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A Statistical Model of Fraud Risk in Financial Statements. Case for Romania Companies

Author

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  • Andrada-Ioana Sabău (Popa)

    (Faculty of Economics and Business Administration, Cluj-Napoca University Babes-Bolyai, 400591 Cluj-Napoca, Romania)

  • Codruța Mare

    (Faculty of Economics and Business Management, Cluj-Napoca University Babes-Bolyai, 400591 Cluj-Napoca, Romania)

  • Ioana Lavinia Safta

    (Faculty of Economics and Business Management, Cluj-Napoca University Babes-Bolyai, 400591 Cluj-Napoca, Romania)

Abstract

Tax avoidance is one of the most frequent reasons for which companies tend to resort to creative accounting techniques. The purpose of the study is to identify which of the eight-variables from the Beneish influences the most or least the outcome of the final score, as a percent, by developing a statistical model. The sample was selected from the Bucharest Stock Exchange and consists of 66 companies traded on the main market, for the years 2015–2019. The results show that from the total of the eight variables, GMI (Gross Margin Index), AQI (Asset Quality Index), DEPI (Depreciation Index) and TATA (Total Accruals to Total Assets) are significantly influencing the probability to commit fraud. The developed model is validated with only 10% of the non-fraud companies being mistakenly considered as fraud based on our model and vice versa.

Suggested Citation

  • Andrada-Ioana Sabău (Popa) & Codruța Mare & Ioana Lavinia Safta, 2021. "A Statistical Model of Fraud Risk in Financial Statements. Case for Romania Companies," Risks, MDPI, vol. 9(6), pages 1-15, June.
    • Handle: RePEc:gam:jrisks:v:9:y:2021:i:6:p:116-:d:572590
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    References listed on IDEAS

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    2. Alina Beattrice Vladu & Oriol Amat & Dan Dacian Cuzdriorean, 2014. "Truthfulness in accounting: How to discriminate accounting manipulators from non-manipulators," Economics Working Papers 1434, Department of Economics and Business, Universitat Pompeu Fabra.
    3. Ines Hakim Hbaieb & Mohamed Ali Brahim Omri, 2019. "Tax management and tax fraud: evidence from Tunisian companies," International Journal of Managerial and Financial Accounting, Inderscience Enterprises Ltd, vol. 11(2), pages 132-144.
    4. Lucia Svabova & Katarina Kramarova & Jan Chutka & Lenka Strakova, 2020. "Detecting earnings manipulation and fraudulent financial reporting in Slovakia," Oeconomia Copernicana, Institute of Economic Research, vol. 11(3), pages 485-508, September.
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    Cited by:

    1. Pavol Durana & Roman Blazek & Veronika Machova & Miroslav Krasnan, 2022. "The use of Beneish M-scores to reveal creative accounting: evidence from Slovakia," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 17(2), pages 481-510, June.
    2. Paolo Vanini & Sebastiano Rossi & Ermin Zvizdic & Thomas Domenig, 2023. "Online payment fraud: from anomaly detection to risk management," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-25, December.
    3. Elena G. Popkova & Bruno S. Sergi, 2021. "Dataset Modelling of the Financial Risk Management of Social Entrepreneurship in Emerging Economies," Risks, MDPI, vol. 9(12), pages 1-20, November.
    4. SAFTA (PLESA) Ioana Lavinia & SABAU (POPA) Andrada Ioana & BORLEA Sorin Nicolae, 2022. "Selecting Indicators Of Predicting Fraud Risk. Case Study For Romanian Business Environment," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 74(4), pages 75-90, December.

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