This PhD thesis deals with the performance of clusters. Even though cluster studies are numerous, a coherent framework to analyze cluster performance is lacking. In this thesis, such a framework is developed, drawing from different schools that deal with clusters. Central in the framework is a distinction to variables of cluster performance related to the structure of a cluster and variables related to the governance of a cluster. Four structure variables - agglomeration ands disagglomeration forces, internal competition, heterogeneity of the cluster and the level of entry and exit barriers - and four governance related variables - the presence of trust, the presence of intermediaries, the presence of leader firms and the quality of collective action regimes - are identified and discussed. The validity of these variables is confirmed in the three case studies, of the port clusters of Rotterdam, Durban, and the lower Mississippi. The strengths and weaknesses of the three port clusters, the importance of the variables discussed above and opportunities for policy and management to improve the performance of clusters are discussed.The results of this study are relevant for cluster scholars and for scholars specializing in port studies. The thesis is also relevant for (port) cluster managers and for managers of firms in (port) clusters, since implications of this study for policy and management in (port) clusters are discussed.

Land Reform Revisited engages with contemporary debates on land reform and agrarian transformation in South Africa. The volume offers insights into post-apartheid transformation dynamics through the lens of agency and state making. The chapters written by emerging scholars are based on extensive qualitative research and their analysis highlights the ways in which people negotiate and contest land reform realities and politics. By focusing on the diverse meanings of land and competing interpretations of what constitutes success and failure in land reform Brandt and Mkodzongi insist on looking beyond the productivity discourses guiding research and policy making in the field towards an informed view from below. Contributors are: Kezia Batisai, Femke Brandt, Sarah Bruchhausen, Nerhene Davis, Elene Cloete, Tariro Kamuti, Tarminder Kaur, Grasian Mkodzongi, Camalita Naicker, Fani Ncapayi, Mnqobi Ngubane, and Chizuko Sato.

The book provides detailed descriptions, including more than 550 mathematical formulas, for more than 150 trading strategies across a host of asset classes and trading styles. These include stocks, options, fixed income, futures, ETFs, indexes, commodities, foreign exchange, convertibles, structured assets, volatility, real estate, distressed assets, cash, cryptocurrencies, weather, energy, inflation, global macro, infrastructure, and tax arbitrage. Some strategies are based on machine learning algorithms such as artificial neural networks, Bayes, and k-nearest neighbors. The book also includes source code for illustrating out-of-sample backtesting, around 2,000 bibliographic references, and more than 900 glossary, acronym and math definitions. The presentation is intended to be descriptive and pedagogical and of particular interest to finance practitioners, traders, researchers, academics, and business school and finance program students.