Evaluating sentiment in annual reports for financial distress prediction using neural networks and support vector machines
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F13%3A39899272" target="_blank" >RIV/00216275:25410/13:39899272 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-642-41016-1_1" target="_blank" >http://dx.doi.org/10.1007/978-3-642-41016-1_1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-642-41016-1_1" target="_blank" >10.1007/978-3-642-41016-1_1</a>
Alternative languages
Result language
angličtina
Original language name
Evaluating sentiment in annual reports for financial distress prediction using neural networks and support vector machines
Original language description
Sentiment in annual reports is recognized as being an important determinant of future financial performance. The aim of this study is to examine the effect of the sentiment on future financial distress. We evaluated the sentiment in the annual reports of U.S. companies using word categorization (rule-based) approach. We used six categories of sentiment, together with financial indicators, as the inputs of neural networks and support vector machines. The results indicate that the sentiment information significantly improves the accuracy of the used classifiers.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
AE - Management, administration and clerical work
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GA13-10331S" target="_blank" >GA13-10331S: The role of text information in corporate financial distress prediction models – country-specific and industry-specific approaches</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Engineering Applications of Neural Networks: 14th International Conference (EANN 2013), Part II
ISBN
978-3-642-41015-4
ISSN
1865-0929
e-ISSN
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Number of pages
10
Pages from-to
1-10
Publisher name
Springer
Place of publication
Berlin
Event location
Halkidiki
Event date
Sep 13, 2013
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000345333000001