Rao’s quadratic entropy and maximum diversification indexation
基于Rao二次熵提出最大分散化指数化策略的新公式，阐明其投资问题、业绩来源及改进方向，并验证改进在换手率、风险、估计窗口和协方差矩阵估计下的稳健性。
This paper proposes a new formulation of the maximum diversification indexation strategy based on Rao’s Quadratic Entropy. It clarifies the investment problem underlying this diversification strategy, identifies the source of its out-of-sample performance, and suggests new dimensions along which this performance can be improved. We show that these potential improvements are quantitatively important and are robust to portfolio turnover, portfolio risk, estimation window, and covariance matrix estimation.