Back
 JFRM  Vol.6 No.2 , June 2017
Research on P2P Network Loan Risk Evaluation Based on Generalized DEA Model and R-Type Clustering Analysis under the Background of Big Data
Abstract: Internet financial risk is not only directly related to the operation and development of the Internet financial system itself, but also has a very important impact on the country’s macroeconomic operation because of its rapid development speed and growing scale of development. As of February 2017, there were 2335 network loan platforms, among which 55 platforms for problem existed. The event, similar to the platform responsible person absconded with money frequently occurred due to lax supervision, credit risk and so on. Therefore, it is very important to evaluate the financial risks of Internet scientifically. This paper takes the top 100 P2P network loan platform risk controls, obtained the net loan home’s rating authentication, as the main research object. The evaluation index system is structured from three dimensions, respectively as follows: liquidity risk, market risk and credit risk. The R-type cluster analysis is used to reduce the dimension of the index system, and the core index evaluation system is obtained finally. On the basis of this, the risk control capability efficiency of that was evaluated for the first time by the classical DEA-CCR model, and then carried out the excellent, the good, the medium and the poor risk control capacity efficiency rating according to the pre-set step size. The excellent refers to the network loan platforms whose ranking is in the first quarter of the comprehensive efficiency derived by DEA-CCR; non-excellent network loan platform refers to the study of 100 network lending platforms in addition to the excellent lending platform other than the research platform. Taking the Excellent P2P network loan platforms as the reference set and the Non-excellent as the evaluation set, this paper also uses the new generalized DEA model to carry on the research of the “catch-up efficiency” and projection analysis, and obtains the projection value of the non-excellent network lending platform, that is, the improvement value of the non-excellent network lending platform in each research index, and provides a feasible way for the non-excellent P2P network loan platforms to change to the excellent P2P network loan platforms.
Cite this paper: Lv, X. , Zhou, L. and Guo, X. (2017) Research on P2P Network Loan Risk Evaluation Based on Generalized DEA Model and R-Type Clustering Analysis under the Background of Big Data. Journal of Financial Risk Management, 6, 163-190. doi: 10.4236/jfrm.2017.62013.
References

[1]   Afsharian, M., Ahn, H., & Neumann, L. (2016). Generalized DEA: An Approach for Supporting Input/Output Factor Determination in DEA. Benchmarking: An International Journal, 23, 1892-1909.
https://doi.org/10.1108/BIJ-07-2015-0074

[2]   Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the Efficiency of Decision Making Units. European Journal of Operational Research, 2, 429-444.
https://doi.org/10.1016/0377-2217(78)90138-8

[3]   Jain, A. K., & Dubes, R. C. (1988). Algorithms for Clustering Data. Upper Saddle River, NJ: Prentice-Hall, Inc.
http://dl.acm.org/citation.cfm?id=SERIES10022.42779

[4]   Lin, X. D. (2015). Research on Credit Risk Measurement of Loan Platform in P2P Network Loan in China. Thesis, Guangzhou: South China University of Technology.

[5]   Liu, C. Y., & Lv. X. M. (2016). Research on “Catching Efficiency” of Urban Commercial Banks Based on Generalized DEA. Report of Inner Mongolia Finance Society, 2016, 11.

[6]   Lv, X. M. (2016). Research on Evaluation of Innovation Ability of A-Share Listed Banks under the Impact of Internet Finance—Based on Panel Data Generalized DEA Model. Journal of Accounting and Economic, 30, 96-114.

[7]   Ma, Z. X. (2012a). DEA Model with Generalized Reference Set and Its Properties. Systems Engineering and Electronics, 34, 709-714.

[8]   Ma, Z. X. (2012b). Generalized Data Envelopment Analysis. Beijing: Science Press.

[9]   Ma, Z. X., & Lv, X. M. (2007). Research on Sample Data Envelopment Analysis Method of Preference Cone. Journal of Systems Engineering and Electronics, 29, 1275-1282.

[10]   Ma, Z. X., & Zhao, C. Y. (2016). Measure Method for Efficiency of Generalized DEA. Journal of Systems Engineering and Electronics, 38, 2572-2585.

[11]   Ouyang, Z. S., & Mo, T. H. (2016). Research on Internet Financial Risk Measurement and Evaluation. Journal of Hunan University of Science and Technology (Social Science Edition), 19, 173-178.

[12]   Sha, H. J. (2015). P2P Network Loan Platform Risk Research—“Jin Rong Yun” Collapse Case as an Example. Journal of Anhui University of Finance and Economics.

[13]   Si, S. K., & Sun, X. J. (2011). Mathematical Modeling Algorithm and Application. Beijing: Defense Industry Press.

[14]   Wang, L. Y., & Shi, Y. (2016). Research on Internet Financial Risk Mechanism and Risk Measurement—Taking P2P Network Loan as an Example. Journal of Southeast University (Philosophy and Social Sciences), 18, 103-112.

[15]   Zhang, T., Ramakrishnan, R., & Livny, M (1996). BIRCH: An Efficient Data Clustering Method for Very Large Databases. ACM SIGMOD Record, 25, 103-114.
https://doi.org/10.1145/235968.233324

 
 
Top