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Data mining in finance: From extremes to realism

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Abstract

This paper describes data mining in finance by discussing financial tasks and 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.

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  • Kovalerchuk, Boris & Vityaev, Evgenii, 2004. "Data mining in finance: From extremes to realism," Journal of Financial Transformation, Capco Institute, vol. 11, pages 81-89.
    • Handle: RePEc:ris:jofitr:1366
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    More about this item

    Keywords

    Data mining; financial services; relational methodologies;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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