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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.

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.

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.

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.

Forecasts 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.

The 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.

The 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.

Through 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.

The 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.

The 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.

The 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.

Results 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.

Understandings of house prices and their interrelationships have undoubtedly drawn a great amount of attention from various market participants. This study aims to investigate the monthly newly-built residential house price indices of seventy Chinese cities during a 10-year period spanning January 2011–December 2020 for understandings of issues related to their interdependence and synchronizations.

Analysis here is facilitated through network analysis together with topological and hierarchical characterizations of price comovements.

This study determines eight sectoral groups of cities whose house price indices are directly connected and the price synchronization within each group is higher than that at the national level, although each shows rather idiosyncratic patterns. Degrees of house price comovements are generally lower starting from 2018 at the national level and for the eight sectoral groups. Similarly, this study finds that the synchronization intensity associated with the house price index of each city generally switches to a lower level starting from early 2019.

Results here should be of use to policy design and analysis aiming at housing market evaluations and monitoring.

This study aims to examine how supervisor bottom-line mentality (BLM) influences subordinate unethical pro-organizational behavior (UPB), considering the mediating role of subordinate moral disengagement and the moderating role of their power-distance orientation.

The theoretical model was tested using two-wave data collected from employees of five firms in southern China.

Subordinate moral disengagement was found to mediate the positive relationship between supervisor BLM and subordinate UPB. Furthermore, for subordinates with high power-distance orientation, the positive relationship between supervisor BLM and subordinate moral disengagement and the indirect positive relationship between supervisor BLM and subordinate UPB were both strengthened.

First, organizations should train their employees to pursue goals ethically based on established standards and policies for acceptable behavior and to punish UPB. Second, organizations should strengthen employees' ethics and reduce their likelihood of moral disengagement. Finally, organizations should create an environment that allows subordinates to question their supervisors’ BLM.

First, the results demonstrate that supervisor BLM is an antecedent of subordinate UPB. Second, the study sheds important new light on how employees respond to supervisor BLM through cognitive processes. Third, it examines the moderating role of subordinate power-distance orientation between supervisor BLM, moral disengagement and UPB.

The purpose of this paper is to investigate the double-edged effects of supervisor bottom-line mentality (BLM) on subordinates' work-related behaviors (work performance and knowledge hiding) and the moderating role of subordinate gender.

The theoretical model was tested using a sample of 218 three-wave multi-source data collected from employees of five firms in southern China.

The results revealed that supervisor BLM is positively associated with subordinate BLM. Although subordinate BLM can enhance their work performance, it can also lead to knowledge hiding toward coworkers. Furthermore, these indirect effects are moderated by subordinate gender.

Managers should pay more attention to the potential positive and negative consequences of supervisor BLM and intervene to mitigate the negative impact of BLM.

This study is among the first to examine how supervisor BLM can be a mixed blessing and elicit both positive and negative behaviors from their subordinates. Moreover, by illuminating how subordinate gender moderates the relationship between supervisor BLM and subordinates' work-related behaviors, we enrich and extend the BLM literature.

With the rapid-growing house market in the past decade, the purpose of this paper is to study the important issue of house price information flows among 12 major cities in China, including Shanghai, Beijing, Xiamen, Shenzhen, Guangzhou, Hangzhou, Ningbo, Nanjing, Zhuhai, Fuzhou, Suzhou and Dongguan, during the period of June 2010 to May 2019.

The authors approach this issue in both time and frequency domains, latter of which is facilitated through wavelet analysis and by exploring both linear and nonlinear causality under the vector autoregressive framework.

The main findings are threefold. First, in the long run of the time domain and for timescales beyond 16 months of the frequency domain, house prices of all cities significantly affect each other. For timescales up to 16 months, linear causality is weaker and is most often identified for the scale of four to eight months. Second, while nonlinear causality is seldom determined in the time domain and is never found for timescales up to four months, it is identified for scales beyond four months and particularly for those beyond 32 months. Third, nonlinear causality found in the frequency domain is partly explained by the volatility spillover effect.

Results here should be of use to policymakers in certain policy analysis.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

Chinese housing market has been growing fast during the past decade, and price-related forecasting has turned to be an important issue to various market participants, including the people, investors and policy makers. Here, the authors approach this issue by researching neural networks for rent index forecasting from 10 major cities for March 2012 to May 2020. The authors aim at building simple and accurate neural networks to contribute to pure technical forecasting of the Chinese rental housing market.

To facilitate the analysis, the authors examine different model settings over the algorithm, delay, hidden neuron and data spitting ratio.

The authors reach a rather simple neural network with six delays and two hidden neurons, which leads to stable performance of 1.4% average relative root mean square error across the ten cities for the training, validation and testing phases.

The results might be used on a standalone basis or combined with fundamental forecasting to form perspectives of rent price trends and conduct policy analysis.

The purpose of this study is to investigate the relationship between supervisor bottom-line mentality (BLM) and employee work-to-family conflict (WFC) through employee psychological detachment, and the moderating role of employee trait optimism.

The research model was empirically tested using a sample of 225 two-wave data gathered from five Chinese companies.

The results revealed that employee psychological detachment mediated the impact of supervisor BLM on employee WFC. Moreover, employee trait optimism buffered the negative relationship between supervisor BLM and employee psychological detachment and the indirect effect of supervisor BLM on employee WFC through employee psychological detachment.

Supervisors should pay more attention to the spillover effect of supervisor BLM on employees’ family life and take some training measures to help employees effectively psychological detach from supervisor BLM.

The findings, therefore, provide a more comprehensive understanding of the adverse effects of supervisor BLM beyond the work domain and the buffering role of employee trait optimism on work–family intervention.