Fraudes Contábeis: uma estimativa da probabilidade de detecção

Wuerges, Artur Filipe Ewald; Borba, José Alonso

doi:10.7819/rbgn.v16i52.1555

RESUMO

Fraudes nas demonstrações financeiras (FDF) custam caro para os investidores e podem prejudicar a credibilidade dos auditores. Para prevenir e detectar fraudes, é útil conhecer suas causas. Os modelos de escolha binária (por exemplo, logit e probit), frequentemente utilizados na literatura, porém, não levam em consideração os casos de fraudes não detectados e, portanto, apresentam testes de hipóteses pouco confiáveis. Usando uma amostra de 118 empresas acusadas de fraude pela Comissão de Valores Mobiliários dos Estados Unidos (Securities and Exchange Commission, SEC), estimamos um modelo logit que corrige os problemas oriundos de fraudes não detectadas em empresas dos Estados Unidos. Para evitar problemas de multicolinearidade, extraímos sete fatores a partir de 28 variáveis, usando o método dos componentes principais. Nossos resultados indicam que apenas 1,43% dos casos de FDF foram divulgados pela SEC. Das sete variáveis significativas incluídas em um modelo logit tradicional e não corrigido, três na realidade não foram consideradas significativas em um modelo corrigido. A probabilidade de FDF é 5,12 vezes maior quando o auditor da empresa emite um parecer adverso ou com ressalvas.

Palavras-chave:
Fraude contábil; AAER; Erros de classificação; Logit; Análise fatorial

RESUMEN

El fraude en los estados financieros (FEF) es costoso para los inversionistas y pueden minar la credibilidad de los auditores. A fin de prevenir y detectar el fraude, es útil conocer sus causas. Sin embargo, los modelos de elección binaria (logit y probit, por ejemplo) a menudo utilizados en la literatura, no tienen en cuenta los casos de fraudes detectados y consecuentemente presentan pruebas de hipótesis poco fiables. Utilizando una muestra de 118 compañías acusadas de fraude por la Comisión de Bolsa y Valores de EE.UU. (Securities and Exchange Commission, SEC), hemos estimado un modelo logit que corrige los problemas derivados de los fraudes no detectados en las compañías estadounidenses. Para evitar problemas de multicolinealidad, hemos extraído siete factores de 28 variables, utilizando el método de componentes principales. Nuestros resultados indican que sólo el 1,43% de los casos de FEF se han dado a conocer por la SEC. De las siete variables significativas incluidas en un modelo logit tradicional y no corregido, tres en efecto no fueron consideradas significativas en un modelo corregido. La probabilidad de FEF es 5,12 veces mayor cuando el auditor de la compañía emite una opinión adversa o con reservas.

Palabras clave:
Fraude contable; AAER; Errores de clasificación; Logit; Análisis factorial

ABSTRACT

Financial statement fraud (FSF) is costly for investors and can damage the credibility of the audit profession. To prevent and detect fraud, it is helpful to know its causes. The binary choice models (e.g. logit and probit) commonly used in the extant literature, however, fail to account for undetected cases of fraud and thus present unreliable hypotheses tests. Using a sample of 118 companies accused of fraud by the Securities and Exchange Commission (SEC), we estimated a logit model that corrects the problems arising from undetected frauds in U.S. companies. To avoid multicollinearity problems, we extracted seven factors from 28 variables using the principal factors method. Our results indicate that only 1.43 percent of the instances of FSF were publicized by the SEC. Of the six significant variables included in the traditional, uncorrected logit model, three were found to be actually non-significant in the corrected model. The likelihood of FSF is 5.12 times higher when the firm’s auditor issues an adverse or qualified report.

Keywords:
Accounting fraud; AAER; Misclassification; Logit; Factor analysis

Texto completo disponível apenas em PDF.

Full text available only in PDF format.

REFERÊNCIAS

ABBOTT, L. J.; PARK, Y.; PARKER, S. The effects of audit committee activity and independence on corporate fraud. Managerial Finance, Bradford v. 26, n. 11, p. 55-68, 2000.
ALBRECHT, W. S. et al. Fraud examination. 3rd. ed. Mason: South-Western Cengage, 2009.
ALTMAN, E. I. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, Malden, v. 23, n. 4, p. 589-609, Sept. 1968.
AMERICAN INSTITUTE OF CERTIFIED PUBLIC ACCOUNTANTS (AICPA). Consideration of fraud in a financial statement audit. AU Section 316. New York: AICPA, 2002.
BEASLEY, M. S. An empirical analysis of the relation between the board of director composition and financial statement fraud. The Accounting Review, Sarasota, v. 71, n. 4, p. 443-465, Oct. 1996.
BECKER, G. S. Crime and punishment: an economic approach. Journal of Political Economy, Chicago, v. 76, n. 2, p. 169-217, Mar./Apr. 1968.
BENEISH, M. D. Detecting GAAP violation: implications for assessing earnings management among firms with extreme financial performance. Journal of Accounting and Public Policy, New York, v. 16, n. 3, p. 271-309, Autumn 1997.
BENEISH, M. D. The detection of earnings manipulation. Financial Analysts Journal, Charlottesville, v. 55, n. 5, p. 24-36, Sept./Oct. 1999a.
BENEISH, M. D. Incentives and penalties related to earnings overstatements that violate GAAP. The Accounting Review, Sarasota, v. 74, n. 4, p. 425-457, Oct. 1999b.
BEST, P. J.; BUCKBY, S.; TAN, C. Evidence of the audit expectation gap in Singapore. Managerial Auditing Journal, Bradford, v. 16, n. 3, p. 134-144, 2001.
BRAZEL, J. F.; JONES, K. L.; ZIMBELMAN, M. F. Using nonfinancial measures to assess fraud risk. Journal of Accounting Research, Chicago, v. 47, n. 5, p. 1135-1166, July 2009.
CHEN, G.; FIRTH, M.; GAO, D.; RUI, O. Ownership structure, corporate governance, and fraud: evidence from China. Journal of Corporate Finance, London, v. 12, n. 3, p. 424-448, June 2006.
CHOI, J. H.; GREEN, B. P. Assessing the risk of management fraud through neural network technology. Auditing: A Journal of Practice & Theory, Sarasota, v. 16, n. 1, p. 14, Mar. 1997.
CHOO, F.; TAN, K. An “American dream” theory of corporate executive fraud. Accounting Forum, [S.l.], v. 31, n. 2, p. 203-215, June 2007.
CHUI, L.; PIKE, B. Auditors’ responsibility for fraud detection: new wine in old bottles? In: AMERICAN ACCOUNTING ASSOCIATION - ANNUAL MEETING, 2011, Denver. Proceedings… Sarasota: American Accounting Association - AAA, 2011.
COLEMAN, J. W. Toward an integrated theory of white-collar crime. American Journal of Sociology, Chicago, v. 93, n. 2, p. 406-439, Sept. 1987.
CRUTCHLEY, C. E.; JENSEN, M. R. H.; MARSHALL, B. B. Climate for scandal: corporate environments that contribute to accounting fraud. The Financial Review, Knoxville, v. 42, n. 1, p. 53-73, Feb. 2007.
DECHOW, P. M.; SKINNER, D. J. Earnings management: reconciling the views of accounting academics, practitioners, and regulators. Accounting Horizons, Sarasota, v. 14, n. 2, p. 235-250, June 2000.
DESHMUKH, A.; TALLURU, L. A rule-based fuzzy reasoning system for assessing the risk of management fraud. Intelligent Systems in Accounting, Finance & Management, Chichester, v. 7, n. 4, p. 223-241, Dec. 1998.
DOORNIK, J. A.; HANSEN, H. An omnibus test for univariate and multivariate normality. Oxford Bulletin of Economics and Statistics, Oxford, v. 70, p. 927-939, Dec. 2008.
ENGLE, R. F. Wald, likelihood ratio, and Lagrange multiplier tests in econometrics. In: GRILICHES, Z.; INTRILIGATOR, M. D. (Eds). Handbook of econometrics. Amsterdan: Norht-Holand, 1984. v. 2, p. 775-826.
ERICKSON, M.; HANLON, M.; MAYDEW, E. L. How much will firms pay for earnings that do not exist? Evidence of taxes paid on allegedly fraudulent earnings. The Accounting Review, Sarasota, v. 79, n. 2, p. 387-408, Apr. 2004.
ERICKSON, M.; HANLON, M.; MAYDEW, E. L. Is there a link between executive equity incentives and accounting fraud? Journal of Accounting Research, Chicago, v. 44, n. 1, p. 113-143, Mar. 2006.
ETTREDGE, M. L. et al. Is earnings fraud associated with high deferred tax and/or book minus tax levels? Auditing: A Journal of Practice & Theory, Sarasota, v. 27, n. 1, p. 1-33, May 2008.
FABRIGAR, L. R. et al. Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, Washington, D.C, v. 4, n. 3, p. 272-299, Sept. 1999.
FANNING, K.; COGGER, K. O. Neural network detection of management fraud using published financial data. Intelligent Systems in Accounting, Finance & Management, Chichester, v. 7, n. 1, p. 21-41, Mar. 1998.
FENG, M. et al. Why do CFOs become involved in material accounting manipulations? Journal of Accounting and Economics, Amsterdam, v. 51, n. 1-2, p. 21-36, Feb. 2011.
FRANCIS, J. R.; KRISHNAN, J. Accounting accruals and auditor reporting conservatism. Contemporary Accounting Research, Toronto, v. 16, n. 1, p. 135-165, Mar. 1999.
GAGANIS, C. Classification techniques for the identification of falsified financial statements: a comparative analysis. Intelligent Systems in Accounting, Finance & Management, Chichester, v. 16, n. 3, p. 207-229, July 2009.
GERETY, M.; LEHN, K. The causes and consequences of accounting fraud. Managerial and Decision Economics, Chichester, v. 18, n. 7/8, p. 587-599, Nov./Dec. 1997.
GUJARATI, D. N. Basic econometrics. 3rd ed. New York: McGraw-Hill, 1995.
HAUSMAN, J. A.; ABREVAYA, J.; SCOTT-MORTON, F. M. Misclassification of the dependent variable in a discrete-response setting. Journal of Econometrics, Amsterdam, v. 87, n. 2, p. 239-269, Sept. 1998.
JAMAL, K. Mandatory audit of financial reporting: a failed strategy for dealing with fraud. Accounting Perspectives, Toronto, v. 7, n. 2, p. 97-110, May 2008.
JENSEN, M. C.; MECKLING, W. H. Theory of the firm: managerial behavior, agency costs and ownership structure. Journal of Financial Economics, Amsterdam, v. 3, n. 4, p. 305-360, Oct. 1976.
JOHNSON, S. A.; RYAN, H. E.; TIAN, Y. S. Managerial incentives and corporate fraud: the sources of incentives matter. Review of Finance, Dordrecht, Oxford, v. 13, n. 1, p. 115-145, May 2008.
JÖRESKOG, K. G. A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, New York, v. 34, n. 2, p. 183-202, June 1969.
KAMINSKI, K. A.; WETZEL, T. S.; GUAN, L. Can financial ratios detect fraudulent financial reporting? Managerial Auditing Journal, Bradford, v. 19, n. 1, p. 15-28, 2004.
KIRKOS, E.; SPATHIS, C.; MANOLOPOULOS, Y. Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, [S.l.], v. 32, n. 4, p. 995-1003, May 2007.
LEE, T. A.; INGRAM, R. W.; HOWARD, T. P. The difference between earnings and operating cash flow as an indicator of financial reporting fraud. Contemporary Accounting Research, Toronto, v. 16, n. 4, p. 749-786, Winter 1999.
LEE, T. H.; GLOECK, J. D.; PALANIAPPAN, A. K. The audit expectation gap: an empirical study in Malaysia. Southern African Journal of Accountability and Auditing Research, [S.l.], v. 7, p. 1-15, 2007.
LENNOX, C.; PITTMAN, J. A. Big five audits and accounting fraud. Contemporary Accounting Research, Toronto, v. 27, n. 1, p. 209-247, Spring 2010.
LIN, J. W.; HWANG, M. I.; BECKER, J. D. A fuzzy neural network for assessing the risk of fraudulent financial reporting. Managerial Auditing Journal, Bradford, v. 18, n. 8, p. 657-665, 2003.
MILLER, G. S. The press as a watchdog for accounting fraud. Journal of Accounting Research, Chicago, v. 44, n. 5, p. 1001-1033, Dec. 2006.
OGUT, H. et al. Prediction of financial information manipulation by using support vector machine and probabilistic neural network. Expert Systems With Applications, Tarrytown, NY ,v. 36, n. 3, p. 5419-5423, Apr. 2009.
PEDNEAULT, S. Fraud 101. 3rd ed. Hoboken: John Wiley & Sons, 2009.
PERSONS, O. S. Using financial statement data to identify factors associated with fraudulent financial reporting. Journal of Applied Business Research, Littleton, v. 11, n. 3, p. 38-46, June 1995.
POIRIER, D. J. Partial observability in bivariate probit models. Journal of Econometrics, Amsterdam, v. 12, n. 2, p. 209-217, Feb. 1980.
POWERS, D. A.; XIE, Y. Statistical methods for categorical data analysis. San Diego: Academic Press, 2000.
SKOUSEN, C. J.; WRIGHT, C. J. Contemporaneous risk factors and the prediction of financial statement fraud. Journal of Forensic Accounting, Tampa, Florida, v. 9, n. 1, p. 37-62, 2008.
SPATHIS, C. Detecting false financial statements using published data: some evidence from Greece. Managerial Auditing Journal, Bradford, v. 17, v. 4, p. 179-191, 2002.
SUMMERS, S. L.; SWEENEY, J. T. Fraudulently misstated financial statements and insider trading: an empirical analysis. The Accounting Review, Sarasota, v. 73, n. 1, p. 131-146, Jan. 1998.
WANG, T. Y. Corporate securities fraud: insights from a new empirical framework. Oxford, 2011. (Journal of Law, Economics, and Organization). No prelo.

2
Processo de avaliação: Double Blind Review

Datas de Publicação

Publicação nesta coleção
Jul-Sep 2014

Histórico

Recebido
20 Fev 2013
Aceito
01 Out 2014

Este é um artigo publicado em acesso aberto sob uma licença Creative Commons

[1] ABBOTT, L. J.; PARK, Y.; PARKER, S. The effects of audit committee activity and independence on corporate fraud. Managerial Finance, Bradford v. 26, n. 11, p. 55-68, 2000.

[2] ALBRECHT, W. S. et al. Fraud examination. 3rd. ed. Mason: South-Western Cengage, 2009.

[3] ALTMAN, E. I. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, Malden, v. 23, n. 4, p. 589-609, Sept. 1968.

[4] AMERICAN INSTITUTE OF CERTIFIED PUBLIC ACCOUNTANTS (AICPA). Consideration of fraud in a financial statement audit. AU Section 316. New York: AICPA, 2002.

[5] BEASLEY, M. S. An empirical analysis of the relation between the board of director composition and financial statement fraud. The Accounting Review, Sarasota, v. 71, n. 4, p. 443-465, Oct. 1996.

[6] BECKER, G. S. Crime and punishment: an economic approach. Journal of Political Economy, Chicago, v. 76, n. 2, p. 169-217, Mar./Apr. 1968.

[7] BENEISH, M. D. Detecting GAAP violation: implications for assessing earnings management among firms with extreme financial performance. Journal of Accounting and Public Policy, New York, v. 16, n. 3, p. 271-309, Autumn 1997.

[8] BENEISH, M. D. The detection of earnings manipulation. Financial Analysts Journal, Charlottesville, v. 55, n. 5, p. 24-36, Sept./Oct. 1999a.

[9] BENEISH, M. D. Incentives and penalties related to earnings overstatements that violate GAAP. The Accounting Review, Sarasota, v. 74, n. 4, p. 425-457, Oct. 1999b.

[10] BEST, P. J.; BUCKBY, S.; TAN, C. Evidence of the audit expectation gap in Singapore. Managerial Auditing Journal, Bradford, v. 16, n. 3, p. 134-144, 2001.

[11] BRAZEL, J. F.; JONES, K. L.; ZIMBELMAN, M. F. Using nonfinancial measures to assess fraud risk. Journal of Accounting Research, Chicago, v. 47, n. 5, p. 1135-1166, July 2009.

[12] CHEN, G.; FIRTH, M.; GAO, D.; RUI, O. Ownership structure, corporate governance, and fraud: evidence from China. Journal of Corporate Finance, London, v. 12, n. 3, p. 424-448, June 2006.

[13] CHOI, J. H.; GREEN, B. P. Assessing the risk of management fraud through neural network technology. Auditing: A Journal of Practice & Theory, Sarasota, v. 16, n. 1, p. 14, Mar. 1997.

[14] CHOO, F.; TAN, K. An “American dream” theory of corporate executive fraud. Accounting Forum, [S.l.], v. 31, n. 2, p. 203-215, June 2007.

[15] CHUI, L.; PIKE, B. Auditors’ responsibility for fraud detection: new wine in old bottles? In: AMERICAN ACCOUNTING ASSOCIATION - ANNUAL MEETING, 2011, Denver. Proceedings… Sarasota: American Accounting Association - AAA, 2011.

[16] COLEMAN, J. W. Toward an integrated theory of white-collar crime. American Journal of Sociology, Chicago, v. 93, n. 2, p. 406-439, Sept. 1987.

[17] CRUTCHLEY, C. E.; JENSEN, M. R. H.; MARSHALL, B. B. Climate for scandal: corporate environments that contribute to accounting fraud. The Financial Review, Knoxville, v. 42, n. 1, p. 53-73, Feb. 2007.

[18] DECHOW, P. M.; SKINNER, D. J. Earnings management: reconciling the views of accounting academics, practitioners, and regulators. Accounting Horizons, Sarasota, v. 14, n. 2, p. 235-250, June 2000.

[19] DESHMUKH, A.; TALLURU, L. A rule-based fuzzy reasoning system for assessing the risk of management fraud. Intelligent Systems in Accounting, Finance & Management, Chichester, v. 7, n. 4, p. 223-241, Dec. 1998.

[20] DOORNIK, J. A.; HANSEN, H. An omnibus test for univariate and multivariate normality. Oxford Bulletin of Economics and Statistics, Oxford, v. 70, p. 927-939, Dec. 2008.

[21] ENGLE, R. F. Wald, likelihood ratio, and Lagrange multiplier tests in econometrics. In: GRILICHES, Z.; INTRILIGATOR, M. D. (Eds). Handbook of econometrics. Amsterdan: Norht-Holand, 1984. v. 2, p. 775-826.

[22] ERICKSON, M.; HANLON, M.; MAYDEW, E. L. How much will firms pay for earnings that do not exist? Evidence of taxes paid on allegedly fraudulent earnings. The Accounting Review, Sarasota, v. 79, n. 2, p. 387-408, Apr. 2004.

[23] ERICKSON, M.; HANLON, M.; MAYDEW, E. L. Is there a link between executive equity incentives and accounting fraud? Journal of Accounting Research, Chicago, v. 44, n. 1, p. 113-143, Mar. 2006.

[24] ETTREDGE, M. L. et al. Is earnings fraud associated with high deferred tax and/or book minus tax levels? Auditing: A Journal of Practice & Theory, Sarasota, v. 27, n. 1, p. 1-33, May 2008.

[25] FABRIGAR, L. R. et al. Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, Washington, D.C, v. 4, n. 3, p. 272-299, Sept. 1999.

[26] FANNING, K.; COGGER, K. O. Neural network detection of management fraud using published financial data. Intelligent Systems in Accounting, Finance & Management, Chichester, v. 7, n. 1, p. 21-41, Mar. 1998.

[27] FENG, M. et al. Why do CFOs become involved in material accounting manipulations? Journal of Accounting and Economics, Amsterdam, v. 51, n. 1-2, p. 21-36, Feb. 2011.

[28] FRANCIS, J. R.; KRISHNAN, J. Accounting accruals and auditor reporting conservatism. Contemporary Accounting Research, Toronto, v. 16, n. 1, p. 135-165, Mar. 1999.

[29] GAGANIS, C. Classification techniques for the identification of falsified financial statements: a comparative analysis. Intelligent Systems in Accounting, Finance & Management, Chichester, v. 16, n. 3, p. 207-229, July 2009.

[30] GERETY, M.; LEHN, K. The causes and consequences of accounting fraud. Managerial and Decision Economics, Chichester, v. 18, n. 7/8, p. 587-599, Nov./Dec. 1997.

[31] GUJARATI, D. N. Basic econometrics. 3rd ed. New York: McGraw-Hill, 1995.

[32] HAUSMAN, J. A.; ABREVAYA, J.; SCOTT-MORTON, F. M. Misclassification of the dependent variable in a discrete-response setting. Journal of Econometrics, Amsterdam, v. 87, n. 2, p. 239-269, Sept. 1998.

[33] JAMAL, K. Mandatory audit of financial reporting: a failed strategy for dealing with fraud. Accounting Perspectives, Toronto, v. 7, n. 2, p. 97-110, May 2008.

[34] JENSEN, M. C.; MECKLING, W. H. Theory of the firm: managerial behavior, agency costs and ownership structure. Journal of Financial Economics, Amsterdam, v. 3, n. 4, p. 305-360, Oct. 1976.

[35] JOHNSON, S. A.; RYAN, H. E.; TIAN, Y. S. Managerial incentives and corporate fraud: the sources of incentives matter. Review of Finance, Dordrecht, Oxford, v. 13, n. 1, p. 115-145, May 2008.

[36] JÖRESKOG, K. G. A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, New York, v. 34, n. 2, p. 183-202, June 1969.

[37] KAMINSKI, K. A.; WETZEL, T. S.; GUAN, L. Can financial ratios detect fraudulent financial reporting? Managerial Auditing Journal, Bradford, v. 19, n. 1, p. 15-28, 2004.

[38] KIRKOS, E.; SPATHIS, C.; MANOLOPOULOS, Y. Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, [S.l.], v. 32, n. 4, p. 995-1003, May 2007.

[39] LEE, T. A.; INGRAM, R. W.; HOWARD, T. P. The difference between earnings and operating cash flow as an indicator of financial reporting fraud. Contemporary Accounting Research, Toronto, v. 16, n. 4, p. 749-786, Winter 1999.

[40] LEE, T. H.; GLOECK, J. D.; PALANIAPPAN, A. K. The audit expectation gap: an empirical study in Malaysia. Southern African Journal of Accountability and Auditing Research, [S.l.], v. 7, p. 1-15, 2007.

[41] LENNOX, C.; PITTMAN, J. A. Big five audits and accounting fraud. Contemporary Accounting Research, Toronto, v. 27, n. 1, p. 209-247, Spring 2010.

[42] LIN, J. W.; HWANG, M. I.; BECKER, J. D. A fuzzy neural network for assessing the risk of fraudulent financial reporting. Managerial Auditing Journal, Bradford, v. 18, n. 8, p. 657-665, 2003.

[43] MILLER, G. S. The press as a watchdog for accounting fraud. Journal of Accounting Research, Chicago, v. 44, n. 5, p. 1001-1033, Dec. 2006.

[44] OGUT, H. et al. Prediction of financial information manipulation by using support vector machine and probabilistic neural network. Expert Systems With Applications, Tarrytown, NY ,v. 36, n. 3, p. 5419-5423, Apr. 2009.

[45] PEDNEAULT, S. Fraud 101. 3rd ed. Hoboken: John Wiley & Sons, 2009.

[46] PERSONS, O. S. Using financial statement data to identify factors associated with fraudulent financial reporting. Journal of Applied Business Research, Littleton, v. 11, n. 3, p. 38-46, June 1995.

[47] POIRIER, D. J. Partial observability in bivariate probit models. Journal of Econometrics, Amsterdam, v. 12, n. 2, p. 209-217, Feb. 1980.

[48] POWERS, D. A.; XIE, Y. Statistical methods for categorical data analysis. San Diego: Academic Press, 2000.

[49] SKOUSEN, C. J.; WRIGHT, C. J. Contemporaneous risk factors and the prediction of financial statement fraud. Journal of Forensic Accounting, Tampa, Florida, v. 9, n. 1, p. 37-62, 2008.

[50] SPATHIS, C. Detecting false financial statements using published data: some evidence from Greece. Managerial Auditing Journal, Bradford, v. 17, v. 4, p. 179-191, 2002.

[51] SUMMERS, S. L.; SWEENEY, J. T. Fraudulently misstated financial statements and insider trading: an empirical analysis. The Accounting Review, Sarasota, v. 73, n. 1, p. 131-146, Jan. 1998.

[52] WANG, T. Y. Corporate securities fraud: insights from a new empirical framework. Oxford, 2011. (Journal of Law, Economics, and Organization). No prelo.

Brasil