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1.0	Introduction

The fuel of the world is oil; therefore oil price volatility affects everyone, from the way people drive to the way people live. British Petroleum (BP) is one of the world’s largest petro-chemical companies and its success or failure largely depends on the price of oil. It would be very interesting to see the effect of oil price change on the share returns of BP an oil company, which is greater than the effect of the market. This will be done by finding out whether or not the oil price change and the BP share residuals and have a relationship, or if some other factors are responsible for the share return residuals.

It is argued that securities are valued by converting forecasts about company fundamentals and economic data into a course of action regarding individual securities (Elton et al. 2003). The initial introductory chapters will examine the history of the oil industry and the oil price as well as the effect oil has on the economy, also an analysis of BP and its activities. This will be followed by a critical literature review of research on the effect of oil price volatility on share returns. The methodology will provide the reader with understanding of how the analysis will be carried out illustrating the models and data to be used and the justification for this approach.

Oil is one of BP’s main products therefore according to the Single Index model it is part of one of the two variables, which affect the share price the other being the market. Using the Single Index Model it will be possible to predict the share return and then calculate the residuals. Then it will be then possible to carry out a number of time series regression analyses to see whether or not the oil price change and the share return residuals of BP have a relationship.

The objective of this paper is:

1.	To ascertain whether or not the oil price change and the share price of BP have a relationship, that is greater than the effect of the market.

The hypothesis of this paper is that:

1.	There is a positive linear correlation between the oil price change and the BP share return residuals.

2.0 The oil industry

Rockefeller was the founding father of the Oil Company. Rockefeller started the first integrated Oil Company called Standard Oil Trust. The ethos was control the supply of oil, control prices, control the market and form cartels. Originally the main uses for oil was cooking and lighting. The US government saw oil as so important to the economy that it had to intervene and help create a freer market. Intervention by the state meant that Standard Oil could no longer be a monopoly. Standard Oil is now called Exxon and in Europe is more commonly known as Esso. Other oil companies that emerged are called Gulf and Texaco.

America is where the oil industry began, but the discovery of oil in Russia and Asia at the beginning of the century, led to the creation of Shell and Royal Dutch who are now merging. And before the First World War a discovery of oil in the Middle East led to the creation of British Petroleum. This handful of companies had control of the world oil industry and through vertical integration were extremely powerful (Kearton, 1985).

2.1 Oil Price History Oil prices were around $3.00 throughout the period after World War Two to 1970 (see graph 2.0). This is the period were oil companies operated freely, which led to conflict with the oil exporting countries. Oil exporting countries felt they were being cheated.

Five major oil exporting countries, Saudi Arabia, Iraq, Iran, Kuwait and Venezuela set up an organisation called the Oil Producing Economic Countries (OPEC) in 1960. By 1971 six other nations had joined OPEC they were Libya, Nigeria, Algeria, Qatar, Indonesia and United Arab Emirates. During the period after the war oil exporting countries found growing demand for oil (Odell, 1986). OPEC managed to get effective control of its oil in 1973, and started operating like a true cartel.

OPEC’s strategy was to set up a market price for Saudi Arabian oil, and leaving other members to set similar prices for their oil, a dominant firm price leadership. Demand was stable, and demand was fairly inelastic, which allowed OPEC to raise prices, which led to increase in revenues, with only a small fall in demand (See graph 2.1).

2.2 Middle East war

The Arab Israeli war started on the 5th of October 1973. The Arab nations cut supply and caused the price of crude oil to rise over $12.00 (Sloman, 1997). The Arab nations reduced output by about 5 million barrels per day (mbpd). The price sensitivity to supply volatility showed when prices jumped by 400% in just a few months (Shwadran, 1977). The price of oil was kept around $12 per barrel until 1979, without a major decrease in quantity demand.

The revolution in Iran led to a decrease of around 3 mbpd, in the period 1978 to 1979. Due to the start of the Iran-Iraq war, the oil production of Iraq reduced by 3 mbpd and Iran's production reduced by a further 0.5 mbpd. These events caused crude oil prices to rise from $12 in 1978 to over $30 a barrel in 1981.

2.3 OPEC's Failure as a cartel After 1980 demand for oil did fall following the previous period of ever growing prices. Saudi Arabia explained to the other members of OPEC that such high prices of crude would lead to a decline in demand. OPEC tried to set low quotas to calm prices up to 1985. These attempts didn’t work, as members of OPEC would cheat by producing beyond their quota limits. The theory of cartel quotas is to equalise the marginal cost of the efficient firm (MCe) & inefficient firm (MCi), the output of the inefficient producer is fixed at OXi and the output of the efficient producer will be OXe. This is done to rationalise, as it would be better to concentrate on the efficient producer. Thus OXi + OXe= OX (See fig 2.3).

Due to cheating the Saudis linked their oil prices to the market for crude and doubled production to 5 mbpd. The increase in production caused the price to fall by 50% by the middle of 1986 (Odell, 1986).

Iraq’s assault on Kuwait caused the oil prices to reach new highs. But at the end of the Gulf War there was a recession, and the oil price fell. The United States economy and the growing Tiger economies of the pacific region were booming (Abdalla, 1995). World oil consumption increased by over 6 mbpd after the Gulf War to 1997. In 1997 OPEC increased its production quota to 27 mbpd as a result of the depression in the world economy. The prices fell because there was more oil being produced and a reduction in use.

2.4 Recent oil history

Oil prices became extremely volatile after the terrorist attacks on September 11 2001; the following wars in Afghanistan and Iraq have caused further ups and downs. Brent crude oil began 2003 at around $30 per barrel and remained just under $30 per barrel on 10 March, a week before the start of war in Iraq.

Oil prices then fell to reach a low of under $27 at the end the Iraq conflict it ended the year above $30 per barrel. At the beginning of 2004 oil price was $30 it has climbed to historic levels, above $50 a barrel in October. The BP plant accident in Texas and the announcement from Goldman Sachs the investment bank that crude oil prices could rise to $105 a barrel has increased the price to over $55 a barrel.

2.5 The effect of oil price volatility on economies Oil is a key material in the production of many goods, as well as being an energy source. Changes in the price of oil affect a company’s average cost leading to higher prices. If oil prices increase, companies may try to avoid raising charges as this will lead to a loss of customers. Also transport costs are affected by rising prices for crude oil. Higher living costs will lead to a demand for increased pay; this can also lead to inflation. The oil shocks of 1973 and 1979-80 sent inflation soaring and damaged economies.

Now economies rely about half as much on oil, with most economies using a combination of energy sources such as gas, nuclear, coal, renewable and oil (Duncan, 2004), which proves the fact that demand in the long run becomes elastic. Firms have to accept the price for oil because they are price takers and the level of output takes a long time to increase, along with the lack of viable substitutes makes oil demand inelastic in the short run.

Most firms are in business to maximise profit the firm will make abnormal profit between average cost (AC) and average revenue (AR). Therefore any increase in the average cost will reduce the abnormal profit or potentially the firm will enter in to a position of no profit or loss. All firms will try to reduce their costs; this is the main reason behind the existence of the international oil markets where it is possible to reduce risk by purchasing oil ahead of time. However oil prices have recently maintained a high price and all firms will eventually have to deal with higher costs.

As oil prices rise the average cost will increase. This increase will lead to profit reducing or loss-making position for firms. Figure 2.6 shows that the average cost curve (AC) has risen above the average revenue curve at all levels of output, which means that between points AR & AC the firm will make a loss.

Region	Billion barrels	Billion tonnes Middle east	726.6	99.0 Europe & Eurasia	105.9	14.5 South & Central America 	102.2	14.6 Africa	101.8	13.5 North America	63.6	8.8 Asia Pacific	47.7	6.4 Total	1,147.8	156.8

The value and use of crude oil depends on whether it has a high tar and sulphur content, the different types of oil, which are known as sweet or sour are found all over the world. Three fifths of world output is sour, including the medium sour from Saudi Arabia, Iraq, Iran and Russia. It is more expensive to refine sour oil and remove the pollutants.

2.6 Factors for oil price increase and trends International oil prices are above $50 a barrel a situation caused by a combination of factors, increases in demand for fuel by the Chinese and Indian economies, the war in Iraq and international terrorism and the inability to refine some types of oil (keynote, 2004). Further more Russian State intervention of its oil industry, the government started the process of breaking up Yukos, the country’s biggest oil producer, over a tax dispute. Strikes by oil workers in Nigeria and Norway are further decreasing the supply.

Oil prices are increasing because there is an assumption that future oil demand will outstrip future oil supply. Analysis by Odell & Rosing (1980) suggests a 90% chance that the oil industry could grow until 2011, a 50% chance that it could grow until 2033. They also suggest that there is no incentive to look for new oil sources due to the cost in terms of capital and environmental damage.

Economies that rely on oil have become too dependent on oil reserves, which are located in a small number of countries i.e. Middle East. Odell & Rosing (1980) also argue that oil companies should be given more incentive to look for new oil reserves and invest in new refineries, as this will reduce prices.

Odell & Rosing’s strategy will pose a problem for environmentalists, as this policy will lead to more pollution. It might be better to invest in renewable energy sources such as hydroelectric and solar power. For example oil companies of today are already planning for the eventual decline of oil, the level of investment in renewable energy is increasing as well as the level of government incentives. Also the Kyoto agreement will make it more expensive to put pollutants in to the air with the introduction of the carbon trading market. However Odell and Rosing’s strategy suggestion will eventually reduce oil price volatility by increasing supply.

2.7 British Petroleum British Petroleum (BP) plc is a group of companies with activities structured around the following areas, exploration and production (oil & gas), gas power & renewable, refining & marketing, chemicals and other businesses. BP is a diversified company as it is involved in exploration and retail sale of crude oil and petroleum. The profit for BP therefore depends on the price of crude oil sold on the international oil markets and the price they pay for crude to refine and sell on the retail markets. This means that if the crude oil price increases the exploration division will make a greater profit as the reserves are worth more the refining division will have to pass on the extra cost to their customers in order to maintain their margins.

Turnover 1	£130 billion (year 2003). Replacement cost profit 2	£9.6 billion (year 2003) Number of employees	103,700 Number of shareholders	1.2 million + Reserves	18.3 billion barrels of oil and gas equivalent Service stations	27,800 Exploration	Active in 26 countries Refineries	23 Chemicals revenues	$16.1 billion Solar power Capacity sold 71 million watts per year

An oil company only involved in the exploration industry will make a greater profit if the market price for crude increases and a loss if the price decreases as their reserves will increase and decrease in value. An oil company only involved in refining will be forced to pass on the extra cost to their customers if they wish to maintain their profits, as there is greater competition in this industry. The exploration industry is less competitive as oil companies acquire licences from nations, which have oil resources.

BP employs over 100,000 and serves 13 million customers in more than a 100 countries and in 2003, group turnover was £130bn. BP has 21,641,840,000 shares in issue, and has a historical beta of 0.9, a correlation of 0.6 (Datastream, 2004). BP has had rising profits for the period of 1994-2003. Also it can be perceived from graph 2.10 that the oil price change and BP share returns are correlated, whether this is true or not needs to be proven statistically.

Graph 2.9: BP annual profit 1994-2003

Graph 2.10: BP Share Price return & Brent Oil price change activity for period 08/11/02-08/11/04

3.0 Critical literature review

3.1 The effect of the oil price on financial markets via the companies International financial markets are affected by oil price and industry developments because oil is the most significant commodity needed by all major economies to satisfy their energy needs. Changes in oil price affect the balance of payments, foreign debts, international investments and credits, foreign exchange rates, inflation and securities yields (Rees & Odell 1987). Most oil companies secure loans or new equity on future oil price and production level assumptions; therefore volatility of oil price affects them greatly.

Jones & Kaul (1996) carried out research into the effects of oil on the stock markets of the USA, Canada, Japan and the UK. Their research discovered that the reaction of stocks in the USA and Canada could be completely accounted for by the impact of oil price shocks on real cash flows of companies. However the stock markets of Japan and the UK appear to have larger changes then can be justified by changes in cash flows or changing expected returns.

The assumption made by Jones & Kaul is that the reason for the overreaction in Japan and the UK is caused by (a) their expected return models didn’t capture the way the oil price shocks effected the stocks in these markets, or (b) these markets overreact to oil price shocks.

The data used for the analysis included the producer price indices for the oil price analysis. Quarterly data was used for all indices, the periods being 1974-1991 USA, 1960-1991 Canada, 1970-1991 Japan, & 1962-1991 UK. The regression analysis of the effects of oil shocks on real stock returns shows that the US & Canadian, stock indices had a R² of 0.069 & 0.028 respectively, while the Japanese and UK had a R² 0.260 & 0.122. These R² statistics show that the Japanese and UK stock markets are more likely to show a greater volatility caused by an oil shock than the US & Canadian markets.

3.2 Empirical research on the effects of oil price volatility on oil stock markets and oil stocks

Balabanoff (1995) applied cointegration analysis and causality testing to the monthly average of commercial (non-strategic) primary oil stocks and monthly averages of West Texas Intermediate (WTI) spot and futures prices 1 month & 3 months delivery, over the period January 1985 to June 1993. The results show a relationship between futures prices and oil stocks.

Balabanoff’s research was concerned with monthly average spot and future oil prices and the monthly average oil stocks. The causality testing involved regression analysis, this was done to know whether or not the increases or decreases in WTI spot or future prices resulted in petroleum stock increases or decreases or was there an inverse relationship.

The regression analysis shows that the oil stocks and spot price had a R² of 0.156, the oil stock and 1 month future price had a R² of 0.167 and the oil stocks and 3 month future price had a R² of 0.136. The R² for all three regressions shows a relationship between the oil stocks and the oil prices; however Balabanoff’s research was not dealing with daily oil prices rather monthly averages. The research was also analysing monthly averages of oil stocks rather than daily share returns of individual oil companies.

Balabanoff’s research was mainly for the benefit of hedgers and speculators in the management of crude oil inventories and how they can protect themselves against variations in oil price. To that end the research is beneficial as it shows a causal relationship between the oil stocks and the oil spot and future prices.

Hammoudeh et al (2002) discovered that on a daily basis, all oil price returns with the exception of the 3-month futures could explain the future movements of each other. Also none of the daily oil industry stock indices can explain the daily future movements of the New York Mercantile Exchange (NYMEX) futures prices, whereas these prices can explain the movements of independent companies engaged in exploration, refining, and marketing.

The research used daily data for the available period July 17, 1995 to October 10, 2001. The U.S. oil markets include the West Texas Intermediate (WTI) spot and the New York Mercantile Exchange (NYMEX) 1- to 4-month futures prices. The U.S. oil industry’s sector stock indices as classified by S&P includes companies grouped into five categories, oil exploration and production; oil and gas refining and marketing; oil-domestic integrated; oil-international integrated; and oil composite.

Hammoudeh et al discovered that there is a causal relationship between the oil spot and future prices and independent companies involved in oil exploration, refining and marketing. This is different from Balabanoff’s research, which was looking at causal relationships between monthly average stock prices and oil prices.

The research showed that the oil price volatility could be used to predict the movement of oil companies stocks. It increased the volatility of companies stocks engaged in exploration & production and oil-domestic integrated. The analysis also implied that this oil volatility had a dampening effect on the volatility of the stocks of the oil international integrated, and oil and gas refining and marketing companies.

Hilliard & Danielsen (1984) discovered that world oil spot prices were significantly related to US oil companies stocks. The four oil companies analysed were Exxon, Mobil, Texaco and Standard Oil of California. The world oil price and US gasoline price series were also used in the analysis. The time period of the analysis was from 1970-1979, using monthly data.

Hilliard & Danielsen employed regression analysis to show a relationship between the US oil company stock returns and the oil price. The analysis shows that the oil price and Exxon stock had a R² of 0.4435, oil price and Mobil stock had a R² of 0.3840, oil price and Texaco stock had a R² of 0.3957 and oil price and Standard Oil of California had a R² of 0.4303. The R² for all four regressions shows a causal relationship between the oil stocks and the oil prices as Balabanoff’s study did.

While Hilliard & Danielsen study shows a causal relationship between the oil price and oil stocks the study was analysing a period of time, which is now very old and a time, which was very volatile economically and politically with regards to oil price and oil politics. This volatility could have influenced the outcome of the research by Hilliard & Danielsen, providing a causal relationship when there might have not been one.

Sadorsky (2001) used a multi-factor market model to estimate the expected returns for Canadian oil and gas industry share prices. Sadorsky used a single index model because it is the best method for evaluating correlation. The results show that exchange rates, crude oil prices and interest rates have a large and significant impact on share price returns in the Canadian oil and gas industry. Sadorsky’s research suggests that an increase in the market or oil price, increases the return for Canadian oil and gas share prices while an increase in exchange rate or interest rate decreases the returns. Sadorsky used the two following multi factor models to carry out his research.

(1)	Rit =  +  o Rot +  m Rmt + t

(2)	Rit =  +  o Rot +  m Rmt +  r R tpt +  e R et + t

Rit is the monthly excess equity returns on the oil and gas stock index, Rmt is the monthly excess return on the market index, and Rot is the monthly return to oil prices. The parameters,  m and  o are the market beta and oil beta, respectively. Model (1) doesn’t include interest rate factors and exchange rate factors. Model (2) has an interest rate factor, R tpt and exchange rate factor R et.

The data used in Sadorsky’s research is monthly, from 1983-1994. The oil price shares were gathered from the Toronto Stock Exchange oil & gas index. Oil prices were measured using 1-month oil futures prices on West Texas Intermediate crude oil.

Sadorsky’s correlation matrix for the data indicates that market returns are positively correlated with the oil and gas share price returns (R= 0.207). Oil price return is also positively correlated with the oil and gas share price returns (R= 0.439). The exchange rate returns (R= -0.207) and the interest rate (R= -0.083) are each negatively correlated with oil and gas share price returns. The results also show that the adjusted R² shows that 22% of the variation in oil and gas share price returns can be explained by market returns and oil price returns.

Sadorsky’s research shows that there is positive linear relationship between the oil price change and the oil and gas share price returns in Canada. Sadorsky also discovered that the exchange rate and interest rate has the effect of pushing the returns down. However the research was based on an oil & gas index, which includes many companies involved in different levels within the petrochemicals and energy sector. Sadorsky’s research also pointed to the fact that 90% of the Canadian energy exports go to the US, therefor the exchange rate between the Canadian and US dollar is a critical factor in the oil and gas share price returns.

The analysis of all the studies shows a positive correlation, as oil companies are profit maximisers. Profit is maximised when marginal cost equals marginal revenue. Usually when price goes up quantity demanded goes down leading to reduction in revenue, thus the effect of an oil price increase should lead to a decrease in the performance of oil stocks.

In the short run demand for oil is fairly inelastic (Sloman, 1997), meaning that when price goes up the quantity demanded falls slightly, the price elasticity of demand is close to 0, leading to an increase in revenue. Graph 3.0 shows that the profit return for BP follows the oil price change in a linear fashion and graph 2.10 shows that the oil price change and BP share returns do the same. This perceived correlation may be due to random chance, it needs to be proved statistically.

Graph 3.0 Cumulative Brent oil price change & BP annual profit return 1995-2003

3.3 Summary Jones & Kaul show that the stock markets of the USA, Canada, Japan & UK are affected by oil price shocks. Hammoudeh et al study involved five US oil indices and five US oil prices it suggests a causal relationship between future oil price and independent oil companies, which is the hypothesis of this project.

However Balabanoff discovered the same causal relationship, but between the monthly averages of spot, 1 month & 3 month future oil prices and stock price. Hilliard & Danielsen also show a similar relationship as Balabanoff did, as the all four regressions show a R² statistic, which is significant and shows that a proportionate variation in oil stocks were caused by variations in oil price. As this project involves analysing only one share price to calculate the residuals, exchange rate and interest rate factors are to be assumed as part of the residuals as opposed to Sadorsky’s research.

However Sadorsky’s research is the most relevant to this project in terms of the methodology and tests involving regression analysis, and the use of a multi-factor model similar to Sharp’s Single Index Model. In the short run demand for oil is inelastic, which means an increase in price leads to a small decrease in quantity demanded and an increase in revenues. The correlation can be seen in the graph 3.0 as the oil price change and the annual profits follow each other in a linear fashion. Whether this is in reality true or not needs to be examined using statistical techniques, which follow in the methodology.

4.0 Methodology

4.1 Model to calculate expected return In 1964 William Sharpe developed the Single Index Model. The basis of the model is that the returns of a share derive form only two variables, the first being the return of the stock market, and secondly something specific to the firm alone. Basic observation of stock prices reveals that when the market goes up shares tend to go up and vice versa. This suggests that share returns are correlated to market returns (Elton et al, 2003).

This paper is concerned with using the Single Index Model to calculate the expected share price then subtract this from the observed price to derive the share return residuals. Then it will be then possible to carry out a number of time series regression analyses to see whether or not the oil price change and the share residuals have a positive linear relationship. The Single Index Model is the equation below.

E(Ri) = i + i E(Rm)

E(Ri) = 	represents the expected return on share i. i       = 	the part of the security return, which is independent of the market performance, a random variable or the intercept term. i              = 	the constant that measures the expected change in Ri when Rm changes. E(Rm) = 	is the observed return of the market, it is a random variable

4.2 Regression analysis The next stage will involve subtracting the actual return from the expected return, which will provide the BP share residuals. After this a series of regression analyses of daily Brent oil change on the BP share residuals over four 6-monthly, two 1-yearly and one 2-year period will be carried out.

Regression analysis is a statistical technique applied to data to determine the degree of correlation of a dependent variable (y) with an independent variable (x). The analysis is used to see whether or not there is a cause and effect relationship between two variables (Edwards, 1984). The regression equation for this paper is the following.

Y =  + x

Y 	= Predicted BP share price residual return     	= Intercept 	= Slope (x variable 1) x	= Oil price change

The regression analysis will provide correlation coefficient, correlation of determination, significance of regression, significance of slope and intercept. From this information it will be possible to determine whether or not the regressions are significant and if the two variables have a positive linear relationship. T-statistics and F-statistics will be used to determine the significance of regressions, slope and intercept, if they are inside the critical values they will be significant if they are outside they will not be significant. 4.3 The Data The daily FTSE All Share Index, BP Share Price and Brent Oil Current Month prices will be gathered by using the Datastream resource available for the Moorgate Library of London Metropolitan University. The period of analysis will be from 8th November 2002 to 8th November 2004.

The FTSE ALL Share Index was selected because the Sharpe Single Index requires the use of the broadest market with all shares of that market in it. Brent Oil price Current Month (spot price) is for immediate delivery was chosen as it was the immediate price for that day reflecting any major changes up to the moment of delivery, which effect oil price. Also as researched by Hammoudeh et al (2002) the oil markets are positively correlated therefore any change in any oil market will mean a change in the Brent oil market. The period of two years was selected because daily prices where being used and the short run effect was being analysed, therefore it is a long enough period to find a relationship or not.

The first stage prior to the analysis involves downloading data on the FTSE All Share Index, BP share price and Brent Crude Oil price from Datastream. Then the returns are to be calculated using the Microsoft Excel Spreadsheet software. The data was gathered for a two-year period from the 08/11/02-08/11/04. In order to find relationship between the BP share residuals and the oil price returns, the residuals needed to be calculated. The Single Index model is to be used to discover the residuals. The model is the following, E(Ri) = i + i E(Rm). E(Rm), the return on the market is known however the (i) Alpha and (i) Beta are not known a regression of the FTSE returns and BP returns  is to be carried out as this will provide the Alpha and Beta.

5.0 Findings

5.1 FTSE All Share on BP Share Price returns Regression

Table 5.0 FTSE All Share on BP Share Return Regression Output SUMMARY OUTPUT FTSE (x) BP (y) 2 year Regression Statistics Multiple R	0.483654723 R Square	0.233921891 Adjusted R Square	0.232398873 Standard Error	0.011945752 Observations	505 ANOVA df	SS	MS	F	Significance F Regression	1	0.021917589	0.021917589	153.5910108	5.73199E-31 Residual	503	0.071778596	0.000142701 Total	504	0.093696185 Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95% Intercept	0.000305896	0.000531989	0.575003363	0.565546122	-0.000739298	0.00135109 X Variable 1	0.694133192	0.05600927	12.39318405	5.73199E-31	0.584092314	0.80417407 (Source: students own)

Analysis of the regression output shows that correlation coefficient (Multiple R) is 0.48, this means that there is a positive linear relationship between the FTSE returns and the BP returns. The coefficient of determination (R²) is 0.23; this means that 23% of BP returns are being explained by the FTSE returns.

A significance test on the regression as a whole can be carried out to determine the probability that the variables are linked by chance. Significance of F = 153.59, is out side the critical value of 3.86. The P Value shows that there is a 0.00% probability of this test statistic by chance if the null hypothesis is correct. Basically the significance F shows that the regression is significant that the coefficients are not 0.

The  = 0.00, the T-statistic for the  = 0.57, it’s inside the critical value of 1.96 this means that it’s not significant. The regression shows that the  = 0.69, this means that when the FTSE return goes up by 1% the BP return goes up by 0.69%. The T-statistic for the  = 12.39, it’s outside the critical value of 1.96, this means that it’s significant. This is evidence that the  and  have a relationship. The residual is the difference between the observed return and the expected. The expected share returns can now be calculated by the Single Index Model, the results are in appendix 6.

5.2 Brent Crude Oil Price return & BP Share Residual Regression

5.2.1 Correlation coefficient, correlation of determination & significance of regression

Period	Multiple R	R²	F 	F-Critical 08/11/2002 - 09/05/2003	0.15	0.02	3.00	3.92 12/05/2003 - 13/11/2003	0.02	0.00	0.06	3.91 14/11/2003 - 14/05/2004	0.15	0.02	3.08	3.92 17/05/2004 - 08/11/2004	0.28	0.08	10.65*	3.92 08/11/2002 - 13/11/2003	0.09	0.00	2.26	3.90 14/11/2003 - 08/11/2004	0.22	0.04	12.71*	3.90 08/11/2002 - 08/11/2004	0.14	0.02	11.07*	3.86

The above table shows the correlation coefficient (Multiple R). It also shows the coefficient of determination (R²), the significance of the regression (F) and the critical value for significance test (F-Critical) for Brent oil return on Residual Regressions of all periods.

The initial 3 6-month periods of regression 08/11/02-09/05/03, 12/05/03-13/11/03 & 14/11/03-14/05/04 shows that Multiple R is 0.15, 0.02 & 0.15 respectively. This means that there is a positive but weak linear relationship between the oil price change and BP share residuals. The R² is 0.02, 0.00 & 0.02 this means that 2%, 0% & 2% of the residuals are being explained by the oil price change. It can be assumed that the relationship is very weak and almost non-existent. F is 3.00, 0.06 & 3.08, which are inside the critical value of 3.92, 3.91 & 3.92 therefore they are not significant.

The final 6-month period of regression 17/05/04-08/11/04 shows that Multiple R is 0.28; this means that there is a positive linear relationship between the oil price change and share residuals. Also it is the strongest linear relationship of all the 6-month periods. The R² is 0.08; this means that 8% of the residuals are being explained by the oil price change. It can be assumed that the relationship between the variables is stronger than any of the other 6-month periods. F is 10.65 outside the critical value of 3.92, and is therefore significant.

The 1-year period of regression 11/11/02-13/11/03 shows that Multiple R is 0.09; this means that there is a positive but weak linear relationship. The R² is 0.008; this means that less than 1% of the Residuals are being explained by the oil price change. The relationship is almost non-existent. F is 2.26 inside the critical value of 3.90, and isn’t significant.

The 1-year period of regression 14/11/03-08/11/04 shows that Multiple R is 0.22; this means that there is a positive and stronger linear relationship than the other 1-year period. The R² is 0.04; this means that 4% of the residuals are being explained by the oil price change. It can be assumed that the relationship is still weak but an improvement on the other similar period of time. F is 12.71 outside the critical value of 3.90, and is therefore significant.

The entire 2-year period of regression 11/11/02-08/11/04 shows that Multiple R is 0.14; this means that there is a positive linear relationship. The R² is 0.02; this means that 2% of the residuals are being explained by the oil price change. The linear relationship is not as strong as the 2nd 1-year period, but is still strong. F is 11.07 outside the critical value of 3.86, and is therefore significant.

5.2.2 Significance of intercept

Period	Intercept	T-statistic	T-Critical 08/11/2002 - 09/05/2003	0.0000083253	-0.05	1.98 12/05/2003 - 13/11/2003	-0.000946812	-1.05	1.98 14/11/2003 - 14/05/2004	0.000821321	0.85	1.96 17/05/2004 - 08/11/2004	-0.00000413257	-0.04	1.98 08/11/2002 - 13/11/2003	-0.000548104	-0.66	1.97 14/11/2003 - 08/11/2004	0.000378739	0.58	1.97 08/11/2002 - 08/11/2004	-0.000080122	-0.15	1.96

The above table shows the intercepts. It also shows the T-statistic for the intercepts, and the critical values for Brent oil return on Residual Regressions of all periods. Analysis of the table shows that the intercept for all periods is very close to 0, further more it can be seen that the T-statistic’s for all periods are inside the critical values. From this it suggests that the intercepts are not significant, the intercepts are probably 0. The intercepts are zero because a major assumption of regression analysis is that the average value of the residuals is 0, this finding confirms this assumption.

5.2.3 Significance of slope

Period	Slope	T-statistic	T-Critical 08/11/2002 - 09/05/2003	0.09	1.73	1.98 12/05/2003 - 13/11/2003	-0.01	-0.25	1.98 14/11/2003 - 14/05/2004	0.08	1.75	1.96 17/05/2004 - 08/11/2004	0.12	3.26*	1.98 08/11/2002 - 13/11/2003	0.05	1.50	1.97 14/11/2003 - 08/11/2004	0.10	3.56*	1.97 08/11/2002 - 08/11/2004	0.07	3.32*	1.96

The above table shows the slope, it also shows the T-stat for the slope and the critical values for Brent oil price change on share residual regressions of all periods.

The initial 3 6-month periods of regression 08/11/02-09/05/03, 12/05/03-13/11/03 & 14/11/03-14/05/04 show the slopes are 0.09, -0.01 & 0.08. This means that when the oil price goes up by 1% the residual goes up by 0.09% and down by 0.01% and up 0.08% respectively on average. The T-statistic for the slope’s are 1.73, -0.25 & 1.75, which are inside the critical value of 1.98 & 1.96 this means that they are not significant. This is evidence that the slopes are probably 0. These 3 6-month periods show no relationship between the oil price change and the residuals.

The final 6-month period of regression 17/05/04-08/11/04 shows that the slope is 0.12; this means that when the oil price goes up by 1% the residual goes up by 0.12%. The T-statistic for the slope is 3.26 outside the critical value of 1.98, and is significant. This 6-month period shows a relationship between the oil price change and the share residuals, it is the only 6-month period to do so.

The 1st 1-year period of regression 11/11/02-13/11/03 shows the slope is 0.05, this means that when the oil price goes up by 1% the residual goes up by 0.05%. The T-statistic for the slope is 1.50 inside the critical value of 1.97, and isn’t significant. This 1-year period along with the 3 initial 6-month periods shows no relationship between the oil price change and the residuals.

The 2nd 1-year period of regression 14/11/03-08/11/04 shows the slope is 0.10, this means that when the oil price goes up by 1% the residual goes up by 0.10%. The T-statistic for the slope is 3.56 outside the critical value of 1.97, and is significant. This 1-year period along with the 4th 6-month period shows a relationship between the oil price change and the residuals.

The entire 2-year period of regression 11/11/02-08/11/04 shows the slope is 0.07, this means that when the oil price goes up by 1% the residual goes up by 0.07%. The T-statistic for the slope is 3.32 outside the critical value of 1.96, and is significant. The 2-year period along with the fourth 6-month period, and the 2nd 1-year period show a relationship between the oil price change and the share residuals.

5.3 FTSE All Share return, BP Share return & Oil price change activity and discussion

Period	FTSE	FTSE return	BP Share Price	BP return	Brent Price	Brent change 08/11/2002-09/05/2003	1942.17		4.03		14.84		1918.49	-0.01	4.13	0.02	15.71	0.05 12/05/2003-13/11/2003	1928.37		4.17		15.49		2163.67	0.10	4.12	-0.01	17.52	0.11 14/11/2003-14/05/2004	2173.5		4.16		17.7		2203.32	0.01	4.9	0.15	22.04	0.19 17/05/2004-08/11/2004	2180.85		4.93		21.95		2344.63	0.06	5.36	0.08	24.66	0.10

Table 5.4 shows that the FTSE returns where low apart from the 2nd period where it increased by 10%. The BP return & Brent change show a 15% and 19% increase in the 3rd period, and an 8% and 11% increase in the 4th period however the regression only shows a statistical relationship between the slope and intercept in the 4th period only.

Period	FTSE	FTSE return	BP Share Price	BP return	Brent Price	Brent change 08/11/2002-13/11/2003	1942.17		4.03		14.84		2163.67	0.10	4.12	0.02	17.52	0.15 14/11/2003-08/11/2004	2173.5		4.16		17.7		2344.63	0.07	5.36	0.22	24.66	0.28 08/11/2002-08/11/2004	1942.17		4.03		14.84		2344.63	0.17	5.36	0.24	24.66	0.39

Table 5.5 shows the FTSE returned 17% over 2-years, 10% in the 1st year period and 7% in the 2nd. BP returns were 24% over 2-years, 2% in the 1st 1-year period and 22% for the 2nd. The oil price change was 39% over 2-years, 15% in the 1st 1-year period and 28% in the 2nd. The regression analysis shows that there was a statistical relationship between the oil price change and the share residuals in the 2nd 1-year period. The 2nd 1-year period is where the BP return and oil price change where increasing the fastest.

As seen from table 5.5 the BP share returns increased in the 2nd 1-year period by 22%, where as the 1st 1-year period increased by only 2%. The oil price increased in the same periods by 28% in the 2nd 1-year period and 15% in the 1st 1-year period. The oil price change and the share price return increased significantly during the 2nd year of analysis where as the FTSE All Share index only increased by 7% in the 2nd 1-year of analysis.

During the period of analysis BP announced that it was selling assets to focus more on key areas and in 2003 the company sold £3bn of assets, this would have resulted in a positive effect on the share price and have nothing to do with the oil price.

BP have entered in to a 50/50 joint venture with a Russian oil company to form Russia’s third largest oil company, it cost them $6.8bn. BP has also announced investment of $20bn in new production areas in place of older assets in the UK and US. This would have the effect of making BP more efficient and increasing the share price in a period where the oil prices are increasing and at the same time increasing the value of reserves.

The company announced in January 2004 that it was to sell 270 retail fuel stations in Asia. As discussed earlier the increase in oil price reduces the profitability of refining leading to the extra cost being transferred to the customer; this strategy of selling fuel stations will have a positive effect on the share price where there is an increasing oil price. The analysis shows that the correlation exists in periods where the oil price change is significantly increasing beyond the levels of the market returns. The residuals can be analysed to see whether or not they are significant (Residual t-stat = Residual/Standard Error).

Periods	Index return	BP return	Predicted	Residual	Residual t-stat 08/11/2002-09/05/2003	-0.012343041	0.024213	-0.0082618	0.0324748	2.71 12/05/2003-13/11/2003	0.10875041	-0.01214	0.0757932	-0.0879332	-7.36 14/11/2003-14/05/2004	0.013534121	0.15102	0.0097004	0.1413196	11.83* 17/05/2004-08/11/2004	0.06985329	0.080224	0.0487934	0.0314306	2.63 08/11/2002-13/11/2003	0.102372358	0.0218447	0.0713659	-0.0495212	-4.14 14/11/2003-08/11/2004	0.072988062	0.2238806	0.0509693	0.1729113	14.47* 08/11/2002-08/11/2004	0.171651817	0.2481343	0.1194551	0.1286792	10.77*

The above table shows that the residual T-statistic for the 2nd 6-month & the 1st 1-year period are inside the critical value of 1.96 and not significant. The 1st 6-month & 4th 6-month periods are just outside the critical value of 1.96. This is curious, as the regression of oil price change on share residuals was statistically significant for only the 4th 6-month period. The 3rd 6-month, 2nd 1-year & the overall 2-year periods, residual T-statistics are well outside the critical value of 1.96 and are therefore significant. This is probably why the regressions for the two latter periods are significant and show a relationship between the oil price change and the share residuals. However the 3rd 6-month period regression for oil price change and the share residuals was not significant.

6.0 Conclusion

Period	Multiple R	R²	F 	F-Critical	Intercept	Intercept   t-stat	slope	slope  t-stat	T-Critical 08/11/2002 09/05/2003	0.15	0.02	3.00	3.92	0.00	-0.05	0.09	1.73	1.98 12/05/2003 13/11/2003	0.02	0.00	0.06	3.91	-0.00	-1.05	-0.01	-0.25	1.98 14/11/2003 14/05/2004	0.15	0.02	3.08	3.92	0.00	0.85	0.08	1.75	1.96 17/05/2004 08/11/2004	0.28	0.08	10.65*	3.92	-0.00	-0.04	0.12	3.26*	1.98 08/11/2002 13/11/2003	0.09	0.00	2.26	3.90	-0.00	-0.66	0.05	1.50	1.97 14/11/2003 08/11/2004	0.22	0.04	12.71*	3.90	0.00	0.58	0.10	3.56*	1.97 08/11/2002 08/11/2004	0.14	0.02	11.07*	3.86	-0.00	-0.15	0.07	3.32*	1.96

The oil price change has a positive linear correlation with the share return of British Petroleum plc above the effect of the market return. The above table shows that the slope and intercept are correlated for the entire 2-year period, 08/11/2002-08/11/2004. Also the relationship is stronger during the 4th 6-month period, 17/05/2004-08/11/2004 where the correlation coefficient was 0.28, which is the strongest positive linear relationship of all periods. The 2nd 1-year period, 14/11/2003-08/11/2004 also show’s a significant relationship between the intercept & slope and also shows the second strongest correlation coefficient of 0.22. The initial 3 6-monthly periods, 08/11/2002-09/05/2003, 12/05/2003-13/11/2003 & 14/11/2003-14/05/2004 and the 1st 1-year period, 08/11/2002-13/11/2003 show no relationship.

The part of the return that is not explained by the market is 17% for the 2nd 1-year period. The part of the return that is not explained by the market is 12% for the 2-year period. The residual T-statistic for the 2nd 1-year period is 14.47 and for the 2-year period is 10.77. Both the residual T-statistics are outside the critical value of 1.96 and are significant. This is probably why the regressions for these periods are significant and show a relationship. However while the 4th 6-month regression period was statistically significant, the residual T-statistic is just outside the critical value of 1.96 at 2.63.

The correlation coefficient and coefficient of determination are both very low for the entire 2-year period of regression, however the research by Hammoudeh et al & Sadorsky show why this might be. The research by Hammoudeh et al showed that the oil price volatility increased the volatility of companies stocks engaged in exploration & production and oil-domestic integrated. The analysis also implied that this oil volatility had a dampening effect on the volatility of the stocks of the oil international integrated, and oil and gas refining and marketing companies. Sadorsky’s research showed that the interest rate has a negative effect on the returns of oil stocks. The Bank of England raised the base rate of interest four times in 2004.

BP is involved in all stages of the oil industry. The companies activities are, exploration and production (oil & gas), and refining & marketing. This would suggest that if Hammoudeh et al were correct, that when the oil price becomes volatile some business units of BP benefit like exploration and production, while other business units like refining and marketing suffer. This along with being involved in other business areas means that BP is more diversified than an oil exploration company and the oil price volatility explains some of the movement of the share return residuals, it can’t explain all of it. The increase in interest rates may have also a negative influence in the findings of this paper.

Further research needs to be carried out to prove if this is true statistically. This research will need to include companies engaged to varying degrees in the oil industry and the model would need to take into account the interest rate factor. Then it will be possible to see the degree to which diversification of companies protects them against the volatility of international oil prices.

7.0 References

Abdalla, K.L (1995). The Changing Structure of the International Oil Industry, Energy Policy, Vol 23, No 10, pp 871-877

Balabanoff, S (1995). Oil futures prices and stock management, Energy Economics, Vol 17, No 3, pp 205-210

BP (2004). Statistical review of world energy 2004 [online]. Available at<URL: http://www.bp.com/subsection.do?categoryId=95&contentId=2006480 [Accessed 12 October 2004]

Datastream, BP Company Overview Moorgate Library, [Accessed 8th November 2004]

Duncan, G. (11/10/2004). How an oil price change affects the inflation rate The Times Newspaper, p42

Edwards, A. L (1984) An Introduction to Linear Regression and Correlation W H Freeman and Company p1-4

Elton, E.J., Gruber, M. J., Brown, S. J. & Goetzmann, W. N (2003) Modern Portfolio Theory and Investment Analysis John Wiley & Sons Inc. p132 & p669

Hammoudeh, S., Dibooglu, S., & Aleisa, E (2002). Relationships among U.S. oil prices and oil industry equity indices, International Review of Economics and Finance. Vol 13, pp. 427-453

Hilliard, J.E. & Danielsen, A.L (1984). World oil prices and equity returns of major oil and auto companies, Resources & Energy, Vol 6, pp.259-276

Jones, C.M. & Kaul, G (1996). Oil and the Stock Markets, The Journal of Finance, Vol 51, No 2, pp 463-491

Kearton, Lord. (1985)The oil industry some personal Recollection and opinions in Hawdon, D. The Changing Structure of the World Oil Industry Croom Helm Ltd. p1-2

Keynote (2004). The Offshore Oil and Gas Industry Mintel. p73-74

Odell, P. (1986). Oil and world power Penguin Books. Ch ‘Postscript’

Odell, P., Rosing, K. (1980). The future of oil Kogen Page Ltd p23-24, 246-259

Rees, J., Odell, P. (1987). The International Oil Industry The Macmillan Press Ltd. Ch 8.

Sadorsky, P (2001). Risk factors in stock returns of Canadian oil and gas companies, Energy Economics, Vol 23, pp. 17¬¬-28

Shwadran, B. (1977). Middle East Oil Schenkman Publishing Inc. Ch 2 & 7.

Sloman, J. (1997). Economics Prentice Hall Ch 2,6,7 & p204-205 8.0 Bibliography

Adamson, A (1990) A Students Guide for Assignments, Projects and research 4th edition, Thamesman

Brook, A., Price, R., Sutherland, D., Westerlund, N., & André, C. (2004). Oil price developments: drivers, economic consequences and police responses. Economics department working papers, No. 412 OECD p5

Claes, D.H (1992) The Politics of Oil Producer Co-operation, The Political conomy of Global Interdependence Westview Press

Datastream, FTSE All Share Index, BP share price & Brent Crude Oil price Moorgate Library, [Accessed 8th November 2004]

El-Mukadem, A.M, Hawdon, D, Robinson, C & Stevens, P.J (1984) OPEC and The World Oil Market 1973-1983 Eastlords Publishing ltd

Grayson, L.E (1981) National Oil Companies John Wiley & Sons

Mitchel, J, Morita, K, Selley, N & Sten, J (2001) The New Economy of Oil, Impacts on Business, Geopolitics and Society Earthscan Publications ltd

Verdin, M. (11/10/04) Price of factory goods soars as oil rises The Times Online Available at<URL: http//www.thetimes.co.uk [Accessed 15 October 2004]

Wolcott, H (1990) Writing up Qualitative research Sage

Yergin, D (1991) The Prize Simion & Schuster ltd

9.0 Appendix

9.1 Appendix contents

Appendix 1a-g: Brent crude oil price change on BP Share residuals, Regressions			42 Appendix 2: FTSE All Share Index v BP Share returns scatter graph-trend line			45

Appendix 3a-g: Oil price change v BP share residuals scatter graph-trend line			46

Appendix 4a-g: FTSE All Share return, BP Share Price return & Brent Oil price change activity	49

Appendix 5: BP profit, return, cumulative return & Brent oil price, return and cumulative change 1994-2003									53

Appendix 6: FTSE All Share price & return, BP Share price & return, Brent Oil price & change and Residuals								54

Appendix 1: Brent crude oil price change on BP Share residuals, Regressions

Appendix 1a: Oil price change & BP share residual regression for period 08/11/02-09/05/03 SUMMARY OUTPUT Oil price change (x) BP residual (y) 08/11/02-09/05/03 Regression Statistics Multiple R	0.154950385 R Square	0.024009622 Adjusted R Square	0.016009701 Standard Error	0.015766903 Observations	124 ANOVA df	SS	MS	F	Significance F Regression	1	0.000746092	0.000746092	3.0012323	0.085728385 Residual	122	0.03032862	0.000248595 Total	123	0.031074712 Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95% Intercept	-8.3253E-05	0.001416134	-0.058789	0.9532164	-0.002886635	0.0027201 X Variable 1	0.095111873	0.054901591	1.732406514	0.0857284	-0.013571405	0.2037952

Appendix 1b: Oil price change & BP share residual regression for period 12/05/03-13/11/03 SUMMARY OUTPUT Oil price change (x) BP residual (y) 12/05/03-13/11/03 Regression Statistics Multiple R	0.022516943 R Square	0.000507013 Adjusted R Square	-0.007181395 Standard Error	0.010328853 Observations	132 ANOVA df	SS	MS	F	Significance F Regression	1	7.03536E-06	7.035E-06	0.0659451	0.79774088 Residual	130	0.013869076	0.0001067 Total	131	0.013876111 Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95% Intercept	-0.000946812	0.000899835	-1.0522065	0.2946567	-0.002727026	0.0008334 X Variable 1	-0.011966207	0.046597788	-0.2567978	0.7977409	-0.104154262	0.0802218

Appendix 1c: Oil price change & BP share residual regression for period 14/11/03-14/05/04 SUMMARY OUTPUT Oil price change (x) BP residual (y) 14/11/03-14/05/04 Regression Statistics Multiple R	0.156329792 R Square	0.024439004 Adjusted R Square	0.016507614 Standard Error	0.010702461 Observations	125 ANOVA df	SS	MS	F	Significance F Regression	1	0.000352941	0.0003529	3.0813014	0.08168672 Residual	123	0.014088749	0.0001145 Total	124	0.01444169 Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95% Intercept	0.000821321	0.000961533	0.8541781	0.3946669	-0.001081975	0.0027246 X Variable 1	0.086595063	0.049331695	1.7553636	0.0816867	-0.011053965	0.1842441

Appendix 1d: Oil price change & BP share residual regression for period 17/05/04-08/11/04 SUMMARY OUTPUT Oil price change (x) BP residual (y) 17/05/04-08/11/04 Regression Statistics Multiple R	0.283453456 R Square	0.080345862 Adjusted R Square	0.072807713 Standard Error	0.00956796 Observations	124 ANOVA df	SS	MS	F	Significance F Regression	1	0.000975748	0.0009757	10.658567	0.001422089 Residual	122	0.011168594	9.155E-05 Total	123	0.012144342 Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95% Intercept	-4.13257E-05	0.000859901	-0.0480587	0.9617481	-0.001743588	0.0016609 X Variable 1	0.122620793	0.03755906	3.2647461	0.0014221	0.048268817	0.1969728

Appendix 1e: Oil price change & BP share residual regression for period 08/11/02-13/11/03 SUMMARY OUTPUT Oil price change (x) BP residual (y) 08/11/02-13/11/03 Regression Statistics Multiple R	0.094004809 R Square	0.008836904 Adjusted R Square	0.004934687 Standard Error	0.013252084 Observations	256 ANOVA df	SS	MS	F	Significance F Regression	1	0.000397701	0.0003977	2.2645856	0.133604079 Residual	254	0.044606903	0.0001756 Total	255	0.045004604 Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95% Intercept	-0.000548104	0.000828594	-0.6614873	0.5088995	-0.002179893	0.0010837 X Variable 1	0.054969649	0.036528227	1.504854	0.1336041	-0.016967108	0.1269064

Appendix 1f: Oil price change & BP share residual regression for period 14/11/03-08/11/04 SUMMARY OUTPUT Oil price change (x) BP residual (y) 14/11/03-08/11/04 Regression Statistics Multiple R	0.221255045 R Square	0.048953795 Adjusted R Square	0.045103405 Standard Error	0.010127453 Observations	249 ANOVA df	SS	MS	F	Significance F Regression	1	0.001304014	0.001304	12.713985	0.000435677 Residual	247	0.025333631	0.0001026 Total	248	0.026637645 Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95% Intercept	0.000378739	0.000643144	0.5888864	0.5564757	-0.000888008	0.001645486 X Variable 1	0.107895207	0.030259469	3.5656676	0.0004357	0.048295692	0.167494722

Appendix 1g: Oil price change & BP share residual regression for period 08/11/02-08/11/04 SUMMARY OUTPUT Oil price change (x) BP residual (y) 08/11/02-08/11/04 Regression Statistics Multiple R	0.146802442 R Square	0.021550957 Adjusted R Square	0.01960573 Standard Error	0.01181633 Observations	505 ANOVA df	SS	MS	F	Significance F Regression	1	0.001546897	0.0015469	11.07889213	0.00093716 Residual	503	0.070231699	0.0001396 Total	504	0.071778596 Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95% Intercept	-0.000080122	0.00052637	-0.1522167	0.879077051	-0.001114277	0.000954 X Variable 1	0.079671759	0.023936257	3.328497	0.00093716	0.032644424	0.1266991

Appendix 3: Oil price change v BP share residuals scatter graph-trend line

Appendix 3a: Oil price change & BP share residual scatter graph-trend line for period 08/11/02-09/05/03 Appendix 3b: Oil price change & BP share residual scatter graph-trend line for period 12/05/03-13/11/03

Appendix 3c: Oil price change & BP share residual scatter graph-trend line for period 14/11/03-14/05/04

Appendix 3d: Oil price change & BP share residual scatter graph-trend line for period 17/05/04-08/11/04 Appendix 3e: Oil price change & BP share residual scatter graph-trend line for period 08/11/02-13/11/03

Appendix 3f: Oil price change & BP share residual scatter graph-trend line for period 14/11/03-08/11/04 Appendix 3g: Oil price change & BP share residual scatter graph-trend line for period 08/11/02-08/11/04 Appendix 4: FTSE All Share return, BP Share Price return & Brent Oil price change activity

Appendix 4a: FTSE All Share return, BP Share Price return & Brent Oil price change activity for period 08/11/02-09/05/03 Appendix 4b: FTSE All Share return, BP Share Price return & Brent Oil price change activity for period 12/05/03-13/11/03 Appendix 4c: FTSE All Share return, BP Share Price return & Brent Oil price change activity for period 14/11/03-14/05/04

Appendix 4d: FTSE All Share return, BP Share Price return & Brent Oil price change activity for period 17/05/04-08/11/04 Appendix 4e: FTSE All Share return, BP Share Price return & Brent Oil price change activity for period 08/11/02-13/11/03

Appendix 4f: FTSE All Share return, BP Share Price return & Brent Oil price change activity for period 14/11/03-08/11/04 Appendix 4g: FTSE All Share return, BP Share Price return & Brent Oil price change activity for period 08/11/02-08/11/04

Appendix 5: BP profit, return, cumulative return & Brent oil price, return and cumulative change 1994-2003

Year	Brent Crude oil price £	BP Turnover	BP profit	Oil price change	BP profit return	Cumulative oil price change	Cumulative BP profit return 1994	9.5	33,116	2,281				1995	10.43	36,106	1,946	0.089165868	-0.172147996	0.089165868	-0.172147996 1996	12.58	44,731	3,667	0.1709062	0.469320971	0.260072068	0.297172975 1997	12.49	43,460	3,646	-0.007205765	-0.005759737	0.252866303	0.291413238 1998	7.19	41,053	2,911	-0.73713491	-0.252490553	-0.484268606	0.038922685 1999	7.83	51,457	4,326	0.081736909	0.327092002	-0.402531697	0.366014687 2000	17.62	98,054	11,215	0.555618615	0.614266607	0.153086918	0.980281294 2001	17.19	120,867	9,088	-0.025014543	-0.234044894	0.128072375	0.7462364 2002	16.97	111,589	7,033	-0.012964054	-0.292193943	0.115108321	0.454042457 2003	19.53	129,920	9,167	0.131080389	0.232791535	0.24618871	0.686833992