Gaussian process regression ́s hyperparameters optimization to predict financial distress

Kód výsledku v 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>

Výsledek na webu

<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

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Jazyk výsledku

angličtina

Název v původním jazyce

Gaussian process regression ́s hyperparameters optimization to predict financial distress

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Gaussian process regression ́s hyperparameters optimization to predict financial distress

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

50200 - Economics and Business

Projekt

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Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

RETOS-REVISTA DE CIENCIAS DE LA ADMINISTRACION Y ECONOMIA

ISSN

1390-6291

e-ISSN

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Svazek periodika

13

Číslo periodika v rámci svazku

26

Stát vydavatele periodika

ES - Španělské království

Počet stran výsledku

17

Strana od-do

273-289

Kód UT WoS článku

001077774200006

EID výsledku v databázi Scopus

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