文章基本信息

标题：Localized distress prediction models in the economic environment of Iran
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作者：Samira Zarei ; Iman Dadashi ; Mir Askari Akbari 等
期刊名称：African Journal of Business Management
印刷版ISSN：1993-8233
出版年度：2012
卷号：6
期号：29
页码：8651-8658
DOI：10.5897/AJBM11.3083
语种：English
出版社：Academic Journals
摘要：Business bankruptcies in world current economic climate intensified the importance of default predictions in societies more. So, a great deal studies is accomplished for finding more effective empirical method to predict corporate bankruptcy. In this research, we try to localize the eight known distress prediction models such as: Thai DA, CA-score, SAF, Springate, Zemijewski, Altman, Taffler and Deakin model and tested them for distresses prediction according to Iran economical conditions, 36 hypotheses of present research categorized in two groups. In first group hypotheses, the power of each model tested for distress prediction and in second group, the presence of meaningful relation between aforementioned models was investigated. For corporate categorization and hypotheses test, the 141 amendment to the Iran business law, approved in 1968 March, was used for determination of bankrupt companies and simple Q-Tobin was used for determination of non-bankrupt companies. For testing the first group of hypotheses, logit correlation was used, while for testing the second group of hypotheses, meaningful test of difference in two correlation coefficients was used. Based on the test results of the first group of hypotheses, all the research models except Deakin model can predict bankruptcy or activity stopping according to the effective ratios of companies accepted in the Stock Exchange of Tehran. Based on the test results of second group of hypotheses, all the models - Taffler model, Deakin model, Thai DA model with CA-score, Zemijewski model, Zemijewski and springate model, and Altman model, have meaningful difference in activity stopping prediction.
关键词：Distress models; bankruptcy; simple Q-Tobin index; the 141 amendment to the business law