| Summary: | 碩士 === 國立交通大學 === 管理學院資訊管理學程 === 101 === This research applies market profile, which is mentioned by the theory of market logic, and considers the change in trade volume to establish parameters to back propagation neural network to construct a better model than random trading behavior. This research also checks whether the model has a better investment performance with considering the change in trade volume and eventually extrapolates market logic and knowledge.
To let back propagation neural network learn the key to market trend changes, this study uses the indicators of price biases in market profile, rotation factor, the change in value area and the change in trade volume to see if the model constructed with the indicators derived from market profile has better predicting ability than random trading behavior does. This study also tests if the market profile model with considering the change in trade volume predicts the stock price more accurately than the model without considering the change in trade volume does.
Experimental results show that the model based on market profile has a better performance than random trading does no matter whether the change in trade volume is considered, especially the 5-day prediction. Therefore, market profile is an effective instrument for making investing decisions. Moreover, experimental results also show that the model that includes the parameter of the change in trade volume does not significantly predict the market trend better. The reason may be that the indicator of the change in trade volume does not include the relating price variation. As a result, the subsequent researchers can establish an indicator which contains both price and volume information and experiment its effectiveness on investment performance.
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