Exploring applications of topological data analysis in stock index movement prediction

TL;DR

This paper investigates how topological data analysis can be applied to predict stock index movements by constructing point clouds, extracting topological features, and evaluating various machine learning models across multiple datasets.

Contribution

It introduces a comprehensive framework for applying TDA to stock prediction, exploring different point cloud constructions, features, and models to optimize classification accuracy.

Findings

Certain TDA configurations outperform traditional methods

Topological features improve prediction accuracy

Different datasets respond uniquely to TDA features

Abstract

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.