Predicting Abnormal Bank Stock Returns Using Textual Analysis of Annual Reports - A Neural Network Approach

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Predicting Abnormal Bank Stock Returns Using Textual Analysis of Annual Reports - A Neural Network Approach

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Predicting Abnormal Bank Stock Returns Using Textual Analysis of Annual Reports - A Neural Network Approach

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GA16-19590S" target="_blank" >GA16-19590S: Analýza témat a sentimentu vícenásobných textových zdrojů pro finanční rozhodování podniků</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Communications in Computer and Information Science

ISBN

978-3-319-44187-0

ISSN

1865-0929

e-ISSN

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Počet stran výsledku

10

Strana od-do

67-78

Název nakladatele

Springer

Místo vydání

Dordrecht

Místo konání akce

Aberdeen

Datum konání akce

2. 9. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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