Indexology^® Blog

In my last post, I wrote about how we approached financial advisors with SPIVA.  In that effort to share SPIVA results, we were pleasantly surprised to find that some financial advisors were already embracing SPIVA.  Here are two more examples of that to add to Phil Dodson’s example presented in the last post.

Todd Green and Chris Mirrione represent Alesco Advisors, a Registered Investment Advisor (RIA) firm in Pittsford, New York.  They have used SPIVA and Persistence data for years as part of their new business presentations and as reference material when reviewing portfolios with current clients.  Use of SPIVA and Persistence support their investment approach, which includes constructing a strategic asset allocation, broadly diversifying risk, and controlling costs. They employ this strategy as a consultant and investment manager for institutional accounts as well as with high net worth clients. In their view, establishing a proper asset allocation is essential to helping clients meet their investing goals. They believe that implementing a portfolio using passive investments to ensure participation in the long-term returns of the market is the most effective way for their clients to experience the benefits of their asset allocation structure.  SPIVA and Persistence data support their choice to use ETFs and passively-managed mutual funds to obtain market returns within each asset class instead of attempting to generate alpha or chase managers who have had strong recent returns.

Rick Ferri, Founder and Managing Partner of Portfolio Solutions, an RIA based in Troy, Michigan, is a long-time proponent of indexing.  Rick has written books about the power of indexing and in his blog for Forbes about how SPIVA and Persistence pertain to financial advisors as data in support of indexing for low-cost investing.  In June 2013, Rick co-wrote a paper titled A Case for Index Fund Portfolios with Alex Benke of Betterment.  In that paper, the co-authors extended research beyond SPIVA and S&P Persistence Scorecard’s case for index effectiveness to determine if a portfolio of index funds would outperform a portfolio of actively managed funds. The authors modeled and analyzed data from three consecutive periods of five years and the entire 15-year period to find that a portfolio of index funds outperformed in all four scenarios. The complete results of their research can be seen at https://us.spindices.com/resource-center/thought-leadership/spiva/.

These three examples (including Phil Dodson’s example from my previous post) are representative of a growing body of wealth managers who are using SPIVA and Persistence to their advantage.  Their practices may differ, but they have in common that they reject the seeking of alpha as their objective.  They also reject the practice of picking managers.  What they retain is the ability to construct portfolios for high net worth clients where they themselves manage the asset allocation.  Informed by SPIVA, their method is to use index-based tools as the most efficient building blocks for that asset allocation.

Our 2014 end of year US SPIVA report, published last week, breaks new ground.  For the first time, we present 10-year numbers.  This new section of our US SPIVA analysis will enable financial advisors to perform robust analysis across business cycles of comparative performance of index vs active.

If we’re talking about the worst performing months for commodities, March 2015 should be dominating the conversation. The S&P GSCI Total Return is down 9.1% month-to-date (as of 3/17/15) and is on track to post the 4th worst March in history of the index since 1970; only 2003 (-14.4%), 1980 (-14.0%) and 1974 (-11.5%) lost more. All 24 of the benchmark’s commodities are negative for the year with just four positive performers for the month: feeder cattle, natural gas, live cattle and Kansas wheat.

In the history of the index there have only been 11 of 545 months with as few positive performers as shown in the table below.

To add more to the madness, commodities have been the most volatile this March (annualized 90-day volatility of 27.1%) in almost six years – that is since December, 2009.  This is after the index experienced annualized 90-day volatility of just 8.3% in August 2014, its lowest since December, 1995.

However, there might be a few bright spots in this March “Commodity” Madness. One statistic is that of the 24 commodities, 10 are currently in backwardation: live cattle, heating oil, feeder cattle, gasoil, unleaded gasoline, copper, wheat, silver, cocoa and Kansas wheat. Gasoil, unleaded gasoline, silver and Kansas wheat have flipped from contango with gasoil moving enough to produce a positive roll return for the year thus far.

Finally, we can’t ignore the Fed, especially today. They dropped the word “patient” and 15 of the bank’s top 17 officials think the Fed will raise rates sometime this year but with the economy seeming somewhat unsettled, at least temporarily, the Fed chairwoman effectively ruled out a rate increase in April and perhaps longer.

If the central bank starts to raise rates in June, analysts said that the U.S. dollar could be close to its peak and such an outcome would be supportive for dollar commodity prices. The good news for commodities is that when rates rise, it has been generally supportive of prices.

Source: S&P Dow Jones Indices LLC. All information presented prior to the index launch date is back-tested. Back-tested performance is not actual performance, but is hypothetical. The back-test calculations are based on the same methodology that was in effect when the index was officially launched. Past performance is not a guarantee of future results.  Please see the Performance Disclosure at http://www.spindices.com/regulatory-affairs-disclaimers/ for more information regarding the inherent limitations associated with back-tested performance.

This is a series of blog posts relating to the in depth analysis of performance differential between the S&P SmallCap 600 and the Russell 2000.

Benchmarks are designed to represent a passive strategy in a given universe. Given that purpose, the risk/return profiles among various benchmarks in the same universe should be relatively similar in nature. This similarity appears to be borne out in the U.S. large-cap equity universe when comparing the returns of the Russell 1000® and the S&P 500®. Using monthly total returns from 1994 to 2014, Exhibit 1 charts the growth of a hypothetical investment of USD 1.00 in the S&P 500 and the Russell 1000, as well as in the S&P SmallCap 600 and the Russell 2000. In the U.S. large-cap universe, USD 1.00 invested in the S&P 500 and the Russell 1000 from January 1994 through December 2014 would have returned USD 6.63 and USD 6.80, respectively. However, in the small-cap universe, the returns of the Russell 2000 and the S&P SmallCap 600 are considerably different. An investment of USD 1.00 in the S&P SmallCap 600 over the same time period would have returned USD 8.59, while it would have returned USD 6.18 if invested in the Russell 2000.

Since its launch in 1994, the S&P SmallCap 600 has outperformed the Russell 2000 in 14 of 21 calendar years. From January 1994 through December 2014, the returns of the S&P SmallCap 600 exceeded those of the Russell 2000 by 1.72% on an annualized basis. It is also important to note that the S&P SmallCap 600 has exhibited lower volatility than the Russell 2000 historically, leading to a higher Sharpe Ratio. These results are similar to what was seen in the previous research paper on this topic (2%), when the time range reviewed was from 1994 to 2009. Exhibit 2 highlights the risk/return profiles of the two indices.

The continued return differential between the two small-cap indices merits further study, and an understanding of the factors contributing to the divergence. We will be covering those in detail in upcoming blog posts!

From the end of 2013 there has been a 10.2% increase in the number of issues paying a dividend in the S&P SmallCap 600. Initiating a dividend represents a broad commitment of future earnings, to which companies need to be very sure of their future cash-flow.  On an index market-size level, SmallCap yields remains at the lower end of the spectrum, as the traditional growth characteristics (and priority) plays out more than the income component.  However, comparisons on an issue level within the size classifications show a much closer relationship of yields, with size not being a significant differentiator.  Starting with recent growth in SmallCap payers, and the history of payers to continue to pay and increase, SmallCaps issues appear to be trending up, and if the trend continues, eventually, the index level characterizes will change.

S&P SmallCap600 dividend stats:

• 324 of the 600 pay a regular cash dividends
• The weighted index yield is 1.34%, with the 324 payers yielding 2.24%
• The average market value is $1.24 billion, with the average market value of dividend payers being $1.37 billion
• 182 payers have a dividend rate less than 50% of their 12 month net GAAP income, with 235 being less than 75%
• 18 issues have increased their cash dividend payment for at least 20 consecutive years, with 19 more increasing it for at least 10 years
• 176 have paid cash dividends for at least 10 consecutive years, with 231 paying for at least 5 consecutive years
• Based on the current dividend rate, 202 issues will pay more than they paid in 2014; 255 paid more in 2014 than 2013 and 139 paid more in 2013 than 2012

More Small-caps are paying, with their yields higher

20-year-look

Who pays what in the SmallCap

Cross-indices yields

Long-term SmallCap payers

Four SmallCaps in the S&P High Yield Dividend Aristocrats

S&P 1500 breakdown

SmallCaps have underperformed, which also has helped yields

Long-term total returns

It can be interesting to try and explain the world of indices and benchmarks to people from non-financial backgrounds because, at times, it can pose a bit of a challenge.  For me, it is a revelation to find out that what I consider as generic information and common knowledge is not quite as simple for many.  The general questions being: “What is a benchmark?” Why do you need it?”, and “How do you choose the correct one?”

I thought I would try and simplify this for common understanding.  A benchmark is an ideal comparative measure that forms a standard or norm and can be used to gain an understanding of a relative market area or segment.  For example, the S&P BSE SENSEX is considered the barometer of Indian markets.  Hence, one can understand whether Indian markets have fared well or not based on the index’s movements.  The S&P BSE SENSEX’s growth percentage provides periodical statistics on the performance of Indian equity markets.

S&P BSE SENSEX – Price Returns

Source: Asia Index Pvt. Ltd.  Data as of Feb. 28, 2015.  Charts and tables are provided for illustrative purposes only.  Past performance is no guarantee of future results.

But would this index fit all comparative analysis?  The answer is no.  If one wants to merely check on how the infrastructure companies are faring in the Indian market, the S&P BSE SENSEX would not be the ideal measure, as it is a generic, overall market indicator.  One would have to review an index that would be a representation of all infrastructure companies, like the S&P BSE India Infrastructure Index.

S&P BSE India Infrastructure Index – Price Returns

Source: Asia Index Pvt. Ltd. Data as of Feb. 28, 2015.  Charts and tables are provided for illustrative purposes only.  Past performance is no guarantee of future results.

So how are such benchmarks or indices crafted to be able to provide ideal measuring tools within the segment they represent?  Indices are all created based on strict rules.  These rules take into account market dynamics and suitability for the region it will represent.  For example, rules that work in the U.S. may not necessarily be applicable to the South Asia region.  This is true within regions, too: if we were to look at South Asia, the markets in Sri Lanka are largely different than those in Bangladesh, for example.  Hence, when rules are crafted, we need to see the maturity and depth of the markets.  Furthermore, market consultation also proves to be very beneficial in ensuring that benchmarks are suitable for market participants.

It is important to ensure comparisons are made among suitable benchmarks.  As the saying goes, we must compare “apples to apples” in order to understand which is better.  Similarly, for investments, the comparison should be made with the similar index benchmark, in order for investors to understand the performance of their portfolios or investments in comparison to the market standards.  So if I am investing in an infrastructure fund, it would be most ideal to compare the fund with the market benchmark, meaning, an infrastructure index rather than a generic market index.