GRU vader: Sentiment-Informed Stock Market Prediction

Akhila Mamillapalli, Bayode Ogunleye (), Sonia Timoteo Inacio and Olamilekan Shobayo
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Akhila Mamillapalli: School of Architecture, Technology and Engineering, University of Brighton, Brighton BN2 4GJ, UK
Bayode Ogunleye: School of Architecture, Technology and Engineering, University of Brighton, Brighton BN2 4GJ, UK
Sonia Timoteo Inacio: School of Architecture, Technology and Engineering, University of Brighton, Brighton BN2 4GJ, UK
Olamilekan Shobayo: School of Computing and Digital Technologies, Sheffield Hallam University, Sheffield S1 2NU, UK

Mathematics, 2024, vol. 12, issue 23, 1-18

Abstract: 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 GRU vader , 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.

Keywords: autoregressive integrated moving average; ARIMA; generative adversarial networks; GAN; gated recurrent unit; GRU; machine learning; natural language processing; sentiment analysis; time series analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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