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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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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Inflation and precious metal prices

December 12, 2020

Theory and plausibility suggest that precious metal prices benefit from inflation and negative real interest rates. This makes gold, silver, platinum, and palladium natural candidates for hedges against inflationary monetary policy. Long-term empirical evidence supports the inflation-precious metal link. However, there are important qualifications. First, the equilibrium relation between consumer and metal prices can take many years to re-assert itself and short-term excesses in relative prices are common. Second, the relationship between precious metal and consumer prices can change over time as a consequence of evolving market structures or diverging supply and demand conditions. And third, the equilibrium relationship works better for gold, platinum and palladium than for silver.

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Forecasting energy markets with macro data

November 7, 2020

Recent academic papers illustrate how macroeconomic data support predictions of energy market flows and prices. Valid macro indicators include shipping costs, industrial production measures, non-energy industrial commodity prices, transportation data, weather data, financial conditions indices, and geopolitical uncertainty measures. Good practices include a focus on “small” models and a reduction of the dimensionality of large datasets. Forecasts can extend to predictions of the entire probability distribution of prices and – hence – can be used to assess the probability of breakouts from price ranges.

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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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Understanding international capital flows and shocks

October 3, 2020

Macro trading factors for FX must foremostly consider (gross) external investment positions. That is because modern international capital flows are mainly about financing, i.e. exchanges of money and financial assets, rather than saving, real investments and consumption (which are goods market concepts). Trades in financial assets are much larger than physical resource trades. Also, financing flows simultaneously create aggregate purchasing power, bank assets and liabilities. The vulnerability of currencies depends on gross rather than net external debt. Current account balances, which indicate current net payment flows, can be misleading. The nature and gravity of financial inflow shocks, physical saving shocks, credit shocks and – most importantly – ‘sudden stops’ all depend critically on international financing.

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Market-implied macro shocks

August 22, 2020

Combinations of equity returns and yield-curve changes can be used to classify market-implied underlying macro news. The methodology is structural vector autoregression. Theoretical ‘restrictions’ on unexpected changes to this multivariate linear model allow identifying economically interpretable shocks. In particular, one can distinguish news on growth, monetary policy, common risk premia and hedge premia. Monetary and growth news capture shocks to investors’ expectations of discount rates and cash flows, respectively. The common risk premium is a price for exposure to risks that drive stock and bond returns in the same direction. The hedge premium is a price for exposure to risks that drive stock and bond returns in opposite directions. Identifying shocks helps to uncover trading opportunities, including market trends and reversion of relative market returns that were inconsistent with actual macro developments.

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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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