Market Trend Prediction using Sentiment Analysis:Lessons Learned and Paths Forward
Table 2. Sentiment attitudes Granger-causality on the FT I dataset.
['Stock', 'Model', 'Lag', 'Attitude', 'Price⇒']
In all the experiments, we failed to discover any sign that sentiment attitudes Granger-cause stock price changes, which would suggest that in general sentiment attitudes probably cannot be useful for the prediction of stock price movements. However, in many cases, we found that the opposite was true — stock price changes Granger-cause sentiment attitudes in the news, with the strongest causality found using the temporal sentiment analysis model. The individual stocks also produced mixed results, with each company behaving differently. For the Apple stock, we failed to detect any causality. For the Google stock, we identified that the prices would Granger-cause sentiment attitudes, but only with a two-day lag. For the HP stock, we detected causality only in temporal sentiment and only with a one-day lag. For the JPM stock, we found causality using standard sentiment, but it was absent using temporal sentiment. It is difficult to draw general conclusions from such varying results. According to the Granger-causality test with a one-day or two-day lag, sentiment attitudes do not seem to be useful for predicting stock price movements. However, the opposite seems to be true — the sentiment attitudes should be predicted using stock price movements. It is still possible that the Granger-causality from sentiment attitudes to stock price changes is present at a finer time granularity (e.g., minutes), but we are unable to perform such an analysis using our current datasets.