Industry Based Fundamental Analysis: Using Neural Networks and a Dual-Layered Genetic Algorithm Approach

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.

Name thesis:degree_name

Ph. D.

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Accounting and Information Systems

Department dc:contributor.department

Accounting and Information Systems

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

1998

Author dc:creator

• Stivason, Charles T.

Chair dc:contributor.committeechair

• Sen, Tarun K.

Committee members dc:contributor.committeemember

• Sumichrast, Robert T.
• Maher, John J.
• Brown, Robert M.
• Easterwood, Cintia M.

• neural networks
• genetic algorithms
• fundamental analysis

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

etd-121498-112557

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/40422