| Summary: | 碩士 === 國立成功大學 === 財務金融研究所碩士在職專班 === 106 === In this study, the sentiment lexicon is taken as one of the variables of the model mainly through text mining technology and the machine learning model to reinforce the analytic ability of the model, and the misclassification rate is used as the basis for the accuracy of the model.
The three different sentiment lexicon lagged from 1 to 5 variables into the model, respectively. Using machine learning methods about Gradient Boosting Decision Tree , Decision Tree, Logistic Regression, Neural Networks and Support Vector Machines, we can find that misclassification rate stability and accuracy are not the same. In order to select a more stable variable, each method calculates the misclassification rate and variance, and finally take the sentiment index Opinion Lexicon lagged 5,using the neural network model as the forecast model.
The target variable for this research model uses the Dow Jones Precious Metals Index, which is highly correlated with the gold price .This study chose the futures price delivered by COMEX Gold Futures in December 2017 when making the investment strategy, and buys and sells the futures respectively .The holding strategy is compared with the investment strategy results under the forecasting model. Empirical results show that the Opinion Lexicon lagged 5 using the neural network model. When at the long strategy, it can maintain a certain rate of return; while under the short strategy, investment loss can be avoided.
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