IDEAS home Printed from https://ideas.repec.org/a/hur/ijaraf/v3y2013i4p146-152.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://hrmars.com/hrmars_papers/Article_16_The_Bankruptcy_Prediction.pdf
    Download Restriction: no
    File URL: http://hrmars.com/hrmars_papers/Article_16_The_Bankruptcy_Prediction.pdf
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    2. Adler Haymans Manurung & Derwin Suhartono & Benny Hutahayan & Noptovius Halimawan, 2023. "Probability Bankruptcy Using Support Vector Regression Machines," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 13(1), pages 1-3.
    3. Ilyes Abid & Rim Ayadi & Khaled Guesmi & Farid Mkaouar, 2022. "A new approach to deal with variable selection in neural networks: an application to bankruptcy prediction," Annals of Operations Research, Springer, vol. 313(2), pages 605-623, June.
    4. Mohammad Mahdi Mousavi & Jamal Ouenniche, 2018. "Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions," Annals of Operations Research, Springer, vol. 271(2), pages 853-886, December.
    5. Bhanu Pratap Singh & Alok Kumar Mishra, 2016. "Re-estimation and comparisons of alternative accounting based bankruptcy prediction models for Indian companies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-28, December.
    6. Deni Memic, 2015. "Assessing Credit Default using Logistic Regression and Multiple Discriminant Analysis: Empirical Evidence from Bosnia and Herzegovina," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 13(1), pages 128-153.
    7. Youssef Zizi & Mohamed Oudgou & Abdeslam El Moudden, 2020. "Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach," Risks, MDPI, vol. 8(4), pages 1-21, October.
    8. Ilyes Abid & Farid Mkaouar & Olfa Kaabia, 2018. "Dynamic analysis of the forecasting bankruptcy under presence of unobserved heterogeneity," Annals of Operations Research, Springer, vol. 262(2), pages 241-256, March.
    9. Fayçal Mraihi, 2016. "Distressed Company Prediction Using Logistic Regression: Tunisian’s Case," Quarterly Journal of Business Studies, Research Academy of Social Sciences, vol. 2(1), pages 34-54.
    10. Sami BEN JABEUR, 2014. "Prévision de la détresse financière des entreprises françaises: Approche par la régression logistique PLS," Working Papers 2014-321, Department of Research, Ipag Business School.
    11. Sami Ben Jabeur & Youssef Fahmi, 2014. "Les modèles de prévision de la défaillance des entreprises françaises : une approche comparative," Working Papers 2014-317, Department of Research, Ipag Business School.
    12. Philippe Jardin, 2021. "Forecasting bankruptcy using biclustering and neural network-based ensembles," Annals of Operations Research, Springer, vol. 299(1), pages 531-566, April.
    13. Alessandra Amendola & Francesco Giordano & Maria Lucia Parrella & Marialuisa Restaino, 2017. "Variable selection in high‐dimensional regression: a nonparametric procedure for business failure prediction," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(4), pages 355-368, August.
    14. Fayçal Mraihi & Inane Kanzari & Mohamed Tahar Rajhi, 2015. "Development of a Prediction Model of Failure in Tunisian Companies: Comparison between Logistic Regression and Support Vector Machines," International Journal of Empirical Finance, Research Academy of Social Sciences, vol. 4(3), pages 184-205.
    15. Giordani, Paolo & Jacobson, Tor & Schedvin, Erik von & Villani, Mattias, 2014. "Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(4), pages 1071-1099, August.
    16. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
    17. Lauren Stagnol, 2015. "Designing a corporate bond index on solvency criteria," EconomiX Working Papers 2015-39, University of Paris Nanterre, EconomiX.
    18. Lin, Hsiou-Wei William & Lo, Huai-Chun & Wu, Ruei-Shian, 2016. "Modeling default prediction with earnings management," Pacific-Basin Finance Journal, Elsevier, vol. 40(PB), pages 306-322.
    19. Chiara Pederzoli & Grid Thoma & Costanza Torricelli, 2013. "Modelling Credit Risk for Innovative SMEs: the Role of Innovation Measures," Journal of Financial Services Research, Springer;Western Finance Association, vol. 44(1), pages 111-129, August.
    20. Enrico Supino & Nicola Piras, 2022. "Le performance dei modelli di credit scoring in contesti di forte instabilit? macroeconomica: il ruolo delle Reti Neurali Artificiali," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2022(2), pages 41-61.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hur:ijaraf:v:3:y:2013:i:4:p:146-152. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Hassan Danial Aslam (email available below). General contact details of provider: http://hrmars.com/index.php/pages/detail/Accounting-Finance-Journal .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.