Title of article :
Application of Nonlinear Mathematical Modeling in Panel data analyses of the pecking order theory in capital structure Using Multiple Regression: Evidence from the Tehran Stock Exchange
Author/Authors :
Heydari, Mahmoud Department of Accounting - Islamic Azad University Borujerd Branch, Borujerd, Iran , Hematfar, Amin Department of Accounting - Islamic Azad University Borujerd Branch, Borujerd, Iran , Janani, Mohammad Hassan Department of Accounting - Islamic Azad University Borujerd Branch, Borujerd, Iran
Pages :
19
From page :
2383
To page :
2401
Abstract :
Financing decisions for investment are one of the important tasks of the company in determining the best composition. On the other hand, how to finance the company’s assets for individuals and institutions is important, and how much debt and stocks the company has used to finance its assets, is important because it depends on the company’s financing decisions will be affected. This study investigates the application of nonlinear mathematical modeling in panel data analyses of the pecking order theory in capital structure using multiple regression in the Tehran stock exchange. The research period is from 2014 to 2019. In this research, panel data regression has been used to test the hypotheses. To collect information and data, the library method, and to test the research hypotheses, panel data and panel analysis using multiple regression have been used. The main hypothesis of this research is based on the fact that in order to Management decisions about the optimal capital structure of the company can be based on the theory model of the pecking order. In the results of this study, it seems that the companies in question to finance themselves, in fact, follow the mathematical model used in the theory of pecking order.
Keywords :
capital structure , financial deficit , Nonlinear statistical model of pecking order theory
Journal title :
International Journal of Nonlinear Analysis and Applications
Serial Year :
2021
Record number :
2702021
Link To Document :
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