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Data and Dimension Reduction for Visual Financial Performance Analysis

Peter Sarlin, Data and Dimension Reduction for Visual Financial Performance Analysis. TUCS Technical Reports 1049, TUCS, 2012.

Abstract:

This paper assesses the suitability of data and dimension reduction methods, and data-dimension reduction (DDR) combinations, for visual financial performance analysis. Motivated by no comparable quantitative measure of all aspects of dimension reductions, this paper attempts to capture the suitability of methods for the task through a qualitative comparison and illustrative experiments. While the discussion deals with differences of DDR combinations in terms of their properties, the experiments illustrate their general applicability for financial performance analysis. The main conclusion is that topology-preserving DDR combinations with predefined 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.

Files:

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BibTeX entry:

@TECHREPORT{tSarlin_Peter12a,
  title = {Data and Dimension Reduction for Visual Financial Performance Analysis},
  author = {Sarlin, Peter},
  number = {1049},
  series = {TUCS Technical Reports},
  publisher = {TUCS},
  year = {2012},
  keywords = {financial performance analysis; visualization; data reduction; dimension reduction},
  ISBN = {978-952-12-2754-7},
}

Belongs to TUCS Research Unit(s): Data Mining and Knowledge Management Laboratory

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