| Abstrakt: |
Accurate prediction of stock trends is of great crucial to guiding investment management and financial policymaking. In recent years, text content generated by investors on various media platforms has had a big impact on stock movements. However, most of the existing studies apply equal weight to the text content to construct the sentiment index and ignore the fact that the influence of the sentiment index on the stock market will decrease with the increase of time interval. In this paper, we propose a model based on dual classifier coupling and sentiment analysis to study the trend of stock index. (1) We come up with a sentiment index weighted based on the reading volume to predict the trend of the stock index. (2) The exponentially weighted moving average (EWMA) model is used to modify the weighted sentiment index to obtain the modified sentiment index. (3) The maximum information coefficient (MIC) is applied to calculate the correlation between the closing price of the stock index and the modified sentiment index to obtain the best-modified sentiment index. (4) Two frequently used classifiers, convolutional neural network (CNN) and support vector machine (SVM), are used to build a dual-classifier coupling prediction model and adopt it for the final classification prediction. Two Chinese stock indexes (SSE 50 and CSI 300) are used to evaluate the predictive effect of the proposed model. In the practice case of the SSE 50 Index, the precision, accuracy, recall rate, F1-score, and AUC reached 81.38%, 80.99%, 80.99%, 81.04%, and 81.22%, respectively, after adding the modified sentiment index. In the practical case of the CSI 300 Index, the precision, accuracy, recall rate, F1-score, and AUC reached 80.07%, 80.07%, 80.07%, 80.06%, and 80.03%, respectively, after adding the modified sentiment index. It can be found through the experimental results that adding the investor sentiment index can advance the trend prediction of the stock index, and adding the modified sentiment index has a more obvious improvement effect. After adding the modified investor sentiment index, the prediction results of our proposed two-classifier coupled CNN-SVM model are much better than other benchmark models. [ABSTRACT FROM AUTHOR] |
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