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The Language of Quarterly Reports as an Indicator of Change in the Company'S Financial Status

Camilla Magnusson, Antti Arppe, Tomas Eklund, Barbro Back, Hannu Vanharanta, Ari Visa, The Language of Quarterly Reports as an Indicator of Change in the Company'S Financial Status. Information and Management 42(4), 561–574, 2005.

Abstract:

This paper adopts a multi-methodological approach to information systems research in order to produce new information through data mining. This approach is particularly suitable for mining material that consists of both qualitative and quantitative information. The contents of quarterly reports from three telecommunications companies were compared. The study focused on the years 2000-2001, a period of economic decline for many IT companies. The central quantitative data, reflected by seven financial ratios, were visualised using self-organising maps. The qualitative data, consisting of the textual contents of the reports, were visualised using collocational networks; these showed the relationships between the central concepts in the texts. As the visualisations of the contents were compared, certain patterns could be found. The results seemed to suggest that changes in the networks indicated future changes in the self-organising maps. In the cases studied, a change in the textual data usually indicated a change in the financial data in the following quarter. This may be a consequence of the fact that the texts reflected the plans and future expectations of management, whereas the financial ratios reflected the current financial situation of the company.

BibTeX entry:

@ARTICLE{jMaArEkBaVaVi05a,
  title = {The Language of Quarterly Reports as an Indicator of Change in the Company'S Financial Status},
  author = {Magnusson, Camilla and Arppe, Antti and Eklund, Tomas and Back, Barbro and Vanharanta, Hannu and Visa, Ari},
  journal = {Information and Management},
  volume = {42},
  number = {4},
  pages = {561–574},
  year = {2005},
  keywords = {Data mining; Quarterly reports; Multiple methods; Collocational networks; Self-organising maps},
}

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

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