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1 – 10 of over 2000Xiaojie Xu and Yun Zhang
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…
Abstract
Purpose
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.
Design/methodology/approach
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?
Findings
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.
Originality/value
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.
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Jun-Hwa Cheah, Wolfgang Kersten, Christian M. Ringle and Carl Wallenburg
Joe F. Hair, Jun-Hwa Cheah, Christian M. Ringle, Marko Sarstedt and Hiram Ting
Daniela Pinheiro dos Reis and Katia Puente-Palacios
The purpose of this study was to identify the explanatory power of the affective, cognitive and evaluative aspects of identity with work teams in predicting team effectiveness…
Abstract
Purpose
The purpose of this study was to identify the explanatory power of the affective, cognitive and evaluative aspects of identity with work teams in predicting team effectiveness, represented by the variables: satisfaction with the team, manager-assessed team performance and objective indicators of performance.
Design/methodology/approach
Data were collected from 131 work teams of a Brazilian public organization with units in all state capitals of the country. Work team identity scale, the work team satisfaction scale, the team performance scale and objective performance indicators collected based on the achievement of the goals set for the units that make up the organization were used. To test the predictive model, three regressions were conducted using the stepwise method.
Findings
Regression analysis results showed that the evaluative dimension explains about 6% of the performance assessment given by managers, whereas the affective dimension explains 63% of the satisfaction with work teams. No significant results were found for the objective performance indicators.
Originality/value
The observed findings demonstrate the pertinence of understanding the work team identity as a collective and multidimensional phenomenon, as well as the contribution of its different components in explaining variables that represent effectiveness.
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Ahamuefula Ephraim Ogbonna and Olusanya Elisa Olubusoye
This study aims to investigate the response of green investments of emerging countries to own-market uncertainty, oil-market uncertainty and COVID-19 effect/geo-political risks…
Abstract
Purpose
This study aims to investigate the response of green investments of emerging countries to own-market uncertainty, oil-market uncertainty and COVID-19 effect/geo-political risks (GPRs), using the tail risks of corresponding markets as measures of uncertainty.
Design/methodology/approach
This study employs Westerlund and Narayan (2015) (WN)-type distributed lag model that simultaneously accounts for persistence, endogeneity and conditional heteroscedasticity, within a single model framework. The tail risks are obtained using conditional standard deviation of the residuals from an asymmetric autoregressive moving average – ARMA(1,1) – generalized autoregressive conditional heteroscedasticity – GARCH(1,1) model framework with Gaussian innovation. For out-of-sample forecast evaluation, the study employs root mean square error (RMSE), and Clark and West (2007) (CW) test for pairwise comparison of nested models, under three forecast horizons; providing statistical justification for incorporating oil tail risks and COVID-19 effects or GPRs in the predictive model.
Findings
Green returns responds significantly to own-market uncertainty (mostly positively), oil-market uncertainty (mostly positively) as well as the COVID-19 effect (mostly negatively), with some evidence of hedging potential against uncertainties that are external to the green investments market. Also, incorporating external uncertainties improves the in-sample predictability and out-of-sample forecasts, and yields some economic gains.
Originality/value
This study contributes originally to the green market-uncertainty literature in four ways. First, it generates daily tail risks (a more realistic measure of uncertainty) for emerging countries’ green returns and global oil prices. Second, it employs WN-type distributed lag model that is well suited to account for conditional heteroscedasticity, endogeneity and persistence effects; which characterizes financial series. Third, it presents both in-sample predictability and out-of-sample forecast performances. Fourth, it provides the economic gains of incorporating own-market, oil-market and COVID-19 uncertainty.
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Oussama Saoula, Amjad Shamim, Munawar Javed Ahmad and Muhammad Farrukh Abid
Entrepreneurship is an important paradigm for enhancing the economic well-being of nations. However, despite heated debate about the significant role of entrepreneurial education…
Abstract
Purpose
Entrepreneurship is an important paradigm for enhancing the economic well-being of nations. However, despite heated debate about the significant role of entrepreneurial education (EE) in developing favourable entrepreneurial intention (EI), little is known about the role of individuals’ entrepreneurial self-efficacy (ES), entrepreneurial motivation (EM) and family support (FS), which the authors investigated in this study.
Design/methodology/approach
This study has used a quantitative research design to collect data from 334 young people from various Malaysian higher education institutes using a purposive sampling technique and a deductive approach based on the theory of planned behaviour (TPB).
Findings
The findings revealed interesting insights into the criticality of young people’s ES, EM and FS in learning methods, techniques and skills to start new enterprises. Moreover, EE was a significant mediator of the relationship between individual self-efficacy, FS, EM and EI.
Originality/value
This study is among the few to contribute to strategic management scholarship by designing a framework based on the idea that EE relies on diverse factors, particularly ES, EM and FS. These factors encourage Malaysian young people to seek the necessary education to develop favourable EI and launch successful businesses.
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Nicola Cangialosi, Adalgisa Battistelli and Carlo Odoardi
How to design jobs to support innovation is an issue that has received plenty of consideration over the past years. Building on the job characteristics model, the present study is…
Abstract
Purpose
How to design jobs to support innovation is an issue that has received plenty of consideration over the past years. Building on the job characteristics model, the present study is set up to identify configurations of perceived job characteristics for innovation.
Design/methodology/approach
By adopting a fuzzy-set configurational approach (fsQCA), the research question is addressed through a two-wave self-report survey of 199 employees of an Italian manufacturing company.
Findings
Results reveal four compatible configurations of job characteristics leading to high levels of innovative work behavior and two for low levels.
Practical implications
The results offer guidance for managers and organizations that aim to strengthen employee-driven innovation by offering different recipes of job design to maximize the chance of boosting innovative behaviors among their workers.
Originality/value
This research is one of the first to empirically test the relation of job characteristics for innovative behavior using a configurational approach. By doing so it contributes to the literature by advancing the notion that innovative endeavors are determined by the holistic effects of different interdependent configurations of job characteristics.
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Stavros Kourtzidis and Nickolaos G. Tzeremes
The purpose of this paper is to use tenets of the complexity theory in order to study the effect of various determinants of firm’s performance, such as CEO’s compensation and age…
Abstract
Purpose
The purpose of this paper is to use tenets of the complexity theory in order to study the effect of various determinants of firm’s performance, such as CEO’s compensation and age, for the case of 72 insurance companies.
Design/methodology/approach
The authors identify the asymmetries in the data set by creating quantiles and using contrarian analysis. Instead of ignoring this information and use a main effects approach, all the available information in the data set is taken into account. For this purpose, the authors use qualitative comparative analysis to find alternative equifinal routes toward high firm performance.
Findings
Five configurations are found which lead to high performance. Every one of the five configurations is found to be sufficient but not necessary for high firm performance.
Originality/value
The research findings contribute to a better understanding of the determinants of firm’s performance taking into account the asymmetries in the data set. The authors identify alternative paths toward high firm performance, which could be vital information for the decision maker inside a firm.
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