Fundamental Analysis and Mean-Variance Optimal Portfolios
54 Pages Posted: 27 Dec 2018 Last revised: 21 Oct 2020
Date Written: November 5, 2019
We integrate fundamental analysis with mean-variance portfolio optimization to form fully optimized fundamental portfolios. We find that fully optimized fundamental portfolios produce large out-of-sample factor alphas with high Sharpe ratios. They substantially outperform equal-weighted and value-weighted portfolios of stocks in the extreme decile of expected returns, an approach commonly used in fundamental analysis research. They also outperform the factor-based and parametric portfolio policy approaches used in the prior portfolio optimization literature. The relative performance gains from mean-variance optimized fundamental portfolios are persistent through time, robust to eliminating small capitalization firms from the investment set, and robust to incorporating estimated transactions costs. Our results suggest that future fundamental analysis research could implement this portfolio optimization approach to provide greater investment insights.
Keywords: Fundamental Analysis, Portfolio Optimization, Return Prediction, Accounting-Based Valuation
JEL Classification: G11, G12, G17
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