Textual analysis of stock market prediction using breaking financial news: The AZFin text system

@article{Schumaker2009TextualAO,
  title={Textual analysis of stock market prediction using breaking financial news: The AZFin text system},
  author={Robert P. Schumaker and Hsinchun Chen},
  journal={ACM Trans. Inf. Syst.},
  year={2009},
  volume={27},
  pages={12:1-12:19}
}
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… CONTINUE READING
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