Board composition, ownership structure and dividend policies in an emerging market: further evidence from CASE 50

Purpose – The purpose of this study is to examine dividend policies in an emerging capital market, in a country undergoing a transitional period. Design/methodology/approach – Using pooled cross-sectional observations from the top 50 listed Egyptian firms between 2003 and 2005, this study examines the effect of board of directors’ composition and ownership structure on dividend policies in Egypt. Findings – It is found that there is a significant positive association between institutional ownership and firm performance, and both dividend decision and payout ratio. The results confirm that firms with a higher return on equity and a higher institutional ownership distribute higher levels of dividend. No significant association was found between board composition and dividend decisions or ratios. Originality/value – This study provides additional evidence of the applicability of the signalling model in the emerging market of Egypt. It was found that despite the high institutional ownership and the closely held nature of the firms, which imply lower agency costs, the payment of higher dividend was considered necessary to attract capital during this transitional period.

The role of social agents in the translation into English of the novels of Naguib Mahfouz

This research investigates the field of translation in an Egyptain context around the work of the Egyptian writer and Nobel Laureate Naguib Mahfouz by adopting Pierre Bourdieu’s sociological framework. Bourdieu’s framework is used to examine the relationship between the field of cultural production and its social agents. The thesis includes investigation in two areas: first, the role of social agents in structuring and restructuring the field of translation, taking Mahfouz’s works as a case study; their role in the production and reception of translations and their practices in the field; and second, the way the field, with its political and socio-cultural factors, has influenced translators’ behaviour and structured their practices. In this research, it is argued that there are important social agents who have contributed significantly to the structure of the field and its boundaries. These are key social agents in the field namely; the main English language publisher in Egypt, the American University in Cairo Press (AUCP); the translators: Denys Johnson-Davies, Roger Allen and Trevor Le Gassick; and the author, Naguib Mahfouz. Their roles and contributions are examined and discussed through the lens of Bourdieu’s sociology. Particular focus is given to the author Mahfouz and his award of the Nobel Prize, and how this award has influenced the field of cultural production and its social agents. Also, it is argued that socio-cultural factors in the field, in the period between 1960s and 2000s, affected the translators’ practices in terms of modes of production of Mahfouz’s works. To investigate the influence of these factors on translators’ practices in the field, empirical examination is conducted, at the textual level, on a corpus of six translated novels written by the same author, Mahfouz. It is shown that the translators have an increased tendency, over time, towards applying a foreignising approach in their translations of culture-specific items. The translators’ behaviour, which is a result of their habitus, is correlated to political and socio-cultural factors in the field of translation. That is, based on interviews conducted with the translators, it has been found that there are particular factors influenced their translational habitus and, thus, their practices during the production process of the translations.

Linked knowledge sources for topic classification of microposts:a semantic graph-based approach

Short text messages a.k.a Microposts (e.g. Tweets) have proven to be an effective channel for revealing information about trends and events, ranging from those related to Disaster (e.g. hurricane Sandy) to those related to Violence (e.g. Egyptian revolution). Being informed about such events as they occur could be extremely important to authorities and emergency professionals by allowing such parties to immediately respond. In this work we study the problem of topic classification (TC) of Microposts, which aims to automatically classify short messages based on the subject(s) discussed in them. The accurate TC of Microposts however is a challenging task since the limited number of tokens in a post often implies a lack of sufficient contextual information. In order to provide contextual information to Microposts, we present and evaluate several graph structures surrounding concepts present in linked knowledge sources (KSs). Traditional TC techniques enrich the content of Microposts with features extracted only from the Microposts content. In contrast our approach relies on the generation of different weighted semantic meta-graphs extracted from linked KSs. We introduce a new semantic graph, called category meta-graph. This novel meta-graph provides a more fine grained categorisation of concepts providing a set of novel semantic features. Our findings show that such category meta-graph features effectively improve the performance of a topic classifier of Microposts. Furthermore our goal is also to understand which semantic feature contributes to the performance of a topic classifier. For this reason we propose an approach for automatic estimation of accuracy loss of a topic classifier on new, unseen Microposts. We introduce and evaluate novel topic similarity measures, which capture the similarity between the KS documents and Microposts at a conceptual level, considering the enriched representation of these documents. Extensive evaluation in the context of Emergency Response (ER) and Violence Detection (VD) revealed that our approach outperforms previous approaches using single KS without linked data and Twitter data only up to 31.4% in terms of F1 measure. Our main findings indicate that the new category graph contains useful information for TC and achieves comparable results to previously used semantic graphs. Furthermore our results also indicate that the accuracy of a topic classifier can be accurately predicted using the enhanced text representation, outperforming previous approaches considering content-based similarity measures. © 2014 Elsevier B.V. All rights reserved.