Predictions of steel price indices through machine learning for the regional northeast Chinese market

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(33), P. 20863 - 20882

Published: Aug. 19, 2024

Language: Английский

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Forecasts of thermal coal prices through Gaussian process regressions

Ironmaking & Steelmaking Processes Products and Applications, Journal Year: 2024, Volume and Issue: 51(8), P. 819 - 834

Published: July 23, 2024

Given thermal coal's significance as a tactical energy source, price projections for the commodity are crucial investors and decision-makers alike. The goal of current work is to determine whether Gaussian process regressions useful this forecast problem using dataset closing prices coal traded on China Zhengzhou Commodity Exchange from January 4, 2016, December 31, 2020. This significant financial index that has not received enough attention in literature terms forecasting. Our forecasting exercises make use Bayesian optimizations cross-validation. 02, 2020, 2020 successfully predicted by generated models, with out-of-sample relative root mean square error 0.4210%. shown be problem. outcomes projection might used independent technical forecasts or conjunction other policy research entails developing viewpoints patterns.

Language: Английский

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Thermal coal futures trading volume predictions through the neural network

Journal of Modelling in Management, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 16, 2024

Purpose Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose this study is concentrate on the energy sector explore volume prediction issue for thermal coal traded in Zhengzhou Commodity Exchange China with daily data spanning January 2016–December 2020. Design/methodology/approach nonlinear autoregressive neural network adopted performance examined based upon variety settings over algorithms model estimations, numbers hidden neurons delays ratios splitting series into training, validation testing phases. Findings A relatively simple setting arrived at that leads predictions good accuracy stabilities maintains small errors up 99.273 th quantile observed volume. Originality/value results could, one hand, serve as standalone technical predictions. They other be combined different (fundamental) forming perspectives trends carrying out policy analysis.

Language: Английский

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Forecasts of China Mainland New Energy Index Prices through Gaussian Process Regressions

Deleted Journal, Journal Year: 2024, Volume and Issue: 01

Published: Jan. 1, 2024

Energy index price forecasting has long been a crucial undertaking for investors and regulators. This study examines the daily predicting problem new energy on Chinese mainland market from January 4, 2016 to December 31, 2020 as insufficient attention paid in literature this financial metric. Gaussian process regressions facilitate our analysis, training procedures of models make use cross-validation Bayesian optimizations. From 2, 2020, was properly projected by created models, with an out-of-sample relative root mean square error 1.8837%. The developed may be utilized investors’ policymakers’ policy analysis decision-making processes. Because results provide reference information about patterns indicated they also useful building similar indices.

Language: Английский

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Machine learning price index forecasts of flat steel products

Mineral Economics, Journal Year: 2024, Volume and Issue: unknown

Published: July 22, 2024

Language: Английский

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Peanut oil price change forecasts through the neural network

foresight, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 14, 2025

Purpose For a wide range of market actors, including policymakers, forecasting changes in commodity prices is crucial. As one essential edible oil, peanut oil’s price swings are certainly important to predict. In this paper, the weekly wholesale index for period January 1, 2010 10, 2020 used address specific challenge Chinese market. Design/methodology/approach The nonlinear auto-regressive neural network (NAR-NN) model method used. Forecasting performance based on various settings, such as training techniques, delay counts, hidden neuron counts and data segmentation ratios, assessed build final specification. Findings With training, validation testing root mean square errors 5.89, 4.96 5.57, respectively, produces reliable accurate forecasts. Here, paper demonstrates applicability NAR-NN approach predictions. Originality/value On hand, findings may be independent technical movement Conversely, they included forecast combinations with forecasts derived from other models form viewpoints patterns policy research.

Language: Английский

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Predicting Scrap Steel Prices Through Machine Learning for South China

Materials Circular Economy, Journal Year: 2025, Volume and Issue: 7(1)

Published: Jan. 6, 2025

Language: Английский

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Machine learning gold price forecasting

International Journal of Management Science and Engineering Management, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 13

Published: Jan. 21, 2025

Language: Английский

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Machine learning-based scrap steel price forecasting for the northeast Chinese market

International Journal of Empirical Economics, Journal Year: 2024, Volume and Issue: 03(04)

Published: Aug. 30, 2024

Throughout history, governments and investors have relied on predictions of prices for a broad spectrum commodities. Using time-series data covering 08/23/2013–04/15/2021, this study investigates the challenging problem predicting scrap steel prices, which are issued daily northeast China market. Previous research has not sufficiently taken into account estimates significant commodity price measurement. In instance, Gaussian process regression methods created using Bayesian optimisation approaches cross-validation processes, resulting forecasts constructed. This empirical prediction methodology provides reasonably accurate out-of-sample period from 09/17/2019 to 04/15/2021, with root mean square error 9.6951, absolute 5.4218, correlation coefficient 99.9122%. Governments can arrive at informed decisions regarding regional markets by pricing models.

Language: Английский

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Predicting open interest in thermal coal futures using machine learning

Mineral Economics, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 23, 2024

Language: Английский

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