Nowcasting macro trends with machine learning

August 12, 2023

Nowcasting economic trends can make use of a broad range of machine learning methods. This not only serves the purpose of optimization but also allows replication of past information states of the market and supports realistic backtesting. A practical framework for modern nowcasting is the three-step approach of (1) variable pre-selection, (2) orthogonalized factor formation, and (3) regression-based prediction. Various methods can be applied at each step, in accordance with the nature of the task. For example, pre-selection can be based on sure independence screening, t-stat-based selection, least-angle regression, or Bayesian moving averaging. Predictive models include many non-linear models, such as Markov switching models, quantile regression, random forests, gradient boosting, macroeconomic random forests, and linear gradient boosting. There is some evidence that linear regression-based methods outperform random forests in the field of macroeconomics.

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Pure macro FX strategies: the benefits of double diversification

July 29, 2023

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Pure macro(economic) strategies are trading rules that are informed by macroeconomic indicators alone. They are rarer and require greater analytical resources than standard price-based strategies. However, they are also more suitable for pure alpha generation. This post investigates a pure macro strategy for FX forward trading across developed and emerging countries based on an “external strength score” considering economic growth, external balances, and terms-of-trade.

Rather than optimizing, we build trading signals based on the principles of “risk parity” and “double diversification.” Risk parity means that allocation is adjusted for the volatility of signals and returns. Double diversification means risk is spread over different currency areas and conceptual macro factors. Risk parity across currency signals diminishes vulnerability to idiosyncratic country risk. Risk parity across macroeconomic concepts mitigates the effects of the seasonality of macro influences. Based on these principles, the simplest pure macro FX strategy would have produced a long-term Sharpe ratio of around 0.8 before transaction costs with no correlation to equity, fixed income, and FX benchmarks.

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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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FX trend following and macro headwinds

May 20, 2023

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Trend following can benefit from consideration of macro trends. One reason is that macroeconomic data indicate headwinds (or tailwinds) for the continuation of market price trends. This is particularly obvious in the foreign-exchange space. For example, the positive return trend of a currency is less likely to be sustained if concurrent economic data signal a deterioration in the competitiveness of the local economy. Macro indicators of such setback risk can slip through the net of statistical detection of return predictors because their effects compete with dominant trends and are often non-linear and concentrated. As a simple example, empirical evidence shows that standard global FX trend following would have benefited significantly merely from adjusting for changes in external balances.

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Macroeconomic cycles and asset class returns

May 6, 2023

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Indicators of growth and inflation cycles are plausible and successful predictors of asset class returns. For proof of concept, we propose a single balanced “cyclical strength score” based on point-in-time quantamental indicators of excess GDP growth, labor market tightening, and excess inflation. It has clear theoretical implications for all major asset markets, as rising operating rates and consumer price pressure raise real discount factors. Empirically, the cyclical strength score has displayed significant predictive power for equity, FX, and fixed income returns, as well as relative asset class positions. The direction of relationships has been in accordance with standard economic theory. Predictive power can be explained by rational inattention. Naïve PnLs based on cyclical strength scores have each produced long-term Sharpe ratios between 0.4 and 1 with little correlation with risk benchmarks. This suggests that a single indicator of cyclical economic strength can be the basis of a diversified portfolio.

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Excess inflation and asset class returns

March 25, 2023

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Excess inflation means consumer price trends over and above the inflation target. In a credible inflation targeting regime, positive excess inflation skews the balance of risks of monetary policy towards tightening. An inflation shortfall tips the risk balance towards easing. Assuming that these shifting balances are not always fully priced by the market, excess inflation in a local currency area should negatively predict local rates market and equity market returns, and positively local-currency FX returns. Indeed, these hypotheses are strongly supported by empirical evidence for 10 developed markets since 2000. For fixed income and FX excess inflation has not just been a directional but also a relative cross-country trading signal. The deployment of excess inflation as a trading signal across asset classes has added notable economic value.

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Fiscal policy criteria for fixed-income allocation

January 28, 2023

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The fiscal stance of governments can be a powerful force in local fixed-income markets. On its own, an expansionary stance is seen as a headwind for long-duration or government bond positions due to increased debt issuance, greater default or inflation risk, and less need for monetary policy stimulus. Quantamental indicators of general government balances and estimated fiscal stimulus allow backtesting the impact of fiscal stance information. Empirical evidence for 20 countries since the early 2000s shows that returns on interest rate swap receiver positions in fiscally more expansionary countries have significantly underperformed those in fiscally more conservative countries. Indicators of fiscal stance have been timely, theoretically plausible, and profitable criteria for fixed-income allocations across currency areas.

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Detecting trends and mean reversion with the Hurst exponent

January 14, 2023

The Hurst exponent is a statistical measure of long-term memory of time series. The existence and form of such memory are of great interest in financial markets, as financial returns are not generally governed by random walks.
The Hurst exponent is a single scalar value that indicates if a time series is purely random, trending, or rather mean reverting. Thus, it can validate either momentum or mean-reverting strategies. The Hurst exponent uses the variance of a log price series to assess the rate of diffusive behavior. If a time series follows a random walk, its variance simply increases linearly with time elapsed. If instead variance increases with time to the power of an exponent, then a low (Hurst) exponent would indicate mean reversion and a high exponent trending behavior. The Hurst exponent depends on the period used for return calculation. For example, monthly returns can display a memory that is different from daily returns.
The Hurst exponent is estimated rather than calculated. Most methods regress rescaled ranges of the return series on the time span of observations. Code examples are available for Python and R.

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Modified and balanced FX carry

December 31, 2022

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There are two simple ways to enhance FX carry strategies with economic information. The first increases or reduces the carry signal depending on whether relevant economic indicators reinforce or contradict its direction. The output can be called “modified carry”. It is a gentle adjustment that leaves the basic characteristics of the original carry strategy intact. The second method equalizes the influence of carry and economic indicators, thus diversifying over signals with complementary strengths. The combined signal can be called “balanced carry”. An empirical analysis of carry modification and balancing with economic performance indicators for 26 countries since 2000 suggests that both adjustments would have greatly improved the performance of vol-targeted carry strategies. Modified carry would also have improved the performance of hedged FX carry strategies.

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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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