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Crude oil is one of the most important commodities in the current global world. Simple logic says that oil prices should predict the economy’s performance and, therefore, should also have some predictive ability for equity returns.
Academic research confirms it and what’s more - it shows that oil isn‘t the only commodity with prediction ability (prices of some industrial metals could also be used as equity indicators). Higher oil prices predict lower future equity returns and vice versa. Therefore a simple market timing system that is using oil prices as an indicator of time equities can be constructed.
Fundamental reason
Equity predictability is explained by the underreaction hypothesis. It seems that it takes time before information about oil price changes become fully reflected in stock market prices. Underreaction can occur due to a possible difficulty for investors to assess the impact of information on the value of stocks, or when investors react to information at different points in time.
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Keywords
Market Factors
Confidence in Anomaly's Validity
Period of Rebalancing
Number of Traded Instruments
Complexity Evaluation
Financial instruments
Backtest period from source paper
Indicative Performance
Notes to Indicative Performance
Estimated Volatility
Notes to Estimated Volatility
Maximum Drawdown
Notes to Maximum drawdown
Sharpe Ratio
Regions
Simple trading strategy
Several types of oil can be used (Brent, WTI, Dubai, etc.) without big differences in results. The source paper for this anomaly uses Arab Light crude oil. Monthly oil returns are used in the regression equation as an independent variable, and equity returns are used as a dependent variable. The model is re-estimated every month, and observations of the last month are added. The investor determines whether the expected stock market return in a specific month (based on regression results and conditional on the oil price change in the previous month) is higher or lower than the risk-free rate. The investor is fully invested in the market portfolio if the expected return is higher (bull market); he invests in cash if the expected return is lower (bear market).
Hedge for stocks during bear markets
Partially – The selected strategy is a class of "Market Timing" strategies that try to rotate out of equities during the time of stress. Therefore the proposed strategy isn't mainly used as an add-on to the portfolio to hedge equity.
Out-of-sample strategy's implementation/validation in QuantConnect's framework(chart, statistics & code)
Related picture
Source paper
Driesprong, Jacobsen, Maat: Striking Oil: Another Puzzle?
Abstract: Changes in oil prices predict stock market returns worldwide. In our thirty year sample of monthly returns for developed stock markets, we find statistically significant predictability for twelve out of eighteen countries as well as for the world market index. Results are similar for our shorter time series of emerging markets. We find no evidence that our results can be explained by time varying risk premia. Even though oil price shocks increase risk, investors seem to underreact to information in the price of oil: a rise in oil prices does not lead to higher stock market returns, but drastically lowers returns. For instance, an oil price shock of one standard deviation (around 10 percent) predictably lowers world market returns by one percent. Oil price changes also significantly predict negative excess returns. Our findings are consistent with the hypothesis of a delayed reaction by investors to oil price changes. In line with this hypothesis the relation between monthly stock returns and lagged monthly oil price changes becomes substantially stronger once we introduce lags of several trading days between monthly stock returns and lagged monthly oil price changes.
Other papers
Jacobsen, Marshall, Visaltanachoti: Return Predictability Revisited
Abstract: We are able to predict a huge amount of variation in monthly stock market returns when we use a very simple and theoretically more appealing approach of refining the observation intervals of the variables used to predict these returns. In contrast to normal short-term predictability studies which have notoriously low R2s of around one or two percent, we find R2s, based on price changes in economically important commodities, of up to 18 percent. Shorter intervals reveal return predictability consistent with near efficient markets based on price changes in industrial metals, and more historical intervals expose predictability consistent with the gradual information diffusion hypothesis based on price changes in energy series. This predictability is statistically and economically significant, robust to data mining adjustment, does not appear to be a consequence of time-varying risk premia, and provides investors with the opportunity to make out-of-sample economic profits. Taken together, our results provide strong support for Kendall’s (1953) conclusion that “the interval of observation may be very important” for stock market return predictability tests.
Haibo Jiang, Georgios Skoulakis and Jinming Xue: Oil and Equity Index Return Predictability:The Importance of Dissecting Oil Price Changes
Abstract: Using data until 2015, we document that oil price changes no longer predict G7 country equity index returns, in contrast to evidence based on earlier samples. Using a structural VAR approach, we decompose oil price changes into oil supply shocks, global demand shocks, and oil-specific demand shocks. The conjecture that oil supply shocks and oil-specific demand shocks (global demand shocks) predict equity returns with a negative (positive) slope is supported by the empirical evidence over the 1986-2015 period. The results are statistically and economically significant and do not appear to be consistent with time-varying risk premia.
McMillan, David G. and Ziadat, Salem Adel: The Predictive Power of the Oil Variance Risk Premium
Abstract: This paper examines the ability of the oil market variance risk premium (VRP) to predict both financial and key macroeconomic series. Interest in understanding movement in such variables increasingly considers measures of investor risk and the variance risk premium, which incorporates both implied and realised volatility, has recently come for the fore. It is well established that oil price movement impacts both the stock market and wider economy and thus, we examined whether this is also true of the oil VRP. Using monthly US data over the period from 2009 to 2021, we demonstrate the nature of oil VRP predictive power for oil and stock returns, as well as output growth, unemployment, and inflation. Of notable interest, while predictability from the oil VRP series dominates at the one-month horizon and (largely) wanes at over longer time periods, the reverse is found for the stock VRP. These results are robust to the inclusion of additional, established, predictor variables. This indicates that the impact of oil market risk has a more immediate effect on both the stock market and economy, with stock market risk reflecting longer term considerations.
Haykir, Ozkan and Yagli, Ibrahim and Aktekin-Gok, Emine Dilara and Budak, Hilal: Oil Price Explosivity and Stock Return: Do Sector and Firm Size Matter?
Abstract: The paper aims to examine whether the oil price series contains price explosivity, and if exists whether this price explosivity offers excess return for oil-related, oil-substitute, and oil-user companies in US stock markets. Moreover, we examine whether the size effect moderates the relationship between oil price explosivity and stock returns. We use monthly West Texas Intermediate crude oil prices which spans between January 1986 and December 2019 and employ the Generalized Supremum Augmented Dickey-Fuller test to detect the price explosivity. The results indicate multiple episodes of price explosivity which mostly coincides with the 2008 financial crisis. The price explosivity leads to an excess return for oil-related companies; whereas, there is a negative impact of oil price explosivity on oil-substitute and oil-user firms. However, the effect of oil price explosivity on stock returns is heterogeneous across size groups. The results provide key insightful information to policymakers and investors. Policymakers should prevent the occurrence of price explosivity increasing the efficiency of an oil futures market. Given the diverse impact of oil price explosivity on the stock return across sectors and sub-size groups, investors should maximize their profits rebalancing their portfolio based on oil dependency and the size of the firm.