An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network

The stock market, which has been investigated by various researchers, is a rather complicated environment. Most research only concerned the technical indexes (quantitative factors), instead of qualitative factors, e.g., political e ect. However, the latter plays a critical role in the stock market environment. Thus, this study develops a genetic algorithm based fuzzy neural network (GFNN) to formulate the knowledge base of fuzzy inference rules which can measure the qualitative e ect on the stock market. Next, the e ect is further integrated with the technical indexes through the arti cial neural network (ANN). An example based on the Taiwan stock market is utilized to assess the proposed intelligent system. Evaluation results indicate that the neural network considering both the quantitative and qualitative factors excels the neural network considering only the quantitative factors both in the clarity of buying-selling points and buying-selling performance. c © 2001 Elsevier Science B.V. All rights reserved.