面向光伏项目投资风险的大数据监测指标甄选研究——以Solarbao平台为例  

Detecting Investment Risks of Photovoltaic Projects with Big Data: Case Study of Solarbao.com

作　　者：杨旸[1] 林辉[1] 胡广伟[2] 

机构地区：[1]南京大学商学院,南京210093 [2]南京大学信息管理学院,南京210093

出　　处：《现代图书情报技术》2016年第11期11-19,共9页New Technology of Library and Information Service

基　　金：国家电网科技开发项目"提升电力营销服务能力的大数据关键技术研究"(项目编号:SGTYHT/14-JS-188);国家自然科学基金面上项目"双维度流动性调整的期权定价模型研究"(项目编号:71271110);江苏省"六大人才高峰"项目"政务大数据资源开发技术与实现方法研究"(项目编号:2015-XXRJ-001);中国经济改革研究基金会课题"互联网金融的风险与监管制度研究"的研究成果之一

摘　　要：【目的】在构建光伏项目投资风险监测模型的过程中,为了甄选面向互联网金融平台的大数据应用监测指标,尝试提出系统的甄选方案并结合实际案例进行验证。【方法】应用大数据监测模型,整合Solarbao平台多源异构数据,以专家判断为项目投资风险分析依据,运用CHAID决策树归纳多维监测指标组合,并运用R-Q型因子分析方法提炼识别投资风险的关键指标。【结果】得到8条监测光伏项目投资风险的指标组合和10项识别投资风险的关键指标。【局限】R-Q型因子分析中的专业指标有待进一步细分并形成动态更新机制。【结论】该甄选方案能够满足大数据监测模型对指标采集的要求,对投资者评估光伏项目风险、平台筛选合适项目以及监管部门排查该领域系统性风险具有借鉴意义。[Objective] This research proposes a selection scheme for the big data application to monitor the Intemet financial platforms, which is verified by the real world cases. [Methods] First, we adopted a big data model to integrate multi-source heterogeneous data from the Solarbao platform. Second, we utilized the CHAID decision tree to summarize multi-dimensional monitoring indicators based on analysis of each project＇s investment risks. Finally, we employed the R-Q factor analysis method to extract the key investment risks. [Results] We got 8 indicators to track the investment risks, which could be identified by the other 10 indicators for the photovoltaic projects. [Limitations] More research needs to be done with indicators of the R-Q factor analysis, which also requires a dynamic update mechanism. [Conclusions] The proposed scheme could help investors assess the risks of individual projects and then select the appropriate ones. It will also support the risk management work of the regulatory agencies.

关 键 词：大数据监测指标 光伏项目 投资风险 CHAID决策树 R-Q型因子分析 

分 类 号：F426.61[经济管理—产业经济]