha) of seven different portfolios which are ranked according to their market value. Portfolios range from 2000-2005 and are reformed every quarter.

Portfolio 1 (high market value) 0,005288






Portfolio 7 (low market value) -0,01059

From these numbers, there is obviously no distinguishable relationship between company size

and financial success for Swedish companies, whether measured by the Sharpe ratio or by the

Jensen’s Alpha. Indeed, the results do seem quite uncertain.

4.1.4 Liquidity

Company liquidity does exhibit somewhat of a positive pattern to the returns measured in the

Sharpe ratio. This is highlighted by a recorded positive return from the portfolio with the high

24

liquidity companies and a negative return from the portfolio consisting of low liquidity

companies. This positive relationship is not clear-cut however. Regard the following table.

Table 7

This table presents the average returns (provided by the Sharpe Ratio) of seven different portfolios which are ranked according to their rate of liquidity. Portfolios range from 1995-2005 and are reformed every quarter.

Portfolio 1 (high degree of liquidity) 0,023

Portfolio 2 -0,10

Portfolio 3 0,007

Portfolio 4 -0,007

Portfolio 5 -0,03

Portfolio 6 -0,016

Portfolio 7 (low degree of liquidity) -0,057

Turning now to the latter part of the time period, it exhibits a similar pattern. Even though

portfolio number two records a surprising result both with regards to the Sharpe ratio and to

the Jensen’s Alpha, the same pattern emerges. Portfolios consisting of companies with larger

degrees of liquidity tend to outperform low-liquidity companies. The resuults from the Jensen

Alpha analysis is presented in table 8.

Table 8

This table presents the average returns (provided by Jensen’s Alpha) of seven different portfolios which are ranked according to their liquidity. Portfolios range from 2000-2005 and are reformed every quarter.

Portfolio 1 (high degree of liquidity) 0,059775






25

Portfolio 7 (low degree of liquidity) -0,00449

With regards to previous research, Johnson and Soenen (2003) do not find any significant

relationships between the level of liquidity of the companies and their financial performance.

Somewhat surprisingly, their study also records negative relationships between liquidity and

the performance metrics. At the same time, however, their analysis does record poor p-values

for this variable with 0,87 for the Jensen’s Alpha and 0,32 for the Sharpe ratio. Therefore it

will be interesting to see if the positive relationship discerned from the Swedish companies

will hold in the parametric analysis that will follow.

4.1.5 Capital structure and the cash conversion cycle

These two variables do not record any discernable nor apparent pattern in relation to the

performance measures. In a similar vein as with the analysis on company size these variables

do not seem to exhibit any clear-cut correlation to the Swedish companies’ Sharpe ratios or

their Jensen’s Alphas. Therefore, the portfolio analyses of these variables have been placed in

Appendix B.

4.2 Parametric analysis

In order to provide further evidence of the relationship between the chosen indicator variables

and financial success an OLS regression is performed. The following table presents the results

of the time period 1995-2005 where the indicator variables are regressed on the corresponding

Sharpe ratios for the companies.

Sharpe Ratio regression Coefficients Standard Error t- ratio p-value Constant -0,313170942 0,131216669 -2,38667 0,017115Market Value -1,66397E-07 3,51313E-07 -0,47364 0,635817Book-to-Market ratio 0,140010717 0,043623171 3,209549 0,001355Return on Assets 2,621615564 0,42608279 6,152831 9,56E-10Liquidity 0,226817826 0,155333559 1,460198 0,144428Capital Structure -0,281748743 0,219213232 -1,28527 0,19888

Cash Conversion Cycle -0,000340069 0,000718532 -0,47328 0,636075

The regression shows a particularly strong relationship between the ROA and the Sharpe

ratio. This relationship is also statistically significant and supports the conclusions drawn

26

from the portfolio analysis performed earlier. In addition, the book-to-market ratio records a

positive relationship to the Sharpe ratio albeit smaller than the relationship recorded by the

ROA. This observation, being statistically significant, is also one which further strengthens

the evidence that was presented by the portfolio analysis. In accordance with the portfolio

analysis, company liquidity records a positive relationship to the Sharpe ratio. This

relationship is not statistically significant, however, with a p-value of 0,144.

These observations, with a positive relationship between the ROA and the book-to-market

value are also observable in the results from the Jensen’s Alpha regression (2000-2005).

However, the positive relationship between the Jensen’s Alphas and the book-to-market ratio

is not statistically significant until the 93% level. The complete set of coefficients is presented

below.

Jensen Alpha regression Coefficients Standard

Error t-ratio p-value Constant -0,07551 0,028639 -2,63648 0,008533Market Value -8,4E-09 6,38E-08 -0,13178 0,895188Book-to-Market ratio 0,016199 0,00884 1,832604 0,067218Return on Assets 0,407863 0,085277 4,782801 2,04E-06Liquidity 0,088285 0,033939 2,60131 0,009452Capital Structure -0,03745 0,049204 -0,76107 0,446833Cash Conversion Cycle 0,000209 0,000174 1,198744 0,230969

Aside from the remarks made in relation to the book-to-market ratio and the ROA measure, a

couple of interesting observations can be made from these two regression analyses. First, both

regressions record a negative relationship between size and financial success but this

relationship is highly insignificant (statistically speaking) in both instances. This is indeed

quite a surprising result, since size has recorded statistically significant relationships in much

previous research. Second, with regards to the Jensen’s Alpha analysis for 2000-2005 the role

of company liquidity exhibits a positive and statistically significant relationship. This is in

line with the results from the portfolio analysis performed earlier. Evidently, the shorter time

period studied for the Jensen’s Alpha provides for more precise parametric analysis.

Moreover, the variable of capital structure and the cash conversion cycle does not generate

any significant relationships to neither the Sharpe ratio nor the Jensen’s Alpha. This result

27

was anticipated due to the quite inconclusive evidence from the portfolio analyses performed

on these variables.

4.2.1 Reliability

In order to ascertain the reliability of the parametric analysis some tests are necessary. An

aspect which is important, especially regarding time series analysis, is that of autocorrelation.

We can get an approximation of the degree of autocorrelation by looking at the Durbin-

Watson statistic. A rule of thumb is that values near 2 indicate that the null hypothesis of no

autocorrelation cannot be rejected. If that statistic is close to the number 2 we therefore

assume that no autocorrelation is present. In this study the Sharpe ratio regression records a

Durbin-Watson statistic of 2,00 whereas the Jensen’s Alpha regression displays 1,86 for the

same statistic. This suggests that there is no autocorrelation present, a conclusion which is

further strengthened by looking at the residual plots for the two regression analyses. These

plotted residuals are found in appendix d and do not display any signs of autocorrelation.

Furthermore, it is important to determine that the independent variables in the study are not

severely correlated to each other, i.e. that the study does not suffer from multicolinearity.

Thus, a correlation matrix for the X-variables was incorporated and the conclusion is that no

disturbing degree of multicolinearity can be distinguished. These numbers are found in

appendix c.

Finally a test of heteroskedasticity is necessary. Heteroskedasticity refers to unequal variance

in the regression errors which might upset the results of the regression analysis. The most

used method for testing for heteroskedasticity is the test labelled as White’s test.

Consequently, such a test was performed for the two regression analyses. The Sharpe ratio

regression posted a p-value of 0,79 and the Jensen’s Alpha regression got a p-value of 0,11.

These findings suggest that no heteroskedasticity is present in the regression analyses.

The conclusion from these three tests is that the statistical reliability of the parametric analysis

is quite robust. There is no evidence of multicolinearity, autocorrelation, or heteroskedasticity.

Therefore, the results from the regression analyses should indeed be reliable. On a final note,

however, it should be noted that the two models both exhibit quite poor R-square and adjusted

R-square values, reported in appendix c. Nevertheless, since Johnson and Soenen (2003) do

not present the R-square values for their investigation, there is no point of comparison.

28

Studies of this kind might in fact be characterised by low R-square values. Still, the relatively

poor fit of the models seems a bit surprising.

29

Chapter 5. Concluding remarks

The search for winning bets on the stock market has generated huge interest within the

research community. Both academics and practitioners alike are preoccupied with finding

models that would accurately predict stock returns. Evidently, much remains to be done

before any assertive models are found. Still, being able to define the characteristics of

companies that beat the overall performance of the stock market is a major driving force of

financial economists. Obviously, the quest for correct indicators of financial success is also

fraught with many obstacles and the solutions are not always as straightforward as one would

have hoped for.

This study has analysed the relationship between certain specific financial indicators and

financial success of Swedish companies. The indicator variables were chosen from previous

studies and the predictive power of these variables in terms of generating returns to

shareholders was examined. In terms of measuring financial success, the Sharpe ratio and the

Jensen’s Alpha were used. In order to determine the relationships between financial success

and the financial indicator both a method of portfolio analyses and a parametric method were

used.

The results point to the fact that profitable companies with a high book-to-market ratio are

successful in generating excess returns to shareholders. In addition, the liquidity of the

companies seems to be a good indicator of financial success, particularly in recent years

(2000-2005). Contrary to previous studies, the size of the companies does not present any

clear-cut nor statistically significant relationship to stock returns. Evidently, it is always

necessary to keep in mind that we are comparing this relatively modest study of 42 Swedish

companies to studies that analyse other markets (predominantly the American stock market)

which are characterised by other types of companies. It should therefore not be surprising that

somewhat diverging results might emerge, which is also the case in respect to contemporary

research. The fact that no statistical significance is reached with regards to company size is

still surprising however.

Nevertheless, this study has hopefully provided some insights into the existing literature on

which are the indicators of financial success. Furthermore, the application of the model onto

the Swedish stock market provides many thought-stimulating topics for discussion. As was

30

evidenced in the preceding discussion, there is always a difficulty when dealing with time

series that stretch over a long period of time. Such time series are always susceptible to

historical occurrences that might upset the results. At the same time, such instances also

provide many opportunities to remodel the investigation and find ideas to new research. Such

research will undoubtedly be performed and the quest for successful stock picks will go on.

This paper constitutes a humble addition to contemporary research within this field.

5.1 Suggestions for future research

As previously noted, this field of study has generated enormous interest in recent years.

Nevertheless, it would undoubtedly be interesting to see further elaborations on the models

used within existing research. It could be contemplated to use other measures of both the

explanatory and the explaining variables. Concentrating on a shorter time period and using a

larger sample of companies could also be worthwhile. Further expansions of the statistical

methods used could also be considered.

31

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Appendices

Appendix A: List of Companies 1 AB ANGPANNEFOERENINGEN 2 ASSA ABLOY AB 3 ATLAS COPCO AB 4 BEIJER ALMA AB 5 G & L BEIJER AB 6 BERGMAN & BEVING AB 7 BILIA AB 8 BONG LJUNGDAHL AB 9 BRIO AB 10 BURE EQUITY AB 11 CONSILIUM AB 12 ELEKTA AB 13 ELANDERS AB 14 ELECTROLUX AB 15 ERICSSON AB 16 FENIX OUTDOOR AB 17 GAMBRO AB 18 GETINGE AB 19 AB GEVEKO 20 HEXAGON AB 21 HALDEX AB 22 HOGANAS AB 23 HOLMEN AB 24 IBS AB 25 INDUSTRIVARDEN AB 26 INVESTMENT AB LATOUR 27 MIDWAY HOLDING AB 28 NCC AB 29 NOLATO AB 30 PEAB AB 31 SANDVIK AB 32 SCRIBONA AB 33 SECURITAS AB 34 SKANSKA AB 35 SKF AB 36 SSAB SVENSKT STAL AB 37 TRELLEBORG AB 38 VBG AB 39 AB VOLVO 40 BORAS WAFVERI AB 41 AB WESTERGYLLEN 42 XANO INDUSTRI AB

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Appendix B: Additional portfolio analyses

Table 9

This table presents the average returns (provided by the Sharpe Ratio) of seven different portfolios which are ranked according to their capital structure. Portfolios range from 1995-2005 and are reformed every quarter. Portfolio 1 (high degree of leverage) -0,17737



Portfolio 4 0,02655



Portfolio 7 (low degree of leverage) -0,00265

Table 10

This table presents the average returns (provided by Jensen’s Alpha) of seven different portfolios which are ranked according to their capital structure. Portfolios range from 2000-2005 and are reformed every quarter. Portfolio 1 (high degree of leverage) -0,03198






Portfolio 7 (low degree of leverage) 0,025897

Table 11

This table presents the average returns (provided by the Sharpe Ratio) of seven different portfolios which are ranked according to their cash conversion cycles. Portfolios range from 1995-2005 and are reformed every quarter. Portfolio 1 (short cash conversion cycles) -0,10996





35


Portfolio 7 (long cash conversion cycles) -0,08118

Table 12

This table presents the average returns (provided by Jensen’s Alpha) of seven different portfolios which are ranked according to cash conversion cycles. Portfolios range from 2000-2005 and are reformed every quarter. Portfolio 1 (short cash conversion cycles) -0,02208





Portfolio 6 (long cash conversion cycles) 0,008678

Portfolio 7 (long cash conversion cycles) 0,021527

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Appendix C: Complete regression analyses The Sharpe ratio regression: Dependent Variable: SHARPE_RATIO Method: Least Squares Date: 06/13/06 Time: 14:51 Sample: 1 1638 Included observations: 1638 White Heteroskedasticity-Consistent Standard Errors & Covariance

Variable Coefficient Std. Error t-Statistic Prob.

BOOK_TO_MARKET 0.140011 0.046709 2.997510 0.0028 CAPITAL_STRUCTURE -0.281749 0.215953 -1.304677 0.1922

CASH_CONVERSION_CYCLE -0.000340 0.000716 -0.474768 0.6350 LIQUIDITY 0.226818 0.154828 1.464969 0.1431

MARKET VALUE -1.66E-07 2.91E-07 -0.571247 0.5679 ROA 2.621616 0.465535 5.631408 0.0000

C -0.313171 0.130894 -2.392558 0.0168

R-squared 0.032850 Mean dependent var -0.026112 Adjusted R-squared 0.029292 S.D. dependent var 1.006020 S.E. of regression 0.991176 Akaike info criterion 2.824416 Sum squared resid 1602.344 Schwarz criterion 2.847498 Log likelihood -2306.197 F-statistic 9.233006 Durbin-Watson stat 2.002267 Prob(F-statistic) 0.000000

Test for Multicolinearity

Correlation Matrix

Market Value Book-to-Market ratio

Return on Assets

Liquidity Capital Structure

Cash Conversion Cycle

Market Value 1 Book-to-Market ratio

-0,185496168 1

Return on Assets

0,02341662 -0,233138101 1

Liquidity 0,141701464 0,025173061 -0,09946 1 Capital Structure

-0,073941364 0,088763425 -0,29952 -0,24065 1


0,226303166 -0,104585785 0,104096 0,031506 0,046231 1

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The Jensen’s Alpha regression: Dependent Variable: Jensen’s Alpha Method: Least Squares Date: 06/13/06 Time: 14:55 Sample: 1 840 Included observations: 840 White Heteroskedasticity-Consistent Standard Errors & Covariance

Variable Coefficient Std. Error t-Statistic Prob.

Market Value -8.41E-09 3.48E-08 -0.241431 0.8093Book-to-Market ratio 0.016199 0.011232 1.442272 0.1496

Return on Assets 0.407863 0.108311 3.765675 0.0002Liquidity 0.088285 0.034801 2.536827 0.0114

Capital Structure -0.037448 0.048220 -0.776599 0.4376Cash Conversion Cycle 0.000209 0.000185 1.126725 0.2602

C -0.075505 0.030299 -2.492031 0.0129

R-squared 0.044569 Mean dependent var 0.011255Adjusted R-squared 0.037687 S.D. dependent var 0.166148S.E. of regression 0.162987 Akaike info criterion -0.781991Sum squared resid 22.12852 Schwarz criterion -0.742546Log likelihood 335.4362 F-statistic 6.476261Durbin-Watson stat 1.856770 Prob(F-statistic) 0.000001

Test for Multicolinearity

Correlation Matrix

Market Value

Book-to-Market ratio

Return on Assets

Liquidity Capital Structure


Market Value 1 Book-to-Market ratio

-0,18408 1

Return on Assets

0,033129 -0,18831 1

Liquidity 0,150746 0,045373 -0,06929 1 Capital Structure

-0,06851 0,092104 -0,23638 -0,2972 1


0,217555 -0,06127 0,139499 -0,0176 0,090439 1

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Appendix D: Tests of Heteroskedasticity and autocorrelation The Sharpe ratio regression: White Heteroskedasticity Test:

F-statistic 0.662220 Prob. F(12,1625) 0.788898 Obs*R-squared 7.971238 Prob. Chi-Square(12) 0.787374

Residual Plot:

6

-4

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4

6

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250 500 750 1000 1250 1500

Residual Actual Fitted

The Jensen’s Alpha regression: White Heteroskedasticity Test:

F-statistic 1.514633 Prob. F(12,827) 0.113071Obs*R-squared 18.06430 Prob. Chi-Square(12) 0.113752 Residual Plot:

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Residual Actual Fitted

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