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Evaluating Sentiment in Annual Reports for Financial Distress Prediction Using Neural Networks and Support Vector Machines

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Book cover Engineering Applications of Neural Networks (EANN 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 384))

Abstract

Sentiment in annual reports is recognized as being an important determinant of future financial performance. The aim of this study is to examine the effect of the sentiment on future financial distress. We evaluated the sentiment in the annual reports of U.S. companies using word categorization (rule-based) approach. We used six categories of sentiment, together with financial indicators, as the inputs of neural networks and support vector machines. The results indicate that the sentiment information significantly improves the accuracy of the used classifiers.

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Author information

Authors and Affiliations

  1. Institute of System Engineering and Informatics, Faculty of Economics and Administration, University of Pardubice, Studentská 84, 532 10, Pardubice, Czech Republic

    Petr Hájek & Vladimír Olej

Authors
  1. Petr Hájek
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  2. Vladimír Olej
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Editor information

Editors and Affiliations

  1. Department of Forestry & Management of the Environment and Natural Resources, Democritus University of Thrace, GR-68200, Orestiada, Hellas

    Lazaros Iliadis

  2. Frederick University of Cyprus, Cyprus

    Harris Papadopoulos

  3. Faculty of Engineering and Computing, Coventry University, UK

    Chrisina Jayne

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Hájek, P., Olej, V. (2013). Evaluating Sentiment in Annual Reports for Financial Distress Prediction Using Neural Networks and Support Vector Machines. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 384. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41016-1_1

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  • DOI: https://doi.org/10.1007/978-3-642-41016-1_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41015-4

  • Online ISBN: 978-3-642-41016-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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