Credit rating optimal weight vector method based on maximum default discriminating ability approximating ideal point

A technology approaching the ideal point and identifying ability, applied in the field of credit services, can solve the problems of not reflecting the relationship between rating results and real default status

Inactive Publication Date: 2018-08-03
DALIAN UNIV OF TECH
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Problems solved by technology

[0011] Although the above-mentioned objective weighting reflects the relationship between the index weight and

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  • Credit rating optimal weight vector method based on maximum default discriminating ability approximating ideal point
  • Credit rating optimal weight vector method based on maximum default discriminating ability approximating ideal point
  • Credit rating optimal weight vector method based on maximum default discriminating ability approximating ideal point

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Embodiment Construction

[0059] The specific implementation manners of the present invention will be further described below in conjunction with the accompanying drawings and technical solutions.

[0060] The purpose of the present invention is to provide an optimal weight determination method that maximizes the default identification ability of credit rating results.

[0061] The purpose of the present invention is achieved through the following technical solutions:

[0062] The first objective function is to take the minimum of the algebraic sum of the Euclidean distance from the credit score of the non-defaulting enterprise to the positive ideal point, and the minimum of the algebraic sum of the Euclidean distance from the credit score of the defaulting enterprise to the negative ideal point. Taking the minimum degree of dispersion of "the distance between the score of non-defaulting enterprises and the positive ideal point" and the minimum degree of dispersion of "the distance between the score of...

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Abstract

The invention provides a credit rating optimal weight vector determining method based on the maximum default discriminating ability approximating an ideal point, which belongs to the technical field of credit services. The minimum algebraic sum of the Euclidean distance from the credit score of a non-defaulting enterprise to a positive ideal point and the minimum algebraic sum of the Euclidean distance from the credit score of a defaulting enterprise to a negative ideal point are uses as the first objective function. The minimum dispersion degree of the distance between the score of the non-defaulting enterprise and the positive ideal point and the minimum dispersion degree of the distance between the score of the defaulting enterprise and the negative ideal point are used as the second objective function. A multi-objective programming function is constructed to reverse a group of optimal weights of a credit rating equation. The size of the score of the credit rating equation can significantly distinguish non-defaulting or defaulting customers. The rating result of the credit rating equation is that the score of the non-defaulting enterprise is the highest, while the score of the defaulting enterprise is the lowest. Crossover between two types of samples is minimized.

Description

technical field [0001] The invention provides a method for determining the optimal weight vector of a credit rating index, which maximizes the default identification ability of a credit rating system and belongs to the technical field of credit services. Background technique [0002] Credit rating has an extremely important impact on today's economy and society. Whether it is sovereign credit rating, corporate credit rating, bank credit rating, or personal credit rating. If the credit rating results are unreasonable and the risk of default cannot be accurately assessed, it will definitely mislead investors and the public. Small enough to lead to the collapse of banks and enterprises, big enough to trigger a financial crisis, and even the entire economic and social disorder. A reasonable credit rating system must have strong default identification capabilities, be able to effectively distinguish default customers from non-default customers, and accurately identify customers...

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Application Information

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IPC IPC(8): G06Q30/00G06Q40/02
CPCG06Q30/018G06Q40/03
Inventor 迟国泰李鸿禧周颖
Owner DALIAN UNIV OF TECH
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