Re-evaluating Short- and Long-Term Trend Factors in CTA Replication: A Bayesian Graphical Approach
This work addresses a controversial issue in quantitative finance for CTA practitioners, but it appears incremental as it builds on existing debates without claiming broad breakthroughs.
The paper tackled the problem of understanding the relative contributions of short- and long-term trend factors in Commodity Trading Advisor (CTA) replication by dynamically decomposing returns using a Bayesian graphical model, and it showed how the blend of horizons affects risk-adjusted performance, though no concrete numbers were provided.
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.