Inferring Multi-Period Optimal Portfolios via Detrending Moving Average Cluster Entropy
arXiv:2104.09988 · doi:10.1209/0295-5075/133/60004
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.
arXiv comment7 pages, 5 figures
References in corpus (7)
- Colloquium: Statistical mechanics of money, wealth, and income
- Agent-based simulation of a financial market
- Entropy-Based Financial Asset Pricing
- Detrended cross-correlations between returns, volatility, trading activity, and volume traded for the stock market companies
- Information Measures for Long-Range Correlated Sequences: the Case of the 24 Human Chromosome Sequences
- Information measure for financial time series: quantifying short-term market heterogeneity
- Long-Range Dependence in Financial Markets: a Moving Average Cluster Entropy Approach