Inferring Multi-Period Optimal Portfolios via Detrending Moving Average Cluster Entropy

Abstract

Despite half a century of research, there is still no general agreement about the optimal approach to build a robust multi-period portfolio. We address this question by proposing the detrended cluster entropy approach to estimate the portfolio weights of high-frequency market indices. The information measure produces reliable estimates of the portfolio weights gathered from the real-world market data at varying temporal horizons. The portfolio exhibits a high level of diversity, robustness and stability as it is not affected by the drawbacks of traditional mean-variance approaches.