We extend Byoun's (2008) modelling of the relationship between deficits and surpluses and adjustment speed, to demonstrate how industry characteristics identified by Kayo and Kimura (2011), including industry concentration, industry munificence and industry dynamism, impact on speed of adjustment. Using New Zealand firms as a case study, we find significant evidence that, as well as firm financial position, industry characteristics also impact on adjustment speed. The firm financial position results are the most robust, and we recommend further research to confirm the nature of the relationship between industry characteristics and the speed at which firms adjust towards target capital structures.

Abstract:The relationship between burglary and socio-demographic factors has long been a hot topic in crime research. Spatial dependence and spatial heterogeneity are two issues to be addressed in modeling geographic data. When these two issues arise at the same time, it is difficult to model them simultaneously. A cross-comparison of three models is presented in this study to identify which spatial effect should be addressed first in crime analysis. The negative binominal model (NB), Bayesian hierarchical model (BHM) and the geographically weighted Poisson regression model (GWPR) were implemented based on a three-year residential burglary data set from ZG, China. The modeling result shows that both BHM and GWPR outperform NB as they capture either of the spatial effects. Compared to the NB model, the mean absolute deviation (MAD) of BHM and GWPR was decreased by 83.71% and 49.39%, the mean squared error (MSE) of BHM and GWPR was decreased by 97.88% and 77.15%, and the R 2 d of BHM and GWPR was improved by 26.7% and 19.1%, respectively. In comparison with BHM and GWPR, BHM fits the data better with lower MAD, MSE and higher R 2 d . The empirical analysis indicates that the percentage of renter population, percentage of people from other provinces, bus line density, and bus stop density have a significantly positive impact on the number of residential burglaries. The percentage of residents with a bachelor degree or higher, on the other hand, is negatively associated with the number of residential burglaries.

PurposeThe purpose of this paper is to investigate the risk factors for A‐shares listed on both Shenzhen and Shanghai Stock Exchange in China using variables from Akgun and Gibson.Design/methodology/approachThe paper applies cross‐sectional regression on the orthogonal components by rearranging these risk variables into several principal components.FindingsThe results produced strong evidence that size and book‐to‐market (BM) ratio could be well explained by these alternative risk variables. Additionally, the alternative variables are better at explaining returns in terms of adjusted R‐squares.Practical implicationsThe practical implication of the study is that investors can improve both their pricing of the investment risk and their management of the risk factors with the alternatives identified in the study.Originality/valueThe paper provides evidence in explaining the size and BM effects in China's stock markets.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract：We compare environmental impacts associated with incoming foreign direct investment versus domestic capital in China. We use aggregate data on Chinese provinces' economic and pollution indicators to explore the effects of the financial origin of fixed capital. Our simultaneous models consider three prime channels through which these effects work: economic scale, sectoral composition, and pollution intensity. Results show that emissions associated with foreign financed capital are lower than with domestically financed capital for some but not all of the considered types of pollution. Terms of use: Documents in

No abstract