Forecasting corporate financial performance using sentiment in annual reports for stakeholders' decision-making
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F14%3A39898554" target="_blank" >RIV/00216275:25410/14:39898554 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.3846/20294913.2014.979456" target="_blank" >http://dx.doi.org/10.3846/20294913.2014.979456</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3846/20294913.2014.979456" target="_blank" >10.3846/20294913.2014.979456</a>
Alternative languages
Result language
angličtina
Original language name
Forecasting corporate financial performance using sentiment in annual reports for stakeholders' decision-making
Original language description
This paper is aimed at examining the role of annual reports' sentiment in forecasting financial performance. The sentiment (tone, opinion) is assessed using several categorization schemes in order to explore various aspects of language used in the annualreports of U.S. companies. Further, we employ machine learning methods and neural networks to predict financial performance expressed in terms of the Z-score bankruptcy model. Eleven categories of sentiment (ranging from negative and positive to activeand common) are used as the inputs of the prediction models. Support vector machines provide the highest forecasting accuracy. This evidence suggests that there exist non-linear relationships between the sentiment and financial performance. The results indicate that the sentiment information is an important forecasting determinant of financial performance and, thus, can be used to support decision-making process of corporate stakeholders.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
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
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Technological and Economic Development of Economy
ISSN
2029-4913
e-ISSN
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Volume of the periodical
20
Issue of the periodical within the volume
4
Country of publishing house
LT - LITHUANIA
Number of pages
18
Pages from-to
721-738
UT code for WoS article
000346354400006
EID of the result in the Scopus database
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