Basic factor investment for bonds

November 23, 2019

Popular factors for government bond investment are “carry”, “momentum”, “value” and “defensive”. “Carry” depends on the steepness of the yield curve, which to some extent reflects aversion to risk and volatility. “Momentum” relates to medium-term directional trends, which in the case of fixed income are often propagated by fundamental economic changes. “Value” compares yields against a fundamental anchor, albeit some approaches are as rough as medium-term mean reversion. Finally, “defensive” seeks to benefit from some bonds’ status as a “safe haven” in crisis times. A historic analysis over the past 50 years suggests that all of these factors have been relevant in some form. Yet, without more precise and compelling macroeconomic rationale factor investing may lack stability of performance in the medium term. The scope for theory-guided improvement seems vast.

(more…)

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.

(more…)

Tradable economics

October 26, 2019

Tradable economics is a technology for building systematic trading strategies based on economic data. Economic data are statistics that – unlike market prices – directly inform on economic activity. Tradable economics is not a zero-sum game. Trading profits are ultimately paid out of the economic gains from a faster and smoother alignment of market prices with economic conditions. Hence, technological advances in the field increase the value generation or “alpha” of the asset management industry overall. This suggests that the technology is highly scalable. One critical step is to make economic data applicable to systematic trading or trading support tools, which requires considerable investment in data wrangling, transformation, econometric estimation, documentation, and economic research.

(more…)

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.

(more…)

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.

(more…)

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.

(more…)

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.

(more…)

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.

(more…)

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.

(more…)

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.

(more…)

Tags

Tags
Research
Quantamental Academy
About Us
Macro-quantamental investment handbook