Artificial intelligence in predicting the bankruptcy of non-financial corporations

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Artificial intelligence in predicting the bankruptcy of non-financial corporations

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Artificial intelligence in predicting the bankruptcy of non-financial corporations

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50201 - Economic Theory

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Oeconomia Copernicana

ISSN

2083-1277

e-ISSN

2353-1827

Svazek periodika

13

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

PL - Polská republika

Počet stran výsledku

37

Strana od-do

"1215 "- 1251

Kód UT WoS článku

000907675800008

EID výsledku v databázi Scopus

2-s2.0-85147224931