CAPM vs. Fama–French Three-Factor Model
Comparing Asset Pricing Models for Equity Returns
Comparing asset pricing models to explain stock returns
This project evaluated two foundational financial models—the Capital Asset Pricing Model (CAPM) and the Fama–French Three-Factor Model—to determine which better explains equity returns. The analysis focused on estimating factor exposures, comparing model fit, and interpreting the economic significance of each factor.
Role
Financial Data Analyst
Domain
Quantitative Finance
Stack
R · Linear Regression
Methods
CAPM · Fama–French · Factor Modeling
Project Snapshot
Question
Does the Fama–French Three-Factor Model explain stock returns better than CAPM?
Approach
Estimated both models using historical market data and compared explanatory power, residual behavior, and factor significance.
Finding
The Fama–French model captured additional variation in stock returns through size and value factors, providing a better overall fit than CAPM.
Overview
Asset pricing models attempt to explain why different stocks generate different expected returns. While CAPM attributes returns primarily to market risk, the Fama–French Three-Factor Model extends this framework by incorporating company size and value characteristics.
This project compared both models using historical financial data to evaluate whether the additional factors meaningfully improved model performance and interpretation.
The Challenge
Financial market variability
Stock returns are influenced by multiple sources of systematic risk that may not be captured by a single-factor model.
Model comparison
The project required comparing two competing statistical models using both quantitative metrics and economic interpretation.
Factor estimation
Regression coefficients were interpreted as measures of exposure to market, size, and value risk factors.
Model diagnostics
Residual analysis and goodness-of-fit metrics were used to evaluate model quality.
Analysis Pipeline
Financial Data
Collected historical stock returns and factor data.
Data Preparation
Computed excess returns and aligned market factor observations.
CAPM
Estimated the single-factor market model using linear regression.
Fama–French Model
Estimated the three-factor regression including market, size, and value factors.
Model Comparison
Compared explanatory power, residuals, and statistical significance.
Interpretation
Evaluated which model better explained observed stock returns.
Regression workflow for comparing competing asset pricing models.
Technical Stack
- R
- Linear Regression
- CAPM
- Fama–French
- Financial Modeling
- Regression Diagnostics
- Statistical Inference
- Data Visualization
- Quantitative Finance
What I Built
CAPM Regression
Estimated market beta and evaluated the relationship between market risk and excess stock returns.
Three-Factor Model
Extended the analysis by incorporating size (SMB) and value (HML) factors to explain additional return variation.
Model Evaluation
Compared model fit using regression diagnostics, residual analysis, and statistical significance tests.
Financial Interpretation
Connected regression coefficients to economic intuition and discussed how additional factors influence expected returns.
Key Results
Asset pricing models compared
Systematic risk factors analyzed
Linear regression framework used for estimation
What I Learned
This project strengthened my understanding of factor-based regression models and how statistical modeling can be used to explain financial market behavior. Comparing CAPM and the Fama–French model demonstrated how incorporating additional explanatory variables can improve predictive performance and economic interpretation.
It also reinforced the importance of evaluating models using both statistical evidence and domain knowledge. A model with better fit should also produce results that align with established financial theory.
Research Deliverable
The complete report includes the regression methodology, coefficient estimates, diagnostic analysis, and model comparison.