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

  • Petr HájekEmail author
  • Vladimír Olej
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Bankruptcy prediction Word categorization Sentiment analysis Machine learning Meta-learning 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute of System Engineering and Informatics, Faculty of Economics and AdministrationUniversity of PardubicePardubiceCzech Republic

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