Network analysis of comovements among newly-built residential house price indices of seventy Chinese cities

Ironmaking & Steelmaking

2023

Self Cite

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.

Section: Datamentioning

confidence: 67%

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

Ironmaking & Steelmaking

2023

Self Cite

“…With these reviews, although not exhaustive, it appears that the neural network model is one of the most useful techniques in terms of constructing price forecasts for agricultural commodities (Bayona-Oré, Cerna, & Tirado Hinojoza, 2021). More specifically, a wide variety of time-series variables that are chaotic and noised could be well forecasted through the neural network model (Karasu, Altan, Bekiros, & Ahmad, 2020; Wang & Yang, 2010; Wegener, von Spreckelsen, Basse, & von Mettenheim, 2016; Xu, 2015, Xu, 2018, Xu, 2018, Xu, 2018; Yang, Cabrera, & Wang, 2010, Yang, Su, & Kolari, 2008), including many different types of economic and financial time series (Xu & Zhang, 2022). This fact could stem from the good capability of the neural network model for self-learning (Karasu, Altan, Saraç, & Hacioğlu, 2017, Karasu, Altan, Saraç, & Hacioğlu, 2017) and characterizing nonlinear features (Altan, Karasu, & Zio, 2021; Karasu et al.…”

Section: Introductionmentioning

confidence: 99%

Yellow corn wholesale price forecasts via the neural network

2023

ECON

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PurposeForecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present study, the authors assess the forecast problem for the weekly wholesale price index of yellow corn in China during January 1, 2010–January 10, 2020 period.Design/methodology/approachThe authors employ the nonlinear auto-regressive neural network as the forecast tool and evaluate forecast performance of different model settings over algorithms, delays, hidden neurons and data splitting ratios in arriving at the final model.FindingsThe final model is relatively simple and leads to accurate and stable results. Particularly, it generates relative root mean square errors of 1.05%, 1.08% and 1.03% for training, validation and testing, respectively.Originality/valueThrough the analysis, the study shows usefulness of the neural network technique for commodity price forecasts. The results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.

“…During the past decade, the Chinese housing market has witnessed rapid growth. Undoubtedly, housing price forecasting problems have turned to be one of key concerns of investors and policymakers (Xu and Zhang, 2022g). It is essential to have good understandings of trends and fluctuations of housing prices as they have direct influences on people's investment decisions in the real estate market and choices of cities to reside and work, as well as regulatory agencies' policy analysis, design and implementations.…”

Section: Introductionmentioning

confidence: 99%

Office property price index forecasting using neural networks

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.