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. / Ph. D.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/40422 |
| Date | 06 January 1999 |
| Creators | Stivason, Charles T. |
| Contributors | Accounting and Information Systems, Sen, Tarun K., Sumichrast, Robert T., Maher, John J., Brown, Robert M., Easterwood, Cintia M. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
| Relation | 03vitae.pdf, 01body.pdf |
Page generated in 0.0021 seconds