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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The predictability of relative asset returns

November 4, 2017

Empirical research suggests that it is easier to predict relative returns within an asset class than to predict absolute returns. Also, out-of-sample value generation with standard factors has been more robust for relative positions than for outright directional positions. This has been shown for bond, equity and currency markets. Importantly, directional and relative predictability have been complementary sources of investment returns, suggesting that using both will produce best performance.

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Fixed income relative value

October 28, 2017

Relative value can be defined as expected price convergence of contracts or portfolios with similar risk profiles. For fixed income this means similar exposure to duration, convexity and credit risk. The causes of relative value are limited arbitrage capital and aversion to the risk of persistent divergence. Relative value in the fixed income space has been pervasive and persistent. Relative value trades can be based on parametric estimation of yield curves or comparisons of individual contracts with portfolios that replicate their essential features. The latter appear to have been more profitable in the past.

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How bank regulatory reform has changed macro trading

October 21, 2017

The great regulatory reform in global banking has altered the backdrop for macro trading. First, greater complexity and policymaker discretion means that investment managers must pay more attention to regulatory policies, not unlike the way they follow monetary policies. Second, changes in capital standards interfere with the effects of monetary conditions and probably held back their full impact on credit conditions in past years. Third, elevated capital ratios and loss-absorption capacity will plausibly contain classical banking crises in the future and, by themselves, reduce the depth of recessions. Fourth, regulatory tightening seems to have reduced market liquidity and may increase the depth of market price downturns.

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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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The latent factors behind commodity price indices

October 7, 2017

A 35-year empirical study suggests that about one third of the monthly changes in a broad commodity price index can be attributed to a single global factor that is related to the business cycle. In fact, for a non-fuel commodity basket almost 70% of price changes can be explained by this factor. By contrast, oil and energy price indices have been driven mainly by a fuels-specific factor that is conventionally associated with supply shocks. Short-term price changes of individual commodities depend more on contract-specific events, but also display a significant influence of global and sectoral factors. The latent global factor seems to help forecasting commodity index prices at shorter horizons.

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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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Hedging FX trades against unwanted risk

September 23, 2017

When FX forward positions express views on country-specific developments one can shape the trade to its rationale by hedging against significant unrelated global influences. Almost all major exchange rates are sensitive to directional global market moves and USD-based exchange rates are typically also exposed to EURUSD changes. A simple empirical analysis for 29 currencies for 1999-2017 suggests that the largest part of these influences has been predictable out-of-sample and hence “hedgeable”. Even volatility-adjusted relative positions across EM or FX carry currencies may sometimes be hedged against market directional influences.

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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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FX forward returns: basic empirical lessons

September 9, 2017

FX forward returns for 29 floating and convertible currencies since 1999 provide important empirical lessons. First, the long-term performance of FX returns has been dependent on economic structure and clearly correlated with forward-implied carry. The carry-return link has weakened considerably in the 2010s. Second, monthly returns for all currencies showed large and frequent outliers beyond the borders of a normal random distribution. Simple volatility targeting would not have mitigated this. Third, despite large fundamental differences, all carry and EM currencies have been positively correlated among themselves and with global risk benchmarks. Fourth, relative standard deviations across currencies have been predictable and partly structural. Hence, they have been important for scaling FX trades across small currencies.

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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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