Corporate Governance and Equity Valuation: The Model by Using Artificial Neural Network

In this study will be investigated the relationship between the corporate governance mechanisms, including board size, duality, outside directors and institutional investors, with the share value. for create a link between elements of corporate governance and equity value, is used from the Ohlson`s valuation model (1995), and corporate governance mechanisms replace with the "Other Information" in this model. Background research indicates that there isn`t linear and Specified relationship between the mechanism of corporate governance and the company's stock price. Therefore, the artificial neural network is used to identify any relationships between model variables. In other words, assumption of linear information dynamics, in the Ohlson`s model will be challenged. After The design of artificial neural network model, the results is compared with ordinary least squares method. For the design of Neural network model and the linear regression equation, totally is used from 776 firm-years information and for to test the neural network and linear regression is used of 62 firm-years information in the years 1380 to 1389. Neural network used in this researchis of multi-layer perceptron with error back propagation learning algorithm and is used on it from two hidden layer to simulate the relationships between variables. The results suggest that: 1.using of Mechanisms of corporate overnance as part of other information in the ohlson valuation model (1995) cause to increase the explanatory power of the valuation model, and 2. Using of artificial neural networks raises the explanatory power and accuracy of the model than ordinary least squares method, to analyze relationships between variables