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A Decision Support System to Predict Financial Distress. The Case Of Romania

Author

Listed:
  • Liviu Tudor

    (Ph.D. Student at The Bucharest University of Economic Studies)

  • Mădălina Ecaterina Popescu

    (The Bucharest University of Economic Studies and Scientific Researcher at the National Scientific Research Institute for Labour and Social Protection)

  • Marin Andreica

    (The Bucharest University of Economic Studies)

Abstract

Financial distress prediction has become a topic of great interest for most decision makers over the last decades, especially because of the valuable insights and effective early warnings of potential bankruptcy yielded by such prediction models. Therefore, discovering a suitable model for predicting financial distress is likely to be of great significance to global investors. Thus, this paper aims to offer a practical solution to predict financial distress in Romania by focusing on developing an integrated decision support system and on analysing the effectiveness of several prediction models based on decision trees, logit and hazard models, as well as neural networks.

Suggested Citation

  • Liviu Tudor & Mădălina Ecaterina Popescu & Marin Andreica, 2015. "A Decision Support System to Predict Financial Distress. The Case Of Romania," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 170-179, December.
    • Handle: RePEc:rjr:romjef:v::y:2015:i:4:p:170-179
    as

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    References listed on IDEAS

    as
    1. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
    2. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    3. Chae Woo Nam & Tong Suk Kim & Nam Jung Park & Hoe Kyung Lee, 2008. "Bankruptcy prediction using a discrete-time duration model incorporating temporal and macroeconomic dependencies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(6), pages 493-506.
    4. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    5. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. José Alejandro Fernández Fernández & Virginia Bejarano Vázquez & Juan Antonio Vicente Virseda, 2019. "Evaluación de riesgos con Data Mining: el sistema financiero español," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 14(3), pages 309-328, Julio - S.
    2. Dejan JOVANOVIĆ & Mirjana TODOROVIĆ & Milka GRBIĆ, 2017. "Financial Indicators As Predictors Of Illiquidity," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 128-149, March.
    3. Madalina Ecaterina POPESCU & Marin ANDREICA & Ion-Petru POPESCU, 2017. "Decision Support Solution To Business Failure Prediction," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 11(1), pages 99-106, November.
    4. Madalina Ecaterina Popescu & Victor Dragotă, 2018. "What Do Post-Communist Countries Have in Common When Predicting Financial Distress?," Prague Economic Papers, Prague University of Economics and Business, vol. 2018(6), pages 637-653.
    5. Marin ANDREICA & Peter LANGER & Eugen ALBU & Paul LANGER, 2015. "Management Implications Of Implementation Of Danube Strategy In Refloating Of Ships," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 9(1), pages 97-104, November.
    6. repec:prg:jnlpep:v:preprint:id:664:p:1-17 is not listed on IDEAS

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    More about this item

    Keywords

    financial distress; decision support system; decision tree; logit and hazard model; neural networks;
    All these keywords.

    JEL classification:

    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General

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