• 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