Summary of Exploring Applications Of Topological Data Analysis in Stock Index Movement Prediction, by Dazhi Huang et al.
Exploring applications of topological data analysis in stock index movement prediction
by Dazhi Huang, Pengcheng Xu, Xiaocheng Huang, Jiayi Chen
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Analysis, Statistics and Probability (physics.data-an)
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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 The paper presents a study on using Topological Data Analysis (TDA) for predicting stock index movements. The authors investigate the impact of different point cloud construction methods, topological feature representations, and classification models on prediction results. They construct point clouds for various stock indices using three methods, apply TDA to extract topological structures, compute four distinct features to represent patterns in the data, and input 15 combinations of these features into six machine learning models. The authors evaluate the predictive performance of different TDA configurations on datasets such as CSI, DAX, HSI, and FTSE. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study uses Topological Data Analysis (TDA) to predict stock index movements. Researchers built point clouds for several indices using three methods, then used TDA to find patterns in the data. They looked at four different features that represent these patterns and tried 15 combinations of these features with six different machine learning models. The results show which TDA configurations work best. |
Keywords
* Artificial intelligence * Classification * Machine learning
