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Classification techniques for the identification of falsified financial statements: a comparative analysis

GaganisChrysovalantis

Intelligent Systems in Accounting, Finance and Management: An International Journal2009https://doi.org/10.5555/1598951.1598953article
ABDC B
Weight
0.34

Abstract

In this study, I develop 10 alternative classification models using logit analysis, discriminant analysis, support vector machines, artificial neural networks, probabilistic neural networks, neares...

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https://doi.org/https://doi.org/10.5555/1598951.1598953

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@article{gaganischrysovalantis2009,
  title        = {{Classification techniques for the identification of falsified financial statements: a comparative analysis}},
  author       = {GaganisChrysovalantis},
  journal      = {Intelligent Systems in Accounting, Finance and Management: An International Journal},
  year         = {2009},
  doi          = {https://doi.org/https://doi.org/10.5555/1598951.1598953},
}

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Classification techniques for the identification of falsified financial statements: a comparative analysis

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Evidence weight

0.34

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.00 × 0.4 = 0.00
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.