| dc.description.abstract | At present, most research on stock price prediction is based on technical data. However, With the development of social media, investors can easily receive numerous information so that they will make different decisions, and thus cause the short-term inflation of the stock price. Therefore, considering both text and technical data will be an important direction for improving the accuracy of stock price prediction.
This research is mainly discussed the efficiency about using several improved dynamic and static word embedding methods to conduct the sentiment scores of social media texts, and then combine them with structured data to predict stock trend by a deep learning classifier. In the deep learning classifier, we figure out the best Transformer configuration which called Enhanced Transformer for sentiment analysis. In the area of stock price prediction, we propose an improved classifier (Multi-head Self-Attention with Convolutional Neural Network LSTM, MAC) by combining CNN in hybrid model and Multi-head Self-Attention mechanism in Transformer architecture. MAC-LSTM) to enhance the attention of important information to grasp the trend of stock rise and fall more accurately.
This research collects data from two kinds of social media text data (Twitter and Stocktwit) and uses Amazon (AMZN) and Netflix (NFLX) as the predict targets, with different sentiment analysis methods (dictionary method, API, and dynamic word embedding sentiment analysis method), and three deep classifiers, ATLSTM, CNN-LSTM, and MAC-LSTM, for investigating which combination is most suitable for stock prediction. At the end of the research, we found that the improved dynamic word embedding sentiment analysis method finBERT with the stock prediction classifier MAC-LSTM can predict the future stock trend with the best combination of 71.70% accuracy. | en_US |