The quantitative investing landscape has enjoyed a remarkable revival since the ‘Quant Winter’ of 2018-2020. After several favourable years, some investors are naturally questioning whether this positive momentum can continue. Here, we address this question by examining how the evolution of systematic equity investing has created opportunities for genuine differentiation. 

The root causes

Our research identifies two primary drivers of past underperformance: adverse macro environments and the inherent sensitivity of traditional factors to them. This sensitivity introduces risk that cannot be easily diversified away through traditional portfolio construction techniques.

While individual macro relationships provide valuable insights into factor behaviour, economic conditions reflect the complex interaction of multiple variables, including interest rates, inflation, growth expectations, policy settings, and market sentiment. To capture these interactions, we leverage Man Numeric’s MacroScope model,¹  a framework that identifies how variables combine to create distinct market environments and their implications for systematic investment strategies.

We identified four distinct macroeconomic regimes:

• Crisis/recession periods (Regime One): Times of acute economic and financial stress, characterised by defensiveness, elevated volatility, correlation spikes between asset classes, and significant liquidity constraints. 
• Recovery/early expansion periods (Regime Two): Post-crisis phases when accommodative policy measures remain active and markets begin to normalise, often presenting favourable conditions for systematic investment strategies.
• Mid-cycle expansion (Regime Three): Periods of stable economic growth, moderate volatility and balanced policy settings, featuring normal factor performance patterns and economic conditions that often represent the optimal environment for quantitative investment strategies.
• Late cycle/overheating (Regime Four): Periods of extended economic expansion with potential overheating concerns, policy tightening, and compressed risk premiums. These phases typically precede Quant Winters, featuring ‘everything rally’ dynamics where correlations between different investment approaches increase. 

Figure 1 illustrates how the economy has cycled through these regimes over the past three decades.

Figure 1: Macroeconomic regimes (Aug 1994-July 2025)

^{Source: Man Numeric. 1=Crisis/recession periods, 2=Recovery/early expansion periods, 3=Mid-cycle expansion, 4=Late cycle/overheating.}

Macro-dependent performance

This regime framework demonstrates that generic factor performance can be highly dependent on macro conditions. 

Using momentum as an example, during crisis/recession periods (Regime One), momentum factors show consistent underperformance across all geographic regions, as established trends reverse rapidly and correlations between securities spike.

Volatility in cross-sectional momentum factors increases substantially during recession periods compared to normal market conditions. 

Figure 2a: Momentum² factor performance by macroeconomic regime
Long-short quintile spreads, sector-neutral
 

Figure 2b: Momentum factor volatility by macroeconomic regime

^{Source: Man Numeric, S&P Capital IQ, Jan 2005-July 2025.}

Multi-factor: not so diversifying after all?

Importantly, these patterns extend beyond momentum to encompass all major factor categories, meaning that the benefits of multi-factor diversification may be overstated during periods when macro regime shifts create correlated headwinds.

During normal market conditions, macro factors typically explain 15-25% of generic factor return variation, leaving substantial room for idiosyncratic factor performance and diversification benefits. However, during stress periods, this percentage can potentially spike dramatically — reaching peaks of over 40% during the GFC around 2009 and again during 2018-2019. These substantial increases in R-squared values during Quant Winters demonstrate that macro factors often become the dominant driver of factor returns (putting bottom-up company fundamentals in the backseat). This occurs precisely when diversification is needed most, fundamentally altering the risk-return characteristics of factor-based strategies.

This pattern becomes even more concerning when examining crowding among quantitative managers. Macro factors consistently explain a larger proportion of variation in live manager returns compared to generic factor returns. This difference likely reflects several aspects of real-world portfolio construction that amplify macro sensitivity beyond what pure factor returns would suggest.

Constraints and timing

Quant portfolios are constructed with sector constraints, position sizing rules and risk budgeting frameworks that can inadvertently concentrate macro exposures. Many managers also employ similar risk management overlays such as volatility targeting that trigger synchronised portfolio adjustments during stress periods, effectively creating common macro exposures that don’t exist in the underlying factors themselves.

The timing of factor exposures also matters – managers who rebalance on similar schedules or respond to similar signals may inadvertently synchronise their macro sensitivities even when their underlying factor philosophies differ.

When macro sensitivity increases, much of the diversification benefit that investors expect from allocating across multiple quant managers disappears. As these shared macro sensitivities dominate returns precisely when diversification is most valuable, the result is reduced portfolio-level diversification benefits and increased systemic risk during Quant Winters.

Preparing for future market cycles

Some quant managers have addressed the core structural vulnerabilities behind past Quant Winters, simultaneously creating new sources of alpha generation and risk management. This transformation encompasses three dimensions to create a more robust and resilient framework:

1.    Enhanced diversification through alternative data and novel data science techniques

Alternative data models have nearly tripled in recent years, providing real-time insights into business fundamentals before they appear in financial statements. This creates temporal advantages and independence from traditional accounting frameworks that reduce susceptibility to macro sensitivities.

Machine learning techniques capture complex, non-linear relationships in high-dimensional datasets where traditional statistical approaches struggle, providing pattern recognition capabilities that extend far beyond conventional analytical capacity.

2.    Dynamic factor selection and model combination

Our dynamic approach utilises machine learning and macro-informed weighting to create a forward-looking, adaptive factor combination. This methodology continuously evaluates changing market conditions and factor relationships to optimise combinations in real time, moving beyond backward-looking metrics to embrace predictive analytics and regime-aware positioning. 

3.    Macro regime resilience

Perhaps the most critical evolution in systematic investing is the development of macro regime resilience. This does not necessarily require explicit regime detection or regime adaptive modelling. Rather, it involves constructing alpha sources and factor combinations that are inherently more robust to macro regime changes through their fundamental design and diversification properties. 

Conclusion

The opportunity for transformation is undeniable. This evolution represents more than technological advancement; it reflects a deeper understanding of market dynamics and systematic risk. The integration of alternative data, machine learning, and regime awareness has created what we believe is a new foundation for systematic investing – one that potentially maintains the benefits of quantitative approaches while finally addressing their historical vulnerabilities.

This is an abridged version of a paper originally published on Man Institute. 

^{1 See: https://www.man.com/insights/under-the-macroscope}

^{2 Momentum factor defined as 39-week price momentum lagged by four weeks from S&P Capital IQ Alpha Factor Library.}