Indexology^® Blog

Explore how high volatility and high dispersion can impact passive and active managers’ performance with S&P DJI’s Craig Lazzara.

https://www.youtube.com/watch?v=zdU50uYbvs0

Get the latest Dispersion, Volatility, & Correlation dashboard: https://spdji.com/documents/commentary/dashboard-dispersion-2020-03.pdf

Managed futures strategies generally tend to be trend following, which means that when an individual asset shows a clear price uptrend (or downtrend), the strategy will hold a long (or short) position in the asset. The strategies use a wide variety of quantitative models that utilize highly liquid, regulated, exchange-traded financial derivatives across equity, fixed income, foreign exchange, and commodity markets.

Traditionally, managed futures strategies have been utilized by investors as a complement or alternative to active or less-liquid alternative strategies. Such strategies have been touted by investors for their ability to offer liquidity and capital preservation during periods of broad equity market malaise.

Managed futures strategies have a unique profile relative to traditional investment strategies, including:

• Long-term positive historical returns, achieved with unlevered risk levels that are on average one-half that of equities;
• Low and sometimes negative correlations to equities and other asset classes; and
• Strong historical performance during equity bear markets.

Managed futures strategies are well suited to indexing, given that they are based on transparent, rules-based quantitative models. S&P Dow Jones Indices offers three headline managed futures indices. All three reflect the price momentum of futures contracts across different asset classes.

• The S&P Strategic Futures Index (SFI) reflects the price momentum of 24 futures contracts on physical commodities, interest rates, and currencies. The index uses an enhanced rolling schedule for long commodities and applies a risk parity weighting scheme by sector.
• The S&P Dynamic Futures Index (DFI) reflects the price momentum of 24 futures contracts on physical commodities, interest rates, and currencies. It applies an equal weighting scheme between commodities and financials, and individual commodities weights are based on the S&P GSCI Light Energy.

The S&P Systematic Global Macro Index (SGMI) reflects the price momentum of 37 constituent futures contracts, covering equities, commodities, interest rates, and currencies. Each sector contributes equally to index risk, and each constituent contributes equally to the risk of the sector in order to hit a target volatility. Leverage is used to help achieve the volatility target.

In a subsequent post, we will examine the recent performance of these indices in light of the current market conditions and identify the benefits of passive managed futures strategies.

COVID-19 driven volatility has caused market participants to refocus on defensive strategies. As investors turned to quality, the S&P 500^® Quality Index demonstrated better downside protection and outperformed. Furthermore, it offered a sizable dividend yield of 2.2%. This analysis investigates attributes of the index.

Breaking Down Components

From all-time highs on Feb. 19, 2020, to April 15, 2020, the S&P 500 dropped about 18%. In contrast, the S&P 500 Quality Index outperformed its benchmark by 3.3% (see Exhibit 1).

The S&P Quality Index Series uses three components to define constituents’ overall quality scores:

• Balance sheet accruals ratio (BSA),
• Return on equity (ROE); and
• Financial leverage ratio (leverage).[1]

In Exhibit 2, we dissect the index’s performance into three components: BSA, leverage, and ROE attributions.[2] As seen in Exhibit 2, BSA had the highest contribution to the outperformance, followed by ROE.

ROE and leverage are commonly used metrics. Market participants also generally use earnings quality or growth. The S&P 500 Quality Index uses BSA to capture earnings quality[3] instead of earnings variability (EV),[4] another popular measure to capture earnings growth. Recently, investors have been more focused on earnings quality than growth,[5] resulting in BSA outperforming EV (see Exhibit 3).

Besides its outperformance, the S&P 500 Quality Index also yielded about 2.2%. This level was second only to the S&P 500 Bond Index (see Exhibit 4).

In conclusion, the S&P 500 Quality Index showed its defensive characteristics during this uncertain period. In addition, with a 2.2% dividend yield, it also provided income for investors, especially when 10-year U.S. Treasuries were yielding close to zero.

[1] The detailed factor definition and index construction are laid out in the S&P Quality Indices Methodology.

[2] S&P 500 Quality BSA Attribution: Securities in the eligible universe are selected for index inclusion based on their accruals ratio z-score determined during the semiannual rebalancing of the S&P 500 Quality Index. The values for all securities are ranked in ascending order.

S&P 500 Quality Leverage Attribution: Securities in the eligible universe are selected for index inclusion based on their financial leverage ratio z-score determined during the semiannual rebalancing of the S&P 500 Quality Index. The values for all securities are ranked in ascending order.

S&P 500 Quality ROE Attribution: Securities in the eligible universe are selected for index inclusion based on their return-on-equity z-score determined during the semiannual rebalancing of the S&P 500 Quality Index. The values for all securities are ranked in ascending order.

[3] Richardson, Sloan, Soliman, and Tuna, Accrual Reliability, Earnings Persistence and Stock Prices, Journal of Accounting & Economics, Vol. 39, No. 3, 2005.

[4] EV is usually calculated as the standard deviation of year-over-year earnings per share growth over (n-) number of previous fiscal years.

[5] We selected the top quintile (Q1) of EV factor to form a cap-weighted hypothetical portfolio using the S&P 500 as the underlying universe. The higher the EV, the less stable the earnings growth. For details, please refer to https://www.indexologyblog.com/2018/10/01/measuring-earnings-quality-balance-sheet-accruals-ratio-versus-earnings-variability/.

How can VIX data help us understand the current market environment? S&P DJI’s Tim Edwards explores what recent historical highs for VIX could mean for equity and commodity markets moving forward.

https://www.youtube.com/watch?v=ATWjE9s8ukQ

Get the latest Risk & Volatility dashboard on Indexology: https://spdji.com/indexology/risk-management/risk-volatility-dashboard

Explore how a transparent, rules-based approach to risk management is designed to offer participation and downside protection with S&P DJI’s Tianyin Cheng.

https://www.youtube.com/watch?v=M65z4NCARhg

To learn more, read Tianyin’s latest blog, “The Trade-Off between Upside Participation and Downside Protection.”