Does the term structure of crude oil futures predict stock market returns? In their October 2016 paper entitled “Do Oil Futures Prices Predict Stock Returns?”, I-Hsuan Chiang and Keener Hughen examine the ability of crude oil futures prices to predict U.S. stock market returns. They identify the first three principal components of the nearest six oil futures prices. After finding that one of these components (related to the term structure) predicts stock market returns, they define a simple oil futures term structure curvature factor as:
- Short-term slope (natural logarithm of the second nearest price minus natural logarithm of the nearest price), minus
- Long-term slope (natural logarithm of the sixth nearest price minus natural logarithm of the third nearest price).
They test the ability of this curvature factor to predict U.S. stock market performance and industry performance in-sample (based on returns) and out-of-sample (based on R-squared explanatory power) at a one-month horizon. They compare its out-of-sample predictive power with those of nine other widely used predictors: dividend-price ratio, dividend yield, earnings-price ratio, book-to-market ratio, long-term U.S. Treasuries yield, long-term U.S. Treasuries return, U.S. Treasuries yield spread, U.S. Treasury bills yield and default yield spread. Using daily prices for the six nearest WTI light crude oil futures contracts and monthly returns for the broad U.S. stock market, 49 value-weighted industries and stocks in four crude oil subsectors during March 1983 through December 2014, they find that:
- In-sample, a 1% (one standard deviation) increase in the oil futures curvature factor predicts a next-month U.S. stock market return of -0.4% (-2.3%). This negative relationship holds in two equal subperiods.
- Out-of-sample, expanding (inception-to-date) window regressions indicate that changes in the oil futures curvature factor explain 2.3% of the variation in next-month U.S. stock market return (R-squared = 0.023).
- The curvature factor outperforms other predictors in most non-oil industries, but does not forecast oil-related industry/subsector performance.
- The predictive power of the curvature factor derives from anticipation of oil supply shocks, adversely affecting non-oil stocks but favorably affecting oil-related stocks.
In summary, evidence indicates that the term structure of oil futures may be able to predict U.S. stock market and non-oil industry stock returns.
Cautions regarding findings include:
- Tests are academic and do not involve any trading strategies designed to exploit findings, which may not be economically meaningful.
- Results are gross, not net. Accounting for costs of trading to exploit curvature factor predictions would reduce apparent exploitability.
- The methodology employed involves snooping for predictive power among the principle components of oil futures price behavior, thereby overstating expectations. There may also be snooping of contract nearness in specification of the curvature factor.
