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

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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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Macro-aware risk parity

May 24, 2025

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Risk parity is an investment strategy that allocates risk exposure equally across asset types through volatility-based calibration and leverage. A most profitable risk parity strategy in the past decades has been the equity-duration “long-long”, which harvests combined equity and long fixed-income risk premia, while containing return volatility through diversification. Alas, this position is vulnerable to “autonomous” monetary tightening shocks, i.e., surges in real interest rates without commensurate economic improvements.
The probability of such downside shocks depends on the macroeconomic environment. Hence, by adapting volatility-targeted risk-parity positions to economic overheating measures, strategies become macro-aware and more information-efficient. We show that simple point-in-time macro scores of excess inflation, growth, and overconfidence have predicted equity-duration returns across eight developed markets and for over three decades. Macro-aware signals have materially outperformed simple volatility-targeted positions, both in terms of absolute and risk-adjusted returns. The long-term effects of macro awareness on cumulative net asset value have been huge.

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Equity trend-following with market and macro data

May 10, 2025

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The popularity of trend-following bears the risk of market excesses. Medium-term market price trends often fuel economic trends that eventually oppose them (”macro headwinds”). Fortunately, relevant point-in-time economic indicators can provide critical information on the sustainability of medium-term market movements and are a natural complement to standard trend signals.
This post illustrates the benefit of combining market and macro trends in equity markets. Since the 1990s, “robust” market price trend signals alone have created value for equity index future strategies in developed markets. However, risk-adjusted returns would have been enhanced materially if one adjusted market signals for natural macro headwinds, such as the state of the business cycle, inflation, and equity valuations.

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Boosting macro trading signals

April 26, 2025

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Boosting is a machine learning ensemble method that combines the predictions of a chain of basic models, whereby each model seeks to address the shortcomings of the previous one. This post applies adaptive boosting (Adaboost) to trading signal optimisation. Signals are constructed with macro factors to guide positioning in a broad range of global FX forwards.
Boosting is beneficial for learning from a wide and heterogeneous set of markets over time, because it is well-suited for exploiting the diversity of experiences across countries and global economic states. Empirically, we generate machine learning-based signals that use regularized regression and random forest regression, and compare processes with and without adaptive boosting methods. For both regression types, machine learning prefers boosting as datasets get larger and, by doing so, creates more profitable signals.

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Quantamental economic surprise indicators: a primer

April 12, 2025

Jupyter Notebook

Quantamental economic surprises are point-in-time measures of deviations of economic indicators from expected values. There are two types of surprises: first-print events and pure revisions. First-print events feature new observation periods, and the surprise element depends on market expectations of the indicator. Market surveys can approximate such expectations, but only for a limited number of indicators. Quantamental surprises use econometric prediction models and can be calculated for all indicators and transformations, principally using the whole information state.
This post introduces economic surprises in global industry and construction and shows how they can be transformed into short-term macro trading signals for commodities. There is clear empirical evidence for the predictive power of such surprises for a basket of industrial commodity futures at a daily and weekly frequency. Related simulated PnL generation produces risk-adjusted alpha, albeit mainly in seasons of large swings in manufacturing and construction.

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EM sovereign bond allocation with macro risk premium scores

March 29, 2025

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Macro risk premium scores are differences between market-implied risk and point-in-time quantified macroeconomic risk. Two principal types of scores can be calculated for credit markets: spread-based risk premium scores and rating-based risk premium scores. This post proposes a small set of these scores for EM foreign-currency sovereign debt, targeting 24 country sub-indices of the EMBI Global. The macroeconomic component captures four risk dimensions: general government finance, external balances, international investment flows, and foreign debt sustainability.
Macro risk premium scores are constructed on a point-in-time basis, making them suitable for backtesting. Both individual and aggregated scores have shown strong and statistically significant predictive power for subsequent returns of country indices. Portfolios of EM sovereign bonds weighted by risk premium scores have consistently outperformed those based on equal weights or risk parity. Risk premium scores have also generated material cross-country relative value. Most importantly, macro risk premia offer a responsible and profitable approach to adjusting weights of emerging market bond indices.

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Macro trading signal optimization: basic statistical learning methods

March 15, 2025

Jupyter Notebook

A key task of macro strategy development is condensing candidate factors into a single positioning signal. Statistical learning offers methods for selecting factors, combining them to a return prediction, and classifying the market state. These methods efficiently incorporate diverse information sets and allow running realistic backtests.
This post applies sequential statistical learning to optimal signal generation for interest rate swap positions. Sequential methods update, estimate, and select models over time, adapting to growing development data sets, and apply signals based on the latest optimal model each month. These methods require intelligent choices on model versions, hyperparameters, cross-validation splitters, and model quality criteria. Sequential statistical learning has generally done a good job in discarding irrelevant information and has produced greater accuracy and higher risk-adjusted returns than simple factor averages.

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ABOUT US

Macrosynergy is a London based macroeconomic research and technology company whose founders have developed and employed macro quantamental investment strategies in liquid, tradable asset classes, across many markets and for a variety of different factors to generate competitive, uncorrelated investment returns for institutional investors for two decades. Our quantitative-fundamental (quantamental) computing system tracks a broad range of real-time macroeconomic trends in developed and emerging countries, transforming them into macro systematic quantamental investment strategies. In June 2020 Macrosynergy and J.P. Morgan started a collaboration to scale the quantamental system and to popularize tradable economics across financial markets.

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