Recent theoretical literature argues that total skewness matters to the pricing of assets, assets with higher degrees of skewness or lottery-like features should earn lower expected returns. The impact of skewness on asset’s future returns has been investigated mainly for equities and options, but researches are moving to review also other assets.
A recent article by Perez, Frijns, Fuertes, and Miffre studies the pricing of skewness in commodity futures markets. The time-series tests show that a fully-collateralized portfolio that buys commodities with low skewness and shorts commodities with high skewness earns 8.01% a year with a t-statistic of 4.08. The performance of the low-minus-high skewness portfolio is not fully explained by a standard commodity benchmark, which makes it a perfect addition to commodity-based multi-strategy portfolios.
Academic studies explain skewness effect in all assets by investors are willing to pay a higher price, and to earn a lower expected return, on assets with lottery-like payoffs. A recent study shows that skewness affects commodity futures returns and documents that skewness is priced in different markets than the ones where retail investors typically trade. Hence, its findings suggest that the effect of skewness on asset returns is not merely a consequence of retail investor preferences. Rather this indicates that other groups of traders have these heterogeneous preferences.
Confidence in anomaly's validity
Backtest period from source paper
Notes to Confidence in Anomaly's Validity
Period of Rebalancing
Notes to Indicative Performance
per annum, data from table IV panel A for long-short portfolio
Notes to Period of Rebalancing
Number of Traded Instruments
Notes to Estimated Volatility
data from table IV panel A for long-short portfolio
Notes to Number of Traded Instruments
Notes to Maximum drawdown
data from table 3 for long-short portfolio
Notes to Complexity Evaluation
Simple trading strategy
The investment universe consists of 27 futures contracts on commodities. Each month, investor calculates skewness (3rd moment of returns) from daily returns from data going 12 months into the past for all futures. Commodities are then sorted into quintiles and investor goes long quintile containing the commodities with the 20% lowest total skewness and short quintile containing the commodities with the 20% highest total skewness (over a ranking period of 12 months). The resultant portfolio is equally weighted and rebalanced each month.
Hedge for stocks during bear markets
Yes - Based on the source research paper (see Appendix B), the strategy has a significantly negative correlation to the equity market; therefore, it probably can be used as a hedge/diversification to equity market risk factor during bear markets.
Strategy's implementation in QuantConnect's framework