Summary of Dcc: Differentiable Cardinality Constraints For Partial Index Tracking, by Wooyeon Jo et al.
DCC: Differentiable Cardinality Constraints for Partial Index Tracking
by Wooyeon Jo, Hyunsouk Cho
First submitted to arxiv on: 22 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computational Engineering, Finance, and Science (cs.CE)
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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 approach to index tracking, aiming to optimize portfolios while minimizing transaction costs. The authors address the limitations of existing methods by introducing a differentiable cardinality constraint (DCC) and a floating-point precision-aware method (DCCfpp). These innovations enable accurate calculation of cardinality and enforcement with polynomial time complexity. Experimental results demonstrate the effectiveness of DCCfpp across various datasets, outperforming baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Index tracking is a popular way to invest in the stock market. The goal is to create a portfolio that follows a specific index, like the S&P 500. However, this process can be complex and expensive. To make it more efficient, researchers have developed new ways to track indices. This paper proposes two new methods: DCC and DCCfpp. These approaches help calculate the right mix of investments (cardinality) accurately and quickly. The authors tested these methods on different datasets and found that they work better than other existing methods. |
Keywords
» Artificial intelligence » Precision » Tracking
