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Calendar Effects in Chinese Stock Market

Author

Listed:
  • Lei Gao

    (Institute of Behavioral Finance, College of Business, Hangzhou Dianzi University)

  • Gerhard Kling

    (Utrecht School of Economics)

Abstract

Our paper examines calendar effects in Chinese stock market, particularly monthly and daily effects. Using individual stock returns, we observe the change of the calendar effect over time. In Shanghai and Shenzhen, the year-end effect was strong in 1991 -- but disappeared later. As the Chinese year-end is in February, the highest returns can be achieved in March and April. Studying daily effects, we found that Fridays are profitable. Chinese investors are "amateur speculator" who often embezzles business fund for private trading; thus, these funds are used for short-term speculations before they are paid back prior to weekends.

Suggested Citation

  • Lei Gao & Gerhard Kling, 2005. "Calendar Effects in Chinese Stock Market," Annals of Economics and Finance, Society for AEF, vol. 6(1), pages 75-88, May.
    • Handle: RePEc:cuf:journl:y:2005:v:6:i:1:p:75-88
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    References listed on IDEAS

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    Citations

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    2. El Hedi Arouri, Mohamed & Lahiani, Amine & Nguyen, Duc Khuong, 2015. "World gold prices and stock returns in China: Insights for hedging and diversification strategies," Economic Modelling, Elsevier, vol. 44(C), pages 273-282.
    3. Naz Farah & Lutfullah Tooba & Zahra Kanwal, 2024. "COVID-19 and Seasonality in Monthly Returns: a Firm Level Analysis of PSX," Zagreb International Review of Economics and Business, Sciendo, vol. 27(1), pages 201-230.
    4. Meher Shiva Tadepalli & Ravi Kumar Jain, 2018. "Persistence of calendar anomalies: insights and perspectives from literature," American Journal of Business, Emerald Group Publishing Limited, vol. 33(1/2), pages 18-60, May.
    5. Lei Gao & Gerhard Kling, 2008. "Equity Transfers and Market Reactions," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 7(3), pages 293-308, December.
    6. Guo, Biao & Luo, Xingguo & Zhang, Ziding, 2014. "Sell in May and Go Away: Evidence from China," Finance Research Letters, Elsevier, vol. 11(4), pages 362-368.
    7. Degenhardt, Thomas & Auer, Benjamin R., 2018. "The “Sell in May” effect: A review and new empirical evidence," The North American Journal of Economics and Finance, Elsevier, vol. 43(C), pages 169-205.
    8. Gao, Ya & Xiong, Xiong & Feng, Xu, 2020. "Responsible investment in the Chinese stock market," Research in International Business and Finance, Elsevier, vol. 52(C).
    9. Harald Kinateder & Kimberly Weber & Niklas F. Wagner, 2019. "Revisiting Calendar Anomalies In Brics Countries," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 22(2), pages 213-236, July.
    10. Qiao, Kenan & Ji, Zhehan & Xie, Haibin, 2023. "Unrealized return dispersion and the equity risk premium," Finance Research Letters, Elsevier, vol. 58(PA).
    11. Tianlun Fei & Xiaoquan Liu & Conghua Wen, 2023. "Forecasting stock return volatility: Realized volatility‐type or duration‐based estimators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1594-1621, November.
    12. Xiong, Xiong & Meng, Yongqiang & Li, Xiao & Shen, Dehua, 2019. "An empirical analysis of the Adaptive Market Hypothesis with calendar effects:Evidence from China," Finance Research Letters, Elsevier, vol. 31(C).
    13. Ramona DUMITRIU & Razvan STEFANESCU, 2017. "The Behavior of Stock Prices during Lent and Advent," Risk in Contemporary Economy, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, pages 95-112.
    14. Doyle, John R. & Chen, Catherine Huirong, 2009. "The wandering weekday effect in major stock markets," Journal of Banking & Finance, Elsevier, vol. 33(8), pages 1388-1399, August.
    15. Jiang, Lin & Kling, Gerhard & Bo, Hong & Driver, Ciaran, 2017. "Why do firms adopt stock options and who benefits? A natural experiment in China," Pacific-Basin Finance Journal, Elsevier, vol. 46(PA), pages 124-140.
    16. Lin, Yu En & Chu, Chien Chi & Omura, Akihiro & Li, Bin & Roca, Eduardo, 2020. "Arbitrage risk and the cross-section of stock returns: Evidence from China," Emerging Markets Review, Elsevier, vol. 43(C).
    17. Ayman Abdalmajeed Ahmad Al-Smadi & Mahmoud Khalid Almsafir & Nur Hanis Hazwani Binti Husni, 2018. "Trends And Calendar Effects In Malaysia’S Stock Market," Romanian Economic Business Review, Romanian-American University, vol. 13(2), pages 15-22, June.
    18. Ramona DUMITRIU & Razvan STEFANESCU, 2017. "The Behavior of Stock Prices during Lent and Advent," Proceedings RCE 2017, Editura Lumen, vol. 0, pages 95-112, November.
    19. Luo, Kevin & Tian, Shuairu, 2020. "The “Black Thursday” effect in Chinese stock market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    20. Rajesh Elangovan & Francis Gnanasekar Irudayasamy & Satyanarayana Parayitam, 2022. "Month-of-the-Year Effect: Empirical Evidence from Indian Stock Market," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 29(3), pages 449-476, September.
    21. Shuang Feng & Jon T. Stewart, 2015. "A Review of Market Segmentation and Inefficiencies of the Chinese Stock Market," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 4(4), pages 18-28, October.
    22. Gao, Lei & Kling, Gerhard, 2006. "Regulatory changes and market liquidity in Chinese stock markets," Emerging Markets Review, Elsevier, vol. 7(2), pages 162-175, June.
    23. 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.
    24. Chaoran Cui & Xiaojie Li & Juan Du & Chunyun Zhang & Xiushan Nie & Meng Wang & Yilong Yin, 2021. "Temporal-Relational Hypergraph Tri-Attention Networks for Stock Trend Prediction," Papers 2107.14033, arXiv.org, revised Mar 2022.

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    More about this item

    Keywords

    Year-end effect; China; Anomalies; Tax-loss selling;
    All these keywords.

    JEL classification:

    • K22 - Law and Economics - - Regulation and Business Law - - - Business and Securities Law
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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