abstract - lund universitylup.lub.lu.se/.../record/1973769/file/1973781.docx · web viewbachelor...

Bachelor Thesis in Economics

May 2011

Stock returns explained- using a volume filter, interest rates, and the

oil price

Supervisors: Author:

Hossein Asgharian Pierre R.M. Carlsson

Title: Stock returns explained - using a volume filter, interest rates, and

the oil price.

Seminar date: 2011-05-31

Course: Bachelor thesis in Economics, 15 ECTS

Author: Pierre R.M. Carlsson

Advisor: Hossein Asgharian, Department of Economics

Key Words: Econometrics, Information asymmetry, Interest rate, Oil price, and

Volume.

Purpose: Account for trade volume activity and investigate the explanatory

power of a few generally accepted variables ability to explain index

and stock returns.

Empirical The Swedish stock index OMXS30 and constituents has

foundation: empirically been studied to obtain the data needed.

Theoretical The theory is derived from research on macroeconomic variables

perspective and the price-volume relation. By combining them value is added.

Methodology: A quantitative approach using regression analysis have been used.

Conclusions: Filtering for volume provides additional insights of when an

explanatory variable is useful. It further provides insights that the

sign and size of these impacts could vary, significantly, depending

on the trade volume activity. The most reliable and consistent

variables was the oil price, showing a positive relation, followed by

the term spread, also positive. The results further demonstrate

significant differences between high and low turnover stocks.

AcknowledgementsFirst of all I would like to recognize my parents who are always there for me. Thank

you for always letting me walk my own way. Since this is my last major coursework

at Lund University I would also like to take the opportunity to thank everyone at Lund

University and all friends I have made here. Thank you for having made my time in

Lund the very best. Third, I would like to thank my supervisor, Hossein Asgharian,

for the brilliant knowledge and wisdom you have taught me over the years. Last but

not least I would like to thank the city of Lund for a great city to live, study and work

in.

AbstractUsing a volume filter on daily index and stock price data the daily return has been

researched. The explanatory variables used in the study are the 1 M T-Bill, the term

spread - 10 Y Treasury bond versus a 3 M T-Bill -, and the oil price. The results

revealed that accounting for trade volume is an important part in explaining the return

of a stock or index. The volume activity provides additional insights of when a

relation between the explanatory variables and the stock return are valid. It also

reveals that the relation varies significantly across different volume activity. The most

reliable and consistent variables was the oil price and the term spread, both

demonstrating a positive relation. The results also revealed that there are differences

between high and low turnover stocks.

Table of Content

1 INTRODUCTION................................................................................................1

1.1 Background..............................................................................................................................1

1.2 Problem discussion..................................................................................................................3

1.3 Purpose.....................................................................................................................................5

1.4 Delimitations............................................................................................................................5

1.5 Thesis outline...........................................................................................................................5

2 THEORETICAL FRAMEWORK......................................................................6

2.1 Volume......................................................................................................................................6

2.2 Interest rate..............................................................................................................................9

2.3 Oil price..................................................................................................................................11

3 METHODOLOGICAL FRAMEWORK.........................................................13

3.1 Research design.....................................................................................................................13

3.2 Data selection.........................................................................................................................15

3.3 Construction of explanatory variables................................................................................15

3.4 Regression model...................................................................................................................16

3.5 Hypothesis discussion............................................................................................................17

3.6 Methodological difficulties....................................................................................................19

4 EMPIRICAL RESULTS....................................................................................20

4.1 The OMXS30 index...............................................................................................................20

4.2 The OMXS30 index constituents..........................................................................................23

4.3 Concluding remarks and main results................................................................................27

5 ANALYSIS AND DISCUSSION.......................................................................28

6 CONCLUSION...................................................................................................33

6.1 Criticism of research.............................................................................................................33

6.2 Further studies.......................................................................................................................34

7 REFERENCES...................................................................................................35

APPENDIX.................................................................................................................45

Table 1 –Volume characteristics for different volume groups of OMXS30................20Table 2 – Return characteristics for different volume groups for OMXS30...............21Table 3 – Correlation matrix........................................................................................21Table 4 – Regression results from the OMXS30 index...............................................22Table 5 –Summary of the volume within different volume groups for all firms.........23Table 6 –Summary of the return within different volume groups for all firms...........24Table 7 – Summary of the regression results for ONLY significant variables among all firms........................................................................................................................25Table 8 – Hypothesis table, OMXS30 index...............................................................28Table 9 – Hypothesis table, OMXS30 index constituents, relation and significance. .29Table 10 –Firms in the study with corresponding sector, industry group and sub-industry (GICS)............................................................................................................45Table 11 – Descriptive statistics of firms sorted by turnover (1 of 2).........................46Table 12 – Descriptive statistics of firms sorted by turnover (2 of 2).........................47Table 13 – Descriptive statistics of firms sorted by sector (1 of 3).............................48Table 14 – Descriptive statistics of firms sorted by sector (2 of 3).............................49Table 15 – Descriptive statistics of firms sorted by sector (3 of 3).............................50Table 16 – Results from regression model for firms sorted by sector, 1 of 2 (read together with table 17 and 21)......................................................................................51Table 17 – Results from regression model for firms sorted by sector, 2 of 2 (read together with table 16 and 21)......................................................................................53Table 18 – Results from regression model for firms for high turnover, 1 of 2 (read together with table 19 and 20)......................................................................................54Table 19 – Results from regression model for firms for low turnover, 2 of 2 (read together with table 18 and 20)......................................................................................55Table 20 –Results associated to the regression models for firms sorted by turnover (see 18 and 19 )............................................................................................................56Table 21 –Results associated to the regression models for firms sorted by sector (see 16 and 17).....................................................................................................................56Table 22 - The average value for each explanatory variable in the three volume groups......................................................................................................................................57Table 23 – OMXS price graph with the return, and high and low volume days.........58Table 24 – A graph of the explanatory variables.........................................................59

1 Introduction

In this introductory chapter choice and motives behind the research topic are presented

and this leads up to the purpose of the thesis. The chapter is ended by delimitations and a

disposition of the thesis.

1.1 Background

Several studies have revealed that past trading volume contains information not

accounted for in the past stock return. (Gervais et al., 2001) Moreover, intuition and

research have concluded that the market consists of different investors; institutional and

individual, which further interprets information differently, and trade accounts of

different size. Therefore, in trying to explain, and predict, stock returns the best results

would emerge if one could capture and model each investor group separately. More

accurate information could be discovered in doing so. In capturing the behaviour of each

investor group one could potentially acquire better models with higher predictive ability,

which exogenous factors that are important in explaining the return, and an overall

improved foundation for intelligent decision making. This paper will be a first effort in

trying to separate the high volume, suggestively consisting of a high share of institutional

volume, from the low volume, containing a suggestively low share of institutional

investors.

The rationale behind separating the different trade volume activity is that the information

content in high (low) volume periods is concluded to be positive (negative) and very

robust. It has further been suggested that this remains in effect independent of how

trading volume is measured, if it is adjusted for firm announcements, return effects, and it

has been viable for several decades. (Gervais et al., 2001; Lee and Swanminathan (1998)

among others) The institutional investor is expected to be better informed than the

individual investor, as well as demonstrate more investor intelligence. The institutional

would by experience automatically filter away a lot of noise in the economic, the firm

and the graph specific information/announcements. It is therefore important to distinguish

between these two major investor groups.

1

In summary, different investors do not necessarily “see” the same firm fundamentals or

future which contributes to different actions and behaviour. This in turn, could potentially

be extracted from the past trade volume activity.

2

1.2 Problem discussion

In the financial market different investors has, naturally, different access to information.

This information asymmetry is one contributing factor to the framework suggested by the

efficient market hypothesis (EMH). According to the EMH there are three different

efficiency levels: weak efficiency, the price already reflects all past publicly available

information, semi-strong efficiency, the price reflect all publicly available information

and instantly change to reflect new, and strong efficiency, price instantly reflect private

information. In other words, no information advantage and information advantage. When

one has (no) information advantage he/she is expected to conduct in (in)significant trade

activity. This give rise to, on average, high volume days with suggestively a high share of

institutional investors, average volume days consisting of a more symmetric mix of

investors, and third, low volume days with a high share of individual investors. However,

using just daily historical volume records to determine the exact ratio between

institutional and individual investors is not possible. On the other hand, it is impossible

for individual investors to consistently generate days with high trading volume. That said,

it is probable that an individual investors have an information advantage, and as this

spreads to the investment community the flow of information could act as a trigger for

the high volume to enter the market. Altogether, using volume as a filter one should

approximately be able to separate the days with high institutional volume activity from

days with low institutional volume activity (a high share of individual volume activity).

In addition to information asymmetry and the information contained in the past volume

activity several variables and ratios have been used and developed to gain insights about

the future stock return. The two major groups categorizing most of them are financial-

and macroeconomic variables. Starting with the financial variables, such as market

capitalization, cash flow yield, dividend yield, foreign exchange, earning-to-price ratio,

price-to-book ratio, and turnover etc. several authors have analyzed them trying to find

the most useful and significant variables. (Fama and French, 1992; Chan et al., 1991;

Banz,, 1981; Basu, 1983; Litzenberger and Ramaswamy, 1982; Adler and Dumas, 1983;

Roll, 1992; Dumas and Solnik, 1995; Clasessens et al., 1995; Keim and Stambaugh,

1986; Pontiff and Schall, 1998; Lewellen, 2004) However, there is limited consensus on

3

which variables are consistent predictors of equity return and when one should use them.

In addition, these ratios cannot always be calculated for all firms, e.g all firms do not pay

dividend etc., which is why they will not be considered in this study. Shifting to the

macroeconomic variables, such as aggregated output, default spread, imports and exports,

inflation rate, industrial production, interest rates, money stock, term spread, and

unemployment rate etc. the general consensus is that stock returns are predictable using

macroeconomic variables (Ang and Bekaert, 2001; Balvers et al., 1990; Bodie, 1976;

Campbell, 1987, 1990; Chen et al., 1986, Chan et al., 1998; Chen, 1991; Chen, 2009;

Conover et al., 1999; Cutler et al., 1989; Fama, 1981; Fama and Schwert, 1977; Fama

and French, 1989; Ferson and Harvey, 1993; Flannery and Protopapadakis, 2002; Geske

and Roll, 1983; Hodrick, 1989; Jaffe and Mandelker, 1976; Lamont, 2001; Nelson, 1976;

Pearce and Roley, 1983, 1985; Pesaran and Timmermann, 1995; Rapach et al., 2005)

That said, consistent with the financial variables there is limited consensus on which

variables are robust predictors of stock returns. As regards of the macroeconomic

variables the interest rate appears to be the most useful one followed by, to a lesser

extent, inflation. (Rapach et al., 2005 and Chen, 2009)

An alternative measure/proxy to inflation, and economic activity, is oil. Oil is the fundamental driver of modern economic activity and various studies have shown that the oil price have a significant effect on the macro economy; GDP growth, inflation, and the stock market. (Apergis

and Miller, 2008; Chen, 2010; Driesprong et al, 2008; El-Sharif et al., 2005; Faff and

Brailsford, 1999; Gisser and Goodwin, 1986; Hamilton, 1983; Huang et al, 1996; Jones

and Kaul, 1996; Kilian and Park, 2009; Keane and Prasad, 1996; Lescaroux and Mignon,

2008, Lardic and Mignon, 2006, 2008; Mork, 1989; Mory, 1993; Mork et al., 1994; Mussa, 2000; Nandha and Faff, 2008; Park and Ratti, 2008; Sadorsky, 1999;) Hence, its high dependence in our society and driver of the economy seems to make it an acceptable proxy to account for inflation and contribute to explaining stock returns.

4

In summary, research have documented that different level of trade activity contain different information content. Together with the fact that the market is made up of differently informed investors one could suspect that classical exogenous variables is more viable under certain volume characteristics.

1.2.1 Research questions

i. Do interest rates and the oil price explain the return of index and stocks?

ii. Investigate if the above mentioned exogenous variables have different

explanatory power under different volume activity.

iii. Study if there are differences between high and low turnover stocks and

sectors among the firms listed in OMXS30, the major Swedish stock index.

1.3 Purpose

The purpose is to account for the daily trade volume activity and investigate the

explanatory power of the oil price, the term spread, and a short interest rate and their

ability to explain index and stock returns.

1.4 Delimitations

The research is conducted on the Swedish OMXS30 index and its constituents. The study

relies upon data from 1991-01-01 to 2011-04-29.

1.5 Thesis outline

The rest of the thesis is organized as follows. Chapter 2 gives an overview of the, for the

thesis, relevant theoretical framework with focus on the selected variables. Chapter 3

discusses the methodology and data collection. In Chapter 4 the empirical findings from

the study is described in text and tables. Chapter 5 contains an analysis and discussion of

the empirical findings. Chapter 6 concludes the thesis accompanied by suggestions for

future research.

5

2 Theoretical Framework

In this chapter I present the theoretical context for the study. It narrows down into a

summary accompanied by a few hypotheses at the end of each section.

2.1 Volume

2.1.1 Volume and return

It is apparent that institutions trade in larger sizes than individuals, and research has

further suggested that institutions are better informed and/or more sophisticated than

individuals. (Cohen et al., 2002; Daniel et al., 1997; Holden and Subrahmanyam, 1992;

Nofsinger and Sias, 1999) Consequently, investigating the trade volume could reveal

information about future stock return. An extensive body of research have conducted an

investigation on aggregated trade volume and stock return, and found a relation. (Gallant

et al., 1992; Karpoff, 1987; Llorente, et al., 2002; Schwert, 1989) Shu, 2010 distinguish

the trading volume from institutional and individual investors and show that the

allocation of volume has significant impact on stock returns. The results reveal that

stocks with lower fractions of institutional trading volumes underperform stocks with

higher institutional volume.

Chordia and Swaminathan (2000) find that daily and weekly returns on high volume

portfolios lead returns on low volume portfolios. They argue that these patterns arise

because returns on low volume portfolios respond more slowly to information in the

market than high volume portfolios. The spread of information in the market was

examined by Michael and Starks (1988). They investigate the relationship between stock

prices and volume using the Granger causality1 technique and found that information is

processed by investors sequentially rather than simultaneously or all at once. This finding

was consistent with Copeland (1976) sequential arrival of information model in which

information is disseminated to only one trader at a time and that implies a positive

correlation between volume and change in price. Hence, past trading volume should hold

1 A statistical tests used to determine whether one time-series is good at forecasting another.

6

information that is not, always, fully incorporated in the price. This conclusion was

reached by Lee and Swaminathan (1998) which argue for an “investor expectation

hypothesis” in which past trading volume is a proxy for the level of investor interest in a

stock. Low-volume stocks (low investor interest) have a greater upside potential on

average, while high-volume stocks (high investor interest) face greater risk. They also

provide an explanation arguing that investors holding illiquid stock demand a return

premium.

An extensive amount of research has further studied volume effects in relation to news

announcements and price movements. According to Easley and O´hara (1992) and

Bessembinder and Segin (1993) an unusual high or low volume are potential signs of the

arrival of new information. Stickel and Verrecchia (1994) reason that as volume

increases, the probability that the price change is information driven increases. This

seems to be the evidence as large price changes on days with weak volume support tend

to reverse the next day. While large price increases with strong volume support tends to

be followed by another price increase the next day. (Abbondante, 2010) This was also

found by Gervais et al. (2001) which show that stocks experiencing unusually high

trading volume over a day or a week tend to appreciate over the course of the following

month. Their rationale for this investor behavior appears to be that the high-volume

return premium is consistent with the idea that shocks in the trading activity affects its

visibility and in turn the subsequent demand and price for that stock. Their results also

indicate that the flow of information is related to volume and not the occurrence of news

events, contrary to Easley and O´hara (1992) among others.

2.1.2 Volume and volatility

According to Poon and Granger (2003) several characteristics of financial markets

volatility have been documented. Some among the many are volatility clustering, in

which volatility vary over time where high (low) absolute return are followed by high

(low) absolute returns (Mandelbrot, 1963), asymmetric reactions to shocks, in which the

volatility of returns increases more following negative shocks than positive shocks of

7

equal size (Black, 1976), fat tailed distributions of risky asset returns and long memory of

volatility. (Mandelbrot and van Ness, 1968)

Models by Clark (1973), Epps and Epps (1976), and Tauchen and Pitts (1983) concluded

that there is an existence of a positive contemporary relationship between volatility and

trading volume. Lamoureax and Lastrapes (1990) argues that the daily trading volume is

a measure of the amount of information flowing into the market every day. They find that

trading volume seems to be a good proxy for the arrival of information into the market

and for explaining the persistence of the volatility of the return of individual shares. In

other words, ARCH2 effect tend to disappear when contemporaneous trading volume is

added to the conditional variance function of a GARCH(1,1)3 specification. Fleming et al.

(2005) study the degree to which the dynamics of trading volume can explain ARCH in

stock returns. In contrast to previous authors they find that trading volume, inserted into

the conditional variance function, do not reduce the importance of lagged squared returns

in capturing volatility dynamics. They use a EGARCH4 (2,2) model that allows for both

short- and long-term volatility components and find little support for the proposition that

volume explains ARCH effects. However, the model does imply that volume is strongly

associated with return volatility.

2.1.3 Concluding remarks of volume

It has been concluded that institutional investors trade in larger size and are more

sophisticated as they account for more information. It is further suggested that high

volume activity lead as these investors behavior act as a signal to other investor groups.

Conditional on volume the return is further expected to exhibit different characteristics,

such as higher volatility for high volume than low volume stocks. Presented that high

volume incorporate more information than low volume the explanatory variables should

provide better explanatory power on high volume days than on low volume days. It is

further possible that volume clustering will be found, which some argues correlates with

2 Engle, (1982) (ARCH, Autoregressive Conditional Heteroskedasticity, a method used to account for time-variation in the past error term in time-series analysis.3 Bolleslev, (1986), (GARCH, Generalized ARCH, a method used to, in addition to ARCH, also account for time-variation in the past variance4 Nelson, (1991), EGARCH, the exponential-GARCH allow for asymmetries between the volatility and the return, and it is the log of variance is used, which consequently will always be positive.

8

the well documented return volatility clustering. (Clark, 1973; Chordia and Swaminathan,

2000; Gervais et al., 2001; Karpoff, 1987; Lee and Swaminathan, 1998)

9

2.2 Interest rate

Macro variables such as the term structure of interest rate are commonly associated with

expectations of future economic events that may affect the stock market. (Chen, 2009)

Avramov and Chordia (2006) conduct further research on the topic and find that returns

are predictable out-of-sample by the term spread (the difference between treasury bonds

with >10 years to maturity and the T-bill that matures in three months) and the one month

T-bill yield, among other variables which are not as robust. Several authors have used a 3

month T-Bill, one of them are Perez-Quiros and Timmermann (2000) which uses it as a

proxy for investors’ expectations of future economic activity. They also argue, consistent

with others5 that it is a proxy for firms’ interest costs and find it to be negatively

correlated with future returns. In a larger study, accounting for a wide range of

macroeconomic variables across twelve industrialized countries, Rapach et al., (2005)

find that the relative6 3-month treasury bill rate, the relative4 long term bond yield, the

relative4 money market rate, demonstrate the most consistent and reliable in-sample and

out-of-sample predictors of stock returns. As regards of the term spread they find limiting

evidence for this to predict stock returns. However, the usefulness of term spread has

been investigated by Estrella and Mishkin (1998) which found it to be a good predictor of

recessions (both in-sample and out-of-sample). The discovery that some macro variables

are good at predicting bear markets has been given further attention. Chen (2009)

concluded that macroeconomic variables, and the term spread in particular, are better

predicting bear markets rather than stock returns. Chang (2009) account for the fact that

bear and bull markets should be treated separately. This is taken into account as the

interest rate, dividend yield and default premium is analyzed on US stock return

movements using a regime switching model. The results show that stock returns and

volatility depend on macro factors and the degree of influence do change with the stock

market conditions. It is also concluded that the contribution of default premium and

interest rate to stock returns is significantly greater in a volatile regime compared to that

in a stable regime.

5 Fama and Schwert, 1977; Campbell, 1987; Glosten et al., 1993; and Whitelaw, 19946 Defined as the difference between itself and its 12 month moving average

10

Besides Chen (2009) finding that the term spread predicts bear markets it was also found

that the inflation rate do so. Regarding the relationship between the interest rate and

inflation, Feldstein and Eckstein (1970) concluded that a one percent increase in the

interest rate was approximately the result of a one percent increase in the anticipated

inflation. A theory trying to explain the relationship between inflation and interest rate is

the Fisher theory. Put into a stock market context the Fisher theory suggest that the

relation between stock returns and inflation should be positive. (Patro et al. 2002) In

contrast Fama (1981) argues and find support for, using US data, that an increase in

inflation is expected to be followed by a decline in real economic activity and corporate

profits. Hence, stocks will react negatively to a rise in inflation. According to the study

by Rapach et al. (2005) the inflation rate demonstrate a significant in-sample and out-of-

sample stock return predictive ability. However, the low frequent nature of inflation rate

data makes it a less attractive variable for this study.

2.2.1 Concluding remarks of interest rates

Altogether, the variables which have demonstrated the most reliable and consistent

explanatory power of the interest rate variables are a short rate, the term spread and the

inflation. Therefore, according to previous research the one month T-bill should be a

good measure to reflect the expectations of future economic activity, influences on

volatility and the cost of capital. It is further expected to be negatively related to stock

returns. (Perez-Quiros and Timmermann, 2000) A second interest rate variable which has

been considered useful is the term-spread. It will be used as measure to capture the state

of the economy. The term spread is expected to demonstrate a positive relation to stock

returns. (Chen, 2009) To account for the low frequent data of inflation the oil price will

be considered a possible proxy.

11

2.3 Oil price

According to research investigating the consumer price index several studies have

concluded that an oil price increase represents an inflationary shock. (Fuhrer, 1995;

Gordon, 1997; Hooker, 2002) Barsky and Kilian (2004) show that oil price increases

generate high inflation while LeBlanc and Chinn (2004) conclude that it has only a

moderate impact on inflation. The advantage of accounting for oil is that it is the one

fundamental driver of modern economic activity in our world today. It is therefore

concluded that the oil price should be an acceptable proxy for inflation.

Different studies have investigated the relation between oil price movements on gross

domestic product and on prices. The general conclusion has been that rising oil prices

leads to a reduction of potential output (Brown and Yücel, 1999, 2002; Hamilton, 1983,

2005; Gisser and Goodwin, 1986; Mussa, 2000). According to available research it has

been demonstrated that the impact of oil price changes on the macro economy is

asymmetric. (Brown and Yücel, 2002; Ferderer, 1996; Lardic and Mignon, 2006, 2008;

Mork, 1989; Mork et al., 1994; Mory, 1993) Lescaroux and Mignon (2008) extend the

analysis and investigate various links between oil prices and several macroeconomic and

financial variables. Their short-term analysis indicates that when Granger-causality

exists, it generally runs from oil prices to other variables. Their long-term analysis

reveals that GDP and oil prices evolve together (for 12 countries). According to

Lescaroux and Mignon a rise in energy prices causes a drop in productivity, which is

passed on to (i) real wages and employment; (ii) selling prices and core inflation; and (iii)

profits and investments, as well as stock market capitalization. (Brown and Yücel, 2002

and Lardic and Mignon, 2006)

Caruth et al. (1998) and Davis and Haltiwanger (2001) investigated the impact of oil

price movements on the labour market and the natural rate of unemployment. Their

results, consistent with Keane and Prasad (1996), conclude that the impact of oil price

movements can differ with the considered horizon; in the short run prices tend to reduce

employment, but in the long run it tend to increase it.

12

Several authors have studied the link between oil prices and the stock market. According

to Nandha and Faff, 2008 there is a common market perception that stock markets react

to oil price shocks. And Sardosky (1999) has concluded that the oil price influence share

prices. Driesprong et al. (2008) discover that changes in oil prices predict stock market

returns worldwide, and that an oil price increase drastically lowers future stock returns.

Nandha and Faff (2008) show that oil price rises have a negative impact on stock returns

for all 35 global equity industry sectors but mining, oil and gas they investigate. Jones

and Kaul (1996) argue that the oil prices impact the US stock market through its

influence on expected dividends and cash flows. In the study by Lescaroux and Mignon

(2008) their analysis reveals that there exists a strong negative Granger-causality running

from oil prices to the stock market and share price. And for almost every country in their

study oil prices are found to lead countercyclically share prices. Altogether the oil price

appears to be a good proxy for inflation as well as be able to capture the economic

activity. However, as Lescaroux and Mignon points out, our dependence on oil today is

not as high as it was some decades ago, suggesting a lower impact of the oil price on

economic activity than previously throughout our history.

2.3.1 Concluding remarks of oil prices

Research on the oil price has demonstrated an inverse relation to dividend, cash flows

and corporate profits. Given societies high dependence on oil it will primarily be used as

a proxy for inflation, but also reflect the economic activity. Hence, a negative impact on

stock returns is one possible relation. On the contrary, if firms successfully adjust their

prices to account for higher oil prices it could demonstrate a positive relation on stock

returns. (Patro et al. 2002, Driesprong et al., 2008 and Nandha and Faff, 2008) Also, if oil

prices are increasing the global economy is booming, contributing to increased corporate

profit across sectors which suggest a positive relation.

13

3 Methodological framework

In this third chapter I give a description of the methodology used in order to perform my

proposed research. I describe how data for the study was collected and constructed.

Towards the end of the chapter a discussion and formulation of hypotheses are presented.

3.1 Research design

Research design is a framework for gathering and analysis of data. The choice of research

design reflects the stands the researcher have taken regarding what priority be given to

the number of dimensions and aspects in the research process (Bryman and Bell, 2003)

3.1.1 Research philosophy and research approach.

The research philosophy is associated with the view that was taken on the research

process. The philosophy captures the way the researcher view the world and subsequently

affects the research design, the data collection and the analysis of the study (Saunders, et

al., 2003). In this study I will focus on objective and quantifiable observations that can be

analysed and result in consistent and regular generalizations. Therefore, certain

limitations were made already in the introductory chapter.

Research approach can be described as the theoretical design of the research. Of the two

main approaches, the one that is best suited for my study is the deductive one. The

deductive approach has a structured design in which existing theories are examined

through hypothesis testing (Saunders et al., 2003), which fit best with how I want to carry

out my study. The study is an extension of previous econometric research on stock return

and its relation to the information content provided by the historical price volume records

and macroeconomic variables.

This research is a complement to Lee and Swaminathan (1998) work on the relation

between volume and return, Rapach et al. (2005) study using macroeconomic variables to

explain/predict stock returns, and Nandha and Faff (2008) research investigating the

14

relation between oil prices and stock returns, among others. The results should be of

interest for those with a passion for indices and stocks and add to their expertise.

3.1.2 Reliability, validity, and generalisation

It is of importance that the research results are trustworthy and reliable. Reliability is often of concern in quantitative research since the researcher is interested in whether the measurement technique is stable or not. A high degree of reliability is assured if it were to generate the same results if performed again. (Bryman and Bell, 2003)

For a study to have validity it should measure what it sets out to measure i.e. does the data really measure what the authors intended and can the conclusions drawn from the study actually be made based on it? (Bryman and Bell, 2003; and Saunders et al, 2003)

The research depends on data from highly accredited sources. Moreover, the software

packages used, MS Excel 2007 and Eviews 7.0, are commonly used by the academic

community as well as industry practitioners. The econometric methodology follows

standard statistical procedure used by practitioners researching financial data.

As mentioned previously it is of course impossible to surely conclude which investors,

institutional or individual, contribute to the daily volume on any particular day. On the

other hand it is intuitive that only the sophisticated investors, institutional, would have

the ability to contribute to the above normal volume. When they are less active it would

result in a below normal volume. Relying on close to 20 years of daily data for most

stocks potential bias and a miss-categorization of investors within the different volume

groups are reduced significantly.

Since all the explanatory variables that have been used are general for an economy (also

publically available) it should be possible to generalize the results to other stocks.

15

3.2 Data selectionThe research accounted for in this research is found up to 85% using the Electronic Library Information Navigator (ELIN) and the remaining 15 % are contributed to the Social Science Research Network (SSRN). The data this thesis is built upon is secondary data from Nasdaq OMX Nordic’s website (stock data), The Swedish Centralbank, Riksbanken’s website (interest rates data) and Datastream Advance (OMXS30, oil price and USDSEK data). The data used are daily observations. Adjusted data for OMXS30 and its constituents are used.

3.3 Construction of explanatory variables

R = the first difference in the log of the daily price.

TB = the first difference in the log of the 1 month T-bill, SSVX 1M7.

TS = term spread, the difference between treasury bonds with >10 years to maturity, SE

GVB 10Y8, and the T-bill with 3 months to maturity, SSVX 3M6.

OP =the first difference in the log of the OPEC basket of oil price.

In the study by Gervais et al. (2001) they construct volume groups based on the past 50

days, which will also be use in this study. The criteria used to determine a high and low

volume day is consequently today’s volume evaluated on the past 50 days MA (out-of-

sample). If the volume is above (below) one standard deviation, assuming normality, of

the 50 day MA it results in a high (low) volume day. Otherwise it is determined a normal

volume day.9

Dummy for volume group G1 (high volume group):

1 if > one stdev of the 50 day MA of volume, otherwise 0.7 SSVX 1M and SSVX 3M, a Swedish Treasury Bill with 1 and 3 months maturity, respectively.8 SE GVB 10 Y, a Swedish Government Bond with 10 years maturity9 In the study by Gervais et al., (2001) they determines their formation periods using the top/bottom 10% of the daily volumes over the whole trading interval. A 10% limit is determined to be too strict and would further capture too few observations. Based on the fact that only firm and macro announcements, contributing to high volume, occurs around 6-12 times per year. Where the volume is expected to be high before and after the announcement for a few days. This alone would results in >10% of the total trading days in any given year.

16

Dummy for volume group G3 (low volume group):

1 if < one stdev of the 50 day MA of volume, otherwise 0.

G#TB = Dummy for group G# times the TB

G#TS = Dummy for group G# times the TS

G#OP = Dummy for group G# times the OP

where # = 1 or 3.

3.4 Regression model10

The standard Ordinary Least Square method is used where the regression model is

presented below.

Rt=α +TBt+TS t+OP t+G 1Dummy∗(TB¿¿ t+TS t+OPt)+¿¿

G 3Dummy∗(TBt+TS t+OPt)+εt❑

ε t❑ N (0 , σ t

2 )

Newey and West (1987) heteroskedasticity-consistent standard errors are used, consistent

with the general perception about the characteristic of financial data. This approach is

used to handle issues with autocorrelation and heterskedasticity common in financial

data. Graphical tests are performed along with standard econometric tests;

heteroskedasticity tests, (White, 1984) autocorrelation tests Breusch-Godfrey Serial

correlation LM test and Durbin-Watson, (Godfrey, 1978, 1981; Durbin and Watson,

1951), unit root test (Dickey and Fuller, 1979), and normality11 tests (Jarque and Bera,

1980) for the relevant time-series used in the study. However, no peculiar finding should

be found since the explanatory variables are constructed using standard procedure for

financial data. Consequently, following this procedure for the input data the risk of

jeopardizing the reliability of the results are reduced, and the coefficient estimates should

be the best linear unbiased estimators (BLUE).

10 Brooks, (2008)11 Even though it is empirically concluded several decades ago the returns are not normally distributed (Mandelbrot, 1997), they are assumed to be normally distributed in this study. This assumption is consistent with most academic research on financial data. Given the large sample size the impact of non-normality is further limited.

17

3.5 Hypothesis discussion

Group 1, G1, consists of days with high volume, in general generated by large

institutional investors. Informed and sophisticated they are expected to signal information

which reflects the conditions in the market. Given their size and experience they are

primarily expected to act when they have an information advantage and/or when new

information is released. In doing so they are likely to move the market. Consequently, the

activity from the high volume days is expected to be related to the explanatory variables.

(Cohen et al., 2002; Daniel et al., 1997; Holden and Subrahmanyam, 1992; Nofsinger and

Sias, 1999, Shu, 2010) There is also the possibility that the explanatory variables are not

a good reflection. This as the high volume days are expected to be correlated to firm and

macro specific news announcements limiting the power of the selected explanatory

variables, shifting it to the actual news event and other variables not accounted for.

Group 2, G2 consisting of days with normal volume. Made up of a mix of institutional

and individual investors continuously active in the market the relation with the

explanatory variables is expected to be consistent with that observed in G1. The variables

in normal days are further expected to demonstrate a higher relation to the explanatory

variables than for the other two groups.

Group 3, G3, consists of days with low volume. The expectation from days with low

activity is that they are poorly related to the explanatory variables as the investors’

uncertainty is higher during low volume when they are awaiting new information.

Expressed differently, little consensus which one can act upon is available. (Lee and

Swaminathan, 1998) Second, during days with low volume there is an expected greater

share of uniformed investors active in the market. (Stickel and Verrecchia, 1994) This

group of investors does suggestively not rely as much on the explanatory variables in

their decision making as the other more sophisticated groups. (Cohen et al., 2002; Daniel

et al., 1997; Holden and Subrahmanyam, 1992; Nofsinger and Sias, 1999) Hence, the

potential impact it could have on the return, positive or negative, is undetermined. This

further provides an explanation to why the return could be poorly associated to the

explanatory variables during low volume days.

18

3.5.1 Hypotheses table

Relation 1M T-Bill Term spread Oil price

G1 - + +/-

G2 - + +/-

G3 +/- +/- +/-

In the table above a summary of the expected relation among volume activity and

variables are presented.

To support the results and the study firms will be sorted by sector, following the global

industry classification standard, GICS, and by turnover. The turnover will be calculated

as the average price times the average volume. The firms will then be divided into a high

and a low turnover group. This will be conducted so that the sizes of the two groups are

not too skewed while still reflecting an intuitive break of the turnover.

19

3.6 Methodological difficulties

Stocks lacking a continuous historical data records will be excluded from the study. This

was the case for Securitas and Nokia. If data is missing for the explanatory variables the most recent value were used. The start day for the regression model is 1992-01-02 and the final day is 2011-04-29. A list of all firms and sectors can be found in table 10 in the appendix.

Since this study relies upon parts rather than the whole of other researchers work, with a

different approach and purpose the assumptions of using a 50 day moving average and

one standard deviation as filtering criteria was carefully investigated. Test results

revealed that using longer moving averages, 100 and 150 days, would cause limited

changes on the number of observations in the normal volume group. However, it

contributed to an asymmetric size of each extreme volume group. Hence, a 50 day

moving average was determined good. However, tests of using one standard deviation to

filter out high and low volume days was not satisfactory to create symmetric sized groups

consisting of approximately 16 % of the observations each. This due to the non-normality

characteristics of the volume data. Therefore, using trial and error it was concluded that

0,82 standard deviations was best. This assured that each group contained a satisfactory

share of the observations approximately corresponding to the characteristics of a normal

distribution.12

12 1,00 stdev, 0,75 stdev, 0,80 stdev, 0,85 stdev and 0,84 stdev has been tested to reach consensus for the complete sample of stocks. Note also that it was optimized to fit all firms in the sample and and individual optimization could improve the results.

20

4 Empirical Results

In this fourth chapter I present the empirical findings from the conducted study. All

results are revealed in graphs and tables accompanied by brief comments. In reflecting

over the results there will be a focus on OMXS30, a summary of the index constituents

and brief comments from a turnover/sector comparison.

4.1 The OMXS30 index

4.1.1 Descriptive statistics of the volume and returnTable 1 –Volume characteristics for different volume groups of OMXS30

OMXS30Start: 2/1/1992End: 4/29/2011

G1 G2 G3 Compl. Per.Observations 929 3094 831 4854Obs./total 19.1% 63.7% 17.1% 100.0%Turnover in M SEKAverage (turn) 72,688 60,972 31,284 57,114Stdev (turn) 91,421 55,086 29,268 62,347Skewness 2.85 0.55 0.59 2.73Kurtosis 18.64 -0.76 -0.72 25.67

Descriptive statistics for the G2 group is presented to give the reader an idea about the characteristics during a day with normal volume.

The volume distribution in all groups are demonstrating a non-normal distribution with

rather different characteristics. We note that each volume group G1 (high volume) and

G3 (low volume) contains more observations than desirable using the same specification

as for individual firms, 0.82 standard deviations. This contributes to G2 containing too

few observation and the extreme groups being asymmetric in size. (After using 0.87

standard deviations as filtering characteristic more symmetric size volume groups was

obtained. The overall impact of the change was on the other hand very small and the

relations between the groups persisted.)

21

Table 2 – Return characteristics for different volume groups for OMXS30

G1 G2 G3 Compl. PerMin -8.527% -6.544% -5.551% -8.527%

25th percentile -1.107% -0.759% -0.614% -0.790%50th percentile 0.000% 0.077% 0.060% 0.068%75th percentile 1.012% 0.868% 0.670% 0.858%

Max 11.023% 9.865% 4.963% 11.023%Average R -0.042% 0.065% 0.037% 0.040%Stdev R 2.04% 1.44% 1.15% 1.53%Skewness 0.14 0.26 -0.20 0.16Kurtosis 2.51 3.24 3.36 3.76

The return characteristic does exhibit considerably normal distributional characteristics

for all volume groups. The highest average return occurs in the normal volume days,

0.065%. For the high volume days the return is negative (-0.042%), while for low volume

days it is positive (0.037%) on average. It further demonstrates that large extreme returns

do, not unexpectedly, occur under high volume.

Please see Appendix, Table 23 for a graph of the high and low volume day’s market in

the price graph together with the daily returns. The graph reveals that there is a large

tendency to volume clustering, where low volume in general characterize short-term

consolidation periods.

Table 3 – Correlation matrixTB, 1M-T-Bill TS, Term Spread OP, Oil Price OMXS30 SEKUSD

TB, 1M-T-Bill 1.00TS, Term Spread 0.00 1.00OP, Oil Price 0.03 0.04 1.00OMXS30 0.01 0.05 0.08 1.00SEKUSD -0.02 -0.01 -0.17 -0.13 1.00

(The SEKUSD time-series does not start until 1994-01-03). Please see appendix, table 24 for a graph of the

explanatory variables.

Above it is noted that the variables demonstrate very low cross-correlation within the

sample period. The highest relation is observed for the oil price and SEKUSD, -0.17.13

13 The author considered it important to investigate the relation between USDSEK and oil price to develop an idea about the possibility of USDSEK price changes being incorporated in the oil price.

22

4.1.2 Results from the regression model, OMXS30In discussing the results for the normal volume activity this is reflected by the TB, TS,

and OP coefficients.

Table 4 – Regression results from the OMXS30 index14

Coefficient C TB TS OP G1TB G1TS G1OP G3TB G3TS G3OPOMXS30 -0.0002 -0.0037 0.0005 0.0341 -0.0182 -0.0003 0.1271 0.0213 0.0000 0.0212

(0.5882) (0.5568) (0.014) (0.1284) (0.1779) (0.2194) (0.0199) (0.1827) (0.8711) (0.5936)

The average value for each the exogenous variables among the different volume groupsTB TS OP G1TB G1TS G1OP G3TB G3TS G3OP

Average value 1 -0.0140% 0.4372 0.0136% -0.0352% 0.204 -0.0001 -0.0208% 0.253 0.0028%

Each explantory variables average impact on the return in realtion to the constant and the mean return (R).Relative constant 100% -0.28% -118.27% -2.52% -3.50% 35.93% 4.72% 2.42% 4.85% -0.32%Relative R -45.64% 0.13% 53.97% 1.15% 1.60% -16.40% -2.15% -1.10% -2.21% 0.15%

Mean dependent var 0.0004S.D. dependent var 0.0153R-squared 1.32%Adjusted R-squared 1.14%Durbin-Watson stat 1.98F-statistic 7.21Prob(F-statistic) 0

The regression results above reveals that the term spread, TS, and the oil price on high

volume days, G1OP, are positive and significant. It is moreover concluded that the term

spread by itself account for ~54% of the mean return for the OMXS30 index under

normal volume, which is noteworthy. However, when also accounting for days with high

volume it drops down to ~38%. (A regression was also run when 0,87 standard deviations

was used as the filtering criteria. The above presented coefficient estimates was still a

good approximation where the p-value for OP, G1TB and G1TS approached, but never

reached, significance at the 10% level.)

14 A regression of only the TB, TS and OP without a volume filter resulted in TS and OP being significant.

23

4.2 The OMXS30 index constituents

4.2.1 Descriptive statistics of the volume and returnTable 5 –Summary of the volume within different volume groups for all firmsVolume/ (Turnover) 25th 50th 75thG1 Min percentile percentile percentile Max Average StdevAverage (Turnover) 105,679,016 271,023,543 448,180,477 836,597,434 4,333,395,079 682,305,520 802,146,409 Observations 277 497.5 680 795.5 876 634 186Obs./total 13.59% 14.84% 16.25% 17.23% 18.32% 16.06% 1%Skewness 1.50 2.28 3.81 5.27 14.42 4.62 3.52 Kurtosis 2.33 8.12 20.48 47.69 302.21 49.23 75.06

G2Average (Turnover) 31,694,857 108,639,828 193,932,272 362,202,047 2,144,385,721 306,629,871 401,402,718 Observations 1102 2046 3077 3223 3557 2,673 690Obs./total 62.23% 65.80% 67.92% 70.25% 75.02% 68.15% 3%Skewness 0.24 0.92 1.20 1.37 3.74 1.35 0.77 Kurtosis (0.14) 0.89 1.61 3.40 28.95 4.02 6.19

Volume/ (Turnover) 25th 50th 75thG3 Min percentile percentile percentile Max Average StdevAverage (Turnover) 21,861,218 60,756,555 102,686,339 203,295,759 1,120,485,402 171,255,475 209,425,950 Observations 232 506.5 601 761 907 619 180Obs./total 11.39% 14.58% 15.74% 17.26% 19.45% 15.79% 2%Skewness (0.01) 0.50 1.05 1.62 7.62 1.81 2.23 Kurtosis (0.82) (0.34) 0.48 2.85 97.29 13.28 30.76

Complete PeriodAverage (Turnover) 41,784,570 136,206,104 221,053,162 411,845,926 2,307,703,913 344,212,051 430,067,358 Observations 1611 3024.5 4136 4854 4854 3,926 1,012Obs./total 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 0%Skewness 2.02 2.98 4.64 6.90 34.60 7.01 7.09 Kurtosis 6.87 18.03 50.76 108.99 1,629.55 163.42 333.59

This table illustrate the turnover statistics for the different volume groups. It also presents the distributional characteristics for each volume group and the spread among all the firms. Example, in volume group G1 in the Average (turnover) row one see the distribution characteristics for the turnover among all firms. On the Obs./total row the distributional characteristics for the ratio of observations amongst all firms are presented. Example, in the dark grey cell above, representing the high volume group, we see that for the ratio of observations as part of the total for the median firm in the sample is 16.25 %. (Average (turnover) is calculated as the average volume * the average price for each group.) 15

In the table above we note the 50 day moving average of the volume with 0,82 standard

deviations as filtering criteria for each group is a good specification. The percentage

share of the average and the median volume is seemingly equal, 16.06% for the high

volume group and 15.79% for the low volume group. This even though there is a

considerable skewness and kurtosis within the high volume group (G1) and low volume

group (G3).

15 See table 11-15 in appendix for details of volume distribution for each individual firm, sorted either by sector or turnover.

24

Table 6 –Summary of the return within different volume groups for all firmsReturn 25th 50th 75thG1 Min percentile percentile percentile Max Average StdevMin -92.434% -21.005% -17.659% -14.360% -7.955% -23.507% 18.243%25th percentile -3.282% -1.900% -1.587% -1.372% -0.969% -1.744% 0.590%50 th percentile 0.000% 0.000% 0.256% 0.351% 0.683% 0.221% 0.193%75th percentile 1.564% 1.859% 2.026% 2.462% 4.200% 2.224% 0.580%Max 10.323% 14.347% 17.506% 22.309% 36.011% 19.001% 6.652%Average -0.197% 0.168% 0.250% 0.341% 0.708% 0.250% 0.191%Stdev 2.396% 3.168% 3.510% 4.131% 6.967% 3.833% 1.136%Skewness (6.11) (0.21) 0.04 0.15 0.75 (0.47) 1.58 Kurtosis 1.20 2.22 3.66 8.70 103.64 11.58 22.73

Return 25th 50th 75thG2 Min percentile percentile percentile Max Average StdevMin -29.267% -14.254% -12.032% -10.350% -6.638% -13.286% 5.189%25th percentile -1.758% -1.178% -1.110% -1.006% -0.826% -1.135% 0.204%50 th percentile 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000%75th percentile 0.861% 1.010% 1.107% 1.268% 1.641% 1.164% 0.217%Max 7.054% 11.517% 12.783% 15.024% 23.215% 13.574% 3.913%Average -0.070% -0.010% 0.015% 0.036% 0.128% 0.017% 0.042%Stdev 1.532% 1.902% 2.088% 2.296% 3.067% 2.150% 0.400%Skewness (0.56) (0.07) 0.13 0.25 0.45 0.08 0.22 Kurtosis 1.54 2.60 3.43 4.57 13.54 4.08 2.40

Return 25th 50th 75thG3 Min percentile percentile percentile Max Average StdevMin -11.179% -7.843% -7.048% -6.025% -3.779% -7.195% 1.771%25th percentile -1.303% -0.972% -0.908% -0.782% -0.584% -0.897% 0.153%50 th percentile -0.234% 0.000% 0.000% 0.000% 0.000% -0.009% 0.045%75th percentile 0.490% 0.681% 0.784% 0.879% 1.316% 0.790% 0.172%Max 4.500% 6.023% 7.565% 9.814% 14.086% 8.184% 2.724%

Average -0.220% -0.116% -0.080% -0.026% 0.159% -0.065% 0.085%Stdev 1.175% 1.418% 1.592% 1.748% 2.135% 1.581% 0.239%Skewness (0.45) (0.18) (0.06) 0.31 1.09 0.08 0.37 Kurtosis 1.05 2.21 3.49 4.65 10.93 3.85 2.33

Return 25th 50th 75thComplete Period Min percentile percentile percentile Max Average StdevMin -92.434% -27.096% -17.659% -14.360% -7.955% -24.140% 18.241%25th percentile -1.737% -1.220% -1.129% -1.010% -0.838% -1.146% 0.200%50 th percentile 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000%75th percentile 0.904% 1.054% 1.169% 1.295% 1.783% 1.210% 0.222%Max 10.323% 14.963% 17.506% 22.309% 36.011% 19.201% 6.455%Average -0.021% 0.030% 0.044% 0.061% 0.092% 0.043% 0.026%Stdev 1.670% 2.157% 2.326% 2.515% 3.751% 2.436% 0.528%Skewness (6.25) (0.08) 0.09 0.29 0.65 (0.36) 1.54 Kurtosis 2.45 4.46 6.14 10.31 177.16 19.81 40.68

On the horizontal axis the return for all firms using different statistical features are presented. Example, in the Complete Period section directly above, the average in the left column (y-led, dark grey) is the average return for a firm. Then go right, (x-led, light grey) to the 50th percentile and you see the median average return among all firms, 0.044%. If you look further to the rights, in the stdev column (x-led, lightest grey) you see the standard deviation of the average returns for all firms in the group Complete Period, 0.026%16

The return characteristics corresponding to each volume group, found in the table above,

illustrate interesting results. The average return and standard deviation (stdev)

demonstrates a decaying pattern for the average, 25th, 50th, and 75th percentile separation

across volume groups (look vertical/top-down). These figures are higher in high volume

group and lower in the low volume group. The difference is considerable where the

average (standard deviation) is 0.250% (3,833%) for the high volume days, 0.017%

(2.150%) for the medium volume days, and -0.065% (1.581%) for the low volume days 16 See table 11-15 in appendix for details of volume distribution for each individual firm, sorted either by sector or turnover.

25

while over the complete period it is 0.043% (2.436%). It is also noted that the skewness

and kurtosis for medium and low volume is in between -0.18 – 0.31 and 2.21 – 4.65

respectively. Hence, it is fairly normally distributed around its average. While in the high

volume group the kurtosis is considerably higher. (The overall pattern also persists after

removing two outlier firms, ABB and SKF).

4.2.2 Descriptive statistics for OMXS30 constituents sorted by turnover and sector

If the reader is interest in descriptive statistics for each individual firm in the OMXS30,

tables can be found in Appendix sorted by turnover and sector (table 11-15).

4.2.3 Results from regression model, OMXS30 index constituentsTable 7 – Summary of the regression results for ONLY significant variables among all firms

7 9 15 22 6 13 6 11 8 426% 33% 56% 81% 22% 48% 22% 41% 30% 15%

Coefficient: C TB TS OP G1TB G1TS G1OP G3TB G3TS G3OP

Average -0.0026 -0.0084 0.0012 0.1113 0.0313 0.0021 0.3660 0.0355 -0.0009 -0.0227(0.03968) (0.05134) (0.03299) (0.01194) (0.03171) (0.02615) (0.03418) (0.03166) (0.02056) (0.01742)

Stdev 0.0011 0.0222 0.0006 0.1146 0.0957 0.0011 0.2999 0.0290 0.0002 0.1688(0.03731) (0.03421) (0.02704) (0.01161) (0.02624) (0.02267) (0.03384) (0.03506) (0.02628) (0.01729)

Variables impact compared to constant C TB TS OP G1TB G1TS G1OP G3TB G3TS G3OP(using average values) 100% -0.01% -35.83% -1.24% 0.23% -13.88% 0.59% 0.09% 6.89% 0.12%

% an # of significant coefficients:

In the table above the average coefficient values for the significant variables accompanied by the average p-value are presented. The standard deviation is calculated for the coefficient values and the p-values. Example, for the TS variable it is significant for 15 out of the 27 firms. Among these 15 firms the average coefficient value is 0.0012, with a standard deviation of 0.0006. As regards of the average p-value for the significant variables it is 0.03299 with a standard deviation of 0.02704. At last we note that the TS impact in relation to the constant, C, is -35.83%. If the reader is interested in the coefficient and p-values for each individual firm these can be found in appendix in table 16-17 sorted by sector and in table 18-19 sorted by turnover.

In the table above we conclude that the constant is negative and significant for 26 % of

all the firms in the sample. The oil price (OP) is the variable demonstrating the best

explanatory characteristics. It is significant for 81 % of all firms on normal days and is

moreover significant for 22% of the firms among the high volume group and 15% among

the low volume group. For those firms which the variables are significant it is interesting

to note that 11.13%17 of the oil price return is transferred over to the average stock return

on normal volume days, and as much as 47.73% (36.60%18 + 11.13%) on high volume 17 9.28% excluding Lundin Petroleum18 25.24% excluding Lundin Petroleum

26

days. However, the overall impact transferred to a firm’s average return is just a few

percent, at most. It is noted that for the normal and high volume days the oil price

coefficient is positive. Important to stress is that the sign of the explanatory variable

G1OP is negative on average, while for the low volume group G3OP it is positive.

(Please see Appendix, Table 22)

The second most significant variable, for 56 % of the firms, is the term spread (TS) under

normal volume. Concerning the high volume group the relation is also positive and

significant for 48 % of the firms. In the low volume days the relation is negative and

significant for 30 % of the firms. Noteworthy is that the TS across the three volume

groups is the single most important factor in explaining the return across all firms.

Shifting focus to the 1 month T-bill the explanatory power is the lowest. It is negative

and significant for 33 % of the firms in the normal volume group, positive and significant

for 22 % of the firms in the high volume group, and positive and significant for 41 % of

the firms in the low volume group. However, the impact on the return is very small.

4.2.4 Results from regression model results, OMXS30 index constituents sorted by

turnover and sector

In the Appendix results for individual firms are presented sorted by turnover and sector.

According to statistical test no difference was found for sectors versus the whole sample.

In the table illustrating the sector group separation, each firm’s regression coefficients are

presented accompanied by its impact in relation to the stocks mean return. It reveals that

the term spread indeed can have a very high impact on the return for many firms. (Please

see Appendix, Table 16-17,21)

When firms are sorted by turnover more interesting results are found. It is statistically

concluded that the explanatory variables are better explaining high turnover stocks than

low turnover stocks. The average number of explanatory variables for the high turnover

stocks is 4.07 versus an average of 3.38 for low turnover stocks, with a standard deviation

of 1.60. For the high turnover stocks the variables more significant than the low turnover

27

stocks are the constant (C), the term spread (TS and G3TS), the 1 M T-Bill (G1TB), and

the oil price (OP). The opposite relation is found for the term spread (G1TS), which is

significantly more frequent among the low turnover stocks. In the table presenting firms

sorted by turnover, each firm’s regression coefficients is presented in relation to the

constant. (Please see Appendix, Table 18-19, 20)

4.3 Concluding remarks and main results

It was found that the term spread and oil price was the most important variables to

explain the index and stock return. The term spread demonstrated the most significant

impact on the return. For individual firms it was found that high turnover stocks return

was better explained using these variables than low turnover stocks.

28

5 Analysis and discussion

This chapter begins with a discussion of OMXS30 and its constituent firms. It is followed

by a discussion of the explanatory variables and the sector/turnover comparison.

In analyzing the return characteristics and the volatility for each volume group the results

are consistent with what was expected according to Lee and Swaminathan (1998). They

argued, and the results illustrate it, that high volume days are subject of greater risk and

further demonstrate a higher return volatility, while the opposite holds true for the low

volume days. (Please see appendix, table 11-15) Shu, (2010) found that stocks with

higher fraction of institutional trading volume outperform stocks with lower fraction of

institutional trading volume. Although this study have not performed a explicit separation

between institutional and individual investors consistent with Shu (2010), the return

characteristics demonstrate that under high volume the return is significantly larger than

for the low volume group across firms. As argued previously it is not unlikely that these

group formations would be a good representation of institutional and individual investors

respectively. Interestingly it was noted that on high volume days for the index the

average return was negative contrary to individual stocks. This is likely due to Ericsson,

Sandvik and ABB which throughout the sample period demonstrate a negative return in

the high volume period and further has a high weighting in the OMXS30 index. This

illustrates the importance of considering individual analysis in relation to index research.

Table 8 – Hypothesis table, OMXS30 index

Relation and significance 1M T-Bill, TB Term spread, TS Oil price, OPG1, High. - - + and significant

G2, Normal - + and significant +

G3, Low + + +

The above table illustrates the sign of the coefficients and if it was significant for the OMXS30 index.

In relation to formulated hypothesis the results for the OMXS30 index illustrate fairly

consistent results. As hypothesised, the impact on low volume days was difficult to

determine but it turned out to be positive for all variables. For the normal and high

29

volume days the TB had a negative impact which was expected. The OP had a positive

impact which was close to being significant for both volume groups. Regarding the TS

the results were mixed.

The regression results for the OMXS30 index revealed limited significant variables,

especially for the extreme volume groups. This is interesting since the weighting of

stocks in the OMXS30 do contain a high share of high turnover stocks, a group that

demonstrated more significant variables than that for the low turnover group.

Consequently, a low explanatory power for the exogenous variables during high and low

volume groups for the OMXS30 index could be due to the index being subject of changes

more related to, and reflecting, other macroeconomic figures. Figures such as the GDP,

industrial production, export and import, money growth, and global economic figures

among others.

Table 9 – Hypothesis table, OMXS30 index constituents, relation and significance

Relation 1M T-Bill, TB Term spread, TS Oil price, OP

G1, High

volume

+

(12 firms have + sign)

+


+


G2, Normal

volume

-

(16 firms have – sign)

+


+


G3, Low

volume

+ (20)


- (24)

(24 firms have - sign)

- (18)

(18 firms have - sign)

Significance 1M T-Bill, TB Term spread, TS Oil price, OP

G1, High

volume

3 firms, a significant + sign

3 firms, a significant - sign





G2, Normal

volume






1 firm, a significant - sign

G3, Low

volume


1 firm, a significant - sign





The relation hypothesis table above illustrates the sign, on average, for all firms in the study. Under each sign, in parenthesis, the number of firms which demonstrate that sign is presented. Example, in the normal period there is on average a negative relation between the 1 month T-bill and the stock return, and 16 firms out of 27 demonstrate a negative sign. In the significance hypothesis table the number of firms with

30

significance and the relation is presented. If the reader is interested in the relation and significance for an individual firm please see appendix in table 16-17 sorted by sector and table 18-19 sorted by turnover.In analyzing the results from the OMXS30 firm constituents several findings were

observed, see table 9. As indicated in the theory section the explanatory variables relation

in the low volume days was expected to show low significance and mixed results. This is

what the results illustrate concerning the OMXS30 index and across firms. For the three

variables in the low volume group only one is consistent with what was observed for the

index, which is the TB. In the normal volume group the TB is negative on average and

that for the majority of the firms. In contrast to the hypothesis and what was found for the

OMXS30 index there is a positive relation, on average, for the TB in the high volume

group. However, it is among the minority of the firms that this positive relation is

demonstrated. The results for the TS and OP in the normal and high volume groups are

clearer and demonstrate a positive sign for almost all firms, consistent with the

hypothesis. As regards of the negative relation among firms in the low volume group for

TS and OP this likely reflects that little information is available in the market. And with

little information the uncertainty increases and a negative or undetermined relation would

be expected as demonstrated. Concerning the significance for individual firms it is clear

that the TS is indeed a good and consistent explanatory variable under the three volume

groups, followed by the OP.

In discussing the explanatory variables, starting with the TB, it was in line with the

expectations demonstrating a negative relation for the OMXS30 index (table 8) and that

also for the majority of the firms under worth mentioning volume. It is interesting to note

however that under low volume the relation is positive and significant for 10 firms.

Important to remember is that the impact on the return for the TB was low. This is not

unlikely as the effect of the financing costs on a firm’s total profit is small in most cases

and of less importance to the stock return, in comparison to many other factors.

Moreover, most of the firms within the OMXS30 have a significant cross-border

operation. This enables these firms to raise capital on the international market and would

possibly be more subject of international interest rates movements. In large the results for

the short term interest rate variable were consistent with previous researchers, such as

Avramov and Chordia (2006) and Perez-Quiros and Timmermann (2000) among other.

31

The 1 month T-Bill demonstrated a negative relation with the return most likely

reflecting higher financing costs.

As regards of the term spread a consistent relation with previous research was found,

with the exception of high volume days for the OMXS30 index, and the underlying

reason for this is hard to determine. For Swedish stocks and its major index the term

spread was the most important variable across all volume groups and firms. It is

concluded a good measure in general and not only a good predictor of recessions as

suggested by Estrella and Mishkin (1998) and bull and bear markets as suggested by

Chen (2009). These results are contrary to Rapach et al., (2005) which found limiting

evidence for the terms spread as an explanatory variable. Important to note is that the

impact of the term spread in relation to the average return over the sample period,

especially on days with normal volume is extreme for several firms (please see appendix,

table 16-17). That the term spread should account for several hundred percent of the

average return is spurious. The conclusion from this is that additional interest rate

variables seem to be justified as they could help narrow down the relation between the

observed return and that of other explanatory variables.

The peculiar finding, in contrast to previous research such as Driesprong et al. (2008) and

Nandha and Faff (2008) suggesting a negative relation with the oil price, is that it

demonstrates a clear positive relation with the return for all firms but one. And on normal

days this relation is significant for four fifths of the firms. This seems to suggest that

Swedish firms benefit from higher oil prices. This could have several explanations. As

mentioned previously one is that higher oil prices reflect a booming economy which

would be positive for most firms. Another explanation is that the Swedish economy and

its firms, although representing several energy intensive sectors, are less dependent on

fossil fuels usage than perhaps many other firms and countries. Hence, with less

dependence on oil as an input variable they are compensated when oil prices increases.

This either from adjusting their own prices resulting in an improved profit margin, or an

increased competitiveness increasing the demand for their goods and services. Using oil

as a proxy for inflation seems not unlikely given its importance in society today. In doing

32

so the expected relation suggested by the fisher theory, predicting that stocks provide a

hedge against inflation, holds. If the average sign of the explanatory variable (the input

data) is incorporated in the analysis, found in table 22 in appendix, one note that the total

effect is that under high and low volume the impact form the oil price impact is on

average negative for the stock return, while under normal volume the impact is positive

on average for the stock return.

Concerning the sector and high versus low turnover stock comparison it was hard to

reveal any differences for sectors. First, most of the different sector groups were small

with only a few firms within each, plus within each sector the sub-industry each firm

operated in were considerable different. A more intuitive comparison could have been

services providers versus goods producers. This could capture some firms’ high physical

capital costs and energy intensive production contrary to other firms low capital costs and

high human capital costs. The separation of stocks into a high and low turnover group

revealed more interesting findings. The volume for high turnover stocks contained

information which was to a higher degree related to the explanatory variables than the

lower turnover stocks. The average return was further significantly higher for high

turnover stocks than low. This in line with what several other researchers have suggested.

(Copeland, 1976; Easley and O´hara, 1992, Bessembinder and Segin, 1993; Gervais et

al., 2001) However, as regards of the standard deviation of the return it was significantly

lower for high turnover stock than for low turnover stocks. One explanation for this is

that the high turnover stocks are more governed by institutional investors and finance

journalists, nationally and internationally. This reduces information asymmetries and the

high turnover stocks would possibly face lesser degree of news announcement surprises

than lower turnover stocks might. Algorithm trading, more implemented on high turnover

stocks, could also play a role in which the computerized trading contribute to lower

volatility as they seize mispricing faster. In summary, Lamoureax and Lastrapes (1990)

presenting that trading volume is a good proxy for the arrival of new information, Stickel

and Verrecchia (1994) arguing that as volume increases the likelihood that the price

change is information drive, and finally Clark (1973) conclusion that there is a relation

between trading volume and volatility are supported by the results.

33

6 Conclusion

In this final chapter I present my conclusions from the performed study. I reflect on the

research and finally I give suggestions to further research around some of the fields this

study have touched upon.

A considerable amount of research was analyzed to find consistent and intuitive

explanatory variables. Three was found, the 1 M T-Bill, the term spread - 10 Y Treasury

bond versus a 3 M T-Bill -, and the oil price. In trying to reveal and better understand the

complexity of the financial markets these three variables was used together with a

volume filter for high, normal and low volume days. The results from using this

methodology have revealed that accounting for trade volume is important in trying to

explain the return. The chosen explanatory variables do indeed explain Swedish index

and stock returns as suggested by previous research. Moreover, filtering for the volume

provides additional insights of when the explanatory variables are useful. It provides

insights on the relation; sign and size of the impacts, which varied significantly across

different volume activity across firms. The results revealed a significant difference

between high and low turnover stocks. The most reliable and consistent variables were

the oil price followed by the term spread, both demonstrating a positive relation with the

return.

6.1 Criticism of research

The results rest upon an in-sample study revealing the sign and size an explanatory

variable has in relation to the stock return. However, it does not say anything about

whether a change in the oil price or the interest rate variables comes pre or post a stock

price movement. Hence, it is important to further investigate the causality between the

stock return and the explanatory factors for the Swedish case. It is also important to be

aware of that a lot of economic research conducted using macroeconomic data use lower

frequent data such as weekly or monthly observations. This data does not include as

much noise as the daily observations does used in this study.

34

Although, an extensive literature review was conducted to find appropriate variables

general for all firms more could have been chosen and rather let the regression decide

which are useful and which are not. However, using the chosen volume filter three times

as much information must be interpreted. That and given that hardly any other research

has been found using a similar methodology it was necessary to impose some restrictions

to be able to target the possible relations and reach a conclusion if the methodology

chosen to work with was promising or not.

6.2 Further studies

The results and the methodology are indeed promising and a bigger study with large cap,

medium cap and small cap stocks included would be interesting to conduct. This would

make the sector and turnover group study more intuitive, and accurate. In such a study it

would also be appropriate to include more explanatory variables trying to find a good

model fit.

It should further be interesting to study the trade records from higher frequent data. In

doing so the focus would be to separate the trade activity relating to the institutional

investors from that of the individual investors. In this and above mentioned scenario it

would moreover be important to conduct out-of-sample test to reveal if the results are

useful in practice.

An extensive amount of data have been researched and analyzed. The results are

encouraging and reveal a lot of interesting relations, some of which have been discussed

around in the text while many more can be revealed if the reader study the tables and

graphs more closely. The filtering methodology show potential and deserve further

research.

35

7 ReferencesArticles and Books

Abbondante, P., 2010, Trading volume and Stock indicies: A test of Technical Analysis,

American Journal of Economics and Business Administration 2, p. 287-292

Adler, M., and Dumas, B., 1983, International portfolio choice and corporate finance: A

synthesis, Journal of Finance 38, p. 925-984

Ang A., and Bekaert, G., 2001, Stock return predictability: Is it there?, National Bureau

of Economic Research, Working Paper No. 8207

Apergis N., and Miller, S.M., 2008, Do structural oil-market shocks affect stock prices?

Energy economics 4, p. 569-575

Avramov D., and Chordia, T., 2006, Predicting stock returns, Journal of Financial

Economics 82, p. 387-415

Banz, R.W., 1981, The relationship between returns and market values of common

stocks, Journal of financial economics 9, p. 3-18

Basu, S., 1983, The relationship between earnings yield, market value, and return for

NYSE common stocks: Further evidence, Journal of Financial Economics 12, p. 129-156

Balvers R.J., Cosimano, T.F., and McDonald, B., (1990), Predicting Stock Returns in an

Efficient Market, Journal of Finance 45, p. 1109-1128

Bessembinder H., and Segin,P.J., 1993, Price volatility, trading volume, and market

depth: evidence from the futures market, Journal of Financial and Quantitative Analysis

28, p. 21-39

Bodie, Z., 1976, Common stocks as a hedge against inflation, Journal of Finance 3, p.

459-470

Barsky R.B:, and Kilian, L., 2004, Oil and the Macroeconomy since the 1970s, Journal of

Economic Perspectives 18, p. 115-134

36

Black, F., 1976, Studies in stock price volatility change, Proceedings of the 1976 business

meeting of the business and economic section, American Statistical Association, p. 177-

181

Bolleslev, T., 1986, Generalized Autoregressive Conditional Heteroskedasticity, Journal

of Econometrics 31, p. 307-327

Brown, S.P.A., and Yücel, M.K., 1999, Oil prices and U.S. aggregate economic activity,

Federal Reserve Bank of Dallas Economic Review 14, p. 16-53

Brown, S.P.A., and Yücel, M.K., 2002, Energy prices and aggregate economic activity:

an interpretative survey, Quarterly Review of Economics and Finance 42, p. 193-208

Brooks, C., (2008), Introductory Econometrics for Finance, Cambridge University Press, Cambridge

Bryman, A., Bell, E., (2003) “Business Research Methods”, Oxford University Press,

Oxford

Campbell, J. Y., 1987; Stock returns and the term structure, Journal of Financial


Campbell, J.Y., 1990, Measuring the persistence of expected returns, The American

economic review 80, p. 43-47

Caruth, A.A, Hooker, M.A. and Oswald, A.J., 1998, Unemployment equilibria and input

prices: theory and evidence from the United States, Review of Economics and Statistics

80, p. 621-628

Clark, P.K., 1973, A subordinated stochastic process model with finance variance for

speculative prices, Econometrica 41, p. 135-155

Chan, L.K., Hamao, Y., and Lakonishok,J., 1991, Fundamentals and stock returns in

Japan, Journal of Finance 46, p. 1739-1789

Chan, L.K.C., Karceski, J., and Lakonishok, J., 1998, The risk and Return from factors,

The Journal of Financial and Quantitative Analysis 33, No 2, p. 159-188

37

Chang, K-L., 2009, Do macroeconomic variables have regime-dependent effects on stock

return dynamics? Evidence from the Markov regime shifting model, Economic

Modelling, 1283-1299

Chen, N-F., Roll, R., and Ross S.A., 1986, Economic forces and the stock market, Journal

of Business 56, p. 383-403

Chen, N.F., (1991), Financial investment opportunities and the macroeconomy, Journal

of Finance 46, p. 529-554

Chen, S-S., 2009, Predicting the bear stock market: Macroeconomic variables as leading

indicators, Journal of Banking and Fiannce, 211-223

Chen, S-S., 2010, Do higher oil prices push the stock market into bear territory?, Energy

Economics, 490-495

Claessens, S., Dasgupta, S., and Glen, J., 1995, The Cross-Section of Stock Returns,

Evidence from Emerging Markets, Policy Research Working Paper, 1-20

Cohen, R.B., Gompers P.A., and Vuolteenaho, T., 2002, Who underreacts to cash-flow

news? Evidence from trading between individuals and institutions, Journal of Financial

Economics 66, 409-462

Conover, Jensen & Johnson, 1999, Monetary environments and international stock

returns, Journal of Banking and Finance 23, p. 1357-1381

Copeland, T.E., 1976, A Model of assets trading under the assumption of sequential

information arrival, Journal of Finance 31, p. 1149-1168

Chordia and Swaminathan (2000), Trading volume and Cross-Autocorrelations in stock

returns, Journal of Finance 55, p. 913-935

Cutler, D, Poterba and Summers, 1989; International evidence on the predictability of

stock returns, Working paper, Massachusetts Institute of Technology

Davis S.J. and Haltiwanger J., 2001, Sectoral job creation and destruction responses to

oil price changes, Journal of Monetary Economics 48, p. 465-512

Daniel, K., Grinblatt, M., Titman, S., Wermers, R., 1997, Measuring mutual fund

performance with characteristic-based benchmarks, Journal of Finance 52, p. 1035-1058

38

Dickey D., and Fuller, W., 1979, Distribution of the estimators for autoregressive time

series with unit root, Journal of American Statistical Association 74, p. 427-431

Driesprong G., Jacobsen, B., Maat, B., 2008, Striking oil: Another puzzle?, Journal of

Financial Economics 89, p. 307-327

Dumas B., and Solnik B., 1995, The world price of foreign exchange risk, Journal of

Finance 50, p. 445-479

Durbin, J., and Watson, G.S., 1951, Testing for Serial Correlation in Least Squares

Regression, Biometrika 38, p. 159-71

Easley, D., and O’hara M., 1992, Time and process of security price adjustments, Journal


El-Sharif, I., Brown, D., Bruce, B., Nixon, B., and Russel A., 2005, Evidence on the

nature and extent of the relationship between oil prices and equity values in the UK ,

Energy economics 27, p. 819-830

Engle , R.F., 1982, Autoregressive conditional heteroskedasticity with estimates of the

variance of United Kingdom inflation, Econometrica 50, p. 987-1007

Epps T.W., and Epps M.L., 1976, The stochastic dependence of security price changes

and transaction volumes: implications for the mixture-of-distributions hypothesis,

Econometrica 44, p. 305-321

Estrella, A., and Mishkin, F.S., 1998, Predicting U.S. recessions: Financial variables as

leading indicators, Review of economics and statistics 80, p. 45-61

Faff and Brailsford, 1999; Oil price risk and the Australian stock market - some further

results, Journal of Energy Finance and Development 4, p. 69-87

Fama, E., 1981, Stock returns, real activity, inflation and money, American Economic

Review 71, p. 545-565

Fama, E., and French, K., (1992) "The Cross-Section of Expected Stock Returns”, Journal


Fama, E.F., and Schwert, W., 1977, Asset returns and inflation, Journal of Financial


39

Fama, E., and French, K., 1989, Business conditions and expected returns on stocks and

bonds, Journal of Financial Economics 25, p. 23-49

Ferson W., and Harvey, C., 1993, The risk and predictability of International Equity

Returns, Review of Financial Studies 6, p. 527-566

Fleming, J., Kirby., Ostdiek, B., 2005, ARCH Effects and Trading Volume, Rice

University and Clemson University Working Paper

Flannery, M.J., and Protopapadakis, A.A., 2002, Macroeconomic factors do influence

aggregate stock returns, Review of Finanical studies 15, p. 751-782

Feldstein M.S., and Eckstein, O., 1970, The fundamental determinants of the interest

rate, Review of economics and Statistics 52, p. 363-376

Ferderer, J.P., 1996, Oil price volatility and the macroeconomy, Journal of

Macroeconomics 18, p. 1-26

Fuhrer, J.C., 1995, The Phillips curve is alive and well, New England Economic Review

of the Federal Reserve Bank of Boston March/April, p. 41-56

Gallant, A., Ronald, G.A., Rossi P.E., and Tauchen, G., 1992; Stock price and volume,

Review of Financial Studies 14, p. 1-27

Gervais, S., Kaniel, R., and Mingelgrin, D., (2001), The High Volume Return Premium,

Journal of Finance 56, p. 877-919

Geske, R., and Roll, R., 1983, The fiscal and monetary linkage between stock returns and

inflation, Journal of Finance 38, p. 1-33

Gisser M., and Goodwin, T.H., 1986, Crude oil and the macroeconomy: tests of some

popular notions, Journal of Money, Credit and Banking 18, p. 95-103

Glosten, L.R., Jagannathan, R.R., Runkle, D., (1993), On the relation between the

expected value and the volatility of the nominal excess return on stocks, Journal of

Finance 48, p. 1779-1801

Godfrey, L.J., 1978, Testing against general autoregressive and moving average error

models when the regressors included lagged dependent variables, Econometrica 46, p.

1293-1302

40

Godfrey, L.J., 1981, On the invariance of the Lagrance multiplier test with respect to

certain changes in the alternative hypothesis, Econometrica 49, p. 1443-1455

Gordon, R.J., 1997, The time-varying NAIRU and its implications for economic policy,

Journal of Economic Perspectives 11, p. 11-32

Hamilton, J.D., 1983, Oil and the macroeconomy since World War II, Journal of Political

Economy 91, p. 228-248

Hamilton, J.D., 2005, Oil and the macroeconomy, Working paper UCSD

Hodrick, R.J., 1989, Dividend yields and expected stock returns: Alternative procedures

for inference and measurement, Review of Financial Studies 5, p. 357-386

Holden C.W:, and Subrahmanyam, A., 1992, Long-lived private information and

imperfect competition, Journal of Finance 47, p. 247-270

Hooker, M.A., 2002, Are oil shocks inflationary? Asymmetric and nonlinear

specifications versus changes in regime, Journal of Money, Credit and Banking 34, p.

540-561

Huang, R.D., Masulis, R.W., Stoll, H.R., 1996, Energy shocks and financial markets,

Journal of futures markets 16, p. 1-27

Jaffe, J., and Mandelker, G., 1976, The “Fisher Effect” for risky assets: An empirical

investigation, The Journal of Finance 31, p. 447-458

Jarque, A., and Bera, A., 1980, Efficient test for normality, heteroskedasticity and serial

dependence of regression residuals, Economic Letters 6, p. 255-259

Jones, C.M., and Kaul, G., 1996, Oil and the stock markets, Journal of Finance 51, p.

463-491

Karpoff, J.M., 1987, The relation between price changes and trading volume: A survey,

Journal of Financial and Quantitative Analysis 22, 109-126

Keim, D., and Stambaugh, R.F., 1986, Predicting returns in the stock and bond markets,

Journal of Financial Economics 17, p. 357-390

41

Kilian, L., and Park, C., 2009, The impact of oil price shocks on the U.S. stock market, , International economic review 50, p. 1267-1287

Keane, M.P., and Prasad, E.S., 1996, The employment and wage effects of oil price

changes: a sectoral analysis, Review of Economics and Statistics 78, p. 389-400

Lamont, O., 2001, Economic tracking portfolios, Journal of Econometrics 105, p. 161-

184

Lardic, S., and Mignon, V., 2006, The impact of oil prices on GDP in European

Countries: an empirical investigation based on asymmetric cointegration, Energy Policy

34, p. 3910-3915

Lardic, S., and Mignon, V., 2008, Oil prices and economic activity: an asymmetric

cointegration approach, Energy enomics 30, p. 847-855

Lee, C.M.C., and Swaminathan, B. 1998, Price momentum and trading volume, Johnson

Graduate School of Management Cornell University

LeBlanc, M. and Chinn M.D., 2004, Do high oil prices presage inflation? The evidence

from G5 countries, Business Economics 34, p. 38-48

Lescaroux, F., and Mignon, V., 2008, On the influence of oil prices on economic activity

and other macroeconomic and financial variables, OPEC energy Review December 2008

Litzenberger R.H., and Ramaswamy, K., 1982, The Effects of Dividends on Common

Stock Prices Tax Effects or Information Effect?, The Journal of Finance 37, p. 429-443

Lewellen, J., 2004, Predicting returns with financial ratios, Journal of Financial

Economics 74, No 2, p. 209-235

Llorente, G., and Michaely, R., Gideon, S., and Wang, J., 2002, Dynamic volume return

relation of individual stocks, Review of Financial studies 15, p. 1005-1139

Mandelbrot, B.S., 1963, The variation of certain speculative prices, Journal of Business,

36, p. 394-419

Mandelbrot, B.B., and van Ness, J.W., 1968, Fractional Brownian motion, fractional

noises and applications, parts 1,2,3, SIAM Review 10, p. 422-437

42

Mandelbrot, B.B., 1997, Fractals and Scaling in Finance, Springer-Verlag, New York

Berlin Heidelberg

Michael S., and Starks L.T., (1988), An Empirical Analysis of the Stock Price-Volume

Relationship, Journal of Banking and Finance 12, p. 31-41

Mork, K.A., 1989, Oil and the macroeconomy when prices goes up and down: an extension of Hamilton’s results, Journal of Political Economy 97, p. 740-744

Mork, K.A., Olsen, O., and Mysen, H.T., 1994, Macroeconomic responses to oil price increases and decreases in seven OECD countries, The Energy Journal 15, p. 19-35

Mory, J.F., 1993, Oil prices and economic activity: is the relationship symmetric? The

Energy Journal 14, p. 151-161

Mussa, M., 2000, The impact of higher oil prices on the global economy, International

Monetary Fund 2000, 8 december,

http://www.imf.org/external/pubs/ft/oil/2000/oilrep.PDF

Nandha, M., and Faff, R., 2008, Does oil move equity prices? A global view, Energy

Economics 30, 986-997

Nelson, D. B. (1991), Conditional Heteroskedasticity in Asset Returns: A new Approach,

Econometrica 59, p. 347-370

Newey, W.K., and West, K.D., (1987), A simple, Positive Semi-Definite,

Heteroskedasticity and Autocorrelation Consistent Covariance Matrix, Econometrica 55,

p. 703-708

Nelson, C.R., 1976, Inflation and rates of return on common stock, Journal of Finance

31, p. 471-483

Nofsinger J.R. and Sias, R.W., 1999, Herding and feedback trading by institutional and

individual investors, Journal of Finance 54, p. 2263-2295

43

Park, J., and Ratti, R.A., 2008, Oil price shocks and stock market in the U.S. and 13

European countries, Energy Economics 30, p. 2587-2608

Patro D.K., Wald, J.K., and Wu, Y., 2002, The impact of macroecomnomic and FInanical

variables on market risk: evidence form international equity returns, European Financial

Management 8, p. 421-477

Pearce D.K., and Roley, V.V., 1983, The reaction of stock prices to unanticipated

changes in money: A note, Journal of Finance 38, p. 1323-1333

Pearce D.K., and Roley, V.V., 1985, Stock price and economic news, Journal of Business

58, .p 49-67

Pesaran, M.H., and Timmermann, A., 1995, Predictability of stock returns: Robustness

and economic significance, Journal of Finance 50, p. 1201-1228

Perez-Quiros, G., and Timmermann A., 2000, Firm size and cyclical variations in stock,

Journal of Finance 55, p. 1229-1262

Pontiff and Schall, 1998, Book-to-market ratios as predictors of stock return, Journal of

Financial Economics 49, p. 141-160

Poon, S-H., Granger, C., 2003, Forecasting volatility in financial markets: A review,

Journal of Economic Literature 41, p. 478-539

Roll, R., 1992, Industrial structure and the comparative behavior of international stock

market indexes, Journal of Finance 47, p. 3-42

Rapach, D. E., Wohar, M. E., and Rangvid, J., 2005, Macro variables and international

stock return predictability, International journal of Forecasting, 137-166

Sadorsky, P., 1999, Oil price shocks and stock market activity, Energy Economics 21, p.

449-469

Schwert, W.G., 1989, Why does stock market volatility change over time?, Journal of

Finance 52, p. 35-55

Shu, T., 2010, Trader composition and the cross-section of stock returns, The University

of Georgia

44

Stickel S.E., and Verrecchia R.E., 1994, Evidence that trading volume sustains stock

price changes, Financial Analysts Journal, November-December, p. 57-67

Saunders, M., Lewis, P., Thornhill, A., (2003) Research Methods for Business Studies

3rd ed”, Pearson Education Limited, EssexSimon et al. (2001

Tauchen G.E., and Pitts M., 1983, The price variability-volume relationship on

speculative markets, Econometrica 51, p. 485-505

Whitlaw, R.F., (1994), Time variations and covariations in the expectation and volatility

of stock returns, Journal of Finance 49, p. 515-541

Databases:

Datastream Advance 5.0, Thomson Financial Limited

Internet

NASDAQ OMX NORDIC,

http://www.nasdaqomxnordic.com/nordic/Nordic.aspx

Riksbanken,

http://www.riksbank.se/templates/SectionStart.aspx?id=8720

45

http://www.riksbank.se/templates/SectionStart.aspx?id=8720

http://www.nasdaqomxnordic.com/nordic/Nordic.aspx

AppendixTable 10 –Firms in the study with corresponding sector, industry group and sub-industry (GICS)

Company Sector Industry Group Sub-industryElectrolux Consumer Discretionary Consumer Durables & Apparel household appliances Hennes & Mauritz Consumer Discretionary Retailing apparel retail Modern Times Group Consumer Discretionary Media broadcasting Swedish Match Consumer Staples Food, Beverage & Tobacco tobacco Lundin Petroleum Energy Energy oil and gas exploration and production Investor Financials Diversified Financials multi-sector holdings Nordea Financials Banks diversified banks SEB Financials Banks diversified banks Svenska Handelsbanken Financials Banks diversified banks Swedbank Financials Banks diversified banks AstraZeneca Health Care Pharmaceuticals, Biotechnology & Life Sciences pharmaceuticals Getinge Health Care Health Care Equipment & Services health care equipment ABB Industrials Capital Goods industrial machinery Alfa Laval Industrials Capital Goods industrial machinery Assa Abloy Industrials Capital Goods building products Atlas Copco B Industrials Capital Goods industrial machinery Sandvik Industrials Capital Goods industrial machinery Scania Industrials Capital Goods construction and farm machinery; heavy trucks Skanska Industrials Capital Goods construction and engineering SKF Industrials Capital Goods industrial machinery Volvo Group Industrials Capital Goods construction and farm machinery; heavy trucks Ericsson Information Technology Technology Hardware & Equipment communications equipment Boliden Materials Materials diversified metals and mining SCA Materials Materials paper products SSAB Materials Materials steel Tele2 Telecommunication Services Telecommunication Services integrated telecommunication services TeliaSonera Telecommunication Services Telecommunication Services integrated telecommunication services

The industries represented by the OMXS30 index constituents are diverse. Many firms operate with the whole world as their market, along with is large size it is

also possible that an international rate, rather than the Swedish, should be used. Moreover, it is also highly likely that the USD and EUR exchange rate could

have a significant impact. Of greater weight for the following comments and notes on the sector comparison is the diversity. Hence, a focus on the financial and

industrial firms will be keep as these sectors demonstrate most similarities within their sub-industry group.

46

Table 11 – Descriptive statistics of firms sorted by turnover (1 of 2)Company V O L U M N R E T U R N S T A T I S T I C S(Sub-Industry Volume Turn- Obs./ 25th 50th 75th(Sector) group over total Skew Kurt Min percentile percentile percentile Max Average Stdev Skew KurtEricsson G1 4333 15% 4.2 34.8 -27.39% -2.90% 0.00% 2.59% 22.31% -0.20% 5.27% (0.5) 3.6

G2 2144 70% 1.2 1.5 -18.85% -1.44% 0.00% 1.63% 19.23% 0.13% 2.68% 0.2 3.8 G3 1120 16% 1.3 1.6 -9.31% -0.98% 0.00% 0.78% 10.13% -0.12% 1.85% (0.1) 4.0

Compl. Per. 2308 0% 5.3 75.5 -27.39% -1.47% 0.00% 1.59% 22.31% 0.04% 3.10% (0.3) 7.9 Nordea G1 1076 17% 4.0 32.8 -12.22% -1.98% 0.00% 1.86% 13.00% 0.11% 3.37% 0.1 1.4

G2 572 65% 0.2 -0.1 -12.03% -0.98% 0.00% 1.02% 14.91% 0.02% 2.13% 0.4 4.9 G3 316 18% 0.0 -0.4 -7.05% -0.93% 0.00% 0.85% 7.56% -0.04% 1.68% (0.1) 2.9

Compl. Per. 617 0% 4.6 57.8 -12.22% -1.08% 0.00% 1.09% 14.91% 0.03% 2.33% 0.3 4.2 AstraZeneca G1 1215 15% 2.8 16.6 -12.51% -1.55% 0.00% 1.93% 12.10% 0.04% 2.96% (0.2) 1.8

G2 543 68% 1.2 2.0 -7.75% -0.90% 0.00% 0.94% 7.05% 0.01% 1.53% (0.0) 1.8 G3 319 17% 3.1 15.1 -3.78% -0.76% 0.00% 0.49% 8.35% -0.12% 1.17% 0.7 5.6

Compl. Per. 607 0% 3.6 28.7 -12.51% -0.96% 0.00% 0.95% 12.10% -0.01% 1.77% (0.1) 4.6 TeliaSonera G1 1259 15% 9.9 120.1 -13.82% -1.59% 0.00% 1.78% 19.48% 0.08% 3.67% 0.1 3.3

G2 538 70% 2.5 14.1 -11.00% -1.05% 0.00% 1.02% 11.72% -0.03% 2.03% (0.1) 3.4 G3 280 15% 7.3 94.5 -5.86% -0.86% 0.00% 0.83% 5.22% -0.07% 1.48% (0.2) 2.1

Compl. Per. 604 0% 19.5 550.3 -13.82% -1.06% 0.00% 1.05% 19.48% -0.02% 2.28% 0.1 6.1 Sandvik G1 892 18% 1.8 6.8 -16.10% -1.96% 0.00% 1.94% 10.33% -0.02% 3.23% (0.2) 1.7

G2 487 62% 0.5 -0.1 -11.16% -1.12% 0.00% 1.25% 13.20% 0.08% 2.00% 0.2 3.2 G3 264 19% 0.4 0.3 -7.77% -0.88% 0.00% 0.85% 7.59% -0.01% 1.59% (0.2) 3.5

Compl. Per. 520 0% 2.0 8.7 -16.10% -1.16% 0.00% 1.25% 13.20% 0.04% 2.21% (0.1) 3.8 Hennes & Mauritz G1 897 15% 4.0 25.4 -35.31% -1.57% 0.00% 1.85% 15.59% 0.18% 3.74% (1.0) 13.6

G2 367 73% 0.9 1.4 -13.72% -0.97% 0.00% 1.07% 14.92% 0.08% 1.81% 0.3 4.0 G3 214 12% 0.6 -0.1 -5.72% -0.58% 0.00% 0.81% 5.74% 0.07% 1.26% (0.2) 3.3

Compl. Per. 431 0% 6.0 72.3 -35.31% -0.97% 0.00% 1.11% 15.59% 0.09% 2.16% (0.6) 21.5 Volvo Group G1 758 17% 3.8 26.9 -15.38% -1.42% 0.48% 2.44% 15.13% 0.50% 3.36% (0.1) 2.1

G2 371 67% 0.5 0.7 -14.01% -1.15% 0.00% 1.11% 8.99% -0.02% 1.93% (0.1) 2.6 G3 209 17% 0.4 0.1 -7.22% -0.95% -0.23% 0.58% 8.75% -0.13% 1.43% 0.4 3.7

Compl. Per. 413 0% 4.5 48.1 -15.38% -1.13% 0.00% 1.17% 15.13% 0.05% 2.18% 0.1 4.4 ABB G1 889 15% 3.8 20.5 -92.43% -2.39% 0.24% 2.16% 36.01% -0.07% 6.97% (5.0) 70.1

G2 357 67% 2.9 13.0 -15.99% -1.14% 0.00% 1.26% 15.03% 0.03% 2.49% (0.2) 4.6 G3 198 18% 1.4 3.0 -6.36% -0.94% 0.00% 0.81% 10.27% -0.03% 1.73% 0.3 3.5

Compl. Per. 411 0% 5.8 58.6 -92.43% -1.22% 0.00% 1.29% 36.01% 0.00% 3.47% (6.2) 177.2 Electrolux G1 785 16% 6.5 80.9 -13.26% -1.62% 0.38% 2.49% 19.18% 0.47% 3.71% 0.2 2.5

G2 335 69% 1.2 3.8 -9.01% -1.17% 0.00% 1.10% 11.00% -0.02% 2.02% 0.1 2.1 G3 167 15% 0.5 -0.5 -6.55% -0.97% 0.00% 0.75% 10.18% -0.09% 1.63% 0.7 4.4

Compl. Per. 382 0% 7.5 153.7 -13.26% -1.18% 0.00% 1.16% 19.18% 0.04% 2.33% 0.4 5.0 Swedbank G1 736 17% 2.7 9.5 -20.53% -1.37% 0.29% 2.04% 17.36% 0.20% 3.67% (0.1) 5.3

G2 336 65% 2.4 7.6 -13.48% -1.10% 0.00% 1.07% 15.12% -0.01% 2.18% (0.1) 4.5 G3 198 18% 1.8 2.7 -10.96% -0.77% 0.00% 0.89% 14.09% 0.04% 1.79% 0.3 9.1

Compl. Per. 378 0% 3.6 21.3 -20.53% -1.08% 0.00% 1.17% 17.36% 0.03% 2.44% (0.0) 7.8 SEB G1 551 18% 1.7 4.7 -35.34% -1.83% 0.00% 2.49% 34.66% 0.30% 5.09% 0.1 11.4

G2 268 63% 1.3 3.0 -18.63% -1.22% 0.00% 1.27% 23.21% 0.01% 2.63% 0.1 6.7 G3 156 19% 1.3 2.1 -10.51% -1.01% 0.00% 0.68% 9.50% -0.16% 1.76% 0.3 4.7

Compl. Per. 300 0% 2.3 9.4 -35.34% -1.22% 0.00% 1.27% 34.66% 0.03% 3.10% 0.3 18.9 Boliden G1 614 14% 4.1 30.4 -19.14% -3.28% 0.36% 3.49% 21.51% 0.28% 5.67% 0.2 1.5

G2 207 71% 0.8 0.8 -14.49% -1.42% 0.00% 1.64% 14.20% 0.03% 2.87% (0.1) 3.1 G3 165 15% 0.3 -0.8 -7.91% -0.78% 0.00% 1.03% 6.90% 0.04% 1.86% (0.1) 3.1

Compl. Per. 258 0% 6.3 89.9 -19.14% -1.43% 0.00% 1.65% 21.51% 0.07% 3.30% 0.1 5.1 Tele2 G1 464 15% 4.0 22.3 -16.52% -2.08% 0.30% 2.54% 21.69% 0.22% 3.80% 0.0 2.9

G2 194 70% 1.1 1.6 -12.85% -1.28% 0.00% 1.27% 11.65% 0.02% 2.32% 0.0 2.6 G3 103 15% 0.4 -0.5 -4.88% -1.00% 0.00% 0.89% 7.03% -0.02% 1.48% 0.2 1.1

Compl. Per. 223 0% 6.2 69.1 -16.52% -1.28% 0.00% 1.33% 21.69% 0.05% 2.51% 0.1 4.7 G1 409 18% 1.5 4.6 -21.44% -1.40% 0.00% 1.70% 24.86% 0.25% 3.34% 0.8 9.7 G2 198 64% 0.9 0.6 -11.21% -1.05% 0.00% 0.98% 16.18% 0.01% 2.09% 0.4 5.7 G3 117 19% 0.8 0.2 -8.73% -0.79% 0.00% 0.74% 7.16% -0.04% 1.48% (0.3) 3.8

(construction and farm machinery; heavy trucks)(Industrials)

(multi-sector holdings)(Consumer Discretionary)

(industrial machinery)(Industrials)

(diversified banks)(Financials)




(diversified metals and mining)(Materials)

(household appliances)(Consumer Discretionary)

(integrated (telecommunication services))

Svenska Handelsbanken

(pharmaceuticals)(Health Care)


(communicationsequipment)(Informaiton Technology)


For comments see under Descriptive statistics of firms sorted by turnover (2 of 2).

47

Table 12 – Descriptive statistics of firms sorted by turnover (2 of 2) V O L U M N R E T U R N S T A T I S T I C S

Company Volume Turn- Obs./ 25th 50th 75th(Sub-Industry group over total Skew Kurt Min percentile percentile percentile Max Average Stdev Skew KurtSKF G1 389 16% 1.9 4.8 -68.20% -1.82% 0.34% 2.03% 12.60% 0.17% 4.04% (6.1) 103.6

G2 179 69% 1.4 2.4 -10.91% -1.10% 0.00% 1.11% 11.82% 0.03% 1.98% 0.3 2.4 G3 80 14% 2.1 4.9 -7.26% -0.96% 0.00% 0.72% 5.43% -0.10% 1.53% (0.2) 1.9

Compl. Per. 199 0% 2.8 13.8 -68.20% -1.14% 0.00% 1.19% 12.60% 0.03% 2.39% (4.6) 137.9 Alfa Laval G1 412 14% 7.7 77.3 -38.20% -1.84% 0.00% 2.42% 14.28% 0.17% 4.34% (2.2) 20.4

G2 147 70% 1.8 6.2 -10.33% -1.11% 0.00% 1.21% 12.75% 0.04% 2.15% 0.1 3.6 G3 95 15% 7.6 97.3 -7.19% -0.69% 0.00% 0.94% 5.81% 0.16% 1.68% 0.1 2.3

Compl. Per. 176 0% 13.9 312.9 -38.20% -1.13% 0.00% 1.28% 14.28% 0.08% 2.52% (1.5) 28.0 Investor G1 330 17% 6.4 60.6 -14.90% -0.97% 0.44% 1.77% 22.31% 0.40% 2.75% 0.6 7.7

G2 154 65% 1.1 2.7 -13.01% -0.99% 0.00% 1.00% 9.64% 0.01% 1.79% (0.1) 3.2 G3 83 17% 0.5 -0.3 -5.64% -0.87% 0.00% 0.57% 5.20% -0.22% 1.27% (0.3) 1.7

Compl. Per. 172 0% 7.7 128.1 -14.90% -0.96% 0.00% 1.01% 22.31% 0.04% 1.93% 0.3 8.0 Assa Abloy G1 388 14% 3.2 15.6 -18.35% -1.72% 0.42% 2.41% 15.86% 0.36% 3.51% (0.0) 3.8

G2 130 75% 1.0 0.9 -10.37% -1.19% 0.00% 1.26% 12.45% 0.06% 2.27% 0.3 2.4 G3 96 11% 0.3 -0.4 -6.81% -1.08% 0.00% 1.01% 4.50% -0.11% 1.70% (0.4) 1.1

Compl. Per. 160 0% 5.1 50.8 -18.35% -1.22% 0.00% 1.34% 15.86% 0.08% 2.43% 0.2 4.2 Scania G1 448 14% 14.4 265.0 -12.56% -1.25% 0.26% 2.00% 22.30% 0.34% 3.23% 0.7 6.6

G2 112 74% 3.7 29.0 -9.87% -0.93% 0.00% 0.92% 12.57% 0.01% 1.88% 0.3 4.5 G3 57 12% 4.8 41.4 -7.35% -0.84% 0.00% 0.58% 12.72% -0.15% 1.67% 0.6 10.9

Compl. Per. 153 0% 34.6 1629.6 -12.56% -0.95% 0.00% 0.97% 22.30% 0.04% 2.10% 0.6 8.8 SCA G1 261 18% 2.6 15.5 -12.94% -1.04% 0.33% 1.63% 17.51% 0.30% 2.61% 0.1 4.0

G2 131 65% 0.9 0.6 -8.42% -0.91% 0.00% 0.86% 11.38% -0.04% 1.63% 0.3 2.8 G3 76 17% 0.7 -0.2 -6.33% -0.78% 0.00% 0.64% 6.24% -0.03% 1.28% 0.3 2.4

Compl. Per. 146 0% 3.0 23.4 -12.94% -0.90% 0.00% 0.90% 17.51% 0.02% 1.80% 0.3 5.4 Swedish Match G1 281 16% 3.0 14.0 -7.96% -1.25% 0.15% 1.56% 10.32% 0.20% 2.40% 0.1 1.2

G2 105 67% 1.9 8.2 -6.64% -0.83% 0.00% 0.90% 7.23% 0.05% 1.54% 0.1 1.5 G3 63 17% 7.2 94.7 -5.41% -0.70% 0.00% 0.70% 6.91% -0.04% 1.24% (0.1) 3.5

Compl. Per. 127 0% 3.8 27.8 -7.96% -0.84% 0.00% 0.93% 10.32% 0.06% 1.67% 0.2 2.4 Skanska G1 213 18% 13.8 302.2 -20.57% -1.34% 0.34% 1.86% 26.08% 0.24% 3.08% 0.0 9.8

G2 92 66% 1.0 0.8 -26.80% -1.05% 0.00% 1.00% 12.78% -0.01% 2.01% (0.2) 13.5 G3 59 16% 1.0 0.5 -8.28% -0.75% 0.00% 0.65% 8.70% -0.08% 1.30% (0.2) 5.6

Compl. Per. 109 0% 16.1 627.6 -26.80% -0.99% 0.00% 1.06% 26.08% 0.03% 2.16% (0.0) 15.0 Lundin Petroleum G1 213 17% 1.9 4.7 -19.90% -3.12% 0.40% 4.20% 27.01% 0.51% 6.36% 0.2 2.2

G2 83 68% 1.3 1.8 -13.64% -1.76% 0.00% 1.59% 23.07% -0.03% 3.07% 0.4 4.7 G3 52 14% 0.8 -0.3 -5.95% -1.03% 0.00% 1.32% 13.82% 0.03% 2.13% 1.1 7.3

Compl. Per. 102 0% 3.0 14.8 -19.90% -1.74% 0.00% 1.78% 27.01% 0.07% 3.75% 0.5 6.9 G1 173 16% 2.9 14.0 -18.41% -1.84% 0.68% 2.88% 18.92% 0.71% 4.23% 0.1 2.2 G2 71 67% 1.3 2.0 -29.27% -1.49% 0.00% 1.46% 16.79% -0.07% 2.88% (0.6) 7.7

(broadcasting) (Consumer Discretionary)

G3 36 17% 1.5 2.6 -7.41% -1.30% 0.00% 0.87% 12.76% -0.22% 1.91% 0.3 4.6 Compl. Per. 82 0% 3.3 23.0 -29.27% -1.47% 0.00% 1.51% 18.92% 0.03% 3.02% (0.1) 6.5

SSAB G1 161 17% 1.5 2.3 -16.89% -1.37% 0.26% 2.08% 14.42% 0.34% 3.35% (0.1) 3.0 G2 66 68% 1.3 1.3 -14.92% -1.15% 0.00% 1.16% 15.02% 0.02% 2.16% (0.0) 4.4 G3 46 15% 1.2 0.5 -11.18% -0.91% 0.00% 0.77% 7.78% -0.08% 1.59% (0.4) 4.9

Compl. Per. 79 0% 2.1 6.9 -16.89% -1.14% 0.00% 1.20% 15.02% 0.05% 2.34% (0.0) 5.2 Atlas Copco B G1 169 16% 1.6 3.8 -17.66% -1.39% 0.24% 1.99% 16.59% 0.34% 3.11% 0.2 4.1

G2 61 67% 1.1 1.5 -11.51% -1.13% 0.00% 1.28% 14.20% 0.04% 2.18% 0.1 2.8 G3 34 16% 1.1 1.0 -6.73% -1.17% 0.00% 0.90% 7.46% -0.12% 1.77% (0.1) 1.4

Compl. Per. 75 0% 2.6 10.6 -17.66% -1.17% 0.00% 1.30% 16.59% 0.07% 2.31% 0.2 4.2 Getinge G1 106 15% 8.7 113.4 -16.75% -1.19% 0.07% 1.94% 11.61% 0.32% 2.65% (0.3) 3.7

G2 32 72% 0.9 1.2 -8.84% -1.02% 0.00% 1.03% 10.36% 0.03% 1.80% 0.2 2.2 G3 22 13% 0.5 -0.4 -6.10% -0.91% 0.00% 0.68% 5.16% -0.10% 1.41% (0.0) 1.9

Compl. Per. 42 0% 11.8 291.4 -16.75% -1.02% 0.00% 1.09% 11.61% 0.06% 1.92% 0.1 3.8

Modern Times Group

(apparel retail)(Financials)

(building products)(Industrials)

(paper products)(Materials)

(tobacco)(Consumer Staples)



(construction and farm machinery; heavy trucks)(Industrials)

(construction and engineering)(Industrials)

(oil and gas exploration and production)(Energy)

(health care equipment)(Health Care)

(steel) (Industrials)


Comments to Descriptive statistics of firms sorted by turnover 1-2.

One can note that the skewness (skew) is largest for group G1 followed by G2 and G3, the same relation

holds for the kurtosis (kurt). The average return for G1 is 0,25 %, being negative for 3 out of 27 firms. The

average return for G2 is 0,02 %, and is positive for 19 out of 27 firms. The average return for G3 is -0,06%,

being negative for 22 out of 27 firms. The standard deviation of the return (third column from the right) is

largest in G1 and is gradually decaying for all firms to being the lowest for G3, the standard deviation for

group G1 is larger than that for the complete period among all firms. The turnover for G1 is 3,5 to 5 times

larger than that found for G3.

48

Table 13 – Descriptive statistics of firms sorted by sector (1 of 3) V O L U M N R E T U R N S T A T I S T I C S

Company Volume Turn- Obs./ 25th 50th 75thSector (Sub-Industry group over total Skew Kurt Min percentile percentile percentile Max Average Stdev Skew Kurt

Co Electrolux G1 785 16% 6.5 80.9 -13.26% -1.62% 0.38% 2.49% 19.18% 0.47% 3.71% 0.2 2.5 ns G2 335 69% 1.2 3.8 -9.01% -1.17% 0.00% 1.10% 11.00% -0.02% 2.02% 0.1 2.1 um G3 167 15% 0.5 -0.5 -6.55% -0.97% 0.00% 0.75% 10.18% -0.09% 1.63% 0.7 4.4 er Compl. Per. 382 0% 7.5 153.7 -13.26% -1.18% 0.00% 1.16% 19.18% 0.04% 2.33% 0.4 5.0

Hennes & Mauritz G1 897 15% 4.0 25.4 -35.31% -1.57% 0.00% 1.85% 15.59% 0.18% 3.74% (1.0) 13.6 Di G2 367 73% 0.9 1.4 -13.72% -0.97% 0.00% 1.07% 14.92% 0.08% 1.81% 0.3 4.0 sc G3 214 12% 0.6 -0.1 -5.72% -0.58% 0.00% 0.81% 5.74% 0.07% 1.26% (0.2) 3.3 re Compl. Per. 431 0% 6.0 72.3 -35.31% -0.97% 0.00% 1.11% 15.59% 0.09% 2.16% (0.6) 21.5 ti Modern Times Group G1 173 16% 2.9 14.0 -18.41% -1.84% 0.68% 2.88% 18.92% 0.71% 4.23% 0.1 2.2

on G2 71 67% 1.3 2.0 -29.27% -1.49% 0.00% 1.46% 16.79% -0.07% 2.88% (0.6) 7.7 ar G3 36 17% 1.5 2.6 -7.41% -1.30% 0.00% 0.87% 12.76% -0.22% 1.91% 0.3 4.6 y Compl. Per. 82 0% 3.3 23.0 -29.27% -1.47% 0.00% 1.51% 18.92% 0.03% 3.02% (0.1) 6.5

Cons- Swedish Match G1 281 16% 3.0 14.0 -7.96% -1.25% 0.15% 1.56% 10.32% 0.20% 2.40% 0.1 1.2 umer G2 105 67% 1.9 8.2 -6.64% -0.83% 0.00% 0.90% 7.23% 0.05% 1.54% 0.1 1.5 Stap- G3 63 17% 7.2 94.7 -5.41% -0.70% 0.00% 0.70% 6.91% -0.04% 1.24% (0.1) 3.5

les Compl. Per. 127 0% 3.8 27.8 -7.96% -0.84% 0.00% 0.93% 10.32% 0.06% 1.67% 0.2 2.4 Lundin Petroleum G1 213 17% 1.9 4.7 -19.90% -3.12% 0.40% 4.20% 27.01% 0.51% 6.36% 0.2 2.2

En- G2 83 68% 1.3 1.8 -13.64% -1.76% 0.00% 1.59% 23.07% -0.03% 3.07% 0.4 4.7 er- G3 52 14% 0.8 -0.3 -5.95% -1.03% 0.00% 1.32% 13.82% 0.03% 2.13% 1.1 7.3 gy Compl. Per. 102 0% 3.0 14.8 -19.90% -1.74% 0.00% 1.78% 27.01% 0.07% 3.75% 0.5 6.9 F Investor G1 330 17% 6.4 60.6 -14.90% -0.97% 0.44% 1.77% 22.31% 0.40% 2.75% 0.6 7.7

G2 154 65% 1.1 2.7 -13.01% -0.99% 0.00% 1.00% 9.64% 0.01% 1.79% (0.1) 3.2 I G3 83 17% 0.5 -0.3 -5.64% -0.87% 0.00% 0.57% 5.20% -0.22% 1.27% (0.3) 1.7

Compl. Per. 172 0% 7.7 128.1 -14.90% -0.96% 0.00% 1.01% 22.31% 0.04% 1.93% 0.3 8.0 N Nordea G1 1076 17% 4.0 32.8 -12.22% -1.98% 0.00% 1.86% 13.00% 0.11% 3.37% 0.1 1.4

G2 572 65% 0.2 -0.1 -12.03% -0.98% 0.00% 1.02% 14.91% 0.02% 2.13% 0.4 4.9 A G3 316 18% 0.0 -0.4 -7.05% -0.93% 0.00% 0.85% 7.56% -0.04% 1.68% (0.1) 2.9

Compl. Per. 617 0% 4.6 57.8 -12.22% -1.08% 0.00% 1.09% 14.91% 0.03% 2.33% 0.3 4.2 N SEB G1 551 18% 1.7 4.7 -35.34% -1.83% 0.00% 2.49% 34.66% 0.30% 5.09% 0.1 11.4

G2 268 63% 1.3 3.0 -18.63% -1.22% 0.00% 1.27% 23.21% 0.01% 2.63% 0.1 6.7 C G3 156 19% 1.3 2.1 -10.51% -1.01% 0.00% 0.68% 9.50% -0.16% 1.76% 0.3 4.7

Compl. Per. 300 0% 2.3 9.4 -35.34% -1.22% 0.00% 1.27% 34.66% 0.03% 3.10% 0.3 18.9 I Svenska Handelsbanken G1 409 18% 1.5 4.6 -21.44% -1.40% 0.00% 1.70% 24.86% 0.25% 3.34% 0.8 9.7

G2 198 64% 0.9 0.6 -11.21% -1.05% 0.00% 0.98% 16.18% 0.01% 2.09% 0.4 5.7 A G3 117 19% 0.8 0.2 -8.73% -0.79% 0.00% 0.74% 7.16% -0.04% 1.48% (0.3) 3.8

Compl. Per. 221 0% 2.1 8.4 -21.44% -1.03% 0.00% 1.02% 24.86% 0.04% 2.28% 0.7 11.8 L Swedbank G1 736 17% 2.7 9.5 -20.53% -1.37% 0.29% 2.04% 17.36% 0.20% 3.67% (0.1) 5.3

G2 336 65% 2.4 7.6 -13.48% -1.10% 0.00% 1.07% 15.12% -0.01% 2.18% (0.1) 4.5 S G3 198 18% 1.8 2.7 -10.96% -0.77% 0.00% 0.89% 14.09% 0.04% 1.79% 0.3 9.1

Compl. Per. 378 0% 3.6 21.3 -20.53% -1.08% 0.00% 1.17% 17.36% 0.03% 2.44% (0.0) 7.8 AstraZeneca G1 1215 15% 2.8 16.6 -12.51% -1.55% 0.00% 1.93% 12.10% 0.04% 2.96% (0.2) 1.8

Health G2 543 68% 1.2 2.0 -7.75% -0.90% 0.00% 0.94% 7.05% 0.01% 1.53% (0.0) 1.8 G3 319 17% 3.1 15.1 -3.78% -0.76% 0.00% 0.49% 8.35% -0.12% 1.17% 0.7 5.6

Compl. Per. 607 0% 3.6 28.7 -12.51% -0.96% 0.00% 0.95% 12.10% -0.01% 1.77% (0.1) 4.6 Getinge G1 106 15% 8.7 113.4 -16.75% -1.19% 0.07% 1.94% 11.61% 0.32% 2.65% (0.3) 3.7

Care G2 32 72% 0.9 1.2 -8.84% -1.02% 0.00% 1.03% 10.36% 0.03% 1.80% 0.2 2.2 G3 22 13% 0.5 -0.4 -6.10% -0.91% 0.00% 0.68% 5.16% -0.10% 1.41% (0.0) 1.9

Compl. Per. 42 0% 11.8 291.4 -16.75% -1.02% 0.00% 1.09% 11.61% 0.06% 1.92% 0.1 3.8

(pharmaceuticals)

(diversified banks)

(diversified banks)

(broadcasting)

(multi-sector holdings)

(diversified banks)

(diversified banks)

(apparel retail)

(oil and gas exploration and production)

(tobacco)

(household appliances)

(health care equipment)

The financial companies demonstrate a large spread in turnover between themselves. Each firm has 17-19

% of total trade observations in G1 and G3 demonstrating considerably symmetric groups individually and

cross-sectional. The average return is positive in group G1 and G2 and negative in G3. The exception is

Swedbank which demonstrate a negative return on average in G2 and positive in G3.

49


Company Turn- Obs./ 25th 50th 75th(Sub-Industry Volume Group over total Skew Kurt Min percentile percentile percentile Max Average Stdev Skew KurtABB G1 889 15% 3.8 20.5 -92.43% -2.39% 0.24% 2.16% 36.01% -0.07% 6.97% (5.0) 70.1

G2 357 67% 2.9 13.0 -15.99% -1.14% 0.00% 1.26% 15.03% 0.03% 2.49% (0.2) 4.6 I G3 198 18% 1.4 3.0 -6.36% -0.94% 0.00% 0.81% 10.27% -0.03% 1.73% 0.3 3.5

Compl. Per. 411 0% 5.8 58.6 -92.43% -1.22% 0.00% 1.29% 36.01% 0.00% 3.47% (6.2) 177.2 Alfa Laval G1 412 14% 7.7 77.3 -38.20% -1.84% 0.00% 2.42% 14.28% 0.17% 4.34% (2.2) 20.4

N G2 147 70% 1.8 6.2 -10.33% -1.11% 0.00% 1.21% 12.75% 0.04% 2.15% 0.1 3.6 G3 95 15% 7.6 97.3 -7.19% -0.69% 0.00% 0.94% 5.81% 0.16% 1.68% 0.1 2.3

Compl. Per. 176 0% 13.9 312.9 -38.20% -1.13% 0.00% 1.28% 14.28% 0.08% 2.52% (1.5) 28.0 D Assa Abloy G1 388 14% 3.2 15.6 -18.35% -1.72% 0.42% 2.41% 15.86% 0.36% 3.51% (0.0) 3.8

G2 130 75% 1.0 0.9 -10.37% -1.19% 0.00% 1.26% 12.45% 0.06% 2.27% 0.3 2.4 G3 96 11% 0.3 -0.4 -6.81% -1.08% 0.00% 1.01% 4.50% -0.11% 1.70% (0.4) 1.1

U Compl. Per. 160 0% 5.1 50.8 -18.35% -1.22% 0.00% 1.34% 15.86% 0.08% 2.43% 0.2 4.2 Atlas Copco B G1 169 16% 1.6 3.8 -17.66% -1.39% 0.24% 1.99% 16.59% 0.34% 3.11% 0.2 4.1

G2 61 67% 1.1 1.5 -11.51% -1.13% 0.00% 1.28% 14.20% 0.04% 2.18% 0.1 2.8 S G3 34 16% 1.1 1.0 -6.73% -1.17% 0.00% 0.90% 7.46% -0.12% 1.77% (0.1) 1.4

Compl. Per. 75 0% 2.6 10.6 -17.66% -1.17% 0.00% 1.30% 16.59% 0.07% 2.31% 0.2 4.2 Sandvik G1 892 18% 1.8 6.8 -16.10% -1.96% 0.00% 1.94% 10.33% -0.02% 3.23% (0.2) 1.7

T G2 487 62% 0.5 -0.1 -11.16% -1.12% 0.00% 1.25% 13.20% 0.08% 2.00% 0.2 3.2 G3 264 19% 0.4 0.3 -7.77% -0.88% 0.00% 0.85% 7.59% -0.01% 1.59% (0.2) 3.5

Compl. Per. 520 0% 2.0 8.7 -16.10% -1.16% 0.00% 1.25% 13.20% 0.04% 2.21% (0.1) 3.8 R Scania G1 448 14% 14.4 265.0 -12.56% -1.25% 0.26% 2.00% 22.30% 0.34% 3.23% 0.7 6.6

G2 112 74% 3.7 29.0 -9.87% -0.93% 0.00% 0.92% 12.57% 0.01% 1.88% 0.3 4.5 G3 57 12% 4.8 41.4 -7.35% -0.84% 0.00% 0.58% 12.72% -0.15% 1.67% 0.6 10.9

I Compl. Per. 153 0% 34.6 1629.6 -12.56% -0.95% 0.00% 0.97% 22.30% 0.04% 2.10% 0.6 8.8 Skanska G1 213 18% 13.8 302.2 -20.57% -1.34% 0.34% 1.86% 26.08% 0.24% 3.08% 0.0 9.8

G2 92 66% 1.0 0.8 -26.80% -1.05% 0.00% 1.00% 12.78% -0.01% 2.01% (0.2) 13.5 A G3 59 16% 1.0 0.5 -8.28% -0.75% 0.00% 0.65% 8.70% -0.08% 1.30% (0.2) 5.6

Compl. Per. 109 0% 16.1 627.6 -26.80% -0.99% 0.00% 1.06% 26.08% 0.03% 2.16% (0.0) 15.0 SKF G1 389 16% 1.9 4.8 -68.20% -1.82% 0.34% 2.03% 12.60% 0.17% 4.04% (6.1) 103.6

L G2 179 69% 1.4 2.4 -10.91% -1.10% 0.00% 1.11% 11.82% 0.03% 1.98% 0.3 2.4 G3 80 14% 2.1 4.9 -7.26% -0.96% 0.00% 0.72% 5.43% -0.10% 1.53% (0.2) 1.9

Compl. Per. 199 0% 2.8 13.8 -68.20% -1.14% 0.00% 1.19% 12.60% 0.03% 2.39% (4.6) 137.9 S Volvo Group G1 758 17% 3.8 26.9 -15.38% -1.42% 0.48% 2.44% 15.13% 0.50% 3.36% (0.1) 2.1

G2 371 67% 0.5 0.7 -14.01% -1.15% 0.00% 1.11% 8.99% -0.02% 1.93% (0.1) 2.6 G3 209 17% 0.4 0.1 -7.22% -0.95% -0.23% 0.58% 8.75% -0.13% 1.43% 0.4 3.7

Compl. Per. 413 0% 4.5 48.1 -15.38% -1.13% 0.00% 1.17% 15.13% 0.05% 2.18% 0.1 4.4

(industrial machinery)


(building products)



(construction and farm machinery; heavy trucks)

(construction and farm machinery; heavy trucks)

(construction and engineering)


From the industrial companies we note that the volume groups are not as symmetric as for the financial firms, there is also a larger spread cross-sectional where

some groups having to many and other too few observations compared to what was sought after. The average return is positive (negative) in the complete period,

G1, and G2 (G3).

50


Company Turn- Obs./ 25th 50th 75th(Sub-Industry Volume Group over total Skew Kurt Min percentile percentile percentile Max Average Stdev Skew Kurt

Infor- Ericsson G1 4333 15% 4.2 34.8 -27.39% -2.90% 0.00% 2.59% 22.31% -0.20% 5.27% (0.5) 3.6 mation G2 2144 70% 1.2 1.5 -18.85% -1.44% 0.00% 1.63% 19.23% 0.13% 2.68% 0.2 3.8 Tech- G3 1120 16% 1.3 1.6 -9.31% -0.98% 0.00% 0.78% 10.13% -0.12% 1.85% (0.1) 4.0

nology Compl. Per. 2308 0% 5.3 75.5 -27.39% -1.47% 0.00% 1.59% 22.31% 0.04% 3.10% (0.3) 7.9 Boliden G1 614 14% 4.1 30.4 -19.14% -3.28% 0.36% 3.49% 21.51% 0.28% 5.67% 0.2 1.5

M G2 207 71% 0.8 0.8 -14.49% -1.42% 0.00% 1.64% 14.20% 0.03% 2.87% (0.1) 3.1 A G3 165 15% 0.3 -0.8 -7.91% -0.78% 0.00% 1.03% 6.90% 0.04% 1.86% (0.1) 3.1 T Compl. Per. 258 0% 6.3 89.9 -19.14% -1.43% 0.00% 1.65% 21.51% 0.07% 3.30% 0.1 5.1 E SCA G1 261 18% 2.6 15.5 -12.94% -1.04% 0.33% 1.63% 17.51% 0.30% 2.61% 0.1 4.0 R G2 131 65% 0.9 0.6 -8.42% -0.91% 0.00% 0.86% 11.38% -0.04% 1.63% 0.3 2.8 I G3 76 17% 0.7 -0.2 -6.33% -0.78% 0.00% 0.64% 6.24% -0.03% 1.28% 0.3 2.4 A Compl. Per. 146 0% 3.0 23.4 -12.94% -0.90% 0.00% 0.90% 17.51% 0.02% 1.80% 0.3 5.4 L SSAB G1 161 17% 1.5 2.3 -16.89% -1.37% 0.26% 2.08% 14.42% 0.34% 3.35% (0.1) 3.0 S G2 66 68% 1.3 1.3 -14.92% -1.15% 0.00% 1.16% 15.02% 0.02% 2.16% (0.0) 4.4

G3 46 15% 1.2 0.5 -11.18% -0.91% 0.00% 0.77% 7.78% -0.08% 1.59% (0.4) 4.9 Compl. Per. 79 0% 2.1 6.9 -16.89% -1.14% 0.00% 1.20% 15.02% 0.05% 2.34% (0.0) 5.2

Tele- Tele2 G1 464 15% 4.0 22.3 -16.52% -2.08% 0.30% 2.54% 21.69% 0.22% 3.80% 0.0 2.9 comm- G2 194 70% 1.1 1.6 -12.85% -1.28% 0.00% 1.27% 11.65% 0.02% 2.32% 0.0 2.6 unica- G3 103 15% 0.4 -0.5 -4.88% -1.00% 0.00% 0.89% 7.03% -0.02% 1.48% 0.2 1.1 tion Compl. Per. 223 0% 6.2 69.1 -16.52% -1.28% 0.00% 1.33% 21.69% 0.05% 2.51% 0.1 4.7

TeliaSonera G1 1259 15% 9.9 120.1 -13.82% -1.59% 0.00% 1.78% 19.48% 0.08% 3.67% 0.1 3.3 Ser- G2 538 70% 2.5 14.1 -11.00% -1.05% 0.00% 1.02% 11.72% -0.03% 2.03% (0.1) 3.4 vices G3 280 15% 7.3 94.5 -5.86% -0.86% 0.00% 0.83% 5.22% -0.07% 1.48% (0.2) 2.1

Compl. Per. 604 0% 19.5 550.3 -13.82% -1.06% 0.00% 1.05% 19.48% -0.02% 2.28% 0.1 6.1



(steel)

(paper products)

(diversified metals and mining)

(communicationsequipment)

Companies in the materials sector is, just as with the industrials, operating in very different sub-industries. The size of each volume could be better adjusted with

individual determined standard deviations rather than using 0,82 which was optimized for the complete sample. The average is positive for the complete period

and G1. Whilst for G2 and G3 the sign of the returns are mixed.

51

Table 16 – Results from regression model for firms sorted by sector, 1 of 2 (read together with table 17 and 21)Company Significant

Sector (Sub-Industry) C TB TS OP G1TB G1TS G1OP G3TB G3TS G3OP variablesElectrolux B 0.00015 0.00922 0.00004 0.05728 -0.00555 0.00148 -0.00629 0.01087 -0.00034 -0.00624

Consumer (household appliances ) (0.7593) (0.3122) (0.84) (0.0318) (0.7263) (0.0281) (0.9393) (0.6846) (0.326) (0.8982) 2 of 10The coefficients impact compared to mean return. 34.3% 0.1% 9.2% 4.2% 0.0% 63.8% 0.0% -0.1% -15.8% -0.1%

Hennes & Mauritz -0.00028 -0.00101 0.00079 0.05199 -0.01583 -0.00048 0.05199 0.00442 -0.00123 0.00746(multi-sector holdings ) (0.6198) (0.9268) (0.028) (0.0126) (0.5012) (0.4362) (0.4435) (0.852) (0.0001) (0.8402) 3 of 10

The coefficients impact compared to mean return. -75.1% 0.1% 212.7% 4.0% 0.0% -20.3% -0.8% 0.0% -56.0% 0.4%Discretionary Modern Times Group -0.00363 0.00734 0.00196 0.16776 0.03744 0.00434 0.05791 -0.01494 -0.00074 -0.11419

(broadcasting ) (0.0018) (0.5011) (0.001) (0.001) (0.3606) (0.0003) (0.6375) (0.2648) (0.1852) (0.1277) 4 of 10The coefficients impact compared to mean return. -1152.7% -0.3% 408.9% 16.8% 0.0% 201.8% -0.4% -1.0% -37.6% -4.2%

Significant coefficients by sector: 1 of 3 0 of 3 2 of 3 3 of 3 0 of 3 2 of 3 0 of 3 0 of 3 1 of 3 0 of 3 9 of 30p-value (H0: sector = average(total)) 0.6818

Consumer Swedish Match 0.00002 -0.01199 0.00027 0.01535 0.01057 0.00113 0.04693 0.01058 -0.00040 -0.01587staples (tobacco ) (0.9636) (0.0132) (0.3477) (0.4425) (0.6001) (0.041) (0.3986) (0.1296) (0.1562) (0.7294) 2 of 10

The coefficients impact compared to mean return. 4.0% 0.2% 37.1% 0.5% 0.0% 33.7% -0.2% -0.5% -14.2% -0.5%Significant coefficients by sector: 0 of 1 1 of 1 0 of 1 0 of 1 0 of 1 1 of 1 0 of 1 0 of 1 0 of 1 0 of 1 2 of 10

Lundin Petroleum -0.00340 0.02974 0.00191 0.51390 -0.04384 0.00446 0.93415 -0.04724 -0.00058 -0.13494(oil and gas exploration and production ) (0.0334) (0.0479) (0.0312) (0) (0.3677) (0.0463) (0) (0.0047) (0.4598) (0.2388) 7 of 10

The coefficients impact compared to mean return. -483.8% 0.4% 91.8% 13.3% 0.0% 49.7% -2.3% 0.3% -6.6% -1.7%Significant coefficients by sector: 1 of 1 1 of 1 1 of 1 1 of 1 0 of 1 1 of 1 1 of 1 1 of 1 0 of 1 0 of 1 7 of 10

F Investor B 0.00063 0.00472 0.00014 0.04147 -0.00670 0.00025 0.06330 -0.01396 0.00042 -0.03793I (multi-sector holdings ) (0.1873) (0.5469) (0.581) (0.0403) (0.6885) (0.6765) (0.4137) (0.3007) (0.1258) (0.501) 1 of 10

The coefficients impact compared to mean return. 68.2% 0.0% 12.5% 1.5% 0.0% 5.7% -0.3% -0.1% 12.1% -0.5%N Nordea -0.00143 -0.00796 0.00097 0.10899 -0.04522 0.00084 -0.07084 0.04009 -0.00014 0.03333A (diversified banks ) (0.0763) (0.4873) (0.0344) (0.0004) (0.2468) (0.2936) (0.373) (0.0163) (0.7213) (0.5374) 4 of 10

The coefficients impact compared to mean return. -546.4% 0.1% 249.9% 13.1% 0.0% 53.9% 2.1% -0.9% -10.3% 2.0%N SEB A -0.00071 -0.04466 0.00076 0.10262 0.02908 0.00102 0.19237 0.05494 -0.00087 -0.06936C (diversified banks ) (0.3432) (0.0001) (0.0307) (0.0122) (0.7109) (0.2294) (0.0869) (0.0004) (0.0109) (0.2195) 6 of 10

The coefficients impact compared to mean return. -257.4% -1.0% 225.2% 14.5% 0.0% 78.5% -5.6% -4.8% -86.4% -2.4%I Svenska Handelsbanken B -0.00048 -0.02357 0.00064 0.07023 -0.05507 0.00078 0.03963 0.04948 -0.00047 -0.02248A (diversified banks ) (0.4068) (0.0855) (0.044) (0.0127) (0.0291) (0.1663) (0.4884) (0.0269) (0.1372) (0.5484) 5 of 10

The coefficients impact compared to mean return. -108.8% 0.9% 119.9% 3.1% 0.0% 35.6% -0.1% 1.0% -29.9% -1.1%L SWEDBANK A -0.00270 -0.01066 0.00161 0.12361 0.15453 0.00169 0.09691 0.02196 0.00011 -0.07882S (diversified banks ) (0.0012) (0.4011) (0.0008) (0.0002) (0.0023) (0.069) (0.2566) (0.2447) (0.7873) (0.114) 5 of 10

The coefficients impact compared to mean return. -852.1% 0.7% 428.7% 9.3% 0.0% 110.7% 2.5% 0.5% 8.1% -1.9%Significant coefficients by sector: 2 of 5 2 of 5 4 of 5 5 of 5 2 of 5 1 of 5 1 of 5 3 of 5 1 of 5 0 of 5 21 of 50

p-value (H0: sector = average(total)) 0.7529Health ASTRA ZENECA -0.00067 0.00759 0.00044 -0.07501 0.04181 0.00040 -0.02771 -0.01694 -0.00061 0.10645

(pharmaceuticals ) (0.3441) (0.1021) (0.2373) (0.0008) (0.107) (0.5925) (0.7287) (0.2742) (0.0386) (0.0014) 4 of 10The coefficients impact compared to mean return. 835.0% -1.7% -361.7% 25.8% 0.0% -69.3% -2.5% -2.6% 125.7% -31.5%

Care Getinge B 0.00048 -0.01671 0.00000 0.04341 0.02582 0.00125 0.07616 0.04000 -0.00084 0.01272(health care equipment ) (0.4015) (0.0221) (0.9942) (0.0313) (0.3365) (0.0358) (0.1628) (0.0003) (0.0062) (0.7731) 5 of 10

The coefficients impact compared to mean return. 78.9% 0.1% -0.4% 2.7% 0.0% 43.8% -1.5% -0.8% -24.7% 0.3%Significant coefficients by sector: 0 of 2 2 of 2 0 of 2 2 of 2 0 of 2 1 of 2 0 of 2 1 of 2 2 of 2 1 of 2 9 of 20

p-value (H0: sector = average(total)) 0.8155

C o e f f i c i e n t s w i t h p - v a l u e b e l o w i n (C a r a n t h e s i s). G r e y s h a d o w a n d b o l d p < 0 . 1 0.

-

Energy

52

The single energy stock in the sample is the one demonstrating best features. For the health care sector it is particular the TB, OP, and G3TS that have good

explanatory power, which could relate to high R&D costs and a high dependence on energy. However, the sign for the TB and OP is opposite each other for the

two firms. For financial firms TS, OP, and G3TB show highest accuracy. Moreover, if one looks beyond a p-value of <0.10 versus 0.15-0.20 the TS emerges as

unquestionably the one with most explanatory power.

53

Table 17 – Results from regression model for firms sorted by sector, 2 of 2 (read together with table 16 and 21)Company Significant

Sector (Sub-Industry) C TB TS OP G1TB G1TS G1OP G3TB G3TS G3OP variablesI ASSA ABLOY B -0.00077 -0.00805 0.00078 0.03600 -0.01981 0.00252 0.12476 0.04834 -0.00093 0.06213

(building products ) (0 .3132) (0.2372) (0.0533) (0.2272) (0.4118) (0.0005) (0.2104) (0.0591) (0 .0251) (0.2197) 4 of 10The coefficients impact compared to mean return. -90.5% 0.2% 95.1% 1.3% 0.0% 52.5% -1.3% -0.2% -17.1% 1.3%

N ABB -0.00202 -0.00228 0.00127 0.10694 0.03200 0.00126 0.20883 0.02452 -0.00057 -0.06926(industrial machinery ) (0 .1079) (0.716) (0.0446) (0.0207) (0.307) (0.451) (0.2419) (0.0881) (0.1425) (0.2495) 3 of 10

The coefficients impact compared to mean return. -5952.8% -1.5% 2412.0% 122.8% 0.0% 493.2% -104.8% -2.9% -290.4% -41.1%D ALFA LAVAL -0.00074 0.01080 0.00078 0.13456 -0.02896 0.00103 0.08515 0.00045 -0.00012 0.04693

(industrial machinery ) (0 .5201) (0.1983) (0.1765) (0.0089) (0.3328) (0.3625) (0.4965) (0.9765) (0.7765) (0.5346) 1 of 10The coefficients impact compared to mean return. -96.8% 0.1% 52.1% 4.8% 0.0% 13.5% -0.8% 0.0% -1.9% 0.8%

U Atlas Copco B 0.00012 -0.01324 0.00031 0.07061 -0.01539 0.00094 0.13222 0.04898 -0.00037 -0.03025(industrial machinery ) (0 .7908) (0.0655) (0.1746) (0.0164) (0.6517) (0.0542) (0.135) (0.0185) (0.3318) (0.5427) 4 of 10

The coefficients impact compared to mean return. 17.6% 0.1% 40.9% 2.6% 0.0% 28.8% 0.4% -0.2% -12.2% -0.6%S Sandvik -0.00153 0.00245 0.00136 0.10054 0.00858 0.00030 0.22252 0.01253 -0.00071 0.01716T (industrial machinery ) (0.0827) (0.7581) (0.0055) (0.0166) (0.8135) (0.7659) (0 .0216) (0.4428) (0 .0769) (0.8118) 5 of 10

The coefficients impact compared to mean return. -346.5% 0.2% 164.1% 2.9% 0.0% 10.1% 2.9% -0.6% -27.2% 0.5%R SKF B -0.00036 0.01445 0.00047 0.06189 -0.04684 0.00065 0.11486 -0.00127 -0.00043 -0.00758I (industrial machinery ) (0 .4827) (0.0526) (0.0472) (0.0162) (0.0125) (0.2222) (0.1305) (0.9384) (0.1824) (0.86) 4 of 10

The coefficients impact compared to mean return. -110.6% -0.1% 136.5% 6.3% 0.0% 35.0% -1.3% 0.0% -24.5% -0.3%Skanska B -0.00083 -0.01613 0.00042 0.03477 -0.04491 0.00233 0.15452 0.01871 -0.00009 -0.01131

A (construction and engineering ) (0.216) (0.15) (0.2018) (0.1898) (0.3415) (0.0004) (0 .0195) (0.2391) (0.7317) (0.7858) 2 of 10The coefficients impact compared to mean return. -316.3% -0.6% 132.8% 3.0% 0.0% 202.4% 3.9% -2.0% -9.0% -0.5%

Scania B -0.00127 0.00226 0.00078 0.07343 0.07673 0.00206 0.03589 0.03286 -0.00042 0.00019L

(construction and farm machinery; heavy trucks ) (0 .1543) (0.8283) (0.0839) (0.0024) (0.0196) (0.0093) (0.6498) (0.0977) (0.1888) (0.9973) 5 of 10

The coefficients impact compared to mean return. -357.2% -0.1% 204.8% 9.9% 0.0% 96.6% -1.1% 0.1% -18.2% 0.0%Volvo B 0.00013 -0.01696 0.00037 0.08317 -0.00230 0.00081 0.02482 0.05711 -0.00120 -0.00096

S(construction and farm machinery; heavy trucks ) (0 .8293) (0.0731) (0.2895) (0.0059) (0.9367) (0.3775) (0.7427) (0.0022) (0 .0019) (0.9848) 4 of 10

The coefficients impact compared to mean return. 26.1% 0.3% 67.0% 8.3% 0.0% 30.2% -0.8% -1.6% -62.5% 0.0%Significant coefficients by sector: 1 of 9 3 of 9 5 of 9 7 of 9 2 of 9 4 of 9 4 of 9 5 of 9 3 of 9 0 of 9 32 of 90

p-value (H0: sector = average(total)) 0.4732Information Ericsson B -0.00018 0.00036 0.00069 0.00108 -0.05724 -0.00054 0.22234 -0.02263 -0.00095 -0.01340Technology (communications equipment ) (0 .7704) (0.9565) (0.0088) (0.9751) (0.0683) (0.5281) (0 .0647) (0.1733) (0 .0048) (0.8319) 4 of 10

The coefficients impact compared to mean return. -42.7% 0.0% 150.8% 0.1% 0.0% -21.9% -4.6% 1.0% -47.0% -0.5%Significant coefficients by sector: 0 of 1 0 of 1 1 of 1 0 of 1 1 of 1 0 of 1 1 of 1 0 of 1 1 of 1 0 of 1 4 of 10

MA Boliden -0.00382 -0.01442 0.00268 0.32092 0.11574 0.00193 0.47037 0.04141 -0.00077 -0.23434T (diversified metals and mining ) (0.008) (0.2609) (0.0006) (0) (0.0585) (0.2495) (0 .0124) (0.0341) (0.1876) (0.0037) 7 of 10

The coefficients impact compared to mean return. -582.0% 0.3% 215.8% 12.7% 0.0% 30.7% -5.8% 0.0% -14.9% -7.5%ER SCA B -0.00014 -0.00806 -0.00003 0.05230 -0.03323 0.00147 0.05889 0.01189 0.00033 -0.08312I (paper products ) (0 .7438) (0.3363) (0.9092) (0.0144) (0.1774) (0.0029) (0.2931) (0.3641) (0.267) (0.0345) 3 of 10

The coefficients impact compared to mean return. -59.6% 0.8% -9.6% 5.0% 0.0% 133.9% -0.4% 0.2% 33.6% -6.7%AL SSAB A -0.00051 -0.00398 0.00048 0.16006 0.04085 0.00147 0.05930 0.01957 -0.00047 -0.04788S (steel ) (0 .4777) (0.7889) (0.1875) (0) (0.2461) (0.0281) (0.4865) (0.3723) (0.12) (0.3686) 2 of 10

The coefficients impact compared to mean return. -94.1% 0.1% 87.4% 7.0% 0.0% 65.5% 0.4% 0.2% -20.3% -1.2%Significant coefficients by sector: 1 of 3 0 of 3 1 of 3 3 of 3 1 of 3 2 of 3 1 of 3 1 of 3 0 of 3 2 of 3 12 of 30

p-value (H0: sector = average(total)) 0.8892Telecom- Tele 2 B -0.00108 -0.00214 0.00070 0.07840 0.02840 0.00175 -0.01080 0.00780 -0.00010 -0.08457

munication(integrated telecommunication services ) (0.243) (0.8559) (0.1352) (0.0179) (0.3321) (0.0241) (0.9018) (0.6822) (0.7929) (0.1447) 2 of 10

The coefficients impact compared to mean return. -230.6% 0.1% 129.7% 2.9% 0.0% 71.6% -0.1% -0.3% -3.7% -3.1%- TeliaSonera -0.00153 0.00681 0.00075 0.02546 -0.01838 0.00073 -0.09831 -0.02038 -0.00018 0.12014

Services(integrated telecommunication services ) (0.0744) (0.3209) (0.0809) (0.4598) (0.3973) (0.4273) (0.439) (0.1577) (0.602) (0.0301) 3 of 10

The coefficients impact compared to mean return. 723.1% -0.2% -211.3% 1.1% 0.0% -40.8% 9.1% -0.3% 11.3% -11.0%Significant coefficients by sector: 1 of 2 0 of 2 1 of 2 1 of 2 0 of 2 1 of 2 0 of 2 0 of 2 0 of 2 1 of 2 5 of 20

p-value (H0: sector = average(total)) 0.7058

C o e f f i c i e n t s w i t h p - v a l u e b e l o w i n (C a r a n t h e s i s). G r e y s h a d o w a n d b o l d p < 0 . 1 0.

Moderately consistent results within sectors are found. The industrials sector demonstrating most, where OP, TS, G1TS, and G3TS have best explanatory power.

54

Table 18 – Results from regression model for firms for high turnover, 1 of 2 (read together with table 19 and 20)Company, with turnover Significant

(Sector) + Coefficients impact to C (%) C TB TS OP G1TB G1TS G1OP G3TB G3TS G3OP variablesEricsson B -0.00018 0.00036 0.00069 0.00108 -0.05724 -0.00054 0.22234 -0.02263 -0.00095 -0.01340

2,307,703,913 (0.7704) (0.9565) (0.0088) (0.9751) (0.0683) (0.5281) (0.0647) (0.1733) (0.0048) (0.8319) 4 of 10(Information Technology) 100.0% 0.0% -353.1% -0.2% -7.8% 51.2% 10.8% -2.3% 110.2% 1.1%Nordea -0.00143 -0.00796 0.00097 0.10899 -0.04522 0.00084 -0.07084 0.04009 -0.00014 0.03333

616,802,253 (0.0763) (0.4873) (0.0344) (0.0004) (0.2468) (0.2936) (0.373) (0.0163) (0.7213) (0.5374) 4 of 10(Financials) 100.0% 0.0% -45.7% -2.4% -0.3% -9.9% -0.4% 0.2% 1.9% -0.4%ASTRA ZENECA -0.00067 0.00759 0.00044 -0.07501 0.04181 0.00040 -0.02771 -0.01694 -0.00061 0.10645

606,855,871 (0.3441) (0.1021) (0.2373) (0.0008) (0.107) (0.5925) (0.7287) (0.2742) (0.0386) (0.0014) 4 of 10(Health Care) 100.0% -0.2% -43.3% 3.1% 1.0% -8.3% -0.3% -0.3% 15.1% -3.8%TeliaSonera -0.00153 0.00681 0.00075 0.02546 -0.01838 0.00073 -0.09831 -0.02038 -0.00018 0.12014

604,393,943 (0.0744) (0.3209) (0.0809) (0.4598) (0.3973) (0.4273) (0.439) (0.1577) (0.602) (0.0301) 3 of 10(Integrated telecommunication services ) 100.0% 0.0% -29.2% 0.2% -0.2% -5.6% 1.3% 0.0% 1.6% -1.5%Sandvik -0.00153 0.00245 0.00136 0.10054 0.00858 0.00030 0.22252 0.01253 -0.00071 0.01716

519,802,114 (0.0827) (0.7581) (0.0055) (0.0166) (0.8135) (0.7659) (0.0216) (0.4428) (0.0769) (0.8118) 5 of 10(Industrials) 100.0% 0.0% -47.4% -0.8% 0.1% -2.9% -0.8% 0.2% 7.8% -0.1%Hennes & Mauritz -0.00028 -0.00101 0.00079 0.05199 -0.01583 -0.00048 0.05199 0.00442 -0.00123 0.00746

431,471,361 (0.6198) (0.9268) (0.028) (0.0126) (0.5012) (0.4362) (0.4435) (0.852) (0.0001) (0.8402) 3 of 10(Consumer Discretionary) 100.0% -0.2% -283.0% -5.3% 0.1% 27.1% 1.1% 0.0% 74.5% -0.5%Volvo B 0.00013 -0.01696 0.00037 0.08317 -0.00230 0.00081 0.02482 0.05711 -0.00120 -0.00096

412,957,368 (0.8293) (0.0731) (0.2895) (0.0059) (0.9367) (0.3775) (0.7427) (0.0022) (0.0019) (0.9848) 4 of 10(Industrials) 100.0% 1.2% 256.4% 31.6% 0.4% 115.7% -2.9% -6.0% -238.9% -0.1%ABB -0.00202 -0.00228 0.00127 0.10694 0.03200 0.00126 0.20883 0.02452 -0.00057 -0.06926

410,734,483 (0.1079) (0.716) (0.0446) (0.0207) (0.307) (0.451) (0.2419) (0.0881) (0.1425) (0.2495) 3 of 10(Industrials) 100.0% 0.0% -40.5% -2.1% 0.4% -8.3% 1.8% 0.0% 4.9% 0.7%Electrolux B 0.00015 0.00922 0.00004 0.05728 -0.00555 0.00148 -0.00629 0.01087 -0.00034 -0.00624

381,693,446 (0.7593) (0.3122) (0.84) (0.0318) (0.7263) (0.0281) (0.9393) (0.6846) (0.326) (0.8982) 2 of 10(Consumer Discretionary) 100.0% 0.2% 26.7% 12.4% 1.5% 186.0% -0.1% -0.4% -46.0% -0.3%SWEDBANK A -0.00270 -0.01066 0.00161 0.12361 0.15453 0.00169 0.09691 0.02196 0.00011 -0.07882

377,969,611 (0.0012) (0.4011) (0.0008) (0.0002) (0.0023) (0.069) (0.2566) (0.2447) (0.7873) (0.114) 5 of 10(Financials) 100.0% -0.1% -50.3% -1.1% 1.1% -13.0% -0.3% -0.1% -1.0% 0.2%SEB A -0.00071 -0.04466 0.00076 0.10262 0.02908 0.00102 0.19237 0.05494 -0.00087 -0.06936

299,504,385 (0.3432) (0.0001) (0.0307) (0.0122) (0.7109) (0.2294) (0.0869) (0.0004) (0.0109) (0.2195) 6 of 10(Financials) 100.0% 0.4% -87.5% -5.6% 1.0% -30.5% 2.2% 1.9% 33.6% 0.9%Boliden -0.00382 -0.01442 0.00268 0.32092 0.11574 0.00193 0.47037 0.04141 -0.00077 -0.23434

258,082,203 (0.008) (0.2609) (0.0006) (0) (0.0585) (0.2495) (0.0124) (0.0341) (0.1876) (0.0037) 7 of 10(Materials) 100.0% 0.0% -37.1% -2.2% 0.1% -5.3% 1.0% 0.0% 2.6% 1.3%Tele 2 B -0.00108 -0.00214 0.00070 0.07840 0.02840 0.00175 -0.01080 0.00780 -0.00010 -0.08457

223,321,689 (0.243) (0.8559) (0.1352) (0.0179) (0.3321) (0.0241) (0.9018) (0.6822) (0.7929) (0.1447) 2 of 10(Integrated telecommunication services ) 100.0% 0.0% -56.2% -1.3% -0.1% -31.1% 0.0% 0.1% 1.6% 1.4%Svenska Handelsbanken B -0.00048 -0.02357 0.00064 0.07023 -0.05507 0.00078 0.03963 0.04948 -0.00047 -0.02248

221,053,162 (0.4068) (0.0855) (0.044) (0.0127) (0.0291) (0.1663) (0.4884) (0.0269) (0.1372) (0.5484) 5 of 10(Financials) 100.0% -0.8% -110.3% -2.8% -3.9% -32.8% 0.1% -0.9% 27.5% 1.0% Total:

Significant coefficients across firms: 5 of 14 4 of 14 10 of 14 12 of 14 4 of 14 3 of 14 4 of 14 6 of 14 6 of 14 3 of 14 57 of 140p-value (H0: High = Low turnover) 0.003 0.649 0.012 0.100 0.052 0.007 0.196 0.673 0.014 0.222 0.000

C o e f f i c i e n t s w i t h p - v a l u e b e l o w i n (p a r a n t h e s i s). G r e y s h a d o w a n d b o l d p < 0 . 1 0.

Above we note that the term spread (10 of 14) and oil price (12 of 14) is most significant variables for the high turnover group (>200,000,000). We can also note

that the G3TB, G3TS, and G3OP is more significant than they are for G1TB, G1TS, and G1OP for firms > 400,000,000. Could higher monitoring explain this?

55

Table 19 – Results from regression model for firms for low turnover, 2 of 2 (read together with table 18 and 20)Company, with turnover Significant(Sector) + Coefficients impact to C (%) C TB TS OP G1TB G1TS G1OP G3TB G3TS G3OP variablesSKF B -0.00036 0.01445 0.00047 0.06189 -0.04684 0.00065 0.11486 -0.00127 -0.00043 -0.00758

199,185,161 (0.4827) (0.0526) (0.0472) (0.0162) (0.0125) (0 .2222) (0.1305) (0.9384) (0.1824) (0.86) 4 of 10(Industrials) 100.0% 0.1% -123.4% -5.7% -6.1% -31.6% 1.2% 0.0% 22.1% 0.2%ALFA LAVAL -0.00074 0.01080 0.00078 0.13456 -0.02896 0.00103 0.08515 0.00045 -0.00012 0.04693 1 of 10

176,007,590 (0.5201) (0.1983) (0.1765) (0.0089) (0.3328) (0.3625) (0.4965) (0.9765) (0.7765) (0.5346)(Industrials) 100.0% -0.1% -53.8% -4.9% 0.2% -14.0% 0.9% 0.0% 1.9% -0.8%Investor B 0.00063 0.00472 0.00014 0.04147 -0.00670 0.00025 0.06330 -0.01396 0.00042 -0.03793

172,086,542 (0.1873) (0.5469) (0.581) (0.0403) (0.6885) (0.6765) (0.4137) (0.3007) (0.1258) (0.501) 1 of 10(Finanicals) 100.0% 0.1% 18.3% 2.2% 0.6% 8.4% -0.4% -0.2% 17.8% -0.7%ASSA ABLOY B -0.00077 -0.00805 0.00078 0.03600 -0.01981 0.00252 0.12476 0.04834 -0.00093 0.06213

160,371,930 (0.3132) (0.2372) (0.0533) (0 .2272) (0.4118) (0.0005) (0.2104) (0.0591) (0.0251) (0 .2197) 4 of 10(Industrials) 100.0% -0.3% -105.0% -1.5% 0.0% -57.9% 1.5% 0.3% 18.9% -1.4%Scania B -0.00127 0.00226 0.00078 0.07343 0.07673 0.00206 0.03589 0.03286 -0.00042 0.00019

153,081,678 (0.1543) (0.8283) (0.0839) (0.0024) (0.0196) (0.0093) (0.6498) (0.0977) (0.1888) (0.9973) 5 of 10(Industrials) 100.0% 0.0% -57.3% -2.8% 0.9% -27.0% 0.3% 0.0% 5.1% 0.0%SCA B -0.00014 -0.00806 -0.00003 0.05230 -0.03323 0.00147 0.05889 0.01189 0.00033 -0.08312

145,879,477 (0.7438) (0.3363) (0.9092) (0.0144) (0.1774) (0.0029) (0.2931) (0.3641) (0.267) (0.0345) 3 of 10(Materials) 100.0% -1.3% 16.1% -8.4% -5.5% -224.7% 0.7% -0.4% -56.4% 11.2%Swedish Match 0.00002 -0.01199 0.00027 0.01535 0.01057 0.00113 0.04693 0.01058 -0.00040 -0.01587

126,532,730 (0.9636) (0.0132) (0.3477) (0.4425) (0.6001) (0.041) (0.3986) (0.1296) (0.1562) (0.7294) 2 of 10(Consumer Staples) 100.0% 4.1% 919.7% 13.5% 5.0% 837.5% -5.6% -12.3% -351.2% -12.6%Skanska B -0.00083 -0.01613 0.00042 0.03477 -0.04491 0.00233 0.15452 0.01871 -0.00009 -0.01131

108,622,561 (0.216) (0.15) (0.2018) (0.1898) (0.3415) (0.0004) (0.0195) (0 .2391) (0.7317) (0.7858) 2 of 10(Industrials) 100.0% 0.2% -42.0% -0.9% -1.3% -64.0% -1.2% 0.6% 2.8% 0.2%Lundin Petroleum -0.00340 0.02974 0.00191 0.51390 -0.04384 0.00446 0.93415 -0.04724 -0.00058 -0.13494

101,798,044 (0.0334) (0.0479) (0.0312) (0) (0.3677) (0.0463) (0) (0.0047) (0.4598) (0.2388) 7 of 10(Energy) 100.0% -0.1% -19.0% -2.8% -0.1% -10.3% 0.5% -0.1% 1.4% 0.3%Modern Times Group -0.00363 0.00734 0.00196 0.16776 0.03744 0.00434 0.05791 -0.01494 -0.00074 -0.11419

81,782,079 (0.0018) (0 .5011) (0.001) (0.001) (0.3606) (0.0003) (0.6375) (0.2648) (0.1852) (0.1277) 4 of 10(Consumer Discretionary) 100.0% 0.0% -35.5% -1.5% 0.2% -17.5% 0.0% 0.1% 3.3% 0.4%SSAB A -0.00051 -0.00398 0.00048 0.16006 0.04085 0.00147 0.05930 0.01957 -0.00047 -0.04788

79,430,678 (0.4777) (0.7889) (0.1875) (0) (0.2461) (0.0281) (0.4865) (0.3723) (0.12) (0.3686) 2 of 10(Materials) 100.0% -0.1% -92.9% -7.4% 2.9% -69.6% -0.4% -0.2% 21.6% 1.2%Atlas Copco B 0.00012 -0.01324 0.00031 0.07061 -0.01539 0.00094 0.13222 0.04898 -0.00037 -0.03025

74,816,520 (0.7908) (0.0655) (0.1746) (0.0164) (0.6517) (0.0542) (0.135) (0.0185) (0.3318) (0.5427) 4 of 10(Industrials) 100.0% 0.6% 232.4% 14.8% 4.4% 163.6% 2.2% -1.2% -69.2% -3.6%Getinge B 0.00048 -0.01671 0.00000 0.04341 0.02582 0.00125 0.07616 0.04000 -0.00084 0.01272

41,784,570 (0.4015) (0.0221) (0.9942) (0.0313) (0.3365) (0.0358) (0.1628) (0.0003) (0.0062) (0 .7731) 5 of 10(Health Care) 100.0% 0.1% -0.5% 3.4% -1.0% 55.5% -1.9% -1.0% -31.4% 0.4% Total:

Significant coefficients across firms: 2 of 13 5 of 13 5 of 13 10 of 13 2 of 13 10 of 13 2 of 13 5 of 13 2 of 13 1 of 13 44 of 130p-value (H0: High = Low turnover) 0.003 0.649 0.012 0.100 0.052 0.007 0.196 0.673 0.014 0.222 0.000

C o e f f i c i e n t s w i t h p - v a l u e b e l o w i n (p a r a n t h e s i s). G r e y s h a d o w a n d b o l d p < 0 . 1 0.

For the low turnover group (<200,000,000) we note that it is in particular the oil price (10 of 13) and high volume term spread, G1TS, (10 of 13) that demonstrate

significant variables. The interesting finding here is that the term spread G1TS rather than TS is most significant for the low turnover group. Moreover, in total

there are more significant variables for the high turnover group than it is for the low turnover group.

56

Table 20 –Results associated to the regression models for firms sorted by turnover (see 18 and 19 )

CompanyEricsson B 0.00042 0.03098 0.4% 0.2% 1.90 2.28 0.0152Nordea 0.00026 0.02326 1.4% 1.1% 2.10 5.14 0.0000ASTRA ZENECA -0.00008 0.01772 0.9% 0.6% 1.94 3.20 0.0007TeliaSonera -0.00021 0.02277 0.5% 0.2% 2.05 1.49 0.1469Sandvik 0.00044 0.02213 2.4% 2.1% 2.00 7.46 0.0000Hennes & Mauritz 0.00037 0.01930 0.8% 0.7% 1.92 4.61 0.0000Volvo B 0.00048 0.02178 1.2% 1.0% 1.87 6.68 0.0000ABB 0.00003 0.03471 1.0% 0.7% 1.88 3.17 0.0008Electrolux B 0.00045 0.02326 0.6% 0.4% 1.90 3.20 0.0007SWEDBANK A 0.00032 0.02442 2.7% 2.5% 2.04 12.16 0.0000SEB A 0.00028 0.03102 1.5% 1.3% 1.87 7.99 0.0000Boliden 0.00066 0.03305 6.7% 6.3% 1.99 18.72 0.0000Tele 2 B 0.00047 0.02508 0.7% 0.5% 1.92 2.91 0.0020Svenska Handelsbanken B 0.00044 0.02276 1.8% 1.7% 1.96 10.14 0.0000SKF B 0.00033 0.02391 0.9% 0.7% 1.92 4.65 0.0000ALFA LAVAL 0.00076 0.02523 1.5% 1.1% 2.02 3.86 0.0001Investor B 0.00092 0.02157 0.3% 0.1% 1.88 1.65 0.0963ASSA ABLOY B 0.00085 0.02426 1.1% 0.9% 2.13 4.94 0.0000Scania B 0.00036 0.02099 1.5% 1.3% 1.96 6.42 0.0000SCA B 0.00024 0.01800 1.2% 1.0% 1.96 6.37 0.0000Swedish Match 0.00061 0.01670 0.5% 0.2% 2.22 1.93 0.0441Skanska B 0.00026 0.02156 1.9% 1.8% 1.91 10.66 0.0000Lundin Petroleum 0.00070 0.03752 16.9% 16.4% 1.94 36.06 0.0000Modern Times Group 0.00032 0.03023 2.6% 2.3% 1.83 8.86 0.0000SSAB A 0.00054 0.02336 2.2% 2.0% 1.97 11.41 0.0000Atlas Copco B 0.00067 0.02307 1.0% 0.8% 1.97 5.33 0.0000Getinge B 0.00061 0.01917 0.8% 0.6% 2.03 4.01 0.0000

Mean dependent var

S.D. Dependent var R-squared

Adjusted R-squared

Durbin-Watson stat F-statistic

Prob(F-statistic)

Table 21 –Results associated to the regression models for firms sorted by sector (see 16 and 17)

CompanyElectrolux B 0.00045 0.02326 0.59% 0.41% 1.901 3.195 0.0007Hennes & Mauritz 0.00037 0.01930 0.85% 0.66% 1.921 4.606 0.0000Modern Times Group 0.00032 0.03023 2.59% 2.29% 1.832 8.860 0.0000Swedish Match 0.00061 0.01670 0.46% 0.22% 2.220 1.926 0.0441Lundin Petroleum 0.00070 0.03752 16.86% 16.40% 1.940 36.063 0.0000Investor B 0.00092 0.02157 0.31% 0.12% 1.881 1.647 0.0963Nordea 0.00026 0.02326 1.36% 1.10% 2.104 5.138 0.0000SEB A 0.00028 0.03102 1.46% 1.28% 1.873 7.988 0.0000Svenska Handelsbanken B 0.00044 0.02276 1.85% 1.67% 1.957 10.142 0.0000SWEDBANK A 0.00032 0.02442 2.68% 2.46% 2.039 12.159 0.0000ASTRA ZENECA -0.00008 0.01772 0.94% 0.65% 1.938 3.199 0.0007Getinge B 0.00061 0.01917 0.80% 0.60% 2.033 4.012 0.0000ASSA ABLOY B 0.00085 0.02426 1.07% 0.85% 2.131 4.944 0.0000ABB 0.00003 0.03471 0.95% 0.65% 1.879 3.167 0.0008ALFA LAVAL 0.00076 0.02523 1.53% 1.13% 2.022 3.857 0.0001Atlas Copco B 0.00067 0.02307 0.98% 0.80% 1.971 5.334 0.0000Sandvik 0.00044 0.02213 2.39% 2.07% 2.003 7.462 0.0000SKF B 0.00033 0.02391 0.86% 0.67% 1.919 4.645 0.0000Skanska B 0.00026 0.02156 1.94% 1.76% 1.907 10.661 0.0000Scania B 0.00036 0.02099 1.51% 1.27% 1.956 6.420 0.0000Volvo B 0.00048 0.02178 1.23% 1.04% 1.870 6.682 0.0000Ericsson B 0.00042 0.03098 0.42% 0.24% 1.897 2.278 0.0152Boliden 0.00066 0.03305 6.70% 6.34% 1.990 18.722 0.0000SCA B 0.00024 0.01800 1.17% 0.99% 1.960 6.367 0.0000SSAB A 0.00054 0.02336 2.20% 2.00% 1.970 11.411 0.0000Tele 2 B 0.00047 0.02508 0.69% 0.46% 1.924 2.910 0.0020TeliaSonera -0.00021 0.02277 0.49% 0.16% 2.052 1.486 0.1469

F-statisticProb

(F-statistic)Mean

dependent varS.D. Dependent

var R-squaredAdjusted

R-squaredDurbin-

Watson stat

Comments for Results associated to the regression models for firms sorted by sector/turnover.

Above it is easily noted that only two stocks demonstrate a negative average return over the complete

period, Astra Zeneca and Telia Sonera. Of more interest the model specification using, the 1 month T-bill,

the term spread –the difference a 10 year treasury bond and the 3 month T-bill, and the oil price using a

volume filter is modest. The explanatory power for the whole model is, not unexpectedly, best for the

energy sector with Lunding Petroleum, followed by the materials, industrials and financials respectively.

57

Table 22 - The average value for each explanatory variable in the three volume groupsCompany TB TS OP G1TB G1TS G1OP G3TB G3TS G3OP G2TB G2TS G2OPEricsson B -0.014% 0.437 0.014% -0.025% 0.172 -0.009% -0.018% 0.210 0.015% 0.001% 0.930 0.034%Nordea -0.006% 0.343 0.013% -0.011% 0.168 -0.008% -0.006% 0.187 0.016% -0.002% 0.672 0.031%ASTRA ZENECA -0.003% 0.323 0.015% -0.016% 0.140 -0.007% -0.012% 0.166 0.024% 0.018% 0.662 0.028%TeliaSonera -0.005% 0.282 0.010% -0.018% 0.118 0.020% -0.003% 0.134 0.019% 0.006% 0.594 -0.009%Sandvik -0.005% 0.284 0.010% -0.023% 0.148 0.006% -0.022% 0.170 0.013% 0.030% 0.533 0.013%Hennes & Mauritz -0.014% 0.437 0.014% 0.002% 0.156 -0.006% 0.000% 0.166 0.018% -0.044% 0.989 0.028%Volvo B -0.014% 0.437 0.014% -0.020% 0.180 -0.015% -0.013% 0.251 0.007% -0.009% 0.880 0.048%ABB -0.003% 0.317 0.014% -0.027% 0.132 -0.017% -0.004% 0.174 0.020% 0.022% 0.645 0.039%Electrolux B -0.014% 0.437 0.014% -0.041% 0.192 0.001% -0.005% 0.207 0.006% 0.004% 0.912 0.033%SWEDBANK A -0.011% 0.429 0.013% -0.019% 0.208 0.008% 0.007% 0.238 0.008% -0.021% 0.842 0.024%SEB A -0.014% 0.437 0.014% -0.024% 0.214 -0.008% -0.024% 0.274 0.009% 0.006% 0.824 0.039%Boliden -0.005% 0.253 0.013% -0.003% 0.104 -0.008% 0.000% 0.127 0.021% -0.012% 0.528 0.026%Tele 2 B -0.009% 0.414 0.013% 0.004% 0.192 0.004% -0.017% 0.179 0.017% -0.013% 0.872 0.017%Svenska Handelsbanken B -0.014% 0.437 0.014% -0.034% 0.203 -0.001% 0.009% 0.282 0.022% -0.016% 0.827 0.019%SKF B -0.014% 0.437 0.014% -0.048% 0.178 -0.004% 0.007% 0.186 0.011% -0.001% 0.947 0.033%ALFA LAVAL -0.006% 0.242 0.011% 0.006% 0.099 -0.007% -0.027% 0.119 0.012% 0.004% 0.508 0.027%Investor B -0.014% 0.437 0.014% -0.058% 0.208 -0.004% 0.010% 0.262 0.011% 0.007% 0.841 0.033%ASSA ABLOY B -0.010% 0.455 0.013% -0.001% 0.176 -0.009% -0.004% 0.156 0.018% -0.024% 1.034 0.032%Scania B -0.009% 0.418 0.012% -0.016% 0.166 -0.011% 0.001% 0.154 0.000% -0.013% 0.935 0.048%SCA B -0.014% 0.437 0.014% -0.024% 0.218 -0.002% 0.005% 0.247 0.019% -0.023% 0.846 0.023%Swedish Match -0.008% 0.414 0.013% 0.012% 0.181 -0.003% -0.029% 0.213 0.020% -0.008% 0.847 0.022%Skanska B -0.014% 0.437 0.014% -0.024% 0.228 0.007% -0.027% 0.252 0.012% 0.010% 0.832 0.022%Lundin Petroleum -0.001% 0.165 0.008% -0.006% 0.078 -0.002% -0.004% 0.080 0.009% 0.008% 0.337 0.018%Modern Times Group -0.003% 0.321 0.014% -0.017% 0.147 -0.002% 0.021% 0.160 0.012% -0.014% 0.656 0.032%SSAB A -0.012% 0.490 0.013% -0.036% 0.242 0.003% 0.006% 0.235 0.013% -0.007% 0.994 0.024%Atlas Copco B -0.014% 0.437 0.014% -0.033% 0.205 0.002% -0.003% 0.219 0.014% -0.005% 0.887 0.025%Getinge B -0.011% 0.490 0.013% -0.018% 0.212 -0.012% -0.013% 0.179 0.014% -0.003% 1.080 0.037%Average -0.010% 0.387 0.013% -0.019% 0.173 -0.003% -0.006% 0.194 0.014% -0.004% 0.795 0.028%Stdev 0.004% 0.084 0.001% 0.017% 0.042 0.008% 0.013% 0.051 0.006% 0.016% 0.183 0.011%Share of positive varaibles 0% 100% 100% 15% 100% 30% 37% 100% 100% 41% 100% 96%

This table provide insights of sign and value of each explanatory input variable. An interesting finding is that on days with medium and low volume the average

return in the oil price is positive for 26 and 27 of the firms respectively, while on high volume days it is only positive for 30 % of the firms on average.

58

Table 23 – OMXS price graph with the return, and high and low volume days

100

1000

OMXS30

Low volume

High volume

Price

-10.0%

-6.0%

-2.0%

2.0%

6.0%

10.0%

1/2/1991 1/2/1992 1/2/1993 1/2/1994 1/2/1995 1/2/1996 1/2/1997 1/2/1998 1/2/1999 1/2/2000 1/2/2001 1/2/2002 1/2/2003 1/2/2004 1/2/2005 1/2/2006 1/2/2007 1/2/2008 1/2/2009 1/2/2010 1/2/2011

R

A strong tendency though out the 20 year sample periods of volume clustering.

59

Table 24 – A graph of the explanatory variables

1

10

100

1000

-5

-2.5

0

2.5

5

7.5

10

12.5

15

1M T-Bill, TB

Term Spread, TS

Oil Price, OP

The interest rate and oil price movements over the 20 year sample period.

60

abstract - lund universitylup.lub.lu.se/.../record/1973769/file/1973781.docx · web viewbachelor...

Documents