Accounting and Auditing Studies

Accounting and Auditing Studies

Designing and Explaining the Bankruptc Forecasting Model of Companies Using Deep Learning Model Optimized with Whale Meta-Heuristic Algorithm.

Document Type : Original Article

Authors
Mostafa Taghimollaei 1
Azar Moslemi 2
Abdolkarim Moghadam 3
1 PhD student, Department of Accounting, Faculty of Humanities, Khomein Branch, Islamic Azad University, Khomein, Iran
2 Assistant Professor, Department of Accounting, Faculty of Humanities, Khomein Branch, Islamic Azad University, Khomein, Iran
3 Associate Professor, Department of Accounting, Payam Noor University, Tehran, Iran
10.22034/iaas.2024.211394
Abstract
Today, businesses need to properly manage their resources and expenses in order to survive, in the competitive arena, the flexibility of companies has decreased drastically and this factor has caused them to not have the ability to react correctly and appropriately in different economic conditions and with the risk of bankruptcy. to face Predicting the bankruptcy of companies is one of the important topics that contribute to the success and continuity of companies. The purpose of this research is to design and explain the model of predicting the bankruptcy of companies using a deep learning model optimized with the whale meta-heuristic algorithm. It has been implemented on the data of 328 examples of listed companies including 246 healthy companies and 82 bankrupt companies in the period of 2016-2021. The financial ratios are the independent variables of this research, which were optimized using the whale meta-heuristic algorithm and extracted as one of the artificial intelligence models. The results showed that the ratios of operating profit to total assets, cash to net sales, Cash to total assets, cash to current liabilities, current liabilities to total assets have been the most effective variables in determining bankruptcy and in all evaluation criteria of classification models, fit function and area under the ROC curve of the whale algorithm compared to the swarm algorithm. The particles provided better performance.
Keywords
Bankruptcy prediction
Financial Ratios
Deep Learning
Whale Optimization Algorithm
  1. بیات ، علی ؛ احمدی ، سید علیرضا ؛ محمدی ، مجید ، (1397). "پیش بینی ورشکستگی شرکت های پذیرفته شده در بورس اوراق بهادار تهران با استفاده از الگوریتم کرم شب تاب (FA )"، مهندسی مالی و مدیریت اوراق بهادار،37 (4)، 234-260
  2. حسینی ، سید علی؛ مرشدی ، فاطمه (1399). " ریسک ورشکستگی شرکت ها؛ مطالعه مقایسه ای " ، تحقیقات حسابداری و حسابرسی، انجمن حسابداری ایران، شماره 47، 55-72
  3. رسول زاده، مهدی. "بررسی کاربرد مدل آلتمن برای پیش بینی ورشکستگی شرکت های پذیرفته شده در بورس اوراق بهادار تهران"، ماهنامه بورس.1380،شماره 30 ، 62-65
  4. سلمانیان، مریم ؛ دارابی ، رویا. "بررسی تحلیلی درماندگی مالی "، مطالعات حسابداری و حسابرسی، انجمن حسابداری ایران، 1396، شماره 23 ، 47-64
  5. فرهنگ ، منوچهر ، فرهنگ علوم اقتصادی ،انتشارات نشر البرز ،چاپ هفتم ،(1374)
  6. قلی زاده سالطه ، توحید ؛ اقبال نیا ، محمد ؛ آقابابائی ، محمد ابراهیم (1398) . "پیش بینی ورشکستگی با مدل یادگیری ماشین سریع مبتنی بر کرنل بهینه شده با الگوریتم گرگ خاکستری"، تحقیقات مالی ، 21 (2) ، 187-212
  7. کرمی، غلامرضا ؛ سید حسینی ، سید مصطفی(1391). "سودمندی اطلاعات حسابداری نسبت به اطلاعات بازار در پیش بینی ورشکستگی " ، دانش حسابداری ، 10، 93-116
  8. Chou, C.-H., Hsieh, S.-C., & Qiu, C.-J. (2017). "Hybrid genetic algorithm and fuzzy  clustering for bankruptcy prediction". Applied Soft Computing, 56, 298-316
  9. 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
  10. Kim, Myoung-Jong & Dae-Ki Kang. (2012). “Classifiers selection in ensembles using genetic algorithms for bankruptcy prediction”. Expert Systems with Applications, pp. 1-7.
  11. Kim, s . Y, & Upneja. A (2014) "predicting restaurant financial  distress using decision tree and adaboosted decision tree models". Economic modelling . 36: 354-362
  12. Olson David L , dursun Delen & yanyan meng . (2012). "Comparative analysis of data mining methods for bankruptcy prediction" . Decision support systems , 52 : pp.464-473
  13. S Mirjalili and A. Lewis, "The whale optimization algorithm,"Advances in Engineering Software, vol. 95, no. 6 pp. 51-67,May 2016

W. J. Chen, Y. H. Shao, C. N. Li, Y. Q. Wang, M. Z. Liu, and Z.Wang,"NPrSVM: nonparallel sparse projection support vector machine with efficient 

Accounting and Auditing Studies
Volume 13, Issue 51
Autumn 2024
Pages 83-100