Indexology^® Blog

How has the market’s response to the pandemic impacted the potential opportunity set? S&P DJI’s Anu Ganti and Hamish Preston examine where market reversals took place and what they could mean for active management and asset allocation strategies moving forward.

Read on: Style Bias and Active Performance

This blog, the final in a series of three, reviews the performance of the new S&P Dividend Growers Indices and highlights some of their defensive characteristics. They are designed to track companies with consistently increasing dividends while excluding the top 25% highest-yielding eligible companies. Only companies that increase dividends consecutively for at least 10 and 7 years for the S&P U.S. Dividend Growers Index and S&P Global Ex-U.S. Dividend Growers Index, respectively, are eligible to be included.

Risk/Return Statistics

Exhibit 1 shows these indices’ back-tested risk/return statistics over various periods. Over the full period, the S&P Global Ex-U.S. Dividend Growers Index outperformed its benchmark by 2.44% annualized, while the S&P U.S. Dividend Growers Index slightly underperformed by 0.16%. It is important to frame the U.S. index’s underperformance in the context of the index’s defensive qualities (examined in the next section) and from a risk-adjusted perspective.

Here, risk-adjusted return is the ratio of annualized return and annualized volatility. With respect to volatility, both indices were substantially lower than their benchmarks. The full period volatilities of the S&P U.S. Dividend Growers Index and S&P Global Ex-U.S. Dividend Growers Index were 2.15% and 2.60% lower, respectively. Thus, on a risk-adjusted basis, both indices displayed superior risk-adjusted returns.

Down-Market Performance

The first blog in this series showed that companies with consistently increasing dividends displayed greater financial strength. As expected, higher-quality companies tended to outperform during periods of market stress.

Exhibit 2 shows the S&P Dividend Growers Indices experienced lower drawdowns than their benchmarks across the full back-test period. More recently, the S&P U.S. Dividend Growers Index and S&P Global Ex-U.S. Dividend Growers Index outperformed by 4.21% and 1.36%, respectively, during Q4 2018 and by 5.11% and 3.84%, respectively, during the COVID-19-related drawdowns in March 2020.

The defensive nature of the S&P Dividend Growers Indices is further evidenced by their historical capture ratios. A downside capture ratio of less than 100 indicates that a strategy has lost less than its benchmark during months when the benchmark return was negative. Exhibit 3 shows the S&P U.S. Dividend Growers Index and S&P Global Ex-U.S. Dividend Growers Index had downside capture ratios of 78.97 and 78.84, respectively, across the full back-test period.

Conversely, the upside capture ratio measures how a strategy has performed relative to the benchmark during months when the benchmark return was positive. Both S&P Dividend Growers Indices had upside capture ratios of less than 100, indicating they tend to participate less fully during up months.

Another interesting way to examine the performance of the S&P Dividend Growers Indices is during periods of increasing volatility. Exhibit 4 shows the average monthly over/underperformance of each index across different thresholds of monthly increases in the CBOE Volatility Index^® (VIX^®).

Market volatility can cause significant price fluctuations in a portfolio; however, companies exhibiting sustained dividend growth tend to be higher quality and may help reduce volatility. When VIX increased more than 20% over the course of the month, the S&P U.S. Dividend Growers Index and S&P Global Ex-U.S. Dividend Growers Index outperformed by 50 bps and 53 bps per month on average, respectively.

Dividend Yield

Finally, let’s examine the historical dividend yield of the S&P Dividend Growers Indices. Compared with their benchmarks, the full-period average yield of the S&P U.S. Dividend Growers Index was 0.35% higher, while the S&P Global Ex-U.S. Dividend Growers Index was 0.55% lower.

Let’s recall that the objective of the index is to track companies that consistently raise dividends while excluding the top 25% highest yielding. The goal was not to generate superior yield but to select companies that are well managed and demonstrate sustainable growth. Thus, while the realized yield of the S&P Global Ex-U.S. Dividend Growers Index has generally been lower than its benchmark, this approach has produced superior total returns on absolute and risk-adjusted bases.

While dividend-paying stocks offer yield and the potential for price appreciation, not all dividend stocks are the same. Furthermore, certain dividend strategies may not be designed with the goal of earning a higher yield but rather greater price appreciation or lower risk. Overall, dividend growers tend to be higher-quality companies that exhibit superior risk-adjusted return and tend to outperform during down markets.

While India has taken to passive investing in a big way, there are still a few hurdles preventing serious growth. S&P DJI’s Tyler Carter & Ved Malla explore how passive’s rise in other markets could help inform a path forward in India.

Learn more about passive investing in India: Charting a Course for the Continued Rise of Passive in India

Earlier this year, S&P DJI launched three S&P Quality, Value, and Momentum Top 90% Multi-factor Indices (the “S&P QVM Top 90%” Indices) across our large-cap, mid-cap, and small-cap universes. Each of these indices is tracked by an ETF.

Compared to S&P DJI’s other flagship multi-factor indices, this new series represents a differentiated approach to multi-factor index construction because it selects a high percentage of the universe and weights proportionally to float market cap. Based on internal back-tested research, this approach historically demonstrated moderate outperformance while retaining many of the core benchmark characteristics.

Methodology Overview

The S&P QVM Top 90% Indices are designed to track companies in the top 90% of their respective underlying index universe, ranked by their multi-factor score, which is based on the average of three separate factors: quality, value, and momentum.

Back-tested data shows that removing the lowest-ranked decile led to performance improvement. Exhibit 1 shows the cap-weighted return of each decile in the S&P 500^®. Here, stocks have been ranked by their multi-factor score, placed into deciles (D1 = the lowest ranked, D10 = the highest ranked), and rebalanced quarterly.

For the S&P 500, the lowest-ranked decile exhibited the lowest performance over the period tested. Similarly, for the mid- and small-cap universes, the bottom decile has been the lowest, or close to lowest, performing decile.

Moreover, removing only the lowest decile also resulted in improved returns over the benchmark at relatively low tracking error. Exhibit 2 plots the ratio between excess returns over the S&P 500 and its resulting tracking error for a series of indices, each differentiated by the number of deciles removed. For example, T90% removes only the lowest-ranked decile (ranked by multi-factor score), T80% removes the two lowest-ranked deciles (i.e., the 20% lowest-ranked stocks), and so on.

Generally, as further deciles were removed from the back-tested strategy, the increase in tracking error did not result in proportional gains in excess returns. This is represented in Exhibit 2 by the slope of the line.

Headline Performance Statistics

The index construction methodology is such that the potential for substantial outperformance over the benchmark is limited, but so is the risk of significantly underperforming. Exhibit 3 shows the back-tested risk/return statistics for each of the S&P QVM Top 90% Indices. Since the start of the back-test period, the large-, mid-, and small-cap S&P QVM Top 90% Indices outperformed their benchmarks by 74, 107, and 107 bps per year, respectively, each with low tracking error.

Benchmark Characteristics

Removing only the lowest-ranked decile and weighting stocks proportionally to float market capitalization results in the S&P QVM Top 90% Indices having retained many of the qualities of the underlying benchmark. The back-tested analysis in Exhibit 4 shows that the active share, tracking error, and turnover historically remained low across the cap range.

Conclusion

The design of multi-factor strategies affects the expected performance characteristics and impacts positioning within a portfolio. For the S&P QVM Top 90% Indices, the construction methodology demonstrates moderate outperformance while retaining “benchmark-like” characteristics.

How does viewing companies through the prism of price-to-sales influence risk/return? Explore a systematic approach to indexing factors with a focus on valuations with Justin Lowry, President and Chief Investment Officer at Global Beta Advisors and S&P DJI’s Michael Mell.