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.
Acknowledgements
First 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.
Abstract
Using 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
2
3
4
INTRODUCTION ................................................................................................ 1
1.1
Background ............................................................................................................................. 1
1.2
Problem discussion ................................................................................................................. 3
1.3
Purpose .................................................................................................................................... 5
1.4
Delimitations ........................................................................................................................... 5
1.5
Thesis outline .......................................................................................................................... 5
THEORETICAL FRAMEWORK ..................................................................... 6
2.1
Volume .................................................................................................................................... 6
2.2
Interest rate ............................................................................................................................. 9
2.3
Oil price ................................................................................................................................. 11
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
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
7
Further studies...................................................................................................................... 34
REFERENCES ................................................................................................... 35
APPENDIX ................................................................................................................. 45
Table 1 –Volume characteristics for different volume groups of OMXS30 ............... 20
Table 2 – Return characteristics for different volume groups for OMXS30 ............... 21
Table 3 – Correlation matrix ........................................................................................ 21
Table 4 – Regression results from the OMXS30 index ............................................... 22
Table 5 –Summary of the volume within different volume groups for all firms ......... 23
Table 6 –Summary of the return within different volume groups for all firms ........... 24
Table 7 – Summary of the regression results for ONLY significant variables among
all firms ........................................................................................................................ 25
Table 8 – Hypothesis table, OMXS30 index ............................................................... 28
Table 9 – Hypothesis table, OMXS30 index constituents, relation and significance .. 29
Table 10 –Firms in the study with corresponding sector, industry group and subindustry (GICS) ............................................................................................................ 45
Table 11 – Descriptive statistics of firms sorted by turnover (1 of 2) ......................... 46
Table 12 – Descriptive statistics of firms sorted by turnover (2 of 2) ......................... 47
Table 13 – Descriptive statistics of firms sorted by sector (1 of 3) ............................. 48
Table 14 – Descriptive statistics of firms sorted by sector (2 of 3) ............................. 49
Table 15 – Descriptive statistics of firms sorted by sector (3 of 3) ............................. 50
Table 16 – Results from regression model for firms sorted by sector, 1 of 2 (read
together with table 17 and 21) ..................................................................................... 51
Table 17 – Results from regression model for firms sorted by sector, 2 of 2 (read
together with table 16 and 21) ..................................................................................... 53
Table 18 – Results from regression model for firms for high turnover, 1 of 2 (read
together with table 19 and 20) ..................................................................................... 54
Table 19 – Results from regression model for firms for low turnover, 2 of 2 (read
together with table 18 and 20) ..................................................................................... 55
Table 20 –Results associated to the regression models for firms sorted by turnover
(see 18 and 19 ) ............................................................................................................ 56
Table 21 –Results associated to the regression models for firms sorted by sector (see
16 and 17) .................................................................................................................... 56
Table 22 - The average value for each explanatory variable in the three volume
groups ........................................................................................................................... 57
Table 23 – OMXS price graph with the return, and high and low volume days ......... 58
Table 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 financialand 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.
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
4
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
information that is not, always, fully incorporated in the price. This conclusion was
1
A statistical tests used to determine whether one time-series is good at forecasting another.
6
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
equal size (Black, 1976), fat tailed distributions of risky asset returns and long memory of
volatility. (Mandelbrot and van Ness, 1968)
7
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
the well documented return volatility clustering. (Clark, 1973; Chordia and Swaminathan,
2000; Gervais et al., 2001; Karpoff, 1987; Lee and Swaminathan, 1998)
2
Engle, (1982) (ARCH, Autoregressive Conditional Heteroskedasticity, a method used to account for timevariation 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 variance
4
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
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
6
Fama and Schwert, 1977; Campbell, 1987; Glosten et al., 1993; and Whitelaw, 1994
Defined as the difference between itself and its 12 month moving average
9
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-ofsample 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.
10
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.
11
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.
12
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
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.
13
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.
14
3.2 Data selection
The 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-ofsample). 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.
Dummy for volume group G3 (low 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.
SE GVB 10 Y, a Swedish Government Bond with 10 years maturity
9
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.
8
15
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 + 𝑇𝑆𝑡 + 𝑂𝑃𝑡 + 𝐺1𝐷𝑢𝑚𝑚𝑦 ∗ (𝑇𝐵𝑡 + 𝑇𝑆𝑡 + 𝑂𝑃𝑡 ) +
𝐺3𝐷𝑢𝑚𝑚𝑦 ∗ (𝑇𝐵𝑡 + 𝑇𝑆𝑡 + 𝑂𝑃𝑡 ) + 𝜀𝑡
𝜀𝑡 ~𝑁(0, 𝜎𝑡2 )
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)
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 nonnormality is further limited.
11
16
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.
17
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.
18
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.
19
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 return
Table 1 –Volume characteristics for different volume groups of OMXS30
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.)
20
Table 2 – Return characteristics for different volume groups for OMXS30
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 matrix
(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.
21
4.1.2 Results from the regression model, OMXS30
In 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
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.
22
4.2 The OMXS30 index constituents
4.2.1 Descriptive statistics of the volume and return
Table 5 –Summary of the volume within different volume groups for all firms
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
15
each group.)
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.
23
Table 6 –Summary of the return within different volume groups for all firms
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%
16
See table 11-15 in appendix for details of volume distribution for each individual firm, sorted either by
sector or turnover.
24
(2.150%) for the medium volume days, and -0.065% (1.581%) for the low volume days
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 constituents
Table 7 – Summary of the regression results for ONLY significant variables among all firms
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
17
9.28% excluding Lundin Petroleum
25
on normal volume days, and as much as 47.73% (36.60%18 + 11.13%) on high volume
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
18
25.24% excluding Lundin Petroleum
26
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
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.
27
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, OP
G1, 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
volume days the TB had a negative impact which was expected. The OP had a positive
28
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
+
+
+
(12 firms have + sign)
(25 firms have + sign)
(22 firms have + sign)
-
+
+
(16 firms have – sign)
(25 firms have + sign)
(26 firms have + sign)
+ (20)
- (24)
- (18)
(20 firms have + sign)
(24 firms have - sign)
(18 firms have - sign)
1M T-Bill, TB
Term spread, TS
Oil price, OP
G1, High
3 firms, a significant + sign
13 firms, a significant + sign
6 firms, a significant + sign
volume
3 firms, a significant - sign
0 firms, a significant - sign
0 firms, a significant - sign
G2, Normal
3 firms, a significant + sign
15 firms, a significant + sign
21 firms, a significant + sign
volume
6 firms, a significant - sign
0 firms, a significant - sign
1 firm, a significant - sign
10 firms, a significant + sign
0 firms, a significant + sign
2 firms, a significant + sign
8 firms, a significant - sign
2 firms, a significant - sign
volume
G2, Normal
volume
G3, Low
volume
Significance
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
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.
29
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.
30
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
31
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.
32
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.
33
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.
34
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44
Appendix
Table 10 –Firms in the study with corresponding sector, industry group and sub-industry (GICS)
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.
45
Table 11 – Descriptive statistics of firms sorted by turnover (1 of 2)
For comments see under Descriptive statistics of firms sorted by turnover (2 of 2).
46
Table 12 – Descriptive statistics of firms sorted by turnover (2 of 2)
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.
47
Table 13 – Descriptive statistics of firms sorted by sector (1 of 3)
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.
48
Table 14 – Descriptive statistics of firms sorted by sector (2 of 3)
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).
49
Table 15 – Descriptive statistics of firms sorted by sector (3 of 3)
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.
50
Table 16 – Results from regression model for firms sorted by sector, 1 of 2 (read together with table 17 and 21)
51
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.
52
Table 17 – Results from regression model for firms sorted by sector, 2 of 2 (read together with table 16 and 21)
Moderately consistent results within sectors are found. The industrials sector demonstrating most, where OP, TS, G1TS, and G3TS have best explanatory power.
53
Table 18 – Results from regression model for firms for high turnover, 1 of 2 (read together with table 19 and 20)
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?
54
Table 19 – Results from regression model for firms for low turnover, 2 of 2 (read together with table 18 and 20)
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.
55
Table 20 –Results associated to the regression models for firms sorted by turnover (see 18 and 19 )
Table 21 –Results associated to the regression models for firms sorted by sector (see 16 and 17)
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.
56
Table 22 - The average value for each explanatory variable in the three volume groups
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.
57
Table 23 – OMXS price graph with the return, and high and low volume days
1000
OMXS30
Low volume
High volume
Price
10.0%
6.0%
2.0%
100
-2.0%
R
-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
A strong tendency though out the 20 year sample periods of volume clustering.
58
1/2/2004
1/2/2005
1/2/2006
1/2/2007
1/2/2008
1/2/2009
1/2/2010
1/2/2011
Table 24 – A graph of the explanatory variables
15
1000
12.5
10
100
7.5
5
1M T-Bill, TB
Term Spread, TS
Oil Price, OP
2.5
10
0
-2.5
-5
1
The interest rate and oil price movements over the 20 year sample period.
59