Support Vector Machine Methods and Artificial Neural Networks Used for the Development of Bankruptcy Prediction Models and their Comparison

Horák, Jakub; Vrbka, Jaromír; Šuleř, Petr

doi:10.3390/jrfm13030060

Cited by 59 publications

(46 citation statements)

References 35 publications

(34 reference statements)

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“…Even in the case of our research, neural networks have proven to be a suitable tool for problems related to the prediction of company bankruptcy, thereby specifying the software that is most suitable for data processing and which produces the best results. A similar result was achieved by Horák, Vrbka, and Šuleř [80], whose aim was to create a model for predicting the potential bankruptcy of companies using appropriate classification methods, especially Support Vector Machine and artificial neural networks, and to evaluate the results of those methods. They came to the conclusion that the most successful model applicable in practice is the model determined by the neural structure 2.MLP 22-9-2.…”

Section: Discussionsupporting

confidence: 55%

“…Information on the fulfilment of these conditions is entered in the Commercial Register, which is maintained as a public database by the Ministry of Justice of the Czech Republic. The 3:1 ratio is based on previous experience [80,81]. It was not possible to use population distribution because the proportion of companies in liquidation is relatively small.…”

Section: Datamentioning

confidence: 99%

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Creating a Comprehensive Method for the Evaluation of a Company

et al. 2020

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For investment purposes, the evaluation of a company is not only a matter for a company itself, but also for shareholders and external persons. There are many methods for evaluating a company. This contribution therefore focuses on the creation of a comprehensive method for the evaluation of an industrial enterprise, one that can be used to predict potential future bankruptcies, using a dataset of financial statements of active companies and those in liquidation in the period 2015–2019. Artificial neural networks were used to process the data, specifically logistic regressions from the data processed in the Statistica and Mathematica software programmes. The results showed that the models created using the Mathematica software are not applicable in practice due to the parameters of the obtained results. In contrast, the artificial neural structures obtained using the neural network model in the Statistica software were prospective due to their performance, which is almost always above 0.8, and the logical economic interpretation of the relevant variables. All the generated and retained networks show excellent performance and few errors. However, one of the artificial structures, network no. 4 (MLP 16-16-2), produces better results than the others. Overall, accuracy is almost 81%. In the case of the classification of companies capable of surviving financial distress, accuracy is almost 90%, with that for the classification of companies at risk of going into bankruptcy at nearly 55%.

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Section: Discussionsupporting

confidence: 55%

Section: Datamentioning

confidence: 99%

Creating a Comprehensive Method for the Evaluation of a Company

et al. 2020

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“…[19][20][21]. Neural networks can even predict the potential viability of a company [22]. In conclusion, the application of neural networks can provide important guidance in implementing effective monetary and fiscal policies.…”

Section: Introduction and Literature Researchmentioning

confidence: 99%

The Dependence of the CZK Exchange Rate on the Development of the GDP in the Czech Republic in the Conditions of an Expected Crisis

Machová

Krulický

2021

SHS Web of Conf.

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Research background: In the past, some studies proved that the development of a currency exchange rate predicts the development of the whole national economy. The monetary market overtakes the development of the actual economy for a few months. Does this apply also in the case of the Czech koruna, in the era of the global Coronavirus pandemics and in the world affected by the pandemics? Purpose of the article: The main objective is to analyze a dependence of the Czech koruna (CZK) to Euro (EUR) exchange rate development on gross domestic product of the Czech Republic in the conditions of an expected crisis. Methods: The data used of the analysis are represented by the information about the CZK and EUR exchange rate from the beginning of 1999 to the 15th June 2020 and by the quarterly development of the Czech GDP. To measure the dependence and predict the development of the GDP based on the CZK exchange rate development, the method of AI is used, namely the regression analysis using the artificial neural networks. Findings & Value added: The effect of EUR/CZK on GDP can be quantified reaching around 31%. It is assumed that the GDP will fall significantly in 2020 with a certain growth only being possibly expected in 2021 (even more significantly in the second quarter of 2021). Due to the GDP development, the development of the EUR/CZK could then be forecasted as well.

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“…In turn, cloud computing technologies are being used to analyze and detect emerging patterns [ 7 ] and facilitate near real-time monitoring and visualization. Support vector machine methods and artificial neural networks have been used for a variety of prediction tasks, starting from business and financial tasks [ 8 ], to a variety of ecological problems [ 9 ]. Even more beneficial are predictive air pollution systems because they can help governments employ smarter solutions and preventive measures to address air quality problems.…”

Section: Introductionmentioning

confidence: 99%

Multi-Horizon Air Pollution Forecasting with Deep Neural Networks

Arsov

Zdravevski

Lameski

et al. 2021

Sensors

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Air pollution is a global problem, especially in urban areas where the population density is very high due to the diverse pollutant sources such as vehicles, industrial plants, buildings, and waste. North Macedonia, as a developing country, has a serious problem with air pollution. The problem is highly present in its capital city, Skopje, where air pollution places it consistently within the top 10 cities in the world during the winter months. In this work, we propose using Recurrent Neural Network (RNN) models with long short-term memory units to predict the level of PM10 particles at 6, 12, and 24 h in the future. We employ historical air quality measurement data from sensors placed at multiple locations in Skopje and meteorological conditions such as temperature and humidity. We compare different deep learning models’ performance to an Auto-regressive Integrated Moving Average (ARIMA) model. The obtained results show that the proposed models consistently outperform the baseline model and can be successfully employed for air pollution prediction. Ultimately, we demonstrate that these models can help decision-makers and local authorities better manage the air pollution consequences by taking proactive measures.

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Support Vector Machine Methods and Artificial Neural Networks Used for the Development of Bankruptcy Prediction Models and their Comparison

Cited by 59 publications

References 35 publications

Creating a Comprehensive Method for the Evaluation of a Company

Creating a Comprehensive Method for the Evaluation of a Company

The Dependence of the CZK Exchange Rate on the Development of the GDP in the Czech Republic in the Conditions of an Expected Crisis

Multi-Horizon Air Pollution Forecasting with Deep Neural Networks

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