Treasury basis and dollar overshooting

September 28, 2019

Safe dollar assets, such as Treasury securities, carry significant convenience yields. Their suitability for liquidity management and collateralization means that they provide value over and above financial return. The dollar exchange rate clears the market for safe dollar-denominated assets. Hence, when the convenience value of such assets turns positive the dollar appreciates above its long-term equilibrium, similar to classical exchange rate overshooting. Changes in convenience yields are common responses to financial crises, monetary policy actions, and regulatory changes. A proxy for such fluctuations is the Treasury basis, the difference between an actual Treasury yield and the yield on a synthetic counterpart based on foreign-currency yields and FX hedges. There is empirical support for the link between the Treasury basis on the dollar exchange rate.

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The quantitative path to macro information efficiency

September 21, 2019

Financial markets are not information efficient with respect to macroeconomic information because data are notoriously ‘dirty’, relevant economic research is expensive, and establishing stable relations between macro data and market performance is challenging. However, statistical programming and packages have prepared the ground for great advances in macro information efficiency. The quantitative path to macro information efficiency leads over three stages. The first is elaborate in-depth data wrangling that turns raw macro data (and even textual information) into clean and meaningful time series whose frequency and time stamps accord with market prices. The second stage is statistical learning, be it supervised (to validate logical hypotheses), or unsupervised (to detect patterns). The third stage is realistic backtesting to verify the value of the learning process and to assess the commercial viability of a macro trading strategy.

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Reinforcement learning and its potential for trading systems

September 14, 2019

In general, machine learning is a form of artificial intelligence that allows computers to improve the performance of a task through data, without being directly programmed. Reinforcing learning is a specialized application of (deep) machine learning that interacts with the environment and seeks to improve on the way it performs a task so as to maximize its reward. The computer employs trial and error. The model designer defines the reward but gives no clues as to how to solve the problem. Reinforcement learning holds potential for trading systems because markets are highly complex and quickly changing dynamic systems. Conventional forecasting models have been notoriously inadequate. A self-adaptive approach that can learn quickly from the outcome of actions may be more suitable. A recent paper proposes a reinforcement learning algorithm for that purpose.

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The low-risk effect: evidence and reason

September 7, 2019

The low-risk effect refers to the empirical finding that within an asset classes higher-beta securities fail to outperform lower-beta securities. As a result, “betting against beta”, i.e. leveraged portfolios of longs in low-risk securities versus shorts in high-risk securities, have been profitable in the past. The empirical evidence for the low-risk effect indeed is reported as strong and consistent across asset classes and time. The effect is explained by structural inefficiencies in financial markets, such as leverage constraints for many investors, focus on the performance of portfolios against benchmarks, institutional incentives to enhance beta and – for some investors – a preference for lottery-like securities with high upside risks.

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How to build a quantamental system for investment management

August 31, 2019

A quantamental system combines customized high-quality databases and statistical programming outlines in order to systematically investigate relations between market returns and plausible predictors. The term “quantamental” refers to a joint quantitative and fundamental approach to investing. The purpose of a quantamental system is to increase the information efficiency of investment managers, support the development of robust algorithmic trading strategies and to reduce costs of quantitative research. Its main building blocks are [1] bespoke proprietary databases of “clean” high-quality data, [2] market research outlines that analyse the features of particular types of trades, [3] factor construction outlines that calculate plausible trading factors based on theoretical reasoning, [4] factor research outlines that explore the behaviour and predictive power of these trading factors, [5] backtest outlines that investigate the commercial prospects of factor-based strategies, and [6] trade generators that calculate positions of factor-based strategies.

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Analyzing global fixed income markets with tensors

August 24, 2019

Roughly speaking, a tensor is an array (generalization of a matrix) of numbers that transform according to certain rules when the array’s coordinates change. Fixed-income returns across countries can be seen as residing on tensor-like multidimensional data structures. Hence a tensor-valued approach allows identifying common factors behind international yield curves in the same way as principal components analysis identifies key factors behind a local yield curve. Estimated risk factors can be decomposed into two parallel risk domains, the maturity domain, and the country domain. This achieves a significant reduction in the number of parameters required to fully describe the international investment universe.

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The power of R for trading (part 2)

August 17, 2019

The R environment makes statistical estimation and learning accessible to portfolio management beyond the traditional quant space. Overcoming technicalities and jargon, managers can operate powerful statistical tools by learning a few lines of code and gaining some basic intuition of statistical models. Thus, for example, R offers convenient functions for time series analysis (characterizing trading signals and returns), seasonal adjustment (detecting inefficiencies and cleaning calendar-dependent data), principal component analysis (condensing the information content of large data sets), standard OLS regression (simplest method to check quantitative relations), panel regression (estimating one type of relation across many countries or companies), logistic regression (estimating the probability of categorical events), Bayesian estimation (characterizing uncertainty of trading strategies comprehensively), and supervised machine learning (delegating the exact form of forecasts and signals to a statistical method).

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The power of R for trading (part 1)

August 10, 2019

R is an object-oriented programming language and work environment for statistical analysis. It is not just for programmers, but for everyone conducting data analysis, including portfolio managers and traders. Even with limited coding skills R outclasses Excel spreadsheets and boosts information efficiency. First, like Excel, the R environment is built around data structures, albeit far more flexible ones. Operations on data are simple and efficient, particularly for import, wrangling, and complex transformations. Second, R is a functional programming language. This means that functions can use other functions as arguments, making code succinct and readable. Specialized “functions of functions” map elaborate coding subroutines to data structures. Third, R users have access to a repository of almost 15,000 packages of function for all sorts of operations and analyses. Finally, R supports vast arrays of visualizations, which are essential in financial research for building intuition and trust in statistical findings.

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Retail investor beliefs

August 3, 2019

Survey evidence suggests that retail investors adjust positions rather sluggishly to changing beliefs and the beliefs themselves contradict classic rationality. Sluggishness arises from two features. First, the sensitivity of portfolio choices to beliefs is small. Second, the timing of trades does not depend much on belief changes. Contradictions to classic rationality arise because different investors cling stubbornly to different beliefs with little convergence. Also, retail investors associate higher returns with higher economic growth expectations and lower returns with fears of large drawdowns (contradicting the notion of tail risk premia). Overall this suggests that retail investors feel better informed than the market, with no need for updating their beliefs quickly and thoroughly. Opportunities for professional macro trading may arise through front running retail flows and applying more consistent rationality.

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Commodity trends as predictors of bond returns

July 27, 2019

Simple commodity price changes may reflect either supply or demand shocks. However, filtered commodity price trends are plausibly more aligned with demand, economic growth and, ultimately, inflationary pressure. All of these are key factors of fixed income returns. Empirical analysis based on a basket of crude oil prices shows that their common trend is indeed closely associated with empirical proxies for demand and has predictive power for economic output. More importantly for trading strategies, the oil price trend has been able to forecast returns in 20 international bond markets, both in-sample and out-of-sample.

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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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