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

ArXiv ID: 2507.15876 “View on arXiv”

Authors: Eric Benhamou, Jean-Jacques Ohana, Alban Etienne, Béatrice Guez, Ethan Setrouk, Thomas Jacquot

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.

Keywords: Trend Following, CTA, Bayesian Graphical Model, Risk-Adjusted Performance, Momentum, Managed Futures / CTA

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced Bayesian graphical models and detailed mathematical proofs of option-pricing theory, indicating high math complexity. It also presents extensive empirical backtesting on CTA replication over 2010-2025 with transaction cost analysis, showing strong empirical rigor.

  flowchart TD
    A["Research Goal<br>Disentangle Short vs. Long-Term<br>Trend Factors in CTA Returns"] --> B["Methodology<br>Bayesian Graphical Model"]
    
    B --> C["Data/Inputs<br>Historical CTA Return Indices<br>& Market Data"]
    
    C --> D["Computational Process<br>Dynamic Decomposition<br>of Returns into 3 Factors:"]
    
    D --> D1["Short-Term Trend<br>(Momentum)"]
    D --> D2["Long-Term Trend<br>(Breakout)"]
    D --> D3["Market Beta<br>(General Exposure)"]
    
    D1 & D2 & D3 --> E["Outcome<br>Risk-Adjusted Performance<br>Analysis by Horizon Blend"]