Stock market analysis using persistent homology

• Main Author: Lim, Lara Gabrielle F.
• Format: text
• Language: English
• Published: Animo Repository 2025
• Subjects: Stock exchanges—Mathematical models; Homology theory; Mathematics
• Online Access: https://animorepository.dlsu.edu.ph/etdm_math/12; https://animorepository.dlsu.edu.ph/context/etdm_math/article/1012/viewcontent/2025_Lim_Stock_market_analysis_using_persistent_homology.pdf
• Institution: De La Salle University

Methods in machine learning have been used in recent decades to aid market participants in determining the future direction of stock markets, which is imperative for any investment decision to yield high financial returns and minimize risks. Several studies have integrated persistent homology into machine learning, and it has been shown that this approach improves accuracy in inferencing imaging datasets, recognizing patterns and predicting time series data. In computational topology, persistent homology is a tool that keeps track of data features that persist across different scales. Application of persistent homology obtains invariant topological features which may be used as input data for machine learning models. In this study, we choose indices from the Philippine Stock Exchange as our data for prediction: the Composite Index, the Service Index and the Industrial Sector Index. The stock returns, technical indicators and topological features obtained from the historical data are used in the machine learning models, artificial neural network and support vector machine. We compare performance of the models using the various inputs to show that the method using persistent homology is a strong option for investors on their stock market predictions and analysis.