Featured Image

Asset Class Analysis and Allocation Implications in Changing Economic Environments

Risk, Performance, and Reporting

By Martin Georgiev  |  June 25, 2024

Amid increased asset class volatility, continued geopolitical uncertainty, and dynamic US monetary policy, it can be challenging at times to keep an eye on the big picture and the fundamental drivers of asset class returns. In addition, the relationship between asset class performance and factor exposures can dim over time.

Indeed, one can think of plenty of factors that explain asset returns. Yet, in times where the fight against inflation is still not over and Treasury yields are experiencing elevated volatility from post-Covid low/negative levels to the current values, we decided to test how breakeven inflation (BE) and real yields (RY) explain asset class historical performance. BE and RY are forward-looking market factors (as opposed to recorded CPI and GDP growth), hence they are better and timelier reflections of investors’ changing economic expectations that can influence decision making.

The intention of our analysis is to offer insights and key considerations for asset allocation analysis, to hint at possible tactical rebalancing or positioning ideas, and to serve as a potential starting point in the investment assessment process. You could apply the approaches discussed here to any set of asset classes, and the FactSet quant tools ecosystem would easily automate the process for you.


Data ingestion and subsequent analysis are executed in the FactSet Programmatic Environment (FPE) across these datasets:

  • Date range between January 2003 and April 2024

  • Economic data

  • Breakeven inflation: difference between 10Y nominal and 10Y real yields

  • Real yield: yield on 10Y Treasury inflation protected securities (TIPS)

  • Asset class data

For our analysis, we chose the following market indices’ returns as proxies for asset class returns. FPE enables easy, on-the-fly performance of similar studies for other asset classes.

Identifier name class
SPGSCL S&P GSCI Crude Oil Crude Oil
SPGSIN S&P GSCI Industrial Metals Industrial Metals
SPGSPM S&P GSCI Precious Metals Precious Metals
183657 Euro STOXX 50 Europe Equity
SP50 S&P 500 US Equity
MLG0QJ ICE BofA US Government US Treasury

Regime Breakdown

We distinguished the four regimes of the economy based on BE inflation and RY, with the latter a proxy for expected growth. We took the 24-month difference in both variables to arrive at the following signals:

  • Positive signal: BE or RY is trending up

  • Negative signal: BE or RY is trending down

We defined the four environments:

Regime BE signal RY signal
Growing - +
Overheating + +
Slowing - -
Stagflation + -


Sensitivity Plots: Regime Breakdown


The distinctions across economic environments allow us to assess how each asset class is affected by the chosen factors in each regime alone. They also provide information for the consistency or dynamics of the findings (i.e., calculated betas). The analysis provides asset allocation insights and ideas for positioning.

Thus, it turns out crude oil and industrial metals are almost always positively correlated to BE inflation (of course betas have varying magnitude) and have no meaningful relationship with the RY variable. On the other hand, precious metals seem to be negatively influenced by real yields. Only under the stagflation environment, this asset class has significant sensitivity to both BE and RY. US Treasury reveals the most consistent results given both calculated coefficients overlap across the four regimes, and both denote a negative relationship with the asset returns. Coupled with the high R2 (see next plot), US Treasury returns can be well forecasted with this type of analysis. US equity is also positively affected by changes in inflation expectations, with only one regime overheating when both variables significantly affect the asset class.


All asset classes but US Treasury have low R2s. Yet theoretically, this doesn’t invalidate the model. Low R2 values coupled with statistically significant coefficients can still reveal important conclusions about the relationships between the variables. Betas continue to represent the mean change in the dependent asset class given a one-unit shift in the independent factor. Simply stated, the variability of that mean will be greater for lower R-squared models, and that indicates low predictive power. Yet, the descriptive insights remain relevant.

The US Treasury model has the highest and most consistent explanatory power across all regimes, thus making BE inflation and RY nearly perfect in explaining the returns.

Crude oil and industrial metals, with their positive relationship to changes in inflation expectations, have the highest R2 in the growing scenario where BE is trending down. This might imply negative returns for the two asset classes during a growing environment.

US equity and Europe equity have their peak R2s in the overheating and growing regimes, respectively.

Returns Under the Regime Paradigm


Returns analysis under the regime paradigm confirms and challenges some of the preceding findings. For example, crude oil being positively affected by expectations for higher inflation brings in a high positive mean return during the stagflation environment. But the return is negative in the other regime when BE inflation is rising (overheating). Slowing and growing scenarios (diminishing BE inflation) are intact, resulting in negative average returns for the asset class. Industrial metals are fairly in line with the sensitivity findings as only in the slowing scenario (diminishing inflation) does the asset class still produce a positive average return.

Precious metals generate positive average performance in the scenarios where RY is trending down (negative correlation with the latter) and mediocre returns in the other two regimes.

The two equity asset classes have a positive mean return across all regimes. It’s a partial contradiction considering the resulting betas toward the two explanatory variables.

The presence of confronting findings can be considered normal since the previous regression analyses are imperfect—the explanatory power of the models is not high, and it varies across regimes. This indicates other variables affecting asset performance.

On the other hand, when geopolitical uncertainty is high and central banks’ monetary policies and fiscal measures are highly stimulative (as they were since the Great Financial Crisis), significant deviations from the systematic drivers of asset returns could be expected. The idiosyncratic portion of total asset risk is more than relevant and, once again, it affirms the benefits of timely and proper diversification.

Volatilities Under the Regime Paradigm


The asset classes’ downside volatility in each regime provides additional perspective. Given crude oil is an inflation hedge (as revealed by the sensitivities above), it has the lowest risk in both overheating and stagflation scenarios where, by definition, BE is trending up. On the other hand, this asset class exhibits the highest downside deviation in the slowing scenario when inflation expectations are drifting down.

Industrial metals, another inflation hedge, exhibit low return dispersion in the overheating regime and the highest return dispersion in the growing scenario. US equity and Europe equity have relatively low deviation in overheating and stagflation regimes, but they are much more volatile in the other two scenarios.

Sensitivity Plot: All Time


As a final step in the analysis, we exited the regime paradigm and tested for sensitivities between January 2003 and April 2024. The preceding findings are reinforced. For example, on the commodity front, crude oil and industrial metals hedge against higher inflation only. And precious metals, given their negative correlation with yields, immunize against falling RY. The two equity asset classes along with US Treasury have meaningful correlation with both variables.


Our analysis indicates that industrial commodities can serve as a good inflation hedge, providing positive returns and low volatility in the regimes where inflation expectations are increasing.

Precious metals are confirmed to move inversely with real yields as high interest rates discourage investors to hold non-yielding assets and vice versa. On the other hand, precious metals are often considered in popular media to be an inflation hedge as well. Our findings do not fully support that view.

Equities’ positive performance seems to prevail across all regimes. That might be explained by distorted fundamentals resulting from persistently stimulative monetary and fiscal policies around the globe coupled with abundant liquidity.

Treasuries are well explained by the two variables and produce similar findings across all economic scenarios.

In closing, here are a few notes on possible model modifications for your consideration.

  • Definitions of the historical economic regimes could be based on recorded CPI and GDP growth. Alternatively, you could decide whether to fine tune other methods based on BE and RY movements to shape economic regime periods.

  • Regressions explanatory power could be boosted by calibrating the model inputs.

  • For a larger sample size, the analysis period could be extended beyond the current 20 years.

  • The analysis could be expanded by calculating rolling stats that better assess changing coefficients and R2s over time.


This blog post is for informational purposes only. The information contained in this blog post is not legal, tax, or investment advice. FactSet does not endorse or recommend any investments and assumes no liability for any consequence relating directly or indirectly to any action or inaction taken based on the information contained in this article.


Martin Georgiev

Senior Consultant, Client Solutions

Mr. Georgiev is Senior Consultant of Client Solutions at FactSet, based in Sofia, Bulgaria. In this role, he is responsible for developing FactSet’s data science solutions. Martin helps clients achieve workflow automation and efficiency by utilizing FactSet’s quant product offerings and services. Prior to FactSet, he worked 10+ years in various finance functions, including financial planning and analysis, corporate finance, portfolio management, and asset allocation. Martin earned an MBA diploma from CEU Business School in Budapest. He is also a CFA charterholder.


The information contained in this article is not investment advice. FactSet does not endorse or recommend any investments and assumes no liability for any consequence relating directly or indirectly to any action or inaction taken based on the information contained in this article.