Funda H. SEZGİN

ESTIMATION FINANCIAL INFORMATION MANIPULATION BY NEGATIVE BINOMIAL HURDLE MODEL

Manipulation is one of the important issues in securities markets because manipulative actions send false signals to investors and make them buy or sell securities. There are different types of manipulations that can deceive investors, one type of which called financial information manipulation. Manipulators, who use this kind of manipulation, distort information on financial statements in order to give false information about the prospects of the issuing firms. The aim of the manipulators is to deceive the investors and gain advantage at their expense. In this study, it is aimed to develop an appropriate model in order to determine the factors affecting the number of companies which has published false financial statements at Istanbul Stock Exchange in 2010 year. Zero-inflated count data is analyzed with Negative Binomial Hurdle Model in order to determine the effective financial ratios.

Keywords:

Financial Information Manipulation , Negative Binomial Hurdle model Caunt data,

PDF

___

Beneish, M. D. (1997), “Detecting GAAP Violation: Implications for Assessing Earnings Management Among Firms with Extreme Financial Performance”, Journal of Accounting and Public Policy, 16(3), pp. 271-309.
Beneish, M. D. (1999), “The Detection of Earnings Manipulation”, Financial Analysts Journal, 55(5), pp. 24-36.
Beasley, M. (1996), “An Empirical Analysis of the Relation Between Board of Director Composition and Financial Statement Fraud”, Accounting Review, 71(4), pp.443–66.
Cameron, A. C. and Trivedi, P. K. (1998), “Regression Analysis of Count Data”, Cambridge University Pres., Cambridge.
Green, B. P. and Choi, J. H. (1997), “Assessing the Risk of Management Fraud Through Neural Network Technology”, Journal of Practice and Theory, 16(1), pp. 14–28.
Hoffman, V. B. (1997), “Discussion of the Effects of SAS No. 82 on Auditors’ Attention to Fraud Risk-factors and Audit Planning Decisions”, Journal of Accounting Research, 35(5), pp. 99–104.
Hollman, V. P. and Patton, J. M. (1997), “Accountability, the Dilution Effect and Conservatism in Auditors’ Fraud Judgments”, Journal of Accounting Research, 35(2), pp. 227–37.
Kirschenheiter, M. and Melumad, N. D. (2002), “Can Big Bath and Earnings Smoothing Co-Exist As Equilibrium Financial Reporting Strategies?”, Journal of Accounting Research, 40(3), pp.761-796.
Küçüksözen, C. (2004), “Finansal Bilgi Manipülasyonu: Nedenleri, Yöntemleri, Amaçları, Teknikleri, Sonuçları ve İMKB Şirketleri Üzerine Ampirik Bir Çalışma”, Unpublished PhD Thesis, Ankara.
Lev, B. (2003) , “Corporate Earnings: Facts and Fiction”, The Journal of Economic Perspectives, 17 (2), pp.27-50.
Mulford, C. W. and Comiskey, E. (2002), “The Financial Numbers Game: Detecting Creative Accounting Practices”, John Wiley & Sons, Inc., New York.
Mullahy, J. (1986), “Specification and Testing of Some Modified Count Data Models”, Journal of Econometrics, 33(1), pp. 341-365.
Ridout, M., Hinde, J. and Demetrio, C. B. (2001), A Score Test for Testing a Zero-Inflated Poisson Regression Model Against Zero-Inflated Negative Binomial Alternatives, Biometrics, 57(2), pp.219-223.
Sheu, M. L., Hu, T. W., Keeler, T. E., Ong, M. and Sung, H. Y. (2004), “ The Effect of a Major Cigarette Price Change on Smoking Behavior in California: a Zero-Inflated Negative Binomial Model”, Health Economics, 13(8), pp.781-91.
Spathis, C. (2002), “Detecting False Financial Statements Using Published Data: Some Evidence From Greece”, Managerial Auditing Journal, 17(4) , pp. 179- 191.
Spathis, C., Doumpos M., and Zopounidis, C. (2004), “Detecting Falsified Financial Statements Using Multicriteria Analysis: The Case of Greece”, Working Paper, http://papers.ssrn.com/sol3/papers.cfm?abstract_id=250413, 12.06.2011].
Accessed Stolowy, H. and Lebas, M. J. (2006), “Financial Accounting and Reporting: A Global Perspective”, Thomson Learning, 2nd Edition, London.
Wilson, M. and Shailer, G. (2007), “Accounting Manipulations and Political Costs: Tooth&Co Ltd., 1910-1965”, Accounting and Business Research, 37(4), pp.247-266.
Zimbelman, M. F. (1997), “The Effects of SAS No. 82 on Auditors’ Attention to Fraud Risk Factors and Audit Planning Decisions”, Journal of Accounting Research, Vol: 35(5), pp: 75–97.