英文标题：
《Multi-Likelihood Methods for Developing Stock Relationship Networks
  Using Financial Big Data》
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作者：
Xue Guo, Hu Zhang, Tianhai Tian
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最新提交年份：
2019
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英文摘要：
  Development of stock networks is an important approach to explore the relationship between different stocks in the era of big-data. Although a number of methods have been designed to construct the stock correlation networks, it is still a challenge to balance the selection of prominent correlations and connectivity of networks. To address this issue, we propose a new approach to select essential edges in stock networks and also maintain the connectivity of established networks. This approach uses different threshold values for choosing the edges connecting to a particular stock, rather than employing a single threshold value in the existing asset-value method. The innovation of our algorithm includes the multiple distributions in a maximum likelihood estimator for selecting the threshold value rather than the single distribution estimator in the existing methods. Using the Chinese Shanghai security market data of 151 stocks, we develop a stock relationship network and analyze the topological properties of the developed network. Our results suggest that the proposed method is able to develop networks that maintain appropriate connectivities in the type of assets threshold methods.
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中文摘要：
发展股票网络是探索大数据时代不同股票之间关系的重要途径。虽然已经设计了许多方法来构建股票相关性网络，但如何平衡显著相关性的选择和网络的连通性仍然是一个挑战。为了解决这个问题，我们提出了一种新的方法来选择股票网络中的关键边，并保持已建立网络的连通性。这种方法使用不同的阈值来选择连接到特定股票的边，而不是在现有的资产价值方法中使用单个阈值。我们算法的创新之处在于，在选择阈值的最大似然估计中加入了多个分布，而不是现有方法中的单一分布估计。利用中国上海证券市场151只股票的数据，我们构建了一个股票关系网络，并分析了该网络的拓扑性质。我们的结果表明，所提出的方法能够开发在资产阈值方法类型中保持适当连接的网络。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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