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How Do Different Time Spans Affect The Prediction Accuracy Of Business Failure?

Author

Listed:
  • Càmara-Turull, X.

    (Department of Business Management. Faculty of Business and Economics. Universitat Rovira i Virgili. Av. Universitat, 1, E-43204 Reus, Spain.)

  • Fernández Izquierdo, M.A.

    (Department of Finance and Accounting. Faculty of Law and Economics. Universitat Jaume I, Av. Sos Baynat, s/n, E-12071 Castelló, Spain.)

  • Sorrosal Forradellas, M.T.

    (Department of Business Management. Faculty of Business and Economics. Universitat Rovira i Virgili. Av. Universitat, 1, E-43204 Reus, Spain.)

Abstract

The prediction of business failure has been widely studied by many authors. Most of the studies focused on improve the results by applying new methodologies or by using more suitable financial information. This study aims to analyze the impact of the input data timeframe on the prediction accuracy of business failure. Using an artificial neural network, the self-organizing maps (SOM), we compare the results obtained by using 9, 6 and 3 years of input data. We concluded that the 3-year case provides a better global results despite of the 6-year case presents the lowest error type I.

Suggested Citation

  • Càmara-Turull, X. & Fernández Izquierdo, M.A. & Sorrosal Forradellas, M.T., 2015. "How Do Different Time Spans Affect The Prediction Accuracy Of Business Failure?," Fuzzy Economic Review, International Association for Fuzzy-set Management and Economy (SIGEF), vol. 0(1), pages 71-89, May.
    • Handle: RePEc:fzy:fuzeco:v:xx:y:2015:i:1:p:71-89
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    More about this item

    Keywords

    business failure; timeframe data; self-organizing maps;
    All these keywords.

    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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