Data and dimension reduction for visual financial performance analysis

Peter Sarlin

    Research output: Contribution to journalArticleScientificpeer-review

    9 Citations (Scopus)

    Abstract

    This article assesses the suitability of data and dimension reduction methods, and data-dimension reduction combinations, for visual financial performance analysis. Motivated by no comparable quantitative measure of all aspects of dimension reductions, this article attempts to capture the suitability of methods for the task through a qualitative comparison and illustrative experiments. While the discussion deals with differences of data-dimension reduction combinations in terms of their properties, the experiments illustrate their general applicability for financial performance analysis. The main conclusion is that topology-preserving data-dimension reduction combinations with predefined, regular grid shapes, such as the self-organizing map, are ideal tools for this task. We illustrate advantages of these types of methods with a visual financial performance analysis of large European banks.
    Original languageUndefined/Unknown
    Pages (from-to)148–167
    Number of pages20
    JournalInformation Visualization
    Volume14
    Issue number2
    DOIs
    Publication statusPublished - 2015
    MoE publication typeA1 Journal article-refereed

    Keywords

    • data reduction
    • dimension reduction
    • Financial performance analysis
    • visualization

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