The impact of crude oil on stock markets, an automobile
industry investigation for 2004 up and until 2013
Amsterdam Business School
Name Ralph de Bruijne
Student number 10334367
Program Economics & Business Specialization Finance & Organization Number of ECTS 12
Supervisor Dr. Ilko Naaborg Completion Abstract
The aim of this thesis is to investigate the relationship between the oil price return and the return on stock of automobile manufacturers. The timeframe researched is from 2004 up and until 2013. A linear model is used to investigate this relationship between the weekly return on stock for the twenty biggest automobile manufacturers worldwide and the return on Brent oil price. The findings confirm previous literature and show a significant negative relationship between the oil price and the stock return of automobile manufacturers. However the level of significance differs depending on macro-‐economic growth.
Verklaring eigen werk
Hierbij verklaar ik, Ralph de Bruijne, dat ik deze scriptie zelf geschreven heb en dat ik de volledige verantwoordelijkheid op me neem voor de inhoud ervan. Ik bevestig dat de tekst en het werk dat in deze scriptie gepresenteerd wordt origineel is en dat ik geen gebruik heb gemaakt van andere bronnen dan die welke in de tekst en in de referenties worden genoemd.
De Faculteit Economie en Bedrijfskunde is alleen verantwoordelijk voor de begeleiding tot het inleveren van de scriptie, niet voor de inhoud.
Table of Contents
1. Introduction ... 3
2. Literature review ... 4
2.1. Oil in the macro economy………..4
2.2. Automobile industry………..7
3. Hypothesis, Methodology and Data ... 10
3.1. Hypothesis ... 10
3.2. Methodology ... 10
3.3. Data and descriptive statistics ... 12
4. Analysis ... 16
4.1. Empirical Results ... 16
4.2 Robustness check ... 19
5. Conclusion and discussion ... 21
References ... 24
Appendix ... 26
1. Introduction
In the years from the first week of January 2004 up and until the last week of 2013 the Brent oil price has been fluctuating heavily, with a maximum of $145.61 on July 11th 2008 and a minimum of $34.58 at the 26th of December 2008. Crude oil being the largest commodity market in the world, with a total world consumption of 70-‐80 million barrels a day (Driesprong et al, 2008, p. 309), fluctuations in oil price have a to a great extent impact on the world economy, and therefore on the stock returns of companies. Previous research finds that there is a significant negative relationship between the returns of oil price return and the return on stock value. However this relationship differs per sector. Therefore I want to do research on the effect of oil price return for the automobile industry. In most developed economies the main way of transportation is the car, consequently there were 806 million cars on the road in 2007. These automobiles consume 806 billion liter of gasoline on a yearly basis, which makes the automobile industry one of the world’s most important economic sectors. The automobile industry is affected by oil price fluctuation in two ways. One-‐way, the industry is affected due to production costs. In general higher oil price leads to higher production costs, which leads to lower profit margins, which result in lower to negative stock returns. While on the other side there is the indirect effect caused by more expensive gasoline. This increase could consumers make less use of the car then they would in the case gasoline prices are at a lower level. Arouri (2011) already tested this relationship for the period: January 1st 1998 to June 30th 2010 for the European automobile market. However only seven out of the twenty biggest automobile manufacturers are located in Europe. Therefore I want to expand my model to the global automobile market, in order to create a better view on the relationship between the oil price return and stock return for the automobile industry. This in combination with a distinct period of will add new relevant information to current literature. The period on which this thesis will be focusing is from the first week of January 2004, up and until the last week of December in 2013. The difference between this research and previous research is that this research will be solely focused on the automobile industry, where in previous research the focus was on multiple industries and the differences among their
relation to the oil price. This research will be relevant for automobile manufacturers, because this will give them an insight in the reaction of their stock price upon fluctuations of oil prices. If the effects are significant this is useful information for automobile manufacturers because they could act upon the fluctuations timely and use this in their advantage. Procedures, which can be used to protect themselves, are for instance the use of hedging instruments. In order to research this relationship a regression will be performed with the return on stock for automobile manufacturers as dependent variable, return on oil price as main explanatory variable and multiple other explanatory variables will be added. In the literature review the main theories in existing literature and their predictions on oil price in the macro economy and in the automobile industry will be discussed. Subsequently the hypotheses, methodology and a description of the data will be discussed in part three. In part four the main results and their economic meaning are interpreted, after which the robustness will be explained. This thesis will be concluded in part five with a description of the research and the results, limitations of this thesis and the implications of the findings.
2. Literature review
This thesis will be focusing on the relationship between the oil price and the stock return for automobile manufacturers. The research question can be formulated as: is there a relationship between the return on oil price and the return on stock for the automobile industry. The time frame of this research will be from the first week of 2004 up and until the last week of 2013. In the first part of this literature review general information about oil and their position in the macro-‐economic environment will be provided after which in the second part existing literature concerning the relationship between oil and stock return of automobile manufacturers will be discussed.
2.1. Oil in the Marco economy
According to Driesprong et al (2008) crude oil is the largest commodity market in the world, with a total world consumption of 70-‐80 million barrels a day (p. 309).
Driesprong et al (2008) state that due to stabilization of the oil price by a few large U.S. oil companies known as the Seven Sisters, the oil price did not use to fluctuate as much until 1973. As a result of the Yom Kippur War in 1973, control moved to OPEC, which led oil prices to behave like prices of other commodities (p. 307). OPEC stands for “Organization of the Petroleum Exporting Countries”, and is an intergovernmental organization with the goal of stabilizing the oil markets. OPEC’s thirteen members consist of six Middle Eastern, four African, two South American and one Southeast Asian oil producing countries. Since 2000 OPEC is used as a benchmark for crude oil prices. Prices of crude oil are based among others on variety, grade, delivery date and location. Because these factors differ among OPEC’s members, the OPEC benchmark for crude oil is calculated as a weighted average of prices for the oil produced by their different members. In part 4.2. The OPEC oil price is used to check for robustness.
Last decade, the oil price has been fluctuating heavily due to multiple causes. These fluctuations are shown in figure one which represents the movements in the Brent Oil Price. The Brent Oil Price is oil, which is retracted from the North Sea, is used as a benchmark for approximately two-‐third of the worlds traded crude oil. The Brent Crude Oil price will be used in this thesis as the major benchmark for the world crude oil price. In the period from the beginning of January 2004 up and until the last week of December 2013, the Brent Oil Price noted some extremes. The Brent Oil Price noted during this time frame a maximum of $145.61 at 11-‐07-‐2008. A few months later on 26-‐12-‐2008 a minimum was noted of $34.58, this means a decline of around 76.25% in little less than half a year. Even though this was the biggest fluctuation in this time frame, this was not the only one, which is shown in figure one. According to Sadorsky (1999) due to the relationship between oil price movements and inflation in the economy and the implications of inflation, oil price movements are an important and interesting topic to research (p. 468). Sadorsky’s results are shared by Hamilton (1983) who finds that a dramatic increase in oil price have resulted into seven of the eight post-‐war recessions in the United States (p. 245)
Park and Ratti (2008) find that major political events in the Middle East, which could lead to uncertainty about oil prices leads to extreme values for daily crude oil prices (p. 2588). This statement is confirmed by Hamilton (2003), who finds that exogenous eruptions have a major effect on the world oil supply. Hamilton (2003) comes up with five examples namely: the Suez-‐Crisis, Arab-‐Israel war, the Iranian revolution, the Iran-‐Iraq war and the Persian Gulf War (p. 390). The Suez crisis took place at November 1956 and led to a 10.1% drop in world production of oil. The Arab-‐Israel war during 1973 led to a decrease in world oil production of 7.8%. In 1978 the Iranian revolution causes an 8.9% drop in oil production. In October 1980 the Iran-‐ Iraq war entailed a decrease of 7.2%. And the Persian Gulf War, which took place during the 1990’s, engendered an 8.8% drop in world production (Hamilton, 2003, p. 390). According to Austvik (1992) high oil prices could be initiated by the destruction of production facilities, caused by a new a new war in the Gulf or other places in the world, which would lead to a decrease in supply (p. 1104). These findings imply that unrest in oil producing countries have a major effect in oil supply and thus in price. During the examined time frame the Arab Spring started, namely on 19th December
2010. This event could have contributed to the rise in oil price.
Sadorsky (1999) finds that the oil price movements can influence US economic variables. However, the inverse relation has limited impact (p. 457). Sadorsky’s (1999) research finds a significant negative relationship between oil price and stock returns. Oil price changes affect the earnings of companies for which oil is a cost of production. Consequently, an increase in oil prices will lead to a decline in
earnings. In efficient stock markets the consequence of this decline will lead to a decline in stock prices (p. 458).
According to Hamilton (2009) the fall of the oil price in 2008 can be explained due to financialization of oil. Financialization of commodities arises when investors buy oil as a financial asset instead of a commodity to use. This financialization of oil led to speculation, which consequently induced an oil price bubble, which burst in 2008 (p. 235).
2.2. Automobile industry
The dependent variable of this research is the return on stock for the automobile industry. In order to test the effect of oil on this industry the return on stock for the twenty biggest automobile manufacturers will be used. In the appendix a table is provided which displays which manufacturers are added to the data and their amount of vehicles sold in 2014. Figure three shows the movement of stock of these manufacturers from the first week of 2004 up and until the last week of 2013; however Hyundai is excluded from this table due to displaying complications. The figures four until six, in the appendix display these movements. They are grouped into three figures due to scaling complications.
Figure two shows the movement of the Stoxx 3000 Automobiles & Parts over a period from 2004 up and until 2013. The Stoxx 3000 Automobiles & Parts is an index of automobile industry companies and behave as a benchmark for the automobile industry. In figure three and even more so in figure two it is shown that the movement of the automobile industry is moving slightly in the same direction as the Brent crude oil price which is shown in figure one.
Previous research by Lee and Ni found evidence on the negative effect between the oil price and the return on stock for automobile manufacturers. Lee and Ni (2002) found that rising cost of oil has harmed the automobile industry in the past. This effect has arisen because the demand for large cars plummeted. They based their findings on the 1973-‐74 oil crisis and the 178-‐81 oil crisis (p. 829). Lee and Ni’s (2002) empirical findings show us that the automobile industry responses are explained by a shift in the demand following an oil price shock (p. 847), which means that an increase in the oil price leads to a reduction in demand. Arouri’s (2011) results show that oil price increases affect stock prices of the automobile & parts sector negatively. These results were expected: due to the fact that higher oil prices reduce automobile manufacturer returns. However on the demand-‐side there could be argued that higher oil price lead consumers to drive less or demand more efficient vehicles (p.1719). However the direct effect of oil price increases is at the production side. Oil being a main commodity used in the production process, increases in price affects the production costs and firm profitability. However, Arouri
(2011) shows that the negative effect of oil price increases on automobile & parts stock returns is only weak (p. 1719). Nevertheless Park and Ratti (2008) show there exists a statistical significantly positive relationship between the oil price and stock return, in oil exporting countries (p. 2588). In order to test whether the positive effect also holds for the automobile industry, a dummy variable will be added. This dummy variable will test if the country of origin of the manufacturer is in the top ten of oil producing countries among the world.
Cameron and Schnusenberg’s (2008) findings suggest that efficiency is an important variable in the relationship between oil prices and profitability for automobile manufacturers. They conclude that manufacturers whose focus is on SUV’s and large trucks are impacted more by oil shocks compared to manufacturers specialized in fuel-‐efficient vehicles. They corroborate their findings on a study by the University of Michigan, which state that between 2001 and the end of 2004 profits from SUVs dropped 40% ($7 billion) (p. 375). Data provided by the United States Department of Transportation shows by using the average miles per gallon (MPG) that vehicles have become more efficient during the researched period of time. Therefore a variable is added to the model researched in this thesis, which corrects for the increase in fuel-‐efficiency over the years. Ramey and Vine (2011) find that for automobiles, fluctuations in the price of energy change the desired characteristics of the capital in use. Because the energy efficiency of the existing stock of consumer durables available in the short run is largely fixed, demand for new goods can shift between products, more fuel-‐efficient vehicles will become more attractive (p. 338).
This research focusses compared to previous research solely on the automobile industry, where previous research focused on multiple industries. Additionally this research focusses on a distinct period of time. Previous research ended at 2010 where this research also focusses on the period after the financial crisis, which according to Arouri (2011) took place from August 2007 till June 2010 (p. 1718). Nevertheless, the model tested is constructed from models used during previous research by Arouri (2011), Narayan and Sharma (2011) and Cameron and Schnusenberg (2009).
3. Methodology and Data 3.1. Hypothesis
The tested hypotheses for this thesis are focussing on the relationship between the oil price return and the return on stock for automobile manufacturers and are defined as:
H0: The oil price return is not related to the return on stock for automobile manufacturers.
H1: The oil price return is negatively related to the return on stock for automobile manufacturers.
3.2. Methodology
In order to research the relationship between the oil price and the return on stock of automobile manufacturers a model will be constructed inspired on models used in existing literature. The most influential articles used to create this model are by Arouri (2011), Narayan and Sharma (2011) and Cameron and Schnusenberg (2009).
𝑅𝑒𝑡𝑢𝑟𝑛 𝑠𝑡𝑜𝑐𝑘 𝑐𝑎𝑟 𝑚𝑎𝑛𝑢𝑓𝑎𝑐𝑡𝑢𝑟𝑒𝑟 = 𝛽0 + 𝛽1 𝑅𝑒𝑡𝑢𝑟𝑛 𝐵𝑟𝑒𝑛𝑡 𝑜𝑖𝑙 𝑝𝑟𝑖𝑐𝑒 𝑖 + 𝛽2 𝐺𝑙𝑜𝑏𝑎𝑙 𝐷𝑜𝑤 𝐽𝑜𝑛𝑒𝑠 𝑖 + 𝛽3 !"#$!%#&'(!"# !"#$% 𝑖 +
𝛽4 𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝑓𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑐𝑟𝑖𝑠𝑖𝑠 𝑖 + 𝛽5 𝑂𝑖𝑙 𝑝𝑟𝑜𝑑𝑢𝑐𝑖𝑛𝑔 𝑐𝑜𝑢𝑛𝑡𝑟𝑦 𝑖 + 𝜀 𝑖
Dependent variable is the weekly return on stock price of a car manufacturer, Arouri, Narayan and Sharma and Cameron and Schnusenberg also use this variable. Data used for this variable is the weekly return on stock of the twenty biggest automobile manufacturers worldwide.
Explanatory variable 1 is the weekly return on the oil price, as a benchmark the return on the Brent crude oil price will be used. This variable is also used in researches by Cameron and Schnusenberg, Narayan and Sharma and Arouri. The expectation is that this variable will be negatively correlated with the dependent variable.
Explanatory variable 2 is the return on the Global Dow Jones, which represents an index of global companies; this will be used as a benchmark for the average return on stock as an indicator for the economic environment. The models used by Arouri, Narayan and Sharma and Cameron and Schnusenberg, also include variables, which correct for market returns. This variable is expected to have a positive relationship with the return on stock for automobile manufacturers. The Global Dow Jones is a benchmark for the behavior of stocks among the world, therefore it can be expected that stocks for the automobile industry move in the same direction then the Global Dow Jones.
Explanatory variable 3 is the average miles per gallon divided by the oil price, this variable should correct for the increase in efficiency of cars over the years, which should have made the sales of automobiles less dependent on the oil price. For this variable there will be a different dataset for US automobile manufacturers and non-‐ US manufacturers. In the case of automobiles becoming more energy efficient, due to lower fuel costs it will become cheaper to use the vehicle for consumers. Therefore the usage will become less dependent on fluctuations of oil price, which consequently leads to an increase of automobile sales. This increase in sales will lead to an increase in stock price for automobile manufacturers. Therefore a positive relationship leads to the increase in efficiency and the positive return on stock for automobile manufacturers can be expected.
Explanatory 4 is used by Arouri (2011) and is a dummy variable equals to 1 during the current international financial crisis (August 2007 – June 2010) and 0 otherwise (p. 1718). Arouri et al. (2012) states that due to government aid for the automobile industry an insignificant relationship between oil and the return on automobile manufacturer stock exists. Arouri et al. (2012) also states that their volatilities are only driven by their own past news and volatilities (p. 616). Therefore this variable is expected to have an insignificant negative relationship with the return on stock as the financial crisis led to a decline for the stock market.
Explanatory variable 5 is a dummy variable in the case where the country of origin for the manufacturer is in the top ten oil producing countries. According to Park and Ratti (2008) there exists a statistical significant positive relationship between the oil price and stock return in oil exporting countries (p. 2588). However
Sadorsky (1999) finds that a positive shock leads to a decline in stock return (p. 468). Therefore it is interesting to test which of these statements hold for the automobile industry, during the researched timeframe.
The main explanatory variable of this model is the return on Brent oil price. If the results show that this variable is significant and negative, hypothesis one can be rejected. Consequently we can conclude there is a negative relationship between oil price movement and the return on stock for automobile manufacturers.
3.3. Data and descriptive statistics
In order to find the appropriate companies, which were used to find the return, annual sales per automobile manufacturer, is retrieved from DataStream. However the data provided by DataStream contained some errors, for example motorcycle manufacturers, and a design firm and a semiconductor manufacturer were also included. Therefore the data was adapted to fit the requirements. The selected companies accompanied with their annual sales for 2014 can be found in table eight in the appendix. The stock prices of the twenty biggest automobile manufacturers are retrieved from DataStream. In order to calculate the weekly return the following model will be used.
𝑅𝑒𝑡𝑢𝑟𝑛 = 𝑃1 − 𝑃0
𝑃1 ∗ 100%
The above model will also be used to calculate the weekly return for the Brent crude oil price. The weekly prices have been retrieved from DataStream. The movements of the Brent crude oil price during the selected timeframe can be found in figure one.
The Global Dow Jones is used as a benchmark for the global return on stock, the weekly Global Dow Jones prices are retrieved from DataStream and the return on these prices are calculated with the model above. Figure seven shows the movement of the Global Dow Jones during the selected timeframe.
The Bureau of Transportation Statistics provides the average fuel efficiency; this bureau is a subsidiary of the United States Department of transportation. The bureau of transportation statistics provided date about the fuel efficiency of US domestic passenger cars. Conjointly the bureau provided information about the fuel efficiency of imported passenger cars. The fuel efficiency is provided by the average miles per gallon (MPG); these can be found at table nine in the appendix. These numbers will be divided by the Brent Oil Price to be used as the fourth variable in the model.
The Central Intelligence Agency (CIA), provided date about the amount of oil barrels produced per day per country. This data will be used to examine whether or not the country of origin of the manufacturer is in the top ten of oil producing countries worldwide. Table eleven gives the top ten of oil producing countries and their amount of barrels per day produced, and can be found in the appendix.
Table 1 summary statistics total timeframe 2004 up and until 2013
Variable Obs Mean Std. Dev. Min Max
Auto return 10061 0.254 5.811 -‐50.864 125.680 Brent Oil Price 10061 0.353 4.372 -‐19.238 12.446 OPEC Oil Price 10061 0.343 4.261 -‐22.343 19.754 Dow Jones 10061 0.133 2.600 -‐19.835 12.198 MPG 10061 0.455 0.158 0.214 1.032 Financial Crisis 10061 0.287 0.452 0 1 Oil producing 10061 0.275 0.447 0 1
Table 2 summary statistics financial crisis 03/08/2007 to 25/06/2010
Variable Obs Mean Std. Dev. Min Max
Auto return 2888 0.022 7.835 -‐50.864 125.689 Brent Oil Price 2888 0.165 5.717 -‐19.238 12.446 OPEC Oil Price 2888 0.183 5.737 -‐22.343 19.754 Dow Jones 2888 -‐0.143 3.770 -‐19.835 12.198 MPG 2888 0.456 0.153 0.214 0.920 Oil producing 2888 0.263 0.440 0 1
Table 3 summary statistics excluding financial crisis from 02/01/2004 to 03/08/2007 and 25/06/2010 to 27/12/2013
Variable Obs Mean Std. Dev. Min Max
Auto return 7173 0.348 4.756 -‐38.287 34.985 Brent Oil Price 7173 0.428 3.692 -‐12.308 9.970 OPEC Oil Price 7173 0.408 3.493 -‐13.253 11.645 Dow Jones 7173 0.245 1.927 -‐8.751 8.276 MPG 7173 0.455 0.160 0.259 1.032 Oil producing 7173 0.280 0.449 0 1
Table 4 Cross-‐correlation table Brent Oil Price
Correlation Auto
return Brent Oil Price
Dow
Jones MPG Financial crisis Oil producing
Auto return 1.000 Brent Oil Price 0.152 1.000 Dow Jones 0.430 0.386 1.000 MPG 0.028 -‐0.014 0.023 1.000 Financial crisis -‐0.025 -‐0.027 -‐0.068 0.005 1.000 Oil producing 0.006 -‐0.001 0.000 -‐0.032 -‐0.017 1.000
Table 5 Cross-‐correlation table OPEC Oil price
Correlation Auto return OPEC Oil Price Dow Jones MPG Financial crisis Oil producing Auto return 1.000 OEPC Oil Price 0.155 1.000 Dow Jones 0.430 0.396 1.000 MPG 0.028 -‐0.012 0.023 1.000 Financial crisis -‐0.025 -‐0.024 -‐0.068 0.005 1.000 Oil producing 0.006 -‐0.007 0.000 -‐0.032 -‐0.017 1.000
4. Analysis
4.1. Empirical Results
Table six shows the main results of the performed regression. In this regression the dependent variable is the return on stock for automobile manufacturers, the main explanatory variable is the return on Brent Oil price. The remaining explanatory variables are: the return on the Global Dow Jones, average MPG divided by the oil price, a dummy variable for the financial crisis and a dummy variable for the situation where an automobile manufacturer is originated from a major oil producing country. Three tests are performed, all focusing on different timeframes. The first test focusses on the whole timeframe from the beginning of 2004 up and until the last week of 2013, the second one focusses on the financial crisis which according to Arouri (2011), lasted from August 2007 until June 2010 (p. 1718), and the third regression is focused on the timeframe from 2004 up and until 2013 excluding the financial crisis.
Table 6 Empirical Results
This table looks at the effects on the return on stock of automobile manufacturers for different timeframes. Column one focusses on the timeframe of 02/01/2004 up and include 27/12/2013. Column two focusses on the effects during the financial crisis, starting from 03/08/2007 and ending at 25/06/2010. Column three focusses on the total timeframe excluding the financial crisis, which is therefore from 02/01/2004 up to 03/08/2007 and from 25/06/2010 up and include 27/12/2013. The regressions uses return on stock of automobile manufacturers as the dependent variable and the Brent Oil Price return as the main explanatory variable. For definitions of all variables see 3.2. Methodology. Robust t-‐statistics are reported in parentheses. *, **, and *** indicate significance at a one sided 10%, 5%, and 1%, level respectively.
Return automobile manufacturer
(1)
Total time Frame Financial crisis (2) Non-‐financial crisis (3) Brent Oil Price Return -‐0.021* 0.006 -‐0.035***
(0.016) (0.034) (0.014)
Return Dow Jones 0.974*** 0.959*** 0.963***
(0.043) (0.075) (0.030) Average MPG 0.643** 3.466*** -‐0.359 (0.364) (1.035) (0.318) Financial Crisis 0.047 (0.142) Oil producing 0.085 0.316 -‐0.008 (0.138) (0.342) (0.138) Intercept -‐0.198 -‐1.506*** 0.293** (0.180) (0.466) (0.156) N 10061 2888 7173
The results of regressions one to three shows a negative significant relationship for the Brent Oil price return and the return on automobile manufacturer stock, at a 10% one sided significance level during the total time frame, a positive non-‐significant relationship during the financial crisis and a negative significant relationship at a 1% one sided significance level. The negative relationship matches with the expectations. This can be explained by the fact that oil is an important commodity used in the production process of automobiles. Therefore rising oil prices will lead to an increase in production costs, which will depress the firm’s profits, which leads to a decrease in return. The positive relationship in the second regression can be explained by the government aid during the financial crisis for the automobile industry, as mentioned by Arouri et al. (2012). During the financial crisis China reduced automotive taxes in order to increase automobile sales. The United States government rescued both General Motors and Chrysler and offered Ford a line of credit. These interventions would lead to more confidence by investors in the industry, and disturbed the free market forces (p.616).
For variable 2 the results show a positive significant relationship at the 1% significance level during all of the three timeframes. This was expected because the Global Dow Jones is an index of companies around the world, and is used as a benchmark for the market return or economic environment. It can be expected that the return on stock of automobile manufacturers move in the same direction over time as the return on other industries, therefore it is not unexpected that there is a positive significant relationship between the return on Global Dow Jones and the return on stock manufacturers.
For the variable average miles per gallon (MPG) divided by oil, the regression shows a significant positive relationship at 5% one sided significance level for the total time frame, a at the 1% one-‐sided significance level positive significant relationship during the financial crisis, and an insignificant positive relationship during the timeframe excluding the financial crisis. This positive relationship was expected considering that for more energy efficient vehicles, it would become cheaper to use the vehicle for consumers, due to lower fuel costs. Therefore the usage will become less depend on fluctuations of oil price; this could make it more interesting to purchase a new vehicle, which consequently could lead to an increase
for automobile sales. This increase in sales will result in an increase in stock price for automobile manufacturers. Therefore a positive relationship between efficiency and the return on stock for automobile manufacturers can be expected. An explanation to the difference between non-‐financial crisis and the timeframe during the financial crisis could be that consumers are more price sensitive during times of economic stagnation.
The dummy for the financial crisis is solely added to the regression for the total timeframe, it is excluded for the remaining two regressions. This was a necessary procedure due to the otherwise arising omitted variable bias. The dummy variable in regression one shows an insignificant positive relationship. This result is unexpected as the financial crisis led to a decline for the stock market. An explanation for this is given by Arouri et al. (2012), who claim that during the financial crisis, the industry was supported by the government (p. 616). During the financial crisis China reduced automotive taxes, this led to lower prices for automobiles, which consequently led to an increase in sales. The United States government rescued both General Motors and Chrysler and offered Ford a line of credit. These interventions by governments could have led to a disturbance of the free market forces, which consequently made the industry behave differently than expected. Example given due to the rescue by the United States government and the line of credit investors gained more confidence in the stocks of the supported companies, while under normal circumstances this wouldn’t be the case. Therefore the industry wasn’t hurt as much as expected, which led to an insignificant impact on stock return for the industry.
For the dummy variable correcting for the effect of being located in a major oil producing country there is a positive non-‐significant relationship for all three regressions. This positive relationship is in line with the results provided by Park and Ratti (2008). Their results show a statistical significantly positive relationship between the oil price and stock return in oil exporting countries (p. 2588). Nonetheless Park and Ratti (2008) find a statistically positive relationship where in this research the positive relationship is not significant at a one-‐sided 10%, 5% and 1%, level respectively.
4.2 Robustness check
In order to perform a robustness check, a different benchmark for the oil price is researched, on top of that again there will be tested if differences exist between three timeframes, namely: the whole timeframe from the beginning of 2004 up and until the last week of 2013, the financial crisis which according to Arouri (2011) lasted from August 2007 until June 2010 (p. 1718), and the third on is the timeframe from 2004 up and until 2013 excluding the financial crisis. In comparison to the main results in part 4.1. one adaption took place, the Brent Oil Price return is replaced by the return on the OPEC Oil price. The OPEC Oil price return is a benchmark introduced in 2000, which represents the weighted average of the oil price produced by the thirteen OPEC members. The relationship between the Brent crude oil price and the OPEC oil price are displayed in figure eight. The results of the robustness check are presented in table seven.
Table 7 Robustness check
This table looks at the effects on the return on stock of automobile manufacturers for different timeframes. Column one focusses on the timeframe of 02/01/2004 up and include 27/12/2013. Column two focusses on the effects during the financial crisis, starting from 03/08/2007 and ending at 25/06/2010. Column three focusses on the total timeframe excluding the financial crisis, which is therefore from 02/01/2004 up to 03/08/2007 and from 25/06/2010 up and include 27/12/2013. The regressions uses return on stock of automobile manufacturers as the dependent variable and the OPEC Oil Price return as the main explanatory variable. For definitions of all variables see 3.2. Methodology. Robust t-‐statistics are reported in parentheses. *, **, and *** indicate significance at a one sided 10%, 5%, and 1%, level respectively.
Return automobile
manufacturer
(1)
Total time Frame Financial crisis (2) Non-‐financial crisis (3) OPEC Oil Price Return -‐0.025* -‐0.015 -‐0.029**
(0.016) (0.032) (0.014)
Return Dow Jones 0.976*** 0.976*** 0.959***
(0.042) (0.073) (0.030) Average MPG 0.642** 3.404*** -‐0.366 (0.364) (1.040) (0.318) Financial Crisis 0.048 (0.142) Oil producing 0.085 0.316 -‐0.008 (0.138) (0.342) (0.138) Intercept -‐0.197 -‐1.472*** 0.293** (0.180) (0.467) (0.156) N 10061 2888 7173 R-‐sq 0.1853 0.2231 0.1475
For the OPEC oil price return there is a significant negative relationship at the 10% one-‐sided significance level. During the timeframe excluding the financial crisis this effect is also negative and significant at a 5% one-‐sided significance level. During the financial crisis this relationship was negative, however not significant. Compared to the results of the regression with Brent Crude Oil the effect during the non-‐ financial crisis timeframe got less significant, and the positive relationship during the financial crisis turned into a negative relationship. The negative relationships match with the expectations. This can be explained by the fact that oil is an important commodity used in the production process of automobiles. Therefore rising oil prices will lead to an increase in production costs, which will depress the firm’s profits, which leads to a decrease in return.
Concerning the relationship between the Global Dow Jones return and the return on stock for automobile manufacturers, no differences occur changing from Brent oil price to the OPEC oil price. For all three timeframes there is a positive relationship at the 1% one-‐sided significance level. This is not an unexpected effect with the Global Dow Jones being an index of stock return of companies around the world. Hence it can be expected that both returns move in the same direction. For the averages miles per gallon divided by oil, there also appears no difference with respect to the main results with Brent crude oil as the main explanatory variable. During the total timeframe there is a positive significant effect at the 5% one-‐sided significance level. During the financial crisis the positive effect is significant at the 1% one-‐sided significance level. Nevertheless the positive effect during the
Figure'8'Brent'and'OPEC'price'movement' ! Source:'DataStream' $0,00! $20,00! $40,00! $60,00! $80,00! $100,00! $120,00! $140,00! $160,00! 1*2*2004! 1*2*2005! 1*2*2006! 1*2*2007! 1*2*2008! 1*2*2009! 1*2*2010! 1*2*2011! 1*2*2012! 1*2*2013! Brent!crude!oil! OPEC!oil!price!
timeframe excluding the financial crisis is insignificant at the 10%, 5% and 1%, level respectively. This positive relationship was expected considering that for more energy efficient vehicles, it would become cheaper to use the vehicle for consumers, due to lower fuel costs. Therefore the usage will become less depend on fluctuations of oil price, which could make it more interesting to purchase an automobile; this will lead to an increase for automobile sales. This increase in sales will lead to an increase in stock price for automobile manufacturers. Therefore a positive relationship will result in an increase in efficiency and the positive return on stock for automobile manufacturer.
For the robustness check the dummy for financial crisis is solely added to the regression focusing on the whole timeframe from 2004 up and until 2013. Like the main results there appears to be an insignificant positive relationship. This could be explained by the government aid the automobile industry received during the financial crisis, as Arouri et al. (2012) mentioned (p. 616). Therefore the industry wasn’t hurt as much as expected, which led to an insignificant impact on stock return for the industry.
For the final variable there appears to be no difference between the regressions with OPEC oil as the main explanatory variable compared to the regression with Brent crude oil as the main explanatory. There is a non-‐significant positive relationship for all three timeframes. This positive relationship is in line with the findings by Park and Ratti (2008), who find a positive response of real stock return to an oil price shock increase (p. 2588).
5. Conclusion and discussion
In this thesis the relationship between the oil price return and the return on stock for automobile manufacturers has been researched. The dependent variable used in this model is the return on stock for automobile manufacturers. The model also includes five explanatory variables namely: return on oil price, return on the Global Dow Jones, average miles per gallon, a dummy variable during the financial crisis and a dummy variable for the situation where the manufacturer is originated from a country which produces a major amount of oil. The model is tested during three
timeframes: 2004 up and until 2013, the financial crisis and the third timeframe is 2004 up and until 2013 excluding the financial crisis.
During the total timeframe there has been a significant negative relationship at the 10% one-‐sided significance level, during the financial crisis a positive insignificant relationship is found and during the timeframe excluding the financial crisis the negative relationship is significant at the 1% one-‐sided significance level. In previous literature by Arouri (2011) and Lee and Ni (2002) a significant negative relationship has been found as well. Nevertheless during the financial crisis the results show a positive insignificant relationship between the return on oil price and the return on stock for automobile manufacturers. This can be explained by the government aid the automobile sector received during the financial crisis (Arouri et al., 2012, p. 616). During the financial crisis China reduced automotive taxes, this led to lower prices for automobiles, which consequently led to an increase in sales. The United States government rescued both General Motors and Chrysler and offered Ford a line of credit. These interventions by governments could have led to a disturbance of the free market forces, which consequently made the industry behave differently than expected. Example given due to the rescue by the United States government and the line of credit investors gained more confidence in the stocks of the supported companies, while under normal circumstances this wouldn’t be the case. Figure one till three show the movements of oil price and stock price for the automobile industry. These movements show a decline during the financial crisis and correlate with each other, therefore the relationship is positive nevertheless insignificant.
One of the limitations of this research is explanatory variable three: MPG divided by the oil price. Due to the fact that the same oil price is used in variable one as the denominator of variable three, multicollinearity could occur in the results, which would lead to unbiased results. Another limitation of this research is the absence of consumer preferences concerning fuel efficiency of the vehicles. When demand for fuel-‐efficiency vehicles gets higher the effect of oil price on the demand side reduces.
The implications of the findings are helpful for automobile manufacturers. With oil being one of the main production costs and furthermore affecting the
company on the demand side by consumer preferences, knowledge about the effect of oil price fluctuations can help them in order to protect themselves against these fluctuations. For example companies can hedge against future oil price increases by the purchase futures, which could keep the oil price at a stable price, and therefore their production costs at a balanced level. For future research I would suggest to add a different variable for the increase in efficiency, which is less correlated to the oil price.
