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Sentiment analysis of news articles for financial signal prediction. Anand Atreya Nicholas Cohen Jinjiang James Zhai. Motivation. Financial markets can be swayed by sentiment Bearish sentiment can make a down market worse and lessen the impact of positive news

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sentiment analysis of news articles for financial signal prediction

Sentiment analysis of news articles for financial signal prediction

Anand Atreya

Nicholas Cohen

Jinjiang James Zhai

motivation
Motivation
  • Financial markets can be swayed by sentiment
    • Bearish sentiment can make a down market worse and lessen the impact of positive news
    • Vice versa for bullish sentiment
  • Firms which take advantage of sentiment information quickly can gain an edge
  • Computers analyzing sentiment can work far faster (and for less money) than human analysts
  • Our hypothesis: sentiment can be discovered in news articles about finance
methods
Methods
  • Data sets:
    • New York Times articles about finance (from the business section, containing the word “stock”, and with the metatag “financial desk”) from the LDC corpus
    • Articles from 2006 were used
    • S&P 500 data used as representative of market
  • Stanford MaxEnt classifier was used
methods continued
Methods (continued)
  • Two approaches were tried
    • Manual sentiment training: manually classified articles into positive, neutral, or negative sentiment, used these sets as training and test
    • Automatic: used the market return for the day preceding the news article with thresholds for positive, neutral, negative
results classification
Results: classification
  • F1 for manual classification (positive, neutral, negative):
    • 0.581, 0.614, 0.568 (141 test cases)
  • F1 results for automatic classification with and without metadata filtering:
  • Decent results for manual classification; mixed results for automatic classification
  • Using metadata filtering appears to help in most cases (except negative sentiment)
results correlation with market
Results: correlation with market

Not clear that article sentiment is correlated with market movements

future work
Future work
  • Classify different portions of an article
    • Some articles discuss several stocks or events with different sentiment
  • Select news articles only discussing companies in the S&P 500 index
  • Classify articles that come in throughout the day (i.e. over a wire) and correlate with market movements intra-day
  • Use a time window of more than one day for market returns: sentiment may correlate with longer term movements
    • Could use a moving average for this