Summary of Degree Of Irrationality: Sentiment and Implied Volatility Surface, by Jiahao Weng and Yan Xie
Degree of Irrationality: Sentiment and Implied Volatility Surface
by Jiahao Weng, Yan Xie
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: General Finance (q-fin.GN)
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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 constructs daily high-frequency sentiment data using text entries from the East Money Stock Forum and predicts the next day’s implied volatility surface using the VAR method. It employs deep learning models like BERT and LSTM to build market sentiment indicators, which are then decomposed using FFT and EMD methods. The research finds that high-frequency sentiment is strongly correlated with at-the-money options’ implied volatility, while low-frequency sentiment is more correlated with deep out-of-the-money options’ implied volatility. By incorporating sentiment information, the study demonstrates improved accuracy in predicting implied volatility surfaces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses big data from a stock forum to predict how stock prices will change tomorrow. It breaks down daily feelings about the market into high- and low-frequency signals. The researchers find that short-term market feelings are connected to the price of at-the-money options, while long-term feelings are more related to deep out-of-the-money options. By combining these sentiment signals with other information, the study shows it can better predict how stock prices will change. |
Keywords
» Artificial intelligence » Bert » Deep learning » Lstm
