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Variable Selection In Forecasting Models For Corporate Bankruptcy

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Author Info

  • Alessandra Amendola

    ()

  • Marialuisa Restaino

    ()
    (Dipartimento di Scienze Economiche e Statistiche, Università degli Studi di Salerno)

  • Luca Sensini

    ()
    (Dipartimento di Studi e Ricerche Aziendali (Management & Information Technology), Università degli Studi di Salerno)

Abstract

In this paper we develop statistical models for bankruptcy prediction of Italian firms in the limited liability sector, using annual balance sheet information. Several issues involved in default risk analysis are investigated, such as the structure of the data-base, the sampling procedure and the influence of predictors. In particular we focus on the variable selection problem, comparing innovative techniques based on shrinkage with traditional stepwise methods. The predictive performance of the proposed default risk model has been evaluated by means of different accuracy measures. The results of the analysis, carried out on a data-set of financial ratios expressly created from a sample of industrial firms annual reports, give evidence in favor of the proposed model over traditional ones.

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File URL: http://www.dises.unisa.it/RePEc/sep/wpaper/3_216.pdf
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Bibliographic Info

Paper provided by Dipartimento di Scienze Economiche e Statistiche, Università degli Studi di Salerno in its series Working Papers with number 3_216.

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Date of creation: Nov 2010
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Publication status: Published in Working Papers, Novembre 2010, pages 1-43
Handle: RePEc:sep:wpaper:3_216

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Related research

Keywords: Forecasting; Default Risk; Variable Selection; Shrinkage; Lasso.;

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  1. Lennox, Clive, 1999. "Identifying failing companies: a re-evaluation of the logit, probit and DA approaches," Journal of Economics and Business, Elsevier, vol. 51(4), pages 347-364, July.
  2. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, 09.
  3. Wolfgang Härdle & Yuh-Jye Lee & Dorothea Schäfer & Yi-Ren Yeh, 2009. "Variable selection and oversampling in the use of smooth support vector machines for predicting the default risk of companies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(6), pages 512-534.
  4. S. Balcaen & H. Ooghe, 2004. "35 years of studies on business failure: an overview of the classical statistical methodologiesand their related problems," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/248, Ghent University, Faculty of Economics and Business Administration.
  5. Carlos Serrano-Cinca, 1997. "Feedforward neural networks in the classification of financial information," The European Journal of Finance, Taylor & Francis Journals, vol. 3(3), pages 183-202.
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