Goldman Sachs constructs seven FX factors, emphasizing macro scenarios rather than standalone returns in evaluating their trading value

Overview

The report constructs dynamically weighted long-short FX factors across 26 currencies, including the USD, to explain cross-sectional currency performance and exposure to global macro themes. The authors explicitly distinguish descriptive thematic factors from systematic trading strategies targeting standalone returns, arguing that the former should be used in conjunction with the macro environment.

Core views

Most macro FX factors exhibit high correlations, particularly along the risk-appetite dimension: carry, equity risk, low-debt, and commodity factors generally move in the same direction, while the net international investment position factor moves in the opposite direction. Currencies that are exceptions between factors and trade-offs across factors create more attractive hedging and trading opportunities. Re-normalizing related factors into blended factors through weight differences or weight sums can improve exposure efficiency or performance in specific environments.

Analysis framework

Time-varying positive and negative weights are assigned to each currency's total return to form long-short factor portfolios. Daily factors are resampled to quarterly frequency and regressed against S&P 500 returns, changes in 2-year and 10-year U.S. Treasury yields, GS Commodity Index returns, trade-weighted USD realized volatility, and realized USD–10-year Treasury yield correlation. The report also compares linear weights with more concentrated exponential-transformation weights and constructs two-factor blended portfolios.

Methodology notes

• Macro Factor FrameworkDynamically Weighted FX Factors

  Thematic factors constructed from long-short weights across 26 currencies

  Each factor contains currencies with positive and negative weights, with positive and negative weights normalized separately to +1 and -1; the USD is represented by its equal-weighted performance relative to the other 25 currencies.

• Statistical AnalysisQuarterly Macro Regressions

  Tests factor sensitivity to key macro market variables

  The sample spans the first quarter of 2010 through the fourth quarter of 2025, assessing relationships between factors and equities, rates, commodities, USD volatility, and USD–yield correlations at quarterly frequency.

• Portfolio ConstructionBlended Factors

  Optimizes thematic trade-offs through two-factor weight differences or weight sums

  Weight differences are taken for positively correlated factors and weight sums for negatively correlated factors, followed by re-normalization to select more efficient currency exposures.

Asset mapping & comparison

Structured mapping from thesis to named assets (strengths, weaknesses, peers, risks).

• Carry Factor

  Risk-on, generally positively correlated with the equity-risk factor

  Strengths

  Provides exposure to high-interest-rate currencies and can improve risk-adjusted performance when blended with other factors.

  Weaknesses

  Vulnerable to unwinds during risk events, with an unremarkable full-sample standalone Sharpe ratio.

  Comparison

  Generally negatively correlated with the net international investment position factor; blending with mean reversion can enhance total returns.

  Risks

  High-yielding currencies may face sharp depreciation, default, or high-inflation risk, and the currency sample is subject to survivorship bias.

• Net International Investment Position Factor

  Risk-off, generally negatively correlated with risk-appetite factors

  Strengths

  May perform well amid rising USD asset volatility, falling USD–yield correlation, and capital repatriation.

  Weaknesses

  Typically has low-yield characteristics, making direct carry costs potentially high.

  Comparison

  Can be combined with carry to create more cost-efficient capital-retrenchment thematic exposure; the report specifically notes NOK and the portfolio-screening value of TWD, BRL, COP, and ILS.

  Risks

  Capital flows, USD hedging demand, and changes in cross-asset correlations are highly regime-dependent.

• Energy and Metals Commodity Factors

  Strongly associated with commodity prices and risk-appetite environments

  Strengths

  Can provide thematic exposure to commodity cycles and net commodity exporters.

  Weaknesses

  Highly correlated with other risk-appetite factors, potentially limiting portfolio diversification benefits.

  Comparison

  The energy factor applies a 25% adjustment to NOK's net energy-export scale to reflect reduced oil-price sensitivity due to its fiscal framework.

  Risks

  Commodity-price volatility, changes in terms of trade, and shifts in export structures may weaken historical relationships.

• Mean Reversion Factor

  A technical factor that assigns higher weights to currencies with weaker total returns over the prior six months

  Strengths

  Has weaker correlations with macro factors and major macro variables, allowing it to independently complement carry exposure.

  Weaknesses

  Lacks a fundamental explanation directly corresponding to a macro theme.

  Comparison

  When blended with carry, the report observes higher full-sample total returns and similar volatility.

  Risks

  Persistent trends and changes in market structure may cause mean-reversion signals to fail.

Key data

• Currency Coverage26 currenciesIncludes the USD; the USD is represented by its equal-weighted performance relative to the other 25 currencies.
• Number of Factors7 categoriesCarry, equity risk, government debt, net international investment position, energy commodities, metals commodities, and mean reversion.
• Regression Sample PeriodFirst quarter of 2010 to fourth quarter of 2025Factors are resampled from daily data to quarterly frequency.
• Full-sample total return of the carry/mean reversion portfolio relative to pure carryApproximately 40 percentage points higherThe report states that the two have similar levels of volatility.
• Relative-performance environment for the carry/risk hedge portfolioQuarterly equity-market drawdown of less than 3%The report states that the portfolio outperforms both standalone constituent factors during moderate equity drawdowns.

Impact & implications

For FX investors, factors should serve as tools for identifying macro regimes, measuring currency exposures, and constructing relative-value portfolios. The high collinearity among risk-appetite factors implies that apparent diversification may be limited; more valuable allocations seek currencies that combine carry and safe-haven characteristics, or provide capital-retrenchment thematic exposure at lower holding costs. More concentrated weights may strengthen thematic exposure and historical returns, but do not give any individual factor a particularly strong unconditional Sharpe ratio.

Risks

• The unconditional Sharpe ratios of the factors are generally unremarkable, and historical backtests do not imply consistently achievable risk-adjusted returns.
• Risk-appetite factors are highly correlated and may draw down simultaneously during market stress.
• Factor performance is sensitive to the currency universe, weighting method, and rebalancing frequency, and is subject to survivorship-bias risk.
• The relative advantages of blended factors are macro-regime-dependent; correlation breakdowns or capital-repatriation scenarios may not recur.
• More concentrated weights may increase thematic purity but also raise individual-currency and liquidity risks.

What to watch

• Whether global equity markets enter a moderate drawdown and the relative performance of the carry/risk hedge portfolio.
• Whether the correlation between the USD and U.S. Treasury yields declines, and the resulting repatriation of overseas assets and demand for USD hedging.
• Changes in trade-weighted USD realized volatility, U.S. fiscal concerns, and global capital flows.
• Whether energy risks abate, thereby extending the advantage of pro-cyclical currencies over pure high-carry currencies.
• Changes in the trade-offs between carry and safe-haven themes for NOK, ZAR, and the report-cited TWD, BRL, COP, and ILS.