How much should portfolios shrink?
提出一个惩罚偏离参考投资组合的模型，得到在参数不确定下表现更优的稳健组合，并给出数据驱动的惩罚程度确定方法，模拟和实证表明该模型显著优于现有模型。
Abstract This paper develops a portfolio model that penalizes the deviation from a reference portfolio. The proposed model renders a robust portfolio that performs superior under parameter uncertainty. Penalizing the deviation also improves the performance of existing shrinkage portfolio models that are suboptimal due to model parameter uncertainty. The equal‐weight portfolio turns out to be a better reference portfolio than the currently holding portfolio even in the presence of transaction costs. A data‐driven method for determining the degree of penalization is offered. Comprehensive simulation and empirical studies suggest that the proposed model significantly outperforms various existing models.