Recent work has emphasized the diversification benefits of combining trend signals across multiple horizons, with the medium-term window-typically six months to one year-long viewed as the "sweet spot" of trend-following. This paper revisits this conventional view by reallocating exposure dynamically across horizons using a Bayesian optimization framework designed to learn the optimal weights assigned to each trend horizon at the asset level. The common practice of equal weighting implicitly assumes that all assets benefit equally from all horizons; we show that this assumption is both theoretically and empirically suboptimal. We first optimize the horizon-level weights at the asset level to maximize the informativeness of trend signals before applying Bayesian graphical models-with sparsity and turnover control-to allocate dynamically across assets. The key finding is that the medium-term band contributes little incremental performance or diversification once short- and long-term components are included. Removing the 125-day layer improves Sharpe ratios and drawdown efficiency while maintaining benchmark correlation. We then rationalize this outcome through a minimum-variance formulation, showing that the medium-term horizon largely overlaps with its neighboring horizons. The resulting "barbell" structure-combining short- and long-term trends-captures most of the performance while reducing model complexity. This result challenges the common belief that more horizons always improve diversification and suggests that some forms of time-scale diversification may conceal unnecessary redundancy in trend premia.

翻译：近期研究强调了跨多个时间尺度组合趋势信号的多样化优势，其中中期窗口——通常为六个月至一年——被视为趋势跟踪的“最佳区间”。本文通过采用贝叶斯优化框架动态调整不同时间尺度的暴露度，重新审视了这一传统观点。该框架旨在学习资产层面各趋势时间尺度的最优权重。常见的等权重做法隐含假设所有资产均等受益于所有时间尺度；我们证明这一假设在理论与实证上均非最优。我们首先在资产层面优化时间尺度权重，以最大化趋势信号的信息量，随后应用贝叶斯图模型——结合稀疏性与换手率控制——动态配置资产。关键发现是：一旦纳入短期与长期成分，中期区间对增量绩效或多样化的贡献甚微。移除125日层级可在保持基准相关性的同时提升夏普比率与回撤效率。我们通过最小方差模型阐释这一结果，表明中期时间尺度与其相邻尺度存在显著重叠。由此形成的“杠铃”结构——结合短期与长期趋势——在降低模型复杂度的同时捕获了大部分绩效。这一结果挑战了“更多时间尺度总能提升多样化”的普遍认知，并提示某些时间尺度多样化形式可能掩盖趋势溢价中不必要的冗余。