Summary of Natural Language Processing and Multimodal Stock Price Prediction, by Kevin Taylor and Jerry Ng
Natural Language Processing and Multimodal Stock Price Prediction
by Kevin Taylor, Jerry Ng
First submitted to arxiv on: 3 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 AI-based approach uses specialized BERT models to predict stock price trends by analyzing publicly released news articles. Unlike traditional methods that rely on raw currency values, this study employs percentage change as training data to provide context about price fluctuations’ significance and impact on a given stock. By leveraging natural language processing (NLP) techniques and long short-term memory networks (LSTMs), support-vector machines (SVMs), the model aims to improve the accuracy of predicting overall stock trends. The results show that this strategy can effectively predict stock price movements, highlighting the importance of sector-specific data features. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Stock prices are hard to predict! Researchers use special AI models like BERT to look at news articles and try to figure out what will happen to a company’s shares. Instead of using just the dollar value of the stock, they use how much the price has changed compared to before. This helps the model understand why prices might go up or down. The results show that this method can be pretty good at guessing which way the stock market will move! |
Keywords
* Artificial intelligence * Bert * Natural language processing * Nlp
