Summary of Predicting Stock Prices with Finbert-lstm: Integrating News Sentiment Analysis, by Wenjun Gu et al.
Predicting Stock Prices with FinBERT-LSTM: Integrating News Sentiment Analysis
by Wenjun Gu, Yihao Zhong, Shizun Li, Changsong Wei, Liting Dong, Zhuoyue Wang, Chao Yan
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper presents a deep learning approach to predicting stock prices using financial text mining. By integrating weighted news categories into a forecasting model, the authors enhance predictive precision. They develop FinBERT, a pre-trained NLP model designed to discern sentiments within financial texts, and then advance it by incorporating LSTM architecture, creating the innovative FinBERT-LSTM model. This model uses news categories related to stock market structure hierarchy and combines them with previous week’s stock price situation for prediction. The authors train the model on Benzinga news articles using NASDAQ-100 index stock data and evaluate its performance using MAE, MAPE, and Accuracy metrics. The results show that FinBERT-LSTM performs best, followed by LSTM, and DNN model ranks third in terms of effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses computers to predict how the stock market will change. It does this by looking at old news articles about stocks and trying to figure out what they mean for the future. The authors make a special computer program that can understand these articles and use them to make predictions. They test their program on real data from the NASDAQ-100 index, which is made up of big companies like Apple and Google. The results show that their program does a good job at predicting how the market will change. |
Keywords
» Artificial intelligence » Deep learning » Lstm » Mae » Nlp » Precision
