- Factor-timing activity is persistent. About 70% of all funds in the lowest (highest) timing decile remain in the lowest (highest) three deciles after one year. Funds sorted into decile portfolios with the lowest (highest) factor timing in year t have a 53% (50%) likelihood of remaining in the lowest (highest) three deciles in year t+3. Thus, factor timing seems to be an investment strategy prevalent in different market situations and periods of economic booms and recessions.
- High timing activity of an individual factor does not necessarily imply high timing activity to another factor.
- Risk factor timing is associated with future fund underperformance. A portfolio of the 20% of funds with the highest timing indicator underperforms a portfolio of the 20% of funds with the lowest timing indicator by a risk-adjusted 134 basis points per year with statistical significance at the 1% confidence level (t-stat: 3.3).
- Sorting funds on individual MKT-, SMB- or UMD-timing measures results in underperformance of the most actively timed funds relative to the least actively timed funds by 126 basis points (t-stat: -3.6), 70 basis points (t-stat: -2.5) and 85 basis points (t-stat: -2.3) per year, respectively, with statistical significance at least at the 5% level. Funds with high HML timing underperform funds with low HML timing by a statistically insignificant 7 basis points (t-stat: -0.3) per year.
- A one-standard-deviation increase of market, size, value and momentum timing leads to a decrease of annualized abnormal returns by 34 basis points, 19 basis points, 5 basis points and 19 basis points per year, respectively. The economic impact of the overall timing measure is also substantial: A one-standard-deviation increase of timing reduces abnormal future returns by 46 basis points per year.
- Risk factor timing is particularly prevalent among smaller mutual funds and those with long management tenure, high turnover, high total expense ratios and high past fund inflows.
- Funds with higher factor timing activity were more likely to drop from the authors’ sample within the next years, highlighting the importance of accounting for survivorship bias in the data.
To test the robustness of their findings, Ammann, Fischer and Weigert also examined performance while including in their analysis the additional factors of betting against beta, profitability, investment, sentiment and liquidity. They found their results remained qualitatively unchanged and statistically significant for all alternative factor models (even more significant for some of the additional models).
This led the authors to conclude: “The underperformance of risk factor timing by mutual funds is not explained by alternative asset pricing risk factors.” Having split the data into subperiods, they also confirmed their results were independent of the time period they examined. Their results also held up to various other tests of robustness.
Summarizing their results, Ammann, Fischer and Weigert also concluded: “Our results do not support the hypothesis that deviations in risk factor exposures are a signal of skill and we recommend that investors should resist the temptation to invest in funds that intentionally or coincidentally vary their exposure to risk factors over time.”
Hubert Dichtl, Wolfgang Drobetz, Harald Lohre, Carsten Rother and Patrick Vosskamp contribute to the literature on the ability to successfully time factor exposures with their January 2018 study “Optimal Timing and Tilting of Equity Factors.”
They compiled investable global long/short equity factor portfolios and computed their returns net of the transaction costs that arose in their monthly construction. Their data set covered 20 factors assembled from a large sample (about 4,500 to 5,000) of companies in the period 1997 through 2016. The factors they examined were:
- Value: cash flow yield, dividend yield, book to market, earnings yield, profitability
- Momentum: 12-month price momentum, short-term reversal, long-term reversal
- Quality: asset turnover, change in long-term debt, change in shares outstanding, asset growth, cash productivity, profit margin, leverage, return on assets, sales-to-cash, sales-to-inventory, accruals
Following is a summary of their findings:
- Ignoring the additional transaction costs necessary to follow an active factor allocation strategy, factor timing with time-series predictor variables is statistically and economically relevant, using both fundamental macroeconomic (such as the term spread, default spread, inflation, and price-to-earnings and dividend ratios) and technical predictors (factors with positive price momentum are overweighted relative to the benchmark, while factors with negative price momentum are underweighted).
- Optimal factor tilting favors factors with positive short-term momentum and wide spreads in valuations, but avoids factors that are close to the market factor or that exhibit crowding in the short leg (exhibited by narrow spreads).
- Due to higher turnover, transaction costs tend to erode much of the value added of factor predictability—the predictability is hard to exploit.