Portfolio optimization based on empirical mode decomposition
Portfolio optimization based on empirical mode decomposition
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DOI：
10.1016/j.physa.2019.121813
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发表时间：
2019-10
期刊：
影响因子：
--
通讯作者：
Li Yang;Longfeng Zhao;Chao Wang
中科院分区：
文献类型：
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作者：
Li Yang;Longfeng Zhao;Chao Wang
近年来，金融资产之间的相关性研究引起了广泛的关注。由于金融时间序列的非线性和非平稳特性，例如，股票收益率时间序列中，不同波动水平的互相关对于学术界和金融从业者来说都是非常重要的。本文利用经验模态分解（EMD）方法分析了金融资产不同波动水平之间的互相关结构。然后，基于相关性的网络来确定股票市场的聚类性质。然后，我们提出了几个投资组合优化策略的基础上的EMD相关网络。利用网络的拓扑信息，我们可以构造一些高收益、低风险的投资组合。在两个投资组合评价框架下，我们证明了这些投资组合具有一贯的良好表现。
The investigation about the cross-correlation among financial assets has drawn broad attention recently. Due to the nonlinear and non-stationary identities of the financial time series, e.g., stock return time series, the cross-correlation for different level of fluctuations are quite important for both academia and financial practitioners. Here we use the empirical mode decomposition (EMD) method to analyze the cross-correlation structure among different level of fluctuations for financial assets. The correlation-based networks are then employed to determine the clustering property of stock market. We then propose several portfolio optimization strategies based on the EMD correlation-based networks. Using the topological information of the networks, we can construct some portfolios with high return and low risk. Under two portfolio evaluation frameworks, we prove that these portfolios have consistently good performance.