FX trading strategies based on output gaps

October 19, 2019

Macroeconomic theory suggests that currencies of countries in a strong cyclical position should appreciate against those in a weak position. One metric for cyclical strength is the output gap, i.e. the production level relative to output at a sustainable operating rate. In the past, even a simple proxy of this gap, based on the manufacturing sector, seems to have provided an information advantage in FX markets. Empirical analysis suggests that [1] following the output gap in simple strategies would have turned a trading profit in the long-term, and [2] the return profile would have been quite different from classical FX trading factors.

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Crowded trades: measure and effect

October 12, 2019

One measure of the crowdedness of trades in a portfolio is centrality. Centrality is a concept of network analysis that measures how similar one institution’s portfolio is to its peers by assessing its importance as a network node. Empirical analysis suggests that [1] the centrality of individual portfolios is negatively related to future returns, [2] mutual fund holdings become more similar when volatility is high, and [3] the centrality of portfolios seems to reflect lack of information advantage. This evidence cautions against exposure to crowded trades that rely upon others’ information leadership or are motivated by widely publicized persuasive views.

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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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