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This article originally appeared in ETF.COM here.
The academic literature, including Russ Wermers’ 2000 study “Mutual Fund Performance: An Empirical Decomposition into Stock-Picking Talent, Style, Transactions Costs, and Expenses,” and Eugene Fama and Ken French’s 2010 study “Luck Versus Skill in the Cross Section of Mutual Fund Returns” has shown that, while mutual funds demonstrate stock-picking skills on a gross-of-fee basis, they fail to outperform appropriate risk-adjusted benchmarks net of fees.
There’s also strong evidence that mutual funds don’t outperform by successfully timing the market. For example, a study in the Spring/Summer 2009 issue of Vanguard Investment Perspectives examined the performance of mutual funds in bear markets. Defining a bear market as a loss of at least 10%, the study covered the period 1970 through 2008. The period included seven bear markets in the U.S. and six in Europe.
Once adjusting for risk (exposure to different asset classes), Vanguard’s researchers concluded that “whether an active manager is operating in a bear market, a bull market that precedes or follows it, or across longer-term market cycles, the combination of cost, security selection, and market-timing proves a difficult hurdle to overcome.”
They also confirmed that “past success in overcoming this hurdle does not ensure future success.” Vanguard was able to reach this conclusion despite the fact that the data was biased in favor of active managers because it contained survivorship bias.
Failing those two tests, can active managers perhaps outperform by successfully timing their exposures to factors that have been found to provide premiums over the long term? The idea is tempting, because the evidence shows that factor premiums are time-varying and regime-dependent.
For example, Arnav Sheth and Tee Lim’s December 2017 study “Fama-French Factors and Business Cycles” examined the behavior of six Fama-French factors – market beta (MKT), size (SMB), value (HML), momentum (MOM), investment (CMA) and profitability (RMW) – across business cycles, splitting them into four separate stages: recession, early-stage recovery, late-stage recovery and very-late stage-recovery. Their data, including the results shown in the following table, covered the period April 1953 through September 2015.
Cumulative returns for six factors across economic stages (%)
| |
Recession
|
Early
|
Late
|
Very Late
|
|
SMB
|
-1.5
|
9.0
|
4.8
|
1.7
|
|
HML
|
12.5
|
17.1
|
9.8
|
2.2
|
|
MKT
|
-6.9
|
29.0
|
32.2
|
29.7
|
|
MOM
|
11.2
|
17.9
|
13.7
|
13.7
|
|
RMW
|
1.9
|
6.7
|
5.5
|
7.0
|
|
CMA
|
18.3
|
13.5
|
3.5
|
1.2
|
As you can see, factor premiums vary and are regime-dependent. That, of course, makes timing them tempting. In their September 2017 paper “How Can 'Smart Beta' Go Horribly Wrong?”, Research Affiliate’s Robert Arnott, Noah Beck, Vitali Kalesnik and John West advocate using risk factors’ value spread as a signal to time them, which begs the question of whether timing factors has been a successful strategy for actively managed funds.
The evidence
In their August 2018 study “Risk Factor Exposure Variation and Mutual Fund Performance,” Manuel Ammann, Sebastian Fischer and Florian Weigert examined whether actively managed mutual funds were successful at timing factor premiums (net of fees).
To determine mutual funds’ degree of risk factor timing activity, they measured the volatility of funds’ factor exposures (loadings) on market beta (MKT), size (SMB), value (HML) and momentum (UMD). To express a fund’s overall level of factor timing, they computed an aggregated (overall) timing indicator by averaging and standardizing the individual market, size, value and momentum timing measures.
Their study covered the period from late 2000 through 2016. Following is a summary of their findings:
- 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.”
Further evidence
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
- Size
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.
The findings from the two studies we examined are consistent with those of Cliff Asness, Swati Chandra, Antti Ilmanen and Ronen Israel, authors of the study “Contrarian Factor Timing Is Deceptively Difficult,” which appeared in the 2017 Special Issue of The Journal of Portfolio Management.
They found “lackluster results” when investigating the impact of value timing (in other words, whether dynamic allocations can improve the performance of a diversified, multi-style portfolio). They write: “Strategic diversification turns out to be a tough benchmark to beat.”
Takeaways
An obvious takeaway is that the research indicates you should not invest in funds that try to time factor premiums. Perhaps a less obvious – though equally important – takeaway is that, because different factors outperform at different stages, diversification across factors, rather than concentrating risk in a single factor, is the prudent strategy.
We can see the benefits of diversifying across factors in the following table, which shows the correlation of returns among factors across all four economic stages examined in the aforementioned study “Fama-French Factors and Business Cycles.” The table is from that paper.
Correlation across economic stages
With the exception of the correlation between the value and investment factors, the correlations are all low to negative. However, correlations are not static. They change depending on the economic regime.
For example, during a recession, the correlation between HML and RMW switches from 0.1 to -0.3, indicating that value and profitability have a small positive correlation, on average, but are negatively correlated in a recession. This demonstrates the benefits to value investors of adding exposure to the profitability factor. Similarly, the correlation between MOM and CMA switches from 0.0 to 0.4, indicating a fairly strong positive correlation between momentum and investment during a recession.
Summary
As tempting as the proposition might be, there isn’t convincing evidence that a style-timing strategy will be profitable. That said, if you are going to “sin” by trying to time factors based on either relative valuations (as Arnott suggests) or economic regime forecasts, I’d recommend following Asness’s advice to “sin a little.”
The most prudent strategy for investors is to build portfolios strategically (as opposed to tactically) diversified across factors that show persistence in their premiums, have low correlation to other factors, are pervasive around the globe and across asset classes, have intuitive reasons to believe the premiums should persist (whether behavioral-based or risk-based) and are implementable (meaning they survive transaction costs).
Larry Swedroe is the director of research for The BAM Alliance, a community of more than 130 independent registered investment advisors throughout the country.
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