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International Conference on Text, Speech, and Dialogue

TSD 2015: Text, Speech, and Dialogue pp 122–130Cite as

Word Categorization of Corporate Annual Reports for Bankruptcy Prediction by Machine Learning Methods

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9302))

Abstract

The language of company related documents is recognized as being an important indicator of future financial performance. This study aims to extract various word categories from corporate annual reports and examine their effect on bankruptcy prediction. We show that the language used by bankrupt companies is characterized by stronger tenacity, accomplishment, familiarity, present concern, exclusion and denial. Bankrupt companies also use more modal, positive, uncertain and negative language. We used neural networks, support vector machines, decision trees and ensembles of decision trees to predict corporate bankruptcy. The prediction models utilized both financial indicators and word categorizations as input variables. We show that both general dictionary and financial dictionary categories can significantly improve the accuracy of the prediction models.

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Author information

Authors and Affiliations

  1. Institute of System Engineering and Informatics, Faculty of Economics and Administration, University of Pardubice, Pardubice, Czech Republic

    Petr Hájek & Vladimír Olej

Authors
  1. Petr Hájek
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  2. Vladimír Olej
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Correspondence to Petr Hájek .

Editor information

Editors and Affiliations

  1. University of West Bohemia, Pilsen, Czech Republic

    Pavel Král

  2. University of West Bohemia, Pilsen, Czech Republic

    Václav Matoušek

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Hájek, P., Olej, V. (2015). Word Categorization of Corporate Annual Reports for Bankruptcy Prediction by Machine Learning Methods. In: Král, P., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2015. Lecture Notes in Computer Science(), vol 9302. Springer, Cham. https://doi.org/10.1007/978-3-319-24033-6_14

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  • DOI: https://doi.org/10.1007/978-3-319-24033-6_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24032-9

  • Online ISBN: 978-3-319-24033-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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