# Exploring applications of topological data analysis in stock index movement prediction

arXiv: https://arxiv.org/abs/2411.13881 · HTML: https://mlsanity.com/paper/2411.13881/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 38 references that are in the map.
- First posted on arXiv 21 Nov 2024.

## Builds on (most cited first, 2 of 2)

- [Stock price direction prediction by directly using prices data: an empirical study on the KOSPI and HSI](https://mlsanity.com/paper/1309.7119/) (2013)
- [Time Series Featurization via Topological Data Analysis](https://mlsanity.com/paper/1812.02987/) (2018)

## Built on by (most cited first, 1 of 1)

- [TopicProphet: Prophesies on Temporal Topic Trends and Stocks](https://mlsanity.com/paper/2512.11857/) (2025)

## Abstract (arXiv)

Topological Data Analysis (TDA) has recently gained significant attention in the field of financial prediction. However, the choice of point cloud construction methods, topological feature representations, and classification models has a substantial impact on prediction results. This paper addresses the classification problem of stock index movement. First, we construct point clouds for stock indices using three different methods. Next, we apply TDA to extract topological structures from the point clouds. Four distinct topological features are computed to represent the patterns in the data, and 15 combinations of these features are enumerated and input into six different machine learning models. We evaluate the predictive performance of various TDA configurations by conducting index movement classification tasks on datasets such as CSI, DAX, HSI and FTSE providing insights into the efficiency of different TDA setups.