Summary of Trading Through Earnings Seasons Using Self-supervised Contrastive Representation Learning, by Zhengxin Joseph Ye and Bjoern Schuller
Trading through Earnings Seasons using Self-Supervised Contrastive Representation Learning
by Zhengxin Joseph Ye, Bjoern Schuller
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Trading and Market Microstructure (q-fin.TR)
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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 In this paper, researchers aim to develop an AI model that can accurately predict stock prices based on earnings release data. They propose the Contrastive Earnings Transformer (CET) model, which uses a self-supervised learning approach rooted in Contrastive Predictive Coding (CPC). The authors evaluate CET’s performance against benchmark models across various sectors and find that it outperforms them in extrapolating the value of earnings data over time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Earnings release is important for predicting stock movements. But, because the timing of releases can be irregular, it’s hard to use this data in trading models that need to make predictions quickly. The researchers developed a new AI model called CET that can use earnings data better than other models. They tested CET against other models and found that it does a great job of predicting stock prices even when the earnings data is old. |
Keywords
» Artificial intelligence » Self supervised » Transformer
