Graduation Year

2008

Document Type

Dissertation

Degree

Ph.D.

Degree Granting Department

Accounting

Major Professor

Kaushal Chari, Ph.D.

Co-Major Professor

Jacqueline L. Reck, Ph.D.

Committee Member

Uday S. Murthy, Ph.D.

Committee Member

Manish Agrawal, Ph.D.

Keywords

Earnings management, Discretionary accruals, Unexpected productivity, Information markets, Combiner methods

Abstract

The goal of this dissertation is to improve financial statement fraud detection using a cross-functional research approach. The efficacy of financial statement fraud detection depends on the classification algorithms and the fraud predictors used and how they are combined. Essay I introduces IMF, a novel combiner method classification algorithm. The results show that IMF performs well relative to existing combiner methods over a wide range of domains. This research contributes to combiner method research and, thereby, to the broader research stream of ensemble-based classification and to classification algorithm research in general. Essay II develops three novel fraud predictors: total discretionary accruals, meeting or beating analyst forecasts and unexpected employee productivity. The results show that the three variables are significant predictors of fraud.

Hence Essay II provides insights into (1) conditions under which fraud is more likely to occur (total discretionary accruals is high), (2) incentives for fraud (firms desire to meet or beat analyst forecasts), and (3) how fraud is committed and can be detected (revenue fraud detection using unexpected employee productivity). This essay contributes to confirmatory fraud predictor research, which is a sub-stream of research that focuses on developing and testing financial statement fraud predictors. Essay III compares the utility of artifacts developed in the broader research streams to which the first two essays contribute, i.e., classification algorithm and fraud predictor research in detecting financial statement fraud.

The results show that logistic regression and SVM perform well, and that out of 41 variables found to be good predictors in prior fraud research, only six variables are selected by three or more classifiers: auditor turnover, Big 4 auditor, accounts receivable and the three variables introduced in Essay II. Together, the results from Essay I and Essay III show that IMF performs better than existing combiner methods in a wide range of domains and better than stacking, an ensemble-based classification algorithm, in fraud detection. The results from Essay II and Essay III show that the three predictors created in Essay II are significant predictors of fraud and, when evaluated together with 38 other predictors, provide utility to classification algorithms.

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