Abstract
The applications of fuzzy expert systems in aiding stock market investment are increasingly emphasized in recent years. Fuzzy expert systems mimic human reasoning approach that synthesizes approximate input information into crisp and precise decision. This advantage helps mass investors using the If-Then-Else terms of rules to support the stock trading decision. This research proposes a Genetic Algorithm-based approach to construct the fuzzy expert system by inferring the historical trading data implicitly. Several candidate technical indexes are considered in the fuzzy rules set to guide trading strategies. By adopting the Genetic Algorithms approach, the selection of appropriate indexes along with related fuzzy rules and corresponding membership functions can be effectively determined. The learning data derived from Taiwan's stock market is used for developing the fuzzy rules. A prototype GA-Fuzzy system is developed and tested. The results indicate that the system is able to improve the investment performance.
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