Acta Informatica Pragensia 2025, 14(1), 63-87 | DOI: 10.18267/j.aip.2533617

Forecasting Financial Distress for Shaping Public Policy: An Empirical Investigation

Soumya Ranjan Sethi ORCID..., Dushyant Ashok Mahadik ORCID...
School of Management, National Institute of Technology Rourkela, Odisha, India

Background: Prediction of financial distress has been made more accurate and reliable through machine learning methods. Financial stress affects the business corporate entity, society and the general economy. Analysing such nonlinear events is essential for preventing the dangers and supporting a favourable economic climate.

Objective: This paper seeks to develop a robust predictive model for identifying firms in the Indian context other than the financial service sector that may face financial distress and also to check the impact of one essential predictor, i.e., future cash flow, on financial distress prediction. Besides, the study also aims at making research that can inform public policy and provide recommendations.

Methods: The study employs financial information from the Prowess Database but is confined to non-financial service sector firms in India. Logistic regression, linear discriminant analysis (LDA), and artificial neural networks (ANNs) are applied to predict financial distress and their ability to foretell future cash flows. Other methods adopted in evaluating the models include accuracy, sensitivity, and specificity.

Results: ANNs outperform the other models based on accuracy and predictability, which are higher than the rates given by the other two models, namely logistic regression and LDA. The ANN model performs well in identifying financially distressed firms; thus, it is informative in evaluating their financial position. Also, results suggest that future cash flow substantially affects financial distress prediction, an essential new variable that needs to be considered in future research.

Conclusion: This predictive model of financial distress further gives a sound platform for the corresponding sector in India. In general, ANNs offer profound opportunities for managers, investors, policymakers, regulators and shareholders as an effective tool for preventive decision-making to reinforce the corporate world. This research demonstrates that high-level machine-learning approaches are still crucial in financial analysis and policymaking.

Keywords: Forecasting; Public policy; Sustainability; ANN; Future cash flow; FCF; Logistic regression; LR; Linear discriminant analysis; LDA.

Received: August 13, 2024; Revised: October 28, 2024; Accepted: November 14, 2024; Prepublished online: January 5, 2025; Published: January 31, 2025  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Ranjan Sethi, S., & Ashok Mahadik, D. (2025). Forecasting Financial Distress for Shaping Public Policy: An Empirical Investigation. Acta Informatica Pragensia, 14(1), 63-87. doi: 10.18267/j.aip.253
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Agrawal, K., & Maheshwari, Y. (2016). Predicting financial distress: revisiting the option-based model. South Asian Journal of Global Business Research, 5(2), 268-284. https://doi.org/10.1108/sajgbr-04-2015-0030 Go to original source...
    2. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589. https://doi.org/10.2307/2978933 Go to original source...
    3. Altman, E. I. (1984). The success of business failure prediction models. Journal of Banking & Finance, 8(2), 171-198. https://doi.org/10.1016/0378-4266(84)90003-7 Go to original source...
    4. Altman, E. I., Haldeman, R. G., & Narayanan, P. (1977). ZETATM analysis: A new model to identify bankruptcy risk of corporations. Journal of Banking & Finance, 1(1), 29-54. https://doi.org/10.1016/0378-4266(77)90017-6 Go to original source...
    5. Altman, E. I., Marco, G., & Varetto, F. (1994). Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking & Finance, 18(3), 505-529. https://doi.org/10.1016/0378-4266(94)90007-8 Go to original source...
    6. Altman, E.I., & Narayanan, P. (1996). Business failure classification models: an international survey. Working Paper Series. New York University. https://archive.nyu.edu/bitstream/2451/26900/2/wpa96005.pdf
    7. Anandarajan, M., Lee, P., & Anandarajan, A. (2001). Bankruptcy prediction of financially stressed firms: an examination of the predictive accuracy of artificial neural networks. Intelligent Systems in Accounting Finance & Management, 10(2), 69-81. https://doi.org/10.1002/isaf.199 Go to original source...
    8. Appiah, K. O., Chizema, A., & Arthur, J. (2015). Predicting corporate failure: a systematic literature review of methodological issues. International Journal of Law and Management, 57(5), 461-485. https://doi.org/10.1108/ijlma-04-2014-0032 Go to original source...
    9. Balasubramanian, S. A., GS, R., P, S., & Natarajan, T. (2019). Modeling corporate financial distress using financial and non-financial variables. International Journal of Law and Management, 61(3/4), 457-484. https://doi.org/10.1108/ijlma-04-2018-0078 Go to original source...
    10. Balcaen, S., & Ooghe, H. (2006). 35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems. The British Accounting Review, 38(1), 63-93. https://doi.org/10.1016/j.bar.2005.09.001 Go to original source...
    11. Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111. https://doi.org/10.2307/2490171 Go to original source...
    12. Billah, N. B., Yakob, N. A., & McGowan Jr, C. B. (2015). Liquidity analysis of selected public-listed companies in Malaysia. Issues in Economics and Business, 1(1), 1-20. Go to original source...
    13. Blum, M. (1974). Failing Company Discriminant analysis. Journal of Accounting Research, 12(1), 1-25. https://doi.org/10.2307/2490525 Go to original source...
    14. Boedeker, P., & Kearns, N. T. (2019). Linear Discriminant Analysis for Prediction of Group Membership: A User-Friendly Primer. Advances in Methods and Practices in Psychological Science, 2(3), 250-263. https://doi.org/10.1177/2515245919849378 Go to original source...
    15. Boughorbel, S., Jarray, F., & El-Anbari, M. (2017). Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric. PLoS ONE, 12(6), e0177678. https://doi.org/10.1371/journal.pone.0177678 Go to original source...
    16. Chen, W., & Du, Y. (2009). Using neural networks and data mining techniques for the financial distress prediction model. Expert Systems With Applications, 36(2), 4075-4086. https://doi.org/10.1016/j.eswa.2008.03.020 Go to original source...
    17. Claessens, S., Djankov, S., & Lang, L. H. (2000). The separation of ownership and control in East Asian Corporations. Journal of Financial Economics, 58(1-2), 81-112. https://doi.org/10.1016/s0304-405x(00)00067-2 Go to original source...
    18. Cox, D. R., & Snell, E. J. (1989). The Analysis of Binary Data. Chapman and Hall.
    19. Dimitras, A., Zanakis, S., & Zopounidis, C. (1996). A survey of business failures with an emphasis on prediction methods and industrial applications. European Journal of Operational Research, 90(3), 487-513. https://doi.org/10.1016/0377-2217(95)00070-4 Go to original source...
    20. Eftekhar, B., Mohammad, K., Ardebili, H. E., Ghodsi, M., & Ketabchi, E. (2005). Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data. BMC Medical Informatics and Decision Making, 5(1), 1-8. https://doi.org/10.1186/1472-6947-5-3 Go to original source...
    21. Elhoseny, M., Metawa, N., Sztano, G., & El-Hasnony, I. M. (2022). Deep Learning-Based Model for Financial Distress Prediction. Annals of Operations Research, (in press). https://doi.org/10.1007/s10479-022-04766-5 Go to original source...
    22. Farshadfar, S., & Monem, R. (2013). The usefulness of operating cash flow and accrual components in improving the predictive ability of earnings: a re-examination and extension. Accounting and Finance, 53(4), 1061-1082. https://doi.org/10.1111/j.1467-629x.2012.00486.x Go to original source...
    23. Flannery, M. J., & Hankins, K. W. (2013). Estimating dynamic panel models in corporate finance. Journal of Corporate Finance, 19, 1-19. https://doi.org/10.1016/j.jcorpfin.2012.09.004 Go to original source...
    24. Firmansyah, J., Siregar, H., & Syarifuddin, F. (2018). Does working capital management affect the profitability of property and real estate firms in Indonesia? Jurnal Keuangan Dan Perbankan, 22(4), 694-706. https://doi.org/10.26905/jkdp.v22i4.2438 Go to original source...
    25. Foster, B. P., Ward, T. J., & Woodroof, J. (1998). An analysis of the usefulness of debt defaults and going concern opinions in bankruptcy risk assessment. Journal of Accounting Auditing & Finance, 13(3), 351-371. https://doi.org/10.1177/0148558x9801300311 Go to original source...
    26. Geng, R., Bose, I., & Chen, X. (2015). Prediction of financial distress: An empirical study of listed Chinese companies using data mining. European Journal of Operational Research, 241(1), 236-247. https://doi.org/10.1016/j.ejor.2014.08.016 Go to original source...
    27. Hosamani, B., Ali, S. A., & Katti, V. (2021). Assessment of performance and exhaust emission quality of different compression ratio engine using two biodiesel mixture: Artificial neural network approach. Alexandria Engineering Journal, 60(1), 837-844. https://doi.org/10.1016/j.aej.2020.10.012 Go to original source...
    28. Hosmer, D. W., Hosmer, T., Le Cessie, S., & Lemeshow, S. (1997). A comparison of goodness-of-fit tests for the logistic regression model. Statistics in medicine, 16(9), 965-980. https://doi.org/10.1002/(SICI)1097-0258(19970515)16:9<965::AID-SIM509>3.0.CO;2-O Go to original source...
    29. Htet, T. Z., & Oo, W. M. (2024). Mutual Information Ratio-Based Approach for Rainfall Prediction with Multicollinearity. In 2024 IEEE Conference on Computer Applications, (pp. 193-197). IEEE. https://doi.org/10.1109/ICCA62361.2024.10532808 Go to original source...
    30. Hua, Z., Wang, Y., Xu, X., Zhang, B., & Liang, L. (2006). Predicting corporate financial distress based on integration of support vector machine and logistic regression. Expert Systems With Applications, 33(2), 434-440. https://doi.org/10.1016/j.eswa.2006.05.006 Go to original source...
    31. Idrissi, T. E., Idri, A., & Bakkoury, Z. (2019). Systematic map and review of predictive techniques in diabetes self-management. International Journal of Information Management, 46, 263-277. https://doi.org/10.1016/j.ijinfomgt.2018.09.011 Go to original source...
    32. Isayas, Y. N. (2021). Financial distress and its determinants: Evidence from insurance companies in Ethiopia. Cogent Business & Management, 8(1), 1951110. https://doi.org/10.1080/23311975.2021.1951110 Go to original source...
    33. Jemaa, O. B., Toukabri, M., & Jilani, F. (2015). The Examination of the Ability of Earnings and Cash flow in Predicting future cash flows: Application to the Tunisian context. Accounting and Finance Research, 4(1), 1-16. https://doi.org/10.5430/afr.v4n1p1 Go to original source...
    34. Johnston, R. (2024). Discriminant analysis. In Encyclopedia of Quality of Life and Well-Being Research, (pp. 1663-1664). Springer. https://doi.org/10.1007/978-94-007-0753-5_750 Go to original source...
    35. Keasey, K., & Watson, R. (1991). Financial Distress Prediction Models: A Review of Their Usefulness. British Journal of Management, 2(2), 89-102. https://doi.org/10.1111/j.1467-8551.1991.tb00019.x Go to original source...
    36. Kasgari, A. A., Salehnezhad, S. H., & Ebadi, F. (2013). The bankruptcy prediction by neural networks and logistic regression. International Journal of Academic Research in Accounting, Finance and Management Sciences, 3(4), 191-199.
    37. Krishnan, G. V., & Largay, J. A., III. (2000). The predictive ability of direct method cash flow information. Journal of Business Finance & Accounting, 27(1-2), 215-245. https://doi.org/10.1111/1468-5957.00311 Go to original source...
    38. Kristanti, F. T., & Dhaniswara, V. (2023). The accuracy of artificial neural networks and logit models in predicting the companies' financial distress. Journal of Technology Management & Innovation, 18(3), 42-50. https://doi.org/10.4067/s0718-27242023000300042 Go to original source...
    39. Kumar, S., & Ranjani, K. S. (2018). Financial Constraints and Cash Flow Sensitivity to Investment in Indian Listed Manufacturing Firms. In Contemporary Trends in Accounting, Finance and Financial Institutions: Proceedings from the International Conference on Accounting, Finance and Financial Institutions, (pp. 57-69). Springer. https://doi.org/10.1007/978-3-319-72862-9_5 Go to original source...
    40. Kumar, P. R., & Ravi, V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review. European Journal of Operational Research, 180(1), 1-28. https://doi.org/10.1016/j.ejor.2006.08.043 Go to original source...
    41. Kumar, A., Rao, V. R., & Soni, H. (1995). An empirical comparison of neural network and logistic regression models. Marketing Letters, 6(4), 251-263. https://doi.org/10.1007/bf00996189 Go to original source...
    42. Laitinen, E. K., & Laitinen, T. (2000). Bankruptcy prediction. International Review of Financial Analysis, 9(4), 327-349. https://doi.org/10.1016/s1057-5219(00)00039-9 Go to original source...
    43. Lin, T. (2009). A cross model study of corporate financial distress prediction in Taiwan: Multiple discriminant analysis, logit, probit and neural networks models. Neurocomputing, 72(16-18), 3507-3516. https://doi.org/10.1016/j.neucom.2009.02.018 Go to original source...
    44. Liu, S. (2015). Corporate governance and forward-looking disclosure: Evidence from China. Journal of International Accounting Auditing and Taxation, 25, 16-30. https://doi.org/10.1016/j.intaccaudtax.2015.10.002 Go to original source...
    45. Lukić, R. (2023). Influence of net working capital on trade profitability in Serbia. European Journal of Interdisciplinary Studies, 15(1), 48-67. https://doi.org/10.24818/ejis.2023.04 Go to original source...
    46. Magee, L. (1990). R2 measures based on Wald and likelihood ratio joint significance tests. The American Statistician, 44(3), 250-253. https://doi.org/10.2307/2685352 Go to original source...
    47. Mahadik, D., & Mohanty, P. (2024). Does Prospect Theory Explain Risk Management by Firms?. SSRN. https://ssrn.com/abstract=4919435 Go to original source...
    48. Meyer, P. A., & Pifer, H. W. (1970). Prediction of bank failures. The Journal of Finance, 25(4), 853-868. https://doi.org/10.2307/2325421 Go to original source...
    49. Michalski, G. (2013). To Standardize or Not to Standardize Financial Liquidity Ratios? The Answer Using Financial Liquidity Efficiency of Investment Model (FLEIM). SHODH GANGA Management Journal, 3(1), 17-24. Go to original source...
    50. Midi, H., Sarkar, S., & Rana, S. (2010). Collinearity diagnostics of binary logistic regression model. Journal of Interdisciplinary Mathematics, 13(3), 253-267. https://doi.org/10.1080/09720502.2010.10700699 Go to original source...
    51. Mishra, N., Ashok, S., & Tandon, D. (2021). Predicting financial distress in the Indian banking sector: A comparative study between the Logistic Regression, LDA and ANN models. Global Business Review, 25(6), 1540-1558. https://doi.org/10.1177/09721509211026785 Go to original source...
    52. Morris, R. (1997). Early Warning Indicators of Corporate Failure: A Critical Review of Previous Research and Further Empirical Evidence. Routledge.
    53. Murty, A. V. N., & Misra, D. P. (2004). Cash flow ratios as indicators of corporate failure. Finance India, 18(3), 1315-1325.
    54. Neter, J., Wasserman, W., & Kutner, M. H. (1983). Applied linear regression models. Richard D. Irwin.
    55. Nick, T. G., & Campbell, K. M. (2007). Logistic regression. In Ambrosius, W.T. (eds) Topics in Biostatistics, (pp. 273-301). Humana Press. https://doi.org/10.1007/978-1-59745-530-5_14 Go to original source...
    56. Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131. https://doi.org/10.2307/2490395 Go to original source...
    57. O'Leary, D. E. (1998). Using neural network to predict corporate failure. International Journal of Intelligent Systems in Accounting Finance and Management, 7(3), 187-197. https://doi.org/10.1002/(SICI)1099-1174(199809)7:3%3C187::AID-ISAF144%3E3.0.CO;2-7 Go to original source...
    58. Powers, D. M. (2011). Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. International Journal of Machine Learning Technology, 2(1), 37-63.
    59. Prabhakar, B., & Japee, G. (2023). Analysing Selected Cement Companies of India: A Liquidity Scenario Perspective. VIDYA - A Journal of Gujarat University, 2(1), 117-127. https://doi.org/10.47413/vidya.v2i1.152 Go to original source...
    60. Safiq, M., Selviana, R., & Kusumastati, W. W. (2020). Financial and nonfinancial factors affecting future cashflow and their impacts on financial distress. International Journal of Research in Business and Social Science, 9(5), 212-226. https://doi.org/10.20525/ijrbs.v9i5.859 Go to original source...
    61. Salcedo-Sanz, S., Deo, R. C., Carro-Calvo, L., & Saavedra-Moreno, B. (2016). Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms. Theoretical and Applied Climatology, 125(1-2), 13-25. https://doi.org/10.1007/s00704-015-1480-4 Go to original source...
    62. Scott, J. (1981). The probability of bankruptcy. Journal of Banking & Finance, 5(3), 317-344. https://doi.org/10.1016/0378-4266(81)90029-7 Go to original source...
    63. Sehgal, S., Mishra, R. K., Deisting, F., & Vashisht, R. (2021). On the determinants and prediction of corporate financial distress in India. Managerial Finance, 47(10), 1428-1447. https://doi.org/10.1108/mf-06-2020-0332 Go to original source...
    64. Senaviratna, N. a. M. R., & Cooray, T. M. J. A. (2019). Diagnosing multicollinearity of logistic regression model. Asian Journal of Probability and Statistics, 5(2), 1-9. https://doi.org/10.9734/ajpas/2019/v5i230132 Go to original source...
    65. Sethi, S. R., Mahadik, D. A., & Bilolikar, R. V. (2024). Exploring Trends and Advancements in Financial Distress Prediction Research: A Bibliometric study. International Journal of Economics and Financial Issues, 14(1), 164-179. https://doi.org/10.32479/ijefi.15472 Go to original source...
    66. Sharma, A., & Goyal, M. K. (2015). Bayesian network model for monthly rainfall forecast. In 2015 IEEE international conference on research in computational intelligence and communication networks, (pp. 241-246). IEEE. https://doi.org/10.1109/ICRCICN.2015.7434243 Go to original source...
    67. Sharma, S. K., Sharma, H., & Dwivedi, Y. K. (2019). A hybrid SEM-Neural network model for predicting determinants of mobile payment services. Information Systems Management, 36(3), 243-261. https://doi.org/10.1080/10580530.2019.1620504 Go to original source...
    68. Shayan, Z., Mezerji, N. M. G., Shayan, L., & Naseri, P. (2015). Prediction of Depression in Cancer Patients With Different Classification Criteria, Linear Discriminant Analysis versus Logistic Regression. Global Journal of Health Science, 8(7), 41-46. https://doi.org/10.5539/gjhs.v8n7p41 Go to original source...
    69. Sherry, A. (2006). Discriminant analysis in Counseling Psychology Research. The Counseling Psychologist, 34(5), 661-683. https://doi.org/10.1177/0011000006287103 Go to original source...
    70. Shrivastava, A., Kumar, K., & Kumar, N. (2018). Business distress prediction using Bayesian logistic Model for Indian firms. Risks, 6(4), 113. https://doi.org/10.3390/risks6040113 Go to original source...
    71. Singh, B. P., & Mishra, A. K. (2016). Re-estimation and comparisons of alternative accounting based bankruptcy prediction models for Indian companies. Financial Innovation, 2(1), Article 6. https://doi.org/10.1186/s40854-016-0026-9 Go to original source...
    72. Soni, R. (2019). Application of discriminant analysis to diagnose the financial distress. Theoretical Economics Letters, 9(4), 1197-1209. https://doi.org/10.4236/tel.2019.94077 Go to original source...
    73. Taffler, R. J. (1985). The use of the Z-score in practice. Working Paper 95/1. City Business School.
    74. Tay, F. E., & Shen, L. (2002). Economic and financial prediction using rough sets model. European Journal of Operational Research, 141(3), 641-659. https://doi.org/10.1016/s0377-2217(01)00259-4 Go to original source...
    75. Teo, A., Tan, G. W., Ooi, K., Hew, T., & Yew, K. (2015). The effects of convenience and speed in m-payment. Industrial Management & Data Systems, 115(2), 311-331. https://doi.org/10.1108/imds-08-2014-0231 Go to original source...
    76. Tinoco, M. H., & Wilson, N. (2013). Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables. International Review of Financial Analysis, 30, 394-419. https://doi.org/10.1016/j.irfa.2013.02.013 Go to original source...
    77. Wang, Z., Wang, Q., Nie, Z., & Li, B. (2022). Corporate financial distress prediction based on controlling shareholder's equity pledge. Applied Economics Letters, 29(15), 1365-1368. https://doi.org/10.1080/13504851.2021.1931656 Go to original source...
    78. Zamani, H., & Nadimi-Shahraki, M. H. (2024). An evolutionary crow search algorithm equipped with interactive memory mechanism to optimize artificial neural network for disease diagnosis. Biomedical Signal Processing and Control, 90, 105879. https://doi.org/10.1016/j.bspc.2023.105879 Go to original source...
    79. Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 59-82. https://doi.org/10.2307/2490859 Go to original source...
    80. Zopounidis, C., & Dimitras, A. I. (1998). Multicriteria Decision Aid Methods for the Prediction of Business Failure. Springer. https://doi.org/10.1007/978-1-4757-2885-9 Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content