This paper provides an analysis of the link between the oil market and the U.S. stock market returns at the aggregate as well as industry levels. We empirically model oil price changes as driven by speculative demand shocks along with consumption demand and supply shocks in the oil market. We also take into account in our model all the factors that affect stock market price movements over and above the oil market, in order to quantify the pure effect of oil price shocks on returns. The results show that stock returns respond to oil price shocks differently, depending on the causes behind the shocks. Impulse response analysis suggests that consumption demand shocks are the most relevant drivers of the stock market return, relative to other oil market driven shocks. Industry level analysis is performed to control for the heterogeneity of the responses of returns to oil price changes. The results show that both cost side and demand side effects of oil price shocks matter for the responses of industries to oil price shocks. However, the main driver of the variation in industries' returns is the shock to aggregate stock market.

JEL classification: C3 G11 Q41 Q43 A B S T R A C TRecent studies of the oil market demonstrate endogeneity of the oil price by modelling it as a function of consumption and precautionary demands and producers' supply. However, studies analysing the effect of oil price uncertainty on investment do not disentangle uncertainties raised by underlying components which play a role in the oil market. Accordingly, this study uses a new approach to investigate the relationship between investment and uncertainty for a panel of U.S. firms operating in the oil and gas industry. We decompose oil price volatility to be driven by structural shocks that are recognized in the oil market literature, over and above other determinants, in order to study whether the investment uncertainty relationship depends on the drivers of uncertainty. Our findings suggest that oil market uncertainty lowers investment only when it is caused by global (consumption) oil demand shock. Stock market uncertainty is found to have a negative effect on investment with a year of delay. The results suggest no positive relationship between irreversible investment and uncertainty, but interestingly, a positive relation exists for reversible investment. This finding is in line with the option theory of investment and implies that the irreversibility effect of increased uncertainty dominates the traditional convexity effect.

This paper provides an analysis of the link between the oil market and the U.S. stock market returns at the aggregate as well as industry levels. We empirically model oil price changes as driven by speculative demand shocks along with consumption demand and supply shocks in the oil market. We also take into account in our model all the factors that affect stock market price movements over and above the oil market, in order to quantify the pure effect of oil price shocks on returns. The results show that stock returns respond to oil price shocks differently, depending on the causes behind the shocks. Impulse response analysis suggests that consumption demand shocks are the most relevant drivers of the stock market return, relative to other oil market driven shocks. Industry level analysis is performed to control for the heterogeneity of the responses of returns to oil price changes. The results show that both cost side and demand side effects of oil price shocks matter for the responses of industries to oil price shocks. However, the main driver of the variation in industries' returns is the shock to aggregate stock market.

This thesis will show that the addition of Explanation-Based Fuzzy Neural Networks (EBFNN) to Q-learning improves the learning process of a self-learning visual servoing robot manipulator system. Two new self-learning visual servoing systems for robot manipulators are proposed based on the following methodologies: Self-learning visual servoing of a robot manipulator using a Q-learning algorithm and fuzzy neural networks. Self-learning visual servoing of a robot manipulator using EBFNN and a Qlearning algorithm.Both learning methodologies do not require robot or camera models, or calibration.These systems apply Q-learning to find the optimal policy using reinforcement learning. This policy is used by the robot to reach a predetermined object that has been randomly placed in the environment. In the first system the Q-learning algorithm is implemented using fuzzy neural networks to estimate the Q-evaluation function for each robot action.This system learns the optimal policy in order to select the best basic action that maximizes the cumulative reward received at each time step. Simulation results demonstrate the effectiveness of the system to learn the highly non-linear mapping between the continuous work-space and the optimal action policy.In the second system an analytical learning component is added to the induction learning. This system includes two main properties: on-line training and lifelong learning that are implemented by the Q-learning algorithm and the EBFNN respectively. It is demonstrated that the number of training samples, and therefore the training time for a specific robot positioning accuracy task, can be reduced using fuzzy explanation-based neural networks and the Q-learning algorithm. Background knowledge about the robot and its environment is transferred to the robot agent during the learning process using a set of neural networks which have been previously trained.The on-line learning and real-time performances of these two systems are compared and simulation results show the effectiveness of the EBFNN to improve the learning process and performance of the self-learning visual servoing system. The T-test and Wilcoxon-Mann-Whitney U test are used to justify the statistical significance of the results.iv ACKNOWLEDGEMENTS

Multicell coordination increases the cell-edge users' throughput in cellular wireless communication if the inter-cell interference (ICI) is mitigated properly. In this paper, we use the null space concept to design precoders and decoders to remove ICI for cell-edge users in the downlink of a network MIMO system. We propose two novel methods that are able to eliminate ICI assuming no channel state information (CSI) is available at the basestations. Calculating signal-to-interference-ratio (SINR), we show that our proposed methods reaches to higher SINR in comparison to the block diagonalization (BD) with full CSI. By simulations, we show our methods outperform the BD method with full CSI in terms of BER since the SINR is higher for our proposed methods. We also show that to obtain most of sumrate provided by coordination strategy, it is not necessary to have global coordination and having four cells coordinating with each other is enough to reach more than 85 percent of maximum achievable sum-rate.