Empirical Project 2

Empirical Project 2 Name Institution Course Instructor Due Date Empirical Project 2 Empirical Project 2 expands on the analysis conducted in project 1 by evaluating the robustness of the three-factor model, made up of the market excess return (RM(t) − RF(t)), size (SMB), and value (HML) factors, in capturing the time series variations in monthly portfolio returns. In the first part of the analysis, the returns of 25 stock portfolios formed based on Size and Book-to-Market ratios are analyzed by applying a multivariate linear regression model that assesses the explanatory power of the three factors. The second part expands on the analysis to include new stock portfolios formed on Size and Operating Profitability, as well as Size and Investment, but the focus remains on examining the robustness of the three-factor model in explaining variation in portfolio returns compared to the CAPM model used in empirical project 1. The results will contribute to understanding the strengths and limitations of multi-factor models in analyzing the returns of a given portfolio. Data The analysis uses multiple sets of stock portfolios formed using different criteria. The first set includes 25 portfolios formed at the end of each June based on size (market equity) and book-to-market ratios, with monthly return data available from July 1926 to October 2024 (Fama & French, 2024). These portfolios combines five portfolios based on market equity and five based on the book-to-market ratio. The portfolios formed on size and operating profitability has monthly returns data from July 1963 to October 2024. These portfolios are made of size breakpoints and profitability. The portfolio formed on size and investment also covers monthly returns data from July 1963 to October 2024 (Fama & French, 2024). Portfolios in this set are constructed using size breakpoints and investment (Fama & French, 2024). Additionally, the Fama/French factors are used to analyze the stock portfolios. Model and Findings The analysis in the two parts relies on the following multivariate linear regression model developed by Fama and French (1993): Rt-RFt=a+bRMt-RFt+sSMBt+hHMLt+e(t) A descriptive analysis of the FAMA French factors from July 19...