Investment Themes

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Commodities "To Be Or Not To Be" GROWN

Are Investors Prepared for What may Be on the Horizon?

What is SPIVA®?

Low Volatility and High Beta: When Opposite Paths Meet

Why Risk Control Works

Commodities "To Be Or Not To Be" GROWN

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Jodie Gunzberg

Managing Director, Head of U.S. Equities

S&P Dow Jones Indices

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There are no conclusive definitions of an asset class or definitive lists of asset classes, but asset allocation depends on how one chooses to define asset classes that collectively form the opportunity set. A company owned by Morningstar called Ibbotson Associates together with PIMCO pulled together some research on the topic that I think is interesting. They present a framework based on three super asset classes:

1. Capital assets, such as stocks, bonds and real estate, provide an ongoing source of value that can be measured using the present value of future cash flows technique.

2. Consumable or transformable assets, like commodities, only provide a single cash flow.

3. Store of value assets such as currency and fine art are not consumed and do not generate income but do have a monetary value.

The identification of the investable opportunity set significantly changes the potential risk and return possibilities. In another model, asset classes can also be thought of as risk factors or market exposures that produce a return that is not based on skill. These market exposures include sensitivities to financial markets, interest rates, credit spreads, volatility and other market-related forces.

Commodities offer an inherent or natural return that is not conditional on skill. Coupled with the fact that commodities are the basic ingredients that build society, commodities are a unique asset class and should be treated as such. Together, their sources of return have provided an important portfolio function.

The component of return called expectational variance that is caused by unexpected inflation – or supply shocks- is the main driver of the return differences between commodities and also between commodities and other asset classes.  It is typical to observe this in the low correlations between commodity sectors as shown in the table below.

Source: S&P Dow Jones Indices. S&P GSCI Sectors monthly data from 2/83 - 12/14. Charts and graphs are provided for illustrative purposes only.  Indices are unmanaged statistical composites and their returns do not include payment of any sales charges or fees an investor would pay to purchase the securities the index represents.  Such costs would lower performance.  It is not possible to invest directly in an index.  Past performance is not an indication of future results. The inception date for the S&P GSCI Sectors was May 1, 1991, at the market close.  All information presented prior to the index inception date is back-tested. Please see the Performance Disclosure at the end of this document for more information regarding the inherent limitations associated with back-tested performance.
Source: S&P Dow Jones Indices. S&P GSCI Sectors monthly data from 2/83 – 12/14. Charts and graphs are provided for illustrative purposes only. Indices are unmanaged statistical composites and their returns do not include payment of any sales charges or fees an investor would pay to purchase the securities the index represents. Such costs would lower performance. It is not possible to invest directly in an index. Past performance is not an indication of future results. The inception date for the S&P GSCI Sectors was May 1, 1991, at the market close. All information presented prior to the index inception date is back-tested. Please see the Performance Disclosure at the end of this document for more information regarding the inherent limitations associated with back-tested performance.

However, there is an important distinction between the commodities “to be grown” and commodities “in the ground”.  The commodities in the ground are energy and metals and are finite, while the agriculture and livestock are commodities “to be grown” and are able to be replenished.  As David Jacks points out, since 1950, there is a major historical performance difference of real prices for “commodities in the ground” that rose by roughly 180% versus real prices for “commodities to be grown” that fell by roughly 33%. Source: http://apebc.ca/resources/Jacks_2015.pptx

Source: http://apebc.ca/resources/Jacks_2015.pptx

This can be due to the scarcity factor of commodities “in the ground” but rather than a constant premium for scarcity, Jacks observes that typically, cycles in commodities “to be grown” are preceded by those “in the ground”. This time may be different from the transition of fixed capital accumulation to a consumption-based economy and suburbanization, but relies on the success of the CPC (Communist Party of China.) If the suburbanization is successful then we may see an increase in demand for goods “to be grown” and an inflection in long-run trend and we may also observe below-trend prices for goods “in the ground” and formation of new cycle in medium run.

Source: http://apebc.ca/resources/Jacks_2015.pptx
Source: http://apebc.ca/resources/Jacks_2015.pptx

It is difficult to see these long-term cycles since the shorter term trends in place are noisy, particularly for the “to be grown” commodities. The prices are sensitive to immediate inventories driven by the balance of the individual supply and demand models where supply is impacted by the planting decisions, weather patterns, crop disease and technology that determine the crop yields from season to season.

For example, below is the chart of the one-year index levels of the S&P GSCI Energy & Metals versus the S&P GSCI Agriculture & Livestock:

Source: http://us.spindices.com/indices/commodities/sp-gsci-energy-metals
Source: http://us.spindices.com/indices/commodities/sp-gsci-energy-metals

Yesterday, the USDA (U.S. Department of Agriculture) released Prospective Plantings report, one of its most important reports of the year. Soybeans in the index rose 57 basis points but corn and wheat fell 4.6% and 3.4%, respectively, after farmers were seen cutting their corn plantings by 500k acres less than expected even as supplies grow to highest levels in nearly 20 years. Today, corn and wheat rebounded, gaining 1.5% and 3.1%, respectively while soybeans continued to rise 1.7%. The next factors that may impact the crops are short term weather patterns and conditions that impact crop yield.

However, when thinking of long-term investing, it is important not to miss the forest for the trees and remember the asset class is providing diversification and inflation protection through time. Long-term investors like pensions may be able to use the points in the cycle rather than the short term trends to identify true opportunities that may help meet long-term liabilities.

 

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

Are Investors Prepared for What may Be on the Horizon?

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Kevin Horan

Director, Fixed Income Indices

S&P Dow Jones Indices

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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.
S&P Global Developed Sovereign Inflation-Linked Bond Index

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.

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

What is SPIVA®?

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Paul Murdock

Manager, Content & Delivery

S&P Dow Jones Indices

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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

 

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

Low Volatility and High Beta: When Opposite Paths Meet

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Fei Mei Chan

Director, Index Investment Strategy

S&P Dow Jones Indices

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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.

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

Why Risk Control Works

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Tianyin Cheng

Senior Director, Strategy and Volatility Indices

S&P Dow Jones Indices

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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.

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