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Influence of different factors on prices of upstream, middle and downstream products in China's whole steel industry chain: Based on Adaptive Neural Fuzzy Inference System

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  • Liu, Yanxin
  • Li, Huajiao
  • Guan, Jianhe
  • Liu, Xueyong
  • Guan, Qing
  • Sun, Qingru

Abstract

The steel industry in China has been developing slowly, and the prices of this industry’s products have recently fluctuated. To predict the trends of price changes in the future, we must discover the factors that play leading roles in determining prices. In this paper, an adaptive neuro fuzzy inference system (ANFIS) is used to measure the factors influencing the prices of steel products in China's upper, middle and lower reaches from the perspective of the whole industry chain and to identify the most influential variables. The analysis uses daily data pertaining to relevant variables from December 2013 to October 2017 and selects the upper, middle and lower reaches of iron ore, ferrosilicon and rebar as the research objects. The results show that the main factors affecting steel products at different stages are varied. The most significant factors affecting the prices of upstream products, midstream products and downstream products are midstream product prices, market supply and demand, and inventory, respectively. Therefore, from the perspective of the whole industry chain, when the prices of upstream products are high, China should regulate the prices of midstream products. For midstream products, China can consider improving the market structure to improve supply and demand. In response to rising prices of downstream products, China should optimize its inventory structure. This paper provides policy suggestions for the regulation and control of the development of the steel industry.

Suggested Citation

  • Liu, Yanxin & Li, Huajiao & Guan, Jianhe & Liu, Xueyong & Guan, Qing & Sun, Qingru, 2019. "Influence of different factors on prices of upstream, middle and downstream products in China's whole steel industry chain: Based on Adaptive Neural Fuzzy Inference System," Resources Policy, Elsevier, vol. 60(C), pages 134-142.
    • Handle: RePEc:eee:jrpoli:v:60:y:2019:i:c:p:134-142
    DOI: 10.1016/j.resourpol.2018.12.009
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    2. Sen Wu & Shuaiqi Liu & Huimin Zong & Yiyuan Sun & Wei Wang, 2023. "Research on a Prediction Model and Influencing Factors of Cross-Regional Price Differences of Rebar Spot Based on Long Short-Term Memory Network," Sustainability, MDPI, vol. 15(6), pages 1-12, March.
    3. Ewees, Ahmed A. & Elaziz, Mohamed Abd & Alameer, Zakaria & Ye, Haiwang & Jianhua, Zhang, 2020. "Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility," Resources Policy, Elsevier, vol. 65(C).
    4. Qi, Yajie & Li, Huajiao & Liu, Yanxin & Feng, Sida & Li, Yang & Guo, Sui, 2020. "Granger causality transmission mechanism of steel product prices under multiple scales—The industrial chain perspective," Resources Policy, Elsevier, vol. 67(C).
    5. Mehmanpazir, Farhad & Khalili-Damghani, Kaveh & Hafezalkotob, Ashkan, 2022. "Dynamic strategic planning: A hybrid approach based on logarithmic regression, system dynamics, Game Theory and Fuzzy Inference System (Case study Steel Industry)," Resources Policy, Elsevier, vol. 77(C).
    6. Liu, Yanxin & Li, Huajiao & Guan, Jianhe & Feng, Sida & Guo, Sui, 2019. "The impact of Chinese steel product prices based on the midstream industry chain," Resources Policy, Elsevier, vol. 63(C), pages 1-1.

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