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


1)     Abhinav, R, N., Pindoriya, M., Wu, J., and Long, C. (2017). Short-term wind power forecasting using wavelet-based neural network. Energy Procedia, 142, 455-60.

2)     Antunes, A, Bonfim, D., Monteiro, N and Rodrigues, P. M, M. (2018). Forecasting banking crises with dynamic panel probit models. International Journal of Forecasting, 34 (2), 249-75.

3)     BAO, H. and Haotong li, S. (2016). Overconfidence and real estate research: a survey of the literature. The Singapore Economic Review, 61(2). Doi: 10.1142/S0217590816500156

4)     Bamber, M. and McMeeking, K. (2016). An examination of international accounting standard-setting due process and the implications for legitimacy. The British Accounting Review, 48 (1), 59-73.

5)     Ben-David, I, Graham, J.R. and Campbell, R .H. (2007). Managerial overconfidence and corporate policies. International Bureau of EconomicResearch. Working Paper, Available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1640552

6)     Bharati, R, Doellman, T. and Xudong, F. (2016). CEO confidence and stock returns. Journal of Contemporary Accounting & Economics, 12 (1), 89-110.

7)     Bradley, M., Jarrell, G.A. and Kim, E.H (1984). On the existence of an optimal capital structure: Theory and evidence.  The journal of Finance, 39 (3), 857-78.

8)     Chen, Sh., Lai, Sh., Liu, Ch., and McVay, S. (2014). Overconfident managers and internal controls. Working paper, National Taiwan University and University of Washington. Available at SSRN: https://ssrn.com/abstract=2510137 or http://dx.doi.org/10.2139/ssrn.2510137 .

9)     Fonseca Costa, D, Carvalho, F., Melo, B., Moreira, B.S and Prado, J. (2017). Bibliometric analysis on the association between behavioral finance and decision making with cognitive biases such as overconfidence, anchoring effect and confirmation bias. Scientometrics, 111 (3), 1775-99.

10)  Fazzari, S., Hubbard, R, G. and Petersen, B, C. (1987). Financing constraints and corporate investment. International Bureau of Economic Research Cambridge, vol.1. Mass., USA.

11)  Han, S and Vytlacil, E, J. (2017). Identification in a generalization of bivariate probit models with dummy endogenous repressors. Journal of Econometrics, 199 (1), 63-73.

12)  Hartog, J. and Zanten, H. (2018). Nonparametric Bayesian label prediction on a graph. Computational Statistics & Data Analysis, 120, 111-31.

13)  Hribar, P. and Yang, H. (2016). CEO Overconfidence and Management Forecasting. Contemporary Accounting Research, 33(1), 204-27.

14)  Jensen, F. (1996). An introduction to Bayesian networks. Vol. 210: UCL press London.

15)  Kang, J., Kang, J., Kang, M.  And Kim, J. (2018). Curbing Managerial Myopia: The Role of Managerial Overconfidence in Owner-Managed Firms and Professionally Managed Firms. Available at SSRN: https://ssrn.com/abstract=2944998 or http://dx.doi.org/10.2139/ssrn.2944998.

16)  Kaplan, S. and Zingales, L. (1997). Do investment-cash flow sensitivities provide useful measures of financing constraints? The quarterly journal of economics, 112 (1), 169-215.

17)  Magnus, J. R., and Wang, W. (2014). Concept-Based Bayesian Model Averaging and Growth Empirics. Oxford Bulletin of Economics and Statistics, 76(6), 874-897.

18)  Malmendier, U., Tate, G. and Yan, J. (2011). Overconfidence and early life experiences: the effect of managerial traits on corporate financial policies. The journal of Finance, 66 (5), 1687-733.

19)  Martinetti, D. and Geniaux, G. (2017). Approximate likelihood estimation of spatial probit models. Regional Science and Urban Economics, 64, 30-45.

20)  Marucci-Wellman, H.R, Corns, H.L. and Lehto, M.R. (2017). Classifying injury narratives of large administrative databases for surveillance-a practical approach combining machine learning ensembles and human review. Accident Analysis & Prevention, 98, 359-71.

21)  Nanzad, B, Anderson, K.B. and Conder, J.A. (2017). Evaluation of the logit/probit transform method to modeling historical resource production and forecasting compared to conventional Hubbert modeling. International Journal of Coal Geology, 182, 42-51.

22)  Panayiotis, A., Doukas, J.A., Koursaros, D. and Louca, C. (2017). CEO Overconfidence and the Valuation Effects of Corporate Diversification & Refocusing Decisions. Social Science Network of America, Available at: https://ssrn.com/abstract=2898469

23)  Rutledge, T., L. Groesz, M., Linke, S.E, Woods, G. and Herbst, K. L. (2011). Behavioral weight management for the primary care provider. Obesity Reviews, 12 (5), e290-e7. Doi:10.1111/j.1467789X.2010.00818.x.

24)  Salama, K, M. and Freitas, A. (2012). ABC-miner: an ant-based Bayesian classification algorithm. Paper presented at the International Conference on Swarm Intelligence.

25)  Schrand, C.M, and Zechman, S.LC.  (2012). Executive overconfidence and the slippery slope to financial misreporting. Journal of Accounting and Economics, 53 (1-2), 311-29.

26)  Smith, J.r., Clifford, W. and Watts, R. (1992). The investment opportunity set and corporate financing, dividend, and compensation policies. Journal of financial Economics, 32 (3), 263-92.

27)  Soleimani Rasa, M., Taherabadi, A., Karimi Pouya, M. (1395). Accounting ratios in the analysis of financial statements: Financial ratios of companies accepted in Tehran Stock Exchange, first edition, Tehran: secret covert.

28)  Titman, S. and Wessel, R. (1988). The Determinants of Capital Structure Choice. The Journal of Finance, Vol 43(1), March.

29)  Wang, H., Dash, D. and Druzdzel, M.  (2002). A method for evaluating elicitation schemes for probabilistic models. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 32 (1), 38-43