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

【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.