The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Activity monitoring: noticing interesting changes in behavior
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
IEEE Transactions on Knowledge and Data Engineering
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search
IEEE Transactions on Knowledge and Data Engineering
WWW '05 Proceedings of the 14th international conference on World Wide Web
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Analyzing feature trajectories for event detection
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Can the Content of Public News Be Used to Forecast Abnormal Stock Market Behaviour?
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
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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.