Jin Zhang, Jun Shan and Susheng Wang
Inspired by the recent opening up of the Chinese banking market and by the ensuing location strategies adopted by foreign banks, the purpose of this paper is to develop an…
Abstract
Purpose
Inspired by the recent opening up of the Chinese banking market and by the ensuing location strategies adopted by foreign banks, the purpose of this paper is to develop an empirical analysis on location strategies. The paper enriches the existing literature by including many important realistic aspects, some of which have never been analyzed in theory before, especially for firms entering an unfamiliar and risky foreign market.
Design/methodology/approach
The authors carried out an empirical study on foreign banks' entry to the newly opened up Chinese banking market and modelled location strategies of foreign banks in China as a conditional logit problem, in which the dependent variable is the market chosen by an investor. To investigate the determinants of foreign banks' location choices in China, the authors collected data of foreign banks' entries into China during 1980‐2006.
Findings
The main empirical results are: asymmetric information, firm size and entry sequence are significant determinants of foreign banks' location strategies.
Originality/value
The paper presents a new set of results.
Details
Keywords
The purpose of the study is to review and understand firm selection mechanism involved in government venture capital (GVC) funding and identify key factors influencing selection…
Abstract
Purpose
The purpose of the study is to review and understand firm selection mechanism involved in government venture capital (GVC) funding and identify key factors influencing selection of tech-based firms for GVC funding.
Design/methodology/approach
This paper is based on real-time methodology. The data was generated from interviews of 60 young applicants, who applied for startup funding, and analyzed using statistical techniques to draw the results.
Findings
This review identifies financial viability, market viability and technological innovation to have the strongest predictive ability in firm selection process of the GVC funding program for tech-based youth-owned startups in the first round of interview. This review also highlighted that social impact is not a statistically significant variable in firm selection process in GVC funding.
Originality/value
This study tests the validity of the theory of GVC based on quantitative analysis of field data and identifies key factors with strong predictive abilities for GVC funding, more particularly for the youth-owned tech-based startups. This study brings to light the mechanism adopted for GVC funding and addresses gaps in the literature relevant to firm selection mechanism in GVC programs. This study would help GVC Fund Managers to review their own GVC programs in terms of selection mechanism and help them in appropriate designing of such programs.
Details
Keywords
Xiaojie 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.