This study examines the price reaction to the announcements of joint venture and strategic alliance formation, the main determinants of the partnering firm's choices to enter a specific joint venture and a specific strategic alliance, and the impact of such alliance formation on partnering firms' valuation. The analysis of the price reaction at the announcement of alliance formation indicates that market can distinguish between value creating and non-value creating alliances. I also provide evidence supporting the argument that alliance formation is not a random process. A firm's choice of entering an alliance designed as diversifying or non-diversifying strategy is a result of a complex interaction of external factors and internal needs. Finally, using the change in excess value from the year prior to the year following the alliance formation, I document that alliance formation negatively impacts the valuation of the single segment partnering firms relative to their industry peers, and has no impact on the valuation of multiple segment firms. Thus, single segment firms entering alliances are facing the trade-off between the longterm benefit provided by the alliance and the immediate costs affecting the activity developed in the house.

An ideal inductive machine learning algorithm produces a model best approximating an underlying target function by using reasonable computational cost. This requires the resultant model to be consistent with the training data, and generalize well over the unseen data. Regular inductive machine learning algorithms rely heavily on numerical data as well as general-purpose inductive bias. However certain environments contain rich domain knowledge prior to the learning task, but it is not easy for regular inductive learning algorithms to utilize prior domain knowledge. This thesis discusses and analyzes various methods of incorporating prior domain knowledge into inductive machine learning through three key issues: consistency, generalization and convergence. Additionally three new methods are proposed and tested over data sets collected from capital markets. These methods utilize financial knowledge collected from various sources, such as experts and research papers, to facilitate the learning process of kernel methods (emerging inductive learning algorithms). The test results are encouraging and demonstrate that prior domain knowledge is valuable to inductive learning machines.

An ideal inductive machine learning algorithm produces a model best approximating an underlying target function by using reasonable computational cost. This requires the resultant model to be consistent with the training data, and generalize well over the unseen data. Regular inductive machine learning algorithms rely heavily on numerical data as well as general-purpose inductive bias. However certain environments contain rich domain knowledge prior to the learning task, but it is not easy for regular inductive learning algorithms to utilize prior domain knowledge. This thesis discusses and analyzes various methods of incorporating prior domain knowledge into inductive machine learning through three key issues: consistency, generalization and convergence. Additionally three new methods are proposed and tested over data sets collected from capital markets. These methods utilize financial knowledge collected from various sources, such as experts and research papers, to facilitate the learning process of kernel methods (emerging inductive learning algorithms). The test results are encouraging and demonstrate that prior domain knowledge is valuable to inductive learning machines.

Includes bibliography. / ORGANIZATION OF THESIS: In Chapter 2, the important contributions to the development of Capital Market Theory will be discussed. Greater emphasis will be given to the more classical contributions and only a brief outline of the mathematical development will be presented where it is deemed necessary for the ensuing development of the thesis. In Chapter 3, a modified approach for portfolio selection in thinly-traded environments is proposed. This proposal concentrates on improving estimation of the inputs for practical implementation of the usual Markowitz portfolio selection routine. The estimation procedure adopted makes corrections for thin-trading and also makes use of the CAPM to improve the vector of return inputs. Chapter 4, basically consists of four sections. The first, gives a brief outline on historical estimation problems associated with the market model. In the second section the extent of thin-trading is investigated on the JSE. Furthermore a suitable beta estimation procedure which corrects for the effects of thin-trading is investigated empirically. In the third section an extended market model is proposed. This model leads to a more detailed, yet tractable structure of the risk components of stocks on smaller markets. An empirical investigation is subsequently conducted to investigate the risk structure of JSE stocks. In the last section of Chapter 4, an example of an empirical study using risk-adjusted returns is presented. The example considers the choice between bullion and South African gold shares from the international diversification perspective. In Chapter 5, empirical tests of the CAPM are conducted. This chapter consists of 2 main sections, namely, univariate tests and multivariate tests of the CAPM. Both of the tests also consider possible extensions of the CAPM by encorporating additional factors in the tests. Chapter 6 represents the main focus of this thesis, here the power of the univariate and multivariate tests of the CAPM are investigated using a simulation approach. The power investigation is conducted on simulated data that charaterizes the NYSE, however the JSE parameters are also considered. In the final section of this chapter the power of these tests are compared using various structured residual variance-covariance matrices. Lastly, some final thoughts and directions for future research are offered.

I examine the relation between a firm's dividend policy and its strategic tax decisions. I posit that the capital market pressure associated with paying a dividend leads dividend-paying firms to seek predictable cash flows. I specifically focus on the volatility of a firm's cash effective tax rate (ETR) due to the observability, large size, variability, and periodicity of cash tax payments. Consistent with dividend payments altering a firm's strategic tax preferences, I find that firms that pay a higher dividend exhibit more predictable cash ETRs. Further, I find that the predictability of a dividend-initiating (eliminating) firm's cash ETR subsequently increases (decreases). Additionally, I find that, consistent with prior research suggesting that financially constrained firms "borrow" cash from their tax account, financial constraint moderates the positive relation between the predictability of a firm's cash ETR and its dividend payments. Importantly, my results hold for firms initiating a dividend in response to the exogenous shock of the Bush tax cuts. Finally, I also examine specific tax strategies dividend-paying firms use to help increase the predictability of their cash tax payments. My results contribute to the academic literature by examining whether, and how, dividend-paying firms alter their strategic tax decisions. Additionally, I contribute to ongoing public policy debates over the value of dividend payments by demonstrating a positive relation between dividend payments and the predictability of a firm's cash tax payments.

The objective of this thesis was to analyse the empirical applicability of the Arbitrage Pricing Theory to international asset markets (UK stock market and US stock market) and to identify the set of economic variables which correspond most closely with the stock market factors obtained from the traditional factor analysis. Factor analysis and canonical correlation analysis were used as the principal tools for the empirical testing. Although factor analysis is frequently used, canonical correlation analysis is an new technique in this area and provides a method of linking factors extracted from the two sets of data. Various economic indicators were investigated as systematic influences on stock returns. It was shown that, based on the foundations of the APT and the characteristics of the factor scores from the factor analysis on the security returns and the economic indicators, canonical correlation analysis is an approximate technique to link the stock market and the economic forces. The results using the UK data imply that there is a good correspondence between factor scores generated by the factor analysis on the UK security returns and on the UK economic indicators. The results using the US data show that there is also a fair correspondence, but lower than that for the UK data, between factor scores generated by the factor analysis on the US security returns and on the US economic indicators. The APT was also investigated in an international setting by considering the UK data and the US data together. The results show that the canonical correlation analysis successfully links the stock returns and economic forces. The conclusion of these empirical findings is that security returns are influenced by a number of systematic economic forces. The validity and applicability of the APT were also empirically evaluated. The regression results show that the explanatory power of the APT model is fairly good. The overall results obtained here appear to suggest that the APT pricing relationship is supported by the testing methodology. In addition, the international correlation structure of financial markets movements between the UK economy and the US economy has been analysed. On balance, the evidence favours the APT and there is available evidence of inter-market linkage between the UK and the US. Individual sets of economic variables have been identified which correspond most closely with the UK and the US stock market factors by using the canonical correlation analysis. The results, at least partially, contribute to the understanding of security market pricing.

No description available.

Thesis (M.M. (Finance & Investment))--University of the Witwatersrand, Faculty of Commerce, Law and Management, Graduate School of Business Administration, 2013. / This study investigates the current private equity market in African frontier markets as well as inherent investment opportunities in these African frontier markets. The research includes an analysis of, inter-alia, the following: the development of capital markets in Africa, the classification of African frontier markets, the measurement of liquidity, the relationship between liquidity and asset prices and the history of private equity. This study will highlight to policymakers both in African and in donor capitals the need to implement strategies that will support investment (especially private equity investment) into the continent. The research carried out in this study should contribute to a better understanding of illiquidity risks of African frontier markets and show how these can be mitigated. This study will also provide key information on African frontier markets to investors and fund managers in order for them to understand that a typical investment strategy for investing in developed markets cannot be applied to frontier markets. The study analyses data of listed stocks on selected African stock exchanges and compares this to data for similar stocks listed on developed world stock markets to examine the relationship between liquidity, earnings multiples and market capitalisations for these stocks. Interestingly, results show that, while there is no relationship between the liquidity of stocks and the Price Earnings (PE) multiples of stocks, there is strong evidence to suggest that a relationship exists between the liquidity of stocks and the Enterprise Value to EBITDA (EV/EBITDA) multiples of stocks. Furthermore, we find strong evidence that African frontier market stocks are significantly less liquid and have lower earnings multiples than stocks with similar market capitalisations listed on stock exchanges in the developed world.

The purpose of this research is to explore the motivation of business group firms to adopt pyramidal ownership structures. The traditional approach claims that pyramids are useful in tunneling resources to other affiliates by transferring value to firms with high cash flow rights of controlling shareholders. Using a unique dataset of 7,180 Ecuadorian firms, I analyze the transmission of profits' shocks among group firms to assess the existence and the amount of tunneling. The comprehensive ownership information allows me to identify pyramidal and horizontally owned group firms separately and better understand the nature of their ownership structure. The results provide support for the existence of tunneling in Ecuadorian business groups. About 70% of the profits of the average group firm are transferred to another affiliate, although only half of this money shows up on its books. An alternative explanation for the flow of money among group firms is the existence of internal capital markets to substitute for imperfections in the external market. I test this hypothesis by comparing the impact of cash flow availability in the investment decision of group firms with that of stand-alone firms. Group firms' cash flow to investment sensitivity appears to be only half of the value for comparable standalone firms. Moreover, group liquidity is also a determinant of the average group firm's investment, especially for pyramidal firms. The analysis sheds light on the nature of business groups in Latin America, their ownership patterns, and their resource allocation decisions.

Diss. Uppsala : Uppsala universitet, 2006.