Earticle

초록

영어

Finance fraud of companies is an international difficult problem with a long history. The finance fraud problem is concerned by lots of people. Some researchers make a lot of qualitative or quantitative researches and get some valuable conclusions. In this article , we mainly applies empirical research method, combined with normative research method. First of all, this paper reviews the relevant literatures of financial fraud detecting of listed companies, expounds existing research results from the aspects of motives, signs and detecting methods. We appraise these results are ordering to national conditions and characteristics, analyze the definition of financial fraud. We established a new method which is partial least squares (PLS) and support vector regression (SVR) to solve the above problem in finance. The PLS are able to reduce dimension effectively, acquire nonlinear factor matrix, and SVR has many advantages, such as high imitation degree, effective classification and strong robustness. The model which combines PLS and SVR has great recognition effect.

목차

Abstract
 1. Introduction
 2. The Basic Model of SVR
 3. The Types and Selection of Kernel Function
 4. The PLS-SVR Model with Mixed Kernel Function
  4.1 PLS Regression Model
  4.2 PLS-SVR Model
 5. The Simulation and Conclusion
  5.1 Sample selection
  5.2 Select the index variable
  5.3 Results and conclusion
 References

키워드

저자

• Zhang Chen [ Logistics Management Department, Hunan Communication Polytechnic No. 635, South Shaoshan Road, Yuhua District, Changsha City, Hunan Province, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605