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Combining data and text mining techniques for analysing financial reports

Author

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  • Antonina Kloptchenko
  • Tomas Eklund
  • Jonas Karlsson
  • Barbro Back
  • Hannu Vanharanta
  • Ari Visa

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.

Suggested Citation

  • Antonina Kloptchenko & Tomas Eklund & Jonas Karlsson & Barbro Back & Hannu Vanharanta & Ari Visa, 2004. "Combining data and text mining techniques for analysing financial reports," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 12(1), pages 29-41, January.
    • Handle: RePEc:wly:isacfm:v:12:y:2004:i:1:p:29-41
    DOI: 10.1002/isaf.239
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    Cited by:

    1. Shianghau Wu, 2020. "A Fuzzy Association Rules Mining Analysis of the Influencing Factors on the Failure of oBike in Taiwan," Mathematics, MDPI, vol. 8(11), pages 1-18, October.
    2. Yubin Qian & Ya Sun, 2021. "The Correlation Between Annual Reports’ Narratives and Business Performance: A Retrospective Analysis," SAGE Open, , vol. 11(3), pages 21582440211, July.
    3. Kumar, Rahul & Deb, Soumya Guha & Mukherjee, Shubhadeep, 2020. "Do words reveal the latent truth? Identifying communication patterns of corporate losers," Journal of Behavioral and Experimental Finance, Elsevier, vol. 26(C).
    4. Daniel E. O'Leary, 2009. "Downloads and citations in Intelligent Systems in Accounting, Finance and Management," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 16(1‐2), pages 21-31, January.
    5. Chung, Wingyan, 2014. "BizPro: Extracting and categorizing business intelligence factors from textual news articles," International Journal of Information Management, Elsevier, vol. 34(2), pages 272-284.
    6. Hyun Jung Kim & Keun Tae Cho, 2022. "Analysis of Changes in Innovative Management of Global Insurers in the Pre- and Post-COVID-19 Eras," Sustainability, MDPI, vol. 14(16), pages 1-21, August.
    7. Eachempati, Prajwal & Srivastava, Praveen Ranjan & Kumar, Ajay & Tan, Kim Hua & Gupta, Shivam, 2021. "Validating the impact of accounting disclosures on stock market: A deep neural network approach," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    8. Ece Acar & Görkem Sarıyer & Vipul Jain & Bharti Ramtiyal, 2023. "Discovering Hidden Associations among Environmental Disclosure Themes Using Data Mining Approaches," Sustainability, MDPI, vol. 15(14), pages 1-14, July.

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