Prediction of stock values changes using sentiment analysis of stock news headlines

• Main Authors: László Nemes, Attila Kiss
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2021-07-01
• Series: Journal of Information and Telecommunication
• Subjects: sentiment analysis; bert; recurrent neural network; stock values; dataset building
• Online Access: http://dx.doi.org/10.1080/24751839.2021.1874252

The prediction and speculation about the values of the stock market especially the values of the worldwide companies are a really interesting and attractive topic. In this article, we cover the topic of the stock value changes and predictions of the stock values using fresh scraped economic news about the companies. We are focussing on the headlines of economic news. We use numerous different tools to the sentiment analysis of the headlines. We consider BERT as the baseline and compare the results with three other tools, VADER, TextBlob, and a Recurrent Neural Network, and compare the sentiment results to the stock changes of the same period. The BERT and RNN were much more accurate, these tools were able to determine the emotional values without neutral sections, in contrast to the other two tools. Comparing these results with the movement of stock market values in the same time periods, we can establish the moment of the change occurred in the stock values with sentiment analysis of economic news headlines. Also we discovered a significant difference between the different models in terms of the effect of emotional values on the change in the value of the stock market by the correlation matrices.