A method for de-trending asset prices

November 2, 2019

Financial market prices and return indices are non-stationary time series, even in logarithmic form. This means not only that they are drifting, but also that their distribution changes overtime. The main purpose of de-trending is to mitigate the effects of non-stationarity on estimated price or return distribution. De-trending can also support the design of trading strategies. The simplest basis for estimating trends is to subtract moving averages. The key challenge is to pick the appropriate average window, which must be long enough to detect a trend and short enough to make the de-trended data stationary. A neat method is to pick the window based on the kurtosis criterion, i.e. choosing the window length that brings the ‘fatness of tails’ of de-trended data to what it should look like under a normal distribution.

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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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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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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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The dangerous disregard for fat tails in quantitative finance

July 28, 2018

The statistical term ‘fat tails’ refers to probability distributions with relatively high probability of extreme outcomes. Fat tails also imply strong influence of extreme observations on expected future risk. Alas, they are a plausible and common feature of financial markets. A summary article by Nassim Taleb reminds practitioners that fat tails typically invalidate methods and conventions applied in quantitative finance. Standard in-sample estimates of means, variance and typical outliers of financial returns are erroneous, as are estimates of relations based on linear regression. The inconsistency between the evidence of fat tails and the ongoing dominant usage of conventional statistics in markets is plausibly a major source of inefficiency and trading opportunities.

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Directional predictability of daily equity returns

March 3, 2018

A new empirical paper provides evidence that the direction of daily equity returns in the Dow Jones has been predictable over the past 15 years, based on conventional short-term factors and out-of-sample selection and forecasting methods. Hit ratios have been 51-52%. The predictability has been statistically significant and consistent over time. Trading returns based on forecasting have been economically meaningful. Simple forecasting methods have outperformed more complex machine learning.

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Statistical remedies against macro information overload

February 4, 2017

“Dimension reduction” condenses the information content of a multitude of data series into small manageable set of factors or functions. This reduction is important for forecasting with macro variables because many data series have only limited and highly correlated information content. There are three types of statistical methods.The first type selects a subset of “best” explanatory variables (view post here). The second type selects a small set of latent background factors of all explanatory variables and then uses these background factors for prediction (Dynamic Factor Models). The third type generates a small set of functions of the original explanatory variables that historically would have retained their explanatory power and then deploys these for forecasting (Sufficient Dimension Reduction).

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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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