Management Ability and its Consequences on Firms Value Creation by Employing Neural Networks (A Comparative Approach to Chemical & Food Industries)

The purpose of this study is to investigate the effect of management ability on the value creation in the chemical and food industry in 2012-2016 by using artificial neural network. Initially, with the implementation of the DEA model with a native model, the relative efficiency is determined for each company and by estimating a regression model, the management ability to each company is also determined. Then the 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. To evaluate the relative efficiency is used the GAMS optimization software and to measure the management ability used the E-Views and MATLAB software is used to train and test the neural networks model.
The results show that the artificial neural network model in the food industry is better than the chemical industry. This model estimates the companie's value creation through the management ability and other input variables. Other research results, the management ability score in both industries is equal, but the average value creation of the companies in the food industry is higher than the chemical industry.