| dc.creator | Papadamou, S. | en |
| dc.creator | Stephanides, G. | en |
| dc.date.accessioned | 2015-11-23T10:42:45Z | |
| dc.date.available | 2015-11-23T10:42:45Z | |
| dc.date.issued | 2005 | |
| dc.identifier.issn | 11092769 | |
| dc.identifier.uri | http://hdl.handle.net/11615/31654 | |
| dc.description.abstract | Over the last years, trading systems are widely used for market assessment however parameter optimization of these systems has adopted little concern. This paper, paper provides an answer to the question Can Genetic Algorithms Improve Trading Decisions in Financial Markets? Our proposed MATLAB based tool uses the power of genetic algorithms to generate fast and efficient solutions in real trading terms. By testing our trading system extensively on historical data of Emerging Stock markets we found that GATradeTool outperformed commonly used, non-adaptive, software tools with respect to the stability of return and time saving over the whole sample period. | en |
| dc.source.uri | http://www.scopus.com/inward/record.url?eid=2-s2.0-29844448469&partnerID=40&md5=6f5a1b6c112b050411351e632ce5b92a | |
| dc.subject | Financial markets | en |
| dc.subject | Genetic algorithms | en |
| dc.subject | Non-linear technical rules | en |
| dc.subject | Prediction | en |
| dc.subject | Commerce | en |
| dc.subject | Computer simulation | en |
| dc.subject | Decision making | en |
| dc.subject | Evolutionary algorithms | en |
| dc.subject | Finance | en |
| dc.subject | Forecasting | en |
| dc.subject | Marketing | en |
| dc.subject | Optimization | en |
| dc.subject | Market assessment | en |
| dc.subject | Nonlinear technical rules | en |
| dc.subject | Trading systems | en |
| dc.title | Can genetic algorithms improve trading decisions in financial markets? | en |
| dc.type | journalArticle | en |
