Machine learning scrap steel price forecasts for the regional east Chinese market

Bingzi Jin · Xiaojie Xu

27 citations0/0 files storedJournal of Modelling in Management

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Forecasting Techniques and Applications Market Dynamics and Volatility Stock Market Forecasting Methods

Abstract

Purpose Investors and governments have traditionally depended on estimates of prices of various commodities. Using data from 08/23/2013 to 04/15/2021, this study aims to investigate the challenging problem of forecasting scrap steel prices that are published on a daily basis for the east China regional market. The research has not given much attention to predictions of this important commodity price indicator. Design/methodology/approach Gaussian process regression models, which are estimated using cross-validation approaches with Bayesian optimizations, are used to provide price forecasts. Findings Having a relative root mean square error of 0.4357%, the constructed models appropriately generate price forecasts for the out-of-sample testing stage from 09/17/2019 to 04/15/2021. Originality/value Models designed to research prices can be used by governments and investors to make well-informed decisions.

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References

1996A test for independence based on the correlation dimension 1997Testing the equality of prediction mean squared errors 2002Comparing Predictive Accuracy 2006Chapter 4 Forecast Combinations 2020A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series 2011Convergence rates of efficient global optimization algorithms 2021Corn cash price forecasting with neural networks 2024Forecasting wholesale prices of yellow corn through the Gaussian process regression

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2025Forecasting Scrap Steel Prices via Machine Learning for the Central Chinese Market

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