Evaluating and comparing the ability to predict the bankruptcy prediction models of Zavgren and Springate in companies accepted in Tehran Stock Exchange | Zendy

Ghodratollah Talebnia | Zendy; Fatemeh Karmozi | Zendy; Samira Rahimi | Zendy

Open Access

Evaluating and comparing the ability to predict the bankruptcy prediction models of Zavgren and Springate in companies accepted in Tehran Stock Exchange

Author(s) -

Ghodratollah Talebnia,

Fatemeh Karmozi,

Samira Rahimi

Publication year - 2016

Publication title -

marketing and branding research

Language(s) - English

Resource type - Journals

ISSN - 2476-3160

DOI - 10.33844/mbr.2016.60238

Subject(s) - bankruptcy prediction , bankruptcy , stock exchange , logistic regression , logit , business , linear discriminant analysis , explanatory power , econometrics , actuarial science , economics , computer science , artificial intelligence , finance , machine learning , philosophy , epistemology

Recent bankruptcy of large companies at international level and volatilities of securities in Iran have highlighted the necessity of evaluating the financial power of companies. One of the evaluating tools is using bankruptcy prediction models. Bankruptcy prediction models are one of the tools for estimating the future condition of companies. The aim of this research is to present theoretical foundations and compare the results of investigating two models of Zavgren (1985) and Springate (1978) in Iran’s exchange market through main and adjusted coefficients according to statistical techniques of Logit and Multiple Discriminant Analysis (MDA). The data was gathered and tested from 2009 to 2013. The results indicated that the adjusted Springate Model was more efficient than other models in the bankruptcy year.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research