Neural Network VS Genetic and Particle Swarm Optimization Algorithms in Bankruptcy

Iranian Journal of Accounting, Auditing and Finance

مقاله 10، دوره 9، شماره 2 - شماره پیاپی 31، مرداد 2025، صفحه 175-196   

نوع مقاله: Original Article

شناسه دیجیتال (DOI): 10.22067/ijaaf.2025.45564.1456

نویسنده

Alireza Azarberahman^*

Assistant professor, Shandiz Institute of Higher Education, Mashhad, Iran

چکیده

The goal of the study is to estimate an artificial neural network (ANN) model for bankruptcy prediction and optimize processes using the Particle Swarm (PSO) and Genetic (GA) algorithms. 21 variables that were related to the likelihood of bankruptcy were chosen for the study. Neural networks (NNs) choose the optimal network with the least error in training and evaluating patterns in the second phase. The neural network's weights and biases were optimized in the final stage by combining GA and PSO with the neural network. The results showed that the ability to explain the initial pattern has risen using GA and PSO. The evaluation of ANN performance demonstrates the superiority of the models over linear regression. Finally, four variables—current ratio, sales to current assets ratio, economic value added, and gross profit margin ratio—that may reliably predict bankruptcy were found using the ANNs-PSO and ANNs-GA hybrid approach. The evidence reveals the effectiveness of the metaheuristic algorithms compared to linear ones in predicting bankruptcy. This further highlights the new breed of computational tools available to techno-savvy financial analysts and investors.

کلیدواژه‌ها

Artificial Neural Networks؛ Bankruptcy؛ Genetic Algorithm؛ Particle Swarm Algorithm

مراجع

1. Ainan, U.H., Por, L.Y., Y. Chen, L., Yang, J and Ku, C.S. (2024). Advancing bankruptcy forecasting with hybrid machine learning techniques: Insights from an unbalanced Polish dataset, IEEE Access, 12, pp. 9369-9381. https://doi.org/10.1109/ACCESS.2024.3354173
2. Alam, N., Gao, J. and Jones, S. (2021). Corporate failure prediction: an evaluation of deep learning vs discrete hazard models. Journal of International Financial Markets, Institutions and Money, 75, A. 101455. https://doi.org/10.1016/j.intfin.2021.101455
3. Alibabaee, G. and Khanmohammadi, M. (2022). The study of the predictive power of meta-heuristic algorithms to provide a model for bankruptcy prediction. International Journal of Finance & Managerial Accounting, 7(26), pp. 33-51. https://doi.org/10.30495/ijfma.2022.19523
4. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), pp. 589-609. https://doi.org/10.2307/2978933
5. Altman, E. I. and Hotchkiss, E. (2010). Corporate financial distress and bankruptcy: Predict and avoid bankruptcy, analyze and invest in distressed debt (Vol. 289). John Wiley & Sons, 3rd edition, New Jersey, United States. DOI:10.1002/9781118267806
6. Altman, E. I., Haldeman, R. G. and Narayanan, P. (1977). ZETATM analysis a new model to identify bankruptcy risk of corporations. Journal of Banking & Finance, 1(1), pp. 29-54. https://doi.org/10.1016/0378-4266(77)90017-6
7. Ashori, S. (2012). A comparative evaluation of the results of bankruptcy prediction: Shirata model vs. firm efficiency with coverage analysis model, Master dissertation, Islamic Azad University-Kashan, Kashan, Iran (In Persian).
8. Azayite, F.Z., and Achchab, S. (2018). Topology design of bankruptcy prediction neural networks using Particle swarm optimization and backpropagation, Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications, 49, pp. 1-6. https://doi.org/10.1145/3230905.3230951
9. Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 1(1), pp. 71-111. https://doi.org/10.2307/2490171
10. Bhattacharya, M., Inekwe, J. N. and Valenzuela, M. R. (2020). Credit risk and financial integration: An application of network analysis. International Review of Financial Analysis, 72, A. 101588. https://doi.org/10.1016/j.irfa.2020.101588
11. Campbell, J. Y., Hilscher, J. and Szilagyi, J. (2008). In search of distress risk. The Journal of Finance, 63(6), pp. 2899-2939. https://doi.org/10.1111/j.1540-6261.2008.01416.x
12. Charalambous, C., Martzoukos, S. H. and Taoushianis, Z. (2023). A neuro-structural framework for bankruptcy prediction. Quantitative Finance, 23(10), pp. 1445-1464. https://doi.org/10.1080/14697688.2023.2230241
13. Chava, S. and Jarrow, R. A. (2004). Bankruptcy prediction with industry effects. Review of Finance, 8(4), pp. 537-569. https://doi.org/10.1093/rof/8.4.537
14. Dasilas, A. and Rigani, A. (2024). Machine learning techniques in bankruptcy prediction: A systematic literature review. Expert Systems with Applications, 255, A. 124761. https://doi.org/10.1016/j.eswa.2024.124761
15. Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accounting Research, 10(1), pp. 167-179. https://doi.org/10.2307/2490225
16. Ellis, R.P., and Mookim, P.G. (2013). K-Fold cross-validation is superior to Split sample validation for risk adjustment models, Department of Economics, working paper. Boston University, Boston, United States. https://www.bu.edu/econ/files/2016/01/Ellis_Mookim_R2paper_20130605.pdf
17. Fagerland, M. W., Hosmer, D. W. and Bofin, A. M. (2008). Multinomial goodness‐of‐fit tests for logistic regression models. Statistics in Medicine, 27(21), pp. 4238-4253. https://doi.org/10.1002/sim.3202
18. Foroughi, D. and Yadegari, S. (2010). Application of artificial neural networks in accounting. Accounting and Financial Management, 4, pp. 31-58. https://www.noormags.ir/view/en/magazine/number/46282
19. Fulmer, J. G., Moon, J. E., Gavin, T. A. and Erwin, M. (1984). A bankruptcy classification model for small firms. Journal of Commercial Bank Lending, 66(11), pp. 25-37. https://archive.org/details/sim_rma-journal_1984-1985_67_index/page/48/mode/2up
20. Ghaderi, E., Amini, P., Noravesh, I., and Mohammadi Moqrny, A. (2018). Explaining the model of earning management measurement using an intelligent hybrid method of neural networks and meta heuristic algorithms, Financial Engineering and Portfolio Management, 9(36), pp. 99-127. https://journals.iau.ir/article_544303.html?lang=en
21. Gitman, L. (1996). Principle of managerial finance, 7th edition. New York, USA: Harper Collins College. The https://www.amazon.com/Principles-Managerial-Finance-Standalone-Pearson/dp/0133546403
22. Goletsis, Y., Exarchos, T. P. and Katsis, C. D. (2009). Can Ants predict bankruptcy? A comparison of Ant Colony Systems to other state-of-the-art computational methods. New Mathematics and Natural Computation, 5(03), pp. 571-588. https://doi.org/10.1142/S1793005709001532
23. Goodfellow, I. Bengio, Y, and Courville, A. (2016). Deep learning. Boston: MIT Press. Boston, MA. https://mitpress.mit.edu/9780262035613/deep-learning/
24. Graham, J. R., Harvey, C. R. and Rajgopal, S. (2005). The economic implications of corporate financial reporting. Journal of Accounting and Economics, 40(1-3), pp. 3-73. https://doi.org/10.1016/j.jacceco.2005.01.002
25. Hosaka, T. (2019). Bankruptcy prediction using imaged financial ratios and convolutional neural networks. Expert Systems With Applications, 117, pp. 287-299. https://doi.org/10.1016/j.eswa.2018.09.039
26. Jaki, A. and Ćwięk, W. (2020). Bankruptcy prediction models based on value measures. Journal of Risk and Financial Management, 14(1), p. 6. https://doi.org/10.3390/jrfm14010006
27. Jamali, J., Mottaghi, A. and Mohammadi, A. (2021). A comparative study of bankruptcy Prediction Models and Presenting an Optimized Model for Iran's Economic Environment. Journal of Development and Capital, 6(2), pp. 111-134. https://doi.org/10.22103/jdc.2022.18728.1187
28. Khani, A. H. and Guruli, M. R. (2015). Predicting bankruptcy by liquidity ratios analysis. Jurnal UMP Social Sciences and Technology Management, 3(2), pp. 372-380.
29. Kou, G., Chao, X., Peng, Y., Alsadi, F.E., H4errera-Viedma, E. (2019). Machine learning methods for systemic risk analysis in financial sectors. Technological and Economic Development of Economy, 25(5), pp. 716-742. https://doi.org/10.3846/tede.2019.8740
30. Kumar, K. and Bhattacharya, S. (2006). Artificial neural network vs linear discriminant analysis in credit ratings forecast: A comparative study of prediction performances. Review of Accounting and Finance, 5(3), pp. 216-227. https://doi.org/10.1108/14757700610686426
31. Kumar, R., Gupta, R. A. and Bansal, A. K. (2013). Economic analysis and power management of a stand-alone wind/photovoltaic hybrid energy system using biogeography based optimization algorithm. Swarm and Evolutionary Computation, 8, pp. 33-43. https://doi.org/10.1016/j.swevo.2012.08.002
32. Letkovský, S., Jenčová, S. and Vašaničová, P. (2024). Is artificial intelligence really more accurate in predicting bankruptcy? International Journal of Financial Studies, 12(1), pp. 8. https://doi.org/10.3390/ijfs12010008
33. Lu, Y., Zeng, N., Liu, X. and Yi, S. (2015). A new hybrid algorithm for bankruptcy prediction using switching particle swarm optimization and support vector machines. Discrete Dynamics in Nature and Society, 2015(1), pp. 294930. https://doi.org/10.1155/2015/294930
34. Mai, F., Tian, S., Lee, C. and Ma, L. (2019). Deep learning models for bankruptcy prediction using textual disclosures. European Journal of Operational Research, 274(2), pp. 743-758. https://doi.org/10.1016/j.ejor.2018.10.024
35. Marso, S. and El Merouani, M. (2020). Predicting financial distress using hybrid feedforward neural network with cuckoo search algorithm. Procedia Computer Science, 170, pp. 1134-1140. https://doi.org/10.1016/j.procs.2020.03.054
36. McCulloch, W. S. and Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5, pp. 115-133. https://doi.org/10.1007/BF02478259
37. Merton, R. C. (1974). On the pricing of corporate debt: the risk structure of interest rates. The Journal of Finance, 29(2), pp. 449-470. https://doi.org/10.2307/2978814
38. Mirarab-Bayegi, S.A., Mokari, H, and Azariyon, A. (2020). Dynamic forecasting of financial bankruptcy using the Malm Quest method (Case study: companies listed on the Tehran Stock Exchange), Quarterly Scientific Journal of Technical and Vocational University, 17(3), 211-229 (In Persian). https://doi.org/10.48301/kssa.2020.124675
39. Moghaddam, A. and Taghi Mollayi, M. (2015). Review of Flamer and Zimsky’s financial models in bankruptcy predicting of companies. New Researches on Accounting, 1(1), pp. 11-25 (In Persian). https://nra.journals.pnu.ac.ir/article_1672.html?lang=en
40. Motie Ghader, H. Lotfi,Sh. and Seyyed-Sfahlan M.M. (2010). An overview of intelligent optimization methods. Shabestar publication, East Azerbaijan province, Iran
41. Mousavi, M. M. and Ouenniche, J. (2018). Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions. Annals of Operations Research, 271(2), pp. 853-886. https://doi.org/10.1007/s10479-018-2814-2
42. Ogachi, D., Ndege, R., Gaturu, P. and Zoltan, Z. (2020). Corporate bankruptcy prediction model, a special focus on listed companies in Kenya. Journal of Risk and Financial Management, 13(3), p. 47. https://doi.org/10.3390/jrfm13030047
43. Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 1(1), pp. 109-131. https://doi.org/10.2307/2490395
44. Parejo, J. A., Ruiz-Cortés, A., Lozano, S. and Fernandez, P. (2012). Metaheuristic optimization frameworks: a survey and benchmarking. Soft Computing, 16, pp. 527-561. DOI:10.1007/s00500-011-0754-8
45. Qin, X., Ji, X., Zhang, S. and Xu, D. (2024). A credit scoring ensemble model incorporating fuzzy clustering particle swarm optimization algorithm. Journal of Intelligent & Fuzzy Systems, (Preprint), 1(1), pp. 1-18. https://doi.org/10.3233/jifs-233334
46. Rocha, A. L. and Stern, F. (2004). The asymmetric n-player gambler's ruin problem with equal initial fortunes. Advances in Applied Mathematics, 33(3), pp. 512-530. https://doi.org/10.1016/j.aam.2003.07.005
47. Sharifzadeh, H. and Amjady, N. (2014). A review of metaheuristic algorithms in optimization. Journal of Modeling in Engineering, 12(38), pp. 27-43. https://doi.org/10.22075/jme.2017.1677
48. Shetty, S., Musa, M. and Brédart, X. (2022). Bankruptcy prediction using machine learning techniques. Journal of Risk and Financial Management, 15(1), p. 35. https://doi.org/10.3390/jrfm15010035
49. Shin, K. S., Lee, T. S. and Kim, H. J. (2005). An application of support vector machines in bankruptcy prediction model. Expert systems with applications, 28(1), pp. 127-135. https://doi.org/10.1016/j.eswa.2004.08.009
50. Shirata, C. Y. (1998). Financial ratios as predictors of bankruptcy in Japan: an empirical research. In Proceedings of the second Asian Pacific interdisciplinary research in accounting conference, Tsukuba College of Technology Japan, Japan (pp. 437-445). https://www.shirata.net/eng/APIRA98.html
51. Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. The Journal of Business, 74(1), pp. 101-124. https://doi.org/10.1086/209665
52. Thompson, J. (2016). Statistics for bioinformatics: methods for multiple sequence alignment. Elsevier, Book chapter, pp. 43-51. https://doi.org/10.1016/B978-1-78548-216-8.50004-X
53. Vaghfi, S. H. (2019). Using artificial intelligence algorithm in financial bankruptcy by macro-economic and accounting variables in listed companies for stock exchange in Tehran. Journal of Decisions and Operations Research, 4(2), pp. 158-173. https://doi.org/10.22105/dmor.2019.174674.1106
54. Valizadeh Larijani, A. and Banimahd, B. (2022). Financial statement items, life cycle and bankruptcy. Empirical Research in Accounting, 12(2), pp. 91-110. https://doi.org/10.22051/jera.2020.30270.2643
55. Wallace, W. A. (2004). Risk assessment by internal auditors using past research on bankruptcy. The IIA Research Foundation, Florida, USA.
56. Wang, H., Kou, G. and Peng, Y. (2021). Multi-class misclassification cost matrix for credit ratings in peer-to-peer lending. Journal of the Operational Research Society, 72(4), pp. 923-934. https://doi.org/10.1080/01605682.2019.1705193
57. Zahra, B. Salhi, H, and Mellit. (2017). Wind turbine performance enhancement by control of pitch angle using PID controller and Particle Swarm Optimization, 5th international conference on electrical engineering- The International Conference on Electrical Engineering, Boumerdes (ICEE-B), Boumerdes, Algeria. DOI:10.1109/ICEE-B.2017.8192221
58. Zainol, A. Z., Saian, R., Kin, T. Y., Razali, M. H. M. and Bakar, S. A. (2024). An enhanced ant colony optimization algorithm with the Hellinger distance for Shariah-Compliant securities companies bankruptcy prediction. Journal of Information and Communication Technology, 23(1), pp. 1-24. https://doi.org/10.32890/jict2024.23.1.1
59. Zarin, J., Jamshidinavid, B., Ghanbari, M. and Baghfalaki, A. (2021). Develop a bankruptcy forecasting model for state-owned companies using macroeconomic, managerial, financial and political variables. Governmental Accounting, 7(1), 79-96 (In Persian). https://doi.org/10.30473/gaa.2021.45164.1249