标题： 重新评估CTA复制中的短期和长期趋势因素：一种贝叶斯图方法 显示英文标题

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

摘要： 商品交易顾问（CTAs）历来依赖于在截然不同的时间框架上运作的趋势跟随规则，从捕捉主要方向性变动的长期突破到在快速变动的市场中表现良好的短期动量信号。 尽管有关趋势跟随的研究成果丰富，但短期与长期趋势系统的相对优势和相互作用仍存在争议。 本文通过（i）使用贝叶斯图模型将CTA收益动态分解为短期趋势、长期趋势和市场贝塔因子，以及（ii）展示不同时间框架的混合如何影响策略的风险调整后表现，来加入这场争论。 显示英文摘要

摘要： 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.