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2016 | OriginalPaper | Buchkapitel

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

verfasst von : Petr Hájek, Jana Boháčová

Erschienen in: Engineering Applications of Neural Networks

Verlag: Springer International Publishing

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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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Metadaten
Titel
Predicting Abnormal Bank Stock Returns Using Textual Analysis of Annual Reports – a Neural Network Approach
verfasst von
Petr Hájek
Jana Boháčová
Copyright-Jahr
2016
Buch
Engineering Applications of Neural Networks

Print ISBN: 978-3-319-44187-0

Electronic ISBN: 978-3-319-44188-7

Copyright-Jahr: 2016

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

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