1. The united states stock and Oil Prices1
The Relationship between the United States Stock
Return and the Oil Prices
SubjectCode: ECON939
Submittedby :
NourMutair 4553573
AhmedAbuSharkh
Hassan Wahdan
2. The united states stock and Oil Prices2
Abstract
Our project will examine the relationship between crude oil price(cp), stock market and
macroeconomic variables, the duration of research for the last 10 years for the macroeconomic
variables, we will include industrial production(ip), money supply(M2), inflation(IR)and it’s relation
with oil price and stock market. These factors have an effect on the performance of the economy that
can be analyzed and presented. Changes in oil prices coincide with changes in the stock prices of
some stocks but not necessarily the stock index not unless the change is experienced over a long
period.
3. The united states stock and Oil Prices3
Contents
The Relationship between the United States Stock and the Brent...........................................................1
Abstract............................................................................................................................................2
METHODOLOGY.................................................................................................................................5
CONCLUSION ...................................................................................................................................11
4. The united states stock and Oil Prices4
Introduction
Many studies concentrate on the macroeconomic variables and their direct effects on the stock
market. The vague effect on stock market due to change in oil price as well as other economic factors
might lead to delay of investor decision to go further for investment sin the stock market. As oil price
have a direct effect on other macroeconomic factors, hence oil price increase will lead to inflation and
reduction in customers spending. On other hand, increase in money supply leads to an increase in
liquidity for purchasing securities, while an increase in money supply leads to inflation which will
lead to higher interest rates and a fall in stock prices (Billah Dar, Shah, Bhanja & Samantaraya,
2014). Industrial production followed by company high sales and increase in profit will lead an
increase in company capital and stock prices that Leeds to confidence in investment of stock market.
This is witnessed in the economy when oil prices change; it is accompanied by changes in food
prices, stock price and a general improvement in the performance of the sectors of economy
(Büyüksalvarci & Abdioglu, 2010).
Literature review
Oil prices play a key role in the performance of the world economy. Studies have found
out that changes in oil prices affect consumer behaviors (Anoruo & Elike, 2009). When oil prices
decrease, consumers spend more on luxuries, which in turn drives the stock prices of companies
offering luxury goods. The prices of oil stocks drop since the companies record reduced income
that translates to low profits or even losses. Therefore, investors are lose faith in them and off
load them leading to a drop in their stock value. This shift in prices usually results to negligible
changes in overall stock index (Kapusuzoglu, 2011). This is because some stocks drop in price
while others increase. There are those whose companies are not affected in any way by the
5. The united states stock and Oil Prices5
changes in stock price. When oil prices increase, the stock prices of oil companies increase.
Consumers spend a significant amount of their money on oil leaving them with little money to
spend on luxuries. Consequently, the stocks of luxury good producers remain constant or do not
record significant changes (Asteriou, Dimitras & Lendewig, 2013).
METHODOLOGY
Historical data of the United States of America was collected from 1959 to 2014 to test
the significance of oil prices in the United States in determining the return on the U.S. stock
market. Theoretically, oil prices have been seen to impact greatly on prices of most items in the
market and we therefore wanted to see what impact it has had on the U.S. stock market.
Considering that oil prices affect most of the major indicators of a country’s development rate,
we develop a model to determine the impact of oil prices on various historical indices as follows:
OP=∝ + βGDP+β2IG+β3CPI+β4PCI+β5PCE+β6NE+ε;
Where;
OP is the adjusted inflation price of oil;
GDP is the gross domestic product;
IG is the industrial growth;
CPI is the consumer price index;
PCI is the per capita income;
PCE is the personal consumption expenditure;
NE is the Net Exports; and
ε is the error term .
On running a Pearson correlation test of the variables, the following was obtained:
6. The united states stock and Oil Prices6
Correlations
Nominal
Price
Inflation
Adjusted
Price
GDP Indus
Growth
consum
er price
index
per
capita
income
personal
consumptio
n
expenditure
net
exports
Nominal Price 1 .826** -.399** .764** .820** .779** .800** -.795**
Inflation
Adjusted Price
.826** 1 -.406** .435** .486** .460** .482** -.471**
GDP -.399** -.406** 1 -.570** -.549** -.604** -.580** .339*
Idus Growth .764** .435** -.570** 1 .974** .990** .972** -.871**
consumer price
index
.820** .486** -.549** .974** 1 .986** .998** -.856**
per capita
income
.779** .460** -.604** .990** .986** 1 .987** -.854**
personal
consumption
expenditure
.800** .482** -.580** .972** .998** .987** 1 -.833**
net exports -.795** -.471** .339* -.871** -.856** -.854** -.833** 1
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
7. The united states stock and Oil Prices7
As can be observed, the inflation-adjusted prices are correlated to all the variables listed. The
correlations however are negative for GDP and net exports but positive for all the other
variables. From this, we can comfortably say that since none of the variables exhibits a
correlation higher than o.65, the multi-collinearity does not need to be performed for this data.
Next, we carry out a linear regression analysis of the variables using the model obtained above.
Model Summaryb
Mode
l
R R Square Adjusted R
Square
Std. Error of the Estimate
1 .619a .383 .307 21.35042
a. Predictors: (Constant), net exports, GDP, personal consumption expenditure,
IndusGrowth, per capita income, consumer price index
b. Dependent Variable: InflationAdjustedPrice
From the SPSS output on the summary, we can tell that the 6 predictors used explain on
38.3% of the impact of oil prices on the stock market. This means that the variables have
quite an impact on the oil prices though they may not be the only predictors.
The coefficients of regression were obtained as follows:
8. The united states stock and Oil Prices8
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 99.221 38.234 2.595 .012
GDP -.041 .016 -.448 -2.532 .015
IndusGrowth -1.180 .956 -1.147 -1.235 .223
consumer price index .904 .954 2.429 .948 .348
per capita income -.001 .004 -.502 -.361 .720
personal consumption
expenditure
-.855 2.194 -1.026 -.390 .698
net exports -.055 .032 -.521 -1.723 .091
a. Dependent Variable: Inflation AdjustedPrice
9. The united states stock and Oil Prices9
It can be observed that the coefficients of regression are negative with only one positive
for consumer price index. This means that the other variables have a decreasing impact on
the prices of oil as opposed to the consumer price index. Therefore, we can say that GDP,
industrial growth, per capita income, personal consumption expenditure and net exports
have negative significance but the consumer price index has a positive significance on oil
prices.
We can further determine the auto-correlation of the variables using the Durbin-Watson
statistic test. The following results were obtained:
Model Summaryb
Mode
l
R R Square Adjusted R
Square
Std. Error of
the Estimate
Durbin-
Watson
1 .619a .383 .307 21.35042 .368
a. Predictors: (Constant), net exports, GDP, personal consumption
expenditure, IndusGrowth, per capita income, consumer price index
b. Dependent Variable: InflationAdjustedPrice
Autocorrelation is used to determine the correlation error terms or residuals over
time and it violates the regression assumption that residuals are random and independent.
We can therefore use this test to tell how useful our regression model is by taking the
Durbin-Watson statistic, which is obtained as 0.368. Theoretically, the statistic ranges
from 0 to 4 and indicates the strength of the autocorrelation. If the value is between 0 and
2, there is positive auto correlation while if it is between 2 and 4, there is negative
autocorrelation.
Since our value stands at 0.368, then there is positive autocorrelation.
On testing for heteroskedasticity, the White’s test was used. From the diagram above,
Number of observations = 55 and R^2=0.383.
10. The united states stock and Oil Prices10
From the tables of Chi-square, we can average the chi-square as 11.514. Comparing the two
values with df=54 and 0.05 level of significance, we can clearly justify that there is very high
heteroskedasticity in our model.
This can also be observed from the scatterplot shown below which shows a non-uniform and
unequal distribution as the values increase in our model.
11. The united states stock and Oil Prices11
CONCLUSION
From the tests carried out, we can therefore say that most of the variables in our regression
model had negative coefficients, which include GDP, Industrial growth, per capita income,
personal consumption index and net exports. These therefore impact the oil prices negatively.
Only consumer price index had a positive coefficient hence showing that it had a positive impact
on oil prices. However, it is noteworthy that only GDP had a significance of 0.015 which is less
than 0.05 hence is the only significant predictor of oil prices in the U.S. according to the model.
All the other predictors have higher significances hence cannot be relied upon as significant
predictors.
It is also noteworthy that the variables used in the model only account for 38.3% of the
dependent variable, hence there are other variables, which strongly impact oil prices in the
market.
On multicollinearity, it was observed that none of the variables has a correlation higher or equal
to 0.65, hence we can say that no multicollinearity exists between the variables in the model.
On heteroskedasticity, the values obtained from the chi-square test were used to compare with
value of R^2 and the null hypothesis was rejected. We therefore, conclude that very high
heteroskedasticity exists between the variables. The scatter plot obtained also can be used
informally to explain the same finding
12. The united states stock and Oil Prices12
Bibliography
Anoruo, E., PhD. & Elike, U., PhD. 2009, "An Empirical Investigation into the Impact of High
Oil Prices on Economic Growth of Oil-Importing African Countries", International Journal of
Economic Perspectives, vol. 3, no. 2, pp. 121-129,152.
Asteriou, D., Dimitras, A. & Lendewig, A. 2013, "The Influence of Oil Prices on Stock Market
Returns: Empirical Evidence from Oil Exporting and Oil Importing Countries", International
Journal of Business and Management, vol. 8, no. 18, pp. 101-120.
Billah Dar, A., Shah, A., Bhanja, N. & Samantaraya, A. 2014, "The relationship between stock
prices and exchange rates in Asian markets", South Asian Journal of Global Business
Research, vol. 3, no. 2, pp. 209.
Büyüksalvarci, A. & Abdioglu, H. 2010, "The Causal Relationship between Stock Prices and
Macroeconomic Variables: A Case Study for Turkey", International Journal of Economic
Perspectives, vol. 4, no. 4, pp. 601-610.
Kapusuzoglu, A. 2011, "Relationships between Oil Price and Stock Market: An Empirical
Analysis from Istanbul Stock Exchange (ISE)", International Journal of Economics and
Finance, vol. 3, no. 6, pp. 99-106.
13. The united states stock and Oil Prices13
Appendixes
Historical oil prices
Annual Average
Domestic Crude Oil Prices (in $/Barrel)
Inflation Adjusted to November 2014
1946-
Present
Year Nominal
Price
Inflation Adjusted
Price
1946 $1.63 $19.41
1947 $2.16 $22.81
1948 $2.77 $27.21
1949 $2.77 $27.48
1950 $2.77 $27.19
1951 $2.77 $25.20
1952 $2.77 $24.64
1953 $2.92 $25.72
1954 $2.99 $26.30
1955 $2.93 $25.80
1956 $2.94 $25.56
1957 $3.14 $26.38
1958 $3.00 $24.55
1959 $3.00 $24.30
1960 $2.91 $23.26
1961 $2.85 $22.52
1962 $2.85 $22.25