Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.15846 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916374438215680 |
|---|---|
| author | Tang, Ruijie |
| author_facet | Tang, Ruijie |
| contents | This study investigates the application of causal discovery algorithms in equity markets, with a focus on their potential to build investment strategies. An investment strategy was developed based on the causal structures identified by these algorithms. The performance of the strategy is evaluated based on the profitability and effectiveness in stock markets. The results indicate that causal discovery algorithms can successfully uncover actionable causal relationships in large markets, leading to profitable investment outcomes. However, the research also identifies a critical challenge: the computational complexity and scalability of these algorithms when dealing with large datasets. This challenge presents practical limitations for their application in real-world market analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_15846 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Trading with Time Series Causal Discovery: An Empirical Study Tang, Ruijie Computational Finance This study investigates the application of causal discovery algorithms in equity markets, with a focus on their potential to build investment strategies. An investment strategy was developed based on the causal structures identified by these algorithms. The performance of the strategy is evaluated based on the profitability and effectiveness in stock markets. The results indicate that causal discovery algorithms can successfully uncover actionable causal relationships in large markets, leading to profitable investment outcomes. However, the research also identifies a critical challenge: the computational complexity and scalability of these algorithms when dealing with large datasets. This challenge presents practical limitations for their application in real-world market analysis. |
| title | Trading with Time Series Causal Discovery: An Empirical Study |
| topic | Computational Finance |
| url | https://arxiv.org/abs/2408.15846 |