Trend following: combining market and macro information

July 9, 2022

Classic trend following is based on market prices or returns. Market trends are relatively cheap to produce, popular, and plausibly generate value in the presence of behavioral biases and rational herding. Macro trends track relevant states of the economy based on fundamental data. They are more expensive to produce from scratch and generate value due to rational information inattentiveness. While market trends are timelier, macro trends are more specific in information content. Due to this precision, they serve better as building blocks of trading signals without statistical optimization and are easier to predict based on real-time information. Reason and evidence suggest that macro and market trends are complementary. Two combination methods are [1] market information enhancement of macro trends and [2] market influence adjustment of macro trends.

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The power of macro trends in rates markets

June 25, 2022

Broad macroeconomic trends, such as inflation, economic growth, and credit creation are critical factors of shifts in monetary policy. Above-target trends support monetary tightening. Below-target dynamics give grounds for monetary easing. Yet, markets may not fully anticipate policy shifts that follow macro trends, for lack of attention or conviction. In this case, macro trends should predict returns in rates markets. In the past, even a very simple point-in-time macro pressure indicator, an average of excess inflation, economic growth, and private credit trends, has been significantly correlated with subsequent rates receiver returns, both in large and small currency areas. Looking at the gap between real rates and macro trend pressure delivers even higher forward correlation and extraordinary directional accuracy with respect to fixed income returns.

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Six ways to estimate realized volatility

June 11, 2022

Asset return volatility is typically calculated as (annualized) standard deviation of returns over a sequence of periods, usually daily from close to close. However, this is neither the only nor necessarily the best method. For exchange-traded contracts, such as equity indices, one can use open, close, high, and low prices and even trading volumes. These provide different types of information on the dispersion of prices and support the calculation of different volatility metrics. A recent paper illustrates the application of the volatility concepts of Parkinson, Garman-Klass, Rogers-Satchell, and Yang-Zhang, as well as intrinsic entropy, a method of econophysics. Intrinsic entropy seems to be more suitable for estimating short-term fluctuations in volatility.

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Duration volatility risk premia

May 28, 2022

Jupyter Notebook

The analysis of this post has been updated on June 22, 2023

Duration volatility risk premium means compensation for bearing return volatility risk of an interest rate swap (IRS) contract. It is the scaled difference between swaption-implied and realized volatility of swap rates’ changes. Historically, these premia have been stationary around positive long-term averages, with episodes of negative values. Unlike in equity, simple duration volatility risk premia have not been significant predictors of subsequent IRS returns. However, they have helped predict idiosyncratic IRS returns in non-USD markets.
Moreover, two derived concepts of volatility risk premia hold promise for trading strategies. [1] Term spreads are the differences between volatility risk premia for longer-maturity and shorter-maturity IRS contracts and are related to the credibility of a monetary policy regime. Historically, term spreads have been significant predictors of returns on curve positions. [2] Maturity spreads are the differences between volatility risk premia of longer- and shorter-maturity options and should be indicative of a fear of risk escalation, which affects mainly fixed receivers. Indeed, maturity spreads have been positively and significantly related to subsequent fixed-rate receiver returns. These premia are best combined with fundamental indicators of the related risks to give valid signals for fixed-income positions.

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Inflation as equity trading signal

May 14, 2022

Jupyter Notebook

Academic research suggests that high and rising consumer price inflation puts upward pressure on real discount rates and is a headwind for equity market performance. A fresh analysis of 17 international markets since 2000 confirms an ongoing pervasive negative relation between published CPI dynamics and subsequent equity returns. Global equity index portfolios that have respected the inflation dynamics of major currency areas significantly outperformed equally weighted portfolios. Even the simplest metrics have served well as warning signals at the outset of large market drawdowns and as heads-ups for opportunities before recoveries. The evident predictive power of inflation for country equity indices has broad implications for the use of real-time CPI metrics in equity portfolio management.

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Economic growth and FX forward returns

April 30, 2022

Economic growth differentials are plausible predictors of foreign exchange return trends because they are related to differences in monetary policy and return on investment. Suitable metrics for testing growth differentials as trading signals must replicate historic information states. Two types of such metrics based on higher-frequency activity data are [i] technical GDP growth trends, based on standard econometrics, and [ii] intuitive GDP growth trends, mimicking intuitive methods of market economists. Both types have predicted FX forward returns of a set of 28 currencies since 2000.
For simple growth differentials, the statistical probability of positive correlation with subsequent returns has been near 100% with a quite stable relationship across time. Excess growth trends, relative to potential growth proxies, would have been more appropriate predictors for non-directional (hedged) FX forward returns. Correlations with hedged returns have generally been lower but accuracy has been more balanced. Finally, balanced growth differentials that emphasize equally the performance of output and external balances are theoretically a sounder predictor. Indeed, these indicators post even higher and more stable correlations with subsequent directional returns than simple growth differentials.

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How to use FX carry in trading strategies

April 16, 2022

FX forward-implied carry is a valid basis for trading strategies because it is related to divergences in monetary and financial conditions. However, nominal carry is a cheap and rough indicator: related PnLs are highly seasonal, sensitive to global equity markets, and prone to large drawdowns. Simple alternative concepts such as real carry, interest rate differentials, and volatility-adjusted carry metrics have specific benefits but broadly fail to mitigate these shortcomings. However, the consideration of a market beta premium, adjustment for inflation expectations, and the consideration of other macro-quantamental factors make huge positive differences. Not only do these modifications greatly enhance the theoretical plausibility of value generation, but they also would have almost doubled the PnL generation over the past 20 years, removed most of its equity market dependence, and greatly reduced seasonality.

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Equity convexity and gamma strategies

April 2, 2022

Equity convexity means that a stock outperforms in times of large upward or downward movements of the broad market: its elasticity to the market return is curved upward. Gamma is a measure of that convexity. All else equal, positive gamma is attractive, as a stock would outperform in market rallies and diversify in market stress. However, gamma is not observable, changeable, and needs to be estimated. Only a subset of stocks displays statistically significant gamma. Empirical analysis suggests that convex stocks can mostly be found in the materials, telecom, industrials, and energy sectors. High past volatility and price-to-book ratios have also been indicative of high gamma. Macroeconomic drivers that trigger gamma performance have been interest rates and oil prices. Systematic long-convexity strategies that seek to time convexity exposure have reportedly produced significant investor value.

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Predicting volatility with neural networks

March 19, 2022

Predicting realized volatility is critical for trading signals and position calibration. Econometric models, such as GARCH and HAR, forecast future volatility based on past returns in a fairly intuitive and transparent way. However, recurrent neural networks have become a serious competitor. Neural networks are adaptive machine learning methods that use interconnected layers of neurons. Activations in one layer determine the activations in the next layer. Neural networks learn by finding activation function weights and biases through training data. Recurrent neural networks are a class of neural networks designed for modeling sequences of data, such as time series. And specialized recurrent neural networks have been developed to retain longer memory, particularly LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit). The advantage of neural networks is their flexibility to include complex interactions of features, non-linear effects, and various types of non-price information.

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How to estimate credit spread curves

February 19, 2022

Credit spread curves are essential for analyzing lower-grade bond markets and for the construction of trading strategies that are based on carry and relative value. However, simple spread proxies can be misleading because they assume that default may occur more than once in the given time interval and that losses are in proportion to market value just before default, rather than par value. A more accurate method is to estimate the present value of survival-contingent payments – coupons and principals – as the product of a risk-free discount factor and survival probability. To this, one must add a discounted expected recovery of the par value in case of default. This model allows parametrically defining a grid of curves that depends on rating and maturity. The estimated ‘fair’ spread for a particular rating and tenor would be a sort of weighted average of bonds of nearby rating and tenor.

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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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