A Bayesian Survival Model Approach for Business Distress Prediction

Arvind Shrivastava; Kuldeep Kumar; Nitin Kumar

doi:doi:10.11648/j.jim.20211003.12

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A Bayesian Survival Model Approach for Business Distress Prediction

Arvind Shrivastava, Kuldeep Kumar, Nitin Kumar

Published in Journal of Investment and Management (Volume 10, Issue 3)

Received: 15 June 2021 Accepted: 28 June 2021 Published: 10 November 2021

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Abstract

The early warning signals of corporate distress and failure have been a major area of concern for shareholders, policy makers and academicians alike. Numerous approaches have been applied to examine firm insolvency ranging from the famous Altman’s Z-score, traditional econometrics, financial ratio analysis to the more contemporary tools of Artificial Intelligence and Machine Learning. The Cox proportional survival hazard model is a commonly applied technique not only in the field of medical sciences for estimating occurrences of a specific event but also in failure prediction of private firms. The study investigates distress prediction of firms in context of emerging nation like India where otherwise the application of Bayesian survival models is limited. A rich panel of firms spanning over ten years and representing varied sectors like manufacturing, services, mining and construction is compiled for the purpose. The study contributes by developing hazard (survival) modelling using Bayesian perspective. The advantage of Bayesian method lies in dealing effectively with censored and small samples over usual frequentist methods. Both standard Cox survival model for censored failure time and Bayesian estimation have been performed to assess and compare their performance. It is found that prediction accuracy of Bayesian Cox model is significantly higher than of the classical Cox model. The study contributes by providing useful insights in detecting early signs of distress in Indian corporate sector that is otherwise scant in literature.

Published in	Journal of Investment and Management (Volume 10, Issue 3)
DOI	10.11648/j.jim.20211003.12
Page(s)	43-51
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Firms, Corporate Liquidation, Bayesian Analysis, Survival Modelling

References

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[2]	Andersen, P. K., & Gill, R. D. (1982). Cox's regression model for counting processes: a large sample study. The annals of statistics, 1100-1120.
[3]	Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research (Supplement), 4 (3): 71–111.
[4]	Campbell, J. Y., Hilscher, J., & Szilagyi, J. (2008). In search of distress risk. The Journal of Finance, 63 (6), 2899–2939.
[5]	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society, 34 (2), 187-220.
[6]	Cox, D. R. (1975). Partial likelihood. Biometrika, 62 (2), 269-276.
[7]	Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on pattern analysis and machine intelligence, 6, 721-741.
[8]	Ibrahim, J. G., Chen, M. H., & Sinha, D. (2001). Bayesian semi parametric models for survival data with a cure fraction. Biometrics, 57 (2), 383-388.
[9]	Kalbfleisch, J. D., & Prentice, R. L. (2011). The statistical analysis of failure time data (Vol. 360). John Wiley & Sons.
[10]	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American statistical association, 53 (282), 457-481.
[11]	Kumar, D., & Klefsjö, B. (1994). Proportional hazards model: a review. Reliability Engineering & System Safety, 44 (2), 177-188.
[12]	Lin, S. M., Jake Ansell, & Galina Andreeva. (2012). Predicting default of a small business using different definitions of financial distress. Journal of the Operational Research Society 63, 539–48.
[13]	Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18 (1), 109–131.
[14]	Shrivastava, A., Kumar, K., & Kumar, N. (2018). Business distress prediction using Bayesian logistic model for Indian firms. Risks, 6 (4), 113.
[15]	Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. The Journal of Business, 74 (1): 101–124.
[16]	Sinha, D., & Dey, D. K. (1997). Semi parametric Bayesian analysis of survival data. Journal of the American Statistical Association, 92 (439), 1195-1212.
[17]	Zmijewski, M. E. (1984). Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research, 22, 59-82.

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APA Style

Arvind Shrivastava, Kuldeep Kumar, Nitin Kumar. (2021). A Bayesian Survival Model Approach for Business Distress Prediction. Journal of Investment and Management, 10(3), 43-51. https://doi.org/10.11648/j.jim.20211003.12

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ACS Style

Arvind Shrivastava; Kuldeep Kumar; Nitin Kumar. A Bayesian Survival Model Approach for Business Distress Prediction. J. Invest. Manag. 2021, 10(3), 43-51. doi: 10.11648/j.jim.20211003.12

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AMA Style

Arvind Shrivastava, Kuldeep Kumar, Nitin Kumar. A Bayesian Survival Model Approach for Business Distress Prediction. J Invest Manag. 2021;10(3):43-51. doi: 10.11648/j.jim.20211003.12

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@article{10.11648/j.jim.20211003.12,
  author = {Arvind Shrivastava and Kuldeep Kumar and Nitin Kumar},
  title = {A Bayesian Survival Model Approach for Business Distress Prediction},
  journal = {Journal of Investment and Management},
  volume = {10},
  number = {3},
  pages = {43-51},
  doi = {10.11648/j.jim.20211003.12},
  url = {https://doi.org/10.11648/j.jim.20211003.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jim.20211003.12},
  abstract = {The early warning signals of corporate distress and failure have been a major area of concern for shareholders, policy makers and academicians alike. Numerous approaches have been applied to examine firm insolvency ranging from the famous Altman’s Z-score, traditional econometrics, financial ratio analysis to the more contemporary tools of Artificial Intelligence and Machine Learning. The Cox proportional survival hazard model is a commonly applied technique not only in the field of medical sciences for estimating occurrences of a specific event but also in failure prediction of private firms. The study investigates distress prediction of firms in context of emerging nation like India where otherwise the application of Bayesian survival models is limited. A rich panel of firms spanning over ten years and representing varied sectors like manufacturing, services, mining and construction is compiled for the purpose. The study contributes by developing hazard (survival) modelling using Bayesian perspective. The advantage of Bayesian method lies in dealing effectively with censored and small samples over usual frequentist methods. Both standard Cox survival model for censored failure time and Bayesian estimation have been performed to assess and compare their performance. It is found that prediction accuracy of Bayesian Cox model is significantly higher than of the classical Cox model. The study contributes by providing useful insights in detecting early signs of distress in Indian corporate sector that is otherwise scant in literature.},
 year = {2021}
}

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TY  - JOUR
T1  - A Bayesian Survival Model Approach for Business Distress Prediction
AU  - Arvind Shrivastava
AU  - Kuldeep Kumar
AU  - Nitin Kumar
Y1  - 2021/11/10
PY  - 2021
N1  - https://doi.org/10.11648/j.jim.20211003.12
DO  - 10.11648/j.jim.20211003.12
T2  - Journal of Investment and Management
JF  - Journal of Investment and Management
JO  - Journal of Investment and Management
SP  - 43
EP  - 51
PB  - Science Publishing Group
SN  - 2328-7721
UR  - https://doi.org/10.11648/j.jim.20211003.12
AB  - The early warning signals of corporate distress and failure have been a major area of concern for shareholders, policy makers and academicians alike. Numerous approaches have been applied to examine firm insolvency ranging from the famous Altman’s Z-score, traditional econometrics, financial ratio analysis to the more contemporary tools of Artificial Intelligence and Machine Learning. The Cox proportional survival hazard model is a commonly applied technique not only in the field of medical sciences for estimating occurrences of a specific event but also in failure prediction of private firms. The study investigates distress prediction of firms in context of emerging nation like India where otherwise the application of Bayesian survival models is limited. A rich panel of firms spanning over ten years and representing varied sectors like manufacturing, services, mining and construction is compiled for the purpose. The study contributes by developing hazard (survival) modelling using Bayesian perspective. The advantage of Bayesian method lies in dealing effectively with censored and small samples over usual frequentist methods. Both standard Cox survival model for censored failure time and Bayesian estimation have been performed to assess and compare their performance. It is found that prediction accuracy of Bayesian Cox model is significantly higher than of the classical Cox model. The study contributes by providing useful insights in detecting early signs of distress in Indian corporate sector that is otherwise scant in literature.
VL  - 10
IS  - 3
ER  -

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Author Information

Arvind Shrivastava

Reserve Bank of India, Mumbai, India
Kuldeep Kumar

Bond Business School, Bond University, Queensland, Australia
Nitin Kumar

Reserve Bank of India, Mumbai, India

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APA Style

Arvind Shrivastava, Kuldeep Kumar, Nitin Kumar. (2021). A Bayesian Survival Model Approach for Business Distress Prediction. Journal of Investment and Management, 10(3), 43-51. https://doi.org/10.11648/j.jim.20211003.12

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ACS Style

Arvind Shrivastava; Kuldeep Kumar; Nitin Kumar. A Bayesian Survival Model Approach for Business Distress Prediction. J. Invest. Manag. 2021, 10(3), 43-51. doi: 10.11648/j.jim.20211003.12

Copy | Download

AMA Style

Arvind Shrivastava, Kuldeep Kumar, Nitin Kumar. A Bayesian Survival Model Approach for Business Distress Prediction. J Invest Manag. 2021;10(3):43-51. doi: 10.11648/j.jim.20211003.12

Copy | Download

@article{10.11648/j.jim.20211003.12,
  author = {Arvind Shrivastava and Kuldeep Kumar and Nitin Kumar},
  title = {A Bayesian Survival Model Approach for Business Distress Prediction},
  journal = {Journal of Investment and Management},
  volume = {10},
  number = {3},
  pages = {43-51},
  doi = {10.11648/j.jim.20211003.12},
  url = {https://doi.org/10.11648/j.jim.20211003.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jim.20211003.12},
  abstract = {The early warning signals of corporate distress and failure have been a major area of concern for shareholders, policy makers and academicians alike. Numerous approaches have been applied to examine firm insolvency ranging from the famous Altman’s Z-score, traditional econometrics, financial ratio analysis to the more contemporary tools of Artificial Intelligence and Machine Learning. The Cox proportional survival hazard model is a commonly applied technique not only in the field of medical sciences for estimating occurrences of a specific event but also in failure prediction of private firms. The study investigates distress prediction of firms in context of emerging nation like India where otherwise the application of Bayesian survival models is limited. A rich panel of firms spanning over ten years and representing varied sectors like manufacturing, services, mining and construction is compiled for the purpose. The study contributes by developing hazard (survival) modelling using Bayesian perspective. The advantage of Bayesian method lies in dealing effectively with censored and small samples over usual frequentist methods. Both standard Cox survival model for censored failure time and Bayesian estimation have been performed to assess and compare their performance. It is found that prediction accuracy of Bayesian Cox model is significantly higher than of the classical Cox model. The study contributes by providing useful insights in detecting early signs of distress in Indian corporate sector that is otherwise scant in literature.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - A Bayesian Survival Model Approach for Business Distress Prediction
AU  - Arvind Shrivastava
AU  - Kuldeep Kumar
AU  - Nitin Kumar
Y1  - 2021/11/10
PY  - 2021
N1  - https://doi.org/10.11648/j.jim.20211003.12
DO  - 10.11648/j.jim.20211003.12
T2  - Journal of Investment and Management
JF  - Journal of Investment and Management
JO  - Journal of Investment and Management
SP  - 43
EP  - 51
PB  - Science Publishing Group
SN  - 2328-7721
UR  - https://doi.org/10.11648/j.jim.20211003.12
AB  - The early warning signals of corporate distress and failure have been a major area of concern for shareholders, policy makers and academicians alike. Numerous approaches have been applied to examine firm insolvency ranging from the famous Altman’s Z-score, traditional econometrics, financial ratio analysis to the more contemporary tools of Artificial Intelligence and Machine Learning. The Cox proportional survival hazard model is a commonly applied technique not only in the field of medical sciences for estimating occurrences of a specific event but also in failure prediction of private firms. The study investigates distress prediction of firms in context of emerging nation like India where otherwise the application of Bayesian survival models is limited. A rich panel of firms spanning over ten years and representing varied sectors like manufacturing, services, mining and construction is compiled for the purpose. The study contributes by developing hazard (survival) modelling using Bayesian perspective. The advantage of Bayesian method lies in dealing effectively with censored and small samples over usual frequentist methods. Both standard Cox survival model for censored failure time and Bayesian estimation have been performed to assess and compare their performance. It is found that prediction accuracy of Bayesian Cox model is significantly higher than of the classical Cox model. The study contributes by providing useful insights in detecting early signs of distress in Indian corporate sector that is otherwise scant in literature.
VL  - 10
IS  - 3
ER  -

Copy | Download