ReadingResearchRadarInvestment framework
Sign in / Sign up
← Back to reading
NewsMaterial4 min readDisconfirmAI Editorial

Man Group: Macro Dual-Perspective Country Allocation Model Supplements Systematic EM Strategy with Low-Correlation Alpha

Man Numeric proposes a country allocation framework that combines time-series and cross-sectional perspectives, using machine learning to process macroeconomic data. Backtesting shows a Sharpe ratio of 0.85 and a daily return correlation of only 0.08 with fundamental country allocation models, but results are gross of transaction costs and turnover is high.

2026-10-21 06:00 GMT+8

In systematic emerging markets (EM) equity strategies, stock selection is the primary source of alpha; company fundamentals—earnings dynamics, balance sheet quality, and valuation signals—do indeed have predictive power at the security level. But country-level returns are a different matter: macro forces such as monetary policy cycles, terms of trade, external demand, and financial conditions often dominate. Man Numeric researchers Mickael Nouvellon and Dawei Zhou point out a key but long-ignored fact: risk contribution at the country level in EM is significantly higher than in developed markets (DM), meaning the opportunity set for country allocation is structurally larger, while stock-selection models are typically not designed to exploit it.

The most intuitive approach is to standardize relative valuation and 12-month price momentum, then combine them into a cross-sectional long/short country signal. However, Man Numeric finds structural flaws in this method: both valuation and momentum are slow-moving variables, resulting in country exposures that can remain static for years rather than rotating with the macro cycle. Take South Korea as an example: due to its chronic valuation discount relative to EM peers (driven by corporate governance concerns and a concentrated, export-oriented industrial structure), the model has maintained a long position in Korean equities for almost the entire past two decades, largely independent of the prevailing macro environment. More critically, this signal overlaps heavily with the information used by stock-selection models; its country tilts are often positively correlated with a fund manager's existing stock-picking alpha, offering limited diversification benefits.

Sign in / Sign up
ReadingResearchRadarInvestment framework

Man Numeric’s alternative is to combine basic economic logic with machine learning techniques, utilizing high-quality, point-in-time macroeconomic data to build a country allocation model. This framework operates through two complementary perspectives: the time-series perspective compares each country against its own history, capturing idiosyncratic dynamics (e.g., whether Brazil’s current macro environment is improving or deteriorating relative to its historical norm); the cross-sectional perspective compares all countries simultaneously, simulating a global allocator’s relative value judgment (e.g., whether Brazil is more attractive right now than Indonesia or South Africa). These two signals are fused into a single view that considers both country-specific dynamics and relative positioning, enabling the identification of how oil price increases affect net importers like South Korea differently than net exporters like Saudi Arabia, and modeling the spillover effects of US interest rates or Chinese industrial output on specific EM economies through trade linkages.

The case of South Korea from 2021 to 2026 demonstrates the model’s dynamic adaptability. In 2022, the model’s conviction shifted from sovereign credit concerns to external trade vulnerabilities—the energy price volatility triggered by the war in Ukraine made the trade balance the largest drag, with the score hitting bottom in August. In early 2023, a sector rotation signal indicated that domestic momentum was turning before the global backdrop caught up; thereafter, the model remained bullish during periods of stabilizing trade balances and softening core inflation, until a new sector rotation in September 2024 pulled the score back to cycle lows. In early 2026, a sharp drop in silver and gold prices, combined with strong semiconductor exports and softening oil prices, reinvigorated Korea’s export engine, leading to the most significant improvement in the score. This stands in stark contrast to the static, multi-year tilts generated by fundamental models.

In backtests from 2008 to 2026, this macro model produced a realized Sharpe ratio of 0.85, a substantial improvement over the fundamental model’s level of under 0.5, while turnover was 28% lower and maximum peak-to-trough drawdown was 4.6% compared to the fundamental model’s 8.7%. Because it is built on macroeconomic data rather than valuation or price information, its daily returns correlate with those of the fundamental model at only 0.08, constituting a largely uncorrelated source of alpha.

However, Man Numeric explicitly notes several important boundaries: all results are gross returns before transaction costs; both models rebalance daily, implying considerable annualized turnover, which means translating them into actual tradable strategies requires addressing cost issues through position smoothing, reduced rebalancing frequency, or explicit trading constraints; the single-country study on Korea is merely an illustration of the model’s mechanics, and its results should not be generalized to other markets; the fundamental model generated over 40% of its value added since 2008 in the last 12 months alone, and the model was particularly vulnerable when Russia became uninvestable in 2022. These are self-reported backtest results by Man Numeric as a strategy provider and have not yet been independently verified.

Man Numeric concludes that this is not a replacement for stock selection, but a complement to it—a systematic overlay that transforms country risk from an unmanaged residual into an intentional, data-driven source of alpha. As macro differentiation across emerging markets continues to deepen, they believe the rationale for integrating this capability into institutional investment processes will only strengthen.

Sources

  • man.com↗
Continue researching →