Detecting Management Fraud in Public Companies

Published Online:https://doi.org/10.1287/mnsc.1100.1174

References

  • Abbot L. J., Parker S., Peters G. Audit committee characteristics and restatements. Auditing (2004) 23(March):69–77CrossrefGoogle Scholar
  • Agresti A.Categorical Data Analysis (1990) (John Wiley & Sons, New York) Google Scholar
  • American Institute of Certified Public Accountants (AICPA) What does new audit standard SAS no. 99, consideration of fraud in a financial statement audit, mean for business and industry members? The CPA Letter (2002) November). http://www.aicpa.org/pubs/cpaltr/nov2002/supps/busind1.htmGoogle Scholar
  • Asare S. K., Wright A. M. The effectiveness of alternative risk assessment and program planning tools in a fraud setting. Contemporary Accounting Res. (2004) 21(2):325–352CrossrefGoogle Scholar
  • Bell T. B., Carcello J. V. Research notes, a decision aid for assessing the likelihood of fraudulent financial reporting. Auditing: J. Practice Theory (2000) 19(1):169–175CrossrefGoogle Scholar
  • Beneish M. Detecting GAAP violation: Implications for assessing earnings management among firms with extreme financial performance. J. Accounting Public Policy (1997) 16(3):271–309CrossrefGoogle Scholar
  • Beneish M. The detection of earnings manipulation. Financial Analysts J. (1999) 55(5):24–36CrossrefGoogle Scholar
  • Callen J. L., Livnat J., Segal D. The impact of earnings on the pricing of credit default swaps. Accounting Rev. (2009) 84(5):1363–1394CrossrefGoogle Scholar
  • Cristianini N., Shawe-Taylor J.An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods (2000) (Cambridge University Press, Cambridge, UK) CrossrefGoogle Scholar
  • Cristianini N., Shawe-Taylor J., Lodhi H. Latent semantic kernels. J. Intelligent Inform. Systems (2002) 18(2–3):127–152CrossrefGoogle Scholar
  • Dechow P. M., Ge W., Larson C. R., Sloan R. G. Predicting material accounting misstatements. (2009) . AAA 2008 Financial Accounting and Reporting Section (FARS) Paper. http://ssrn.com/abstract=997483Google Scholar
  • Durtschi C., Easton P. Earnings management? The shapes of the frequency distributions of earnings metrics are not evidence ipso facto. J. Accounting Res. (2005) 43(4):557–592CrossrefGoogle Scholar
  • Eisenbeis R. Discussion, supplement to Srinivasan, V. and Kim, Y. H. Credit granting: A comparative analysis of classification procedures. J. Finance (1987) 42(3):681–683CrossrefGoogle Scholar
  • Fanning K., Cogger K. O., Srivastava R. Detection of management fraud: A neural network approach. Proc. 11th Conf. Artificial Intelligence Appl. (1995) (IEEE Computer Society, Washington, DC) 220–223CrossrefGoogle Scholar
  • Fawcett T. An introduction to ROC analysis. Pattern Recognition Lett. (2006) 27(8):861–874CrossrefGoogle Scholar
  • Fisher R. A. The use of multiple measurements in taxonomic problems. Ann. Eugenics (1936) 7(7):179–188CrossrefGoogle Scholar
  • Francis J., LaFond R., Olsson P., Schipper K. The market pricing of accruals quality. J. Accounting Econom. (2005) 39(2):295–327CrossrefGoogle Scholar
  • Genton M. G. Classes of kernels for machine-learning: A statistics perspective. J. Machine Learn. Res. (2001) 2(12):299–312Google Scholar
  • Graf A. B. A., Borer S. Normalization in support vector machines. Proc. 23rd DAGM-Sympos. Pattern Recognition (2001) (Springer-Verlag, London) 277–282CrossrefGoogle Scholar
  • Green P., Choi J. H. Assessing the risk of management fraud through neural network technology. Auditing: J. Practice Theory (1997) 16(1):14–29Google Scholar
  • Hackenbrack K. The effect of experience with different sized clients on auditor evaluations of fraudulent financial reporting indicators. Auditing: J. Practice Theory (1993) 12(1):99–100Google Scholar
  • Hansen J. V., McDonald J. B., Messier W. F., Bell T. B. A generalized qualitative-response model and the analysis of management fraud. Management Sci. (1996) 42(7):1022–1033LinkGoogle Scholar
  • Haykin S.Neural Networks: A Comprehensive Foundation (1998) (Prentice Hall, Upper Saddle River, NJ) Google Scholar
  • Joachims T. Text categorization with support vector machines: Learning with many relevant features. Proc. 10th European Conf. Machine Learning (1998) (Springer-Verlag, London) 137–142CrossrefGoogle Scholar
  • Khan M., Watts R. L. Estimation and empirical properties of a firm-year measure of accounting conservatism. J. Accounting Econom. (2009) 48(2–3):132–150CrossrefGoogle Scholar
  • Loebbecke J. K., Eining M. M., Willingham J. J. Auditors' experience with material irregularities: Frequency, nature, and detectability. Auditing: J. Practice Theory (1989) 9(1):1–28Google Scholar
  • McNichols M., Wilson P. Evidence of earnings management from the provision for bad debts. J. Accounting Res. (1988) 26:1–31CrossrefGoogle Scholar
  • Messier W. F., Hansen J. V. Inducing rules for expert system development: An example using default bankruptcy data. Management Sci. (1988) 34(12):1403–1416LinkGoogle Scholar
  • New York Stock Exchange Final NYSE corporate governance rules. (2003) . Report, http://www.nyse.com/pdfs/finalcorpgovrules.pdfGoogle Scholar
  • Pincus K. V. The efficacy of a red flags questionnaire for assessing the possibility of fraud. Accounting, Organ. Soc. (1989) 14(1–2):153–163CrossrefGoogle Scholar
  • Quinlan J. R., Tucker A. B. Decision trees and instance-based classifiers. CRC Handbook of Computer Science and Engineering (1996) (CRC Press, Boca Raton, FL) 521–535Google Scholar
  • Ragothaman S., Carpenter J., Buttars T. Using rule induction for knowledge acquisition: An expert systems approach to evaluating material errors and irregularities. Expert Systems with Appl. (1995) 9(4):483–490CrossrefGoogle Scholar
  • Rüping S., Klinkenberg R., Rüping S., Fick A., Henze N., Herzog C., Molitor R., Schrüder O. SVM kernels for time series analysis. LLWA 01—Tagungsband der GI-Workshop-Woche Lernen—Lehren—Wissen—Adaptivität (2001) Dortmund, Germany:43–50Google Scholar
  • Securities and Exchange Commission (SEC) Selected accounting and auditing enforcement releases. (1995) . http://www.sec.gov/divisions/enforce/friactions.shtmlGoogle Scholar
  • Shawe-Taylor J., Cristianini N.Kernel Methods for Pattern Analysis (2004) (Cambridge University Press, Cambridge, UK) CrossrefGoogle Scholar
  • Standard & Poor's. Compustat database (2005) . Accessed June 2009, http://www.compustat.com/Google Scholar
  • Summers S. L., Sweeney J. T. Fraudulentyl misstated financial statements and insider trading: An empirical analysis. Accounting Rev. (1998) 73(1):131–146Google Scholar
  • Takimoto E., Warmuth M. Path kernels and multiplicative updates. J. Machine Learning Res. (2003) 4:773–818Google Scholar
  • Tam K., Kiang M. Managerial applications of neural networks: The case of bank failure predictions. Management Sci. (1992) 38(7):926–947LinkGoogle Scholar
  • Tsai L., Koehler G. The accuracy of concepts learned from induction. Decision Support Systems (1993) 10(2):161–172CrossrefGoogle Scholar
  • U.S. CongressSarbanes-Oxley Act of 2002HR 3763. 107th Cong., 2nd Sess. Pub. L. 107-204, 116 Stat. 745 (July 30, 2002)Google Scholar
  • Vapnik V.Statistical Learning Theory (1995) (Springer Verlag, New York) CrossrefGoogle Scholar
  • Yu H., Yang J., Han J. Classifying large data sets using SVMs with hierarchical clusters. Proc. Ninth ACM SIGKDD Internat. Conf. Knowledge Discovery and Data Mining (2003) (ACM, New York) 306–315CrossrefGoogle Scholar
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