Indexology^® Blog

The Dow Jones Industrial Average ended 2016 at 19,762.60 – up 2.337.57 points for a 13.42% annual return, the best year since 2013 when the market surged over 26%.

• Biggest Themes – markets were driven by the crash (and partial recovery) in oil prices, Fed-watching, China, Brexit and questions regarding the US economy. Oh yeah, there was a Presidential Election too – it was so under-reported I almost forgot to mention it.
• Leader & Laggard – Goldman Sachs (GS) contributed the most to the DJIA’s advance during the year; Nike (NKE) was the biggest detractor.
• Sector Performance – Financials were the best performing sector during 2016; the Consumer Discretionary sector posted the worst performance.
• Best Day YTD (In Points & Percent) – January 29th, an otherwise bright spot in a rather tough month.
• Worst Day YTD (In Points & Percent) – June 24th, when the UK votes for Brexit.
• New Highs – the DJIA posted 26 new highs in 2016, including two separate streaks of 7 straight records in July and December.

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The high-yield corporate bond segment, as measured by the S&P U.S. High Yield Corporate Bond Index, was the top-performing asset class for 2016, posting a total return of 17.2%.  Despite a rather tumultuous first quarter, 2016 finished with a clear “risk-on” sentiment as evidenced by the asset classes that topped the list.

On Feb. 11, 2016, it looked like quite the opposite, as high-yield bonds were down over 4%, and U.S. equities (S&P 500^®), REITs (Dow Jones U.S. Select REIT Index), and commodities (S&P GSCI) were down 11%, 12%, and 13%, respectively, to start the year.  Then, as if lifted by the price of oil bouncing off its bottom, higher-risk asset classes all entered positive teritory by the end of April 2016.  The second quarter ended with some increased volatility, as markets reacted to (and then quickly moved on from) the surprise Brexit referendum that was announced on June 24, 2016.  A brief flight to quality increased demand for U.S. Treasuries (S&P U.S. Treasury Bond Index), municipals (S&P Municipal Bond Index), and investment-grade corporates (S&P U.S. Investment Grade Corporate Bond Index), and U.S. equities briefly dipped back into the red.

Excluding commodities, the third quarter saw relativley low volatility.  Positive economic results and strong earnings from higher beta sectors propelled equities, high-yield corporate bonds, and leveraged loans (S&P/LSTA U.S. Leveraged Loan 100 Index).  The fourth quarter largely hinged on market participant sentiment leading up to and then reacting to the unexpected result of the U.S. presidential election.  Post-election, a focus on potential inflationary conditions and increased interest rates put upward pressure on bond yields.  Market participants ended 2016 favoring higher risk asset classes such as equities (S&P 500), commodities (S&P GSCI), and REITs (Dow Jones U.S. Select REIT Index).

Exhibits 2 and 3 show the total return performance of the major asset classes, taking into account their respective volatility.  In Exhibit 2, the column on the left ranks the major asset classes in terms of total return for 2016.  The column on the right re-ranks the asset classes by risk-adjusted returns, or Sharpe ratio, based on volatility (i.e., daily standard deviation throughout 2016).  The Sharpe ratio is used to compare investment options in a manner that shows total return per unit of risk.  Perhaps most surprisingly, leveraged loans (S&P/LSTA U.S. Leveraged Loan 100 Index) jumped to the top with a Sharpe ratio of 6.4, helped by extremely low volatility for the asset class.  Investment-grade corporate bonds also benefitted from relatively low volatility moving to the top-three performing asset classes on a risk-adjusted basis.  Not surprisingly, commodities (S&P GSCI) experienced the greatest amount of volatility (standard deviation of 23.8%) and fell out of the top-three on a risk-adjusted basis.

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.

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.

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.

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.

[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.

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.

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.