Factor Momentum, Return Autocovariance, and Stock Momentum Crashes

Summary

This literature review explains time-series and cross-sectional momentum strategies applied to equity anomaly factors. The time-series approach buys factors with positive trailing returns and shorts those with negative returns; the cross-sectional approach buys factors that outperformed their peers and shorts relative laggards. It reviews evidence from a 1963–2015 sample and reports that both approaches outperformed an equal-weight factor benchmark, with returns largely attributed to positive factor-return autocovariance.

The review further connects factor autocorrelation to stock momentum. A composite strategy conditioned on factor returns is reported to explain momentum-sorted portfolio returns at least as well as the Carhart model in some tests. Periods of negative factor autocorrelation are associated with severe momentum losses, while positive autocorrelation aligns with more favorable momentum outcomes. The evidence is drawn from historical US and global anomaly data and cited research; it does not establish that these relationships will persist or that they transfer directly to Chinese equities. The review identifies the origins of factor autocovariance as an open question.

Key ideas

• Time-series factor momentum goes long factors with positive trailing returns and shorts factors with negative trailing returns.
• Cross-sectional factor momentum buys past factor winners relative to peers and sells relative laggards.
• The reviewed decompositions attribute much of factor momentum’s return to positive autocovariance, especially in short portfolios.
• A composite factor-momentum measure is reported to track stock momentum and explain momentum-sorted portfolio returns.
• Negative factor autocorrelation is associated with momentum crashes, but historical evidence does not guarantee future performance.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.