New Managerial Overconfidence Assessment Model and Earnings Forecasts: Generalized Method of Moments (GMM)

Management earnings forecast is one of the most important information resources in capital markets. The literature suggests managerial overconfidence is an effecting factors on the earnings forecasts’ accuracy. Because of users' relying to forecasted information, examination of the bias's effects on forecasts' accuracy is important. By using a new managerial overconfidence assessment model and Generalized Method of Moments (GMM) regression analysis, the paper examines this managerial bias’s effect on management earnings forecasts’ error in the firms that have listed at Tehran Securities Exchange (TSE) during the period from year 2007 to year 2016. The results show Chief Executive Officers’ overconfidence has a significant positive effect on earnings forecasts’ error, how overconfident Chief Executive Officers overestimate earnings forecasted above than actual earnings. This finding is consistent with recent researches’ ones and suggests information's users should be aware of Chief Executive Officers' overconfidence's negative effects on reliability of managerial forecasted information.