A generalized model for bankruptcy prediction of the electricity industrial firms: Empirical evidence for the restructured Iranian distribution companies

In today´s world, security of capital investment is one of the most important concerns for existing economic environment. Insurance of productive capital investment and reducing economical risk causes more fundraising and therefore the greater economic boom cycle. One way to arrive capital investment security is to predict bankruptcy of a business unit. Predicting possibility of a company´s bankruptcy not only can prevent losing the principle and capital interest of investing, but also facilitate the most important decision makings. Considering the importance of this subject, many researches have been done in this area. Since the Iranian electricity stock exchange is going to start working in 2012, it would be appropriate to propose a new model for bankruptcy perdition of Electrical and energy industries which will participate in the new stock exchange. This paper presents a generalized model for bankruptcy prediction of an electrical business unit. Prevalent linear models are changed into a nonlinear model and all unknown coefficients are determined through optimization process using a Learning Automata based algorithm. The developed model is conducted for bankruptcy prediction of electrical and energy industrial companies listed in Tehran Stock Exchange (TSE). In addition obtained results are compared to those of multiple discriminant analysis (MDA), Logit, Altman model and results of the prevalent linear model. Moreover, linear Altman model is reformed into a nonlinear model relevant to the Iranian firms. Detailed numerical studies and comparisons presented in the paper show that proposed model could improve noticeably the quality of prediction and can be used as an effective model for bankruptcy prediction of an electrical firm in the new stock market.