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 to 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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Markets Traded
equities

Financial instruments
CFDs, ETFs, funds, futures

Confidence in anomaly's validity
Strong

Backtest period from source paper
1988-2003

Notes to Confidence in Anomaly's Validity

Indicative Performance
11.9%

Period of Rebalancing
Monthly

Notes to Indicative Performance

per annum, strategy’s performance from table VI for US (compared to 10,7% benchmark return)


Notes to Period of Rebalancing

Estimated Volatility
9.8%

Number of Traded Instruments
1

Notes to Estimated Volatility

volatility from table VI for US (compared to 15,1% benchmark volatility)


Notes to Number of Traded Instruments

Maximum Drawdown
0%

Complexity Evaluation
Simple strategy

Notes to Maximum drawdown

not stated


Notes to Complexity Evaluation

Sharpe Ratio
0.81

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

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.

Strategy's implementation in QuantConnect's framework (chart+statistics+code)
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.

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