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

Robert P. Schumaker, Hsinchun Chen

Research output: Contribution to journalArticlepeer-review

552 Scopus citations


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.

Original languageEnglish (US)
Article numbera12
JournalACM Transactions on Information Systems
Issue number2
StatePublished - Feb 1 2009


  • Prediction
  • SVM
  • Stock market

ASJC Scopus subject areas

  • Information Systems
  • Business, Management and Accounting(all)
  • Computer Science Applications


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