The Behavior of Nonlinear Models in Predicting the Financial Strength of Stock Exchange Companies

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.

Shah Bazadeh Zafarani Seyedeh Fatemeh; ABBASI EBRAHIM; Didehkhani Hosein

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

Journal Paper

Paper Information

Journal: IRANIAN JOURNAL OF INSURANCE RESEARCH (SANAAT-E-BIMEH) Year:2020 | Volume:34 | Issue:4 Page(s): 103-129

Download Full-Text

View:

161

Download:

Cites:

Information Journal Paper

Title

The Behavior of Nonlinear Models in Predicting the Financial Strength of Stock Exchange Companies

Author(s)

Shah Bazadeh Zafarani Seyedeh Fatemeh | ABBASI EBRAHIM | Didehkhani Hosein | Issue Writer Certificate

Keywords

Financial Markets

Classification

Data Mining

Finance

Artificial Intelligence

Abstract

May 2 Abstract Objective: Since surviving on obligations to insurers and maximizing the benefits of the company is one of the most important goals of insurance companies, and on the one hand, the regulatory agencies of each country are trying to enact specific laws and regulations and managers by adopting precautionary measures and modern technical and financial strategies. The purpose of this study was to present an intelligent model for predicting financial empowerment in insurance companies as a decision support system. Methodology: In order to present a model of predicting the financial health of insurance companies by examining the background of 17 variables as predictor variables for predicting the class of financial wealth, extracted from reliable sources of central insurance site of the Islamic Republic of Iran during 2013-2017. In this study, first, the results of applying different prediction models based on Artificial Intelligence including: decision tree, neural network, nibbles were compared. Then, the ranking of predictive algorithms was evaluated. Findings: The results of this study showed that the decision tree with 99% accuracy has the best performance in predicting financial ability, considering that the decision tree is a nonlinear cognition and mapping models and patterns of turbulence between goal and decision variables. Conclusion: The accuracy of the decision tree model in predicting financial robustness is high and the model extracted using decision tree has high accuracy and capability in estimation.

Cites

No record.

References

No record.

Cite

APA: Copy

Shah Bazadeh Zafarani, Seyedeh Fatemeh, ABBASI, EBRAHIM, & Didehkhani, Hosein. (2020). The Behavior of Nonlinear Models in Predicting the Financial Strength of Stock Exchange Companies. IRANIAN JOURNAL OF INSURANCE RESEARCH (SANAAT-E-BIMEH), 34(4 ), 103-129. SID. https://sid.ir/paper/652468/en

Vancouver: Copy

Shah Bazadeh Zafarani Seyedeh Fatemeh, ABBASI EBRAHIM, Didehkhani Hosein. The Behavior of Nonlinear Models in Predicting the Financial Strength of Stock Exchange Companies. IRANIAN JOURNAL OF INSURANCE RESEARCH (SANAAT-E-BIMEH)[Internet]. 2020;34(4 ):103-129. Available from: https://sid.ir/paper/652468/en

IEEE: Copy

Seyedeh Fatemeh Shah Bazadeh Zafarani, EBRAHIM ABBASI, and Hosein Didehkhani, “The Behavior of Nonlinear Models in Predicting the Financial Strength of Stock Exchange Companies,” IRANIAN JOURNAL OF INSURANCE RESEARCH (SANAAT-E-BIMEH), vol. 34, no. 4 , pp. 103–129, 2020, [Online]. Available: https://sid.ir/paper/652468/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops