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New Approaches to Financial and Bankruptcy Risk

Author

Listed:
  • Bogdan POPA

    (University of Craiova)

  • Jenica POPESCU

    (University of Craiova)

Abstract

A consistent direction in which financial risk and bankruptcy analysis models were developed was the inclusion of artificial intelligence algorithms in the methodology, they are being used in most of the cases to achieve some classifications. The artificial intelligence (machine learning) algorithms widely used for the analysis of financial or bankruptcy risks, presented in the paper, are: KNN (K-Nearest Neighbor) algorithm; Support Vector Machine (SVM); Random Forest; Neural networks (ANN – Artificial Neural Networks). Using these algorithms, companies can be classified into different categories, based on some variables, and the final result is to obtain a certain probability of bankruptcy or insolvency for that company. Obviously, there are limitations of the models and problems that can arise from their estimation, among the most well-known being overfitting (the risk of learning the model to perform very well only for the data series being used on). In recent years, ESG (Environmental, Social and Governance) factors have played a very important role. We believe that this is a direction in which the analysis of bankruptcy risk and financial risks could go, by including sustainability aspects in the models.

Suggested Citation

  • Bogdan POPA & Jenica POPESCU, 2023. "New Approaches to Financial and Bankruptcy Risk," Finante - provocarile viitorului (Finance - Challenges of the Future), University of Craiova, Faculty of Economics and Business Administration, vol. 1(25), pages 8-13, November.
  • Handle: RePEc:aio:fpvfcf:v:1:y:2023:i:25:p:8-13
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    References listed on IDEAS

    as
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    2. Nicola Raimo & Alessandra Caragnano & Marianna Zito & Filippo Vitolla & Massimo Mariani, 2021. "Extending the benefits of ESG disclosure: The effect on the cost of debt financing," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 28(4), pages 1412-1421, July.
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    5. Cheng-Wu Chen & Chun-Pin Tseng, 2012. "Default risk-based probabilistic decision model for risk management and control," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 63(2), pages 659-671, September.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    artificial intelligence algorithms; sustainability; corporate governance; comparative analysis;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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