Market Trend Prediction using Sentiment Analysis: Lessons Learned and Paths Forward
This work addresses the problem of improving financial market forecasting for investors and analysts, but it is incremental as it builds on existing sentiment analysis methods with mixed results.
The paper investigated using sentiment attitudes and emotions from financial texts to predict stock price movements, finding that sentiment attitudes generally do not Granger-cause price changes, while sentiment emotions sometimes do but inconsistently, and integrating emotions as features improved model accuracy for certain stocks.
Financial market forecasting is one of the most attractive practical applications of sentiment analysis. In this paper, we investigate the potential of using sentiment \emph{attitudes} (positive vs negative) and also sentiment \emph{emotions} (joy, sadness, etc.) extracted from financial news or tweets to help predict stock price movements. Our extensive experiments using the \emph{Granger-causality} test have revealed that (i) in general sentiment attitudes do not seem to Granger-cause stock price changes; and (ii) while on some specific occasions sentiment emotions do seem to Granger-cause stock price changes, the exhibited pattern is not universal and must be looked at on a case by case basis. Furthermore, it has been observed that at least for certain stocks, integrating sentiment emotions as additional features into the machine learning based market trend prediction model could improve its accuracy.