Fundamental Analysis and Mean-Variance Optimal Portfolios

• 成果类型: Article
• 署名作者: Lyle, Matthew R.; Yohn, Teri Lombardi
• 署名单位: Northwestern University; Emory University
• 刊物名称: ACCOUNTING REVIEW
• ISSN/ISSBN: 0001-4826
• DOI: 10.2308/TAR-2019-0622
• 发表日期: 2021
• 页码: 303-327
• 关键词: cross-section; COVARIANCE-MATRIX; RISK; INFORMATION; anomalies; returns; diversification; optimization; valuation; forecasts

摘要

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.