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Re-estimation and comparisons of alternative accounting based bankruptcy prediction models for Indian companies

Author

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  • Bhanu Pratap Singh

    (University of Hyderabad)

  • Alok Kumar Mishra

    (University of Hyderabad)

Abstract

Background The suitability and performance of the bankruptcy prediction models is an empirical question. The aim of this paper is to develop a bankruptcy prediction model for Indian manufacturing companies on a sample of 208 companies consisting of an equal number of defaulted and non-defaulted firms. Out of 208 companies, 130 are used for estimation sample, and 78 are holdout for model validation. The study reestimates the accounting based models such as Altman EI (Journal of Finance 23: 19189–209, 1968) Z-Score, Ohlson JA (Journal of Accounting Research 18:109–131, 1980) Y-Score and Zmijewski ME (Journal of Accounting Research 22:59–82, 1984) X-Score model. The paper compares original and re-estimated models to explore the sensitivity of these models towards the change in time periods and financial conditions. Methods Multiple Discriminant Analysis (MDA) and Probit techniques are employed in the estimation of Z-Score and X-Score models, whereas Logit technique is employed in the estimation of Y-Score and the newly proposed models. The performance of all the original, re-estimated and new proposed models are assessed by predictive accuracy, significance of parameters, long-range accuracy, secondary sample and Receiver Operating Characteristic (ROC) tests. Results The major findings of the study reveal that the overall predictive accuracy of all the three models improves on estimation and holdout sample when the coefficients are re-estimated. Amongst the contesting models, the new bankruptcy prediction model outperforms other models. Conclusions The industry specific model should be developed with the new combinations of financial ratios to predict bankruptcy of the firms in a particular country. The study further suggests the coefficients of the models are sensitive to time periods and financial condition. Hence, researchers should be cautioned while choosing the models for bankruptcy prediction to recalculate the models by looking at the recent data in order to get higher predictive accuracy.

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  • 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.
    • Handle: RePEc:spr:fininn:v:2:y:2016:i:1:d:10.1186_s40854-016-0026-9
    DOI: 10.1186/s40854-016-0026-9
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    Cited by:

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    2. Frank Ranganai Matenda & Mabutho Sibanda & Eriyoti Chikodza & Victor Gumbo, 2022. "Bankruptcy prediction for private firms in developing economies: a scoping review and guidance for future research," Management Review Quarterly, Springer, vol. 72(4), pages 927-966, December.
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    4. Sebastian Klaudiusz Tomczak & Piotr Staszkiewicz, 2020. "Cross-Country Application of Manufacturing Failure Models," JRFM, MDPI, vol. 13(2), pages 1-10, February.
    5. Qiwei Xie & Xi Chen & Lin Li & Kaifeng Rao & Luo Tao & Chao Ma, 2019. "Image Fusion Based on Kernel Estimation and Data Envelopment Analysis," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(02), pages 487-515, March.
    6. Daniela Rybárová & Helena Majdúchová & Peter Štetka & Darina Luščíková, 2021. "Reliability and Accuracy of Alternative Default Prediction Models: Evidence from Slovakia," IJFS, MDPI, vol. 9(4), pages 1-33, November.
    7. Oz, Ibrahim Onur & Simga-Mugan, Can, 2018. "Bankruptcy prediction models' generalizability: Evidence from emerging market economies," Advances in accounting, Elsevier, vol. 41(C), pages 114-125.
    8. Sebastian Klaudiusz Tomczak & Edward Radosiński, 2017. "The effectiveness of discriminant models based on the example of the manufacturing sector," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 27(3), pages 81-97.

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