Type of Document Dissertation Author Stivason, Charles T. Author's Email Address firstname.lastname@example.org URN etd-121498-112557 Title Industry Based Fundamental Analysis: Using Neural Networks and a Dual-Layered Genetic Algorithm Approach Degree PhD Department Accounting and Information Systems Advisory Committee
Advisor Name Title Sen, Tarun K. Committee Chair Brown, Robert M. Committee Member Easterwood, Cintia M. Committee Member Maher, John J. Committee Member Sumichrast, Robert T. Committee Member Keywords
- neural networks
- genetic algorithms
- fundamental analysis
Date of Defense 1998-11-16 Availability mixed AbstractThis research tests the ability of artificial learning methodologies to map market returns better than logistic regression. The learning methodologies used are neural networks and dual-layered genetic algorithms. These methodologies are used to develop a trading strategy to generate excess returns. The excess returns are compared to test the trading strategy's effectiveness. Market-adjusted and size-adjusted excess returns are calculated.
Using a trading strategy based approach the logistic regression models generated greater returns than the neural network and dual-layered genetic algorithm models. It appears that the noise in the financial markets prevents the artificial learning methodologies from properly mapping the market returns. The results confirm the findings that fundamental analysis can be used to generate excess returns.
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