Because of the magnitude, persistence, pervasiveness and robustness of their related premiums, several factors have dominated the academic literature. Among them are market beta, size, value, momentum and profitability. However, despite their persistence, each factor has undergone even fairly long periods during which it produced negative returns.
Said another way, while investors can raise expected returns by increasing their exposure to the market, size, value and profitability premiums, over any given time period—no matter how long—the realized premiums can be negative. And that fact is what’s tempted many to find a way to “time the premiums”—to tactically allocate by shifting from stocks to bonds, small-caps to large-caps, value to growth and so on.
Asset class (or factor) rotation is a strategy that many active managers employ in an effort to enhance performance. As a strategy, it certainly has appeal, assuming you can identify (in advance) the best-performing asset class (factor). It would seem that a logical way to do this would be to rely on relative valuations.
When an asset class looks cheap relative to its historical relationships, rotate into it. For example, when the spread between the book-to-market (or price-to-earnings) ratios of value and growth stocks is wider than the historical average, then investors should load up on value stocks. On the other hand, when the ratio is relatively low, they should abandon value stocks and move to growth stocks.
This would seem to make sense, as the historical data shows that when the spread in book-to-market (BtM) ratios between value stocks and growth stocks is high, the subsequent value premium tends to be larger. The reverse is also true. Based on that information, if next year’s value premium is expected to be high, it would seem logical to own value stocks. If it were expected to be negative, then growth stocks would seem to become the logical choice.
But is it really that simple to earn abnormal returns? Does a statistical relationship always translate into a viable portfolio strategy?
A DFA Study
Wei Dai of the research team at Dimensional Fund Advisors (DFA) sought the answer to those questions in her March 2016 paper, “Premium Timing with Valuation Ratios.” Dai studied the performance of the market, size and value premiums over the period July 1926 through June 2015, as well as the performance of the profitability premium for the period July 1963 through June 2015.
Looking for trading rules that outperformed the long-only benchmarks by at least 25 basis points per year and where that outperformance was reliably different from zero, Dai examined timing strategies that trade back-and-forth between the long and the short sides of the premiums in an attempt to generate abnormal returns. The strategies rebalanced annually on June 30.
Dai considered nonparametric trading rules (a test that no assumptions regarding the specific form of the relationship between the spread and the premium, other than the direction of the relationship) that invest in the long side of a premium and then move into the short side when the valuation spread of interest is small. For example, when the book-to-market spread between value and growth is small, it suggests the subsequent value premium may be low, so the trading rule invests in the growth side.
To implement such a strategy, it is necessary to define a “small” valuation spread. Small spreads are defined as those below the 10th, 20th or 50th%ile (breakpoint) of the historical distribution. Dai also considered various strategies for when to switch back, including when the spread crosses a breakpoint, and until it crosses the 50th%ile.
She also examined data that covered all past data up to the trading day, as well as data that covered only the most recent 20-year periods. For each pair of premiums and valuation spreads, Dai ran a variety of trading rules defined by breakpoint, switchback and window. In all, he tested 200 trading rules.
Following is a summary of her findings:
Dai also tested 480 parametric trading rules (rules that employ a regression approach to forecast future premiums). Since a premium is constructed by subtracting the return on the short side from the return on the long side, they reflect the relative performance of each side. Thus, the trading rule invests in the side of a premium that is predicted to do better (or the one that is more likely to do so). She found that only about 2% of simulations produced a reliably positive excess return greater than 0.25.
Given the large number of simulations, we should expect some chance of what is called a “false discovery.” For example, under a simplified multiple-testing framework in which trials are independent, about 5% of trials will appear statistically significant even if all the signals are pure noise.
Lack Of Robustness
To have confidence in an investment strategy, the evidence should be not only persistent and pervasive, but robust to various definitions. For example, we would not have the level of confidence we do in the value premium if it only existed when we measured value using the metric of price-to-book. There’s a very similar premium when we measure value using other metrics, such as price-to-cash flow, price-to-earnings, price-to-sales and (for markets outside of the United States) price-to-dividends.
Similarly, there’s a strong momentum premium whether we measure momentum by 12 months, nine months or six months. The profitability premium is strong whether it’s measured by profits-to-sales or profits-to-assets, or even cash-flow-to-assets.
Given that Dai found only about 2% of trading rules passed the test, we cannot achieve the same level of confidence in the robustness of a premium timing strategy. Dai concluded that “the percentages observed here do not constitute strong evidence that one can reliably time the subsequent year’s premiums using valuation spreads.”
Furthermore, there is other research investigating the variables that can be used to time markets.
The 2007 study “Does Predicting the Value Premium Earn Abnormal Returns?” by Jim Davis of DFA examined style-timing strategies over the period July 1927 through June 2005. Davis found that style-timing rules did not generate high average returns, despite being able to use future information about BtM spreads.
In fact, he concluded the expected excess return of style timing is probably negative, for the same reasons that efforts to time the overall market are likely to fail. Just as ex-ante there should always be an equity risk premium, ex-ante there should always be a value risk premium. And as is the case with the equity risk premium, the value premium is so large that any trading strategy would have to be right a large majority of the time to deliver successful results.
It would be like switching from the high-speed carpool lane to the center lane on a crowded freeway. Your “freeway algorithm” might help predict when the carpool lane or center lane will move faster or slower than normal, but will it be accurate enough to justify switching into the slower lane in an effort to get to your destination quicker? The evidence suggests you are better off staying in the carpool lane.
The lesson for investors is that just because a statistical relationship exists does not necessarily imply that a profitable trading strategy based on that relationship exists as well, especially after taking into account trading and other costs.
What About Valuation?
There’s also the study “Is There Value in Valuation?” which appeared in the Winter 2013 issue of The Journal of Portfolio Management. The authors, Martin Fridson and John Finnerty, studied whether a valuation-based rotation strategy worked for high-yield bonds.
They first showed that a rotation strategy seemed to have great appeal. For example, for the period 2007 through 2011, even though mean returns for bonds rated BB, B and CCC were 9.9%, 8.3% and 15.2%, respectively, choosing the best-performing sector would have produced a return of 21.0%. Clearly, there’s a large opportunity to add value.
Before getting into their findings, it’s important to understand that, in general, you should expect the lowest-rated bonds (CCC) to outperform when the risk premium (or incremental yield over Treasurys) for high-yield bonds either remains constant or decreases and to underperform when it increases. (They can even outperform if the increase in the risk premium is small relative to the size of the premium.)
This should certainly be true if the change in risk premiums is large. (If the risk premium widens by a small amount, it’s possible that the higher yield of the lower-rated bond would provide enough of a cushion for them to still outperform.)
This is all intuitive. If a risk premium falls, the riskier asset class should outperform, and vice versa. And if this is true, then we don’t need information about relative valuations to inform us about which sector/asset class will outperform. We only need to forecast whether the premium will widen or not. (The evidence on active management demonstrates managers don’t have much success at that endeavor.) Studying the quarterly results for the period 1997 through 2011, this is exactly what Fridson and Finnerty found.
Forecasting Premium Direction
Using what is called the option-adjusted spread (OAS) to take into account the call risk inherent in corporate bonds, they found this relationship held true 76% of the time for B and BB bonds, 83% of the time for B and CCC bonds and 86% of the time for BB and CCC bonds. This finding very clearly calls into question a valuation-based trading strategy.
To know which sector to move to, all you need is to accurately forecast whether the premium for the entire asset class will rise or fall. If it’s going to narrow, then own the lowest-rated bonds. If it’s going to widen, then own the highest-rated bonds.
To test whether a valuation-based approach was likely to add value, Fridson and Finnerty built a five-factor valuation model (credit availability, capacity utilization, industrial production, speculative-grade default rate and yield to maturity on the five-year Treasury). If the OAS spread was greater than the model predicted, the asset was cheap (you should rotate into it). The asset would be expensive (you should rotate out of it) if the spread was less than fair value. The model explained a high percentage, between 74% and 80%, of the variance in performance.
Unfortunately, using various tests, the authors found no statistically significant benefit from a sector rotation strategy based on the valuation model. It’s also important to observe that high-yield bonds are an expensive asset class to trade. As a result, even if there appeared to be some theoretical advantage to a rotation strategy, the advantage could very well be ephemeral, with trading costs creating a large hurdle to overcome.
As tempting as the proposition might be, there doesn’t seem to be much, if any, evidence that a style-timing strategy can be expected to be profitable going forward. But that doesn’t mean the information contained in the size of the spreads isn’t useful. Valuations do matter. They should be used in setting longer-term return expectations (with the understanding that a wide range of potential outcomes must be expected). It’s just that using the information to time markets isn’t likely to prove successful.
This commentary originally appeared March 18 on ETF.com
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