Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (10): 13-21.doi: 10.11896/jsjkx.250100136

• Digital Intelligence Enabling FinTech Frontiers • Previous Articles     Next Articles

Research on Portfolio Construction Based on Topological Structure Features

LI Ruiyang, LI Shuyi, YANG Yuexi, PENG Chuhan, XING Jingyu, QIAO Gaoxiu   

  1. School of Mathematics,Southwest Jiaotong University,Chengdu 611756,China
  • Received:2025-01-21 Revised:2025-05-06 Online:2025-10-15 Published:2025-10-14
  • About author:LI Ruiyang,born in 2003,is a member of CCF(No.O3787G).His main research interests include data mining and deep learning.
    QIAO Gaoxiu,born in 1982,Ph.D,associate professor.Her main research interests include time series forecasting,derivatives pricing,machine learning and energy economics.
  • Supported by:
    Fundamental Research Funds for the Central Universities of Ministry of Education of China(202410613071,2682025ZTPY001) and National Natural Science Foundation of China(72001180).

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Abstract: In recent years,the application of topological data analysis(TDA) in the financial field has gradually demonstrated its value.TDA,through methods such as persistent homology,constructing complexes that effectively quantify the shape of data,facilitating the extraction of data information.This provides unique advantages for time series analysis,particularly in the clustering of financial time series and the construction of portfolios.Based on this,by deeply mining the time series data of China's stock market using TDA methods,combined with clustering algorithms,and applying these insights to portfolio construction,the effectiveness of such approaches is analyzed.The results,validated through the sliding window method,indicate that portfolios constructed based on TDA(denoising) clustering perform well in terms of return-risk ratio and stability,outperforming the overall market.Therefore,the TDA method can more effectively mine information from stock data,providing a scientific basis for investors to optimize returns.

Key words: Topological data analysis,Portfolio,Time series,Clustering

CLC Number: 

  • TP399
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