Explaining effective factors on information asymmetry of management earnings forecast with Earnings Forecast from Fuzzy Neural Networks

The goal of explaining effective factors on information asymmetry of management earnings forecast with other earnings forecast methods is to recognize information asymmetry degree among in and out organizational individuals. The research methodology is an inferential from the goal dimension and an induction from the logical dimension. The statics population includes accepted companies in stock exchange that 158 samples have been selected during 1380-1388 by elimination sampling. Firstly, the forecasting power comparison and superiority determining of each of the statistical forecasting methods with management earnings forecast were done. Based on grounded theory, the effective factor on management earnings forecast information asymmetry with the least- error earnings forecast method was recognized. Then, the relationship between each of recognized effective factors on profit information asymmetry was tested. The results showed at first the accuracy of fuzzy neural networks forecast is more than other linear and non-linear forecasting methods. This superiority isn’t conformed in comparison with management earnings forecast that is resulted from error mean test. So existence of informational advantage in management earnings forecast was accepted. Secondly industry concentration, institutional ownership, earnings quality, disclosure quality, economic uncertainty, management’s support and conservatism in earnings forecast have relationship with information asymmetry.