Detection of financial information manipulation by an ensemble-based mechanism

Title:

Detection of financial information manipulation by an ensemble-based mechanism

Creator:

Shin , Ching-Hui, Lin, Sin-Jin, and Hsu , Ming-Fu

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:6e1440e2-b3a8-4d47-985c-3b6b2ee374fe
uuid:6e1440e2-b3a8-4d47-985c-3b6b2ee374fe
doi:10.14311/NNW.2014.24.028

Subject:

Feature selection and extraction ensemble, decision making, extreme learning machine, and financial information manipulation

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Complicated financial information manipulation, involving heightened offender knowledge of transactional procedures, can be damaging to the reputations of corporations and the auditors, as well as cause serious turbulence in financial markets. Unfortunately, most incidents of financial information manipulation involve higher level managers who are truly knowledgeable and comprehend the limitations of standard auditing procedures. Thus, there is an urgent need for additional detection mechanisms to prevent financial information manipulation. To address this problem, the author proposes an ensemble-based mechanism (EM) consisting of feature selection and extraction ensemble and extreme learning machine (ELM). The model not only counters the redundancy-removing problem, but also gives direction to auditors who need to allocate limited audit resources to abnormal client relationships during the auditing procedure and protect the CPA firms reputation. The experimental results demonstrate that the model is a promising alternative for detecting financial information manipulation, and one that can ensure both the confidence of investors and the stability of financial markets.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2014 Volume:24 | Number:5

Harvested from:

CDK

Metadata only:

false