Fundamental Analysis and Mean-Variance Optimal Portfolios

54 Pages Posted: 27 Dec 2018 Last revised: 21 Oct 2020

Date Written: November 5, 2019

Abstract

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

Suggested Citation

Lyle, Matthew R. and Yohn, Teri Lombardi, Fundamental Analysis and Mean-Variance Optimal Portfolios (November 5, 2019). Kelley School of Business Research Paper Forthcoming, Available at SSRN: https://ssrn.com/abstract=3300976 or http://dx.doi.org/10.2139/ssrn.3300976

Matthew R. Lyle (Contact Author)

Northwestern University - Kellogg School of Management ( email )

2001 Sheridan Road
Evanston, IL 60208
United States

Teri Lombardi Yohn

Emory University Goizueta Business School ( email )

201 Dowman Drive
Atlanta, GA 30322
United States

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