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Patterns of trading profiles at the Nordic Stock Exchange. A correlation-based approach

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  • Federico Musciotto
  • Luca Marotta
  • Salvatore Miccich`e
  • Jyrki Piilo
  • Rosario N. Mantegna

Abstract

We investigate the trading behavior of Finnish individual investors trading the stocks selected to compute the OMXH25 index in 2003 by tracking the individual daily investment decisions. We verify that the set of investors is a highly heterogeneous system under many aspects. We introduce a correlation based method that is able to detect a hierarchical structure of the trading profiles of heterogeneous individual investors. We verify that the detected hierarchical structure is highly overlapping with the cluster structure obtained with the approach of statistically validated networks when an appropriate threshold of the hierarchical trees is used. We also show that the combination of the correlation based method and of the statistically validated method provides a way to expand the information about the clusters of investors with similar trading profiles in a robust and reliable way.

Suggested Citation

  • Federico Musciotto & Luca Marotta & Salvatore Miccich`e & Jyrki Piilo & Rosario N. Mantegna, 2015. "Patterns of trading profiles at the Nordic Stock Exchange. A correlation-based approach," Papers 1511.06873, arXiv.org.
    • Handle: RePEc:arx:papers:1511.06873
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    Cited by:

    1. Duarte Queirós, Sílvio M. & Anteneodo, Celia, 2016. "Complexity in quantitative finance and economics," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 1-2.
    2. Margarita Baltakienė & Kęstutis Baltakys & Juho Kanniainen & Dino Pedreschi & Fabrizio Lillo, 2019. "Clusters of investors around initial public offering," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-14, December.
    3. 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.
    4. Wojciech Wisniewski & Yuri Kalnishkan & David Lindsay & Si^an Lindsay, 2024. "Temporal distribution of clusters of investors and their application in prediction with expert advice," Papers 2406.19403, arXiv.org.
    5. Bongiorno, Christian & Miccichè, Salvatore & Mantegna, Rosario N., 2022. "Statistically validated hierarchical clustering: Nested partitions in hierarchical trees," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).

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