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Detecting management fraud in public companies / Mark Cecchini in Management science, Vol. 56 N° 7 (Juillet 2010) 

Public ISBD [article]  in Management science > Vol. 56 N° 7 (Juillet 2010) . - pp. 1146-1160 Titre : Detecting management fraud in public companies Type de document : texte imprimé Auteurs : Mark Cecchini, Auteur ; Haldun Aytug, Auteur ; Gary J. Koehler, Auteur Année de publication : 2010 Article en page(s) : pp. 1146-1160 Note générale : Management Langues : Anglais (eng) Mots-clés : Management  fraud  Classification  Support  vector  machines  Financial  event  detection  Kernel  methods Index. décimale : 658 Organisation des entreprises. Techniques du commerce Résumé : This paper provides a methodology for detecting management fraud using basic financial data. The methodology is based on support vector machines. An important aspect therein is a kernel that increases the power of the learning machine by allowing an implicit and generally nonlinear mapping of points, usually into a higher dimensional feature space. A kernel specific to the domain of finance is developed. This financial kernel constructs features shown in prior research to be helpful in detecting management fraud. A large empirical data set was collected, which included quantitative financial attributes for fraudulent and nonfraudulent public companies. Support vector machines using the financial kernel correctly labeled 80% of the fraudulent cases and 90.6% of the nonfraudulent cases on a holdout set. Furthermore, we replicate other leading fraud research studies using our data and find that our method has the highest accuracy on fraudulent cases and competitive accuracy on nonfraudulent cases. The results validate the financial kernel together with support vector machines as a useful method for discriminating between fraudulent and nonfraudulent companies using only publicly available quantitative financial attributes. The results also show that the methodology has predictive value because, using only historical data, it was able to distinguish fraudulent from nonfraudulent companies in subsequent years. DEWEY : 658 ISSN : 0025-1909 En ligne : http://mansci.journal.informs.org/content/56/7.toc [article] Detecting management fraud in public companies [texte imprimé] / Mark Cecchini, Auteur ; Haldun Aytug, Auteur ; Gary J. Koehler, Auteur . - 2010 . - pp. 1146-1160. Management Langues : Anglais (eng) in Management science > Vol. 56 N° 7 (Juillet 2010) . - pp. 1146-1160 Mots-clés : Management  fraud  Classification  Support  vector  machines  Financial  event  detection  Kernel  methods Index. décimale : 658 Organisation des entreprises. Techniques du commerce Résumé : This paper provides a methodology for detecting management fraud using basic financial data. The methodology is based on support vector machines. An important aspect therein is a kernel that increases the power of the learning machine by allowing an implicit and generally nonlinear mapping of points, usually into a higher dimensional feature space. A kernel specific to the domain of finance is developed. This financial kernel constructs features shown in prior research to be helpful in detecting management fraud. A large empirical data set was collected, which included quantitative financial attributes for fraudulent and nonfraudulent public companies. Support vector machines using the financial kernel correctly labeled 80% of the fraudulent cases and 90.6% of the nonfraudulent cases on a holdout set. Furthermore, we replicate other leading fraud research studies using our data and find that our method has the highest accuracy on fraudulent cases and competitive accuracy on nonfraudulent cases. The results validate the financial kernel together with support vector machines as a useful method for discriminating between fraudulent and nonfraudulent companies using only publicly available quantitative financial attributes. The results also show that the methodology has predictive value because, using only historical data, it was able to distinguish fraudulent from nonfraudulent companies in subsequent years. DEWEY : 658 ISSN : 0025-1909 En ligne : http://mansci.journal.informs.org/content/56/7.toc