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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. In: Proceedings of the 2002 Eighth Americas Conference on Information Systems (AMCIS2002), 20–28, AIS, 2002.

Abstract:

There is a vast amount of financial information on companies financial performance available to investors today. While automatic analysis of financial figures is common, it has been difficult to automatically extract meaning from the textual part of financial reports. The textual part of an annual report contains richer information than the financial ratios. In this paper, we combine data 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:

@INPROCEEDINGS{pKlEkBaKaVaVi02a,
  title = {Combining Data and Text Mining Techniques for Analyzing Financial Reports},
  booktitle = {Proceedings of the 2002 Eighth Americas Conference on Information Systems (AMCIS2002)},
  author = {Kloptchenko, Antonina and Eklund, Tomas and Back, Barbro and Karlsson, Jonas and Vanharanta, Hannu and Visa, Ari},
  publisher = {AIS},
  pages = {20–28},
  year = {2002},
  keywords = {Self-organizing map, text mining, annual reports, prototype-matching clustering},
}

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

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