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Predicting Abnormal Bank Stock Returns Using Textual Analysis of Annual Reports – a Neural Network Approach

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Engineering Applications of Neural Networks (EANN 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 629))

Abstract

This paper aims to extract both sentiment and bag-of-words information from the annual reports of U.S. banks. The sentiment analysis is based on two commonly used finance-specific dictionaries, while the bag-of-words are selected according to their tf-idf. We combine these features with financial indicators to predict abnormal bank stock returns using a neural network with dropout regularization and rectified linear units. We show that this method outperforms other machine learning algorithms (Naïve Bayes, Support Vector Machine, C4.5 decision tree, and k-nearest neighbour classifier) in predicting positive/negative abnormal stock returns. Thus, this neural network seems to be well suited for text classification tasks working with sparse high-dimensional data. We also show that the quality of the prediction significantly increased when using the combination of financial indicators and bigrams and trigrams, respectively.

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Acknowledgments

This work was supported by the scientific research project of the Czech Sciences Foundation Grant No: GA16-19590S “Topic and sentiment analysis of multiple textual sources for corporate financial decision-making”.

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Authors and Affiliations

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

    Petr Hájek & Jana Boháčová

Authors
  1. Petr Hájek
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  2. Jana Boháčová
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Correspondence to Petr Hájek .

Editor information

Editors and Affiliations

  1. Robert Gordon University, Aberdeen, United Kingdom

    Chrisina Jayne

  2. Lab of Forest Informatics (FiLAB), Democritus University of Thrace Lab of Forest Informatics (FiLAB), Orestiada, Greece

    Lazaros Iliadis

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Hájek, P., Boháčová, J. (2016). Predicting Abnormal Bank Stock Returns Using Textual Analysis of Annual Reports – a Neural Network Approach. In: Jayne, C., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2016. Communications in Computer and Information Science, vol 629. Springer, Cham. https://doi.org/10.1007/978-3-319-44188-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-44188-7_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44187-0

  • Online ISBN: 978-3-319-44188-7

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