| Summary: | 碩士 === 國立交通大學 === 管理科學系所 === 99 === In the times of financial information explosion, if we invested the stock market with the right decision support system, we can get more profits. How to build the decision support system with the most appropriate artificial intelligence methods plays an important role. It also be one of the important issues in the financial research for these years.
The research about the technical analysis has been many years. The technical analysis includes two parts, one is the technical indicators, and the other is about the trends. Now many researches about the technical indicators have been applied in the financial decision area, but there are a few researches about the wave principle of the trends. Ju-Chi Chen in 2010 indicate that extract N-wave under the Elliott Wave characteristics by using the back-propagation neural network (BPNN) method. It can have great performance in accuracy and profitability, as well as prove that stock market analysis can exactly be obtained by the physical quantity. But there were no researches about integration of technical indicators with the wave principle. For the technical indicators of the technical analysis, this thesis will through BPNN to integrate the physical quantity about the technical indicators and the wave principle. Hopefully to built one model which have the better accuracy predictions and profitability predictions than the model only with the wave principle.
The results showed that through scientific method to integrate RSI and MACD with the N-wave will have the better accuracy and profitability predictions than only considering N-wave physical quantity. Considering that RSI represents the short-term disturbance and MACD represents the long-term trends, the results manifested that long-term and short-term physical quantities also have the control ability on the stock market. After establishing an intelligent model, this thesis will retrospect indicators original value, gives more confidence recommendations to investors.
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