Nowcasting macro trends with machine learning

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

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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

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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Equity trend following and macro headwinds

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Market price trends often foster economic trends that eventually oppose them. Theory and empirical evidence support this phenomenon for equity markets and suggest that macro headwind (or tailwind) indicators are powerful modifiers of trend following strategies. As a simple example, we calculate a macro support factor for equity index futures in the eight largest developed markets based on labor markets, inflation, and equity carry. This factor is used to modify standard trend following signals. The modification increases the predictive power of the trend signal and roughly doubles the risk-adjusted return of a stylized global trend following strategy since 2000.

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Merchandise import as predictor of duration returns

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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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Finding (latent) trading factors

Financial markets are looking at a growing and broadening range of correlated time series for the operation of trading strategies. This increases the importance of latent factor models, i.e., methods that condense high-dimensional datasets into a low-dimensional group of factors that retain most of their underlying relevant information. There are two principal approaches to finding such factors. The first uses domain knowledge to pick factor proxies up front. The second treats all factors as latent and applies statistical methods, such as principal components, to a comprehensive set of correlated variables. A new paper proposes to combine domain knowledge and statistical methods using penalized reduced-rank regression. The approach promises improved accuracy and robustness.

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FX trend following and macro headwinds

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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

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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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Rational inattention and trading strategies

The theory of rational inattention supports the development of trading strategies by providing a model of how market participants manage the scarcity of attention. In general, people cannot continuously process and act upon all information, but they can set priorities and choose the mistakes they are willing to accept. Rational inattention explains why agents pay disproportionate attention to popular variables, simplify the world into a small set of indicators, pay more attention in times of uncertainty, and limit their range of actions. In macroeconomics, rational inattention elucidates why forecasters underreact to shocks and why pure nominal variables, such as money and interest rates have persistent real effects. In finance, rational inattention explains why markets ignore a wide range of relevant data, leave pockets of information advantage, exaggerate price volatility, and propagate financial contagion.

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Terms of trade as FX trading signal

All other things equal, an improvement in a country’s terms of trade, the ratio of export to import prices, translates into increased demand for its currency and a boost for its growth outlook. However, terms of trade are a rather subtle and sporadic influence. Therefore, many market participants are rationally inattentive to smaller changes and unwilling to trade on large changes in times of turmoil. This points to investor value in the systematic consideration of monthly or annual terms-of-trade dynamics, which can be approximated by commodity-based export and import price indices. Empirically, standard terms-of-trade dynamics have indeed predicted FX returns positively since 2000, across developed and emerging market countries. However, while this relation has been fairly stable in the developed world since 2000, for emerging markets the trading value of terms-of-trade indicators has only become evident since the great financial crisis.

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