Predicting Abnormal Bank Stock Returns Using Textual Analysis of Annual Reports - A Neural Network Approach
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F16%3A39902124" target="_blank" >RIV/00216275:25410/16:39902124 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/chapter/10.1007/978-3-319-44188-7_5" target="_blank" >http://link.springer.com/chapter/10.1007/978-3-319-44188-7_5</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-44188-7_5" target="_blank" >10.1007/978-3-319-44188-7_5</a>
Alternative languages
Result language
angličtina
Original language name
Predicting Abnormal Bank Stock Returns Using Textual Analysis of Annual Reports - A Neural Network Approach
Original language description
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 (Na?ve 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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GA16-19590S" target="_blank" >GA16-19590S: Topic and sentiment analysis of multiple textual sources for corporate financial decision-making</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Communications in Computer and Information Science
ISBN
978-3-319-44187-0
ISSN
1865-0929
e-ISSN
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Number of pages
10
Pages from-to
67-78
Publisher name
Springer
Place of publication
Dordrecht
Event location
Aberdeen
Event date
Sep 2, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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