Re-evaluating Short- and Long-Term Trend Factors in CTA Replication: A Bayesian Graphical Approach

TL;DR

This paper introduces a Bayesian graphical model to dynamically decompose CTA returns into short-term, long-term, and market factors, providing insights into how different trend horizons influence risk-adjusted performance.

Contribution

It presents a novel Bayesian approach to analyze the interaction of short- and long-term trend factors in CTA strategies, enhancing understanding of their combined effects.

Findings

Decomposition of CTA returns into distinct trend components.

Insights into how horizon blending affects performance.

Improved risk-adjusted return analysis.

Abstract

Commodity Trading Advisors (CTAs) have historically relied on trend-following rules that operate on vastly different horizons from long-term breakouts that capture major directional moves to short-term momentum signals that thrive in fast-moving markets. Despite a large body of work on trend following, the relative merits and interactions of short-versus long-term trend systems remain controversial. This paper adds to the debate by (i) dynamically decomposing CTA returns into short-term trend, long-term trend and market beta factors using a Bayesian graphical model, and (ii) showing how the blend of horizons shapes the strategy's risk-adjusted performance.