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Research on a Prediction Model and Influencing Factors of Cross-Regional Price Differences of Rebar Spot Based on Long Short-Term Memory Network

Author

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  • Sen Wu

    (School of Economics & Management, University of Science and Technology Beijing, No. 30 Xueyuan Road, Beijing 100083, China)

  • Shuaiqi Liu

    (School of Economics & Management, University of Science and Technology Beijing, No. 30 Xueyuan Road, Beijing 100083, China)

  • Huimin Zong

    (School of Economics & Management, University of Science and Technology Beijing, No. 30 Xueyuan Road, Beijing 100083, China)

  • Yiyuan Sun

    (School of Economics & Management, University of Science and Technology Beijing, No. 30 Xueyuan Road, Beijing 100083, China)

  • Wei Wang

    (School of Economics & Management, University of Science and Technology Beijing, No. 30 Xueyuan Road, Beijing 100083, China)

Abstract

In this paper, taking rebar steel as an example, we study the causes and influencing factors of spot price differences in rebar steel in different regions, and put forward a prediction model of rebar steel regional price differences based on the spot price of rebar from 2013 to 2022, supply and demand, cost, macroeconomics, industrial economic indicators, and policy data. Through correlation analysis, we consider all influencing factors step by step, select indicators with high correlation to add to the model, and select the optimal combination of influencing factors by comparing the results of five groups of experiments. Using the long short-term memory network, we predict the weekly spot price differences of rebar in different regions. Based on the historical-price time series, the optimal time window setting is given as the final price difference prediction model. The experimental results show that the prediction model of rebar spot price differences can support a 72.3% effective trading rate by combining the influencing factors with the LSTM model. This study has a guiding role for spot trading and can help spot enterprises, determine arbitrage trading strategies based on the prediction results, obtain sustainable returns under low risk, and realize the maximization of cross-regional arbitrage.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:4951-:d:1093551
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    1. Shenjie Cheng & Panke Qin & Baoyun Lu & Jinxia Yu & Yongli Tang & Zeliang Zeng & Sensen Tu & Haoran Qi & Bo Ye & Zhongqi Cai, 2024. "Multi-strategy modified sparrow search algorithm for hyperparameter optimization in arbitrage prediction models," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-24, May.

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