Search results

The purpose of this paper is to discuss how venture capital institutions can use their syndicated investment network to help listed companies to achieve better performance in mergers and acquisitions (M&A) activities.

This paper builds a fixed effect unbalanced panel regression model to study the impact of venture capital network on the M&A performance of listed companies.

Evidence indicated that the stronger the information resource acquisition ability of venture capital institutions in the network, the better the listed company's M&A performance supported; the stronger the information resource control ability of venture capital institutions in the network, the better the listed company's M&A performance supported; the higher the participation of venture capital institutions, the more significant the positive impact of information resource acquisition and information resource control abilities on M&A performance in the network.

The data in this paper are from China's Growth Enterprise Market (GEM), other markets may be considered in the future research studies.

The research conclusions of this paper affirm the positive role played by venture capital institutions through syndicated investment in eliminating information asymmetry in M&A of invested companies. The information resource acquisition and control abilities and participation degree of the venture capital network have positively promoted the M&A performance of the invested enterprises.

The conclusions of this paper not only provide useful supplements to existing research literature on venture capital network functions and corporate M&A but also have certain guiding value for venture capital institutions and start-ups to better use venture capital practices to improve their capabilities and performance.

The purpose of this paper is to empirically analyze the effects of stock price synchronicity and herding behavior of qualified foreign institutional investors (QFII) on stock price crash risk, especially the mediating effect of herding behavior of QFII on the relation of stock price synchronicity and stock price crash risk.

Taking China’s A-share listed companies from 2005 to 2014 and QFII holding shares data as the research sample, this study calculates herding effect index, sock price synchronicity index and stock price crash risk index, and perform linear regression.

This study concludes that, either herding behavior of QFII or the stock price synchronicity can increase the stock price crash risk. Further study reveals that, the herding behavior of QFII also improves the effect of stock price synchronicity on stock price crash risk. Namely, herding behavior of QFII acts as the mediating role between stock price synchronicity and stock price crash risk.

This study empirically analyzes and verifies the mediating roles of herding behavior of QFII in affecting the relation of sock price synchronicity and stock price crash risk for the first time. The findings of this study contribute to the study of the role of QFII in stabilizing Chinese security market.

Project portfolio benefit (PPB) evaluation is crucial for project portfolio management decisions. However, PPB is complex in composition and affected by synergy and ambidexterity. Ignoring these characteristics can result in inaccurate assessments, impeding the management and optimization of benefit. Considering the above complexity of PPB evaluation, this study aims to propose a refined PPB evaluation model to provide decision support for organizations.

A back propagation neural network optimized via genetic algorithm and pruning algorithm (P-GA-BPNN) is constructed for PPB evaluation. First, the benefit evaluation criteria are established. Second, the inputs and expected outputs for model training and testing are determined. Then, based on the optimization of BPNN via genetic algorithm and pruning algorithm, a PPB evaluation model is constructed considering the impacts of ambidexterity and synergy on PPB. Finally, a numerical example was applied to validate the model.

The results indicate that the proposed model can be used for effective PPB evaluation. Moreover, it shows superiority in terms of MSE and fitting effect through extensive comparative experiments with BPNN, GA-BPNN, and SVM models. The robustness of the model is also demonstrated via data random disturbance experiment and 10-cross-validation. Therefore, the proposed model could serve as a valuable decision-making tool for PPB management.

This study extends prior research by integrating the impacts of synergy and ambidexterity on PPB when conducting PPB evaluation, which facilitates to manage and enhance PPB. Besides, the structural redundancy of existing assessment methods is solved through the dynamic optimization of the network structure via the pruning algorithm, enhancing the effectiveness of PPB decision-making tools.