Does Dividend Policy Foretell Earnings Growth? Arnott, Robert D. and Asness, Cliff S. (December 2001)

Higher dividend payout ratios tend to predict higher earnings growth going forward.

0:00 - Introduction

This is a paper called "Does Dividend Policy Foretell Earnings Growth?" by Cliff Asness and Robert Arnott.  Back in the 60s, Miller and Modigliani developed the Dividend Puzzle phenomenon, which states that investors should be indifferent as to the level of dividends a company pays (because the dividend or foregone dividend is either put in his own pocket to reinvest or kept in the company's pocket to reinvest on his behalf; he doesn't lose or gain wealth in either scenario).

Although elegant, the authors want to understand whether that thought process has proved to be true throughout history; and whether investors should take into consideration the level of dividends when making investment decisions.  And in fact, one might be swayed to believe that if a company retains more of its earnings for reinvestment, then the company should exhibit higher earnings growth going forward, and vise versa.  So does dividend policy foretell earnings growth?

1:14 - Exhibit 1. S&P 500 Payout Ratio

First, to get an idea of the average amount of dividends that have historically been paid out as a percentage of earnings, the authors have charted the S&P 500's dividend payout ratio (i.e., dividends divided by earnings) from 1950 - 2001.

They find that the historical payout ratio averages around 50% and is volatile, because dividends tend to be sticky, while earnings move up and down over time.  At the date of this paper (i.e., 2001), the payout ratio was at its lowest level in recent history.

2:09 - Exhibit 2a. Payout Ratios and Subsequent ten-year Earnings Growth

Next, the authors want to understand whether historical payout ratios have had any bearing on future earnings growth (and indirectly, whether the Miller Modigliani dividend indifference theory holds).  By creating a scatter plot with the historical payout ratio on the x-axis and the subsequent 10-year earnings growth on the y-axis, they find a positive relationship between the two; for example, the higher the payout ratio (i.e., the less earnings are being retained), the higher the next 10-years' earnings growth.  This is the complete opposite of what might be expected (i.e., one would think if the company retained more earnings, they would likely see more earnings growth in the future; but that's not the case according to this figure).

3:03 - Exhibit 2b. Ten-year Earnings Growth, as function of Payout Ratios

For additional analysis, the authors create regressions over various time periods going back to 1871, with the 10-year earnings growth as the dependent variable and the payout ratio as the independent variable.  They find over all time periods that the coefficient on the payout ratio is positive in all time periods and statistically significant.  As such, this analysis supports the prior figure in confirming that higher payout ratios have resulted in higher earnings growth over the next 10 years.

3:44 - Exhibit 3. Payout Ratios and Subsequent ten-year Earnings Growth

Next, the authors split the payout ratios of the population into quartiles (i.e., 4 groups) to assess the amount of earnings growth (i.e., the average, maximum, and minimum) at different levels of payout ratios.  They find that the average subsequent 10-year earnings growth increases as the quartile of payout ratios increases; and in fact, the quartile with the lowest payout ratio exhibited negative earnings growth.  Further, the worst earnings growth period in the highest payout ratio quartile exhibited higher earnings growth than the average payout ratio at the lowest quartile of payout ratios.  They found a monotonically increasing relationship of payout ratios and subsequent 10-year earnings growth.

5:19 - Potential Explanations for the Positive Relationship of Payout to Growth

The authors have come up with several reasons why this counter-intuitive result might be occurring:

1. Management prefers not to cut dividends: If management prefers not to cut dividends (because it makes the company look bad), they would want to pay out a level of dividends that is sustainable; therefore, the dividend policy might be a reflection of management's confidence in the stability and growth of future earnings.  As such, if management is fairly sure the future earnings growth is attainable, they would be more comfortable paying out more dividends.  And in that case, higher payout ratios might foretell future earnings growth.
2. Marginal attractiveness of reinvestment opportunities: If management has several projects that it can reinvest the earnings in, then it will select the best projects first and each subsequent project will be marginally less profitable.  As such, if management retains a lot of earnings, then it might be forced to reinvest them in less compelling projects.  Therefore, a higher payout ratio might force management to be more selective in the projects it invests in; thereby efficiently managing the company's capital.  And in that case, higher payout ratios might foretell future earnings growth through efficiency and reinvestment selectivity.
3. Empire building: Management might retain too much of its earnings, which gives them the incentive to possibly spend frivolously.  With less earnings retained, management might be more frugal, reduce conflicts of interest, and perhaps curtail empire building.  And in that case, a higher payout ratio might foretell future earnings growth through better management of cash and expenses.
4. Sticky dividends and mean reversion of earnings: Earnings might be temporarily depressed (which produces high payout ratios, because dividends amounts are generally less volatile), then move back up to their long-term mean; thereby giving that appearance that high payout ratios predicted high earnings growth, when in factor it was just the nature of earnings volatility.  
5. Data or experimental design error: The results might be isolated to the period under study, but might not be true across other time periods not studied; the results might be due to another economic variable (as opposed to the payout ratio); or perhaps the recent increase in share repurchases are being done in lieu of dividends, causing misinterpretations of the payout ratios and earnings per share.

The authors have left 1-3 above for other researchers to explore; however, they use robustness tests below to evaluate the likelihood of 4 and 5.

8:24 - Exhibit 2b. Robustness Test Over Time Periods

For the first robustness check, they go back to exhibit 2b and look at the different time periods under study.  The initial time period under study was 1950 - 2001, which showed supporting results for the positive relationship between the payout ratio and subsequent 10-year earnings growth.  So, they next performed the same regression over the 1926 - 2001, 1871 - 2001, and 1871 - 1941 time periods, finding the same results as before (albeit, a bit muted); as such, it does not appear there is an error of isolated time period.

9:54 - Exhibit 4a. Earnings Growth as function of Prior Earnings Growth

The next robustness check is to test the possibility that maybe the positive relationship between payout ratio and subsequent earnings growth is merely the result of mean reversion of earnings and sticky dividends.  The authors created a regression that has 10-year earnings growth as the dependent variable and both payout ratio and lagged 10-year earnings growth as the independent variables.  If high earnings growth (and a high payout ratio) is due to a reversion of earnings upward to the long-term mean, then we would expect a negative coefficient on the lagged 10-year earnings growth variable (because earnings would have had to go down previously, resulting in their go back up to the mean in the subsequent 10 years).

The authors find the coefficient on the lagged 10-year earnings growth variable is not statistically significant; so we might conclude that the prior earnings growth is not related to the future earnings growth; and as a result, the positive relationship of payout ratio and subsequent 10-year earnings growth does not appear to be caused by a mean reversion of earnings and sticky dividends.

11:47 - Exhibit 4b. Earnings Growth as Function of Current dividend by 20-Year Avg

As a second robustness test of the potential for the reversion of earnings to their mean, the authors prepared the same regression; only this time, they replace the lagged earnings variable with a ratio of current earnings to the prior 20-year average of earnings.  As in the prior regression, if the coefficient on the ratio of current earnings to 20-year average is negative, then we might conclude that earnings were temporarily depressed and merely reverted to their mean, thereby causing a positive relationship between earnings growth and payout ratios.  Instead, they find the same result as before: the coefficient on the ratio of earnings to 20-year average is statistically insignificant and therefore determined to not explain the subsequent 10-year earnings growth.  As such, we again find that the positive relationship between payout ratios and subsequent earnings growth is likely not caused by a mean reversion of earnings and sticky dividends.

13:28 - Exhibit 5. Five-Year Growth as function of Payout Ratios

Next, as another robustness test, the authors form a regression to see the relationship between payout ratios and subsequent 5-year earnings growth (whereas, prior exhibits were of 10-year earnings growth); by reducing the 5-year periods, the authors are adding more individual data-points to the analysis at the sacrifice of longer time periods.  They find the same results as before where the payout ratios are significantly positively related to the subsequent five-year earnings growth.

Next, as a robustness check of whether the recent propensity to repurchase shares in lieu of paying dividends, the authors form the same regression; only this time they halt the time period at 1979, because this is around the time that companies started taking part in more stock buybacks.  If these share buybacks are affecting the payout ratios (i.e., companies are repurchasing shares instead of paying dividends), we would expect a less significant relationship between the payout ratio and earnings growth.  But instead, we still find a significant and positive relationship between the payout ratio and earnings growth before 1979; and the 1980 - 2001 period even has a 50% R^2, suggesting that this regression explains a very large portion of the variation in returns, even though more dividends are potentially being "paid out" as stock buybacks.

16:03 - Exhibit 6. Consistency of R2 and T-Stat

Next, the authors want to understand how consistently the a + b*PR regression has explained the variation in 5-year earnings growth over time.  To do so, they do this regression on 30-year rolling periods and plot the R^2.  They find that the R^2 is fairly volatile, ranging from 0.15 to 0.63, but is always at a respectable level.

Next, the authors chart the t-statistics of the coefficient on the payout ratio for the 30-year rolling a + b*PR regression.  They find the t-statistic is always at a significant level (except for maybe the 1910ish period), signaling that the payout ratio significantly explains the subsequent 5-year growth throughout the entire time period under study.

17:57 - Exhibit 7. Growth as function of YCS and Payout Ratio

Next, the authors want to understand whether other economic variables might be causing the significance of the positive relationship between payout ratios and subsequent earnings growth (rather than the payout ratios themselves).  To do so, they form regressions with the 5 and 10 year earnings growth as the dependent variable and the yield curve slope (i.e., the ratio of 10-year treasury to 3-month treasury) as the independent variable.  Historically, higher yield curves have predicted higher earnings growth.  The regression on the 10-year earnings growth results in an insignificant coefficient on the yield curve variable; however, the regression on the 5-year earnings growth results in significant and positive between yield curve slope and 5-year earnings growth over all time periods under study.

To determine whether this yield curve slope is supplanting the payout ratios, the authors formed a regression with 5 and 10-year earnings growth as the dependent variable and the payout ratio and the yield curve slope as independent variables.  In both cases, they find that the yield curve slope is statistically insignificant and the payout ratio is positively and significantly related to the subsequent 5 and 10-year earnings growth.  As such, the yield curve slope is a poor predictor of subsequent earnings growth when compared with the explanatory power of the payout ratio.

20:48 - Exhibit 8. Ten Year Growth as function of earnings yield and payout ratio

Finally, as another example of another variable that might be supplanting the payout ratio as a powerful explainer of subsequent earnings growth, the authors form the same regression as above, only this time they've replaced the yield curve slope variable with an earnings yield (i.e., earnings divided by price) variable.  They do find that earnings yield is a significant predictor of earnings growth on its own; however, its explanatory power is crushed when payout ratios are added to the regression.  As such, the payout ratio is much better at predicting earnings growth than is the earnings yield.

As such, in assessing/predicting future earnings growth, it might be more prudent to follow the lead of company management (i.e., by paying attention to their dividend policy) than to investors (i.e., by paying attention to P/E levels).

Abstract

Many market observers point to the very high fraction of earnings retained (or low dividend payout ratio) among companies today as a sign that future earnings growth will be well above historical norms. This view is sometimes interpreted as an extension of the work of Miller and Modigliani. They proved that, given certain assumptions about market efficiency, dividend policy should not matter to the value of a firm. Extending this concept intertemporally, and to the market as a whole, as many do, whenever market-wide dividend payout ratios are low, higher reinvestment of earnings should lead to faster future aggregate growth.

However, in the real world, many complications exist that could confound the expected inverse relationship between current payouts and future earnings growth. For instance, dividends might signals managers' private information about future earnings prospects, with low payout ratios indicating fear that the current earnings may not be sustainable. Alternatively, earnings might be retained for the purpose of "empire-building," which itself can negatively impact future earnings growth.

We test whether dividend policy, as we observe in the payout ratio of the market portfolio, forecasts future aggregate earnings growth. This is, in a sense, one test of whether dividend policy "matters." The historical evidence strongly suggests that expected future earnings growth is fastest when current payout ratios are high and slowest when payout ratios are low. This relationship is not subsumed by other factors such as simple mean reversion in earnings. Our evidence contradicts the views of many who believe that substantial reinvestment of retained earnings will fuel faster future earnings growth. Rather, it is fully consistent with anecdotal tales about managers signaling their earnings expectations through dividends, or engaging in inefficient empire building, at times; either of these phenomena will conform with a positive link between payout ratios and subsequent earnings growth.

Our findings offer a challenge to optimistic market observers who see recent low dividend payouts as a sign of high future earnings growth to come. These observers may prove to be correct, but history provides scant support for their thesis. This challenge is potentially all the more serious, as recent stock prices, relative to earnings, dividends and book values, rely heavily upon this expectation of superior future real earnings growth.

Suggested Citation

Arnott, Robert D. and Asness, Cliff S., Does Dividend Policy Foretell Earnings Growth? (December 2001). Available at SSRN: https://ssrn.com/abstract=295974 or http://dx.doi.org/10.2139/ssrn.295974

Fight the Fed Model: The Relationship between Stock Market Yields, Bond Market Yields, and Future ReturnsAsness, Cliff S., (December 2002)

Using the Fed Model to determine appropriate stock market P/E levels is flawed, primarily due to stocks being "real" assets and bonds being "nominal" assets.

0:00 - Introduction

This is a paper called "Fight the Fed Model" by Cliff Asness of AQR Capital Management, LLC.  In the paper, he discusses the relationship between stock market yields, bond market yields, and future returns in an effort to analyze the validity of the Fed Model.

The Fed Model states that the stock market yield (i.e., Earnings divided by Price, or E/P) should generally be equal to the bond market yield (i.e., the yield on the 10-year treasury, or Y).  When E/P exceeds Y, stocks are considered cheap; when E/P is less than Y, stocks are considered expensive.

 
0:41 - Section 3. Arguments in Favor of the Fed Model

There are generally 3 arguments in favor of the Fed Model:

• The Competing Assets Argument: This rationalizes that an investor could either buy stocks or he could buy bonds, so those securities are competing.  If stocks are cheaper than bonds (i.e., they have a higher yield) or are expected to have a higher risk-adjusted return, investors should buy stocks instead of bonds.
• The Present Value Argument: In present value models, the interest rate is embedded in the denominator; therefore, decreases in interest rates should produce higher stock valuations and ultimately a lower earnings yield (or equivalently a higher P/E ratio).  As such, movements in the P/E ratio should be inversely related to movements in interest rates.
• The Historical Data Argument: Historically (i.e., since 1965), the S&P 500 E/P has moved in line with 10-Year treasury yields, and actually has a 0.81 correlation!  In addition, the S&P 500 P/E ratio has historically been inversely related to the level of inflation (which is a primary component of interest rates).

 

2:18 - Section 3. Arguments Against the Fed Model

Next, the author starts with the dividend discount model, and after making several substitutions, comes to the following model of real returns for stocks.  The model generally says that real returns to owning a stock should be equal to half of the earnings yield (assuming a dividend payout ratio of 50%) plus real long-term earnings growth:

In a scenario where the inflation rate changes, the real return to owning the stock should stay the same as well (i.e., because it is net of inflation); therefore, the right side of the equation has to remain the same also.  Since the real growth rate is net of inflation, then the nominal growth rate must have to change to counteract the change in inflation; which makes since, because changes in inflation should change the revenues and expenses earned by the company, and ultimately its profit level, in line with that inflation changed.

Fed Modelers would argue that the E/P in the equation should change with the change in inflation; but our analysis above points to the more likely scenario that the nominal growth rate changes instead.

6:10 - Section 3. Arguments Against the Fed Model (cont')

To verify this empirically, the author forms a regression with the nominal earnings growth as the dependent variable and inflation as the independent variable.  He finds that historical changes in the inflation rate change the nominal earnings growth rate with almost a 1:1 relationship (i.e., the beta on inflation has a 0.94 coefficient).  This means that, on average, 94% of decade-long inflation showed up in nominal earnings growth, explaining 36.5% of earnings' variation.  This is in line with our analysis above, and in stark contrast to the thought process of Fed Modelers.

This leads us to conclude that Fed Modelers are incorrectly trying to compare a real asset (i.e., stocks; because their returns are not affected by inflation) to a nominal asset (i.e., bonds; because their returns ARE affected by inflation).  This can be thought of like the "coupon" of a stock is its earnings (which move with inflation); however, the bond's coupon does not move with inflation.

So now that we understand the arguments for and against the Fed Model, we can revisit each of the Arguments and conclude on their efficacy (or lack thereof):

• The Competing Assets Argument: The argument was that the yields of stocks and bonds should be about the same; and investors should choose the asset class that yields more than the other.  However, as we've seen, the Fed Modelers have left out the understanding that stocks have an earnings growth rate that moves with inflation, while bonds do not.  As such, comparing the two without adjusting for this growth causes an error in thinking.
• The Present Value Argument: The argument is that changes to inflation (and therefore interest rates) should adjust the denominator in the present value of cash flows formula, resulting in a change to the present value.  Which is true; however, the Fed Modelers have failed to take into account that the change in inflation will also change the cash flows in the numerator of the equation, which counteracts the change in the discount rate in the denominator.  As such, a change in inflation should not materially change the present value of cash flows.
• The Historical Data Argument: In Figure 1 and Table 1, we saw that P/E ratios move inversely to interest rates and inflation rates over the 1965 - 2001 period, with a high correlation.  However, if we were to consider this relationship back to 1926, we would see that the relationship falls apart during the 1926 - 1965 period, with a very low correlation.  Therefore, have investors had a mistaking in thinking in more recent times when deciding appropriate P/E ratios? 

15:54 - Table 2. Forecasting 10-Year Real S&P 500 Returns

Next, the author runs a few regressions with the dependent variable being the average 10-year rollings S&P 500 returns, and the independent variables being the E/P, Y, and E/P-Y.  The thought process is that if the Traditional Model holds (i.e., the primary driver of returns is the earnings yield), the E/P should be statistically significant, while the other two variables are insignificant; if the Fed Model holds (i.e., the primary driver of returns is the difference between the earnings yield and 10-year treasury yield), the E/P-Y variable should be significant.

The authors find that over the 1881-2001, 1926-2001, and 1955-2001 time periods, the E/P is significantly positively related to the real S&P 500 returns, while neither the 10-year treasury yield nor the E/P-Y variable are statistically significant.  This would lead us to believe the earnings yield is the primary driver of returns, and the traditional model holds (and the Fed Model fails).

19:11 - Table 3. Forecasting 20-Year Real S&P 500 Returns

Next, the author performs the same regression over the 1881-2001 and 1926-2001 time periods, only this time the dependent variable is average rolling 20-year returns (rather than 10-year returns).  He comes to the same conclusion that the E/P is significantly positively related to the real S&P 500 returns, while neither the 10-year treasury yield nor the E/P-Y variable are statistically significant. Again, this is in favor of the Traditional Model and against the Fed Model.

20:13 - Table 4. Forecasting 1-Year Real S&P 500 Returns

Next, the author performs the same regression over the 1881-2001, 1926-2001, 1955-2001, and 1982-2001 time periods, only this time the dependent variable is average rolling 20-year returns (rather than 10- or 20-year returns).  In this study, the author generally finds that over the more recent periods (i.e., 1965- 2001 and 1982-2001) the E/P nor the Y or E/P-Y do a good job of explaining the real S&P 500 returns over the rolling 1-year periods; in fact, in all cases, the R^2 is less than 10%.  There tend to be significant alphas during the more recent times, which means that something other than the traditional or fed models are explaining the 1-year returns; one would have had to know to be long equities over this period in order to capitalize on this alpha.

22:43 - Section 5. How P/Es and Real Rates Really Move Together

Next, the author creates a regression with the earnings yield (i.e., E/P) being the dependent variable and the 10-year treasury yield (i.e., Y) being the independent variable.  If the Fed Model is appropriate, we should see the earnings yield move with the 10-year treasury yield.  Instead, we get a regression that has a very low R^2 and the coefficient on Y is minuscule.  This would mean that the 10-year treasury yield does not explain a significant portion of the variation in E/P over the time period; as such, there must be other variables that drive the E/P.

Plotting that regression against actual P/E over the years, we would get the following figure, where we see that the equation does a poor job of predicting P/E:

Next, the author adds the ratio of stock volatility to bond volatility as an independent variable.  The thought process is that the E/P of the stock, should be equal to Y plus a risk premium (which might stem from the relative volatility of stocks to bonds).  In adding the relative volatility variable, the R^2 jumps to 58.1% during the 1926-2001 period:

and to 78.9% during the 1955-2001 period:

As such, we see that the E/P figure has moved in an almost 1:1 ratio with the 10-year treasury when we also take into account the volatility of stocks relative to bonds.  The following chart shows how well the new regression fits the actual P/Es over the 1926-2001 period:

The difference in results between the 1926-1965 and the 1965-2001 periods in the earlier figures and tables is due to relative volatility of stocks-to-bonds in recent years being stable, while the pre-1965 volatility ratio was less stable.  This is why the Fed Model seemed to work in the post-1965 period; the volatility ratio piece did not have as much bearing on the results.  As such, investors should consider the relative volatility of stocks to bonds in their assessment of the appropriate P/E ratio (and not just the level of the 10-year treasury yield).

28:34 - Section 6. The International Cross-Sectional Evidence

Finally, as a robust test, the author performs an out-of-sample test to verify the results we found above in the US market.  The author forms the same regression of E/P (dependent variable) and Y (independent variable) across 10 developed countries over the 1987-2002 period.  He finds a significant positive relationship between Y and E/P, with an R^2 of 32.2%; as such, countries with higher/lower interest rates tend to have higher/lower earnings yields. 

Next, the author forms another regression of the stock market's real return (dependent variable) and E/P (independent variable); and another regression that add Y as a dependent variable.  In doing so, the author hopes to learn how well do a country's stock earnings yield and interest rates explain the returns to the stock market.  The traditional model would hold if the E/P is positive and significant while the Y is insignificant; and the Fed Model would hold if the E/P is positive and significant and the Y is negative and significant.

The results are that the E/P is significant and positive while the Y is insignificant.  As such, the Traditional Model holds and the Fed Model fails.  In summary, the real returns to the stock market are primarily driven by the the earnings yield at time of purchase, while the level of interest rates are inconsequential.

Abstract

The "Fed Model" has become a very popular yardstick for judging whether the U.S. stock market is fairly valued. The Fed Model compares the stock market's earnings yield (E/P) to the yield on long-term government bonds. In contrast, traditional methods evaluate the stock market purely on its own without regard to the level of interest rates. My goal is to examine the theoretical soundness, and empirical power for forecasting stock returns, of both the "Fed Model" and the "Traditional Model". The logic most often cited in support of the Fed Model is that stocks should yield less and cost more when bond yields are low, as stocks and bonds are competing assets. Unfortunately, this reasoning compares a real number to a nominal number, ignoring the fact that over the long-term companies' nominal earnings should, and generally do, move in tandem with inflation. In other words, while it is a very popular metric, there are serious theoretical flaws in the Fed Model. Empirical results support this conclusion. The crucible for testing a valuation indicator is how well it forecasts long-term returns, and the Fed Model fails this test, while the Traditional Model has strong forecasting power. Long-term expected real stock returns are low when starting P/Es are high and vice versa, regardless of starting nominal interest rates. I also examine the usefulness of the Fed Model for explaining how investors set stock market P/Es. That is, does the market contemporaneously set P/Es higher when interest rates are lower? Note the difference between testing whether the Fed Model makes economic sense, and thus forecasts future long-term returns, versus testing whether it explains how investors set current P/Es. If investors consistently confuse the real and nominal, high P/Es will indeed be contemporaneously explained by low nominal interest rates, but these high P/Es lead to low future returns regardless. I confirm that investors have indeed historically required a higher stock market P/E when nominal interest rates have been lower and vice versa. In addition, I show that this relationship is somewhat more complicated than described by the simple Fed Model, varying systematically with perceptions of long-term stock and bond market risk. This addition of perceived risk to the Fed Model also fully explains the previously puzzling fact that stocks "out yielded" bonds for the first half of the 20th century, but have "under yielded" bonds for the last 40 years. Finally, I note that as of the writing of this paper, the stock market's P/E (based on trend earnings) is still very high versus history. A major underpinning of bullish pundits' defense of this high valuation is the Fed Model I discredit. Sadly, the Fed Model perhaps offers a contemporaneous explanation of why P/Es are high, but no true solace for long-term investors.

Suggested Citation

Asness, Cliff S., Fight the Fed Model: The Relationship between Stock Market Yields, Bond Market Yields, and Future Returns (December 2002). Available at SSRN: https://ssrn.com/abstract=381480 or http://dx.doi.org/10.2139/ssrn.381480  

The Siren Song of Factor Timing. Asness, Cliff S. (April 12, 2016)

Asness, Cliff S., The Siren Song of Factor Timing (April 12, 2016). Journal of Portfolio Management, Vol. Special Issue, No. 1, 2016. Available at SSRN: https://ssrn.com/abstract=2763956 or http://dx.doi.org/10.2139/ssrn.2763956 

 

Abstract

Everyone seems to want to time factors. Often the first question after an initial discussion of factors is “ok, what’s the current outlook?” And the common answer, “the same as usual,” is often unsatisfying. There is powerful incentive to oversell timing ability. Factor investing is often done at fees in between active management and cap-weighted indexing and these fees have been falling over time. Factor timing has the potential of reintroducing a type of skill-based “active management” (as timing is generally thought of this way) back into the equation. I think that siren song should be resisted, even if that verdict is disappointing to some. At least when using the simple “value” of the factors themselves, I find such timing strategies to be very weak historically, and some tests of their long-term power to be exaggerated and/or inapplicable.
 

Asness, Cliff S. and Brown, Aaron, Pulling the Goalie: Hockey and Investment Implications (March 1, 2018).

Asness, Cliff S. and Brown, Aaron, Pulling the Goalie: Hockey and Investment Implications (March 1, 2018). Available at SSRN: https://ssrn.com/abstract=3132563 or http://dx.doi.org/10.2139/ssrn.3132563  

Abstract

We build a simple, but powerful and intuitive, model for when a hockey coach should pull the goalie when trailing. When the model reports that the coaches aren’t doing it nearly early enough, we then ask why, and take away some key lessons for portfolio and risk management, and business in general.

Fact, Fiction, and Value Investing. ASNESS, C., FRAZZINI, A., ISRAEL, R., & MOSKOWITZ, T. (2015)

ASNESS, C., FRAZZINI, A., ISRAEL, R., & MOSKOWITZ, T. (2015). Fact, Fiction, and Value Investing. Journal of Portfolio Management, 42(1), 34–52. https://doi.org/10.3905/jpm.2015.42.1.034

Value investing has been a part of the investment lexicon for the better part of a century, with the diversified systematic value factor (or value effect) studied extensively since at least the 1980s. The authors aim to clarify the many remaining areas of confusion about value investing, focusing on the diversified systematic value strategy, but also exploring how this strategy relates to its more concentrated implementation. They highlight many points about value investing and attempt to prove or disprove each of them, referencing an extensive academic literature and performing simple, yet powerful, tests based on easily accessible, industry-standard public data.

Global Tactical Cross-Asset Allocation: Applying Value and Momentum Across Asset Classes. Blitz, David and van Vliet, Pim (2008)

Introduction

The authors are using a Global Tactical Asset Allocation strategy to tilt their portfolio toward asset classes that are more favorable.  Typically this is done through a "building block" model, where each of the asset classes uses a different model to build the overall allocation strategy.  There are a few problems with this: securities within asset classes aren't compared to securities in other asset classes, it takes a lot of time to build all of the different models, and there has to be a good risk-management process in place to mitigate unnecessary risk.  As such, the authors propose a single model, they name the Global Tactical Cross-Asset Allocation (GTCAA) strategy, that is not subject to those limitations.  This allocation approach selects asset classes, rather than securities within asset classes, as has been done in previous studies. If they find this approach to work, it could be a challenge to market efficiency.

The authors then summarize the results of a few previous studies in favor of momentum strategies; for example, Jegadeesh and Titman's 6-month results, Fama and French 12-1 momentum strategy, and Rouwenhorst's international markets, and Pirron's futures market studies.  Based on those previous studies, they will look at the 1-month return strategy, 12-1 momentum strategy, and value strategies across asset classes.  In their study, they found these value and momentum strategies to exhibit statistically and economically significant returns of 7-8% over the 1986 - 2007 period.  In addition, when they combined the value and momentum strategies, they find excess returns of 12% over the same period.  These strategies also outperformed in the 1974-1985 out-of-sample period, overcame transaction costs, and overcame typical risk factors such as the Fama-French and Carhart 4-factor models.  These results are significant, because it provides a single model for practitioners to use easily; also they use variables that can be used across all the asset classes.

Data and Methodology

The authors use 12 asset classes; these include 3 US equity, 3 international equity, 3 US bonds, 2 international bonds, and 1 month libor over the 1985 - 2007 period.  They selected these asset classes to achieve ease of data retrieval, ease of modeling, liquidity, large capitalization, and lack of correlation with other asset classes in the study.  For each of the asset classes, they found the excess returns in local currency and subtracted the local risk-free rate to simulate the return of a typical futures contract.  Over the 1985 - 2007 period, emerging markets returned the highest at 10.8% and Japan equity returned the worst at 0.7%.  When matched against their standard deviations, all assets seemed to have similar Sharpe ratios, excluding Japan equity which seemed to have very high volatility.


To form portfolios of these asset classes, at the beginning of each month the authors rank them according to their momentum or value scores and put them into quartiles (with 3 asset classes in each quartile).  In doing so, they will calculate the average 1-month returns of each quartile as well as the return of the top quartile minus the return of the bottom quartile.  They will use the 1-month momentum and 12-1 momentum strategies, and they will use yield measures for the value strategy.  In addition, they will use a combination strategy which allocates 25% to the 1-month momentum strategy, 25% to the 12-1 momentum strategy, and 50% to the value strategy.

The authors realize this strategy is somewhat simplistic, so they make a few adjustments to the yields to adjust for risks; otherwise, for example, high yield bonds will most likely always get high allocations over risk-less bonds.  So, they subtract 1% yield for government bonds, 2% from US investment grade bonds, 6% from US high-yield bonds, 1% from emerging market equities, and 2% from US REITS.  

Main Results

Over the 1986 - 2007 period, each of the individual strategies' top quartiles outperformed the bottom quartiles by 7-8%, and the combination strategy's top quartile outperformed the bottom quartile by 12%.  There was also a somewhat monotonic relationship of these returns across the quartiles.  Each of these results were statistically significant.  The information ratios were about 0.60 for the individual strategies and 1.19 for the combination strategies.  As would be expected, the returns of the momentum strategies were positively correlated with each other; however, the momentum strategies were negatively correlated with the value strategy.  And that diversification is why the combined strategy performs so well.

The authors also look at a chart of the returns for each strategy over time.  Each of the 3 individual strategies perform similarly over the period; however, the combination strategy's return was stable and significantly outperformed the individual strategies over the period.

Next, the authors wanted to determine whether these returns were caused by biases to certain asset classes; so for the combined strategy, they look at the percentage of time that each asset class is allocated to a particular quartile.  The US REIT seems to be the most frequent asset class in the top quartile, and UK equity in the bottom quartile; however, no asset class seems to have too big or too small of an allocation in a particular quartile.  Therefore, the authors find this be evidence there is no bias toward any particular asset class.

Next, the authors analyze the loadings toward the Fama/French and Carhart 4 factors (i.e., market, size, value, and momentum factors).  The returns of the 1-month momentum strategy seem to be explained by the size factor and alpha.  The returns of the 12-1 momentum strategy seem to be explained by the market and momentum factors.  The returns of the value strategy seem to be explained by market, size, momentum, and alpha; although interestingly, those returns have a negative relationship with the market factor and the momentum factor.  Finally, the returns of the combination strategy seem to be explained by alpha, size, and value factors, but the alpha figure is a significant 11%. 

Robustness Tests

The authors now analyze the transaction costs of utilizing these strategies.  The 1-month strategy has the highest turnover at 1675%, while the valuation strategy has the lowest turnover at 234%; since the 1-month strategy had the lowest returns and the highest transaction costs, its net returns are the lowest.  However, the high returns of the combination strategy allowed it to have the highest net returns of all the strategies, when the transaction costs are estimated to be below 0.40%.  When the transaction costs exceed 0.40%, the value strategy has the highest net returns, because of its lower turnover.  Even at a transaction cost of 0.50%, the combination strategy has excess returns of 4.6%.

Next, the authors replicate the prior results over the 1974 - 1985 period to see if the strategies work over an out-of-sample period.  Due to lack of data, there are only 8 asset classes in this analysis.  The results are quite similar to those found in the 1986 - 2007 period, with the top quartile returns exceeding those of the bottom quartiles, with high information ratios, and t-statistics across all strategies; these measures were, however, a bit lower than was found in the 1986 - 2007 period.  As was found before, the combination strategy significantly outperformed the individual strategies.

Next, the authors understand that some assets are more volatile than others; so the more volatile ones may have more extreme ranks (i.e., end up in the top quartile or bottom quartile more often) than the less volatile asset classes.  So, they re-perform the tests with adjusted allocations based on tilting the weights to obtain a 10% volatility for each of the asset classes.  The results are similar to the returns we found in the prior sections, with the top quartile outperforming the bottom quartile, the information ratios significant, and the t-statistics significant; as we found before, the combination strategy continues to outperform the individual strategies.

Finally, the authors are concerned that the returns of the strategies are principally because of a single or few asset classes.  So the authors provide a chart of the average returns of each asset class when allocated to each quartile.  In the top quartile, no asset class averages a return less than 0.3%, and the average return across all asset classes is 0.8%; in the bottom quartile, no asset class averages a return greater than 0.2% (except US mid-cap equities).  The authors find this to be evidence that no individual asset class causes the majority of returns in the strategies; the returns seem to be spread across all the asset classes.

Discussion

These results could certainly be due to data mining or chance; however, the authors don't expect this to be the case, and expect these results to continue going forward.  The authors also caution, that it could be possible that these returns could just be compensation for a risk that was not modeled; the authors also think that explanation is likely not true, either, because the alpha was 12%, so that presents a very high hurdle to be consumed by risk measures.  Also, there didn't seem to be any risk-differences between top and bottom quartile portfolios (e.g., volatility, skewness, etc.).  Further, the authors look at how the strategies would have performed in different regimes (e.g., high/low interest rate environment, high/low term spread environment, high/low credit spread environment, and high/low volatility strategy).  They find the returns for each strategy to be similar across all 4 strategies in different regimes; however, the value and combination strategies seem to perform differently in different credit spread and interest rate regimes.


These findings provide a challenge to the efficient market hypothesis; however, the authors note that it is challenging to use a risk-model that works across asset classes (because different asset classes have different risks).  The authors propose the results of these strategies may be due to behavioral effects that make it difficult for the smart money to arbitrage away these results.  For example, practitioners may find cross-asset allocation to be too challenging or the strategy and valuation measures may be too simplistic.  Secondly, typical asset managers specialize in individual asset classes; so they are more concerned with selecting individual securities within the asset class, rather than how the asset class as a whole will perform.  Finally, allocations by end-investors may be primarily driven by long-term considerations (e.g., pension funds' ALM), fixed allocation mechanisms (e.g., 401ks), herding behavior, or recent performance.  The authors believe these constraints will continue going forward, so the results of these strategies will continue as well.

The authors note that hedge funds have the greatest ability to capture this alpha and arbitrage away the results.  So to understand whether they currently are doing so, the authors regress the returns of the strategies against the returns of various hedge fund strategies.  They find the returns of the 12-1 momentum strategy seem to explain the returns of global macro, long-short equity, managed futures, and multi-strategy.   The returns of the combined strategy seem to be related to the managed futures and multi strategy returns.  But for the most part, the individual strategies' returns don't seem to be related to hedge fund strategies' returns, so maybe these returns are not being arbitraged away by hedge funds.

Summary, implications and extensions

In summary, the individual strategies (i.e., 1-month momentum, 12-1 momentum, and value) all earned significant return premiums of 7-8% over the 1986 - 2007 period, and the combined strategy (i.e., 25% 1-month momentum, 25% 12-1 momentum, and 50% value) earned an alpha of 12%.  Even after adjusting for the Fama/French (i.e., market, size, and value) and Carhart factors (i.e., momentum), there still remains a significant excess return for the strategies.  The authors argue against risk-based explanations and instead suggest the market to be macro-inefficient; this is because there is not enough smart money to arbitrage away these alphas due to various constraints.  Future researchers could extend this study by expanding the number of asset classes, expanding the number of predictor variables, or introducing portfolio optimization to the allocations.

Fact, Fiction and Momentum Investing. Asness, C., Frazzini, A., Israel, R., & Moskowitz, T. (2014).

The authors note empirical studies have found that momentum strategies have been found to exist across 2 centuries, several asset classes, and geographies.  However, there have also been several rebuttals claiming that momentum strategies may not work effectively for various reasons.  The authors in this paper intend to defend 10 myths about momentum strategies.

The first myth says that momentum strategies offer returns that are too small and sporadic.  The authors reference several prior studies that show the robustness of momentum strategies across several countries, regimes, and asset classes.  They then use Ken French’s Up-minus-Down (Winners minus losers) strategy over the 1927 – 2013 period, 1965 – 2013 period, and the 1991 – 2013 period.  Across all periods, they find that this momentum strategy earned about 8%, significantly outperforming other common factors such as RMRF (market premium), SMB (size premium), and HML (value premium) strategies on both nominal returns and Sharpe ratios.  The authors also examine the percentage of annual periods and five year periods that the momentum strategy was profitable.  They find that about 80% of the periods were profitable, while the other factors’ positive return percentages were less.  Finally, the authors built a value/momentum portfolio, which outperformed the other factors even further on both a Sharpe ratio and percent positive metrics.  Therefore, the authors find this to be good evidence to refute the claim that momentum strategies are small and sporadic.

The second myth says that momentum can only be exploited on the short side (and is not very useful to long-only investors).  The authors use the same time periods as in Myth 1 and split the alphas of the returns of the UMB strategy into the long and short pieces.  In doing so, they find that about half of the return relates to the profit from going long the past winners, and half of the return is due to going short the past losers.  The authors also reference a paper that finds the same results over 86 years of US equity data, 40 years of international equity data, and 40 years of data from other asset classes.  Therefore, the authors find this to be good evidence to refute the claim that momentum strategies are only profitable on the short side.

The third myth says that momentum is only present in small-cap stocks and not in large-cap stocks.  However, several researchers have found that small-cap and large-cap stocks contribute rather equally to momentum returns.  The authors use Ken French's data to analyze the momentum returns (UMB) of large-cap versus small-cap stocks; in doing so, they find that small companies tend to have a larger momentum return than large-cap companies, but the difference is fairly small.  Doing the same strategy using value/growth (HML), they find that small-cap companies have significantly higher returns than large-cap returns; and in fact, large-cap value premiums are significantly zero.  So, it seems the momentum strategies do not prefer small over large-cap stocks as much as value strategies do.  And actually, they note that if Fama/French had not normalized for company size, there would likely have been no value premium found at all.  The authors also make note that Fama and French found in 2012 that over a 1989-2011 period in international equities, small cap momentum stocks had slightly higher returns than large cap momentum stocks, but the large-cap momentum returns were significant.  This was also found in the 1927 - 2013 period used in the current paper.

The fourth myth says that momentum returns do not exceed trading costs.  The thought is that momentum strategies have higher turnover and therefore higher trading costs.  The authors use real-world trading costs from AQR Capital's trades over the 1998-2013 period in 19 developed equity markets across different factor strategies.  They find that per-dollar trading costs for momentum strategies are actually quite low compared to other strategies (e.g., value, size, etc.).  They also note that trading strategies should be used to reduce trading costs (e.g., limit orders rather than market orders).  They find that prior studies using estimated trading costs often inflate them due to averages that include trades of retail investors (rather than just institutional investors).  Using the AQR data, they find that since momentum strategy returns significantly exceed the returns of small-cap strategies and since trading costs of momentum strategies are lower than trading costs of small-cap strategies, the net returns to momentum strategies significantly exceed those of small-cap strategies.

The fifth myth says momentum strategies do not work for taxable investors; this of course relates to the higher turnover of momentum strategies.  Prior research has found, however, that the tax burden of momentum strategies are about the same as value strategies, despite their having 5-6 times the turnover.  This is due to momentum strategies instructing the investor to hold winners longer (which typically results in long-term capital gains status) and to sell losers (which results in deductions).  Also, value strategies typically have high dividend exposure, which is taxed at ordinary rates; however, momentum strategies do not have as high of dividend exposure.  As such, since momentum strategies' returns are significantly higher than value strategies' and they typically both have about the same tax burden, then momentum strategies outperform on an after-tax basis.  Another item to note is that tax optimization is a lot easier to implement when concentrating on capital gains (because you can control when to sell securities) rather than dividends (because you can't control when you receive dividends). 

The sixth myth says momentum strategies are best used for a screen rather than as an actual factor.  However, it seems counter intuitive to say that momentum strategies are good or useful but to denounce it at the same time.  This may be due to naysayers anchoring to the concept of market efficiency.  The authors suggest that if all the previously discussed myths were true, it might be useful as a screen; but since they were debunked, we might say that momentum has more of a right to be an actual factor than value or size do.

The seventh myth says that investors should be worried about momentum returns disappearing.  The authors argue that this should be the case for any factor, not just momentum.  They argue, however, that momentum has had a more stable record than other factors over time (as was found in the first few myths in this paper).  They note that naysayers may believe this myth due to the relatively newness of momentum studies in academia, and the use of behavioral reasons rather than risk-based causes of momentum.  The authors remind us, however, that momentum has been found to exist going back 200 years and across dozens of equity markets; also, they note that any factor could be due to behavioral or risk-based factors (e.g., the promotion of value stocks could increase demand/price and reduce their premium to zero).  The authors reference a 2013 paper that finds the out-of-sample period did not result in reductions to momentum returns (which might be evidence of continuity of the strategy).  And importantly, the authors used Ken French's data to form portfolios of stocks with both momentum and value characteristics; they find that even if the returns of the momentum factor are zero, including them in the portfolio increases the sharpe ratio of the portfolio due to the diversification benefits.

The eight myth says that momentum is too volatile to rely on.  However, we remember that the sharpe ratios of momentum strategies found in the previous myths are significantly higher than those of other factor strategies; and since volatility is taken into account when calculating the sharpe ratio, we know this myth to be untrue.  The authors note the myth may come about due to the recent 2009 crash in momentum returns when the market significantly increased after the recession; in that instance, the momentum strategy would have held low-beta winners and high-beta losers, meaning the market would have moved up faster than the winners in the momentum strategy did.  The authors note, however, that 1999 was bad for value investors and 2008 was bad for passive investors; so, they argue that an entire strategy should not be denounced just because of a few bad periods.  They also note 1932 was bad for momentum and 1930 was bad for value.  But, it was found that using momentum and value together during these bad times would have significantly reduced the volatility; using Ken French's data, the authors find that the worst drawdown for value-only was 43%, the worst drawdown for momentum-only was 77%, but when using value and momentum together, the worst drawdown was 30%.  Also, being long-only would have also fared ok, because most of the loss in the momentum strategy is due to being short the high-beta stocks that suddenly increase in value.   

The ninth myth says different measures of momentum can give different results over different periods.  This is actually true, but may be a good characteristic, rather than bad.  The argument of the naysayers is that data mining could be used to find the best strategy; but the authors note that out-of-sample evidence is robustly in favor of momentum strategies.  Also, other factor strategies use different measurements as well (e.g., P/B, P/E, D/P, for value strategies).  In the same vein, such metrics as past returns, past earnings, and analyst revisions are used in ranking momentum stocks; it was found in other studies, however, that each of these measures are effective independently and together.  As such, this should be taken as a sign of robustness, rather than a critique, of momentum strategies.

The tenth myth is that there is no theory behind momentum.  The authors note this is not fair, because other factors (e.g., value and size) do not have definite supporting theories either, and are in fact still heavily debated.  The two behavioral reasons for momentum are that investors typically under-react to new information or delay-overreact to new information; both of these have been found to occur.  The other possibility is that momentum occurs due to compensation for risk; for example, growth companies have a risk that they will not have enough cash to support their growth.  The authors argue that no matter the reason that momentum occurs, there certainly does seem to be a persistence of momentum over time and should continue to exist going forward.  In addition, the naysayers might be anchoring to the efficient market hypothesis and claim that the momentum premium should be arbitraged away; however, we've seen this to not be the case in the prior myths.

In conclusion, in this paper, the authors debunked 10 myths about momentum strategies; they welcome further debate around this paper or the effectiveness of momentum strategies.

Asness, Cliff S. and Frazzini, Andrea and Israel, Ronen and Moskowitz, Tobias J., Fact, Fiction and Momentum Investing (May 9, 2014). Journal of Portfolio Management, Fall 2014 (40th Anniversary Issue); Fama-Miller Working Paper. Available at SSRN: https://ssrn.com/abstract=2435323 or http://dx.doi.org/10.2139/ssrn.2435323

About Sawyer Investment Management Company:
SIMCO is a Texas-registered Investment Adviser with its principal place of business in Dallas, Texas. It was formed on January 1, 2015 and is wholly owned by Ryan Sawyer, who is a CFA Charterholder and a Certified Public Accountant.

SIMCO specializes in the construction of equity portfolios, and is therefore an ideal resource for long-term investors. The firm goes through a rigorous process for selecting each and every holding in the portfolio. Rooted in the empirical research of academia, the portfolios are generally characterized as large-cap value momentum. For more information about how the portfolios are managed, see our website.

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