The choice of the type of image for graphical processing of input data for corporate bankruptcy prediction using CNN

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26510%2F21%3APU141810" target="_blank" >RIV/00216305:26510/21:PU141810 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
The choice of the type of image for graphical processing of input data for corporate bankruptcy prediction using CNN
Original language description
This paper deals with the application of Convolutional Neural Networks (CNN) for the bankruptcy prediction of firms in the Czech Republic. It proposes several variants based on the GoogLeNet architecture that predict the bankruptcy of a company 1 to 3 years in advance. The inputs of the model are financial ratios whose values are converted into several types of images. The various types of images are searched to improve the accuracy of company bankruptcy prediction and the right type of image is found. CNN networks can effectively distinguish between active and bankrupt enterprises. The predictive accuracy of the best proposed model ranges between 85 and 93% (depending on the number of years before bankruptcy).
Czech name
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Czech description
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Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Financial Management of Firms and Financial Institutions
ISBN
978-80-248-4548-7
ISSN
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e-ISSN
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Number of pages
9
Pages from-to
164-172
Publisher name
VSB - Technical University of Ostrava
Place of publication
Ostrava
Event location
Ostrava
Event date
Sep 6, 2021
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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