Paper: arXiv 2507.15876

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.

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: 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.

Research Flowchart

  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"]

Cited References (31)

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  7. Beyond Trend Following: Deep Learning for Market Trend Prediction — → 2407.13685
  8. Demystifying time-series momentum strategies: Volatility estimators, trading rules and pairwise correlations (2020)
and 23 more
  1. Autoencoder asset pricing models (2021)
  2. High-order Hidden Markov Model for trend prediction in financial time series (2019)
  3. Machine Learning: A Probabilistic Perspective (2012)
  4. Time Series Momentum (2012) — link
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  6. Implementing Models in Quantitative Finance: Methods and Cases (2008)
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  9. Forecasting, structural time series models and the Kalman filter (1990)
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  19. Trend Following Struggles to Return to Vogue (2024) — link
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  21. Novel Risk-Off CTA Strategy Passes Tariff Test (2025) — link
  22. Cliff Asness: AI is ‘Still Just Statistics’. The AQR Head on Passive Investing, Private Equity, Tail Insurance, Trend-Following and Much More (2024) — link
  23. Designing robust trend-following system: Behind the scenes of trend-following (2018)

1 of 31 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.