A Validation Test Naive Bayesian Classification Algorithm and Probit Regression as Prediction Models for Managerial Overconfidence in Iran's Capital Market

Document Type : Original Article

Authors

1 PhD student of accounting, Department of Economics and Accounting, Islamic Azad University, South Tehran Branch, Tehran, Iran

2 Faculty Member, Department of Economics and Accounting, Islamic Azad University, South Tehran Branch, Tehran, Iran. (Corresponding Author)

3 Faculty Member, Department of Economics and Accounting, Islamic Azad University, South Tehran Branch, Tehran, Iran

4 Faculty Member, Department of Economics and Accounting, Islamic Azad University, South Tehran Branch, Tehran, Iran,

Abstract

Corporate directors are influenced by overconfidence, which is one of the personality traits of individuals; it may take irrational decisions that will have a significant impact on the company's performance in the long run. The purpose of this paper is to validate and compare the Naive Bayesian Classification algorithm and probit regression in the prediction of Management's overconfident at present and in the future. Financial during the years are 2012 to 2017. To support the theoretical results, the samples 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 library method, and for calculating data, Excel software was used, and in order to test the research hypotheses Matlab 2017 and Eviews10.0 were used. The empirical findings demonstrate that, Gained nonlinear prediction model of the Naive Bayes Classification algorithm, has high ability to predict, and the Probit regression model, has limited ability to predict the over-confidence of management. Finally, the artificial intelligence prediction model (naive Bayesian classification algorithm) has better result compared with statistical binary regression prediction model (probit regression).
 
 

Keywords

References

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International Journal of Finance & Managerial Accounting
Volume 4, Issue 13
April 2019
Pages 25-38

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