Predicting Downside in Stock Market Using Knowledge and News Data

Abstract

Traditionally, decision making in the stock market greatly depends on human expertise in sophisticated information processing, such as analyzing various financial reports and related news. However, limitations of expertise, time, and resources make investors suffer from information overload and information imbalance and may pose a negative impact on the investment market. Recent improvements in computing power, the availability of large volumes of data, and the advanced Artificial Intelligence (AI) techniques empower us with the ability to assist decision making in the stock market. In this paper, we present an integrated system that comprehensively monitors the downside risks of individual stocks and the overall market. Specifically, the stock downside risk is predicted based on quantitative data of related stocks, where the relationship between stocks is measured by constructing an Enterprise Knowledge Graph (EKG) using public knowledge. On the other hand, the market downside risk is predicted based on information extracted from daily news. For each risk, a Temporal Convolutional Network (TCN) is trained to output a continuous risk level that reveals both the direction and amplitude of incoming changes. Finally, key information and the predicted risk levels are organized into a condensed and understandable dashboard to interact with investors. Experiments on three focal stocks in the U.S. market suggest convincing accuracy in both stock risk and market risk modeling. Further visualization analysis demonstrates that our model has the potential to inform reverse changes of a stock movement ten days in advance.