Artikel

Credit rating as a mechanism for capital structure optimization: Empirical evidence from panel data analysis

This paper empirically examines the significance of credit ratings for optimal capital structure decisions. Non-financial Asian listed companies, evaluated by Standard and Poor's, are selected from 2000 to 2016. Panel data analysis with pooled ordinary least square (OLS), fixed effect (FE), and generalized method of moment (GMM) estimation techniques are employed to test the effect of each credit rating scale on capital structure choices. For the problem of heteroskedasticity in OLS, the heteroskedastic white consistent variance is used for the best fit of the model. Findings of all estimation techniques show that the relationship between credit rating scales and leverage ratio is a non-linear inverted U shape. High- and low-rated companies have a low level of leverage, whereas mid-rated companies have a high level of leverage. It is evident that costs and benefits of each rating scale have a substantial effect on the behavior of a company's choices for optimal capital structure. The study suggests that policymakers, investors, and financial officers should consider credit rating as an important measure of financing decisions.

Sprache
Englisch

Erschienen in
Journal: International Journal of Financial Studies ; ISSN: 2227-7072 ; Volume: 6 ; Year: 2018 ; Issue: 1 ; Pages: 1-14 ; Basel: MDPI

Klassifikation
Wirtschaft
Firm Behavior: Empirical Analysis
Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
Thema
credit rating
leverage
capital structure
Asian markets

Ereignis
Geistige Schöpfung
(wer)
Sajjad, Faiza
Zakaria, Muhammad
Ereignis
Veröffentlichung
(wer)
MDPI
(wo)
Basel
(wann)
2018

DOI
doi:10.3390/ijfs6010013
Handle
Letzte Aktualisierung
10.03.2025, 11:46 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Sajjad, Faiza
  • Zakaria, Muhammad
  • MDPI

Entstanden

  • 2018

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