Factor-Targeted Asset Allocation: A Reverse Optimization Approach
提出一种基于因子风险溢价的逆向优化方法，构建权重更稳定的因子目标投资组合，在多种预期收益假设下获得更高且更稳定的夏普比率。
We demonstrate that using a mean-variance portfolio to obtain implied factor risk premia can result in stable weights for a factor portfolio when assets’ expected returns follow a factor structure that is subject to pricing errors. We propose a methodology to construct asset portfolios based on these factor portfolio weights, taking into account the possibility of pricing errors. Our simulation shows that these “factor-targeted” portfolios have higher and more stable Sharpe ratios than traditional allocation methodologies in various scenarios involving expected return assumptions. Furthermore, while our factor-targeted portfolios exhibit similar Sharpe ratios to the mean-variance portfolio built using factors for high levels of pricing errors, the factor-targeted portfolios have more stable portfolio weights, which makes them more appealing in practice.