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Regional steel price index forecasts with neural networks: evidence from east, south, north, central south, northeast, southwest, and northwest China

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Abstract

For policy makers and a diverse spectrum of market participants, understandings of commodity price forecasts are considered as an important matter. In this work, we focus on the metal business sector and examine forecast problems for seven daily regional steel prices in the Chinese market over an eleven-year period spanning 2010–2021. Specifically, our analysis covers steel prices of east, south, north, central south, northeast, southwest, and northwest China through the use of non-linear auto-regressive neural networks. For each price series, we test forecast accuracy based upon one hundred and twenty settings over training algorithms for model estimations, numbers of hidden neurons, numbers of delays, and ratios used for segmenting the data into training, validation, and testing phases. Our analysis leads to the construction of a rather simple model that generates high forecast accuracy and stabilities for each of the seven price series. Correspondingly, forecast accuracy measured by the relative root mean square error is lower than 0.60% for all considered series based upon the overall sample. Forecast results here could be utilized on a standalone basis or combined with forecasts from other models by different forecast users as part of evaluating price trends and performing policy analysis.

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References

  1. Li B, Ding J, Yin Z, Li K, Zhao X, Zhang L (2021) Optimized neural network combined model based on the induced ordered weighted averaging operator for vegetable price forecasting. Expert Syst Appl 168:114232. https://doi.org/10.1016/j.eswa.2020.114232

    Article  Google Scholar 

  2. Robles M, Torero M, Von Braun J (2009) When speculation matters, Technical Report

  3. Zhou K, Yang S (2016) Emission reduction of china’s steel industry: progress and challenges. Renew Sustain Energy Rev 61:319–327. https://doi.org/10.1016/j.rser.2016.04.009

    Article  Google Scholar 

  4. Liu Y, Li H, Guan J, Liu X, Guan Q, Sun Q (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. Resour Policy 60:134–142. https://doi.org/10.1016/j.resourpol.2018.12.009

    Article  Google Scholar 

  5. Guo Z, Fu Z (2010) Current situation of energy consumption and measures taken for energy saving in the iron and steel industry in china. Energy 35:4356–4360. https://doi.org/10.1016/j.energy.2009.04.008

    Article  Google Scholar 

  6. Xu X (2020) Corn cash price forecasting. Am J Agric Econ 102:1297–1320. https://doi.org/10.1002/ajae.12041

    Article  Google Scholar 

  7. Xu X (2019) Contemporaneous and granger causality among us corn cash and futures prices. Eur Rev Agric Econ 46:663–695. https://doi.org/10.1093/erae/jby036

    Article  Google Scholar 

  8. Bessler DA (1990) Forecasting multiple time series with little prior information. Am J Agric Econ 72:788–792. https://doi.org/10.2307/1243059

    Article  Google Scholar 

  9. Bessler DA, Yang J, Wongcharupan M (2003) Price dynamics in the international wheat market: modeling with error correction and directed acyclic graphs. J Reg Sci 43:1–33. https://doi.org/10.1111/1467-9787.00287

    Article  Google Scholar 

  10. Li J, Li G, Liu M, Zhu X, Wei L (2020) A novel text-based framework for forecasting agricultural futures using massive online news headlines. Int J Forecast. https://doi.org/10.1016/j.ijforecast.2020.02.002

    Article  Google Scholar 

  11. Ribeiro CO, Oliveira SM (2011) A hybrid commodity price-forecasting model applied to the sugar-alcohol sector. Aust J Agric Resour Econ 55:180–198. https://doi.org/10.1111/j.1467-8489.2011.00534.x

    Article  Google Scholar 

  12. Xu X, Zhang Y (2021) Corn cash price forecasting with neural networks. Comput Electron Agric 184:106120. https://doi.org/10.1016/j.compag.2021.106120

    Article  Google Scholar 

  13. Kanchymalay K, Salim N, Sukprasert A, Krishnan R, Hashim UR (2017) Multivariate time series forecasting of crude palm oil price using machine learning techniques. In: IOP Conference Series: Materials Science and Engineering, volume 226, IOP Publishing, p 012117. https://doi.org/10.1088/1757-899X/226/1/012117

  14. Sohrabi P, Jodeiri Shokri B, Dehghani H (2021) Predicting coal price using time series methods and combination of radial basis function (rbf) neural network with time series. Miner Econ. https://doi.org/10.1007/s13563-021-00286-z

    Article  Google Scholar 

  15. Saâdaoui F (2017) A seasonal feedforward neural network to forecast electricity prices. Neural Comput Appl 28:835–847. https://doi.org/10.1007/s00521-016-2356-y

    Article  Google Scholar 

  16. Wang B, Wang J (2019) Energy futures prices forecasting by novel dpfwr neural network and ds-cid evaluation. Neurocomputing 338:1–15. https://doi.org/10.1016/j.neucom.2019.01.092

    Article  MathSciNet  Google Scholar 

  17. Tang B-Q, Han J, Guo G-F, Chen Y, Zhang S (2019) Building material prices forecasting based on least square support vector machine and improved particle swarm optimization. Archit Eng Design Manag 15:196–212. https://doi.org/10.1080/17452007.2018.1556577

    Article  Google Scholar 

  18. Shyu Y-W, Chang C-C (2022) A hybrid model of memd and pso-lssvr for steel price forecasting. Int J Eng Manag Res 12:30–40. https://doi.org/10.31033/ijemr.12.1.5

    Article  Google Scholar 

  19. Mir M, Kabir HD, Nasirzadeh F, Khosravi A (2021) Neural network-based interval forecasting of construction material prices. J Build Eng 39:102288. https://doi.org/10.1016/j.jobe.2021.102288

    Article  Google Scholar 

  20. Ganokratanaa T, Ketcham M (2021) Deep index price forecasting in steel industry. In: 2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE), IEEE, pp. 1–6. https://doi.org/10.1109/JCSSE53117.2021.9493843

  21. Liu Z, Wang Y, Zhu S, Zhang B, Wei L (2015) Steel prices index prediction in china based on bp neural network. In: LISS 2014, Springer, pp. 603–608. https://doi.org/10.1007/978-3-662-43871-8_87

  22. Benrhmach G, Namir K, Namir A, Bouyaghroumni J (2020) Nonlinear autoregressive neural network and extended kalman filters for prediction of financial time series. J Appl Math 58:2020. https://doi.org/10.1155/2020/5057801

    Article  MathSciNet  MATH  Google Scholar 

  23. Wu B, Zhu Q (2012) Week-ahead price forecasting for steel market based on rbf nn and asw. In: 2012 IEEE International Conference on Computer Science and Automation Engineering, IEEE, pp. 729–732. https://doi.org/10.1109/ICSESS.2012.6269570

  24. Jabeur SB, Mefteh-Wali S, Viviani J-L (2021) Forecasting gold price with the xgboost algorithm and shap interaction values. Ann Oper Res. https://doi.org/10.1007/s10479-021-04187-w

    Article  Google Scholar 

  25. Huynh TLD (2020) The effect of uncertainty on the precious metals market: new insights from transfer entropy and neural network var. Resour Policy 66:101623. https://doi.org/10.1016/j.resourpol.2020.101623

    Article  Google Scholar 

  26. Majid R (2018) Advances in statistical forecasting methods: an overview. Econ Aff 63:295479. https://doi.org/10.30954/0424-2513.4.2018.5

    Article  Google Scholar 

  27. Xu X, Zhang Y (2022) Commodity price forecasting via neural networks for coffee, corn, cotton, oats, soybeans, soybean oil, sugar, and wheat. Int Syst Account Financ Manag 29:169–181. https://doi.org/10.1002/isaf.1519

    Article  Google Scholar 

  28. Yang J, Cabrera J, Wang T (2010) Nonlinearity, data-snooping, and stock index etf return predictability. Eur J Oper Res 200:498–507. https://doi.org/10.1016/j.ejor.2009.01.009

    Article  MATH  Google Scholar 

  29. Karasu S, Altan A, Bekiros S, Ahmad W (2020) A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series. Energy 212:118750. https://doi.org/10.1016/j.energy.2020.118750

    Article  Google Scholar 

  30. Altan A, Karasu S, Zio E (2021) A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer. Appl Soft Comput 100:106996. https://doi.org/10.1016/j.asoc.2020.106996

    Article  Google Scholar 

  31. Yang J, Zhang J, Leatham DJ (2003) Price and volatility transmission in international wheat futures markets. Ann Econ Financ 4:37–50

    Google Scholar 

  32. Yang J, Li Z, Wang T (2021) Price discovery in chinese agricultural futures markets: a comprehensive look. J Futures Markets 41:536–555. https://doi.org/10.1002/fut.22179

    Article  Google Scholar 

  33. Kapl M, Müller WG (2010) Prediction of steel prices: a comparison between a conventional regression model and mssa, statistics and its. Interface 3:369–375. https://doi.org/10.4310/SII.2010.v3.n3.a10

    Article  MathSciNet  MATH  Google Scholar 

  34. Supattana N (2014) Steel price index forecasting using arima and arimax model, National Institute of Development Administration

  35. Ming-Tao C, Bo-Ching H (2010) An analysis of the relationship between forward freight agreements and steel price index: an application of the vector arma model. Afr J Bus Manag 4:1149–1154

    Google Scholar 

  36. Adli KA, Sener U (2021) Forecasting of the us steel prices with lvar and vec models. Bus Econ Res J 12:509–522

    Article  Google Scholar 

  37. Faghih SAM, Kashani H (2018) Forecasting construction material prices using vector error correction model. J Constr Eng Manag 144:04018075. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001528

    Article  Google Scholar 

  38. Liu Y, Yang C, Huang K, Gui W (2020) Non-ferrous metals price forecasting based on variational mode decomposition and lstm network. Knowl-Based Syst 188:105006. https://doi.org/10.1016/j.knosys.2019.105006

    Article  Google Scholar 

  39. Tuo J, Zhang F (2020) Modelling the iron ore price index: a new perspective from a hybrid data reconstructed eemd-goru model. J Manag Sci Eng 5:212–225. https://doi.org/10.1016/j.jmse.2020.08.003

    Article  Google Scholar 

  40. Wang Z-X, Zhao Y-F, He L-Y (2020) Forecasting the monthly iron ore import of china using a model combining empirical mode decomposition, non-linear autoregressive neural network, and autoregressive integrated moving average. Appl Soft Comput 94:106475. https://doi.org/10.1016/j.asoc.2020.106475

    Article  Google Scholar 

  41. Wang J, Li X (2018) A combined neural network model for commodity price forecasting with ssa. Soft Comput 22:5323–5333. https://doi.org/10.1007/s00500-018-3023-2

    Article  Google Scholar 

  42. Xu X, Zhang Y (2022) Contemporaneous causality among one hundred chinese cities. Empir Econ 63:2315–2329. https://doi.org/10.1007/s00181-021-02190-5

    Article  Google Scholar 

  43. Levenberg K (1944) A method for the solution of certain non-linear problems in least squares. Q Appl Math 2:164–168. https://doi.org/10.1090/qam/10666

    Article  MathSciNet  MATH  Google Scholar 

  44. Marquardt DW (1963) An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11:431–441. https://doi.org/10.1137/0111030

    Article  MathSciNet  MATH  Google Scholar 

  45. Møller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6:525–533. https://doi.org/10.1016/S0893-6080(05)80056-5

    Article  Google Scholar 

  46. Selvamuthu D, Kumar V, Mishra A (2019) Indian stock market prediction using artificial neural networks on tick data. Financ Innov 5:16. https://doi.org/10.1186/s40854-019-0131-7

    Article  Google Scholar 

  47. Baghirli O (2015) Comparison of lavenberg-marquardt, scaled conjugate gradient and bayesian regularization backpropagation algorithms for multistep ahead wind speed forecasting using multilayer perceptron feedforward neural network https://www.diva-portal.org/smash/get/diva2:828170/FULLTEXT01.pdf

  48. Hagan MT, Menhaj MB (1994) Training feedforward networks with the marquardt algorithm. IEEE Trans Neural Netw 5:989–993. https://doi.org/10.1109/72.329697

    Article  Google Scholar 

  49. Paluszek M, Thomas S (2020) Practical MATLAB Deep Learning: A Project-Based Approach, Apress https://link.springer.com/content/pdf/10.1007/978-1-4842-5124-9.pdf

  50. Jamieson P, Porter J, Wilson D (1991) A test of the computer simulation model arcwheat1 on wheat crops grown in new zealand. Field Crops Res 27:337–350. https://doi.org/10.1016/0378-4290(91)90040-3

    Article  Google Scholar 

  51. Heinemann AB, Van Oort PA, Fernandes DS, Maia Ad HN (2012) Sensitivity of apsim/oryza model due to estimation errors in solar radiation. Bragantia 71:572–582. https://doi.org/10.1590/S0006-87052012000400016

    Article  Google Scholar 

  52. Li M-F, Tang X-P, Wu W, Liu H-B (2013) General models for estimating daily global solar radiation for different solar radiation zones in mainland china. Energy Convers Manag 70:139–148. https://doi.org/10.1016/j.enconman.2013.03.004

    Article  Google Scholar 

  53. Despotovic M, Nedic V, Despotovic D, Cvetanovic S (2016) Evaluation of empirical models for predicting monthly mean horizontal diffuse solar radiation. Renew Sustain Energy Rev 56:246–260. https://doi.org/10.1016/j.rser.2015.11.058

    Article  Google Scholar 

  54. Batra D (2014) Comparison between levenberg-marquardt and scaled conjugate gradient training algorithms for image compression using mlp. Int J Image Process (IJIP) 8:412–422

    Google Scholar 

  55. Brock WA, Scheinkman JA, Dechert WD, LeBaron B (1996) A test for independence based on the correlation dimension. Econ Rev 15:197–235. https://doi.org/10.1080/07474939608800353

    Article  MathSciNet  MATH  Google Scholar 

  56. Diebold FX, Mariano RS (2002) Comparing predictive accuracy. J Bus Econ Stat 20:134–144. https://doi.org/10.2307/1392185

    Article  MathSciNet  Google Scholar 

  57. Harvey D, Leybourne S, Newbold P (1997) Testing the equality of prediction mean squared errors. Int J Forecast 13:281–291. https://doi.org/10.1016/S0169-2070(96)00719-4

    Article  Google Scholar 

  58. Breiman L (2017) Classification and regression trees. Routledge, England

    Book  Google Scholar 

  59. Shimizu S, Hoyer PO, Hyvärinen A, Kerminen A, Jordan M (2006) A linear non-gaussian acyclic model for causal discovery. J Mach Learn Res 7:2003–2030

    MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. North Carolina State University, Raleigh, NC, 27695, USA

    Xiaojie Xu & Yun Zhang

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  1. Xiaojie Xu
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  2. Yun Zhang
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XX:Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Validation; Visualization; Writing. YZ:Writing.

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Correspondence to Xiaojie Xu.

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Xu, X., Zhang, Y. Regional steel price index forecasts with neural networks: evidence from east, south, north, central south, northeast, southwest, and northwest China. J Supercomput 79, 13601–13619 (2023). https://doi.org/10.1007/s11227-023-05207-1

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