Title page for ETD etd-121498-112557


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 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
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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