Thesis (M.S.)--Florida State University, 2003. / Advisor: Dr. James R. Simpson, Florida State University, College of Engineering, Dept. of Industrial and Manufacturing Engineering. Title and description from dissertation home page (viewed Mar. 2, 2004). Includes bibliographical references.

Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003. / Includes bibliographical references (leaves 69-70). Also available in electronic version. Access restricted to campus users.

Thesis (Ph. D.)--University of Texas at Austin, 2003. / Vita. Includes bibliographical references. Available also from UMI Company.

Thesis (Ph. D.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 451-486).

Thesis (M. Phil.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 138-147).

Thesis (Ph. D.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 381-406).

Thesis (Ph. D.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 379-401).

Thesis (Ph. D.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 354-392).

published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

Current research carried out on financial forecasting has highlighted some limitations of classical econometric methods based on the assumption that the investigated time series can be described as stationary stochastic processes with Gaussian probability density functions. Chaotic behaviour, fractal characteristics and non-linear dynamics have been emerging in different aspects of the financial forecasting problem. The objective of this thesis is to take a system level perspective of the financial forecasting problem and to explore a number of approaches to enhance more 'traditional' decision making flows for stock market forecasting, with particular emphasis on stock selection and timing. To achieve this purpose, a number of stock selection and timing computational 'modules' are investigated. From a computational point of view, the investigation performed in this work encompass techniques such as artificial neural networks, genetic algorithms, chaos theory and fractal geometry, as well as more traditional methods such as clustering, screening, ranking, and statistics based models. From a financial data point of view, this research takes advantage of both fundamental and technical information to enhance the stock selection and timing processes and to cover several investment horizons. Three computational modules are proposed. First, a multivariate stock ranking module which uses fundamental information and is optimised through genetic algorithms. Second, a multivariate forecasting module which uses technical information and is based on artificial neural networks. Third, a univariate price time series forecasting module based on artificial neural networks. In addition, an integrated flow that takes advantage of some synergies and complementary properties of the devised modules is proposed. The effectiveness of the developed modules and the viability of the proposed integrated flow are evaluated over a number of investment horizons using (out-of-sample) historical data.