The Stock Market Reaction to Oil Price Changes
Sridhar Gogineni
Division of Finance
Michael F. Price College of Business
University of Oklahoma
Norman, OK 73019-0450
Abstract
In this paper, I test the reaction of the stock market as a whole and of different industries to oil
price changes. While the market reacts negatively to daily oil price changes, this reaction is
economically significant only for large oil price changes. I find no evidence of under or overreaction of the market to oil price changes or an asymmetry in market’s reaction to oil price
increases and decreases. The level of oil prices and the risk of US being involved in a war are
significant factors in determining market’s sensitivity to oil price changes. Finally, I document
the sensitivity of individual industries to oil price changes. In addition to oil-intensive industries,
industries that do not use oil to any significant extent are also sensitive to oil price changes.

I am grateful for the helpful comments received from Louis Ederington, Chitru Fernando, Vahap Uysal,
Cynthia Rogers, Carlos Lamarche and my colleagues in the PhD program. I also acknowledge support from
the Center for Financial Studies and the Summer Research Paper support fund at the University of
Oklahoma.
25 January 2006. “Stocks Manage to Keep Rally Going Amid Falling Oil Prices, Rising Profits”
4 March 2006. “Oil Price Rises Have Contributed To Global Imbalances, Study Says”
2 June 2006. “Stocks Surge as Inflation Fears Ease on Factory Data, Falling Oil Prices”
21 September 2006. “Unchanged Rates, Oil-Price Dip Rally Stocks”
(Headlines from The Wall Street Journal)
As the above headlines illustrate, in the recent months the popular financial press has talked
repeatedly about how changes in oil prices are impacting the stock market. In fact, during 2005,
oil prices figured in the headlines1 of The Wall Street Journal on 112 days and out of these, the
stock market movement is attributed to oil price changes on 30 days. As reviewed below, a
considerable economics literature has been devoted to study the impact of oil prices on
macroeconomic variables such as inflation, growth rates, and exchange rates. However, there is
very little research in finance literature on how the stock market reacts to contemporaneous oil
price changes. While the financial media assumes that the stock market is strongly influenced by
oil prices, no one has measured how strong the relation is.
Petroleum is an essential energy source in the US accounting for 40% of total energy
requirements. The total demand for oil was approximately 21 million barrels per day in 2005 and
is projected to increase to 28 million barrels per day by 2030. Given the importance of oil, its
short-term demand price inelasticity, and the attention oil prices receive in the financial press, an
understanding of the impact of oil price changes on contemporaneous market returns is essential
to market participants. In this paper, I investigate how daily oil price changes affect the stock
market as whole and particular industries.
The first goal of the paper is to provide a systematic investigation of the impact of oil
price changes on the stock market. Specifically, I test how the market reacts to daily oil price
changes and whether the market’s reaction is conditional on different scenarios such as the
1
The search terms used are “oil prices”, “oil price”, “oil prices and stocks” and “oil price and stocks”
2
direction of the price change, the current level of the oil prices, etc. Also, I test for any underreaction or over-reaction of the market to oil price changes and explore the time series patterns of
market sensitivity to oil price changes.
Using daily data from 1983 to 2005, I find that the market reacts negatively to oil price
changes but the magnitude of this reaction is quite small. For example, a 10% increase in the oil
price on average is associated with a reduction in market returns by about 0.2%. Market
sensitivity to oil prices is directly related to the level of oil prices. Evidence for the existence of
an asymmetry in market’s reaction to oil price increases and decreases is weak. A time series
analysis shows that significant negative relation between the market returns and oil price changes
is concentrated in periods that are witnessed by high level of oil prices and the US involvement in
an armed conflict in the Middle East.
The second goal of this paper is to examine the impact of oil price changes on individual
industries. Oil-intensive industries2, led by air transportation, trucking and chemicals are more
sensitive to oil price changes than industries that are not oil-intensive. Exceptions include power
generation and mining industries which are oil-intensive, but are less sensitive to oil price
changes than non oil-intensive industries such as entertainment. It is surprising to find that
industries that do not use oil to any significant extent are also sensitive to oil price changes. This
suggests a demand effect of oil price changes. That is, the market expects that when oil prices go
up, consumers have less to spend on other goods.
My paper differs from the existing literature in two aspects. First, most of the existing
studies test the impact of oil price changes on longer periods. Table I presents the frequency of
data and the response variables used in some of the most important studies in this area. Most of
these studies use quarterly or monthly data and have a macroeconomic focus. In this paper, I use
2
As explained in Section III, an industry is classified as an oil-intensive or non oil-intensive based on the
Benchmark input requirement coefficients published by Survey of Current Business (2002).
3
daily returns data and investigate the impact of oil price changes on market and industry returns
from a financial markets perspective. Using daily data offers several advantages. Oil prices are
reported daily and since stock prices usually respond quickly to public information, using daily
data helps measure how investors see oil prices impact the economy. I also test for any under or
over-reaction of the market to oil price changes which provides a good measure of market
efficiency in responding to publicly available information. In addition, I test whether the market
is more sensitive to oil price changes in certain periods than others and if it is so, what the
possible reasons might be. .
Second, this paper also contributes to the existing literature by studying the impact of oil
price changes on industry returns. It is widely accepted that the stock returns of oil-intensive
industries are more sensitive to oil price changes than those of non oil-intensive industries. But no
one has documented these largely subjective views of the investing public. I classify industries
into oil-intensive and non oil-intensive groups3 and document their sensitivity to
contemporaneous oil price changes. To my knowledge, this is the first paper to do so. The
findings of this paper might have important implications on the hedging decisions for individual
industries.
The rest of the paper is organized as follows. Section I discusses related literature,
Section II presents the development of testable hypotheses. Section III contains the data
collection methods adopted and defines the sample and the variables. Section IV presents the
empirical results. Section V concludes the paper.
I. Related Literature
A long line of empirical work in economics finds that oil price increases negatively
impact measures of macroeconomic activity. To review some of the most important studies,
3
based on the input requirement coefficients
4
Hamilton (1983) documents a significant negative relation between oil price changes and future
GDP growth in the United States finding that all but one of the U.S recessions since World War II
have been preceded, typically with a lag of three quarters, by a dramatic increase in the price of
crude petroleum. Subsequent research by Gisser & Goodwin (1986) largely confirms Hamilton’s
findings while Burbidge & Harrison (1984) report similar, although slightly weaker, results using
data from five OECD countries4. Mork (1989) extends Hamilton’s results and documents an
asymmetric relation between oil prices and output growth. Mork presents evidence that GNP
growth has a significant negative correlation with increases in real price of oil, but an
insignificant positive correlation with decreases in real price of oil. However, Hooker (1996a)
reports that the oil price-macroeconomic relationship and the evidence for asymmetric oil price
effects are considerably weaker when the sample period is extended to the 1990’s5.
Golub (1983) examines exchange rate reactions to oil price changes and notes that the
country’s dependence on imported oil and the direction of wealth transfer associated with the oil
price change explains the reaction. Among the many other studies finding that oil price shocks
impact the economy are Davis & Haltiwanger (2001), Davis, Loungani & Mahidhara (1996), and
Keane & Prasad (1996) for employment effects; Hamilton & Herrera (2002), Bernanke, Gertler &
Watson (1997), Barsky & Kilian (2001) on the role of monetary policy responses to oil price
shocks; Lee & Ni (2002) on demand and supply effects on industries and DeLong & Bradford
(1997) and Hooker(1997) on the inflationary effects of oil price shocks6.
A much smaller finance literature on oil price effects addresses whether stock market
reactions to oil price shocks are rational and whether oil prices have any predictability. Kaul and
Seyhun (1990) investigate the effects of relative price variability on output and stock market.
4
Organization for Economic Co-operation and Development. The countries include US, Japan, Germany,
UK and Canada.
5
Although these results are contrary to the notion that oil price movements have large effects on the
behavior of economy, they are consistent with the opinion of Darby (1982) that the relevance of oil prices
as a regular element in business cycle fluctuations has been overstated.
6
Hamilton & Herrera (2002) provide a comprehensive list of studies conducted on oil shocks.
5
They find a negative effect of relative price variability on output and stock returns and suggest
that these results were largely driven by the oil shocks of the 1970s. Jones & Kaul (1996) test
whether the reaction of international stock markets to oil price shocks can be justified by current
and future changes in real cash flows. Using quarterly data, they find that the US and Canadian
stock markets are rational while the Japanese and the UK stock markets tend to over-react to oil
price shocks. Using monthly data, Sadorsky (1999) finds that an oil price shock has a negative
and statistically significant initial impact on stock returns7. Huang, Masulis and Stoll (1996)
provide evidence for a significant causality from oil futures to stocks of individual companies, but
showed no impact on a broad based market index like the S&P 500. Chen, Roll & Ross (1986)
find that the risk associated with oil price changes is not priced in the stock market. In a recent
working paper, Bittlingmayer (2005) finds that oil price changes associated with war risk cause
larger declines in stock prices, larger increases in treasure yields and larger increases in implied
stock volatility than oil price changes associated with other causes.
Three recent papers have examined whether future stock market returns can be predicted
based on past oil price changes. Dreisprong, Jacobsen & Maat (2003) test if oil price changes can
predict stock market returns worldwide. Using monthly data of eighteen developed and thirty
emerging markets, they find that twelve of the eighteen developed markets exhibit statistically
significant predictability. Emerging markets show the same effect, though with less significance.
Hong, Torous & Valkanov (2002) document a negative relation between lagged petroleum
industry returns and the U.S stock market returns. Pollet (2002) finds that expected changes in oil
prices are able to predict excess market returns as well as excess returns for most U.S industries.
II. Hypotheses
7
In a related paper, Papapetrou (2001) estimates that real stock returns in Greece are affected negatively by
oil price increases. This impact lasts for approximately 4 months.
6
A. Relation between Oil Price changes and Market Returns (Hypothesis I)
If investors believe oil has an important impact on the economy, then oil price changes
should impact the stock market immediately as stock prices usually respond very quickly to
public information. Also, financial commentators often attribute negative (positive) stock market
movements to oil price increases (decreases) at roughly the same time. As mentioned earlier,
during the year 2005, oil prices figured on the headlines of The Wall Street Journal on 112 days
and out of these, the stock market movement is attributed to oil price changes on 30 days. Given
the apparent presumption in the financial press that oil prices strongly impact the stock market, it
is surprising that little research has been conducted to measure the impact of oil price changes on
contemporaneous market returns. This leads to my first hypothesis.
Hypothesis I: Markets react negatively (positively) to oil price increases (decreases).
I use the following regression specification to test this hypothesis.
Rst    Rot   t
(1)
Where Rst is the return on the value-weighted NYSE index (which is used as a measure of
market return in this paper) day ‘t’ and Ro t is the log return of the real price of oil on that day. A
significant negative relation between oil price changes and market returns would support the
presumption in the financial press.
Financial press seems to pay more attention to oil prices when the prices are high.. For
instance, oil prices are in the headlines of The Wall Street Journal on 112 days in 2005 when they
were relatively higher and only on 17 days in 1997 when they were relatively lower8 It would be
interesting to explore the relationship between the market sensitivity to oil price changes and the
level of oil prices. This is the focus of my next hypothesis.
8
Average oil price for the entire sample is $16.91 per barrel, and the average oil price in 2005 and 1997 is
$28.93 and $12.84 respectively.
7
Hypothesis I.I: The stock market’s sensitivity to oil price changes is directly related to
the level of oil prices
B. Sensitivity of the Stock Market to Oil price changes over time (Hypothesis II)
I conjecture that the stock market is more sensitive to oil price changes in recent times
than it was a decade or two ago. A variety of reasons provide support to this line of thought. On
the economic front, there is an increased competition for oil and nearly half of the projected
increase in demand is attributed to emerging Asian countries9. On the political front, unrest in the
Middle East is attributed as a significant factor escalating the uncertainties associated with oil
prices. Given these reasons, the US dependence on foreign oil, and the fact that oil prices have
been increasing over the past few years and are at their historic highs (see Figure 1), the market
might be more sensitive to oil prices in recent times. Also, the findings of Sardosky (1999) and
Ciner (2001) (using quarterly oil price data and daily closing prices of oil futures respectively)
that the market sensitivity to oil price changes has changed over time provide support to this
hypothesis.
Hypothesis II: The stock market is more sensitive to oil price changes in recent times
than it was earlier.
C. Under/Over reaction of Market to Oil Price Changes (Hypothesis III)
Jones and Kaul (1996) find that quarterly stock returns of most of the countries in their
sample including the U.S. are negatively affected by both current and lagged oil price variables.
Similarly, Pollet (2002) and Jacobsen & Maat (2003) find that monthly oil price changes have
9
The world demand for oil has almost doubled from 46.5 million barrels per day in 1970 to approximately
82.4 million barrels per day in 2005. During the same period, the share of U.S demand has gone down from
31.5% to approximately 24% indicating the growing demand from other countries as well. The demand for
oil is projected to shoot up to 103 million barrels per day by 2015 and to 119 million barrels per day by
2025, with emerging Asian countries (lead by China and India) accounting for nearly 45% of this increase.
8
predictive ability for excess market returns and returns of most US industries. If markets are
efficient and investors correctly anticipate the impact of oil price changes on the economy, then
stock prices should adjust almost simultaneously so that these changes have no predictive ability.
On the other hand, if investors underestimate (overestimate) the true impact of an increase in oil
price on the economy, then as the true impact becomes clearer, stock prices will fall (rise) further
and the oil price changes will have predictive ability. I hypothesize that the market under reacts to
oil price changes. This is because only the stocks of industries directly dependent on oil will react
immediately to any oil price changes and others will not, as investors might not be able to assess
the impact of oil price changes on these industries. As they realize the wider effects of oil, the
returns of these industries will exhibit negative correlation with lagged oil price changes.
Hypothesis III: The stock market under- reacts to oil price changes.
D. Asymmetry in Market’s reaction to Oil price increases and decreases (Hypothesis IV)
In an interesting observation, Mork (1989) using quarterly data finds an asymmetric
relation between oil prices and output growth and presents evidence that while GNP growth
displays a statistically significant negative correlation with oil price increases, the correlation
with oil price decreases is insignificant. In a related study, Mork, Olsen and Mysen (1994)
confirm the asymmetry in oil price effects on the growth rates of other OECD countries. Oil price
increases seem to slow down economic growth in the U.S. to a greater extent than in Germany,
France and Japan, all of which are more dependent on imported oil than the U.S. If oil price
increases impact GNP more than oil price decreases and investors recognize this, then their
impact on the stock market should also be asymmetric. Hence, motivated by the results of Mork
(1989) and Mork, Olsen & Mysen (1994), I test for an asymmetry in market’s reaction to oil price
changes. Also, Brown, Harlow & Tinic (1988) show that stock price reaction to unfavorable news
events tends to be larger than reaction for favorable events. See also Campbell & Hentschel
9
(1992). These results would support my hypothesis, assuming an oil price increase as an
unfavorable event and an oil price decrease as a favorable event10. This leads to my next
hypothesis
Hypothesis IV: The stock market’s reaction to oil price increases is larger than its
reaction to oil price decreases.
E. Sensitivity of Industry Returns to Oil Price Changes (Hypothesis V)
As mentioned earlier, not all industries are equally dependent on oil. However, no one
has explored the impact of oil price changes on the stock returns of oil-intensive11 industries and
non oil-intensive industries. The small academic literature examining the impact of oil shocks on
industries includes Lee and Ni (2002) who study the long run supply and demand effects of oil
shocks using industry level data and Hong, Torous & Valkanov (2002) and Pollet (2002) who
study whether monthly market and individual industry returns can be predicted using oil prices.
To the best of my knowledge, there are no studies in the finance literature that explore the effects
of oil price changes on the contemporaneous returns of individual industries12. This leads to my
next hypothesis.
Hypothesis V: Stocks of oil-intensive industries are more sensitive to oil price changes
than the stocks of non oil-intensive industries.
10
Furthermore, it seems oil price increases receive more attention in the financial press than oil price
decreases. For example, during 2005, news related to oil price increases figured in the headlines of The
Wall Street Journal on 74 days while news related to oil price decreases figured in the headlines on 47
days.
11
For this paper, an industry is classified as oil-intensive or non oil-intensive according to the input
requirement coefficients provided by the Survey of Current Business. Details are provided in Section III
12
Economists investigated the effects of oil shocks on the employment and wages on different sectors.
According to the economic theory on transmission mechanism of oil shocks, labor migrates from sectors
that are directly affected by oil price shocks to sectors that are not, and aggregate output decreases during
this process. However, Keane and Prasad (1996) find that oil price shocks are correlated with the decline in
employment and real wages in all sectors. Along the same lines, Bohi (1991) finds no cross-industry
correlation between changes in employment and energy intensities.
10
III. Sample Data and Descriptive Statistics
I gather data from three sources. Daily value weighted returns of NYSE index are
obtained from CRSP. I use these returns as measure of stock market returns. Daily price data of
NYMEX Light crude oil are obtained from Normans’ historical data13. Daily value weighted
industry returns data are obtained from Ken French’s website when available or calculated in
some cases using the returns of individual firms from CRSP. The real price of oil is calculated as
the nominal price divided by CPI. The base year is 1982. Consumer Price Index numbers are
obtained from the website of Federal Reserve Bank of St.Louis. The sample period spans April
1983 to December 2005.
Oil return is calculated as the difference between the log percentage change in the
nominal price of oil and the rate of inflation. After matching daily market returns with the
corresponding oil returns, there are 5701 observations14. Table II presents descriptive statistics for
oil price returns and market returns. The mean oil return and the market return are close to zero.
However, the standard deviation of oil returns is 2.4%, nearly one and a half times higher than
that of market returns. Panel B presents percentile distributions of oil returns and market returns.
Table III presents the details of industry groups used in this study and their classification
into oil-intensive or non oil-intensive categories. An industry is classified as oil-intensive or non
oil-intensive based on the input requirement coefficients obtained from Benchmark Input-Output
Accounts15 (2002) of United States. These values show the amount of oil required to produce a
dollar’s worth of an industry’s product. Higher coefficients imply that an industry is oil-intensive
and vice versa. Column 1 of Table III presents the industry groups listed according to their
dependence on the oil industry. Power generation & supply industry is the most oil-intensive16
13
www.normanshistoricaldata.com
There are 5745 daily market returns available and 44 observations are lost during the matching process.
15
Published by The Survey of Current Business. A total of 130 industry groups are used in this survey.
16
Four industry groups namely Oil and gas extraction (1.159), Petroleum and Coal products manufacturing
(0.6881), Natural gas distribution (0.5148) and Pipeline Transportation (0.153) are excluded in this study.
14
11
with an input requirement coefficient of 0.098, followed by industrial & agricultural chemicals
industry (0.091), rubber industry (0.084) and air transportation industry (0.065). In this paper, I
use 19 industry groups. Out of these, 12 are classified as oil-intensive industries and the
remaining 7 as non oil-intensive. The 12 oil-intensive industry groups represent the 15 industries
with the highest input requirement coefficients. The 7 non-oil intensive industry groups represent
the 15 industries with the lowest input requirement coefficients. I combine the industries into
groups of 12 and 7 for two reasons. First, some of the industries in the list are very similar (the
SIC codes match until the second or third digit) and the input requirement coefficients are also
close. For the purpose of this study, it seems logical to combine them into one industry group. For
example, basic chemical manufacturing and agricultural chemical manufacturing have input
requirement coefficients of 0.0913 and 0.0876 respectively, and to combine them into one
industry group allows me to include one more industry group in the analysis. The second reason
is the availability and ease of calculating industry returns. Returns data of some of the industries,
especially those with low input requirement coefficients are not available and when these
industries are grouped together, they closely match the industry definitions available at Ken
French’s website. Most of the industries in the oil-intensive group belong to manufacturing and
transportation sectors while industries in the non oil-intensive group span services, financials and
communications sectors. Returns of 11 industry groups are obtained from the 48 industry
portfolio returns available at Kenneth French’s website. Returns of the 8 remaining industry
groups are calculated using daily returns for individual firms from CRSP. Daily price, volume
and return data for each firm in an industry are obtained from CRSP based on the SIC codes.
Relative market value weights of each of the firms are calculated and weighted returns are
While these industries rely heavily on oil for their operations, oil or close substitutes of oil are also the end
products of these industries. It is, therefore, very difficult to distinguish whether oil price changes have a
bigger effect on the supply or demand of these industries.
12
calculated as the product of relative weights and returns of the individual firms. The sum of all
the individual weighted returns gives the valued weighted return for an industry on a given day.
Table IV contains descriptive statistics on industry returns. Panel A presents the mean,
median, first order serial correlation coefficient and standard deviation of the returns of oilintensive and non oil-intensive groups. Panel B presents similar statistics for each of the
industries in the oil-intensive and non oil-intensive groups. Among the oil-intensive industries, air
transportation (0.02896), trucking (0.0221) and paints17 (0.0248) have higher standard deviation
of returns than others in the same group. In the non oil-intensive group, entertainment industry
(0.0154) is the most volatile..
IV. Empirical Results
A. Relation between Oil Price changes and Market Returns (Hypothesis I)
As a first step towards exploring the effects of oil price changes on market returns, I form
oil returns quintiles and present the mean oil and market returns for the corresponding quintile.
This provides some non-regression evidence of the relation between oil price changes and market
returns. Also, mean oil returns and market returns of observations in the top and bottom 1%, 5%
and 10% of the sample are presented. This enables me to infer the relation between oil prices
changes and market returns at the extremes. The results are presented in Table V. It appears that a
negative relationship between oil price changes and market returns is persistent only at these
extremes, suggesting that the market is sensitive to oil price changes only when there are large oil
price changes.
To document further evidence between market returns and oil price changes, I estimate
the following regression specification.
17
The maximum daily return for the paints industry is 0.992 and this corresponds to the buy out of Lilly
Industries by The Valspar Corporation for 2.5 times then current share price (about $762 million).
13
Rst    Rot   t
(2)
Where Rst is the return on the value weighted NYSE index on day ‘t’ and Ro t is the return of
the real price of oil. Results are presented in Panel A of Table VI. Market returns covary
negatively and significantly with oil price changes, providing support to hypothesis I. For
example, if the oil return increases by 10% points, market return on average will go down by
0.22% points, or 22 basis points. This suggests that while oil price changes have a negative
impact on stock market returns, this impact has economic significance only when there are very
large oil price changes. It seems that although the financial media writes about a strong negative
connection between oil prices and the stock market, on average the connection is very weak with
an R2 of less than 1%.
As mentioned earlier, the higher sensitivity of stock returns to oil price changes could be
partly due to the higher level of oil prices. To test this relation, I adopt the following regression
specification.
Rs t    1 Ro t   2 ( RP * Ro t )   t
(3)
Here RP is the real price of oil and the other variables follow the same definition as in regression
specification (1). The sensitivity of market returns to oil price changes is 1   2 * RP and if the
level of oil prices is an important factor,  2 should be negative and significant. The results are
presented in Panel B of Table VI. The estimates of 1 and  2 are 0.04543 and -0.0043
respectively and they are significantly different from each other at the 1% level (results not
presented). It is surprising to see that the relation between market returns and oil prices is positive
at lower levels of oil price. Specifically, there is a positive but declining relationship between oil
prices and market returns as long as the real price of oil is below $10.5918. After this, there is a
negative relationship between stock returns and oil price changes and the magnitude of the
18
That is, 0.0454352/0.0042902 = 10.59. Oil price that corresponds to the top 10 percentile is $11.25.
14
reaction increases with the level of oil prices. Results suggest that the stock market reaction to oil
price changes is proportional to the level of oil prices, confirming hypothesis I.I.
B. Sensitivity of the Stock Market to Oil price changes over time (Hypothesis II)
In this part of the paper, I examine the time series patterns of the market’s sensitivity to
oil price changes. Since the points of structural breaks are unknown, I use a recursive least
squares model to test this hypothesis. More specifically, I estimate a rolling 125-day regression of
stock market returns on oil returns. For each successive regression, I use a step size of 21 trading
days19. I use regression specification (1) to test this hypothesis.
Figure 2 presents the results of this estimation process. It is interesting to note that the coefficient
estimates vary considerably over time and are even positive at times. However, these estimates
are statistically insignificant most of the times20. Panel A of Table VII presents partial results of
rolling window estimation process used to test this hypothesis. Specifically, coefficients that are
significant at 10% level or lower are presented along with the corresponding time periods, pvalues and R-squared. Out of the 74 sub-periods where a significant relationship between the
stock market returns and oil returns is found, 41 periods are before 1999 and 33 periods are after
1999 providing support to hypothesis II that the stock market is more sensitive to oil price
changes in recent periods than earlier.
Several interesting observations can be drawn from the results presented in Panel A of
Table VII. First, significant relationship between stock market returns and oil prices seems to be
concentrated in a few periods, namely between 1984 and mid-1987, 1990-1994 and from 1999
onwards. As can be seen from Figure I, either the real price of oil (as measured in 1982 dollars) is
19
This step size is used for two reasons. The default step size is 1 day and it seems unlikely that the market
reaction to oil price changes between day 1 and day 125 will greatly differ from day 2 and day 126 and so
on. The second reason is the ease of interpretation of results. With a step size of 1, I have to estimate nearly
5575 regressions and with a step size of 21 days, I estimate 267 regressions.
20
Out of 267 coefficient estimates, 193 are statistically insignificant.
15
high or it is more volatile during these periods. It is worth mentioning that the Organization of
Petroleum Exporting Countries (OPEC) played a significant role in controlling the supply of oil
in the mid 1980s21. Second, the coefficient estimates and R2 are higher during the periods
between March 1990 and July 1991 and between October 2002 and September 2003, more so in
the former period. It should be pointed out that the US is in a war with Iraq during these two
periods22. Third, there is a positive relationship between oil price changes and stock market
returns mostly between September 1992 and July 1993 and between December 2001 and
December 2002. The US economy is in a recession23 or has been preceded by a recession in both
these periods. Also, most of the major oil companies reported lower earnings and there is a drop
in oil prices24 because of ample supply during these periods. While this issue requires further
investigation, it seems that the decline in aggregate demand due to recession and oil prices at
roughly the same time is a reason for the positive relationship between stock returns and oil price
changes.
At this point, it is imperative to answer the question whether the US involvement in a war
and high level of oil prices impact market sensitivity independently. That is, controlling for the
real price of oil, is the market more sensitive to oil price changes during wars? Or, is the market
more sensitive to oil prices when real price of oil is high? To test this, I estimate the following
regression specification.
21
Appendix A, which presents the details about the thirty largest oil price movements (fifteen positive and
fifteen negative) also reveals that most of the large movements in oil prices are either associated with U.S
involvement in the Middle East or the decision of OPEC to regulate the supply of oil at their discretion
22
US –Iraq war I (Persian Gulf War-1): Tensions began escalating when Iraq invaded Kuwait on August 2
1990. The war started in January 1991 and ended with Iraq accepting the ceasefire on March 3 1991.
However, military operations such as establishing no-fly zones, and sporadic retaliatory strikes continued
until mid-1993.
US Iraq war II (Persian Gulf War-2): Tensions began escalating from September 2002 when US and British
forces increased air strikes against targets in Iraq. War officially started on March 19 2003 and ended on
May 1 2003. But US military is involved in Iraq to date.
23
According to the National Bureau of Economic Research (NBER), the US economy is in a recession
from July 1990 to March 1991 and again from March 2001 to November 2001. (www.nber.org/cycles)
24
The average real price of oil during these periods is $14 compared to $17 for the entire period.
16
Rs t    1 Ro t   2 ( RP * Ro t )   3 (WAR * Ro t )   t
(3)
Where Rst is the return on the value weighted NYSE index on day ‘t’ and Ro t is the return of
the real price of oil. RP is the real price of oil (in 1982 dollars) and WAR is a dummy variable that
is equal to 1 if US is in a war25 or 0 otherwise. In this case, the effect of oil prices on stock returns
would be 1   2 * RP when there is no war and 1   2 * RP   3 when there is a war. Results
are presented in Panel B of table VII. The coefficient estimates on 1 ,  2 and  3 are .029790,
-.002330 and -.070069 respectively and are significantly different from each other at the 1% level
(results not reported). Results suggest both the level of oil prices and war risk are significant, but
war risk is the major factor affecting the stock market’s sensitivity to oil price changes.
C. Under/Over-reaction of Market to Oil Price Changes (Hypothesis III)
I do not find strong evidence for the under reaction of market to oil price changes. The
results are presented in Panel A of Table VIII
Rs t    1 Ro t   2 Ro t 1   3 Ro t 2   t
(4)
Assuming 1 <0, and 1 ,  2 and  3 are significant,  2 <0 &  3 <0 indicates under reaction and
 2 >0 &  3 >0 indicates over reaction.  2 ≠ 0 or  3 ≠ 0 indicates predictability. As can be seen
from the results, there is no evidence of any under-reaction or over-reaction of the market to daily
oil price changes. A significant under or over reaction of market returns to oil price changes
would indicate predictability. My results therefore, contradict the findings of Pollet (2002) and
those of Dreisprong, Jacobsen & Matt (2005). One of the major differences between the above
mentioned studies and this paper is that they use monthly returns data while I use daily returns
data. The results suggest that the stock market is efficient in responding to daily oil price changes.
25
I use reports in media to determine whether US is in a war or not.
17
D. Asymmetry in Market’s reaction to Oil price increases and decreases (Hypothesis IV)
In this section, I test whether the market’s reaction to oil price increases is different from
its reaction to oil price decreases. The specification that I examine is
Rs t    1 Rot   2 ( Rot D)   t
(5)
Here D is a dummy variable that equals 1 if the real return of oil is positive on day ‘t’ and 0
otherwise. Rejecting the null hypothesis of equal  ’s indicates an asymmetry in market reaction
to oil price changes. Also,  2 being negative and significant indicates that the market is more
sensitive to oil price increases than to oil price decreases. Panel B of Table VIII presents the
results. The coefficient estimate on  2 is insignificant and the estimates of  1 and  2 are not
significantly different from each other, providing no evidence of asymmetry in market’s reaction
to daily oil price increases and oil price decreases, even when daily oil price changes are large.
Assuming that an oil price increase is viewed as bad news and an oil price decrease as good news,
the results are not consistent with the findings of Brown, Harlow & Tinic (1988) who show that
stock price reaction to unfavorable news events tends to be larger than reaction for favorable
events.
E. Sensitivity of Industry Returns to Oil Price Changes (Hypothesis V)
In this part of the paper, I examine the reaction of individual industries to oil price
changes. As mentioned earlier, I use 19 industry groups, 12 of them classified as oil-intensive
industries and the remaining 7 industry groups as non oil-intensive industries. I estimate
regression specification (4) for each industry. Results are presented in Panel A of Table IX.
Ri t     i Ro t   it
(6)
18
Here Ri t is the return of industry ‘i’ on day‘t’ and Ro t is the real return of oil as defined
earlier. It can be seen that most of the oil-intensive industries are more sensitive to oil price
changes than non oil intensive industries, providing support to hypothesis V. Industries in
transportation sector such as air transportation, trucking and courier services are most sensitive to
oil price changes. For example, a 10% increase in oil prices leads to a 1.4% decrease in the
returns of air transportation industry, suggesting that the airlines industry is six times more
sensitive to oil price changes than the market. Similarly, trucking and courier industries are twice
as sensitive as the market for an equal change in oil prices. On the other hand, even though power
generation industry is the most oil-intensive as per the input requirement coefficients, its returns
do not covary significantly with oil price changes. It seems that the degree of sensitivity of an
industry to oil price changes depend on the proportion of oil price impact an industry can transfer
to consumers. Industries such as power generation might be able to pass on a large portion of the
effect of oil price changes to consumers, while industries such as air transportation, trucking and
couriers might not be able to. This is probably because of the nature of the industry, competition
and the necessity of the products offered by that industry. Oil price increases are expected to have
a greater impact on the future earnings of those industries that absorb the entire effect of the price
change.
Industries in the non oil-intensive group mostly span the services, financial and
telecommunication sectors. All the industries in this group react negatively to oil price changes
and some of them are more sensitive to oil price changes than oil-intensive industries. For
example, the stock returns of entertainment industry and insurance industry are more sensitive to
oil price changes than the returns of power generation industry which is oil-intensive. While it is
not a surprise to find that oil-intensive industries are highly sensitive to oil price changes, it is
surprising to find that industries that do not use oil to any significant extent are also sensitive to
19
oil price changes. This suggests a demand effect of oil price changes. That is, the market expects
that when oil prices go up, consumers have less to spend on other goods.
I find that the returns of gold industry covary positively and significantly with oil price
changes. A 10% increase in oil prices increases the gold returns by approximately 1%. This is
probably because oil price hikes are viewed as inflationary and therefore increase the demand for
gold, which is considered a natural hedge against inflation. Using the daily gold and oil price data
from 1983 to 2005, I find the correlation coefficient between gold returns and oil returns to be
0.39. Oil price changes also have small but positive effects on the returns of mining industry. This
is due to the fact that some products of the mining industry such as coal are viewed as substitutes
to oil.
To further explore the relation between industry returns and oil price changes, I include
the market returns along with oil returns as a second independent variable in the regression
specification (6). This should allow for an estimation of oil’s incremental impact. I estimate the
regression
Ri t     i1 Ro t   i 2 Rs t   it
(7)
Controlling for the market returns, a large negative i1 indicates that the industry ‘i’ is
more sensitive to oil prices than overall market. Results are presented in Panel B of Table IX.
While the coefficients on oil returns decrease across all the industries, few industries such as
airlines and couriers still exhibit a high sensitivity to oil price changes. Similar results were found
for gold and mining industries, the two groups that are positively correlated with oil price
changes.
To summarize, I document the sensitivities of individual industries to oil price changes. I
find that the most oil-intensive industry (as measured by input requirement coefficients) need not
be the one most sensitive to oil price changes. Factors such as the ability of industry to transfer
20
the impact of oil price changes to its consumers, the nature of industry and competition and the
necessity of the products might offered by the industry might play a crucial role in determining an
industry’s reaction to oil price changes. While it is not a surprise that stock returns of oilintensive industries are negatively correlated with oil price changes, it is interesting to find that
the returns of industries that virtually use no oil are also negatively and significantly correlated
with oil price changes. This finding indicates oil prices impact these industries and market as a
whole is through the demand side, not just costs and supply side. That is, investors reason that if
oil prices rise, consumers will have less to spend on everything else.
V Conclusion
This paper provides a comprehensive study of the stock market’s reaction to oil price
changes. The conclusions and contributions that I consider as most important follow.
First, I find that a significant negative relationship between market returns and oil price
changes exists and that the market’s reaction to oil price changes depends on the level of oil
prices. There is no evidence of an asymmetry or in market’s reaction to oil price changes. I find
no evidence for any over or under-reaction of market to oil price changes, suggesting that the
market is efficient in responding to daily oil price changes.
Second, significant negative relation between the stock market and oil prices is
concentrated in a few periods that are often associated with higher oil prices, US involvement in
an armed conflict in the Middle East or the involvement of OPEC trying to regulate oil supply.
Results suggest both the level of oil prices and war risk are significant, but war risk is the major
factor affecting the stock market’s sensitivity to oil price changes.
Third, I document the relationship between stock returns of individual industries and oil
price changes. I classify industries into oil-intensive and non oil-intensive groups based on the
input requirement coefficients and document their sensitivity to contemporaneous oil price
21
changes. To my knowledge, this is the first paper to do so. I show that oil-intensive industries are
more sensitive to the oil price changes than non oil-intensive industries. It is interesting to find
that the returns of industries that virtually use no oil are also negatively and significantly
correlated with oil price changes. This finding indicates oil prices impact these industries and
market as a whole is through the demand side, not just costs and supply side. That is, investors
reason that if oil prices rise, consumers will have less to spend on everything else. When
controlled for the market returns, however, oil has a significant incremental impact only on a few
industry groups such as airlines and couriers. The findings of this paper might have important
implications on the hedging decisions of individual industries.
22
References
Barsky, B. Robert, and Lutz Kilian (2001), “Do We Really Know that Oil Caused the Great
Stagflation? A Monetary Alternative”, NBER Working Paper Series 8389
Bernanke, S. Ben, Gertler, Mark and Watson, (1997), “Systematic Monetary Policy and the
Effects of Oil Price Shocks” Brookings Papers on Economic Activity Vol.1, pp91-157
Bittlingmayer, George (2005), “Oil and Stocks; Is it War Risk?” Working Paper Series
Brown, C. Keith, Harlow W.V., and Seha M. Tinic, (1988), “Risk Aversion, Uncertain
Information and Market Efficiency”, “Journal of Financial Economics”, Vol. 22 pp 355385
Burbidge, John and Alan Harrison, (1984), “Testing for the Effects of Oil-Price Rises Using
Vector Autoregressions”, International Economic Review, Vol. 25, No.2 pp 459-484
Ciner, C. (2001), Energy shocks and Financial Markets: Nonlinear Linkages, Studies in Nonlinear
Dynamics and Econometrics, October, 5 (3), 203-212
Chen, N., Roll, R., and Stephen A. Ross, (1986), “Economic Forces and the Stock Market”, The
Journal of Business, Vol. 59, No.3 pp 383-403
Davis, J Steven., and John Haltiwanger (2001). “Sectoral Job Creation and Destruction Responses
to Oil Price Changes.” Journal of Monetary Economics 48, 465–512.
Darby, R. Michael, (1982), “The Price of Oil and World Inflation and Recession”, The American
Economics Review, Vol. 72, No. 4 pp 738-751
Driesprong, G., Jacobsen, B. and Benjiman Maat. (2003), “Striking Oil: Another Puzzle?,
Working Paper, Erasmus University Rotterdam
Gisser, Micha, and Goodwin, Thomas H., (1986), “Crude Oil and the Macroeconomy: Tests of
Some Popular Notions: Note”, Journal of Money, Credit and Banking, Vol. 18, No.1 pp
95-103
Golub, Stephen S. (1983), “Oil Prices and Exchange Rates”, The Economic Journal, Vol.93, No.
371, pp 576-593
Hamilton, James D. (1983), “Oil and the Macroeconomy since World War II”, The Journal of
Political Economy, Vol 91, No.2, pp 228-248
Hamilton, James D., Herrera, M. Ana (2002), “Oil Shocks and Aggregate Macroeconomic
Behavior”, Journal of Money, Credit & Banking Vol 36, No.2, pp 265-286
Hong, H., Torous, W., and Rossen Valkanov. (2002), “Do Industries Lead the Stock Market?
Gradual Diffusion of Information and Cross-Asset Return Predictability”, Working
Paper, Stanford Univeristy & UCLA
23
Hooker, Mark A., (1996), “What happened to the oil price-macroeconomy relationship?”, Journal
of Monetary Economics, No.38, pp 195-213
Hooker, Mark A., (2002), “Are Oil Shocks Inflationary? Asymmetric and Nonlinear
Specifications versus Changes in Regime”, Journal of Money, Credit and Banking, Vol
34, No.2, pp 540-561
Huang, R.D.; Masulis, R.W.; Stoll, H.R. (1996), Energy shocks and financial markets, Journal of
Futures Markets, 16, 1-27
Jones, Charles M., and Gautam Kaul, (1996), “Oil and the Stock Markets”, The Journal of
Finance, Vol LI, No.2, pp 463-491
Kaul, Gautam and H. Nejat Seyhun (1990), “Relative Price Variability, Real Shocks, and the
Stock Market” The Journal of Finance, Vol .45, No.2, pp 479-496
Keane, P. Michael, and Eswar Prasad (1996). “The Employment and Wage Effects of Oil Price
Changes: A Sectoral Analysis.” Review of Economics and Statistics 78, 389–400.
Mork, A. Knut, (1989), “Oil and the Macroeconomy When Prices Go Up and Down: An
Extension of Hamilton’s Results”, The Journal of Political Economy, Vol. 97, No. 3, pp
740-744
Mork, K.A., Olsen, O. and Mysen, H.T. (1994), Macroeconomic responses to oil price increases
and decreases in seven OECD countries. Energy Journal 15 4, 19-35.
Papapetrou, E. (2001), “Oil price shocks, stock market, economic activity and employment in
Greece”, Energy Economics Vol. 23, No. 5 pp 511-532.
Pollet, Joshua. (2002),“Predicting Asset Returns With Expected Oil Price Changes”,
Working Paper, Harvard Univeristy
Sadorsky, P. (1999), “Oil price shocks and stock market activity”, Energy Economics, No. 2,
pp449-469
24
Figure I
Daily Oil Prices
This figure graphs the nominal price and the real price of oil during the sample period (April
1983 to December 2005). Daily oil price data are obtained from Normans’ historical data
(www.normanshistoricaldata.com). Real Price of oil is calculated as the nominal price divided by
the CPI (base year 1982). Consumer Price Index numbers are obtained from the website of
Federal Reserve Bank of St.Louis.
Nominal Vs Real Price of Oil
80
70
50
40
30
20
10
Apr-05
Apr-04
Apr-03
Apr-02
Apr-01
Apr-00
Apr-99
Apr-98
Apr-97
Apr-96
Apr-95
Apr-94
Apr-93
Apr-92
Apr-91
Apr-90
Apr-89
Apr-88
Apr-87
Apr-86
Apr-85
Apr-84
0
Apr-83
Dollars
60
Time
Nominal Price
Real Price
25
Figure II
Apr-05
Apr-04
Apr-03
Apr-02
Apr-01
Apr-00
Apr-99
Apr-98
Apr-97
Apr-96
Apr-95
Apr-94
Apr-93
Apr-92
Apr-91
Apr-90
Apr-89
Apr-88
Apr-87
Apr-86
Apr-85
Apr-84
0.25
0.2
0.15
0.1
0.05
0
-0.05
-0.1
-0.15
-0.2
-0.25
Apr-83
Coefficient Estimates
This figure graphs the coefficient estimates from rolling regression of daily return of the value
weighted NYSE index on the return of the real price of crude oil. The length of the window is 125
days and the step size is 21 days. Daily returns data from April 1983 to December 2005 are used.
Time
26
Study
Darby (1982)
Table I
Summary of Data Used in Previous Empirical Studies on the Effect of Oil Price Shocks
Sample Period Frequency
Key dependent variable/variables
Focus
1957 – 1976
Quarterly
Employment level or real output, money supply, Oil prices and world inflation
govt. expenditures and ratio of exports to GNP
Hamilton (1983)
1948 – 1972
Quarterly
Golub (1983)
1972 – 1980
Annual
Burbidge & Harrison (1984)
1973-1982
Monthly
Macroeconomic variables from OECD
Countries
Chen, Roll & Ross (1986)
1953 – 1983
Monthly
Returns on NYSE index, growth rates in
industrial production, measures of inflation,
measures of risk premium etc.
Gisser & Goodwin (1986)
1961 – 1982
Quarterly
Six-variable system presented by Sims (1980
b)*
Oil price – macroeconomy
relationship
Mork (1989)
1949 – 1988
Quarterly
Kaul & Seyhun (1990)
1947 – 1985
Annual
Six-variable system presented by Sims (1980
b)*
Growth rate of output and market returns
Hooker (1996)
1948 – 1994
Quarterly
GDP and unemployment rate
Huang, Masulis & Stoll (1996)
1983 – 1990
Daily (futures)
S&P 500 index returns, industry returns
Oil price – macroeconomy
asymmetric relationship
Effect of relative price
variability on output and
stock market
Oil price – macroeconomy
relationship
Relation between oil futures
and stock returns
Jones & Koul (1996)
1947 – 1991
Quarterly
Returns of market indices of several countries
and Cash flows
Market’s ability to evaluate
the impact of oil shocks
Keane & Prasad (1996)
1966 – 1981
Survey data
Bernanke, Gertler & Watson (1997)
1965 – 1995
Monthly
Weekly wage rates and proxies for human
capital
GDP, GDP deflator and federal funds rate
Employment and wage
effects of oil price changes
Effects of systematic
Six-variable system presented by Sims (1980
b)*
Exchange rates
Oil price – macroeconomy
relationship
Response of FOREX markets
to oil price changes
Oil price – marcroeconomy
relationship in OECD
countries
Asset pricing
Study
Sample Period
Frequency
Key dependent variable/variables
Focus
monetary policy and oil price
shocks
Impact of oil prices and
volatility on stock returns
Sardosky (1999)
1947 – 1996
Monthly
Index of Industrial Production, interest rates &
real stock returns
Ciner (2001)
1983 – 2000
Daily (futures)
Same as Huang, Masulis & Stoll (1996)
Barsky & Kilian (2001)
1960 – 2001
Annual
Growth rate of GDP
Davis & Haltiwanger (2001)
1972 – 1988
Quarterly
Job flows between industries
Lee & Ni (2002)
1959 – 1997
Monthly
Industry level output
Hamilton & Herrera (2002)
1965 – 1995
Monthly
Same as Bernanke et.al (1997)
Hong, Torous & Valkanov (2002)
1972 – 2001
Monthly
Thirty-four industry portfolios
Hooker (2002)
1962 – 2000
Quarterly
Rate of inflation
Pollet (2004)
1973 – 2002
Monthly
Value-weighted market and industry returns
Bittlingmayer (2005)
1983 – 2004
Daily
Returns on S&P 500 index
War risk and impact of oil
price changes
Driesprong, Jacobsen & Maat (2005)
1973 – 2003
Monthly
Returns on market indices of several countries
and world market index
Predictive ability of oil price
changes
Non-linear linkages between
energy shocks and financial
markets
Role of oil price shocks in
causing stagflation
Oil shocks and employment
effects
Output responses to oil price
shocks
Oil price – macroeconomy
relationship and the role of
monetary policy
Predictability of market by
industries
Oil price changes and
inflation
Predictive ability of expected
oil price changes
*
This system includes two output variables (real GNP and unemployment rate), three price variables (implicit price deflator for nonfarm business
income, hourly compensation per worker, and import prices) and Money supply
28
Table II
Descriptive Statistics.
This table reports descriptive statistics for oil and market returns. Daily oil price data are obtained
from Normans’ historical data. Oil return is the return of the real price of oil calculated as the
difference between the log percentage change in the nominal price of oil and the rate of inflation.
Inflation is calculated as the log percentage change in CPI (base year 1982). Consumer Price
Index numbers are obtained from the website of Federal Reserve Bank of St.Louis. Market
Return is the return on valued weighted NYSE index obtained from CRSP. Panel A summarizes
the data (daily data from April 1983 to December 2005) rho1 is the first order serial correlation
coefficient. Panel B presents the percentile distribution of the oil and market returns data.
Variable
Real Oil Return
Market Return
Panel A: Summary Statistics
Observations
Mean
Median
5701 .00000445
0.0000
5701
.0005095
0.0007
Percentile
1%
5%
10%
25%
50%
75%
90%
95%
99%
rho1
-0.0100
0.0620
Panel B: Percentile Distributions
Oil Return
Market Return
-.06779
-.03626
-.02461
-.01031
0
.01125
.02505
.03484
.05985
-.02373
-.01376
-.00938
-.00386
.00070
.00505
.01049
.01421
.02373
Std. Deviation
0.02418
0.00927
Table III
Classification of Industries into Oil-Intensive and Non-Oil Intensive Groups
This table presents the details about the classification of an industry as an oil-intensive industry or as a non oil-intensive industry. Column 1
lists the industry groups used in the study. Column 2 contains the input requirement coefficients. An industry is classified as oil-intensive or
non oil-intensive based on the input requirement coefficients obtained from Benchmark Input-Output Accounts (2002) of United States. These
values show the amount of oil required to produce a dollar’s worth of an industry’s product. Higher coefficients imply that an industry is oil
intensive and vice versa. Column 3 lists the subgroups included in the major industry group. Column 4 contains the source of industry returns.
Industry
Power Generation & Supply
Input Requirement
Coefficient
0.0980
Basic Chemicals;
Agricultural Chemical
Manufacturing;
Other Chemical product and
preparation manufacturing;
Resin, Rubber and Artificial Fibres
0.0913
0.0875
Air Transportation
Paint, coating and adhesive
manufacturing
Metal ores mining;
Nonmetallic mineral mining and
quarrying;
Waste management and remediation
services
0.0649
0.0488
Pulp, paper and paperboard mills
0.0390
Truck Transportation
Textile Mills
0.0374
0.0360
Couriers and messengers
0.0349
Definition
Source
4911 Electric Services
CRSP
Chemicals and allied products; Industrial inorganical chems; Plastic material &
synthetic resin; Paints; Industrial organic chems; Agriculture chemicals; Misc
chemical products
K.French’s 48 industry
portfolio
Reclaimed rubber; Rubber & plastic hose and belting;Gaskets, hoses, etc;
Fabricated rubber products; Misc rubber products; Misc plastic products
4512 Air Transportation, Scheduled
2851 Paints, Varnishes, Lacquers, Enamels, and Allied Products
K.French’s 48 industry
portfolio
CRSP
CRSP
Metal mining; Iron ores; Copper ores; Lead and zinc ores; Bauxite and other
aluminum ores; Ferroalloy ores; Mining; Mining services; Misc metal
ores;Anthracite mining; Mining and quarrying non-metalic minerals
4952 Sewerage Systems
4953 Refuse Systems
4959 Sanitary Services, Not Elsewhere Classified
2611 Pulp Mills
2621 Paper Mills
2631 Paperboard Mills
4213 Trucking, Except Local
Textile mill products; Floor covering mills; Yarn and thread mills; Misc textile
goods; Nonwoven fabrics; Cordage and twine; Misc textile products; Textile
bags, canvas products; Misc textile products
4513 Air Courier Services
K.French’s 48 industry
portfolio
(Mines)
CRSP
0.0451
0.0838
0.0449
0.0444
0.0423
CRSP
CRSP
K.French’s 48 industry
portfolio
(Textiles)
CRSP
4215 Courier Services, Except by Air
Employment Services;
Software Publishers;
Legal Services;
Architectural and engineering
services;
Accounting and bookkeeping
services;
Data Processing services; Computer
System design and related services;
Machinery and equipment rental and
leasing;
0.0001
0.0030
0.0032
0.0033
Insurance carriers and related
activities
0.0019
Telecommunications;
Cable networks and program
distribution; Radio and Television
broadcasting;
Funds, trusts, and other financial
vehicles
0.0043
0.0039
Motion Picture and Sound Recording
Industries
0.0054
Ambulatory Health Care Services
Hospitals
0.0057
0.0097
Wholesale Trade
0.0065
0.0033
0.0034
0.0036
0.0047
Commercial printing, Signs, advertising specialty, industrial launderers, business
services, advertising, credit reporting agencies, collection services, mailing,
reproduction, commercial art, services to dwellings, other buildings, cleaning and
building maint, misc equip rental and leasing, medical equip rental, heavy
construction equip rental, equip rental and leasing, personnel supply services,
computer programming and data processing, information retrieval services,
computer rental and leasing, computer maintenance and reapir, computer related
services, misc business services, security, new syndicates, photofinishing labs,
telephone interconnections, misc business services, R&D labs, management
consulting &P.R, detective and protective, equipment rental and leasing, trading
stamp services, commercial testing labs, business services, trailer rental and
leasing, engg, accounting, research, management, surveying, auditing,
consulting, architect etc.
K.French’s 48 industry
portfolio
(Business Services)
Insurance;Life insurance; Accident and health insurance; Fire, marine, propertycasualty ins; Surety insurance; Title insurance; Pension, health, welfare funds;
Insurance carriers; Insurance agents
Communications; Telephone communications;Telegraph and other message
communication; Radio-TV Broadcasters;Cable and other pay TV services;
Communications;Communication services (Comsat); Cable TV
operators;Telephone interconnect; Communication services
Security and commodity brokers;Holding, other investment offices; Holding
offices; Investment offices; Management investment, closed-end; Unit
investment trusts; Face-amount certificate offices; Unit inv trusts, closed-end;
Trusts; Investment offices; Miscellaneous investing; Oil royalty traders;
Commodity traders; Patent owners & lessors; Mineral royalty traders; REIT
Investors, NEC
motion picture production and distribution; motion picture theatres; video rental;
amusement and recreation; dance studios
bands, entertainers; bowling centers; professional sports; misc entertainment
Services – health;
K.French’s 48 industry
portfolio
(Insurance)
K.French’s 48 industry
portfolio
5000-5199
K.French’s 48 industry
portfolio (Wholesale)
K.French’s 48 industry
portfolio
(Trading)
K.French’s 48 industry
portfolio
(Entertainment)
K.French’s 48 industry
portfolio (Healthcare)
31
Table IV
Descriptive Statistics of Industry Returns Data
This table presents the descriptive statistics for industry returns. Industries are classified as oilintensive or non oil-intensive based on oil requirements as reported in the Survey of Current
Business. Returns of 11 industry groups are obtained from the 48 industry portfolio returns
available at Kenneth French’s website. Returns of the 8 remaining industry groups are calculated
using daily returns for individual firms from CRSP. The * sign represents the industries for which
returns are calculated manually. Panel A presents the mean, median, first order serial correlation
coefficient and the standard deviation of oil-intensive and non oil-intensive groups. Panel B
presents similar statistics for each of the industries in both groups. Daily returns data from April
1983 to December 2005 are used.
Panel A: Summary statistics for oil-intensive and non oil-intensive groups.
Mean
Median
rho1
Oil-intensive group
0.00104
0.00115
0.1410
Std Dev
0.00978
Non oil-intensive group
0.1020
0.00968
Panel B: Summary statistics for the Industry Returns.
Mean
Median
rho1
Std Dev
0.00051
0.00084
Power Generation*
Chemicals
Rubber
Air Transportation*
Paints*
Gold*
Mines
Waste Management*
Paper*
Trucking*
Textiles
Couriers*
Oil-intensive Industries
0.00072
0.0010
0.00052
0.0004
0.00043
0.0005
0.00174
0.0005
0.00275
0.0012
0.00035
-0.0007
0.00031
0.0004
0.00105
0.0008
0.00135
0.0009
0.00144
0.0010
0.00031
0.0050
0.00122
0.0000
0.1030
0.0850
0.0810
0.0660
0.0180
0.0270
0.0830
0.0880
0.0530
0.0930
0.1040
0.0830
0.013404
0.011223
0.010521
0.028963
0.024827
0.02039
0.011847
0.020662
0.016448
0.022148
0.011224
0.020565
Business Services
Insurance
Telecommunications
Trading
Entertainment
Healthcare
Wholesale
Non oil-intensive Industries
0.00049
0.0010
0.00053
0.0006
0.00042
0.0005
0.00059
0.0007
0.00054
0.0004
0.00044
0.0007
0.00046
0.0007
0.0860
0.1500
0.0500
0.0560
0.0380
0.1560
0.1380
0.014586
0.00956
0.012168
0.010775
0.015361
0.012748
0.009923
Table V
Mean Oil and Market Returns based on Oil Return Quintiles
This table presents the mean oil and market returns for quintiles formed on oil returns. The
numbers in column (A) represent the quintiles that are formed, with 1 being the lowest quintile.
Also, the mean oil returns and market returns for lowest and highest 1%, 5% and 10%
observations are presented. Estimates that are significantly different from zero at the 10%, 5%
and 1% are marked with *, **, and *** respectively. Daily returns data from April 1983 to
December 2005 are used.
Mean Oil and Market returns based on Oil return quintiles.
Oil Return Quintiles
Mean Oil Return
Mean Market Return
Column(A)
Column (B)
Column (C)
1
-0.0315
0.0005
**
1%
-0.1043
0.0052
5%
-0.0583*
0.0023
10%
-0.0442
0.0015
2
-0.0079**
.00076
3
0.0003
0.0001
4
0.0087***
0.0011
5
0.0301*
0.0850***
0.0523**
0.0410**
0.0001
-0.0026
-0.0010
-.00047
1%
5%
10%
33
Table VI
Regression Analysis of the Effect of Oil Price Changes on Market Returns
Panel A presents the estimates of regression specification (1)
Rs t    Ro t   t
(1)
Panel B presents the estimates of regression specification (2):
Rs t    1 Ro t   2 ( RP * Ro t )   t
(2)
Here Rst is the value weighted market return on day ‘t’ and Ro t is return of the real price of oil
calculated as the difference between the log percentage change in the nominal price of oil and the
rate of inflation. RP is the real price of oil. Coefficients that are significant at the 10%, 5% and
1% levels are respectively marked with *, **, and ***. Intercepts are not reported. Daily returns
data from April 1983 to December 2005 are used.
Panel A
Sample Specification
Whole sample
ˆ
t-stat
R2
Obs.
-0.0219*** (-4.34) 0.0033 5701
Panel B
Sample specification
Whole Sample
ˆ1
.04543***
(2.78)
ˆ2
R2
Obs.
-.0043*** 0.0066 5701
(-4.34)
34
Table VII
Regression Analysis of the Time Series Patterns of Market Sensitivity to Oil Price Changes.
Panel A presents partial results of rolling window regression of value weighted NYSE index
returns on daily return of real price of crude oil (regression specification (1)). Specifically,
coefficients that are significant at 10% or lower are presented. The length of the window is 125
days and the step size is 21 trading days.
Panel B presents the estimates of the following regression specification:
Rs t    1 Ro t   2 ( RP * Ro t )   3 (WAR * Ro t )   t
(3)
Where Rst is the return on the value weighted NYSE index on day ‘t’ and Ro t is the return of
the real price of oil. RP is the real price of oil (in 1982 dollars) and WAR is a dummy variable that
equals 1 if US is in a war or 0 otherwise. Estimates that are significantly different from zero at the
10%, 5% and 1% are marked with *, **, and *** respectively. t stats are presented in parentheses.
Daily returns data from April 1983 to December 2005 are used.
Start date End date
Aug-84
Nov-84
Apr-85
May-85
Jun-85
Oct-85
Nov-85
Dec-85
Dec-86
Jan-87
Feb-90
Mar-90
Apr-90
May-90
Jun-90
Jul-90
Aug-90
Sep-90
Oct-90
Nov-90
Dec-90
Jan-91
Jun-91
Jul-91
Aug-91
Mar-92
Jan-85
Jun-85
Oct-85
Nov-85
Dec-85
Apr-86
May-86
Jun-86
Jun-87
Jul-87
Aug-90
Sep-90
Oct-90
Nov-90
Dec-90
Jan-91
Feb-91
Mar-91
Apr-91
May-91
Jun-91
Jul-91
Dec-91
Jan-92
Feb-92
Sep-92
Panel A: Estimates of rolling-window regression
p-value
R2
Start date End date
ˆ
0.1635
0.0868
-0.1153
-0.1363
-0.1576
-0.0332
-0.0372
-0.0320
0.0969
0.1269
-0.0616
-0.1067
-0.1242
-0.1153
-0.1181
-0.1194
-0.0857
-0.0725
-0.0607
-0.0504
-0.0410
-0.0420
-0.1001
-0.1012
-0.0812
-0.0866
0.0289
0.0764
0.0544
0.0335
0.0049
0.0402
0.0193
0.0409
0.0959
0.0348
0.0068
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0003
0.0046
0.0056
0.0427
0.0230
0.0561
0.0539
0.0382
0.0253
0.0298
0.0362
0.0625
0.0338
0.0437
0.0335
0.0224
0.0357
0.0580
0.1767
0.2343
0.2442
0.2874
0.3083
0.2564
0.2013
0.1558
0.1027
0.0635
0.0606
0.0330
0.0413
0.0293
0.0299
Oct-93
Nov-93
Dec-93
Nov-94
Jul-99
Aug-99
Sep-99
Oct-99
May-00
Jun-00
Jul-00
Aug-00
Sep-00
Dec-00
Feb-01
Apr-01
May-01
Jun-01
Dec-01
Jan-02
Feb-02
Mar-02
Apr-02
May-02
Jun-02
Oct-02
Apr-94
May-94
Jun-94
May-95
Jan-00
Feb-00
Mar-00
Apr-00
Nov-00
Dec-00
Jan-01
Feb-01
Mar-01
Jun-01
Aug-01
Oct-01
Nov-01
Jan-02
Jun-02
Jul-02
Aug-02
Sep-02
Oct-02
Nov-02
Dec-02
Apr-03
ˆ
p-value
R2
-0.0690
-0.0617
-0.0541
-0.0640
-0.0925
-0.0858
-0.0979
-0.0678
-0.0726
-0.0830
-0.0798
-0.0915
-0.0661
0.0812
0.0921
-0.0853
-0.0663
-0.0489
0.0875
0.1075
0.1458
0.1744
0.2007
0.2041
0.1624
-0.1156
0.0059
0.0127
0.0327
0.0225
0.0098
0.0184
0.0100
0.0916
0.0077
0.0019
0.0052
0.0019
0.0791
0.0789
0.0523
0.0295
0.0311
0.0906
0.0044
0.0041
0.0099
0.0069
0.0045
0.0190
0.0746
0.0162
0.0600
0.0495
0.0366
0.0416
0.0530
0.0444
0.0528
0.0230
0.0564
0.0756
0.0618
0.0757
0.0249
0.0249
0.0303
0.0379
0.0372
0.0231
0.0642
0.0649
0.0528
0.0578
0.0636
0.0439
0.0256
0.0461
35
Apr-92
May-92
Sep-92
Oct-92
Nov-92
Dec-92
Jan-93
Jun-93
Jul-93
Aug-93
Sep-93
Oct-92
Nov-92
Mar-93
Apr-93
May-93
Jun-93
Jul-93
Dec-93
Jan-94
Feb-94
Mar-94
-0.0999
-0.0949
0.0835
0.0832
0.0866
0.0960
0.0778
-0.0436
-0.0486
-0.0574
-0.0685
0.0401
0.0521
0.0394
0.0289
0.0233
0.0179
0.0560
0.0448
0.0096
0.0069
0.0018
0.0338
0.0303
0.0340
0.0382
0.0411
0.0447
0.0294
0.0323
0.0532
0.0578
0.0761
Nov-02
Dec-02
Jan-03
Feb-03
Mar-03
Jun-03
Jul-03
May-04
Jun-04
Jul-04
Sep-04
May-03
Jun-03
Jul-03
Aug-03
Sep-03
Dec-03
Jan-04
Nov-04
Dec-04
Jan-05
Mar-05
Panel B: Estimates of Regression (3)
R2
ˆ
ˆ
ˆ
1
2
-0.0986
-0.1039
-0.1072
-0.1205
-0.1112
-0.0501
-0.0527
-0.0536
-0.0564
-0.0389
-0.0389
0.0126
0.0050
0.0022
0.0004
0.0004
0.0984
0.0749
0.0304
0.0176
0.0923
0.0981
0.0495
0.0623
0.0736
0.0971
0.0980
0.0221
0.0256
0.0375
0.0449
0.0229
0.0221
Obs.
3
.02979* -.00233** -.07007*** 0.0115 5701
(1.80)
(-2.21)
(-5.35)
36
Table VIII
Regression Analysis for Under/Over reaction and Asymmetry of Market to Oil Price Returns
Panel A contains the estimates of the following regression specification:
Rs t    1 Ro t   2 Ro t 1   3 Ro t 2   t
(4)
Panel B contains the estimates of the following regression specification:
Rs t    1 Rot   2 ( Rot D)   t
(5)
Here Rst is the value weighted market return on day ‘t’ and Ro t is return of the real price of oil
calculated as the difference between the log percentage change in the nominal price of oil and the
rate of inflation. Ro t 1 and Ro t 2 are lagged one day and two day returns of oil respectively.
D is a dummy variable that takes a value of 1 if the real return of oil is positive on day ‘t’ and 0
otherwise. Coefficients that are significant at the 10%, 5% and 1% levels are respectively marked
with *, **, and ***. Daily returns data from April 1983 to December 2005 are used.
Panel A: Under/Over reaction of Market
F-stat R2
ˆ
ˆ
ˆ
1
2
***
-.0215
(-4.25)
3
.0036 .0069
(0.72) (1.36)
7.06 0.0037
Panel B: Asymmetry in Market’s reaction to Oil Price changes
Sample Specification
F-stat R2
ˆ
ˆ
1
Whole Sample
2
***
-.02525
(-3.16)
Price change greater than $0.5 -.0478***
(-3.67)
Price change greater than $1
-.0593***
(-3.35)
.00717
(0.53)
.02211
(0.86)
-.00459
(-0.13)
20.71 0.0033
18.69 0.0492
26.55 0.2576
37
Table IX
Regression Analysis of the Effect of Oil Price Changes on Industry Returns
Panel A presents the estimates of regression specification (6)
Rit    i Rot   it
(6)
Panel B presents the estimates of regression specification (7)
Rit     i1 Ro t   i 2 Rs t   it
(7)
where Rit is the return of industry ‘i’ on day ‘t’, Ro t is the real return of oil as defined earlier and
Rst is the value weighted market return on day ‘t’. Intercepts are not reported. Coefficients that
are significant at the 10%, 5% and 1% levels are respectively marked with *, **, and ***. t statistics
are reported in the parentheses. Daily data from April 1983 to December 2005 are used.
Industry Category
Panel A
Panel B
Linear Regression Estimates Market Model Regression Estimates
t-stat
R2
R2
ˆi 2
ˆi1
ˆ
Oil-Intensive Industries
-0.0129*
(-1.76)
0.0005
Chemicals
-0.0316***
(-5.15)
0.0046
Rubber
-0.0249***
(-4.33)
0.0033
Air Transportation
-0.1399***
(-8.88)
0.0136
Paints
-0.0313**
(-2.31)
0.0009
Gold
0.1017***
(9.17)
0.0146
Mines
0.0233***
(3.61)
0.0023
Waste Management
-0.0467***
(-4.15)
0.0030
Paper
-0.0381***
(-4.25)
0.0032
Trucking
-0.0417***
(-3.45)
0.0021
Textiles
-0.0226***
(-3.69)
0.0022
Couriers
-0.0543***
(-4.83)
0.0041
Power Generation
0.0013
(0.19)
-0.0105***
(-2.81)
-0.0074*
(-1.81)
-0.1104***
(-7.76)
-0.0157
(-1.20)
0.1065***
(9.64)
0.0397***
(7.51)
-0.0250**
(-2.47)
-0.0161**
(-2.17)
-0.0197*
(-1.79)
-0.006
(-1.26)
-0.0325***
(-3.23)
0.6456***
(37.62)
0.9582***
(98.06)
0.7926***
(73.73)
1.3422***
(36.16)
0.7082***
(20.68)
0.2184***
(7.58)
0.7420***
(53.84)
0.9862***
(37.26)
1.003***
(51.76)
1.001***
(34.85)
0.7557***
(60.31)
0.9892***
(37.65)
0.1994
0.6296
0.4899
0.1978
0.0707
0.0244
0.3387
0.1983
0.3220
0.1774
0.3911
0.2025
38
Industry Category
Linear Regression Estimates Market Model Regression Estimates
t-stat
R2
R2
ˆi 2
ˆi1
ˆ
Non Oil-Intensive Industries
Business Services
-0.0221***
(-2.78)
0.0013
Insurance
-0.0347***
(-6.65)
0.0077
Telecommunications
-0.0298***
(-4.48)
0.0035
Trading
-0.0291***
(-4.95)
0.0043
Entertainment
-0.0448***
(-5.35)
0.0050
Healthcare
-0.0272***
(-3.90)
0.0027
Wholesale
-0.0275***
(-5.08)
0.0045
0.0046
(0.92)
-0.0163***
(-5.35)
-0.0066*
(-1.68)
-0.0061**
(-2.41)
-0.02037***
(-3.28)
-0.0082
(-1.52)
-0.0073***
(-2.65)
1.2118***
(92.34)
0.8349***
(104.92)
1.0529***
(101.57)
1.0499***
(159.77)
1.1131***
(68.62)
0.8599***
(60.51)
0.9197***
(127.35)
0.60
0.6615
0.6455
0.8183
0.4552
0.3928
0.7412
39
Appendix A
This table contains the largest changes in the oil returns (WTI spot). Panel A contains the fifteen observations with the largest decline on oil
returns and Panel B contains the fifteen observations with the largest increase in oil returns. Rot is the oil return on day ‘t’ and Rst is the valueweighted market return on that day. ‘News’ is the corresponding reports found in the press (primarily The Wall Street Journal and Lexis Nexis)
that attribute the potential link between changes in oil prices and market returns.
Date
19910117
19901022
19860722
20010924
19910128
19980423
19860408
19860120
19900827
19860623
19960223
19901130
20001221
20011115
19900808
Rot
Rst
-0.4020 0.0341
-0.1744 0.0078
-0.1739 0.0085
-0.1653 0.0345
-0.1630 0.0008
-0.1556 -0.0089
-0.1382 0.0202
-0.1306 -0.0036
-0.1298 0.0300
-0.1283 -0.0058
-0.1278 0.0007
-0.1227 0.0167
-0.1215 0.0062
-0.1213 0.0007
-0.1179 0.0106
Panel A: 15 largest drops in Oil Prices
News
Initial successful strike against Iraq. The drop remained steady. No sudden reversal
Oil price tumbles on rumors of peace in the middle east.
Technical Factors. Rumors that Saudi might increase oil production. Discord among on going OPEC meetings
Fears of recession rise after the terrorist attacks
Nothing particular
Four major oil companies reported lower earnings.
Over supply in the short run.
Over production by OPEC and mild weather curbed demand.
Speculation of a resolution in Middle east and an increase in oil production by OPEC ministers
Nothing particular. News about an increased onshore drilling activity
Nothing particular
OPEC is pumping oil at the highest levels and unless war disrupts supply, there is an over supply.
Nothing particular
Traders faced the prospect of a price war among global producers
Crude-Oil Prices Fall as Saudis and Others Plan to Boost Output to Offset Shortages.
Date
Rot
Rst
20000
124
0.09
62
20010
0.09
- Nothing Particular
0.02
17
0.00 Nothing Particular
Panel B: 15 largest hikes in Oil Prices
News
40
426
19860
224
77
0.09
91
19900
822
0.10
14
20011
226
19910
107
0.10
39
0.10
53
19910
121
0.10
58
19910
114
0.10
72
19860
407
0.11
68
19980
622
19980
323
0.11
91
0.12
51
19900
806
0.14
69
19910
122
0.15
06
19980
0.18
94
0.00
11
0.01
52
0.00
44
0.01
62
0.00
12
0.00
87
0.00
17
0.00
14
0.00
38
0.03
07
0.00
65
-
Nothing Particular. Issues with Saudi Arabia declining responsibility for Oil Price plunge happening for the last six months
Fear of war in the Persian Gulf escalated.
Crude Oil Rises on Prospect of OPEC Output Cuts
Renewed war fears.
Nothing Particular
War worries swept world oil markets on Jan 14, 1991 and drove petroleum prices $2 to $4 a barrel higher.
Political: Reagan Aides Dispute Bush and Affirm Free-Market Oil Policy; Prices Up Again
Speculation about the three largest exporters to U.S cutting output
The surprise production cutback by the Organization of Petroleum Exporting Countries.
Prices surged around the world as Iraqi troops occupying Kuwait appeared to be digging in on Saudi Arabia's border.
Iraqis Set Fire to Oil Sites In Kuwait
Nothing particular
41
427
29
19860
805
0.19
86
0.02
00
0.00 Oil prices, continuing a sharp turnaround, soared to $15 a barrel in U.S. markets as the Organization of Petroleum
40 Exporting Countries reached a two-month accord cutting oil production.
42