Interpretable Nonlinear Model for Enterprise Bankruptcy Prediction

Purvinis, Ojaras; Virbickaitė, Rūta; Šukys, Povilas

doi:10.15388/na.2008.13.1.14589

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…Next, we used several indicators in our neural network on specific types of financial aspects of a firm which have been used in the literature. First, the indebtedness indicators were represented by the financial leverage (Purvinis et al, 2008;Kim & Kang, 2010), and total indebtedness (Purvinis et al, 2007;Purvinis et al, 2008;Chen et al, 2009;Nyitrai & Virag, 2019). Second, activity indicators were represented by the asset turnover ratio (Lee et al, 1996;Merkevicius et al, 2006;Purvinis et al, 2008;Kim & Kang, 2010;Nyitrai & Virag, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…First, the indebtedness indicators were represented by the financial leverage (Purvinis et al, 2008;Kim & Kang, 2010), and total indebtedness (Purvinis et al, 2007;Purvinis et al, 2008;Chen et al, 2009;Nyitrai & Virag, 2019). Second, activity indicators were represented by the asset turnover ratio (Lee et al, 1996;Merkevicius et al, 2006;Purvinis et al, 2008;Kim & Kang, 2010;Nyitrai & Virag, 2019). Among liquidity indicators, we included the quick ratio (Tsakonas et al, 2006;Purvinis et al, 2007;Purvinis et al, 2008;Nyitrai & Virag 2019) and net working capital to assets ratio (Merkevicius et al, 2006;Kim & Kang, 2010).…”

Section: Discussionmentioning

confidence: 99%

“…Second, activity indicators were represented by the asset turnover ratio (Lee et al, 1996;Merkevicius et al, 2006;Purvinis et al, 2008;Kim & Kang, 2010;Nyitrai & Virag, 2019). Among liquidity indicators, we included the quick ratio (Tsakonas et al, 2006;Purvinis et al, 2007;Purvinis et al, 2008;Nyitrai & Virag 2019) and net working capital to assets ratio (Merkevicius et al, 2006;Kim & Kang, 2010). Profitability indicators were ROI (Merkevicius et al, 2006), ROE, and ROS (Purvinis, Sukys & Virbickaite, 2007;Nyitrai & Virag, 2010).…”

Section: Discussionmentioning

confidence: 99%

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Artificial intelligence in predicting the bankruptcy of non-financial corporations

Gavurová

Jenčová

Bačík

et al. 2022

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Research background: In a modern economy, full of complexities, ensuring a business' financial stability, and increasing its financial performance and competitiveness, has become especially difficult. Then, monitoring the company'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 & 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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Artificial intelligence in predicting the bankruptcy of non-financial corporations

Gavurová

Jenčová

Bačík

et al. 2022

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Intelligent Credit Risk Decision Support: Architecture and Implementations

Danėnas

Garšva

2016

Artificial Intelligence in Financial Markets

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Review of Research into Enterprise Bankruptcy Prediction in Selected Central and Eastern European Countries

Prusak

2018

IJFS

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Abstract:In developed countries, the first studies on forecasting bankruptcy date to the early 20th century. In Central and Eastern Europe, due to, among other factors, the geopolitical situation and the introduced economic system, this issue became the subject of researcher interest only in the 1990s. Therefore, it is worthwhile to analyze whether these countries conduct bankruptcy risk assessments and what their level of advancement is. The main objective of the article is the review and assessment of the level of advancement of bankruptcy prediction research in countries of the former Eastern Bloc, in comparison to the latest global research trends in this area. For this purpose, the method of analyzing scientific literature was applied. The publications chosen as the basis for the research were mainly based on information from the Google Scholar and ResearchGate databases during the period Q4 2016-Q3 2017. According to the author's knowledge, this is the first such large-scale study involving the countries of the former Eastern Bloc-which includes the following states: Poland, Lithuania, Latvia, Estonia, Ukraine, Hungary, Russia, Slovakia, Czech Republic, Romania, Bulgaria, and Belarus. The results show that the most advanced research in this area is conducted in the Czech Republic, Poland, Slovakia, Estonia, Russia, and Hungary. Belarus Bulgaria and Latvia are on the other end. In the remaining countries, traditional approaches to predicting business insolvency are generally used.

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Interpretable Nonlinear Model for Enterprise Bankruptcy Prediction

Cited by 4 publications

References 8 publications

Artificial intelligence in predicting the bankruptcy of non-financial corporations

Artificial intelligence in predicting the bankruptcy of non-financial corporations

Intelligent Credit Risk Decision Support: Architecture and Implementations

Review of Research into Enterprise Bankruptcy Prediction in Selected Central and Eastern European Countries

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