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Integrating corporate governance and financial variables for the identification of qualified audit opinions with neural networks

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Abstract

Prior studies have contributed to the development of models that help predict audit opinions, and have applied several methodologies in the search of better predictions. Nevertheless, and even though the existing literature on the prediction of audit opinions is profuse, the results achieved by the existing modelling are still considerably far from having obtained high levels of prediction, and the results are not in excess of 80 % in terms of classification. In previous research, prediction models of audit opinions have used financial variables. The main contribution of this paper is to show that combining these financial variables with variables relating to corporate governance of companies, the predictive ability of the models is significantly higher. For this, a sample was selected from Spanish Listed companies during the financial years between 2008 and 2010. From this sample, financial and corporate governance information was obtained, enabling us to rely on a new set of variables, more complete than those used in prior research. The validity of the variables was studied by means of univariant tests, upon completion of the database. Then, the results of the models built upon said variables were compared through neural network tests that have proven to yield a higher level of prediction, according to prior literature, specifically, multilayer perceptron and probabilistic neural network.

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Notes

  1. This paper has the finality of analysing the most relevant aspects of the supervision activities carried out by CNMV, in relation to the annual accounts of listed companies, as well as the audit reports corresponding to this accounts, with the purpose of increasing transparency on their actions.

  2. Excel spreadsheet provided annually on the corporate website of the CNMV, called “Individual details of listed societies, ordered by stock capitalisation”.

References

  1. Abbot LJ, Parker S, Peters GF, Raghunandan K (2003) The association between audit committee characteristics and audit fees. Audit J Pract Theory 22(2):17–32

    Article  Google Scholar 

  2. Ancona F, Colla AM, Rovetta S, Zunino R (1997) Implementing probabilistic neural networks. Neural Comput Appl 5(3):152–159

    Article  Google Scholar 

  3. Baldwin A, Brown CE, Trinkle B (2006) Opportunities for artificial intelligence development in the accounting domain: the case for auditing. Intell Syst Account Finance Manag 14:77–86

    Article  Google Scholar 

  4. Bartov E, Gul Ferdinand A, Tsui Judy SL (2001) Discretionary-accruals models and audit qualifications. J Account Econ 30:421–452

    Article  Google Scholar 

  5. Beasley M (1996) An empirical analysis of the relation between board of director composition and financial statement fraud. Account Rev 71(4):443–465

    Google Scholar 

  6. Beasley M, Carcello J, Hermanson D (1999) Fraudulent Financial Reporting (1987–1997). An analysis of US public companies. Committee of Sponsoring Organizations of the Treadway Commission, New York

    Google Scholar 

  7. Bell TB, Tabor R (1991) Empirical analysis of audit uncertainty qualifications. J Account Res 29(2):350–370

    Article  Google Scholar 

  8. Bierstaker JL, Burnaby P et al (2001) The impact of information technology on the audit process: an assessment of the state of the art and implications for the future. Manag Audit J 16(3):159–164

    Article  Google Scholar 

  9. Bishop C (1995) Neural networks for pattern recognition. Oxford University Press, Oxford, pp 353–368 Section 9

    Google Scholar 

  10. Calderon TG, Cheh JJ (2002) A roadmap for future neural networks research in auditing and risk assessment. Int J Account Inf Syst 3(4):203–236

    Article  Google Scholar 

  11. Caramanis C, Spathis Ch (2006) Auditee and audit firm characteristics as determinants of audit qualifications: evidence from the Athens stock exchange. Manag Audit J 21(9):905–920

    Article  Google Scholar 

  12. Carcello JV, Hermanson DR, Neal TL, Riley RA (2002) Board characteristics and audit fees. Contemp Account Res 19(3):365–384

    Article  Google Scholar 

  13. Chen K, Church B (1992) Default on debt obligations and the issuance of going concern opinions. Audit J Pract Theory 11(2):30–49

    Google Scholar 

  14. Cybenko G (1989) Approximation by superposition of a sigmoidal function. Math Control Signals Syst 2:303–314

    Article  MathSciNet  MATH  Google Scholar 

  15. Coderre GD (1999) Fraud detection. Using data analysis techniques to detect fraud. Global Audit Publications, Vancouver

    Google Scholar 

  16. Comité de Supervisión Bancaria de Basilea (1999) La mejora del gobierno corporativo en organizaciones bancarias. Bank for International Settlements, Basilea

    Google Scholar 

  17. De Andrés P, Azofra V, López F (2001) Discrecionalidad directiva, dirección de resultados y gobierno de la empresa: un análisis empírico internacional. AECA, Madrid

    Google Scholar 

  18. Dechow P, Sloan R, Sweeney A (1996) Causes and consequences of earnings manipulation: an analysis of firms subject to enforcement actions by the SEC. Contemp Account Res 13(1):1–36

    Article  Google Scholar 

  19. Dopouch N, Holthausen R, Leftwich R (1987) Predicting audit qualifications with financial and market variables. Account Rev 62(3):431–454

    Google Scholar 

  20. Doumpos M, Gaganis C, Pasiouras F (2005) Explaining qualifications in audit reports using a support vector machine methodology. Intell Syst Account Finance Manag 13:197–215

    Article  Google Scholar 

  21. Duda RO, Hart P (2001) Pattern classification. Wiley, Hoboken

    MATH  Google Scholar 

  22. Dunn P (2004) The impact of insider power on fraudulent financial reporting. J Manag 30:397–412

    Google Scholar 

  23. Fama E (1980) Agency problems and the theory of the firm. J Polit Econ 88:288–301

    Article  Google Scholar 

  24. Fama E, Jensen MC (1983) Agency problems and residual claims. J Law Econ 26:327–349

    Article  Google Scholar 

  25. Fanning K, Cogger K (1998) Neural detection of management fraud using published financial data. Int J Intell Syst Account Finance Manag 7(1):21–41

    Article  Google Scholar 

  26. Fanning K, Cogger K, Srivastana R (1995) Detection of management fraud: a neural network approach. Int J Intell Syst Account Finance Manag 4(2):113–126

    Article  Google Scholar 

  27. Flórez R, Fernández JM (2008) Las Redes Neuronales Artificiales. Fundamentos teóricos y aplicaciones prácticas. Netbiblo

  28. Flury B (1988) Common principal components and related multivariate models. Wiley, New York

    MATH  Google Scholar 

  29. Gaganis Ch, Pasiouras F, Tzanetoulakos A (2005) A comparison and integration of classification techniques for the prediction of small UK firms failure. J Financ Decis Mak 1(1):55–69

    Google Scholar 

  30. Gaganis C, Pasiouras F (2007) A multivariate analysis of the determinants of auditors opinions on Asian Banks. Manag Audit J 22(3):268–287

    Article  Google Scholar 

  31. Ghafran C, O´Sullivan N (2011) The impact of audit committee characteristics on audit fees: an empirical analysis of large UK companies. Financial Reporting and Business Communication Research Unit. In: 15th annual conference, Bristol

  32. Goodwin-Stewart J, Kent P (2006) Relation between external audit fees, audit committee characteristics and internal audit. Account Finance 46:387–404

    Article  Google Scholar 

  33. Haykin S (2008) Neural networks and learning machines. Prentice Hall, Upper Saddle River

    Google Scholar 

  34. Hornik K (1991) Approximation capabilities of multilayer feedforward networks. Neural Netw 4:251–257

    Article  Google Scholar 

  35. Ireland JC (2003) An empirical investigation of determinants of audit reports in the UK. J Bus Finance Account 30(7):975–1015

    Article  Google Scholar 

  36. Isasi P, Galván I (2004) Redes de Neuronas Artificiales. Un enfoque práctico. Pearson Prentice Hall, Upper Saddle River

    Google Scholar 

  37. Jackson JE (1991) A user´s guide to principal components. Wiley, New York

    Book  MATH  Google Scholar 

  38. Jolliffe IT (1986) Principal components analysis. Springer, New York

    Book  MATH  Google Scholar 

  39. Keasey K, Watson R, Wynarczyk P (1988) The small company audit qualification: a preliminary investigation. Accounting Bus Res 18(72):323–333

    Article  Google Scholar 

  40. Kirkos E, Spathis C, Nanopolulos A, Manolopoulos Y (2007) Identifying qualified auditors´opinions: a data mining approach. J Emerg Technol Account 4:183–197

    Article  Google Scholar 

  41. Klein A (2002) Audit committee, board of director characteristics, and earnings management. J Account Econ 33(3):375–400

    Article  Google Scholar 

  42. Kleinman G, Anandarajan A (1999) The usefulness of off-balance sheet variables as predictors of auditors’ going concern opinions: an empirical analysis. Manag Audit J 14(6):273–285

    Article  Google Scholar 

  43. Koskivaara E (2004) Artificial neural network in auditing: state of art. ICFAI J Audit Pract 1(4):12–33

    Google Scholar 

  44. Koskivaara E (2004) Artificial neural network in analytical review procedures. Manag Audit J 19(2):191–223

    Article  Google Scholar 

  45. Laitinen EK, Laitinen T (1998) Qualified audit reports in Finland: evidence from large companies. Eur Account Rev 7(4):639–653

    Article  MathSciNet  Google Scholar 

  46. Lenard M, Alam P, Madey G (1995) The application of neural networks and a qualitative response model to the auditor’s going concern uncertainty decision. Decis Sci 26(2):209–227

    Article  Google Scholar 

  47. Lennox C (2000) Do companies successfully engage in opinion-shopping? Evidence from the UK. J Account Econ 29:321–337

    Article  Google Scholar 

  48. Levitan AS, Knobblet JA (1985) Indicators of exceptions to the going-concern assumption. Audit J Pract Theory 5(1):26–39

    Google Scholar 

  49. Marra A, Mazzola P, Prencipe A (2011) Board monitoring and earnings management pre-and post-IFRS. Int J Account 46:205–230

    Article  Google Scholar 

  50. Menon K, Schwartz KB (1987) An empirical investigation of audit qualification decisions in the presence of going-concern uncertainties. Contemp Account Res 3(2):302–315

    Article  Google Scholar 

  51. Muirhead RJ (1982) Aspects of multivariate statistical theory. Wiley, NewYork

    Book  MATH  Google Scholar 

  52. Mutchler JF (1985) A multivariate analysis of the auditors going-concern opinion decision. J Account Res 23(2):668–682

    Article  Google Scholar 

  53. Pasiouras F, Gaganis C, Zopounidis C (2004) Replicating auditors’ opinion: multicriteria approaches. In: 2nd meeting of the greek working group on multicriteria decision aid, 21–22 Oct, Chania

  54. Pasiouras F, Gaganis C, Zopounidis C (2007) Multicriteria decision support methodologies for auditing decisions: the case of qualified audit report in the UK. Eur J Oper Res 180:1317–1330

    Article  MATH  Google Scholar 

  55. Peasnell KV, Pope PF, Young S (2005) Board monitoring and earnings management: do outside directors influence abnormal accruals? J Bus Finance Account 32:1311–1346

    Article  Google Scholar 

  56. Porter B, Cameron A (1987) Company fraud-what price the auditor? Account J 12:44–47

    Google Scholar 

  57. Pourheydari O, Nezamabadi-pour H, Zeinab A (2012) Identifying qualified audit opinions by artificial neural networks. Afr J Bus Manag 6(44):11077–11087

    Google Scholar 

  58. Principe JC, Euliano NR, Lefebvre WC (2000) Neural and adaptive systems. Wiley, Hoboken

    Google Scholar 

  59. Rezaee Z (2005) Causes, consequences, and deterrence of financial statement fraud. Crit Perspect Account 16(3):277–298

    Article  Google Scholar 

  60. Seber GAF (1977) Linear regression analysis, vol 13. Wiley, Hoboken

    MATH  Google Scholar 

  61. Spathis C, Doumpos M, Zopounidis C (2002) Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques. Eur Account Rev 11(3):509–535

    Article  Google Scholar 

  62. Spathis C (2003) Audit qualification, firm litigation and financial information: an empirical analysis in Greece. Int J Audit 7:71–85

    Article  Google Scholar 

  63. Specht D (1990) Probabilistic neural networks. Neural Netw 3:109–118

    Article  Google Scholar 

  64. Titman S, Trueman B (1986) Information quality and the valuation of new issues. J Account Econ 8(June):159–172

    Article  Google Scholar 

  65. Tukey JW (1977) Exploratory data analysis. Addison-Wesley, Reading, Massachusetts

    MATH  Google Scholar 

  66. Vafeas N (1999) Board meeting frequency and firm performance. J Financ Econ 53:113–142

    Article  Google Scholar 

  67. Xie B, Davidson WN III, DaDalt PJ (2003) Earnings management and corporate governance: the roles of the board and the audit committee. J Corp Finance 9(3):295–316

    Article  Google Scholar 

  68. Yao X, Lin Y (1997) A new evolutionary system for evolving artificial neural networks. IEEE Trans Neural Netw 8:694–713

    Article  Google Scholar 

  69. Zaman M, Hudaib M, Haniffa R (2011) Corporate governance quality, audit fees and non-audit services fees. J Bus Finance Account 38(1, 2):165–197

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Finance and Accounting, University of Málaga, Málaga, Spain

    M. A. Fernández-Gámez & J. R. Sánchez-Serrano

  2. Department of Electronic Technology, University of Málaga, Málaga, Spain

    F. García-Lagos

Authors
  1. M. A. Fernández-Gámez
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  2. F. García-Lagos
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  3. J. R. Sánchez-Serrano
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Corresponding author

Correspondence to F. García-Lagos.

Appendix 1

Appendix 1

In this appendix, we show the results of the MLP and the PNN models without using corporate governance variables.

See Tables 12, 13, 14.

Table 12 Architecture of MLP (Excluding corporate governance variables)
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Table 13 Architecture of PNN (excluding corporate governance variables)
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Table 14 Comparative classification results (%) (excluding corporate governance variables)
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Fernández-Gámez, M.A., García-Lagos, F. & Sánchez-Serrano, J.R. Integrating corporate governance and financial variables for the identification of qualified audit opinions with neural networks. Neural Comput & Applic 27, 1427–1444 (2016). https://doi.org/10.1007/s00521-015-1944-6

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