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Data Mining for Financial Applications

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Data Mining and Knowledge Discovery Handbook

Summary

This chapter describes Data Mining in finance by discussing financial tasks, specifics of methodologies and techniques in this Data Mining area. It includes time dependence, data selection, forecast horizon, measures of success, quality of patterns, hypothesis evaluation, problem ID, method profile, attribute-based and relational methodologies. The second part of the chapter discusses Data Mining models and practice in finance. It covers use of neural networks in portfolio management, design of interpretable trading rules and discovering money laundering schemes using decision rules and relational Data Mining methodology.

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Author information

Authors and Affiliations

  1. Central Washington University, Ellensburg, Washington, USA

    Boris Kovalerchuk

  2. Institute of Mathematics, Russian Academy of Sciences, Novosibirsk, Russia

    Evgenii Vityaev

Authors
  1. Boris Kovalerchuk
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  2. Evgenii Vityaev
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Editor information

Editors and Affiliations

  1. , Dept. Industrial Engineering, Tel Aviv University, Ramat Aviv, 69978, Israel

    Oded Maimon

  2. , Dept. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel

    Lior Rokach

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Kovalerchuk, B., Vityaev, E. (2009). Data Mining for Financial Applications. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09823-4_60

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  • DOI: https://doi.org/10.1007/978-0-387-09823-4_60

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  • Print ISBN: 978-0-387-09822-7

  • Online ISBN: 978-0-387-09823-4

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