Yellow corn wholesale price forecasts via the neural network

Xu, Xiaojie; Zhang, Yun

doi:10.1108/econ-05-2022-0026

Cited by 17 publications

(3 citation statements)

References 176 publications

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“…Here, we also consider the scaled conjugate gradient (SCG) (Møller, 1993) and Bayesian regularization (BR) (MacKay, 1992) algorithms. The SCG and BR algorithms, as well as the LM algorithm, have been explored in different varieties of fields (Xu and Zhang, 2021a, 2022a, b, d, h, 2023f, j, p, t, u; Doan and Liong, 2004; Xu and Zhang, 2023n; Kayri, 2016; Khan et al ., 2019; Selvamuthu et al ., 2019). Comparative research of these algorithms can be seen from the literature (Baghirli, 2015; Xu and Zhang, 2022m, 2023a, k, m; Al Bataineh and Kaur, 2018).…”

Section: Methodsmentioning

confidence: 99%

Composite property price index forecasting with neural networks

Xu,

Zhang

2023

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PurposeThe Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021.Design/methodology/approachThe goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios.FindingsThe authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases.Originality/valueResults here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis.

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Section: Methodsmentioning

confidence: 99%

Composite property price index forecasting with neural networks

Xu,

Zhang

2023

Self Cite

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“…We implement the test with embedding dimensions of 2–10 and normalϵ (distance used for testing proximity of data points) values of 0.5, 1.0, 1.5, 2.0, 2.5 and 3.0 times the standard deviation of the price series, and find that the corresponding p -values are all nearly zero, suggesting non-linear patterns in the price data across the 10 cities. Under this circumstance, NNs could have advantages in terms of self-learning for forecasting purposes (Karasu et al , 2020; Xu and Zhang, 2023o) and capturing non-linear features (Altan et al , 2021) inhabiting the price data. For example, as compared to some other approaches that model nonlinearities using a particular nonlinear function, NNs could be used to approximate a large class of functions with a class of multi-layer networks that combine many “basic” nonlinear functions (Yang et al , 2008, 2010; Wang and Yang, 2010).…”

Section: Datamentioning

confidence: 99%

Office property price index forecasting using neural networks

Zhang

2023

JFMPC

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Purpose The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021. Design/methodology/approach The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios. Findings The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases. Originality/value The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

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“…In recent years, as computing resources and tools are becoming continuously easier and more affordable to reach [100], researchers across the globe have started to show continuously increasing interest in exploring applications of machine learning techniques [101] for the purpose of forecasting commodity prices. Corresponding studies in the literature have covered many different commodities from different economic sectors and industries, including but not limited to those in the agricultural sector, such as soybeans [102][103][104][105][106][107][108], soybean oil [109][110][111], palm oil [112], sugar [113][114][115][116][117][118], corn [102,113,[119][120][121][122][123][124][125][126][127][128][129][130][131], wheat [105,[132][133][134][135][136][137][138][139], coffee [140][141]…”

Section: Introductionmentioning

confidence: 99%

Scrap steel price forecasting with neural networks for east, north, south, central, northeast, and southwest China and at the national level

Zhang

2023

Ironmaking & Steelmaking

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There is little doubt about importance of forecasting commodity prices to policy makers and diverse varieties of market participants. In this present work, we analyse price forecasting problems for scrap steel for east, north, south, central, northeast, and southwest China and at the national level with daily data during 08/23/2013-04/15/2021. We focus on exploration of usefulness of non-linear autoregressive neural networks and examine forecasting performance based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays, and ratios for splitting the data into training, validation, and testing phases. We arrive at relatively simple models for the seven price series, which lead to forecasts of high accuracy and stabilities with relative root mean square errors below 0.85%. Our results could serve as standalone technical forecasts. They could be combined with other forecasts for forming perspectives of price trends and carrying out policy analysis.

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Yellow corn wholesale price forecasts via the neural network

Cited by 17 publications

References 176 publications

Composite property price index forecasting with neural networks

Composite property price index forecasting with neural networks

Office property price index forecasting using neural networks

Scrap steel price forecasting with neural networks for east, north, south, central, northeast, and southwest China and at the national level

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