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

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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

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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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Global FX management with systematic macro scores

March 1, 2025

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Global foreign exchange markets are subject to a wide range of macroeconomic influences. The sheer breadth of related information and required analyses often prevent their systematic use in trading. However, modern macro-quantamental scorecards can condense ample point-in-time macroeconomic data into thematic scores for easy systematic visualization and empirical evaluation.
This post demonstrates how to create structured macro-quantamental scorecards for FX forward trading in Python. It uses indicators related to economic growth differentials, monetary policy divergences, external balances, valuation metrics, and price competitiveness. Resulting scorecards provide point-in-time snapshots of macroeconomic conditions across all liquid currencies. They also summarize historical and thematic perspectives. Empirical analysis highlights the predictive power and trading value of macro-quantamental scores.

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Classifying credit markets with macro factors

February 15, 2025

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Macro credit trades can be implemented through CDS indices. Due to obligors’ default option, long credit positions typically feature a positive mean and negative skew of returns. At the macro level, downside skew is reinforced by fragile liquidity and the potential for escalating credit crises. To enhance performance and create a chance to contain drawdowns, credit markets can be classified based on point-in-time macro factors, such as bank lending surveys, private credit dynamics, real estate price growth, business confidence dynamics, real interest rates, and credit spread dynamics. These factors support statistical learning processes that sequentially select and apply versions of four popular classification methods: naive Bayes, logistic regression, nearest neighbours, and random forest.
With only two decades and four liquid markets of CDS index trading, empirical results are still tentative. Yet they suggest that machine learning classification can detect the medium-term bias of returns and produce good monthly accuracy and balanced accuracy ratios. The random forest method stands out regarding predictive power and economic value generation.

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Cross-country equity risk allocation with statistical learning

February 1, 2025

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Macroeconomic factors plausibly cause divergences in equity market returns across countries. Factors related to monetary policy, financial conditions, and competitiveness should all systematically help detect such divergences. In the presence of rational inattention, point-in-time indicators should also have predictive power.
We apply statistical learning to investigate the signalling value of nine candidate macro factors for cross-country excess returns within major equity sectors for a set of 12 countries. Most factors turn out to be relevant predictors. Cross-country trading signals based on point-in-time macro factors and models would have added material uncorrelated PnL value to an equity portfolio.

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Conditional short-term trend signals

January 18, 2025

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There are plausible relations between past and future short-term trends across and within financial markets. This is because market returns affect expected physical payoffs, risk premia, and the monetary policy outlook. However, the relations between past and future returns are unstable and often depend on the economic environment. As an example, this post shows that the impact of short-term commodity future trends on subsequent S&P500 future returns depends on the inflationary pressure in the U.S. economy. Empirical analysis suggests that macro-conditional trend signals outperform unconditional short-term trend signals regarding predictive power, accuracy and naïve PnL generations.

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Crowded trades and consequences

January 4, 2025

A crowded trade is a position with a high ratio of active institutional investor involvement relative to its liquidity. Crowding is a form of endogenous market risk as it arises not from contracts’ fundamentals but from the market itself. The risk of crowding has increased in past decades due to the growing share of institutional investors in the market, particularly the activity of hedge funds. Liquidations of crowded positions can trigger price distortions and, in cases of self-reinforcing deleveraging, even systemic pressure.
Crowdedness can be measured by the total value of active institutional positioning in an asset relative to its trading volume. It indicates how long it would take institutions to exit their trades under normal market conditions. For U.S. stocks, these ratios can be calculated based on reported data. Crowding typically skews risk to the downside. This point has been proven empirically for the U.S. equity market. However, crowdedness should also command excess premia. Historically, crowded stocks have outperformed non-crowded stocks materially and with high statistical significance.

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FX trading signals: Common sense and machine learning

December 21, 2024

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Two valid methods to combine macro trading factors into a single signal are “conceptual parity” and machine learning. Conceptual parity takes a set of conceptually separate normalized factors and gives them equal weights. Machine learning optimizes models and derives weights sequentially, potentially with theoretical restrictions. Both methods support realistic backtests. Conceptual parity works best in the presence of strong theoretical priors. Machine learning works best with large homogenous data sets.
We apply conceptual parity, and two machine learning methods to combine 11 macro-quantamental trading factors for developed and emerging market FX forwards in 16 currencies since 2000. The signals derived by all methods have been highly significant predictors and produced material and uncorrelated risk-adjusted trading returns. Machine learning methods have failed to outperform conceptual parity, probably reflecting that theoretical priors in the FX space are abundant while data are limited and heterogeneous.

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asset management Asset Purchase Program banks bond returns bond yields bubbles capital regulation carry China collateral commodities credit returns default ECB economic data EM equity returns euro area crisis Fed financial crisis FX FX interventions FX returns government debt government deficits herding inflation information insurances Japan leverage liquidity machine learning market turmoil price factors regulation risk management shadow banking statistical methods term structure trend following variance risk premium VIX volatility ZLB
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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