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The detection of earnings manipulation: the three-phase cutting plane algorithm using mathematical programming

Author

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  • Burcu Dikmen

    (Scientific and Technological Research Council of Turkey, Space Technologies Research Institute, Ankara, Turkey)

  • Güray Küçükkocaoğlu

    (Faculty of Economics and Administrative Sciences, Baskent University, Ankara, Turkey)

Abstract

The primary goal of this study was to propose an algorithm using mathematical programming to detect earnings management practices. In order to evaluate the ability of this proposed algorithm, the traditional statistical models are used as a benchmark vis-à-vis their time series counterparts. As emerging techniques in the area of mathematical programming yield better results, application of suitable models is expected to result in highly performed forecasts. The motivation behind this paper is to develop an algorithm which will succeed in detecting companies that appeal to financial manipulation. The methodology is based on cutting plane formulation using mathematical programming. A sample of 126 Turkish manufacturing firms described over 10 financial ratios and indexes are used for detecting factors associated with false financial statements. The results indicate that the proposed three-phase cutting plane algorithm outperforms the traditional statistical techniques which are widely used for false financial statement detections. Furthermore, the results indicate that the investigation of financial information can be helpful towards the identification of false financial statements and highlight the importance of financial ratios|indexes such as Days' Sales in Receivables Index (DSRI), Gross Margin Index (GMI), Working Capital Accruals to Total Assets (TATA) and Days to Inventory Index (DINV). Copyright © 2009 John Wiley & Sons, Ltd.

Suggested Citation

  • Burcu Dikmen & Güray Küçükkocaoğlu, 2010. "The detection of earnings manipulation: the three-phase cutting plane algorithm using mathematical programming," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(5), pages 442-466.
    • Handle: RePEc:jof:jforec:v:29:y:2010:i:5:p:442-466
    DOI: 10.1002/for.1138
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    2. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
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    4. Amani, Farzaneh A. & Fadlalla, Adam M., 2017. "Data mining applications in accounting: A review of the literature and organizing framework," International Journal of Accounting Information Systems, Elsevier, vol. 24(C), pages 32-58.
    5. repec:zbw:bofrdp:2015_015 is not listed on IDEAS
    6. Gaganis, Chrysovalantis & Hasan, Iftekhar & Pasiouras, Fotios, 2016. "Regulations, institutions and income smoothing by managing technical reserves: International evidence from the insurance industry," Omega, Elsevier, vol. 59(PA), pages 113-129.
    7. Li, Jing & Li, Nan & Xia, Tongshui & Guo, Jinjin, 2023. "Textual analysis and detection of financial fraud: Evidence from Chinese manufacturing firms," Economic Modelling, Elsevier, vol. 126(C).
    8. repec:zbw:bofrdp:urn:nbn:fi:bof-201508181354 is not listed on IDEAS
    9. Joanna Wyrobek & Lukasz Poplawski & Marcin Surowka, 2020. "Identification of a Fraudulent Organizational Culture in Enterprises Listed in Warsaw Stock Exchange," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 622-637.
    10. Abdullah Albizri & Deniz Appelbaum & Nicholas Rizzotto, 2019. "Evaluation of financial statements fraud detection research: a multi-disciplinary analysis," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 16(4), pages 206-241, December.

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