Evaluation of Intelligent and Statistical Prediction Models for Overconfidence of Managers in the Iranian Capital Market Companies

Document Type : ŮŽApplied-Research Paper


1 Faculty Member, Department of Accounting, Sama Technical and Vocational College, Karaj Branch, Islamic Azad University, Karaj, Iran

2 Department of Economics and Accounting, Tehran South Branch, Islamic Azad University, Tehran, Iran



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.


Main Subjects

  • Abhinav, R., Pindoriya, N. M., Wu, J., Long, C., Short-term wind power forecasting using wavelet-based neural network, Energy Procedia, 2017, 142(1), P. 455- 460. Doi: 10.1016/j.egypro.2017.12.071
  • Ahmed, A. S., Duellman, S., Managerial overconfidence and accounting conservatism, Journal of Accounting Research, 2013, 51(1), P. 1- 30. Doi:10.2139/ssrn.887301
  • Aliahmadi, S., Jamshidi, A., Mousavi, R., The Managerial Ability and Value of Cash: Evidence from Iran, Advances in mathematical finance & applications, 2016, 1(1), P. 19-31. Doi: 10.22034/AMFA.2016.526241
  • Ali Nejad Saroklaei, M., Sobhi, M., The Effect of More Trusted Managers on Capital Structure, Financial Accounting and Audit Research, 2016, 8(31), P. 93-109. Doi:10.30699/ijf.2020.121887
  • Antunes, A., Bonfim, D., Monteiro, N., Rodrigues, P. M., Forecasting banking crises with dynamic panel probit models, International Journal of Forecasting, 2018, 34(2), P. 249-275.

 Doi: 10.1016/j.ijforecast.2017.12.003

  • Baig, M. M., Awais, M. M., El-Alfy, E.-S. M., AdaBoost-based artificial neural network learning, Neurocomputing, 2017, Doi: 10.1016/j.neucom.2017.02.077
  • Bamber, M., McMeeking, K., An examination of international accounting standard-setting due process and the implications for legitimacy, The British Accounting Review, 2016, 48(1), P. 59-73.

 Doi: 10.1016/j.bar.2015.03.003

  • Berk, J., Stanton, R., Managerial ability, compensation, and the closed-end fund discount, Journal of Finance, 2007, 62(2), P. 529-556. Doi: 10.1111/j.1540-6261.2007.01216.x.
  • Bharati, R., Doellman, T., Fu, X., CEO confidence and stock returns, Journal of Contemporary Accounting & Economics, 2016, 12(1), P. 89-110. Doi: 10.1016/j.jcae
  • Chen, S. S., Lai, S.-M., Liu, C.-L., McVay, S., Overconfident managers and internal controls, working paper, National Taiwan University and University of Washington, 2014, Available at SSRN.

 Doi: 10.2139/ssrn.2510137 .

  • Cordeiro, L., Managerial overconfidence and dividend policy, 2009, Available at SSRN.

Doi: 10.2139/ssrn.1343805

  • Demerjian, P., Lev, B. McVay, S., Quantifying managerial ability: A new measure and validity tests, Management Science, 2012, 58(7), P. 1229-1248. Doi:10.1287/mnsc.1110.1487
  • Deshmukh, S., Goel, A. M., Howe, K. M., CEO overconfidence and dividend policy, Journal of Financial Intermediation, 2013, 22(3), P. 440-463. Doi: 10.1016/j.jfi.2013.02.003
  • Abbasi, I., Vakilifard, H., Marouf, M., Investigating the Impact of Managerial Uncertainty on Financial Reporting Quality and Conditional Conservatism in Tehran Stock Exchange, Journal of Accounting and Auditing Management, 2018, 7(25), P. 193-205
  • Fonseca Costa, D., Carvalho, de Mel F., de Melo Moreira, B. C., do Prado, J. W., Bibliometric analysis on the association between behavioral finance and decision making with cognitive biases such as overconfidence, anchoring effect and confirmation bias, Scientometrics, 2017, 111(3), P.1775-1799. Doi: 10.1007/s11192-017-2371-5
  • Foroughi, D., Moazzeni, N., The Effect of Excessive confident of Management on Company Value, Financial Accounting Knowledge, 2016, 4(4), P. 65-84
  • Han, S., Vytlacil, E. J., Identification in a generalization of bivariate probit models with dummy endogenous repressors, Journal of Econometrics, 2017, 199(1), P. 63-73. Doi: 10.1016/j.jeconom.2017.04.001
  • Hirshleifer, D., Low, A., Teoh, S. H., Are overconfident CEOs better innovators, the journal of Finance, 2012, 67(4), P. 1457-1498. Doi:10.1111/j.1540-6261.2012.01753.x
  • Hribar, P., Yang, H., CEO Overconfidence and Management Forecasting, Contemporary Accounting Research, 2016, 33(1), P. 204-227, Doi: 10.1111/1911-3846.12144.
  • Jokar, H., Shamsaddinia, K., Daneshi, V., Investigating the Effect of Investors' Behavior and Management on the Stock Returns: Evidence from Iran, Advances in mathematical finance & applications, 2018, 3(3), P. 41-52, Doi:10.22034/AMFA.2018.544948
  • Jongjaroenkamol, P., Laux, V., Insider versus outsider CEOs, executive compensation, and accounting manipulation, Journal of Accounting and Economics, 2017, 63(2-3), P. 253-261. Doi: 10.1016/j.jacceco.2017.01.002
  • Jun, W., Yuyan, L., Lingyu, T., Peng, G., Modeling a combined forecast algorithm based on sequence patterns and near characteristics: An application for tourism demand forecasting, Chaos, Solutions & Fractals, 2018, 108, P. 136-147. Doi: 10.1016/j.chaos.2018.01.028
  • Junior, B., do Carmo Nicoletti, M., An iterative boosting-based ensemble for streaming data classification, Information Fusion, 2019, 45, P. 66-78. Doi: 10.1016/j.inffus.2018.01.003
  • Kang, J., Kang, J.-K., Kang, M., Kim, J., Curbing Managerial Myopia: The Role of Managerial Overconfidence in Owner-Managed Firms and Professionally Managed Firms, Social Science Network of America, 2018, Doi: 10.2139/ssrn.2944998.
  • Libby, R., Rennekamp, K., Self-serving attribution bias, overconfidence, and the issuance of management forecasts, Journal of Accounting Research, 2012, 50(1), P.197-231. Doi: 10.1111/j.1475-679X.2011.00430.x
  • Malmendier, U., Tate, G., Who makes acquisitions? CEO overconfidence and the market's reaction, Journal of financial Economics, 2008, 89(1), P. 20-43. Doi: 10.1016/j.jfineco.2007.07.002
  • Malmendier, U., Tate, G., Yan, J., Overconfidence and early life experiences: the effect of managerial traits on corporate financial policies, 2011, the journal of Finance, 66(5), P. 1687-1733.


  • Martinetti, D., Geniaux, G., Approximate likelihood estimation of spatial probit models, Regional Science and Urban Economics, 2017, 64, P.30-45. Doi: 10.1016/j.regsciurbeco.2017.02.002
  • Marucci-Wellman, H. R., Corns, H. L., Lehto, M. R., Classifying injury narratives of large administrative databases for surveillance- a practical approach is combining machine learning ensembles and human review, Accident Analysis & Prevention, 2017, 98, P. 359-371. Doi: 10.1016/j.aap.2016.10.014
  • Nasirzadeh, F., Abbaszadeh, M. R., Zolfaghararani, M. H., Investigating the effect of managers' optimism and information asymmetry on stock price fall risk, Journal of financial accounting, 2017, 9(34), P. 35-70
  • Panayiotis C, A., Doukas, J. A., Koursaros, D., Louca, C., CEO Overconfidence and the Valuation Effects of Corporate Diversification & Refocusing Decisions, Social Science Network of America, 2017.

 Doi: 10.1016/j.jbankfin.2019.01.009

  • Parsamehr,H., Kasravi ,A., and Fazli, M., Impact of the Management Performance Evaluation Methods on the Data Quality in Accounting, Advances in mathematical finance & applications, 2017,  2(1), P. 41-53,
  • Rezaei, N., Elmi, Z., Behavioral Finance Models and Behavioral Biases in Stock Price Forecasting, Advances in mathematical finance & applications, 2018, 3(4), P. 67-82
  • Schrand, C. M., Zechman, S. L., Executive overconfidence and the slippery slope to financial misreporting, Journal of Accounting and Economics, 2012, 53(1), P. 311-329. Doi: 10.1016/j.jacceco.2011.09.001
  • Soleimani, M., Taherabadi,A., Karimipouya,M., Accounting Ratios in the Analysis of Financial Statements: Financial Ratios of Companies Listed on the Tehran Stock Exchange, The Secret Secret, 2015, 1, P.51-58(in Persian).
  • Zanjardar, M., Rafiei, Z., Effect of the Earnings Reaction Coefficient on Extreme Confidence Relationships and Contingent Conservatism, Financial Accounting and Audit Research, 2017, 9 (34), P.113-135
Volume 7, Issue 1
January 2022
Pages 245-260
  • Receive Date: 21 November 2018
  • Revise Date: 13 July 2019
  • Accept Date: 04 August 2019
  • First Publish Date: 01 January 2022