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

arXiv: https://arxiv.org/abs/2507.15876 · HTML: https://mlsanity.com/paper/2507.15876/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 30 references that are in the map.
- First posted on arXiv 17 Jul 2025.

## Builds on (most cited first, 1 of 1)

- [Kalman filter demystified: from intuition to probabilistic graphical model to real case in financial markets](https://mlsanity.com/paper/1811.11618/) (2018)

## Built on by (most cited first, 1 of 1)

- [Revisiting the Structure of Trend Premia: When Diversification Hides Redundancy](https://mlsanity.com/paper/2510.23150/) (2025)

## Abstract (arXiv)

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.