摘要:
随着互联网技术的快速发展，在线P2P借贷市场投资推荐已经成为网络金融领域的重要研究方向.对于P2P借贷市场潜在投资者来说，需解决的关键问题包括2个方面：1)如何选择真正符合自己投资需求和偏好的投资项目；2)如何将自己的投资金额在这些投资项目中进行合理分配.以往关于这两者的研究主要是侧重在借贷项目的违约风险预测、投资项目全局推荐及投资组合优化等方面.而随着研究的深入可以发现，仍在投资者效用无差异假设及投资者历史交易数据的基础上设计推荐模型，将难以满足不同风险偏好投资者的投资决策需求，保证推荐的有效性.鉴于此，1)基于Prosper平台历史数据建立P2P关联网络模型，并分别计算借贷项目和投资者的概念特征，得出相应的概念模型；2)进一步考察P2P关联网络模型中的投资者朋友关系，以捕获投资者之间投资行为的相互影响，发掘投资者投资行为的影响因子，并将其应用于借贷项目兴趣度的预测，以提高投资项目推荐的有效性；3)在此基础上，从预期效用理论出发，进一步考虑投资者风险偏好对投资需求的影响，建立个性化投资组合推荐框架，以提高其投资的满意度和经济性能；4)将其推荐结果与其他基准模型的推荐结果进行对比分析，以综合评价其推荐效果.在Prosper平台真实数据的基础上进行了详细的实验测试，结果表明:该方法相较于传统的投资推荐方法具有更好的推荐效果.
Abstract:
With the rapid development of Internet technology, online P2P lending market investment recommendation has become an important research direction in the field of online finance. For potential investors in P2P lending market, there are two key issues which need to be solved. One is how to choose the right investment projects considering their investment needs and preferences, the other is how to reasonably allocate their investment amount to these projects. Previous studies on these two questions mainly focused on default risk prediction of lending projects, global optimal product recommendation and portfolio optimization. With further research, limitations of recommendation model which was designed based on historical transaction data and utility indifference of investors have become increasingly prominent. It does not meet the investment decision needs of investors with different risk preferences. In view of this, firstly, based on the historical data of Prosper platform, this paper establishes the P2P relationship network model and calculates the concept features of the lending projects and investors separately, and then obtains the corresponding conceptual model. Secondly, we build friend network model to capture the mutual influence of investment behavior among investors, excavate investment behavior influence factor and take it as indicator variables of investment interest prediction, then generate a candidate investment recommendation list. Thirdly, in order to optimize the shares of each recommended candidate, individualized portfolio recommended framework is constructed based on expected utility theory considering the influence of risk preference difference on investment demand of investors. Finally, the recommendation results of our model are compared with other benchmark models to comprehensively evaluate its recommend effect. We implement experiments on real datasets of Prosper platform, experimental results demonstrate that our method has better recommendation quality than traditional investment recommendation method.
百度学术
下载:
