Sentiment Analysis of Financial News Articles 1
Robert P. Schumaker, Yulei Zhang, Chun-Neng Huang, New Rochelle · 2009
We investigated the pairing of a financial news article prediction system, AZFinText, with sentiment analysis techniques. From our comparisons we found that news articles of a subjective nature were easier to predict in both price direction (59.0 % vs 50.4 % without sentiment) and through a simple trading engine (3.30 % return vs 2.41 % without sentiment). Looking into sentiment further, we found that news articles of a negative sentiment were easiest to predict in both price direction (50.9 % vs 50.4 % without sentiment) and our simple trading engine (3.04% return vs 2.41 % without sentiment). Investigating the negative sentiment further, we found that AZFinText was best able to predict price decreases in articles of a positive sentiment (53.5%) and price increases in articles of a negative or neutral sentiment (52.4 % and 49.5 % respectively).