Type of Document	Dissertation
Author	Stivason, Charles T.
Author's Email Address	cstivaso@cnu.edu
URN	etd-121498-112557
Title	INDUSTRY BASED FUNDAMENTAL ANALYSIS: USING NEURAL NETWORKS AND A DUAL-LAYERED GENETIC ALGORITHM APPROACH
Degree	PhD
Department	Accounting (Academic)
Advisory Committee	Advisor Name Title Dr. Tarun K. Sen Committee Chair Dr. Cintia M. Easterwood Committee Member Dr. John J. Maher Committee Member Dr. Robert M. Brown Committee Member Dr. Robert T. Sumichrast Committee Member
Keywords	neural networks genetic algorithms fundamental analysis
Date of Defense	1998-11-16
Availability	mixed
Abstract This 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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