Artificial intelligence in predicting the bankruptcy of non-financial corporations

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28120%2F22%3A63551978" target="_blank" >RIV/70883521:28120/22:63551978 - isvavai.cz</a>

Result on the web

<a href="http://economic-research.pl/Journals/index.php/oc/article/view/2149" target="_blank" >http://economic-research.pl/Journals/index.php/oc/article/view/2149</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.24136/oc.2022.035" target="_blank" >10.24136/oc.2022.035</a>

Result language

angličtina

Original language name

Artificial intelligence in predicting the bankruptcy of non-financial corporations

Original language description

Research background: In a modern economy, full of complexities, ensuring a business&apos; financial stability, and increasing its financial performance and competitiveness, has become especially difficult. Then, monitoring the company&apos;s financial situation and predicting its future development becomes important. Assessing the financial health of business entities using various models is an important area in not only scientific research, but also business practice.Purpose of the article: This study aims to predict the bankruptcy of companies in the engineering and automotive industries of the Slovak Republic using a multilayer neural network and logistic regression. Importantly, we develop a novel an early warning model for the Slovak engineering and automotive industries, which can be applied in countries with undeveloped capital markets.Methods: Data on the financial ratios of 2,384 companies were used. We used a logistic regression to analyse the data for the year 2019 and designed a logistic model. Meanwhile, the data for the years 2018 and 2019 were analysed using the neural network. In the prediction model, we analysed the predictive performance of several combinations of factors based on the industry sector, use of the scaling technique, activation function, and ratio of the sample distribution to the test and training parts.Findings &amp; value added: The financial indicators ROS, QR, NWC/A, and PC/S reduce the likelihood of bankruptcy. Regarding the value of this work, we constructed an optimal network for the automotive and engineering industries using nine financial indicators on the input layer in combination with one hidden layer. Moreover, we developed a novel prediction model for bankruptcy using six of these indicators. Almost all sampled industries are privatised, and most companies are foreign owned. Hence, international companies as well as researchers can apply our models to understand their financial health and sustainability. Moreover, they can conduct comparative analyses of their own model with ours to reveal areas of model improvements.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

50201 - Economic Theory

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

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

Name of the periodical

Oeconomia Copernicana

ISSN

2083-1277

e-ISSN

2353-1827

Volume of the periodical

13

Issue of the periodical within the volume

4

Country of publishing house

PL - POLAND

Number of pages

37

Pages from-to

"1215 "- 1251

UT code for WoS article

000907675800008

EID of the result in the Scopus database

2-s2.0-85147224931