The Relationship between Crude oil prices and stock performance of
European Automobile manufacturersPerformance of European Automobile
Manufacturers
SCHOOL OF ECONOMICS AND MANAGEMENT, UNIVERSITEIT VAN TILBURG
The Relationship between Crude oil prices and stock performance of
European Automobile manufacturers
Supervisor
Submitted by
Faraz Inayat
Date: 30.09.2010
Abstract
Abstract
This paper aims to analyze the relationship between oil prices and
stock performance of European
automobile manufacturers. Up till now, the focus of research has
been North American data. Due to the
crucial importance of auto manufacturing industry, it is imperative
to carry out similar analysis in
Europe. This paper explores the relationship by adding an oil
factor to the three factor fama-french
model and carrying out regression by using the OLS method. The
results indicate that oil is not having a
significantly adverse impact on auto returns. The relationship only
turns negative during the credit crises
years 2007-2009, where factors other than rising oil prices impact
performance. Luxury car
manufacturers have shown volatile trends during the analysis
period, but this was due to economic and
industry factors rather than oil price rises. Finally, oil adds no
significant value to the asset pricing model.
Acknowledgements
I thank Prof. Peter de Goeij for his valuable comments and guidance
throughout the writing of this
thesis. I also thank Sohail Ahmed, PhD Student, Tilburg University,
for helping me with using the
statistical software for my data analysis. Finally, I would also
like to thank the library staff for their
cooperation in guiding me on how to use the financial
databases.
Contents 1. Introduction
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1
2. Literature Review
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5
4. Hypothesis development
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12
5.1 Methodology
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16
6.2 Luxury and non-luxury Auto indices
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24
7. Analyses
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28
8. Conclusion
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34
9. References
..............................................................................................................................................
35
10. Appendix
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37
1. Introduction
The global economy is witnessing its most testing times in recent
history. Ever since the credit
crises originated from USA, most of the major economies in both
developed and developing
countries are engulfed in recessionary phases. While most of the
talk in news and press is
regarding the financial crises, the recent years has also witnessed
unprecedented rise in
commodity prices. This has exacerbated the problems facing the
global economy. Out of the
major commodities, none has a more widespread and pronounced affect
than oil. Oil is used
either as a raw material for various industries, or consumed by the
products of these industries.
Energy and transportation prices which are critical for industries
as they can influence their
cash flow and profitability; all are linked to the price of oil.
Oil prices can also affect the cash
flows of firms depending on the nature of the industry. Moreover,
oil prices also play a role in
asset pricing as they affect the level of inflation and real
interest rates, thereby influencing the
discount rate estimations. For all these reasons, oil and its
relationship to the global economy
and aggregate macro-economic indicators have been the focus of a
great deal of research.
Economists have tried to empirically establish a relationship
between oil and aggregate
economic performance. According to an International Energy Agency
(IEA) paper in 2004, that
investigated the impact of high oil prices on global economy, it
estimated a 0.4% reduction of
GDP of OECD countries, equivalent to $255 billion, in the year
following a $10 rise in oil prices.
The economy of European Union (EU) is the largest in the world
($14.51 trillion) in terms of GDP
(based on purchasing power parity)1. To fuel its energy needs,
Europe has to rely on fuel
imports as its domestic production is insufficient to meet all its
requirements. As a region, EU is
the third highest in terms of oil consumption after North America
and Asia Pacific 2(figures in
table 1). Out of the top ten net importers of oil, five of the
countries are from Europe3.
According to EU Commission’s Green Paper on Security of Energy
Supply, based on current
trends, by 2020, the EU will be importing 90% of its oil
requirements.
1 CIA World Fact book
2 BP Statistical Review of World Energy 2010
3 International Energy Agency (IAE) Key World Energy Statistics
2009
2 BP Statistical Review of World Energy 2010
3 International Energy Agency (IAE) Key World Energy Statistics
2009
2
Such dependency can have serious consequences for EU’s economy, as
world demand for fossil
fuels is expected to grow in the future as well. With developing
economies led by China and
India fueling the higher demand for oil and concentration of oil in
few but unstable regions, the
price of oil can be expected to remain high in the coming years.
The high prices can have
detrimental effects on a region trying to recover from economic
recessions triggered mainly by
sovereign debt and fiscal deficit crises in some European
economies. This point has factored
high on the EU planners and policy makers, who in December 2008
adopted an integrated
energy and climate change policy which aims to achieve the
following targets by year 2020:
Cut greenhouse gas emissions by 20%.
Reduction in energy consumption by 20% through increased energy
efficiency
Meeting 20% of energy needs from renewable sources
In order to reduce energy consumption by 20%, the EU has identified
three key sectors for
which energy-efficient technology needs to be developed and
implemented; buildings,
transport, and manufacturing. Focusing on the road transport
sector, it consumes 26% of EU’s
energy requirements. As part of the new policy, Car emissions are
to be restricted, energy-
efficient vehicles to be promoted, along with promoting
alternatives to car travel such as public
transport. Apart from this, fuel prices in EU are heavily taxed and
EU policy makers depend on
regulatory measures to influence energy consumption in transport
industry. This, they hope,
Table 1: World oil consumption
Region %age of Total world oil consumption
Asia Pacific 31,10%
North America 26,40%
Europe & Eurasia 23,50%
Middle East 8,70%
3
will help reduce fossil fuel consumption and promotion of cleaner
and greener technologies,
which shall help in combating climate change.
These policy changes coupled with increasing fuel prices bring new
challenges to the auto
manufacturing industry. A look at the figures of oil prices and
passenger vehicle demand in
Europe over the last decade is interesting reading. The oil prices
have consistently increased
from 2001 onwards, reaching their peak in July 2008 where the price
touched $132.70/bl. Since
then it has come down to around $70/bl, which is still considered
high. Looking at vehicle
demand during the same time period, we notice vehicle registrations
falling steadily beginning
from year 2000 till 2003, the same time oil prices are rising.
However, after the year 2004 there
is a steep rise in registration, which reaches its peak in 2007,
after which demand nosedives in
year 2008 and 2009. This was also the time when the financial and
credit crises began, and oil
prices reached their peak. According to the latest figures made
available by the European
Automobile Manufacturers Association (ACEA), total vehicle
production in 2009 was at its
lowest level since 1996.
Given this background, the aim of this paper will be to analyze any
linkage between the oil
prices and performance of auto manufacturing companies stock
returns. The returns will help
give an idea how well the companies have been performing in a high
oil price environment, and
whether oil price should be considered an important element for
European auto industry
managers as well as EU policy makers. The approach will be using
the three factor fama-french
model, where a fourth factor of oil will be included to study its
impact on the stock returns of
auto manufacturing companies.
Figure 1: Annual Oil Price Trend for Brent spot prices
Figure 2: Commercial vehicle registrations in EU
0
20
40
60
80
100
120
1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009
5
2. Literature Review
The real determinants or the linkage of oil price shock to
recessionary trends is still debated
among academics. This topic first came into focus after the 1973
oil crises. Hamilton’s (1983)
pioneering paper on Oil and the Macro economy set the tone, in
which he stated that all but
one of U.S recessions since World War II were followed by rises in
oil prices. This negative
relation between oil and aggregate economic activity is confirmed
in subsequent studies by Lee
et al. (1995), and Hooker (1996). However, since then some
refinements in the nature of this
relationship have taken place, like the nonlinearity feature where
the affect of an oil price
increase is bigger than an oil price decrease (Hamilton, 2003).
This result is also confirmed by
Lardic and Mignon (2006) for 12 European countries. In their study
they use an asymmetric
cointegration framework rather than the standard linear
cointegration model used by most
empirical studies for similar topics. They conclude for 12 European
countries that an increase in
oil price hinders aggregate economic activity more compared to the
positive benefits of an oil
price reduction. Secondly, the present day economy is growing more
resilient to oil shocks
compared too historically. Blanchard and Gali (2007) have analyzed
the macroeconomic effects
of oil shocks since 1970, in which they find that the effects of
oil price shocks have decreased
over time, and this can be attributed to increasing energy
efficiency in the economy, smaller
effects of oil on wages as well as output and employment and
improvements in monetary
policies.
As has been established that how high oil prices can affect the
macro economy; it is then
natural for this impact to be felt by the major industries in an
economy as well. Most industries
can be categorized into those that use oil as an input (example
chemical industry), or produced
an output (example petroleum refining), so the impact can be either
demand side or supply
side. Lee and Ni (2002) investigate the effects of oil price shocks
on supply and demand in
various industries. They conclude that for oil-intensive industries
like petrochemicals and
industrial chemicals, the impact of oil price shock is on the
supply side, and for other industries,
specially the automobile industry the impact is on the demand
side.
6
Subsequently over the last decade or so, the focus has shifted to
the oil prices and its effects on
the financial markets, most notably the stock markets. Various
academics and economists have
worked on analyzing the linkage between performances of equities
and oil price shocks. The
presence of oil shocks (both positive and negative) over the past
decade has made this topic
even more relevant, as before not much attention was paid to the
relationship between oil
prices and stock returns. Faff and Nandha (2008) conclude that out
of 35 industry indices
analyzed by them, oil price increases negatively impact equity
returns for all sectors except
mining, and oil and gas industries. A paper by Sharif et.al (2005)
analyzing the link between oil
prices and equity values of UK-listed oil and gas companies,
concluded that the relationship is
always positive and often highly significant. A rise in oil prices
or equity market will most likely
increase the return on the UK oil and gas index.
The above results indicating a positive relationship between high
oil prices and returns in oil
and gas stocks should come as no surprise. Understandably so, such
a price environment will
increase the cash flows of oil and gas firms and prove beneficial
for them. It is the impact of oil
prices on stock returns of other industry and market indices which
is a source of interest to
academics. Studying in more detail the effects of oil shocks on
stock market returns, Park and
Ratti (2008) analyze data from U.S.A, and thirteen other European
countries’ stock markets
from the period 1986 to 2005. Their results indicate a
statistically significant impact on real
stock returns by oil price shocks in the same month or within one
month. They concluded that
using real world oil prices rather than national level oil prices
yielded higher statistically
significant results. This implies that markets anticipate
significant and pervasive effects of oil
price shocks in most countries and markets that will have
implications for own firm
circumstances reflected in stock price movement. For most European
countries, volatility in oil
prices negatively affected the real stock returns. In a similar
research, Miller and Ratti (2009)
analyze the long-run relationship between world price of crude oil
and international stock
markets from 1971 to March 2008. Over the long run they find a
negative relationship between
stock indices and oil prices. However, this link appears less
likely after year 1999. According to
their analysis, the findings may suggest presence of stock market
and/or oil price bubbles since
the turn of the century.
7
To study the affect of oil price volatility on stock fluctuations
in an emerging market, Masih et
al. (2010) analyzed data on South Korea. The case of South Korea is
very relevant as it is entirely
dependent on imports for its energy requirements making it the
world’s fifth largest importer
of oil. They use a vector error correction (VEC) model to study the
effect and relationship
various economic variables like interest rates, economic activity,
real stock returns, and oil price
volatility will have on the stock market. Their results conclude
that oil price volatility had the
most pronounced affect on real stock returns, and this trend
increased over time.
This linkage between oil price shocks and stock returns can lead
some investors to predict the
direction of the stock market in case of an unusual move in the oil
prices. Driesprong et al.
(2008) indicate that changes in oil price can help predict stock
market returns worldwide. Stock
returns seem to decrease after a rise in oil price. However, this
reaction takes time to be
reflected in stock markets. According to the authors, this
observation is in line with the gradual
information diffusion hypothesis proposed by Hong and Stein (1999),
whereby investors react
at different points in time to changes in oil prices, or have
difficulty in assessing the impact of
these changes on value of stocks not related to the oil
sector.
The above papers discuss different aspects of the relationship
between oil prices and stock
market. They show how this impact is felt across various
industries. Papers discussing the direct
impact of oil prices on one of the largest consumer of oil; the
transport sector is almost non-
existent. Cameron and Schnusenberg (2008) are one of the first to
investigate a direct
relationship between oil prices and stock prices of automobile
manufacturers in U.S.A. They use
the three-factor Fama-French model, in which they add an oil price
factor measured by the
change in WTI crude oil prices in excess of the risk free rate, or
alternatively measured by
excess return on energy Exchange Traded Fund (ETF). Their results
show in general an inverse
relationship between oil prices and stocks of auto manufacturers.
This result becomes
statistically significant for manufacturers of SUV vehicles, and
using the energy ETF instead of
crude oil prices as the fourth factor. Secondly, the authors had
divided their time period into
pre and post Iraq invasion. Not much change in coefficient was
witnessed in these two periods.
The only significant change came when the index comprising of SUV
vehicles was used as the
8
dependent variable, where the post-Iraq invasion saw a significant
increase in coefficient as
well as higher statistical significance.
Fama and French have conducted extensive studies on the subject of
equity price returns. Their
studies aimed at improving the results explained by CAPM which
compares an individual’s risk
and return with the overall market return. The Fama-French paper
(1993) show that most of
the returns in a portfolio can be explained by cross section
returns on stocks using firm size
measured by market capitalization and book to market value factors.
Along with the market risk
premium they constitute the three factor model. The growth stocks
or small-cap stocks are
represented by SMB (Small minus Big) and HML (High minus Low)
factors. Using the stock
return data from 1963 to 1990, regressions were run. The results
showed small-cap stocks and
high book-to-market stocks having higher average returns, and these
factors explain
considerable amount of variation in portfolio returns. Their
results have been generally
accepted by academics and portfolio investment managers as well. As
for international
evidence, Fama-French have analyzed data from 13 countries and
concluded that value firms
generate higher returns than growth firms, but based on a two
factor model that includes a risk
factor for relative distress. For European dataset, Malin and
Veeraraghavan (2004) checked for
the robustness of the Fama and French multifactor model based on
evidence from France,
Germany, and the United Kingdom. They observe a small firm effect
in France and Germany and
a big firm effect in the United Kingdom. Secondly, they observe a
growth effect rather than a
value effect for these markets. Moerman (2005) applies the
Fama-French asset pricing model to
the Euro area to see the affects of integration. For this purpose
he uses the time period 1992-
2002. He concludes that a domestic three factor model outperforms
the euro area three factor
model. But, for countries with high number of listed stocks, the
relative performance of the
Euro area is increasing. This could be evidence of increasing
integration among equity markets
and decreasing investment barriers.
3. Motivation for research topic
The papers above serve as a motivating point for me to conduct
further research into this very
relevant and important topic. Oil prices have continued to remain
volatile, but have significantly
climbed down since then but remain in the USD 70s range. The equity
markets have been
showing mixed results, given the severe shocks they suffered in the
aftermath of credit crises
and general recessionary trends in the USA and leading developed
nations of the world.
However, during this time, emerging markets such as China, India,
and Brazil among others
have given investors a good return. Notwithstanding long term
structural issues in their
economies, these economies are expected to grow handsomely in the
coming years. These
growing economies have been instrumental in driving up the demand
for fossil fuels, thus
leading to higher oil prices.
Building on this report, I have chosen to analyze the relationship
between oil prices and stock
performance of European Auto manufacturers. The auto industry is
the largest employer in
Europe, as well as its highest export revenue earner, according to
the European Auto
Manufacturers Association (ACEA). They provide direct employment to
more than 2.3 million
people and indirectly support another 10 million jobs. Annually,
ACEA members annually invest
over €26 billion in R&D, or about 5% of turnover (ACEA
website). Therefore; the significance of
this industry in Europe cannot be underestimated. Over the past
years the industry has seen
declining sales in Europe as it struggles in a fiercely competitive
market, highly taxed and
regulated environment, exacerbated by the credit crises originating
from the U.S. These years
also saw higher fuel prices, with oil peaking at $148 in mid 2008.
The ACEA in its annual reports
state two major challenges; macroeconomic situation and regulatory
issues. In terms of general
macroeconomic situation given the fuel prices, the Secretary
General of ACEA had this to say in
the annual industry report (2005) “The taxation burden placed on
vehicles is also rising. High oil
prices have caused combined with increased excise duties to create
a sharp overall increase in
fuel costs. This, together with the increasing use of charges to
deter vehicle use, particularly in
cities, has added to the operating costs that users face and may
cause them to defer the
purchase of new vehicles”. The above statement indicates the
concern amongst the industry of
10
rising fuel prices along with economic and regulatory pressures
facing the industry. The demand
for vehicles has been negatively associated with these factors. As
we know now, the years after
2005 saw unprecedented rise in fuel prices. This means that
investors in the auto industry
should also have been vary of this factor. If sales of an auto
company are to suffer, it inevitably
affects the company’s revenues, thereby its profits as well.
Therefore, the purpose of this paper
would be to study the impact on the stock performance of these auto
companies, and whether
the high oil price environment had any detrimental impact on
investor returns or not.
The auto companies for which their stock performance is to be
analyzed are chosen from the
members of the European Automobile Manufacturer’s Association
(ACEA). The focus is on
manufacturers of passenger vehicles, as this vehicle segment
attracts buyers from a range of
income brackets, thus covering a broad range of customers. In other
words, the consumers of
this vehicle segment are more likely to be price elastic. I focus
on the top nine companies which
have a combined market share of 86% based on sales from the years
2006-2009. The one
exception is General Motors which was excluded from the selection
by virtue of it declaring
bankruptcy and delisting from stock markets. Among other auto
companies, Ford and Toyota
are the only non-European origin companies. The largest company by
market share is
Volkswagen, followed by the PSA (Peugeot Citroen). At the bottom of
the table are Daimler and
BMW, the two German luxury car manufacturers. Therefore, I have
further analyzed the impact
of oil on luxury car manufacturers, comparing them with the other
manufacturers of mid-level
passenger vehicles. Although, other companies have luxury brands of
their own, but the
percentage of sales contribution by the particular brand is minimal
to qualify the company as a
luxury car manufacturer.
Rank Company 2006 2007 2008 2009 Average
1 Volkswagen 20,10% 18,30% 19,00% 19,90% 19,33%
2 PSA 12,90% 13,20% 13,10% 13,60% 13,20%
3 Ford 10,50% 10,70% 9,90% 10,30% 10,35%
4 GM 10,20% 9,60% 9,00% 8,40% 9,30%
5 Renault 9,20% 9,40% 9,50% 9,80% 9,48%
6 Fiat 7,40% 8,80% 9,00% 9,30% 8,63%
7 Toyota 5,80% 5,40% 4,90% 4,70% 5,20%
8 Daimler 5,90% 5,90% 6,20% 5,40% 5,85%
9 BMW 5,00% 4,60% 4,80% 4,40% 4,70%
Total Market share 87,00% 85,90% 85,40% 85,80% 86,03%
Source: ACEA
4. Hypothesis development
The main hypothesis will be developed by applying the concept of
negative relationship
between oil price and stock performance of auto companies. Lee and
Ni (2000) showed that for
U.S manufacturers, increase in oil prices led to a decrease in auto
sales. U.S manufacturers
were more sensitive compared to their foreign counterparts, mainly
the Japanese origin auto
manufacturers. Most of the literature based on related topics
showed oil prices having an
adverse impact on stock market returns. Therefore, extending these
results to my research, I
hypothesize that oil prices will have a negative relationship with
an index of European auto
manufacturers.
= No relationship between oil prices and European auto manufacturer
stock prices.
= Negative relationship between oil prices and European auto
manufacturer stock
prices.
Secondly, I have divided the auto companies into luxury and
non-luxury manufacturers. Luxury
companies produce expensive vehicles which are also prone to higher
fuel consumption than
non-luxury vehicles. For this reason, I want to analyze whether the
luxury car consumers were
sensitive to oil prices or not. Given the high oil price
environment in my chosen time period, I
predict a more pronounced negative effect of oil prices on luxury
vehicle manufacturer stock
prices.
= There is the same level of relationship between oil prices and
European luxury car
manufacturers as the level of relationship between oil prices and
non-luxury auto
manufacturers.
= There is a more negative relationship between the oil prices and
stock prices of
European luxury auto manufacturers.
Third, I have divided this time period into three parts. Based on
their paper, Cameron and
Schnusenberg (2008) observe considerable variation in oil prices
going from pre to post-Iraq
invasion period. This makes the relationship between oil prices and
stock price of auto
13
manufacturers more negative. While this observation is true, it is
also pertinent to observe that
there was a period of commodity boom right after the collapse of
the U.S housing market, or
the onset of the credit/banking crises. In the year 2008, which saw
the collapse of renowned
Investment banks like Bear Stearns, Lehman brothers, oil peaked at
$147 per barrel. In this
regard, I predict a more negative relationship in the period
2007-2009 (herein referred to as the
Credit Crises years or CC-years), compared to the other two periods
of pre-Iraq and post-Iraq
invasion. I choose to start credit crises years from 2007 since
this was the time when signs of
trouble started emerging. The mortgage markets in the U.S started
declining with consumers
defaulting on their payments, leading some financial institutions,
especially those dealing with
sub-prime mortgages, to cut staff and filing for bankruptcy (case
of New Century Financial
which filed for ch.11 bankruptcy protection in April 2007).
Therefore, I want to see whether
European equity market investors in general and auto company
investors in particular picked
up those signs or not.
= The level of negative relationship between oil prices and stock
prices of auto
manufacturers to be same in all three periods
= There will be a more negative relationship between oil prices and
stock prices of
European auto manufacturers in the credit crises years, compared
with pre-Iraq and post-Iraq
invasion phases.
5. Data and Methodology
This paper aims to study the impact of oil prices on an index of
European auto manufacturers’
stock index by using the three factor fama-french model. The
Fama-French factors used by
most of the previous research were downloaded from the Kenneth
M.French’s website, which
uses U.S data. But for the purpose of this paper, it is more
appropriate I use those factors which
are representing the European markets as compared to the U.S
market. When talking about the
Euro zone, there is general consensus that the German market, more
specifically the Frankfurt
Stock exchange by virtue of its being the largest and most liquid
exchange, can be an
appropriate representative market. Secondly, the yields on German
10-year bonds are also
sometimes used as the risk free rate for the Euro zone, which
further justifies the status of
German economy being representative one for Europe. Similarly, I
have also selected the
Frankfurt Stock Exchange as the market to be used to calculate the
required Fama-French
factors.
The selection of companies from the Frankfurt Stock Exchange was
done by using factors
mentioned on their website. The criteria I used were all those
companies using the Prime
standard of transparency, continuous trading of ordinary shares,
and covering all market
segments. The total companies selected were 348. The prime
standard, are those companies
which adhere to the highest international transparency standards
such as adherence to
international accounting standards (IFRS/IAS or US-GAAP), and
operate under the EU defined
regulated market criteria. Once, the companies were shortlisted,
for each of them the
following data variables were downloaded from DataStream.
Closing Prices: for each of the companies, the daily closing prices
were downloaded for the
period 31-12-1999 to 31-12-2009. The daily closing prices will be
used to calculate daily log
returns to be used in Fama-French factors calculation.
Market Value: The number of outstanding shares multiplied by market
share price. This data
was downloaded on annual basis for the time period 31-12-1999 to
31-12-2009. For Book Value
I used DataStream data type Equity Capital and Reserves
(305).
15
Calculation of the factors was done using the same method
Fama-French used in their paper
and as mentioned on their website. It involved making six
portfolios based on market
capitalization and book-to-market ratios.
First, year wise portfolios of companies based on their market
capitalization were made. Then
the first sort was applied using market capitalization as the
criteria. The first 50% companies
were denoted ‘Small’ size companies and the next 50% companies
‘Big’ size. Then, B/M ratios
were calculated by dividing Equity Capital and Reserves values with
Market Values. A second
sort was made on this portfolio using the B/M ratios as the
criteria, on the basis of 30-40-30
percentiles. The first 30% of the companies were denoted Low
stocks, the next 40% Medium,
and the final 30% High stocks. This step was repeated for each
year, and only those companies
were used which had data for each of the ten years in the time
period under scrutiny. These
steps created the six portfolios divided into growth stocks;
Small-Low (SL), Small-Medium (SM),
and Small-High (SH), and value stocks; Big-Low (BL), Big-Medium
(BM), and, Big-High (BH).
In the final stage, the daily log returns of those companies that
constituted the value and
growth portfolios of each year were calculated. For example, to
make the Value and Growth
portfolios for year 2001, the annual data available as of 31
December 2000 was used, on which
the two sorts were applied via the method described above. Then the
returns were calculated
for only those companies forming the portfolio. The portfolio
components kept changing every
year, depending on which equity fulfilled the criteria. Therefore,
year-wise portfolios were
made. Once, I had the returns, the Fama-French factors of Small
minus Big (SMB) and High
minus Low (HML) were created by applying the following
formulas:
SMB = 1/3*(Small-Low + Small-Medium + Small-High) – 1/3*(Big-Low +
Big-Medium + Big-High)
HML = ½*(Small-High + Big-High) – ½*(Small-Low + Big-Low)
These steps were repeated for each year, until I had daily SMB and
HML factors for the time
period 31-12-1999 to 31-12-2009.
Once the Fama-Fench factors were calculated, the following
regression equation estimated by
Ordinary Least Squares (OLS) method was used:
The dependent variable is an index comprising of auto companies
based in Europe. It will be
regressed on the standard variables contained in a Fama-French
model, along with the fourth
oil factor. The details of calculating and assembling data
regarding the variables of this equation
are explained as follows:
= Return on the auto index. The auto index is a value-weighted
index. Daily Market Value
figures for each of the eight auto companies were downloaded for
the proposed time period via
DataStream. Then, on daily basis, the Market Values of the eight
component stocks were
summed and divided by a divisor. I choose such a divisor that the
index value on the first date
of my analysis (01-01-2000) becomes 100. This date is the base
value, over which the market
returns for the subsequent days are calculated. Then I take log
returns and subtract the daily
risk free rate to get the excess returns on the auto index.
= I have taken the 1-month EURIBOR rate to be the risk free rate. I
choose to use a short-
term risk free rate due to the daily data frequency I was using. In
this regard, the 1-month
EURIBOR rate is an appropriate risk free rate, as it represents the
short-term borrowing rate
between the European financial institutions. The data was
downloaded using DataStream.
= Is the return on the market index. I took the daily closing index
prices of CDAX,
which is the composite DAX index at the Frankfurt stock exchange.
According to the Deutsche
Bourse website, the CDAX index reflects the performance of the
German equity market as a
whole, and is well suited for analytic purposes. Therefore, this
index is appropriate for my
analyses. I then take the log returns of the index prices, and
subtract the risk free rate to get
the required excess market return. The index prices were downloaded
using DataStream.
17
= The last is the oil factor, based on the daily closing prices of
Dated Brent UK crude oil
downloaded from DataStream. Since the prices were in U.S dollars,
they were converted to
Euro by using the daily Euro-US exchange rate. In oil trading,
various types of oil pricing
benchmarks have been created. These benchmarks are based on the
quality of oil which is
determined by factors like density and sulphur content of the crude
oil. The lower the sulphur
content, the more ‘sweet’ the oil is, which is used to produce
gasoline and is in high demand,
particularly in industrialized nations. The WTI Texas Crude is
considered to be of the best
quality among the various crude oil benchmarks and is priced at a
premium. The Brent Crude
comes in second based on its characteristics, followed by Dubai
crude and OPEX reference
basket. According to the Intercontinental Exchange (ICE), the
leading trading exchange for oil
futures, Dated Brent is the basis of pricing approximately 65% of
the world’s trade. Secondly,
the Dated-Brent UK is also used as the pricing benchmark for crude
oil in Europe. On this basis,
Dated Brent should serve as an appropriate benchmark for my
analysis. After calculating the log
returns of daily prices, the risk free rate was subtracted to
arrive at the excess returns from oil.
5.2 Descriptive statistics
This section summarizes some of the important statistics generated
by implementing the data
and methodology method discussed in the above sections. The focus
will be on the three
variables that are important for analyses in this paper; the auto
index, market index, and crude
oil returns. Table 3 shows the results for auto index. This index
comprises all eight auto
companies being analyzed for the combined time period of 1999-2009.
I have tabulated a
sample of descriptive statistics for the three variables. The
tables show year-wise values for
mean (annualized returns in percentage) and standard deviation
(volatility) for the three
factors. From 1999-2003 the pre-Iraq years, the index returns show
a mixed trend with
relatively high volatility. Moving towards the post-Iraq years, one
can see stable performance in
the years 2004, 2005, 2006 and to some extent 2007 also, as
standard deviation drops, and
mean values turn positive. In the final phase of credit crises
years, a lot of volatility can be
18
witnessed, with the standard deviation jumping from 1.65 to 2.93.
The returns also get negative
in 2007-2008, but only slightly returning to positive 0.04% in
2009.
Table 3: Descriptive statistics - Auto index
Year mean std.dev
1999 0.0969 1.534
2000 -0.1162 1.489
2001 -0.0719 1.683
2002 -0.1247 1.858
2003 0.0573 1.571
2004 -0.0068 1.160
2005 0.0897 0.909
2006 0.0534 1.098
2007 -0.0248 1.166
2008 -0.2708 2.937
2009 0.0414 1.922
Moving towards the market index return statistics (Table 4), almost
the same trend can be
observed. Initial years of pre-Iraq phase show negative returns,
and volatility remains almost
constant around 1.4, with a spike in year 2002 to 2.18. The markets
return to positive territory
in the post-Iraq phase, with low volatility levels. The statistics
for the credit crises years reflect
the turmoil at the time, with standard deviation suddenly jumping
to 2.18 in 2008, the year
when crises was at its peak.
Table 4: Descriptive statistics - Market Index
Market mean std.dev
1999 0.0889 1.1449
2000 -0.0619 1.4154
2001 -0.1001 1.4898
2002 -0.2512 2.1832
2003 0.1027 1.7597
2004 0.015 0.9217
2005 0.0775 0.708
2006 0.0627 0.9346
2007 0.0458 0.9717
2008 -0.277 2.1826
2009 0.0672 1.705
19
The statistics for crude oil returns (table 5) present a different
trend. It indicates more volatility
in returns, with consistently high standard deviation values of
above 2. The mean values also
keep fluctuating between negative and positive. The variations
witnessed appear to diverge
from the general trend in the market return index. In the post-Iraq
period, where the markets
were performing steadily, the oil markets are showing more
volatility.
Table 5: Descriptive statistics - Crude oil
Oil mean std.dev
1999 0.3924 2.4617
2000 -0.0394 2.927
2001 -0.0419 2.8303
2002 0.0962 2.1936
2003 -0.0909 2.0778
2004 0.0717 2.2543
2005 0.1915 2.0568
2006 -0.0499 1.8263
2007 0.1216 1.6541
2008 -0.3484 2.7431
2009 0.2666 2.8128
Panel 1 shows the descriptive statistics for the other auto indices
created for the purpose of
closer analysis of the three hypotheses. These auto indices are
labeled luxury, non-luxury, Euro-
origin and excluding-Volkswagen. The significance of theses results
are explained in more detail
in the regression and analysis sections.
20
Year mean std.dev mean std.dev mean std.dev mean std.dev
1999 -0.0266 1.7201 0.1498 1.77 -0.0132 1.5329 0.1081 1.5561
2000 -0.1532 1.5625 -0.1044 1.8237 -0.0969 1.237 -0.1219
1.5374
2001 0.0163 2.2682 -0.1031 1.7494 -0.0188 1.9472 -0.0739
1.7014
2002 -0.1799 2.4882 -0.1049 1.8742 -0.1463 2.2519 -0.1221
1.8592
2003 0.0838 2.0519 0.0483 1.6202 0.0779 1.8822 0.0551 1.5667
2004 -0.037 1.2097 0.00164 1.2766 -0.025 1.097 -0.00198
1.1822
2005 0.0562 1.0754 0.1001 0.9717 0.0669 0.9611 0.089 0.9217
2006 0.0311 1.2758 0.0598 1.1698 0.0717 1.1689 0.045 1.0986
2007 0.0783 1.5164 -0.0549 1.1707 0.0879 1.3759 -0.0486
1.2006
2008 -0.3536 3.2765 -0.1655 3.2228 -0.1936 4.2915 -0.3219
2.7855
2009 0.1569 2.8715 0.00916 1.9061 -0.0573 2.6135 0.1701 1.922
Average -0.02979 1.9378 -0.01491 1.6868 -0.02243 1.8508 -0.02028
1.5755
The values indicate similar trend witnessed in the combined auto
index including all eight auto
manufacturing companies. The pre-Iraq years show negative returns,
but returns become
positive in the post-Iraq phase before the credit crises years. The
last column shows the
average values for all the variables. It indicates the luxury index
to be showing the lowest
returns as well as highest volatility. The steadiest index appears
to be the euro-origin index in
terms of volatility.
21
6. Regression Results: This section will discuss the regression
results by using the equation mentioned in section 5.
Table 6 shows the results when the auto index comprising of all
eight companies is regressed
using the fama-french factors and the fourth oil factor. This
regression is for the entire time
period from January 1999 till December 2009. The results provide
for interesting reading and
provide a new perspective on this relationship between oil prices
and stock performance.
Surprisingly, it shows a positive relationship between the auto
index and dated-Brent UK crude
oil, but this is not significant. The market coefficient is
positive and highly significant. These
results in general imply that oil prices are not having any adverse
effect on the stock
performance of auto companies, but this cannot be termed
statistically significant. However,
the auto companies are highly and positively correlated to the
market index. This is not
surprising, considering the fact that auto manufacturers are
affected by the same macro-
economic factors that investors are sensitive too. Both the
Fama-French factors of SMB and
HML show positive relationship, but only HML being statistically
significant. This indicates that
the top eight auto companies of Europe combine to form a value
portfolio. The other factor to
note here is the adjusted r-squared value of 43% which is low
compared with the results from
the study on North American auto manufacturers.
Table 6: Combined auto index
C MKT SMB HML OIL adj R-sqrd
All years -0.00018 0.9349 0.1439 0.2307 0.0092 0.4386
t-statistic (-0.7538) (16.001) (1.6330) (6.7396) (0.8222)
Pre-Iraq -0.00028 0.7401 0.0361 0.1444 0.0236 0.3532
t-statistic (-0.6905) (17.7760) (0.5334) (3.9783) (1.3947)
Post-Iraq -0.00011 1.0013 0.0020 0.1322 0.0242 0.5248
t-statistic (-0.4398) (26.5237) (0.0336) (2.6070) (1.8767)
CC years -0.00016 1.2520 0.5234 0.2933 -0.0399 0.5116
t-statistic (-0.2860) (6.1664) (1.7295) (3.0078) (-1.4513)
22
The reasoning can be deducted after further breaking down the time
period into the three
phases of Pre-Iraq, Post-Iraq and Credit Crises years.
Table 6 also shows the regression results for the three phases the
time period is divided into.
The market coefficient is positive and statistically very
significant, according to expectations.
However, dated Brent crude oil coefficient is positive, but
statistically not significant. This is
something which negates the general perception of negative
relationship between oil prices
and stock performance of auto manufacturers. The results for
post-Iraq invasion period show
similar conclusions, positive coefficient for oil with statistical
significance increasing slightly, and
drastic increase of statistical significance for the coefficient
for market index. This shows, auto
companies’ stocks doing quite well in the post-Iraq period, with no
adverse effect from rising oil
prices. This is in contradiction with the results for North
American manufacturers. An
interesting observation is the change in values of adjusted
r-squared, which increases
considerably from 35% in pre-Iraq phase to 52% in post-Iraq
phase.
Coming towards the final phase of credit crises years from
2007-2009, the results show a
negative coefficient for crude oil, although statistically not
significant. Secondly, the market
coefficient increases, and remains statistically significant,
despite its significance level dropping
considerably from post-Iraq years. The adjusted r-squared in this
period drops slightly to 51%
and so does the statistical significance levels. These results do
give an indication of the turmoil
the markets and economies were facing at the time, where factors
other than rising commodity
prices were making investors nervous, and this is reflected in the
regression results.
In terms of the FF factors of SMB and HML, we notice the same
trends as witnessed when
regressing the equation for the entire time period. The evidence
points towards a value
portfolio rather than a growth portfolio. This means that generally
investors look at the stocks
of automobile manufacturers as value stocks.
23
6.1 The influence of Volkswagen:
Table 2 in third section of the paper showed the market shares of
each of the individual auto
companies. Volkswagen (VW) had by far the largest market share,
with 20%. Its nearest rival
was PSA with 13%. This makes Volkswagen a dominating player in the
European auto
manufacturing sector. In passenger vehicles category, it has
several brands under its umbrella,
competing with almost every auto brand sold in Europe. In terms of
my analysis, the role of VW
also needs to be scrutinized, especially after the events of
October 2008, when VW was target
of an acquisition by Porsche. The company Porsche announced on
October 26, 2008, an
intention to acquire complete control of VW. At that time, it
already possessed 42.6 percent of
Volkswagen's ordinary shares and stock options on another 31.5
percent. This news made
speculators and those hedge funds that had ‘shorted’ VW shares,
scramble to purchase VW
shares as they saw prices rising. The problem was that VW shares
were in limited supply in the
market as 74% was directly and indirectly in control of Porsche,
20% equity stake in the hands
of the State of Lower Saxony, so this left only 6% free float
shares in the market. Speculators
and Hedge funds were willing to purchase the share at any price,
due to which on 28-October
VW shares drove up to euro 1000 and above, making it briefly the
world’s largest company. On
the next trading day, on news that Porsche will be supplying the
market with VW shares after
cancelling some of its options, the price halved, but was still
double its price before the
announcement by Porsche was made on 26 October, 2008. This
distorted the German equity
markets on the day, and the exchange operator Deutsche Bourse
responded by lowering the
weighting of VW share to 10% from the artificially high point of
27%.
This fact created a distortion for my auto index, which showed a
return of 39% on the day and
for this reason the dataset for this date (28-October-2008) has
been excluded from my analysis
in this paper. Secondly, this activity almost doubled VW shares
briefly from October 27
onwards. For this reason, the year 2008 shows the maximum return on
the auto index as well
as high volatility. To check the degree of influence of VW on my
analysis, and whether there is
any significant distortion, I excluded VW from the auto index, and
ran a regression for the
entire time period (table 7). The adjusted r-squared value goes up
slightly to 45% and in terms
24
of market and oil coefficients, the results show a positive but
statistically insignificant
relationship between oil prices and returns on auto index for all
three phases. The increase in
adjusted r-square values is maximum in the Credit Crises years,
which saw wild fluctuations in
stock prices of VW. These results do indicate the kind of
influence, Volkswagen share can have
in a study conducted on the European automobile market. This fact
can have major implications
for investors also, as one company is seen to single handedly
affect the performance of a
portfolio.
C MKT SMB HML OIL adj R-sqrd
All years -0.00019 0.8807 -0.0188 0.2053 0.023171 0.4562
t-statistic (-0.8295) (22.1751) (-0.3236) (6.4583) (1.9550)
Pre-Iraq -0.00027 0.7156 0.0487 0.1319 0.0219 0.3221
t-statistic (-0.6391) (16.741) (0.7003) (3.5150) (1.2567)
Post-Iraq -0.00011 0.9855 0.0086 0.1302 0.0247 0.4991
t-statistic (-0.4245) (25.0375) (0.13824) (2.4781) (1.8620)
CC-years -0.00021 1.0478 -0.0052 0.1354 0.0083 0.5917
t-statistic (-0.4467) (9.3203) (-0.0316) (1.3567) (0.3119)
6.2 Luxury and non-luxury Auto indices
Regression results for luxury (Table 8) and non-luxury (Table 9)
auto indices provide an
interesting insight into the performance of the European auto
manufacturers. For both the auto
indices, the oil coefficient is positive, although both are
statistically insignificant. The market
coefficient is again positive for both the auto indexes, with the
luxury auto index showing
higher significance levels. Breakdown into the three periods show
both indices having positive
coefficients in pre-Iraq and post-Iraq phases, with negative
coefficient in credit crises years. This
pattern is similar to the combined auto index including all eight
companies. However, the major
difference can be noted in the adjusted r-squared values, where the
non-luxury auto index
show 28%, compared with the luxury auto index value of 63%.
Secondly, the luxury-auto index
is very much a value oriented portfolio, with high statistical
significance levels of HML factor,
25
compared with the negative SMB coefficient in all the periods. This
is understandable, since
both BMW and Daimler are established groups representing some of
the larger market
capitalization companies listed on the Frankfurt Stock Exchange.
Also, for the luxury auto index,
high adjusted r-squared values are observed throughout the three
phases, with highest in credit
crises years of 72%. This result is different from the trend
witnessed in the other regressions,
and it shows that for the luxury companies at least their
performance in the credit crises years
can be explained by the rising oil prices, although this cannot be
said conclusively due to the
low statistical significance.
C MKT SMB HML OIL adj R-sqrd
All years -0.00031 1.1969 -0.3034 0.3347 0.00075 0.6340
t-statistic (-1.3191) (31.840) (-5.0085) (9.4072) (0.0654)
Pre-Iraq -0.00042 1.1257 -0.2122 0.3162 0.0027 0.5657
t-statistic (-1.00069) (27.0167) (-2.8807) (7.7246) (0.1667)
Post-Iraq -0.00044 1.1556 -0.1781 0.0887 0.00462 0.5880
t-statistic (-1.5866) (29.354) (-2.7516) (1.6669) (0.3536)
CC-years 0.00014 1.3172 -0.3812 0.3673 -0.0330 0.7205
t-statistic (0.2639) (11.755) (-2.2191) (3.5326) (-1.2344)
The point to note here is the negative coefficient in credit crises
years. Since, the portfolio
excludes Volkswagen; apparently the luxury auto manufacturers did
feel the negative
consequences of oil price rises. The statistical significance also
goes up, as well as adjusted r-
squared, which is the highest for all regression results. There is
further evidence of this point
when reviewing the descriptive statistics. This point will be
further elaborated in the next
section.
26
C MKT SMB HML OIL adj R-sqrd
All years -0.00013 0.8405 0.2388 0.1995 0.0147 0.2834
t-statistic (-0.4491) (11.7683) (2.2261) (4.9008) (1.0900)
pre-Iraq -0.00021 0.5952 0.1161 0.0816 0.0302 0.1858
t-statistic (-0.4309) (11.9415) (1.4217) (1.8808) (1.4584)
post-Iraq -6.79E-06 0.9471 0.0643 0.1474 0.0313 0.3943
t-statistic (-0.0218) (20.0733) (0.8765) (2.3581) (1.9684)
CC-years -0.00025 1.2091 0.6779 0.2597 -0.0373 0.3713
t-statistic (-0.3670) (4.8429) (1.8251) (2.2234) (-1.1145)
Both these results indicate that apparently oil prices were not
having any significant
relationship or impact on their stock performance. Negative
coefficient is only witnessed in the
credit crises years, which saw an unprecedented rise in commodity
prices and large fluctuations
in equity market returns. Secondly, for the non-luxury auto indices
one does witness low
adjusted r-squared values, especially in the pre-Iraq phase. This
could be due to the dominant
affect of Volkswagen.
To have a further understanding of these results, I excluded Ford
and Toyota companies from
the auto index and regress the equation while retaining the other
variables. This created a
European origin auto index. The results for the first time show a
negative oil coefficient for the
combined time period, although statistically insignificant. The
adjusted r-squared values are
61% for pre-Iraq and 62% for post-Iraq. Since, the Fama-French
factors were calculated using
European data sets, such levels of adjusted r-squared should be
expected. However, the
surprising observation is in the credit crises years, when the
adjusted r-squared value drops to
36%, which is against the trend witnessed in the previous
regressions, but could be due to the
share price distortions introduced by Volkswagen in the year 2008.
Finally, the SMB and HML
coefficients also support the fact that stock of auto manufacturers
form value oriented
portfolios.
27
C MKT SMB HML OIL adj R-sqrd
All years -0.00022 1.1775 0.1271 0.3313 -0.0104 0.4598
t-statistic (-0.7593) (11.7172) (0.8676) (7.0840) (-0.7626)
pre-Iraq -0.00033 1.0508 -0.1376 0.2982 0.00907 0.6149
t-statistic (-0.9411) (28.8153) (-2.3254) (8.7444) (0.6402)
post-Iraq -0.00023 1.1140 -0.0718 0.0968 0.0163 0.6287
t-statistic (-0.9716) (33.8966) (-1.3535) (2.1765) (1.3998)
CC-years -2.12E-05 1.5516 0.7598 0.5169 -0.0774 0.3679
t-statistic (-0.0228) (4.1160) (1.3731) (3.3830) (-1.8400)
Table 10 also indicates how the stocks of European-origin auto
manufacturers have highly
positive correlations with market returns. Their relationship with
oil prices is a weak one, with
no evidence of negative effects of oil price rises in the pre-Iraq
and post-Iraq phases. It only
turns negative in the credit crises years, but the low adjusted
r-squared levels indicate there are
other factors and variables which can better explain the results.
As explained above, one of the
factors could be the influence of Volkswagen has on the portfolio,
specially the takeover related
activity that occurred in end-2008.
28
7. Analyses
This section discusses the possible reasons for the regression
results described above. Going to
the first hypotheses wherein a negative relationship between oil
prices and stock performance
of European auto manufacturers was expected; the results for the
combined period do not
show a negative relationship between the two variables. However, on
closer analysis while
breaking down the time period into three phases a weak link between
the two variables is
established. The negative coefficient is only observed in the
credit crises years, which means
that the main reason for this nature is the economic environment
which prevailed at the time
and it will be unjustified to pin the negative returns for auto
investors solely due to rising oil
prices.
I further analyze this relationship between different time periods
by adding a dummy variable
for the pre-Iraq and post-Iraq phases (Table 11). The results show
negligible changes in the
values of the coefficient for the pre-Iraq and post-Iraq phases,
with low statistical significance.
This confirms the findings that oil prices are not having
significant affects on the performance
of auto manufacturing companies.
C MKT SMB HML OIL PRE POST adj R-sqrd
All years -0.00027 0.9345 0.1434 0.2306 0.00921 8.29E-05 0.00018
0.4382
t-statistic (-0.4897) (15.8359) (1.61775) (6.6967) (0.8224)
(0.1242) (0.2830)
The second aspect of this analysis was to test whether oil factor
is adding value to the asset
pricing model. In the previous part, the regression results did
show high adjusted r-squared
values for luxury auto index, and European origin index. To check
whether the oil factor has any
explanatory power, I run the regressions without the oil factor
using the normal three factors of
market, SMB and HML. The comparison reveals only a 0.07% increase
in incremental r-squared
values. This again shows the lack of explanatory power by the
fourth oil factor.
29
C MKT SMB HML R-squared adj R-sqrd
All years -0.00017 0.9373 0.1444 0.2313 0.4393 0.4386 t-statistic
(-0.7352) (38.4040) (3.5487) (8.6340)
pre-Iraq -0.00025 0.7449 0.0397 0.1444 0.3542 0.3524
t-statistic -0.6323 (17.9528) 0.5849 (3.9615)
post-Iraq -9.95E-05 0.9990 0.0075 0.1274 0.5249 0.5235
t-statistic (-0.3838) (28.9712) (0.1257) (2.5732)
CC-years -0.00017 1.2320 0.5209 0.2785 0.5122 0.5103
t-statistic (-0.3038) (6.1841) (1.7194) (2.8851)
While reviewing the financial statements of some of the auto
companies and annual auto
industry reports issued by ACEA, one notices decline in auto sales
in Euro region. At the same
time, the stock performance of these companies has shown stable
performance especially in
the post-Iraq phases except for the credit crises years. The
regressions and descriptive statistics
also confirm such pattern of behavior.
To have a better understanding of the reasons underlying the nature
of these relationships, I
will analyze the descriptive statistics discussed in section 4
above via a graphical representation
of these statistics. The tables show year-wise values for mean
(annualized returns in
percentage) and standard deviation (volatility) for the three
factors.
30
Figure 3: Mean values
Figure 4: Standard Deviation
As can be observed in figure 3 and figure 4, the lines for auto and
market index almost mimic
each other. This could explain the positive coefficient between the
two variables and high
statistical significance seen in the regression results. Auto
companies are well integrated within
the European economic scenario, and they are affected by the same
macro-economic factors
that investors take into account. Therefore, it is according to
expectations for the auto index to
reflect the general performance of the equity markets, and the
regression results prove this
point. Secondly, according to this analysis, the post-Iraq phase
can be seen as a stable
environment for equities, as they gave positive returns with low
volatility. This shows that the
oil price variations following the invasion had no adverse affect
on the equity markets in
general, and the automobile manufacturers in particular. The graphs
also indicate the affects of
-0,5
-0,4
-0,3
-0,2
-0,1
0
0,1
0,2
0,3
0,4
0,5
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Auto
mkt
oil
0
0,5
1
1,5
2
2,5
3
3,5
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
auto
mkt
oil
31
credit crises in year 2008, with returns turning negative, and
volatility rising. For auto index,
however, the events relating to the failed takeover bid of
Volkswagen and the share prices
doubling overnight, has influenced the performance for the auto
index for year 2008. The line
for oil returns indicates the fluctuating nature of crude oil
prices in the last decade. For most of
the period the standard deviation figures remain high, and returns
keep fluctuating. This
behavior pattern differs from the markets and auto stocks. This
could explain the low statistical
significance of oil coefficients, as well as the lack of
explanatory power of oil factor in the asset
pricing model.
The last hypothesis relates to the performance of luxury auto
index. The two companies, BMW
and Daimler seemed to have performed generally well for investors
in the time period. They
were not affected by the post-Iraq variations in oil prices. In
fact they appeared to perform
rather well in the time period, with a positive oil coefficient and
quite high statistical
significance of their market coefficient. The reason can be
attributed to their strategy of risk
diversification with increasing focus in emerging markets of China,
India, and Middle East. This
strategy has enabled them to successfully navigate the sea of
challenges auto manufacturers
faced around the world in the last decade. This observation can be
validated by the fact that
the major U.S manufacturers like GM and Chrysler had to file for
bankruptcy in the year 2009,
prompting the U.S government to bail them out with emergency
funding. This was not the case
for BMW and Daimler, who continued to perform comparatively better
than their trans-Atlantic
rivals, despite being more prone to negative developments taking
place in the credit crises
years. This is to be expected in a recession, since luxury vehicles
are expensively priced, and
their sales also decreased in North America, thus impacting their
financial performance. In their
latest annual report, BMW stated that by year 2012, they expect 50%
of their car sales outside
Europe. This increasing focus on emerging markets has helped these
European brands in
retaining their profitability, and generating cash to build fuel
efficient vehicles which comply
with the strict EU emission standards.
However, looking at the graphical representation of the descriptive
statistics, and the
comparison with the other auto indices, we notice the luxury-auto
index to be the most
32
volatile. You can see fluctuations in the returns, and standard
deviation values remaining high.
But the regression results indicated positive oil coefficients,
which mean factors other than oil
were influencing the luxury car manufacturers. The overall returns
of luxury-auto index were
influenced by the huge drop in returns in year 2008. This is in
line with the regression results for
the auto index, showing a significantly negative oil coefficient in
year 2008. This proves that
credit crises years were hard for the luxury manufacturers
compared.
Figure 5: Mean values other indices
Figure 6 plots the standard deviations of other auto indices. All
of them are following a similar
trend, with the luxury auto index showing higher values in both the
pre-Iraq and post-Iraq
phase. In the credit crises years, the major deviation is seen in
the Euro-origin auto index line,
not surprisingly as it contains the Volkswagen share, otherwise the
graph shows low volatility
levels. And, as soon as Volkswagen is excluded, the standard
deviation figure drops down for
year 2008.
-0,4
-0,3
-0,2
-0,1
0
0,1
0,2
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Luxury
non-Luxury
Euro-origin
ex-Volks
33
0
0,5
1
1,5
2
2,5
3
3,5
4
4,5
5
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
luxury
non-luxury
euro-origin
ex-volks
34
8. Conclusion
The purpose of this paper was to explore the nature of relationship
between crude oil prices
and stock performance of European automobile manufacturers by
adding a fourth oil factor to
the three-factor Fama-French model. This topic has gained credence
in Europe, as EU policy
makers tighten regulation relating to fossil fuel consumption, and
auto manufacturers face the
challenge of operating in a recessionary economy aggravated by the
high oil price environment.
The paper analyses data from 1999-2009 time period, which saw two
major events that
influenced oil prices; Iraq invasion (2003) and the Credit crises
(2008). The aim was to
investigate whether high oil prices had a detriment affect for auto
investor returns or not, and
if this affect was more negative in the years following the credit
crises. In addition the paper
also analyses the performance of luxury auto manufacturers in a
high oil price environment.
The results indicate that crude oil prices generally have no major
impact on the stock
performance of European auto manufacturers. But, for most of the
time period analyzed, crude
oil prices appeared to have negligible affect on stock performance.
It was only in the credit
crises years when the relationship turned negative, but apparently
this was caused by the
economic and financial turmoil prevalent at the time, rather than
extremely high oil prices.
Secondly, the analyses have brought to fore the influence
Volkswagen has on the European
auto industry, specially the events of October 2008. The stocks of
BMW and Daimler, the two
luxury car manufacturers, were not affected by rising oil prices, a
surprising conclusion given
the fate of their North American counterparts. Apparently they were
more successful in driving
growth and increasing sales in emerging markets, specially China,
which helped them survive
the negative fallout stemming from the credit and financial crises.
Finally, a fourth oil factor in
the asset pricing model of three factor fama-french model does not
seem to add much value,
but neither does it have any detrimental affect.
The results of this study apparently indicate that investors in
European auto manufacturing
industries remained unscathed by rising commodity prices in the
last decade. However, they
need to be vary of the affect big auto companies like Volkswagen
can have on their investment
35
portfolio. Secondly, for future investments, those companies should
be favored which are
successful in increasing international sales outside Europe, as
this has proved to be an effective
hedging strategy. Factoring oil in their asset pricing models can
be useful for those industries
which are more sensitive to oil price movements.
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0
50
100
150
200
250
300
350
400
450
500
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
VW
Daimler
BMW
Renault
Fiat
PSA
Ford
Toyota
38
-15,0%
-10,0%
-5,0%
0,0%
5,0%
10,0%
15,0%
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Graph 1: Returns on Auto index
-10,0%
-5,0%
0,0%
5,0%
10,0%
15,0%
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Graph 2: Returns on market
39
-15,0%
-10,0%
-5,0%
0,0%
5,0%
10,0%
15,0%
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Graph 3: Returns on oil
-15,0%
-10,0%
-5,0%
0,0%
5,0%
10,0%
15,0%
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Graph 4: Returns on Luxury auto index
40