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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (12): 76-83    DOI: 10.11925/infotech.2096-3467.2019.0357
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Choosing Portfolios Based on Bipartite Graph of P2P Lending Networks
Yong Ding1,2,Lu Cheng1(),Cuiqing Jiang1,2
1 School of Management, Hefei University of Technology, Hefei 230009, China
2 Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education, Hefei 230009, China
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Abstract  

[Objective] This paper proposes a method based on recommendation algorithm, portfolio theory and the actual data of China’s online lending market, aiming to help investors make better decisions. [Methods] We collected data from Renren’s Loan Transaction and constructed a bipartite graph network graph for the P2P scenario. Then, we used the recommendation algorithm and Markowitz portfolio theory to choose the investment products. [Results] Under different K values, the accuracy of the improved bipartite graph recommendation algorithm with simple weight were 0.055, 0.044, 0.039, 0.035, 0.036 and 0.032. These results were higher than those of the user-based collaborative filtering algorithms UCF (0.022, 0.019, 0.032, 0.032, 0.033, 0.034) and item-based collaborative filtering algorithms ICF (0.007, 0.013, 0.014, 0.014, 0.014, 0.014). The recall rate was also higher than those of the other two algorithms. [Limitations] The sample dataset needs to be expanded. [Conclusions] Combining recommendation algorithm with group theory could find portfolios with better return of investments.

Key wordsP2P Network Lending      Bipartite Graph      Recommendation Algorithm      Investment Portfolio      Decision Method     
Received: 03 April 2019      Published: 25 January 2020
ZTFLH:  TP391  
Corresponding Authors: Lu Cheng     E-mail: chengluhs@163.com

Cite this article:

Yong Ding,Lu Cheng,Cuiqing Jiang. Choosing Portfolios Based on Bipartite Graph of P2P Lending Networks. Data Analysis and Knowledge Discovery, 2019, 3(12): 76-83.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.0357     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I12/76

投资者 v1 v2 v3 v4 v5 v6 v7 v8 v9 v10
1 15 309 11 737 17 135 16 944 15 181 14 584 11 633 19 976 12 988 14 349
2 11 988 12 097 18 336 9 429 9 387 11 780 7 095 12 056 12 988 11 575
3 13 640 13 073 13 084 14 352 14 062 12 988 19 102 19 976 13 681 13 946
4 15 919 11 737 15 112 18 901 11 633 15 579 19 976 16 944 20 084 11 735
5 15 919 17 973 17 065 15 563 19 172 16 944 16 149 18 296 19 976 19 085
投资者 w1 w2 w3 w4 w5 w6 w7 w8 w9 w10 Ri Ei
1 1.38% 11.19% 1.46% 1.29% 0.03% 5.01% 64.45% 2.13% 12.01% 1.05% 0.54% 15.00%
2 4.18% 6.67% 28.27% 1.04% 6.43% 0.16% 0.07% 32.16% 11.46% 9.56% 0.52% 12.27%
3 3.09% 6.37% 4.12% 0.02% 72.47% 10.13% 0.72% 1.80% 1.02% 0.27% 0.49% 9.88%
4 0.68% 12.63% 0.00% 2.23% 72.80% 0.06% 2.41% 1.46% 2.44% 5.28% 0.57% 14.62%
5 0.67% 1.76% 1.51% 5.47% 10.41% 1.42% 73.77% 0.01% 2.35% 2.63% 0.56% 12.31%
K
算法
5 15 25 35 45 50
PNBI 0.055 0.044 0.039 0.035 0.036 0.032
UCF 0.022 0.019 0.032 0.032 0.033 0.034
ICF 0.007 0.013 0.014 0.014 0.014 0.014
K
算法
5 15 25 35 45 50
PNBI 0.032 0.077 0.113 0.143 0.167 0.170
UCF 0.053 0.072 0.079 0.079 0.081 0.083
ICF 0.016 0.032 0.034 0.034 0.034 0.034
K
算法
5 10 20 30 40 50
PNBI 0.139 0.136 0.134 0.133 0.132 0.132
UCF 0.138 0.133 0.128 0.129 0.131 0.129
ICF 0.137 0.134 0.132 0.13 0.128 0.125
无推荐 0.114 0.114 0.114 0.114 0.114 0.114
K 5 10 20 30 40 50
PNBI 9.929 8.726 6.253 5.576 4.738 4.092
UCF 9.125 8.247 7.125 5.236 4.978 3.968
ICF 7.719 4.125 3.283 3.015 2.863 1.238
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