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A factor score clustering approach to analyze the biopharmaceutical sector in the Chinese market during COVID-19

Author

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  • Jiahui Xi

    (Xi’an Jiaotong-Liverpool University)

  • Conghua Wen

    (Xi’an Jiaotong-Liverpool University
    Xi’an Jiaotong-Liverpool University)

  • Yifan Tang

    (Xi’an Jiaotong-Liverpool University)

  • Feifan Zhao

    (Xi’an Jiaotong-Liverpool University)

Abstract

The biopharmaceutical sector is of considerable interest during the COVID-19 pandemic. This study aims to investigate the biopharmaceutical sector using the Shenwan Industry Classification and provides insights into investment strategies. We combine factor and cluster analyses to reduce data dimensions and detect their latent similarities. Specifically, the biopharmaceutical sector is divided into six categories based on second-level industry classification. It is observed that medical devices, medical services, biological products, and chemical pharmaceuticals maintained their upward tendency, while Chinese medicine and pharmaceutical commerce declined slightly. We also develop optimal investment strategies using various metrics for different investor types.

Suggested Citation

  • Jiahui Xi & Conghua Wen & Yifan Tang & Feifan Zhao, 2024. "A factor score clustering approach to analyze the biopharmaceutical sector in the Chinese market during COVID-19," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-28, December.
  • Handle: RePEc:spr:fininn:v:10:y:2024:i:1:d:10.1186_s40854-024-00654-y
    DOI: 10.1186/s40854-024-00654-y
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    References listed on IDEAS

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    1. Leippold, Markus & Wang, Qian & Zhou, Wenyu, 2022. "Machine learning in the Chinese stock market," Journal of Financial Economics, Elsevier, vol. 145(2), pages 64-82.
    2. Theodoros Daglis & Ioannis G. Melissaropoulos & Konstantinos N. Konstantakis & Panayotis G. Michaelides, 2022. "The impact of COVID-19 on global stock markets: early linear and non-linear evidence for Italy," Evolutionary and Institutional Economics Review, Springer, vol. 19(1), pages 485-495, April.
    3. Alexander F. Wagner, 2020. "What the stock market tells us about the post-COVID-19 world," Nature Human Behaviour, Nature, vol. 4(5), pages 440-440, May.
    4. Emre Cevik & Buket Kirci Altinkeski & Emrah Ismail Cevik & Sel Dibooglu, 2022. "Investor sentiments and stock markets during the COVID-19 pandemic," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-34, December.
    5. Bai, Jushan & Ng, Serena, 2006. "Evaluating latent and observed factors in macroeconomics and finance," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 507-537.
    6. David Morelli, 1999. "Tests of structural change using factor analysis in equity returns," Applied Economics Letters, Taylor & Francis Journals, vol. 6(4), pages 203-207.
    7. Wagenvoort, Rien J.L.M. & Ebner, André & Morgese Borys, Magdalena, 2011. "A factor analysis approach to measuring European loan and bond market integration," Journal of Banking & Finance, Elsevier, vol. 35(4), pages 1011-1025, April.
    8. HaiYue Liu & Yile Wang & Dongmei He & Cangyu Wang, 2020. "Short term response of Chinese stock markets to the outbreak of COVID-19," Applied Economics, Taylor & Francis Journals, vol. 52(53), pages 5859-5872, November.
    9. Wang, Yudong & Ma, Feng & Wei, Yu & Wu, Chongfeng, 2016. "Forecasting realized volatility in a changing world: A dynamic model averaging approach," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 136-149.
    10. Takyi, Paul Owusu & Bentum-Ennin, Isaac, 2021. "The impact of COVID-19 on stock market performance in Africa: A Bayesian structural time series approach," Journal of Economics and Business, Elsevier, vol. 115(C).
    11. Baker, H. Kent & Haslem, John A., 1974. "The impact of investor socioeconomic characteristics on risk and return preferences," Journal of Business Research, Elsevier, vol. 2(4), pages 469-476, October.
    12. Christopher S. Jones, 2006. "A Nonlinear Factor Analysis of S&P 500 Index Option Returns," Journal of Finance, American Finance Association, vol. 61(5), pages 2325-2363, October.
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    More about this item

    Keywords

    Factor analysis; Cluster analysis; Biopharmaceutical sector; Investment analysis;
    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
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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