Swedroe Spotlight: Explaining The Low Risk Effect

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as mechanically related to volatility as it is more purely about the shape of the return distribution. Behavioral theories imply that these idiosyncratic risk factors should have positive alphas. And the data confirmed the hypothesis.


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The authors also found that BAB and BAC “are predicted by measures of leverage constraints, while these factors are not predicted by investor sentiment.”(3) In contrast, Asness, et al found that their behavioral measures MAX and IVOL are related to sentiment, but not measures of leverage constraints. They concluded: “This evidence is consistent with both of the alternative theories playing a role and that the alternative factors may, to some extent, capture different effects.”

There were other interesting findings:


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  • The BAC factor loads substantially on the small-minus-big factor as firms with, for the same volatility, low correlation often are small, undiversified firms.
  • The BAC factor has a positive loading on the value factor (HML), consistent with the theory of leverage constraints. The theory of leverage constraints predicts that safe stocks, those with low correlation and volatility, become cheap because they are “abandoned” by leverage constrained investors, giving rise to a positive HML loading.
  • For the BAC factor, the loadings on the profitability factor RMW (robust minus weak) and the investment factor CMA (conservative minus aggressive) also tend to be positive, especially those of RMW. This should be expected as they are measures of quality and safety.
  • While low-correlation stocks, holding volatility constant, tend to be small stocks, low-volatility stocks, holding correlation constant, tend to be big stocks.
  • The idiosyncratic (behavioral) factors tend to load on the quality variables RMW and CMA.
  • Both BAC and BAB have higher future returns when leverage constraints are high (margin debt is low) and contemporaneous increases in margin debt are associated with positive returns to BAB and BAC. This is consistent with the theory that investors shift their portfolios toward low-risk stocks when leverage constraints decrease.
  • IVOL has higher returns when ex-ante investor sentiment is high, consistent with behavioral demand, but is unrelated to margin debt (and thus is unrelated to leverage constraints).

Summary

The bottom line for investors is that in the case of the low-risk phenomenon, the world isn’t black or white.(4) As the authors concluded: “The low-risk effect can be driven by more than one economic effect and the evidence is not inconsistent with both leverage constraints and lottery demand playing a role.” This is very similar to the findings on the value factor. The source of the value premium is one of the great debates in finance: Is it risk or behavior? With strong evidence for both explanations, it seems likely that while the value premium isn’t a free lunch (there are simple/logical risk-based explanations), it might just be a free stop at the dessert tray (there are good behavioral explanations as well).

The strong evidence demonstrating the superior risk-adjusted performance of low-risk stocks has led to a dramatic increase in investor interest, and flows into funds seeking to capture to returns of low-risk strategies.

Appendix: Have Low-Risk Strategies Become Overgrazed?

As is the case with so many well-known anomalies and factors, the problem of potential overgrazing does exist.(5) Published research on the premium, combined with the bear market caused by the financial crisis of 2008, led to a dramatic increase in the popularity of low-volatility strategies. For example, as of February 2017, the iShares Edge MSCI Minimum Volatility USA ETF (USMV) had more than $12 billion in assets. Strong cash inflows have raised the valuations of defensive (low-volatility/low-beta) stocks, dramatically reducing their exposure to their value premium from quite high to negative, and thus lowering expected returns. Specifically, as low-volatility stocks are bid up in price, low-volatility portfolios can lose their value characteristics, which reduces their forward-looking returns (see here for a detailed discussion on the issues of buying high valuation low-volatility stocks).

We will take a look at the valuation metrics the PowerShares S&P 500 Low Volatility Portfolio (SPLV). We will then compare its value metrics to those of the iShares Russell 1000 EFT (IWB), which is a market-oriented fund, and the iShares Russell 1000 Value ETF (IWD). Data is from Morningstar as of February 9, 2017.

USMV IWB IWD
Price-to-earnings 20.4 18.8 17.4
Price-to-book 3.1 2.6 1.8
Price-to-cash flow 12.1 10.6 9.7

 

What is clear from the data is that the demand for these strategies has altered their very nature. In the past, the valuation metrics of USMV were more value-oriented than the Russell 1000. However, now these metrics certainly do not mirror the holdings of a classic value-oriented fund. These funds’ price-to-earnings, book-to-market, and price-to-cash flow ratios are all quite a bit higher than those of IWD. In fact, the metrics indicate that USMV is now more “growthy” than the market-like IWB. In other words, because there is an ex-ante value premium, what low volatility is predicting at this point in time is not higher returns, just low future volatility. While investors should always prefer buying stocks at lower valuations, what we do not know is how big of an impact valuations will have on low-volatility strategies. Research from a 2012 white paper by Pim van Vliet, “Enhancing a Low Volatility Strategy is Particularly Helpful When Generic Low Volatility is Expensive,” sheds some light on this question. Using data from 1929 through 2010, he found that while on average low-volatility strategies tend to have exposure to the value factor, that exposure is time-varying. The low-volatility factor spends about 62 percent of the time in a value regime and 38 percent of the time in a growth regime.

This regime-shifting behavior impacts the performance of low-volatility strategies. When low-volatility stocks have value exposure, they have outperformed the market, returning an average of 9.5 percent annually versus the market’s 7.5 percent. The low-volatility factor has also exhibited lower volatility, with an annual standard deviation of 13.5 percent versus the market’s 16.5 percent. However, when low-volatility stocks have growth exposure, they have underperformed, returning an average of 10.8 percent annually versus the market’s 12.2 percent. The low-volatility factor did continue to have lower annual volatility, at 15.3 percent versus 20.3 percent for the market. The result has been a higher risk-adjusted return in either regime. The bottom line is that in either regime, low volatility predicts future low volatility. However, when low volatility has negative exposure to the value factor (as it did in mid-2016), it also forecasts below-market returns.

It seems likely that at least some investors have taken notice of the high valuations and become concerned because, despite rising equity prices, UMLV actually has slightly less assets under management than it did in April 2016.

Appendix: Summary

The evidence suggests that you might be better served by investing in vehicles that screen out high-volatility (or high-beta), high-risk stocks. In other words, consider investing directly in size, value and profitability/quality rather than doing so indirectly (like defensive strategies do).


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