Portfolio-based risk factors are identified through diversified, zero-cost, long/short portfolios that may link stock returns to systematic risk. There is a substantial amount of evidence in the academic literature that some portfolio-based risk factors explain well the cross section of stock returns.
Using a size factor and value factor in addition to the market factor, in 1993, Eugene Fama and Kenneth French introduced their three-factor model, which explained stock returns better than the traditional one-factor CAPM model. This seminal asset pricing model ushered in an era that has seen many risk factors (such as momentum) introduced in the literature. But not all proposed risk factors hold up under scrutiny, nor do they all come with a clear risk/return explanation.
My colleague, Sean Grover, a member of the investment strategy team at Buckingham and The BAM Alliance, furnished the following analysis of new research that puts some recently proposed risk factors to the test.
New Research On Factors
In their study, “Identifying Portfolio-Based Systematic Risk Factors in Equity Markets,” which appeared in the May 2016 issue of Finance Research Letters, authors Klaus Grobys and Jesper Haga tested four factors new to the literature. The names of those factors, and the papers that proposed them, are listed below:
- Betting Against Beta factor (BAB): “Betting Against Beta” by Andrea Frazzini and Lasse Pedersen, Journal of Financial Economics (2014).
- Quality factor (QMJ): “Quality Minus Junk” by Cliff Asness, Andrea Frazzini and Lasse Pedersen, working paper (2014).
- Investment and Profitability factors (CMA and RMW): “A Five-Factor Asset Pricing Model” by Eugene Fama and Kenneth French, Journal of Financial Economics (2015).
To examine this set of candidate risk factors, the authors used an approach outlined in a 2008 study, “Identifying Risk-Based Factors” by Anchada Charoenrook and Jennifer Conrad. The approach statistically tests if a risk/return relationship exists for the proposed factors. Grobys and Haga suggest that a failure to pass the test could mean a nonrisk-based (behavioral) explanation. The data used for their study is from the Kenneth French and AQR data libraries and covers the period July 1963 to August 2015. The authors examined the candidate risk factors across the full sample and two subsamples.
They found evidence that the CMA and BAB factors both pass the statistical test for a risk/return relationship, and thus qualify as risk factors. In contrast, the evidence indicates that the QMJ and RMW factors don’t pass, and so they do not qualify as risk factors. This suggests that these factors may have a behavioral explanation.
The nature of evidence-based investing requires that investors consider a breadth of academic findings prior to making portfolio decisions. While, based on their specific statistical test, the authors find a lack of evidence for QMJ and RMW as risk factors, Grobys and Haga’s conclusion does not close the door on those two factors, or the research behind them. It simply suggests that additional research will be required to clarify their underlying sources, just as has been done with many proposed factors that have come before.
Factors do not necessarily need to have a risk-based explanation for us to believe they can persist (as is the case with momentum). But the lack of a risk-based explanation can raise concerns about a factor’s ability to persist post-publication.
Larry Swedroe is the director of research for The BAM Alliance, a community of more than 140 independent registered investment advisors throughout the country.