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Chinese stock market sectoral indices performance in the time of novel coronavirus pandemic

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  • Liew, Venus Khim-Sen
  • Puah, Chin-Hong

Abstract

This paper aims to quantify the effect of the deadly novel coronavirus (COVID-19) pandemic outbreak on Chinese stock market performance. Shanghai Stock Exchange Composite Index and its component sectorial indices are examined in this study. The pandemic is represented by a lockdown dummy, new COVID-19 cases and a dummy for 3 February 2020. First, descriptive analysis is performed on these indices to compare their performances before and during the lockdown period. Next, regression analysis with Exponential Generalized Autoregressive Conditional Heteroscedasticity specification is estimated to quantify the pandemic effect on the Chinese stock market. This paper finds that health care, information technology and telecommunication services sectors were relatively more pandemic-resistant, while other sectors were more severely hurt by the pandemic outbreak. The extent to which each sector was affected by pandemic and sentiments in other financial and commodity markets were reported in details in this paper. The findings of this paper are resourceful for investors to avoid huge loss amid pandemic outburst and the China Securities Regulatory Commission in handling future pandemic occurrence to cool down excessive market sentiments.

Suggested Citation

  • Liew, Venus Khim-Sen & Puah, Chin-Hong, 2020. "Chinese stock market sectoral indices performance in the time of novel coronavirus pandemic," MPRA Paper 100414, University Library of Munich, Germany, revised 28 Apr 2020.
  • Handle: RePEc:pra:mprapa:100414
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    References listed on IDEAS

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    1. Chenyu Han & Yiming Wang & Yingying Xu, 2019. "Efficiency and Multifractality Analysis of the Chinese Stock Market: Evidence from Stock Indices before and after the 2015 Stock Market Crash," Sustainability, MDPI, vol. 11(6), pages 1-15, March.
    2. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    3. Yousaf, Imran & Hassan, Arshad, 2019. "Linkages between crude oil and emerging Asian stock markets: New evidence from the Chinese stock market crash," Finance Research Letters, Elsevier, vol. 31(C).
    4. Zhang, Bing & Li, Xiao-Ming, 2014. "Has there been any change in the comovement between the Chinese and US stock markets?," International Review of Economics & Finance, Elsevier, vol. 29(C), pages 525-536.
    5. Phillips, Peter C B & Ouliaris, S, 1990. "Asymptotic Properties of Residual Based Tests for Cointegration," Econometrica, Econometric Society, vol. 58(1), pages 165-193, January.
    6. Chong, Terence Tai-Leung & Lam, Tau-Hing & Yan, Isabel Kit-Ming, 2012. "Is the Chinese stock market really inefficient?," China Economic Review, Elsevier, vol. 23(1), pages 122-137.
    7. Zhuang, Xiaoyang & Wei, Yu & Ma, Feng, 2015. "Multifractality, efficiency analysis of Chinese stock market and its cross-correlation with WTI crude oil price," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 430(C), pages 101-113.
    8. Teng, Chia-Chen & Yang, J. Jimmy, 2018. "Chinese Lunar New Year effect, investor sentiment, and market deregulation," Finance Research Letters, Elsevier, vol. 27(C), pages 175-184.
    9. Casalin, Fabrizio, 2018. "Determinants of holiday effects in mainland Chinese and Hong-Kong markets," China Economic Review, Elsevier, vol. 49(C), pages 45-67.
    10. Yuan, Tian & Gupta, Rakesh, 2014. "Chinese Lunar New Year effect in Asian stock markets, 1999–2012," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(4), pages 529-537.
    11. World Bank, "undated". "World Bank East Asia and Pacific Economic Update, April 2020," World Bank Publications - Reports 33477, The World Bank Group.
    12. He, Hongbo & Chen, Shou & Yao, Shujie & Ou, Jinghua, 2015. "Stock market interdependence between China and the world: A multi-factor R-squared approach," Finance Research Letters, Elsevier, vol. 13(C), pages 125-129.
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    Cited by:

    1. Qing Liu & Huina Jin & Xiang Bai & Jinliang Zhang, 2023. "Prediction and Analysis of the Price of Carbon Emission Rights in Shanghai: Under the Background of COVID-19 and the Russia–Ukraine Conflict," Mathematics, MDPI, vol. 11(14), pages 1-16, July.
    2. Aristovnik, Aleksander & Yang, Guo-liang & Song, Yao-yao & Ravšelj, Dejan, 2023. "Industrial performance of the top R&D enterprises in world-leading economies: A metafrontier approach," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    3. Josua Sinaga & Ting Wu & Yu-wang Chen, 2022. "Impact of government interventions on the stock market during COVID-19: a case study in Indonesia," SN Business & Economics, Springer, vol. 2(9), pages 1-35, September.
    4. ATM Adnan & Sameer Al Johani, 2023. "Stock Market Reaction to COVID-19: A Cross-Sectional Industry Analysis in Frontier Market," IIM Kozhikode Society & Management Review, , vol. 12(2), pages 157-181, July.

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    More about this item

    Keywords

    Novel coronavirus; COVID-19; SARS-CoV-2; pandemic; Chinese stock market; Exponential Generalized Autoregressive Conditional Heteroscedasticity;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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