Re-evaluating Short- and Long-Term Trend Factors in CTA Replication: A Bayesian Graphical Approach
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912496112107520 |
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| author | Benhamou, Eric Ohana, Jean-Jacques Etienne, Alban Guez, Béatrice Setrouk, Ethan Jacquot, Thomas |
| author_facet | Benhamou, Eric Ohana, Jean-Jacques Etienne, Alban Guez, Béatrice Setrouk, Ethan Jacquot, Thomas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15876 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Re-evaluating Short- and Long-Term Trend Factors in CTA Replication: A Bayesian Graphical Approach Benhamou, Eric Ohana, Jean-Jacques Etienne, Alban Guez, Béatrice Setrouk, Ethan Jacquot, Thomas Artificial Intelligence Pricing of Securities Statistical Finance Trading and Market Microstructure 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. |
| title | Re-evaluating Short- and Long-Term Trend Factors in CTA Replication: A Bayesian Graphical Approach |
| topic | Artificial Intelligence Pricing of Securities Statistical Finance Trading and Market Microstructure |
| url | https://arxiv.org/abs/2507.15876 |