| Summary: | 碩士 === 國立交通大學 === 管理學院資訊管理學程 === 99 === The stock market in Taiwan is a shallow-plate market, which is full of high level of systemic risk. Stock price in this market goes up and down seriously because it affects by lots of economic factors. This study proposes a multi-neural network model with monthly data on fundamental analysis (long-term) and weekly data on technical analysis (short-term), and tries to find the knowledge rules of the trends in stock price behavior. By using multi-neural network, we make integrated evaluation of long-term and short-term sub-network together, and we analyze the result to increase reliability of the neural network’s output. After that, a Taiwan Stock Market trend of stock wave forecast model is established.
The results show that multi-neural network is significantly more effective than single neural network and random walk model in forecasting accuracy and trading profitability. This thesis built the multi-neural network model of integrating long-term and short-term factors with physical quantity in stock wave. It prove multi-neural model has the better prediction result than single neural network. In addition, we prove the theory by CAPM which claims the value of individual company will be affected by the whole market.
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