Predicting Bankruptcy Using Artificial Intelligence: the Case of the Engineering Industry

• Autorzy: Stanislav Letkovsky , Sylvia Jencova , Petra Vasanicova , Stefan Gavura , Radovan Bacik
• Języki publikacji: EN

Abstrakty

EN

Bankruptcy prediction is a powerful early-warning tool and plays a crucial role in various aspects of financial and business management. It is vital for safeguarding investments, maintaining financial stability, making informed credit decisions, and contributing to the overall health of the economy. This paper aims to develop bankruptcy prediction models for the Slovak engineering industry and to compare their effectiveness. Predictions are generated using the classical logistic regression (LR) method as well as artificial intelligence (AI) techniques (artificial neural networks (ANN) and support vector machines (SVM)). Research sample consists of 825 businesses operating in the engineering industry (Manufacture of machinery and equipment n.e.c.; Manufacture of motor vehicles, trailers and semi-trailers; Manufacture of other transport equipment). The selection of eight financial indicators is grounded in prior research and existing literature. The results show high accuracy for all used methods. The SVM outcomes indicate a level of accuracy on the test set that is nearly indistinguishable from that of the ANN model. The use of AI techniques demonstrates their effective predictive capabilities and holds a significant position within the realm of tools for forecasting bankruptcy. (original abstract)

Słowa kluczowe

EN

Enterprises bankruptcy forecasting; Artificial neural networks (ANN); Support Vector Machines (SVM); Logistic regression; Industry

PL

Prognozowanie upadłości przedsiębiorstwa; Sztuczne sieci neuronowe (SSN); Metoda wektorów nośnych (SVM); Regresja logistyczna; Przemysł

• Czasopismo: Economics & Sociology
• Rocznik: 2023
• Tom: 16
• Numer: nr 4
• Strony: 178--190

Twórcy

autor

Stanislav Letkovsky

• pplied Meters a.s., Prešov, Slovakia

autor

Sylvia Jencova

• University of Prešov in Prešov, Slovak Republic

autor

Petra Vasanicova

• University of Prešov in Prešov, Slovak Republic

autor

Stefan Gavura

• Technical University of Košice, Košice, Slovakia

autor

Radovan Bacik

• University of Prešov in Prešov, Slovak Republic

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Identyfikatory

DOI

10.14254/2071-789X.2023/16-4/8