Performance Evaluation of Unit Trusts in UK Market

Abstract Because of the low risk and professional management, mutual funds have become one of the most popular types of investments for individuals. Since the 1960s, the evaluation of mutual fund performance has aroused great interests in academic researches. However, previous studies have shown that mutual funds often underperform. Therefore, in this dissertation, the situation in the UK market is selected to examine whether unit trusts can ‘beat’ the market in the past five-year period from December 2003 to December 2008. This study employs Jensen’s alpha, Sharpe’s ratio, and Treynor’s ratio to analyse the selectivity abilities of unit trusts. The results suggest that the UK unit trusts cannot beat the market and that the actively managed unit trusts do not have a superior performance to the passively managed ones. Consequently, based on previous researches, two main reasons are determined relating to why unit trusts underperform: liquidity risk and poor managerial investment choices

Improvements on Uncertainty Quantification for Node Classification via Distance-Based Regularization

Deep neural networks have achieved significant success in the last decades, but they are not well-calibrated and often produce unreliable predictions. A large number of literature relies on uncertainty quantification to evaluate the reliability of a learning model, which is particularly important for applications of out-of-distribution (OOD) detection and misclassification detection. We are interested in uncertainty quantification for interdependent node-level classification. We start our analysis based on graph posterior networks (GPNs) that optimize the uncertainty cross-entropy (UCE)-based loss function. We describe the theoretical limitations of the widely-used UCE loss. To alleviate the identified drawbacks, we propose a distance-based regularization that encourages clustered OOD nodes to remain clustered in the latent space. We conduct extensive comparison experiments on eight standard datasets and demonstrate that the proposed regularization outperforms the state-of-the-art in both OOD detection and misclassification detection.Comment: Neurips 202

Perceived teacher support, peer relationship, and university students’ mental health: The mediation of reality and Internet altruistic behaviors

Studying in universities is a crucial development stage for students, whose thoughts, feelings, and actions are affected by interactions with their teachers and peers. This study explored the relationships between perceived teacher support and mental health as well as those between peer relationship and mental health among university students, and examined the mediating effects of reality and Internet altruistic behaviors on these relationships. Perceived teacher support questionnaire, peer relationship satisfaction questionnaire, self-reported altruism questionnaire, Internet altruistic behavior questionnaire, and general health questionnaire were administered to 553 university students. Results demonstrated that perceived teacher support and peer relationship positively predicted reality and Internet altruistic behaviors and positively predicted mental health. Reality and Internet altruistic behaviors positively predicted mental health and exerted significant mediating effects on the correlations between perceived teacher support and mental health as well as those between peer relationship and mental health. The male and female students differed insignificantly in the mediating effects of reality and Internet altruistic behaviors. Therefore, no matter for males or females, teachers should provide sufficient support for the students and establish favorable relationships with them. Friendly relationships, comfort, and active communication among peer students are also essential for creating a healthy and harmonious interaction environment. Those various factors of the school have impacts on the mental health of university students through their altruistic behaviors. This study suggests that further emphasis on teacher support and peer relationship is needed to promote the positive development of altruistic behaviors among university students, and ultimately provide a viable contribution to the university students’ mental health interventions

The dominance of big teams in China's scientific output

Modern science is dominated by scientific productions from teams. A recent finding shows that teams with both large and small sizes are essential in research, prompting us to analyze the extent to which a country's scientific work is carried out by big/small teams. Here, using over 26 million publications from Web of Science, we find that China's research output is more dominated by big teams than the rest of the world, which is particularly the case in fields of natural science. Despite the global trend that more papers are done by big teams, China's drop in small team output is much steeper. As teams in China shift from small to large size, the team diversity that is essential for innovative works does not increase as much as that in other countries. Using the national average as the baseline, we find that the National Natural Science Foundation of China (NSFC) supports fewer small team works than the National Science Foundation of U.S. (NSF) does, implying that big teams are more preferred by grant agencies in China. Our finding provides new insights into the concern of originality and innovation in China, which urges a need to balance small and big teams

The dominance of big teams in china’s scientific output

Modern science is dominated by scientific productions from teams. A recent finding shows that teams of both large and small sizes are essential in research, prompting us to analyze the extent to which a country’s scientific work is carried out by big or small teams. Here, using over 26 million publications from Web of Science, we find that China’s research output is more dominated by big teams than the rest of the world, which is particularly the case in fields of natural science. Despite the global trend that more papers are written by big teams, China’s drop in small team output is much steeper. As teams in China shift from small to large size, the team diversity that is essential for innovative work does not increase as much as that in other countries. Using the national average as the baseline, we find that the National Natural Science Foundation of China (NSFC) supports fewer small teams than the National Science Foundation (NSF) of the United States does, implying that big teams are preferred by grant agencies in China. Our finding provides new insights into the concern of originality and innovation in China, which indicates a need to balance small and big teams. © 2020 Linlin Liu, Jianfei Yu, Junming Huang, Feng Xia, and Tao Jia. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license