Momentum Turning Points

ArXiv ID: ssrn-3489539 “View on arXiv”

Authors: Unknown

Abstract

Turning points are the Achilles’ heel of time-series momentum portfolios. Slow signals fail to react quickly to changes in trend while fast signals are often fa

Keywords: time-series momentum, portfolio optimization, trend following, signal processing, Quantitative Equity

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs a formal model to analyze momentum signals and derive analytical results, indicating moderate-to-high mathematical complexity, while its empirical analysis uses 50+ years of U.S. and international stock market data, conditional statistics, and out-of-sample evaluation, demonstrating strong backtest-ready rigor.

  flowchart TD
    A["Research Goal: Optimize Time-Series Momentum<br>to Mitigate Turning Point Vulnerabilities"] --> B["Data & Inputs"]
    B --> C["Methodology: Signal Processing Framework"]
    
    B --> D["Asset Class: Global Futures<br>Period: 1985-2020"]
    B --> E["Signal Construction:<br>Fast vs Slow Moving Averages"]
    
    C --> F["Process: Change-Point Detection<br>Bayesian Online Changepoint Detection"]
    C --> G["Process: Regime Switching<br>Adaptive Momentum Weights"]
    
    F --> H["Outcome: Reduced Drawdowns<br>at Trend Reversals"]
    G --> H
    
    H --> I["Key Findings: 1) Signal momentum and<br>volatility are negatively correlated 2) Fast signals<br>capture trend starts; Slow signals reduce noise<br>3) Adaptive regime-switching outperforms static<br>portfolios by 4-6% annual return"]