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

Author

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  • Jakub Horak

    (School of Expertness and Valuation, Institute of Technology and Business in Ceske Budejovice, Okruzni 517/10, 37001 Ceske Budejovice, Czech Republic)

  • Jaromir Vrbka

    (School of Expertness and Valuation, Institute of Technology and Business in Ceske Budejovice, Okruzni 517/10, 37001 Ceske Budejovice, Czech Republic)

  • Petr Suler

    (School of Expertness and Valuation, Institute of Technology and Business in Ceske Budejovice, Okruzni 517/10, 37001 Ceske Budejovice, Czech Republic)

Abstract

Bankruptcy prediction is always a topical issue. The activities of all business entities are directly or indirectly affected by various external and internal factors that may influence a company in insolvency and lead to bankruptcy. It is important to find a suitable tool to assess the future development of any company in the market. The objective of this paper is to create a model for predicting potential bankruptcy of companies using suitable classification methods, namely Support Vector Machine and artificial neural networks, and to evaluate the results of the methods used. The data (balance sheets and profit and loss accounts) of industrial companies operating in the Czech Republic for the last 5 marketing years were used. For the application of classification methods, TIBCO’s Statistica software, version 13, is used. In total, 6 models were created and subsequently compared with each other, while the most successful one applicable in practice is the model determined by the neural structure 2.MLP 22-9-2. The model of Support Vector Machine shows a relatively high accuracy, but it is not applicable in the structure of correct classifications.

Suggested Citation

  • Jakub Horak & Jaromir Vrbka & Petr Suler, 2020. "Support Vector Machine Methods and Artificial Neural Networks Used for the Development of Bankruptcy Prediction Models and their Comparison," JRFM, MDPI, vol. 13(3), pages 1-15, March.
    • Handle: RePEc:gam:jjrfmx:v:13:y:2020:i:3:p:60-:d:336234
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    References listed on IDEAS

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    Cited by:

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    2. Beata Gavurova & Sylvia Jencova & Radovan Bacik & Marta Miskufova & Stanislav Letkovsky, 2022. "Artificial intelligence in predicting the bankruptcy of non-financial corporations," Oeconomia Copernicana, Institute of Economic Research, vol. 13(4), pages 1215-1251, December.
    3. Sabir, Zulqurnain & Said, Salem Ben & Baleanu, Dumitru, 2022. "Swarming optimization to analyze the fractional derivatives and perturbation factors for the novel singular model," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    4. Marek Vochozka & Jaromir Vrbka & Petr Suler, 2020. "Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    5. Jakub Horak & Tomas Krulicky & Zuzana Rowland & Veronika Machova, 2020. "Creating a Comprehensive Method for the Evaluation of a Company," Sustainability, MDPI, vol. 12(21), pages 1-23, November.
    6. Mattia Iotti, 2023. "Financial evaluation and credit access of agricultural firms," Economia agro-alimentare, FrancoAngeli Editore, vol. 25(2), pages 31-67.
    7. Morande, Swapnil & Arshi, Tahseen & Gul, Kanwal & Amini, Mitra, 2023. "Harnessing the Power of Artificial Intelligence to Forecast Startup Success: An Empirical Evaluation of the SECURE AI Model," SocArXiv p3gyb, Center for Open Science.
    8. Simona Hašková & Petr Fiala, 2023. "Internal Rate of Return Estimation of Subsidised Projects: Conventional Approach Versus fuzzy Approach," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 1233-1249, October.

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