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Stock Index Pattern Discovery via Toeplitz Inverse Covariance-based Clustering

Author

Listed:
  • Hongbing OUYANG

    (School of Economics, Huazhong University of Science and Technology, Hubei Province, P.R. China)

  • Xiaolu WEI

    (Corresponding author. Business School, Hubei University, Hubei Province, P.R. China.)

  • Qiufeng WU

    (School of Science, Northeast Agricultural University, Heilongjiang Province, P.R. China.)

Abstract

In this study, we attempt to discover repeated patterns of stock index through analysis of multivariate time series. Our motivation is based on the notion that financial planning guided by pattern discovery of stock index may be more effective. A two-stage architecture constructed by combining Toeplitz Inverse Covariance-Based Clustering (TICC) with betweenness centrality is applied for pattern discovery of stock index. In the first stage, TICC is used to discover repeated patterns of stock index’s multivariate time series. Then, in the second stage, betweenness centrality scores that reveal the relative “importance” of each influencing factor are plotted by Markov random field (MRF) which is derived from the first experiment. The Hangseng Stock Index and five influencing factors are used in the experiment. Empirical results show that six kinds of time-invariant patterns with flexible and long-term time periods are discovered in Hangseng Stock Index, and that different influencing factors have different betweenness scores in each cluster. This empirical research provides new ideas of stock index prediction and portfolio construction for scholars and investors. Moreover, the long-period repeated patterns that are discovered in this paper increase the possibility to reduce transaction cost for portfolio construction which then, maybe more applicable to the real financial markets.

Suggested Citation

  • Hongbing OUYANG & Xiaolu WEI & Qiufeng WU, 2020. "Stock Index Pattern Discovery via Toeplitz Inverse Covariance-based Clustering," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 58-72, July.
    • Handle: RePEc:rjr:romjef:v::y:2020:i:2:p:58-72
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    References listed on IDEAS

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    More about this item

    Keywords

    pattern discovery; Toeplitz Inverse Covariance-Based Clustering (TICC); multivariate time series; stock index; influencing factors; betweenness centrality scores;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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