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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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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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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How to construct a bond volatility index and extract market information

November 27, 2021

Volatility indices, based upon the methodology of the Cboe volatility index (VIX), serve as measures of near-term market uncertainty across asset classes. They are constructed from out-of-the-money put and call premia using variance swap pricing. Volatility indices for fixed income markets are of particular importance, as they allow inferring market expectations about discount factors and credit premia, which have repercussions on all assets and the broader economy. There is a step-by-step construction plan for building a bespoke index for any rates market with liquid futures and options. Such a volatility index supports asset management in two ways. First, it is a valid basis for portfolio risk management and volatility targeting. Second, it can be used for extracting forward-looking market information, including changing probability quantiles for prices and rates, probabilities of certain extreme events, and the skewness of expectations.

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Equity factor timing with macro trends

October 30, 2021

Plausibility and empirical evidence suggest that the prices of equity factor portfolios are anchored by the macroeconomy in the long run. A new paper finds long-term equilibrium relations of factor prices and macro trends, such as activity, inflation, and market liquidity. This implies the predictability of factor performance going forward. When the price of a factor is greater than the long-term value implied by the macro trends, expected returns should be lower over the next period. The predictability seems to have been economically large in the past.

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Macro trends for trading models

August 21, 2021

Unlike market price trends, macroeconomic trends are hard to track in real-time. Conventional econometric models are immutable and not backtestable for algorithmic trading. That is because they are built with hindsight and do not aim to replicate perceived economic trends of the past (even if their parameters are sequentially updated). Fortunately, the rise of machine learning breathes new life into econometrics for trading. A practical approach is “two-stage supervised learning”. The first stage is scouting features, by applying an elastic net algorithm to available data sets during the regular release cycle, which identifies competitive features based on timelines and predictive power. Sequential scouting gives feature vintages. The second stage evaluates various candidate models based on the concurrent feature vintages and selects at any point in time one with the best historic predictive power. Sequential evaluation gives data vintages. Trends calculated based on these data vintages are valid backtestable contributors to trading signals.

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Factor momentum: a brief introduction

May 29, 2021

Standard equity factors are autocorrelated. Hence, it is not surprising that factor strategies have also displayed momentum: past returns have historically predicted future returns. Indeed, factor momentum seems to explain all return momentum in individual stocks and across industries. Momentum has been concentrated on a subset of factors, most notably those related to “betting against beta”, a leveraged strategy that is long high-beta stocks and short low beta stocks. Also, factor return autocorrelation has been changing over time. Measures of continuation in factor returns can indicate “momentum crashes”. A plausible cause of factor momentum is mispricing, i.e. drifts of prices in accordance with fundamental gravity, if positions that exploit the mispricing bear systematic risk.

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The macro forces behind equity-bond price correlation

May 15, 2021

Since the late 1990s, the negative price correlation of equity and high-grade bonds has reduced the volatility of balanced portfolios and boosted Sharpe ratios of leveraged “long-long” equity-bond strategies. However, this correlation is not structurally stable. Over the past 150 years, equity-bond correlation has changed repeatedly. A structural economic model helps to explain and predict these changes. The key factor is the dominant macro policy. In an active monetary policy regime, where central bank rates respond disproportionately to inflation changes, the influence of technology (supply) shocks dominates markets and the correlation turns positive. In a fiscal policy regime, where governments use debt financing to manage the economy, the influence of investment (financial) shocks dominates and the correlation turns negative. In a world with low inflation and real interest rates, the fiscal regime is typically more prevalent.

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Macro uncertainty as predictor of market volatility

January 23, 2021

Market volatility measures the size of variations of asset returns. Macroeconomic uncertainty measures the size of unpredictable disturbances in economic activity. Large moves in macroeconomic uncertainty are less frequent and more persistent than shifts in market volatility. However, macroeconomic uncertainty is an important driver of market volatility because it is related to future earnings and dividend discount rates. One proxy of macro uncertainty is a weighted average of forecasting errors over a wide set of macroeconomic indicators. Empirical evidence suggests that this proxy of latent macro uncertainty is a significant predictor of volatility and volatility jumps.

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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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