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 over- reaction 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 under- reaction 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 daily returns data and investigate the impact of oil price changes on market and industry returns

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 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,

Hamilton (1983) documents a significant negative relation between oil price changes and future

3 based on the input requirement coefficients

4 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.

They find a negative effect of relative price variability on output and stock returns and suggest

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 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

A. Relation between Oil Price changes and Market Returns (Hypothesis I)

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 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 Rot 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 lower 8 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.

Hypothesis I.I: The stock market’s sensitivity to oil price changes is directly related to

the level of oil prices

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 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 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

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 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

(1992). These results would support my hypothesis, assuming an oil price increase as an

9 unfavorable event and an oil price decrease as a favorable event 10. 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.

III. Sample Data and Descriptive Statistics

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 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 data 13. 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-intensive 16

13 www.normanshistoricaldata.com 14 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. 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.

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 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.

12 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 oil- intensive 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.

Rst   Rot t (2)

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 Where Rst is the return on the value weighted NYSE index on day ‘t’ and Rot 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.

Rst   1Rot  2 (RP * Rot )   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, p- values 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 Rst   1Rot  2 (RP * Rot )  3 (WAR * Rot )   t (3)

Where Rst is the return on the value weighted NYSE index on day ‘t’ and Rot 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

Rst   1Rot   2 Rot1  3Rot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.

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

Rst   1Rot   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.

Rit   i Rot   it (6)

18 Here Rit is the return of industry ‘i’ on day‘t’ and Rot 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

Rit   i1Rot  i2 Rst   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 oil- intensive 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 355- 385

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

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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.

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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 60

s 50 r a l

l 40 o

D 30 20 10 0 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 8 8 8 8 9 9 9 9 9 0 0 0 8 8 8 9 9 9 9 9 0 0 0 ------r r r r r r r r r r r r r r r r r r r r r r r p p p p p p p p p p p p p p p p p p p p p p p A A A A A A A A A A A A A A A A A A A A A A A Time

Nominal Price Real Price

Figure II

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.

0.25 0.2 s e

t 0.15 a

m 0.1 i t

s 0.05 E

t 0 n e

i -0.05 c i

f -0.1 f e

o -0.15 C -0.2 -0.25 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0 0 ------r r r r r r r r r r r r r r r r r r r r r r r p p p p p p p p p p p p p p p p p p p p p p p A A A A A A A A A A A A A A A A A A A A A A A Time

26 Table I Summary of Data Used in Previous Empirical Studies on the Effect of Oil Price Shocks Study Sample Frequency Key dependent variable/variables Focus Period Darby (1982) 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 Six-variable system presented by Sims (1980 Oil price – macroeconomy b)* relationship Golub (1983) 1972 – 1980 Annual Exchange rates Response of FOREX markets to oil price changes Burbidge & Harrison (1984) 1973-1982 Monthly Macroeconomic variables from OECD Oil price – marcroeconomy Countries relationship in OECD countries Chen, Roll & Ross (1986) 1953 – 1983 Monthly Returns on NYSE index, growth rates in Asset pricing industrial production, measures of inflation, measures of risk premium etc.

Gisser & Goodwin (1986) 1961 – 1982 Quarterly Six-variable system presented by Sims (1980 Oil price – macroeconomy b)* relationship

Mork (1989) 1949 – 1988 Quarterly Six-variable system presented by Sims (1980 Oil price – macroeconomy b)* asymmetric relationship Kaul & Seyhun (1990) 1947 – 1985 Annual Growth rate of output and market returns Effect of relative price variability on output and stock market Hooker (1996) 1948 – 1994 Quarterly GDP and unemployment rate Oil price – macroeconomy relationship Huang, Masulis & Stoll (1996) 1983 – 1990 Daily (futures) S&P 500 index returns, industry returns Relation between oil futures and stock returns

Jones & Koul (1996) 1947 – 1991 Quarterly Returns of market indices of several countries Market’s ability to evaluate and Cash flows the impact of oil shocks Study Sample Frequency Key dependent variable/variables Focus Period Keane & Prasad (1996) 1966 – 1981 Survey data Weekly wage rates and proxies for human Employment and wage capital effects of oil price changes Bernanke, Gertler & Watson (1997) 1965 – 1995 Monthly GDP, GDP deflator and federal funds rate Effects of systematic monetary policy and oil price shocks Sardosky (1999) 1947 – 1996 Monthly Index of Industrial Production, interest rates & Impact of oil prices and real stock returns volatility on stock returns

Ciner (2001) 1983 – 2000 Daily (futures) Same as Huang, Masulis & Stoll (1996) Non-linear linkages between energy shocks and financial markets Barsky & Kilian (2001) 1960 – 2001 Annual Growth rate of GDP Role of oil price shocks in causing stagflation Davis & Haltiwanger (2001) 1972 – 1988 Quarterly Job flows between industries Oil shocks and employment effects Lee & Ni (2002) 1959 – 1997 Monthly Industry level output Output responses to oil price shocks

Hamilton & Herrera (2002) 1965 – 1995 Monthly Same as Bernanke et.al (1997) Oil price – macroeconomy relationship and the role of monetary policy Hong, Torous & Valkanov (2002) 1972 – 2001 Monthly Thirty-four industry portfolios Predictability of market by industries Hooker (2002) 1962 – 2000 Quarterly Rate of inflation Oil price changes and inflation Pollet (2004) 1973 – 2002 Monthly Value-weighted market and industry returns Predictive ability of expected oil price changes

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 Predictive ability of oil price and world market index changes

28 * 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

29 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.

Panel A: Summary Statistics

Variable Observations Mean Median rho1 Std. Deviation Real Oil Return 5701 .00000445 0.0000 -0.0100 0.02418 Market Return 5701 .0005095 0.0007 0.0620 0.00927

Panel B: Percentile Distributions Percentile Oil Return Market Return

1% -.06779 -.02373 5% -.03626 -.01376 10% -.02461 -.00938 25% -.01031 -.00386 50% 0 .00070 75% .01125 .00505 90% .02505 .01049 95% .03484 .01421 99% .05985 .02373 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 Input Requirement Definition Source Coefficient Power Generation & Supply 0.0980 4911 Electric Services CRSP

Basic Chemicals; 0.0913 Chemicals and allied products; Industrial inorganical chems; Plastic material & K.French’s 48 industry Agricultural Chemical 0.0875 synthetic resin; Paints; Industrial organic chems; Agriculture chemicals; Misc portfolio Manufacturing; chemical products Other Chemical product and 0.0451 preparation manufacturing; Resin, Rubber and Artificial Fibres 0.0838 Reclaimed rubber; Rubber & plastic hose and belting;Gaskets, hoses, etc; K.French’s 48 industry Fabricated rubber products; Misc rubber products; Misc plastic products portfolio Air Transportation 0.0649 4512 Air Transportation, Scheduled CRSP Paint, coating and adhesive 0.0488 2851 Paints, Varnishes, Lacquers, Enamels, and Allied Products CRSP manufacturing Metal ores mining; 0.0449 Metal mining; Iron ores; Copper ores; Lead and zinc ores; Bauxite and other K.French’s 48 industry Nonmetallic mineral mining and 0.0444 aluminum ores; Ferroalloy ores; Mining; Mining services; Misc metal portfolio quarrying; ores;Anthracite mining; Mining and quarrying non-metalic minerals (Mines) Waste management and remediation 0.0423 4952 Sewerage Systems CRSP services 4953 Refuse Systems 4959 Sanitary Services, Not Elsewhere Classified Pulp, paper and paperboard mills 0.0390 2611 Pulp Mills CRSP 2621 Paper Mills 2631 Paperboard Mills Truck Transportation 0.0374 4213 Trucking, Except Local CRSP Textile Mills 0.0360 Textile mill products; Floor covering mills; Yarn and thread mills; Misc textile K.French’s 48 industry goods; Nonwoven fabrics; Cordage and twine; Misc textile products; Textile portfolio bags, canvas products; Misc textile products (Textiles) Couriers and messengers 0.0349 4513 Air Courier Services CRSP 4215 Courier Services, Except by Air

Employment Services; 0.0001 Commercial printing, Signs, advertising specialty, industrial launderers, business K.French’s 48 industry Software Publishers; 0.0030 services, advertising, credit reporting agencies, collection services, mailing, portfolio Legal Services; 0.0032 reproduction, commercial art, services to dwellings, other buildings, cleaning and (Business Services) Architectural and engineering 0.0033 building maint, misc equip rental and leasing, medical equip rental, heavy services; construction equip rental, equip rental and leasing, personnel supply services, Accounting and bookkeeping 0.0033 computer programming and data processing, information retrieval services, services; computer rental and leasing, computer maintenance and reapir, computer related Data Processing services; Computer 0.0034 services, misc business services, security, new syndicates, photofinishing labs, System design and related services; telephone interconnections, misc business services, R&D labs, management Machinery and equipment rental and 0.0036 consulting &P.R, detective and protective, equipment rental and leasing, trading leasing; stamp services, commercial testing labs, business services, trailer rental and leasing, engg, accounting, research, management, surveying, auditing, consulting, architect etc.

Insurance carriers and related 0.0019 Insurance;Life insurance; Accident and health insurance; Fire, marine, property- K.French’s 48 industry activities casualty ins; Surety insurance; Title insurance; Pension, health, welfare funds; portfolio Insurance carriers; Insurance agents (Insurance) Telecommunications; 0.0043 Communications; Telephone communications;Telegraph and other message K.French’s 48 industry Cable networks and program 0.0039 communication; Radio-TV Broadcasters;Cable and other pay TV services; portfolio distribution; Radio and Television Communications;Communication services (Comsat); Cable TV broadcasting; operators;Telephone interconnect; Communication services Funds, trusts, and other financial 0.0047 Security and commodity brokers;Holding, other investment offices; Holding K.French’s 48 industry vehicles offices; Investment offices; Management investment, closed-end; Unit portfolio investment trusts; Face-amount certificate offices; Unit inv trusts, closed-end; (Trading) Trusts; Investment offices; Miscellaneous investing; Oil royalty traders; Commodity traders; Patent owners & lessors; Mineral royalty traders; REIT Investors, NEC Motion Picture and Sound Recording 0.0054 motion picture production and distribution; motion picture theatres; video rental; K.French’s 48 industry Industries amusement and recreation; dance studios portfolio bands, entertainers; bowling centers; professional sports; misc entertainment (Entertainment) Ambulatory Health Care Services 0.0057 Services – health; K.French’s 48 industry Hospitals 0.0097 portfolio (Healthcare)

32 Wholesale Trade 0.0065 5000-5199 K.French’s 48 industry portfolio (Wholesale)

33 Table IV Descriptive Statistics of Industry Returns Data

This table presents the descriptive statistics for industry returns. Industries are classified as oil- intensive 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 Std Dev Oil-intensive group 0.00104 0.00115 0.1410 0.00978

Non oil-intensive 0.00051 0.00084 0.1020 0.00968 group

Panel B: Summary statistics for the Industry Returns.

Mean Median rho1 Std Dev

Oil-intensive Industries Power Generation* 0.00072 0.0010 0.1030 0.013404 Chemicals 0.00052 0.0004 0.0850 0.011223 Rubber 0.00043 0.0005 0.0810 0.010521 Air Transportation* 0.00174 0.0005 0.0660 0.028963 Paints* 0.00275 0.0012 0.0180 0.024827 Gold* 0.00035 -0.0007 0.0270 0.02039 Mines 0.00031 0.0004 0.0830 0.011847 Waste Management* 0.00105 0.0008 0.0880 0.020662 Paper* 0.00135 0.0009 0.0530 0.016448 Trucking* 0.00144 0.0010 0.0930 0.022148 Textiles 0.00031 0.0050 0.1040 0.011224 Couriers* 0.00122 0.0000 0.0830 0.020565

Non oil-intensive Industries Business Services 0.00049 0.0010 0.0860 0.014586 Insurance 0.00053 0.0006 0.1500 0.00956 Telecommunications 0.00042 0.0005 0.0500 0.012168 Trading 0.00059 0.0007 0.0560 0.010775 Entertainment 0.00054 0.0004 0.0380 0.015361 Healthcare 0.00044 0.0007 0.1560 0.012748 Wholesale 0.00046 0.0007 0.1380 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* 0.0001 1% 0.0850*** -0.0026 5% 0.0523** -0.0010 10% 0.0410** -.00047

35 Table VI Regression Analysis of the Effect of Oil Price Changes on Market Returns

Panel A presents the estimates of regression specification (1)

Rst   Rot   t (1)

Panel B presents the estimates of regression specification (2):

Rst   1Rot  2 (RP * Rot )   t (2)

Here Rst is the value weighted market return on day ‘t’ and Rot 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 2 Sample Specification ˆ t-stat R Obs. Whole sample -0.0219*** (-4.34) 0.0033 5701

Panel B 2 Sample ˆ ˆ R Obs. specification 1 2 Whole Sample .04543*** -.0043*** 0.0066 5701 (2.78) (-4.34)

36 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:

Rst   1Rot  2 (RP * Rot )  3 (WAR * Rot )   t (3)

Where Rst is the return on the value weighted NYSE index on day ‘t’ and Rot 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.

Panel A: Estimates of rolling-window regression 2 2 Start End ˆ p- R Start End ˆ p- R date date value date date value Aug-84 Jan-85 0.1635 0.0289 0.0382 Oct-93 Apr-94 -0.0690 0.0059 0.0600 Nov-84 Jun-85 0.0868 0.0764 0.0253 Nov-93 May-94 -0.0617 0.0127 0.0495 Apr-85 Oct-85 -0.1153 0.0544 0.0298 Dec-93 Jun-94 -0.0541 0.0327 0.0366 May-85 Nov-85 -0.1363 0.0335 0.0362 Nov-94 May-95 -0.0640 0.0225 0.0416 Jun-85 Dec-85 -0.1576 0.0049 0.0625 Jul-99 Jan-00 -0.0925 0.0098 0.0530 Oct-85 Apr-86 -0.0332 0.0402 0.0338 Aug-99 Feb-00 -0.0858 0.0184 0.0444 Nov-85 May-86 -0.0372 0.0193 0.0437 Sep-99 Mar-00 -0.0979 0.0100 0.0528 Dec-85 Jun-86 -0.0320 0.0409 0.0335 Oct-99 Apr-00 -0.0678 0.0916 0.0230 Dec-86 Jun-87 0.0969 0.0959 0.0224 May-00 Nov-00 -0.0726 0.0077 0.0564 Jan-87 Jul-87 0.1269 0.0348 0.0357 Jun-00 Dec-00 -0.0830 0.0019 0.0756 Feb-90 Aug-90 -0.0616 0.0068 0.0580 Jul-00 Jan-01 -0.0798 0.0052 0.0618 Mar-90 Sep-90 -0.1067 0.0000 0.1767 Aug-00 Feb-01 -0.0915 0.0019 0.0757 Apr-90 Oct-90 -0.1242 0.0000 0.2343 Sep-00 Mar-01 -0.0661 0.0791 0.0249 May-90 Nov-90 -0.1153 0.0000 0.2442 Dec-00 Jun-01 0.0812 0.0789 0.0249 Jun-90 Dec-90 -0.1181 0.0000 0.2874 Feb-01 Aug-01 0.0921 0.0523 0.0303 Jul-90 Jan-91 -0.1194 0.0000 0.3083 Apr-01 Oct-01 -0.0853 0.0295 0.0379 Aug-90 Feb-91 -0.0857 0.0000 0.2564 May-01 Nov-01 -0.0663 0.0311 0.0372 Sep-90 Mar-91 -0.0725 0.0000 0.2013 Jun-01 Jan-02 -0.0489 0.0906 0.0231 Oct-90 Apr-91 -0.0607 0.0000 0.1558 Dec-01 Jun-02 0.0875 0.0044 0.0642 Nov-90 May-91 -0.0504 0.0003 0.1027 Jan-02 Jul-02 0.1075 0.0041 0.0649 Dec-90 Jun-91 -0.0410 0.0046 0.0635 Feb-02 Aug-02 0.1458 0.0099 0.0528 Jan-91 Jul-91 -0.0420 0.0056 0.0606 Mar-02 Sep-02 0.1744 0.0069 0.0578 Jun-91 Dec-91 -0.1001 0.0427 0.0330 Apr-02 Oct-02 0.2007 0.0045 0.0636 Jul-91 Jan-92 -0.1012 0.0230 0.0413 May-02 Nov-02 0.2041 0.0190 0.0439 Aug-91 Feb-92 -0.0812 0.0561 0.0293 Jun-02 Dec-02 0.1624 0.0746 0.0256

37 Mar-92 Sep-92 -0.0866 0.0539 0.0299 Oct-02 Apr-03 -0.1156 0.0162 0.0461 Apr-92 Oct-92 -0.0999 0.0401 0.0338 Nov-02 May-03 -0.0986 0.0126 0.0495 May-92 Nov-92 -0.0949 0.0521 0.0303 Dec-02 Jun-03 -0.1039 0.0050 0.0623 Sep-92 Mar-93 0.0835 0.0394 0.0340 Jan-03 Jul-03 -0.1072 0.0022 0.0736 Oct-92 Apr-93 0.0832 0.0289 0.0382 Feb-03 Aug-03 -0.1205 0.0004 0.0971 Nov-92 May-93 0.0866 0.0233 0.0411 Mar-03 Sep-03 -0.1112 0.0004 0.0980 Dec-92 Jun-93 0.0960 0.0179 0.0447 Jun-03 Dec-03 -0.0501 0.0984 0.0221 Jan-93 Jul-93 0.0778 0.0560 0.0294 Jul-03 Jan-04 -0.0527 0.0749 0.0256 Jun-93 Dec-93 -0.0436 0.0448 0.0323 May-04 Nov-04 -0.0536 0.0304 0.0375 Jul-93 Jan-94 -0.0486 0.0096 0.0532 Jun-04 Dec-04 -0.0564 0.0176 0.0449 Aug-93 Feb-94 -0.0574 0.0069 0.0578 Jul-04 Jan-05 -0.0389 0.0923 0.0229 Sep-93 Mar-94 -0.0685 0.0018 0.0761 Sep-04 Mar-05 -0.0389 0.0981 0.0221

Panel B: Estimates of Regression (3) ˆ ˆ ˆ R2 Obs. 1  2 3 .02979* -.00233** -.07007*** 0.0115 5701 (1.80) (-2.21) (-5.35)

38 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:

Rst   1Rot   2 Rot1  3Rot2   t (4)

Panel B contains the estimates of the following regression specification:

Rst   1Rot   2 (Rot D)   t (5)

Here Rst is the value weighted market return on day ‘t’ and Rot 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. Rot1 and Rot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 2 ˆ ˆ ˆ F- R 1 2 3 stat -.0215*** .0036 .0069 7.06 0.0037 (-4.25) (0.72) (1.36)

Panel B: Asymmetry in Market’s reaction to Oil Price changes 2 Sample Specification ˆ ˆ F- R 1 2 stat Whole Sample -.02525*** .00717 20.71 0.0033 (-3.16) (0.53) Price change greater than $0.5 -.0478*** .02211 18.69 0.0492 (-3.67) (0.86) Price change greater than $1 -.0593*** -.00459 26.55 0.2576 (-3.35) (-0.13)

39 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    i1Rot   i2 Rst   it (7)

where Rit is the return of industry ‘i’ on day ‘t’, Rot 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.

Panel A Panel B Industry Category Linear Regression Market Model Regression Estimates Estimates ˆ t-stat R 2 ˆ ˆ R 2  i1 i2 Oil-Intensive Industries

Power Generation -0.0129* (-1.76) 0.0005 0.0013 0.6456*** 0.1994 (0.19) (37.62) Chemicals -0.0316*** (-5.15) 0.0046 -0.0105*** 0.9582*** 0.6296 (-2.81) (98.06) Rubber -0.0249*** (-4.33) 0.0033 -0.0074* 0.7926*** 0.4899 (-1.81) (73.73) Air Transportation -0.1399*** (-8.88) 0.0136 -0.1104*** 1.3422*** 0.1978 (-7.76) (36.16) Paints -0.0313** (-2.31) 0.0009 -0.0157 0.7082*** 0.0707 (-1.20) (20.68) Gold 0.1017*** (9.17) 0.0146 0.1065*** 0.2184*** 0.0244 (9.64) (7.58) Mines 0.0233*** (3.61) 0.0023 0.0397*** 0.7420*** 0.3387 (7.51) (53.84) Waste Management -0.0467*** (-4.15) 0.0030 -0.0250** 0.9862*** 0.1983 (-2.47) (37.26) Paper -0.0381*** (-4.25) 0.0032 -0.0161** 1.003*** 0.3220 (-2.17) (51.76) Trucking -0.0417*** (-3.45) 0.0021 -0.0197* 1.001*** 0.1774 (-1.79) (34.85) Textiles -0.0226*** (-3.69) 0.0022 -0.006 0.7557*** 0.3911

40 Industry Category Linear Regression Market Model Regression Estimates Estimates ˆ t-stat R 2 ˆ ˆ R 2  i1 i2 (-1.26) (60.31) Couriers -0.0543*** (-4.83) 0.0041 -0.0325*** 0.9892*** 0.2025 (-3.23) (37.65) Non Oil -Intensive Industries

Business Services -0.0221*** (-2.78) 0.0013 0.0046 1.2118*** 0.60 (0.92) (92.34) Insurance -0.0347*** (-6.65) 0.0077 -0.0163*** 0.8349*** 0.6615 (-5.35) (104.92) Telecommunications -0.0298*** (-4.48) 0.0035 -0.0066* 1.0529*** 0.6455 (-1.68) (101.57) Trading -0.0291*** (-4.95) 0.0043 -0.0061** 1.0499*** 0.8183 (-2.41) (159.77) Entertainment -0.0448*** (-5.35) 0.0050 -0.02037*** 1.1131*** 0.4552 (-3.28) (68.62) Healthcare -0.0272*** (-3.90) 0.0027 -0.0082 0.8599*** 0.3928 (-1.52) (60.51) Wholesale -0.0275*** (-5.08) 0.0045 -0.0073*** 0.9197*** 0.7412 (-2.65) (127.35)

41 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 value- weighted 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.

Panel A: 15 largest drops in Oil Prices Date Rot Rst News 19910117 -0.4020 0.0341 Initial successful strike against Iraq. The drop remained steady. No sudden reversal 19901022 -0.1744 0.0078 Oil price tumbles on rumors of peace in the middle east. 19860722 -0.1739 0.0085 Technical Factors. Rumors that Saudi might increase oil production. Discord among on going OPEC meetings 20010924 -0.1653 0.0345 Fears of recession rise after the terrorist attacks 19910128 -0.1630 0.0008 Nothing particular 19980423 -0.1556 -0.0089 Four major oil companies reported lower earnings. 19860408 -0.1382 0.0202 Over supply in the short run. 19860120 -0.1306 -0.0036 Over production by OPEC and mild weather curbed demand. 19900827 -0.1298 0.0300 Speculation of a resolution in Middle east and an increase in oil production by OPEC ministers 19860623 -0.1283 -0.0058 Nothing particular. News about an increased onshore drilling activity 19960223 -0.1278 0.0007 Nothing particular 19901130 -0.1227 0.0167 OPEC is pumping oil at the highest levels and unless war disrupts supply, there is an over supply. 20001221 -0.1215 0.0062 Nothing particular 20011115 -0.1213 0.0007 Traders faced the prospect of a price war among global producers 19900808 -0.1179 0.0106 Crude-Oil Prices Fall as Saudis and Others Plan to Boost Output to Offset Shortages.

Panel B: 15 largest hikes in Oil Prices Date Rot Rst News

20000124 0.0962 -0.0217 Nothing Particular 20010426 0.0977 0.0094 Nothing Particular 19860224 0.0991 -0.0011 Nothing Particular. Issues with Saudi Arabia declining responsibility for Oil Price plunge happening for the last six months 19900822 0.1014 -0.0152 Fear of war in the Persian Gulf escalated.

42 20011226 0.1039 0.0044 Crude Oil Rises on Prospect of OPEC Output Cuts 19910107 0.1053 -0.0162 Renewed war fears. 19910121 0.1058 -0.0012 Nothing Particular 19910114 0.1072 -0.0087 War worries swept world oil markets on Jan 14, 1991 and drove petroleum prices $2 to $4 a barrel higher. 19860407 0.1168 -0.0017 Political: Reagan Aides Dispute Bush and Affirm Free-Market Oil Policy; Prices Up Again 19980622 0.1191 0.0014 Speculation about the three largest exporters to U.S cutting output 19980323 0.1251 -0.0038 The surprise production cutback by the Organization of Petroleum Exporting Countries. 19900806 0.1469 -0.0307 Prices surged around the world as Iraqi troops occupying Kuwait appeared to be digging in on Saudi Arabia's border. 19910122 0.1506 -0.0065 Iraqis Set Fire to Oil Sites In Kuwait 19980427 0.1829 -0.0200 Nothing particular 19860805 0.1986 0.0040 Oil prices, continuing a sharp turnaround, soared to $15 a barrel in U.S. markets as the Organization of Petroleum Exporting Countries reached a two-month accord cutting production.