Probabilistic neural networks for the identification of qualified audit opinions

Expert Systems with Applications

Volumn 32, Issue 1, 2007, Pages 114-124

  Gaganis, Chrysovalantis ^a   Pasiouras, Fotios ^a,b   Doumpos, Michael ^a  

^a TECHNICAL UNIVERSITY OF CRETE   (Greece)

^b Department of Economics Finance and Accounting   (United Kingdom)

Author keywords

Auditing; Probabilistic neural networks; Qualified audit reports

Indexed keywords

COMPUTATIONAL METHODS; EXPERT SYSTEMS; PROBABILISTIC LOGICS; REGRESSION ANALYSIS; SENSITIVITY ANALYSIS;

AUDITING; COMPUTATIONAL POWER; PROBABILISTIC NEURAL NETWORKS; QUALIFIED AUDIT REPORTS;

NEURAL NETWORKS;

EID: 33748202031     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2005.11.003     Document Type: Article

References (45)

1. Beasley, S. M, Carcello, J. V., & Hermanson, D. R. (1999). Fraudulent financial reporting: 1987-1997: an analysis of US public companies, Research Report, COSO.
2. Bell T., and Tabor R. Empirical analysis of audit uncertainty qualifications. Journal of Accounting Research 29 (1991) 350-370
3. Calderon T.G., and Cheh J.J. A roadmap for future neural networks research in auditing and risk assessment. International Journal of Accounting Information Systems 3 4 (2002) 203-236
4. Charitou A., Neophytou E., and Charalambous Ch. Predicting corporate failure: empirical evidence for the UK. European Accounting Review 13 3 (2004) 465-497
5. Chen K., and Church B. Default on debt obligations and the issuance of going concern opinions. Auditing: A Journal of Practice and Theory Fall (1992) 30-49
6. Chen A.-S., Leung M.T., and Daouk H. Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index. Computers & Operations Research 30 6 (2003) 901-923
7. Craswell A. Does the provision of non-audit services impair auditor independence?. International Journal of Auditing 3 (1999) 29-40
8. Craven P., and Wahba G. Smoothing noisy data with spline functions. Numerische Mathematik 31 (1979) 377-403
9. DeAngelo L. Auditor size and audit quality. Journal of Accounting and Economics 3 (1981) 183-199
10. Dopuch N., Holthausen R.W., and Leftwich R.W. Predicting audit qualifications with financial and market variables. Accounting Review LXII 3 (1987) 431-453
11. Etheridge H., and Sriram R. A comparison of the relative cost of financial distress models. International Journal of Intelligent Systems in Accounting Finance and Management 6 3 (1997) 235-248
12. Fanning K., and Cogger K.O. Neural network detection of management fraud using published financial data. International Journal of Intelligent Systems in Accounting Finance and Management 7 1 (1998) 21-41
13. Fanning K., Cogger K.O., and Srivastava R. Detection of management fraud: a neural network approach. International Journal of Intelligent Systems in Accounting Finance and Management 4 2 (1995) 113-126
14. Fawcett, T., (2003). ROC graphs: notes and practical considerations for researchers, Technical Report HPL-2003-4, HP Laboratories. Available from www.hpl.hp.com/techreports/2003/HPL-2003-4.pdf.
15. Hamid S.A., and Iqbal Z. Using neural networks for forecasting volatility of S&P 500 Index futures prices. Journal of Business Research 57 10 (2004) 1116-1125
16. Hansen J.V., McDonald J.B., and Stice J.D. Artificial intelligence and generalized qualitative-response models: an empirical test on two audit decision-making domains. Decision Sciences 23 3 (1992) 708-723
17. Hastie T., Tibshirani R., and Friedman J. The elements of statistical learning: data mining, inference and prediction (2001), Springer, New York
18. Huang Z., Chen H., Hsu Ch.-J., Chen W.-H., and Wu S. Credit rating analysis with support vector machines and neural networks: a market comparative study. Decision Support Systems 37 4 (2004) 543-558
19. Hung S.-Y., Liang T.-P., and Liu V.W.-C. Integrating arbitrage pricing theory and artificial neural networks to support portfolio management. Decision Support Systems 18 3-4 (1996) 301-316
20. Ireland J. An empirical investigation of determinants of audit reports in the UK. Journal of Business Finance & Accounting 30 7 & 8 (2003) 975-1015
21. Keasey K., and Watson R. Non-financial symptoms and the prediction of small company failure: a test of Argenti's hypotheses. Journal of Business Finance and Accounting 14 3 (1987) 335-354
22. Keasey K., Watson R., and Wynarzcyk P. The small company audit qualification: a preliminary investigation. Accounting and Business Research 18 72 (1988) 323-333
23. Kim S.H., and Chun S.H. Graded forecasting using an array of bipolar predictions: application of probabilistic neural networks to a stock market index. International Journal of Forecasting 14 3 (1998) 323-337
24. Kleinman G., and Anandarajan A. The usefulness of off-balance sheet variables as predictors of auditors' going concern opinions: an empirical analysis. Managerial Auditing Journal 14 6 (1999) 273-285
25. Koh H.C. Model predictions and auditor assessments of going concern status. Accounting and Business Research 21 84 (1991) 331-338
26. Krishnan J., and Krishnan J. The role of economic trade-offs in the audit opinion decision: an empirical analysis. Journal of Accounting, Auditing and Finance 11 4 (1996) 565-586
27. Laitinen E.K., and Laitinen T. Qualified audit reports in Finland: evidence from large companies. The European Accounting Review 7 4 (1998) 639-653
28. Li S.-T., Shue L.-Y., and Shiue W. The development of a decision model for liquidity analysis. Expert Systems with Applications 19 4 (2000) 271-278
29. Loebbecke J., Eining M., and Willingham J. Auditor's experience with material irregularities: frequency, nature, and detectability. Auditing: A Journal of Practice and Theory 9 (1989) 1-28
30. Luther R.K. An artificial neural network approach to predicting the outcome of Chapter 11 bankruptcy. The Journal of Business and Economic Studies 4 (1998) 57-73
31. McKeown J.C., Mutchler J.F., and Hopwood W. Towards an explanation of auditor failure to modify the audit opinions on bankrupt companies. Auditing: A Journal of Practice and Theory 10 (1991) 1-13
32. Pendharkar P.C. A threshold-varying artificial neural network approach for classification and its application to bankruptcy prediction problem. Computers & Operations Research 32 10 (2005) 2561-2582
33. Reynolds J., and Francis J. Does size matter? The influence of large clients on office-level auditor reporting decisions. Journal of Accounting and Economics 30 (2001) 375-400
34. Salchenberger L.M., Cinar E.M., and Lash N.A. A new tool for predicting thrift failures. Decision Sciences 23 (1992) 899-916
35. Spathis Ch. Detecting false financial statements using published data: evidence from Greece. Managerial Auditing Journal 17 4 (2002) 179-191
36. Spathis Ch., Doumpos M., and Zopounidis C. Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques. The European Accounting Review 11 3 (2002) 509-535
37. Spathis Ch., Doumpos M., and Zopounidis C. Using client performance measures to identify pre-engagement factors associated with qualified audit reports in Greece. The International Journal of Accounting 38 (2003) 267-284
38. Specht D. Probabilistic neural networks. Neural Networks 3 (1990) 109-118
39. Summers S.L., and Sweeney J.T. Fraudulently misstated financial statements and insider trading: an empirical analysis. The Accounting Review 73 1 (1998) 131-146
40. Wines G. Auditor independence, audit qualifications and the provision of non-audit services: a note. Accounting and Finance 34 (1994) 75-86
41. Wong B.K., Lai V.S., and Lam J. A bibliography of neural network business applications research: 1994-1998. Computers & Operations Research 27 11-12 (2000) 1045-1076
42. Wong B.K., and Selvi Y. Neural network applications in finance: a review and analysis of literature (1990-1996). Information & Management 34 3 (1998) 129-139
43. Yang Z.R., Platt M.B., and Platt H.D. Probabilistic neural networks in bankruptcy prediction. Journal of Business Research 44 (1999) 67-74
44. Yim J., and Mitchell H. Comparison of country risk models: hybrid neural networks, logit models, discriminant analysis and cluster techniques. Expert Systems with Applications 28 1 (2005) 137-148
45. Zhang G., Hu M.Y., Patuwo B.E., and Indro D.C. Artificial neural networks in bankruptcy prediction: general framework and cross-validation analysis. European Journal of Operational Research 116 1 (1999) 16-32