We investigate 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)
