GRUvader: Sentiment-Informed Stock Market Prediction

作     者：Mamillapalli, Akhila Ogunleye, Bayode Inacio, Sonia Timoteo Shobayo, Olamilekan 

作者机构：School of Architecture Technology and Engineering University of Brighton BrightonBN2 4GJ United Kingdom School of Computing and Digital Technologies Sheffield Hallam University SheffieldS1 2NU United Kingdom 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Generative adversarial networks 

摘      要：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. © 2024, CC BY.