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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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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The dominance of price over value

October 1, 2016

Market prices reveal information about fundamental value indirectly. Private research produces information about fundamental value directly. Neither is a perfect indicator of fundamental value: the former due to non-fundamental market factors, and the latter due to limitations of private research. However, plausible theoretical research shows that overtime the information content of prices in respect to (known) fundamentals improves faster due to aggregation and averaging. When this happens investors rationally neglect their own fundamental research. This can erode information efficiency of the market and lead to sustained misalignments if the market as a whole misses key risks and value factors.

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Selecting macro factors for trading strategies

September 17, 2016

A powerful statistical method for selecting macro factors for trading strategies is the “Elastic Net”. The method simultaneously selects factors in accordance with their past predictive power and estimates their influence conservatively in order to contain the influence of accidental correlation. Unlike other statistical selection methods, such as “LASSO”, the “Elastic Net” can make use of a large number of correlated factors, a typical feature of economic time series.

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asset management Asset Purchase Program banks bond returns bond yields bubbles capital regulation carry China collateral commodities credit returns default ECB economic data EM equity returns euro area crisis Fed financial crisis FX FX interventions FX returns government debt government deficits herding inflation information insurances Japan leverage liquidity machine learning market turmoil price factors regulation risk management shadow banking statistical methods term structure trend following variance risk premium VIX volatility ZLB
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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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