An empirical analysis of IPOs and SEOs : evidence from the Chinese stock markets

Initial Public Offerings (IPOs) have drawn much attention among financial economists recently. However, gaps still exist and more empirical research is warranted, especially for immature stock markets, such as China. This research mainly concentrates on the aspects of “Credit rating effect on IPOs and SEOs’, ‘Complicated IPO allocation mechanisms’ and ‘Links between IPOs and SEOs, and SEOs motivations’ in the Chinese case using data from 1990 to 2011, which covers the entire history of the Chinese stock market development.First of all, this thesis confirms that the presence of credit rating is able to reduce information asymmetry and lower the IPO/SEO underpricing level no matter the rating is from the Chinese domestic rating agency or top three international rating agencies (S&P, Fitch and Moody’s), where the so-called ‘Non-creditable rating’ system does work in Chinese case. Further, this thesis proves additional evidence that multiple credit ratings' presence can lower the IPO/SEO underpricing level. What is more, this research confirms that what matters on IPO/SEO underpricing is not only the presence of credit rating, but also the level of credit rating. In order to analyse the credit-rating effect, this thesis has also divided sample into four sub-samples based on a pricing model in China and provides additional results that credit-rating presence is only able to reduce information asymmetry in time periods two and three for IPO, but the presence of credit rating can lower underpricing for SEO in all time periods.Secondly, we examine the determinants of the allocation mechanism choice and the how effective each allocation mechanism is in reducing the IPO underpricing for the Chinese market. Our results show that among the several IPO allocation mechanisms in China, the “bookbuilding” (BB) is most effective in reducing the underpricing level, and that the market conditions, firm’s risk level, information asymmetry and capital demand all play important roles in the choice of the IPO allocation mechanism. Our results also attest that firms with larger board size and or a higher proportion of legal persons sharing ownership are less likely to use the BB allocation mechanism. A higher proportion of tradable shares is negatively associated with the likelihood of using BB allocation mechanism, and the short-term and the long-term performance of IPOs vary significantly across the allocation mechanisms.Thirdly, regarding the link between IPOs and SEOs, the results provide new evidence that firms do underpricing IPOs as strategy and will compensate the loss from following SEOs with higher price and larger sizes. Additionally, this thesis also captures the link between the IPO and SEO effect in different time lengths (doing SEOs within 12 months, 24 months, 36 months and more than 36 months after IPOs). The thesis confirms corporate governance can influence SEO decisions as well. Incentives of SEOs in the Chinese case also be evaluated in this thesis.All our results in the thesis provide empirical evidence of difference areas about IPO and SEO in the Chinese case, and the results can be used as references directly in the real world

A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability

Graph Neural Networks (GNNs) have made rapid developments in the recent years. Due to their great ability in modeling graph-structured data, GNNs are vastly used in various applications, including high-stakes scenarios such as financial analysis, traffic predictions, and drug discovery. Despite their great potential in benefiting humans in the real world, recent study shows that GNNs can leak private information, are vulnerable to adversarial attacks, can inherit and magnify societal bias from training data and lack interpretability, which have risk of causing unintentional harm to the users and society. For example, existing works demonstrate that attackers can fool the GNNs to give the outcome they desire with unnoticeable perturbation on training graph. GNNs trained on social networks may embed the discrimination in their decision process, strengthening the undesirable societal bias. Consequently, trustworthy GNNs in various aspects are emerging to prevent the harm from GNN models and increase the users' trust in GNNs. In this paper, we give a comprehensive survey of GNNs in the computational aspects of privacy, robustness, fairness, and explainability. For each aspect, we give the taxonomy of the related methods and formulate the general frameworks for the multiple categories of trustworthy GNNs. We also discuss the future research directions of each aspect and connections between these aspects to help achieve trustworthiness

Flexible shear stress sensor skin for aerodynamics applications

Packaging for a large distributed sensing system is a challenging topic. Using flexible skin technology solves many of these problems. Combining with the newly developed backside contact technique, sensor packaging is made even easier by completely avoiding the fragile bonding wires. This paper describes the improved flexible MEMS technology and its application to the fabrication and packaging of practical shear stress sensor skins. An airflow separation detection system including these skins, MOSIS bias circuits and a data acquisition unit has been successfully tested in windtunnel and is being used for the aerodynamic study of a MEMS controlled super-maneuverable low-altitude unmanned aerial vehicle (UAV)

Sentinel-2 satellite imagery for urban land cover classification by optimized random forest classifier

Land cover classification is able to reflect the potential natural and social process in urban development, providing vital information to stakeholders. Recent solutions on land cover classification are generally addressed by remotely sensed imagery and supervised classification methods. However, a high-performance classifier is desirable but challenging due to the existence of model hyperparameters. Conventional approaches generally rely on manual tuning, which is time-consuming and far from satisfying. Therefore, this work aims to propose a systematic method to automatically tune the hyperparameters by Bayesian parameter optimization for the random forest classifier. The recently launched Sentinel-2A/B satellites are drawn to provide the remote sensing imageries for land cover classification case study in Beijing, China, which have the best spectral/spatial resolutions among the freely available satellites. The improved random forest with Bayesian parameter optimization is compared against the support vector machine (SVM) and random forest (RF) with default hyperparameters by discriminating five land cover classes including building, tree, road, water and crop field. Comparative experimental results show that the optimized RF classifier outperforms the conventional SVM and the RF with default hyperparameters in terms of accuracy, precision and recall. The effects of band/feature number and the band usefulness are also assessed. It is envisaged that the improved classifier for Sentinel-2 satellite image processing can find a wide range of applications where high-resolution satellite imagery classification is applicable

Phase interaction induced texture in a plasma sprayed-remelted NiCrBSi coating during solidification: An electron backscatter diffraction study

Although considerable endeavors have been dedicated to investigate the microstructures of the remelting-enhanced NiCrBSi coatings, the textures in the remelted coatings, which may result in property anisotropy, are rarely studied. In this work, the recrystallized fractions, grain orientations and interphase boundaries for Ni, Ni3B and CrB in a plasma sprayed-remelted NiCrBSi coating were investigated by electron backscatter diffraction. The results demonstrate that the texture is induced by phase interaction during solidification. Cooling from the liquid, the firstly formed Ni grains possess a cube fiber texture of {001}〈001〉. The successively formed Ni3B colonies are randomly oriented and keep specific orientation relationships with the surrounding Ni grains, resulting in formation of some weak texture components of Ni. The finally formed CrB grains have a considerably high frequency (40.8%) of lattice correlation boundary of (002)Ni//(040)CrB, but no specific orientation relationships with Ni3B grains. Hence, the interaction of Ni and CrB grains leads to the formation of more texture components of Ni. As such, the phase interaction induced texture forms in the remelted NiCrBSi coating. This work would give an insight into the anisotropy in the remelted NiCrBSi coatings and provide a theoretical basis of further optimizing the remelting process technologies

Flame temperature reconstruction through multi-plenoptic camera technique

Due to the variety of burner structure and fuel mixing, the flame temperature distribution is not only irregular but also complex. Therefore, it is necessary to develop an advanced temperature measurement technique, which can provide not only adequate flame radiative information but also reconstruct complex flame temperature accurately. In this paper, a novel multi-plenoptic camera imaging technique is proposed which is not only provide adequate flame radiative information from two different directions but also reconstruct the complex flame temperature distribution accurately. An inverse algorithm i.e., Non-Negative Least Squares is used to reconstruct the flame temperature. The bimodal asymmetric temperature distribution is considered to verify the feasibility of the proposed system. Numerical simulations and experiments were carried out to evaluate the performance of the proposed technique. Simulation results demonstrate that the proposed system is able to provide higher reconstruction accuracy although the reconstruction accuracy decreases with the increase of noise levels. Meanwhile, compared with the single plenoptic and conventional multi-camera techniques, the proposed method has the advantages of lower relative error and better reconstruction quality even with higher noise levels. The proposed technique is further verified by experimental studies. The experimental results also demonstrate that the proposed technique is effective and feasible for the reconstruction of flame temperature. Therefore, the proposed multi-plenoptic camera imaging technique is capable of reconstructing the complex flame temperature fields more precisely

Genome-wide analysis of the TIFY family and function of CaTIFY7 and CaTIFY10b under cold stress in pepper (Capsicum annuum L.)

TIFY [TIF(F/Y)XG] proteins are a plant particular transcription factor family that regulates plant stress responses. Therefore, to fill this gap, we investigated CaTIFY genes in pepper. Gene structure and conserved motifs of the pepper TIFY gene family were systematically analyzed using sequence alignment analysis, Cis-acting element analysis, transcriptomic data, and RT-qPCR analysis, and their expression patterns were further analyzed using Virus-Induced Gene Silencing (VIGS) and cold stress reactive oxygen species (ROS) response. We identified 16 CaTIFY genes in pepper, which were dispersed among seven subgroups (JAZI, JAZII, JAZIII, PPD, TIFY, and ZIM/ZML). Several CaTIFY members had stress-related harmonic-responsive elements, and four (CaTIFY7, CaTIFY10b, CaTIFY1b, and CaTIFY6b) had low-temperature-responsive elements. Transcriptomic data and RT-qPCR analysis revealed that the TIFY genes in pepper displayed different expression patterns in the roots, stems, leaves, flower fruits, and seeds. In particular, CaTIFY7 was highly expressed in young leaves, and CaTIFY10b was highly expressed in roots. CaTIFYs participated in the regulation of several different abiotic stresses and CaTIFY7 and CaTIFY10b were significantly induced by cold stress. Additionally, Virus-Induced Gene Silencing (targeting CaTIFY7 and CaTIFY10b) resulted in plants that were sensitive to cold stress. Conversely, overexpression of CaTIFY7 and CaTIFY10b enhanced plant cold tolerance by promoting the expression of genes related to cold stress and the ROS response. CaTIFY7 and CaTIFY10b interacted with themselves and CaTIFY7 also interacted with CaTIFY10b in the yeast two-hybrid (Y2H) system. Our data provide a basis for further analysis of the role of pepper TIFY genes in cold-stress responses in the future