Mamillapalli, A., Ogunleye, B., Timoteo Inacio, S., & Shobayo, O. (2024). GRUvader ‐ Sentiment Informed Stock Market Prediction. Preprints. https://doi.org/10.20944/preprints202410.2465.v1
Chicago/Turabian Style
Mamillapalli, A., Sonia Timoteo Inacio and Olamilekan Shobayo. 2024 "GRUvader ‐ Sentiment Informed Stock Market Prediction" Preprints. https://doi.org/10.20944/preprints202410.2465.v1
Abstract
Stock price prediction is challenging due to global economic instability, high volatility and complexity of financial markets. Hence, this study compared several machine learning algorithms for stock market prediction and further examine the influence of sentiment analysis indicator in predicting stock price. Our results are in two-fold. Firstly, we present a lexicon-based sentiment analysis approach for identifying sentiment features and thus, evidence the correlation between the sentiment indicator and stock price movement. Secondly, we propose the use of GRUVader, an optimal gated recurrent unit networks for stock market prediction. Our findings suggest stand-alone models struggle when compared with AI-enhanced models. Thus, our paper made further recommendations on latter systems.
Keywords
ARIMA; GRU; machine learning; sentiment analysis; time series analysis
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
