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Pattern Recognition in Financial Data Using Association Rule

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Computer Vision and Graphics (ICCVG 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11114))

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Abstract

The paper is devoted to study patterns between the world’s financial markets. The classical Association Rules method was adopted to study the relations between time series of stock market indices. One revealed the comovement patterns are predominant over the anti comovement ones. The strength of the relations depends on the distance between markets. One extracted the strongest patterns what allowed to distinguishing the groups of financial markets. The strongest links between Polish and other stock markets were discovered.

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Author information

Authors and Affiliations

  1. Warsaw University of Life Sciences, Nowoursynowska 166, 02-787, Warsaw, Poland

    Krzysztof Karpio & Piotr Łukasiewicz

Authors
  1. Krzysztof Karpio
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  2. Piotr Łukasiewicz
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Corresponding author

Correspondence to Krzysztof Karpio .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Warsaw, Poland

    Leszek J. Chmielewski

  2. Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Warsaw, Poland

    Ryszard Kozera

  3. Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Warsaw, Poland

    Arkadiusz Orłowski

  4. Institute of Computer Science, Silesian University of Technology, Gliwice, Poland

    Konrad Wojciechowski

  5. Technion, Israel Institute of Technology, Haifa, Israel

    Alfred M. Bruckstein

  6. University of Groningen, Groningen, The Netherlands

    Nicolai Petkov

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Karpio, K., Łukasiewicz, P. (2018). Pattern Recognition in Financial Data Using Association Rule. In: Chmielewski, L., Kozera, R., Orłowski, A., Wojciechowski, K., Bruckstein, A., Petkov, N. (eds) Computer Vision and Graphics. ICCVG 2018. Lecture Notes in Computer Science(), vol 11114. Springer, Cham. https://doi.org/10.1007/978-3-030-00692-1_44

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  • DOI: https://doi.org/10.1007/978-3-030-00692-1_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00691-4

  • Online ISBN: 978-3-030-00692-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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