Method for evaluating P2P (Peer-to-Peer) network lending platform development index

A P2P network and platform technology, applied in the evaluation field of P2P network lending platform development index

Inactive Publication Date: 2017-05-10
WUHAN UNIV OF SCI & TECH
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  • Abstract
  • Description
  • Claims
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Problems solved by technology

[0006] Furthermore, the P2P online lending industry has accumulated a certain amount of operational data. These operational data imply key information such as the importance of indicators. Ho

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  • Method for evaluating P2P (Peer-to-Peer) network lending platform development index
  • Method for evaluating P2P (Peer-to-Peer) network lending platform development index
  • Method for evaluating P2P (Peer-to-Peer) network lending platform development index

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[0059] The following describes in detail a method for evaluating the development index of a P2P network lending platform involved in the present invention with reference to the accompanying drawings and embodiments.

[0060] figure 1 It is a flowchart of a method for evaluating the development index of a P2P network lending platform in an embodiment of the present invention.

[0061] Such as figure 1 As shown, a P2P network lending platform development index evaluation method has the following steps:

[0062] Step A: Establish a non-linear mathematical programming model of the improved TOPSIS method with data-driven weighting, and proceed to step B.

[0063] Step A includes the following steps:

[0064] Step A-1: ​​Determine the evaluation object A i The (standardized) evaluation value of (i=1,2,...,m) is X i =(x i1 ,x i2 ,...,X in ), go to step A-2.

[0065] Step A-2: Define evaluation object A i And A k Similarity

[0066]

[0067] Where ||X i -X k ||On behalf of X i And X k For th...

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Abstract

The invention provides a method for evaluating a P2P (Peer-to-Peer) network lending platform development index, provides an improved TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) evaluation model based on data-driven weighting, can provide a reliable evaluation model for P2P network lending evaluation by use of an intelligent optimization solution model, and provides a reasonable reference advice for investment decision-making. The method is characterized by comprising the following steps: step A, establishing a non-linear mathematical programming model of data-driven weighting with an improved TOPSIS method; step B, solving the non-linear mathematical programming model by use of an intelligent optimization algorithm to obtain an optimal weight as shown in the specification of the TOPSIS method; and step C, solving by use of operational data, which are grabbed by data crawlers, of a P2P network lending platform and by use of the intelligent optimization algorithm to obtain an optimal weight as shown in the specification, and establishing a TOPSIS evaluation model for the P2P network lending platform development index.

Description

technical field [0001] The invention relates to the field of multi-attribute decision-making, in particular to an evaluation method for the development index of a P2P network lending platform. Background technique [0002] P2P network lending (peer-to-peer lending) is an emerging personal-to-personal direct credit model in recent years. my country's first P2P online lending platform was established in 2007, and then it showed an explosive growth trend. According to the first network lending data, as of December 2015, the total number of P2P online lending platforms in my country reached 4,948, with a total transaction amount of 1,180.565 billion and a loan balance of 558.22 billion. [0003] The operation of P2P network lending has had an important impact on economic operation and investment decision-making. How to scientifically evaluate the development potential of P2P network lending platforms has become an important practical issue. At present, most of the research on P...

Claims

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Application Information

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IPC IPC(8): G06Q40/02G06Q40/06
CPCG06Q40/06G06Q40/03
Inventor 刘翱邓旭东李维刚徐芳芳任亮
Owner WUHAN UNIV OF SCI & TECH
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