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Bankruptcy Prediction: A Model Based on Cash Flow Ratios: Evidence From Selected European Countries

Author

Listed:
  • Lorenzo Rizzo
  • Giorgio Valentinuz
  • Dario Obratil
  • Valentino Pediroda

Abstract

The importance of assessing the financial distress risk of a company is a topic that has been of central value in many different economic fields and since a long time. Until the twenty-first century, most of the studies were concentrated primarily on using mathematical and statistical methods to assess the health of businesses. Many of these studies employed either accounting-based ratios or cash flow-based ratios; even if there is not a unique conclusion, the use of cash flows seems to improve the predictive capacity of the models significantly. Especially in the last twenty-five years, methods derived from different fields started to be applied in forecasting corporate failures, such as artificial neural networks, genetic algorithms, and fuzzy logic. The objective of this study was to test the goodness of the discriminatory power of ratios based only on cash flows using a model that employs genetic algorithms and fuzzy logic. Five countries (Germany, Spain, France, Great Britain, Italy) and five Nace macro sectors (Agriculture, Industry, Services, Construction, Commerce and Food) have been considered in the analysis for a total of around 719-thousand companies. The model has proven to be well-performing on most of the countries and sectors that have been tested. The results obtained are almost all adequate; in particular, in Germany and Spain, results have been particularly good. The main weaknesses of this work are the limited availability of financial data in some countries and the time delay from the reporting of financial statement to the availability of the data through web services. It means that a large-scale risk assessment requires ¨C being useful for the public and the private sectors ¨C greater and faster disclosure of information at European level, and standardization of financial information transparency among countries.

Suggested Citation

  • Lorenzo Rizzo & Giorgio Valentinuz & Dario Obratil & Valentino Pediroda, 2020. "Bankruptcy Prediction: A Model Based on Cash Flow Ratios: Evidence From Selected European Countries," International Journal of Business Administration, International Journal of Business Administration, Sciedu Press, vol. 11(6), pages 89-108, November.
  • Handle: RePEc:jfr:ijba11:v:11:y:2020:i:6:p:89-108
    DOI: 10.5430/ijba.v11n6p89
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    References listed on IDEAS

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    1. Stephen Satchel & Wei Xia, 2006. "Analytic Models of the ROC Curve: Applications to Credit Rating Model Validation," Research Paper Series 181, Quantitative Finance Research Centre, University of Technology, Sydney.
    2. Casey, C & Bartczak, N, 1985. "Using Operating Cash Flow Data To Predict Financial Distress - Some Extensions," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 23(1), pages 384-401.
    3. repec:eme:mfppss:03074350110767114 is not listed on IDEAS
    4. 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.
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