Application of Artificial Neural Network to Estimate the Strategic Value Creation Via Relative Efficiency in the Automotive Industry

The purpose of this study is to investigate the effect of relative efficiency of companies on value creation in the automotive industry accepted in Tehran Stock Exchange. The data were extracted from the financial statements of selected companies during in the 2013-2017. Initially, with the implementation of the DEA model with a native model, the relative efficiency is determined for each company. Then the strategic value creation of the companies is measured by the average of the factors such as return on equity, Q Tobin ratio, return on investment, and wealth creation for shareholders. The neural network model used in this study is a multilayer perceptron with back propagation error training pattern. The results show that the implementation of the artificial neural network model in the automotive industry explains the strategic value of the companies to a satisfactory level through the relative efficiency index and other input variables. Although some of the companies are efficient, such as Rana Investments Co., Khawar Spring Co., Saipa Diesel, Bahman Group and Charkheshgar Co., But in recent years, the automotive industry has been inefficient. At the same time, companies in this industry have somehow been able to strategically create value for their shareholders and their owners.