The Relationship between Crude oil prices and stock performan.pdf

7/28/2019 The Relationship between Crude oil prices and stock performan.pdf

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Master Thesis

The Relationship between Crude Oil Prices and Stock

Performance of European Automobile Manufacturers


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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-frenchmodel 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.


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Contents1. Introduction .............................................................................................................................................. 1

2. Literature Review ...................................................................................................................................... 5

3. Motivation for research topic ................................................................................................................... 9

4. Hypothesis development ........................................................................................................................ 12

5. Data and Methodology ........................................................................................................................... 14

5.1 Methodology ..................................................................................................................................... 16

5.2 Descriptive statistics ......................................................................................................................... 17

6. Regression Results: ................................................................................................................................. 21

6.1 The influence of Volkswagen: ........................................................................................................... 23

6.2 Luxury and non-luxury Auto indices ................................................................................................. 24

7. Analyses .................................................................................................................................................. 288. Conclusion ............................................................................................................................................... 34

9. References .............................................................................................................................................. 35

10. Appendix ............................................................................................................................................... 37


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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 Pacific2(figures in

table 1). Out of the top ten net importers of oil, five of the countries are from Europe3.

According to EU Commissions Green Paper on Security of Energy Supply, based on current

trends, by 2020, the EU will be importing 90% of its oil requirements.

1CIA World Fact book

2BP Statistical Review of World Energy 2010

3International Energy Agency (IAE) Key World Energy Statistics 2009

2BP Statistical Review of World Energy 2010

3International Energy Agency (IAE) Key World Energy Statistics 2009


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Such dependency can have serious consequences for EUs 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 EUs

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%

S. & Cent. America 6,60%

Africa 3,70%

Source: BP


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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.


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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


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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. Hamiltons (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 asymmetriccointegration 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, smallereffects 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.


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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.


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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 worlds 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


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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 individuals 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.


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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, thusleading 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


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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 companys 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 Manufacturers 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.


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Table 2: Yearly Market Shares

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


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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


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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.


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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.Frenchs 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 ofGerman 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 logreturns 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).


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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 = *(Small-High + Big-High) *(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.


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5.1 Methodology

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 Valuefigures 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.


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= 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 worlds 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


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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.dev1999 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 2.9372009 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 yearwhen 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


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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.


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Panel 1 Luxury auto index non-Luxury Euro-origin Ex-Volkswagen

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 1.922Average -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.


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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.


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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 worlds 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


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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.

Table 7: Auto index excluding Volkswagen

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,


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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.

Table 8: Luxury auto index


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.


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Table 9: Non-luxury Auto index


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.


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Table 10: Euro-origin auto index


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.


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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.

Table 11: Regression with dummy variable

C MKT SMB HML OIL PRE POST adj R-sq

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.


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Table 12: Regression excluding oil factor

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 representationof these statistics. The tables show year-wise values for mean (annualized returns in

percentage) and standard deviation (volatility) for the three factors.


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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

0,10,2

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


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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


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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 2009Luxury

non-Luxury

Euro-origin

ex-Volks


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Figure 6: Standard deviation other indices

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


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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 paperalso 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


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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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So Different from the 1970s? NBER Working Paper 13368. Available at

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Cameron, K., and Schnusenberg, O., 2009. Oil prices, SUVs, and Iraq: An investigation of

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Driesprong, G., Jacobsen, B., Maat, B., 2007. Striking oil: Another puzzle. Journal of Financial

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El-Sharif, I., Brown, D., Burton, B., Nixon, B., Russell, A., 2005. Evidence on the nature and

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Fama, Eugene F. and French, Kenneth R., (1993). Common Risk Factors in the Returns on Stocks

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Hamilton, James D. (1983), Oil and the Macroeconomy since World War II.Journal

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Hamilton, J.D., 2003. What is an oil shock?Journal of Econometrics, 113, pp.363398.

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10. Appendix

Monthly closing prices - 1999-2009

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


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-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


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-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


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