Using SVAR for macro trading strategies

January 21, 2017

Structural vector autoregression may be the most practical model class for empirical macroeconomics. Yet, it can also be employed for macro trading strategies, because it helps identifying specific market and macro shocks. For example, SVAR can identify short-term policy, growth or inflation expectation shocks. Once a shock is identified it can be used for trading in two ways. First, one can compare the type of shock implied by markets with the actual news flow and detect fundamental inconsistencies. Second, different types of shocks may entail different types of subsequent asset price dynamics and may, hence, be a basis for systematic strategies.

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Volcker Rule and liquidity risk

January 7, 2017

The Volcker Rule has banned proprietary trading of banks with access to official backstops. Also, market making has become more onerous as restrictions and ambiguities of the rule make it harder for dealers to manage inventory and to absorb large volumes of client orders in times of distress. This increases liquidity risk, particularly in market segments with longer turnover periods, such as corporate bonds. A new empirical paper confirms that the Volcker Rule has indeed reduced corporate bond liquidity and aggravated the price impact of distress events, such as significant rating downgrades.

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Inflation: risk without premium

December 31, 2016

Historically, securities that lose value as inflation increases have paid a sizable risk premium. However, there is evidence that inflation risk premia have vanished or become negative in recent years. Macroeconomic theory suggests that this is related to monetary policy constraints at the zero lower bound: demand shocks are harder to contain and cause positive correlation between inflation and growth. Assets whose returns go down with higher inflation become valuable proxy-hedges. As a consequence, inflation breakevens underestimate inflation. Bond yields would rise disproportionately once policy rates move away from the zero lower bound.

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Improving asset return forecasts with wavelets

December 23, 2016

Time series that are used for forecasting asset returns can carry information on trends of different persistence. Therefore, frequency decomposition of standard signals based on wavelets can improve and expand potential predictors. Similarly, asset returns can be decomposed into parts of different persistence. These can be forecast separately and summed up eventually. This “sum-of-parts” method seems to improve forecast accuracy because its aligns predictors and return trends and helps separating signal from noise.

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The drivers of commodity cycles

December 17, 2016

Demand shocks have been the dominant force behind non-oil commodity price cycles, according to a 145-year empirical analysis. They have been linked to global recessions or recoveries and displayed persistent effects of 10 years or more. The second most import driver has been so-called “inventory shocks”, which have been less long-lived. Supply shocks have not played an important role in driving long-term price deviations of most commodities. They were mostly commodity-specific and transient.

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Credit market herding and price distortions

December 10, 2016

Corporate credit markets have historically been especially prone to herding. The main drivers of herding have been past returns, rating changes and liquidity. Sell herding has been particularly strong and flows have been disproportionate after very large price moves. Herding can be persistent and lead to significant price distortions. Non-fundamental price overshooting is a valid basis for profitable contrarian trading strategies.

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Inflation differentials and equity returns

December 3, 2016

Inflation differentials and equity returns

There is evidence that equity markets fail to adjust to persistent cross-country shifts in inflation in a timely and efficient manner. While equity investors focus on tracking firm-specific price effects and cash flows, they seem to pay less attention to aggregate local inflation and appear sluggish in adjusting long-term discount factors across countries. Since equity is a long duration asset even small calibration errors in discount factors have a large impact. Empirically, real equity returns in lower-inflation markets tend to outperform those in higher-inflation markets. No such effect can be found in fixed income markets.

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Policy rates and equity returns: the “slope factor”

November 26, 2016

A long-term empirical analysis suggests that faster expected monetary policy tightening in future months leads equity market underperformance. The predictive factor can be modelled as a change in the slope in future implied future policy rates. It has had a meaningful and consistent effect on weekly U.S. equity returns for more than 25 years. Faster future policy tightening can mean either that the central bank has become more hawkish or that it has acted dovishly but thereby fallen behind the curve.

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The irrational neglect of optimal betting strategies

November 5, 2016

A recent betting experiment among finance students and professionals based on biased coin flipping revealed a wide gap between rational and actual behavior. The optimal strategy, which would have been constant and moderate risk taking (“Kelly criterion”), was not widely applied, notwithstanding education and training in finance. Instead, the experiment revealed a range of common behavioral biases. It challenges the general assumption of rational decision-making of finance professionals under uncertainty.

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The importance of statistical programming for investment managers

November 1, 2016

Almost every portfolio manager uses some form of quantitative analysis. Most still rely on Excel spreadsheets, but this popular tool constrains the creativity of analysis and struggles to cope with large data sets. Statistical programming in R and Python both facilitates and widens the scope of analysis. In particular, it allows using high-frequency data, alternative data sets, textual information and machine learning. And it greatly enhances the display and presentation of analytical findings.

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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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