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A quadratic interval logit model for forecasting bankruptcy

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  • Tseng, Fang-Mei
  • Lin, Lin

Abstract

This paper proposes a quadratic interval logit model (or quadratic interval logistic regression analysis) based on a quadratic programming approach to deal with binary response variables. This model combines the advantages of logit (or logistic regression) and Tanaka's quadratic interval regression model. As a demonstration, we applied this model to forecasting corporate distress in the UK. The results show that this model can support the logit model to discriminate between groups, and it provides more information to researchers.

Suggested Citation

  • Tseng, Fang-Mei & Lin, Lin, 2005. "A quadratic interval logit model for forecasting bankruptcy," Omega, Elsevier, vol. 33(1), pages 85-91, February.
    • Handle: RePEc:eee:jomega:v:33:y:2005:i:1:p:85-91
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    References listed on IDEAS

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    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. Laitinen, Erkki K. & Laitinen, Teija, 2000. "Bankruptcy prediction: Application of the Taylor's expansion in logistic regression," International Review of Financial Analysis, Elsevier, vol. 9(4), pages 327-349.
    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.
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    5. L. Lin & J. Piesse, 2004. "Identification of corporate distress in UK industrials: a conditional probability analysis approach," Applied Financial Economics, Taylor & Francis Journals, vol. 14(2), pages 73-82.
    6. Wood, Douglas & Piesse, Jennie, 1988. "The information value of failure predictions in credit assessment," Journal of Banking & Finance, Elsevier, vol. 12(2), pages 275-292, June.
    7. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    8. Tanaka, Hideo & Hayashi, Isao & Watada, Junzo, 1989. "Possibilistic linear regression analysis for fuzzy data," European Journal of Operational Research, Elsevier, vol. 40(3), pages 389-396, June.
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    Cited by:

    1. 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.
    2. Akkoç, Soner, 2012. "An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish cred," European Journal of Operational Research, Elsevier, vol. 222(1), pages 168-178.
    3. Salwa Kessioui & Michalis Doumpos & Constantin Zopounidis, 2023. "A Bibliometric Overview of the State-of-the-Art in Bankruptcy Prediction Methods and Applications," World Scientific Book Chapters, in: Emilios Galariotis & Alexandros Garefalakis & Christos Lemonakis & Marios Menexiadis & Constantin Zo (ed.), Governance and Financial Performance Current Trends and Perspectives, chapter 6, pages 123-153, World Scientific Publishing Co. Pte. Ltd..
    4. Vicente García & Ana I. Marqués & J. Salvador Sánchez & Humberto J. Ochoa-Domínguez, 2019. "Dissimilarity-Based Linear Models for Corporate Bankruptcy Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1019-1031, March.
    5. Beata Gavurova & Sylvia Jencova & Radovan Bacik & Marta Miskufova & Stanislav Letkovsky, 2022. "Artificial intelligence in predicting the bankruptcy of non-financial corporations," Oeconomia Copernicana, Institute of Economic Research, vol. 13(4), pages 1215-1251, December.
    6. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.

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