Grey Wolf Optimization Evolving Kernel Extreme Learning Machine: Application to Bankruptcy Prediction

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Golizadeh Salteh Tohid; Eghbalnia Mohammad; AGHABABAEI MOHAMMAD EBRAHIM

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: FINANCIAL RESEARCH Year:2019 | Volume:21 | Issue:2 Page(s): 187-212

Download Full-Text

Persian Verion

View:

965

Download:

Cites:

Information Journal Paper

Title

Grey Wolf Optimization Evolving Kernel Extreme Learning Machine: Application to Bankruptcy Prediction

Author(s)

Golizadeh Salteh Tohid | Eghbalnia Mohammad | AGHABABAEI MOHAMMAD EBRAHIM | Issue Writer Certificate

Keywords

Bankruptcy predictionQ1

Learning machineQ1

Grey Wolf Optimizer (GWO)Q1

Financial ratiosQ2

Abstract

Objective: In the present era, businesses have developed to a large extent which has, in turn, forced them to manage their resources and expenditures wisely for the sake of competition. This is mainly because the competitive market has severely reduced the flexibility of companies, which means that their ability respond to different economic situations has reduced and this puts most firms at the constant risk of bankruptcy and contraction. Therefore, in this study, we have tried to predict the bankruptcy of manufacturing companies through preventing the occurrence of such risks. Methods: In this study, the "Kernel Extreme Learning machine" has been used as one of the artificial intelligence models for predicting bankruptcy. Given that machine learning methods require an optimization algorithm we have used one of the most up-to-date, "Gray Wolf Algorithm" which has been introduced in 2014. Results: The above model has been implemented on the 136 samples that were collected from the Tehran Stock Exchange between 2015 and 2018. All of the performance evaluation criteria including the classification, accuracy, type error, second-order error and area under the ROC curve showed better performance than the genetic algorithm which was presented and its significance was confirmed by t-test. Conclusion: Considering the gray wolf algorithm’ s high accuracy and its performance compared to the genetic algorithm, it is necessary to use the gray wolf algorithm to predict the bankruptcy of Iranian manufacturing companies either for investment purposes and for validation purposes, or for using internal management of the company.

Cites

No record.

References

No record.

Cite

APA: Copy

Golizadeh Salteh, Tohid, Eghbalnia, Mohammad, & AGHABABAEI, MOHAMMAD EBRAHIM. (2019). Grey Wolf Optimization Evolving Kernel Extreme Learning Machine: Application to Bankruptcy Prediction. FINANCIAL RESEARCH, 21(2 ), 187-212. SID. https://sid.ir/paper/91139/en

Vancouver: Copy

Golizadeh Salteh Tohid, Eghbalnia Mohammad, AGHABABAEI MOHAMMAD EBRAHIM. Grey Wolf Optimization Evolving Kernel Extreme Learning Machine: Application to Bankruptcy Prediction. FINANCIAL RESEARCH[Internet]. 2019;21(2 ):187-212. Available from: https://sid.ir/paper/91139/en

IEEE: Copy

Tohid Golizadeh Salteh, Mohammad Eghbalnia, and MOHAMMAD EBRAHIM AGHABABAEI, “Grey Wolf Optimization Evolving Kernel Extreme Learning Machine: Application to Bankruptcy Prediction,” FINANCIAL RESEARCH, vol. 21, no. 2 , pp. 187–212, 2019, [Online]. Available: https://sid.ir/paper/91139/en

Related Journal Papers