Risk control of metal raw materials based on deep learning

Abstract

Stabilizing the purchase cost of metal raw materials is of great significance to the metal manufacturing industry. Most enterprises use futures hedging strategies to cope with the risks arising from fluctuations in the prices of metal raw materials. However, the difference between spot and futures prices of metals makes it impossible to fully control the risk. In order to further improve the efficiency of hedging and controlling corporate risks, it is necessary to accurately predict futures prices. However, the decomposition algorithms in traditional mixed models are prone to modal aliasing and have limited ability to extract nonlinear features from futures prices. Therefore, this paper proposes a variational modal decomposition-sample entropy-Cascaded Long Short-Term Memory Neural Network Model (VMD-SE-CLSTM). This paper proposes SE combined with VMD algorithm to determine the decomposition number to suppress the aliasing phenomenon of subsequence patterns, and introduces CLSTM network to improve the extraction ability of nonlinear features in futures data. The experiments are compared with 10 mainstream model methods and the method proposed in this paper. The experimental results show that the model reduces the prediction error and improves the prediction accuracy of the model, which is of great significance for enterprises to improve hedging efficiency, reduce operating risks and control production costs.