Trend Premia: Redundancy Between Medium-Term and Adjacent Horizons
Summary
The paper questions whether combining trend signals across many horizons always improves diversification. It dynamically selects horizon weights for each asset using Bayesian optimization, then applies sparse Bayesian graphical models with turnover control to allocate across assets. This contrasts with equal weighting, which assumes every asset benefits equally from each horizon.
The authors find that the medium-term component adds little performance or diversification after short- and long-term trends are included. Removing the 125-day layer reportedly improves Sharpe ratios and drawdown efficiency while preserving benchmark correlation. A minimum-variance analysis supports the interpretation that medium-term signals overlap with neighboring horizons, motivating a simpler short-and-long-term barbell. The claims are specific to the paper’s framework and evidence; the excerpt does not provide sample details or robustness results, so it does not establish that the same horizon mix is optimal across markets or regimes.
Key ideas
- Equal horizon weighting assumes each asset benefits equally from every trend timescale.
- The authors optimize horizon weights at the asset level before allocating across assets.
- The medium-term trend component contributes little incremental value alongside short- and long-term signals in their analysis.
- Removing the 125-day layer reportedly improves Sharpe ratios and drawdown efficiency while maintaining benchmark correlation.
- A short-and-long-term barbell can reduce redundancy and model complexity.
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Full text
# Revisiting the Structure of Trend Premia: When Diversification Hides Redundancy # Revisiting the Structure of Trend Premia: When Diversification Hides Redundancy 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.
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