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Detecting Investment Risks of Photovoltaic Projects with Big Data: Case Study of Solarbao.com |
Yang Yang1,Lin Hui1,Hu Guangwei2() |
1School of Business, Nanjing University, Nanjing 210093, China 2School of Information Management, Nanjing University, Nanjing 210093, China |
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Abstract [Objective] This research proposes a selection scheme for the big data application to monitor the Internet 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.
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Received: 25 July 2016
Published: 20 December 2016
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