Abstract
Buy and sell of stocks is an essential activity in financial markets. Financial experts invented many market indicators to monitor the movement of stock prices. Trading rules were defined on these indicators for generating buy-sell signals. These rules are fuzzy in nature and they can predict to certain extent but not always the price movement of stocks. Their accuracy is time-varying and it is impossible to have the best trading rule. Certain combination of trading rules will generate more reliable buy-sell signals for a particular stock in a certain period of time. The selection of these trading rules can be formulated as an optimization problem. In this paper, we propose a new stock market timing system by integrating a genetic algorithm with a fuzzy expert system. Genetic algorithm is used to optimize the selection of fuzzy trading rules. Experiments were conducted to evaluate the performance of the system and the results indicate that the system can generate more reliable buy-sell signals even in a declining market.
| Original language | English |
|---|---|
| Pages | 410-417 |
| Number of pages | 8 |
| Publication status | Published - 2001 |
| Event | Congress on Evolutionary Computation 2001 - Soul, Korea, Republic of Duration: 27 May 2001 → 30 May 2001 |
Conference
| Conference | Congress on Evolutionary Computation 2001 |
|---|---|
| Country/Territory | Korea, Republic of |
| City | Soul |
| Period | 27/05/01 → 30/05/01 |
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