Fundamental Analysis and Mean-Variance Optimal Portfolios
将基本面分析与均值方差投资组合优化相结合，形成完全优化的基本面投资组合，发现其产生高夏普比率的大样本外因子阿尔法，优于等权重和价值权重组合及因子和参数化组合方法。
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. JEL Classifications: G12; G14; G17.