Combining data and text mining techniques for analysing financial reports: Research Articles

Authors:
Antonina Kloptchenko;Tomas Eklund;Jonas Karlsson;Barbro Back;Hannu Vanharanta;Ari Visa
Affiliations:
Turku Centre for Computer Science, Åbo Akademi University, Finland and IAMSR, Åbo Akademi University, Finland;Turku Centre for Computer Science, Åbo Akademi University, Finland and IAMSR, Åbo Akademi University, Finland;IAMSR, Åbo Akademi University, Finland;Department of Information Systems, Åbo Akademi University, Finland;Pori School of Technology and Economics, Tampere University of Technology, Finland;Department of Information Technology, Tampere University of Technology, Finland
Venue:
International Journal of Intelligent Systems in Accounting and Finance Management
Year:
2004

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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 parts 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 analysing quantitative and qualitative data from ﬁnancial reports, in order to see if the textual part of the report contains some indications 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. Copyright © 2004 John Wiley & Sons, Ltd.