Trend-Following Strategy Design for Asset and Industry Allocation

Summary

This research summary reviews trend-following indicators and how to build strategies for broad asset allocation and industry allocation. It groups 41 indicators by their input data, filtering, moving-average construction, and signal generation, arguing that many share similar behavior while differing in how they weight past prices or returns. It uses trend signature plots and Monte Carlo simulations of synthetic price series to study how underlying return, volatility, autocorrelation, and cross-asset correlation affect strategy results.

The summary reports that higher-return, lower-volatility assets tend to produce higher backtest Sharpe ratios, while stronger autocorrelation may reduce maximum drawdown. It also cautions that strategies with higher Sharpe ratios can have less stable parameters and greater overfitting risk. Low cross-asset correlation favors time-series momentum for broad asset allocation, while correlated industry indexes may suit cross-sectional momentum. The described framework matches assets and indicators, tests generalized synthetic data, and screens for overfitting. Reported historical results are specific to the study’s samples and do not establish future performance.

Key ideas

• The review classifies 41 trend-following indicators by their inputs, filters, averaging, and signal rules.
• Trend-strategy results depend strongly on the underlying assets’ return, volatility, autocorrelation, and correlation structure.
• Higher backtest Sharpe ratios can coincide with weaker parameter stability and greater overfitting risk.
• The study favors time-series momentum for broad assets and cross-sectional momentum for correlated industry indexes.
• It recommends matching the asset pool and indicator to the use case and applying overfit checks.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.