Indexology^® Blog

In April, the S&P 500 (TR) gained 0.4%, ending its first consecutive monthly loss in almost two years, but the index is still down 0.4% year-to-date (ending April 30, 2018.)  Mid caps are also down for the year, -1.0%, after the S&P 400 (TR) lost 0.3% in April.  However, Small caps pushed into positive territory, up now 1.6% year-to-date, from the S&P 600 (TR) gain of 1.0% in April.

Overall, 6 of 11 sectors gained in large and mid caps while 8 of 11 gained in small caps.  Energy led the gains across the size spectrum with total returns of 9.4%, 13.4% and 16.6%, respectively in large, mid and small caps that more than tripled the next best sector’s returns (S&P 500 Consumer Discretionary gained 2.4%, S&P 400 Utilities gained 3.9%, and S&P 600 Telecommunications gained 5.9%.) It  was the S&P 500 Energy’s 17th best month on record since October 1989, and it gained most since Sep. 2017.  Consumer Staples posted its 28th worst month in history, losing 4.3%, making it the 3rd consecutive monthly loss and worst 3-month loss (-12.5%) since the 3 months ending in Feb. 2009.

Energy’s outperformance not only propelled small caps to outperform large caps (since smaller energy companies rise more with oil) but drove value to outperform growth.  The S&P 500 Value has 12.5% more energy than the S&P 500 Growth, which has nearly none. While the value outperformed growth across all sizes for the first time in 2018, the mid and small caps had a much greater premium (respective 1.8% and 1.3%) than the large cap premium at 0.2%.  The mid cap premium was the most since Nov. 2016 and the small cap was most since Sep. 2017.  It is also the first time large cap value outperformed growth for 4 of 6 months since the second half of 2016.

As shown at the end of 2017, when growth and large caps outperform as much as they did in 2017 (that was the most since 1999,) the trend reverses.  That’s what seems to be happening now.

In a previous blog, we performed preliminary exploration of rising interest rate exposure of the S&P 500^® Low Volatility Index. In this blog, we continue the analysis to see if there is a relationship between the magnitude of interest rate change and magnitude of active return of the low volatility index relative to the S&P 500. To do so, we run a regression line by plotting the historical monthly excess returns (y-axis) against the monthly interest rate changes (x-axis).

Looking at the trend line in Exhibit 1, there is a downward sloping, negative relationship between the degree of interest rate movements and the excess return of the low volatility index relative to the S&P 500. The regression equation, also shown in the chart, confirms the negative relationship.

The regression equation has a slope coefficient of -3.07 and an r-squared value of 8.8%. The coefficient indicates that for every 1% change in interest rate, the excess return of the low volatility index is expected to change by -3.07 times. For example, if interest rates rise by 1%, the relative return is expected to be -3.07%. Conversely, if rates decline by 1%, the excess return is expected to be 3.07%.

The r-squared value is the trend line’s “goodness of fit” to the data; in essence, it is the explanatory power of interest rate movements on excess returns. We note that the r-squared value is relatively low; however, the coefficient to interest rates is statistically significant. Ensuring that coefficients are statistically significant when it comes to factors that have low explanatory power, such as macroeconomic factors, on equity performance is especially critical. In this case, the t-stat of the interest rate change coefficient is -5.61, which is significant at the 99^th percent confidence interval.

Combined with the findings in the first blog, we can conclude that, historically, the S&P 500 Low Volatility Index tends to be negatively affected by rising interest rates. In a subsequent blog, we will explore an alternative low volatility index strategy that is designed to reduce interest rate exposure while still preserving low volatility properties.

In my recent book, The Behavioral Economics Approach to Winning New Clients (and Keeping the Ones You Have!), I offer a dozen recommendations to financial advisors charged with stewarding their clients’ assets to ultimately improve the relationship between client and advisor.  Several lessons are devoted to communication, specifically ways to employ trust, loss aversion and regret aversion when helping clients save for future goals, and most often, those goals are retirement goals.

The heart of any retirement discussion must include the role of today’s defined contribution (DC) plan.  DC plans today are not like yesterday’s supplemental, savings-oriented plans and the more we rely on these plans to provide a true retirement, the more we may also change our focus from wealth accumulation to a different goal such as an income-oriented goal.  Income-oriented goals are those targeting a specific standard of living, withdrawal rate, or income replacement ratio in retirement.  This shift in focus would require plan sponsors, practitioners and plan sponsors alike to change the way they communicate to and with each other; moving from a mere savings discussion to a discussion encompassing both saving and spending over one’s lifecycle.

When we communicate with clients (and/or DC plan participants), we must communicate in terms familiar and relevant to them.  Historically, the retirement industry has communicated from the wealth accumulation lens, focusing on growing an account balance or what stocks or funds to pick.  The problem with this communication approach is two-fold.  First, participants generally do not know how much money is enough for retirement.  Lump sum account balances are confusing – it may be more natural for participants to make decisions based on periodic amounts such as an annual salary or monthly bills.  Secondly, when participants do retire, their nest egg is probably the biggest amount of money they have ever seen, let alone have to manage.  This conundrum causes participants to exhibit either the illusion of wealth (spending too much too quick) or alternatively spending too little because they haven’t fully graduated from the savings mindset.

The retirement industry (financial advisors, plan sponsors, asset manager and index providers) can help participants by creating products and services that focus on retirement income rather than just wealth accumulation.  I am pleased to see a resurgence in new products, white papers, retirement income topics at conferences and regulatory proposals such as the Retirement Enhancement and Savings Act of 2018.  What’s needed now are innovative solutions designed to provide clarity in savings outcomes to help participants understand the risks they face as they progress to and in retirement.  I am confident that our industry will create these solutions and help turn America’s defined contribution system into America’s retirement system!

Video Link: https://www.assettv.ca/video/communicating-income-lessons-behavioral-finance

A topic commonly brought up when interest rates rise is the impact that rates have on the performance of low volatility indices. Several studies[1][2] have shown that low volatility portfolios have exposure to rising interest rate risk. One of the main drivers of this exposure stems from the bond-like characteristics of sectors usually favored by low volatility strategies, such as utilities and consumer staples. Thus, rising interest rates can negatively affect the performance of indices, such as the S&P 500^® Low Volatility Index. In this blog, we review the historical performance of the S&P 500 Low Volatility Index to the S&P 500 in rising interest rate periods to confirm whether or not this is the case.

The S&P 500 Low Volatility Index outperformed the S&P 500 in the long term on an absolute and risk-adjusted basis, but in periods of rising interest rates, the index has underperformed.  Using the 10-year U.S. Treasury Bond yield as the proxy for interest rates, Exhibit 1 shows the historical performance of the S&P 500 Low Volatility and S&P 500 indices in periods of significantly increased interest rates. For the purpose of this study, rising interest rate periods are classified as significant when rates rose by 1% or more on a month-end basis.

Going back to 1991, there have been 10 non-overlapping periods of rising interest rates. This includes the most recent environment through the end of February 2018, as interest rates have been trending upwards since the summer of 2016. The S&P 500 Low Volatility Index underperformed the S&P 500 in 9 of the 10 periods, with an average excess return of -8.92% and median excess return of -5.44%.

The largest underperformance was seen during the technology boom in the late 90s as interest rates rose by over 2%. In this period, the S&P 500 Low Volatility Index underperformed by nearly 42% from October 1998 through January 2000. One of the main drivers of underperformance stemmed from the S&P 500 Low Volatility Index having no exposure to the information technology sector, which unsurprisingly was the best performing sector during the technology boom. The information technology sector is cyclical and generally has performed well in increased interest rate periods, while the utilities sector has significantly underperformed.[3]

To further isolate the impact of interest rate movements, monthly hit rates and average excess returns were calculated. Exhibit 2 shows these two statistics for the S&P 500 Low Volatility Index relative to the S&P 500 going back to 1991. The time period was broken out between months when interest rates rose and when rates fell. No minimum change threshold was incorporated; thus, all months were included in the analysis.

It is evident that there is a difference in relative performance based on the direction of interest rate changes. The S&P 500 Low Volatility Index underperformed the benchmark 60% of the time when interest rates rose and underperformed by an average of -0.60%. Conversely, the S&P 500 Low Volatility Index performed better than the benchmark when interest rates declined.

In a forthcoming blog, we will use regression analysis to further examine the robustness of the results found in this blog. In particular, we will test if there is a relationship between the magnitude of interest rate changes and resulting excess returns.

[1]   Blitz, D., B. van der Grient, and P. van Vliet. “Interest rate risk in low-volatility strategies.” 2014.

[2]   Driessen, J., I. Kuiper, and R. Beilo. “Does Interest Rate Exposure Explain the Low-Volatility Anomaly?” 2017.

[3]   Based on the same periods in Exhibit 1, the Information Technology sector had a median excess return of 4.26% versus the S&P 500, while the utilities sector had a median excess return -13.89%.

Previously I discussed why preference for dividend-paying stocks may not have a strong theoretical footing, but could be grounded in behavioral and practical reasons. Furthermore, due to possible economic signaling generated by dividends, such strategies may be correlated with widely accepted factors like quality and value. This post demonstrates how specific dividend strategy indices may be related to several major factors. Exhibit 1 lists major characteristics of four popular S&P DJI dividend indices. Dividend strategies can be very different from one another so it pays to understand individual indices behind various index-tracking products. However, despite their differences, indices are generally much more transparent than actively managed dividend strategies.

The indices in Exhibit 1 highlight four combinations of constituent selection and weighting. For selection, we have either indicated dividend-yield ranking or persistent historical dividend growth, while for weighting we have yield weighting or equal weighting. The various combinations of selection and weighting methods contribute to significantly different index performance.

Exhibit 2 shows summary statistics of the four dividend indices regressed on Fama-French factor returns including market beta (Mkt-rf), small size (SMB), value (HML), and momentum (MOM). AQR’s quality factor, Quality Minus Junk (QMJ), is added to form a five-factor regression model.

• Market Beta (Mkt-rf): All of the indices had statistically significant market betas in a tight range from 0.84 to 0.89, indicating somewhat less equity risk than a broad benchmark.
• Small Size (SMB): These strategies generally did not present positive SMB loadings and, based on the presence of several negative coefficients, the regressions may indicate a tendency toward larger constituents. However, none of the regression coefficients were statistically significant (at 95% confidence) so the data does not support an inference.
• Value (HML): With respect to HML loadings, we see differentiation among the indices along the line of their primary weighting methods. The yield-weighted indices had positive and statistically significant coefficients (95%), while the equal-weighted indices had somewhat lower, positive coefficients that failed statistical significance.
• Momentum (MOM): All of the indices’ returns loaded negatively on MOM, but they differed with respect to coefficient size and significance. The S&P 500 High Dividend Index had the largest negative loading with the most significant t-stat. The S&P 500 Dividend Aristocrats and S&P High Yield Dividend Aristocrats were in the middle of the pack in terms of coefficient size, and both were significant. Finally, the Dow Jones U.S. Select Dividend Index had a lower negative coefficient, and its t-statistic was somewhat below the 95% confidence level of significance.
• Quality (QMJ): Whereas value loadings seemed to be differentiated along the line of constituent weighting methods, QMJ loadings were potentially related to constituent selection. The two Dividend Aristocrat indices, both of which select members with long records of dividend increases, had positive and significant QMJ loadings. The indices that select constituents by dividend yield were less related to QMJ, if at all.
• Alpha: As expected, when controlled for multiple factor exposures, none of the indices produced alpha over the sample period.

Little differentiation was found between factor loadings of the indices with respect to market beta, small size, or momentum, and none created alpha. On the other hand, value and quality factor loadings were different between indices, and they generally aligned either by constituent selection or weighting. The two yield-weighted indices produced positive and significant value exposure, while the Dividend Aristocrats indices produced positive and significant quality exposure. Interestingly, only the S&P High Yield Dividend Aristocrats had positive and significant loadings on both value and quality over the sample period.

Through this analysis, we see that dividend strategies are not only about income or yield, but also about how their various combinations of factor loadings may compliment portfolios through factor diversification. Market participants ought to carefully consider which strategies can be reasonably expected to enhance their specific investment program before jumping in.

¹ http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/index.html

² https://www.aqr.com/Insights/Datasets/Quality-Minus-Junk-Factors-Monthly