IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v109y1998i2p390-402.html
   My bibliography  Save this article

A hybrid intelligent system for predicting bank holding structures

Author

Listed:
  • Hashemi, R. R.
  • Le Blanc, L. A.
  • Rucks, C. T.
  • Rajaratnam, A.

Abstract

No abstract is available for this item.

Suggested Citation

  • Hashemi, R. R. & Le Blanc, L. A. & Rucks, C. T. & Rajaratnam, A., 1998. "A hybrid intelligent system for predicting bank holding structures," European Journal of Operational Research, Elsevier, vol. 109(2), pages 390-402, September.
  • Handle: RePEc:eee:ejores:v:109:y:1998:i:2:p:390-402
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(98)00065-4
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Slowinski, R. & Zopounidis, C. & Dimitras, A. I., 1997. "Prediction of company acquisition in Greece by means of the rough set approach," European Journal of Operational Research, Elsevier, vol. 100(1), pages 1-15, July.
    2. R. Slowinski & C. Zopounidis, 1995. "Application of the Rough Set Approach to Evaluation of Bankruptcy Risk," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 4(1), pages 27-41, March.
    3. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
    4. William F. Messier, Jr. & James V. Hansen, 1988. "Inducing Rules for Expert System Development: An Example Using Default and Bankruptcy Data," Management Science, INFORMS, vol. 34(12), pages 1403-1415, 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. Mak, Brenda & Munakata, Toshinori, 2002. "Rule extraction from expert heuristics: A comparative study of rough sets with neural networks and ID3," European Journal of Operational Research, Elsevier, vol. 136(1), pages 212-229, January.
    2. Senan Amer, 2021. "The Role of Using CAMELS Model in Analyzing the Factors Affecting the Performance of The Jordanian Commercial Banks (2014-2019)," Theory Methodology Practice (TMP), Faculty of Economics, University of Miskolc, vol. 17(02), pages 3-11.
    3. Landajo, Manuel & de Andres, Javier & Lorca, Pedro, 2007. "Robust neural modeling for the cross-sectional analysis of accounting information," European Journal of Operational Research, Elsevier, vol. 177(2), pages 1232-1252, March.
    4. Tay, Francis E. H. & Shen, Lixiang, 2002. "Economic and financial prediction using rough sets model," European Journal of Operational Research, Elsevier, vol. 141(3), pages 641-659, September.
    5. Zhang, Faming & Tadikamalla, Pandu R. & Shang, Jennifer, 2016. "Corporate credit-risk evaluation system: Integrating explicit and implicit financial performances," International Journal of Production Economics, Elsevier, vol. 177(C), pages 77-100.
    6. Bekiros, Stelios D., 2010. "Fuzzy adaptive decision-making for boundedly rational traders in speculative stock markets," European Journal of Operational Research, Elsevier, vol. 202(1), pages 285-293, April.

    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. Beynon, Malcolm J. & Peel, Michael J., 2001. "Variable precision rough set theory and data discretisation: an application to corporate failure prediction," Omega, Elsevier, vol. 29(6), pages 561-576, December.
    2. Thomas E. Mckee, 2000. "Developing a bankruptcy prediction model via rough sets theory," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(3), pages 159-173, September.
    3. Thomas E. McKee, 2003. "Rough sets bankruptcy prediction models versus auditor signalling rates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(8), pages 569-586.
    4. fernández, María t. Tascón & gutiérrez, Francisco J. Castaño, 2012. "Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 15(1), pages 7-58.
    5. Pablo de Llano Monelos & Manuel Rodríguez López & Carlos Piñeiro Sánchez, 2013. "Bankruptcy Prediction Models in Galician companies. Application of Parametric Methodologies and Artificial Intelligence," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(1), pages 117-136.
    6. S. Balcaen & H. Ooghe, 2004. "Alternative methodologies in studies on business failure: do they produce better results than the classical statistical methods?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/249, Ghent University, Faculty of Economics and Business Administration.
    7. Kattan, Michael W. & Cooper, Randolph B., 2000. "A simulation of factors affecting machine learning techniques: an examination of partitioning and class proportions," Omega, Elsevier, vol. 28(5), pages 501-512, October.
    8. Capotorti, Andrea & Barbanera, Eva, 2012. "Credit scoring analysis using a fuzzy probabilistic rough set model," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 981-994.
    9. Fernando García & Francisco Guijarro & Ismael Moya, 2013. "Monitoring credit risk in the social economy sector by means of a binary goal programming model," Service Business, Springer;Pan-Pacific Business Association, vol. 7(3), pages 483-495, September.
    10. Low Sui Pheng & Jiang Hongbin, 2006. "Analysing ownership, locational and internalization advantages of Chinese construction MNCs using rough sets analysis," Construction Management and Economics, Taylor & Francis Journals, vol. 24(11), pages 1149-1165.
    11. Barniv, Ran & Mehrez, Abraham & Kline, Douglas M., 2000. "Confidence intervals for controlling the probability of bankruptcy," Omega, Elsevier, vol. 28(5), pages 555-565, October.
    12. Bose, Indranil & Pal, Raktim, 2006. "Predicting the survival or failure of click-and-mortar corporations: A knowledge discovery approach," European Journal of Operational Research, Elsevier, vol. 174(2), pages 959-982, October.
    13. Lili Li & Jun Yang & Xin Zou, 2016. "A study of credit risk of Chinese listed companies: ZPP versus KMV," Applied Economics, Taylor & Francis Journals, vol. 48(29), pages 2697-2710, June.
    14. Sancho Salcedo‐Sanz & Mario DePrado‐Cumplido & María Jesús Segovia‐Vargas & Fernando Pérez‐Cruz & Carlos Bousoño‐Calzón, 2004. "Feature selection methods involving support vector machines for prediction of insolvency in non‐life insurance companies," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 12(4), pages 261-281, October.
    15. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
    16. Llano Monelos Pablo De & Piñeiro Sánchez Carlos & Rodríguez López Manuel, 2014. "DEA as a business failure prediction tool. Application to the case of galician SMEs," Contaduría y Administración, Accounting and Management, vol. 59(2), pages 65-96, abril-jun.
    17. Mark Cecchini & Haldun Aytug & Gary J. Koehler & Praveen Pathak, 2010. "Detecting Management Fraud in Public Companies," Management Science, INFORMS, vol. 56(7), pages 1146-1160, July.
    18. Tay, Francis E. H. & Shen, Lixiang, 2002. "Economic and financial prediction using rough sets model," European Journal of Operational Research, Elsevier, vol. 141(3), pages 641-659, September.
    19. Zopounidis, Constantin & Doumpos, Michael, 2001. "A preference disaggregation decision support system for financial classification problems," European Journal of Operational Research, Elsevier, vol. 130(2), pages 402-413, April.
    20. Kattan, MW & Cooper, RB, 1998. "The predictive accuracy of computer-based classification decision techniques.A review and research directions," Omega, Elsevier, vol. 26(4), pages 467-482, August.

    More about this item

    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:eee:ejores:v:109:y:1998:i:2:p:390-402. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.