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The Bankruptcy Prediction by Neural Networks and Logistic Regression

Author

Listed:
  • Ahmad Ahmadpour Kasgari

    (Mazandaran University)

  • Seyyed Hasan Salehnezhad

    (Payame Noor University)

  • Fatemeh Ebadi

    (Islamic Azad university)

Abstract

Today, the intensity of industry competition has led many companies going bankrupt and pulling out of race. The early warning against the possibility of bankruptcy enables the managers and investors to take pre-emptive actions when it is necessary. The bankruptcy prediction models reveal the latent problems in financial structures like a warning bell and provide timely feedback to managers and investors as well as other people who benefit from this. The bankruptcy of manufacturing companies in Tehran Stock Exchange Market has been predicted in this study using artificial neural network in this respect. It has been also used the logistic regression to do compare with neural network as well. All information which has been used here is related to time periods from 2001 to 2011 and the bankrupt groups have been selected on the basis of Article 141 of the Commercial Code of Iran. In the years before bankruptcy, the financial management has the chance to predict the probability of bankruptcy by using this model and take necessary actions in this regard since the results derived from the neural network predictions are very consistent with reality. Moreover, this model is more accurate than that of logistic regression in prediction process.

Suggested Citation

  • Ahmad Ahmadpour Kasgari & Seyyed Hasan Salehnezhad & Fatemeh Ebadi, 2013. "The Bankruptcy Prediction by Neural Networks and Logistic Regression," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 3(4), pages 146-152, October.
    • Handle: RePEc:hur:ijaraf:v:3:y:2013:i:4:p:146-152
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    References listed on IDEAS

    as
    1. Hossein Rezaiedolatabadi & Saeed Sayadi & Amirhossein Hosseini & Mohammadhossein Forghani & Morteza Shokhmgar, 2013. "Modeling and Forecasting Stock Prices Using an Artificial Neural Network and Imperialist Competitive Algorithm," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 3(1), pages 296-302, January.
    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, September.
    3. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    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. Chris Charalambous & Andreas Charitou & Froso Kaourou, 2000. "Comparative Analysis of Artificial Neural Network Models: Application in Bankruptcy Prediction," Annals of Operations Research, Springer, vol. 99(1), pages 403-425, December.
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    Cited by:

    1. Javed Iqbal & Furrukh Bashir & Rashid Ahmad & Hina Arshad, 2022. "Predicting Bankruptcy through Neural Network:Case of PSX Listed Companies," iRASD Journal of Management, International Research Alliance for Sustainable Development (iRASD), vol. 4(2), pages 299-315, june.
    2. Jaroslav Mazanec & Viera Bartosova & Patrik Bohm, 2022. "Logit Model for Estimating Non-Profit Organizations’ Financial Status as a Part of Non-Profit Financial Management," Mathematics, MDPI, vol. 10(13), pages 1-18, June.

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