All For One, One For All
- Following the economic disruption of the COVID-19 pandemic and the unprecedented stimulus response, supply bottlenecks and a rebound in demand have been a feature of 2021.
- We observe that a diversified portfolio approach has had the highest correlation to upside inflation and the least frequent incidents of correlation breakdown.
- Much of the upside performance in the model portfolio has come due to the presence of equity exposure in the mix, which continued to perform well during the moderate inflation of the post-financial crisis environment.
Available data suggests that a balanced risk allocation to real assets may be the most robust way to mitigate the impact of upticks in inflation.
Following the economic disruption of the COVID-19 pandemic and the unprecedented stimulus response, supply bottlenecks and a rebound in demand have been a feature of 2021. The inflation runup in the last few U.S. Consumer price Index (CPI) reports is now beginning to look significant relative to history (figure 1).
The coincident lack of clarity in the recent Fed forward guidance may be a good opportunity for investors to re-examine their allocation options and identify investments that provide robust inflation protection, yet still be a good fit from a risk/return perspective.
Figure 1: An Inflation Surprise in 2021
U.S. CPI, year-on-year change
The Inflation Protection Toolbox
In this article, we compare several categories of investments that have traditionally been regarded as having inflation-protection characteristics: commodity futures, standalone gold and Real Estate Investment Trusts (REITs), as well as equities from specific sectors, such as Base Metal and Gold Mining, Energy and Agriculture. Note that given the current low level of real interest rates, we have excluded Treasury Inflation Protected Securities (TIPS) here.¹
The inflationary periods of the 1970s have been studied extensively, although refined equity sector returns data dating back that far is hard to come by. We therefore consider monthly returns and year-on-year changes in U.S. CPI in the 26-year period since 1995, when a large number of the relevant S&P Global Industry Classification Standard (GICS) sub-indices became available.
Focus on Upticks in Inflation
Figure 2 shows the performance statistics for each asset class, as well as for a combined portfolio, over the period. Given that positive inflation is the scenario in which the typical investor seeks some mitigation via positive real returns, we show correlations with changes in inflation conditional on that change being positive (rather than calculating correlations over the entire period). To assess the robustness of these correlations over time, we also examined them on a five-year rolling basis and calculated the percentage of time the rolling correlations were negative, as shown in the last line of the table.
Figure 2. Comparing Different Asset Classes Against Inflation-Hypothetical Backtest
Summary Statistics, January 1995 to June 2021
Source: Bloomberg, Neuberger Berman. Indices used are the Bloomberg Commodity Total Return Index (BCOMTR Index); Gold spot price (XAU Currency); S&P 1500 Composite Gold Sub Industry GICS Index (S15GOLD Index); S&P United States REIT Index (SPREIUSRT Index); S&P 1500 Composite Agricultural Products Sub Industry GICS Index and S&P 1500 Composite Fertilizers and Agricultural Chem Sub Industry GICS Index (S15AGRI Index and S15FERT Index); S&P 1500 Composite Energy Sector GICS Index (SP1500-10); and the S&P 1500 Composite Metals and Mining Industry GICS Index (SP1500-151040). The Model Portfolio is equally weighted by notional exposure across the six asset classes, with Gold and Gold Mining Equities grouped together into a single category, rebalanced monthly.
Interestingly, we find that the equities from the selected sectors have had comparable, if not higher correlation to inflation increases than the index of commodity futures. There could be many reasons for this, including leverage, the absence of the roll costs of futures markets in contango, and in some cases, perhaps, being a step closer to the CPI consumer basket than the raw materials themselves.
Take the Portfolio Approach
Most importantly, however, we observe that a diversified portfolio approach has had the highest correlation to upside inflation and the least frequent incidents of correlation breakdown. This is not surprising because none of the other options has consistently dominated the rest, and the averaging effect of the portfolio therefore provided more stable positive correlation. For instance, in 2015 - 19 the best hedge would have been gold, while the full commodities index was at the bottom of the pack. On the other hand, during periods such as 2000 - 01, or over the past 12 months, the commodities index has followed inflation closely, while the gold price has stalled. Indeed, on a five-year rolling basis, there have been periods when each of the options has been most correlated to an increase in the CPI.
Moreover, the portfolio approach came a close second in terms of risk-adjusted performance. Figure 3 helps visualize this by superimposing the portfolio return series onto the commodity index return series and the year-on-year inflation rate.
Figure 3. The Portfolio Approach Has Provided More Stable Inflation Mitigation
Source: Bloomberg, Neuberger Berman. For the indices used and the portfolio construction method, see the note to figure 2.
We can see the runups in the portfolio's performance during commodity price rallies, such as 2003 - 2008 and over the past year, as well as some shared downside risk when commodities have sold off, during periods such as 2008 and the 2020 pandemic. Nonetheless, what stands out is the much more consistent performance generated in the immediate recovery from the 2008 - 09 financial crisis and the following years.
The available historical data suggests that upticks in U.S. CPI have been positively correlated to commodity futures, precious metals, equities in the Energy, Agriculture and Mining sectors, and to some extent REITs.
We find that combining these asset classes into a model portfolio appears to have been a more reliable way to mitigate against rising inflation than picking any one of them. The diversification effect in our model portfolio has appeared to boost risk-adjusted performance while preserving strong upside potential.
Much of the upside performance in the model portfolio has come due to the presence of equity exposure in the mix, which continued to perform well during the moderate inflation of the post-financial crisis environment. This is an important point for an asset allocator to consider, as it potentially makes it less onerous to shift into an inflation-protected portfolio; for example, a 60/40 investor seeking to mitigate the impact of higher inflation could rotate some of its equity exposure into more inflation-resilient sectors, while replacing some of its fixed income with a mix of gold, commodity futures and real estate securities.
¹ While a single TIPS can be used effectively to hedge specific future cash flows from the impact of inflation, the volatility of real yields has led portfolios of TIPS held as an asset class to be a poor inflation hedge, historically. See Andrew Ang, "'Real' Assets" (September 2012), Columbia Business School Research Paper No. 12-60, at https://ssrn.com/abstract=2161124 or http://dx.doi.org/10.2139/ssrn.2161124
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Commodity futures and forward contract prices are highly volatile and the commodity markets can also lack sustained movements of prices in one direction, whether up or down, for extended periods. Participation in a market that is either volatile or trendless could produce substantial losses. Price movements of commodity interests are influenced by, among other factors: changing supply and demand relationships; governmental, agricultural and trade programs and policies; climate; and national and international political and economic events. None of these factors can be controlled by the manager.
The Bloomberg Commodity Index (BCOM) is designed to be a highly liquid and diversified benchmark for commodities investments. The index provides broad-based exposure to commodities as an asset class, since no single commodity or commodity sector dominates the Index. This index is composed of futures contracts on 20 physical commodities traded on U.S. exchanges, with the exception of aluminum, nickel and zinc, which are traded on the London Metal Exchange (LME).
The S&P Composite 1500 Index covers approximately 90% of US market capitalization. It is designed for investors seeking to replicate performance of the US equity market or benchmark against a representative universe of tradable stocks. The S&P Composite 1500 is a broad measure of the investible US equity market. By looking beyond large caps to include mid-caps, the index captures stocks that have successfully navigated the challenges specific to smaller companies, but are dynamic and not so large that continued growth is unattainable. By including small caps (but not micro-caps), the index captures stocks that may have high growth potential but also meet investability and financial viability criteria.
The S&P Composite 1500 Sector / Industry / Sub Industry GICS Indices are designed to measure the performance of stocks in the S&P Composite 1500 Index that are classified in the relevant GICS sector, industry, or sub-industry.
The S&P United States REIT Index defines and measures the investable universe of publicly traded real estate investment trusts domiciled in the United States.
Hypothetical Backtested Performance Disclosures
The hypothetical performance results included in this material are for a backtested model portfolio and are shown for illustrative purposes only. Neuberger Berman calculated the hypothetical results by running a model portfolio on a backtested basis using the methodology described herein. The backtest was designed to exhibit the risk-adjusted performance and inflation correlation of a model portfolio of liquid real assets, between January 1995 and June 2021. The model portfolio included six asset classes, represented by the following indices: Commodities (Bloomberg Commodity Total Return Index, BCOMTR); Gold (an equal-weighted mix of XAU Currency and S&P 1500 Composite Gold Sub Industry GICS Index, S15GOLD); Real Estate Equities (S&P United States REIT Index, SPREIUSRT); Agriculture Equities (an equal-weighted mix of S&P 1500 Composite Agricultural Products Sub Industry GICS Index and S&P 1500 Composite Fertilizers and Agricultural Chem Sub Industry GICS Index, S15AGRI and S15FERT); Energy Equities (S&P 1500 Composite Energy Sector GICS Index, SP1500-10); and Metals and Mining Equities (S&P 1500 Composite Metals and Mining Industry GICS Index, SP1500-151040). The indices were equally weighted by notional exposure and rebalanced monthly. The results do not represent the performance of any Neuberger Berman managed account or product and do not reflect the fees and expenses associated with managing a portfolio. If such fees and expense were reflected, returns referenced would be lower. The model portfolio may not be appropriate for any investor. There may be material differences between the hypothetical backtested performance results and actual results achieved by actual accounts. Backtested model performance is hypothetical and does not represent the performance of actual accounts. Hypothetical performance has certain inherent limitations. Unlike actual investment performance, hypothetical results do not represent actual trading and accordingly the performance results may have under- or over-compensated for the impact, if any, that certain economic or other market factors, such as lack of liquidity or price fluctuations, might have had on the investment decision-making process or results if assets were actually being managed. Hypothetical performance may also not accurately reflect the impact, if any, of other material economic and market factors, or the impact of financial risk and the ability to withstand losses. Hypothetical performance results are also subject to the fact that they are generally designed with the benefit of hindsight. As a result, the backtested models theoretically may be changed from time to time to obtain more favorable performance results. In addition, the results are based, in part, on hypothetical assumptions. Certain of the assumptions have been made for modeling purposes and may not have been realized in the actual management of accounts. No representation or warranty is made as to the reasonableness of the assumptions made or that all assumptions used in achieving the hypothetical results have been stated or fully considered. Changes in the model assumptions may have a material impact on the hypothetical returns presented. There are frequently material differences between hypothetical performance results and actual results achieved by any investment strategy. Neuberger Berman did not manage any accounts in this manner reflected in the models during the backtested time periods shown.
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