The study examines social entrepreneurship from the perspective of: objective and philosophy (why social entrepreneurs are social entrepreneurs), opportunity identification (how social entrepreneurs recognise opportunities), implementation (how social entrepreneurship is implemented), and social entrepreneurship's contribution to entrepreneurship. The dual objective (some profit, social impact), and strong focus on social impact of the social entrepreneur are highlighted. Although certainly possible, opportunities are encountered and experienced, and thus recognised, rather than actively sought. There are both strong similarities and differences between social entrepreneurship and entrepreneurship opportunities. Innovation is as much a component of social entrepreneurship. Also, the proactiveness and innovativeness of social entrepreneurship are important aspects and concepts. Social entrepreneurship can also be radical and disruptive. Implementation of social entrepreneurship entails both the micro-and macro-level. Both the individual implementation of social entrepreneurship, and the overall extent and sophistication of social entrepreneurship opportunities and practice at a broader level, are relevant. Sustainability is a very important consideration for the social entrepreneur. Emphasis has shifted to self-sufficiency and financial sustainability. There are marked similarities between start-up for the social entrepreneur, and the entrepreneur, particularly given that social enterprises are run as businesses just as much. The various contributions of social entrepreneurship to entrepreneurship are highlighted. It is evident that social entrepreneurship and entrepreneurship can be compared on several grounds, including the level of risk and the level of difficulty.

The paper examines an axiomatic structural approach to term structure decomposition. From this perspective, term structure decomposition is modelled as an non-parsimonious optimization problem, with the structure delineated by constraints related to the likely attributes thereof, rather than by a linear combination of splines or functions. The motivation for the model lies in its perceived flexibility or power. Also, the model is seen as a likely candidate to implement issue-level term structure decomposition. Consequently, issue-level term structure decomposition is also briefly introduced. The power of the model is tested on a simulated and market sample. Even though it may go against notions of structure smoothness, the relationship or correlation between structure smoothness, goodness of fit, and systematic/ unsystematic risk is also touched on.

The study examines the creative process of entrepreneurs and innovators. It considers how several types of thinking-analytical, analogical, imaginary, intuitive-are involved in creativity and the creative process. It further considers how learning and composite thinking-the integration of the different types of thinking-are incorporated in the creative process. The subsequent analysis covers a number of aspects of creativity and the creative process: 1) attributes, 2) traits, 3) skill, 4) stimulants, 5) process, 6) method and technique, 7) imagination, 8) intuition and the subconscious , 9) problem statement, 10) referencing past solutions, 11) the solution space, 12) teams, and 13) factors of success in the market. The prominent role of the intuition and the subconscious in creativity is clear. Both the left-brain and rightbrain typically contribute to creativity. Perspective formation forms a key component of creativity. There is a semi-formal process to creativity. Both intuition and creativity itself can be developed. Given the array of factors that influence success in the market, it may be questioned whether creativity is essentially a prominent factor of entrepreneurship. Pure or raw creativity by itself is certainly not a sufficient factor of entrepreneurship but must first be combined with general business sense or acumen to guarantee innovation success.

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The study examines rating migration, and default probability term structures obtained from rating migration matrices. It expands on the use of rating migration matrices with reduced form bond valuation models, by formally delineating the probability of default according to the likely rating paths of a bond, as implied by the rating migration matrix. Further, two alternatives are also considered. First, the cost of default is stipulated as the recovery of par according to the exit rating upon default. Also, in addition to stating the value of a bond in terms of expected cash flows, when considering the probability of default, the value of a bond is alternatively stated as the present value of all likely rating paths of the bond, discounted against the market risk-bearing bond forward rates of the different rating categories. The impact of term structure volatility and rating migration uncertainty on bond valuation is also considered.It is shown that the relationship between rating migration and default probability is complex, and the default probabilities of different rating categories are time-dependent and not isolated from each other. Also, rating migration resembles a delayed default process that influences default probabilities of subsequent intervals. The implications of a rating migration matrix may perhaps only be fully understood through simulation. This form one of the first points by which to evaluate rating migration matrices. The results of the valuation model show that historical rating migration matrices may not be optimal for pricing bonds ahistorically. A principal premise of the study is the dichotomy between historical values and ahistorical estimates, particularly with regards to rating migration. It is argued that historical estimates face two key shortcomings: they must be able to accurately forecast future rating migration and rating category intensities as a result, and they must specify a method to include rating migration uncertainty. An optimization model is delineated to extract ahistorical rating migration matrices from market prices. This too has implications that should be considered. In light of the above, reduced form models may have an advantage over structural models, in their ability to portray a far more sophisticated default process.