Oil Prices and the Stock Market - Semantic Scholar

Oil Prices and the Stock Market

Robert C. Ready

First Version: September 1, 2012

This Draft: December 13, 2013

Abstract This paper develops a novel method for classifying oil price changes as supply or demand driven and documents several new facts about the relation between oil prices and stock returns. Demand shocks are strongly positively correlated with market returns, while supply shocks have a strong negative correlation. The negative effects of supply shocks are concentrated in firms which produce consumer goods, and are also strongest for oil importing countries. Demand shocks are identified as returns to an index of oil producing firms which are orthogonal to unexpected changes in the VIX index. Supply shocks are oil price changes which are orthogonal to demand shocks and changes in the VIX. Theoretical and empirical evidence are presented in support of this strategy.

Keywords: oil prices, stock markets, supply and demand JEL codes: G01, Q04, E02

I would like to acknowledge the helpful comments of Nikolai Roussanov, Ron Kaniel, Mark Ready, Bob Jarrow, and seminar participants at Cornell University, The University of Pennsylvania, and the European Financial Association. All errors are my own.

The Simon School of Business - University of Rochester

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1 Introduction

Over the past three decades, oil prices have received substantial attention as an economic indicator both from academics and the popular press.1 An extensive macroeconomic literature suggests a strong link between oil prices and economic output. Hamilton [2003] and others find a strong negative relation between increases in oil prices and future GDP growth, and Hamilton [2011] points out that 10 of the 11 postwar economic downturns have been immediately preceded by a significant rise in oil prices.

Given the apparent importance of oil prices, it is natural to examine the relations between oil prices and other traded assets, such as equities, to help better understand the link between oil prices and the economy. However in doing this a puzzling fact emerges; oil price changes and stock market returns seem to be unrelated! Indeed, from 1986 to 2012, a simple regression of monthly aggregate U.S. stock returns on contemporaneous changes in oil prices suggests essentially zero relation between the two variables. More simply put: Where is the Oil Price Beta?2

This finding is particularly surprising when considered in the context of recent macrofinance models featuring long-run risks, following the work of Bansal and Yaron [2004]. These models have been successful in explaining empirical asset pricing facts using the premise that expected future changes in personal consumption impact the utility of the consumer today, and hence have a large contemporaneous effect on the aggregate wealth portfolio and stock prices. The fact that oil is a strong empirical predictor of macroeconomic growth, including growth in personal consumption, while having little relation with aggregate stock returns, is potentially at odds with this intuition.

This paper attempts to address this puzzle by introducing a novel method of classifying changes in oil prices as demand driven (demand shocks) or supply driven (supply shocks). The simple intuition behind the identification strategy is that oil producing firms are likely

1As an illustration, a search for occurrences of the term "oil price" in the Wall Street Journal from 1986 to 2012 yields 10,821 articles; more than one article per day.

2This disconnect between the observed importance of oil prices for the macroeconomy and the lack of stock market reaction to changes in oil prices has received little attention in the academic literature. The relation between oil prices and stock markets is mostly studied in the context of Vector Autoregressions (VAR). There is some evidence for relations at various leads and lags, but uniformly weak results in terms of contemporaneous correlations. See for instance; Jones and Kaul [1996], Kilian and Park [2009], Chen, Roll, and Ross [1986], Huang, Masulis, and Stoll [1996] and Sadorsky [1999].

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to benefit from increases in oil demand, but may have a natural hedge against shocks to oil supply. If oil becomes more difficult to produce, producers will sell less, but at higher prices. If this is the case, oil producer returns can be used as a control to separate shocks to demand and supply.

Following this intuition, demand shocks are defined as the portion contemporaneous returns of an index of oil producing firms which is orthogonal to unexpected changes in the VIX index, which are included to control for aggregate changes in discount rates.3 Supply shocks are constructed as the portion of contemporaneous oil price changes which is orthogonal to demand shocks as well as to innovations in the VIX.4 By construction, innovations to the VIX (henceforth risk shocks), demand shocks, and supply shocks, are orthogonal and account for all of the variation in oil prices. Since the VIX has very low correlation with oil prices, nearly all of the variance is captured by the demand and supply shocks, with supply shocks accounting for 78.5% of the total variation and demand shocks another 20%.

When the supply and demand shocks are examined separately, it becomes clear that the apparent lack of relation between oil prices and stock returns is an artifact of the conflicting effects of the two types of shocks. Instead of no relation, both supply and demand shocks are strongly correlated with aggregate stock returns over the sample period. Changes in oil prices classified as supply shocks have a strong statistically significant negative relation with stock returns, with oil supply shocks explaining roughly 4% of the monthly variation in aggregate U.S. stock market returns over the 1986 - 2011 sample period. The presence of large outliers of returns during the financial crisis increases this to roughly 9% when the sample is restricted to the period prior to June 2008, with nearly all of the effect coming from 1985 - 1992 and 2003 - 2008, two periods of high uncertainty in oil markets. Similar results hold for international stock returns, with the impact of supply shocks being strongest for oil importing countries.

In contrast, increases in prices classified as oil demand shocks have a strong positive relation with stock returns, and explain roughly 10% of monthly U.S. stock market variation over the time period. Demand shocks are significantly positively correlated with both U.S.

3Unexpected innovations are the residual from an ARMA(1,1) regression. 4Returns to a position in short-term oil futures are used to focus on the unanticipated change in the oil price.

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and world returns in all subperiods, with the exception of a weak correlation with U.S. returns during the 2003-2008 period. However, during this period the demand shocks are still strongly positively correlated with world returns, suggesting world wide growth as the source of increases in demand over this period.

In addition to providing evidence for the importance of oil in explaining aggregate stock returns, construction of these shocks also allows for an investigation of how oil shocks affect different types of firms. Regressions of industry portfolios find that all industries, with the exception of gold and coal producers, have significantly negative relations with oil supply shocks. However, it is not the industries with the highest oil use that are the most negatively affected by oil supply shocks. While some high oil use industries such as Airlines are near the top of the list, in general producers of consumer goods and services (ie. Apparel and Retail) tend to have greater exposures than high oil use manufacturing firms. This result suggests that the main effect of an oil shock may be a reduction in consumer spending, which in turn impacts firms that produce consumer goods, consistent with the hypothesis of Hamilton [2003] that oil shocks act through a reduction in consumer demand. The least impacted firms appear to be high tech and telecom firms, which have low oil use and may be less directly dependent on consumer spending. All industries have positive betas on oil demand shocks, with high oil use industries having the strongest loadings. High oil use firms tend to be manufacturing firms, suggesting that increases in manufacturing activity may be the primary driver of oil demand shocks.

The interpretations here rely crucially on the validity of the identification strategy. To understand the logic, it is important to understand the potential reason for the lack of correlation between oil prices and stock prices. If an exogenous increase in oil prices is, ceterus paribus, bad news for stock prices, what possible cause is there for the negligible relation between the two variables? One potential explanation is an omitted variable, with an obvious candidate being shocks to aggregate oil demand, which would intuitively be associated with rising oil prices and positive equity returns.5 In order to account for this

5This logic has been emphasized by several authors, most notably Kilian [2009]. A different logic, suggested in recent work by Casassus and Higuera [2013], builds on the fact (se Driesprong, Jacobsen, and Maat [2008] and Casassus and Higuera [2012]) that high oil prices predict low future aggregate market returns. If high oil prices mean low future cash flows but also lower expected future returns, the two effects can also counteract each other when examining stock market returns. It is shown here that the identified supply and demand

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and sign the causal relation between oil prices and stock returns, one might potentially find instruments for oil price changes that are exogenous with respect to the rest of the economy, such as a time series of events affecting oil production.6 However these events tend to be rare so this technique is less suited to answering the question of how much the variance in stock prices is driven by oil supply shocks.

Another potential issue with directly classifying shocks lies in the definition of a supply shock. Although oil supply shocks are typically thought of as discrete events, such as a war or hurricane which disrupts oil production, there is the possibility for more subtle effects. For instance, how would one classify a month where oil prices rise slowly day by day, as the amount of oil produced fails to meet expectations? The resulting change in prices is as much a supply shock as a one time major disruption in production from a natural or political event, but is much more difficult to identify from news reports.

Perhaps the most direct technique to account for this problem is to examine data on oil production. Kilian [2009] attempts to disentangle demand and supply shocks using a structural vector autoregression (SVAR) with data on oil production and shipping activity as proxies for supply and demand, and Kilian and Park [2009] extend this methodology to examining different shocks' impact on the U.S. stock market. However, they find very little contemporaneous explanatory power (less than 2% combined), mostly concentrated in changes in oil prices related to neither supply nor aggregate demand.

One weakness of this framework is that the data included in the SVAR needs to correlate with contemporaneous or future changes in oil prices to effectively identify shocks. For instance, the identified supply and demand shocks in Kilian [2009] explain only 2% of the contemporaneous variation in oil prices from 1987 to 2011, and less than 1% one percent when the sample is truncated in 2008 to remove effects of the financial crisis. Of the remaining variation in oil price changes, 30% is classified as predictable by the VAR, suggesting overfitting, and the remaining 68% is classified as "precautionary demand shocks".7 Unfortunately, there is no way to ascertain if these changes in precautionary demand are driven by concerns

shocks both predict negative future market returns, suggesting that their difference in correlations with contemporaneous market returns is not driven by discount rate effects.

6This strategy is pursued by several authors, including Hamilton [1983], Hamilton [2003], Kilian [2008], and Cavallo and Wu [2006].

7See Web Appendix section ??.

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