Laboratory of Industrial and Information Management, Tampere University of Technology, Tampere, Finland.
Predictive Medicine and Data Analytics Lab, Faculty of Biomedical Sciences and Engineering, Tampere University of Technology, Tampere, Finland.
Sci Rep. 2018 May 29;8(1):8198. doi: 10.1038/s41598-018-26575-2.
Multilayer networks are attracting growing attention in many fields, including finance. In this paper, we develop a new tractable procedure for multilayer aggregation based on statistical validation, which we apply to investor networks. Moreover, we propose two other improvements to their analysis: transaction bootstrapping and investor categorization. The aggregation procedure can be used to integrate security-wise and time-wise information about investor trading networks, but it is not limited to finance. In fact, it can be used for different applications, such as gene, transportation, and social networks, were they inferred or observable. Additionally, in the investor network inference, we use transaction bootstrapping for better statistical validation. Investor categorization allows for constant size networks and having more observations for each node, which is important in the inference especially for less liquid securities. Furthermore, we observe that the window size used for averaging has a substantial effect on the number of inferred relationships. We apply this procedure by analyzing a unique data set of Finnish shareholders during the period 2004-2009. We find that households in the capital have high centrality in investor networks, which, under the theory of information channels in investor networks suggests that they are well-informed investors.
多层网络在包括金融在内的许多领域引起了越来越多的关注。在本文中，我们开发了一种基于统计验证的新的可扩展的多层聚合方法，并将其应用于投资者网络。此外，我们还提出了另外两个改进其分析的方法：交易引导和投资者分类。聚合过程可用于整合关于投资者交易网络的安全和时间信息，但它不仅限于金融领域。实际上，它可以用于不同的应用，如基因、交通和社交网络，无论是推断的还是可观察的。此外，在投资者网络推断中，我们使用交易引导来进行更好的统计验证。投资者分类允许网络具有固定的大小，并且每个节点具有更多的观测值，这在推断中非常重要，特别是对于流动性较低的证券。此外，我们观察到用于平均的窗口大小对推断关系的数量有很大的影响。我们通过分析 2004-2009 年期间芬兰股东的独特数据集来应用此过程。我们发现，资本中的家庭在投资者网络中具有很高的中心性，根据投资者网络中的信息渠道理论，这表明他们是消息灵通的投资者。
