Mathematics, Vol. 12, Pages 3801: GRUvader: Sentiment-Informed Stock Market Prediction

Mathematics doi: 10.3390/math12233801

Authors:
Akhila Mamillapalli
Bayode Ogunleye
Sonia Timoteo Inacio
Olamilekan Shobayo

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.

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