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Multilayer Aggregation with Statistical Validation: Application to Investor Networks

Author

Listed:
  • Kk{e}stutis Baltakys
  • Juho Kanniainen
  • Frank Emmert-Streib
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.

Suggested Citation

  • Kk{e}stutis Baltakys & Juho Kanniainen & Frank Emmert-Streib, 2017. "Multilayer Aggregation with Statistical Validation: Application to Investor Networks," Papers 1708.09850, arXiv.org, revised May 2018.
    • Handle: RePEc:arx:papers:1708.09850
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    References listed on IDEAS

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    Cited by:

    1. Baptiste Barreau & Laurent Carlier & Damien Challet, 2019. "Deep Prediction of Investor Interest: a Supervised Clustering Approach," Papers 1909.05289, arXiv.org, revised Feb 2021.
    2. Frank Emmert-Streib & Aliyu Musa & Kestutis Baltakys & Juho Kanniainen & Shailesh Tripathi & Olli Yli-Harja & Herbert Jodlbauer & Matthias Dehmer, 2017. "Computational Analysis of the structural properties of Economic and Financial Networks," Papers 1710.04455, arXiv.org.
    3. Sindhuja Ranganathan & Mikko Kivelä & Juho Kanniainen, 2018. "Dynamics of investor spanning trees around dot-com bubble," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-14, June.
    4. Baptiste Barreau & Laurent Carlier & Damien Challet, 2019. "Deep Prediction of Investor Interest: a Supervised Clustering Approach," Papers 1909.05289, arXiv.org, revised Feb 2021.
    5. Baltakys, Kȩstutis & Baltakienė, Margarita & Kärkkäinen, Hannu & Kanniainen, Juho, 2019. "Neighbors matter: Geographical distance and trade timing in the stock market," Finance Research Letters, Elsevier, vol. 31(C).
    6. Siikanen, Milla & Baltakys, Kęstutis & Kanniainen, Juho & Vatrapu, Ravi & Mukkamala, Raghava & Hussain, Abid, 2018. "Facebook drives behavior of passive households in stock markets," Finance Research Letters, Elsevier, vol. 27(C), pages 208-213.
    7. Baltakienė, Margarita & Kanniainen, Juho & Baltakys, Kęstutis, 2021. "Identification of information networks in stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 131(C).
    8. Mishra, Abinash & Srivastava, Pranjal & Chakrabarti, Anindya S., 2020. "'Too central to fail' firms in bi-layered financial networks: Evidence of linkages from the US corporate bond and stock markets," IIMA Working Papers WP 2020-06-02, Indian Institute of Management Ahmedabad, Research and Publication Department.
    9. Li, Liqiang & Liu, Jing, 2020. "The aggregation of multiplex networks based on the similarity of networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).

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