Predictive analytics of the copper spot price by utilizing complex network and artificial neural network techniques

Chao Wang, Xinyi Zhang, Minggang Wang, Ming K. Lim and Pezhman Ghadimi

Resources Policy, 2019, vol. 63, issue C, -

Abstract: The copper prices in international trade markets are volatile. An accurate copper price prediction may guide commodity trading and firm profits in the copper industry. In this paper, a hybrid predictive technique combining complex network and traditional artificial neural network (ANN) techniques is developed for copper price forecasting. This technique first transforms the original price time series to a price volatility network (PVN) and extracts the volatility characteristics from the topological structure of the PVN. After the original data are reconstructed, three widely used ANN techniques, the BPNN, RBFNN, and ELM, are applied to forecast the future copper price. To examine the forecasting performance of the proposed PVN-ANN techniques, the published data of copper spot prices from the New York Commodity Exchange (COMEX) are used. The empirical results show that the proposed hybrid PVN-ANN techniques can obtain a favorable prediction effect in both level and directional predictions compared to those of the traditional ANN techniques. This result clearly demonstrates the effectiveness of the proposed hybrid predictive techniques in revealing the underlying nonlinear patterns of international copper prices.

Keywords: Artificial neural network; Price volatility network; Copper price forecasting; New York commodity exchange (COMEX); Complex network (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jrpoli:v:63:y:2019:i:c:54

DOI: 10.1016/j.resourpol.2019.101414

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