Method for identifying abnormality of customers in futures market

An identification method and customer technology, applied in the information field, can solve the problems of high misjudgment, low efficiency, abnormal identification method, etc.

Inactive Publication Date: 2012-06-13
HARBIN INST OF TECH
View PDF3 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] In order to solve the problem of low efficiency and high degree of misjudgment for futures companies to analyze the credit risk of customers based on manpower, the present invention provides a method for identifying abnormalities of customers in the futures market

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method for identifying abnormality of customers in futures market
  • Method for identifying abnormality of customers in futures market
  • Method for identifying abnormality of customers in futures market

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment approach 1

[0044] Specific implementation mode one: the following combination figure 1 Describe this implementation mode, this implementation mode comprises the following steps:

[0045] Step 1: Obtain the registration information and transaction data of G customers, PF k Indicates the kth customer, G is an integer greater than 600, k=1, 2, 3...G;

[0046] Step 2: According to the registration information and transaction data of G customers, extract the feature quantity F of each customer k (l), wherein l is an integer, l=1,2,3...24;

[0047] Step 3: According to the customer's characteristic quantity F k (l), using p-norm distance to measure customer PF b and customer PF g The similarity distance between Dist(PF b , PF g ):

[0048] Dist ( PF b , PF g ) = ( Σ ...

specific Embodiment approach 2

[0059] Specific implementation mode 2: This implementation mode is a further description of implementation mode 1. In the step 4, the PF of each customer is obtained k The cluster center sample fc to which it belongs k The specific method is:

[0060] Step 41: From the global distance matrix D G×G Get customer PF b and customer PF g The similarity between Sim(b, g), establish a global similarity matrix S G×G :

[0061] S G × G = Sim ( 1,1 ) Sim ( 1,2 ) · · · · · · ...

specific Embodiment approach 3

[0068] Specific embodiment three: this embodiment is a further description of embodiment two, the feedback matrix R after LT times of iterative update is obtained in the step four or three G×G and validity matrix A G×G The specific process is:

[0069] Step 431: The feedback matrix R obtained in step 42 G×G All elements of r(i, j) are updated to r(i.j) * =λr tmp (i,j)+(1-λ)r old (i,j),

[0070] where λ is the update coefficient, r old (i, j) is the r(i, j) obtained from the previous iteration update, r tmp (i, j) is shown in the following formula:

[0071] r tmp ( i , j ) = Sim ( i , j ) - max ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a method for identifying the abnormality of customers in a futures market, belongs to the technical field of information, and solves the problems of low efficiency and high mal-judgment degree in manual analysis of the credit risk of the customers. The method comprises the following steps of: acquiring the registration information and transaction data of G customers; according to the registration information and transaction data of the G customers, extracting the characteristic quantity Fk (1) of each customer; according to the characteristic quantity Fk (1) of each customer, establishing a global distance matrix D(G*G); establishing an active matrix A(G*G) and a feedback matrix R(G*G), and acquiring a cluster center sample fck of each customer PFk according to a similar distance Dist (PFb, PFg) between a customer PFb and a customer PFg; and gathering the customers PFk with the same cluster center samples fck into a customer cluster, and determining that the customer cluster having minimum customers is abnormal. The method is applicable to identification of the customers in the futures market.

Description

technical field [0001] The invention relates to a method for identifying abnormalities of customers in a futures market, belonging to the field of information technology. Background technique [0002] The introduction of stock index futures is an objective requirement for the development of my country's capital market to a certain stage. It enables stock holders to transfer the risks of stocks in their hands through stock index futures, thereby increasing the market balance and is conducive to the stability of the entire market. At the same time, stock index futures also contain huge risks. Once stock index futures are used or managed improperly, it may bring huge losses to investors and even disrupt the country's financial order. Therefore, how to effectively prevent and manage the risk of stock index futures is particularly important. [0003] Due to the imperfection of our country's market system, our country's stock index futures have risks brought about by the transact...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06Q40/04
Inventor 刘金福贺惠新俞福福
Owner HARBIN INST OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products