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Separating Short- and Long-Term Trend Factors in CTA Returns

Article arXiv papers · Author: Eric Benhamou et al.
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Summary

The paper examines how trend systems operating over different horizons contribute to commodity trading advisor (CTA) returns. It proposes a Bayesian graphical model to dynamically separate returns into short-term trend, long-term trend, and market beta components. This provides a way to study how shorter and longer trend signals interact within a CTA strategy, rather than treating trend following as a single uniform exposure.

The document says the analysis considers how mixing trend horizons shapes risk-adjusted performance, but it provides no empirical results, sample details, or model specifications in the supplied text. Its claims therefore describe the research question and method, not evidence that one horizon or blend performs better. The framing also leaves open how the factors are identified and how broadly the conclusions apply across CTAs or market conditions.

Key ideas

  • CTA returns can be decomposed into short-term trend, long-term trend, and market beta factors.
  • A Bayesian graphical model is proposed to make that decomposition dynamic.
  • The study examines how combining trend horizons affects risk-adjusted performance.
  • The supplied description does not report factor estimates or comparative performance results.

Tags

CommoditiesTrend followingMomentumStatisticsFactor investing
Full text
# Re-evaluating Short- and Long-Term Trend Factors in CTA Replication: A Bayesian Graphical Approach


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









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.