Fei Zhang, Xiao-Hua Zhou, Jiafu Su, Sang-Bing Tsai and Yu-Ming Zhai
The purpose of this paper is to examine how signals of uncertainty in the media affect retail investor decisions and initial public offering (IPO) underpricing through theoretical…
Abstract
Purpose
The purpose of this paper is to examine how signals of uncertainty in the media affect retail investor decisions and initial public offering (IPO) underpricing through theoretical and empirical methods.
Design/methodology/approach
The authors construct a theoretical model of the influence of media signals on IPO pricing, which describes the micro process in which uncertain signals in media influence retail investors’ decisions and IPO underpricing. Besides, the authors take 516 small and medium-size enterprises (SMEs) listed in A-share from July 2009 to December 2012 as samples for empirical tests and establish an in-depth learning model for text analysis with Java programming to measure Chinese media tone. Finally, the results of the model analysis are verified by empirical results.
Findings
The results show that authoritative media with high credibility can reduce the uncertainty of information sources attract more investors’ attention and improve the valuation and demand of retail investors. The higher the media credibility is the higher the IPO underpricing rate is. The uncertain tone of the media will increase the decision-making cost of investors reduce the valuation expectation and demand of the secondary market and lead to a lower IPO underpricing rate.
Originality/value
The authors study the influence of the uncertainty of media source and media content on the degree of IPO underpricing of SMEs. This is a useful supplement to the Chinese media tone research system that is still in the exploration stage. The research has reference value for government regulation and investor decision-making.
Details
Keywords
Sijia Shen, Ketai He, Biqiang Yu, Chenlong Zhai and Tianyan Ji
This paper proposes a new intra-layer partition adaptive slicing algorithm for FDM 3D printing, aiming to further improve forming efficiency based on the adaptive slicing…
Abstract
Purpose
This paper proposes a new intra-layer partition adaptive slicing algorithm for FDM 3D printing, aiming to further improve forming efficiency based on the adaptive slicing algorithm while preserving the surface finish quality of the formed model.
Design/methodology/approach
This method initially applies a large layer thickness for primary slicing, then refines layer thickness in layer height ranges with significant cross-sectional contour changes. Refined layers are partitioned: the internal region uses the large layer thickness for efficiency, while the external region uses a smaller layer thickness for surface quality. A thickness ratio and transition zone between regions prevent overlaps and gaps in printing paths.
Findings
The experimental results show that, compared to traditional adaptive slicing algorithms, the intra-layer partition adaptive slicing algorithm can effectively improve forming efficiency for most models while ensuring the model’s surface finish, with minimal impact on the bonding strength of the model.
Originality/value
The intra-layer partition adaptive slicing algorithm is a novel algorithm improved upon the traditional adaptive slicing algorithm, enabling models to achieve higher printing efficiency while maintaining the surface finish provided by the conventional adaptive slicing algorithm. This algorithm is of significant importance to vendors and individual users who provide printing services for large-sized fused deposition modeling models, as it can greatly enhance their production efficiency.