Investor trading networks are attracting growing attention in the financial market literature . In this paper, we propose three improvements to their analysis: information aggregation, transaction bootstrapping , and investor categorization. These components can be used individually or in combination. For information aggregation, we introduce a tractable multilayer aggregation procedure to integrate security-wise and time-wise information about investor category trading networks. We use transaction bootstrapping to capture the properties of the actual data generation process and to have a more robust statistical testing procedure . Investor categorization allows for inferring constant size networks and  more observations for each node, which is important  especially for less liquid securities. We apply this procedure by analyzing a unique data set of Finnish shareholders during the period 2004--2009. We find that households play a central role in investor networks, as they have the most synchronized trading. Furthermore, we observe that the window size used for averaging has a substantial effect on the number of inferred relationships. Importantly, the use of our proposed aggregation framework is not limited to the field of investor trading networks; in fact, it can be used for different non-financial applications, with both observable and inferred relationships, spanning a number of different information layers .
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 .
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Investor trading
Multilayer
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the financial market literature
many fields, including finance
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propose three
develop a new tractable procedure for multilayer aggregation based on statistical validation, which we apply to investor networks. Moreover, we propose two other
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information aggregation, transaction bootstrapping ,
transaction bootstrapping
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These components
The aggregation procedure
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individually or in combination. For information aggregation, we introduce a tractable multilayer aggregation procedure
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category trading networks. We
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
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to capture the properties of the actual data generation process and to have a more robust statistical testing procedure
for better statistical validation
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inferring
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having
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in the inference
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We apply this procedure by analyzing a unique data set of Finnish shareholders during the period 2004--2009. We find that households play a central role in investor networks, as they have the most synchronized trading.
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Importantly, the use of our proposed aggregation framework is not limited to the field of investor trading networks; in fact, it can be used for different non-financial applications, with both observable and inferred relationships, spanning a number of different information layers
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