Detection models for unintentional financial restatements
Abstract
The aim of manuscript is to analyze and identify determinants of honest accounting errors leading to financial restatements based on data from SEC database and from annual reports. Reason for this study is that accounting errors are expensive for companies that need to change already published financial statements and have impact on company reputation and stock price. Most of authors focus on prediction of accounting frauds and financial restatements remain in the background of research. This study initially tests existing accounting fraud detection model of Beneish on a sample of 40 financial restatement companies over 10 years and develops two new pioneer prediction models, one based on linear discriminant analysis (LDA) and another based on logistic regression. In testing dataset, LDA model has achieved accuracy 70.96%, specificity 25.00% and sensitivity 79.83% and logistic regression model has achieved accuracy 62.22%, specificity 41.66% and sensitivity 66.67%, performance of both models is better than existing Beneish model or other studies in this field. Developed models can be widely used by both internal and external users of financial statements, who would like to determine if financial statements of analyzed company include accounting errors or not, thanks to easily interpretable results in equation form.
First published online 28 November 2019
Keyword : unintentional financial restatement, financial restatements, accounting fraud, accounting error, linear discriminant analysis, logistic regression, prediction modelling, fraudulent financial statements, accounting manipulation, auditing
How to Cite
Papík, M., & Papíková, L. (2020). Detection models for unintentional financial restatements. Journal of Business Economics and Management, 21(1), 64-86. https://doi.org/10.3846/jbem.2019.10179
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Abbasi, A., Albrecht, C., Vance, A., & Hansen J. (2012). Metafraud: A meta-learning framework for detecting financial fraud. MIS Quart Manage Inf Syst MIS Quarterly: Management Information Systems, 36(4), 1293-1327. https://doi.org/10.2307/41703508
Ahmed, T., & Naima, J. (2016). Detection and analysis of probable earnings manipulation by firms in a developing country. Expert Systems with Application Asian Journal of Business and Accounting, 9(1), 59-82.
Albashrawi, M. (2016). Detecting financial fraud using data mining techniques: a decade review from 2004 to 2015. Journal of Data Science, 14(3), 553-570.
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance 23(4), 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
Apparao, G., Singh, A., Rao, G. S., Bhavani, B. L., Eswar, K., & Rajani, D. (2009). Financial statement fraud detection by data mining. Corporate governance, 3(1), 159-163. Retrieved from http://www.ijana.in/papers/3.3.pdf
Ata, A. & Seyrek, I. (2009). The use of data mining techniques in detecting fraudulent financial statements: an application on manufacturing firms. The Journal of Faculty of Economics and Administrative Sciences, 14(2), 157-170.
Audit Analytics. (2015). 2014 financial restatements: a fourteen year comparison. USA. Sutton: Audit Analytics.
Bateineh, H., Abuaddous, M., & Alabood, E. (2018). The effect of family ownership and board characteristics on earnings management: evidence from Jordan. Academy of Accounting and Financial Studies Journal, 22(4), 1-17.
Beneish, M. D., Lee, C., Press, E., Whaley, B., Zmijewski, M., & Cisilino, P. (1999). The detection of earnings manipulation. Financial Analysts Journal, 55(5), 24-36. https://doi.org/10.2469/faj.v55.n5.2296
Beneish, M. D., Lee, C. M. C., & Nichols, D. C. (2012). Fraud detection and expected return. Financial Analysts Journal, 69(5), 14-14. https://doi.org/10.2139/ssrn.1998387
Bhardwaj, A., & Gupta, R. (2016). Financial frauds: data mining based detection-a comprehensive survey. International Journal of Computer Applications, 156. https://doi.org/10.5120/ijca2016912538
Bhasin, M. L. (2013). Corporate governance and forensic accountants’ role: global regulatory action scenario. International Journal of Accounting Research, 1(1), 1-19. https://doi.org/10.2139/ssrn.2676468
Callao, S., & Jarne, J. (2010). Have IFRS affected earnings management in the European Union. Accounting in Europe, 7(2), 159-189. https://doi.org/10.1080/17449480.2010.511896
Cressey, D. R. (1973). Other people’s money: study in the social psychology of embezzlement. Montclair, N. J., Patterson Smith.
Cecchini, M., Aytug H., Koehler, G. J., & Pathak, P. (2010). Detecting management fraud in public companies. Management Science, 56(7), 1146-1160. https://doi.org/10.1287/mnsc.1100.1174
Chen, K. Y., & Zhou, J. (2010). Audit committee, board characteristics, and auditor switch decisions by Andersen’s clients. Contemporary Accounting Research, 24(4), 1,085-1,117. https://doi.org/10.1506/car.24.4.2
Chen, Y-J., Wu, Ch-H., Chen, Y-M., Li, H-Y., & Chen, H-K. (2017). Enhancement of fraud detection for narratives in annual reports. International Journal of Accounting Information Systems, 26, 32-45. https://doi.org/10.1016/j.accinf.2017.06.004
Chorvatovičová, L., & Saxunová, D. (2016a). Earnings management after IFRS implementations across the European Union. In Bilaterálne akademické fórum – Slovensko a Francúzsko v perspektíve zahraničnej politiky SR (pp. 84-90). Czech Republic: Wolters Kluwer. Retrieved from https://www.fm.uniba.sk/fileadmin/fm/Veda/Forum_bilateralne/Zbornik_Bilateralne_akademicke_forum_Slovensko_a_Francuzsko_v_perspektive_zahranicnej_politiky_SR_FMUK_2016_finale.pdf
Chorvatovičová, L., & Saxunová, D. (2016b). Usefulness of financial statements and annual reports in the process of accounting fraud detection. In Managing Global Changes: Proceedings of the joint international conference (pp. 233-247). Croatia: University of Primorska Press. Retrieved from http://www.hippocampus.si/ISBN/978-961-6984-81-2/130.pdf
Dechow, P. M., Ge, W., Larson, C. R., & Sloan, R. G. (2011). Predicting material accounting misstatements*: predicting material accounting misstatements. Contemporary Accounting Research, 28, 1782. https://doi.org/10.1111/j.1911-3846.2010.01041.x
Drábková, Z. (2015). Analysis of possibilities of detecting the manipulation of financial statements in terms of the IFRS and Czech accounting standards. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 63, 1859-1866. https://doi.org/10.11118/actaun201563061859
Duska, R., Duska, B., & Ragatz, A. (2011). Accounting ethics. New Jersey, USA: John Wiley & Sons Publishing. https://doi.org/10.1002/9781444395907
Dutta, I., Dutta, S., & Raahemi, B. (2017). Detecting financial restatements using data mining techniques. Expert Systems with Applications, 90, 374-393. https://doi.org/10.1016/j.eswa.2017.08.030
EDGAR Online. (2018). List of companies. 10-K.
Fanning, K., & Cogger, K. (1998). Neural network detection of management fraud using published financial data. International Journal of Intelligent Systems in Accounting, Finance & Management. https://doi.org/10.1002/(SICI)1099-1174(199803)7:1<21::AID-ISAF138>3.0.CO;2-K
Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27, 861-874. https://doi.org/10.1016/j.patrec.2005.10.010
Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. United Kingdom. London: SAGE Publications Ltd.
Franceschetti, B. M., & Koschtial, C. (2013). Do bankrupt companies manipulate earnings more than the non-bankrupt ones? Journal of Finance and Accountancy, 12, 4-24.
Gepp, A. (2015). Financial statement fraud detection using supervised learning methods. Retrieved from http://epublications.bond.edu.au/cgi/viewcontent.cgi?article=1227&context=theses
Girgenti, R., & Hedley, T. (2011). Managing the risk of fraud and misconduct. New York, USA: McGraw Hill.
Glancy, F. H., & Yadav S. B. (2011). A computational model for financial reporting fraud detection. Decision Support Systems, 50, 595-601. https://doi.org/10.1016/j.dss.2010.08.010
Gleason, C. A., Jenkins, N. T., & Johnson, W. B. (2008). The contagion effects of accounting restatements. The Accounting Review, 83(1), 83-110. https://doi.org/10.2308/accr.2008.83.1.83
Herawati, T. N. (2015). Application of Beneish M-Score models and data mining to detect financial fraud. Procedia – Social and Behavioral Sciences, 211, 924-930. https://doi.org/10.1016/j.sbspro.2015.11.122
La Torre, I. (2009). Creative accounting exposed. New York, USA: Palgrave Macmillan.
Lemus, E. (2014). The financial collapse of the Enron corporation and its impact in the United States Capital Market. Global Journal of Management and Business Research, 14(4), 40-51. Retrieved from https://journalofbusiness.org/index.php/GJMBR/article/view/1539/1442
Lin, C.-C., Chiu, A.-A., Huang, S. Y., & Yen, D. C. (2015). Detecting the financial statement fraud: The analysis of the differences between data mining techniques and experts’ judgments. KnowledgeBased Systems, 89, 459-470. https://doi.org/10.1016/j.knosys.2015.08.011
Kang, S.-A., & Kim, Y.-S. (2012). Effect of corporate governance on real activity-based earnings management: evidence from Korea. Journal of Business Economics and Management, 13, 29-52. https://doi.org/10.3846/16111699.2011.620164
Kara, E., Korpi, M., & Ugurlu, M. (2015). Using Beneish model in identifying accounting manipulation: an empirical study in BIST manufacturing industry sector. Journal of Accounting, Finance and Auditing Studies, 1(1), 21-39.
Kim, Y. J., Baik, B., & Cho, S. (2016). Detecting financial misstatements with fraud intention using multi-class cost-sensitive learning. Expert Systems with Applications, 62, 32-43. https://doi.org/10.1016/j.eswa.2016.06.016
Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32, 995-1003. https://doi.org/10.1016/j.eswa.2006.02.016
MacCarthy, J. (2017). Using Altman Z-score and Beneish M-score models to detect financial fraud and corporate failure: a case study of Enron corporation. International Journal of Finance and Accounting, 6, 159-166. https://doi.org/10.5923/j.ijfa.20170606.01
Messner, M. (2004). Exploring options in forensic accounting. National Public Accountants, 19-20.
Nakashima, M. (2017). Can The Fraud Triangle predict accounting fraud?: Evidence from Japan. Proceedings: The 8th International Conference of the Japanese Accounting Review. Japan: Rokkodai Campus of Kobe University. Retrieved from http://www.rieb.kobe-u.ac.jp/tjar/conference/8th/CC2_MasumiNAKASHIMA.pdf
Oyebisi, O., Wisdom, O., Olusogo, O., & Ifeoluwa, O. (2018). Forensic accounting and fraud prevention and detection in Nigerian banking industry. COJ Reviews & Research, 1(1), 1-8. https://doi.org/10.31031/cojrr.2018.01.000504
Özcan, A. (2018). The use of Beneish model in forensic accounting: evidence from Turkey. Journal of Applied Economics and Business Research, 8(1), 57-67.
Ozili, P. K. (2015). Forensic accounting and fraud: a review of literature and policy implications. International Journal of Accounting and Economic Studies, 3(1), 63-68. http://doi.org/10.2139/ssrn.2628554
Ozkul, F. U., & Pamukcu, A. (2012). Fraud detection and forensic accounting. Emerging Fraud, 19-41. https://doi.org/10.1007/978-3-642-20826-3_2
Paolone, F., & Magazzino, C. (2014). Earnings manipulation among the main industrial sectors. evidence from Italy. Business and Management Sciences International Quarterly Review, 5(4), 253-261.
Penman, S. H. (2010). Financial statement analysis and security valuation. New York, USA: McGraw Hill.
Pervan, I., Pavić, P., & Pervan, M. (2014). Firm financial distress prediction with statistical methods: prediction accuracy improvements based on the financial data restatements. 8th International Days of Statistics and Economics. Retrieved from https://msed.vse.cz/msed_2014/article/278-Pervan-Ivica-paper.pdf
Purda, L., & Skillicorn, D. (2014). Accounting variables, deception, and a bag of words: assessing the tools of fraud detection. Contemporary Accounting Research, 32(3). https://doi.org/10.1111/1911-3846.12089
Ramirez-Orellana, A., Martinez-Romero, M., & Mariño-Garrido, T. (2017). Measuring fraud and earnings management by a case of study: Evidence from an international family business. European Journal of Family Business, 7(1-2), 41-53. https://doi.org/10.1016/j.ejfb.2017.10.001
Rezaee, Z. (2005). Causes, consequences and deterrence of financial statement fraud. Critical Perspectives on Accounting, 16(3), 277-298. https://doi.org/10.1016/S1045-2354(03)00072-8
Repousis, S. (2016). Using Beneish model to detect corporate financial statement fraud in Greece. Journal of Financial Crime, 23(4), 1063-1073. https://doi.org/10.1108/JFC-11-2014-0055
Roy, M. (2013). Financial statement fraud detection and prevention. Amsterdam, Netherlands: World Press.
Sadaf, R., Oláh, J., Popp, J., & Mate, D. (2018). an investigation of the influence of the worldwide governance and competitiveness on accounting fraud cases: a cross-country perspective. Sustainability, 10(3), 1-11. https://doi.org/10.3390/su10030588
Savčuk, O. (2007). Internal audit efficiency evaluation principles. Journal of Business Economics and Management, 8, 275-284. https://doi.org/10.3846/16111699.2007.9636180
Song, M., Oshiro, N., Shuto, A. (2016). Predicting accounting fraud: evidence from Japan. The Japanese Accounting Review, 6, 17-63. https://doi.org/10.11640/tjar.6.2016.01
Stanley, J. D., & DeZoort, F. T. (2007). Audit firm tenure and financial restatements: an analysis of industry specialization and fee effects. Journal of Accounting and Public Policy, 26(2), 131-159. https://doi.org/10.1016/j.jaccpubpol.2007.02.003
Tucker, J. W., & Zarowin, P. A. (2006). Does income smoothing improve earnings informativeness? The Accounting Review, 81(1), 251-270. https://doi.org/10.2308/accr.2006.81.1.251
Unegbu, A. O. (2013). Advances in modeling for falsified financial statements. International Journal of Finance and Accounting, 2, 37-54. Retrieved from http://article.sapub.org/10.5923.j.ijfa.20130201.06.html#Sec1
West, J., Bhattacharya, M., & Islam, R. (2014). Intelligent financial fraud detection practices: an investigation. In International Conference on Security and Privacy in Communication Systems (pp. 186203). Springer. https://doi.org/10.1016/j.cose.2015.09.005
Ahmed, T., & Naima, J. (2016). Detection and analysis of probable earnings manipulation by firms in a developing country. Expert Systems with Application Asian Journal of Business and Accounting, 9(1), 59-82.
Albashrawi, M. (2016). Detecting financial fraud using data mining techniques: a decade review from 2004 to 2015. Journal of Data Science, 14(3), 553-570.
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance 23(4), 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
Apparao, G., Singh, A., Rao, G. S., Bhavani, B. L., Eswar, K., & Rajani, D. (2009). Financial statement fraud detection by data mining. Corporate governance, 3(1), 159-163. Retrieved from http://www.ijana.in/papers/3.3.pdf
Ata, A. & Seyrek, I. (2009). The use of data mining techniques in detecting fraudulent financial statements: an application on manufacturing firms. The Journal of Faculty of Economics and Administrative Sciences, 14(2), 157-170.
Audit Analytics. (2015). 2014 financial restatements: a fourteen year comparison. USA. Sutton: Audit Analytics.
Bateineh, H., Abuaddous, M., & Alabood, E. (2018). The effect of family ownership and board characteristics on earnings management: evidence from Jordan. Academy of Accounting and Financial Studies Journal, 22(4), 1-17.
Beneish, M. D., Lee, C., Press, E., Whaley, B., Zmijewski, M., & Cisilino, P. (1999). The detection of earnings manipulation. Financial Analysts Journal, 55(5), 24-36. https://doi.org/10.2469/faj.v55.n5.2296
Beneish, M. D., Lee, C. M. C., & Nichols, D. C. (2012). Fraud detection and expected return. Financial Analysts Journal, 69(5), 14-14. https://doi.org/10.2139/ssrn.1998387
Bhardwaj, A., & Gupta, R. (2016). Financial frauds: data mining based detection-a comprehensive survey. International Journal of Computer Applications, 156. https://doi.org/10.5120/ijca2016912538
Bhasin, M. L. (2013). Corporate governance and forensic accountants’ role: global regulatory action scenario. International Journal of Accounting Research, 1(1), 1-19. https://doi.org/10.2139/ssrn.2676468
Callao, S., & Jarne, J. (2010). Have IFRS affected earnings management in the European Union. Accounting in Europe, 7(2), 159-189. https://doi.org/10.1080/17449480.2010.511896
Cressey, D. R. (1973). Other people’s money: study in the social psychology of embezzlement. Montclair, N. J., Patterson Smith.
Cecchini, M., Aytug H., Koehler, G. J., & Pathak, P. (2010). Detecting management fraud in public companies. Management Science, 56(7), 1146-1160. https://doi.org/10.1287/mnsc.1100.1174
Chen, K. Y., & Zhou, J. (2010). Audit committee, board characteristics, and auditor switch decisions by Andersen’s clients. Contemporary Accounting Research, 24(4), 1,085-1,117. https://doi.org/10.1506/car.24.4.2
Chen, Y-J., Wu, Ch-H., Chen, Y-M., Li, H-Y., & Chen, H-K. (2017). Enhancement of fraud detection for narratives in annual reports. International Journal of Accounting Information Systems, 26, 32-45. https://doi.org/10.1016/j.accinf.2017.06.004
Chorvatovičová, L., & Saxunová, D. (2016a). Earnings management after IFRS implementations across the European Union. In Bilaterálne akademické fórum – Slovensko a Francúzsko v perspektíve zahraničnej politiky SR (pp. 84-90). Czech Republic: Wolters Kluwer. Retrieved from https://www.fm.uniba.sk/fileadmin/fm/Veda/Forum_bilateralne/Zbornik_Bilateralne_akademicke_forum_Slovensko_a_Francuzsko_v_perspektive_zahranicnej_politiky_SR_FMUK_2016_finale.pdf
Chorvatovičová, L., & Saxunová, D. (2016b). Usefulness of financial statements and annual reports in the process of accounting fraud detection. In Managing Global Changes: Proceedings of the joint international conference (pp. 233-247). Croatia: University of Primorska Press. Retrieved from http://www.hippocampus.si/ISBN/978-961-6984-81-2/130.pdf
Dechow, P. M., Ge, W., Larson, C. R., & Sloan, R. G. (2011). Predicting material accounting misstatements*: predicting material accounting misstatements. Contemporary Accounting Research, 28, 1782. https://doi.org/10.1111/j.1911-3846.2010.01041.x
Drábková, Z. (2015). Analysis of possibilities of detecting the manipulation of financial statements in terms of the IFRS and Czech accounting standards. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 63, 1859-1866. https://doi.org/10.11118/actaun201563061859
Duska, R., Duska, B., & Ragatz, A. (2011). Accounting ethics. New Jersey, USA: John Wiley & Sons Publishing. https://doi.org/10.1002/9781444395907
Dutta, I., Dutta, S., & Raahemi, B. (2017). Detecting financial restatements using data mining techniques. Expert Systems with Applications, 90, 374-393. https://doi.org/10.1016/j.eswa.2017.08.030
EDGAR Online. (2018). List of companies. 10-K.
Fanning, K., & Cogger, K. (1998). Neural network detection of management fraud using published financial data. International Journal of Intelligent Systems in Accounting, Finance & Management. https://doi.org/10.1002/(SICI)1099-1174(199803)7:1<21::AID-ISAF138>3.0.CO;2-K
Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27, 861-874. https://doi.org/10.1016/j.patrec.2005.10.010
Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. United Kingdom. London: SAGE Publications Ltd.
Franceschetti, B. M., & Koschtial, C. (2013). Do bankrupt companies manipulate earnings more than the non-bankrupt ones? Journal of Finance and Accountancy, 12, 4-24.
Gepp, A. (2015). Financial statement fraud detection using supervised learning methods. Retrieved from http://epublications.bond.edu.au/cgi/viewcontent.cgi?article=1227&context=theses
Girgenti, R., & Hedley, T. (2011). Managing the risk of fraud and misconduct. New York, USA: McGraw Hill.
Glancy, F. H., & Yadav S. B. (2011). A computational model for financial reporting fraud detection. Decision Support Systems, 50, 595-601. https://doi.org/10.1016/j.dss.2010.08.010
Gleason, C. A., Jenkins, N. T., & Johnson, W. B. (2008). The contagion effects of accounting restatements. The Accounting Review, 83(1), 83-110. https://doi.org/10.2308/accr.2008.83.1.83
Herawati, T. N. (2015). Application of Beneish M-Score models and data mining to detect financial fraud. Procedia – Social and Behavioral Sciences, 211, 924-930. https://doi.org/10.1016/j.sbspro.2015.11.122
La Torre, I. (2009). Creative accounting exposed. New York, USA: Palgrave Macmillan.
Lemus, E. (2014). The financial collapse of the Enron corporation and its impact in the United States Capital Market. Global Journal of Management and Business Research, 14(4), 40-51. Retrieved from https://journalofbusiness.org/index.php/GJMBR/article/view/1539/1442
Lin, C.-C., Chiu, A.-A., Huang, S. Y., & Yen, D. C. (2015). Detecting the financial statement fraud: The analysis of the differences between data mining techniques and experts’ judgments. KnowledgeBased Systems, 89, 459-470. https://doi.org/10.1016/j.knosys.2015.08.011
Kang, S.-A., & Kim, Y.-S. (2012). Effect of corporate governance on real activity-based earnings management: evidence from Korea. Journal of Business Economics and Management, 13, 29-52. https://doi.org/10.3846/16111699.2011.620164
Kara, E., Korpi, M., & Ugurlu, M. (2015). Using Beneish model in identifying accounting manipulation: an empirical study in BIST manufacturing industry sector. Journal of Accounting, Finance and Auditing Studies, 1(1), 21-39.
Kim, Y. J., Baik, B., & Cho, S. (2016). Detecting financial misstatements with fraud intention using multi-class cost-sensitive learning. Expert Systems with Applications, 62, 32-43. https://doi.org/10.1016/j.eswa.2016.06.016
Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32, 995-1003. https://doi.org/10.1016/j.eswa.2006.02.016
MacCarthy, J. (2017). Using Altman Z-score and Beneish M-score models to detect financial fraud and corporate failure: a case study of Enron corporation. International Journal of Finance and Accounting, 6, 159-166. https://doi.org/10.5923/j.ijfa.20170606.01
Messner, M. (2004). Exploring options in forensic accounting. National Public Accountants, 19-20.
Nakashima, M. (2017). Can The Fraud Triangle predict accounting fraud?: Evidence from Japan. Proceedings: The 8th International Conference of the Japanese Accounting Review. Japan: Rokkodai Campus of Kobe University. Retrieved from http://www.rieb.kobe-u.ac.jp/tjar/conference/8th/CC2_MasumiNAKASHIMA.pdf
Oyebisi, O., Wisdom, O., Olusogo, O., & Ifeoluwa, O. (2018). Forensic accounting and fraud prevention and detection in Nigerian banking industry. COJ Reviews & Research, 1(1), 1-8. https://doi.org/10.31031/cojrr.2018.01.000504
Özcan, A. (2018). The use of Beneish model in forensic accounting: evidence from Turkey. Journal of Applied Economics and Business Research, 8(1), 57-67.
Ozili, P. K. (2015). Forensic accounting and fraud: a review of literature and policy implications. International Journal of Accounting and Economic Studies, 3(1), 63-68. http://doi.org/10.2139/ssrn.2628554
Ozkul, F. U., & Pamukcu, A. (2012). Fraud detection and forensic accounting. Emerging Fraud, 19-41. https://doi.org/10.1007/978-3-642-20826-3_2
Paolone, F., & Magazzino, C. (2014). Earnings manipulation among the main industrial sectors. evidence from Italy. Business and Management Sciences International Quarterly Review, 5(4), 253-261.
Penman, S. H. (2010). Financial statement analysis and security valuation. New York, USA: McGraw Hill.
Pervan, I., Pavić, P., & Pervan, M. (2014). Firm financial distress prediction with statistical methods: prediction accuracy improvements based on the financial data restatements. 8th International Days of Statistics and Economics. Retrieved from https://msed.vse.cz/msed_2014/article/278-Pervan-Ivica-paper.pdf
Purda, L., & Skillicorn, D. (2014). Accounting variables, deception, and a bag of words: assessing the tools of fraud detection. Contemporary Accounting Research, 32(3). https://doi.org/10.1111/1911-3846.12089
Ramirez-Orellana, A., Martinez-Romero, M., & Mariño-Garrido, T. (2017). Measuring fraud and earnings management by a case of study: Evidence from an international family business. European Journal of Family Business, 7(1-2), 41-53. https://doi.org/10.1016/j.ejfb.2017.10.001
Rezaee, Z. (2005). Causes, consequences and deterrence of financial statement fraud. Critical Perspectives on Accounting, 16(3), 277-298. https://doi.org/10.1016/S1045-2354(03)00072-8
Repousis, S. (2016). Using Beneish model to detect corporate financial statement fraud in Greece. Journal of Financial Crime, 23(4), 1063-1073. https://doi.org/10.1108/JFC-11-2014-0055
Roy, M. (2013). Financial statement fraud detection and prevention. Amsterdam, Netherlands: World Press.
Sadaf, R., Oláh, J., Popp, J., & Mate, D. (2018). an investigation of the influence of the worldwide governance and competitiveness on accounting fraud cases: a cross-country perspective. Sustainability, 10(3), 1-11. https://doi.org/10.3390/su10030588
Savčuk, O. (2007). Internal audit efficiency evaluation principles. Journal of Business Economics and Management, 8, 275-284. https://doi.org/10.3846/16111699.2007.9636180
Song, M., Oshiro, N., Shuto, A. (2016). Predicting accounting fraud: evidence from Japan. The Japanese Accounting Review, 6, 17-63. https://doi.org/10.11640/tjar.6.2016.01
Stanley, J. D., & DeZoort, F. T. (2007). Audit firm tenure and financial restatements: an analysis of industry specialization and fee effects. Journal of Accounting and Public Policy, 26(2), 131-159. https://doi.org/10.1016/j.jaccpubpol.2007.02.003
Tucker, J. W., & Zarowin, P. A. (2006). Does income smoothing improve earnings informativeness? The Accounting Review, 81(1), 251-270. https://doi.org/10.2308/accr.2006.81.1.251
Unegbu, A. O. (2013). Advances in modeling for falsified financial statements. International Journal of Finance and Accounting, 2, 37-54. Retrieved from http://article.sapub.org/10.5923.j.ijfa.20130201.06.html#Sec1
West, J., Bhattacharya, M., & Islam, R. (2014). Intelligent financial fraud detection practices: an investigation. In International Conference on Security and Privacy in Communication Systems (pp. 186203). Springer. https://doi.org/10.1016/j.cose.2015.09.005