Algorithms for clustering data
Algorithms for clustering data
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Language models for financial news recommendation
Proceedings of the ninth international conference on Information and knowledge management
News Sensitive Stock Trend Prediction
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Online event-driven subsequence matching over financial data streams
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
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In this paper, given a news article referring to one company, we decide whether it is a piece of good news that is followed by a moving-up trend in the company's stock market or a piece of bad news reversely. Additionally, we predict how will the fluctuation of stock price be influenced by the news article. The existing research work did not support flexible identification of the trends in stock price series, or take account of the case that temporal consecutive news articles may influence the stock market sensitively. In our proposed methods, we realize a more flexible and accurate investigation of correlation between news articles and stock prices. Experiments of our proposed methods yield high accuracy of prediction. The proposed mechanism for dynamically choosing sliding window to identify trends is also proven to be effective.