Eugene Fama and Kenneth French’s seminal 1992 paper, “The Cross-Section of Expected Stock Returns,” resulted in the development of the Fama-French three-factor model. This model added the size and value factors to the market beta factor.
One of the benefits of adding the value factor (the tendency for relatively cheap assets to outperform relatively expensive ones) to asset pricing models was that its inclusion went a long way toward explaining the superior performance of “superstar” investors from the value school of Benjamin Graham and David Dodd.
The value premium has been highly persistent; almost as persistent as the market beta premium (also known as the equity risk premium). This table shows the persistence of the market beta and value premiums over various time frames for the period 1927 through 2015.
The value premium has also been pervasive. For example, for the period 1975 through 2015, the Fama-French International Growth Index returned 8.6% and the Fama-French International Value Index returned 13.8%, which comes to a 5.2 percentage point advantage for value. And for the period 1989 through 2015, the Fama-French Emerging Markets Growth Index returned 9.3% and the Fama-French Emerging Markets Value Index returned 13.0%, a 3.7 percentage point advantage for value.
The value premium has also been robust to various definitions. While Fama and French used the book-to-market (BtM) ratio as their measure of value, other metrics can be used to separate cheap from expensive stocks.
For example, in the United States, for the period 1952 through 2015, the annualized value premium as measured by the BtM ratio was 4.1% (t-stat = 2.4), 4.7% (t-stat = 2.9) as measured by the cash flow-to-price ratio, and 6.3% (t-stat = 3.4) as measured by the earnings-to-price ratio. Not only do we see a value premium despite the various definitions, some of the alternates have even higher returns.
Using The Dividend-To-Market Ratio
Yiqing Dai contributes to the literature with the July 2016 study “Value Investing with Dividend-to-Market Ratio.” Dai proposes employing the dividend-to-market (DM) ratio as a less “noisy” and more parsimonious (simpler) value metric.
With this model, dividends represent the maximum possible dividend that could be paid out as determined by profits less investment (the reinvestment of earnings required to generate future cash flows).
Dai explains: “Because expected dividends are a strong indicator of intrinsic value, the dividend-to-market ratio effectively distinguishes between undervalued stocks and overvalued ones. A high (low) DM indicates the firm’s expected future cash flows are currently discounted at a high (low) rate, hence its stocks are in the value (growth) category. If two firms are identical in market valuation but different in dividend, the firm with the larger dividend must have a higher market discount rate.”
“Likewise, if two firms are identical in dividend but different in market valuation, the firm with higher market valuation should have a lower market discount rate. Value investors could thus maximize their economic gain per dollar of investment by constructing a high DM portfolio, holding stocks with strong fundamentals at moderate prices, as well as stocks with average fundamentals at discount prices.”
Dai’s choice of DM is consistent with the latest research on factor models. In their paper, “A Five-Factor Asset Pricing Model,” which appeared in the April 2015 issue of the Journal of Financial Economics, Eugene Fama and Kenneth French took a close look at a new five-factor model.
Fama & French Weigh In
Their objective was to determine whether two additional factors (the same metrics Dai uses to determine the DM ratio)—profitability (RMW, or robust-minus-weak profitability) and investment (CMA, or conservative-minus-aggressive investment)—added explanatory power.
In other words, if Fama and French knew in 1992 (when they constructed the original three-factor model) what they know today, which model would they have chosen?
Following is a summary of their findings:
While a five-factor model doesn’t fully explain the cross section of returns (there are still anomalies), it provides a good description of average returns.
Importantly, Fama and French further found that the five-factor model performs well. They write: “Unexplained average returns for individual portfolios are almost all close to zero.”
More On The DM Ratio
Returning to DM as a value measure, Dai, whose study covered the period July 1963 through December 2013, found the following:
Thus, Dai concluded: “DM is superior to BM for value investing from both theoretical justification and empirical regularity.”
Focus On Profitability
Because profitability is the source of dividends, Dai next conducted a sort of horse race between DM and several other prominent profitability measures used for predicting average stock returns: the earnings-to-price ratio, the cash flow-to-price ratio, gross profitability (revenue minus cost of goods sold), operating profitability (revenue less cost of goods sold less selling, general and administrative expenses excluding expenditure on research and development) and the return-to-equity ratio.
Dai found that the DM factor outperforms the other profitability factors: “In particular, none of the other profitability factors exhibits statistically reliable alpha after controlling for DM. In contrast, the DM factor consistently produces a large, highly significant alpha after controlling for other profitability factors. These results show that the DM factor is much closer to the efficient frontier than other profitability factors.”
Dai’s results are consistent with financial theory. However, research from Dimensional Fund Advisors suggests it is important to consider relative price and profitability separately.
In their recent paper, “Capturing Value: Why Less Can Be More,” Marlena Lee, Savina Rizova and Antonio Picca found that book-to-market and profitability, measured as profits-to-book, both contain reliable information about differences in average returns, while composite measures such as price-to-earnings, price-to-cash flow and price-to-sales did not contain reliable information about average returns once controlling for the other variables.
This commentary originally appeared November 28 on ETF.com
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The opinions expressed by featured authors are their own and may not accurately reflect those of the BAM ALLIANCE. This article is for general information only and is not intended to serve as specific financial, accounting or tax advice.
© 2016, The BAM ALLIANCE
Asset pricing models imply that equity portfolios’ time-varying exposure to the market risk and uncertainty factors carries with it positive risk premiums. Turan Bali and Hao Zhou contribute to the body of literature on this topic through the study “Risk, Uncertainty, and Expected Returns,” which appeared in the June 2016 issue of the Journal of Financial and Quantitative Analysis.
Their study seeks to investigate whether the market price of risk and the market price of uncertainty are significantly positive, and whether they may help explain the cross-sectional and time-series variation in stock returns. According to the authors’ model, the premium on equity is made up of two separate terms. The first term compensates for standard market risk. The second term represents an additional premium for variance risk.
Measures Of Uncertainty
Economic uncertainty is proxied by the variance risk premium (the price of volatility insurance as implied in options prices) in the U.S. equity market. The second set of uncertainty measures that they use is based on the extreme downside risk of financial institutions and is obtained from the left tail of the time-series and cross-sectional distribution of financial firms’ returns.
The third uncertainty variable is related to the general health of the financial sector, and is proxied by the credit default swap index. The final uncertainty variable originates from the aggregate measure of investors’ disagreement about the individual stocks trading at the NYSE, AMEX and Nasdaq. The dispersion in analysts’ earnings forecasts acts as a proxy for the divergence of opinion.
Bali and Zhou’s study covers the period January 1990 to December 2012. Following is a summary of their findings, all of which are intuitive:
Conclusions
Bali and Zhou found that the difference between the implied and expected variances not only positively covaries with stock returns, but it covaries negatively with future growth rates in GDP.
They explain: “Intuitively, when VRP is high (low), it generally signals a high (low) degree of aggregate economic uncertainty. Consequently agents tend to simultaneously cut (increase) their consumption and investment expenditures and shift their portfolios from more (less) to less (more) risky assets. This in turn results in a rise (decrease) in expected excess returns for stock portfolios that covaries more (less) with the macroeconomic uncertainty, as proxied by VRP.”
Having intuitive explanations for why a premium exists gives us greater confidence that results are not just random outcomes or the result of data mining exercises.
Bali and Zhou’s findings also provide further support for risk-based explanations for the size and value premiums, as small and value firms are more exposed to uncertainty risks, which in turn can lead investors to flee to the stocks of less risky large and growth companies.
This commentary originally appeared November 7 on ETF.com
By clicking on any of the links above, you acknowledge that they are solely for your convenience, and do not necessarily imply any affiliations, sponsorships, endorsements or representations whatsoever by us regarding third-party Web sites. We are not responsible for the content, availability or privacy policies of these sites, and shall not be responsible or liable for any information, opinions, advice, products or services available on or through them.
The opinions expressed by featured authors are their own and may not accurately reflect those of the BAM ALLIANCE. This article is for general information only and is not intended to serve as specific financial, accounting or tax advice.
© 2016, The BAM ALLIANCE
Larry Swedroe discusses his new book, “Your Complete Guide To Factor-Based Investing,” while taking on smart beta, the investment factor “zoo” and how to think differently about diversification in a recent interview with ETF.com’s Drew Voros.
By clicking on any of the links above, you acknowledge that they are solely for your convenience, and do not necessarily imply any affiliations, sponsorships, endorsements or representations whatsoever by us regarding third-party Web sites. We are not responsible for the content, availability or privacy policies of these sites, and shall not be responsible or liable for any information, opinions, advice, products or services available on or through them.
The opinions expressed by featured authors are their own and may not accurately reflect those of the BAM ALLIANCE. This article is for general information only and is not intended to serve as specific financial, accounting or tax advice.
© 2016, The BAM ALLIANCE