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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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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Tradable economics

October 26, 2019

Tradable economics is a technology for building systematic trading strategies based on economic data. Economic data are statistics that – unlike market prices – directly inform on economic activity. Tradable economics is not a zero-sum game. Trading profits are ultimately paid out of the economic gains from a faster and smoother alignment of market prices with economic conditions. Hence, technological advances in the field increase the value generation or “alpha” of the asset management industry overall. This suggests that the technology is highly scalable. One critical step is to make economic data applicable to systematic trading or trading support tools, which requires considerable investment in data wrangling, transformation, econometric estimation, documentation, and economic research.

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FX trading strategies based on output gaps

October 19, 2019

Macroeconomic theory suggests that currencies of countries in a strong cyclical position should appreciate against those in a weak position. One metric for cyclical strength is the output gap, i.e. the production level relative to output at a sustainable operating rate. In the past, even a simple proxy of this gap, based on the manufacturing sector, seems to have provided an information advantage in FX markets. Empirical analysis suggests that [1] following the output gap in simple strategies would have turned a trading profit in the long-term, and [2] the return profile would have been quite different from classical FX trading factors.

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U.S. dollar exchange rate before FOMC decisions

August 15, 2018

Since the mid-1990s the dollar exchange rate has mostly anticipated the outcome of FOMC meetings: it appreciated in the days before a rate hike and depreciated in the days before a rate cut. This suggests that since fixed income markets usually predict policy rate moves early and correctly their information content can be used to trade the exchange rate. A recent paper proposes a systematic trading rule for trading USD before FOMC meetings based upon what is priced into the each Fed meeting from Fed fund futures and claims that such a strategy would have delivered a respectable Sharpe ratio.

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Watching U.S. financial conditions

April 15, 2017

The U.S. financial system wields dominant influence over the national and global economies. Moreover, securities and derivatives markets play a greater role relative to banks and compared to other developed countries. Medium-term shifts in financial conditions, rather than short-term changes, should be consequential for economic growth and monetary policy. Therefore, a timely and consistent measurement of U.S. financial conditions is crucial for macro trading strategies. A broad econometric measure of U.S. financial conditions based on risk, liquidity and leverage is produced and regularly updated by the Chicago Fed.

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Nowcasting GDP growth

April 8, 2017

Financial markets have long struggled with tracking GDP growth trends in a timely and consistent fashion. However, over the past decade statistical methods for “nowcasting” various economies have improved considerably, benefiting macro trading strategies. Dynamic factor models have become the method of choice for this purpose: they extract the communal underlying factor behind timely economic reports and translate the information of many data series into a single underlying trend. The estimation process may look daunting, but its basics are intuitive and calculation is executable in statistical programming language R.

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FX returns and external balances

March 29, 2017

A new paper supports the view that currency excess returns can to some extent be viewed as compensation for risk to net capital flows in imperfect markets. An increase in current account uncertainty can be approximated by economists’ forecast dispersion. Historically, a rise in current account uncertainty has reduced returns on carry currencies and investment currencies, i.e. those of countries with net capital inflows. There is also evidence that markets have been sluggish in adapting to higher uncertainty.

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The impact of U.S. economic data surprises

October 29, 2016

A new paper estimated the short-term effects of U.S. economic data surprises on treasury notes and USD exchange rates over the past 20 years. All of 21 commonly followed data releases produced highly significant surprise effects at least for parts of the sample. However, only non-farm payrolls produced a consistently highly significant impact. After short-term interest rates reached the zero lower bound, the importance of surprises to CPI inflation, housing indicators and weekly jobless claims increased noticeably, possibly related to the Fed’s struggle with its dual mandate.

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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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