No Abstract

Equity Risk Premiums (ERP): Determinants, Estimation and Implications - The 2023 Edition ArXiv ID: ssrn-4398884 “View on arXiv” Authors: Unknown Abstract No abstract found Keywords: N/A, Insufficient Data, No Abstract Complexity vs Empirical Score Math Complexity: 6.5/10 Empirical Rigor: 7.0/10 Quadrant: Holy Grail Why: The paper employs sophisticated financial models (DCF, option pricing, risk decomposition) requiring advanced math, and its empirical component is data-heavy, featuring historical time-series analysis, implied premium calculations from market prices, and country-specific risk scores, making it backtest-ready despite lacking explicit code or GitHub links. flowchart TD A["Research Goal: Determine & Estimate Equity Risk Premiums for 2023"] --> B{"Data Inputs: Historical Market & Government Bond Returns"} B --> C["Methodology: Cross-Sectional & Time-Series Analysis"] C --> D["Computation: Discounted Cash Flow & Risk Models"] D --> E["Outcome 1: Estimated Equity Risk Premium for 2023"] D --> F["Outcome 2: Key Determinants of ERP Identified"] D --> G["Outcome 3: Implications for Investors & Valuation"]

Understanding Risk and Return, the CAPM, and the Fama-French Three-Factor Model ArXiv ID: ssrn-481881 “View on arXiv” Authors: Unknown Abstract No abstract found Keywords: N/A, Insufficient Data, No Abstract Complexity vs Empirical Score Math Complexity: 6.5/10 Empirical Rigor: 3.0/10 Quadrant: Lab Rats Why: The paper introduces and derives the mathematical formulas for the CAPM and beta, involving covariance and variance calculations, which is moderately complex. However, it lacks backtest results, code, or heavy implementation details, relying primarily on conceptual explanation and historical data charts for illustration rather than rigorous empirical testing. flowchart TD A["Research Goal: Understand Risk & Return<br>Test CAPM vs. Fama-French Model"] --> B{"Data Collection & Preparation"} B --> C["CRSP & Compustat Datasets"] C --> D["Market, Size, Value Factors"] D --> E["Portfolio Formation<br>Size/BM Sorted Portfolios"] E --> F["Computational Analysis<br>Time-Series Regressions"] F --> G["Key Outcomes"] G --> H["CAPM Fails to Explain<br>Returns (Size & Value Effects)"] G --> I["Fama-French 3-Factor Model<br>Significantly Improves Fit"]

What is BehavioralFinance? ArXiv ID: ssrn-256754 “View on arXiv” Authors: Unknown Abstract No abstract found Keywords: N/A, Insufficient Data, No Abstract Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 0.5/10 Quadrant: Philosophers Why: The excerpt is a descriptive overview of behavioral finance concepts with no mathematical formulas or advanced statistical analysis, and it presents no data, backtests, or implementation details. flowchart TD A["Research Goal: Define Behavioral Finance"] --> B["Key Methodology: Review Existing Literature"] B --> C{"Data/Input: Academic Papers & Theory"} C --> D["Computational Process: Conceptual Analysis & Synthesis"] D --> E["Key Outcome: Framework of Behavioral Biases & Market Anomalies"]