Summary of A Hybrid Deep Learning Framework For Stock Price Prediction Considering the Investor Sentiment Of Online Forum Enhanced by Popularity, By Huiyu Li and Junhua Hu
A Hybrid Deep Learning Framework for Stock Price Prediction Considering the Investor Sentiment of Online Forum Enhanced by Popularity
by Huiyu Li, Junhua Hu
First submitted to arxiv on: 17 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Statistical Finance (q-fin.ST)
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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 proposed novel hybrid deep learning framework leverages XLNET to analyze investor sentiment in online forums, combining sentiments with post popularity factors to compute daily group sentiments. This information is then integrated with stock technical indicators into an improved BiLSTM-highway model for predicting stock prices. Comparative experiments involving four Chinese stocks demonstrate the effectiveness of this approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to predict how well the price of a stock will do based on what people are saying about it online. They used special computer programs called “deep learning” to look at messages from investors on websites and figure out what they think about the stock. This information is then combined with other facts about the stock, like its past performance, to make a better prediction of how well it will do in the future. |
Keywords
» Artificial intelligence » Deep learning
