Summary of Contrastive Learning Of Asset Embeddings From Financial Time Series, by Rian Dolphin et al.
Contrastive Learning of Asset Embeddings from Financial Time Series
by Rian Dolphin, Barry Smyth, Ruihai Dong
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistical Finance (q-fin.ST)
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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 a novel contrastive learning framework to generate informative asset embeddings from financial time series data. The goal is to learn representations that can be used for tasks like sector classification, risk management, and portfolio optimization. To address the complex and stochastic nature of financial markets, the approach leverages the similarity of asset returns over many subwindows to generate positive and negative samples using a statistical sampling strategy based on hypothesis testing. Various contrastive loss functions are explored to capture different relationships between assets in a discriminative representation space. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to learn useful information from financial data. It’s like trying to find patterns in a big mess of numbers! The authors developed a new way to do this by looking at how similar the values are for different types of investments over time. They used this method to create a special kind of map, called an embedding, that can help us group similar investments together and make better decisions about where to put our money. |
Keywords
* Artificial intelligence * Classification * Contrastive loss * Embedding * Optimization * Time series
