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Gaussian process regression ́s hyperparameters optimization to predict financial distress

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F75081431%3A_____%2F23%3A00002637" target="_blank" >RIV/75081431:_____/23:00002637 - isvavai.cz</a>

  • Result on the web

    <a href="https://retos.ups.edu.ec/index.php/retos/article/view/7417" target="_blank" >https://retos.ups.edu.ec/index.php/retos/article/view/7417</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Gaussian process regression ́s hyperparameters optimization to predict financial distress

  • Original language description

    Predicting financial distress has become one of the most important topics of the hour that has swept the accounting and financial field due to its significant correlation with the development of science and technology. The main objective of this paper is to predict financial distress based on the Gaussian Process Regression (GPR) and then compare the results of this model with the results of other deep learning models (SVM, LR, LD, DT, KNN). The analysis is based on a dataset of 352 companies extracted from the Kaggle database. As for predictors, 83 financial ratios were used. The study concluded that the use of GPR achieves very relevant results. Furthermore, it outperformed the rest of the deep learning models and achieved first place equally with the SVM model with a classification accuracy of 81%. The results contribute to the maintenance of the integrated system and the prosperity of the country's economy, the prediction of the financial distress of companies and thus the potential prevention of disruption of the given system.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    50200 - Economics and Business

Result continuities

  • Project

  • Continuities

    V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Others

  • Publication year

    2023

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    RETOS-REVISTA DE CIENCIAS DE LA ADMINISTRACION Y ECONOMIA

  • ISSN

    1390-6291

  • e-ISSN

  • Volume of the periodical

    13

  • Issue of the periodical within the volume

    26

  • Country of publishing house

    ES - SPAIN

  • Number of pages

    17

  • Pages from-to

    273-289

  • UT code for WoS article

    001077774200006

  • EID of the result in the Scopus database