Indexology^® Blog

With the stock market in the midst of a historic slump, many investors may be looking to alternatives to protect against a prolonged downturn. The S&P Strategic Futures Indices are designed to measure the performance of passively constructed, liquid, and transparent solutions by spreading risk evenly across global futures markets utilizing a long/short trend-following strategy to deliver results with little correlation to traditional markets. The recent performance of these indices has reflected their usefulness in providing liquidity and capital preservation during broad market downturns (see Exhibit 1).

The unlevered risk of these indices has historically been nearly half that of U.S. equities (see Exhibit 2).

The S&P Systematic Global Macro Index (SGMI) has had a relatively modest correlation to the S&P 500®, while the S&P Dynamic Futures Index (DFI) and the S&P Strategic Futures Index (SFI) have been negatively correlated to equities (see Exhibit 3). Low and negative correlations can make these strategies attractive to investors looking to diversify their portfolios and preserve capital during periods of broad equity market stress.

While the absolute performance of the S&P Managed Futures Indices was modest over most of the past decade, their performance during equity market drawdowns has been admirable. During the drawdown of close to 50% in the S&P 500 during the global financial crisis, all three indices rallied, and the same has occurred in the first quarter of 2020 (see Exhibit 4).

There are a number of advantages of passive managed futures strategies. Passive strategies may offer an enhanced level of liquidity and lower fees as compared with active managed futures strategies and other alternative strategies, such as real assets and private equity. The transparent, rules-based approach of passive managed futures strategies also improves the ease with which investors can track and benchmark relative performance. Style drift has become a major concern of investors in the managed futures space; many fear managers have made changes to their investment processes over recent years to improve short-term performance relative to the bullish equities market. Passive managed futures solutions based on an index eliminate the risk of style drift.

Finally, from a benchmark perspective, the S&P Strategic Futures Indices seek to represent the performance of a pure strategy, not the fund of fund approach adopted by other benchmarks that combine the actual performance of individual managed futures strategies.

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

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.

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