Mapping Asset Returns to Economic Regimes: A Practical Investor’s Guide

Asset owners, institutional managers, and wealth managers looking to sharpen their investment strategies may need to move beyond conventional assessments of historical risk and return. A more refined, macro-aware perspective—one that organizes market history into distinct economic regimes defined by core drivers like growth and inflation—can provide deeper and more actionable insights. By framing asset performance through the lens of the prevailing macro environment, such regime-based models can offer a clearer (though still grounded in the theory) alternative to traditional approaches and help investors better prepare for what lies ahead.

This article explains how identifying and analyzing such regimes can enhance strategic asset allocation, investment decisions, and risk management in today’s dynamic global markets. We examine this approach with the following goals:

• Explore whether market history can be sorted into a larger but still manageable set of qualitatively different and nuanced economic regimes.

• Root our analysis in fundamental macro factors and signals of wider economic conditions.

• Discern whether identified macro patterns translate in a consistent and robust way across the performance of a wide range of markets and asset classes. 

Why Consider a Regime Approach?

By evaluating investment strategies through the lens of economic cycles, investors can gain additional clarity into opportunities and vulnerabilities in three areas.

Investment evaluation

Regime-based analysis allows a more nuanced view of investment alternatives. Instead of evaluations solely with average returns or risk-return measures, one can use the regime approach to assess strategy or vehicle performance across economic conditions as well as inform capital market assumptions.

Asset allocation

Asset allocation can move beyond basic portfolio analysis towards a more realistic multi-period investment framework to construct strategically optimal portfolios. Decision makers can also adjust tactical exposures before the next economic regime.

Risk management

Regime mapping offers a complementary lens to traditional historical volatility or value-at-risk measures with more realistic risk estimates conditional on well-defined macro environments. Stress testing with regime-specific drawdowns and volatilities can also uncover hidden vulnerabilities.

Building an Investment Model Around Economic Regimes

A regime-based approach is relevant given the unexpected events over the last five years that continue to impact the global economy: Covid supply shock followed by the post-pandemic inflation spike and, just as those events subsided, new uncertainties from the 2025 U.S. tariff plans that have caused speculation about a global recession.

A practical way to address these time periods is to sort history into a handful of economic regimes and evaluate how broad asset classes behaved within them. If patterns are clear and durable, they become a useful tool for portfolio construction and risk management. 

We propose a model that identifies regimes with the help of two macro signals constructed with the U.S. Composite Leading Indicator (CLI) from the Organization for Economic Cooperation and Development (OECD; FactSet ID USA.LOLITOAA.STSA) and the U.S. inflation index CPI-U from the Bureau of Labor and Statistics (BLS; FactSet ID BLSCUUR0000SA0). The macro signals include:

• A growth signal derived from the monthly percentage change in the U.S. OECD CLI.

• An inflation‑trend signal (ITS) based on the gap between 3‑month and 36‑month annualized CPI (consumer price index) inflation.

While this model is U.S.-centric, it can be easily extended to other regions and markets by adopting the respective regional factors.

Depending on the realization of each of the signals we assign each month to one of four regimes:

• Increasing CLI and increasing ITS - Heating Regime

• Increasing CLI and decreasing ITS - Growing Regime

• Decreasing CLI and decreasing ITS—Slowing regime

• Decreasing CLI and increasing ITS—Stagflation regime[1]

Constructed this way, our regime model is on one hand simple but on the other hand enables full-scale economic insights rooted in aggregate demand and supply.

The CLI is not selected arbitrarily; it indicates shifts in expected economic activity by capturing movements in aggregate demand through the combined effect of various forward-looking indicators. For example, net new orders of durable goods, consumer confidence surveys, financial market prices, and interest rate spreads.[2]

The ITS acts as a measure of inflation momentum, highlighting whether short-term inflation is accelerating or decelerating relative to its long-term trend. By simultaneously incorporating economic (CLI) and price (ITS) factors, our regime model disentangles whether changes in economic activity are supply or demand driven, which eventually influences the performance of various asset classes.

This feature of the model is important one because, for example, linking falling oil prices to a weak global economy or lower rents to a troubled housing market reflects a common way of interpreting price movements. Yet a price drop may just as well result from rising supply as from falling demand—or some mix of the two. Understanding which force is at play is essential to grasp where the economy might be heading.

A rising CLI with rising ITS suggest that economic expansion is based on demand outpacing supply, a situation typically expected to favor cyclical and risky assets like commodities and emerging market equities. Alternatively, a rising CLI with falling ITS would indicate non-inflationary growth where rising demand can accommodate positive supply shocks. This scenario supports both equities and bonds.

Conversely, a falling CLI with rising ITS reflects stagflation risk (weak demand growth combined with negative supply shocks), a regime historically challenging for most asset classes, including safe government bonds. Alternatively, a falling CLI with falling ITS is associated with a typical demand-driven recession, where defensive assets like government bonds tend to perform best.

All in all, mapping asset class performance to these empirically defined states allows for a more dynamic and economically grounded investment framework.

Evaluating the Regime Model: What the Data Says

A natural starting point in evaluating the performance of the regime model is to determine whether it aligns with historical economic regimes. As shown in Figure 1, which is fed only with CLI growth and inflation-momentum (ITS) signals, the full 1958–2025 history[3] confirms the empirical regime model maps correctly to past macro episodes.

Figure 1. Performance of Empirical Regimes and Regime Factors Over Time

Periods of strong, demand-led expansions such as the beginning of the 1980s and recovery from the global financial crisis are Growing or Heating regimes, while the periods of high inflation and deep recession from 1973–1975 and 1978–1980 are Stagflation regimes.

The sharp downturn in 2008–2009 and the Covid-related contraction are classified as Slowing or Stagflation regimes, while the rebound immediately after the pandemic is a Heating regime given the surging inflation and recovering economic activity.

A pair of historical examples from 1997–1998 are the Asian-currency crisis and the Long-Term Capital Management hedge fund turmoil. Both are tagged as Slowing regimes, with a demand-side shock in which activity slipped and inflation pressure faded.

In the early 2000s, the tech unwind began in a late-cycle Stagflation regime—a period when growth was decelerating while headline CPI was moving up—before flipping to Slowing once a recession began in 2001. 

The model also identifies benign expansion periods like 1983–1984, the mid-1990s, and 2023–2024 as Growing regimes when rising economic activity and improving aggregate demand was accompanied by moderating inflation. The early 2010s, a period of stable growth after the Great Recession, was also part of this regime.

Regime Duration and Transitions

The persistence of the model is good. Average regime lengths cluster around four months (Growing - 4.7, Stagflation - 4.5, Slowing - 3.6, Heating - 3.5), which is long enough to make tactical investment adjustments before the next regime. The chance of staying in the same regime for that period of time is 75%.

The most common transitions are Stagflation to Slowing (about 18 % of all moves), followed closely by Growing to Heating (17%). The sequence Heating to Growing is also important as it ranks third at roughly 15%, while Slowing to Stagflation is fourth (14%).

In other words, the model more often records inflation pressure building as the expansion matures (Growing to Heating) than the reverse (Slowing), and it more often shows price pressures easing before growth recovers (Stagflation to Slowing) than the other way around.

The frequency of unreasonable transitions such as Slowing to Heating or Stagflation to Heating is relatively low at 3% and 4%, respectively, giving further support for the performance of the model.

Model Limitations

As a model it is not a perfect replication of the real world, so there are occasions when it behaves inconsistently. For example, periods when the regime categorization is ambiguous, less economically intuitive, or unstable due to implausibly rapid (monthly) transitions.

Additionally, on some occasions the model shows inconsistent transitions between regimes, such as Stagflation to Heating. There also can be mislabeled periods, such as the 2021–2022 inflationary period that is mapped to the stagflation regime. Even though this period experienced a spike in inflation rates, it was hardly a recessionary one given the lack of falling aggregate demand.

It’s also worth noting that the model has not yet been tested using point-in-time macro data that accounts for real-time availability or future data revisions, which could affect the performance of historical regime signals when applied in practice.

Even when the model has shortcomings, they tend to be as minor or as acceptable as possible. For example, when the model oscillates[4] between regimes, it mostly happens between Slowing to Stagflation (30% of all oscillation periods) and Growing to Heating (31% of all oscillation periods). This makes the impact of errors relatively minor and suggests that a two-state model could be more robust, with only a slight reduction in precision as a potential trade-off.

The regime model is intentionally simple, relying on just two macro signals—CLI and inflation momentum. While adding more variables or refining the transformations (such as distinguishing trend-level changes in CLI) could sharpen regime detection, there’s a trade-off: more complexity risks overfitting. Striking the right balance between simplicity and signal precision remains a key challenge.

Another way to assess the regime model is by comparing how different asset classes perform in each regime to see if the results match economic expectations. However, this analysis is limited because market indices data only goes back to 1997, so the evaluation covers a shorter period than the regime model itself, which starts in 1958. As a result, the analysis has constraints due to the limited historical data available for asset classes[5].

Before we turn to specific asset class performance, analyzing the overall asset class performance across the regimes confirms the model does a good job separating between Growing to Slowing as well as Heating to Stagflation. As shown in Figures 2 and 3, this is visible from the distinction in the mean returns of the asset classes across the regime pairs, which form a clear fanning-out pattern when going from safer to riskier asset classes.

Figure 2. Historical Asset Class Average Monthly Return and Standard Deviation under Growing and Slowing Regimes

Figure 3. Historical Asset Class Average Monthly Return and Standard Deviation under Heating and Stagflation Regimes

Regime Asset Class Performance

Next, we analyze specific regime-by-regime asset class performance, which paints a nuanced picture to both confirm many economic expectations and surface subtler dynamics.

Unsurprisingly, risky assets prosper when growth is strong, and inflation stays low. Broad equities, cyclical equities, and emerging market debt deliver their best return numbers in Growing to Heating and turn lower or negative in both Slowing to Stagflation.

Emerging-market equities display the highest average return among all asset classes but at the cost of higher volatility. Data also reveals that almost all asset classes perform better in terms of realized average return under the Growing to Heating regime.

This might look surprising at first, but it is not without logic once accounting for the fact that in Heating the growth in demand might start to collide with real-world supply constraints. It is also natural to expect that economy’s potential returns have reached their limit, and expected and realized returns would begin reverting to their long-term means. This is further supported by the fact that only cyclical commodities, precious metals, and inflation-linked bonds have their best performance under Heating regimes.

Another noteworthy finding consistent with the nature of the asset class is the asymmetry of returns for equity sub-sectors; cyclical versus defensive equities widen the performance gap dramatically, depending on the state of the regime. The return differential is +1.9 % per month in Growing and +0.9 % in Heating but flips to –1.4 % in Stagflation.

Figure 4. Monthly Historical Asset Class Performance under Growing Regime

Figure 5. Monthly Historical Asset Class Performance under Slowing Regime

Figure 6. Monthly Historical Asset Class Performance under Heating Regime

Figure 7. Monthly Historical Asset Class Performance under Stagflation Regime

Fixed income behaves almost as the textbooks suggest. Government bonds barely keep up with cash in the two expansionary regimes but post a healthy 0.8 % a month (Sharpe 0.5) once demand collapses in the Slowing regime. Importantly, they still earn a positive nominal return in the Stagflation regime.

Riskier debt tells the mirror scenario. Emerging market credit thrives in expansionary regimes such as Growing and Heating when risk premia narrow. Once activity stalls and risk premia rises, returns decrease in Slowing and Stagflation. Non-U.S. sovereign bonds echo this pattern, even turning negative in Stagflation while, as expected, inflation-linked bonds outperform in Stagflation and Slowing relative to Growing and Heating regimes.

Another nuance in the regime model is that high yield credit earns the full return premium in Growing and Heating (with Growing delivering 0.3% more return than Heating, on average) and turns flat to negative in Slowing and Stagflation. The asymmetry suggests that its risk exposure belongs in the same growth bucket as equities, despite the asset class often being grouped with bonds in strategic allocations.

Taken together, the regime lines up neatly with economic intuition: High-quality paper protects capital when demand and economic activity suffer, whereas riskier credit and emerging market risks are rewarded primarily when the expansion is still gathering momentum and inflation has not yet built up speed.

Commodities split neatly between the demand and supply stories. Cyclical industrial commodities perform best under Growing and Heating regimes when demand is strong but show similar performance under Stagflation, thus displaying their ability to adjust quickly to rising prices.

Their worst performance is during the Slowing regime, delivering negative returns when demand weakens and prices fall.

Precious metals, by contrast, shine once growth weakens, gaining 1.4% a month in Slowing and perform well in Heating. They suffer most under Stagflation and, based on past performance, challenge the view that precious metals are a persistent portfolio diversifier and inflation hedge.

Foreign-exchange exposure shows the opposite behavior. When the economy is doing well (the Growing and Heating regimes) the dollar tends to drift lower and deliver negative returns. When the cycle shifts to Slowing or Stagflation, a slowdown in the U.S. economy usually leads to or coincides with a global downturn. This increases demand for Treasuries and strengthens the dollar.

Cash is the king of consistency. Modest but stable returns and Sharpe above 1 in almost every regime except Heating are hardly exciting, yet they illustrate the opportunity cost of leaving liquidity idle during the right parts of the cycle.

The regime analysis also uncovers a set of patterns that are both familiar and yet richer than those offered by unconditional statistics alone. The historical data affirms that no single asset class dominates across all environments. Periods of outperformance are typically clustered in specific regimes, and the risk of significant underperformance is never fully eliminated.

For investors and risk managers, this underscores the practical value of regime thinking—not only as a prediction tool, but as a means of understanding the conditions under which certain assets are likely to deliver on their intended roles within a portfolio.

Conclusion

In conclusion, regime-based analysis provides a more adaptive and informed framework for asset allocation and risk management. By linking asset class performance to distinct macroeconomic environments, you can identify opportunities and vulnerabilities that traditional models may miss. The evidence shows that no single asset class consistently outperforms across all regimes, reinforcing the value of a dynamic, regime-aware approach. Incorporating regime signals enables you to respond more strategically to changing market conditions and build portfolios better suited for long-term resilience.

Up next

Watch for our next article, which will extend the analysis by testing its validity through the practical application of a scenario closer to a real-world use case. We will test and report model performance in an asset allocation/investment evaluation set up consisting of:

• Building a forecasting model for the next regime that uses past regimes as determined by the empirical regime mode in combination with a set of other contemporary factors.

• Using the regime forecast together with regime-dependent CMAs (based on historical regime performance) to back test conditional asset allocation strategy and compare its performance relative to a traditional, unconditional strategy.

FactSet Programmatic Environment

To test your ideas faster in one cohesive, multi-asset class data environment, consider the FactSet Programmatic Environment. FPE enables you to easily evaluate and implement your own assumptions and models in an open, programmatic environment. You can simulate strategy performance over current and historical data and iterate on ideas and optimize portfolio construction and performance through the entire workflow.

[1] These regime definitions are used in a very broad semantic way to distinguishing between four different states of the economy as indicated by the macro factors that are only remotely connected to specific economic conditions. This naming convention doesn’t have the intention to load narrowly on any concrete economic meaning that might be accepted within a particular investment community. Thus, for example “Stagflation” indicates any environment which shows slowing CLI and rising CPI without distinguishing in magnitude of these changes or pointing to any dire economic environment.

[2] For a detailed review and description of the CLI construction and methodology see Composite Leading Indicators (CLI) | OECD.

[3] This is the longest common history period for which both CLI and CPI factors have time series data.

[4] Model oscillation is defined as a period longer than two months where the model oscillates between two regimes and do not spend more than two consecutive months within any of the two regimes.

[5] Asset classes are represented by widely used market indices—e.g., International Developed Equity is represented by MSCI ACWI ex USA (FactSet ID: 899901) and Cash by ICE BofA U.S. 3-Month Treasury Bill (FactSet ID: MLG0O1). Other asset classes follow the same logic. All index return series are measured in U.S. dollars (USD).

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