Year: 2025
Type: conference-paper
Abstract: Investment is a crucial factor in economic growth and the accumulation of personal wealth. Making informed investment decisions in an ever-changing market is of paramount importance. This paper explores a financial investment decision making methods based on Topological Data Analysis (TDA), aiming to uncover the intrinsic structures and patterns within financial market data through TDA, thereby enhancing the accuracy and efficiency of investment decisions. With the advent of the big data era, the complexity and volume of financial market data have increased dramatically. Traditional investment analysis methods face limitations when dealing with high-dimensional, nonlinear, and complex data. As an emerging data analysis approach, TDA can identify features such as clusters, voids, and connectivity within data, revealing the topological structure of data at different scales. This provides a new perspective for financial investment decision-making.
Author: Xilai Qin
Institution: City University of Macau
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: Universidade de Macau
OpenAlex ID: W4413812555