IDEAS home Printed from https://ideas.repec.org/a/ibn/ijefaa/v8y2016i11p159.html
   My bibliography  Save this article

Predicting Financial Distress: Multi Scenarios Modeling Using Neural Network

Author

Listed:
  • Mohamed Gameel
  • Khairy El-Geziry

Abstract

This paper aims to investigate the best scenario to predict financial distress in the Egyptian stock market using a neural network model. The sample consists of 37 company listed on the EGX100. The sample period is eight years from 2001 to 2008, so we can isolate the effects of global financial Depression in the end of 2008, and the effect of economic instability, which coincided with the Egyptian revolution in 2011 tell now. The results show evidence that the best scenario for predicting distress in Egypt is that the company will be distressed if there is a decreasing in liquidity, decreasing in generating cash from sales with increasing in financial leverage.

Suggested Citation

  • Mohamed Gameel & Khairy El-Geziry, 2016. "Predicting Financial Distress: Multi Scenarios Modeling Using Neural Network," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 8(11), pages 159-159, November.
    • Handle: RePEc:ibn:ijefaa:v:8:y:2016:i:11:p:159
    as

    Download full text from publisher

    File URL: https://ccsenet.org/journal/index.php/ijef/article/download/62736/34368
    Download Restriction: no
    File URL: https://ccsenet.org/journal/index.php/ijef/article/view/62736
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, Wiley Blackwell, vol. 22, pages 59-82.
    4. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    5. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    6. Mensah, Ym, 1984. "An Examination Of The Stationarity Of Multivariate Bankruptcy Prediction Models - A Methodological Study," Journal of Accounting Research, Wiley Blackwell, vol. 22(1), pages 380-395.
    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. Sunday Nosa UGBOGBO (Ph.D) & Sunday Nosa UGBOGBO (Ph.D), 2023. "Capital Structure and Corporate Financial Distress of Quoted Non-Financial Firms in Nigeria," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 7(6), pages 1302-1314, 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. Mohammad Mahdi Mousavi & Jamal Ouenniche & Kaoru Tone, 2023. "A dynamic performance evaluation of distress prediction models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 756-784, July.
    2. Leila Bateni & Farshid Asghari, 2020. "Bankruptcy Prediction Using Logit and Genetic Algorithm Models: A Comparative Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 335-348, January.
    3. 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).
    4. Adriana Csikosova & Maria Janoskova & Katarina Culkova, 2020. "Application of Discriminant Analysis for Avoiding the Risk of Quarry Operation Failure," JRFM, MDPI, vol. 13(10), pages 1-14, September.
    5. Trueck, Stefan & Rachev, Svetlozar T., 2008. "Rating Based Modeling of Credit Risk," Elsevier Monographs, Elsevier, edition 1, number 9780123736833.
    6. Ahsan Habib & Mabel D' Costa & Hedy Jiaying Huang & Md. Borhan Uddin Bhuiyan & Li Sun, 2020. "Determinants and consequences of financial distress: review of the empirical literature," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(S1), pages 1023-1075, April.
    7. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.
    8. Hamid Waqas & Rohani Md-Rus, 2018. "Predicting financial distress: Applicability of O-score model for Pakistani firms," Business and Economic Horizons (BEH), Prague Development Center, vol. 14(2), pages 389-401, April.
    9. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    10. 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.
    11. Kumar, Rahul & Deb, Soumya Guha & Mukherjee, Shubhadeep, 2020. "Do words reveal the latent truth? Identifying communication patterns of corporate losers," Journal of Behavioral and Experimental Finance, Elsevier, vol. 26(C).
    12. Arvind Shrivastava & Kuldeep Kumar & Nitin Kumar, 2018. "Business Distress Prediction Using Bayesian Logistic Model for Indian Firms," Risks, MDPI, vol. 6(4), pages 1-15, October.
    13. Amin Jan & Maran Marimuthu & Muhammad Kashif Shad & Haseeb ur-Rehman & Muhammad Zahid & Ahmad Ali Jan, 2019. "Bankruptcy profile of the Islamic and conventional banks in Malaysia: a post-crisis period analysis," Economic Change and Restructuring, Springer, vol. 52(1), pages 67-87, February.
    14. 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.
    15. Ivana Podhorska, 2016. "Success of prediction models in Slovak companies," GATR Journals gjbssr446, Global Academy of Training and Research (GATR) Enterprise.
    16. Juan García Lara & Beatriz Osma & Evi Neophytou, 2009. "Earnings quality in ex‐post failed firms," Accounting and Business Research, Taylor & Francis Journals, vol. 39(2), pages 119-138.
    17. Yung-Ho Chiu & Yu-Chuan Chen & Yu Han Hung, 2009. "Basel II and bank bankruptcy analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 16(18), pages 1843-1847.
    18. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    19. Duc Hong Vo & Binh Ninh Vo Pham & Chi Minh Ho & Michael McAleer, 2019. "Corporate Financial Distress of Industry Level Listings in Vietnam," JRFM, MDPI, vol. 12(4), pages 1-17, September.
    20. Tamara Ayœs, Armando Lenin & Villegas, Gladis Cecilia & Leones Castro, María Cristina & Salazar Bocanegra, Juan Antonio, 2018. "Modelaci—n del riesgo de insolvencia en empresas del sector salud empleando modelos logit || Modeling of Insolvency Risk in Health Sector Companies Using Logit Models," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 26(1), pages 128-145, Diciembre.

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    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:ibn:ijefaa:v:8:y:2016:i:11:p:159. 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: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

    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.