Multilayer Aggregation with Statistical Validation: Application to Investor Networks

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    Abstract

    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.

    Original languageEnglish
    Article number8198
    JournalScientific Reports
    Volume8
    Issue number1
    DOIs
    Publication statusPublished - 1 Dec 2018
    Publication typeA1 Journal article-refereed

    Publication forum classification

    • Publication forum level 2

    ASJC Scopus subject areas

    • General

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