The Capital Asset Pricing model (CAPM) is the most widely used equity valuation model in both the United States of America (U.S.) and South Africa, thus its importance in corporate finance cannot be underestimated. The largest criticism of the CAPM lies in the difficulties with estimating its parameters and in particular the return on the market parameter. Roll (1977) believed that it is impossible to estimate the market portfolio let alone find a good proxy for it. The common trend amongst practitioners is to use a broad based stock index such as the S&P 500 or in South Africa‟s case the All Share Index (ALSI) as a proxy for the market portfolio. However these methods are questionable, as the market portfolio theoretically contains all risky assets held in proportion to their market value, and stock indices ignore large asset classes such as bonds. Furthermore, using a broad based stock index in the South African context ignores South African specific problems such as the supposed segregation of the market to the Resource and Financial and Industrial sectors. Therefore the purpose of this study was to determine whether simply using the broad based stock index, the ALSI, as a proxy for the market portfolio would suffice or whether the inclusion of debt instruments and the acknowledgement of the segregation on the JSE would enhance the proxy‟s performances. First a set of theoretical requirements that a proxy must satisfy to be considered a suitable proxy for the market portfolio were derived. Then a review of literature on the matter was undertaken, which showed that studies in both the U.S. and South Africa had had mixed results. Next, the various proxies were formed, and tested using the two-pass regression method. The two-pass regressions that were run with the model comprising solely of the ALSI as a proxy, produced a negative sloping SML. This result suggested an inverse relationship between risk and return, which is contradictory to the theory set out in chapters two and three. Thus robustness tests were performed on the model, but none solved the problem. Next the proposed multifactor models were tested to see if they would enhance the results of the first model. Although the results improved slightly, they too did not solve the problem. Thus, in conclusion it was found that incorporating a bond index into the proxy for the market portfolio did not significantly enhance the use of the CAPM in South Africa. / Thesis (M.Com.)-University of KwaZulu-Natal, Pietermaritzburg, 2011.

Machine learning methods have become powerful tools used in multiple industries. They have been successfully applied to problems such as image recognition, speech recognition and machine translation, among others. In this report, we investigated several machine learning methods for forecasting five different bond indexes. We have implemented and analyzed Feedforward Neural Nets, LSTMs, Q-Networks and Gradient Boosted Trees, and compared them to the Buy&Hold strategy. We performed manual feature extraction based on some popular features used in the industry. The features were extracted from several financial instruments and were used as predictor variables. The results showed that XGBoost and Feedforward Neural Networks were consistently able to beat the Buy&Hold strategy for three of five bond indexes. / Maskininlärningsmetoder har blivit kraftfulla verktyg som används i flera problemområden. De har framgångsrikt tillämpats på problem som bland annat bildigenkänning, taligenkänning och maskinöversättning. I denna rapport har vi undersökt flera maskininlärningsmetoder för att förutse fem olika obligationsindex. Vi har implementerat och analyserat Feedforward Neural Nets, LSTMs, Q-Networks och Gradient Boosted Trees, och jämfört dem med Buy\&Hold strategin. Vi har utfört manuell extraktion av features baserat på några populära funktioner som används inom industrin. Dessa features beräknades från flera finansiella instrument och användes som prediktorvariabler. Resultaten visar att XGBoost och Feedforward Neural Networks kan konsekvent slå Buy\&Hold strategin för tre av fem obligationsindex.

Snow algae as typical extremophiles are good model organisms for study of adaptation for life on the boundary of physiological possibilities. So far, it is not clear, how these microorganisms ensure on the molecular level the optimization of photosynthetic processes in conditions around 0 řC, often with very high light intensity. The aim of this work was to find out light and temperature growth optima of two less studied strains and to assess the composition of fatty acids in selected psychrophilic and psychrotrophic strains from the genera Chloromonas and Chlamydomonas (Chlamydomonadales, Chlorophyta) from culture collections UTEX and CCCryo and collections in Europe including the Czech Republic and Slovakia. Using crossed gradients method, this thesis describes optimal temperature and light conditions of two strains of snow algae isolated from sites in the Krkonoše Mountains that are characterized by different ecological conditions. The strain Chloromonas reticulata Luční originates from alpine zone and according to its growth characteristics, it can be classified as psychrotrophic alga requiring high light. The second tested strain was Chloromonas pichinchae Meandry from forest environment, which is also characterized as psychrotrophic, In contrast to previous strain, it grows in a wide range of...