Diversified trend following in emerging FX markets

October 25, 2025

Jupyter Notebook

Trend-following strategies exploit sustained past price movements, capitalizing on inefficiencies such as the gradual dissemination of information, inertia in portfolio reallocation, and disposition effects. However, the exclusive reliance on price data makes them highly sensitive to prevailing market regimes, neglects accompanying economic dynamics, and often leads to crowded positioning. These limitations can be mitigated by incorporating point-in-time information on concurrent macroeconomic trends.
We demonstrate the advantages of diversifying trend signals in the emerging-market FX space. While purely price-based strategies performed exceptionally during the EM boom and the global financial crisis of the 2000s, they struggled in the largely trendless markets of the 2010s and 2020s. By integrating macro support and headwind factors, one can construct macro-enhanced trends, i.e., signals that draw on a broader and more stable information set. Such approaches have delivered significantly higher risk-adjusted returns and sustained positive PnL even during periods of weak market trends.

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Terms of trade as trading signals

October 11, 2025

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Terms of trade measure an economy’s ratio of export prices to import prices. Changes in these ratios drive divergences in economic performance across currency areas and influence foreign exchange and other asset market returns. Consequently, timely estimates of terms-of-trade dynamics, often derived from commodity prices, have long been valued as leading indicators by investors. Today, point-in-time versions of these measures provide a robust foundation for statistical analysis and backtesting of systematic strategies.
This post illustrates the trading value of real-time terms-of-trade changes through five simple strategies. Individually, such single-signal strategies yield only modest standalone performance ratios. However, their profit-and-loss streams are largely uncorrelated with major market benchmarks and only moderately correlated with each other. This suggests that, when applied systematically and across a broad set of markets, terms-of-trade dynamics can serve as a significant and independent source of true alpha in systematic macro trading.

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A scorecard for global equity allocation

September 27, 2025

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Macro-quantamental scorecards are systematic enhancements of discretionary portfolio management. They offer (a) information efficiency by structuring and condensing key macroeconomic data series, and (b) empirical validation of predictive power and trading value using historic point-in-time information. Scorecards can be readily built in Python, with pandas and existing classes and methods.
Macro-quantamental scores support capital allocation across country equity markets, which is critical for long-term wealth generation by professional investment managers and private investors alike. This post demonstrates how to construct a simple tactical scorecard based on real equity carry, real exchange rate valuation, terms-of-trade dynamics, external balance strength, international investment position changes, and economic confidence trends. There is strong evidence that such a systematic approach delivers predictive power and sustained value generation.

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Macro trading factors: dimension reduction and statistical learning

September 13, 2025

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Macro trading factors are information states of economic developments that help predict asset returns. A single factor is typically represented by multiple indicators, just as a trading signal often combines several factors. Like signal generation, factor construction can be supported by regression-based statistical learning. Dimension reduction is particularly useful for factor discovery. It is the transformation of high-dimensional data into a lower-dimensional representation that retains most of the information content. Dimension reduction methods, such as principal components and partial least squares, reduce bias, increase objectivity, and strengthen the reliability of backtests.

This post applies statistical learning with dimension-reduction techniques to macro factor generation for developed fixed-income markets. The method adapts to the degree of theoretical guidance and the complexity of the data. Several dimension-reduction approaches have successfully produced factors for interest-rate swap trading, delivering positive predictive power, strong accuracy, and robust long-term PnL.

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The (hidden) trading value of central bank liquidity information

August 30, 2025

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Central banks regularly adjust the economy’s monetary base through foreign exchange interventions and open market operations. Point-in-time information on such intervention-based liquidity expansion has predictive power for asset returns. That is because such operations often come in longer-term trends, and there are lagged effects, for example, through private sector portfolio rebalancing. Alas, the discovery of the economic value of intervention-liquidity signals is often obscured by “ugly backtests”: as a single type of information applied to a single asset type, simulations typically show patchy relevance and uneven value profiles. However, across strategy types and asset classes, intervention-driven liquidity growth has consistently contributed value. A simulation combining two directional and two relative-value strategies demonstrates steady and meaningful PnL generation with low correlation to major market benchmarks.
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Systematic equity allocation across countries for dollar-based investors

August 16, 2025

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This post demonstrates that country allocation with macroeconomic factors can materially enhance the returns on international equity portfolios in dollar terms. We identify a range of economic developments that, according to standard theory and in conjunction with market inattention, should predict the outperformance of countries either through exchange rate appreciation or higher local-currency equity returns. These developments are captured in backtestable economic factor scores, built from point-in-time macro-quantamental indicators. To select and combine these factors into trading signals without hindsight bias, we employ sequential machine learning. Empirical evidence based on 19 international markets shows highly significant predictive power and consistent material value arising from cross-country allocation alone. A simulation of U.S. dollar-based cross-country allocation of real money investment also reveals material long-term outperformance relative to an equally-weighted international portfolio or a U.S. portfolio.

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Machine learning for international trading strategies

July 26, 2025

Jupyter Notebook

Financial markets’ broadening access to point-in-time economic indicators across countries offers a robust foundation for diversified international trading strategies. The central challenge lies in combining multiple macro factors into a single positioning signal for each country—drawing on statistical patterns from both global and country-specific (local) experiences. To address this, we propose a novel “global-local” method of machine learning for generating international macro trading signals. This method manages the bias-variance tradeoff by regularizing country-level coefficients toward their global counterparts. Crucially, the strength of this regularization diminishes when historical evidence supports the value of emphasizing local relationships over global ones. We demonstrate the approach by applying it to international equity index futures strategies. The “global-local” method has generated stronger predictive power and higher risk-adjusted returns than either fully country-specific models or globally pooled alternatives.

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Estimating emerging markets sovereign risk premia

July 12, 2025

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Macro risk premia compensate investors for bearing broad economic uncertainty. For systematic strategies, these premia can be approximated by the difference between market-implied risk and risk implied by fundamental macroeconomic data. In the emerging market sovereign credit space, market-implied risk is represented by credit spreads and ratings, while fundamental risk factors may include government finances, external and investment balances, foreign-currency debt, political stability, medium-term economic growth, and inflation.
Macro risk premium scores, derived from this comparison, capture variations in premia paid across time and countries. Applied to 24 sovereigns over the past 25 years, these scores show significant positive predictive power for volatility-targeted returns on U.S. dollar-denominated bond indices. Compared to strategies based solely on market risk metrics, those guided by macro risk premia de-emphasize “reach for yield” behavior and deliver superior risk-adjusted returns. Moreover, the long-term performance of relative cross-currency allocations suggests that sovereign risk premia could serve as a robust foundation for adjusting bond index weights.

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Economic surprises and commodity future returns

June 28, 2025

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Surprises in industry and construction activity are plausible predictors of short-term future returns for commodities that are heavily used in these sectors. This hypothesis can be evaluated using quantamental economic surprises, which are point-in-time differences between estimated market expectations and actual reported values of economic indicators, measured at a daily frequency.
We specifically assess the predictive power and economic value of global surprises in survey and production indicators across 32 economies. Country-level surprises are first standardised and then aggregated into global composites, weighted by each economy’s share in global industry. Since 2000, the resulting global surprise indicator has been a statistically significant predictor of returns for an industrial commodity futures basket. A simple daily trading strategy based solely on this signal would have generated material risk-adjusted returns with little correlation to global equity markets. Further hedging against non-growth-related factors, such as monetary risk proxies, can improve performance.

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Cross-country relative duration strategies with macro factors

June 7, 2025

Jupyter Notebook

Cross-country relative duration strategies take vol-adjusted fixed receiver positions in interest rate swaps of one currency area versus others. Similar to directional IRS strategies, cross-country trading factors can be based on point-in-time indicators of economic developments. The main difference is that cross-country factors are typically related to relative economic performance.
This post constructs 12 conceptual macro factors that theoretically should help predict cross-country vol-targeted duration returns. Empirically, all of them have displayed at least some modest predictive power, while their cross-correlations have been mostly modest and very diverse. Composite signals can be generated by conceptual parity or sequential machine learning methods. All of these have produced material, consistent, and uncorrelated risk-adjusted returns over the past two decades with few major drawdowns.

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