GRUvader: Sentiment-Informed Stock Market Prediction
This work addresses stock market prediction for investors and analysts, but it appears incremental as it combines existing methods (GRU and sentiment analysis) without major breakthroughs.
This study tackled stock price prediction by comparing machine learning algorithms and examining the influence of sentiment analysis, finding that AI-enhanced models outperformed stand-alone models and demonstrating a correlation between sentiment indicators and stock price movement.
Stock price prediction is challenging due to global economic instability, high volatility, and the complexity of financial markets. Hence, this study compared several machine learning algorithms for stock market prediction and further examined the influence of a sentiment analysis indicator on the prediction of stock prices. Our results were two-fold. Firstly, we used a lexicon-based sentiment analysis approach to identify sentiment features, thus evidencing the correlation between the sentiment indicator and stock price movement. Secondly, we proposed the use of GRUvader, an optimal gated recurrent unit network, for stock market prediction. Our findings suggest that stand-alone models struggled compared with AI-enhanced models. Thus, our paper makes further recommendations on latter systems.