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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U.S. Treasuries and macro-enhanced trend following

December 7, 2024

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Trend-following strategies rely on the persistence of market trends. Such persistence can arise from the gradual dissemination of information or behavioural biases. In light of these inefficiencies, trends that coincide with supporting economic information (macro tailwinds) are more likely to persist than those accompanied by opposing macro information (macro headwinds). As a result, a macro-enhancement of standard trend-following signals should produce better investment returns.
This post supports this proposition for the U.S. Treasury market over the past 32 years. It tests simple macro enhancement types for directional return trends and curve-flattening return trends. In all cases, macro enhancement would have materially improved predictive power and backtested trading profits. This echoes previous research for other asset classes that illustrated the complementarity of price and economic information in systematic trading.

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Macro information changes as systematic trading signals

September 28, 2024

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Macro information state changes are point-in-time updates of recorded economic developments. They can refer to a specific indicator or a broad development, such as growth or inflation. The broader the economic concept, the higher the frequency of changes. Information state changes are valuable trading indicators. They provide daily or weekly signals and naturally thrive in periods of underestimated escalatory economic change, adding a layer of tail risk protection.
This post illustrates the application of information state changes to interest rate swap trading across developed and emerging markets, focusing on six broad macro developments: economic growth, sentiment, labour markets, inflation, and financing conditions. For trading, we introduce the concept of normalized information state changes that are comparable across economic groups and countries and, hence, can be aggregated to local and global signals. The predictive power of aggregate information state changes has been strong, with material and consistent PnL generation over the past 25 years.

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Macro-quantamental scorecards: A Python kit for fixed-income markets

August 3, 2024

 Jupyter Notebook

Macro-quantamental scorecards are condensed visualizations of point-in-time economic information for a specific financial market. Their defining characteristic is the combination of efficient presentation and evidence of empirical power. This post and the accompanying Python code show how to build scorecards for duration exposure based on six thematic scores: excess inflation, excess economic growth, overconfidence, labour market tightening, financial conditions, and government finance. All thematic scores have displayed predictive power for interest rate swap returns in the U.S. and the euro area over the past 25 years. Since economic change is often gradual and requires attention to a broad range of indicators, monitoring can be tedious and costly. The influence of such change can, therefore, build surreptitiously. Macro-quantamental scorecards cut information costs and attention time and, hence, improve the information efficiency of the investment process.

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Reported economic changes and the Treasury market: impact and payback

July 13, 2024

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Financial markets pay great attention to reported changes in key economic statistics, particularly when they are unexpected. For quantitative analysis, we introduce the concept of information state changes and the methods of aggregating them across time and indicators. We apply these to a few popular U.S. indicators and investigate how information state changes have affected the bond market. In line with theory, monthly changes in economic growth, inflation, and employment growth have all been negatively correlated with concurrent Treasury returns over the past 25 years. However, there has been subsequent payback: the correlation reverses for subsequent monthly Treasury returns. This supports the hypothesis that high publicity volatile indicators are easily “overtraded.” Cognitive biases may systematically exaggerate positioning toward the latest “surprises” or publicized changes.

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Tracking systematic default risk

December 30, 2023

Systematic default risk is the probability of a critical share of the corporate sector defaulting simultaneously. It can be analyzed through a corporate default model that accounts for both firm-level and communal macro shocks. Point-in-time estimation of such a risk metric requires accounting data and market returns. Systematic default risk arises from the capital structure’s vulnerability and firms’ recent performance, as reflected in equity prices. The metric is both an indicator and predictor of macroeconomic conditions, particularly financial distress. Also, systematic default risk has helped forecast medium-term equity and lower-grade bond returns. This predictive power seems to arise mostly from the price of risk. When systematic default risk is high, investors require greater compensation for taking on exposure to corporate finances.

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The predictive power of real government bond yields

September 23, 2023

Real government bond yields are indicators of standard market risk premia and implicit subsidies. They can be estimated by subtracting an estimate of inflation expectations from standard yields. And for credible monetary policy regimes, inflation expectations can be estimated based on concurrent information on recent CPI trends and the future inflation target. For a data panel of developed markets since 2000, real yields have displayed strong predictive power for subsequent monthly and quarterly government bond returns. Simple real yield-based strategies have added material economic value in 2000-2023 by guiding both intertemporal and cross-country risk allocation.

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Equity versus fixed income: the predictive power of bank surveys

September 9, 2023

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Bank lending surveys help predict the relative performance of equity and duration positions. Signals of strengthening credit demand and easing lending conditions favor a stronger economy and expanding leverage, benefiting equity positions. Signs of deteriorating credit demand and tightening credit supply bode for a weaker economy and more accommodative monetary policy, benefiting long-duration positions. Empirical evidence for developed markets strongly supports these propositions. Since 2000, bank survey scores have been a significant predictor of equity versus duration returns. They helped create uncorrelated returns in both asset classes, as well as for a relative asset class book.

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A model for bond risk premia and the macroeconomy

July 15, 2023

An empirical analysis of the U.S. bond market since the 1960s emphasizes occasional abrupt regime changes, as defined by yield levels, curve slopes, and related volatility metrics. An arbitrage-free bond pricing model illustrates that bond risk premia can be decomposed into two types. One is related to continuous risk factors, traditionally summarized as the level, slope, and curvature of the yield term structure. The other type is related to regime-switching risk. Accounting for regime shift risk adds significant explanatory power to the model. Moreover, risk premia associated with regime shifts are related to the macroeconomic environment, particularly inflation and economic activity. The market price of regime shifts is strongly pro-cyclical and largely explained by these economic indicators. Investors apply a higher regime-related discount to bond values when the economy is booming.

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Merchandise import as predictor of duration returns

June 17, 2023

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Local-currency import growth is a widely underestimated and important indicator of trends in fixed-income markets. Its predictive power reflects its alignment with economic trends that matter for monetary policy: domestic demand, inflation, and effective currency dynamics. Empirical evidence confirms that import growth has significantly predicted outright duration returns, curve position returns, and cross-currency relative duration returns over the past 22 years. A composite import score would have added considerable economic value to a duration portfolio through timing directional exposure, positioning along the curve, and cross-country allocations.

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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
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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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