The predictive power score

May 16, 2020

The predictive power score is a summary metric for predictive relations between data series. Like correlation, it is suitable for quick data exploration. Unlike correlation, it can work with non-linear relations, categorical data, and asymmetric relations, where variable A informs on variable B more than variable B informs on variable A. Technically, the score is a measurement of the success of a Decision Tree model in predicting a target variable with the help of a predictor variable out-of-sample and relative to naïve approaches. For macro strategy development, predictive power score matrices can be easily created based on an existing python module and can increase the efficiency of finding hidden patterns in the data and selecting predictor variables.

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Equilibrium theory of Treasury yields

May 2, 2020

An equilibrium model for U.S. Treasury yields explains how macroeconomic trends and related expectations for future short-term interest rates shape the yield curve. Long-term yield trends arise from learning about stable components in GDP growth and inflation. They explain the steady rise of Treasury yields in the 1960s-1980s and their decline in the 1990s-2010s. Cyclical movements in yields curves result from learning about transitory deviations of GDP growth and inflation. They explain why curves have been steep out of recessions and inverted in mature economic expansions. Finally, since the 2000s pro-cyclical inflation expectations and fears for secular stagnation have accentuated the steepness of the Treasury curve; positive correlation between inflation and growth expectations means that the Fed can cut rates more drastically to support the economy.

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Public finance risk

April 25, 2020

Fiscal expansion was the logical response to the 2020 health and economic crisis. Alas, public deficit and debt ratios had already been historically high before. The IMF estimates that this year’s general government deficit in the developed world will reach 11% of GDP, while the government debt stock will exceed 120% of GDP. Fiscal sustainability relies on low or negative real interest rates. Yet, on smaller and lower-grade countries credit spreads have risen and become more volatile. In the large developed economies, central bank purchases help to absorb the glut of debt issuance but cannot prevent balance sheet deterioration. Concerns over sovereign credit risk or – more realistically – debt monetization are rational. Fiscal risk and related government strategies will likely be key drivers of financial market trends for years to come.

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Factor timing

April 18, 2020

Factors beyond aggregate market risk are sources of alternative risk premia. Factor timing addresses the question when to receive and when to pay such risk premia. A new method for predicting the performance of cross-sectional equity return factors proposes to focus only on the dominant principal components of a wide array of factors. This dimension reduction seems to be critical for robust estimation. Forecasts of the dominant principal components can serve as the basis of portfolio construction. Empirical evidence suggests that predictability is significant and that market-neutral factor timing is highly valuable for portfolio construction, over and above directional market timing. Factor timing is related to macroeconomic conditions, particularly at business cycle frequency.

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Macro trading and macroeconomic trend indicators

April 11, 2020

Macroeconomic trends are powerful asset return factors because they affect risk aversion and risk-neutral valuations of securities at the same time. The influence of macroeconomics appears to be strongest over longer horizons. A macro trend indicator can be defined as an updatable time series that represents a meaningful economic trend and that can be mapped to the performance of tradable assets or derivatives positions. It can be based on three complementary types of information: economic data, financial market data, and expert judgment. Economic data establish a direct link between investment and economic reality, market data inform on the state of financial markets and economic trends that are not (yet) incorporated in economic data, and expert judgment is critical for formulating stable theories and choosing the right data sets.

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A statistical learning workflow for macro trading strategies

April 4, 2020

Statistical learning for macro trading involves model training, model validation and learning method testing. A simple workflow [1] determines form and parameters of trading models, [2] chooses the best of these models based on past out-of-sample performance, and [3] assesses the value of the deployed learning method based on further out-of-sample results. A convenient technology is the ‘list-column workflow’ based on the tidyverse packages in R. It stores all related objects in a single data table, including models and nested data sets, and implements statistical learning through functional programming on that table. Key steps are [1] the creation of point-in-time data sets that represent information available at a particular date in the past, [2] the estimation of different model types based on initial training sets prior to each point in time, [3] the evaluation of these different model types based on subsequent validation data just before each point in time, and [4] the testing of the overall learning method based on testing data at each point in time.

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The basics of low-risk strategies

March 28, 2020

Low-risk investment strategies prefer leveraged low-risk assets over high-risk assets. The measure of risk can be based on price statistics, such as volatility and market correlation, or fundamental features. The rationale for low-risk strategies is that leverage is not available for all investors (but required to increase the weight of low-risk longs) and that many investors pay over the odds for assets with lottery-like upwardly skewed return expectations. Popular versions of this strategy principle include “betting against beta”, “betting against correlation”, “stable minus risky” or “quality minus junk”. Empirical research suggests that low-risk strategies have delivered significant risk-adjusted returns for nearly a century and that this performance has not deteriorated over time.

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How loss aversion increases market volatility and predicts returns

March 21, 2020

Loss aversion means that people are more sensitive to losses than to gains. This asymmetry is backed by ample experimental evidence. Loss aversion is not the same as risk aversion, because the aversion is disproportionate towards drawdowns below a threshold. Importantly, loss aversion implies that risk aversion is changing with market prices. This means that the compensation an investor requires for holding a risky asset varies over time, giving rise to excessive price volatility (relative to the volatility of fundamentals), volatility clustering across time, and predictability of returns. All these phenomena are consistent with historical experience and form a useful basis for trading strategies, such as trend following.

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Reward-risk timing

March 14, 2020

Reward-risk timing refers to methods for allocating between a risky market index and a risk-free asset. It is a combination of reward timing, based on expected future risk asset returns, and volatility timing, based on recent price volatility. A new paper proposes to use machine learning with random forests for estimating both risk premia (return expectations) and optimal lookback windows for volatility estimates This method allows for non-linear prediction interaction and averages forecasts across a range of simplistic valid prediction functions. In an empirical analysis with data going back to 1952 the random forest method for reward-risk timing has outperformed other methods and earned significantly higher risk-adjusted returns than a buy-and-hold strategy.

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Lagged correlation between asset prices

March 7, 2020

Efficient market theory assumes that all market prices incorporate all information at the same time. Realistically, different market segments focus on different news flows, depending on the nature of the traded security and their research capacity. Such specialization makes it plausible that lagged correlations arise between securities prices, even though their specifics may change overtime. Indeed, there is empirical evidence for lagged correlation between the price trends of different U.S. stocks. Such lagged correlation can be identified and tested through a neural network. Academic research finds that price trends of some stocks have been predictable out-of-sample based on information about the price trends of others.

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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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