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The main aim of this study is to give an overview of literature in accounting and finance regarding the performance of Auditors’ GCOs, Statistical Failure Prediction Models (SFPMs), and Artificial Intelligence Technology (AIT). The study reviews the accounting and finance literature regarding (SFPMs) and presents the most important types of SFPMs and AIT that have been developed to evaluate a company’s financial position from 1968 to date. The study focuses on studies that compare the relative performance of auditors’ GCOs with SFPMs and AIT. Our findings illustrated that SPFMs and AIT are better in predicting companies’ failure than auditors’ GCOs. We found that the prediction power of SFPMs is in many instances very high. Their accuracy differed from one model to another, depending on several factors such as industry, time period, and economic environment. The most commonly used and accurate models are the Altman models, logit models, and neural networks models, although overall the NNs models produce better results. We found that SFPMs and AIT can be very useful to users when assessing a company’s future position. Incorporating the use of SFPMs and AIT in the audit program can provide further evidence that the auditors exerted professional competence and due care. This study provides a comprehensive overview of research on Auditors’ GCOs, SFPMs, and AIT. The study provides a clear picture of the best tools used in failure/bankruptcy prediction in the last decades. Thus, it is an aid to future research in the area.
References
Altman, E. (1976). A financial early warning system for over-the-Counter Broker Dealers. Journal of Finance, 1201-1217. https://doi.org/10.1111/j.1540-6261.1976.tb01969.x
Alareeni, B. A., & Branson, J. (2013). Predicting listed companies' failure in Jordan using Altman models: A case study. International Journal of Business and Management, 8(1), 113-126. https://doi.org/10.5539/ijbm.v8n1p113
Alareeni, B. and Aljuaidi, O. (2014). The Modified Jones and Yoon Models in detecting earnings management in Palestine Exchange (PEX). International Journal of Innovation and Applied Studies, 9(4), 2028-9324.
Alareeni, B. and Branson, J. (2011). The relative performance of auditors' going-concern opinions and statistical failure prediction models in Jordan. Accountancy & Bedrijfskunde, 8(8), 23- 35.
Alareeni, B. and Deghish, H. (2017). Applicability of the balanced scorecard to assess performance of Al-Aqsa Media Network Institution in Gaza Strip. IUG Journal of Economics and Business, 24(3), 21-46. https://doi.org/10.12816/0035571
Alareeni, B., Branson, Joel. (2010). The Effectiveness of Auditors' Opinions and Statistical Models in Predicting Failure of Listed Companies in Jordan. Working paper, 33rd Congress of the European Accounting Association (EAA).
Alareeni, B.A. (2019). The associations between audit firm attributes and audit quality-specific indicators A meta-analysis. Managerial Auditing Journal, 34(1), 6-43. https://doi.org/10.1108/MAJ-05-2017-1559
Alqallaf, H., and Alareeni, B. (2018). Evolving of Selected Integrated Reporting Capitals among Listed Bahraini Banks. Journal of Accounting and Applied Business Research,1(1), pp. 1-21. https://doi.org/10.51325/ijbeg.v1i1.10
Altman, E. (1968). Financial ratio, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
Altman, E. (1993). Corporate financial distress: A complete guide to predicting, avoiding and dealing with bankruptcy. 2nd Ed., John Wiley & Sons, New York (1993).
Altman, E., Eisenbeis, R. (1978). Financial applications of discriminant analysis: A clarification. Journal of Financial and Quantitative Analysis, 85-195 (1978). https://doi.org/10.2307/2330534
Altman, E., Haldeman, R., Narayanan, P. (1977). ZETA analysis: A new model to identify bankruptcy risk of corporations. Journal of Banking and Finance, 1, 29-54. https://doi.org/10.1016/0378-4266(77)90017-6
Altman, E., Marco, G., Varetto, F. (1994). Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking and Finance, 18, 505-529. https://doi.org/10.1016/0378-4266(94)90007-8
Altman, E., McGough, T. (1974). Evaluation of a company as a going-concern. The Journal of Accountancy, 50-57.
Beaver, W. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111. https://doi.org/10.2307/2490171
Dimitras, A., Slowinski, R., Susmaga, R., Zopounidis, C. (1999). Business failure prediction using rough sets. European Journal of Operational Research, 114, 263-80. https://doi.org/10.1016/S0377-2217(98)00255-0
Edwards, V., Yu, H., Chan, P., Manger, G. (2005). Corporate failure prediction (Bankruptcy) in Australia - from Zeta to neural networks. Working Paper, available at: http://ssrn.com/abstract=1347351
Field, A. (2009). Discovering statistics using SPSS (and sex and drugs and rock' n' roll). (3rd ed.). Publisher: London, Thousand Oaks, CA: Sage.
Frydman, H., Altman, E., Kao, D. (1985). Introducing recursive partitioning for financial classification: the case of financial distress. Journal of Finance, 40, 269-291. https://doi.org/10.1111/j.1540-6261.1985.tb04949.x
Grice, J., Dugan, M. (2001). The limitations of bankruptcy prediction models: Some cautions for the researcher. Review of Quantitative Finance and Accounting, 17, 151-166. https://doi.org/10.1023/A:1017973604789
Hopwood, W., McKeown, J., Mutchler, J. (1994). Re-examination of auditor versus model accuracy within the context of the going-concern opinion decision. Contemporary Accounting Research, 11, 295-310.
Jo, H., Han, I. (1996). Integration of case-based forecasting, neural network and discriminant analysis for bankruptcy prediction. Expert Systems with Applications, 11(4), 415-422. https://doi.org/10.1016/S0957-4174(96)00056-5
Kida, T. (1980). An investigation into auditors' continuity and related qualification judgments. Journal of Accounting Research, 18(2), 506-523. https://doi.org/10.2307/2490590
Kinney, W. (1999). Commentary - auditor independence: a burdensome constraint or core value Accounting Horizon, 13(1), 69-75. https://doi.org/10.2308/acch.1999.13.1.69
Koh, H. (1991). Model predictions and auditor assessments of going-concern status. Accounting and Business Research, 21(84) 331-338. https://doi.org/10.1080/00014788.1991.9729848
Koh, H., Low, C. (2004). Going-concern prediction using data mining techniques. Managerial Auditing Journal, 19(3), 462-476. https://doi.org/10.1108/02686900410524436
Lennox, C. (1999). The accuracy and incremental information content of audit reports in predicting bankruptcy. Journal of Business Finance & Accounting, 26(5&6), 557- 778 (1999). https://doi.org/10.1111/1468-5957.00274
Li, H., & Sun, J. (2009). Gaussian case-based reasoning for business failure prediction with empirical data in China. Information Sciences, 179(1-2), 89-108. https://doi.org/10.1016/j.ins.2008.09.003
McKee, T. (2000). Developing a Bankruptcy prediction model via rough sets theory. International Journal of Intelligent Systems in Accounting. Finance & Management, 9(3), 159-173. https://doi.org/10.1002/1099-1174(200009)9:3<159::AID-ISAF184>3.0.CO;2-C
Nam, J., Jinn, T. (2000). Bankruptcy prediction: Evidence from Korean listed companies during the IMF Crisis. Journal of International Financial Management and Accounting, 178-197. https://doi.org/10.1111/1467-646X.00061
Neophytou, E., Molinero, C. (2004). Predicting corporate failure in the UK: A multidimensional scaling approach. Journal of Business Finance & Accounting, 31(5-6), 677-710. https://doi.org/10.1111/j.0306-686X.2004.00553.x
Odom, M., Sharda, R. (1990). A neural network for bankruptcy prediction. IJCNN International Conference on Neural Networks, San Diego. https://doi.org/10.1109/IJCNN.1990.137710
Ohlson, J. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18, 109-131. https://doi.org/10.2307/2490395
Padawy, M. (2004). Comparing between audit report and Altman's Z-score in predicting failure of Jordanian companies. Unpublished Master thesis-Yarmouk University, Jordan.
Pawlak, Z. (1982). Rough sets. International Journal of Computer and Information Science, 11, 341-356.https://doi.org/10.1007/BF01001956
Pompe, P., Bilderbeek, J. (2005). Bankruptcy prediction: The influence of the year prior to failure selected for model building and the effects in a period of economic decline. Intelligent Systems in Accounting, Finance & Management, 13(2), 95 -112. https://doi.org/10.1002/isaf.259
Russell, E., Chiang, L., Braatz, R. (2000). Data-driven methods for fault detection and diagnosis in chemical processes. London. Springer-Verlog, Berlin Heidelberg New York.
Shin, K., Lee, Y. (2002). A genetic algorithm application in bankruptcy prediction modeling, Expert Systems with Applications. Working Paper, available at: http: www.elsevier.com/. https://doi.org/10.1016/S0957-4174(02)00051-9
Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. Journal of Business, 74(1), 101-124. https://doi.org/10.1086/209665
Sun, L. (2007). A re-evaluation of auditors' opinions versus statistical models in bankruptcy prediction. Review of Quantitative Finance and Accounting, 28(1), 55-78. https://doi.org/10.1007/s11156-006-0003-x
Varetto, F. (1998). Genetic algorithm applications in the analysis of insolvency risk.Journal of Banking and Finance, 22, 1421-1439. https://doi.org/10.1016/S0378-4266(98)00059-4
Wilson, R., Sharda, R. (1994). Bankruptcy prediction using neural networks. Decision Support Systems, 11, 545-557. https://doi.org/10.1016/0167-9236(94)90024-8
Yip, A. (2004). Predicting business failure with a case-based reasoning approach, lecture notes in computer science, in: M.G. Negoita, R.J. Howlett, L.C. Jain (Eds.), '' Knowledge- Based Intelligent Information and Engineering Systems: 8th International Conference, KES 2004, Wellington, New Zealand, September 3215/2004, Proceedings, Part III, 20-25.
Zavgren, C. (1985). Assessing the vulnerability to failure of American industrial firms: A logistic analysis. Journal of Business Finance and Accounting, 12, 19-45. https://doi.org/10.1111/j.1468-5957.1985.tb00077.x
Zmijewski, M. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 59-82. https://doi.org/10.2307/2490859
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