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We study the predictability of stock risk premia in the Chinese stock market using oil factors. In contrast to existing findings that oil price changes render little forecasting power for stock returns, we propose an oil price trend factor which strongly predicts excess stock returns in-sample and out-of-sample at both market- and industry-level. Notably, the predictive power of the oil trend factor is unspanned by the existing predictors and macroeconomic risks. We find that the trend factor negatively predicts equity risk premia which is due to the fact that our trend factor represents oil demand-driven force in determining asset return premium. • An oil trend factor strongly predicts stock returns at market- and industry-level. • The predictive power is unspanned by the existing predictors and macroeconomic risks. • The oil trend factor's return predictability is demand-driven. [ABSTRACT FROM AUTHOR]

Stock variance, which is the sum of squared daily returns on the S&P 500, implies the level of investor fear in stock market and performs well at predicting the return of index commodity futures. This prediction still holds after controlling for the sample period and macroeconomic variables. Also, the fear index performs very well at predicting index commodity futures returns out-of-sample, and the asset allocation exercise based on predictive regressions also shows that stock variance generates economic performance. The results remain robust while considering different macroeconomic conditions, such as recession (expansion), contango (backwardation), or inflation up (down). • Stock variance, the fear index, performs well in commodity return prediction. • The predictability test spans from 1926 to 2016. • The results remain robust after controlling various financial and economic variables. • The predictive power remains strong under different macroeconomic conditions. [ABSTRACT FROM AUTHOR]