Abstract
ABSTRACT Having effective and reliable risk management is essential for the development of futures trading companies in China. Research analyzing early warnings on related risks for futures trading companies in China have important theoretical and empirical value. In this paper, we take the period during which fluctuation in extreme market risk occurred as the verification period and design overall risk indicators and equations to measure market risk for futures trading companies. We built an early risk warning model using extreme learning machine technology. We tested our model’s validity using statistics from China’s futures market. Empirical evidence shows that our model is more accurate than models based on the support vector machine, logistic regression, and the back-propagation neural network.
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