Indexology^® Blog

Demand for protection against rising costs is showing up in the S&P Global Developed Sovereign Inflation-Linked Bond Index, with the yield tumbling to -0.65%, the lowest level since April 2013.  The index has returned 1.09% MTD and 2.70% YTD, as of March 31, 2015.

Janet Yellen has been quoted saying “oil is having a transitory negative effect on inflation,” and she is taking “comfort” in the longer-term inflation expectations.  Reading between the lines, once oil normalizes, a higher inflation level (headed toward the Fed’s inflation target) is to be expected.

Source: S&P Dow Jones Indices LLC.  Data as of March 31, 2015.  Charts and tables are provided for illustrative purposes only.  Past performance is no guarantee of future results.

SPIVA Scorecards are issued every six months in a number of markets around the globe.  But what is SPIVA?  Where does the data used to generate the scorecards come from and who does it apply to?  In a recent video interview, I spoke with one of the Scorecard’s authors, Aye Soe, Senior Director of Global Research & Design, to answer these questions.

https://www.youtube.com/watch?v=llGiz8t_f5U&feature=youtu.be

By design, the S&P 500^® Low Volatility Index sometimes takes large positions in sectors.  Particularly in times of turmoil, the rankings-based methodology of the S&P 500 Low Volatility Index offered refuge by steering clear of sectors such as financials in 2008 and the technology sector during the 2000-2002 deflation of the bubble. On the flip side, there have also been times when having large sector concentrations caused a performance drag, particularly during strong markets.

However, we’ve also illustrated that while large positions in relatively less volatile sectors tend to account for most of the low volatility strategy’s overall risk reduction, it has not explained all of the S&P 500 Low Volatility Index’s success historically.

We see another, perhaps more intuitive, manifestation of this when we compare the sector holdings of the S&P 500 Low Volatility Index and its not-quite-polar opposite, the S&P 500 High Beta Index. Exhibit 1 provides a good summary of the contrast between the two strategies.  From 1992 through 2014, the low volatility strategy consistently had a significant concentration in the relatively stable utilities sector, while quite often the volatile technology sector was a significant holding of the S&P 500 High Beta Index.  Characteristically, the S&P 500 Low Volatility Index owned very little, and rarely at that, of the technology sector (likewise for the high beta strategy and the utilities sector).

However, notably, the low volatility and high beta indices’ paths crossed at the sector level more often than we would have surmised.  The two indices follow the same rebalancing schedule, and of the total 92 rebalances in the period from 1992 to 2014, they overlapped in at least one of their two highest sector allocations 23% of the time.  For example, at the end of 2014, the largest sector concentration in the S&P 500 Low Volatility Index was financials.  This sector was also the second highest concentration—by a very slim margin behind the largest sector (technology)—in the S&P 500 High Beta Index.

Reassuringly, that’s where the similarities end.  While both indices may have had top sectors in common, holdings at the stock level were virtually always mutually exclusive.  Although sectors may play a big role in both strategies, they are not just sector bet strategies.

Recently, institutional investors with long-term investment horizons have responded with aversion to market volatility by considering a number of risk control strategies.  Risk control strategies use dynamic asset allocation (between an index and cash) to target a stable level of volatility in all market environments.  For institutional investors with long-standing liabilities, ranging from defined benefit plans and variable annuities offered at insurance companies, a risk control strategy may provide a smoother path of asset returns and could more closely align the performance of the institution’s assets to the characteristics of its liabilities.

The basic idea is that the investor sets a target volatility, which is then matched by allocating funds to the risky asset and the risk-free money market.  If the realized historical volatility is above the target, money is shifted to cash.  On the other hand, if the realized historical volatility is below the target, leverage is taken in order to achieve the target.

This strategy takes advantage of the negative relationship between volatility and return, as well as the persistence of volatility.  As illustrated in Exhibit 1, the monthly volatility of the S&P 500^® is negatively correlated with its monthly returns.  This relationship is present in most equity indices.  As a result, a strategy that reduces exposure in periods when volatility is high and increases exposure in periods when volatility is low would be more likely to outperform in risk-adjusted terms over the long run.

In addition, the S&P 500 daily returns are not independent across time, as large returns tend to be followed by large returns and small returns tend to be followed by small returns.  In Exhibit 2, the sample autocorrelation function shows significant autocorrelation in the squared residual series, calculated by the square of daily total return of the S&P 500 subtracted by the long-term average daily return.  This illustrates that periods of high and low volatility tend to cluster together for extended periods of time.  Therefore, a risk-control strategy based on realized historical volatility is likely to add value over the long run as well; even though we do not forecast volatility.

Over the long term, actively managed bond funds have not outperformed their benchmarks  as evident in the SPIVA U.S. Scorecard for year-end 2014.  In a recent blog post, I analyzed the performance data of this scorecard.  Many wonder what might be causing the results to be one-sided.  For example, in a recent post on Practical Stock Investing, the author states that “the numbers are stunning” but questions what the reason is for this  “woeful showing.”   Here  are some possible explanations of why actively managed bond funds might underperform their benchmark:

• Interest rate call. Sounds easy but it isn’t and getting the timing right is equally as critical.
• Duration risk. A component of the right interest rate call is managing duration risk.  What is the funds duration target?  How will it achieve that target and when?
• Sector allocation.  For multi-asset class funds and corporate bond funds, sector weights can make a difference.  When to reallocate and place more/less weight in select sectors?
• Credit selection. A more pressing issue for corporate bond funds includes which credits should the fund invest in, how much to invest in them and when to do so?
• Turnover & transaction costs.  Indices do not take into account the costs of transactions. Turnover in a fund translates into transaction costs, the higher the turnover, the higher the ‘friction’ or erosion on returns.
• Timing.  Timing is mentioned in almost all of these possible explanations.

There may be other explanations but these are the ones that I believe have the most impact.