Predicting the Effective Factors on Concurrency of Stock Price Considering Corporative Governing Based on Neural Network

The aim of this research is predicting the effective factors on concurrency of stock price considering corporative governing based on neural network. This study is based on Neural Network. The data of 93 financial companies listed on Tehran Stock Exchange during the period of 6 years (2009-2015) have been studied. The sample is divided into two categories of testing and training. The results of analysis suggest that since the amount of error in testing sample is equal to training sample, thus model fitness is acceptable; Also, the results of table 7 represent that financial leverage, company size, growth opportunity, standard deviation of unlevered cash flow, standard deviation of daily yield, and controlling shareholders is effective on the concurrency of stock price.