Nicholas J. Johnson, Klaus Thuerbach
After the challenges in commodity markets in recent years, many investors are looking to bring their exposures back in line with their longer-term
allocation targets. As a result, they are revisiting the issue of whether it is better to invest in commodities directly through commodity futures
or indirectly through exposure to natural resource equities (NREs).
An argument is often made that investing in NREs results in better performance, in large part because investors avoid the potential headwinds of roll
yield. Commodity futures investors are exposed to roll yield when they sell a contract before delivery and “roll into” a more distant contract. Roll yield
can be positive (with downward sloping futures curves) or negative (with upward sloping futures curves). While it’s true that NREs are not directly
impacted by roll yield, they carry their own risks, namely equity beta and higher volatility, for which investors should be compensated.
To investigate further, we created a framework for comparing the performance of NREs and commodity futures on an apples-to-apples basis. We found that two
main factors contribute to NRE portfolio returns: a broad equity market factor and a commodity factor. Assuming that the market is efficient, we reasoned
that an NRE portfolio would neither underperform nor outperform a portfolio that matches the same risk factors by taking exposure to a mix of broad
equities and commodity futures.
Our key findings include:
The implications of these findings are very relevant for investors today: More often than not, it seems there is better return per unit of risk when
investing in a combination of commodity futures and broad stocks than in natural resource equities.
Analyzing and comparing returnsOur analysis focused on broad NREs as well as five individual NRE sectors (see Figure 1).
To construct a replicating portfolio for each NRE sector, we used regression models to find the combination of S&P 500 and commodity futures investment
weights (also called betas) that most closely represented the returns posted by a basket of NREs. To capture changes in the relationship over time, a
monthly rolling regression was estimated using a 10-year window. For each month, a replicating portfolio was then created with the corresponding betas in
the S&P 500 and commodity futures as well as a cash component in case the two factor betas didn’t sum to 100% (they typically didn’t). For example, if
the commodity beta was 0.4 and the equity beta was 0.8, then the allocation to cash would be ‒0.2 to bring the total exposure in percent market value terms
to 100%. This approach essentially eliminated in-sample bias as the replicating portfolio was generated using past data only. (The appendix contains a
comprehensive description of the methodology.)
Figure 2 illustrates the evolving nature of factor betas. For broad NREs, the S&P 500 beta ranged from about 0.6 to 1.1 while the commodity beta
generally increased over time from a low beta of 0.2 to a recent beta of 0.6.
Figure 3 displays the average regression results for broad NREs and select NRE sectors over the fully available time periods.
In general, most NRE sectors showed a higher beta to equity markets than to their respective commodity markets. The exception was gold, which demonstrated
a high commodity beta and the lowest beta to the equity market. All replicating portfolios outside of agriculture observed high R2 and
explanatory power. The appendix provides summary statistics on the regressions (Figure 9).
Real implications are risks and returnsWhile it is academically informative to expose the underlying economics of NREs, namely the extent to which they are influenced by the equity risk premium
and their underlying commodities, the real implications for investors are risk and return characteristics. Figure 4 offers summary returns and volatilities
for each sector with the maximum time periods available.
The top half of Figure 4 shows the performance of NREs. The bottom half displays the performance of the replicating portfolio. The replicating portfolio
was a blend of S&P 500 equity exposure and commodity sector exposure based upon the trailing 10-year beta, as previously described.
The replicating portfolio outperformed NREs in absolute terms in four of the six examples. Further, in all six cases the replicating portfolio yielded
higher risk-adjusted returns.
Figure 5 presents the same data from 1993‒2015 as this period is available for all sectors. Notably, the results didn’t seem materially influenced by
aligning time periods. While the absolute return levels changed, the relative conclusions remained largely intact. The replicating portfolios outperformed
in absolute terms in three out of the six sectors, and all six performed better in risk-adjusted terms.
Given the consistently lower Sharpe ratio of NREs, it seems investors in NREs take more risk than they are compensated for. One explanation could be that
some investors face constraints in accessing commodities directly and are willing to accept a lower return in NREs for their commodity-related,
inflation-hedging properties. But for investors without asset class constraints, a replicating portfolio seems the preferred choice.
Preferred choice if not asset class constrainedAnother consideration in favor of NREs is their intrinsically levered nature. The replicating portfolios, with the exception of the oil and agriculture
sectors, observed factor betas that sum to more than 100%. In other words, to build the replicating portfolio requires leverage, which for some investors
can be a challenge and might be a partial cause of the relative underperformance of NREs.
It is important to note that relative NRE underperformance was not continuous, but rather cyclical. Figure 6 reveals the cumulative return dispersion
between broad NREs and the replicating portfolios. The excess return (green line) in Figure 6 shows the return of the replicating portfolio minus the return of NREs.
Prolonged periods of out-performance and under-performance were the norm over this period. This raised the question of whether certain factors can help
explain and possibly predict relative performance. Figure 7 highlights this point by focusing on three-year rolling excess returns. NREs consistently
outperformed during the “Goldilocks” period from the bursting of the tech bubble in the early 2000s through the global financial crisis (GFC), while the
replicating portfolio consistently dominated before and afterwards.
To home in on the cyclical aspects, Figure 8 shows the performance statistics for the oil and mining NRE sectors and their replicating portfolios both
before and after the financial crisis. When focusing on risk-adjusted returns, we saw a similar picture: the Goldilocks period led to stellar NRE
performance while the replicating portfolio outperformed after the global financial crisis. Over the whole period, oil stocks out-performed and mining
stocks underperformed on a risk-adjusted basis.
One potential explanation could be the cyclical return behavior of energy and mining companies. Most commodity-producing projects are long-term investments
with high upfront/fixed costs and relatively low marginal costs of production. During commodity boom cycles, elevated expectations for future commodity
prices increase the attractiveness, or net present value (NPV), of new projects. This was especially the case during the period of “peak oil” concerns in
2007‒2009 when the back end of the oil futures curve appreciated significantly, and elevated long-term price expectations might have influenced project
valuations. Whether or not these projects deliver on this NPV depends on the future path of commodity prices in the years following project completion.
Since many NRE companies tend to invest in high NPV projects at the same time, this can lead to an oversupplied market and negatively affect commodity
Commodity super cycle hits NREs moreIn the case of the recent commodity price decline, we can clearly see that equity value was destroyed as substantial capacity was added in the years just
preceding the commodity price collapse in late 2014. This phenomenon is often referred to as the commodity super cycle, and our analysis suggests that NREs
are affected more by the super cycle than replicating portfolios that invest directly in a mix of broad equities and commodity futures.
SummaryWhen comparing performance between NREs and commodity futures, it is important to account for the equity beta present in NREs to make an apples-to-apples
comparison, especially since the beta of NREs to broad equities is often higher than it is to the commodity market. While some investors may be accustomed
to taking commodity exposure through natural resource equities, our study suggests that beta-replicating portfolios allocating directly to broad equities
and commodity futures have historically provided superior risk-adjusted returns. It also shows that periods of outperformance and underperformance can be
prolonged and are likely due to the long investment cycles in NREs. Going forward, if history is a guide (which it may or may not be), we should look for
similar absolute and better risk-adjusted returns from a basket of commodity futures and broad equities compared to investments in NREs.
PIMCO’s Tapio Pekkala, a senior vice president, contributed to this article.
Appendix – methodologyNREs and commodities lend themselves to a beta replication analysis as both asset classes have been around for many decades, thus allowing observation of
behavior during various economic regimes. Going back as far as 1970, our analysis covers almost five decades of financial returns.
To prevent over-fitting and in-sample biases, all of our results were out of sample: Using monthly return data, we estimated the long-run betas of NREs to
both commodity markets and broad equities over a 10-year window. While providing a reasonable sample size of 120 observations, this monthly rolling 10-year
beta also allowed us to capture structural changes in sensitivities over time.
Using these betas, we then created a replicating portfolio consisting of X% of stocks, Y% of commodities, and 1-(X+Y)% of cash where X and Y represented
the beta of NREs to stocks and commodities, respectively, and the cash allocation represented required leverage. We then held this portfolio for one month
at the end of which we reconstituted the replicating portfolio based on the updated betas. As a result, the replicating portfolio returns were based solely
on historical information that was available at the time of investment (i.e., out-of-sample portfolio).
1Broad natural resource equities were represented by an equally weighted total return index of seven sector total return indexes (agriculture, oil, mines,
gold, building materials, steel and coal). Companies were included in the respective sector indexes using their Standard Industrial Classification (SIC)
codes. Return series for equity price data and natural resource equities were obtained from the Kenneth R. French data library.
Broad equities are represented by the S&P 500 Total Return Index.
The Broad commodities composite was based on monthly returns from 1970–2015. It represented a fully collateralized total return index, whose methodology
was based on Ibbotson’s Strategic Asset Allocation and Commodities (2006). The index model was an equally weighted, monthly rebalanced composite of the
following six commodity indexes: S&P Goldman Sachs Commodity Index Total Return (since 1970), Dow Jones-UBS Commodity Index Total Return (since 1991),
Reuters/Jefferies CRB Total Return Index (since 1994), Gorton and Rouwenhorst Commodity Total Return Index (1959-2007), JPMorgan Commodity Futures Index (1970-2001) and Credit Suisse Commodity Benchmark
Total Return Index (since 2001). It is not possible to invest directly in an unmanaged index. Nothing contained herein is indicative of the past or future
performance of any PIMCO product.
This material contains hypothetical analysis based on a series of assumptions detailed herein. There is no guarantee that these
assumptions are accurate or complete. They are for illustrative purposes only. It is not possible to invest directly in an unmanaged index. Nothing
contained herein is indicative of the past or future performance of any PIMCO product. Hypothetical and simulated examples have many inherent limitations
and are generally prepared with the benefit of hindsight. There are frequently sharp differences between simulated results and the actual results. There
are numerous factors related to the markets in general or the implementation of any specific investment strategy, which cannot be fully accounted for in
the preparation of simulated results and all of which can adversely affect actual results. No guarantee is being made that the stated results will be
contain risk and may lose value. Commodities contain heightened risk, including market, political, regulatory and natural conditions, and
may not be suitable for all investors. Derivatives and commodity-linked derivatives may involve certain costs and risks, such as
liquidity, interest rate, market, credit, management and the risk that a position could not be closed when most advantageous. Commodity-linked derivative
instruments may involve additional costs and risks such as changes in commodity index volatility or factors affecting a particular industry or commodity,
such as drought, floods, weather, livestock disease, embargoes, tariffs and international economic, political and regulatory developments. Investing in
derivatives could lose more than the amount invested. There is no guarantee that these investment strategies will work under all market conditions or are
suitable for all investors and each investor should evaluate their ability to invest long-term, especially during periods of downturn in the market.
Investors should consult their investment professional prior to making an investment decision.
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