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Language models for financial news recommendation
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Ranking related news predictions
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Correlating financial time series with micro-blogging activity
Proceedings of the fifth ACM international conference on Web search and data mining
Media coverage in times of political crisis: A text mining approach
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Evaluating sentiment in financial news articles
Decision Support Systems
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PAISI'12 Proceedings of the 2012 Pacific Asia conference on Intelligence and Security Informatics
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Decision Support Systems
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Transfer joint embedding for cross-domain named entity recognition
ACM Transactions on Information Systems (TOIS)
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Information Retrieval with Time Series Query
Proceedings of the 2013 Conference on the Theory of Information Retrieval
What's buzzing in the blizzard of buzz? Automotive component isolation in social media postings
Decision Support Systems
Evaluating and understanding text-based stock price prediction models
Information Processing and Management: an International Journal
BizPro: Extracting and categorizing business intelligence factors from textual news articles
International Journal of Information Management: The Journal for Information Professionals
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Our research examines a predictive machine learning approach for financial news articles analysis using several different textual representations: bag of words, noun phrases, and named entities. Through this approach, we investigated 9,211 financial news articles and 10,259,042 stock quotes covering the S&P 500 stocks during a five week period. We applied our analysis to estimate a discrete stock price twenty minutes after a news article was released. Using a support vector machine (SVM) derivative specially tailored for discrete numeric prediction and models containing different stock-specific variables, we show that the model containing both article terms and stock price at the time of article release had the best performance in closeness to the actual future stock price (MSE 0.04261), the same direction of price movement as the future price (57.1% directional accuracy) and the highest return using a simulated trading engine (2.06% return). We further investigated the different textual representations and found that a Proper Noun scheme performs better than the de facto standard of Bag of Words in all three metrics.