Stock risk mining by news

Due to the fast delivery of news articles by news providers on the Internet and/or via news datafeeds, it becomes an important research issue of predicting the risk of stocks by utilizing such textual information available in addition to the time series information. In the literature, the issue of predicting stock price up/down trend based on news articles has been studied. In this paper, we study a new problem which is to predict the risk of stocks by their corresponding news of companies. We discuss the unique challenges of volatility prediction, volatility ranking and volatility index construction. A new feature selection approach is proposed to select bursty volatility features. Such selected features can accurately represent/simulate volatility bursts. A volatility prediction method is then proposed based on random walk by considering both the direct impacts of bursty volatility features on the stocks and the propagated impacts through correlation between stocks. Finally, we construct a volatility index, called VN-index, which is a time series of predicted stock volatility. Moreover, stocks are ranked based on the predicted volatility values. Such information provides investors with knowledge on how widely a stock price is dispersed from the average, as an important indicator of stock risks in a stock market. We conducted extensive experimental studies using real datasets and report our findings in this paper.