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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Nowcasting for financial markets

July 25, 2020

Nowcasting is a modern approach to monitoring economic conditions in real-time. It makes financial market trading more efficient because economic dynamics drive corporate profits, financial flows and policy decisions, and account for a large part of asset price fluctuations. The main technology behind nowcasting is the dynamic factor model, which condenses the information of numerous correlated ‘hard’ and ‘soft’ data series into a small number of ‘latent’ factors. A growth nowcast can be interpreted as the factor that is most correlated with a diverse representative set of growth-related data series. The state-space representation of the dynamic factor model formalizes how markets read economic data in real-time. The related estimation technique (‘Kalman filter’) generates projections for all data series and estimates for each data release a model-based surprise, called ‘news’. In recent years machine learning models, such as support vector machines, LASSO, elastic net and feed-forward artificial neural networks, have been deployed to improve the predictive power of nowcasts.

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External imbalances and FX returns

June 13, 2020

Hedge ratios of international investment positions have increased over past decades, spurred by regulation and expanding derivative markets. This has given rise to predictable movements in spot and forward exchange rates. First, on balance hedgers are long currencies with positive net international investment positions and short those with negative international investment positions. With intermediaries requiring some profit for balance sheet usage these trades command negative premia and widen cross-currency bases. Second, hedge ratios increase in times of rising FX volatility. An increase in the hedge ratio for a currency puts downward pressure on its market price in proportion to its external imbalance and bodes for higher medium-term returns. Also, the dispersion of cross-currency bases increases in times of turmoil.

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Predicting volatility with heterogeneous autoregressive models

May 30, 2020

Heterogeneous autoregressive models of realized volatility have become a popular standard in financial market research. They use high-frequency volatility measures and the assumption that traders with different time horizons perceive, react to, and cause different types of volatility components. A key hypothesis is that volatility over longer time intervals has a stronger impact on short-term volatility than vice versa. This leads to an additive volatility cascade and a simple model in autoregressive form that can be estimated with ordinary least squares regression. Natural extensions include weighted least-squares estimations, the inclusion of jump-components and the consideration of index covariances. Research papers report significant improvement of volatility forecasting performance compared to other models, across equity, fixed income, and commodity markets.

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Joint predictability of FX and bond returns

May 23, 2020

When macroeconomic conditions change rational inattention and cognitive frictions plausibly prevent markets from adjusting expectations for futures interest rates immediately and fully. This is an instance of information inefficiency. The resulting forecast errors give rise to joint predictability of currency and bond market returns. In particular, an upside shock to the rates outlook in a country heralds positive (rationally) expected returns on its currency and negative expected returns on its long-term bond. This proposition has been backed by empirical evidence for developed markets over the past 30 years.

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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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