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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Inflation as equity trading signal

May 14, 2022

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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Ten things investors should know about nowcasting

September 4, 2021

Nowcasting in financial markets is mainly about forecasting forthcoming data reports, particularly GDP releases. However, nowcasting models are more versatile and can be used for a range of market-relevant information, including inflation, sentiment, weather, and harvest conditions. Nowcasting is about information efficiency and is particularly suitable for dealing with big messy data. The underlying models typically condense large datasets into a few underlying factors. They also tackle mixed frequencies of time series and missing data. The most popular model class for nowcasting is factor models: there are different categories of these that produce different results. One size does not fit all purposes. Also, factor models have competitors in the nowcasting space, such as Bayesian vector autoregression, MIDAS models and bridge regressions. The reason why investors should understand their nowcasting models is that they can do a lot more than just nowcasting: most models allow tracking latent trends, spotting significant changes in market conditions, and quantifying the relevance of different data releases.

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Markets’ neglect of macro news

June 12, 2021

Empirical evidence suggests that investors pay less attention to macroeconomic news when market sentiment is positive. Market responses to economic data surprises have historically been muted in high sentiment periods. Behavioral research supports the idea that investors prefer heuristic decision-making and neglect fundamental information in bullish markets, but pay more attention in turbulent times. This allows prices to diverge temporarily from fundamentals and undermines the conventional risk-return trade-off when sentiment is high. Low-risk portfolios tend to outperform subsequently. The sentiment bias also means that fundamental predictors of market prices work better in low-sentiment periods than in high-sentiment periods.

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Macro information waste and the quantamental solution

May 1, 2021

Financial markets are not macro information efficient. This means that investment decisions miss out on ample relevant macroeconomic data and facts. Information goes to waste due to research costs, trading restrictions, and external effects. Evidence of macro information inefficiency includes sluggishness of position changes, the popularity of simple investment rules, and the prevalence of herding.  A simple and practical enhancement of macro information efficiency is the construction of quantamental indicators. A quantamental indicator is a time series that represents the state of an investment-relevant fundamental feature in real-time. The term ‘fundamental’ means that these data inform directly on economic activity, unlike market prices, which inform only indirectly. The key benefits of quantamental indicators are that [1] they fit machine learning pipelines and algorithmic trading tools, thus making a broad set of macro information tradable, [2] they support the consistent use of macro information, [3] they can be applied across traders (or programs), strategy types and asset classes and are, thus, cost-efficient.

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Classifying market states

January 9, 2021

Typically, we cannot predict a meaningful portion of daily or higher-frequency market returns. A more realistic approach is classifying the state of the market for a particular day or hour. A powerful tool for this purpose is artificial neural networks. This is a popular machine learning method that consists of layers of data-processing units, connections between them and the application of weights and biases that are estimated based on training data. Classification with neural networks is suitable for complex structures and large numbers of data points. A simple idea for a neural network approach to financial markets is to use combinations of price trends as features and deploy them to classify the market into simple buy, sell or neutral labels and to estimate the probability of each class at each point in time. This approach can, in principle, be extended to include trading volumes, economic data or sentiment indicators.

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What traders should know about seasonal adjustment

December 26, 2020

The purpose of seasonal adjustment is to remove seasonal and calendar effects from economic time series. It is a common procedure but also a complex one, with side effects. Seasonal adjustment has two essential stages. The first accounts for deterministic effects by means of regression and selects a general time series model. The second stage decomposes the original time series into trend-cycle, seasonal, calendar and irregular components.
Seasonal adjustment does not generally improve the quality of economic data. There is always some loss of information. Also, it is often unclear which calendar effects have been removed. And sometimes seasonal adjustment is just adding noise or fails to remove all seasonality. Moreover, seasonally adjusted data are not necessarily good trend indicators. By design, they do not remove noise and outliers. And extreme weather events or public holiday patterns are notorious sources of distortions. Estimated trends at the end of the series are subject to great uncertainty. Furthermore, seasonally adjusted time series are often revised and can be source of bias if these data are used for trading strategy backtests.

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Real-time growth estimation with reinforcement learning

December 5, 2020

Survey data and asset prices can be combined to estimate high-frequency growth expectations. This is a specific form of nowcasting that implicitly captures all types of news on the economy, not just official data releases. Methods for estimation include the Kalman filter, MIDAS regression, and reinforcement learning. Since reinforcement learning is model-free it can estimate more efficiently. And a recent paper suggests that this efficiency gain brings great benefits for nowcasting growth expectations. Nowcasting with reinforcement learning can be applied to expectations for a variety of macro variables.

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Fundamental trend following

October 31, 2020

Fundamental trend following uses moving averages of past fundamental data, such as valuation metrics or economic indicators, to predict future fundamentals, analogously to the conventions in price or return trend following. A recent paper shows that fundamental trend following can be applied to equity earnings and profitability indicators. One approach is to pool fundamental information across a range of popular indicators and to sequentially choose lookback windows for moving averages in accordance with past predictive power for returns. The fundamental extrapolation measure predicts future stock returns positively and would historically have generated significant profits. Most importantly, fundamental trend following returns seems to have little correlation with price trend following returns, supporting the idea that these trading styles are complementary.

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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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