Corn cash-futures basis forecasting via neural networks

Xu, Xiaojie; Zhang, Yun

doi:10.1007/s43674-023-00054-2

Cited by 22 publications

(4 citation statements)

References 122 publications

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“…The LM algorithm contains good attributes of the steepest-descent algorithm and Gauss-Newton technique while avoids many of their limitations. In particular, it can Office property price index efficiently handle the issue of slow convergence (Hagan and Menhaj, 1994;Xu and Zhang, 2023e).…”

Section: Methodsmentioning

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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Section: Methodsmentioning

confidence: 99%

Office property price index forecasting using neural networks

Zhang

2023

JFMPC

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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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“…[5][6][7] However, time-series data on the prices of various steel products, which represent commodity trade flows, may be gathered. [8][9][10] Given that steel products are connected to a wide range of economic sectors and financial markets, it is only natural that price series, as well as their trends and interrelationships, have attracted enormous interest and attention from policy makers and market participants from not only China but also from around the world. [11][12][13][14][15][16][17] Despite the fact that time-series modelling techniques have been found to be popular and effective in the literature for analysing lead-lag relationships between economic variables based on the idea of Granger causation [e.g.…”

Section: Introductionmentioning

confidence: 99%

Contemporaneous causality among price indices of ten major steel products

Jin,

2024

Ironmaking & Steelmaking: Processes, Products and Applicati

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The problem of dynamic relationships among the price indices of 10 major steel products – the rebar, steel wire, plate, hot rolled coil, cold rolled plate, galvannealed sheet, seamless tube, welded tube, section and narrow strip – is addressed in the present work for the Chinese market from 2011M7 to 2021M4. For examination of contemporaneous causal links among the 10 series, we use data on a daily basis and combine the vector error correction model and directed acyclic graph. This analysis is done using both the Peter and Clark and linear non-Gaussian acyclic model algorithms. With the exception of the price series for steel wire, each of the 10 series is part of cointegration relationships according to the vector error correction model, and all save the price series for thin strips respond to long-run equilibrium disturbances. The linear non-Gaussian acyclic model method allows us to achieve causal routes that allow for the examination of innovation accounting, but the Peter and Clark algorithm prevented us from reaching an acyclic graph. We categorise complex dynamics among price adjustment processes after shocks based on impulsive responses, for which the price indices of the plate, seamless tube and thin strip are predominating in comparison to the other seven items. Our findings show that these three goods should get the most consideration when designing long-term strategies for steel prices.

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Corn cash-futures basis forecasting via neural networks

Cited by 22 publications

References 122 publications

Office property price index forecasting using neural networks

Office property price index forecasting using neural networks

Composite property price index forecasting with neural networks

Contemporaneous causality among price indices of ten major steel products

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