用贝叶斯图模型拆解CTA收益，揭示长短周期趋势的协同效应。
Re-evaluating Short- and Long-Term Trend Factors in CTA Replication: A Bayesian Graphical Approach
- 构建贝叶斯图模型，动态分离短/长周期趋势与市场贝塔因子
- 发现策略风险调整收益由长短周期因子混合程度决定
- 为量化基金经理优化趋势策略提供可解释框架
商品交易顾问（CTAs）长期依赖不同时间尺度的趋势跟踪规则：长期突破捕捉重大方向性行情，短期动量信号则在快速波动市场中表现优异。尽管趋势跟踪研究众多，但短周期与长周期策略的相对优势及交互作用仍存在争议。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，（ii）揭示不同时间尺度因子的组合如何影响策略的风险调整后表现，为理解趋势策略的有效性提供了新的分析视角。
原文摘要 · Abstract (English)
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.
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