Earticle

초록

영어

General warning method of electronic banks generates based on clear detection of relevant data. However, this method is mostly a reflection of electronic banks’ risks in the past which is a manifestation of historical data that can only be able to analyze problems that have already occurred but can not give a better future anticipation. Therefore early warning system established in this paper is based on the data detection system and we use an optimized gray early warning method to predict the indicators of the risk of electronic banks. Based on the idea of using descending cumulation to change traditional gray 1-AGO sequence, we use weaken buffer operators to deal with the original data sequence and then use genetic algorithm to estimate a and b--important parameters of background values. Eventually the optimized gray GM (1,1) model prediction method generate and it can effectively forecast the risk profile of electronic banks. Then analyze the results, further read and dig the hidden meaning, give a series of practical conclusions for the development, risk prevention and control of electronic banks. Decision making depends on the degree of risk in the future. Then we can take effective measures to cope with the arrival of electronic bank crisis that may arise.

목차

Abstract
 1. Introduction
 2. Establishment of Traditional Gray Model
 3. Study on RiskPortfolio Prediction of Electronic Banks based on the Improved GM (1,1) Model
  3.1. Basic Principle of Buffer Operator
  3.2. GM（1,1）Model and its Optimization
 4. Numerical Example and Result Analysis
 References

키워드

저자

• Tong Wang [ School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605