M.Com. (Financial Management) / Over the last three decades, socially responsible investing (SRI) has emerged as one of the foremost issues faced by individuals and institutions in their daily activities. While the roots of responsible investing date back to the 18th century, the recent focus on responsible investing has been impactful. There has been growth in understanding the impact of investors’ decisions on long-term sustainability of business and society. In South Africa, the recent amendment of Regulation 28 of Pension Funds Act of 1956 and the introduction of the Code for Responsible Investing in South Africa (CRISA) are some of the latest developments in support of SRI. This minor dissertation evaluates the performance of SRI funds relative to traditional funds from January 2006 to June 2011. Specifically, the focus is on four main measures. Firstly, SRI funds relative to SRI funds’ own mandated benchmark; secondly, SRI funds relative to proxy market benchmark indices; thirdly, SRI funds relative to a matched sample of traditional unit trust funds; and lastly, SRI indices relative to traditional market indices. Twenty-seven funds were analysed in the study. The first finding was that SRI funds outperform their respective benchmarks on an unadjusted basis. Secondly, SRI funds showed slightly better risk-adjusted performance compared to proxy benchmark indices. Thirdly, SRI funds underperformed against a matched sample of traditional peers. Lastly, the FTSE/JSE SRI Equity Index underperformed against the general market equity index, but outperformed both the bonds and money market indices.

Fu, Zhengjia. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 107-116). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese.

Os rankings musicais destinam-se principalmente a fins de marketing, mas também ajudam os usuários a descobrir novas músicas, bem como a comparar artistas, álbuns, etc. Este trabalho apresenta uma ferramenta interativa para visualizar, encontrar e comparar rankings musicais usando diferentes técnicas além de exibir atributos das músicas. A técnica foi concebida após uma pesquisa remota que coletou dados sobre como as pessoas escolhem música. As técnicas de visualização tornam mais fácil obter informações sobre artistas e faixas, e também comparar os dados obtidos a partir dos dois principais rankings de música, Billboard e Spotify. A ferrament também permite a interação com dados pessoais. Resultados de experimentos conduzidos com usuários potenciais mostraram que a ferramenta foi considerada interessante, com um layout atrativo. Comparando com as formas tradicionais de visualizar rankings de músicas, usuários preferiram a ferramenta aqui desenvolvida, mas a diferença para Billboard e Spotify não foi grande. Entretanto, quando avaliada a usabilidade da ferramenta, os resultados foram melhores, principalmente no que se refere à filtragem e às técnicas de comparação. MusicVis foi também considerado fácil de aprender. / Music rankings are mainly aimed at marketing purposes but also help users in discovering new music as well as comparing songs, artists, albums, etc. This work presents an interactive way to visualize, find and compare music rankings using different techniques, including the display of music attributes. The technique was conceived after a remote survey we conducted to collect data about how people choose music. Our visualization makes easier to obtain information about artists and tracks, and also to compare the data gathered from the two major music rankings, namely Billboard and Spotify. The tool also provides interaction with personal data. The results obtained from experiments with potential users showed that the tool was considered interesting, with an attractive layout. Compared to traditional music ranking tools users preferred ours, but with not such a large difference from using Billboard or Spotify. However, when evaluating the usability of our tool, results are positive, mainly concerning to data filtering and comparison features. MusicVis was also considered easy to learn.

Li Ka-leung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 85-88). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Structural models of credit risk --- p.9 / Chapter 2.1 --- The original Merton model --- p.10 / Chapter 2.2 --- The extended Merton model --- p.11 / Chapter 2.3 --- The Black and Cox model --- p.12 / Chapter 2.4 --- The LS model --- p.14 / Chapter 2.5 --- The CDG model --- p.16 / Chapter 2.6 --- Comments on structural models --- p.19 / Chapter 3 --- Proxies and their implications --- p.20 / Chapter 3.1 --- Reviews of the EHH's empirical studies --- p.20 / Chapter 3.2 --- The proxy for market values of firms --- p.23 / Chapter 3.2.1 --- Zero coupon bonds under the Merton model --- p.23 / Chapter 3.2.2 --- Coupon bearing bonds under the extended Merton model --- p.25 / Chapter 3.2.3 --- Zero coupon bonds under the LS model --- p.26 / Chapter 3.2.4 --- Coupon bearing bonds under the LS model --- p.28 / Chapter 3.3 --- Implications of other proxies --- p.29 / Chapter 4 --- Maximum Likelihood Estimation --- p.33 / Chapter 4.1 --- The MLE approach for the Merton model --- p.33 / Chapter 4.2 --- The MLE approach for the barrier dependent models --- p.35 / Chapter 4.3 --- Survivorship consideration --- p.36 / Chapter 4.4 --- Simulation tests --- p.37 / Chapter 4.5 --- Simulation results --- p.39 / Chapter 4.5.1 --- Simulation results for the Merton model --- p.39 / Chapter 4.5.2 --- Simulation results for the LS model --- p.42 / Chapter 5 --- Empirical test --- p.47 / Chapter 5.1 --- Criteria of bond selection --- p.47 / Chapter 5.2 --- Parameters of models --- p.51 / Chapter 5.2.1 --- Firm specific parameters --- p.51 / Chapter 5.2.2 --- Interest rate parameters --- p.54 / Chapter 5.2.3 --- Stationary leverage process parameters --- p.55 / Chapter 5.2.4 --- Bond specific parameters --- p.57 / Chapter 5.3 --- Empirical results --- p.58 / Chapter 5.3.1 --- Empirical results for the Merton model --- p.59 / Chapter 5.3.2 --- Empirical results for the LS model --- p.66 / Chapter 5.3.3 --- Empirical results for the CDG model --- p.71 / Chapter 6 --- Conclusion --- p.77 / Appendix --- p.80 / Chapter A.1 --- Appendix 1 --- p.80 / Chapter A.2 --- Appendix 2 --- p.82 / Chapter A.3 --- Appendix 3 --- p.84 / Bibliography --- p.85

Wang, Wei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 106-122). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgement --- p.vi / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Thesis Contributions --- p.5 / Chapter 1.3 --- Thesis Organization --- p.8 / Chapter 2 --- Background and Literature Review --- p.9 / Chapter 2.1 --- Label Ranking in Machine Learning --- p.11 / Chapter 2.1.1 --- Label Ranking --- p.11 / Chapter 2.1.2 --- Semi-Supervised Learning --- p.12 / Chapter 2.1.3 --- The Development of Label Ranking --- p.14 / Chapter 2.2 --- Question Retrieval in Community Question Answering --- p.16 / Chapter 2.2.1 --- Question Retrieval --- p.16 / Chapter 2.2.2 --- Basic Question Retrieval Models --- p.18 / Chapter 2.2.3 --- The Development of Question Retrieval Models --- p.21 / Chapter 2.3 --- Ranking through CTR by Building Click Models --- p.24 / Chapter 2.3.1 --- Click Model's Importance --- p.24 / Chapter 2.3.2 --- A Simple Example of Click Model --- p.25 / Chapter 2.3.3 --- The Development of Click Models --- p.27 / Chapter 3 --- Semi-Supervised Label Ranking --- p.30 / Chapter 3.1 --- Motivation: The Limitations of Supervised Label Ranking --- p.30 / Chapter 3.2 --- Label Ranking and Semi-Supervised Learning Framework --- p.32 / Chapter 3.2.1 --- Label Ranking and Semi-Supervised Learning Setup --- p.32 / Chapter 3.2.2 --- Information Gain Decision Tree for Label Ranking --- p.37 / Chapter 3.2.3 --- Instance Based Label Ranking --- p.39 / Chapter 3.2.4 --- Mallows Model Decision Tree for Label Ranking --- p.40 / Chapter 3.3 --- Experiments --- p.40 / Chapter 3.3.1 --- Dataset Description --- p.41 / Chapter 3.3.2 --- Experimental Results --- p.42 / Chapter 3.3.3 --- Discussion --- p.42 / Chapter 3.4 --- Summary --- p.44 / Chapter 4 --- An Application of Label Ranking --- p.45 / Chapter 4.1 --- Motivation: The Limitations of Traditional Question Retrieval --- p.45 / Chapter 4.2 --- Intention Detection Using Label Ranking --- p.47 / Chapter 4.2.1 --- Question Intention Detection --- p.48 / Chapter 4.2.2 --- Label Ranking Algorithms --- p.50 / Chapter 4.2.3 --- Some Other Learning Algorithms --- p.53 / Chapter 4.3 --- Improved Question Retrieval Using Label Ranking --- p.54 / Chapter 4.3.1 --- Question Retrieval Models --- p.55 / Chapter 4.3.2 --- Improved Question Retrieval Model --- p.55 / Chapter 4.4 --- Experimental Setup --- p.56 / Chapter 4.4.1 --- Experiment Objective --- p.56 / Chapter 4.4.2 --- Experiment Design --- p.56 / Chapter 4.4.3 --- DataSet Description --- p.57 / Chapter 4.4.4 --- Question Feature --- p.59 / Chapter 4.5 --- Experiment Result and Comments --- p.60 / Chapter 4.5.1 --- Question Classification --- p.60 / Chapter 4.5.2 --- Classification Enhanced Question Retrieval --- p.63 / Chapter 4.6 --- Summary --- p.69 / Chapter 5 --- Ranking by CTR in Click Models --- p.71 / Chapter 5.1 --- Motivation: The Relational Influence's Importance in Click Models --- p.71 / Chapter 5.2 --- Click Models in Sponsored Search --- p.75 / Chapter 5.2.1 --- A Brief Review on Click Models --- p.76 / Chapter 5.3 --- Collaborating Influence Identification from Data Analysis --- p.77 / Chapter 5.3.1 --- Quantity Analysis --- p.77 / Chapter 5.3.2 --- Psychology Interpretation --- p.82 / Chapter 5.3.3 --- Applications Being Influenced --- p.82 / Chapter 5.4 --- Incorporating Collaborating Influence into CCM . --- p.83 / Chapter 5.4.1 --- Dependency Analysis of CCM --- p.83 / Chapter 5.4.2 --- Extended CCM --- p.84 / Chapter 5.4.3 --- Algorithms --- p.85 / Chapter 5.5 --- Incorporating Collaborating Influence into TCM . --- p.87 / Chapter 5.5.1 --- TCM --- p.87 / Chapter 5.5.2 --- Extended TCM --- p.88 / Chapter 5.5.3 --- Algorithms --- p.88 / Chapter 5.6 --- Experiment --- p.90 / Chapter 5.6.1 --- Dataset Description --- p.90 / Chapter 5.6.2 --- Experimental Setup --- p.91 / Chapter 5.6.3 --- Evaluation Metrics --- p.91 / Chapter 5.6.4 --- Baselines --- p.92 / Chapter 5.6.5 --- Performance on RMS --- p.92 / Chapter 5.6.6 --- Performance on Click Perplexity --- p.93 / Chapter 5.6.7 --- Performance on Log-Likelihood --- p.93 / Chapter 5.6.8 --- Significance Discussion --- p.98 / Chapter 5.6.9 --- Sensitivity Analysis --- p.98 / Chapter 5.7 --- Summary --- p.102 / Chapter 6 --- Conclusion and Future Work --- p.103 / Chapter 6.1 --- Conclusion --- p.103 / Chapter 6.2 --- Future Work --- p.105 / Bibliography --- p.106

La thèse vise à comprendre le lien existant entre les classements internationaux, le système de publication ainsi que les acteurs de l'enseignement et de la recherche en gestion. Nous approfondirons leurs relations en nous basant sur la théorie de la structuration de Giddens et les concepts d'habitus et de champ chez Bourdieu. Les classements construisent des indicateurs de performance généralement basés sur les articles dans des revues scientifiques internationales. Les critères des classements sont internalisés par les établissements et influencent leur stratégie ainsi que leur système d'évaluation. C'est tout un système qui se construit autour des publications pour mesurer la qualité des établissements. Or, les enseignants-chercheurs ne partagent pas les mêmes attitudes par rapport au système de publication. Nous avons élaboré une typologie afin de rendre compte de cette diversité des acteurs et de leurs stratégies d’adaptation face aux nouvelles injonctions institutionnelles. / The goal of the thesis is to understand the link between the international rankings, the publication system, and the academics in business studies. We will study their relations by using the Giddens’ theory of structuration and the concepts of habitus and field of Bourdieu. Rankings build performance indicators are usually based on articles in international journals. Rankings criterias are internalized by institutions and influence their strategy and their evaluation system. It’s a system which is built around the publications to measure the quality of institutions. However, the academics do not have the same attitude towards the publication system. We have developed a typology to reflect this diversity of agents and their adaptation strategies towards the new institutional injunctions.

What are the top journals in the field of mathematics education? This presentation will examine how quality of academic journals can be assessed and how to determine what journal metrics, if any, are of concern to scholars in the field.

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Excerpt: Publish or perish has long been the mantra academics live by. For decades, this referred to a list of publications on a researcher’s Curriculum Vitae.

What are the top journals in the field of mathematics education? This presentation will examine how quality of academic journals can be assessed and how to determine what journal metrics, if any, are of concern to scholars in the field.