| dc.description.abstract | The natural language processing develops rapidly and be used in multiple purposes such as sentiment interpretation, bankruptcy prediction. Moreover, the twitter is used for stock price prediction, which is the main focus of research. In the past, relevant researches used financial news to predict stock prices by sentiment analysis or TF-IDF. Recently, word vectors have been used in related pre-processing methods. It uses sentence vectors to strengthen the contextual relevance of articles.
This research extracts the sentence vector and word direction from European and American financial news. Then, the prediction accuracy produced by different types of deep learning models are compared. Particularly, the models are trained with single and multiple news sources individually. In addition, the feature representations by news headlines and news content are also compared. As a result, the word vector will remove the commonly used word sentence vector, which is because the difference in word orders and syntax meanings, so that there is no need to make it. In this case, the prediciton models based on the word vectors with and without sentence vectors are also compared. The experimental results show that the sentence vector under CNN performs slightly better than the word vector. On the other hand, the news sources have an impact on the prediction performance due to their spread and exposure. Mixed news sources are more useful for long-term forecasts, while short-term forecasts are exposed to a wide range of news websites. Compared with news headlines and contents, the models trained by news contents perform better, which are different from the findings of previous researches. Finally, if the last sentence vector removes the commonly used words, the training efficiency will increase, and the prediction accuracy will slightly decrease. | en_US |