Should Advisors Time Factor Exposure? – ValueWalk Premium

Should Advisors Time Factor Exposure?

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Q4 hedge fund letters, conference, scoops etc


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This article originally appeared in ETF.COM here.

Q4 hedge fund letters, conference, scoops etc

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.

Read the full article here by Larry Swedroe, Advisor Perspectives

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