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Combining Data and Text Mining Techniques for Analyzing Financial Reports

Antonina Kloptchenko, Tomas Eklund, Barbro Back, Jonas Karlsson, Hannu Vanharanta, Ari Visa, Combining Data and Text Mining Techniques for Analyzing Financial Reports. Intelligent Systems in Accounting, Finance and Management 12(1), 29–41, 2004.

Abstract:

There is a vast amount of financial information on companies' financial
performance available to investors in electronic form today. While
automatic analysis of financial figures is common, it has been difficult
to extract meaning from the textual part of financial reports
automatically. The textual part of an annual report contains richer
information than the financial ratios. In this paper, we combine data
and text mining methods for analyzing quantitative and qualitative data
from financial reports, in order to see if the textual part of the
report contains some indication about future financial performance. The
quantitative analysis has been performed using self-organizing maps, and
the qualitative analysis using prototype-matching text clustering. The
analysis is performed on the quarterly reports of three leading
companies in the telecommunications sector.

BibTeX entry:

@ARTICLE{jKlEkBaKaVaVi04a,
  title = {Combining Data and Text Mining Techniques for Analyzing Financial Reports},
  author = {Kloptchenko, Antonina and Eklund, Tomas and Back, Barbro and Karlsson, Jonas and Vanharanta, Hannu and Visa, Ari},
  journal = {Intelligent Systems in Accounting, Finance and Management},
  volume = {12},
  number = {1},
  pages = {29–41},
  year = {2004},
  keywords = {Self-organizing maps; text mining; annual reports; prototype-matching clustering},
}

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

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