Evidence on the effect of Claw-Back provisions on IPO share allocation and underpricing in Hong Kong

The article examines share allocation practices of over 300 initial public offerings (IPOs) in Hong Kong during the years immediately following the enacting of a ‘Claw-Back’ provision for IPO share reallocation. The examination of exhaustive micro-level data reveals that small (uninformed, retail) investors earn higher initial returns than large investors. Before the enacting of the ‘Claw-Back’ provision, small investors were unfavourably treated in relation to large investors. The pattern now prevailing in the proportion of shares allocated to small and large investors also differs from that observed previously. When attempting to isolate the determinants of IPO underpricing in Hong Kong, the article also shows that both the ‘informed demand’ hypothesis and the signalling effect of underwriters’ reputation are significant determinants of underpricing. Such result, not visible when pooled OLS regressions are used, becomes apparent through the use of a system of simultaneous equations.info:eu-repo/semantics/acceptedVersio

Generic system for human-computer gesture interaction

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for humancomputer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of vision-based interaction systems can be the same for all applications and thus facilitate the implementation. In order to test the proposed solutions, three prototypes were implemented. For hand posture recognition, a SVM model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications.(undefined

A comparative study of different image features for hand gesture machine learning

Vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition. Hand gesture recognition for human computer interaction is an area of active research in computer vision and machine learning. The primary goal of gesture recognition research is to create a system, which can identify specific human gestures and use them to convey information or for device control. In this paper we present a comparative study of seven different algorithms for hand feature extraction, for static hand gesture classification, analysed with RapidMiner in order to find the best learner. We defined our own gesture vocabulary, with 10 gestures, and we have recorded videos from 20 persons performing the gestures for later processing. Our goal in the present study is to learn features that, isolated, respond better in various situations in human-computer interaction. Results show that the radial signature and the centroid distance are the features that when used separately obtain better results, being at the same time simple in terms of computational complexity.(undefined

A comparison of machine learning algorithms applied to hand gesture recognition

Hand gesture recognition for human computer interaction is an area of active research in computer vision and machine learning. The primary goal of gesture recognition research is to create a system, which can identify specific human gestures and use them to convey information or for device control. This paper presents a comparative study of four classification algorithms for static hand gesture classification using two different hand features data sets. The approach used consists in identifying hand pixels in each frame, extract features and use those features to recognize a specific hand pose. The results obtained proved that the ANN had a very good performance and that the feature selection and data preparation is an important phase in the all process, when using lowresolution images like the ones obtained with the camera in the current work.The authors wish to thank all members of the Laboratorio de Automacao e Robotica, at University of Minho, Guimaraes. Also special thanks to the ALGORITMI Research Centre for the opportunity to develop this research work

Hand gesture recognition for human computer interaction: a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition

Vision-based gesture recognition system for human-computer interaction

Hand gesture recognition, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. This work intends to study and implement a solution, generic enough, able to interpret user commands, composed of a set of dynamic and static gestures, and use those solutions to build an application able to work in a realtime human-computer interaction systems. The proposed solution is composed of two modules controlled by a FSM (Finite State Machine): a real time hand tracking and feature extraction system, supported by a SVM (Support Vector Machine) model for static hand posture classification and a set of HMMs (Hidden Markov Models) for dynamic single stroke hand gesture recognition. The experimental results showed that the system works very reliably, being able to recognize the set of defined commands in real-time. The SVM model for hand posture classification, trained with the selected hand features, achieved an accuracy of 99,2%. The proposed solution as the advantage of being computationally simple to train and use, and at the same time generic enough, allowing its application in any robot/system command interface

A Portuguese East Indiaman from the 1502-1503 Fleet of Vasco da Gama off Al Hallaniyah Island, Oman: An interim report

Two Portuguese naus from Vasco da Gama's second voyage to India, left behind to disrupt maritime trade between India and the Red Sea, were wrecked in May 1503 off the north-eastern coast of Al Hallaniyah Island, Oman. The ships, Esmeralda and São Pedro, had been commanded by da Gama's maternal uncles, Vicente and Brás Sodré, respectively. A detailed study and scientific analysis of an artefact assemblage recovered during archaeological excavations conducted in Al Hallaniyah in 2013 and 2014 confirms the location of an early 16th-century Portuguese wreck-site, initially discovered in 1998. Esmeralda is proposed as the probable source of the remaining, un-salved wreckage

Attention, emotions and cause-related marketing effectiveness

Purpose – The purpose of this study is to explain how cognitive and emotional responses may influence decisions to purchase cause-related products. Design/methodology/approach – An experimental design clarifies how autonomic reactions determine altruistic choices in a simulated shopping environment. Eye-tracking and electrodermal response measurements were set to predict choices of hedonic vs utilitarian cause-related vs unrelated products. Findings – Emotional arousal, pleasure and attention to the cause-related bundle are associated with altruistic behaviour in hedonic choices. When facing utilitarian choices, customers focus on brand logo and donation amount while experiencing pleasure, but emotional arousal does not increase marketing effectiveness in this case. Research limitations/implications – The experiment may be replicated in the real-world shopping environment, but spurious influences will be difficult to control. Distracting cues such as background music and scents used to increase positive emotions may affect intensity of emotive and cognitive processes. Practical implications – The results highlight the prominence of automatic reactions in customers’ choices. In the present instance, managers’ effort should be directed to the raising of altruistic visual cues of the donation-based promotion and positive emotional responses through guilt reducing effects. Originality/value – The study pioneers the use of eye-tracking coupled with skin conductance measurement in experimental designs aimed at clarifying the role of autonomic reactions such as emotional arousal, pleasure and attention in the effectiveness of emotionally charged marketing campaigns.info:eu-repo/semantics/publishedVersio

A text mining-based review of cause-related marketing literature

Cause-related marketing (C-RM) has risen to become a popular strategy to increase business value through profit-motivated giving. Despite the growing number of articles published in the last decade, no comprehensive analysis of the most discussed constructs of cause-related marketing is available. This paper uses an advanced Text Mining methodology (a Bayesian contextual analysis algorithm known as Correlated Topic Model, CTM) to conduct a comprehensive analysis of 246 articles published in 40 different journals between 1988 and 2013 on the subject of cause-related marketing. Text Mining also allows quantitative analyses to be performed on the literature. For instance, it is shown that the most prominent long-term topics discussed since 1988 on the subject are “brand-cause fit”, “law and Ethics”, and “corporate and social identification”, while the most actively discussed topic presently is “sectors raising social taboos and moral debates”. The paper has two goals: first, it introduces the technique of CTM to the Marketing area, illustrating how Text Mining may guide, simplify, and enhance review processes while providing objective building blocks (topics) to be used in a review; second, it applies CTM to the C-RM field, uncovering and summarizing the most discussed topics. Mining text, however, is not aimed at replacing all subjective decisions that must be taken as part of literature review methodologies.info:eu-repo/semantics/publishedVersio