Investment Themes

Sign up to receive Indexology® Blog email updates

In This List

Most Major Islamic Indices Lag Conventional Benchmarks in 2016 as Strong Q4 Financials Sector Gains Detract From Performance

A New Metric for Smart Beta: The Cost-Adjusted Factor Efficiency Ratio

Smart Beta in India

Positive Commodity Years Typically Don't Show Up Alone

Remarkably Unremarkable

Most Major Islamic Indices Lag Conventional Benchmarks in 2016 as Strong Q4 Financials Sector Gains Detract From Performance

Contributor Image
Michael Orzano

Senior Director, Global Equity Indices

S&P Dow Jones Indices

two

Most of S&P Dow Jones Indices’ Shariah-compliant benchmarks lagged their conventional counterparts for the year, as the financials sector—which is largely absent from Islamic indices—outperformed, and health care—which tends to be overweight in Islamic Indices—was the worst-performing sector globally.

capture

The S&P Global BMI Shariah and Dow Jones Islamic Market World finished the year up 4.2% and 3.8%, respectively, lagging their conventional counterparts by approximately 2% each.  Meanwhile, in the U.S., the S&P 500® Shariah gained 6.3% in 2016, underperforming the conventional S&P 500 by 320 bps.  The Dow Jones Islamic Market (DJIM) Europe Index and DJIM Asia/Pacific Index performed relatively close to their conventional counterparts.

U.S. Equities Lead All Major Regions for the Year
U.S. equity markets led all major regions for the year, driven by enthusiasm following the U.S. presidential election in November.  Emerging markets experienced declines in the fourth quarter, as prospects for higher U.S. interest rates and concerns about increased protectionism weighed on emerging market currencies and equities.  Despite weakness late in the year, the DJIM World Emerging Markets Index finished 2016 up 8.5%.  Europe was the only major region to close the year in the red, as uncertainty over BREXIT and continued economic weakness contributed to declining equity markets.

capture

MENA Equities Close 2016 Strong, Reversing Losses From Earlier in the Year
After declining nearly 8% through the end of September, the S&P Pan Arab Composite gained 12.6% in the fourth quarter, closing the year up 3.7%.  The S&P Pan Arab Composite Shariah gained an even stronger 16.5% in the fourth quarter, outperforming the conventional S&P Pan Arab Composite by nearly 3% in 2016 due to the S&P Pan Arab Composite Shariah’s greater exposure to Saudi Arabia.

The posts on this blog are opinions, not advice. Please read our Disclaimers.

A New Metric for Smart Beta: The Cost-Adjusted Factor Efficiency Ratio

Contributor Image
Daniel Ung

Director

Global Research & Design

two

With an increasing number of smart beta strategies that track the same factor in the marketplace, it is more important than ever to understand the underlying drivers of risk and return of these strategies, which can vary greatly.  This is because the underlying portfolio construction of these strategies determines risk and return and, ultimately, the factors to which a portfolio is exposed.  Portfolio construction also determines how investable a strategy is, and this is often manifested through both financial and non-financial costs.  For example, consider two strategies that are all but identical except their rebalance frequency.  The strategy that rebalances more frequently may have a higher factor exposure, but it is also likely to rack up higher transaction costs.  For this reason, if having the maximum possible factor exposure is one of the portfolio objectives, then looking at factor exposure via a risk model may be useful in understanding how much risk exposure you obtain from a strategy—but this should be seen in the context of how much cost is incurred in the process of achieving that exposure.

To that end, we have come up with the cost-adjusted factor efficiency ratio (ca-FER), which seeks to address this trade-off.  This new metric is built on Hunstad and Deskahyer’s[1] factor efficiency ratio (FER), and it may be used in conjunction with other criteria that are already at the disposal of market participants to judge smart beta portfolios.

DOES MORE CONCENTRATION ALWAYS MEAN HIGHER FACTOR EXPOSURE?

Moving away from the benchmark is necessary, but portfolio concentration alone may not yield exposure to the desired factor, in terms of the percentage of active risk taken on a total basis.  Exhibit 1 indicates how the level of FER in relation to the momentum factor, portfolio turnover, and amount of risk derived from non-momentum common factors changed for portfolios with a varying number of stocks.  All these stylized portfolios have the same aim: to maximize the amount of momentum exposure as far as possible by conducting optimizations via the Northfield U.S. Fundamental Equity Risk Model.

As can be expected, when we move away from the benchmark, the level of momentum exposure initially increases with fewer stocks in the portfolio, and this comes with a higher portfolio turnover rate.  Meanwhile, active risk derived from exposure to other common factors (excluding momentum and industry risks) also gradually rises with portfolio concentration and eventually overtakes the amount of risk derived from momentum, which is our targeted exposure.

Consequently, “high conviction” concentrated portfolios may experience a double whammy effect.  If they are too concentrated, they may experience falling efficiency to the targeted factor, and they may rack up higher portfolio turnover as well.

For more details, see our research paper Smart Beta Efficiency Versus Investability.

capture

[1]   Hunstad M. and Dekhayser J. (2015), Evaluating the Efficiency of “Smart Beta” Indexes, The Journal of Index Investing, Summer 2015, Vol. 6, No. 1: pp. 111-121.

The posts on this blog are opinions, not advice. Please read our Disclaimers.

Smart Beta in India

Contributor Image
Akash Jain

Associate Director, Global Research & Design

S&P BSE Indices

two

2016 has been an unpredictable year on many fronts, whether it was Leicester City FC winning the Premier League, the Brexit, or the U.S. election results.  In India, “demonetization” and the Goods and Services Tax (GST) are fundamentally altering fund managers’ target portfolios.  Active institutional fund managers have the benefit of professionally run research teams.  The question is, therefore, how do individual market participants churn their portfolios in times of such volatility?

Many active portfolio managers have been adopting risk factors to achieve portfolio diversification and deliver excess returns.  These common risk factors include size, dividend, volatility, momentum, quality, and value.  In recent years, an increasing number of passive investment products have been designed to capture the potential benefits of factor-based investing (also referred to as “smart beta”) as well as the transparency and cost effectiveness of passive investing.

We recently published a report called Factor Risk Premia in the Indian Market, which studies the risk/return characteristics of common risk factors in the Indian equity market.  The research analyzed four common equity risk factors—low volatility, risk-adjusted momentum, quality, and value—based on the S&P BSE LargeMidCap universe back-tested from Sept. 30, 2005, to April 30, 2016.  Using the monthly return of the S&P BSE LargeMidCap to define up and down markets, we summarized the performance of different factors under these two market conditions.

The low volatility portfolio delivered significant excess return in the overall period, and the excess return was more pronounced during down markets.  The quality portfolio, which was constructed using a combined score on return on equity (ROE), the balance sheet accruals ratio, and the financial leverage ratio, demonstrated similar defensive characteristics as the low volatility portfolio.  In contrast, the value portfolio, constructed using book-to-price, earnings-to-price, and sales-to-price ratios, tended to outperform during up markets but significantly underperforms in down markets.  The risk-adjusted momentum portfolio did not deliver significant excess returns in the overall period, despite significantly outperforming the benchmark during down markets.

The analysis shows that different risk factors in the Indian equity market have distinct characteristics and, therefore, they can be used for the implementation of active investment views.  Moreover, blending risk factors with low return correlation may also provide portfolio diversification to mitigate risk.

smart-beta-blog-2016

 

Above risk factor portfolios are hypothetical equal-weight portfolios.

Source: S&P Dow Jones Indices LLC.  Performance data is based on total return in INR.  Data from Sept. 30, 2005, to April 30, 2016.  Past performance is no guarantee of future results.  Table is provided for illustrative purposes and reflects hypothetical historical performance.  Please see the Performance Disclosure available in the research paper for more information regarding the inherent limitations associated with back-tested performance.  Up months are those months when the float-market-cap-weighted S&P BSE LargeMidCap had positive returns.  Down months are those months when the float-market-cap-weighted S&P BSE LargeMidCap had negative returns.  Percentage of months that outperformed the market and average monthly excess returns were calculated using the float-cap-weighted S&P BSE LargeMidCap as the benchmark.

*Implies significance at a 5% level.

The posts on this blog are opinions, not advice. Please read our Disclaimers.

Positive Commodity Years Typically Don't Show Up Alone

Contributor Image
Jodie Gunzberg

Managing Director, Head of U.S. Equities

S&P Dow Jones Indices

two

Commodities ended 2016 by posting the first positive returns in 4 years. The S&P GSCI Total Return gained 11.4% and the DJCI (Dow Jones Commodity Index) gained 13.3%.  Energy was the best performing sector gaining 18.1% in the S&P GSCI, and livestock performed worst, losing 7.3%. Agriculture, industrial metals and precious metals returned -4.2%, 17.6%, and 8.4%, respectively. It was the energy sector’s best year since 2007, when it gained 41.9%. It was also the industrial metal’s best year since 2009, when it gained 82.4%. The comeback in these two sectors together is meaningful since they are the most economically sensitive. The last time these two sectors were up this much together after two consecutive negative years was in 1999, that led commodities to return roughly 360% through 2007.  Through history since 1970, the average number of consecutive positive years is 3.5 years with rarely a single positive year.

Source: S&P Dow Jones Indices
Source: S&P Dow Jones Indices

In 2016, there were 17 positive commodities of 24 total, that is the most since 2010 when 21 were positive. It is also the greatest comeback (from only 2 positive commodities last year, namely cocoa and cotton) since the 2009 improvement when 18 were positive after only 2 gained in 2008 – cocoa and gold.  This year, cocoa was the worst performing commodity, losing 33.0%, while zinc performed best, gaining 57.4%. For cocoa, 2016 was the 3rd worst on record but it was the 3rd best year for zinc.  Despite some relatively high and low rankings, the overall index performance rank fell exactly in the middle of its historical performance ranking both its 24th best and 24th worst year.  Also, although many commodities of energy and industrial metals did well, the sectors’ returns only ranked as their 14th (energy) and 12th (industrial metals) best years.

Source: S&P Dow Jones Indices
Source: S&P Dow Jones Indices

One potential concern is there were only six commodities (cocoa, cotton, feeder cattle, natural gas, sugar and wheat) in backwardation in December which is just below the average number of 6.8 for this time of year, which means there still needs to be more inventory drawn down before there are persistent shortages.

However, while the S&P 500 beat the S&P GSCI in 2016, by 59 basis points, extending the commodity consecutive annual under-performance to a new record of nine years, the gap is closing that could indicate a turning point in the cycle for commodities.

The three most powerful influences on commodities in 2017 are OPEC’s ability to manage oil production in the face of US competition, Trump’s impact on inflation and growth, and a potential weakening dollar. 

OPEC

  • OPEC’s decision gave energy a huge bump up in the last month, its biggest gain since April, but the upside can be capped depending on three things: 1. Whether all the participants follow through on the agreement.
  •  How the US producers respond  – since US inventories need to be low for OPEC’s decision to matter.
  •  Whether China might slow buying as prices rise – or even worse, start exporting their stockpiles which are somewhat unknown. (China could also put a stop on the metals rally from exporting stockpiles – like it did on the nickel rally in 2014).

Trump and Inflation

  • Historically Republican presidencies are favorable for grains and gas which are key to rising inflation. Moreover, copper, lead and nickel have had their best performance with Republicans. That in itself does not promise growth since rising inflation from commodity prices can cause stagflation – BUT stagflation is less likely if Trump builds infrastructure and jobs growth which may propel GDP and help the metals.
    • Industrials have outperformed as much as 15% annualized on average of industrial metals during Republican (over Democratic) rule.
    • Also,  industrial metals outperformed precious metals last month by the most in 26 years. This is considered extremely bullish. The bullish sentiment is also showing itself in the falling correlation between the metals, meaning investors are specifically seeking growth in industrial metals instead of hiding in the safe haven of gold.

Weakening Dollar?

  • Although the dollar theoretically should rally from rising rates, that relationship hasn’t held. So, even if rates rise more, there is a chance the dollar will revert after continually hitting new recent highs – that can lift commodities substantially. In fact, every single one of the 24 commodities we track rises from a falling dollar, especially industrial metals which can rise as much as 7% for every 1% the dollar falls.

 

 

 

 

The posts on this blog are opinions, not advice. Please read our Disclaimers.

Remarkably Unremarkable

Contributor Image
Fei Mei Chan

Director, Index Investment Strategy

S&P Dow Jones Indices

two

In geopolitical terms 2016 was a tumultuous year. From the outcome of the Brexit referendum to the surprising conclusion of the U.S. presidential election, 2016 was a year of political surprises. The markets, braced or not, reacted differently in each case. We saw heightened correlation in the aftermath of Brexit and observed higher dispersion immediately after the U.S. presidential election.  Heightened dispersion and/or correlation levels can accompany market weakness, but in both of these cases, the markets rallied and dispersion and correlation readings returned to average levels in fairly short order.

Despite being buffeted by remarkable political events, 2016 looks unremarkable in terms of our dispersion-correlation map. For the U.S., November’s spike in dispersion after the election was resolved by the end of December. Dispersion returned to below-average levels and, for all of 2016, dispersion and correlation essentially plot at the midpoint for the last 26 years. One would be hard-pressed to find a more nondescript point. It’s a very similar story in Europe, even though the U.K.’s exit from the European Union is a work in progress. In Asia, dispersion is lower than average while correlation is right around average.  Market dynamics can certainly change quickly, but dispersion levels suggest that adding value by active management will continue to be challenging.

Dispersion-Correlation Maps

remarkably-unremarkable1 remarkably-unremarkable2 remarkably-unremarkable3

The posts on this blog are opinions, not advice. Please read our Disclaimers.