Financial distress prediction in Slovakia: An application of the CART algorithm

Ďurica, Marek; Frnda, Jaroslav; Švábová, Lucia

doi:10.14254/2071-8330.2021/14-1/14

Cited by 4 publications

(1 citation statement)

References 37 publications

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“…Nowadays the wide implementation of ICT-based technologies in business environments (Bilan et al, 2023;Roshchyk et al, 2022;Dias et al, 2023) and the increase in data availability facilitate the development of new approaches to analyze and predict business results, including financial distress (Durica et al, 2021). With the advancement of technology, AI-based methods have emerged, and the work of Odom and Shard (1990) is considered pioneering in the prediction field using ANN.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting bankruptcy using artificial intelligence: The case of the engineering industry

Letkovsky,

Jencova,

Vasanicova

et al. 2023

Economics &Amp; Sociology

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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.

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Section: Literature Reviewmentioning

confidence: 99%

Predicting bankruptcy using artificial intelligence: The case of the engineering industry

Letkovsky,

Jencova,

Vasanicova

et al. 2023

Economics &Amp; Sociology

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Family Businesses in Global Economy and Evaluating their Financial Health Using Bankruptcy Models

Trúchliková

2021

SHS Web Conf.

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Research background: Predicting and assessing financial health should be one of the most important activities for each business especially in context of turbulent business environment and global economy. The financial sustainability of family businesses has a direct and significant influence on the development and growth of the economy because they still represent the backbone of the economy and play an important role in national economies worldwide accounting. Purpose of the article: We used in this article the financial distress and bankruptcy prediction models for assessing financial status of family businesses in agricultural sector. The aim of the paper is to compare models developed by using three different methods to identify a model with the highest predictive accuracy of financial distress and assess financial health. Methods: The data was obtained from Finstat database. For assessing the financial health of selected family businesses bankruptcy models were used: Chrastinova’s CH-Index, Gurcik’s G-Index (defined for Slovak agricultural enterprises) and Altman Z-score. Findings & Value added: This article summarizes existing models and compares results of assessing financial health of family businesses using three different models.

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Prediction of Urban and Rural Tourism Economic Forecast Based on Machine Learning

Zhou

2021

Scientific Programming

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With the rapid development of tourism, tourism revenue, as one of the important indicators to measure the development of the tourism economy, has high research value. The quasi-prediction of tourism revenue can drive the development of a series of related industries and accelerate the development of the domestic economy. When forecasting tourism income, it is necessary to examine the causal relationship between tourism income and local economic development. The traditional cointegration analysis method is to extract the promotion characteristics of tourism income to the local economy and construct a tourism income prediction model, but it cannot accurately describe the causal relationship between tourism income and local economic development and cannot accurately predict tourism income. We propose an optimized forecasting method of tourism revenue based on time series. This method first conducts a cointegration test on the time series data of the relationship between tourism income and local economic development, constructs a two-variable autoregressive model of tourism income and local economy, and uses the swarm intelligence method to test the causal relationship and the relationship between tourism income and local economic development, calculate the proportion of tourism industry, define the calculation result as the direct influence factor of tourism industry on the local economy, calculate the relevant effect of local tourism development and economic income, and construct tourism income optimization forecast model. The simulation results show that the model used can accurately predict tourism revenue.

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Financial distress prediction in Slovakia: An application of the CART algorithm

Cited by 4 publications

References 37 publications

Predicting bankruptcy using artificial intelligence: The case of the engineering industry

Predicting bankruptcy using artificial intelligence: The case of the engineering industry

Family Businesses in Global Economy and Evaluating their Financial Health Using Bankruptcy Models

Prediction of Urban and Rural Tourism Economic Forecast Based on Machine Learning

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