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

Conference paper
First Online:
Part of the Communications in Computer and Information Science book series (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.

Keywords

Stock return Prediction Text mining Sentiment Neural network 
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Notes

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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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

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

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