Revisiting the Structure of Trend Premia: When Diversification Hides Redundancy

2025-10-27

AI Summary

This paper challenges the conventional assumption that intermediate-term momentum signals (6–12 months) are optimal, investigating redundancy and efficiency limits in multi-horizon signal ensembles. Method: We propose a dynamic horizon-weighting framework grounded in Bayesian optimization and sparse graphical models, enabling asset-level adaptive allocation of weights across time scales. Contribution/Results: Empirical analysis reveals that intermediate-term trend signals contribute minimally when short- and long-term components coexist—yielding a “dumbbell-shaped” efficacy structure. Removing the 125-day horizon improves the portfolio’s Sharpe ratio by 18% and reduces maximum drawdown by 23%, significantly enhancing risk-adjusted returns while preserving high correlation with the benchmark. This study is the first to systematically document temporal-scale redundancy in trend signals, refuting the “more horizons, better performance” paradigm. It provides both theoretical grounding and empirical validation for parsimonious, robust trend-following strategies.

Technology Category

• Search and Optimization: Other Foundations of Search & Optimization
• Machine Learning: Ensemble Methods
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

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.

Problem

• Optimizing dynamic allocation across trend horizons using Bayesian framework
• Challenging equal weighting assumption across trend-following time horizons
• Identifying redundancy in medium-term trend signals through variance analysis

Innovation

• Dynamic horizon allocation using Bayesian optimization
• Asset-level weight optimization for trend signals
• Sparsity-controlled graphical models for asset allocation