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Multilayer networks are attracting growing attention in many fields,including finance. In this paper, we develop a new tractable procedure formultilayer aggregation based on statistical validation, which we apply toinvestor networks. Moreover, we propose two other improvements to theiranalysis: transaction bootstrapping and investor categorization. Theaggregation procedure can be used to integrate security-wise and time-wiseinformation 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 transactionbootstrapping for better statistical validation. Investor categorization allowsfor constant size networks and having more observations for each node, which isimportant in the inference especially for less liquid securities. Furthermore,we observe that the window size used for averaging has a substantial effect onthe number of inferred relationships. We apply this procedure by analyzing aunique data set of Finnish shareholders during the period 2004-2009. We findthat households in the capital have high centrality in investor networks,which, under the theory of information channels in investor networks suggeststhat they are well-informed investors.

Multilayer Aggregation with Statistical Validation: Application to Investor Networks