Indexology^® Blog

The S&P GSCI (WTI) Crude Oil posted a 3-day gain of 14.4% ending Feb. 17, 2016. This is the biggest 3-day gain in about 6 months for the index, and gains of this magnitude have only happened near oil bottoms.

After the index hit its lowest since Nov 4, 2003, on Jan. 20, 2016, it capitulated, reaching near that level again, a few days ago on Feb. 11. Then oil spiked on news of the possibility of a production freeze from OPEC and non-OPEC producers, but quickly waned after hopes diminished and the reality of the lack of potential impact hit.  OPEC has the ability to be the swing producer given its large market share, spare capacity, low production costs and capability of acting alone or in a cartel; however, U.S. inventories need to be low for it to matter.

The production coordination of U.S. shale producers is difficult since there are too many players that are always aiming to produce as much oil profitably as possible. Today, the American Petroleum Institute  reported an unexpected decline in U.S. inventories that excited the market.

Without the decline in inventories from the U.S., the potential impact of an OPEC production freeze is far less. This makes the U.S. inventory arguably more important than the production decisions of the swing producer, Saudi Arabia, but as the U.S. inventory drops, the picture may shift the oil price power back towards Saudi Arabia.

Financial advisors who implement style investing seek to outperform the blended benchmark whenever they believe that market conditions will favor either a growth or a value approach.  Their view could be influenced by research, fundamental factors, or technicals.  To implement such a view does not require 100% conviction in either growth or value.  In fact, some prefer to “tilt” toward growth or value by maintaining a reduced position in the less-favored style.  As a hypothetical example, advisors have shown me portfolios with a 60% weighting in value and 40% in growth.  Such an example overweights value stocks compared to the blended benchmark.

If we accept that some style investors “tilt” as their implementation technique, then we recognize that these advisors are creating a mixed basket of stocks that have a combination of growth and value characteristics.  In accepting this mix, many advisors overlook that the overlap in styles is more profound than they may expect based on a “technical” term that I call “the muddle in the middle.”

This “muddle in the middle” is a side effect of the way most index providers create style baskets.  Using the S&P 500^® as an example, Exhibit 1 shows how the S&P 500 Growth and S&P 500 Value are constructed.

While this index construction technique has existed for many years and is widely practiced, it is less understood that the stocks in the middle, or blend basket, are assigned to both the growth and value indices.  This “muddle in the middle” currently leads to 172 overlapping stocks in the S&P 500 Style Indices that don’t have strong growth or value characteristics (see Exhibit 2).

The S&P Pure Style Indices were created to measure concentrated style expressions while eliminating the blend basket.  The S&P Pure Style Indices measure, select, and retain only the strongest growth and value stocks and weight the constituents in accordance with their style score.  The two approaches to basket selection can be compared in Exhibit 3 with the S&P Style Indices on the left and the S&P Pure Style Indices on the right.

The S&P Pure Style Indices seek to create style baskets without any stock overlap.  The S&P Pure Style Indices eliminate the “muddle in the middle” problem for style investing (see Exhibit 4).

The ramification for advisors employing a style tilt is that the same 60% value and 40% growth tilt imagined before now results in zero stocks overlapping in the resulting portfolio, instead of 172.  S&P Dow Jones Indices has over 10 years of live index history for the S&P Pure Style Indices (launched on Dec. 16, 2005) that can be shared with advisors in the S&P 500, S&P MidCap 400^®, and S&P SmallCap 600^® size classifications.

Sam Stovall, Equity Strategist at S&P Global Market Intelligence, plans to cover S&P Pure Style Indices as Smart Beta in one of his “7 Rules of Wall Street Strategies” at our FA Forum in Miami on Feb. 24, 2016.  Advisors may register at no cost to attend in person or by live streaming.

One sign that people are worried about a recession is getting questions about one on days the market rises.  There seem to have been more down days than up days since the start of the year and the recession question is being asked more on all days.

The current recovery is weaker than other recent rebounds. The first chart compares the last several recoveries and expansions by indexing each one to 100 at the quarter of the pre-recession peak.  The horizontal axis is the time dimension measured in quarters. The thick black line at the bottom is the current recovery. The low point took a bit longer to reach than some other recession/recovery cycles; the track is below all the other cycles shown on the chart.

To paraphrase Mark Twain, predicting recessions is quite difficult, especially about the future.  One theory popular in the press is that as recoveries get older they get weaker and eventually collapse into the next recession. Though this may be an appealing analogy to nature, the statistics don’t support it.  Research, including a recent note published by the San Francisco Federal Reserve Bank, shows that since the end of the Second World War, there is no link between longevity and the timing of the next recession.  Then there is the stock market which Paul Samuelson noted successfully predicted nine of the last five recessions. Doubters can examine the second chart which shows the S&P 500 and the recessions since 1947.

In fact, most recessions weren’t even recognized until a few months after they began.  At the moment recession fears seem to be overdone – the unemployment rate is falling, weekly initial unemployment claims are far below any danger zone and consumers are busy buying houses and cars.

I will speak more on recessions and the outlook during my keynote at the upcoming CBOE Risk Management Conference.  Register: http://www.cboermcus.com/

After identifying value and low volatility as factors that can effectively explain the return and volatility of an investment-grade corporate bond portfolio, we proposed a two-factor model to capture the security selection process of active corporate bond managers.

The underlying universe for our study is the S&P U.S Issued Investment Grade Corporate Bond Index from June 30, 2006, through Aug. 31, 2015.  To enhance the liquidity profile and tradability of the index portfolio, we first derived an investable subuniverse, applying seasoning and outstanding amount as criteria.  Of that investable subuniverse, the bonds were then divided into groups based upon their effective duration and credit rating.  Within each group, bonds were selected by credit spread and low volatility factors; bonds with Libor OAS wider than the median level of the group were ranked by yield volatility, and only the 20% of those ranked bonds with the lowest volatility were then selected.  The weights of selected bonds were designed in such a way that the weight for each grouping matched that of the underlying base universe.

Utilizing a volatility factor is a key step in the index component selection process.  Without the overlay of this factor one is simply picking the cheapest bonds with the widest OAS and, therefore, most likely piling on credit risk.  Screening first by OAS results in a pool of bonds that have higher potential for spread tightening and better carry.  The subsequent low-volatility screening is designed so that bonds with less risk, as demonstrated by their trading pattern, are selected, while duration and credit rating are held equal.  One can think of our selection process as identifying the cheapest bonds with wide credit spreads that are not justified by their historical trading volatility in their respective duration and credit groups.

Exhibit 1 shows the improved risk-adjusted return of the two-factor portfolios versus the underlying universe.  The Sharpe ratio for the two-factor model improves to 1.15 (monthly rebalancing) or 1.05 (quarterly rebalanced) from 0.87 of the base universe.  The two-factor model also provides better downside protection, demonstrated by a maximum drawdown of 13.28% (monthly rebalancing) or 13.69% (quarterly rebalanced) compared to 14.57% of the base universe.

The calendar year (CY) 2015 started with high optimism among domestic and international investors that the recently (September 2014) formed Modi Government would be able to push a reform agenda to help clock GDP growth in India from 8%-10%, if not more. The high expectations could be seen as the S&P BSE SENSEX reached an all-time high of 30,024.74 on March 4, 2015.

Exhibit 1: S&P BSE SENSEX Performance During CY 2015

Key macroeconomic factors such as falling inflation, falling crude oil prices, current account deficits, and fiscal deficits showed improvement during CY 2015. However, dismal corporate earnings, a poor (or below average) monsoon season, fear of the U.S. Fed increasing interest rates, and concern over the expected slowing of China’s economy were the negative factors for India’s economy throughout the year.

For CY 2015, the S&P BSE SENSEX noted a total return of -3.68% (and price return of -5.0%). The March quarter (Q1) showed the best total return, at 1.85%; in line with global stock markets, the September quarter (Q3) noted the worst total return, at 5.45%, which was primarily on account of growing concern over the slowing of the Chinese economy.

Let’s See the Contribution of Stocks and Sectors to the S&P BSE SENSEX Total Returns During CY 2015

Stock Contribution

As opposed to other information technology stocks in the index, Infosys Ltd. noted an impressive total return of 14.79%, contributing the most out of all other constituents during the CY 2015 and helping the S&P BSE SENSEX gain 1.15% for the year. Reliance Industries, with its enhanced oil-refining margins and high expectations for the launch of Reliance Jio (an upcoming provider of mobile telephony, broadband services, and digital services), noted positive total returns of over 15%, which pulled S&P BSE SENSEX up by 0.97% at the end of 2015.

The S&P BSE Bankex was down by over 9% during 2015, which can be seen from leading bank constituents that are part of the S&P BSE SENSEX, such as the State Bank of India and ICICI Bank Ltd.; each were down by over 25% and dragged the S&P BSE SENSEX down by 1% and 2%, respectively. However, the HDFC Bank noted total returns of over 14%, helping S&P BSE SENSEX gain by 1.12% for the year.

BSE Sectors Contribution

Historically, the service sectors such financials and information technology have had significant weights in the broader Indian market and S&P BSE SENSEX.

Out of the 10 BSE sectors, information technology, consumer discretionary, and energy were the only sectors that pulled the S&P BSE SENSEX up in CY 2015. Impressive performance by Infosys Ltd, the falling Indian rupee, low commodity prices, and falling crude oil prices were considered the key reasons for the good performance of these sectors.

Industrials, financials, and materials pushed the S&P BSE SENSEX down the most out of the other BSE sectors. Dismal corporate earnings, low credit growth, rising NPAs, and falling commodity prices were the potential causes of poor performance by these sectors.

Exhibit 4: BSE Sector Contribution to the S&P BSE SENSEX Total Returns During CY 2015 

Outlook

Despite global concerns, India is considered by many to be a bright spot in the coming years (for the medium to long term), assuming internal demand picks up and macroeconomic factors improve; however, there are differences of opinions over the short-term outlook.

The first month of 2016 noted negative total returns of 4.75%, which was the third consecutive month with negative returns. Many hopes have been set on the passage of important legislation such as the GST and Land bills, as well as on the coming budget at the end of February.