Summary of Diffstock: Probabilistic Relational Stock Market Predictions Using Diffusion Models, by Divyanshu Daiya et al.
DiffSTOCK: Probabilistic relational Stock Market Predictions using Diffusion Models
by Divyanshu Daiya, Monika Yadav, Harshit Singh Rao
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Computational Finance (q-fin.CP); Portfolio Management (q-fin.PM)
GrooveSquid.com Paper Summaries
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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 This paper proposes an innovative approach to generalize denoising diffusion probabilistic models for predicting the stock market and managing portfolios. Building upon previous works, which demonstrated the effectiveness of modeling inter-stock relationships for forecasting and portfolio management, this study aims to address the limitations of deterministic approaches in handling uncertainties. The authors showcase the application of Denoising Diffusion Probabilistic Models (DDPM) to develop an architecture that provides better market predictions conditioned on historical financial indicators and inter-stock relations. Additionally, a novel deterministic architecture called MaTCHS is proposed, which utilizes Masked Relational Transformer (MRT) to exploit inter-stock relationships and historical stock features. The study achieves state-of-the-art performance for movement prediction and portfolio management. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better predict the stock market and make smart investment decisions. Right now, we use special math formulas to try to figure out what will happen with stocks in the future. But sometimes these formulas don’t work very well because they can’t handle all the unknowns and surprises that happen in the market. The people who did this research tried a new way of thinking about it by using something called Denoising Diffusion Probabilistic Models (DDPM). This helps them make more accurate predictions about what will happen with stocks. They also came up with a special formula, called MaTCHS, that uses other information like how different stocks relate to each other to make even better predictions. |
Keywords
* Artificial intelligence * Diffusion * Transformer
