Portfolio Optimization with Fama-French Model

Abstract

This paper explores the method of using Fama-French Three Factor Model and mean variance analysis to optimize portfolios, reaching more accurate predictions, and achieving maximum return and minimum risk. Using historical data of stocks from different industries, three factors from the Fama-French database is applied, and new expected return is calculated. Then mean variance analysis is performed to find the optimum Sharpe ratio weights. From the optimized weights, it can be seen that COST, ROM, and JPM are strong performing stocks. Whereas AAPL and small cap stocks like PERI are considered less favorable by both the CAPM model and Fama-French Model. Reducing weight of these stocks could decrease the portfolio risk, hence lowering variance, reaching higher Sharpe ratio. The results in this paper would be beneficial to public and private investors in different financial markets. As shown in this paper and also other works, the use of Fama-French Model and mean variance analysis could increase profit in most cases.