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Clustering stocks using partial correlation coefficients

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  • Jung, Sean S.
  • Chang, Woojin

Abstract

A partial correlation analysis is performed on the Korean stock market (KOSPI). The difference between Pearson correlation and the partial correlation is analyzed and it is found that when conditioned on the market return, Pearson correlation coefficients are generally greater than those of the partial correlation, which implies that the market return tends to drive up the correlation between stock returns. A clustering analysis is then performed to study the market structure given by the partial correlation analysis and the members of the clusters are compared with the Global Industry Classification Standard (GICS). The initial hypothesis is that the firms in the same GICS sector are clustered together since they are in a similar business and environment. However, the result is inconsistent with the hypothesis and most clusters are a mix of multiple sectors suggesting that the traditional approach of using sectors to determine the proximity between stocks may not be sufficient enough to diversify a portfolio.

Suggested Citation

  • Jung, Sean S. & Chang, Woojin, 2016. "Clustering stocks using partial correlation coefficients," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 410-420.
  • Handle: RePEc:eee:phsmap:v:462:y:2016:i:c:p:410-420
    DOI: 10.1016/j.physa.2016.06.094
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    Cited by:

    1. Zhang, Xingwei & Zheng, Xiaolong & Zeng, Daniel Dajun, 2017. "The dynamic interdependence of international financial markets: An empirical study on twenty-seven stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 472(C), pages 32-42.
    2. Zhang, Peipei & Sun, Mei & Zhang, Xiaoling & Gao, Cuixia, 2017. "Who are leading the change? The impact of China’s leading PV enterprises: A complex network analysis," Applied Energy, Elsevier, vol. 207(C), pages 477-493.
    3. Xi, Xian & An, Haizhong, 2018. "Research on energy stock market associated network structure based on financial indicators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1309-1323.
    4. 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).
    5. Choi, Insu & Kim, Woo Chang, 2023. "Estimating Historical Downside Risks of Global Financial Market Indices via Inflation Rate-Adjusted Dependence Graphs," Research in International Business and Finance, Elsevier, vol. 66(C).
    6. Qian, Biyu & Wang, Gang-Jin & Feng, Yusen & Xie, Chi, 2022. "Partial cross-quantilogram networks: Measuring quantile connectedness of financial institutions," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    7. Bu, Hui & Tang, Wenjin & Wu, Junjie, 2019. "Time-varying comovement and changes of comovement structure in the Chinese stock market: A causal network method," Economic Modelling, Elsevier, vol. 81(C), pages 181-204.

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