Identification of the Factors Determining the Number of Industrial Companies Traded at BIST that Declare False Financial Statements: Poisson Hurdle Model
Keywords:
False financial statements, financial information manipulation, Poisson Hurdle model, count dataAbstract
Financial information is important for all companies and organizations. Decision makers such as investors, customers and banks need accurate financial data. When this financial information is misrepresented or fails to meet the needs in a transparent and accurate way, problems arise both ethically and financially. Companies that are obliged to present their financial information in a transparent and complete manner may occasionally choose to misrepresent their data and resort to manipulation. Proper functioning of financial markets requires that financial information is understandable, sufficient, reliable and timely. For current and potential investors the most important source of information about companies is financial statements. Investors, who make financial decisions by relying on inaccurate information may be dragged into major economic losses. A manipulation or misrepresentation of these statements will prevent public savings from participating widely and effectively in financial markets.
The study aims to determine the factors that affect the number of industrial companies traded at BIST and present false financial statements. In order to achieve this, zero-inflated count data is analyzed with Poisson Hurdle Model in order to determine the effective financial ratios.
References
ACFE (Association of Certified Fraud Examiners), (2003), Fraud Examiners Manuel, Erişim:[ http://www.acfe.com/ ], (Erişim Tarihi: 15.12.2019).
Beneish, M. D. (1999), The Detection of Earnings Manipulation, Financial Analysts Journal, 55(1), 24-36.
Bhasin, M. L. (2015). Creative Accounting Practices in the Indian Corporate Sector: An Empirical Study, International Journal of Management Sciences and Business Research, 4(10), 35-52.
Chen, H., Tang, Q., Jiang, Y. & Lin, Z. (2010). The Role of International Financial Reporting Standards in Accounting Quality: Evidence from the European Union. Journal of International Financial Management and Accounting, 21(3), 220-278.
Cameron, A., Trivedi, P. (1998). Regression Analysis of Count Data, Cambridge: Cambridge University Press.
Cohen, L., Chrıstopher, M., ve Lukasz, P. (2012). Decoding Inside Information, The Journal of Finance, 67(3), 1009-1043.
Coons, C. & Weber, M. (2014). Manipulation Theory and Practice, Oxford: Oxford University Press.
Cox, R. & Weirich, T. (2002). The Stock Market Reaction to Fraudulent Financial Reporting. Managerial Auditing Journal, 17(7), 374-382.
Crocker, K. J. & Slemrod, J. (2007). The Economics of Earnings Manipulation and Managerial Compensation, The RAND Journal of Economics, 38(3), 698-713.
Dalrymple, M. L., Hudson I. L. V & Ford R. P. K. (1998). Finite Mixture, Zero-inflated Poisson and Hurdle models with application to SIDS, Computational Statistics and Data Analysis, 41(1), 491-504.
De Bondt, W. F. & Thaler R. R. (1985). Does the Stock Market Overreact?, The Journal of Finance, 40(2), 793-805.
Feng, M., Ge, W., Luo, S. & Shevlin, T. (2011). Why Do Cfos Become Involved in Material Accounting Manipulations?, Journal of Accounting and Economics, 51(1), 21-36.
Guay W., Samuels D. & Taylor D. (2016). Guiding Through the Fog: Financial Statement Complexity and Voluntary Disclosure, Journal of Accounting and Economics, 62(1), 234-269.
Hastuti, T. D. & Gozali, I. (2015). Manipulation Detection in Financial Statements, International Journal of Humanities and Management Sciences, 3(4), 222-229.
Healy, P. M. & Wahlen J. M. (1999). A Review of the Earnings Management Literature and Its Implications for Standart Setting, Accounting Horizons, 13 (1), 365-383.
Küçükkocaoğlu, G. & Küçüksözen, C. (2005). Gerçeğe Aykırı Finansal Tabloların Ortaya Çıkarılması: İMKB Şirketleri Üzerine Ampirik Bir Çalışma, Muhasebe ve Finansman Dergisi, 28(1), 45-63.
Küçüksözen, C. (2005). Finansal Bilgi Manipülasyonu: Nedenleri, Yöntemleri, Amaçları, Teknikleri, Sonuçları Ve İMKB Şirketleri Üzerine Ampirik Bir Çalışma, SPK Yayınları, No.183, Ankara.
Küçükkocaoğlu, G. & Küçüksözen, C. (2005). Kurumsal Şirket Yönetimi’nde Finansal Tabloların Rolü, Erişim:[http://www.baskent.edu.tr/~gurayk/kisiselkurumsalsirket.pdf], (Erişim Tarihi: 15.12.2019).
Lambert, D. (1992). Zero-Inflated Poisson Regression, With an Application to Defects in Manufacturing, Technometrics, 34(2), 1-14.
Long, J. S. & Freese, J. (2006), Regression Models for Categorical Depentent Variable Using Stata, Stata Pres Publication, StataCorp LD Collage Station, Texas, USA.
Mamo, J. & Aliaj, A. (2014). Accounting Manipulation and its Effects in the Financial Statements of Albanian Entities, Interdisplinary Journal of Research and Development , 1(2), 55-60.
Mohamed, N. & Handley-Schachelor, M. (2014). Financial statement fraud risk mechanisms and strategies: The case studies of Malaysian commercial companies, Procedia - Social and Behavioral Sciences, 145, 321-329.
Mao, Y. & Renneboog, L. (2013). Do Managers Manipulate Earnings Prior to Management Buyouts?. Journal of Corporate Finance, 35(2), 43-61.
Mullahy, J. (1986). Specification and Testing of Some Modified Count Data Models, Journal of Econometrics, 33(2), 341-365.
Nelson, M. W., Elliott, J. A. & Tarpley, R. L. (2002). Evidence From Auditors About Managers and Auditors Earnings Management Decisions, The Accounting Review, 77(1), 175-202.
Stolowy, H. & Breton, G. (2004). Accounts Manipulation: A Literature Review and Proposed Conceptual Framework, Review of Accounting and Finance, 3(1), 5-92.
Stolowy, H. & Lebas, M. J. (2006). Financial Accounting and Reporting: A Global Perspective (2. Baskı), Boston: Cengage Learning Pbc.
Welsh, A. H., Cunningham, R. B., Donnelly, C. F. & Lindenmayer, D. B. (1996). Modelling Abundance of Rare Species: Statistical Models for Counts With Extra Zeros, Ecological Modelling, 88(1), 297-308.
Yeşilova, A. (2009). Sıfır Değer Ağırlıklı Sayıma Dayalı Verilerin Analizinde Hurdle Modelin Kullanılması, Anadolu Üniversitesi Bilim ve Teknoloji Dergisi,10(1), 467-475.
Zeileis, A., Kleiber, C. & Jackman S. (2007). Regression Models for Count Data in R, Erişim:[ http://statmath.wu-wien.ac.at/ ], (Erişim Tarihi: 25.11.2019).
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Gökçe Tekin Turhan (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.