Summary of Gruvader: Sentiment-informed Stock Market Prediction, by Akhila Mamillapalli et al.
GRUvader: Sentiment-Informed Stock Market Prediction
by Akhila Mamillapalli, Bayode Ogunleye, Sonia Timoteo Inacio, Olamilekan Shobayo
First submitted to arxiv on: 7 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Applications (stat.AP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study compares various machine learning algorithms for predicting stock prices and investigates the impact of sentiment analysis on these predictions. The results show a correlation between sentiment indicators and stock price movements, and propose the use of GRUvader, an optimal gated recurrent unit network, for stock market prediction. The findings suggest that standalone models struggle compared to AI-enhanced models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps us predict stock prices better by using special machine learning tools. It shows how words in news articles can make it easier to guess what will happen to the stock market. The best model they found is called GRUvader, which uses a special kind of artificial intelligence to make predictions. What’s surprising is that just using one type of model isn’t as good as combining different models together. |
Keywords
» Artificial intelligence » Machine learning
