Asset Pricing Models

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

The Link between Fama-French Time-Series Tests and Fama-Macbeth Cross-Sectional Tests ArXiv ID: ssrn-1271935 “View on arXiv” Authors: Unknown Abstract Many papers in the empirical finance literature implement tests of asset pricing models either via Fama-French time-series regressions or via Fama-Macbeth cros Keywords: Asset Pricing Models, Fama-French Regressions, Fama-MacBeth Regressions, Empirical Finance, Cross-Sectional Returns, Equity Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper’s mathematical framework relies on established econometric and asset pricing models, which are advanced but not unusually dense; however, it heavily emphasizes empirical implementation, using real financial data and detailed testing methodologies. flowchart TD A["Research Goal: Test Asset Pricing Models"] --> B{"Choose Methodology"} B --> C["Fama-French Time-Series Regressions"] B --> D["Fama-MacBeth Cross-Sectional Regressions"] C --> E["Input: Time-Series Data Portfolio Returns & Factors"] E --> F["Compute: Regression R_it - R_ft = α_i + β_i Factor_t + ε_it"] D --> G["Input: Cross-Sectional Data Cross-Section of Returns at Each Time t"] G --> H["Compute: Regress Returns on Risk Factors Across Assets at Each t"] F --> I["Key Finding: Link & Equivalence Under Null Hypothesis"] H --> I style A fill:#e1f5fe style I fill:#f3e5f5