Indexology^® Blog

In November 2017, according to the Energy Information Administration, U.S. crude oil production surpassed 10 million barrels per day for the first time since November 1970. After 1970, it began to decline, dropping bellow 7 million barrels per day in February 1993, below 6 million barrels in March 1999, and then remaining at that level until November 2011, when it began steadily increasing.

How has the performance of crude oil futures contracts fared in comparison to crude oil production? The S&P GSCI Crude Oil was launched on May 1, 1991—with history dating back to 1987—to provide market participants with a reliable and publicly available benchmark for investment performance in the crude oil market.

Exhibit 1 depicts the performance of the S&P GSCI Crude Oil and the daily barrel production of U.S. crude oil from January 1987 until November 2017.

The two series had a negative correlation of 0.23 for the period studied and low or negative annual correlation, which implies that as overproduction and -supply occur in the market, futures prices tend to decline, a pattern that can be more clearly seen after September 2011.

Can increases or decreases in production be seen in the roll yield? The answer is yes and no. The roll yield or roll return, as measured by the excess return of the index minus the spot return, is the difference in the price of the expiring contract and the next eligible contract. Futures contracts expire on a regular basis, and futures-based indices must roll their positions into the next contract to maintain their exposure.

If a commodity’s forward price curve is downward sloping (in backwardation), then the roll process involves rolling into (buying) a futures contract that is trading cheaper than the current futures contract, hence a positive roll yield. However, if the commodity’s forward price curve is upward sloping (in contango), then the roll process would involve rolling into a futures contract that is trading at a higher price than the current futures contract, which results in a negative roll yield.

If the futures in the index are in backwardation regularly over time, and if the incidence of backwardation is higher relative to the degree of contango, then market participants tracking the indices will tend to profit from the roll process.

Analyzing the S&P GSCI Crude Oil roll return for the period between January 1987 and November 2017 (a total of 370 observations) showed that during this period, daily oil production increased or decreased relatively equally. The roll yield appeared more sensitive to declines in production, resulting in a negative yield almost 60% of the time, while an increase in production only resulted in a positive roll yield 43% of the time, implying that shortages or undersupply in the market was more likely to drive up prices than oversupply to weigh down on futures prices. That is likely because crude oil prices are sensitive to factors other than production, such as geopolitical risk and inventory levels.

In continuation to our previous blog titled “Factor Investing 101,” this blog investigates the performance of single factor indices in the Indian equity market. Over the period from October 2005 to June 2017, portfolios for all risk factors we examined—low volatility, momentum, value, quality, dividend, and size (small cap)—outperformed the S&P BSE LargeMidCap (see Appendix A for methodology of factor portfolios). However, only low volatility, quality, and momentum delivered better risk-adjusted return (return per unit of risk) than the S&P BSE LargeMidCap. Among the six factors, low volatility and quality recorded lower return volatility than the benchmark and had the highest risk-adjusted return, while value, dividend, and size displayed much more volatile return than the benchmark (see Exhibit 1).

Macroeconomic and market events affected each factor portfolio in different ways. Factor returns tended to exhibit cyclicality with periods of outperformance and underperformance in different phases of the cycles. In our report, we also examined how various risk factors performed in the Indian equity market across different macroeconomic regimes between October 2005 and June 2017.

Based on our factor performance analysis across business cycles,[1] we observed that value, dividend, and size exhibited strong pro-cyclical characteristics and tended to outperform the benchmark when business activities expanded. In contrast, low volatility, quality, and momentum outperformed the benchmark in both cycle phases but with a higher tendency to outperform the benchmark during business cycle contraction (see Exhibit 2). 

Apart from business cycles, factors also displayed different cyclical behavior across market cycles[2] that we divided into bearish, recovery, and bullish phases based on historical price trends of the S&P BSE SENSEX. Quality and low volatility tended to perform the best in bearish markets. Conversely, value, dividend, and size gained the highest excess returns when the market recovered from equity market troughs. In bullish markets, momentum had the strongest performance among all factors see Exhibit 3).

In addition, we also studied factor performance over investor sentiment regimes, which changed more frequently than market and business cycle phases. We used the rolling 22-day realized return volatility of the S&P BSE SENSEX Price Return as a proxy to measure investor sentiment in the Indian equity market. We divided the examined period into three sentiment regimes: bullish, neutral, and bearish. Bearish investor sentiment is signaled by high levels of realized volatility (values in the bottom decile), whereas bullish investor sentiment is represented by low realized volatility values (values in the top decile), and neutral investor sentiment makes up the periods when the realized volatility values lie between the top and bottom deciles. When market participants were bullish, results showed that value delivered the most excess return, while low volatility had the worst performance. In contrast, momentum and size underperformed, and high-quality stocks were favored by market participants when they were bearish. 

Source: S&P Dow Jones Indices LLC. The S&P BSE Dividend Portfolio and S&P BSE Equal-Weighted Portfolio are hypothetical portfolios. Data as of October 2017. Table is provided for illustrative purposes.

Please refer to Factor Performance Across Different Macroeconomic Regimes in India for more information on this research paper.

[1]   A business cycle is defined by the monthly movement of the Organisation for Economic Co-operation and Development (OECD) Composite Leading Indicator (CLI) for India. A rising CLI signals business cycle expansion and a falling CLI signals business cycle contraction (see Appendix A in report for the OECD Composite Indicator business cycles).

[2]   A bearish phase is defined as a period during which the S&P BSE SENSEX goes from peak to trough.  A recovery phase is defined as the 12-month period after the S&P BSE SENSEX trough.  A bullish phase is defined as a period from the end of the recovery phase to the next S&P BSE SENSEX peak (see Appendix B in report for Illustrative Market Cycles).

[3]   The eligibility criteria for the dividend portfolio require that each eligible stock maintains a ratio of dividend-per-share to par value-per-share above 10% for two consecutive years.

The story line for a number of years now has been the “search for yield” and how the recent low-interest-rate environment has been forcing investors down in credit or out the maturity curve in an effort to maintain income though adding risk. Now that interest rates have begun reversing the low-rate environment, fixed-coupon securities may experience downward price pressure to varying degrees depending on the terms of each bond. But for floating-rate securities, the story could be different. As rates have started to increase, income investors are getting the opportunity to earn higher returns on their investments when considering their options.

The U.S. Treasury began issuing floating rate notes (FRNs) in January 2014. To date, these notes have been issued for a term of two years. The FRNs pay varying amounts of interest quarterly until maturity. Interest payments rise and fall based on discount rates in auctions of 13-week Treasury bills. The S&P U.S. Treasury Bond Floating Rate Index and the S&P U.S. Treasury Bond Floating Rate Current 2-Year Index seek to measure the performance of current and previously issued U.S. Treasury floating-rate issuance representing the U.S. Treasury floating-rate market or the most recent 2-year issuance.

Exhibit 1 shows how the yield of these indices had been relatively flat since their inception, which starts with the U.S. Treasury’s inaugural issuance. From October, 2015 to Feb. 13, 2018, the index yields increased from 0.09% and 0.13%, respectively, to their current levels of 1.55% and 1.58%, respectively.

Total rate of returns, of floating-rate indices, have outperformed the similar duration  S&P U.S. Treasury Bill 3-6 Month Index, which has returned 0.01% for the month and 0.13% YTD as of Feb. 13, 2018 (see Exhibit 2).
Exhibit 1: Historic Yield-to-Worst

Exhibit 2: Total Rate of Returns

As anticipation continues for future rate hikes, FRN performance could further benefit from the interest rate changes. There are strategies designed to meet the needs of those looking to gain income as rates rise.

The S&P 500^® is widely considered one of the best single gauges of the U.S. equity market. Composed of 500 companies that are domiciled in the U.S., the index captures approximately 82%[1] of the total U.S. equity market value. An index of U.S. companies may lead one to assume that the index is only reliant on the health and growth of the U.S. economy. In reality, the index is much more global than that. Many U.S. corporations have a global presence, with assets and revenues in foreign markets. Therefore, global market events and economic shocks can have a material effect on S&P 500 companies, thus overall index performance.

To better understand where S&P 500 companies’ revenues are coming from, we used the FactSet Geographic Revenue Exposure (GeoRev^TM) dataset,[2] which gives a geographic breakdown of revenues for all companies with available data, down to the country level. Since there are no standardization rules for the reporting of geographic revenue segments, the dataset uses a normalization/estimation process to assign revenues to specific countries. The ability to have detail of revenues at the country level is an important tool in understanding potential country exposures or risks for a company or index.

We first look at total revenues at the regional level (see Exhibit 1). The Americas region, which combines the North and South American continents, is unsurprisingly the largest regional exposure, at 76% of total revenues. The Asia Pacific region (11.1%) and Europe (10.6%) follow in terms of total revenue, with Africa & Middle East having the lowest total revenues, at 2.4%.

At the country level, nearly 71% of S&P 500 revenues comes from the U.S., with the remaining coming from foreign markets. Internationally, the largest individual countries by total revenue include China (4.3%), Japan (2.6%), and the UK (2.5%).

Given the diverse mix of countries, it is important to examine the potential foreign currency exposure of the S&P 500, which we determined by mapping the currency used in each country (see Exhibit 3). Several additional observations can be made from the currency-based revenue chart. First, it shows that the euro is the foreign currency the S&P 500 has the most exposure to, coming in at 6%. Second, the chart shows the number and the mix of currencies the S&P 500 has exposure to—six foreign currencies with a total exposure of 1% or more and 19 currencies coming in at a minimum of 0.25%.

Given the results, we can see that the S&P 500 has meaningful exposure to foreign markets. As such, events both domestic and globally, as well as policies that change the dynamic between the U.S. and other foreign markets, can potentially have an effect on the S&P 500. In future posts, we will look at how the global market exposure of companies in the S&P 500 affect the performance of the index.

[1]   Source: S&P Dow Jones Indices LLC. Calculation as of Dec. 29, 2017.

[2]   More information on the dataset can be found here: https://www.factset.com/data/company_data/geo_revenue

On May 16, 2014, the Lok Sabha election results were announced and Mr. Narendra Modi’s Bharatiya Janata Party got a clear mandate to form the government. Narendra Modi was sworn in as the Prime Minister of India on May 26, 2014. Since taking charge, the Narendra Modi government has made several landmark policy decisions. Some of these initiatives are listed below.

1. GST: The Goods and Services Tax is the biggest tax reform since Indian independence.
2. Aadhaar Linking: Linking Aadhaar to bank accounts, PAN Card, mobile number, etc.
3. Jan-Dhan Yojana: Aimed at bringing banking services to every household in India.
4. EPFO Investment in ETFs: Employees’ Provident Fund permitted to invest in ETFs.
5. Demonetization: Aimed at cracking down on black money.
6. Digital India: Aimed at digitizing India and moving to cashless transactions.
7. Make in India: Aimed at making India a global manufacturing hub.
8. Skill India: Aimed at providing skill development training to youth.
9. Startup India: Aimed at promoting entrepreneurship.

The S&P BSE SENSEX is the oldest and most-tracked index in India, and it acts as an indicator of India’s economic growth. Any national or international change in economic activity is likely to have an impact on the S&P BSE SENSEX.

The S&P BSE SENSEX’s total return index value increased from 27,648.13 on Jan. 31, 2014, to 51,281.74 on Jan. 31, 2018, and the highest close was at 51,729.13 on Jan. 29, 2018. This represents a four-year CAGR of 16.70% for the period.

Every year, the Finance Minister presents the Union Budget, which is perhaps the most important economic activity in India. “Budget Day” comes with a lot of expectations, and it therefore has a bearing on the capital markets in both the pre- and post-budget sessions. Mr. Arun Jaitley has been the Finance Minister since this Government was formed. Arun Jaitley has presented four budgets during this term and will be presenting his fifth budget on Feb. 1, 2018.

In Exhibit 2, we can see that in most years, the S&P BSE SENSEX witnessed high volatility in the 30-day pre- and post-budget sessions. The highest 30-day pre- and post-budget volatility was observed in the budget year 2016. The lowest volatility in the 30-day pre-budget session was seen in 2018.

To conclude, we can say that the budget sessions are usually volatile for capital markets in India. The pre-budget movement is caused by market participant expectations for the budget, while the post-budget movement is based on the actual budget presented by the Finance Minister. The budget may be the most important economic activity affecting capital markets in India, and its relevance is captured in the movement of S&P BSE SENSEX.