evaluation of intelligent and statistical prediction models for overconfidence of managers in the iranian capital market companies

Behavioural characteristic managerial overconfidence of managers effects on the investment and financing decisions and company performance in the long run. the purpose of the present study was to validate the adaboost machine learning and probit regression in the prediction of management's overconfidence at present and in the future. it also compares the predicted models obtained during the years 2012 to 2017. the samples of the research were the companies admitted to the tehran stock exchange, data collection in the theoretical section of this study uses content analysis of international scientific articles in the library method and calculating the data used by excel software using matlab 2017 and eviews10.0 to test the research hypothesis. the empirical findings demonstrate that the adaboost's algorithm nonlinear prediction model represents the highest power in learning and prediction (performance of this model) the managerial over-confidence for this year and the next year, proved to be better than the probit regression prediction model.