Exploring applications of topological data analysis in stock index movement prediction
This work addresses stock market prediction for financial analysts, but it is incremental as it focuses on evaluating existing TDA components rather than introducing new methods.
This paper tackles the problem of predicting stock index movements by systematically evaluating different configurations of Topological Data Analysis (TDA), including point cloud construction methods, topological features, and machine learning models, and finds that certain TDA setups improve prediction efficiency on datasets like CSI, DAX, HSI, and FTSE.
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.