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 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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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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Natural language processing for financial markets

June 15, 2019

News and comments are major drivers for asset prices, maybe more so than conventional price and economic data. Yet it is impossible for any financial professional to read and analyse the vast and growing flow of written information. This is becoming the domain of natural language processing; a technology that supports the quantitative evaluation of humans’ natural language. It delivers textual information in a structured form that makes it usable for financial market analysis. A range of useful tools is now available for extracting and analysing financial news and comments. Examples of application include machine-readable Bloomberg news, news analytics and market sentiment metrics by Refinitiv, and the Federal Reserve communication score by Cuemacro.

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U.S. Treasuries: decomposing the yield curve and predicting returns

May 25, 2019

A new paper proposes to decompose the U.S. government bond yield curve by applying a ‘bootstrapping method’ that resamples observed return differences across maturities. The advantage of this method over the classical principal components approach would be greater robustness to misspecification of the underlying factor model. Hence, the method should be suitable for bond return predictions under model uncertainty. Empirical findings based on this method suggest that equity tail risk (options skew) and economic growth surveys are significant predictors of returns of government bonds with shorter maturities.

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Systematic trading strategies: fooled by live records

May 11, 2019

Allocators to systematic strategies usually trust live records far more than backtests. Given the moral hazard issues of backtesting in the financial industry, this is understandable (view post here). Unfortunately, for many systematic strategies live records can be even more misleading. First, the survivor bias in published live records is worsening as the business has entered the age of mass production. Second, pronounced seasonality is a natural feature of many single-principle trading strategies. This means that even multi-year live records have very wide standard deviations across time depending on the conditions for the strategy principle. If one relies upon a few years’ of live PnL the probability of investing in a losing strategy or discarding a strong long-term value generator is disturbingly high. This suggests that the use of live record as allocation criterion, without sound theoretical reasoning and backtesting, can be highly inefficient.

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Survival in the trading factor zoo

March 30, 2019

The algorithmic strategy business likes quoting academic research to support specific trading factors, particularly in the equity space. Unfortunately, the rules of conventional academic success provide strong incentives for data mining and presenting ‘significant’ results. This greases the wheels of a trial-and-error machinery that discover factors merely by the force of large numbers and clever fitting of the data: some 400 trading factors have so far been published in academic journals (called the “factor zoo”). The number of unpublished and rejected tested factors is probably a high multiple of that. With so many tryouts, conventional significance indicators are largely meaningless and misleading. As with the problem of overfitting (view post here), the best remedies are plausible mathematical adjustments and reliance upon solid prior theory.

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The dollar as barometer for credit market risk

March 9, 2019

The external value of the USD has become a key factor of U.S. and global credit conditions. This reflects the surge in global USD-denominated debt in conjunction with the growing importance of mutual funds as the ultimate source of loan financing. There is empirical evidence that USD strength has been correlated with credit tightening by U.S. banks. There is also evidence that this tightening arises from deteriorating secondary market conditions for U.S. corporate loans, which, in turn, are related to outflows of credit funds after USD appreciation. The outflows are a rational response to the negative balance sheet effect of a strong dollar on EM corporates in particular. One upshot is that the dollar exchange rate has become an important early indicator for credit market conditions.

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Algorithmic strategies: managing the overfitting bias

February 16, 2019

The business of algorithmic trading strategies creates incentives for model overfitting and backtest embellishment: researchers must pass Sharpe ratio thresholds for their strategies to be considered, while managers lack interest in realistic simulations of ideas. Overfitting leads to bad investment decisions and underestimated risk. Sound ethical principles are the best method for containing this risk (view post here). Where these principles have been compromised one should assume that all ‘tweaks’ to the original strategy idea that are motivated by backtest improvement contribute to overfitting. Their effects can be estimated by looking at the ratio of directional ‘flips’ in the trading signal. Overfitting typically increases with the Sharpe ratio targets of the business and the scope for applying ‘tweaks’ to the strategy. Realistic strategy performance expectations should be based on a range of plausible strategy versions, not on an optimized one.

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ABOUT US

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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