Applying Back Propagation Neural Network for Discovery Behavior of Opening Patterns of Taiwan Stock Market

• Main Authors: Huang, Wan-Cheng, 黃萬成
• Other Authors: Chen, An-Pin
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/92053619424594342371

碩士 === 國立交通大學 === 管理學院碩士在職專班資訊管理組 === 98 === Because the rapid change of information transmission, Taiwan stock market is also easy to influence by unexpected events. Many investors’ property changes in the stock market overnight sometimes shrink substantially. Therefore, it is urgent that the stock market investors seeking low risk investment opportunities to reduce the unpredictable risk. In order to prevent the dramatic overnight losses, the long-term investors need to build a hedging model to save their own property. In this study, we apply the theory of artificial intelligence in the field of back-propagation neural network to clustering the historical data of the behavior of opening patterns after 15 minutes in Taiwan weighted index price by the time 09:05, 09:10, 09:15 of the closing price. Produce eight types of groups, then each group of data entry to the back-propagation neural networks to predict relative to the same day's closing price of the Taiwan stock price index, and tests the investors in Taiwan's futures index as trading partners.The experimental result confirmed that after the experimental model through the combination of clustering propagation neural network to predict the exact rate was significantly better than the control group which only using back propagation neural network and the random walk model. In addition, the model with M2 (up, up, down), M7 (down, down, up) of the investment transaction accuracy and the profitability are the best profit performance model. Therefore, these experiments assisted by clustering has better grasp of the changes in the environment to make dynamic learning. Thus provide investors with more specific transactions information to assist decision-makers to make the right choice.