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Clustering Approaches for Financial Data Analysis: a Survey

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  • Fan Cai
  • Nhien-An Le-Khac
  • Tahar Kechadi

Abstract

Nowadays, financial data analysis is becoming increasingly important in the business market. As companies collect more and more data from daily operations, they expect to extract useful knowledge from existing collected data to help make reasonable decisions for new customer requests, e.g. user credit category, confidence of expected return, etc. Banking and financial institutes have applied different data mining techniques to enhance their business performance. Among these techniques, clustering has been considered as a significant method to capture the natural structure of data. However, there are not many studies on clustering approaches for financial data analysis. In this paper, we evaluate different clustering algorithms for analysing different financial datasets varied from time series to transactions. We also discuss the advantages and disadvantages of each method to enhance the understanding of inner structure of financial datasets as well as the capability of each clustering method in this context.

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  • Fan Cai & Nhien-An Le-Khac & Tahar Kechadi, 2016. "Clustering Approaches for Financial Data Analysis: a Survey," Papers 1609.08520, arXiv.org.
    • Handle: RePEc:arx:papers:1609.08520
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    References listed on IDEAS

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    1. Halima Bensmail & Ramon P. DeGennaro, 2004. "Analyzing imputed financial data: a new approach to cluster analysis," FRB Atlanta Working Paper 2004-20, Federal Reserve Bank of Atlanta.
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    Cited by:

    1. Jeongwoo Kim, 2019. "Optimally adjusted last cluster for prediction based on balancing the bias and variance by bootstrapping," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-31, November.
    2. Iulia LUPU & Adina CRISTE & Anca Dana DRAGU & Teodora Daniela ALBU, 2024. "Volatility Transitions in European Stock Markets: A Clustering-Based Approach," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 65-80, October.
    3. Shi, Yong & Li, Bo & Du, Guangle & Dai, Wei, 2021. "Clustering framework based on multi-scale analysis of intraday financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    4. Mishra, Abinash & Srivastava, Pranjal & Chakrabarti, Anindya S., 2020. "'Too central to fail' firms in bi-layered financial networks: Evidence of linkages from the US corporate bond and stock markets," IIMA Working Papers WP 2020-06-02, Indian Institute of Management Ahmedabad, Research and Publication Department.
    5. Nixon, Paul & Gilbert, Evan, 2022. "Unsupervised machine learning to reveal South African risk behaviour archetypes in the domain of discretionary investment decisions," Journal of Behavioral and Experimental Finance, Elsevier, vol. 36(C).
    6. Vipul Satone & Dhruv Desai & Dhagash Mehta, 2021. "Fund2Vec: Mutual Funds Similarity using Graph Learning," Papers 2106.12987, arXiv.org.
    7. Chen, James Ming & Rehman, Mobeen Ur & Vo, Xuan Vinh, 2021. "Clustering commodity markets in space and time: Clarifying returns, volatility, and trading regimes through unsupervised machine learning," Resources Policy, Elsevier, vol. 73(C).
    8. Dhagash Mehta & Dhruv Desai & Jithin Pradeep, 2020. "Machine Learning Fund Categorizations," Papers 2006.00123, arXiv.org.

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