Abstract
Improving the accuracy of interest rate risk measure is an effective means of early warning of interest rate risks. Based on the analysis of the drives and effects of interest rate on Internet finance, interest rate risks are identified, and 15 evaluation indicators are initially constructed in this paper. Through the questionnaire survey of internet financial professionals, the risk size and importance degree of the initial indicators are scored, and the rough set mining reduction based on improved genetic algorithm is applied to improve the search efficiency. The evaluation indicators are reduced from fifteen to nine, and the core indicators affecting the measure of internet financial interest rate risk are obtained. Using these core indicators, an internet financial interest rate measure model is established, which is based on data mining and combined with 30,000+ data items from an internet finance company from January to July 2018. By comparing with the initial indicators, the validity of the measure model under the genetic rough set reduction is proved. The empirical results show that the model established by the optimized reduction improves the accuracy of measure.
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Mu, S., Xiong, Z. Internet financial interest rate risk measure based on genetic rough set reduction. SOCA 13, 309–321 (2019). https://doi.org/10.1007/s11761-019-00274-w
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DOI: https://doi.org/10.1007/s11761-019-00274-w