To complete this assessment, a tutor must be able to work step-by-step as follows: first, import and clean the Excel dataset in Python (using Pandas), select six appropriate stocks, and compute monthly returns; second, calculate and interpret mean returns, standard deviations, Sharpe ratios (using the 0.1% monthly risk-free rate), and construct the covariance and correlation matrices; third, systematically generate all feasible long-only portfolio weight combinations (weights between 0 and 1 summing to 1), then compute each portfolio’s expected return, standard deviation (using matrix algebra , and Sharpe ratio, and plot the feasible set; fourth, identify and explain the minimum variance portfolio and maximum Sharpe ratio portfolio, and demonstrate how to mix the optimal risky portfolio with the risk-free asset to achieve a 1% monthly target return; fifth, derive and plot the theoretical efficient frontier using the matrix formulas from Modern Portfolio Theory; sixth, compute the monthly returns of the optimal portfolio, import the factor dataset, run a multivariate regression (using Statsmodels) on market, size, value, profitability, and investment factors, and interpret the statistical and economic meaning of the betas and R²; finally, research ESG ratings, adjust portfolio construction to reflect ESG or socially responsible investing principles (as discussed by Laura T. Starks), and present all results clearly in a structured 10-page report with proper financial interpretation and economic intuition.