Do Auditors Reflect the True Image of the Company Contrary to the Clients’ Interests? An Artificial Intelligence Approach

Abstract

In recent years, after various scandals, the role of auditors has been called into question, even casting doubt on whether their reports reliably reflect the true financial situation of the auditee, especially when this situation is not good. Normative changes in the way auditors have to rate certain questions provide a good opportunity to study this problem. These changes have acquired great relevance among the factors involved in studying audit quality. Thus, the present study analyzed the effect of the normative change that took place in Spain in December 2010, related to opinions modified for going-concern uncertainties. Until that date, the auditor’s uncertainty about the company’s going-concern status led to a qualified opinion. However, under the new regulation, it became an opinion that included an explanatory paragraph stating the reasons for concern, which was considered less serious. In all, 152 small- and medium-sized enterprises that had begun bankruptcy proceedings were studied. Expert systems were used for their analysis, based on classification trees assembled through boosting and bagging. In addition, the logistic regression was used as baseline to compare previous methods. The main result obtained was that a change in the norm that catalogs the going-concern issue as less serious made auditors more likely to report this situation, thus questioning the audit quality.

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References

  1. Alfaro, E., Gamez, M., & García, N. (2013). ADABAG: An R package for classification with boosting and bagging. Journal of Statistical Software, 54(2), 1–35.

    Article  Google Scholar 

  2. Altman, E. I. (1983). Corporate financial distress. New York: Wiley Interscience.

    Google Scholar 

  3. Altman, D. G., & Bland, J. M. (1994). Diagnostic tests (1)—Sensitivity and specificity. BMJ, 308, 1552. doi:10.1258/phleb.2012.012J05.

    Article  Google Scholar 

  4. Altman, E. I., & Sabato, G. (2007). Modelling credit risk for SMEs: Evidence from the U.S. market. Abacus, 43(3), 332–357. doi:10.1111/j.1467-6281.2007.00234.x.

    Article  Google Scholar 

  5. Altman, E. I., Sabato, G., & Wilson, N. (2010). The value of non-financial information in small-sized enterprise risk management. The Journal of Credit Risk, 6(2), 1–33.

    Article  Google Scholar 

  6. Arnedo, L., Lizarraga, F., & Sanchez, S. (2008). Going-concern Uncertainties in pre-bankrupt audit reports: New evidence regarding discretionary accruals and wording ambiguity. International Journal of Auditing, 12(1), 25–44.

    Article  Google Scholar 

  7. Auditing and Assurance Standards Board (AUASB). (2007a). Auditing Standard ASA 570: Going Concern. Melbourne: Auditing and Assurance Standards Board.

  8. Auditing and Assurance Standards Board (AUASB). (2007b). Auditing Standard ASA 701: Modifications to the Auditor′s Report. Melbourne: Auditing and Assurance Standards Board.

  9. Barnes, L. (2008). Banking sector governance: Lessons from Hong Kong listed banks. A three years perspective. ICFAI Journal of Corporate Governance, 7, 22–35.

    Google Scholar 

  10. Bates, T. (2005). Analysis of young, small firms that have closed: Delineating successful from unsuccessful closures. Journal of Business Venturing, 20(3), 343–358. doi:10.1016/j.jbusvent.2004.01.003.

    Article  Google Scholar 

  11. Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36(1–2), 105–139.

  12. Blanco Oliver, A., Irimia, A., & Oliver Alfonso, M. D. (2012). The prediction of bankruptcy of small firms in the UK using logistic regression. Análisis financiero, (118), 32–40. https://dialnet.unirioja.es/servlet/articulo?codigo=3992172&orden=354854&info=link%5Cnhttps://dialnet.unirioja.es/servlet/extart?codigo=3992172

  13. Boone, J. P., Khurana, I. K., & Raman, K. K. (2010). Do the Big 4 and the second-tier firms provide audits of similar quality? Journal of Accounting and Public Policy, 29(4), 330–352. doi:10.1016/j.jaccpubpol.2010.06.007.

    Article  Google Scholar 

  14. Boter, H., & Lundström, A. (2005). SME perspectives on business support services. Journal of Small Business and Enterprise Development, 12(2), 244–258. doi:10.1108/14626000510594638.

    Article  Google Scholar 

  15. Bradley, A. P. (1997). The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145–1159.

    Article  Google Scholar 

  16. Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees. Monterey, CA: Wadsworth & Brooks, Wadsworth International Group.

    Google Scholar 

  17. Bureau Van Dijk. (2016). SABI. Sistema de Análisis de Balances Ibéricos. https://sabi.bvdinfo.com/. Accessed july 2016.

  18. Carcello, J. V., Hermanson, D., & Huss, F. (1995). Temporal changes in bankruptcy-related reporting. AUDITING: A Journal of Practice & Theory, 14, 133–143.

    Google Scholar 

  19. Carcello, J., Hermanson, D. & Huss, F. (1997). The effect of SAS No. 59: How treatment of the transition period influences results. Auditing: A Journal of Practice and Theory, 16, 114–123.

  20. Carcello, J. V., & Neal, T. L. (2003). Audit committee characteristics and auditor dismissals following “new” going-concern reports. The Accounting Review, 78(1), 95–117.

    Article  Google Scholar 

  21. Carcello, J. V., Vanstraelen, A., & Willenborg, M. (2009). Rules rather than discretion in audit standards: Going-concern opinions in Belgium. Accounting Review, 84(5), 1395–1428. doi:10.2308/accr.2009.84.5.1395.

    Article  Google Scholar 

  22. Chan, K. H., Lin, K. Z., & Mo, P. L. (2006). A political-economic analysis of auditor reporting and auditor switches. Review of Accounting Studies, 11(1), 21–48.

    Article  Google Scholar 

  23. Chen, K. C. W., & Church, B. K. (1992). Default on debt obligations and the issuance of going-concern opinions. AUDITING: A Journal of Practice & Theory, 11(2), 30–50.

    Google Scholar 

  24. Choi, J. H., Kim, J. B., Liu, X., & Simunic, D. (2008). Audit pricing, legal liability regimes, and Big 4 premiums: Theory and cross-country evidence. Contemporary Accounting Research, 25(1), 55–99.

    Article  Google Scholar 

  25. Chrzanowska, M., Alfaro, E., & Witkowska, D. (2009). The individual borrowers recognition: Single and ensemble trees. Expert Systems with Applications, 36(3 PART 2), 6409–6414. doi:10.1016/j.eswa.2008.07.048.

    Article  Google Scholar 

  26. Chung, S., & Narasimhan, R. (2001). Perceived value of mandatory audits of small companies. Managerial Auditing, 16(3), 120–123.

    Article  Google Scholar 

  27. Collis, J. (2008). Directors’ views on accounting and auditing requirements for SMEs. London: Department for Business Enterprise and Regulatory Reform.

    Google Scholar 

  28. Deangelo, L. E. (1981). Auditor size and audit quality. Journal of Accounting and Economics, 3(May), 183–199.

    Article  Google Scholar 

  29. Dechow, P., Ge, W., & Schrand, C. (2010). Understanding earnings quality: A review of the proxies, their determinants and their consequences. Journal of Accounting and Economics, 50(2–3), 344–401. doi:10.1016/j.jacceco.2010.09.001.

    Article  Google Scholar 

  30. DeFond, M. L., & Lennox, C. S. (2011). The effect of SOX on small auditor exits and audit quality. Journal of Accounting and Economics, 52(1), 21–40. doi:10.1016/j.jacceco.2011.03.002.

    Article  Google Scholar 

  31. Defond, M., Raghunandan, K., & Subramanyamk, K. R. (2002). Do non-audit service fees impair auditor independence? Evidence from going concern audit. Journal of Accounting Research, 40(4), 1247–1274.

    Article  Google Scholar 

  32. DeFond, M., & Zhang, J. (2014). A review of archival auditing research. Journal of Accounting and Economics, 58(2–3), 275–326. doi:10.1016/j.jacceco.2014.09.002.

    Article  Google Scholar 

  33. Devi, S. S., & Samujh, R. (2010). Accountants as providers of support and advice to SMEs in Malaysia. London: ACCA Research Report.

    Google Scholar 

  34. Dietterich, T. G. (2000). An experimental comparison of three methods for constructing ensembles of decision trees. Machine Learning, 40, 139–157. doi:10.1023/A:1007607513941.

    Article  Google Scholar 

  35. Fargher, N. L., & Jiang, L. (2008). Changes in the audit environment and auditors’ propensity to issue going-concern opinions. AUDITING: A Journal of Practice & Theory, 27(2), 55–77. doi:10.2308/aud.2008.27.2.55.

    Article  Google Scholar 

  36. Francis, J. R. (2004). What do we know about audit quality?*. The British Accounting Review, 36, 345–368. doi:10.1016/j.bar.2004.09.003.

    Article  Google Scholar 

  37. Francis, J. R. (2011). A Framework for Understanding and Researching Audit Quality, 30(2), 125–152. doi:10.2308/ajpt-50006.

    Article  Google Scholar 

  38. Francis, J. R., & Krishnan, J. (2002). Asia-Pacific Journal of Accounting and Economics evidence on auditor risk- management strategies before and after the private securities litigation reform act of 1995. Journal of Accounting and Economics, 9(2), 135–157. doi:10.1080/16081625.2002.10510607.

    Article  Google Scholar 

  39. Gaeremynck, A., & Willekens, M. (2003). The endogenous relationship between audit-report type and business termination: Evidence on private firms in a non-litigious environment. Accounting and Business Research, 33(1), 65–79. doi:10.1080/00014788.2003.9729632.

    Article  Google Scholar 

  40. Galar, M., Fern, A., Barrenechea, E., & Bustince, H. (2012). Hybrid-Based Approaches. IEEE Transactions On Systems, Man, and Cybernetics—Part C: Applications and Reviews, 42(4), 463–484.

  41. Geiger, M. A., Raghunandan, K., & Rama, D. V. (1998). Cost associated with going-concern modified audit opinion: An analysis of auditor changes, subsequent opinions and client failures. Advances in Accounting, 16, 117–140.

    Google Scholar 

  42. Geiger, M. A., Raghunandan, K., & Rama, D. V. (2005). Recent changes in the association between bankruptcies and prior audit opinions. AUDITING: A Journal of Practice & Theory, 24(1), 21–35.

    Article  Google Scholar 

  43. Gómez-Aguilar, N., & Ruiz-Barbadillo, E. (2003). Do Spanish firms change auditor to avoid a qualified audit report? International Journal of Auditing, 7(1), 37–53. doi:10.1111/1099-1123.00004.

    Article  Google Scholar 

  44. Graham, J. R., Harvey, C. R., & Rajgopal, S. (2005). The economic implications of corporate financial reporting. Journal of Accounting and Economics, 40(1–3), 3–73. doi:10.1016/j.jacceco.2005.01.002.

    Article  Google Scholar 

  45. Gramling, A. A., Krishnan, J. & Zhang, Y. (2011). PCAOB inspections of small accounting firms and auditor reporting decisions. Working Paper, Kennesaw State University.

  46. Hair, J. F., Hult, G. T., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks, CA: SAGE.

    Google Scholar 

  47. Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). Advances in international marketing. Advances in International Marketing, 20(2009), 277–319. doi:10.1108/S1474-7979(2009)0000020014.

    Article  Google Scholar 

  48. Homaie-Shandizi, A.-H., Nia, V. P., Gamache, M., & Agard, B. (2016). Flight deck crew reserve: From data to forecasting. Engineering Applications of Artificial Intelligence, 50, 106–114. doi:10.1016/j.engappai.2016.01.028.

    Article  Google Scholar 

  49. Hopwood, W., Mckeown, J. C., & Mutchler, J. F. (1994). A reexamination of auditor versus model accuracy within the context of the going-concern opinion decision*. Contemporary Accounting Research, 10(2), 409–431.

    Article  Google Scholar 

  50. Ireland, J. C. (2003). An empirical investigation of determinants of audit reports in the UK. Journal of Business Finance and Accounting, 30(7–8), 975–1015. doi:10.1111/1468-5957.05417.

    Article  Google Scholar 

  51. King, M. A., Abrahams, A. S., & Ragsdale, C. T. (2015). Ensemble learning methods for pay-per-click campaign management. Expert Systems with Applications, 42(10), 4818–4829. doi:10.1016/j.eswa.2015.01.047.

    Article  Google Scholar 

  52. Knechel, R., Krishnan, G. V., Pevzner, M., Shefchik, L. B., & Velury, U. K. (2013). Audit quality: Insights from the academic literature. AUDITING: A Journal of Practice & Theory, 32(Supplement 1), 385–421.

    Article  Google Scholar 

  53. Knechel, W. R., Niemi, L., & Sundgren, S. (2008). Determinants of auditor choice: Evidence from a small client market. International Journal of Auditing, 12, 65–88.

    Article  Google Scholar 

  54. Kotsiantis, S. B. (2007). Supervised machine learning: A review of classification techniques. Informatica, 31, 249–268. doi:10.1115/1.1559160.

  55. Krishnan, J. (1994). Auditor switching and conservatism. The Accounting Review, 69(1), 200–2015.

    Google Scholar 

  56. Krishnan, J., Andrishnan, J., & Schauer, P. C. (2000). The differentiation of quality among auditors: Evidence from the not-for-profit sector. AUDITING: A Journal of Practice & Theory dating, 19(2), 9–25.

    Article  Google Scholar 

  57. Lechner, C., & Dowling, M. (2003). Firm networks: External relationships as sources for the growth and competitiveness of entrepreneurial firms. Entrepreneurship & Regional Development, 15(1), 1–26. doi:10.1080/08985620210159220.

    Article  Google Scholar 

  58. Lennox, C. (2000). Do companies successfully engage in opinion-shopping? Evidence from the UK. Journal of Accounting and Economics, 29(3), 321–337.

  59. Li, C. (2009). Does client importance effect auditor independence at the office level? Empirical evidence from going-concern opinions. Contemporary Accounting Research, 26(1), 201–230.

    Article  Google Scholar 

  60. Liu, D., Li, T., & Liang, D. (2014). Incorporating logistic regression to decision-theoretic rough sets for classifications. International Journal of Approximate Reasoning, 55(1 PART 2), 197–210. doi:10.1016/j.ijar.2013.02.013.

    Article  Google Scholar 

  61. Mata, J., & Portugal, P. (2002). The survival of new domestic and foreign-owned firms. Strategic Management Journal, 23(4), 323–343. doi:10.1002/smj.217.

    Article  Google Scholar 

  62. Mo, P. L. L., Rui, O. M., & Wu, X. (2015). ScienceDirect auditors’ going concern reporting in the pre- and post-bankruptcy law eras: Chinese affiliates of Big 4 versus local auditors*. The International Journal of Accounting, 50, 1–30. doi:10.1016/j.intacc.2014.12.005.

    Article  Google Scholar 

  63. Morgan, J., & Sonquist, J. (1963). Problems in the analysis of survey data, and a proposal. Journal of the American Statistical Association, 302, 415–434.

    Article  Google Scholar 

  64. Omoteso, K. (2012). The application of artificial intelligence in auditing: Looking back to the future. Expert Systems with Applications, 39(9), 8490–8495. doi:10.1016/j.eswa.2012.01.098.

  65. Pryor, C., & Terza, J. (2002). Are going-concern audit opinions a self-fulfilling prophecy? Advances in quantitative. Analysis of Finance and Accounting, 10, 89–116.

    Google Scholar 

  66. Raghunandan, K., & Rama, D. V. (1995). Audit reports for companies in financial distress: Before and after SAS no. 59. AUDITING: A Journal of Practice & Theory, 14(1), 50–63.

    Google Scholar 

  67. Ruiz-Barbadillo, E., Gomez-Aguilar, N., & Cabrera-Pena, N. (2009). Derogación de la rotación obligatoria de auditores y calidad de la auditoría*. Revista Española de Financiación y Contabilidad, XVII(49), 105–134.

    Google Scholar 

  68. Segura, A. S. (2003). Salvedades y Cambio de Auditor. Spanish Journal of Finance and Accounting/Revista Española de Financiación y Contabilidad, 32(119), 983–1012. doi:10.1080/02102412.2003.10779504.

    Article  Google Scholar 

  69. Senkow, D. W., Rennie, M. D., Rennie, R. D., & Wong, J. W. (2001). The audit retention decision in the face of deregulation: Evidence from large private Canadian corporations. AUDITING: A Journal of Practice & Theory, 20(2), 101–113. doi:10.2308/aud.2001.20.2.101.

    Article  Google Scholar 

  70. Sercu, P., Vander Bauwhede, H., & Willekens, M. (2006). Post-enron implicit audit reporting standards: Sifting through the evidence. De Economist, 154(3), 389–403.

    Article  Google Scholar 

  71. Shmueli, G., Patel, N., & Bruce, P. (2010). Data mining for business intelligence: Concepts, techniques, and applications in microsoft office excel with XLMiner. Hoboken, NJ: Wiley.

    Google Scholar 

  72. Tauringana, V., & Clarke, S. (2000). The demand for external auditing: Managerial share ownership, size, gearing and liquidity influences. Managerial Auditing, 15(4), 160–168.

    Article  Google Scholar 

  73. Tsai, C. F., & Chiou, Y. J. (2009). Earnings management prediction: A pilot study of combining neural networks and decision trees. Expert Systems with Applications, 36(3 PART 2), 7183–7191. doi:10.1016/j.eswa.2008.09.025.

    Article  Google Scholar 

  74. Vanstraelen, A. N. N. (2003). Going-concern opinions, auditor switching, and the self-fulfilling prophecy effect examined in the regulatory context of Belgium. Journal of Accounting, Auditing and Finance, 18(2), 231–253.

    Article  Google Scholar 

  75. Xu, Y., Carson, E., Fargher, N., & Jiang, L. (2013). Responses by Australian auditors to the global financial crisis. Accounting and Finance, 53(April 2011), 301–338.

    Article  Google Scholar 

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Correspondence to Agustín J. Sánchez-Medina.

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Sánchez-Medina, A.J., Blázquez-Santana, F. & Alonso, J.B. Do Auditors Reflect the True Image of the Company Contrary to the Clients’ Interests? An Artificial Intelligence Approach. J Bus Ethics 155, 529–545 (2019). https://doi.org/10.1007/s10551-017-3496-4

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Keywords

  • Reliable image
  • Audit quality
  • Normative changes
  • Classification trees
  • Boosting
  • Bagging