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 .............................................................................................................................................. 1
2. Literature Review ...................................................................................................................................... 5
4. Hypothesis development ........................................................................................................................ 12
5.1 Methodology ..................................................................................................................................... 16
6.2 Luxury and non-luxury Auto indices ................................................................................................. 24
7. Analyses .................................................................................................................................................. 28
8. Conclusion ............................................................................................................................................... 34
9. References .............................................................................................................................................. 35
10. Appendix ............................................................................................................................................... 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.
9. References
Blanchard, O,J., Gali, Jordi., 2007. The Macroeconomic Effects of Oil Shocks: Why are the 2000s
So Different from the 1970s? NBER Working Paper 13368. Available at
www.nber.org/papers/w13368 [Accessed march 2010]
Cameron, K., and Schnusenberg, O., 2009. Oil prices, SUVs, and Iraq: An investigation of
automobile manufacturer oil price sensitivity. Energy Economics, 31(3), pp.375-381.
Driesprong, G., Jacobsen, B., Maat, B., 2007. Striking oil: Another puzzle. Journal of Financial
Economics, 89, pp.307–327.
El-Sharif, I., Brown, D., Burton, B., Nixon, B., Russell, A., 2005. Evidence on the nature and
extent of the relationship between oil prices and equity values in the UK. Energy Economics, 27,
pp.819–830.
Fama, Eugene F. and French, Kenneth R., (1993). Common Risk Factors in the Returns on Stocks
and Bonds. Journal of Financial Economics, 33 (1), pp.3–56.
Fama, Eugene F. and French, Kenneth R., Value Versus Growth: The International Evidence
(August 1997). Available at SSRN: http://ssrn.com/abstract=2358.
Faff, R., Brailsford, T., (1999). Oil price risk and the Australian stock market. Journal of Energy
Finance and Development, 4, pp.69–87.
Hamilton, James D. (1983), Oil and the Macroeconomy since World War II. Journal
of Political Economy, 91, pp. 228-248.
Hamilton, J.D., 2003. What is an oil shock? Journal of Econometrics, 113, pp.363–398.
Hong, H.G., Lim, T., Stein, J., 2000. Bad news travels slowly, size, analyst coverage and the
profitability of momentum strategies. The Journal of Finance, 55, pp.265–295
Hooker, M.A., 1996. What happened to the oil price–macroeconomy relationship. Journal of
Monetary Economics 38, pp.195–213.
IEA (International Energy Agency)Analysis of the impact of high oil prices on the global
economy. Available at: http://www.iea.org/papers/2004/high_oil_prices.pdf Accessed May,
2004.
Lardic, S ., Mignon, V .,2006. The impact of oil prices on GDP in European countries: An
empirical investigation based on asymmetric cointegration. Energy Policy 34(18), pp.3910-3915
Lee, Kiseok, Ni, Shawn, 2002. On the dynamic effects of oil price shocks: a study using industry
level data. Journal of Monetary Economics, 49, pp.823–852.
Masih, R., Peters, S., De Mello, L., 2010. Oil Price Volatility and Stock Price Fluctuations in an
Emerging Market: Evidence from South Korea, available at
http://www.iese.edu/en/Research/CentersandChairs/Centers/CEM/Home/HomeCEM.asp
[Accessed July 2010]
Malin, M., Veeraraghavan, M., 2004. On the Robustness of the Fama and French Multifactor
Model: Evidence from France, Germany, and the United Kingdom. International Journal of
Business and Economics. 3(2),pp.155-176
Moerman, G.A., 2005. How Domestic is the Fama and French Three-Factor Model? An
Application to the Euro Area. Paper number ERS-2005-035-F&A, available at
http://ideas.repec.org/s/dgr/eureri1.html [Accessed June 2010]
Miller, J. Isaac & Ratti, Ronald A., 2009. "Crude oil and stock markets: Stability, instability, and
bubbles," Energy Economics, 31(4), pp.559-568.
Miller, J.I., Ratti, R.A., 2009. Crude oil and stock markets: Stability, instability, and bubbles.
Energy Economics, 31(2009), pp.559-568
Park, J ., Ratti, R.A.,2008. Oil price shocks and stock markets in the U.S. and 13 European
countries. Energy Economics, 30, pp.2587–2608.
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