Key Takeaways

  • Macroeconomic regimes (e.g., inflation, growth) materially drive long-term portfolio risk and return, yet they are rarely explicitly integrated into Strategic Asset Allocation (SAA) frameworks.
  • A regime-based SAA approach improves robustness by combining portfolios designed to perform in specific economic environments, rather than optimizing for a single, static set of capital market assumptions.
  • When benchmarked against traditional asset-based SAA methodologies, regime-based portfolios – particularly risk-based combinations – deliver more resilient outcomes, including stronger out-of-sample performance and lower drawdowns.

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Summary

Motivation: SAA decisions are a primary driver of long-term pension fund performance. However, traditional SAA often relies on static capital market assumptions that are difficult to estimate and fail to account for regime shifts. Although there is ample evidence that macroeconomic regimes shape portfolio returns, much of the existing literature focuses on addressing regimes through tactical asset allocation. The authors analyze how regimes can be incorporated directly into SAA approaches, without relying on market timing or frequent reallocations.

Methodology: Using 50 years of U.S. monthly data since 1973, the authors define macroeconomic regimes along two dimensions, economic growth and inflation, resulting in four regimes: Goldilocks, overheating, downturn, and stagflation. For each regime, a maximum Sharpe ratio portfolio is constructed based on seven asset classes (U.S. equities, Treasuries, U.S. credit, TIPS, REITs, gold, and commodities ex. gold). These regime-specific portfolios are then combined using four regime-based portfolio construction approaches that vary by capital- versus risk-based allocation and by equal versus historical probability weighting. The resulting portfolios are benchmarked against traditional asset-based strategic allocation methodologies and evaluated both in-sample and out-of-sample to assess robustness, risk-adjusted performance, and drawdown behaviour.

Findings:

  • Asset class performance varies substantially across economic regimes, highlighting the limits of single-regime capital market assumptions: equities, credit, and REITs lead in Goldilocks (high growth-low inflation) environments, commodities and gold during overheating (high growth-high inflation) regimes, gold and TIPS in stagflation periods, and Treasuries and credit in downturns.
  • Goldilocks regimes dominate historically (~75% of observations) and are the only environment in which all asset classes deliver positive returns. Asset class volatilities are significantly higher in low-growth regimes.
  • In out-of-sample tests, regime-based portfolios, especially the equal-risk contribution (rg-ERC) approach, can produce superior risk-adjusted performance and more resilient drawdown behaviour than traditional asset-based allocations.
  • The rg-ERC methodology delivers the tightest dispersion of outcomes across regimes and reduces maximum drawdowns by approximately 5-10 percentage points relative to asset-based approaches, while maintaining the highest Sharpe ratio.
  • Regime-based approaches face practical limitations, as they rely on timely macroeconomic data and long historical samples; data publication lags and the low frequency and reduced relevance of some regimes (e.g., stagflation) can constrain their application, particularly in emerging markets or private asset classes with limited data availability.
  • Framing strategic asset allocation in terms of economic regimes can improve governance and communication, making portfolio trade-offs more transparent to boards and stakeholders than purely statistical allocation outputs.

Q&A Highlights:

  • Rethinking capital market assumptions: Regime-based SAA embeds capital market assumptions across economic regimes, producing more robust allocations than single-scenario optimization.
  • Choosing the right level of regime granularity: Defining too many regimes introduces estimation noise and data constraints, while overly broad definitions risk missing meaningful differences in asset behaviour.
  • Changing economic regimes: Structural changes in policy and financial markets may alter the relevance of historically defined regimes such as stagflation, underscoring the need for judgment when translating historical regime analysis into future-oriented SAA decisions.
  • Incorporating dynamic risk tolerance: Varying risk tolerance across regimes could improve alignment between long-term strategic allocations and investor behaviour in stressed environments.