Combining Factor Momentum with a Market Portfolio

Summary

The document describes a monthly strategy that blends momentum across equity factors with a broad market portfolio. It forms fast and slow signals from each factor’s recent one-month and twelve-month returns, ranks signal magnitudes to allocate factor weights, and combines the signals with a heavier weight on the fast measure. It then compares the factor strategy with the market using twelve moving averages of past returns; the number of winning averages determines each portfolio’s allocation.

The article reports that the factor strategy alone lagged the market, but its returns were significantly negatively correlated with the market. In the cited US and EAFE tests, the combined approach reportedly achieved higher returns and lower volatility and drawdown than either component, including a stated US cumulative wealth comparison through August 2020. These are historical results from the described study, not a guarantee. The page gives limited detail on implementation costs and robustness beyond its cited sample and should not be read as proof of future performance.

Key ideas

• The strategy combines one-month and twelve-month time-series momentum signals across equity factors.
• Factor weights scale with the cross-sectional rank of each signal’s absolute magnitude and preserve its direction.
• The blend assigns three-quarters of factor weight to the fast signal and one-quarter to the slow signal.
• Twelve return moving averages compare the factor portfolio with the market and set their relative allocations.
• The reported benefit comes from combining negatively correlated strategies, although the evidence is historical.

Tags

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