Overconfidence and inattention as asset return factors

January 20, 2018

Overconfidence in personal beliefs and inattention to new trends are widespread in financial markets. If specific behavioural biases become common across investors they constitute sources of mispricing and – hence – return factors. Indeed, overconfidence and inattention can be quantified as factors to an equity market pricing model and seem to capture a wide range of pricing anomalies. This suggests that detecting sources of behavioural biases, such as attachment to ideological views or laziness in the analysis of data, offers opportunities for systematic returns.

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Passive investment vehicles and price distortions

November 11, 2017

The share of passive investment vehicles in financial markets has soared over the past 20 years. In the U.S. equity market it has risen from 12% to 46%. There is reason and evidence suggesting that this will lead to more market price distortions. First, index effects on prices have gained importance relative to other price factors. Second, there has been a reduction of differentials across equity returns within indices. And third, the rise in passive investment vehicles has coincided with the expansion of momentum strategies in active funds, giving rise to vast flows that all explicitly neglect fundamental value.

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How to use financial conditions indices

October 14, 2017

There are two ways to use financial conditions indicators for macro trading. First, the tightening of aggregate financial conditions helps forecasting macroeconomic dynamics and policy responses. Second, financial vulnerability indicators, such as leverage and credit aggregates, help predicting the impact of an initial adverse shock to growth or financial markets on the subsequent macroeconomic and market dynamics. The latest IMF Global Financial Report has provided some clues as to how to combine these effects with existing economic-financial data.

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Critical transitions in financial markets

September 30, 2017

Critical transitions in financial markets are shifts in prices and operational structure to a new equilibrium after reaching a tipping point. “Complexity theory” helps analysing and predicting such transitions in large systems. Quantitative indicators of a market regime change can be a slowdown in corrections to small perturbations, increased autocorrelation of prices, increased variance and skewness of prices, and a “flickering” of markets between different states. A new research paper applies complexity theory to changes in euro area fixed income markets that arose from non-conventional policy. It finds that quantitative indicators heralded critical structural shifts in unsecured money markets and high-grade bond markets.

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Financial econometrics and machine learning

September 16, 2017

Supervised machine learning enhances the econometric toolbox by methods that find functional forms of prediction models in a manner that optimizes out-of-sample forecasting. It mainly serves prediction, whereas classical econometrics mainly estimates specific structural parameters of the economy. Machine learning emphasizes past patterns in data rather than top-down theoretical priors. The prediction function is typically found in two stages: [1] picking the “best” form conditional on a given level of complexity and [2] picking the “best” complexity based on past out-of-sample forecast performance. This method is attractive for financial forecasting, where returns depend on many complex relations most of which are not well understood even by professionals, and where backtesting of strategies should be free of theoretical bias that arises from historical experience.

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What traders can learn from market price volatility

July 1, 2017

Equity and bond market volatility can be decomposed into persistent and transitory components by means of statistical methods. The distinction is relevant for macro trading because plausibility and empirical research suggest that the persistent component is associated with macroeconomic fundamentals. This means that persistent volatility is an important signal itself and that its sustainability depends on macroeconomic trends and events. Meanwhile, the transitory component, if correctly identified, is more closely associated with market sentiment and can indicate mean-reverting price dynamics.

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Why financial markets misprice fundamental value

June 24, 2017

Experimental research has produced robust evidence for mispricing of assets relative to their fundamental values even with active trading and sufficient information. Academic studies support a wide range of causes for such mispricing, including asset supply, peer performance pressure, overconfidence in private information, speculative overpricing, risk aversion, confusion about macroeconomic signals and – more generally – inexperience and cognitive limitations of market participants.

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Building international financial conditions indices

April 29, 2017

IMF staff has developed global financial conditions indices for 43 global economies. Conceptually, these indices extract the communal component of range of indicators for local financing conditions, independent of economic conditions. The idea looks like a good basic principle for building FCIs for macro trading strategies. The research on these indices suggests that [1] financial conditions are a warning sign for recessions, and [2] global financial shocks have a powerful impact on local conditions, particularly in the short run and in emerging economies.

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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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Measuring non-conventional monetary policy surprises

March 18, 2017

A new paper proposes a measure for monetary policy surprises that arise from asset purchases and forward guidance. The idea is to estimate the change in the first principal component of government bond yields at different maturities to the extent that it is independent of changes in the policy reference rate and on days of significant policy statements. Such identified non-conventional policy shocks have had a persistent impact on yield curves and exchange rates since 2000. Their monitoring is important for so-called “long-long” risk parity trades.

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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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