The efficacy of red flags in predicting the SEC's targets: an artificial neural networks approach

Ehsan Feroz, Taek Mu Kwon, Victor S. Pastena and Kyungjoo Park

Intelligent Systems in Accounting, Finance and Management, 2000, vol. 9, issue 3, 145-157

Abstract: This paper illustrates the application of artificial neural networks (ANNs) to test the ability of selected SAS No. 53 red flags to predict the targets of the SEC investigations. Investors and auditors desire to predict SEC targets because substantial losses in equity value are associated with SEC investigations. The ANN models classify the membership in target (investigated) versus control (non‐investigated) firms with an average accuracy of 81%. One reason for the relative success of the ANN models is that ANNs have the ability to ‘learn’ what is important. The participants in financial reporting frauds have incentives to appear prosperous as evidenced by high profitability. In contrast to conventional statistical models with static assumptions, the ANNs use adaptive learning processes to determine what is important in predicting targets. Thus, the ANN approach is less likely to be affected by accounting manipulations. Our ANN models are biased against achieving predictive success because we use only publicly available information. The results confirm the value of red flags, i.e. financial ratios available from trial balance in conjunction with non‐financial red flags such as the turnover of CEO, CFO and auditors do have predictive value. © 2000 John Wiley & Sons, Ltd.

Date: 2000
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https://doi.org/10.1002/1099-1174(200009)9:33.0.CO;2-G

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