Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Grouping stocks using dynamic linear models

  • Sihyeon, Kim (Department of Applied Statistics, Chung-Ang University) ;
  • Byeongchan, Seong (Department of Applied Statistics, Chung-Ang University)
  • Received : 2022.07.02
  • Accepted : 2022.08.17
  • Published : 2022.11.30
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Abstract

Recently, several studies have been conducted using state space model. In this study, a dynamic linear model with state space model form is applied to stock data. The monthly returns for 135 Korean stocks are fitted to a dynamic linear model, to obtain an estimate of the time-varying 𝛽-coefficient time-series. The model formula used for the return is a capital asset pricing model formula explained in economics. In particular, the transition equation of the state space model form is appropriately modified to satisfy the assumptions of the error term. k-shape clustering is performed to classify the 135 estimated 𝛽 time-series into several groups. As a result of the clustering, four clusters are obtained, each consisting of approximately 30 stocks. It is found that the distribution is different for each group, so that it is well grouped to have its own characteristics. In addition, a common pattern is observed for each group, which could be interpreted appropriately.

Keywords

Acknowledgement

This research was supported by the Chung-Ang University Graduate Research Scholarship in 2021. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1F1A1A01073864).

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