مطالب مرتبط با کلیدواژه

managerial overconfidence


۱.

The Moderating Effect of Firm Value and liquidity on the relationship between Managerial Overconfidence and R&D(مقاله علمی وزارت علوم)

کلیدواژه‌ها: managerial overconfidence R&D company liquidity Firm Value

حوزه های تخصصی:
تعداد بازدید : ۲۲۷ تعداد دانلود : ۱۷۳
Overconfident managers, who tend to overestimate their capabilities, underestimate the possibility and impact of side effects in projects. The purpose of this study is to review the effect of managerial overconfidence on research and development expenditures and the moderating effect of firm value and liquidity on this relationship. For this purpose 51 companies were chosen from oil/gas, and petrochemical Companies listed on the Tehran Stock Exchange over the period 2012-2017. This research, within three basic hypotheses, is analyzed by Eviews software and shows that managerial overconfidence has positive effect on research and development. Company liquidity has direct effect on relationship between managerial overconfidence and R&D, but firm value has no meaningful effect on the relationship between managerial overconfidence and R&D. <strong>Keywords</strong>: managerial overconfidence, R&D, liquidity, firm value
۲.

Evaluation of Intelligent and Statistical Prediction Models for Overconfidence of Managers in the Iranian Capital Market Companies(مقاله علمی وزارت علوم)

کلیدواژه‌ها: managerial overconfidence Machine learning Adaboost algorithm Probit Regression

حوزه های تخصصی:
تعداد بازدید : ۲۶۹ تعداد دانلود : ۱۹۷
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, (financial data of 1292 companies/year in total). Data collection in the theoretical part of the study benefitted from the content analysis international research paper in library method and for calculating the data's Excel software was used, and in order to test the research hypotheses, Matlab 2017 and Eviews10.0 were used. 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 following year, proved to be better than the probit regression prediction model.