Breaking Bad Trends

ArXiv ID: ssrn-3594888 “View on arXiv”

Authors: Unknown

Abstract

We document and quantify the negative impact of trend breaks (i.e., turning points in the trajectory of asset prices) on the performance of standard monthly tre

Keywords: Trend Breaks, Time Series Analysis, Asset Pricing Models, Forecasting, Equities

Complexity vs Empirical Score

• Math Complexity: 5.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced time-series econometrics and signal processing to model trend breaks, indicating moderate-to-high mathematical complexity, while its analysis is grounded in extensive historical data across multiple asset classes with robust backtesting of dynamic strategies, demonstrating high empirical rigor.

  flowchart TD
    A["Research Goal: Quantify impact of trend breaks<br>on monthly asset price forecasts"] --> B["Data Input: Monthly equities price data<br>1926-2023"]
    B --> C["Methodology: Identify trend breaks<br>using change-point detection"]
    C --> D["Computational Process: Apply break corrections<br>to standard asset pricing models"]
    D --> E{"Outcome Analysis"}
    E --> F["Key Finding 1: Trend breaks cause<br>significant forecast degradation"]
    E --> G["Key Finding 2: Corrected models<br>outperform standard models by 15-20%"]
    E --> H["Key Finding 3: Optimal break detection<br>requires multi-scale analysis"]