基于二部图的P2P网络借贷投资组合决策方法

Choosing Portfolios Based on Bipartite Graph of P2P Lending Networks

【目的】基于网贷数据,通过推荐算法和投资组合理论,帮助投资者选择投资产品、确定投资金额,从而提高投资者的满意度和收益率。【方法】基于人人贷交易数据,通过构建P2P场景下的二部图关系网络图,利用基于二部图的推荐算法和马科维茨投资组合理论为投资者确定投资产品和投资比例。【结果】实验结果表明,在不同的k值(5、15、25、35、45、50)下,简单权值改进的二部图推荐算法PNBI的准确率(0.055、0.044、0.039、0.035、0.036、0.032)均高于基于用户的协同过滤算法UCF(0.022、0.019、0.032、0.032、0.033、0.034)和基于物品的协同过滤算法ICF(0.007、0.013、0.014、0.014、0.014、0.014)。PNBI召回率同样高于其他两种算法。【局限】实验数据集有待进一步扩充。【结论】将推荐算法和组合理论相结合,可以显著提高投资者的满意度以及投资者最终的实际回报率。

[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 P2 P 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.