Stock price prediction through sentiment analysis of corporate disclosures using distributed representation

Many researches have exploited textual data, such as news, online blogs, and financial reports, in order to predict stock price movements effectively. Previous studies formed the task as a classification problem predicting upward or downward movement of stock prices from text documents. Such an app roach, however, may be deemed inappropriate when combined with sentiment analysis. In financial documents, same words may convey different sentiments across different sectors; if documents from multiple sectors are learned simultaneously, performance can deteriorate. Therefore, we conducted sentiment analysis of 8-K financial reports of firms sector by sector. In particular, we also employed distributed representation for predicting stock price movements. Experiment results show that our approach improves prediction performance by 25.4% over the baseline model, and that the direction of post-announcement stock price movements shifts accordingly with the polarity of the sentiment of reports. Not only does our model improve predictability, but also provides visualizations, which may assist agents actively trading in the field with understanding the drivers for the observed stock movements. The two main aspects of our model, predictability and interpretability, will provide meaningful insights to help decision-makers in the industry with time-split trading decisions or data-driven detection of promising companies.